From d557b8125f1eb5c9f730b00c7f3e2d161345bd47 Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Wed, 27 Mar 2024 14:14:52 +0000 Subject: [PATCH 01/43] Setting up GitHub Classroom Feedback From c4f839a88b5c1f32798953d0dfc0400c6e90ccf2 Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Wed, 27 Mar 2024 14:14:54 +0000 Subject: [PATCH 02/43] add online IDE url --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index da21a6d..3f72673 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +[![Open in Codespaces](https://classroom.github.com/assets/launch-codespace-7f7980b617ed060a017424585567c406b6ee15c891e84e1186181d67ecf80aa0.svg)](https://classroom.github.com/open-in-codespaces?assignment_repo_id=14492363) # Autograding Example: Python This example project is written in Python, and tested with pytest. From c3ccfcf656383b725405dc97e8b5ac146a0f8611 Mon Sep 17 00:00:00 2001 From: Joe Manning Date: Wed, 27 Mar 2024 15:31:40 +0000 Subject: [PATCH 03/43] initial schwefel funciton definition --- schwefel_functions.py | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 schwefel_functions.py diff --git a/schwefel_functions.py b/schwefel_functions.py new file mode 100644 index 0000000..323c518 --- /dev/null +++ b/schwefel_functions.py @@ -0,0 +1,37 @@ +import numpy as np + +def schwefel_1d(x): + + return 418.9829 - x * np.sin(np.sqrt(np.abs(x))) + +def schwefel_nd(args): + output = 0 + + for dim in range(args): + output += schwefel_1d(args[dim]) + +def add_gaussian_noise(signal, noise_level): + + return signal + np.random.normal(0, noise_level, 1)[0] + +def schwefel_1d_with_noise(x, noise_level = 0.01): + # Calculate the Schwefel function value + + schwefel_value = schwefel_1d(x) + + # Add Gaussian noise to the Schwefel function value + + noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level) + + return noisy_schwefel_value + +def schwefel_nd_with_noise(args, noise_level = 0.01): + # Calculate the Schwefel function value + + schwefel_value = schwefel_nd(args) + + # Add Gaussian noise to the Schwefel function value + + noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level) + + return noisy_schwefel_value \ No newline at end of file From 4c704e9bab3e68501b0149e53cec7357b9e9cc6e Mon Sep 17 00:00:00 2001 From: Joe Manning Date: Wed, 27 Mar 2024 15:32:15 +0000 Subject: [PATCH 04/43] moved into src --- schwefel_functions.py => src/schwefel_functions.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename schwefel_functions.py => src/schwefel_functions.py (100%) diff --git a/schwefel_functions.py b/src/schwefel_functions.py similarity index 100% rename from schwefel_functions.py rename to src/schwefel_functions.py From b3419c1ec8eb16b77d646eb02cedfc4d9fc77202 Mon Sep 17 00:00:00 2001 From: Joe Manning Date: Wed, 27 Mar 2024 17:02:08 +0100 Subject: [PATCH 05/43] initial commit of BayBE quickstart tutorial --- src/baybe_playground.ipynb | 204 +++++++++++++++++++++++++++++++++++++ 1 file changed, 204 insertions(+) create mode 100644 src/baybe_playground.ipynb diff --git a/src/baybe_playground.ipynb b/src/baybe_playground.ipynb new file mode 100644 index 0000000..7bb01e5 --- /dev/null +++ b/src/baybe_playground.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1-dimensional continuous data BO test using BayBE" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.targets import NumericalTarget\n", + "from baybe.objective import Objective\n", + "\n", + "target = NumericalTarget(\n", + " name=\"Yield\",\n", + " mode=\"MAX\",\n", + ")\n", + "objective = Objective(mode=\"SINGLE\", targets=[target])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.parameters import NumericalContinuousParameter\n", + "\n", + "parameters = [\n", + " NumericalContinuousParameter('schwefel1', bounds=(-500,500)),\n", + " NumericalContinuousParameter('schwefel2', bounds=(-500,500))\n", + "\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.recommenders import SequentialGreedyRecommender\n", + "\n", + "recommender = SequentialGreedyRecommender()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.searchspace import SearchSpace\n", + "\n", + "searchspace = SearchSpace.from_product(parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe import Campaign\n", + "\n", + "campaign = Campaign(searchspace, objective, recommender)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__annotations__',\n", + " '__attrs_attrs__',\n", + " '__attrs_own_setattr__',\n", + " '__attrs_types_resolved__',\n", + " '__class__',\n", + " '__delattr__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getstate__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__setstate__',\n", + " '__sizeof__',\n", + " '__slots__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_cached_recommendation',\n", + " '_measurements_exp',\n", + " '_measurements_parameters_comp',\n", + " '_measurements_targets_comp',\n", + " '_validate_strategy',\n", + " '_validate_tolerance_flag',\n", + " 'add_measurements',\n", + " 'from_config',\n", + " 'from_dict',\n", + " 'from_json',\n", + " 'measurements',\n", + " 'n_batches_done',\n", + " 'n_fits_done',\n", + " 'numerical_measurements_must_be_within_tolerance',\n", + " 'objective',\n", + " 'parameters',\n", + " 'recommend',\n", + " 'recommender',\n", + " 'searchspace',\n", + " 'strategy',\n", + " 'targets',\n", + " 'to_dict',\n", + " 'to_json',\n", + " 'validate_config']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(campaign)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "can only convert an array of size 1 to a Python scalar", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mcampaign\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m display(df)\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\campaign.py:298\u001b[0m, in \u001b[0;36mCampaign.recommend\u001b[1;34m(self, batch_size, batch_quantity)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_measurements_exp\u001b[38;5;241m.\u001b[39mfillna({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitNr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_fits_done}, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 297\u001b[0m \u001b[38;5;66;03m# Get the recommended search space entries\u001b[39;00m\n\u001b[1;32m--> 298\u001b[0m rec \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommender\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearchspace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 300\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 301\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_measurements_parameters_comp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 302\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_measurements_targets_comp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 303\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 305\u001b[0m \u001b[38;5;66;03m# Cache the recommendations\u001b[39;00m\n\u001b[0;32m 306\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_recommendation \u001b[38;5;241m=\u001b[39m rec\u001b[38;5;241m.\u001b[39mcopy()\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\recommenders\\pure\\bayesian\\base.py:139\u001b[0m, in \u001b[0;36mBayesianRecommender.recommend\u001b[1;34m(self, searchspace, batch_size, train_x, train_y)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _ONNX_INSTALLED \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msurrogate_model, CustomONNXSurrogate):\n\u001b[0;32m 137\u001b[0m CustomONNXSurrogate\u001b[38;5;241m.\u001b[39mvalidate_compatibility(searchspace)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup_acquisition_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43msearchspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecommend(searchspace, batch_size, train_x, train_y)\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\recommenders\\pure\\bayesian\\base.py:94\u001b[0m, in \u001b[0;36mBayesianRecommender.setup_acquisition_function\u001b[1;34m(self, searchspace, train_x, train_y)\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m train_x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m train_y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m 91\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBayesian recommenders do not support empty training data yet.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 92\u001b[0m )\n\u001b[1;32m---> 94\u001b[0m best_f \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 95\u001b[0m surrogate_model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(searchspace, train_x, train_y)\n\u001b[0;32m 96\u001b[0m acquisition_function_cls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_acquisition_function_cls()\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\pandas\\core\\base.py:418\u001b[0m, in \u001b[0;36mIndexOpsMixin.item\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan only convert an array of size 1 to a Python scalar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mValueError\u001b[0m: can only convert an array of size 1 to a Python scalar" + ] + } + ], + "source": [ + "df = campaign.recommend(batch_size=3)\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "BO", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a2b654ba2046ce4bc550708f33125281253d4fa0 Mon Sep 17 00:00:00 2001 From: Joe Manning Date: Wed, 27 Mar 2024 17:26:20 +0100 Subject: [PATCH 06/43] fixed minimum working example of a BayBE campaign --- src/baybe_playground.ipynb | 335 ++++++++++++++++++++++++++++--------- 1 file changed, 253 insertions(+), 82 deletions(-) diff --git a/src/baybe_playground.ipynb b/src/baybe_playground.ipynb index 7bb01e5..7a4b998 100644 --- a/src/baybe_playground.ipynb +++ b/src/baybe_playground.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -33,58 +33,25 @@ "\n", "parameters = [\n", " NumericalContinuousParameter('schwefel1', bounds=(-500,500)),\n", - " NumericalContinuousParameter('schwefel2', bounds=(-500,500))\n", - "\n", "]" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from baybe.recommenders import SequentialGreedyRecommender\n", - "\n", - "recommender = SequentialGreedyRecommender()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from baybe.searchspace import SearchSpace\n", - "\n", - "searchspace = SearchSpace.from_product(parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from baybe import Campaign\n", - "\n", - "campaign = Campaign(searchspace, objective, recommender)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['__annotations__',\n", + "['__abstractmethods__',\n", + " '__annotations__',\n", " '__attrs_attrs__',\n", " '__attrs_own_setattr__',\n", - " '__attrs_types_resolved__',\n", " '__class__',\n", + " '__class_getitem__',\n", " '__delattr__',\n", + " '__dict__',\n", " '__dir__',\n", " '__doc__',\n", " '__eq__',\n", @@ -101,6 +68,7 @@ " '__module__',\n", " '__ne__',\n", " '__new__',\n", + " '__parameters__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", @@ -111,60 +79,122 @@ " '__str__',\n", " '__subclasshook__',\n", " '__weakref__',\n", - " '_cached_recommendation',\n", - " '_measurements_exp',\n", - " '_measurements_parameters_comp',\n", - " '_measurements_targets_comp',\n", - " '_validate_strategy',\n", - " '_validate_tolerance_flag',\n", - " 'add_measurements',\n", - " 'from_config',\n", - " 'from_dict',\n", - " 'from_json',\n", - " 'measurements',\n", - " 'n_batches_done',\n", - " 'n_fits_done',\n", - " 'numerical_measurements_must_be_within_tolerance',\n", - " 'objective',\n", - " 'parameters',\n", - " 'recommend',\n", - " 'recommender',\n", - " 'searchspace',\n", - " 'strategy',\n", - " 'targets',\n", - " 'to_dict',\n", - " 'to_json',\n", - " 'validate_config']" + " '_abc_impl',\n", + " '_is_protocol',\n", + " '_is_runtime_protocol',\n", + " '_recommend_continuous',\n", + " '_recommend_discrete',\n", + " '_recommend_hybrid',\n", + " '_recommend_with_discrete_parts',\n", + " 'allow_recommending_already_measured',\n", + " 'allow_repeated_recommendations',\n", + " 'compatibility',\n", + " 'recommend']" ] }, - "execution_count": 22, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dir(campaign)" + "import baybe.recommenders\n", + "\n", + "dir(baybe.recommenders.pure.RandomRecommender)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.recommenders import (\n", + " SequentialGreedyRecommender, \n", + " SequentialMetaRecommender, \n", + " RandomRecommender\n", + ")\n", + "\n", + "recommender = RandomRecommender()#SequentialMetaRecommender([SequentialGreedyRecommender(), RandomRecommender()])" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.searchspace import SearchSpace\n", + "\n", + "searchspace = SearchSpace.from_product(parameters)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe import Campaign\n", + "\n", + "campaign = Campaign(searchspace, objective, recommender)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "can only convert an array of size 1 to a Python scalar", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mcampaign\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m display(df)\n", - "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\campaign.py:298\u001b[0m, in \u001b[0;36mCampaign.recommend\u001b[1;34m(self, batch_size, batch_quantity)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_measurements_exp\u001b[38;5;241m.\u001b[39mfillna({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitNr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_fits_done}, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 297\u001b[0m \u001b[38;5;66;03m# Get the recommended search space entries\u001b[39;00m\n\u001b[1;32m--> 298\u001b[0m rec \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommender\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearchspace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 300\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 301\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_measurements_parameters_comp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 302\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_measurements_targets_comp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 303\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 305\u001b[0m \u001b[38;5;66;03m# Cache the recommendations\u001b[39;00m\n\u001b[0;32m 306\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_recommendation \u001b[38;5;241m=\u001b[39m rec\u001b[38;5;241m.\u001b[39mcopy()\n", - "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\recommenders\\pure\\bayesian\\base.py:139\u001b[0m, in \u001b[0;36mBayesianRecommender.recommend\u001b[1;34m(self, searchspace, batch_size, train_x, train_y)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _ONNX_INSTALLED \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msurrogate_model, CustomONNXSurrogate):\n\u001b[0;32m 137\u001b[0m CustomONNXSurrogate\u001b[38;5;241m.\u001b[39mvalidate_compatibility(searchspace)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup_acquisition_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43msearchspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecommend(searchspace, batch_size, train_x, train_y)\n", - "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\recommenders\\pure\\bayesian\\base.py:94\u001b[0m, in \u001b[0;36mBayesianRecommender.setup_acquisition_function\u001b[1;34m(self, searchspace, train_x, train_y)\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m train_x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m train_y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m 91\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBayesian recommenders do not support empty training data yet.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 92\u001b[0m )\n\u001b[1;32m---> 94\u001b[0m best_f \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 95\u001b[0m surrogate_model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(searchspace, train_x, train_y)\n\u001b[0;32m 96\u001b[0m acquisition_function_cls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_acquisition_function_cls()\n", - "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\pandas\\core\\base.py:418\u001b[0m, in \u001b[0;36mIndexOpsMixin.item\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan only convert an array of size 1 to a Python scalar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mValueError\u001b[0m: can only convert an array of size 1 to a Python scalar" - ] + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1
0356.991347
1-309.004730
2-55.674007
\n", + "
" + ], + "text/plain": [ + " schwefel1\n", + "0 356.991347\n", + "1 -309.004730\n", + "2 -55.674007" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -174,10 +204,151 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "from schwefel_functions import schwefel_1d\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 93, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "campaign.add_measurements(df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1
0-428.432368
1-347.173847
2395.345514
\n", + "
" + ], + "text/plain": [ + " schwefel1\n", + "0 -428.432368\n", + "1 -347.173847\n", + "2 395.345514" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "campaign.recommend(batch_size=3)" + ] } ], "metadata": { From e5dc25195d5e013010afc54998f2c2a9d5cd5be8 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 00:50:46 +0800 Subject: [PATCH 07/43] schwefel class --- src/schwefel.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 src/schwefel.py diff --git a/src/schwefel.py b/src/schwefel.py new file mode 100644 index 0000000..731851d --- /dev/null +++ b/src/schwefel.py @@ -0,0 +1,32 @@ +import numpy as np + +class SchwefelProblem: + def __init__(self, n_var=1, noise_level=0.01): + """ + y = f(x) + eps + """ + self.noise_level = noise_level + self.n_var = n_var # Number of variables/dimensions + self.bounds = np.array([[-500] * self.n_var, [500] * self.n_var]) + + def _schwefel_individual(self, x): + return x * np.sin(np.sqrt(np.abs(x))) + + def f(self, x): + return 418.9829 * self.n_var - np.sum(self._schwefel_individual(x), axis=1) + + def eps(self, x): + # Assuming the noise is independent of x for simplicity + return np.random.normal(0, self.noise_level, x.shape[0]) + + def y(self, x): + return self.f(x) + self.eps(x) + +# Test code if this file is the main program being run +if __name__ == "__main__": + # Create a SchwefelProblem instance with 3 variables/dimensions + schwefel = SchwefelProblem(n_var=3, noise_level=1.) + x_test = np.array([[420, 420, 420], [420, 420, 420]]) # Example input vector + print("Objective function value (f):", schwefel.f(x_test)) + print("Noisy objective function value (y):", schwefel.y(x_test)) + From f12d41137e0949ceda184b830ba6515802001cb9 Mon Sep 17 00:00:00 2001 From: Joe Manning Date: Wed, 27 Mar 2024 17:53:12 +0100 Subject: [PATCH 08/43] Initial minimum working example of BayBE with no noise --- src/baybe_no_noise.ipynb | 367 +++++++++++++++++++++++++++++++++++++ src/baybe_playground.ipynb | 17 ++ src/seed_data.csv | 4 + 3 files changed, 388 insertions(+) create mode 100644 src/baybe_no_noise.ipynb create mode 100644 src/seed_data.csv diff --git a/src/baybe_no_noise.ipynb b/src/baybe_no_noise.ipynb new file mode 100644 index 0000000..8eb5fcb --- /dev/null +++ b/src/baybe_no_noise.ipynb @@ -0,0 +1,367 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1-dimensional continuous data BO test using BayBE" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno 11001] getaddrinfo failed)\"))\n", + " warnings.warn(\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from baybe.targets import NumericalTarget\n", + "from baybe.objective import Objective\n", + "\n", + "target = NumericalTarget(\n", + " name=\"Yield\",\n", + " mode=\"MAX\",\n", + ")\n", + "objective = Objective(mode=\"SINGLE\", targets=[target])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.parameters import NumericalContinuousParameter\n", + "\n", + "parameters = [\n", + " NumericalContinuousParameter('schwefel1', bounds=(-500,500)),\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.recommenders import (\n", + " SequentialGreedyRecommender, \n", + " SequentialMetaRecommender, \n", + " RandomRecommender\n", + ")\n", + "\n", + "recommender = SequentialGreedyRecommender()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.searchspace import SearchSpace\n", + "\n", + "searchspace = SearchSpace.from_product(parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe import Campaign\n", + "\n", + "campaign = Campaign(searchspace, objective, recommender)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv('seed_data.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from schwefel_functions import schwefel_1d\n", + "\n", + "df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "campaign.add_measurements(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "example_data = pd.DataFrame(columns = ['x', 'y'])\n", + "\n", + "example_data['x'] = np.linspace(-500,500,5000)\n", + "example_data['y'] = example_data['x'].apply(schwefel_1d)\n", + "\n", + "fig, ax = plt.subplots()\n", + "example_data.plot('x', 'y', ax=ax)\n", + "df.plot.scatter('schwefel1', 'Yield', ax=ax, c='red')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEiCAYAAAD9DXUdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABjMklEQVR4nO2dd3wU1fr/P7M9vSebngAJARKKNAUh9C4qICp4ryCoSBNBUS5+BRFBUIQrXPWqiChSfleKqEhvUlRIKKEHSO9103eT3fP7Y3cm2dTdbJnd5Lxfr31BzpyZObNlPvOc5znPwxBCCCgUCoVC0SHgewAUCoVCsS2oMFAoFApFDyoMFAqFQtGDCgOFQqFQ9KDCQKFQKBQ9qDBQKBQKRQ8qDBQKhULRgwoDhUKhUPSgwkChUCgUPagw2DHfffcdGIbB5cuXubZDhw5h5cqV/A3KgHGEhYVhxowZVh0PS3V1Nd5//32EhYVBKpUiKioKmzdvNuoYq1atQteuXaHRaPTa8/Pz8frrr3PH9vPzw9ixY1FYWGjOS7A5Vq5cCYZh+B5Gi3z++ef47rvvWrXv1q1bERgYiPLycvMOylYhFLtl27ZtBAC5dOkS1zZv3jxiCx9rc+OIj48n9+/ft/KItMyePZtIpVKyfv16curUKfLOO+8QhmHIhx9+aND+GRkZxMnJifzvf/9r0N6hQwcSGRlJvvnmG3LmzBmyd+9eMn/+fJKVlWWJS7EZ0tLSyMWLF/keRot069aNxMbGtmrf6upqEhERQd577z3zDspGEfGsSxQ7oaKiAo6OjmY5Vq9evcxyHGO5efMmtm7dig8//BBvvfUWAGDIkCEoKCjA6tWrMWfOHHh6ejZ7jH//+99wd3fHpEmT9Nrnzp0LpVKJy5cvw8PDg2uv368twX4ngoKCEBQUxPdwLIpIJMKrr76KDz74AG+//bbZfgs2C9/KRGk99S2GF198kQBo8EpKSiKEEKLRaMh//vMf0qNHDyKTyYi7uzuZPHkyefDggd5xY2NjSbdu3ciZM2fIY489RhwcHMizzz5LCCFk9+7dZOTIkUQulxOZTEaioqLI22+/TcrKyrj9WxpHaGgoefHFF/XOmZKSQqZPn058fHyIRCIhUVFR5JNPPiFqtZrrk5SURACQjz/+mGzYsIGEhYURJycn8uijjxr0xLp69WoCoMET/IULFwgA8uOPPza7v1KpJF5eXuStt97Sa09KSiIMw5CVK1e2OIamUCgUZMmSJSQsLIyIxWISEBBAXn/9db339dVXXyVSqZRcvnyZa1Or1WTYsGHE19eXZGZmEkJqvxdHjx4lM2bMIB4eHsTR0ZFMmDChwWdNCCHHjh0jw4YNIy4uLsTBwYEMGDCAHD9+XK/PihUrCAASFxdHJk+eTNzd3YlcLtfbVpfQ0FAyfvx48ssvv5CePXty35VffvmFG2NUVBRxdHQkffv21bN6WS5dukSeeOIJ4uHhQaRSKenZsyfZs2ePXh/2Wk+ePEnmzJlDvLy8iKenJ3n66adJRkaG3njqfx9DQ0O59/CDDz4gkZGRRCaTETc3NxITE0M2bdqkd66srCzCMAzZunVr4x9iG4IKgx1TXxju379PpkyZQgCQixcvcq+qqipCCCEvv/wyEYvFZMmSJeTw4cNk586dJCoqivj5+ZHs7GzuuLGxscTT05MEBweTzZs3k1OnTpEzZ84QQgj54IMPyMaNG8lvv/1GTp8+Tb788ksSHh5Ohg4dyu3f0jjqC0Nubi4JDAwkPj4+5MsvvySHDx8m8+fPJwDIa6+9xvVjhSEsLIyMGTOGHDhwgBw4cIDExMQQDw8PUlxc3Oz79dxzzxEfH58G7WVlZQQAWbZsWbP7nz17lgAghw4d0mv//vvvCQDy1Vdfkeeee444OTkRqVRKYmNjyYULF5o9JiGElJeXk549exJvb2/y6aefkuPHj5N///vfxM3NjQwbNoxoNBpCCCGVlZWkZ8+epEOHDqSoqIgQQsh7771HBAIBOXr0KHc89nsRHBxMXnrpJfL777+Tr776ivj6+pLg4GBuX0II+eGHHwjDMOSpp54i+/btI7/88guZMGECEQqFeuLA3vxDQ0PJ22+/TY4dO0YOHDigt60uoaGhJCgoiERHR5Ndu3aRQ4cOkf79+xOxWEzee+89MnDgQLJv3z6yf/9+EhkZSfz8/EhFRQW3/8mTJ4lEIiGDBg0ie/bsIYcPHyYzZswgAMi2bdsaXGuHDh3IggULyJEjR8g333xDPDw89L6T8fHxpEOHDqRXr17c9zE+Pp4QQsjatWuJUCgkK1asICdOnCCHDx8mmzZtalTou3TpQiZNmtTiZ2rvUGGwY4zxMVy8eJEAIBs2bNBrT0tLIw4ODmTp0qVcW2xsLAFATpw40ez5NRoNqa6uJmfOnCEAyLVr11ocByENheGdd94hAMhff/2l1++1114jDMOQu3fvEkJqhSEmJobU1NRw/f7++28CgOzatavZ8Y4cOZJ07ty50W0SiYS88sorze6/bt06AkBPRAnR3lgAEFdXV/Lkk0+Sw4cPk71795Lu3bsTmUym9740xtq1a4lAIGjw1PzTTz81EKLExETi6upKnnrqKXL8+HEiEAjIu+++q7cf+714+umn9drPnz9PAJDVq1cTQrSC5OnpSZ544gm9fmq1mvTo0YP069ePa2Nv/o3NsTclDA4ODiQ9PZ1ru3r1KgFA/P39SXl5Odd+4MABAoAcPHiQa4uKiiK9evUi1dXVesedMGEC8ff35yxJ9lrnzp2r12/9+vUNrMOmfAwTJkwgPXv2bNDeGNOnTyd+fn4G9bVnaFRSO+HXX38FwzB44YUXUFNTw73kcjl69OiB06dP6/X38PDAsGHDGhzn4cOHmDZtGuRyOYRCIcRiMWJjYwEAt2/fbtXYTp48ia5du6Jfv3567TNmzAAhBCdPntRrHz9+PIRCIfd39+7dAQApKSktnqu56JmWImsyMzPBMAy8vb312tnopKCgIOzduxejR4/GpEmTcPjwYQgEAqxfv77Z4/7666+Ijo5Gz5499T6b0aNHg2EYvc+mU6dO+Prrr3HgwAFMmDABgwYNajL6a/r06Xp/DxgwAKGhoTh16hQA4MKFCygsLMSLL76od16NRoMxY8bg0qVLDaJwJk+e3Oy11KVnz54IDAzk/u7SpQsArV+n7hw9285+fvfv38edO3e48dcd27hx45CVlYW7d+/qnWvixIl6fxvznejXrx+uXbuGuXPn4siRIygpKWmyr6+vL3Jzc1FTU9Pice0Z6nxuJ+Tk5IAQAj8/v0a3d+jQQe9vf3//Bn3KysowaNAgyGQyrF69GpGRkXB0dERaWhomTZqEysrKVo2toKAAYWFhDdoDAgK47XXx8vLS+1sqlQJAi+f38vLC1atXG7SXl5dDpVK16HiurKyEWCzWE6W64xkxYoTeNn9/f/To0QPx8fHNHjcnJwf379+HWCxudHt+fr7e3+PHj4efnx9ycnKwePHiBuNhkcvljbax72dOTg4AYMqUKU2OrbCwEE5OTnrXZCj130+JRNJse1VVld643nzzTbz55puNHrv+e9La7wQALFu2DE5OTtixYwe+/PJLCIVCDB48GOvWrUOfPn30+spkMhBCUFVVBWdn5xaPba9QYWgneHt7g2EY/PHHH9yPpi712xp7ej558iQyMzNx+vRpzkoAgOLiYpPG5uXlhaysrAbtmZmZ3NjNQUxMDHbv3o3s7Gy9m2ZCQgIAIDo6utn9vb29oVKpUF5ernezZJ9OG4MQAoGgecPc29sbDg4O+Pbbb5vcXpc5c+agtLQU3bp1w8KFCzFo0CC9SCiW7OzsRts6deqkd9zNmzfj0UcfbfTc9R8krLFegR3XsmXLmozq6ty5s9nOJxKJsHjxYixevBjFxcU4fvw4/vWvf2H06NFIS0vTs24KCwshlUrbtCgAdIFbm6OpJ6UJEyaAEIKMjAz06dOnwSsmJqbFY7M3hfoi8t///tfgcTTG8OHDcevWrQZP1t9//z0YhsHQoUNbPIYhPPnkk2AYBtu3b9dr/+677+Dg4IAxY8Y0u39UVBQA4MGDB3rt/fv3R1BQEI4ePQq1Ws21Z2Zm4tq1a03edFkmTJiABw8ewMvLq9HPpq419c0332DHjh3YsmULDh48iOLiYsycObPR4/744496f1+4cAEpKSkYMmQIAGDgwIFwd3fHrVu3Gj1vnz59uKd5a9K5c2dERETg2rVrTY7LxcXF6ONKpdIWv4/u7u6YMmUK5s2bh8LCQiQnJ+ttf/jwIbp27Wr0ue0NajG0Mdgb/Lp16zB27FgIhUJ0794dAwcOxCuvvIKZM2fi8uXLGDx4MJycnJCVlYVz584hJiYGr732WrPHHjBgADw8PDBnzhysWLECYrEYP/74I65du2bwOBq70bzxxhv4/vvvMX78eKxatQqhoaH47bff8Pnnn+O1115DZGSkGd4ZoFu3bpg1axZWrFgBoVCIvn374ujRo/jqq6+wevXqFqeS2Bvqn3/+qWclCAQCbNy4EVOnTsWTTz6J1157DeXl5fjggw8gkUiwbNmyZo+7aNEi7N27F4MHD8Ybb7yB7t27Q6PRIDU1FUePHsWSJUvQv39/JCQkYOHChXjxxRc5Mdi6dSumTJmCTZs2YdGiRXrHvXz5MmbPno1nnnkGaWlpWL58OQIDAzF37lwAgLOzMzZv3owXX3wRhYWFmDJlCnx9fZGXl4dr164hLy8PX3zxhZHvsnn473//i7Fjx2L06NGYMWMGAgMDUVhYiNu3byM+Ph7/+9//jD4mazHu2bMHHTp0gEwmQ0xMDJ544glER0ejT58+8PHxQUpKCjZt2oTQ0FBERERw+2s0Gvz999+YNWuWOS/VNuHV9U0xicaikpRKJZk9ezbx8fEhDMPorR8ghJBvv/2W9O/fnzg5OREHBwfSsWNH8s9//lMvNp5dx9AYFy5cII899hhxdHQkPj4+ZPbs2SQ+Pr5BGGFz42hqHcO0adOIl5cXEYvFpHPnzuTjjz9uch1DfQCQFStWtPieqVQqsmLFChISEkIkEgmJjIwkn332WYv7sQwaNIiMGzeu0W0HDhwgffv25WLhJ06cSG7evGnQccvKysi7775LOnfuTCQSCRdL/8Ybb5Ds7GxSVlZGoqKiSNeuXfUiegjRRoCJxWIuqqvuOoZ//OMfxN3dnTg4OJBx48aRxMTEBuc+c+YMGT9+PPH09CRisZgEBgaS8ePH663uZiOP8vLyGuzf3DqG+gAg8+bN02tr6nO9du0amTp1KvH19SVisZjI5XIybNgw8uWXX3J9GvsNEELIqVOnCABy6tQpri05OZmMGjWKuLi46K1j2LBhAxkwYADx9vYmEomEhISEkFmzZpHk5GS9Y544cYJby9HWYQghhAc9olDskr179+LZZ59FSkqKXsSNLfHdd99h5syZuHTpUgPnKaX1/OMf/8DDhw9x/vx5vodicaiPgUIxgkmTJqFv375Yu3Yt30OhWJEHDx5gz549WLduHd9DsQpUGCgUI2AYBl9//TUCAgIaZFeltF1SU1OxZcsWPP7443wPxSrQqSQKhUKh6EEtBgqFQqHoQYWBQqFQKHpQYaBQKBSKHnSBG7QLVzIzM+Hi4mIXJQopFArFWAghKC0tRUBAQItpWqgwQJu6IDg4mO9hUCgUisVJS0trseIeFQaAy7uSlpYGV1dXnkdDoVAo5qekpATBwcEG5ZmiwoDa5HCurq5UGCgUSpvGkOly6nymUCgUih5UGCgUCoWiBxUGCoVCoehBfQy2ypEjwF9/AY89BowcyfdoKJQ2g1qtRnV1Nd/DMDuNlZ1tLVQYbI0HD4D+/YG6dY69vIBLl4DwcP7GRaHYOYQQZGdnm1yK1pZxd3eHXC43eT0WFQZbo39/lJVWIC78EfiVFiAqP0UrEn37AvUKoFMoFMNhRcHX1xeOjo5tajErIQQVFRXIzc0FAPj7+5t0PCoMtsSRI/jbQY7Xnl+GAid3AMDTN05i/e//hrigADh2jE4rUSitQK1Wc6Lg5eXF93AsgoODAwAgNzcXvr6+Jk0rUeezDZF68QpemrICBU7u8C4rglCjxv7oYVg9bLa2w8WL/A6QQrFTWJ+Co6MjzyOxLOz1mepDocJgQ7zn2B1lUkf0Tr+Fc/+dhf/u+xAAsL33E4gLjNI6oikUSqtpS9NHjWGu66PCYCPEpRTidCGBWF2Djw9tgqxGhREP/sbU60cBAJ8OnUmnkSgUilWgwmAjfHX2IQBgUjdvdBAoufaF53dBpKnB+cBuuJGh4Gt4FAqlHcGrMNTU1ODdd99FeHg4HBwc0KFDB6xatUqvli4hBCtXrkRAQAAcHBwwZMgQ3Lx5U+84SqUSCxYsgLe3N5ycnDBx4kSkp6db+3JaTV6pEsdu5QAAZo/tro0+OnoUeP99BP30I0b31GZ+/X+X0/gcJoVCaSfwKgzr1q3Dl19+iS1btuD27dtYv349Pv74Y2zevJnrs379enz66afYsmULLl26BLlcjpEjR6K0tJTrs2jRIuzfvx+7d+/GuXPnUFZWhgkTJkCtVvNxWUZzKCELGgL0CHZHhJ8u8+HIkcB77wEjR+K5vlph2H8lA1XV9nFNFArFfuFVGC5evIgnn3wS48ePR1hYGKZMmYJRo0bh8uXLALTWwqZNm7B8+XJMmjQJ0dHR2L59OyoqKrBz504AgEKhwNatW7FhwwaMGDECvXr1wo4dO5CQkIDjx4/zeXkGc/BaJgBgYo+ARrcP7OgNuasMpVU1uPigoNE+FAql7fH999/Dy8sLSqVSr33y5Mn45z//abHz8ioMjz/+OE6cOIF79+4BAK5du4Zz585h3LhxAICkpCRkZ2dj1KhR3D5SqRSxsbG4cOECACAuLg7V1dV6fQICAhAdHc31qY9SqURJSYneiy9yS6oQl1IEhgEmdG98UYpAwGBkVz8AwNFb2dYcHoXSJiGEoEJVw8uLEGLwOJ955hmo1WocPHiQa8vPz8evv/6KmTNnWuKtAcDzAre3334bCoUCUVFREAqFUKvV+PDDD/H8888D0K5UBAA/Pz+9/fz8/JCSksL1kUgk8PDwaNCH3b8+a9euxfvvv2/uy2kVfyRqVzPHBLrBz1XWZL9R3fzww58pOHYrB6ufIhAK2nbYHYViSSqr1ej63hFezn1r1Wg4Sgy79To4OGDatGnYtm0bnnnmGQDAjz/+iKCgIAwZMsRiY+TVYtizZw927NiBnTt3Ij4+Htu3b8cnn3yC7du36/WrH5tLCGkxXre5PsuWLYNCoeBeaWn8OXXPJuYBAAZH+DTbr3+4F5ylIuSXqXA7iz8Lh0KhWJeXX34ZR48eRUZGBgBg27ZtmDFjhkXXZPBqMbz11lt455138NxzzwEAYmJikJKSgrVr1+LFF1+EXC4HoLUK6ub+yM3N5awIuVwOlUqFoqIiPashNzcXAwYMaPS8UqkUUqnUUpdlMBoN4SyGQRHezfaViAToH+6JE3dyceFBPqID3awxRAqlTeIgFuLWqtG8ndsYevXqhR49euD777/H6NGjkZCQgF9++cVCo9PCq8VQUVEBgUB/CEKhkAtXDQ8Ph1wux7Fjx7jtKpUKZ86c4W76vXv3hlgs1uuTlZWFGzduNCkMtsKtrBIUlqvgLBXhkVCPFvsP6KQVjwvUAU2hmATDMHCUiHh5teZJf/bs2di2bRu+/fZbjBgxAsHBwRZ4V2rh1WJ44okn8OGHHyIkJATdunXDlStX8Omnn+Kll14CoP3wFi1ahDVr1iAiIgIRERFYs2YNHB0dMW3aNACAm5sbZs2ahSVLlsDLywuenp548803ERMTgxEjRvB5eS1yObkQANAnzANiYcsaPaCjNvnX30mFUNVoIBHR9YkUSntg+vTpePPNN/H111/j+++/t/j5eBWGzZs34//+7/8wd+5c5ObmIiAgAK+++iree+89rs/SpUtRWVmJuXPnoqioCP3798fRo0fh4uLC9dm4cSNEIhGmTp2KyspKDB8+HN99953ZilZYirjUYgBAHwOsBQDo7OcCTycJCstVuJ5ejD5hnhYcHYVCsRVcXV0xefJk/Pbbb3jqqacsfj6GGBM71UYpKSmBm5sbFAoFXF1drXbegR+dREZxJXa+3B8DOjbvY2CZ80McDt/Mxr/GReGVwR0tPEIKpW1QVVWFpKQkhIeHQyZrOvrPlhk5ciS6dOmCzz77rMk+zV2nMfc5OhfBE1mKSmQUV0IoYNAjyN3g/XqFaPte0VkbFAqlbVNYWIjdu3fj5MmTmDdvnlXOSQv18ERcShEAoIu/C5ykhn8MvUK0005UGCiU9sEjjzyCoqIirFu3Dp07d7bKOakw8ER8SjEAoHeIYf4FlphANwgFDLJLqpClqIS/m4MFRkehUGyF5ORkq5+TTiXxBJtCu7sR00gA4CARIkqudbxTq4FCoVgCKgw8oNEQ3NKtXu4WaLyzm/UzxOumoygUCsWcUGHggbSiCpQpayARCdDRx9no/Vkr42YmTY1BoRhD3VovbRFzXR/1MfAAe0OPkrsYtLCtPl39XXXHURiUN4pCae9IJBIIBAJkZmbCx8cHEomkTf1uCCFQqVTIy8uDQCCARCIx6XhUGHjgZqbWv8De4I0lws8ZIgGDkqoaZBRXIsjD0ZzDo1DaHAKBAOHh4cjKykJmZibfw7EYjo6OCAkJaZBqyFioMPDALZ3F0C2gdcIgFQkR4eeC21kluJVZQoWBQjEAiUSCkJAQ1NTU2E11R2MQCoUQiVqXi6k+VBh4gJ1K6hrQ+gypXf1dtcKQVYJR3eTmGhqF0qZhGAZisRhisZjvodg01PlsZfLLlMgtVYJhwIWdtoauAayfgTqgKRQASCuswOI9VzH5iwv49/FEKGvanlVgLajFYGXuZZcCAEI9HY1a8VwfdhrqFhUGCgVJ+eV4+vPzKK6oBqDNLHA5pRDbZvSFqBUBHu0d+o5ZmcTcMgBAhF/rrQUA6KJzXGcUV0Kh+zFQKO0RtYZg3o/xKK6oRrcAV6x4oiscJUL8kZiPL04/4Ht4dgkVBiuTmKu1GDr5Gr9+oS5uDmIEuMn0jkmhtEf2XErDrawSuMpE2DazL2YODMfqp6IBAJ+ffoC8UiXPI7Q/qDBYmcQcncVgojAAtVbHPd0xKZT2hlpD8MWZ+wCA10dEwtdF+7D0dK9A9Ah2R2W1Gt+ce8jnEO0SKgxW5kEeKwymTSVpj6EVl3s51GKgtE9O3slFWmEl3BzEmNYvhGtnGAbzhmjrlfzvcjp1RBsJFQYrUliuQn6ZCgDQ0dfJ5ONF6iyG+7nUYqC0T3b/nQoAeK5vMBwk+hUbh0X5Qu4qQ2G5Ckdv5vAxPLuFCoMVYW/gQR4OcJSYHhAW4UctBkr7RVFZjbOJeQCAyb2DGmwXCQWY3DsQAHAoIcuqY7N3qDBYEdZJbA7/AlDrwM4tVdLIJEq74/itHFSrCSL9nDnruT5jo/0BAKfv5qFSRaeTDIUKgxXhHM8mhqqyuMhoZBKl/cJaAeNjAprs0y3AFUEeDqisVuPMvTxrDc3uocJgRdippE6tSLXdFJ1oZBKlHaKsUePCgwIAwKhufk32YxgGI7tqt5+5l2uVsbUFqDBYEVYYOpppKgkAImlkEqUdEp9SjMpqNXxcpC2mlhkU4Q0AOH+/wBpDaxNQYbASlSo1skuqAAAdvE2PSGJh51bpVBKlPfGHzun8eCfvFrOJ9gv3gkjAILWwAmmFFdYYnt1DhcFKpBSWA9CuWPZwMq2IRl1Y6+NhXrnZjkmh2Drn7ucD0ApDSzhLRegZ7A4AOK/bj9I8VBisRHK+9sYdZkZrAQA6+miPl6WoQoWqxqzHplBskeIKFRIytMWuHo9oWRgAYIBOQC4+pNNJhkCFwUok5WtN2DAv8xbVcXeUwMNRm1s+OZ+ayZS2z5XUYhAChHs7wc9VZtA+fUI9uH0pLUOFwUqkFOgsBi/zWgyA9gcCAA/zaWQSpe0Tl1IEAOitu9kbQs8QdzAMkFpYgfwymlSvJagwWIkk3VRSuJmnkrTH1PoZkqifgdIOaI0wuMrE3MLSeN3+lKahwmAlknUWQ6iZp5IAoIPOz8CKD4XSVqlRa3AtvRgA8EiI4cJQt388nU5qESoMVqBCVYOcEq35agmLoQM3lUSFgdK2uZNdigqVGi5SkdGpZXqFuAMA4lOpxdASVBisQEqB1ins5iCGu6P5QlVZwnUWw8O8MhBCzH58CsVWYG/qvUI9IBA0v36hPqzFkJCugFpDfyfNQYXBClgqVJWFdWiXVNWgsFxlkXNQKLYAG1X0iO7p3xg6+DhDJhagslpNp11bgAqDFUjWWQzhFvAvAIBMLESguwMA+/UzEELwy7VMzNsZj1W/3EKWopLvIVFskBu69Qs9gtyN3lcoYBAl19ZKv5mpMOew2hy8C0NGRgZeeOEFeHl5wdHRET179kRcXBy3nRCClStXIiAgAA4ODhgyZAhu3rypdwylUokFCxbA29sbTk5OmDhxItLT0619KU3CWgyhFghVZQm3cz/DmkO3sWDXFfx2PQvfnk/ChM/O0QJEFD0qVWquAmK3ANdWHYPd71ZWidnG1RbhVRiKioowcOBAiMVi/P7777h16xY2bNgAd3d3rs/69evx6aefYsuWLbh06RLkcjlGjhyJ0tLa3ECLFi3C/v37sXv3bpw7dw5lZWWYMGEC1GrbyL+eVGC5UFUWe45MOnwjG1//kQQAmDkwDFFyFxSUqzB/ZzwtyUjhuJ1dAg0BfFyk8DVwYVt9ugW4AQBuZVJhaA7Ty4iZwLp16xAcHIxt27ZxbWFhYdz/CSHYtGkTli9fjkmTJgEAtm/fDj8/P+zcuROvvvoqFAoFtm7dih9++AEjRowAAOzYsQPBwcE4fvw4Ro8ebdVraowUC4aqsrCiY29rGaqq1fjg11sAgDmxHfHO2CjklSoxZtNZ3Mkuxa6/UjFjYDjPo6TYAjd100ittRbq7nszswSEkBYT8LVXeLUYDh48iD59+uCZZ56Br68vevXqha+//prbnpSUhOzsbIwaNYprk0qliI2NxYULFwAAcXFxqK6u1usTEBCA6Ohorg+fKGvUXKhqiKflhcHeVj8fvJaJjOJKyF1leH14BADtE+EbIyMBAP85/QBV1dRqoGhv5gAQrXvqbw2d5S4QChgUlqu436U9odYQaKwQUcWrMDx8+BBffPEFIiIicOTIEcyZMwcLFy7E999/DwDIzs4GAPj56Rfi8PPz47ZlZ2dDIpHAw8OjyT71USqVKCkp0XtZimyFNtW2TCyApxmzqtang271c3JBhVW+OOaAEIJt55MBADMGhukVc5/aJxj+bjLklSpx5GbjnyOlfXEj03SLQSYWcokn7dEB/dfDAnT+v9/xwjd/WfQ8vAqDRqPBI488gjVr1qBXr1549dVX8fLLL+OLL77Q61ff3DPEBGyuz9q1a+Hm5sa9goODTbuQZsgo0kbXBLg7WNRsDXCXQSRgoKrRIKe0ymLnMScJGQrcziqBTCzAc331PwOJSIBndW27/k7lY3gUG0JVo8HdbK1fMTqw9RYDAHT11wrLnWz7q2GSUVyJajWBpWfAeBUGf39/dO3aVa+tS5cuSE3V3gjkcjkANHjyz83N5awIuVwOlUqFoqKiJvvUZ9myZVAoFNwrLS3NLNfTGBnFWmFgw0kthUgoQKCH9hzsgjpb57fr2pq9w6P8Gl34N7VPMAQM8OfDQlpgpZ2TmFuKajWBq0yEIA/TfksRXDlc+xOGzGLtQ1+Am2XvJ7wKw8CBA3H37l29tnv37iE0NBQAEB4eDrlcjmPHjnHbVSoVzpw5gwEDBgAAevfuDbFYrNcnKysLN27c4PrURyqVwtXVVe9lKawlDECtD4N1dtsyhBD8qhOGCd39G+0T4O6A/uFeAECnk9o5NzO0073dAtxMtry5qod2WCc9s7h2BsKS8CoMb7zxBv7880+sWbMG9+/fx86dO/HVV19h3rx5ALRTSIsWLcKaNWuwf/9+3LhxAzNmzICjoyOmTZsGAHBzc8OsWbOwZMkSnDhxAleuXMELL7yAmJgYLkqJT9ipJGsIA7sC2h4shhsZJcgoroSjRIihUb5N9hsTrbUaD9+gwtCeuat7uu/ib/pDXKSf1h/3IK/M7lJjZCpYYWhduK6h8Bqu2rdvX+zfvx/Lli3DqlWrEB4ejk2bNmH69Olcn6VLl6KyshJz585FUVER+vfvj6NHj8LFpbYA+MaNGyESiTB16lRUVlZi+PDh+O677yAUChs7rVWp/SAtLwxsOGyKHUy7nK1Ts1cmbvpzGtXNDysO3kRcahFyS6vg62LZHwTFNmGnfdibuikEeThCKhJAWaNBamGFRdcXmZtMa01NW/ToBjBhwgRMmDChye0Mw2DlypVYuXJlk31kMhk2b96MzZs3W2CEpsFZDCbOixoCO5WUagcWw9l7WmEYFOnTbD9/NwfEBLohIUOBP+7lY3LvIGsMj2JjsKvgI8wgDEIBg06+zriZWYJ7OaV2IwyEEM7H4N+Wp5LaOhoNQaYuXNUaU0lsyo3kgnKbzrJapqzhsmQONqBmL1vX9xwt5N4uKa2qRpbud9TJx6WF3oZR62ewHwe0orIalbo1Pf5ulrWcqTBYkPxyJVQ1GggYQG7hDxKotRhKq2pQXFFt8fO1lr8eFqBaTRDi6WhQ/qhBnWqFwZYFj2IZWGvB10UKN119c1NhLY9EO8rHxQayeDtLmp1+NQdUGCwIO43k5yqDWGj5t9pBIoSfqxSAbfsZ/koqBAAM7ORlUP/eYR6QiQXIK1Xinh1GklBMg715s0/55iDSlw1ZtZ/vEzeNZOFQVYAKg0XhYo6tMI3EEurJRibZbsgqW7O3T6inQf2lIiH66cJW6XRS+4O1GDoZWbGtOSLsMDKpNlTV8rMPVBgsSEax9qndGv4FlhAv23ZAK2vUSEjXpiIwppj7ox20InI5udAi42q33LsH/P47kJjI90iahI1IMofjmSXYwxEysQAqXWSSPWDNCEcqDBaEtRisEZHEEqYThmQbFYYbGQqo1Bp4OUmMyjbbN0wnDClF1M9gDgoLgTFjgM6dgXHjQCIjtX8X2V49ZHYhWoSv+aaSBAKGW/eTZCeJJ7n7CRUG+ya9yHoKzxKi+7KnFtrmVBI7jfRIqIdRK1hjAt0gEWr9DPbyhGfTTJsGHD+OVDc/THv2Q3R862eMCXwCl2Yv5ntkepQrazina4QZp5IAoKOP9ngP7SRVPTuVRH0Mdg77hQ6yqo+BTYthmzfPWv+C4dNIgDYrZkyQNnnapWTbe6q1K+7dA44cQZ7UBVOmr8eFsB7QCIS44xOG6eETcf1CAt8j5GArtnk7S+Bh5uzE9lb1kPoY2gjWymtSF3Z6JrdUiQpVjdXOawiEEMSzxdyNFAagVkziUqifwSQePAAALBszH7kuXuiUn4qD2xch9uFlqEQSvHE8FTVqDc+D1MJOI5nT8cxiT8WtatQa5JTQqSS7p0xZA0Wldi2BNX0M7o4SuDloY71tbcolt1SJvFIlBEzriq300fkZqMVgIh074s/gaByPeBRCjRqf//wRumffx2cHP4ZXeTEeVAB7Llsu47Ax3MtlU2GYz7/AYk/lcHNKldAQQCxk4O0stfj5qDBYCNZacHMQw1lq3cwjXM4kG5tOuqErzdjJ11mvKI+hsFFM93PLoLDhBXw2T2QktjwxFwDw/NXDiMzXprl3q6nCvKLrAICvzz60iYJP9znHs+UshuySKpQrbcu6rg97P5G7ySAQWL4cKRUGC5HBg+OZxVZzJplamtHTSYJgT+37mZBhf9W3bIWHeWU45xIChhDM+eun2g0jRuDZDW/BRSpCckGFTawZSeTWMJjfYnB3lHBVFW3dauCmpa3geAaoMFgMa9ZhqE9tllXb+rKzFkM3Eypw9QhyBwBcSy82w4jaJzv/0loIw7r4IejyeeDQIa1D+vBhOPl5c4kK+Z5OqlSpkVakfbgx5xqGunTwto/pJGuGqgJUGCxGrTBYP010qI3WZai1GFqfU58VhutUGFqFWkPw87VMAMC0/iFARAQwdqz2Xx1P9woEAJy8nYtKlZqXcQLaiCRCAA9HMbwsVC+di0yycQc0F6pqpfsJFQYLYc102/WxxZDVwnIVJ5ZdTRCG7rqQ1WtpdCqpNcSlFCGvVAlXmQiDIhpPed49yA1BHg6orFbj9N1cK4+wFi7Vtq+Lxeqlh/vYxyI3a0c4UmGwELUFNQxf3WsuWItBWzjcNsIOb2Zqb+RhXo5wkbU+Q2Z0oBsEjNZhmKsL36MYzqEEbTnVkV3lkIga//kzDINxMdpyq7/zWDmPTYXRyULTSADQwVt7bJufSlJYN+8aFQYLkWHFxSj18XWRQioSQK0hnEDxzQ22Zq8J/gUAcJKKuJj2a+nUajAGjYbg9xtaYRgXI2+273BdudVz9/N5i07isqpaICKJhQ1ZfZhn2zVMrFW5jYUKgwWorrsYhYepJIGA4SKTbGU6ibUYupkwjcTSnfoZWsWNTAVySpRwloq44kdN8UioB5ylIhSWq3Ajkx8Brq3aZv6IJJYQT0cwDFCqrEF+mcpi5zGF8jproixdoIeFCoMFyFZUQUMAiVAAbyfLL0ZpDFur/3zPjMXce7B+BmoxGMUfidrw08c6ekEqan4diVgowGMdtanO2TKs1qSqWs2ljrfEGgYWmVjIPYXb6nQSay24yEQmTcMaAxUGC1B3Gskai1EaI0RXlyHVBuoyVKs13I/OHD/yuhaDtcz/1IIKLP3pGmZs+xt7LqXa9LRDU5zTCcMgA8qpAsBgXT3us4nWX8+QlF8ODQFcZSL4uFj24ap2QSj/v5XGsGZ5YBaDluROmjTJ4APu27ev1YNpK3DzgTxMI7HY0urnlIIKVKsJHCVCsyzQifJ3gUQoQHFFNVILKwwqD2oK93PLMOnz8yip0q6OPX03DzczS7DqyWiLntecVKrUXALDgZ0ME4aBOovhamoxqqrVFi8nWZfEOtNIlopIYgn1csL5+wU2l0KGhY+cawZZDG5ubtzL1dUVJ06cwOXLl7ntcXFxOHHiBNzcTHMsthW4Vc9WWqXYGFzBHhv4st/X5bvp5OtsFgtKKhIiyl8773zdwtNJNWoN5v0Yj5KqGsQEumH+0E4QMMD3F1Pw89UMi57bnPydXAiVWoMANxm3qKslwr2d4O0sgUqtsfj7XJ9EtjiPBaeRWGwxvLsu1syqymKQxbBt2zbu/2+//TamTp2KL7/8EkKh9glCrVZj7ty5cHU1ff64LcBWWuLVYvCsFQZCiMWfuprDEoVWuge54Xq6AgkZCjzRI8Bsx63P/7ucjrs5pfBwFOPbGX3h4yKFUMDg3ycS8eFvtzGyqx8cJdbNhdUaziVq/QSPR3gb/F1gGAZ9wzzx+41sXEouRL9ww0qxmgPuO2NBxzMLZ10/zAQSnfUW+9kCGVasw8BitI/h22+/xZtvvsmJAgAIhUIsXrwY3377rVkHZ6/wUaCnPkEejhAwQIVKjbwyJW/jAIB73LSA+Z7+uge6A7BsZJJaQ7DlpLbk5cLhEdxc99yhHRHs6YDcUiW+v5hisfObE9bx/HgTi9qaoi+X0da6qc4Tc61kMRQWIvTdNwEAKbklgA1WsrN2qCrQCmGoqanB7du3G7Tfvn0bGo1tLKbim0weCvTURyIScE8YfCfTs8S0AFu050ZGicXi7E/eyUWmogoejmI83y+Ea5eKhFg4TPtUuf1Css0sImyKvFIl7mRrPwPWb2AorJUQl1wEtZXWM6hqNFxpWkvlSOKYNg0hR34GABQ7uEIhdQKOHweef96y5zWCLCsvbgNaIQwzZ87ESy+9hE8++QTnzp3DuXPn8Mknn2D27NmYOXOmJcZoVxBC6kQl8ScMgG04oGvUGq5CljmnkiJ8nSETC1CmrLFYBa4f/tRaA1P7BDdwvE7sGQBvZwmyFFW8rg42hPO6LKld/V3hZWQu/y7+rnCWilCqrMGd7BJLDK8ByQXlUGsInKUiyF0tOK+uq2TnVFUO7zKthZDqLgfUauDIESAx0XLnNhCNhiCrmBUG6/kYjBaGTz75BO+88w42btyIwYMHY/Dgwdi4cSOWLl2Kjz/+2BJjtCsKy1WoqtY+QVor4VVT2MJahrSiSqhqNJCJBQgyo89FJBSgmy59d0JGsdmOy5JfpuTm5etaCyxSkRDT+4cCAHbpspXaKn8YGaZaF6GAQa8QdwDAFV31PUtTt2qbRX1jukp2ABBarF0RnuLhX7v9/n3LndtA8suVUKk1EDCAnyVFsh5GC4NAIMDSpUuRkZGB4uJiFBcXIyMjA0uXLtXzO7RX2PS42rQU/L4ftrCWgZ1GMldEUl1iAi2XUO/ozRxoiNbJHdZEFM/UvsFgGODiwwKk2UD0V2MQQjiLoaXVzk3RK9gdAHAtrdhMo2qee9aKSOrYkftvaJFOGNzrCEOnTpY9vwGw9xM/VxnEQustOzPpTK6urjQSqR4ZxdobBN/TSIBtWAyJueaPSGLpEcxaDOYXBjan0Nho/yb7BLo7YGBH7c32p7h0s5z3Xk4pZmz7G4PXn8LKgzdNTnv9IK8M2SVVkIgEnCPZWHqwwmClFCT3LRCs0CiRkcDo0YBQqG8xCIXadhuITsriIpKsO/tgUJxdr169DDbp4uPjTRqQvZNRzF+OpPrYQr6kuhaDuYnRRSbdzFSgRq2ByExPVEXlKlx4UAAAGBvdfLK5Z/oE4dz9fPwUl47Xh0eYZBXdyynF1P9eRLGubOl3F5LxIK8M383sB2Erj8tOI/UL82z1AjV2pXlibhnKlDUWL1XLRSRZIVQVu3YBzz+P0DStnyjF3R8YMULbbgPw5a806BN+6qmnLDyMtgNXh8GGLIbCchVKq6qtlmelLrUWg/mFoYO3E5ylIpQpa5CYW2aWPEwAcDYxD2oNQZTcpclpJJbR3eRwkYmQUVyJiw8LDF5VXJ9qtQaLdl9FcUU1egS745+PhmL5gQT8kZiP7y8mY+bA8FYdl02D0dpxAYCPixSB7g7IKK5EQrqCy6FkCcydPqVFPDyAw4cRev468EsaUrv0Av7vHcuf10CsXbmNxSBhWLFihaXH0WZgp5JsQRhcZNrKVwXlKqQUVCDaxJTXxqLWEG5aINICT38CAYPoQFf8+bAQCekKswkDezNlcwU1h0wsxMQeAfjxr1TsuZTW6hvwl6cf4FZWCdwdxfjmn33g4yJFZbUa7x64gY3H7mHSI0FwczBO2FU1Gvz5UGv5tMbxXJcewW7IKK7EtfRiiwqDudOnGEpozyjglzRklddYPf1Hc/CRDgNopY+huLgY33zzDZYtW4bCQu3Cl/j4eGRk2E+KAEvBl8I3BZ+pMdKLKqCs0UAiEiDY0zIFi7iEemaKTNJz1hp4k3+urzZq6fCNbBSVN5+6ObWgAj9fzUCWorZORmJOKTaf1EbArHyiG7eQ7vl+IYj0c0ZJVQ12/W185NOV1CKUq9TwcpKgq4miydXatrAD2tzpUwzFw1EMF90UmS0FErBZFGxeGK5fv47IyEisW7cOn3zyCYqLiwEA+/fvx7Jly8w9PrvDVtYwsPCZB4YNO+zo49zqOfKWYCOTEsyUy+dhfjkyFVWQCA131kYHuqKrvytUag0ONJM/6fCNLAz/9DRe330Vwz45g5/i0lGt1uDtvdehUmswtLMPnuxZm95DKGDw8qAOAIBt55OgqjFuId25+7XTSKbeZHtYKTLpXp1QVWvCMAz3EGVLOZPYB01rO5+NFobFixdjxowZSExMhExWO9ixY8fi7NmzrR7I2rVrwTAMFi1axLURQrBy5UoEBATAwcEBQ4YMwc2bN/X2UyqVWLBgAby9veHk5ISJEyciPd08ESLGUqlSo1D3xGgLzmcACNFlHk0ttH7IqiX9Cyzsk+ztrFKjb5yNwU4j9QnzgIPEsOkEhmHwXL9gAMDuv9MaTcmdnF+ON/ZcQ7WawFUmQmW1Gm/+7xpi159CfGoxnCRCrH46pkGQx8SeAfBxkSKnRImTd3KMupazJqxfqE90oBsYRpsCOrfUciVV2VBVS0w9tkSY7reSbCPpt6uq1cjXpbOx9gyE0cJw6dIlvPrqqw3aAwMDkZ3duhWgly5dwldffYXu3bvrta9fvx6ffvoptmzZgkuXLkEul2PkyJEoLS3l+ixatAj79+/H7t27ce7cOZSVlWHChAlQq00L82sNrLXgLBXBVWYbidV4tRhy2R+55YQh2NMBbg5iqNQa3M0ubXmHFqjNKWTczfTJHoGQigS4m1OKS8n6eXY0GoKlP11HZbUaj3XwQtz/jcTikZHcjVYiEmDztF6N/vilIiGm9A4CoE3oZyjFFSok6MJLBxmZH6kxnKUiTuAtsW6EpTbhonUtBsC2MhID2oJfAOAgFsLd0bqBI0YLg0wmQ0lJw6Xxd+/ehY+P8V/AsrIyTJ8+HV9//TU8PDy4dkIINm3ahOXLl2PSpEmIjo7G9u3bUVFRgZ07dwIAFAoFtm7dig0bNmDEiBHo1asXduzYgYSEBBw/ftzosZhK3WRXfGYzrQufaTFYx3MnC6xhYGEYBt11eZNM9TNUq+s4azsZ9112cxRj0iPaG/hmXeI93LsH/P47fvz1Mv5OLoSjRIj1U7pDLBRg4fAI/LZgEFY/FY0jiwZjWJRfk8d+RicMp+/mciVjW+LCgwJoiPYGKzfTNATrz0mw0HqGarUGD/MtF6zQEraWfrtuum1r30+MFoYnn3wSq1atQnW1NtaaYRikpqbinXfeweTJk40ewLx58zB+/HiMGDFCrz0pKQnZ2dkYNWoU1yaVShEbG4sLFy4A0NaBqK6u1usTEBCA6Ohoro81ybCBAj31YZ+CshSVZplqMRSNhtRJnWzZpz/Wz3DdxCfZ6+nFKFPWwN1RjK6tqE09d0hHiAQM/kjMx9lnXgY6d0bms//E+lNJAIClg0P0nPBdA1zxwqOhCG8hJLaDjzP6hHpAQ4C98YZZDWw5ztaudm6M7hYuqZpSUI5qNYFDnXKb1iSUm3a1DWHg01/ZqlxJeXl58PX1RWVlJWJjY9GpUye4uLjgww8/NOpYu3fvRnx8PNauXdtgGzst5een/yTl5+fHbcvOzoZEItGzNOr3aQylUomSkhK9lzngCvTwnCOpLj7OUjhKhNAQbZSQtcgorkRltRpiIcM9iVmK2sgk025Y7DTSwI7erXKWB3s64oVHtfmT3vIdhJMd+uDlSe+iVOqEHln38I9PlrR6bFP7aH0Y/7uc3mJZUbWG4PhtrT9iWJRvq89ZH85iyFBYpLRp3QcJPkristZ1WmEFamwgYy6XVZWHgl9GC4OrqyvOnTuHvXv34qOPPsL8+fNx6NAhnDlzBk5OhpdYTEtLw+uvv44dO3boObHrU9+EMqToTEt91q5dq1eVLjg42OBxN0ftVJJlb4TGwDBM7QpoKz4JsdNIHbydzbYiuSnYJ9l7OaWoqm69b+lcK/0LdVnaUYiI/BTkuHjhpWdW4qa8EzwqFNjy8zoIjxxudcbO8d394SQRIim/HH8nNV8b4WpaEfLLVHCRidA/3HxrDrr4u0AsZFBYruJqjpiTexYo6GQMclcZJCIBajSEuynzCV9rGAATciUNGzYMb775JpYuXdpgGsgQ4uLikJubi969e0MkEkEkEuHMmTP47LPPIBKJOEuh/pN/bm4ut00ul0OlUqGoXlGNun0aY9myZVAoFNwrLS3N6PE3RjoPJfgMgRUGa9ZlqE1rYHknor+bDN7OEqg1BDczW2f9lVZV44ouFNPQ9QuN4ZiahF27/oVxd87Bo0KBx5Ov4KcflyJYoYsoamXGTiepCBO6a0NZ91xu/vt69GattSARmU+UpSIhOsu1N21L5Ke6Z4VgheYQCBgE66aBbcHPkMHj/cSg0JnPPvsMr7zyCmQyGT777LNm+y5cuNCgEw8fPhwJCQl6bTNnzkRUVBTefvttdOjQAXK5HMeOHUOvXr0AACqVCmfOnMG6desAAL1794ZYLMaxY8cwdepUAEBWVhZu3LiB9evXN3luqVQKqdS4vPSGwBXosSEfA8CPA9qaT39aB7Q7Tt7JRUJ6MXqHerS8Uz3+elgItYYg1MvRtMV4HTvCu0KBz3/+qPHtJmTsnNo3GHsup+FQQhZWTuwG10ZSnBBCcPim9mFqZNemH45aS/cgd9zIKMG19GKMi2k6wWBrSOQxVJUl1MsJD/LKkVJYjsdhPv9Ma+CjchuLQcKwceNGTJo0CUFBQdi4cWOT/RiGMVgYXFxcEB0drdfm5OQELy8vrn3RokVYs2YNIiIiEBERgTVr1sDR0RHTpk0DALi5uWHWrFlYsmQJvLy84OnpiTfffBMxMTGtsmJMQa0hXHiZLU0lAfysZUi0VoZMHTGBbjh5J7fVfoZzRq52bhI2Y+fx49qCLyxCoTY5mwkZOx8JcUeErzMSc8vwy7VMrh5EXeJSipBSUAFHiRBDO5vPv8DSI8gNO/8y3dFfH70cSTxZDAA/1nVjEEJ4qdzGYpAwJCUlwd3dHZs3b0ZSUpKlx8SxdOlSVFZWYu7cuSgqKkL//v1x9OhRuLjUPlFs3LgRIpEIU6dORWVlJYYPH47vvvvO6rUhckqqUKMhEAkYLqWBrWDtMDxCCO5bK6e+Di5ippUrc//QFeUxx2IwNmMnjhypbTNDxk6GYfBs32Cs/u02dv2dimn9Qhr40tiopXEx/nCyQBZUNqPtjQwFNBpiNidxcn45LzmS6mMLVQ8BQFFZjQpdynVzhRsbg8ETkGvWrMG8efMwefJkFBQUWGQwp0+fxqZNm7i/GYbBypUrkZWVhaqqKpw5c6aBlSGTybB582YUFBSgoqICv/zyi9mcycbAmn3+7jKLpX9oLaF1Fu5Yqj5yXbIUVShXqSESMFwIoKXpqUvZ8CCvnFt9biiZxZV4kFcOAQM81tEMwqDL2Il794BDh7T/Hj6sbTeRp3tpF9LdyCjB+fv6v8PiChUOXs0EAEzWrakwN5F+zpCKBChV1iDJjCuE79VZ2MZHRBKLLdQwAWr9C97OEl4S+hksDHPnzsW1a9dQVFSEbt264eDBg5Ycl93BOYp4fNppigB3BwgFDJQ1GuSWKi1+PnYaKdzbyazOz+bwcpZy1klLUTv1YaORegS7G53BtFkiIoCxY81a8MXLWcqVGv33iXt6YaPfXUhGuUqNLv6ueLRD64rytIS2pKp2jcd1My50s2oNhmaoW/XQEiG5hpJZzN80EmBkVFJ4eDhOnjyJd999F5MnT0b37t3xyCOP6L3aK7a4uI1FLBRwDqwUK+SBYZ2I1p4r7q+7Gf6VZJxF+4fOvzDIVP+ClZgT2xESkQCXkouwN16btC+juBJfnX0IAHhtSEeLrpTl1o2YcaEbu4aBr4gklmBPBzAMUK5So8BIy9OcZPL8oGn0JGRKSgr27t0LT09PPPnkkxCJbCMnEN+wi9uCbCSran1CvRyRWliBlIIK9O9guXz6QN1i7tZ9+usf7oUdf6YaZTFoNHVrIpueU8gayN1keH14BD4+chfvHkhASWU1/t/lNFSo1Ogb5oEJZo4Wqg9bUtWcwnA3xzYsBqlICH9XGTIVVUgpqIC3Mz/+Qr7SbbMYdVf/+uuvsWTJEowYMQI3btxoVW6ktoqtpduuT+0iNytYDLnWdTyz9A/XWgy3skqgqKw2aFroVlYJCstVcJII0SvE3cIjNB+vDu6Ay8mFOHU3D6t+vQVAOx+94ZmeFp+jN3dJ1apqNR7maR8mTK0bYQ5CvByRqahCamF5q0KfzUHtVBI/a6IM/kTHjBmDt99+G1u2bMG+ffuoKNQj04ankgDrRVsQQjgfg7Xj0X1dZQj3dgIhwOVkw6wGNg3GYx29ILbwCm1zIhIK8OU/emPxyEg8EuKOp3sFYv/cgVxuLEvSwdsJLlIRqqo13GdtCnezS6EhgJeTBL42ENEXqvMzJOfz54Dmc9UzYITFoFarcf36dQQFWSbawZ4hhNTJk2SbwsA51SwcbZFbqkRpVQ2EAgZh3tZfz9E/3BNJ+eU4f78Aw7u0vMDr1J1cAOZJTW1tpCIhFg6PwMLh5nNuG4K2pKobLj4swPX0YpNLqt7O0q5W7+LvahNZiW0h/TYX5chDqCpghMVw7NgxKgpNUFJZg3JdzLGtlPSsj7UsBrbQSqiXI6Qi64fZxerqNJ+6m9ti34IyJS6naC2LERZYJdyW4VKdm8HPcEsnDK3JaGsJ2II91gjUaAxVjQbZuvTqQR78LJa1H9vZhkkv1t5s+Yo5NgTWx6CorIaiotpi5+Gz0AqgTYAnFjJIyi/nVtI2xYk7udAQoFuAq80Kuq1izsikWouBX8czSyjPFkO2ogqEAFKRAN7OEl7GQIXBDPAdc2wITlIRF2FhSQd0bTlPfn7kLjIxV6v55J3mrYZjt7TJ5iyRU6itw1oMd7JLoKxpfUZbjYbgdpbWyuzq72aWsZkKO5WUX6ZCmbLG6udn0+MHefBX8IsKgxnI0H2Qtv7UGab7widbcDqJrzUMdWFrEBy/1XSNZEVlNVfMZlRXuVXG1ZYI8nCAh6MY1WqCO1mtL6maXlSJMmUNJEIBOvhYZ5V8S7jKxPDQldLkI2cSm9Kcr2kkgAqDWbD1UFUW9ofHhgaam7oRSXxZDEDtjf7PpAIusWF9DiVkQVmjQaSfs81MYdgTbEZbwLQV0Kx/IVLubFNRYXwknmSpazHwhe18EnYMO5Vk6xZDBx/tU/yDPMt82fNKlVBUVkPAgNenvxAvR/QN8wAhwIGrGY322RunTTY3+ZEgm4iEsUfM4YBmhaGL3DYczyx81n9mLQY+Q9+pMJiBdBtfw8DSUScMlrIY2ERoYV5OvDvh2SRy/+9yWoPEgTcyFLicUgShgMFTvQL5GF6bwBwO6Bu6NOndbCQiiYXPZHp0KqmNwGdBDWOonUoqt0iW1Xs24F9gGd/dHy4yER7mleNoPV/Df3U5hSZ094efq21V27MnWIshMbcUFSrjnbSEEC5Nes8QflYYNwWfdRkybKDgFxUGE6mqViNPl7HU1oUhxNMRIgGDymo1FydtThJz+a/AxeIiE+PFx8IAAJ8euwtVjba4e3xqEX65pk1N/crgDnwNr03g5yqDn6sUGoJWlVRNL6pEQbkKYiFjc34eNl28NVLI1KVarUGWggqD3cNWWXIQC+HuaMaUzRZALBRwoXgPLDCdxOXUtwFhAIBZj4fDy0mCezllWHHwJu7nlmLR7qsAgCm9g9AtwDbCI+0ZdjqpNQWSrumc1l39XXlZDNkc7FRSZnEVqtUaq503W1EFjW4Ngw9PCfwAKgwmUzdHkj04MTt4s34G8z4JEUJqp5J4WtxWHw8nCT58OgYMA+z6OxUjPj2L1MIKhHg6Yvm4LnwPr03QPbD1DmhWTHroiizZEr4uUsjEAqg1telurEFandB3Pu8nVBhMxNZzJNWno69lQlZzSmpzJNlKPDoAjImW49/P9eLKrT7awRM/zu4PDyd+VpS2NdibenxqkdH7XtPVje6hszpsCYZh6mQktp6fwRYikoBW1GOg6JNhJ45nlo7elglZ5TtHUnNM7BGACTH+qKpRw1FCv/LmpHeoB4QCBulFlUgrrECwp2GRNNVqDRJ0EUlsfQdbI8TTCfdyypBaUA7AOkkWM2wgIgmgFoPJ2EIEgTFYymJghSGSx4VtzSEQMFQULICTVMRFJ/1lRIGkGxkKVFar4e4o5qY3bY1QK2QKqE9tqCq/9xMqDCZSmzfdPsIe2R9hpqKqVSGGTWErpRkp1ucxXUXAiw8ML6nKikjfME+LFxZqLdbKSFwXW1j1DFBhMJnaqSR+TT9D8XCScHlgzOmAvmcjxdwp1udRnTD8+dAIYdD1Zavu2SLcWgYrhqxSi6ENoNEQZPFcgq81dORSY5hnOokQgvs5/FRto/BP71APiAQMMoq1foaWUGsILidrndWPWrj+uCmEetUWtyLE/AtC61Oj5r8OAwsVBhPIL1NCpdZAKGAgt6MVtJwwmKEsI6Bdy1Gq1EYkhXvbTkQSxTo4SUVcdBJbKrU5bmYqUKqsgYtMZHL1N0sS6O4AAQNUVWuQq1vEakmyFFVQawgkQn7XMABUGEyCzZEkd5WZXBDdmrApK+7mtD5dcl3uZGtXvXb0cYJEZD/vA8V8DO2sjdo5eafpVOcsp+9q050/2sELQhv1LwCARCTgwkat4WfIqLMmim+/C/0Vm4C9OZ5ZonSZLO9mm0cYbunSIXS14ac/imVh62v/kZiPSlXzhXvYAkps3QxbJtTTemU+2Wk4Wwh9p8JgAmzMsS18kMbQWa71A6QUVpglMomtwGXL0wIUyxIld0GguwOUNRqcv9/0dFJBmZJLhTG0s+0LQ4gVy3yy52DPySdUGEzAXgr01MfHRQovJwkIqQ0zNQVbK+ZOsT4Mw2B4F+2N/vcb2U32O347B4RorUu5m+1b2tasy8CeI9TARYKWhAqDCWTaSR2GxmCtBlOnk8qVNUjWmdnUYmjfPNEjAADw+40slDdRK/nAFW1m2/Hd/a02LlOwZl0G9hyh1GKwb9LtdCoJqBWGOyYKw53sUhCiTTrmzXMkBYVf+oR6INTLERUqNQ43YjVkFlfizyTt+oUnewZYe3itIkTnY0i1go+BPYehaUUsCRUGEyjXzc/bozBEsRZDjvF59Otymy3NSK2Fdg/DMJiiq5z3/cXkBrH/P/yZAkK0iQz5jtM3FHa+v6iiGiVV1RY7T0lVNYoqtMdn10/wCRUGE/hj6TAkrBzF1VK2JzqbKTLpFhUGSh2e6xcCmViAa+kKnLmXx7UrKqux488UAMBLA8P5Gp7ROEtF8HbWZuK1ZDU39theThI4S/nP6UWFwURcZGKbjsVuikg/ZzAMkF+mQn5Z6xfvcKGq1PFMgTawYXr/UADAB7/eQlW1NnT14yN3UFpVgwhfZ4zQhbbaCyFWcEDbUkQSQIWh3eIoEXFf+DtZrbMaatQabnFbVxsrzUjhj/lDO8HHRYoHeeWYvf0yVv96Czv+TAUAvD+xG++Lt4zFGmU+bSkiCeBZGNauXYu+ffvCxcUFvr6+eOqpp3D37l29PoQQrFy5EgEBAXBwcMCQIUNw8+ZNvT5KpRILFiyAt7c3nJycMHHiRKSnp1vzUuySaF1pSzYvvrHcyylDVbUGzlKRzaZOplgfDycJPnuuF2RiAc7dz8c355IAAAuHR2BAJ2+eR2c8nMWQb0mLoVzvXHzDqzCcOXMG8+bNw59//oljx46hpqYGo0aNQnl5rTKvX78en376KbZs2YJLly5BLpdj5MiRKC2tfcpdtGgR9u/fj927d+PcuXMoKyvDhAkToFY3vwKzvcPm0U/IKG7V/uxCpe5Bbnb3FEixLI919MK+1wbi6V6BGBbli8+e74U3RkTwPaxWEeatvVknWTAyqXYqiX/HM8BzBbfDhw/r/b1t2zb4+voiLi4OgwcPBiEEmzZtwvLlyzFp0iQAwPbt2+Hn54edO3fi1VdfhUKhwNatW/HDDz9gxIgRAIAdO3YgODgYx48fx+jRo61+XfZCTFDr6/UCtl2zl8I/XQNcsfHZnnwPw2QsVSe9LtxUEvUxNESh0N6gPD21OdqTkpKQnZ2NUaNGcX2kUiliY2Nx4cIFAEBcXByqq6v1+gQEBCA6OprrQ2mcaF0h9/SiShSWq4ze/yorDDZYs5dCMRdsDfP8MiUUleYPWVXVaLjFstTHUA9CCBYvXozHH38c0dHRAIDsbO0iGT8//SgGPz8/blt2djYkEgk8PDya7FMfpVKJkpISvVd7xFUm5r7013XTQoZSoapBoi5td09qMVDaMC4yMXxdtIs3zV0SF9Cm1tEQQCYWwMfFNhaJ2owwzJ8/H9evX8euXbsabGMY/flrQkiDtvo012ft2rVwc3PjXsHBwa0fuJ3TXWc1JBg5nXQzswRqDYGfq9Quct5QKKbA1jCxxHQSm7k1xNOxxfuatbAJYViwYAEOHjyIU6dOISgoiGuXy+UA0ODJPzc3l7Mi5HI5VCoVioqKmuxTn2XLlkGhUHCvtLQ0c16OXRGjmwa6ZqQwxKdo3286jURpD7CWtbmqHtYl+XYyACBUavkqcYbCqzAQQjB//nzs27cPJ0+eRHi4/orI8PBwyOVyHDt2jGtTqVQ4c+YMBgwYAADo3bs3xGKxXp+srCzcuHGD61MfqVQKV1dXvVd7pVeIOwAgLqUQGo3hX0y2mHs/G67ZS6GYC4tYDIWFwJgxePifbQCADv9vOzBmDFDvIZcPeBWGefPmYceOHdi5cydcXFyQnZ2N7OxsVFZqHTEMw2DRokVYs2YN9u/fjxs3bmDGjBlwdHTEtGnTAABubm6YNWsWlixZghMnTuDKlSt44YUXEBMTw0UpUZomJtANDmIhiiqqcd/ApyG1huCSThj6h9tuzV4KxVywFsPDfDNaDNOmAceP46FXIACgY2EGcPw48Pzz5jtHK+E1XPWLL74AAAwZMkSvfdu2bZgxYwYAYOnSpaisrMTcuXNRVFSE/v374+jRo3BxqV1pu3HjRohEIkydOhWVlZUYPnw4vvvuOwiFQmtdit0iFgrQO9QD5+7n46+HBYj0a3kF8+2sEm3NXqmIpsKgtAtYiyE5vwJqDTE9Dc69e8CRIwCAh55aYehQmA6o1dr2xEQggr91H7xPJTX2YkUB0FoNK1euRFZWFqqqqnDmzBkuaolFJpNh8+bNKCgoQEVFBX755Zd27VA2FnY6iJ0eagm2X58wD7vME0WhGEuguwOkIgFUag3Si8ywAvrBAwBApUiKTFdtgaMOhRm12+/fN/0cJmATzmcKv7DC8HdSYYNUyY1x8UG+bj86jURpHwgEDMK9zeiA7tgRAJDkoa1L4V5ZAs/KOmHznTqZfg4ToMJAQc9gd0hFAuSWKnGvhVKfVdVqnL+vLbYSG+ljjeFRKDaBWR3QkZHA6NF46KOd2eCsBaEQGD2a12kkgAoDBYBMLMSAjtqn/5N3cpvt++fDAlRWq+HvJkMXmlGV0o7oaO6Q1V278LDPYAA6/wIAjBgBNLKWy9pQYaAAAIbpcuSfvJPTbL9TOuEY0tnXZhbjUCjWgC3I9cBcIaseHng4VpsDrsPEUVqH9OHDQL0sDnxAhYECABgWpXWAxaUUNZk3SaMhOHorR68/hdJe6OSrFYb7uWUG+eIM4WG+VmQ6DHyE9+mjulBhoADQRl10C3CFhgC/Xc9stM9fSYXIUlTBRSbCoAj7y6tPoZhCJ19nCBigsFyFPBOqHrIQQjh/BTtNZStQYaBwTNIVcv8prvEiRweuaB1k42P8IRPTNSKU9oVMLESYrl6CqbXSASC3VIkyZQ2EAsZmSnqyUGGgcDzZMwAiAYNr6QrczNTPnVRYrsLP17TC8HSvQD6GR6HwTme5NuDCHMLAHiPMyxFSkW09aFFhoHB4O0sxJlqbuPDzUw/0tm2/kIyqag2iA11pfiRKu4UVhjtmFIYoue1lD6DCQNFj/jDtwppDN7Lwt26Fc1phBb46+xAA8FpsJxqNRGm3RJnTYsjRHsOQNDTWhgoDRY8ouSum9gkCIcD8nfHYfyUds7dfRmW1Gv3CPTEuRs73ECkU3uise7q/l1MKtRHZiBvjnk4YOsudTR6XuaHCQGnA/03oikg/Z+SWKvHGnmu4m1MKb2cpNj7bk1oLlHZNiKcjZGIBlDUarsBOa1BrCCcM1GKg2AUuMjH2vPIYXng0BJF+zniiRwB+nj8Qge4OfA+NQuEVoYBBhK/p00lphRWoqtZAIhIg1Mu2QlUBntNuU2wXDycJVj8Vw/cwKBSbo7PcBQkZCtzJLsXYGP9WHYP1L0T4OttkhmJqMVAoFIoRsA7o21klLfRsGtbaYKOcbA0qDBQKhWIEMYFuAICEDOPqpNeFFZXONuhfAKgwUCgUilF0C3QDwwBZiirklla16hisqLAiY2tQYaBQKBQjcJaKuNoMN1phNRSVq5BepK1r340KA4VCobQNugdpb+jX040XBtZaCPNyhJuD2KzjMhdUGCgUCsVIurN+BhOEISbI3ZxDMitUGCgUCsVI2Jv69QyF0bUZWDGJCbS9HEksVBgoFArFSLr6u0IoYJBXqkR2iXEOaNZiiLZR/wJAhYFCoVCMxkEi5NYzXEouMni/nJIqZBRXgmFsNyIJoMJAoVAoraJ/uBcA4O+kAoP3uZSszVjcRe4KF5ltOp4BKgwUCoXSKti6JGx6ekO4pOtr6zVNqDBQKBRKK+gb5gEAuJdThsJylUH7sNNOfcOoMFAoFEqbw8tZighf7UI3doqoOUqqqnE7W5sKgxUVW4UKA4VCobQSdkro4oOW/QwXHxSAECDc2wm+rjJLD80kqDBQKBRKK4mN9AEAnLiT0+J6htN38/T2sWWoMFAoFEoreTzCGxKRAGmFlbifW9ZkP0IIztzNBQDEdqbCQKFQKG0WR4kIAzpqw1ZP3Mltst+9nDJkKqogEQnwqC7M1ZahwkChUCgmMDzKFwBw+EZ2k31+vZ4JABjUyRsOEqFVxmUKVBgoFArFBMZE+0MoYHA1rRj3cxvWgSaE4OerWmGY2DPA2sNrFVQYKBQKxQR8XKQYqvMb/C8uvcH2S8lFSC2sgINYiJFd/aw9vFbRZoTh888/R3h4OGQyGXr37o0//viD7yFRKJR2wjN9ggEAu/9OQ2lVtd62receAgCe7BkAR4nI6mNrDW1CGPbs2YNFixZh+fLluHLlCgYNGoSxY8ciNTWV76FRKJR2wIgufujo4wRFZTW2nU/m2m9kKHD0Vg4AYPagcJ5GZzxtQhg+/fRTzJo1C7Nnz0aXLl2wadMmBAcH44svvuB7aBQKpR0gFDBYODwCALDl5H3cyFCgQlWDpT9dByHAxB4B6OTrwvMoDcc+7JpmUKlUiIuLwzvvvKPXPmrUKFy4cKHRfZRKJZRKJfd3SUmJRcdIoVDaPhN7BODAlQycupuHqf+9CHcHMTIVVfB0kuBf47rwPTyjsHuLIT8/H2q1Gn5++k4dPz8/ZGc3Hj62du1auLm5ca/g4GBrDJVCobRhGIbBpud6oV+YJypUamQqquDtLME3L/aB3M22U2DUx+4tBhaGYfT+JoQ0aGNZtmwZFi9ezP1dUlJCxYFCoZiMm4MYu195FH8+LICishoDI7zhasN1F5rC7oXB29sbQqGwgXWQm5vbwIpgkUqlkEql1hgehUJpZwgEDAZ08uZ7GCZh91NJEokEvXv3xrFjx/Tajx07hgEDBvA0KgqFQrFf7N5iAIDFixfjH//4B/r06YPHHnsMX331FVJTUzFnzhy+h0ahUCh2R5sQhmeffRYFBQVYtWoVsrKyEB0djUOHDiE0NJTvoVEoFIrdwZCWkoi3A0pKSuDm5gaFQgFXV1e+h0OhUChmx5j7nN37GCgUCoViXqgwUCgUCkWPNuFjMBV2No2ugKZQKG0V9v5miPeACgOA0lJtDnW6yI1CobR1SktL4ebm1mwf6nwGoNFokJmZCRcXlyZXS9sK7CrttLS0Nusop9fYNqDXaFsQQlBaWoqAgAAIBM17EajFAEAgECAoKIjvYRiFq6urzX8RTYVeY9uAXqPt0JKlwEKdzxQKhULRgwoDhUKhUPSgwmBnSKVSrFixok0nAaTX2Dag12i/UOczhUKhUPSgFgOFQqFQ9KDCQKFQKBQ9qDBQKBQKRQ8qDDaOUqlEz549wTAMrl69qrctNTUVTzzxBJycnODt7Y2FCxdCpVLp9UlISEBsbCwcHBwQGBiIVatWGbQk3tIkJydj1qxZCA8Ph4ODAzp27IgVK1Y0GL89X2NTfP755wgPD4dMJkPv3r3xxx9/8D0kg1m7di369u0LFxcX+Pr64qmnnsLdu3f1+hBCsHLlSgQEBMDBwQFDhgzBzZs39foolUosWLAA3t7ecHJywsSJE5Genm7NSzGYtWvXgmEYLFq0iGtra9fYAEKxaRYuXEjGjh1LAJArV65w7TU1NSQ6OpoMHTqUxMfHk2PHjpGAgAAyf/58ro9CoSB+fn7kueeeIwkJCWTv3r3ExcWFfPLJJzxciT6///47mTFjBjly5Ah58OAB+fnnn4mvry9ZsmQJ18fer7Exdu/eTcRiMfn666/JrVu3yOuvv06cnJxISkoK30MziNGjR5Nt27aRGzdukKtXr5Lx48eTkJAQUlZWxvX56KOPiIuLC9m7dy9JSEggzz77LPH39yclJSVcnzlz5pDAwEBy7NgxEh8fT4YOHUp69OhBampq+LisJvn7779JWFgY6d69O3n99de59rZ0jY1BhcGGOXToEImKiiI3b95sIAyHDh0iAoGAZGRkcG27du0iUqmUKBQKQgghn3/+OXFzcyNVVVVcn7Vr15KAgACi0Wisdh2Gsn79ehIeHs793RavsV+/fmTOnDl6bVFRUeSdd97haUSmkZubSwCQM2fOEEII0Wg0RC6Xk48++ojrU1VVRdzc3MiXX35JCCGkuLiYiMVisnv3bq5PRkYGEQgE5PDhw9a9gGYoLS0lERER5NixYyQ2NpYThrZ0jU1Bp5JslJycHLz88sv44Ycf4Ojo2GD7xYsXER0djYCAAK5t9OjRUCqViIuL4/rExsbqxViPHj0amZmZSE5Otvg1GItCoYCnpyf3d1u7RpVKhbi4OIwaNUqvfdSoUbhw4QJPozINhUIBANznlpSUhOzsbL1rlEqliI2N5a4xLi4O1dXVen0CAgIQHR1tU+/DvHnzMH78eIwYMUKvvS1dY1NQYbBBCCGYMWMG5syZgz59+jTaJzs7G35+fnptHh4ekEgkyM7ObrIP+zfbx1Z48OABNm/erFenu61dY35+PtRqdaPjtbWxGgIhBIsXL8bjjz+O6OhoALXveXPXmJ2dDYlEAg8Pjyb78M3u3bsRHx+PtWvXNtjWVq6xOagwWJGVK1eCYZhmX5cvX8bmzZtRUlKCZcuWNXu8xjLBEkL02uv3ITqnrKWyyBp6jXXJzMzEmDFj8Mwzz2D27Nl622zxGk2lsfHa6libY/78+bh+/Tp27drVYFtrrtFW3oe0tDS8/vrr2LFjB2QyWZP97PkaW4JmV7Ui8+fPx3PPPddsn7CwMKxevRp//vlng2X2ffr0wfTp07F9+3bI5XL89ddfetuLiopQXV3NPcnI5fIGTye5ubkAGj7tmAtDr5ElMzMTQ4cOxWOPPYavvvpKr5+tXmNr8fb2hlAobHS8tjbWlliwYAEOHjyIs2fP6mUmlsvlALRPzP7+/lx73WuUy+VQqVQoKirSe6LOzc3FgAEDrHQFTRMXF4fc3Fz07t2ba1Or1Th79iy2bNnCRWHZ8zW2CE++DUozpKSkkISEBO515MgRAoD89NNPJC0tjRBS65jNzMzk9tu9e3cDx6y7uztRKpVcn48++shmHLPp6ekkIiKCPPfcc41GarSFa6xPv379yGuvvabX1qVLF7txPms0GjJv3jwSEBBA7t271+h2uVxO1q1bx7UplcpGHbN79uzh+mRmZtqMY7akpETv95eQkED69OlDXnjhBZKQkNAmrrElqDDYAUlJSU2Gqw4fPpzEx8eT48ePk6CgIL1QzuLiYuLn50eef/55kpCQQPbt20dcXV1tIpQzIyODdOrUiQwbNoykp6eTrKws7sVi79fYGGy46tatW8mtW7fIokWLiJOTE0lOTuZ7aAbx2muvETc3N3L69Gm9z6yiooLr89FHHxE3Nzeyb98+kpCQQJ5//vlGQzmDgoLI8ePHSXx8PBk2bJhNh3LWjUoipG1eY12oMNgBjQkDIVrLYvz48cTBwYF4enqS+fPn64VtEkLI9evXyaBBg4hUKiVyuZysXLnSJp6kt23bRgA0+qqLPV9jU/znP/8hoaGhRCKRkEceeYQL9bQHmvrMtm3bxvXRaDRkxYoVRC6XE6lUSgYPHkwSEhL0jlNZWUnmz59PPD09iYODA5kwYQJJTU218tUYTn1haIvXWBeaXZVCoVAoetCoJAqFQqHoQYWBQqFQKHpQYaBQKBSKHlQYKBQKhaIHFQYKhUKh6EGFgUKhUCh6UGGgUCgUih5UGCgUCoWiBxUGCsUIGIbBgQMHrHrOAwcOoFOnThAKhXrlJZsjLCwMmzZtsui4KG0XKgwUio3z6quvYsqUKUhLS8MHH3zQqmN89dVXGDJkCFxdXcEwDIqLi807SEqbggoDhWLDlJWVITc3F6NHj0ZAQABcXFxadZyKigqMGTMG//rXv8w8QkpbhAoDpV3y008/ISYmBg4ODvDy8sKIESNQXl4OAPj222/RrVs3SKVS+Pv7Y/78+Xr75ufn4+mnn4ajoyMiIiJw8OBBblvv3r2xYcMG7u+nnnoKIpEIJSUlALQ5/BmG4XL6q1QqLF26FIGBgXByckL//v1x+vRpAMDp06c5IRg2bBgYhuG2XbhwAYMHD4aDgwOCg4OxcOFCbvyNsWjRIrzzzjt49NFHTXvjKO0CKgyUdkdWVhaef/55vPTSS7h9+zZOnz6NSZMmgRCCL774AvPmzcMrr7yChIQEHDx4EJ06ddLb//3338fUqVNx/fp1jBs3DtOnT0dhYSEAYMiQIdzNmxCCP/74Ax4eHjh37hwA4NSpU5DL5ejcuTMAYObMmTh//jx2796N69ev45lnnsGYMWOQmJiIAQMGcAKyd+9eZGVlYcCAAUhISMDo0aMxadIkXL9+HXv27MG5c+caCBiF0mr4Te5KoVifuLg4AqDRGggBAQFk+fLlTe4LgLz77rvc32VlZYRhGPL7778TQgg5ePAgcXNzI2q1mly9epX4+PiQN954g7z11luEEEJeeeUV8uyzzxJCCLl//z5hGIZkZGTonWP48OFk2bJlhBBCioqKCABy6tQpbvs//vEP8sorr+jt88cffxCBQEAqKysJIYSEhoaSjRs3Nhj/qVOnCABSVFTU5DVSKLS0J6Xd0aNHDwwfPhwxMTEYPXo0Ro0ahSlTpqC6uhqZmZkYPnx4s/t3796d+7+TkxNcXFy4cqKDBw9GaWkprly5gvPnzyM2NhZDhw7F6tWrAWinh9jIovj4eBBCEBkZqXd8pVIJLy+vJs8fFxeH+/fv48cff+TaCCHQaDRISkpCly5djHo/KJT6UGGgtDuEQiGOHTuGCxcu4OjRo9i8eTOWL1+OEydOGLS/WCzW+5thGGg0GgCAm5sbevbsidOnT+PChQsYNmwYBg0ahKtXryIxMRH37t3DkCFDAAAajQZCoRBxcXEQCoV6x3R2dm7y/BqNBq+++ioWLlzYYFtISIhB10ChNAcVBkq7hGEYDBw4EAMHDsR7772H0NBQHDt2DGFhYThx4gSGDh3a6mMPGTIEp06dwl9//YVVq1bB3d0dXbt2xerVq+Hr68s90ffq1QtqtRq5ubkYNGiQwcd/5JFHcPPmzQa+DwrFXFDnM6Xd8ddff2HNmjW4fPkyUlNTsW/fPuTl5aFLly5YuXIlNmzYgM8++wyJiYmIj4/H5s2bjTr+kCFDcPjwYTAMg65du3JtP/74I2JjY7l+kZGRmD59Ov75z39i3759SEpKwqVLl7Bu3TocOnSoyeO//fbbuHjxIubNm8dZIgcPHsSCBQua3Cc7OxtXr17F/fv3AQAJCQm4evUq5zSnUOpCLQZKu8PV1RVnz57Fpk2bUFJSgtDQUGzYsAFjx44FAFRVVWHjxo1488034e3tjSlTphh1/MGDBwMAYmNjwTAM9/9NmzbpCQMAbNu2DatXr8aSJUuQkZEBLy8vPPbYYxg3blyTx+/evTvOnDmD5cuXY9CgQSCEoGPHjnj22Web3OfLL7/E+++/32CM27Ztw4wZM4y6Pkrbh9Z8plAoFIoedCqJQqFQKHpQYaBQKBSKHlQYKBQKhaIHFQYKhUKh6EGFgUKhUCh6UGGgUCgUih5UGCgUCoWiBxUGCoVCoehBhYFCoVAoelBhoFAoFIoeVBgoFAqFogcVBgqFQqHo8f8BGbeNX6xaiNkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from copy import deepcopy\n", + "df_cumulative = deepcopy(df)\n", + "for iteration in range(5):\n", + " df = campaign.recommend(batch_size=3)\n", + " df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + " campaign.add_measurements(df)\n", + " df_cumulative = pd.concat([df_cumulative, df])\n", + " fig, ax = plt.subplots(figsize=(4,3))\n", + " ax.set_title(f'Iteration {iteration} ({len(df_cumulative)} experiments)')\n", + " example_data.plot('x', 'y', ax=ax)\n", + " df_cumulative.plot.scatter('schwefel1', 'Yield', ax=ax, c='red')\n", + " plt.tight_layout()\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "BO", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/baybe_playground.ipynb b/src/baybe_playground.ipynb index 7a4b998..44659ef 100644 --- a/src/baybe_playground.ipynb +++ b/src/baybe_playground.ipynb @@ -279,6 +279,13 @@ "df" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 93, @@ -349,6 +356,16 @@ "source": [ "campaign.recommend(batch_size=3)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for iteration in range(5):\n", + " " + ] } ], "metadata": { diff --git a/src/seed_data.csv b/src/seed_data.csv new file mode 100644 index 0000000..4ca1035 --- /dev/null +++ b/src/seed_data.csv @@ -0,0 +1,4 @@ +schwefel1,Yield +356.99134680215377,403.0454233719017 +-309.0047300290056,123.75848185395103 +-55.674007109553486,470.42364275539364 From 9cd61b266c05d654cf5b9d71e5a2e3c948767ce0 Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Wed, 27 Mar 2024 19:04:50 +0000 Subject: [PATCH 09/43] Update GitHub Classroom Autograding Workflow From 6d310ba32a7fa97261dffff87665d6c66e5d9314 Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Wed, 27 Mar 2024 19:04:55 +0000 Subject: [PATCH 10/43] Update GitHub Classroom Autograding Workflow From e62944b568dc255df998cb54b04c8ed75fea3306 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 05:37:41 +0800 Subject: [PATCH 11/43] BO loop, run experiment comparing noisy GP vs noiseless GP --- .gitignore | 1 + run_experiment.py | 158 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 159 insertions(+) create mode 100644 run_experiment.py diff --git a/.gitignore b/.gitignore index 4ded053..fdf3903 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ *.pyc .coverage *.egg-info/ +*.pt \ No newline at end of file diff --git a/run_experiment.py b/run_experiment.py new file mode 100644 index 0000000..13b03ae --- /dev/null +++ b/run_experiment.py @@ -0,0 +1,158 @@ +# %% +import matplotlib.pyplot as plt +import numpy as np +import torch + +from botorch.models.gp_regression import ( + SingleTaskGP, +) +from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood +from botorch.fit import fit_gpytorch_model +from botorch.models.transforms.outcome import Standardize + +from botorch.optim.optimize import optimize_acqf +from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement +from botorch.sampling.normal import SobolQMCNormalSampler +from botorch.utils.transforms import normalize, unnormalize +import os +import gc + +tkwargs = { + "dtype": torch.double, + "device": torch.device("cuda" if torch.cuda.is_available() else "cpu"), +} +SMOKE_TEST = os.environ.get("SMOKE_TEST") +# SMOKE_TEST = True +print("SMOKE_TEST", SMOKE_TEST) +NUM_RESTARTS = 10 if not SMOKE_TEST else 2 +RAW_SAMPLES = 512 if not SMOKE_TEST else 4 +MC_SAMPLES = 128 if not SMOKE_TEST else 16 +batch_size = 1 + + +# %% +def generate_initial_data(problem, n: int, bounds: torch.Tensor) -> tuple: + X_init = draw_sobol_samples( + bounds=bounds, n=n, q=1, seed=torch.randint(100000, (1,)).item() + ).squeeze(-1) + Y_init = torch.tensor(problem.y(X_init.numpy())) + return X_init, Y_init + +# %% +def initialize_model(train_x, train_y, noise_bool=True) -> tuple: + # define models for objective and constraint + train_y= -train_y # negative because botorch assumes maximization + + if noise_bool: + model = SingleTaskGP( + train_X=train_x, + train_Y=train_y.unsqueeze(-1), + outcome_transform=Standardize(m=1), + ) + else: + model = SingleTaskGP( + train_X=train_x, + train_Y=train_y.unsqueeze(-1), + train_Yvar=torch.full_like(train_y.unsqueeze(-1), 1e-6), + outcome_transform=Standardize(m=1), + ) + + mll = ExactMarginalLogLikelihood(model.likelihood, model) + + return mll, model + +# %% +def optimize_acqf_loop(problem, acq_func): + + standard_bounds = torch.zeros(2, problem.n_var, **tkwargs) + standard_bounds[1] = 1 + options = {"batch_limit": batch_size, "maxiter": 2000} + + while options["batch_limit"] >= 1: + try: + torch.cuda.empty_cache() + x_cand, acq_val = optimize_acqf( + acq_function=acq_func, + bounds=standard_bounds, + q=batch_size, + num_restarts=NUM_RESTARTS, + raw_samples=RAW_SAMPLES, # used for intialization heuristic + options=options, + ) + torch.cuda.empty_cache() + gc.collect() + break + except RuntimeError as e: + if options["batch_limit"] > 1: + print( + "Got a RuntimeError in `optimize_acqf`. " + "Trying with reduced `batch_limit`." + ) + options["batch_limit"] //= 2 + continue + else: + raise e + + return x_cand, acq_val + + +# %% +from botorch.utils.sampling import draw_sobol_samples +from src.schwefel import SchwefelProblem +from time import time + +def run_experiment(n_init, noise_level, budget, seed, noise_bool): + + + torch.manual_seed(seed) + np.random.seed(seed) + + problem = SchwefelProblem(n_var=1, noise_level=noise_level) + + bounds = torch.tensor(problem.bounds, **tkwargs) + + train_X, train_Y= generate_initial_data(problem, n_init, bounds) + + start_time = time() + + for i in range(budget): + print(f"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.") + + train_x = normalize(train_X, bounds) + mll, model = initialize_model(train_x, train_Y, noise_bool) + fit_gpytorch_model(mll) + + # optimize the acquisition function and get the observations + X_baseline = train_x + sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES])) + + acq_func = qNoisyExpectedImprovement( + model=model, + X_baseline=X_baseline, + prune_baseline=True, + sampler=sampler, + ) + + x_cand, acq_func = optimize_acqf_loop(problem, acq_func) + X_cand = unnormalize(x_cand, bounds) + Y_cand = torch.tensor(problem.y(X_cand.numpy())) + print(f"New candidate: {X_cand}, {Y_cand}") + + # update the model with new observations + train_X = torch.cat([train_X, X_cand], dim=0) + train_Y = torch.cat([train_Y, Y_cand], dim=0) + + train_x = normalize(train_X, bounds) + mll, model = initialize_model(train_x, train_Y, noise_bool) + fit_gpytorch_model(mll) + + os.makedirs('results', exist_ok=True) + fname = f"results/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" + torch.save((train_X, train_Y, model), fname) + + return train_X, train_Y, model + + +if __name__ == "__main__": + run_experiment(5, 0.1, 5, 0, True) + run_experiment(5, 0.1, 5, 0, False) \ No newline at end of file From 41b824552c959a3e176d8280ccc32eb3414ed3f4 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 05:38:14 +0800 Subject: [PATCH 12/43] Run grid study using ray to parallelize workload --- analyse_grid_experiment.ipynb | 2340 +++++++++++++++++++++++++++++++++ run_grid_experiments.py | 60 + 2 files changed, 2400 insertions(+) create mode 100644 analyse_grid_experiment.ipynb create mode 100644 run_grid_experiments.py diff --git a/analyse_grid_experiment.ipynb b/analyse_grid_experiment.ipynb new file mode 100644 index 0000000..bc83b29 --- /dev/null +++ b/analyse_grid_experiment.ipynb @@ -0,0 +1,2340 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST None\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-03-28 05:16:37,097\tINFO worker.py:1724 -- Started a local Ray instance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 2 noise 0.01 budget 10 seed 0, time: 1.15s\n", + "Started problem 2 noise 0.01 budget 10 seed 0, time: 2.33s\n", + "Started problem 2 noise 0.1 budget 10 seed 0, time: 3.49s\n", + "Started problem 2 noise 0.1 budget 10 seed 0, time: 4.71s\n", + "Started problem 2 noise 1.0 budget 10 seed 0, time: 5.95s\n", + "\u001b[36m(pid=13260)\u001b[0m SMOKE_TEST None\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", + "Started problem 2 noise 1.0 budget 10 seed 0, time: 7.22s\n", + "Started problem 4 noise 0.01 budget 10 seed 0, time: 8.49s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4035], dtype=torch.float64)\n", + "Started problem 4 noise 0.01 budget 10 seed 0, time: 9.76s\n", + "Started problem 4 noise 0.1 budget 10 seed 0, time: 11.04s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 4 noise 0.1 budget 10 seed 0, time: 12.30s\n", + "\u001b[36m(pid=11152)\u001b[0m SMOKE_TEST None\u001b[32m [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 5.538 seconds.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "Started problem 4 noise 1.0 budget 10 seed 0, time: 13.61s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5945], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "Started problem 4 noise 1.0 budget 10 seed 0, time: 14.93s\n", + "Started problem 6 noise 0.01 budget 10 seed 0, time: 16.24s\n", + "\u001b[36m(pid=26380)\u001b[0m SMOKE_TEST None\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 3, total time: 9.158 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.7961], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(pid=5304)\u001b[0m SMOKE_TEST None\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 4, total time: 12.772 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4124], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 4, total time: 12.941 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[139.5930]], dtype=torch.float64), tensor([514.2790], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 8, total time: 26.648 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-210.6462]], dtype=torch.float64), tensor([614.8742], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 5, total time: 16.357 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", + "Started problem 6 noise 0.01 budget 10 seed 0, time: 40.08s\n", + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[14.5189]], dtype=torch.float64), tensor([427.9994], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 6 noise 0.1 budget 10 seed 0, time: 41.45s\n", + "Started problem 6 noise 0.1 budget 10 seed 0, time: 42.82s\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 7, total time: 22.562 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11308)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-366.4503]], dtype=torch.float64), tensor([524.8431], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 6 noise 1.0 budget 10 seed 0, time: 47.45s\n", + "Started problem 6 noise 1.0 budget 10 seed 0, time: 48.73s\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 8 noise 0.01 budget 10 seed 0, time: 50.09s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5816], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 0.01 budget 10 seed 0, time: 53.88s\n", + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 4, total time: 14.870 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started problem 8 noise 0.1 budget 10 seed 0, time: 55.83s\n", + "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[-33.9809]], dtype=torch.float64), tensor([403.9808], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 6, total time: 18.962 seconds.\u001b[32m [repeated 15x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[151.9244]], dtype=torch.float64), tensor([455.2001], dtype=torch.float64)\u001b[32m [repeated 16x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 5, total time: 13.611 seconds.\u001b[32m [repeated 16x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-194.1547]], dtype=torch.float64), tensor([609.2873], dtype=torch.float64)\u001b[32m [repeated 16x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 0.1 budget 10 seed 0, time: 71.24s\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 7, total time: 19.449 seconds.\u001b[32m [repeated 16x across cluster]\u001b[0m\n", + "Started problem 8 noise 1.0 budget 10 seed 0, time: 74.59s\n", + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[165.3069]], dtype=torch.float64), tensor([373.0400], dtype=torch.float64)\u001b[32m [repeated 15x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 1.0 budget 10 seed 0, time: 76.03s\n", + "Started problem 10 noise 0.01 budget 10 seed 0, time: 77.84s\n", + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.01 budget 10 seed 0, time: 79.89s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5710], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.1 budget 10 seed 0, time: 83.05s\n", + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 2, total time: 6.233 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.1 budget 10 seed 0, time: 84.39s\n", + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.4688], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 2, total time: 6.261 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-349.1740]], dtype=torch.float64), tensor([362.2102], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started problem 10 noise 1.0 budget 10 seed 0, time: 91.98s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=23920)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=23920)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 1, total time: 3.567 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[19.4747]], dtype=torch.float64), tensor([437.5959], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 8, total time: 25.884 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[-330.7235]], dtype=torch.float64), tensor([215.2656], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 10 noise 1.0 budget 10 seed 0, time: 104.44s\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 9, total time: 31.145 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.01 budget 10 seed 1, time: 107.08s\n", + "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-350.3297]], dtype=torch.float64), tensor([374.1686], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.01 budget 10 seed 1, time: 109.48s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 6.148 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.1 budget 10 seed 1, time: 110.79s\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[431.1796]], dtype=torch.float64), tensor([13.1537], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11152)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n", + "\u001b[36m(worker pid=11152)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 8, total time: 31.750 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.1 budget 10 seed 1, time: 117.00s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.7161], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 7, total time: 29.613 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 2 noise 1.0 budget 10 seed 1, time: 122.75s\n", + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[349.8151]], dtype=torch.float64), tensor([469.7429], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started problem 2 noise 1.0 budget 10 seed 1, time: 125.72s\n", + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 3, total time: 9.940 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-395.2393]], dtype=torch.float64), tensor([758.0537], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 2, total time: 8.417 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "Started problem 4 noise 0.01 budget 10 seed 1, time: 132.43s\n", + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4024], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11152)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n", + "\u001b[36m(worker pid=11152)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 9, total time: 31.250 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 4 noise 0.01 budget 10 seed 1, time: 139.13s\n", + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[219.4622]], dtype=torch.float64), tensor([247.8814], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 7, total time: 33.871 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 4 noise 0.1 budget 10 seed 1, time: 143.49s\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[242.8150]], dtype=torch.float64), tensor([388.5509], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 23.435 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[127.6187]], dtype=torch.float64), tensor([539.9996], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started problem 4 noise 0.1 budget 10 seed 1, time: 153.02s\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 15.195 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 4 noise 1.0 budget 10 seed 1, time: 154.37s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 4 noise 1.0 budget 10 seed 1, time: 156.93s\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-303.9571]], dtype=torch.float64), tensor([118.4931], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 2, total time: 5.840 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=23920)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=23920)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 6 noise 0.01 budget 10 seed 1, time: 161.86s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=23920)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=23920)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5807], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 3, total time: 11.494 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[339.0733]], dtype=torch.float64), tensor([563.8048], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started problem 6 noise 0.01 budget 10 seed 1, time: 169.41s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 6 noise 0.1 budget 10 seed 1, time: 170.92s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 3, total time: 10.153 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 6 noise 0.1 budget 10 seed 1, time: 172.38s\n", + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-59.2630]], dtype=torch.float64), tensor([476.7675], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 1, total time: 5.100 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[33.2251]], dtype=torch.float64), tensor([435.6392], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 6, total time: 20.879 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[156.7309]], dtype=torch.float64), tensor([426.5164], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 17.665 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[259.5776]], dtype=torch.float64), tensor([520.8659], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 6 noise 1.0 budget 10 seed 1, time: 190.66s\n", + "Started problem 6 noise 1.0 budget 10 seed 1, time: 192.20s\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 9, total time: 39.471 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 0.01 budget 10 seed 1, time: 194.30s\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-50.0829]], dtype=torch.float64), tensor([455.0102], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 0.01 budget 10 seed 1, time: 198.54s\n", + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-299.6165]], dtype=torch.float64), tensor([119.5035], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "Started problem 8 noise 0.1 budget 10 seed 1, time: 201.87s\n", + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 3, total time: 13.371 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 0.1 budget 10 seed 1, time: 205.59s\n", + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[419.3371]], dtype=torch.float64), tensor([0.3389], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 4, total time: 17.544 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 8 noise 1.0 budget 10 seed 1, time: 210.01s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[419.3050]], dtype=torch.float64), tensor([0.3809], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 4, total time: 13.287 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[480.9437]], dtype=torch.float64), tensor([389.0365], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 8 noise 1.0 budget 10 seed 1, time: 218.17s\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 3, total time: 10.274 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-130.3979]], dtype=torch.float64), tensor([300.1079], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 23.496 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4084], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 4, total time: 12.458 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[421.2228]], dtype=torch.float64), tensor([0.0195], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.01 budget 10 seed 1, time: 233.20s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.01 budget 10 seed 1, time: 236.07s\n", + "Started problem 10 noise 0.1 budget 10 seed 1, time: 237.55s\n", + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[25.8501]], dtype=torch.float64), tensor([442.6817], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.1 budget 10 seed 1, time: 238.98s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 2, total time: 9.002 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 10 noise 1.0 budget 10 seed 1, time: 243.38s\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-283.2684]], dtype=torch.float64), tensor([163.7023], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 10 noise 1.0 budget 10 seed 1, time: 246.47s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[423.2956]], dtype=torch.float64), tensor([0.6630], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 5, total time: 20.089 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.01 budget 10 seed 2, time: 252.94s\n", + "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.3731], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 3, total time: 10.805 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.01 budget 10 seed 2, time: 258.51s\n", + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[58.5060]], dtype=torch.float64), tensor([361.3186], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11152)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11152)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 7, total time: 27.107 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-283.2569]], dtype=torch.float64), tensor([163.8564], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 5, total time: 26.755 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-289.6109]], dtype=torch.float64), tensor([139.1617], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.1 budget 10 seed 2, time: 272.20s\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 4, total time: 16.254 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "Started problem 2 noise 0.1 budget 10 seed 2, time: 275.67s\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[421.4555]], dtype=torch.float64), tensor([-0.0801], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 1, total time: 5.039 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[379.1839]], dtype=torch.float64), tensor([197.8249], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 2 noise 1.0 budget 10 seed 2, time: 283.55s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 7, total time: 33.795 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[401.1191]], dtype=torch.float64), tensor([48.1779], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 2 noise 1.0 budget 10 seed 2, time: 289.18s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 3, total time: 17.535 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "Started problem 4 noise 0.01 budget 10 seed 2, time: 292.32s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[422.6121]], dtype=torch.float64), tensor([0.3322], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 4 noise 0.01 budget 10 seed 2, time: 293.80s\n", + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 1, total time: 4.613 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[220.0513]], dtype=torch.float64), tensor([250.1605], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 3, total time: 13.537 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[400.4539]], dtype=torch.float64), tensor([51.5641], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 4 noise 0.1 budget 10 seed 2, time: 305.51s\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 4, total time: 15.496 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 4 noise 0.1 budget 10 seed 2, time: 310.05s\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[60.1855]], dtype=torch.float64), tensor([359.0799], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 7.593 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-71.2434]], dtype=torch.float64), tensor([478.3039], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 3, total time: 14.358 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[352.6976]], dtype=torch.float64), tensor([443.3986], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 3, total time: 15.323 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 4 noise 1.0 budget 10 seed 2, time: 326.65s\n", + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[421.4453]], dtype=torch.float64), tensor([0.0870], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 4 noise 1.0 budget 10 seed 2, time: 328.39s\n", + "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 1, total time: 4.222 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[60.7442]], dtype=torch.float64), tensor([358.8514], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 9, total time: 43.902 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 6 noise 0.01 budget 10 seed 2, time: 336.45s\n", + "Started problem 6 noise 0.01 budget 10 seed 2, time: 337.94s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.9766], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=26380)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=26380)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 6 noise 0.1 budget 10 seed 2, time: 339.92s\n", + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "Started problem 6 noise 0.1 budget 10 seed 2, time: 341.56s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:389: RuntimeWarning: Optimization failed on the second try, after generating a new set of initial conditions.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[430.7882]], dtype=torch.float64), tensor([12.0632], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 4, total time: 18.488 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-349.7572]], dtype=torch.float64), tensor([367.4822], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 3, total time: 13.924 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "Started problem 6 noise 1.0 budget 10 seed 2, time: 354.14s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-428.2759]], dtype=torch.float64), tensor([831.1028], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 5, total time: 21.075 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 6 noise 1.0 budget 10 seed 2, time: 358.25s\n", + "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[125.5204]], dtype=torch.float64), tensor([541.7065], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 7.223 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[67.0226]], dtype=torch.float64), tensor([355.6261], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 29.338 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 8 noise 0.01 budget 10 seed 2, time: 371.70s\n", + "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-296.3666]], dtype=torch.float64), tensor([123.4420], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 17.151 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 8 noise 0.01 budget 10 seed 2, time: 374.80s\n", + "Started problem 8 noise 0.1 budget 10 seed 2, time: 377.56s\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[386.8818]], dtype=torch.float64), tensor([136.1810], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 6, total time: 21.760 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 8 noise 0.1 budget 10 seed 2, time: 381.39s\n", + "Started problem 8 noise 1.0 budget 10 seed 2, time: 382.78s\n", + "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.8966], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "Started problem 8 noise 1.0 budget 10 seed 2, time: 384.18s\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 8, total time: 28.449 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[367.1162]], dtype=torch.float64), tensor([306.6477], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 4, total time: 16.961 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 10 noise 0.01 budget 10 seed 2, time: 392.68s\n", + "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[367.0235]], dtype=torch.float64), tensor([306.7043], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 1, total time: 3.617 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.01 budget 10 seed 2, time: 397.27s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-325.9922]], dtype=torch.float64), tensor([186.4192], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 1, total time: 4.216 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[195.3344]], dtype=torch.float64), tensor([226.2150], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 25.392 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[304.3039]], dtype=torch.float64), tensor([719.1803], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 9, total time: 39.304 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 10 noise 0.1 budget 10 seed 2, time: 416.41s\n", + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-113.2019]], dtype=torch.float64), tensor([312.8168], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "Started problem 10 noise 0.1 budget 10 seed 2, time: 418.20s\n", + "Started problem 10 noise 1.0 budget 10 seed 2, time: 419.63s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started problem 10 noise 1.0 budget 10 seed 2, time: 421.12s\n", + "0\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-131.4509]], dtype=torch.float64), tensor([301.6516], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 1, total time: 4.467 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[351.2055]], dtype=torch.float64), tensor([456.3065], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 1, total time: 11.225 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-281.5970]], dtype=torch.float64), tensor([171.6314], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 2, total time: 9.821 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5762], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 5, total time: 22.026 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[399.0926]], dtype=torch.float64), tensor([58.5408], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 6, total time: 25.794 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[40.5840]], dtype=torch.float64), tensor([415.4415], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 7, total time: 31.019 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[413.1906]], dtype=torch.float64), tensor([7.6418], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 19.580 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:389: RuntimeWarning: Optimization failed on the second try, after generating a new set of initial conditions.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-94.1265]], dtype=torch.float64), tensor([393.3504], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 8, total time: 37.378 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=26380)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=11308)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[179.8027]], dtype=torch.float64), tensor([287.0650], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 2, total time: 7.422 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[420.9739]], dtype=torch.float64), tensor([-0.0748], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 9, total time: 35.767 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[407.8553]], dtype=torch.float64), tensor([21.4103], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 6, total time: 17.385 seconds.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[421.8449]], dtype=torch.float64), tensor([1.2719], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 8, total time: 24.529 seconds.\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[422.5996]], dtype=torch.float64), tensor([0.3448], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 9, total time: 27.854 seconds.\n", + "0\n", + "all experiments done, time: 490.79s\n" + ] + } + ], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from run_grid_experiments import run_grid_experiments\n", + "\n", + "seeds = [0,1,2]\n", + "n_inits = [2, 4, 6 ,8, 10]\n", + "noise_levels = [0.01, 0.1, 1.]\n", + "# budgets = [10, 20, 50]\n", + "noise_bools = [True, False]\n", + "budget = 10\n", + "\n", + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_15664\\3020836113.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
020.010True238.403534
020.011True42.152557
020.012True118.625458
020.100True238.491608
020.101True42.097500
..................
0100.101False-0.108989
0100.102False-0.074771
0101.000False-0.549255
0101.001False-1.931614
0101.002False-0.017878
\n", + "

90 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 0.01 0 True 238.403534\n", + "0 2 0.01 1 True 42.152557\n", + "0 2 0.01 2 True 118.625458\n", + "0 2 0.10 0 True 238.491608\n", + "0 2 0.10 1 True 42.097500\n", + ".. ... ... ... ... ...\n", + "0 10 0.10 1 False -0.108989\n", + "0 10 0.10 2 False -0.074771\n", + "0 10 1.00 0 False -0.549255\n", + "0 10 1.00 1 False -1.931614\n", + "0 10 1.00 2 False -0.017878\n", + "\n", + "[90 rows x 5 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " for seed in seeds:\n", + " X, Y, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y.shape[0]):\n", + " sliding_min[i] = Y[:i+1].min().item()\n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n", + " \n", + "df " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 best
 meanstd
noise_level0.0100000.1000001.0000000.0100000.1000001.000000
n_init      
2133.060516133.059017133.04409098.91860198.99353799.743231
423.64979479.13051123.14113517.57767396.07697618.302944
648.19026748.11009347.31825351.47901551.48155751.493613
867.55036967.65741568.74347331.23158431.13585630.332036
1055.20263754.27264854.80385642.23444159.17468541.636230
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 best
 meanstd
noise_level0.0100000.1000001.0000000.0100000.1000001.000000
n_init      
274.09683174.09221874.063016126.082897126.206706127.244658
479.06701779.10704879.73454468.43010568.32782067.513591
60.1688980.195790-0.2214620.2718620.2018100.777112
80.0088230.014245-0.2993560.0060000.1007650.695021
100.001599-0.083678-0.8329150.0157370.0222380.987899
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_no_noise = df[df[\"noise_bool\"] == False]\n", + "df_noise = df[df[\"noise_bool\"] == True]\n", + "# df = df.groupby([\"n_init\", \"noise_level\", \"noise_bool\"]).agg({\"min\": [\"mean\", \"std\"]})\n", + "df_no_noise = df_no_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "df_noise = df_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "display(df_noise.unstack().style.background_gradient(cmap='viridis'))\n", + "display(df_no_noise.unstack().style.background_gradient(cmap='viridis'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/run_grid_experiments.py b/run_grid_experiments.py new file mode 100644 index 0000000..d699d6d --- /dev/null +++ b/run_grid_experiments.py @@ -0,0 +1,60 @@ +import ray +import argparse +from time import time, sleep +from run_experiment import run_experiment +from datetime import datetime +import gc + +MAX_NUM_PENDING_TASKS = 12 + + +@ray.remote +def worker(n_init, noise_level, budget, seed, noise_bool): + + try: + run_experiment(n_init, noise_level, budget, seed, noise_bool) + # saved file looks like this: results\Schwe_n_init_6_noiselvl_0_budget_0_seed_2_noise_False.pt + except Exception as e: + print(e) + print(f'problem {n_init} noise {noise_level} budget {budget} seed {seed} failed') + return 1 + + return 0 + +def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget): + + # ray.init(local_mode=True) + ray.init() + start_time = time() + tasks = [] + + for seed in seeds: + for n_init in n_inits: + for noise_level in noise_levels: + for noise_bool in noise_bools: + if len(tasks) > MAX_NUM_PENDING_TASKS: + completed_tasks, tasks = ray.wait(tasks, num_returns=1) + ray.get(completed_tasks[0]) + + sleep(1) + task = worker.remote(n_init, noise_level, budget, seed, noise_bool) + tasks.append(task) + print(f'Started problem {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') + gc.collect() + + while len(tasks) > 0: + completed_tasks, tasks = ray.wait(tasks, num_returns=1) + print(ray.get(completed_tasks[0])) + + + print('all experiments done, time: %.2fs' % (time() - start_time)) + +if __name__ == "__main__": + + seeds = [0] + n_inits = [2, 4, 6 ,8, 10] + noise_levels = [0, 0.01, 0.1, 0.5] + # budgets = [10, 20, 50] + noise_bools = [True, False] + budget = 10 + run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget) From 99f5d5a72a3acdf78df56d4fc14fc546b4fc02e5 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Wed, 27 Mar 2024 15:11:46 -0700 Subject: [PATCH 13/43] pull karims schwefel class onto this branch --- src/schwefel.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 src/schwefel.py diff --git a/src/schwefel.py b/src/schwefel.py new file mode 100644 index 0000000..731851d --- /dev/null +++ b/src/schwefel.py @@ -0,0 +1,32 @@ +import numpy as np + +class SchwefelProblem: + def __init__(self, n_var=1, noise_level=0.01): + """ + y = f(x) + eps + """ + self.noise_level = noise_level + self.n_var = n_var # Number of variables/dimensions + self.bounds = np.array([[-500] * self.n_var, [500] * self.n_var]) + + def _schwefel_individual(self, x): + return x * np.sin(np.sqrt(np.abs(x))) + + def f(self, x): + return 418.9829 * self.n_var - np.sum(self._schwefel_individual(x), axis=1) + + def eps(self, x): + # Assuming the noise is independent of x for simplicity + return np.random.normal(0, self.noise_level, x.shape[0]) + + def y(self, x): + return self.f(x) + self.eps(x) + +# Test code if this file is the main program being run +if __name__ == "__main__": + # Create a SchwefelProblem instance with 3 variables/dimensions + schwefel = SchwefelProblem(n_var=3, noise_level=1.) + x_test = np.array([[420, 420, 420], [420, 420, 420]]) # Example input vector + print("Objective function value (f):", schwefel.f(x_test)) + print("Noisy objective function value (y):", schwefel.y(x_test)) + From 504934b71d52b9bd0a1b3080237c0dcf3bf3e218 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Wed, 27 Mar 2024 15:12:14 -0700 Subject: [PATCH 14/43] notebook of BayBE grid search example --- BayBE_grid_search.ipynb | 259 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 259 insertions(+) create mode 100644 BayBE_grid_search.ipynb diff --git a/BayBE_grid_search.ipynb b/BayBE_grid_search.ipynb new file mode 100644 index 0000000..b95d12a --- /dev/null +++ b/BayBE_grid_search.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "fc7d83ca-d889-49f9-a332-785d3267512d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from src import schwefel\n", + "from src import baybe_utils\n", + "from src import visualization" + ] + }, + { + "cell_type": "markdown", + "id": "3e9d8019-5827-4325-b0e4-cc1f80745270", + "metadata": {}, + "source": [ + "## BayBE Schwefel function optimization examples\n", + "\n", + "### Brenden Pelkie\n", + "\n", + "This notebook walks through a quick grid search to explore the impact of measurement noise on optimization performance of a vanilla BO implementation in BayBE." + ] + }, + { + "cell_type": "markdown", + "id": "44701cdf-29e6-47f7-84f5-abfd5112a2e9", + "metadata": {}, + "source": [ + "### 1. Pick parameters\n", + "\n", + "First define parameters for optimization. Here we set the number of BO iterations/cycles to run, the number of random initial observations to include, the dimensionality of the schwefel function to optimize, the noise level of the schwefel observations, and the number of obserations to make per iteration/BO batch cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b158f689-b0e5-478e-b7a6-2cb586acf4c5", + "metadata": {}, + "outputs": [], + "source": [ + "NUM_ITERATIONS = 15\n", + "NUM_INIT_OBS = 5\n", + "N_DIMS_SCHWEF = 1\n", + "NOISE_LEVEL_SCHWEF = 0\n", + "ITERATION_BATCH_SIZE = 1" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e07e3-3daa-4196-b17f-8982e0598db9", + "metadata": {}, + "source": [ + "For the grid search over number of BO iterations and noise, select the desired values here" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0670300a-6f94-4b37-9854-d4b0b5251b40", + "metadata": {}, + "outputs": [], + "source": [ + "num_iterations = [5,10,15]#[5,10,20,40,60,80]\n", + "noise = [0, 0.1]# [0, 0.1, 0.2, 0.5]\n" + ] + }, + { + "cell_type": "markdown", + "id": "4705ebb3-86ad-41f5-8c23-0f883b79a871", + "metadata": {}, + "source": [ + "### 2. Run grid search" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6a78944a-2833-407a-9bbc-86206155b2d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00, 4.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 6.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 7.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 5.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:02<00:00, 5.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:02<00:00, 5.63it/s]\n" + ] + } + ], + "source": [ + "grid_results = baybe_utils.iteration_noise_grid_search(num_iterations, noise, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE)" + ] + }, + { + "cell_type": "markdown", + "id": "4f403581-1dd2-4a10-b98e-fcd147ec0bba", + "metadata": {}, + "source": [ + "### 3. Process and visualize results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a10e5944-1038-4bec-bb54-66a60f22ccec", + "metadata": {}, + "outputs": [], + "source": [ + "n_its, noise, performance_matrix = baybe_utils.process_grid_searh_results(grid_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "524e2642-4a6d-458e-bade-05106c17e4ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualization.grid_search_heatmap(n_its, noise, performance_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1f0cda8467448c551166cf27ae5e99b76b903af6 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Wed, 27 Mar 2024 15:12:36 -0700 Subject: [PATCH 15/43] utilities for baybe and visualization --- src/baybe_utils.py | 150 +++++++++++++++++++++++++++++++++++++++++++ src/visualization.py | 25 ++++++++ 2 files changed, 175 insertions(+) create mode 100644 src/baybe_utils.py create mode 100644 src/visualization.py diff --git a/src/baybe_utils.py b/src/baybe_utils.py new file mode 100644 index 0000000..dbee7b0 --- /dev/null +++ b/src/baybe_utils.py @@ -0,0 +1,150 @@ +from baybe.targets import NumericalTarget +from baybe.objective import Objective +from baybe.parameters import ( + NumericalContinuousParameter +) + +from baybe.recommenders import ( + SequentialGreedyRecommender, + RandomRecommender +) + +from baybe.searchspace import SearchSpace +from baybe import Campaign + +from src import schwefel + +import numpy as np + +from tqdm import tqdm + + +def run_optimization_campaign( + NUM_ITERATIONS, + NUM_INIT_OBS, + N_DIMS_SCHWEF, + NOISE_LEVEL_SCHWEF, + ITERATION_BATCH_SIZE, + recommender_init = None, + recommender_main = None +): + """ + Utility function for running a bayesian optimization campaign. + + NUM_ITERATIONS: Number of bayesian optimization iterations to run for + NUM_INIT_OBS: number of random initial observations to make before starting BO + N_DIMS_SCWEF: number of x dimensions of schwefel function to optimize + NOISE_LEVEL_SCHWEF: variance of noise added to schwefel function + ITERATION_BATCH_SIZE: number of observations to make per BO batch + reccomender_init: (BayBE Reccomender): recommender to use for initial sampling. Default random + recommender_main: (BayBE reccomender): recommender to use for main BO loop. Default baybe sequential greedy with EI + + """ + # Define Schweffel oracle + schweffer = schwefel.SchwefelProblem(n_var = N_DIMS_SCHWEF, noise_level=NOISE_LEVEL_SCHWEF) + target = NumericalTarget(name = 'schwefel', mode = "MIN") + parameters = [ + NumericalContinuousParameter(f'schwefel{i+1}', bounds = (-500,500)) for i in range(N_DIMS_SCHWEF) + ] + + objective = Objective(mode = "SINGLE", targets = [target]) + searchspace = SearchSpace.from_product(parameters) + + if recommender_init is None: + recommender_init = RandomRecommender() + if recommender_main is None: + recommender_main = SequentialGreedyRecommender(acquisition_function_cls='EI') + + print("Collecting initial observations") + campaign_init = Campaign(searchspace, objective, recommender_init) + random_params = campaign_init.recommend(NUM_INIT_OBS) + + y_init = schweffer.f(random_params.to_numpy()) + + random_params.insert(N_DIMS_SCHWEF, 'schwefel', y_init) + + optimization_campaign = Campaign(searchspace, objective, recommender_main) + optimization_campaign.add_measurements(random_params) + + print('Beginning optimization campaign') + for i in tqdm(range(NUM_ITERATIONS)): + reccs = optimization_campaign.recommend(ITERATION_BATCH_SIZE) + + y_vals = schweffer.f(reccs.to_numpy()) + + reccs.insert(N_DIMS_SCHWEF, 'schwefel', y_vals) + + optimization_campaign.add_measurements(reccs) + + return optimization_campaign + + + +def iteration_noise_grid_search(iterations_list, noise_list, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE): + """ + Utility to run a parameter grid experiment varying noise level and number of BO iterations. Runs full grid in serial. + Params: + ------- + iteraitons_list: list of ints - number of BO iterations to run + noise_list - list of floats - noise values to run + NUM_INIT_JOBS - int + N_DIMS_SCWEF - int + ITERATION_BATCH_SIZE - int + + returns: + --------- + iteration_results: an abomination of dictionaries. Outer level: results keyed by number of iterations; next layer: results keyed by noise level. Values are BayBE campaign objects of completed campaign + """ + iteration_results = {} + + for its in iterations_list: + noise_results = {} + for noise_level in noise_list: + opt_campaign = run_optimization_campaign(its, NUM_INIT_OBS, N_DIMS_SCHWEF, noise_level, ITERATION_BATCH_SIZE) + noise_results[str(noise_level)] = opt_campaign + iteration_results[str(its)] = noise_results + + return iteration_results + +def process_grid_searh_results(grid_search_results): + """ + Process results from iteration_noise_grid_search function to extract a performance matrix of best result for each campaign + + Params: + -------- + grid_search_results: dict of dicts of BayBE campaigns + + Returns: + ------- + n_its - list - iteration numbers used + n_noise - list - noise values used + performance_matrix: matrix of best observed values (min schwefel val) for each campaign from grid search, arranged with iteration varying on axis 0 and noise on axis 1 + """ + + + n_its = len(grid_search_results) + n_noise = len(grid_search_results[list(grid_search_results.keys())[0]]) + + performance_matrix = np.zeros((n_its, n_noise)) + + + + # fill out performance matrix + iteration_vals = [] + noise_vals = [] + + first_pass = True + for i, (its, entry) in enumerate(grid_search_results.items()): + iteration_vals.append(its) + for j, (noise, camp) in enumerate(entry.items()): + if first_pass: + noise_vals.append(noise) + best_result = camp.measurements['schwefel'].min() + # hack to flip matrix BO iterations upside down for plotting + performance_matrix[n_its - 1 - i, j] = best_result + first_pass = False + + iteration_vals = iteration_vals[::-1] + + return iteration_vals, noise_vals, performance_matrix + diff --git a/src/visualization.py b/src/visualization.py new file mode 100644 index 0000000..2f75dbe --- /dev/null +++ b/src/visualization.py @@ -0,0 +1,25 @@ +import seaborn as sn +import pandas as pd + +def grid_search_heatmap(iterations_list, noise_list, performance_matrix): + """ + plot a heatmap + + Params: + ------- + iterations_list: list of number of iterations used + noise list: list of noise values used + performance matrix: np array of dims len(iterations_list) x len(noise_list) with smallest noise, smallest iterations in lower left corner ('origin') + + returns: + ------ + matplotlib ax object + """ + + df_heatmap = pd.DataFrame(performance_matrix, index = [str(its) for its in iterations_list], columns = [str(noise) for noise in noise_list]) + + ax = sn.heatmap(df_heatmap, annot=True, fmt = '.3g', cmap = 'crest') + ax.set_xlabel('Noise level') + ax.set_ylabel('Number of BO iterations') + + return ax \ No newline at end of file From 7f6a5739552772b74fb14873f35e4ac98ae6af57 Mon Sep 17 00:00:00 2001 From: brendenpelkie Date: Wed, 27 Mar 2024 15:14:52 -0700 Subject: [PATCH 16/43] nuke grading --- .github/workflows/classroom.yml | 29 +---------------------------- 1 file changed, 1 insertion(+), 28 deletions(-) diff --git a/.github/workflows/classroom.yml b/.github/workflows/classroom.yml index 92a4edd..8b13789 100644 --- a/.github/workflows/classroom.yml +++ b/.github/workflows/classroom.yml @@ -1,28 +1 @@ -name: Autograding Tests -'on': -- push -- workflow_dispatch -- repository_dispatch -permissions: - checks: write - actions: read - contents: read -jobs: - run-autograding-tests: - runs-on: ubuntu-latest - if: github.actor != 'github-classroom[bot]' - steps: - - name: Checkout code - uses: actions/checkout@v4 - - name: Hello world test - id: hello-world-test - uses: education/autograding-python-grader@v1 - with: - timeout: 5 - max-score: 5 - - name: Autograding Reporter - uses: education/autograding-grading-reporter@v1 - env: - HELLO-WORLD-TEST_RESULTS: "${{steps.hello-world-test.outputs.result}}" - with: - runners: hello-world-test + From d8d0e3cb7dafe566d48ecdefa8591b34254a3526 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Wed, 27 Mar 2024 15:16:42 -0700 Subject: [PATCH 17/43] remove template workflows --- .github/workflows/classroom.yml | 28 ---------------------------- .github/workflows/docker-image.yml | 18 ------------------ src/baybe_no_noise.ipynb | 6 +++--- 3 files changed, 3 insertions(+), 49 deletions(-) delete mode 100644 .github/workflows/classroom.yml delete mode 100644 .github/workflows/docker-image.yml diff --git a/.github/workflows/classroom.yml b/.github/workflows/classroom.yml deleted file mode 100644 index 92a4edd..0000000 --- a/.github/workflows/classroom.yml +++ /dev/null @@ -1,28 +0,0 @@ -name: Autograding Tests -'on': -- push -- workflow_dispatch -- repository_dispatch -permissions: - checks: write - actions: read - contents: read -jobs: - run-autograding-tests: - runs-on: ubuntu-latest - if: github.actor != 'github-classroom[bot]' - steps: - - name: Checkout code - uses: actions/checkout@v4 - - name: Hello world test - id: hello-world-test - uses: education/autograding-python-grader@v1 - with: - timeout: 5 - max-score: 5 - - name: Autograding Reporter - uses: education/autograding-grading-reporter@v1 - env: - HELLO-WORLD-TEST_RESULTS: "${{steps.hello-world-test.outputs.result}}" - with: - runners: hello-world-test diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml deleted file mode 100644 index 763a95f..0000000 --- a/.github/workflows/docker-image.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: Docker Image CI - -on: - pull_request: - branches: [ "main" ] - # Allow mannually trigger - workflow_dispatch: - -jobs: - - build: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - name: Build the Codespaces container image - run: docker build . --file .devcontainer/Dockerfile diff --git a/src/baybe_no_noise.ipynb b/src/baybe_no_noise.ipynb index 8eb5fcb..e316a89 100644 --- a/src/baybe_no_noise.ipynb +++ b/src/baybe_no_noise.ipynb @@ -345,7 +345,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BO", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -359,9 +359,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.12.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 8a2a61d9412815c72dcf7acee84d449b4aeb2179 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Wed, 27 Mar 2024 15:17:36 -0700 Subject: [PATCH 18/43] remove classroom.yml --- .github/workflows/classroom.yml | 1 - 1 file changed, 1 deletion(-) delete mode 100644 .github/workflows/classroom.yml diff --git a/.github/workflows/classroom.yml b/.github/workflows/classroom.yml deleted file mode 100644 index 8b13789..0000000 --- a/.github/workflows/classroom.yml +++ /dev/null @@ -1 +0,0 @@ - From bcf7794009a56e0d332aff770cf4134cbabebe03 Mon Sep 17 00:00:00 2001 From: brendenpelkie Date: Wed, 27 Mar 2024 15:19:47 -0700 Subject: [PATCH 19/43] Update README.md --- README.md | 17 ++--------------- 1 file changed, 2 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 3f72673..8ca827a 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,3 @@ -[![Open in Codespaces](https://classroom.github.com/assets/launch-codespace-7f7980b617ed060a017424585567c406b6ee15c891e84e1186181d67ecf80aa0.svg)](https://classroom.github.com/open-in-codespaces?assignment_repo_id=14492363) -# Autograding Example: Python -This example project is written in Python, and tested with pytest. +# Project 23: Reliable Surrogate Models of Noisy Data -## The assignment -The tests are failing right now because the method isn't outputting the correct string. Fixing this up will make the tests green. - -## Setup command - -See `postCreateCommand` from [`devcontainer.json`](.devcontainer/devcontainer.json). - -## Run command -`pytest` - -## Notes -- pip's install path is not included in the PATH var by default, so without installing via `sudo -H`, pytest would be unaccessible. +TODO: Figure out a real readme From 2d8205bb4f9cdfdffa037638f54310eb666c9432 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 06:30:55 -0700 Subject: [PATCH 20/43] notebook updates --- BayBE_grid_search.ipynb | 319 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 319 insertions(+) create mode 100644 BayBE_grid_search.ipynb diff --git a/BayBE_grid_search.ipynb b/BayBE_grid_search.ipynb new file mode 100644 index 0000000..9899cb9 --- /dev/null +++ b/BayBE_grid_search.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "fc7d83ca-d889-49f9-a332-785d3267512d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from src import schwefel\n", + "from src import baybe_utils\n", + "from src import visualization" + ] + }, + { + "cell_type": "markdown", + "id": "3e9d8019-5827-4325-b0e4-cc1f80745270", + "metadata": {}, + "source": [ + "## BayBE Schwefel function optimization examples\n", + "\n", + "### Brenden Pelkie\n", + "\n", + "This notebook walks through a quick grid search to explore the impact of measurement noise on optimization performance of a vanilla BO implementation in BayBE." + ] + }, + { + "cell_type": "markdown", + "id": "44701cdf-29e6-47f7-84f5-abfd5112a2e9", + "metadata": {}, + "source": [ + "### 1. Pick parameters\n", + "\n", + "First define parameters for optimization. Here we set the number of BO iterations/cycles to run, the number of random initial observations to include, the dimensionality of the schwefel function to optimize, the noise level of the schwefel observations, and the number of obserations to make per iteration/BO batch cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b158f689-b0e5-478e-b7a6-2cb586acf4c5", + "metadata": {}, + "outputs": [], + "source": [ + "NUM_ITERATIONS = 15\n", + "NUM_INIT_OBS = 5\n", + "N_DIMS_SCHWEF = 1\n", + "NOISE_LEVEL_SCHWEF = 0\n", + "ITERATION_BATCH_SIZE = 1" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e07e3-3daa-4196-b17f-8982e0598db9", + "metadata": {}, + "source": [ + "For the grid search over number of BO iterations and noise, select the desired values here" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0670300a-6f94-4b37-9854-d4b0b5251b40", + "metadata": {}, + "outputs": [], + "source": [ + "num_iterations = [5,10,15]#[5,10,20,40,60,80]\n", + "noise = [0, 0.1]# [0, 0.1, 0.2, 0.5]\n" + ] + }, + { + "cell_type": "markdown", + "id": "4705ebb3-86ad-41f5-8c23-0f883b79a871", + "metadata": {}, + "source": [ + "### 2. Run grid search" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6a78944a-2833-407a-9bbc-86206155b2d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00, 4.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 6.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 7.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 5.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:02<00:00, 5.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:02<00:00, 5.63it/s]\n" + ] + } + ], + "source": [ + "grid_results = baybe_utils.iteration_noise_grid_search(num_iterations, noise, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE)" + ] + }, + { + "cell_type": "markdown", + "id": "4f403581-1dd2-4a10-b98e-fcd147ec0bba", + "metadata": {}, + "source": [ + "### 3. Process and visualize results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a10e5944-1038-4bec-bb54-66a60f22ccec", + "metadata": {}, + "outputs": [], + "source": [ + "n_its, noise, performance_matrix = baybe_utils.process_grid_searh_results(grid_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "524e2642-4a6d-458e-bade-05106c17e4ab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = visualization.grid_search_heatmap(n_its, noise, performance_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e321112f-2613-45ff-adad-f586336f30be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.savefig('test.png', dpi = 300)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7ca74e30-9b2d-40bf-b5b2-a84991804034", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sn" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "48b0d45b-ce95-41ba-8fc7-358835b36912", + "metadata": {}, + "outputs": [], + "source": [ + "fig = viz" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9ff98ed7-b654-4e4e-9307-31c85ce9b5eb", + "metadata": {}, + "outputs": [], + "source": [ + "fig = viz.get_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cb278944-a389-48e7-a5ef-bdb2928968d3", + "metadata": {}, + "outputs": [], + "source": [ + "fig.savefig('test.png', dpi = 300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cde098ef-b077-4229-8b23-05fba56ed7ed", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 3c82b8796b7ad6d049a2af520c8be0a43713a695 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 06:34:19 -0700 Subject: [PATCH 21/43] fix merge --- BayBE_grid_search.ipynb | 78 +++++------------------------------------ 1 file changed, 9 insertions(+), 69 deletions(-) mode change 100644 => 100755 BayBE_grid_search.ipynb diff --git a/BayBE_grid_search.ipynb b/BayBE_grid_search.ipynb old mode 100644 new mode 100755 index 9899cb9..b95d12a --- a/BayBE_grid_search.ipynb +++ b/BayBE_grid_search.ipynb @@ -197,45 +197,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "524e2642-4a6d-458e-bade-05106c17e4ab", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", "text/plain": [ - "
" + "" ] }, + "execution_count": 7, "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "viz = visualization.grid_search_heatmap(n_its, noise, performance_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e321112f-2613-45ff-adad-f586336f30be", - "metadata": {}, - "outputs": [ + "output_type": "execute_result" + }, { "data": { + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -243,53 +223,13 @@ } ], "source": [ - "plt.savefig('test.png', dpi = 300)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7ca74e30-9b2d-40bf-b5b2-a84991804034", - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sn" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "48b0d45b-ce95-41ba-8fc7-358835b36912", - "metadata": {}, - "outputs": [], - "source": [ - "fig = viz" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9ff98ed7-b654-4e4e-9307-31c85ce9b5eb", - "metadata": {}, - "outputs": [], - "source": [ - "fig = viz.get_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cb278944-a389-48e7-a5ef-bdb2928968d3", - "metadata": {}, - "outputs": [], - "source": [ - "fig.savefig('test.png', dpi = 300)" + "visualization.grid_search_heatmap(n_its, noise, performance_matrix)" ] }, { "cell_type": "code", "execution_count": null, - "id": "cde098ef-b077-4229-8b23-05fba56ed7ed", + "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", "metadata": {}, "outputs": [], "source": [] From e9119ef9f2edb0758c5c3a7273a48f86c2f2321c Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 21:56:12 +0800 Subject: [PATCH 22/43] update --- analyse_grid_experiment.ipynb | 2116 +++++---------------------------- comparison.ipynb | 1 + run_experiment.py | 13 +- run_grid_experiments.py | 2 +- src/schwefel.py | 4 +- 5 files changed, 282 insertions(+), 1854 deletions(-) create mode 100644 comparison.ipynb diff --git a/analyse_grid_experiment.ipynb b/analyse_grid_experiment.ipynb index bc83b29..b55e5a5 100644 --- a/analyse_grid_experiment.ipynb +++ b/analyse_grid_experiment.ipynb @@ -2,1606 +2,197 @@ "cells": [ { "cell_type": "code", - "execution_count": 40, + "execution_count": 6, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SMOKE_TEST None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-03-28 05:16:37,097\tINFO worker.py:1724 -- Started a local Ray instance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 2 noise 0.01 budget 10 seed 0, time: 1.15s\n", - "Started problem 2 noise 0.01 budget 10 seed 0, time: 2.33s\n", - "Started problem 2 noise 0.1 budget 10 seed 0, time: 3.49s\n", - "Started problem 2 noise 0.1 budget 10 seed 0, time: 4.71s\n", - "Started problem 2 noise 1.0 budget 10 seed 0, time: 5.95s\n", - "\u001b[36m(pid=13260)\u001b[0m SMOKE_TEST None\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", - "Started problem 2 noise 1.0 budget 10 seed 0, time: 7.22s\n", - "Started problem 4 noise 0.01 budget 10 seed 0, time: 8.49s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4035], dtype=torch.float64)\n", - "Started problem 4 noise 0.01 budget 10 seed 0, time: 9.76s\n", - "Started problem 4 noise 0.1 budget 10 seed 0, time: 11.04s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 4 noise 0.1 budget 10 seed 0, time: 12.30s\n", - "\u001b[36m(pid=11152)\u001b[0m SMOKE_TEST None\u001b[32m [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 5.538 seconds.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "Started problem 4 noise 1.0 budget 10 seed 0, time: 13.61s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5945], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "Started problem 4 noise 1.0 budget 10 seed 0, time: 14.93s\n", - "Started problem 6 noise 0.01 budget 10 seed 0, time: 16.24s\n", - "\u001b[36m(pid=26380)\u001b[0m SMOKE_TEST None\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 3, total time: 9.158 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.7961], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(pid=5304)\u001b[0m SMOKE_TEST None\u001b[32m [repeated 3x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 4, total time: 12.772 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4124], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 4, total time: 12.941 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[139.5930]], dtype=torch.float64), tensor([514.2790], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 8, total time: 26.648 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-210.6462]], dtype=torch.float64), tensor([614.8742], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 5, total time: 16.357 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", - "Started problem 6 noise 0.01 budget 10 seed 0, time: 40.08s\n", - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[14.5189]], dtype=torch.float64), tensor([427.9994], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 6 noise 0.1 budget 10 seed 0, time: 41.45s\n", - "Started problem 6 noise 0.1 budget 10 seed 0, time: 42.82s\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 7, total time: 22.562 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11308)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-366.4503]], dtype=torch.float64), tensor([524.8431], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 6 noise 1.0 budget 10 seed 0, time: 47.45s\n", - "Started problem 6 noise 1.0 budget 10 seed 0, time: 48.73s\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 8 noise 0.01 budget 10 seed 0, time: 50.09s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5816], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 8 noise 0.01 budget 10 seed 0, time: 53.88s\n", - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 4, total time: 14.870 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "Started problem 8 noise 0.1 budget 10 seed 0, time: 55.83s\n", - "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[-33.9809]], dtype=torch.float64), tensor([403.9808], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 6, total time: 18.962 seconds.\u001b[32m [repeated 15x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[151.9244]], dtype=torch.float64), tensor([455.2001], dtype=torch.float64)\u001b[32m [repeated 16x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 5, total time: 13.611 seconds.\u001b[32m [repeated 16x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-194.1547]], dtype=torch.float64), tensor([609.2873], dtype=torch.float64)\u001b[32m [repeated 16x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 8 noise 0.1 budget 10 seed 0, time: 71.24s\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 7, total time: 19.449 seconds.\u001b[32m [repeated 16x across cluster]\u001b[0m\n", - "Started problem 8 noise 1.0 budget 10 seed 0, time: 74.59s\n", - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[165.3069]], dtype=torch.float64), tensor([373.0400], dtype=torch.float64)\u001b[32m [repeated 15x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 8 noise 1.0 budget 10 seed 0, time: 76.03s\n", - "Started problem 10 noise 0.01 budget 10 seed 0, time: 77.84s\n", - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.01 budget 10 seed 0, time: 79.89s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5710], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.1 budget 10 seed 0, time: 83.05s\n", - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 2, total time: 6.233 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.1 budget 10 seed 0, time: 84.39s\n", - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.4688], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 2, total time: 6.261 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-349.1740]], dtype=torch.float64), tensor([362.2102], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "Started problem 10 noise 1.0 budget 10 seed 0, time: 91.98s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=23920)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=23920)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 1, total time: 3.567 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[19.4747]], dtype=torch.float64), tensor([437.5959], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 8, total time: 25.884 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[-330.7235]], dtype=torch.float64), tensor([215.2656], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 10 noise 1.0 budget 10 seed 0, time: 104.44s\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 9, total time: 31.145 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.01 budget 10 seed 1, time: 107.08s\n", - "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-350.3297]], dtype=torch.float64), tensor([374.1686], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.01 budget 10 seed 1, time: 109.48s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 6.148 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.1 budget 10 seed 1, time: 110.79s\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[431.1796]], dtype=torch.float64), tensor([13.1537], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11152)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n", - "\u001b[36m(worker pid=11152)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 8, total time: 31.750 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.1 budget 10 seed 1, time: 117.00s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.7161], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 7, total time: 29.613 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 2 noise 1.0 budget 10 seed 1, time: 122.75s\n", - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[349.8151]], dtype=torch.float64), tensor([469.7429], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "Started problem 2 noise 1.0 budget 10 seed 1, time: 125.72s\n", - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 3, total time: 9.940 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-395.2393]], dtype=torch.float64), tensor([758.0537], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 2, total time: 8.417 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "Started problem 4 noise 0.01 budget 10 seed 1, time: 132.43s\n", - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4024], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11152)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n", - "\u001b[36m(worker pid=11152)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 9, total time: 31.250 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 4 noise 0.01 budget 10 seed 1, time: 139.13s\n", - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[219.4622]], dtype=torch.float64), tensor([247.8814], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 7, total time: 33.871 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 4 noise 0.1 budget 10 seed 1, time: 143.49s\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[242.8150]], dtype=torch.float64), tensor([388.5509], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 23.435 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[127.6187]], dtype=torch.float64), tensor([539.9996], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "Started problem 4 noise 0.1 budget 10 seed 1, time: 153.02s\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 15.195 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 4 noise 1.0 budget 10 seed 1, time: 154.37s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 4 noise 1.0 budget 10 seed 1, time: 156.93s\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-303.9571]], dtype=torch.float64), tensor([118.4931], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 2, total time: 5.840 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=23920)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=23920)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 6 noise 0.01 budget 10 seed 1, time: 161.86s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=23920)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=23920)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=23920)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5807], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 3, total time: 11.494 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[339.0733]], dtype=torch.float64), tensor([563.8048], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "Started problem 6 noise 0.01 budget 10 seed 1, time: 169.41s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 6 noise 0.1 budget 10 seed 1, time: 170.92s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 3, total time: 10.153 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 6 noise 0.1 budget 10 seed 1, time: 172.38s\n", - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-59.2630]], dtype=torch.float64), tensor([476.7675], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 1, total time: 5.100 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[33.2251]], dtype=torch.float64), tensor([435.6392], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 6, total time: 20.879 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[156.7309]], dtype=torch.float64), tensor([426.5164], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 17.665 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[259.5776]], dtype=torch.float64), tensor([520.8659], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 6 noise 1.0 budget 10 seed 1, time: 190.66s\n", - "Started problem 6 noise 1.0 budget 10 seed 1, time: 192.20s\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 9, total time: 39.471 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 8 noise 0.01 budget 10 seed 1, time: 194.30s\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-50.0829]], dtype=torch.float64), tensor([455.0102], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 8 noise 0.01 budget 10 seed 1, time: 198.54s\n", - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-299.6165]], dtype=torch.float64), tensor([119.5035], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "Started problem 8 noise 0.1 budget 10 seed 1, time: 201.87s\n", - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 3, total time: 13.371 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 8 noise 0.1 budget 10 seed 1, time: 205.59s\n", - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[419.3371]], dtype=torch.float64), tensor([0.3389], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 4, total time: 17.544 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 8 noise 1.0 budget 10 seed 1, time: 210.01s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[419.3050]], dtype=torch.float64), tensor([0.3809], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 4, total time: 13.287 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[480.9437]], dtype=torch.float64), tensor([389.0365], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 8 noise 1.0 budget 10 seed 1, time: 218.17s\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 3, total time: 10.274 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-130.3979]], dtype=torch.float64), tensor([300.1079], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 23.496 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.4084], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 4, total time: 12.458 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[421.2228]], dtype=torch.float64), tensor([0.0195], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.01 budget 10 seed 1, time: 233.20s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.01 budget 10 seed 1, time: 236.07s\n", - "Started problem 10 noise 0.1 budget 10 seed 1, time: 237.55s\n", - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[25.8501]], dtype=torch.float64), tensor([442.6817], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.1 budget 10 seed 1, time: 238.98s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 2, total time: 9.002 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 10 noise 1.0 budget 10 seed 1, time: 243.38s\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-283.2684]], dtype=torch.float64), tensor([163.7023], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 10 noise 1.0 budget 10 seed 1, time: 246.47s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[423.2956]], dtype=torch.float64), tensor([0.6630], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 5, total time: 20.089 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.01 budget 10 seed 2, time: 252.94s\n", - "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.3731], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 3, total time: 10.805 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.01 budget 10 seed 2, time: 258.51s\n", - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[58.5060]], dtype=torch.float64), tensor([361.3186], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11152)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11152)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 7, total time: 27.107 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-283.2569]], dtype=torch.float64), tensor([163.8564], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 5, total time: 26.755 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-289.6109]], dtype=torch.float64), tensor([139.1617], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.1 budget 10 seed 2, time: 272.20s\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 4, total time: 16.254 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "Started problem 2 noise 0.1 budget 10 seed 2, time: 275.67s\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[421.4555]], dtype=torch.float64), tensor([-0.0801], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 1, total time: 5.039 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[379.1839]], dtype=torch.float64), tensor([197.8249], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 2 noise 1.0 budget 10 seed 2, time: 283.55s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 7, total time: 33.795 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[401.1191]], dtype=torch.float64), tensor([48.1779], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 2 noise 1.0 budget 10 seed 2, time: 289.18s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 3, total time: 17.535 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "Started problem 4 noise 0.01 budget 10 seed 2, time: 292.32s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[422.6121]], dtype=torch.float64), tensor([0.3322], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 4 noise 0.01 budget 10 seed 2, time: 293.80s\n", - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 1, total time: 4.613 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[220.0513]], dtype=torch.float64), tensor([250.1605], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 3, total time: 13.537 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[400.4539]], dtype=torch.float64), tensor([51.5641], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 4 noise 0.1 budget 10 seed 2, time: 305.51s\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 4, total time: 15.496 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 4 noise 0.1 budget 10 seed 2, time: 310.05s\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[60.1855]], dtype=torch.float64), tensor([359.0799], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 7.593 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-71.2434]], dtype=torch.float64), tensor([478.3039], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 3, total time: 14.358 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[352.6976]], dtype=torch.float64), tensor([443.3986], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 3, total time: 15.323 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 4 noise 1.0 budget 10 seed 2, time: 326.65s\n", - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[421.4453]], dtype=torch.float64), tensor([0.0870], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 4 noise 1.0 budget 10 seed 2, time: 328.39s\n", - "\u001b[36m(worker pid=26380)\u001b[0m Starting iteration 1, total time: 4.222 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[60.7442]], dtype=torch.float64), tensor([358.8514], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 9, total time: 43.902 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 6 noise 0.01 budget 10 seed 2, time: 336.45s\n", - "Started problem 6 noise 0.01 budget 10 seed 2, time: 337.94s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.9766], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=26380)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=26380)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 6 noise 0.1 budget 10 seed 2, time: 339.92s\n", - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "Started problem 6 noise 0.1 budget 10 seed 2, time: 341.56s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:389: RuntimeWarning: Optimization failed on the second try, after generating a new set of initial conditions.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[430.7882]], dtype=torch.float64), tensor([12.0632], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=11308)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 4, total time: 18.488 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-349.7572]], dtype=torch.float64), tensor([367.4822], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 3, total time: 13.924 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "Started problem 6 noise 1.0 budget 10 seed 2, time: 354.14s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[-428.2759]], dtype=torch.float64), tensor([831.1028], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 5, total time: 21.075 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 6 noise 1.0 budget 10 seed 2, time: 358.25s\n", - "\u001b[36m(worker pid=5304)\u001b[0m New candidate: tensor([[125.5204]], dtype=torch.float64), tensor([541.7065], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "2024-03-28 21:40:41,708\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 2, total time: 7.223 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[67.0226]], dtype=torch.float64), tensor([355.6261], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + "Started problem 4 noise 10 budget 10 seed 0, time: 1.00s\n", + "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", + "Started problem 4 noise 10 budget 10 seed 0, time: 2.11s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 29.338 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 8 noise 0.01 budget 10 seed 2, time: 371.70s\n", - "\u001b[36m(worker pid=10364)\u001b[0m New candidate: tensor([[-296.3666]], dtype=torch.float64), tensor([123.4420], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([257.0693], dtype=torch.float64)\n", + "Started problem 4 noise 100 budget 10 seed 0, time: 3.26s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 17.151 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 8 noise 0.01 budget 10 seed 2, time: 374.80s\n", - "Started problem 8 noise 0.1 budget 10 seed 2, time: 377.56s\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[386.8818]], dtype=torch.float64), tensor([136.1810], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 6, total time: 21.760 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "Started problem 4 noise 100 budget 10 seed 0, time: 4.38s\n", + "Started problem 10 noise 10 budget 10 seed 0, time: 5.58s\n", + "\u001b[36m(worker pid=12024)\u001b[0m Starting iteration 2, total time: 4.455 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 10 noise 10 budget 10 seed 0, time: 6.80s\n", + "Started problem 10 noise 100 budget 10 seed 0, time: 8.03s\n", + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([609.0729], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "Started problem 10 noise 100 budget 10 seed 0, time: 9.30s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 8 noise 0.1 budget 10 seed 2, time: 381.39s\n", - "Started problem 8 noise 1.0 budget 10 seed 2, time: 382.78s\n", - "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([238.8966], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "Started problem 8 noise 1.0 budget 10 seed 2, time: 384.18s\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 8, total time: 28.449 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "Started problem 4 noise 10 budget 10 seed 1, time: 10.58s\n", + "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 4, total time: 10.661 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 4 noise 10 budget 10 seed 1, time: 11.82s\n", + "Started problem 4 noise 100 budget 10 seed 1, time: 13.08s\n", + "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[-17.1415]], dtype=torch.float64), tensor([499.5804], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "Started problem 4 noise 100 budget 10 seed 1, time: 14.33s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[367.1162]], dtype=torch.float64), tensor([306.6477], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 4, total time: 16.961 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + "Started problem 10 noise 10 budget 10 seed 1, time: 15.56s\n", + "\u001b[36m(worker pid=18444)\u001b[0m Starting iteration 2, total time: 8.578 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[-56.0295]], dtype=torch.float64), tensor([456.1113], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 10 noise 0.01 budget 10 seed 2, time: 392.68s\n", - "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[367.0235]], dtype=torch.float64), tensor([306.7043], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 1, total time: 3.617 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.01 budget 10 seed 2, time: 397.27s\n" + "\u001b[36m(worker pid=5244)\u001b[0m Starting iteration 4, total time: 16.479 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[135.0684]], dtype=torch.float64), tensor([529.8546], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[-325.9922]], dtype=torch.float64), tensor([186.4192], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m Starting iteration 1, total time: 4.216 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 6, total time: 24.424 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5244)\u001b[0m New candidate: tensor([[27.8300]], dtype=torch.float64), tensor([445.8539], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[195.3344]], dtype=torch.float64), tensor([226.2150], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m Starting iteration 7, total time: 25.392 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 7, total time: 29.584 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5244)\u001b[0m New candidate: tensor([[370.4692]], dtype=torch.float64), tensor([290.3323], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[304.3039]], dtype=torch.float64), tensor([719.1803], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 9, total time: 39.304 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m Starting iteration 6, total time: 29.239 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[-345.6434]], dtype=torch.float64), tensor([480.1781], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "Started problem 10 noise 10 budget 10 seed 1, time: 41.78s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 10 noise 0.1 budget 10 seed 2, time: 416.41s\n", - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-113.2019]], dtype=torch.float64), tensor([312.8168], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "Started problem 10 noise 0.1 budget 10 seed 2, time: 418.20s\n", - "Started problem 10 noise 1.0 budget 10 seed 2, time: 419.63s\n" + "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 1, total time: 4.131 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[-325.8501]], dtype=torch.float64), tensor([197.8004], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started problem 10 noise 100 budget 10 seed 1, time: 46.98s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 10 noise 1.0 budget 10 seed 2, time: 421.12s\n", + "Started problem 10 noise 100 budget 10 seed 1, time: 48.25s\n", "0\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[-131.4509]], dtype=torch.float64), tensor([301.6516], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 1, total time: 4.467 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 1, total time: 4.420 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m New candidate: tensor([[435.1405]], dtype=torch.float64), tensor([56.5343], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", "0\n" ] }, @@ -1609,32 +200,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[351.2055]], dtype=torch.float64), tensor([456.3065], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m Starting iteration 1, total time: 11.225 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m New candidate: tensor([[-281.5970]], dtype=torch.float64), tensor([171.6314], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 2, total time: 9.821 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 2, total time: 8.928 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "0\n", + "0\n", + "\u001b[36m(worker pid=5244)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([614.1931], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", "0\n" ] }, @@ -1642,148 +219,104 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([599.5762], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 5, total time: 22.026 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[399.0926]], dtype=torch.float64), tensor([58.5408], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 6, total time: 25.794 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "0\n" + "\u001b[36m(worker pid=5244)\u001b[0m Starting iteration 2, total time: 8.105 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[490.3498]], dtype=torch.float64), tensor([639.7774], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5304)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=5304)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=5304)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=5304)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=11152)\u001b[0m New candidate: tensor([[40.5840]], dtype=torch.float64), tensor([415.4415], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11152)\u001b[0m Starting iteration 7, total time: 31.019 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 4, total time: 17.851 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([235.9000], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=5984)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=5984)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", + "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[413.1906]], dtype=torch.float64), tensor([7.6418], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 5, total time: 19.580 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "0\n" + "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 7, total time: 31.495 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[-76.1500]], dtype=torch.float64), tensor([443.0088], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:389: RuntimeWarning: Optimization failed on the second try, after generating a new set of initial conditions.\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0\n", - "\u001b[36m(worker pid=11308)\u001b[0m New candidate: tensor([[-94.1265]], dtype=torch.float64), tensor([393.3504], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=11308)\u001b[0m Starting iteration 8, total time: 37.378 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m Starting iteration 5, total time: 21.873 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[221.3941]], dtype=torch.float64), tensor([402.0225], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=26380)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=26380)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=26380)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=11308)\u001b[0m [NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]\n" + "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 8, total time: 35.729 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", "0\n", - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[179.8027]], dtype=torch.float64), tensor([287.0650], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 2, total time: 7.422 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "0\n" + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[-62.3537]], dtype=torch.float64), tensor([477.4399], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=10364)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m Trying again with a new set of initial conditions.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=10364)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\u001b[36m(worker pid=1928)\u001b[0m Starting iteration 8, total time: 35.362 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m New candidate: tensor([[392.9702]], dtype=torch.float64), tensor([85.1805], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "0\n", "0\n", - "\u001b[36m(worker pid=13260)\u001b[0m New candidate: tensor([[420.9739]], dtype=torch.float64), tensor([-0.0748], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m Starting iteration 9, total time: 35.767 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13260)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13260)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=13260)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=13260)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ "0\n", - "\u001b[36m(worker pid=23920)\u001b[0m New candidate: tensor([[407.8553]], dtype=torch.float64), tensor([21.4103], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 6, total time: 17.385 seconds.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", "0\n" ] }, @@ -1791,46 +324,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[421.8449]], dtype=torch.float64), tensor([1.2719], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 8, total time: 24.529 seconds.\u001b[32m [repeated 2x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=29676)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=29676)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=29676)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=29676)\u001b[0m New candidate: tensor([[422.5996]], dtype=torch.float64), tensor([0.3448], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=29676)\u001b[0m Starting iteration 9, total time: 27.854 seconds.\n", + "\u001b[36m(worker pid=5984)\u001b[0m Starting iteration 8, total time: 37.963 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m New candidate: tensor([[-312.4408]], dtype=torch.float64), tensor([136.6988], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "0\n", "0\n", - "all experiments done, time: 490.79s\n" + "0\n", + "all experiments done, time: 100.01s\n" ] } ], @@ -1839,9 +346,9 @@ "import pandas as pd\n", "from run_grid_experiments import run_grid_experiments\n", "\n", - "seeds = [0,1,2]\n", - "n_inits = [2, 4, 6 ,8, 10]\n", - "noise_levels = [0.01, 0.1, 1.]\n", + "seeds = [0,1]\n", + "n_inits = [4, 10]\n", + "noise_levels = [10, 100]\n", "# budgets = [10, 20, 50]\n", "noise_bools = [True, False]\n", "budget = 10\n", @@ -1851,14 +358,23 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[455.3359]], dtype=torch.float64), tensor([200.7786], dtype=torch.float64)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_15664\\3020836113.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\n", + "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_16892\\636701015.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n" ] }, @@ -1893,115 +409,157 @@ " \n", " \n", " 0\n", - " 2\n", - " 0.01\n", + " 4\n", + " 10\n", " 0\n", " True\n", - " 238.403534\n", + " 207.341003\n", " \n", " \n", " 0\n", - " 2\n", - " 0.01\n", + " 4\n", + " 10\n", " 1\n", " True\n", - " 42.152557\n", + " 42.158676\n", + " \n", + " \n", + " 0\n", + " 4\n", + " 100\n", + " 0\n", + " True\n", + " 120.417244\n", " \n", " \n", " 0\n", - " 2\n", - " 0.01\n", - " 2\n", + " 4\n", + " 100\n", + " 1\n", " True\n", - " 118.625458\n", + " 42.158676\n", " \n", " \n", " 0\n", - " 2\n", - " 0.10\n", + " 10\n", + " 10\n", " 0\n", " True\n", - " 238.491608\n", + " 1.374434\n", " \n", " \n", " 0\n", - " 2\n", - " 0.10\n", + " 10\n", + " 10\n", " 1\n", " True\n", - " 42.097500\n", + " 42.158676\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 0\n", + " 10\n", + " 100\n", + " 0\n", + " True\n", + " 102.431664\n", " \n", " \n", " 0\n", " 10\n", - " 0.10\n", + " 100\n", " 1\n", + " True\n", + " 42.158676\n", + " \n", + " \n", + " 0\n", + " 4\n", + " 10\n", + " 0\n", " False\n", - " -0.108989\n", + " 118.466827\n", " \n", " \n", " 0\n", + " 4\n", " 10\n", - " 0.10\n", - " 2\n", + " 1\n", + " False\n", + " 0.138172\n", + " \n", + " \n", + " 0\n", + " 4\n", + " 100\n", + " 0\n", + " False\n", + " 118.751045\n", + " \n", + " \n", + " 0\n", + " 4\n", + " 100\n", + " 1\n", " False\n", - " -0.074771\n", + " 13.000295\n", " \n", " \n", " 0\n", " 10\n", - " 1.00\n", + " 10\n", " 0\n", " False\n", - " -0.549255\n", + " 0.007694\n", " \n", " \n", " 0\n", " 10\n", - " 1.00\n", + " 10\n", " 1\n", " False\n", - " -1.931614\n", + " 0.265530\n", " \n", " \n", " 0\n", " 10\n", - " 1.00\n", - " 2\n", + " 100\n", + " 0\n", + " False\n", + " 25.227522\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 100\n", + " 1\n", " False\n", - " -0.017878\n", + " 0.034306\n", " \n", " \n", "\n", - "

90 rows × 5 columns

\n", "" ], "text/plain": [ - " n_init noise_level seed noise_bool best\n", - "0 2 0.01 0 True 238.403534\n", - "0 2 0.01 1 True 42.152557\n", - "0 2 0.01 2 True 118.625458\n", - "0 2 0.10 0 True 238.491608\n", - "0 2 0.10 1 True 42.097500\n", - ".. ... ... ... ... ...\n", - "0 10 0.10 1 False -0.108989\n", - "0 10 0.10 2 False -0.074771\n", - "0 10 1.00 0 False -0.549255\n", - "0 10 1.00 1 False -1.931614\n", - "0 10 1.00 2 False -0.017878\n", - "\n", - "[90 rows x 5 columns]" + " n_init noise_level seed noise_bool best\n", + "0 4 10 0 True 207.341003\n", + "0 4 10 1 True 42.158676\n", + "0 4 100 0 True 120.417244\n", + "0 4 100 1 True 42.158676\n", + "0 10 10 0 True 1.374434\n", + "0 10 10 1 True 42.158676\n", + "0 10 100 0 True 102.431664\n", + "0 10 100 1 True 42.158676\n", + "0 4 10 0 False 118.466827\n", + "0 4 10 1 False 0.138172\n", + "0 4 100 0 False 118.751045\n", + "0 4 100 1 False 13.000295\n", + "0 10 10 0 False 0.007694\n", + "0 10 10 1 False 0.265530\n", + "0 10 100 0 False 25.227522\n", + "0 10 100 1 False 0.034306" ] }, - "execution_count": 41, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2012,10 +570,10 @@ " for n_init in n_inits:\n", " for noise_level in noise_levels:\n", " for seed in seeds:\n", - " X, Y, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " X, Y, Y_real, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", " sliding_min = torch.zeros(Y.shape[0])\n", - " for i in range(Y.shape[0]):\n", - " sliding_min[i] = Y[:i+1].min().item()\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n", " \n", "df " @@ -2023,101 +581,46 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GP with noise\n" + ] + }, { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2125,118 +628,70 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 bestbest
 meanstdmeanstd
noise_level0.0100000.1000001.0000000.0100000.1000001.0000001010010100
n_init     
2133.060516133.059017133.04409098.91860198.99353799.743231
423.64979479.13051123.14113517.57767396.07697618.302944
648.19026748.11009347.31825351.47901551.48155751.4936134124.7581.29116.8055.34
867.55036967.65741568.74347331.23158431.13585630.332036
1055.20263754.27264854.80385642.23444159.17468541.6362301021.7772.3028.8442.62
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GP without noise\n" + ] + }, { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2244,61 +699,28 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 bestbest
 meanstdmeanstd
noise_level0.0100000.1000001.0000000.0100000.1000001.0000001010010100
n_init     
274.09683174.09221874.063016126.082897126.206706127.244658
479.06701779.10704879.73454468.43010568.32782067.513591
60.1688980.195790-0.2214620.2718620.2018100.777112
80.0088230.014245-0.2993560.0060000.1007650.695021459.3065.8883.6774.78
100.001599-0.083678-0.8329150.0157370.0222380.987899100.1412.630.1817.81
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2311,8 +733,10 @@ "# df = df.groupby([\"n_init\", \"noise_level\", \"noise_bool\"]).agg({\"min\": [\"mean\", \"std\"]})\n", "df_no_noise = df_no_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", "df_noise = df_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "display(df_noise.unstack().style.background_gradient(cmap='viridis'))\n", - "display(df_no_noise.unstack().style.background_gradient(cmap='viridis'))" + "print(\"GP with noise\")\n", + "display(df_noise.unstack().style.format(\"{:.2f}\").background_gradient(cmap='viridis'))\n", + "print(\"GP without noise\")\n", + "display(df_no_noise.unstack().style.format(\"{:.2f}\").background_gradient(cmap='viridis'))" ] } ], diff --git a/comparison.ipynb b/comparison.ipynb new file mode 100644 index 0000000..a4f3653 --- /dev/null +++ b/comparison.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Starting iteration 0, total time: 0.000 seconds.\n"]},{"ename":"RuntimeError","evalue":"Expected Y.shape[:-2] to be torch.Size([]), matching the `batch_shape` argument to `Standardize`, but got Y.shape[:-2]=torch.Size([50]).","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)","Cell \u001b[1;32mIn[5], line 21\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStarting iteration \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, total time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m seconds.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 20\u001b[0m train_x \u001b[38;5;241m=\u001b[39m normalize(train_X, bounds)\n\u001b[1;32m---> 21\u001b[0m mll, model \u001b[38;5;241m=\u001b[39m \u001b[43minitialize_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbounds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 22\u001b[0m fit_gpytorch_model(mll)\n\u001b[0;32m 24\u001b[0m \u001b[38;5;66;03m# optimize the acquisition function and get the observations\u001b[39;00m\n","Cell \u001b[1;32mIn[3], line 5\u001b[0m, in \u001b[0;36minitialize_model\u001b[1;34m(train_x, train_y, bounds)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minitialize_model\u001b[39m(train_x, train_y, bounds):\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# define models for objective and constraint\u001b[39;00m\n\u001b[0;32m 3\u001b[0m train_y\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mtrain_y \u001b[38;5;66;03m# negative because botorch assumes maximization\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mSingleTaskGP\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_X\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# train_Yvar=torch.full_like(train_y.unsqueeze(-1), 1e-6),\u001b[39;49;00m\n\u001b[0;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43moutcome_transform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStandardize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m mll \u001b[38;5;241m=\u001b[39m ExactMarginalLogLikelihood(model\u001b[38;5;241m.\u001b[39mlikelihood, model)\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m mll, model\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\models\\gp_regression.py:160\u001b[0m, in \u001b[0;36mSingleTaskGP.__init__\u001b[1;34m(self, train_X, train_Y, train_Yvar, likelihood, covar_module, mean_module, outcome_transform, input_transform)\u001b[0m\n\u001b[0;32m 156\u001b[0m transformed_X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_inputs(\n\u001b[0;32m 157\u001b[0m X\u001b[38;5;241m=\u001b[39mtrain_X, input_transform\u001b[38;5;241m=\u001b[39minput_transform\n\u001b[0;32m 158\u001b[0m )\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m outcome_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 160\u001b[0m train_Y, train_Yvar \u001b[38;5;241m=\u001b[39m \u001b[43moutcome_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_Yvar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tensor_args(X\u001b[38;5;241m=\u001b[39mtransformed_X, Y\u001b[38;5;241m=\u001b[39mtrain_Y, Yvar\u001b[38;5;241m=\u001b[39mtrain_Yvar)\n\u001b[0;32m 162\u001b[0m ignore_X_dims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ignore_X_dims_scaling_check\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\models\\transforms\\outcome.py:294\u001b[0m, in \u001b[0;36mStandardize.forward\u001b[1;34m(self, Y, Yvar)\u001b[0m\n\u001b[0;32m 292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Y\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_shape:\n\u001b[1;32m--> 294\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 295\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected Y.shape[:-2] to be \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, matching \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 296\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe `batch_shape` argument to `Standardize`, but got \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 297\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY.shape[:-2]=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mY\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 298\u001b[0m )\n\u001b[0;32m 299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Y\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_m:\n\u001b[0;32m 300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 301\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWrong output dimension. Y.size(-1) is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mY\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m; expected \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 302\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 303\u001b[0m )\n","\u001b[1;31mRuntimeError\u001b[0m: Expected Y.shape[:-2] to be torch.Size([]), matching the `batch_shape` argument to `Standardize`, but got Y.shape[:-2]=torch.Size([50])."]}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=10)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","n_init = 50\n","budget = 2\n","\n","train_X, train_Y= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, bounds)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, bounds)\n","fit_gpytorch_model(mll)\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 374.5851697961611\n","Best solution found: [50.]\n","Total number of evaluations: 52\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACtG0lEQVR4nO2deZwT9f3/X5Nz7w3swi7HcsktKCpq8a5QQPGqtGqL59evRwsK1vtbrWirqNWqVFv9VStqVdB6tNUWpSh4gIIoCoqouMAqLAsse+8m2WR+f3zymZnMTiYzk8lmkryfj0ceyeb8JJvMvOf1fr3fb0EURREEQRAEQRA5iivTCyAIgiAIgkgnFOwQBEEQBJHTULBDEARBEEROQ8EOQRAEQRA5DQU7BEEQBEHkNBTsEARBEASR01CwQxAEQRBETuPJ9AKcQDQaxa5du1BaWgpBEDK9HIIgCIIgDCCKIlpbWzFw4EC4XIn1Gwp2AOzatQs1NTWZXgZBEARBEBaoq6vD4MGDE95OwQ6A0tJSAOzDKisry/BqCIIgCCILaG8HBg5kl3ftAoqLe30JLS0tqKmpkfbjiaBgB5BSV2VlZRTsEARBEIQR3G75cllZRoIdTjILChmUCYIgCILIaSjYIQiCIAgip6E0FkEQBEEQ5vF4gIsuki87GGevjiAIgiAIZ+L3A0uWZHoVhqA0FkEQBEEQOQ0pOwRBEARBmEcUgY4OdrmoCHBwU15SdgiCIAiCME9HB1BSwk486HEoFOwQBEEQBJHTULBDEARBEEROQ8EOQRAEQRA5DQU7BEEQBEHkNBTsEARBEASR01Cwk0+EnO2WJwiCIIh0QMFOvvDVm8CiwcD6JzK9EoIgCCIXcLuBn/yEnZQT0B0INRXMF75bB4gR4LuPgCMvzfRqCIIgiGynoAB48cVMr8IQpOzkC8E2dt7dldl1EARBEEQvQ8FOvhBqZefdwcyugyAIgiB6GQp28gVSdgiCIAg7aW9n87AEgV12MBTs5AshHuyQskMQBEHkFxTs5AuSstOZ2XUQBEEQRC9DwU6+QMoOQRAEkadQsJMvBLlBmTw7BEEQRH5BwU6+EIqZx0jZIQiCIPIMCnbyhRBVYxEEQRD5CXVQzgci3XKQE6ZghyAIgrABtxs49VT5soOhYCcf4A0FAVJ2CIIgCHsoKABefz3TqzAEpbHyAV52DgDRMBCNZG4tBEEQBNHLULCTD4Ta4v8mkzJBEASRRzgm2Ln77rshCAIWLFgQd/3atWtx8skno7i4GGVlZTjhhBPQ2Sk3xmtsbMScOXNQVlaGQCCASy+9FG1tqp17vhNUBzuUyiIIgiBSpL0dKC5mJxoXkZz169fjsccewyGHHBJ3/dq1azFz5kxMnz4d69atw/r16zFv3jy4XPKy58yZg88//xwrVqzAa6+9hnfeeQeXX355b78FZ6P07AAU7BAEQRD20NHBTg4n4wbltrY2zJkzB3/5y1/wu9/9Lu62a665BldffTVuuukm6boxY8ZIl7ds2YLly5dj/fr1mDx5MgDgj3/8I0499VTcd999GDhwoOZrBoNBBINyKqelpcXOt+Q8SNkhCIIg8piMKztz587FrFmzMG3atLjrGxoa8OGHH6J///445phjUFVVhRNPPBHvvfeedJ+1a9ciEAhIgQ4ATJs2DS6XCx9++GHC11y0aBHKy8ulU01Njf1vzEmQZ4cgCILIYzIa7CxduhQff/wxFi1a1OO2b7/9FgCwcOFCXHbZZVi+fDkOP/xwTJ06FV9//TUAoL6+Hv379497nMfjQd++fVFfX5/wdW+++WY0NzdLp7q6OhvflQMhZYcgCILIYzKWxqqrq8P8+fOxYsUKFBQU9Lg9Go0CAK644gpccsklAIDDDjsMK1euxF//+lfNAMkofr8ffr/f8uOzjh6eHVJ2CIIgiPwhY8rOhg0b0NDQgMMPPxwejwcejwerV6/G4sWL4fF4UFVVBQAYP3583OPGjRuHnTt3AgCqq6vR0NAQd3t3dzcaGxtRXV3dO28kGwipXPLhTu37EQRBEEQOkjFlZ+rUqdi0aVPcdZdccgnGjh2LG2+8ESNGjMDAgQOxdevWuPt89dVXOOWUUwAAU6ZMQVNTEzZs2IAjjjgCAPDWW28hGo3i6KOP7p03kg30SGORskMQBEGkiMsFnHiifNnBZCzYKS0txYQJE+KuKy4uRkVFhXT99ddfj9tuuw2HHnooJk2ahKeeegpffvkl/v73vwNgKs/MmTNx2WWX4dFHH0U4HMa8efNw3nnnJazEykt6GJTJs0MQBEGkSGEhsGpVpldhiIyXnuuxYMECdHV14ZprrkFjYyMOPfRQrFixAgcddJB0n2effRbz5s3D1KlT4XK5MHv2bCxevDiDq3YgQfLsEARBEPmLIIqimOlFZJqWlhaUl5ejubkZZWVlmV6O/TzzY2DbW/Lfpy8Gjrgoc+shCIIgCBswuv92dpKNsAfu2XF52TmlsQgi/ez+DFh8GLD55UyvhCDSQ3s70K8fO9G4CCLjcM9OcSU7p2CHINLPN/8FGr8Fvng10yshiPSxbx87ORwKdvIBruwU8WCHPDsEkXb4QUZXc2bXQRAEBTt5AW8qSMoOQfQevDCgsymjyyAIgoKd/CCoTmORskMQaSdIyg5BOAUKdnKd7iAQDbPLPI1FHZQJIv1wRZWCHYLIOBTs5DrK7slFFeyclB2CSD9KZYc6fBBERnF0U0HCBrhJ0lMA+IrZZfLsEET64b89McIu+0szux6CsBuXC5g8Wb7sYCjYyXX4BtdXAnhik94p2CGI9KPsXN7ZRMEOkXsUFgLr12d6FYZwdihGpA6X0v0lgLeQXaY0FkGkH2UKmXw7BJFRKNjJdbhJ0ldKyg5B9CYhhbLT1ZSxZRAEQcFO7qNUdjwF7DIFOwSRXkSRlB0i9+noAIYNY6eOjkyvRhfy7OQ65NkhiN6nu4sZkznUWJDIRUQR2LFDvuxgSNnJdTSVHfLsEERaUZqTAVJ2CCLDULCT60ienRLAww3KpOwQRFrpEew0ZWQZBEEwKNjJdSRlR2FQDlOwQxBpJdQW/zcpOwSRUSjYyXXiPDuUxiKIXiGoCnbIs0MQGYWCnVwnzrNDBmWC6BVI2SEIR0HVWLmOpOwUy8pOJMic84KQuXURRC5Dnh0iHxAEYPx4+bKDoWAn15GCnVLAWyBf390ld1QmCMJeeLDjLQLCHaTsELlJURHw+eeZXoUhKI2V62iVngOUyiKIdMIPMsoHs3MKdggio1Cwk+soDcouDyDE/uVkUiaI9BFUBTtkUCaIjELBTq6jVHYEgUZGEERvoFZ2wu1AJJy59RBEOujoAA4+mJ1oXASRUZSDQAEW7IQ7SNkhiHQSbGHnZYPk67qageLKzKyHINKBKAJffCFfdjCk7OQyymGE/hJ2zpWdcGdm1kQQ+QD/3RWUywca5NshiIxBwU4uoxxG6OPBDu+1Q8oOQaSNkKJzeWGAXSbfDkFkDAp2nE6kG1g6B3h7kfnHKru4+lTKDnl2CCJ9BBWFAQXl7DL12iGIjEHBjtNp+AL48jVg7cPmH8v9Ot5iwBX7V5OyQxDph//2/CVAQYBdpmCHIDIGBTtOp2MfOw+1Ad0hc49V+3UAuZEgKTsEkT6CisIASdkhzw5BZAqqxnI67fvly50HgNIq449V9tjh0Hwsgkg/ygMN8uwQuYogAEOHypcdDAU7TqcjhWBHS9khzw5BpB+lQZmUHSJXKSoCtm/P9CoMQWksp8PTWADQ2WjusVKPHS1lhzw7BJEWImH5YMJHnh2CcAIU7DgdtbJjhlA7O/eRskMQvYZy4jkpOwThCCjYcTrtSmXHZLBDaSyC6H14CsvtB9xeCnaI3KWzEzjySHbqdHajWvLsOJ2ORu3LRtA0KPMOyhTsEERaCCr8OgAZlIncJRoFPvpIvuxgSNlxOh2pKDu810epfB1VYxFEegmpFNV8VHbMHpgRRJqhYMfpxHl2bFR2yKBMEOkhqBq+m28G5Q1PAfcOBz5dmumVEIQEBTtOJhqNP0KyxbNDyg5BpJWgonsyEK/sOHwytC3s3sjOd23M5CoIIg4KdpxMV5M8yBOwUI2loexIHZRJ2SGItKD+3XHPTrRbrpDMZfhBVqhV/34E0YtQsONklCksAOiw07PjbOc8AKB1D+tZQmQ3HY3Ac+cCX/wj0yvpHdQGZW8R4IrVguSDb4cHe8pBxASRYSjYcTLqYMcOZSdbPDv7twF/GAu8dGmmV0KkytcrgK+WAx88mumV9A4hVRpLEPLLt8O3O/mgYhFAZSU7ORwqPXcyvMdOUSWryjJrUM7mPjt7twJiFNj1SaZXQqRK+152Hs6TnR//3fkUimpBOfsN54OyI6WxSNnJeYqLgb17M70KQ5Cy42S4slM5ip2HO8z1x9EdBOpwZYevvX2//v0I58PbJ4Q6MruO3kJtUAbyq9cOV3QojUU4CAp2nAzfSfQZDgixf5UZGTyoFexwg7LDlR0e7ITbgXAW+IuIxHCFMl/+j1oHGfnUaydEBmXCeVCw42R42XlxpZzzN9qsSxR7NjcDZGXH6R2Ulfl+5cgMIvvg39l8S2MpCwPyybMTJINy3tDZCZx0EjvRuAjCMnwnX1wJFPZhnh2jJuVwB4BYT49sHASqDHY69gGBmsythUiNjnxTdjSqIPNF2VEeZJFnJ/eJRoHVq+XLDoaUHSfDPTtFFUBRX3bZqElZOqoSAF+xfH22eHaUk6PJt5Pd8KC9uwuIRvTvmwtopY95sJPrnp3uLrk3WHcXEOnO7HoIIgYFO06mQ1GNVdiHXTaq7Ch9A4IgX5+tyg6RvSj/f/mg7ugZlHNd2VGnrsi3QzgECnacjFLZKeTKjsFgR2uDCwDeLOmzQ56d3CASjt/Bh/OgIiufDcrq1BX5dgiHQMGOk+Hpm+IKWdkxalDW2uACCmWn09lzekjZyQ3UjTHzIdjJZ4OyOtgh3w7hECjYcSrhTrl6pajCfBpLq6EgIHt2ACASSm2N6UQpf5Oyk72og51c77UTVwWZhwZldddkUnYIh0DVWE6F7yRcXsBfZt6gnEzZAZhvRxn8OIk4ZYcMylmLOlDNdc9OqB2aVZD50lSQPDv5R1FRpldgCAp2nIrSryMICmWnydjjtYaAAoDbB0AAIDrbt0OendxAnYLM9V47/HcnuABvoXy9lMbKdWVHFdzQfKzcprgYaM+O/zGlsZyKsscOkFo1lhJByI6KLLs8O421wH8XAm0NKS+JsIC6bUDOKzuKuVjKKkge7IRac7scm9JYhEOhYMepcCMyT1+ZNSgn8uwA2dFF2a4+O2sfBt57APj4qdTXRJhHHajm+pF+IkW1oExxn5beW09v0yONRcEO4Qwo2HEqyh47QArKTnHP27JN2Qk2A90WzdTN37PztuyYzJtz9KjGyhNlR32Q4fbKKqvR33A2ok5jBcmzk9N0dQGzZrFTl4P3JyDPjnNRenYAWeHp7mQ7DKUfQAulnK7G6V2Uu0NANBx/Xcd+oGyA+edq28POc3kH42R6GJRzvBpLq3syp6Cc/S5z2bejVu5I2cltIhHg3/+WLzsYUnacitqz4y8DBDe7bGTHrZvGcriyo9xASuk7i74d7tXJ9f4mToUH7W4fO8/5YCdBM08gP3rtqNNY5NkhHAIFO05FrezEVWQZCHYSGZSB1Loo7/oEWPNwemcc8bV7CoCSanbZSkWWKJKyk2n4/618MDvP9T47PI2TSNkB8kPZkQzZFOwQzoCCHaeiDnYAcyZlQ8qOBf/E8puBN38NbH/X/GONwjeYvmJZ2bLSa6fzgJwOo2AnM3BFLjCEnee8ssN/d2U9b8uHYaA82CuNpZzJs0M4BAp2nIpesGNI2eFHmDZ7dviRessu8481ijLY4e/firKjLDfP5R2MU4lG5cC8vIad56tBGciPYaA82CutYuek7BAOgYIdp8J37spgp8jEMNB0eXZ4IJLOrsZKc7Wk7FgJdvbIlzsPOHsWWC7S1QSIsXSnFOzkurJDaSwACmWHgh3CGVCw40SiUXksBN/ZAwplx0AaS8+zIyk7Dg12goqyeV56n6qyI0ZIUu9t+HfEXyarGjkf7OgcZOSDQZlvd0pI2SGchWOCnbvvvhuCIGDBggU9bhNFEaeccgoEQcCrr74ad9vOnTsxa9YsFBUVoX///rj++uvR3Z3lHUq7mgAxyi4X9pWvL7RL2YmVrZtNYymHHKZzhIOmZydFZQcg305vo1QneauEvDEoa6SP80HZkdJYMWWHgp3cpriY7RdEkV12MI7os7N+/Xo89thjOOSQQzRvf/DBByEoW6/HiEQimDVrFqqrq7FmzRrs3r0bF154IbxeL+666650Lzt9SEfE5YDHJ19v1KAcjcoziPQ8O2b9E91BOS1htJOzFZQNESXPjgUlSR3sdDUBGJrKyggzdCjaJ3hjwwLzRtnR+N3lwzBQ/tstjVVRUhqLcAgZV3ba2towZ84c/OUvf0GfPn163L5x40bcf//9+Otf/9rjtjfffBNffPEF/va3v2HSpEk45ZRT8Nvf/haPPPIIQiGLHXedgHRE3Df++iKDBmXl0ZSuZ8eksqPcUaXVsxML1PypenZU87BI2eldpO9xHgU7egblfFB21MEOKTuEQ8h4sDN37lzMmjUL06ZN63FbR0cHfv7zn+ORRx5BdXV1j9vXrl2LiRMnoqqqSrpuxowZaGlpweeff57wNYPBIFpaWuJOjoIHEkq/DmB88jnfwAhuObBRYtWzo9xw9YpBOVXPDqWxMoqyotDHg50cr8bSNSgH2Hmuena6Q0AkdpDJg51IyPqoF8L5dHUBP/0pOzl8XERGg52lS5fi448/xqJFizRvv+aaa3DMMcfgzDPP1Ly9vr4+LtABIP1dX1+f8HUXLVqE8vJy6VRTU2PxHaSJDo1KLMC4QVnp19FI/1lWduImkfeCsqP07HQeMN/IkCs73lguOZfTB05ECtorZGUn5weB6qSxskHZSWUiu/JgqKRK+3oit4hEgL//nZ1oXIQ2dXV1mD9/Pp599lkUFPRUH/75z3/irbfewoMPPmj7a998881obm6WTnV1dba/RkpIR8RqZcegQVmviyug6KBsVtlR7KisBB+GX0dRSSYZtEXzPqG2WMDbbzQ7J2Wnd4lLY8UMyrmu7Oj99pSeHSe2QWjfB9w/GvjHPGuP579bt4/9v/lBFVVBEg4gY8HOhg0b0NDQgMMPPxwejwcejwerV6/G4sWL4fF4sGLFCmzbtg2BQEC6HQBmz56Nk046CQBQXV2NPXviUxX8b620F8fv96OsrCzu5Cj4Tl3t2VEalPU2lpIykiDYsdpnJ+4ITUxf8KBcv9tjbT5WJCwHjZVj2DkFO70LGZTj4cpONOzMoG/3p+w38+0qa49Xb3f4OSk7hAPIWDXW1KlTsWnTprjrLrnkEowdOxY33ngjKisrccUVV8TdPnHiRDzwwAM4/fTTAQBTpkzBnXfeiYaGBvTv3x8AsGLFCpSVlWH8+PG980bSgXoIKIcHP5Eg21hyH4QavbJzIIVgR5WC6Njfc412oOyzAzBloPMA0L4XwDhjz9G+l527PEDfEexyrnolnEoig7IoaqdXs53uoDyeROu35ythPjoxwr6LiX6/mYKn17osehjV2x1/CQt4qSKLcAAZC3ZKS0sxYcKEuOuKi4tRUVEhXa+lzgwZMgTDhw8HAEyfPh3jx4/HBRdcgHvvvRf19fW45ZZbMHfuXPj9/vS/iXShNSoCYBtLlweIdrOdf6KNpV5DQcD6uAh1j5R0+XZCqmCnuBLY/7U5kzI3Jxf3N9d5mrAPLYOyGGXfO6+Gcd4pRCPAMz8GKg4CTnvA+OOU6Rqt354gMHWns5EFFmUDU1+rnfBgJ9jC2le4TAr/6u0Ob3sRojQWkXkyXo2VCm63G6+99hrcbjemTJmC888/HxdeeCHuuOOOTC8tNToUR8RK4iaf6/hX+EZXS0oHbEpjIY3BjkoO50Gfmdfj5uSS/sar2Aj7EEVtgzLg/FTWge1A7WpgwxJzvjT+u/MWAS639n2cPAxUUj5FawGKOtjhCg8pO4QDcERTQc6qVat0bxc1fCpDhw7Fv//97zStKEMkUnYAZtht36uvUiRVdmyoxlKu026kPjux9fNUmRVlp6QqP5q5qQm2AvWbgZqjzR+h20GoXQ6miyoBtxdweWN+lQ4AfXUfnlH470eMst+Z0VRtst8dwL6LB+DMiizlmrpa5MDMKOo0Fnl2CAeR1cpOztKuOCJWY6SLslHPjlmTZK8FOxqeHcCcQVkKdvqbmxafKyy/CXhyJvDNisy8Pv9feQrk/6M3S3rtKNO16saUeuiZkzlS+XmT6WWlnbhgx0Iwpv7dkrKT+xQVAW1t7FTkMA+aCgp2nEa4Ux71oKXsGPGfpM2zo9poWRnhYOZ1fKkoOzyNVSU3c8unYKfhS3a+/5vMvH67on0CNyP7sqTXjnJ93Ohu6HFJDjIARWNBhys7QQsmZel3Gwv28k3ZCXcC333E/E75giCwmVjFxY4vOqBgx2lwtcTlZdOi1RhRKSTPTpqqsfgg0XQoO6IY31QQUCg7Zjw7yjRW7DMLt+dPN1f+/jMV4Ell54qAPVt67YQtBjs8QNCaR8dxcmPBVJUddRUlV7jyJdj570Lg8anA1tczvRJCAwp2nIbSr6MVKRsxKKuPsNRYVXa4sTQwJH6tdhIJsWozQFGNxYeBWlF2+sd2MLHP0onpA7sRRQcEOxq+M97JOux0ZUeRxjIV7BhQdpzsH1N7dsyi9tr58iyN1fgtO9/3VWbX0ZsEg8DFF7NT0OT+pJehYMdpJOqxwzGk7CTZ6PIjbKvVWOkMdpQpBKkaKxXPThWrjJGqYPIgldV5QJ5RlKn3265RUZgtyo5SiTDj2TFiUM4WZcdSGkvVPdqfZ2ksvt11YiCbLrq7gaeeYqfuFEaN9AIU7DiNRN2TOZJB2Q7PjsU0Vp+hsTWkIdjhKThPoVy+K00+bzSeD1cqO4DiiNrhwU40Cnzz39TWqdxBZzyNpQh2smUYaDhVZUcvjRVg505UGJU7aSvrS9RBOV/GRfBgz+nbmDyFgh2nkajHDseIQTmYLNhReHbMzOjhG7PeUHZ4CguQUyG882wygm1ywMcHEmZLr50t/wD+NhtYfrP151BOe8+YsqOVxsoWg7LFYCeUxCsHZI+yYyWNle+l5/z9OzGQJSjYcRx6PXYAY2msZFUhXNkRo7I/xgh8J1VeI79O2KQ6ZPQ1lMGOxy+btY34dtoV0875Z5At5efb3mbne7dafw4nBDtayk7WlJ5bTGNxBUPPoOxUz064i42h4aRUjZWnTQX5tstp/1sCAAU7zsOwZ8eIQTmJsgOYS2Xx5y0byGb8JFuHFUIJUgFSF2UDwQ7fQZVWyddlS/l53Tp23lpv/TmcEOxInh2taiyHKztxaSwTPjEjBmWnlp6r15NSn508VXb4+3T6NiZPoWDHaSRVdhRprEQpKKNNBQFzFVlc3veVWBvhYOg1VOWrHDO9dpTmZA4PEp0sMXceAPZuYZfb9pgbVaBEGex0NVt/nlToUPTZ4fD/qeOVHWWw02A81WvIoBxg5077HvYIdmxIY+WTshONyEEyKTuOhIIdp2E0jRUJaXsfIt1Ad2xnkkhOFwTAHUtlmdnxKFNMaQt2NNJYgLmKLLU5GciONFbdevmyGDGnKihp3RP/dyZUBGkulkY1lnqgrNNQKhHdXcaVCTMdlIMtmQlCE2GrshP77ebTIFDld8TJ25g8hoIdp5Es2PEVA24fu6z1o1L+6PTkdLPzsaJROf3gK1FUSPWWssN77Rh4PU1lJ8DOnbwhqvsw/u/W3daep00V7PT2e+4Oyp4PzT47Dg921Osz6tuRDMoGgh3AWaks9VoseXZU1VhKZcdMIUQ2ojzwzKfmpUVFQEMDO9G4CMIUyTw7cZPPdYIdl1c2ImthtvxcuQPwFctVYXaPjJA2mGrPjgllh/tdNJWdppSWl1Z6BDsWfTvqnXNvBzs8ABbcctoGUHh2HB7sqJUnowpbUNVnRguPTzZqOyrYaWLnUprNZLCjTOPwYI9/DmLEfJuLbEOdqnNamjJdCALQrx870bgIwjDRqGz4TaTsAArfjoY52IhJEjCv7EhHLgLbaaUrjaVuOc8x5dlRzMXiOD2NFQkD329gl/sMZ+eWlZ1YkMRTlb39npXmZOXE9axpKqhKD7cbVHaM/vacOAyUryUQq7Q0G4jFNQPlaawS7dtzEXWqzqnbmTyGgh0n0dXEysEBOaDRwoiyo1f+CgBek/OxwgovjSA43LOjkcZyejVW/SZ2ZFxQDow4kV1nRdnpDsrvsXI0O8+UsqMO2Pn/1Ok7Pv5d579Bo712jBiUAWdWZPG1BGINQ7s7WQBuFP7eBbd8IOVyyanLXG8sqFZ2nKwg20kwCMydy040LoIwDN9J+MuZ3J0IqYuylrJjoLEZoEhjGTzK7jGcM93VWKr1S8qOEc+OjkHZSUfTSnjJ+eCjgLJB7LIVZYfvmF1eoG9MIcpUsKNOxWaNshNLx/QZxs7bDAQ7cWkcjQG+SpzYWJCvpXyw4joTqSylqqVMZ+TLyAh1AJ/Kb+7LfwMbn0ttPb1Fdzfwpz+xE42LIAwjyf86qg4AFBlRdtKUxkp7sKMaJsgx2mcnGpXTDonSWEZHTvQmdR+w8yFHA6XV7LIVZYdXYpX0lz+zTKaxlGTNIFA+FmUYOzei7CiVi2QHGk5sLMiDnaIK+f9k5sAg0XYnX4aBqoM5qwdV0Sjw0qXAq780FmQThqFgx0kkOiJWo5fGMu3ZMZjGUldJScFHmpoK6nl29Co7Og/IXaGL+8nX8x2MGHVmKSxXdmqOBkoHsMtWlB1lCi9TPiWt7slAdig7oigHY1KwY8CzY7QwAHC2slNQHl8eb5REwU6+KDvqNJ3V31xXU0whFGXvHWELFOw4iY4ER8RqCnXmYxlWdngay6yywyeRm+hobIZknp1oWH8jzHf2RRWA2ytf7y2UAzyn+Xaa6oCW75nfYdARqSk7fAOZyWBHa+I5IA8CdXKfne6g7Jszk8YyepABOLOxYFywUxZ/nRES/W7zZRioOpiz+ptTPi4dswfzGAp2nIRW11ktDBmUDSo7Ro+y+caMl80q01h29tBIKIcXya+tV5GlZU7mOLX8nJecV09kOwuu7LTvNWcSBeL9SplWdnqksfhsLAcHO0rvRZ+YWddIGstoYQDgcGUnoFifRc+OknwZGdHDs9Nk7XmUAQ4FO7ZCwY6TkCZFJ/Hs6BqU01x6rk5jRUL2bsjUCpISqSJLZyOgZU7mpLLz//hp4D83pafrLQ92hvyAnRf2ZekQiOYGUQJysFdancFgJ/a9LM7CYIensDwFQElMYTOSxpIKAwwEO0707PC1FJTLBmtTaawEQ1DzZWREUJHGBKz/5uKCHZstAnkOBTtOwqhnp8iONJZZz44qCPEVAZ6YB8POI5BEfXYAY7129JSdVMrP37wF+PDPwLerzD82GTzYqTmKnbtc1lNZSoOy09JYPNiJhNhYEyfCU2zeIjlg7mpO3hHXaBUk4HBlJ01prJxXdmL///JYJaXVFKUywKFgx1Yo2EkT0UgEax+/BpsXnYimfQZ3WIY9O3YYlK16dhQbs3RUZOkpO9KICiPBjo6yY3ZDFGqXN/y1q809NhnBNqB+M7tc8wP5einY2WXu+ZxsUPYp2sk7Vd1Rfv8KAsxHBSRPZRk9yACc59kRRW2Dsh1pLK505bpnh7//8lhTRluUnSxIYxUWArW17FRYmOnV6ELBTppwud0Y/P1yTAhuxI5N7xp7kGHPjqKDstovk0hOVmNZ2VEEO2bmVRlBFBNXYwHy56Kr7GiUnXOs7vyV6sq3Ngc7329g7fTLBstHhYB1ZUd6/9XxHqXemk0UjchHpOrvsacAQKwHi1MrsqTmmUVMYeMVfcmCHVMGZYcpO+FOZvwHUkhjJfjd5o2yE/ve8D5F+RLsuFzAsGHs5HJ2OOHs1WU59WUTAQAd335g7AGSZ8egshPt7rkRMbrRNdtBWevI1W5lpzvIdvyA9vqLDbyerkE5wM5TCXZ2f2qvvCz5dY6Ov95K+bmoKFct6a8ot4/03pF15wEAscBK7T0TBOf7dpRpLMB4sGP0IANwnmeHB12CmwUrltJYCQzaUum5w3srpUpIrew0WXse5QigbAh2sggKdtJIdNBkAEDx3k+MPUDy7CQJdryF8twj9Y437Z4dRSrC7mBHGbh5i3rebkrZ0UpjBdi52Q1RXMAhAtvfM/d4PSS/jjrYsaDsdDUxPwzAgj1voeyr6q1UFv8uFJTHl/5znD4MVK1QlJhVdrKwGkuZwhIEa+tLVo2V8wblWLAbUKSxrKipHVkW7IRCwPXXs1PI2ZPeKdhJIxVjjgUADOvagmgkSRVPuFOW0JMpO4KQ2KScLs8O3zml07PDdzTeIsDl7nm7Kc9OmtJYgH2+nWgUqFvPLvcIdiwoOzzQKyiXlbve9u0kMidzeLDs2DSW6nvOlZ1kVXFWDMqRoDM+B2WwA7BxNYDJNFYCgzIP/pzYyNNOJGUnlsYSI9ZSd8ptqdP6gWkRDgP33cdOYZNtMnoZCnbSyLDxR6JD9KMMHaj7aqP+nfmX3OVNPlsHSLwTS5tnpxfSWIk2mNLrJVF2ukOyDKxbjdVkbl084ODTyO2qyNr7JRBsZu35qybE32ZF2eH3zeS090TmZA5X7Jya1rCcxjJhUPaVAkJs0+sEdYcbpbnyaUXZyftxEfxAtVJW3a385rLNs5NFULCTRjxeH2r9bPJ0w5YkqQ/lPCHlIL1EKE3KSrK5GkuvEgtQKDsJXk85BJPv5JVYrcbiQcTEn7Kd1P5vgObvzT2HFnwe1uAjALcn/rZUlJ1MBjvJlB2v05Ud1fecp0OTprFMKDsul3xA4wTfjlrZkTw7ZqqxEvQZyptxEYrtbiq/OWUaK9zh7G7jWQYFO2mmpeIwAID43Xr9OxrtscNJZLY17NkxOaeoN4IdvsFMtHb+eomUHWUKSytgTDWN1W8MMGASu2xHKmtnAr8OICs7nQeAsEH1TSuFZ9WUbZVkvjMp2HGqsqP6nhtNY5npoAzI/xdHKDvqNJadfXZif+eyshONKoLkUuud2qPRngev6r8Jy1Cwk2YKhrMdWb+mTfp3lMrOk3RP5khdlBU7se6QbFDNamUnQRqLB4LdndppEL0eO0AK1VgxdaV0ADDiRHbZjhJ0yZz8g563FQTkVKPRgYBtDkhjJZp4znH6MNAeaSyu7CSZAWfGoAw4y6TMlU5J2VF4doyabBOmsUrjb89FlO/NX2J9O9PVJM9l48o9pbJsg4KdNFMz4XgAwNDIDrQ260TpRnvscLQMysofne3VWNw8nMFgx1ci58O1dj565mRA3vGHO4wHeaIoKzul1cDwWLBTuzq13jVtDcCBWgACMHhyz9sFwbxvh6sPpZn07CT5Hjt9GGhYVXUode220aAMOKuxYKI0VrTbeFAaTBDsKNNYvdXrqbfh2y3BxbarVtPl/DfqK5V/+xTs2AYFO2mmcuBQ7EY/uAQROz7TaS6Y7IhYjdZOjAckbr922a8S08qOTjVW5wF7ZkbpNRQEWACgV5GlV3YOxKpMYuktoxJzsFXeAZZWs/lVbj9Te/Z9bew5tOCqTv9x8pGgGrO+Hc00Vi8PPzVqUHZs6bnKN1aiUHaiUZ3HmTAoAw5TdlTBjq/EnIFa2Qw0Uem5GHXu/zxVlClMQbA+lkap7ksHkpTGsgsKdnqBXaWsuWDrtrWJ72Tas6NhUDbTxdWMshMJszJZQBXsxNYgRu3ZaIcMpAIk347GEU8yZcflMi8xc1XFX8beu7dQnmGVim9nZ8yczJ9LC7PKTqtGGq/X01hJlB3HBzuqNBZ/H2JE/zM0m8ZyUmNB5cRzgO2wzXRRDndAaiTZI41VDOkAI1d9O2pVz+oBRlyww9NYDg92CguBzZvZicZFEOEBRwAACvfoNBc0OheLo6fsGDm6NNNBWemPUT632ysfDdohtyZLYwFJlJ0knh3A/FGX5Neplq8bcRI7T6UEvW4dO9fy63AsKzuKtWas9DyZZ8ehwY66Gsvjk78ziSqyRFHR8sGsstNkZZX2olZ2AHMVWcrtg7oZqCDk/sgItRpo1bMjjVmpSM/cwXTgcgEHH8xONC6C6DP6GADA0M4vICaSwpVfdCNoRf6Jyj+1MKPs8B+zy8s2/nHrSFIhZQYjwY7Ua0djx6M3F4tjNp+u9OtweLCz/V1r6btwF7B7I7usHhOhxIyyk6jHUG8GO6KYPB3L/7dO9exozoDjvXYS+HbCnbKx1LBBOcDOnRDscAWCrwkwl2ZTVlFq7fC44pGrw0DV6XervzlJ2cmiYCeLoGCnFxg24QcIiR70QQu+//YL7TvZ6dkxcnRpxrOjF4TY+aNM5tkBFIZRCwZlwPyGSFmJxRkwicn8Xc1sVpZZdm9kVXPF/eRGhVqYUXb4jljdY6g3g51gqzxQMmEaK8uqsYDkvXaknbig/91V4mTPDqDoomxgfcm2O5Ky49B2A6mitg9YNSjzbWhh3+wJdkIhYOFCdqJxEYS/oAjfekcBAOq/SGBSNu3ZUezEeJVDujw7upPIbfxRBhVGv0Qkej1RTG5QBqx7dpTKjtsDDDuOXbbi25H8OkfrN5A0o+woU3jKo2ut70m64Cksb1H8DDUlvJrPqX121GksQNFrJ0Gwo9zZG2kICvS+cVwP3TSWkWAniSKb640F1V3rrRqUOxXqfraUnofDwO23sxONiyAAoKniUABAZOe6njcqm0mZVXbEiGwiNKXsxIKdaDcQ6da/r9ZcLI6tyo4Jz45a2Qm1yevUDXZM7mS0lB1ALkG30m9H8uvopLCUr2kk2NEyJwPy++2NOUzJzMlAFig7emmsJMqO0bJzwDnKjigmCHb4+gx4dpIdZPlyPY0V+870MCib/N9KVgYbqrFadgMPHgKs/r21x+cgKQU733zzDd544w10drINl5irfRRswDuUVd30PfBZzxuVzaSMBjtaE63NbHR5sAMkV3d001g2HoGY8eyoDcpc1fGV6j/edBpLQ9kB5OaCOz8wXr4PsJ0LLzsfomNOVr5msCV5JYuWORlgn4Ur1oYg3amsZOZkwPl9dnTTWAk8O2bLzgHnGJRD7eyACVClsUxUYyUzZ0vDQHNU2QmqPTsBdp6SZyfBOCCjbH8PaNoBbHrB2uNzEEvBzv79+zFt2jSMHj0ap556KnbvZke/l156Ka699lpbF5grDJpwAgBgWHctOttVRzj8S+4vT94fR4napGymZT337ADJd9Z6G3Mp+LDTs6Oz00ik7BipxAJSqMZSKTv9xjJvUHenrNQYofFbFhS4/cCAQ/Xv6y+VPwv+/hKRKIUnCL3n20k2Fwtwdul5NML+n4BK2Ym9n0RpLLNl54DCoJxhZYe/vssrq26AxTRWMmUnR4Md9XaL/95Craxth1E0lZ391tLPvJt68/e528zRJJaCnWuuuQYejwc7d+5EUZF8BHTuuedi+fLlti0ul6gafBD2og+8QgS1m96Pv1HaSRgcFcFR78TMeHZcbvmIPyVlJw0GZb31JwqujJiTAXPmQXX3ZCWCAAxnAawp3w736ww8LD7gTITk20liUtYaFcHprWDHSPsEJwc7yjXFBTsGDcqW0lgt+s0K040yhaX0G9maxuIVeA4OdlJJq6qrYJUKmZlgVqsaq7vL2m+Fb7fC7ZlXDx2CpWDnzTffxD333IPBgwfHXT9q1Cjs2LHDloXlGoLLhbriCQCAlq/XxN9o1pzMUe/EzMrpRk3KPNhR99AAMuDZib1eqC1+QKYRczJgbsffeUBupqhODwHWfDvSPCydZoJKjPp2tEZFcHprGKiR73Eqwc7er4CVv02fGiKl1oT4NG+y0nO1QdUI0g5RNJYqShfquVgcU2ksgwZlp3p26jcDdw8BVvzG2uPVypbLLVezGf3NqX2bvmJ5NI6VbatycG3z9+Yfn4NYCnba29vjFB1OY2Mj/H4DR6t5Sqj6cACAr/7j+BvMNhTk9FB2+EbXYPmr0fLzXlN2ksjhAJP/XZ7YaypSWYaVnQA7N7IR4gFGYR+5CaMS7tv5foOxI2DAuF+Hw4Odll3699N7/72WxlIcmSaCe3asHEm/ez/w7n3AhqfMP9YIykospcpRwoOdBL2kzCiqHG+BHFBlMpXFX1s9ssSMgTpZsOf0YaC7PmatIHbodLjXQ6ta1WyHbPUQUEFIzQ+pHB7cQsEOYDHYOf744/H0009LfwuCgGg0invvvRc//OEPbVtcrlE2ijUXrGnfHN9c0OwQUE4PZYdXBRg8wuQ5+u4kOx69IMSuGS7K+Tp6wZogaDcyNOrZMVONlcivwwkMYX1yxAiwY432fZR0NAJ7v2SXBxtVdgyWn7c6INhJNhcLkL9zVgzKXFn5foP5xxohUVDPlZ1wh7bvxMiYEy2c0FhQqxILMNdBOVmw53e4Z4e/R6v/B633b1ZNVQ4B5Y1bUzmQbFV4/Jq/M/94oxQUAOvWsVOBxgGhg/BYedC9996LqVOn4qOPPkIoFMINN9yAzz//HI2NjXj//feTP0GeMnzisej+jwv9hAOo/24bqoew3jvyEbFJz05Cg7LRNJYdyk5sDcEW1sVX3WHZKMoutMnWX1TJgps4ZcdA92Qg3rMTjeq3OE/k11Ey4kRgQy3z7YyZmfh+3SHgpf9ll/uNk9WCZBhpLCiKDlF2jBiUeZ+dDrZuo31pAHnHzLtP241WJRbAvo+eQnZQ0L635049mKQaKREF5ewI3AnKTo80lhllJ0kay+njIvj/z+r/QUvZMvub69DYB0jbdwu/295Sdtxu4Mgj0/f8NmJJ2ZkwYQK++uorHHfccTjzzDPR3t6Os88+G5988gkOOuggu9eYMxQWl2K7h3XM/X7zO/INdnl2zMrpZj07WhuzggAguNnlVFJZevN11HDfjnIYqNE0Fj+aFqPJ/QjJlB3AmG8nGgX+8Utg20r23s74o/7rKjGi7HQ1K7xFDlB2dA3KvOJHNNbQUglX4w5sT8+ARK2GggALyKRUloZJ2YpBGTCe6tiwBFhyWnrec0Jlh3dQNuLZSXKQ5XRlh79Hqw0e1X12APNdlDWDHYvKTrgrPnAjzw4Ai8oOAJSXl+PXv/61nWvJC/b3ORQj921DeMc6AJeyK+3y7JgpPQdMKDs6GzOXi/1A2/eyH2WZTmBg5DW8xckHymn12jFqUPYWyEfpnQd6ehWUGFF2eEVWw+dsDerXF0XgjZuBTS8yr9E5zwA1Jo6EjCg7PNArKNf2FvVasBPbGRsxKANMzVOWOydDuQHf/SlwkM0pc72gvrgf0LRTO9gx+7vjGPHFtNYD/7mRBYZb/gUccZG510hGsjRWsDW5App0XAT37DjUoMyDnUiQBQpavyHdx2uk3822uNCajWg12FG3qUinshMKAQ89xC7Pnw/4LCr7vYAlZWfkyJFYuHAhvv76a7vXk/O4hzCvRmC/YqaSZc+OqvGU2SNM08pOAsXFDpOykUosjrrXTjSiqEbSCUw4Ro+6jCg7xZVA1UR2ufadnre/ez/w4aPs8lmPAqOmJV+fEqWyk6hfRjJVqzeCnXCXvNPTC9rdHsAd2yCamZWk7PQLpCeVlSiNBcjl520aFVlWDMqAMc/Oew/Iv0/u97KTZNVYRqrFjHp2nDobS+lLsuLb0Qr2zHZq79Aw99sV7KTTsxMOAzfcwE65OC5i7ty5eP311zFmzBgceeSReOihh1Bfb6ClPYHq8ccDAIaHv0GwK7ZxNVLFooV67pHV0vNwkmBHb1wEYFOwY2KHoVZ2OhpjXWAFYwGj0Z2/EWUHkKuyvl0Vf/2GJcBbv2WXZ94DHPLT5GtTw1+7uzOxAqBnTgbMV4ZYgf8vXN6eO041VkZGhDvlIaMAsOsTc+sz9Bp6yo7OAFrLBuUkyk7z98BHT8p/pyPYkSaeq/5n3gK59DlZsGN0EKhj01gKxcmsbyca1f7/mzUoK4eAcqxuV/l2i28PWnZRY0Gk0FRw/fr1+PLLL3HqqafikUceQU1NDaZPnx5XpUX0ZNCI8TiAMviFMLZvjjWYkzw7JoMdpUG5O8jmXAFpUHaSbMzsGBlhpBKLo/bs8COZ4kqmHCTD6IZICnaSpOa4b0fZXPCLfwKvXcMuH38d8IMrk69LC2+hrAAk8u04QdlpV6Rik5mOrQwDVR9x79po/LFG0VMX9UZGWDUoJwtC3/sDS61wVakhHcoOT2MFet5mtCIrWcsIpw8CVQZzZg8I4hpRaik7RquxNNJYVoeB8u3BgEkABPYdStQ2IY9IaTbW6NGjcfvtt+Orr77Cu+++i7179+KSSy6xa205ieByYUfheADAga/WsCNWvtG3qux0NcUfnfRmNRZgz8gIIz12erxe7Ads1JzMMSIxR6Py8yZTdoYew/w4TTuBxlqWznrpUmaCPvwi4ORbjK0rEcl8O04Idsz4zqwoO3ynzOfBNe2w37Crm8YyYlC2UdlpqpP7CZ3+IDtv3WV/5ZZusGOwIitZ+lzZZyeT3aITkYqywwM4wRXvPzNtUFaMiuBI87FM/m759qB8sLxNaK4z9xw5SMpTz9etW4cFCxbgxz/+Mb766iv89KcWpPo8o7PqMACAd/cGOUBweRV5coNIk8+jsgnNW8Q6eBrBjmosIPOeHaPmZI4RZaezUU6bJAui/CXAoMns8tpHgOd/zpqUjT0NmPUHc+XVWiSryErWY4h/T8Lt5oaWmqHdhDppZRgo3wmVDWC9jQD7fTu6aaxYsKM1H8vKIFBAfxjou/ex79/wE4Cxs4DSgez6vVvNvUYyEhmUAWNdlI30x1IGQWbUvN4iFc9OUPG/jxu3EWDnKZWep5jGKq0Gygexy9RY0Fqw89VXX+G2227D6NGjceyxx2LLli245557sGfPHixdutTuNeYcpQdNAQAMaNtsTv5X4/HLKQEeuZvZ4JpWdhKlsWwIdoImdhipKjtGNkRcRSnuZ2w4K/ftrP8LqzoZehww+wljabVkGFV2EilQ/nIAse9Wunw7Zkz2VkZGKL0lA9nBgu2prGTVWECCNFaqBmWVmnBgO/DJ39jlk/6Pnfcbw87t9u3oBTtGhoFGQnL6PNFv11PAlA/Amb6dlJSdBClM0wblJNVYZjw3yu1hWSzYofJza8HO2LFjsXz5csydOxffffcd3njjDVx44YUoKTH5Y89Thh5yPKKigIFiA1p2fsauNNtjh8N/VE072bmplvUGOigrj9wS9b+x06BsJNjhn1VXM5sqbFrZMSAxGzUnc7hvBwCqJwI/e858CWsikio7Sd6/y5X++VhGuidzrAQ7ynTLwEnsst0mZb00luTZUSk73SG5x5HVNJZ6h/jO71kAcdDJwFB2YIT+49i5nb6dqKLXlGawY2AYqDJ4SfTbFQTnjoyIdMerTWaVHa0eO0D8781IoKJXjRUJmfvclMFOeWx+ZUsaK7KyBEuHnVu3bsWoUaPsXkveUFreF7XuIRge3YGOza+jDDDfPZlT1Id9kZvSpOx0BxWdjR2SxirsA6ZUiOw10+HZMVJ2rmTwkUDVBLZhO//l5BVJZkim7EjVFzqBWWEftuFN12gCI92TOSkFO+Ux4yUyk8bqPMACbK72KXdCZvvs8B2iUk3Yvw3Y+Dy7zFUdID3KTqhN/m3rprF01A7+/j0F+iqmv4Q9j9OGgap7/5hVPrV67ADyNiYaZts2vYNQ9RBQjq9I7gnWsd94MM2rM0t7QdkpKADeflu+7GAsBTsU6KTO3sAhGN64A312xXqzmO2xw+E/KktpLAOeHWVvjITBjmpshRXMBDsuN3vNjv1sJ2s62Amwc900lkllx+MDrnyPBTvJmiKaRU/Z6Q7JG0q9959uk7KZikJLnp0mdl5QDgw4lF1u2sm+c1YPFNTw76CWslPYl3UKFyPsO8ebZ/Kdd7KdvRZanp3V97LXGDU9vvlkv5iyY2eww4Mst19bhTRiUDaqyDp1ZIRatbJqUFa/f28R82FGw+z/qxfsBJvjh4AqKerL/DYd+4E+w5KvJxqRU60lveDZcbuBk05Kz3PbjOGtct++fbFvHzt669OnD/r27ZvwRCRHGMw2ZP5obINvthKLk0oay4iyIx25FSY2PisNw1b7OYSSVHT0eM3YkXbHPutpLDuVHYDJ9XYHOso1aAU7PK3i8sjvS4t0BztK71kyUqnGKgywU98R7G87U1khnX5SLpfie67w7Vg1JwOyZ6e7i/W62vsVsOkFdt1JN8fflys7Ld8bG85phEQNBaX1mUhjJfvdOnVkhFppMm1QTlCJJwjGf3P8IFE5BJRj9kCyY38scBLYNrIslsYiz45xZeeBBx5AaWmpdFlItcIkz+k/7jjgM8UVlj07sR9DupUdPcVFyi0Hk0u2SV/H4GOLFAGW5TSWjcpOOpGUnd09h2fygX8lVfqBVm8pO4bSWCn02eE74AGTgMZvWSpr5FTjz6OHXhoLYDuPtj3xvh2r5mQgliaKpWODLcDqe9iOaswsYNDh8fctDLCgt3U3q8gyM3IkEXrmZGl90K/GynZlR/3eTCs7Otutwj4sME6WGtOqxOJIFgGDwQ7fbvGeY1zZad3NVB+jlbpGCYeB//f/2OXLLwe8Boo5MoThYOeii+SZLBdffHE61pJX1IyehBYUoQw2KTt8J2ZK2THQQdlIsOMtYs/V3RXLLacS7BhIYwFyuqRll7wjNKrsmKnGMqPspAsexEXDbKOnTBUZVbXSHuyYMSinoOzwHfPAw4DPX7a3IksvjQVol5+HEhzZG8HlYhVPXc3Azg+AzS+x60+6Sfv+/cbEgp0v7Q12Es2HszON5XeoQVmtWpn17OiV3RstCjAU7Bj0Q0oHfrEDpJIqpvpGu1kgxIMfuwiFgHnz2OWLL3Z0sGNJc3e73Who6FmCuX//frjdNkeOOYrL7cb2gnHyFVaDHfUPxIxJ0pCyY6CzsSCkblI2G+xwBaFhCzt3+7Qbo2nBd/zdnYkDPScpOx6f/H7VJmWjqlY6g51It/y8RpQdXyql5wF2LlVkbTT+HMnQS2MB2o0Fpe7JFoIdQA4oVvwGgAiMOwMYcIj2fe327SRTdox0UE5k0FVjZWTEdx8BHz+d3lEHkucqFoCbVXb0GiqaTWNp7QPMblel7VZse+Byywdsed5rx1KwIyb48gWDQfgcPPXUabT3O0z+I1Vlh2O3ZyfZXCxOqiZlszsNriA0fM7OS6qM9ynyl8l9P7Ry9NGIoneNA5QdILFvJ9lcLE46gx1ukIag7xvieK0YlFWdfrlJuXmnfZ2Uk6WxtEZGpJLGAuT3c6AWgNDTq6PE7ooso2ksXWUnQel1j+eykMZ6+XLgn1exCffpgleaBWrYuenSc52J90YGvQLaZeecVJUdQFGRld/l56bKBxYvXgwAEAQBjz/+eFxfnUgkgnfeeQdjx461d4U5TNGIHwB1jwMA3twRQcuBxF/Gw4cEMKKf1tGDWtnJgGcHyJyywzvKGk1hAbH0QTnb8Xce6KnetO9l3gnBJR/NZ5rSamDPJmcqO9ycXBgwVpEklZ6nkMYqKAf6HgQ0bmMmZTt8O3p9dgDtYaCpGJSB+EDj4B8DVeMT39fuXjtJlZ3Y9bqeHYMHKWaVnWiENVcE2G+cK3l2ww+yymuAfV8xFSsaNV5ooBfsGVZ2NIaAciwHO4rtYfkgoA55r+yYCnYeeOABAEzZefTRR+NSVj6fD8OGDcOjjz5q7wpzmGGHnADEZkf+3xv12IfEG//yQi+WLzgeA8oL429It7JjdGOu7mpsFqueHR6oGTUnc3jfGa0NEQ8oSqrsN/RZJVH5ebJREZx0BjvSXCyDJnsp2EnBoAywHaBdwU53SB4PkjCNFfuM22xUdiS/jJDYq8ORKrK+Yztlnmayih1pLKO/W0nZMdhnp7WeleADMdUrTfD3xpUdbhZP5GNSo5fGM9pFWavHjvo5zBqUS7WUnfwOdkylsWpra1FbW4sTTzwRn376qfR3bW0ttm7dijfeeANHH320pYXcfffdEAQBCxYsAAA0NjbiqquuwpgxY1BYWIghQ4bg6quvRnNzvKS6c+dOzJo1C0VFRejfvz+uv/56dHd3W1pDb1NeUYUPRt+Al8svwoQxI3HSmH6ap0GBQjR3hnHtC58iGlWlENXBjhnvgJEOyk5XdjhmlB1Af0PkJL8OJ1FjQaPDStMa7PAeOwaDHZ9JZScalXdKyp0QHxthR3NBZeCV1LOjLD3nng2LgQcP0if+VA5mElHYR05P7PvK2uspUY7g0IJf393JgkEtjAZ7fLtkVNlRply4wpMOuLJT3E9Wus34dvQOBg0blDWGgHL4drXTYLCjpfRSF2UAFpsKvs07JtrE+vXr8dhjj+GQQ2Rj3q5du7Br1y7cd999GD9+PHbs2IErr7wSu3btwt///ncALHU2a9YsVFdXY82aNdi9ezcuvPBCeL1e3HXXXbauMV384Oe/BgCcrXOfb/e2Ydbi97Bm2348/t63uPyEg+Qb1T8Q25WdXgh24oYJGu2zow52LCg7gL6y4xS/DhBffq7ESWkso74zHmQb9eyEWgHEgnxlUME7KdthUuZrcXkTz0Ir4cGOQr2UvGYWlZ1jrmY72qMuN3b/fmNYu4G9XwKDJ1t7TY5Rzw7A1A6PRjBruPS8OP7+yVDumBvTqOzwFJ2/jH0ObV0xFXGowcfrVOOZTWPZWY1Fyk4PLBmUZ8+ejXvuuafH9ffee6/pqedtbW2YM2cO/vKXv6BPH1mlmDBhAl566SWcfvrpOOigg3DyySfjzjvvxL/+9S9JuXnzzTfxxRdf4G9/+xsmTZqEU045Bb/97W/xyCOPIBRKcCSShYzoV4LfnM5y+b9/Yys+36U48lBXINnu2UkyF4sjGZQtBDvhDsg7M5N9djhmlR298vNsUXZE0bxBuauZ+SHsxKyyY3ZcBN8pewriO/3yqqXmOnnqulWMBPU8jdW+l6lNQOpprD5DWfrKaBdoybezxdrrKUkW7Ljc8vYkkdphuPTcpGcnTtlJZ7CjCFYSDWbVQ6/PjmGDssFqrGRVaXHbA5VnB0iPZ8fvB157jZ38fvuf30YsBTvvvPMOTj311B7Xn3LKKXjnnXdMPdfcuXMxa9YsTJs2Lel9m5ubUVZWBo+HCVJr167FxIkTUVUlb+hnzJiBlpYWfP755wmfJxgMoqWlJe7kdM47sgbTx1chHBExf+lGdIZiOyyPLz51ZaXPjq6yw6uxknl2TDa/insNnkIQ5BLQZKh3DlaVHa0NkaOVHYVnp6tZHkKZLNhTBsVmy2uTYVrZMRnsJEq3FJQDFSPZ5d0pdlJOVokFyMFctFv+3qRqUDaLVJG1NfXnUle4aZGs147ZNJZRz45ShWjbEz+2xk54erSgLPFgVj10++yYVXa0gp3Ydi7arW8UB9jt3JIQV40VS2O1NSROR1rF4wFmzWInj6VEUa9hKdhpa2vTLDH3er2mAoelS5fi448/xqJFi5Led9++ffjtb3+Lyy+X5d76+vq4QAeA9Hd9fYIJ0QAWLVqE8vJy6VRTU5Pwvk5BEATcPfsQ9C/145uGNiz6j+LITunbsTQI1MZqrHYLBmXlBsNoFYTbG7+R1huCqYVuGsvByk7bHlmZ4UZZf7mcGkqE2yOnJexOZVk2KJtUdrR2ylIqK8VgJ1klFsB+L3yHyHvt6KUx0oGdvXaMBDvJuiibNShbUXaA9Pl2pDRWufZg1qSP58GeXhpL5/mUQ0C1qrG8hXLH8WSqOVd1/GWyLw6IdVP2AxCB1l36z5HDWAp2Jk6ciGXLlvW4funSpRg/Xqd0UkFdXR3mz5+PZ599FgVJpqW2tLRg1qxZGD9+PBYuXGhlyXHcfPPNaG5ulk51dXUpP2dv0LfYh/t+yvqLPL12B97+MrazU5o2TQU7sR1kJJQ4tWGkqSAgH/VaSWMZbUyW6DUBCwblADvPFs9OcT9WCi9G5R0tHxVRalDVMmqYNItVg7JRz45eusWu5oJGd9rqxoIhnZ1dOuDKTnNd6hPEk6WxAEVFVoppLLPjItRm2nT5dqRgp1R7MKseybyG/PcWbGaNNzVfXzEENFEq02gPs0T+PUEAygayy3b7dsJhYMkSdgqH7X1um7GkO9166604++yzsW3bNpx88skAgJUrV+L555/Hiy++aOg5NmzYgIaGBhx+uDwDhvfqefjhhxEMBuF2u9Ha2oqZM2eitLQUr7zyCryKdtTV1dVYt25d3PPu2bNHui0Rfr8ffofnFxNxwuh+uOTYYXjy/e24/u+fYvmCE1Cp/JFYMSgDLJXl0ziqNTqzSlk1YKZPhZnX6PGalcD+b9jlXK/GcnuYZ6StngVjpdWKUREmZoI17bQ/2GnXkeG14OpJNAxEwokNwRy9gZVSRVaKjeeMpLEA9j/Y/4382adqUDZLUV/2/27bwwaHDj7C2vNEo/KOXjfYSTIM1KiyZVXZqRwD7NuaPt8OX39BmXnPTjKvoTp1XKzx+4gbAppgn1TUlwW3VoMdgFVkHai137cTCgGXXMIu//SnuTcu4vTTT8err76Kb775Br/85S9x7bXX4rvvvsN///tfnHXWWYaeY+rUqdi0aRM2btwonSZPnow5c+Zg48aNcLvdaGlpwfTp0+Hz+fDPf/6zhwI0ZcoUbNq0KW50xYoVK1BWVmZYYcpGbpw5FmOqSrGvLYQb/v4ZRGUay2tCHfEoPs9EqSyjR7xcghWjFrqQmiw753AlwUgaR02iNFYkLB+1O0nZAXr6doz22OGkqyLLzFwsID5VZCSVpTfDqVppUrbY4wkwlsYCejYWTNWgbAU7OikHmyHtqPX69diVxuKene7O5Ab5UIesFg4/np2nQ9kRRTmIi1N2DAY7UuAmaH9v3B75fSfaJupVYnGMVmSpR0UooS7K1pQdAJg1axZmzZpl+YVLS0sxYcKEuOuKi4tRUVGBCRMmSIFOR0cH/va3v8UZifv16we3243p06dj/PjxuOCCC3Dvvfeivr4et9xyC+bOnZu1yo0RCrxuPPSzSTjj4ffx1pcN+GqUF2MAdnRpRlFxewDBzZp3JTIpSxuzJDsBj49tGIMt7AjEaHUJYN3kyTcCZlUdIHE1Fg8gXF7tHHomKR3AesrwNBvfuBn1K6Uj2BFFcxPPgdgRbGzad7hTX1kA9NMtBWXMpLz/G5bKGpW80EEToztt9ciI3jYoA8y3U/sOsDeFiiypwq0wsaIAJA8AzFZj8cfo/c9bdsnPyT1Z6VB2urvkRpL+MkWat8nY45XvPdGomsI+zJSd6Den12OHYzTY0RoVwUlnRVaWYEnZAYCmpiY8/vjj+L//+z80NrJ/2Mcff4zvv7fnw/z444/x4YcfYtOmTRg5ciQGDBggnbjHxu1247XXXoPb7caUKVNw/vnn48ILL8Qdd9xhyxqczNjqMtw4k43meGsH7/xqYYObrPw8bCLFZLX83KgvSA0/yjZbiQUkrsZSprDMBI69QQ9lx+DEc046gp2uJlYpAhhPYwmCou+KgSqbZN4SKZWVgknZcBpL4dmJRnvfswPYU5FlxK8D6HdRjnTL241k79/jZwcQQPJUVnPMQ1k2COg7nF1Oh7IjeZ4Etn0zq+yEDKh6yQIovUosjulgR2N7QL12rCk7n332GaZNm4by8nJs374d//u//4u+ffvi5Zdfxs6dO/H0009bWsyqVaukyyeddFLCgaNKhg4din//+9+WXi/bueSYYVi1tQH7vi0CXIDoK4HBUZgy3gK2oU81jQWwH+WB7eZHRhgdJqiG/4ADFqrplJ4dpcdIMic7yK/DUffaMdpQkJOOYEfyHJTE98BJhreQ7SyMdFFWTzxXM2ASsOnF1EzKhtNYsWCnbW+82bY3gx07ZmQZDXb00ljK929k++AvYd+9ZCZlrj6UDwb6xIKd5joWXBmZvWYUZQrL5TLeF4cTNKBqJSsKsDPY0fMaUhdla8HOr371K1x88cW49957UVoq/8hPPfVU/PznP7dtcYQ+LpeA+356KB55IACIwJbGKC5dtDLh/Qu8btx+xsE4YbRiuGUyZcdUsGOxIsuqsnPIOWx9488w9zhA4f8QmX+BBwJONCdzylSTz6VuqRkMdqQeOyZTfmbKz5MqO5PYeUrBjoU0Fv/eCu54/1u66Rcbtty8k+1wrfiF9HxQSvTUDv7+XV79VBjHV8q+e0mVndgOuXwwC/DdftZPqrlOVnrsQFmJBVhXdvS+M8l+c3pDQDlGFXO9gx9SdqylsdavX48rrriix/WDBg3S7W9D2E9VWQFmTJuOqChgU/cQ7G7uSniq3deOlz9WRfZ6IyOiEXlnZCiNZXFkhNVqLH8pcOzVQJ9h5h4HsPfNd7jKDZETy845jlR2TPp1OHYGO9WHABDYUWvbXnPr4FhJYynNyYk8G+mgqK/czXmfxVSW4TSWXrBjsrDA6DBQZbDjcsm/b7t9O8pREYB5z46RSrRkapHeEFAOD4SMVmNpKjuD5Ncz2vIhx7Ck7Pj9fs3mgV999RX69eun8QginRx77En4fuinOFgsxWsJpnS//WUD7l/xFfa3qzpo6ik7yh2RIWXHqmfHYjVWqhT2Ye9RuXGTTL8WfEDpRunZiYTlzzmTBmWzlVgcM7129ErPAYVJ+Wtm4B71I3NrUa7DVBqLl533YgqL038sUNvAfDuDLJSf25HG0muopwX/fZtRdgCm5uzbynw7ByV+mGnUwYppZcfAQVpSZceEQVlvGGh3UH4NrW1XQYBV6obbmQG8cmTi5zKD3w+88IJ82cFYCnbOOOMM3HHHHXgh9iYFQcDOnTtx4403Yvbs2bYukDDGoMFDMUjndh7k7G1VKTh6IyOkHZFgTKa3OjLCSO47HRT2Yf6AbFN22vfKvgaXJ76Dth5688CsYsRzoIUVZUcv5TLwMBbs7NpoMdgxqeyE2xUdrHv5ewuwVFbtO9ZnZNmi7JjsMWS0sSD/bvPUC/ft2K3sKEdFAKop78HkqTlbDcopVmNxVcft094eCAJTd/Z9xRRQu4Idj4f118kCLKWx7r//frS1taF///7o7OzEiSeeiJEjR6K0tBR33nmn3WskbKCimI33SKjsaBlFjZRWKrGcxspQsKO183eyZ6ewr1zRUr+JnRf3N141ltY0ltVgx4BB2ciOWfLtWKzIMprG8pfKvxleIdSb5mQO9+1YrchKNG9MjV41ltU0ll7nZ1HUVnYA+yuy1MqOvxzgJR5G1B0jnd8NKztGDMqN8gBaNcqBwIm21fzzzFPfjiVlp7y8HCtWrMB7772Hzz77DG1tbTj88MMNDfMkMkO/UnaU0tgeQjQqwuWK/SD0PDtmN2bqhmtGyVgaK8DOlfl0Jys7LhcLwprrZDOuUXMyEL/hNdvlOhFmuydzeBPIZMpOpFsOhvVmOPF+LLs3mlsHx2gaSxCYutNcBzR+y67r7SAdUAQ7vajsiGL8jtRsQ0VpGKiOstN5QP5O9FB2tht7HaOoPTsuV6xXWDMLBpO1dDCibOkNHAYMVmPxhq2R+GIKJUb8e/zztLPXTnc38Mor7PKPf+zoYaAprey4447DcccdZ9daiDTSp4gpO5GoiKbOMPrGlB5dz47ZICRlg3IGPDuAfNQV7pIvO1HZAeRgh49HMOMt4sGdGGUb6mQ7OiNYVXb4/zpZsKM8wuY7JS0GcJPy9yy9ZLbRpNHmmYAc7PC0SibSWLz8vGknW7vZ345Zz44YK1ZQvo7ZNJaRkRFc1SnuJ7cyUCo76oArFYKqNBYAFJazgMKIsiO1zDBgUNZSdpINAeV4/CxQDLUydUcz2DHgNZSUHRvLz4NB4JxzYmtoy41gZ/Hixbj88stRUFCAxYsX6963pKQEBx98MI4++uiUF0jYg8/jQnmhF82dYexvCyqCHRuVHaueHb7R7O2dhjqfzjcYbr9xH0xvw4MwrmCY2al7C1nH3O5OtvG1M9gxa1Dmyk4ygzI/IvaV6vdY8ZcClaOYJ2HXRmD0dHPrMdM8k/t2JGUnA2msor5sHe17Yyblw5M/RonRYMdXLHdZ72pRBTsmqyiNeHb4jrhM4UAMDAEgsP9R+15rHdO16FIpO4C5YaCpprGMDAHlFMU6MXfsByo0XNqtBtpQpEPZySIMBzsPPPAA5syZg4KCAjzwwAO69w0Gg2hoaMA111yD3//+9ykvkrCHyhIfmjvD2NsWxKiq2Aaa73Q0lR2TXhoe7ASbjQ14lF7HYul5qqiHgSr9Or1ZSmwGpUkZMF6JxSnsA7TGgh0rJftqeDVWugzKRnfKAEtl7fuKBYJmgx2jaSwAKIkFO0072XkmlB2ApbJSDnYC+vcTBKZ8dB5gjylTpHfNprH8BoIdZUNBjsfPdtQt3zF1x65gR/LsKIOdADs3pOyYaSrY1FOVMjIElFNUwb5viQ4k9UZFcHj5eZ56dgwn7Wtra1FRUSFd1jvt2rUL//nPf7BkyZJ0rZuwQEUJ+0Htb1OYlCVlR6f03Ii0D7AdkhD7SplRd5ySxnKyX4ejTq9ZnvZuk0nZiMFSC8PBThM7NxLs8LERVpoLmk1jAfKYjEwYlIHUfDtmgshE5edmf7dcAdNNY8VGRSiDHUBOZdlZkaVuKgjIn4eR34eRPjv89xYJ9jTjSylgAypyMouAkQajZbyLMgU7tnLcccfhlltuSdfTExboFwt29rUpUlZ2enZcbvnHbWZkRKaCHXU+3cmVWBx1IGZ2rXYGO91BeYdh2aCcpBrLaKdfwHpFliiaa55ZrAowM2FQBlivHcBaRZaZYCdRRZbZPkNGlJ1mDWUHkFVIOyuy1KXngKJowYRnR+//7yth7SGAnqkxMwcKyYIdI/3BuLITbNGurtOi4UvgH/OsN+t0EJaDnZUrV+K0007DQQcdhIMOOginnXYa/vvf/0q3FxYWYv78+bYskrCHipJY+bmmsqPl2bFQEm52ZIRymGJvex/UlRJZqeyYbH5otkusHnxjLbiTp0PUGB0EamanzDspt+6Se+AYIdwJIDaHz1AaSxXsZFrZMdtrJ9ItBypG/m+JugCbrsYyYVBOqOxsN/ZaRtBSZiylsXQO0gQhsUnZjLnfqLKjtz3wFctrMaruvPlr4JNngI1/M3Z/B2Mp2PnTn/6EmTNnorS0FPPnz8f8+fNRVlaGU089FY888ojdayRsojLdyg5gviLLbJdmO+mRxspCZSeTaSxlQzSzZexmlR1D6ZYS1kkZAPZ8bnwtyoDLSLCjNmNnStnpp6rIMooyHVWgU+HGsSuNZcazU6ZWdtKZxtLy7DQZeLzBYC/Rb85UsKPTnT4alYP7ZNsuM712Qh1A7bux1zVZdOJALNWJ3XXXXXjggQcwb9486bqrr74axx57LO666y7MnTvXtgUS9sGVnX1xyo7dwY7JkRHSRlqQd4C9hXoicV4oO3YGOxbNyYBxz06yiedqygawTsrtJmR3XonlLTIWtKnTWJkyKBdXMCW1Yx8zZnPPUjL4jtxbbKyIIFEXZbPKr6TsJGgqGOlmowyAxMqOnWksTYOyiZERRhXpRGqqkbJzjl6la8d+Vi0HQfaTJaJsELBns7Hp59vfZV4jQHtcCAD4fMCTT8qXHYwlZaepqQkzZ87scf306dPR3GxwrgjR6+gqO2GdaiyvFWXH4JGA2S7NdsJ3/N1dTGHIBmWnICD/z/zl5gNEdQVaKljtsQOkpxoLMJ9GBcxVYgE9dyiZSmMBcr8dM74ds59pIs8ODxbsKj1vq2c7bZenp2LJlZ32huSztYwQjcrr1/LsJPt9iKKxcRGAPcpOoc5BJG+ZUVSRPHiVKrIMBDtfr5AvJ/L4eL3AxRezk9dg9W2GsBTsnHHGGXiFd01U8I9//AOnnXZayosi0kMl9+y0K4MdnWosvhNIZxrL6AYjHfjLmN8EYBs3KdhxsLIjCHIwZqUE11Zlx2IlFmB8EKjpYCe2FjNdvM1UYgFMvRQUm85M9Nnh9BvDzs34dsx+psnSWGZLzxMFKzy1UjaQFTsoKQzI3107fDuhNkg+La1qrGTKTrhT7pGTbPuYqIuykSGgHL1hoFKPHQMHaWUGy89FEfj6DfnvRMpOFmGqqSBn/PjxuPPOO7Fq1SpMmTIFAPDBBx/g/fffx7XXXmv/KglbkJSdVq00lg1NBQHzO5tMVWIBMfNgOduAtHwv/6CdrOwALBg7sN3aOu0MdtrtSGMl8+w0sXOjO+ZiC8qOmYaCANsRF1XIqbJMpbEAazOyzFS4AfansSJB7T5cvOxc7dfh9BnOvrcHaoHqCcZeMxH8t+7yxg85NurZUapTyZTvpAZlM2ksLWXHgDmZw9ODydJY+76W+0gBiZWd7m7gjVhQNGNGbnRQVjcS7NOnD7744gt88cUX0nWBQAB//etfqeTcofA+O53hCDpC3SjyeZIoOwaqDdSY3dlkMtgB2M6/s1E+MvYWZzYtYQTHKDt2pLEMVmMZ3TFLOwUzyo7JNBbAfDs82MmUQRmw1mvHrjSWaYOy4ncVbO25k9dqKKik73Bg18f2+HaUKSxl+tyosqNM4SXzeSVMY1koPe88AEQj8cpXm4n0u1Fl5+s32bmvhO0HEik7wSDAszm5Mi6itrbnF2zfPrZBqaw02SqeyAjFPjcKvC50haPY1xrCkAqPooOynrJjpvTcYhorUzsMviHa+yU7d3L3ZA7fGZQNNP/YdBiUzY6KANJTjQVYG1liNo0FxL/nTCo73LNzYAcL2oy8BzvSWMqWEUYPDtxeNoolEmSPVQc7Utn5oJ6PBeytyJJGRajWruyzozcs18y2MZEPyEo1lhhla1N+dtLEcwMHP/yzbflef87YNzG/zthZwGfLjPflcTCmPTtNTU2YO3cuKisrUVVVhaqqKlRWVmLevHloampKwxIJuxAEARXFsVQW9+1Iyo7GTielaiyDOxsj82XSCd/5N8QUSif7dThHXQFMmQccdbn5xyqDHVFMbR2pKDvKQaDRaOL7SdVYZoOdNKaxgPgdSyaVneLK2HsWWUWWEcx+plpqh1KRM/P+pfJzDUUvUUNBjp0VWVqVWID8XsVoYiM1YE711jrAMDoElOP2soIEoOd3WxoCakLZ6e5KvI0OtgHb32eXJ/wkdl32BzumNKfGxkZMmTIF33//PebMmYNx49hRxRdffIElS5Zg5cqVWLNmDfr0cegQRQKVpX5839SJfa082DHi2TFxxGta2cnQXCwOP+riaSyn+3UAIFADzLjT2mPV7evN/G/VpGJQVlaRdXclXofRGU4crriYMihbTGMBLO2pNtP2Nv3GATveY74d3kVaDzvSWPx3K7jMVQT6Sti2QcukbMSzA9ij7ARjn4E62PEWyupTV1PiPkRmGipqGZTNDAHlFPVhj+vYD2CUfL3UY8eAZ8fjlwfItnzH2heoqV0NRMOsazWfuRZq65k+yzJMBTt33HEHfD4ftm3bhqqqqh63TZ8+HXfccUfSQaFE5qgs5hVZMZOyXp8dK0e8fMfX3WlMVrfiC7ITviGSeuxkQbCTCr5iZsqMhtmRZirBjh0GZYCpO1rrCHfJfT7MKjudjfppiLjXSSGNlckUFqffmFiwY9C3YzrYCcQ/DlAosiZbRvC0Ee/grMSIZwcAmurMDRrWQqvsnFNQzkrc9Xw7ZkZlaBmUzQwB5RRVsMIEtSJjZFSEkrJBLNhp/h4YcGjP27lfZ9T0+GAw2CJvL7MQU2msV199Fffdd1+PQAcAqqurce+992qWpBPOQa7IUqexbKrG8pWwIyNArhLQI+PKjurHmw1prFQQBHt8O6KYWhrL5Za/J4l67fCdjeAy/v3gaxGjxrrgAta+gzyNlckUFof7dhq+NHZ/q56dUCs7ugesH6QkGhkR6pC/T4mCnZJqdnAmRmQVyCqJPDuAsV47ZsrutX5vZoaAcrRUc1E0V40FKCqyNEzKogh8HRv7NPJHgMcnHxBnuW/HVLCze/duHHzwwQlvnzBhAurr61NeFJE+pPlYkrLDjaIqZac7BERi9zGzQRMEoO8Idnn/N8nvn2llR50eyXVlB7An2Am2MnUIsBbsALKSksikzIMVf5nxcRR63oZEWEljldewcysVcXbDe+3sTVOwo1Q/uCJitbAg0cgI3jnZV5J4XS6XfQNBE3l2AGMVWWa8hpLpuUUOFq2kgLWCnVCbfLBgdNslVWRplJ83bGHpLU8BMOw4dl2iPktZhqlgp7KyEtu3b094e21tLfr2NZh/JDICV3b2tqmVHVWwozQgmumgDCg2vgZ6f5htTGY3+absAPYEO7wSy1tkPRXGg4tEc53Mlp1zuA/BqG/HShpr2PHAqfexU6bhM7IObE/epBEw74Py+BVH97HHmh0Cykmk7HClpnywflrMLt9OUEfZkYKdpsSPNxPsSZ+zKH9+VlRRrWCHV2L5So0fMCorstTwFNaw4+XfQ6LWAwAbEfHww+yUS+MiZsyYgV//+tcIhUI9bgsGg7j11ls1x0gQzkGefK4yKEeC8dU5fAfk9jEp0wxS7w8DR5pOKT3nkLJjjFTMyZxkjQXNKhCc3jDJu1zAUZel3tzODoorY2MyRDb3KBlWPlf10b3V3600MkLl2eEqQ1mCsnOOXRVZfMet6dkJxO6jp+xoTExPhMcnv28eQPHvppFKLI5WpatUiWVCYdTrtcNHRIyaLl+np+x4vcDcuezk8HERpg3KkydPxqhRozB37lyMHTsWoihiy5Yt+NOf/oRgMIhnnnkmXWslbKBfTNnZz4eBKs1x3UHAGwt++A7AjLQvvYgFZSdjBuVA/N8U7BgjFXMyR+q1k0CNMFsizZHmYxnt4m0hjeUkBAEYOgXY8i82vLHmKP37m+1KDbCgoL1BDhJSTWOplZ1k5mSOpOxsN/e6aowoO0Y8O0bff0GAfWb8N9dpUxrLyjy/RF2Uu5qBug/Y5VHT5Ov1lJ0swlSwM3jwYKxduxa//OUvcfPNN0OMKQGCIOBHP/oRHn74YdTU1KRloYQ9VKiHgSpbpXd39Qx2rCguymBHr3EV4Jw+OwDzemRqHb2JLcpOCuZkjrLXjhbSTjlg7nnNKjv89VOpTMs0w45nwU7tu8DxOiN7ukPy+zUV7Kh8LKmmsdSeHWUaSw+7lB0p2NH4DJSNBRNh1mtY2IcFF/w3Z+X3w1Ug5XwsXnZu1JwMyMpOy+74isVvVwHRbqBipOy7BPSVnUgEePdddvn44wG3c0vTTfd2Hj58OP7zn//gwIED+PrrrwEAI0eOJK9OlsCHgR7oCKM7EoXH7WXVLmI03reTiuJSMZI9Z7CZHXmU6fhgnFSNlQ+qDmBvsGOlezKHKzuJfCZW01iSZydLunjbwbDj2XndhyygSZR6Vu6wtMy5ieiRxrK4fUik7CRrKMhRKjvJDqT00EtDmTEoGw321BVeZoaAcrSCeDOjIjilA9j2ORpmah1/rLLkXImk7Gh8Hl1dwA9/GFtLG1Ds3INFS1PPATYb66ijjsJRRx1FgU4WESjywRXbPjS2h9jGQqvXTirBjscvHxkk8+1kekejVA3yJtgJsHM7DMq2eHZsDnZMe3ayPI0FsPLzokr2WX6/IfH9+GfqKwXcJo511QGA1Gemlz07gRoAAjOVc1XDCoY8O02JH2+mzw7Q8zdnV7BjZlQEx+2Ruy3zIDOu5Hxa/P25+pVP1VhE9uN2Cegbayy4T+3bUfbaSbUk3Og05kx7djw+udosHyqxAJvTWCkc6CQNdprYuek0lknPTi6ksQRBLhXe/m7i+1nx6wA9fRtWFVmupCiVHVE07tnx+OX7pFKRlWrpudkqUnUAlUo1VmcTEOlml82MilAiVWTFgsz6Tey5vEXA0GPj75sjnh0KdvKQykS+HU1lx6Liwn07+5IFOxn27ADyzj9vlB0e7DRZfw7pyNSGNFayaiyzpedWh9GabbHgNIbHUlm17yS+j1W1TEpj2eXZUbQb6DwgB5zJlB3Anl47egZlI00FzXoN1b85S54dnnIX5aDJzKgIJeqKLJ7CGn6i7Nvk5GOfHSI3kBsL6nRRTvVo14iyE43Ir2N0cnI64Bs3UnaMY0c1Ft9RJOuzY9qzw+djmUxjZbs5fdgJ7LxuXc8moRyrAaS6HNvOpoI8hVXcr+eOVgtuUraq7HSH5AO7ROMiAIMGZaOeHcVvzuwQUI7bI/8feLDUalXZUXVRlkrOf9TzvqTsENmKPDJC3UVZcYSdchrLQFdXZfoikzsafqRYOTJza+hNnGZQTqTsWC495/1IDAQ7kW55/la2BzuVo1hVTiQIfP+R9n2sfqY90lhW++xopLF4sJMshcVJtfw8qPALaXluDHl2uLJl1rPTZG0IKEf53e4OyUGTmWosIL6LcucB4Lt17G+tYIeUHSJbqSiOBTt6yk6qaayKUQAE9qNM1MlWOTnZY+CILl2ceh9w7rPAiJMzt4behAc74XbtmWhGsNWgnEzZCZh7Xr6mcHviQIoT1yk8iz07QMy3w1NZCXw7KaexYjs8q2ksv4ZB2ag5mZNq+Tl/D95ibZM2/2zCHSygUCOK8YNQjaA8wLAyBJQjpWgb5ZlYLq/5oEnZRXnbWyz46jcWCAzpeV9SdohspbI0ZlCWlB2bq7EAlv7iP5xE6o7Vycl2UzYAGHea8flL2Y6/jAWYgDXfTiQs7zSd2EHZX8Z2AEBydYensAS3+R2PE+G+nUQmZaufaY9qLBsHgXKTLJ83loxUR0bo+XWA+M9GK5XV3cWGkQLG379SLbIyBJSj9KMpe+yY3X6WxVS05u/1U1hA8g7K997LTg7voJwnW3dCSWVM2dH17NhRJZVsbESmy87zFZdL3vhaSWVJjxF6jtswA/eDafXZEUXrO2ZBkHcKyeZjhRV+nUwG3HbBlZ3v1msHkZaDHbuqsWL3j4bl7Y2UxjKp7LTvjU9JGYU/RsuvAwAut7yD1wp2lIFaKsqOlQOFuGCH99gxmcIC5M+6rV4OdkYmCHakQDfBbKzrr2enXJqNReQGkrKjW41lQyAi+Xa+0r4902Xn+Uwqvh0eQBT2YTsGq+iVnofa5KNnsztmQPYSJVV2eCVWlqewOH1HAKUDgUiINRhUY5eyEzTpWeEoK974cxhtKKhcCzf2WvHtdCVRdgB9346yes+oGhwX7KTQfVzp2ZHMyRaCneL+TP0Uoywl7SsBhkzRvi8P/EKt8tT2LISCnTyEe3ak+VherWDHhkZrSZUdCnYyRiqNBe0YFQHoBzt8p+r2yUZmMxg1KedKJRZHEBQl6BqpLDs8O6JoPY3l9sgFEdy3I3l2DAY7QGq+HSmNpdNBWm/yeciCX4n/3rq7gJZdsess9KiK8+xYGBXBcbmAsoHy3yNOStx1W6mAqZW0SARYv56dIs4OhCjYyUMqS+VgRxTF9Hh2gOTl51a7sBKpk4qyY0clFqBfjaXcKVtJLxUZVHa4QTmbGwqqGabj20k1jdXdFQt4uGfFwm9XOTIi0g207mZ/G1V2gNR8O0Ymluv12rEyz89fxnxhANC4jZ1bOVgoVATxVkZFKFF+3uoREUo8fsAdszqofTtdXcBRR7FTV4J2Bw6Bgp08pCLWQTkUiaKlq1vh2bE7jTWanbfVa+9UzXYhJewjpWDHhkosQL/PjtUSaY5Rzw5/7WxvKKiEKzvfb+g5g8pqhZtSBeHKBGDtYEg5DLStngVOLo+5kQepKDvSZ2BE2dHw7FjZNgqC/Jz7Uwh2pC7KjYpRERaUHSC++k09IkKN3nysLIGCnTykwOtGqZ+VXO5rCyqUHZsNyv5SWZrW8u1QGitzpBTspGCwVGJI2QlYe27Dnp0cS2MBrG9U+RA2wbrug/jbrI6LcLnlnjS8EZ23yJpnS6nscL9O2UBzz2WLsqMX7ATYuW4ay6Rfif/m9n/DzlOuxkrBswPIJuWqCcnN4f7sLz+nYCdPkboot4USKDs2BSJc3dHy7ThhVES+YkcaK+VgJ/Z/t7NqiCPtFJJVY+VgGgtI7NtJ5XPlR/dc2bGq+vKgKdQGNNexy2b8OkAvenZ0qrHMvn/pN2dXNZbFURGcMaey/8XRVya/b4FO+XmWQMFOnhI3H0vqoJyOYEfHt2N1o0GkDt/w6nWJTYQdoyIAhbKjkcayLdhp1L9fLqaxAG3fTrhLMSbBSrATe4wU7Fj8zJQjI8x2T+ZwZaf5O9b3yQzJSs8B+b1qeXasHqSpR3SkEux0NctNBc2OiuDUHAX833fA4Rckvy8pO0S2Iis7wZ7KjigqjnhTDXZ0BoKm2qWZsI4TDMpcTYl299xhWU23cAx7dnIwjQXIys6ujfIOSjoqF/RVjUTwx/A0llWvnbKxoNFp52pKq9lBmhgBmnaae6yR0nMemOgpO2bfv7onlSWDcgBAzLAfjU0+N+N1sgopO0S2wpWdvW2hnp6d7i55dks6lR3y7GQOJxiUlW0N1CZlqwMrOUY9O7maxiofzNQPMQLsjPl2+GfqL7PWLbyHsmNxeK9yZITZhoIcQZBn2pn17Rjy7BgxKJt8/2r/mZXSc5c7PmgqqgDcvdC52G9gOKrDoWAnT6ko4eXnGsqOcseTarO1yphnp7muZ48G8uxkDlsMyhY21krcPnlshdq3Y1caq7ORTZlORK6msQBg2HHsfPs77DzVz7SHZ8fqKBnFMFAp2DE4KkKJVd9OsnERgEGDcqrKjsXfj/JxVlNYZuHfGbWy4/UCt93GTjQugnAi/UoUXZTVyo6yq2wqHXIB9sPk1QL7VBVZNC4ic1gNdkRRYVBOMY0lCAqTsqqxoF2l52I0yfTqHE1jAcDwE9g5Nymnmhq0K42l5dkxOgRUidXp53yHrfc5GDIom/XsKIIdK0NAOUpF1ao52SyJhoH6fMDChexE4yIIJyIrOyHZKNodO7q2O70kjY1QpbKoz07mkAzKzeZawIfaZQUw1TQWoDApq4KdVEvP3V5ZetdLZeVqGguQTcr1n7HgkQeQVlOD6gDAcjVW7HFtDXJlklnPDmBd2THj2dE1KJtVdgLyZStl59JjFb87q2XnZtEbBpolULCTp3DPzv72UM9BoHYHO5U82FGVn5NnJ3MogwgzeXgeOHgK7Pm/JRoGmmrKBQCKDZiUczmNVTYAqBjJ1K0da+xLY3EsBzuxz5orvb4Sa2uy4tkRRfOeHVGMvy3VPjtAagcKcWmsDCs70Sjw+efspJcudgAU7OQpvBprX2uw57gIu3cACZUdSmNlDLdH3tibSWUpe+zYMSU80XwsKeUSsP7cyp4kicjlNBYQX4KearCjDg4sp7FiQQJvrlc+2Np3SZnGUgckiQh3yKMujHh2xIi8neJYbZmh/C6nFOwo01i95NlJpOx0dgITJrBTp0a/LAdBwU6eUhkbBtoa7EYIMWNZupSdRBVZVnPfhD1wWT1ZebYSKdhJ0ZzMSRjs2KDsSPOxdN5fLqexgPjmgikrO6rHWTYox4IEXjptxa8DAIEhAAT23Wnfa+wxXJkQXPrr9xayqeBAT+XTcp8dhbJjpRKLk4k0ViJlJ4ugYCdPKSv0wOtmR1PN4ZgJWa3s2B3sHNgeX3VDaazMEhjKzs2kAewyJ3O0RkZEI/IRpFV/CWBQ2cnhNBYgKzt7Nsn/Z9uCnRQNyhwrfh2ATekuHcAuN9UZe4xyCKiemiQIiX07lvvsBOTLqSg7hRlIY5Fnh8hWBEFARUzdORDm5b882LFZcSmujP1ARWDf1+y6aEQ2RFvt10GkRsVIds7TCUawq3syR2sYqHKDaqX5HUfy7ORxGqukv3yw8c1Kdm5XsGPWs8JRB0lWgx0gpu4AaNph7P5S2bmBzyBRRZYdfXayLY0lfRYU7BBZSGUp8+00BmNHOD2UHZu8NILQM5Wl3Lnl6o7G6VSOYudmgh275mJxtJQdfiTtLWJH71YxouyEczzYAWR1h++k7fLsWB4XoQoSUgp2Yv15mo0qO7zs3EAQrdVrRxSt99nxFshp22ytxgq1mqvedBAU7OQxXNnZ1xX7GqTLswMoTMqxiiy+wRDc1vtNEKlhRdmxa1QER+qzowh+Uy075yTz7IiiIo2Vo54dQPbtcDJejWWjssObERpNYxkpO+doKTvdQdlrZGX7yL/TqRwslMVSd4V9eq9th/J/r24OmyV4Mr0AInNIIyP4QXV3l2oulo07gB7BjkI9sqOqhzCPFOxsY/93I/8H2w3KGsqOHeZkILmy0x2UK3NyWdkZelz831aDSLvSWOodtFWDMmBB2TFQds7R8uwoK7OsBHuBIUDrLrls3gqBIcBpD1rrOm0Vjx9w+4FIkKljqXjpMgQFO3lMZaz8fG8n38mJbCBjOgZ0SgNBY701rErBhH0EhgAuD0vltOwyNp/IboOyVp+dVDv9crj6lMizo6wAy+Vgp7gC6H8w0PA5+9vq5+otYkpsqgGi2gyeUrDDPTsGh4EaGRXB0VJ2Uu0u/+NHgYYtwMDDzD9WyeRLUnu8FQrKWNWb0rfj9QLXXSdfdjAU7OQxXNmpV1b9dnemKY0V8+zs3wZ0h6gSywm4vewIc/837GQq2LHLs6NRep7qEFAOV58SKTv8O+j2pz4WxekMPz71YEcQ2A6P92WyejDkcrHHhtqA4n7My2KVch7s1BlTJ7myY9WzY7XHDqfvcLnzc7bhjwU7ygICnw/4/e8ztyYTkGcnj+GNBRvaFQ25uoPpCURKB7AfixgBGrdRjx2nYNa3Y3c1ll6wY1caK9zec9AooPie57BfhzNM4dtJ5XNVPjaV3y4PFlLx6ygfH2rVn4HGSdWzk8/Di7O81w4FO3kMV3b2tYfiuyino7OxIMT7dqh7sjNQ+naSEY3IR/W2GZQ1ZmPZFez4y+TGcFrqTjgN6VqnMuxYVipd3D+196v0ulj17ABysJBKCgtggWpxP3bZSCpLSmNZ9OxY7bGTC2j12olGge3b2YnGRRBORRoZ0aaaj5WuFJNybEQ6fEGEeSoOYudGlJ3OAwBiKmBhCqWzSvh3TKv0PNVgRxBkdUerSzT3CeVyJRansA9wxWrgspUsjWQV/j9x+1ka1Co8WLDDZGumIstMsKOr7ORhbzBJ2VF8Hp2dwPDh7ETjIgin0i+m7DS2ByHGKTtp2glIvXa+JM+OUzCTxuLqSEF5ajs6JVzZCWkpO4HUn58rUFrKTj6lsQAW2HJDr1V4AJDq75YHC0Z8YskwU5FlybOjEezkpbIT+99naRdlCnbymD7FTNmJikDUHVN2wmlKYwHxjQXzOfftJHiwc2A7q8TTw+5KLEDRZycNaSxA36ScT2ksu+CKSKo7+4GTAAhAzQ9SXZGiIstAsGPJs9MkX5fPXkPy7NjD3XffDUEQsGDBAum6rq4uzJ07FxUVFSgpKcHs2bOxZ8+euMft3LkTs2bNQlFREfr374/rr78e3d3dvbz67MTrdqFPETtC73bxNFZX+lSXytHsfP838g6NdjSZpXQACzjECHAgSct9u83JQALPThM7tyXY0VN28iiNZReSspPi73b674AbvgVqjkx9TeUmRkaknMaKKUP5uN3K8vlYjgh21q9fj8ceewyHHHJI3PXXXHMN/vWvf+HFF1/E6tWrsWvXLpx99tnS7ZFIBLNmzUIoFMKaNWvw1FNPYcmSJfjNb37T228ha6mIpbLCiLXlT6dnp7yG7VgiIWDPZnZdPsrBTkIQjPt27O6eDCiqsTSaCtrRuEzXs5NnaSw74Ef3qe7sBcG+xpRW0lhGlB3uSwu1yaon/86kYs7OVkjZSY22tjbMmTMHf/nLX9Cnj2x6bG5uxhNPPIE//OEPOPnkk3HEEUfgySefxJo1a/DBBx8AAN5880188cUX+Nvf/oZJkybhlFNOwW9/+1s88sgjCIVCCV8zGAyipaUl7pSv8MaCISHmwQi3KwZ02hyIuFyyuvP9J7HXyEM52GlIvp2v9e9nd/dkQNFUUGtchA3Kjp5nJ0y+MdPYlcayEzMGZb6jNuLZUao//HGp9tnJZkjZSY25c+di1qxZmDZtWtz1GzZsQDgcjrt+7NixGDJkCNauXQsAWLt2LSZOnIiqKnkY2owZM9DS0oLPP/884WsuWrQI5eXl0qmmphfbbjsMrux0ibFgh5cWA+k54uW+nSBPY9GOJuMYNSnb3VAQSO+4CEAxMkKvGou+g4apGs/O+e/YCXBlp7NRDka0iEbkANfI1HO3RzZS89RqPnsNs1zZyWgH5aVLl+Ljjz/G+vXre9xWX18Pn8+HQCAQd31VVRXq6+ul+ygDHX47vy0RN998M371q19Jf7e0tORtwMMrsjp5sMPlfsEl996x9QXHxP+dj0dITsNor510GpS7O1mfjmi37N+xNdhp7HkbpbHMc9DJwNWfyD4ZJ1BQzk5dzSyV1X+c9v2UioTRNFRBeXzDQikNlofbLS1lx+MBfvlL+bKDydjq6urqMH/+fKxYsQIFBWnYqerg9/vh99OkbQCoiFVkdUR587XYTiFdAzrVR4QU7GQeo8pOOg3KQGxUCTcqC8aOvpOh59mhNJY1+o7I9Ap6Uj4E6NrEUlkJg51YoOIpADw+Y89bGABavpN7P0kBMnl2AAB+P/DII5lZj0kylsbasGEDGhoacPjhh8Pj8cDj8WD16tVYvHgxPB4PqqqqEAqF0NTUFPe4PXv2oLq6GgBQXV3dozqL/83vQ+hTWcqCvvZIbDYQP3pP1w6gh7JDO5qMUxHbebXu1k8DpNOgDLBAh6ew/GWpNb/j6PbZoTRWzsDLz5t1uiibKTvnqCuyqM8OeXbMMnXqVGzatAkbN26UTpMnT8acOXOky16vFytXrpQes3XrVuzcuRNTpkwBAEyZMgWbNm1CQ0ODdJ8VK1agrKwM48eP7/X3lI1wZac1EhP50h3s9BnGuq9ySNnJPIV95NRUo04qi6t+dio7LkW6NNxhb9k5IK+1s7FnO3tKY+UO3LejNzJCSkEZMCdz1MNAqc8O+xz5b0kUgb172UkUEz/WAWQsjVVaWooJEybEXVdcXIyKigrp+ksvvRS/+tWv0LdvX5SVleGqq67ClClT8IMfsEZU06dPx/jx43HBBRfg3nvvRX19PW655RbMnTuX0lQG4cpOazgW96Y72HG5gcpRcul5Pm40nEjFSGbi3f8NMOBQ7ftwk6+d1VgAU3e6u+KDnUKbgx0xyp5buXZqKpg7GKnICtqh7ORxGksKEkX2WRYGgI4OoH9/dnVbG1Ds3O15xqux9HjggQdw2mmnYfbs2TjhhBNQXV2Nl19+Wbrd7Xbjtddeg9vtxpQpU3D++efjwgsvxB133JHBVWcXlcUs2GkKq9JY6ZT2lamsfJSDnUgyk3KoQzYO22lQBuInn9s5KgJgYy24/K727VBTwdzBSK8dM6MiOOphoKE8Nih7CwB3zOuUhaksR9mnV61aFfd3QUEBHnnkETyiY4AaOnQo/v3vf6d5ZbkLHwbaHvWy0FcyKKcz2FGYlOmo2hlUJjEpd8a+Fy6v/Q3VpF47HfaWnXOKK1irA7Vvh9JYuYM0MkLPs6PwgxlFrezkcxoLYJ9dx76sLD93tLJDpJ9ivweFXjeCvPRcaofeS8pOvm40nEayiixlJZbdVXrKXjt2TTxXIpWfq4IdSmPlDrwUvm0Pm++nRaqene4gEI11Us7X70xB9jYWpGCHQEWJD0Goplin88fMlR23X5ZFiczCg51932gbDdNRicWRhoG225/GAhTzsSiNlbMU9ZX/jy3fa98nVc+Osst3vgY7/uxtLOioNBaRGSpL/Ai2qIOdNCoulaOBY65mQyjT0cuHME+f4QAElu5p3weU9Iu/PR2jIjhKZScdaaxEyg6lsXIHQWCprL1fslQWn/emJBXPTlezok9PIeuunI9YVXbWP8GqLseckp5tiAHy9D9GKKks8aELKoUlncGOIADTf5u+5yfM4y1gJs+mnSyVlTDYSYOyo5yPZXfpOcA8OwDQrgh2otH0zYAjMkN5jRzsaJFKn53OpvweFcGRlJ1m/fupWXk7e8wvP6Bgh8gclSV+eVwEh45284+KkXKwM3RK/G3pmIvFUU4+t3PiOUdL2eGVZcrXJ7KbZBVZUhrLimenWTYn52MlFqdA1VjQ4wEuuki+rIWy8KB0QHrXpwMFOwQqSnzY1kPZyeMfdL5SMRLY9pa2STkdoyI4WsGOrWksDc+O5L8Q4kdWENlLsl47VtJYkmenSVG8kYc9djhqz47fDyxZov+Y1t3s3Ftk7+/aJGRQJphnp4dBOY+l2nxFryIrrQZlHuy0955nRzkXi3xjuYE0MiJBsGOl9JwrjNFuoG1v7PF5fCCoVnaM0LKLnWfYo0nBDoEKCnYIQDZ1ajUWlEZFpCHf7lMoO+koPecBmtKzQ5VYuUeyXjtWSs+9RYArlgBp+Y6d5/O2UT0MVBSB9nZ2SjQugis7ZQPTvz4dKNghmEFZpDRW3sOVncZvgWgk/raOdKaxYmmkUBo6KANygKZUdqgSK/fgaayWXUCku+ftVkrPBUEOvJt5sJPH20a/qhqrowMoKWGnjg7tx3Blh4IdItNQGosAwHYWbj8QCcobdk46q7G4utKxX27alg7PTridqUf8MkATz3OJkirWt0uMAK27et5uxbMDyIF3c6x/T16nsSz02eHKTgbNyQAFOwQSBDsk7+cfLjfQdwS7rPTtRKPpmXjO4d81voMS3PYG2/5SNuYCkIM2nsaioD53cLmA8sHssjqVFe4CIiF22ey4Ex5482aFZFA26dmJfW6k7BCZJlDoRbdAaSwCCt+OItjpamJHy0B6g52W2BFgYcBeI6MgyOvmVWWUxspNElVkcVUHMB+scJMyV3byOUC2ouy0kLJDOASXS0BhkeoHnM8/6HxGqyKLqzr+MsCThvEePOBoj1W7pKM8lZuUubJDaazcJFGvHa5E+EqZAmQGqQKJV3Pl8YGg30I1FhmUCSdRXKT6AVOwk59oBjvcnJymzqdSn5tYNUc6gh21SZnSWLlJYCg7V6ex+M7ZrF8H6GmWz2fVWxoX0crS28mIRoDWenaZlB3CCZSVUrBDIEGwk0ZzMtBTXUlLsKNWdniwQ2msnEJKY6mCHSujIjjq76OV58gVpLJ9UW6yqEf7XpYCF1zMQJ5BqIMyAQAoL1HscNx+wO1NfGcid+HBTlMdM3V6C9I7KgLo2cHYzrJzTg/PTqz1P6WxcouEaSwLPXY46tEl+Xwg6C1gFW+REAsgC/oBP/kJu83t7nl/XnZeUpXx4akU7BAAgIrSQgRFD/xCNx3t5jPFlSwvH2wGDtQC/celd1QE0HPn0RueHUpj5SZSF+XvWJqF+3Os9NjhqL+P+ZzGAljA2LGPfaaBGuDFFxPfV9k9OcNQGosAoOqinO8/5nxGEHpWZEmjInpL2UlHGouPjIgFbpTGyk1KB7LWBZEQ0LZHvr7LRs9OPqexAHMVWQ4xJwMU7BAxKkt8CPJhoHS0m9+ofTtpT2OpAg47J55zpGAnVllGaazcxO2Rd6zKVFYqaSxSduIx02uHlB3CacQ1FqRgJ7+pHMXOewQ76TIoq4KddCo7kmeH0lg5i5ZJWSobt6DKkGcnHqWy097O1GBBYJfVkLJDOI2KEh+CIgU7BOQ01r5eUnY8PnnYIpAeg3KPPjuUxspZtKafS6MiLATSPdJYpOwAkANIPRwyFwugYIeIwZQdlsYSKdjJb9RprHQblIF4dSedyk5nIzOuUhordwloKDsplZ4H4v/O53ERgPz7NOPZoTQW4RT6FvukNFbYVZjk3kRO0zem7HTsAzoPyD6X4jSlsQBVsBOw//l5sCNG2fgLSmPlLlojI1Ly7Cge4ynIeAl1xrHi2SFlh3AKBV43wi4/AKBToGAnr/GXyEdiDVvk5mHp6qAMxFdkpUPZcXvlVvft+yiNlctoprFSUHbcXtmUTMGx8WqsrhZZQSVlh3ASopsFOx2iP8MrITIOT2XVfcjOBbccLKQD5U4kHcEOIJfOd+ynNFYuw4Odpp2AGBtBInl2LCg7gPydzPdKLMC4ssNTWP4yR/ic8lyPI5QIHj8QAXZ1urH/ewPmMyLrGRgoRN9ijeGeFQcB298F6taxv4sqzA9QNEO6lR2AvYfGb1l6jtJYuUvZIHYe7mAp2OKK1Dw7AEuttnxPPXYA48qOg1JYAAU7hBJvIRAE3vy6DY99+V6mV0P0Aj63C3/8+WGYcXB1/A1qZSed5mRADnY8BawlfTrgpfOt9UA0zC5TGiv38Baw8QRte4DmnSzYkTw7FgNpUnZklMqO2w2ceir7Wz0uwkHmZICCHUKBb9K52LKmDhuLfoAqgVJZuU44IqKxPYSrnvsE/+/CI3DSmP7yjTzYkbonp9GcDMjppHSpOoAcsCmrdCiNlZsEhrBgp6kOqD40Nc8OIPfaISUwXtkpKABef137fqTsEE5l0rTzgGnnYVmmF0L0Ct2RKOYv3YjXN+3GFc9swJOXHIljDooFNTzY4aTTnAzIyk46gx3u2eHGVZeX9fghco/yGuC79SywDbcDiHl3UvXsOMB7knG4OpbMs+Og7skAGZQJIm/xuF144NxJmDauP4LdUfzvUx9hw45YmXmfYcyUzEl3Gounk9JRds5RKzuUwspdlNPPubfE5WVpUitIaSzy7MjKThJfp9Q9mYIdgiAyjM/jwsM/PxzHj6pERyiCi/+6Hp9918TKbfsMk++YrlERHN5nJ61prNh74MEOpbByF6kiqy4+hSUI1p6v74j4581nJM9OK9DaChQXs5N6XISUxhrUu+tLAAU7BJHnFHjd+H8XTMZRw/uiNdiNC55Yhy27W+JTWWk3KPdGsMPnY+1l5+S/yF3KFeXnqZadA8ARFwPnvwwcc1XKS8t6pM9RZD24OjrYSY3DDMoU7BAEgUKfG3+9+EhMqgmguTOM8x//EE1FiqPYdBuUywezc6WaZDfq90BprNxFSmPtTL3sHAA8fmDkVPrOACwV6IrNUexq1b5PJAy0NbDLDjEoU7BDEAQAoMTvwVP/cxQOHliG/e0hPLpZIfmn26B8+IXAz5YBx12TvtdQvwdKY+UufGREVzPrjwOktylmPiEIsrqTyKTctgeAyIKidKfADULBDkEQEuWFXjxz6dEYXVWCTzv7SdeLhX0himL6Tm4fxNEzIPqKbXk+TdQbXTpKz138JUBhLLjd83nsOjIX24bSt6OFVIlVnd5mpCag0nOCIOLoW+zD3/73aMz9814gloqfsvgz1OP7zC7MIJUlfjx32dEYXaXauflL2ZGm1FCQlJ2cJlDDptzzYCcVzw4RT4HRYMcZfh2AlB2CIDToX1qAhy47FVtdI7E1Ohh7Ecj0kgyzry2Iuc9+jM5QJP4GQYg3WlMaK7fhqawGUnZsx59kZIRUdu4Mvw5Ayg5BEAkY2KcI0V+vQ1NHCOtd7uQPcACtXWH85NG1+LqhDbf/63PcPfuQ+DsUVwJt9ewypbFym8BQdt55gJ37SdmxDV41GW4FTjyRXVamqxzWPRmgYIcgCB1cbjf6lhYmv6ND6Fvsw4PnTsL5T3yIpevrcMzISpxxqGKDqzQpUxort+EVWRxSduxDChy7gFWret7usLJzgNJYBEHkGMeOrMS8H7IeQf/38ibs2K9odqY0KVMaK7cpVwU75Nmxj2TVWC3OS2NRsEMQRM4xf+ooHDWsL9qC3bjq+U8Q6o6yG5SeHUpj5TbqbseUxrKPZJ4dXu5Pyg5BEET68LhdePC8SQgUefHZd824Z/mX7AZlY0FKY+U2PdJYFOzYBld2mvcD/fqxEx8XIYqONChTsEMQRE4yMFCI3//kUADAE+/VYuWWPVSNlU8UBOIHd1Iayz64QbmrFdi3j504nQeA7i52mZQdgiCI9POj8VW45NhhAIDrXvwUB6DY+VEaK7cRhHh1hwzK9sFVspBGGourOoV9Aa/FKfNpgIIdgiBymptOGYsJg8pwoCOMxR8ckG+gNFbuo/TtUBrLPrhKpjUby4HmZICCHYIgchy/x42Hf3Y4in1urNmtuIHSWLlPOSk7aYHPGdPqoNzqvO7JAPXZIQgiDxhWWYy7zp6I3y2VvQXd7gIgEs3gqgg7cLsECIKgfWNcGouUHdvQKz2XlB0KdgiCIHqdMycNwtqvxwGx6QEnP7wBO8XvMrsoImXcLgFlBR6UFXpRHjvxy0d1eHAWgLCrAM+srcv0UrOSkgIPzjh0IAq8ii7qeoNAedl52aD0L84EFOwQBJE3/ObMQ/Hx1sMQCO3GbrEi+QMIxxOJijjQEcaBjnCP2z4VRJzlB/ZESnDHa19kYHW5QUtnGP97/Aj5Cq7sCCJw+GGAyy2Pi3Bg92SAgh2CIPKIIp8Hk25+Cy2dwayZ90Xo0xWOorkzjJauMJo7wmjuVJw6huLf312KOt8InF7iLMNsNlDX2IGNdU1Yu21/fLDjKQBcXsAbBt76J1A+WL7NoQZlCnYMEo1GEQqFMr0MggAAeL1euN20s7aCy+1CoCR75n0Ryaku1ytx/kOvrSPX2FjXhLMeeR8bdh5ANCrC5Yp5owSBqTsd+1kX5XLFg8ignL2EQiHU1tYiGiUzI+EcAoEAqqurE5szCYIgUuDggWUo8LrQ1BHGtr1tGFWlqGjzx4IdpUm5O8iuA0jZyTZEUcTu3bvhdrtRU1MDl4uq9YnMIooiOjo60NDQAAAYMMBZR1AEQeQGXrcLhw4O4MPaRny040B8sFNQBoRF4JizWFrriy+AINsmwVMAFPbJyJoTQcFOErq7u9HR0YGBAweiqIg6rhLOoLCQpWEaGhrQv39/SmkRBJEWJg/rw4Kd7Qfws6NUTRpFAN/vYX+LItCiSGE5THEmmSIJkUgEAODz+TK8EoKIhwff4XDPKhSCIAg7mDy0LwBgw47G+BsKynvemQc7DkthARTsGIZ8EYTToO8kQRDp5vAhLB21fX8H9rYG5Ru0mjQ6tOwcoGCHIAiCIIgElBd5MbqqBACwYYditpzWFHmHdk8GKNgheomFCxeiqqoKgiDg1VdfzehaLr74Ypx11llpfY2FCxdi0qRJaX0NgiCI3mDyMI1Ulqaywz07lMYieomLL74YgiBIp4qKCsycOROfffaZba9hdIe+ZcsW3H777Xjsscewe/dunHLKKbatgSAIgkgvk4eyVNZHhpUdCnaIXmTmzJnYvXs3du/ejZUrV8Lj8eC0007r9XVs27YNAHDmmWeiuroafr+/19dAEARBWIOblDd/34yuMCvagb8MEAAMKgPGj2fVV2RQzh1EUURHqDsjJ1EUTa3V7/ejuroa1dXVmDRpEm666SbU1dVh79690n3q6upwzjnnIBAIoG/fvjjzzDOxfft26fZVq1bhqKOOQnFxMQKBAI499ljs2LEDS5Yswe23345PP/1UUo+WLFnSYw0LFy7E6aefDgBwuVySqTYajeKOO+7A4MGD4ff7MWnSJCxfvjzudQVBQFNTk3Tdxo0bIQiCtL4lS5YgEAjgjTfewLhx41BSUiIFeJxIJIJf/epXCAQCqKiowA033KD7Oba0tKCwsBD/+c9/4q5/5ZVXUFpaio6ODgDAjTfeiNGjR6OoqAgjRozArbfeqlsVddJJJ2HBggVx15111lm4+OKLpb+DwSCuu+46DBo0CMXFxTj66KOxatWqhM9JEATRG9T0LUS/Uj/CERGffdfMriwoA7wCcMcxwOefAwUFjjYoU58dk3SGIxj/mzcy8tpf3DEDRT5r/7K2tjb87W9/w8iRI1FRwQYghsNhzJgxA1OmTMG7774Lj8eD3/3ud1K6y+Vy4ayzzsJll12G559/HqFQCOvWrYMgCDj33HOxefNmLF++HP/9738BAOXlPUsRr7vuOgwbNgyXXHJJXBDy0EMP4f7778djjz2Gww47DH/9619xxhln4PPPP8eoUaMMv6+Ojg7cd999eOaZZ+ByuXD++efjuuuuw7PPPgsAuP/++7FkyRL89a9/xbhx43D//ffjlVdewcknn6z5fGVlZTjttNPw3HPPxaXbnn32WZx11llSuXdpaSmWLFmCgQMHYtOmTbjssstQWlqKG264wfDa1cybNw9ffPEFli5dioEDB+KVV17BzJkzsWnTJlOfCUEQhJ0IgoDJQ/vgP5vr8dGORhw1vK/s2emKBT8d+4FoGIAAlFZnbK2JoGAnh3nttddQUsJc9O3t7RgwYABee+01qQv0smXLEI1G8fjjj0uKy5NPPolAIIBVq1Zh8uTJaG5uxmmnnYaDDjoIADBu3Djp+UtKSuDxeFBdnfiLXVJSgkAgAABx97vvvvtw44034rzzzgMA3HPPPXj77bfx4IMP4pFHHjH8HsPhMB599FFpffPmzcMdd9wh3f7ggw/i5ptvxtlnnw0AePTRR/HGG/rB6pw5c3DBBRego6MDRUVFaGlpweuvv45XXnlFus8tt9wiXR42bBiuu+46LF261HKws3PnTjz55JPYuXMnBg5kEvB1112H5cuX48knn8Rdd91l6XkJgiDs4IhYsLNhe8y3w/vs8HER3Jxc3A9we3t/gUnIaLDz5z//GX/+85+ltMTBBx+M3/zmN9IRdX19Pa6//nqsWLECra2tGDNmDH79619j9uzZ0nM0Njbiqquuwr/+9S+4XC7Mnj0bDz30kLSTt5tCrxtf3DEjLc9t5LXN8MMf/hB//vOfAQAHDhzAn/70J5xyyilYt24dhg4dik8//RTffPMNSktL4x7X1dWFbdu2Yfr06bj44osxY8YM/OhHP8K0adNwzjnnpDyeoKWlBbt27cKxxx4bd/2xxx6LTz/91NRzFRUVSYEOwEYn8DEKzc3N2L17N44++mjpdo/Hg8mTJ+umsk499VR4vV7885//xHnnnYeXXnoJZWVlmDZtmnSfZcuWYfHixdi2bRva2trQ3d2NsjINw55BNm3ahEgkgtGjR8ddHwwGJSWOIAgiU0gVWXwoqD82LuLurcDjBwPP3snu6MCycyDDwc7gwYNx9913Y9SoURBFEU899RTOPPNMfPLJJzj44INx4YUXoqmpCf/85z9RWVmJ5557Dueccw4++ugjHHbYYQDYUfju3buxYsUKhMNhXHLJJbj88svx3HPPpWXNgiBYTiX1NsXFxRg5cqT09+OPP47y8nL85S9/we9+9zu0tbXhiCOOkFI+Svr16weAKT1XX301li9fjmXLluGWW27BihUr8IMf/CCta+fqkzIo0fLEeL3xRxCCIJj2Nqnx+Xz4yU9+gueeew7nnXcennvuOZx77rnweNj/fe3atZgzZw5uv/12zJgxA+Xl5Vi6dCnuv/9+3fejXpfy/bS1tcHtdmPDhg09Rj+kK3AnCIIwinIo6Lf72jCyMDYuoqEbaPhC9uuUDcroOhORUYPy6aefjlNPPRWjRo3C6NGjceedd6KkpAQffPABAGDNmjW46qqrcNRRR2HEiBG45ZZbEAgEsGHDBgCspHn58uV4/PHHcfTRR+O4447DH//4RyxduhS7du3K5FtzJIIgwOVyobOzEwBw+OGH4+uvv0b//v0xcuTIuJPSf3PYYYfh5ptvxpo1azBhwgQpkPT5fNI4DTOUlZVh4MCBeP/99+Ouf//99zF+/HgAcrCl9Pls3LjR1OuUl5djwIAB+PDDD6Xruru7pe+PHnPmzMHy5cvx+eef46233sKcOXOk29asWYOhQ4fi17/+NSZPnoxRo0Zhx44dus/Xr1+/HsbpzZs3S38fdthhiEQiaGho6PG/0EsTEgRB9AZ8KCgAfLT9QM8+Oy3ONScDDqrGikQiWLp0Kdrb2zFlyhQAwDHHHINly5ahsbER0WgUS5cuRVdXF0466SQA7Ag7EAhg8uTJ0vNMmzYNLpcrbgenJhgMoqWlJe6UiwSDQdTX16O+vh5btmzBVVddhba2Nqk6as6cOaisrMSZZ56Jd999F7W1tVi1ahWuvvpqfPfdd6itrcXNN9+MtWvXYseOHXjzzTfx9ddfS76dYcOGoba2Fhs3bsS+ffsQDAb1lhPH9ddfj3vuuQfLli3D1q1bcdNNN2Hjxo2YP38+AGDkyJGoqanBwoUL8fXXX+P111/XVU4SMX/+fNx999149dVX8eWXX+KXv/xlXIVXIk444QRUV1djzpw5GD58eFwqbNSoUdi5cyeWLl2Kbdu2YfHixXF+Hi1OPvlkvP7663j99dfx5Zdf4he/+EXcOkaPHo05c+bgwgsvxMsvv4za2lqsW7cOixYtwuuvv276fRMEQdjN5GGKfjveQsClyHK01rNzh6axMh7sbNq0CSUlJfD7/bjyyivxyiuvSEf3L7zwAsLhMCoqKuD3+3HFFVfglVdekVIz9fX16N+/f9zzeTwe9O3bF/X19Qlfc9GiRSgvL5dONTU16XuDGWT58uUYMGAABgwYgKOPPhrr16/Hiy++KAWLRUVFeOeddzBkyBCcffbZGDduHC699FJ0dXWhrKwMRUVF+PLLLzF79myMHj0al19+OebOnYsrrrgCADB79mzMnDkTP/zhD9GvXz88//zzhtd29dVX41e/+hWuvfZaTJw4EcuXL8c///lPqerI6/Xi+eefx5dffolDDjkE99xzD373u9+Z/gyuvfZaXHDBBbjoooswZcoUlJaW4sc//nHSxwmCgJ/97Gf49NNP41QdADjjjDNwzTXXYN68eZg0aRLWrFmDW2+9Vff5/ud//gcXXXQRLrzwQpx44okYMWIEfvjDH8bd58knn8SFF16Ia6+9FmPGjMFZZ52F9evXY8iQIQmelSAIovfg/XY+2t7I+ur4FX7PNq7sOK/HDgAIYqoGhxQJhULYuXMnmpub8fe//x2PP/44Vq9ejfHjx+Oqq67CunXrcNddd6GyshKvvvoqHnjgAbz77ruYOHEi7rrrLjz11FPYunVr3HP2798ft99+O37xi19ovmYwGIxTIVpaWlBTU4Pm5uYeJtOuri7U1tZi+PDhKCgosP8DIAiL0HeTIIjepLkjjEPveBMAsP7X09Dvz5OBG2Pp+PsnAy1fARe8Ahyk3dojHbS0tKC8vFxz/60k405bn88nKTVHHHEE1q9fj4ceegg33HADHn74YWzevBkHH3wwAODQQw/Fu+++i0ceeQSPPvooqqurpcobTnd3NxobG3V9Dn6/n7r4EgRBEIQJ+FDQr/a0YcOOA5ippeyQQdkY0WgUwWBQ6lTLq3I4brcb0WgUADBlyhQ0NTXFGU7feustRKPROI8FQRAEQRCpc8RQxVDQgnKgXAAGVMr9dsig3JObb74Z77zzDrZv345Nmzbh5ptvxqpVqzBnzhyMHTsWI0eOxBVXXIF169Zh27ZtuP/++7FixQppYvW4ceMwc+ZMXHbZZVi3bh3ef/99zJs3D+edd57UmI0gCIIgCHuIGwpa1gdYUAo8eTkbHeEr0R4Q6gAymsZqaGjAhRdeiN27d6O8vByHHHII3njjDfzoRz8CAPz73//GTTfdhNNPPx1tbW0YOXIknnrqKZx66qnSczz77LOYN28epk6dKjUVXLx4cabeEkEQBEHkLLwia/P3zYhUlcINAHu/ZDc6VNUBMhzsPPHEE7q3jxo1Ci+99JLuffr27Zu2BoIEQRAEQcgM6VuEyhI/9rUFsTfsRzUA7I0VCTm07BxwoGeHIAiCIAhnIggCjoypO983A/hLG3DfFjY6wqFl5wAFOwRBEARBmOCImG9ne7MA7IqykwigjIIdgiAIgiByAD4U9KtmIf4GCnYIgiAIgsgF+FDQhqCqX52DDcoU7OQxq1atgiAI0oymJUuWIBAIZHRNAHDSSSdhwYIFvfJagiDg1Vdf7ZXXIgiCyAX4UNBWFMXfQAZlIlOsXbsWbrcbs2bNyvRSMsrChQsxadKkHtfv3r0bp5xySu8viCAIIouZPKwPWsXC+CvJoExkiieeeAJXXXUV3nnnHezatSvTy3Ec1dXVNDqEIAjCJJOH9kUbFMGO4AZK+id+QIahYMcsogiE2jNzMjmzta2tDcuWLcMvfvELzJo1C0uWLEn57dfV1eGcc85BIBBA3759ceaZZ2L79u0AgDfffBMFBQVSWowzf/58nHwyGwy3f/9+/OxnP8OgQYNQVFSEiRMnJp2WrpVqCgQCce/nxhtvxOjRo1FUVIQRI0bg1ltvRTgcBsDSc7fffjs+/fRTCIIAQRCkx6qfe9OmTTj55JNRWFiIiooKXH755Whra5Nuv/jii3HWWWfhvvvuw4ABA1BRUYG5c+dKrwUAf/rTnzBq1CgUFBSgqqoKP/nJTwx8sgRBENnD4UP6oEUsBIoEdiqpAlzuTC8rIRkfBJp1hDuAuzIk1f3fLsBXbPjuL7zwAsaOHYsxY8bg/PPPx4IFC3DzzTdDEITkD9YgHA5jxowZmDJlCt599114PB787ne/w8yZM/HZZ59h6tSpCAQCeOmll3DppZcCACKRCJYtW4Y777wTAJvUfcQRR+DGG29EWVkZXn/9dVxwwQU46KCDcNRRR1laFwCUlpZiyZIlGDhwIDZt2oTLLrsMpaWluOGGG3Duuedi8+bNWL58Of773/8CAMrLy3s8R3t7u/T+1q9fj4aGBvzv//4v5s2bFxdYvf322xgwYADefvttfPPNNzj33HMxadIkXHbZZfjoo49w9dVX45lnnsExxxyDxsZGvPvuu5bfF0EQhBMpL/IiMHgIcH1sGGilMweAckjZyWGeeOIJnH/++QCAmTNnorm5GatXr7b8fMuWLUM0GsXjjz+OiRMnYty4cXjyySexc+dOrFq1Cm63G+edd15cR+uVK1eiqakJs2fPBgAMGjQI1113HSZNmoQRI0bgqquuwsyZM/HCCy+k9F5vueUWHHPMMRg2bBhOP/10XHfdddJzFhYWoqSkBB6PB9XV1aiurkZhYWGP53juuefQ1dWFp59+GhMmTMDJJ5+Mhx9+GM888wz27Nkj3a9Pnz54+OGHMXbsWJx22mmYNWsWVq5cCQDYuXMniouLcdppp2Ho0KE47LDDcPXVV6f03giCIJzIhKHVCIsxNcfB5mSAlB3zeIuYwpKp1zbI1q1bsW7dOrzyyisAAI/Hg3PPPRdPPPEETjrpJEsv/+mnn+Kbb75BaWlp3PVdXV3Ytm0bAGDOnDn4wQ9+gF27dmHgwIF49tlnMWvWLKnKKxKJ4K677sILL7yA77//HqFQCMFgEEVFxt+bFsuWLcPixYuxbds2tLW1obu7G2Vl5gbSbdmyBYceeiiKi2X17Nhjj0U0GsXWrVtRVVUFADj44IPhdsty7YABA7Bp0yYAwI9+9CMMHToUI0aMwMyZMzFz5kz8+Mc/Tvn9EQRBOI3Jw/qi9bNC9EWbo83JAAU75hEEU6mkTPHEE0+gu7s7bvq7KIrw+/14+OGHNdM4yWhra8MRRxyBZ599tsdt/fr1AwAceeSROOigg7B06VL84he/wCuvvBKXAvr973+Phx56CA8++CAmTpyI4uJiLFiwAKFQKOHrCoIAUeVXUnpk1q5dizlz5uD222/HjBkzUF5ejqVLl+L+++83/R6N4PV6e6wvGo0CYOm0jz/+GKtWrcKbb76J3/zmN1i4cCHWr1/viLJ+giAIuziyqgBFTzUCCKPx8DJ0HOjQvX9VWQG87swklCjYyUG6u7vx9NNP4/7778f06dPjbjvrrLPw/PPP48orrzT9vIcffjiWLVuG/v3766omc+bMwbPPPovBgwfD5XLFlb2///77OPPMM6X0WjQaxVdffYXx48cnfL5+/fph9+7d0t9ff/01OjrkH9WaNWswdOhQ/PrXv5au27FjR9xz+Hw+RCIR3fc3btw4LFmyBO3t7ZK68/7778PlcmHMmDG6j1Xi8Xgwbdo0TJs2DbfddhsCgQDeeustnH322YafgyAIwunUBAog7OgCACx65wBeXPO27v3fuvZEjOhX0htL6wF5dnKQ1157DQcOHMCll16KCRMmxJ1mz56ddNp8IubMmYPKykqceeaZePfdd1FbW4tVq1bh6quvxnfffRd3v48//hh33nknfvKTn8SVdo8aNQorVqzAmjVrsGXLFlxxxRVxfhgtuHfmk08+wUcffYQrr7wyTl0ZNWoUdu7ciaVLl2Lbtm1YvHixlL7jDBs2DLW1tdi4cSP27duHYDCo+f4KCgpw0UUXYfPmzXj77bdx1VVX4YILLpBSWMl47bXXsHjxYmzcuBE7duzA008/jWg0aipYIgiCyAaUxS6fuA6G3+PSPVktjrEDUnZykCeeeALTpk3TTFXNnj0b9957Lz777DPTz1tUVIR33nkHN954I84++2y0trZi0KBBmDp1apzSM3LkSBx11FFYt24dHnzwwbjnuOWWW/Dtt99ixowZKCoqwuWXX46zzjoLzc3NCV/3/vvvxyWXXILjjz8eAwcOxEMPPYQNGzZIt59xxhm45pprMG/ePASDQcyaNQu33norFi5cGPe+X375Zfzwhz9EU1MTnnzySVx88cU93t8bb7yB+fPn48gjj0RRURFmz56NP/zhD4Y/o0AggJdffhkLFy5EV1cXRo0aheeffx4HH3yw4ecgCILINv5727lAsXMtHoKoNkPkIS0tLSgvL0dzc3OP9ExXVxdqa2sxfPhwFBQUZGiFBNET+m4SBJFR2tuBklhaqq0tI8GO3v5bCaWxCIIgCILIaSjYIQiCIAgipyHPDkEQBEEQ1siSHmIU7BAEQRAEYZ7iYubbyQIojWUQ8nETToO+kwRBEMagYCcJfCyAXodfgsgEvLGiuqMzQRAEEQ+lsZLg8XhQVFSEvXv3wuv1wuWi+JDILKIooqOjAw0NDQgEAnFzugiCIHqNri4gNuQZL70EOLgFBgU7SRAEAQMGDEBtbW2PEQQEkUkCgQCqq6szvQyCIPKVSAT497/lyw6Ggh0D+Hw+jBo1ilJZhGPwer2k6BAEQRiEgh2DuFwu6lJLEARBEFkIGVAIgiAIgshpKNghCIIgCCKnoWCHIAiCIIichjw7kJuztbS0ZHglBEEQBJElKLsnt7RkpCKL77eTNVmlYAdAa2srAKCmpibDKyEIgiCILGTgwIy+fGtrK8rLyxPeLojUcx7RaBS7du1CaWkpBEGw7XlbWlpQU1ODuro6lJWV2fa8hAx9xumFPt/0Qp9v+qHPOL1k+vMVRRGtra0YOHCgbtNfUnbAysoHDx6ctucvKyujH1maoc84vdDnm17o800/9Bmnl0x+vnqKDocMygRBEARB5DQU7BAEQRAEkdNQsJNG/H4/brvtNvj9/kwvJWehzzi90OebXujzTT/0GaeXbPl8yaBMEARBEEROQ8oOQRAEQRA5DQU7BEEQBEHkNBTsEARBEASR01CwQxAEQRBETkPBThp55JFHMGzYMBQUFODoo4/GunXrMr2krOSdd97B6aefjoEDB0IQBLz66qtxt4uiiN/85jcYMGAACgsLMW3aNHz99deZWWwWsmjRIhx55JEoLS1F//79cdZZZ2Hr1q1x9+nq6sLcuXNRUVGBkpISzJ49G3v27MnQirOPP//5zzjkkEOkxmtTpkzBf/7zH+l2+nzt5e6774YgCFiwYIF0HX3GqbFw4UIIghB3Gjt2rHS70z9fCnbSxLJly/CrX/0Kt912Gz7++GMceuihmDFjBhoaGjK9tKyjvb0dhx56KB555BHN2++9914sXrwYjz76KD788EMUFxdjxowZ6Orq6uWVZierV6/G3Llz8cEHH2DFihUIh8OYPn062hVD/q655hr861//wosvvojVq1dj165dOPvsszO46uxi8ODBuPvuu7FhwwZ89NFHOPnkk3HmmWfi888/B0Cfr52sX78ejz32GA455JC46+kzTp2DDz4Yu3fvlk7vvfeedJvjP1+RSAtHHXWUOHfuXOnvSCQiDhw4UFy0aFEGV5X9ABBfeeUV6e9oNCpWV1eLv//976XrmpqaRL/fLz7//PMZWGH209DQIAIQV69eLYoi+zy9Xq/44osvSvfZsmWLCEBcu3ZtppaZ9fTp00d8/PHH6fO1kdbWVnHUqFHiihUrxBNPPFGcP3++KIr0HbaD2267TTz00EM1b8uGz5eUnTQQCoWwYcMGTJs2TbrO5XJh2rRpWLt2bQZXlnvU1taivr4+7rMuLy/H0UcfTZ+1RZqbmwEAffv2BQBs2LAB4XA47jMeO3YshgwZQp+xBSKRCJYuXYr29nZMmTKFPl8bmTt3LmbNmhX3WQL0HbaLr7/+GgMHDsSIESMwZ84c7Ny5E0B2fL40CDQN7Nu3D5FIBFVVVXHXV1VV4csvv8zQqnKT+vp6AND8rPlthHGi0SgWLFiAY489FhMmTADAPmOfz4dAIBB3X/qMzbFp0yZMmTIFXV1dKCkpwSuvvILx48dj48aN9PnawNKlS/Hxxx9j/fr1PW6j73DqHH300ViyZAnGjBmD3bt34/bbb8fxxx+PzZs3Z8XnS8EOQRASc+fOxebNm+Ny8YQ9jBkzBhs3bkRzczP+/ve/46KLLsLq1aszvaycoK6uDvPnz8eKFStQUFCQ6eXkJKeccop0+ZBDDsHRRx+NoUOH4oUXXkBhYWEGV2YMSmOlgcrKSrjd7h5O9D179qC6ujpDq8pN+OdJn3XqzJs3D6+99hrefvttDB48WLq+uroaoVAITU1Ncfenz9gcPp8PI0eOxBFHHIFFixbh0EMPxUMPPUSfrw1s2LABDQ0NOPzww+HxeODxeLB69WosXrwYHo8HVVVV9BnbTCAQwOjRo/HNN99kxXeYgp004PP5cMQRR2DlypXSddFoFCtXrsSUKVMyuLLcY/jw4aiuro77rFtaWvDhhx/SZ20QURQxb948vPLKK3jrrbcwfPjwuNuPOOIIeL3euM9469at2LlzJ33GKRCNRhEMBunztYGpU6di06ZN2Lhxo3SaPHky5syZI12mz9he2trasG3bNgwYMCA7vsOZdkjnKkuXLhX9fr+4ZMkS8YsvvhAvv/xyMRAIiPX19ZleWtbR2toqfvLJJ+Inn3wiAhD/8Ic/iJ988om4Y8cOURRF8e677xYDgYD4j3/8Q/zss8/EM888Uxw+fLjY2dmZ4ZVnB7/4xS/E8vJycdWqVeLu3bulU0dHh3SfK6+8UhwyZIj41ltviR999JE4ZcoUccqUKRlcdXZx0003iatXrxZra2vFzz77TLzppptEQRDEN998UxRF+nzTgbIaSxTpM06Va6+9Vly1apVYW1srvv/+++K0adPEyspKsaGhQRRF53++FOykkT/+8Y/ikCFDRJ/PJx511FHiBx98kOklZSVvv/22CKDH6aKLLhJFkZWf33rrrWJVVZXo9/vFqVOnilu3bs3sorMIrc8WgPjkk09K9+ns7BR/+ctfin369BGLiorEH//4x+Lu3bszt+gs43/+53/EoUOHij6fT+zXr584depUKdARRfp804E62KHPODXOPfdcccCAAaLP5xMHDRoknnvuueI333wj3e70z1cQRVHMjKZEEARBEASRfsizQxAEQRBETkPBDkEQBEEQOQ0FOwRBEARB5DQU7BAEQRAEkdNQsEMQBEEQRE5DwQ5BEARBEDkNBTsEQRAEQeQ0FOwQBEEQBJHTULBDEAQBYNiwYXjwwQczvQyCINIABTsEQfQ6F198Mc466ywAwEknnYQFCxb02msvWbIEgUCgx/Xr16/H5Zdf3mvrIAii9/BkegEEQRB2EAqF4PP5LD++X79+Nq6GIAgnQcoOQRAZ4+KLL8bq1avx0EMPQRAECIKA7du3AwA2b96MU045BSUlJaiqqsIFF1yAffv2SY896aSTMG/ePCxYsACVlZWYMWMGAOAPf/gDJk6ciOLiYtTU1OCXv/wl2traAACrVq3CJZdcgubmZun1Fi5cCKBnGmvnzp0488wzUVJSgrKyMpxzzjnYs2ePdPvChQsxadIkPPPMMxg2bBjKy8tx3nnnobW1Nb0fGkEQpqFghyCIjPHQQw9hypQpuOyyy7B7927s3r0bNTU1aGpqwsknn4zDDjsMH330EZYvX449e/bgnHPOiXv8U089BZ/Ph/fffx+PPvooAMDlcmHx4sX4/PPP8dRTT+Gtt97CDTfcAAA45phj8OCDD6KsrEx6veuuu67HuqLRKM4880w0NjZi9erVWLFiBb799luce+65cffbtm0bXn31Vbz22mt47bXXsHr1atx9991p+rQIgrAKpbEIgsgY5eXl8Pl8KCoqQnV1tXT9ww8/jMMOOwx33XWXdN1f//pX1NTU4KuvvsLo0aMBAKNGjcK9994b95xK/8+wYcPwu9/9DldeeSX+9Kc/wefzoby8HIIgxL2empUrV2LTpk2ora1FTU0NAODpp5/GwQcfjPXr1+PII48EwIKiJUuWoLS0FABwwQUXYOXKlbjzzjtT+2AIgrAVUnYIgnAcn376Kd5++22UlJRIp7FjxwJgagrniCOO6PHY//73v5g6dSoGDRqE0tJSXHDBBdi/fz86OjoMv/6WLVtQU1MjBToAMH78eAQCAWzZskW6btiwYVKgAwADBgxAQ0ODqfdKEET6IWWHIAjH0dbWhtNPPx333HNPj9sGDBggXS4uLo67bfv27TjttNPwi1/8AnfeeSf69u2L9957D5deeilCoRCKiopsXafX6437WxAERKNRW1+DIIjUoWCHIIiM4vP5EIlE4q47/PDD8dJLL2HYsGHweIxvpjZs2IBoNIr7778fLhcTrl944YWkr6dm3LhxqKurQ11dnaTufPHFF2hqasL48eMNr4cgCGdAaSyCIDLKsGHD8OGHH2L79u3Yt28fotEo5s6di8bGRvzsZz/D+vXrsW3bNrzxxhu45JJLdAOVkSNHIhwO449//CO+/fZbPPPMM5JxWfl6bW1tWLlyJfbt26eZ3po2bRomTpyIOXPm4OOPP8a6detw4YUX4sQTT8TkyZNt/wwIgkgvFOwQBJFRrrvuOrjdbowfPx79+vXDzp07MXDgQLz//vuIRCKYPn06Jk6ciAULFiAQCEiKjRaHHnoo/vCHP+Cee+7BhAkT8Oyzz2LRokVx9znmmGNw5ZVX4txzz0W/fv16GJwBlo76xz/+gT59+uCEE07AtGnTMGLECCxbtsz2908QRPoRRFEUM70IgiAIgiCIdEHKDkEQBEEQOQ0FOwRBEARB5DQU7BAEQRAEkdNQsEMQBEEQRE5DwQ5BEARBEDkNBTsEQRAEQeQ0FOwQBEEQBJHTULBDEARBEEROQ8EOQRAEQRA5DQU7BEEQBEHkNBTsEARBEASR0/x/irsKztbsr/AAAAAASUVORK5CYII=","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n","\n","\n","#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","colors = [c_unnormed[i] / max(c_unnormed) for i in range(len(c_unnormed))]\n","\n","# Plot initial samples\n","plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', c=colors, cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} diff --git a/run_experiment.py b/run_experiment.py index 13b03ae..2d072c5 100644 --- a/run_experiment.py +++ b/run_experiment.py @@ -36,7 +36,8 @@ def generate_initial_data(problem, n: int, bounds: torch.Tensor) -> tuple: bounds=bounds, n=n, q=1, seed=torch.randint(100000, (1,)).item() ).squeeze(-1) Y_init = torch.tensor(problem.y(X_init.numpy())) - return X_init, Y_init + Y_init_real = torch.tensor(problem.f(X_init.numpy())) + return X_init, Y_init, Y_init_real # %% def initialize_model(train_x, train_y, noise_bool=True) -> tuple: @@ -111,7 +112,7 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): bounds = torch.tensor(problem.bounds, **tkwargs) - train_X, train_Y= generate_initial_data(problem, n_init, bounds) + train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds) start_time = time() @@ -133,14 +134,16 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): sampler=sampler, ) - x_cand, acq_func = optimize_acqf_loop(problem, acq_func) + x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func) X_cand = unnormalize(x_cand, bounds) Y_cand = torch.tensor(problem.y(X_cand.numpy())) + Y_cand_real = torch.tensor(problem.f(X_cand.numpy())) print(f"New candidate: {X_cand}, {Y_cand}") # update the model with new observations train_X = torch.cat([train_X, X_cand], dim=0) train_Y = torch.cat([train_Y, Y_cand], dim=0) + train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) train_x = normalize(train_X, bounds) mll, model = initialize_model(train_x, train_Y, noise_bool) @@ -148,9 +151,9 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): os.makedirs('results', exist_ok=True) fname = f"results/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" - torch.save((train_X, train_Y, model), fname) + torch.save((train_X, train_Y, train_Y_real, model), fname) - return train_X, train_Y, model + return train_X, train_Y, train_Y_real, model if __name__ == "__main__": diff --git a/run_grid_experiments.py b/run_grid_experiments.py index d699d6d..1e3e7f5 100644 --- a/run_grid_experiments.py +++ b/run_grid_experiments.py @@ -24,7 +24,7 @@ def worker(n_init, noise_level, budget, seed, noise_bool): def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget): # ray.init(local_mode=True) - ray.init() + ray.init(ignore_reinit_error=True) start_time = time() tasks = [] diff --git a/src/schwefel.py b/src/schwefel.py index 731851d..e06f92e 100644 --- a/src/schwefel.py +++ b/src/schwefel.py @@ -7,7 +7,7 @@ def __init__(self, n_var=1, noise_level=0.01): """ self.noise_level = noise_level self.n_var = n_var # Number of variables/dimensions - self.bounds = np.array([[-500] * self.n_var, [500] * self.n_var]) + self.bounds = np.array([[-50] * self.n_var, [50] * self.n_var]) def _schwefel_individual(self, x): return x * np.sin(np.sqrt(np.abs(x))) @@ -17,7 +17,7 @@ def f(self, x): def eps(self, x): # Assuming the noise is independent of x for simplicity - return np.random.normal(0, self.noise_level, x.shape[0]) + return np.random.normal(0, self.noise_level, x.shape[0]).reshape(-1, 1) def y(self, x): return self.f(x) + self.eps(x) From a403cc266645ee3342895baa9db579ed374fe8b0 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 07:05:16 -0700 Subject: [PATCH 23/43] add range params to schwefel generator --- src/schwefel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/schwefel.py b/src/schwefel.py index 731851d..384a690 100644 --- a/src/schwefel.py +++ b/src/schwefel.py @@ -1,13 +1,13 @@ import numpy as np class SchwefelProblem: - def __init__(self, n_var=1, noise_level=0.01): + def __init__(self, n_var=1, noise_level=0.01, range = (-50, 50)): """ y = f(x) + eps """ self.noise_level = noise_level self.n_var = n_var # Number of variables/dimensions - self.bounds = np.array([[-500] * self.n_var, [500] * self.n_var]) + self.bounds = np.array([[range[0]] * self.n_var, [range[1]] * self.n_var]) def _schwefel_individual(self, x): return x * np.sin(np.sqrt(np.abs(x))) From a61d359b2ce6e4514edf3304e2b8983f69e5d2f7 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 07:05:34 -0700 Subject: [PATCH 24/43] add range parameters; --- src/baybe_utils.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/baybe_utils.py b/src/baybe_utils.py index dbee7b0..b22e1d5 100644 --- a/src/baybe_utils.py +++ b/src/baybe_utils.py @@ -25,6 +25,7 @@ def run_optimization_campaign( N_DIMS_SCHWEF, NOISE_LEVEL_SCHWEF, ITERATION_BATCH_SIZE, + SCHWEFEL_RANGE = (-50,50), recommender_init = None, recommender_main = None ): @@ -41,7 +42,7 @@ def run_optimization_campaign( """ # Define Schweffel oracle - schweffer = schwefel.SchwefelProblem(n_var = N_DIMS_SCHWEF, noise_level=NOISE_LEVEL_SCHWEF) + schweffer = schwefel.SchwefelProblem(n_var = N_DIMS_SCHWEF, noise_level=NOISE_LEVEL_SCHWEF, range = SCHWEFEL_RANGE) target = NumericalTarget(name = 'schwefel', mode = "MIN") parameters = [ NumericalContinuousParameter(f'schwefel{i+1}', bounds = (-500,500)) for i in range(N_DIMS_SCHWEF) @@ -80,7 +81,7 @@ def run_optimization_campaign( -def iteration_noise_grid_search(iterations_list, noise_list, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE): +def iteration_noise_grid_search(iterations_list, noise_list, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE, SCHWEFEL_RANGE = (-50, 50)): """ Utility to run a parameter grid experiment varying noise level and number of BO iterations. Runs full grid in serial. Params: @@ -100,7 +101,7 @@ def iteration_noise_grid_search(iterations_list, noise_list, NUM_INIT_OBS, N_DIM for its in iterations_list: noise_results = {} for noise_level in noise_list: - opt_campaign = run_optimization_campaign(its, NUM_INIT_OBS, N_DIMS_SCHWEF, noise_level, ITERATION_BATCH_SIZE) + opt_campaign = run_optimization_campaign(its, NUM_INIT_OBS, N_DIMS_SCHWEF, noise_level, ITERATION_BATCH_SIZE, SCHWEFEL_RANGE=SCHWEFEL_RANGE) noise_results[str(noise_level)] = opt_campaign iteration_results[str(its)] = noise_results From cf0aad41bf0fae32e03cfd7158583275e3c819f3 Mon Sep 17 00:00:00 2001 From: Joe Manning Date: Thu, 28 Mar 2024 15:20:33 +0100 Subject: [PATCH 25/43] simulate expriment MWE --- src/baybe_simulate_expreiment example.ipynb | 765 ++++++++++++++++++++ 1 file changed, 765 insertions(+) create mode 100644 src/baybe_simulate_expreiment example.ipynb diff --git a/src/baybe_simulate_expreiment example.ipynb b/src/baybe_simulate_expreiment example.ipynb new file mode 100644 index 0000000..bcb54d7 --- /dev/null +++ b/src/baybe_simulate_expreiment example.ipynb @@ -0,0 +1,765 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1-dimensional continuous data BO test using BayBE" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.targets import NumericalTarget\n", + "from baybe.objective import Objective\n", + "\n", + "target = NumericalTarget(\n", + " name=\"Yield\",\n", + " mode=\"MIN\",\n", + ")\n", + "objective = Objective(mode=\"SINGLE\", targets=[target])" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.parameters import NumericalContinuousParameter\n", + "\n", + "parameters = [\n", + " NumericalContinuousParameter('schwefel1', bounds=(-500,500)),\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.recommenders import (\n", + " SequentialGreedyRecommender, \n", + " SequentialMetaRecommender, \n", + " RandomRecommender\n", + ")\n", + "\n", + "recommender = SequentialGreedyRecommender()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.searchspace import SearchSpace\n", + "\n", + "searchspace = SearchSpace.from_product(parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe import Campaign\n", + "\n", + "campaign = Campaign(searchspace, objective, recommender)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv('seed_data.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from schwefel_functions import schwefel_1d, schwefel_1d_with_noise\n", + "\n", + "\n", + "df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "campaign.add_measurements(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "example_data = pd.DataFrame(columns = ['x', 'y'])\n", + "\n", + "example_data['x'] = np.linspace(-500,500,5000)\n", + "example_data['y'] = example_data['x'].apply(schwefel_1d)\n", + "\n", + "fig, ax = plt.subplots()\n", + "example_data.plot('x', 'y', ax=ax)\n", + "df.plot.scatter('schwefel1', 'Yield', ax=ax, c='red')" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "# from copy import deepcopy\n", + "# df_cumulative = deepcopy(df)\n", + "# for iteration in range(5):\n", + "# df = campaign.recommend(batch_size=3)\n", + "# df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + "# campaign.add_measurements(df)\n", + "# df_cumulative = pd.concat([df_cumulative, df])\n", + "# fig, ax = plt.subplots(figsize=(4,3))\n", + "# ax.set_title(f'Iteration {iteration} ({len(df_cumulative)} experiments)')\n", + "# example_data.plot('x', 'y', ax=ax)\n", + "# df_cumulative.plot.scatter('schwefel1', 'Yield', ax=ax, c='red')\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n", + "10\n", + "11\n", + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n", + "10\n", + "11\n", + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n", + "10\n", + "11\n", + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n", + "10\n", + "11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n", + "19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from baybe.simulation import simulate_experiment\n", + "from random import randint\n", + "\n", + "results = {}\n", + "for noise_level in [0,0.01,0.1,1]:\n", + " eval_eqn = lambda x: schwefel_1d_with_noise(x, noise_level)\n", + " results[noise_level] = {}\n", + " placeholder = {}\n", + " for x in range(20):\n", + " print(x)\n", + " placeholder[x] = simulate_experiment(\n", + " campaign,\n", + " eval_eqn,\n", + " n_doe_iterations = 20,\n", + " random_seed=x\n", + " )\n", + " placeholder_df = pd.DataFrame({k:v['Yield_CumBest'] for k,v in placeholder.items()})\n", + " results[noise_level] = placeholder_df.T.describe().T[['mean', 'std']].reset_index(names='N_exp')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results_df = pd.DataFrame()\n", + "for k,v in results.items():\n", + " v['noise'] = k\n", + " results_df = pd.concat([results_df, v])\n", + "ax = sns.lineplot(data=results_df, x='N_exp', y='mean', hue='noise')" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Could not interpret value `N_exp` for parameter `x`", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[111], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlineplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresult_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mN_exp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\relational.py:618\u001b[0m, in \u001b[0;36mlineplot\u001b[1;34m(data, x, y, hue, size, style, units, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, estimator, errorbar, n_boot, seed, orient, sort, err_style, err_kws, legend, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m 615\u001b[0m errorbar \u001b[38;5;241m=\u001b[39m _deprecate_ci(errorbar, ci)\n\u001b[0;32m 617\u001b[0m variables \u001b[38;5;241m=\u001b[39m _LinePlotter\u001b[38;5;241m.\u001b[39mget_semantics(\u001b[38;5;28mlocals\u001b[39m())\n\u001b[1;32m--> 618\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43m_LinePlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 619\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 620\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrorbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrorbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 621\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_style\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merr_style\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merr_kws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlegend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 623\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 625\u001b[0m p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[0;32m 626\u001b[0m p\u001b[38;5;241m.\u001b[39mmap_size(sizes\u001b[38;5;241m=\u001b[39msizes, order\u001b[38;5;241m=\u001b[39msize_order, norm\u001b[38;5;241m=\u001b[39msize_norm)\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\relational.py:365\u001b[0m, in \u001b[0;36m_LinePlotter.__init__\u001b[1;34m(self, data, variables, estimator, n_boot, seed, errorbar, sort, orient, err_style, err_kws, legend)\u001b[0m\n\u001b[0;32m 351\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m 352\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m 353\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, variables\u001b[38;5;241m=\u001b[39m{},\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 359\u001b[0m \u001b[38;5;66;03m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[0;32m 360\u001b[0m \u001b[38;5;66;03m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[0;32m 361\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_default_size_range \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 362\u001b[0m np\u001b[38;5;241m.\u001b[39mr_[\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlines.linewidth\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 363\u001b[0m )\n\u001b[1;32m--> 365\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 367\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimator \u001b[38;5;241m=\u001b[39m estimator\n\u001b[0;32m 368\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merrorbar \u001b[38;5;241m=\u001b[39m errorbar\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[0;32m 636\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[0;32m 637\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[0;32m 638\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[0;32m 639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m} \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[1;32m--> 640\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 642\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var, \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_semantic_mappings\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 643\u001b[0m \n\u001b[0;32m 644\u001b[0m \u001b[38;5;66;03m# Create the mapping function\u001b[39;00m\n\u001b[0;32m 645\u001b[0m map_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mmap, plotter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m 699\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 701\u001b[0m plot_data, variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assign_variables_longform(\n\u001b[0;32m 702\u001b[0m data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mvariables,\n\u001b[0;32m 703\u001b[0m )\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data \u001b[38;5;241m=\u001b[39m plot_data\n\u001b[0;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables \u001b[38;5;241m=\u001b[39m variables\n", + "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:938\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[1;34m(self, data, **kwargs)\u001b[0m\n\u001b[0;32m 933\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[0;32m 934\u001b[0m \n\u001b[0;32m 935\u001b[0m \u001b[38;5;66;03m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[0;32m 937\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret value `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` for parameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 938\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[0;32m 940\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 941\u001b[0m \n\u001b[0;32m 942\u001b[0m \u001b[38;5;66;03m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[0;32m 943\u001b[0m \n\u001b[0;32m 944\u001b[0m \u001b[38;5;66;03m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[0;32m 945\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, pd\u001b[38;5;241m.\u001b[39mSeries):\n", + "\u001b[1;31mValueError\u001b[0m: Could not interpret value `N_exp` for parameter `x`" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = sns.lineplot(data=result_df, x='N_exp', y='mean', hue=)\n", + "ax.fill_between(result_df_ave['N_exp'], result_df_ave['mean']-result_df_ave['std'], result_df_ave['mean']+result_df_ave['std'], alpha=0.2, )\n", + "ax.legend()\n", + "ax.set_xlabel('Number of iterations')\n", + "ax.set_ylabel('Cumulative best guess')\n", + "ax.set_title('Backtracing 1-dimensional noisy Schwefel function')" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Backtracing 1-dimensional noisy Schwefel function')" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "result_df_ave = result_df.T.describe().T[['mean', 'std']].reset_index(names='N_exp')\n", + "ax = sns.lineplot(data=result_df_ave, x='N_exp', y='mean', label='noise=0.01')\n", + "ax.fill_between(result_df_ave['N_exp'], result_df_ave['mean']-result_df_ave['std'], result_df_ave['mean']+result_df_ave['std'], alpha=0.2, )\n", + "ax.legend()\n", + "ax.set_xlabel('Number of iterations')\n", + "ax.set_ylabel('Cumulative best guess')\n", + "ax.set_title('Backtracing 1-dimensional noisy Schwefel function')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 863ceac2e02ca2ab54f2ab91f961baeb1f1a3117 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 22:30:13 +0800 Subject: [PATCH 26/43] make schwefel word in nd --- run_experiment.py | 4 ++-- src/schwefel.py | 6 ++++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/run_experiment.py b/run_experiment.py index 2d072c5..eb4d79e 100644 --- a/run_experiment.py +++ b/run_experiment.py @@ -34,7 +34,7 @@ def generate_initial_data(problem, n: int, bounds: torch.Tensor) -> tuple: X_init = draw_sobol_samples( bounds=bounds, n=n, q=1, seed=torch.randint(100000, (1,)).item() - ).squeeze(-1) + ).squeeze(1) Y_init = torch.tensor(problem.y(X_init.numpy())) Y_init_real = torch.tensor(problem.f(X_init.numpy())) return X_init, Y_init, Y_init_real @@ -108,7 +108,7 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): torch.manual_seed(seed) np.random.seed(seed) - problem = SchwefelProblem(n_var=1, noise_level=noise_level) + problem = SchwefelProblem(n_var=2, noise_level=noise_level) bounds = torch.tensor(problem.bounds, **tkwargs) diff --git a/src/schwefel.py b/src/schwefel.py index e06f92e..2b63fd7 100644 --- a/src/schwefel.py +++ b/src/schwefel.py @@ -17,10 +17,12 @@ def f(self, x): def eps(self, x): # Assuming the noise is independent of x for simplicity - return np.random.normal(0, self.noise_level, x.shape[0]).reshape(-1, 1) + return np.random.normal(0, self.noise_level, x.shape[0]) def y(self, x): - return self.f(x) + self.eps(x) + f = self.f(x) + eps = self.eps(x) + return f + eps # Test code if this file is the main program being run if __name__ == "__main__": From cbedbb843b37ecd8b386c7f574b4957cbf87c19c Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 22:30:26 +0800 Subject: [PATCH 27/43] todo fix plots --- comparison.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comparison.ipynb b/comparison.ipynb index a4f3653..7b778b3 100644 --- a/comparison.ipynb +++ b/comparison.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Starting iteration 0, total time: 0.000 seconds.\n"]},{"ename":"RuntimeError","evalue":"Expected Y.shape[:-2] to be torch.Size([]), matching the `batch_shape` argument to `Standardize`, but got Y.shape[:-2]=torch.Size([50]).","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)","Cell \u001b[1;32mIn[5], line 21\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStarting iteration \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, total time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m seconds.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 20\u001b[0m train_x \u001b[38;5;241m=\u001b[39m normalize(train_X, bounds)\n\u001b[1;32m---> 21\u001b[0m mll, model \u001b[38;5;241m=\u001b[39m \u001b[43minitialize_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbounds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 22\u001b[0m fit_gpytorch_model(mll)\n\u001b[0;32m 24\u001b[0m \u001b[38;5;66;03m# optimize the acquisition function and get the observations\u001b[39;00m\n","Cell \u001b[1;32mIn[3], line 5\u001b[0m, in \u001b[0;36minitialize_model\u001b[1;34m(train_x, train_y, bounds)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minitialize_model\u001b[39m(train_x, train_y, bounds):\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# define models for objective and constraint\u001b[39;00m\n\u001b[0;32m 3\u001b[0m train_y\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mtrain_y \u001b[38;5;66;03m# negative because botorch assumes maximization\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mSingleTaskGP\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_X\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# train_Yvar=torch.full_like(train_y.unsqueeze(-1), 1e-6),\u001b[39;49;00m\n\u001b[0;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43moutcome_transform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStandardize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m mll \u001b[38;5;241m=\u001b[39m ExactMarginalLogLikelihood(model\u001b[38;5;241m.\u001b[39mlikelihood, model)\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m mll, model\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\models\\gp_regression.py:160\u001b[0m, in \u001b[0;36mSingleTaskGP.__init__\u001b[1;34m(self, train_X, train_Y, train_Yvar, likelihood, covar_module, mean_module, outcome_transform, input_transform)\u001b[0m\n\u001b[0;32m 156\u001b[0m transformed_X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_inputs(\n\u001b[0;32m 157\u001b[0m X\u001b[38;5;241m=\u001b[39mtrain_X, input_transform\u001b[38;5;241m=\u001b[39minput_transform\n\u001b[0;32m 158\u001b[0m )\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m outcome_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 160\u001b[0m train_Y, train_Yvar \u001b[38;5;241m=\u001b[39m \u001b[43moutcome_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_Yvar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tensor_args(X\u001b[38;5;241m=\u001b[39mtransformed_X, Y\u001b[38;5;241m=\u001b[39mtrain_Y, Yvar\u001b[38;5;241m=\u001b[39mtrain_Yvar)\n\u001b[0;32m 162\u001b[0m ignore_X_dims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ignore_X_dims_scaling_check\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[1;32mc:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\models\\transforms\\outcome.py:294\u001b[0m, in \u001b[0;36mStandardize.forward\u001b[1;34m(self, Y, Yvar)\u001b[0m\n\u001b[0;32m 292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Y\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_shape:\n\u001b[1;32m--> 294\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 295\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected Y.shape[:-2] to be \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, matching \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 296\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe `batch_shape` argument to `Standardize`, but got \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 297\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY.shape[:-2]=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mY\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 298\u001b[0m )\n\u001b[0;32m 299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Y\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_m:\n\u001b[0;32m 300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 301\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWrong output dimension. Y.size(-1) is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mY\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m; expected \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 302\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 303\u001b[0m )\n","\u001b[1;31mRuntimeError\u001b[0m: Expected Y.shape[:-2] to be torch.Size([]), matching the `batch_shape` argument to `Standardize`, but got Y.shape[:-2]=torch.Size([50])."]}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=10)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","n_init = 50\n","budget = 2\n","\n","train_X, train_Y= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, bounds)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, bounds)\n","fit_gpytorch_model(mll)\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 374.5851697961611\n","Best solution found: [50.]\n","Total number of evaluations: 52\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n","\n","\n","#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","colors = [c_unnormed[i] / max(c_unnormed) for i in range(len(c_unnormed))]\n","\n","# Plot initial samples\n","plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', c=colors, cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} +{"cells":[{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Starting iteration 0, total time: 0.000 seconds.\n","New candidate: tensor([[-26.2945, 50.0000]], dtype=torch.float64), tensor([773.9874], dtype=torch.float64)\n"]},{"data":{"text/plain":["ExactMarginalLogLikelihood(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (model): SingleTaskGP(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (mean_module): ConstantMean()\n"," (covar_module): ScaleKernel(\n"," (base_kernel): MaternKernel(\n"," (lengthscale_prior): GammaPrior()\n"," (raw_lengthscale_constraint): Positive()\n"," )\n"," (outputscale_prior): GammaPrior()\n"," (raw_outputscale_constraint): Positive()\n"," )\n"," (outcome_transform): Standardize()\n"," )\n",")"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=10)\n","\n","\n","seed = 0 \n","n_inits = 10\n","noise_level = 5\n","noise_bool = True\n","\n","n_init = 50\n","budget = 1\n","\n","\n","torch.manual_seed(seed)\n","np.random.seed(seed)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","\n","train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, noise_bool)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, noise_bool)\n","fit_gpytorch_model(mll)\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 773.9874075531119\n","Best solution found: [-26.29448786 50. ]\n","Total number of evaluations: 51\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACj/UlEQVR4nO2dd5xTZdbHfynTWxjKDEMbpEgVUJAFXQuy0lRQXGF3FrG8VlDRtfEq1kWEtSBY2H11QXdpuoq7KyuKKKCA0jsCIk1gGGCYPpPJJPf948lzc5NJuTU3yZzv55NPLkkm90lIcn/3nN85xyIIggCCIAiCIIgExWr2AgiCIAiCIIyExA5BEARBEAkNiR2CIAiCIBIaEjsEQRAEQSQ0JHYIgiAIgkhoSOwQBEEQBJHQkNghCIIgCCKhsZu9gFjA4/Hg5MmTyMrKgsViMXs5BEEQBEHIQBAEVFZWoqCgAFZr6PgNiR0AJ0+eRLt27cxeBkEQBEEQKjh+/Djatm0b8n4SOwCysrIAsDcrOzvb5NUQBEEQRIJQXQ0UFLDtkyeBjAxdn76iogLt2rUTj+OhILEDiKmr7OxsEjsEQRAEoRc2m287O1t3scOJZEEhgzJBEARBEAkNiR2CIAiCIBIaSmMRBEEQBGEMdjswcaJv26xlmLZngiAIgiASm5QUYMECs1dBaSyCIAiCIBIbiuwQBEEQBGEMggDU1LDt9HTApMa9FNkhCIIgCMIYamqAzEx24aLHBEjsEARBEASR0JDYIQiCIAgioSGxQxAEQRBEQkNihyAIgiCIhIbEDkEQBEEQCQ2JnUSi3jynO0EQBEHEKiR2EoXvXgdebgccXW/2SgiCIAiCYbMBN9/MLtIJ6FGGmgomCkc3AJ4G4MQWoMNgs1dDEARBEEBqKvDRR2avgiI7CYOzgl3XV5u7DoIgCIKIMUjsJAp1XrHjrDR3HQRBEAQRY5DYSRTEyE6VuesgCIIgCE51NZuHZbGwbZMgsZMoiJEdEjsEQRAEIYXETiLg8ZBnhyAIgiBCQGInEaivBCB4tymyQxAEQRBSSOwkAjyFBZBBmSAIgiACILGTCDglYofSWARBEAThB4mdREAa2aE0FkEQBEH4QR2UEwFpZIeqsQiCIIhYwWYDRo70bZsEiZ1EIDCyIwispwFBEARBmElqKrB8udmroDRWQlBXJvmHALho+jlBEARBcEjsJALSNBZAqSyCIAiCkEBiJxGoCxA7ZFImCIIgYoHqaiAjg11MHBdBnp1EIDCyQ2KHIAiCiBVqzLdWUGQnEQiM7FAaSx3lJ4D/Pg6c/cnslRAEQRA6QmInEWgU2aHGgqrYvhDY+Bfgh3lmr4QgCILQERI7iUBduf+/62lkhCpqy9h1dYmpyyAIgiD0hcROIsDTWEnp7JrSWOpweSNitefNXQdBEAShKyR2EgGexspqza4pjaWOeq+JjsQOQRBEQkHVWIkAj+xkFwClh6gaSy1cJNaQ2CEIgtAFqxW48krftkmQ2Il3PG6fRye7Dbt2kmdHFZTGIgiC0Je0NGD1arNXQWmsuEcqbLIpjaUJnsZyVQMNTnPXQhAEQegGiZ14h1di2VOBtFy2TWksdUhnilF0hyAIImEgsRPvcHNySjaQkum9jcSOKqQRMRI7BEEQ2qmuBlq2ZBcaF0GohpuTU7OB5Cy2TZEddUgjOzWl5q2DIAgikTh71uwVUGQn7pFGdpIz2DaJHXVQZIcgCCIhIbET70gjO5TGUo8gkNghCIJIUEjsxDs8spOaAyR7xQ5VYymnoQ6A4Pt3LaWxCIIgEgUSO/FOXRm7TsmWiB3qs6OY+hr/f1NkhyAIImEwVey43W5MmzYNHTt2RFpaGjp16oQXX3wRguA7w66qqsLkyZPRtm1bpKWloUePHpg3z38qdV1dHSZNmoTmzZsjMzMTY8eOxenTp6P9csyhThLZkaaxJO8hIQNXQDSMxA5BEETCYGo11syZM/HOO+/g/fffR8+ePbF582bcfvvtyMnJwYMPPggAeOSRR/D111/jH//4BwoLC/Hll1/i/vvvR0FBAW644QYAwMMPP4zly5fjo48+Qk5ODiZPnoybbroJ69atM/PlRQc/g7JX7Ahu1hQvKdW8dcUbgak/qsYiCILQjtUK9O/v2zYJU8XO+vXrMXr0aIwaNQoAUFhYiMWLF2Pjxo1+j5k4cSKuuuoqAMDdd9+Nv/zlL9i4cSNuuOEGlJeX47333sOiRYswZMgQAMD8+fPRvXt3fP/99/jVr37VaL9OpxNOp69DbkVFhYGv0mD8Ss8zfLfXVxkndo6sA9bOAkb8GWjZ1Zh9RBtKYxEEQehPWhqwaZPZqzA3jTV48GCsWrUKBw4cAADs2LED3333HUaMGOH3mH//+984ceIEBEHAN998gwMHDuDaa68FAGzZsgUulwtDhw4V/6Zbt25o3749NmzYEHS/M2bMQE5Ojnhp166dga/SYKSRHasNSEr33m6gb2fLAuDn1cDeT43bR7RplMYqM2UZBEEQhP6YGtl58sknUVFRgW7dusFms8HtdmP69OkoKioSHzN37lzcfffdaNu2Lex2O6xWK/7v//4PV1xxBQCguLgYycnJcDgcfs+dl5eH4uLioPudOnUqHnnkEfHfFRUV8St4pJEdgKWyXDXGVmRVnvLuu9y4fUQbHtmx2FgakKqxCIIgEgZTxc6HH36IhQsXYtGiRejZsye2b9+OKVOmoKCgABMnTgTAxM7333+Pf//73+jQoQPWrl2LSZMmoaCgwC+ao4SUlBSkpKTo+VLMgwuO1Bx2nZIJVJcY21iwqsR/34kAj+xkFwDlxymNRRAEoQc1NUCPHmx7714gPd2UZZgqdh577DE8+eSTGD9+PACgd+/eOHr0KGbMmIGJEyeitrYW//u//4tly5aJvp6LLroI27dvxyuvvIKhQ4ciPz8f9fX1KCsr84vunD59Gvn5+Wa8rOgiTWMBPt+OkY0Fq0777zsR4JGd7DZM7LhqAFcdmbwJgiC0IAjA0aO+bZMw1bNTU1MDa4A722azwePxAABcLhdcLlfYx1xyySVISkrCqlWrxPv379+PY8eOYdCgQQa/ghigURrL4PlYrjpfb59EiuzwtF9WPmDxft4oukMQBJEQmBrZuf766zF9+nS0b98ePXv2xLZt2/Daa6/hjjvuAABkZ2fjyiuvxGOPPYa0tDR06NABa9aswQcffIDXXnsNAJCTk4M777wTjzzyCHJzc5GdnY0HHngAgwYNClqJlVC4G3zplxRJGgswTuxUl/i26xIossPfx+RMIK0ZUHOOiZ3s1uauiyAIgtCMqWJn7ty5mDZtGu6//36UlJSgoKAA99xzD5555hnxMUuWLMHUqVNRVFSE0tJSdOjQAdOnT8e9994rPub111+H1WrF2LFj4XQ6MWzYMLz99ttmvKToIk0jpUYpjVUlETuJmMZKzpCIHTIpEwRBJAKmip2srCzMnj0bs2fPDvmY/Px8zJ8/P+zzpKam4q233sJbb72l8wpjHC427GmALYltJxsc2amUVLglVGSHi510JnYASmMRBEEkCDQbK54JrMQCgBSDPTtVkjEcCRXZ8aaxkjKAtFy2TWKHIAgiITA1skNoJNCcDEQhjSUROw11QEM9YE82Zl/RhIsdaWSHRkYQBEFow2LxlZ5bLKYtg8ROPBNYdg5I0lgGNRWsChiw6qwA7C2M2Vc04WmsJEpjEQRB6EZ6OrBnj9mroDRWXBMssiNWYxk0LkJqUAYSp/xcjOxkAOmUxiIIgkgkSOzEM+EiO0alsSoDRnAkim/HFVCNBVA1FkEQRIJAYieeCerZMbgaq1FkJ0HETn2wNFaZacshCIJICGpqgJ492aWmxrRlkGcnnnEGq8Yy0LMjCD7PTnYboOJEAqWxvOIwOQMQWHduSmMRBEFoRBDYTCy+bRIU2YlnuNBIkYgdI9NYtecBj4ttN+/k3U+CRHaCGZSpGosgCCIhoMhOPBM2jWWAQZlHddKaAekt/NcQ79RLmgrCW75PkR2CIIiEgMROPBPMoGxkGoubkzPzfAIrESI7giAxKGcC9hS23VALuGqBpDTz1kYQBEFohtJY8Uy4poLuetbwT0+4OTkzz+cTSoTIjqsWgDeXnJTOxKPFxv5N0R2CIIi4h8ROPBO09DzLt613RVaVJLLD9+lMAIOyS1IhkJTOunxSY0GCIIiEgcROPBMssmOzA/ZUtq272PFGdrISLLLD3yd7GmD1fiXIpEwQBKEdiwXo0IFdaFwEoYpgg0ABlspqqNO/IosblP0iO4kgdqTmZC8U2SEIgtBOejpw5IjZq6DITtzidjEDLeCfxgKMaywYzKCcCJEdafdkTlMbGeGsAr54Cjix1eyVEARB6A6JnXhFKjICxU6K17djVBor4SI73sq1JInYaWojI3YuBTa8CayZZfZKCIIgdIfETrzCjcFJGcynI4VHKHRPYyV6ZKcJp7FO7WDXdWWmLoMgiASjthYYMIBdamtNWwZ5duKVYOZkjhFpLFedzyOUlefbfyKMixAjO1Kx08TSWMW72LVRM9UIgmiaeDzA5s2+bZMgsROvBCs75xjRWLDam8KyJQOpDt/tbifQ4PQ14otH+Psk9eykOdh1U6jGcjcAJd7ZNUY0oyQIgjAZSmPFK6EqsQDJfCwdR0ZUSiqxLBZ/kRXvqSzpXCxOU5p8fu4nVr0H+CrTCIIgEggSO/FKtNNY0rJzALDaJKIqzsVOsMhOU6rG4iksgCI7BEEkJCR24pVop7Gk5mSO2Fgwzn07wUrPm1I1VvFO33Z9FZsVRhAEkUCQ2IlXwkZ2DKjGEsvOW/luS5Ty8/pwaawmFtmB4J0VRhAEkTiQQTleCRfZ4fOx6nX07PA0Vla+77ZEKT/n6b7kINVYDXVMDEnvSyQEIUDsgEW6EvX1EgQRfVq0MHsFFNmJW0SDcpTSWKJBOQEjO6JBWZLGSskCrN5zgUSO7lQWAzVnAYuVVdoBVH6uFmcV8M5lrBM1QRCMjAzgzBl2yciI/HiDILETr3CxkxKsGsuINBYXO4kY2Qni2Wkqk895VKdFV594JZOyOop3Aad3A1s/IN8TQcQYJHbiFR5NCVd6bmQ1FpBAkZ0g1VhA0zApc3Nyfm/f6yexow7+fXNWNI3+TAQRR5DYiVfCGZT1no3l8QQ3KIuRnTivxgpmUAaaVmQnv7dEJJPYUYW0r9X5w+atgyBiidpa4Kqr2MXEcREkduKVsAZlndNYdWWAx8W2g3l24j6NxSM7gWKnCfTa8RM7FNnRhFTslJLYIQgA7GR5zRp2MXFcBImdeCWaTQUrvT120pr5j4XgKTRnnEd2XEGmngO+yE6ipiSclUDpz2w7r7dP7JHYUYf0+0aRHYKIKUjsxCthmwp601gNdWzukVaCmZOl+477yE4QgzKQ+Gms03sBCEBWayCzpTFer6aENJJKkR2CiClI7MQjDU7fLKNwTQUBfQ5cwfw6gCSyE+diR+ygHJDGSk9wsSOaky9i1/xz46L5WKqoJ88OQcQqJHbiEWkkJVhkx54CWJPYti5iJ8ioCCAxSs89nuB9doDEj+xI/ToAeXa0QpEdgohZSOzEIzySkpzFBnIGQ8/GgjyykxUgdhKh9LxBUh3QyKDcxMQOr0ajNJY6pO9bVTFNkFeDxw0sGgcs/r2pZlYi8SCxE4+E657MESeS63DgqkzgyI5UDNrT/O9L5GosdwNQspdti5EdKj3XROB37fwRU5YR1/z8DXBgBbB/OXBym9mrIfQiPZ1dTITETjwSzpzMEQ9cOszHCtZQULp/t5P5iOIRfmBPSgesAV+HRK7GOvcT830lZwLNOrLbxDQWRSRUERgRI9+OcrYv8m0f+Ny8dRD6kZEBVFezC42LIBQRruycY0Qaq5HYyWq8pnjDFaISC/BPYyVa+3+ewsrr5RN5otihNJYqeJ8dPoiXfDvKqC0Dflzu+/f+FaYthUg8SOzEI+JcrHCRHR0bC4YyKFttvh/2eO2iHKp7MgCke9NYbifgMq/zpyFIx0RwyKCsDS52+HtKkR1l7FnGoo2ODmww7eldQNlxs1dFJAgkduKRcHOxOHqlsVx1PiETaFAGfNGleG0sGGouFsDeQ3HyeYKlsgLNyQCJHa3wiBh/TymyowyewhrwP0C7gWz7AEV34p66OmDUKHapqzNtGSR24hFZaSw+H0vjgYv7dWzJQKojyH7i3KQs9ewEYrEkpklZEMKLHeqzow5ngNihyI58zv4E/LIRsNiAi24Bug5nt5PYiX/cbuC//2UXt9u0ZZDYiUdkGZR1SmNJ/ToWS+P7U+O8/Lw+RENBTiKalCuLgZqz7MDSqrvvduqgrB6Pxxcl5GKn7Jg+HcybAju8UZ3O1wBZ+cCFI9i/D6/Vb8Yf0aQhsROPyIns6HXgClWJxYn3yI6YxsoMfn8i9trhUZ0WXYEkSbl9Es3GUo30e9aiC2BLATwNQMUv5q0pXvC4gR1L2Hbf37PrFl1ZlaC7npWjE4RGSOzEI04ZBuUUvcROCHMyJ95HRoQzKAM+k3JCiZ0g5mSAPDta4N8zi419lpp1YP8m305kDq8BKk6wNHlXb0THYvFFd/ZTCTqhHRI78YjYVFCGQVmvNFYwczIQ/40FxchOhDRWIhmUg/l1AN9nxlVD3WuVwr9nKVnsQM17F5FvJzLbF7PrXmOBpFTf7aJv5wsW/SEIDZDYiUfqlDQV1Ch2QnVP5sT7yIj6EHOxOAmZxooQ2QHIpKwUXvXICwNyvWKHIjvhqSsH9v2Hbfct8r+vw2AgJYf5y05sif7aiISCxE48Iqf0XK+mgqEmnnPiPbJTLzeykyBix1kJlP7MtgPFTlIaAK8JnVJZyuCRHX6SQZEdeez5lM2na3Eh0OZi//tsScywDFAqi9CMqWLH7XZj2rRp6NixI9LS0tCpUye8+OKLEAK61e7btw833HADcnJykJGRgQEDBuDYsWPi/XV1dZg0aRKaN2+OzMxMjB07FqdPn472y4keSgzKTo19dkSDcn7w+1MSuM8OIKnGShCxc3oPu84qADJa+N9nsVAXZbXw94ufZIiRnSOmLCdu2OFNYfX9ffBqzwtHsutYL0HfsRT49lWzVxGbZGSwdheC0HTHRcycORPvvPMO3nzzTezbtw8zZ87ErFmzMHfuXPExhw4dwuWXX45u3bph9erV2LlzJ6ZNm4bUVF9u9+GHH8Z//vMffPTRR1izZg1OnjyJm266yYyXFB0UzcYyuBqLR5fiNrLTxNJYofw6HOq1ow5xVESQyE6ijRrRi3OHgGMbWLfki8YFf0yXocz0XbIXOH80uuuTi8cD/OchYNUL2td4eg/w01f6rIvww27mztevX4/Ro0dj1KhRAIDCwkIsXrwYGzduFB/z1FNPYeTIkZg1a5Z4W6dOncTt8vJyvPfee1i0aBGGDBkCAJg/fz66d++O77//Hr/61a8a7dfpdMLp9A2urKiIowO1q46VYwLyZmNpMSh7PJHTWGLpebxGdiL02Um0aqxQfh1OU6zIctUCm94F2l4KtB+o7jm42OHfu2YdAFjYyUb1WSCzpS5LTSh4VKfTECC7dfDHpDUD2g8Cjn7HojsD74ne+uRSXcJScQA7OeSVeGpY8nsmmB7eDeS01Wd9BACTIzuDBw/GqlWrcODAAQDAjh078N1332HECFZy6PF4sHz5cnTt2hXDhg1Dq1atMHDgQHz66afic2zZsgUulwtDhw4Vb+vWrRvat2+PDRs2BN3vjBkzkJOTI17atWtn3IvUG1FUWHxzqYKRrINnp/Y84HGx7Uienbg1KIfpoAwkXjVWpMhOUhNLY50/Arz3G+DLp4Hlf1T/PPz94t9JewqQ3ca7D/LtNMLjadxbJxQXequyYtW3Uy7ppVR9Vv3zeNzeyJDgKwxJBOrqgN/+ll2a6riIJ598EuPHj0e3bt2QlJSEfv36YcqUKSgqYq78kpISVFVV4eWXX8bw4cPx5Zdf4sYbb8RNN92ENWvWAACKi4uRnJwMh8Ph99x5eXkoLg7+gZk6dSrKy8vFy/HjcTRsTkxhZfmmVQdDLCOuVl9GzFNYac3Yj3cwjG4qKAhAxUnjUgH1Mj07iTD53N0AnN7LtimyAxz8CvjLlT4BWF2i/rmcAZ4dgCqywnHkW6D8OKu2unBU+Mfy3jtHvovNdHm55PhRo0Hs1J4H4P2N0eq1jCXcbuCf/2SXpjou4sMPP8TChQuxaNEibN26Fe+//z5eeeUVvP/++wBYZAcARo8ejYcffhh9+/bFk08+ieuuuw7z5s1Tvd+UlBRkZ2f7XeKGOhmVWID/j67as/RI5mTA+MjOpneB17oDWz8w5vldEZoK8tlY7vr497GcO8gmuCdn+jwlgYhiJ85fazg8HmDNLGDhzUBdGdC8M7tdy4G0PqAaCwCaFbJriuw0hg/97HWTf2+dYLTozP6PPC7g0NfGr00pekV2pH/bVCKrUcRUsfPYY4+J0Z3evXtjwoQJePjhhzFjxgwAQIsWLWC329GjRw+/v+vevbtYjZWfn4/6+nqUlZX5Peb06dPIzw9zkI5X5HRPBgB7KjP2AerP0kWxEyKFJV2Hu575ifTml03s+tj3+j83IJmNFWJcRHIGYE1i2/E+H4tHMPJ6hY4KJno1Vm0Z80V8Mx2AAFxyO3DbcnZfQy3gdql7XorsyMdZCez7N9sO7K0Tiq4xnMqSip2ac+qfRxoVonlgumOq2KmpqYE14EfXZrOJEZ3k5GQMGDAA+/fv93vMgQMH0KEDM4FdcsklSEpKwqpVq8T79+/fj2PHjmHQoEEGvwITkFN2DnjLiDVWZEWqxAK8YsdbMmpEdKfiJLs+f0T/5wYid1C2WBLHpBzJnAwkdhrr9F7g/64GDnzOZleNfgu4fjaQLinBVxvdEZsKSr6XPHrG+xoRjL3/YlHS5l2Atv3l/Q0fHXHwy9jrpmxEZCeR0lgxgqnVWNdffz2mT5+O9u3bo2fPnti2bRtee+013HHHHeJjHnvsMYwbNw5XXHEFrr76aqxYsQL/+c9/sHr1agBATk4O7rzzTjzyyCPIzc1FdnY2HnjgAQwaNChoJVbcI6fsnJOSySJBar84lV6xE2pUBMAiBClZbF11FeGjQGrgPyRGiZ1Is7EA5tupOh3/JuVI5mQgemLn3CFWbRLKC6Y3u/4J/PsBdpDNaQeM+ztQ0I/dZ7OzE4P6KvZ9yWiu/PkDmwoCvsgOpbH84Smsvr8L3lsnGO1+xWZn1ZYCxzcCHWLoRFYvz071Gd92PYkdvTE1sjN37lzcfPPNuP/++9G9e3c8+uijuOeee/Diiy+Kj7nxxhsxb948zJo1C71798a7776Ljz/+GJdffrn4mNdffx3XXXcdxo4diyuuuAL5+fn45JNPzHhJxiPOxZIhdrRWZMmJ7ADGNRbk5mQAqDypf5rM4/aVjIYyKAOJ0WtHEJSJHSP9Scc3AXMvBj6937h9cAQB+OIp4OM72Wu64Crg7jU+ocPR2kIhsKkg4IvsVJ+hM3VO6c/A0XUALMBF4+X/nc0OdPkN2z4QY6ms8hO+bS2RHWkKTGsa6/gmYNtCbc+RYJga2cnKysLs2bMxe/bssI+74447/KI9gaSmpuKtt97CW2+9pfMKYxA5c7E4Wv0XcgzKABNeFdC/UqLmHDPUcsqOAi0v1O/5pQf0sJGdBEhjVZ5i76fFBrTqHvpxejWjDMd+r0fm9G7j9sH58TNgw5ts+/JHgCFPA1Zb48elZjNBrfYzHNhUEADSHEwo155nkclwIrOpwMvNO10N5LRR9rcXjgB2fQTsXwH85gX916YGV61/NEeLZ0dPg/Kn97GChBZdgHaXanuuBIFmY8UbcuZicbQ2FpRjUAaMGwYqzYUD+qeyxIoji3cuVAjEkRFxnMbiUZ0WXcO/Vi76jExjHf6WXVdFYaQLr+IbNBkY+mxwoQNo/wxLp55LaUYmZRFB8DUS7BOht04wOg8FrHbg7P7Y8UFJozqAxsiOjp4d3gw2Froxp6cDVVXskh7mpNJgSOzEG3INyoCvwZmRBmXpWvTuosxTWBy9xY50LlY470Cag13Hc2RHjjkZMN6z46wETm5j27XngYZ6Y/YDsM8P/7G/5Pbwj9U60LY+SGQHMN+3U1/Nog+xQPUZoOwYAAvQLUJvnWCk5rBJ6ACL7sQC3K/DG0g21Kpv26CXQVkQfKI9Fkr1LRY2Eysjwu+swZDYiTeUGJS1pLFctT7xEs6gLF2L3mmsioCzJqMiO+FSWICkGqtM3/1HEzl+HUCfztvhOPY9IEiqaaSmTL3ZvggQPED7waxXSzjEGW8qBXuw0nPA3MiOsxJ4vRfw16tio7qOnyyk5oSufowEbzAYK74dHn1u2Q2wJbNttSZlaQpMSxqrvhpic8ITW+L7JE1HSOzEG6JB2eA0Fg+D2pJZFUQ4jGosyMWO3Zt20T2yE2EuFicRRkbIFjsGp7GOfOv/b6NSWR4PsO0fbLvfHyI/Xksaq8HpG6sSS5GdswfZZ/bMj8A3L0V//4Hwgy7/PqmBj444uj42Tj642HG087UwUJvKkgp/LZEd6d8KHl/a2CycTuC229hFMpMy2pDYiTe42JEV2dFgNhUHgOZFDj0aFdnh+XBusNP77Ji/L6EmnnPivRrLWenzOJidxjrynf+/jYrsHF3HBEZyFtBzTOTHa0ljSU8mGomdC9i1GZEd6Xv7/dvAia3RX4MUPcRO7gVAiwsBT0Ns+FG42Mlp62tZoMak7PH4ewK1VGMFCiWzU1kNDcD777NLQ4NpyyCxE284lXh2tIgd71yxSH4d6Vp0j+x4PTuF3jYD54/oO59K7J4cSezEeTXW6T3sOqsAyGgR/rFGprHqKoCT29l2q57s2qjIDo/q9Lop8v8voK19AvfrJKWzEmkpPI1V/ov67sxqqZLM+hI8wH8ejP4apOghdgBfdOdADPh2uGcnR2Nkp67MP72rJY0VKHZ+/kb9cyUQJHbiDSWl55rSWDLNyYB2v0MoKrxnTe0GAhYrM/9VaRjWGIjSNFa8VmOd+ZFd5/UI/zhA0mfHALHD/TrNOvr63Oj5/8mpK2ddegHg4lvl/Y34GdYQ2Qk2ciQrn6VhBbfXnBtF+GDTLteyVHTxLmCDie05+PdHq9jhvp2DK83vpuwX2fGKHTWenUCBpCmy4/0MO9qz6rXzR2Knes1ESOzEE1KXvRzPjpazdDndkzkp3rXoGdmRNhRsVghkt2Xbevp2+PuiJI0Vj5PP+Q9ploxZcUaWnh9Zy64LL/e1MzBC7Oz6JxPGLbsBbS6R9zdaBHuwhoIci8W8gaBV3jRWqx7AMK9nZ/UM1r3aDPSK7LQdAMDCoiFa+tpoRRD8xY6WyA4XSOJvdqX63xr+ecwqYCeKAHCIojskduIJVy3LVQMy01gaqrEURXYM8OxUn2XDRWEBsloDuYXsdj3FjtzIDq/G8rhio6pFKfyMOl3GGAT+Y9tQB7h1zq9zv07HK3xip9oAsSMakyfIL3XVYlAO1lBQilkDQfl7m9kK6Pt7oOOV7P/1s4fNEe16iR2b3SdOzUwtV5/1Nj21eFPE3LOjIbLDhbHgUd/FnH8eU7JY80ZAvW+nphQ4sz/y4+IAEjvxBP8htlhD/7BK4Q3O1Dj7qyQ/lBH3Y4Bnh1diZbYC7MnGnB2LkZ0IYicp3VdWGo8VWbVKxI4kyqVnKquuHDi1g20bGdk5vQc4uZWF7/soGEegyaAsObgEg/t2jJrvFgr+3ma0YqLvutcBeypweI1vPlU00UvsAJLeV2Xan0st3K+Tlc9+o8TIjopoEzeT57SDb7CyylSW9PN4wRC2fXit8pMXQQD+fiPw9qCEEDwkduIJ0a+TJe+MVUsaSzQoy0h9GBHZ4WKHN+sSxc4R/fZRL2kqGA6LJb5NyjzUL0fs2FPYSAlAfXO0YBzdwM5WczsB2QXsAAzoL3a2/p1dXzgishlbihbBXh/GswOYGNnxHkAzW7Lr5p2Aq6ay7S/+15gUYjh0FTsxUCEpTWEB2jw7/Dua0cInmtWalJ2S40RBX+bXclawkwAlHN8InNrO/GY/r1G3lhiCxE48IZady/DrABrTWJLS80hIDxR6hce5Xye7gF0bIXZcMquxgPg2KSsROxaLMeXnvL8Or6zjnys9D7gNTmCnd/ZSP5nGZI4Wz06ohoIcMbITbc+OJLLDGTSZtR+oKwNWPBnd9egpdnjvr7oy7c+llkCxo8Wzw/8mo6VPNKseXSKJ7FhtwAVXsn8rTWVtfd+3fWKzurUAbERESQm70LgIQha8LFaOXweQpLEUih2Px/dDKcegzNfjrmeeAD0I/CExJLIjs4MyEBtnkmpRInYA7QNkgyGKnV+zax5tcJbrN81+/3/Z/09WAdD5GmV/K/0MK12P3MiO3q0TwuF2+dKX0lS0zQ7cMJelwnd/DBz4MjrrAZpQZEdFGotHgzJaaJ9pKIod72e6kzeVpcSkXFcO7Fnm+/eJLerWArATqJYt2YXGRRCyqFNQiQX499lR8iNbe97XETajpYz9ZEHMM+uVyhLTWDyy4z1gVJ7Sb9aPS2YaC5CMjIhHscNLfnPlPV7vyE5tGXDKO5uLR3ZSHT4flF4mZZ7C6vv70AM/QyH9DCs9o47k2clpx8SFqyY6w08BX6TAYmv8/17QD/jV/Wz7s4e1D52UC/fXJJpnJ6cdu+YnE84K5TPf+P9Xuh5pLP559P7+X+A1Kf+ySX7kctc/2ec1pz3797mf4vO3TwKJnXhCyVwsQHIQF5Q5+/kPcloz5uGIhNUqiSLpJXZ4GquNby08fadXvxJFkR0Hu443g3JDve//JF2h2FFbDRLI0fUABKB5ZyC7NbvNYpH4dnToolx23Bem71ek/O+ln2Glgl0sPQ8hduzJvrP/aPl2uIDMaMFeWyBX/y/rw1LxC/D1n4xfj7vBF5lOtMgO/41Kdfj8bkqjO6Jnp7kkjaVShAaK72YdmFdOcDfuYB6KrR+w64H3+E401XbgdjqBSZPYhcZFELJQMvEc8B60VDj7lZiTOXqPjAj8IbFY2JcW0O+AIRqUlaSxyvTZd7Tg4sxijTzjjJOkcxqL/8DyFBZHrMjSIdqxYzEAge2Dj2hQitouyuGaCnKi7dvhAjIjRDVlcgZw3Wy2/cNfgF80eDLkII0o8BMHLcSiZ8dq9Z1QKDUpczO5NLKjl9gBlJWgn9rBjMnWJKDP74C2/dntasVOQwPw9tvsQuMiCFkojexYLOpGRigpO+ekqjxQBMPjYekqAMhp47tdb9+OmMaSUcYfr9VY/IwxLTf4GX4w9E5jSZsJStGr147HA2zzprD6TVD/PGpNyuGaCnKiXZEl9tgJk4bufA1w0TgAAvDfR41dD//eJGcBtiTtz2d2ZKfB6XuPeRoLUGdSFgTjqrE4Snw7PKrT/ToWaeKNObX4dmIAEjvxhDjxXKbYAdSZTZU0FORoabcfSE1AQ0GO3mJHjUE53qqxlDQU5OgpdmpKgeLdbDtkZEej2DmylqU2U7KB7terfx61LRQiNRUETIjsBKnECsbVT7Hrk9uZaDQKPc3JgPmeHe4ptKf5p4fVmJTrynzNYtNbSNJYOhmUAfbds9iA0kPhfz/ra4CdH7Htiyeya1HsbI7PDvJeSOzEE0rmYnHUOPuVjIoQ96NjY0GxoWCe/1mg7pEdmR2UAfPPJNWitBIL0HcY6LENAASgRdfGnye9eu3wjsm9b5b3fxkKtZ/hSAZlwITITkCPnVCIJzSCb6CpEYhix6HP85n9fZSmsKQVRvx7piSyw5sQJmcBSamS32y1aawgHrLUbO+YDYSP7uz9F4vOOzqwjtsAkH8Ra9JZfcZnyo5DSOzEE0rmYnHUHLhURXZ09OyUe8WONIUF+Jfw6oHc2VhA/FZjiWJHpjkZ8AkGPcTO4YD+OlLEXjsaPDu154G9/2bbWlJYgPrPcKTScyB2IztJqYDNW4Sg9yBfKfx7o+RzGA6zPTuBfh2OmsaCYtm5VyhJ52OpIZT45qmscFPQeQrr4gm+tHdSKpDXi20b7e0yEBI78YRSgzKg7osjih0VBmVdIjsBDQU50siOHuFUVQZlSmMpIpQ5GfBFHao1VGPt+iebT5TXyzdJXS2iyV6lQVmOZ6fmnL6dxkNRrcB3p6Wholx0T2OZPJw3lNhR49mRlp0Dkt9SFWmsBqd3XheCiB2vSfnnNcGnxZ85ABxbz4oZ+gZUNCaAb4fETjzBzb9yOygD6tJYothRYVDW44e8gldiBfyQ8H4lDbXaK3g8bt+PgiyDcpxOPlcV2dGpGqumFDi9i20bFdkRjcl/0N6wjB/0lQp2OZGdlCzfwSwa0R2xGktGn6xUlSJPCUZ5djwN5gznDeyxw1ET2eFin/8t/81W8/2T/s4Hfh4LLmbHjroy5tEKZJs3qtNlWOMTTa0VWTEAiZ14QlNkx+A0ltqz4mCEiuzYknxnUlpTWdL3Q5ZBmU8+b9C3s3AkSg8HPwuTixbPjtY+O0fXseuW3YILZ619dk7vZWWytmRvVZFG1Ah2jydynx1ONH07iR7Z8RvOa0JqOWRkh3t2FBiUawIiO1r67HChnpzZuLGmzQ509EZYA0vQG+qB7YvZ9sVBRq3wyM6p7coHiqalAYcPs0tamrK/1RESO/GEOBvLwGosV61vP0oMyqk6prFCeXYA/UzK/EBuscprnJiU5vM2RKsia//nwJy+wKoX1D+HGrGTpJNnJ5xfB/AdiOsr1Q0dLfZGjdoN1McLoiYVK50MH0nsRMu343FLSpkTVOxYLOb6dvT07HBhFBjZUZPGimSWD+Xb2f9ftubMfKDLtY3/rnkX9v1w1QBn9ilbk9UKFBayi9z2FwZAYideEATfB1lJZEdpgypubLSlyG9CB/hSa7pEdgImnktpppNJWWpOlpP+sFiiXwFychu7Lt6p/jlURXZ08uyIfp0QYiclC7Cnsm01vXbKvZ20HR2U/20w1Bz0+QHJYvO9llBEK7JTc45NmIdF3v+7nm0jQqG32JE+V7QjO4Kgr2dHOhcL8IluNQblSG0QuG/n+A/+xwQ+9LNfEYsABWK1+jxxcerbIbETL9RXs3bfgMLIjsI0ljSFpcQDoVdkx+NpPCpCCo/saD1gKCk754gVWVGK7PDGilpKs1UZlDV4BjjV54CSPWy7QwixY7Fo67VT5vVNONqFf5xc1IgdaUPBSN+XaEV2+HuZ3jz4gSuQeIzsAOb12qk97/v9CPyN4t+z2vPy08+BBmVNaawIkZ3cC9hvqKfBdzJy/qivHD1cRSNPZSmtyKqvBx57jF3qFc4M0xESO/ECFxEWm7zBlRylaSw15mRAv3ERNWe9Q0gtQFaQajC90lhiZEeB2In2mWSld2yHFrHDhZkqg7IGz85R7w9py+7he71o6bXDZ6QFmkTVoiaNJXokIqSwAElk54iiZSlGiV8HiGOxY1Jkh5uTM1qxsmwp4vdMkL+uRpEdA9NYQONuytv+AUBgfXX4ZzQYak3KLhfwyivs4nIp+1sdIbETL0jNyUoiLkq/OGrMyXxdgPbIDg8PZ+UHbyuvt9iRU4nFifaPa4U3slNzVp1J2VXnE7mKIjs6eHb4WSM3RIZCS0VWud6RHRWCXU7ZOYdHdip+UT4VWwlKKrGA+BU7Znl2QqWwAPabxdclN5VVHZBq5kLF42Kl5EoINioiED4F/dDX7HeFN+W8ZGL45+aRnTP71Hd3NhESO/GC0rlYHH7GKTeyo6Z7MuCf99dSmi36dQqC38/FTlWxtsiDmjSWODIiWpEdr9gRPMqnKAO+qI7VrjL1qeEHLZI5maO2146fb8KAyI7cz7CcsnNOZivmERM8vqiUESiN7IhR2TJDlgOPx/fcCRHZCSN2AGUmZUFoXHou/SwpFRXBRkUE0vEKVphx7iCwZQFQeZJVm3a7LvxzZ+WztJ3gYVVZcQaJnXhBzVwsQEUai088Vyh2+JfL4wIa6pT9rZRwfh2A/cBxM7SWA4aSuVjSfQPR+XFtqPf/sVST5pEOAVUSDdRqUK4646vYCOXX4aiN7FSfYZ8zizX0Z0Up/LvlaZBfdq8ksmOxSCKTBvp25HZP5ogREoMiO84Kr2EayooeImGWZydUjx2OEpOys8Kbtpf8ndXm+10yYnRJmsMXpVn5DLvu8zt5Valx3FyQxE68IJadK2goCChPY507xK75j7JckjMBeA+oWnw7kc6aLBYgt5BtazlgiBPPFfifojkyIvDgr6ZaSU0lFuB7TzwudekW7tdp1dPXAj8Uag3K3Jyc1RqwJyv721AkZzLxBMj/DNdHqH4JJBoVWXLnYnGMTmPx70tSemOPixYSIbLDBVFShn+UWW10VW7PJ+7b4Y8P1lsnGGpNyjEAiZ14wSnx7ChByZdGEICSvWy7ZTdl+7Fa9RkZEaqhoBQ9fDuaDMpRqMbi5mSOlsiOUrEjnRXmUhHdiVRyLkWtQbnsKLvWK4UFMCGt9DMcbOhiOMRqwp8VLU0RiiM7KjtHy8UIvw7gixLFmthR0lhQ7IcU8B0VW4aoTWNF+Dxy3w7A+lS1kvl7H8edlEnsxAuiQVlhZEeJ2Kkq8f5wWICWFyrbD6DPyIhwPXY4uogd7tlRENmJ5pkk9+twVIkdFZVYAIuUWL3mcDWpLLnmZMCXxlIaudLbnMxR+hmWeybNEYfZGhnZibFqLKPEDn++WDIoA+oiOzyFxVE7MkKOQRlgooX7OeVGdQCgdV8W/az4pfEJWYxDYideUGtQlqaxIpkuuc8ityPrGKwUPYyO0RI7atJYaVFMYwX+kEQzjQWo9+1UlQBnfgRgATpcFvnxPNVSVaLM2F4WwTehFp4mdso88PMzb7lprGZRSGNpqcYyYu6bYWLH4f/80aCh3vfdDBnZUeDZCSw753AhotqzE+E4YUsCrnsNuPQeoPdv5T9/SqYv6i/Xt5OWBuzezS40LoKIiJq5WIDvR1hwRzYOl3jFTsvuyvbB0Vp+7vH4yq2DjYrg6BnZUZPGisa4CB7Z4R6SaKaxAHUz1QDg+EZ23aqHvIgST7W4apSdxRoe2ZEpdrhnR45BGQAc7dl1YOROLzweiWdHbmTH+5oFj7YKvFCIYseh7/OKkVYDS+YDqTwFQGAd5gOjMRxFkR3v/1WoyI5RaSwAuOgWYOQsecZkKUpNylYr0LMnu9C4CCIiauZiAf6Ri0gHLi52WqkUO1obC1afYaZYi5XNaAmFdGSE2jNRLaXn0Zh8zs8em3dh15rSWGrEjspeO9xYLdfgnpLp8wgpeY1i9+T28v9GDkpHJ4jt+WWmsXjazlmhrXVCKGrP+zqty43sJKWz9gSAMaksXi1llGfHWa5tWK4SxBRWm9AHbiWeneoInh3FaSyF4lsNcWpSJrETL6g1KPuVMUZoP65V7GiN7FR4f0gy88O3uc9pyzpJN9SpzxtLZ2PJhUcqBLe6Vu5K4Gf+rfuwa6V9aABz0lhqOjYrrcgSBEn3ZJ3FjmqDssyDS0oWYPeG8tU0UowET3emNQvelDMYFouxvh2j01iAsQ0RpUTy6wDKIjtiGitAmCZHIbKjFi52Tm5jkcRI1NcDzz3HLjQugohInUrPDiDPpCwIXq8FzIvsyKnEAtiPOP+xUZvKqlfh2UlK8w17NLoii4u41hexazUHRlHsqJgIrrb0lTdcVCN25PqS6sp86aNwBx01qDUoy/XsaJ0HFgmllVgcI4eBGiV2bEm+9z1avp1IPXYAX0qq5lzkCHAkg7IRfXa00qoHE+zOCuDcT5Ef73IBzz/PLjQugoiIGNlRWI0FyDtLrzjJ9mGxAc07K9+HdG1qIzvlXnNyOL8OR6tvR00aC4heRRaP7OR7xU7NOeWherXVWIDvMyO3uZ64T0kjQ7koPfjzFFZ6C+X/f5EwOrIDaBuREQmlfh1OPEZ2pM8ZrcaCciI7PJLqaYj8foYyKIuTzxWcbHjckupAFSfFcrHZgYK+bPtE/KSyNImdn376CV988QVqa2sBAILRPoamjNrSc0Ce2Y2nsJp3Um5Y42gtPZdTicXRKnbUpLEA30HcSJOyq9ZX0Zbfm3mYBI/8WTscLWmsJJWeHTVpLKW9dowyJwPKD/r1Cj07gETcGSB2xMiOTL8Ox1Cx4/1MGCF2ot1rR47YSUr1RZwijXkR52IFVmOpSGNJhZGRkR0gLjspqxI7586dw9ChQ9G1a1eMHDkSp06xs9A777wTf/zjH3VdIOHFqdKgDEjmY4XxmZzR6NcBtDcVjKbYieXIDo/q2NPY/kTDo4K0R30N0MBOQrR5dpSmsfiBTUlkR2GkwyhzMqBcsGuK7BiQxlLaY4cTt5EdB7uOVq8dOWIHkHxnw5yg+M3FCjQoc7GjwBvIH2tLVn/CKpemInYefvhh2O12HDt2DOnpvoPFuHHjsGLFCt0WR3jxeNSXngPy0lglXr+O2rJzQPsPJk9jRfLsANqbs6kpPQeA9GiIHa9fJyufeTzUdBnmZ5S2ZGWT3TlqS89rVVSAKR0GKpqTDYjsKBXsSpsKAsamsZT22OGIfrs4FTvRiOwIgjzPDiDPpFxfBbi9U81DRXaUnGxEw6/D4WKneDfg0jALMYqoEjtffvklZs6cibZt/dVtly5dcPToUV0WRkiorwLgTRGqiezISmN5x0SYGtnxGpTlmE41p7G4sVRpGiuKkZ2s1uxajaFVmsJSMgSUIwpkpZ4dNdVYCg/+5V6xY3Zkp6EecHurS5QISiMNypojO2W6LgeCkDienbpy3+9GpOiznMaC/D57auPfIfG3VElkR4XwVoujPRPUHhdQvMv4/emAKrFTXV3tF9HhlJaWIiXF4PBZU4SLB6tdXWdj8SwhxBfH4wHO7GfbWsSO0oZsfmtwA5URJp5L4WKn6rS6fiViGkul2DHSsyON7ADKq5UAbX4dQF2fHXeD72CpJI0lRq7kRnYM6p4M+Dooy/kMS8+6FYkdIyM7aquxHOxa7/lY9VXMqAvEv2eHp7DSm0dOf8uJ7IhzsVo2PiFRMy5C7qgIPbBYJKms+DApqxI7v/71r/HBBx+I/7ZYLPB4PJg1axauvvrqMH9JqEJadq7qLD1CSqL8OBufYE0Cci9Qt0a+PkCdQbn6DPtRtFh9B4NwpDXznY2WKYwmuht8Z+RK01i8oeHx75X9nRJ4ZIen83hKQkkkgP/4q6nEAtSF0aVRASUHNqlhV06RQzQMynLGRfCDiz0tfF+oQAz17CiceM4xyrPDP4e2ZOXfNTlEcz6WXL8OIK+xoFh2HuSEJFmDZ8fISiwpcn07qanAxo3skqrj1HuFqBI7s2bNwl//+leMGDEC9fX1ePzxx9GrVy+sXbsWM2fOlP08brcb06ZNQ8eOHZGWloZOnTrhxRdfDFnVde+998JisWD27Nl+t5eWlqKoqAjZ2dlwOBy48847UVVlQNtzs9BSdg5ETmPxSqwWXeU3IguGtKmg0so87tfJai3/wKE2lSWd5K00stPtOlaef3IbcO6Qsr+VS6PIjoqDo5oScClqmgryaFdKjsKDv1fsuJ2RIwv11b7XZkRkJ1WSPoj0GVZjTgaUizu5+BleY8SgLE1hqTlRi0Q0PTty/TqAzMhOiLJzQN3U82h6dgD5YsdmAwYMYBebzfh1hUCV2OnVqxcOHDiAyy+/HKNHj0Z1dTVuuukmbNu2DZ06dZL9PDNnzsQ777yDN998E/v27cPMmTMxa9YszJ07t9Fjly1bhu+//x4FBY3Nq0VFRdizZw9WrlyJzz77DGvXrsXdd9+t5qXFJlrMyUDks3SxEqubuufn8DMKTwMrn1ZChQJzMkftUEWe9rLY2BmnEjJbAhdcxbZ3/VPZ38pFFDsBnp2oprFU9NlRU3YOsNQs/+xEEnQ8hZWSo/+sJcC3DjlzopQ2FOTw/0+PS9+DdF2ZL2IZK6XnRvp1pM8bDc+OosiOHM9OiLlYgE+wNNSySLQcxNElBo6KkNLmYnZd+nN05gVqRMHplz85OTl46qmnNO18/fr1GD16NEaNGgUAKCwsxOLFi7Fx40a/x504cQIPPPAAvvjiC/GxnH379mHFihXYtGkT+vfvDwCYO3cuRo4ciVdeeSWoOIo71M7F4kQqIy7R2DlZ3E8mAAsAgZ2hKynrVlJ2zlEd2ZH4ddScbfb+LXBoFbDrI+DKx/U/YxUNygGeHbmeFkC72ElSUXqupYlhRkv2makqAVp0Cf04I1NYABNeVru3IVxF+LNktZEdewo7SNeeZ69XbaoxEC4UU3JYrxclxKvYMcOzI0fsyInsVIeJ7EgFS32VPGEf7chOWjPWgPbcT8CJrUCXocEfV18PvPEG237oISBZ4QmmTqiK7HTu3BnPPfccDh48qGnngwcPxqpVq3DgwAEAwI4dO/Ddd99hxIgR4mM8Hg8mTJiAxx57DD179mz0HBs2bIDD4RCFDgAMHToUVqsVP/zwQ9D9Op1OVFRU+F1iGu4fUJ3GihAS5ZVYWsrOATYYT61vJ5piR20lFqfbKFZBce4gULxT3XOEIzCykyFJe8hFr8iOojSWhtSZXNOukWXngLI5UWoaCnKMMClzsaPUrwNoKy4IR7QiOzHn2eGRnTCenXDfUXuyL+os17cTTYMyR04qy+UCHn+cXeJtXMSkSZOwfPlyXHjhhRgwYADeeOMNFBcrH8j45JNPYvz48ejWrRuSkpLQr18/TJkyBUVFReJjZs6cCbvdjgcffDDocxQXF6NVK//8tN1uR25ubsg1zZgxAzk5OeKlXTuDfjj1QstcLCB8GsvjBs4ysak5sgOoHxmhZFQER7XYUdljh5OaDXQdzrZ3faTuOULhrPT9P/EDIo/s1JyTH9I2Q+yoTWMB8nvtGB3ZAeS3UFAb2QGM6aJcrbISC/AXeHr6iAwXOw7//RiJKHbkeHa837twHZTFyE4Icap08nm0DcpAXFVkqW4quGnTJvz4448YOXIk3nrrLbRr1w7XXnutX5VWJD788EMsXLgQixYtwtatW/H+++/jlVdewfvvvw8A2LJlC9544w0sWLAAFh1TBVOnTkV5ebl4OX78uG7PbQhqJ55zxDLGIAeu80fY9HB7qk88aEHtGaLcIaBS+HrLjsqbvsvhBmUtc5V638yud3+ibN+R4FGdlGzf/1t6c1alBkHeJGVAW0oJkAhkBZ4dNd2TObIjOwaWnXPk9tpR69kBDIrsqKzEAnxiR43fLhzRiuw01Om77kDcDb7WGEoiOw21oU8YwhmUAeUjI6KdxgKANt6Myokt+opkA9A0G6tr1654/vnnceDAAXz77bc4c+YMbr/9dtl//9hjj4nRnd69e2PChAl4+OGHMWPGDADAt99+i5KSErRv3x52ux12ux1Hjx7FH//4RxQWFgIA8vPzUVLib2psaGhAaWkp8vPzg+43JSUF2dnZfpeYRstcLCD8l0ZaiWXVwSmvtrGgmMZSMMU6px0zGTfUAVUKIotiZEdlGgsAOv+GeSMqTgDHNqh/nkAC/ToA+3/hP55yK7J067NTJf9HTE33ZI7cLtE8jWVEQ0GO4shOjKSxtER2kjO9ghr6prJEsePQ7zmlJGf51m2kSbnyFDOtW5Pkvb/JGYDN23MulEk51Fwsjmg/kDu6xASxk9+LvSc159Q3eI0Smqeeb9y4EVOmTMGNN96IAwcO4Le//a3sv62pqYHV6r8Em80Gj/dMecKECdi5cye2b98uXgoKCvDYY4/hiy++AAAMGjQIZWVl2LLFlzP8+uuv4fF4MHDgQK0vLzZw6pXGCpL71WMmlhQ1w0A97sa9ZeRgs/vSGUq+aGrnYklJSgV6XM+29UxlVQQRO4CyiixB0C+NJbiBBqe8vxGjSSrO4uV2FY5GGktuN+F6DQcXI7ooi54dFWJHiVdJCVyAqG2BEAmr1WdSNtK3I60Wtco4bFoskU3KoeZicZT2ujJD7NhTfAUFakf3RAlV1VgHDhzAwoULsXjxYhw+fBhDhgzBzJkzcdNNNyEzU35I9/rrr8f06dPRvn179OzZE9u2bcNrr72GO+64AwDQvHlzNG/u/0FISkpCfn4+LrzwQgBA9+7dMXz4cNx1112YN28eXC4XJk+ejPHjxydGJRbg+1KoDQWHS2OV6Cx21ER2qkq8DQVtjQ/ykWhWyITO+SNAh8Hy/karQZnT62Zg2z+AvZ8CI2YxU6FWAkdFcDJbAach7+BYX+0rQVabxpJGveqr5VX3aEpjyRBzDfW+NF+OgZEd8aAfKbKjodTXkMiOyrlYnNQcFokxJLJjUBoLYFGj2lJjfTtK/Dqc9OZMJAUzKddXSwb1RorsyBQ7aua06YH4mx/bve1UiZ1u3bphwIABmDRpEsaPH4+8PBkdb4Mwd+5cTJs2Dffffz9KSkpQUFCAe+65B88884yi51m4cCEmT56Ma665BlarFWPHjsWcOXNUrSkmKdN4NssP6u56dsCQHpT1GAAqRY1nh581ZeUrT6WpMSnrkcYCgI5XsJB2dQnw8zdA12Hang9o3FCQo2QYKI/q2FPVm7Btdvb3DXVej5OMCJEmg7KM11fxCwCBdSwO5XPQg6galGMksgMYMww0KmInCr12xIaCCtLs4SI7PLVlSw4tTpSOjNCaAVCLmmIGE1Aldvbv348uXcL0wpBJVlYWZs+e3agjcjiOHDnS6Lbc3FwsWrRI83piEo9H3VmFFGlpbH0VYPcejNwuVj4N6B/ZUZLGUlN2zlEjdvRIYwFMmPUaC/zwDktl6SJ2QkV2FEwG1zoElJOUzsSO3B+xGp08O4IQfN1lkgOOEd14OfFqUFbbPZljSBorCmInGr12lJSdc8I1FhTNyUHmYnFEr2UMe3aAyKIsNRX45hvftkmo8uzoIXQImVSfYW30YVEnBgDfWTrg/4Es/ZlFe5Iy9KtuSVWRxlJTds5RFdnxHrz1mNXDq7J+XK7PmU1gjx2OkoOj1kosTqSZalIEwRfZ0ZLGCtdVOBrmZEBBZEeLZ8f7/1lzjp10aEUQtPXZAZTNBZNLNCM7Rnp21IidsJEdGZ46JekhQTBP7ETyFtlswFVXsUs8jIvIzc3F2bPsP61Zs2bIzc0NeSF0hIdPs1pr84TwUKP0i8P9Oi0vlGe6k0PUIzsqRkaonXgejDaXMMHlqgH2f679+UJFdtSksdSakzmROm9LcVb6plurEVn2FN/BNlT0KhrmZEB+KlZLZCctl3nUAHnRukg4K7wnRdAQ2XGwa70iO65aFhkEjPfsALHp2QGCe3YilZ0DytJYDXW+75+atKoWlJbIm4TsNNbrr7+OrKwscVvPvjdEGMSzWY0/8MmZ7CAo/eKc4WMiemh7bilqmgrqkcaqLmERCDkCRs/IjsXCxkes/TOblcUjPWoQhNCeHbVpLC2IYkdGrx3RJ5TGRi6oITOPHWirTjMBHkg0euwACgzKGjw7ViuLZlWeYq9XSRViMHiPneRM9elZvdNYXHxYbMZGG+LZsxPKnAwoExFil2WLdi+iUiJ5dlwu4K9/Zdt33w0kaRg2rQHZYmfixIni9m233WbEWohgKJm0G45g3Tj5mAitA0D99qPGoKyioSAnzcHOSOvKgPNHgTwZwo1/KfWI7ACsKmvtn4GfVrIUktr0Ue1539l5I7GjJI2ll9jhvXZkpLG0mJM5Ga1YN+9Q0SsxshMjaSwtkR1AInZ0MCmLPXZUprAA48SOURPPOUZ7duoqfO+JklR7OM+O6K+SEdmRMy5CmsLSK0ovFzECFWKd9fXA5Mls+7bbTBM7qt4Vm83WqJEfAJw7dw42E3NyCYnWSixO0DSWTgNApaiJ7IieHQVnTVKU+nb0TGMBTCzm9WZh5L3/Uv88PKqTlsvSOlJ4aqKmNLLHQzexoyCMXuM90GgRO5EqlIyei8WRa1DW0lQQADK9glYPk7LWSizAWLFjJEZ7dnjkOdWh7P86XGRHzneUi+5QIkKKGXOxOEq8fSaiSuwIITqqOp1OJJs00TRh0etsNvDA1eBk02oB/crOAeVNBdU2FJSiVOxonY0VDHF8xMfqnyOUXwdgIsJiAyCE7sjK0TKQU4qSklIt5mROuF47HrfvoGO0ZydFxkFfELT3NdFzPpbWHjuA/sNAoyZ2HP770xu11bA8ssMLBqSEm3jOUZPGMlPsJIpnB4DYu8ZiseDdd9/1ayDodruxdu1adOumY0qEkPgUNIqdwMaC535i3XFTsrX7Bfz2I0kBhCohllJ1mq3DYvOlapSSy03KP8t7vEvnNBbAStC/ehY48h2LVKmpLAvl1wFYmXtGC/Z+VZcA2UEEEadWhygL4Ht/XAo8O0ZFdipPsciZ1R5cDOoJj3DUVzKRFaz3U301AO9Jn+o0lvfzXhlrkR2Fo15CEe3IjlGeHTV+HcDXGdlZwU4updFaael5KNSmsaJNIvbZef311wGwyM68efP8UlbJyckoLCzEvHnz9F1hU0evCpTkgC+OtHOynvl0fnbIBwpGMktyv05Wa/WzuXIvYNelh+Q93ojIjqMd0H4Qm5O15xNg8APKnyNcZAdgqayq0z4zaij0SmMlKajG0tI9mROu4oyL/uw2+sxwC4d04K6zMvhcJ/49sli1GbIBnSI7GuZiceI1jWW0Z0dN2TnAIoQWGzuZqznnf1KpxKAstxoSMEfsRPLsxAiKxM7hw6y89+qrr8Ynn3yCZs0M/hA3dWrLfLlYtX4WTuAXRyw71zkSxwcKCh72oxlJ7Ig/JCp7CAFAbid2fU6u2DEgsgOwVNaxDawqS5XYCRPZASQjIyIcHHWvxoqSQTncwT9a5mSAnYHbUphZ3FkRXOyI5uQs9ScLenZR1jLxnBOvYkfq2fF49DfoqhU7Viv7DlaXMHEjFTv8OxrWoKxgXEQspLFiPLKj6lPxzTffkNCJBtyQmd5c+4E5MI1lRNk5wH74lUzr1VKJxWnuFTvlx9k4jEgYkcYCgB5j2Jncqe3A2YPK/z7YxHMpcuZH6TEElKOo9FxD92ROuPL6aJmTOZG8Z+LBRUNPE4rs6AMXo4LHmOiCWrEDBDcpu2p9YjmsQVlSQesdjh2SWBA7Me7ZUSV2xo4di5kzZza6fdasWYqmnhMR0KvsHGhcjSWmsQzwWKUoyP1r6bHDycxjXzjBI8+kbEQaC2A/bJ2GsO1d/1T+9xHTWF4xEC6N5azQ1txPipKmgroYlPnBv6Txj7te/abkEqmFgtayc0DnyI6Onh23E3DVaV9TtMROUpqvQ7wRvh0tv8PBGgvyFJY1yfeeB0P8bAm+E7RQiGInynOxgMgR4JQU4LPP2CUlJfhjooAqsbN27VqMHDmy0e0jRozA2rVrNS+K8KJX2Tngm49VX8nOLLiZV89KLI44MkLGGWKFxrJzgEWTRJNyhFSW28VGEgDaZ2MFo7dX7O/+J4uyKCHUqAiOnEgAj+okZaj3knCUpLH0MChzMSe4feKJE800FhC5hYKWhoIc/v/pqtZ+VqxHNVZyFgBvSk6P6E60xI50H3r7djweX/RZTao9WGSHb0eaXZeU5uuyHenzwcWOFvGtFh5NclUHj0DZ7cCoUexiVzWOUxdUiZ2qqqqgJeZJSUmoqNDJyU9Izih0+IGXprHOHgAgsLNwLWeCIfeloPyc99jRWhEm17cjPXAb0Wm020h2lnnuJ5bOkovH4xM7oSqt5KSx9EgncRSJHX5g0yB2bEm+vw+MdkSrezInUhpLj8hOSqbvM6glleWs8lXMafk+W636Tj6PptgRR12U6fu8NWe9kVKLumrRYI0FeZQnkjC1WOSPjIiFaiwgcgTKRFSJnd69e2Pp0qWNbl+yZAl69NDZA9KU0TN0L01jSZsJGtHZVMkwUDGNpdGAzX07kSI7/MBtTdI2aywUKVnAhSPY9u5P5P9dzVkW0YAltO9CThpLjLDocIARS8+VGJQ17jdY7xlB8Pkmop3GChnZ0engokcqi4tfe5r2M3s1TUFDwVNK8RzZ4VGdzFZMjCslXGQnQ8YJCY/IRyo/N1Ps2FPDR6BcLmDBAnZx6TD0ViWqYkrTpk3DTTfdhEOHDmHIEOZRWLVqFRYvXoyPPvpI1wU2aXT17EjOEPiYCL0rsThyIzvuBkk0I0qRHbF7sgEpLE7XEcCeZcCx7+X/DffrZLZiU+qDoSSNpUtkR2aVhavO975q3W9mK2ael5qUq88CDbUALNpFsVzEyE5Z8Pu1NhTkZOYB5w9ri+xIK7G0nryk5gDl0CdCIkZ2HNqfKxJiY8EyfZ83UoVkJETPjjSyI6PsnCO3146ZHZQtFvZb4SwP/ltRXw/cfjvb/u1vY382lpTrr78en376KV566SX885//RFpaGi666CJ89dVXuPLKK/VeY9OlTEefgtTZf8aAMRFS5J4d8oaCVrv2dJoY2YnQWFAcAmrgsLy2/dn1qR2sOkxOBEnOjyp/j2q9IyOCnWnqmcbiBu5IYodHdax27QbJjCCRnXJvhDMr35hoXDAimeydOqSxAH0jO1oqsTh6VWQ11PsEYTxHdip5HzCVJ2NiZEdqUJYxF4sTbKZhMMw0KANMlDnLY7rXjmq30KhRozBq1Cg910JIqa/xhTt1T2NJGgoagdyREXo0FOTwyE75LyzSkJQa/HHRiOzkXuAbTlqyByjoF/lvIlViAczPwpuUVZ8N7u3RNbIj07MjjqfQYeCjtCKLE+2yc0CGQVmH0nNAn/JzPSqxOHqJHVF0WMJXHOmFUZ6dSD66SATz7NQoiOzILes2M40FxEUXZdXdl8rKyvDuu+/if//3f1Fays7stm7dihMnTui2uCYN9ygkZ/m+yFrgX5q6MqDsKNs2ohILkD81usL7GrWUnXMyWnj3K7C0QCiMKjuXYrEAbS5h279slvc3ciI7VqvEtxPi4GhUGitcZZke3ZM5vNeOn9iJciUWIMOgrFP1ix5iR49KLI7eYic1x/iO14Dxnh21I0qCRna4QVnGd1RMY0UaSmu22In9XjuqxM7OnTvRtWtXzJw5E3/+859RVlYGAPjkk08wdepUPdfXdCmXmJP1MBHzLwHvwZLRSt6XTQ1KIzt6zOayWHxjI8L5doxqKBgIFzsntsp7vJzIDhC+8R6gTwk4R3yPBNauIBR6dE/m8IO/tOJMr5EpSohoUNbJs5MVa5EdnaqxolmJBUTBs6MxslN7ns1ZAxRGdpSmscyO7CSY2HnkkUdw22234eDBg0hN9aULRo4cSX129ELvUtvAg7sRzQQ5cspXBQE4up5taxkVIUVORZZRoyIC4b6dE1vkPV6uETJSJMAIzw4QPjyt5z6DzceKdtk5EPmgr0fpOaBTZMcIz47Gaqyoix2jPDsyT0JCkdYMrHeR4PueiBPPZUTi5IyMcDd4DfwwT+zI9RaZiCqxs2nTJtxzzz2Nbm/Tpg2Ki4s1L4qA/h1j7Sms3Jqj95gIKaLfIYzY2bEE+PEz5kHpeaM++5VTkSUalA1MYwFAwcXs+uwBeWfJcn9Uww3LBPRNY1mtEpNymB8xsXuyDge2YIbdaDcUBCIf9PVoKgjoY1DWYy4WR+80VrTEjlGeHTH6rFLs2Oy+qBOP6MiZi8WR02dHago2PbKTYJ6dlJSUoM0DDxw4gJYtdfjCEfqWnXOkP8xGlZ0DkUvPz/4ELP8j2756qi/loxU5FVmiQdngyE5mS+/BWQBObov8eNmRHZlpLD38M4Ck106Y+Vg1BqSxas76wv5mGJQjpbF0j+wEGZEhl1isxjItslOm33M2OH1CXm1kB/A3KTc4fZ8pOSckyTJKz/l99jR1vYD0IJxnJyUF+PBDdom3cRE33HADXnjhBbi8DYIsFguOHTuGJ554AmPHjtV1gU0WPUdFcKQ/zIZGdsIcKBrqgY/vZN6Zwl8Dlz+i335lRXaiYFDmtJGZynK7fGf2ET07YdIeHo/EP6OTH0vOGZueBuX05gAsbM5ZzTl28OKfo2h6dmQPAtVaah9mRIZcxMhOUxY7Du9+y/R7Th5ttaVoex3SxoI8hWWxySs8EdNYMsSO1iijFsJ5dux21l/nt7+Nv3ERr776KqqqqtCqVSvU1tbiyiuvROfOnZGVlYXp06frvcamiZ6jIjhSsdPyQv2eNxBpZCewiufrF9gYhbRmwI1/0bdSg0d2Kk+GntQtGpSjIXZ4RVYEsVNVAkBgfWoimRbDpbGc5UwkAPpEWQBfPyI5aSw99mmz+w4OVad934P05sZH46TwPjuuaiZGA6nXKY1lS/IJUzW+HVetL40Ri9VY0Y7s1FcG//9Sg7TsXEuRiLSxoHQullXG4VeOF8Zsc7J03zHs2VEls3JycrBy5Up899132LlzJ6qqqnDxxRdj6NCheq+vaeJ2+c4q9Dyb5T/MWQXGdjXlZ8WCm6U/+EHqp6+A9XPZ9ui39DMmc9Jzff1tSn8G8ns1foxoUI7CWZBoUt7MRF+oH0z+o5qZH/kHMFwai0dYkrOYR0sPlER29IomZbRir6+qhIX9geimsADfZxhgB5NAIadXU0GARetqzjGxk9dT2d9y0WtL1qefjV6zsaLu2ZG89rpyeX6YSGgtO+dIy89Fc7LM9ckp6Y4FsRPud6KhAVi2jG3feKNp0R1Ne7388stx+eWX67UWglNxgp2h21L0ycNz+AfSqGaC4n4yAYuVvYa6CrbfqhJg2b3s/gF3Ad0MakjZvBNLG5UeCiF2opjGyr+IhaurTrP/01CT3UVzsoyW9OHSWHqWnXPEH7Ewnp1aHdNYAEvHlOxhnxkzUlgAi7gkpTOxXlfu/5421ANurwjTI3WQ2YqNcFFjUhZ77LTSp0WFXrOxoi12rDYWjXOWs33rIXa0lp1zpJ4dJeZkQN64CHFUhEndk4HwoszpBG65hW1XVcW+2JkzZw7uvvtupKamYs6cOWEfm5mZiZ49e2LgwIGaF9gkEUtt28oLdcqFfyCNFjsWC/vi1ZWxL6Injwmd6jNAq57AtS8at+9cr9gJ5duJZhorOR3I6wEU72Jr0kPscPFbe77xyAg9K7E4cvpn6C2ypNPduQDQM50rl5RsJnYCD/zS9yJZh7NpLeXnYo8dnQpDuNhx1cgfdRKMaIsdAEjjYqdMn+er1Duyc1bZXCwgftJY0tmLMYpssfP666+jqKgIqampeP3118M+1ul0oqSkBA8//DD+/Oc/a15kk8OoJmrtLgX2/xfoHIV0Y6pX7NRVAN+/BRxaxaoFbv4bkJRm3H4j9doRIztR8n+06e8TOz1GB3+MkjPItGbM2+NpYOJR2pDRULETIo3lbvClPPSM7ADsQK53CwYlpGYDVcWNTcpi9Utq6KGtStBSfq5nJRbgHx1wVgB2lRESU8ROM/Z50avXToX3JERt2TlHGtlRMhcLkDf1PBbEjpwSeZOR/U09fPhw0O1QrFy5Er///e9J7KjBqCZqlz0E9L8zOq59bvA8vBpYPZNtD59hbDNDQFKRFaL8PBqzsaS0uQTYMj+8SVnJZGU+MqLyFIsEmC12pH1N9DqwSU3YZvTY4YTyr+hVds7RFNnRsccOwMRbchYz+mrxvvDoSjTFjt69dvRKY/FO9TXnlHVPBvzTWKF8f7EgdhK1z44cLr/8cjz99NNGPX1iI46KMOAHPlrlidzg+c1LgMcFdL8BuOQ24/fb3DsyImRkJ0odlDm8IuvkNl/fmECUdmkV52MFmJSN9Oy4QvyIcXNyao4+UQ7A/+BvRvdkTqgWCno1FORoETt6R3YASUVWmbq/dzf4Gorq+VmMhN5dlPVKY/lFdhTMxQJ8glpwAw11wR8TE2InQWdjAcCqVatw3XXXoVOnTujUqROuu+46fPXVV+L9aWlpeOihh3RZZJPDzB94veBnxYIHyG4L3DBHHwNlJHhkp+p08NCv2EE5SmKn5YXsh8BVDZzZH/wxSiI7QOiDoyFiRzIMNBh6m5MBX5Si7KjvTNiUNFaILsriEFCdDi5a0lh6zsXiaC0/l/6dHkOM5aJnrx1B0D7xnONXjeU9QZEb2ZFGD0MJCdGgHANiJ4bTWKrEzttvv43hw4cjKysLDz30EB566CFkZ2dj5MiReOutt/ReY9PDTJ+CXvCzYosVGPtudJuL8TROsE7K0U5jWW1AQT+2fSLEBHSlkR2pgVeK3iXggGRcRKjIjgECi4u580fYdXJWdA+anFBdlPUaAsrJ9IrcShWjdvSceM7ROgyUR1ZSsvWL9slBz8hOXbnvt0JzZMf7fRTcvt8kuf9fVqtESITw7ejV4FILUs9OYG+1GEHVJ/Gll17C66+/jsmTJ4u3Pfjgg7jsssvw0ksvYdKkSbotsMnh8bAyZSC+Izut+wA7lwJDngY6DIruvnM7sYPwuUNsHVKibVAGWCrryLfMpHzxrf73+bWklxnZCZnGMkDsRMrF69k9mROYknG0i05UMJBQB329GgpyuHitK2OfByU9kgyN7KgsPxfNyQ5dliMbPT07XHimOrQXVNhTmBBxVvgilUq8UMmZ7DMXyqSst/hWA/+d8DQA7nr/z3ByMjB/vm/bJFRFdsrKyjB8+PBGt1977bUoL9fYjKqpU3WafVgsVn/zabzxq/uBR34Efv3H6O87XEVWNEvPOeE6KfMfVSUt6SOmsfQUOzLTWHpGdtJzWX8ijhnmZMBnsg8UO3o2FAS8FXbeFgJKU1mGenY0RnaiaU6W7k+PyI5efh1O4PdDbhoLiDz5nIugaDRKDUW4dFtSEnDbbeySZNLsLmiYjbWMd0SU8K9//QvXXXed5kU1aXj1SVaBeUPd9MBi0Z7rVkuoiqyGenbmAUSnqSCHi52SvY1Fg7THjtzohZjGCmVQNiGyo+c+rTb/M1+zIpyhGuzpPYvIYvEfCCqXBqdPkMSSZ8c0sePw7r9M+3PpVXbOkYobi1XZexOprDsWDMpWG2stAsSsb0dRU0FOjx49MH36dKxevRqDBrEUxffff49169bhj3804Uw+kUgEv47ZhKrI8msGF8U0Vk4bdoZYeQo4tdM/rafUrwNI0liSA6PH7TvI6Cp2Inh2jDAoA+zgzSNXZn0XQg0D1dugDLDXW/GLsoosLnatdn09TXErdvSM7PDvpU7Rdal4T8tV1iw20uTzWBA7ABNlDbWNxU5DA/DFF2x72LDY76Ac2EiwWbNm2Lt3L/bu3Sve5nA48Le//Y1KzrVgZl+RRCHU9HNuOLQlRz9q1uYS4MfPmEnZT+yoqPgIlsaqLQPgNQbqeZCJVGUhRnZ0PrBJ0zJmRXYiGpR1TBuoKT/nYjejpb6d1uNV7Ojq2VHQ1VwO0siO0t5FkSafx4JBGWAnkNVnGp8YOZ0Az/jEw7iIYI0Ez55lZqsWLXSYQ0IwEqHs3Gy4Z6fmLPvB5j/e0ZyLFYgodgJ8O2oiO36GVm9Lf57CSsnRV8iJfXZCzMYywqAM+A7+gHnCP5JBWU+PhJrycyMqsQDtw0BjIbITbvCuHPQqO+dI++oo8esA4UdGCEJslJ4D8ro9m4ji04GysjJMmjQJLVq0QF5eHvLy8tCiRQtMnjwZZWVlBiyxiWHUqIimREqWLzIgje64otxQUEook7LSHjsAO4O1es9T+AHPiBJwILJnxwiDMuDfEdjsyE6jcRExFtnR068DaB8GarZnx10PuGq1PZdeE885WiI74Rr21VdDjOiaLnZiu4uyonhSaWkpBg0ahBMnTqCoqAjdu7OBknv37sWCBQuwatUqrF+/Hs2aRflDnkhQZEcfmndilSqlPwNtLma3mRnZKegHwMK6Y1eV+A5QaiI7VisTc5Un2cExp40x5mTAV6LvqmG+IKvN/34jDMqA7+BvT9X/YC6XUAd9ozw7gELPjgGVWED8prGSM31z42rPa6u41GtUBCdDSxorTCqZR1EsNmNnDsohxudjKRI7L7zwApKTk3Ho0CHk5eU1uu/aa6/FCy+8EHFQKBECQSDPjl7kdgKObQiI7PCGgiZEdlKzWTflMz8CJ7YCF3pbN6iJ7AAs8lF5MkhkR2fRIX2vXDX+Z4+CYJxBmR/Ac9qa02MH8KWxGur8+98Y0ddETTWW3nOxOPEqdiwWFvWsOctSvDlt1D2Px+0TnUZEdpSmscT0UJBIm9ScbNb3hBPjkR1FaaxPP/0Ur7zySiOhAwD5+fmYNWtW0JJ0Qia1532qOKetuWuJd4JVZIleCxPEDuBLZUk7Kas9gwxMe9QaFGFJSgPg/RGtD/DtOCt8pfx6p7EKLwOy2wC9btb3eZUgNXxKU1l6NxUE1KWxKLLTGD0qsqpKWLdji02/qKLUs6PaoBwmsmO2ORlILM/OqVOn0LNnz5D39+rVC8XFKlqeEwxedp7R0vyQZLwTrCLLzDQW4EuncZOys8p3tqY0spMRYGg1yrNjsYSuyOIpLHua/p/X7ALg4T3A1VP1fV4lWG0Sv4RE7OjdVBDwNyjLbbdvmGfHwa7rq9hQT6WYKnYc3jWUqX8OnlrOzGuctlWLX2RH4QlJ2DRWjJiTgZiP7ChKY7Vo0QJHjhxB27bBow6HDx9Gbm4Up9wmGuXk19GNYF2Uoz0XK5A2/dn1iS3sgMbP4pMzlf9Y8dSFmMYyKLIDsPervrLxj5hR5mSO2WF5gEU56qv8oxxG9DXhgqWhlj1/qowzdaOqsaT7dlYo+//1eHyl3/Ea2dG77BzQ5tkJ12en3oCUqlpCibLkZODNN33bJqEosjNs2DA89dRTqK+vb3Sf0+nEtGnTgo6RIGRSRpVYupHrTWPVnvcJgWhPPA8krycz3NaVs4iTlh/VwLSHUZ4dIPQZWw1vYpjAJziBvXYEwZjS8+QMXxpAbirLqMiOLckX/VSaynJWAIKHbZsxvFWPXjv8e6nnuJ7kDN/vjtK0o6w0lomjIjji70SQcRGTJrGLieMiFBuU+/fvjy5dumDSpEno1q0bBEHAvn378Pbbb8PpdOLvf/+7UWtNfCiyox/JGb6uxaU/swOymQZlgB1EWvcBjv/Aojs8RK7GBBk4DNSoNBYg6bUTIrKjtzk5lgjsouxX6qvzASYrDzhXycROiy7hH+t2+d5/vT07AItouWqUix0eUUlKB5JS9V9XJPSI7FQYENkBgKHPAmcPskIFJYh9doJEdmKlezIg8ezEZjWWoshO27ZtsWHDBvTo0QNTp07FmDFjcOONN+Kpp55Cjx49sG7dOrRrJ/9A7Xa7MW3aNHTs2BFpaWno1KkTXnzxRQjenLXL5cITTzyB3r17IyMjAwUFBbj11ltx8uRJv+cpLS1FUVERsrOz4XA4cOedd6Kqytw3XPB4sPHj17F3+mUoOdG4IWNQxFERHYxbWFMi0LfDIxNmpbEAf5OypsgOn48V6NkxIrITYhiokQIrVghssMfPWi1W/b1fSkzK1Wd96zDi/VdrUjbTrwPo69nRqxKLM/AeYNQrytOz4frsxINnx+0GVq9mF7c72qsSUdy3uWPHjvj8889x/vx5HDx4EADQuXNnVV6dmTNn4p133sH777+Pnj17YvPmzbj99tuRk5ODBx98EDU1Ndi6dSumTZuGPn364Pz583jooYdwww03YPNmX0VLUVERTp06hZUrV8LlcuH222/H3XffjUWLFilek15YrFZk/fgRurv2YMOqv6HVrS9G/iNqKKgvzS8Ajn7n8+2YncYCJGJni2/SdaynsZJCzMcyqntyLJEakMaSmpP19hQp6aL8y0Z27eign4lWimqxwz8TZokdPT07Jg0yDiQljGcnlqqxQnl26uqAq69m21VVQIY5v7+qh1Q0a9YMl156qaadr1+/HqNHj8aoUaMAAIWFhVi8eDE2bmRf5JycHKxcudLvb958801ceumlOHbsGNq3b499+/ZhxYoV2LRpE/r3ZwbQuXPnYuTIkXjllVdQUKBj3lUhld1uAXY9i4Ijn0DwPA9LpPk11FBQX8TIzk/s2myDMuATO8W7fD+maoYN8jRWXTkTIfygFE3PjtEG5VhAPOjzNBZvKGiAR0JJZGf3J+y6+/X6rwPQIHbK2LVZYkcPz47eE8+1wqM2bidLX0rHwcRUGiuEZydG0HF6nHIGDx6MVatW4cCBAwCAHTt24LvvvsOIESNC/k15eTksFgscDgcAYMOGDXA4HKLQAYChQ4fCarXihx9+CPocTqcTFRUVfhcj6D70VtQKyejg+QUHtq4O/+D6at/BgyI7+tA8RBrLrNJzAGhWyCIh7nrg5zXsNjWRnbRmvsjQmf3eGy3GmEJDprEMrACLFQINykaMiuDIjezUVwMHvFOke96o/zoAHdJYDl2XIxtdIzvmnSj7Ie3UHRjdiSmxk0CeHb158sknMX78eHTr1g1JSUno168fpkyZgqKioqCPr6urwxNPPIHf/e53yM5mP0LFxcVo1crfoGe325Gbmxuy58+MGTOQk5MjXpT4jJSQlZOLPTlXAgDKNrwf/sE8qpOS4/uhIbTBIzulP7MqGrMNygBLffDoDo8SqAmXWyy+g+OZH9l1mgOwGTBROGQ1ljd11hTSWIGeHSMOLnIjOwe+YCXqzQq9Y0gMoKl6dly1vqiQ3gZltdjsrIoTaBw1iSmxE9t9dkwVOx9++CEWLlyIRYsWYevWrXj//ffxyiuv4P33GwsDl8uFW265BYIg4J133tG036lTp6K8vFy8HD9+XNPzhSN1wAQAQPdzX6KuNsyHgPw6+pPbkV07K5ihsz4GxA4AtO3v/2+1P6o8lVWyj10bFWFJDuHZaQpprECDshENBTlyxc4ebwqr543G9SIKjGjJxXSxozGyw6M6SemxddIZqtdOLImdGJ+NZarYeeyxx8ToTu/evTFhwgQ8/PDDmDFjht/juNA5evQoVq5cKUZ1ADamoqTEP+zb0NCA0tJS5OcHP4ikpKQgOzvb72IUPQZfh2K0QDZqsOfrxaEfyCuxyK+jH0lpQLa3AWbpodhIYwG+yA5HrdjhB0ce2TFM7ITqoMwPbAksdgKHgRpZ/SInjeWsBA56fYxGpbCA+I3siJ6dctbgUCnSsvNYaGrJCdVrJ6aqsby/E3xocIxhqtipqamBNcC0a7PZ4JF8SLnQOXjwIL766is0b+7/gz5o0CCUlZVhy5Yt4m1ff/01PB4PBg4caOwLkIHVZsPhtjcAAJJ2LQn9QLHsnMSOrvAZWecO+frEmB3ZkYqdVIf6UQuZAZEdo0SH2GcnYDaWGNkx6cAWDRoZlKMQ2ak+E/pgsX8FG0ya2wnIv0j/NXDiVeyIXiEBcKqY7RVrfh2OGDUJFdmJgWos6XciBlNZpoqd66+/HtOnT8fy5ctx5MgRLFu2DK+99hpuvJGdsbhcLtx8883YvHkzFi5cCLfbjeLiYhQXF4tdnLt3747hw4fjrrvuwsaNG7Fu3TpMnjwZ48ePN7USS0q7q+4EAPSs3Ry65w41FDQG0bdzyPzZWJz0XKCZN8WmpbyVN5Pjnx3DIjtBcvGuWp/4IYOyPqS3AGBhHYi5HyqQPd5By0amsID4FTv2FN/3W41vx4hREXoQashmLKWx7ClseCrg/1uRlATMmsUuJnZQNlXszJ07FzfffDPuv/9+dO/eHY8++ijuuecevPgi60lz4sQJ/Pvf/8Yvv/yCvn37onXr1uJl/fr14vMsXLgQ3bp1wzXXXIORI0fi8ssvx1//+lezXlYj2nbuhX1JPWGzCDi06m/BH0SjIoxBWpEVCwZlDo/uaPlR5ZEAjlHemaQgJaW8Estqj42zSqMIZVA2IrJjs/vmJgXz7dRVAD95U1i9btJ//1LiVexI963GtxNrZeeckGksAz+PSrFYgvt2kpOBxx5jFxNnYxlQuiGfrKwszJ49G7Nnzw56f2FhodhNORy5ubmmNhCUQ8SeO6JBuX30F5fI+EV2YiSNBQAdrwB2/1N563gpPI3FiWZkR9o8Lpa8DXojGpQrWEWf0bOIMvNYGqvqNIDe/vft/y9rWdCiK9CqhzH750i9L0qIBbGT6gAqTqjrtRPzaSyJiGhwst47QGxEdgAmuurKY9KkbGpkpykRtudOQz1Q6S2TzyGxoys8snPmACB4fRBmp7EAoG8RUPQxMORp9c8ROBPJcLEj8ew0he7JgC/C4XExr4wY2THo4MJNypVBIjtiCusm4wVm4EwwOQhCbIgdLZGdmE1jBRkZId2OJbED+K/N7QY2bWIXE8dFkNiJEmF77lT8AkAA7Gm+MDahD80K2fwgfgYExEZkx2YHugzV9iPVKI1ldDVWkMhOIpedA97X7hUWdRXGenYAINN7kA1MY9WWAT+tYts9xxizbynSKjS5VU31VYCngW2bKnYc7FqLZ0fPied6IKaxJOKTbydlGDMyRA3BosB1dcCll7JLXZ056wKJnagSsueOOCaibWKnBMzAnsLeV44tJXZ+GLQStTQW77MTxLOTyOZkALBa/XvtGNlUEAhdfv7jchZdatkdaNXdmH1LEX1YgvxeOzySYks2N3oqih2FkR1BMG7iuVbEyefSyE4MmZM5Mdxrh8ROFAnZc4caChoL9+0A5s7F0ptUBzuwcKLp2amReHYSHekwUKMNoaEaC/IUltHGZE5Sqq9rr1zfjjSFZeZJm9r5WLXnfRHgWBkCygmaxopBsROqJ1cMQGInioTsuUMDQI2luVTsxEDVgl5YLP6+HaNSSvw9czsBtzdN0VTSWIB/ZMfoJm7BIjs1pcDP37BtIxsJBqK0IisW/DrS/SuN7PAUVnpzFhGOJYJFTGJZ7MTgfCwSO1HGv+fOEXYjRXaMRRrZiQVzsp7wVJbFalx7e6nHiTdmbCoGZcDfv2Jk6TkQPLLz42fMC5PXG2jRxZj9BkO12DH5M6HWsyOmsGIsqgMEHxcRk2IndudjkdiJMv49d95jN4qjIqgSyxCaJ2gaC/AdHNOaGedFsiU3bhbWlCI70sokww3KXOxIIjtiFdYYY/YZisBRGZGIuchOmbK/q4xhsSM2t5SKnRgaFcERxU5l+MeZAIkdE6jsdgsAeHvueGhUhNH4RXZioBJLT/gwUCONwhZL44qspmJQBnwHmpqzPk+HYZEdbxrLWc66VFefBX5ew26LZgoLaDwENRKxInbUenZitewciJDGiqGmnqKROvYiO6Y2FWyqdB96K2p3TkcHzy/Yv/VrXFhxgt1Bnh1jaNaBRSYEdwJGdrwHR6NFR3IGOwCLYsc7zsDslEU04JEdnuYAjDubTs1hFYNuJ4vuHPqafW5b9/GPUEYD1WkshyHLkY1Wz06slZ0D8ZfGknp2kpKAZ5/1bZsEiR0TyMrJxeacK9G/YiU8a19j+XirPTbDp4mALYl1pj5/OPE8O9lt2HVmq/CP00pgLr4ppbH4mXPFSXZtS2GfKSOwWFgqq/wYEzvSWVjRJm4Nyg52rdqzE4uRnSDjIuoNTqmqIVhPruRk4LnnTFmOFEpjmYTYc6diHbshu4A1miOMgZ8VJ1I1FgD0GgsMfgC44nFj9yP22qlmFVn8ANgkIjveg37FL+za6IMLF66ndwFHvmXbcSF2yti16WLHu39XNetOL5dKr5iNtVERgE/suKp9TR5jMrITYjp7DEBixyR4zx0RMicbS/PO7DqWzoL0IM0BXPsnIL+XsfuR9s+QeiHMPrBFg9SAyI7RBxduUt70HpuAXnAx6wQebeI1spOSA1/X6zL5f8dH9sTaEFDA/ySNR3Ri0aCcEiSy4/EAe/awi9xu3AZAYsckpD13AJA52WguuQ3oOgLo+3uzVxKf8DSWq8ZnTk7NaRrRSJ7Gqj7Dro2ai8URIzu72XW0GgkGEq9ixyppwyDXt+N2+SrgYtFOYE9hVgfAF9GJRYNyMM9ObS3Qqxe71Naasy6QZ8dU2l11J/CPvwEAVp9Owep/7zF5RQlO5jRgEwDQ+xwMm9WCcQPaoWtekIO51LPTlMzJQOP+RYansQJmnvUYY+z+QhGvYgdgEc+6Mvm+naoSAAITFOkxOJ/QYmERnNrzkshOLKexqBqLkNC2cy/sTeqNHq5d+ORYJv595IjZSyKaONuPl+Hj+wY3voOX7NdXNS1zMtBY7Bjt+5Kazdteal7UN67FTjPg/BH5kR1eiZWZzyJDsUiyV+w440HsxJ5nh8SOyWT/YT6+WvsxOrS8AZMtBjWFI4gIuAUB76w+hC1Hz+NUeS1a56T5P8AvstOEuicDjdMERkd2pNVAZhiTOUrEjqsWaPBOtI4FsaO0145Ydh6DKSxOSoCQiEWxI/XsCEJMDbYmsWMybTt0QdsJT2Ko2QshmjybDpdi89HzWLG7GLdf1tH/TlHs1DTByE6A2DE8siNJY/UYbey+wqFE7JzZz66TM2Pj4Ku0104sl51zAnvtxKLY4b8TngagwckGysYIMRqvIwgi2ozszc5qP99V3PjOZEkaqyl1TwaCRHYMPrjkXwR0GgIMmgzktDF2X+GQjouIVEXDB5UWXh4bZ/NKe+3Ectk5R9prx+OW9NmJJYOytGostnw7JHYIggAADO/Fzmo3HS1FSUWd/51N2aCcnOGbDQYYH9mxJwMTlgHDphu7n0hwsSN4/McUBOOQV+xccLWxa5KL0shOLJedc6QjI6T/H7EU2bHaALs3BR5jvh0SOwRBAAAKHGno194BQQBW7AmI7kjFDj+ApMeANyMaWCz+qaxE69UUCnsqGwILhB8G6qoFjn3PtjvFiNhR6tnhPZRiseycw1seOCt8KSxbMitLjyUCe+0kJQGPPsouJo6LILFDEITIKG8q67+7TvnfEazPTlOJ7AD+qYJYOpM2EotF3jDQo+vZLK+sAqBF1+isLRJqIzuxLHakaSwudmKxI3xgr53kZODPf2aX5GTTlkVihyAIEZ7K2ni4FGcqnb47pB2Ua5uYZwfwj+wY3VQwlpBjUuZ+nU5Xx4ZfB1Dh2eEG5VgWO5LvIBcSsSi8+fcjUuozypDYIQhCpG2zdPRpmwOPAHwhTWUlSWZj1TSxaizAO4KAb8fg2bRRyBI7q9l1rPh1AGWRHWeVL00Xy54dsRpLst5YMidzpMUMADO3HznCLjQugiCIWGEEr8raLUllSUPTtU0wjeUX2SGxI1J1BijexbYvuCoqS5IF9+zIETs8hRUrZfOh4CJb6tmJxfUGenZqa4GOHdnFxHERJHYIgvBjZC8mdr7/uRTnqrypLH6Ary5hPTSAphXZSaXITlAOr2HXeb2BzJbRWZMceGSnrow1twtHZRyYkwFfFKe+KrbFTrD5WDEAiR2CIPxo3zwdvdpkw+0R8OXe0+zGZG8aiwsdexqQlBb8CRKRlKbu2QlRjcVLzjtdFZXlyIZ7djwNQNXp8I+Nh7JzICCNFctihzw7BEHECSN6BVRl8bM1TlMyJwNNs/Qc8L3uYCXcguAzJ8eSXwdgn9eCfmx79Yzwj42HsnMgwKAcy2InwLMTI5DYIQiiEbyb8vpD53C+ut43CJTTVHrscFLIs9OIsweBihOALQXoEGR4rNkMe4ldb3kfOLUj9OPioewc8B8XIRqUY1DsBHp2YgQSOwRBNKJjiwx0b81SWSv3nWZdfW2SHhlNyZwMSDw7lsZRrkRGbM4XROzwqE77X8VmSrPDYKDXzQAE4L+Ph/buxJtnxy+NFcPVWOTZIQgiHhjp7bkjprJ4+TnQtMzJgC+dk5IVO71kokG4yM4hSX+dWOU3L7DP7fHvgd0fB39MRRxMPAf8p57HcmSHPDsEQcQTvAR93U9nUV7j8k/fNLXIDj+DbkopLCC02HG7gCPfse1Y8+tIyWkD/PoRtv3ltOCpFTGNFcNDQAHfZ0/wAFUlbDsmxU6AZ8duB+6/n13sdtOWRWKHIIigdG6ViQvzsuByC/hq32n/9E1TMyi37AZYk4C8nmavJLqEEju/bGYRhvTmbEp7LDPoAcDRgaWrvn3N/z6PR9I9OT/6a1NCcgYAb1SRrzkWzfKBnp2UFOCtt9glxbw5XiR2CIIIyYjeklSWn9hpYpGdnDbAI/uA8YvMXkl04WIncBAo9+t0vBKwxvhhJCnVZ1ZePxcoPey7r7YU8LjYdqyLHYvFF8nhqbdYjuyQZ4cgiHiBV2V9e/AsGuwSE2pTS2MBrGme3bxBhqYgHQQqNfjGg19HSrdRrMOz2wl8+bTvdl52ntESsJk3kVs2PJXV4O1EHJMG5QDPjiAAZ86wS6QGjwZCYocgiJB0zctC51aZqHd7cNYpORg0tchOU4VHdjwNbOI9wITPiS1sO5b9OlIsFmD4TMBiA378DDj0Nbs9XsrOOYFpq1iO7HCxU1MDtGrFLjU1pi2LxA5BEGHhVVnHqiRVSE0xstMUSc5gAgHw+XYOfwsIbqB5Z8DRzry1KaVVN+DSu9n2508yk3W8lJ1zAsVNLIod6rNDEEQ8wquyjlRKxA5FdpoGFktjk3Ksdk2Ww1VPMlP12f3Axv+Ln7JzTmA1YCyKHb5GVw3gcZu7FgkkdgiCCEu3/Cxc0CIDVR6JX4XETtMhUOzEm19HSpoDuOYZtr36Zd/E9lgvO+f4iRtL487msYBUkMVQdIfEDkEQYbFYLBjROx/VSGU3WO2xaYwkjEE6DLTsGFB6iKW2Ci83d11q6TcBaN0HcJYD+5ez22K9EosjFRIpWbFZCWdP8aU+Y6ixYAy+UwRBxBojerVGjcDEjpDarGl1EW7qpEoqsn5ezbbb9peM0IgzrDZgxCz/27LjMLITiykswFsiH3u+HfPaGRIEETf0LMjGV+lZgAsot2ThdHGl2UsidKRD83SkJtmC3ylGdsqAo+vZdjz6daS0/xXQ+xZg14fs3/ES2UkJiOzEKsmZTBw7K4EYGZtGYocgiIhYLBZ0aZcP/AwcqEzCLbPXmr0kQkfyslPw3sQB6NUmSLSGi53aMuDwGrYdj36dQH7zPLD/czZ+wdHB7NXIIzCNFaskSyI7djswcSL7t4njIkjsEAQhi35XjsaBI4vxufUatEhuYs31EpjaejdOVzhxy1824K3fX4yru7XyfwCffH7kW6DmHGsa1+aSqK9Td7ILgHvWAA1OX6ou1pF65WJ5Tpu0105KCrBgganLAUjsEAQhk4IOXYBntuBZAM+avRhCNyrqXLj/H1vx3U9n8T8fbMYLo3uiaKAk0sEjO3zwZ+Hl8dFtWA7NO5m9AmXESxorBj07ZFAmCIJowmSnJuFvtw3AzZe0hdsj4Kllu/Hy5z/C4/G29udiR/D2TEmEFFa84pfGiuFoFF+ns5KNiKiuZhcaF0EQBEGYRbLdij/ffBEe+U1XAMC8NYfw0NLtqHO5G1ddxbs5OZ6Jl8iO1LNTUwNkZrJLUx0X4Xa7MW3aNHTs2BFpaWno1KkTXnzxRQgS9ScIAp555hm0bt0aaWlpGDp0KA4ePOj3PKWlpSgqKkJ2djYcDgfuvPNOVFXFTn0/QRBErGOxWPDgNV3w2i19kGSz4D87TmLCez+gCum+B2W3AVp0MW+RTR1pNCemxU7AfKwYwFSxM3PmTLzzzjt48803sW/fPsycOROzZs3C3LlzxcfMmjULc+bMwbx58/DDDz8gIyMDw4YNQ11dnfiYoqIi7NmzBytXrsRnn32GtWvX4u677zbjJREEQcQ1N13cFu/ffimyUu3YdOQ8nl5x3HfnBVdTjyUziZdqLNGzEztix1SD8vr16zF69GiMGjUKAFBYWIjFixdj48aNAFhUZ/bs2Xj66acxevRoAMAHH3yAvLw8fPrppxg/fjz27duHFStWYNOmTejfvz8AYO7cuRg5ciReeeUVFBTESbMogiCIGGFw5xb4+L7BuH3+JvxYZgVS2O3bk/vi1K5T5i4uwcnNSMbFHZohyRYkFhFvaSwniR0AwODBg/HXv/4VBw4cQNeuXbFjxw589913eO211wAAhw8fRnFxMYYOHSr+TU5ODgYOHIgNGzZg/Pjx2LBhAxwOhyh0AGDo0KGwWq344YcfcOONNzbar9PphNPpFP9dUVFh4KskCIKIP7rmZWHZ/YPx2N/KgTJ2251rM3AOW01dV1MgO9WOId1a4dqe+biia0tkpngP1fES2UmOvWosU8XOk08+iYqKCnTr1g02mw1utxvTp09HUVERAKC4uBgAkJeX5/d3eXl54n3FxcVo1cq/L4Tdbkdubq74mEBmzJiB559/Xu+XQxAEkVC0yk7F2/deh3V/+x1+rk1Hp/yOiLNi7bhCgICfz1TjXHU9Pt1+Ep9uP4lkmxWDOzfHb3rk4TcXtoB4tIvpaqzY8+yYKnY+/PBDLFy4EIsWLULPnj2xfft2TJkyBQUFBZjIOy4awNSpU/HII4+I/66oqEC7du0M2x9BEES8kpGahMvun4fLAEwwezFNALdHwNZj57Fy72l8uacYR87VYPX+M1i9/wyeAvBjagpS4cQ/tpfil0M/mr1cAECbZmn4w8D2sHA/F486kdhhPPbYY3jyyScxfvx4AEDv3r1x9OhRzJgxAxMnTkR+PptXcvr0abRu3Vr8u9OnT6Nv374AgPz8fJSUlPg9b0NDA0pLS8W/DyQlJQUpKSkGvCKCIAiCUI/NasGAwlwMKMzF1BHd8FNJFb7cexor957G9uNlKPFko731DN7dVoUjwiGzlyvSLT8LAwpz2T94ZMdZBdhswM03s3/bQsxfiwKmip2amhpYA0bU22w2eDweAEDHjh2Rn5+PVatWieKmoqICP/zwA+677z4AwKBBg1BWVoYtW7bgkktYC/Ovv/4aHo8HAwcOjN6LIQiCIAgdsVgs6JKXhS55WZh0dWecrqjDjnWzsOP0QQxtMdjs5QEAvtp3GkfO1eDnM1USsSPx7KSmAh99ZN4CvZgqdq6//npMnz4d7du3R8+ePbFt2za89tpruOOOOwCw/+gpU6bgT3/6E7p06YKOHTti2rRpKCgowJgxYwAA3bt3x/Dhw3HXXXdh3rx5cLlcmDx5MsaPH0+VWARBEETCkJedimtH3AQAuN7ktXDqGtw4cu4Yfjlf67uRPDv+zJ07F9OmTcP999+PkpISFBQU4J577sEzzzwjPubxxx9HdXU17r77bpSVleHyyy/HihUrkJqaKj5m4cKFmDx5Mq655hpYrVaMHTsWc+bMMeMlEQRBEESToW0z1nTyeKmkO3IMenYsgmDisIoYoaKiAjk5OSgvL0d2dgw73AmCIAgihli+8xQmLdqK/h2a4Z/3eVNrFSeB17oDFhvwx2NAllf8VFUBGRm67l/u8ZtmYxEEQRAEoYq2zdIAICCN5fXsCG6gwRnkr6IPiR2CIAiCIFTBxc7pyjo4G9zsxmRJ9CZGUlkkdgiCIAiCUEVuRjLSk20QBOBkmXdmpdUGJHkHyMZIF2USOwRBEARBqMJisUhSWRKTcoxVZJHYIQiCIAhCNb6KrCC+nfqaIH8RfUjsEARBEAShmnZBIztc7FSasKLGmNpnhyAIgiCI+IZHdvwqslK8YsddB4wcybab6rgIgiAIgiDiG+7ZOR7Ms4N6YPny6C8qAEpjEQRBEAShmna5QSI70vlYMQCJHYIgCIIgVMMjO2cqnahz8V47seXZIbFDEARBEIRqctKSkJnCXDEnyrzRHe7ZKT/PRkRkZADV5kV5SOwQBEEQBKEaaa8dcSCotM9OTQ27mAiJHYIgCIIgNNGoIov67BAEQRAEkUg0GghKnh2CIAiCIBKJRuXnKRTZIQiCIAgigWhUfk6zsQiCIAiCSCR4ZOcEj+zEmGeHOigTBEEQBKEJblA+W1WPmvoGpHOx46oCrrySbVvNi6+Q2CEIgiAIQhM5aUnITrWjoq4BJ87Xogv37KAWWL3azKUBoDQWQRAEQRA64Fd+zj07TvLsEARBEASRIPhVZCVnsRsbagGP28RVMUjsEARBEAShGb+KLLEaSwDy8oCWLU0dF0GeHYIgCIIgNONrLFgD2FMAqx2ACzh7ztyFgSI7BEEQBEHoAPfsHC+tBSwWX3QnBiCxQxAEQRCEZtrlSiI7gM+3EwOQ2CEIgiAIQjNtHEzsnK9xocrZQJEdgiAIgiASi6zUJDjSkwB4oztirx3zIbFDEARBEIQutOO9dkprKbJDEARBEETi4VeRlZwFWAB0LwT69zd1XASJHYIgCIIgdMHXWNAb2UmyAO8+AmzaBKSlmbYu6rOjALfbDZfLZfYyCAJJSUmw2WxmL4MgCMIPX2PBGqAZn3xuXjNBDokdGQiCgOLiYpSVlZm9FIIQcTgcyM/Ph8ViMXspBEEQAKRprFogj8/HqjRxRQwSOzLgQqdVq1ZIT0+ngwthKoIgoKamBiUlJQCA1q1bm7wigiAIhq+xoNez4xKAW+cCaQuBvXuB9HRT1kViJwJut1sUOs2bNzd7OQQBAEjz5r5LSkrQqlUrSmkRBBET8MhORV0Dai2pSBMAlFQCqAQEwbR1kUE5Atyjk26SGiWIUPDPJPnICIKIFdKT7WiekQwAON+QYvJqfJDYkQmlrohYgz6TBEHEIjy6c6Y+dpJHJHYIgiAIgtAN7ts5XZdk8kp8kNghosJzzz2HvLw8WCwWfPrpp6au5bbbbsOYMWMM3cdzzz2Hvn37GroPgiCIWKStdyDoydrYkRixsxJCV2677TZYLBbx0rx5cwwfPhw7d+7UbR9yD+j79u3D888/j7/85S84deoURowYodsaCIIgiNhCrMiqip3CCRI7Cczw4cNx6tQpnDp1CqtWrYLdbsd1110X9XUcOnQIADB69Gjk5+cjJSV2TGsEQRCEvnDPztFKsHERrexAjx6AiT5DEjsKEQQBNfUNplwEhWV7KSkpyM/PR35+Pvr27Ysnn3wSx48fx5kzZ8THHD9+HLfccgscDgdyc3MxevRoHDlyRLx/9erVuPTSS5GRkQGHw4HLLrsMR48exYIFC/D8889jx44dYvRowYIFjdbw3HPP4frrrwcAWK1W0VTr8XjwwgsvoG3btkhJSUHfvn2xYsUKv/1aLBa/Ro7bt2+HxWIR17dgwQI4HA588cUX6N69OzIzM0WBx3G73XjkkUfgcDjQvHlzPP7442Hfx4qKCqSlpeHzzz/3u33ZsmXIyspCTU0NAOCJJ55A165dkZ6ejgsuuADTpk0LWxV11VVXYcqUKX63jRkzBrfddpv4b6fTiUcffRRt2rRBRkYGBg4ciNWrV4d8ToIgiFiEDwP9ucLCxkXcnwXs3m1ajx2A+uwoptblRo9nvjBl33tfGIb0ZHX/ZVVVVfjHP/6Bzp07i/2CXC4Xhg0bhkGDBuHbb7+F3W7Hn/70JzHdZbVaMWbMGNx1111YvHgx6uvrsXHjRlgsFowbNw67d+/GihUr8NVXXwEAcnJyGu330UcfRWFhIW6//XY/EfLGG2/g1VdfxV/+8hf069cPf/vb33DDDTdgz5496NKli+zXVVNTg1deeQV///vfYbVa8Yc//AGPPvooFi5cCAB49dVXsWDBAvztb39D9+7d8eqrr2LZsmUYMmRI0OfLzs7Gddddh0WLFvml2xYuXIgxY8aI5d5ZWVlYsGABCgoKsGvXLtx1113IysrC448/LnvtgUyePBl79+7FkiVLUFBQgGXLlmH48OHYtWuXoveEIAjCTHhkp8RpB1IBCG6goQ5IotlYhAF89tlnyMxks0mqq6vRunVrfPbZZ7B6J88uXboUHo8H7777rhhxmT9/PhwOB1avXo3+/fujvLwc1113HTp16gQA6N69u/j8mZmZsNvtyM/PD7mGzMxMOBwOAPB73CuvvIInnngC48ePBwDMnDkT33zzDWbPno233npL9mt0uVyYN2+euL7JkyfjhRdeEO+fPXs2pk6diptuugkAMG/ePHzxRXixWlRUhAkTJqCmpgbp6emoqKjA8uXLsWzZMvExTz/9tLhdWFiIRx99FEuWLFEtdo4dO4b58+fj2LFjKCgoAMCE4ooVKzB//ny89NJLqp6XIAgi2qQm2dAiMwWlVR7fjfXVJHbiibQkG/a+MMy0fSvh6quvxjvvvAMAOH/+PN5++22MGDECGzduRIcOHbBjxw789NNPyMrK8vu7uro6HDp0CNdeey1uu+02DBs2DL/5zW8wdOhQ3HLLLZrHE1RUVODkyZO47LLL/G6/7LLLsGPHDkXPlZ6eLgodgI1O4GMUysvLcerUKQwcOFC83263o3///mFTWSNHjkRSUhL+/e9/Y/z48fj444+RnZ2NoUOHio9ZunQp5syZg0OHDqGqqgoNDQ3Izs5WtHYpu3btgtvtRteuXf1udzqd1LmbIIi4o11uGs5WOdHgToX9L2eBJYOBrdtpXES8YLFYVKeSok1GRgY6d+4s/vvdd99FTk4O/u///g9/+tOfUFVVhUsuuURM+Uhp2bIlABbpefDBB7FixQosXboUTz/9NFauXIlf/epXhq6dR5+koiSYJyYpyb+Pg8ViUextCiQ5ORk333wzFi1ahPHjx2PRokUYN24c7Hb2/75hwwYUFRXh+eefx7Bhw5CTk4MlS5bg1VdfDft6AtclfT1VVVWw2WzYsmVLo9EPPDpHEAQRL7Rtlo5tx8pQb02F/YwHOHOQxkUQ0cFiscBqtaK2thYAcPHFF+PgwYNo1aoVOnfu7HeR+m/69euHqVOnYv369ejVqxcWLVoEgIkCt9uteB3Z2dkoKCjAunXr/G5ft24devToAcAntqQ+n+3btyvaT05ODlq3bo0ffvhBvK2hoQFbtmyJ+LdFRUVYsWIF9uzZg6+//hpFRUXifevXr0eHDh3w1FNPoX///ujSpQuOHj0a9vlatmzZyDi9e/du8d/9+vWD2+1GSUlJo/+LcGlCgiCIWIT7dmot5qWupJgqdgoLC/16wfDLpEmTALBp4xMmTEB+fj4yMjJw8cUX4+OPP/Z7jtLSUhQVFSE7OxsOhwN33nknqqqqzHg5MYfT6URxcTGKi4uxb98+PPDAA6iqqhKro4qKitCiRQuMHj0a3377LQ4fPozVq1fjwQcfxC+//ILDhw9j6tSp2LBhA44ePYovv/wSBw8eFH07hYWFOHz4MLZv346zZ8/C6XTKXttjjz2GmTNnYunSpdi/fz+efPJJbN++HQ899BAAoHPnzmjXrh2ee+45HDx4EMuXLw8bOQnFQw89hJdffhmffvopfvzxR9x///1+FV6huOKKK5Cfn4+ioiJ07NjRLxXWpUsXHDt2DEuWLMGhQ4cwZ84cPz9PMIYMGYLly5dj+fLl+PHHH3Hffff5raNr164oKirCrbfeik8++QSHDx/Gxo0bMWPGDCxfvlzx6yYIgjATXpFVI8RIqxHBREpKSoRTp06Jl5UrVwoAhG+++UYQBEH4zW9+IwwYMED44YcfhEOHDgkvvviiYLVaha1bt4rPMXz4cKFPnz7C999/L3z77bdC586dhd/97neK1lFeXi4AEMrLyxvdV1tbK+zdu1eora3V9FqjzcSJEwUA4iUrK0sYMGCA8M9//tPvcadOnRJuvfVWoUWLFkJKSopwwQUXCHfddZdQXl4uFBcXC2PGjBFat24tJCcnCx06dBCeeeYZwe12C4IgCHV1dcLYsWMFh8MhABDmz58fdC3Lli0TAj9qbrdbeO6554Q2bdoISUlJQp8+fYTPP//c7zHfffed0Lt3byE1NVX49a9/LXz00UcCAOHw4cOCIAjC/PnzhZycnLD7crlcwkMPPSRkZ2cLDodDeOSRR4Rbb71VGD16dMT38PHHHxcACM8880yj+x577DGhefPmQmZmpjBu3Djh9ddf91vLs88+K/Tp00f8d319vXDfffcJubm5QqtWrYQZM2YIo0ePFiZOnOj3mGeeeUYoLCwUkpKShNatWws33nijsHPnzqDri9fPJkEQic+a/SVChyc+E3Y9PUAQWAJLEKqqdN9PuOO3FIsgmJhEC2DKlCn47LPPcPDgQVgsFmRmZuKdd97BhAkTxMc0b94cM2fOxP/8z/9g37596NGjBzZt2oT+/fsDAFasWIGRI0fil19+EataIlFRUYGcnByUl5c3MpnW1dXh8OHD6NixI1JTU/V7sQShEfpsEgQRq/x8pgpDXl2DhZYZuOxlr2WhqgrIyNB1P+GO31JixrNTX1+Pf/zjH7jjjjvEMujBgwdj6dKlKC0thcfjwZIlS1BXV4errroKADOKOhwOUegAwNChQ2G1Wv18GoE4nU5UVFT4XQiCIAiC0Ic2Xs9OhSc20lgxI3Y+/fRTlJWV+XWU/fDDD+FyudC8eXOkpKTgnnvuwbJly8QKo+LiYrRq1crveex2O3Jzc1FcXBxyXzNmzEBOTo54adeunSGviSAIgiCaIil2G/KyU1CDFCDHAuQ3o3ERAPDee+9hxIgRfqmnadOmoaysDF999RU2b96MRx55BLfccgt27dqlaV9Tp05FeXm5eDl+/LjW5RMEQRAEIaFts3RUJGUCU7KAvz9E4yKOHj2Kr776Cp988ol426FDh/Dmm29i9+7d6NmzJwCgT58++Pbbb/HWW29h3rx5yM/PFxvIcRoaGlBaWhq2XDclJYWGURIEQRCEgbRrlobqE14/YX21qWuJicjO/Pnz0apVK4waNUq8jQ9c5M3lODabDR4Pa0E9aNAglJWV+fVN+frrr+HxePxKhQmCIAiCiC5tm6WjWvD22ak3tyWM6WLH4/Fg/vz5mDhxotihFgC6deuGzp0745577sHGjRtx6NAhvPrqq1i5ciXGjBkDgM1pGj58OO666y5s3LgR69atw+TJkzF+/HjZlVgEQRAEQehP22ZpcLpswP9VAX/8EPA2tDUD08XOV199hWPHjuGOO+7wuz0pKQn//e9/0bJlS1x//fW46KKL8MEHH+D999/HyJEjxcctXLgQ3bp1wzXXXIORI0fi8ssvx1//+tdovwyCIAiCICS0y01HrScFOOkBfj4HeDyR/8ggTPfsXHvttSFnGXXp0qVRx+RAcnNzxfEFBEEQBEHEBm2bpaEasdEDzPTIDkEQBEEQiUfrnDTUkNghzGb16tWwWCzijKYFCxbA4XCYuiYAuOqqqzBlypSo7MtiseDTTz+Nyr4IgiCaEsl2K1IzssxeBgASOwnPhg0bYLPZ/CrdmiLPPfcc+vbt2+j2U6dOYcSIEdFfEEEQRBMgK8th9hIAkNhJeN577z088MADWLt2LU6ePGn2cmKO/Px86rlEEARhEA5HM7OXAIDEjnIEgTVHMuOicGZrVVUVli5divvuuw+jRo3CggULNL/848eP45ZbboHD4UBubi5Gjx6NI0eOAAC+/PJLpKamimkxzkMPPYQhQ4YAAM6dO4ff/e53aNOmDdLT09G7d28sXrw47D6DpZocDoff63niiSfQtWtXpKen44ILLsC0adPgcrkAsPTc888/jx07dsBiscBisYh/G/jcu3btwpAhQ5CWlobmzZvj7rvvRlWVrz/EbbfdhjFjxuCVV15B69at0bx5c0yaNEncFwC8/fbb6NKlC1JTU5GXl4ebb75ZxjtLEASReDRr1gxIt7CLu8G0dZhejRV3uGqAl0zq4fO/J4Fk+RNjP/zwQ3Tr1g0XXngh/vCHP2DKlCmYOnWqOGhVKS6XC8OGDcOgQYPw7bffwm63409/+hOGDx+OnTt34pprroHD4cDHH3+MO++8EwDgdruxdOlSTJ8+HQCb1H3JJZfgiSeeQHZ2NpYvX44JEyagU6dOuPTSS1WtCwCysrKwYMECFBQUYNeuXbjrrruQlZWFxx9/HOPGjcPu3buxYsUKfPXVVwCAnJycRs9RXV0tvr5NmzahpKQE//M//4PJkyf7CatvvvkGrVu3xjfffIOffvoJ48aNQ9++fXHXXXdh8+bNePDBB/H3v/8dgwcPRmlpKb799lvVr4sgCCKeyW3bFnjM69tJNm8dJHYSmPfeew9/+MMfAADDhw9HeXk51qxZI06NV8rSpUvh8Xjw7rvvioJp/vz5cDgcWL16Na699lqMHz8eixYtEsXOqlWrUFZWhrFjxwIA2rRpg0cffVR8zgceeABffPEFPvzwQ01i5+mnnxa3CwsL8eijj2LJkiV4/PHHkZaWhszMTNjt9rBjRBYtWoS6ujp88MEHyMhgovLNN9/E9ddfj5kzZyIvLw8AO1N58803YbPZ0K1bN4waNQqrVq3CXXfdhWPHjiEjIwPXXXcdsrKy0KFDB/Tr10/16yIIgohnCprnwCXYkGRxA84qILXxiWY0ILGjlKR0FmExa98y2b9/PzZu3Ihly5YBYNPgx40bh/fee0+12NmxYwd++uknZGX5u+vr6upw6NAhAEBRURF+9atf4eTJkygoKMDChQsxatQoscrL7XbjpZdewocffogTJ06gvr4eTqcT6RoHxC1duhRz5szBoUOHUFVVhYaGBmRnZyt6jn379qFPnz6i0AGAyy67DB6PB/v37xfFTs+ePWGz2cTHtG7dWhxO+5vf/AYdOnTABRdcgOHDh2P48OG48cYbNb8+giCIeKRdbgZqkIIc1MDjrDLNO0NiRykWi6JUklm89957aGho8BubIQgCUlJS8OabbwZN40SiqqoKl1xyCRYuXNjovpYtWwIABgwYgE6dOmHJkiW47777sGzZMr8U0J///Ge88cYbmD17Nnr37o2MjAxMmTIF9fX1IfdrsVgaNZ6UemQ2bNiAoqIiPP/88xg2bBhycnKwZMkSvPrqq4pfoxySkpIarY/Pa8vKysLWrVuxevVqfPnll3jmmWfw3HPPYdOmTTFR1k8QBBFNWicLcL9fBsCF8zedQvNWF5qyDhI7CUhDQwM++OADvPrqq7j22mv97hszZgwWL16Me++9V/HzXnzxxVi6dClatWoVNmpSVFSEhQsXom3btrBarX5l7+vWrcPo0aPF9JrH48GBAwfQo0ePkM/XsmVLnDp1Svz3wYMHxUGxALB+/Xp06NABTz31lHjb0aNH/Z4jOTkZbrc77Ovr3r07FixYgOrqajG6s27dOlitVlx4ofwvqN1ux9ChQzF06FA8++yzcDgc+Prrr3HTTTfJfg6CIIhEwG4B7EfrALACleYmrYOqsRKQzz77DOfPn8edd96JXr16+V3Gjh2L9957T9XzFhUVoUWLFhg9ejS+/fZbHD58GKtXr8aDDz6IX375xe9xW7duxfTp03HzzTf7lXZ36dIFK1euxPr167Fv3z7cc889OH36dNj9DhkyBG+++Sa2bduGzZs349577/WLrnTp0gXHjh3DkiVLcOjQIcyZM0dM33EKCwtx+PBhbN++HWfPnoXT6Qz6+lJTUzFx4kTs3r0b33zzDR544AFMmDBBTGFF4rPPPsOcOXOwfft2HD16FB988AE8Ho8isUQQBJGI1DXlQaCE/rz33nsYOnRo0FTV2LFjsXnzZuzcuVPx86anp2Pt2rVo3749brrpJnTv3h133nkn6urq/CI9nTt3xqWXXoqdO3eiqKjI7zmefvppXHzxxRg2bBiuuuoq5Ofni1PsQ/Hqq6+iXbt2+PWvf43f//73ePTRR/08MDfccAMefvhhTJ48GX379sX69esxbdq0Rq97+PDhuPrqq9GyZcug5e7p6en44osvUFpaigEDBuDmm2/GNddcgzfffFP2e+RwOPDJJ59gyJAh6N69O+bNm4fFixejZ8+esp+DIAgiEbnoyhtN27dFCDWFswlRUVGBnJwclJeXN0rP1NXV4fDhw+jYsSNSU2NjxgdBAPTZJAgiDqiuBjIz2XZVFZChr+c13PFbCkV2CIIgCIJIaEjsEARBEASR0FA1FkEQBEEQxhEDfcZI7BAEQRAEYQwZGcy3YzKUxpIJ+biJWIM+kwRBEPIgsRMB3s9F2sSOIGIB/pkM7OhMEARB+ENprAjYbDY4HA6UlJQAYL1Y1E4NJwg9EAQBNTU1KCkpgcPh8JvTRRAEEVPU1QHeQdD4+GPApDYZJHZkwCdlc8FDELGAw+EIO8WdIAjCdNxu4L//9W2bBIkdGVgsFrRu3RqtWrXyG0BJEGaRlJREER2CIAiZkNhRgM1mowMMQRAEQcQZZFAmCIIgCCKhIbFDEARBEERCQ2KHIAiCIIiEhjw78DVnq6ioMHklBEEQBJFASLsnV1ToXpHFj9uRmqyS2AFQWVkJAGjXrp3JKyEIgiCIBKWgwLCnrqysRE5OTsj7LQL1nIfH48HJkyeRlZWla8PAiooKtGvXDsePH0d2drZuz0s0ht7r6EDvc3Sg9zk60PscHYx8nwVBQGVlJQoKCmC1hnbmUGQHgNVqRdu2bQ17/uzsbPoiRQl6r6MDvc/Rgd7n6EDvc3Qw6n0OF9HhkEGZIAiCIIiEhsQOQRAEQRAJDYkdA0lJScGzzz6LlJQUs5eS8NB7HR3ofY4O9D5HB3qfo0MsvM9kUCYIgiAIIqGhyA5BEARBEAkNiR2CIAiCIBIaEjsEQRAEQSQ0JHYIgiAIgkhoSOwYyFtvvYXCwkKkpqZi4MCB2Lhxo9lLimvWrl2L66+/HgUFBbBYLPj000/97hcEAc888wxat26NtLQ0DB06FAcPHjRnsXHMjBkzMGDAAGRlZaFVq1YYM2YM9u/f7/eYuro6TJo0Cc2bN0dmZibGjh2L06dPm7Ti+OSdd97BRRddJDZaGzRoED7//HPxfnqPjeHll1+GxWLBlClTxNvovdaH5557DhaLxe/SrVs38X4z32cSOwaxdOlSPPLII3j22WexdetW9OnTB8OGDUNJSYnZS4tbqqur0adPH7z11ltB7581axbmzJmDefPm4YcffkBGRgaGDRuGurq6KK80vlmzZg0mTZqE77//HitXroTL5cK1116LaslAv4cffhj/+c9/8NFHH2HNmjU4efIkbrrpJhNXHX+0bdsWL7/8MrZs2YLNmzdjyJAhGD16NPbs2QOA3mMj2LRpE/7yl7/goosu8rud3mv96NmzJ06dOiVevvvuO/E+U99ngTCESy+9VJg0aZL4b7fbLRQUFAgzZswwcVWJAwBh2bJl4r89Ho+Qn58v/PnPfxZvKysrE1JSUoTFixebsMLEoaSkRAAgrFmzRhAE9r4mJSUJH330kfiYffv2CQCEDRs2mLXMhKBZs2bCu+++S++xAVRWVgpdunQRVq5cKVx55ZXCQw89JAgCfZ715NlnnxX69OkT9D6z32eK7BhAfX09tmzZgqFDh4q3Wa1WDB06FBs2bDBxZYnL4cOHUVxc7Pee5+TkYODAgfSea6S8vBwAkJubCwDYsmULXC6X33vdrVs3tG/fnt5rlbjdbixZsgTV1dUYNGgQvccGMGnSJIwaNcrvPQXo86w3Bw8eREFBAS644AIUFRXh2LFjAMx/n2kQqAGcPXsWbrcbeXl5frfn5eXhxx9/NGlViU1xcTEABH3P+X2EcjweD6ZMmYLLLrsMvXr1AsDe6+TkZDgcDr/H0nutnF27dmHQoEGoq6tDZmYmli1bhh49emD79u30HuvIkiVLsHXrVmzatKnRffR51o+BAwdiwYIFuPDCC3Hq1Ck8//zz+PWvf43du3eb/j6T2CEIIiSTJk3C7t27/fLuhH5ceOGF2L59O8rLy/HPf/4TEydOxJo1a8xeVkJx/PhxPPTQQ1i5ciVSU1PNXk5CM2LECHH7oosuwsCBA9GhQwd8+OGHSEtLM3FlZFA2hBYtWsBmszVymZ8+fRr5+fkmrSqx4e8rvef6MXnyZHz22Wf45ptv0LZtW/H2/Px81NfXo6yszO/x9F4rJzk5GZ07d8Yll1yCGTNmoE+fPnjjjTfoPdaRLVu2oKSkBBdffDHsdjvsdjvWrFmDOXPmwG63Iy8vj95rg3A4HOjatSt++ukn0z/TJHYMIDk5GZdccglWrVol3ubxeLBq1SoMGjTIxJUlLh07dkR+fr7fe15RUYEffviB3nOFCIKAyZMnY9myZfj666/RsWNHv/svueQSJCUl+b3X+/fvx7Fjx+i91ojH44HT6aT3WEeuueYa7Nq1C9u3bxcv/fv3R1FRkbhN77UxVFVV4dChQ2jdurX5n2nDLdBNlCVLlggpKSnCggULhL179wp333234HA4hOLiYrOXFrdUVlYK27ZtE7Zt2yYAEF577TVh27ZtwtGjRwVBEISXX35ZcDgcwr/+9S9h586dwujRo4WOHTsKtbW1Jq88vrjvvvuEnJwcYfXq1cKpU6fES01NjfiYe++9V2jfvr3w9ddfC5s3bxYGDRokDBo0yMRVxx9PPvmksGbNGuHw4cPCzp07hSeffFKwWCzCl19+KQgCvcdGIq3GEgR6r/Xij3/8o7B69Wrh8OHDwrp164ShQ4cKLVq0EEpKSgRBMPd9JrFjIHPnzhXat28vJCcnC5deeqnw/fffm72kuOabb74RADS6TJw4URAEVn4+bdo0IS8vT0hJSRGuueYaYf/+/eYuOg4J9h4DEObPny8+pra2Vrj//vuFZs2aCenp6cKNN94onDp1yrxFxyF33HGH0KFDByE5OVlo2bKlcM0114hCRxDoPTaSQLFD77U+jBs3TmjdurWQnJwstGnTRhg3bpzw008/ifeb+T5bBEEQjI8fEQRBEARBmAN5dgiCIAiCSGhI7BAEQRAEkdCQ2CEIgiAIIqEhsUMQBEEQREJDYocgCIIgiISGxA5BEARBEAkNiR2CIAiCIBIaEjsEQRAEQSQ0JHYIgiAAFBYWYvbs2WYvgyAIAyCxQxBE1LntttswZswYAMBVV12FKVOmRG3fCxYsgMPhaHT7pk2bcPfdd0dtHQRBRA+72QsgCILQg/r6eiQnJ6v++5YtW+q4GoIgYgmK7BAEYRq33XYb1qxZgzfeeAMWiwUWiwVHjhwBAOzevRsjRoxAZmYm8vLyMGHCBJw9e1b826uuugqTJ0/GlClT0KJFCwwbNgwA8Nprr6F3797IyMhAu3btcP/996OqqgoAsHr1atx+++0oLy8X9/fcc88BaJzGOnbsGEaPHo3MzExkZ2fjlltuwenTp8X7n3vuOfTt2xd///vfUVhYiJycHIwfPx6VlZXGvmkEQSiGxA5BEKbxxhtvYNCgQbjrrrtw6tQpnDp1Cu3atUNZWRmGDBmCfv36YfPmzVixYgVOnz6NW265xe/v33//fSQnJ2PdunWYN28eAMBqtWLOnDnYs2cP3n//fXz99dd4/PHHAQCDBw/G7NmzkZ2dLe7v0UcfbbQuj8eD0aNHo7S0FGvWrMHKlSvx888/Y9y4cX6PO3ToED799FN89tln+Oyzz7BmzRq8/PLLBr1bBEGohdJYBEGYRk5ODpKTk5Geno78/Hzx9jfffBP9+vXDSy+9JN72t7/9De3atcOBAwfQtWtXAECXLl0wa9Ysv+eU+n8KCwvxpz/9Cffeey/efvttJCcnIycnBxaLxW9/gaxatQq7du3C4cOH0a5dOwDABx98gJ49e2LTpk0YMGAAACaKFixYgKysLADAhAkTsGrVKkyfPl3bG0MQhK5QZIcgiJhjx44d+Oabb5CZmSleunXrBoBFUziXXHJJo7/96quvcM0116BNmzbIysrChAkTcO7cOdTU1Mje/759+9CuXTtR6ABAjx494HA4sG/fPvG2wsJCUegAQOvWrVFSUqLotRIEYTwU2SEIIuaoqqrC9ddfj5kzZza6r3Xr1uJ2RkaG331HjhzBddddh/vuuw/Tp09Hbm4uvvvuO9x5552or69Henq6rutMSkry+7fFYoHH49F1HwRBaIfEDkEQppKcnAy32+1328UXX4yPP/4YhYWFsNvl/0xt2bIFHo8Hr776KqxWFrj+8MMPI+4vkO7du+P48eM4fvy4GN3Zu3cvysrK0KNHD9nrIQgiNqA0FkEQplJYWIgffvgBR44cwdmzZ+HxeDBp0iSUlpbid7/7HTZt2oRDhw7hiy++wO233x5WqHTu3Bkulwtz587Fzz//jL///e+icVm6v6qqKqxatQpnz54Nmt4aOnQoevfujaKiImzduhUbN27ErbfeiiuvvBL9+/fX/T0gCMJYSOwQBGEqjz76KGw2G3r06IGWLVvi2LFjKCgowLp16+B2u3Httdeid+/emDJlChwOhxixCUafPn3w2muvYebMmejVqxcWLlyIGTNm+D1m8ODBuPfeezFu3Di0bNmykcEZYOmof/3rX2jWrBmuuOIKDB06FBdccAGWLl2q++snCMJ4LIIgCGYvgiAIgiAIwigoskMQBEEQREJDYocgCIIgiISGxA5BEARBEAkNiR2CIAiCIBIaEjsEQRAEQSQ0JHYIgiAIgkhoSOwQBEEQBJHQkNghCIIgCCKhIbFDEARBEERCQ2KHIAiCIIiEhsQOQRAEQRAJzf8DrK+0FoxdJY0AAAAASUVORK5CYII=","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"ename":"ZeroDivisionError","evalue":"division by zero","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)","Cell \u001b[1;32mIn[5], line 49\u001b[0m\n\u001b[0;32m 46\u001b[0m c_unnormed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(train_X_np[n_init:])))\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# Normalize the colors to be between 0 and 1\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m colors \u001b[38;5;241m=\u001b[39m [c_unnormed[i] \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mmax\u001b[39m(c_unnormed) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(c_unnormed))]\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# Plot initial samples\u001b[39;00m\n\u001b[0;32m 52\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(train_X_np[:n_init], train_Y_np[:n_init], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInitial samples\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n","Cell \u001b[1;32mIn[5], line 49\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 46\u001b[0m c_unnormed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(train_X_np[n_init:])))\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# Normalize the colors to be between 0 and 1\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m colors \u001b[38;5;241m=\u001b[39m [\u001b[43mc_unnormed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mc_unnormed\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(c_unnormed))]\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# Plot initial samples\u001b[39;00m\n\u001b[0;32m 52\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(train_X_np[:n_init], train_Y_np[:n_init], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInitial samples\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n","\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n","\n","\n","#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","colors = [c_unnormed[i] / max(c_unnormed) for i in range(len(c_unnormed))]\n","\n","# Plot initial samples\n","plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', c=colors, cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} From a5dcec5f6863c5d5ab6b5017ae64f7dba2568475 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Thu, 28 Mar 2024 22:41:21 +0800 Subject: [PATCH 28/43] fix --- analyse_grid_experiment.ipynb | 325 +++++++++++++--------------------- comparison.ipynb | 2 +- 2 files changed, 127 insertions(+), 200 deletions(-) diff --git a/analyse_grid_experiment.ipynb b/analyse_grid_experiment.ipynb index b55e5a5..64251c5 100644 --- a/analyse_grid_experiment.ipynb +++ b/analyse_grid_experiment.ipynb @@ -2,14 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-03-28 21:40:41,708\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n" + "2024-03-28 22:39:28,614\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n" ] }, { @@ -17,201 +17,148 @@ "output_type": "stream", "text": [ "Started problem 4 noise 10 budget 10 seed 0, time: 1.00s\n", - "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", - "Started problem 4 noise 10 budget 10 seed 0, time: 2.11s\n" + "\u001b[36m(worker pid=5984)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", + "Started problem 4 noise 10 budget 10 seed 0, time: 2.12s\n", + "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([257.0693], dtype=torch.float64)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([257.0693], dtype=torch.float64)\n", - "Started problem 4 noise 100 budget 10 seed 0, time: 3.26s\n" + "Started problem 4 noise 100 budget 10 seed 0, time: 3.27s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 4 noise 100 budget 10 seed 0, time: 4.38s\n", - "Started problem 10 noise 10 budget 10 seed 0, time: 5.58s\n", - "\u001b[36m(worker pid=12024)\u001b[0m Starting iteration 2, total time: 4.455 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 10 noise 10 budget 10 seed 0, time: 6.80s\n", - "Started problem 10 noise 100 budget 10 seed 0, time: 8.03s\n", - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([609.0729], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "Started problem 10 noise 100 budget 10 seed 0, time: 9.30s\n" + "Started problem 4 noise 100 budget 10 seed 0, time: 4.39s\n", + "Started problem 10 noise 10 budget 10 seed 0, time: 5.53s\n", + "Started problem 10 noise 10 budget 10 seed 0, time: 6.66s\n", + "\u001b[36m(worker pid=12024)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 4 noise 10 budget 10 seed 1, time: 10.58s\n", - "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 4, total time: 10.661 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 4 noise 10 budget 10 seed 1, time: 11.82s\n", - "Started problem 4 noise 100 budget 10 seed 1, time: 13.08s\n", - "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[-17.1415]], dtype=torch.float64), tensor([499.5804], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "Started problem 4 noise 100 budget 10 seed 1, time: 14.33s\n" + "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[-221.4744]], dtype=torch.float64), tensor([591.3097], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "Started problem 10 noise 100 budget 10 seed 0, time: 7.82s\n", + "Started problem 10 noise 100 budget 10 seed 0, time: 9.02s\n", + "Started problem 4 noise 10 budget 10 seed 1, time: 10.27s\n", + "Started problem 4 noise 10 budget 10 seed 1, time: 11.52s\n", + "\u001b[36m(worker pid=5244)\u001b[0m Starting iteration 3, total time: 7.398 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "Started problem 4 noise 100 budget 10 seed 1, time: 12.77s\n", + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[455.8119]], dtype=torch.float64), tensor([160.6682], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12436)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12436)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 10 noise 10 budget 10 seed 1, time: 15.56s\n", - "\u001b[36m(worker pid=18444)\u001b[0m Starting iteration 2, total time: 8.578 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[-56.0295]], dtype=torch.float64), tensor([456.1113], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "Started problem 4 noise 100 budget 10 seed 1, time: 13.98s\n", + "Started problem 10 noise 10 budget 10 seed 1, time: 15.18s\n", + "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 2, total time: 8.225 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5244)\u001b[0m Starting iteration 4, total time: 16.479 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[135.0684]], dtype=torch.float64), tensor([529.8546], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[-310.3727]], dtype=torch.float64), tensor([130.3428], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12436)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12436)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 6, total time: 24.424 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5244)\u001b[0m New candidate: tensor([[27.8300]], dtype=torch.float64), tensor([445.8539], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 4, total time: 14.192 seconds.\u001b[32m [repeated 16x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[451.2381]], dtype=torch.float64), tensor([123.9714], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "Started problem 10 noise 10 budget 10 seed 1, time: 27.88s\n", + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 7, total time: 23.087 seconds.\u001b[32m [repeated 15x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[370.4692]], dtype=torch.float64), tensor([290.3323], dtype=torch.float64)\u001b[32m [repeated 15x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=5244)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5244)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 7, total time: 29.584 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5244)\u001b[0m New candidate: tensor([[370.4692]], dtype=torch.float64), tensor([290.3323], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "Started problem 10 noise 100 budget 10 seed 1, time: 32.42s\n", + "Started problem 10 noise 100 budget 10 seed 1, time: 33.60s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m Starting iteration 6, total time: 29.239 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[-345.6434]], dtype=torch.float64), tensor([480.1781], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "Started problem 10 noise 10 budget 10 seed 1, time: 41.78s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 1, total time: 4.131 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[-325.8501]], dtype=torch.float64), tensor([197.8004], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "Started problem 10 noise 100 budget 10 seed 1, time: 46.98s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started problem 10 noise 100 budget 10 seed 1, time: 48.25s\n", + "\u001b[36m(worker pid=18444)\u001b[0m Starting iteration 1, total time: 2.755 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([608.2261], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", "0\n", - "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 1, total time: 4.420 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m New candidate: tensor([[435.1405]], dtype=torch.float64), tensor([56.5343], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 2, total time: 8.928 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "0\n", - "0\n", - "\u001b[36m(worker pid=5244)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([614.1931], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", "0\n" ] }, @@ -227,95 +174,84 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5244)\u001b[0m Starting iteration 2, total time: 8.105 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[490.3498]], dtype=torch.float64), tensor([639.7774], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 4, total time: 17.851 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([235.9000], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=12024)\u001b[0m New candidate: tensor([[420.7220]], dtype=torch.float64), tensor([-8.5333], dtype=torch.float64)\u001b[32m [repeated 15x across cluster]\u001b[0m\n", + "0\n", + "0\n", + "0\n", + "\u001b[36m(worker pid=5984)\u001b[0m Starting iteration 6, total time: 18.163 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([235.9000], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=5984)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=5984)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n", - "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=13296)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 7, total time: 31.495 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[-76.1500]], dtype=torch.float64), tensor([443.0088], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 4, total time: 12.347 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[47.6366]], dtype=torch.float64), tensor([387.5126], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "\u001b[36m(worker pid=13296)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "\u001b[36m(worker pid=13296)\u001b[0m Trying again with a new set of initial conditions.\n", + "\u001b[36m(worker pid=13296)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m Starting iteration 5, total time: 21.873 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[221.3941]], dtype=torch.float64), tensor([402.0225], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 3, total time: 15.013 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[407.8906]], dtype=torch.float64), tensor([-10.9544], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 8, total time: 35.729 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "0\n", - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[-62.3537]], dtype=torch.float64), tensor([477.4399], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 7, total time: 22.743 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[395.9327]], dtype=torch.float64), tensor([74.1132], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m Starting iteration 8, total time: 35.362 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m New candidate: tensor([[392.9702]], dtype=torch.float64), tensor([85.1805], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", "0\n", "0\n", + "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 9, total time: 29.110 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[364.4596]], dtype=torch.float64), tensor([332.3226], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n", "0\n", "0\n" ] @@ -324,20 +260,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=13296)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m Starting iteration 8, total time: 37.963 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m New candidate: tensor([[-312.4408]], dtype=torch.float64), tensor([136.6988], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", "0\n", "0\n", + "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 9, total time: 31.273 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[455.3359]], dtype=torch.float64), tensor([200.7786], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", "0\n", - "all experiments done, time: 100.01s\n" + "all experiments done, time: 74.30s\n" ] } ], @@ -353,27 +289,18 @@ "noise_bools = [True, False]\n", "budget = 10\n", "\n", - "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)\n" + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[455.3359]], dtype=torch.float64), tensor([200.7786], dtype=torch.float64)\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5984)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=5984)\u001b[0m warnings.warn(\n", "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_16892\\636701015.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n" ] @@ -559,7 +486,7 @@ "0 10 100 1 False 0.034306" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -581,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -595,32 +522,32 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -632,24 +559,24 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 bestbest
 meanstdmeanstd
noise_level10100101001010010100
n_init
4124.7581.29116.8055.344124.7581.29116.8055.34
1021.7772.3028.8442.621021.7772.3028.8442.62
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -666,32 +593,32 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -703,24 +630,24 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 bestbest
 meanstdmeanstd
noise_level10100101001010010100
n_init
459.3065.8883.6774.78459.3065.8883.6774.78
100.1412.630.1817.81100.1412.630.1817.81
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/comparison.ipynb b/comparison.ipynb index 7b778b3..d9d45f5 100644 --- a/comparison.ipynb +++ b/comparison.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Starting iteration 0, total time: 0.000 seconds.\n","New candidate: tensor([[-26.2945, 50.0000]], dtype=torch.float64), tensor([773.9874], dtype=torch.float64)\n"]},{"data":{"text/plain":["ExactMarginalLogLikelihood(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (model): SingleTaskGP(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (mean_module): ConstantMean()\n"," (covar_module): ScaleKernel(\n"," (base_kernel): MaternKernel(\n"," (lengthscale_prior): GammaPrior()\n"," (raw_lengthscale_constraint): Positive()\n"," )\n"," (outputscale_prior): GammaPrior()\n"," (raw_outputscale_constraint): Positive()\n"," )\n"," (outcome_transform): Standardize()\n"," )\n",")"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=10)\n","\n","\n","seed = 0 \n","n_inits = 10\n","noise_level = 5\n","noise_bool = True\n","\n","n_init = 50\n","budget = 1\n","\n","\n","torch.manual_seed(seed)\n","np.random.seed(seed)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","\n","train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, noise_bool)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, noise_bool)\n","fit_gpytorch_model(mll)\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 773.9874075531119\n","Best solution found: [-26.29448786 50. ]\n","Total number of evaluations: 51\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"ename":"ZeroDivisionError","evalue":"division by zero","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)","Cell \u001b[1;32mIn[5], line 49\u001b[0m\n\u001b[0;32m 46\u001b[0m c_unnormed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(train_X_np[n_init:])))\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# Normalize the colors to be between 0 and 1\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m colors \u001b[38;5;241m=\u001b[39m [c_unnormed[i] \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mmax\u001b[39m(c_unnormed) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(c_unnormed))]\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# Plot initial samples\u001b[39;00m\n\u001b[0;32m 52\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(train_X_np[:n_init], train_Y_np[:n_init], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInitial samples\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n","Cell \u001b[1;32mIn[5], line 49\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 46\u001b[0m c_unnormed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(train_X_np[n_init:])))\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# Normalize the colors to be between 0 and 1\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m colors \u001b[38;5;241m=\u001b[39m [\u001b[43mc_unnormed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mc_unnormed\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(c_unnormed))]\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# Plot initial samples\u001b[39;00m\n\u001b[0;32m 52\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(train_X_np[:n_init], train_Y_np[:n_init], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInitial samples\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n","\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n","\n","\n","#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","colors = [c_unnormed[i] / max(c_unnormed) for i in range(len(c_unnormed))]\n","\n","# Plot initial samples\n","plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', c=colors, cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} +{"cells":[{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Starting iteration 0, total time: 0.000 seconds.\n","New candidate: tensor([[-26.2945, 50.0000]], dtype=torch.float64), tensor([773.9874], dtype=torch.float64)\n"]},{"data":{"text/plain":["ExactMarginalLogLikelihood(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (model): SingleTaskGP(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (mean_module): ConstantMean()\n"," (covar_module): ScaleKernel(\n"," (base_kernel): MaternKernel(\n"," (lengthscale_prior): GammaPrior()\n"," (raw_lengthscale_constraint): Positive()\n"," )\n"," (outputscale_prior): GammaPrior()\n"," (raw_outputscale_constraint): Positive()\n"," )\n"," (outcome_transform): Standardize()\n"," )\n",")"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=10)\n","\n","\n","seed = 0 \n","n_inits = 10\n","noise_level = 5\n","noise_bool = True\n","\n","n_init = 50\n","budget = 1\n","\n","\n","torch.manual_seed(seed)\n","np.random.seed(seed)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","\n","train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, noise_bool)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, noise_bool)\n","fit_gpytorch_model(mll)\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 773.9874075531119\n","Best solution found: [-26.29448786 50. ]\n","Best real value found: 778.4647403590802\n","Best real solution found: [-26.29448786 50. ]\n","Total number of evaluations: 51\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Best real value found: {train_Y_real.min().item()}\")\n","print(f\"Best real solution found: {train_X[train_Y_real.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["\n","\n","#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","\n","# Plot initial samples\n","# plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","# plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} From 168611d26c62f280c1938246cf82b1b573cd67d6 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 07:59:00 -0700 Subject: [PATCH 29/43] initial baybe run experiment --- run_experiment_baybe.py | 187 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 run_experiment_baybe.py diff --git a/run_experiment_baybe.py b/run_experiment_baybe.py new file mode 100644 index 0000000..8d191ac --- /dev/null +++ b/run_experiment_baybe.py @@ -0,0 +1,187 @@ +# %% +import matplotlib.pyplot as plt +import numpy as np +import torch + +from botorch.models.gp_regression import ( + SingleTaskGP, +) +from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood +from botorch.fit import fit_gpytorch_model +from botorch.models.transforms.outcome import Standardize + +from botorch.optim.optimize import optimize_acqf +from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement +from botorch.sampling.normal import SobolQMCNormalSampler +from botorch.utils.transforms import normalize, unnormalize +import os +import gc + + + +from baybe.targets import NumericalTarget +from baybe.objective import Objective +from baybe.parameters import ( + NumericalContinuousParameter +) + +from baybe.recommenders import ( + SequentialGreedyRecommender, + RandomRecommender +) + +from baybe.searchspace import SearchSpace +from baybe import Campaign +from baybe import simulation + +tkwargs = { + "dtype": torch.double, + "device": torch.device("cuda" if torch.cuda.is_available() else "cpu"), +} +SMOKE_TEST = os.environ.get("SMOKE_TEST") +# SMOKE_TEST = True +print("SMOKE_TEST", SMOKE_TEST) +NUM_RESTARTS = 10 if not SMOKE_TEST else 2 +RAW_SAMPLES = 512 if not SMOKE_TEST else 4 +MC_SAMPLES = 128 if not SMOKE_TEST else 16 +batch_size = 1 + + +# %% +def generate_initial_data(problem, n: int, bounds: torch.Tensor) -> tuple: + X_init = draw_sobol_samples( + bounds=bounds, n=n, q=1, seed=torch.randint(100000, (1,)).item() + ).squeeze(-1) + Y_init = torch.tensor(problem.y(X_init.numpy())) + Y_init_real = torch.tensor(problem.f(X_init.numpy())) + return X_init, Y_init, Y_init_real + +# %% +def initialize_model(train_x, train_y, noise_bool=True) -> tuple: + # define models for objective and constraint + train_y= -train_y # negative because botorch assumes maximization + + if noise_bool: + model = SingleTaskGP( + train_X=train_x, + train_Y=train_y.unsqueeze(-1), + outcome_transform=Standardize(m=1), + ) + else: + model = SingleTaskGP( + train_X=train_x, + train_Y=train_y.unsqueeze(-1), + train_Yvar=torch.full_like(train_y.unsqueeze(-1), 1e-6), + outcome_transform=Standardize(m=1), + ) + + mll = ExactMarginalLogLikelihood(model.likelihood, model) + + return mll, model + +# %% +def optimize_acqf_loop(problem, acq_func): + + standard_bounds = torch.zeros(2, problem.n_var, **tkwargs) + standard_bounds[1] = 1 + options = {"batch_limit": batch_size, "maxiter": 2000} + + while options["batch_limit"] >= 1: + try: + torch.cuda.empty_cache() + x_cand, acq_val = optimize_acqf( + acq_function=acq_func, + bounds=standard_bounds, + q=batch_size, + num_restarts=NUM_RESTARTS, + raw_samples=RAW_SAMPLES, # used for intialization heuristic + options=options, + ) + torch.cuda.empty_cache() + gc.collect() + break + except RuntimeError as e: + if options["batch_limit"] > 1: + print( + "Got a RuntimeError in `optimize_acqf`. " + "Trying with reduced `batch_limit`." + ) + options["batch_limit"] //= 2 + continue + else: + raise e + + return x_cand, acq_val + + +# %% +from botorch.utils.sampling import draw_sobol_samples +from src.schwefel import SchwefelProblem +from time import time + +def run_experiment(n_init, noise_level, budget, seed, noise_bool): + + N_DIMS_SCHWEF = 2 + ITERATION_BATCH_SIZE = 1 + + + torch.manual_seed(seed) + np.random.seed(seed) + + problem = SchwefelProblem(n_var=N_DIMS_SCHWEF, noise_level=noise_level) + + bounds = torch.tensor(problem.bounds, **tkwargs) + + target = NumericalTarget(name = 'schwefel', mode = "MIN") + parameters = [ + NumericalContinuousParameter(f'schwefel{i+1}', bounds = (-50,50)) for i in range(N_DIMS_SCHWEF) + ] + + objective = Objective(mode = "SINGLE", targets = [target]) + searchspace = SearchSpace.from_product(parameters) + + if recommender_init is None: + recommender_init = RandomRecommender() + if recommender_main is None: + recommender_main = SequentialGreedyRecommender(acquisition_function_cls='EI') + + + print("Collecting initial observations") + campaign_init = Campaign(searchspace, objective, recommender_init) + random_params = campaign_init.recommend(n_init) + + y_init = problem.y(random_params.to_numpy()) + y_init_real = problem.f(random_params.to_numpy()) + + random_params.insert(N_DIMS_SCHWEF, 'schwefel', y_init) + + optimization_campaign = Campaign(searchspace, objective, recommender_main) + optimization_campaign.add_measurements(random_params) + + y_real = [] + print('Beginning optimization campaign') + for i in range(budget): + reccs = optimization_campaign.recommend(ITERATION_BATCH_SIZE) + + y_vals = problem.y(reccs.to_numpy()) + y_real = problem.f(reccs.to_numpy()) + + reccs.insert(N_DIMS_SCHWEF, 'schwefel', y_vals) + + optimization_campaign.add_measurements(reccs) + + measurements = optimization_campaign.measurements + + y_real = np. + + + os.makedirs('results', exist_ok=True) + fname = f"results/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" + torch.save((train_X, train_Y, train_Y_real, model), fname) + + return train_X, train_Y, train_Y_real, model + + +if __name__ == "__main__": + run_experiment(5, 0.1, 5, 0, True) + run_experiment(5, 0.1, 5, 0, False) \ No newline at end of file From f6c4ae304bbba4495eaae494e060160fcb169a06 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 08:18:46 -0700 Subject: [PATCH 30/43] baybe with karims framework --- run_experiment_baybe.py | 88 +++++++++-------------------------------- 1 file changed, 19 insertions(+), 69 deletions(-) diff --git a/run_experiment_baybe.py b/run_experiment_baybe.py index 8d191ac..8b77d60 100644 --- a/run_experiment_baybe.py +++ b/run_experiment_baybe.py @@ -47,72 +47,6 @@ batch_size = 1 -# %% -def generate_initial_data(problem, n: int, bounds: torch.Tensor) -> tuple: - X_init = draw_sobol_samples( - bounds=bounds, n=n, q=1, seed=torch.randint(100000, (1,)).item() - ).squeeze(-1) - Y_init = torch.tensor(problem.y(X_init.numpy())) - Y_init_real = torch.tensor(problem.f(X_init.numpy())) - return X_init, Y_init, Y_init_real - -# %% -def initialize_model(train_x, train_y, noise_bool=True) -> tuple: - # define models for objective and constraint - train_y= -train_y # negative because botorch assumes maximization - - if noise_bool: - model = SingleTaskGP( - train_X=train_x, - train_Y=train_y.unsqueeze(-1), - outcome_transform=Standardize(m=1), - ) - else: - model = SingleTaskGP( - train_X=train_x, - train_Y=train_y.unsqueeze(-1), - train_Yvar=torch.full_like(train_y.unsqueeze(-1), 1e-6), - outcome_transform=Standardize(m=1), - ) - - mll = ExactMarginalLogLikelihood(model.likelihood, model) - - return mll, model - -# %% -def optimize_acqf_loop(problem, acq_func): - - standard_bounds = torch.zeros(2, problem.n_var, **tkwargs) - standard_bounds[1] = 1 - options = {"batch_limit": batch_size, "maxiter": 2000} - - while options["batch_limit"] >= 1: - try: - torch.cuda.empty_cache() - x_cand, acq_val = optimize_acqf( - acq_function=acq_func, - bounds=standard_bounds, - q=batch_size, - num_restarts=NUM_RESTARTS, - raw_samples=RAW_SAMPLES, # used for intialization heuristic - options=options, - ) - torch.cuda.empty_cache() - gc.collect() - break - except RuntimeError as e: - if options["batch_limit"] > 1: - print( - "Got a RuntimeError in `optimize_acqf`. " - "Trying with reduced `batch_limit`." - ) - options["batch_limit"] //= 2 - continue - else: - raise e - - return x_cand, acq_val - # %% from botorch.utils.sampling import draw_sobol_samples @@ -172,14 +106,30 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): measurements = optimization_campaign.measurements - y_real = np. - + # get X and noisy y values + x_names = [f'schwefel{i+1}' for i in range(N_DIMS_SCHWEF)] + x_train = measurements[x_names].to_numpy() + y_train = measurements['schwefel'].to_numpy() + + # compile noise-free ground truth vals + y_real_complete = np.zeros(len(y_init_real) + len(y_real)) + + for i, val in enumerate(y_init_real): + y_real_complete[i] = val + + for i, val in enumerate(y_real): + y_real_complete[i+len(y_init_real)] = val os.makedirs('results', exist_ok=True) fname = f"results/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" torch.save((train_X, train_Y, train_Y_real, model), fname) + + train_X = torch.from_numpy(x_train) + train_Y = torch.from_numpy(y_train) + train_Y_real = torch.from_numpy(y_real_complete) - return train_X, train_Y, train_Y_real, model + return train_X, train_Y, train_Y_real, None + if __name__ == "__main__": From 27a1d0d54e70c7f1d46c0c03fa69f77c876a8950 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 08:26:09 -0700 Subject: [PATCH 31/43] add baybe karim grid search notebook --- analyse_grid_experiment_BAYBE.ipynb | 432 ++++++++++++++++++++++++++++ 1 file changed, 432 insertions(+) create mode 100644 analyse_grid_experiment_BAYBE.ipynb diff --git a/analyse_grid_experiment_BAYBE.ipynb b/analyse_grid_experiment_BAYBE.ipynb new file mode 100644 index 0000000..f25534a --- /dev/null +++ b/analyse_grid_experiment_BAYBE.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'ray'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrun_grid_experiments_baybe\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m run_grid_experiments\n\u001b[1;32m 5\u001b[0m seeds \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 6\u001b[0m n_inits \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m10\u001b[39m]\n", + "File \u001b[0;32m~/Code/project-project-noisy-nerds/run_grid_experiments_baybe.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mray\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01margparse\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m time, sleep\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'ray'" + ] + } + ], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from run_grid_experiments_baybe import run_grid_experiments\n", + "\n", + "seeds = [0]\n", + "n_inits = [4, 10]\n", + "noise_levels = [0]\n", + "# budgets = [10, 20, 50]\n", + "noise_bools = [True, False]\n", + "budget = 10\n", + "\n", + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_16892\\636701015.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
04100True207.341003
04101True42.158676
041000True120.417244
041001True42.158676
010100True1.374434
010101True42.158676
0101000True102.431664
0101001True42.158676
04100False118.466827
04101False0.138172
041000False118.751045
041001False13.000295
010100False0.007694
010101False0.265530
0101000False25.227522
0101001False0.034306
\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 4 10 0 True 207.341003\n", + "0 4 10 1 True 42.158676\n", + "0 4 100 0 True 120.417244\n", + "0 4 100 1 True 42.158676\n", + "0 10 10 0 True 1.374434\n", + "0 10 10 1 True 42.158676\n", + "0 10 100 0 True 102.431664\n", + "0 10 100 1 True 42.158676\n", + "0 4 10 0 False 118.466827\n", + "0 4 10 1 False 0.138172\n", + "0 4 100 0 False 118.751045\n", + "0 4 100 1 False 13.000295\n", + "0 10 10 0 False 0.007694\n", + "0 10 10 1 False 0.265530\n", + "0 10 100 0 False 25.227522\n", + "0 10 100 1 False 0.034306" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " for seed in seeds:\n", + " X, Y, Y_real, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n", + " \n", + "df " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GP with noise\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 best
 meanstd
noise_level1010010100
n_init    
4124.7581.29116.8055.34
1021.7772.3028.8442.62
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GP without noise\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 best
 meanstd
noise_level1010010100
n_init    
459.3065.8883.6774.78
100.1412.630.1817.81
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_no_noise = df[df[\"noise_bool\"] == False]\n", + "df_noise = df[df[\"noise_bool\"] == True]\n", + "# df = df.groupby([\"n_init\", \"noise_level\", \"noise_bool\"]).agg({\"min\": [\"mean\", \"std\"]})\n", + "df_no_noise = df_no_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "df_noise = df_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "print(\"GP with noise\")\n", + "display(df_noise.unstack().style.format(\"{:.2f}\").background_gradient(cmap='viridis'))\n", + "print(\"GP without noise\")\n", + "display(df_no_noise.unstack().style.format(\"{:.2f}\").background_gradient(cmap='viridis'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 389927d798a1e641c882af11fab2d87acb5f27b3 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Fri, 29 Mar 2024 00:22:37 +0800 Subject: [PATCH 32/43] changes --- analyse_grid_experiment.ipynb | 1125 +++++++++++++++++++++++++++------ comparison.ipynb | 2 +- line_plot.ipynb | 252 ++++++++ run_grid_experiments.py | 4 +- 4 files changed, 1192 insertions(+), 191 deletions(-) create mode 100644 line_plot.ipynb diff --git a/analyse_grid_experiment.ipynb b/analyse_grid_experiment.ipynb index 64251c5..9b17e32 100644 --- a/analyse_grid_experiment.ipynb +++ b/analyse_grid_experiment.ipynb @@ -2,163 +2,556 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-03-28 22:39:28,614\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n" + "2024-03-28 23:45:15,122\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 4 noise 10 budget 10 seed 0, time: 1.00s\n", - "\u001b[36m(worker pid=5984)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", - "Started problem 4 noise 10 budget 10 seed 0, time: 2.12s\n", - "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([257.0693], dtype=torch.float64)\n" + "Started 1 2 noise 10 budget 30 seed 0, time: 1.00s\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\n", + "Started 2 2 noise 10 budget 30 seed 0, time: 2.13s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\n" + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 4 noise 100 budget 10 seed 0, time: 3.27s\n" + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[ 39.7946, -50.0000]], dtype=torch.float64), tensor([882.1972], dtype=torch.float64)\n", + "Started 3 2 noise 20 budget 30 seed 0, time: 3.25s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\n" + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 4 noise 100 budget 10 seed 0, time: 4.39s\n", - "Started problem 10 noise 10 budget 10 seed 0, time: 5.53s\n", - "Started problem 10 noise 10 budget 10 seed 0, time: 6.66s\n", - "\u001b[36m(worker pid=12024)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + "Started 4 2 noise 20 budget 30 seed 0, time: 4.37s\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-22.9021, -8.9259]], dtype=torch.float64), tensor([838.9029], dtype=torch.float64)\n", + "Started 5 10 noise 10 budget 30 seed 0, time: 5.51s\n", + "\u001b[36m(worker pid=21600)\u001b[0m Starting iteration 2, total time: 4.108 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "Started 6 10 noise 10 budget 30 seed 0, time: 6.65s\n", + "Started 7 10 noise 20 budget 30 seed 0, time: 7.79s\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 28.1781, -50.0000]], dtype=torch.float64), tensor([916.3048], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[-221.4744]], dtype=torch.float64), tensor([591.3097], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "Started problem 10 noise 100 budget 10 seed 0, time: 7.82s\n", - "Started problem 10 noise 100 budget 10 seed 0, time: 9.02s\n", - "Started problem 4 noise 10 budget 10 seed 1, time: 10.27s\n", - "Started problem 4 noise 10 budget 10 seed 1, time: 11.52s\n", - "\u001b[36m(worker pid=5244)\u001b[0m Starting iteration 3, total time: 7.398 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "Started problem 4 noise 100 budget 10 seed 1, time: 12.77s\n", - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[455.8119]], dtype=torch.float64), tensor([160.6682], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n" + "Started 8 10 noise 20 budget 30 seed 0, time: 8.94s\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-12.7701, -7.1403]], dtype=torch.float64), tensor([880.6680], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0mStarted 9 2 noise 10 budget 30 seed 1, time: 10.13s\n", + "\n", + "Started 10 2 noise 10 budget 30 seed 1, time: 11.48s\n", + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 3, total time: 9.211 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", + "Started 11 2 noise 20 budget 30 seed 1, time: 12.75s\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 19.8383, -13.8344]], dtype=torch.float64), tensor([851.0293], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "Started 12 2 noise 20 budget 30 seed 1, time: 14.01s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12436)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12436)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 4 noise 100 budget 10 seed 1, time: 13.98s\n", - "Started problem 10 noise 10 budget 10 seed 1, time: 15.18s\n", - "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 2, total time: 8.225 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "Started 13 10 noise 10 budget 30 seed 1, time: 15.27s\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[-4.0885, -4.4151]], dtype=torch.float64), tensor([859.9963], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 4, total time: 14.475 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ -2.3311, -25.0347]], dtype=torch.float64), tensor([845.3985], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18916)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[-310.3727]], dtype=torch.float64), tensor([130.3428], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[-43.2568, -9.9327]], dtype=torch.float64), tensor([858.0034], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m Starting iteration 6, total time: 20.949 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 50., -50.]], dtype=torch.float64), tensor([853.1866], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12436)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12436)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12436)\u001b[0m Starting iteration 4, total time: 14.192 seconds.\u001b[32m [repeated 16x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12436)\u001b[0m New candidate: tensor([[451.2381]], dtype=torch.float64), tensor([123.9714], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "Started problem 10 noise 10 budget 10 seed 1, time: 27.88s\n", - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 7, total time: 23.087 seconds.\u001b[32m [repeated 15x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[370.4692]], dtype=torch.float64), tensor([290.3323], dtype=torch.float64)\u001b[32m [repeated 15x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[4.6972, 0.5243]], dtype=torch.float64), tensor([852.7343], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 6, total time: 23.759 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=5244)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5244)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Started problem 10 noise 100 budget 10 seed 1, time: 32.42s\n", - "Started problem 10 noise 100 budget 10 seed 1, time: 33.60s\n" + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 7.8979, -27.8577]], dtype=torch.float64), tensor([816.3171], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-41.7001, -19.7992]], dtype=torch.float64), tensor([834.9580], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 9, total time: 32.832 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ -3.4078, -32.0686]], dtype=torch.float64), tensor([820.5409], dtype=torch.float64)\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 50.0000, -32.3497]], dtype=torch.float64), tensor([791.0505], dtype=torch.float64)\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[-34.1875, 36.2613]], dtype=torch.float64), tensor([836.2319], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 10, total time: 38.054 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 5.2522, -8.2017]], dtype=torch.float64), tensor([866.1509], dtype=torch.float64)\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-46.5142, 50.0000]], dtype=torch.float64), tensor([829.1963], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 10, total time: 41.094 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 5.2899, -32.8627]], dtype=torch.float64), tensor([812.7245], dtype=torch.float64)\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-18.3506, -27.5281]], dtype=torch.float64), tensor([798.8012], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 11, total time: 45.223 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 3.2589, -33.8143]], dtype=torch.float64), tensor([825.7982], dtype=torch.float64)\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-30.2566, -27.0914]], dtype=torch.float64), tensor([797.1991], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 12, total time: 50.454 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 7.2373, -29.8487]], dtype=torch.float64), tensor([795.9076], dtype=torch.float64)\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-50.0000, -19.9661]], dtype=torch.float64), tensor([857.3711], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 13, total time: 55.355 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 11.8526, -28.3617]], dtype=torch.float64), tensor([767.2271], dtype=torch.float64)\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-24.7510, -30.9211]], dtype=torch.float64), tensor([808.5573], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 50.0000, -17.2480]], dtype=torch.float64), tensor([736.8433], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m Starting iteration 12, total time: 57.665 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[38.4051, 50.0000]], dtype=torch.float64), tensor([835.7031], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[ 40.3226, -12.8266]], dtype=torch.float64), tensor([827.7604], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 16, total time: 69.325 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[50., 50.]], dtype=torch.float64), tensor([762.9765], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 50.0000, -23.6988]], dtype=torch.float64), tensor([775.0076], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m Starting iteration 14, total time: 69.723 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-50., -50.]], dtype=torch.float64), tensor([915.1133], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 41.1306, -24.0083]], dtype=torch.float64), tensor([815.3022], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m Starting iteration 15, total time: 75.540 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[50.0000, -2.2373]], dtype=torch.float64), tensor([789.9104], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 50.0000, -28.7868]], dtype=torch.float64), tensor([762.5755], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 20, total time: 86.525 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[50.0000, -9.9561]], dtype=torch.float64), tensor([815.4584], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ -8.7837, -32.1145]], dtype=torch.float64), tensor([866.3542], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 21, total time: 92.081 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[40.0832, -6.2144]], dtype=torch.float64), tensor([840.2569], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 3.3151, 50.0000]], dtype=torch.float64), tensor([816.5994], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 22, total time: 98.173 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[-25.1044, 50.0000]], dtype=torch.float64), tensor([776.6532], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 8.3266, 50.0000]], dtype=torch.float64), tensor([785.5709], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n", + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=16224)\u001b[0m Starting iteration 20, total time: 103.254 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[-27.1338, -29.3408]], dtype=torch.float64), tensor([806.4444], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 11.7550, -20.6524]], dtype=torch.float64), tensor([817.1873], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 20, total time: 106.290 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[50.0000, 20.1912]], dtype=torch.float64), tensor([867.6270], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 50.0000, -14.1050]], dtype=torch.float64), tensor([823.7826], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 25, total time: 118.353 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[-50.0000, -17.6397]], dtype=torch.float64), tensor([828.9475], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[ 50.0000, -13.8943]], dtype=torch.float64), tensor([795.2972], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 22, total time: 116.780 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-50.0000, -7.0270]], dtype=torch.float64), tensor([884.2838], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 11.5881, -28.4256]], dtype=torch.float64), tensor([820.9236], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 10.3265, -38.3447]], dtype=torch.float64), tensor([842.7911], dtype=torch.float64)\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 27, total time: 128.574 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m New candidate: tensor([[50.0000, 46.3396]], dtype=torch.float64), tensor([753.8016], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 50.0000, -27.0164]], dtype=torch.float64), tensor([809.2819], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m Starting iteration 20, total time: 132.525 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[50., 50.]], dtype=torch.float64), tensor([763.6005], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 25.7630, -16.6807]], dtype=torch.float64), tensor([808.9346], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 25, total time: 133.565 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[42.8940, 50.0000]], dtype=torch.float64), tensor([792.8038], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 30.9688, -28.6224]], dtype=torch.float64), tensor([828.3876], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "Started 14 10 noise 10 budget 30 seed 1, time: 145.49s\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 1, total time: 4.049 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-50., -50.]], dtype=torch.float64), tensor([903.5702], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ -8.4299, -28.6028]], dtype=torch.float64), tensor([820.0672], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "Started 15 10 noise 20 budget 30 seed 1, time: 151.66s\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 2, total time: 9.295 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[31.3218, -5.0777]], dtype=torch.float64), tensor([886.3668], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ -3.9800, -50.0000]], dtype=torch.float64), tensor([873.1629], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started 16 10 noise 20 budget 30 seed 1, time: 156.41s\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 3, total time: 14.962 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[-26.1165, 44.1587]], dtype=torch.float64), tensor([810.5547], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[ -3.1083, -11.8278]], dtype=torch.float64), tensor([846.2030], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 2, total time: 9.647 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[-25.6619, 50.0000]], dtype=torch.float64), tensor([790.4702], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 7.2330, -26.9222]], dtype=torch.float64), tensor([789.4201], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m Starting iteration 1, total time: 2.755 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m New candidate: tensor([[500.]], dtype=torch.float64), tensor([608.2261], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", "0\n", + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 4, total time: 19.373 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[-15.2542, -10.3626]], dtype=torch.float64), tensor([835.2637], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 13.9418, -34.5552]], dtype=torch.float64), tensor([809.1237], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", "0\n" ] }, @@ -166,57 +559,69 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=12024)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12024)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 2, total time: 9.338 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-24.6997, 47.8755]], dtype=torch.float64), tensor([765.0407], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ -0.6654, -12.1565]], dtype=torch.float64), tensor([788.3081], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 0, total time: 0.000 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=12024)\u001b[0m New candidate: tensor([[420.7220]], dtype=torch.float64), tensor([-8.5333], dtype=torch.float64)\u001b[32m [repeated 15x across cluster]\u001b[0m\n", - "0\n", - "0\n", - "0\n", - "\u001b[36m(worker pid=5984)\u001b[0m Starting iteration 6, total time: 18.163 seconds.\u001b[32m [repeated 14x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=5984)\u001b[0m New candidate: tensor([[-500.]], dtype=torch.float64), tensor([235.9000], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 8, total time: 37.944 seconds.\u001b[32m [repeated 11x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[-5.1859, -7.0773]], dtype=torch.float64), tensor([799.1559], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[ -0.8872, -24.8199]], dtype=torch.float64), tensor([812.2661], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13296)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m warnings.warn(\u001b[32m [repeated 9x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=16224)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 4, total time: 12.347 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[47.6366]], dtype=torch.float64), tensor([387.5126], dtype=torch.float64)\u001b[32m [repeated 12x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 5, total time: 21.111 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-50.0000, 40.9712]], dtype=torch.float64), tensor([879.9357], dtype=torch.float64)\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ -5.2461, -25.6446]], dtype=torch.float64), tensor([852.7321], dtype=torch.float64)\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 43.2992, -38.5375]], dtype=torch.float64), tensor([819.8991], dtype=torch.float64)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=1928)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=1928)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\optimize.py:367: RuntimeWarning: Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", - "\u001b[36m(worker pid=13296)\u001b[0m [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", - "\u001b[36m(worker pid=13296)\u001b[0m Trying again with a new set of initial conditions.\n", - "\u001b[36m(worker pid=13296)\u001b[0m warnings.warn(first_warn_msg, RuntimeWarning)\n" + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 3, total time: 15.013 seconds.\u001b[32m [repeated 13x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[407.8906]], dtype=torch.float64), tensor([-10.9544], dtype=torch.float64)\u001b[32m [repeated 13x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m Starting iteration 6, total time: 25.745 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[-32.0303, 50.0000]], dtype=torch.float64), tensor([806.4923], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 50.0000, -10.4556]], dtype=torch.float64), tensor([790.5641], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", "0\n" ] }, @@ -224,35 +629,351 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 10, total time: 48.259 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[-14.4521, -20.6974]], dtype=torch.float64), tensor([788.0802], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m New candidate: tensor([[ 48.5349, -38.2677]], dtype=torch.float64), tensor([807.3869], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 10, total time: 49.010 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-12.7355, -9.1832]], dtype=torch.float64), tensor([792.5068], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 19.2634, -43.2288]], dtype=torch.float64), tensor([865.4372], dtype=torch.float64)\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 50., -50.]], dtype=torch.float64), tensor([820.4086], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 11, total time: 54.396 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-28.7328, -10.3676]], dtype=torch.float64), tensor([807.7967], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ -2.0508, -20.0832]], dtype=torch.float64), tensor([849.6891], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m Starting iteration 11, total time: 47.945 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[-37.2390, 32.6825]], dtype=torch.float64), tensor([837.7983], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ -9.7445, -15.3912]], dtype=torch.float64), tensor([819.6380], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 12, total time: 55.075 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-29.2553, 50.0000]], dtype=torch.float64), tensor([791.5271], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ -0.4768, -12.8039]], dtype=torch.float64), tensor([826.4063], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 5.7003, -22.8672]], dtype=torch.float64), tensor([809.5272], dtype=torch.float64)\n", + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 13, total time: 60.470 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[43.7032, 50.0000]], dtype=torch.float64), tensor([797.4740], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ -6.5204, -50.0000]], dtype=torch.float64), tensor([873.5776], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 18, total time: 87.896 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[40.3901, 50.0000]], dtype=torch.float64), tensor([805.4399], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 7.3687, -16.1100]], dtype=torch.float64), tensor([805.0210], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 15, total time: 70.966 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m New candidate: tensor([[41.4368, 14.5369]], dtype=torch.float64), tensor([823.0945], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 40.3352, -35.4813]], dtype=torch.float64), tensor([829.6786], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 20, total time: 98.177 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-23.9160, 21.9873]], dtype=torch.float64), tensor([847.8563], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ -0.0502, -19.0402]], dtype=torch.float64), tensor([816.6353], dtype=torch.float64)\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 50.0000, -32.5339]], dtype=torch.float64), tensor([773.7052], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 17, total time: 81.008 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-31.4983, -50.0000]], dtype=torch.float64), tensor([852.5990], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 50.0000, -30.7047]], dtype=torch.float64), tensor([793.2208], dtype=torch.float64)\n", + "\u001b[36m(worker pid=7436)\u001b[0m Starting iteration 22, total time: 108.773 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[50.0000, 17.2285]], dtype=torch.float64), tensor([822.1293], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 50.0000, -13.6179]], dtype=torch.float64), tensor([786.0627], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 19, total time: 91.827 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-15.4671, -7.8007]], dtype=torch.float64), tensor([826.9511], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[ 38.7456, -31.9759]], dtype=torch.float64), tensor([826.4608], dtype=torch.float64)\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 45.8373, -13.7136]], dtype=torch.float64), tensor([814.5100], dtype=torch.float64)\n", + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 20, total time: 96.914 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=7436)\u001b[0m New candidate: tensor([[-30.4446, 50.0000]], dtype=torch.float64), tensor([774.5900], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 50.0000, -17.3952]], dtype=torch.float64), tensor([805.6346], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 23, total time: 113.456 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[50.0000, 10.5149]], dtype=torch.float64), tensor([796.6671], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=19872)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 41.7309, -34.3034]], dtype=torch.float64), tensor([812.4763], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 24, total time: 123.603 seconds.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[-33.2006, -1.5149]], dtype=torch.float64), tensor([816.0258], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 50.0000, -28.5068]], dtype=torch.float64), tensor([772.5629], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m Starting iteration 25, total time: 124.341 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-8.5319, 50.0000]], dtype=torch.float64), tensor([801.9322], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ 3.1303, -18.8035]], dtype=torch.float64), tensor([803.7331], dtype=torch.float64)\n", + "\u001b[36m(worker pid=19872)\u001b[0m Starting iteration 26, total time: 134.097 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=19872)\u001b[0m New candidate: tensor([[-50.0000, 2.1180]], dtype=torch.float64), tensor([861.9473], dtype=torch.float64)\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 50.0000, -31.2472]], dtype=torch.float64), tensor([768.8106], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=11148)\u001b[0m Starting iteration 24, total time: 118.792 seconds.\u001b[32m [repeated 10x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[-24.4003, 50.0000]], dtype=torch.float64), tensor([773.3922], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[ 50.0000, -14.8577]], dtype=torch.float64), tensor([785.9328], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 7, total time: 22.743 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[395.9327]], dtype=torch.float64), tensor([74.1132], dtype=torch.float64)\u001b[32m [repeated 10x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=8932)\u001b[0m Starting iteration 24, total time: 129.253 seconds.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[-20.3774, 46.9667]], dtype=torch.float64), tensor([784.1892], dtype=torch.float64)\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 42.7733, -13.8050]], dtype=torch.float64), tensor([806.1760], dtype=torch.float64)\u001b[32m [repeated 3x across cluster]\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=18444)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 8x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=18444)\u001b[0m warnings.warn(\u001b[32m [repeated 8x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=16224)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\u001b[36m(worker pid=16224)\u001b[0m Starting iteration 29, total time: 141.661 seconds.\u001b[32m [repeated 9x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[-23.1438, 44.4639]], dtype=torch.float64), tensor([791.5253], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=16224)\u001b[0m New candidate: tensor([[ 1.3242, -19.8745]], dtype=torch.float64), tensor([803.6881], dtype=torch.float64)\u001b[32m [repeated 2x across cluster]\u001b[0m\n", "0\n", "0\n", - "\u001b[36m(worker pid=20068)\u001b[0m Starting iteration 9, total time: 29.110 seconds.\u001b[32m [repeated 12x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=20068)\u001b[0m New candidate: tensor([[364.4596]], dtype=torch.float64), tensor([332.3226], dtype=torch.float64)\u001b[32m [repeated 14x across cluster]\u001b[0m\n", "0\n", + "\u001b[36m(worker pid=2788)\u001b[0m Starting iteration 29, total time: 141.226 seconds.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[-23.2098, 50.0000]], dtype=torch.float64), tensor([796.0394], dtype=torch.float64)\u001b[32m [repeated 7x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=18916)\u001b[0m New candidate: tensor([[ 50., -50.]], dtype=torch.float64), tensor([830.0307], dtype=torch.float64)\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 50.0000, -26.6882]], dtype=torch.float64), tensor([783.2225], dtype=torch.float64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 5x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0\n" ] }, @@ -260,20 +981,52 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m(worker pid=13296)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 7x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m warnings.warn(\u001b[32m [repeated 7x across cluster]\u001b[0m\n" + "\u001b[36m(worker pid=21600)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 3x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m warnings.warn(\u001b[32m [repeated 3x across cluster]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m Starting iteration 25, total time: 116.711 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=21600)\u001b[0m New candidate: tensor([[48.4517, 3.3870]], dtype=torch.float64), tensor([790.9030], dtype=torch.float64)\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=2788)\u001b[0m New candidate: tensor([[ 5.9724, 50.0000]], dtype=torch.float64), tensor([806.1060], dtype=torch.float64)\n", + "\u001b[36m(worker pid=11148)\u001b[0m New candidate: tensor([[ 43.3958, -23.9999]], dtype=torch.float64), tensor([816.2181], dtype=torch.float64)\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=7436)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\botorch\\optim\\initializers.py:432: BadInitialCandidatesWarning: Unable to find non-zero acquisition function values - initial conditions are being selected randomly.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=8932)\u001b[0m c:\\Users\\queim\\micromambaenv\\envs\\mobo\\lib\\site-packages\\gpytorch\\likelihoods\\noise_models.py:148: NumericalWarning: Very small noise values detected. This will likely lead to numerical instabilities. Rounding small noise values up to 1e-06.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m warnings.warn(\u001b[32m [repeated 6x across cluster]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(worker pid=21600)\u001b[0m Starting iteration 27, total time: 120.961 seconds.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", + "\u001b[36m(worker pid=8932)\u001b[0m New candidate: tensor([[-16.5342, 45.6331]], dtype=torch.float64), tensor([811.4356], dtype=torch.float64)\u001b[32m [repeated 6x across cluster]\u001b[0m\n", "0\n", "0\n", - "\u001b[36m(worker pid=13296)\u001b[0m Starting iteration 9, total time: 31.273 seconds.\u001b[32m [repeated 6x across cluster]\u001b[0m\n", - "\u001b[36m(worker pid=13296)\u001b[0m New candidate: tensor([[455.3359]], dtype=torch.float64), tensor([200.7786], dtype=torch.float64)\u001b[32m [repeated 11x across cluster]\u001b[0m\n", - "0\n", - "all experiments done, time: 74.30s\n" + "all experiments done, time: 324.22s\n" ] } ], @@ -282,26 +1035,24 @@ "import pandas as pd\n", "from run_grid_experiments import run_grid_experiments\n", "\n", - "seeds = [0,1]\n", - "n_inits = [4, 10]\n", - "noise_levels = [10, 100]\n", - "# budgets = [10, 20, 50]\n", + "seeds = list(range(2))\n", + "n_inits = [2,10]\n", + "noise_levels = [10, 20]\n", "noise_bools = [True, False]\n", - "budget = 10\n", - "\n", + "budget = 30\n", "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_16892\\636701015.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_18664\\636701015.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n" ] }, @@ -336,35 +1087,35 @@ " \n", " \n", " 0\n", - " 4\n", + " 2\n", " 10\n", " 0\n", " True\n", - " 207.341003\n", + " 767.079651\n", " \n", " \n", " 0\n", - " 4\n", + " 2\n", " 10\n", " 1\n", " True\n", - " 42.158676\n", + " 767.079651\n", " \n", " \n", " 0\n", - " 4\n", - " 100\n", + " 2\n", + " 20\n", " 0\n", " True\n", - " 120.417244\n", + " 767.079651\n", " \n", " \n", " 0\n", - " 4\n", - " 100\n", + " 2\n", + " 20\n", " 1\n", " True\n", - " 42.158676\n", + " 767.079651\n", " \n", " \n", " 0\n", @@ -372,7 +1123,7 @@ " 10\n", " 0\n", " True\n", - " 1.374434\n", + " 767.079651\n", " \n", " \n", " 0\n", @@ -380,55 +1131,55 @@ " 10\n", " 1\n", " True\n", - " 42.158676\n", + " 767.079651\n", " \n", " \n", " 0\n", " 10\n", - " 100\n", + " 20\n", " 0\n", " True\n", - " 102.431664\n", + " 767.079651\n", " \n", " \n", " 0\n", " 10\n", - " 100\n", + " 20\n", " 1\n", " True\n", - " 42.158676\n", + " 767.079651\n", " \n", " \n", " 0\n", - " 4\n", + " 2\n", " 10\n", " 0\n", " False\n", - " 118.466827\n", + " 767.079651\n", " \n", " \n", " 0\n", - " 4\n", + " 2\n", " 10\n", " 1\n", " False\n", - " 0.138172\n", + " 781.773621\n", " \n", " \n", " 0\n", - " 4\n", - " 100\n", + " 2\n", + " 20\n", " 0\n", " False\n", - " 118.751045\n", + " 767.079651\n", " \n", " \n", " 0\n", - " 4\n", - " 100\n", + " 2\n", + " 20\n", " 1\n", " False\n", - " 13.000295\n", + " 794.947937\n", " \n", " \n", " 0\n", @@ -436,7 +1187,7 @@ " 10\n", " 0\n", " False\n", - " 0.007694\n", + " 778.442871\n", " \n", " \n", " 0\n", @@ -444,23 +1195,23 @@ " 10\n", " 1\n", " False\n", - " 0.265530\n", + " 779.081116\n", " \n", " \n", " 0\n", " 10\n", - " 100\n", + " 20\n", " 0\n", " False\n", - " 25.227522\n", + " 801.972839\n", " \n", " \n", " 0\n", " 10\n", - " 100\n", + " 20\n", " 1\n", " False\n", - " 0.034306\n", + " 794.947937\n", " \n", " \n", "\n", @@ -468,25 +1219,25 @@ ], "text/plain": [ " n_init noise_level seed noise_bool best\n", - "0 4 10 0 True 207.341003\n", - "0 4 10 1 True 42.158676\n", - "0 4 100 0 True 120.417244\n", - "0 4 100 1 True 42.158676\n", - "0 10 10 0 True 1.374434\n", - "0 10 10 1 True 42.158676\n", - "0 10 100 0 True 102.431664\n", - "0 10 100 1 True 42.158676\n", - "0 4 10 0 False 118.466827\n", - "0 4 10 1 False 0.138172\n", - "0 4 100 0 False 118.751045\n", - "0 4 100 1 False 13.000295\n", - "0 10 10 0 False 0.007694\n", - "0 10 10 1 False 0.265530\n", - "0 10 100 0 False 25.227522\n", - "0 10 100 1 False 0.034306" + "0 2 10 0 True 767.079651\n", + "0 2 10 1 True 767.079651\n", + "0 2 20 0 True 767.079651\n", + "0 2 20 1 True 767.079651\n", + "0 10 10 0 True 767.079651\n", + "0 10 10 1 True 767.079651\n", + "0 10 20 0 True 767.079651\n", + "0 10 20 1 True 767.079651\n", + "0 2 10 0 False 767.079651\n", + "0 2 10 1 False 781.773621\n", + "0 2 20 0 False 767.079651\n", + "0 2 20 1 False 794.947937\n", + "0 10 10 0 False 778.442871\n", + "0 10 10 1 False 779.081116\n", + "0 10 20 0 False 801.972839\n", + "0 10 20 1 False 794.947937" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -508,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -522,32 +1273,28 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -559,24 +1306,24 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 bestbest
 meanstdmeanstd
noise_level101001010010201020
n_init
4124.7581.29116.8055.342767.08767.080.000.00
1021.7772.3028.8442.6210767.08767.080.000.00
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -593,32 +1340,32 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -630,24 +1377,24 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 bestbest
 meanstdmeanstd
noise_level101001010010201020
n_init
459.3065.8883.6774.782774.43781.0110.3919.71
100.1412.630.1817.8110778.76798.460.454.97
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/comparison.ipynb b/comparison.ipynb index d9d45f5..61d7847 100644 --- a/comparison.ipynb +++ b/comparison.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Starting iteration 0, total time: 0.000 seconds.\n","New candidate: tensor([[-26.2945, 50.0000]], dtype=torch.float64), tensor([773.9874], dtype=torch.float64)\n"]},{"data":{"text/plain":["ExactMarginalLogLikelihood(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (model): SingleTaskGP(\n"," (likelihood): GaussianLikelihood(\n"," (noise_covar): HomoskedasticNoise(\n"," (noise_prior): GammaPrior()\n"," (raw_noise_constraint): GreaterThan(1.000E-04)\n"," )\n"," )\n"," (mean_module): ConstantMean()\n"," (covar_module): ScaleKernel(\n"," (base_kernel): MaternKernel(\n"," (lengthscale_prior): GammaPrior()\n"," (raw_lengthscale_constraint): Positive()\n"," )\n"," (outputscale_prior): GammaPrior()\n"," (raw_outputscale_constraint): Positive()\n"," )\n"," (outcome_transform): Standardize()\n"," )\n",")"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=10)\n","\n","\n","seed = 0 \n","n_inits = 10\n","noise_level = 5\n","noise_bool = True\n","\n","n_init = 50\n","budget = 1\n","\n","\n","torch.manual_seed(seed)\n","np.random.seed(seed)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","\n","train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, noise_bool)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, noise_bool)\n","fit_gpytorch_model(mll)\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 773.9874075531119\n","Best solution found: [-26.29448786 50. ]\n","Best real value found: 778.4647403590802\n","Best real solution found: [-26.29448786 50. ]\n","Total number of evaluations: 51\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Best real value found: {train_Y_real.min().item()}\")\n","print(f\"Best real solution found: {train_X[train_Y_real.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["\n","\n","#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","\n","# Plot initial samples\n","# plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","# plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n","SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"ename":"NameError","evalue":"name 'n_init' is not defined","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[1;32mIn[2], line 22\u001b[0m\n\u001b[0;32m 18\u001b[0m problem \u001b[38;5;241m=\u001b[39m SchwefelProblem(n_var\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, noise_level\u001b[38;5;241m=\u001b[39mnoise_level)\n\u001b[0;32m 20\u001b[0m bounds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(problem\u001b[38;5;241m.\u001b[39mbounds, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtkwargs)\n\u001b[1;32m---> 22\u001b[0m train_X, train_Y, train_Y_real\u001b[38;5;241m=\u001b[39m generate_initial_data(problem, \u001b[43mn_init\u001b[49m, bounds)\n\u001b[0;32m 24\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time()\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(budget):\n","\u001b[1;31mNameError\u001b[0m: name 'n_init' is not defined"]}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","\n","seed = 0 \n","n_inits = 60\n","noise_level = 10\n","noise_bool = True\n","budget = 1\n","\n","\n","torch.manual_seed(seed)\n","np.random.seed(seed)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","\n","train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, noise_bool)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, noise_bool)\n","fit_gpytorch_model(mll)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 764.5328592764758\n","Best solution found: [-25.51163547 38.93445618]\n","Best real value found: 801.2781865671863\n","Best real solution found: [ 43.27939814 -25.89256121]\n","Total number of evaluations: 51\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Best real value found: {train_Y_real.min().item()}\")\n","print(f\"Best real solution found: {train_X[train_Y_real.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","F = torch.tensor(problem.f(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), F.cpu().numpy(), label='True function without noise', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","\n","# Plot initial samples\n","# plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","# plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import torch\n","\n","x1 = torch.linspace(bounds[0, 0], bounds[1, 0], 100) # 100 points along x1\n","x2 = torch.linspace(bounds[0, 1], bounds[1, 1], 100) # 100 points along x2\n","X1, X2 = torch.meshgrid(x1, x2) # Create a meshgrid\n","X = torch.stack([X1.flatten(), X2.flatten()], -1) # Stack and flatten to create [N, 2] input tensor\n","\n","\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach().view(X1.shape) # Reshape mean to match the grid\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower.view(X1.shape) # Reshape to match the grid\n"," upper = -upper.view(X1.shape)\n","\n","# True function\n","Y = torch.tensor(problem.y(X.cpu().numpy())).view(X1.shape)\n","\n","# Plotting\n","fig = plt.figure(figsize=(18, 6))\n","plt.suptitle(\"noise_10\")\n","ax = fig.add_subplot(1, 2, 1, projection='3d')\n","ax.plot_surface(X1.numpy(), X2.numpy(), mean.cpu().numpy(), cmap='viridis', alpha=0.7, label='GP Mean')\n","ax.set_title('GP Mean')\n","\n","ax2 = fig.add_subplot(1, 3, 2, projection='3d')\n","ax2.plot_surface(X1.numpy(), X2.numpy(), Y.cpu().numpy(), cmap='viridis', alpha=0.7, label='True Function')\n","ax2.set_title('True Function')\n","\n","\n","for ax in [ax, ax2]:\n"," ax.set_xlabel('X1')\n"," ax.set_ylabel('X2')\n"," ax.set_zlabel('Objective')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["prob = problem\n","n_obj = 1\n","bounds = prob.bounds\n","n = 30\n","\n","x1 = np.linspace(bounds[0, 0], bounds[1, 0], n)\n","x2 = np.linspace(bounds[0, 1], bounds[1, 1], n)\n","x1, x2 = np.meshgrid(x1, x2)\n","X = np.stack([x1.flatten(), x2.flatten()]).T\n","f = prob.f(X).reshape(n, n, n_obj)\n","eps = prob.eps(X).reshape(n, n, n_obj)\n","y = prob.y(X).reshape(n, n, n_obj)\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} diff --git a/line_plot.ipynb b/line_plot.ipynb new file mode 100644 index 0000000..1fbc1ab --- /dev/null +++ b/line_plot.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from run_grid_experiments import run_grid_experiments" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_12420\\752471085.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
02100True767.079651
02101True767.079651
02100False767.079651
02101False781.773621
\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 10 0 True 767.079651\n", + "0 2 10 1 True 767.079651\n", + "0 2 10 0 False 767.079651\n", + "0 2 10 1 False 781.773621" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "seeds = list(range(2))\n", + "n_inits = [2]\n", + "noise_levels = [10]\n", + "noise_bools = [True, False]\n", + "budget = 30\n", + "\n", + "sm_list = {}\n", + "df = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + "df " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(2,\n", + " 10,\n", + " True): (tensor([803.5202, 802.7020, 802.7020, 802.7020, 802.7020, 802.7020, 802.7020,\n", + " 802.7020, 802.7020, 795.6658, 795.6658, 786.6938, 786.6938, 782.3187,\n", + " 782.3187, 778.4468, 778.4468, 772.7598, 772.7598, 772.7598, 767.0797,\n", + " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797,\n", + " 767.0797, 767.0797, 767.0797, 767.0797]), tensor([1.2123e+01, 1.0966e+01, 1.0966e+01, 1.0966e+01, 1.0966e+01, 1.0966e+01,\n", + " 1.0966e+01, 1.0966e+01, 1.0966e+01, 1.0151e+00, 1.0151e+00, 1.1673e+01,\n", + " 1.1673e+01, 5.4855e+00, 5.4855e+00, 9.8401e-03, 9.8401e-03, 8.0329e+00,\n", + " 8.0329e+00, 8.0329e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00])),\n", + " (2,\n", + " 10,\n", + " False): (tensor([803.5202, 802.7020, 802.7020, 802.7020, 802.5529, 802.5529, 796.6227,\n", + " 796.6227, 795.3466, 795.3466, 795.3466, 779.7377, 779.7377, 779.7377,\n", + " 779.7377, 779.7377, 779.7377, 779.7377, 778.0778, 778.0778, 775.8955,\n", + " 774.4266, 774.4266, 774.4266, 774.4266, 774.4266, 774.4266, 774.4266,\n", + " 774.4266, 774.4266, 774.4266, 774.4266]), tensor([12.1230, 10.9659, 10.9659, 10.9659, 10.7551, 10.7551, 2.3685, 2.3685,\n", + " 4.1732, 4.1732, 4.1732, 17.9011, 17.9011, 17.9011, 17.9011, 17.9011,\n", + " 17.9011, 17.9011, 15.5536, 15.5536, 12.4675, 10.3902, 10.3902, 10.3902,\n", + " 10.3902, 10.3902, 10.3902, 10.3902, 10.3902, 10.3902, 10.3902, 10.3902]))}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sm_list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAG1CAYAAADX6N+4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABinklEQVR4nO3dd3gUdf4H8PfMbE02ZdNDCYQAiSAlFIETBYXDcuiJ4KkHothFwQ6o2A/1BESlCqJYQDks6P2wHXbvINItNEMNkErKpmyf+f2xZGFJ2WST7E6S9+t58pDMzsx+58Pu7Ge/VVAURQERERGRComhLgARERFRXZioEBERkWoxUSEiIiLVYqJCREREqsVEhYiIiFSLiQoRERGpFhMVIiIiUi0mKkRERKRaTFSIqE1RwxyWaigDUVvBRIUoQPv378f999+P888/H+eeey6GDx+O++67D3v37vXZ7+KLL8asWbMAAMeOHUN6ejo++uijes995jEAkJ6ejoULFzb/RTSThl5Xc1u4cCHS09O9f2/btg233357yMplsVgwY8YMbN26NSjPN2vWLFx88cVBeS6iUNGEugBErdEff/yBa6+9Fv3798fs2bMRGxuLvLw8vPvuu/jb3/6Gt99+G/37969xXEJCAtauXYuUlJRGPd/atWuRlJTUTKVvfoFeV1Ndc801uOCCC7x/r1u3DgcOHAhqGc60Z88efPLJJxg/fnxQnm/q1KmYPHlyUJ6LKFSYqBAF4M0334TZbMaKFSug0Zx+G40ePRqXXnoplixZguXLl9c4TqfT1ZrA+BPIMcEU6HU1VVJSkqoTuJYW7MSQKBTY9EMUgKKiIiiKAlmWfbaHhYXh0UcfxWWXXVbrcbU1RezduxdTpkxBZmYmLrroInz66ac1jjuz6ScrKwvp6enYtGkTbr75ZvTr1w/nn38+5s6dC7fb7T2moqICTzzxBIYNG4bMzEzcf//9WLVqlU9TSV3l++KLLzB16lT0798ff/rTn7BkyRJUVFTg0UcfxcCBA/GnP/0Jc+fO9fbFOPu6PvroI/Tq1Qu7du3Ctddeiz59+uCiiy7CypUr63zut99+GxkZGSgpKfFuW7x4sfdaq23cuBEZGRnIz8/3afqZNWsWPv74Yxw/frxGjAsLCzF9+nRkZmbivPPOw+OPP47Kykrv4263G6tXr8YVV1yBvn37YuTIkZg3bx7sdrt3nxtuuAE33HCDT5mr/y+ysrKQlZXlrd2YPHlyjX3PjvHnn3/uU6bZs2ejqqqqUWU6u+nnt99+w4033oiBAwciMzMTN910E3bu3Onz/Fu3bsWkSZPQr18/nHfeeZg5cyaKi4vr/H8hCjUmKkQBGDlyJE6cOIHrrrsOq1evxoEDB7wf2pdeeinGjRvXoPPk5+dj0qRJKC8vx9y5c3Hvvfdi3rx5yM/P93vsQw89hIEDB2LZsmUYO3YsXn/9daxbt877+NSpU/H5559j2rRpWLBgASorKzF//vwGlWv27Nno2bMnli5dimHDhuGVV17BhAkTYDAYsGjRIowZMwavv/46vvjiizrPIcsy7rvvPlx++eVYvnw5BgwYgBdffBE//vhjrfuPHDkSiqJg8+bN3m3Vv2/ZssW77YcffkCvXr2QmJjoc/zUqVMxYsQIxMfHY+3atRg5cqT3sVdeeQXJyclYsmQJbrzxRvzrX//CokWLvI8/8cQTeP755zF69GgsXboUEydOxLvvvoupU6c2uGNs79698cQTT3jP9+STT9a7/5NPPomOHTtiyZIluOWWW/DBBx9g6dKlAZepoqICt956K8xmMxYuXIgFCxbAarXilltuQXl5uTeON910EwwGA15++WU8+uij+PnnnzF58mTYbLYGXSdRsLHphygAf//731FYWIiVK1fimWeeAQCYzWYMHz4ckydPRt++fRt0nlWrVsHtdmP58uWIiYkBAKSmpuJvf/ub32OvueYa3H333QCAYcOGYePGjfjuu+9w3XXXYdOmTcjKysLChQsxZswYAMCFF16IsWPHNqgPxwUXXID77rsPANCjRw/83//9H2JjY70fxEOHDsW///1vbN++vc7aI0VRMHXqVFxzzTUAgIEDB+I///kPvvvuO59+JdVSUlKQmpqKTZs24bLLLoPVasWOHTvQu3dvn0Tlxx9/xNVXX13r8TExMT7NUNU1FJdccgkeeeQRb6z++9//epOg7OxsfPDBB3jwwQe9HXHPP/98JCQkYMaMGfjhhx8wYsQIvzEzmUzo3r07AKB79+7e3+syYsQIzJw506dM3333HR588MGAypSdnY2SkhJMnjwZAwYMAAB069YNa9euRWVlJSIiIjB//nykpqbitddegyRJAIB+/frhL3/5Cz788ENMnDjR73USBRtrVIgCdO+99+LHH3/E/PnzMWHCBJhMJvz73//2dqZtiG3btqF///7eJAXwfHB06NDB77GZmZk+fyclJXk/mDdv3gytVovRo0d7HxdFEZdffnmDynXmuePi4gDAJ/kSBAFRUVHeb+oNOY9Op0NMTIxP88bZRo4cif/9738APLHRarWYPHkydu3aBYfDgezsbJw4ccKntqQhBg0a5PN3p06dYLFYAAA///wzAOAvf/mLzz5/+ctfIEkSsrKyGvVcDXV2n54z//8CKVOPHj0QExODO++8E0888QT+85//IC4uDg8//DCSkpJgtVqxa9cujBgxAoqiwOVyweVyoXPnzkhLS8N///vfFrlOoqZijQpRE0RFRWHs2LEYO3YsAGD37t14+OGHMXfuXFxxxRUwm831Hl9WVoZOnTrV2B4fH+/3uQ0Gg8/foih6mwRKSkoQHR0NUfT9LhIbG+v3vICnduBsYWFhDTq2oWWszYgRI/Dmm2/i2LFj2LRpEwYMGIBhw4bBbrdj165d+O233xAfH49zzz23UeUwGo11lqOsrAxAzZhrNBqYzWa/yVigmrtM4eHhWL16NZYuXYrPP/8ca9euhcFgwF//+lfMnj0bFosFsixjxYoVWLFiRY3j9Xp9c10aUbNiokLUSPn5+Rg/fjzuvfdeb7NGtV69euH+++/H3XffjZycHL+JitlsRlFRUY3tpaWlTSpjYmIiSkpKIMuyT7Jy8uTJJp23pQ0aNAgmkwmbNm3C5s2bcckllyAxMRFdu3ZFVlYWtm3bhpEjR0IQhGZ7zqioKACeDrcdO3b0bnc6nSgpKfH5PzyzszKAemuHglWmM3Xr1s3bqfqXX37BJ598gvfeew8pKSm47rrrIAgCbrrppho1NUDNxIlILdj0Q9RIcXFx0Gg0WLNmjc8IjGoHDx6EXq9Hly5d/J5r6NCh2LFjh0/n2ezsbOTk5DSpjOeddx5cLhe++eYb7zZFUbBx48YmnbelabVanH/++fj666+xZ88enHfeeQA8cfruu++wdetWXHTRRXUef3YNUkNUP8eGDRt8tm/YsAFutxsDBw4E4KllysvL89ln27ZtPn9X9/toqoaW6UxffPEFhg4disLCQkiShMzMTDz11FOIjIzEiRMnYDKZ0KtXLxw8eBB9+vTx/vTo0QMLFy5ssSYuoqZijQpRI0mShKeeegp33303xo8fj4kTJyItLQ1WqxX//e9/sXr1atx7773eb8X1ufHGG/HBBx/glltuwbRp0+B2u7FgwQJotdomlXHw4ME4//zz8dhjj6GoqAgdOnTABx98gH379jVrbURLGDFiBB599FGEhYV5m3iGDBmC999/H3q9Hn/605/qPDYyMhJFRUX4/vvvcc455zTo+bp3745x48bh1VdfhdVqxeDBg7Fnzx4sWrQIQ4YM8Xb8veiii/DNN9/g+eefx8UXX4ytW7di/fr1PueKiIgAAHz33XeIiopCRkZGABFoeJnONGDAAMiyjLvvvhu33347wsPD8fnnn6O8vNzbofqBBx7A7bffjgcffBBXXnkl3G433njjDezatQtTp04NqKxELY2JClEARo4ciX/9619YuXIlli1bhuLiYuh0OvTq1QsLFizwfjD4Yzab8d5772HOnDmYNWsWwsPDceutt+Kzzz5rchkXLFiAF154AfPnz4fL5cKoUaNw/fXX1/hwVZsRI0ZAEAQMGDDAO5nekCFDIAgChgwZUm8TxdVXX43vv/8ed999N6ZPn97gzsNz5sxBly5d8OGHH2LFihVISEjA5MmTMXXqVG8tzfjx43H06FF8/PHHeP/99zF48GC8+uqruP76673n6dGjB8aOHYvVq1fjxx9/xP/93/8FHIeGlOlMCQkJeP311/HKK6/gscceg9Vq9daWDB06FAAwfPhwrFy5EosWLcL06dOh1WrRu3dvvPnmm6qfVJDaL0Hh6llEbc7x48exc+dOjBo1yqdD6/Tp05GTk4OPP/44hKUjImo41qgQtUGiKGLWrFkYNWoUJkyYAEmS8OOPP+Krr77C888/H+riERE1GGtUiNqozZs3Y/HixdizZw9cLhfS0tIwZcoU71BqIqLWgIkKERERqRaHJxMREZFqMVEhIiIi1WKiQkRERKrFRIWIiIhUq00MT1YUBbLc/H2CRVFokfO2JYyRf4yRf4xR/Rgf/xgj/9QWI1EUGjRTdptIVGRZQXFxZbOeU6MRYTaHw2KpgsslN+u52wrGyD/GyD/GqH6Mj3+MkX9qjFFMTDgkyX+iwqYfIiIiUi0mKkRERKRaTFSIiIhItZioEBERkWoxUSEiIiLVahOjfoiIGkuWZbjdrlAXwy9ZFmCzSXA47HC71TO0VE0YI/+CHSNJ0kAUm6cuhIkKEbUriqLAYimG1VoR6qI0WFGRCFlWx5BStWKM/At2jIxGEyIjYxo0V0p9mKgQUbtSnaSYTGbodPom30SDQZIE1hT4wRj5F6wYKYoCh8OOiooSAEBUVGyTzsdEhYjaDVl2e5MUkyky1MVpMI1GVM0kXWrFGPkXzBjpdHoAQEVFCSIizE1qBmJnWiJqN9xuN4DTN1EiajnV77Om9gVjokJE7U5raO4hau2a633GRIWIiIhUi4kKEVE7NXz4IHz22b8btG9u7gkMHz4I27dvBQBYrVZ8+OG/Gv2cGzZ8ismTr8Xo0cNx3XXj8M47q7xNcsFyzz23Y86cp4L2fHPmPIV77rk9aM+3cuVrmDDhioCPf+edN2st7x9/7MM999yO0aOHY8KEK7Bu3ftNKWaDsTMtEVE79cknX8BkMjVo34SERHzyyReIjIwCALz33jv47LN/Y/z4vzX4+b766nPMnfsc7r9/BgYNOg979+7Biy/+Ay6XE1Om3BbQNQTiuefmQhSloD1fa/LRR+uwYsVS9O3b32d7WVkp7r//bpx//oV46KFH8Pvvv2L+/H8iLCwMf/nLlS1aJiYqRETtVGxsXIP3lSTJZ39Fafww148//gCXXTYWf/3r1QCAjh07ISfnCD799OOgJirVyRadVlRUiBdffA47dmxF584pNR7/9NOPodFo8fDDj0Kj0aBr11QcO5aDd99dxUQllBRFAUQRgqAE9KYkImopw4cPwqxZj+M///kSv/66CxERJlx11YRGfeAPHz4Ijz76JC6//ApvU0hUVDS++GIDrNYqDBw4GDNmPIa4uHjk5p7ANddciVdfXYYdO7bhzTdXeM+xbt2nmDPnKezcub3W55ky5TbccssduOuuaYiONvs8JggCysvLG1zme+65Hb1790FpaQm+//4byLKC88+/ALNmPQa93ggAOHz4EJYufRW//voL3G4XBg8egnvuuR9JScnecyQnd8Bjjz0Ft9uN115bjI0bv0RJSTGSkzvgb3+7HlddNcH7nBs2fIo1a95Gbm4ukpOT8de/jseECdcGPOS2sLAAixYtQFbWJoiihD59+uKee+5H584p2L59K6ZPvxOrV3+ALl26eo+ZPv1OxMXF44knnkVFRQUWL34FP/74LZxOJ9LTz8HUqdORkdGrxnNV/7/VZd26T5Gc3AF79+6BVqvBqlXvYdWq15Gbe8Jnv127dqB//wHQaE6nDQMGDMI777yJ4uKTiIlp2lwp9WGiUg9FAXLyy5EUGwaNyMmEiNoqRVHgcIZuDg6dVgxohMSiRS/j/vsfxsyZj2Hjxi+xfPkSZGYORP/+AwIqx8aNX+LPf74UixevQHHxSTz11KNYvnwJHn30SZ/9rr/+BlitVnzzzX+wYsVbiI4247nn5sLpdNZ6XqMxDABqNCdUVFRg/foPMWTIsEaV81//WoPrrpuEFSvexpEjh/DUU4+ha9euuPHGW5GXl4s775yCQYOG4NVXl8Fut2PRogW4++7b8Pbb7yM83Lep6+OP1+Hbb7/G008/h/j4BPz3vz9g3rwXkJraHf369ccnn3yE115bjAcemIFzzumNP/7YhwULXkRRUQGmTr23UeUGPH17pk27A+npGVi4cDkkScT776/G7bffhLfffh+ZmQORnNwRX331OW677S4AQEFBPnbu3I758xdCURQ8/PB06HQG/POfL8NkMuGLLzbgrrtuwWuvvYmePTN8nq+6yQ6ofR6V6sRx+PALMXz4hXWWu7CwAN26dffZFhcX7y0fE5UQKim34WSZDekp0TDqJE4oRNTGKIqC59/djuzjZSErQ/dOUXhk4oBGJyuXXTYWl1xyOQBg8uSbsWbNO/j1110BJyrh4SbMmPEYNBoNunTpilGjxmDTpv/W2C8sLAxGoxGiKHqbgxrbnFJVVYVZsx6A3W7H3Xc37gO/a9dU3HHH3QCAzp1TMHjwUPzyy04Anj4WRmMYnnjiWeh0OgDAP/7xT1xzzV/x5Zef4+qrr/E51/Hjx2E0GpCc3BFxcXEYP/5apKR0RUqKp/njrbdW4qabbsHo0ZcA8DRXVVZWYv78f+KWW+6EXt+4OXm+/vpLVFSU4/HHn/XWTsya9Th27NiGTz/9GLfccgcuu+wv+OKLDd5EZePGLxEXF4+BAwdj27Yt+O23X7Fhw0ZvzO+44278+usurFv3Ph577Cmf5zuzya4pE77ZbDZvPKtV/223OwI6Z0MxUfFDlhVk55TCaneid2ososJ1cDqD20OdiFpYK51W5cymAQAwmUx11mo0RMeOnXyq9sPDTXC5GjZZ14MPTscvv+yo9bEbbpiCyZNv9v598mQRZsy4HydOHMeCBYuQnNyhUeVMSenq87fJZEJ+vmftpoMHs5GRcY7Ph2psbBxSUrrg4MHsGue6+upr8MMP3+Lqqy9Hjx7pGDx4CEaNGgOzOQYlJSUoKMjHsmWLsWLFUu8xsizD4bAjN/cEunZNbVTZ9+3bB4vFgssuu8hnu8PhwJEjhwF4EtA331yB3377Beee2xdffvk5Lr30LxBFEfv374WiKBg/fmyN4+12e43ny8vLww03XFNje7V33lmHpKQkv+XW6/VwOHwTkuq/jUaD3+ObgolKAx0vqIDd4UbvtDgkRBmYrBC1EYIg4JGJA1pl08/Z33CBwDq5VtNqtQGfb9as2bV+UAJAZOTp5QqOHDmMBx64B4qiYPHiFejWLa3R5azvuusqrqLIPklYtc6dU7B27Xrs2LEVW7Zk4X//+xGrV7+FRx990tskNX36/Rg0aEiNYxMT/X/A11aOlJQueOGFl2o8ZjR6+tgkJ3dAZuZAfPXV5wgLC8OBA39gzpwXAXiSpPDwcKxc+W6N42v7/4uLi8Obb64BUPtaP3FxDetQnZCQiJMnC322FRV5/o6PT2jQOQLFRKURikqt2L4nH73TYtEp3gS3y13nm4KIWg9BEKDXcbhqY5ydWDXkw+rEieOYPv0OREREYv78hQF90PuTltYdX331ORwOhzehKS4+iZycHIwbN6HG/uvWvQ+z2YzRoy/B4MFDMXXqvbjvvqn4+uuvcOmlf0F0tBknThxHp06dvcd8/fVX+OGHb/HYY083unypqWn44osNMJkiEB0dDQBwuVx46qlHcdFFf8aoUX8GAFx++RVYunQhwsNN6NOnn/f5u3XrjsrKSjidTqSmdvOe95///Ae6d++B8eOv9Xk+jUbjPbYpTT/9+g3AJ598CLfbDUnyvFe2b9+KlJQuMJtjAjpnQ3HCt0Yqr3Jgx74CHDheBogiRLGV1hkTETWB0RiG8nILjh490uDmoeeeexoOhxNPPjkHGo0GJ08WeX+qlZSUoKKiIuByjRs3AVVVVXj22SeQnf0Hdu/+DY8/PgvR0dEYNeqSGvuXlpZgwYIX8dNP3yMvLxdZWZuQnb0f557bF4IgYOLEG/HBB2vx4Ydrcfz4MXz//beYN+8F6PWGWmt2/LnkkssRGRmF2bNn4Pfff8ORI4fxj388ic2b/4e0tNOdVUeOHOWdVO/yy09P3jZkyDD06NETTz75CLZv34pjx3KwcOFL+Oyzf6Nr1261PWWzGDv2SlRWVuKFF57FoUMH8dln/8batWtwww1TWuw5q7FGJQB2hxu/ZBfC5nCjR6doaCURbjc72RJR+zFy5MX4978/xk03XY+FC5ejd+9z692/qKjQO3x5ypS/13j8p588M97edttkZGYOrNEptKGSkztg0aLXsGTJq7jjjpug1epw3nlD8fjjzyIiIqLG/lOm3Aan04kFC+Z6h9leddUE7wfw9ddPgl6vxwcfvI+FCxcgJiYWV145DrfcckdA5TOZTFi0aDkWL34ZDz54D9xuGenpGViwYLFPfxeDwYCLLx6NjRu/xMUXj/ZulyQJCxYswZIlr+CJJ2bBarWia9dumDNnLgYOHBxQmRrCbI7BSy8txMsvz8Mtt0xCbGwc7r57Oi67bKz/g5tIUNrABCFut4zi4spmPadGIyIqKgzfbzuK7JzSWvcRBKBrhyhkdImByaBpd/1WNBoRZnM4SkoqORqqDoyRf8GMkdPpwMmTuYiNTYZW2/hvw6HSlCr71mbPnt+xYcOneOihRxp1XHuKUaCCHSN/77eYmHBIkv+GHTb9NIGiAIeOl2HHvgIUV9ihZRs3EVGTvPXWSu9QYCKATT/NIu9kJewOF3qnxSE5Jqzd1awQkbrMmHE/duzYWu8+K1e+i5SULkEqUcM9++w/ax29okZff/0VXnjh2Xr3ue66SQE3E5EHE5VmUlJux459BbB3i0VKggmym9PuE1FozJjxKGw2W737VE8nrzatJUkBgGHDhnuH/taltn4x1DhMVJpRpdWJXfsLYHe40TU5skFtb81JgGecvADPRHWy7EmWmC8RtS/VU5tTywoLC0NYWFioi9HmMVFpZg6njN8OFKGguAqSFNyhywI8c0FEhOsQYdRBr5Og04rQSiI0kgBJFKEoik8SQ0REpGZMVFqALCvIO9m8o5ACoddKMOgl6HUaGPQahBu0iAzXIcyghU4jQqeVoJEEaCQBnnqYxiUu1TVGAUyoSRRSTNKJWl5zvc+YqLRhdqcbdqcbgO/6DJIowKDXQK+TYNBpEKbXQK9v/EtBFICEWBPiI3VwhnD6caKGqp5R0+GwQ6dr3GJyRNQ4DodnSQVJalqqwUSlHXLLCiqtTlRaA1+8DABEUYCkkZAQxRs+tQ6iKMFoNKGiogQAoNPpA1pjJ9hkueYaLeSLMfIvWDFSFAUOhx0VFSUwGk0Qxab112SiQk1SZXPBxZsDtSKRkZ51SaqTldZAFEXIMmst68MY+RfsGBmNJu/7rSmYqFCT2BwuODkbJLUigiAgKioWERFmuN0NW6MmlCRJQFRUGMrKqlhjUAfGyL9gx0iSNE2uSanGRIWaxO5ww+GSoZfqXl6dSI1EUYQoqn8afY1GhMFggNXq5hTxdWCM/GvNMeIU+tQkNocbDqebq0gTEVGLYKJCTeJyy7DaXRAEvpSIiKj58dOFmsxS6WCNChERtQgmKtRkFVYHZJkdVIiIqPkxUaEms9pdcLpbV+csIiJqHZioUJPZTiUqrWDeLCIiamWYqFCT2Rxu2J1yq5jhk4iIWhcmKtRkDqcbNoeLHWqJiKjZMVGhZmGp4MgfIiJqfkxUqFmUVznAgT9ERNTcGp2ouFwuvPLKK7jooouQmZmJiRMnYufOnTX2KykpwfDhw5GVleWzXZZlvPrqq7jgggvQv39/3HbbbcjJyQn4AkgdbHYXXBz5Q0REzazRicrSpUuxbt06PPvss1i/fj1SU1Nx6623oqCgwLtPfn4+brnlFhQWFtY4fsmSJVizZg2effZZvP/++5BlGbfeeiscDkfTroRCyrM4IatUiIioeTU6Udm4cSPGjh2L4cOHo0uXLpg1axbKy8u9tSoffPABrrzyylqPdTgceOONNzB9+nSMHDkSGRkZWLBgAfLy8vDVV1816UIotKx2F+wurvlDRETNq9GJSmxsLL799lscO3YMbrcba9euhU6nQ0ZGBgDgP//5D+6//3688sorNY7du3cvKisrMWzYMO+2yMhI9OrVC1u2bGnCZVCoORxu2B1MVIiIqHlpGnvAY489hnvvvRejRo2CJEkQRRELFy5ESkoKAOC1114DABw7dqzGsXl5eQCA5ORkn+0JCQnexwKl0TRvv2BJ8pxPEAR++NahOi6iKEAGUGF1omNceGgLpTLVr6Pqf6kmxqh+jI9/jJF/rTlGjU5UsrOzERERgcWLFyMxMRHr1q3DQw89hHfffRfnnHNOvcdarVYAgE6n89mu1+tRVlbW2KJ4iaIAs7n5PyBlWYFWK8Fo1PnfuR3T67UAPBO/GcN00LTCN0JLi4w0hroIqscY1Y/x8Y8x8q81xqhRiUpubi4efPBBrFq1CoMGDQIA9OnTB9nZ2Vi4cCGWLFlS7/EGgwGAp69K9e8AYLfbYTQGHjxZVmCxVAV8fG0kSYTJZIDT6YbVyo6+tRFFAXq9Fna70/N/UGFDmcUGEexUW02SRERGGmGxWOHmqKhaMUb1Y3z8Y4z8U2OMIiONDarhaVSismvXLjidTvTp08dne79+/fDDDz/4Pb66yaegoMDbVFT9d3p6emOKUoPL1TKBVxSFKwP7IcueGFXZXLA73dCypawGt1tusddoW8EY1Y/x8Y8x8q81xqhRdfRJSUkAgH379vls379/P7p27er3+IyMDJhMJp+5VSwWC3bv3o3Bgwc3piikQja7Cw4nO9QSEVHzaVSNSt++fTFw4EDMnDkTTz75JJKSkrB+/Xps2rQJ7733nt/jdTodJk2ahHnz5iEmJgYdO3bE3LlzkZSUhDFjxgR8EaQONoenRiUqTMtaKCIiahaNSlREUcTSpUvx8ssv45FHHkFZWRl69uyJVatWoV+/fg06x/Tp0+FyuTB79mzYbDYMHjwYK1euhFarDegCSD0UBaiociIxuvV11iIiInUSFEVp9V993W4ZxcWVzXpOjUZEVFQYvt92FNk5pc167rZCFAUYjTpYrQ5vDUr3zmYM6BkPh8MV4tKpg0YjwmwOR0lJZatrFw4Wxqh+jI9/jJF/aoxRTEx4gzrTchwpNasqmxMOlfQoJyKi1o+JCjUrm8OlmqFvRETU+jFRoWZls7tgd8oc+UNERM2CiQo1K5vdDYfTDUFgokJERE3HRIWalawoqLA6WaNCRETNgokKNbuyCjtYoUJERM2BiQo1O6vNBae71Y96JyIiFWCiQs3O6nDBxZE/RETUDJioULOz2V1wuGR2qCUioiZjokLNjosTEhFRc2GiQs3OLSuotLqYqBARUZMxUaEWUVbJkT9ERNR0TFSoRVRZnXDJHPlDRERNw0SFWoRn5A8TFSIiahomKtQiPB1qOfKHiIiahokKtYjqNX/YoZaIiJqCiQq1CJdbRpWdI3+IiKhpmKhQi/Gs+cNEhYiIAsdEhVpMpdUJt8yp9ImIKHBMVKjF2OxcnJCIiJqGiQq1GJvDBadL5sRvREQUMCYq1GKsdhfsHPlDRERNwESFWozTJcNmd0EQ+DIjIqLA8BOEWpSl0sEaFSIiChgTFWpR5VUOuBV2qCUiosAwUaEWZbO74HJziDIREQWGiQq1KJvDzZE/REQUMCYq1KJsdhfsXJyQiIgCxESFWpTd6YbNwTV/iIgoMExUqMWVc+QPEREFiIkKtThLpQMyB/4QEVEAmKhQi+PIHyIiChQTlTq43DJ+PVAEq90V6qK0ep41f1ilQkREjacJdQHU6pvtx/Dul/shigISog3oGGdCYowRGom5XWPZ7C7YXW7oDRrIbAMiIqJGYKJSh95dY5CaHIlDuRbkFVuRV2yFJApIjg1Dx/hwxEcZ2UG0gewON+wON8QwLRMVIiJqFCYqdegYb8LLD4zEh1/vw6bf8nC8sBJVdheOFVbiWGEldBoRHeLC0TE+HDERes4TUg8Fnqn0k2OMoS4KERG1MkxU/IiLNuKcLmZkpESjpMKO44WVOF5UCYdTxuG8chzOK4dRL6FjnAmd4sMRGa4LdZFVyVLpAJf8ISKixmKi0kCCICAmwoCYCAN6p8agqNSG40UVyD1ZBavdjezjZcg+XoaIMC06xoUjNsqAYNexiIIAjUaETiNCoxEhqqiWx2pzwulmpkJERI3DRCUAoiAgwWxEgtmIvt1k5JdYcaywEgUlVSivcmLv0dJQFxEAIImCN2nRVicwkuizTauRoJWEgJIaQRQQFSHDqPXfwdjmcMPllqFRT+5EREStABOVJpIkT1+VDnHhcLrcyD1ZheNFlai0BX9YsywrcLpkuE91WHXLCqwON+Bwt+jzXtgvGdEmfb37eNb8cUPHkT9ERNQITFSakVYjISUxAimJESEth6wocLlkOFyy51+3519n9Ta353fvj1uGEkAHErvDDavDjUO55cjs4SdRcbjgcHLkDxERNQ4TlTZIFATotBJ0WqlFn6e0wo4fduXieFElzk01Q6up+/kUBSi3OpEQzZE/RETUcJy9jAJmjtAjyqSDLCs4Vljpd39LhQMq6t9LREStABMVCpggCEjrGA0AOJJf7rf5qIojf4iIqJGYqFCTdO0QCVEQYKl0oqzCUe++NgcXJyQiosZhokJNotdK6BAXBsBTq1Ifz8gfmUsPEBFRgzFRoSbrkuQZ5XS8qLLeGhObww2H083lBoiIqMGYqFCTxUUZEG7QwOVWcKKo7k61sqyg0upkjQoRETUYExVqMkEQvHPHHMmvqHffsgo7gr62ABERtVpMVKhZdE4IhyAAJeV2WKrq7lRbZXPBzZE/RETUQI1OVFwuF1555RVcdNFFyMzMxMSJE7Fz507v43v27MGkSZPQv39/XHzxxXj77bd9jpdlGa+++iouuOAC9O/fH7fddhtycnKafCEUWgadBolmT6fao3l116rYHC44OfKHiIgaqNGJytKlS7Fu3To8++yzWL9+PVJTU3HrrbeioKAAJSUlmDJlClJSUvDhhx/i7rvvxrx58/Dhhx96j1+yZAnWrFmDZ599Fu+//z5kWcatt94Kh6P+oa2kfl0STQCAnMIKuOXakxGr3QWHS2aHWiIiapBGJyobN27E2LFjMXz4cHTp0gWzZs1CeXk5du7ciX/961/QarV45plnkJaWhvHjx+Omm27C8uXLAQAOhwNvvPEGpk+fjpEjRyIjIwMLFixAXl4evvrqq2a/OAquBLMRBp0Ep0tG3smqWvex2V1wcIgyERE1UKMTldjYWHz77bc4duwY3G431q5dC51Oh4yMDGzduhXnnXceNJrTSwgNHToUhw8fRlFREfbu3YvKykoMGzbM+3hkZCR69eqFLVu2NM8VUch4OtV6alXq6lTrlhVU2jjyh4iIGqbRicpjjz0GrVaLUaNGoU+fPliwYAFeffVVpKSkIC8vD0lJST77JyQkAAByc3ORl5cHAEhOTq6xT/Vj1LqlJHhG/xSV2VBpdda6j6XCzjV/iIioQRq9enJ2djYiIiKwePFiJCYmYt26dXjooYfw7rvvwmazQafT+eyv1+sBAHa7HVarFQBq3aesrCzQawAAaDTNO4BJkjznEwSB3/7rUB2XM+NjCtMiwWxEQYkVOYUV6NU1psZxVrsLMpr//0yNql9H1f9STYxR/Rgf/xgj/1pzjBqVqOTm5uLBBx/EqlWrMGjQIABAnz59kJ2djYULF8JgMNToFGu32wEAYWFhMBgMADx9Vap/r97HaDQGfBGiKMBsDg/4+LrIsgKtVoLRqPO/czum12t9/u6ZYkZBiRVH8yuQmZ5YI9FTBAGSRkJkuD6YxQypyMjAX9/tBWNUP8bHP8bIv9YYo0YlKrt27YLT6USfPn18tvfr1w8//PADOnTogIKCAp/Hqv9OTEyEy+XybktJSfHZJz09PaALADwJhcVSe+fNQEmSCJPJAKfTDauVI5JqI4oC9Hot7HYnZPn03CgxJh10WhE2hxuHT5QiOdY3iSwVFJRX2CE7XfCz4HKrJ0kiIiONsFiscHNYdq0Yo/oxPv4xRv6pMUaRkcYG1fA0KlGp7n+yb98+9O3b17t9//796Nq1K/r164f3338fbrcbkiQBADZv3ozU1FTExsYiIiICJpMJWVlZ3kTFYrFg9+7dmDRpUmOKUoPL1TKBVxTF50OYapLlmjHqnGDCgeMWHM4t986vUq3K6oLN4YZRKwXtDSMIgCiKQW/Gq+6L43bLLfYabSsYo/oxPv4xRv61xhg1KlHp27cvBg4ciJkzZ+LJJ59EUlIS1q9fj02bNuG9995Dp06d8Prrr+Oxxx7Drbfeil9++QWrVq3C008/DcDTN2XSpEmYN28eYmJi0LFjR8ydOxdJSUkYM2ZMi1wghUaXxAgcOG5BfokVVrsLRv3pl5rLLaPK5kJshB5ud8s8vygK3h+nW4HDKcNa5UCFzQkliImnIAqIKnfAbNJBcMttvgaJiKi5NSpREUURS5cuxcsvv4xHHnkEZWVl6NmzJ1atWoV+/foBAF5//XXMmTMH48aNQ3x8PGbMmIFx48Z5zzF9+nS4XC7Mnj0bNpsNgwcPxsqVK6HVaut6WmqFTEYtYiP1OGmx42hBBdI7R/s8XlZhR0qCqdmeT5IEiKKnCtHhklFpd6PK7sLJMhvKKjwjkCqqnPWu7twSRFGAOcqILkkR6JIYAZE1dEREjSIoSuv/jud2yygurnvV3kBoNCKiosLw/bajyM4pbdZztxWiKMBo1MFqddT64ZtTUIEdfxTBqJcwemAnn9loU5IjMTgjEUodM9g25LlFUYBLPlVb4nChvMqJk6VWlFc5UGl1osrmDHkNRnWMHHYnunWKRo9O0TBoxVZX9dqSNBoRZnM4SkoqGZdaMD7+MUb+qTFGMTHhzd9HhagxOsSG4beDIqx2NwpLbUgwn+5tXmVzoqTcHtgQZUWB3elGSbkdpeU2VFidqKxywu5soXakZuCWFew7XIyKKgd6pcYiOlwHp4rLS0SkFkxUqMVIkohOCeE4lFuOo/nlPolKUYkV//v1BALt2mp3ultlE8rxggpYbS706haL5JgwuFzukNf6EBGpGRMValEpiRE4lFuO3OIq2J1u6LWS9zGb3RXCkoVOscWG7XvzcU5qLFKSIiDWMmqKiIg8Wt8UddSqRIXrEG3SQVE8fVbIo8rmwq79hdh7uAROWWkXs/QSEQWCd0dqcSmJnvV/juaXow303W42LreMvYdOYtcfRbBYXdCeUdtEREQeTFSoxXWKC4ckCqiwulBcbg91cVRFAXA0z4Lte/ORX2qFVsdkhYjoTExUqMVpNCI6xnmm0T+SVx7i0qhTUakV2/bk43BeOURN8GfQJSJSKyYqFBQpSZ7J3XJPVsHp4rDc2lRandi5rwD7jpbCpbTOVU6JiJobR/1QUJhNekSEaVFe5cSxwkqkJkeGukiq5HTJ2H2wCFVWJ9K7mGEyaj3tQ22YKAlwumSIkghNwAPWAyAALs5lQ6R6TFQoKARBQJfECPx2qBhH8ivQNSnCZ6ZaOk1RgEMnymCpciAirO0vLSEIAnQ6DRwOVxA7WwtI6xgFs0mnmlk6iah2TFQoaDrFh2P34WJYKh0oq3Qg2qQPdZFU7WSpFSdLraEuRovztxRDSxEA9O8ZH7TnI6LAsBGcgkanlZAcy061pA7HCsqRX2zlsHAilWOiQkHVJdHTqfZ4UWXQVzImOpPTJePg8VJYHW42QxKpGJt+KKhiowwIM2hQZXMh+3gZYiMNjT6HViMiKlzHDxdqsvziShwvqkT3DpFcJJJIpZioUFB5OtWasOdIKfbnlAEoC+g8sZF69OoaA3ME+7lQ4BQFOHisFAnRRpgMEtzuNj7EiqgVYqJCQdclKQJFZTbYHIF9g620uXDSYsePv+SiQ2wYzuliRrix7Y+OoZZRVmHHoVwL+nSLBcBaFSK1YaJCQafTSBjWOyng4612F/YeLUVOQQVOnKxCbnEVUpMi0KNztM/qzEQNdTS3DB3iwhEXaYCLExISqQo701KrY9RrkNkjDiP6d0B8tMFTfZ9bjq+3HcMfx0rhZiddaiSbw43sY6VwyjLY9YlIXZioUKsVFa7DsN5JGNo7EZHhOrjcCvYcKcXX249zpWZqtNzCCuSerIJGw1o5IjVhokKtXkK0ESP6JSOzRxyMOgk2hxs7s0/i+50nUFBiZcJCDeKWFRw8VopKu4uLQhKpCBMVahMEQUDnBBMuHtgRvbqYoZEEWKqc2Lw7H5t+z0dZhT3URaRWoKjUipz8ci4ISaQi7ExLbYokiujeKQopiSbsP1aGQ7kWFJXZ8P2uXHSKD0eHuPBgLnsHQRSQGCsG9TmpaQ4dL0NiTDiiwrTs70SkAkxUqE3SaSWcmxqD1OQI7D1SiuNFlThW6PkJtjD9Sfx5cKegPy8FpsLqxMHjZejXMy7URSEiMFGhNi7coMXA9HikdYjE/mNlsDlcQX3+0goHquwu2BxuDp1uRXLyLegQH47kmDDOWEsUYkxUqF2IjtDjvHMSgv68X28/hkqrCxVWJxOVVsTpknHgWCnMEXpoBM8MtkQUGuwxRtSCTKdmzK2wOkNcEmqsvJOedYA0TDCJQoqJClEL8iYqVUxUWhtFAQ4dK4WlysnhykQhxESFqAWxRqV1Kym340iuBYLIWyVRqPDdR9SCmKi0fkdyLThpsUGj4e2SKBTYmZZCwmjQIMEcFvDxhSVVqLIFdwRPIKoTlSqbC7KssAmhFbLaXTh4ogzRpvhQF4WoXWKiQkFn1GvQt0c8OsebgEBGUwjAkfxy7NxXAKdL3RNyGXQSNJIAl1tBlc0FU5g21EWiABwvKEeHuHB0jjdxuDJRkDFRoaDS6ySc2z0OneNNcDXhht85wYTySgf2Hi5uxtI1P0EQEBGmQ0m5HRVWJxOVVsrtVnDgWBliIw3Qa0TIMscrEwULG10paHRaEb3T4tAlMaJJSQoAQFaQ1ikaHeJNzVO4FhQRrgMAVNjYT6U1KyqpQk5BJUSuA0QUVKxRoaDQakT07haH1KQIuF1NrzqXZQVhOgkZXWNQUeWApdLRDKVsGRFhnkSlkh1qWzUFwKHjpUiIMcJs0gXcbOl0sOmIqDGYqFCLkyQB56TGIrVDFGS3u9lm+XQ63YiN1CMjNRY79uartr9KRBhH/rQV5VUO/PJHIcINjW/CEwSga4comMN1XOyQqBGYqFCLEkUBGV1jkdYxCopbbvapyJ0ONzrFm1BR5cCegycD+pLb0iKrm36s6h+lRP4VFFcFfKxGIyEmLRZgpQpRg7GxlVqMIAA9u8SgR6doCIoCpaUWTJFldOsQhQ4J6uyvUt30Y3e6VVvrQ8GRV1SBcitnuiVqDCYq1CIEAeiREoP0ztEQobToKAlZVmDQisjoGoMok77FnidQOq0EvdbzVqtkh9p2zVLpQN7JKkjskEvUYHy3UIvo1ikaGSlmSAKCMpTT5ZIRY9IjIzUGOq36XtacoZaqHS8oR5XDDYGVKkQNor47OrV6qR2icE7XGGil4CQp1ZxONzrFhaN7ZzPU9hkQfipRqWQ/lXbvZJkVRaVWaDRclZmoIZioULNKSY5Er9QY6CURbnfwu7YqsoK0jlHomBgR9OeuTwRrVOgURQFy8svh4MgfogZhokLNpmOCCb1TY2HUSSEbfinLCvQaERldYhAdoZ7+KuFMVOgMhSVVKLHYudAhUQPwXULNIik2HOemxSFcL8EV4pEtLpcMc4QO56TGQq9VR/V6dR+VSpuz5UY/UavhdMk4ml8O1qkQ+cdEhZos3hyGPt3jEGnUhjxJqeZ0uNEhNhw9uphV0WmxukbF5VZg56J2BCC/uBJllU6OACLygxO+tXGCAIiCAEEQzvgdEETh9O+CEFDnU1EUEB1pRI9OUYgO16luVVnZLaNbchQslQ4czbWEtCySKCDMoEGVzYVKqwsGHd967Z3V5sLxgnLEdIuFW11vHSJV4d2yiQQBMOg00Osk6HUaGHQSwgxamIxaCCGY1EkUBEiiAFE8KzE5lYwIZ/4tAAKq/278cwmCAKNRCw0U2GzqG82iKAp0GhHpKWaUVzpQYrGFtDwmgxZVNhcqrE7ERhlCWhZSh9yiSnRNjkKYjisyE9WFiUoDCAKg13oSEb1OgkEnwWjQIjJMhzCjFjqNCJ1GgkYSoJEESKIYsn4InqdVzvq7+nel3n0bS5JEmCMMKCmpDPgcLc3lkhFt0uGcrjHYtjcf9hAuCBdu1ACl7FBLp5VV2JFfXInunaIgc7FColoxUamHIHj6X3SIN0GnlaDTiNBK4qlkRICieEaZKMqpmVcVBW6XAje7yKmK0+FGcmw4enaJwW/Zhc2+3lBDndmhlqjasYJydIw3QSsiZK9NIjVjolIPQRDQJdEEl0v2JiSKNxmh1kR2y0hNjkSV1YnSCnvQnlcUBGi0EqxWxxlDlNXXTEahc7LUhqIyGzrHh6uunxeRGjBR8cPlklUzkoUCpygKtJKIPmlxkIP4tVUQBFidbpSWWX1qVGRFgaiG4UgUcrKiICffgqQYY6iLQqRKTFSo3aiehC6Yg0G1GgGCqIFGI8KokyCKAmRZgdXm8tawEBUUV6GkwoH4SD2/GBGdpVH37KysLKSnp9f6M2rUKADAL7/8gkmTJiEzMxN//vOf8fbbb/ucQ5ZlvPrqq7jgggvQv39/3HbbbcjJyWm+KyJSEUXxdDrWSCIEQUC4wfPdgB1q6UxOl4ycvPImdG0narsalahkZmbip59+8vlZtGgRBEHA1KlTcfToUdxwww2IjY3F2rVrMXv2bCxfvhyLFy/2nmPJkiVYs2YNnn32Wbz//vuQZRm33norHA5Hs18ckRpIogDNqUm9vKsos0MtnSXvZAXKqjgBHNHZGvWO0Ol0iI+P9/6Eh4fj+eefx7hx4zB+/Hi88847iImJwdy5c9GzZ0+MGDECM2bMwPLly2Gz2eBwOPDGG29g+vTpGDlyJDIyMrBgwQLk5eXhq6++aqlrJAopSRSh0Xj6o5i4ijLVocrmwonCCogS+y4RnalJqfuyZctgtVoxc+ZMAMCRI0fQp08f6HQ67z69evWCzWbDr7/+ir1796KyshLDhg3zPh4ZGYlevXphy5YtTSkKkWpJ0ukaFS5OSPU5UViBCqsLYggmiyRSq4A70xYXF2PVqlV48MEHER0dDQBISEjAvn37fPY7fvw4AODkyZMQRc/NOjk52WefhIQE5OXlBVoUAGj2VUirq19ZDVs3xsi/6v4pWo2nI21k2OmRP/ww8qiOA+MBWCodKCitQo+OUd5OtXyf+ccY+deaYxRworJmzRpERETg2muv9W7761//ikmTJuH111/H5MmTkZ+fj5dffhmCIMDpdEKWPW+8M2tcAECv16OsrCzQokAUBZjN4QEfX5/ISA4Z9Icxqp+iKDAatDAadYiTPKs5W+1uaLWaZk+wWzO9nqOgAKCo1Ia0TmaYzb7vK77P/GOM/GuNMQo4UVm/fj2uuuoqGAyn1ywZPHgw/vGPf+DFF1/E/PnzYTab8fDDD+ORRx5BREQE7HbPRFsOh8PnOLvdDqMx8ODJsgKLpSrg42sjSSIiI42wWKzeYa3kizHyrzpGGlGA1erpMK7ViHC6ZBSVVCLKpA9xCUNPFAXo9VrY7U6udwPguN2J4/kmiIpnDie+z/xjjPxTY4wiI40NquEJKFHZu3cvcnJycMUVV9R47JprrsGECRNQUFCA2NhYHD58GIqioHPnzqis9KwJU1BQgJSUFO8xBQUFSE9PD6QoXi0194DbzQnf/GGM/NNpTy86ZzJqUVJuR3mVExFhOj9Hth+yrDBRgScOR/IsiI82AvLp9xXfZ/4xRv61xhgFVO+8detWxMbGIiMjw2f7l19+ienTp0MQBCQmJkKj0eDLL79Ehw4dkJaWhoyMDJhMJmRlZXmPsVgs2L17NwYPHty0KyFSMb1W8v5uMnIuFapfQXEVSivsbBokQoA1Krt37661BqR79+745ptv8Prrr+PSSy9FVlYWli5diueeew6Ap2/KpEmTMG/ePMTExKBjx46YO3cukpKSMGbMmKZdCZGKaTUSBMEzAVy4gSN/qH4Opxs5BeWIjTRA4DRw1M4FlKgUFhZ6R/qcKS0tDa+++ioWLFiAhQsXolOnTnjuuedw5ZVXeveZPn06XC4XZs+eDZvNhsGDB2PlypXQatmRjtqu6iHKTpd8xpo/nEuF6pZXVAlLhyiYTWwepPZNUJTWv7C42y2juLiyWc+p0Ygwm8NRUlLZ6trzgoUx8q86Rkdzy/DD9mOw2l0oq3Tg+50noNWIuPS8zhDa+eKEoijAaNTBanWwj8pZeqfF4dxuMYiMMPJ9Vg/ei/xTY4xiYsIb1JmWDaBEQaA5NZ8KAO96P06XDIdKbhikTtUTwBG1Z0xUiIJAEgVvx0iNJMKo93SurWQ/FapHqcWG/JIqtIGKb6KAMVEhCgJROD2NPsAOtdQwCoDj+eUor+KirdR+MVEhCgJRFKA5Y7E5dqilhioqtaKwxMqhytRu8ZVPFATiGU0/wOlEhTUq5I9bVnAo14Iqh7vdd7ym9omJClEQaET4Nv1w0jdqhMKSKhzJLYfEWhVqh/iqJwoCQRDOmp32VNOP1cmOkuSXogAHjpWgqMwGjUbyfwBRG8JEhSgIFAXQ607Prxim10AQAFnxrKRM5E+VzYU/ckrgcMlsAqJ2hYkKUVAo0OtOfxMWBOH0yB8bm3+oYU4UVuBoQTk71lK7wlc7URAoiu/ChMDpxQk5lwo1lKIAfxwtwUkLFyyk9oOvdKIgUBQFGkmAJJ6usg/nyB8KQKXVif05JXC4FbAFiNoDJipEQSKJos/IHw5RpkCdKKjAsYIKdqyldoGJClEQKIriWUH5zLlUDNUjfzjpGzWOrCj442gJiiscbAKiNo+vcKIgUJRT6/34zKXiSVSq7C64uWowNVJ5lQPZOSVwyWATELVpTFSIguTsph+9VvROq1/JkT8UgGP55cgpKGcTELVpTFSIgkQU4LPejyAIPhO/ETWWW/Y0AZVUOiBJvJ1T28RXNlGQnL3eD3DmyB/2U6HAWCodOJBTCrfCJiBqm5ioEAWJeFYfFeDMDrWsUaHAHc2z4HhRBbRaNgFR28NEhShIJAE1+hJUT/rG2WmpKaqbgEor2AREbQ9f0URBI8Bw1jdeTvpGzaW03I7sY2WQQ10QombGRIUoSBRFgV5/do2KJ1FxOGU4XFyckJrG0wRUCa2OTUDUdjBRIQoSRQH0ZzX9aCQRhlMfKpz4jZrK5Zbxx9ESWCqdbAKiNoOvZKKgUaDTSjh7YIZ3FWU2/1AzKLHYcOB4KWSOAKI2gokKUZBUT6N/9jddrqJMze1IrgW5RZUcBURtAhMVoiDxTqNf51wqTFSoeThdMvYfLUG51QlJYtUKtW6aUBeAqL3w1KiIPrPTAmesomxjHxVqPsVlNhw8XoY+aXHQ69t2siKKnuvTaiX2zalDU2PkdLohh2hNMiYqREFSXaOirdH0c3rSN0VRIHB6UWomh06UweGSIYlt+zUliAJ0Og0cDhcULvBZq6bEyBxhQLcOEXA4QjMykYkKURBJogDprKafML0GAjyTdtkcbhj1fFtS83C6ZBw+URbqYrQ4URRgNOpgtTpC9q1f7ZoSoz7d41uoVA3DOjKiIBKFmtPoi6KAMAM71BIR1YaJClEQiSJqJCrAGf1UmKgQEflgokIURFItCxMCZ4z8YYdaIiIfTFSIgkiAAJ227hoVNv0QEfliokIUVAoMupqdZU2n+qiw6YeIyBcTFaIgUhRAX8uCcdVNP1U2F0ctEBGdgYkKURApime9n7MZdBIkUYACT7JCREQeTFSIgkhRAK0kQjxrUjdBEM6YoZbNP0RE1ZioEAWRdxp9Tc2ZQsON7KdCRHQ2JipEQaQogKaOIcomA0f+EBGdjYkKUVApkCQ/c6lY2UeFiKgaExWiIKpemFCj4ey0REQNwUSFKMhqW+8HAEyn+qjYnW64XHKwi0VEpEpMVIiCTKijj4pWI3lnreXIHyIiDyYqREEmCbUvTAhwKn0iorMxUSEKMkkU605UDOxQS0R0JiYqRCFQ2zT6wJmrKLNGhYgIYKJCFHSKosBQR6JS3aGWTT9ERB5MVIhCQF/LCsqA7xBlReHihERETFSIgkxRFOhqmUcFAMJO9VFxuRXYnRyiTETERIUoyBTFM+pHkmqu9yOJAsL0bP4hIqrGRIUoyDwLE9Y+lwoArqJMRHSGRiUqWVlZSE9Pr/Vn1KhRAICjR4/izjvvxKBBgzB8+HA88cQTKC8v9znP6tWrMWrUKPTt2xd///vfsXv37ua7IiKVq55GX1tHosJVlImITmtUopKZmYmffvrJ52fRokUQBAFTp06F0+nEbbfdBo1Gg7Vr1+Lll19GVlYWZs+e7T3Hxx9/jBdffBH33nsvPvroI3Tq1AlTpkxBcXFxs18ckRopiuKZS6WOfiqc9I2I6LRGJSo6nQ7x8fHen/DwcDz//PMYN24cxo8fj+zsbBw+fBjTpk1DWloaBg0ahIkTJ+LHH3/0nmPZsmWYNGkSrrzySnTv3h3PPfccjEYj1q1b1+wXR6RWolj37LRcRZmI6LQm9VFZtmwZrFYrZs6cCQAwm80QRRH/+te/4HA4UFxcjC+++AL9+vUDAJw8eRKHDx/GsGHDvOfQaDQYNGgQtmzZ0pSiELUqUh3r/QCnZ6ettHGIMhFRwIlKcXExVq1ahTvvvBPR0dEAgKSkJMyePRsfffQR+vXrh2HDhqG8vBwvvfQSACAvLw8AkJyc7HOuhIQE72NE7YFQxwrKAGDUSxBFAYoCVNlZq0JE7Vvts041wJo1axAREYFrr73Wu83hcGDfvn0YM2YMJk6ciJKSErz44ou477778MYbb8BqtQLwNCGdSa/Xw263B1oUAKizvT9Q0qkPEamODxNijBqirhiJGhFarQhRrDlEGRBgMmhgqXKiyuZCRJiuln3ajuoY1B4LYnz8Y4z8a0qMBNFzD9NoQlPDG3Cisn79elx11VUwGAzebatWrUJWVhY+++wzSJJnivCuXbtizJgx+Pbbb5GUlATAk9CcyW63w2g0BloUiKIAszk84OPrExkZeLnaC8bIv9piFB6mh9FYexISZdLDUuXEzuyT0GlLWrp47Y4gCOgYb0LvbrGQWsmHm16vDXURVI8x8i+QGOm0GphMBv87tpCAEpW9e/ciJycHV1xxhc/2bdu2oVevXt4kBQC6dOkCs9mMw4cPIzMzEwBQUFCAtLQ07z4FBQVITEwMpCgAAFlWYLFUBXx8bSRJRGSkERaLFW43ZwitDWPkX10x0moliFBgtTpqPS4qXIccAFa7C9amVTZSHUrL7ThWUI5B6fGqrrUSRQF6vRZ2uxOyzD5LtWGM/GtKjBxOFyoqbHA63c1apshIY4Nq5ANKVLZu3YrY2FhkZGT4bE9MTMT27duhKAoEwfMtJT8/H6WlpejatStiY2ORmpqKrKwsb4dal8uFrVu34u9//3sgRfFyuVrmg9Ltllvs3G0FY+Tf2TESBAE6rVjnDSM1OQLRETq43W3/pisKAnR6DRx2F+QgdR622l34/XAJyioc+G7HCfROjUGXRJP3vqVGsqzwQ9gPxsi/QGKkyKG9zweUqOzevRvp6ek1tk+cOBEff/wxHn/8cUyZMgXl5eV4/vnnkZGRgREjRgAAbr75ZsyZMwddunRBnz59sHz5cthsNkyYMKFpV0LUinjW+6n77ScIAmIiQlfVGkyiKMBo1MFqdQT1QyYh2ojtfxShqMyGXw6cREGJFf26x0KvrX1layIKjYASlcLCQu9InzOlp6fjnXfewUsvvYRrr70WRqMRw4cPx8MPPwyt1tMu9re//Q3l5eV4+eWXUVpainPPPRdvvvkmYmJimnQhRK2JogBarQhB8PxOwWfQazCsdyIOnLBgz5ES5BVXoWSHHQN6xiE+mv2uiNRCUNrARA1ut4zi4spmPadGI8JsDkdJSSWbNerAGPlXV4wkSUBppRM/7DgGZzuPXahqVM5UVmHHtv1F3mUL0jpEIqOLWRUdbdUQH7VjjPxrSoz6dI/HOV2i4XA0bx+VmJjwBvVR4bhSohBQlOrhfnwLqkGUSY8L+yWja1IEAODACQt+/CUX5VW1d3YmouDhXZIoBDzr/dQ96RsFn0YS0TctFudlJECnEWGpdOD7Xbk4lGvhDMFEIcS7JFEIVK+gzERFfZJiwzAyswPiow2QZQW/HizGz3sKYG/moZlE1DC8SxKFiMhERbUMOg2G9kpE71QzRAHIL7Hiux0nUFBiDXXRiNqdgGemJaKmEQVAI4W+sybVThAEpHWIQlyUEdv3FaLc6sTm3flISTR5F45s3AmB+GgjosLVO7kckRoxUSEKEUkU2Jm2FYgK1+HCfsnYfaQEh3LLcTS/IuBziUIJBqbHIzm2ZZb8IGqLmKgQhYgoCtCy6adVkCQRfbrFItEchhNFlQHNoFtlc6G43I4tewvRv4eClARTC5SUqO1hokIUIgIE6DgLaquSYDYiwRzYZHCyomBX9knkFFRg5x9FcLlkdOsQ2cwlJGp7+HWOKGQU6HX8rtBeiIKA/t1j0S3Zk5z8dqgY+3JKOfSZyA8mKkQhoiiAXscalfZEEAT0TjUjvXM0AGDf0VL8friEyQpRPZioEIWIoihcAK8dEgQB6SnRODfVs77ZwRMW7Mo+yWSFqA5MVIhCRFE8s6GqYT0ZCr5uHSLRv3ssAOBoQQW27iuEm+vUENXARIUoRLzT6HOIcruVkhiBQRnxEAUg92QVft6TD5e7fS9SSXQ23iGJQsSzMCFnp23vOsSG47xeiZBEAYWlNmz+PR9OF6frJ6rGOyRRiHBhQqqWEG3EsN6J0Eoiisvt+O9v+bA7mKwQAUxUiEJKEkUmKgQAiIk04E99kqDTelZu/unXXFjtrlAXiyjkeIckCiGu90NnigrXYXifZBj1EiptLvz0ay4qqpyhLhZRSDFRIQohkZ1p6SwmoxbD+yTDZNTAanfjx19OoMRiC3WxiEKGd0iiEBLZR4VqYdRrcP65yYgK18HulPH11hwcL6rkXCvULvEOSRRCkgBoNZz0jWrS6yT86dwkxEbq4XTJ2LKnAD/vKWC/FWp3mKgQhZTAafSpTlqNiD/1SULvbrEQBCC/xIpvth/HwRMW1q5Qu8FEhSiEFEVhokL1kkQRfbvH4aLMjoiJ0MMtK/jtUDF+/CUXZRX2UBePqMUxUSEKIUUB9Gz6oQaIDNfh/D5J6JsWC40koLTCgR925eL3Q8WczZbaNCYqRCGlQKeVwAHK1BCCIKBrUgQuHtARHWLDoAA4cMKC73acQEFJVaiLR9QimKgQhZCiKJAkARJH/lAjGHQaDMpIwJBzEmDUS6iyu7B5dwG27SuEjTPaUhvDuyNRCCkKIIkCtJxLhQKQGBOGizI7oluHSADA8aJKfLv9OI7klbOzLbUZvDsShZCnRoXT6FPgNJKIc1NjcGE/z7wrTreMXQdO4n+/5aG8yhHq4hE1mSbUBSBqz6prVDiNPjVVtEmPC/ol49AJC/YeLcVJix3f7zyBxJgwCG385SVAgKQR4XbJUMCapNo0JUbHiirRKb5XyDr+M1EhCjFJ9NxAiJpKFASkdYxCcmw4fj14EvklVuSeZCdbapoTRVXY3acEmd3jQvL8TFSIQkwUOI0+Na8wgwbnnZOAwlIbKqxtf1FDQQB0Og0cDhfYNad2TYlRWqdoDEyPh+wOTXCZqBCFmCiCiQo1O0EQkGA2IsFsDHVRWpwoCjAadbBaHZBlZiq1aUqMzu0WC40kwuEOzYgy3h2JQkwSBGiZqBAR1Yp3R6IQEwQBOi3fikREteHdkSjkFOh1bIUlIqoNExWiEFMUcGFCIqI6MFEhCjFF8az3Q0RENTFRIQoxRQG0kgixrc/KRUQUACYqRCHmnUafk74REdXAOyNRiCkKoOE0+kREtWKiQhRyCiSJs9MSEdWGd0aiEPMuTMimHyKiGnhnJFIBrvdDRFQ73hmJVEAUmagQEdWGd0YiFRAFsOmHiKgWvDMSqYDEGhUiolrxzkikCgL0nJ2WiKgGJipEKqAoCgxc74eIqAYmKkQqwRWUiYhqYqJCpAKehQn5diQiOhvvjEQq4JlGX2SHWiKis/CuSKQCnoUJud4PEdHZGtUonpWVhcmTJ9f6WKdOnTB48GB8/PHHtT4+bdo03HPPPQCAzz//HAsXLsSxY8fQrVs3zJw5E8OGDWtk0YnaDu80+pIIwB3q4hARqUajEpXMzEz89NNPPtt27tyJadOmYerUqRgzZgwefPBBn8eff/55/Pzzz7jmmmsAAJs3b8bDDz+MGTNm4Pzzz8cHH3yA22+/HevXr0daWloTL4eodVIUBZIoctI3IqKzNCpR0el0iI+P9/5dVVWF559/HuPGjcP48eMBABEREd7Hv/nmG3z22Wd46623kJiYCABYsWIFRo8e7a2ZmTlzJnbs2IG33noLzzzzTJMviKi1EkWwjwoR0VmadFdctmwZrFYrZs6cWeMxu92OOXPmYPz48RgyZAgAQJZlbN++vUYzz5AhQ7Bly5amFIWo1ePstERENQU8cUNxcTFWrVqFBx98ENHR0TUeX7duHYqKinDfffd5t1ksFlRVVSEpKcln34SEBOTl5QVaFADNv06KdOoDQ+IHR50YI/8aFSNBgFYrQhTbV4fa6uttb9fdUIyPf4yRf02JkSB67mEajdLcxWqQgBOVNWvWICIiAtdee22Nx2RZxltvvYVrrrnGp6nIZrMB8DQhnUmv18NutwdaFIiiALM5PODj6xMZaWyR87YljJF/DYmRyyUjzKiD0ajzu29bpNdrQ10EVWN8/GOM/AskRjqtBiaToQVK0zABJyrr16/HVVddBYOhZuG3b9+Oo0eP4vrrr/fZrtfrAQAOh8Nnu91uh9EY+IedLCuwWKoCPr42kiQiMtIIi8UKt1tu1nO3FYyRf42JkVYrQZEVWK2Oevdra0RRgF6vhd3uhCyH5hubmjE+/jFG/jUlRg6nCxUVNjidzTsiMTLS2KDa5oASlb179yInJwdXXHFFrY//5z//Qa9evWqM4omOjkZYWBgKCgp8thcUFHg72wbK5WqZD0q3W26xc7cVjJF/DYmRKHoWJmyvN1pZVtrttTcE4+MfY+RfIDFS5NDe5wPqXLB161bExsYiIyOj1se3bNlS67wogiBgwIAB+Pnnn322Z2VlYdCgQYEUhajNUBRAr2N/HyKiMwV0V9y9ezfS09NrfcztdmP//v11JjFTpkzBhg0b8Oabb+LAgQN48cUXsWfPHtx4442BFIWozVAUBToNFyYkIjpTQIlKYWFhrSN9AKC0tBROp7POx4cPH47nnnsO7733HsaNG4fNmzdj2bJlnOyN2j1FAXRaEQIHLhAReQX09W3FihV1PhYbG4t9+/bVe/xVV12Fq666KpCnJmqzPLPTeuZScbLPDxERAC5KSKQqnrkK+LYkIqrGOyKRSpxZo0JERB68IxKphO8KykREBDBRIVIVkYkKEZEP3hGJVEQSAC37qBARefGOSKQinhoVjk8mIqrGRIVIRdj0Q0Tki3dEIhURIECnlUJdDCIi1WCiQqQqCvQ6TqNPRFSNiQqRingWJmSNChFRNSYqRCqiKAr0bPohIvJiokKkIooCaCQRksiRP0REABMVIlXxTqPPuVSIiAAwUSFSFUUBJIlDlImIqvFuSKQiXJiQiMgX74ZEKiOJIhMVIqJTeDckUhlRAKfRJyI6hYkKkcqI7ExLROTFuyGRynC9HyKi03g3JFIZSQC0Gk76RkQEMFEhUiGB0+gTEZ3CRIVIZRRFYaJCRHQKExUilVEUQM+mHyIiAExUiFRIgU4ngQOUiYiYqBCpDtf7ISI6jXdCIpVRFHAafSKiU3gnJFIZRVEgSZxGn4gIYKJCpDqna1TYS4WIiIkKkQpJogCJfVSIiJioEKmRKAjQsumHiAiaUBeAiGryrKDMRIUCY9RrEBGuQ5RJD3OkAaLQtpsRRQHQ6jRwOlyQlVCXRp2aEiOdNrT3IiYqRCrEUT/UGHqthIhwHSLD9Yg3GxEZroNRr4H+VPOh0sY/vCVJQGSkERaLFW53G7/YADU1Rk6nuwVK1TBMVIhUSBCEkH+LIfXSakREhOkQEa5DXHQYoiN0CNNroNd6Jgp0u2XIshLSD5fg8rxX3G4ZLpcc4rKoVeuNERMVIlVSoNfx7UkeoiAgyqSHyahFbJQR5kgDwgwaGLQiREGALMtwuxW42k1iQu0J74REKqQo4MKEBFOYFgkx4UjtGA2DVoReK0IjCnC7FbjdMtwuGUxNqK1jokKkQoqiQK9lotIeaTUiYqOMSI4zIeFUf5PoKCNKSyvhdMpwhLqAREHGRIVIhRTFM+pHFAXIHMbQ5gkAIk16JJjD0DHBhMhwHfQaEW63DEX29Cdo6x1iierCRIVIhc6cRt8hs3K/rdLrJMRGGdEpwYTYKCPCDRpAUeByyd6OsKLYtocWE/nDRIVIhRQF0JyaRt/hDHVpqDkJAmCONCAxJhzJceGIDNNBpxHgdMrsDEtUCyYqRKqkQJI4l4ra6LQSIsN1Af+/6LQSOiaYEBNpQJhOgix7OsU62PGEqE5MVIhUyLswIdf7CTmTUYuoCD3iosMQG2VAuEEDSQzs/0UUAUkQ4HK529EcJ0RNw0SFSKVEgTUqoSBJAqLC9YiKMCAxJgxR4Z7J1LSnOre63TIgBzZhliwDrWuqLaLQY6JCpFJMVILHqNd418VJiAmDyaiFUXfmLK8yHA6mGEShwESFSKVEETDoNDDq2/bbVBQFGHQSIGuCOhRbpxVPDQkOhzlSj3C9BjqtCOVUvxF2bCVSh7Z9ByRqxURRQPfO0ejeOTrURWlRggDo9RrY7a6gzhUiiQKMegkaUYDLdWptHAeTEyK1YaJCpFJOhxsmfdufnVaSRERHh6G0tMrT/yNIFEWB7OJMr0Rqx0SFSMXaw6y0oui5RkVR2sX1ElHjsKceERERqRYTFSIiIlItJipERESkWo1KVLKyspCenl7rz6hRowAAFRUVePLJJzF06FAMHDgQd955J3JycnzO8/nnn+Pyyy9H3759cdVVV2HTpk3Nd0VERETUZjQqUcnMzMRPP/3k87No0SIIgoCpU6cCAKZNm4asrCwsXrwYq1evRnl5Oe666y7Ip2Zy3Lx5Mx5++GFcd911+PjjjzFs2DDcfvvtOHDgQPNfHREREbVqjUpUdDod4uPjvT/h4eF4/vnnMW7cOIwfPx5ZWVnYtGkTXnnlFQwcOBAZGRl4+umnUVlZicOHDwMAVqxYgdGjR2Py5MlIS0vDzJkz0bt3b7z11lstcX1ERETUijWpj8qyZctgtVoxc+ZMAMBPP/2Enj17Ij093btP9+7d8e2336Jbt26QZRnbt2/HsGHDfM4zZMgQbNmypSlFISIiojYo4HlUiouLsWrVKjz44IOIjo4GABw6dAhdunTBmjVrsHr1algsFgwcOBCPPPIIEhMTYbFYUFVVhaSkJJ9zJSQkIC8vr2kX0syrzEqn1liRuNZKnRgj/xgj/xij+jE+/jFG/rXmGAWcqKxZswYRERG49tprvdsqKirw+++/o6SkBE8//TQAYN68eZg8eTI+/fRT2Gw2AJ4mpDPp9XrY7fZAiwJRFGA2hwd8fH0iI40tct62hDHyjzHyjzGqH+PjH2PkX2uMUcCJyvr163HVVVfBYDCcPplGA7vdjsWLFyMqKgoAsGjRIlxwwQX45ptvMHToUACAw+E7abXdbofRGHjwZFmBxVIV8PG1kSQRkZFGWCzWoE7r3ZowRv4xRv4xRvVjfPxjjPxTY4wiI40NquEJKFHZu3cvcnJycMUVV/hsT0pKQmJiojdJAYC4uDhER0fj2LFjiI6ORlhYGAoKCnyOKygoQGJiYiBF8XK5WibwbrfcYuduKxgj/xgj/xij+jE+/jFG/rXGGAXUWLV161bExsYiIyPDZ/vgwYNx4sQJn0SkoKAAJSUl6NKlCwRBwIABA/Dzzz/7HJeVlYVBgwYFUhQiIiJqwwJKVHbv3u0zsqfaZZddhq5du+Lee+/Fb7/9ht27d+OBBx5AamoqRo4cCQCYMmUKNmzYgDfffBMHDhzAiy++iD179uDGG29s0oUQERFR2yMoitLo5Upvu+02mEwmLFiwoMZjhYWFeOGFF/Ddd99BURScf/75mD17tk/Tzvr167FkyRLk5eWhe/fuePjhh2sMWW6Mllp1VZJE1bTlqRVj5B9j5B9jVD/Gxz/GyD+1xUgUBQiC4He/gBIVIiIiomBofQOqiYiIqN1gokJERESqxUSFiIiIVIuJChEREakWExUiIiJSLSYqREREpFpMVIiIiEi1mKgQERGRajFRISIiItViokJERESqxUSFiIiIVIuJChEREakWE5VayLKMV199FRdccAH69++P2267DTk5OaEulqrk5+cjPT29xs9HH30U6qKF3GuvvYYbbrjBZ9uePXswadIk9O/fHxdffDHefvvtEJVOHWqL0ezZs2u8ni6++OIQlTD4SktL8cQTT+DCCy/EgAEDcP3112Pr1q3exzdt2oSrr74a/fr1w6WXXooNGzaEsLSh4S9GU6ZMqfEaOvt11tadPHkSDz/8MIYOHYrMzEzcfvvtOHDggPfxVnkvUqiGhQsXKkOGDFG+/fZbZc+ePcrNN9+sjBkzRrHb7aEummp89913Sp8+fZT8/HyloKDA+2O1WkNdtJB69913lYyMDGXSpEnebcXFxcqQIUOURx55RMnOzlY++OADpU+fPsoHH3wQwpKGTm0xUhRFmTBhgvLSSy/5vJ5OnjwZolIG35QpU5SxY8cqW7ZsUQ4ePKg8/fTTSt++fZUDBw4o2dnZSp8+fZSXXnpJyc7OVl5//XWlV69eyv/+979QFzuo6ouRoijKsGHDlDVr1vi8hkpKSkJb6CC79tprlWuuuUbZtWuXkp2drUybNk0ZPny4UlVV1WrvRUxUzmK325XMzExl9erV3m1lZWVK3759lX//+98hLJm6LF++XLniiitCXQzVyMvLU+644w6lf//+yqWXXurzIbxs2TJl+PDhitPp9G6bP3++MmbMmFAUNWTqi5Esy0r//v2Vr776KoQlDJ3Dhw8rPXv2VLZu3erdJsuyMnr0aOXll19WHn/8cWXChAk+xzzwwAPKzTffHOyihoy/GBUVFSk9e/ZUfv/99xCWMrRKS0uVBx54QNm3b5932549e5SePXsqu3btarX3Ijb9nGXv3r2orKzEsGHDvNsiIyPRq1cvbNmyJYQlU5d9+/YhLS0t1MVQjd9//x1arRaffvop+vXr5/PY1q1bcd5550Gj0Xi3DR06FIcPH0ZRUVGwixoy9cXo6NGjqKqqQrdu3UJUutAym81Yvnw5+vTp490mCAIEQYDFYsHWrVt97kmA5zW0bds2KIoS7OKGhL8Y7du3D4IgIDU1NYSlDK2oqCjMnz8fPXv2BAAUFxdj1apVSEpKQvfu3VvtvYiJylny8vIAAMnJyT7bExISvI8RsH//fhQXF2PixIn405/+hOuvvx4//PBDqIsVMhdffDEWLlyIzp0713gsLy8PSUlJPtsSEhIAALm5uUEpnxrUF6P9+/cDAN555x1cfPHFGD16NJ555hmUl5cHu5ghERkZiREjRkCn03m3ffnllzhy5AguuOCCOl9DVqsVJSUlwS5uSPiL0f79+xEREYFnnnkGF154IS699FK8/PLLcDgcISx16Dz++OMYNmwYNmzYgDlz5iAsLKzV3ouYqJzFarUCgM+bAQD0ej3sdnsoiqQ6LpcLBw8eRFlZGaZNm4bly5ejf//+uP3227Fp06ZQF091bDZbra8nAHxNnbJ//36IooiEhAQsW7YMs2bNwk8//YSpU6dCluVQFy/otm/fjkceeQRjxozByJEja30NVf/dXj+Iz47R/v37Ybfb0bdvX7z++uu46667sG7dOsyePTvURQ2JG2+8ER9++CHGjh2Lu+++G7///nurvRdp/O/SvhgMBgCeN3/174DnP9FoNIaqWKqi0WiQlZUFSZK8MTr33HPxxx9/YOXKlTWqqNs7g8FQ48Ok+qYQFhYWiiKpzl133YW///3vMJvNAICePXsiPj4ef/vb3/Drr7/WaCpqyzZu3IiHHnoIAwYMwLx58wB4PkzOfg1V/90e70u1xeiZZ57BzJkzERUVBcDzGtJqtbj//vsxY8YMxMXFhbLIQde9e3cAwJw5c7Br1y68++67rfZexBqVs1Q3+RQUFPhsLygoQGJiYiiKpErh4eE+iRwA9OjRA/n5+SEqkXolJSXV+noCwNfUKaIoepOUaj169ACAdtXk+u6772LatGm46KKLsGzZMu+33eTk5FpfQ2FhYYiIiAhFUUOmrhhpNBpvklKtvb2GiouLsWHDBrhcLu82URTRvXt3FBQUtNp7EROVs2RkZMBkMiErK8u7zWKxYPfu3Rg8eHAIS6Yef/zxBwYMGOATIwD47bffvFk8nTZ48GBs27YNbrfbu23z5s1ITU1FbGxsCEumHjNmzMBNN93ks+3XX38FgHbzmlqzZg2effZZTJw4ES+99JJPFf2gQYPw888/++y/efNmDBgwAKLYfm7j9cXohhtuwCOPPOKz/6+//gqtVouuXbsGuaShUVRUhAceeMCnCd7pdGL37t1IS0trtfei9vMKbyCdTodJkyZh3rx5+Prrr7F3717cf//9SEpKwpgxY0JdPFVIS0tDt27d8Mwzz2Dr1q04cOAAnn/+eezcuRN33XVXqIunOuPHj0dFRQUee+wxZGdn46OPPsKqVatwxx13hLpoqnHJJZdg06ZNWLRoEY4ePYrvv/8ejz76KMaOHdsuRpcdOnQIzz33HP785z/jjjvuQFFREQoLC1FYWIjy8nLccMMN+OWXXzBv3jwcOHAAb7zxBr744gvceuutoS560PiL0SWXXIJPPvkE7733HnJycvDZZ5/hxRdfxC233AKTyRTq4gdFz549ceGFF+If//gHtmzZgv3792PWrFmwWCy46aabWu29SFDay9i2RnC73XjppZfw0UcfwWazYfDgwXjiiSfQqVOnUBdNNYqKijB//nz8+OOPsFgs6NWrFx566CEMGjQo1EULuVmzZuH48eN45513vNt++eUXzJkzB7t370Z8fDxuvvlmTJo0KYSlDK3aYvT5559j+fLlOHjwICIiInDFFVfgvvvu81btt2XLli3DggULan1s3LhxeOGFF/DDDz9g7ty5OHz4MDp16oRp06bh8ssvD3JJQ6chMVq9ejVWr16NnJwcbx+n22+/vV3VOpWXl2P+/PnYuHEjysvLMWjQIMyaNcvbDNYa70VMVIiIiEi12k+aSURERK0OExUiIiJSLSYqREREpFpMVIiIiEi1mKgQERGRajFRISIiItViokJERESqxUSFiIiIVIuJChEREakWExUiIiJSLSYqREREpFpMVIiIiEi1/h/+AlND4/P1zwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# slide_min line plot\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df_no_noise = df[df[\"noise_bool\"] == False]\n", + "df_noise = df[df[\"noise_bool\"] == True]\n", + "\n", + "\n", + "\n", + "# # df = df.groupby([\"n_init\", \"noise_level\", \"noise_bool\"]).agg({\"min\": [\"mean\", \"std\"]})\n", + "df_no_noise = df_no_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "df_noise = df_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "df_no_noise\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in df_no_noise.iterrows():\n", + " mean = sm_list[(idx[0], idx[1], False)][0]\n", + " std = sm_list[(idx[0], idx[1], False)][1]\n", + " plt.plot(mean, label=f\"n_init={idx[0]}, noise_level={idx[1]}\")\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.5)\n", + " \n", + "plt.legend()\n", + "plt.title(\"Sliding min without noise\")\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/run_grid_experiments.py b/run_grid_experiments.py index 1e3e7f5..37cd724 100644 --- a/run_grid_experiments.py +++ b/run_grid_experiments.py @@ -28,6 +28,7 @@ def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget): start_time = time() tasks = [] + iter = 0 for seed in seeds: for n_init in n_inits: for noise_level in noise_levels: @@ -39,7 +40,8 @@ def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget): sleep(1) task = worker.remote(n_init, noise_level, budget, seed, noise_bool) tasks.append(task) - print(f'Started problem {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') + iter+=1 + print(f'Started {iter} {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') gc.collect() while len(tasks) > 0: From e251950a1ffaf9b179d60981c90d65bf988d6b42 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Thu, 28 Mar 2024 09:33:39 -0700 Subject: [PATCH 33/43] baybe grid search stuff --- analyse_grid_experiment_BAYBE.ipynb | 205 +++++++++++++++++++++++++--- baybe_result_plots.ipynb | 144 +++++++++++++++++++ run_experiment_baybe.py | 15 +- 3 files changed, 341 insertions(+), 23 deletions(-) create mode 100644 baybe_result_plots.ipynb diff --git a/analyse_grid_experiment_BAYBE.ipynb b/analyse_grid_experiment_BAYBE.ipynb index f25534a..a537a8b 100644 --- a/analyse_grid_experiment_BAYBE.ipynb +++ b/analyse_grid_experiment_BAYBE.ipynb @@ -4,17 +4,30 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'ray'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrun_grid_experiments_baybe\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m run_grid_experiments\n\u001b[1;32m 5\u001b[0m seeds \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 6\u001b[0m n_inits \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m10\u001b[39m]\n", - "File \u001b[0;32m~/Code/project-project-noisy-nerds/run_grid_experiments_baybe.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mray\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01margparse\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m time, sleep\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'ray'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/brendenpelkie/miniconda3/envs/noisybo_ray/lib/python3.10/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST None\n" ] } ], @@ -22,13 +35,173 @@ "import torch\n", "import pandas as pd\n", "from run_grid_experiments_baybe import run_grid_experiments\n", - "\n", - "seeds = [0]\n", - "n_inits = [4, 10]\n", - "noise_levels = [0]\n", + "import run_experiment_baybe\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 22.37s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 40.27s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 57.96s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 0, time: 75.61s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 0, time: 88.79s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 0, time: 101.12s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 0, time: 115.53s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 0, time: 129.22s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 0, time: 141.80s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 0, time: 156.04s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 0, time: 166.04s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 0, time: 178.00s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 0, time: 191.24s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 0, time: 202.44s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 0, time: 214.49s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 0, time: 226.22s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 1, time: 240.88s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 1, time: 252.93s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 1, time: 268.69s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 1, time: 281.57s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 1, time: 294.19s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 1, time: 307.24s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 1, time: 324.61s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 1, time: 337.33s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 1, time: 360.94s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 1, time: 379.74s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 1, time: 401.20s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 1, time: 421.99s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 1, time: 444.00s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 1, time: 469.75s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 1, time: 485.24s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 1, time: 508.65s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 2, time: 534.37s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 2, time: 555.21s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 2, time: 570.75s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 2, time: 582.51s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 2, time: 596.20s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 2, time: 612.98s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 2, time: 628.34s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 2, time: 643.01s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 2, time: 657.63s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 2, time: 669.18s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 2, time: 683.32s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 2, time: 703.73s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 2, time: 719.24s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 2, time: 737.70s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 2, time: 763.14s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 2, time: 786.99s\n", + "all experiments done, time: 786.99s\n" + ] + } + ], + "source": [ + "seeds = list(range(3))\n", + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", "# budgets = [10, 20, 50]\n", - "noise_bools = [True, False]\n", - "budget = 10\n", + "noise_bools = [True]\n", + "budget = 30\n", "\n", "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)" ] @@ -424,7 +597,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/baybe_result_plots.ipynb b/baybe_result_plots.ipynb new file mode 100644 index 0000000..21a3a1e --- /dev/null +++ b/baybe_result_plots.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 21, + "id": "d6c817ba-cab1-419e-97b0-d5d5f7fd4f01", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sn\n", + "import numpy as np\n", + "import torch\n", + "import pandas as pd\n", + "\n", + "from src import visualization\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "783c52b7-cf3b-44b7-b14f-445227c15754", + "metadata": {}, + "outputs": [], + "source": [ + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", + "\n", + "n_inits = n_inits[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9a3411fe-7c63-47eb-b080-800019cd1458", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix[i,j] = best_y\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f0925240-1130-40f6-9835-51e6a4fcf89f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "62d7a546-5ca9-4836-b075-dfa5792c7907", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.savefig('BAYBE_heatmap.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1e8781e6-ed60-4959-91fb-8ea7ef3ec956", + "metadata": {}, + "outputs": [], + "source": [ + "fig.savefig('BayBE_heatmap.png', dpi = 300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce3eb3a5-5150-4b5b-bd41-636edd081feb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_experiment_baybe.py b/run_experiment_baybe.py index 8b77d60..c3dab96 100644 --- a/run_experiment_baybe.py +++ b/run_experiment_baybe.py @@ -74,10 +74,9 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): objective = Objective(mode = "SINGLE", targets = [target]) searchspace = SearchSpace.from_product(parameters) - if recommender_init is None: - recommender_init = RandomRecommender() - if recommender_main is None: - recommender_main = SequentialGreedyRecommender(acquisition_function_cls='EI') + + recommender_init = RandomRecommender() + recommender_main = SequentialGreedyRecommender(acquisition_function_cls='EI') print("Collecting initial observations") @@ -120,13 +119,15 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): for i, val in enumerate(y_real): y_real_complete[i+len(y_init_real)] = val - os.makedirs('results', exist_ok=True) - fname = f"results/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" - torch.save((train_X, train_Y, train_Y_real, model), fname) + train_X = torch.from_numpy(x_train) train_Y = torch.from_numpy(y_train) train_Y_real = torch.from_numpy(y_real_complete) + + os.makedirs('results', exist_ok=True) + fname = f"results/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" + torch.save((train_X, train_Y, train_Y_real, None), fname) return train_X, train_Y, train_Y_real, None From c7078c2873332c16b164e635f25ffcd161b815ce Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Fri, 29 Mar 2024 00:37:12 +0800 Subject: [PATCH 34/43] update --- line_plot.ipynb | 582 ++++++++++++++++++++++++++++++++++++++++++++---- results.zip | Bin 0 -> 1039332 bytes 2 files changed, 539 insertions(+), 43 deletions(-) create mode 100644 results.zip diff --git a/line_plot.ipynb b/line_plot.ipynb index 1fbc1ab..b3e4256 100644 --- a/line_plot.ipynb +++ b/line_plot.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 23, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -13,14 +13,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_12420\\752471085.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_12420\\2067356850.py:23: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" ] }, @@ -55,15 +55,95 @@ " \n", " \n", " 0\n", + " 10\n", + " 1\n", + " 0\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 1\n", + " 1\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 1\n", " 2\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 1\n", + " 3\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", " 10\n", + " 1\n", + " 4\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", " 0\n", " True\n", " 767.079651\n", " \n", " \n", " 0\n", + " 10\n", + " 5\n", + " 1\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", " 2\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 3\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 4\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 0\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", " 10\n", " 1\n", " True\n", @@ -71,19 +151,227 @@ " \n", " \n", " 0\n", + " 10\n", + " 10\n", " 2\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", " 10\n", + " 3\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 4\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 0\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 1\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 2\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 3\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 4\n", + " True\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 1\n", " 0\n", " False\n", " 767.079651\n", " \n", " \n", " 0\n", + " 10\n", + " 1\n", + " 1\n", + " False\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 1\n", " 2\n", + " False\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", " 10\n", " 1\n", + " 3\n", " False\n", - " 781.773621\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 1\n", + " 4\n", + " False\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 0\n", + " False\n", + " 790.102966\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 1\n", + " False\n", + " 778.442139\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 2\n", + " False\n", + " 789.921326\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 3\n", + " False\n", + " 778.506653\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 5\n", + " 4\n", + " False\n", + " 767.079651\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 0\n", + " False\n", + " 778.442871\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 1\n", + " False\n", + " 778.503662\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 2\n", + " False\n", + " 778.479553\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 3\n", + " False\n", + " 798.732605\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 10\n", + " 4\n", + " False\n", + " 778.500122\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 0\n", + " False\n", + " 801.972839\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 1\n", + " False\n", + " 794.947937\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 2\n", + " False\n", + " 804.242676\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 3\n", + " False\n", + " 809.956909\n", + " \n", + " \n", + " 0\n", + " 10\n", + " 20\n", + " 4\n", + " False\n", + " 791.936157\n", " \n", " \n", "\n", @@ -91,22 +379,58 @@ ], "text/plain": [ " n_init noise_level seed noise_bool best\n", - "0 2 10 0 True 767.079651\n", - "0 2 10 1 True 767.079651\n", - "0 2 10 0 False 767.079651\n", - "0 2 10 1 False 781.773621" + "0 10 1 0 True 767.079651\n", + "0 10 1 1 True 767.079651\n", + "0 10 1 2 True 767.079651\n", + "0 10 1 3 True 767.079651\n", + "0 10 1 4 True 767.079651\n", + "0 10 5 0 True 767.079651\n", + "0 10 5 1 True 767.079651\n", + "0 10 5 2 True 767.079651\n", + "0 10 5 3 True 767.079651\n", + "0 10 5 4 True 767.079651\n", + "0 10 10 0 True 767.079651\n", + "0 10 10 1 True 767.079651\n", + "0 10 10 2 True 767.079651\n", + "0 10 10 3 True 767.079651\n", + "0 10 10 4 True 767.079651\n", + "0 10 20 0 True 767.079651\n", + "0 10 20 1 True 767.079651\n", + "0 10 20 2 True 767.079651\n", + "0 10 20 3 True 767.079651\n", + "0 10 20 4 True 767.079651\n", + "0 10 1 0 False 767.079651\n", + "0 10 1 1 False 767.079651\n", + "0 10 1 2 False 767.079651\n", + "0 10 1 3 False 767.079651\n", + "0 10 1 4 False 767.079651\n", + "0 10 5 0 False 790.102966\n", + "0 10 5 1 False 778.442139\n", + "0 10 5 2 False 789.921326\n", + "0 10 5 3 False 778.506653\n", + "0 10 5 4 False 767.079651\n", + "0 10 10 0 False 778.442871\n", + "0 10 10 1 False 778.503662\n", + "0 10 10 2 False 778.479553\n", + "0 10 10 3 False 798.732605\n", + "0 10 10 4 False 778.500122\n", + "0 10 20 0 False 801.972839\n", + "0 10 20 1 False 794.947937\n", + "0 10 20 2 False 804.242676\n", + "0 10 20 3 False 809.956909\n", + "0 10 20 4 False 791.936157" ] }, - "execution_count": 33, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", - "seeds = list(range(2))\n", - "n_inits = [2]\n", - "noise_levels = [10]\n", + "seeds = list(range(5))\n", + "# n_inits = [2, 4, 8, 10]\n", + "n_inits = [10]\n", + "noise_levels = [1, 5, 10, 20]\n", "noise_bools = [True, False]\n", "budget = 30\n", "\n", @@ -136,37 +460,111 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{(2,\n", - " 10,\n", - " True): (tensor([803.5202, 802.7020, 802.7020, 802.7020, 802.7020, 802.7020, 802.7020,\n", - " 802.7020, 802.7020, 795.6658, 795.6658, 786.6938, 786.6938, 782.3187,\n", - " 782.3187, 778.4468, 778.4468, 772.7598, 772.7598, 772.7598, 767.0797,\n", + "{(10,\n", + " 1,\n", + " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 800.8542, 791.7416, 791.2345, 791.2345,\n", + " 790.3158, 788.2984, 786.3238, 780.9172, 780.9172, 778.6176, 778.6176,\n", + " 773.8997, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797,\n", " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797]), tensor([1.2123e+01, 1.0966e+01, 1.0966e+01, 1.0966e+01, 1.0966e+01, 1.0966e+01,\n", - " 1.0966e+01, 1.0966e+01, 1.0966e+01, 1.0151e+00, 1.0151e+00, 1.1673e+01,\n", - " 1.1673e+01, 5.4855e+00, 5.4855e+00, 9.8401e-03, 9.8401e-03, 8.0329e+00,\n", - " 8.0329e+00, 8.0329e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00])),\n", - " (2,\n", + " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 13.1364, 9.5984, 10.3388, 10.3388, 11.3995, 8.9474,\n", + " 7.4343, 4.9843, 4.9843, 8.0457, 8.0457, 10.1687, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", + " (10,\n", + " 5,\n", + " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 799.7986, 790.9266, 785.5935, 781.9990,\n", + " 781.5400, 781.5178, 781.3954, 781.3954, 781.3954, 781.3680, 779.0939,\n", + " 778.7161, 776.4197, 774.0198, 774.0198, 774.0068, 774.0068, 774.0068,\n", + " 774.0068, 772.3127, 771.3956, 769.1237, 769.1237, 767.0797, 767.0797,\n", + " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 13.1392, 15.0903, 7.7353, 6.2436, 6.4401, 6.4505,\n", + " 6.5145, 6.5145, 6.5145, 6.4820, 9.1904, 8.4899, 9.9619, 10.4045,\n", + " 10.4045, 10.3789, 10.3789, 10.3789, 10.3789, 7.2685, 5.9237, 4.5706,\n", + " 4.5706, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", + " (10,\n", + " 10,\n", + " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 799.6551, 797.7939, 797.7939, 787.2091,\n", + " 784.4460, 783.4135, 783.4135, 783.4135, 779.7010, 776.2844, 773.9120,\n", + " 773.9120, 771.6389, 771.6389, 771.0857, 771.0857, 771.0857, 771.0857,\n", + " 771.0857, 771.0857, 769.3610, 769.3610, 769.3610, 769.3610, 767.0797,\n", + " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 13.4219, 14.8111, 14.8111, 7.8954, 6.9691, 7.4013,\n", + " 7.4013, 7.4013, 10.1493, 9.5644, 10.1911, 10.1911, 10.1947, 10.1947,\n", + " 8.9578, 8.9578, 8.9578, 8.9578, 8.9578, 8.9578, 5.1012, 5.1012,\n", + " 5.1012, 5.1012, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", + " (10,\n", + " 20,\n", + " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 799.2902, 793.1466, 793.1466, 786.7264,\n", + " 783.2312, 783.2312, 780.6354, 778.1125, 774.2051, 774.2051, 774.2051,\n", + " 774.2051, 774.2051, 774.2051, 774.2051, 774.2051, 771.6734, 771.6734,\n", + " 771.6734, 769.3727, 769.3727, 769.3727, 769.3727, 769.3727, 767.0797,\n", + " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 19.4279, 19.8661, 19.8661, 17.1929, 12.3169, 12.3169,\n", + " 14.0070, 12.7139, 6.5223, 6.5223, 6.5223, 6.5223, 6.5223, 6.5223,\n", + " 6.5223, 6.5223, 6.2902, 6.2902, 6.2902, 5.1275, 5.1275, 5.1275,\n", + " 5.1275, 5.1275, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", + " (10,\n", + " 1,\n", + " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 800.6844, 789.4686, 787.9406, 785.3496,\n", + " 784.1904, 781.8794, 781.0068, 778.8218, 776.4470, 776.3942, 776.3308,\n", + " 774.0087, 774.0087, 771.8953, 771.6793, 771.6649, 769.3792, 769.3792,\n", + " 769.3792, 769.3792, 767.5079, 767.5079, 767.5079, 767.0797, 767.0797,\n", + " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 13.4569, 10.1973, 11.1412, 8.8125, 7.6181, 10.8091,\n", + " 9.6961, 8.2987, 9.8125, 9.7956, 9.6816, 10.3434, 10.3434, 6.6052,\n", + " 6.2983, 6.2786, 5.1419, 5.1419, 5.1419, 5.1419, 0.9575, 0.9575,\n", + " 0.9575, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", + " (10,\n", + " 5,\n", + " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 804.5387, 797.1251, 792.2640, 792.2640,\n", + " 792.2640, 787.7631, 785.6548, 784.4960, 782.9075, 782.7043, 780.9127,\n", + " 780.9127, 780.9054, 780.9054, 780.8105, 780.8105, 780.8105, 780.8105,\n", + " 780.8105, 780.8105, 780.8105, 780.8105, 780.8105, 780.8105, 780.8105,\n", + " 780.8105, 780.8105, 780.8105, 780.8105, 780.8105]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 6.3592, 12.9019, 9.8306, 9.8306, 9.8306, 15.1025,\n", + " 12.7848, 11.5005, 10.1976, 9.9938, 9.7140, 9.7140, 9.7163, 9.7163,\n", + " 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022,\n", + " 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022])),\n", + " (10,\n", " 10,\n", - " False): (tensor([803.5202, 802.7020, 802.7020, 802.7020, 802.5529, 802.5529, 796.6227,\n", - " 796.6227, 795.3466, 795.3466, 795.3466, 779.7377, 779.7377, 779.7377,\n", - " 779.7377, 779.7377, 779.7377, 779.7377, 778.0778, 778.0778, 775.8955,\n", - " 774.4266, 774.4266, 774.4266, 774.4266, 774.4266, 774.4266, 774.4266,\n", - " 774.4266, 774.4266, 774.4266, 774.4266]), tensor([12.1230, 10.9659, 10.9659, 10.9659, 10.7551, 10.7551, 2.3685, 2.3685,\n", - " 4.1732, 4.1732, 4.1732, 17.9011, 17.9011, 17.9011, 17.9011, 17.9011,\n", - " 17.9011, 17.9011, 15.5536, 15.5536, 12.4675, 10.3902, 10.3902, 10.3902,\n", - " 10.3902, 10.3902, 10.3902, 10.3902, 10.3902, 10.3902, 10.3902, 10.3902]))}" + " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 804.2871, 803.7126, 797.7743, 797.7743,\n", + " 795.0626, 794.2285, 791.1630, 787.4348, 787.4348, 786.3362, 786.1907,\n", + " 786.1907, 783.3408, 782.7313, 782.6476, 782.6476, 782.6476, 782.6476,\n", + " 782.6476, 782.6476, 782.6473, 782.6473, 782.6473, 782.6473, 782.6473,\n", + " 782.6473, 782.6473, 782.6473, 782.5318, 782.5318]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 6.4661, 6.8784, 10.5844, 10.5844, 13.6058, 14.6343,\n", + " 11.2435, 9.4880, 9.4880, 10.2008, 10.3328, 10.3328, 8.7235, 8.9490,\n", + " 8.9956, 8.9956, 8.9956, 8.9956, 8.9956, 8.9956, 8.9958, 8.9958,\n", + " 8.9958, 8.9958, 8.9958, 8.9958, 8.9958, 8.9958, 9.0566, 9.0566])),\n", + " (10,\n", + " 20,\n", + " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", + " 806.7228, 806.7228, 806.7228, 806.1791, 804.1471, 800.8330, 800.8330,\n", + " 800.7966, 800.7966, 800.7966, 800.7365, 800.7365, 800.7365, 800.7365,\n", + " 800.7365, 800.7365, 800.7365, 800.7365, 800.7278, 800.7278, 800.7278,\n", + " 800.6722, 800.6722, 800.6722, 800.6722, 800.6722, 800.6113, 800.6113,\n", + " 800.6113, 800.6113, 800.6113, 800.6113, 800.6113]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", + " 7.4257, 7.4257, 6.8573, 6.8794, 7.6061, 7.6061, 7.5450, 7.5450,\n", + " 7.5450, 7.4451, 7.4451, 7.4451, 7.4451, 7.4451, 7.4451, 7.4451,\n", + " 7.4451, 7.4309, 7.4309, 7.4309, 7.3395, 7.3395, 7.3395, 7.3395,\n", + " 7.3395, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407]))}" ] }, - "execution_count": 36, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -184,18 +582,89 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
best
meanstd
n_initnoise_level
101767.0796510.0
5767.0796510.0
10767.0796510.0
20767.0796510.0
\n", + "
" + ], "text/plain": [ - "
" + " best \n", + " mean std\n", + "n_init noise_level \n", + "10 1 767.079651 0.0\n", + " 5 767.079651 0.0\n", + " 10 767.079651 0.0\n", + " 20 767.079651 0.0" ] }, + "execution_count": 87, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -212,7 +681,27 @@ "df_no_noise = df_no_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", "df_noise = df_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", "\n", - "df_no_noise\n", + "# df_no_noise\n", + "df_noise" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "\n", "fig, ax = plt.subplots()\n", "\n", @@ -220,10 +709,17 @@ " mean = sm_list[(idx[0], idx[1], False)][0]\n", " std = sm_list[(idx[0], idx[1], False)][1]\n", " plt.plot(mean, label=f\"n_init={idx[0]}, noise_level={idx[1]}\")\n", - " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.5)\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", " \n", - "plt.legend()\n", - "plt.title(\"Sliding min without noise\")\n", + "for idx, row in df_noise.iterrows():\n", + " mean = sm_list[(idx[0], idx[1], True)][0]\n", + " std = sm_list[(idx[0], idx[1], True)][1]\n", + " plt.plot(mean, label=f\"n_init={idx[0]}, noise_level={idx[1]}\", linestyle=\"--\")\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(\"Sliding min without noise\")\n", "plt.show()\n" ] } diff --git a/results.zip b/results.zip new file mode 100644 index 0000000000000000000000000000000000000000..2516abfa24e85e0c1f8c6e517c997d57b5cba060 GIT binary patch literal 1039332 zcmaI7b8s&~*Y6wKwr%a$wv!#(wr$(CZJRr`lV5BnJ5J7i&VB2i_rAC4O!b;sy;fKE zUo%s^zMoc<0R@8r`p*TW@1ya*o&UN)0pS5TnL4}LxHvPasX_yR5+#G15&tt+4_F{z zuxDT(ATYFlV-^3+@Si6Xkg=YRMtT$3EGiZdkhCxmkkEgdLB-g@-BjOB-_p*~MW03A z&fe15)W*$5pP5PD$koK$)J30_N#EJj)I^{4|4HhrI=PxMIJo@pr7se}&HiKmW9bns zXb)7e4~1bh5)SY`z9LgfUP{v7O?bA2rL864L&r%JTXxb}E@m9ZO#za`mdGI>Bz{B@ zP(?mzDuM}K0TD2%xadMiz~Dy-BEVlK>sKb)ciwpdbL0JU$tG@QyPoEL{d*s?S9VKD z)v7D!AfT?P-!WZ(yIiU{l>oUtJC^BhlDp?kGC`z!gAyT+nReQ3M2|}QJ!DYq{88)= z*z@#H4Vor08oOW;LjkK@3qPM6B7{b%y7M$)_jVgM32?kzyQiqJ^McudVcLY;s%?M*3n>&;ARtgZ#6|5!ffD0zfUP3c zlD8%b*s*JomZcD=z+8Jt*efW5-znw=jgg>nl!J*z3y|3@WZ|#H%YL?W;U zTbb~i-ZOK6I8O|VNpurn=>pJ9KZ!*|DI!|f-)Sg((17(jOFd zpa6;W4<arDAO%y1WDJntP7ql= zk4lkN6`ODR5hzLKv_BPk5sPDjFpY}HG=%D9g8~ws+wvAfUbES$T-lUKE7dlg9hxNu z5Mch+XPOD0+6z7fl^69 zk0w!%DZ)%EV0wxU42lkUV-9(B0A7*7H!<+jOqf|lYR)-j2L@G#yj*2z$2=7vUbIuZ znC3OGC^k+^TFyE6dr)q~OQ6JWvtGES`=k|JqIOY;`m6zEl-vnvpn6LiuOQRq6IX$3 z|4D;R=o$wVhdnAVS~-JMkeh6nj%w@aN(TTzDm16%=7j z6!;;Np?wL$%~CKPxSRTn@GY=U0>YHyj+xoD`16AGOVoH1)<;@S;E{EQHrSWqu5)PZ zTlg*%8c*@{U3veKc5H18MjsNr4}sbzmDb0%_|8>h|1x4|O)0eA7)F15wDu2$mHWvD zuy2r%6pCQRCKDc5!qaL!ZamTC8BAhruE08O{SNmxp7QcpeJX%7X-XwV%k z+*M6hlta@Xg-coi#Z^Ya4kPa3R`*c!Q(rnMUSK-XjJ zxG0psHDx7*|AXN%6O0O*duvaUd+3^o;4ad0fn4u7f#Xb{{K^ZN)}tbbje|d9M*&Hc zXbgP}V*#qtHmexGcLrp8)*9;^{+0AHy%5`h=ani+1)Mx zQF=1pFJ(J@5ERuzlJ!O3d0>yCLL@i!2p&{1QxfAssS-(DXpcezeGTP$$f`r~2)6pL zNMW!6qK||mEU-h;1P8Ie-BCDqc(q+=Kc~u^E`FWQhPr+*;@da}m^g0IogH zr_Ij~e!8D67Gj(J=IDD9T_c%48_YXei2T=wdPieK=$#8k!aZ zU8PN__KBik^vxwTSZHL?D^0dGKHmG76ZnJW=ED#-RBciMj*UO5Tc<~W%lD`>ftJu4 z&$M13a{`Fo8}w_#8EV!>v*Zfi3(OmtDX*zK-4Mi-kqc-?d|1>)=%!@fK()vRf!K#4 zQH`S72Z7wDEMZesfIna47|;KE5z2p9QUdjmG50>baNnNu&>rm2p6qaL`W1Kd6?f(p zclZ@I`fW@44OhazTkp8Oe~3__jJ=PGTEO7^8??3qm-|Tw7h7+>DJfK& znrWk?R8&Tk3~hy}$$FS7e1Jbtb~ccwx9nGJP-j)8iuDCandb;vfxTB%KmrqSqE#mI zzM1Tibw-jr!IV8TD_4JdZE6#2#03D8SXh_Y5R?`Vn6K{!WI>x=lnHnOy$7H6U3V6E|2RfZApPr}&ERb_czP4Ky$S5@2u4pPy$7uJoqCSSz3%Ta zB5eSB4ld|~Zxfxc5Q;7W?kCW1i2WKzVP5Jy=}~Wio#S@MitbN@98|}mi9so0DZZz` zB#J`7ATXZp5dBSoMDg;bt z+YwVBq5A%5tjXFJSiP7+UIUCL$|ssG#b4STclYDfYR2T+QM2(=;%WeouTR)gPbYaE z)=rTJKjOJ6f7hk1s>9LxUqM2L4B^b0TQjRIY*9?|3bo1^6lp=W=K z7c#8)WNJDTXnHH$=MdKGvr;DuhMtmPPgUqWPT)NUOZ{d1L~8Fo{K`|=>JtX@S8m3x z8QeB8+&1~j3l^w96U6s2lS4l9T>$f40`ncFF~EK-K)#*@so*#E{G}R1)!9EgI9SbD zAv>Wd3$yb2^-EP^6p!w2&KKLNc4egyDjM0Z<@rNN#N|WR)#c_JA@uGK<={XpUJ*E} zb4RO+TJy?6guikbeI;`6T%4#SCl)Az2+)7=Q2x+a)z;|*^05cFE<~X3`7}^M8b$>Y z&%QLT$S#5e7F z{rYq8FThv*ySOG^FSe=gU*NrwinWRAP$T8pT!|fsk#b()%b|e4Udrp?0tkTwh$fOL zPnK86lx*#WtY$!zFU|Aw$n$jQbgYpGe>%toieWKBYDmm0U2p0|IJPe**}#e1s(4=! zhJ|t%EmZ5X3#IJB_eFEAa=8q7Jv`Ae3u^V;qscWzqF@4%JH!NbLP?xlf^Xm9q9PVf z;uQ~lC=o0`*g`!whD2T*mD2d`?iE;_0PRi&%OyZ1-)f){Ofo5g%%um}hmp`xf-?^6 z@{x=zxK*KQ=rf;Lh`?3q9rr8o#pXMnVTgv`6sz71zH)u#wvMaj;JOw(n{UL-md!qF z=uS~W zA8syF!IHnXW8Ej$RnryH-4)D7p1l_S+j#!?TwQ(LO>)o5Epi?c0Q;x1ka&FVEM=l_ zFW&0qJl;Y44q-N`RiRbt4bFDVhpE0j$c?qY!_v@3nczN_< z-?f~&!Is#iU4Ek{-F|;}3A3d@r9Q`wOAdJFvR0fLy*X|`Z@06W#>Ty$)avIbXeYvT zGK;!INX2#Ep4W@CTf4TFO6|}2WeIL$Y*$LF#{K^A4#&I0p+EzB?)PIZAFZ^zFSDd+46@Gwswx2|YsWe$cS?T7?@^O-;mBlVlgN5_{?B{=-rGFm3GK z*MW8K1>JS=DrCPGkzH~$t+iXFXL$+vy^i>JeRm&oUhCc;ZEM1JE1QkQsg1afp)u2! zXSm%0tf#r^)2QEe$#LV9UHq|XxA)8RTK&m*+PONz&#%iX#IcxE z##j$KC0tj6;AZ*snVV(VYk&VG$}f_%k%~1|eyABAKfo{J!;%i#sjvbtreG#A75E(&WHgZ;D0b?Mn7Zrr{g6u_muHv8S1%N0$oMsGE%Sgzby8g3 z(w#RAO^tdoJm-0i4?kyK@?Yd#?4RuEu<*Vmq-*nb^?cM2_UO23*B@*`^6B^*Z)SGo zdv^5p`!rjb|7LV==Ka2z%`me2h%anx?|X5m*7t8X$eNwX?6ddfVKiiTIXoao464R( zWbEZ~|Ek0u)fviA%R990)d|1g*`ZzH)j2@%^qLEI?l{js;m6}29k$h2z8yX@V*Kr7 zvN()f6n^RBBq2_D!uS07m;LfT>3>PS-~0Xh zBl5Gjp?-1^Q1BGbe%%2$_QUn6x>+O^+lG`nv@3@|cb*xkN5GcHaPID_WH+A1U^Ji5 z(f8^LOrNscwep#lMRb4t6ROHp5P^%kQ(dyj#mKR-eWtg4OK`JQk&ftoeRC|%9pCHp zL5X;3x39bY;f4HC>FI6}4~O{M%V?vu8j)9LQq)+}b5*C#=dogo#|zC-%DN{MmVn4# z<+8^X@A%kD2To?MZFX3$7im@v`*MHN+x#}tG(ap=A6P{zu6BoQQVxWcJbX$d>k%+rg6*taxE1Q?_KVL z^{e0hRU1zK!QK*IZ20{xAjgw7mjBc{EGXDNmfujvse;PQY9@c>y~*9(j*ETV-FF8I zwV$ZLd8zs0`D#7McJLNoseZ5nUuGN&qiW5(IzDMtlz<~Hf2G)^^EAp)dboDS3W2k- z?ZYpW#}qVfm%CPPu6ys}J)xB{##or&ZruuCdZ<(BK7$=Qi^XcDn_=1U>Pr4G9ZIO6 zZ!AzgtNr2LtNiu(c3FqX(P`1s#z5=m6n;3yt;xz5I>&9b?fx7yzv@%7_)0o!8~bPC zJ!yrKd>pQ~Y0cwtj(k$$Pd$#ie~04>0KSIrpn7NWJ@9yp(MY=EcgSsQKY`}DywARb z@oqz0;6cgb*zlaQIYxfJ&GJr<4okx*|H_TuUDOJXcpB~=&XHep!=vV5yNLGBs>d}~ ztM71Mmy-EZ|A)e@8{wiT);O>aoo>;a@^@ol5nFSb{FYn$bxFDe>Q#KkYf{mV>VZtm za-=D<^$)lij3$~%iX@gML}>^Kl(|U+GAFg1)HH{SRvGIbF`UjVjXAz}(;Gu*Cp?>v zyY5H7xv#!QosWm{w|3a758V&QtU5AmSK|S79aPW0s(9^}ARMj1u}4V9AEo*=|D!hwy}$GYaFwC{&%2T}>z=<_AsjZjr` za;@&pUK{nth;|VHnYP_sNy?m@plNbbvE`x71^gm8s|(z4C6OFkV$keuS0;d-d=H!! z*?5TGadDu*w{bGJBYL_?Pm6601%|6=GzqgiT87D9CZJ0jGgt9|1748_Ob5ftK41P# zi1(@+{p}YwWUy18AIlkUFln3+m+ugCjWn;r5bv3qW4Ys$=M9icYb~g4LS{&~Sm3Ai zaXm#;ip(T9O~L7bji~J# z$1p92PAe4Q;=E8jkWly8Phj$u;NxBp9$JK6eo17+C_5yLmMfQ8m4BlZiWOquG2GP>D@Tjddk_m^7#3Kb zkUOYQ9o-N#H?V!nNCi{cNvbQLp*w|4}1UsK}M66mOz*q`9=b2n9Dutlg706a=?(K1cQX z{P|{XW_g}{k2#N5U`{Ncm(*y3gE0AsrDru0D~-i)F`>*_G3*q1jT*5zgF04bMj1Zc zee(v!2SwPk2J_vg8F00ktWS&9wwo16B+lZSs8Rd=Uzr6l^BXmaE?bxuo+#Nx-|tN4b|eY&B9SZ%H`1MAz6XStrTS12+>*0GD4wZv<$#% zJ-}*h2wMNLD4uG6Xu{NfhG4~1(+DbI41tIf!BsiNp$|11cGjx1Fo=U`xn@Uhp#|MX z&JvH{F>b_URqw7Hu{#_%%mmId^O&~~@}XcmBXCfxQzRw%-+s2Bj5PZF&A z>w03W2BQot7Kh73FgqK92v({iL0)2+`H+t!%bDU9V@~mMvA7b*$PlJTj^J#?6yi+Ta*NHM{T8?;4&v)# z%P1+X>{T;^rwLYYaoV#3ceBj`CYP6{KMXe+46U(s_x>t-SJ34^WsAnAE#K4ZETjRlB^ zc1HsM8j~bE1S%#e48U*@shltW(09`OLw&Y3LbmD0+(QD59ieH$z%4{PRD5QPBzPmD z;u)Z+YdDFeC+mn7n!OrFB4=$lBBU1P*dml4>%1Xo_!rv|<%ozJjvn*^s;xQs*qAu| zc;|fmXIwI^{Zod{-Z<=- zK6Ct`Y|)HMM8hWJlcgZq0I~u5N)h8+u^s{#RB zvxU$FbI`JJGhWX!V|t)t&oTv_YbH()NwQZD(K8soLyH$5cj~lyoX9B@-N}F`3r{2- zDHU9ddP=(mxi{N(ib_buPDF@Vbcw$JdB=n)cC=a~8{1v=EzAh(J|J82 zs%dJ_Hge}2p*pLWHr$_C*G9H}bNA9ud1eoT-=>5$En9}|mNA0mu z6;VU8JHyWiTJ6b^9lB!^i{m4-O=nFb@MDVo3N!Q5ih`}$xvo^yoL(S2A&3Xw8fu&Y z(%hCne7dVOw8Fr7%RT712FuVOSfP$FSW{7+)a-rRm$D`5;(12^hVzoonDy-vyoc z-w=#;{$|O!+Id!}Ya8r;BE`HjL1-3ynlSDZ?Nd*>Fscx}a>XJ91Aq-V0CqLnXV5G1 z_l+rTM-jwt&ttu6ptTi1n06<{p-e{LDcaMMyOo)AOvQ#Oy z!BgXPpqa?36K5B+b0}!-Uy9l~H8X>AG*~ee|Mosfz7-Sv+wWoVhJrk&X<2}q4+ ziegd1$mnZKV9AoL`Czz*#8(Rq&|WCHIAIWgq7G{0I_!4}bR-1J_3dmK7Pv22?+?wvgO=QfA7lAVSY*{zyVkQa*B=O4_tWWPLmDB>0#CurqiNH&XBG8)BA} z1L1tii+zd7Fwx=n*A{tL)lhxP-~}O?t14Gg|;!#u2ka z)Aj)M$Utu-z_$UAk%_N1Z)fIPI-T-b%7Rno)$yF{TnxpL<>0O5lFoL(@HOesb;bZH z>OPs!IVHHD3lVw4qT!NGT-0q6b@LE&#-RDni@tfNIb+~DTL_6g{h$y^;)CW?t7{!l zH)?aH;Y{I($igD~ZI^H=8VolaFM4tsIn9Wa`KxI92icT>A^A;A=>)lGnp7-PAH~#6 zH1kn3v$bgYt!SEC$*h+L{;0T}z(SBuHT>5I;~brruX(K*AKgH(;j3;L^N*b#&Hb}* zCTj8!^&i%$N+#x0C9KOx5}gp7Rx>;~H&C_*VB^zj2#cXD(oj&kA&SAb<&Z~G`MJ;R zO1krd2e_<-Pc#i#7i-6*Fwdn6MeDqn>44N4Zi~C{uX4(E}@j zaqq)9tuX3AHn{MhX&76){5k-Y=qb^?Y2)xh2EA^4|Dy0DI3ZtBl{lx@7(fn|@{h(r zI^)!rQ_fkssmB-@kANiI_`q-8MqWLFDO5>11v0Dbkix8wnz+zs!B1kNevB8gg zQN9Xw>)@h|x5>5)BZ_^{+yo2{HF|XVy&*Mv((3+1S;rzqY4HXuLaRwR4RKICyK=ZEa+kA2xKPh)A@8_JLRIA0BcZ^NE2pm}NT~8y z8x)#(Oo=%LB*=^q?Xy}o2}hf;oNHb?Em589(i#PS#3se#S};4%Q38DS1CbKhD^ySFZZn!zu4kg0kTJ{}$vWg>P8~IffbJ zpbuYTqS&2I+<7u@Ta7w&6T!Ae8+GWS3|}{4+5H}I=%xwV`o+9m7rpN$0@t2x)bSNH z3Y`W9^a<}xotQu!){hDvW%+=y@nIXU6cvkMgrypPIKrXZ$pO%HL7&n$U+Ai?i+ zvoGlMO<6=Oy1h7#`$$&q$_NI16lHVbGuqL=z6rEGx*R>M2M-N)!tv31t?4yCGcw zy!TP`u;3gHf=k=TAjNQ?=xFNjvl|F)h)N!}rbr(5XZ0bwp6Wao-6l#hj8~MO7n_9zf7x7Kw zMO)&uf0Fo>m~pc3GXBkJ^WV!YOU0q>`xvgfZl~!N>Y_rKxJ{?rM zb9PF&o9nI}=DD&@iosSsDglqvHC3wN^vF`&qlxNCq)d-M!UBU!CM#JvPUsX*jQqne*RPeRFqLq_>j! z2*&)j^02<|fY8L-IbN*WymHKbkJ~XwTW_!TnoR<)&o{KSA>wdC`E0&`xILJyFOBRg zm>t)X8MT)oK9}^Y7Y|DQE9!K!{ikDrld}02hCBu;C)a0yN4u_k|GM$RHb>62Npo;l%6`tLw1o;lGbB7i^C3dB;G5Qt# z?T{A=R66T@kgz36RYR7#qns^GpWW3{vpi=5LDvEb*_Q<}&H2%ptX?#|jT>ER!8bgX&x6sH+SOHOQmgjn z_`iA1yX+8y=OEM2to!ETuluoL5?M@m)Hmy68&w4w5g? zM)szV&eY!JVt;=)dq1lzKhEhJ6US9I5m2)^FdJK(;BN8^#NA5wv+kht%B zLe3BZ`_*9b<72+ihu&+uZN6-f4#aB-C?{tygji9`-Lpp`o*P8wZ26sPW z;|S8K)%Y(qCwSVV3S#0gM3aviDF30gU!$l`(cG{Z4 zhFtIak=Ck%52aeO3tC6NHDqg*h{xrEf?c^?yatHY+`0(?ikmM`IQU9KC=`uD7cGg))9hF=Jgg#M{n%(nNH`5(E7i(FX@`~e^NetzLK zh|H^oUZZ)Mvoa}Gt4fau7ZQjFe6yX!$0vNspYwcWsU58`uw2q({nG-W+7g4w)ec)O zFcNo7FZ}*tN5{{<-06P&zED2mFEHnMABA{S`=54s3vG7;rMnU`GuP}ollu~D)MQVp z&J-Be{G3gU$$g1AB+-*^O3K$h*#Nv&zZqP86&n|X1iqu)#v2uKo~)NR+Tiu~3ov6R zJB>B33;L)0sjYab^xK%4yZctUlMr4lv(H82X}Aq1y4DBPA^BWyX4GO|NZ)6WS4le& z-XC&f5*mr+-$O}R5st*T&mrvh>U~jG7^CQ|$9(awOGN4%cQAMA3c_t)EA$VzAo1^K z>vDb5IM1+)sn%|SDqE<{Y*fd2^6&SHB!8Z2s_kEm z({L?&PsUeCV&&1R?t3>37BZ(zS2heDH-{wX)%7*`9<^QQOs^g%-|?n!epIR|vstNI zbq%kS0=Bo9sA+9(Sdxt^}2KpBSwxAR%3Q zyVCTl?V7&q>y9eWR-8*@^ z+1k1;Pc%C>4b7_K-d5#RX1ny^T>czJ98L7E`BxS7>Ay>u$_5y{TBonkb+np__5awv zVO;jVQcoTD>;K$~{&2XX`}(qfA~k*l6yWcD`FSZA@cR|P<-1*+)UfH-`wek7?JUjh5qW9sydJ@PGP;QTm=S)T3q+pfpQ6hi^%JbN zp6sOyXi^BwHUApaM{@Z)<4-7XjCe=5@F7F}?Y^KM-QpLkVtB;&2phitp?s`QVov@3 zqjE$=!fhTPUc14W_DM2_^^9$~{L^cc;BRDPba0S-pn{@&aG-LKtVqel&dp8E%tg%2 z&c#K|K+VO>&rM8?EYoT7d>ea@Zv~c!qR$lVBz-I98_g~7Lm0%7@l=(Z1{`bs5&f(`8g_mH}g zPj?O;p04w4MDA%Ub<5Usba6nb-o%mi1g;j>I<8-X_HV#cDz8|o%QNB>Wlza1vQs0s z%OmO^N--&GvrY}zu8;6z)X7B_O@x)ga(_ksK)Tw9W5wprkcUWP%LUqSEBtf96a_4q zOC3h-H|S&3(Z!@ynJTKE0-qoauMCfkKMGX&SZQh14+B;^mxLnFqm3MCEf&*BhH^ir z8vZ&6rzQ~K&uAI#h%rwY!|~XZ`Nn;LZ4aeA$jFrjQ^*) zPX4dDekpi1V)>8t|MP!i{r`J){r|`M|JOl){}1c$_K)-bkNuD8`hQq|w9z(s3g96m zD4q%Vb(9gLBtanPB|!~iVHRL~qIFrbtS7SM%*u;A(GVaQVpeFHh$cM@Xp(d>3{%tu zB_ts*a3OUuA+W%P%8r0ntMl_L`GJqVad*$RzLM(d*-Dku&mS(eo!qCCg!zJkhK2$f zfv^HMp-hZMMF4)!jA8N{*zSIdR8a50tb6ov^Uf-s(8-~(rxa3+4<7!JgkbypidEVS za~E0Cqvx znvvt?b&yya7k-i-XA6vC1H~3y6G4a=u8G>SS36K;)@1{=#Q3NG}Pmk@5L>D)fUzD)U+#Z-0tx`~e8;rq{B*9d$bH+BN*}hy8aTuY|zdTn(eSYd-fYEgwRHF0z={$es1ieZuDTMe=`VCJ7GO0wN$`~YR0cmw7 zx-vjFrHBX-Ia65-yxPcXKjp}|00Ol|#^eEkWd!!`-IWBY&}5pvoK2%jucF(degjPu zUj2b1FKTi~kmg=0lJ#1)Za;>0!%b=n+E2q<1G}KO6gxL_E0$&D@mQ@Sta;)a?h@p7 z;wy&2DjspuOEeQKlavw=jzK{Vm%s=%j|FfgI<1f?>&2mTmqw;o7K=%jGLx7}r%6&X ztCx5PFe;mPCLVCemjGp|AgZD|o;61YkW1rHA)v5|qH$FtLy<(LNRl$^mCT+dcr0q! zIAUZFBN1Nlq8Elr2#jRjraXXEYbM#IvGEo`vYY&BhdKiHu;o}0O%`z`rvFRyLtLs& zkL3lJM;rA3h>9_5KrrZ_De-~5BL0)gk^*hO><~MtsenVp?}AE3JV9a~qel&jkY$w* zbpCZ&q94)7XH`-;`%6aNOvz`LQaBZUAL!JKc#2>6;hZLs016#vM;-s-2vt(dno8%G zi-Uu0g4@bN=@|x6;48y*78auzsr&VD;kQ2n)J)D>NtkGf+M3Z7^y67p#=6s&427Oi zv+fDNPb`WufOZR%{%L*8+*#uGb#N!xgFZ>Qk}^QBY}`A8!d1;wf~Wc{8a7}u+iV>& z(St0c9x~83PUIcObRD!QfWBHV_fnK?_Y^>#a?po8c}I2XzY_pP?dp*`++CsIH+;8I z9`;)HV7O-)iUAym+--?5wk$y}7UYafTekn;0LfmTJ(;Fli9#pIik6e$8Os|Rm0`i6 zB3TDX6YbQ>aiU!V$p#ez*CE(3QAn1ngEE`0R{ z71SpJ15Eh$aA2W5deD4iA5qnvioSO>fz=9%AU;)B`+y5dmUVNmd>W5yMhM6l04sQX zFpOS4YYzgo2fq54 zO?KW`a)ig&ePt(wDF41<+caY{4%G$j5XD`SqXT$LpzA-%! z&;+jc0vHlr^A;(w?x9{hw~It4xbF&o&9L_ibl+e;#s{gIKeKS-A;(I@Q=!rq#LZ}O z{hkII+<~c{p-R51^$T!HkFp1C`Hrn2D38@g{$#MC^Pa#faierHFVv6V#Qh=a1R+_ zg5!XWH^~a!=O_LwR@Ju8c1l*aS9M=AYgw`xGl4U zz&%7rvTT61L*XAN>yV-X&L5B)f?0>-pk`)z1tv#h9~ZGjrX8C68`$O-iqQa$V>mU7 zS^z3wBYAd4bN3{lA8D^_f!ikfFmbIua=kiYDmn0(kU{U zdx=cCh9N{l0eKWlyrwQjqY=l*)p#W8Uo#((HW;~>jaUgrrcFeyrnD*}DAPF?3tHWp ztJc92m8~tGTzr3R{2hnN6lu7Jc}Z0v?PpN1AGm&e_%*o?$(XwRws1)0^)(^O4H>||NfzRB~pDsR>e>RGq3+2-k*p#(JqsC-%JwCB7w?8vS%P6 zV<5pJk(l{kizHSWNyv!FW@}Q>FeyKv;36JMpXkyX!^a(c8F}d?;ky1+EqxwW#J12u z1FK+>AupQ*fPQz`xX*R7Mv3B%I*qjK)p#S@N^i)qJx!!+p|J_hi4`{wG&O*8c zNTFBgq{jqNhmL)IUV2RFJ<(#YanoObmco=3g)dWA0pfXJ`aphTFz(larcF@4y0R@| z+b(>H^S|JctLcbT)sc4*RbHyq@90W=;;Qk@vR_$9FJR~VlM&ug$S*Bn9p+e$b1Vmb z!x10!q&HmR+qYPb{pKTla}nNw$S;uU>WoRQU#|^KI9P81O#Yq$Q?sS z9q$EN8>o6J!y1L~hps4NxF)(Bgo?D@gW6RWwzU^?qR*H`@3bNS1R(&&;unty05NPJ z9|!D)1?+|y?Ar)x+Z^h11_^4h5(?~670iVjJrF53N|4IVZkC>s>vt!c>QZHCA-C0u zQPb&7dFQS)J$zOPQE6cqg1J`)!iT2H_z12##!ET`pHhVw+K_7p+wyATq9WuLg{1y! zT9}Rw^rF)*aH4cFZw4vTv{nrBz5zYl0Vp=(V@ATO8KIb>f;myOktk`zZztf(3#h4! zbEc__OEx;|0u{|wr1*>~>iAI zR`NuPp#S7_k_~-=!4wUvdvNV?rZ(j4ws>x5Ew?jSW+#4TCtqGCg|BTu*$Fuik7_0# zf}5)OEgGkH3b%I)x3@XVN3zLZ2JYhpXn#WDNLr13m5a|mTj}5OT>iNi%XF8Ou+vR?+`p#0E|=-NwaU#j33Ibd zrTzupo9I{@=ngf~o{cqE3A<4-2K+b$!`sR5-&}x_rGQd}ITPr75~3vgW`q<2gIzOD zKq-5?_}$Fx(@I1%k`PAavRH>5lKD%+KLjck;+;`sD?w~Qe8M2OG{Y)kT3(&WWtZOa zo5QN-%aR++6HIZWYFO`$E~)AT6Y*W3CXo{gk=5W_!$~U(C9(N0IaJ~DNr9=tC{`w@ zj7->=Ss)bR^$j`;0tAK%Ky%*JLCq2clZ43%4`Ts8uMyz;8c8*C$tgBfzAEUbuW`^1 zk|yhWXW>5Pus*KHDsS||oN8=UO%?+x&a%hj+N|myu+^;#KG;#vuZk$eeGFk68lU_q z?%mC%7DDdK=hVN3_*)p->#fkyLj0Eq-*<9gFFyUh?DF`8Fmhh4p9;S5f0n|XkH_tj zO}hhr|ITsQkGz=R|Mq>oND3k$IIi|vTNnDW24{emdy`Fe^z}W<>Na=sXP~ke_Ep3R zud^+&F6J)uv%6^@=kgKY-8X@B!N=Ti(F3`#fAlSW^5gHSD&)6b{qu4CcjWzQ3gsj8 z#rF30w256^`?n8i(&L`M)#mGV#)&N@nR8ch87txT%?}foekpCc?c=qzHBb8;IKEEP z^Wx5~+EJMq8$vf$*&IPt zFFN>AGQw@N;iaCNU*GdL^V1p2y`lE~BU2I8+MB+!YlXav2OE1egi-IaB%$2- zUpvjVo`m_&(^2ROaM&nF=}$8!mKuHLQ*PD}uj0#DHo9mfo6n&)yf0GeZ#lP*#sYh{ zTDw~>R?QQ=^s6J)kM%YW&l#7DP8VNKsrdxacNC6%h%OnuTfFn(nH#UGw3Z&IBmF-k z(>L)W({{n{?jQOTaszXXrPdUydMC#R<$YK1nu>ppKUYlOrX!T?)X#Ii7VNz{fhDx$ zbMn;xT+i{VTAvdmx;GiTJ{*WLx)+zVa39T})b-pcPSwKlZZGCAMAvUWIBdONuX`HF zQgf_&JL)vcl;%J+iQB+49zv8M&~mKhP14U*-9PyC#Z<4qt(ML{_H55CeVEn%p+BzM zu3L8%{_BJvcAd}X+mSA_*xzicWQhG@nVp&K#{S?iCDV^(#O~24{=XP|r{LPwK;1UB zZD)ovp3#hL+qP}nwr$(CZQC|Z*4k&^y$|lKTa{jw>ecfyMjl4;{jIgzcQ8fnvt2Ky z4)?eBCOoNYu6eqN{lg)xwALN$ss7CHa!$up6xi=VTjWakEA2x8h)Ro0Ivv zm}RlnMOA&U3(l+ECD!(g$+NS=v+Oktt|uPI=F*Dy%S#Q+;9D%r%~PrKZSOklFB2z3**=B53M=O?nUN0n_U$wls4L1Z zPZq7=0eMTJ8}E^YPxx~ef$#mA?+3DoflsA=DPuhU?_1xmdA4+<_wh3Aj8BXUGKI$I z%SDL#%y1Mu|IaJM%k2!mOKG~M?H7!TXBVGuk}q%S*6kho73m6t?C|Jq{<~`11Lbpk z|Ie)N>6d3WrjK|(GwwLggr`5NT!&if?wyasKmW%*jk+%WO>XrcAv8HwiU7|}bb?dKKhMp-N9Xik2= z>y+)bx>hUGD&7)NH>p$|nxtTQ>B8ZTH~0QJ>QIC<+<0}{f6v@JKBP&ko(yYDwhPYh zRF`IYvIB=-^=3?TWz)u=f4v7a+(^tfmUtd=X;CMubfhVAX^juw+GH?H4qgY{hUh-N zvw3#Ayj-Z-7%vQFQhT*63U1$9sc-T~Nch-f@gV@+WKgB)v_{-Y_ya^%k^BS zGd!v($GfUm+eXRCI@moDR${uMo9{1FtPow#5P8OTaZfw%{`uXH{WR8}bAMK!x{I?H z3ucM>{@6a&&M^9>SjAj)H8Azwa~|T&eblePM>pYSvt8dSN8ToRsxMl`e8jjgf6CUe zXnAcld$HvH%J%hU+Sr+e$&VzRep1!p8?jGLOP@L`)tKn1(Aqh<*7 z@k@8v>u{Tfk-41CW5)S(qmOwRk4X9TxQNjHvqy+RzvMNBL85Xteny^6#rJ#5L+OH@WO4kqvzYz^-H50yjCZk zm(_+>zp-{g5~(zs{!BL_*5m!_1B7eaz3t5Eh{Q zx@vt*BK$Y*&U9B16j_{MmTIwzhWU!6x&|hDV<#i$VKHqI%w68#RbFtx*tW!2M8u<6 z!=!|wSlLlD!9%42KIaJDI9$0SxzJQJY}};HB-Mqf3XAKQ0Z)tf=^8r5?9VCpYnJY7 zXD2XWZ9ri;m;I0;v-NA3gDp6&&PE^wp+Fq%`#Pus_G_a$By)@5WW~Yhi7sW`cdqgM z7&GHB^Bt{aJ62uyQV;D5UFt>WEA2j1j?IUL2U3Y-*ojN=V{8tF$h|F(;vvXaYe9S4 zd68e0QXMYYEC*moVT_!Ms@(OHdQVk-BfgeKI$akQ3b5sX&F|EJllN;ruSNk3l{>H@ zTO*j(Zd|}&zPo?6)wDxiyXUJGi@cHCr6{&~4LuQ^54)%+WE`oKQ;J$69L-SGr`Uhb z52J@M4lNtPOnOsTv6kL$$9n=Qyk*QLv1|Z}O1gfGl;G!i_mkHEJo`*{rEyG zvMI!(6~{a;O4;T#8E%_U7QN#L{cVQ5`|{;0{02S_2@4MAk?=a&S{sZrz3kA#O?uy2 z+B+F_Y;NndZ(kWb(Y-}y6|?`!o>BeM{h9A`leyN(|NPRSJ-08HEVZJgU?G0K0sUJx zxbvxVo3lep%UQHII?LANyGF~cn#A?b2Di->Qfr$@Ad02<^wt+|l&rvtI9%*uF|~Gj zi-F;u(5p2}IC8R}6qu%T#teKpnu`*G19kaQfE~NgFr-=#B3~nQ|G7hi(j-)pCR0Sg zoj_%KHHv$5tfH7S`uABty=k3ljz=_faIfq=2?)u{8f<;wHY;otd_Te(|GViD9ky`q zD0-OMzHqRfEeeiPAaTiVKDlfFHR}wYYG4C3A3BbnZrcZis#yjoxKav@8d`Aujt!8Z zE4WOZO9tq+2%TmFHSClhKbgd`iC9vS%BW0Fz-gZ0X&arEKDjC%`cQZhYS_<@fc%!- z@^zrrbM1A7pd3dK2qh9j$?(+a|C1Cam_W{Y+!GX@B&fm9y4K?WDqk0Z1p2O!kSIL< z^VCJH3|fAk0U{~Xlj2E&wZVzX0BOK)l(4k%x!e#ch;~wx#}p(%4W1PY-LeG-^1Olm zLIioiP$EhW3s*Wt9NaMSo0@uF*9h)Ui`vLVj{D}TFcq`LqE0mJa zcJ|O#+^uu28L$NdO~)J+>)}}EOLiF#c-cg`pQ@#Qh@2ej@$wU%o3Gx%|7+to`G+8I zOB3+I1OR{%`2QdXSpOSA@PBL^|8oHOUp9_|A9~=Q>;K$198dKoC(~8Xp`a$#SkgGKRD5u4h%!S;;H+VK!n=*QHL?-f?J{l@kg(08p%@EUO`4hG&BVb ze|;Hap<60y%uo(O;SFBJL;|7Qb2ERsQ z9sC{OP%{U~U;}Xs18$sXoLM}3@>x^mN0l*FNg^z_M^gqoGqP1BM9s;XHgjad$y2Ot zdaG(pZa!8T~76hgq#2YN)+Ve+MeY)#}NVK?J2Ay}L`tMCajXt0|hrz*fB>-0l@%< z6M>>{g419UIxmi@3kk~niC6x4AZLzcZlaY>>4V8aQ!x>qK%aa3d@q+oc!3U00 zuy>bkX)p{@cwmqxf15Kwk~#?@-2i|xoba}hrm8Pq7}+MMh#B^p@$%LAW)&#i$tTeF$9n6wR^H; zIfzfvSmXs|&xb0W*oM@({edFR9lS)d^pEvLEFk=Zr3UuTnLBj-?$Bb#9I@CYou&m|GNI5MiwP2iU`vM#LE;t56NyMZ*@K42VnSEf97KaCSf|Z$$_*W6T~R!U zBMV^;uUM&MYZ-*N7%|tyOhITc3vT7Y!ch}5sRI{u7THwmqXR0Yq4))aE*3@zWwT!p za!1(XO&}Ro2ZVxr<1#9FD_9)2Z*^aG}+?x-2E5C;WH;l9A9%b+qVlV|G zYzqjqLkEa9?nFR|$VJpR9^^~VQVXumRS@t;bHoP^`rK$&GXkQe+^bpP*B;>e9t3qI zhq_@%*)pzZqg=AnRqFSi1a-Ae=|rV?p)~uO+T~jH_im?YBxFUQr5^njhsiqoT+#T5|<_{HRn4lKkQ-mYk#YUu3LXMQdu)2g&lUh4qcDPeVj~r&= z!m3fn8o7fju$aVfV}+pjtyJn3ZPv3(iI^L6eaC94Qx5l#rGpLX>5g#n7iEXV_c^o_ z{BYUlR4UVy_6G3JCaOW3I-tA64u4{G=iv@9@{7vhIdWpPG^X~uLKm0(>ss}lBhTTv zePXpgrM7m7hiB=XqsZZTO@rvBz~$5)K~6LsxhPP`VwwT*6-E0Hl~u4XvtK@-eY;*W zE1f?cB2GTEk~BFc`6|_#>=hLdpS4Yf-;E;z#5VbkIx_3-=<^KcoL+tIeQ`hO_{m6! zhCk)pM5BA&{xKA)$)du%SmDL=SDisni~L z#@=X#M%X#%mrxiltu<;(S#??Dt?C=M-f1nj23Z!yxg0BB^bXeAnb$9(SvIYN6Px6M z?9Mq*T2=knL&)CCo;c?)a{m1M)x!z|5vZfr7zUCp{Gfn0$ zK;@X4>LlN7zW~q33A|v*9)u5?Q$`njn@qVoVFXfd${kwd7GB2Z$<&=BNGuuSKd$`< z3cun4aVQh})lRA*mxdYz+H2rwkY4-RS}Qa*A}poned-3!9N4BmOvyD6bppc-pTr4e$Gxh+P11`eo)sgz*rIupz$ zq0ia6^%%ou(s4KukS6}-1M(=vJT4y{^%cB!W3}BIMtPhIAM;u*9bpB%INwaZ8~-7H zkejI!+F7^=zQRU{Gax8W5SfJ%NGMA0uRfh6$`9QL} z%jkl%klw7C z;DF-K_$Gs!Gr90*o}%k&k%6x{%yhv*Hw5ryUcnunHU!Y zs;km>^qfi^(VKhZ6x3vm2LZl(Rh(=RJIRVKp2Vw;&U=kE2e9QQNxR@D%=SHNoXcpI zZ?15wY8Fl&cy(dE^TRY5N0XJe11D&t5LO$)F|+g0(3A|G2m$S~+_aD&#o$-H{dbM0 z=_V*Utd(Gy%nJ=~x&)P1q(E;Xv^mkKK3uJuYL}4Qi+z^aTpL`(yG_|Sjyuezjp!d5 zjGQGbpMEJ`q7v}6BAgNd?g0w;hJBnv0q==YEyJlc-9~+%K|gk*UmM60Es7E?+w{9x z27R1^0q$vqi;MXFg6)OJin#%bC_wYC^h)>s@%*<`b|r7X(-Y3YSFI>Yqx~NVcT*DN^|jo zi8&Mc&nu>DuMij8!R7p8F#-_%!EmHtVcC+fuynTrPVL_oWI`BurE+NiQQ~xHNpdIh zmd+@VR?UG|Fd|hx$HCq05z4VJ?q$$wa~J%(HF|8KN-7|O6`k;lj z!wp@yt)P}9`EV?TKpo~YpmF}$H$aVL#Nlmx`7Ooas3_tH=Ak}GiO0h#Re~zbBXHo? zO^BOe@me#P?c6O7;0z9;3=X7R4&ELb0kPC#06l7kP9SUQncWm_PG;9m=+{m(M^4~l zXWQ-0@{zx|_<)ofdac%8&eoHelE)EUUV-#$A#c+l+Re50Sv3;|k?|o1go%m;RpH@; zRgp@sMondx!sqiUSkQ3{Jz0N`#j*V!d$UtZc-(IwKc6<0W%dzNg$ifL5>%xOuVd=S zm?3!V_r=+YW4$~5M`lzON|!0Y!LMI1@{i0&0&bk6^Wik!wmx1p2F{3=9R%HUf+HgQ zfufUl#~_i$D5KXdZZky8w+Sg< zV?8Y3sDIur`Au5s)H}7&^4Id>i|I8k57OZ~cZasQbG$+|6z`6Q8*)w@H4)kKO!haZFz38M zJ(W>j=F?Zc>PYm&&0)=4G4uzHaUHv_~jcMm#P+IQOp^^W?Ln>aKzwOt( z?>p^rrWp28UAgx@>ib5tvcr9Hl$oQG+oI0*!GqZJ=%wZdtr~9O-Y&@ZVR|Ri};9On& zCCee~{conisnLUAe0%H6Z!P7@@1ukMOeHwMmzHIwbUG_fF&!`8rT8YtmWe6>E|em&1vq3d2TrEm&$CV?Z#W$t3=JW7iH3D)?0g*I_9Y_ZHucoec+E# zcw7s4V-t37CbPld^(nK-K6F)EZ?pXFeVpRv}X~Kd~ER?Tf zNB4Rf`ko#i2~Nha*3*0FMhn(IM&)xnYwuP+j;KRZzGi#~CEvYGAGJ@6icfp34rc=x7n71r?D4;3uBT1j+PF z<6l6?54YwO;Mtk8)p6Wi2Fk~uu$>=O=hXttmFN4|uuoR2UBlza*AK@>aODTzP}3If zo}tOsbVWq^XC$=TBYytlR7;d>R+0(Yh7Mv zrtCLM6B9l9g@W0wRCW#}jf)$wR1`+LT~0DryNzBTvhlT@*KW^MKHQDDz%kf2akI%4 z8{dWb;%RYA5#n?R?xEaY#UQdEzCPPs|FpEb66OY)-QRW6uw-?9?M3O%PgiLnxtoG# z37-!7x8Hi71bp{%m~C%zATi7V=?BI=YKMied%-VdV{~}28$s`R%Wg8-U;BRJ{rS}Q ze&o9(V-I=m@s^YPMlVL%lFVG%1pel*5j*&oNz^6j3J4?l4yyw+*Iz1=Ap%*Hs{Co8TN!@V;k&F=Ktx4!J)@;d6hSPrLEzO$!k z*0LeDVD|RX-v%U}?J}yukS&>?3a~3Lv}W>gr7@%}>j*6)N<2aYDVhD~|3!`oq*W z46ls|Dqa0_yVp9B-cIcK(Q;+%o*jC7+gLH(vSu*3SAJL2;Z!*?jK4y6cGkW*%cbh2 zR%S!=e$1`8uHTZZ&0hO8A3EOY(OF5{#qk{*9jvI$?RZX0yM2?lVK{ot`1;Gdn=_tm zY~0!6W%lG+rkz9oa%IiDqvM=M=z)xew)?PsVolW2yX|6Aqx_yI8-Ln->qxi%`}s|B=2F|g z89senau<>Y*%CDU=Pe7+`JPu6oM5*p&_^8}3 zaz4he)QBvH27>1xy@T=c!f@_*mi55pnC?W;zIJ!$clC&*T7CG_)#PTsqve5H-@O`r z!hU&h{C8^4Tw-8H+osd?J?i(1+sEfjf6LbEX_EX)*Ha8RcUO|aBS%Q};rLwnjic_; z5Wd_}Bt0!gtFiW2sbuhSC8i5Uq3#q_SM=uJ@A>xDVFB9#+m9IIkfz%`|2Z2JSM{ka z`{ydapb(?e+Jo+f!H8$M2cyH7E^_*pVzv__&4VQPrVS&M6zf z243^Y@AC>g7bCQ#PLW{FE!zmt#aF&VbrOT{X*4$;3ltSXw&)T;%Nop;m*B|ilyY8d zd1@ENSPdQVt;4PP@m0!~u=Yg9U_9&Kx!N9|b!Zv3C-%*i*dU&JUzyFV-oRg;^#*y4 zuP<{|<=v`yX#l0LjJEzqW^W%!Px!}M7FKRwAy(ZD!d#t^*0MDdlG#rf%1_O+kNC9Y zl9ZqAr*8FX?u~M@ka)iyz=9W}fF}t@l{-N>MY=EM2*-?fQ%>;-WOPwCYK$Ycvi|65 z+cuQKvdReviP}-vk3b+yPqEk`YA%!ctl<1 z&t1@u5!UrK59%k4A%)66U7CTLD$Ailv!Is%F9*YQW99>KKP$UQ3bFu9j&q|Hi-Gx| zd(-?Pa!+bQRY@#3l`d{^6&z)bCM)$hfoPw5p14}-E}Zx=3qnRjs&Q>#mln!>8g9dn zCFH!-8)v5rYk?EnvOPTo&hw<|k!D?Bg7Ib{C#QQW%Lm5#roU`R)#$I>=a|m}+b};p zb{Ti5p6PrjRNrm7?QCmhCo{1KXpXQPf)APS`qhaDeK3KzNoi5ugu4KZ#1<>5$K%U4 zdB-|+8%Y9cl~LAKr~#j)lMHT>9r2310;B(Jr|MZh9f`-jCnlg^Ib5+^g<-|ZlU&Dr zLs96uIhPTXl%XNTU(2kSjnuwmdoJ^pH6tH*PBXS`*5yDY&|e?BrlpNyTyi;^E9f8V zH|^_ANn+DBXRVHQZgZ;m{t)g<`95K%o|s(3c!9)x?^gHjdO-a!2kUal9$t96hfJu( zBeH<~NCW(OdOVIN&%Xbj@-~F|3KG23cO|&T|Fo-Lg}RtjvDjSjM!ruyf#T}Ob+eD= zecoQ{u=RZ0R{eBlgS@@@5VWWyb4D3BO9p!|PyXhG+k2@>>=;Mz(wiW&jV0Tk!u$fe zlhtV0wyJR(j(kN9GeF9f5P}LMD%FsH5~+Hdz58f`}u%6 z4rUcZ{#af=ZriuTr6U;iii-o~U+IPqCSufWgwg5ZGpF;u5Y8)KnQ5c}!y{W$KP-@V z-NO$li(;|l$|zGRl|rthQbRawu*#}mr()at3nyMNWE?4%dc}r+&#u=XR2I#micXcS zg&NYlBL^K4DVge*cvzjzJWMEbvS1GN3c=4~{RXk+7_=Hm7O4A&QKNDB)GHmjMI|^A zXy> zSW$^&MQ>y(VBp2^NGK&xsCxUyf<8+EEt z27;)lxKKMyF&@qn;K?OfGbO3@shma4dxMWsJpNgVL~Cpfyv4}M?X11*{;%Oj^Jn;> z9bfo?gA}O|T*$1Q z*9^ImoZBwqc1E0U=sl8#lZZz=G?-{1{UW3D^*>xr5Z!*z64Eiw&DaEtqVbR9ZX(YB zMJL{Jj6rvUc7BE%nUT8{TxKn_j5z4K*IKZg@DRFXLGiel0#acBUWGxrjl)qp2+W3M zfQKUj5)@d!$=;LYpUp?DGy}#gT70oEsPOFApS|>LA?G!x>fg=BPqC>zBTg5l0aB(0 zwprZ}7$TI(*NuTLg~t;y71dvW-5kb*U`Y%%o|m6No!%q?O92M!U7%t{08COcCsl|L zjj0$aH;fOuVkVBVA&%D^*+hTRPImiVgSnSb@USKJnN22!q=AkoF zBz8m(!3ZdGLb9e|Fg`yP7X5E3J-mSlughP19^>p*E;I-A98&2RP;y@ygICQ^oIM^V z=Bo=K_V)Dk*ud6|4(!J@QjnaBWM1l6-hjko`lhejVW3-qis)8PNBZ#m9tQcJgoOYJwg`crCk2cb?-4&Y!^ZYT?T~Kd zD4>8tPAdgkqCq(I4fBA6VIHr6Lrtq-qO6e_L@b@$G!9L8r4fBp2(09~qOvG=F>?LP zJ_5<%a*y6!<_MLqi(WosvP9=)KXcFSaFG6vlY-0f>qH}AVm)N$3<_^8!#Yq?X2-h{0Ut z2r9$_hFc7CDAR=M4Qy- zZ$kiAViz~~=s0`KgIn>^aMYko8o^zkDK^b|=>Uq0$cF&YDZ_XfZ1(d)&Io(7W6%Ue z!RjDgIu3}sK~Pe9U_?XjC9sZF2X`QmQz{2qWkT=csi*7{SkWnLv&sivDM+WJscf@~ z2U=A^?>`!FnWjhsd&KuM!uKy2edQ!G(8+DJFc1+;SQ^?1T!KK;97JRf{G!A{R9#(@ zYFjJ;!ea^t19e1sERrd+xUhAtLY!%n=flp(8g@9LsOJUf@u-VsHGK0hFh%p9Ghmd` zdhSX7#wGzMnL51rpqEUA1G3YO?ieDw4D$QGo6)OS0##G=dTK&mVWDj_irH+-_`Me) zZ8ZO2E%JWtT98-kKlfzyo~w}BVI82MH{7?7Z?_s|L6~Rh|EX9wSTJI`Z-X?=h-F#- ztUfKjV%%C6h5yA`#D1_AU(3@hYI_p`S=nE#q;-{}I)C{?AH{qv`591V9&9>HY_WR4 zAyiju7M8JY2cl1H4rNep=kzW*g&wt34e3+kDg;q9|3$F4FrSi?&k5~&GN(&WA zOPkWgvvB9Ju|;zAXZ88;;mOv?B>^Q6!;OPck+U-#|5<$)eB!+v+bw=pA3r=m4}M%F zDRR-0mGLv#D-=jR?wjAReWyMI8L_?2zigJ`4|ClM295Z^WrD@yW?~Uj!Hz>#Tz^)d zICu>LPxL|KfkD>BL`D=zEsctv=lfn#{?!`v}IsW8$+%ZGoWR$9>>r#up7`1 zo?qXx8#GrkLbC{4)ivEaWVD=Xf^S4l7N${o9;Tt})# zH&)<9KNSnqpNfT-6f7UfFv=M63|NsxW;Y#=O>>_a^cabUDMpb-;V!fdHsZS!;maSN zmG0QZ{f1jAzxKQ)zi04nd66${fKhygmwAw{yu^1c$d`2xM-df@EjoxsNW)yb#xPyF zH|AcomSh|Br6EZ^Z542qPnaObTU0`Nm|n>~eHC~X$i!Sg7RYp6&kd4*AXuAt8!YAl zNj~!xh(E-0PVwyU*!kgnDA+#OC zkyI$X*q=ue2lco~I35tpJ!oI2oJ^BC;v2jSuuXlEec&rtERdu+*c&7jK9;ueNWlL1 zGJLv!ITj#m^8o*?Sj+%52@_ka&V&B9iiN>WxWZZxZVZQAJF)FM;$BlW5gm+XTt2H& zs2#AMqb`|6maSm)b!wh2>(!vgPqHFh7R(8t0nkh9aF0OG43j^v@Eta=Mtk5}Z1EdxVGv1}ZkV8L7g#T346kVVk6#QQ6ENMP zFx?VC+Xg|~`xxDwDBTn!7V?T8L6k4I2B!06d&WPs3*v`OD;xy%rgsi%crHJFL+mN& zZ^~7d1h8Mh5!CZQh|q$6A}`jM#z=LurO=Lh_{tl~Z0V#G9nBOq1r{Y00N;v=p&%fU zkWP~@k??{9y1+t#f(BrM1|)(8sDlQOVM147LivIQ;#)+C{{{;-L9mQMB2JN>yFocy zpf!i6d;UNzjg?aWKrLLAqIvJQ??Y`Qf2(M~Sfh(%=zk7@pj9A&B_&5(%nEVfy)A?! zn-O>U2zU_M?qX6q$ds>v)5s~Jn=2+GEEWnps)8*s04>?gb^7@};sBc$f>j)2s+41> z(9+dt8R&El^th$^K0*QiG!2%_z?RHgS}Wi9LFHOUHzNr9y=elf=Xt6LeYMJAAF!!s ztifL45v&n|mw&z(gSU;~y7O&cG>>k;j&9ullV_RyL0VG#)l>S_Q~TF#BnO=}RsAe{ z*KI9fh@MC5AV5VO*eq^hahhYUL@K7rCNwZHV>Wm{)=jX|DRL~4HGl|)i2u> zWCA=jMGdx|zD@yMeAyN0GCR!D;Rh94fb>DtICTlPQAprI4Kllw9{BQ`d!f2>yn(Kh zQDmjw-@Wj&hIcWN!QSefm&L{-OM3PD_c4k$*<4(GhFcqev=LUa9M?k(9RXKf8Zt>I zkpvt82OT7c8F(%j(&LMH(s{Ce`*m10YcgYJ!(Vl8kc2acKU9k$$Xi8GT^c}i*{PF6 zx>a_(!(XX9MJ;~SOX%1#Ens#S5|TS6S0V#jZpkIVj&ymM+mHlh)wxV#KIzvjM}_xnA*H(xu&93 z%TSdZkA7L{pNItzJm}poW~`=#ch`BbYwmv{7UrE&rj4`j@Qs#_DWaDm25b4&4D}@I zccd%1YZLS}`Rw<^zKp@8jLve#aAb_mzZji+8JF{;VP_UPKy;SP>WBNR&Mr2AjiY6Z zqZN$36DGF&C${9PH`sTdiAiihu`ddzsyRGG9a?d|f@$fX$`Q(KYnsjes8x^FVdxPj zt%S^%tD;p8t1bmQkew`2pO-7wS7dY1jlZ2QQe)>R*IVRhP1fFR8HvxQP0D{pEXV*ApmrhZZb(pLIojiMfZ`uhj);wD+7LQz0L14|k_w1z z)18YzA)Ui? ztO$(}%NT&;^4WnHn0Wl*_uK(7BbNw@euYKt4tR6dg4O-qRpkp%Nj^>t`HYj`BSmT1Nx_85gPFy6${^Jn!GYD!<3>h zAq7;}7W1ewH+S=1GjH8Sy2qhW8Lwh$Sq)Aus^@mO|0%{Uxjb(PD_AhTnP=SS=J8Q5tW zN00jF)~skKER#6DycE@0(nRjICoDLuV0U^Kp2|w`PsHL~ti0lY!B(`UW_KP9r;#fT zd&bKp6S>tTLyM~W%<)xS8eZi^MPN0svT^`SGuZAqetmXVL{~6*rVewEiq<)OCfxlw zo)g@ss@=Q8Vtx#lZ8F%25_)}(iq=DdZ_$-i-6B54zIXTRaqTX7T&)vLxrw;8KcmGp zQ}J(f6{~06By|)tW zL!oghzl&&>n@7&tcgOyy-Et*!8Mm?)KJzEgiF3D0!l_qA-T6flo}syWcO#Ro#C6>Y z8g6~N6rP$=)4N)yqGyYlm!mk|AIJA$(Z#p;ql)VdLZ-m>bs~3VAPZGAC2r@XiBT{P zE5s@|;isgE$!{HiWTj!3t~w8%RW|EG&Hm??miC)__s+Dp7VSxI6sR9eMaN#Qq_#df9dq_1xx1U@kV8_;qUpLd$(-e3fjQvo7}8uKSO67eo0u(ADQN<^1cc77eS{(H&kNAIn)%?h(Q{h}GGM{R7w+E2r1;ME4=j<{_^M-^oJV{1=~nKoyitnr2JOjI+YF~u zQVrV{oh@EZ2YGUjZfXZ8uKXF%;*_a~ssT^g{P$KB;#mzoRNlp_m5srk8lq2&v6ir0 zR_^!3S@h^`tySCIPEMxK_|3g?=b?%w->S7=S?|@iBU7Uh6x-Q1w!QI`dj8SwtwO|E zT^jVWYA{?5j$@r@ zYrDD(w&qsS-|ij9t!CDex}aTmqJ7z2U%ukzU9oAeZ`_u-`4;Zag*U*k~kqKd~fxRc;o9_qN$x*ss3zz z{X~|+621#n|7vsp?6T`_WQ*~9yfNKR(?*^;u>RU=u~8pOdtS~^I9#&}-1MlI!mO{} zfYF|N6-#ubjFjYKJwCigUoc;v&B#xzg4#aA^(%Y?d0ebxzI`wv&* zP`@WMTmenDY~^=RjWkT{HdEN?yjll&7V{L>t=;iBEF_I!yZ36CSGZJ_mdj#`M8@3b zM*DEYkb$o4y!`g=*Ls&cORYw?(X{?|*b~cN!HcIC?f(o}Y&cK$6(nf!AiK}nrLAvX zS($Pb8XO2HOaz2A))~ZBi;939a-)9EOOP;9o}P8dz8)$?as& zvKHiBGGDYFV}`n`c;ogpO@Cf>Y}?03 zP0Czq^93sY4Q%bx?m|Xs%T32_-yQ?=zL&j_&e!~M8*k8~+0Xfju-wKDXCYuI8(Or! zcd%|f=<2z?2TMaa?b#mTE9H1{_~%IQLCVF1=C|sMBe}iKWhbbLY3MSO(6ldkN>1D( z*ps3*JY}83Qs2qZCM=^Watw>IfxwMZ4VOipu6-)31Q#5mVRt5Y(6)O$7d}0&W&P`J zd=;n*$JN**^S4({)CI*t!;}w8^?WKVpwg|EdN+b{x7~Mf#y7_F{Wi}jt23A2uP=O( zjFPd(*SBf0W$$6|Eo;--P_II+>dE7-x%h3;w+Y?VhH1~J8%ZOp@{?!mc$U|VW6166 z?yYY~Iqn|MYkakhm+@Ce!x^Y2Fsyakb8KHtVA{pSFlBN6J;xTo>7_Tj&+Y?{iu^jh zx-Z6EDc6F|i;{N0Z~iGq9Ng;zl-iGQV{?Z!=(h2_Y6zybxeJNoXnhbR8i)>!Dw#42 zu0&%HsVRDxd<0gd)Uh%bt-%tM40~Gb3GPNV{e1yj+RW{#&rNBj^+|%Kj4L+s4Ze7_ zQ$H$;`z5uhC|elV<2lllUh@f5(0wEo_aCwvO;j%SBVG3KQNVl3YatgTO83pii zO5doxpX1Hs6uPL#l(A8AyC_)FBJXqLxPn?rA^?QHDZSw{1s+J5%zLCzNd0v0c3A1HqGS(c z^*B)>P82&(K(c+90R$@#l_E-GU;C;_Ls3AQouBOm`R9%rJG+-iAyJSR5j#7(m5ybf z`6%bc4hlQtfmjLOlKR*^Q+zcXWNa(>6j_KEe^zBOjs%}j} z3!9L0&f2|m&FW9!|5_Y;G(A*4Rebkqe{@$Lv&#ewcWFqHp=G~tc` zHTviJzbp>_kR~wdDF_e%iOxK9U zr9y^~kL>BoGUNXcCSe4K34^F0pb4JtK0X9_In>^Yj?&8xNDm*}!S}}*KRT_eO7&;2 z?am*amIQy}8J46?P0^e*x8z^Rq`>TfTDYa7notYprQHU|jLw;GEPY`Jdng%6rG+oR1MIY6}S&W!O+vG*7uIufuBL70W}7=sXUFpPuW&Q;vz zz-ezHQ25FGj1DE{5R_n#9UMv!$h@A!COL8<0I;NiHmbRZN+#44Oo+Vl#3n|gwqjlA ztd$v>A(M`T*Y<>D5GJ~9)ak^_Q;|&2ul@{*$L89dotPeE?8C8A3>j zLYiu*hm;!FRFs0<90-hXP849+v3b$u`56jVW%op`w5YMe&6sNd?f#YW^k3n9`^oM!s#|KrxiCu9;1RunyfPnKP zOEgSG48Y5F{!wX#$$-zjqQzwc4%Kp)k2|u)dh#&?!cYSGN9sW6V%%yoKxf^z*@J|N zB`RH`eAv>(9Kt8MkkjJ85NBe}{=z+LT)$&$hZR-twGpo}4?FmEDY*Kh(h^|&tFl&# zP4Wy|s7Zj_%9Zz{(t0JQr6uJ-qqX%S!Jj0Cilzk1kJN#om0cVVZ@?KJHoifct#b_( zoxVqs)!HEyir6P@8NNrVp{DI+@lC#=$F{m>y<-#XL7Id=n14m&fXY~eV9f#vO9rBk zP4<{3-Axdy9aJ?5@~K{P78RiU+txb;R2(y{I_YOpZ?J=iTSHjxDoDWHW7=NIoQ9J| zCfbOH!jWr3b;t*fQx=y3kx@bd7TOaiPE-~I@a-!niXuRO<-5*r1ts1~LYzW9qMJn& z_&TU}Lrd%;3TGdSb5zPSF@HcOo!rmC4|gxYHbFbCn?)Mjssd*px{C&BZ;E&$qC3^E z`-J5yje5kuHc_7sym#TpOa@|ak|7$5AZ}G43wT?W(~}Xp&VB<`$J7kbfrYT^ z{*BfvN33#`q}SeoQ9d)SFI@{*FRsr+!IDptQ;De4ECu=pk(cFM$Af3&;U zO4M)WUF!y7$%^FIO4(xclbpj)4snJvz3^ms@;|V)NqaAfb3mhZN zX;TAeGHS7x*!ATQ4BbH($@=AIi9)(Y1=8f`PY_%bOzc|E58gyv!bJ^)56aWXA9SW4 z3hZ=D0$0Z-)!Yx|ROXij;v~xZO+s+eE+}IKl8vOuQdZ@)$Vs`FrBhw-rs{KcO!Q;Z zdN=HVL-F?)x(SR}PG->3mhI?Dx_uKGI#gTr|)Ul&MUjY4#=-w8OV@!${@F4R((vgM8= zAo0&GAjp7$=%YZ)VD@9Y!f9^cO{-LLcx(6z3Ydu=Yig4el1V%)$o8*I%Qw4!da(32 zeon!`U?Y?UHDGeY4g+R|IQVZ9EtZ2GZ}A>f;ubZiD#$o10PUV>gS+z&<}ZF%6xiKO zsCQ^9mje69j>K3%SKyA*-atXMYub*6A@vTXP0O#K;OS~dYZpQ9sc~FSefHq ze?SQ1^r28~Oy1*Irv9QJZ2YcYi(r7W}>mnuz>TMBqfid6zmCLZO`%9PcXNKT2`I#jki*?DZkktCyN-!;3{Kq*e zg4YD;V4h?2ywOqJRkAC}0hfA6w{Nf!Eky+SNoTv^>B6&=5!6+hp_j@0nA?t|bjj8x zTVFoP{Ubl>+Y3qjBW=M)8CO79AhC7if2NI}Zg#v45c9BPC>q9_4-l04PA1f3lKID`#jB(4ATG6=LVQ;b zz7&YK!eJPO#!WNWE3y&33Y0Gsjs(^O-8YSKDcbQjr#pbHhS)5z&twi8xhGrdpV=Ye ze=zorF}6j2x8^CIvTfV8ZQD9!+dgI6_9@%8ZQI`Eu7CG^`}Tc1ebdRF$y_6ACo6k@ zS^14Q#)G#4bthRX;GeB%9!fA*0iCa^UVtHOu@pT|B_~?xE>#ICRVgA>d7Y%jOHrLb zQSGUuMOs+0=O&blg4Cert9G=9VPy0~MtyM*rpG7U+l}((t04}qdV2u;q$;HD&rTkO z-(h$-4Ub10u#!tdq6(|0Md0o_9Z!9aHi;?r&w|ivTp5Fa+Np z(gi0=1+slbO1d_aLl;&!$T;1aFKrz8mufmR{0Y80c;z+A4P5+uRN;E0akm9bI2JWv{vYE3lK@XoQHQUyUsq%gxu7-^<;YPfrdW8W^gnf4oj-M2)94gO zM5BtpJ;nX>SYtUI5bp(~HENuxzg`M91V7oX-i?Qrv7JVzjhglr;zAyBdHDuZw-=%4 zW!CdGmP1T6fOuay@+l!`YLI{MtKWp ze$tag=}^}ofc~pMY!yQUv{h)O!_-h4cILw!$RZ8vkG9*YDpD)&+K4`>H=W}aykRSL zNh1QX32q_Iy2TM&P;lto#1hDX|^2SkyMU3C(;s#4s%f^;M^uSVo&RrSyLdDNlLVnJ~fp}#`7 zv~;lfT3T4=MjN1K)aUbYVbp?RITgPS<4tCW%Vipt_i1)dsX;X0r!?*oA$+V5sHsou z6;SI6=7ab(dl_gAHGOE!G(oB(Okta#Ci?IGmWO^w(DaJAQ^c`DI@H!J&t#HmeW#;k zn`BzDI##AH`<2-I8FW^xK<`f1bRjF{Yn zo6*tsH1#hr1N*h1p1}*`s$h15!Qs`&IEIr2zQ@Ejuf2v;PQqKO@yI}^Jout9rAr@%Q|+Y2C|#bPE7WLdE;XuxEW&5~BqlNYuxW zQ?99ercklnej=H~k2=N9!N2yR0rrWS_qjtMBmOvx&iRKOuSUqXm{mO^R5q#lAVdGsX{q5J`jJ}w46kK+O+D@L$&&*%e+drEvx5tcP7bw8CI;2zGCqwol5>@A^U~M z$wz;V&>x@UcgUh#;pmw->_AUh7MSktkki-4l3h}EpSwj$%U2g_5#}^J44gVT-^uVM zPyb<&$&Q;jFSOchE;}??)W$7npA|^Fp7h#vdGCa|DrcquSi7-H7i`npS5cE3^DW(O4eJUkA1<#}%Yqk*<^H_7M+uJzc zlW}+@^WK>_zdkk%ly#_vr`zgm;vw@=r5x{O{7`qU5F z{-K(mIwG&wczXR3w_G!Mn45%PEN{|63-lT zA$)(~GAHJQrr({FLw-+e_VKfqq=^G~cE*;k74Io7IN&=y6mrIvTt6_NA7{I_nz&*vxNMg~pU^!q_3&ZL?Y`+3PZpFP zJLtdMGoAIf=ds~>%ZPm6HK6qz8&8Ln(4-Byr^Bw5V~e@_$lRkIZ>bjYPS(8*`LvV1 zr3GE^d^9KOl{kDWdqqr{PUda*O(SdK9Y5;G(q=B?ez5491J>D& zEY9j`mF|lhK57p7!ar*B{|LC+H(o@r$_NPN0 zf736JyN?yP6?n@Sj-9FU^5~UpjE!8W%}^S*%DR4OWu)#VH#SUgeYoO$^I_I>XJE;P zpi_|wW?W{F^J6%f`1lyLOyA-A_J-cx?5x51P<8yJXrfef)gC`@)^o9N6R}=oho;wG zi(YX6n6a{d!Kc^yX^i}I>@3i zugJgo4|4C5055ZkK3MM*S1v&BD$#2$IXgdQfip)(-Bbn*z0=;sQ*;jW6XPBvd3$h5 z3ZuG~DeD^YnX&bbI3`#26Qi&#ja2(%c$meU&03dmnJu)j5#Axq15>@KZGPdkDIV z`z=ja+q@^mXMI9PC%(RwEAsMd)#Y{A8s6#8!)gb<%L8M!r)$jEW%M&MK>RX60sg_l z?62#lc z=!A@}NSfOHF`I@^t#`%8{NkHlwN?et__$9Z7EX}TTZPMxd_i4>XZX-yUfkQv#fKr@2AEWr}xg+d805O zqqB6IzP0CJqm=Hz%gTGvjEKMV)%o)^#I5-n!%6Pj>FnbS`$T6}AK;u)Y1&o!)|I*9 z_d0eI`=r15-t=;i5ZoRQ*yAy)2SM-u+pWoqQ{BHJsZPO=llIqH~h_KnlVuGG3LR%jNs{Q{9ec9wvu zTos{j@B%(Myz(xBXj}0BOL&prl~=)4c(P$_=yX^&PKCBWDNcA_khd0tV4a;507slp z4_Kk6&-TcDdG9*2^X<8rw0^7ZxnMn52|tdcv!dpRVCCl(Qrnt&ps&Y#eaR*6@Ya_ku9sHR zrk?Xi(cQ>QdNSZhUxlGOXkG`(10tD(1YC;c9SwOB+WoE;pT{oHHMlZ&XIR&E)|1cC5RP zsi6WE!xPhP*ro>Pp}*#13$O~dp!|UR)w=YnAysTEnz|C4|4^r+=iHh%n6urKJ;9sc zPSR6k4}qP=hWgKXSH&;5rlajC?j>OCsC@Fsm3M*Fy#)D1FYv!STE zcvcO_z!6yO;(1_Gq1YdY#vKq-R70PHMD4vW!l!r#%%ySPqZ{jvwWm*Fwc_J7dOs{^ zMatI!Wn-*z`)t+rJzhP2jIp;q{=M{rksXSUOOT$dONZ`9)-SLS`swBFYlrUeJ-;2m z1dRTZ?u9t>82s5O?i<~>mAJg@yK^7jPq6%fajy8 zo2kCi|$<=Nx1VkFJBCuGl|f-i10%k95MRIwLh6Pw^3Jlu0v9 z1Q?|#iVM>@>-h=B`ZW^C0?jy+#60%V0>tWjrGPgArJBMd)q=2K{pE1!N#^wA2cIy} z2K(Z)&N^u0AX68qRTdcZZzIUnpgcn$$_%o-dQ?d*?tn;=SrVPHdW0eetN=_9jw&w_*o=Q~en4i{zN@ox!mmPjg%;*8RLRD6sF&t)e zk3=V}pgk0B1pF5oF-cvE4<#}6wj@F1$e|#uGx$ZAM2bHMe&~?26O_EhB{m0oXb|4c zRU$!lmxyt1U;``R@R-SzY$~N;Pslt(9VfXgbWNx9F75DM-udk98?j zEPNCQk!=~5eT*0TvYLks=^p9E##N&{q3@9wX%)& zTd(Q1)XmMo^_`2vmB=1nkFL=+^X{34&fF8u8QwWSE0wBfK?bWRTrM6FyrNMAmpJK6 zk`)uHr-y+*k8-J@%DOeJuN)i2f{Leyy?>TsdD1;Wvoc=cEEAPqu~5zJ1Xg}@xFsRh zTe*DBDz|7&5Tzx5n_$fx6B4HiDK?8%u};mniL*iuE(s8kot-9ZBPn>aN>RB#G`A>H zH8Y8oYPfYJ=wDO1KMKFfh$;ielW0K7u+VEDmXP*E9JU?y$MQV0IHpaR$_!;B=Pa5k zP`boY$0Wc7ilHi!<}y&aKo=k51GPK};se($K`UJq8!EvAxy~ybwmo;H)FvQY4s`AX zc?+@R%W5ECi+W`l2!FRWAY(ZqW9>mkuR-RX2_jSe$w;U&Hl3NJRMi@$r-hcx1y_X$ zVk47WG%2xA1wozsyXp@4=a?CfWdvAmNF`GenRS3G77X+{itJv_; ziv1&ClJ&qacSW0bQlf;>;xZzzH{yq%>yJ&0+hsh^ESDJm3iu3Rm=JsJW)r0v+dgH; zwCMzDk@TKe%@X1}Ck4texz!gDnTo^!Qypn4kg8ue6JzX3tGy&HAwM?wd==3PET>7# zj4H!wRolb-zND{=^ip=2H8Vm;$Dpw~o+X``J6&?@6u<%z8m(1WJBb-US;rZo5X&hL z2rU9NG%?_i79JAN3;jKhOKJ5wXA5Lv&eRxL1%x+#it3rH6Hb$;T}FL&MP_BPG9m0j zPE4W3R4teP0bt00xtOz)rJep{Vy;8QYNQIKCcW=dh)*t7dKN<_A%I%bpV2|Mq7}Xq z5>eTZ?j?r;hw8$U5{PQ&q%MQy!lPd)GCiLK|J%e!;L3DL3#N~NOblgUD2QK3m~Ln@ zX86ukB82E*^6jH3e8>j(7C-#8%>-$=WGPnGh{d7>30_oW8f`Rms2$%y2{XEYlJ3~8 zh*XGKuJk+-Pm^+SHQ^TMUKAXxL-3^5!dRiHFQL+K%&%}&Ew=n&%7;M$7PM5gG`?6B z0;dS^tlq%Jt}=zSq;9!v7Fw=(0MMFZ;@6pSq)~5>AuHFcK>1W#Q8GqKa1xAYX;0-6 z%gz(yQjrRfl65XIb4jQ$TZ$87nE*dR(QO6>ei~Wog)}6D&JxGYF1e z)RH@gQ9GaX&PxoWk+bZTg!L%Ll(Xa|Zwg4{%1q=6OZ5B+Uog7$CvT;zUOotx!G1#sCu+WMHTh?`zpqWI)9STUag3 zQ-Cj#?t`I$>=;S6bk*M}U$dE1lBZNCT&mN-jEKLdp?waMK`k6%^khSS$3#UEplm7j zlJgU0stbTYt#_F2IKF4|&&1>>Y5^9t)Jc9kwEVOOn-q@7&4uo6A@l`Ogi<*ir6vmu z`F8Zzj)jHPAej~atr7LOR{+6J-~#~EdTJCvh$_`K`m3bK0!}8fPkvhAj!A783VF;X`79EmFks0Rg@@%@q8V9PUfiP<;M4AIj>8?1 z%mF(b$bf9-fUPV@111}J>xOj@#VAO^D0O@5y%u&MFlGm{zC0%>Qgf>W5uI@3+&(;# zrEQ(snb3pJ!>S=1CrofWc(}3WaT*GwUZ1`!3KFydlC!B^853@*4GkW_;y~DJwK}N51<7cRmOVyY)tIy?z zLERZJb`7H!BO^PhK3c(#)+t5%k<0}q<$OAY{jzD8LGsC}S#y4+N`+J1U{`wd%@)N| ze61@%Y4=Hz<}~F4NTm#gP{-v9^BlRe z;7prBRJUgJwy;)+s)8z1Wr3acR=;IyKpDE)2(ko4!yG7ftRd{;NM%O!oNrM-n6+dT z5wS30Y%Xan8CLYOV&tW;(6eH`odz}tQdch*=xlPUgc;PhL_BrDAy)f7Ve>kCn2Q5) z^B)a(-a9eai;mr*Rv^S_Sf50g-yJN@E&8OuY>ofsC0eD7>&MMalF3dt1XcrO=ZyKR z#WFGv+Je~toW3NJeQ1EXK%P~kmEV4~sJ0EY&8({ec09kj1O@QxSKyWfE3C6l8-logs8=H3uls@|pVMl8 zSUIr99TK2F6*}TgEF487$byviVNPSQ?&6mw=hUs`%rOWUkTZ=po;8tbj9EBlB@76x zCe_U(0~r1cHr3Vk&^^ z)@i733=RHZzJIwk{9D#-#S&F39JAzgcM2yuo=;>Ee>k3xWKYEiXdApgp3h`YrFVBa z8_1N#v7__xa5@{wl=j=!0lz&)ip5)C^oX%TB3VTaOC&j0deYPaAr63~Ayg8QD2R&- z;33rC7bkikAbKz$YKThp_)Y(y5HP8K#{_?1`jwID!9m}UmC6~Os@XS@H93*hKT%cn z8)WRX8lpl0_Q`PG$=a2Y2BD*YoR&HHj7(96qzfh*q_v$Ti5hRuX(T&h`S>J zj^*=-gW&Egac>VE#SKuHqiY84-*S!9IV4%x@@Dd64W+m7dH(Jp@hq9{HGq_th;rbX zv|%q%!W+~DkD9bTuOXRj>h6OsBeWaTh96G$yqOKZWzU!p7a#nL6R+|kQq9@@&^w3U(%SUD+8JhT^}jUST>^*N=cAo3GpIVrL{v^}?t*2flW z{R{QUrKZ}V{&FLXsgfgoNaHBdb-;@duL+zP;Avn=RoWwtzdQpxA*hQFYsf|3F{^ zSVo}x<$(+%I12pbeg?W%1tYL>S%Gr|1ocjyAM8RPmG@2Ly!Zt!R2B74xT)^1;8=3S z{tFxr!ZNb4Up4)(-kON}!;!g{IT&^xp$}gkeA{|U7w=sIcWhp!OIgdOLZ&t_e+N6- zX-sLC_ySC*39t2EOW!*OR!hCk2HRI+=gYkwfZyj8t<>RD&-Zf*k5tY4-cF-YftKIf z2>aul<)ddc)D2CKw&cS3e^}q|(-iTQPCZa>5Q8F1`z;|Um+zWNZqQT3d6ud4rqW8UazaEN9GxO?CZ>L>>%D!jJ zM!K&<#siDQHVPEIw#PqewD#I71f@6i4j1Oh(VDblf}`MsOa(HFLc4BTH8I897U?q< z4TGtv4)kB{3TeKBvpx#GlU*OLhsBN5a*AjR%pM}-Jo!qFU5fSj+_7!yIx|%8w*IA% z8mfCt?{#m{pgHsh?Dun}EK|=!b-2dYf3%stW-=B$KV~V@$5?o|wE#fQLY=UMXd?CS zS@GKNWyxuuu$A3hi%6{_)l1n^J?H#Zp0|1ml*^8!gu<}CN{vse70+BcuJUQzItHKF zZBIqbS+}yijlpdsT{k!yYFR?@xTdRP++7<*YwV#{mpDUHwpq|wE9SQx-7`+wy|#=_ zS?R&~;CbaABl+}a$89r9J*%&cs&5iY#r)Vw=C!_?InMC@7d7Au3Z0dzmjM@x5zQeA$TFzCZi|$5-85QT@*?vNuw2y6 z#EPvi7;ol0;gPMkNcQEughfBDZu9SGeho{A({r^Rj(QRMY_rpSRY!(T!{}(bHU`Dh z`7UV{#QCDE>PnTZfaxeS(IX*eM0Nfy5OPDmY1rhVarqH zv4^;=pXxRE(>dho`KgNlWh?JL*lQaB>AJaTManirJ!S(v$;yXj{(1XKfi`WDbsoFj zYWW3WuDoL>+wwRMZfIBOk7P9257I*YqJ%YSG|J1zSns46QB zUp}GvpjC-nZrgHs`RS(AJM8iE+Y=+kU68n7_Na~@{vWq)fHfWZl~p-t3-ZY6e}wkKy)0OC_%P?1WEc zg|gHcD7K1>ao5{kV7-uX_u#L!(5a)g+w_qx z84cU$cq(=mMQJVo7>cpl9UQY&Nt1;523a4Ud@a+|`~D5hYcC!j_oU$Gc8AP&)BGH_ z-*Pz@W_39as}nCb+u&w>`gr0ZgwD@vayInAHyA^th1ZJg5M6FyI_gVmi@ zA3(pThheWYw(bYu<&;zAb za~Nm%1oNxK3dPGtK0mBe*PkIT_NI$!wugxFmLttEU3r}nxnQ#5Lp&Z?T-y#e*#ii7 z9X&w5zjkm<_63^~i1-NE@Via{IIVuc1znFs^u?JHAL}zy)51Kd)p35Mit9udbA${; z88t{Vt`GMOQ&VexRNMAD(-WbOSFGUEcv(zNW}w<5zwl6227h(2NqGFzR%N`D;5Ey5BlPlu3c|2#ggXt$wcIdOPc(~Y#Lpu_7 zI6OnNI88Tz>w$fjA+hz(z4{N#8>m0IhzlR)r%10|`@ z%Fw4Jc3O{Xja4- zw*GA9cZtVfpYc2D_26Qe7jf&H@qZ2JAmg?*jRaAnN3pE(x&HiX$hzGkbF@ZH@i`1W z*D_iBn&bMo_mjpCrEQ3nJ(d60kBMB!Z{gha*|7F9xLT~M*U3b^9ecq|_0rv@7ADwJ zd}70#o8`jr-XIM>31$~_lDx@f^;MgfO6oI)+hA&T?u(&h^qediCbgZCrV8%~bLQC` zJkHT@dj@`F0h$aC>Vzs)_zU0$)A*7K3iq9F8YY`*e@k97gtcKX`$ zImhFsPa~IpCM84OTJBWR949Y>Dk^)6&pN+z^Iyup?gxLR8!Ny2mNIFTpxU3A38dZR zO?UfAPsO&e(;`E*$>o{3S@EWg<=zsrijXs)QSjm={iU_(nZn0_&G-vyuk+*kc*f%! z`s?qvEcKSw@$5j@0B%Ond5%I=CMp{+`|1fse7)hlb2$nXCH)+;=v=V!^uH zHAry{E7db&?N~BM4H^cduZc#to9S-Y+U@`fe@{uy5>55Hw}(D`rSSbJ`@=c!;I~lw zm)~V!Y2v$p1f00l4O~+8K2p+-tT@Ud);v6RGB1L1y+~JPODC>AyneDWuf`Ng7 zy@b1j$sdy+LahWxg$O7Zs3<5HI7Fy8C`1StCOL`pCiwp!Ix-+hL-mYTqiv(o!8Ssn%2kLA$^M zz@HF4-cj%Jyx*hc;wq=il!N>3_diV(f*%t_ufI;267AQoYp(wd+T?%OB>(YHSpR=n zC{{oA$$!>=vrrB-J=~F1SLC5XkWzqYI|wp05TwwXg$zT1t2LlVLsDzuESRQ>iX^YI zC9Nt$EGccI0>%seT0^M~R?rYd8VGBepa&D()+dWTiwX)Ly5BI3kBN*+#GnEm5UHDSl_ z2#@@IB$C74I^2qpt$Emi<(xn_$To(9{8?`~`GZXJ33^IEJr6;crc0kJ$O!UI*fL?W zN3uh#j+2H~2T`@JZ6;e!_-l3^#dDi4y$DkMOv0Qi;kDGOei1|#5@@+@S+=xsndgZ^ z4b-#eP|>>V(7hlK&9TFgYmrwXT+@;<)v3p%Nn7BF!EzhN4#RRr%6fZK2Gm57x9qOz%8K2`8*BSq3dexe$-_lmpLs9IVDdz}$uav3x;ZYDEylJcx%}qdHM! zHbeq`4%|I@q3)jvjmk!v2EF5$Yr~os+;8lP>M-%d>RDq$4o!@F8$Hv%((IDJVPF`d zv(Yja|9r~NS+Y5GQwbeCafuRu9tn`WlrVIVQL#Pa_atm8OX0lAFJ()f=h%9cMJ37a zvVa(s(NILi-Yv0Ju~b!_h?y}g)7L7Pd3>;>OP*qP4#>lDhXOVKus;-RRl??)~M$tDqG{fnrjAk5o%@R~LDr>+1N;b)@aish~g z0*)Mk2%*rl3me8OS)@}@?^Ai|D1x*0jUXlBaX^sc3P9Re~Ms=KtezcGXtDqke@A_R~JX2@D_^@2sU^e z-?qz-#UPbewFH%vUl>xG4}7$V5s+Y6CBVrnmv@LkuW(5I8^u^IPeKlbLZuzB zNC|x!6e1`T#h@d=$MWYFJwh(zBKa7wOHjOfN{ML~t1Lvotuy;9B@3A1yKV_Ge^~&e zh3pt`B2N|Dgj%UmC2d=81rXbsC4n|9YNhC!rDXFuopv^vb~c^%XNk2-sI`mP4-ETB zTXWaO(gmuSOB#gCOOsc$nJW`KX4*iYG+~V@;uI&KoX9;as#`h+5e1x#dR&{c$c{?3 z%+nbqZZZtXk`6OX3_^ka7>^HFP>)YmnTc`CU*_b)yE=Dbr+3cE|s3(Z1tbYR%X^z)Inc=B+~Sp)6pjV{pc@{ z6Udj&{AZH>02Mq3otS?wsBpWsjKQnb!s%E$6%eCtcK~p2e;j^(Xzx>?58!>2GU*c? z=sxioj^3@2P%N6qdvW&K->(DWz>)F%T&_jF8bkrbSho&*ZLcKoGz86z?Db*>O&14+D^u=rIG`FzDYu+*q zE?Bp;`I;YmT-|)o)&d89HzIJVbgoIKmuxD(?}91HSk3Im%(CN&PqxK~mgo}Q5@oh; zOrWe&C~HMadS95p7}9dcUCU>|1*3on|8_9AQf*`o3Pb;+5pkBg!|75c-_PXmgl#6O z9Ix+XgO6%SY(O)nQO9uSTP1WaT*bXCqWIbk*r$WMT=wTh8S3&d$OXT2%Xjy`)DNrp z$C5RpsPM(1QZ$M;2^Pk(OOClVz*H}eVlrWB5<5QnMMLZ*Blg@HbqR^N_B}qD6U8qO zz?}>xr))66P;1VzTc!S07Dvc|PUEHN+ys5=SV3NYWH9C+`(wS;RP^(jo?qklnFgZRA`@ z*##1y1^YQ?-59H7f^S(5pW0(+QxyjQTPB3MNI4uyY7z) z1v-@?WUaahirfbuYz;k4RT1DQGBhxy>@tYcmLj&Jj1EsC6OmGWv?Bl0XLibj&PKjP zd#eSE?G34qbZ8*$eFhBQ-0ud;4`AvhLHO}V67qg;>`DWZSVz)Brqtqgli2q^0}_bd zpt_3)ToeR6Q8zTgQZ}wlK!WZ-A3=e*6S`a-<@Xot^X19Ajx!e1WzGZ1 zNB4-(L-Twg%I7&i|B8x`I#69kaQ%2Bot9PZSYO*;QZ?Ho?#j499#LFJ5DG)p=hNmmD@tEYa-q>(>?4W+w} z(hj3|ic~XeS|*fg2&IcOYYGKkbDjt)`Ao9A7`|*WX4V{O$&m$IKBt@TouESTdyDO|8e^syUV7 z$t)Fr@{%W;_oFcU1|+Dn zcFG33I%99xEls~muWY3WPD=+tuBdqG(ODT_v8it$-9+a!;g}CNaUg=Fqe>CUsx#nD zO_1N2)(x=NX*6^B6o({0^Lg@AV7;D>^=78XC&~0rv~+sBHuq1^@FeWJ_{M3#1to6P z@eFaNG)U6)2yZ^qsiA6^x^PT7)a0CZaivz}h^d`WrW53fgakRGQz$a(hDQk1l%?g1 z49SpEnNcb#7KsdbNvTY#6&S@Jp*kg(rIm^dy|1#paNYr}t$S~V#fG4GV-&?r<|=V6 zc@gc$pYsn=AAWTC_uj{|(?FwYpdo5>-m^MbgX&zmULx6z(2>S`rddBAS5;UBUW2E$ zsnfsceNt-b%BcC3^wtOV-gBPmmG|C<0859X)96#xdKdTJHvvyjfu$@#(O98qEdNM_ zz4dYdN80Lva($fYGJRv@VC2b3QkW)P34(09hDd1XEYwMJF#D+7Bth0BoQBAZ0PdB| zym2R8OcUnxu8_&E9FsVuSeX4mD9Cf@3X?dLEQZh$_oHx>1Y{+NLF&HhmXdqCHdpHD zrsVBLsx~$ey*xRQv0;BE6klbi35v63#Ce++vMsm}XYRY-9@peE0k>gPpyzOIZIF$b zNWYVx`4)EZvc5)_^l*?wHu;3gKLX6fT;q(WH`zcfT0;oWCxU*k#-b2k&f z6DGnB3pE^R-z-7Gk41j@{Uhok=Q&RC-Bf;qYImq$D+^*J1e=!qqDn8e=2 zZ1H~eac*3)bnR*hGrvPBXhN^Dx8gm)wLJ-sffgANCEfHT+;a_g)>rGlia@ee@9MwG zK(bv)w(rOf-jF5Tw58m0)rM@*Pqu%Tg>2!LYTX(-26%z~`oL{S(iko7_Zke4%IK7T z{_2I5su&aU5AZ`MfgexvplhscK`Qd25`06@pC5gN)}LT)ck^rQ= zr6=zqW9ojuL#n*)3u6{u99#&kte{U+9!uw3dDnG(dh3EgE(J*u5XYh{-BuS-@SrR| zl16Ar&+e$JB|%#mvhuYlT&XE#Qj`lPEe`G9d9h(1-g)V(@U`#c_j-1(rjnqCm$mNe ze!K04GxVa5So!GVB+GW~c!#)mHw41YCb8$_!Fx2_ygri@&3k}ix0iye=bYcA5ERWL zL&FSC;N_dTdL@7c&FV@B>BxSkuZNVc%+-bUK+b&JM60?0dcIy#k_hkAK4R5moHzX5 z9$nTH;<>u5}cqwA^}iWWRs)sYFr{aYngp@mJ#3yDtsXdx>;hSB_S1 z;;%C?Qh-Sjw$ld!dw z&TCxweN6teHoLB!VK3XCbt6N;t;NQ(Ok4BdL&@1qGmcrTKezcT3Ge3jsfPU=Ll5uP ztVax6`;0NVjl)z{R0LFbd5~Plhg$%WjsEtpE9H1+*uzDya7nOK^W-V@8pn$P<)-Dd zda+-xOt$QYaV@cErVl2Iz2|Cq6_OpFE%$!FU&pUUFR1`4^f?_gRufj>Y9ZvUiGCTT ztH_T}FAA`cH}N#?GLJ^rE^yrkT5ZO*T;sht-1SnW`3cuYrms97v~4-;_wDo;>lL0D z>1oI~2UP&wN!_)IVXJelCis(HfP1U-im^AtNAm6&P|H=nn(b3Sa9i)UK^KGISQ{>M zlfJ|r*Oep+r`=S;xmvo29W8XlsqR=Mkpoa`x4Jd35|x~|D*5J0s=Vd+yb8A$!>|iV z8Xm%CwrhEfxi%hCmr|z#yj_hOCgajSaW2Qn)@?_Ka4x@IA;d+a?Q5mm(POVzXZmb) z|9D}PQ5(1@matp^yxIJ&n)>AwUoVriWq%U=F0}*3*fQx4u6&u>){bJnvB1s4&EfE| zAzT_=#=L$#9J##s^f{+ln!VR=?GxD9#~iD*qA|Xh<~WzG`TIG65) z48?nm!292I@eu6$ZeD+ct3Fy7p6#nY7<;9to;UQpZOp(US|RTzHXw)ieNcKCmP ze^J?eQtgNFqw&Le1HBDUpP3UAH8X1X9W;ma_yWBV=NR=sU9e|cO!WNv+^RVD%Lll< zEcQ^EAGks0a%O4xz4O)hJ_A3#+%Ce`{KEmf8vZ0DK6y*kI)8og@;$pxH9}L3u?VZNxDD^Ev^zQeAnWZ@tV|j)>6W&pHk@N!_EgC40TauCnvF z8GUSCZFBAGwKOz4 zf%4z8B&u5PFmv95J3Y^Kar=;cj|N)_J;T#-^y}a=wDWy?aUOhFu2`1b$WLky3%6Yj zt1=wW0UCGE6s8}SEE@Q(zUs8~{9}!|vD;HEm`XG^tFnZzzc!ty#Ynzf)8?G_$!I!U zvMFt`-MIhF(7m`zE-b3){>wJGR>S$|ZoLIfRAZ>Q-4NbLw;bVjb#9Y;p4h_oMrh%l zx#1O9$Cj4cf8MO)9Gsq_T|x8{ft>_^w+O~NO)%OHv5$`e4ve*@cb`ofJD?LuN4IEuSXl^Fa3JHe*0N|8q$})HyKt2+4LNCL}ZDAUG_B<`t7VX z`P05pIv+lZ!8J>sB$L|7yVbhbMP{e)jv7~@;%wDBR|RD!PUrPk&hqkG3L8#Ouaq=I zgY=zcYra~CJ@4Gy)=(IvqRZlM>`$H1JgA*Cf<->8O(v#{j`T_BJKsd8I>50}S_$Yc zNkDAu#+6+qUmBd`#K3v*wE2G)b^2WisQA%Pz+?fyQ)ke6{Cs^l)<-hhe0h-xDwzBG zF2#_cRw-+eeXYX&N0N}$A9`hGb@Z7>J!rG?@h$xuGwhOhyG;-@yy)6TNPa@?F>FI!JnDeOqnAeIX4dryk_Iq zn0)BQElm0Cdsea6fN035^Wu(=q5_o;zvc5=d^yG)J;1Dc`H+p-;MkgQKa^Xm8Vb20k2&$pYG=U4ett<6Mc*l+k@TM7%n%!!}A>ek6R1XYhE zr?3FkQ3B`Hx}L0mQ*4VEN9vl-@HIT!zGPQb*c;3M!apoJ!@u+xXcG zsVuTo5B$HNf)o$%fO+Z2L!$%brGoC}*$Qa?)fO#bqktu%M4cadPzWm4YHkabSqFqO zne-WeHoWPin3|Dv|QqhNOoWl6jiLpZAC_joSG$p0Xs33|c6du{J z#*!yVabWgef(HX9728M>n5;2$-SI`Z=E2qNUA(F4e$a_|C9y(GlsqP{@6r>;ru^q} z;<@#i3odafZI?XVx}M!T?EjomNB+#Hf2~0ujQ&s|{h#&UX4IXUklr%t=($m}n9?e<>yXg^1QH=4lYeob@e*Vy`R-)Q^d8J)%xy1S zkcz_X!Rd8LN}`lov`vkc>LnCSHN~!qR+JU4<8C)TcH5bf>%KcT7dOm3JCi;)+fFk- z^Xc!C&G&7S64kOYB`}D}ai7MD;|gnH_6Sq!s5LXRG>&02_J^vb+3`UpPB>*eY6iwD z%$)y&v2%{`Bx>9Aw2f(F+O}N0y*_P(2rCapR7D{ntt9mIrC1rm4DDickdG0n*L;TsUdI97 zus8@=8QnruOs@f=93THi=_bLQ%zRKsFP@oyYVLZVwrW+U3bOcyPrZPv0#NO(s_O-! zD*j<<7gaJVYY3-%09R%f%fO`ns%VWC?N}Vg0&CbDwtOQ%j#N{WZIB1_?x=8L5b{UE zs~py<|D=XBx%e%%MwtHwu9Rs?>cuh_AfaEFiictWdr5#_f=)$*8yJLYDuQwz4Qk&D zN&$O`fL~%dS`pd4za(867du4NyS_)9xOdY}w`!o78GmBCAyQ;wsr9K~(*q&1!YHc= zX<}j(<27`J1cUbvjh+j5Xc8}34Y^MH6R3B9kpjrWy(;FxC}{7A~RD-Z;*ES zj%bSBE)!f3@)sj|Nl*-PBaDD5y=I0e@Mkl<3jG^hlG-Nt=GjpBB=G%Fd2k~GXv7IS zmv?d8=AyNNhdiMDJvA=QB^})qj~S*m_zD&|aF$qy+JCIKR{)i1&4b0UE#mw3E|P$@pGSo;M-lMahi24Dq4 z0)UqmU}OQk#FIU^GAm39vFE`NA*>v~WVPU71N@ljgDvqn&rGvMnU1(KaOnK%qeIJ( z_2_Ueq$xx}@Y936H)8(i94NFMuDk2v&z-!zM+pBzgP%7y6)n^W^sG9HuUw2OlCI5R zsL2&~Qal|xHL=@~Vp_o{QcN_!ol0jEr;$t&eWwhhM}{DnfO3x4a%3Yr5@aQt>)}Hm z(`Q8>_XlydLnNzLnF)u*nRFiA5uS$I6;|jP4$s6K4?n;d4j)B07LLM{5q1D26Eqgr z)eR=XKVZ*eNRT{IXr+c90Tc#Oiy01wGmIS$(jUx@Mi{S}(J|wXe!61_|E>cK|9aOd zu?O%oHCx#C$NT7LlGNOb)L4@Mssq6imgVlSD0J=h!&m?)|_h-7nuumWQM5ZQnw z7FOvou0j`wY4#}rbr%HAhmq}Jl{oITZygRAPwtSmvsY%lB-vt2p{PmluBhqYvv?FM zy}P4v=^1?N`p^x;3vtBF7{o}fvPka!Iz1{V-3Hex8>=b~bxM)3*?DKbX~?pR!MkxZ zmy6R!+0TIFdx?b8+G1t4)8Xgl?0;p)6#)J~94SzyVt|JMZIy%IbC}JDCF_@Ux>3|I zST!Y_;HC4|J=q})H;yDprjb``Rx(&kL7GUGpw^_VvkD%pPePjLPEdPtHhL3{H>t?H z!Hzd+fSOV{-@*FZ@F2bSqCut=I`YjM2Fu&VZkOkXvJ;3X86L3SNl3bEF=}|y-I8tL zEPk+wAMaWq>@ST3)}2INtDgT0Xx?{ZtDj{65t*NSk2k7o{7$rfR8$6yd#glEv~%7l zF@yOQ%O|vJ?JQ{hgMLfqStFp%sUQFD#*-FtNUc>!*uDfWf)COz1^x<6euL@v)ncDF zFUs@$Blb;MMcjg8=06BVx8JLQBsX#JxXE!mgan>S0#9O4tA&k zj=XEj={ADCvCYAe>MFUzgVRX|cJGfe8TywVPomF@8tgtCWwJzp{yQ$brCI4?>UUdq+pe|pmnL7 zCZTZbb})EFnvzh+9|w7qE7y5NtU#c;+ZXZ7*tK1mdgDd994emQ5WE8K|C}FHi%AiZF6G(OH4SoEX%@jto3Eg?6Uena09V;R71Z`ZPymT?o+Mo` zZ5L|$gZxQCF`%eMFvTgD786?g=Pzk&Fa-@*=amdfE$@`d|nYuPeNPADZWf}$60^~)qVZ>*dbD>?5mIWIP9 zj&wI;Twsm|V&wG=2XzoPx8uugR)eNAd5Y5#=?o*wpy|CJU-dCqTq?%^ zUS_7q0ip$~nglC{nh_fEFhMp}TrhPk?jL|SFjQFyngX7LJ6etuNCYiMHUSlGj^CZR zl{N3`nz3>83vsk8^QcS_OeynV5AyH+o=!I@0d5BIxp^dM&(pji!E;e24a!kPx*x*~ zR$t<{m(tS;tY$wkc(07~R)>+m;0oU?!Dq?Dw9K-oN~cPJ;6ZI~qIy>Q8sk)8!hsw1 zu?z8?A%|d5eGc>~x(twLQVRi*+axqvTBGw@u0?T8+287ojRjG8onAa~DTb&zoMT}m zdf&Ni8$i*Cj_zK1wCM9%K7k>Z(gu{38y98|$=vUky{jZnPoSKh`3g{4%v2kV>jg$b z#3WRt5Jp2NBvi$e>bEfosPq3SQ>%@JI{vCm%GDbbC!k(as53;qkX|ES&lgRRdK+oh z#!dAKQuP#Bl|73M)-22rFcrS>r1akj;8WkDs2wS)0iUtJS;4-^!CwUQ_R<#me}>WC z8yb{*`Codf zps01L=&AoiuA_k`EWnakx`C|;SW;Tz_6!L`h7^heDwQcmIT@wq4WiUgn${=r!~gn97O~Ak*ALYMxP=Quylpt34hlkxhLGgSsF_LE0-{tC z*3D*2KP@g@3zw4J=gSw?$jt*tvC*-StK|-)Db^?xY_Lf-pZMpzYvODmF!N?UA2<*% zsl{^D9s;FuwF&)iW_itk*QCesy4Hjg0?OAVDVk!zP5Hix(%t)%Mhid=nOy}TjHg0B zSn!fr@ZwqUiqd@x(_?>65$sjKRi^I}aO%i-nm!~}eg^eCCDuPF)<4ZFyop7fKRt@JVteyq;I=!c@wTfG7Da>6mO1DrozLzK1 zEdO(QC&AyWraE;IlK2TtJ9U&5uG5fg+EAT7WhB_R{tRnNv~&z+>Ggo}KSN5Eg-Y?Z z%Yu~t8-sAVL@<`Sn_`8A^$_;PN9S6^c>-YgJs?q7aKkA*+<^dKDT`a_fC>zu*HNY*8*z zZBAowe*pFTy(bVdUyfyLIo%>5sRp$BHR8_KG+3vf&^vn{!Mo}D`-!-G(kIAbEI-~@ zaX3P4DSW_*3&54J>9n_NO{=zV^2#ap?_1N zZ=*s&|Mz^3?TaA`Oh6I|)zABK>>6KTef@eIZ!Z)49_tCXTq_|~e$+)%;mBtvMBQ<5 ze5~HI@3>P7U|OnB;Q6>Y%v9P4Fdaihvd`cs-L7Qk*6H+I^*W!su}n+T@VzD7t?TQucaWiq-08Rs9V5u2O?H|x!t_e8uG6*Es`cG&IMK-!BkD0P zb1f~dwJdT)H}5ggEQH=1W9H4d_AcaB)u(^NddCg0b^G)Tf9yLLKzFrqYI(qw8b8aI zg#+umzV?25shFUgX#&OFTUpo`Vze0DH5Rlg?uY_ooKwcn6R4p|rS_yzD3yFjV- ze?Db>{7}y!am)Ea)W^J$$aEcOj}j@q*Ea4b2pwjNzFd;&w&i>`Gs&vk?3Cua_VGC# zW8JE-gwWBnZ%k~_Xb7(D=Gi)LclP{90>SBAWNk4Hq$TkWx&3SzVM9n+R{eo$NCZFX z_oB|JpxTrD?nJ|8u8>z^vz6|iE)sC|U&eGDd?at|U~6#m5)}?ZYFV7TEA^q!uESaK z!Q7W4$>7^KJg?rWMnLb&l2B;V$;USyO>t+VpJ`J;c-Q2Gzh5L(3($ z14{f85qG)ZGjIw0R|x+t(x3E7pM)21(2NUcm+X%|t8wo=5|EWOBK71ykXoVaUVw|? z-Hy{Zu7sf0VmO=EF~y3qLmgEd}@H?L8|E78C=uJuzxSlc8 z&NUmU-&>cU<-e)Kd%JSGOO@#MjW*3MAL1K8PAM<`{w+7Vba`u}Ez*vr8y_5ZyY`=#CgZoDH4n*YW#2VSEyhnq;M^(`EY@jX>AqJCglS{r!IAxO)LpyIt4$ zi5}rw+4Ct@v>#Zo-Wj-`z|nO7wDKYy8i3#dvqHM{@pZWUok;&0*yHT<*P44V+9G^iMZ$6`(=uRr`Kp>j)bS@FgT~J zUe+K{A&=`pzxf>QTlF(deu+lW)bi*j8^vSKa&ykV)nidxi&&|}Y#5*AHm_US*RQL$ zs*K^{i$yHk=kL9x%Z#<&xQ(UhbRMc|#o1to&#dxgY95VGvdjM4nnt^;O|o+ss|jBn zo#pSz0o_wQShoR1{ zwP+THps1k0wU80c=~a|^v883|(YD9@zv^OFDw3&jjhgER6DM=H>#^o=_NqZz`vjNe zcsX>NU)Z!=V{|EWqxZ2r9VaV~m~NX8K^yI=)#lrF z-W6)s@pdn+p!6 z5%;kV8wEIXUZq_3lQfs*{sQ{XbU!yY9k?aev={MkekMU!!oHhp`|^74K74<_QoQ+c z@;y#&eLTKN+SpZGhXYo-cyhOfhb)7yaOti0TvyjMJbTfZ5?GtMj)1D&`4~h3Z(l8J z3f6h7Y!KhxH_|=4wy)DHipMU1XL8DPbCS=2^(B<&kE)#f11rF{HMgfhICa|w9P1MK z?Nhh{32k@pLkCRo0J@kt41dpgQn@fY9j{WjHzCMREnRY!Z0ro`TOPPXV>|KSwwrHJBD_q#t{-@+YU#2S08IX!W8hyWhvzXzAOhYFo`;yj-puD15d4P-N%4v2CuMfBX7GeAP_L7PNR$ z_AH+6B^!g7US>4yttHzkhRS|E6Aoryejqq{eHmWEk5y^h8-M$XW_#W_CeNJ1NGB$6vTjAAso$Ni;)Lc?^=_=`Fd71lGc2>OB zn5tgp6zOc9xg4+iOr=F(A|LnEZjb9+8EHMO_{6*w7WH}ZuGf6eV36nEk99x3qAcx0 z<6ZI{oP0mAe!))19#WZ3ll<{*hAl@K!mCXy5@+Xo>fUVMd9Qj$0%|gA=&V)GTta0(CXbMA@9-_KT0*=RrP?$EI;p)9Gcno zU`BjOxGVQ?pfmnyMcUeVFzA!DWABV(kpr!YL&M*JSBcmFvKnS&R>=0J&L@a6B5{8lyb9vGPWaK zHsPsVj#P|fM0RAX=%pguV^ZA6ypXWH%>@<`5;8<~M3z*PG(?s}cElg)9|f!|^iKjd zdRHd}+21k1*Q4S_JkIH6q8LZ%H=>$MNAbkmAB@J1tDK;PiY%uTS8bBgMSmum0 z!SP;1Tj1PZX3&OM889O{i*5aEQ13=j%9ZgB$Q#BHHz^PLz`0N=;K}98P(xbcAd=-S zBjgGJw6MMe2!wfS&VdRYQGiQ9T06i;k?>DK+u+DD+}}$^ffg8de8?%?ag;UV2pjaf zT!i!<@}>#cP1*w>TxQ=gIkOawcI{C^PP4E41zH%VjySHBQWvmt1+x^ApZ+Yq1;TOu zK*kRGdUOXqutiN+4HS)Fd0~Q`@M%bBg@L3`#6$E*hU;j%&(EE`P_M$7bbuoYasucQvU={v134!7NmgcVH;A~85 z=cKFz^$X>wDQ2TtAC3?uL1_&TSFixJk)Sx_|8Z8A| zY3dJkpoI{VbJ``-V^OY0F)vdg%EFBXwM>V0?I>%B5F@O=3J~;?Q-%nu9{_`>lmaC1 z_X5&vtwET$r}VF;q4Km#V?pZ`|bKNa?BisZ;T$=ph+J0bd;80OligKqUq@u622| z$1%>8;MQrfwv9UQ6qAtDqg~OI|JAEjgVk?$3FZz^#4>!ANmfjt1;S#`M&&$+mjbeA>o$E1MGySNdBZQvR<*gfg6g@X)hTJnc|qe49NU$!K~_S$Ys~j0PgvxFS1= z7#ab|QSlp+P~dN&coh2S-aTYCGp1&yhzaE2IJ&H~*&YL|9U5m5aGyo8Q3eTi3x*$D zbh<7qbbyLA+oEum&^>7SrNAzTLQ6V5Y;T@KghX$6fq&Y0w@ z|A9Dr0t-U3iwdygg7zaYvg?DAZMNC8i~R?t@T-Z71PTXp7O*2`;1ZFEk+cI+BC#pE zC%H%cXKfra0-R1pWk-sF-4gUz+>qU}G1q5P?iXK5z`f|r1gxDvez$-J5W2*1%3#wT zXAvsP?k98&y4Dk*Ai6#+9^4qFbFVKvc=}m2e)_gtyrY9u=P68;+bBBtD76cE&ycI92D1J8_kf%hF=q&ieY`j(mR)=)=2E{1p_v*sdU&xu1}&2MfSpp* z^qSBecV(bWgg$6B;p!Lo_){%yni|hVfTPdS@LqDPa}TUjSc#=T#&8P zRK{RzOUC^T!OAae@OIaE`oL&AuyE!T4D@hZJ!CE71i#o_LU}N&h)5-j)Q?;g=#1TB z6>-$2u_ym#9}%=0`-T2uZG3(RpOrYktn$ujmOe)JSfTOCpVb0mu>|85{0Vq*&}QM5 z1M(Q0Zx;m{Sxzg0~4FeZXfM}kmmeyac@s}LfqAR((TBCDX2 zR#r=?ZKl>L$T3*bE&g={)`<11zGDbe(~ylTdxW*C`9r2hH|%YDQBX?>7Uo};jfrtbiT?*PjO&)AD+#4TIE zt*OCBw9!ZO&(Y+AC;Ih0wwSe=(AQ*Gmv$GS z#suI6fx;t7js~P8fYBMke8thc5KRT}nZW2wqkTYW-at3+Bw6)UtOW2G!{|(*b%xNi z{yA3qSV*E8HEBgV9!9*&q>hw}6E4|=<{LGN?~vzam^;pL1a7QMnQoIwYT< zD`@e9aSqm^`oTEURouM)V4Q9GEN2thDgz_~G-s{9x2RgSHPn1wN$Q)_tY5f|egl`p z*4U-9v5X)WXrjz?)WXr_mEe*6BYDGzY$4!6fC3H0wi(%UP(IBtL)l@k0?%Uo*Vs-J zr{anPbU6oY#)b^Y9-p&NeQCn7*)|)nrh{tn2Ce!`DqOU=(Oi7=TYBW$^N4H3HHh~} zg?#2Ke1k;3k|*7`X6fe{4)V-|dWIm~K$dDHOSAJe>gO2>@=S+%CL`ToqOQs&x#xV& zZN!T5lGFIagb^J}I)(!2F~e-_#b{Miua@#pQNp0J)bAA3`$(ERw;*Y&=jtqO|0;Iw zlF{-dt7gLhVS@r`5r4EE?!}Ap=fVR2GJ^dW!g$GGuuEaEi)F~2uYe6YQ3ikTNb;xr zDdnwd**#%{T0;wR(lx~)XQipJL)krv!ybj{Sj++f3PNx^F%NP)I;PS*Otx~f+;Ld2 zgL!E7rBx$k1X42!LhQI1^xirdSa zS_JQp@#dq&zehlY{8NxlbbYo5%ecqSh38ZG)JYF%6gL%7u95ViO?VUf%x;Jbw0e`|1Faz**3 zCFt*YEfRP2pzriBO(+)jW|HVN@!Y1d@)a_K^zi-`n-i-c3?#y->iFl|0|#5>dtnAjASQ&u zu?^}S5wn6EiPOW86s!+_l2*35;UXO+N)&hoAiVyp@3SA8f@X+)xw(_bd~V4PO(C|$ z3V#j@;`5e54pk66W98`iPzRom#w<#-$raAn}gXc?rj{P%87JPZE&i2J6)y}Ww= z`?kaVidfxlFLoBwT>krQSL?NI-Ds6|XEo{J?X>R9%Uw`c<=}*q(E9yhBfru)X;oZFH32AR~R+*$jX|5uZ$Mb1`{?7ePR z1L0LmJk%GCd!p!8_Qob(kDF3eq-Dq3#IsE!@$~r_0@P=r?M63>?@X73s}&LFE73ah z#`cFt#CFQU{>~rSh_xJtL+I=*wv!qJ&ll4i=pK!K3({2xT0O0Xot-}FoT_zO8c6Q1 z1@ND(L)8?AeCSR}pI?9BbGXKH5Nj3B(YM3hX{-eQocCz1Ve6j47lB`>{FS^{95LX# z5@e4(zBPQHJI}v$HhWaz0X#kpie%8+@L^rQY3%wS*>+)uUXXQfS|VoWP+6RsrnV$W z7Ry`(uW6f$gxd4470I|>%tU_q}!d~7}9z}PkDPiw#-d$(z zGo1Lmbu3AJZJVd;dK2s$^ES$_?#Bz1*%Wh)jx%9M5B(wTdUKsLS zS4V>2eSam^cko8lBQwq+eJ|(2ysaks5EM^~(z!l(y}tjx_u5#``Es{iV8kP%=HewL zoSsM;GUDmF=D}O`-3EKlSoF$#HLcyWUzxCeZ@zb7?;nx2R$~t4i}^tKI{VNIOqtkg zTf*DAw|Cw0q0O_Pp=-I@SgIOnc4yC{_PNb<(V>0kIvp)`KyYRFjJ06nd}+nyp!>J* zGC44$l=bEy-4)uAt?EH>92A;yi>2z?ON&6(mEdkO0))`wvD$FCWJR~Hvk*P??$H&x z?rmUHQvgoa^5nIx>d2O4pTWIXUCg0#wd9wm4Ds}6Z@j8W6xHCp(J54LjO#r#a$1<( zAo=Fe)+e3qu< zwV8t!S{C&CYreA|*Fs0!jAgY`w3EfiSgKc_Ey6Z#`$!zoKvXIci8}i>8ttZb^yTBY%Fa!u@uu_?7c|{T27| zyb#Pc`9gjHCtyDNX}<1!iO}NvIEsC~58$0w)53{-s;R9|2o-5DwaW=c zNauNp3T4WnN}UJ0)cw7@^t|c0Fre*Dl>hLIW4z%yV*1tmd({*cE55lXJ7qnC%kgg_ zjj@*c>g`^KP}j!gMRd;*{EfY#Mpuga4h^1b`Bx(^#l5%I-IydgTahQndRJ`r>Dt7| zW~vVpk3(_n#Fl|=od46|B27%Qr2D``*;@*!sbQ}q#&AVk5?eqr(`C2BRB^H ztr)jEta?85irW`$TQ)M)9#ppie$xkNx#mIk6KY#G)n(^bw+wf;7ghJt%>sK&js6su zLC@(AmHgJ&>-!~QWNACa8vPQ4rcq~YT({QOs;*@CaFgYI$J)1g)n2Q%XVYm{BxVN8a{>QT z&zB>Xt?l7T{k@@NdF)cL!wdmAbe1B==Fe!IS0ur-mz2t<p5$hO`F|MdxIID=smB>!`3_5E2PF5l%6*vpl>D<(M_yGB&quM=O$jz>?LQ{KkD85Mm?akq)y%iF7XcWpWA zmgkI3uaZ8^e6?PRc9i>5pU+=-=~ELQt6!g8?#SLBU+YY5l(?Q~UwuBa5YKUoVti{8 z%ybr2@-JrJJHnq#KaUC$eI3Nh*YPm6wIW5UtanTGzEjU#lFtPTfx_$9UEbhfhL`i) zSZsRr`B@jS`WdUOdzqsfr{d&!Rq@%Ln1LYa<7T8gNNlQ{?dIBJJsp$8Xm0xi@sQ#3 z`h5!Uag9{xW)NU|?og&og(CIZ-rnOQun2$Q70F0*;g*AEx#_NEpIYZ5Exp9c^@lN;{(x-e^9|VklfG^n zG4++C_a43JyfwSw!vy<&iv4wj@x%mZ`n2@7;6rY_9>8V2r88?!3-;R-{I>#U^>&|k z82;g^ijbw!205LwIxERGU;n89zLuPr6FTR^a0I;SWv|0&X7P*JHGISbPwynov;lYJ zeW*+%Eu14wc}E%lHjTnYCU;00jWh%e?IfUm%>>*8T=XazEd+dcn6wz2 zbeL2W+yq}$=hB4|9PZuHPa7&-Tcj1)-D(Sd;PBt2q0oT-a;W?N>(5T9rAdB7*xLV3tC(svDGcrpQPSy^$)H5{fo?>>#@%_h3xKfp6V!Yx3om5uHk!tM%z$E&a6dmQwh5m*5I zFJb5ChnEdr?1Ovtvo{3d2Ld7Z{{?~gugVU?{{UwHADctIe`wkNDgQ;KX(fx~-xIeC%~O@Z(f~)0J8RKN{tB&j%m^{4@b^x&TNzvq>7a8PN1mensPQ|9PH)T!r(M zUS^^>Ubu2~G;68!%Egk?1qx^vFRcU=W|&m?bkjhzuRDz}t^zhwdhmuZGuFCX$ zUfhK<`5X!5)PGnZi{w!jfQS~QdKV-{xpTii08#L@@pA)ciK%`) z3HtQSq*dAag=WIgz@mZE9J!IOPDyO|)TraP>d>VIQF2peupzh9C!(zopWQ^^K%(ftZDL_)&3>aV>UaiYN6b z$m*hH{4s^VyFs;N5P?~80D=H1v2o2V=QRl9wf9fZVs(qM_H(m2z*sJFOPa(AK> zbpa*jyj=DvC-C62OCx52^9;M;Pz1g%T+cl95|dYzaoAAk0{JQ|KQwUx*t;HtdI3IP zj&XD(gS9Uiw|uKy`m|h>L5p<9h=@W%57CTbwp=(wTSI{jYI=Tfa3pjh@SVW`0ToBe ziPwbnC%DQ@#9O29Kn}43Hh82_z5E`n2vO&?TZ=3-`HEgd<8*t%l4AOZerbKt_+kK4 ze=f5Mmq69u!7}QY92{z?0Aiz{yjA|A8gtu4ySli_-3>mG{ThL-4}EwHQjh_b%;j;G z%+MGBD47L&4tCjY<{1&(F;f9^reoBS-DvEr9R?E?{8$|pJf%p#IUXE10Sh+yC(9>G zU3wmtkx&wBDa)V<)->rpb0+3om~mTWC9@GFKA>|G_77A_0Q=`j=2Buq-$>@t zsoJ>}X6jzyqms_Kxz-N35~UOQDJ*s5f9MYZr$#Q|9HMd4p7SBKMCKj za)MI?$PKMf%n{x{^auRq{vwJkhB46VX|%}B^3AQ@Gbfaw0PZXD(c{%sYroE#^rX_njh`Loz-2kg83#++FXd-;u9@ z`R-|NV6K5Z-OzjWB4jUsr8Pkrz=QB@Mpj6zY&)^e?2Y^K9sf?*@t8AS4LZ)~(aoJL zTh+=2-TF8z8AQE8gUknlV40$L`UU=p2o)yBW%VawwEHJ?t47OC3qp25o=yC&np;kV zeC&wwuLMaQezpIPod)>y%_!RKXf|rxXS7)JkHLG8*wdfb3wPXA818y>cOyCy6vy7r zY&crS-meJ%E<%tZnb&I?JowphanhoN@VqbiIG~YbY!Wi!xR3wUF2qFnfUlT z@%|YFH5~Z@?*;t#xCHN&OE^md|3TW?A8VG3pfH_qN(GdN(${f?xKR6sMEDlF=sn$-hR%U^1lK?cU2wX=!CdQ3F zH6*n^%!hb<2+ch)F1K3LtKT1bmLKByLDn}!0>6JC51`^J@O((r`ko)+J3%-%M4@J4 zAEW*Y62DHpD-CI?H(ZFZPhz0}*r2fZo|hN+3Z{F4IYeg$WM_ZF+Vu$)d`u~wG&12Z zwGR6EnQcgvu>yb1Hk1-i`)$tR^VX-gjYOO{g$T&l5OOvT&CC&lR1AfSLjQctpa^1+ z&MwIH0}3ASV*(6yO-B`kNEh{%D)d^ZdgE(LKz1Na{fMA?yTbOm>h@C8FqHu=Cf-96oN|_oI*#ChDi~f>u0Vfvubs+R1 z?u0Aqge&8e48s|V=!AP(crO+(T>zRT_S63DWCEHs0?kPUnxzPql@u^d>4_ySV#^~J z?eXJkrw?3Fu`RJa`9X>Q zcUvQWIcz8v{_QrzeA{FyMf^yxy&EY6f`URb2{QKr8VS^wDs)|~odLY`TJO#=<><9h zL8h}|5nGNIufv%7)q_qYb9?C`rF&zz$-%R6&Al#rQR;$$`5lV$`zE zF^A^F$tB1QQ@#0EtyS>;IqWJkPC8jaDl>!?j=@rT1oHYM1MY%K2Qc!tWr|9ZX!(0g z1eCn!KW0XVsE)ye%E*i(tLk)6=DuGM)& z-=8%ZJ#yB+Spp?Lf+oWmyfRGQOf9F-vmyy%}NrY5yl-&LgzWFLYb!{cKb5qLG~Z)!|r6dQW`b);V{P^G*0#$G)Lb39j${MuVlNgXgk{B zOiMde>@b!AV70yBA6KK}9Uq;^&w#*p5r}8Wp!e&bcVoKlyT0c-;+REBx1qkAp4b=$ z45uegPql~?#IoNvwT<8`)aUMG>H1IRvvw|P;sk9W-l=aMxL!iONXDkOTC;n^7#2|tt1m7)s(c#E_+<{1l2~c zM=Z5%e_V|+ZM%VoTvcju2{9kQy+;?jU!I5ohui^=IE7!;h54ER{3&c`E5GPGqm$F7 z%1BobAAuA1DwMD1cWwgTPgVweCB9$QHFYIMzCYGA&x8}#{CwvkqRrpw_`36AWh@y_ z7u-xaq7)7!+ok5hpa~KJ!=?mdePlf;BkZULtTCh3AGV z3_+lq1onBU&jU5~hdF-trP{oeSEE>zc*FNV3-1ThHOrjFzBO|qH@gU5dWhile3@-D z=MmOmTAV)eEVYPSjw3~~pv`N46v zTa~$4Ll;2E)pT>;8@guPx}~f?`@%Q#nCY+{pA~I2_tk59qkTxP^DoZgJYB&zNt>xW zEH6@eTX&a*5(D9htiw~N(8|EZ*7KA%d3IJKP7LMvGjFeDqj-BWlTr@5e4RfFwii9r z)71Wp<#nw{xisIr>gtMlbOOlwe)MwX{w=hY6rYHDN_?NEN_U~Tw7$3;pXqB+6!Az& z&G;SjO7UzFla=}B>?FsU@8BZtsj=YN`f5rg@u8Vyssov|TGn*rB}jb;0YvhB4u=Hw z4NO8l=2o#I-zJm}&h>$bj+Dd)d272aHlC>PPBZImmXAIbxPEvTV2w2tgVAd5XXrE0 z@BHd*GuU>Oan(XRp|(~OnV|#6g1Jm9VR&lO7L9c{9n(0o?gCfk`}JtL`;Z(e?VYd$ zfd53-rpHO|sf`Yto<1ILDt+4|eQXAkU#e&J#(uLiJ=}l~UA9YX>{H3;qplU2>JZkze5$`_w!^-dX!t4Bl2>}x9U^xZATwIFyb z(K2Pd4)|-k$&%o~GAH=W@$vQOnRR!Pc6=~_5Yr4ufyes{Y*IVHRMZsfnXCiopIb^_ z;+Phh{x`<%F*wpNTo?6BII*osCg#Mp*|BZgoY=N)+qP|VFtP1qXV%7AXYD$t&X2e2 z>$kr~RabRY_ube1Sej~hgp5f5k6G+&%4^YIUP87fx`Q;1;+*%tv%k_fO5E0re3!!C z+<&Jj_jpx3y4vQPB;iMYS!dNKGYg}+!!gr*oC|Kf+r{yaf8eT_5vF@6<#CG#Omiz# zg!04yfREfx9BxuP=NNcf=FxKk(cQ-MBumrz^xo5{i9UPb$ZwC+PjYb(_`AXKDtId2 z*tgIWu6anuI4lSKu7T|ja`XLOKSj$(?c(SNou3?@fpC<9-XZm#988V`f%~l1Dh;g+ ze}mgj>3VM(Rn|(W1ie$z-^K>JgT=g8%gpSRiW2|U0tKv_s$idYh@S7^WkNn~)4K*f z8_vpR#$td9#6HWbu@+K{2Pn_=yK(tn4;~=ag@L~DCZY5nvl!fpw_os~qaq@&9mM%U zF2~${g~@gjnW3~jfLW)*wYtDG>z?;>mSie7b<$arXqbFBp*I(brsEEs`$w|cY%Alx zm{M6!Yd&&DALm=4jN6V=n|r}U6C7&^t<_F@Soc`{{re}}b?ZTR(tbmyM*rGq!<)5H zfe>GZzQ&ls8g1~ox#Yzn5Ar$VGjgny`Eq8o(S*WE=$Xughf5}riJjZvAmM%Cvq;8pKF1c`6>j}!U zJET2kt}d%)Ydr4ec~0r8Y-`FTk}T1Z=jf>07@mxpJ}ajz&1SluhQcm_mzqgy-J(PE zJ~9s8lJ~g8z;mf4n(D?L!1)6C+6b@4-aRo(w|3?6dTFy&Tr|Y3V|Tsmkx-DS^Re@5 zKkB)}=$OBZf8Y9*)ibd!M@c_)-SvU4tnuXlq2wW*ARrsZaV*#5s^3a*>+a1T#DOT8 zR@-Hn-rKz1NbF?^CaNnFaF|HJ?=_8I++_)#*i~L7*K|RMRtB7o?dU!_S>sNW|O5D<1J6ju8xIH4eeJHPsS8iK1 zHa>=Izdah4OZM5|8I`~geZM<6aHQINX?d_a@8jZ_{FLuYL#66GS%2sCdKnXd+_whC zuPjM?F0mS@H8Y~Ws7kEvr?po)%JL7~GGu$cR2vjVhp}oLZJSWwz}YJjyjxqjRrn@O zb;j0-uuKSGSmRvZ4PUt3>0yOvX6fe|bZda+7ipO8rPH5h<+9v}LDcM-Y@FHINki4e&GfY7Eqn^OLmC0m-Vu&9r8coI(=!*b$OC9l=1I>%GGIDj7z z21ETdFR0b~j-US%iN@SWnnP*k<6{cJOM{noMXhzp^=`~D$TR^j zHxcb>tvNwwEsXXI&R}u|6YK$P>--CY~$|MsjIw)T6FSZUuLv> zByG3vQu@Lua@K+69eqRDUZMT_=9?4i!QY4P2FJh+b??W9G5N|!3emb7#o{Li-d+z~nFSohwWx#Bl zde%8;V)Y{4YfCr%%SIwyb)3WBLq?8li5LRfq--9hqvvBpx^?y^w$rr`i~)FoWU)TTy{1aF^s}Gd!mm>RTU%RG z)#;iM{McN*SUqkzG9qF&@(NK^q3D?_eEwcNZoj@hK|v3F8WWzaO*Ek1D+Wn;b+aq_a@SPdn&YEvk_KD&sU}7zSK>bU zxG0XAx*-32iYU_33-Bm`{Tttz3X!?oppGm{Q7a@V~g593{8aHhz@c%(b znEq#&@E_Br|9jBzzi!j{uY{!gtAOg?`foYI7a?&(SzHG6D-%ij*8LdiLY=voNi-&J z_|sTeK>gj9P`W5Hf$8`wIguO7Ym)YZCjT))Agl%!i-)TA0R|>)sKCc3>n!x0A63}s zd5a-6Iaj#*){aeQ`l|!OP>vN29eSE_9Lhe*or;)_mm#_PlOz_v!$Go4ev zM*Y#Y6SEC*JYn(xt*B59d1%v;!Ftg#_aOP35sm6cl1Ad_qbBZH+IBu~7}W^$618S1 zMQ*HDFJfT1P;WM!gCmo{i~st)t*C;|2n(fHGhK`hfh+Ot384B%R`_~!2L z133(0abj_1QGFL;vHoQwXQ+o-OSzrei@hYZa2lEdvav(Ce0&G7qHc3P0Twhz`j1~_ z^d=<^pD6}P2@R+}6c;H`{g)vziTv&~aH4i@D4RT?3~^!2&D&@6Rmz(Mj(ZR#G{Y?68s=8-duX&iWdR2fna0KWt10O)r^wgTN)JXXL5>Ari>Cw9waCAVxwH;fB#I`9IJPKO7j5DHyV{1(vP6iQS!D%rIFeOp< zCMK!iuJ_LBM)shfNe}@8ZRr}y==E$vYD1D8i+&-7pxhz4Ac2HGM?A%d`^68GmEbL; zNKs}{9YComN53XUScRmH36&Z)6t`Y`SOopaC?V0+f*@ijnsTBb)P`?5I%kdr zV18Q0^8xK=romoN!eYP4)^D3tbL!-2mNj@Lqy7*=SNmj42pQ|+XE~k-{f#4A-OnRp zyAs;~2U~-l6j5wlr=4Jp&N{{v9&eHv)FlCAtbqATv<#U9q7bmMMKuRIf@pZxA$_ciGFJHx)8>IkDbZ&O}3P~iA zCAxT%hQn3J_m$YiJo(?GBNoA}ENQsvL$l@xVFa>mi++0FWTfRHKxq|G1r25W!ima) zzkT{PkV`PcNcWr*vKmnJu%Woh$nSDx4Lv_IT=_vs$?j^F4Y(17-x8Bd#V4`>>kqo08kD~B{FnEe?U7zHMIY9O$1MK6pQ^E z6eSU&HaM81xxw;hIi}F8P}g17(u6##3tz7)+=VWAKJ0|7VVfJeBR5o^D@8n?4Mw#7H45mZkX$TH2 zrS)*Aj}EKjI^$G96=+vI##tW9m1H1ORu1Y4MsWgh@ud_KpkzWVEf}6catGWCx0`b5 zP-pIJV@&O^dxn3MMHX(y|14plkhLnpwQU)KikWt4&19(&M!Cb+rGk6$@%QVJhVVvN zkx|?%>4H#nF;ztADOPkci8-Og@-zm?rgA-}1=UIE!3VFVL+f~8=y;%TJy*Hn$k}$1 z?LDOeudYVxfTwW9Q@-NJ+rE!%;a?RxpQ`2e>ALO`ZmUJmunQ zGWZfu^#G6;;dWtKq)5Aoyos$9MP?DVs%yHp%YyN(5+);H$u+j8@ikSC+^a(!BbS9a zHtBYiqMz!6pHwZ~qUh{&$2h?ib7yDIM3jFJu@}kK{$crTbo!R~-LD-@Swog|M?=tN zpB4xB2Z&f}ljH~V4|#`FBx4c=nn$3a|y zYJ=&w7EmV}t!`9<{bJ#_Xh*k0Tj84?&{acf4S@-3vBN2#j~SL8FkHi8jZ7&BYK_d& zKWrP1h`rP+-DjqX!3tHW$Gs3SqAhd6RS|71p@(@vwD>_<_E7U(O;xbM&%*m5Sg{Ey z7OuGN6ZbzTN!Qf{vS#j&ts`}|EIM0N=<9!!1hU9wp2=U@7IKtn1(g+wK$mfYIN?w9 z9;qI|BH|8|4NrOfq9*+rxRKSC8MLN&A`wKLA1BovH>y{DWAS4gT7wXR{|+ryY0$b;cZu^t|{ z62&)ICgo5q`mOAY?^kuBTHm;%@|MG?iM7JDFLm#5{pw}WDcrrv_~;;vW$*47CJK&> z0&|8kX$jnv{8p*TXp|SudboGi{*@Ujf@mm=`xgc|#<$im&f_3`}%?wIYXG2UqR~R}h*C*9t0KKQ*>RTUdf(4h_`;{D|9dS)S-GV273VN?fJ5vi$!+5*yer zNCL2x9tix3a-a(DW-uhu{QRQJZBekPg44xuo4`VYfpq^MiE`eRPF_uW5~E994*x87w|&iiW68&qB$S2<`rd`MW1$mVr6 zV6^oHrj5Co=h_7}eueD*V#lwRhu~+65qBFSWZSl5;;LT7;e`^3jGMfeGan?c%!`n) zxEa8wV+z#}jC3?!HfD~{q9WbwwdS}Em^=R#y3|WKW)AZ8)6=_pxqCG;Z0Ny041c^8@^9M&^;iY<;05()1@&+T{gJ6ALd;V^nmJVhD?9#L zdj~;zfy9Rwba2df=4K_KX~HRI!tG{N&GM4|q!eOiC56MO_+`W1OG}K`SFeMhg}r>J z_`jf??pl}@7VGC^5!Ok?bmo$Rh%}+>!!@*~Q!Kt(sE{oqg8kf3aNyF9Ks(IJ z5OB?B<(g8piyQElA>kV*?^DES#0=H*x($uRn!|8$ck60JSDTy4#*Q_ zU{+k7K@hFcu=QxB*M|}*`3PJlaI(3RBzgp5NoGW~Q~lwU#T_8R&NGAsvV1`hN(>8l z@I^suqqbr_vRJqhAhHmXWwCk#Bewc~)wsSOi4q9-8xjQG_ChnZEf^Frg%C2w)(_w4 zXjQ~0;!%#U0z=00^sz6wM!RlLTNs-IPGQ#0lbU*D^Fj!qxao9R+LG=3c*jp~S7|8u4NlK0;?l{s4b1BFO z+HeQ;($ejT-Z(ZZp9zy0W*a91@Q2;^KCbc^`>x+tAEL2loo+h2&_ot|t}@EaT&$fk z6V3D-cHbSH-ro<;Z_fOz=s%XHt(bK*2+pgQT_y_@tmfV|E@Wl^-mT$4*}L7d{aE^s z!mf38FK6ak4g1O3aQJ~&jBuDXR?e0F&(XQP9J^@6&ySmMD<9mXGXx7q@2xiA>(jLM zXQhijkJ*P753q}+9r-;uyx33+Ct>$8p~lc*p~2ASs37YhrKX^Vqov1P8*kHn^r~?5 zgXGdk#;Z&`jMw_GbIM{1aGibR+H^cyp{S&-&9UG`#l_zV#9_!cQ!OUH?;1*cTktJH z_33YWpFhDL+g6@zUC*<$j`Nw?UHw(5(4V?F7dT&}ZS$h)4M0sh3E^s)V5&~l;n;V^K+YlHsN#aW z`+7`FaK|HC?@A+nexNXVaKSPfFw7f#HjAU&#h->|y~K?VD{(q%u)efJOa4x_w&`9O zH*#Fl#%A%RBuhbvQ0rP5zL&z4MNB)}Z<5)t;%MI*$J5Ao885r+-0)3ct9dF3_HaK} zzHHjZr)@Z>v)J+I(A;BGu}-eOwl$MBe>7{G>VLkhsyiHhX#qk2*EB!6f+eR2YTXZv z5tiTjwi>@as$4FHx}Z3DZf!lPBRurp>+vt{UN;{AOKz>^n_-_EtCQpB=AD1Vf%V!` zKhde5j;En%k23>{1$gdk0hOsv56g-8Q&$0p10^^-7lYO~?cS_|!;KY*>BsdiElM~C zlkK&m!^|m24yOU*UUG4Nj!ebQ^w*7 zSLt?dueu%XC+1wJ+9e4AM?s?Z&cG?UA0*Czj^<`qoWAZ)!{!UYRbiph$q?i&2EC+@ zPJ-si60A z4g#^Fx4b>k9%?()a~~AW14M1QJ+r1~gL~Q+_tGerTRdkw z$|k@xJEzWI&gQ}Ywz z^&mrYXKzTTS!U~@Bl)x!uEKx;us;Cea4)A(Y+q@<)S1DaQHikkJJ082K(y;~VDA0! zIxO`Tq2u`8puMvWDCYC`Eqw8U7ytH9;~-)`1lYY5>2^K49=*8oNnd+T1ycnedw6Yb zo%z~ZzThQXu?<&XzFPNw;|2XRWqZqo&5tCjn1ezBaMcci|7wfiiFJk5dF_;|!_>in zS91Z^$se|23$3O}zTb+h{sqtU45dYu{cgT4yf@@FuaW(HVqu^g+wVaG_-b)kNjau_ zvk;v^+MAFY)ct#QKE&&Mmd^M%BDa^+y9dBu#-sOMx*CJXeGoU`AH7fbPSwrgEYbbe zALCaY@o2ALY{THeedIc0YPG&< zSGdNr_w1!&f-UfXQ+=o5K)HAG-3V`a@!nEgTEqV6j%V)_Z%}O`$7|@ateLi*)1~^_ zIg^{@vtD!<(7#xW>tb(L*Y@KO*l5Rb(r*Vh)%`9hF&Ft>@h)U>*kuI-uBBI0rplXm zZE5SMG*)=8Og7Qyar}GwHu4us8MVq4!a(%cwwCiNKac!O?aT~Z7 zOo28h|9n;vS+Dl%TQ4yfW<>1nCB35!aehAlsck-Wh7V~{aC3zf0#-=2z~+7O+PnQg z2=;$9E~HZAYBKR~E6A=rqtN71xodQNtewZ)f!OnpE1pjB7YKNzYl1C8B?0KR55(NT zOBKZK_Z@AkyfSnCUCPe-aQiH{U%OtmsoJt>@fyBv*7kYi&E|X^9oIdv*iaO z{e8U854~GQXLr#1eRY(rS)ZhM*1I#@{=7F1gWA4s%FlLW+rI1d_k0f0ID=rxe4Ys_nVN>T ziu9@WyUi35#6+Q4M=;8f5Q4p`MVM z60ZiXJ&2f{x_bboo?)P;Z>pzfZE9+0Zf0q%Yi+4#Y^rZ&ZEkICtg8nBmP87Xgg&%w zEHh(>HB-|Fv67gx${)ZPJDha>TT^D0hx)6&e7lf0en8 z#vfKVe?5T9GSFPX7g?AEEAQ)ytWmgrydU(JW^jdeqlW$uhY`3#^>>GiNvBlaI1x$2 zTol_(;1=zRZRCHkjgmj+i2>L~%BeXTftnxr&tU@PQvVbNrNC_>$Jh2h+Cdp`Km}dN zKt@>gPi*-8huDyGERP9hlpQC5k`DXo%2FNy#VpBE&k;_Q#@t7~Q3Y35G*kF89IXLd z)V}CSUp-q$e?~x&OuOcCC;kbJR)tXWMIq?QKJw(4SHTixtfT*V_6E3s^eF-?c^&kx zw-`>&5*A@hdBgv0HQM;U9z8dB!8CFvU zk;f1*IT0>T6utsK5Bs=eoSV|f;Nk|EJ(Hyu$Ra!APf;A*IN z0$diTB1F|In|cbC^d!Pn5ep+R^q)_Ro2W)ATqCrDKePvr<0e6nZ#!g?1!aYr&-nWW zTp3|Q!|Ij>6yjWDHSCE4*6?P`}UscT5s{#^31iipnYI=g<;IC^49YSVsv5|s@CUQeT($#etJ+gu; zJ6HA)wd|$%p|rieBO74>Cb;^JX>CJ)S=q}OM`5u#wO8QZ+N(f<=5;FI(gM%(`wBa_@_B1WM46Gf{{9b8_UsDf- zM>XvCY=_bHSX58POj?PlR8@N`(&QSvz^$6m&00tzX=t3lf}tNMRVv;+9fcpj06iY>wX-7Nt}T!D773 zm_?=3s*ZZaN>vPgB_5lU9vdXc6K=GjHJSOQYBGq>Lt&n031UD|*)oirt%jj7>Sb=p zdr2fw{aNG7dnj}#t=v+T@eaKq6$KKWe~mv@k^Vaz zwZEC1at56MdBZUTj~zn|1vEoHY$>>g>CDl9wk>)Kw=0OE6~jaZ9HiQ|E+swmHym_$ zxlT8RdHKX+kH6m#)f_(rQxO|O`z_VksmIKR8fi^pkL>j=AaSkf$WtehMlI}GRy)|{ zEUkT2(ij`tayy_8whIrF_$3n-v=ddqB4SdGL?z^qm#*^nZ4oo9uzfEBjWWu?{(7~ubnEj>dHp@7A0)$Nu7^bpt=>4U z4&xGKSxCLyzw1$8&8?S`yB>pzk^@OLC<~qxQxI}XNbG|s8CC~^26OjvG&#Utvj9Ri ziaiyNHKj1NgM$845af^=bgLM3%LI3Lj6DS~W1F6_%_`mDlI(BQ?00j1kO;cXU^p^h z!$8hZf~oku&85=srmcuLLo#yAe`N;GR2gG%1ZR<0TNf5?#a>_;bbBoZGq%mhze2m9 z+(tg8o_wZ_-;8$%F#P3#s%gnbk3_3S&J!*d5oLTtqqNNc;~6DF_JF+e^)Dm*EAWF` z{g`Pzb-6>-&*uIRrfMOdG}~Og135?^h8tJ zb`LH4+Pltxd{$oH*A6X{WmF4d>jX1*@hm>QNbQ}53@yt=R!=i`b zWnP5x^AQ}%&VeE3vyK|;Q+V7YA}6b`^rL)tDc)^p{o${TiDzab7S zz`!-g_r&odzy{TI4P@H&w!<>IfgShh>G)=Zfrnu?cSp_KIVuaYaoZm_*LFKMKL>)m zwuTi3PA9H>)xLV=2d-TZxzjNl^?FZ)e4Ny;yI^8pk+{N6(_q(6z3AoCb*Zs zdS4++ZBLY|k7T2}w)9Lz7NBTR*0du=)O_H(rp40g+pVVuB==|a5AX_~$_kL~LE1`+ z!0Nzw%apqj{c7Z*yy8WF!WiFhIq2!8AqT^;7{v+PWk{rE*pX*13;I--b&{8C3^$J< zN~CiSK?Q{tVIIXpqlf+_TxOscj#`=!n~GYRCTAsI;3wiF7Hf{ZNFq#Wff^Mqw_0wN zC3=4)ja3FHGi~bjt>-r9BYXI5N660W0atG&X7wxOoL7)~I~ctJqgO+K0d*fo2Zj{p znO5UFJ6O70KHsA@x^W48X-;fVS`5)5sJTR#B60~9g}hSELBU7>{|JIaZV9-VdAO2X zur-0}>9V1(`Yt-pIT2FpXn#;dbX8u@#l)`>X>_W5#uZRWExct4q1 zp3Uf{&*-kr=pKghvB2`#Be&dh`hYNfgK{)Sp}aNOd)t@$5Nm@6Njm2pk(s#4MPN65 z%!^I*oE!T?kwX(6DKzj)9N||neFQYcnXscht^QrdEc17?(<1jpm?E9lF?W=77=1v3Y zu4TIYtu~9+6;$i0+_M+w+3(Xx^~%DtH~qRs`|3^3_%WxAdIK9e^MbIeAqX zi(@FzU`-A8l%I6vBd;QDrxzp^^s}rL`k@g#Jfu^ z`)_D7{P}-ACc*EnH|NX&ccJ*!!4W&oDM9PSsFl+famw*CNSpU1v-b`sFW^q^aiS;m zlpA{uFJKrKDw-{1a9{mffv?OM7p$5s#jUo}wmVt-ecZ!=uQV7JZnPVhuIin9hfdep zX3Ut+ISnt|ND)a?baD{hzZtH6nQRxg?~fTess&$%Aq|zI4p}&A^8!g!9t66Ns+J?G zy5*OFC8uyxJRv0?~}u}(`2cDEs82fhos zMevQKf_Nz6a8A-(%mMMW6!XaurMWSNetLmHV6tNVoiRZS(dzBRs(-8DWVtsC_UQ=T zCix={bn$)bLqLo1Z}veJS>+O$eG5^2PRdUNoZ zq4Lon70rj597UjVZYH25m=Q4mvbjFjSKK1@)#2=Nn`zL}NySEUNjg*`5&PfA0E_T2IZFVEeI7G8pfgj z8X<|9XUC*EJRf6e?x}!=@)iXJwPH8|dmUq|H$dSeVS zt$%#9!u~dwzpjhU8vABatmPaC{4)lbwGuxJg^)7z#c z@h;tSM_`T*+wMtE@=?2)%Un%z`abyCKuKrgz;Tqscx$N{-Sts;c)6hN&bgXBcNn|u zvypksYv1Jlsf^+NN}~Z(bIR9_^SL=%Nitv|ee#%ACuq7>Ciq<47(ZtG2)P41U6?z% z8|DE&rLs{ti=6I#T>onLj;hkH(pZAK>tn3zT&ybbWt{2e_Y@d@~W4wn6 zWbQ5}pFf%!t*}@20M**utay&CEpkUrh);+8ah=2F4_7)VNCZ5mo^hW8XXY3W@2F65 zZSc>>?!vshrd_X(XM~IVI!=ovXKuA04TsT>(Je_(AvZdBOrN`sxCMV7vl9sHe8!^3 z#VU13$WhP{@EaV;pyjRn_l#*vzo~hg=Pz+8B6gdvW^|N5*NAnZFB`-{w{fILtEp7^ z5vpW$+qP+eA2rK?3mXKEUM_V}4s*JyOIU`1MVuZ)q-j;gbWlm=5Od z*>g_sfZo~~`yz>elt7T8jc1A5#hBhOv&JolHDYa4->tRTbeg+~|1xRGZr zCC2%VUh+=nKX^@n!ju~8;!8(eTynX65?s=BE!<3ngcEu*Aj|`YS>irtNk&?SyUL%zLW+;+n?v8jsiH-isgiHY#hSo2_f}|6IP63?AAQ;oo9E3TryEUM z;(g=cz(5{E7d=JwVg(-Aw{tepU`sUWHb(b}!|sxwKz5tKULv#(r|(tO&7+V+*ovH8 zj=>1HA8l8=+x%$Tih8qUlO|rab+5z#OB)fxz074eG=$aHVE0k2ps>x=?0q=`IKEf1pb}?^3+dJm z`~WBxI?bsF$es-w0fEv^I-(31=%IqmIWcaX_}86{6&^Q6%_^N z{D%djF?$!=#snv9M&CBt+;^cV(6aNF=Xj8@4+%?p*n zjM`XVN~il+n}74*6bF3;Y}+2r_zRr$K8ckz$O|I3e#4|XZ_bi4H&d?xf;azgJyp#+ zj9Ix(M`>~Pi+t~z$2dnJxwm`g}fe(*W;5fk^RK`jo>;8R#lAQ9rxZrY` zc?OVlJ&Lr;F##7(y>tHw106XKh~12RG{GoDvsj*M_c``0J;LI52RjXER&@~G_??Tf zVcAamTVJhzGVtP>ibV(MV5cn5D_OodROr5k!px&lQwJuI=KJg~-gYRtdzB|)jl~{9 zRHfH73*U~GuBme=X4rAnmfp=75Z)y^t>)(yP*VqIe{}|K{6cZ;fYi7ml`spNWkE z{Z#Fb_7p$pw^7OO$4-z>6JbW#94}lLe4ga+JV$fPsf)?sW93%eM}w z(w}etT48uF7f5MFlBZ0zt?M@PHwxvI<~Eq?4SvSXy3?jmJ1)zRZv%F@8b8N^H{S#F zru2)`MT1L51_1diuxyHO;$I=v@`N>Edm65EX}MC(fvJI-kjgK%s@;nz=QfCBn6a~) zw92;QjDzWz=4zdlmvq<6kEtz=&JK#aA$tOYn}}O?z0Y3)kGu!)jPvvB)Nd=DH#+XV zbjG)6=sffqGixxOQ2y17Vj^l!$rtVjm*8Cqz0Jq_@71vT>&_Mg7kz(|62 zN~2Xtk)TNeB5oMiC>8h<@wjNT|z*&1ilyVT&I;d^BX8fT9GG zs=k}CF+)tL%jnk482-#GeEAW-KW>-{#rqOvQ_V>mzK@xmCw^w@9Q36G%xW4!~+4fB1mY;5o$r>`fx;) zc}Eq(V5H&9K_$BSPl4n5;GRs>3~$JKb*k6+Xo9faU(aP59fr?AB@{8LFYKXs@Oz0K zR=K%EzfZQ2u(9S@CZcuRX`le4LB<5iLl0E_?st#RzXj*|Jm@~hwft8whVLLiy1 zvax^b|H`i*T~UQ!0ZHglGlG}g-(e}h1SY=`eismE`G(K0N$Vo{(6>*^*p3kN5mgA00tr!0wsx*Sr7%PS<$E`U2WK|EdIkBEkQ4SSTax2u?4pbldf$8UfBMCbMT0qe%xR>Ns*pKLXmis zR-~5S4)aDV^GtaWJC$`Rb)i{J_JH?O3aQVk3}^MP*la#WMwqyeeA4hvLwMO=`3>~RLWGvqK{V9NAq|W1Rxwr-%{7Sl zB_mipnL(G!)l;wW5XvF<=y9poYoty};4mK;61`io`} zZqKp;1uwjAp>T_8S2cRxEWvlyEXmx^u>uF2d`NasRkEK!aMv}MGThM565P2+-!5s$ zXs}}HdMPx_v|gWRRS$!5C9!uK<1f#^{El@}(IXy9%9)I?hpIWUoN!}Voayky0_lmk zsE67~0``89yFw_~LF&9!BTxrcww9-~7>wp!leSMFT0y>{T-*~^?e>QF^;?CmpBMe- z3tG>BEld>#KU2dyoa?T+u9A|WpSb<61JgfFM06Z=TGk!k##q2f0sf#9wm%Co;l44# zKt6M+LGQ_@?zf}){FFa>fn+2yhY2oiV5Hg5@9UHqW*a^3nR8|vNXlf5Z5{QnYg6N9 zo3EgabxDhB?CR}+DKc0NM(1;A*%%|MhaHK*Iu0J)VK}(D5@73tB}m3np<&qD7h~s; zHkKMsz)BPuji3OGL?GuI1wF_Id*f-=8&$O+R;g$i0Bp#?4Rw4Q(Ptf7JBcQR{eJB=h_np-%?;bMOMdMEkS?X@8%FB3$rim@x!{jhG%Zw(+go=oe) zxRl9$k`}ZZF$be{0pQ4JCVrB+>uJ!uf61@4NouSlM3A%a;o>W4Q8q$y*A&0;LjN(Z zhgckr0F`-kg$h-(jvj|^#`N0)w>IJjq9fj&Q&Q9S14_yumsPq?;Qh^2TH;GDpk?0` z1KTIB7~!h91Fswy-vs(Cs)Lq!qL!1fwROx~y()Jab^1?n;HqtyJG$ntUbOp9A>pbC z_Tkn@Pi*%g!Ct;0kkEXKeTmU0iVOQ6guP>oEm6a^dD^yZ+tz8@wr$(CZQHg^+qP}H zd-{3anat$Nmv4SlQhTjrrz-o$u3GoHugkM4`M{0miVk|Q?~oB(AU?Z^FQK|!) zGQPBgu2MYn7qm$7C=Lk&B!Tp}p;9_CMRI&NGexF6%6w6fNPdwxM|4F3X)#MhNakL7 zsd={O(w#Iwg+PZ{b8k!~PlY&|N@Q7FMy^N5*m6mOzSMKBAoYGI>Ll7+q9`TuFqUp3 zS%hKsvXuikYd+`q84vZ8oc6o`6U-8k^f;JOE;9v*9MF7G6+eu4B0``ap4?w~BF06) zM#7~3=2vF^mJS}DFQXX62YQbnRl)4bjNWPVOt~Q*sr3&_2fJa zlG&|>XRO{1nbRusVB46a^@q;I!k^HUqYj78hr&^e@l}|~RG`UJsL51JWvbX^s%W#+ zv^nbc?U|mi4V+n=(6|Ok{~lUd@!(k^+7*F96v?uK5CU^XiJ^Cr~C~@t}+*?i(psN;cOU2zndN{ z742F@zdK{zXDWkY3ZI27w7||NuI$lM@{X_Q5mMGY)|)EP>;BuRd!#qDr`K)r=fW9w zSqpZV8+O?Xb{Px1ij6}Jid_wgLoJeB%{Klxq=62_?5ISRsRa6`9iGO-U*|7S_ageK zG5735;1^gJ%Hm#Y`5;yM;Ei3VDh9UB6pl|SS62vER|?lBuIsD!^)2W1t;h8(*ybKg zQAt8+4Yhc40i3usbji$EMHI2H3)hty(HS(6@`0b19B-1yvro%}E#$K6TdbHgaWG*h zGDP)b9hyW)z~7ikGbY^35Z)5|RYZJT6%8$}zJh8ceJtfwnMwzU>XA#eI|v5J2IA7V z=XoShnRe7idI24k_?Kv>l`R1;gi|aP3RwZJJ8OrV7ndEaroDTqj^csIuKsJT?WCXZ$eUVF#TIa9X%^=@O8mwM?_D-J&YcEY_xm_w?SX z>wMt%O|d*WcK_$?+A0@;k4LAE`7|5dIj4^d5z8O%aSPSQ=$iLx`6sT5cOK<0f7A2W zq&6_K7Xg_~!K}7Wm?55l@DC)iYk8SX!>qOu4trU<{hY%gp5buM45Sxmx=Pctj%l8< zDo_#Lys8|spgD^pkchq+5n!J^aIG%g>L}6FM|vIBkUL12gIXtT)f+{s&ioyxrJd*s zU%4eqr6*3&T~Og&(yN|fgx5qOyA8iSU2K0YZ2wMde@|@xUJgKkfEfO|JR*E>PQW8z z9RK^Gb^=p~srLEe{D8dFkchT)!O@(?&?67UZf5j^fRqpkz_CYo|5tVO-F^My;jiL; zA(RTaU^)M!kP1LLn58i5h({1Z^HG9ie3}utKMrOMC$mb&>Xwct5}(Zg6)~pNUq^wx z9AS&dQW$L!tM+FEy0mgI{tZO@+Pjx~@0SSgmse+(IH#95AXjtYUU>Q8RBLmF z@1N@%sXaEW3qSWD!iXpqEU3=&)gllf)R85u24&G|0y7QMfMX5p>s!LJja8L-rs`er zX~tW?JQE#I9w|@okJJ|wr|Qd+zmse;4Zu0Zex~YpImX`MTm%4@qF=xbBoL1a01`z2 zi;()h9k;|d@misg1px^le;>Y)OtT|0=CaAo{^S$^i6P%F7u+{rwkgsLkBfWY(wXXmmEBNhRMYxtM{~ z11${dMIOGWi&jWY=mQt&$*oS=u-7^Im?S~aNH>M^>9UZAf1ml?cXNzi_Vx4B>Aoza z?2uSHC+@xRy%FkoHF74WTq>lZx_aRh7_rLx=6!qa0ix`iR{Ne^knpYop@o^=IJk9- z=yk{I$PA(PBUpJ^5nd~g*Afvs@}>BX?veE1*JC`TcyR0d*W|(H%l@Z;GW`K;_abM2 z#Qu-2Bx+N|8e(j$yZ7k=&+Sr!owS|qX70+}5mWZvSBXB2DG+0?+v-BjBo_Cbp4(Xe zdNkYTyL~R=@fLrc_R~jU`39{r*G}W%bdPNdUS=jZsx`*xGz`m`0|8iy%crLfa$VJ` z@dfb4G0smYm4Vc*j#p@y;Vy@dsJ6x-+erHe#BSt)@_=^O(m^k9N3dM$yaMO(vXp zCxHmD|I&uUzRiw#?M$!z@N(*L(~-}lrrtVxH|*_Bs@i~p4BX}_Oc5Vff&KnUwIpU~ zOYM@4uqHm5c2-71iD3HEkS*NL+3u*2-HBagu`=6m@?GIZPO)I3gJ-fA*25f{v(^}X z?M2mOY@z45L*zhU!f(q4=un(vV$t)f&z5Y(R+GNjb8Mj|aB99qBzZGK)1hpGiAU1M zYUS$PME_5i+uldpOvMu|!K6%vCr>Uh@7oud$p{Z}V`1XE$WN*`+O1;e{Wq&9-A7;R ze(;DYC9hr4(-4WH2uk3thU+$iYc4^UmDD|2_(W;oBo4~G z=H-LGs|$)t#!n8~<+)b<=jEe;UC9;_O{uzZH zxC%R_bsik{YwgpwLu9%(`dWut|CR1Mt>C0#aT;%Ym*j>Yp}!2PE#I{Og1qE_M^>IM zE__PBI_{p^`=`k|ab3KhiIy|&{ap}(ozdct{^6E0*Bo2*=$n7IUP7{{0h7O9iHheQ zbn1#5)LzE3&-7N69iNM$hxKwf3(R(ZpV7|OQ_Zgez4MMXSKra7YT&l7^EdPY0`^kZ6Q-S8_N!YrQ@8&!2OS zVQX71d=0tOMcqEzrVja>EIubV$N4rn4HDkpPayN#K2eviM~QCvSZ94%O}#CA<%b*T zcmO}LC)UZHDTCQ#T9a`7zuFp}Yh_5E$ucy1f50zY+hc#2zkO-jR6h__Z#2ev!=ksu zA8KtTZkBlgKkj~JUR^!eKRrQ|y~#h=uc#gx)bvG8%c>2%@~*;Jrcz8_`_AZ8moSu2 zZd4gUIaO#fVCQ|JJl#hsd2dGyG?!{@Q3G46v20(avS3YeGPI6LR&q;=|i zzeX?coGC${wxg_3lg@##cvv)a!*gK&f-P1*?Q0JAIf_&4SOuXGW6x-{uo`pP;iw*0 zrF8A4c5IN{klzLn3yXWu{R8vhs1W^{Z9!M+u4vMAs04?;w5?#)Pe-e&X|`T>UxJG1 zcBtu3?e5B3wx5~4O7zYn_Hf;dyW^GfJr9~zAMD<(PwoK^dKu2f`?~%2M##xW&0`M0!2rs^G|H; zxxLkB7d2j;Pn}4$*}Xr|9mny~!`gT9s-E)7&(}A~gYK2?TjqxrE{RbsjGUI*>{DVC9qDmaVzts`!z3=c4BI zh}qmI)T}POXTp*pSg}qmxg=eALYdAYT)~BU(ZOGt(w_GH`{+ZXBl%den928pHt%cL z^zx@Yga(c$4W1R>YIM4t3$ckaMbGKY;_q}`!CH3U3#r#!+t*l;Oezv@?U{D#u#-aS z#p_o&ir~TwDRuA1hpz|QOI}$8dkh_BWU>fA$>?nZu~6Succ&{|8nzOBGAa>EkpU|- zkjWn$3dO1-L~=5ah*4v?RDb4B3Uq&mv|9;jdj8QobXj+*Sdb(FbkNcD?un|Cy`esg zP2cOR5BA&-o|Ekzj^3L!OJ)5v=$_LL+ROXXTp{mVQC~|uokid(&5jqGimIWQ9WdF@ z?p?&!erd%u#ZS-Iv=57J(R+%*?N(WgKJ4wa2 zWIl9Iigt4iW#8trwi^R^r%&1Jye2U%9ZeqaeQm@Z+omp9Ufiy#@KqluXpbLDgd8oH z9CH@9Q>bpRODwkPQP~RQe@9{Y*I11wy1axOTL;y-XyV6Ne#ze^npZb!GrD1A9?eWe z;B77;VN-+Gw*u-dPo_|lHE%qXEE}D*fplAW*}NTHp}yNMQyqn>rLRO7!Y1LJ6|Yx2 zk4r6D9Z;Ojn;NpYxIR^ldneiFeEd2oBRN7}?oa8xrc`h-x00XpHyvji%|(3kd~L1) zvYYaE>PZNx8i|C({KGNYuIoHhX!Z!Bsn5IbL$M^K{S0?7EMVOw4g`|W3EmmS!N zO3<{{CaqLGPfW>94*0}GROi0a3GP6AB-xmjp3@TC9Yt6n* z=nuMbfEnz)K|Lh-Yq`=6)G_O+b$;SCNHiJu7|9CwY<>&M>(9yG4eanQ%I|5i1-5h9 zoXS!j94f>pN)$5&qZKBRW5gtna!`zrV=PNj8e*uF%aL;gjvIDwe6vzxwLwzCR5rpn z4{Y<|Cn@@Xg%0XfZleL%$2^3jqr%x?TIJdZnfXrTnrK1y$=5Z87C@>j zD-A>@k^e=t8qKdtwj@^%qFoIVRM*f*fi~?A!77;uP8i;$Ueyv%H#Jjt$O3^QPOC{J zSBK03$uvo$)j*AG)C+`OIFcAsn9lDtQ{TjXx1=GNfHLhrhQTbDmTL%GfqFKSQ}4MT zmlp~P=tfIg5}P3Wi+tIbSJi!;L|p)*Zway>{GXw$lY%OII0;mQf?aHAV{zgnYC@rY zca-artc`)v*9EmZSdpW5e}-2y*^U zKsToUojC^nm*)1L<9`Wqy3`@ukd~fp2UP}olBJO;^(GV`X)61mXF~owa5P-jP=p@0j2RP~&VPP*dPRHa z($hU>cusO2KC&HeHi?H-%h2KBk&)%D2Z6hA20qnrM zf|X<7)c{Nm83G#$9kL`19KXwW?HkLB*f48Sr|yzx+^8yWp=Z8}V-wDdPCx|?KI2!0 zFmuk!@SBA62V7c5P(q{p7b=zqI+p@w@el0SU&96fu8hW?lT&WRmY+;67PgEI&dd)C zcmV*0WL69e6HC#0@r61wyftj^%;nIV^H3N;S}N13r9|^JAC!TKRvaoLC>l&UdpB9m z+qo#ak+Xq4Q?P-H7=bwwZgFKe+JwsESOF$Mepjep@1(!dp(tU>P~J}mY657=IWM?} zHHDRZ{>w-(2-RZ}2m%v+d)eu>LsJ(9Y8_@aZdj5VSwNEUOXZj7hED^-)dzzUL zzE+im)0`m;oOhV~OLXHei$9=mNmv`V*H|n7(Wl6h#x<)1x_W#HlL%yRYy!w&=Q`x* zH<>^d@GRib(w(TyeRjt8MtIlT?;b7Yphoq@m}QXHMS3Y&%bV$wG14pQ5pSopX;MdI zufo>`mKUmO{7$|%0G%)sj2Xp%2@@lo4F-29rgsBT#_Nc`zJ6EzY1)`Ef+Kt{>48N> zAqgCEBps4cmzSO2kle|Wsq|Fw|wWubZ2 za8xBC4M?#gK@gT$C#e#gG89KRI)D5f;fW+Esd<8hwWE2}`+rz%Unv1%a{Q?Q6!!}B zD0CnTrwa7ifOw~U(3cR|2U7mimMQ$DclmKQh;c5Lsww)=rmeePb@v59mXD$iS7`2s ze&G+no-PGM$#(dWqS&d8mNDZ1o75ply)b182cp(|ZxjH0e(mJ94_SIWaJNuma(Vj9 zCph4!-@|}4)L0sIqjb3PdJGYwdBD6Pci>Qk_OC*Kl1_T%k=$MxaJ&otzLAU*yrtA1 zgmBag{&dDEG^y&($>R+uj1wy3)S4uXR?U+QEQ}M7#wpdw>MgX)`aH#J9%QTh=B#iF zR)w%$(q<`j{?E1!0aIl^H-V%1*^xjfoS`_@L2iGMoup^>=BY!RVyZkf!&E}0wyPQ4 zTgQ-BX%+QCrTnuQLFm(Zs9fS_0R^G}2*NxWp34>{4)alNsD+wj?y$Y*Ne@EZJm6di zYF)Vcaq*&F5}HvBsef@z+^-_HqV-IxqVk(Jf%^auUX?cIZ|fr6ZNtC9N%jJ`nY z&c+8GF$wo(1!D0u(?BCmyKPaI5EyGW%0{x|qd5ujJTyd}A|lUKQI~Lx4Sc^tlCK$n zuGLI%lW!T1ZShRM{UhIwVIkV@{rhxAEppTdhD*M32ya3%b zz>5Q#3nc!ak0R-QE&y!u{49uV!4pFG+jZL|Sj$|@ZLTD@iLjf6{oT26xTk%Bwsv?B z67+Z{{$!lDjo6dAhkCrqfp1c<$F&ahxfA`L(y+%+9BIA3XMvzdpn2+~R>XHf{-@gz zET^d=&A`GaD*UmWY6xHN|4>y1V2Hcw7;*U}(q)OwctVuraxpD53J1pmwd$JfL#n6+ z5qnk!@QI_3<&!4`C6D)_*{VWd659IChxA|)2vvyI;|r98GA-4BB40&%sj9*==?nE~ zoJtcgmtlu8_c@2!J5EzC{p=(LJ&c>30-sQ5U_P6Hk!WDTQ$$Wz1ZD%G=d5w9{$^zE z*8{V@LiUi1_M5yeL*aU%@F9HeTE3?6gJ&LS1`t~L+d{w^4EJh-b zDB_bZP%3aD73y!dt&ct?NVRZOldh9g_v;+2f$ZD*1h|&@b1i`Fi^LU^%rOhQURhOI z%LwCB9_81KY~B6U%pim%+4LlTHlI3MjAtIBa)wwqPqG|KYfq{DUg?C9H*cILk?NLA z8>rMlT;?RMbjGMwEQ#1L)wi4Mn}|Q0L!8CBmO{El11k>|D#z~x9B1UqA5!55J=i+* z`$BetFl5?&{-9YgL-gh4qxtlPvvh*IbmFR<%dvDKUoiu^bfUCmT6o?#rU|MrrwMmB zhqNB5JWMwO=;4iKg^DCK82xnDDJA`GlXErro?c?Y_022c-JD#b&Cxlefcaw8fr@Lop4Y?nqG+_hFr}1SvDoY_Q z^QJ<7RzhpC`9ELBf?+qeGFxVhTVU5|&m;uBQkY6BC$n_@U*j*5|9j9#nph*P-+d@0&L zdwgk660~q-2Ur0zD^zWq$k^pEc*cC4a4~q&qo!4<{3DHmGnvuK)ER`QBFcT#8KkEo z&MZ{C@=S)IS*T1Z)EOkFBH)&)Ud5&&)+|&e`+F!ZhO`))-P>U3pfKDRM)8yR3a!dt zgojEOXGVw)-@E<#8fNiQgej>bA*g+xGJshTeX4+;k$(DU3H-GsO5P|~m3#O_`)88o zexC8ZI556IFuJ9Jk0b#n9OgfhAtxYFQj&e@suc9qs|OlHf{W#XjjJIiI8jobD5MZ^HKzNQk*@*&tE-}ZLv|FJ@4V(Z_922?R`4{pF_iu1HY#r zf?Hn!kqrj6ea3g**h#=Uhk_TwChlSj^a?eSr#A?@HDw*s)~U^TO{zZF3qL>zKfDM% zh!?yeFKZ>^TH`Nef$yF>C=`-zRYXP?|WTGrE>V zk!zS#0fhDvNyANK`jQx0#I;EHt{^>g^pa#cVe^udg{bhHxjp*C z_$DZf{2}lM!0aM7bOK`0abm%&stpL~+Az0kqcZQpLbL$0_Mdk*Dr+c-%9lZ?iwHr7 zjMs}3VNC+7G+^>~m#aiC7l~3L6S2^Hdd-@Gb@#fCY>6GmfUGL-uP1I66UwC8{_q7u z4Qgg)*R`~?Q(?X5dxQLCO$9i|jrQ@)hGJiRkYS3o|!WmCaBHxTUk9s=>i| z`iF2}mm^li<_!GWVSu|imp)HnHJ)$T&ggzT4S_U%T7SBWkHxxRehN`G*baZ*A7H$x zO~e~+-AvrOYglWdtPXB;XR1{Bt9|cl^+Ll|dzQRPgOy^&t#L*QE~YVgKJqO0%Gt8B zc>n-p`E1fKLu)Q?7*IS4E$74f9Xs;BHv=nUmPJ)(qLR`|a5*ahF26`tn$GZ847+FE z-}lE1sXp+=CaEyu2@wo5MFxJ#;hglWx28t8U#@%mfb zzvZ(Js5;y(qiJ_^Mw{=Y%wM;&_DaxY733gSzC8Ry(Yg0T?#Ah72vs^{ncxUgCO3%oL?#TV<*}S_&<}{zXHOC=;Cf!n-N8k$Z*u@jOa+Ocr`g}L62sdXI zv0nRzuV5`<@Az>QY%AG*dZIFI5YL5{?WXjmhC+?D%%f|C)!?r+Z5y9w@BNH8kT29z zR0qSyXU=(ZGW(2JmJK&k5fzuB87j_3!jl!w@6zxk^>9wN#Z2uK-nJx^-O znwz;+zpLJZ^=3RB3cXyby5^0~$zh$Uwr*eZpiFNkrcR%0X*Mo0o`=nUtRR;JgD_L4 zP34!hk)ghN))ywSm*sJI&J)Aa5WG#V4QhQ6H++*kP1{rJNGg`0)>L|E(JUq`vE+I@ zZ;>M`=ocm|)!5Qc={C=a|DGNHQGi_9nQpe5QrNV}-xjTs25MO~<2c*)ex*cu*M5yx z>2soONP|andQA@du#h?S&Fj;$T3PI+@2c3eoPW$jq${Cu8I2^@@_NS4TsM{8WU$W- zbJmVHtP#+Ar`?4t4MtMJm1|s z=>&Yq7+*HV$9tk%@v$EcWu)ry-OGN$^*v9a3BLY*sOb7ijqo+q{n$#yy>xp4Vk=i{ zePPRbM!a;rYv!}Q3d1;%X$-L0I=4xXBlwymKxJn11c&Cy|RQvX5xH}zb`x#b>p_A-nIh8dJ?{0@WjSf)J9 zX`(Bu!u|aQVxmzk$hM5bKGifD)&8v$cpx=e?v5qWLaFiy&6)iwkz5uy{^0Lvs@YZM zEfgK-ID@stipnHim1&7f{wZ#*tMz_#t}GZsUmST4*Q4J%A!f=AVBM=Yaxw zMZtCRv>+JSAo{6P^YwXxcJR++({Q@lOl_Uu7P|4#WIX8Ui;n6$_u_o@Ugg=@L`q@* zQ@-+YRb_o1E0hMr$|yDqLzrr->f35b zaY6-!{D2mPHFlX}>oS0sz#q%nrsZzWbLn}KMlZJ%$9LVTEi-ykwQ^@FpO&@QdahZ~ zOjiM>@2!`#a}_BwNS&?MzCMt0%iczG=1G@319xS6N`>xD$tUaDz;Y99!|RzjcgpQ# z!9J958={8I zVz)qhxQY)4OU>@oRI(m8N1bZ9HqFsmr!N$Z6`ib}3Aif~be-r$t<$lm=+SVu)yg%UVXv?cta#hXVd^k=wIa0izmk{}qc4W=Wv{CIl zAl1ose%E;*i;6?}HLr2j^zR{;X3IN&j{CX(3+ZTJB4PRF<-=!YpNZ#3kB|KGsCCnl z6#Kgcx(Yd%uMT5y6n^Q$#GnpiM8Pqrc*I*Q4C58wHY1rZ+~`ShG6Z<Dd7LNlD;dLyDq?D1xDg(;1=y3AF@IlAp&{bILHRg!9Vbr zPU4D@l(&a4iM(k7c#HNwqI6$*i&TmZ?a@G7vu}lxSz4xFx)>Py?5A+K_VK9;L9BV+ zn`H3cPU_jKzt?#M>(22Coz!!`=UVxw@*w9)Yr*l|aUTd}+X}Nqzan-*$o8qI^J)-{ z|4bytXO5MrP@Mk9+%6(wgHWi!68L+G_Y4k&XiJn3+P|{)(SM^?T224)y+>(9Z59bX zB;y*Z=o=Xvjsw%wkT*dR!4a{k$}^<|@vf-|V2%7<5F4hq{AGqvkM?Tj;Q#2=2mhm2 zM-?xaQ*6U`3I+%QJqNO;GT5yTxg{8!t+ImWNzi+OZ~p(#x(a{G4x=2!vo-&JWe0}; zyU_3->j}&M3hVmcl^y(liw^%e{+BiK|5#6uhnHnCNQ}hrs{r+3!jMwplD#PagZRb3 z6aNgZOPORm5+|mYpXUh8j+*F^>LX~$s{`lu391{R$PW+}aOZ-T;TKSk_fT*J^i-VZ z)SLl(oqT4Eqzn>v3a*|I_qC#{fwsKuD;-O7& zX}gHqA#=VG^Ntu!pcwHIr2J}6ex-$8toPvAR;X);2h*-a#I4|%UoU)$9EHj*1eNH6 zo<^O5jJIN=cdIz8I;dF*POO4PYtN;Xkvmkg6#TnGEa6r?bF@gexyZF+!I%CiiKW_mkAM$Pz;$Fjg4`I6Fh$)#Vh( z=_;G?BV$rR%8u2=L$J0SwwOIuSMvOThY@HAaB-{=_tGk%i2#&Uhy#X=7)QLYb zqpL$_VovG1xon?N>}un#UB#2EQq!zzr8=!=w@sa>8C8x-77Ww$9aoe#Tqf2BY6zwF zrI4WY%=g-LKNZ<-YCb2~F>TZKVphk*Npf$irDYoS|zZ zz`7j`{OdfqCs~c3#x<;|#!~&qIajadDA?f{o_lJ~{>VsAJE#>ICF7L;XV|SGP0vQb zJdCbkf>4%pG+B=jMUs)Nn%cx3S)Ek;*Lf0d6qsGEhXw-Li@zbL%DFkU`I$e+nYdMM z2Iq)F_&?H4`1noakEmpm9Bq)^FLN=L~I8EyA7?0*VJ#KtU3S z4^e&7ravVD1)3h?p;njS(w6za3qeSmi$9cX8WdGs1T>9Mgc1v;PUg_vvt}U}jem`_ z-=9gp0s^)Gxe!)va-g4AHm1m~eEwcLVF$t7=}QoqumURdtchvRjCEHhlte-%q5_nnDjfTP_&^F&ms{PzSw5OtJ3;K89EH zyImxt0m7rJNuC`FHGQ`+;rv8AB9|<+??md>eQ9R6B3D<>C#T@uz2f}Dydt->(kCbX z{Y`jjhNL3bv?N#Yle-e#1BLGdf$u}APU+-?acQP88X(4ns1vH6V{)!ZFd`quSZQFA zlDy6+p#xxgIOP~+Fcr{?OLyxGu#h7wONX`;PdW?R+!#or+aB!|aR$bq$*rT>>sCnp9AXKclT^Dzs| zMxNd^yI;2yo{|H7JW*>WjJih{H(D)}O7k z6!=i7>zpdn47FKZb`2r-B^BZ+ed~x)>rHYCh4iT~Y}p&J(u=O6TkiIyIecM>QVT?> zvs31VqjbkvwC}7oe4&X_dov_`PGI4R3d;WOc`T9f$DoN|jGvffMQH8nRt(f_sMaR+ z3Ki_P@6cB`9Uj2YYKK+{Qu0geH zzu1$yj(egLC+J=Vz|_o(<%5R?JuyqJjK2>o&BIB@)Gy8^c?lI;yvY2tv}Il#XuEHy zM;+Q01pb3nn+F1`m#~ZIoXL&NP1XNU=MS;(=xq-KuN>n`ZsV>lDRNNo(qVLfeYOIs zeVa-^#cnUDQu3q1b%8~Z#93rTVnuF0c~BF1xIBzPG52r7L83egx=3Oz$|8Z1gc*`Z zdE6XXrXp#%47CPxfo`5V*5W{fAp&2Ud1Gg24RHvf;A8Hx9}j5mLF0SD1zw`p+tcvN2E}k#2+o5+yKZz zpd5@2{ON}rK{zHyH5ozg;k8?+TMS|mA-dY|0CGqShlSYF0ogUrXcRB9H?gx>a1A)&8SBJU(Ly6&L8ZIE8mwXM-IKV7~wm_1?WzH#qwT=8y zxXv@Rvqoi{-#+~V+DxP}7DDryULCgy3Prq#>uMqfSyNS2)MNnI}6>jEMO?hmkK|iV7KS;(X8(3u08-8> zWqT;2DLX4Fhy75tLI#l|B3}KPPjEwo@?paXLxvim!^NS)$DqScek~|W#1R;Xba55? zYZQ}>isH%J@N>)^HD^VJ-E9J!r$}ekwc^GSU|F+fA+%w*&Q}vo;w}l=(5J#AK@#L@ zOiy}K272yiqb}VeNb#}{Zspqt#)}7yXQ;>U`Pv>!#LTLS*`=!9;T6-R6;SAEbc%I4 z`CGM&bvlN6-8DcLFrdqSL60A?N z^>0rCjS_Yb4(G>C57D1Mj`wc89}okOty_tXJ!PxGA0sTC>W$kEt3kXvm|A@-oe9>i z7;874joVPGL9Epfwgwnm!v8Y6z#BlUN!uCHc*!OR5sBxH>hfud8hOVscOpfH3Na2Z zRb}3)``0Oc5XQoZGn$T&=N?F1mL|xj=GrPvv8YIrJyP( zZ42!E5}PpXHslu0PrjQvVi8ZFc>H^efY*QOn$|JCB%xYsANQVZF>Q1eZE@VhG_C@A z=9J*hmEq)<29=&PQOkGa_kCnLW9j#CjfcJjL^lni+V~jvaZQK5!Vz9fMK+P5+Q=~M zWf}H!OoqOKiMN;``?Rtd5|pSf6+x11ldOv1ZfO13B6@h>k7J z4eNt2)-Z9}tCPRWo>So0m*{N$p$;Sv{LrJ-IkN zJvlwWWW1dScSOy0Az2sYGj+W`5-uG$KIwGjpyl&bkmSgwET~H(8`v#~a@NImo5&>( z#ga(ouBWq5jkVcn#ycGR<(H!u3aYqDh1KC?A{%mkC%S(pJQ~R_1`D9;$S)=esMD=V zMgDxq^Tl9`V}CjMy(@xMgxmemc8drR^WPGe7Xae;`v8!5oD-3~l?`G}2{kWJR53KE zr=XlOKlq8anDlEtk*EZ=Cd4dY=THf;c3C?&&kP zWtmkw7t07E_UcCX#KQ~6#}o~8=7t#)d1q5*@V`lej}->}3HCj8ZRt}*V?fzql$sTa zMiBS%D6 zgMXXyI&^h}U%dW#-{Lk+pl%yoIZZlj`MDZwe?O?3Xb{A&q&jo%uy?k~{p9s3~R^ zi?h|R^JlZS>G-r&!M?rL$Q`lI_-%jLx#0FSh%JO?2PpNJ#4;e!zPEEQKo`;8)6!*e z!f$fsXV+Zx7$(OlXjPl@>wu2)0MUxqjs-Th)3ckM5jXY2X3Ju|_tVE^K8dQA7f2`L zmq+8M9Cftk*vPYx@Sa4qjSg(08S}V$EVHyuTLHRbw~r-A#}Xka~i4~V&a(vzXFF{5d29cfpVsQbfzSw*s%r{$;xLmJMNiqH9R zsI^+P)X&xB1%m6*Th-djCUM6@%W_B9()Vt}G7(kev5Fn<)$VRPEc;m_{-wRZ)PLE&FC7te@7~T= z;yJhPfmmu7FLp64L*>MnWv|&gP^78Mdf%HuP4q$a+Hr{Y>)d+$eOtR^rW?L_ZR{)@ z)s(#dF#GfXTk_@pv^$R|1I}}s-}SM-x#Bg#!P<$)7J*LO>D3fnJ*WmkDi3Li8D81HRTx zZ$xPBZF;YL$o2ag8h;M&j>CM;Ees;l<126YvQ!NF5-(xB_??@ETjP8B*&6m;_5^(H zRI2+0YPl@DTB19fT_M%&JpEw&*u;kV*5=)gG{g?_CKdNvG!FjrV(bT7&rw_0@219M z|5`c+4IGpd_0)^Hfzy zhl%RW-v1j4H>pnVp znaUCI;14Q`1GmOF0A8k(bvg-R`+$u_7evusZz>~Z2dv*Wh)w)D2&pCrtdSdKS6$?o+Gu^J24)LVJk z?^iEeEB^sA655&L=e_lYA_Xn_trhK6jsNp-+ZuAglcVotw^Sqchw-jGkI37Oq4}hN zsx8ao?Bg@B=Je@Xvr8c4Wz9w)<5;(&6q(+@(Vg+PQM&Q{O*o3&fMI*UUOfL#3d5=gl? z-i4d%IOtL2#)p~Bb+v7jSZk1F=KOJ|$TTa0O!@BCk+y=82pQM+XsW@ct) zW@ct)=2W=CNrjo2vBIe^Gcz+g%*@>Rrl)o9bZh3%NItUl*vGPD$+F&k)?RLQSii_R z_*!mwC?TY+JyT1G4CR;6A^C5(m!U&Dw`Ame7mF>G)h8=Q-uq&PkH!hjWE|Ire##HC z4Om#Dmr^t|pkH#PHj;SS1MFN@Qw~Hgi_4@<6PKBwU?lu~1S#4v>FQX09XyjE(j|S@ zjMg4FSAW~*b-Rz6-W257k@3``Yw4~pRUUx$&VN+d>|ZfE1H`QCuG#sAwvD2eWf@>l zHe1QdYfngmn#IOL4v5M$pv-nCZU6W_W#J;y{{s7s(ve#>M$F{cUD33m4sM5Dua0c- z+3T!WEWFZBFn<^O&Cu@|$QB{sUBYC96+OmERt@QT&zW#P|F)KA*!sxIZ2Di5l9Mogf{-php@B!Vl)oF$C1Vi_rr8KA0rT%~vruFrX+I-32xWB>~c;zqx>Wy4N#$-m!Z)7C=~mf9U3cq)sbD!qk@TO^>E>J}F&Sh(@a_-)jOm2c8#RPx6wWhiS8e=| z6zTOJ_{_>xXyD$AavG=%Q7MH+c_CwOtI$b9IHIdM!ar}MMGf=MG@}YJ`)3Op_=?_W zQ(vE1oeG+sCNtyfAs-)xEz~XwOj`$&ausrLB{_u+c|e#6B9R&vamCLF4Y5>244n{h zX#B=;EU2tstPX`wZ;eU$2!s!fwrAik+}&m-uT8t7h_<8f?u1bW%<*Jqo7&xunbirK zuXAO{G}|al#7c~tlUiAn>Q2#i(Bi~ndM@-OGpOj6dsXA7o9{R9f58-GKS2?oPG1J~ zCnz%F2LclPe{041??KW3sdf2ZNs)xHor^hxqpP|aG!SUiPfYZm^}j_$m)@RQsw2;S z;of@4O;@ls(CP{LR44e#PR7VZ)pHfh@lt`rW0UeiYG~DT5wiL^w${qFnkzOq+i%Ys8#rSSf%F@ZRU@IjKD;jfHyH$w=EJj#VEBs|Df2;zgo(CaUB1;)R z-zBf}C}z=y=WnG>41jPcRNzmvO%bZw1Vy|HYdUDUVIQ*!Z8AlXtDO$VQjTVm$Ob(h z4magG}4L*3r)fS!nL*; zk&>@vPDzE&8uTz5D8Ycwn4JXUH?M752o^dSlDFzpw34Q1+@t6;53Dw?l~*YYSm0mE zS2@?}F(X|Tz%JLuV3cD~sZ?Apm!w`B3qmEUt-hvPWk79C8^<+1h3Vo}H)hI;wm^mD z9rPH@N|LB15Tr#(DItpqT%wAxSxsnJl)6;2%U9@Lt8IY$oxn+2lyYQNTDP6a;JgP( zQ9S5#o@A^U)k%zPrAA_2v0f}ecOj9slM_2Yz+{bL?UTjvHo7qvg8cwP`fI5^T}bs2 z>w=VmN?wHus)eH3pdcQA0sVlFxHA^YZ&q8n85OBUA+MlGlF}c(2ouxClo8TZ<|^o^ zTI%<&hV$}1t)+0iAyV2RXWA1EE6)Yyb52aC)>w@5bOAGTra=WWJM5UC9iKI%O5>G9nr0AoNa2V4%KEw6P_%F&)_tROZHcIUA%w&otkr4hQ zOf+fl_i^3!ho5Gl6R6MyIZLGq1Ae$!9KGB2*`Q&g0HrgN zMIbynJPT&Z`fSD}t?qzo4ly`=f4bxFkA7o3`{=ZB@>ccIoT_E(TPt+D++@2+z#^KC zQ!Dip*L<>9$ShUFvVwBuo-_sS* zowU9=oN3!boN3V!E08_*?3tJ)M}^0L(EAK0oN4Z1TdpHYE3Q~{oY=7loLCz3P4&9a z`1o|_q~c8BOsgp<_~rr`h$S2wR(Q)KX`E@evBi2l6$O@JYUIVvb+}C=25_f0%}Rw! zq5z%f%tUBPUaRW;CWXVsWlPZ@I2_Mv;u?08Mhnks`OakvgL5{6b610N>ds~C&Slm8 ze~vne6Tu7I)vDpk87y1_(&rhS%i4MXDs`Gp%2PPHsAuHl@qX<_caALyM=UFNE(c$! zjUz{Bc4!B1w)@@SOnF}CR)kF%pE$b!CeAon)lSoSgzcRBy92k1OL%$(U16j~XWc$M zKiYK3=D^oYjUVB?U}9g080xQ>D7MXZ4BJ+7y19cR>sr@B%?jtp{xZ_re9Z=m#3I2X z7s1M5m@>3jSAP@-g<^sDieZ6i$rY78NGaBH$w!SnIe7wd(3euKJ0QWKP{UMcK^G!{ zJRET91@tA<-Q(~2fQ=S-=#Ty}g1%ayje!BMG*j=FH|O74#(}ivHxqnswKSN1cy$O@wPAf3`gE8|1M+H&igAsjyvTwNY4#JZIpY!RwpbH3%>h>h&-koGjTAB!Bm~R*0ve@eA0N}(0j5@ zAHTFjVi?vH!BwBmhY_=-nzb4bo3g7h~d@l!pVfzvplrYDTd-_DdZ8<)QN%p1|xlb}fX8w_bwf?}htDfB9LuKjKHJ{at( zgJ^F?%n) zIP4Unc-1?Q?8NeeMarr^lw9mKN5YXNiWPb;AM&(BDD$He>M~e1#%c5O+A_2>hx9am z*}phco0V{VvSQZfkA9mm{Dp6#Y)R%`4A|623gbzn949;z`$Zj6#@hCmu8`p%6(yo` zjQ$YCjl3mUVKHXYIr=wc3!bzCEZHj(KCaVYGyfV9HTIt!qIgFAbKWQ6Gd7hImmsg7 z%h=M8k0e1>uQ?@)*3ukm@g$h9yT4(p=h4!U_@iX^)E03$6X`4Mr?i|_a}s)zvbgLv zVxJSiKr6{3BmzOWB%zCAQ4TJs`T~oBl_ZJlYFLXa6G-NamMfLYX!~}{;&K2?mVpD* zeb*{Oq~UvF>DZdHOZ_DHArWJ;e+AEM)0StnaYX+D(^U5W7?c7w#!XW?V!z-iMQ*Ds-2&+Pnr58-3zz-bua8cS0(!hzGZE;qQfe~6#D~eQ3?6cQ zm0V){s~O?)#pbuOI(oekid`8TO}0@XN!(Mb@E#ZB{vA5a5~5U~#2Y#2;_5~#=>pFp zSb6k=>NGsP_$k7SaNog)G6{RL1Z+Wss}`s4{S?8i9K~nZl+;BvX`AjUVQVpcPsnSru%BJwGcMn9G3BoEucf43e3t#cgX&xu6 zr~SC~ZzkmCllB_@XSEQ7STE}M7PwOztmtM6EwbeEJ8dQr#0b|dXPk0#n!sU%^_&@*u}Gxd3%{>_}ETTk9i5$K3~(NjXqa$6PO_M;K>-JKJxIm!KoYY9sO&y zwB^P<#S?ns`8E_MMh_{zPboYlOkgc?7XX~dUQXfI+alv%_ zkgV3IX6vQ%4V_B2kZP~EA82hFm20mc|_O|Nii^%RL&}qPtFR@v~KOb(+ba70M~bE zMBkrd)r?X17x3b9V93yYMaPsDw5?+vAYg58SLfrTCSa; z2THE}46nsS2dclvhJKHgmLIUH-Cc`0gI-yIMrqU{zeh(UhEnlTavbLU|H4``_3P3{E6Gc)f18+G@$8)i z1`qU+E7jwGE}?e-fxi`bnZS^-7rdDeP~ox@F%hgunLt`g3F9co4~y|}Te6a{b3YFq z7Jb5jvm>MG&g%{C?aAnWdP8D|XGThQa-b%a*3D0@@aE&oGrcl(aD};{AI5xJQofD1wl^}N7ij3bR z?Y`AaYdVhwK3@>xbpR*RbxHDb74Pi=BO004;*YHqw zQ-P1TW~3bik=h+@ME%CTas|yH47Za^l~^Zv&WnMXlj@5p(hs}d#IVLq=VmcEwIc*% zCEhLsIRx1|PsCz(S8fD!CNd^rzC1T=QE#{2UIbm`lOV(C9uQjFem^#A+k3wPrE@mk z6)?;ert}PVZdwNLbf4c;@HGPO!Zgho`N4|VWZJg4KeE@v7iJZiSGH8dPHo9m| ztFO#=^L3Bft8z+feT#Di1hGX$_h=05EP?-dVnu6~Rj z$sb+Q_g$M0AAe5DO8x<_kz0;+Rg`PQd$vvVyUEh!d+4|dt=Z6)weHNhuWfl6y>DZL zMA>N|Wp9o@g^j|bDsa7PKLoW2wfj6|@^HXHU3!=fFK1!I-#>n|Fmk5DVpQut)jvYe zYGu8()_t4N-n66V^30e3qx-TnWpPe{OU+;JuBBnr&Z8k)_wR3?Ej>8Z9_s<&PV(s$ zHv=t5l4qgBY4>V)xY{os^3@JChZd&9 zfJbl5-MuGV>jpT)zZ6##fe$WyRvl(Dd;aO-=xj920QUiC*YP;~1%EjEhe&o2spssH78X0sZr?R2 zfKUc~=mVqa&mJLDimNy2i!n$yB=9fyHUw^XqMo=;jMW8wz|Be7@HBKdR5vw^@eMhx zvpRUU#TGa3D~;#X*YPCk0Zwy^HM#Y5-PxYoT@}npdq!;D4o8%p`_9<9;=io(6Wt_2i@j9^JNc9PAeRU2b1qb;%B%6PiKq<7zoKrG zS7@xf-)pJ@R_pd9>sHGJ-m4eYr5QWXPPPa>t{JyxxMsnwJOg%CHwRfmvYO1t_UAF6 z9D3%{_*EUPHJ5lU2Z4DulO>hW&LSQ+f#W;r8StfFk7CX#85#m*OIP;q>64b)TQaCJ z87}*B6CaTX%vqHsO!Yn{x3$~%Ye*+GCLoU~`Dsl#&@cE>XYZ(rHdsF^y-df`B|*D5 z)F?2STg+1?nzyM;{g6uO`Bq!Ptyc?=!%Mv<(#vG77waGunFU{)p)-+n#t#h3pKXt8 z5Lg@BW4ZOt2UsA~JiGK=E5kFgHC%a*N?C>{%4{;!l?J09VU_u!X-zf$&^uxGT$?z5 zdVf&}7!3Q&+5rAd#UQZk>DQm`*Q7FA4MjpH@J6QV>;=q3Ied+M?77hHnDXW3rTDoR zesM!^Ol`*|My7viI;Xo8vRJ&dcbE%k#xgl+4l4R%IVZIl<~k4Q_=IQ7dswZ+8vLEi zLA}O2GI@`-e&RZ8u1^j)Ow>g=LYjJ~l${yriFl*FL4Q=u@r)5zWuF7t?yfIbXd$J~ zzc^V>9l+5Qj_MWk;+W2X-SbD(?tDgt^=PFjcgh2yeKBae)LvX|Gx~a*`g~8->UGbH zlI(d$TcNHD#C_ng0*@2K{%D*l)i4_Mq|RmE0I_Joqu^add(KmwiPI9m9EAAh?)2yY zuR_3AUZWd;S$E6h*&5k^A<*aD_7RT-KVY|mFzv4WqulcQ)PY=A=zE8cK9kwpYJlAYAb4~)62GV?4+Kj-5axJe_Y?H zAd_C}^gN=~Q`=aj!+)$LG&k$3K7pb2=Qv^sU$Ln!&`DsRKIWVbP!mbnK$Ob*`wnv- zv8gGLa>chNJt^uqtZLMYdkuunc%%CDv)U-9*x4kC86!XH(T9sCr83VCuk~m#t1J)1 zP+72xi9FgG+q1j&rJ8lh-MzJ6i(c8lw40iiZq86H%dFbc*1&uJDTh#>@x#kvC}jVK zM{ACCP=7k#!VDRii2IAj<;QHVtvIIrxE3 zw^hI6rnf7(a*}+(TQn5kLj!BW<~de3i-Xy^0RuZtG8xlvwN7;=#rhcrvuPzVy6GS5 z%|+PhLVK>6#H8WlQ~Wgn3oQsA0wjdzA71?hdoFFR2E)T~R%7xITZkV@ z_rg0@TZh)|7O+nVdv7RUy}^~AXkANuV$7^MbTQ^h=`P;~Y+~Qh? zUGu$>a`WtxB$(R+{S|1~XL!&A{j6$>i9XLVT$=yrzoHmjR*-exduaP}4omDpA$5?E zp~#Pq^lJE&Gl1~ykAI8bmqQd}Q*#C(-UjcLHouOftY!vAf?Q2+7O{lzTj~1-a3jzsHbF{k2h0tcrb0i42o{(dd%XT>YjySXP3jcl!dDgR z9GJO14bSyH*G8MWk4(HDyi(DXb@uq=4(`G?;|yWDJf>d@q{%cQ3JU=HUD&W;#IEMT zSLiH}n85P$KgCzowZ@*t2c)?6RGFwLP$S}K6SgaiES_D-ZIy(KZj>XmV>Gp zxTxQVvwQTiykDi$;Jyh_nb3-7>uR~A@ZgA9En&xtu{Ui5>)i&cLE4C4`OC6)+3zZF zDco*kejVE`;pFYZHMl~&KAvE@Q_>1tr@)%%4AFV5Mdzy;>U*OLF-F#*E-z_C5N#GX$S^rxa^M8Rm z;yAf*z=^>9=+i(bZ4|hMM;^64Q zVnP^TfuqiF<9{omNCFE3^Z$(Q27pA?x;?ncQv$kMI?eA5=9knTR}3G0cfK{<+?^ZF zg3nxyjoWnu%i9&&`6{(2ZWOk!8Yg^aJho5kWdn;A`zY_h33L;P?jvqH$)Gp{BARbs zXGtGwwasL+y$eZ6FM+qxd%nwOajWZLb%waMNjLBl1HwcCMy_ga>jBAn@r?3JKsj?( z|Kdu>3UX6PfP4x6_%X`cagn z{hfiyfIfS#(nuR1)-vyypvEQ$;Q)qdCjMP%2V~4lfy4kB1kHw&pvx>)T=WD6KQv2M z1c#F_MD+5t2pC8lIE52f|R}YOq z{ckiz!w?KyS_GB#U@b=+fp~{ResYriPc!p^uo}0OB^#$$(Y=Bq^!457uQxpqq2{0jVg$82Mm4NvH0_pYdQYuukThDr-}C;uJ^DcfZtZ(~tt3^AS-Z;G#8h zM>fZQQ_kQj#Q+aRL)N9_cR=%*{Za^R)S^t)&W~{y4Zzg@7lGoD9!+5RtIE36o?lA0 zvyEK4YY@{555b;rmURR#Ko?e@X_P4diz0Jl*=BFzqJDHAIFc-0tere-W9iDmDOsCB z_6ObxHUsUF8re<`6_RWYMVsFo+1!nt!b$U$Uhz9BX{{vFH4A7VHm3o7q^t&=q_=5g z(FB!_Q)_1ln=t8c;f!^~P5x91M)6ai)F>DsmKtB`6@;Mw)nbGj2?s|6f9l}Kjb*}C zc2OLo}xHKs_G4z21eW2I6!B#KpV zXjT43qn48mMz1Ts(ev|Clkp&JwPFg8Y&@FNigeAB1wirac4n8kqFBaOQA)& z-im`MW=2*2Q-KBB%fVFam>o35I-_xtM*W}Y4&J5-(C>q$Ry;X4ou&c2SctGxmdp#` zF@2SmOeVPAa_Pv3-jGC-aJWq_>Ovo=8$zk1LNd@bFg^okRUX*nj9xP7K2Me*PZ@HL zey|c6t~6RM!7<0&5`bO-fKR{gl^XoG6g}ILic6D*Yi9CaKGzHlq(P($^YEG_h&oQT zB`wzsGX(Hu=x;G^& zhl*Bx-e_9I!pzWovdTUP1z4z}bKV9JQStzAl$=o+AVwRz#6H{VI~3$k9aR0wE(m&tY%LSc@3f5Bwuirrv{3knE~_)k~U%u zy1}0R48luw!W$&CLk`*leL+z|kZcHAa&0Z=9rRp&noG3M#j@UxRwt{lm-`*;90DHb`BGgwa&`nAb zYTPR23OC@CAcc+Nm{5kz(JM}3wTgrpQDRK0M^+z-TxK=tmwDj1inWzIK+zg$_P(Rk z940?Dm}!ifecxa?+dU<{Beni~O?)&b3Of&b?X(Xk{vxqbIObC@pc0JO2eC@R)EO3v zZ5Kf+Roti1D)j)QyHAn;W)~DChF(k0pki!v7o8Q{n(+HW$4i8~ioBIr#HA5&2NY;;%=UYxv2bKY z?)FAlFX|D5w*Vyx?gB56kgXl5x1+E}jJFn-W-3rZ1sAnAU+V&$aWyb^P3G3RRxEH! z**sP(z!7P=A=Rijfvi$9|lCA}6Uqc$9zWK7XctT^vTMWZYbhAcoxTsf)oil^{0 zNzueD>?064KGgSdi1H;7A4@T8NU+anIItr%yhAm-V;U{Uy8lCFI>7Ba(Cs_m9n~b> zapv|uwT|x)g$U-#*^9`3(M9)w^b;zq2=~r^Lh3kix}F4auyp2||3zw1F^c@DKcz>8 zR#F=n{RdxU0`e}#!VP)#P`J#7oLW|*P+cCC-X1|MaLn9Y1k#-L(W%4|mKn2?RM>&jA7>NvEqN8!|>PA;*GBC)I@u{;r7 zrHQ3o!&0BhTyHQibhgfjE_Fnq$Qs(&+lItYDpW%x)bkJAiyZVVCcYQg1YP?k%dD?* zEpVk4yk?2+g}xc!X4m&^1ZyyZ(HTJhV&4pivFpoP4diS9*WAyJ%xi}MTLWnUY+{Mz zCYd9{B%V000(e_lP%L9i_ndB{@H(Ft^!;qZ+b4RWTu2nqhPb4u%EP*q=E=}}^g=e2 zHsoFu0|%b+uM*fhXd$&)gRm~hAUdQpo|1gz;?O(lbn(cCQi@ZvnUqGwlLo;2v_p0{ z6ZN0@J2Ch@2WVQ!Uj*8x@LG*`n#`BdjuNYfEJlurE0MT-FZ!&vHwhG@e<|}_5BqvR z^MAJH)6GE&w_-ltCki=w0R;HyFlBbcihxF}*-sh^ikChE8omBNoq$&V5ikRJTgjQX zVSGuz`9)|ua7hH+q6noEb``JTJupylTB@@^I{3h9;*~6Zb}ZI9BK7D*vB{Y3*s}=r z42F88Dc5Sqy6ZO(?41bxjD~vEBG+1);W)#z>o+og_H1Tgd7Y{elz=Exd0cChtmm1_ zbQ6>s02>`Zc%4kE3J^yhmes*xM8&F!_xyAX(_4f5s>=K-WdBS_Hy|(RVTSOdgwrEk zTEPb8!~*rV!g@(!c1&Wv!88S6m;yLVg-SKCq2?;#P9AAM-5zK_3tF}GI|B#xrWTbn zYf3`B%Cw?~41E3!dqJ|ZTLlIdhrW5?of>;}PCN3S6KFSh6R~533xk@>D#fXalJd*K zBd>BAzjbo3qwUMZt5!(^4Uw%zNmDhHHMPrtJ*@+ANRTdn-1Tka4N^md@+FJgQ9S|} zF>51o%c{x=DyuRKit3NN)XfuGhx9Z(=~9hgH_@uHf<0DM2xsMKeE~{QvGvuDJbxD@ z2)P!UmrTjUkloGDqHE%}_eeC_{) z9+3leEaLD3|5lg0#^7sL#^;y7=TD8^&WZktH}G`r-;tF10d`($dAjeO$fpjS!3?^4 zx$HzbDf$U2Ry?{W#=TGop$8=k4PxraetwzERJLSmB(xp6aLlRS095BinnhJLKhODj zv5&uX114E z6|Dewxp+x^g0>jHQTJwmCZ-en53!@vfn)m{-peMo^3}t(&%)ohAvTjy)Jd37X78s* zw-(5~C#2n*51gg3u%mUe>dp9Gg0s@2AxI(2-{tykD|=z+t7T^jmSQ3h)<0%i>ijKF zAe&YYp@WVWjIU!H*m8YQ^y~I|%aT)Q<;9@;>^Xc!$`*-_oy}}0fzaDg>!<{;$gs5I z%X#z_&h?V3Y2~)BRp4&5U&_b7T{TF}wYoKjn-+bEkZ0c}CHmh8tpR^Z#I;}!Dlb?Q zqpaR`8)Wyd587(n3U@{ACSv$4&M_>tpVzfl!YBI6EuDU>#dZdixjVxug_1@5a~KF z2;OSHD+MilJTKe+eci8g!^=;_RC6OTTnvUR;a2EYJO`X--Yo8U-CFBSz5RW6AY0wE&ClZT=xEiY++*P$!9 zI$>cWFlO_jDC}HOpK1zMjWt(rFRw+iTq&zxS6^xsY(A|F^;` z{gw19KkIzvGQxP=@YRo)r=s;Aa+<-Of7u?zqisU2!RbCmE}*#awV^GFz;FG+htRU@ ztcl;dD-LzJYMZ5}CGOqjzQOj*pla`B)C*t0U~iviCMqMTr+;MZe5Z}ge;seSX@$)% zt~`>q%7^9Gy*8t!gQVId(bz=Jr}O^Y2ZO<{qrH^oBT3)Qe-ts61iteVhXV7BId$6T zD{ZZ%)(24yZTDUNTj?1WKQ(BA$%1<~bXOiGL0Omt&rUt=+&=YoDQWMcuV3@=Z3b5# z?(m_FUAz0CKF%zz4$*PA-j8vwmYQDN8*??9OZZ$_-tJmiCcE^E&yPbc^XdogS9ev2 zvC&V?-SkB)EPGv>)9&=A`p<8zQJni((H{L1DMu|khRUUp-sVn4i47}0n^W0qzAM#F zPQ1(VJ)gpE=qnx7jr2}2X<12kcM&~&R@>o%TSEeIyDh0N&sQJti{jh3{#0)4<>G*c zFV*+aB-}(#JU+uMWZvN(f6lnjXAVTNVkS3&>P$v`AM>F$eRONs?MVLL=9sx7mjtUrk)4?bpVHEoF2|{$-8` zY41bba|06UP7}BeP7XY0hY}WC(;o~TZK3$QbpF&c6WS&JVqkZ!_r3T(Ee3u^b#1zz zM!{v|y|xYrGLAB2^JHZ$vI}mdob2O#k`4W96^P2>|9sfP-}lk$3QCw^^M=3l|40Dd zclT(M#?=&aH9XqK2%pev*~tGyxZuxL0~`{p8h^~|MnBaY$q(z(J~>bHA79kQh`yIu z9+#3h1Q?Y?mi z9ge2^A@z6-ppTo@9s zIxUX1zu1C*<%RXQZGLRy6~$CK$pZrd@}l1e_z_PjvZc6VYeX^IH1}*9l(QK2Y<;wU zwCU@=9s!^)JXcAnn6piL54-w5O=%R)C+K<(+ikYA-MOpyPu&09{Td(Px)Jf0r{ZeN zOTBy}Yf>DF^WNOAj|kAz(MOyhz&MPc7sDm{4r-xb;NG{@_x_}(O7V#iDExr~F(Cpg z!ro7oPh1NY>L(NF$ONbjgtJB>th-`jVI;Ay=D@2hYcNG0gNOto(p>4UPxGgdaErdr zI9%Pk_&;yH^B%wRdyi4Uc5n#|QYis3bBUEo5-qdGHcMwKJ^G+*|8<=AWYj znm2t&mZTvg5!a5EaoF-HoPF0ZXdgGfOpV%W#f%g_jC7{Gc&|bH^?A!+6V_Ia-~vns zsz4bfUDKQWo15>8*xE4owz%HfEKY8xa1dVH? zSpvWYTl){$Muc^rS%I=czqu%qsv8wm**cD%hDH!Nb>1}Y1w@{;T)tHn!=5u-}oL^sa1Cyl-wE#>P!yQuDHR1epEp0M*v=t`|ytd^xtIqApxR12ENW-Ui4cFlR z)E{Yhzm);|dMGZx*T}d$sii++i$Ys>X8kTxcuDiu<~Joc3n(RE&8qG9cAzmQbWAM6 zAvOV3T3ar@IK?%(s;<>l$i-AUk!LVCKou{l)6f(}PQ+iz(t)qDz zS$XV0LS_qU?T@5|vrjg!-R*v}fE8joZx&z_no8Rdt^Geatur)&_8^Wgf^T-`s>tL9 zZQ|D8|Ru_F5wNw7`KAM53 z_^dZI`@`{-!M|G`8R-<6r+R0xG=Cp* zf74U&@-fvN`u8|W>1)d%fp*AY@x80{!H0RA^WA@>2L)%xN8w#-By~D|VQK=yh}Wdf zGVIIljkor@PEP-aNX%}tU4XbDw7>HIaPmX})+9l8Y2zzMW<)@`Cocp#y+Bc<+Lisw zmzzv^03HrHO2^&i)z?nkM&HL`2TiY^&)d={ppY$753Zz3MdeGrmWbz4F*Ydy7 z#4NXYWIC|`iT5cG;4js|{7-5JQ%p3)!=w<)x-S$j=Ww#mjQK85o{Q%kjUnm>ycvKW zn}AIklGLtk6S2(cHHTKh*i0butR6OlR>rW)B=+2`7yA#j6W`g%ti5Mx#i*BhfW1Be znM5wGU8^n1s7%SE9sM8w$%(c2zacSZ8MKS8S#3@y?PwJ6cRE*%PDY9-C&V8C${1|g z5S4Om?B9mMkDo0l;I<40Yf!yrKRc=_PvSa35#xqhLu!LXwi)Em_7VDRB4VA7_6x8$ zkuW5rG}5UgBEn>e6H|Z?hsthh4}|Oc#h79Es_ah6QbhRBin<3>AWmB_;xbVcs9-VB zcG;Gl=L)s&W&e&c(bDd;C3Wb&{G1Dx;&ZT!ULY~e=8yeAn7=qz+OMB0Y;I&B*jKo8 z*nC#}uaM~Y$AHqYvgZqn1q5`(_y1u)`EM%w|D)XPe`Q4f4`gTe$A0pk^}mHgKgfd3o)FKm%IT4y!ktx#vY?(EsWvV0PZee~)}94VuqA7+Pwghr-X3S|4K)?>wM!)t!o z3u(q?=)JodGIDQDqq17PhqWQ=;@8Qet-)i>kIA*ZgLURdGIadOH^av}R~#&pf(}DS zC?10N*Qy*Vx!>Xpi?oRIj9!k-lH>8qt_5M`1$j##b(Txc8LD znjJ&F*0YhA;mJP|+5dCw7D1*i-3SJEQj6XTwA3u0VXcDG3Oj0gG=qqhLJk^DSoqAw zN*XV5vH0{4rl8o`gqb|Y3B+x+450c+M1(j$u^pwoB)vM{S;QZ;LsI-CXsHdH!xDM1A66MuUbVHb2LrFRqS7k?+jLHuksCrf3A(v&Re1gLx|xGTbv61mdGR zv23Am>10&b>W*;1AqlAJ5xfrX?-wSq#zQLBDAWk@Yn;xPoY%SeEtAO|rKxHszFSgP zD7AovU8js)N?5so(A!35aB|7`JCMvQIABc(eXzN)!G{jc#PgYO5~6N2E9@Y!fQBjh zuuEg=j({f{9>4cafHUwg=L|pfIU|AY8t=NE@BX@uZv<|Fl{@mVe4eb^Flva5%rYpM zsF@RY1i>r;zKJH^G-66?o1&b~A(YKTTY$baeb|b~=zA5e6Dlvz<1?oMm6s}jprFJu zXbz}SYW`#EAfCbb?V1rt zjT_H0k1jaHcw;V^Rn7optVE@FM`GWglNDhAHl9b`g960{6bxt=^kFrk)CQVBK}mk~ zA2WeuB^T)twQkTRlbr!wqS1z1YZ=EMBT8$-RLQ@+M$X!a=0L~z83%Ciehnsg2k?d0 zYNJr_NwolNEmXgl25mom7cD<*mo+}zD2ovULYR7n~<^9+O(7l8#0xNi%DYx|5>HvW7f?=*ww*pb9wwq!$0Ee5%K^bq3)rsnDTj8_qjR%UpC&%vQB?sMzg82V< zQL2Ai2Jxf(uoqnzE_7;GS_BuShjbEwpL|g}(pr2*z>w+y@7y6#&wMd(1YT`Ek>wg; z^%bn_8D%8`{NCG1f(h^ubwF_Mg>=B0DoH~rxWx?AWG;F-KP8j5fzeZ7?}d24nrwus z=N^M)5G`L_2XmdKIv|tI|AR=$kzcBo<(Lh#Re3HOJ6zGqb)7YVj~m_#wP0%;;x9_j z_HXRPeAW_et?hjGO*kCz@p$1bdH{n0szIw!*9d0SVW-ozZZ9ah$0+WzGQo>7xSuo5 zv*oh+%QD*(z1}*A3gdt%V_dAkl7zI`c;JKNVYG%#=}$uT*usmp^oxF zKHQuY&m5O$NJ^&h>Y)55o5jiQ5wB{9lg(AE8m(u!*yj|8|PJhqr3Wc#-jGO`H77&JB= z3Jcf{N>13%4LN72aC$&IM{?BFPjb<-xa_{8aQZrehqZ9JNGylFaC)q8n!0dW#}Y?M z!hv5YELRC%1HXr_;p8!g9lMbUo%3zqJwxZC;}t8SrgSVj9ZjL{pMgR|2zOeamL}RR ziCPUaz^eW%KNk^;){wo|wUbYUt9LFPC0lX9$#3v9!=E*UTwgC&81yU;hBe$iMnT)( zeGN@bx+oGNGrTcF3iKfdlKAcDg$3Lh3n|Rf3(J~ZT$X1H7nn>P&|=`@7{KXo)%SEu zA<53e3R!)^-2EdXRZK(M%2RlfttMDeqvk273NWp0KjXYzf9Xm!p41B6Rf^BZ57}Qp zpZsZ|L@|`0#fvKaOM5S=L)8YTmQ-1S(pAJTXsj)YCiUYsxJpA6>zN$pKdt`^8g!=0 zK8ZY(zSEi}uKN&R-Y|nZ)HHU*`sii%iu7@1z~)bho3bbRc8NU*3UZ+GG~HHM`s{IWEE_Sf&0g&>a|Pvz4B}~(tsL#S1nO-Pqqh#({UzCR$z<9= z7_63pZtZNa>m&=&Y)7|w8MgDp57`i;-^2~ydoDz`;RQT3qT9sQbMPem@{_GgMTAL* zeubjXOC!xqXy$c{PsnFQG}x^jTJSWTZo_4$jzll&4d;Ky-nox|;Ose)g|Oh5y?xsO zW_vC8-AR8-Vtew9`YNG^_9O5cW!p`0w*Nu0KHaL)YH(=Zoa{)Wh1CZQQ3ZRh$XM{3RsimLTk7mlCLvc+a|)>o$u$AEaSQK zTUrhKdg|}%QPxNLQ)<*e*jlRAjc5^N1eWiQhh%5P#pgrSei;J$dgh`@0mb92u9ff+ zo=QykLpkys>cp*%qPlTxWVhOTXf7}LMx8!k+s7I zCrAtHPXb?(1H6_?5W1}0Vb+1`(+4UxVZyUL_T2YGbINyZB8S`(`F6yZV9uq3$0I{B zVMAZ93oCPM75L37I_rCGOH2-0D<-hISIK2hJI{I9UjxY{`>K*^Ni}F*?uB<0K(+s=aq#)C(VQm;30QCMR7ed1Tf1ODH3uLv41Mq;Ma`UJ9b4 zwb{#{$(PagO*n_nmb&JuqJw1g!Q2PF6(I{qkh31sH_O8$z1QQ*%z-Rn2SgFipWCIn zWri8%XJf*y9`lYqu_dij=htcT`b!Oc6!lK-SN-)jIdG2Swh&eoSM!-Jez~1pt!-72 zhBK;IH`LvzSEs!fZM_W5frbldQ4%Y-BWwW&vLsEk5cVK%($9ajagH!~+pk7#HI)c%#U7Fw8_KV&8vVy`y(YTbElkR$uO-qdn&&J)eT zMn}SYfdid9Q@ES{%>0Uz1z+ya9ZNb|Fzj5u*y3D~ne7pCtY?Heb@ax7{s zt7Tj2Y^mbQ+}N1y({nV)tUd8yt~~g~ef^I5FWJ-G8(q$mrj!B#rWypWdZ{I*UaE1M=TSd>w7!%VX|X-|CN^0DFba5i;oyp${jozvsTtPsmh zIkqP;QxZ^Ar79~n5TEr-QRpW2%BQB5iovyb@6=E3LH zGqsI9(X|Kb$9n0Cvgk~!$FqqqToCN~IoF0snwc(>LP6BWzg2;D+n$rJ72`)<9xG$P zjDR`5g|yiR@Ac==+Vt0(*|*CYvm=aLa%JWpO#$~R(Zt)12Bm72{ovi+UJX_0+2vsD zX;FevRrt=&j?>~D%RV^A!P3FO7diA^pUA`uU0|PAywlpg*Y`?lr&=K10>W?0BVL7w zUged`jS{FU;#nN)p4Df}+rpyPcv}lvyb`m0UwWTR=31cMM&uu!)WsS)Q`CN6kJI~$ z+aEKE9yBl;hh|6~n7-cFPm;P^(>{9p`sOVp#2hz+M9s1s`KS(bZ&+PGe>&~xN{1k8 z>Vc)0$sCE7bw|eukQa`7)3>(Mbh}Si8?x@k`-^+n#u8^_Z~sbUr`OD`i{Usc>#gHa zk2UsdhCB5+NY8Q2sW*%+8i^G}A&cOS=tF|5ybOu&=A!ElL9h4XlqYA`a%;C(G45B@ zvdt5>giVC#;v&}4Y!X{fY1S-o3(QWJ!@l@=@LNT>@;1f9oS<*n_nTtPH>kER=MK@? zpJYb{8ha%2(jK@AdE}mtGE1G>SMa|k)Et7!s)rYt$~+Rvx{TeRTdhGB41l}-qn1<2 zJ8weBcs^(p6T4=8>sptESJ;-Sn%P}PZwflN$T2z%ucgtpH7CS8vpYt$W?5Y>v+V3X z)hU_nZLWZ_Rpd6;?v%lOy!w`Mc@D?53~LP+-%Gqjs%*aFFuUHQ0?TLN6rF3*mO!5* z%ZI^z%TmgD_Y-Oz^w-DdYlrVfawGIcLrcr*WoLTWx(l;B>gg`V%Phgmpezl+Z8>~n z&r6v|ue{ynNqR$BSSz`_c$E=1IXY(_2N#n@^qX>j=+Ez)uck(gP0K0~a9nsO*aeoD zpl@6gIi#C05ma6D_U#dPA2#B(YcC7Z?%LIJSvn;_zn8m@TqCp>_s`&>E<>gldot{D zl)aMFaa@vxLjR$c~W$vB7o9jLQz-txYBl^j*(V`1uP`AmrAz4KaWvqa39eZFv8 znl$O+VJ-h+6Y607=$B5Gdby!jh*pwI)8xeGymYiY5y*V?xFXINXmZ&+ci<`#I@@@% z!0YVxI=5Ku^|ctx*?8Ssn>+9=|8bzm}6*?(JC9!|^>%G;wP(>k)svprm;LK#elEi%qyK?HRd! zft{b<>w26QaM5zdI_O@sZmG{FmyeNfR8r7+GNky`+&b= z%LCpUb7`=&C6>}2gMFSS9EYEW-Bn{qcS?0Awl#j--n=bTW#*3q?O7vLsc>`=y*aRd z|0*DneX%|L2F$8lM!a3LS(b@f1}d0vh@X<^+n)ZHXhCQ@Rf~#vr_+acB{UKcGaQEznXg}!hSTN z=W|?9Ni5D(4P`*`J%>Qh-=;(uiG2Mszlh5~QJtFh!6V z`Wd=H09!IrhkcO?@F|1pn`V*)nQsm;l!D1qQkrZu-2W&OgcWCoW2qA4@GFPuo3~TK zRk{D*N|46@E-!3Y09|Qw+B@=o;J`+aZ?ObBa5zor>;%N@(CJflSUW55_7^ksM>Ex6 zUGtOC7h1QNlD7mQ{>1nP?tiBc{eTprEP7YP8{n{CbNs)g5Dfogt^Yp~Gyjh%L^>dW z_;3B+Qivl>cQ+*E71==*fqcGvc=X~bSdOefe=8}gnK=2q!<|?_CL}@zu(@u#-}!q zKCjt(9cQ>5fsq(8nN`aJ0+oGylPYp4N5_)uqLS$~r(sEB>er{ZEGb&(!lh~!l4d#5 zUeQBL)sYhMAL%i`pk_rfpu4d& zjMwmLj}{*HGcY(4eoysVUasa27+e!pUVfN!T^xA4(c9NJ+aa*Kd)uAQJR9FmHr14g z@nzZ46wz-SX~VcwHoxUDx-`wuwFx@R#R4GUT#&$temxxY0yrWes1?_T3j>KM*qHv` z_wI3gAtdKF4|pbZN$3*pBW8RtM!&_4^cwgC7x=_Ok>|53Zrn$0Ko8{@9{Ih&xsqJb zv8fCNaws4@!X9B4T%XU9^XA9?Nc@{JU`)H_iXt5hF|D?s4#{1L=>BJ}?|xUIj~fiu znav!{kHn-0a^D+ote2z`dmkK;#-iO%CIJo>3T(j#wxTVk6cRJacWP$r8t2KCGz%|wi2YmFfVJY#Lu&>rSeBd5Kx=jh)8G`Yjuftz6sj%_*GP@f zke$^aF#B@CNFy-Q_G_kD@Ha))m_=^RAp!YMW$#QF9(e$S5W-w;DUUp&EeOb9CRh`i zvxzNh0s_nh{}Vz?ii-yS^Vg`q*`tP^vWGC2Z~}x79gUO4wsS_dyDCz<-5kH7PvSD90zak-9Iv=O$Gmb)NlCjV*;}D-pV_00zR(oJluX+gb;J ze6VY@xHQ3X+7;Y4QNMxu!kX+8E&I6^F_Jsl7Yd&-7Y&A�J%VkWA-_B*-QB*d`@h zqTsk0EqL%D?C3L@18oVTxh+5aohJ1bqdob=k8CLL=Rg|Uv*-w=;a|jGDBLSWDk3g( zhg-oCJ2)Oe4Qp!P4I^w{nP-iudNuvsJ3Y=)BYK2Oc(qS~k$oBP)nZ^>NUG}`;GTK^ z4pP*YxF;;RCFQ6GcF2j^MtA?U?jyZi*h{o@ZhQt$IfLi8u}fCM#+`^MT-5Y08KY;< z_%)CCwI362eE1hZ3^qdeP86h#JYZ3jw}UjXe*0=OX)f~#-9Pu3-)}yFLAnSckowraGF0O z-E82W1;D8pQJ(1!m=7xscEaE_5hGosm=7IBQ&}gbwh-$( z`1Yqnea_7wZp@GZ&hc{n4}2|TSNsWQp`D`N0|YRGUd2UyGCCC5$qEDENi;$dAnz`K z7(&%a6Sgz{_}YyoA7(q$7Q(R(yvLdl?s-Tyh<6og2N6==)=N??$osz-;u#QANUp;D z7ef&DprBvH+JYurr;1_vIYT2%Slc%EYjFJ+L--q22X+On8fcwD1%k1{U*b zcdqKuA6Sj;5~7LZyReqeqYH&4i;~zQM2SSZO`ad){cYI77$M&jYO$WYVMem*!XU$h zi9*39iD8rInwLi;78NIs2{7C-_^VHlXz8dSO*fvptCV(0!X_+kM@isDF8|42#fEw; zgyNJ~7kW6b_*%>W2Kyi(sVGqx#*`t3Mbkd{XP<^h{DFL2d!EE~T&``>Y+QRp?3zKv zA=N&i(@5g_zV47l9dIOh&7k9u3US)oV&`zREYsQO!7A43KWO%|IWt--&$Fp%S>bayk) zkD|sDzItz=>3w@8%+w2tj+nz!Rv@S&yriMA;C5~?pQVfSQxOMygj3#FLhfb%LD;3c zK66g2-6X6{j$ispHdWy_RS{2D5^HQHnz`%pPg;bhz!7UKi#08ZHB}{>y~PSmasb-H z5UH_?ZQFNMIZiZAuRqbi|>wKw=0Xa1sYv5wh19nfdeBh>po55gRi# zpJ?PJs!*(mgd;Jrldq-8g$$4t+g0v5ZGK!_Z-Mkth;2-i9)~bj>H-&pAO>hq_PYc!k5SIz?>`r(YC%Ji%;Mq;~ ziWB1zBK8d=X+@1;i8BiUzt!YD8^5hneRp*@+wm-3@htvH75niQ<3%J^;d$js8KSog zfYEu|cYpkoIX&-W0~fnb0m2N5`%r+Z8lp+m&&E$DBeCZa5wu}G|5=D^V_Ia#S^1~c zQK9XNIC#UIV$&yO;3@#w2CdlcLm0AEQlU*7a6+|8rjF{@F3M%GkYLFl9TKpl`9tQ0 z=L;Q$h$$5T=q`RP?(WCo1A1k_V*G9j&@*bUzd|#%uPSULf6O@s(R|=`_pEtc6!&fd zvdzIi1&#iTA(Sn;C=6W!7o{&Nmeh0@ncZ6z4INwMSW$?7!&3U+tAi{mTmd7a0c(~+ z$k~EJt3>i0)iH-aP=;dCieddm6yRGoxciT2R|gR76QVa5kXEH5EyV3%qQfW~kDlPT ze*~AWzH*vwdX72o~*BDq4LYm*3PzrBu*8~PeC$$!Hw?MYsfd1 zN$B9fesVIL<>fk}{=sZ+tyLN&Q=Benp&^4vQ#>tHc$V|77E`phN^12}nbL|b+&ew6 zw&$!E2Uo(k^TJ*R4c7b_;5HiF#%hl?QMF+;jV_Pe*eZ9KYVM44@9j(dp7PlxkKE>b zO^an$srzhM-OW3%ACl5}vz=#oUS#Qan|?c8?AfGp=U$vkytCxS)|-?#Nme3m=9yD8 zysI>^*>g_&!$lvMq#Leek1?R*#p?B!6WY zljXR3A=!_ryHH3|*XNjrsvP?~(@D4WW?CyBcXtME&#bd**AZcNlTDwmOVg7=ZZtCO z8JMs7*il?x7eirvEZKFuPKkk(Qg;(B=g%v2$|XgHde^1zL$_JkzaLpSKb{$?vFGzu z2Op^}`LVc@Wy`WwXXSA7zk7Vh&sS<|zPWI;xe*Qo?_MEbDyPyy0n9MDwXmzSjZP_E z+VNWFVIc*Htp&GzXD_#}@w=U@vdeY+1;*XB^N&|~-N!VV=)!YT?T32Iu%}3sbZoAL z)ScdJr~MM!FEZTE_RJG!*s%u#MiHl5xUkxs)jM-MIq{vH>(9!mbP12k@R>l=l6Lcr zLnRvx6Ot{1Q5BDsKWN<_ja9+y7g~?08Mvt{vCebKQ%F##Ev|Fas!TgP9-W!E>U$o~ zk#jcd^(F^z?u?bE)w*5u?b@8IU)w4Dw7C{#MW$S+-Na7!S{^53p(WhBW;EQ?&zsMQ zhFXuc(Ug-1*4mF3k-6AEYnC=0`yY*1x@Ws3)T)FkS!q0}*SC@$)0g5l?}^=pI^2xy z2{M>0_GhoP+D*OMbK#3%Qe zsf9Z(3%r%OEV4bl2|4$Vf=i2fXx!{o8;+q+e>TX@1pCY43&^swU z)oc1S*_{t}GtUuSPmH>rFPbi=jLr+vQt^+B^ilYOl6K>P9-W$q1v*l{oOsd}+BO4;!WO7_#Iy zBdr(Ncr+*O+wJO2+0QwY>4=v{gasu75V%`4XXsAe5DJ%?kat@mluk*Kx{rCqS&O^+kWsgS@H};;|awv;RR!K~YQ7U)t z7YxcgsV`dR1AKFhS6 z!;vx=ph=T65iW8w;5SZi&EBwyR5Vt2`@f<;*ZN&rx#krN&hmus&KD+{j|{ zdXBwcg$Y~~p2@o8{dG*Dpvc$5ljtVS1sr_Jo2RQL3HRfJ`c?^UMUi4ak$@B{?~W=M8ZlmO~;k6&%cHX$5Y#PF2<8*5hdR#&2Ee4 z1!-FHd@j$2ic{k!97ohVj{QfogZ@NM%CoIeQ<-%h6PmBai=9H3vy*xDz22!xyz>Gm zvR)5!b6+%^&OH6qxzbH1uj@AFNnhu=Ugi{77>aDkN?u;Y?`ddO>#H?R^M@e`j)Oyk zt+i|#4b4a4(kfj<+=> z*6tS7`3@XK$}>TA%Xi+a>jKNOcFz=_zl(rRA&Btf^@ZR%`fjLW)0mMwDzkzbs-)tq z_e0$3x8Y3rbLc;)C(W$=i zzA*wM{9a4poln-Zc7)XIB~beU371=-QkoT|#k|KPd}S+jmfIeCUPr0FBmMFFJf9IK zN?R7Mx_j0Ab}H%OqWsVne1`XBeSdxU#D!W1cT=u1yZLlEm`R6I($~Ll!`)ziEcQ59-SB}nWe;!bg?1ppMl>74i z^vx@^#FFQncVF4u6tm#%!JVD!67ag0G@al2@*jsW6oKAHFcc#lQS-JQah zScA`-1Zx%dFO1s$v5)PsTw>H;uKY6!(Vv}T@?|atEHpeMq!bvI7;j#8lwKj_B!Ind z9dX&W_9ERo^wS^UThK&2=6T3GjIAG*w3polG-;nMCMkbZA6>o*;n7!taan~VIoc;^ zXv$?YiSLH25D-UpO9 zR=z3_U8*v|4q1|OX^wtJ`gzY z9?HmFxGE?D9T7cA7ke^NheXNQPtHf5s@5@Ct{d+U^aO3h5lMBd3Xs4XXuTKX-5w<+ z9eFQ$N@^vK`|XWj_;5XKN|)J`f}V5K_lBA)O(>aBixs;N@^<4IuufhL&q^@no(H!^ zTY>v)?^9Vt2;}Sly+dTmKvs=W7v1yE|H)R*0_=NJhNiCf{C}!wV*H;?@&8LjlPX}( z`@i*n+xH&Q0C!UwewIz?3DPMdnrDF*fb=I$lKM>nk2nz0;ODk>xqWD}dTDaJEgjbL zC&GWLSWFZYq26y*E(6~f1ztEek34`}*h&lm0h#KyWV5@G6=>(>>kaqEOPAp)i=FZ0 zh12wSJG1K&&>Ayvz5-Wymx#E`OtnLr92p>K*M=Pdk44nB39Igu!X6Bg-)BgRh(f?4 z5+^)IGt@}04}BlzeWfytsm-yJ9>2_Pwa@!AMVDq3fwfj-)?dh$)gaGj;6J+81Ql_b zuq8$w$v8llIIaLkD?XC2Oo$M|h;l&JTG~f(+^9puq&8452VG@c9xSO@4!%4yqo|iX zx2C^VVl_ym#<~%Q!vnSaux4dbs@OyU+bYQtBjAD%{;Ut1X%O^8G+pQaJKAJbZos72 zE_@mjil}IPjTDrnpi#MhWNlBmE?kxnQW|A-3Oe%wd0!8zIn44 zI^?F+4T(+XX;NXN#NBmd9QmZ1{)ks-7_yL)SlysfsSQmJ}nBhLq5y^TREz=sgj;Ji4$2I9|?K94i81L z&Z&&T#5qOtuTj6ruu#L`x02njKmziympHM|M5=5X_A177DPeGp3BK(i@3HoVx*r}c)U-PQ374)s7k-R37d|ARa{SA=Mpv<0grXdxcr+M8sfGXU%*ARFXd}I0JEcl{|wGTw3d->5wB6 zHffbFt^UtXJru7o=%3>Ec7>;h!OhtPW!_o2ug|da6H&mC(${C&`3WTeQFlP46DSUUYp%|Jg9@`VK!*yl4iYqh^jGjnSCm~tpQeYi;Tb(~hw&M@8 zMXTV{HfmrZEKS`o9(+SmAIAViyXablqtVyVis(ki81HJ8@}Hc(h4_zF50X81Lflo9 zG8GP+kpShf4yojtYM)i=Uov^EI^SfjaLhfJ@z!y^hN^JXRm$#EalYnQYNsuq(XQoq zFkWweN%+0&v|HrB`qSVeRcg2Wh zE(tE~Q9{{2qHQKK0SQ?OHpGifTniL0p2bJsn#pbDvsK9iY`YB0xaP^C6c=cVvP|1gI$8Iu>#~H3<)k2U0S$>z~5C*31G>vJf zVFD6la@gxH#n>5LcKjku<7O05M03p0ifZh6KUKQ=T9ttvfv_)5fUKUGN7zMr#^{FF zHG}^~1d_mu@|ueAJ?rI5v;Gz>|SW6RhlFWrd27>e5=b?A?-ooDvzU3{3z@! z&OD~46#Jj7?){&v4kMPVfPn$9&M}yYl_kuO`74qZ$`HyGGzv%BEGE>Y9=I$5u#(Ft z&I;DPDQBL`FtpVi3j`c|W)J}*P7^Ig4L_F#K)g+QJ%4f(67tEn+o&qwA+whW0kV35 zO8*%*)s&p&JUM43zSqExZ1Tj4gHj9V^ogr_>2MXL4#h)+O4rv)@a zTqhC!09ifnLyUG0>O8_Si=j-D&Hdl049+(2Pw^%{x4ktB!`${5JlGQ zV3J0QSgywtQEKu@YTjiAOk^s$GF9j}^#<(q`)u|5?Dg;V1|)U{PbWi|ec{PrmF$z~ zstIKa34vXdq%y>dm}03cAK<*xq{VUVw>shs_H$YWsWCmT=S-arDL~ zU`HWj$LPEToas|R`4gn0iDK!l(cu%4+=o~@lr1HjcT8mHwis^h?6CkhS%6^zTTw_9 z72v81CZLMIdZVK#w#pVqIwIziJyqP52`M_CDQ_t?Cshc1rfyNf7l@5DY3_-+u^}3B z5KpipF0dh@V26XVBf{Aee-Fo(9g1;DRZKN5w40j7QFap)h@N)flHk~iVHHdBgl|8u zFBLhBK}4IJ2b{XB^l-Ia;;#*d;TjSs3HN6dgIltIPfCiqoN{l&y)ngpJmKaFa`U9x z=;I=IsMcoq9a~n!UMQo&Ty+T8hW>pk~XC{)+E`~;>)iB9F#bdB&NKaM>~)!GH=$zWC=p@QVBsB9;;9!+5!YC z%1t=2(*m}NyqA9fvmx4A-F*ewO8RKZtI~}Qe#eF`)o!6_B=QPc!k%X^z@b`b9_jeB zm17?w9_Kxh&jxw6YQqW`SN+pd1X$Z2-cQF+52temu5ll6($L#H&k0PnXJV)$G35N@ zBn$yjxjvg>@oRzj>!JU->RPyuxap{iCjZM-clgg$|JKv+&P6;0=JtoR`|a&k*|d)5 zT@<|0`?U8=57QdBs2OS#uL7PY%d{l>R1sQ!ZmRU;h+8g7Zr_`?CCY9eYrpSPPIPTA zvsue#-+Mg7-5>6mfb@b)c5N=RY0G9mYq#$+31}8~(%#vjsSferA+G-B`%?%&A}M~G zKK=O|LSS>P)JhMVv9#&Rb3G6~AIUxIpxGi+CC%4mTAK7&(k(wnsqjc9y2TRSbzS$4 zCbG*Wx($W(=VkxxYY*~${AY9b&*tHuAPgpu0T|5RV1Xfg)PQBJu%-2h9o>Fjo*%<6 z3FBWzAl-L#ko1(|_)jv-dG!W!qzLT7iEvNRl6-Y2>0gT zpMgm}nSt?VG6Tzr%2NNI+Hyja+Id;*FoT6~h=4m1TA&72Kv(-HeAnWBr4^Q7nuew2 zA03Mgj6aF%inC6A09(DmfHqNn6q9kZKxTa^ThO#eHM2dE<-yzOxW?(&#^@OAvKc5x zBaYx)$MBhLQ82UH^z@R{`dQ5SS;y&l+v%A+@cmS%C-z?-%2in`OYb|Ns_DuFOt&Y` ztU#c8q_8Jr2|bWz!k|1Fz^iZP39Iu0hc|$%Z|&p>ul)){H4YE?M=r@=CZA&UufQsg zA5hE$Mm+(B)kKapP)HRlim@x7GEqnsZB^>?=VML|m^}gdaRCTZ1gDI2z^UUF8YUhw zG_oLo0Ve494CSFWJUw$NkSP-U%ud0Wo-sgaP0>T>HW2fJjjZqz^WU=|QF`J7a#Epy zB#&x=mf$VBWfGV*uJeakhZN(JGmpBJ;S&mZOCT+QFd++5Nk~2jQwjzHg_+hrITTD; z9JyesjChE}F(C^h#Ppo5K1yN2w?BcjcI%ojfE_tvX(5K5?XdpXSRr`nlK%H!Fykx$ zTzxM8CN0iSSY&gE_mNZcv@-Ja+!mw6tf1t)fIxP7ZVAt}TIkBQz@_$jQqE~CG{Nl3 znVu4VpS7-SR51@9%SQObc%^dZ_MyJE>H-;6#OJ~0V+%Xz>Aly7m9|Vyi1iIKJAV0~ z*PBh%Hw#wSKElXZIdHbAI`S%UXQS4}PA!r1tzw6jl&D(&bpAF;(d2Oqq-}G_OpcO< z6&ph>9sUngXUlm`fRt7M)>(7NE`#l7nQ^$A*NDF=T+5KLUjO0abDQr-S*{0h-19XM z2De?i-uCynknNKsE6oVfSM0;2#;!wuJb`IgcF70a0Krg<^C;)9msHru%uY&Of0HjhR%J-duUIf5p<9{*n_Z$dZKhgO51QPm zoJ(nS->tFe2-!)z?g51P>F~8_Ejt)!(V6r1yOliZtIa|og7aT6i+A8eNky{KVza9^ zU)ihRFSo1KY5u40A#WTOr?>V%7T;`T-7XWJ=@%3#S7@f($!MvSb)4@{&y%e7bd65) zqa9UJUQCR(1kYlf(MWOs3WPxK!u>?xn#^>xL1_s8Y)8d)sW)zle;(ia;&2_{VB3*9 z?+tAFa=W9M2QAcLQIN#A7#;l{O}=^`V>F!0UUBe#D-p9yYaDEKZzsiYy~=Ue_*1jC zJI>G0|1s;iX*=gJX?U6O%T*?Fkj}OKk32sAINf*OL=r#sR$hB!j*370%^{+sIIe(m zw>ERu&V%r(e7Zm1s;qcV(|NtVk`3_McHf-u7k_76)%(cZ9%0619f8Vr$H8}-*P%_} zK1pqbaCh|~?(F_s_vWE%ws=VYWa71>qHd^>d8yanoCeayV?XEC%K-Lp zo$P5hEmyU-=ugLMRoM_ukwRhJ2;28%+At#AtZ;oGr} z3%IcMEKif2N)Q~A6hUIyADVxwMky>?dr`m_Q{>6dz*%sa6DYM z(9ckrg`b9)b=Q`dwy36EttSfD-lj|UPnt_!9;Y9x8J5NG^Nt$XTXn=`> z!h1Gzi-)~@8gqG2XWO^1TlcALYx6M!?t|-IHI6j+m3Os4YptyCrPqJydjInH9M}vf z9D8KH0N+j)4UT|#_42GZD9)$BGrPY~l%{UewrlS9yK+AEW6jp*oDk+!d$G=yVMn@k zZ*)~V;DNsIA8x7Ly5qFWXCiV-! ztFvvr9ohI?haO}rmg8yYPFFjlU5*B?_dZW;I$$yA%f0S;4esnzi%|f^FZGe5# z;Qv0^!-HvUV;p?lwNS|ZWwgHot1Z<(Tu%Ko6MugZlq4&juFU1jOi?Q?$=i1nh2$;s z$v65@%PT$c$h(3~cBK08>?1otOG?8gI3XF?s@QE`9fD1IG(NcVS?cN-8&ME(@Cp{7 zXBL5-1E!{D$+cC^nwtmN+3)FOLayiXAAOsCB7 z&eDAj?rCbk;i7VZDBMS1sxh-Cjq9Rf?!tz7+R9aQmM8P#w-^<bXXRaO?>GA$EZ{{S>B`2Z!2^ak2hoR*do572?u3J@t$%EuI3Jr> z1)G-TT;JO@q+lm6c$S7&tQ3q|chRDp&YzR!WJlA(M^!!)b_pr9-xaYDa6X==vDEL4 zE6K>X*JqTLASFb9+4e0c=z7|@E**-H(KXDjsMzJe8=`Vg-%}*~<%itjE3SahyLqh~ zI`R-t?PPA0Wybuzt|4n|_^3}4Le|`>%76*9F0S+U9O&#$l8)jEldxO=3VwPYujr0F z$ug;aajrYt%^z8?>Krm>7*wx?-bjvCxP^M$Ip(hNvPf8sJXj?Jezgz2Zfm(qhD87F zMZtQ#t43q6s~t2St=jOsjqlkkeZq^{cMlx|mMlefyOf(*_X9y$J?bzqqAEw8eTtNY zKu8UI46Tgz=~)AFilN2eXx^j0PHz`v@DhTk$(h4)V#Wf&q^#~LM@O+ckWDGlVA(fT zCFK2_EvH}apxuKBb8Ii6XJ;Q; zweP{&=4rQ(@i0Pp>#Q}4VHil6=UC8S@9Tso=Hq>vHr#+)#~4D?dADEN*Fos$=#_}g z=6%D@5sz)4YaMOY{9HDwg>pl}q<95X_%| zm$IAF^6E8*{b`jzyK1u0`|1@nZfiX%CGI00nLW4vFf7G$@{|1|VdfQ$anhaPvav*1p{xH z`Xk_S&_$C6IO8!iSSu=;(PDQ?K1 zpU&dWPaGhvn_^10*kyRo`vnJNadA`}lQ1FYh6hY{NHZw~pb$nRqgsv4sgEp&QK{(E zL5P|f7c^j}QLWk;9yY_qlj!|NtplV?V~BJrH+=oWCrCocA*22h3?f=3)Dfe8Fu(x~ zq2wp@3sf?Rd&oyKZv4?0VB;C4Tc{YnK`j>WRd#{$g=V@CAh!EV-&p z(2Z0F7n%eiC4xdhvSLc&Y0_ERlpo%;w6t7JjZZUQk;kzYc9)deh|)WAdSl<=5|d2| z<5W^xP+LFIA0NIf?R-J~?|}&tKyb8_eW5xA7?`l({C^pk_+LX2{|!vg>l!#4nHoFk zGScZf8XFtw{;y#Q-TzB%lXE;EH2QD3LHz;(c>?6yL6G+q7VC0JYL^q233h>7WRPKHuQxFaVn;#+q z`AnVPV!Z4DiKrN@3gO`a@F%}g=Qy5>0MWb?U*biZRt+trQS2%2+P zI^4HGP#GFesWkQx)2U>pn^GT8OHn0W(4o}*M#3AsrLr{GA7UrnGTU1-OO&S*SsPWK zSHxS2dV82)fweuSz3cD#}M#M~o$p2BrFg zn0h*I6{7`}NfdMLw+tT?X28cS$h~;HhLh(blJN^OYV4npkdfIW-ypQLh#ZF5;lyO4 zI+az9UW>Z>mYtaafu#iF2?PiHrXU<96kjR0G#khys6F9|8vMZ0Y#2Nr`JM`7h6zY= zfC&vOtr?A9=wn{61wXbK1&tXAwHrFE88wX=kzb3)KN{K<(g__S#(GqqioTd4T0g4U z!ss)Hspa2LA4TzatZbU#>o0CVLCJow@geX-+4UR|=$V3boFKSd2?$1J{@~Lpi>40I z0=T2MS#vW1F$pCOQ;Bf_xL5L!15bI0`RpYu9FR&)2wG&o&%i*K@!i#+d5x3*K# z|EUc@QXTT6HGI_s!M^eN@ZesGfOTRGjEz{SjKqjcb1~%Y=JV?i`i*gg^tYf)sf5hs zBjB9gQJtzoeiwsxLsLUY8g92ih?p5%#u>9^T4Td`AYIFcm{hpi(_~ByqiZ0Yxs&eb z*O!^|S*7Gtkj&x+`naBt?h%AeDo_o&Z6N zI6yoHjVquYV#JVbaQfQ0Ksh%0$6R+%Ff9o!>i1+?oT)_Ad;HpWNT|1oaT!oh03u1_ zYE;P%Ss}#Q!T&+oI|j+pM(vty+qP}nwtKg2+uUv2wr%XTZQHh|-)~ODi8(WWrXn)x z$rY9Lt1_cvt>?P#^jt9MJ+o#*dS?v{T}ziq`2aHy4iQ+!v()E&aO{co9wVoJi5q zin8u3Kqmo9LP=QDQa3>>uE~#s3Xcm)@{k{_B}Fzq z2+0!R1CJk&w@Fz-ImS2^d^jyvnjav((^t#|wJ#Xrjv|v96Nvdrkw%FJS_`qO%9|EK zium%1MSgwZBDXngU9&kxc&$-GbRhD-U;BBJ`6ytRrkYu|}^ds@Q$EJ4u2SxBvfa0I`D~(O0Nb0uyd2Wa$q5sgh zZ$M2*^&#?DlNy_-AfeYL^;j#7Pp=eZ*VX~zL*BZSy)?CY%*$xgJT}|^Tt~X)%=mF4 zWh!%Y$P+oKD-;SNjR{;2G$h+Gq|qO3e*xV)kIQ&#AGO+8pMsc&-@A|*_;nzt+MKl%xP60T?_@h z5Hyvw9^uo6HW#r&Rn?{nuyfYWHW#rm(GSP_x@|O+ZTusq;u1abh68$3L!;Ytd99>S z3C=H(vPG=vT5G-LlVI{9ox)3?`J89GCY)dbFP-wG*l-y?X*p*vF5SmCSBG6^5PQ)N zsrp@(C}Yp0G|zOSh|Y0TJ56LPZ~k=3ocPEC#Tz|4o#gLFjpF$m(G6`yi@>3nf8vdj z0pTkIwFI@xBZn;bD{Ue`t3nq&(nabs;cX+Lda>keD*<2O9q;b0mBQg0VqkPCIn*1nW$_Kk*a)O;BhY^|~Rfy~ntR1>^ocqJtI z1N|sVMSwG3S4^L3ygEK{WWbub94w9fubB4T6~U_C^|T(Pg?zxkot6>%(jPzkXqWhP z&WL9f@g?kQx9~MS4I%VE+)#8Ck{?O#EW_MNA0@+>)vj@KfO1#>K5^j7P@s`1oQj}V+LJQV!hY&F#_G<5jeiEV zXxm)y#z8}85t19GMCgLC8zxDobjP?3*u-zJx@#)=Z-J^)I`oomz}RgpX^T|aHFd9{ z)3|U%$eQPe9WvokvhU2%`Ymv4f7iu6*c^eEQ6grYKzFo|GxCpeTO+3;=E#C1MUTMJ3sJcFQ5zRr@4m)Y zv%4dyijKH);z(wfL~)O30M$`NCiU0WL`7vfu6P%LgIYVT$S55Xv2s}Et`ifn=BP3m zU$_s;Np)E`@pv2usJlAoZGKWVBVDa`~EQT;ssGU9nH@d~=VE4XQFC?DRBVUQJ zue#KaSlw%Jh?m0Pk9vag`~H`Dk{C^7p6es}E%DNIYqXheUY>wdghgYG6H(rBxK?G5 z1D1Xc;=dccl5N?f11E>}AChpb4;A*E1b;U~IXCIX_FOgLTB#k|bdm?IGH`5Hj_f-L z|8B_A$kh=6?9%?y5<(>q+hkBl3x~K3FP*xDDCyOM47XmR<59d84J`c%b>>r5!dUL{saD( zmSElbzP7V#9|E!(=ru1c75iL@wL3LpgboZD;-&5Zm@Uo}7XM+94@}es)x;k$*zq!`9UDLzLyl#^VIp78t?tl%}lVqs5K0$l^4f!%D zeZ?KZ8sk=b)^2D1yYFPHUWc{4S!|p)U622^UOb`Ox&N4${@Ji_GgqBdWu!ZCzsHTG zB9@kF2>1JJL@(4g42EQWF4L2aiRWPpz%CbW%le}u5J5|O#d$C?Na?Jc{T=qrvn-Xq z+rwjM-bZJ?`)hH0KPyb9e%qJdNav%Wt!KMO{vs#mR&Ed3M|qR}FN_QZ7S|y6&tCT- z!mDzD8?KI*_{-DN_O@@g!%K~9_wveCt+csjw^Pum;#qcW_p8-7{@(1r(3J!HuNQ&E z(o~oENAatE=HhyDOplK>D<~Gl| zXy-Y9M}w>{=$6+RHGc6?=c(8R_YC!%Y-#0nl^ySUTw+eIr^|~Ncb2nCZVh6wI`t`M z|S#d|UGt8uCe7R+k= zw0GZ;acsHwltS_IV(p_xs*Pn#NiOuQ0)e!r>tv|+aO2yBZ9&w>ZT4QrMN;mw&CJq~ zC?>U8l{%kqxhohh>)Bh<#2lwN6?el~H7n3=Renf3MZV z&sE+4uXnw~ylVZrIgv zJnR>R^GD<)9hEB6N#a^8e4mM}Gx8q3+x1~w6;J-7f6;3?1Zdq)!fS(qx3)bLT(~F$ zwf3&~ef9>zx6m)2o=>7YDBo=#U$%QXmdu|~T=`e_8g*0*ca&#n*>&!w)Ux#~kBQc1_!x%kl;vrwPW#C!9~OwG4pczGmM)}A z!#?koW<5>{8^I-Ay39LW=zY6xWJA5Tx@^R-X`B_UJ*mD;T(qA?V!P_$2cM@B0_S*J zo}EHK-Uucx;__;=J3(uVOo@vI{hL!6u9qP>1>E$|`t@NHDb(=UtTD`feLqOkZpg4JR zVW&Q=BU{rEgR9Nkz~fjBkQ4sE{t$br+Jt&fr_Q9dTgnyGqsVP4bWDq!M&CYC(yPk5 zS=n%qfIz)SWm{@-{Y5R1#LrXTCb2;qCt8-8IB)NoF8|gnJ>FM&EzhqYVoM$Z!N}N%O_QTxpw%WhS z0F8`^rYW3Jb|V+SoI3NenpqMNh5t0Z{k%X)(=>tG>TEOB z%^d9)_iGao{q$6|!gI;SvTt*qx8l#P7;9f4bDxW+PO^7;-jWorRi3~vB=R!NE}VR( zmm;U@kSmtaW$kf2l$7HB`ZiN{-74mjT$AEmyyj^38Y?As72U2$?=&*8`Ce^gHF%Nj zcjIf9WKDOjnhTIUvD&oR_hp%>IEks!xumPv6uAnYPD@pFt|#lF_&zo{3CF7Xq22n` z1zLT*tgt{x{FFsO&PSztSjZk_)2af0_o8Z7{S??PzkGXqkt2My>Nvhmq_aOGceZAE z1AV%D&FUdzA<$9nU`=>52;V=VqGyICw9Q~j$bO&PA*;Sp_v$b{nUA+;m_*KWe*wA# zoaP@qV!6OMz&}sKUsCb5wFnEVT9jEH~U3om1DlT#TSO1@<30CMS38;iRLh7&MTMynuqef z2Zx7;$w^3y9wo!&B_t(;vJcx<4_5~V4-a1t2M5~@57S;>iw;R>Ovb{VD{pZvN#c?8 z7$zL4ZbT{h$ACZmAbRx2E9JC6SvW84f;{MTeddx@)W1~CtGPi)w49^plkSg2VO5tH z8Ok*DD2^tc0Z~`he~}A^DjLrv1sgB$-|Fx<*UHF$$c1QqL(x#K^r6W|6d+MUZFI z&qG$HV6a21R}kG2AA0}0h5qr|LeFfq7|ZxgKG5d{01yVi0dO>Na)%Pq~>e2{^SZ5y>mJ`(dyNk zZnsbwvij#2?|Yulrvs1C?9QH-td}gOnN0ylLtwLjzA0J#7i8L7q@t2aG71x|bgjze zi9jF|t?Udn%X^kn&Z)fLs9}@-04A++l~JKIInAy=!qmVhj@_I5?Aplq3z7`)KKvm< zJI>v3MU|f>VBK26k;Ve)rV3UlOx;S-7ydNzGzB0%Jxc0rq*yUYW>l2qrV$i!W)ce& z%IRu|q)A-;f&nVP7Qh-MCs-6GgT}l|45W&2rhi4pm8goc$k`EPrevrY zvw!59;}mE;kOx($O@*vn|ACteVVeqR^DfB=HOtE{FgBc=qDaAB<)%Xy&wxl}gkmEX zS2U+%9ochcXUEYLaJ~8nd7voZ`V>d+oikMvikC2-I>CBooF|=<*o0#(X=$Vuk3F4H zVOA%cP?b+tl_)JG<^^`VqAKPfjZ3fNM5kagc^Gw8bQO)lJqp0REt@KBlUWe6FsQ>F za!Pe0%_hNb`?B-EX$gpdSzg9qdr9!#4m)dU(pjr6#0mIpVVDzpj;%GLnpQL&o4B< z1pOL%g+WOrGYht1aO*NChLMAcaW6ZJkrzFaVQAwdl?mN7^en_b3>>FGJosp(Q-y_< z6!P8mF@%M;1mr^PH*pECKBsN*jQM$3l-7Y@i=Q03l}h|m#^@IDU*O_=^%o`tMfVUv zh||T$)w{ef!vBiGPg(YN=PgWPZaDKNb4492F)NVX=S0YeknV)iAn}YSbIN`ddXFYb-?pUxEU$YSfaHYE(#! z5-4doMkaQ(VU%T5M16Wa)i4L28slWFG9%7}3QXdGDvauV{y7?zh>#Xw@^vLvS%i9V zSsolJl2rLWE@eipF$G4;(ZLLpK*tU`c{qxt&ZRyW10e)rzwA>Dcp@oS#YU(yYwB{= zszuJI#dRVn@_a|la!NJJK1ta|r3=k+*s4X3szqdta`FbHlSU`mu;!}#WDI8Fz z$HhOtU3tlWmkvD8x9k z2{0C`V2(+%!BS-0B<0gTO#KZ zew=-@BcWkh!wcdN%{b=+lJeF5@3#*|kGIJ=REY|D*bXKpj8D!2SywIZ04e_$A9F&z!CiUvc0CMLZ#L&b6^2TrzQ-Bp3Dy7=pd zKbWn6dh5vQGFbz7s~`!Os^u>olhKPX5Cbu54^}5_=(Qo6C>rzjIE#}QSrw@&NSJ08 zbt#s5fGm4aM|$@g(HtgCG0b^f1cuD&>0ROs?&44*|2{1uLb@aw+-Wen{1N(68`+2K z^5dNBxL4r$E9b!!Zkkq2Q9wnyNSU&>K+|Z(VLH<=nPZgBJHwEaS58T7D>~X z8fuBT|4orc$2@o~Jg6J5pzd*ZfF<4UqNg_DoZV4|{d_(d;0g0GhcB%=oxoh4A@74v6xGkRDBS;Jq_5_4w)JL`|+H}T198v?3Xf+E0Tfgj! zVIGkYCmLjW8_sMwy9r4;L|bQ1yGK6lA1588)>0W9w49>Kw_7_8Ry&VYI}hiJ#h{(X z>rDfMYu^Z-5orO8J4b8^=PVTKau_gO7WxLPGfP~8;9ej;Npvd|n!k%o`JhlR19Ps*UdE2M2oC%2 z%q{0nq1c>4Xu_Xe_zhVO=|n-HCKj1+CjkI!5pgweFTHxMY(at_JE{-gPC5@1R;bXfsm3% z$V^$p#vq=9On$*8c;O}~j!d!p#&}bS9x&t&8gj?6oarMDOcNaGBTh^coaqZ3=?k3c zs4fNsusu%zq*eVqlepbHmAe<6Ql*OiFpH~L-4iS(+fxr!*1U;kgB%X`t&j6WENLwi zGA4IKNx>CISckG;qzcS%M*0n4aUm^#k-_7lDFp9Wpohd!a?;tAb!SD-FlYO|(+R*Y z)U?@Kny*%!(8k44a1qHwa%_g@MwqXoAbcje29o{{JXI1kL4H)qwSUIwqcJDxV~|N=N>1E^4i2M44~+rtxmGZC_y%HMC98Iz~AHiZ@>PupmAYlgnJ#(Z~aNQZ<`ubyV_1bKqm z$WcpiKA{$q zVuM4w!KvkH-*kCs{?mLS!%vH8&U?IilUa8S2>8;~Pz2sMDvJ;T+ezHY3Q7jnwl^Pr(bl zSo?;~^w#y=<`v(GEj)*t*J%3&@$?p+)Ae((Q>*3lR*vKKbCgr-i3AQiH8=o$T(Tjg z3Fj_-l>nX0@@{u;Q(QvfvY}xgwe=bB!^t0bnyV4cGplLIAFj{KIgCg7Z^8Ahw0Ck|H?qX@icQLD zuM{e^f&4Pr(Pp7!D?=7wLRomtVXeN$!fR~#Yp8hc0Pws{+28bfy&@4`KW}!ri;pgn zxgN9CyrWJ&dcA}Av6^*j6$`RaVcX`mK@AIc{B|%2&G%PZ0|5i>7_T>5b`wHMZ@}Y5 zO-UttOape09838R49+-7o2by5Tlx5VSu6BjY(@GSZgYbTfm+lzXPd8}Co2Bc){y1B zm(S-o)b-N17s~@swJr@m`d_stF589&JRRT7k@^@=x^I`=_y`d&z%Lbv_4_(1?W4+T z@U;`4ivIh)i@o-G5_c#6cLv~iML$v6!4t)zw^hddvq!WG5i$CU=@R#P8%CTJOgU`D(p=0Z(gsl z(pV!LxfTEDL5H7K8{hEzGxqDawbE$ptYon*sn};qRox3Tz^-6wOp&-I7k6sv$F}Y7 z@ranAlZwhO8C|@}Q>PPPCu<$ne%bMYE;+@WQM%KX#jJp1Fdde=S^BQZqRHy(F}1lM zcwAONmfI79WBk4JU?F4?yh`)tNv`S3JiAFvak{sIPCV6d(7%$n9-R62bvPz01J$I) z-C@}0y#>&CICY>igeQi1hJw$D=tnNjV|O~*iwvq@$}D9X_Yd~U)TJAjbebd4dY4V) z$N1_8qN&f(LAFgy!Z9vCjV7lkj`=j-<7Famx8y?$O!rOh$-&sQ&2ZP89wc*b*}9|1 z1-hZ{*VND?yUE<~Ht!FfZ-=(u!wh7(q&nLRJMjd)?(C%;p|ir{2j7I|q%66#M2%`e^C? z_kMG>%pplGWwj^w4ABWKE$)JAclaZnqudIX%wcFW{5!~}C6Qlb+e6dBD$;M#Vxl_Q zHcLXk-VK!umXgr5FAM*pk{2s z)~e%$1o5v}{GwoXNJMu=X6i0wz$5$5BP*zwqx1@rXeS=oypz*UrS6B!;prUnNX@n> zNv2D0wGtgeJS<5@xlW+W;Y)S}@%t1FVeE}AktPWp;YaWLaJ6zsy!X{MfzWemy!9U! zil>Yfng)ln?4;k{YlVv}vD>NaVdMa^_omP;7aseue3vPapDi~P%@HuNhd5%(nXx$X zTe|#}$Bh3CJVW+9o1}m1a`3^bWBuiTXFvSWt#s$JmRGShNlR}m32(|0v@gbA`VST(0qj=8sGpZS zeD>cG$KRV^f8H7vkygOdkN6j$R(li2XW0AyEPA(N4(`oD^t&&HWvbu?YNn!eb{zSZEoVpsFLN1X~D4z(Mci#5DG7ho0K z@Ye=nN5Pf(r3#u&rn{;tH4>W+=*g5eEqdZd71tAR1&;2LSBxyZ^{ZEC^_&-TBO;Fu z`z;DO9UO;9j4x})zB9ZnNcruGGoRj5gPDavi3w)vNX&m06@9#^AeVC?dd_nYI^xB- zUO0V6=jqqHE!w#r*!MfRq4{E?yhO6L{jB#rO9xWEukNR(8TW%yJZ~P6;yo4+rN6wV zx}q+#$;0PTd$~H#YrzNOIE%rFvgEYhjr8a@nptx;^aga?N}c=7@8iWR3Fn;5TJd4k;HU5rOgiF=KE21Nu$IU4_BgcLxlXaCWMfhg{_H; zJ%7{vsPC&4DkG1-}uv-~>84hy3g$SH!iMMV^>gE^R{73{VgX5uyn4=!O>^R{-Kh4Y=OBnV$@Wk3mTSlyr7tpM zi-*MS`|J3hP0TRH&XzueK?kCZ$H)1GtU@zKgTekS{T=!DoVBLfZLocG)nza>W{4~a zH-}-EnK^o{r0(!HM9k4QbqqcWD}B_PelOaVl0^Ir5!sPt2de+iwOnc>F4YB z|Grr53|p+pd))75a8KZ1r+Tn+{ag#)8TQgSSR6e?4c^j9qVZ{d_IMohy`C1I+^i-F z#iHIRWz{r$%6_^AP6B)A)O*i67%eAnjwEwvhyP+!P9T5luGrgb$`3D0$X%q$)3!NdL2`+CY2&#OP*WSUrua0RUtdjjt#LWBCD8|U|)g^}bi zvS9UKQm_S@%ej|46Q-=wAUa&VBr?7bmUcK|7Iq{g*knqtB$lom(3(qfA7J`t2X;>{uFfIHx=zI1-?b-IZ3~f-ujNUcdq98Y+@9k z$z(h?;ni6QXH*=~a=`M~FCh6_f>^<7Z(c86+9tJ5H&E>wFMXuzP zLvz)5O!eGrmS^_&=Xl292io`V&y;UlW7Y9)O4*Ozw;gE@vgNy%jn6ImJ=`}+B#5%! z1$}a%Pn7B(r?;EWa0C`ddwd2Tm+B4hc=ddkPDkhs2mTd)((Hb>kR;m#u#jxfPRAjA7I8_ zNN}WC+&20YBp<=PF$0Xfq#8+<~Bs{XKxJqw`I)nfHjeAKUqo!k~Rn zko7>#9kaXs3^xZ9no+dL_xZrX09AJ`7*q0$Csb5?+4CcYjBM*=bS3X5s*$;4j9vu)#Wz^{7L3MMF#HP7CGNHeO0)i zwf5#G#R2d{Fs|RrmX^n8$5JN|8Q79r77A4pIHUfOK{^6lZJjwe7DP(hxxP)qzH8*I zZzO?>%3#u&N^!t{5vT9k_?1lm`DAxCIBps_NU#3^|KDSg7aebnxI=(*um3+|5vKpw zScLI^IjR01Yyr_PSMcBQe~v}8-84`}KKSeW|E~4{v57?M_ZJ4)s3XvX?IS)}YreJK@Y8XX|&7H0!P=t|MM%7zct9Y#pA{F|YnZDTC!5jI`yyT4Z z`trcDb2Hy`<$7Fnaf3rTuv$uy)YK$c@^_Qkkc5wAKw65;m_KO|BU2bXg3(qko1CT* zU?v(X6_-WJ)I}LAS)wA3$}-hU;Z|N(C}AZ7o*lTr}C68R{fKlNqnGG8fn(kSQIPe=gRrj8V)|lBm!E0 zoM(BEn{JgCMK>|KDWe(FqRz;Op|yrNDY5nhtMQ)FNs1XY2kNj)IqZGm~tK|W0rl8nm(rSXoS(+7LHD_b2dwKJP@fd7z z@IJ#}ToqWzv6)ZPomnbO;sK-4unvjEc2g%AR$IiXa}0{iMW8-M{A1Sg5^+!0d7 z5ATqZ6o!g~Sqm94h8y#s_we${=rc}^I}$k43n1fjOor2)xfo#~aEi+QnvG!QP5mQi zV9?mpv582}W3|MXBnSZ|45m?;E8t33Vi)8LH%UkyjaMOvLI`y-twVrf5{WyL5Uq5R z`|WWc&R<7_rX{gbMO%u6@Fbco22DPzu(V{N=6NDQMI_3YDO7CBiXk^_-;G$_?Uk+= z(X*T_((Fj3?CkejVoVJ1`aiz@fn~WF}}#r&ieP^l70Wia+zmIf?il5ahtX-78DX zmC14IRxc1)qGDuaCGdqKD`Wg-LbK#cD4$~*C}34)m#M-2ORu8m=T=Aqfs}w0W(wGp zUs$f%GbB_TX#W|(;)lII&R^3{!+}|`*R7nj$Kw)0=8zb8+mwJkQ7L4}nu%JrpJo&r z(ELV*H8c8scdQC!PGRt)k zqg=LY5wmL%*}a_nUP*qxI0wfsIMGvz>@_Ko3spLs5?onSN&cXCdLKxIaZXWy316yg&6wzKra9M0lgqsXg zf2WgeO+kw2P9F^yRG=W98_MB>ct4Fd%H(lOAbnJx0Nk;@WE-z<`Q9_D`hbm5j;U!4 zn8S!;F(oB*P-p~;FQFtnQC)lZ;|bQi$Fdhfv3(Szg-0N)5GVoN%{ z*hU?F>~+@$j7Bs>KMoRU1JfH!0ZK*AkP@Jq31gsLy+2g6&TKA$Qvl-IZ#y-+k$|J;C;% z+Y<89AhbQva@$Jvcbu&9lT}z7w7YDo93s3t*;r`YRK{LECfuIjx6a^ z=Bx}x30YjwW}L({OD_FioRb~TG$rf6Q&D&u@(6FxmqbN^jD6qmF!-2eq|Kx{Mj9T@d{{LG z6r7XV=h|?Dc+_qg4$eW<>x7K`A2{D;jXRLFUL;i=Ope^byiXg13C(9?HV#toQjdw8 zJz6idx=YUOh7*qo-CbI*_}0stE))8@lN zs!w;?Ml_dz@$+*T;pbq_NWKHfnSxoWU{_w`%{s+X*-9Z7OP>hE0m{deY@vZZn<51> z;pNe{_hgj>|QXHOxN#>}p_%kqOdm(B?wBShtGpVVWs%&L zj*#YC07d$$X!JN0(?Uo(d|`AJ^0JJHIl!U;a%&4c6Riv;Kz`|b>H@KkGT~=J(vP}R z><$Bg0(6a3fR03jIr72?ln^4SntX1u`XyhEs=t5h#0TC_CG9O&`ykPWV8iXBXUWU` z?Incaz332U7siY0)1;CA6;tlb!}w%`HRqvwPKIt9w{hx9Pl9RhiW_pSBJpcQ{qB0l ztH-yF#t(3%0o>W+ZtWiXBX;qxB^@4!*i2Hk*5Xjc1-04~*y1$GmVnYGGXy z;Fs~=CdlJovl6F{=K@B5$5AAQRx}e@1VxU&=N2ws*#DnU2CzH_=HE~TV+7tvseP7J zRM>suiB8X|)vRd+GDqZ0qm5_H41N7(jyVYff)fhCLB+tK7$hPNDGCP^oDZ%65LV*^ z7_x4t&^2rhq)=^G96~mG5=MJ=B6l|$4pUPh)TR(kcx}_^*J33wFRk_`B&R|M_6++A z+BRtvhg5T>O>z5FjD8(M(d&72`&8e+&R=<&iT^I0LtrNi$|k9{`LGn{~#c$7yiPLpypi)1v5X0)oID1hWQm0_vu?|B{9>yc)V?hY$hHdM?& zP|mAP3Eqosj={kNB@sDUh_b&+O0uEhas@*)YlJ;wh3dIIOZuY11ZY%vC6+$b|8NHG z|8WM&{}T@F#feqL5GdbgKH|kl`k$q;1x<6It)6Zucz(V5gKuj`-HN8R5-GI@ z6WGbVeJZW}ZU-~kfg&TUP*Fisf3Xy4b$g(K3DUb__Wu7sgT&7N8#G{3h4p)s`ImVt zpENc|UmEsxuiQ`1d?!TF@J}Z6A-HT5y}&Sqg31(H@h$`b6;w!N60u-knvm)kN|8}MEaFW_g$AKu zpPDA~DN945r3E5(&xjBeAFQ0@p>(~}Q#*3C=zYi?G`BYuq0yU`Z9=J^W(T1$?4DK8 zn&FSB>ZN17p*q4)4avrzko*erQ-rqlj(6?5b5`Rgtp?9*{gkIc8tl6`tlS( zC5L|)NmS#=IsR;= ze2`MVOsHL}L%dW5f3TAG{NJF#5|xJK(aesxI3^6&L^m(ZfHZ_92u*8&TG&8!g1!o3 z)P+#n7QvotBFqlbzpEAmTQ`wCS9OS1uirBIzpDTQTlf%rFOuL5Bf`xbh`nbOh*thO zjv6*VfS*BKDH7xPUFHG-LRmg?FFylTsSGuF7+^gl`PlSF(GY7jc0!3Cq`*g1-$SAk zyU)=rq{ZgVJ*OS;x?3043$cIfSvkm2x3qRTpRkZg90$Uf+ACvghB{_uleMAD9<5Hp z=QT&xloA{`DnP|$hzd4l+<63;&JJV(zGA26lfWheTo}WY*izL5wjf1xfDR*1eZ^J! zY-}U~bUr|^H6*;RC9ZEGfvk4d3&3|>vTB(!kz(ePpM#PNwzs8R-L&lDaXsJK{p0t2 zK_j(z>GgX_VHqo9+r4QrEcD*~Il_LQvxpYU#=4>DG%HrO36A#r@i0po+^V+&?Ho5O zJcrm2qOp^J2SYDx`7V6eQji9R)5DXJCgGF2;-y#|ABOGT0d79SJ`n*i>^P(5RxX>O z^49~;yGx{Rk&71m+J8rGZ4tlnG>P?OH1GNiYvi$8oWBrWX%)PRfNnq5ka6>{i&ZP* zWvYBL^Od>0Z~{oc+;yZ(54QQzwNoUe`ogQdDw2A#cb9Vb0NHunf+l-pVg zjXiJR^}(4;dnNwiUC4jcblHxYYnxo;-q9G?@aX1)n0><7kzTixCGFFS@<}p_eu1Y- zQ$cb6eAb@oyAhl6nRAzS`xa6Vx0Jg4rbhU+wHfzq?}MrO@ZiHMw~{nh(~2b3TKiDK zu7k}(>nRIEyWwZQU4vAf?5E~7wAg-9qxEK%&5Pziz2}J7T}rCzmyf=+`xzgA-UFK# z`{hVDZzIB=fu~xf{!*J)(eR`DS>T1QrMBMX;{N-UZKftcoWlQ3X>yqhWyGn<8t$*7 z<;0!~p)=byv*stGYnX~#t%{=xCQ1}~lY=`T?|qycj+17kEccu5>Fnxxz>zo5WxA9W zD^|SMMyb2c8cKNiwk$2hYV9zdj+UEzozXsAx7UE|u)MxwYYLMzDFp6rK6rKp1ZUu* zqATq1v>f~x_73Bc9-5<)$zq{uiPDy?hw8`K2MHiyW-_a zrs7z05DHtA_%L5@>nUVN*CuYAa~VtGRlBIuI^e}gr@~Cf^)hCj!EHw#-|U!_cxB;c zJW=q(@+oNa*l*)7XEW?i!lMr#&sPo`zw@pemsp9#Xx5)-GgqIEU-G0FmKe$VvFo%f z%XHFSvUXYLrRSincztcvdptI$pf;dyo35i=BG#YT(&4t$h#ODbnRb-Qzx&^&ntHx9 zlFXpWJDugGs4V*?*p+PXJ=*!WyW>^gY|_y>n$czN_!v(l4Dw`9J!kL59M%*hZmYPm zFKj52%5w*Jdv-{#mz`>w8uk{F(iYr`LVkaleat0@#jL{@*Lb>u9lG;pguXCJZrf6W zx%$|F6_YB7zXafvw+y1`2|eCAu3^W*2jY|bD-8;Q|BaLmze%UX2jOqa_(Y1TrF~NM zL-<7S`h>UsQ2^{+X64lB>i-jUrw0mW{v>!i0P<;-%HL zI}wuc2J%ClTJ6RM;T!CK6C9A4y8N#d@b$k*2)i>12NT=4PL@By4yu!QR{u$l{B4Gw z!v*i_HSA^}Yph)%+r{_uc3ZM__T7r+W(uyAD@ZFmn{MsDYNFRMwauKGsu%8(8&#e& zKHGHBYDr#)rVq^d7$3YIz}9%>Px^zm{n|~u{qN7C$l5*Eho)G${kx;Dt#SKGo?O46 zjHhFZ#zAmZ6`7!sb9(laho>>(^QRYaX0975)c4ewojYFVt@Rt%=fRPo?^Fr~<0~L# zb4Zs}(p&qJDzD|Y?k2CqNwTBG6P1&0w_(0KxM)s?8jl$zsa)R{Gqpt@s&~*$R}&l5 zVhDv+8yw$Qs7wcw(Y!@R%ga&qLg)y5rOJaY>aTOi*X_GbRkzL*&5zQ)WN1|R;4j^5 zZho&7pMajpPL}i02&D@7+xCz(JA9>}6D)OKZ6zs}oHahX=m+5QGqzGZEAA9UJ|nde zv5!Bcp3zy~Yfo&bDusjxYF2bxr9#>)QjQiRx3!NNevWVOKtBh)lwVj^l*Q{F&2RY* zTTl_tPn4J5jS(3PhTl`q(0EFIe>euRU?>XVmh7_;ZXfYj<(Dn`n$%E}+)M<&_O5J| ztjORrS2WbbK)KJdKTG+Gj8P-e)3H*OC)qohjNmtmubmB4sWTy7ZQx?ocR1^!JhS4M z&u9}$yFM*{Qn;ndHDcTQgC5<==VKno`9BM@uyh^z{3a{@E*`JyaQYGd^j-TspL7TD ziF~0v#=Mu)Q1ZI;hy2k}=CU;@Eq`-9@IR}^6~Nmv`&Rj3R5kUKPIntBUARAGF1b3o zM>bvef^KB-gs+hI)ZOUzX-MbWc^GM3Da|}v9%+`}l0Tkn-B90xlNu}ZiyRxnT?VPv zi)$~x24dcF{)o~0qn%+98(pclBj$b5bNV!wncDt%e0i$=u#$Gx{Pk`N5jj?v3Tu_T z{C{!wPC=FhZJTy=*>;z0+qP}nwv8^^HoNSqU1pbU+xFBu@lAX&Gx5*CAF*>suFPCJ z_Q5(>k=K*=#mW$8rd=v42`lEonaAR>(I>Or$xQb&n%Dji|8y}ZnQQhoO($$#k(qhQ znnx<^9^yRUKi;0sYvv{S<<5(N`QvV!t^>EFWtqQ6?${a=y`RqX(!Gq)>SfR6o|ygW z@oyjQ}zMKA+Ni^rw_!DdqF{{n23IYL%tr zt718eK@X0=t>P4;wN14*`j0;s!!+-dk)UG-OZDAsaDogXJKB6Y^YeA~n7usfvV-Lb zx3OoGbe{UlMGljqIGx^?EFKC{3K7 zitDJ^WhB*mn)1FSgU#tt!-JyjJRWvhV6cqC22(PC?iI ze$#b#IpANHM-iD7{4Q`<6>W--l&03P{Mr`v;^=ga*2o9ymk!rsm+l;GmcSb2j!Si` zME^kQ+BD5(h5Q9`3jW;?X94jXIIeM~Pk-`t7``4&J+>dm%dPW;_!eD1i$Btc;xIVf5+lbx= z;!3=DP#+P}X>H5;xt8pIQV#@kZ%tg%ptq)W5Qn8IXK$W$Y9XnR<>JubJ=c`g;MFbj zNC0+|8!F8V-sIC<5PD$FgCk~jrbUaoiV=(G@9^2mF*9#MQnAhx4y}0JOxw4=NmI*) zO*`M6n{-^U=tgm7-pwZD+S=i>LYK2P^obWH&N#G_cvDYnqjD{7@I|5PMXOtAt7AtT zS|PldmsRbUwi4by*yCrhKFLrdHDFq?$!gc^eTf*oMo>H)P)SO+j;%|xHYOKas2Xj% zE~Ep?Ru)}kuqd?Na)utv3gduUt!1*Fx@1%@3LmTrW3%q10edvRs9101Xsi7Z1)$@C zRimx8jl*V47doQQXfAJ|wX^(d5*u(^()9EH6F4`I~kA2O5yjerNiBhWV4VC@jO04Rpm~A|%f0@XtBn=9&s-^}TQ*~fa zCQR01G^_gtMHqP|urn|n|41%rd$i;z(w`7Ji)ke$VONEH+?#iCZ4j*F2S%G*1da7u zV~(1w$)Xbz*Q;%TXKYx&DTIcf1J5RUkW@??1?F*x&JUPPH`QYq)q$70`bkhgznp)} zF2)-}q$pYZ0xd2pSIR6dCl69rTA=hp5zL=AsMn;a7Gzkh?mB|AFd?+QFl2(nR^SwZ zse}|L0u#hxns{|ATzVo%uS%X10U{L9{#Bm2rw&xf$;ngD0i?GWgw2OiC@AYFRL)X> zo~xjptsoBM+MJ(DI8XMLYs=h#&c{Rz;h6`fnJW%6Z<1cEiT)(Z%io~x4_A(Vi)Rs! zfE{)ol;AdOHEm|&MmR!uv(t>quM4+f*-*%qRDl7DGvNG&gU*sjlwyb-47|Wm2*ffQ zRN8T-p=CdPmtZ^0x@d?8sStP5VitHLr;CU_IVA7TrP>NXuIAx2|lVl09ZdS-2P( zL<6jOW6Xh|b|J5l`joj2j^+7avE(At@JHpV09#VNeEpi!8kP`k7#6k{7GkAca;d3SK%5{iM<32HF2#9c> z>kSol`^*R+VujMovD-7%FAw)Bi(e6j|XMRQ%AW#RzkWo|;yR zZHBmTd{95UWf25ULipu64Z+**TCP_vVd4F@6FDBJhnNpe;tVGu-d%0%Ih<_TTmXjH za+|*_GNTRih8UFq6k#T)U&fn+(XL|n%C?0Pf!grpM}$-o8muyxfnGbGuNQ4eV$Dcs zN$fEYpTex=qI@cPTH0D9Gp~(RqMT%dJ%l~)KzmLa^P~(e=#Lum;FAw`oXO3}h{42> z@MZ6`i(_;j0L-8O1MWyjI~nYb))hfSq-pST=!(5!pMrGCdHf9I8a z@Mtl1&RCHbrPrH;FdU_q^>_JI>!vntd7q?iBs#K)9ecLcU0`gFy$LH2GtGdQYeECbe{-bq~#YR@>*+faynx&_AvnAt=}>m0iFp#ZBH( z@N`!|o^TNGqvs!UqV#w6-)&vC7JWQ<9xg!liaZ~n^*}?!L_&WCgnrS?1r5Q^z4#z& z6oN^6Mw(8Dq|(v@>01DxBTe(lpwne{Grv8(5JimZWI3$^Y8;dYtJb znEk#3h$Wci8D-ke1xrno4Pqh&wLuuSHgqM>H0>pU#XV7` zB57yBZb%?q1D;2#@b3U}^nd(GylICuxC{+E>!a^Q8PQQADXyM`9OHnVtN`*vQSW4d zdCfZ*2vP52Z;vMnDbeSgQsW?&T@e=mU~~yLY*k4YbcC14aV$M$e$>sLFSYu zo?qP|)o91T6MDb8i69pGwHiU?U{4mEe#L++D+nwCx5Z9`;Pcg|MnEhnF}AUTIw-@8 z5AJMbg4$UFaOEAocMc4;Xn~%ac6v}JA(R;KsQ@3!&_~3S>2W0ndcBwppcci;8H! zOHf)3giZ9mx8lxUxzYx~aJRIA@I7JnygLA~Hqo2lm|NOFL^ePxsOSpnHZp-BvyXIr z$kfjuN=siWQDNP_Hc~MISaIsgh{0_eEyi!mSg;us&_4v$wxlqB+h6kOp_`u|n+b(% z{~~hw9iP#X>A+$6jD{wZZ%fKq!UF#UDG@ue*@Sc5rmFTD)p}h1V;B!7(_3$O?k??( zVc!)h3cM686hSE*d_gV=7E1nkAhX}mweeswWU@A`LL3i&U4ibK25`RtZ$tuR3*cP; z!5taDI03y>TpH5Oz~)yG1JpLO*!8SY6vmLPA)|GWAa%HqZ}OscJgA%`UI?e@LF;-^ zyPOuyl++2Op@!0ZqIMKhxkxIX+=U6HeVr`*0aU$^2uATra>$0+hQu5Hy~j?c11=;D zFnIyKD+L|}@{#tzlkmY4@mZqeLs0X<+uOfa7&#cd2q~JqP_gSLmGpFMF zjhYve%M>4(`xB$2b%LV?#?Rw?a+}kr`NskD3U+9kNp;BlUWdNc{53uuC!6R{Q7<^u zlhYtOTl59dYEIjMZB^3(8D*F*7dsz|J`M+GM*M^w$W!8<;N65(cu~DpN8*bV3>rq)qeqTaag8~PUQ<)iAHpu5k+d{pG`*vJ zq{itlL8o?JAN7BSjz3Ymxd(UNBsSN^%B<9rv0*3&tN1Vf=m;9g6IYXxrFIB3cS$zs zNU6!FH5rG+SFO7_(?Dl{dm$ z!I-yTCyVd0Cepm@MY^Ou%{{_YdXEt`j^6s2aM1ggH=nzfoopF0(WprmxehCGdf(0H*&wZuOqiL{F)Z3^StQPk*tSaYEwH@7=>G({xm8Var#@i=DH-I4|HDMB~h2jrF?k9F8TFqze{SqQ)<3j7JkCc`Yp+G zz3%*^D8*?K^N^pXZHbew{-h{*dwbx?6RL6dZ<|(O_{?7t{dJ67p-`KAXs^BbDk%p*nRFmTb-X=~14#NlUh_OLc52&RrsqZeAI=Q+bmDb%kq6@t8F1 z`XURI$nX(Z`%?rb+BP5??Bs=<@`}rMBC?VSO3sN(CGrio8XfTfTKBYr45qn#Zoh%> zzEpp0%H338nrj72lepaSlPHu7XEBK_%U=qr*9^aXEFP@N#SGx)KjBD-aGyXX4-pC1 zl}}(r#Rdgh8I?x@QCE=)gVjT`?W+T^HY5-jk)&fn0nTo{TX07Ao_kJSD0+nhRTnu@ zi_vdS9EQBUuKR-OGqI1YU0MH)cl6lz5Or^B0@M>Ew#`q3^)9`8c_V3B@d4Nk79O68 zb*-QYhq)RCvOJgcEK_#Vs0I{PqEGTr)fet97w1Y%m%H}gy`R@HYN4O{-`*2AwDoKs zacWLVjsEZVaK1ETg7x(treM1COWt6s!`r?3n(C^}7_aK}x++de@y`wMZ({Q`V7&R|w1)a%0|FBe*HOdvj;WM|WL(+Uj^#DEQ>9 zw6pdm!&9eZttC|(xtiWv*ZcoS+tw!|GPa9;i8_~DL$E*>Z<=pVzo&-}t7o^C%-UU= zUv9#!-oq$slg{9G7Ve?fS<%%^yYX1)mWT|+?Vx=jaAAmcVyIwozt7I)KAbUzH9me6 zZ7O#ByDrT-?+BM|^4*^!Q`J~@*Z%VOyn%??J{;bRj`C~5-P91j(yu>g?tg>pXkV7o z*yz4GGic`@;lC}q%EF%Ms7iXasYE%v!q76cZ|a2deN8%1Ff$0!ZZ0>Ox_U6X+C^bA zH=~Ov^0!K{bjV7rZ4kx zo-Rf~0lMzuuU3~tTjfr4u$dM_vwEtvyElQzKmCq!qND$DoCNW@o^5Mi=f5v$@^N|g zME_gg+^>3^#&Nl5at;RJ>Nt4=E8AY>*bqdxz*c%cHGtr4m~R=W!RJ1OdIm2K7UMjj zqUO_B3UHiNw7yfSqIa2z{e4~2>$4)-=~nq{$9r5jQtNc7byE=RR&!o|E}##A%iNoont5*~qn{~&*B79ibyQWq&v4th)wbtBQOXkZLYNzTYoh6J-z9f_9{zyZ6!Tl0HCv|6`#kHgr9tu`LkxG#hiyTSd})=1 zWagCMR^ADRcIf))=J|SmTNdrj=vKT!zZ)2+@Jf6ChD*#^yLuq(_Bn3}>BHz;K%~6- zuDOoL@x>W1pGgn!bK!h)bdUej0$+l=-RR>X$D-7HUWKHjYM6ow-rPz{}b?k1bNl zHi7Xo24@;YWSPp_1Hd$zJ}-H_y>qUR|Kv4zLrwNa?JTn5b~dIiZU&3#q{j|M3Au4xTuS;@x>D#mteU-AKR$9E^7AEom!#ci{Wo**ET>&&=J!Xw za569R5$rI1?5(*mHm|exi`2VL^Pfw?J{=nxqql72Wb}9k1?6F+@SR@sqwOAN|12(# z>#%H>u*|k>2yXw`s`o1y5Y(>1r-nW|FV177?{Rw&POG-J8O>Sya?W{}zPu>tZtHnAyDvBQYP^&f^8NLJ^to{#Ub@;o$Zj-^zV;~IB0{c1g){fQ zG(~L~UP1mF*zOsfPWg&n?nxs-L8vA=^U-c0F;Qs7(oyYg4n}2EUJ>NDEQg-PYAq%7 z*1f#2=~38xx-9-yU`@Cc zdb$OEgKfjJ^G3t#tuh(gbfJ_EL>?@Nza0Jjal)DHqLi=Ftf;aC1LKZ6cbGf`mv)j< zz1>>mitB8v6NFk(CZN-0J4z!ip=-ey`o%?<}E9S1!R&=#$Z6}G{&?6xZAx2;4P zxVD?}^Bh0ENZJ;yO$PVQY(WJ=#o<8ZQ!eip{Nk<4(nb!cX~=iV^P%1A^Gh$qw{h;o zcLh5et(QVm%lh{+b!=~qsNtkHjP^B7s)}A}AAJ&?xW3_saDtLB2F<_ab zdloQ3dicBc>nJhXE<6J39GL7!N0F;)NeF~a_nmWsBv04u4r+E0;QTwZRL8P>oE z4L?B*O_e}c$fC)Bsi(|IfNnuklUN`tChM0FHV2KxWrSvmZb4BKTX3qXXiFqTZG7Uas!}Q;Y^e%M zORFR*VkrviFEi6kHLIWnC)^qJX5gY?5z7dZIo#2oNg;0b*fOxL9i|8=pNmK9CP>+| z^=IO-ie~wrBfy&X?>T8Zt?@w{VVq>-=V1^3e{WVB{1oZ0r=3~p{uJp;3H?9f|NkrE z|9{5+|6euw|CMX>zj>oU;=#=R)Bcam>On0i4;0bgc4^$S3i%t5hji+vqU~dILNK6G zl7V6ZsVu8*^QDqzZo+~zJBpc2ko@@pQ9%JZR6zoYh>0t%)%6*yT*5#Fg)9>`Q#7m(~L8SXr~QaqDSkEzeHOluMzi>98(}!pPG(+gKZJ{yCbX(&AHr zBzXFuQm}l@VoKDQf1Deh+4HpR8;Ov)jG$R(ds0~-y3QG9V?+*x25z_c?LjIXm!6;s z(eR-J{g7Ij{SNkx+}iW4DKm2+t^|V2d+P~sTHdmRZABBXQ=VOe!bnYo^Y+qjgit1? zyW?)f4c!%#8R_$Yc7+;Q5m2cwJ_!=!h$aV|Bee!j4!LkEm;3?ed}G2B6VM#l$I*B* zX}caQEl!LBPQd9Tnji*~S8_#5sjl6*)LNk|+~}Do-Gptc^?N|NiZbm+=3&UMu)l*0 z{}zgcP}1y^K;aSMX@flGYO*RchwRL}dpsLPH76w`)ZzuW+|n#>+>5PG1T(!eGx-}d z_Zu)y*Va`a+|fuI2c6Z5WlZz=iks^y{MyydD)CPX($vk|Gi&0mh7;{R;d-fOj~xt< zIxf4)KhD}Ws)R{F0z(W6n!O0=5eP&&DqsS2x7G-i?L^kXL7qv7h}Z4b9-Zyx5}fTR z$C%cdDLQ5*IYi54Agt#MEnwGIcv*^V;b8GEO__JpTV_vfJ`|pVg_04TLs1GgH2|he zBpL44SCEqwDk%Y0;JPp!8G4`(aXJYVgM6x?Uk{=TV-N;vIt*%hnNoTgC4;=WfuCaF zD=FAf8d|!(q5++XL9QVMF#`q~P#ml?eAucr=n!_gG-|rJ82HXb5F0sYol>@BD5fag zff~{&75#K}*aQ~?jAy1U&=(reC5~xqOn zOuRx+b{lEdITghWESdVdn`DU-G(}j%lNG^6%GErxAH+2Z7(kfFD+^qbOsYih?`6labA=4S0({Ne~G{1@bG6(rY;d4m~s*eTW3$3d|>v>Y{=}~-nQ`Ruv3JHeVejP zoU$2#?eKJv(W`gjkrb=YQ<`=hfr8}igTe~Q0~X+*49tt!N#>rXwU0NeLc`^|lv5Nr z7CpeuCfocMzh8*#vqMwCP2NQ{Ib2YKlYg)?)DE4U_lph_AUdZJ&89WeW;Yrv&D>{7 z{89q>K{ej;%j=i==#F}*(k@B~8td*RbRCAy{pc~aDK# z2Hb9@c-s}B^(3?Wo{k%hCqx`F3s>5w9i$l12=e&;>{E$NHajI@o0eiTmMC)10x6~X zzztYiXozno&;}IZlP;4t4x68_gZvEHP3{gtMj}55#j7;%e`@@*+rH_55n>o1UDce} zVZ}^|G)ME52&+SSV(OV{N!LPE9TG+{)P_WRgA7@HL`7h$_K9X0s{^A&#udV&MWz@) zF(UH~Dd>=82Qu3vOyH~wjEZ;3ZIdqyahd8mR)EWV259G+@lLVn z+&|3&I9+;!7W&(DIrx!UX6>I$=qBv`4Ik^{R$t?9(n4U_1W-$41H#%Q7Jz>55+Q-w z{D~GpsfVH)Z|GZqAW>LBWp5VAge1-S5x<}*_L-H3%5kTK!~5hB7AjLukEwUK_!!1d z++Z!>({^%dy1_Kq^a>b&c94`gR3ja3fvoqZuJEct2ivC=8dB>-F9K^9A)~?Cp!~UJ zTtn_!FR(AfS;0$*G9anAM9j~NU7^;i$J(rw9V7?vAh`*pR20%w{S#5sjW9$_QB|j^ zvWTfRDz7%GsNO5D-m9qYP}C$<&~z2XT; z^(4Rft%wn-KHLL8#ud{c*vrB5yAO<}>{(eMY<)&jN@oN;)9OOPsEAak1^Jz*N=7{3fC`ICm`n~OGU+H;2uVAZFJ%&-osL6G0-jw zp`Sop9v{ytEd7(FI3cXXH&29z&`q^Bq%|lFxyL+)tT&Y7W+B@N(5yVQsX6V9IK?sI zN=syoi+{wf@XJYL%}Z<+5MC0JTKb(_%}%Lluh@XD*sxV<(k(ouB{QZaH0FhY!LI6) zEpW%yo-|wXJ{{sv0sFuN{NO}8bFG-K4*HIOKs^?uo&Z)?3Zo~C@zG-jz@Gv0m%!*s zrF$Xn*l~3MoSz2qm%?lbV00zWd7#Kuyu?x`rA^kOmL(cEMbkjVV}O-yLbLCWJ7lxQ zLW+kPS9lz!J-^5YE%JUTXCpfnjshfwBzf~YDUBdUO%OY6G`V*9fN1Vp7C?TNi;t?*2S2)Be?&2*&K7*oN$*Z)HS2*~W_VbE{ zdS^mE!=hbjQf)Y^v>jV&bqgH1-a1;cBEAMRc|pO5jisbPf%IBny`(TX94fXqKy+31 zd&t7tj{kUtUo_MRjEG_xK{kxVt?Q zn?+DiNthK}+{+cbt`S_gGd%73N*<2O2vG=D2rC=giiw4Fp}z-eKCQWe5W+lY0Y?^i zln{_BER-QuGn6qhFAiSMhW+=08sTk$K#Fmuq=?2yv;v~HA;1t;Q4<%us+z7yQIl6p zRg=SxQB~0{Qd9$<9qf#Z4V%s2qr-CaWxDBvWIX~(Nh${GJEH|EIAtN3U>?>tDM56M zOEF|x1(Oq;Oe~|;{f}P7=(p|9C)Rr|Kx9d4wu(^-z@Rs zEO&X4J4ul{QIWe@@^A7FqYzL!`%vA5+Z1y8NV&4j?;S8PLzF#McsJEy{1_(Q7^YM) zO@HhkgE8kK$O1wVW)~^rLCG&10U#CRT>++BlavIWtl)ZJWlWm;z$EjWnB3K9@KXZp z;$TO`*bKrpitVECx9#FF`$G|nasgW&9ID_+DTj0+T0eOxWvp-8C5MzX7T|YCCRt(t z7p{Cu>fH;wCd8M@mF0cM((%H&VLT%K2VmMe!O`V+ z)0kxCy2KOiaBY^cRQ~SP!)6HenjuFy3Mzfvi4>2DjFW?#-(s7aS29pqGpZ_?1zL~1 zyPI`g>pT>Y{&RTm{n>C)ulD-s=5nbXH5Mg3WmcHGaz{2ZQl_SbL1zyb%ut_|pqXV>Cg8EGFu>55INSI>Zt58w;3uf76rv zZFmOs;K7X0Q+B5BME7oeDAerJ`Xc$eR@_Nh{$6r--mDFKQ?rQ+tPMp!)~exvi7XWA zCU%Zyvp4$Iq(y7XxpLMCvS#HGZe6Z(rM+^gv&Zr1N2?r}$j3_qYth(w(!Wc>&h{Hw z$Q3R6c6V=c38s!0lXLp&4~EJsq29eOM0_lvu8UJ^9PgW;PdyC1|1Pt?sPHbb9LvmQ zt^0HNIg7_Ch$+#I*mqy^vgk17kDqn@Qq0xKp6?`8<=VE_){|;Bi_a5nxxL=&?`pG0 z$4AQWKVHcX7yXG2he*hmDlmUPTKOzL9k9}S$9HD`)p}?UexKT4x0i#;x}D!EU-B3YN}xeDjn+3MLIl?HZA ztB=cH_Y)sMX0)-&i|6)md3kx3d*3is4Cb!$C(V|F<{~S>ocAIw z?+EwhA13z^vT_e`syR0f#9g0}W%XZeeUB5yY(!saD%}jnlrM30C;0DGYMXAAXy2$G z?o}tfHrP-6t1D)o$~Sp85sxSz-yX-;Uh)9{XQ!{#wzeFSFYKnb-J-Ic-jVN&?_Bv| z|Ly!t`2+k=U${*0s!3SfPTQa~TX!$pXtYy3w}X=D_hOQidd_EkkN2YqJvKuBSLx3V z)JAG-I_|eOBzo`Op}ka$nJt%FcB>u9N! z$K_OfIyv!osnPu5S?aEfn$6v2_RahHl<7NVc)u&d_W4d>sb$ng)``K`i7T|fmX28F z`jL+f*Y`0|N#I_ZNByyhcA9uC&hMLzHUPb+vegN$?hU-lxFqJKnv6@stbbefdE3qD zw07&?3$x97Yy%H_fbE$!q(|<&Q_iN4Ztl`y?am^p74Z9O$@iHrReuGEH+^Ovo6Qg7 zX*+>?E~LgL7ys<8?~U{BmJ9#h??FDN?~KG=mwWEtT?aTy588K$-`!tEtnc#sAkF7> z*K>J0^@@Mo_`tsZ27YC2X16@cGNirqX1kZaP`)f*@B>yH=ap5rlwZEZM;>!08P`0l z(lCJVUYuqWdEYX&%uih(xIfPlHke;JF6rlc=JPT+eIC1O)E~bjO}f}l#*hA7Kax+1 z$M&+lw0-ULa}lmu9zX0$>30n1a#n3`dMgPBU)K9QRd&<_^{!Ror#U^PVf@5iTzd1s zei~gxCcT|L{dKO>;GZqqIz}&{zjSSAn|IaV>RJ6^ zdgST~9otR7);_1ZY5!(O|GcIf{`l+VChF^}_FcEsma`ZFz`XTNXy41%v(&IbY5aV8 zTny6^bNNH!U|@{5S0B#-PJSoX&j`nGI&;UBeuMe-VG!S4N(H`a;kMe@I{v!dd`n-IFC01^gKE_uyLR{EzBfBp!&uOU41E@u>MKyY zsbrb-`&rzc_pX?@OEZ!!8!I$WPo=CBw7^r<7Q#uWibzqD=_R#H^s8aL6x)Zr7Llnb zQzluKbHtU|P8%W`FW zE`nN}VsAExp^SRF-gl9n{mwUHZgLe2-ap1)K|)j~W&x_Jev|}bG`skqE9IxzBbU$K z{-Byt8u*9qAYNipe&RPX&rh=DdH|>W{3Q9QRd&T6@hQ%h@$C|mJeK>;&KyKOg6*T% z3zo699Ch8@%9zrMoY!6Z*3aWv(P7_iVCG>969%@(MKH5Xdk4S6iuc@+lE73HF1hwq zB$#j;#;Il7j)Y|nDvM{yU$brdhqcVF(fo?wW+QW($$SjP%y{&XM>iCSMaKfpZfg}m zukzSeubiEE$w^ktW}UvCtJmTvLKJ~*0iI9A)fcEpFiwekabRbGM*B2&g|I$f0c$mR z9lV8?j)Knw3uxLF?d|H7cCu8?*yqxeSlMz>PiLStsBwLdD}C0G>_%s?j1-xg_>mP= z4~jhypA;h0%#^;(*TH!bWYhj;e0ps^!`fhZy&GysSoxjb@|!%P>HGb#XfJJX`EPG9 z$Om)hrv7(H_M828e^iyaf5Qikty0DNYlGy=B8@&WzL^t0Kk(r>98wR>x25|F(ewQw zMc%XjvOh~Wzp>3FKlkMv{)n#h7wSd3h|T-laH_wPO zF6XMlXPO7s==Jz>X)M+a|7c{FhWc2Q#%p+^6KlphE7(OZl$+&~O~kKxY3W-G($W%C z*$GN18fA$CqV|y3{9>oj$*%+rk$LD02^wT&rIvr$SInGq$7t13BeFeEISwMQ42L1p ziUkZDXE8?s6#i+3`&cYe!Ta45@RvpBEavHd3U^4Nqm{tp3n);*!d0y~%a;yAC}hxa zBpfwQ14C$JFmQw%HNBKlF@MtcQ3|Oe;zT2=BW6eWgF^*s+uCh{b7i9 z1@TqUh@mkA8?=s!r>I!azXjSDg;bCV8KT2$5EV(ugbm>lT6rRJQhj`QaJ@|6fjq|CiSIe-+`Y zIJuhAIk>2*KmmbJCW4v$r~ThpLqlb89bZfZ2aSEhVE~ui1Vizl5|S#2Lqo1qH#~#d z-64H9+Z&?cizM}AGQP9KG_&M;u6lBuRe8LjA!Y&gnOAatvSdN2w{fH0oX)^^`gs5T zmd9cGuIHHd`15n?$H_pidFl%6+L2G78<&_f2L#ut8nxt$?>Xw5Wr76Gt$pVc#L4*I`R&W3B7kT*pjbxF6Zl7b+F z;-pR49hw3NISKVKZGk#&9F%=oNTDP%oxu{nOe3h#*NF#EBnK^SVAds{$3bV=jai;uXm!6+%fF)>JmYJ!=Y(ijX z6)L)iSLeHS83Uu!1jd~Mk(qKVnhHGgiPdb0=pxC40i992RcX1TJjj>pmJ&=ya$#c~ znln?c6>EuJSqVddOJU!Fm%~Icg4#Uz)RE_>tS=X&o4P3-DGsy8;h5SMJ4_Opr=?u26Pl7DE zCyzias8OUwc7?AjV`KLykM16=nFtAW1bGk$1uUOnOBm=J@65vACA*k05hAY91=wh* z22FGiYNJx78H2ej6D!S{vYLiX2em%Yk?N9Ro zx=h)GNe#uuBps@y8N@U;oTbz{<`QVb%-jeXh;iG>BJmvLC_3foNc1kz-VJfdjv{e7 zfl_Uz$oRy_V7W{JlWn0y#Y3A!29soQaypGtR~{l|Lzl#D7LiiGBo;j@>fdwcs6bL3 z9KzD5eUSssDkMmv+hhq|M%CizY0|Iav6Vd<7GZ*=9uF!ZlsLm;=50z@lxj1{7ELC{ zxL+&ri(N(xQ}&B)${;KpEEuyk2zS>>9g87`K=86ke}2g&qk@=8r<=CZqDpm-+csqVm{+ek*?HRKL7p9UWsuLRPpf=xQRIqw?|)o}FEOHE|NAPvFR67egu)bR!$7`SozZ zq`?{BHL&zz(%jr%3YnHf_shoIUi}AaeEnbz@xUQ$`r#nbg=l&L`lmC|?&m_-RBQkf za*l*F5WS_#Uf+a@d3)YJY9k@BU>_G??Wb`cS zwrbfZ5-ZMs zSt~*M>?+?f^3IN#R*XUQDLi~Np?jEux}o*-sXcrLZr;=Y=iFd**Jxc-s9k;vH)~}( z&H8}zfS@0+p$9P}J5FZhjQYm;Mt&rf2t>3%Hx@ujdaTmkca;d6KUZd#WNXGPHbWX9 z>;f2vB1I$h*5n<>C6ki?5-&%IedF9shmHLWfKrc*zDoEH*bumslN1scGr`U#F)AC4 zRD|Ml$ZX0%I*z(`w4gSp!0zJE3~X!!a1y^{xPUjx!C)_gfjRiGl`{qD9|gc5FFx+W zv`K(}t8UQzRGleeY}MU(0Z7?-*C?{EP8C}EChW2`PJH}PPI2lYT-vsWr0s4SvZ@)k zKS=ZqMv~njDTIrPRu> zScjx33{Z}l`#1Z*a|Kj2%20Vjq~Q}T`O=V?>F<#S*zw07^>P~+qTfQM->XAB9Zdv1 zH?_do1&~`O8QeK}R_w=n!u7ve9^kGE4Z*c{!I3TNS?Q4Z`qY3T5+X_eAOC?soT8(=8YRd;HKsv#8qQ%_zZtza%ci}H0 zrJM~oEH4j7^0!u~{)07Ep>Hi2^n-jAj7vo#7t;`{@D=|U44zF#pj8m6p%SaH5Uarw ztI-gv;Sj6w5HA6VRfDQbYDl&-#dFZkj#Tknh{)6knpg$4dkibV}3z4034! zKgAXy%wt`-LvX)`5C#!=i8wDqmx`OV;oeKqCA!WyH0{VOC?k?PB=0keh& znUM8zv*5*p;jJ9$){09*isP40Wx#;(tyGN(zE@d%)mj7-aV1pL9qiMR>C+O*)Hg8b z9_`br{AG;=a!v}qGyz+E2wSa=sX_ho!P2N>s#jyI*Bcm;W!0yOpOm#@4zt;AN9>Wq z?at)zidmZdHjVWo?sE_DNTTte)x6fY}?s_{GtB5NqGO z+W_$E!Td+jyP%F$e9p&_Oqe(RI2!}ClTQ$#lFVAR_S3d8f||$V4NBcAkZrx6()F?m zY#z4mW=Eii;>jUPofXh0vp|92tM9rku>bZG`KTX|=PB-Ja|#H>M+walF~k@fKP17Q zHy$Eip@~iGQks*J!=yOiN*Vz5(+u5aPt<)@?n3AF9H2s#aA2=2h4ZwZ&Z}b6&6I9I zuY$`9rwO`#xW$sYFNOCoa*5u11^!9oIeFR-X%L|gx^X5sCJWqg0PuQf(8c#eiv5b6 zuiUm2maNi$nLA4h5zx1UYDz;oIbWNz;`tDRYqf9QeZqxmy^d_9vXZO<*+Hm0{DT??a8r=sL);F}~WD5MD>bg%_^P3)p{Y&h~wIo%C z6Y;CIQg5+RkGOI<@(eDl4<9s;U-DiytdBOV&lV2YpAq!S406K|a>EpI!x-}9feQGS z7uv6GJ<OzVtntc1vQnrsh!nWmj%oVt|GTskqe`v;*grMBY8J$os%40Xi{0|sh z5B{Bv>W+@;j*V{KjKZB*|6mRJUn||iC$sh8Twe0uvjdB>-W5g9WJS+}MbC@W>f|53 zA)vJep`Slkqm_OjTdwo^5av%G)u0vZMV(g6f^{KH)Jg;yo6&<+^3a)M@=(H&M>n=9 z$j>F|$!C#GzFtg&$j?CV&j8#~m11hKdXWuebb|@HDNb}l0nK>W?jh*wUwruGeOgI* zKHNUkJpodrSOOGzRiv?2PDm;S**Hr+qqna4glX~y0^G467gV~rbgY=@ht#dnXTcc1 z>?8v(I1A%l%(DYjEHjFk|H0Qg23Hn+{kq*@$F^;EY#TdC$F^;DY}?k3ZQC|Gw#}RW zdvDcyaL=h)^TQr^GS*hz($eVlFNcvX8o*-^PKc?ZhlMg2dEukT;^wvJ{xa7mu+tj?mD zriI7tXoX;}3v((clK#xmMo;O(wmqjwWhT=du1}z~jP;T% z@wSE_YArr&QCI6KoVU?-y`PY3vB8bZCedZR!1X)2S&lu2m++BNo5!&~NKJc5&@A4S zMg00iVc8=HOaqkl=YKT|?mao0xa~g)F7s}{;530Zj$J#1*#@T}hq$-K5#;cCwsfYn zyZFpl?f^5S9p5t@PU|u}ZRCuav#;D*nqL=mf4#Z2kS9RcazTWEKfFSZB2zc3h{27pG@lFJQ*~c#lN&V|7mzPr9;nJ^ER`s!?4#x(7otPb9Goe z+2LY1b-}qY)d>1Zka~2ua}UhuW#Pf)al6>wvJ_d3+yjoYJYqe?;QSr*?HfZNo6yP2$$WfNLKQRvxjhK7*f$#P-?nev`m^6TasF>TkyS*G5$BQjyDIKY?OAOl z-_@X_+7hV9zAvKrd)>nuy|Wndvl2t_xQ8$Nxx0~mUEfHP52p#Xflr{v5jvmOTW#aY z@Z%}us+Rm_ur@cx60RIB53^iuTIsjoao zE48&bGei9kvItolz@QLKtfZM&H32Nun_ffqriUj77f<0m@R*RdGClK%&VsQPDPd#9 zSrnV9U7hK0%Y+TB1LhIkwev$2*;0-BM?wUQ?lz-|jV+x^y*1Idcvj<$=g17)Wm!I$cZW z|JIi;`G2n1wWB!Stoyw4gMOKD?EAqMM3tCFp%CLg$AphR^VvB#endRG)6%;W_(V@+ zG(+>-hBJ5tWs?oR;qlAA?rn1g<&b=Oe1tm0w8Z0Tm1vKpUz9AoQb@zhrw6M7A2z40 z+Oj)MDmD1qIqhw`!H46k#V3yr8oSA#>fd=gyzc((pGM$Y@@X4z`${a9v*>NDzN=6y zzCJuwdX9DD%WbuPo=SsubLh%`N!o(;nB94Ws<)1L)Sb7un%6n)y*%$0i)EPO4- zD^Z4rd@Gk?v@Kc=mU^z@H&=t+>Uy^ zcpt5vAMI|>Yp^sYUV;15%k<}O4!7>knO`TH8^Y<~yBvJKRii3siD_8Ii<8@$HZN>I zm;I26jL+!OI|+h~G}27a0fD}WVDtXSW6YHE*&_1cHstjfD#e}sGOA`Vev*PD=d{b;G>$_n~NOwdgBKl2tUO& zBbb`kc&7_{+!5j(t#(}aGWh3=f~M;!NMt57)#W5fO1}`(o=%MD)YKe2l$7}GvrwCp z!_Jy_9>YxZI}GN--)Ialex23OP4Lu+Vv^Kt`yzKO^RPy^CmnEF3~Y=NDdT{81#pPML|(XrFGUp=%4dU=^w zTBg#`3AoqlO@$tp?^wFeyoTXm|3oPquNl*ECN|@ecgpXNVb8hN1nCsD`IM7laT_RJAXw{y3Rct~5U7oc@ zpr2v;q(9y^)`zzdTt;IrdYv;JJqJi1(&49ECpu=pd?`4d><12A3D$f6p1f=yPC5J? zHZ_FF(zNU;&ucZuOIT|?G%4~e-}vc};E`1F-e7+S%rX_@YxeILZ%=B^)NL7Mg4`AA z+jfk;Nsg{LC9m?8QM~9Ebsg|(1fx`J`}zotlQ6#>OkpD6)2{q^`*H}QPV9OLD40C^ z2!OaCct*jer+%frWuZL;)j6{K8e%|snMkB;Uw)zOwKX#eiFglRo)zw#T1N63^}Qf? zpxiEz=DwGse5T@|zgUFxM1;$*&3ZE=16B`~G@PHa0@yVfK928AzGCS*RN~{J?-3$p z2$xZ-kt?kKt*-Nr+`ON|3CpaEK- zbc)GaZnP++GK{GfFqM16uSQpZZsKmY$0b%%Niv`gEf4uMf%2G4fy`3ki2!5TE623P zcr5Nd=HR)^0n7GffVlGgijJNJo1(V~O-h41IJ~e=vIbx*B0*$$A|awsgAtVfsFWXC zs`myJAte{0xJS8S-j|;*!o&g1hNhsbhlwW@P_XkFBPtp;w@bM~-IottB3#P>?GjW| zN(pGg->3G6FS-(}q69$Tg0EwlX+z_R2NB!Qtn>&3aPeeEi!6|>l=mp2Nrn)gB3nu8 zmF0^d!TiEOQd0&vQRdV4<|B)uDddN?ft?Yd7G(rH83cZ5RnWyfRq9jkRYMqCF2~! zC6(SJE|A%Lk2J#CMOq+<0r;x2u#`v^0kn_cAER6x{cCG$ax5oi+r&OO{>gHOP3*6^ zq_y}cE;IG+epY$={`_wqV*j1Q01j6Q!C@BZFmR03Bf zsYm;JdRPOZ(9fVBlnz~L{Ik&+x;7X7PpAUgsX>s3@;@l>h33BykFW?2yu*8l+78Oa z816YxqUvHWEXACgY4l8+#uGfWO%mnm^Jt1XwI(X|3;!PmMC9Co57Rp&N)gHq&y0 zwl%T->l)KfInRd@Tfs<;k|faw4r4SGPPKI#HuwMz^jM z???%<=Sz`h05wpoSiCvrK!62Al7-tZ1s4Se-pGj}UxTGFAIl8vR>~)%*S8L>-x~GW z2#YxUr5CB82_m8#@j4a@Zr996z7(4w-6Bd*sw&$G$!*v zj7)x-Hg4#cNH>I1yLHIFc<$KF1m#T+P^lLChh=u>Au7%s&+g9`>J#>oYg>qx1Opmo z#gCU+ioJpgjx89!5c-N`fIAK|CDRPKN@0{B_*F)@3Ic%`jPzBe&Ny>59!r%ZRk=k1 zxu-`yP9Wl5N{LUQWNSudJ`BFWu@=vmAj(>Ce>Xj=86oVBv?bH;uv_YP3Q zkPxjxn1oE0C=1ffhl(W!OqEE-A?IJ$fG}==WXDndsYEV1_(etjJrx9p3P8?F1IUkz zrz8=RM!jT023Lut^fDw1kC}WA2_(shLpYC962I#xty=7$z}|fX}GlxS(lgNr>Tm(_ZKp0fDzoyUQ%Qcafd%PSw#J3IQTR4sO;r$#w`%?5sTD2vs4Q7zelt? z?yM6p7O51qjPfe|Jfd)SFczu0%Kh*0*oP^Lj2NZ**%n=5eLQposB?^_{}|usawRYh z4Ol1UNrZRLgX9pvSky8lLvTfHj!Prmk`fH12dzm`!)QC>5ln&tAO9@QE)!QSUN0Tk zuLEuZ*}mf)%g%!5uv@u;UMS$T)|%KItI%%aAY0_EVGSbJ(Q*{{9n#K+fyWbndOXn? zr^OB5e14|d{)Aarp;tC+4;^%=#a<_~uu1{eeh5TegjiVdD;mCi*W?#gyn#cPEN#9) zHaz2`9ZYo1f2U7UQzKmAxtjbVz4d?U&M{|OI&$K*5XTy{&dEDy3*{;r>q?S%y0&3_o4&aSVbN*!L925UQp|BL-)wR zJj)H9d7@PlVCwJ#-7XYw)+%=!#Rn~`!#?u^wXSbtbcBG}3%9(5!Vlx!}1q=EQZt;Oqo%vaNKE#5@5Ye-Q@3_c; zd(a&EKPkMi_}T>5aN(U^o!pJEvM}Pz?ZQK|$cIH)UwW|STvgJEI{lS2c?D+o2OFW^ znb9)q|LTtFIkTCG-@WGknvw^{pxtRU-^rJ87Jdftp-9k;SCO_{0Q?Sgr8#G4(UO>E zJyj8dSb)2hBBG~t>Qp6b8?r>sx;cGz?I`1AiwO<26>$R5D?P(9-MvdxpicgMR5-><5W%CeUYb5v*S-O9{idK>hb%Mi+)Q#uLg_AAE!KJ_ya8WkgR6 zYWtgm*lm1skR;6o>r5L*C%UVTkop( z?@>M84Z&MjKEI4WxEekiWT7627D7YA;Z+)7RvKVc?qXJQv1kmkXgKRPoQ|0-!m&;^|N7IG zkjjf^^ankfh+G9kT|I{I{!|_0(P-wWkBhq00TIKD8l?@lgR?bY1&-WR@Y)Sp$F?q( z&*K_dxYsf4Qbsg?=YbBi19K(Df0&9f%-14M?i(GiPVb?LKnghA>}l-Wv&x)trnB(S`ac#XCZ(YK4z}PC_Zla zfnz{OQkuAgFRV^tp?-8%OYIo1(*k#5R(aFo4+7u;`On|aITY|fuwQ9TG_b!2K#5f$ z2izbAz#v5MK@EsOcR3*j?qEZ`AO=V`3gfaSh`GVpM^6yD1eP9%FV%>=sVeWK_Y0S6 zeisP4m_EnThv2*HOFxXrX=z4Y8>b6`gH@Dy)D$z-K#z`ac@J<4ccb4Bdk;iN4wRr^ znCRtAdMvteC@Fc1R&zy^c1bDg8tQe*^>_q0b&vL>Pxg37LoThuR=8rST`|`jGS(b2 z*K8T;O@22`_oPqvX#CA6m+F=ybk5)z#hmvy6>5ov@IZpPH}K9)sS#j+gTcq=BZT*r z!2Jy6d}nfeM#<{WVg5dC1<&w(nZo-{;p+6_e)@C1lR3J5Uug7Y&tWve%2sx)^kn+` zNa}I9l+ySqpguv|BzB7&w0LpR#XDI!mp7Ju4ZNS0TFCZ!BYmPmbiDVn@m0Ay1=Q~l zQL;gnlDJDjq2h_CstDC~ynqz}RJQnhT*8A?d}=oRdJgG2XbA>R-Q2|QS>MRdbZ9=a z5|0~`D;Uuo8fcx&8~!$LPp@9pDmWCaSZNgCg7~{W~ zJ#KFVM2ivpt~E(jC6IeB0@kk^yxd=*vB@~-%ZW_k8Y^rNk{9s;1-Ku;UZatY-|EIJ zFrEXFZTL^;tuZ8#B4KR8)jX2ozPO4f{$PZ$Sii z*m*%Put2&~{?N4dN^4*;ob|f}1>R7rFE=t<7`r})6u*|}Ja7Rz1(j`?!EBJgK8ZJ* zu|QlgL43~;TgDMvh7nt)5uZbduKyD0?feGoPJak_w*|PAp#`h*J5$&b)M}c1{6r zom@h9CMB3w3rexh37jy3)DvCOIJwb9K6*q4s|VX`C_aa0MKgf%Pj6~4FcdTi|9uo> zY5&)_g?*}VBPZVYsU7w>@pWjU|EmOTf14X+B75y@4L-*HB#PFxOoUwB182aRMGyFN z103f>p%^K%e15l)iMsv%SdD#InxP(w^Ui79V6I&E6&q6j&P3uP&qsK?1NBGRwok0G`*a0-WngX`&*TbTi;XUXsH$Q+{ zkXlqg*6;G!O!{$y&?q$nlIZ!m3YL(1+H5D+e+{%}W9I5hQ81Hrmzl}N@{StGWOf?K z4?Eu*qu(15#%W>3SkrYC?BjKOD_PfwkNqO>wvxCnW45 zoHBPl?kv+H^1kV0=FHM_KU*XnO$cq~Oz(lc4CeRcnXr>ZxfigIvxIlhxD`SX-Aa9^ z^v|iRA^~hkf!>tr#X$pHa12dtudgVp-{B4!BrGWgV@iDZg5Y#&JGU8ypI*UWxj!^m zNk~HD;s!?Q-8|lJ$6!R?U%>Hp8eFmMP@zbvM3}j@LVK&D)exh}N4bIvjl4fJfu%D~ z(h35^pai)Dw|;c@gQCi#uCRuzvm^2OLdX2lO- z>2Wv7VmorZBg+I4x(eUp!(ACL)35yyLS(alw;MR=&7@7^A^ph@xPyqFw)SYg*hKep zU%#AGd-1yJ>0tqVQf~8Kx~Q9MjjvroOh?urt`m374_GO`q_EuHh7#HcYFrUPLjlJB`$)NQ{DW5)TRPvyeja0& zc0JoL$E2mGlF*~%_*`FgGgWUDFfx#r$zL!#?A;cYP5L^#y;QfA&~kEZOm`zbtOko6 z8f>MTw!Ljy@%wJxp4J8#hCfqRzSydEoF7d_V29-R&P^}dvKQGMq#10rIEB15n(=qf zaFsN6OiwqbaXW5#>SEluf-F|aX6_t~qwyzo`ARV9w!4-iSXe#YJr=nmge!%=7$;xD z;Dlh@LcUVG6d=65gdgz)LpB%RT&Y6i%n~-ob~gvCH=P^^eO751d%i9acim(WpnsZE z9x}*iwY-0(3i&AZh0Qnm(Cwm_PJh@u1`GMDUi+;3*qk0!Z(g)`E&V0#vYx!MZQxZJ z81BOPx-nhNh6L66%&%qfIm|`PGsk6+@EV9EHrLu0_T?+m!1?(65i|cHe^eoVK3(m0xj1aw z?TGbQ+bQ*LDq(VP*p|Nw_HCIwfy>Ceb@yidN-_D1^wah5L+R-IgT@>m0Y>v+;Kj0K zm+#A7^Y+CT*4JfBs4s%>gLrMs+!m%H1dUAf>-lS~PuD}4u{@pZ-CcXi0>KJ@ZK^HH z+m8R9S-0fZ`rJ+gk!Xjft3^yo@!LyL2=Pm=^M}{zV;6%qkLE;4TmDl7laEiQyLYuq z`-j(7arT|VGU|;sTaNEt==k)N+2+leGibQ`K{iSA7CApbFWk#nj^E(>=JTp3(n*k4 z5%?g%!^+7~xpu-jT^n!I^eFD;iFIqNuY7n1YkNyq0%++Uk z6}9}`uj*hD{87g5a7QxnkL+jvf4}&)-D2<*h#KUUZZ~G#6cP+kEp9y>>9n zLp?v(=5exYuqtri)Atv`8p$hoOQW7F9q(kyImKmlp;_uM$gP<0rqA20W{RbYol8t* zwvc_K61WwLHVFcnF6f!QSm*uGBZhzPPN$qsTQr-gcIVR+y;58f+yfKc292y;T_m=b z8B)7CKFjWRcS(v>A6+|?_nyfob+)A=wo=nRg-djIdE}aXbpAQ* z{`s_(+ib^Y^bT$QE<3!=rN3?f41VEUdF^<>1agu-N)+^)5jgS6^TfX=#p z;*oZTv9jcEY13QBU7{PDA9!jnbY{5Pf?S_@fGq#K)_I^f_w+El#O8M6zbSnXc*41^ z**7{!jMriMilXDxy@AEFR?X>>wZx#HljpUR^MN{_;_gmfHOeb^&Ln|ex#_9 z&FbOOwx?|TL|xgid-X=yCUXhWoGujY0O6NLb||Rm+B zFYl6RUs!^PL@&{Q(nvXO!BwytJ^mR}wY8u%%PnARB-+t`ukqUkso3W1m6ZHZYu&B> zf{R0SjqxLTg<=SgvJ)xQ1TA3xQU|EMNO7Rc2P`!(`!KEe;<$RqY2S->U9&UCeOkwm zyL)@B?p^kE4M{>DHx2vN`}k~Kd2Qizi|sm&*5&ctww>Nj?4e%-`V03)+{u@@@b_V=>-I2samxC7q< z{`~ogz`2-(w>=V3crk7q`uLv7*ct$x}fw1dv{Q8l83LHK(*1TBt{IFw4xf%(rFH;pIfYs2QhC=tBF_$X`=hpl4tSy!A- z4F3%)ioaonQD2hl7RHYski0*B2>qYJ8{a1lZEVdQjsI&Z)&GfB{7(vn&~H@npY^}s ziVIJ7H6`&UUjf0W(o!?+(_m?PVV7Py3^Xmo@LnqDT@eomVhtrMbxHUX$Owi-#vqEErIn&J8sCFL zEI5A6sK`Sz`EeOFdMU7vQV=ZETigT+pp-H|!ik^|3kbUiA+qNoWzVE$T}MOxjteLT zI+4(LCIc#GS@N(}C0=1m^)VZm%+sf0^Oj+im{A}Kr3CAv=LPSyu5Chze^kohOsF@^ zT8*(~V9pSaiqHOFKZPxoE1{94rkgZNTE(7;pI1(|fmT!~kABH*#BBh8?aoR$2Af&c zG(~}|`$177Z3n~7ECEZSAWdlUn57Np1Rxqn)@|j7h)^*3@!4GnqpqmZ6QZ!-oY*WD znL-5;9ATM}QGNkte;+OtvdlD~HcAK%*XAnm9n(T;qr#2GODzV5NtG7Y=LIieN1vdFqvz(r2`xZ8H8z1qLmuF44Z$|L1L{b0SFL`Wuy{< zRY=D7(N9G^JZZ37A8r-AM}A?^q-~Wdce`VX1`kqf?|M-!==G?WEm>k#CC-uLF>`Ac z;D?e+2~kFQ_7N4f`_dgR^);>@nkOY-y& zLI#9}A%WM_=+!J{PSrH+*LsueDT^SlejzR)wnhaQ&7~se$L6ba+W3oA3l}Wzc>fW5 zG*Xf2LTk;Ki{z0t&wUXy&$o#e%kmdq{ZZxmYa2>24mtP`#67gB^3V9IP-%Q;Qlgc>r7XjTkQ>AelZi#W3 zBKg;4!60ej5y+oVHo=TiJfJ9-^0=Rvv{SLMGqcV(kSM?`IckjBFd7-;B4e=!USAlI zrd!D=AChW@XkkK&QIN1PJAo@AX)P8Z(jj(I5{{#ch=H|V(t5X$%~A2lPEmfpFnd6f zH|<*+eJiVS`gQ1!pkBsOyU^;u_=oi2x7;bul6|5DD_7#ZSg_#HuL*-5r#bEbT#^wu zCo$A!K4NQ@)xSU?@tKlNr9 z$#DxX>}zvo34bm$#9j_k9Xc6+s$(1NWxCS+v;M)(lJ~8S4qs@Yz8GGNw+Hv~NA3QN z;YCg8g9U(AmMhFc>Amgd?9u*KM|+XaiK9N_p7alcsStWQosXRA7eS$p_F|wbQqlh? z-`8X9S6{_m8e2LHU^vjjGd0$=S82*_u7k@=@}5**6`LAk`!1x_*U^0!zFV7#Mw``H z*3)6S3@5Fq*y^nvYO+$*Z}FPG-n6bkPkq~w%@bQw0l6%u1w0bMu;58p2`4um#t^bo zo!{?*Sxh_l9fsNySiX%fC@X_$JvLKdjA?x+>s8hPltY1tLs_`o&lj>Y0-#g0m`-x$ zW=-U>{Yj*cSnrXA)T7`j`#TuPg8|?> zz7BRHqr25cUs8$A2Rm-jnYVV({mh!#2+e0G$pG&y697B1z+J_F0rthb+!ED>Tm_up>SQ`SSP$GW(IQxWQWH^64yE zMZ%Mz(d$V=Qr6ccmOZOOTjtN25j2kQ6&ex9dH1hj#vX%1Aog$#h{94Q#u2NEjIve) zg#2pU##C6Krf{zzt3vwJqj{I046M8%(MM|PjR};%j3j_c z`Hwc*sn6v(&bWcs$4K(DC*dAHndz@=vvw~;@XpU;I1m?7&x?KR!qMI^KJLjAo2#BHq^%@+D0dW-;d0nK;DMlG1a(_e3XIfcS zW-eT_LdNgmJiiPm;|zqucHzjp=gprQjdwZVqNp`uB^o(TP) z2K@I1Cny74=$E8hQW<>8U;{d}%O8-c$w|rqMvZP;TCd2CC*tXEcZcyk8PfS9S`gQV z%(3h#f*q}}hm&Ms>?uSYt)$<(eHr6#9j%4=!!k12M;TyHj|2Y-&4+G^tvb`<`toRr z9E2p&Q)Bgci283x;@rXF+)3lqd&hagQ`{RQ4w*V82(Fo94vaq$7+5C9d7@L)N5?a# z#w&J>Rk-^FxIE7Mu$6_~)#%=DD@3cWGzxU!hWCaD-|3Z7yxFIbRy=}7LM3-mPfv{t z-5B>2Qvuovna?PhR*{`9nSS$W+>xOE|F@0{)?t*9KpSP zVvsj2|1RL!fg|(u_Q2dgcZ#HoC#%#NBG}@Xi5tXcKb=>JhdXB?Ths*OI<7~kqfaW` zxma>TgU1sttuuMzGrq(aFYkb6t|Pm|7*u|InOKAEw!v!Ge05^JE~w-H zCp+2JU8Cu?!E1QFE~>=1okhTOH+9@-cPAk%K&n_1>?J#fkblQ!7hP$kRH07{G43k% zS6#qBAVF9Uca~wm#xNJjh`iA~J+lXU7@>ulO)fF{xroIivX0SV^92 zSDPdzX_C-J{ynubm-(^bH3b|0-*DhKtla|)P zkn|<_I#zluG~P1R-P+gPLUlOMJz9X$$+G;&zE|ty<~h*#Xs~-=sal+FQ{fj^;g?tG z-sv~yd*(}it`&o0Xj?kEEO^6?@;K_`?FcMETu^gc4e}5@w^4-}V)X~~wy!(y+OSL9 z$!=}>9Bp1Ck8Il9UAlz0Hn(@QZ|-eg@Lky;xVd_cw6BwFZQ|cuK7vIkDee5YrfO0o zCdvZOK?1+kzvWzgBcT7r77#+P6{N}8jP937HLJXC6yCkuC(`dD9#c8bah+tp?6&^Z zcSAB>CqcH`Uo)somytiG8)AG3fsl1QD-4~#K69-;JPe-wxg)!B=~CPD>6!`*92BHd z2!{Ci^49iKG_V)D3yg9A%<|Pi;H3sV_+~Uj5ktZn1X)1fbm!pP8qKz_$EdjU0iQsAZnn?YiSvlg;qBETDUyB>QcR&g=|VQ;qSf>^cMJSPG%51^A&3y66AFkvNsR*ECHpT{WUv5s5&`(hL?fM4{2V|d+3uZ z?t~CY3y-&N|4RAWpC;mvN;kgFSKB}EM14GgyknOadPM{xm8TBp0k4)2ZKomJSE=?v zCkHEuTQ)wc(@@GEEhWVtk`C=RV`-dAdRo?1-x4R+vbk4qT+z1fM2S*-Q-kZ(MxSJ}U;WJCI?K@W^cRbJ` z1Z>=W3)kcm7L6)h1IfaTb)0cz<#-+Zi}!b%$$w$7RquR{87|f|RSne%a;ls0kZp%2 zUmZkIv}f{k=5uc^x8(X&H{1;TvRKS5DbibCi(e|N#3kI$Mk~M?uAB!4Fjlx1;zLCW zNavAm`T9y#$WT0=K_fI<+jB->s0a4zv8Qcr46W0UcQ9Xmcd5+(Zi2+ z&sn!3!=}r@>-^x+W83udvVb6J5z=p?MAI6?Mf(t2=rGCTZ~a*EL!h6(D0j zWIPy{6FIXl{I)u?pkeJ+>=LP85hDxn(RPqimw+R1;;$W&q$%R*I-h;!uUHVzz~T8# z&;VGXIih(jr(Fp5dAdkmW*fT#lCH{OD24~$ z@oh_Mmi83kXi6`jpN>j(XCHB`m+E@8MR#ahSEe9BYZ8M_B>yxexJXEe#iXTr9YCkjXoVs z@wn``H-PrXZ(IAX?mlCs2BD_J^|aS~dkgioA^0;NX3L8xzZcG5Ujla;J5UC{J><6~ zXBYvue;M&g%*y)@)LkKC9o!TYT0;+y-SRZU##WM@Z!MjUH!o)`Rq(d$Ns&48v`(7hmmDoi?-D z>qPbzwlsC|TpnCGs=U}7-{DqW{@m1hbmzozCY5z~w5+D(OpP!n(9AUrex71?w=U+5 z9ej4Tp56)7Iecz-sjyuw-S=fIZPpor&%?g)(>XuzFT!*9I?s@;)>&Nr=|12%`uE?{kG_bQuXa@)RaF`(TPn<-h`KyZJiijo_Eoc zozqr-gYrxa>E^w(;J-e#I9UaS)IOu1uv{)1io{`_lxIK~qC^-Q`Y7qY(BG3EYg>4> zC?wjAKVK%^OggnQS1NBbS2;b+gNuS#t_27pm9#HVQ5Gu7iTG;Otw{0Gt1O;;wnk}`ynl*r z&ju}Io@wrR=F}ifORSF zH7YJf@6@2z+mTCI2HiDE_tlLzxM6g~pjX58=tA{UR~2s-H`Xy*DIJ=#m=m)CaxWqV zJ1^rre;*n+EcOwxMfV;jjR*0ctcL$qN>Uu?7OE$2smZ4s6%I7Tc{gV##k9HfUg)25 zgP^AgcmWZ&CAET+!zsM=3IvH)jp=-~ilEWenfTmmLNAq*+$>|ZY!7=YHdi^(;x7bR zmaPDTf@npw=6)cCBGj^m%+v(E=4IYRTDANCyP?)nzFu2e9$FBOItX5?cNJFSrh^9o0pk~+49(SP`0st6 z26I7i-_P-pD)1+@PM+VaXVHY2#swd1`I8FOlFJp0 z71fEb?OPF-&uFr`**3#Rl!R)k$`>?dg%Et4kL{8=fPG20CXiqaFFg%LRolzt%AU_u zuy2u~Zqu@4Jf*^&`0b&{#=JUj_(E^5KO))#!C2LbOIfITyR8_iaL{V^ARoTdBFC4mTW%r{mvDq~m|ptS(b8 zsvEe?;51IAsKf7ibZ_q?jkXBcebdHs0gykulmkL~)?GAX?Bk(_K|Qa#3izE$Rfx{E zcg#xGJPFo4DXdynzXCEI0lJqXU2C2ZA0KR<*$|~t7sX1e%gwrF4Thg%ASMKFrk@>; z-6QNT+nMd_;n^wHCwE`tzN!YFE(^RLKa|6JK}J`)tyj8~uhaE>koF;l~qTb3An`Lp;+UEFn<$Iq|@bK_313i_s zoV1LTwUk|z!=eOane1yz(b4fx@Nt%S>FKHAr8(!xTLZ3YghLab{c}>3+!$otB`z<% zK<&j*wpfQFV)?8-uV%H_w=eDbV7mB-TZXPYD72axU1|RrCt}jnB&DeRyHx9gfxN+y zaJRIrJ+4|Kg zxwNENW9G{E?;$3`NGD*hB>?KmV}sQ4h)&J*yZRCRU)8n%sO{oGJ>hjv81y4}a<2yz z@$v;khF+cxndfw3blRGPW;Og-sZ^y1aGI-gveXc1m^%}urS~cw19!IuMsajQR;UN$ z@&3FZ_Zm#gvhU_k+6nJLwaRw)u!Db7eLTyu^xt2-L*1>Zwf_Y0~#=Hu&UeJu+&RgE!(@<3ULvLf-Z>mVPH5GfFTpt_|=v3!^|`|^X)|Cewz)d z&h~|aN}%TKX?f)z;OQKYQbXfZWudY37X7MqVro_L_>DK`=Lb)97eK^cu0E zv-ATqV)`8Sp;;FygV;&zy=ikLLHhUGUo!uQt-^8E3MqRFI5O)1e+`O<|24pbAIEPh zjKr{l2#W+3F{j5zdT7*(A_~byj@rlYRW27+CFX(z92EqsH!@pk(cd)_1#weEm{Y|7#>HHGwo-XGY|7j`tlxFU zZ6|Avn=Z3d;>JgLi>AVWC0v^%ihBYJuqcuUfdrwM-+hX3QS>c5t^Vr; z`|hY%zop@77kZQ6G1@SgE5l`xjYiWc{12%Wv7#{$1?Y$)1Y58g8W#FGE3>V%w^bCS zVV3+rDno5m*~~8Ll?US7PoXhN)S$N(oGVqRyd|fv^~}=3~3t=0z>`rsFd(Z zDaLtjD}-W{5*Nz>m{67O?)lz7m>K=~ zBz(PL1RVmw4`4w3nsTD$6X7btL81snErc+z_wk-Ea98Qs(|-UmU|;1yK_~PG<1l~z zZ37SR%9cs9DBHN-boddf%>31>9C|t+h+QTVQ-cYpoM#er;!?2X#xxk8qRAaMYNXt# zz(+M#N$H`E`dDo1t$>CVl_pU6HG!mPnIl3%D4d^9KrbpG(N7ZI>uAELn6J?&p`21s znCcWpZQUSJ^jj=n%3UWv9+L*pH7uE^E|~uwl|m`F)Nn;Dh50%mq!$tUDoHiR2t{Pd z4hkTakVFlm4WsAF*pYOsoy@Wg67aa+HOEMIiv-lPmb1%?D6Y3MD>Rf zEO*fW=Jw_}6U#mlJ~5SPN@I%c%#jUp?1)J+iTrfupvw39bxk0VkYDj*5SB;BA%P!H z=68T(UAW`V!IvO|>wK(t;lWmg!56aNJ0r9sD(Wdw>M4eD4*5U!KB9xIYJ=_?Yl7i- z_d+{G97lSY-X;q9{qDixcN3w6xp%q!aJ{q;zsq6^M&PWI;_4h6D~0V>f0>ZTJ3{wv z5Lz9T(PR-@0QZ(%HLetLVt$=q(0klFDA|nfr=@fe`O|H4ZRT59Uv~D(KeE(TgsK~zRLXHvY{_6&^Dg_ z*e#cS3oi_slWp{SJ*T;#u4K<9^+2sr?rshR*V*Em5GM6dCih=dO8TQ6K}J(Dyh+!< z!IGrK%2+Y1B%h2l`uv0bAHMD}NU}9d7j~D8E_T^gmu+>~wr$(CyKLLGZFaf3Y-jn) zy=UgkocSWoj}?*mtW-qipY^_Z-&Z7YKUtEE3Zs(Ll9HrrDeF*ZJ8S`|GB;D=rL9f(T)!x*=Wm~ zfW@M#XcnoAAYm$?JE*O zIjB`X8Q*(jr<-Z8R^69@Y9Ltp9Y4Una z)xWQzTnG1qDPp;woa49@uf*p(vB#k4dPkTxFASpyvp*?} z8Ep?orx7lgwv-izATXv*SKl28fmxkEE&f*!uz>0W1lT=A3e4`wLYp zzXlHUE-K2zT9BF^D;xY}6yP$Sz`u9g42DL>))WX_M`HoVQ&J6>|BrWZ;V+`k(LpBe zP=)dc0{*Xeu?QQMoBpqN@%WE-kp%QE0tr|)Q2PGl_lj`W@lwl#eJ^5T`Xz{0r`DxU z<7qlMTH3FTbdI&F%PZTb8~sLVUD!Yk6&_F`?wA(!OrhrA#o~ym zA6I(uNRE11XjW_Y(@SVHlWS4L#v49MRF;K0wIe^U^K)T-3NWevj<9;m9(enVdtgU;FgpOi83y1? z18@cbUikOVyoYwQ0XA79dUh%N0n#?!8lP3sz0v~&va5o93m;G$_Uz6lL9R?)WoBe> zt%~{$5|g03-|OWU2FCJ{3UNW-z?nEHt{y_SSrCsbOX$1HGk+g_R{{nilpunGVbYap zqG6DXi1kK?L`H^4M?^r3_8~@wqM{@Gev-ptp)%|di@GuiyTZ~Zq7u^*-+N-IStqke zWcr|RUb50NoaW(S%PvFAAH@0yZ#96KVUl=ksF&9qfY3yWHJ>ilGo?Q7r!{hI0P5 z7^+wP4@+Cj?u~`~b6vyMG8pZ9^bc=Y_?D!q3yPQD_%yEitsIh$krH&A26^#T5{Re( z6LjC5U#?pWH<}!Gp(;~Jd$P`d_$>ynxTRXP(?we_I>BfuT~q?maaJ49iG z@UKY5PEJP=(9`*#2N)Fi8DPNEpsq#+@6b;(|AV{8W?O1{RUxdtA_9HYWXS857I?!? zQSBV~&}_q-3;V-Py4gc!j^_>JE+BgsrIAi$q#{lY3}1PpmPka}#3ddX$u|jp+xsoS zJaeF4!H{p}0`Hg(@Qw$2MZ!E|l5Z|bx0wM~V;Y(~W2esF4;Czc-U(Vfs?q*nRnsMe z*Ir__k6^s{QwVH^>Z(+_Ul|H)mL!oLgtXcMmZGKSwY zf!|zYys;bU(~Ayxiw=N_4hV||0V`!h9kI@J(}KyC`cncLL$QiTpN=3_4kExf zL{$S}XtCUzOx>pZ;W(p3FyI2*o{m$dq#^xwx^WWSB^+PiVwq`ttwU+IJ`kE|Gx6jSV z_l=W;jT5}F6TI=0S;wifh^_2>;;FD*192ev}>1v(S{(+U+F z^+46Fxf_K2!9g*&N`h7swEN|n&yOtBrY#rjRw%}Q((lE_(mCuF+EPXZ_MJr1c?-f?zEHRONFs3_lXlT+r=2jLeG+-1`NmPM7%*bumxUTb@rei{m!@GT(gkND^@}F&eFnGK&MhM`UxJF~`bfHI$NblF@2vD+GU~ z)^wqP+98_`wiCI}A6IE-$L{9Oe^Ch`Y6QFgF)qGsdp8}W;9GvXj;+D4e1}uQ#<2C) zK5zF@Kb)n(oAd!zSW0jl=lY0a+1k12rKaOI9Ui`at7Y`D8q({jZM_zC%}?aBXBTGp zK~WUR8b@&PcXpP)Gy02gIJYT(&Vz(8-LAt<-5_d__XE9#{(C51?6X#H+k^NY9Es0@ zr((Uh%gxbVx~JBM8~&vCR*WewuSyB=uaERet`(iNEj=`s9wEgq-A|CkL}JwuvR66= zJMErV&*apx!$Wwr0B8DHXTvQu)}IdpOOJN_<8bsk|id-Vu+ z$6l3hy&yoz&1Ab)6YQ`VnWU9uAH0*LJ?&VaZ7urc*M%%JOW}RRlG(({vbjF@zA?6* zPGZ!5YOVIra2WsO0dQrsy#n`o`PY7?NL8~OWen`Hr?Lz zvaGl+-oZhdr!Q6-W!g=OF7xykT=VLyXTsa%7}k4rtO`Y|-SHvGEDw(>=fWrfb}Jyv zR>$XE$oKAL{L#nz)!jn%{8IOGG?YC}*G6NYmo{Y9ZkKNFaL$hS>f+(vTbL@9J^YPRYm&@s>w)9S=>ucGmeC(Q(wu5zl-|+`V*VhB%-QzL# z^!+xq{%TveJGl1DL!Zi*Io6M0RxL4qKj}6^F{0nz&&_ZvO^%l(0lwvaw{}h8WXH6Q zRJfVqJ}MZc0;ZU>{#O?S;=xyZ`6IAlD)>BZ_`H-qAN(1| z;4+Rsw72iW0R_fN_Y8ty1ven>c2ppGGl<}R>iAw zcm3RMgF1kBTA4my{^c~zTB&t_^ajN{|NYL#IN;i(>{H}ZVNK28ZK-^!D=S32yUUr$ zN@Fz5^yaZ3QgI^)h3{oKjd(QY@pjAea&Ni^#aF-ldP~#I=Y7v>u`+B!(*2-ya3!VZ z>c9=@iq;NaWgw38d+XDNJwdOf8(;`MQ_8(&rAyZB$@7Kmt!4AQtu}5)kHe)8VX5c( z^WNgE)#HVECvMDqy>FCYljZD*B3lZ8heW}~TIgdt+_y#+^{(49eF=H*c6oc3(t@Bq zm3x0DT8LET^A(sDSCO3SRG$>be@v^>b6Xe3;EvpIMb^~&Vm7f8))o0w2zR?TS>yf^ zI47*O)DIhDd%ha;pmPP$14{w-c%#rJ$!GiC3C!*v)fbW zYXc#nX~cWvDjGbepBECuzcNdr zBx{UJ%&+2k)jTvhnsD9$DP53yvh8JcXqM4{t|3^H>k}8(mE^wQZT?IN*XPEa^sSwl zXYSUFrdVQVUM7qv!mdFphFOFOt zh*d_}l_`HZ30>_g)%rTP;}zh%M>jpg)s$VS7c=`UAH>$K(zwlz-^2i=Ndz8e%@zHc zQh!uxdG(rVvB()doCyPWAWNTXH8MPF5l6yS;08nJZp0!-H1(~6V={*rbw*s7-+gLy^}BYAEZnv*IM!bt1iM;$4DHiFVt1lxz2xT+YioH*c6jH$ z$r>f8ZTpl%sm2r^=KkmbYuInQ>jEHhsH~x!Cd}Kl_jJkjcpnENB3_vnFFLxU-skcW zN@Gy8sBg&p`7HZfF5X&@y(zT|@nSc8jhVZ*xO9Ug2r8c5%*j3G>EdYhgE<->x1CM* zh%nxUJi5i~2KtyREYjx}xMftH-USQ!pW6I7%}0)n-T7HPiSD&|k{n`_p}j6hQKo{cg)Z^7F~Nt}CtNlIn(@XCXC zZhaN2+(EiZRrNH-v8!%>TMkEqwBErj-8D~nWzM;e_}I0&XW`i@qSv=cdffsDjtadvX1#9@AvR zPdFizhB#PN9;TSp@Nl`=k5B0R+lUdXl4!a;Q8F)4if+0t)BO%S>}rkom!K?GKm9W` zgT45O+^pWi&Ha1y*=14=#7m)WJoB(1D*qpB&c@KvUVv(n{q|253{s59|z+}sNonekY)p2ctT zObxF$q&rjHF`@BK@|U;gB68n4cE-U6_Rwe41EJNycT|ceIclZJV#{Vp>J%zasuU5? z@)X5lA))dmrebO8Qpym+5;7y@ktLS2pY^R{MTr_Z?`jkoGGOAIdv4ks@G%*y4FZPJ1j)zhesL{zk=Y97}{II=&*qbOvfrA~6V|iDO`!*K3&l(!kX* z4-Yx@42dTj)G*f%IVCnx;DAE_7WtN{!Du*Uh4lRTsZ&G?F(V{QA-!rTl!{`O4}uD$ zIf^qz-6Nni&OD+qsc4YR^7o)SML0c@>N(;tGs5ajvY&}W93;^)eDntorN;J?DOryD zLZgz0PlZ`F`kY_BH*k=_K9*arNGM{Iisa)*O(%jQ|2tE;(9)$KGIHoZZkD%JE=q9QuH}Ta{N&UT<>?o02=fWt4tC9(s&TZ60<# zX8|3L*-dlB0h}79^v=$zjj+U*u|(EaMy?bIlb=|$74|9P*80vPw1w0nDVED6r?eG> ze&~gZ7AgVSxzw^Y#?Cs;mQfoFPECyTCC%1jdVb`SKCBqbOD7-7s!{!8l*;mHmaIRT zsiS6we&SmQq0@g>TQXlJb+F zXp-_;ar%$+p0bwTfJ~x{xgCg0L=&z&Lq8dm``^>s;8%<}0T^saa_~U0&ES#uU~>0? z%+25uj5%qT3@gRytScX|V>u?g|zVqUDl+yL4*q*8_gyTEjGtem1h1|I*nob|k7 zJ4rlJ9g_yrvME)mlH~z^RTz3I1c$=@;2f25%P6s0!-q3zyqkTx6zd;x@Jk&=h&QT% zY*fHMaW%8bFtt?o$iO~bPHd6|W?(JUU&Pl*>Hb2FM%^RU=ai{jQ@HZgXHy5lJANCzBnQY5IGgFsTY6vA4@H#+L>0sCkU+UggJ@$?hz)iJUz-N zACJ;idfde(I_q(w?<1hcd+a3qkfnsM?8L_`JpA?^gz|!-o?vvHii@z>k3C_h^pPth=PRVN)%3`QN zPiHlyVF~@_2NI(126UrL8KCR zxm^EV)J=iHrs%iF@OY=fsZ#bI!(W@wa5B6RLscmTE_bE*jC-xC1*Z8-d#z({Pp;hQ zw!}U+Ja(m_-|DsmJU7JjMCdu$wou`C54NupUzld5>}u_sMp(6d(yi;$oPAl5RLCD1 zibl+2M8Fc^IiNYq;FMyUD3_Lx^|Z$PYQCH4%y!$x>w>%ZtD}#34Z3qn?>~-qy*B*H z7H%jFZO|2Iq^iDcj>ob$5h(bUEIZ6gm3S)*>W1V>QvGBc)J+`QOW{R#^t<-wjr&cI z?t~#U<(~Q`g`q<)L^5?hNbGP>`>r?{hw|{gL4z-AMMr}@pSYk$5yK#(7YkG}RX=0S z`Tg)wp&UzzH<1xyr!%+4tBqoR9GcXcD-9MZ7?hsMB z+rVIFQZk{PSJ1)9jGQ~CY%(iyr))Au1AS@9)<({B;?gM?!=;oVI*auX8YC#(FJmm< z4zW34F#v&C)!H8Th-kLiiD54XbLBMR6Ay<^-Csz3z_;=z=7kiBMLHULu*vlk-)`Ysj90=}-7@Cg$1AmMh09ZV(>If@@B+ zthLm^*pcsr*m(kqm=eg^yy2}1`1+T$W1mQnya!Z=Dw&yqs=t|2zqH&}yGJ1#zh$oJm8v^auTV$u4HYwr5k`BgTBqf8tRrE=MLJhfEp5 zs-v+S(o&5&t0&&b&hH(P5&6>s&`mHlh3A;3)cL{mNe3BFw)3CRasCC=A0gbe3T*O^XCLTohEe*@q#(= zC0@UdVR+{Q;WRIVTLk(hne0COZD8jE%4TuvF8wv^RU%=V6hXfZ3WVD!O`8;dzfR(u z4HtRsYbLnF6X`D6_>n@YbYY4Da0!%1Qs`Ghay$qq5!fx=ya)393vYo_WZu2L;4u+O zb{H%d5~?#k{RxlIoCetmkK&vL-ANYpN!54f71iPZ<%bn8mlK#}gXWvbM*Qxc^Jy_U z+z=d`1GjX~%}$ThvDHOVS!q}belLc?z96j5{)Z>Z0nsu+o1uB*WkE2^pQ<&M{-^F9 zMfU!MjAR_SB?s@}i_Fia)Dj(SJV9bh{0OGVe=;gY*j#Zd1yfim%=6NtXGjY{US~p( zeVDb=L9yV&2bp=}n-UAivj4`gk*e&r>U!G;u=|+shQs-{l&?#-XCS-UM9*Rk5feaRZ@4PyD|rhP-Xm< zQ$x-RpZcfxi!{}Dg#M#S=arNAgD1haKcv?C#)CG(C>uk!Y4Uis&}hokXt|yD9ZQa= zZ0{x!@3DbplNx#iJlRSE(Gr_#>4BnIW_sY zND@N$Ap^azb4!QGIeSazCMjQf2A})J>mR^SQI7+n=jeK66j1B#ddom6Pd=K-WV$M< zJ6xk3d)Q4xaqa48eT98j5g0ruNG6|w2+}@|=MVZ}Aj1h_X%}$ih))T%GVqh0Q{Dio zTt?swIle=khtF&>n9KVrYC`-C3$}`4w1u=iTyQw0=Ij}f2Uw5q(6y3wP-ace`*Mex ztI-c>KGs<~7lwNoB`5ce`$V!f$G5cVPidTMQ5a=^&O`YJ%jTztCr^%f>5>^EdQyP0q-pw8$dvVMO) zrin`Q;JB~USNm%Fy|HgXl;D#Q8*Mtz>T^BxLzA4w6J`XwppxCm%pPh$CU=yCyC!epF3Mf=th4#0Ev}1l9&N z|2ek%-qS!VaJ^cATML_qFJ6XzF~;j>8TVVrQR+AuXR6O!`}Z|Fn_1oN2KNy`C%Grt z>0{<@K4_y-Uy)gzVi-d;Buo3rM_X|2{5n64mRh89y!NXX|F)k`Q{fP!K8V5f#YwIz zfX^S}h`h{(Gg;N&FCsu|U*)2w5Hzp605o6Kf4=<1GAeghMsIFh}W+p1DmB% z#TxETQz3FwL+WR9J%>y)h}XCg2y$j$cmlK4>wqh^1;1Ldsh?H^Tg_KGKQVP4;E2~T zklZM_-OfgGbfzF161LV1)X1;#Qn5$GFdrwWl0@r-b#k@gDB}zZ2aa~!&pV-AAG63x zy|l0TuWZ~ZPiJJd)?$tx7JC4B*aKv-TLdlkv7+lMee2NY>dAA3EL0 zQ%pPUD)@bUo^3DYBLigi?>ifx7kmp3!d%Sw)n9BD`Qn7sU#<_Yn=+6me0|*a1Ln-W zTW{WYmOdW_H)N92XYJUF{`9iwsHjH9G3`4?;gV7~W&A+J)AwHOlpX$;OC) zgnxZbGzw?kUHG^C_V+`!N6dBAgSTp^ri{})sJC{Cle;_BgT;REES@ecmq37LX7PBq zbC|q4xpU=83!%HWkSWb^ z>#c5@r>7!AO%A&^KY7{)3}mx;e((I)>!4!WaLS+k_TqpuUG(r%g)W1DgRI4a}1cET_#-QH~Jh~G_F zC}UfF%GUAVem3JW%;q_Fe}t!*)jWSG$D4D}Iwf`-Ti&&VKi7#Db42g$rx7zhcEeOAI^w-NIyH9Q_Jo)GIH2JkJa3&qS<7PI{ zTV0M14U4+f`B`^7j16G<_QiLX))$in78@RQYcKul*VSLpT18kLxN@b| zc^Aj85B8ePqz0vaTROSfxoex`k76`sT6r7732GC9_sjms;OrC22eae`hRAq(%B zt@KYLDwp1a#B`}M+VveFu(G(p>#aAFDZ#VV+hGPdJDy)2CJ#4W z&k|V8huhp+JMT@=nm_k0Pw6|6l;Bh`qq5gb5>=LpT2_L2E z#62l7mj&wb?IbHs>pt?a6pu5WMR-JI9$zp;-l``%Uq7RqSy&IY3#Z~0huU!PH6`i6 zkmYtC7IaiaFTDTl^?fWvT@}+w#aT778Y`w^*_57c8t1BnQc_@ zY_PUE?18c{kUuzEKMR-DC`7Ase?-Pyy%*Un7F4r&zPx{4RZ3Yq_r=~9*Oy&!-7c{{ zxVFdmE?FjJa%1mo+LIK{!;n5NPee{svUL=WhNL9bte@x|9%SNkTNiE#k@LoGn-wy6 z4IizFKWe-V*u=?VIz2A-k+5^WKR+t%@>k@(PDgHOHhsz7R?27@Y-n(5JqMbdLF!>~ z7n1JOo$ROYXG_V}`tm7`FhPuGa=W-I(#A#Av~{05+&=CZWwNGx>C&DjvYQDkOz$ro zVNrnqRh!Xl=SRLn&*Y8_P4`yo-NAhTH?z~~Re7Fvt@Dk}&N(0Y&YMQ-rxb&Hs#^6` zuANV&=5~$Nei1{hVzp&!_rpgx^U~fU`KSMR@HE5J%Gn)tnsV+a}VW%GkqG`H~-J_`WLV75W#M5$#X01`}M*!xx)v3 zQQw$})FR~mz2%D@F~x-EyOuG30asxVWb^#bocAc`6hRzJ;g1~}{8I`-LPAOoS`K1T zVoDBT4tU;Rs8^`aFepfH2uM+INC5Cg7ywF4pAPB`{CdP}tw%)4yN}_7iZ89C!s6;L zF#H0JO2E2^k_VIXg+1kaa~V#~_ZgL#*S)?#wcPQ_EfgVFQY4Sml@$bQAB;l4CL)!m zZby+{QBKCEo@iLyp^j*sPW=%YT1yRGfagpaoD5*&C(I`IIjYtp^Z3Ii1pqHu|g9! zDoU4Xi5U1PL+aaRQiMF*T!2%wC@D>->a5@jjG_JMp#T~`;IoG7+O$(3R|@=)hA~Hh zZ(qQu5V-tvSI_9ffio+4_h=;@^XgFylUv>??3@R}?Ss|7v%mw;>oQ*AcAV301i-f4 z%sMjWVSn=b?~N4TWw*LIXMzO|a3e+X{}q1up9aYPFZ}YK$(a8u{1OQS(f>LA*G9^a zCX6%M@Jf!e`aqqq0O42<_)vTZnSg!7U{DzO`C#8PXtyx)f|9G8K_9mecvcLy~O$17(p8@qaQ8(U>i2zuTw&Bt?T8pxZmKymuFDR@Mbs76+zZ{uDVj3dShY zvs$y2owlxT%9fe~BPnUtxG9bXxY^d+<<$`P0s*SQtu#VfHTS_@215NCeo5&+Fn;ifBcg@(%18EqZLMBsE} zxG_|BxV{~%uzdVW{IAeAbx{Ko=W zv}xYail#6GTbwwnI6ZbW!0^m2sM3uYP&a>c7kQH}(gz<8_me{l5S+(QW`HMc3UYBT zu+&#UGY6+qL~~&o;!4HD<)mlV{AQ2|A36moJgxsD{|C%BR=Ci&ZVjeG#gxO&Lw!QU z@mFzq;tDt@iy~&;4MUM`*-_>BQ-KB7p9`r6R7JK8y2-Yx3==GsQu^5=M3y_yD-cZa z=Y^5}oSK%`Y37i|lF1K+#pW;$fN_+cBk&`V$(0xpcaB+TY4i#uz>G@@`LARuR5Hm3 z36x>429X4G(#RdO2vB30`_&Ztlg3DYI!$ca;$MZNXq=r`;V8iF29g9ig=42*JQj2< z?f2Elzn)!lBIgE-aL;C(CY3}xRa9|XoOukv5sg_}p`{USH!R9S$ir4ZH7^9WeI#2| ze@6Ee(?r7b#Y7tZ2IXm^Eb#m%`4aVS@}-N=c50VOBgTm+=DrE#28sE1%}D#N zn0t7P<8;fcsAY~>#e?@I>|?VfjxVJcrwHuhbqhZ1;|1c~LWh}Qho?-zbo!Y?%dBIO zFz^j9JX`3a27x3*&d|*{1uRAyhS`jOKVm2E(RL?bS&EEh20uT zklNq{W_(P&oZp8cIEY6nYIS`UbCLS z^vm_{en>9zql4+7SQ}EK>x_PK&GGwc*)Y6Sa7Bil`y^{j`7&)L>S_;trw;Z>6f8;Q zWElE%Yb)5b0eQo2NAo`%;F0E@9GVRs(fTxjXCK6Mbl?@2)XD_%h#XrG#Apqhx@mac zWpJJhH{TInM}ZnhzP~@#1!;w@vR+U{?uoKjkX-p$4f669?3xi|Zn?XUF6gHTq+Xf( zrONHb=*>p${$p|EDhPUQ5Vfuz@O1UQ%dg?{y2#b<=(U9lrY@*1ZFj<;KG7kGX#PKJ zez6%!tY=rdXX&m($3iXRMGl?~L3@hY2Xa;A$Sca%t2GsFN}vRb*hfO{WyTYFSNboS z+K*%&7JC&f+ln8{m1?EVCwxzbG>cf{bTm{>Ldv4>!7OTD@Xp#e!-^zlpjX}PT;DCg z@|J@$c77%sm8Tj?16Yy@7Wny8; zxAs-?0f_}qztj`#S_$Wl^9A%==Vfv4sYX6B_pIM{^xsv?omt+};TA{SbsyE{ZfJu<3si{xd{z9|prT}Xf!p*t^VQa{Sw zSgdymn&&OWn=~x!_&UulgCCZt6GDj1lKLF4R-w!G414rdv6;a=b&$4@SnyW++(L#w zfI!D+9fmCmwJ?b-3ftgDn*=n%`7Ze$OI1{6@Jb!NrC4c75*N~WVJ7j~H@5^!KR6Yy zwRqa9@2dT*{5FUwHqyo<Hgx6I!YxD(aM1cW9auv zZW7h1SapYJ_1kE5m}qt0AN8J0hL46rXVc*+;Tab9lxnZ>d5;Kp)R<7;l`07|G!>D( z9yvhXm@lC8@z7N`q2XD!P&;|L_}c?_ixWBtJ$Xd!2sM5+;CE99%{xWb;EEQUAap=^ zI@tddjZ@Nx6=h(B=M@_yItk^G7|bsdy~P!~!IkZjUcSK_G#HdADkmd~tK@<}P@l2ptx z*I^*n=@M?iVW~&`P>*uar!&x}Gx^6z_lyX5WC8;+=~kRzRuEw4=#VRwZSC!6QV@#m zjP~ogYd(A_8?7s)ke-GN7;rd2#zOy;G^Ev_W zj&2~2Zp4mmI;ZwzruLqH4`w>Z49n?g6c~nR+1tRA%#KHbi)uJ>U)s}LMGvVQ{C3vn zN{D^-?U?K(D7n1*V=prlLX3$C)BG@pDcKPi7*}b}jh%_WPv`*nDZxlK5 z(_6~4+EHTl@KxV2QRWXGpNj#L?K?`{A6o_B{Xr(+SiB!?`NWG*f^9hCFTaS}y6lm1 zQftL_L<+%2O<4an<)<#s2AVuN3FY~n!aP8b%x!z6EbH3mrrN$eo<5ODw1SapLR~AU zdJWdHEZ06(goU3O6nF-koGA!x8Wp{9kX*rtZ6(BbB7l{7Ei4 zo#NFOV6|2iy(cPm$jg}`j~K$cJKzC-ir&;h`|RKYc3hy}`;prQkpW3W_Gv^nn8JO1 z7SO>C*r2$jM94E1(8`0;)9sbNW>n}AmN~0gmIS7?rJpuuEoxd9B!lRwep!@_?@7n) z*5x*HkmGA;@Tg-)i{!H@lnw4J$fp?<=i~pQq60k;ZEctZCl?69G*U16e>@O2x12Gc zivDUoGV!m9{sZWNL{x<10X-0)ioOdyiS78?B!&ZT659#rf!u}{cf6|84wC=lf!x;D zQ2{*=673uLsMv;S4xk4z1A){`$1znziISdr!jP_^dhx>K-83=fP?y5ybUv5<$DFx! z+0<6t)K<>)%HOO;R1YVw90IE^R>d?fEW2r)J8TBOdU{(lvzsRUgOSOLUDn5WU`PDd zexxg)4d{V9k*#04LhAMO{iqVEBJaD(SXP%!GjSLaWm_G8*Y zBXumqRD(cvvY`iPf_!k$O(p`5SD+1ops!(Ss4>z=i!;_hi`9wxeZUt09@a`4>&@jK z^y}NQ5XWy?9#LV^**)U2J|f(bm)!x6W255-6A7+lveTcNMV!pI?59ee*}G7zuMV=I z}g>HV4BRuM!u6AL?plfJrK}IFwyt#kU$Tl72BTp|9BvAaq7sj#N%A) zMaIk@nH9h1+@ygX$l#X{|6gvclgg;y6Lwf7<^}U9qQW^DM%4VeVqV(=X9)zU;>cI$yOr(~Yp`^D(_* zrKE1Jb_6ra1f{|XS;V?J5?^rBR)lYW=g-P>yRt7ry~obc64F&f9qfz-z?|HchqRNE zr*oe^z(qq{6t<%!efu1M>fN{Ues_c*{9^er`j^g*k1~FNr}Mc|#v`Z3R9&F^?mcCI zn*?J8{^@?0*Hnl51b0I3W4AL`7q@8KYIS*B@3Wb@Yl0_04+>IjWqU<6snWF@Lpreg zWxqJex6Ayr@DT|>_jvfYe8AETx${civZ)?qXU}~tqtkyr7KwQm=PFyVuyf!+ z)~@wR=({tjBPS{Mh$vEp3*_ELe)l!^tKl)T4c*bTW@nCFU*S_y=6DyLe&z&N9_>>5x>1DS?Ez!dz_=pIrz z-4_thz277(Zc1)`$4C*|JlmOGFz$hP5e3nQ_G72!p7nUcH^8*l&AZ##nq6j_SASZI zx#z0-h2MLBG>M7-3d`Q*F+6}QB4aa5Ep=;6hg-K(Zfe70+qNAK>I`x&p|rT@31uSp>SSW@WQ3+7uj*W+ZiqOUZC#r(=Ig6rJg1F=VQ{@nVf zOcN=$)Z3V&+#VX~hkM>yA4XRpMNYley%~OTo!fhwQKa*4e*iD{X|M_*Pq*l`zBoJ9 zxp8tmzGYUf%>2C^KzC0g%KQE4o|%4U`|Nmh!xp3DU1j20hrhgYtt4v)7bYpEhVl@T z{+w%_5CBNSz>+1`2BUb&FWK7g7FZdg8#m4B9r5MLd6DmRAM~=#+US`B4W(K#l zH}sR&)}SBC*G}q=%{%hNbB%GX+%LdV@FlyA+evnqKL0Hc{NO_h_4557F-2WMQ*7CYT;P1`rL*w|(8)j!e)of@RoZG8pG|~QgKJhY!c2KWF$ zNO>%SCvKy$xHaq7+bN&UI5WE1P41@iXho%$_im`Je|rx>Gb78*))qah)pM~@O zG4}8cP-KVgr5pZu|8_7b8&?Ma_>7mwd`wONd{6%%8S|bVNG0K*vFYs|=&a5jca|)E zggvuv-1qMI?O(TlS*PQ{KP}>8h3*dpANX6x2L(J%ESJ&;EBaS2>n5Q8AI{v@1avpTOhktze==Dq zV)WMK1a#I4uwgz`GFg(iaP9+j^|fwXr7PvYx@D@Fn##TJct)1wYq2VAQ%cvX;`~uX z1>_?VY}ViOKGs<*|GJ@LjFMIJyZiXQCaQMIq#QH;7<$Z^SBuSB{mT@^AHJ^n9#rn4 zU4_(AHTPQVCPl#>!Bub?l(MoUfo>*qwKCXtZ|AEuyv=Rb`p5TeWt;k=kQuKQug7cs z?>C%Iy04EsDnefu+oJ<&XpL%a!_x-}6f*azp-8WX`zqC64G$ONGhzI! zmlku@Qn`u3nqP^CCOE#EeSeQ?w;SE0cA1q5+4*B?TAg!G1x$ywz8-9Ah-5@TaM_t5 zy!?N|n8KE@IX2q3xjoB{HA+wX6lIngl$W3y7w1d-#dH~x8cGbU`j9+@6d<%7wo=pm zAhl#_ijqD%<-JcMLFM%SG4>WPbp?#NWpQ`+;#$18ySux)ySr;~_u}sEP~6>%I~?3$ zPX9ZZx%1wexY@%{#*EuJPv7zOm)^mN)T{clTw#V&67={~Fp) zsouMEF7tZrs!kJ?li8P0A-S~vE$6h&8cMsUh&fC4?Kt|(=1`@`GEe_W=01%+mp|eo zTyNChu69x$=C^lydEay4TB4%e(5KpRY$n_6`ALz4FCb}xI(r_nxg~gX1xdI0yrfS; z^9eXERl*Y0MkNQ4z1U1hJc=)4DO8I;PX)jp!^qyuFS#b%D(7ku&?8k|&9 zl8$!HpShh{5VBO?Q!We^$XlU@oJIrm$yIcU&g`Y&G!hWj%N%AUi;PJPLa@XP?=_YuhR2oOfyuLe24d^C;|<7JQHpTWAv^ZgDs3>K4d z^uNyX9CsRDn^Vr1kYA}NL)2oNaY}j5cn3!UokJ+JlzG0*hi70pt`qvbJG0mMM!)e} zeimU)`nysO2NxcVygyl}9P78dzt=>_Od-IeF;8BW}# zQ1uo`pAOvRwMb|Gm@!xAS^Cobst4_PcwgVK(?3NToJnDet|N$8b(Y>)8n6!VR^RvP z-9^juc2&^~5Iz2~8T2KvEC;;Hui=fpo`;hjBRn%4AXi=1WR+{D=lhBMRP^G~cW^S6#XZCpYO(}50 zyrDtpz4};&aUWbQ47lf`L5EcDwgQQ8Wydq4$y3DDxwgfa)tW5DnJ__zVAm85EFs>gkZ%Za(*zi|KYBcJW%*3$cFRIk&*>}@1MNAzI;*Kt8P4w}g2?*lKl;mOo zO@K(y$t{J+92FdWEHZHJK|US?M0>?PAGyw z49nudu?DT^P0fscbnTEV2tVkv=;I)y>5vbNQujzTY4!s@Wgy7bcKD9yszt{Lw4Hb( zQ}xdeX}nhFYnDxM^QG3QHv1|M^;O)R{XazZJdaraFO`S}Qi-~S770C|k_%Ske>U#` zc0Y_Uza|Md7%6 zD)-Z*tVQqf^~?Jm&)C}bQ`d1<@6KDU^R=v+S4xGTAThCEil0)@I=HF!@5sWx$To;s zBxEp%rLvfp5N^cuYNL+Y&Z43c5f~=mfSEf&Z9^ixr~Mt%+d(W zt?!sA!hiqWjS@oBX8`__+(ZpF^`y!%KUL)?uM|l0n9Xp$oRF%YiV8_5AnVp*fLr&F{aK$s0YCLpNFs#OL_Xo43}g~9 zMDg5366V~>$_Kh zZ!{ecVQGS;r-Dsf;1q<+8M|OAVf@D=g#H-G#*SW@8g^~itxN>8$tV->aBahYq3=f; z1yMK5|6-ggkpRv8t`4aisZn^EFm7b_CUQrnS(lEIP?e8|av^63fNS6-OV}kWIeNlh z&bW{i@y<9iRMcdmEi@3FRH5{WiFsrTRmV0hNsJVM-OAXqY}x(?C48CH!YX!;9Skr# zE(@ztAq~k`J9P?Ozbi2Y^hEf7zzTj@pp9b*4mE?hcof5ysh!>txCZ zwZ^Jdx(tt@Z#JV=dc^6-b=IBK=(31YXqF97FHe$NS_6#V!s)3gzEMg=^}{;s{*JgM z?(_dH8`S`cPU_$-QIroZ!x~g-vNV%amPd&dOfAN!`ruq}c3hf~m#6rl^ZV5^^X%AM zm7l-#)wAR5*l1~ng*jE3KMfre>A{)sv2g`j%GEEo{MA#fA+VXp0ca^Vhd$kA1_V<%w+xooIFK?n(1iW{)xrt+V2c4 z_VV3N6`)5r{XVEU64Kzcr!J`dpcQvs0nC{@MAMj(7VC#zh{ zkAWyY5-gs9Yj%x$*#ftRUJQ|LAnoJCMT?n8Y$hGfG6-OiKq(h-lCdlZ9*$I%I${;x ze11ep_#k=X;5RnnGB&KBA0P_0ss-y}oCfZ4=mo#_UAU_ZI;T&!`otGcxk0mgltK7N zS4CB}sCr*j`IpPxpgPOXC_yUnX#TR0KI;ry%QLN7gTJYC^(fq|&j4PPhR*|{R@8xA za@8)xat{Kv8?M5=hvujib{akLVuUkdD`#|Yj#q4Fpp=Vg{g+K&VuN$nUPGrT7{`@* zE3h*{Kzf4N57S}z5SAE~*j=c5*h4NS0ZLhpCI`V$AOlhw`!|MrM0Q4^aVhF5&#jcM zh?bNYp@-DSd@NQGflop1by~8q7|X$;(wz#oi$gQ;+C1P+3;>2xH3<2`LXRE2KNRP0 zA8@%w@{6rmu=gk3xe;+*Y<&*Ef6#Hw>QJt}CqR-qqyRwJfG-TQ3a$=A zYd}_`WMlvUQ=+no3fCf5TO=8Q;(Arp>Jy&@QVDPru76Ih8laO)JIrQNqSSR2eyqrr zopQrYu;sE8+9opdfF@3}kT1YMcNI3vTxS=+qD*HK>&>KZfTv=d9B3PS6a1%WXjk!s zVHeRR+=`qr^S%YiKmp1I!+td;cMTR`%cvhlplDtq6S)+LT*;ysV>om+5P?xis)kRh z#zU$GN~)$VRwW`VZVsdosS;D<_~E=W$F! zTvYWD3{3edQkRb}58n^KEHM#JC_i^Dj5)6!uh%PZe^E%Poz zbbaQX2H^Dr@WucvOz&dA50>{ojPJR9&($T*vE+8%N`tlWQF#KXY&9f)W?}k>{Ya(f z-$v%|ptS0+JMIN(fsyheX2N)-k%Pp9_Ww|c&;(f`G0&k(w9&l_jgz0|03nuz(_gFzeqU@yKtdE_>f50kTQIvBz)u;d?Yn|WI24KP}tByn-uRG{GAO- zO(Gsiw%GCm-IWHVFIDxE)IkfDX2v`SC997dmdI1*qba8;-ZVWVM-I@*1!5gWScM9i z8XjY_;@w4iZ$-$k!Z)7kJr= zJ>HjD3TDj*cK#D`85DB)9KI?PzDg9nY8bwX9a}>hTf=_uENq+sL*j@+o+ZrYs~fpT zl;DHS@m{IBD2++H7ww`~pi7#qE8E5cVgn%6viszMr#H;jm1*mNumO0p8MIytTd7Cz z?_+x>-T-*D>>_ZK?v>WyD>4nnaU@T8DTuM;?OU7`LJ)i#XSsep|Lq86wK_9`l%u3%SO#1KkxRkEQW{=nF)dNp)v zk7|sTqWd5$P^bo@UFL6^>a#B?rz`&-@7+;6wK?*^t+I^NuE$3HG@EBrHcqW7HXovf zh|i4?N{*?l%-zsAQYmB|4Y6Ny3-M?)%)ZDWc_f@jFyP^Meqp7DmGkU#RDF(G2yM1y zmJiPUT3f=gg+AJn zy~b}s@*!Taj(C>6T!WzxHSx`)n6^@E#|dmldEmDUhd$y^p6E#JI7zQK#Wt@swRq*1 z9|k|>t@4k-=FcEx{yDkGpx?{H|5Aqa%{+~#NCFa%{^;M^b;)c#(`v4ycpr=t0xC*g zv-BR^3%z2?K7kNI4hSJ!b_}35O`tdD|DlM#Y{Gx-!}Y#%|O*Ye;8!Q6Y9gN!V3oYS3h6Yo@hjHc#eMCA`YL@KRU+Q zj)E4o(5C&%V-FaUGErxEf<6;7Ooz3;51OTz#RX3_XJ~^@Zz^TA0|A9KvojvEvo?=2 zm9Oo8GUY_otlm5^^Jex~9NtZA-rly4h$fG5CXaI2uO}hB@xS^os_Pp6VTt0}1 zQR0|>1V+KDPsm+K2{#`|Cjqrf6PwZBCY3jD{lZ^17w^n0QwwgZ&Jn`p83D?oVsUdU zl|J^8*BE9sYlhN_L^{EWs&BqOysW4nNWy)Bnn+6`JV=Wp6%wy3tO$$0%dP@1jtYe? zoMdK#&cvLFjT2lt!ceE9FhpcH3nAxS36l3cjx16_Yz1%b>jnjGpq@-4kBoRtCxit^2mK@M5gjzWt*WoMb;5p5Q=H@wQ`MPHmKm}dplqHg*y zPK|d&RQFD2bPK`i^s0MY;v}pT2CZ!#D(Ek>t*1^-qQ%{>*DW6NL=vu>m6OFD#jnR* zosaqr^kMqFC5$J&mET+q@&4Dn9ovG2NVlrJ(l%B*nSs*6%5k(&Qu~VSMUTe53hj>; zwF4sh3bV1pD4dpM2iNdri813<3BvlaLi?g`YW_>eDLtIVP^_XKqQd@BQbU(HTgpWW zoaT?m#`W8Y81D3x1|MK+5C{sK#}>DoHl=Z8g|1Vc#rVC`JPz2duP1<¬XImKt@nKQrw<5v%w&&hb83FdaIT2rU3(fEdNQzU zi(9F2`n&u0M%`Img8cy#uW&uR!t;N;8hD?w?5f=wHATDgdaVc}+So*JyZ6P)<@KEN zWBY15{aG=SfjdCdbkyazydjl!y__!UI*l)N*egk;$<-^*F3ZQj?5TY^sn6UiVQr`*Yl<&B>gzZ3^Q&o^9YCpLLL=6ani&y|ts ztE|)>r!dGJGNjgcT+a=7y~i%j4HD^QA8@B{o9tvZpv&@2t=wnc%h*@*`Y$#&ANH{N zEl)S-^;~WZWZ#*tVzFQ zTW7R#BhVbW|FCp*pQ_Z$ZKT9+_ianwd3P)#BK1nUb++oLo^a}Z|HL~*(OpluUAt@5 zoVlbr97)#iNWnSY-CwV=?f&(23qjeFhx$>Tw$ovCKG|)l$2XbbOWjkMCck5nrGD5I z-kWHY%e8W}D;&=t<6@G0o3o?k;n7fuS(lNi9Az_Id{*0& zsT>c2xICfmXMUHn&k3#yzE!S0<+s;kSmHJv+tsTB;Z=X3zn`bsv5_C-OEbOC$QSO_ zHbfhhC`Q~1T0j_rM!V5_3*W*&w(7PeT(@&c_(lmVf;`Iwha2>IL%jJ24cMXj$7&II zei5(Azxw~_9SPpu;VydwTN5`V8lN}%x*#C3;k-WdCvYOM;p1w}w9&4YJPn>MPt;xO zdUZu0Z|<5C?40+ z;IZz7Bj6Apsh97Elfu^F{dfVQIG~m)*5L*hQ+TQo}Nn3t4fc>IutrehEN7=Ti)cBSZ;A zW8UB@315;51#?!p!qPz7dZdJR2c=l&n~xC~d5)8IZw#86{quEQ@n?-)PMZ6(boORk zyW5q@=6k76^t1D`IP_j;)0w!O=IFfKv@71~rqHxz(Uu-wc<(GUgLyobhksSHBOP!K z+otf(taujzIjaP}Khf5V#d~dvF8R8z56?G>JZ|^smFB>G%V7Nrcp}hTW6l)%0W$?9 z&-#3oZ3MXwhaOG9xzF2&Dc8(-{8gZ;)E8Rh6l6{Jwz{!sZ#GNt8NnyPi1kL>xo-$s z`*n`>?l?wLmiN1QtMlRZRT|$zyF*r6?Mu-r(`cpC3e;w;pY^eh{lOWp`z_u`dYxKL zc75yK>SClCGM27Z3*QDjD?16L@Z)`8?)Af4Q-#`J_7pFZ?yA5}J*B68Qn96>6nk?q z`*vzVP_T|@(X|!aHBV3b%)ykRjh&PjF)Av6Gix|Mh1Q;gk~KFUtbFcjGt z0Q&H!-M`m?ACoe?E(pEJUTr4e%ibYTIZ3gAP=2`KSFo2^lh1>fBBa;d=W{to$z^^A z`8R9D4bbpFd+W`qeh27f6ASuDTy8^~a{+mRMUehz;{8y;)&RZIS?rdlZ|29DM=@)H z`P)v$%Eh<#Cxvkp1jsRgTf5Zed-lHY``&v<#cij%%96#}{o^PO4j#kyN42p6s(yUL zhU?#+h}@=njBKD(MYX0Z)0om#44JPTh`x!FTsGpa$MlgZH5ryngNwF`78 z)EKihCDLkztnyC#5{yH!(V^kYM&Rf5?B%*4b!B^F|Fw07#Ag33+Il8K=euQ@FK*ymN2*+-E#xn%#kE1jH2o4`CNkKc=PKAVD|x8HaBx2l z98%T4kw;@;Sq<#c{rDk03t! z(CJ|nqDFaiQYm&3iY%eV-J&iOco0Oul9JF!p)(8@mHA(?il^-#G*R#YtXfFV?6 z;}}<#3g0_)R~bjXTb@^R9*8t42qJ+r_8d)}^lDIv+B!PH=2~g1^~Hv|C7({qYtErG zX=yCZec+Z592V1FGgIW|SU-DiSlOug_-|mR7zlP;bg}8wfO8WAV8vbde^hb*|0O&B zA9I%(5bONA{V!7?Rn!>$E4TVYtjCQ2zt0+*CAnd9VEj+MD8zG5RNl-l> zX=@bBc(0XCE$!-ct)}&gV#t~0^<^*-bR<9-7L8Wzdc{u0nyedp{o|9*zl{67=P2+` z|DE3v@9`GQZ?X>dM2XCwNgN;W3D1&B3*|bo^ZARoO=hMJzvc}VGg)8pdA;Ns%UD>= zOf7!R|DG#LDY*E}ELY5K`C6%JrfT3;!bkr{ocU9g|3&G`r4i+~a-dYSW+nSrvOtpA z+}V;;;~xWNP*b^xnAGPt+RT9;8Q+U(goQinrA-E7QdzqxQV3G!ltS^yU<H=OPnDzC=#xIwT8O=WH>zk|pKY&mJ;e8ka2?$HYLIAuSdMn@-Jew&(=p z*x47DIk$p>>XPw>oe)a2m10pprd((zs{io>dwx|xF;^;EG)!SU1*9mi`m0Yj)u*fe~TRhXQz-}!ezeCWTH_v^NEVNLIv<$ z=7_sLs5gadra(%k&RZ~RP)#errgJEI4XV$f@RvW&WcYq`FAKq+E_2+CQISqC(MDMowCE8@HutV78011 zOU+EGf;2PZStKXVrCS!h8D0fOEf;#a)$r%SDY^8`XC){7@aj$vLzGkV$X^>1`>uf< zgcU~9!68fS+-xQliEWa8!YG4Tb8;qAGJjlAl9_x;b0T5I1&Sut06ZId%Pg{cY9ZR1 zCKvr3%3wY)iY;Q%L@nc|)X#gFxVbD3w_k54Fva`k$9@PfC2nDI)(Kb-ZK+-hgjGl0eXJM!W z#GRz9!v~|-ee=$WjLQ>6N?U+g9j*h_(C`8`mSm~#IcV}N8Y_tq>H12jB6Y$mO#&D+ z^a??WKdj5d>cN#S-Iv|RT$ER(zf-}pr$C5hj^`ghnL4ghI%1;wn1E9m2=(Iu*ne@`0~v!Tz5app`($~r57=~IsaoVBbIr~EjF&ij;wZmc>r*UW0?5qc z$x?X41<;{=@a2AdBO+ylZxTyyHQTF4S_{cUj0eqrF%bIEht{|X!x80!L=fm=h>}&p zF*1af&5eaYjk|F#%TieMJ(=DtK4@)Ikr9dj!g;O8AUESvLT*MpvCdmZp#DuuqJBd| z1Orp?7k479^iR=@YVjL`+4M;&afTzOUP&+r1=`Rfy+j+yLB3zbfqc`VD@xl?vvt(S z6V*W(xUQzc?RuzZvqZwf3=>6ZaPYmLv=C)ZDJ`M*3j;I&br4-}%k73s4_}@;lJir8$F`$m~Yi z7Qg35CJ5FI+p^K`x2BbYK&oh_|K%)JY~agYIF>D%UC}`>a!Lr&s#Q%~zq<-0U8E0ux*`# z?Ji|afPzG@Tbxc|Kw6HpH&Ys7y0rLYG1_~Rfpy?Cp#Gan|KPU84>r7$D8l-~03@#_ z93&TEUK@S@uGbI+c+UxnHp+8>xQ)at0k4sE5a9_*7TUc?e3huaczjiY*Nxa&tclH7 z>c*o9*gFb{Pw+Pok6QcHNLJ3WS@X!()reQ4Lca*;v>i^GZcI~QavCSW@A&2pDJ3D0 zOz8%a*$}UTf-WYmj&T_Mr|>}{b|f4vp)(EWMUv5|vGCxHai~EA!I1ibkVJw=%`i0tH`2SQO(;YeQUncth5{E<4ku-gI?2(@S=BlHFY~ zN4ck=KbHyrarR5<8zY3urd#4z%dT!sH!)zfM&@Lbs961O>=E1*N?1Q>TtB&AHwmkE z)ascr5^a*(H}Q_ha3`X7w?BmVashlKJ^sB*sVGMVrYMdtvOH+lhyO67c!MT12^Dgri~ z_Rntw>xA_90u;R*Ja*(y@6UU6pol?D9kc8-dzh~0rcDsWS*#KOoC}VKMvLWRy`NTKtz87vDbvXN?h3%7!3n=_mi3YSjBRg=gDX4|gh&9%mHpxhGelB)V02>Ax)E$2l|qkm3Wf(hJTB!$s{6;O+xJzn z-J!r7-Xh?>1bu}`RN)9K%tQSVMQ#IjGgZPV=l%+~s|OeyVC?QLPS z!kua)!xQ5X$uV+-Az$KVzIV>DB1T)S$+4&l}Kdu`jK7uVDRuM}18 z-?!-P2w4xyEP(?$q^7@{Ea*aINOmsbDHQG;gecBKw0Zi6F82G%l`v4@RgyB(9JmRp zNh8B%m0mC_OT9Y7wFPqF0yQk>9^u|GXaU*#wErXkC`tkg4bHnjbO~|&uCd-Z12ub=*bR&ClIfac7WvBXK?Fd69#&S7`#BQ*N*JIA>_#~I?t2iCbpBs;57 z9q(83yh!kZ+$O1DO3oV&@l&JQ)soxQG2_!+`Wpm$&8^^5JxAP6D~Gx1yw3PICftV> zZ*PU*RD(q-1#9tcGQ`#i?0wijZR6n^*BqNQySwK;ZCif_FH5L4?QV9}M#492`8KZr zyKZSntzN*Rn_ag&q*nfC2k(F(OA6?30Ax?;>bEek;Ql5-SJ*Waq>9%2ww zYOdr2*R9`d=TgNHd7bFJ8vT^Fe3v z``a+SWf1&^fV7Mh)FOU7$^C(6G26{RM|9~<)h8U~66u@(gQ~1A@sM0t zc0tzj0*k(_8OZj_WC-p#xb`u$l`;_V7{$Vx^1+=9V-ppetE)c1&0>wEZPG}R1&m=8 zPDavZeG)s5u>_IO`-J_FsMk9>;r@N6*IPQvY8iXZUCWWFr}py};q#=~tWhu6O^xxy z>6ii;@8k1s8d{Qer3d4J2pwx7dk52mle$-(2WF!ixcCO$NE{FJ+%-iVOZrK$uxBXE z7w<|0zme=%lq%N$@}Aerbp)}|@PcOCz3cH=meoC=xPyzc0@reP{K&oHn_=8xixdZsA%3yGHqk=hn8oCjU`V zcT&g3o8bP$n1*hAgT9(?iB=}2JfyGdjnQJuvWaClt#TLMNa6#Z{sqCECDxmt^>kCZ z-punaa^K$37w0hrr}-0W?HRjkiPyzmW48@#rRCF;L;D&s&%`V^viTnw#HO98bXC0y zm!e)f*0$5W#s+Hu&(hqfjThYZqG=BcEuXvwNo`0(x$RRqHd_2=GFy@O2FdFm<(KKt zv-Cvm#@Z*f@wIDjiBC|QxKD??g@Y)0EUz^;CT?$U!7bjmx6AR5_)NqTTRl7b{51=o zFFnb0=QTc^DL-@H$)8_OK)T;A?vnMly-2*dx(9`V4&_Zu2QO?)&ew{RYQ1d}x z=Yp>yGi`6E=%3sFtCvUeVzt+useRu2Ba-UWCNX{9cM-ZU-KL?v?)B|K^tazw?CJTo z$>__$i(pE|@Ig>r{rH15w$pSwv$TVM*agSVe9l$WU^ z%fzaBcu6I&<9mJ$k)xT+(#(x0;z}W|v7~BvPg~8a*Dc7f`>N3=Yjh&C@$PDJ23=L3c)`<8$To@wbd`7l+(h4K{U4?t_yr+Ql2)$ z0)RjBM`CAJqS=H@iI8X1s&M`l9{ZK=&%`7?%&hm{0s-7E3|}3B0O6d2wC_Hb!&jM* z{YcJ3_6Bd^chANnc2+$0gu!>KAUouxc;Ng&BQp$_`0X`Z@Nao9TX^G;{qYAk4}54d zc`%uAH|F@b3Jc$#6?uB_Kp%Tnv9}=euf_FXHvg#pJ!wl<+XD~5=I8Q*%BlUi!`2k}jsc;D>2WFw;(?G|4pD z#^-ICJdyff&euc8%~5J8Os1B1uibL?RxsBJa#C4!D_oW9P;R0$1`ZVc`f+E+N~p&} zP0Q1JS zs_q>wAEn1hQuBx?YWI?J!X4s2CNCDS;Y+A9sNz0O1h?MxN?94_?qhPjcB@xNL|x92 zWtAF@Cwhtmh6+9#PnVU=B1D>!Syv zcNpsfu0QS+3|~cRd<*_r-l-a^o{FWj?$@Z>+*cy@`pVKeo2RLrZjzji&T~`2mK)ZQ z)p!+%R;ene=iT9tUT#gPZCcI+cDprb9M_$}UKPY=rN7b^~?%ph_B;#7QAq( zvbg+vPr2`qH7GHzqL3wN}6_JjT$UeQ=HB=h7Yze_#T7}KjvTKF>+y7<-J#V zoaFb{4&1tbv#Kl05NbOES{Huc+!n_{KdU^8wja1omY6?3w}~P?nd|4ZZ>-9;LNhzZeuOqZtit1Z8KC+@}HyUC~DdB zt}RuCA=L;TrpnKp!dv*p@V`=hdg^JlZR74oS{YvFBBTqJhyqYXIDh^+LB zWQY^zYqD~0ZhO2->#X8tuf_jx9Uv#6D;SPY^5nfy9NGlUdpoW+^nQ}B%Dhcq9gU%e zr{)p$S*9?%>5AcxKp4zx#Sl@ISeiJRt$XgcUjyacNJs|HzVI8PxZT&7pM1@q9B@Ky1f0BeXg0#^C0(QB{C%F zDCus19xYG44cqZDT3f9)^8h2(O=a&Hujk}o(Spj z;ls^4+jAND5zagEBQu2F_XaMkRlcSSzrcWCrmz@fGB2H&`z$^(a65AK`e z)5YXoSYJHyUwR(w-+HdD{So@`NV*t901wx%AMKMK z?N_M-#YEySZl~{L(-O*NXl@w z>HF4drwMLC)I&m`YlYRZhR=x(peed$P=h^tj%YOMNYerHLFj=5Pp&wVlB~NJDcG{R z$IO{YHUn_fOj;@D7Q}HD1NZyiwcXR^gubrbDxC;^OrpqoG#`A|MoOC-Aealml00MXU8J2xl zJ6?lzhFu5JSxRRBSC?-6hOefObGW4r87p||`j{qN*#fT8j!C0bK)pYkL|a2Ex<9oY z{s?__z)J)EXk$l}v9t{nv)8DOCaphvK+9t>q90wbCWuqCxvq_TII$h{Ps%*-jgUAe zL(39gfUZwW948M^$i6yVbhbY=3cI>lAWf{Y(S+FUa^1(9Bp%y0_&)CSI-X8R{virWKWvqR0E*hEqn0JAAkujuAS4(AayBq1i*S={ zLT5RRVfam%DaN6#!N!Qf`lmoQ^*1=teygSQ|!S*)+`D68&#r3-^{F za0bHiZY_gmXpI<^gY%dKhQwmSWPiU}?!wwg ziPMJmTY?nVWr&l&PGPJLKx@BJ5A~_Z5R0f<$fB<^hPJ@kn1>*lm~*bK-w|NWQHURL z-JCfmP)&Fkg~?R%u29IaPBe{NN|l>p24Er6V_w+1pyQy~cYO+q zYGiK)(I~+h3%`TOq;S=RzG_h32K`*Q>O;%7N!lHSa7adpD?u+pG%(x6j;ED2)2U$b z4Zm;|!Oi*75EF6TW6WVQ3(10STQbY}v0kOI4mx6b%muf`>`9Br-Uuh2xke8V82CC1NTmB5#~3&~u2HN@SC;QwS5W zl}n^!P+7>ro@644X(ix$HBrGvvwLa-MRf91!5asQ(WzB4zZ=eI#waA0m69Dr;z$1A zOM6W!-m?)8#1TB5jy5NuyTKOYyd}7`nKMeouy%tyWfGum$y5h7f2&$IvkHL{KjNeV z_NO|3+x;-a&fj97{v!k>7XMKNap5;uwKG9OBpe)UZR^%N!^Yd4{wre~%M*_F%D*XI4uP-o5GsP8oSii4L{q!`zwF(~F+s}?gclqMnNGielDcg%IgYhMaG-_iqQu6Ex<bjHdv{JiLq&~8)SLeWSisNxl0hfC-aLJ;2+*8cu9(%S4MA`P0)4G+fb~$IA z?yHzboljk~3Eh_6D0T+EMj&fE8ara^tz*!%niLF-8&#NzXcj$Hx3d;lb(I&)a@8*m z&SA6@=zG#C_%Lo6cGj?)1B!dq4fb1%sF5BXtcuJV2mpTUifU1ZEF_idas^v+{5sB~ z2#HarwhPSXp1cL9KBV*o{(Hw)La{SC!#&mbm}hp_NzZL7;T=Dl~j9jRCC4>xlz5ElM|BG`4E&ik01cBXc8d zw3eU|iybCl3CRSUQ42?j2mtHa4%PZhTX4ZN=w3rOHF>IXjy2kK#VZb1As(LoY(RB9 znj8%v870@vw0)J(vPA=MvvBmw)W>9~WEY3#k${(}q91BzzU8`@vUr%RX^90LPA{?Q zZ#x8_3*}RQJJ{2ljc_GY#KREeeGZy|K7@ZvW+j{ffJ*RgZW-OAdO56X(o1q4nznUe zQDkS#_iwEd?lAzN9 zRAMjsb1TZ{AoEjoD4+%9v<^60QT$e;JWj$}lTzsQT#K?qS)JTj22!r{S}36Oz>NKq zOg?#tDnf-iUXfI(SVas{sn|v=Rx$+@_b0LHxaDH93U#$2V@+78ztkO%RTxo}g7dsc z;|D_ar6L?xeX2<4!TbG8x(zK2-hj#1)-Y&KlR*zOJ>{4Jy1mX3CM+kLGd(9bo`Ur? zfF9GV48bx#A#6R7<`B$EydoL(1VpL0O5jp51tmD(I!QrJQdi8iT3+NF)syRi)lK z8d2t4wo`z9XZ)Jump%5}J@!CYTu#3X{N6~vXW6}H*`sIK-RBGrKV?q81r9$X$M+Jn zcSOc_GlkZvMF3vO9_4KLR7GrF#(tXU4e9CnNWIOM;F<_L(d(~JM26R*L}9NCV<*cQ z=TCv5?~-t&mHya=imfvxNsTB4Y457WiajM(PwK7;s+6js&kWqE-$fFBSvC$NI5?8Q zzW+)eOh}(dpgNG4+?U{)Oz;RHRUAr4m#>&?jCY#BOrqf>E=1+39g!FmD_5$xmAG$O z>7g(7b+fo{(?{OATxH)T)|PZ*%BR$TPEo4Ed=75@6?|C2d;R9kgXqZ`pZ4P2mF?w& zqC1czVo};_<$s@C(y^-lU|I1Buj-o`{D2Q(4P&b}-tUzg{18N>!QfPV3l2Fe-&%9gt3YtP3il z^(1Iz7D62W(L06JwQS#i{v5Qk3+I={;K8d8fb97j>(ih097OvPRQVjlo|G+R_s^y! zrY&IypZ{bcDpW|vX@hxBG>wAk!*2sP> z(`X7LbOuYzV4V2M8_q`lED|Y}BS((g8HHz{MjI8y@${YBG|xSrk7Gj}eR3st2k@Z@ zumwP!{=|zvf7?gdT9NAFf8gZ3o?yC8+!?llj< z@mr>MYGLxg`pj>f{a|1lV_jQIPGONKMOW&RU9UycZQ@*<(w>@63}JA8Q<#tlKWxpZ zEQ1)aXG$x)`-3Yxl6cE>K~$W3BRJhBk*i0U7Y_s4+0Ao8#Rs_MX*uI`(6+NsR-|mLcD>oiL}o*}+?z{uRLNb6+vYB{q85 z%?$XnnM$0MzTP1%Yn_8vZ@CGJ3`5M`JeeB3PvWFEcDFz{&OZ{Bn})y3OLA2b!0BV9 zh}4E1%{yaWj88z=eh4~@z2DPyzZe|BjZCAl{=D2shs@tE!FSYJjL;I2jOI zO1WInItBz1LU(0l?tvy*7^wKD?t-#8K)!EgbTm?Qy9_glFP#uw_m@?uS%IWz;n`L# zOyY|h5)F;8-evN8e_9bmUD^?)iXnF1E_>xx@sVAxMpvjzUDOeEw**X(`^*J%j`$q0 zeKOtRc*op^ic3kqY@M^XeCj#C=5uMoAG_^(KQS#1`&dpm&wq^d+)k|{zuGySp?e?( zRF<@dKlXcnaz9uQQ9i&&ZE$WPzk;7s=e&GQsn|5O5kEk`tU=e+5zjqY&D~$Uyy^iy zfV*FT9(_Kg)uWv&!)D}SlK3X|nQoFXt$vQ&hmD)J;{vB6@$=cW2$8BR0R5qF$f`cY z?<=B2jNlYTL4%atScAzxzE_$TIy)B~1s;$*1wf6qO#k?QazV?lfBonoybqE+_Vq*6CwFXWHmA&e7SH)s`+KBY}Int+s0yRy^$?dV|@o= zF`k3mw7d&7H7Q=sm)HQqWgWe#uX5Ezpvvkx$;NrK{uVSAFwVOjSm!;VqQ$$~F(ig- zrEvY0^3kC?RDEo1TgSB%_7b)7QIbgD{?2#i!|by9S+C)}cC?~IQVxe*5wQUo5 z`Ju9WnN_WlLahJ@3?>o-{yYpk&mLb39E?kvljUD0Jtn-Ky_X+1y1_zskV^1lYY6}N zP<<@ej(f00a#nAvL-g!bdpe)!dFsqMg=u;DCY}##z4#^C-Zw_HFm!3MM<%vK$*l7T zjJ8LnRYUDH&^vK zEH6^_ax6)_=zia%a6UH|%p!Sm0di?7x6>62&3|qDuHuJUmAjTl2e4ECm~#Ko#kK-K zY0J=m8jJRjaJ^;}>>L!nl5iRSo||aG)^t>BaVBYk{8^M(3Vj$U|E@myC&~GJL&EMj z2Nq@RD(ituQo8ZeaGN*BpiL^b%bmyb;smEuyt@0?wAnMO`;6}+-^84n|J6->v#Iv2 zqpphRtZ|(c4eUdesF|*LzV(CI*7NicHVZOp%uNF$2cxmgyMM~ZAM=LT;J`ULM3+F8|jN5@8KD8K% zoiAL^Pqk`RQ<#EM2Nothc$DE(AwQ*9@L>y1-*Ry-MYqA(gU%Zt)bcMvt9W!D*_tu_N@Zmu~ub{5|A%1i|= z)UP#AD_j)lViQ~2=5jas{i151Dh#5b<3^PK_R;clQrnybJWIht+Sb^&PKMw1yamkC z)$+pmxdCbW{yGlDY0f&9xxMVlJR0F_{kv9X$5*7~!D!2x<`aH7&)!~q5zeH?%a=bz zQ591<%P$$t`5(WN%4L+AX=?hXzgBw?E&@DV-y4>9g4Wl?DFJkIho?^4@TqV5lsS*- z{r$k(sNOo=(~6@tCq@~S;5vaBt5Tke*Mc)_nr zh?dhtoN2uR@aw`eKV9C@M7x&JonX|vud=B}rQCM@EP}N@J`)=fCl9BOMvq+?^y`xq z#-YNFea1nE26mn7gq!Kww&?STGGN(pY5K9s{Gr$l3z8=23T66ZKloceJC@!SEeyhw zWlV?to^I3%8s8}K&&7p(=GCSzKDqt+wfU&Ouqugfk4{r*jR zeXcYJ9BN0kNR~LnLA|VSXe&sXsUShU54dQ!96v+*bZZd8CZMpX+=D%W5zzttybXgv z-wM7ix8<2sAZg<4F$bLEQfRE~)#I$dJ?O_;?)$P6}WPGV88aD0i3 ztI{=-3^CH{2R?04Zv=S&7JFz!xpq1K^X9!J_3IE#Umu*?n5|l*K%g*27Un>|A@dLl z)=x3b76y^NkU26mY~qB$k;hsb!QDP1=2>0__ag=$P^yTybG@{H2?Lx;O1Ypu-NqfE z0P#SJkgCdpw~k-G2$J17RZJ8p911O`m6G)1$~AdnJjdgU_RGpE5Gxm%lwf29b7-!EiLX_tEF+yBDaj*WmbMlF7U#!owo0f7n$FC1Q) zh%PM4ZzgX^l8+%EK)VCJlTqL7kOAS94M3g(OeLJ1O=uWzZe4nrICGkfDwUr&_V%{H z0j}F%wAz$1$*!a~FMf@CvRyi^Q1-?hZnxj!T5BYe+%j*tilbP1B6APL1X|}+Opg9<(!`WT=3=~bxYEKq?n*lyG=#BwD&V!KAjp^cRp4}aq}1C<*Q$+pC=5FcAe zDG4H=IQVsW|FAN60p@u=xQlvAH31cdV= z)-0UZb3s&Cx_qPTw>3r2)y21L9)m(zAB8X<>(K>k{P9N_0j+G|BDYDE{hKC~Q)r{0 zEa$gUl=<6mRni|&_foljBBXe1g>ZS{g5;GbBA9sNxnZzujm9lUs}7nRy`;iM`}qqd z0s`^V0|>{?CdooLW^q*1#kl+p(hdB#VAEu?n)qsmSr-JG`^gR)^$_Y*&}sPF((ULQ zx42j4AXlR$2r}l4JAj7!L*0$YfPO8D z$A+I5YT69!NE0TT#qY~O@Ic`pw_7;m>XRnTRsJ};8kh|Fsoa5kx*hjoQlt}WNZbO@ zI9l789`n5@J7{xdn}Y<$C8$WdgrGsu+u3UZ6e&$ULJfut*$BlS21%i}D#f z?&kp@5=}0RON=zDEY2AG#A%rt9_f30li$u_<+^9v&&3EJH71f~Yp8INps|dm+3vK;g!gfp9|1}z|-6d(cQ?$9Zr_&@L^szXKsx~Yd z*3I=c*C1nb`IV|RNcn~*qTjlo-EV%pN<03X2QEoLu!tSnQqsvJRS39b#?nh0XC(!- z_IJq)&JrIj&v&9fE`QJrgqWn%8N(MDJoiP+@5EpXG{hntegQoQ0W-F6DyaZ>F) zrH8Dk!R&~paV1o`;x68H((FAA^k3qH)21G@8ly6IIe>(C!GNcv1(7?6HSCFxr&svx z)Itf)b*|Dp?i>LQTr%syZPVQkjQ0)itRw<1nF$<0b`=Ob@sYp)Mz%%KxlVuE&hgIb zH{^kr_EU-#;EbcA9 z5ANYpGIkfCjssMm$5{3Q)$Bb7!qgjS(0_mx)^9L>ffZ4H{M34$4Y&$MnuMz= zy;%Dc|9=51klW56b(gU#d%v#f{aNHRdOZT#G2%a=`0ZtYh|ac+dMhzTKnPW-P@lwg zut2F44g7tn5==#IHpBicoud5icFe8paIPH!z#7db_6Z;QAqkXoV2V`1B-(qpX* z$_SjU<*-JR?&oe1H$^jk^twr<1jCFT-#( zm@$q=-I>1rY|(JVJG{r%Q8Icrm2wOmW--XB)?)~h*W366YHe400(%*$5ywdP2a5N! zsSMaA=sV@QAE8_Yj!68bG$_JJWaloV#x$!25g+1#*cI)38l9ZJ6X}3^I*kakzLU|w zNinIii%6xZNF|O)B~hemUF2VHg%`KZJx_!cICf%y~3;>D?$PWw#X zcie|-RZ#(@8*uQbB>j|tN9q#d4h;K4(U^iGL=tq|Qehie5_hEDG|C8!&fcWlB|@aU zqhq5~{uxOmLEw}b>P9TJS!$p<##A1}l*tHPf)Y;%DvyGgjX6kmzufolDBW!STQFp% z`cFlyZzASb_=8vYlUMltSFEgW*+x%Mzz>6`sNtu{q9-X5y^PXbPX68)YK06%A^?0p zQ^0n}xD&tb@dtR7Dgxn4Zd5AvuTsNZIlNQ`mU>zEA8Z&4Z1om%G#P~ta^F}I(m3lh(j8;5KW4G1eX*|5q;XTRsbjIJL$R*eq;fs6 zsnHp1^{lFfCc9MMNc+?bwLg0aGX@!Vm21SLJtpboxphN=nM~u4uj^jU-FgjDf78=O26pp zGE(R=l5H>np%vEJWYZq?u^#oI9`)Zn?m>R{bbnE62H&e}Y#)$jfwR>acx%FI~lzZFki zR<1f*eOXbrboL~whx?ctHffp2(AW)|VGKWXs6$}NDtEp0mTF??D*WP*X)3Up^%B+H!boh@B6=`^U`o=$nD$jU-jD^*3zY7A zf=*kymoPW7aTQW}SEzWwmg^K(v|)y@Mu)hFy+8v#!GAQLXJw^ol7ml5)1pSRy?hVc=^NQh?k+uc8H6p;&1Ur)#Ke zY_o@WSOr-W!W#E^*0n*($AA>#E+^wqbiYc0$H}K=sHhocu*hAit<2LlQllYaR#6~| zpj?JQhPSY1kC3bSfY1Io3SsvA~#OnrHW6A8>;7a57bkv-B2vo`|n@W zCYiT7 zMrgbJ3s`~Uc=~0oI^n1XgG&1v`d%s>_$G-KCP9sQL24VsJXsjd<_4#`VQr zGPNh`v)$Rp-H*W=Dw}~9e87vBlLdy;5|xI^Zr~lR$wnf{Mg-&apbXR7YX)&d1$sfG6^9LQ6UsDI-@su!3xvcnfPTYM z>y=|55GD*7xBayxM@k?BCJQrJo?sG|P&Y8jWl7ZCGZqB)0TX~^d!m8Jl?@S(K?X}` z1MxX5$^b(hYnUY_Umx}$rKGt1ooa+U&V(qR_$G_-YC%}>xfvlrac1NuA5>;iZlBnO zQT5!W607=|1q1Uck3zyr3xT!v37q1>*<@ro{33rg-U8xs%VGDZO_`hUL(1cii$lRh z+2@|iZKS7~?M}=LEco}2(-4RKVZ9Wi5ML9O<)(j9gehxl#WrwqLy zX{NPqwbtTd<2H~zM|h)lT_+%SHMV+moP4gIy%o$oyt~M#nN=*n4?EsB(hxa1t_RR; zy)Q^^=C~P%1wE&nZ^+Yo*y!Z6@D(h@-|F#cZ3a64XCGG-1|2^?th$=HW3&Zoc$%&C z1KYYF;l~)hJ0tB@6B67E{W{~?YK^2iPxc;p-qf`7a?pHL)mh%0UdDZWc}{m!WidQ_ zSLW-7)Hxnp1XuJpTyt6VZhrx>o_#>*+PEt><>PxdQebuDwdbI6{;G1myn5O0c(QMC z>e13Ds>UPV!QN6Umj`Z;3#>``nit6R-?VXkg)Q$l%aGvZSh!^R$ zjT*sHR$RW&tu3i*n{KC^pt~l+u{baT>akG+b$?%a5#5-HS7)qI0EHg@CxZNVm31K4Q4)y$a9DkbrrAe{poRt5V?}Phe~sV{?$sK+y~^B{ z-m2d6|0o?jK;=B?Dc;>tc^oiCuCk{&dLiP8sYtGz1R5-YA4WtAL>5HAzm)(-gR&~! z`Vag(!fub7)0mmcoK;J~x0>>g@i*$rZc)mQx^BGQhX$)4>PtWq+UJsq?&ouH=%mc1 zkEYQn^>ugqjX*!}i6(BJnm;E>M9u(}=hsodM{7P!-aG(QAMcJ%#KQmy4_tqeV$S(3Uhn9*G{--ltChTk%p7PfvqKkH;E?gVxWP67KY#(OJl#mJs zt&@)8%G3(LL+thAPbQbv(IQ4_=_K1QPQdxfrsf#q)~G0P+QI%J6D(h&VNpl|(@*tZ z80Ub^-N;_fkQ7@5fV?8&Hp3k2^ z_=t;ajh}Bink@Ej36C`!KK`|P2PPF&N>`tv!+A0LI95EQm@vMGGBzI(s9IEBOQ&pI zRE!f^5jBXwDJgSJMbCmM-+mt)fyVTD)-$2K2=3$Gvok7@t7Tf85AO~u5A*psksq(! z>CWqzbZ>f=bCv~~R@az~TRx1g4`Q9z>PgK3MD=?C&fOoAZc5SgX|e@>mc{S8j*2t6 zx~`dqAYHoAOsKJG4S-c8p%>podp&+rY-&ue0k%pGT4cjs^|49wrPdoO}xJ4%^K2v~@s z+Be-7jxM7p=@0NYa=0vbtO+@C9d?$=34BhAmUrfUmwKB=7;s#udo4W4+1+S6s>Y?f$I2buHY z;`S}uWa{zpt;^-m;bfcMcJurxRP}~HktsAWgyjeNCY;XqqfJvD7* zQNR8@dv51)I7a27{eur>CS%cwVG3rfM#U(jnP<-%aGqzjzq*#~R?~9(?qBWfFg%+( zhQ@a}Sa`0^>h>;B4j6;I7qwYlIdd*fn0$VlZI&_7tEyl!=2QdtN``%CjF0=&dIg6P z*u~@Wbfm=(EFuPMkK^Wqcb@iKq%C4MI8l;3nBESzUuu~oyQ?_%V&BJl0Wb~PzFj$%#XE}3wrcS;b*IQdD zlV9mCd37dQgW~fhvd1L__`)%%ZX38y6&G%}vNf2XsG^5M<41%n4^!*^sxF(G~^qc zN#lvK=i%ep_YL;Ogj0atyQq}rXHYhqNs^Xo?%USZ_Z;FB+onc>UjGS{?o*@6JmZfW2YcXIeQD zkcV9FT&~6CiA&EB>}-)O7q1XChI@PqmK(tlp-dP}GMAuKl0#rrsz@|%%``Sm6Qooa zr=^r~=!{&q-9a(I54r06ll}YsnELEoM4y-X6$#F$SGvj;ycdFd4DT^o4#)C0BYMv! z7ea$ZFAi*dUSQ$V^A8GK3IC|#eXw{tP+VAexQF<_(nT5FYCp*3AQSQuY2cdNV@qM=*uwp_2r0@34QUnt6PTpo&mpaF zq(WtK7?v_HPGwODL5%dvz9>+b-GPyT$7pBlBnrZzeU7kAn6YK*62W~?C>&^iKTYbF zUf}|iBoa~~y*7v?QL#2S^1ruI_{ETt(ei4q10o0wmEz)RcLK)oNX;4|Lj$9TG#=8c>WTVTS;Kf1F4q&klZwMY1O3NB+PU|Vw3cu*jhn>+El?=P7d;weD7dHQq zY1{!zFThQwB@1BbrBdkslWB1M%V4o|w$x$%@0rH`R(Zh()?NOd|B-3HswyhI5?A9# zhvT=#Q5}GNp~>|Rx7XrF7a(90)u`^D2>Q-7G4bsYDKtnu?6+@ma(XiEiHVu=L6;(T zQc_x)WR8b*Sy7OrS(?tlKST>;<>mA1)0-O+*V=3LW9H+d$CkrIr(y9zsoB@SV7&4- zgs;Ci4b_SSaI3Lf_?^(VmzodY&o1elwj23Z5016Q(YkpF0{0eUO;rqN=P;dD@~JDB_4EB?$v# zAq3lch;m7RD`Y@z#R7+9LIq<@2 zBJixnD_N~bcE5zPx!C4l)P(`iWZ}L2atAM_&b`N@*qTIx=W>SA+>8V35H71(rHqCe z7!?#F@q>*19OWhTX9;luL}6b*t)xh1*I^_zcv`tvoaGB~foMEdU^6Q=&j~9KhqZ?P z^(P`6#%Ma5p&L9-h+t&2H;Yi*gxUc@Ep?o>1sH0GfY5;gLk-9uYLK7?70X%vx<7bD zP~I_)G{M?Z`BfYgSc(R@>!!i1{PBTkyuDPS04Iw_WNV&vX0#Kv0q?{l5NIJC-m^V2 z;e|#1kZg2!vzhRicQ1DWIT)R@Q(3yu2=&X35Veyz33OrCXG|G2VS_we{ zA_XuaPQN;|{c^SZ4g%^yW3fcDJxYlpYKpN$YM>flLJ|HrhbF8XOXH1B0T)a8u8IiT z&ex8kT2KgVyWmlhPObdpPYf-gI03d@toc%&5~vI+Dtf^D6yFcZV5E44^r}5mc;tSm z84g-c_K=I{iYniR_YUN0eV=fe`7;6QKl<(ennrvgQWFSq_0smztMn z$c3pnB|u84cXph2;)|Ox^OZc{P!I6>GgtBOkY!=2A>W_iJlGiiv)iwEQ3N;yQb3x+`+m}*>9~f z?nfzL!v%N?Sa$&yUz(K;tL3~Nqk&x)pwHs@&_TV^U{(d&Au~Jx=IJ_J=VWNv32T{~ z*3q+DyJjm*@@!ua^P}BYb3aked=ff?g=gj9N;f}W%y#ULszQ%(FxqKS!0Aw)?2ozr znfO672n`fPjfmbRlG3IMSug%?+qRhDvLnuJnH*PnzmGe6|N1Qce|;8j^rzHPcUf=5 zQtx~PDUclb1ucQ{9NKLM^e4eV3m4d@S%`;HSFfVo#+04E5(^(@jg4Z*1F-HQd(Bpq|PUa{JDcw**`Ex`*kwgJHU@BCPDmhk7FnE47B4W#yYprsI1uZg7?r-4O ze}YJfN$DZnK1N6_Ee1hdiY^WHZRa~=CX3%(wk0wzIdD#@&6iuVKC(J;gpEsR;CDQJ z0iNeJ!SSnhI^$$-Ue#uv>D8_wgd5lr>~2c8cw4S0#oKZNX7rjhUYq zLzlY~7WQgkY5XYivTucu>ET~#6{Gli+scdlkPXg;>g&NUR z!Qa_?(_2z(u^0Q41a(yLRlpH~oopjwae{a0c9<(8Dgwu*Ln{I&Yl*7S1p8lFB)Aaq zx9RfO5rh^>@6aix`h!d55b$@t#Gl*bTO#8p1hMzhaUGWA6xK_HS#l-4dtk#KeVcsoGxyU(q9m`+1qC2bHmdgl1Ozts0FFNX1LgfNJd0=|NfsRV3oz zyyarRkzdJD=nH$vsfSd>@vJ)I*Pn}y zG=XFIS;-}64l+y5L!(r}lh?ToVIl!o?|sj0nGWDRF&2QK#w*HO?u;K-2l`;}QAv+txVAcsdt|VX zTmTS_*DDpd_Ke*AmU+e^)$ah&%bIm(46*YKZQ<7cdYs}Zip-xXSO=@$<`+^oGeY+; zuD1a$P+!^N3f^)J-a<#{re^lul6%W0v-VK$x5@p-XUSvn#tPQq`0KNf{NuCK{q)yIwD;?u~zyl z|54+yJ>?X#3RJQr6%SDtQgBq6&}$D8E+GHGSX3N!8TEA;jsIFL?oob?fqwVg;1;yt zr-)F>Ksz3{+PeHj-p|nbhRt9CZXJHd{^SUAVCHl7yE~=BT?VNi{D6jGf5k|!N&{l| zBu9rx^N!1M@D`8+jj?iN{D){cT@GGqK(x`t-yGrSfIU=vn@?h-!)n1j974IttQ3z6CR${G`*c=P^0tU_p12k4Kqigh#wA`_7SbgXjdGOIxefYu_9E=gef~DNSXd1)3DQZ>!39_jcmFLD56_l?Jx~wGvk>2xks_P;NAhTajga?d ziwdR*S1lwl)h|KSlBU<)q#!>WkccRKHO^zu7b=7HY4wfb>Z>0jFjhmC9jSnDfT=@<;MN~BTSB_7c*bJK3 zOj01ux_U%KYsp7+o;y(4>O!z@CGbmO`U+KnQgL0|Zrli65>cvMt5jV zh1)zF)DtOffycgAZZTvozmlm$SPcMlS}1_$18B8?t0q!5(Cn!tA{xqZz}Qf;g}&Q< z4T<@OjTZ_9U-*$Kh?QMV8gB#4G`3}t=6H&Z1|4{f?~MewBO#9sS|kjNxvFPN?q2+m z&F}Uy5$4MI*U+PLrHS%-95L13TG5Pc{SGVR@P4HYoEMC+NDVBhxz_0cJ1&OMTELEr zp{XOLQ0F#|d1(O{F)|LFpFtS7M6MbuOEeCyOeBxrU!P_DKlv;a|Mpp!um1WhC1SOF zQiAnCA5uy`uf9{!ki_k{%pgDi#(Fy;E56YPhD|qBG@8jRIY=FgX~wE_hOTH_@B;cQ zFaPpcR#e}ZC@!uI>8HchnbXXi1XG5r*ILS>dVPIZFP}GfR;KqqpEh{f#Za}L3>;5# zr9Yp5K8xKl<{;_rLekx5mywkr@#m(8lX$Sm_Im7w0AIUK3kqH;E%^nTrQ9!8+f*yKVAUZS?Z6Gy5p}e3nr*Rh%mq zU7c&EHN5xTlYTrfHa|oXWuJ8qYN@JWto;7GTS#DLQXY6cH*txvcr*q``Ro;sv(Ex& z^|uyV1)kUD;@R6jfU@)`hi{FUbmQIHa`I=BU5cOh@ahd{C;#*4K{VoR2x8eRKxJ9` zlRqJG>^)tp#cX=q3h7~HwIMts=nb^Z?3|TeCb33myuf))hPBUKubVN(lYYepYOh&T zwcy>u0^4LMO01NJX~^bepw7znGdrlHzb@Oc!I(JG9=FS&jTU3?+#Pwh;`k?yxA=E=qEO8)8oh@`+@zGgv>U@ zYIC#S_Z|-5+sPW=lVi%=MgZ)|!xMaL9)AP~o`9ly>c*>gDq#jU&4mwQTW(GfJX*$Y zz~%nV{ocoXKFh}o+1eYNDDCH7P)?J#UElEM%S9u_Yot7%^S%E}3g6YW3mnv2T1$Ik z4E@>d^J#dDO;#t*^O+p*_ok`cC3%WzA9-nIhx_d2#^jR2!pSi$#pnFDEM-$#j18@q zt>F6U9-#K}w0)nEILFSjFQJ)95U z&V=f+o_gs^>&d=Nx0CM30J%1wiH_Q4<7UyD(rt%SkwNEM++r9Zeb@EP6jvcJ)WpLL zlaw^~{oa)JhlqBw096>A_5ct*0j? zuNZZgn(J%U=&WTr(WGlwov!c8EE~g9S#_OM&4a@=Za%NNOQG@~56CsVcGtqhy$(+! zXLV0b3?H4G+3$#i7w|HPV zeEvSY|;0LquLTR!jeH08tWEcG$s8%rLG)x$lFD$HbrP3^Iq6Cd-np;5lg zR*S2!%6>lY&zeI{2940)jYf=-=T_PuY8J1FmG{~oPoAG8IgMA>5h8>WX@S4t_s0)t z=~G^w{5!P{MDlMB*UJ|2O0=)9o7JPf)ozR6b{C!=#ELn3v$s&WXxmv(<-DwXpKP#p zdj>uKl0)=sYW#BR>%~FSMbVp!=p)mY(VH9UVZ_L_wzct;Ta9)rx6yGK9DL?e&!7}Q z<7Z*nWwiXqKCs>Xk*W3;{ta*lNK+k);r)oRc&=pWI&?KST@CT{{Cxqc5mJ47ed`iv zeYVjVDT8hEUN?8RXG4*=70Q3O6fPYjjXjijGww-S)@U+!dwM@^dfz z;RWPg{1HVntAo0gw?EyUH2QZQjc^DxqsmdhCN(~0TZBeSE%Cc=EHs<&p67%${galM z^6b?Ihx}UDDl@!aH8itV=JYwg)Ks?G5~UnG=`8NtY(MG^*S>-H$U%My5{}92f7f~V z{$mWbo4RtyD90pUX4~$w&s`-j=V zcQ=NqjwI5j&E)|Rh7#uRHD#00E-DmP{G4SAZN*-7T}*pBg}btat8m~v&bPt@KN$5N zu?zCQA{AyzBNK%flmpTuP-{RZD&tUJkmMoE_Qj zlP(Z#bJXPiENna5X&wdN0)l3=CA0PIrJ~^teXxjG82vq0Eq3=&v2|sXk)R5hV^IrQ z9H73HoD${x&}bm2cOTlY&Wk^N-Cnco9N^%sviw~N^D@TZ@bdiwWNEG-#Z|hI`x9X5 z#a#J$o2>?TOao)>)7ZY&8XV4=j+Qb>?{)E%nF`&}?%~ABw2{Oayny$4X@IR^y=Op4 zMBuL&bHO!uVLe5K|3$ji<##@{wvqk`{_FJN(kg(1=eQHk6!M!=v^U{5SFlxn4Ib7n z@;~oYa;w$|#-B9W8scy{qnKo`+`U{(E-haRovhcN<96s+nGS=9rn0|(uJ#m!1~+?Y zQDpdl9UP$P5|uw8hK_f%3Ge&NrL8cXx|r2WC#`jA)GJ{+`ZhimImj&?_0L?2V0p89xYnUehlE3?waj%;aRN>poaEblH%QssK}WC9?%@z+g=U zhAFlXaIPnRN8HBRy2$3l5vhM-B%a{YdUQ%m>G5vRtryq(b@WOBS(J${r;WHfaEHE+ z0zKllirrH`dHFsc1>U|pvEAIKLZz9r@qg@95YNfNkvwPTMm=gErKkRVmyn}0XdEtY<6?$d{ zj@etvCxwPm^oy5HH~9(A)=-e!WePU3ag6z3;)d^G(&g022?x@Aixx~`nW*4rNdyzk z%ZI^N;t!SIrD6Lp&<+D`gG37!eVL#aOoS87E5|xmFlR}0C-M>wot-GCS&Yr|x5J=o zuHBOd#SOnz%>#p5qdiw=L8JbXrw)t1AR4+mK&gnOSFlTANFxoNF9>r042IBCrVA6J zR>>%Z>~_)8nd;;O1b~lf3M!C1s2+}n$!0zXB@mvKk&)Y_BTHEQye zPY^KV2}N=6Vh0viE_gsd1pZ&emH!m-u>bdv=j*=$Af5k0wMfK58vi~2E#>)#YEfB2 z?u8_WyI9oH3=bdaD&^dPD^dTe~a5RDk$@v zh9Rq3=FK-wy67*u9yikrjRl))+)i|KvP0HePcO4?zaOr%a;`FMHu(QM*r%j_%rj&( z1dn%2i(D;T#J@LoN?bLaZHOf8&qY$9R{upCsYZxbXpAdTfwcQFZ-)my?l^5ek=}DB z7u>R3T%YIjF&g(uohJ2F9J{nB^&Uz&FZgG^u+4P&WMmw>fRjil*vQ+vQqoMaDw}*l z9|*E_cz(ucys0jfZ514eid(LLK##(H*jS+>d@0J-8Uo!zOv+$$bczf_((e8>5xRhp zhJ5^}u5SE`CY2{1M2x+^b>pZlQaP@N>?V@nSs%4<5?Ccj<1x2 z;yGZr$-?*w2-Cnb!H@X8ToeFlI&BPQnu>_`T+uOeVqQQgWmTU#$7qbj7X2u@Fc9TV zUS5IDen63xzF;@L0a^U*L@G z>?Aw3ZQD+E?CjXKZQHhO+sTe?+s5?of6g;!X3kv9TvS)p>eY4C-A_IBuJ`*Pn!EfR zSTzLqnYd^%RjpyGT!~gD3cL;nY3(S;O$w#3L?msIAZBclr<#p`**s%}H5~A4^dyiR zTa>U1B#kFR@ATjE<%U5yU#=Zt7iYh!dq@WV$pmQ$Mtx-AxTU1971qZ9f zO2sc@IJRh1e@+6=L_!-z07}e>g#!wRtB`PQsu;gWzA#Ai}feYdeGV799vSzdEW{EKm*cMKO{mUf7&x`$x(HoxEf& zVpG;oFsFpEB3Mi`3(y676=Hg(XoOoD@~c3Dj)pv)i3RD@M-P4zi4M&Eo?C?6 zLjj0aNOl^cP~;D`TloSPq+VAfP8nL{oG>K0xvumB!vj1sp&65K9Mt?G&h1aHvHLkcMHw@pQb zK78=RTSbO+ZkUp^er3|M5YoM84xP~}%aE9j5gDBvl~C1DhhlTF$_4mU{3tlf5k?Pm z{0|KX=scCkv+u50()d*YtU#-*ml}F7?7nF$f_SY!BGwwJj{9aXZQvi#h}>-WHEGvr0OI0 zsx4WK4QTDV)THX;T5D9>6RNPPEg`K9weHj(fL(J}a`i#=rPlVu@`GwiKmQ)OsR?#t zKiSNbi6Qew6_{37I_c)|d#g#Hb=l8sa+z3qBse&iG?!G+Zv%=lnejj5*esM54%gn| zs?E0}o!RW$H}0sb{h57o+h9JsvOtNHeD$!-)5ybVNCRzwhMjfYif}CZQvsqUzY32n zB7oaaW_bg3Bq{*uD_TQ$cn-@3F>i37v5@@dMKmFS-8hjSA~e_RfuK@$afWvacpOz= z!wBfHabK1S%gO!+oDLI(-s&-HhV8p;C`q?dIlJk+lnfuJvzL@PYkvT@{5*}OZYHz$ zQ#l9eUz~e%;+t<)x3JbZ-xuCb;Jysgsq=@ORMB2;O9I8N&H$ zNb5^hcCtltJ6SNS3?si9W0X#vF@nYXuqE;xnA^e!gCOP9ZSA2itmWEWr*>;lHV=|K z`4M>(0zwgv_~tp2-#9RM6az}Tm?qtf`B0PIveb@Yb<^qHjeGW!-!jyWv31jJIgN!d z*Nq2o3*=Aza~kt;yd5>WBbs1%6;+_A^K9#F{)%o2EW%RnL6anfv-A%es$)JuR~#ES z5tQEoYyA^WOer}F%|)AojTZYT6MD`k_^><0Rvq>ep`XhHQYEG{kP*V9bP!F_9<*=^ zQ~!vs8F%=9i;V8_LItKwrvm3y%io(Y|>?dmzdkvJmBhE)+8;lJSUB%%?AO_d1gLuHyAXs`REK@9!kt*Qzm(em56FCeM zIW&_c?q34r?k53=B?UZH@Lryaxr@#jqqza2O?B+vN#&F6X=ag@93n=81P*X6PYrOq zDK4}UM-OBsHswkqI~{j2yP;Fy7=UAK&g17d{P&CGI=)BQ&{EQKGD zG%+Vjr>N>HE;eZ6d5S`RGA6Fpcd3tc*I{L~C8k$gr0|Da4sD3pa>aJMh5^HzGa9Jo()b zgFYrO6*x9;Sn@!#fb$o(4T*Td2Jzjwf^-6CT+`B&85L)cA7xcdrq|)n@n~Bez<7?e z-`d=cP);Vuv#4`PsDDIM`GVDI26U|RikE2oijP!dgG#e2bCGL(mfPPsPtN%hmu3@} zeD8Ld@xAb+KhypVz|K6qeVnQ%j_i4ln)09X3!9O{+^PQ%Gj0&^PdebcAd>|pfvj<-Y=;KCHIcP?~wBV!|%`vwjIOm zb^QW_-a|KP!{Rk~*4~QQbF?t(;!t%R;UF$Fu$T9X zB3Mu$`H)V4P_V8c3#ge*n4S|c3AR%IcdT}qU4Swv!8TYCmM-2-W3e#A&PT!HKlf0O z%E@{LULp5?gm>>^MSH-7Wz4g#dPl~<}w?0tH)bCPbo{4&6^;lqHiapN`^CMBYIX^sG zKmh0qMrVm>DqeG`f3AQ0OFDV&BT8m6CbmvxGe2Ii9(UND{XaIksjzX3dpSBE zTA7t7JKgnMZ?oy9X0~6ub{YzGt~L;C%y*s~@_5~s3gtmFhSptqeA}%K^36UrD@OT# zY?On%oy>x*M7_#DSL8~U^i(uoZ1M|x2(7MgRb+y?<)YcP2KXae_}RKWI!=wnxxdDb zUo#Aeu0kJjHZQYf);>J@xxcQmFJre;qORy;Tj=3ySAA`Qp*n9yk9#J+xYq^)a>fux zo$FosEY`8wM`ynm5->1U{No;5H&#{4U}Q&k^YTupSJppCOX^3~+3buB9K zPpfR(Ql3nu21Zz7CNu?ID zVqC1&E<9f@*-@qE$_&f2ZK3 z$gAl%JS=xaf;ahitlvgOvaLJj-8`3IzC9krAJ98bQ|3*R`+k=Rhez`{Ifs}-x7n}Xg%M@RVvd&BKtil{0ViPb?WIko25Fmp1rX>Zef zUGt~I7zg#&-Y@r5X$b@qpNYD#*(#P}W|NmR)>l^4pD)H-9V6PU1JvLO6JJS+P-kDB zZMBq!BabfRTK?!f9i#2*D_G2oRGA2RVh{J8TiI$?Km7dw`OuK-5(XcgUet?iV4u^Q zG5W#RcUYe(?mk57*dMt!M2rKBX7lQcjK9S8HKwJ)#Wz6wLQ%QiM7*iilE@#hKK12? zxVz#JceT1zrCf8SF#SJyBLy#jYbE{O0R?66Qb68;*xeET{?(S^+4s-Bp05TNn%+yt zB|}J5?u67;G2OA#X|c9#`+Vfpuq}$}zCCnfgO<}5ex?+eVZ^i!bbL`)32n&uxl)AT9>vKHr@4l$QV&%fJPG?Z~(XtKxCHWc7y4Vinjfvi` zFO-SW^-cPKQrl9Z`SUlQh1Xxry?G~fZNZ&ose=5TF5d1zSY9jqj>i!P%9G>C4?MkG zf}CU8c6KCQecE=*roR7*F9Uf;%g5GkJ}z;+l3=E+`RT|C)83t>i>VWH@eA=D>ufxH zjPO}arm((F8AHoMigiNWy7{Ei<;u2a+I+IGsC#SG=i1XWS&k1L=sR%ot}nIHy7#9s zEo!@Da|@Fr%F{b@On16kb2I9Fo>*O0+@*ey&|k8>2Ff98QF6m1iRCyi*g4Nm zEvz{IuxAFBrR`ah9E^G+IO5JdaPQZ!jQ6ZOx+cQ@v?+@} z_;F@}%^P*jKWzO+60Gbp_nu2rprDT9}Pv}eSyWY=&)lB~?uy>FD9L=|4M7(78)9-iNbP+87PA943Ftae@u@u(x z??8JmX6nuzHR=U+w5402dB7%}SAP_#7EmvPEv45s+GRASOQT$gwKTsZgP}=;~ zRAe{YomP<3!o66e+lilq>-%Tb+qqUMJ6pi{k@x;iwI&^6jG0HDZ zP`MwAII%8Io+g|5HS2*jTh*SiuLt2l38uc&dQnx$TA%qWm2Y|<2Z!957^zoZEt5Z~ zzO_T2%{#0dH#;Zvm$r8&zNV$!dsBL_cRi}10aCu%{KI1v?alN_J*vphtg>sSCv(Rb zVLGl)RXSfgqp@+jsWuwG5NvK*uZJDpLfc;S3F5xZj_#(P^hMr32KZ=O>yT20yhfiN zff(VFh!(xR*E^~FQbhc%Cf)(PZSzZKKYTuqH@m%sFRgH&H}kS$_TP9FePg2-C2#{T zOZWVua&gXIrDOggPAUE#(SrOTpOH|_y4V=fUpvfrXXG3l94s_UG+aDfEHqp+C@KX) z0fU1D#KOWv!hwN-1%m;?M1w-Y;l9CetC2T#U*Sow0eW#tUi7lbiJQ|ukoz!XJr*UT z0tZ;%h72det<)K>@AP5;s;%)_1rS6s1W`}l&r_70uURwEQG~;VF|Mjpf z3pyfU1Poerde=AV>Eq0<_f;wxLYl;`42`~ri}gO4W>orS=t3?i`!faVR{tGhDwAO?V}(iAEjM;jU?Wt9 z6Z!t0_b`)&D@Zb>@Q3Xb;Sn&ESfU86FErStauyWn&x>h7uvln-&$AI~z-uce2}Air z)iwow0b2n})yT`Cq?IlGN3rl|k)48!7z{(IOa)Q4@MxgGF93pd>aPxhRS~D292s$h z1;V53jQaW1muNQ$stZl$o)<3}jNx`6Xb62@g<!}u!BX_I3-4$7Tr=Im_`f) zS#hEgJN6`rmJWxBzcn&&I1@lbPzET_Az%q>g;~;L;@`XN*TszFh1R9leTw6!}@}OZR)1Ui3{!L7_fT1e%N^2L|xBeoj0x_qX@{_kLQ1=JY={L!?+%mSRvMvTd`Pl8JfPCrr0xy zs;tki;LJ@wBH5bcyTE<7Cy`%hJ7mkH0RZ8A@2HwZQXcst!$~5cgKFl!2gz*1=S=@HG69TBX;5cM>f$qP=Xhe&1HRuCycvVv9L z=u6?aLjuu7CdUDHAl0l{%NkY7>pXdhepYgTvOBzcHKv>;Z)vmtf(J;cGeYT;h}?x_ z)-pb6hDJ6240xhOHFN!PI|{?F*hP8P^1^CgJoT?rh56Ip5ZS~Gk-!V%mv9bIc?=2{ z3fmcyBYb~REwBJJi(_GE)>xS-1M)eF=l^OJUc~>Q@+1IM9y|IEdt|r&_%Q_(DEOr! zLnb%O@ou3$o#(Hqg}^tIIv~q|Yy13&tra4)&gkI^uR<96Q4iyB@`(mk4!kBQu~^4P z6uK8?sUklivLH|M1j6K3ffN+LLvEc(H~hXq8G?UWF-)x25g(4-BAdXI@q22A0rKE| zwIpKx_c-asgz8U-kZn(^NP)9fm&gdJ#GY-Z{a8k^en86zmQQ&KAbjizGaX)ax*%)} zS*g}W-ItL(b&?=Ghh0|8C&pTC^dAuq4b@qx-qZifk$2Q>34CbC&Pu(ym**MlSi9D) z3w3TpTb+KE>SG_8MnSB9_;3(J&hYI-HZF5u%o8yk6LXGB;DE7UCL5n;y@$i(e0fH# zftYdcJ<59Fo&oPU(cXoe0rxr71`4F)sgG2-LLT~!6wn54$lTj~Csj6_3?4W`SsZm0 z9=az7@&SiHP%Y)qF9MHr7uP*O z=ou~C!c(6Fe(gc`nhxFq2I?scVzYi2tAzvB`o&4jjPB^KhclfXd>f=0$%`sEYcK~x zQq6?%NyTuATPesYWgVhBjP+QPElAwYYowsYTQ*=m0FF`hvKH_JY`JN{>qY_GTR&1g z4%sW+hmU^1_eUf8jRTP<-jAQtknbq!4a0WIG>b9Uam1@1^MTZKs+ZHSXFuvKgO%)8 zz7824*>e!xDJMopf&6ks@>oS3!nu^b&Ld?Ltc0Pd6^%QfZ(;B~4C|^8cr7&Ta?rqb6$zHa#UD>3j~p7=8mmG=k)mK9%fkRA z|EVw!18)Ei9y_0WyF9-YmM#Jt^~h_Eyxh-DVvy`vyCAALZ9y&rb=zAlzHxeiWCX zSTMO&X#R#UmyuW+iJN-@=YB4ax}z4>H3454zik42pfyW1>Uakbx<`!y8}x$fpfX4@ zqN5kEcCqNjd^ik1$s#&aBAHCs&kR!;#k8M*B`mf|HKv}3Bl~Y%bS6}c^W)sYBb3{A1s0I)mYgjut;5Kr_E(}#1 zX68JY^?|C^xO8fta<~xhs?GOxhe9a!47-cbPH7tZ)NdmJ195lF zl;)ZWaOO#FukZ10=)wU!%@tdWbV zx*~t&$7Rsuve5H7h}R>HVBH-Ru4s^w13|q{x`W}KOnIbNkC158!~x1VuE{V)Mnl%L z2`tezD3wW8jtn@dG852|K2>-IRNL5J2zERnU?&SQS@`zr^WL9sq4Hli2+%MW&F%!VHsSyJEr=0j(?`x__8&U=`NT?__=}+w=B?+v8z=I113nBtAvkl_os4bB~dT?@(knD^WA+n{^MkdP66;?)RFQO*R) z>zz{pqxSE_^5k|BNG{~z(qAG`jxgRRGf}#X{{E%79O47asxzj+S+g3PsT!QQ5~5SL332DFAiU59t(MQ{O+e6Y}i)L<q@aC8Ta#t1HZAnB>@~yY6=Yt zT3`r1;v777^X;o$PTn^H?-|H5*v~Ps8R^flZ4@{4>)W*((BH4EpdF02XqG2Nyk67! zk-dceL~33XhE6)#w5J^%20qT(mY^|A<#RPwk@=z#*!@=4+*P&tHL!Pw534D30 zZYH270^#d^r}9v=z?e$}xginer1AEcPKHQ%-9%1^U*N(#It;gvw1)`}C(qw|Ly`qW zYuk5ubncf~{q=l6qv&c3h(;cdTR9EYzJj%t2f3OdJ~bqLY9;>624dl}R@#QNw%1!*MU2IOcm;}+;0eqYhaz=V5 z`<~A0)sW&!8^K1N9R}sQ6~Xq>d#B!8Z4=dAGdb9aM(>gCD$WZ3>Lrubt|275SD%P% zLaFj#7wekz_MTCxRayGNgzw!@H2Q$-&tiGiZ_=64gQHfo!?d=N)p*b;2Tm}sX}J{v z_BCea*q>ZF%*v1w}5MYeJAxd ze#fPUye0qjcl>=q-fzpD9&4BPFaKUytX&2^OT$4r{O9n-XL4NCFCHNri=&6)0{38B zK}S#TsRKdoh_HaxGVIKS46Jk54`8l-S(5@ z-=moegG%ptF2e`4J)>?If6gQLe)ryT<3sP;?Q0WMyTBzwPI!z;f10^zbWY2Bog1b1p2-${D> zmv(CEj=|3XNNsySAub#9LcMC7m=)z;5$U~`#o;;Vinl?XFSK%ukGFC6{wwk0DfaFm zx)?NP8~wO&qh`^w%98nftDSOhC;+VEDDSjC2v64GrMV$I+WPxU?an&k9s%o&+7}Lq zG>3H_7Og_bQ2h59>1=2auMFdSbf*-$YwB#z`dsbf6UO0xuF=QN(lVvje>RBY^)82eO89@O-L!0y zNfw9yY=K1n0!)@7*a1AXCQ2oS4g0hwPIaz=fBOgWMyGCffo=232IR|suiDQi2NM$> z`_v#U5O&j)>@!M%%=b#SEq>LMROm`=?_1&eZM?ZGOIbZ|z&)kp)BjabeloM?V(_*C zX|FY_UW?P3W8Jamyzsr+(2Vu6gL!8}Z^Iru-4;FCxsxL~GOE*77jyen@!`Jd!#Qh; zXj9GxYqQsax)j)Sri0tls0HK)@)c6rO_9nek2I;u^SQ@?dOkk+u_eR4UepMFaND_W`L6N=K>m{R#8^r@uS{KvH6 zm-J)^BDdw0>(i9%#@(9ya*wW3)4RvGIpAmBz1;aPA+ovxcSI`*-E&K8CYGIwjrX*c z-}Z`MkJnR8qt*ekJ0hiv0s-gI59`{Ydb9gJD!E5T5@RJ{;$Ye50e|^Kg#82Uz^$np z{UeZ~y|4nQH6lI{YU@pY5^CE$6k~JHN%w${{M&9%p1*bASXnskfA}5=UU6Yqma$~uberiNo7)zxA{%`g_RigloDD=g&C8jr|u~7U!#oE3el_* z`y-jhMoBv2m%`U!4xUFsd(3&UoFRSGdf<6}5dZp;uUQdjYs)qR%#!>{kM^!uZMtmk z8t?h|cOJ;QTkW-QXx_W|e02NO>@~|gBeGU2E}v1+MmF12i5#~1Eb~NzzhTs)>eu`- zb_j9LmNcYC;c{chS;u#+SI2v;^a#Pw L<2w%9{NuWbCkO`iSqrt=*D0z=0mg~K zG4q+@X=JsM&32Y;LF`H1QrmTac9L z0;dnT-i`26VwLFMPh01;cXQ*DyF|-;9~f_?uV1{tRf*k>ZEi20)UaSp_><`dWH)c~6n42TS5_Db5a9?a>roOr& zGB&B!6*?*dxHiVS?fA+H48-NbH}I&LYIW5bnS>lW)mw%(tTj5bhatJLvR{<%{qC_p z-7o5jJQq*9U$p3TJww-jZzG-@ObiYZOo;4Td0`mUW89|l|$sb zg0S~?ht$V@lfFb+yn&`&uM>dFI$Q+8L zd&w^TczG|c9UE--&bx2!amLr(5>l(}c+1ElUSpd`aMl$3!Xq0=n5`NA?SE6LvqQxx`RgN>6cS|#lT>+<^*nYgS7dB4;&rv> z$4B&IIX5mYuI{$%HZ3fz?l$d~Vlf;69y%H>It&gP0Tux^HYx@#CMp7IM3e1t+B)#R zssrG8f9{Hs8@(*I+4bd*|Ee_VHtS1cJWKnNZ>mZ6TMGtx*Y1TSam^a{3?;@dD|%@+ zOk?WF&g#Slp-W?EdK{}AxS1wL)s#Skfelivzw^&CFphIFZplW}mkkR8LRZ1i4>8#$ zxLIiE&%UGo4GjQ>Y=bUYE>5fJpgjK$T;Ow0XQ88;)62N67?x^~n+A)n!J=;8tePJ; z)tLb%w3=)ErOeJoLpP+Cc|AAHtWNmK{&kFGGO+SXYq+bEi+Yk!3)<2`Yk0=V@lTqt z1nh;0mIkZ`2jwJTK`?7Wbrrh5+=K&^*Ukh&*eD^IAvf-a?sD4L!6Bg2(>113FB?x) zlJ;u7cG&7e4Qivn`Sqt;1lzOG``KrEI$LKUVXm?12lW4x9-08H5}&IU4fy|m7$5&v ztHl4eJgg!>m&AYD|3~3rNE6!W*YI+VjCv^0U&Ie8Xe|P4LaPNZl$1CmG8#~*X`_Gsbk501Wg0pX& zS^Os?Iyx$?HcKzwFRIS2^GX5KXHH8MXA@tKOU(<$>cb%ZuJ?dAtm8s=KpYlyj29&S zsHun5TENt((*dbq-JN1nG(Iq|kXoi_i%5qf?s#~$li|;vX$ao{RcD?`)FE%f79pk^ zg^|Z40yaIY+&K8#ry1y+s7P960i|SFDXA8a;NlQN-_f`e6lT-%f5yjRIW`m#z&4oqddoC0a{#J zpn=efNby>X5=Dh#{z&zIq^L0$Nea>YH7Nx}^IGJGAo7{Y3_+Y(A^zpzv6j{TX6b`~ zFBc@n3`!R;v@D6N;hi-`#rb2@f?dsr4U-T=W-?aC-H1B?6`91%umzOib3pPzO zMQG-sRcy)tBC1R2}yvK`e2 zczaeC_R>13NZ#d5pfXV)6!om3jd5q<34GZI5LXl=ZlnM?>Zw(Ycu14FBP`9_NN0Xu zB%N9fSV!DH;^}7AtEOsd^9vIj303+#4ZKj{4M>sVbj!VE8VopwD6$a7W3r=+SCCGc zdpAET=%EA~`(u-%P7NIJlyMZ>W&*-B0oQrPocZ0GvE{iy7V!X`_GSrSssgDfv$(-8gTEkl$^VNFiEB#WhoZ0HZc%cN8YM}gB znwvKLY*3iL1&HSLeuNkZ2BPkRV+1mb_E3xe7xICgIsYH>@#XH>vJgxrut?_b-`G!p z`dfxX3@Fd0A1RkysX&v8-8=OVk|=n}mjbPT$v#esYJq49LiwWZ1O|co+!}PSYVCU5 zDZL6kICD6VXnOxpqxf&$ANC{_f2XF$z2LrIge_0uEFerGRPV?JL18RB4Ny@`{{M)U zDm02ZaEQfljN*;2HzvmS!nz|=CvMrE+ZYa`)LT7wT5AMSvBnuzk zw?ig0Wd;@`kqG}4P7_bZm$@sMSih8P>n~8{cGmB(}WB>O)Q+zM+S1g*bwYr*eCf zth<8&e2;Qg#7n6Mql^nk7&OrUC8CWCI2Yy=_3B^hY;Zv!Sa>l=v~r-YK#I+!NSE+= z=fc4i-pZf|YW_fPGO45Hp!ntjHwb(sdA=w>?>?BnGW&YN%OQ`{0N7--IS+?~EI&NM zmkdOx%q~BSUT9<=GU}`L-UV!MZDCZcA&lNAT8|usSGMx&`=7mwBtSBMaBc0co*BRp znk!{y*;YC}&?6@VsU%RmMWRt3FR5C}rHdO0D8~)^|M(uvr^tQa|Koc^CH#-?K}PTh zgh2_F3L@|-29YYe8$rGx{2~!K9fFXDkV=wIOzZ&PL%P*NOfE+H+NbizRaTpTZXmxp z>whI-_2j}!$?S5OR}EuTn`tBd>Wg06ErH(50QHG0v-Z>Nvu1k5wQp?`Hy4$4>m9fN zckO;OE3`5x5?llEecT@sI`Nb*a-=~!v*QnU?cR^oZ5ZL1B-y^|OK^pu5H2iS`${4p zaiGcP0k+nkSbk$B22-Q1XvvcpXbt(E(4p!9?j`v9qY32$i}Vc2& zFWMXMSH(+Q1g>(A=pSQMOr*%foL{8KbR7g8ir^rUHc3VZD_|zpve0nYVk966YyGqY z2@so=aOEqyhPO+Q$1LUH9KBV}TTlTSowfIS>2azy}`y z_>inat&mA9tK&lb>r5CDFQcxLUe{qBgf<+6HXVdE7=$(%gf<$qlnqUt16>J8Riay> zDM^s`^msu985mZ)6e&~Pn(KMQ0sO&m{b7I|tTHYS$D%r>gSVZV<#YdMJUf>+reP&k zhD31^5cu(o6Tqy6PU#6srB4q-ruh8GBvW!(43rKHRk zio&NPX{`yAJE*`(9@h(q@3))(H}pEG{~0}Z%bsw@9&yK>ac64s8U6P&+W0ft@RK+8 z?IYx#J*SUN@TfL-0AId@t)9jgeMFCJ4-ka~^gq5w8#Wgp3JX(rt|1{nlpG>YQ0NiWKlXnVefFC09M%-(PyF9a9%)dgVX6YT%)s7?ikeplu@!qRGT1hw zl5N=pX4zE))gy!SDho=Q3kyS!)CL|QqURb!n~p{AJgC>;WSh0D1Kndmp5f4dw4KdK znKrdFyI$jguNdeTD%5LMvdtR}Om)eb5^D0&Kf~mu zB}-*h`PzyqJ{v|g)&G1CAa?L`fbU^d_Ht1YzwKKlD!#_hEl0_^4DdZaq{VDP#cSjMSy=h*(0wNp(RlD1m4%^@ zF?jhg+k&{=#F*WvnB9&4Tlm<8VOtf&1qdGrrw*K-^m_UvY=pXo`f5v-?A>G&1!1vf z`@~|kVj5L=;u<*R*EMQ|G`fIOL~nfn_`XDOQLa{4m8u)vuD}UL)SVt5YN^xr%94WzLqqGWzNPe{emhK-;%QPu#E`M(h0Pvxo z?N6>H?G7ICnI|mK;)fGWaYBI*Qxu~)ZkM#9h>Rl(L=j-JH2!>6|+Stmz8h*{#TgogPV1ASjM88>SYS+<&3QH zOgkvjY$>u<0{}kajRC;N>ZQEZvebhy1=FsuWYK%aZF5r*pV6D+^hD$SV16-j@{pf{{}9%=i8q1OXwjep8BKwGiwtN%EWnpYMA=wpAd@1K+DU z-uNcub^ikX;-!|k1i$q+9;XTyt+q8Hz6wYw8(`fR|5-=pzbW1n#; zf8a_sGgRSJIXwLqH7#@+**AG32N^7~QS{s~=Or1SHPH7*&K*8i%w2Ij+?g6GA{lk`Qup`qfhwuhb*FIJ zsE$S*t@+07n|AlGtxFBd;%IkHPC8Lc+N80D7=*kRLn^iAlGliIsrTkh{s!N=%hn|C zb{gyL%a^XrLIf)M0(RsJAwz)7ws6^d1EKdRWF;_EICg^qp73k-rkR6dT_oP?~B)vR#Y$T z{6!`wt)kQo4mL`gD@FQLH;?K5O4DP9FKnl)%=y8|;9)tqCHQBD-#=OGZ1Tf58+SK} zcQ=KWx5}@DbG1r2evdXMp&}!YiS8_p0fMWH-(Q{Xi(VD(1cb__59$q+f8UPWi$B=2 zzrVNmKCZ7vA2{5t%zk|%rq21Mi650_h&wqL#M#i`R$^)V$UhCz^L;#xnk~zZ!V!P3 zs8VX&Uw)0M?&5z`YEJtUN8bX@RCarvxSsjf7HvKnUv|0+o=`vWd%o4WO>g*ns(k6l zj`NT|Y*&vTJ!iLlV}DG)QF9W0wmUTC5AcJ#2?^kRGS6n*-?CCsYmK_s%-4BrH2mvd zjh4NxCj!ksyS?z)DQIg>AIjP{=fhw4oQEs{e%(cWY1oDL;7snb~uw3{}p+x<1%_h$N@w zx;}yt!$H{Ka-vf+cAojSD)rEw$UXAe(R!gx_oF?Z*JR9B1FL>Nklw>rgPz9U0e-J% z!D)dRy$pFn+uPXgM)xwV@DQxW+_gEZC)L|!Rq`=AsHwEU`|w5HvHwNRcKCNP*Ys_S z)5v#1<%M}gugT@d-23&b`2WGzT?WO`DB8ltgS$g;cZcBa1b26LcXyZI?(XhRaCe8` z?hbeI?tRYL-@RX*D!LeEda9;se$lJfdXP#`px|A3Yc%gx-(r`ZG3Hg3-p<<42EU3B z=6T&7KbziU5nVR!B@f`BzkJVHKNWw7;>eo)0M4~vru*)=7FJzlz=k`pOg3PnJEB^< zTcSB!pGeWBTyuu~SLh>LR~&1 zX<75-)S14!a!WVUrM&g%5~Up9QsZrrb~%gPrPJGd+hN>#|E51l)SF2bi;KU^B;2vY z0>k%mEu?9*WZ{iIJ({=UFe7o;=SnM;vvkyY|2Pu7$)nRw#~J~Psj7nfA=Trn&@yFh z-NJRE(=hYH;qpL;ena_eSrxv+$74%%m=_17o$cZ-hEvy^l=ICW$9VYWD+!yNryf78 zt!A*b_UGJy&x=?zSD=pjU4qwC>&HNJLqKef+{NJZ*K)^dC=1fCy;nD_riR+H!a{`C zXbNS=rZpF(w^^8^q()Mj1acuO%A_Q59Ws~{sDy~Hr##a{1mssKLqG;)b#F$9`5`|MG+81hr?Cnx+r#7F4c=jB^hM*->O=}~SSvjY=X6U=X&FObV({cER- zIupJbKG%)a&UEu(N1yH@<}`g79*^!To_J3sx=TLs2eG!?Pwv8WP4>2K(5ywMBc~$1 zHhf-I#^CWRr@2=#tnD(FxVOq7LRo{I+hs73KN}f4^;*+TxK^`{S=l&i3iqJB%h$yO z7EX{0+fmh@^t#gP@`i?V*-*bM@s%XtYT?Ww6zp8Rhg zM3jLqiE+u_Joxff1WIa7EUq%J4(A;4Qhs-)^UsKX#5@Jo_+hZ8yK`INx}oOaEh_nf zz?IyBWWkdCTrI@(abE`W;>8rIZo;X$`?~P^+Iy8f{yGl+$Z>QRt|%@E!w+p0b{M(* z$U%Day#vRwu$1BEJGz~!a5Z5u+JCYB$lG!`s$lFs?&1Ba z6YSnUZ}_S&jMz?I{xua@tD0Cl8QV=A@9FoNxSMKkUyMLLJw^YFpR_D&-j@0}Lq$Deak&!UZMlW>5(UPP|9O)#UAxNrXw?;`3_D zcb>(=unB}I(kqYd86g`c394 z+W?>3Gz=rxg}FH&-Gw>a-k{FvaZRCQ=0^0vJw;! zs2QLQ@%Q+Df{AZVsEf<8A?V37LiETHP(z3bh#oFxP?(Ul!0PPz)82NwZI_g5g^bMGU#&Ul8Jm>&;XJU98Bq1CoQW*D8T1v->y3|aU$dC()(%i z?QQdIYr@FRqV1NDP+y<0$R|NiBcxEXNbA3t4zPci4&2Zg`QO(oOd=v-v1kCM1Ipe> z8WWnwz%xUnZm@OaM}j9S4Au)hUt*hC#a@64rRX(jVez~U=*~o*I0bqVbBd5^#X9I%$)h}fFku;C*`#6{ zksRYMDL7h?UtTH|))o86e4>nDrEnGgLzXd!J)$;@_{ zRr~%j9Y+5!9SJl&XaJ@IlMCIISBkS>BKmi~*3Z6Zj|(OcIjG+R$rJ=@RFIgOLzk#v zpr?#}O9cWMf-_5CYiX8=l(b9sZRlnw8*->5DVCYA*~ASasTF6#Cu*Kd=$6$|ohiX} zfLwwel(R6cVt2NV4zmzb40V4JP6`gH%|!(H8Z3=ToWS7wWVBSII&NDsw|cDxVmx4R z2ht^<0Sqh+A#2nQ!*QM|)S0XUdDiAIT}?@aCW2xGk$Ew^J#iNAkTW)lNl`+$U!2FW zm8Q9V$c<>pai>Jz;`K*XDj=4is89`Lzy-t-vr7n+nEdpEJzL<-RbW%ElVpRAFvb$8sFxLj+GGoK1`-&5 zCIszAsbR63Xa-Rzm2@vuWCmsLrbg)22NoigRACZ+>kza+D3dRZLn%)coj{?elSDx( zDusHLLK0C)Apbrrj!H1z%c_qI?=1>kD=Ot(WMm!|Ld4QKw|gRXg2d-z(`4*`GPF+mAv{wOvQoZ7;3?26X!c&k7$;0qTK zn|r;G9>R8+oR!&f3G#KSjRhM}$U##(Byjy@xdfRiy;uNyeDr$ezwPmHfITk#*Bf60N75;porH5!=A>Qqbx6u< zqB#m<176y_B#erMGyNkOJ3>5$GKn@HPI{Bm*VJ#v=GP*6lEm}UjAj>3m~Ahu@cBCU z&QHX;FM^5@a}+r+S5j~AE09Zh@k;>HQ3CSGbiU2@1aR_DjJz4s1Ho@tC!oxDDr~MZnUm5`5<7)VS@o~_<_&A*XKlr$kpgu7- zYPcHG&%fD3MWbKzETrZm2NLJaKuA7*6+EHRv|W{ z^0*Dc-avir49FR#TD#ZGiZoU+o{YMBFbf7r{bem=?_{=hoMyvt9%o!M$S9b|C|Jngu;D58iJ~sZk>^GmfN+8%xYCm9QjOS~ ztm;AHPvdeeOHrVU8HY@Ya4gp&oXe<-mNx7)TDo8`SS1mU+7K)aJ=SVvtQ*L^ITHP> zm=|aGGwfc!G)<#+%^Fg7d0BU{YL?m37)&)7EVW2XwX5C3_Bnbq@dGkh=8*f|HblB&e;q-PE+zcqC-q!D zNH|;qUJ`g;VO*VIT%9RgojgZ3Qpab*!)x-1UFC`0#fe?kiCx*WfgHxrWv0;O-Smi@ zb|&E=kmda~Ov&s-G^mLBKMpH9N3F~ts?h~=j3;i`TW=?HUm~T=u5NEi_`S|kf| zLb?Ir}e!-ey{VBrw)MaBn3b7}31~_by4I~wD_X#Kv zl5OG+slD_S*KeZrXEr0lBBb%bc9+Gcq>%eg{J8)~hdTi2AmyN|5Ga9bs=S0wTHFlc_q+#N z8;5MHsjN?j?3tar(DZKi7Mru?3t#eQ8nYmGlT+j=ROOLY>LD)|Q!D;VL42Y(;T;b1 z%tU$$ifJXsvX^Dt%QhM083^-CMtZ{ihaHCjNaPHAI!4A1$m+|nvD6iP@d#8EDUA9H z8ZRgUJElA!s+b_E#K2$7slwWi;G_~bRJyuJt9lDOdvN1 zA>Resub99t0Vu~A;&US5HHOfRZAdR@NUzX8KnLQOIiwxH>iF?fD3{x&dR#-$;c|9P z&VOD~NMvE6-)MRbpe&ZDU^__&ND7g{n|fe|epF5{-h_z_ffn`gqn0BE+rg)Vl;cvv zE!J8G+{?bs#RXFfXe3ty8O0k-6O~QWFP)B|uUdkteV=?j{x77Xuz<=ysOT@!!3sb+ z(g8>ZEdc3Q10Wrc0Hh-qfOOQt<3S(FXwg)TgS#ol!r)rW$~W_3l5HPn4GOHHXz|AZK!0J^fG>r6_ zHmw!}n8q%d*aDD_kcqA2iLID_Asq<-q=UC)vZmYnK2=Lx-Agv0p91)G@c$ zlU$4>H1hq0$MOEc<8C{F*dH9^0}Bc>WWB&Fzr9g8^?b^0lRodmaob*g+0_KG?<3#TsLR{-eVje$rdn8t-2Icw6 zpL9=N3vA>7-*0@69c_$-7hg9W?xNX5?bjp6e{|%(UjIkwKzn@76A%F?9lSoyn?e@c zeX1Xib)#MVf0YjSQg*V@*lKkyoy(!nph_Pghy9tRG%ssm)YGGF4#%L8r$FSGCc$OK z$+KxG{*TG2Wvq>P-Qy^&dP=5MU-z3NNBS?bdv@-5!OZ&M*SfV5*tWgwT3npYgk`?&t5npT`Hsssb*t48o2EBMdpgHF4+;)^-i<9w`nMRh zKXB-&Bd2?k`|-xplZNSF37tUrv@2T-@Zre=o~9TMAL zuO7MdGdN1{@e}Tb!e2_(Ug&eK$gFUB&o^v&HyG;`WUaMhbuw5pTTiFYQ9noM>8CF; z6BI1o*V3DB)85Ujd0Uv}kKa5^zt%FB-(YJNQ|LVoww8HdIP-5nO@GQ}G_fMaj%dB? z9&HrZ=t$ohnhY=5q3_8cetn`aqvQmoeNXTl`mjwTJ1$xLM!&RLyEal&*=?tN)gtxG zWxII(G^5gwL%!HJ-T3-^bi3yL z+glsT_syi$dBvTcZ}+FfPc1n79;=T46jl1hc zsp&T6u0Zd!Sl8_opV*1(+h3;Sw&6L42QTkv`?eWf?;*B(7~_$ho&B~x#DB75`8rn` zPY=R>U*;Y3G|6?ocMD1E8wz>9M@U&>929)6VXV9~cOf%zh;vF5x#$qRKSN}@E zUwX|zlVNjvA^rxzgZu07Aoik)$;q)Zc00+}SaEdj4k?z7o>X#r|ew5GEOt;TSBRr~4M_xHkciZgfGm;InGrz&Rn+hka2qU`pUTh^D; z)a&49eng51pZ6P-@yf^}Rq*OG8%9p|M~v}G4I-Tei5@&RA?R~I;f1>nRi zye0suaYgW=+VZliEW7P<1YpTKxCuWuh01xty^|h8a=phI>QAZG+3>d=c8l-kw`g~D zPrQyeSJe8yhi+XTgKO3O2Y z@5w6i=hZh?jOwzT{mAv4)cL*cl%CqFl{uSkWS^ho<}IGnx3?K$oAf^gZymeoF&WA? zoOrqC+>6QUHFwWP4O3=5ne(&$J3KpTd{3QxY^rB{_0C~GA5SlEAX-m8nQ=JQ z`5r#VV=nnFWa+M1CvM^$$F5Cd)^a$%_U_-NL-u~a{poSrV=vWdy*^owonQTWiHrYe{aVBn(pPpj&UOzFxxMaKMe{y%z9Uu=Mi<%HhRBO zcFN*5dXi1K{wTyA8o6$IkIPcE`?B~N=i7OsPK5v7zkE}|yQRNzGh6=+L-%!M<*NoQ zN7t-j{P+*u)Q^n1Z8taZ_J_VB^5`c%npJnRccg;)aZlgL@crSbkqYPPwOdWYUdp%( z)9Xy1<<_3-X-6@!9QGKS`~*Ym2PoZ_BtK1??xxHrXM9yT&CG&TnB^7 z`(@eg#pN%$mizwhIK1NS`u#(?QLaZ~wCa2h?Mu_T&xhmpEj~+s$aP;D6(;%u*SueD zACYi;pH%>)cE^4WI06D@gJDe*&%}Nx zCw|;8vzxWY`Ytp634RsKb2qtyj6z*tmz-`2)LfZWR-1!MbAOq0JCm8YPa!86bbpDnarD*2g;H$f0h021O0}*73(@>E zb1mI&vTpD7d`5*vjPs}_?ey2L3@%br1P=W6Brl;UdvE93`-f@5d5`>Ny+~D`PWp!@ z+!ItaQBM?X-jfub^u~+qQK%Qw@n8+>W!hB(S&OL(9RJfq+CZ+aHfZ~ns`e`Qp7*i3 z$`0#56uBR2cSAL{Iw|zo^!SU}pNcL#9CmD?%alho7j6N85)?WId>3|GK(;cXL)lV^ zJ3b3bKGe>*Yt~O5if=ygcS{?JtEfkD)19e44+BK_tsXJSWJ?JAo7)oV>nfaoV%EV! zw`Zq%tHX=Krb^8*vndy%JxltuFr7;u%PebGzkB+%$O=T4U2Qy+@XXX67&%F{6<3)b z#^`#9t~c8f``$On@H%%-m1NOsygt|Ipo}u<`m;0>F=$uwiFUKyt5#eBPY~#%()GJ^qQy8R#j-l-t2>zkJk1i zTsOC-GS&4FY6Tzl)6Ah5`!hR*`kc#kbz82+fTF&qkx;dbW5j4jnv8hl#-Lem0S7#B z5DU&jg25y*voDujoZA-lhcP{0b;LEb^_laj4wdOQj7O9UT0V)3AM4&~g~2WnH=ucx zluDM}t}+H@ceXqs+E7?9&G`~g)|!B;%f!;iVv=SBRNoR4mTM)H zi1H=%3)~U}xnzWuqLTO(%oKr3_Ll4AArb`d_>jQw?WQBx^KCcVfpd8PzptLinnKZ1-ps*BJ z7zasM>BI{4SISI2a!Cw>f|8El8DZv}X`MXakrcsu3vZ=qe(8-+qJo2ObRUCc@>t(#*{}JMCK1b7 zR5#*KHJDQwPaS?Q>@7waD;gQSZn%mbu-U7M zLhvP)cJn=N`TobAR1=`ov6)V_YzNqry5#&{{EL6*BmckI$$ynP{y+Xj5unlW_xQiU zlLJk6Hx%V%St)-ut0IE>uzCvtnoW$ttU7*}BteNtFmR#tLYX<1ldi-@RLYD-5?ZcO zNN5r0upJmkqTh4C#%Ra=@E}Dr3NBvisOsIGSJF(0U^m_q#9vy49V;o(SUk*E=636j52XuK*#t$&JP;MVVX9XgO)k-97Ha{zVfc4y zE)lyDG>O0eZHSo!EOyz%0ua4TL}Sfpg3U~!LzTh~?UP_&fB=s>kCXC&nx8DPHN-&^~j2axz;Tbx!S;Pq;gxB5~`TA ztI&x6K#h?(*E=DF8;Jch>R3I#nx&!2()Xd3ehRfKgPkATf=ts&X5RU&&osAG~`S{0)u0cd;Nfh*}g;x{HHyZy7m8#s5adWJ;# z_&^P3SclwIUWmFuSP*4s@W(23QzeSRQW5C(Xpl{qz6_=rp{;;0rSkgQB)O{J{@B4w zMq>uOn!+A&ooR}E%gc%P;NzM|7g!=8Lwa_2pn?$N8GBo>D5x^9q$mS_tT)?H-8man z{K1Ww2r^+K<9haZrOkgU9x_A!LPnB+BO2|cn1Xxs4}hO7&)FMWLNo2kg5Sf69YIHCJ>1VALsfkSdTv)nh@}PM2SuEhH~|vE0h>< zsT>_xPCv~8PzQ-WA1HymK@TW&#ly_se!Ku$9!0CLMFaxtF^Wn-F(cGYX`(2AI0Xk3 zIg;NOPz#|Dh3)MN@Mn|a9Izj?ejyvS2_vch6u?D#2@W)x7aGi*i&a+U62J2BZ89DFow3Be%XM&&n8J`PqKAL zXwWREgQ{5ZbyT{%@);F=CVa`#;KG8iGybYnPd6Aa#VW1zy>JOmH%`r}+gBG)(2RK% zM2LvCs@$M$=;GZfJ$@+mengJj<_z`brKX>?j&N zzku&B$M0hx7VsNq_wypryhp-q)LD!79sPWS{H9Cb>6X?MKaCf5EDkbi2cmk8KE zCPjhAu1VqYVDc*jZugdJ)Ri$sJses83vTdQ<{>VZwq{}nMi-5#G=6^QM&%uVDX?Ky3|EdQrd4OpK?n&emnv$I)J& zsYpIq@NY&txto!pzh8fqo{kN@;DQ?u>4n@`855Q!_*-l>gt|}TA#r;qJ)JXdHH^4V zXYcTMCOn-}ZZ(v+Pk$zOMLBMyf!HNGp8q^wab%L)PvV=pqbr5`Q#G+N2~O9V!Hn~Q zf_?EQO(yFSs3UBa0^MsIFW~Epxsc0flHQalP}7RZg1kT&=#kN7o>1Qwq-`NdW(zwwbmv7AMSIAC%j_kSU(D z)e9CUpSN#8D+Znj!K*)+jH;pG7v-}KwD+u8BRpR{Jf(W*=2k5d1YlDt8cGiZnMf4v z-msnC?+gcA(vnZLca7`sO;op-~a>%csDMDR;x&OAQ_&L{)d#gNMWaR zVWa3a`X22H#3p)wb79-}Y^e&?$P=>`T)9JAv3kYY)4f=LXf`9Uzk;RM2cb+!x#=D9?hMvUe9g%yE@dPxI)vs3Uf zgz%RlsJ=MVF~RC`;MN#RnluwQJxUM*YN3rBrsk;eqA2E9kWS&`PA5X?Z_rLBgEyHB3s} zu^+;sxCy7{aV&j=1J|^(FWUKccKHojGwwmTcb-2vd+3k36=vL%a__zcOhF4A%?3-TWjg0b^5tK!(>r%hAbZKdQ#LPJcHv_Jn@m7;UNbg665mkQTU zi8J;I&)Yz8;?O!x)js6*#7gVRoX{a`J)qu8{#E#5*+*Le)>7%>FwP#J`AOVr9KDm= z6ri~TW%Ht9H}26#iv!l;Mc!&$z$0*GKV^K+@p5dhFxz1icNGj<59OLzi#cu_oTUX} zAMrgwfGsie8dTmb@}IBi5w`wU)Rw5IEjd=9R+4}ktaxX4FKytleLrTU>llhh0?Iwf zUhs}eT0LvNRtM;jMoOXz;WESxN?{qrYN>okKY5WIxmY_s)QOJ+*(RO8Q-?i*`xW_> zzg;YjKhciPH~&X_Fu|fORiusBdHl9FtWYo{S3kK^0>&xAdl?dD6K6V5>J1XHe#y{3 zRCRwWLjMfFkBWu;DGDtWI_D(< z*}_S(Tr&08f5n-1KIKgz3YNeu}=*R$(a?Okm(P+ z-rfuCoK{r|K5=WYy`6&UJS~$K|JH3=&*;Dt{(c z$Ws3n7&$Wrt792I&eXgbb_6BJ4~8gJ!X!?9Y^1nhutF&G*y&Y=T!@^v{M%Xk5ReQH zL(K%O!Q4Gb!>=w-grLbz`_5O-{Zgw7uUj+{-iCn5**JgM*-+ezNLkqctw&ek9*+CKhRCpqox&Q#^VTwDlXwBJ@TB6Ei}=UY>YH|c!qb;eM0TP1jp#*0!1Im>dkbz<2Y>=$&AMkhf6#J-Pb zqEEV-9TQBv5mlY)*!Mrx6;-}3e=Ye&?yI#=chaerdz+v2X)0f+H0kFkm4j@Z;SYqo zGfmZ#7GbP=ZwKZy7e5-8IN@v>dqys=(w$s|s4NW6KR#IFUgvs_rt4H1E3SK&m#2y7+Rv`8JNv+xP!oh=bRTw zC)28|gg#xKqFQO&PMo~OQ0T2>udLc^;(oH#Q8a6{dIKfqok+BbbpX>0w}LY?#>-gL zgZr~#D-_~<7URLV;|s5${MLemE269Y+T01O!^xqg5so3#8#Asaw^<*ygCS$fr;8%y zdovM5U2ZM=+h|xoGpQ-@%JhT27jAgdo?p_NWNb+1gDFkZ7E(Ln)mQPK@r(JYHmJG-d4ivf1b5 za>Jw(l9LueLnTDI@OWJm$BUEYnzhXR*A9c*h!D3t6XO@k}`q% z=n%abqOPcNa#;(+%~%j+$5vNK;@p;;zFw7IBJK+6mb(gX)P=Li;kI4$KQ(9{UsE2S zj@3AE&vU%2e&3SR9LZrAQe8P7dAY4BGy9A^86Bndt(yzPd1xfnZJGNh{vA5GYEgtj zZg~iC-ZZxj&V%P>UGnQE8_nn87CJT0Fx#otf(Z3yx#i}z3KSjm+0*Yq9fK_InD`t{%87{NMLPA2Y!J$a z&SQQ9axJ8NeNtPCPZ_mcKEKOK55#wl^(w{CeW^u03Db+1YTf<0dMz$)B zx3*!aT7lt*PdR+`>sL>gmlf`=?peYmCE$&cQQN0m7DI_4^H86!%9|-YK<{RZ-53xy zuGODRykFcHt-+?fj8Hd|S}2GKnw+MC`q0{3L9|?9HP)e89Z@xUt67{DhoRKXuUVf7 z_4EeY{z&)}4`q2FR<=pm990RN>YQw)TOG;^y#m&}o7eO9RJ;0HGxNbEWp8jEBafeM zpo_dtFH%P-cm*JA-=gnxv_vFiqdYB}mqjcL_7b_)uT&LKB1FBc(_BK*T?cB+)@wY= zB9VHAgL%vHK-hmO({{V1pU!$3v{{&gACzQr9Ze?*EqoMS@0wQo%7Q~kc?N2dmpW%m zZlY*vyH?Uy{j9gWxd~Ld?)+VzVyY~k*HVa51^iaVqxq0y%c&rvUFXhTGg(%+^};}9 ztCnFg(s`icGvLGR<)09gEuH%K30{6tpqf%dTDyRaN#BCHB!6hK>nQ2{Qvv@l#@nsc zm9&Y^$7m~vRvC4%s@~;2Tk>?eP_)Fd40Ow@>j7Ct8Pc4b}Ua$^0ywgdFb_4&9uut!Nj{gZFIbFDkw;(abK(J@CQ zuCwFN2<4?m*Efi_;j>g?de+x#L(+2En8w}F3n~lITXJ*W6dSJU?8pnq`o3l#B)JmT zr~nihL#0zuLAqz#sO!|HHhSs4?CS0Ch|MGFbI&${3Fo8sEqL;*Kk{U5p@9~=vThDR zA!X|(g1UH>xoSEoUCL~{tGv-m>DzONanb{~VNq$h*mlE<-B?L~#l z)Wzf78(BA!%8XeGOvJ`}F4SFbHz9>h7?t-YclDi&QdW&$UGlZl#?)0cv|BHBvA?Fm zu$wCA%h~a~AI?H7#bf&x4|@1G^@>nDj*p^R15Pmx9~3KGHBV}ii(WjG)lOE4K{x?o!V`_E7J&c2qE(g9V6w=c#skOMBK_ODE0XQiVx1cLx0)l$~zpM`MV9>KRf z&h^?O?VMERizC;GbCF8BU3cmav75QD>r39VT_3FdzN^WRZ(m!V*|&V>Y415c`yF%N zI+lk&e7g+ZGTM`LU!N}UKc(NsVIQ`15WVVfYxcG3uJC<(zD6X|-5TMQd|sf=#FNQf zcw0(CuCGtZ8uNYe`R1U$F3u4;*$X}DzOTuA?Ly*r|;Llkkz@Ge0R6SFMvRC%C8+O_Df3G;*BYww5#zn)%M*ohE zg@S~Kj*EqYi;Nr4G?7Ku7BI~K3RrRSQ=L)tqLXMdzuf%-wF5`NXPx`WzrXfj$S~4Z z0+twb&%7Mlai6fGR+7+q6xcRWE3FoyP7XRwRA zw9~m8`D$8o?PdprNt%d}>?jzz;O-Cy=mUEWi=+sjjuhP-ES34bN-)n1c$)tftKCSq zOqC4Xnb^X0!IU$`rR^eNm&EaG#Jg=sjcCM6>7N4DH|$zq=!JI=dBYZI74C@fgK3`F zx!`lxw4QhPpZ%u&fA2T_@%-=orUYTonW4edvpiA>@i_$oKZ0@t%$?gmTtQlEmOvhk zh{%#aO%qKsi>Hr%Yr}xKCFYG%W*4T@l7M#6E=Dt|(xV0qZTL{VUl(*GMwk%RAgETL z=PIi}rz7e7pjepF1Zu)2Lj>_tYiKN}mN;CaxkzbMxGZcCzXHj4k*I@y=axaS%u|pA z94Zki$(#A3^_L1tVKp-OFD-dtJan!6oo~|e$8pyM0+$ekVg9q01rAR*O@szF!RW_U zHyv=zG{ZK$fjBwE#WU17PTav$54MICI5BBgvKfB);uUFy<}T6L3XYg3Ots7l41S!q z;(%xlB7cZTV|{~aX0#qDZ!+8w0rV`?bSHvP5g+ z8>o^08gDWvW|FsM0*p89#mJkK)5nV!Wd9v+nl!*gI!j^A3s3O*Np6xiLMT4A*c%&! z-lACxCkPD@7xTQ_0}C6*Sm2RIoHseM6F%lIlAH%@6tTN73v{NI-GafPZ3Y}AK9J2xVf%9A>U?8T@S}(P! zmnXY@)I+{qott8w=4ZAuHUyQokUGY1focSL&|=KGK-og9IBJEtqi{x|y=%h*y&$M4 zR7%xJqJx8dgJr9DChH{evil(MG$xJ0BsYaZH$GAYBlq|{cF{s0_4;9UB>UsLaX(hk z2}I_hU80E0NieW@VadXHOlpNA7;$cM#^!cO8pJSGE?E(?T}D_3w2nfeK9hK(4py94 zOuz5yv^1b1z<`nj+{~XD1<48?if*wJ6&OHCf>neO>TgLR!T&!>5=I0Wej`#gl2rU< zP*I6V9I++#*>dpNZ2sYrnd3z2gqqja)qTOuWCqq>h7z#pdaUZXEFg-RyS>^uf9Nep2x04}wa43j1 z;))eKNRl(Bg~+Jqc{!Zn&(D{(lQhyy?BjwZU7`kWh6By>D-LzfA2J8JA}VD|@qYrz z_0aV{1swwptx)pw>K@;3^rubFE%B>jIbH_|=+=f%Cc7q-f5?43Ro%>!zkqj9Sd0Zx zavjxcf%2-{X)3|>G62b;a`P_UX-e6BXbV{81+Sq;?d(>%ewFOA>sE>L+nSqpnnjQ5FYV& z!6_zzpfJFY1#$#5;5r(%o7EpB+Y4cnn3fHP(~natAt)y5Hw%Zv;6DpaFTIp=t{Rzi zQ-fuH7o2YQ48Xf{pnVwpnT={0aT@d8dE|ghO9Jp^cC^4b=;b$oy8U2`ZX@@&_DqaX z;dVcu-(cw6Pi93|Cw}|YgX=u*^$RY)C5kV*i4@L%(CFMoq;w6A7>siO7Mzk?p-Tk; z3r_UI1i}u|c?SPlaH0kUbEU3q&XxYxf)hGm!HLk|O9$c;lHf7N=z+ufJz}T4v}%W^ zR~I$G5;#sPr4J6JRf77d3fgWR%voHGa*F}73CcJRuW3x5_I?@6a$l!kTkRV=l()Y2 z#yvCU(tvBwU@fl=GTs10t3)X*T&u(iY@mH2A~xHgOuzXGBs)}DoZCkv^bLQT?KhQfB2ygd=JE#XHFDVJ6_U%VVRJ2f1~u!H|uAR*SkL zR14gEEqj$`4l!W&ZIopNnH>sW4{)h3$*;;DF!#~iCnE_9>%$Kfyz52*U6)U|$~&`E zGXv5rLTt4v0^+n63J3LF8$5dp%i$(EzFD6OP?Crtu;CNlcvJ)v&DV559ifrOOaI%y?;)l1a2PbN~Ny_wO}0UJtlb1os9?UM0u5J1?4JG0goc=ssn~S4X82*r9Doo z0?UDjBz*7ZzFac_3&HbJc15-+g-s3 zpc4@vTxdCah98zKWQYEnNW_*O?$|jP3zeq}mYLFHnBX20?pr?2EeYJWJoF()hCWBg zo4L}xA4xlDA|4H(*SH~T!T!$&i0>gNe>)N>yD24oSBxRskRjXRAvtNgPaOXgB%ngJ z&7yR%hOQ)#n@g15Un!q&03C_T!M_~|^1mGk#Q*F_z-audBLM>FNWcgap)Ru2Th5op z1$BkdO!)`v%$1Y%87ME7DLW%6v&(}Y)AK0vi^s)@w@k!cGmuEKh$k_T3^S4pGn0@p zkYt;POB;zNF_Mt&l0~00)6^&0CuSnkaqjj4zP{+UOq^s_g;9K0gH|A3TOvk~bE!rNkyx(O*ql<1S&UY`?vEl*h{gjs1Z zO=D4A*qKc}YoIq>syAJ%{fN0%M^BG-#_xd$^o$f@Nf2Ub9JW#wQ>})n7M-ycow+ud zp;l*LQpUAMiQrcT_avsn$B|%b5`+g9)cx5SKfS5H5AknbKet4V&H#?igdd#|KRPw` zuY1x4EK>(8(*|q-y$HY$bLh4SysaL*tr5I!KPwua^8rjfcz2+sA)@=wWDyGS{Bixj ztGy{XE!J-IiXq}vx2uwl_d^2b6kmj8VJ!-1`*=A?1n;~;N{Z)J=xs4W#occt@R}fl zLRYA&@U?c-@++dK&57k&BBzWzDqdledo+|Z^Bi>9Zp_c_y4osL^B|Dd@=%V9L@{bul?_mM8{2|mi50&62K*J zMU_71^c9|v{=Bou$BLr4(-u?}ly)`gHaV8_*7y=F^x}=(m3fsep}J+|Z%yUj!cGP9 zJ;IDn^#wO-BwSgEFOfty3b6O^3+)4pDHUq z`Inpzb&OdVZh?&-Xvm@zsC`KwGv|ryvI*_-iZ#-p>C5#y3*q*Sk%x1Bw^uL7QG530 zC@<1oAV1g{OKx&ynZsS3cgt)Yh=K^oXBe_IkfJX80t9_OZ@JSYfV ziW|Dvu)>6?6f6t-b2H-8l0t@783Z2%%`B*Ul5yIllt95Gt1uE&4V6vp5#Wz&Kx_ic zTQ3KJT|dGUQDNIkBUa>2`gG~^!m?{C%kUa1({u*v4?TVv=eZ4PiMi7x>%nlN{>~T# z!z_NfD=zxiev^tv)|^Gv6TZdzF=JxCgwa}lkCBN)^Dpt*O>Jh^V5HLt1n?=`vMJo6 zsg3N34gU$;Xj2=0zwl|rES}lwr$&XW~FW0wryKGZQJ(E z|3=?9eNOj7NA$yt*n7;mo_4I*Yt1poH=_LVCjJ*1PJNm!A>qKLcFjW-|IC*s9j z!*!x*jZ&AD1fBmqdRA-DG2P>IwE_<4?8ZB16|R@LIg5 z<$W4)*`J`qZ{mLv1e5sCGfIdQ&0LU_cDc9<+=d^KijXtUM&g{IB&T{Cg&a&E{|QP6 zeuJ~T*~|D3cjR7zh0No_C+Q~x(R^t^HavbESL#zXau8n(j4?_RFSWVVMJ1``j>2&U zqCkwwZD|L`?-~N+$S|XCuqZ=_#D9(Fsk7X%Gcbl@xC6=lcY;$isULCB{~7rf*Jpmh`TEow&J6fIG*_K4a;nPid^z~*)${#I3q1Q0 zUsyDE`||cQ^)Ow!&9gh@e0su5+@NdB*nZ{%Ez=!ayr68%;qdO@F0%Pr|Mgi>>KafH zy>yDO?(ny#9KGXl%e~ayP5C6WpA@e%{BrKLGp^=w6&Y2RWzBiF?Ql%{!((G8aFwPg zU~}=ky$0oz{>&_=JaE+;jd?qLdaAXS%zQ6O=giG1hHeSBV@tegqDpJISUILzdBNAh zfOW7?#ak`?!?~}Nx4iRP!*g}13D%#nmFs<7bb0!XnGg_tz~wq?5^V-a$3e$4_nvkde$!T{lIX024=t|ImhSm$wf)%&V?B-cU+c&%EQo} zkGjXy@P-jPX7u&2!mt82Y^CXPiU86=i@re{TE_>BjQ8u4%cNujzM;dUnCz=X9Q~%@ zIc#%1|1ZgM*=Du_JDKPWNus>djPSUzaPySd5;Jw<7 z^>-X-_h`!=+786oy4?4_ua06CQFU0t<@XL!pmVKo8a%)L-rJXW_cE}fyF=t&o(y9T z)*ac~_S|0;UB2zU5h|iQ%lX~Zsq|<(5R#tY+@)IoMb(M$@$PzV;iP*mAY|FQKU<(t!SyLn2mto1#_wDRqw@c0a9$x%r7CjRoJXY_z$Z) zuI9b;q4^m5KkL@lc*ZcY@Z5YxX^~kCuM)HK6`T%Cl^$e$y zxAyAKT8~pujxFZ(zD@A7^Ge2BD-p%ILt&$Y&9waW8SJ{(#(0FOqxG$hjPKtK%z!5r zeZg@61xM1xt0bmpkLTn>Qx}?gR{hFIl^M!br3d$BkIap`HQvit(#!Uxu8ZdJbx6&V z@~$(ghUcZn&d1M-o^H;WF77nnU|egcA?rF`=ciEACsh^irBlH*wZZo?_d*`Ht!{@e zsQ$n%+1XAF>WeS#*OJ|dH>L=d>Zo>LFS0Du_irBf(_w7Ms^MjR$IXe`rqxhb^jnrZ zrv-a2>yG+wTrZqY;T3nMEU&YQOVb+`R;O9n*6mK~cM~t3C;iWX*6lXedvh;cPrGAE zFB+b-dOY2U?NRoynutiOnW8k(&GxSSvMO^QvSVv8ADgGGEb}AD46KjVGqtsU*)gb_ z7Opxt&)TTHXWtJtU;Ofk`q<#`Gr4#Plb}CV$SVP)>t@X+mw>nU?7vYs@P2cMS?+r! z0I*?vW1A5|T^hR)z11k(IKkJrI4FTd5D0g$xD1HCUNoM#IG8^;Qsmzj&&ICzYUDmH zr=M!4j_b=G(uL{s>ho)NN3}J+fzg6>ho|7&+>G(DsvgC7^y9gfn`k)_bLpf3kjy9;-A$o-6*kW!f^w^e)1!&FOf zF1Yua@@a>xTg@GGoE>X>k@y2G;K}#imPfcvxEZg}mln$;Rh&&7t99?7%M_nR2EcLv zhmW9n{=VspxN~02){`Zy>C2Qa9H8#=`2n`-G_qjdj6BpcBudXBPjEe6XI%^PXR`S#NGH3q*h*uc`+67QBn8$WE1d7a%ERqZVPM zPdI?|JYI|s2h4-+*Z^nKbD!w$Rdo>&3!w0-ZG9}|jX%Y{VA0Jj-NIo}%Oh3;KAPCA zaJ3x#x48~((vC|`_{u5M(^;NvSts)R70)Gbu7j)O!;~16vTJ3x+d^~&pw|#w^Fwij zKebmyq5aS^Tx!W`0!5ADbGDUWYXS^pfM8;Ql3Gb6-?@XKCNk{!b}m!**vF^J@oprn zgh#si3x1c{<8Y_9-ie5*`e|NpFn`83apN`SGq9M+R0QMe_kMD|@G)}C&+bUR8;zOS z+$(X+_JSzy#EoV9Pg|in_xXqF)FEA3m3MMi5}Pm`TgPOd%ZUDZ$(Jm_bD(MF zGz&p)P}w_hOjuw9hp%`E-vmc(4wJ8Z>zvWV>hnp_Z;#76-O)LhYwAd9{Ylw{;R3;+H%IwNLea?;0A6qu47sWb$Ea z;eAD&7-J#`wDma$aR41|m?=tSk-a?C$Bi6!h2Cpq1fzcb0XdY)I6}q8tsE2zXau2_ z5&@}1SpLpOj9L>Iyl^hL+L=IQzfj!IswRXApc}bz_dzJ+qHNJhoqj>k0&+E*-J>D_ zZj|e0e^8Wa`8+>2VjUg-CM4Q&9OR)1$y(}uVQc7q!CPzyXo67XRPvw62nAY7k;ss} zLy}gVz5GH2ZWKDYJ`#jy4cBh{uw$OqfBaTIQaoqo z7eYp&8nKQ{2sH8_DK`>b(ZC^i3Nc}=SZ5?$3f8EjMPK3S9++?+iy$-jp$sj`d}fz=%{2+^tT(+Cyw_w(0`$@UVh>n zelLaaY(FS0y`Oq;!C%jdg8w&27w`bQ$P%ZEVdQjsKJ9`1x$$ zY-DQeq{~FF>u79jr2GFG=pgdzpoC1S-8j-{~5?}`mX z&_)P@jeHeDF(AlbVkS1kxji^iwG@qYizXZGjprhj3b`h;Dvb^MGRr!f*5x9bp0u`< zjg7&zXRn`z@UBe3qv=Ve8Pja%Y%agX1B*qH_;eN$8@^5ADUwe>J9gE4DFEvc%~-8; z-fX@>V}KmkU8co?u?fvss5E}jf@PfEMj@?~v34!`Pm(aI0j{}Nq%9@pU2zWNyk65# zAfO$gXfdCymXTz>So0AqYCfN(p0p4fl{O%LM=uC|3n_v^IZfkah$illUVQ;}&1a(& zMgmnQl^jJyQE8xR%_?XyVS&0?pXHl&+QmX0zPLc}6w5D3j znlhg^1~vZc=7|t~hZQaP7`ckdG{QBs_9Li0aJ0hyCsvdGWS1a10_KWXN=?f-LbT@m zbzAAvT5uzy*gjUL+NylW&D60{=6!jd2w1bS!aiODzdnni_?4x6Hp(PMZo+`e;J)WO zeNPTA-CbGrasZK|l2`#VQI&!zW0a9T_&AS{yejj|lyjkDUbu*ohf1103&tQbJ+-oY z$fQYX>_Wd?JG}tn32V{hoTigRKYl_`Tq3o^Z8TTfcx=JAagqh93o-145M%)dlM)gX z0z-x|K)lGE2Q$}b zQ=$3&=dcSi5?$k;M(XYH#DLgwt0fDMJy1rBn9mwd+zZ6Y-d6zuN;*kMH8+95+GX9Onf%>z8p(9|xbXm;5Q; z_c|iNImRAv$~+7?XUC#)&Lj~%VOK0u@OhX+Bq}`Scdn)7PbodG7~ll_5Pw9Si}#)4 z^Y#I;;w%#+#!cplQ2d+ci@kAn6rl+{3QpzVm;ggSB{%!LxCJnsGb>pW177;U?4%^D zds!Sl%Y>x$Zc+1{Ld;&#^llOElw{U~BoC=E7TMzp_X`7=Gg=&{EZj&~A?}PMj~_I4 zl1RV|aX^^yEr)--I(yu-O#TQ29B>!pan+cU6;z7c;tP3h8ax@mi&MT=dPjcG^8g}H z(fTLKfs-I}b7j>)VWD(}wI(de4?=wq?Qi}ee*?(@RqQC*2Lqi+5z-J!yn)NNX#bvG zwcmW3W>YR5(uUktuUwF10vrZjklYlD%@Z>7S&0UnE6^lD5N@{epV0z_X$JgV}qY|Wc*N7q7X|esMLZu{+{@K#m;JE=O>02cVk%z zPY10P`Z!mOQxYPK)~W`VvSMa>s`L_t?K5$w^MrG3ynQ@i?9iI|T#wqBQialKg?epV z;ZCPH;JK=?HL-N7)13WW58Ihy4I{JK*S1>B-J0gu&^9>LJi4oLWiEW>biWEUeeN; zp@-o45GHFjK%w5PY@Hbkf2X-o<>2mBzilbI$;HMkZ`{*w_eHI47m4$V4!C<(N^Y<+ z=jA305p|!y-KX)?s=a{RswZ=w$ljyT9M*hdwq0fFG@iLj=1HlKVA{ZQzu`7r*&M7z zKe)*!_tn~yw&O@$X4EXBbsN&oT3yK=RV$l8S*uVw$-$`NhSzacIMIa*`qMZp6%nm? z!h#;;;=f!cP=w|dPzXts+=;=z?Nfz=!k&o zcQ5HRRrLs&KTX!%_-lab9K?U3iny;sYE}F(tn6%YVn#A)yH||Y9S;7*pEySz4zK=1ywd7wIrAO0APWP_ zmMBd-Jm#ioQFk2O??Mm$CW>4$H%b|SW;-na~&mW z!nTmWrHTqkkHc-k^GM-VK+ea%6RQeMPY|^b1Nd_*Xyzv$#HAxQ5yoT+YbJ<_K*p5X+lEY^ovl$U$<(7i#>#En++oXH|+PL=W1r z=c<}dT)`JXOBq}(CBh{ZRmt_NWY3x=S2`e-8EL+6pq?5$6VH#!qo0=VFDnoV#UW9! zOC&!dCw_u0#->py&ZrcDJv*@2QA4?;w~JiqtpoICmF?gm^g%LFRp(NrIoq3%lF%jP zl~x33W~O2Zw*4Nj%Gja31!%V~?Be=&5JOzWlRKmk_i1}TUCm3{rND!_ilc25wd~MR z_;(ckXrgvM;Hp&N$m1_k{sbOac6C0*;32EpdPYDBQ)uR&!kiWSPtX7u{1~MpKcypn zC6gfhdp-Um0>ZQqC`Mo6QEsVN_g4G{g;@7M{050w4((WVn$gNKDd5wWDsXdq_+|a3 z(TN7Qp6+ugFxYYY0NAk(DV(cKuf&8J)9}nxG#P?ubrq3N^^kt2XmUc~(x29YIip2F z9&~z@YKy<>MxKp!e~H4x#dxO(U*if*ucnj|oy6UPVcfcZ87pr7p)$v6pD(v3iK6;r zln*eGfW19d3cMkkNTLWO`A5v%(0@u!qfu|RWhSEt>V8gw{&T~KqUGaJbd}Ggs~Zh% z7PdZ;#Gu=jrOM1mMr8w(ENFC>a^;1zgca-At}2x&rvgRB_VAeqpeKr`5(7@Hn7boY znA2-P7u%?4GPOqM_LUZIL}h7LnSJvk^L#i|E>iHIebPit>V=NMqYmkrrx7Rihw8Zf zhVO*Ro5exKx2>Jo(=XI`ggZHNs%dj8N}$quMGE?Vo@awfQ%Qw}$nr~5X@!Ph2q;aU zC#a=g9Eiu+yH)r$rtsq(zO+0i4tE- zBa z!5X6H?-D#27VNa#K{3kZQKk*Nb91R6 z9nh0s;=T{3Y)D9noBU@AA*Bv*`?BL{O=co>C$4PMS#o6o_lTy`qBxl`9PD@{nX+=z zWRS-i8;f8jRYnAyhr>L7k~ms(T#kH(`#viJ-Z{oU;m1Ea(RE*A-_`z%Xu!7b?96P7 zj$}v$k9&GL#Zl*#GMvN%Ki0EvRLe#3QHZo5*r=MfehP=wK<>I|L$qM!+jUR`uG0`~ zQ2vBHXad)92sW;MavZV{E$;%YJP7@l1c)WYiobGu!cr6@i3* zZ3~0>Wy?y)4yIeN-^5J`#J&;zA28hy2#-134{RVURJV2=x4~;qf-;;9=Okrj%+Vgg zTWDW{LBtCW!c#ZNo35=%RR8|(3R?d%{E%ZC1LDWK=x$Q=+B{p~|j zQXB_9EYJWMrotP;#_vWcWCgt?x)|fhz%(E0qNBybaV0N}PCSV}B1HQss~f-Of*>fT zBDvL&mB?rO>vfO1=VcmnW*85zyNl=1bnWGdwC8}E2L?Ban*>5q`YhISyE+|@f# zx#R?G5$Z``KE}&ZBGj|FkH-5nYP;6s^NdDk{@CODoSbbrmplC3Z4(bukZK|IA@dOAH(#3DX?*-1V6atmk0}^p&cz^)Vri ze{|JVBlIhlR#o5b=EoP)@pi82vjvuc?&=LyKgu66p}RSZf8?JzPWZIj_c}jet7Ns6 z(vfdG=VpJ&{Sf+iREfSF?KbroS%e05$N_eTjHTGx){_#6sqtVx3GnEqqi+}c5~BN9r(8?u9tJYcBM-lNvG3gz7>%d@Gqd-fBL{Y{|c0^jvsGv3gxvh8v+t7BveDD^`Uu15{~gsDdY6Ti6Lx&zL|I#cTkl7snb zY^M!eyL^c<)G)R(HaiVX{vaRZgOnLF`@NXit>3>(4#%`?y{E%Xwn(=8a1zVnO1|fE z@D&f!uN7)>S2>l+$(e9#P~wGdzBxv&!k5y|v84l(Fns>(Z+mxfuiIf=xU33qnz6c` z`cGgRY#hv^Ugc+rhZ=Syc`ur$W(H;^NF_^$GLM?7-JuHblF3XZK1k}>wS;Wxi-TM~ z-*&T73k_3IUxC5Hnj#iFhQWLIVI}JHf53?!-DCHkJP#kWgD>sXztcFmbu6%~z~A{g z9Ge}-nQKi#+piY~1!_I6vw1+7F7(XWhFYy{30~Mu`)@);TyBVGd{0gT&2y*J3<1~L zmwMhJPg8OCmpjWqmg+mQL3-}Pkt_i#0<)DWCjXpTVFNX523wTZddE1Dfy6%rZUhTb zqXO7g|30XeeUjQX{|L&f`!pl(zsw7W1ah06OrPzV+dX_@b<01)Q)iBb+cZTqp{vs*!;Dfl+)z5 zN@6&iIa%w+5R+05?`WNyowMiEnEo`hdoMI7NlFB`?g}`OCIQJ%tQ5=8B33j^aa(B z?M(^NhwQg*7X5*HLtP!_RwD$!zkI^Y}OrIS=JywZT;dtJhF#1@AkDhZG+&@7rjKW#J zqE4gx>VJkiol$4L<~CoBwpElEMt{8P64O{5GgnP}@L;2Z*R@Mqjq|Y(?YK|D;MGZO z$jFEH>b@m(ESf>F_S)qgX;m|JO@o#cfq~#uI%D)r8KV$qzo&m}oP~GixGHSQEaRx0 zie4XjPnGXd*-Bfh>u2z-4Y`1Hyytdw8?T^XNxm>eT5sl5#BdKe{VpA#e*b511X|AK zTKgCrz{}(u2gr5_Q}-TTvpNb|mx|rq|IoxlPm4W%Lsoq3X}c^rc}?&v2$<%4>ru6e zz%gVWDy>>r3>R!k`%0J=y*}3(?)@i@C~Nr4ev+Ni-FQrfWBlc2v-Rb4ZEdnJel-Ez zk*QZ7<730ab_nNq`xXFtS0akR{+Uv}Foh~LVXav`)Z8&7RHOYu-Zg=0GJ|KGeCWbA z#>~k&;?sR;T}*Gin*4Yio;n&|n@;AjZNCHaR?=a7e!3s+W0RExzf^={ctzu3_I8r9 z9yxY>oiw*Kl16`B+yVWxVUm5(?%49ZLFCV!7yr3ziS6=`;)UvSl>16J^mF_OH=uimu7Y5;Eauij*UV#D6W&Z3CrOuS!E zuJu-4Y8Bq9pY4wQgppQ{vF_|-16{4(?%{n5FRmdlpEWbgy2ln+uuZhOqf-v7nCeCI z`$LOg_kbpu$3*KJg?AW5CFNPk%`!mNbdNaU7}QrjQPc7??D|=i%7)=V;6D0$gU+J$ zMDDrA5}U@N@g8S$mT4x!!xihnd>|)=p}QS8VI-zP#fzNM$ohIPYw|t4HRueCEJ9Cr zn?sw6n=XB>xq*lJSbpwdMA94F43ECu`1SL!3_jJfX(_36L>Xm@oyoJO<%u)l_8xvl zkyG1L!wp@R(UqE_{hG~uF6{Djo8Q2f1#j+RzGcrp`*?O^78 zG6bE>g%iU*ilMkm?Wz75PfFIZx2T22c=GElp?mS7RaI&C^U;5GC?pjwhH3ohMN40X zt!n#excFUafoB5z`;UFhtCibXZy>L0+H}j6Nlovq&&KiLuzf-ey(HW7iR;U;H1ijd zg|7MI_R~H07oiTa?*dgbwU1Uk@6~t9x2EL9vtiZ(U3tGxTuoih(y`b6&8=y~=ntPm z#`*pFEiCTEqZMAO`%US}a4|S~-J#z`fv=k7u%@@X_xd0(XT4@Q>B%x1Rhdf7{_1A3 zy)l}X;`5F875dcUyDsJm_X;?rZqiq0_HzWjB_*9XX4`V*$x8WzZdlwGd*)Yo&q~L~ zLV0hD^FU4gbC3K1-MX~MPh2k+@qh@L2|V`Q<`gULU9A1{dXzxPNhRWeGQbDsGXttf zCjn!7kqI6O8ag60GCDFUJSsFYDsnh0^`=&4H*T(OcQ;Rej$v26E^arj4pB&A)~P?` z0FFCVLcALv;lQbT`Ra(YZRA^Y59-)U9f!{Utu+kXF1o@|mHU`V;Fe3Gz?B=N#;}ws z_1-%fqjIYw+drtypF>yU7owVZ?1-LCK8LP+*c{3fkBDQ!)T@9*q{v$uctj$cg&lq3P{^tq^Q%;n)gM;aZ`uQ;i@dg$e2+Am8tN6SBo9??(Vzy8a!`1q;s5ZPpu#`(#m{D1Z= z82=xh#s5jB{5J>{^iM41KjVLT7Mku#$ji_0JN}5&SI~g)e1F3C@x>T|<$I}2S*`pyhL!QEs0?DHYzbz+{e$^Fl8?+FYi6Ef~FMkJ?7 z!OS8PqP|qHa8?lmQ=C3b#aw9`d3S=i(y6+1sfMviP%7yOGO|u`)N4r&&~vX;1EPtA zAV!3GiRmE>z(~!>gT-(OLygGDf#~2b8b+yAWb(nPv+63mw{VJp4Zl`L~b zc;r&dlYwXOLCp-n96-&=g=;m~XnHKzZ{}IWpqPPe>oo|>k`GiMbm}c`LGYN&s(;ZC zBNP6W^bw%a7qH~cgXPPs!faIh_mqYyqVt4Zpiy8dST7~^MqR3}-pJsdJ{?=M+*xfv z1|yRc0LQQR&zAQFTFE1C5i@`!gh6AHG$IX@j;xJBb4MiW_hK4M5{KeMtwxeKFeg;H z#DcU4lm|&Ef|J58-y(S2s(K^wVV9*hsydw1Jg8giQ6TNg@nW}JaZqKBKMi2@`2VbW zs4zpQ^*-D-R-%|)RNvyGFkvGho6gjPa@%ZR>JgbBCXFyAe-Ko97#tRH0lD_Mq68;p zzxu|Ljl`9Y0=ZF@m`F?Y8%6d(&>jdYLdbRFAMSsSq0&jM28+!w?$da5Il(~4%Cio1 z?MKb(8-S!?lwHWxM2sz@HNoiMM}W#3{oH{yU-U`5vMgc7jLM_20Q_K>$V2l|Eb?&# z(f0i$oSbzUzYFQ$CwbNpLG_05N`1g6V^EL|Ntbt0RhEZvsgDuJkYq7iIba4)Btic( zAundf2MoAbAVOyVswmSPxLk@f$^#-D9~^S@3+y1k&rbq{&Pb>!fY>WvFPAYR51ZJ+ z+npb0_SQ`##h5-H7Qxc6Z-Yjuof!F+Hr*+UVjL_&KKug81BK}5V?l?J`nJ-M}GiECm>irPw7_zJ_|x& za6X_CzezoIjZiF$N-T>^j7L7!BOd?pzzCV{as71%N9W9n+PNF3lPd0whyM&{%U;QX zg)`wnBw6?vVp6{kWu7Yun`ji)Nd%>(IIj8m^~EL_}zen znfL>t%56xM`%_2|>_1*9KIzs)89{Ko#adr3Ouu#Me|v=>rB|W{v<*#KD@p%bs(3$4 zyZliU#ZUlZ-x=F+?}^kb_qQn;HD?JkjBY~7t_0FIj0;iO(?LKN9QOC$X^qLcSpA(_ zy&RQs17`Al^^NP^z28jirN2Vl(BNgk`6zqiIMHI_KP%*xk|Opw9L5bPsAsI)3D~QD zk%5@VLQQ5PrSZ_vdy42ik@>Ac`K{)Fnoxn7%=}D9P9KJG<-q&whHVCfb>;r*k=Prt z1p-9}2Bm3mJ_AZQt6&1Zg@ko}lOk>64AK!XQ-0kE4-lwXLoHv%sGro($y=0xiu%1A zE3i4W!wH9M!>(Voi@b!@>0q1QM+4eAMfE)GWT*Pu;ym%aW2t)5GiV1M^hHtb$eTY6 zUdWgiCGk~E?#S@RyZ|xgMNNE_k~`AvWXyxPWLy<^fZUvQ=$tZf;5Ip-`QudihL7NzGvVJR0{k@gI75|CSSB?%JCpnFvpu7>peQg zd-edEVemH}{Y3azS6BoFqr6DC79z>=EEc>yz=zr@IHOj0AZ0ee3@|$aOExqWW=Ati zHukSpav*uSVz5u;Nm)Zu)m%`;TvEk6gqJ3eW(AiAtsky%6@7-Jk)Q8FEH!{`e}wZ+ zPgINOqfhmQC=bDknmZ%w+i|rGqh2g(ci7#RZMF5Ua z*u%yM|8pC+nAcsyo;gh~Wk3oy(!Afmy*PSimm4==wn4#-tZD+L>w}j4CI;A|GCe9e~6kqpjz`nd1PqgR?-;b!cw;?$B8Ap zQY2yVE#fHt@1*fBb6oQD3H&)NB$6XWHRcq8{I*Wg^a;5+t#~0vOwx48dNW#z{5BZ$ zDH2p36T`c+9`RT`S!|hX6Kq@7Q<_Tb=p9IT1cCX&z)o5uMkGF@G*QH~ZG1*5;WW_* z#+bO#k7DwBHTfg^{7Ks|MyCA9)BH(F`6C$lBPn?kXKjqpzqUMb0T$%&mj1fgO(&1q zmVk=gpMN>1o(bmt?I}kbD;`OML1KHTy61Wa?$jnKDa&WV%)m1xj|*uuSimiu;cf$1 zYE+9T5;*&BMgM(s*60{^?t9zf_}qvErc%*QVfv7hB{{a{r27@AMDc=1us>sF$e6IR z$4(G)WD_M6u|xk>cp8YrsKhnu#W!xq^8SOMih%Zd5oB75;c6?NOI0)DU+nu_O4+1a z^>QTTpj+h3R5Z2-Yy>+puur1f@47Xm8A!ds2o{zx2{cKDSEYxKq9Xk=uI&wTQ*Zz6 zTNLCE&Fg_(|7Z6kEs%{SlRP0zyPKwI!d0kaa)f6!{yyNvf!%BQYT{blyY1i09ZG(4 z8AiEr*D9i2y7$xj{A!Sq3t}3Nb|lEqAXR1IDyhd1%p4VOtlN;(9F<9!4uj@HI7hy! z7rDofXuj$sYL`Lw3F^IY%?rka2_G3+Y!%I*^^4l*a0owZ*vIO0-c}r9h+GfE48q>C zfxrZrf<|&^qnCtP+@NLtidZY4dS#|CoaU!-Po%jGTW}G6R{qBE{Q`A&7{*~Gx>JuWrvLm;I z3B}3;L3Zi!^}X%8AmXEdMI@SuWsDM1W2L!uoUWt{qlXEWu7>NDPK%Wzn+Wl-%11xW@ z59r>a`2PX-Z79%5Y+onww@=ek3cGR%YF$^J*P>}(XPMiqmfWZkar&_?IyO-@FOyGf zT7+FYh}ze+=Qb^bUEj5xT0GUTWx>J!`RY-TATe^>$maiLklG^i^wlFH5jDjh>^!@=dA$Z!duhb&xV^!wIaXr&kkmu>6$CQA zdXXExXm4AtJHLuvzPBU0cDYj9gxx$xLc#?v`m6p21TeY@NlpmE>O%I{&*bEk_7`eD zDaLRW!uTb=G-N3H@7v*teNR?oz>cSGBJbL@oF0>Fr6f{>TB4TuS@(4>@UG<5imvxY zooaK!mRAt_mU`cj+30M~EPUIVYbOt+J!gy_^!x1J<2<+)5CR!n>ROWQClu)hHO9$W z)bgN0QkEycLH`unY+Z=2;`dWW3y6BJ@AnmrVZ6HG;I=slsngpr-S%vZjw$Ooi<@bX zH(ir$VD0Vo_H|^ud&g6io|&3~0J6QN*_eL0QI#BlxO1I-NzuZz1A#5*OS_U9>3*O}6!pi9ONl(#$RumNGumbEOwY zz&mqFHmeh4%6b%ZM8+XUs5!qYX4mD>alP2p0!_EiS&P|~n0-ZW%+;VhSF;exjKIAV zkN)m%bryS$?l@EB?dFNaf`8=VtKUXZYe2;`Z`7gDRJL&UK&$G!v6)sg4J4^eNQN3ChS==6oXn8%j< zHbvzSQj`+>PV2ryJZ|fL91=xjUOV*%9obmF)QjMWeQ?JedFw#5MfyN;&pcs=)<0~N@2`Zz7N@zpfuFZv2bWPtdHecNZY*s@3IH%;?}E$EPYy6N1_@)6SLE2|<)(rc;mu>|t9$oi( zx5*B(jrW?(Q^1AaHhffnhp zuLE-iZT3pDueLmDk1@6O5)&5#P1ErjK4j)Khy$u|e@W%$oyv!FJcDibt|(>+Z#+!P|yA-8QOXCG(N( zNSo#HtuW-*r{|ZMI<)GmU|vNUvfsy=>(;<~Y9YkRKffQ+i2poeTgCg|V1U?BC>@c9 zTkC`R1iC*SL0<_G(X#?ZkMwcE_D^_Z)Z=dAhnyu~ItASq<1p;o_aPeb^5C|;Yx%4$ zj$IH;`*I(&GZOkT;}6cgGTQ`S*E6y#U7-u%QyGdk=ezL)994q+K~OG^^CM-3UI+Vm z(=iXW{C;_l^98ZFqO|ie@NZ%MwHvhkSS9cpN&RW11xgz-$x=0F93hWoyTeFrd(WZu z=FPG5i*^Lp(h(;K6g?(Ir_Wfff}q8|!$I}bwUxVL{T{}K?c-&WM9cO@!xb*Yy=OSH z*Lb&_zh}?Ee7@MS6?@KPUs$X9q`43#rDi=Fclf@Gkh`K3Yv2D07m|8W18$fG3m)cps4_I)_GW4$E!|M?EC?{zUsk zcJ94S(gt0UR+=5&%&~%9lVw03e`4`$%lwn6YmU3QPyA?fbig%3Swa3;Fp!`z$tMQ- zy-{X*PeN8kD%!Ye<40CHypG=EM0-m(G+e2MMQ=yvd?;{f8q$1Kppm5FK%6>w>9Hus_mtLN zJ}m$=OFUja4j-|M?>1k3knbVS`a;v^)onaWEx9~&n=*O%LFpdNyn6=V0rj|9$3irG zGTQ2RF5ot=euLf%%Pn}Fp&t}`KhSQow1>%MvjWd=xmkkG#;9onw8Ho5hWRFY%GLJ^ znD&|RDOF?^m3C&Ii#b~x^;4+tW^dBy+WBi3jpgqqMOIFyCC?t2EaM_zv_h*Z?+uBT zOkWN?ig8QYXgFRp%7%GW|7D@06tsoA((4wwy44dM4$yV}%BUx^5{u{D{M2c3zE0f6 zhPi|&5{@!*I{h~gH`ZBobK4nE3L@}aZnBBw`*9|!uJgv*_rt2Hhbh_OWSV5n(~r&V zuR6XdksehJc~A013E6wvbc77O0vc3rbn4yk)97*MI9)HpqLK{deIT&a6WwGj>h!sB z2`b>`8qb%YB;JkZfth`*m6M~ICpzq=S&^IoqEm5itB0G&Hro*Rrn!!6=Gbe9ra48t zY@N?5QmU@EDVp2+{Hh1uALgx&kILH2FI?f)`&Y%3^^Pwu#8<^Z$Geh9(Iy*nzhy~R zcw5vb)yDya^o(`hMsL_5pVVgw>nL#3)Tu$*`?BI@QZl$gMO#G{VBBS=uvWn#SDWjr z`wMyVhNF>s37;&9L$_KM%z_&Ehl|k4#k8trPFR#Al7N;w&v&`{PoT8{tqK{23ox}X?>Kbv4j<)n6?}z(w?tp5Uy=!*#wj@r`m9y)*x5w`0{JCzoC%3J!^r^q_ z&TVu>g~ai)jeC>Zo%Gq&=VdXiWlmzYOxlK>$9hTPGl}nhpLgy-HGjlaNNG*DtG9CFm(8g|^;xotdymuW#Q~OJF-kJ)JbdX- z&29!Kj;@z|<}$^euOE?)&-?bP+$!oAFG5pYKAGUHEJR@n+3Gsme{xNPn z{EVCV-qm;eQ*?+W&`yJFc({o|?FXH{LIrU==y3_aF!1YkV;B7}$dyY5a zTZH2*UXKZvzwUhL*#Np9o-X`%P}le2UXK;qpTQ44h3L2#x~V^5*BzVRi0-YG-@c-7 z1o;hrN)=C?6UMfc=sBGY1az0F$+@|?87WyQc{zC*DS0W;l=lex_X!R94fgj4_lXGm z3HuEV2ln>s5?|jBI9}kmW5vRH@DUE3I+w3>WgSw!V+Jv&Z|gbvfgYo4pzWS298I}L zs01YZmxpJBZv_T2`>?MY3iWtvTA|RcV2<3(`6YYDx!PbN#}8 z#OElrL?i4(9AWrm>YZIMY3_WFtNNWA$xWF@DoDsy+gpDun8+iyxC7#hb2El%v*=|J znR?J4_54Rw{oktnk6Lr2(1@t5EfV1>;^-BZ2v!aFt2%eqma(rHqr{wpOB=8VtrgAD zuGDy!U7f#pZ#^QU1;tvDQla;#wa##!?B4`!k6M{+%2m9iP#g5K+R|B4uMozTvaFFQ-7ua*%fA@H_4noLsiTEL zfkK$A406mC2#k1I>So8|S+uK`O~ zibn)HiYF9huu5|_WS|XN$U}?&ZX1N>Kt&NsR75}}$R`X1-}@a(-jcfkMGoZSXysBb z_u%=(&Hc?Q+tI*u&2azabIsMzF*Q|=TDTAtcyjfIS$bYL(GY$JbbW9s(=kbQCzGO$ zgl!DR>qlp|4h5PP6R}IeUwg~o6gd-Olb13@sF^Prfpbdnla>5BESX0zPST#G6uGxq zy^4+GMuFOJ35iMpEjjS_Qm;fj$0rZiyZf1b$}TSl9%!*ndqunEF#mZp5;w9UHEXa$Q?y>;<9`Qm&TAs%2aR zVSW+hY`oVD=uzlE1rPzSutlH*GZr4lk5sDwdTe-5#^0ba2m)g?GXsJ*M={_hDo4m8 zXC8)|_AQwYQPi2_7qIQCKMJr<`T87YY$^z0* z`KKa(BxjW6+AisqqDkvKfn=6d#KsrMbt&UCgRW*&F)Eo~r1a|tId!;-C;HoBy$Pz3 z$?xe1Ol@tC7BK39u%5vHZ)C>r#s$qUTGNJ$M&0yRG7KMD4k&B%feV>q&<_&X&XoA{ z!oWtH^vL0^sC1c@;Foa^j6oPoekFHIUDBmYHd{U}(FrG-Qw7kbbZ$r+B7=DyT8d_F z6kQMyUAxv0(usJbXvP4`zEdSSW&{HpK}E_7q)mt*6|l!~>iPa0B<=s=>#d^Vi2DFb ztZ{dD2yVgM-Q67$+=IKjYjAf95F8q};O-LK-C;Z5H?y;|vu7WwPyMUv)Js2fb>IHo zi<^%thq*Vwc!9q=e%?4@1{sPT@x>JGK+_#mRo_pOL3d~-C0`LHAzx1K+r}}sk*4gI zIpE|#wgEYv3*Y}BHI!58nFszRv!n}48a)DY+2fPUtO^u2MK#Fmw`f>k7(#0RuJq8f zVV=91Vz(0o73;^onN6$e(k-(k2B8&`ew1higcnVe8y-o2pJVqZbT&@3_zrb!D%U;@ zcKJ^j#%u5$0wfF@$$)25ForF_g#&X#2=m$ipzqHb%(=^a2%9H9qZEm&QzJLZd{azW ziSQzNFa`drs$WGk*Sn${P@hU1TRIiV|Kj#sw3<+U32R_Vh)xsigvclyh%-#I6wZ~F z$zzs`jOeGR>@A|c%8+ie#-?SKit2I^A8S$}FPZ(G$Yv2EP0OlT>=BPa+t?|6O~9-g zz_9Y|o*da%1W+#-X=U2ZtuLPVD>HyJNjj1p9YehRN%X%}bv6Vj(?8K=^Bh@J6V&9}<2xTIH`pQOn9^f0^MHG6Ycn?8T7HrU&`IMt9#*&<|y)Ce=WT znt^A$|ER-iRd@M0UJ+w&o8Y$#oS6EY9XX3gL9x6r2VA!fT65zbi7WD&AY3){y=^BCQ^4k_5TUhFLtufG70`8(p)=`TN zXix(6$N{p2rEYiELb=DHcW|XFcgg&&@9p|l6Y*H;MqQs92WR1Nn{P%ivMhpKz$V%v~)pNKMWheErVXI}^_o+cket^0SUJFE~C)J_DVAA4ZlI>5eNO4pXt70j)b5{?Kj(Z(?)f{5aaB-L}T z)2zE_Htl5J|7{FYR0NdCZ?of7PeIp=)DpmNA69I~z-8w^`z4m!gBkVK4LtEUMYn3; zNr-v$^qm3@1@68V*;<+luRt(+IGPmPcG1l87@}9SjqiHKdjQ22wM{ce(s^0-b08|G zkG}t)l8Xmr)z!)Kk>po+{mnpG_0a!j)iWyBE%pem_{bj?;2jwQmX|=D!qq1%AW30M z(3e0VT1qYm(q}nnE`@FEn)TmhfjtV!;UTHJNcZImGzduL8UUMu9%|eWZ#Z`o7JKxU zzSY*_Z!Fcpm#~p0u>XW%?f-;fvHyf&r#sYS?1+OPVHj@M&ydA>L2F5iPuUZGL@6$; zdhiSKg%7&QhkCNiWif5Q(%YGfdJ9@&Y<}Y>p((LI6KVI=h3bEWVcd~<+x3{cV)ba8 z4?o@K*n<`RBMig*Ck*@gPZ;*+Kf3(3}3MhbM@XCSA{WGLcBr z;Du^>PZ>26quqtuV9Gvl@+SgoJOXPt0&6NFwKrlR9Ic9(LX+Iu(kNeFhFs+I@K6H{ znY>ifpRL(k=wZwW0@-*OWq=pHGKmD2yIk^2_UhyBU2wX>#oLh+W*5+$98||pg`9DL zofOq6J`&J}^y)IfmzW~$ca1VkBNmYCM|2g-EjFPBRn@~$`NNYmEGs;aDLgEbH?zU` zNkq;Ih2Nav{Q4vFBX`n=uf~_Qxq@~cY5GClFpx2fD`|%-YKJRphYNboJ$IKYEyON6 zYRecPlq+xdmh+^8>j!`m(yxVtkb5NgK4^M04P!UZnJ-}hL0l3@OG$CtBs?IhqCnBX z85#H9g0{jfml6=SWSgkU4mF`fmof6tIfQ3 zaL{2ymoTP~YZ>GD;XEn zsP(RNTejV7fu}1$T=f74bA-(S>^E$^D@%Q~Yc;JvQoU_#eS-VHJUZ9~q>)VFNZqk~ z6pjnrq{Nsg``cZ47dLwMCrqEnwUMpLpx#PB3h|4mjGFS56IOThP-)+zG+_%MLTa71 zme6?TRosM6Wm%-MF@E06tNID7V^Bv`UBYV=4zfhw`+s%SF9vK_6S%5HE1-nLf8#+e zUABMB9^8H%T{Qi*cblMbl_x0JXR*1CuM}gi!fzC1;Q=S`-kQ%a10(Xpnsl2W?%2M= zWSfRi=uoHHi+|@it2~Jyo%$8HqMQ1(&GoJ$i;5_ zF>9lz)XH|FsuvEG_poxmakhIt(RIk;S5k@#GRgHzjsby*h*!w}2*gP6ocv7(1jZt~ zW6&PCDK6k7*ORNObIEUced`+Wai0SkU%vixhE1VDO-g6bc@Tqi-p|(n*wko!N8pFPgqP7-@Y%_=TDuneihV|No z^==`8=ZHuMJ1C>E;HpDe*8A%S)x&U&)8?3+Re}A%fPGR zpWr%{hT~rGp5kYC4CL&7%oJql)ciYT3^?`x))dk?c&d7qpEMgJq#>uZD zc(ng1!_59E!RZye%B1n_D2rvNB6iFLyUTjJ6scSiSA{k?xke4 z`80LS4VMPt_*Z45r6^3SrSq!HNJ_G0^PS70VyGY#Q z=+f>a!C4l*NY-go_z=GsM)NIogg^)0{~aKhXCN?E{$)!#-YD`PVOX$ETFLf)2vj{u zDgITOIcXm9cK<(Nm^=lBk`@|m14tOg(jQ=z%X!?k$NKf9f$|T?83x5=H3|AWc}XEY z`OKFTnv`KVW%|Ci!-D_Ugl$4Ie=v{UBo6qQeIEgD#{~E4$fkf1=|-Gu>${Ida}$hA z77F2<;-6jw#T~>Om9kUhC#7-4iXK4sT{9h%DZL?iUGe8vCN?>FH_T|6w(ZbrQGm<_`H!$_e65w1+#CXvZC-(WYKC# zX~9SONvv*4WNk;4CZ*%iiQa012@JcSAMonSOHq~QimosG3G+GYqd9;3R@RmYarpV| zR!4_P$BPV1k&hjzH0_=rkCFZI;uQX)-F`<# zhwI*3j9+(|RP~}ANa!kYzV7+%XDxg1lk9l?bl3&aTS%cJJ)1XPXwiqC-Cifg%l}H+ zYE0e0`~G8jscxueIq)Kr?tD0pO|$BxIXO~l0? zH~UpLyKe5~twPu8`YH%5L2?ykJw^3>Q`&dSrPQn$7@S}YdUBd?W+lxngkN4MTs+L!#8-GBT| z7m4kDaC~Sy#3{WiKOm1@+_e6^I?gzG>T-S}kMfx5R#5xuy`Ufdl-Y1IeXKv5Qq;CL zR_0=Kew4-T78s5YYj=@@db!hg+7Ta;^e$_SmEuR2REUc35hsaLC* z8%}Np2iPBn_m|&av^-ib)wToQ(F~dFovqUy_!rg_f&Seek_gO)I*q3-7&UvAb#|91 zdiy&dG1jl`<3vNI+duD_Vsqad2g7esaPhr(jt6$mqRb5K6`#{9fJR=e(MTr)=jVqz zyokg^M#Ybg3lz!l$=;eSTuWt|SuI|zg(!W{gHsQv{9N~=Z6W`)hw%t6J^!*`;#aL9EHIO{xf90=n&{5*$;U-EC)9t>6k51lY# z*1|u<%78r+6i?j;Xg_lcmtqTBL`PnjrCfR5wwFlCbcC8$VxIgf9x8ZHHvN5&J{o#> z$sY1}VL>ZVqz`5RiIeP#xiFZ^9wmj-FV~+g$LEruQ(JuRE>2D)$bBC_U7x%*f*n;gUkh)>Ws?Re8~ z4f@t|ZoAeU#%oM`)74$-bT+#a*{pfFZtqHgkjP$bO~se?14P32LMFyVm%5rRe7w!A zJU>FyUw!@#v?|^5uhSjI`*;6p&8qS@zwE`CEp<6 zDs^JD+H|`$x^r|32N-p9&i0?LvEQ8Vbyf0sYkZcdR0*tCoZECWo*eDBcM%aNqGD_NUnzXUyU*GcOn%e-}9S+RtqsF?>#lZm8*8zt99_ zM)Vw@fA7a`d0l+wjr#I+DAJ=RGKuW1Qhz0*JVul3~eIPfa7t(}4{@#g(I4|(WN{o~X6 z2R{|};{uS9iRf|Q8uWHl5A{46|Gu)h@ErIYGC=rB(&>NxQ8FB-_kkJHqq+U*`6M-r z_PCF0r;l_&4#nvs>5eK?r%uh>6HuFah>S_n|Clo+*oxQ?`hS4W%KLPi+x_6P_GwG)sjiqSZOr&d(dlL>^))cQbcM2 z3{2P7U$4L3J(u|NkE07-J8U;wXFXj%z2z~b2!~OSX#fnQEi~xD#e~y*ZF`$&xRshQ z`VrF6>MHEt$4B?TQWs^>tVIzQluLA3tdIMP#iQ|6fUpSIsFZ{dhcX@`d*aHQS*V6c zT`(JyldGy3+wWg9*M`6OeeLMUxpe<=PVBrFKY3DyX6(i1qji8s)oPGrNW!&_QmAG0 zJbElf)#iKn1Ztm9=LId^kh}!1>GqoasbN_xRSfu%>pk4|w4I`vP&}Ps5U(7B+0t|& zd&J^N+(g;fH9J8RCR~uHrn){3^k2EW+=8udG2(IJ*+pyfP4$mKVXg|Te*D^nUc15( zfW#S|jn5DU)+6j&5LyC}-TE^R*;`2+jGSfMuh<< z(hE4?Q{jDlo|fu=7kQ7I`<1{@v)fdScvCe6-o#>;1Z3e;JP_8))xrz9rv$ z185I@)oiRTPAIRhhM!2$2=J-$8ZP~{A%0>mQTL6bh@tV_#G*-$Vn<+*v&ZTq=*j6< z1F7dAyNb(>|56oW2pleW|#M|bHcuhkhuK4s5chd2v=&)aNq;K!?(6CYPx`a@y!L&|WS(U?r{&d(PreZW z)@^$5G0j-R$BuO_3+972DxF82IPzRJ{~f)f5hNU;B}Sy9MW)YShPj8yz7d0+PDA-DGQpOg@};^YU{J|U{z8T1Eo;`*#wBUmy3auRSYx^1~)pIqUk zvE}0V+iJL<3!GDyD;N`oFGk{}K zGWCp7R0b}C5>wR{v;?8(eN!(<>mbHDJW4vIc$`4pg|rI z31BHKR%&o0`ZSXp%_oIQ!k&+IDKgEOCEHpA{!mOJAT3Hnn zu*NvwnGzgcxS?4eD2hb44atwr$P6@u!&-=mEZnDB#x5xATk)XLh3S)oMaQC!N=r9Z zk(CH_yi#uJggT|7iy9vko4G+}2rOUX?T0~-0L_Y>k-B3MM=tt;zT70G-8M)!Z3Y7n z(J_c4)8=>X!UrgO$!DB-8RQc(uB4k%kjGKztTIuf#mA58*M)P{5UBq|5nuvrpz#3B z>P^b?rwbElq+h>2oVDd-5GL(~q?FYlS|pArTgT7&R*WonZYpzSjFei2nPuO-5XXKIZY7nq67#z z2eFORw`G;AzGYlJ3b}A68(a%_U^07PmC13!D^J|>8X$|HZ8XBb4@OEFvt(Ag&7_!{ zTa9Cg;JUGvdUdujv4$_=gMUL3lZ4ApYsPM0jqc2E;n4$kll+NfnVhKyM$F=JP!Gtj z-Lijd)h{$~@>*|n!gP&Lb}i;Tk3tI>))5u};4Q}l!okz-9SLu4HhqKl}VOv}4{k0a7m4o`!RXbgZkZzFDh5RDge9liFH_3wbz39#m9dPr7mRGZj=MNWs@$(Almn?YXXe5C91Nl2o%#h2y! z5oo*6LUK_&SIW6d@v{V|QicB192pB78yhy(cs&}?W<@--awxk5X-wlr1c(Dor*;4^ z!a9c3p6y5%x?M-u-9~M;ijEqN^ zjrB~-3KqYSGQ$_Hb#H>Q=J)#uGyI z$sY(M!K>tOk5H!SP*a%0rlv7YZGJd8!nztV2|>Tb8>~=wOXV z%8?krGf-Cq8x^7@P^iqrC(5JGUQt*WR|8^_%Qb7z7xq0WzNLw$a*7nHX3?mCW>(87 zspV2GY0)h-6KMR^sALluZ#UY}!vue0E-R>Jr7*>&p*kZYpi%{;h$m)H&kP=-@mn#q zct_cxO1m5Kg919k+!Ja?i3sB13Yysu4C|&)itN^Yv^20f&EvW#Zpe0XR*f*B?lK#? zeJl{anP{oOQDmb`;A#3~(Dwh)pe#%a01fu`ed5pEP~&#MM1n@P9L;2WT3{yjRYsIc zMbs?=s3VSb$U`?7U&TG8;gnmp%WpK;CO_yYa=;mN+o!r+#C>R%{f@0%+utTS_zb58 zJ1sGN9Jnn5&0dkPH;QDJn%D@BV7Jn03#C+$h@BcVNVY6kQ|lTziXZ6C9KRNK^QCD= z0G_`{tO3U(t*{ld02fDU##<45O0Sm`Tx~W1 zAvk9_8*m+no6Z?$A6fvmg_)hp2hO~XilomsL_(22z(w}cRIC}He*WrgTeK_?l}Ox9 z8*M2KV2q*Cqz_Mv#ZwmF=x0s(V+W;&rm-dq>>yLO)2i@_x9;7~WD+G?+H>RUMJ75L zHU0wd)q*|hz(TDL5Exj9zOk8htD9^_S(Y+*3~wb?7ET2asz39pLvQ{LKvC~TuJZ96 zd3urFKQ&9OQ&R7aEBEoOym3?CKg}3ha!Re6Qtwuj{<)wqf6Rmo{^9~nB?ES?n`}Ff z*7z+;_3zD?q$AtWg?ct8j^#9c5c5&c0){NF>_gO#l-HzyX&fI35pFDqz9smlAqYip zmoC@%CLY40x8s`;HeR;E0#LmC43USm5(qPy)b28yJWEcvdV&`P&8(=o{RZI(gkVY< z3ag%pd$|`y^Z5;?-xzR-69oEg6ln(GDbV$!s7YeAw55=`(`e^Dh+fdENrmg{Y`%S< z(6ggy!R@c_On97)~&JnAXK6OYol;jz;Sv@ru3lxs#}E5w}C z7&bu$j+0aCB6dH~*DU8BF3GT`#Smtrw;B8JqOM|VJ*oQG(-~1nf@14E>%EzgzAi&( zB}fM|Iwc-~OYV>-LiwQLMA8}4>lgR+{tYiyUdHDdB-dakU4vPD(a`DTEDu%a5Du|_ zt0yZes-l_ZGehoXony0&KAUMQ8f{!lw1A<4Fq>xNi84zqMaViiQf9;+v)oJgvu zZ^tkZ5GUNrtoLFup*jj#NQR?NgrkpytHQz1bfB&_r>+*1*Y;Imb-`JX6@t+H%&)y+ zjnLLbNv=SGb*>}+vMoIHPI^|My1MmJa z_9Y3grNM!FtKIgH#Ct@tQytZ=l*D^Nic?P_opD^9aXcMnmPSpM#t-0#?&yeDVq$Yt zq6TOO4u@;Wr$U&G%QK7h0BS1q$^!n*8sZLLVA=+6x-JBOf?AjK9}pDx9}wh!g58WUf-Ue8goK88{>5XWqLA!?ZLBf;U+B>>Ek^g8duv)7@h0)} zBC>-&l|QRsv|E$8 z&&ZKGWHgZzeB|S?yszrHWpBo%8F@_d6QZfY;ochIdmJb`E(|-d<2p^SqRm@THY^cW zweO|&59pPWY6f=(4#_%j`F+oZtu|M&|AC+&k5cN-E|uZldymjb*t$rgppY9JR}OP3sUbqz=@oW5Y1YX zJXIWcsb6}m?9+4J2wTz>_{a6#XHq2ohR|5HN0juqPP3*wtdW!+b3dlu-Ac~-|{)+-#d?X8$V zy_mqY7;wlkR@5OYM91tQVMax>d`>3&Nh1Z*S}*JJeyfTdaP^5{(eXhKCKG(tHfr0eC7W4V|4g!r!_n%zv`f0K=; zz7$LpE?&@LuGl~>!`^GZkHdJopw{u?x+r5i<mV>$!L9fp_a@kNWZ5oWceOYymmecc65PO_w{|`9|?QaoPQ5 z>fL7S-DZv&NzT%3nQS9cS6mcvBMsXv}K@cUn z(U35%akH2=bFQ!&rA$J{h>3md#Ys2i{;-M0J7ccU7OYHSMHqCQ2*VikFVKW*3X+LR zT2ML$aiJe~O1~bu6sR1d4w0V)J2x^g#~}%yW8Viqv0XngC!5Rr^Ge(wySQIF0eM&g z|0{)KqF@OZte}lc@}n%8*;9# z*gX~vJ`9oQjX3*s20r9{Sp0OLJUk%*VL4(P1z3tm73YWt+Xxifi1@mIU!Vq*#2Xqk z(RR~sd`EQfBua8TB8P$PVJTbKFUiI^qM~fFpQUtycSIPN38N8fN8vu-5PaRxjNTdh z)&I<^Z?ONZ;OTzZt3m2`C+HYF>P8$4_o0bgGQ{cExAjBF^zLedZz}3|vUSG3`aP;E;Zl9}H7(x8T!7EU5>;LLl`_Y;mwTpgzHU^#ReFe@A3dg*j zLF40ZjBC9U!yVJ!ieb$BS0i@2t1BF!l(G;?nbCQ%zHifO1u?+ z;?{`Ku4qSc@58K4;!^7QfV%GYHkL@^TEpMedGk+18ZL zhnX{O=swkH#tE-n@XD`F3S)+&pnu-dYWX5l)L(7hK4Yty6vE(_Lreq-p|mQzb)Jk2 zGDCEft}`hGN4@XHp8sDGMYs&vLL=P09muWPRTlHX(}uvkxzkIo;hpF0yq&#F@9Rs(v!$w5zz9?b~VU%#a_Z0J6qQ2MPG zEJPY5L+lG2Fxzah%bY58-5f?s0hnUWNnn+tg%-Vb0)#~$35F#@%5H!U+ioHL)lU75 zS@&PU3D5k#+x!;3f0-}iqY{scUpj$gO+Fw&)L*q@A>@HwLhk1lYEF|on5xORFV?mR zuyps?->IHIe7%h>F`_f-jPQ(EU#S#07*?LWe+_4;T`S9t8fwjBTbCAo+zD$jDl78- zNKKNSaL*tAkeFHiFh6a7MN=d|U@m(A`bZI-M7tT+bD}d9dSZ6QO z)Y;A<{Vv-V-y3J1yz5bmrAFTU&T~HHeZieW6rUGmEJCeGoXJzoRjR6NhwJ6EE=U7E z!)LZtgXY<(DGmlZ`|7J(J>HdSBj4>qt$<7zz8CtZCJBl?#bWiVW0y}}m79Y2_>Hf; zqF!^u4*NKRm*0;L3B>uiytMRrK9}WICgWS4o~Ce?+B2QUhrRjm|CZqR`e@7*RB?W^ zCB#QRaSL8zkbGJ`a8EWMtRP(oR;b7ck@Rq}yuP);cb~+Ld07?t_|()H8L5q?-|Boj zMX^oZJ^b};XruKbDlhL*JE`flFz?c1aegaXd$Om2?Z@O*AH3hz%DQiDiC_1PrR+5d zqwkxzz199zte@Z)>ssH|o@|oKOb+D-^UA?abW41aDo_Z#R_R_U^yWL2Jl)C$0;6AtvI~VRi=gL-}d{k!z(ft`$ zN!A=mhAv+}C;WT7_x$t8c5{cMOpM}7L@r+3H~PkKbNnsSoK}mpE8E( zm)6yJTTL}P-{`D)} zMn?4#@`e|O9hjK4EA0JKdgxWN*!k}^_nQ?Ix(pTDrxWz&Eyl`#t@D%ON|C%5kE7G~ zEAl4)nj^y>SBboSA0wsLOPl8^lkx9%2dU@ZKUMvp7kA42%#tOB1z#tJrQXQh4Sapc zdVUP;6rWAa_&osS(n;3$_O!~>B|otD54{O>Q~gg>0X?>pXY-JnBzZG0X18`OJVIPH z!;KAM?>;6Mwqg^!O(f^JH+O)h&y3{sZdcNp>^hKdD&55ZtMhnyGi^}r?JeGo&W}y< z>4{9p--X?-E6+&KVnKUv*V*seYVVxKXV%j8`Mq7-^t;VMt3&eVOyI(c|^4%>FipQ2uH5OgNK4@8d5D zoZNw>)uJk`tt~0K~rKKe!;G^BgTt^#> zgvOu~A*!Z|8ZkX8z_Ks^yx|gHhSHb6M>z=%*=LKvG7kkGc?t<9g_D4*26;}#qGz>7 zBaNJ49Ky4N2_-TD5K+jD?%YyU#9K0F|o!AK(1GIZPLk zf1iR+YM#C5fGw$*?C!KElg*z z`Z$(l>Zrwudhdh>_#wXPT1!L&xv~_-C@1CzZn)6s`t4Trs`^)PJhCnqK28v&{pHC= zlV+EHpoPjY0c-R27$_e)1(dC45+tqVZGfg^QUPf#mjdK7x2BhDPJ5GPmN(o|-Ck~8 zdrYQjI%?}TwINrTv_mt_svwFIDO8vj9)iEU=B+IKL8c^fB`|)Dr+-5ZuWcQ_d6dnC za<{jgb{Hn%*WLa(5Aj<`OH7~>s#vmm7rrD$l3h*sn6TF1s(pxEEMpe{Ks@zsZz|;0_W~A zWxZe+Si2#0d^&CWy>`K+E%Mus5*0Dyd7mi*CC@LF;UosgZdDA39VUF0+L>9zTz+~a zTw-e{xw4mshyy1Ff5wHnj09aiBnUsn=LUs7EwYCi-ze!gY_Eo}2T1|QuM7^Xgr#&9 zsh1fOHZa&lg9?N6VKn15l^?mUZu431$+hFEe#N}@tGISV-qIy5B-&Jl)@43J6X!9i zr@*8J(h6}uthWT5Gc#;8I3~*Pn10j%bs6qd?~g&D3P7~vvqscbh4J&?RJR);^kjNG z4Z*q}AluA9BdVUs_{G3zYHdX!pEH!6jsPCtjy=`66_zDBeAW6S>((#oc-UY^(C)DCSnL*;FsWy|w+B^C^qxQ&iSA^|o!6?AW&A0o}rb=52lQ32}?ZeeOc{x1A>351GP?Uf{s> zqY=h0{ntCB>j$|u>}XcqX1E-rCJ-G%ZVv;r6P*hKV9o zFvyDsnYtq!VLB>`;4Gi^hfWk*4o5e{tWhzk@rfIOvDKtq6QdCdv{%cblUd9_HYsXE znZYC_ng;u=K;o-B4y|0wuW1Op((%XyD$lrAiLoTX;df_hbI)ytChdMimCO)^#l6r) zvy8^Sb!dg!WbidzEINm_GL#*LuuF# z`-Z~!$fB_dr#qu~{{DF~XVAncq{V!21>HsAQ<65Fa+P1Wv4WLw-$%^yL8?QKzyvd@Cvallz@K zS6kotwFE|pe*vQ1e&|>KQac1*k|H_#xUIFLH^WJ}#|ij$UM%?5|FGQj{N~rQ^*VWG|5aY25*iR10;Tct6Tx3E=m35lLz2##A8{WF!S@c*tQ(sl&SMmD1EE zikqQ(b%IpNw3P={0q?8u{P~rUTEfi*rKDuf{7OX$uEUKv_z#N`>k3pE_IIBS6o?|245~ANe|tu036JZ)9QnE!&06? z%$>#?2z;3*)#va)Ct%NqM#7pNLCkHmXgglL-S{y>JNy?jf8kJAC2eK^F%wxUPL0qq z_4ef(uJGOwj&L&hH2WBOm?VyVt`G2B1)e_&F)K5OQE6K?9KirVM{|%GuF;$kRm26W zLVj2rN;&$-bb1z1SLbARQ&TCf)*)nmEXXZin0|LH6_UIwO?kbc7ry5CA6aUmo^ud; zA%ipjwl~&N+?Z1Wmzl9fg_*A#9s{fX<_jWAs1_Jd6E(Yza6#Y;xWae1)7`kPEWu@r z_oE4#dWHp9tDz7y@JVYqUxMSz?{MXa{*fi|RZ~VJ5LqHy+jBj64VXMmTW#f7LxM!y zi!kXdaVnuMm@bfpASRW9Hjw^Zn^v0Ip_3E32-j%*i=1plglo2euLQW2SVI=b{zW%S zs9xugEz)4Y)r|dXp0oy#ir5- zY0hcDQRHcWytS%Av>NzyWZP$!q_R*nkr0WDGtGyn+*Z^rZNoZb3B;X0RzpjP`oY}0 z^(HaSHFcbpXPu(Kxf7Pw$rEtuzn7Iz;}hy4SXIJ2qsXl5wzGhxGf@pea>+&l;c0ql z(JaIQdPo%_8zC^iUZo^PJC)>5BMyzzO<>SWU_=8czj_#kwn>M2vIToa!QZN59pI>E z7^$21$%H;zU>%rZ9iY!)LAY3<(G;;9nr3uaD%S>kX2IXym4js?15yQS#B$$l= zKwzo%=C%f})fTLdROxGUZ#CHWZ&e7s9Al~o4hHt=NK{JS2MI7UiXa;hJYs{Mcp+GM zvw|MOAP%yCKPfpAQ(+H|ZQFyM=MQSYegfb<))2A1Xj-D9fP9u$wNdA$SZh>>Yme%- z>6Ob&Y7M?dgFo7$&f&4vP}FU;Dwk_i8(tGGi~3@2Nxr%9o^ewon72*JnHmTyx0$DC-2z`+mC~sAmCI;Q_1RYDFzW*w2CVmc8w-Rm zXRaJG%-!&M%OWbLqIwU()$P8MnWvzA0;=&0QXtjDKZ0KGX?M)M6G7ETF zvYjn&2YClEnG9r>CMceo*PIV=r|&d$Y>3IVnXidqFDKY8VawJ&@?n;9jnks!UDr4^ z&2+7Zsb<~cHKcJg9qkZL$)9OE%c8}NB$T5u5?@PiclrRr0v9SPYiN?LN($n=IB<;u zgc9rPZ~_Uy`?_T;PghH|8oJ<^ID(@&EYTBWg5)D1jwt4sc!aw+AVF^Ny;q{p5;rPZe3}Ij zC$iLpQJ=CgtXYM1><&ipwjP-d2iV>kM)`V5v(oQnZm498kbR@zpHzDC>10P?4xe2z?v1uUE!bZu8V@;lwh=7^(1%Qc}2v_(Uk}^#ACY-jZ8E7I#0sb z!K3p7q0zQS*jUb0MsY`*;-m|vCA6mAyA%_7i)pYARzG?HfZrcGGT# zO9k0BT(*QIYF-lNs7&A?3ddIBx6R&_42w}1k+Ma*yv03xqK1?yZom0L$M)wcxS=w%rht+Ppk|T?cMo%%MbJbk;Y0#7<=L5uqjB>) zq`4Wc?3qWzA_u6VslFqE(IbMvBk!~sF8sPf-U0`Z@s209dy~!=B6DRq>P`pGS0_X; zyAsl|#2ck)$Ns1n=G@#vMM;*^zNGbULK=@3@os)GAW@7&ybbD}pN;3s1-p_1KFTeJ zTKeX^f;>V5t?<^JI5-)ZDD?t7MPGjeZW%>k0vc;B8fzdLYa$wJB-%AUMPURQD;^GQ zqZ)3bnp0vn@(zNzA@gn&$E>bZju!csuAx*cqI$mmbXV59dJ3a3y&v1_rUA@zuyoYs zsYj!97UroS_1S%dd!QlGwX?wP9PIAgD7xjg;?|KuN1qn^T&CBY&VVZ?t8Z!|M|i?L zeuZC7;ypBlO>JB?7oN^7uFfr z`O8#@BP+lQ4*W$xU|J4udOYYoJp6GqC_NcaXN>R>!01t853E`a5;8~lh+up&YuR3~ z1=@htCJ2V(*qw2V9s-ILPjU2e>eY^rid4YA*gA>%G;;Q3M1E29{Z5PAq(muxio<+D z8+$?siw{pzPO<}$bfv z852y3J1*F@Im4Ecv1hHIIz+&9t{G8*5Gv_I!5__iMKVN?BCd%7u&oC%y*}!8y&v$M zGEb87MQ;QuiE+;OKP*T6>MX&_#oZ`?Dgb*#Gj~1Q3yYF>fY{-juZw{S8i)M7XYyQm zhsF-?n5_vpiEpjDv7=PAg${l`k8s3CoqtJ^qS*l?X>xo`7hM05==jPdV)F<&YwiA2 zWciZ3>59U-At(RXAibd!;vJw)V&3Hx$hW8|n166RPM4AXlBiDV8<9WS2HNuA5erZlaH0$*g0R z|7_J>4!AunB|8+FD@?YfV3KAO7<`^;ish*;riRKaJ^C5LK>y&xW~*uSce1{X(>b~~ zQO}y<_pD?bDTP#0qTXJj&8!sk_wORm^$I03d&YWpcRvD)q^ydhY>Iw=DRL(*68clY zrT8=m3#UJW%xAqwL5Z;wI`j} zXJcDzfR^a3y)<%vn`q>An`lmfRw|3hbA=55pc76s8$~o*u7r*Qwgs&R4;lDLxG#4n zmDAr6@H06ubqo_Bwrlm+M4#~JdWcsS?SpsfPG`CwUg!w3;0)3*yX3K%?E1xzM&7Ap(as~ zOZC(7oWO^xNikwQ#cZjt5{iJ)gkk>~mEJYSbh=?6CKl?X3n2G<4?=ca{ffk$1tx=@ z7!$`;_*p}70Xv*v>>C$iPVuf*nbCVFrz~tRdr>5G6U2B0l#n0%hG^DYtAf=5z0N+Y z-r3P?`pbK{y5u59S-zJq3HMxub)s+MQ*uKfmjSRktsx&)AvqFvb#b_5M~oRFgoom5 zG701M(bLT`m6ac1|Ax=YQL^XnZC3l~!izabyDNkJ zg0>*=>wwvhR-An?A&cbr(i_;Y`stecX@|40kr1e@8<|nSsp8{K#baQ%Jq8ZbWck_x z*Ld?Nwo`Z4WnYQ`-U3SVzEZCqg+pwsiGJ$5srJPzjnP<6w^W*v>~*sZ!Mv}iYtod% z)=j^Jf+k7r?x}@R_8j1*J4E+Smbt;v8A6R-|2lbhIZxrrZ|UXgl1jY9V5?8d_K~N`|8*}7OGfv7rN~V0 zkHe-8frEybegBF^;_5xb^i|Q8;PFM3uU-A=oQ|)j(yhyqt}~o&hqD=we0VZ<^}e&X z+pWWn)q084?z6j!wafRX@;j(MrFSjPcg7k+p!O0K9%*@PG_V_>NNO$JW)x69@lp-RtO3Fwnl~HT2Z|VB(1awv(C766X zrV2%cb{`yyLDZB#?=jI|>#(r4rFd}lSeTseiM&s|99GDg&{iu>6R!#6*%6g% z)>u6ZHcy4izV~+^f&&#I?Zx-UhFsNDbTr2q_%m1w+*1=d7(DeefZM+>T@?eKY#4`! z*z_B0ZD9?WJASI=tN8qmBROIF+td^vb2nmndM!vmeN)Xn?dMmN#MvWFax=a#pB>6iF@VLN4mdFg_(5OQNdUdHK!6-Wi|#$f zhnrXLw?yMd`_b`fbhF(R=*P$B3mwq|j_Aer{Y~W0oL4z-md}JBOWt@tlqXgVtV%ZC zi`AN8&(~&hKFd0qzY~6aQ>U9fGc0^=e=oVxOzHJr_7mAZWxu^S%3lkNE-fkBP1dyz zHXfW`M_wRTC^ipwJy$A;gO<9VN0B})$RqBr#WP}=PAoTfP1U29DH~LWHU-mNJO#xm z@LKIOL;g?f>(g-e$U-Ie+;<5`p50o*HJW@c7goEvzSgS;`z16cA4h2pPRtPQue=hl zw3J5UFQ>1t;CgB5!{m6of8ppU_Y>%S{~jT!P1`Z(mbupKvTV7u?{*1Ub$*oTxPd=z z>rBs3vb?#P$sOBu&%fN?)}-8bgmJ6d4@|u5=cO>uZ|V9zuf;9%Ukg9y zRBrT_e9J6y8z9d)g`+}d?7Fva;h$b`evf`NJsg{#PRw>*b=n>DeR&UO;kGe-e%iTy z^nM5SBTM(;UkO64FyE`|@-D~Vx^dv%R7Calb<+A-z$uC?mlX-z3z7rNWiHIG|3JC5$lGe*YfH!Zo8jl)$P&J-Kzf}w`bilYkI8rIrfs|qImXM2_mZXWJ%}#Nd?{1CQk?t?Mp{pYINnnR}9Falc3>9Z<>!$QF0QVg6ijI(keL&i&!Vb)sImZLGtSpcLCip8K4wDOQq53YdXnk6%?Rz)ewxw#7wE%w8EqckX%8 z(yJsB{NGjoQo6l@6BJYt;q?np8}lbb8qUb$kuYQv(R9_C8IgkRno*|drg7pKZ4J#? zoMK4NwU;>t`li`FGZ!8`_gxJjz&Rd~RP*msBlPZ@otC=QTOU5K@YhWbs#a6)etaHf z5$riAZvN^j9+~xbe_p*6{)SJj;ldv6Q!~5F?TV!fT$J|J9NALqC%OGC1z;U`3VClf zH0kZ}`h5Mm;GZx17^!P$6;hk;bGg!_PjkD2y^((MK2&BM&vL~7IJxZ#n<(dC&sXz1 z^frCunn#_Hiujj#Y6Gdfg`u*28iWBq&_C(CVI?sV_yfZ)*U(b3ph zf^P&=>*CpM1u!m>PTj16Gia*SERuZfDes%%rM&urJ9t38YJu1}ln`T8l#BL&_$()% zD$BL%y)CA`t;ISSMPSehn0BBqgTb0Cb>A~ziK{}XpWd*@RZxZ!`tVq z;hvsLbCyE#s;<0wXRfz!o!V;<@Bt>}o=p0pwddA+UwLwBhxv z_m|g6G3!T90XdCd$a1S+ur%{z3NBM0*ZLIqMJbQxJ;L%|3pS4A95mX4)L zcJ%gN_eyhro!^^K^i*gy#~v@cf_tgw(2KpYeYpWfpkA62to%l%hQRlgCRYE}Zbqyi zOKo%ZbKX?h+4($!;PS&`$8>~OYJNHWzWxqf?|am{uqI*8k&{Yg+3|}tvLT`+4ZFE> zD?Tc1HY>e*^+UE+Dyoe7!7L?p3qMthlx}j!G9`{`$VrOcz$DpM&y=1=ib9&K2$hy- zqFyjgkL^SC`_$=2&HPL3b&V_s(QqW*sh2VZ%pcDIP=d7)!&J^7Q49Od7EuAKx=d53 z1O=2#V@QSZB4Y|Hkv;>J(jSWlV&VP9eF2HFNfboJ2JJftlA~utCX5u&aG3}?6F)T! z6T!lu1y!*rr2^}B1Ord*$c-fw&~UH_+r>4Q62TyZ!>bR2X@F|b3 zVF-`*(?DId#onO`{;O9JTP__}S4$YIMe)t_&aXub?jR+kiX#&;0Qt!-8crpMVtQMP zN>q-)gmQFaKM_<)By0c<@5Uy$K*op(b??@i^b2Odalv>$xPB*;NVPXIMSU{8T}5%2 zg9B7XcVnGy1?En)S*5y|mYUUue44pi((Es`?HapC=M!A9Q|Ifx%*ZGBf7xQDztsy1 z3^%fcZ}npQ|D|61Cz|GeRxcR!Z0#(ZO#TmB%)+;F@lX37)C-iM=4=?C{!uER)s$LN z8bWHXRI*^upO6N?9F{LoFT#Eu z=Rm&AO0PmP57}>#u3y9lgb9XDn6}*2@5E-t&O|Z&&@nGsq|uO(s}Jtu-Mv( zi)28+95q*8rq*N}90A1?SjAYMApl+2SQQ50v@$NY93vHnD9F;dnMj_OGxz7hO}Khq zF^pweZxmJtg?t7TC^eKzt)Nc%O+3t5ngC!DhWwFt!~*p~(Os49jt?3tJ{ZkZyoIy= zqF+CXg^qIS2Uf5QC@l1^0(mDD7Vc~N!yDO@%=38CSq{$L52C9^&RO0ZgE(dpT_oBz zVDo%os?8@VDEYs&M6;8i=^0jr7E#nv#Mne^>_&K_vw=6R#sO$}_t5GL%fPgpfiwb; zjU*FxA_oXy!+(adswB*?orLNcN`huYy4WCrDBg_`B#aT#$L=w-R*!Z(K+YKbYZY>B z5Y5eiFQ=Nu5|bw#UeU}Co$`94WRE!@oe3JkGOWypPu0bk&}cJT&XsG z6)E|~iIW;a*Fiw32J*$!cyI)@)GglkV-NAm6Fo3kp1B;I3u%oHi${UAc9e<5U$~CY zWnqRZ`PfDSj$HGHK?Z0%SwJ`0Y>a#~sDC;bfVUbZ!m(`QE-RiMVayl+LV=hR5`;3J zhGof?tJ`6d`EBK~pvFi|OVSe9H149o$AA24I6+o$My^Cl8 zUKo}55Huu@=-@7nh+zp#AC*{P8h7WIijhX8SPY9!nIsyXNJ%Ur8KFQl$Q#VKo%sox}CPs@xNgW(UWF+~Tu|Iyi;QIR{AN1B7?6?>mJUxfw(Vs9R4$YP(L zk3kTmt&O6<3;F_ID54M_U=2*i-Zn-J{z`TeSvc6{Km58#bdwGlt+uCKI{4~>cFdm0 zAt%4bqY!bQA(^?YxcAYFbWB7#ouKGC&w-8(%>LVn?N7r9s3eO6oyy*aY^h$ZQ{EJ0 zpD~*GJYh%zHWwA4>EVI;y#4x*Vge~8=)Ml$Iv=^)2Y|d2Yo`|{Zd$3YAoNy2FN+L{ zQ7;5bmC95@k)N-g0HNP|lT{QVL`oq}3(k z+Vs3ZPtvbuLP#icPY(4)4j6sdz_Sj)+9mjM7bxXzCAgKWSKtHYOu8`C%4N_l%wbOe`?7W8jG;e>&X7ACF6x|lB>9H)6=H43BtLa}C9r~(e1CD!2I)!fL{krlJc~yy zMo#CrmXPET6Ene1Bh@(_43?7MbI85Xg(nP^aWo|{qsZuLSN0A`?8HNAlLalV>XW4} zgMrzbAhlWoFGu#(AT2)bL$}J3eaUFk`j$lJ6L#tu`1QfE^R7{3U}DNQ_h#5?tDkt< zML7R%5#idzy$#jsJSF2hQrF#ex=g);15z<}W_jo3{1JzvL?I@Com%hm83h#BfvTz{ zPqwQiWOqmhw(ajnY`9Ge`uq_NfY9KT#m*sWrMIMZSG(~Syeq*c@f+K*Lv|OZel9S) zSpjtsSEt-!_|XJul84t6sSozB3<}~vze`sco)O$#i)8zT0YkmdEog`loDn=y3uTK$ zB?MxN#5RC#pMZd+`VS*b9hM!k%D`m-M&>nc!9yKn6Qw`BHA?xtqjYUR)7XIC5LNqL z=}CTdO_(ip{L?Ai<6$=NrN6_`A%w`iz)BT}f5~>NKTIE_azA5BYA;hq5cg(lEOMXES*?Yne-oL460_YsCh80SLP4g7Xu+dF|~ja zw@_#A1nKo!#&|JXc0yZrQrmY5;EX&iN0bp71w4UTI_p1E%+)8i%u1jzeO;;awP2Qv zFMWQtTm`e^nV`NO}%@U@V|%4Z;1xNWj5HpMG#rdH9gH*>j5HZM8w_7eL#%+dEWRkvK9vkyni{FF#u1jTx-z!l z^$gH#LnNX}=|9k3BNjrZG~F{+p3TV>E(@4nH~g4uHu|+_5V1BwNn+=vpLTwCjRD)4o zOjG$BgP4koxk{EWYf35bLrOLk0g1SHp_Zw5D+ZDW7RenZQVjzM6b4ca6N#>oc(j=~ z%6Df(25XgF-I#objFYfG!dS;{@6X{LltYmiNl6#@A?0O@&_D}|#6zQzUb<5K?wO&# z-Cq&^a;q(g;jt8cd}ehI$TDEuWFT&vbA4P;dHFZ+cKh#dwc0 zK~e_K6vn*Iu^>kZxF;Uu1Nxf4j9I`ZQ}6fIP5?(&9OpBVvooCYqiJ||XmXcya@Q_( zKq_ruB6VOQZD5-zOpPH-?fY{#Ju;`A33L@$ZGR0zAUnYX5i0(qQEL}#12ei}Sm8F+ zod@+A;GFVBC$M?Ayq6vhEy6~JBzc-gpU46RhOf5E#li;ZE4Np>CCAfR+3M&Yf*sy3 znqM2CY(if)@}ORanK7ghuOs|#LYmWm5z=h?x=+TiRSDGswdQOTXH3?=QoGKEjq97# zZC-d1DPLsq`FEJ?Y@^XdTPU(;hL}2ovwbt=lTU&OHbM_JNDy)8T5;tjz~Vyffim0f^5; z)Dq%Fh>7bCt&nI;=sWrJd;WN;#}DVW)w@jEfQ z937oa5*i<&86PFPA5ncH($dpYepsv6K2fY_W_A7ky2v}bFgUxg8N0X~y|{cFFF^j} z6GT$0?T0)2_-3TJm;Al*dIcV+gQ~-V@(VrtRn1a4HpO{J#Uo$OquG)x0hv0Bk^8Oav7GvV)nLA<|A2i_Wo~1 zS{qIyqx}z9$3Pe*6!Y6-$<*>^UNczbJQ-pGeBopZqB_};sH*BtU=i0@LJ}!~Q1BFH z6zDK{AwnjcGiGH_Ju)DQK-0xh218>GMkZjn7(M+%K_I{2Ul1IRRp2-hz!Fe#!GG8T zulI@3f(yj!|Bz8?$h{X+ZG7P(AH<1&jr?Pcx%YEH)P15Iz))dntSsGDa&liC(Sla> z3SUgO=!OY-_o0AP(?cJCq51q7(V>mWG%4)1YcZA-#HM+?hR!y3dSkby)RW=V-u$(E zeS`^CzcZ*;fV{8uRRVuJ9rd%kR0Xv^m#kmb7svxtneKPAACXoNC_?O zw0hX|#Xk+L);^eMFyzdt))1&z6LtQL!F?bK?>q%~+oX$Ax82Kp5_QgEK=nnM zo>O_e$3wpIb7>Q@x>_WLSJlQx-<6qodwQGHRoIXMgZP7j6Z6QGd>KOOhTJN_u7o`k z75%gnUC)kj=fzqrmDSm2>Lye3JeyjKytKXMY&8)BtEqQ((6#OKl1~^l(8|Z%&JJM7 z?{GUl?S0>sIrlKL>v_ofeO|E!3!gFHwGAoTrBd!qLB<73f=a%l)YE#J{jHH}FT*ovewM*-QX?#tK zi=-;6{Vu=l6-U-8iOiZD==cLEC8_v@cf67P4Rq5-#cQACyrK1Ztt$zN^_uGY-~;KC z4=p>pRo|Cc_vCfFYg!FA2h($)2<`L4-%(U#1Yfd~HeWSa+`swS8m5L}G+XN^f0f&r8Fc037$DyE;P~ zLDyk3>pB-lM5;KppA(?$%N8GNLRz>*8`~;1_^WCSiRWxS3W5`uL)wm$cf2uc$@W`N z1v2QHwpS%;Y+pJHeFFhdPQY?@RJ&{&TAV}|l90=7u2X-2AT~B?G-&NS>NhslE(@+` zI?s;S(7@$ZXKNEX+^pSWl4CZt_vqi>!azIP^0_e0Oo{oAY0OcVNoQnk-i&T5Jj?0sJ ztf!q2B83m)UFS3B_WI5GW#qJ+H^C?INo9r4g^ifq>rH{3JR6g{Wb?d8JL~2(QL1CxRek`7RQnxGR!EZ`-VOH2HL>$_SN6~K{maY)lI>Wp z?$(5!JBRsp`|W-ip6!gC#I`oBW`9;rJ1M!R7^T}1N0qU7bH@3r*$hZB0{ruMkw>d# zOHuRv6}r>Y2kU!AwyP6=4Th_m%?Q#J@l4jf5_pPZ@k1a!nvSE>r5Jw2+IwCGw`ZQd zu7VA`n)|)$!RsaIM%!Wx`qjj(ebCGz)`LcS1~wkmf!zE2K$W{$ALS|Gre88!hRZ=u z&zEoyvoYdy>sQNn>6mjFULgRy6$(`SS>lg z$Q%x^stSDmUhQ-M`44Pc_hKU7^_VZS9emhjU$z1B{W688;P9g?z-MjH%f~v6=clf> z4a3cwi8t8&wYJbQB)jTUzuM+q%NI{@jhWjr@$q4Xo?g581Fh{zQVZNsms8)%W#s{9 zNJ!Xk*7Rj<{?`-I{Rc*y>PC1TeUwPNp%2>*1D=<)tsP5&Qe)Q&&dceSUqvo3f+A z!QK@6d*5w+&o8|l-prXfTzj+TcPkEj(HYopf9u5;nhehF)n|h=j@;MfAObv6&(_EL zr|&!M+ilxX_4S1sEpMg==WD!=$J_(4+Y5g0REDv9<-+}Kb(dz6<9NDH&CVYFYrzsW zr?p_KU5c@t-#+I%>RS>Jozm8t7!UKSYO&wuCn3Jj^WKM;y-V(a$JtvsdGI^ED-2m8 zshobtp#K5fC2d$XQ}o+-H*zEgRU!&$ua<>)u<`PRBFH{>Ly{5NeTI zFq&Zu%=~J;HhQ!v7z_TU6vl70@x2eU2=If&zHc%Ozwhbz`{60%^)6Gl(t~8(Q`a%g zy|!)_YWo!=W~!+F4!^aGTJl}4bn?VJ+X65TFyu#Wyza$gyQMd8PYm(j3`ALlwdT6d zM^k7ZTNLei>Vdvuck03s%r~qs|!iX%S(%>i;D;- zB9tFqnH*eLnZ-DeYWTBVAuzCorLPxgijMdxjF-_oa>E(&-a1PwglFk0P&X==Z9TCQ zw0+V{2#>#(zvBAadz~O?^+c60#yd|_C0962CG>j$@4A|&UX&}Cr-HF1h&LfdDt498M2oFb~rm%#$qA9xQlOP`aug4`| zTBxdiiu~9SzS(@K48abDNQwgoxW<$G1-fVkVZ1eg`xLkkR|6Du1g{xFW(9n5eY{~XXWJA<5g4hlg=28k zd4*~JH#11lH)ZT-^0Nx(n=+=)`{Reu51b#4CQdHa&QA17M&@oNdbWBNwieEM|2t*u z|8|2g|3^;|HL!Lvp|f{ZQHJ~hVj2%-`cM0Bg36_qhdQ#_GJLMT0s@$uf_8Z;;d}lb zOn5F)cpoVp2RM=h1IeTeu%T3xO0xc@rOsBnLQ{3qo?=QniRI=Qu%!x>2-q{OTh+SP zbrxZQL?s}beP%!pe@5tPGRte`I{Tjcc!QZe7U5i8j=7R}u&Dr+a6#lDb4MRc7}>3c(~ zQ?Hmj`T%9&RfmgnAjKVytDg~{S8zT=W`6oMU<6jTRrmDTXjJJ_sl#_!C?}ynom7uf zC0I58GYqHy(ru^DZbLwSx!yLMDn+sCHMj#>L?3V zD}zD4U{ZPF?^tuP5t5&$OPj@WHjXFFTYIANTt)LmN|sq{LM4l{*>$da#idGlfzQhI z!Wl#*ERGVP(Y7O+R0@o!SfxHKJZnQ4Hd1hx~ZBXCcU~L;7FxaxF9~lQeGwUD0`|E z@(PIvPd`s>C7hpm+QY)HC|yU0NGTHK%9q$=*mis?L(RBAcU((dZ1a*k|oyIc(gER2b2&O5@06)hkHmH z`YPxz!yP+9E)9-Uh4%B*+yh%wf!UcQTd}Zw!5(O3oyA$2k5^n6ewZ#4wzqd)=%L*h zc3(<$Le0t&7BOm>KhZ(hb=Mi^D*71>+Mq^}QKMM7cTUk$UD8sNt$=J9dO#*WD2_}) zLA!)OlTn21w^$lYs1H}7mpHiqG0aS!$kv^#Cnqv=gyS$kY_#ieHbd-au0Z&~oO8d) zkaIuJ=)m{Zark(69J2(A+|1jwGt=P@3Fp8EDCYnpsaa|!g;{C*po~)zF;cwK35wmT zfJdWuE6$ocRlbR%H8ZDCf`sEROxa4kw4$tnndA@JuU5=dLTM<&hZ+^!^QzmZOI)hw(yHe)s^^rIORJSjO8btORs1t~ zJ^GE`@6yNNC-fegenXa76QF;DsY1Ns6|3fYeLxGXPApzdm))@g407cMI$3d_r}U95 zp*EoGkAfgs;rU0+@OpI*yv(5sp`p@PhPcRld)u81@>+eXx-59@sDI{0rB>;ab)b3& zH3w=;sxoLv%^=P>#gR>79gn5C_)wcAGwSKeQ-saO>7K?@Du*Hs1xXgq<>LTwGu~O5 zO8pa=O1yq`(qX;}w8Ok{VCz70;tOnI;a9eRnp!$j&onbSP z*TQVujM27APvi;V+R%zQd7s7kK=<e{QL zP8s*Sv!{N#Bg`eBs}lOcNJpRl95`J19(SGK`UjPA>2AYOrbhA-Q6K3HRlX>*agLuM&x`=aX9iN zGY(f#=Hf@4kT(tcP_bj)Yv2L2g?sdWzs9p3QBSAVXc&D?Vco-8O}2bHOzMWcXRz); zjHC(tE21X`ogV?Tx)WRT^kV_%SwIZ|+E!gG4+Hv`L%Vl{9i$x@0jXwJm14TM8Wy=nrruV3gRCY%Q5euslqkPr5I-MHU&NU_4=iN? z$?uX%I}4?I6tL9V0h=CW0b(;pY6|C`KcRUi!0jCN3UWC|T8!YHCpAfQ;!npImrL9} zs2wPKg7g=Xt3WDrZf2HLND=){VYT>(sC1i+BwH^wtiK&XF1D_f+g}0=GOCBPU%ZwO zkAp#;dB0jerV}YpDXDSEyEJa+`_;Xr^sICL1cT*v=1Lk0ZH2s*4 z@w>OTVZv}sC&b|vakybZaZKls*)iyJ#T4z70_vU?I@;iy&^v|)k;$OIE0K}6U=gg( zAr$@-IewJf7*uRj-1$40A;bmmw+osRSIn+6-o#%IoRWKY*<%vfW5~>Dl#+X4Su@BI zC#Di7sFH?8HdvB@7CeF=w^}$Wf8FfEn@62$Rmnb*h0}m76YVs&due7pxkFAyx=e3r zIaWaybt9(K4HS{mpDN;s6zR@Px%$AOBg(J@u(a^#aQaO7pof2167dy0O}C^yB+!%0 z1R!&cgPg6kR4&!wC+a9`<3mwl<@e_>-MhztSKQK1oSO1}>h zbv6!_!rMSwm@|tHr14KNEoLwqCZBx;nby9(dr40q@zF|gY97M5RvR*VOIgYwKdO{1FE-?3^)f;z>@X9cofIMuq4K`1Xl(LUCpU1;Uhcb|LHVEXh= zF8NCreh)-ofhrsd=$|y46OMg>+Qya%h@OInuo~Ii*xo%^!xXNN-%eP*hGSS&EJ`4f zRU{BWObG|fNAWZ1Bst6ypCD3vF{F9|@z@Mv*zznzd3_?(r-Zp1Bmg<5(-i4>YU%l} z{(0)UhY45f!=Py+2b_t*ok$5jFFf2bRcfo5%RUtru!7J)Dr8Gm{*7vb=yQyHaCBRC zht|#h&6YO%j)9PM9o^Q={r*cnbX)jVd#}-ubz-^=%kSP~$T~9JMz-Vb6HM8@HQJ9I z&O60Q!X!9deGo^BJ5t?uYGle&C{!}=&f#o61~1p9@9geY0zd}zJo`y}L}trLJSVBS zvRCMgH-0YAb>#{y$TcQ6xp^N%vd#l6B*IF83}&5`nchTo7jOKre$4jeMO@qY`Z5{` z3>-~>q6xz2+!pZYHaf?u5rG}8eY07>od5`^Q3^ybS!^gqAtJcLue~`=E%4YpCp~q( z^l5qj3f_9sX%Q@S?J>)qYXOj7MqVg2-)ak$)}36p_vB2a05XU1D8x%p%+r*doHxxA z@$3T2!n$MWDrG>-5H_^E*x#)_3oe~{H1}TaoM3hGxd`{OQn1^Pdu z`))VI-Ss*xFYB|-aRup9Tx+*E9u+l_3ka@HWlCn&;{CX-*WEgOOK(?4QAZ%?uLQE~ z-Cqlbegh3BOAVw*L!U_u(_ei~eUf$$w{23R(P7U=D|d=tGghlZ*^GlqWy4 zbJQRjjFmZ3n9`lLwC27}_-d+nj&?9Hp4Vl!4a+FY<5WwuIg_Oi1q0Kufx7 zDScye`75>qZ~PotgQjOI*}8z^u#1v%q4ivGHK6s+)Yxx{z*u_rzT^3>3?eeGSV5H; zqcGD@Ogp5^+5%%`HxQUy&Bk8$OnS6u? z$9_bHr&r9m5sI_YqmaE^t-n8*%81?^`+IErNVLmCaba0K9)Q-_u`im<#?tugi*GBO z-7{EV<4Ol3|L{&Z9C zy}o=ncqyHBle?fLy57J8a&wmW6#mzY%<0pEfbnB)kl9P!kw_Ps} zpKN5d4WFPVN$P+*PQIoh>Un6LzqxG@If!CC=rReBpRRI&NSF6?>}{x)3RmY{<)y1K ztDxXb<9^dzt}PUtuPP{sB{GXI>CrjM)7BEN_SCYlF6pcI{CY*Y!qc zyvlMyeC^!KL-I(=zTov)j?wc~6OMdJB5R+dBPprgY-I-M5^Ain-c+i25WW&VL9!lt z195h^jie}zLyb9vlB87fh8X8MeFvFI;CcgyP7|(g;0A>&P+jxu+|QDBK`;1$s4G7;Qa7<^Bi_z|8-C^W_8Mmo)w|`V`xu^balQ81;mUL36 z8TqaKF66aavR<4M5c_K<(Y+n5T__9&9C8E zx%SRfS0`oivIAi<_}&+$dezb)yWq8blTv_cGWh5~4#%dWD?+24r;YDfAAQ1XS71Gu za6VX$Cilm;tvoPX99HK2NgoyQWo5`q@|bxrd9ef~BUzgeX=3b}uBCz?)AVHwB3K2H zaLN<~Uq@ZkTG!j`cC=b$FkKXuzER61Lj3M7=F;r~3sDIXP^-t0xc$ETkPfdFt|D1) z^A&_D94N5XGLfSqe3+N=ENb)Z7J|dLJgBZAvh}?p##(xSanLyI-UNhE37#am(Gu+- zF`o3Z8Eog2hv!M%b4$z65}zrclhbtRib(rVwK*M2gWZmRZ^p~$lt+vS@r>~KD2qdx#;FX|%dAu?S`~@bSqYVD zD#@MzL|GVK_b7D(4sW*Ce2atUDG?zCq$a|1!Fl^AefBPF{IZo-U4B~uvzGmo z4#5TqS1@(e3qyY+Dv1OEGo75xUnGov2`yiRbt=IFQCI`_q$3VndH`{Pim11-FXBff z<-O0U4!e^vF`+N(PIr66Rm+*TthI0=N87%DK_TAA(WZOi=cjfhVKh40lKK&pxc8pt zO3d8(gx@*7*JPp3V>8&nADVt-Z7P&28Kl%iS=S*`6UgUdoS_NGd%GWe=jBUxX3-eGbqV%om%mX(9R`POC%+oi=K?NjL{c-sG`O#iyoxV z1@3R4&G5LwRZpA_4f9x{XdmAaLddz$5rw_D@RWtPs04*-Z3>18zEaz5$ob!s(_!bG zK^?2uzT9n^s?V^_MpUFnJsjkw_BJASMT&b&ZEOJE>NSXFx0A9UJ- z-+c?@e~^P+$Kpx7Ij+MRD1b+nn&w!>*g~8e;QHMuV4Q{d=4%(#6P`DW9^`xFkaptr zqw(QaUs4E_E+ zN6XY))&AgKcl*nj?n)=|ocj{Rx`20iU>mbeP_?ld*>GW}x!s`~pCa3^3`?99sMW z5)Ae>^*9rE-m>|UI4}qd|H55`sHlzu3K1?#jr=MOR1_~D9-kw}twG$qMi?qMbtr&CEVJUPDw(U;pqUNwz-< zTa4o=rC(e5VaCPPGRVwNMB?r~wZ2xWYo{m0aDPw3m#XEZ)%QWuHwjNGj;EfMGNrFt zo`$b*J2!}HE!r?Y^&t=$qiV~Y-b0%1@m4omRWM}^^yRiSZX*WWD9p^SSwCBqFlGKL z+58AX0W$!i34 z-?}PGg*~(1`EJG0(xxdx#h{69!MzIgxdC;$iggD1o_B3qmo-=el+As42K>_eh$=(H zC>wp%tA3OXcmtI6y=N-?(c=2|Y%3eR@2mbHTaZRb)2r$L1oHbDlqu@s(Gcy}@+2BV zb7HgNROKknun_X=s{hmN9Vg+^Tpc-eca{)6vNCnlQ+TX}5Fib0TIfrh%zZni%?$4I zoSVr>bN6;~KPH&}!{_q*Tax*w{Wn==P!sY$l&;_m z$L$udriWa4gi%FF%EaV&B2a{6mQ~lk#S&(&e-(=Y#VqC>!*K#}0(VM?C`M_2K7yh) z{!~Ob0FRr<|5lNDIN7*Yn@{dPJiZH^vPl}4uNm(@eXqG2I%H%^6_^PKC@6Bra~N~2 z1R_<&QNc>|-PTYE1?}4}>Q6=;ej*wOmaiw%Q3)ES5=7<8oCraL)Xma9HEWEQ}(E zEypm_xl#^G74(AjXNDlkV~`)v1_5X`Z04E**Z6B1Aps+Ck=i#1T!PXO+M|{3H)-m? zg_)~d(3MzYz}gU@*wndJ82FWxt6-JmLpGe8K1k zFL{tI*p33S`y`IEv}R>m8=5(CeIA84omw#r9{G~c)Nki~=WkL7G;<3 zU|0fKODXiTW84YoC+8Aq8UI=t@By;`cVvuT6r#Wan}Kn1*f-gsoCA-UpBmi- zPzs-&C7FX4iYGo86(K-6yo>=eOoI+fecN0j?i>SAvPl$*WfCcp$RrXeus2D+%WrC}pvT^F~*t1ro&zNw6YVBIx+CzHKgs71SfZ z0m^{AvY)g(q3&wQQqw3lXc~1|2{++DD{;%w-m#}GmSJslOmMVqjVnQw?}@fm`WXI7 zYAD13(5OQKV8;9T;b(-sda-CC8bFbdt-J?BB?t%Uy(}8Po$!9&{{`E@L{6<9=G6Or z-#|0XyudM|y#JAjb$q>Pyq;vg8E8@@`{*zz<-?F$Pv>-k@Ce-f*^#sAKQ0$w z9h7(f5SGX&c@(dw!a1!G`+$T8VM(!e6FEQ%YdMUA7925lf}Y5k%M@aHDr+Hp|T ziasA5*efiwjV4R`ZTX;geAZUG@;)Ca*sFC*`{lAeAFN%f?A;wl&5J#A`A};BU^~#rFN#Cw)TlKe zFf~C`x_TAvJ_UO!<$EsTL+4tkH3L+-V#=M}O1E58HY)529n2&2vnDi&g+GEVh3bK% z#;aLK*LRa)cT(jxt5;~@qI0!CM^67qfWe{qR{~5nC?Qs6?k|p0^k94x`*Bd&4yDcS z1eg`;?*th8vF`*JqU9YJL~beBXv@0*r^4$_7P0g&B`sc|xr3o0DD6}*y7XRBrj=M& z%{U!)l)jjTUK8Y(G6e0ovTA(2UKPWS^hTMryg&cgT)qQf&hh>g0K@bh0K?AHT08st zNi)ZpEVZy9bJ{M}zDsB|Suc%TkDGcYi}){_3n?MOKQrYPbXq;^6fyGghQaDcPE>1B?6@ZgJ-ejW7nu$RBK ze55>>3-k~h*0y+`XP)fHxyR=3*>=cb#gN9(0H4WPfQt4JJ(cR5F8UScszxD5}hUFTtp2o zK-Yxz*tf$Fj#bpd5vC_Z*cKVwKMmLtA9PzPaEl>uiy^6$n!CjjzLg^B7WZ+B5xIi# z{Y-%LG5l}MW#AUOa_~kxWS2c;*X_SG7yo^>_^pWo>l zOB1i^7(M`dyjIQE@Bav3hW}G@u^#Fw6D5U>UFa)Hj0g1~Aj&H$j@Ql=!6dm%H*$cZ zX;P}if^7VwT&lAytkf7z1>kfk0TJ>GkZnT=4ot_5%@f8?#-)wKjTwslosM%0A!N)Z zjGv8jLt`^HGOZh%8IlGFU}odpxnsFl#WRa(b;7frHkk7gLxh2xP(l+e1t_Q5vgZ36`-g>m`!2)bA z4WPZFXkGENZpfOqA6EUD>jA9*0n9K9>aKHfLPqk zp1C3Ie4Nqs63cD=bNz461&j{K;w&FN$?3m67jQExv@PL-x=qV%#O85wUfeS)`btMX6Z~aud#E00B%L6z_ioFr#j?IPL>fCX=_wP8?phBeT>h z*4;dzRdu>(k*{n+q9kZ$`FUY^W^U3E@qxf`{RaHI)fHgXBmr6HRNw#|k=c%eos-6Qw z|18@+RfLA0BNn{lPA(M&wHX(EU=Uy1i)`IE4{`U0c_tvffQxJ?Mzxh<+Rri^|vjf%*ir3Oar#h_bY(48q$ z?Y)JCv9Ft=;+JskJGFebu#zn^hz~J@Qrz_}=5JpHknbhJTXexaIKe$S!96^|J=+kz zU!{ZyW9ATcSs{F|GJ<(zHa630f(|tf=4Hd?#rdojCVCB~m!<7H{M4`=MFhq9aG}iH zVF3TjB;%b)^00StKOf3DV(>c-9V{%n3Ko{ZF38#B`$C*QHNQeg%`c-kqgjITsfLyF z38ZyHfd56k>hm~|n*%}_6^61bVomO>-BuWo!zsyn7__Wx04TLn42Dc6YpKI`; z4sMbMW0k-dlE2H)2K0>%?6HW{UC8-C;Z7K)L;p>4qqBPcl3Z|0DI&H@Jf0HUDsnx0 z@}^%po#enIQ}S_UG#1NcugS4F<>PQDwlQRYthcg|nn zA|uOBcv*}bj`u6O-K88A6~x5(`fjLy(~suw$}E21VNdARwe++&8~*jR0{lOds|_We zb$RD#xB4EOScxT;bbXHf;jd+$c39qPBt_sfnw_-%&N|~V`D*G)_cY{`;B$969qv;G zzAn%v_TXii&mF~TM|WnBKONzwAJ3uv`)c}D5AZ(OaNN(xuH($7R=l@($l*6`9PPoX zUa!79@#$^FzM=c}dH*F%XSO}Y%|gT7bV2K=c)bj@x8632nbm8RX7ZEQWH`x5DS@r4 z)6jF0^Su3-Avi+D^J2{!Gq|iNSr12<=_cQ~B~KG<`@PZ>5&teZJKdlCtXT z@g~$&V(S@C@YnItS%O~~&YeBpeC~Z&$n|x9d(MZoR337Br9ZiBv$xY=+$}8pXI?%x zh@Z@g)9YVzWouf5g695uy1L?f?*X8EJuNA2`7^-l#=G|{*Xu&P1(=&=(Ekih)|#8@ zcpsF6f1?5abu8(ro0`A7}2e{~%VO*B7vu%nW92kQL`!t z{re`3qB4Jjo&U?MUo^wslTh0hy zZ#1Xh#Xo=4-05EdC#vJT_?T~y-si`_;%tmwR2H(+O;*u{*_xIE`DViTmPq&9YU7N;z z8uYyct7-}vteLrUX9Ja?JTLdSQnkI@H?r`&pW6+mg42<2&NFtSkHUC9Kc5GVsvjs1 zmc2T>b`yzsJx&inn_Qmgcd!%xbS!=7k79+Q!#+ue2l2%_KcyvSCNp!{Ia?1K@q7#i zPIYkE++Wk~z-PKzD#C7B=s0}kq3L%$3^t}&;)Opui*<`!MDDp}SI&~Yno?6`gME9) zm4@i4cbASE_2_@5nzvM434fSncCpT~_V;Z^Tx&TTm^VyalSQVQ++R;3GiB7jLql(S zHgjmJ|AXEVYW|pod{}P>KXDu9FcP+3SpVVvA#(bdZ7&6mat_<`{K znY8tG(#%; zVE7cyW&2h3M9)+C$IEmEaazac=yODyE}PNiV~MGEJZ|CRm6~nm#&szflRVe*so8~_ zmbK;Zxo~vNgVlS1YN<}PlV{yoFj6!|YxR=1!*10Rl~#A_C`b9B&HgC|_mK0)d3mj=AebTl z0r7Aei=%Ruj>GDY+jmFe@>d91Z?Ee13_dFEN)5CG{GZsG*Kswsij}bll3w{+#id9Y}y9V_F zD+wigI9qp@lmF#G*W*6!s?2?Z?Zp5G*72LWifz|F9V+sqv_-$?PU5t>Iy5>@=DSRP z?;V6!nZrurw9b$8YZd*Cm)6V1=CBg*{O0qQ-frmU)RL#Cs)Xfi<9Aujy^Bi{2r-QR}@ zzS$2OyIh6Wp&Q{5eLeoU=3Kc$y1&PgbP7rYmS zsi7G$MO@gox<=U)Gs@~zNxB+t0=ceIx`dxm1fsfm+u+}fWM7AY@s3a-hf6TTsupU^ zQqvAp=>o;^_EA^q5z3ZEE#yq?-4ii+^U2O2Q!;4!gc{u!07SP5=IHi|7RnsI35Wq^cSQ3CVCxJF}o=VaswmXt=Pt4i`m@ za~2$WjY!SGJE+p8KW(6)xMq=9#Z6(0&@;?)4G92HUV(7`I;cyV87*MI0T>Wb(i-3o z%!8<)R|_{$xWNfmqd*q_XLLM}LQnbMQO$sgF=n>#rGQ(|zzm&4#9H8OQ;Z0+PT}% z{ok7nUI3_;&!!U@FkrL6hU@=B3G#n~YBBzws`>xR2=f2vfN%kTTK_rzkIe=^2gD6o zc~y3BCB!?qAqB0DCgmFXH>QL+w7-C2->-u>RzY%7X^$HzjTQ?~iA=u6PDjc^OW(f*4fFuk{16W@PV45A1y-(H(-V$4l@n95 z0Iq43r4r_7FEmNVD#lo#D%HF!5OAur8F z{;)wgblE9p0Ykb$<~U&7@k3XlhdB5s>`*SQ#2Iwh>hZw!Dkr>i7RNBY9TsqRNO*xp zGc3o+%<4bY{M=Y`#xSQEFdb+7u7Zk03u!DpeGt-4ct&O`CfKw7sbP-{f`Y?s3@d?A z!PpuIR~#N$+XnJ8K#1lcU~W!XuoGw^3BmFqDmsEr{^_RXGz6Kz^F|s``Oz|#GB*X7pk>ZKUYet%uc*3;M)6dla^-Ii*c~Vf_Wq7`caNe(onG}5I`a8~;#VjG zDuza_xAFzerohH<$FtyL+SHg+1dBOYJhrNY-A6V(~FB?az}0YhNcoVN@6J0&gg&el#^0*$=_(7L1y;(gy{{ znGh{z2%3sL48_BefvR<^ta2O8SjG2gS;faV4J8y~3|bP^u}O1G(1>6Z-0-z-#F{Nk z_U8;}5Gk=fKzyV=0JR}pKe#B%c#=(^k7pBV0EG|;^1Sm8xyjrwW`Su+kV9h$O`&2( z(BkUhz7lZvf(1S$EC~t6f`zWKCT%57U8N2_C69ozMlK7r80oG}Ok6+@R6q#s= z{+I*5&WCeCUwGvdsE#P+X~+Fer;3Y1H0T_GLk;l5e#_|Nr%_%5H{SBxrqscAO$WL@ z>anf}xs7mC-D)|i?=v3PlNQq?#5_%~$mBiHuwzxhj2?n;>q>p%1fnp^JGAUdH<}`$ zj~T~>M%Fj;IRI@<2P1YzQ%E0{l;o~qMPcvhj@?2-_j@w!pd%LY83J8tKzx6JceZ-Y z&iM^Pc?t!-#B+bT)q$)RN}~6}NxI9GfKN(@9|j`TX~C=56(o97u&+-T+{WWoA!T10 z&X21|%Cv|$HOLB)e<(RMG#4aRBWG7>IW;^1-ilVyDcNNuNVrDfwk~jKocXxtcwpS& zY4jQ?P=^Wo(++8_)U+m_(0G!M6-H=yRv5fbX@cznss)$`W0C*Wg;9%=d&dn-!(P5WpEe>tIE4(D&1rypEu?8f2cjpQ zu`AH3F-^a|t?z*!wepAo>=K22i}dhT6Yp{*@CF3#CCTsRX%8Gd)N}j+TfRRz){z5h z+}k{>@8a9Qjt|}k#hsht{ylN%VwR{jR7eLtvI`H3)mnAnld(qFf$Iz93WTyzg; zp$?x&FpHcbyk&uHySYB?7Jn7A&Vu`h(dj?3yGs zB>K~iWh;$h6~sGn104L1)j1_89;>6Ic}>znfS9|#n0a{5RS*qEm`$WU^};q*EooIh z>uU-E_~wBox@#S-4dT0=r%<^VCwNNbP;Mu073W*u=4D#TTgEocI# zBuRIea~1j0`n3|U;2p^0`LE76)2s8Gp`$}6-L$1?mU6E2N2DB~^J}ld;V4JK=t@nD zkayf7oaaYz=)`QQqCzeaQwk1l%PBZB#Ug2H3k5&_m=O^_krF?l5jRbwhBYd9!5!P{IyHsp zvz?~#ur;+tL&gc^%G_^F0~?TZ@0nU{M3s?>C}DV^DD6+=Xzf>XMcMzoWYx51*1$YR z0}`cOhN%bV^H+hj?=B4~OV-iOXXs4bjZ~RLTPuSf8Y>sbG2FVkrhWSFiAg>c)-v;) zh{;L(`Y-QPzOWvSd@+9e1_gQf@dO#Vf5(kqO zp~!V=6(x2l!3oei9!4zDZBJ4|@U+(yiD9}jD)0YL#_6H |D72eX@|VsG{^cg`QgStIdaN%X2NKe zdqY*j-aHZW|Lsa>ph-4$$(qR@SsJWSv<+FW6-x}JAP&+I$G!oD+{?_(+qHW>LH#?2 zA3skvdJ;vVt%RV(PrP16qO~N^;E|uZFBh8B2v2cFqWy!NJ2vfsjlA{$B$Ak$TWtQfZRg@^)t%Sntr0C z3brm`;wx<8E6pSpOcSVNz&G$tR11?3h%BNJLnM0yfUXJ>=|El6Xwnf+HgDS1Jh2-j zJ6NZ+@g@2vL_-==cnLwZLd(3s+IQeelvXBW*^uKA-h3X>B3$1)t^cE=q3x2jkewK3Z3O&F+M^7tm;ED*cRkG+qm2!(ta^FQ5(MGk{u9GZy z)0uM1Mr_|j5vo;Fp=~>5;3@{i#uacWbonk7^jBAGcaoXd$pH9}pF)P0SWfqbv_#Fi zM3{eUSm{gd>vQ|9I(IRFHx1~S?_D(lNAJGoepFu0#jVaOpp+D*pyd5GBnkAQVivi!w+#2KcXK zA~{BwJ_es*xkIpWNxpN$xOcU`Jcg5jFg$m{{|Y}qKw0GsH50Uk@(&{HzvleO2SO}x zYNMCfsWho%y9bU=l`uS@rR7?eB9{wlhP!#4~$5{F(kPz~K9kRyBMcS9Cmf zU@bjeCk=)gRafHTKZZn{T2-ceXCEdB+ug1G9(txmeF?UYLmW^ z=*I#sYibjJDLi>`ZT)JzVv8^(Z`-s6!>QS>74)50)^VuSJ2a(#Dc(?Ow)WN+LPEX5 z|88Puo8aEzzCP_d_bgYMp5CkliGg>VO#d=_FQb&)R2ebp(ek-m?pZ1)$1YV{6=@s& z**nLog~8_}b5@Sq;Kti}9u+kF&b+h{O`?OtsVha#`RUX1*2-6i{%X?JN5JcS_Ke+a z3QJv)g>x3Xn!w+!j_H5?Y16K?^RjNJvqd_m-)uXS0Wpr$XtBEi8`7+m2I_=bCEyT@ zwTZn69trf3Ia@*2o|S5I!rrXy8hm$U$gRN5<^IMTr>BDQ-Nb+L#b#k)!PI`pu=S1; z06fgA+H#=<9YFkirJIht9;G9Wha>)cwAp*t^zsnDrY-Y($HzVI&m+Q}ZzCo(YCDoI z_qz9D#-h&@v5ty{V*Jit>R+wt;kY6uSN@qP`G*OOv-B2M;d#o)EES1jIIRWS%^d}ciNWqn zO&Mo}he<)DLsrf*TX4ARO&p$;=;!I=RUH?V)&@9~L8eY4O2@wDPXq(j+^tc_QWz0rxg zSlya$H=WX*p&yN1?)po>-1uE8UCeqs)1>&VBKDN-7tZbBzgHijyT#^PNsjO216lvf zcd)FuthG7E(7pg)WnBJ`)QYlIaJE|>m#f>?#S%(mW5aB}*C7D^%` z;PfArN+N3=^$~OD%Ek>I)!S!!mGHsExHGn+F{=l0E9 zFRewN?$?ga>`(lU14`!_3*wMnP3szW?T-ukSYU|*x{gum(Uy!kYXiJqV9s%OkqPCs=Gw5D5!w~x))g{<3lt(i7G zbMBg>+-==sb{{A$u7-#Aj&0tI-mK~X-^T3WMYm5Y+}=glaislr{=36!pyg{u=G_yh zb(4A1SLW^4Mz>=kohbL;=znUp%k5F4*sAlr2N#d1mM(|ibb<5+-H{#Ot%UdRtx$b! z73aBdRsE}(+@f0~?i`mA-JiZaKH;C5Pm8YlU%%Z7{B0&1{R(op3WKYu`bIhL`u*<_Z-5Fx>Ab+1IMp~wA(rPzCuT=c;)>__{B(;g zYpLd!BZAhyyk}Thd|!7@N@s)_Q&DGEIgZ_wH@0W{MPOGF zUko(*WGsBG`+T@1Nm!Zgr&1ef3RD#*3tyfcoaLM7C-fcCT;8S9u!j>E{m* z*-M9kftl9Rj&iOhqB}I2+==(ATX67jjtcrOCS=_imqk7GKA;|WDCen{GLO^$xj`gP zlUoRrxL-|z8#jWXUhV|9MJ*N`-q2RQZ3k*gdsmG_%E`jb7sJJ+c;#;W8!eyj6Pc*G zbU5-!e?3xJ3s=jRx~eCeqYL0;td?KAhOhcDUddJQ_?xt(=iUv+<#J1U_ae%EEj8)sywV6m0eAuT_l$4k(wV`RJ?uo zX1Rb)nw?LydJ5n6?1Tev>qZ|$%FAkFm9zC^vuf0uyISmHukArE+gKdL9hzN+#+s^q z=y{g#c%R}bJ)g~lzqX}m3TJi7!ZKWJuhOv5n?bkfe!BElqa!36eG4^|zRIk%Q|{Wo z!YY`)z3kQ|fJ>?1cym0OEBCF2k8qo3jFvOx{jX+Kei+_ruXZ(+C&@nwnMPz;&Z{pn zC-AOmZXSMA_am-yH`hB3a$9}>)c-tP;<+AYRS|U6n)Nu2>8`?eD?fP;2>;XcOCpaX zyVhztEyoZ4a4rwT`g(Y+hx#nHUByh*ac(W>TE5S~p4}#N5yM{laQ>us!u}d|d^P!D zH}K_8;hq0+X+)3xd3YbCendBFA$c`!rk{E{xkJwJ|H<0Xy}#>?Y*GLBWSIm;jIWl! zjf0z@Z`0K_QXB0WlHr;9+513bm`Z?T~1BzL2EX49yuOFWwW?x7U|1M2_Bl zp^OBOk?mJ&|JF%uj@kWTtfaBbJXqw5#YPMh9mBV z67W6TPEiktRjSBSKK%v->2>Q5x*ij<&D~Cy=S8dBWNF@@E08YyYSkbCA4{b+j+pSA zXQQdTP3RS+ScW=3AWl+^NLZbcXrAbJ5BY~We$S*1iMq7SMp4U`qB;9RWOs2%Eo%lmv^oCLk9rM zh|V846}{=N54Bmk(9m;>)oVH^Th&8SHo3e}=N^$Z83^~-+-~#3GC~wCR3M6sAb`UXjcyr~5`>3iR&s`~ zp~(2f+%_`QW;}1^ARPsbh|D4qj#bM@Rr^QEG|x=Oz1ugJ;IS zrsn-eqvz)1r{bE7wp~OzkdR?c?g|`(6MLch;E%uSy-TU~G4X(V=_DOw%wKRy(>81M zfGN@7hXjJPSBT%+f0Df;-8E&$w)Js)az#MZ@M9>&NyY5!|jG`LQHoOqqZwJ zYIyDe-kfc)wG$*zn`4Op&S~hp!!1YsYYRinXL#>05tQ}mbtU~|`O4rrEM3bq> z4h=>C6)aeSRMD?U!NuzXZ)g(2NWf79!FChbn~@BTkgW-wAV6kP!A&cL9uMUQAIvF) zR7=%0vkVqjagQ1!<0z=9(CK)iA(24JPNmtnns5X$qM2JLWFUft&IDTD>u(}BdT(*v zIGxbJ( zuWpb^m+hVo<+Pe|XuJJZvPP1k3K*2K zV^78a<-QiBUyZlRSGJT!6})WSVCw-T;;q}1nl-{ZthW7s$9bu zr&pncW)4RaPahnq7c+`iV~3A|Q8#vr3s zX9x?Y9u9{o$;)qm$j2`T$;(eT77&Gt7Yw`@$?K<}qkfl!LGh;~$y!8$yhnMN!6G6L z3E4M=#W$ws5hxT1frd-Tt9zjhD~b=RsOEt3-|a`LN+k^$;v7%diz|X=3MX8xC@mNS zx%{J0;i{Ln?`I7ChlwU{VpqxEw2g zYv|4q>YzCpyS9RTPX6BCMuZur{&lLEH>w#&j2TDeENG>ylXCW1ReK+u;Wtp!69Lqd zf+PpflLN0)dAk`hc2DU#q0ools3*aMK71!cozVTlFlHKr!SmR~$^#P<e%vAM(6erD}fA~wQJ92Z4(9e(0#4{{jS41_cC6$1;U$OW0R%pUGw2w^R)*w z{FIk_QOfk!DBwrwjZ+JPm${#qN;kLC3)k4oJOBQvcw&_?x%QAsmt3hwrot;*;f=HU z{)s-hmiYkA8M&G3;nR<2GOq}dK*};9YDmGsc%mJy)hOu1>)K`A#vTD|`}f~k8a3{G zk^>`pOVM^{lpu`PGNRD&|O!A;lk-Ru?0+{67px)F>3pWww|Y0jx>vJ zTHx_rihY<^_|rZTaD7DNNE3*+U?n!$u9$>H8p_R}qDbv5aLP9dW3Zp_XwThVZo9H; zyo>9Jb$1*C-~~K^FQL5@NuTGU}~f~7qx{IZVp1qT3D3)SnWvm-Kf%0iblJo-*h z%Q1rIHsp9g0fL|eP|aI_)`A0|wJ0O6kxE9pC371}ygZ`&4lTUC=1INtOLIf7lBS>0^LK11 zcP})Dwm65gQ?J;wubF+XnPab>F}E+}1G^+pn?})qz`yU`?2V*~Pjw7m%p~4~nlC@) zXM7`4lh?UWt0qtWSqU<8Wxw$PTKco_Frfl85f^U^W5u=7g>jCsd8%$pl!_67ieF(TlS`{`Zd=AS{p!Z4Wa3bp?z{` z-MBREFg0)C5){W3RYM3g=fFwWz~)Yj)P({_I&fGY3CpcS8b;LbvfOx*FTW<}dYSnE zdH?t^t-;Zi!F4uv#@Bh-G>0ajqZXSm&iQlcU;p3eD<7{?76nA z{OaP;lP-nzzU)LGta|GixoADOha8GumPd5q(7$D~E z`yVkkEI`anMtMD^BWqY=VF3t?mFKh4nIPAj;;R^RcmG$+ttf~aBjiCKx}u(HcsyN^ z@u3hH&$(0x_r#Hag=I&>0uoodlAb9>nmIum$`>5Jn^!%q_jVmct50M! zX0y#_W#@A`<8m&jYGLhGyFlbuExJVuY&34RzdYgRJ>Zu;ZumWK#653@%VTAZJ|JMV z`a^P7-c@&dhT65@b|=wMM6^!J2(E^$bm6v}25%;LM zJlf!!sJgFAWCIYu)IDpbkpv5{bYl;h%d@5ltJ6d>^%Yn_L^iNDWbyGn73SltC$PVt z{Tf#MwJemzN1jv0m|*CtpdyDPYLvrZc*nL64PHlpI}+f;PD#bc9DtHs`uN@hZ!s~- z0ZFP_*aDAL?w3VYh?1;v-;Px5&~s8HkJW6B@;vX9K^Ec6nQj&L4uujM2v4Z~8_!It z*}q_3%{UJMllua@3?aJk7Yb1$>zEto7zpQ>&;qx+e{v+qJt7$XUQ;X9DF+mZv=9={ z4n$9E>=1%P zM%X6)P<7$;q+Hr=Xad8PGvfrov9Ez+E0>^%iWhoHiWa%u)6zR4_9iyW5!+S#?sO=I zz|ZXNG_%QAK%`fUOw`4@E>Bh^FI*=L7YI}50RO7S>d0Z@uIQ{y9pPzx4MQgXvdGV6cTf-=}wqG1qKP(-UGEQ;iW;+n$*+B81TCe&I2EVA+q< zLbi4z!!fmZh_Ss>`MU21dH~e(y>p8E}Vc6}7Vlsk@r<ziq}`{T3$N_ypYAm_B8x|h z3%p5mC23OkuRpxG**ADwooznjNtPcKO@V@$xxCJIn`0F|c@C(mFWZ#BeIkzQW#fWi z=2tH_tx+d*BxWwHsXX}a?!swSfR;H zTU5NLZP4kzW4mwy{dlQ`)N!E1`$X$(o=mq9T;p`H<*4`E;j_*Pt6jw%e>E0+n+#PY z?ZR5_-S7yd$A24j6%J(iXb53ewSv7UX+}?@=hp7wfSmfN0dSV*t+WZHH-)acR}>nF zC-8d<&Y(fr#l~-TDSOusDS3$)Yvh~rq_S~F-A6q8_fvg7E%+vW*SQBcKSmEB-R|@Q zgGZow_77b&IG*$)F?ee9AC;l$+!FMj4Lv|Y^R=qaoEipp>1mqGRF_+J?KKj;wC{0~ z>OG}{$%;ox_Q7LX^1r_f>)Dxoeo(u5^if|gKdAn_0wwRXVVlnB{1vpW*=rK>><3+a zUDLf?4zhIWZgduejAv&rIoN>JiykO~9@y6E>gGp%^y&B3*qt}?NnQDVb?29v{ksLX z{U1>0O^fAZxAt$Fr(LhVj@jT&ZS4=&rv~tLG`b%%@vx+N_tm!zJe%Bo3V%xsrq$`& z-XE~b(l0@y=}KdKo;$#+c0J2))0UxKT-mPaP7i>xA2dLkzw$t$D%OygYP!LjFxaT6 zd@8^JP1xtCP|aUb+C5cS+#A3#wAo4-F3Z3fF`9BGUTvxOsqR)DK<<{}KKRqRRr!D_ zd8nf5Uf9o|>4m*F#$SN3~0)%jQ3SIMH+YSR;vC&9-y0q?<0|0efnx? zsq1?u9<^J#@oLn?>Hkcf9Zb&Xc;v6SXM~)k~%Lf7gKc*x{`gJPp^^e+~@vj8#8zBP36UYcks+Y zbK*4bTHAGj1m)_?8>(C&-4fJq#%80t^Qj-|#%joV^ev>G?1*EZf%>neTyt(21F_q+ zw>+tD`FMT2dUkrG`n7#!Z_wDDfx=Q1<6YpR0NbyhbvEDnxOmupWJbz*_InP>*lO_( z17o#R>^^Tl;mN&RPi&0dOY+?|bPSHhy!Z*no9@=OuL7G0#U}5rDf~X(VSuy#ECpAh zn-%+`;sykv>vemzuOmlotbJF~qB95liQ0DRPJgPPQTsd+eNQ=*aZEs}AoS$aC*l z=#?+qNfO<}b4BqkugxjV4Gv*t|!u==zBD;Z9sX*%a8)+ zH~(T2rL2M1&lj~H41nTAd*2-RIXkoqw{^q_K7y?(hYGakvr32U3&l&1$tlwP1lkT3 z+uhtgC(dN3}Ny{FG{J=y>D$3=6m1WB8FHzI=hlD-Sm0bBw2rDKFUOq%c(H}C5*@Qo& zkcEYatz$-1PY~mBA_|Q|#9|t3EIQE>!rX_LM9G8}H_Oh?RpsH#Ppj#U&zC;Wt}ZXF zCm-CMU|&X_4_wv}_pi+IKIgNOr8>q#k1<8tY)2;k`jwuMJEv4 z@b2`nM@@e{uEDQ*sn3JM(5bXIvNO$oPYlnd^{r=Pmx}Lf>zj8_6L}e;?Ks?SaUyBFC z_h#`OSsTxbVq81E&uDF9fquEP)-`OA`903W_LMqZlwYyZ;uQ{tzfi>7`0v_=(ys(y zeq;}mn-R#c19^bhkFY`M1E(n;M_i6N!;a(UeQr3B~l@5)c0xj>9VImu`ZHO+9 zZ}u{+TF;KW!}9(>u8jLSA3hTo;-UIkjy`i$bPFkAl~+Vx;Sw9*+L-COl#_)>6sD$D zoP^2Bo|g*QT>L`QC@dM_r9qk`yIWT@md@KR>k?zvlw}_|GV`L&AZ7x#?r0V;4clfi z?WG=^?Q@ztX~hZ*@e}l^ZHyEG%h|D_N;RuoI*N5Xu7Nb^Hw+x;T~h@QZ)lil?ED1h zo8QdY!21W_6;+njx7$OkIr)%6hjohBW!=v#(ZdAz)h+4+A3HEqYQJl^-yjR1TqWrm zb3wILnESx?9}>KTJevILCKsBn+rzn7)}LFfHvDR|XR9xy*|*APPZm3~=sn`32fq@x z8Al}L_3uhU-w$7LBzT^M$UWI^9ID}IOt@I!n5)2gX;xiDVW3D=`A7aO?Z!!KBs4tp zYmko?b~}pacCw7Wp0QHd{tIR~x@Yb6E4aGX{o?HANL@CUb5&y4-RJ@Z-lqRE6n@rM z1uiW$nWb zc{axIO!CO#=^1uy!|gJ;{6}VknW2~aU*R`G`v9ArE^E`9?+HEPQ7m^p;oN8N3zWD| zoMV8N2Ei)NPauta{pO!HTH)7yAASFwet-Fh8?mn6-d9&vjIc0ps#7On!$feQ)O82p zG{S%D=MsofZr@b?O8TSb&YnQg1Zy#37LV^cMjkZ;mJ>-hIJ?ryxLNNJ5Ib!BoXTNc zKH(R{4}oQt%2&cuz_F@Xo43KiWI1pE8TXqIWRo%v8QIIBS-sAi`k8y$t4HP-`+aW4u-s_;F+|}8W`+Ru>*S?(8%df^s@c~2Jf)p zusGzRhIQ(pqNWirDBVMBgQ4O2e=@EHLp1Xl*E;geD(Q7F514|kut>G9MopL+&@P7w z*C%i>M#j|Y?u5g12SS-bk=V84#%S#gqpZPXw7F^VaQy zX?`O=l0gIJI(`b*m=|nNGj)XmW)(XrYg(lJL*1jxQapkN?Za~6MWv7zU+E6)|w12;8 zyr6rIerOny%hsx<4DyaCkTyb%D2E-|C{O#+f{o_bT)l^nuUcg8rS~g+LQD&lqDgWd zV1-&Y0a&4MTc>T&Q0Iww@)a=6fP~`XOAuMfupU`YA_&tmFu}RDxm=ktSFnN1pvAL) z3BoDEIXAa$<(ZB^f-PZZBPAil_a_H#VCjh4Q$gNo(f;hzZK7l^6=9DGR6~kb5n_-0 z1(dHSt|0LPoTQ9~s8y*>R3aMeu_yxz3A#*PoaP4{bD)f%NNq42HdS6d66-wHP(aBN zp)8wuC|rhe5IVHE1!y%b*Uye8;8NA5$@zBrrZ*kR3FeG6qRr#6=18L{$FDeA5OX{> zxF#xa8xpz4xzoTwT|&<7h{@`)JYDg+a;O?W{JcBPNR2juP{uSBa-&kxWHUz`$mKWn zkA3mE`6b9__D&}kA08<@ISmMCR+1jP_PoYb6%04$^!#=~6;a9{dgHg)IA2NNM2dq6 zNTt`Za=7%gMWQx^YB_OG>MwptQt01Jkif@k^<1F+{+6(Wmc*x8z#UjZZpzBcMe7Ly z!?4wXFcE==&c~B^5LkR0OA4z$H$4+tFqNGeJT=oUTQ(nk1TQ6(LAJQpp+H~{ej9d` zyEKyYbk<(y@&UVVciZ- zkpZNb2^*Rem@m#4B^d<-&_eC&xy8sftJu8#Tt^TEPydFb7IpbUn5YsH#fqg+y`93a zVN=$M7S(Xr5dqLb(Lr-G;)rFe6V*u%A~xHCRsw69pA|+1Fe#m1CYysA%c%S{Dw2e< zc&SIeZ9|GmE>n|3UOwWaaxD@~X4flF@)RzRPA629l1-xAm6uM^6e{?TOP~}wmbLSN z63JDDrHx-Opwh*rBRZ@SCzJ-I3MXdLEQp*Xb6?Q0c0kV{io5Q1-v}YcA5<_4P@ci4 zHr1@x6#s(`Qb$zlF-$06uSO_ z04vo5v_e$h6%j{>i9rMsFIgoghGJGO{f}A9{zmGVVd|N015Sa`KfJ2L9vKl257aXZ zWt_cbe{>4|=wzJbbEcw!APjRM99*&mI^xZsQO_)rfcU!#WugRP!I~=(NhIQIP?MOT zp{6z3uLGG9%Qr>{nEtv2D8jpZ3gu0p zZvO}4TbKnci|mbvj9cH3RTz5D(|PgjDaoJ?FunV|A>otvNYN8_&4NMQczVww8T^J3 z;0NWv$r!R5M8!~XiAH^v!1%HHU}({%fO72p&;pW9N-lsxut%_b;{~U{7-) zPoypStbEAb(+q7=*_KSFJEAq9%-1ltMaH0M2e5F~HDHWzd;=LB(ggo%`}i{8mJu;( zz(0FI=nxxwWr?B)Pvdt%?Otk_HT32>i<8K?00MSnXJ<4pPqVQR4y-2F&HOL(^{ep) zo;Z_tGT^~^I<5$#vT2Fk113To_(zW zu?0$4QKZd^KcLSey8nP>&a-F}@x!5y)!|g&QYjm{Pz;<4!4{_1c`_Rgp9Z5%2BVDz zqs;~<_XQ`91usWJRdS%HAyb>q6ltA_2wq&Qs^Wtq$y5@gYxxO0pF04<8*EV+;UufB z%frgHQ@DY-`#HXhPm;R%KDkfmQKw604s( z;TP^BI*a5K?JKAhc*B!;!&6jF&As3Xy-bldlYqS7hD>+_e3&48i6_R0kLa@>*hR(k zj$-=7vV4s&f5}GmIz;u}MfFl(`k^rTu~7P}JL zrCbZl1$O3(5Huz1i}BGUI?#ZTr#4Nkpddkz(M4ImF;0-wog|L0JCv^eExD@`WN|r_ z%N|-1ll}1~BaeoNOm3`H&G-)x3FDzCW1`42kw*YtS;%t?WqBvlC{1NQKrpBpW|@pj z{}2Ph=N86#vj-8$3)B|P>cpkpE6Zefbs~cp&O*+;M*1nrE&G?h{23Mysfmn}&&R2a zj6EbL#rgg~%>f8ejYL-XcZDCDQV(1Bs!Ohk=y0 zy8`kF58~-i_YHJMuX&5yu=l(cmp-l73FZuTSkd4@E6LrCyxXfw{|0?8{)uQ7 z<-L6ZV1mjgzetAir43p@$fnpHq~Fj;@VTyHdl`iGBMH-*7(7!-?J3?{HWp30P8*lZ zgRCe8V1ycTAq5zr8lZds8KKm=&||s`(R7)<$rHYu7xC`ZFR~##Vkc??1$e{mtcX zIbgN345I*)&xP#w`2(Z=mCxhP0Oj+5HxEfd&MkWi{FGX$UC~0IQ46-cru>p+-LIxM zETlQxZhw9=;0>qRrVSY1{E*GmH~aU9;7z^2l^(C6%(MwIJT*|y!YV6R}PXG)aoXW6#VG zmW1~|EFNLeqcUjx$YHaVS?pt&?7#HdnW6ON47$p~?vMlSFqms6pU0AY_RyyPs&}8M z{_>1Y(=&tp5XWp2s<2}Qw`BtNU1hS*{BJshH`1p&B8Uh8L{t9@Afm4s*B1TtKL8Q+ zv0?p*kGRo)0U{+4$S@MVB!CfWu>RUmwewv$C;)Sb4E&KZ3;W!OmW{Qq2Wn9O0EqBs zKuI2)p$KC^z3`z-(3{cC0><=k`a;czD)jl+GK0*Q`{JS-exnuRe;fg`|M@+E?WAEM z$C*8m;g~ToU>{lj4-gHSwoxf?I55?5ovg~i%=ndwNvx;n#A#U$zalhOb5#|wgQmBf zGGJ+{RfRBHtJ@rEtm)yji|mc5gGxL7g{+^Q-$yV}BjiI?yqD!3(fiJd z=rZ0zNCbBP2n2;2(;%v}9SJ~19HnBK$QYO=KAdzDZ;o0?j|Tu~Gz(xu{)+{)>0+AD zk<2+_n$(dEtX)}iK#zj~xH}1)_h&%x#6T6{_KfN79V3zf(?S{nBAm1j{bDS!qp~-X zfzFgL3x;iyMy8zf3#ETOzYr|{9%R9?R48TwNhrigP%{ENA4uSM|L~AK@Q(yiVk|s6(eF`-Eu^2M{cogD<0)?(s`CLiX?gw~u)UGKV{R>d z>KOB?yUel+9pY+N*tbi#x{r4&QPW>Tru!WXBJ)28qzjr?hZ!NA|dNrRvZD$}{K6`yRW~r1!DZa3?bI|YH2xgWEI^`5EiF0(v{ZXnd zNxom60Zu5yrCT8Y9*UBZuf*YGV?8V?M-p*&-d5@Lqwc7ADbaT*V4yrb#-Vcz&7<)% zvyV1*p2p@mKO$Y?e|$iA4$ZHub^No(UgmpV=7bdf;_Gzt7t*;ptKN#ZMf-LqTjp-< ztMYQ70}OQl+%M*5($b(i=_pWzZw&SKvnTKE~E| zm)WpCpkgf1JGS0VH!wP@wOMMT!?T;PS^ls=$P+Qb*ppMZ9%aS=clhoU`Gn?neptJ@ zff3_zbz2{&akaBL-NX1gtGDslcI<|YD}Qmslfll;_ZjWHqklsnq1oB_`H`T}bKZ6! zz{aq-Or+YG>t1@@+Dr@LO~>Hs{B+3@EZ|ahH$`{+L-nLk_jSUB&$hN^+|73Bv29-` z(C&i9=1AG1+s81hB$~g8dIE#V%`$qTQL3kK8{u%eePdLe#&>yR)^gbDt1T$Z55u(d zRW=M8XC~@e*JWqJW$5+6u&@Mf;kt3;u*K^{GPw3Ny`xs(PvnE#n?-H$6NWlFLNDIM zIA7+(cMH+U$!*3Rzdp@T2qb4*ZnwQxkO|1!o4CtqkImmB-=1NRlaZS%oEw>s?vnsd zkVK!{WiM-5jQPRVORem6eT;{mVRdxdv{mAH3?qiKyOp^w{;ErtS7a#fk9YNnpWcKf zqa~%oU#UVEw~bwcJSVeDWqYw;nWbE2$qxj7+eUIHs89TK1#Hf`3>X9q6;hqN*i#HnwePK?ca*oCxW5*y-fxe z`uTW2qQsm|^tNyI1!g82h7j)WTL%!1bvDoUcXe65xLW5YdwblqGHVf1?!0b%F`o*D zC;i;+Mjl7JCSDh5I`n9^uaDZbR@rZ{^uET4jY>rv@N+VEm>m)-qf#aOk_4P%!x%bx zOw!sb!$!Ti|40}hxWN#WY4;qUy#Q@dluhyG)v)SP1inxtDt zvdeiA1jEm}fQwS27Sb;azz=Uh=Bt3yfiMzsAMR<7MTYx{#mM z>m~E9`zY2M-}fmAb+>1H@%0Jq0r})_#h#2FW@9zgM(fjb>;_)o6J4su_)8f9pZ8A> zp=dD)KJTe529d~~zkt(wle!q&v3UxS)eMRHHIyg0&`ekE zp6#i-ug8uw8b|w9=)K>%{i+DP>|`I(PW_Xchf6>1^yfb>oEuq6zCXXvp7pQv=U1vL z1#*wcPKt0uoLoO|9&t)HdrNu*Nc_~-3<4)h;I2>r;;S~nZ>P{+oi_Iq??1glMsBbF z#8c?KM`C{uMMAz~L)TWyG318ydrFbLi`*FH| zQ1)E!GGAiw{_NJuocZ&xXJz&*g z@FKb@Mi|Y@r(8O?F>N%to^@D&DXPA=lD543KCcftPu}5MtybsDIuTh6=wo4-1?@xz zmp{?@aQC~knDw`TQ~#)*!Kk+i=j|VHz&%tLa^CcJ9ORN6t3CxX(T!UD&^C+1Twf+-Wo(n5?0#<#4u3Vi4F$eOeUfL_%v2kZFW?EQoPynnFep6IOq1gy^yL=5% z~z{sHLY{Gx#s&F7@o?QV{wl;9zp& zr(mfObQN$D;$<6FEgcg$eX~9eZjSBnTB*~V$|#C39!T<{Tfg{2LBKknEM7pU@#n!<@Lz;aDS8O=_4b!u0woxw@kGe6@Fa-98`sezQ0Sgv}?ePK?hW z`y2F+{*)cv@2fzmx9Y6b9mdVs6X4q8%0hwOLt24?2d*S?9|?i%oY<-X0v3i%zf{Wx z7lS{e0>T?FX69j~4-W(zpG^z}^0HkFo}v22&tUQWCE?G*ZMznTv(4|xctlR*JE?p8 z7vFf_(iUHb`x~P!)AhYJi_^;D^=2sRA|ieqZ+!iU$&Dwn>}wZapUcHvPRO1zTu1uneQHp8J}WsJ_iw! zp%TKl~fUW?;U0u}F>5HXn|ImflZT#$VgqDa00!qB*!NgkQ3*4H%LR z$u?h(g_d4PCQ92p2sScO#$0N|zDJVo40A@EFrRFYuE|40UU3wPZsaQD$>`KD1OiDZ z3N_?O?^F$@d{|P^JU7I)8jidPa}?30+Q1=R3$VG;?rdcOng4GWPQ2BfS2!}hV6G#= zcf3(}oOXmrREF~Mnz#s40KCqH6XQSuV2KJ}E<{5`m^mVTe?9>sG9vC5 zse4mHzA`>BU}<+=3EayzIv1Nh%N~Y~5|wpQm)G`Uy;$iI8Y6XYKif+H<4;54PFBrNEtBt#7|$z?cfGK@_*=x!B3o8y|Y z3Id_Q1jLX&P!$Bhi2NalA%&6R-qDdj4?hq;j@K@YwQs$01ZGEjXA_NGO?N!Ze0z7_ zXD)477KJO%oSQArDn77W<~wLBHAruN?p!xbdIR4$uaosFp6;RidBKgPQ;&Nc;?zS1 z#mSY#dEK8Qac|jnCK;=6JjLE;%=oZHrKrGBTd`R>!KV zHOczvtUw}{i_VO<^jEW*K1`%;!ZlTynHRnLAC;1`AV`P*&7-7%@Hu z8)0DB(t^%JAuLJ__`D<}$={P731=x)2n5Bvj7bs*X^BvY$s9n6l!M5be=`SARB-`n zA;76}_LBAOw*R=0Br<>tO`Xdh_ys?k!o!Lo7WH~o8$%qSIk%mJm-NXC+pY`tT^HeEQ-U8jymGJ9Ceo>lERCD|=5 zT!yR=HjdKc`U9CZNK_VqbGBUtQed?wp0`78u~Wln3DrM6Ib|; z#gnztD|?E<;%lb%8`ki*qDi*jihHq2f^!$cRZkrt6Yj0{B!nV2>DIuBKtLqL-!AlF zm3~c8>YqY3fv|cPSR2Hfm;;snV(FM8ylE4#n|0}!1O2^0%4pAxX$%|`J_HH0M2Gq3 z;ZRaVMinqX1T0t-|B6s7tU?aIAY~#RweD|;_vGDgDA8@Rr)gE=MXXC+DYkEVmnYfC z>7izUS+)dcqDqKD=cB>IIQ)B_KVDogMN&*aCkiP(R3gbgo}5v-NQ*5}C3QCZ4vScI z!#rO4%p;!KBubKuNv+5`7KN&@3vh-Hok}R>FUNo=tj|xTx{==?Ln;nwzvCLy{Rxqa zL?edW&7xELzKqbQ(Bj3AAh#bJm8v`Ymgq}g6 znn{%I6Oc7%2z06sd_WC-fPtP-M$0Ot0sx`CkA%R7N$43e=$XYu45chHR1k*$;6f|` zN^mnK&@(JXf&Q-lVj-IUxX^F{%my_PfD36bI{>&4iM&%p{}!S32^EbG*@zCrjUIg| z+L63@zZYs+tx!FTdvd`Gws5@&M8wVovxWgMjFdAm&5y49dMRK#rOehqYxHr0dZ;8X z$~GD4J|z@)z!Xs~blVN1{jO@CZi)YU4)FCmblW7Fy?{cWZoYq~QvbCGSf}4S9j*J# ztK^W^;wX7tL<>}i`$L{hrPL-i%1f~6y>R59?I2gc;iS9|>4pbrva)b_Gf>s^?O_^8 zP)G!EKUKVq9QXyMOqCimJr20=4>*LdNc7SlRT3&bYOwqW1?Mm!6>usb@apkEuehnB z79comd2V3(N^=}w0X>@Fz9?(LvV4@i;IgfjvYi1oOMfc~7i&#!qoKSjZ=1V8z0H7m zPty$~;b}ce{h3+0GjwHOgosCxsS)SEORTf~7=`Vd z3oKzGNWCpiYjuhbVC?ar-o=^P05u0~<0npQ;LyF6vgtj(K0N91-XO|$njitWJ^bmd zXEXm^S%olensJ=aJmz@Xr9verSg~Dl4Y*b0tAX=2$wL^cyuz7#G%dh@u!$8 zBVxoR=6zxXrkJ6TqH~P67~v)VU{OP;iz2{iN|$hA$mgsGdvQRWaAlWgjervu&Nf8O zc~Z(y;6oz0E4lG}Lv%bZ2eSZk<8S~g_9EWfj`Dg3FGK$?6;jxDi;rzu$5cZY51zS4 zJP#5zAVv)J0!ojp+E>yh84@s#Ae~=_WosFtg7ouWDukxNU%mjH{mT0X_uX`8a7+!b zd@Zcz3vqPHzH*s>ma9|4CA#tYkMO>f(*)e%dg&w_L~9sr!)I9j`%?@KVY6VI*l2MhNCIq<0rccbZd;mK{TEqX0e( z;eUZ4;D2B!;y=Jp{5}{R&B1?xp;hE~AThS6%Z>j6LtOTy98D$Rb%JazmU6m7OL71( zR8k5J359}Oo`QvfKO&kR8csYi1UfP#Ix<8$G9-YBl8A*e92HL0CK>M&K*WQFF%E_F zljOk@NxceP07>96{IF@YsiG*z)%+!a{5QVq<)pKeYnCqTtqHa;S#Z^vR)Z;HLsyYW zA^IKtTq&_$g;-Ya6#3s3L5nlm@ z(}cm&v}0`8F*fcV9@82c)0!HiHZ!I(G^R5#?v9SMs^nK8OwZh!#%usS5q^<}{>uY; z>eejCU?kX!fy^n`C6C^fMf(EMvWI5fPs0ZD6-W1?4`4&}V185RT@iHdY}&WjEqn57 zfdVEldXY}lJr@&5#xOczPDYUJ@+o4Jk_D57K^G1tq%+tMLqUJ?VY{9eb-x-BT~fbs z{~HV`A^7AKN&N#uy@EE2`{03tBYD^H$(=M18ZTr_Yof61iB*>ruW9%+Ee5Te()ZGm zbXT39k^?5%N(sKHQlU{|CI^}={q~1$vU2v z-w&Usi#XYCLtx17B)~`_^CW@n760raMa;r~fx}O!6wm!nGIZLMU$kroV&TGrH0P%E zX{Vfnu>gDQMS!YLyJ%5*yY;Lf(`REugMc1 zMtnTE7ZIG^9P1^S$^K5Ly@BULF61r&ePj}JWOux)hL=$N$zNS{(MoGk;P;u!FV7$* z`$R_jNR?ev*eyfY5A8t!4C>2`3ivlt6cx~m3BuhyA^2O*M6gyJ3v#&AGu)ADvwrZ_|r(Du73IWuRM1fv(1HC7%iBwTXCE%m2C)-5FMNnZ?7~m!c zaN~KlOnFhQURV_iSm>dhh@zd~L^t`=NFqBpT7p&*xU3!3PRQxv_NM|BBtwEHM3Gm; zqN?M8qOs4zpY-k^_Be|$w1KY&=^6fuE@P-3u>Q#z2OtT2Tc7t3A+ zSON)1I#1K(9@l|6gVO^aA(p_PJ>Oq{AP*$#O$8)|NH9T^H2)wP(f#!|&p=)bG`}f&l!jEal?LP5fM{)8;&@U-pupA z*I~ZupG%dTbMoErPhN%F?>lf;hXV0!R&t7?;yQU>W9N%dY-VV& zhPxV6)zN*Gdls7iD$7;IhJ&@6u(^(3AfDP7ya|SbrKuhd@Nl9*zd=05pr}|)huAb# zzqBsb5)73F)1Z1GxD%uLj?hu_0Clb${f0DIXXuTYcBea2#%)utU_i z^fq;Fr0`ha4)%Uai{7Bx&dFTZT?y=@MON`YCd*HDmbc07EVXyxosPrRYV>e58)kjm zJ}{p28-;&S>YqQl)ryrpltXql|LJ6(_q}nGnXuFOdGvG{rQga?RJehU$#9F4-g zR&Vox(dk-^=F?_&Kvz>1E|6tfMcWrgw$FK07js|MALm z#O6tQG+au~QHG65(5m5j=WUMVMmxkj%~qPkQiA5iE|KcM=9l4UuO>;warE~hvPLDz z;UpvZ($f0LA$Rx8i-NW?2}dEm@t0P%l5J+>d&My%!Rpy&lkY{N_ZItRdPGEP*Om+f zvL5Yil0$E_J|pjYTWlm8U+eLdCc$$6B3sckEW@+jx2aQje?6Ei-}JK4{f( z8%I#&74Kis$~h(5*=n;C(+Is8>fAFI=;&TD(^>GF2&Eb_jb)Pp7V}Bv9q8FG&K9^NWkd6xb&RybF4(d2Sza4%TbiinEMV?Y}!srtiQ&Rm_*X#SD$k?dz zh5hQ}H~0ko<~e#M!4LYjP5y8S58sSh^E+61=l{3+4S+NU^Dj4rl@F3%ifgN;7K~(8 ziy5=?J-6?3B9G|s^hf-F!n3xxF;?qkw%p>13-v(@MU_j2u&E*^?5M25DX zy(?9#Q>X16zrqAuWDU#Ffl zxHjysw=`&m*2mLZm}>o7$14gn@p~KZt%)BfGfAIk2N5$|J)GX#lWZ=nR<63Y4rT&& z_aBFp1g(YpYYdSj((N19f1j>zWZHwFuoh23rVmSao$TlOMG4%TUgxiG3U=x6W;GdP zB_7r|n+}^B^=y6P=X=dQWNrQUPI8dI_-kg+$`5#2&Fn=^TCqB=@=`NBYv;3CB)UYO zQ;$?h{$)o7K%R4N)l*DxH?K%e;!cZJd){|17=#+XF(XoN+fsLP_vT#g5MTgbZm_}k zGyV2@2AkiHJHH;G`>@#lUej-={+LX>+8){cZ#z5*-VCFryQNB6Al}<)zSQW`lcA0b zjJun8(gVE@9!k3>C8NIftJS0X&v}Tbui?*5IV~Th>Re|x_1feuu*bKOv)T0&af#@w z3JyRg=iukTawykcxY%fvP3w$}i`5oVVecePI!6s26FZ;NFn7Lvw}YhbBl2W1%E&bh zf|*@L$6{MhF564yd}*{1MoMn3(WV*hnzK}}?I_ei$vyDu0Jv8)O-I+#{!$*y2-|x0 zYB*Y0RAmO;IJLF&ggec(>YUMr?LqwW$~ zxILt3$OtX8rpc$ZmA*1}aUoPOSWFSodOdeERM;JCUPw`-93|t>o1`rj&Qg}vu~4syo+eE9(q}NFwJ8vQ+TD%CWs6)FPuX zDUg^auz6?GUv%wR|H5t3b+fRDqCPn0YxVA~cl#Eq5?ILjE1`eG@2+1Ta0RKA%J*l6 zhrN{L{3~_a=CYX9XQOHe!aA9~iShgc;vB3xrp~KY}8`@G}DACnpw8Kxb6CV&h zc6C-Dj-z^~Ofl0rzxW2r^9F3j(e_x|kdiEIJAL(wn5UZWqOaKN`EL*E+(SRV5C2Yt zEu$qLJb`Z+_XodKuvufXR)>$5zjL^w55;`94EElhG5VDUugppM&uZ*h2dU>DVcaVLAQwal~|5C-Rqy{t*2trX*Qy)x=eZfGfv)9O4L<=<$NP%2b;}A?sH4!L6 zQbezm0&CL$14(JsT{z`~;4Ly~_(Q}m98wcH3E(pF4qrfQsM`lS!TM+zAV)4>3=eFC zDe9EeOcMo^OBQq!AvHB{0Ob-T3@VsJ%C)e2&BzbPf15cl={@8q4S?r;(3oi=$2Bxi z2GF6gbb~3Yzzkc5B*c`ADXV@M8U?F}{T?92PZ2Jv0AfZhxve`Asvs6M1V-S&;kSg6 z3my22*TVeUU@i8X{pZ>?JXQoEzE%B@t=g;_m35E8>8g(dgP9-B7|c3)yJl^*+A@}l!a58EFXB(2%IKQXQN?H68XOk#Q=n9wssay zCjaND80P=n)c=3QOU41{LyzktIC^hw@_JR z>vul8oY7wXoUo}W-I<%52cDBH7LK?Ko!Xwv%+yrv>|9@4yzK?%HQ?-xvK5?1Qx}c0 z4f6#jZjl}#Jp78Et>%r+rY@po8H;C4=l1!^*+oqD9eP^PnkWgWMv@VB7f;6mpQz)g zSf(fw1|m!7jXQng%aWNs@mmWjhzW=u|XHVvH)gwgYeR8iboRSR1-r8$c_BAK>p7QfUb`#o6B3JxaoKXPcH>T&+N47yuB#so zINBh-G-j2hw3sEcBQ&dI<%|e7n-%kseH}$Ie!;!VDaM=Ue2;=E&~lElnHDVgC=;Xg z?+T`~X;z}n@O|fB!4)pq^ezEAQx)OQOL!$rsOBRM=df_Dq;!`e5aLj* zOYne_Ny#=-g)76QtG{lZzG*f1n&OV?P=45uen{?0f7BOXQsQP$)p6 z*P{*@!)-_?EyI3}R6c5oqv!!WvctotfkRO5Un~um{Q%YA{M}mQ6v(iw*%MyLCd#b) zZP1T295RTy!6}-baG(~%FJU%!Ft5eQN$Q7rC};9K>RC)l{* z$FCcMa+H!^B(OBW(qK4ajl#4=^?~mQW@xle#~cy+kYu>y(0Y}>z!30;L*Wd;a=jAK zvMgC?UZ%9G_HgYQfgf(_fJZIW3>;x;6pE!Z>CK>^&O#VP`pFxlE*a4`6)J`CK2({yS$3Fcm*@T1#>4rL~rfRaOh*My>RTE?(_*pjGWRx)%pdEe_ zPluS21Z^ShEW1I};bc)hQMj@b=e7n-Y>-}uCX3M;h-FNZMr94wIA!00JVTTLX_Ykz zeUUK=J()I}lGLEVJx`;~qLyt&cxSNWV46_4i{T_tItXpP{N&7l`Ae#lKdy3+pI&@b zcpO5K1w;68WlvTP{|M@jGPH+(WZgan#sHb?ke^_5AS>AUM)>Vu?|PrjW+<-jp+T(V z3sXy2Cl>wi)9H?G-5`Z&n=Djn>OvSAnSC*`??kxXQ~I7sWyHQEnGT8Py4L(8e`=R? z_NmE*7{ex^=Q>6~stszlb>gW>CSajJQj@A}eMR`Cc+WI49B>R_;A&}Fi68B)mpWMg z;LDG_09&0NJ?oU#qYZXJQKEmf%Hdo#h+IbQxf@TVLd>V#;QG9qo!k!2#YFe*t_K~R)&$kXT6W3-5T81sN@T=uf-dEKh=}r zM5lpe#`hiNZdk)ngQUG~HL72LMWEal3K$k4yY(Xb$WIv_bpFKFaXCu#jtROSCCci% zlpntRjf{_$&PUGdrSt14cb z7+IC$7{M|zSSo5ka=RAbc$dp`dgQCx>j*EarC*UZv%Pw%|U@@c{#?2biEI3;C$numP&^Oh+8v@ z)N;{KOpa}&Gh*HLBjEdjVArA=(BQFbT%%m5k-oq0kO zyle>xkq^y4#IM6(W-tfPxL`9^PMcye8TM%$Jc|lKyY9oiKYODNEbjU$p;u8|RqY0L!&=qKhcQgHu%M~nDL~dl;Rn9D^ueUL zkWsjXf>rdfg&K8kUIP2&Q0Um8QZUC1mk zYtkg~Fi|c+xJG*EW zq5R;ucq9ud7nPV7Xz8X*wry_Vq7Ma^F4$TA&F5b}#H-o^!EVLk=>T|0_+S~YXNyxp zDZjthh)__}*IwsJ0tyGXBe^QfW-G@|r=#2n?|He_39)N3{FaH{Q{=Cl$=-p|{8FS| zFcLT#O&LL_DtKp#r%iHiSYBrWn?1?JS|(h64`ZEdH;#}4 zYds07^SVLH)r6V2HYciQbNqKZpK&u-xPOpL9xWcYC7}~AG=f+TGjtIn2}3%hN>mqA zlowONrXaRboH*)G*l&sxYY$0@86SsNa~=_)Xp^B7Rt$%>wuNQiT_JE{eXMYG0j%(e zlo$$qq9-%8GAMlJQ?9RkiT9^c3ZkFyNcXL9s+4)<4wJ{tdV5fQ5pNC zaZASXwXJS?mtHt_UWXiycD-JaZWkZDe$OfVGgU0{H%$qDcs?Fd?OsOnI5Oe58yfzs zO;%$YSl_=t(->`e>%6PgshcZxwKmbxI@(7vIjDJ0@9;3;x3$}Ng2N2lHG{Z$sc>w; zqw8`07<-r-ThzUEku6P$BzEu$J3lV}l>ENS$kqCD(J*1x5&Gu~ry8L<`*&@zQHI@3 z>}I4IMy#!Tj*Hwz{FBM{rVD}pW;F!yXxM?3`Cgi7xbfD9P7l?!TPAkWK~QbEJa`DB zn*Fm!Y%)YGi}E6ROTF6P*5hi(qiQ|fjFBtNLj>$p>+PN<3H?fK zidkj92?7=;8tH_BM~4-r%Fav4-+mJxEhV((@fdYlJT^K{-oYHV0xMR!rWc%6s(;!j z?20`!5`n!|tN_u%jee*1Ce>AwJTsGD{|{sT032Btfa}5&+fF8)*mg3pHL-2m#>BR5 z+qN;WZ9C~Cx4-lMx6Zle$Gug(s#dMt)wQd3byxLz*YmuKtrG`T_yjjk+o=~|74EV7BY*5T=dX5EQ?9ub~`q`OxR6%Uu)U{e**JyJ^>(^>SeEn!>Y; z+KID2$}HROv%~h~^3Fx<6(Y-H(;x?<7tLBC8$)qvj%6(8kexN=PORowmfsV1;_4=+ zz55f{?!_i6uq*c^eEzwqkc2_Io2hy9d-}6)7_+Q74@lJ=0e-XW>iGHeyn=& zer&B@{#Tw@DCMN7B^=o`0ecMvR%yNR6n)X%x96dSMfhM=MT#g6FXJA#()-KfxWgCL z{&7Mnc9%YU*Mp}WaB=)ut?zLdT|Q5+TvipxankwJj5;eX?Jcbp43-4=N<86knSU5e z*!Rf>V>bM{0Be;$6zn>Fs-wC|4LIb(Ayu{VK<@EYlVIFhtD!#b z&&wcFV{oPh(`ofv;Xefy6OfTGXG(?I9lJpY6-x?uxt2}Swl74l z;(fZx!7(M6T6?z9S0=iC$?7JiPKH`p_j$6Pz$Ku)dN#QORQ#DMGzlQ9WkD(^B`h0f zpa&D*z{l^N$%P^mc%sT`YCwN&tqJ?KQl!Gp<+h&H++n`;37+>fL`L!+jXmqrr3AZS zSmh8qR)j5hY5sCVI}8|Y8gv*P<;Wy$rl7r&w&Q#`oje$a2$$X8>gZEB)tudJ( z1#EgWc*$TOLHm({%_heMp#s0#01ys8wYS0Y%6hbbz)POQG42s69y{_r-S0HPfXvM$ z7kzQdUDMbbv8!9-v&Iqz2!TxyLJ^Tm6KLPxrAByqu)ZSrpIgv9UxpN0(*3c{BYxw6 zmy}z(CV_SuPcr!j^`on8I$c;_LKr{mkIsnjWS_EcIvy8`Q{bBuZvD2_-HrlJNh17i z9L4T|ODVToF!q^|1c$8t-U9creX`mRF_IB44l=*hZReq`5E)-PqPX7RdN2_xyBv~v zrCD$Xl>Q611duF1LtIBg+fgq$Q#xFnPIlsJSZjKrt|$J-+{ufkMYk)z_ur#B8D zV7pj9o>`J9U{FI>_7gzkY+T}i30Rtn8hxnM`QAeYqZw_SR@*Twz@zBuJ>4TSeO76G zNH?m)&W&RsK${0KU0hNatTA1ZQ5IFWJY9lHFGA8x`ZcV=Qi%1quGzBW z0qX|xJ@-2X$7RDWfA)a-_{$cok@y1n;7CoCd#p8MO{+nFbRh#Q%5^XlpQ49nvTAmt z?QZCvW@>6RUnTcjWBBxY(}WvWX}rpU=XA5ssWnaN;V%eF1p^CPWoQ#?$}%HW#>rl~ zAWvFw{>m1M5|4Hc)V^27OJin~j5`kSKoQ(I#iVC$^!gU>DPY{MqSRX(vP4Xv zDqtrmK(Bpm+l}X5|6(QjFBN-gK-_09&0h{`FfBDrh6ZtuIDQX5ukI3f^JM}AUG4&S z8JV0RxjlQG1C7Nx7F!1Qw8r8WcmmpVwo;!?6kg8ft9?Z(H21t-*VD2MS^}9m#N0ZH zOej8&9TuIPlK?CyUjHs#r?_dlR?-)Bbx3pJs8}{DB zJG4gX(;S1_HY=H4B)hUu7p__OoM)pK=}vxnLZ?kP{apV}8M_^VEy!U#BTTl3Nm+ik z-c3872g%rxO=I7y&(EmAQ(_i5MMl=y`>-{SdEWP)m!tc`Ho=5iIEi#GXX^%N_b$<$u<)~yvhU#f4z zlX&xQvXjYgO#;AQsuK~eJv)&ByMXVR+>1M`yTI2>jYgZb!U$Q1Z@>Lpyctdm-lc7tJ8gM`?qtA&Y1ql9`2=O$M9=XQt|PQZg>E{ z>#IB>k_@Gxrg}451bxVttOb7dI*AqA8=OV2pN4o~fH%A-yi^=ZjzkWZ98^5iX^!&kwk_8ggLxKZ>eqBwq?I>m-km*viIkA#?1Wz( z%aZX~AD?KOts@rcDAp)IQD`afWRFoTXT5>7AEWQ&*#WCc(R5X5D_)mETobqyZvC;aj$&Ou zsqX^1(L>-0ela(xQpfFJqX*y)%yz}8l2hNXV~f$RP*CeX7iso!fcwej1R+CP47|n2 zseDB92}Z0rU2T5gp$>Co+6X(f+ERpyxXY-0HKCse?#kbot`;&%bbHS_V&eZ%##ZCs>UY0C%>g;xQ1 ztiW6p-&kfWkiNchsX)B| z?%`@&k`BS>>&=WBE4`4@AO@YT4Lwtb9adq=b?s7B5Cy*h4OY}aOg}7&bwx-zA-;%A z4O~!pm~-rGq9roDX$6|o2^lp4WNB(iAzkSdbV-Q?UBO7Cf|!jtnFfqasr_Fbv%+Lz z;z85K@Xz66=dqE!IclMK?y;0YY(e`Zn18DN^Wf(aV^!$C<~582b;><0i$I0Otc!_Q z*~-xWJxHh%%YekD|Mtli`f2_f1GfS;x=zTb;$e~GXa zV^Kt+!SJO8DbP->ZGV?E9UjUz{jOAi78P{lelVE}nIf>MDa9)_(ivBeYG+#Orj@Z@ zyYj?A&a_1wE=0>>{LK_YIg&Y82@iv26Y*;o#?fRhI?RQGqmtURUcL!^QYZ!jWgONM zxjzRTvX^K>7!>E)#MWWQAbWgUq$&1|iI$oMQxv@_Ed5->XWYD?U9nivw1P1HRNl?h zO|$9+>mYp9zo~F(>DGyp5l+WdQB}H-5mplxPV@q~NFQ7>%nwYuFHeGH0t<3~y`P&` zk-d%*mNn3rD$tg-k2~dm*vd427-Yi#ndn1C!A)Ua2y+F5|X&=tb|S6!J*-w}Cqfrfqoj7)-x0G5(~fEH{HOTdYWKx&z9&J=)G z^yxu)X?9UrKwj;WSNQ2cW@)y#B6o5DQAuEy8pQVeg!j@c7RKGhmtO4?AY|-c2$w$% z({Fujz5o=KLE`7*yHEMP zi^A|lT*Qh2MlC6|uDD89Ou0M0+TFX<@I`QwD4&Q;Ou-NFT;rNwFhrVo3DN3*T;&vC zujtpHd!8#d&@+c)P@Kbe(YNY6g(bztb?0jwcaSN5`7i#1*@5KTK2~g9Uh0(rUb*1-9^QLHwof0 z*Tq8bk2TqCfYDBe@J%SU{h`;V!u-Z|=5r?Of{w8F0DFU_+;+Ak#y2(Q?+N~OIT9XH z^MVy#bJrnK+DWX^R-C(K91D>mDU^?|v`t(9A6;Y0p%jEX&J+SG;_P28eo_mMyjk6_ z9!LVi5e>Gaifik?gIw>&0OALZ{IO&F#AywP*i9*#-R64(*g^`NlW#dd1dI_o)Ip z%hnyJH~h!giyFQ!4MF&th60(9i>FGDt`PqL2Y--ha6D1@v6}bMv(epzOk=)@yGh&bxmRaCCG; z16rce+v24z>bMfbH*5u4wi2v2tNK4T_RH500$ z7h%dksiYxQjfzpPk5YGtQooB*-@>l%U@?5sA37foOZKm@prBOqL@9lGexOF;K~bti z($Lfscs_RlC1<|;Fu=!9Swn)q^wYb9y?r~p4N4W$^nG-X*#6NF_lwUH3~atJGDcgp z;1r<)$+O;`Q8ZRbpIMZF4Vq7E5d9yK{DM7ng(B}?RNlYwIwh9w{F1jaq}^vA?ug<~ zUB3a$(7vQ$dEtAhD2QL`Ic0V09+!1;~ zsC_{-)>)G88W{l%Fut5RP@E|_0*p|R`O-jV*_kLGHQFtuiwr%EhX#_!&!i^ChOd5m z$vF2I;yYg9f?#4M9JO+@QpQ=iVHHQ=;Ki|yl7pD&1#*imgmy-t*ER^ zjIIm4Vz#)lo}qrXOkak42Nq+Uu7N(?zknxdunRWG zYU56~am(GZ_hdbYw;IaPfY3Y2-W6`+&a!cfnWc26q}EcVzWc$+0U3W{EE-rq%XOvt z3UX~7);J=0W_6~_&Hr&m^=%O6I$<}=g+dX*oAvA8KViN9YHfOI25ii$;B6^J2bCfzxAOMf861J-e%5SnKA_c&uM4(vGFg#H2$%n0yI{(teD;)b9xoAdnK`Z zi8FgqFngKF`8W^kh)e87sIH1;>iK*m96GOmGU(~EsuHUn7^qEK!UJ1v)D1~vR>Mw! zRcSe({OJH{m)va5J2^_856REVBx7@~1t2tBT)>xuRRunz6{o&Ax z+TZc`&M@N`@%dHMp&f(q{}hTqbMNt}Lkh#dr1CXbm@}|(grlN{7Z0%1LllMx4dx)^ zyedI*$zy(nONg!D&41sazzsM6!IZ=}>jFO|4E)y^7+4A8)t%G7-ez##E+{&0bb~Az z=Ef%T{-wvK6S1wBRjyc-%?my_5s0q}sMX!{p=;^@0#vv528^^I&3T;i=0L5lv~_P4 zxFC>kQ{LN7F1yoLzfap7ovJW702?<~`KRB9ea+XZO@txV-MJLnEJtk-+(wmTv9g^s{HUb6RPS!>4wq;O@PE5dsrv+2NCL{Ay5HVX$a z=A^9Fdbw5>s&tyQwb){yZoD1zJ__bdSp?L zkBL@l(JqdOi@m^1Pfzhpt4FRp=vA688pD7MGVgA1CLhjWoG=?Nq3jF`W2cS9`Vsl^ zMvS6*YwmR|HJW3iklEUx-URvi<7nM%fBv;vGCLHW=oQ|24sv6|eQp`*;3r#E0T%U% z>*@;p!B0%r&vXLpleMeByRR*j9e~8`yOA4Xj+-T*{dT>l1Zx8;mNV^Ck3)9{j}XG> zYbu-H)&n%<`|}XWkBAomLQx)3@IbnPkme%d>Ep$EA#il+MtjrjO&PuD^(8lL=QKZ! zFf;2>eJRh|Nl+;04QrFP>n@t1y)CQvuENDPWNT8d$NgzgUe4=PZ$&%Iu?RIy;%)eb3r0?_m<}<=e$A^z$RixbAJcwU}bjhcJEfWoTduaztwH$lZ=uhxXS3h ziAk;A0PrDyQTAbf@mmCTc{!e6E6b7O=zivS*ljdc&%=g7SNv+4C-&fb4(5a5qU_85 zB1~HDeilRK*{{&x?_#X}_g%n8sS`W#bLzh?&Ks1~8`UNL7B!kx*OKU0=u5X+%kOt| z>)zGJp%HQY-cg@td4S)?v=;(Fz-LbK<-^^b*Lx!G-0|wWxW2&NH_o>%(qF#<;=}w) zpvY``VR!9vpbLUxtIOGRU_mZ%@=a`hF@fg?gMj(hVVMIKFW|j#Pk>ioc#_b`hTn|) zbZ>A4E)vrA;eLn0x7X#Nk>E>Y|GAez?%|fMx9$_yadpx!r@>V6zz|8mV%X3_&IjrH zWcU>@=1bta-tpGjSkwOYqP9Z&QQh))Cza{OwO{%OVCKhu-ptsPHQT$yZ-~V0*YGEF z%5U`im}QpkVwH1wkZ_yz{iRx`+k4EV{h&ds@ip_PG)i2F&wK4QqV2`C?QN1r$oQxt zvzBV(gb?@YxRt&_pk>Pt$&Rz3WF{U*m)|Cq>EOzqqu06I=mNZ{rh4=_8^~=R3D|BN ziEP3$=v}4Ob&{?mKhU`5X83|sTWwUko#3c-yvZ*5#ku7_Tx?_Qn3~bu-y0pDeCTQ@ z%=j%ju1@umOuT{gczjrIoV_jGDp(ACe*q#oZ{AdFOc$Ltp1r(*{}hG9hX6Nkc%?kO zx`c=Ln;u~DfLn);ya?cW0 zz~Rn0c)DAmmj1(hn~WivKlEm_dVl8&HFa;WR(F8R{bK|}S+07O_eW}IG|M+{%w%IO z8<+oN?uvGozqAuzI=YS5lm8Jt<>rK@rmy%mcYB(TvVuD1APII@8bwhbe9Bn`bRFuW zAfS;n^wWMtm6pqs~nG)Px*$B6<6wf1H-- zAW}gu%+o>FbTE)iqTj)!;YM6OU=OJF)+m0`kIv^icqhio7iOzF-M+?eF8sU1(D?a_ znxOa0YGnb!R4v>@{Qmag-T&LDbeLbWIbX+~=6F$OnxkKmu2+a>9f97n=EV7uKH#G> zmX9`+$$0r4aququS&M!=cK#r}3ilOfMC7fVhfxaEu_s8;`GwA1=RkZrSN7s-RookE z;;P^ra?r`vwR}6gK-oZTb0s$C$pi+vKL>vwD1rW_<^v9SQVWNp0vn z>*z}kq@$6Sa9j2}&?p_$U``a29Sqa9f=+0Ze;!`j<2Ac+npkWvE#}!PrOT|WN>^7X zX%rtX1)k{4=vmTLgU)B93be|!i_>Lz2|UP|Vx(bNG`zcr@|?nHo6}7%{m?0Yb4vcp z147{=_eaE#X4RgYzuM;*+NFhd*3stN=lnuyxDxRU$v^t9MHrMH^rf-`ev)*F1!O*6N4kFK(l&Jf<9O0~qwD?8PW zwZvM3(ip_IC8f`x9I~^A9qz>}FQz|19?+VU4T^31@(qGCl6;C7Tz@&4L-Z5=LR#>i1T{I<-Y>%&vL)pSdImZ@J(rwKUoE+RdTpKz4ySq zF{ZJqT+gz7d!SFOGDM$Qzejhpv1A1Fq8_5ZcHU}(`~6(Io|*2{cmpS=Stty&Q0hlZ zzA&e}c5#ZM^^COXbY8SVGFW9U07rbUx^l|DxcyMlgdC@__UtWw)aZP{c?8A3D39AV z0r(7~b|M(fg?IEN5WXOz;B%W`YjRlt65sW;X5Q6I{lXCS(2vZu%a)3!=t%#O?MhJmJ z2J3GeS)WE(`+%`YT*IaV<~vcpGaachj02`+hCzpf2FdbO2)1!r%~3Gzosu@gpOizY zniW`~6m+wEQv?=K6XXWQt~#8fjI(v4k+K;1#VP z7DXq37n@>C8UWfb8dX5Ynf9t(ko4Gpi#3K{hbOn=>Q(=5vzbV}7|?8%MyUmY6T#+h zZV+5fB2xc{Hv^VQ<=U81HB?-hKn8<2B6)XyA~ZaM;+Js!J&)#u{%m!5;RqZ>RtP3K z9(GlG0aYV8eX{oo4qj)J+coFl(htX>eU9UIPTGah`iEVwII)wiC!20jzDaez7*e0E zuhrfk|1&~b(D7FP_a0IBh56%$Ezkd-${W`I3HSW}OpyLBY|lm)SU z5fXTR7+Kl6sI{Posw8c@nn)lt)BH*kLpM2#+Vzkz`N<}ZR)|7)IwhSUmCC>OW!tET@~PZnu)jj|=bf+8uBHbf;OaS&yC*jajGJxOmm#3MrMs z(1{D-^7S(2r%m+~CKj>A`H3M$)5v%;StA*lUx_9x#*M1v+<|)<%l?pKKNAaB?yS{< zW-8!j@oc0dEsH&`W(6R=@;xgd&fD!rOxZHr*}&FlGv|%=pEh?JgSieb-C|LcbL+p3-&FW)A1$R_g6spQ& z-&2J?FM7iW>_9|hC^Lf)#8uF%CxT@~#$-ju>I%kUMaAs0hKOd%3+JCC8OyO^sw3y; z8aAD7)bN-inU>cg zM8cgen>9YG@{HNz(jkD;jL4W^mHQ2Axm1q~ZuQ&BhEJxt(S*iaxNTYU5=ym9_?g`- zh#k>}PA081s6%^sLWrP=Y{O3cG!7{WHw0=Uu^(~W)Yu4 ze2|S5TLz9;wAjDA1L(POWanjnMjNn!Kc$3xs!#|r1jaZshB(tf^ubS2z-=aXvQ>Y(QWWtu38p2ro>$Qt{m!9cK9@B0ve3Iv zOU^@ufgqDGZ8(MRl@~*6*go4)K^dI29?>;tsqb2?NC&EuQ%7;%^C2wbf&sPi{1S*u z1>X526<3vY6jD&!l^7L*uq!gi%Aql5I7TgNf$YD#qM^z~^Ku~{Dd@$}iTVOT5VbHF zUK3WH>AIv*ER4UwAjHu*5EsUG^AxIaqcfrkH4;%2@%r?AT({i?Wh~G`GG{_bWX(*A z1YIH=ep+D%J84vK@+f40<4C{=-v^KVE;zPB_loNYwOX6 z;0+Mt;B=iK69j!F}^M@Tbbm8tw$yq>QOhN@JJGj!}7xjG7ADo{3SiXs8tg_z=Gy z@`E)c$RmPbSR;Ip2m|yxbR?7tj1pkxv#(rj*iw4}gdNaDf(-X4(})Qm?Dh)vOYG67 z8UkC{cJXP$>qghI>T0>N>Z02>oq7JY8MQmQ9HjB0#Xzx4YB|Kvqc{o7l7PTRZEik5 zt~G=q9X5=O4xeT)y!{|J9t_{)PAYd$+Y-MlK0s9WNAG1M?4n@-kQAVo;Z3$-1`hk# z`*+47u(`|U$70ukpAF8!Zi~O4t}4zA{-IGX0hzoidgynq69QAcFG#IM?Xq?uRxbpx z5@q;^JvAYe(ruA@Y&L3(GtN zKbDSu>M3ZaC|2J{QtoQ8ww1A=HL{^YYIOF(vsmL?6b&2@2&E-`{FmOANX{F?5U1>Q z->;h<{Sx1ZxuIUg$jJAaprpx!d5Kl!{@ZjoNcA=u@+WH5>yxv+I@pA^DzDIUt+2`p zcGj$-0s;OITne8mbc|IW%zOid=wwa;?MJlv+G2spY*-2(jpnn~e8Wz73Lk~$v-i7b zzdhD{zi5A*SDgWaMW2c2*Q+jTf3kw&WS6wgZbd7%Q#@b(TJfy&f)mvvu25$VlI`Er)c5qQY|ROKFf4!s~qT zW-ezdGa-6lF57N?84%}TCX}A=CZT@7QcUlSm;lHsA1f)Q&x)AvVTvwk6q)Jw3s7f4z3RYBEdvHBXq~?=T9SX@H zHq>Q4@$g?=4k3L+P?-JGJV&{|y~H47j`qHRFh^=?k~S_1Jp&qj^n7cyIvl%TY)Bmy zj&0})YGeQLYrHyQOJ6MgL4Qh{_$8Fqp17e9b`)VahJh780v<;Amb9MCI*2fw%Rmc2 zsosR|iyrk)m-!DAtx=wWZD%3^{4n?_0;uaRud73H!_i$KJ)-kV5?E3Z9Yz$Y-qBMT zu`C1}?ZW|k)@vda+#quiX}yPEaYe)M!rfI4jgf}9@rKnY$kpk^J{ZOsn_c(f0JN}xPPBe6{eC7Eyox-X zDbI=Yw}aFP=+((-I>qs5l1=l0>efSg$swKhxIN=>op-70gaZ3iW#CNmdI5;{fFyJl zNnSH6IHo#}>p%i$K&}V=F)b+i#!Y@bLg96BjcW&YJ0;!oFScN9&F^tTd)V4iouZz zg)_m0<0C?a$c*gymi}DsUPbo)h4qs5{L}q!D5Yk5)5`JA4jz!Q<~`8Is9PC2O$dAz zPh|;W7zj3c8x<@Z)DaWn|SUIPBE%AC{JvtX^=m@LIp*I2L35SKkp-(9=3`NJVqbe{g zLC0t;Dsu<2JZ?p0X>_8)KdGpXr4$uOM2C5SGo!W5lOfQC<`8#yLO8Bh#xde_fl-#F zNjehgr<;8Y!!KQtsRnh6l$T$Pw8W;0hgJov=C3_k_0q`^I%4RZS=J9=@JHEsM!T>t z=gAXGm1E~i#!tB8v{vC7U&5gRNmC7S%#)rW8tf9LYn16GiN-0@2^uXDrf(9=ld9vi zgwm#Kn?1`1XEz2xXP8-C@L{$GyQVX9Dgf^1*Q#5Ccn2Y$YNEiC1r!+myWV7C` z_JzYD_0hV$LjX4#86Mb#!fOfoHA+IucUw-u1wd;SrrB#imv*6gxaM2H-V{}=3kBu~T3 zsBPgP?(`7jEzI3x&w-gvxO-(MdQq>0;5&^kI>`wKfN~QRyyN;dWCsjbBrw?lbDN%b zVoc*ruZi_71gi4&sxY$r^x&qkvP1Y#xi7zc1*q?+eCvco1%XQby+#WPd;9VuBEo;^ z6GkBnWBC><0M?C{8ya+zmdy3J}m;{yKV+*!Ywz-x)_0FvT;#f>KgTx z8~iln<)_g*%~?Wm6QiSPwKl*EEF^IP$n5-I?W;*Ud7xX4RDDMl_gs>?lMMRB-6#Oz zwxQHKeLBS?1VNF3m$ce5+T;W4^8=Cl`Bra)@atKua{YU2XS-oUO9Sppl!lDzu-4aG zm@skPlC9p|ETp@J9p?Ay>rJfNvb4%dsvbnASkbSg1UtC;uKaDRD-oNmkJ8S>W6}4ec*nYfGLTYGz9Gbh=5` z)*4^_Mr#E_@i@(N>ndAZozd>ZDL!9kV4Dd#5r&xp4t zVqJZgbUH1)F~vm3-_5Xqg+713%2ha}eD?^?u84Cou)8}=WC{?sb|+elmH zZSZdm{iK1f`>L?EXzq*{3qbEGZ%?j+-LFg#?UvKc1Spk!HrJd}^)tKcyR8a3Da_`v zQ*5Dk*2}QJtJkXA|E^tzwW7w?tL&8-q{np z?wN3Q9u-2q7Co^X#rL#Z5cVQ`U*AytguJ=BV7pn*ReC7%o!a+g=7+-)=~;woo=N1o zyY}H^>1fe*zYt9G`ux;zSuPVe3pr+JH6nNttNy&L;_y0nOAG})?5p_5x04}pJU5x9fPahI~)#{VXJOnwoj} zn0#6ySUf-c7@+ru0)m|L^j5KP`>v7aw4LlN{bn{tP>(0mz1F)e`?|_fqbY~FBr`j$ zuvg_UPhQ1$!Scr6klT9wAd&`?^VIb1Ww!QhQ&2!h3){YTK+ec3c@Av-fQJR}GWsl~ ztnxIiOPSo}zLgv0Flx1K+yDFZX4W3eXHYce&)^j$i4qC$_b`{u9pqJ!;MLjrQ5W`r z1D31(3}RROI|e`=xj=*=3qIuedX+JC<8 zxL7Gm*d-&aEV(N9LA&$k`<%VPd<@W6fVQJ}bkgYdoc+PZR=3!Rc7x9;m|W^g`F+1? zac=5NfKBKUq4_3tRd@WiRZ-kfucOVv1&7VWb%VaS1iui|XW3mc+y6~@-YRHt#@JUF zC-Q>N>s|+k%|OyCdMo&O#bZF%>T^k1L(#R~cS)yWx`R4&iIA|kpe!7(*W4q@)AR76 zIKSy4OSPO_k1ld^>HF7`kMDMh{C0KTHGEI1vfC<-Gj=(_v_-X*3I7CE5SdyO@1_Vj?;7b^&~mjSOg(N|Y`%HOR^`o+dw59vh&4aUySSn@v*+B z6u3Fnlp(xkFM5?J3Yxj}y?cC3N7RH0{h-TW3F1$1R}+}fF9mBW&L8!{T1-A)ySpgm zJ}r2=QY|(?UlcXcW5(ACey%0JTr-Fbs$shz?X5-%ekW7$EB+GD>lKNAKia#nUhNR( zD9aZ0nbei@()BEvZDA|5-KO_>-8(3J%tI??>b=^UbGx2inD1;At=ekJ^t?FUgJBaV zCq`2IcjZqb@gPERhzsK=5|=IfMofn~4eUs z#1HQ^Q`LKMlhZ+dT|b^UKwmirv~+^`((F|!!6SoiNn$-EfDeCYwFeep`LM{84$3{4R^SQ|e}KG~8$q|KOnb z@}p@||8BpUrup{x97X<2ed7HGI>oOM;UMtDv$0=$@mIHEoqw<3zPcodVv-)A{I$r- z1@+|e6*kj2SH^-v5vO;EQ%Fn}{@$wwKOt{yYz!nFWQ44RoMnV$ex3uZQbXiG zaq{r+km%5;=&-13q-VX51pgkEhbC1qWxow}|8=`VtObegup zSOr}wDP0ztVa?@n4%%9Yl+9Ep^K!!P*qkdn&q!Yk)a%1%A z%as+6iHuCnY^PBiiB}dbu2q1!?tmf`xxDwSS_FGZ{1{?&Y6&usK_0A|_sQ@-H{MD9 zV>h@D^~`~tA`?RY&orN_5M$**8kcyZwKTy{u}?>#`}Nl z2KsPI4m`*J@^jr7?z2C3LxV*cd2n5DFry&0u?0DctY^abl(LKbDtum?F}Z&PG&E)8 zJ_ZCt5Hzd=da`VsPb$73YuKeOrXC%i&LWKa&dV!3!%ix&X+GQOJnLJaw4yUH=MR;7 zDICna$}7{k<^^-rK4*a2y=$q?@qv5kBt1+FFBBo#u$iZ|M&Q(_*j)nA>MJ()7*YU1 ze&aNW7ReS@%;6AbM=B8AsNf%=?bKuN|L#Zms2!#fm&1#x}xY* zDG*jrC3orEkrTYq+Kr2BkjNY@PhYx0%LEMiOdoW~Sf4TAoP?PMGF;h;6&fQ*Npn08 za$fa^WeGTjza~El+`kk-e6+GklrWH|vfpVW3WRX1d7ua;b7SB+^`;$VH|_M9yKy9q zn`0lez(NttG~&VQDMx5w+COqT&S2)7dl+-S2w9^U|B_sFEhbKO@-}RcO6O@v#kJF4 zH}TB#>uMUX z%=H&!5%B3c{73y{d|490DLY6c}HaPN# zsCTq&wqP=uG~gucS;C2KniKy*W~5*W zA?yWJU>R5_a^io=>Lkm{y$k^cZWN|p)$R~N?3<;7=}|=_4eDU5+0||_Y=Jq-ISC%# zs)-zLod=h`nmn+ZJLY{V7TTGaDd4DX)Bu(K-qntnLp_eEv1=2S9#McbW**q?+YUrG zDAEmza=5fHHwsIEpH}-XJ30B$iJJ+I4^i+>4Wuu2Q`=TxH6bdazsOMU_8Sq^K-76i zOmIsqq`Wc$Z38?4k%qMpK0;m0$-^^kDIA@Doh+yioS*>OsXV0^C=lHM;g>HFX;(IQ zqaP;>LvQ#BND7-65TrhniDbl6s2$89<-9a+L4a>CZKqJ+&|r|_h=el97#?qw6V@xX zBf0=R8++=1>q94Qc@Aduq#n1L}!4*-WBFRw<=JPQ}n02H8w4wL?zHkVgmV2`Jf2Nco0ER^)v^>IfI!=$aYm za0chB!agqx3DKObxtkzkBt(X@MEqfBj8vFLdwYuWCcFPiT=B|7KORVZcD4JvX>dE_ zcDJ@1O$CtL09MV5qYZQ3Vn*+4qYoqEn?D|p>-WHP$u^)#!srKxkrU7sF-L+LZ^!p{ zlGm*Y2Xz+xpk)g%k+R=W3jE-QyLKdNBc$yAOhw&9O5Uwe2>ggg-HZj!lnq;@!nY`# z;ovsC_%ZIbo7TaX=a`6*>(2O(k}jp3HA$`= z`zRE`?9)3=8-sZA6Wty&?1yTQfG#ocLh>6pq{p1MGmK9)k~K8z!n|#;Fkgm10vUt! zfaNFPnto7MYWGW}o445AM(tj!=zwK;Xgx2qjwdSjOZ6M>oE;a1-t(A%WkZL|e^L<- zAb|esmz;5a+*aTYa1w#fWF;w@5?K-s;reb6hI98^qj* z(jjDnJCVK6p2@^h)^Z~m@kxM^VAXQ77>QYgp-qON^@pL2hoKFJp-qP^2LGqg!LcB- zDbhNZ30fJNt-i_`mZCKsU)Z@btj8tS0~+EZPn(}Z{p#;OBw>^s`Aktv(4pdZED@c5 z@<WgUA=UGZ7~ip*Ke|6B*(|>FJ3@uKb`BFk{Sm5otRT&w3H6g*yEOIt?^1 zj*2$z4?3+fmyZ7Xm~deVet;o!ha*LXmFa^S(^rJ`rE}y4XY2-N=!S*mBZTEc&*&w} z;3aC{<+A7{ir5gRa&H;HqM(q*QN|a5>1T-F2_1J4G&ubNsdUHYeh|V@Wca0s6)9n1 zP&YDyONYp$pfW#FC7W-J{DfP_36a$xNP`XIq^ek~wjd_DDT> zA2Tvf$n=+pDT|27pNJ`zh$)=tmRW>28&KTfurMjE8Ic>5brcS&j&bRX;NeGEj>vu& zbzHO3iJb%DVa+I#${WUepVN)QQ^x?wkW()C{advJtUS#Bkc=d~0Vz6=pKWh&Eo_<) zDO*Y>C78me94#uex-_raN^F}juRi=F9kzlCw4zz^SqFTH7ktSZPQ#9+2A#16ow){` zsbY8NCQoThUZL=DUPf!#Ac~*p083APvpJ-D8%ORR+4}Tf) z8I0fQbcahjW znCNCb-_n%hr5$^S*#@&Zj`2%q$>kEM5nvfnM(eCX>l6~TL!Q-!^FH*J+%SgRFo)zdfaEoS{Fp?XIaLJ>c%TOM?2Hg30}Mvns&U`!1D)471%p*^yM%G_(0SO^xo*Ab^o)_k9eChaVzHQyCm|=o0#E50Xg}EVFi6Ln#>_TsK zO{^ySKVyp!-6anH;-Pw{Jk1@tQ#MHq z1&3)syh3lXj+1Yy&V6oEtlCUdy)(^p^TA(pNf$6d08FOO7iqnO0FwZ~g;})Ye`v=! zlT14`lE{qQgn?HOyv}cv3MjJS4UnO7lpx<@f+@Ew;@20Qf4iCj+Uq zXfg}GjRG!~9~`I3o+aOb*q@vfy$R4WL%sg2BT~#VsyTz%G=^zg-x+L@!R(nqK8%`0 z;RDn?M{p<7U#k$H7dTwp&AR&%@al!!M@;p##~$bSJM-Z zcbj77%SBNNu!M@9J&9cEFJU7{4evIqT3N-{BY$dLm-IT1tleA73N8;l7N)O1P!5piWhxePOlI(Q1GsMtq zh(SD?$SPwkwlsp!xBlrR`D<0~Pv>8mij2zg~zPZ@>14gvx`s1YC+h? zdL`IXcyQDf4dWRlPXb3;T32r?Uxl;fzK&JV$5q!Ba|P!ihJEj}GJSZE7F5tIC&tUM`pKFl#Qfoi!Js!Sl?s@Gd(pXwjI4rSh# z#+v(m!_Aa%t@dKyQ(sSo=4o~~^sa5)h29xy_~W>Wj>@|F78`pIB9&aTWm)LcuqUnG zo^Fr653L>+x4BY<%T3LAlYTQv=TE-ZU#A$__9>~lFl+Jy>4aaO6lOyLNG$pBDEZ&X zQYbelJjH(94CL=EqSD_d7ItV(RNGpizQ67rn8uGVOvO>1Amr7s{LE{jtyr0>3epb_ zC!p>peFW%^o*F~&Y-YtaFw((02r=GT*p&I9&$?HJHIGjqf^AbrKRa7gQ&=p)IjpVX zpN+O39CBhS84ZP*ime{K`pNV?TQj^+te_V=Hck$ci{Sar59`#IQAG~Pl_jr#-!bIhNxZe--|E|SG;NG0PS8!XrSI%l zEta%ZN_a6Ob6U(U9iZ#kdiMs|dbkoamtV~FVr3c83(#IS`kI^B2Y$Kji+d< zb@UFWex-Cd?d%a>$ye`aY~-ixBr-jV;(VCle)Hp3XSMB6^ur_~XVO!D+~igkIf+|8 zKOOPK*AM}U5w0TH6ZFJC!+s|8bG6IToNaCyVdvo3Th3O}x#RI@hj`ukjVyd2oVyEu z8`pe4Py`KpPS-q9WCeWJ`hO{BMumMAs?tySLO<67s26S?LEofbessPX*HkOlT2uTOIPHlC`hjW?Y64*In_t&;kzGAkBuI=%`&0bot zTP+G@xhHq1`I-wk>>eE%d=*aDZSs<}*G+8#YcV=>WadFS}D{+1L?qR0n&g6`AxvGRuXLrrtT*m`u-(JjW;Gv&khq!KvskIveC_%hRZYF{*+8>&;C`QV zDpP2`x4?tQ+8m3|d=`tm^yhzneo};72$=8ROJ`+X(hRzykQ8scUf(l_*MH-}R@$A~ z$`Si^E1$0178?kV&wP26X6xN%XbE0SuP1o!Qmrgs2nem%$ImC;uKRunfPDXI*^0*! z$C0nRqYy1{JL@~*`E$~?;QqqloXPF}ei-@U`|~m{{{@@(C6uPcPxkcS{^e^d;q(30 z-hH5;5#xs(C)*C_79f8^*|{N+JpDQ##C%uzj>4$EFWsy*Gm`l6E0pEgS+_x z{A1T#XIz#(9gh*O-_zgv{RRCfL#DvL*Y*k6+T>U8b_w1qCvQ}~3Gc)8Lqnl;WVu&R z#@6)@*W}Zms)^l>ZWfol_o%S*ql95wXn# z3~(E}4cZrMs%LNdy7sHHc+IS}+)QtW-qjah+c_q9^WOoz4gRKJ#=rZaw)a!wMoFcz zC8?=RZkM`Z&nd~_H5h+r(f>?Hi#!=6$n3Ety%-x28yjhp5JM6L$&%V`O{S#Unwe!L zBhi}mFXiOA>7IEYjlUJVO&`v+zdyY5x^=(%bT3PH?&~$D!a%$@F9kLzz7jAipZ(x) zF?xsBCSj6e0+NpYwEyN0fzr@e-ElW_At40&~>z3_hL#rT!3-YgLsNP zDvl^;7*M!8Nliby2KvScPd^50{(UjXVI};bHIO0e5K3bWPiX%>Mx3qvdyM_&L~Pta zaaO|XoiLx0#Z1nJZ~Z~_V=lItt7?-pO%?|xzHBRk>q&F<^~|j1L)OB;;{jvFG2HGZ z`yQEBK{K9Ul}FU{{_v)(Ei}iv-EQi+(k9QrZm&A+$j)FicD|!97p+1WTU(W^S@1!Z zg=6Igz5+ht&4Z{F`k=JpcKoW@-`RLAeqzLRCmYeR$UOqmsG5pfxo#_B8%Jb66wWnl zw;v~9zu6lV$4VYm%~=O!Q6S|mfzP#OT@feVvrg)28wEY{ZSnK7f8<4BGP(ZGqft`D zmrgv-L!v6byqOwo=fUTfzsMWJF%TUXm<_XlkKcyk^}UPVZg$-3#YLF>*73Q6?>vo9 z%tW_sI&0s%H~ulpce5WVqVF~R^-eSAG!Y-Ys&oHSu@R5wF(Z?aX(`X+n-UX z@Sa(mxdc&aJFEdcR;*f?{)daaG$(AHeAD+8{v0Qn`ZXPL)2F0~?Q!tDF-F&nnuQO} zx6yK(>E!9)W6v%2n7C~6B7%Ltq7AQZaEg0xbCGfvJ>{H&S2>~Ox)fA~Fu4=JC3+3j zQ3If{q{E3#di~MIxMjwPoqLJUi>z?Tq$BHhdZ6dU#*G}%apiI<1kuPM`!#Q(Kmkj0 zyR@9nea=zECZ~XtjD~H$G`$gyX#T{JJs5QT(ge={3nc+OfegW6VtNKn1R+z+J}N~~ zfHwrex{X|^E({s$7$6RstqWrc%7>Ft2PKQ7RIHH~)AVSd%@gg#hcPXsQ2uw-JM}|? zozi@57*P-%YQ)dEncOg>piSh6vV2h_5J(&nnZzV`u>|)PRHHIVMbY3;I)tK#LJ=e| zd{=hLv7h0@XFS%XGcYva_n3>O8TRPbltxHujmH(;+J!-_U0q1J5b95<2jEn-hp+xs zrlnJj&Q}zj!^0Jqo@!?iVLslv@05p^y6a!y|107+0Yn^SdHEv**gt+ii~fH^9Gw5F zTkwBI9RFQB(Er~fj)OQbvw!3NCE`F?e3B0ZCKIG;x=?WaB_yrB223{(3`QFqmlq-y z%8JEpF;DtDRY0p$Vz6EyhA89!f`m`}f+*U97x0^qx}0{bh&F(@gc3U9<%&5q-Ej8# z^A-QwTR)!XIm>C9@A#SLcq`Sy*EUljkcf7|e!jRnw?4OrS{4>wPgb%hu#fTt$!nE1f^156Z)DWg4)Y!$N~RwwYspcKK;ezp zB*FosL5|KVA01maVyG3i%^TZr;b+TMbmdk#f!9PS*TB~*Bangy z(*!}lFlBRcgC>3wA!o7yvBn8IwNvFVrB)&b9fzm{kn=;)h*IX1LT%(LOM=F#5dBpB z6@r$g9E=YA%L2HvBAEWnt8|>4`=EuGz%qz*IZHl)@tKloZg zU=!1k_%GMqQz|qa{J~2_lNoxB0dItVAxERaFU5*DLTU77$h<{Kw3XtVAC1wKpm;Jl z`CP!#+Pq@it8z*b|Ba_IM>@G+;TQ<8g=t8Lh=2qjrD(fpVnt9>7=heeNisJlZASWZ zH(JjS=qyIwLP(US3 zE*AxVj$PfKkaga7uE4;GL8ob5U)(oj*O zji6jXWLXScyeEtMgj&GtO9*R*a%-w)SN!~df&KYcnt`@kux`N;;2@K2e( z7D5O{G9vzgqW}*+l%m+l7&8Muw;NvOFQ%A-bRcO{k}tGW+hM@q*fs+Uxo*PP0u9UL zpBrRE`eT8%W&4;afhyTnt4kUllsLJ<50fmf%|1qgJ9IcDs$|)a0SV8Q)CPp4LhNdE zL3Sx}ZgNj3)yx69VxCju5@)DQkl)eAgo|-dGSdfn@;7jJoNodu2@$-20u(;0e<&#+ z;t=)V()x)>uUHfnOPM(IGl6QK^k;Y|S;RBRf6=M-$)ID%5JV+8AA%2iuu0(J91CKR zBQhpIMZ-mZ=EbHEDjr8l@>{3A1al2Nz*C;V)RUNU+k5>Ijt(RTyj;CJQ3$!XF6B9tIoyo3Gpq=5 z270v}XEaV@;O&@Rd-h*q_ntYBQ)>QBTM@v{7G8mP___79m=9;*m#rlharS< z#?Xlozd)3mr?RmKTeM9Z!Az06Sm70_?{2_ez>wY@xn9`9R=Q&s_Zy%n18Cy90*V96 zQRB7)313BkmSuM2wOXsy1G{Breg`e?!HFoOE1)};s6w6R*rC7~m6?lu(KQ@)r3M|eZIT5RMP?c;*0p~WH9XzZ5Be_NW8W>05sJ}^RGI_UPCHC!a-t< zp0Xt}56eD`M>=l+vq9?po6aF#0l2B}U&4X?pM*n*47I6O1P_S@JN6R9=98MvK%2EM zNtaldqH8r~c2*VjB(e5~{=po(b?8Ct`s~2v)4=5qF#`x^secK_864zNFy6m}1Ad!j zuwSkP;~vaaoK75Tg$#82mopjAbx0SKn}ORxSqM`B2YZ+hUybTT4cKOV;_kdbDXdrC z)GjmGQfH9HGtwX@;O!rPNX2bL#%M&wY(&OnM8;xN$Y}IvHt^GQ;HSuB2lH5>Gjanz zkRIATp^@byne;hEj~<^~4{V4J`I4eyB1liqSMxv>>b3mT4|k6P>fiYCv*W}tqf7Z5 zgqVDxC$vhW5nDalImtgD6dyRjjuajoMP_VRasSYdWU}H000NF&(CPQ^$xH>J7X%|&8JykIjT~Jhicb%333kL+rw4sFrg%T+ii5L-! z7$J)oF^d>Mi|i?e45);Na)}sW+s=*1na5W4{0lguV_hbD@a!b*ex7?Hb6qxerOpWf z0*+GYymh?yJKZ`!z(L}jQKUcyRQ?k|jSXa6T6}9+f*0$pA#C}CL{FNSKeFWp6#Y%1 zIt|#|s?6!3Odff$RrHYxkZS-hdCz^af}CT4F9G9eOf%PH85{6T_jvmIKN5pj5rJ4C zft-_pFByR^jkmOZ9fd*{IL5bQ84K;`Kqk)!GLZRyCG*dWYvkxdLE#c6p@iy_!B-o= zSDV0h7dgFbPwc8r>@KJFm#6kyrS#h|hAuOQZkxbY8^c!*v!QLfpaI+=wmpqZK^qh_ z$f%~Hhc&&;_U5Qn*r5gea~S=%Tk>wN%_0rDUwAqw4!M*=V#0KM5B_mgIXnB*-WXA; zz0Z=kTL{R}ZHhX4J$(paed2jF;VPyArNa*zwOHwcno2rSo}*A8g*pJlAv>3uSsQ6IiH2zn@PD=%NCi_BN8=r!j8zQhz1;vblzrbacvjS@R&Xbx3G{M?5A%x)ab=!wzt$mUDv5DAs}+4ZVGd&7>iD zBOu?1>>8b@Gopz%Y)?0n`#rlhaT#cSl{2}anBIz#+RBpJ3X|0eS;6Q>naccr}X8<>wGhkOtAnXm3I8C zo_1d|$+VVseCR3&td;rUM3dRYO=I=*g0*vWkyZxk^1VmJ;O5X+4^e3 z#8*Sbu={MYDL7(*S84mU9i5E^jRLh=5S!__Ejbuu4r}{H8exOgBo5KV3jS)&Xvx{y z0@mCd*OBV^aNr-we(TRi9l68*jOn{3V$J|#I@jvv*XdyEo5Kp)Akp5Us#%7mD#Gfp zU$ee$NbYE6$Ko|wcKjxzyTz}uh|goM+mxfCT_)ea-LbwHC$5v`{)Mejh@Gzz5vTl{ z$I&Uc_x$aMZgBNyiBB)NR;_}Z?S3={A2v+=>-0rqLTvqvh5~7ZkM^hOi>s1tKGWAU zYiGRJc-8m-U$|~o8LsVZgPUV6Xy#W74?mq&bJsM13OObBp?11Y7H~8bj?C?8`A)nu zH+{YLRh->Y?;X#m#l>^WX!@^%;Se^Q8bY=B^tXw@iI`2zuf`l^LlyVDZST_4&fz7q zR{_3tN*tla zai18`t~@++EaTh1PTH=PwH_ZRs6*wu?1}sJAI7@g%zp+P`8nHaF57na4d($6(OFEt z^?TVSd7q4m{G<>6wyJ9tytEdh47S!ofPWlCuaZo6x><*HZ>pMGii_oapG-||`>?Pj z3OrwdLvX}D>yJxndN9*Tr+)`4Y8T&+*bYANH8Cwg`h5*tx^#UMrm}Az)Q-Eccl5m5 zL`@cRPxiZqkF}H?@owJ;o8(@5ejC#)Mfr5n4qjh%QF`v@Li+YX4*!*dd3)iJNjfe_ zUfRdA%f@d?y*{j7`52DNie9=+?rgMlb$oaJmXYs&gAjpuhqFm$t?oXz_nWwgyGhlx z-`nr*Hg+AuW8xvA;Mwh#Y0H_OJzy1?8^Hehcm?cObFMs8zs&ME?H}Ub>O35vIHa(8 zjd9v{6nDG3JubE`zxawzwqu<^_PWt#5Ga+qO1(MI@@=xWJv*u7_qDS*)Hw}{Ca85- zlkbkKf0<7BQ}gWmbc^@B#&+d+t>rtPDjMU%soK4TnsO@b=QgT6rq9~Ey{d9K^ z!JqE1qCffW%F6t4s|_dMI@rXN3*+;lbX>5EUbJ+VI_ntSz{aol(pYmb`^EM#MNyt* z$zGn;&G>80_nMuc+w}3IKyxJfLkthY&U<+{Tkf*UYNpZBcY35Gu^7YaHDB3M`wCm5 z|NE*FJ+-kpq#D#K$WdY3m{(hkcJnX6@Lu=&eyYWZqq%#Y(&8=w5Z;{2Ue_?oUz9Ak z%;yRq2GR>VnqNh^vze7JNOUsV>h(0S=jOC+O{e|npY%%>ex@jT!&Pw86g%HD;V+>s zL0hZb$WC=2PQgezz7c1>2lOx5s22j6^<=f(EnBIrZoR&DlTWpsZ-!4OnFH+x0*7Yr z0&>Go6zOdp4j32DUBQ6JMjb;px&!7(*{)`qg3N~Kb-82yYY}kg+BeJBsauF&NbW7( zs)WV%??f@gRIkpc@{sHL#=OTD5H|1F8UyciRsJufw85Jeo$+VgrY~#wHFfsF==H|8 zyl8=qTCr^@5}%I+q4K&AU%h_6wEA*t-@nn@&26gJF&-yxFMVCht(qSrl~I1%XiEJO zLlwvjy29^0jb{Uul6E59jr;TXrHAXzcTr#PmmKQHgM1qAMakC_1gW~3XNTH$FCbkn z^~rXxj?YvZ>N} zP}_>nd;R>rt)`=EflO@P(+KFFyfrl;&7$UJwo=cx5N8eU$2lj5&y!edJDu;A?oxly zIRvf`Lq(lC1@>eK2->_<2IKJwvfJ9L9*rBOwnON!Pq)LKZd*6y$R-|cMV6W+$2|ZK z-l*oYo&jTe%gQfp#joiw5bi3#XY&g!0Ir!!5u>}Z=llMqIfUeTf^n@SZ9@{0_qKuE zeC9L8@FTXN|HW;D_nGE4X9}5(R>g}533Q-}4O;$V zlO!E>b%bh7VMJnS9vH(fu+m2(6-zV;?MmsMcoAXPF(gt%Fhj%xOtA4;YvK9kb~!Bf zz}I7rc8}HRkFE93&gajr3Y(omZ0frvoh1ba(YgnEG>k*|@}=RhJKnm02wM^F^OE~I zoC8&AbecE5f~oQP>a!AKr|wz{m5n^K@{*SGgy^J*@UK!;M10$1jl=SwA}Q$uTXyWQ zzm@W_p(efEQUuOYomHA@7TirYVT91nsC_>e#)DcmB#Q1MFD{L7x+FtsG|7e;>=Z;+ zeyB>E2RpBnyDp8Wh!we}o!yY|`?Vbw~fCGhT@P#w=6{9 z_pL>72p`qh?>O*U`QGAaLeHb7YjCrTKsVFZa49p7qVsXLLUcTCC!8~Zk;ZIHJrJi5 zh)GrI>%J){BItWav><_6hdKuzTT8o69wH}a8$|2wmy;bIbvDpRFU@mIO{gx{Vw=eC z4zP@a0;B6PSmk#p^A^KcPg}Y7h8~>ppMp;}5a;y~%=6C{z15cLnVnE;udUjFSx13_ zhbB5HWd=7Lt?T+DV}d7M$3I8Mac|PwR$mvC=`J=iuQRq{ z5xUS$3l2ku6|C@F90i5w7eLDzvR^ZQL|QeeS&%e_c(fK0Rn=L2vZay?w0Az96YDjD zY3!VOH-9QSle%qZNkOSanHV1ag|J=aF2Q-TY2Qv@pW80{HCVX9Y%h=XNv)y|q|Y|Q zk(^l_bVpm)?Y#Xw$9F7cYWBKkeNP_7-Q;38%*J*T@)S#(QV`;_pGXzS zw#cY7cX2{u6HpU58o9x2>(6C(d7d<2&e(lOF3|B|x6;{ak#{u%bLaYQ1|XLK6KgL) z^3bM9hGw&>!C?DAh&4E}zq%lKbQ3ufUVHN3Mso|?zV?w7{O0A|vljv@v9sgGOnQUq z*uvx`KGI=1`2G4P<P5N?TTU2PeJ8 zekntIejEN2+SanoRRWHS@5P*jYiIWt(%h238w~m2@G{QJx$nozuj_j;yB!LgmjJA* z#?EOBme)1M+jsD;yo7i3H~qc?r>(2Eul5)S32l4gKZav-ZAf@ZWiun9<41YGUDQVcECh(HZ@oyps`vN|)XpVwzs$ctO zhgNEKEFW+tr)Sf}^1)X;whsu~frxid!hOeHVZ30-S)zhrugJBc!Z%`B#NjE&qZC1) z`hupKYfba9YwY%HCQ_GEU&;TdS2Ru&&pRoM3uVs$#X~G#NGdx*3hM&@W8N(OQ-;|F ztU;cFdGz15ouJS?7R3@xj?|C{`ytWVIbk?1?(r?K_`xw4w%wBoQk^7-_+V_?KOI8` zb)YinmQ^#F4lT4q@xgMWfHk8!QNUUn(_Nowcq;7zq4B|C#AuweafiL8D96w^T>Rmv zN$wF`r|6(zoklbOdo?qEhaVbllnB6H<<>*nYgQl!ztK7;P2j~3o{21I86E_W15T zMI(<;-J4Y!8TlC&YNYC}a>rQLCt3^5TV{PyLPX4qTHckf6vAZph`UIYkwUAY06L@5 zw1;26MVn~$uC3MPrnixcung*GFaUFnX-WXhX%K;zqG}QF{ zzfrFs^}RKO?3&~zF@F4j066W1|Nl7c{}1fd|463(Z`doscrden>WRM3Z(>05L&6~RpjKdGoBL=Do(r8&rCx$C8JB^z-IrMPHw!oqMJX&{LS z0Vu;T%1~TrF@-DXD`hbM?THKW_H;sDf_vVN$MbvEdY1dC_tfVMHwqgw&!{x$;O_3e z^H3#FnmX7E%#fxT^2kx~@_viR#Q~-xVHUvtmWv^sA#sV2dE9=?Q%c{zaB@0y)FFKL#iCe7HJZ8paD|ub@u|wQF{8EV6XI(_8k{r*FeEC1NXU8RflUKM zKYi#yK3>Sm_|?4rrjG7Os0xjXZpakEPG)>aA_F4x)w)LT4y$Z zNqy)|IN@z%5>4w4(o%&yZJjNPpc~G3fg0$+ZG@d4k=)jdri&|Gwf#4T{4wS~on>!w zMg!iQ|8AKXSo~eYV9cq^DW#p~EXZ-qlopCXZ&(I1u5cd= z`$;SbmJlq_pTo%=VpP*GlKI=VCi%=HA#%hy{I;Q#g!)q(SR2tSD#m}^=3~`EDdP105U~|PVkqK_=##aRh%IWK*~_6G zWKCi++fhlPp6H^M3y>)Y@*fn$>2tNO^QJYa?RZBp645xA43zFgq{AG$(Z*DgxosQ`o`JV^tdPo|U(R(kJI zN>|uZ1_lz%FFjPUwDM|nj4{ntJ8lc@oLz2ZB?ss4d^vLQDA>-NV~HygLGwuPu|izR`<^D3yXhh$|f^!AC#P6+gR* zL6;1H;*LlGf&h-j?}yn|mg>-8_8LhTbRNm$eC8Tam?X!5WV-!MYY!@oTre6i25mC; z)#1EkA7b}a%ii%NNWG`h9aAg7I)(eHc|dX{!fAJqTm5uoFZkBvkrBsw30Qhq`lJ5X zw4Wj@d;Y6AO{{ogBpw;J7f@Ls=0E5aU7kehV=F7ya~G-3Wx%~HQifak4zNBH{Y~Qz z@H*%}=vCHAqP5rmfL@gkTt=DYfT^S7au;K!U62v9 z7#euq(9tv-v91hi-|~c`xvlp+Yu$j@4f2^9RHI)wX*vk3cH^t+26JZT=X@{)?$deY zpyDnBhy<7-z$AT2HC#*wm_|b;@lk6uY1LdX_11L*CiEIlqTh!dyS{Yb-M1l|{%~GO zfPUlMuthJZxiBc56TORn{npup#8VJ@GXHygmUQkh;@Yc`y-?mvq-}5-mULCrA)4o? zNG{V3vr@?%xbLt<9|A^&`b)qgb@hf7yS*-q?af%HCb+FduT8S!b&aj*79H4g!%kPu zgaD}N7ZvqAgHS30s3|{c!fSKRghT){0nC_p9}Noi4E;L;$y<=`Nh>DxEJgL=y<~X} z_?e}l>;u&zT}3cNn5Usl$L|8>aPk*#*z+7N#YOMG@ML&IhUH3+udi=19~A*#Vc3`X z^sAu}7yX5H8VDfBojq@$H9$~EkwB^il&Fdha5x6YrBB9&BD_I-*zCgcdCL7i1awe2 z$otrYvI~Iwh~Uya9m50tZRsZubkb1h=^&GnPUht*!1M*93v1A@Y@_}`uh{=Vue!nr zbcX_M9HLhcn);&$f*2tMkZJm!USctk!S^MMq*s6hke&LHZ%FKBlW$4v3jXHBavNfu z-yb&dto9(v{N5roFt1ytw$Ur>h4p-$v^0oXZ$XIUOQ>zRUENXvdXOPTf|iyd7HGrtnQqcn}@1%@>gEFN!S&$ zpmVa!LrUoXgkGs8xg=EfSRbp{6WSrtF-azE5C)0{!VX1?9ubKi<%*hRQr=tTP4t^E zchisY>LhpsQQk``nrS6)X(wnhjAf9FStFXVx6KK{d!GBlp5<~^(EECr&7SjEvEl4e z@?rv*SJ-#gN5RPY(}`S6R2jmX^%>A8PPW0dSCoBXB~o@nvnJ*_;Ru*K`fNSC9ehfh zJ+mvMoY_aaA7S$>-)7X(Z7sZkW~>5WM<|@tM!T3LN;m#$bsw`80;nx&7*c>*i1ki0d*fTSd z#ZjtCVgD#B^$62eU5RyYd{q>l0?UXvQI1s14BehW{}pnEN7hH@!Hry1Vtb{bOVGKQ zB`LKG$}fhr)8bSdLhAMwxk}y_>!uxI@u<5G9H_(YI{katd{C<+Y~%r~xGoC2xM1Z1 znsK1Vr`gq(P?069mK>hiBb3)C6*4|pl0q#$$f~I#gOP6(f{A7auv8I=56Uo6n}q{p z_J1PW21?x3|3q+Uus?I%;;*jh@PRj-$E1lO_P|t3fd;U z=4tYin#CRhm{*c5J(VlLj)U~X5xO!>uSg+xax;qd;a-nQuVDL71wba31BNY5(OPXV0m8eDu zRWV~Ue6rNRNBf~jNaT|!?j+?#NRSx7n|sryB^@taGpzV*wFY%?sA&~Ub4`pzv7omT z`tUmDeMB;Bi#JZY&@yc?vWTuf5BLy$j}HZB?VH#AyN>Z76Q6vQcMv!~V~hFzF|RrY z3ZIY;FC?;qSj<__9d9HUrkHMir{4=v9gEmbg>Gi}5oh?3XG9xJxxALc4Wu3Y;5{4U zLsx5WC5U(Y9-RDaNq>jx`&94ywT^z{fkAydwRsMAwi*8X|~+S#~YlCxWMVY z7Ue09Z)*-U#1-J62ipuox{9M}J^GKGG}$Dm#e~ z{1k*ql3-7Q-|=fG`h!l$XBm(G+vf)afrlmsKL62UhDD$}==q+VWR7q+f~mExK;d}X zPqHI*5vi@q1dYwcgsOm`VIeXF`oQ0oG$Imr&_4*43o~QTA%oFb$Nk8+# z0}apXh2x%}+sjMr_RjC?imrHMNaI#ri%RP9`*57&!I*v3+6?@qi7eMwaE#_~ zwnTNK62nI{Ow$^ zDiqz~rTf^H1qPLyfZ_91*1}R&m9OgJ0)V>ppwb3_E#YjCTvo4?g+tjyhVox19`;t3 z?TI({Oen?bFG_HYU5lKz*l%7g4!v^B%`0~f$(O=MoiLXmJl=LS9J<+wwZ3xx-LQtv zc6NM@ZWv$X*q5|cO@BL{A}{YRv*l;Eni?i+I|dPl-|VQ6D+uboqIqMU9fy);q1A`% z-1jA5)w_FIrD!Y_8IAVy(7;r0Jfw^CsfUiX%ThCx6?azYo3slkr|kv@O~^c#&76tu zWMk$>zXyUr*!z6h<{3BY;*h`ctA#jfrZqO@Yzn;;-p?w5JG0c?nCy&>&>M?kXxnum z<4}_+iCX`JQ3Gt`?Lu^T@Fc6d|Ertqnakgfi96)o??jBUr|5N>$;alI&tAVjdu6$x zmYVO{+-u`I6vvMG^x=6ZMI){7dvDv^!#Bq+t6EM9`UZMLM&e~bYRR!|Xtx_}b$F~) zL-+Oxemq0Parkl&NKCo3YHZ-;+>>H3i_S~lV@Rq~53!N#QkCM-Hmp-bS=7!JHF43U zD1Pm!<}~fCJu{(JySMJ_w}r~-0o2gMGu}c`1;5bgD!k?Lm&@4BMd?B|%YEPpEJPyj z*+GR={a9)E^%QPxMP0_5`&&R({W*83KVMh6g|CiyCT_)N?KvStBJyU1-2ORync+zS z(X}?NO8vo=EY8Pn&Ys=VrfWV@pYF?QBF{eVLhfjKF@o+@fs$TTYUa^(!I7O#i;6>! zRHui5w5x7;{95ATLcOko)V zzej@Wun?FPT6|O57}e8KTL+0#<^oCoMW_otXUR4<{Z$$l#3_mS=Gpx0;efA0Ciuf3 z1{uJca#^g%`Z()L>B4r$N;v&~r0q^1wwBD_-}CaU`Cf9o&mTjws5o+iCW_!k=f3ga zu_G~ZTRJjg$BNnQakJYv4I(h7?F%-u{3@pa$m*8u9LzMNPbDd47N_%Ysw zH=GUO`v=tTqoXz2vN=EOo2olM0B~^ysKA?R_uo%s8c5%((+@?!cgY1@hx%)0R|MOE z+&q&^gWB$q*n9s}5{U*4!s=dq<1I5sT^-&pb69tC?FF#3u8@ z)^`QizwsK$%;DAadTfq+x;SoHY_$)~Ht@Hv{6M3%pKSRf-`VE5Rd-z{N>fBy=ybEZ zp_^gmICO?CdycZRdoSny-n;F(D!k_Nx%+%Ve&45c@qj;+qWMXYs1M4@9vcV#{qyNq zJhb?2DErgZ@&lh^^LU9R8$J2^D2qV=cRQzl3?xmeGTE&iyKOlzTiBst^APKq-TS=A zuAY)_&iyi>{n`?uqB4#5cFv{xw%l+(l3z`4exYND?xL|ZbYuUE?SVtfvV8M1Il8PG zZrfE*c{8Hcw#-A6rCQIrUN?2S{kS`;_O{V#l0ot{dcE0y%nruJEux)2qs*79aqwkFn@7LpB z>pFt*BMFVIIGRm?v9*l4 zXx^D)CFc9&Z6+V)s7<~p`g~>ude#*ThEJ9FTDCIm_S-4?naq@Dd*1}ab9Z@twa}A8 zJPyUxy7VV=mWoNYAFAhZo4>co3v_B+7HGM)bML{IeFt1Mcr!umz9RheDu*a5-V2@7 znR_)OhOS8FujBz>tBlC(p70TwRX zp10x}!QrzVZAtYfJ5FpK*2s$D%whD5VE7yLp7rBGEoy9XbR&btTl$nk3-7MmAz|*9 z&belnPSb3!<3ra@FRk~fOSZu2Yw{7RsZH&3manF#-$GRA8)BSwkZdr;Vnn-syG^rk z{)m4k1%-o|w8&N1MC$1#?7cg6wL*-NE5nCM21ToTCDr4Bs;F(hwsLL&DbpdF zlhN8DcCrU~?X>R5<*|l?w}}x@0>t5Va_M!>(t3>OaD{Vcb5_j$QKjQsbDHQQmAQG| zWnuVqmT9@rTUE2V&QJ1j4Vtw`+`jJD>C^6X5R6`|Ns0GoL-ylI_s6pXTWy&2vw@V4 z(yNUT>>$OF-(k&eIIeXSqYa0+IU?6>f9Qn%gt2JH>Tj#xX!xGJwRc4nPJh-36?&gj zkLp3uHFaDC)gv!gCb=x9d)H@=0mgNm5Bc8TKj#TMm&w2U-?w#adl_*FydUr`Fdy08 zyFEMyXOdu@BM4LJJ^c53R}>?o-g+)UQ|rI3d{5r-W)*FhgANK`jG?RZK>cFS1n-XX z3R^UC>vDRFrmo7{#2#+`{!PJsVJrdtGACsBU5BJG)dwW#AFUv~pNNm0vv;G?hsU13 z$zGyu^7CKuZuYxx^H<(=iN6M2s>M}$C99b3`oY_SZ+%YRy7;OzBetF1cJpqgQOio_{h$8XtkOtGalYXMOpIuP5Amt-1!NTx({fzY5-He zf2VtZsl$R(Lm=Zj48}Dyh3{jsW%U#iVSm?v+*=k2CS5^vy_Uh6jE?FgIPct&BvXAt z+VZqhP1KcuiKlpTvqYL{3)~~}ByB;dYXjboI>6#vx?0~uEn#E8g3zYsna6}lO}cuk zv^;u(Czxlz%8XL=8os`xw*d0RsXU`UfZdM}%VM9~{dooymSrn-oANtbza`u`|G*Ml z;-GJTRUYX!BYgL!n`kTc%x=%FR87}=2F~3#e?$If#U%=$&tSM$@r(KY8QA}y6&I%e z*?;*j6&E#tI^&~I5sVM zv2A6SOn6NB`k%z8%)$av0Q1<^+9TL|vqil>w?9%Fj9jMUA&rv8b_~U&q|6NG*wJ(;lJ@6EKn^t$ta!gonazvM z&vxU-W@45aW;%KzdcIiNK=y^U<~|gy$sfPCF5UV`)^iZXnb5Ss$+RF>=^BBTudG5` z&8(OPI!HKZA)+Q~=9#2w@-P?%jd*mKYtk{a;Lg3pi{^yF;A0yS6^J1?G|)uULMg-w zD~smj!lq+ypk;rn-tx~{qhF|)#ld>#2ZKouz_OaH;c>eew`pf3Bv}X0^Yj4eVhm7{ z$_ab8ZSBtN=i09wrjf7G(9bPKxctR?$(!Yv#7ImRYmyD9U6L|iqu2*(In{y_jb)lz z43Ns+e7U#jkKG9F$!4$&~&Nzx9M_klM}tj)mt#~D$YK`Sot%r zZlql+)>-;ZntY}-bgp=aJAG*FM~Ha`S9UMDDxV z$wFo*Sc_}{Yf~ymMZ~1$iT-p+8*{8gW^ktM*EfLE#M(W#an!8p*B=ggM`UkvsOa}q zg}>4T%&Pl+b>XjY*xRlve(6?^TT+GC!V*H4MME`Cxy8N`8|62JV{;M;mj)T6H$^ zxK!SuU;Y>WN=eQAtVHGzi<4Jk zA~=s)oaJ+acF9XWgJwukS({gwazH)|lSdtB8GD0=Uo#3p?_t1+vNKD5(}nz{0>da3 zRYz#JBhK?mehd0DnkOoJ%uoCyG@XB9USf0VB%lM_;5K2PsOGtk>jJsn zqaxn(NV;oMf$AE`F?2+-$#4OLc^o?Q4y-G%Qui1h0_s#l+ioON%pU%8snf*?)WO8$ zTLa4B75Ux0!z+nHK-@!jQS*l2CJtnr&@bUbH_RVrSHIM0n>O@0mI?^AdKedckE4`s z&#@Xyeb7I)g6Fh!L!4bI`48q2nLJ^zGQE?ebc{_h#UXQ*XjaO^m{3;AbOnR8ibyfh zpOPGj{t{{H*t1;=u{Ud-V`X0-)d-vf2Axa)!6gFr64)PNYEsjRg2iJTWsSs97lz>( zqlokwj9u&EG-$)PhR!fAw{Fe|++es$sM`K+bpOb{1pVE`D2B5klpSDB%c}&Dv zO@^6FhFMI8nN5aSO}Jb5-cAQfN$215u}e9niqb_K@N?<`ur$D#dMB37C(zHLyqD2 zlpno)H1CLwNX$Mbq8dEi1(N1`*yBuPiawlGN0P`e zS`7-nRw*1EBL1ru;Vsv}5{W@sya#ve3*6a&97(f&Jx;s@;9=!l-BNXlTg{aL^ala> zIr?Az{Qv1-$Jc_z(t^eQUk-LzEG=1Vtw}7cNo*}zEUo(FqoXQDbgRQMMbcIh1r>x$VJ9Za2U%KwtK?Qu}VLKfXszr(6E19kwAo zCXU|W^}Qf7-&%~KlUpnD*b+DMfEN7ZN~EJk3_5Y9J1&geaV*w1(^1m*M2$twNm1`z z2-DGUf1fxbkSXv#g?<1hol8fgsotE^)akffDqmk=+w3Wve|rKpZ{hu0Ug=k^xv8j* zsI+vgUG^mu>7C$4Xfnh<72z!=xltU`Cdjf!U^>J<6X6|%{7goAr3h%cVEt5N-ODpJ zf27l2X%A0T{~mi!UCM_xUexi-Be{tt2!w?Tj7F?Vg+58b7?}k0T?&o6WL8`+fm<3O zJL)RGsx-TW)g4rpwgLWiDv;iVt97_QUVxO{1?nT4_=Zw+2SE5D5Zw_FB??$Tf;wOW z<5CbUzyZkEbJ;kL>x#HF+u0U(n*S)Faymn=IX_diZ~fbMDJU((kYwrRko>3_WWG(9 z*z5UYQ~={zDS{*^Bg!-i=*O63llU4K;y6i@kBu#%2k2#8w6mpZFYDxZf_U2nobCTF z5j%-GxF|*)iH4rnSUT)tKr!8qG2}G%J%EUP%9;+An6wP%%=A}CRsymA){t0XSx!9X zG}m?^2BA5i`eJhqJ>y2J7pwDwwe>ciTP>SgF1wvR zw;eyXoj%-eXhs(NFTz&NLsD= z1wh|rJ)Nw4;mZ3ph$IrG1t+@0Mx&TZKTTW}mw8cw4p3^rlWe5Rb93KsraPap)=ukt zwbVug^jz`)9hYt{`bjTmoiuVMowP?|?d5cHY$NUEcyn!(pXI*^->EH zfT}_riS+*wvFA+-D+^QO6~6WbX_psSJeWymO(eM>S<`T{X0WA}KM-w0vLEi}g#4)z z!VM3j5+)!ic0t#^ZI}#A(iCyBIuSiDg^XHv9Fss9CUzAP{l@wK{i06SgA?iiRw(0^z%atc?=k*cU@Zz5>! zW8JuKi@I5Hl>3sFC0yYdZ{v+jsS95%aT{QlZjhzk`Mj63_haF%6S;&GuJ)I6Sgr1M z^DmB1rOjL^-WzD%zD`4Xfb7-ET&@lTI8wH(GA_vdVD=pCt# zD$iF#GOM>H!EK*g17ng3TlbrpRn>lwia+`O`^8}cv4KX9-o~beJ?k|lvq{*jAZaZt zBcii@z1xY)gc&gD2+}$kAVajG>0+s6L0iEcK9(E!2F$Nt-Km4uPgPp&yN8&gkMh9u zc?NAJCz`&rGrcV?)N)s_8V^}nZ=anlH1;x*^%t?%I>gU-J~!di{LQb|ly2Rrc;kDV z?k=C~gUgh`OU9#$mt6fH5Qg8YKI+}P>psur5La!O)Q<}{q*DZ@-lyV8F9*-{8Tj8) zQ*jCkQD2P7GuMjr`@y9D15-uxQ zsqHWhlg@Dw+j{LB_=k!}co^eDkYHK{M!moRaA*Cqq78XUV06D_@79Mf_6cDd>)x>e z!Hq#~GQ6s8TCndA!J2vND9AR6UTk+;e`qE<{Y3WH78b{k;_OBkwEPO$OHl*azUhz&Up!+}Z%+~9zrk?=E`tuRzXB+|l`u<`+{)5eE zrEY`u7iby?Y&Hqg;+nMH6!53Hy+ED&n!gz;rFn1wzFh76-q6Nmcp^XNasE-COBeF- zwbT(rV5gs9)zae1Uj00AL;Y@V``-MqQM)6}*_!Bz_DRD={9U-PxRI|nZ($l23Z`E(m zv|;-a_-o;1TidPv7I4v>C9qK;fOIhHg0n zne8q4rvZ|mlYTa5Q`gSJmU|W)0s|;9b9g6MdRlM{;oh-*{xvwKhE7l?BRy#KG~rhw zTNU0EU+XW!Jz~byEq+c4s%AJm{U_VSjuWSw@yvxsoZ82L9d@84>l1KxO#XRG&os!G zj@2S7{a=`PtLm3eRonzOmm`V2zKbnzdbNq%K9BZ{tIl*__W1k2ywTa0(Ql=2T)Z_~ zP4!ur8+Hp{xyWvaH#2YThwWo5NNOio~jM%r5u@h-#8CbGp5b&(Vwd3*ss(tNE|Nvmt(O0AfQ(tY?ER!F@gKRo%gc z(~S=nNAJm&bb*moXtQyc`zr`!5DGE!*3bkuSRxok&O?#Xf~BQpH$ z_Z{_G|5Sf`J&M`Fbx?X-0>$7o{him#-0?Z-Yq8v;pR8h~_ghbLH@@F5ruS2EbflZu z#Xt8i;ZF;%^HC$iR)27mL3+hSB177-Z@!Px-H~_;EC;1XB zeDVv$e zrJ$h?+Zk09_?{#XMJ$K}c{X&o034&0EJ+f#GkV2&*41nW?o!Zw#y4lyXSVy;-M{yG z4OR>{TF-OpQ5n9%wSQ)jWcB;!$iNE z_2}XP5|90%!RvJ)-;TpTtJZws#{6r{ZW;Ipz|NJs>4xkb-k(`c?U6XPSsQv?Q$4tH zX8K4FnZJX0^EDALxDb;*pL1ne39Nkh{IDhUa~Mjj`y64BTs6MMuHq}G+#^<8lkMyS zJkqa9iB(#^Zl5%`HD>hvUzyuH;-BRVhO*p&EuKIVn|eBeBe?1DyBaSk#xUhVS82VW zwf%3JOEQ&*6H8WU{gsgsf4jZpcPdMp8LYcVGv9H}=W$bPqSgZ$tbJ9RyjMp)*Vbs7amF9D^hz(t-trAq@0gHR4Q;48qf6vj8(@X@{!&zW11(=XfX!559x{hZ`#z>+oTkk*Ox%ckw>J27Zj9uTc*_&_i5c}wSF zxJ4k|vRtnm*9;6wT6y;T85Zfh!jNtrs)qcFK!L!{K^`=F20!OF8GT#sIFBKUAP89* zAz_>h4Ov>;22HYb8Qi0|yRp;!#x=m`FLp39Z~#BQ3>bBd?DPtN_^Vz;O*MU4BGK) zK)Z^PGe2^H5&mR_AzuW+5{y3O!Z;xx>pjn9cKr|8;I;H@x7}bO9N~-y_Z~iq9O09uO%F8j_PX<>h8&K}6^r#V0?W|e?*V0a z_q<$a5W0_FXbFzfLB+zw{4-Aqc7Qvv3 z!`?WWi;DA)4vNn2@#;+wUDt*dTfkDLfMcTVqNTz6_t#LS%On;a9oaYKvdV8tBcZO* z)Wod&zl>Z?xxQT!VGj$B{GKo3VC6IzWXo~jdlvl97)TyKI>VAxhD-&-Kvn?K*?+H3 z^53$#3;;|n{~Z5U4g~7~r@6fyBWp5DAJjHnE2T%0)P{kL3>}Gtn=bBZ>qeOwJ8eIc zxklDZES|Jk(CpP)u!K=d<7!=6N~=?lqaWRYw%tB8vnGA=&YypLd+3?O z?L70E|IOXIef=$HX_1s^P+r%Mgkn>F0yt{T zzH^X*sz}vMxR7ZE3Qk%1$cT*``?2{TiYOurD_Q@_HatW8M{EEqWTC>Zg1B~s4jtHy z0(-AA5I{A7vqZAg`LMu`C!IhIA*frUdU6-3Hfr-QC3e3+KBS0#&0al*i0m&I4ppBO z2t4_L229dPg|Bdh8YjODaO{|sdBIOFC2Xs`)COTya5mOJrd(k>D+Txg^+A+8(@*mcA~g{uq^28bDu8u1KBT8m z2(rtCgfH59(3}fACJC1}G*`L&Zgf5$%wT9nLWIK)L3JTk<|Ma@SiKHjqX#nm1^bln^$)UvP3Pf#WDk zRoX=zIV6wxw92LOsTC<@(rVQ4Qfep*P9-6!`jDsEcTaHx5~*ND z9)#)2@!2Q4cytJqt%-J11x@F!>Zq9#m`ZVz1#@*y(^icINPl*EDudqB0CsNl4R)Hg zAL@?V8vVYK!}hsRo1Op4T$W`$9K2@1IOaHhAvq8<+mTv(Z-a5n%NbbxT(#q(Oz>%6 zlP%I0QsOpG(YfzW`*mgO>`|bkOvYv2SW9h0YZ|#U5om5ogEoKT7-aSnrynw+3Q@N2 zH&9iTT9rT%j$>Pt7?oM)h63)37NnPdLvBzHDbRx=+74@A?$BcFUG;XejwKJ!MNwHk zM225kz$V2_`@~8fOT!9SkJ9U#%HcWf$jSMD>%8z^)EPVgbv6@>3Y&ZF898$sBo^LDmHQ}l%x4z+nr2ei7f61M zrW__A*(7@^EOnqI@=EfzZ?X1XQ39$?L)}g|F~lL3bg>i82b6)-*pM1n6Ex%#Gm{?y zx0kq$;*|A;$T3&Ni8LT78|Gyl%zl2#w-aQ8Ss3)JhQa|WWJ0VlL9~wk%hzzW+TnP;{Qh!&fei-;DRZXjkr5gt_1Bg+blbxa(?;gA@v zh>4fV1E9|2p>3n8rGEDYHS*g^P+)|~JI>6$WIWj^yoD|Fe8c?;AkaxL0CNYPQ3`Qz zna(9Y;G%1PL9z{@{}}AF&H$*>gT!!bL!-qInLscj$&7yJkyQoQIVQq@CKD!z80&5< zLRXTc*b-WuYnlVE7h-T+W|Fwx&0&VK?2#^Ux1IRS4?1k!ia@Hrn=z8A<+Q zbJ6-I{P`W4+u&V>jv&Z7JTTLSaSrY#OiqFQq6DzHoX7xOhkc=3_FYw|0T)Zzxda4x z8P$9AV?0c0L?{NovRp*cj83R&bw@OKp$=kE)CkCF{o*lxG#`jE9f-0VkTDuK8w~-x zv;F~ux-6tNwJO}g5)r_$T3^ivN0PQYXl5h&u~9t=&6V;~5N^GqX2Q?JXivX=a`ZiU zLD2xB{k~~b^(ibllI(O17kMmC)a8f<#hme#;o>orl-fH7M3u_>RQQA$r>qF~jtpM1 z5arOBZI?B+hDCqh8E%&~crgxs#~F|282m0>`UO{!CkxA8JEj+!=Zk{ni-P%!g7s@E zrk6UVw=$;JI;K~W$$y>Ne*xMy-sS^d^A&Y$(PHsncHm`_;$znZABb+#zX07`G+e-n zbTHaW!^x2roFcuMZUGe;lAJ!~;*Dv7v|*Mk*?C8S@l1Z-xX0pbF}owe8kiaUgppre zNGc;ix@924k(C0*RSu1rA_^O|2@ACe8#SJpg1}NPg^A*5heX_sLG(8o<~TI6fbbDJ zqNZ&Mhje!LKdiG#`qKb>oS7By`R+L1Oop|%rf5K}O{^v{MzfNv-amGikQVC#8%*#M>88!xh z8r5cGZZxnk>mC^MN=9^PtzgS-0)KKEddUqkqx=z4HjT_yz7@1iLkc@gbmlL-BiO+O}WU zQDIn7f33(QQ1_=j9Lel>6qvZS6SwURs2+A)(+KftgNqs6*83^Lw{gPnNryphWXdSM z9J0klQA4Uz6c~ZJ0h&b(1VIumAG|sKE(`ZZAlO#aVEmyS)TC5jGQtJZVTB6}NofP@ zN`TD;%_(>6f7@JiV^;{E@(Mlyu z)#nR_{g2IsWC37v!9F`C4%~4W-1N}uVemzb1;Hy{3Rnos(hz(uU*Z=N@_qz)3xYnG z>?~dSx}l}t=vfY5=Oo+cA-B5hl-0op>Rpg~K9}#MEQ5?V6~?F(MA{`FSqjf|jAz>Y zszAL`lxvgBbo93v_zZ=9#zMX7&2Wro-OV!^;2#fpr*F*@yWM7c=S`dspH8=4JWp+$u zy1}gM8Nq6s!D=fs-JvkuaTw{NAL+9j>BAoh2wcE|+GB&@QZOw*C}&o}#B4pNC1KWN zWnbKHUQ{APzi(K*f11;ZUsMTZppvqII&v%tcl5}G=UV>f7WmsRp#+Bw5v2@P79x+T zh?-txGx_S|t?U}cxf0(p|H-b+z6RkU>6i34md9Kxi_A9wv*ctaHsBXlX^wp5Qh zjF=77l4{E_gEX-FE;K)8tZ86J>5ses;SZedPMOf=(C|Q7jv;3)*LNWXzBwsZu#V02 zD`JZsS53)puuQiTMr>^j+fWu;!ygAdOe(nTqB~>wEs4C=r0x`??!=^mA(Hk&hPVZV z;Mje2EfVp;o6Yl`kZ}a1u>`fTTfs3~$uV2es)V^W&&VikMet9{BbwViWbJwg-;D6)`6{xT9zo z(S(}|I4vhrnqb0P2=hdNpIYgaFeisaKTz{8ehbI?(c7TzV8J+*fv~FC+#btt zrQHi!#x7J$lQqOsF0dijG~JltR@Vz961qT*qbC+ax8OYnqgEDEfh*kc(gY_%1DVg& zaA%6-$dN0Q?e5;f)__p$#WS4%m3J=%#3K<&lO)aGN<8@c4B_v(pwz@CCB)42Rz^Yl zjDmqLl3yac5pS=780rr#`b;v3p5v%D{peG5mYW{ch2`iBo#r{~fg1+%p^BV0z#O#Q z_~badaefWOu^n9WNu+C=ZsipP5C&Qf| zM*%?d@CZvyjt8^zw=u!5?&pgzY@F>C%};?})Y2mXm4(Ek%~{M-;dP#!M`X7UgJJ6h zR0#UK6aI3iac8f`e%xdG??0i#JB!X^W{_to3*O>Jbscy{kHgi=Gzj zdU6C%^!ZtJY~>{$ORFc#T>G9)nxyo40l-!s^riPFim5o!l~o4T0m(hT$-C}pVdB=Vh`Et(0;2Z=56@bn3h}N>SXJi z=kx%CJpV_gVo9O7GyeyO{Qv^f%pjs|qBASH$7^{9Ibz4w?BsLAX5+zPzCn+Zcgt?# zIr>LW6?eD-n%9fN2;A7I|DQoa8X81x|J6xBUvE8^ms^;vt{t1dC)*fzBI(GX*>&^> z(Tw!aC?no4u$G2Dxi6*}6O$onT9+`ew*_NWjMj{zPlxaYh1Ue3QOgwyAHLuQ96 ziRp?RJR}#f$gr=3qutFyz`$(Ps)fCE1 zcW|yX{+7h_g7RNaox6OgCI9N?^p6ZXdLAc~(U-SDfvWb0pV0TBM4lyWt7Jss9qIjM ziw}F|MkFgS`sU+Ue)s!Cti%);C-@>=)%1Nu<%{<_p1JA+H_{Go%dU)VKbv zlN;~hGvpyR9?V_uiijY+FF+qB8OTH56K~~tUPxc4Hm{>!L82F9Ux+@Q>%0Aez8?=;vjw5PK_9+eUtaXd{B+&EzPcfI{dc}uzk{!$zm3D@-tXi8 zNJOM@Y@7p#)$6B+ggUqLx3BdYcd!r!{IFFtug-T#5SYB4ukwT(1cWWWlHy-KqO3)% z$a}3*uQQsrCoN~+yMi}W#&wsku3LOm$OS2V>(~sR?ieLyILrL@54l%L0DG7i4rY&U zB@JA?J()dTvrIa}-DB9yBT@N2YQ~X~_2&evKFNKbTjwS_%L$`9{l)~=n7uuWS&-FV`Ta`whO-RAyy64xZ^m7QDd}}Ry=zk9uesFcU zPZ09m2K2qVz)R=Beqg=VE1%4{4!A{u@0v(%8&6vNA1$EYvqOSB2%UY~h6w#6SN`03E94J6J7;U)jjVL+yXW^m;`%r%?7g!(KVOfo>=j>M4XhX` z-YEOnE%s3UrtN-s>S%iD*&L$Py>WH@=t6DlS&4it@f+z6u225Ced-EixWb418`}_d zoss`bushpZNlhJZ)N?E=KuTDPjEQKnSj+7B%9#c6R>Me?N=TlgNrD=}5=kCuFekFG z5hyu;0yzapm;jy)7-0)MqyR-bB*CMNiJAxzla85!&JC0CI8T=lSl05i7sjoAlAC_@WJ(F0_Ay-G6qsSlY<91^FcqyG)@u(kBS|#~T}2PBA0y8id~WVa9A13K3;mQD=oh-iwWePevQ(&xh3%*P=Rrd>#D!3b6O+l@SA4JYebL z=w$s*i9{}pqyGN=2vhR`pfTKa(qFmS5wxePN@DW+>_T}jbJpq?t_{?Vz9YI8`bMH5 zYYYBh?kc0cdUYfsb#hi)>+}Mqx3cG)A0L!jWZ#>=+vrv$7L$HV#GvO%s+*lEt2$Q` zrkb51mgUxVxE`!5)qJ&oCge6dc4c~*_H(M5 zSX30O0Cn-pd-k!AU~G3CERasj?#bETo1T@QhPeGIk1<>F4@!yJ5@qg3B`Ri=oaf?& zmYB7C`}**OhBEcXeQpo~Hn}V2GvGKQhFwt1v#8I8C}7_)5kJvaaye5v#XJq}3s~`g zdTB?v6RSQeTKfgL^GA|KctnEV6Kj$pU}`S;dXt^SO`03J#37y9>_?ffvuvtMxhLk6$?zP$f)nT1Q8H@YEB8)~S$JZiDTCCCw40Sw7O}(HQSMJpYzlfsjJK=5 z8n82CVT2F|Zb_F{1Hgk>%5&E{hy0(~G_MkKS8WY|Qr)L6uC;38!#k|5KWg;pb@#Qm zL`J5j^?5HNB#LIi*Y4os>;|}VV>7>nNd@x-#Od};D-OCkO@n;B#L%89pG--%-++qbu? zLDoi&o+bjCbQnL|`2s_b+9RQUCD87miiixMLW!Y7CXAxC|0F-73gunJKeL0RlutR^ z+zjQ~?FM$2IB46RFJxOi_fM5TS*OdFxD+VhS~c1f8T^b2T(GAspj(A2Y5IG?q^*|% zj_daTL%BqdJ`4rt_IT28yxPVjVb*{y8GdLq=s^Ydw0Y9t2&sn^IAD|iM4x|1%KE0rY}E=E4TOuz-M! z0Dv*k{}y2E{|bcuqZssG2xA_AR?t7k|B(n?dV8v=O1}FG0Y_G}UBfbd_M(d+68qA3c3tqbsF+PMV6m1_|<2XY8hz4BjX)y8Ouor?!HcZ zpsMl^8C3`-QxvgrsVqvVCd(%&G18@$@CoB^sUXru1sjldk>V*)F6cKqX;P-}M`*b; zk+`WKitUgjFc9nq>`K`-Y{6N#krX@ZECwiqSl8T;9;4nP-c!H=%ZqBk3gt5F)^J`L zJRmgcN~JMNC0Qi}8kpEO?7)?9iyEX_E#X=FI3OR3?E9ed1qi!ouxBIQ2SHvM>;^jy zAdlt+fmXs)R%7&5LyT52m~6WkZ0vy+EOUJ9!+qm<#!6K#wn`ny()kGLbkQ6X@_$Yi zT&t8}-0pV7sIBJBDXcQ#b-*V+k=!L5W{lu0lILwrgwH(?Jwf>@=8FZAi({r6OH;A1;Lm(`0|_>rLK&GNPw%ERmjnd z1;v@Y=R)D%1vInqy%1nu&^btRSj@3XuvHl+0?VhejY-N8F{K(e*vaGY4@%8Ppt)oM zbsPoRv&`vlB{Zm&DC89d$c#X#$NV+QmIu*NeB zlavyOqvRwf*SlPGz}B)8o)T9^OKPNzRhVTCGWm*o%zo#0{hF)+TFO^a$}!^!7I}4J z$x497@~ea(^z-6>5@}KxzN<~a21S=Bmf;4YPYA*~@CR)Ywk^OrgoReVBueC0W>LzJ zQ-axBq~{jSonz)NmR`Y~gNSJTWpsnbBnAXotYoyK0NK97K0U5g`&G1b$`{F7T^%@bo62BCFIYFfakJ?0ljJ!+P zkxNq9(PDxE+a&p+nrIma$m9A_U!o&*L~^f+OA#dX9FdwGN5%zNOI$inaI#K5EF6o` zK`BJnqEbTEVJXM0O15Vut7m2Tjf&!7Y2JuzE_+u-vX>V}M!$6Ju!`kmUpPsn;9lv> zE{J5IS`n+XrI9v(H3o+?rAq@VNL# zafjU(v@?^-$3gYxMxfW`xSovf5;u+V zUFmj5-50uBlRW?w8SJ&Wt|#OAi1*r4-)=fL=XxT1&+&#Wwp&v!OG9+q+70c7k1@v! z@N}Htduz5M`~3{yab~N#3-)4P-$?KpMDUIw zAokS9WPK5MY-p8eXfKuHH|%Pwk{7toKXZ+Hq`sW9DvbM_X&x(YC#gM~`DGWqr@ z&M@7VMRQP&kkpF{U|-P-A&?AO*G}N){0i;Q@&$SjG+c)QW1xuUJIbX_gw^;HK1r}& zDS_o(Ecv$+J`7a%;Pq3z9_C-uwD;uoQ<_~YhW8UbgVd-pa}43>NWLP74>|Ds^PJ^N z+5ghUCXD1CE^Bm8{-KQ(avBJ!WF}Lz`M~+ko*>2!~AoB|J18YElM)swtl2WEB+QV7yC!6^jAL4q8^kk)l$PcXm|H<#D ztd|e90_2+v0~O9@don=|!{Ov;O?UJs#L-%&jZJjh!i0VmkN;=@!>oVO-7L52*mBWWVFN+5`$JAm+#n1Sjx6rUr`yDK>(bqy_X z7GwUfKVc`?7#NrV)Gge{U{FIFX|cj7FzNfX(DT11c$k+Pi$lM7)f3W3?q1Qn|* zojO7ke4?#}V%e8xg+!=LUo{f7a0(YJZolVZ%PT003SR?3i9-#yi$|WqC6lqLJOD0v zDvk#}+B3r>4xWh?tX&_1Tn7;smWTi<83~Uk54VvGwFL0AuOndOK})Fun}Gd<9ZOT#f;IH+?da>Kj$w_9x8@Ms^2JtllY;ztdoeR%@#AYsqx0K#U$X8$T-HR`iY8fNfTc|N$#;o zn)DJpYlxg1rH+0EP3?ncQbMsPqM2EMWl}=3(1K;E0zTlGsj1dP;N6cx=m6f>6lO19 z_569~-vZSEHojG;-pR$ot!dU=AAbFVp%ORC2xhWpBGB+_~1`&1`{5Xmx7@M{L zW@mNK#l6eK0bqp@ivCx%=zCIy*^TTiQD91DWh78f&$AA<#k& z6q|sadm^!II?Xa64A8xs#jqr0m1ZmHEhwT&klx3=Xjq>YQPkyl%JwQ{Y;7xv&Hm3; z0#A*G`!63S0~+?WG8y$-bNH#Dy$Rj@?w^+QgILV)gGEHFLa39av|Qlx+EPBJ(~dM2 zvMUF5%}P%FlJTnQgM|DGH)XG=sGBP7Gh?5?q36aonC=!e#_%C!wr zu}b&P>Ye*%XrtsLqk12@)I%5}bj2>RNVuT#pOLz>YBoTM7ua=^jhJOtf$(AaTx!)Q z)nAbndh%-iw5g^bjN<^WE5;yMHc*o-YT~ZaAo&`!-Uex+PL*Mttrs8})L$S@+;tix zX9a4ysghaHPPJB`ljGCMQE8>XDp8pH!M|aFilU*T{&~O@P66nRt;#y};St?zNv(-h zH6&q~;OA}0m-v%l4ICv3Ui|Bgi6+734?+Uf)kJ zg9Oz*?E&F#QczatuPXssl(2QzK*u114b;9%bf{+jksDjWjVs9ACw;}1O|-ttY(%@~ z5C>|EzDgm;W(c7cUchm2h<33d4&H%-rH80LZsactMTE#e_(-8q=MI?bo{!-ROe`J- z7;eCB9|Y(5C}FafwIS!ag9$!y_?_bJk@=mPLH&~4UpB6RXna%$+h%WAi;qQ6eEPL9 zzJ-8EvtJa3mL6{^8mfxPkJS4UI@Udn0RP*mp_BZCrwW);g&bL*2cuxYeDgrg6+o#t z90fTP(uduTgDHgnvj#d4`)i^1;+8i9sqChco(dlqP!6dgUm_h9rJ<@)N$Pet5Ym`N z87}W8|2<~5mr+e{%uTI?mM#Bb$==OXWSDMheHBHS2D{EDo|%j!$7#>0!_<| ztvJ#p<%0Yr#JDh@2LOvjD;B zu)O;|TyTHrD#|{%;0vK(jXsaj|0uqd`skdo5_2Ejr{h@z)aq1M6phegYsLO4Rbc_o z_pL#-;#2E#U6${gHhRnIqsIKE$j)uFb-Ul(*3%~8x_t3%xDLg#HI-7e>cd>OyBbn_^z8M@w;u{!;9ZMP*?Pgg~64mKj9 z@H>kRqW1>(#+B_ozmvOq>Zfrml43PcEzb*c;#P%x+t=^}hxoVY#A#~HlP~V)3|Bi~ zsxR{8$#ggJ?fueL-0!cY!a^XJE3tKBUhWWCrlHy6sAbEgpOKCe(sOjOI{HmHkq8&- zQ~mD0c%AzS-k0yeSMH}VsEFi-XBp)YXz6U{)&=?ZMGMZowIDs%leJQ;*N0b(aRw4U zXKf)jtriO7-Z7x>NoCKLAGfI=m=8bGJ?)~K_^C9o_|CL`cD|)e$2mwgY+|~a-QE`q zpJdNxxO!c#FINpBJ)1%LdKbwn`EOfzkDVAxOEd4CR283|I-fr_+EaX*-3go;yw$Jx z>7zGZ+0w(``?BOK{>1fRPL_quyS$F|)JdLa`F-_WYB`b&!uX`zf6U5ff+<3-Io?ri zgYgu^>wjgxn3n032*`U$i1@{`aNWm^ldJ&u zJRLWw!1!sPy^><;E3fIP(!T6gy)4>%>j8I_7Me_p#w`B6J@$1ewr$)IhkW5a^T)(@ z4k7z;zgSphUJS&i+?noJYjpV76$R~n=PJ|(90m1)huLCUiqQqv(;fTofAaR8Sw}|K z0Vqi3E?UYWg@k!`TTUzIE8Wx&?ko4UdqpI>0{Er!p8d_Gv*0^GVd-QGh8pH99@E1&@B9>~aOXLF=(- z@AgJ*eIe?O)SjFZ*6fWtAztRkwaq17k*A80au_9I%V8Ac;)zPD?e^}RCND8z9@CnRDR=%^P4`MOoMXxFWcRImKNs`okIU9IMeu&6pspk<&v;CUiGN=goOgJ4 zaqXT^N5xsT$Pzlc-nX6?VPTq zUTq`2ppUi?d_phoZtWURMewK$)@U~$g1(uZS@h90(vU4fSL_xLy7d(!uf%0MUb^g0 zQ~VK%<_fFh6N&BNCF7_jz?ro4E_Zw0DqbSao?|9%VZ7v_f{yRC;g)>x z+>D;qWgM)>dc+3C*F1gMV>?ETYuP$K)~F(+EA zo2I#|(1|PzZ*7K2-|eQ_+t%wrkm$AM>A*>@bQB^lhr2iu+n7-Er>N`Y1q#e1bCcu= zHVa?HcH{AD$z=GtHScFah#tyOA>4P5P^{JcGCunuI@jmkTc2YgdK)C&7Lv=15P70K zb;T{@u9s~VD$cL7lJNX^-1ABot?MMd91_RJd6R2kZPx|)Jp0ldXuReRx8o5dZBLDD zW_I<;#+WWXW3AC6@229hgj#+1n>v1*+qTyHk7V1r#`|Mw@2$ox=}JzkeVB;O?Mbv* ze=@xqxUZ*+>R!HO8}^3L2zPh>#!6ms-oI3MYi*j{=T7@uyxL2pD^P_t-dE9WwS4V`;WuCe^uU(#myey7u<|}20x!3tCSn3MP+>B^#!xqeX3>JyA$(6 z;bS$scc30+Zs!p%=P(x1eG9q$R-LoFlCMww2|A;J<=aHJ@Lzc!%NEP;mMzpt(5pVjEU zXkQ%rzew&xb%3yS#8#!%6@2UQE5@(cT>&y^d`a(^jidGN=i;UVvF?}Lt({yq#F;nR zZUsbmb(a^B)&6vxbiOhV47nss5Z^GC-~Ow^0_@KLV;X1r^e113{g;L4qx%Rru3b8~ zZ&6Nj_`~&Bz4SL-ttDJH1?4;H>+kwxUn!U1oeJM!052&`^|avDxA~YmPFw=~<9b9; z#6=B^6*`M7>LUYMtROn-^f((LBse%YL_AddIT9Ww7Cu%at7K;9Bz&Z1XQrfPr$%o! zPcaWkQE^veOy0SF{07&9AP)IkfMDd*rD|#X(2k3wbmE3AhdQkHPVW#SZu~ro)z61D#c5kjX9&y4WNB#OTg12at?-+Mc)Ho zTOU6j@UXwW;(2Fd_2S4)Z*LI)tAm3!)@XtI6>Ac|?AyxcetUg5S!pSE!nyVh^558F z0)Rbu^ePt70UnHv(2pMi|8LmizecCnxY_8l>KnP5n47xjGc)Kro0^*FGyWfHXfpub z@o)Wa%;W#Aq1C{>!vitWq}O29AxlUUK@rjr63S?VHl|ImoI)j}skq7mOKAiLOCmZ5 z0s#Rb0ga*{DJaAVG7u0EjnL&1L_tGS1m@o?&&{*S27Vnoa?l@>=<2%KZ?-+ZUsax6 znG~tQKtqUeD+vt$qA6FQfR^jMYM>U1-f>wqomJdHGzzh4rWIESK7kZO7tKJF z91hN#p>?a*M3wgV5)rp@lISV%e@&YT(L}>&klGE@uH`lf`Y~wJylIGnI!oD}tAJ?w z1IWfmZHoC8_I?h*@W7z%J@qwOD4Ao7?w;yT*$0YAYuv|>P$+| z@)AuUc0vvItQZVE2)qzk&7$O&7}<7<+D_Pq|< zEMeI30r9nSF2%ToVJJ;I^DrTN;|R*TDYk$^F;=cCk_C!cRg@V<<`qxxq1xSMBrKjZ z3=8{EmK%8+MjdUWDzeFFJbhr3)!(`c`GOz3Tcw!6sRG>2!Zfyy$0sK1(w7sd5YP<- z9a)^Y!lkY7=o8HX(%B{93gEb-cR2)3;UFkvJ5%J6<|VDHnyKTI2L*cV(LYptn8Hb+ zA?u-mhtz7h!Fqpr!RF3u%(P;DuT<$t@cmCx$L`C z;9x~Du#<0%_klL|=cNF}VI94BxCFwyQHmiAS~xot66VcRNdRXPh=VhV&@62f)uj$9 zTBOkcEEuAAI2ZCjN7R2Qv^)r`D@#C~7LY>YL6T))@5`?9jl&)8%i zlOaWdB7U6Mty?KW-;dC^eh?P4dgIJW4Y3QOC|3_+0M@#@7uELvYw%+<+}}4+Kq?h$ zG3p2MS{E0OB4-T+G|MlQQdZZ6X|8093r;xZRDGxj`{cBNyO` z{Z{H5q=7LaQiVVXT*Oc+Tq?p$Af5jgpDIK$yuUxP*8v5SP_ZhVbZk6M$s|-HjZG?F z1)El}C_0XUbVv&I+7Ve4;K2CplS2O?{VXSd3gc^s)ad`)rBucuA{1Aj_KPA;LXrg; zh2+c!K7yQfNKHKe47k+lxD=9>C-kd|Cxu6`LDOW=ZnB3M+(2B-IjTp5zZ&;w&BDoo z^6w(v0dKC0HCX?T%(i??IGqv-a4G_P3{e#Xpf3r#}H^yi6foTK2Or1XFB%rsa2+ViKCxd%)JcjRQ&=6M!6%MB)`NyPdlWCMW(Z3 zrnGFX48}31Rb~#%J&X$sBSo$G(Y|%H-mt=Qg znpK6l)2GZwo{=}~@IrU|jWUAO5GZ(IiZm*gWAUn{Fvx?VAzF=S5UJ59MuE{Q!?sFLyBOS+7HiA-!cJEqxO*pdyA!M1 zcb<0Y_tt$=@N*Fy*$q4{3e3%s;4oW!t-GuNO636qgF^{Ggz0&QEUOeX$iP>@FH!K7;M{RQ zi&PkKYt3T2iK9E&>~+I_i2V%g5(d&kS&>xPEa`ev;h!xX3eZ(?ZO%C>hAv?U;HA>@ z24ow^YUBscozbd4VXNs?I^at@d`tIS6$a0#(W-^1^ukm-y3}sD^7mX-*7EK0-OK~@ zb7N~2;(-yX;6}k|DBH;>Ug4WY zr9%?*xSESLc^*pCq5@2p(SuOm8_Tr#xDWFZA>$|NCBIhoJD;#a-_XBT6@hoHBm)ae zp}8lAx20z0@r!7dRWIShWx22%@6;UOmT{U%a>U(Wj9XMCZ+7-VOpAbsvq&L3oQ=Wt z{38h{uw7M6TY+p>TNwY54tM+4He#(kdcgK)xNlSiUU7^((nj(FS_k<{kkDN*K8ZW( zO$yY9EcNr|>P|KkIDuiKN58-VSz^dfE3|&AlWHjNUA;bC4P168UqiT!2S&`L0r%j6 z8gx5ky#BQo$r4z&7ReRZ^BpommWrs1;Q1OnJ7lf?w-T&mztjcKl^>g^y`e2pD(@L3 zYyDy-hB39!Rj;S|%By4|O_WKW$A~Z|d0@Aq4#)e@qBmm8nWKH6VWbfFfp9fx_Fn9= z%qu_L|HuT&>Wd0I^_{7=R0M8#IJMvLm109n8&u+Gy6#FtsknHG5MWOUfp( zxaHG3+#@iuBK=eNcxPCRM{YpW&j)p0QZmM8kXtKtad-1|@DPw9cja?(i`uzmLOAO6 z@b*%0jF`n4DKaZv3fw8Nl9E46$&i$vf*y`nV94(xoc-6(e<9xkp1=d1M6bxw1D?pE zC}9hhp9iAAt%~0-4YZq3WM<%I+~@^vahE-MmpyZrJ#g3D>?H

TdKB1Gv-sf}8ge zLu!aqyt}MDC{G}bd0blVi&I2K>8Yd{7 zRy3K#rBlo0eDWZDJ0HF`t_v;>><%r$z32DA$Sjo|@}ssD;-)=(hUlpxkvAm^;$ zOHSZR(BMl_8yiZmg}|j72bM#B4!JS|%5FV`1PuM|xA;MV)b{hwFT_<*% zQ~S?d;ZvByzWaarWpH)`aDKo$dk{On=pNmW9o?86-Jt6z-zuv&m1yjEx3Gc7ADf5< z=F_zPWP9Pe0(fFWjHfLwL?G9nE-BmCC6{;W4zdFwL>L%QjSn;bB4~lJ}JWRe*gZD>O&Tv+dh-MA1r;eonqjhSkwQ>gQ1MS z|0CkiW(by@jF4}iuNb&rcjmsPFn)%ouem7=`H+?^@QxY4S6x=&7WUrg@-CEP2O-hs zE1Br2;9^O!aAH{@-BbJa3nV|x#a>4|V?9B&NM4ayamL*e`g?ZpsM@DtWSB0%x zYSeITe+dfn$ikRA(ZfC~CK+#wo}=ESf&wTO2%%j$b+FGID_9nWo1o@W9t#OE)B~e+ zbb&@lN3*4s(o8EClgR6q!E1ohZ@=jAFD3l-2hBAMf z%5ub*%HD>zw!LZ4^qYB%o~%fbM{XQJY($vVH*tOj4<8+(76DOn?@v7OMT z0X!HmCZCGAiMX0dw)_1r<}eH9=6N$ad9y14v#W6PDp`OBQws24$f+jrz;YJM5FT@Q zWimPw*u2e|yfK)()#ZF$`nJU-_9EF{64|=no=8^@onG|1dK@Z*D#ZHj(ib7+QcU9p z#87LICM`A5`*&%gNH;e*X~*~eHP0NrG?xciBFvYggVgb09!Q1|NCn1FzwSl_E{>-?As6`FG4;roe3~2MR)oXAOKqI;2NJ6T8wyhHK-fbKRjEv=h@cHN3=Bt{n@7jNyK@SD4UH@ML z?c)pJ!Kl6Cl!u&j!G^s1Qa~;L^I&NFFe2JFSD_p7+ixZs(i^Fkr}GKuUS6G1uAl<{IuG)Wpop=(3Il0&#Kkd+DOAWha2_X zH+bg5?O2Ec+G@<$%JDuCv-dl1=N@3))bhZnd@qvRpUmpm+R&7y7sKil#L8oqstWZg zo*46>?h*}Tzh5voJyiF+em%p_IddQu%qTXo#@D{E=j|j$HsYf=*y|KFXQP;=x<1_O zuK$Xu72oTKkmpb0_EYI#FS?QN_w&qr-rFfOu=mMlcagk`roVoAKo_gFbTaRr)n=?& zd`n2qK4Z?cDpR7&(ul&zj|(SS;Dtle`mJk*xQoE zifJ^_!n}*N-m!?v_Tol7`I1wmA4XITh#ay;<1p!bZFqnKAIw z9U<%k0W(ulS3eC?vC`6iK7Pw$cjtuci+8bCn^Ja{9KZdQ-TS!}@3eo9Z9MiOsjL14 z-;{JWDLrCANC3egB+t#~aDkk-%;X-@W^=N?N0h<^P-p5be@(dN`G}?Fn!sn=?&tS5 zRIpqiM4;bL9-^^g52J5E*&Rz)A9B}=cd7Ep31bhlXZUQ|=*KY4?&NZ|!P0m0y-llt z!TWM|Jg!|U=IMAoRCISEO}snpP07iAjt@gf!?4$6o9qAnX&qs^KG~mhH8=Y)265u$ zA+*EUb z2D5NIaNBjQjc)u$SUyr*c{0Bh2mHCzX^{b%rm#G(*LQ*I*F2{-@;kMaj>qoodTePQ zUz2)$@~4gWEnus%@2k;r<;z5QI3{+>@44)Q{Puglv0WCg^Rwms@_u%87zE9d==5rIA{tEO&zfzzk{2CFnLH(Fkkp>clY zTH5ov_7(zMEag_xzHlwjdU6+|3tp~*j`9#Ax*fDzmn}^Gt70evXD#LSTyMQ`1VrjJ z^mv$@rh9MK+P<7^oVh!?o2ib5;o26#zawpq^?+DqyZ*YZa1zh&{=|UeLm1)Pec|CG znVjdN)0bM!PBN{Z-NN{?-qQ??`x+hj=ee0*DlTYHIR>+ z_oEKR9YVKU;Qs1iz;`7-5rGLkyCe7_I9pSJ(K|CN2i@0T%vqS%XZYl?evbd~=CNut zOMNqYGNl8neP*U1kspJjyxTh|OrQHAWZ>==?`yhW^J7n`?VDrRRrhV=IftU{8u+x0=!39Lz1>^;e7fn6cfP#czrk$dL}f&}2Ekg#{N5ga`uls$81}SJl)Iuh zx?Sm8OWpci%6Kp_`b^mv9;;wqce$m2Jetou&_2h@zCST1I!pdSKbh?YXMYH1ng&PT zal&GS(eLFd)H~)Gd&SO=cdwliTI>A8zxkBp+9y1xLo=U#~l0W(f{TZ_wRhR zyMFTQx~}(q_UXRfT23JmU25px@iJT_i@~EK-+n|Wh;g{zs{32FcxeFtci#Vuy|3xo zcWRP<3*6AMeA@MM2LAfv;i|2D*^-n+ZfeQ#)axVrTj<(rfETP^SFYVrN~VG+!X)PuW$&m_f)Q$-J#{)tQ$0ig#Ma>L5K6 zu7i`YlOp{V3`BD%=NX_zw6w!RA5gvzi%clTO181zH%?iTZT`)o1tK1Rkm=B73Jl8ykOjvMD)B)gemw)KKk2kkhMQ^ z58CVRhB;LFn1tDXtyx+95zfRc`L%cBt7t*~d>x_4ciPnVzV!Z0e?x}oG_xEk7Q5!7 z7}F}@%drYz_UmpsVYZlIi+-!7YJt-v$19JTa_^xZ1c7U$7Tuai=3mhk&VfC4(@wh{9>$+3>8$A8-jzoV$nnrJqeeWcv z9y0dfptQlav{)vLedP0r*ssga@LJyD5&Y;J+u>+6<+~d60lieLhqijWk#g@d{>z8* zoIR}|=y*f@#;5nY%}>S2x4Lw#*Isv)#JP3B9Gp9Rz>O{ozgG#hldSn7Vb-q64!*p2 z{@CgB&-8{`{5z2OrvR;i$-KIzjCDrq(-VH(kf$3@LXLU=cJiy@QxWYd_o_dm&BWG6 zWcsV~Zzi#P6)Fwju_K5hE0Uol#PBsv;7%cgR`@j+D1jOpz%m zGsxlZn$6)66kD&tQMgr!WQ6e^#R#<4ElTWZ1Ki#gKtY2fr12h-xU&8lGA79M#RVZy z=qREjw@O@Eeh!6lOc5!wvra+pAyQGdD(O^q4$zfYEI-Tojn8@)Wt{S>t1Zqtg*yv2 zhKbtmdH<0#~AGmjD>LEE28=QJ*IFD zL|hD;u(VJ-OtX?RJX8=x<6l^N*s}SODtFOhIb=g3F=D>Rvf-N1@A0VvyjPKLMl9IA z|1q8B*C=QA@Vzs<3+?k#W!tv9y{c=1MFo1vJQ&0|)#t#;+If6MpGvl~lUuRw9uX%X zNp|=9C&Y4;wCnnuXGGLlZv>D1{iEaP%%pp>CW*9%TM;o`Q=?rU|Hp($Uqw8OR*1tC z?J915c92im`c;i1@-?dNpYezV5M7PbR`B%ra1z>69!S9Hy0Ffb3PcV`^BIsaynqm| z0IM_WLxWknU~XKJz$2qrR&8S(C8(g-5T*j8l}DvoN`J35Y5O?knYb_xg6BG7yXsuf z&J>w~GO!m)2`NFn&@X~mY#=L`fT1O1NWucNUtB}KfEAXh4MG&47MKIxutOwf1OQ-$ zf~5~S#x&%ZWM%*~T}xT_ZmnL2ZHq9p*t}q|SXd8rcHlwM&Acs0z9~GdANes?^H?+2 zlg|RltSUzAIP;2T>M*%(GafRV9X$1XU#uNvu~j9?1dT(n#^gS?aIci)-Q^%xEel4# z8pXmGesB)my-lpCa%L#nlRyxrZ#@lu&2zG`#-IsItIi`LjL1U1Xggqp5dJTqSNi*J zZ9*$&TI%?yX@OdM_>aVYECv~}-s$I%S{*m1T&&hZt~2zB(vPvT}@8766M$0z!g1;%Z@^1p7oKjxb7W)hwj5 zE7L{h;1Otp^g^!HJ3>zyr1@Q%tP@wSg&1d(%Z_wG+`ohJ*ZM@d?LAFfZMBkdH76XN zVYxp)-z3gs3IJnZQDwa#(s5Kz3X6=wg9Y`{`b}$9X@`(&1q6Eefrkpqj$4&6-k>xWvGDNbF1*bihEE-N9;JT66 zf)rGquD0GWSyKO3k!*BU?31C8Hk&O#q~U?_wFi}~;U3xvlmv?DG$Dim$nj+mm|+QY z2#QI4GH_!2R!Ag}fY~;XK-m&-iLq%sfijWkpWAGjkrAiB=xtO%@DZjngWO-bIq3)$ zhJ6Zza+t&f)2lMSFv5#LS5YhjJ6>G& zMk4?D*Cb2IBw|_br~g5pCXl^%>@zq`ugpKK@WG>edrvbsT}|PaUHS5= zIyem$nFAw^`{z?95rL;Jhq)%FlT4xF_TD-&=Q#M4lM81}7jdHSHyNA}N9jl>k&&8O zGAUBZqXAwW3qL;1&Hbg2X<>C(n()olp_7*4S1@{qcz`H2{S#=EDScN!$4YDcMJGGy zO)?Y>;cP^MM~zCsJWL1edB=Y7eHCbOT8~rn#ReK!?f~ePa^^ezU@LX0^R{|syOiUV z)nF?=#guLVhkNDThClU`Zt;s<=UzL)A?<|!^{YgW_dGbti^kQy^UV?Avn`2lPHIoF z6WMev#|2v^ibU7O0qN`%p*p8%qE>y+5vY4vYaYKMpiCxOS1D-#BO1{Z`DOaaFjBB< zSZCmP)@u#`GOz!DOb)ezZ3+O$$Q}}=oH=pZ8iWmrqC|{{GcZh4*u(|wjZ4p7j#fr0Z zNDt2Cv2XlS`$Z7|yHA%sbc_9EHxL2gQ)tVV~0sbJLn_u^S1B3fYN=(xC%Z z7tvgfrQe~#`be)4T`7CWr|Zz)eGELud$lOAG5agL3T*G;U=(-kt^DU$gKngM6S>Me z7zeLzh-RFHZaskV`rtp(Wa~peM4V*uXz*+e#+E$8fCDqo07Qmlx6H3b2`7Ylr#pP5 z`#7lGZ{RCv0x#?zkwIy|>cDTI7yd_N5J+xjK)>Rdp9_d?RzO`PG$gkeek4Je5(Y_u z)nLC_2L`kxIbf&_iwfyu21oIM_hYw+Nx%j<#@ztejHr-l$*^djnFb67RGy)&4oPMx zVT*(X+gwYPUOM7#(dm zeI_@KIYl75SJAPPCA>5jALzLu@%D`!=Y)arh5!*9UszNyLJbrhvQ$q|i-ZPH#%{h0 zENquPbjYNmI?h1kn#sF>cDE4B3h?8MrTN`V1U(KBSKD2t&OG^Uq#|AaaU749n{?fU zn#n3(WY1EG)bG7i(lId5^FFo}o>`ROWiB*nki@WrQRYCvve1xKJ9^<-H6lYB=Y(ie6~Q31`j zM?g4f#dP;`^c2J%we)qy*z^HBE@HrH7Uv>1JIK9AIxu?c57kW6FJaRMOsJvJ6D*J! zR&01-G^d2nenrV4XV6|}63s%zLFVio&d3I`mt9WmB<7D>X_A*1!a%Y}edIxU&7kcl z)b3i;Zbwu=j?JED&>ncuo@me>dFZ+%S|8eR-2|;$5Zdo&+FD}dizbC%DJXwt)#p#@ zYyO$Y!I?aSwUL_+^dy!IYHqTu2bwuxBq%V}=!-RmF#@}PX)=SnLajxL)WR~Z#xlD6 zbXgUkH|iWJ0uqVvznb|HEtJTd*d#?tBt?oOPL#;=l*sgoBzF|Zuc#3zj*3qguxG1j zEfR9!4(Msi)GdYuH>EYB)PLsn>cLG{hR{Mgug( z8lJ!AVN3hGdFqcIwM+ZeNU_s|(y37)Zq};ay`E)fK9y&{`E7qny5xGbM0&Jj(zkOB zIwgC(q(PRDp_i1Pm)21$+$l7!m1>d|Ym${~)D&u@0GqpeM`QG85=UgsSibSsI*{L_ z++L)P5ARyy)%m~maCf)`d<6cpO{RO(eHyrIiQbV-_afZ36MGWKJ_F`2gV7O3_kz;4 zBYzsmJ_}|)#EG`ursO#a+`O}aDR4C!|HD(!0njF6-dI62kJ;~o+8qY%cph*6So-0b zvK{J7B!`N{AxTjx?w9vhlH#QWnqS;E#DV*^AD;g<6m2jGgl0n$^-CDlEv`aG>aNzO z;sqEGEsa+c&>DpT0hl08sXg@NH}7I~=T}44h9fv?eA>a@3a%Ox!>0a;HeGxvHneQq zd@kBVaaBN?9g}pDS2=7SbXBN^cEUAt9#Fk?U$LaY7 zuYig3X7WN+|LOO_#i&#Usj3bU#Fh%t9ca-VE7jII`i`Qu+qR_`_HZ2gc+VvDIO_La z?Xol5$}`;jH~suKZqXe&!5xG-pI9P?9HN_I@Bj$ZfH2g6ZVKR7z>N}^OH7pDmm-n@ zCR%`1bff?Qo!yj%q*0ZTRmoIIDIp{pGs7zL(^&16k}O<$E z%zRrs!!PiSClu?!vy_EJ(bo#=(Y-QOay;|EpQOZ5@wDQ>)9Toqz)dRRc8B~<)~*L^ zXCt}FYl00 zI$PlFrTf(O`bN5RVb_N+u)|aXR;U+MT9>^R`80tm;UsMNn5<)7OdMlgaYtLx*rvXK z%{Qhm#idxqf(rfuvDF`9F?AMz6#&@D$T9}YGNyrT8f1|;xOWKpUW*TJc9T5tM;^if z(lIw4GAuq~xHQ~kH47AtBf6IbKb`y9CYJNl@(+BmAQv#kVJcQc)E#YS({)h#hofBZ zkkrgm@NHd!Ce&}G+`$}bL*V^(^BA=}SrR*Z;UY_-F1fyh%7PAHQMVaFk}UDicxtRO zF#SZh#^fE1oIGHT-yFF=(p8D~BjTV2Mnrgc`~s5TJU@`Yad#G)u^o^dNhC#xxVCP( zVq-NBi~bm7$q3a&JjtrId~=hH5XQyD&LX@{9etk>b?j+}QkGe0h$?n#J9`YCSAvy) zhAe7X{}+={*T?8rq4|OYFqu;1hI}oL{93V_wiRjm3d_>@-R%K#KX&$$wlDndjN&7y z4jBLF-%*zjvG`L-_+h&7KY6lsdwZN^gZ}t_ydSKJo(tvdmHR#8Io39^Dhmq_@w1Z_ zq4F|6b^IJZbHV2aRwkaUZ}hPBDOOrN3!jtr|5=*N*MN6UDQVa?VZUc>&fD46>>v|s z==*3n+#PwcM3=dl(Ry+8llO2c zSSR&rS}RMVwC~G2-TW_V7$K%mwL13ul$dG+hbM)@cYaL7787avMmk(Re15vR0ZNSpCUj?)J%b%NwOGlh?$t zXjA9;YwkGi7kp20{;GVJy{Xg?{^5$}2C>`WJfOVN&iEK`0@G;$iv4GMU-$TETZ{xxuy&1{=!?9^&Ix-nEzjwd+MFdUT#k%E z%;=Rjb>8acwz8$F6X=79)jUb;VU>6)bzK($VXPM~&Pql-Ki%f;etCGT_OodrWud#z z^y7JPmPlw^7;fv%8SHhUBH+{<>=af=(4RB0wGYBR)PM;ERr=J0#lU5+koe_<>Ar52 zi_=d%)HpwHSZgJ#OgLSt>+5|4hF!XJ+Igi7_E){V+D(U5c3gC=O2C8o+qO6K_Ii7g_@MMCAY36~%J|E^-QM>7?W#&V2mRvP zYolM+1MmJkhr0KB7x@k0zT`{gK0G=KZbtUcFfdNqw73jKr;~`*6Pj!VpC*blpea+tI>B(EY(QohjeH@?Fyk%Ey;I3wm9L0@P2tStIdt!xq_3ojL^lDpZaOH zqf|tDMvwJrAh8%_CBZ6_wozn7aTjks&7)@_cpfO zi%RJ15Q}w;a~p*=9Ds_xz*OQN)xIa#uas zajurWN|Wi}84UTt+Yo603`FO1mezKw_r{kR_cP8Odv>-m&qpYscYoNT+Eo7AB-wUG zSG|AQuFh*{yXHtNR8}y;6XsO;r}ffrT293-`O0MW^>f+-p7za-O)BY*$scF<>uc8j_q_$U5_kP{+joEL(0p|Gj;I%`uy%b z&4%v}QLHU9L7{+?^QVrs8-~^Azjc3bzJ0zu);{LT$3Es=6%<~z8Qa}B`E!~`T6c!3 zvc1cD>o(vy$+FQ4mLEn^YD){D zYhF&!xY5P8;IGd!Ffe!DXZ3${zkZv8ciugf*8jZU>w8Gbf49XS{HS8y*jvArSY|nF zRN+UBBJM{ZS?WZwmh13A)ko$uu(nC}ojVn)LbDf{c6jNzi6S2NxrSZauc)qHGmG;b z`TQ_@Bs$ZUQCp4jDLI$6U1%C*OkDO}wj6zl49&dE`C092;iaB_nPHAz_(dnD%-un8 zsrOtRMb5kT$mHFp(}!I4s&GUVexE0!JvhB}0)<+hbzZ{3PfVA!Iag=zqCjI4VeOlF zf#$tXHJ*ST%i%A)-;REbw9TU4`YtYZ_=*twI@{Rw1@ejC-1Ob8Df(JvQV^BLQ9yTJP=QEw5cGCSVa-uW}=R^jQ}dCndet~lar zdQif|-1{@{*N^WYowJ;*osm|j*QWiLr8VUxm^*b)@yg*RbP4?78*tp|3|FpB#hnQ) z=HUz%uYh^$NAM|%RM6fLn7~zn7$)+^du2+tsbq1=&a8KCIiB@Nb20dLgh;bpN!rzy z*98O3@>PI^-`~0kI)TO_6A@^Mj3*rT_s#`yef~l)n5-wLB?*X_QDk2|39@P{Zm3aN z-##(&A$a}+3$%P>Ng0zN#c#3QydM$R5c#ky(98a1Wi*&$5(PIUVP(+aOtTa`i+N>h zK*DM4HEIH=-2shbp?RR#A;Tzt>05KR;hxeZCgQf?{&w5SLoXGwX~a+9Ut zgc(u7A)r)L1yBp5TP#Tw(XcTA`!?_uFR3s>ja;Cb1ym500u?4ccFn@t$_+@&-W;@r zj-4V6G98tH zodxsA_vioQb#wwa!f3$$(lmf0Kmc-9|NrKj{O@81^M8pQ|4Yp(DIU!1-}>K3!lk#T zhO+pxe-B&%5uNLD+hRUZI|1E+6j}r1paQF1XDEq75TW4_*^XG0eH2M=NSB+UR?<>i zqt#-YHQdssRpHfppmt?s3%&2+^~6TThBu0}zx+MV$K&|@pH^n)qfE|Y=czP6%_?4_ zph)KEnEd`lq3(B`3S2^YK}^F|3H!WZUc>BPbJx(HAh=@1nhjW7%(zHW^;}uWzjuyK zJQ@QQLo61}TpgY~gne|lS4)=t?tDR5%}^E?dK(5S*lV1y&*o^nf|A+Jqbs21ICq^0 zU|>}i1+|Tk3b}+Kd|F8FfXJlN1yjai#saFxAdo@=vCFVa7EQDTCI?WNOITSHlQC0I zxZxJ$9%LWVz|U4#uoHT7=Tjfah^hS%tXvg}adVeb4|}T1kr!A&T9Frw|7Z+_?Y4vl zyva-s2ryd+w4nX?7RdpgnHkyv12UVDfWnqW4-O*R2rT*tWWNbC+Xy@|n-PN2vYM00 zywW7AoazBLHzB@c&i!pVJ9JR$WgIH_+?9@-^M?qcvP}-G@?IcAK-G63i%qM}B4IGo zma#t9HvfO{^-e*eMBSS0F59-b3%hLFwr$(CZQHhO+qUhhuD|a+x8rtnpNAEhxiVu# zK4q?mG3WSV%IRDrg?FW7u;VmkFm_8M|G`>JCz`JugS7cHBHk*{lgRrFd zUwFUNZz4l6^L&3Ur#25?iPUfzm0u1Hp`<^uVPKvcFUF{2J)I_1A${gY&kQjAXhsN% z@1FQS|GiG-AzNJbcnAq&)*P%MTWO~BJx^ok0k#pfOi1;H&Shh53#b|=*cvCAnkQiGi?Gh+g!p88 zb(v`(1=cEtx@9-<+dRRG7}u(Ywq7}lrc+t^R8`b0qJI$}3S*Z(qjdiLyf*x97Y6jt z5Gk@u|F6jkke)8;zf4w;RsWyKs;S(Z?Fz;+z5pL^wXGmGln1i-pgv^|VSj4b$c5=C+G?6F`S>U}g-+L8vzczJS+>=1LR1`#l3_)5&tQ zZw1jq(BEwom)rOs-dcf$$}}w0WUeAacU}B+OOS3JqUXrHc5~=M{Sb(57$VhCmnFuH z(GHaM^RLqC*K(`T=dmWfJ>h-WWr=cQRPj53l==vCV$5^o*OZwt)o0x-4Y7>GBB4G# zWitwdHTQWDC0Z~(wGZQo;WVJG=+gA4uGGHf-O<@W))y>1fw8+W%gb zeu6+l`;WJZ{{Uybh^t`?j~qv*Ratf7|3oO zBIkb8TPn*jQeUc!n*sMh)Ek=3u(=QP#>h|Oe5CS4x%6}=fq9oZw_jN2@lmxFRw zcqODoZZ+bv;Ew)Azq+e$D%$celqe;Vn*YQ|Eo?fH!TK0BEY9<(KJcOh~QBK=7%UtvUK` zK*YYVVB7F%h9WO(p&pV8O_#0$S_(^{W;z1R6hsQ@=?4F-KlPjefTd8d#z7M>^C~D0 z;-Rj_=P58;^8lAWo+lCyBDkK%r>JZo&MgrD;;**~SYj1|2a#3J|~%0!5F*A}H5%Fe8n<-apKi zILattU-bVNtoq;?Lt`qHv+D@iv-C;_r36QsU)8ep^`BVhM)lB7$oB^22n(YTDOkm2 z_Q_eFVTv_z3Kt|%3s8mIfUtoIwEn!r17ZvOxt2n{2|yW{q$>4yroZ@Wd>o-eDWJ!L z)}l(m0f0E4Ow8)ENE4uM}L&%2HG2O}6SYw@o^c*sWjxgw*B=e zjj!e=ax6FK_uoe0opN`wJ>?+SolRh;FaH~(W~|0WYODonpeYv^A=|a?TQHU%;7+4U zrMdKn;qF;<@6U*Vs4e%@;2T1T$@P?CyrZ2PD2%B$<|JV=6T4YP`+~VFNfaffDdFN; zAZG3jKlmKWFIkTr0*>R~g24&iNHWelADQ(%kOBO|5cwAC?QJOz}gVlMYs| zf3-SpU11z{E}q*-70vy}U)6VBzP|q-f0dMp0cN@opRvDjvV_V%v^g~~PYh~XKx2ue z|MFLrmhZ)6hq*3%hS7(;9vPPDNF#jG!_63qPs1Tj=-mpaQ27u?9eE;7D1@zj2M96U zsp7!$I9GiOwzvCMW(zB$L8kSJ=l2T-U-2s?<10E9Rv!Nnj(B8xnf6w4kZW+O-t?1bOMR7jRiv}ifdyl`HiJY3iH_Txk$!J;0Q zeJw`*?q}PA-FM_Dl5UZ(ZQpVY?tDpj;cXom@N@6)q5)eUVS}f<@+$88`pq0I(XkVS zcLocUz#$%fgYiO|u2jny=oi4vEYNL>c(~D@4$;btX=TMD$&5jm9-%vF|Bt;&s^L%e z?Jh2iOnYk%q65#>zW4CZ7uBA3v@OqXD)|3nuabtMZGC-snX@BJ==-%-(e9H7n$xpRTBn3X{uLZnBG{~sv+a-${(G#W}rp2h&jU7CfN% z-X(tpR<}fGY(E1ro78+>Cpg?M9)F;6JaVysbZnia=>}3b+h5TT$sdM7=!I8CR>G=l zXn*BZDV-Z{`VK4iPN>A0*ouHgr?I)~-PZp70(v+)gygf(U!HD!Ahk582MT|VXb7G{ zW8VcufhKWx8jJ*Rx?hBj3NBzl-aqs;;xz~J_r*^>yMU1TgS%O^fjsRLn`5^?0x zD=-B-#i*TIQ=J}qlO4xv-J$X%h-&Vx(`#SQ3-`8n>g}o@-S#GzweP;BV4uXe-Tebg zi4$iICTHi)5!RjLww3@=pAK9aixX}cuN3E+)|ml(G%elz+(X0Swuqd1yPkl8j%SJu z*OsI0V>FLMDKCBM=bR0!i?p@|P|@C*vpMedCv&lf;qWP+t5{NUZcN|@wr)Gnl{`vJs_!%Og~xMAwW zW3SQZH@$;v-K|d?-Gx>^^p|fO@uj<-$hogKPT4z)95ZkcF;KVQqZ2oQxc45q1>8S& z2|5kL97`Tz9!fW?989Mte^sPg>J(BAa6Mmc)3eUwpAJV`2Cf$4Jfg=))R@m#_m5%} zc3Kp4v{V`FG^+^9`&cN}hs-&irc!T9iXN;M=VzkHJ1{C2uOEm8XqDDV&tlfeTT0Z* z+D}HbsZ@=NtZ%BtC45vxQDW2;cvx4&VN$ZTnaUHg$}{Vn_v{ZG%Zb-xcW6pO-;MX) zTj@9|6%Qx!ly+SH=2OxRc1g->ltG@4TRVtgEqPzJ)ef&%DNghD43SYWmwO)79ED>z zkE1e`ISS%&IJsUNOoVi(mIEf2xt8qnpFsx?tr|@VYx3-<*TDA zNZ<-?l=UG6Bga70D?l}Kax#AuCb7FbO5Ro_5>ob9*Qh&KtCeBcjER#DZj^~x2qbK{ z)XAaq1QpMQ|3^Hvb5Ih?bce{3*pAr_G9Xuf9#!b`w-#{-tU|!MQPjI4H(DNLb5){`?#}Mp`qw?>t*BkbJ2mv=WWE2aDKn(E2|J#t7QR6? zdGaBf;nO|xc45eU{vpwd^SaVvV(a0&GHubyMOXexZ(U(*LwXV1gWv(&f)=fqIdpZi z0k+fUdA_pl4D_-x+_E0D5gS4DJ$MkJ;pGhSa?%J*Jv>m*(Gc)R+bk zpfouB@S*t?H54+Q@l`XpRHOQ#dwvjWucGs|*^^v#Z38KV;AL{$AimPtfP=))d;9kh z`-PF`r7UZ@+O1c)bxMVT)GGDMawPen`pyk18DpMC)BX;tiDvXuAtxaGB`53h%jwxp z=yUeUD-8_n?z0o7*r62%&Grr5dgC$68=xicJ$J{>gQaH4ZIh-ZwMkiS{_d>&9ma;z ze|>fZ3zzkHpNA}r8-p8lL#5b?4*ep>M^4#IOwRV|6k%tshZ57ft716O$m-$uhRyzLWZ^ury zD$J9@YsGm9K-tE!WxIL7H8k6j?X)h(lKn(I%<;E#d8@6*dGxij`PgM^6_@oY#l;HQ zSs2mPVsm`6(ha(m;=l(90Og{LhboIZL%F~*x}k=HlXH2L(ZRIzgr#ER({raW`(Gxj z>!EKEn^p1$AJRrE_DS(4i*N1+(()Wk#kwt2Zi+%chcl1oG3D~lk~lb)pw+Uqw($D< z$Fs%ui*4rcRx zxR7aNDPgDeB037limdZByRFmtZfEoBqM$aJfJ(&@py*i7qNw$|tCEtJ;MLO2@c<{o zn9Ie5k^p7rTiSeB7cYln1Hl^w^e%m$)~xi^MxBoF3}thfkMU)SazaY2pZ%FGxXQ)W z+=L3UCxK_#=Ls0^jEWpo>MXk!%=404l_i-5dWq;gmg7llJD(o?{^ez?l|}c$Qu444kH5d2x}qvq*^6v3$TQt7H|qahv-ek* zYr3)dxl|O76|c-0&+`ba=f1LLqVb#^%eqcGP`WIVDKe`6%CO(}-qkvX$J>2YzH zPGS8nQ*VxH?moz^Ks@pIVe-;l`synE7!FsgtqiRpyW;a0@`61u{YJ92vhA>Fl}P@; z=Fop9=a`j__lo17$=+>sAC{05o=%kFSp8TJ!Vw;>Y&@Yd$rxVv^{Cf!tov%jB!x7i< zn(CJp>`%A_DPg%UT!N8!pPU>z=pB`hZe{y=s%sqySkm$a_EHtB$AnRyu-vEh*Va{^ z?tHH+GAb%6!rzRsViQS0LrFu%^G8THs2h~ujBH#SBm@MUAaH(x0{z-#v~|+K1CC4H ze3I^6v`6F}2?gAAkB=YT&7!cI^bZMQDYLJeqBiU9t)Vd5SACNG^rk!Mwpdvgwfzb5 zvPxf;u0)h$8VS!me6Iouo0}>sxS_!o33h7KeD0PZdcG(@`i42XTB*P8MlKTv?PK8t?F+m?SD3 za%xaiwu7TJauaN96;2*EN4PAait$BV>62G;=uA37nAu-fp{(!hp=GwvRXUrZy&O2(I5yivQJ;u=T5mauCzUpZi4#*PMU; z@crKuQLO(>OTvFCqW*s*;r`bT^`G&-k%Ue)2u~y-G#MaO@S-E`aDKBqJvz{;eK>#VLvfl zDrKUXbf`&5IMNN{Q6msi=I}kWM)S04?^aLMcM!_=E%lm+@%BM&jEwQQyN96vvb`n$vJ-p38IP zFG7Oyb*ZhWH_soeYMqDi0hTe6^G3mEWRv?%=o2x#i)MH7Ry3xJm1)tdP_>zWF;oXJ zR#GHkK8){E!h^3tW0+29*mU0 zDD~}157jy$crx>2`8F}2YTz0)LA){hh0GYenqehK7l7ea%!9>9^&F(nTw>1-(JFfuwThi-WnGSn7Bb6FxN##LF=qZ4?>+5X=cP|K5Yk36 zI2}~AvW+4ro+Y=1mW!PjxK^wTd0tD`XLrc6CY(8SN)-h~7XdjCLf`bShl6lrmD@O$ z3GtGAF3j(0j0z>EJq1pt(=`?kcVC#}UN>G6Hd62^3sircv|PgY)`XU=^G^G?5Ir-g zgGXJTq@r!2G$x)uI%x=Y*~Y%0%x_TB+O)&;ni3TrHiET@*J^zz#kbg-;3? z?Ra-k6MZ`D(Z?@h_g{t%l^+qKEiqZW@6zTkA^v7mkAp5Wn# zJ>f7k86jIVQr^7Lb;SSze0@YNx4*=me48G2bRHj&TJ&%vl0p1HnBHhn9LhxfwtB8W z?8n2uP(pnO#4nc)>4RKzXc}4cehBp^4KxbS%a)pNW1nh zntCNHHnsBX>tl$M ztrAtA?F5ej;*2xGca7tWp&+KX=X&kM8zyZA?_?r&Ok`RZ zr?N&5Q=QtNw_ciMtC+(&HAwEgY2u+E>@#gfcdg3-ySVFpGHqEv49`OZj1FjJ(uQ;GS|bc`X_H^0 z>~h0w(&oGBf_@4E!L`eMCRgi($Y_BSW*-!CGNz)%acKg^nC`kD){BrB#*B^Pro?g) z5O~P*KYKu|;Sn3kh>d0^$8sgSRe^7=5xPnPJA(l{cYp`sLv(*?J5b|q*_397NuCLWY*rvxg~2~#qU(ds4c?P4vV4{!m(Y-O(%Nb4_Mj< z$|ywT?nyXQ77Djg3V?7I?ac{EtQ9y%4z4p+C=By8n%9cpR899?M1~(fUn=HA4N+Pq zBEg_TJyn*Bswg4xX;EOBr~FzE46<;&Y)BIX!zDpOVSmbIe+CNIMBswVvzV+Y4=xAZ zR!E-jd>9WH#y>_fgcuYj0ZC>qLn9=}5i5aEXp2!8Jvj2`uJww7ncN?ImkfL{th%W| zB$OH-%uDXntwk1%axn>f?!d)w+LCxFmLNnEIqU=HF>zRzmx_}c@g&c>HNjG?kI7-k z!bMn)oAGYFxvNG=psF(t851QOH4IT84DmuD@tR)FT{b^W zss$3EVu^A%t$D-t!U;zV`~{<2$rR``ngGLg!*NFpss*D^$&|d;dBNU_H)6gBE-BU} zTZ`yeo8~)8MoC~n0RW6^s8J|-eA;-4^9S(r2hsBe;aW?D@4s&I#b~nPU)b z&6y_xIxbcrcVRaagkvs%15PzT*gWB^G{b}_Az?Z_%l$Hdw9)(1K7c5)*jRUS!l$@I zle@9SlGFGD4ElpR`9u)|3%d#CDw>KBGBeM7K4r=*nIeMkEI;_~qag#x{V=}x>|*qF z2{>v}b(ZxxdA@A-u?VR8HzL155iy;m+BeSny^Y3hyese8&xCL_#~#Mt=qK?R@exc# z-pwU$k!8~BCg=niLv&oSG!%$V)oNLSjOFYJuf;mMQ8kvWwqcg^o2(wjf(&F^RRGa|qyGidQ7iWkp zEpd~X*uQ>R#z6tj97mO@6edDxGRl+j4f@TQC=Q2}u2R$CE5Gx9O@=tx{vJ=L-34c& zK!w_5d+kfLHwG2PSVEE3W2BlMu_)0#ZHKxhy|;`)mwD6X*HS$P8HGVlY#^iNxJv>d8kj%uoTbLkK{A5@MnZ z4MUFXBh6vg%V+8nWYCtj?qZ60DsS2ltEze~R;q4_n+IO9)wG0e<&R@2G7S-J&q{RE z`G~hpk!;^G4CH=UIN@#lZ3J-7?ZO4TLV^g4dG;lWjE=H(c_WbWM4B7!!GY|aGmbz0{>$FfBu zhm}$m_1C^#tLV%*%2_96;fjoH%lfr&L+bZ1cBV{FqEVD--E!nu5x;O1ooVfIE|4PWX3eFH#a2li~`9-CzA4lg6z!3KK@&p zW@z#)Kr`G10&w%@cIXGl^|2JRJ?yj&{e%zDoiI=0n}83JWGxY*-CLZcU7Tc2MM5m+ zDv@~8De)R8%oGgI&tA>)6vK!~fKF~yM+_zisG{|xz~?89D4`zV>e;Hl_2UB1hg?Yfwk|7HUte^SQ6qZeqFjoAit!5U zb6Vkb(?S-v^h~t4N`xt=wRl0zXV^rBx)>Tjxdvz+enS{y{JJ6)j@xm7y8S%r za;o$F0*bu%bKzsNh6J)K{yiry1^HFNvvWIQi44pjp{zWAB41%rYjyv1k@O@7{FUJY z(jinty7Z!rs_drpH?$YyFse9iNDFr()DKj!QGQ0Z`qAlwrwJA>*v|dU+8; zxbos0HNo08-DC52g{FkTPRf;5#hqay^sq{JxOr0_b_#mnIi zp;4{f#FO8y56|M}#g_W=d$@WIAdAcXX1jc)Z;cevGTNhnI8e+!=~_~CgQ<`wv`y2( z%nh%y=PAET1M0)q$;6u33NI6O{H2j&o_?cX1xZIpbTUpk=(}2jXc@b`rx1;|d5+_y zq}X2$Zl}}3O0#ofzB9skG-V8*AIs_Vs#u6}P0Svqs zWLWaLr^}8f2B-gWsbFs3jul|+U-~q438YN*xz}HoTmROP@&qalEekS{oa%(w^G6%lMs3d`UWzi&+upUSqYJ;?F%` zG#6`&Px5|P=f?{)Th3L0NsnG$e8?_v^AgVze-NC!-0_vIU zYNDOkdb8{#m_a*r!JaYLTtcc_0%nDh=QTV`0R2ypkz6Hl8OB~}?k+1#e{OCEizpBA z08p%yq=+ukOU-)Xr~*BIthE4sN2;k4O7}AgBMn>%xAi^p<%nVfoW6ledDz!+o(Kyk z$VZ*eevkhuOE#QQj{ny+(-n`>r-$oCjjFbG$4dsR`bo11UZ^|d^FilTK?9D?!oM%_ zAL|+#AI#5)m*yV(IH8V{Nlo*g1%kHX5?!9Yjcz=;&38|nduHEHp8J;Ij8Be%ure{e zB`Cz@4o~Z4IVNiX{T&X`^<4s%nt0T1mlLv0>6+Een8xs^ns4XGMzrUaO^NmKCKZ|l zWh@UDBD0sP5HkKk!TiNtohCyUiH+a;yRCI?}j7Q1)zr5vNJ5m0i-W76(ca)&z7z4 zx$R$1kC&B^2jO3UMx)OT=eb%y+<&ia#k-D@=D@0hamtEo#J3!LCc~J+Ow3oy zXilt0nk-knB7BP2*J#3bauxhvE1cfD@?f~)tSl8X)K4!45#A75s(IEQn+Mx558m=QK8Y@G z@o&xSRC)p>91+$k8YTR74+*<&$;~H=M4tj#QPP?!w9PjnAMZ-#C7{uso$cEQPtF9M zhh2$~PLI76N+s@IH+4E5N{{=|c-V?Nv^_4zvH=_CZ4LUEDO#&V+^6PRiQ0Ap1|ioi zL}1e2KRG%2-z5(VL6@EjSY%he961g9Z)FU84Ws*u(X$ZoCo)%z$Mry#t?-_9O&Qq(;!9NamNX+()ORwsV|9=Inol8=G= zh!T<#MArJ!zec8)Bbo)^yb*wM4X4c+WRe0ML8N%_9S zebCssen#SQIRLm#tGWNF`%@n_eIq2qz1A`+M%&f-mhUz5l3g&xV{_RvMNa9nd0xTQ zQ0Q)Cu?pp(vNg4Elj*6~Q!W;5HZk+^ysWV@^ilP0{3AAVW$1Sy^zqVpxlWUbcF6}=6vV)#v ziGF)G>kW0pf4=}j27q?OLvO9eyC+X;ph|qEq?%;OOVWLdPq<|&K7ps|{M%jeJ180;Th zfEGEY$6K?t|o5bHiHFw2-{h5a<2a1`v78KVN`$18-URv7)-kogpydt;F}kVVxI)cvK&@hm z$~IXM3Tup%RWdAdR5-8>a3pQ@pIKxSgDP-!z!*Lz|J7J>>uGU0!q0bFDJ*k1v*DqD zT8L>gI3}tc1Z>05V0fldEdeu=CXiBJ-+Q>L7+^9ZvHd^Z^!%_oHbg4T&czBpbFV+B zkO4=JN4HC+F1@_e@Op0q>PgLM%3LtYv*k?y3%WunbEXZdtOcgcY9LeihWqd);dEF+ zFqO#p0ZBrobA6PY%oAX@@ypTwLg5yfk@S?xsiBk?Kn?rOX|g3t3HsC7RVQrtSu3ff zN{M7jB}f+&NIOaU-`Q7tQ*{$OW$MycBjD3bN4CibrQvV;8SjiO@`JF<;p0D`mwST= z5_AhBU)^iD!7WUxqLG)6;jduje50BG-qi~iEQhR^`|vT{ly_mAVVUEIP;8_}0L(Gt zK{C#C9lT%&t!#dm;4fZ4L zQdp?t!U9o_k1-V6qJJ_G&ZzXin7>U4np{TQlBAJn+mjaRb$=5{#mUmFc21s zY*Yn?0~+Jfd3q6T!a|p^Sm#C(9jS8Uh+l{Z>jI3d8WdDc5EFfbCI0h~PWn*IA%7|O zN}(R2c7}0P1?)pESnh|Up|)ot4qsG}vcWxLH!L8T=Eo{5_CDvS+vO($2)n- z!3V^Tko!JP(gzN;TP+;yVTr<}2SmP*2OO|_#3SKb#FFg;lJU+NiB0q|iQbrfl2A2m zKzl}70;AdOBM4B4v|04Yk$byOc-)ZL-ySF#fnf;(i~$Kb(L%qZ@cOxAbKY1VJ7i*n zvOi+i9-Jk5v%Cz=>4^{tUYX6K(j}tO8V$KpU|01Evd)+k(oppa`In6X&g@Fg>{8C| zm6wmKYiEvEIOmUGY5xT5*~3_~$^s6)GPChHxa7t$1I7Ydh$E!nZ%O^-fmy?%rHehl zrvVyZ(5fgJZORp;qFnKEjo?%ksM!BWVfJ%yc(ihfchuuE-taf#I1cTB%Mu?ETf<%k>f}`!Z1_p}SsZ#Zmu)h{1Fegig zOgP^DSB*zblTTNs@4Bk!H9QKFX@p3Ycn3mv%lv9M-Tx=4Xb&G?d%D8hIfHvSL7AJz}I8VW{boVE3gp z{1g~@A%?M{u+BqaUBa-30kNC~yxhR?Z2yi|UmZ>?hISEd_fbM4Sq5tj?2ab1{$i^8!mu{Jai;c zRN{#(k@--+>dZh?j1HRg1yd11;=oC<_+oa}$+bVK96o@NiaZpvQXFZ}d{9I#GxSw4 zsvHqvXV*n|Nfd35p*;EqS-b!f;E+)>fn5daXTAYW%v7@biPqUi~| zseu0~44%uS1e3~G9!kx=T_~@0M`*@`3UrWix4{3a{N2024=`AaK?Z+zg!W(bvU~R!1X?ytqPnlkFu z=5<7lb@ky5ru6L;ur8aqkPqd42oL-hcQApCS6apx|x@pD94efeD|1 z2%ixiGD#?bCf+^48H{U3&#jTnPIvpmz7L{0DnB=^Oux5$XU)kVHkfr2Z#HIE!1XIgKyCsNcOa zBM%y0K98$XP+ty}nS137uvA=^{trfohW|z>6o5nw{ly4kdyOf$YVoF9rqU81Zkr@F zRHMr=f{1ubULpf)AcwtE++FrAKTU=MQ)!MJjA>(GODv=o3C-%sO-LBZ zy<)}!`S?kH6N8U38G9;W8Iwyv{N_8TG&M(;4;+f)TC(j7=kBB9rQP2AcxV}rwTl?* zu!AJupr|hYt^LgHt>^PIDk$M4i%b7Y9Jik!7%nbf23apwhR+kPmyUWSue3^`1mCln zhhoT31))(oputt|BtGFzIm%4=g_N`{{Z}+w2E-2O7bAceQHml`paUg*o(m#YL5MLP zBpouZ%d_UFcLXf1%h#wTsbf*f%nlyt8}y@ucM=M5kjx(`%wOG;Y5DR0PmJ)eP{RA@ zfB`6!&!r7`l>{Cr^9-;sOI!Fk*WpD>QriBNC=X_83;T-^)ZnGc0NL1mCDvUuw!^cf;BE28Xf3{RUAZnfyw697^pBEHA(*C?1 z3TO|`a~?~cR;u)T60)xwH@IrA7C0C}Hm!r8cvNM-$h07u9{@Jl$%(Z{FmBN-T>lks z*HW6lO4n)EQklOBj<;q>a_lH7+^{0qx+Xt!RuOCQ5O4p=E8OtfmnO*p=7j^7C=0QK zd`|$CrAY9ZYvOtIkJoRO*PSIAe_XX|m<~B|REU49i!YQJtx9B*8LN7X#vgsS*M2AB z<((PN-9Y)G9>NGcZHuINvj8qzG*lox1uf+|EngDUVPo=YR!Fc-QSpE!j>qu?7C)@b zgFKfH?jMj9Vq#j96iiyI&!bXIONAKa$E=B?Toi?8l20lnyIH*qC9?6nyPNv}1yNE+ zP@PvN-W@l4V;bE{U2!((1n!AtfYlCMj<5NZd zXQm>s!v}rkNwX`>rcK9&YiIlIq8ir?KC|;G_Dn1hE9gmy3w>+tYEI!qT=vegxk5vy zha!B!;$j+4iMBL@Ydq`WbZ$kUzrS}-q04(bV*`n5qmifUpjvf?^M-vLdF1kR4ak=Q zgKNX9_V4_b@#noc+_|F*UL>{Iqb=#54;N_nF1M!Vq7%0oy_Sr}SzpzfZg~~kgJYKK zG@i!$@tEtiqYZyJ4v(X)n2McG(knCV;YH`@gR?DwQzPhhwjR{hfBIX2ep4Tbbt(rR zkXOsNe{Yt0D2wiP-=N&xHmb#oPk(*o2SA`y-rHCGXWOkX)5gHVyB2$UpeX~JCn?uu zU7lt(z1bG+T-GQD-5r((3yV4(Z?A2{CU3puo7T*BV+qNx?C$5PD&XD0UN3t9?Y>-SZr8SPQe?Dj{Si9Od8pFUVb+bPbJXfO zpc>DRnu_*07W~H_vo~8o#zAwM&(Un=S^X?F zw(9&svUUrm(9$Qh(t+B!o%>*6GoGK*1cKj5(#8 z{O?oNX{OybKnYGt^t)G|0S;avJ>i_PxRGuWgN!3ZA6+{j9_Y8-x0#^?|bbTR4d)D0sNQS-P@6 zx~`(jvk&rJ5$g4@hPwH;cBicJOC{W=u;%Mb=;=WE#=b4=i+zSLxCihWgv%j*j4Xy#*}&7P*or>3H4?{|fv74JWEo2QmkcD222&$A7L##-Bx zE-ac{1C!&@+7#d-Y2C@NaP}6*3=P~ln?+8GC~DuZ4X>!{b%j3!#v^x16UmC)hf4}y zUxgnP4cVB%J;GD1q{s~xcvQnZ z-=)oL3Zgt|9{uuRzGBewZNCU2{B?i&Hg+YAkKN<-uJ6ojo31PaVXTSY(%*JFV_@t% ztaI4n&fNzxuVHNy@1YNBs$cp~cSf2FyrPqcn3M!dv5Tlt?b(*Sbl!X6{x{XTZ^SIJ z!&VX&ea=qKM!^BibftK`unSBp_XI4VRd~R}7q7#q)SM#~M%q>=P3RYJ?cH5^xd>?P zj*JGOU6io;nHOHSunlDP{tw3PDyR*{U-v!k?(XhZoZ?QA7I!Z$#oY-6clT0Uio3g( z7PsK;?(Fp4?>cLp^PjV4U!)hI7n#Wn!{n3S^ChbFUf2p}Q>`%hmE&>w(kLKb56|ip zbQwD~_yr)?Tkf;%zy1`2>of`bhl}wvT=~&rZLN zy4siDI^JZ*L1soYpwH#nUh*x<1D^JVSvarRYA&P=*74M|k1as0AnX!;Z#iOVt=iWu zjBzA@ou~G2u!C1%fjSFKq~9FJjvRgLHG^+^X$q^$nEZOJR8b3XqtW`crsZc$#kUm3 zjO2i3g_y*SW9Os=b%MKifj{d!Hj_VWCpy?2V~~(hUGp3thpy5SuP3&<@1u#D%=`@1 zuQLDCueay%eFN={AgTbbNvM&gcH%mQcl6*MV1=e~;1r0u zYL?~g9wTsx%B}8dU~znZ%5u_uTvHnE^cZ$vZ|y%p=&s7edj8;YY*O(RHcEv9er?-uW#PR&|t=Uca-{EPkj;Tnn|7D6G1L+ED-O3VVB zLRwmj<+h45m=qk>vV27~11biIh~PrBTXrc%967+jFOdWC2tn6smj*r+w^C(Nq~)9x z-)ouAzUgVs79IiDr%#sMkM4Y~UrOIE7sklmx1VjPJs$_&;qU$XOMPF-og3nxOqOf* zK8ya?KmUdfa`=eGk{jNAG_8X4vQk_RsAx?i28s82p89PMZ*E%^%y~=9e<>hW{w%6Q zJio#41fr{c@9aD;dwX&d@9tD#-#2POYJIxaA$${^6uqn5Pi*EbcvoM;d%X1ZtG0r8 zqnPHWv!Ru3y~$}jofwm`%mWb^O;sw##%biuro1x%3vC!L7T&ooBWCLQUn4eM9oYW;?#W}<#|$+6Lm1M&U}y)8I4~KKc!eR zpsQm|Dm7e(V??a2v=YX$_ZQUR5oKLsIxIBO_oy)i6TAtU_-c%VVl%w#Kwu?F${+`_ za?=3i*dJLl`CZt3+`>w`^oYc8hJ%8HN`E57a&xPs846J`N%=4o(w`AmGFA*_u?2^? zYF<z~8E$Q6<}fL%qvt(k{_Dzj&q)Dx0(? zE3e!=-N$x;I38z?BrmX!syC8Qwt#(_qG#zUtd3;HS+)dd^;OF zyMvEP(JchdDOXDVALjf2Pv4dQuhkv(Z*CTBPOcj2h!D`);K7~0kN?ld+XK4D+Bh1| zfJ-=HCZoD7OUzXvEyewENt!B-1Vin#g7H;$1%Uho+en@%PiKf zKR4L?_(5o?>z|P}@muN7@84`sIpZhUqyta6-Eq@-`#~-~pzGZ0H~({=sfqY3jS6=; zIc@C`Qqd3z!LS{jV(Y>UoD=1CsvFE~wssal1yYH``12-QATWMWFRQqWxP|IrKeP)7H9S=zCqLzfeE><{8=llfq6t*j z!kl(t0PCXhEJx~vkV&@)qJ%$iFF@!HO+i(SiP;tjKuxmouMGx|S0%UL5;lw?ec=0> zQ?@~V#2aKntTSiC6xW;k;&mq*AF@1{8e)ZQIMY42rCrFYoF!bbbey@*e#r<1TPR{?QrVIsM@JHg%g@rV$Sj-7F z788NP5JGfigIg!`U)HD@CIM8$z6r@%-z&T~>hycm49@Qek-6)K$WmnQ% z22yPtSR%3TS)c>Y$lsiD*uObtuKhq0I*x4BX#T)VSn7^5-;8gEvAgX%fz)*)PqLr? z=9HJda}Qhb<{JY1$`5L(t%(UNSXnZ}o54Bd7I04a_dhvhuDPy+O5~lwsyH@ix}w<` z<{5;MB!VYn@3VH z%i(l0V!@6ic79aqtDa_u)+|b9vydyI-4Vz_jKI1~mJ(5;n3=@K3ixeq=a!;N5pT0D zj9U;n%rT*Hl2`)djjCL%67U>=I2o}~BTOf1umuQ^d_rUWW7Pyd=pnzR+s6S>!p2Ak z1)lFOKw{|qcfLOp#{!mtZsRUq=1`ww66VKvdEQr5QPIz6WFLZt)kV|F{Qa{Z9}(xK zxiv(mKfk_wKiXd^&wB^2F6iH0XPp{~!++=dePx0M1=;W56ie~* z#0{fkir0NKA*VBXK4ay`DZ)%2yC(J^tM7xv1;=qN3K3a6zU+6Yqk(hE}>Vo`@~2FK?$A)bT;OHV_?M zuqij%o+BuRCn-&r#GQBwk;cg3D$xo*z9O4Tp=!|BB~}PTf~y1`sZh=!u|-@ z&IVsl&foEVTQRpZnkm%Jh_6p!HKz{%VZc4>tcY!}t!{!J7Lj1%1+qAv_`JbHf zScFo@AE^_RR?hNX_%2Gnuf?#xsYsTONP%$d%9B5e;PHOqgs=}o;PL*ij)irfBJ6Nd zUVZTYttqE%pnz-27MmyqJtm9;X~xiYMCt3F`cZiMNojs>gtRC;KxAz*6~Nd9RfOW! z(pLYiDTjP!unPk$Q)^1aZ$p#5GO-%l^7N&OXoM=i)KV5zR51KXr9|hevm$L0zp`4f zA8lGmu!m=WDpE_*ALLY=g~zBwUNO6lW+w5sJc1S4Fr2_&#GE6rFfLJj$)CyK7lwPK zT@5l&wP)i=TwkPsa~ssW3}-#asKNokMOiGOy6uf5Yp)^x^u`$Em#NH+*6ws20Dc`% zFdk4aA2^zfNEwJonTSZ~ii{}yXmJFV8iI|d*E<8Z3g6#B6SDx9%Nf)0P zN0N@4;lTbdlEgz2Zi6$u2{Vmpo~A>TOO2Sj^qr3$VOp)!5GUG*t=EwUlZW%=iooB@ z^&!UML1f^9c=&>w`(=pxg^uHe%<|#Wz=Qpdhfh=vS(Tf7+J9@xS)WNok^9;6HUNLz z;>`{pp{uk}44(31l-Q5d7<-ClIm67&f09MOFj$+IF96gqouAd~_~G%q4QnzHqCRMp z7|uznD@HJh1sqmENd$)~wc&<`W>Kn@OPUf%nhHvq!bxsnh7S~>L=F>RS%Cj_PVv4& z+yig-D>#6+`*jwQTFg2d)RuQrM(k^LbCS^n!Q1@?7;C4?X~&7+?S6udZYzWo?ApD9 z-puiytN4Wb8?tlM&dgyy@3?7dvOJZhS_tIR-xYMdVVOr%oi?y)e!$X?li=b09$ksH zJQHJ)@g8qw`1#Lp^VcYq_ypSTmd31R#+@TQx>G&gkwJ~YLAPAh4qey9P^16kl<`~e z`?N}nw6J8m%RO#vCuV8@aBLWk@u=PMyd6M$&krjXL~G$7Q)2%tdr#Jt3n9DyR63Ee1v=`p%v3Ac<;`R;7(JjR!w!0XL7$PNyBz3T>+XwbBd$t2No~szqOG zNC90tcVF^4V9?4ifPXS;Wg~2cS^-0$HkzWSgdAVp{D}hLo)Mtn?t2KnqDqLH+rUN3A5kBGnG?weZ zLFKvz@7T)Y_V(gA<7xQfxpPjFS$(fu3McWn9(0tTs1LhiA1`DlW72LMI+Ru)uyqzv zh`w1Ed1`!l)v0+xR_l~eui=LAr$u;?X>;U&aRA4a7bq{_B{x*UdyG(m^iYCUP=YYP z73JhV7#sYkLAv0mvc3MAenaAb{_U(P;Lm4C19jFubM|Ffv(BniB3E(o&ypf&vfc!M zd%`Ks%hIE*pjl%IBy@~01hkUyClW?P7VqRLTqZ`pVj(EO}paNl&3QBBoS#|oprs_~{v>kzFgYhyfbVxW=BlRUaY z0I=Z8BRO(dge4`vFP_nq_9&tK5%wGEbM&4(9}adUK>oeiqF`nTQ~kT-CZY%a!;`ae zxQ>U|3UTarWwNp!yMKbpdrm?7m1;x{tple;qg1-<5h;C*{v|caggXiA* zY-ZZnC7s2|6}B1{206&1KoVOH9$ipXrMPKX8tl>BkLcY?yfiTHd+WFDUN&;g@7o^N zFNDB@KJp^&-)%430GHcg6D=G-gqGIF3%jVck&kbk&qr{fQtSO%ueqIEzo}dmCBG-tBrybYCa&Ov~J6YtiZ=izdW3(6saE8q$jT< z-AngnzcEeZe%In?V zw`CDyxSk#l&%&?G&?>#g_yr_wpGCCDblf{i=9TSVD})zvDjWI8RC3W4iXV`dNluuF zxV$b$t{R8VIs%p^;W!a534O(;gzt8Lza9R}T%Q1-HRf4;c*r@nB=K)}H}0D5gZ-3N zT<6?)qe8fRw$1H=)#z_M|5jSAjBc|%H|~$z{js*uCpaLl%m6&&Cet*P1HWDyeOS@B zP5s$+m=OMP(x-&PfLBmj@2hccF>8^jF7L0Ci&sPI>+Lc8hoj?dX2Ek2rWbl@xq#zE z5f=bu>mNl+T9fWQcYhxXF7QI(qKIhNb8f%!O+}6=U!grf!1@EZobDET~0M<0W2OL*u46+jF_*k(pI}!$oif=yG1)r zr;9{hmdLMG=-FV(8`-9ry=_g|NEHJ+8{r_E*kyeRe;f2T(W9J?BX7UGmB12w13OOpB)E z`oHzqu?M`}p3m>FTwOd0eMWYGL?gwB3{!6y_cOXq%Zg@65K=h?O^{B|6(kTfVq-s|H1&6MAtG%DaccE2Zn>OPcS zLF4qD@K+-(t)B8?bFZx9$DT zXTg|!r2^klbWXhE{VvI-HeeUgE+D+@&(pJ`XzPzx?01m9f5qr)bKd8*k9p}WLbxlA z^P3|=_aOhgzfpm8>NjW!qSYuroV)P+27aEJZ~aX!hHoVa!^CW)XWM!w+PBqQi^EzI zYZ=Y$S#beo&dWX`Y!9B(b?jk;+cS@M5?RcW*@j)4?CesRYCaZm_Z7T5UJvv8XaSrr z7VH79OSl13pqBuXch9|1jk*sH4H;cw(=}DkCz1EBFA5K!shZ+1?xNp(V;15T8Y|yoU+;6e z5udJYHT}adX1Pb z-i2SPHcNfDqS2~Hgb;%0-x`m;F)=ASmU&>N8PON}hmV^`BWrD3h2Kp0t4|3pZSUNh z3wzxX>i>AZ9MftM{<+_O?({eq3aoGGsPhz>@hLsbr*08;+sVRi5$iaFo8)4c;me>gX;ucEXFp%C-8iS)zE(q{f#*F*2iu*jRApVrc>ezF8 zc<(OqBJ?Pn4cf2oG88)f-f271rr}^Q&3PhnemGj;w@s;^t#Rexs=e8HN z%;pJTspgpcK`2$P&k7f2+uz#iZhUS34W9&JCg9_&J;V&^YKO6(-5kkq@8x@=bJc4_ z2Y7O~cD=#q*^lacD=qHCW51S$T>lnt$u~0kc;f=y=;ewoqN~8!n0h3)vcJ$6{7Gj5 zKirnlvcolsKKp%})+^2Gdz}SGF@qQMigfsQj~u?x7kMi>?KVLL-dMZvYpxa;+o-+mvR>omGg%ffGDWHA+R znx|_WoOzDuJU>!2(WuNji39#zB=rWr@>j|=m2>yW#R2Zl1T_+MV%4{N~v2;jINeb<;XKyniE0 znpJeyx+gFD^W|aqamIhFJdVAuj%-;m(`mic^4>3?ab{2Pp4lPz8NX~U`!@8Lofz(e z!WOo+5)&q7PP@fKAv2G6?;(rnX_skcbAXcSil`Vg{zUd8rd_}rOt%0}GYBW^mAUth zxc#`JsP)y7;7-)joDR0>eZ$YdIdjD^)gtDR0vM3kXRltr9Qtd~xhNt{KflTJiP zNRX~gq?budl$8-Y&Z@PWm2VP&TFWe6UxDC36*swJ(vn^0X9qqBh-)a~rD53_ZAAWf z7w15DwNZi!v{)EvLg7k|bHKi;B`N`}%c%uzmm}W-4UYPz{(W62RSB28w z=Z_?=alua)=!FTFhwU+=j#YBeQ&uxHj0K0b{Qf?{r07}^571K`FD6XZ2}`JohgVe< z+0Fly@rhs;>q;B3T!RmM=uo6DL53&Z$Rq*as^Eaa@S=d3)WXpXQ)8p_P*AHvnk}AS zivEV!+8t_>5(H_XX_BlsVn8w5Fl8*2ph2(+SX~ky*GOIp#q=FnBT~k+yiP)t<~2z^93jQ#5mmFqmPv?ReHCkzA4sOk;!tqiIfK&`k#73* zYYJ6nUHXXoAzz!;T&7xM(T5vy-Of%>wSG6y-^cDESvv zj{$tHTsykOSZYp4;OsKymMJW_yBrm33SYh7YX4YBMKgd@$FLjmL>S?Xo00s*Z`xD#aA0+0~3P85kmYu-edo)kZ8rosU}qGG^2cH(%aT6&B%E zL7P$u^3$jM3}s3(o)V;OBgXrN-o3l27X>P(+q8jRb$_1r*IZhGFKrzOIK%h5xh66)=#2I`LnePL8U;-sF zIXRk)cf14EA4F+Y9GW!SIu!4OC9{cwHfO>4tmwf^y~MTgJ{oX$IStZzX^+m+H4zz( zVF-H=#$`oUQX=vnwdq2^iquYQHL&N5DjN9Agsh27m56#n8SKZ1;xFC=Fy~sGCh=M@ z$r*i zvS5XB%*GmLNL*1Z!HPH*Cz=BF(ov33E{?0n*My>)w7egi7CNd>jCv%V>Ock>oL%nC zJ$EgD#b_EeUR2^(g^|GkVuHW5`Zv2w|AlQ%w@dL(h$^Wv0Ef1)%_&}5u)b(QX7;#N z1@GVN@}qRIYgQ$GSk_<+)#MhAc8PQPG9S<_h@|0`)1n85*6mJ#2kING!qd;s$)Wr& zax2`(WCWrw%UTn8a`e!rugl zUQ?D(g;K(_YAQ;J2Yc6%qSj1JZ9rBGISv5GzUqNRQTc1I zy>@$6zbS{~;Yd?3>yeOI1zH?n*eMiU#(GvEKwYJ0u7=62oF6;&m6^x#eg#i6a2V|m zO4SB>U()Uj9)N50VMv}!Fn9?EQo2;0(j`eShQ)$4um>!fJy_V$DKo$E@`aLAS0- zEJFZzQlGeg)dpYhKh-A8*riEmnuJX5|E)I0Yy2ocD{p?yI{ugqKbSdq3u9Xdb@Fmm zKPhl?IVD1M#HBTdxAxQi&BqP1qnlGmWl#Y`9^wg2xkIwiYkA&*`g5>>`t2WxxizFk zGpM{W&(kW1ysVy&xHatR9e3OvUFw%l3WLXizy)#K8p(g;Cbra*RO9kVcknp9K}SUD zbZQGimi%iq4I+gMabk@2XZKH^fg}z~2+9w0r@*zX9)vb&r$h(s&I0p%^BOCyR>uhJ z#9qdu&_9sM51Npmj2=Rd+^pOLyew1$QBpP?87&!eqHJ1IP!e$wk?#h{p%D9I>fXjL z&2v>APv<%)e9Pb{I;Th!UXu__o*H7{1|VZY7Q00su^Oy4f$SUfT#wVg@eZNga8Ng| zkJpAXhw)M;i$$U>kn^HC`E~BQCLJF-ejWWLFv`#vtA_7LaZ{uR*xB5mb(&0 zk&fXa2yd5b>JF+Cu~WdCN2%Dz&TEVuX{wI)}&rG0_^p=|a%Xu1OGL`ta& z(%c&Qb+KXrkw3yDFBq&g7NQ&+x7CZdTi2t9@a$0yF9(-6oxSFi*4{VaFopXVIgdc8 z8O3g7Z)Yb4>9{Y(aMJAD2qH55CZ*!J-#4Aqi#%f~JY#9vCuT25q%S6@I+q|lV`XlG z#a_N4_?t!KM{cH#Ul3RHpJRrDicq?pQAD}>9fkWHq597`!$I2w-IF-okpuQBRu8C} zm)Ik}b1NhB!Zy=nFZvG!dw$-$1ZlE_v-$m!`GJ31s?-N&n@4l=F0u&Ef7u4o`Puv* zwvn#Mgh2k6ZKD2@Z7L-TLn%4KDAmT|J%N;$Jd&nNl7-QfmnhYrrgNrwIz zZOa`bJ(njg{$v=rdbd3P!?gzy_3J_Kf#E_INt)g8(jY=!5v96#Pi7f>MPkq{CH2Y; znM$bPiY*8`&e`Mkw?OC^S)5?P`_WXplvCkk1a^G zqX@KXIBLNW?9QPc-H9IEz8>AN9`Br>#^|71cJN2|&;P1T`f)dY-!Ay;1QA7`U*Pbv z%S}1OJFMx4rHd@{e%r-=xJIvev+*CR0hti{r`daotX%lm^%F0K^VOj`ni8)^yD<45 zjbY;=uLirAVR$KP(lCrAj+=y>Z%;X8e~4A2|DLq;a8gq80x!PI*p0g{mCbOOyryzt$bS{C!IHSiHcOIP=RZAZkySHACmS^xdoHOt6Y%_iQ{v^~ z)4|~_g(!iUifMjxvmFdnB8S;@pxLJk+Hma`@zm{N^TUpTA}N^*TnW$A6umB)>kJjk zJ4S4X!#s!r)5=GuZafF0qyAH7EC=|AX0&zMs=F`%&s@LvKe=VK*G%anPKnj+@6RyQ zXYw+uj;;eDgAqQNnD=@ztLFsH(;Qp576Y%jnD->qXI?U^$ShTcG{MIw47K>f;6V4o;vmOh2GK8$SJ!UP>q%6;rEd2?lSL3^Q#l9eO1sd2) zlA*WT)O#RjizsA?=w@{aWpu)4bkdi66x=+grg4%cJ}I88>GG3zX(f3}W9NXYz^Fj1 zZMOW7`bS!auza4GhQA)@rO`$#}4_MVinULYt$bi)* z_pjO{B`s0#TfOsjcN1E3Y=E+T{Q1c3BHly;_@P9K|VEXgTHpVx{mH+C*`=|SWrJbnuGtw7iMbaBjYDXIgUeRP`n~UX-;h# zBMw*S^0vysQQwOu?&KhhNjul`pdd-q&jR=2p#2$mvqmt= zxDk9TI?sucd=zS4F!15519a!bq58c)>r!P$UN9>E@W=c7B z&KAvgn1I&MbY7hmjR0{aET{YeF)HMZbq5b8;=ZV@nZ?si{)U4o#B*hz-_T-tt%O2=(-rZJORrbNrlFr$TJuB zB8t9>f;x8e9EJJVLqKgWv#p~6#Sc`mXTEz=%R!l|H-iatGrgm_&o%L}CWO_|y3g8p zb?29eDV&+Bg;8tY%HbPs+|GVhY-KIJb*cFiNOArE7JfMHH_G+EWXYzNTZnn4RN&^Kj*K3?RXuQ~BGNaMD z)IZEJ=E%e;Rc);}p6w*os9({~=P*@gZtWI*rvkxZ04UJECVHm7!yW)cG z23c_{ygj{~H-b{XqI*Bkt#o|(!FqR55AVX)IkKMHkFVS5ygE*w0?1w=_%g`8g0=K` zL&t7(e{fsEY(VyQ*ipLJUHA1e9Cc04UXMQtpwg~&dUz|`?Rxf`5dPlt5!TwY{ii0n zC~bhuY#g-k%S#G6pX)0LdS0XZ4%vb*yXeVo%VxMJvlsu-!|V+k*~dns z#d;G-;TLjxA4fZVgVFA_7PU0DG?9+&J?jFGR=|0>-&Itz=+)IWzB8uAA7SC4^P&93 z9}iWuZ=HPpW#N1Gk>m;n1s@=NMUI9Vn%LhnfeQ(XR6p@~qXXWpNn_4jNF(G>UycbZ z$QDhGQnG}q1Ae-F8o0_5x)4O`)_=l$L(i#wuZyL;Z;gG3ZkT;vV{|zuZ%Z`}-mckP zQx-vZr-gZln_9|TG}3(EibC)6H)VZZGz>9*&QRV8sm)U#f4j9_^e;QyyeA8N{n&V~ z^76j_`)CjRAB{TwK2nsoL{F4f<@o>h}+yc|7RuXr0&Z;3>yAQW#`Je<}vb$=W<+?5RQ z`7jZ^?8QfP_gcHo;vdpqTP0Pw#a@MZwYTH_5ZuDOb;i&Bem7MAB{j_JF~sFdh;G%@ zMUDGn*4(xf?z{H1nZT{A*W*v@y)fnO?al7>kKE0Uw(Lgq)3|Rd(M)d}LzjF_chy^` z@ONx^AKQT{c^9XVGwq$e+sEJTahsVQC9}NIxX`i(hh=>;8n%XYUS1N_LA~KGICg|^r9hl@#EQW7= zCA(RA8Z+1D13$xL$$K901DdbT>1&S;f-?-i#R)nyKJ-CvIQnipdfe@|7+6%mVT;~4 z2-wer)RL1MgM1jh{$w*=N51wQp{0P|u8l|BP*mCMGC;Tc#(TztZ2#oh9}dBm%vlPE z+=sZ3h0kiv?usFKPLKAVk4*H@*gZ`M*&OBB{bI+i&b|Hap$X5g=kHrcynP-S^>f!> z4{7zu@;L5=*=nZW;}1lZsdf?Arbz@mh|&P=u@A7**q31Pdcb!XIKRj>MC-Z}hYA05&kD%)btKEk1O7s$V2Og`M1h=Ul z4WuPaM`C}3j`nve;H_W7E&Vo27X_e>hEkM`3|gTVflRx9;D;03twxQrqim|C%YFq0 ztDy57es)Y6o>6gNV843}#k}~L!{cw-My#F8SC5@FYaT$B>yuQ-wPKyU^8x<8{fdkX zM0#hXr#pHwm%B0G7(t}Se)^LwsWZ#){fWh$y~G}Qjr{gJ7Crqo z5at!G(335evEmQb2T_$b3+*MSOrc?!?(H=S7-y}1qgSiU2*+;Q!C70x1#jue&C_m3 zz}t?-BYb%;;?Mou#1mfChRte?p0rT)Z3s4%l5uYDpmwyJv}@p zDR{wYc+G$+E{v<#yQDF`xzGUP(<9rHtN~zPQES>)c1)Fq4*EfBG#Yqfhx47mGT?MWjFb)Zas=GXWv(r>nAO! zYb_Ql@`{TWfo=E3UFyXq&y&Yu(X1;J9KFi1nq*t0uAq*0&#Cuze|he%#iV3}Dx(MH zn4Mf;bBrGHQ0MeJ1%TrvRmpz_$N%UoIrP8}k?_ZRLPrV){y{VBpPr2?8mMY1s=aSs z5{yPCtb2-5>%x#KG`DzBQl`0L3@H&X5cu=`Ql!2uwjpU<&%bXhI$}Bna6PMe7+9O@Swlt+487EkWFhl{OMVBQanhS=AOjc}7$Jb7{@R zlDFE~TG}1Eib=PVxj4D0nsv^`TT4S)l~tOt{AfP4i2>9)F|{Ob)xCfI_;M{g3eIKw zP4C`6cb*GyCz&iY2BU_@V@Ss2sY}XAR%>L^ASw5rH84xmZMZI3%&47UY?*}#$5n}` zhlXm4<4I>>$P$NP{-SoT*TGSY_9>+1uBzHh2zbjFl-9w;Z&2V2mi;MYUIZ~H9JgkI zjWfg8p07&c2oqk);9|f-OQ@rZB!wYm5Pis|k}Ek;+$htN5fZP)Qfg5hR;}3#5AzjW zQ|xqeDg0!_&Vj8oY0?6I64uS{zpr;cbJyuY+v^;+u$Z%T> znvq}VGbF0KV=)DfP$>@2mHcACPpHiF9zQ{VCx#4Zp9bK^VgaFBi_!nS-kFwOFx>fj zz4N0dM$-sFT1N((dv7@h7R@dbWMz1_zNXZtISL%PNP| zybE1xIp@G|q@F=jKAtv855`{o`?u3Cm}2%tisC}x>m7&aD%M=69DDe(PQqicAiRO* zdI;B>jAJv&W>{UI6xe|dq<(-h7AX;T_P596K0gMw(q@3KcUsh{3}j%K{E2C>V5M|m zAzl8t-jVtsgCT!-c_N(xVq**mEo0IJR_#MyZZDF;IvhUYv?kk*m4f+R|C6PALJl z0v2%;t_{1NEE%qwQcsf0`pnwFWyNwWzB9tw(FNO%i5X8C$4)N8M%GWx7T9XtsL88t zWSXF$wdbT`*UYyasneb9*K6}2kfR+&24JYrD&j!WU3HjA4I=?N&|ysg;=c~`%ktns zYN@A?;THl|ajI(pWRaOqui=Jk0U}1@sBw>eyag0=`CREoHR4@;9kJF~48PzwrC7xjEj8yrXU#yCFqP&Nr z;bxQeuLIr4|8*dyCrq#d@d(%92-`sw(Ufb{OSuW?0*R^Aibbf@7-JGjKMyIWV{Oxn z-FrrIG0P=)BVy5xB-Zn3_x;*Wfv`pZ&E{2TRX+k;6NG6d2qKNq)rO*jt6Vmz1_%OD<$0Du3WdHqu$mAA zeM!T?4n(FMq=0R4{w(!~u^}2yC#?esMD*;~`O$)+#1%AJ2fW4sUeA91d&yJ5pH|90 zZOlKd?wnh^C9F62sD`^gte(AJ%AZ!wKRvl5>CDCqX$W+o99sGcwJVs-4E~ukEW~NJ zJ3dStcfNcWiKK&7MRc-;x;bI^xXc9%k+nafCl|yD?2$qqkc)4iJ#E}t`Vm|Af(#b# zNL2|YiUAmf37nb1=`6v$h(=_goQdp^x~QC@M--Oz%SFHN5{G2Z>`^2`IvX!GE<@}n z3~pC~^$>u**T7xHWolv8a5DPRYfv2At_yqbhP(QK$=z1tWIbO`dt?3jN9g^JW+h`% zd+eyoJ@J+Kq$X6l+62oh)2M!jeu1E!@lPMIPN9Ow7>j0gVR+FmcQX2+!+=!1j7j#N zARW0=wW~<9dC>x?j>ux;S(K?g;nf<8VeC-zj|@`E?bX4DJ2aR`UTkrH4|jsrNjp@; zL1Yk5@JOXRLFkbc@Vi>w$FhP8WMCS4k`FvCL3)^g`Ka}{tcKLZ5tPGZWK`~ZYU=A( zeATG8du*FhBf=655|aF&AVGxK^noS$5M-7sjJ`(A z%1bO5AgEs#9UETNDyAH5a}k97{p|Zq3lBM<$veETH!2DD!I_QU0;)vbjKQ{{ z&|gFq=IdT?p3;UL;e*bPE=X_4eYyQ^IY&jkSQ5~t80quLK`$EoU{=(i_s)T`gk9qTeFy{!8}j zEN}E;_?MM{aBVSkRyp?I;TUe5O=1J&9|(_VijW^+h)uo(+>;Sm))H1zB*2{yOBcc~ zlHfL->bUFG*>y`93%M%BZfVmVK_lFSonBrj{XN_PhLJk4&Lf{hC@68& ze!=knb)WooPfw2iC7_ot%*)oX|6sYAlLXiDR6$gra}URDpk{wtMQ}% z+1xgalF91*;76v4x;%n(Z@n|%#oNwJQkvx1%gJSOr@uLswfEfxp8V1ZIgiruI_ z&Kx0i!;BnBb!nubWJQ}0u9CUmP4Jl1KEh*tgip}0wDm=!^WBSIriAoGliuwUdke>W z3&)nk-eym`fdA83nhd=~FAngedOIa4?}V>mf; zIMw>n(}SXLmNHP5bXw$;Bb<0M7K&O-|GC>q8nI?C{P7*y-RdEoE#l1egm~|tyPZuE zB8lSA%2w~XF#;XK-&eV0mr@6RFxVHU4aG6?WK%Erd7tFYGhy<6>F*yKY9`NquZBGtVtpm&#F>Yhf-jJmQ_3a0^9!EG;vy7+sl0sG~@zQ?s$Pm&uMR zw@0VKJ*5BMiosSS#!fHRT@LRJ3+-(Y{|<3Uq;tVkw=rkeTT>5PGsEi1s&_$b*XvjZ z+dYWa5zFc+qIUt;v=L+1o3$9sx19~lYrB#fgaQq2L8-bk^E{nTQmhf$etwR(^M~Q)fm@m8{)PzwxYD+hxh{`lW zKUF`U#1&^viz+waWs&9qDhuClE)kWl_vSrKU8AsI_?rHQ0F{hPDMIxg2IXx!JQ_dI z5yA2+S3s7aeyH+4Q>hS&2<-ZWY(0(cRa#9@n^pX#Ctuu)pOTAk%>^BP{ey5y4513mf>`#IPT52o=~4) z#Csa-vpANjT&in9zmf)`2wyQ>kr3FTy|Fw5=&!dZFB1g(9_{)WC;>CZ!Zmdi%j_3I-!?;YV+Y}Um zYs<&Z%cu0k;rH%1E$*edxjH%cSTG`oy!HdeLQ2BIfB}#^FT4IL1z-TwpQa~~NB<2q zUXOt^Z#;kTlp1-(5@Z?jLH%P4!ovZvfE&|60sT+jd_dRF?m=7y4HWqDbAH*9T1HtV z4NhyOB~=1IE)bOi?3A1zoqg}UwI%0{z+y(Bk;_zGijMOukKMmd(&9&~dC+Hkw()=} zZQmlBdvw6B`s_9E$937WmX1XSh*<~mSqF-B2fo);ASb$EAm^$X58lXmcOSx!l$ajCkU>G87LnDB0rkdZDGx?)3p@Vl(8M37huC07%aAL&U0c>2aXEp;)9_S zhX0uWIk^CaJox`L0on&lfCkJJ7Bc{`9t1#!2O6++|7!yDp$?b;9Rz}w5VXJP9IVxUB!I`&~R=3x?}Sf*x=ci=HN(RU)7ERmmPCWZ6pz4xMj(kYJZKawwHN2|su5adlq1r48Nx?k(gmZ}sU7E!O|+Fc(5dn5b0S4}lL`6N_q?+ARHcB|dl=Ho__x86{n+NJ=StW1+c$}*P z1o+SPAWIvtt95&a4UK>ppR*^YyYmh6Zsdx)vFpq!vbCT`xZ;<3s0k_*uPI zXTeiEJ@-REipj5@ZaJLEJ?#ynVi2TSoe!jKxw>xKmT+9w_6~BMuM>)yqM7mEXQ>}1 z==i-&&6$RI4-ay<)Y=}yCyYINEOpmIJqt6dhmTQT?^>Wc&09U|O+~>78Xb0mf03bi z4wuu2zsFx5aEzq{4w`*dIjvy9VY|O*G-PM-{hx!UHpm4d6=R+!e~6Nq4B@Fy}OLzybs<#NMRU-tI|F`IP0D~Ami}l zJ-mz0v_S-e@o&=1KRbmW8B+QPI6V;- za}b-oS4|z5KWDj1(ANWQP%NWkhRSga1QrF`A@(R1aKC1yc|TE=N*=qG0&YB%adPA&o>TjE>)=&v02dOza$c_7T;5ACu{Rfl}YsbNR3y952c! zdA&av2XMqlUEf$vJsgstd%Q3NxgEt*B;R?kM|ajXXmR;u@i^#d4A}$VTJl3S8PE1a zJG-9Ua-B}OHjp|wYwDvM3tqRglB0IdJ}LW@1E1s@l}RP-sp_X2B_B9!jUxpH8}rWY zTJ8JWGZYT7br+X_*p)CpI`Yq8~Lbw4y)|> zX^t#9R?G4Zhz`}OgO_N9BF>mT>wt!Psg2 z1>FnM#H)(qQ`!A>cO3%~va!)^wr8QNjaa%>Bo|fHd-UWCMbp{46qs-=-YH|EN!6GWGxg6n~#GabcSJEr)E=7@~T(czW^FzX7da`VJ+|WbG1vlPWISfN-DiQ9S zwNuaMGzja8kIQ~Z?zg)HeG&WC&HtJLN%f?|3K8~`i0b>+6Fv=GZeZ>B@}$0eAN9-p zp|_s|S!)i9#pxRjZj>(tU#;(3`y+O|2%8nBBe%cbjGUXEPVAf>v!<42gd}#T|B`vh zVX^^}Q}}Hv%Rx8ijqfOXi>vEu1?T)lMUn$^a)M7Oqs;I!1!Zlu(C)%ylz}l`)N}8beVV=_3v~QY!oFGWG&k|H!@zQ}{F8dW6e9*CNlKr0yq9iq$}(!+!9Q+!q8+!4Wn*4-i0L_s?V6O$W{?*xwsY3@t?rkG2ch!2)q2~|TX&5($S-vkF zj#DHH7w8ubwqBoc@kFiRlh>G+ZE^xNFDGT)MR1N(a9OP|G3FG|!)P>7-2;5wI|iGi z9Uafhx$6%uxmx`8FNdRIh%*(=NosJu9RUpL52VqvCp#$x6=!VSV?7jA&j)Syx8uHa zzfQPqUC;UoGCF_dh72X1vYPXBT-M!8bU7Y>*K^HO8NEkg?6^;UAh=?m)o)w6pbK5} z-|xk1q+;>`@hUo&U~LMSe0&$sF<(Q!wQ1IVtkUBu44a>L4`H z%{yN&O;%lMIOo>p23=RHE`6Sv0m^5$uXg0e22u@ySQRO`oGE_fCAEFZ_IQWheMW9L zl1{x(Z{tD}9DrW>&_8^lJ?(wtK==715jqrL;i#aec^ex~$$_p`_w?Rod2TT2yoU4H z+r1m$)Ga{qNPUsf{#F$+m9p(Q`Ni{9g!t)D*D@7PIgkiYAl%^_Nsv5DPPld3}&p(}#a0U8N<1OIxOV32qL?(IvjOYJ5V3LBzY1 zkejA|fg@*R*4TlhcI^d!J_Aekmrz3GFhC|klktYdBHxK1|4z&=KMa(etNySxTLdl0zQv%|Pth9LNTz6`7{bpRw2e62(fQl`N+-IxhT zyk`pQ6VkTq(20*^b$>A#rI6G43kuP#biv=9&oH;OE@Aa=9oufV}(1j>V`mCs&XH} zLpbbwNhpnqORzVl(u&w6NQDYSFb@XGGY&;`8j&7;n9$6~d}2l{>_FLfPNrUEq^cxw1z~QgpYg>svsVhYYRQxWs`D^;N&lNZU9kuYBeEn)(vXrdY{JbJ=v+e0uWOO51>yt=O)f0R^`h_3|ZG zPO#b?a;S)ESf(Y$nOHqX_p39}SQ&<7AJCWG6Je+JN8_9v7n2OI%ABFLy_gW2!OZ5G z#9k^7GuhwrF1F~C&c{o8wp}FBtniN({Tbe8z*a|WigF%05k4;{qn?_FtP}4`7G;im zpHO`Kx{ODqO0RLcAXwRh03+HB9Sr?!G3xmJtDm137Bjx|2dvEeq(2%;Lzs7+9$CtW zQIjTDg4v%n&<#SPLqyX{L#8lp%w<&snh5!l5J7-E@^%xy2wwwzm4iv$Tc)f^;joaTYp`bsd%1 zOn)7)&G^0^3YX$Yav0}#nYF`S6ZhqH`2YzVJaYhI?&$!s;};ro=B8yk!mii^QnMS8`*AaO(iT~0gI^$+|Hsk2(2}md-u!g_& z#G$}ra!-XRxYt*v_x~#9&UyLTaBN|vgxI3dY=$PI;jJ?>m%*#XVk$~}rJy8kCJi_f zKJ?(F>QjZ4e)g{`R)InAGyr=9j|Ufvg@wxjI6F6>gYnD#sGDCO{3in|YrU7(b^k&) zWw->y-g9t#fzDzWujP-rl^bBnHBd|e81iF%0ZAropb^^hEBiu7H>i<6?Vw3a=2C=m-z7%;iWxf@2!;@L>|iI{hi_O-+EEjf zb)bc@qbih>WFBcr{NHmz1d-z;WB0p;oB&${^?KzeT(+4hOR6hpup^dQ^z~=@#pi5^ z9b#om{^t#p9v874lkpw+3?6)fkK9_1?gq0aBWFzx+oU6Bj=N6L__B{T?&0ST%2ald zCM=tY70!a)Z;+#i+pOH&S=J<+aJ0@pn@pxV(-Z~Sb}D^U`G3kgoOIKg13e*M#?c`j zauqR?p(MZ$J34d2eam(gPMK0g1iLG$)N5=<8z<80;h9xsR13Vjx8=VP(cEimudjM? zHOGQ^=KV(>VU9#umZlkjz@Z2&6zgJG^7iY4Pt~w##(;M$l!rI6P2ID1rc>nl-Wp_k z+JvNLF+2Z|to5Vt8^&HY!-$_$hp(9*pwhH8FXi{aoulKLu{$1j_Oj?5a~^AW;d5H)aNE?ziMM>PZA|x-;)kQjOz)e zeo(~lW(dQk)^nKhkP6E0K7)gMYrTiQ7B{v-jaxO@=u}0ZWX4aNS8c z*d2fz9?~xVt{S*?vZD_*K;m{?ijRMnt1hu*(_d0Wt2kw+h)+N%F`PXdv~{K*7kQ4r zTq{sEoShiQM@I6N5P5EnwZa0pG&Y=_6vvlRFARL?3Eximtt|yet0Dx$>vV_=%YRJY zJ`qf=c{}NLXS~~$ID;blNBsfRv5M7&CVcxP+BA;j_sUE16~y24+#_*MSFf9z5@nJ@I@+WT8(4re|>N%(5V{YCXF~NSq zD3~%KIMpI51C|{WDp^s_W-*npP?vk2e2c;eMr@TK7wX>%a02#}RX?)IgM3b)LJL`; z>C1~ypcU%p9~TruEPhKh%=dM{hohIp*f=PFtZo24j@0Cd?I#n5U?*rHxRyehE4<4q z*-H_LObpYBBC%04p-P}iT2ldpiIyVU{N#OAp?~WmfWueM>Z07%Gy~v4NZ;N;{Z?li zI*TFgH1=~DpgJy*+ykH+NQCa6vyAICCD@Ech%kcoyfOHS{$L_z_{Q{OmGWJZYN-a? zw(K_W6hIQByZ&0+d}{4+?h%5Hf_I7yMg9?nd?1x*mPE;vNJ;us`FMkCpmYwOfi;i} z*35ER3ba0gu|0{pO+SBA)o+(S?k6{mZ@kf-WIiHn40>8+cD7Ugk3O=fR3ydlS08DM zkxc2N*536zMy{SE6D*PvNTUBxX(cUl#LAr|`%yIZJ&8U*xfOP>=>)X>Rt~v-c`k5{ zY)xrEx?FfVj|iF29a^H6flyfGdvKiA>0QSB(ayYS001*zIEv%vk5Tl(uYBbddw8K+ zFjakUt2nJwc;FI!SSft9dQ9{~eV-5DT<<-q8QtGW8;ZGV->?w-UC0v1JEokFJyF-* zoP8*4;BE`8CI*Iwgr4YoBZ(d-TsmMzwz9t+D=LW@;%M>x#K`S_+jkJ@FFr!?*Dyc% zw_#qpd!s=aFt~Ua2S-I?+53-S?o6Ha1AvcA2t$5B&oY5Q^%MS>x|jHCn4i>Jvz8S3 z^FG95U>Mzr6Nkklcb4ri|HVh1@iP$kwd+1Mc>spF)^OTr|Hu$_qUbssD*zvP)J&cr zWv=i|7>^baq*gQIO?H@lujTq{m_JKZ*W20=X~iu47Ta{M9mzkyGbTPbSnF)hEWBk7 zi7b5kYnb!oj!4qW-Ao+(W=$!Xsga>5_8*&w7GX(TVev9Onq8?oKId&f<}_DrHgoAr zN2^%?+RtH}qb{A&7M+O1-2$iS@+3ZC@;o6l{D+u)od_2+(M;@;`7Fyt6I3wkdKvhu_rG zhcdCyXkHJVW;)KD%a1zvS|y6*%d@7*22PE)9NUC`{VZay`hDV@RlEkb>ZK&pq-D{% z@%W>O2k=d0?jk$g`f0(DMq%#4FWnkp!O^3zc#XlLm5=-q)SV3IHK``ieb^+Ig4Nd| z%~NveQ~DcOpFu%)?iS_To5@rME>lTFdg0Up=||SOWSWwsbx{+j3h&i*(H98leLik$ z){GR@q?gEj2ml`m0X47xi;ujT^Br3>i0FdRc{R?(+d`P}K_HL7^8**zEz67k!V-={ zDveLZhDKA!Rb@|%phjWwZjmU#gvu8ylP6ILbHB}|L$TZMVBqJczLJ+`qYERe6b0u0 zX_x)Q6G~WCMfRd8ELB^Z4Is>I{}Sfp=qFzgpVMk@n>KR2W9Q|OZQ$09^ax& z?xZ;Rk##emT*T{pKJ;dS5`60+M02czsttwD=%NQdCg>ON*XKIUSJGpImmWY}Y}8`h z-1Bfk-^)gBC$xB4#GBsjjzUkEzH2%OOqb}~(GhgvpPIK(XivG@+}(f{!C9L`(^kND z`%&nMZssgZT%@@Wbvbq)g&|3{X--E~)Al5tgFa@uY z&+mES57SUpqr(2-{JI8JAwLMVm)m_u;K?7!uY41QK7Jg02yl+jNox&kI9MfrXd4Je zwLt{)xnSq2!q>a)+uyqZ5p69n5nbNAR$92F9bUVyM8BCvUBPj1AKoVDt?d-p-)dBJ zYfOKhX=P+?tpBORm%Cx1@CEu(I+~SiYt4bEG1wn~cD9;$ z3gKuc0C_q8-q&)oO=og3QMgQhIYNZ?r59t^#mFMQ$)`IDOM&?EiN{j#g~h_t{}BFs z1*LR|(vYj$8$CilJ+@rk7x{E2xT`alfiq5z*y3!bBdN~MSlzjo->@4DV`27LFhInICczm7qhATnZSh@kANtZ{(6sd|?fEiZ)BuOnH}BUw z;$#_1HO`pMSY_oET|{1XejR)0365LUEU^{{FW}S5 z3y`rr2^XNuho{YcEmdph>P=n!w9}<9Q+ieCZ7`RO^)VGad&$b$&i6>1K|TPTYIW91 zDY5e_a}uYODZc<^!FZe=bae11aO~3MeKN#{Tw~np-;0vH{;@Ik6M!`_nrM#1b}m=r zWB7Nq%n~BtesHmx-Dpq@Se9~9#<#POY;_Gr%-ny5xb7(SzUO?SxrY3w{e zua&?3Xmf)2=yNj6SDAfYUYc(HXgoE(F0Uatu_O=?y)X>@;La1Uaz}eC7=5cIdZZi3 zTL;|Kp8PBy5EvZuHannTgE?(p@AvMj+wk!~do=7gzw%r;m0wr={?ha&G8Ur~^bxJ| zSl)#BY83mRZPB&HIWBS%F|yaaW`zcw_^RpNm;e+7Tn zZrwc*ykWznzhy}-cW&#C^`Kc^<(E>;!rYank$;o2$-%&aZwiP^apT$l7BOddbHDFy zxoJw{$XTS-Bga!XyqVMvlG)<vLTi}UyKUgF|6n7weWAeS^Qtw)tH zo2U<{-%LIa!*k*5E2j~6CSUduzguf$og>J$Xg;P==(}27yS+@FCHg8?b#+Yh4>2xQ zoeyi6vw8X57ov;Blbp+lxLlM@kp??m#5OG5p3;ya4BB+06_=EdoeZ4rw~P!^AOX(i}=I!jTVO5|^~^R`HD8b<5^UMLXW4C-wEu7al~%+mj#+06`9Y zFK2u%F1+`@K;@;FzkJxHW$Qv7QZnMX`*CN-OGhq&eYlnetP0 z>~$SZ2p!Vve%yv{BNtyDXrjav2$?{tBVsk&^H+}10k?^C`QORyf7yT9Syi--v=4!4 zR&9Ej4~}{=*3Bh&*sAe2GmMwN*R;SG;bc~)y-k9fN>XrK%}^3JuJV-6kz~P;zU$OA zE^`rhB5Ku|{<^W6cGaJbFDl5)HSWL;+dY!4**BcneW=UamM9a*VVsA$3}r)?`{;$j z59++(Jt^>Hv|Bi0ddo#oZ_M#9Tw0^Qb{HdThry!*1mvFL5U82jz7B!ra8`YO}Y1r zqFL9e?{;!3D|cpMgN)CGTW~5EBzuZ;8&pk;&P{dd5Ok~)`yALKlMv|;1IjY9+ehW$ zX_sz>vMgn-pME4rExI? z*c7mT33};J^H2betX&%2CzH09Z?N*HSI$!Fu=5 zEFp(-@IYXUZy>C|nHcXoH(Iyf{=hjGum*w$`BZ6cB6A0JK~cVQpr2WL3A`}K>26X+bWP)NFqa@8fJ8RhxYCTmwEQu6JQ-;EYX0C9aHYOK# zOH`yqN+5lZv%ZnpzB6N)HVb6~42b|qlh@3yRE$s?V=aq455`}}JSZ+B?hL*DdA4Ml zK5{&AJzSKgn6d_D)kLfb&{2O9VvrX5?Il^?h8)7R?$610nH0mtpf%DkvFZc=X#rS5 zR(S%mE|fk+OGpCd&WTa*84@T}1O=G?MuRV3Foo=1GEcLbC4g9n4g)RYWIUI}K! zVm6;6eVQz!Rs3EQn<&~7B#MNpfrg4maQ%JsG#Dng78=mi#gy8#mPW@cWf!b#*M1C8 z)%D_?vPMLiFa9W$$2kHCOP;`nO!q@9p1lN;xHSAv^-|6iI$!{VWV;ASng~>;UYASt)cSB7X-?2++WM(=BVXIjjjz^@(J@rs@!n|#jU@C`?;9Jc zUcGNRGBb^+gyW)!yi}P5J5??_pss$NNj%ZNRS=41Ua4Cc9kEhL0{G##P-NLO5DjY~ zbQJ3JGI09xU5De9s7}an@?I8yy$1!VF&5o;>d`Id#;$zE&t-un$aH5{?EYk5&V&Z^ zbnWZs2+(TXn-?@JRu*yf0=ts2fK{PRnLkqgv@4#(W>xOgQ*20FrJ^B<)3d;u8fW_E z8C$;-e$h458sJk-2VXw|js)d4kfYzP3i%mwMj!xjTkH+_X}+`tOmmpgHU zf(H`ok7Pvr`9Sr}OGY)30k*&4!_hV0N>2&Z3{*prcEc>yiI$-`6Q4m7NIwJiRSp7# zR*x_b(+-FY1|R12&Umy)a_ni8NIZ4q6++>^bQL4Ee4VCpdBA_MdJxz~9xs$*b`1t- zjtm0P^a+w0Swo)&8^kl<7PB*9N#6m|1}H#J*Da~!YhL^hAzbfvR9^jWeWar*R87=@_<)_sX`Wj*EWv3gp5K4@lZ__XhMmT&%Me{j8SAqNqsOf^Dgk5V` z(*%)$|FvgU3nBXNnywf?O&17*WH=J^^-f{vEm5}*p-gx!Fe=P7ghYWWIHu{hJ>!Kb zl+qlEWd9Ocz4%i=JT24u+cXrw9i}}LT6riI^_2G_nVXCMsr^J+@$U9pB7KJA zbaZ0y4&==GOi^70j|Nb$i63^Qh9mH}<*zy_d|%LOd_^c`k$Z%)WU19foOGb(+*H0r z&~38`I9JN>Qc`s3?zlByp0Z(OCu7l5l^tgX{S{KZu|F-T|V3Dw+;5;Y^bi*bEA*~l|X zDUo@ZxfW5c`GvzCMt7VHeJJx!cH~D6nf!f-O4(C<>Gb7)spP$K9QdyB$OZ~^E?wFA z{^Bli`L_0ZP5{#36&@^6t6MhzRQ!dluvMyL6%C-Pc=ReiQS0S^d6w=zC{R5w1M@4~ zyebb^3iO?^pjA()w0Fwiyej^;wl3Y@u3nY94^j0$_(Zg`cNxVAO#b$C>HO2v^%$+< zY29T7DlT+NKVa-&2>LDPJpJt5#pW&a@k7z0K*=8^%cCS?-&FZabQDRbpB= zvGX0E(yArTBq61NZz27;8i`VXd>mMH8o#*u6(+n+(mUxoS8Mj3gkZNUmJIC@|Vr49MDh z)yP?w94Ct3TX6|JXltV|JgueOVnUf&Thq0f#0ldKq0z#Qfs|q<%V81MD{EJSiJ>^6 zyyjKet$i&+m%CDY1$slhvMTUHWF(N*WACwe;r{9>Sl}bN(9Oo*eCEczJiwa`{lG~p z$ZatFP6KOWg&P#r!9E(g%bHVcF;x3hgml&M)P!M!L_YZdHQre$uff*?2@Lj7X?+69+~5h^tIbEI+^l4$3-H7d{oxi$Dk%K?u;xC6?sTw49NSUh=eQ=yzcx3Y$q$*_o?!jhB%5NFl z)cRmn;pK^#nC~^H(1thiH2vY4`_JFSyCh*SL&TPAVMLF65OA1VTf>2WVcpsGNuSkV z#fz_5%QkNKTk(h>EhDpysNmL#cmega)}XOZHTUkA0^Sp1KN)m(vzHE1f~<*cLqs|E z?_A92_ToJ=mQDDQ&#nLJE*7RuscCR-#4RF6R(a22bVj8uo> zHXvg*AY(EhV=*XVG$>*=aMJHT9SvQ8Q>jyvuPQ_;aYDE$1L8qaqRFDI6o>S9;Qr>p zeD0x-ho-^>3On9R<1G5@c^lY0MqY{N&MR8Yp&?O>-@(6g+ybcPilcP*y7Jc{J{SM1 ztBB69i02NOEff5stM~$R6+h6ap<((7?Cb!@e{>btsh(0D#OS}e3M5iJ<6m7x*y)*X z^4i0Hha+;w6u_*ET;oh$2G)NOWMpSeTQ6Uo4jaL$5~H&q zrR*j0H>9EZjYv?4d;?mzcPf^0k%%dph$)+h$&ZLBj_3we*Z}#T;w~(;-=-yuQ?dhp zYr5nTocIEG7Nu~Cp8SzpF4!uiPij!G`O9JQJ+$BKJ8EG}F-S7Sgi8~B*|ZTBVZl{> zBdz8CqYK(HZD<^me-^`8A~aJz#@aLV;LPft)%0+)#e`ubM85R*YVb(t;Eu@vh%4 z_x6X!Xh?Z5{gSmiDn@|eMF+W;G8p&!2Cd zZFnuEShuof;L^ckbRw(_OkWrx$lfiQxa!}BB7+kAtEMaHpPDWmQVc@B?VCO?Kus6* zziYZ`{;BDLEn0|y^y6PdIYtr7mAgt-ZR`HfZ1yTfXqx)b>?PR#aG*VFq2~G>aBe`2 zPei$kxJyN9!vHqy8Sf>y7 zl%HwfK6A>|$12sx3zi}eF2UUdpnhkCZ{Wh)tZ)S3zO(*$xI@TCul2~bra zWrcnBSV7Bdv4K_WoRoT)ksZ*KgxO@J;1(1bfD$^kNwCi*8s>9hpyRQLxo}Sua5qii zqW?Yx2u0u|3Z*|#E$lb}u?}^M(tuwmg$&0^!rvxAxxiXM<-hxP_y#gZXtp&;7*)gR zX|oy*Fc6$i2)38$jSFk^`za!P+J8H<3p7TsOEsR!j5dDYiZW&bykV5_qD|HZ;YFRy zC`D&*LB=u`UZ5U4K|PTPnPJJ&1ew!P z#O^@z>u}NNklpC8(e)7f-?)mEn)xH?vQEaw&h?7S>N#Hh3QznBPuvQ@&b&CqyH_A+ z)v<4k#a+3LE^~*D%kcqB4OaXwPh_mzCI3DFB_R0j_+OG98)>Dp*rtu z+%$C`K5w>FpUv9gyXZHkk{;RSig+tST~9jNa>5&LfRF|k5lx-R^Xzn@5`y;mxM8}M zz4QTiU(^5+My>pcyv!oR1jz>DO&!lL9q$jI=hOG=80* zv)oXZwUhNFsxiy1W#?)};9evh#i_^49D~ z<+lYvJ3Ti(wNGrcZ1~zz`FhKtvZIK(#S6W|W21u)S)$DsBiYRr4&NwBbF>ROC-P{< z)aLqLBuVAF$f#da5{o&JSEuVt5JAvVo5h7Gx!C1n{}W~Pozu`c2HnT=f)fw_8UOJ9 zUN}x;TOGgzhfnHDaZH`JQqb)*_$thIy0ad9+>3F#x>4Tyjy(6)$BBJ!o!b!O$^WTZ z*_(LlHkpX+fMH7f;UX>$b7JP%^XIhW&GJpgmF^-zqr9x(#@#e2QbajAP3536@fN_O z3vf(l^&E0YZ?JK7l3AoowbT;BS06RrEMW3h+Z>GGzc-`~E%A&Y_b$F21oGhyR<9*P zX<#1V1Zb)CwVUyt8>ry#I?FgBwzL$hb#n~AdE0B37mV;2zkeQ9L*u9>-CP*3S9K_m z3^zSHP9<843r*gYHyF?H*1)$UIK6LmLLYuG-RSjAq&zP)Si$oj@) zxV}ytXRtia+%?5;+Y)u>usnydEEJb3}HY#XEM>aWS$`J58)J`Tk~P=V`$rD zpYYT~#$avaKkaO?xS3|m6f}M!?aNeOzpx{2H@WcrF}p4>_FU-6scdboj*Mw@9>mwc z+;_2Av&mYd9QG05t#)`w66|~^jT2lkdK#=wAv*lL7XDqXLk7fmf<&P!8%1WyE5@gPDj0or4e)xte>)f8 z@&qs~gZ-bgr$>kUKyOq@7ls~j3ROy#0}y%w4YeP-C32y4Jo}KFyy(eB^{L-fCldJHe#_ayws~X}P;vuhLc> z$Cr%Tl~M)B0lS4=vlAw%NA|G-vt`nrR`%40KPYG9+le?9F90s+;kCqR$ynX?c;`IJk#%@Lqd*+0KAHS*o5c>nT9q#inwzs|)O%j0)_kbMGt{SBIkS zLt$?le3E$R+n55H8lLYxuRVQZq`xkjjeUG=++SCaX6mL}I9Ad+>9~doV96^amLnED zem^TxNMUtZOIcTEQL(&awT(-miznU;i8cM=?c(fa_`Tsh0jGo|aPbz^=YRapeWKBP zVD%$b?kfIfb~st^b8#PGBDqM=yF}Tg7B0(&0pODmE+`G^MrCdkj3zPj+jq+;J!9X$ zOH$LBzjq0A#b3I6JGCLDoNr-~KILkksiuEDz01sI>2_d?Xt5lcAPT_Z5^cHzJtp*C zT)Fe}?>c83g?=#D;99-`ou5rsLt1e@Dzm*e0Dn#O31CPy|*h~BpEhUq+Lj`TLzn`)NE1RTww-&1{hflMLkGJ8g$F3IMv*@O7$9Ex=fbJ^rw zW5_}ua~1gt{^ap!!ymAw@-^4i0wJn0VCQ*2R}lFKljhDA;CJ^KB6@8IU%D` zlsQR!OAvFu0VjK2KdGR3@A7p`l%6ngEtI`DEG7dFJZ)`YGvtjmkvx7K4==O~>FxT! z!#ZGpiKYJQ{gC$P($xDX^Q{fg*R?MHWPurTM#haPhtW|H!X#IW?ayR!aKMiVvw_;c z*!enAkFL`-zGMwF?#dHI;A)7-iyz2_p$Oce_6ZxG_<`jq4wstRj(hSd58}EkF3;~c zE2#jXY_3-R(RAAVgimOGbVo<1{ammi)%Wnd3Jv#0+WgxOnZC0&xlPbTRSOMMwY~GJ zNdJoHdqaD#gcdL3AtaSDL6~_l9OGG0kY$}^6dm5r!moH6RObYm)rpHCyPEb%_RX>G zm(2?omuvIzZ?Kg8GuTUOhgVY_h-vKvH< zHs{?+*2lu-e(deOEnHve4!~jR<3b^JJH?$-6{S7SGw_YOLzZoTEP*h5S-`XCdgV|s zx~(*y62&a}Ji@YU-L_X*V~SgtXr>(jK59mdzC2*qeV|OKe6c27wk>k4=Pg|=aq=if z&5(J~7ig-W4Oh@RYi=-!E)@sAmV~W+S6ZH(3|3mG-%&5UJ*%~@p}#oRslH`$U+2Di z9zby-%s&blUk-n(bnH6`Lf>g~Co6Ud+D1SRLee=-)hN0%UZCPj1ORxZNhqWFIC&=OS zs2@1XK`G5wL!2cjKq;c@3+d0JfNjUoVKmeGb0l8)z){4}w$Ipg5D_pS1VYBCyG5Zf zalO#x&foUzDUdn!=JV8r8ezj!?q#Jrl^HxVp?X(?7q%SJ_&K)n^xZ+iR_(FK{gB`D zrU_{$|56uu$Ajx8Wcnjw8b99VpaUcZe0$}fE4I(A2Zqc>e^zeUhn1b2{?Y4j>wCW8 z)+RpaXTiir3;}_zBfCMw2GyE&2@Sx_Q+0Nl(S+Cm zDuvR+-o;=JqQ5_g0>U zS0!dfE$J9f{TFtpU&#p1Q$X;yuEbXJT)%D-B7k5AqBKMLG*Q`jJaGyXWr2Rh4?hi#mL^eVGj*2)#<3EZ9wt|F2iWFB4=}-LD2^uRS zk6umLUUr&Y1vvhB<>8r<0ytLJdYZ}YH2IaIw6HNEQK_O;00lWc|IB2TJ!7Q#EEMfj z!yY1I0q0|(#@N+=@orpjgHY^*A3(5JO^~#SYPGS~rm&WD2%&S{zLFO**!oU&|VO;QfUe*gG@0 z6!%JzWZ`zj>}QMaL5<+>;~WS=p-DC1v1M@1;!AmL; zV&|rIx@jHPxCA>-SD2>H?d=xzB?J+I2|wZJz@c`?l^Iw`>%*vd71$Ufi01=HJ1h4~ zbDU_WR_*a*>w$XNzmcZ+>x;z-QHlW*rh)Z!Tv`e4Dn=dffaww{j=YLXh$Ldb&q|oO zH~)xnzi1rE+z2kL)GH)gQXy^m;i1r)wm~^J4xj9NnsCsGR1afBxFn7SV#xUwnUcax zn2;ioGZ1nY+E*ae?I6Y&oYHKuOQsWjU^wd*X1?bdCb{PpMup-UhK%9{b#>$hrIDO+ zozX7^ls2~rcFlReGSH1YNpbt*of2Pk(gGA$j(9*S?*C!z9D^f^8ZbS{#I`j_IyNV^ zZQHh;Ol;e>ZQHhOJK21T+FI1sR`u!IeeV5nf8Fjn?|GkRWEXJkAjVm!ZfS>>@@EJC zvV)iq74MVIq)Q=XLT%FDw7y^;k*nrUx!a&_UG}oGf)^7T6T0{y-T_aMBPFN+JWg32 z2KY}fVlK4PCxo~t;QP&6N(M!U5ZQD2hZVG*Hy5#kV8o6lZtp`MGDRPRcGQk0Y5#*E z@?Mc`YX0D_O!EFm7|t=KeTK=H9Zl@MW?GYS*nNfmPV8Pr@csp}KZ8=n_~GAaxM%1F z=(P-liCjMtTz-_+*q?RUiVsf*FzfSPO~cbgeY$Ddrs~nGE<9_fAJ*5r9^WH!+ASXF zQF-19G*wJq3xW_mA%EBwHleB^h)vvupgN9f#oQ_ER-EJ3)BI~N#1_3jz?!_}I?1rr*;5`>-1J&M^CobT<7H1>T{z7@Q_FZtT@8daZk#j43 z38PotEP}EpJ(#H}Dx#9rA66bfOSf*Ntcfhzxp4wmoywDC15s4J?=5n4X(C_AE+Iq4 z0bX4aydf6~eS-S3@?tocd7ukt<#^C#^gpZDuNj0dn>H$G6340GfMy#p=GcUzTq%Nb z$G-_gdARiEt481}MD!F>+$`yOQ*4fVhBYsE>ou?z+R3cs^-M&c= zS-=lkR7Wg7GibjXc07%{eiH0I=0_|8VOD1wcEF9f;_k7Irk700?Vz9ZGbI%K5+oP! z$CH4X!kIO4fp)hYD7HqnGUgDTne@w#>%W5pK%#ocv5nyp@r8g77ZPOL{3v`8GZ_5m zuPmDzMA9mi9lshlK!I>EW6f-XLNWoQ!fdZSY;!JI+5O#N3L4x3Vx!^Vs4cQkWf41s zn3jUz)TY|32R()IwyO}{S)f%AaaH&_JxUC(%wGy$jBY>1mrtm7=qitb{K$^fSRgm( zj@_RP2kO_f9SuY29ZZ|lUv0tNyMMTk(sgvToF(1BgDIHU(>~M6gouYx#*SyeNNh5@ z<#=tIC(Iegh&{{kDYgrDv2C#t-=*nW&P6UfevB_xo?$yK+FHHuem)X0AG_eAUWgxN zAU#QofUx-6R)hC2SY~SnY$7f=X_jTd(fppPTSx5uz7qj_?AJC>(zM{wYlO^>O_j&?3i)5bq z3s#8oSKtZKhar`d0+buVROu5`YZFxM5>z|MYTbW~Fu4O~5}~NkX=d5tKh78brS0Kz zPMrVde3^c$NsdOgB^dNZSc;WH_3Yv8C#oGEevj`5*T!nS&m9&$fhB>)%oDaL;c<7K zilw|ma)*rOr3E7qKRFdlA%xc?yW}gA*}F`mH!f%`;toLZ9wk5Pv*T(?u|_6olg8g5?8}j zy~C_8;dOrhf$z z1*18hjQ)aRSJAltRS?fb0#+`?s!&6rg2!AfigKkrQ6kj-IkJVmSnl=VP&Eitu3=9p zDe5SdPZ5<>BQ7qJtKLJZLW@50LoH#WR0vVjY!&EI7wL8i^}46~Kf*&TaiCNHP%2!= zRVBz($88-HCX=BG?Bk0uy^5|iA-3azKe-_vz|VMTi~YWemt1^bV*Ymc|NTI8dNDY= z5l`$dFX_*g4OUSIUX~4BmJeQ*3tmJ zOv-S!w6%Ep2I?RLUJB;=D;|o=M;96lCa+LU#dRRfNl0Um9kR#vgLtb2ZnMT|xqv?j ziEP!_(*St~TE~M|ZaUeV7M(kcEa^Ay-p2&qXL395G1x%$LdQ3eB{}I~D)Y+lNRvU$ z1rU5-jk<{v{@t)e&q^w#?hF-%j2bW7HWQ#J|GrCHw1M#JQbIOm1v$nBy_Lp(B&os2`@775h~-7S4!*t@Xtm`@k%ckuo20zAa`hkMi+g&>RxZQ_0Ahn^vs(e=OA7Ht zAh5?P?8kHE*G&ct|MMaT#>({fRW2sTnJ0%>nEM$+lTvQi@fqdXSZ5~*bF%2n_jIfG zU9t9^o$^?UVtnEPT)0yYzi^-GyQ0FtRnVUk!MNm1c~P*=Y|S7RX6I%g)=5Nll#_!v zH6iEj6+sE2i9PVdjM5s>Ot?jKuzQ9~ht71kPtpU()cCSP%R*2S@jecL!A>E@(@yBd z)6NvBEQ%CV(IP}7oeqZefmntGzB)o9gQy+3w;C2+;TliK*2RlsH7cFD1gx-ejpa3v zQk_p|Q`HbEL4jdP=^bD!t0y*keU75WCNdLJ1x6>PV^aHXCLRly!A#ASf7Lidt{Ui} z8$DHh)dy47i;&g35Y@Yo){C4S{MRsvn8qZKE8+F9nN}b}y8ZemOzQgylW;CdRU?;5 z>!^ZOLI5m#j^as=bGuaMQf==i2~_9h3UyT4$Js`o|CwJVS7YHVHe9V|2aJQAB~W1t z)=%)(N93xpq!_OvTuA%kzwgT8{`?X7iIXG=fD)kf2-RHU{6)Ja`DFb@*(L7>@>-Ac zOrDFy*ds{HpsdB&7;#t&iywlz;LYcT*?@|ce4Eg5{(!Ra{WD|uPn;yV{F+rSOfh2| zQxi=%&J?F=@}qr;`>)y;gt+lP0&2`Kk3snWfm}k*DU%YAs6Rktek6kMci_k|LxWs{PxihmYSjD6!G8^(^@1fK+Z zn%w(1Au4@R_Y(^-b+naiD>x`V4r?$fzeAThDSBas-Mz~q6?V`Bt*U+iA-S~I3z_n{ z$YGW~0!K+&++Bt}VZnY)x?Z?A2+v=Bzio21iymaWSUAqb(0rfvw|*EjhvsGWms4W8 zb~$Mo;(v2{JN;&k%swdho<1q~1N)I2RFuI~DZvm>wf;LBH=@b{9+vD%t916GtH8*a zp^X6^$pV&tIf9;OJ49rE-r1*)7DJ2jSW41TBbE8GPx)^x{5;F^y~t+f*ved43`rtib5ZaBdLUgDA0sx)!kn;eJ@i-#7Pv^u!We`@A+jW!dV zj)QP?W`f^pYiew_8fA-QMkd$7p^;Jw1*$ewUsODdJZH|Lz^gq|WJ6P1?@z+_Z%IOY zzN1n*vqMtp#RT-flEwC)>x=sD%9G6#8H*c^PN!7HQ@NCaN`xd_Izv#xaTm zg)6T$hc+}^YPKe;j3j3}XgA0|$5Rr*D%rm{XdP%r>WbYm?Te;>xObn@4!{dixCn*{Yl{&W(aT=yeCX+KeWSfc{KdU1|*dv~{{Xj|+k!$F`E08|V5J zC%l@kw25`jMdfxsjI@vVL<16x#PEt96Rx$fynFf;xioL$Es~OF(HGz+tkkC_cn<#3 zp-p<+H#;eyvD`6WDyU3tIk#t!PqIHwZiLwvjgAN5m9u)Jrt+;BU87CwlUPmLY?Q6^ zKSVO4WOE(pIa8&7xk;O2I((bED?2bv2UR=~K}maHL@a-1Pe%{h4BcMu-(nS%ej2*w z%ID3ZWPNr#_V@B&*KGPx#<%GTygXlDuy``GhmZk5$z^b0McNL3tH#cC3mNZ4ZByK^ zd#74%`+f_MobXiaLw-gNeo62E5nna?x8wOheGR|9(@w62t>~yXpEVZicB@GJSXVlK zt)I$Xk*{29&3{dnu6W1NTW%`UY#$?j#lL)ej0PCX^m=cbKc%*`REvH!LU7d_k*`F5 z`wVq&$M5pUBEJYe$*nCHzbo<|Jw0labxhC_MX`7@7DkwLKITvE?JyW`RnRhapJ@+u z)JM;PV2`J;w$!>T$xs-;RsD2ZnOY-OyY((Iskz2e;xcE$)tRYGsw`nGcA6PN=xolP041?N6wX%m)>!ZEtIy-?{})BVE6bn9nB*>?kd!m zWAUZ_3XXMm-tHl7;q~`rfFAC6N(g;*gf_>5#NAF$N_F)2^K-Up-8a+LQfKeNrMA7a zTEp8;);IUJzS6r))7Q#gkPYL&HHXH!X}15Ts?R&=RMxtuO^?OE*P2l#a<;$M;#dA) z(0W!?!1mMrH|9?jX)@7Eb_Tcis`!z1BVoO7Me;+NTFnR}0k1`G&M)IaQ{YS7tP=->ai83sk z*~}20b*s})t5>xB_gvf?N3$iTVah_C`TbZDD@?4yp2l{0dv#i7I#^80(MnfR zF>0rF68Q~VzuV!Lyra^}2c`+^zB9PHKzuc}v(t=AhQoabL)MqaLniIq6vZ?Av<73g zj#lBuawjroYW-VB?t~I|@n!^ekc)|@(^|`OL+E?t$UdZ0xE@g^J z)I?nU#?-`8v&##5M&UQ6`L7Z*4k|`TcB)H;wps|vFq$!{vG5wYg)p)}Iw)-kAw3^V zGm9VTle+amt0y0^k}^C6&okV-N9m9R;RRP5U1yM%F> zZl5h3(^}x_mRmP<>mfVV^#^wM`QTZ9KFbR5`JN0B7_ zX?hN%J6HhvL%F+s4MZVwSS8wP0qm)={clO(6v*_Dk41JvEgG1~kT9webC2{Qyg+3u z$Wd}Ks`7SCU-Q_>LFOu#?fu9K05e>hcLe)>Yyd9j)LasNMIV?w+-9nbTFKV@G?~ms z>o=$3yvOkRIPhA99e%Xt6)^HKl^5r{GiO5tw_V#VkSS*T&FM*_wX7S1+ttk>KJQCx zXK_cgihoFOruY1lPhe^8wB#Bwa!KgP)&f&IY6Z_yk{0*GK$8O~qWboSY zv0s>jk&47pix5^7_QzzXvM$@pft!3es9}{QH2cRU%=vJqr)t+ENM6vC>vu($03NMT z{;uHnb+m0D$Gq$6SV}r!?Qu=*U+!+{BV};+*LLr(oc<&DCTO&yBk!cwQ?(%A<@-mU zo#ZjkOaB;}<$Jd{8|x!4qpn(d$By$?;8B&2?brN}TkuCW@M+XzAJOqbfj7Nqef{;K zss3A}#Ehz2N(xu%_oMY^5#Hn}{3r44!~z?UV|evK!K$`8v9PAX#%7-wbjrL;z;xW< zwfJgkle2|-&IU*U4QEMxe3 zF_bjV-4fg{OR^}zTs;+#pu6;;kUYa&KeVYaJtMic8HDf|=FHf_KWe7BLMQr0W@2bq z(mPbyzl4*b40CCb_|YjPWa?&SLT8u*Vh7`?m~;spYwP|2MW_%EN#OLj*4FijqG3ht z0LDl|B89lLP4WE4DPW8Rlt{LqvfJFDHaV2nhR`Vcm%`6LK#nV<4S-{$FDQo_fwl=FOA zncO-zSVVMtP$@tFmjHEk3)9$!^5JR8IqLy8o3PQQasdyq-D721ZghA(`V;bh@~=vN z%qFX*Ob{7A{Hv=U{?-4F+2sFd&HVR9&HswOiu$pd{Ac`cvBwYo%2QGWU52C%qZS-h zk$O=TE0!QnFYBKwVebILk_p58fs~}N{k2E@3csmWIYcR`$cw%ByGY!ZBVmr(vYQ&&;+p}WfDS#s5%BAlZP#im+X-o=Ps$K z#6!bs)R;yrO^GORDjgy44}K=R8SE*GBDHr;h-dJC$6#})%A~OOZJF!!>d3Y=o$93V z6hT@A8Xp$~HroWIM>=~}U_G{Q*l zf*6TJSSofII>{4mg>dhW&X7c{#4Lm+#@2+oiD}6kdEP3|H*L`4{H(WNlz+&wPMhhb zqUU^`?V--Hlz1LRKj+BGVTMwDfnNo@EZB7zy-v*=Ph- zBX0QGVa}8K0-s8D-2yg=@mX!G)G*GtMkO-n1N$LfS@~rxO{G(Tgx;a|Dbz?9Uf^dZ2soiD=hNUeV>gAE_9) zyWD^|$}npzic>t?Lk0PuJ^(ytizKJ3UXF+QOhzjLix(*zY{hES|3E@1$_6zfD5Urp zpNuWf(>8&Z6j{L0oWX)c>R^I|-%2n5hWs=hz?`UtWbKX}%G4RCM6zgCB$h#sXs@Q@ z=9E(x0)|WkGs?e)(3O~(tp6u8dEv1%Ny#xd(-N|$$lkKIzxeIZ82;UOz5Mltkg+sfAdD(z*$1Rc z?f@7IWXHvS@Pv~y1_R%Xn8Zlrpki+vO2y8yM98seX@MkdW>!g!((w&V2L$D4h2!c( zY8Dly)FbNQbxSO2=P+vLl4|E-bxZL^mBqvfqUth}{PV<>3Vi2O)M;0_k3Z)v$!#Bm ziCPmUl=%prA*7LKB-_}-wTXyfB$eZuw+b9=#2^=WtANv9MfoRlq3a7l8KAqsRS44J zDGSM0=}-MHrdnIsb&5M(%@TjF`%BuG5K~z^@ zUmGiEB0(C6Ac7m%KpwveY3v0haox$4P%h=X)meA%P14@Oy$bp?PIJ?G0sO1zJ<`k9 zhK~S@muycuB0OPV>YxUc0@`mw0f#bjL5waW`RxQcPst9A{Uqr2kNYz4kbNM~!->Qe z@*vGg&A&ZFR+Q+llN2=EWLZvnaIZok(oC|fyyNBgGRog~Y>;C<04SuoVuu+_YIaJZ z_r{0|R;aq|v5~C!ST0&34?f{14$ReXhPv(X5p8j@c;EJnz$(!mwBI!6 zf)7LmkTzthK5_Mf{rTT6vX>m=>gtLc{&+C0=D*uR|`1z93glk1MaxU*L3}f z;X3;4ks(hC?0c8hlxahQuRqxLGNUOg;Ta6M32Vq6@!n|4u{xt=g#%?P!7z<2Sg%ga z)K7ts=GqtF*rpT?_5Tv0YkAZZB&iQiWZ7eoo7$?msik-@-pp}$OGeqs<{e5-*)pDn zn0W~rq2UUEE#?qvF+_#L`D0O*TS#JBG=SB63a!r%|Jo^ zM4qc0&B21VwlLis%*EK73AT0!dRw7TZ=^Un*wSTM1ACBLwvaoL+qYmkX721J>j~cW zPv%Pe8K~w>&I{4tO$H9t;7`^M)!<9+3)K)%;eSv%5hrMr<8_C1gh?2)QmO0x!^@jR zV5{N2U6mVk}c|ML*p>{@vUI8PFtKGe&`S|o8djqm&?XdO% zS(nhl`A$@q)BF~RjNtYMrH#VuJ(^Ec7tEX%iH<#*->5EPvKs(7yJWtwb{LXP#b4ED z@gP!9Ogjr=OOX0)t0X122#^TWpv0rdF}Y9#{W%g&c+yUId`?T`?9qyjc!#<7rb5T` zVN(XEnZxAl^FIpI*w~~dC7Zkyy zX)J8PO3E$3OyaN9lcd10lnMBZ2lEIDDKo@Q?9DvYEM1XmjLO8S8$|8>%7-ho}f`fzr3bdaI z!O*b6ob>Z7RpIC+oTD9e(ZF$nMB}f8vC=JE#Ad~Bnv0nGXDBF%E>bE=R8?g;Rn9wR zW=jz5ONI+9nSM0c{;I-ZP4K)jl9MUdPK#%-=}qj@tLKxeXH-=dIaThQ`E*oOZbjYI zJ)m(BC@Kw9)h6;9OBLN$Rlh@I&?J7)BpFmyXR?|LN0Jl8kyd^R;%r4xiqb@e5gI_i zMUW8|nnFQ^ER_*L^p7ph5PlNUqqHxZ%`AXoo}F=oTf8${-bb2o1gIHC9t4>DjIzuy zMo|HaGYlkrGY`H20Z(jzQe-*tsna7H+uH=qG7jQCVjlxny>+NQbZw z`6++X351*OGTW;#nfAZgcE_GegRd6o$2MfgHsr?+J*{mI<^eUvp#P>-cJV@KTUvaM zr#|z--=Ar&lF6RO6W&p6TJQXxYnh_hh`P@9XuG4MYSkyFo}XGknq*5iZ{TT4mpS_{ z*%R#o#>kZVDkFl{rP#eBAe-04TRu+^t$E0Im8<@(;{rCWD0Y=||E+TYHoTSlF42D` zD0iPIOOSWae!T#>k}t+c^~09}B{IE+WPP%tlbMk~gM#rCWo?<ZJ1SA^9Ovgv zJ5$g#SC6U1h}w%nCiEfld6$X%_x0Cwhe4SW?fpumA_G?er{7{zy+o1^fPyUGHwX4` zolc5kbG?=r6Itd!(UbsfDrglY7(_k(=>831XN5Xofr~15D3N8;#lN|&w%hj4uqXRK zKrJ(@`lTj{Xl7rm6J-!`<;M)S2(Vh2Qr>KfeZ81qZuJ^{*@{KgN1v0%dK9>G zY60%qNvZ=u&h9mmWY1ONF}rK=DiXC3p$A;%@;AQo>M<|(I&%}fvzpR@&H1SAPG<+_ z=5#tmC2OL}b#0wFtG#Ox(#{T~K1Za|#0i|4@~$p0v)o~^`^5g7m7%L;q5Y&7!0LVt z5#>BPZY4L;A&?p=(tVIYt53KjJ-vo9s+i}{_`9fTB>N0`7zW3oeQSl=D@)`!PGK@5 zS;c@CT*$iH+ami(wa_kVkuv&NNcx&@Db?r`I=`1@O=O%S{`O@@N~^!eI$7l`FMNxd z?4&E#VLC4YzMHaM*oC^S$rz1BZ6)7SK+)H#z$QHJA||!7%4c=$66do+L-F@J3Lr!3 zmuC2w__7P}J>YGPk4rRt(_UDQHs<`^H-Kf`=Gxt~IT`iESJT)ffUZWM_teshc4yuC zeh%aB_IkIp#V^<5&Zb7%nQn~_F{&HNYaN)%P;`;SwJ-O^__FOTEhc zeX(DC&p*zUah|&iX*>GNuW;Y;hgL=}=kahfj(SQ8yM34b%Za$%i2Xw_xFVT=l~tLf zV|o|8baA`&4EP^q_4l8xz~7gi;QhzbQGa8n|3sJm!2hlLzEDoZ^8UsHncjA z-X*yCY6~`lrA}`>03EZvsP)y58gLA|G9dd6oxWs#1092Z zO4Gfwnpc8Jw3T}|ZGB#I3_5asM6-J&+-0Mh- znXcNS0cz7{=gwEqi*j4b0I$`^yL+F1ufYZm(*oEJ+B=}I>I-?taZ1HZv1wxER*~b( z8gNvrOb3+?X$H}r(^66GrK{Zb43Gc^ z6Ic(O!s`ws>lL%8li>IX)|hK{!9zBBhL!!^I;|&}LlGO8nF% z3hDuW?3SW}T$nAgU!$N^osQWYr>40ktES^wk3a3F8o%65Hsf~7kd{|kd>mG-C+#rs zZDwy-8fGC)rufs_Tqm1nni{UzbIn)^2ddX^4ALgMVWGKwP9ls=ZYM~*bhxV2vafY& z?Y4v_`*kmuB-YNaj#o@PEZ`eP7xT!fba*PYpsd9A1u0BETC4_Vth@C&M2Fv}=Wh>B z2q~JkHbNKRD4STmA=msn9W_(wSRwmj=5NRvo!#PVw@sLw#qTk#4v87fX#U9bE%fQ_o3xEa`GcN?rQ8&t)B?@#0;p z#{2dF&PQMD0+S<3aK`c8%ke34ZWA4KecD_!E2-@=!RKgH^^A7#l;Q?S94UI?DH#{J ztrLY-N6=nDtEnSJtpJOvzbO$}K}N?W>?yQEmYDNihBWo8=AL_K3V8G4)$6QFHZ%(N zSu@t3HV!E}#eCJ*s+XR?|ky$s8OCZ#rXlHE#ov<-Ym(l`MrZ&l>rx$Jtdo{ zQ3skuE*EuHhn=_+Uwt7**$a(~@Z5hJjgfODHdWiNE8Uz)YUuJ@+-fr2T$|@pM5J&d z%*_X@=MC;s{@@axUx_s`e&tVBWSTTGvR&T$oHG(fx8l{&l)hVn-xH{X;9O)63vNF@ zr8;d)35sl$FmtJuWk(fXxxRc?XZL-Vzm*zV4-S>&qci_j;ru$0&qk7(7mkNPf5Ydo zy%3vLb3*54qhod0l~I9`G2M#VsIeT_pZl!`ewHOa$jz<`$H~A`vn(EIlWHSvdCZ6< zC08$&@0^7Da*?l*ZKeKjvRx1FkYd*%e4FLvtD%x>^7;OWJ4T8s`OLL7-E1OJo3!Tg z-gLiL)hE++4R2ejNbWvvo73TWwP0D?{j{d`wXNOi)3kkOQNDYA5dGBt3X^@$mbqDt zlDmtw{kp|1aX$6DI5>T&#^UwrA++EA8G1bCx$U#PI?bs!PI#M5due0+U2{!74gL;4 zd=+|9-EFyTdbsv_x~=e@4zu3X-RWlcU3*y;6#>wBrj#~cBOY^D=k~r2$886`UZGsJ zNnJ#{arzDz$?K~MkAAjT`G{7#x!CtM(s~Ro*F;};L7xZ>GjwagNAXc1QG7v;;?V$8 z3fqHUnUE*W!1vg;*1Gvz0c)2_`-_F*(+1wZW`^%WA~=}6>3Dd@f$SVGuQXNd#R}1! zZHehhZjWB`Iu>fXoUFEng#YN)dpP%5l|621(~TMS**Z?kiK5`z-K{ql3fVos?ccw9 z;xQSYgE|Kcve7FZ%i%kzDhy`&?f3iQJDTvU+3eZ}8s%GHBH`D1x9{bt;M%%P=%6?I zlqX)VKwUo!ADL%R_`OzMoGtQmz_qu=Ggr*)Gxj>Bxy901`l`IO3xt@+=UJ@dhybFtE_=c|aF6VAN661LXw&G?&VC zI`O9eoUmuGXT)4A1JfF-=o=Xsq1IO{Cw?HSha$o!Vs)3ROykQ{!dS&<%?vkB^yfBG z);rKYAfZmHCToBqLMBonF|{+|O;y5h!)R>{ORF`Gu|aLHi?Bww&!0sfr1>LIciFdv z19xL=Fz<&&Gh6}PEQ%DF)d!mEgTZf_!#&eP6NHETL(_tp{=d<*j(%c+h7}0Rte-Xn zIM@F}i}CmWrN#Ik!t?({i}CA6iSeKDzvTj#9&V}%!_VHlLJ?uu>I1ONXqqufJrpj2 zXq5gK4N{*a%e;h;|Nf<>`>NKip%IH#DmN&X@2QtpzH3vLwpU7^p39?9TRQ|S zP0jo(QED1D{~@ddW$#c+no5u~?2~rr`g23AaC1$LoM%}nT0Cg#3d1dN|2F#zM=P0C z#%OrLoUf><%pZpH1|}Jd3#BXKW5h_6$(&NJmcN={z+zXUke6P^e7b;n!?-XC_*9L;Z&jWfsCXchA;pR{)Q8B#D0^^nA2>RdR8N8!+0n(oMXS0v;gl?#RycI9Y=h#6!+I+I zP2`Go1|G&PI5#XeHs$lS0%!jV_pPoD6>}_Akp#AUD2p5dOEeo!zRAgzf?sGJ5vDqI zt3>|$m5w(UR(kFD&%f+m-j11yWm|qq6CHuF44g=JvMhpN`jzh>lpa+dz!W-z`PyF& zZaWs)R7p{|{YG3@A;5_tXZbiC#w~D3vmIS71e{WjN=dlf@0%Lil);Rl+pdEKPa+kY z5W%*u-3}rs=wPu1##J5i;ZAq0B;o4*{6$+qcVTJn>Y|nKekr4&*dm4+{bEjSRT8_+2G0BaM$X}iBoll8q+;>Jhr-~bm>A_>^Pi1&Yp$1~)J?hS zK+NjqjMO&qq3T;OmqALCu#Qb%ojR)zlj&7B@>RnNkvr>@;#Y9ELcs%q7-Mm?NDjQE15jn_<=c({>GLJ< zh88u^(6h`s4?uI1pz@tce-+|2UJ-U_LN|KgYj)95df)*Av<1R^Wt>gkgqjcN*ezD; zG5ygO7l>f!1|BV`J4lw%zQ4BMB&2FIfs#?@(+?SUpg=W8A+?*rp6eC^Zwx>+iCkAn z{gW$EJ1sMJjIyD$pHg>>&OvF{30zl&d&gORE?wm@snnXijT`%%oFHHyF z?qpw+`EuOece4_PQ=&6UJ09U^f}54(@30VMXO^fK)i`di3l*k;9&b|ry*~DbzChH} zt=<9FfuP|XNw``;U8_pb=Z7%r$TBEz>7asTh#B+me@_THygn#BAc^e`($TMcLhboP zih`f%OtPg?#4zOKENL{M$HCiEZx``>&^m-Cf~VrN0XU(EVo@ul(=Jq z8;c*snZ95Ryh#AgLQAPcN#P=5c(3faQ1e@L0IojTm=(dg$DgfB8@Gc}X*?x%jVAEm=mWm7j3%53WkVUlcpL$h7!B^5-H1v~xn7SdBd@ zc?$VS)&*qNLWvfU-DaSI{8){42}q$GR`P3W5@sTW9lV7vJ2s3)(2c{%fg{PzE(8wb zRwYEO1rxc_2X$GT3#l7&U$W@-V&sC}2XS>FZE+If#Am`ZI#;%Pf0QP|l*bXZM3h(mTIE!e!1eBMz?DZR;*GW#7Ap&)`gOWS$Xb=^- zy&S<+^9)7g;6IGNnn))3-YxN#YF$&cgsYiyF`4P2O;)FkoP#5$qIt3Tx?6JmCHeo> zIK)#<$aWXxL@&-G-LM9VFeky7&#pynQNn5vam-|FQ2?&Lls=5|{vsL=p*nWf_{r;C z9TYM^z}AU{Hzg0rn)!-jQ?*IJI3@pS;81o<{yVF+cew%Jb4gGqLK1Sp zq-m3gJE0lPYmY$K1c2%oBi&_e6Pwl*1Ch!wOaJIFCx4#W2as~I{PLHOm;fY(G)?(T zokI3*VJGD<_HLRp&LQdJ?@;z=R3|*rGfwS=R8*%dg0l)FCuw9SX>=#$nzIKM;H#$qCkb5OL9`u0Jikrh9)vXp%Upr z(pkeL0$hAL<1TaFR71B0TVAz)NO3ZZV&253sXmO25^V*X0`4Cj5Jn2?SZBlT98prk ziA+_dF|koox%q#-viuOfj$Rl*?LcDwnHWP{779s_Ok!)9l@Zu<9_;vAevJrT>`OAy<7t7ZusxUn;@Yk-tR&R-xrG+8}yJk>*=UJ$Z-1f@WRnJKE8x`bk&G7grIHHL?| z43?w-ga<3djMquIb0jo)-$t5Z#8?6((_R^I z6*?0T#mc^8CJ^qP`NhWxJ=D38%+w^!YR8usYI03OCu<}wVKt;-+aqWk*!=9JXY@=1 zt)9f+SIbW>79F3438^i}sIzCT8VRY-1=YD5^A8zglO74Fu?5vx!Wt}*%vP!9>Vyi6 z%0(G-=|AvnAsc8E_d!;OGM`h@;I^C6DU+xVZyX2H_?N+D)Zbq)@*) zyepdbSKeUif}^%1M11%dMFWWgR3QnBymA%5Mu{Pb*yd?u4CgXp#E7(Et{e@TgySr_ ziCdR;3zQ}r{rW)MD5P1;Gjd17$k&EBHoear!7nnea!wqBN08sPWVV0L>XCv&iHKOj zIVQLeXs)%R=2nZo<*Vq#$K>oq{iFxdkq*E<3Zoffnd7v8o0! zZUKOg1Rxs_mwqFHt;#gc=Qt(i4#I8|@6AW?$&vm*k^T^__y(ZpT$3vC9C4$HG>F8~ zY8pkK$~TA*_WEq)YU3}GT@Zcfgl!z+-l&p~ut3>w!?0#GZP_f?EH-0pE`M3tcao-!TMsXio zcWZ&7p@6d!(@aMRz#j_zZmw?tqUm`91ucoHi zna?%BZ#ifeq-yF-F~zr)Y)??}=?Ym&PQw+(k6e-Wx%qhB;7^FgCfG+8911o7grlng zpVdeMscYJ5hB5!v3k@Bhw)Pm&VB5~+wn^km{_C!}xwr0zXwcl;;2A$8>OB`0efkE^ z)3qC|ZNhNMY-ki1P}TI@vvP1&FhM66i*~6L{`6xIgS?HN2?A@<^>#;c!KHctjRqyeAoNM$Vkwv{Qh2Sc|* zdx-3L*C~1L);&;**6l2#l_Mt^s~Z!2#tjhHkzyd8H6rxYa1hm!_8Bk<)%FYpF$2QO z!+(pY!!b*To`H~bH0AA5R_65rZ~l(vJh7NR{z^zOif(dK4{(pb!&_|ZUL>_p zw6}2pHf?=dS=JGJsC%Noe|6ZlLF#$&^}p=$01rrrJ*-S7ZdVKTj;VR!f5(vJZfshd zD2%M+u~N4nyZ+2`GM&zq*yn zY6_(0Sl967?m3_m9zV$GbtL6i7T61jYGm(HxX*(Y4>1c6!nE`V^*Iv|IKk^X$26S7 zWPQNCf&UlV4i0yA_f@B@eIn=!eCC3+>ToK7xA4Y_60U^r^K{letFXH7@FjAzk0k^0 z!wA0iqOm9aUycu3kNwiro|^m9Aq@uT8*5rxXxBwxxDF=+i}nd5+12uVx-qpo^p*$*^8j>YZf20(>6r%Y<%!p#oNwtKN=DUuEPBwOu4 z+%P+pz%Ys7RC^kU&TKPyzoMUt_Z(Mq+02H17xV4SV;mnfU^xr!rnF5H#(IF}=huD{I0=r1s}p&=J2k%R?Z8)`?z8Zs zjg?)4EW0(BO;nq4bX(_sbfm3)gj8Bd>nl_i@BAy>Q0p}eFMWic^P44rp{|Ig3YtUR zAQiVOV(qXQAhXU1|AFA)iGbCX{paPfP0897?nzolUF>;Hc?8NBI=Y836{fqO4i1;NNRVBOh)e&rvhh@)m z{n!tmyw`=O2bYGGuo&pzIkp?r`jXIA(K8}hfWOaveA}lpuD4R>q-?+75!db*%xt#| zyxEqc()nC>Zj7b#{sqRY+VjxiUxU+ml@3uQ!^1AgTvpealV3!1)6sHVlGaRN&;!0# zYg`kucjU{z200rL#n|JeSE*}y|8K_ra`S5JC`HEcwDxKvxCMsjuJ(tW%sl)QHSlQs zmLsQl%#(M6(CK&Pi592QXZ^J$2E0UTk30GD)bVy;izv%d!<4+4RnTx7#?yeN_jB+i zdZ%&dXpgU3u6a+D!NqfvYleCn?~eC?IjkdnwV8JvFOSGoZM0R-*4DM4?%iSqktL92 zx^|sr?kok(@e38nPnP_9z>((6wRPo-0+4Tq351Ag_h=I2Mg!XDe3(cot;5(!9T|eV=x}+_k)$m`-w<) z>(f0Ar-|O{AuDh9Td4a@Xx4fZnKMgQk!uhoKt#3i6Ad2Qv0}Z4q}@eQj5V%BN>ud~ zxt5Yka%$UC48wAHkQ!E8VD$ zOks`plE_CjT!jSJe$s~xOyhy4VuOw9#n}#R?<%_yhmvgmFNse_05z$@R;zwaC#Rj- zbtxm?de7-CZ7&jn1`D$;PL&Od=51x_9Z6OAghsbYM$NBn*b5N>=VaeAzQy(?=_X&u zV3o_$f9J3(9|xB_M+v^gTmh0C(TTqMIt-ML-hdr!r`4_Z&HqB#I|f%0{_VQ4ZQFJx z#srg0Y}>YNJ1d&lwr$%^CY;#DS^syRQ+uEH?AldlRdw~Ve%)2;TUW2X`o3;y;WX6C zgU2>I&1-k}!_yMVy-U*4JEFAIfE@weXWp3ev@%xmj_%buh|>WlZzLiEQ2hWia^K!2 zkm(5|YHpR3fT|YrNvHQcUip%xxD7Rg9a~sCfO}(rXU|kX3vp_|vF?elfR!C=y7%a( z*HpZefV*LI^n_8*Pj-M!`o^T?6_u`jiKZCwq`#CkmEV z>z!#CsKiyzF-VL$j`?pBly^ynf2DLjLr#%RG7I zTkM~ue%u^gwcx+ndcL_3y2oZb-^cTBo4R`+_3&wR(|)ZD=aTsTe%fsg>Fv?);XUBF z!G5xT!58v-8}+mQQ%-Vj)#PV);9rwlU^=a%jm@MlV0(^t`fT~pN6B5TPd$2l_DOK_ z{`gf#pap7wu!sCP+7oBh~!(e<4CBgFyH#T&(x-yK|V8$*rQkFk>DXd zrzt8{9)I|r+s3wDeE^bPeR3v zWrAGss%*(OMbL8jfpNiiEj)GxSyi@zjh9Xg{|dp=)VzE0@g$};T*+-{*sN^K70dG z$RLG4C77#=Y`dRGoW{Z+t#{RXw(by|+fWkG4;D8KH6iu-KN!h5OzXk4ogHw;MHvq? z<3tx-lLn-51`AyWd79kn{i5Fj<|n#P@Xip)IWRmHE8D7d4w5YYZsUW^n1UbOfz!ty z7Au3Qbuhr`YutaP;=DmpyDgXf5>GCe3=^m_Z*&&9)j=P+5YBMy|E#0CjT-c7jCf4x zEn{tK)0@o8FM7Rdedx{g}-uya_tZMXD#JN?~T8a|7T!A7U+pu zRpdY?1NMykAIZr7+B3rW-+M;>w}s*TKq~T|?Z1-|ZBG^S)n{ayC{#>LMl01VlvH2I zKwL`^AQ-7DQR9O`OCL&LVTP@tVX_cC-|1K_SkbLqsc2TiZ6>=4&{EWX_ARv9-l*7p z-a*KaGTPj=oVl$fK1s;%oc!$a7JA-sKY?6GI$uitwY5!&Bve#XNFfkSR9@hcqE*VJ zMB1MsSIbq_^r}S{G{HgZtRg|jkj_@7B2AZaUYLZT6-@W5oP`eTUcw?7Z9^*T<3K*Y zQXim*+;A#9VWMJuQ%_0V;XlYVw+Nmh=X%?>=7a@=pcMmfW4yk?w9IpYmb#n7!*Il)%( z$Q$eeqEHba{o2DjNn^UzIi3zBAwg%1w}1k0%jYR4je*wXUY{M~7+8o(9~oy7mqCY* zrSibbC}Atfm<~}W;oU`!NQ@H*c2_-V$g6pXIN!$) zsc={kLuX*XdvN65e~<@nVqPer54_{vfnt`aExa?R+yH^)ScwaiGcx&Tpn-F=Ryn7U zhSM&RX*HC2CMyY#xlQ99CBxJtm!JzsngFMZS~-`*Ne5vNP}mtN$vuZ3@a?|?Xn2FC z+Adhxj7@i~-Vx zf)YIvTEdZI2?@g`Vq}~3&dvU!CGa9L{3(fB{3oJBsG>M+zWH5{k)!>eu29`t5X^W< zls{%#-J-9@h7?3Ae5UOw$U@mjp7~OhY&Yt(oH#UhcPbM-4nZkW0bkF0p!Yqw1jq2O zT($*>xh(iJF%l%ENvB+Y3dmeIa+FR^9~?l}HW6fU%VqR&??K&IZ9tF!|IArU@qXw# zY4q$USVx$2eSQ5Qzk@edaxkvQ)q&G)K0~Fe#*0&918;D;lW?|zt7h54pu(|)Bc$Yz zPF^ZhnNUfYc)RKT43(oo#4|JiCBHA^)3jj1TCkk?WVaB)^SaL?JN}8wo_^e=Cg)7_ zhDw+u?JUS0a{TAaIP*RupW}pJ&?4?t-XhL8>NuT1&RK<42_R)DCTa$nd?ZP|AoU=O zx1m65tisWPJH1Ykbv%_2%s6R!#NgWl#`2Ig-G{iZgi`8XeYS?AB!Q^N2{{FmxT2+Z zt|Moyi&~h*>e;n|P|JzZ#C4;E?^!L|wes4vLhe~Dd8-wF+)R|LG+-&v5`a5uSuLW4 zJ(hmUr6s@D%ybAQnXfV+D^M6?hO>5m>4(}4?gm^5aEU<9 zt~@d&n(1KuN7nf~^QZG<`fqvmMFrjkdUe*QiNyksxpD-FltQ5s=Ux#=Oa&#Qj^1CK zrbfhLsv^WEh1NNH;AQJ55szwpGOMdoaJRFq=Z2m}$eOU;p6N4%o8E zidm1_W;n90@s|X>7r6K~Cs(R-V(k|*p9pqMfa8PfI*uu6XpSO)yY}X*S3$q-61th` zjCKzs_QYPB?(8GlfkespOW2um1wtzkVlZOOV-|W8s{l$^!GPe9tE9M<%t0tX;ru+M zSGL>(X4V^Vkb9#S{o|M(fg^`Uc(VbgKgE%O2%}!Z&ID;ZeUKnpvV>LAbgEtpEB@e- z-e5U3{*DJ>0=*n6Zas>uL}^6jb}HvELjZ_DGR>dNbJp4U8<+VT%{|R#fd@`9J}FHx zCAVrJry%3jd?VJP*^ZNBzuKmh9fspu8}e6lrc@=Jk3|IoYgTe-U&>eVR!KGYOBkGx}8CrIuTYY~>W zP&;m(=@dXdu1-=;zc3$6-&#Em^^y;Ig9zp_Xnko%STrFSD#%?Xc}e2WPXmHDaOB=2 zS}C!fAS%uT6LN{AQ>+M#4}t=Q+DTxz3K%P!jjJNu%u#F(s0wVgeVAnz@t0`ke~G}H z(B!Wv4%iS<>NuppT+q-wHNPd&37ekD@=``LQU8Dv(}ay{q5J`bzJ*S|15XkOnDjHUa@hs(Biy-xuW}OXO*M7C zl}SGvtg0eIaqtHZ7b2qRzFA9atTgVKoqmJu%MFq4pw#SrZp`ca%?^V%h*Go z#ohuY4(w=#qt8DX0}Qm^D@Son22LW{@nseJtvT}+^B-|+Q13??+PwtSFQ%Yp^J^Ex zKL9`bJggby>tc^Y2`16&*QrR$j*%LF_M4nv(6%&2-&~Y?JaLesr#q@>I5^j%VCKRp z-e$CG)3_pArhl&2;fGtAyVh^DK{Un6A zZlKd@)N4JydYVM&?|kR&slbomlqfcsXb{%(XacqfD9RX>SlOJpy+3*VsLT-V06}%i ziDqxbnr1#rOeb3sA(2d^o1WaiS=zldi$|UDF^f zXqoB*_ggTQ@;iotdoPYF&tjr4fE{U}P`Fw3O|$;NS?%9R^@4x@g8%RWbK6ZSGH?8_ zW0g`+wA$;3r%RAtZM8rvblMz73?3qrwqosC*rsZXBcibm_P`|+QfpS*D*(l=HPoTg z7~RetxT6l)^d;RQg4tIi581RAZ4d{Vo75`+eYU-YX0YuaivsuZ~H; zaB9WVI}!&9hokuI*}tfQh0Ui!dIcXd$jkxKPrX56pC)EO0obO^cwzQ)1)=x+0_7m# zPHwC@0$Fd^(hQjli9N_%u*IbFPUQDnqYHty%lzG@-p{M*)}l%OuPa99CdFb&eM?G8 zmzOl=-Mx41~ek$x#yrTK=uv9D`{c=LVLMt>hL z+C&?_;TVKab>hM9_>KWLr=8G63NIoQy=_RnaQBBCVx&!9rq*BQv2XI*wKrj_Nq59P z!G_<}E0ST91C^d0(U;eQPXo*1#I2IE6(yGn-cqe@+i!htPYN&T3Lc1899#6Lu~}Kx zmPqfE${q0*5Ve-fZ;)tJvlSn>l9OK)W#UaE`U_c)E2}Zi$akjhdX`(ocUeEwoT|_K zyfoAs=vqpaAE0h+ZWfNrDb{yPnm3Ccp-+bEZC_bFcqUltE0T?m)*mTTq>PZsTv%nb%Y*F~cZj;5p&C*Gp#)&~`% z4Lzh6R|VQab{W{iPeKR1JmWZ%IlH?NUe}5c?-RZL(y~4rSu1^oX_DJGPfSNGCD-Px zHe>sp&frPp>t3mY5|A;M<2!dH$DA5C{WU7kF1m@RGj^Qm=%XfL4fxLRE3yO_vwGVs z503kf7H+i@uDbfQ8Z8ep#I1?Ql%u>TidSh2xs!O&bvf%X|E@sBK#rqcm?!IU)B}?9RO;t2ta%dqCI2t-JL}c-^P_ zXZ_n;?fgX$=`>c%Hs(-A98;=l4}!UX-n+my2T3~NIesHW9dDd?j?m0>Lk$WP&D$sS z?6cF%{m&#cenPN@$%=eNH@lVu{7j$b%kJUf!$on~_Z~H@N$*;H0-Y$d&q{i+#vl(rxqJI9& z5nuatp&#RH9eamp@c)DyollGnu@)|M(h!q@Vdw0(YCdL8%;a7wwvn?!GpF@XsMxn2 zwq$2n!(Qa?1sS#Mi0IsoWf?$|@BEYUOIOolek^yV_Sofp*i;NHd$KFla!4mLS2A<& zH&3HeMg~7RU+5OtsJ;Ijt;L-oJI_v|aauL~l!>y_M|;k}lu5UK5dN4yTe3b^l!8p) zJs&VKd{YaB)o>kP)=KZkp1l2>EqAswF6x)xDfH%05!&^y(EiIwnDy1_XpWD4Lwih; z(vdJ&K4biAiFTTxEv`)w*q#I1k`U309PAU9AB^_!{``TObS!rVt*nmj4aT$HD=U1C z!+72W3EKH>3`I$Sn5_ftQuKy~?*rR17+qYO*^Bt0pzXuPU?UWGqwU7u{?B$Voac;L zXbbfF@{X8vz`tvE@L970@`Tm*g7V3BPcrIeRVFIt^*{>esdqNIZ=yAj zm~uc}jr!x`VuU8Fc(Ld`tfMS)G)+Lg#ntc$@Fbzs1S=t;es@IoIhJv)z39dAAi)mrEIl zJ?n)Iz>(O?i{)p5nGTtn=lRLm=!aN>wUB<(r37bv>(1ZhWL+fmU1%ljH``um26{fX zz)Zi0wjC$+GRCLYwl7EF3hZ{rMRE$`&@)#`YmxDkr5qF<4|yS8FyPGoT40ryaoT(Y+C*e zIf;E(%fcK&loD>4J&}yath#!K{?L>1Ve#I$%`m2q@V@tdueggx7aQLOmwfz`NAV;a zALRkQbs9uoqgSa-Obw+jO>G5M0kdmgS-7pQhB~Q_B+qGANl|0|hwtCHe?+>^ciT1k zEZkoU3BdLFh-BoSzuV~FGL;l+wLcUO{wa#P^y+w&KS*Gb66drxd+9*3dM-9KmA1yA zYSI@j%9|6AM;*9d-)efNztE)v+@)zL5%in*2oiPV#2T{Ph}v6n$3Da^TlNhg>zDa& zXH_A7iyfWtm;30*g)}R*bX?ZQ{X%Hls^!?HNz7JjtMUHw`};2?2)@!imkhvQ$?@V9 z4fi*DUGZ~7HJ8fFhtK(aM^jZy!dv-xDx)+qN4?ZN)As>o=G_(J`pN_TQTa6MjCwIl zA}ghC=ikS2&17;f168S*Q1|SIf{Y-`l+8_DaOl>X??IrScGP%Zu)GN_(eU94=7o62 zgs7Pu@0wmU4Hc$!LI zrE+fgxdywZ!S$BrwkX3P->T9(f9U7iu)0#unmN_|CdKZ%NpeQjg}UB~WoyRAyzxL_ z$LiE!P@m)YKJ~Q8%2=P9-sV)%X!H^F=s?GW|5Iv#V5%A0;Yjp_Zq~HP;BGKc$Rxwz zy=0EO^oHh`rr_vCFG-#su6a|m7H0B6Qk*{V=A0-}ii*zHXJiJ2*$AT8<0A1!08x)^ z+Z!$arOt&mkZJ9z2xWgaFHM_%EiVKFTcaUYrPOuU_wWdueB>4bzDm>LXia`UKD;++ zPv{rFx7bzxLse7O;%S()&shpyxbHm6?qa>Q{1>|!x{3y#1E3P$u>rm%$ZY$RMzQxi z$}NqZs{7~I^+K)OeY&v3d5{?(Sex0{WxEbKpmO>H=+=~&UomyZ3|=1G^# z<0zvq<}E$~knHX*|8YvH_~Yp%oyXQ1##egvd^PGC<+@tq^R#*QXMz71kC5vo^-zM| zi65cQ4O9L%ux$FZ-#rrX>a|~reij{EtINmrV3l4>%IF`7n;K>P(bq;4DmOIR)qbbD z-((LB;u9RNf}~T@i-v%Cg6BLVncWV1Wb`-d-`8{KNSqg0Y{ac zu7rig_iA)r7lVF(PVt{k8G+a5TjtoSCeyc2iHj;Z!CYqU)nQi`U8!Tu2keC$U{6!W zOf@*5zPVw`k;w0K`SC-0PDrathgQIUhwy)1*=2sn$z+EU z#XP}tp3Ub%WTnz*U+hjKYTJ2WR-tMFP~TDz#cEWPL*MVHmqK45z)z@T=&0CGNa$#Y zWPkYST*6*@LPXhI`e}MX!d%^0#N1e!LD5k~K7HXlZVT}W8sQDRSJZ5=6!diVrx1!a z$E~KbJ${j+?H6C>z0PWv92#u`=`Uhqa*c8` zLE>w{k@5Z)9L{(Q3q&$LE5@ao=H~qc7yUygd~TUMh*-ABRDM}4koarEnsqqdEVq@p zwk^{4&S+DlQ$w}1Em59s?pTwg+L6{)+QW2SKVXkh7XEguV_B;OBx@j{GS(#8)^~wt z4>`eDI1<`KH}`2WgnMAE_OxgSipUx9gfzCzG%rp9hZb1ZjBY-Ve33^eQ-A4Jxcabu z1P-rGJsJEpVq1o)(j4=L_IA$r1JJ3>5n}Gv%?6=YqVGJHpmp~p8qnS6fB4o*O0C~w zcNe@?i_cnwi$59p`d{eG36RcQB>?yY|9|9GfS|_6)x_M?MW3BT-`UjEMF0P3f&YKy zR`!7v9{<_?JDmaMR)BQI4<-T!m0L#zX_tOxzss=8Cvaj=_??yna8Tlez}1-sP7hW0VEEb&a~7U>E?(6E2dggB+P4OH@P3b#`t zttdB#QNW?`QNWcdTtyCurO~R`ChOEJTBJC2Yn2v@6N84L(O}+Mwd;v_LPVDHdP!51 zoK=uiO^6TwLM-N2wTmQtP!V9LPPmhHp)ESgQn3&P7g$!1;cUo2k>E6=@@PcQ%!8H9 zfSrC&ab{f>%_%W5 z;vxD^shae61^a0uqF1I>_9>ZV2;Q>R3xjO(zau)@>LgQoTKyHJ@-xRdVcQ@$S|QdW zX3GGHNrZn~#^5SnXot{VB*8w`i{&j;Qo0;0+E6B%(%ootDtuiZPaXs=fvm;~#?V`9 zihqN#OBSUY4J$LlAkG+#^O8@?h1*r4+LkMumC9jSBFggQ%jH5<%vqPk7jU9e#@U@s zg-NHOq|2pCCyUB!G`#AHdSrzbR}r5>e`Ok7TK>ZcmN4cEgOde#>o;KXCY;e|GF*s zIy&+trDa6ef>(wft*Y>>3FPBQw=ae4g%?j54u6mV$Kt6;R4$(q%Gc z4vQ2HZ%ip6sD=xhVk3}0Bn3wK-@`}Bw}EKs%n)l$pEfTc>%t|g;Has2cZL{8`GvPi zt{rO7*a6+KC2#WiP!Z*UYr}P*z$Z!x;|UV%J%@EZgtn|=WP0BQBdF`4%2I8tJPKq% z)p)KYN`+yCm`jp(4;TCrvNf1tSyLe7vL&fbu>}y18pUL|#SLFJ*9JAo(t#26J>OqYW9ScS0k%}*p+0BYWVcNL|P07<%LrV zyeo2&=n}!v?(e}(7zFTUq~q%Gwo7=b{E2S^ z8ueNN9~;ns4{Cvjj13l4My9!rl(TTTv!b_bqSH5`wizt(7JC_UH;~@H*CUiP+HnjD z*i8m`e?<~P>Mh5OaOb`|`Ep{@b*Iaq|uSDbFeT{s50 zD9Zs>-1OERTdHt09X#HZvNY{Ig|s+wZna<;D~a#N(+Nq zDH1ya(SH3)V-7D}NgOM~my#GEwsNnQ27llxbFiv9(!>c1jXc#NLSrdh`J&Mo!sLY} zVBMLZ@kGAxOmTu z#M*Sji0KSa$kRxYD{NhFj^@{Z^S~w6nJmA3g!_=QoQGG;rCV!+@Yb0v;;rzUFB0l? z1}crtRA%5ihEatX<4F9keAMU+)_7}z?5sS!q5!}Q`-?gXkq*4!u}Z0&LHw&Y=1 z^&LZAS%Arz^8aRe#aRmT{ZpPnKo~>37Q%SOJUNk6oac>t6HvI%d$#8qal^~-1m%iK z(gJ@o6!7Q}VR2MP7Mc$eZLNTexDS+FdE`?a&gThs9sI%La1ar9{8^<#{t`=1g0YZS z-c&$An?Oy(awYHVuWCHY(&nCwgj)9V>Cl*l4!-c3t;vd_z2`W z#Zrd(01i=K3bkb@V1!%Kf6rlU8}W+pxlFQb5N)$jxbFQri*R``nXt8P01dPN@d*YI z@u<$O3QplEn=^-UZ9zB-f_4=Tc6iMuUo2DPN+}@2h8T21uURNcKw(7u{g+`5@jq;) z*tqHj$YzWVsJd*k{F$Za=dDsK#7I&cQ&=u&_FLl6`gx??|HEcDJ9H=$O8y~eL7p1Q zJ8(oap%Iw=cHp3DqgLy}Rw969?}OVQtK!yAN9k4x3lLPZWNlf1VTc`Ccyqc!iw3@d z!9D7HwGi)|27&MXZ?=6BWJVXt>547FIZX_>a=uC$YX8l4!bI$Nl>}zbj^Y_-kM?8z zAOOAiY~-|&6G{srj3p$3m9)Gi1mOq>0u?xVFx73k$k|!Gzz>xs8>lJ_|*w0&qzv`YUR|sOPP{{3-c5AdV z?d;K$V|Osr<^b37v_Qs@-hwA%`d@6u6c0ldic*0c)YzZ`Dj^UM4iB9W31l-I;75`v zIT_r_Sw+zk?0JEo%EDMJ)hlc(n|@UUY72D*puGIN`0*us?x-L)S!NR$!sNmjIk<*x znAnWCKkQ!)R04Or=KCQxJ_3o>B_rakQr0cbGA)RFub4Z8X81 z5;~GrENrLr=u^!KS(WghGLTL!sdNK%e#}H1{I&)$PSjY4C>k_{U8@2|+Bc8M@)5+hl)+US5du zd&A}E4w-_H$HAf7F&!5e(P3jQ+7uY^Vq>zmmAxcIN7c5KrH_jbGceJaIV;|BVq@+) zDBdzIOn1mn_5aR_-k57i2;>Sz`18tsp)8P#s#o!iG;MJI{G1N(Y-#%Fo4+G38K%-V z9t;b9RU2fg~}qS9k>?}4DJdoq)m8Ab0A#Tk3p(lQQ@Cx1e#V&kEfu0l}d1Z)wlg>3$0+M;W7@E!`?G-le$aoATI58YH|+NuD~MnX58 zi{hY}LHghii#2{)VtGU&junrqdtBC57nzhdr{ml}KRx()vfG8poR35P!)N;SE!HY? z*d^XNMH^Upgpm6x5CFA|bRiM!9+M4y-tP_2eSn8WftfFfS#w3zTEm1( zUPaS_LxX|_2>+9YIb>=Ahl2-g4#g=8Ch~yM%oA0B?r*~YOT;W-hEo^ryrJ=6&fpchxKIkpY=CcBV@?S*f+6lFh+OYO6JOrT z%LiRPk0b}u{&m>+_fWV|_~uPAZ5MrS*c8G$Tz9H{Eqlg2M8v%f>me zrPuSH=6p*etL=2DUv$-#`cD5vgRq~_mCv8{f^74n5q!|&b>FaDy@m2tp4-lhY@g-% zqqob5{`3Do#i#Cdg1gYZ*}fMn|_GvhtA-2tuJz@ zX6~f-^=6_Ds)e1a>7$L*IRu-10m`9VEX(_BDt@HV)by3Jo->^o2A9v|L@mCrUA2$xza!Kg-ys(GuO_{-$*`WO zPdKq8wzxtw9+PXnncw92ag|SmRp%QNoEvYG`h!Q@x!vLAU)_B4id=gn&;G?wM&qTc zOH`lh_;Jo5NWAO7zZKQudba$_cHFhV2{^sZ<>wpVnMIO~Kzw!RTlWQO>iNpab!E5! zF*k{7*vZ4wX3+lxITQ2IKd<0cWGGLlmn_}rX2g)_t?S{*J1PKh2`LL*&%bU$`Jo;kKv)9dnyrKF(zF4F?*rO0NqoP{Sp)mQ0)!>}A0UA@ zSx|qrfwdOcB#D zqwWhdG&KJC6QnA0h(cybU9nZr#>TXK>%#pkx;ah?_eo3_+h)2ea~DF_hJWB5jBTDB z4@@0Up2!|S8*NXp6Ml5Un8@w(RWUs zo-^j&C|J$@jK(xiGo$ZLp7`ER))b5BoSWtZM(K))wSN~q6hB8qWu&q`o-tG4W$j>L z>a~es5WH25ce-N3u5Zhk8b9zw=JXDYlj-Y_RRAifCM*0%x6oU9AU&xgiIYkMYq;+i z_Pgt^t>4ZEhZ5hClN*HuBq8K8J#(LSC3GUQiE`Ty85~<~D|w)E3CY(yDD?i-40pxx zN-S-x8N3$pP{VewlC*kn`ca*Q%+GUD~&w==&p{=N$Sa zrtA1VD%y;W4($bg~Mcde!V@@zjG_L+He;`1{HMHi2jj{)w7 zB*NwQb|u(9awGGRhiXZ#T=JY87yKgXSrq;0+f@FqgA~`V7P{O3^1)g(YxkB(Uz(0z zKHG2eBX9cbp1ylYOf{SeLZP2&Z;@7iUS=fKWuEvo=A0g0jo5T~&6{RR>KTWpkHLPfJQ8};q=!5{7uSbUIoM{nQrlJDB}4aI`n7{}wZe9tl1PB*w(FjTx}vL`5||MmYcjaK9$ncbbNFDt zN%?=lgMCke>~rMFLPMFS}1n{ znfYtBZu11JD|)2iDspSy*UWE%<+NZf6~OV%4}LHAPVQp*Pr!=*0er-LwP~{);3*Hlb*FJ<_&EL8~a@|$JZ|I;$8Szz(reYZPRW< z#lCfUX|e3*>a#6XmFbGl!bjz~&=wOT&V%2|V_f`i?kWwv`r_g1p}+ZIg+YSx5tc)p z>%qP4YIupS$BMPE$dT5;yb(y9+mtEh8_b(@Z6ZZd>iix6*Z7y0v65rbdge&8^D~|0 z=XUXwBby1Bm|mGK_$Xb^qh-KC%4_BCG%)~bKDBkUmuIePIBh?W9a)!m^sJv&H*JyczyA1L$&44tppFqi>_LFpF~We zW2Gmh$Uje(2mHn4u!g?O4-Kk#t0Am1Bm|JYlVQrW5>fE)zjbcc{6v5L93&?rFX)sE zmlTr}8wf9}OE^7c*&-k$#6&~G!o)Q@k^3EICh*!4en1Q|vew6Gd-TU?TlAv-C0w~hBeh4)D0IX47MTDBpP@0g zQ3EOJRzA(9pWwYudd(7Rk3r+5_vE5r(E#iS8#1~D`k)tX!>lqv zn7W@}Am)3+s4_h-8TiBNQToQ4bY@o<6zM;I-JhFjR|VAoxr7);uSCnR710F+;HU8Y zxq)_7`-~CK5PR=a^YGpIF>w0h{#=Kz`_HgbgxS}Q(WrU^5O?%X)f(A?+ti?I*i73X zk1$gu+xFpwe!186G9@dcZEtthE%@qoY9(r|>8?hmpPwrn!=2=RUsGzVWO;Qc=oas1!W5gRuf{r^u9$NyHiRR%2K_|Nv= zz{Qcarw6*~s=P8BWlDDgyeX;{&Gtg7?T9G6mh^8pG!eoDs!NI@sjKWF$R;=|OC>lm z5@tIXva*OVGNmm%4EhN&zA-IflSH!0i<@n4^P~)^glFF=@^2sC922uEC%@-EyZ?a~ zfm>C3P;lZFWdqmoN-pY$n03WaBLg^$5`4*h8dVZA228B%w=z9H^-b#7&whll<2;lF zKCou+vYSUNpI>y~Y(hAjd0`K)L4V6CN?@&3*9F8H61Wv+D6`HbT(e|8#%vqxS0&I& zDS(Qb6t$pgp@mDFMIn$x>P2Ks53! zza@#WO~5pSmW^z3nq#0ZBj9ka_Yj5WvnU6xHbF-dw)PWkW7$XL&qox>uO@6-40wWk zRnvPms@yR+Hj1+O^-gB7_oInLHD_MQgezvj5k2gS6r&vR=K^kYVR}99 z1y^`Bc)S^;rS`r=A*2BNE3Y@`q(jw=8Avwd?;~7la&Xs3I%rh9In_S7Ad^y9+5iZ%e+UP))`d#AZAm zvAk_|D9=bjF)S8@;}yFmmKeC^GU27hJa?YcLeTk*6|tfk1JQD%GhBQQma1jmKycs4 z2OL{xO`%=v-*11LY$Kisluuc*j*NvrU~Jj9v@JrDB4JGmv^M)|qiH!eG@;;W{tA=f z0*WpVg=EZ`mSz8@gAqDW2>hY~SN6c;2Nx$)l5)>%x%cffP52y}``Q3_(3r2TBgPPU zmXKJ0{39&$9V%51!4s2E=rS{_l=J^Kl@tK(HWNzhVN!hw}eNv}HpP-R0@R z;~@<`QrER=rMdGn*X%&Du4^{d)Uy|VUlmOkzw}3nPHZ7@J=Sq+_S=Ey%(x6 zJ>-K;0cG5*F5}`C*1AG1C=_5V=EzoQ?9Pma3~;pQ6bQUBgL?b3-~4>R7&^aJ%w7|L zjDcBJfuj1=#09T~eXpfG5vU4o6SN{chZm*g9JJylVjZ668ky%>p7$vBU!tw##^-x2 zA$=&ly=E@M>Pe10@r)H_SS|T|aLLluLfvtjqaRK2eMU$!l-@ow`=%qCfb2`DtFrbE{61F z1Hc%GAAPzdbgg9{LltcA$f~BFZlk6juJalkb##$&ca$qSZWiun@NnO{V_XjXq2EjZ z#vHp+$1cwP7`32+jj$k-*ed@#v;#Z#&xe(KnOI`g;LuW=50I~51fgDpE?UwrMk9rMdpHu@%ws_gzNv_STC;Cph zvZhDvTGYUMNbCT<5!d?aP!)>b!Leg`6T^XM*@Izlq~MMiE{vH&7@4l(2dTpH@2=J!8iJ+hFDS`XBDxNQ- z(Y?_432M8kf!CA-_7vWX#~NiUHy$_O^yaLvOLq)kG=`4JgdatqLiY@IH6Yp?29@E% zwkDsvDz2$}K5;>JX{bQS){|4+PjEN5sVlVfZAP|ENdvdqz6-&?^-r+IUbrbDlym_a z7H^t&82PPzHir=ECq9`AMZ~h?qj+O5$Wd~jWQ)i-5dG_zLBdBgdNgPKaNKxq7iD9d z?WS`mue1w_6Q4oc4ZwYBNgPZS%s!SU&G#dEQ4EDi<9iQ&#ktGz{Bo}g>SG-LSroFZ z3^a{@qI4<4dWhV3L-Hl+%*@$&)ss@ei*C4>rI{R3_)>1+uia}J1XfPBV5wrdR6K@N&rNi?f)zR7Sf;T$%X^pu!k-o^Z6X$xV$EPod@{oF+wz_N+A zn>2)8*g~D9Z0eVL0kA+554RHC8JK^Fy#(vtFy^C3>}d~>T}0W#Cf4EiQ0fd^y#VH5 zz4x?p=0|TB<(1?Ui}W-`S$ejfsPJ(jq4T(5aKu+OHu=UBg5ZIQE#lcc3$`+5BC&~U zwCweWM$+JD9h~3|-6CmY6jCgt)S+P(EY81-mBId1zue4msGZTrTNu05kFZAfD}7F!Ne416eTRC66~I zX)iDrkfT7jg44_2c>0*#ocUOX!X0+to?-UU=@~h+8f_vc16PV;&s;hX%v0O1VUMv- zwp7M`VBv(5mxzo5tmum>RnJ5x&ahuG2Ucvr?$3OmhwF6OPY-^fTi+T5n? zdJRlPsxS&lYuo}K7je!VGUPPtU=m}@P?#X61Y$ut3JZN4UstN4&%XO*56u2+F7C3B zLXr~-ADZyg@iZ^Bc@?XsI#=ZBcQ@*)3M5p1RUoB??q#8e?T6kZ3-IsizCwa#e)hp4 zCWH9FABr9Zq#vl!#rQXpS~Tr@)sjlfFxs;eT^5Ft88RPtp6&WQByXm;SFGbt$Y_(d zH{$jTy%h0Bv=o3+w8rk3e69Csxqw;^^Ip1KT{$y(@<#e0nLP@{eX{9PBVcr-us|9M zlYOWxomOJl8mQMoEBx!qN@oVtYe~jLwHv8)kxC5nS7o{9z94IE_-uu>LZa^=$WYv8 z`TP&PmOuI|P_LyO0%$#I>LOpbW6Bo?)J7UEI|Q!QU5_JnCg8ZCY97DiObCfk4-D_D z6OWlQlQyO$trJHz`G{G(rgJc(TfT}FnYPCOT6II1EAoFa_Kq>SMBln^+r8S> z>eaTb)wXTh{kCn}wr$(Ct<|>Or~l_>=j?lSa&s#+t7hh?N`0wRQsWup_ZZa)GLE{( zqjVF)`254dx#C(*;O{0a=piiVA;BsXiRa(MjII4j)`ei$3uNdgo0I!V&9o`lJoQG*sz!f~efGY?hw^y< zIjf{Usvj(5Q8b$}L3c zPNwEiTW72z?2t$3PKUaU!KcwrhzZyC{iCp?_fzMzN1}+}MuP7o$vaexPpXgr8kv&d zN6^?`%?%Olx(m8LajaOn;AjbPa)sfS4jSn!9r?HC?63DBLN61(_rwL=qFDIah-qUw zXvakb+rsyAA`8~+!n8RavU^vAYk@Ur)kYn-jK{P|8M5o}UxH2o1L_R#N;Dfq9q0)K z9%6Qjs{CwENi--3LIkx%l$}XszIWA9=3@}E4d-`<-MNbc;OyL$fazy5d44?u{o1}Y zWP{)ScPX{8$K$8{0Sm1}ep?*52Cv}0s!UnesYl}0qG$jJA?E>yjtP*+{R0YOQThNZ z5gHJc2u9xjOE3vUj>9Ta-+w%B9Ckj?HxMbdSdE+4w1FR#_gVN~!805$|Zg!@GXxq>#$AG^zx{ zi1%BZz@^}lhL5|sq^D!(4NF#74)mmyRF(#>P2=**O|OJWpRT06#iyvVwv2V@b1dVu z>reE9OA6`p8T9PEs=C)5-+BBI?exn*i#N*{ewUCI2!tH$w`N^lHwCVIi`oOUJv8`tJ<4aMH`Svl% zgh7R3H{MCab=T1|Jb}uG0EE}`>m$l^)D+@7m$I<4*3Jgkb?F*`}X8pP=f~NJl8{T z-fpzrK%J$N!>qBLK?7=Ij9Q(b6z%^_0bF(7P5sKOYAX!{UItLn%yh@v}au01%jiaks--(&bxhoep|wQ z{YG+#FSzyO*V^;Aph(-vLB+eAF?f<=L9r}>J}dj_iiek&E#7%y&+Dbh9QLw!h_}sI z=eG_xk7w4E#8DKl&a^22Bf6^aRMj+!59cu7=d!1Pwabkp ztFh_kX}gUUdn4yk$-3XvFk;!a;JEsdIle)`;GUFioW~SjBVg2_%PyA`avds ztIB3}x7^vHMT`JnK3eJn^)jaLQG8X0rkOlD_+5OlxZ?`-LZR@{mGjSOX0B7_rsOmr znFs7ciNYHZ;>wlTC@j_oA8M16ez$S*;iwxLYD}B`{Z!jkH%g9fExZQL;p+uYw=X`z zwDh{hSXU0uGv|(a|7JO|tT~p`!tpR^V4Pv2ER)4}t-;xewVboLQqm} z*yXTAV80py9)Kj_{vO3T8%&&-lPtL?DmG=B``s0dUCYJ&6*lrDzk&XI;q}l)vpBsK zL(sPV0=LBNt~#dvNn3v16b0Cx-jXRZ@tJeukGWJDs1sa{!EFk{&h(-3pmYD~eBCps zy~dDOyX(EI)(i1<>5J&Fb(rbz@t0LxI`&|DaT-|J_I9cuv!&QBzvmqhF0E;zc#BFm z%VOAH>E|mY(R;hO*dFQp%)%aWvaoZDc~0Ztuo%32(e0e^vMeWRS^ho@pwp3URn-}_ zTTwiZzNlR5s@&e_&Of>7{Itt?ZgbutyksKuAfKqdLm1iO7M#LnwdfmCbV7DhTo!^m zIyOyGJve{0^cwpRR>ydb?R+=L1T6LF`Veone3eeLq;0Pr7p@fqMZ9&gXW=&(egNv= z%TMrY-5jXx=9ZR+#fw0-8Uf&0xwUO4;Lu6@%72r~9_(v;q|g65Q`Y&it+uINiRoC`6aO*ver`j9F*HV;J! z+ucW}73YTXJPED>E}wQJT|E=BY*wXTt@O9UYxW$p?p_USfQHE=M-}kYM1{*j+aM0s znF+vr&dEExZl24$G@V@+hHg!6--9wJTNPk_N`uzz-+qfs``M=9Tajqi1JP%+&iacK z$<|Run)t*u`{}9E!_CxQ(u3uds#f3q>s!GFWKz`k=Nr*p_v6SH55#jyh^#_wQois{ zdnXH?E#`4EBh{Aj*`+dFuvI?qOJnWS{6t6D&`eTd@sUjtddQN<2J*S(bYivIgYtC* zi(-KZP0#B;t8#G#fc2_U5!YLj(=+dScLS;QVf^DnG(q9A(^}k;&A?dtQ}R{9JXW#YSrp_vp;C&@*p`XznoJkU z(#7_hO^Ib@0zsF;<`wnhUDoxr3bA%^G(}C!dcRgazO^f*L+{c21N*J1oBnI+)!xDqS5k_fnHr_~OkuZ~WVDVM~vt zFPC@7v+uL_x9`ZeCfJ&#YTSqK$M8$rOL@TEPK(dJ*QG9Zi7c1mK>&*@o_K}BSI8S6 zH{bm4`QLx4hW2>P`dD5I``B^Zxfu!!7ds=e$$P45oOl)%+^YBYTuw_Z&Dy`6SJPJC z*uOC9X}%^n1wb?)DF^&f1wL^k9(pjh&v$!je9EPKbWFJXdw%#+E8kupjqkgBh0ig_ zAJ_3!=;ohgKQ;<@u;C)G;Ju)+4QK)16b^9mSxkQmrmZriypc>J-zFW;eAx3nc6B{H zJ2@Fh7-%S@vr>~&{qYr0_V@P{6Od67el~?Aa|-A&=(5|xKwE$YKJP#zvBo~A&VSHL z#Q#d%e7_NTF{IoV#U^}pPF`aAIw5nq&E^k^{1_Wqvp^JRSf?>0+~fa*q%U#Mm%8eb zpHAHU#!`w11ukY15~HrRaZ!t>rlA@ zIwWyF6yI$%rl=I5KncTFkCb{AF11*~oMGR#t@OvS(q5bf^EO5#l|cp?TVHH=X})CY?z z9&T+}%PKkD5RYWm#~YoEd~rVXo7PeE+MF{EJ`&(Z&jo9bJ#``Zj0JN5*c-DctZgkO zOHH2cYL_mP5`q}}5D+6{38}l{1E@A|xobpO+-D6LPt;-X8T@sMO^n1EigH)ssybm| zI!_$Y#d6uOc-9xi7GyRI#560*z}343kJ~SutqBlQ^esX)0a^6D^x2rCsq2eo`w`al zqX)ou#Ia)SBx&nQ7beXZ871PKLy>_-;9g=Q-H8xBqRqkHg>5}$13Vclw2-mYpwiNI zs^Y8@AhyEeN)XsW8ju<3s+Pvp!Lj%d?Tj4@R$VS^(T+Ij-38Ie-oGGK@-}G2#%r>D z;L^2e`#6!YPWTz;cWRMik&Ojj|AhYK(i$KehcHnOc8{fOE`o)>iGIIt<_;e=7028OKxM>u`0uR-Dys>lYe0wm4sRGp+3lf?NcoEfBEnDWiuATgAgyBn3; z0xTv>se&}_%)uWsg;1eL8nrBuG)##iCPE20L3sQbHI8v2BC)6J56NiempUGjV2&aP zwfw#vgYqOYl7k8nqL6>0SllSu>Ha-bHZz81rmzXrpa)&Hb6%Jz-@N)sA`DoCdc1Wq z+-5!`>mf@I7%Euhx=ncqM97gr`UU@_Jchvre5rykGR52BM3OPI9lwEe-F1KReD;D^%DO#o}1v_8K zu2%UjH`kV^JuMY5emH=4c+C>*A9f}cYUZ&JIMAg(D;XF7Yqn4zoD`!`Szw%!n%Q)} z9-ljgdx{E}4or_rSN$|XzL>t#g98sekuMMHT7H)VK0HhhSoqdB&>#jMwM*6LrW}cy zcrL6GtX9l00SsL+g9MnhW=yXPi5j42my+U-CQ_>a{HzPpTEk?wBiHMt2>eU~zS)9i zr%JKgUEup(4RYlH<9toE>h-s!wztc)DfGFRMWi;l8Cux&KG*tOY$HUZHrwRxPs5hU z5QpF4L`)~~x*J%Mpg>s@qSdI~acVzkQ23ud!Z>Sb2plZQN);IDbJ4%R@CYMeCL#D% z0!r4jz?vYX>*aDyQ5L;d)abu+4jj2EcMaiByn4w{A7nxQ?$CnfAbE;NZISo9NxPUy zz58b^I;#4f+F*IwfO(*HKPCHniQEMBucW}}zGLa?Qh4IYy}W7co#*ziu)*ktpmujF zzi?LGI0@~YbJ+*fhpqp(3I8Q9mWu~NtYQ^+-&Axs8mH~hoDVSc24q+Ab0OOXx zs>qO+k*`gh$zOSZ2=LkY``a-B1u8PKTZ5Tya@e$srdO#rel>EDK;vP>S>Ax2Iwxmh zChdYAi*3d1YuV%fvi=7r9DqSaW#`S>st1N4ZYa2H#(&VB2jra&PZ_m)%xEW`-wf;y zoHDknIvBNYQiR5`_X$&jq2zWlC$csv7EllVwci&OH2tg^H+@?!7Schg;x?$uZ4?!B zMCO`;C%H|S3l|k_(o+OZ6lVyH24e=Q*gU<1McAq#--IQM+`WjPSUrCW-Wna~RRH#Z z^!p;i=!xCxBX~EtbZYmrFDEg6132-6B)|u!4==s#n-wK?_xbD5iYd*orf(-OR zjP1*QtniyC@B8B0HfI1nFW9+0;QPh~+oTq}`ga_J=Kh~KP^+K_A>>*XT4d#3)>g3~ zzcB={yjmKYaG`Ruzs(4Zk5u)(mvXQf&VE36FD8NlqN;u+s&7`FaUv77JZCYHUaf`SnAghr$K;N~g~3!{AgS0IU`_?14FsZ9AgSP@sBojG zfS{=SK~X_QQ6V6$?3Pmdlw}ZR{rSY{jPtBU1pE*lNK|OW*bRy52dC~r;_VSaYsdIWppjfkWo*OtegGxfKH?bU} zG&FJ(n;6j;M9EVX?$nBK1?TttrQWZ#X&$o74E@t2J-v|L{a5N3WdQ?AoerrUJ@zaG zOTC7^US}z`DK@StG_EN%t|>6?5u3=8g2)n!$dZG|5}e4Agx03?RnE`I#(}M1<|zku zuZMiL1|@fA7UYu@^sP$o8+hNkeplJ3@46PeX9E2r+obQh0or~$vZ4dadP$v1EeY~6kf>&;#IQdH}OiOqN=B$ozmiv z#^N31g@M5bsEEbuP`jA;GefD?kuzKw4v=#lYMRM4P&mRPNWn5~{QT5AY8^7@tY{V%Y7x2lXa z70;tdpL;Az4{TMZ*Mkq73|)PgF9Gy+2}(N#a35ostyTKFbi=(pSfDPLpgv3xSBwz; zj+CH%oJqi0GRC>dGRD8bFxU=zWDKg^ElR5t6$SB>CPxfrb|ekE0@AUW`S}$Dweg}K zdF4fM+5a@9si?}=zfy{yQ(Itv@8^&ysT6rug;WJUT?Q)P+ql( zE?LWA1G6aYNo|#Pe_mWP?Ncf&x~9_pyJ%74m~XLpPM@TSWb{W#G%^xzMi;O3ii$hX zgLFEK_9GH4B@!(sa`{8#LQ3S)N3>E913x?438uG#T`)3Wb9V9rzKBr}z0Ql?PKf-1 z!s<$qX>;no78TotQCZc*({+1`zi?dtM9|e^W+l`?(ThA|Q7H+lXWu8vei553lTR)k zkWUsWe?M6yKd(?MsLY|IAAdVvB*)2BEU?OziK^%4WEqF}&q9CxWN&_?;ZKj0usWlT zjG6D-9zEW6yyM{#uoNK>Wtbg%nrr#ESl$%Bnjev)+R;D>j}NW$G!S|rk$P-&zc4ma zfqmR|5!+3VTmjuNajHtdx)6J?|Ku3Gb{=|9bs!e!x7V^-(z+Cg2YOm@oL*KG*@m1O zSolY-pjfsqZdAn{IdpVM^dF;_(j9pU&qyFS80qo^=)p0eeIsOie7;@>e&C)!en|Gm zG6>I1AaUflz-TuA?|(#BP-F>uSpov}Nl)U+j=M~xqXo1D*ZF*R*)%tE!j6w6aEeRS z@=F~Mn=GQLJzXq@4P2|#0{155oV&u}jc=`(N4r-(1y?pM(Qq^;l$=aeHf9fgEc_Lm zU~#fXUOO28exC1|>nT9-m_JHSl#KM%(~eFim5*}<^#(Y1$9x}Ob9KPS!OW*A8+++o zK<83Sn6z{?0XqxxVQR8s$jhwl*K|ojTW)TXPr$zG0}i2f+U%BSSM(iT0&H`8Fx%e8 z$~fKgc)njp|MAe9%@Rk7)1xD9%hUSuBUO&g9#@O|HwM}fD(%5^cEbn}~ki}6Ad*6%>5a$Jz32l7!MzZ z%%iG$=N)?s+4Qr(PUWzz6~VBwbH-}Z!Y`-H_pe|Plj`yl%Tk@|434U$tMS@};Is3) zi?q9=SJ)W4OzY_=Hxy*{80%-D>ljd__q2wyt*uvae_0pT!${Ja!o{0oCg&WcLK%-8 zYdib9@CSBXA#s_3>lVic`v}`lWpl|r-hWTJX+Y=Q(_C;y_EwU*vSZ(yo_kTcV-@DpuEd(!s^CseHkkUeFl6t_SB8yki* zG6Dx%n1(obSO1QPKhgPKic`+z+mFvWXSg>Xz?Jn&Al+JBo^^X_n_5C7wK@n1@2wHo zPFLPaWPZv%idnZ*rg*i6FV{TX+zS%WHcxi{ZM~7Wx4LYCzHvMy0x-W3FrKvoa$0`g z)LQ~%)Gxi4f>~Rw0AJyZNen=wDZDE_nR5%bAKt+X-&8?gz!WGnp;DG;MW1&5BxH>> zT^Lq7vEy~qr7M0gEI9860d3Ux1h)Xb75;76*jJqZG{Q=M2VdkP8CZ2r^ji3fuKaQT zN60Q;U9rzajZNvL6xkl(i){Jn_Zi%UPkP-%d&q9j49~Lc%{O3eXA0l{yL;&a01eG0 z{?T;uaGCxS-T7wv{v?R}?&H@-I3x)2;z@?sx(=zT^FI1%McZ+9UY>WT-YCDiGU=jf z4LB#;Tt!x#w#IZRr{pcltVlWPSRilD#7SRxzhyY++$<`Ouh9KPbsTPptnh^_uk-!l zA}M3c0XfogmDaI1;Pm-%RD@69&hZ%=8#xftrM0SDc6{D4-m2e$XKYU3wsh-Jow%Gy zpx*bH$jW5eJvWiIAtOQQ-R#emmdO6Pu84ni&Q_8rRrO$3a)hBfb9VVJ9reNr+XLt_ zAVmkeCF0pZk;_UJDusI!mayrXH|26qMARw$-7Ga_BZ~~_rn+(Ng@&Rtq~oOzxrAm!(vi<neZ=(1U*E=b%d5)V4pKl^lr2 z=%-QWSFqM#sA#uzUbVi%G&-L-IA@ake_e*arqjIEu9SAQRUFcWdNR6c#>hfm&@4hu zZrmfWCLd*LIjliUE3bLEc_Bp7EOc=cdBRjU)JSb! zhfu?(l%$kx0k!EVtDz`?^*!6%cQW7VrN7*NGQ6ef^&WU~lZ?Y!_S&JwXx!;#?-?vu z!T<0LZoUYHQ)esH#Th9yat>YDcpc)k6uJ6tyeHtg_&BzVQ(esVq4EBuqD{$j_WGEU zQ28BTtEMD3OYs)WQ~|?tT@idMDU_p)zIEi5GxCYR$}9FTb!`ap6R_Vo|JM12M)rA_ zZR(jxZvc(1q3RN?{VcoaliUfL2jm*RYW84DH@V{9DNa9lIDXDJY_909hE;!GxQ1xC z5Nfk6`hNeedC)klrpB1q=~^q@NzL1#`oiHa_gf( z%PE|LRuv7>Fx&N?rAwRFwFU*lDojXN+P4PL602$JCnm$R%+5kpL@e*y!@`Hww)a!e zJo4evX-K|=9H8-=5ACC4JBw*1Ce8nmp<|wjO)d&6EDS{tGQn8g%WwLb9HFl6h37Yh zdWDLhsA)H%&*jf@C|QsQDQXBs2$<4Zy~h9V*)gS+vdXObAgz!n`Nc(kVJJ_ijHCi( zT3$gYUg$3oHRVLqx&1knQ01b+g3w&A3JT`|E^DT{nYONXZ#O+ z0@4vx`B^qNzBX{%1B&K%Ffk%jAY-GPfWWGp3Nc*9MeLeUL&x=$6%dQRK>QXMDnBBB zxUYY>uR>v{5Ncs)KDl%e)So|70?IcY&r=tjSzsU2uK?aJ4l=jPZI_*no}I4iDJg{n zN+mcL7$;2q)mdT<1%4iEfDP#kL#bsTjgX;caa?Is=Wq-$|S|F9BB$VZfki_}Lbxn#V_7IuSIwmMGf(e9%>4|5tKm>`S zCHz5CikepOEhhuV$i?P`Mzx?UO~zVcB_GV^EEmw0&ci2PR7JxkFU`YZ8K1N(^O$j3 z6AHmi`bXL^)1ObB5R^85F-;x9K2LcJC&7|8e3>ICeJ8&lCW)D*ZRhR)o+4`Rfr`Z< z3Ddk7EYp$D7z9@lPXS$TCCK5u%yxCNUGi-V|O_JcbgUmqkHBwzHym8rs@di zT%DPuSG$LL>WMvjBqKTLkOH0vS-YLD0k!glmARIUSgMgKF)-<9ygUXax~aK_&g5CD zQPv;WG!+42oHGgVjxzce@HPDXQ#%*Q`}bcsQct=0(8(IVDXCP1QLO^g)`fUZ&FCb% z7z-wqY$@kNx6%fTo3?Z#OANdcd=49L0Gxh7_#xz$MdFwNRW>bVI8vxmr38Dr-=56B z=LPPB5uGmof@TxrsG)&p$mK5zZJGHx#bR8j%V4Mj8D~U7pyl_OFb66`a{gtB2!7+n zsvj?Mm~a{M7A2m0L{JPXrNJdxm`)Un2z%hX;K(14DYQ0oHA(geZii=a`y#)6RZqrFv>S0_1t` z%Dtx{_}(W=S9gz*r)HRlKyfdJoiRijQVYyOaD{QsN~KbhGAyNo+M&DtQg@}sm_WWf zkyw`i{?IJ#J~(hNiGK~Muoui~1z>dKrYO@*6X{s(=sa$Ff_E8+z?wNC(;#(O%!UcV zDrX4;bJkFp)nU=xmQHJH_$H^#ou>m*ET^^mGCLFiS2*c_oyE)!3BZ-bRGY2&w3jB} ziYxtk*{s_`m8TzMCc6naQ~G?;tc#QMsBbSXx=VCqo2>xef^??gr&VSY`iI!|FlUAl zT9L^pUe#u%7)~~zz!qCdjGW2Hvy9r9mN)4BueaSC5q<2zwa~~ZeHId|oaWWc5=d!- zxwN?g*RGW`Ol{Q8EtB;jukTcQ?$^>O0XVx?iND0uiDCH};pO2@?5yBX0Y@#*_TFU@Y#wo^P(G(8vI5nxhA0`uI;Mx=QgDK|xTmZn z6LC5LGfNk87FR;`JV4M1855^0WJN=t6k`o!#L2OMV_&2Di|0Z;0f$_Ye}nojyH0$>h54guv>c$Au|NuMqlu!ng;H^Miqgo5&&;%O|x zsVB82eI%=a=dUHtrTW3=f{;2(5%l6eM;;r3xy$!*a@Dz`Kxza+_em8ptYv za|Z#qpV%~Zij4iqSwse8+*YH9XOu7&6NwN}#7)r|NN)9)M~)jbC(WOzeS3xCHT%8D zm%w0-_LB57t^IvWip)Xv|C>^O)X5@GnsYFHt5hQTi|HnBKDV zAH$3v*+wr>pda1}w~{3`I5h_=sspmQk{NR7d{hH;0sD}roV|5tuK`T_ky>xHQ8IMc z@@Y^7<%abqqgq6Wbw9pkqc(5_R%cJR?d*_HT^350kpL&<68R-z%XlZ#2=bhTDglrWz4v@5QPxhpNiUzxQsARvh#xFfWa$fp;VASCnQCAncX{KpHA`V zSlv4V-91US`l%7ujg_JX=uXP%u$4^}mr_d4a3PkpV3sEuJ@7#-c|p(7K-FL|)oK`P z(SLS^S{*|@8WTMleLWguJsLy3u3t;i|8yy5C#7>tq|CoO2z9DC-NBt6Bnk?4^rF3) zR(<@`gxNaNZC+@a_gdD1cNnZ(;WutNtOhLCf>-L`YfN#xW2|4in)l$X25{Db+4yCG zN~+=H>-MK&aRN>O^?&r`leK=~d;xKu3oNr!;w40v^sd!p-)Z_kDtc2L3W=ev#S%9z z7Ie!o5~H?O?^_#HBHEUCztI2fv{ARe1c`8w3yLZ2mq|~OH0M>RK_duI4~@S7S(miM zBtF!bp8)Hu^nZ?eSlj~l*gL^dnWH(76$8j!KYG~EZaBK3^JrK(w;~vae{GDE#2(R1 z>FJ*#kwDqRA{pI19Q21*@wQ0dsKaa^j2Di0|;V^ez6L@yI1w~v{> zGK_2;!Lpad41LEVx_XXm<)h!vHX7s^3iU`wdWIvqiivDB#j>BN`%xyeS6(5Dmj2E_ z7B1!Z(Q8z9{zZBj3hfmR?yY&ANV6BP;y5w!E=lYUX{oE>jgH--Z zDAzHC+RTO8EZl0r1Zlwnae;&S7(#r>Ahb&%w2L9UK@j{Mx_|_A8wln@W{|O=~Fn`9#vsNTHRbV1HUKI3#8ZOsx?D_~OXY2t^didaOE zIxmJ^7qq&Vt}<0koR|cP~_k(GlvbH{ktex z%NWOjU!w}?xMz{|$X8st)HFT5$I(Q+?%!CedRv;I9l+}d-H$2coT+8eG&+~5C7-D! zIIW_z27YwWKhiG4pM8D+#Q~n0-H6`5;A+Mfvb{ffxNW{@{vX zWS0`fTe>JH-TeM##%nhY%O^5((dX|-4?9mNr)+3KuC!G0K>rQN@_3LLnJ~`30gj-U zm07-P1h4U;6{g7k4Oq6rTUR=JLUK$qIXh=ovvRsbBd-&rB~<^zz+9i1$IdOmf?Bpa z=Pd~C3g`3}xcNjRJ6B{_fk?Lh;Ra+PO85mzkk;-^6P`PoGoJKMnwG85_lPLs;3>1( z$U>}@nWrKe+DkMvtdP+O-(iTiDXgaxlJXPH0GKv&eWh8iB0yg)HbtEBgS86_7LK+4-E2?Bel??&Le`gWWo9#G&^Fy~M!&FLM2*Wc< z=3?ck$BTW%8`oLb{^u9nU&j6r^%mvY0_uVQFS#*5NaNr$*_;aE>TAMjF(F0ax@V1az zd%j!+3@fKrZ;%PPu#s9DXEWxZgKL^&C8)6_GBR~y;G-W}7BZF1b)2ELG(l?Rfvuc{HcS>iuLO zaQ-C=zURHd{0~9Hc{T|;iw$aY{5i5U9e<59Kl*eBnM5JlM4SeJZ3*W4o9 z_@>-D#|`z?zrv)u7L#rR+_e1hmRU?nKONHf>R-2;Yu=flDl3_y>OjA5s(+qpZJ(al zaB<1*{T)#+zTrQg^D zSTgWYmq=&j3()zPRlA(-YZ3IAOiZ8!4w0bt>>TrLz;A2xUkBWt<0(Dw-4K;km~a49 zjUaUX7;$U2N3#qhtoZA{e6~0R@@#TK#R)t*F?V{??7h@RaAkPQ>#H0Ln7Z_HmQR`A zV10zhO#L9t!x37dR|mbn+5}F&d@44sIz(%~r~TelkFCTm=_)=JBx8ELQ|_Mnbi_(O zl>%R&E=H1Ce5@%~a|~=gw~1z+~q{r}i++?{6R-oNG8OFF&swnlB z;D%*1SZ2q38@ib^*+fLVE-fQmJ>Y2e&v5qS+142HdN2L4k&4;i^&Y#URmR`ZoODTF zaoGb8JA8$g)!JDvOZE9QB(XRZquZf&vj05uaepmO8hOA_e6>* zNx8A+Q#J_fAo)o9)nL^m>h@}}a`=nN2JJTI$F{|12bpf?<0iW6(xt&I;}JgH!6G3= zSH6rN+*Xs|+fm>gW2#P{bbn#a!l#HRJzyGQp^Qa`8r z!{)}b&&-b6z-yArq15Uek5?Ii2E>*#FUWfx#QWv?N;qwn71=cgv1qP4GE)roMyS61gN$>Wjc^9dBXkGT;?WL8ilz`53NoCG8f4w(N6x0y@k*2&B^kdJ=wEX+pAVe7YQ&1)b|XXtx_00f z&gpi6+_D!_nzfH&n*{>28077_q_`yq32{Ix*DLE1I5RNF33|%Jalt|;iHKPU;f&cZ5i@Ipn1b;ZH-sq!BNPW$UKC3LyO;Rin0tnTj zVYTJcW|!rdMjoeH^vy@{F$QORAoD0z(J8uZ5Dwm;Arx>nevRl}zJ3+%v!THlw80D$C^}Mx9 z{R7qen^?ycprOsF@t!yFxq+@Y-}zA!Uj^!5C;_XdPfhO?WW9SZKOboryC$SIC$&%8 z*13N*=ZWh=c6fg)>Xkki;*_oTDXJ?yyqiGk$p$2es%EfSwg#e_$Tlf4CwL=Ga2U;t zo)$0`@R`W#+4pXX+0(HSJUo=$bhLHiUTh$tw|Rvg^me=ccxt=r9U`%4)~Q4x^IOJt zapB}G+(B3H@-7k@bVRg$LDJl17Vy4=w2=Gyh~e~^Fs=MuEYjnE_bvvXu4=Ss>eT4p zJpinOUq#t`8-NzHl@ry_MRpF_<UHhatOQEwC5vbHobo2B z1g~XcD`J}c>X@HmeaY#=;qysvN6wPr`EJH!={`(#nRyR|xLQI@wXz8z`hZ}?=xH0v z^WMFiKvyNQwnQtb!q&WPdazCefVV1HbKr9dJ$po=x91+C+4e+k)!f-mYLx}uZk0=y zb1owoZKzo}EGb``8?GAL$PfOzc!W1xu;&AO8>Qi#F*Io}vEf&Eqr+M#Qgd>X3=v!P zO0iz;0Y#kEBGt2`16i5P#)0?&zk=Nqwlgq4G?~#|+%@Ks_qDuapU1h1j*GkJ>oI$5 zaFzM&;?41&n1Z;kr78PK_W3Yf@gTCd`h_W$ z&3c*r`E{bU_$mqGNk_&3ozoL4{k!c;iCYWX*D~c>;Fk#qhJ>@|>2=gbLM;=6KHnSG z=u_aTDoC$SV;jLc87c*_K#%|}YIvcJL0gGdWh^v_Lzz-OB1{m|_u-!~u@ee^b!>7) zl|Exoj6@QxEU!eetIPYJ3gqj)IQq@<=L28VC)nUqbObSMo0vBTv$LRu#t2|y7?-=& z()QyMIRWIL3@1yDQY%OaR63f?*n@g2TnhEhPCJz735EH3A!1mz&Ct$Fa&?3FpbRQ4 z6gQv%1xZ)O0_$uxuhH zNsjGr>9i6INK-k&lZ310+g%4uSmYVsN}ygf*r{u7lB;*J4pyQ4q0>OG5!&NSo>(W& zHTsEGw}sa2iI43kk!z{ihja#oMh{sEXbB*>V335b__u28=UWXrlIwu(_lJ&#BM(vC zx>69=$Is@uqT#1-3#lZM>-dc%!DBRONrdb#xoC|e&Qt{zm;|AW^@9u3hZvJ+(p%b; zp|)Pm=+n77*{x_V{|7=(JFFEM{Wkdn_ zqXqqE{9k&|fAO3^wV{ly*T7(kej8J1=LzctgIE_?K_vN3#Ii2PtTUf>B{ZT^qjr!O z#|^Ed2!q85yBaU}!wO6+s|tY$!N81mhsmL@fjxBm$B!Z*w>!oy`|JLnTY6gC^X7ZU z@ekDLc@jQS+Im*52nI^d&C^U7W2x1VHph4}a?YxeaV~Pr;80yLJ1i{PXx=JVYI%H6 zh>?2&J5?+%L3}>LwYi4hc1iTmBqJVKPzmMxPOS2-Cr00Dr3gxm{_hMErgXT- zB$j*h(L-TNUrKJR4EN~_v%Lb#^pmMJOTNDN8ndG?%o?+) zg0-0lWRuw7AZ@{ASyj2vX;rAq(vKA2Q@FVJvzss)X8e*vzGjHTa8T%wjs2h+u%PQ| z{F*doDcL4Sjx%+bYA6J8X%USxfz&evLFU6)Q)#46!aO-y+$x~irwpKtbYOV>PJU+2 z!&Vde)U1}(Q{w^AWxValt#f7~Gg9f>>C+6{B{RnSp=M?F!xW=`v)mK?r!^4EEu)Jb zo27!RFld!R-1LIn4{ZUYP=FI`!#*vZ#p7BP^t)Utb#JymA>^@femFTKQCm)zWBt@7 zq!>-uZijy>6d~dUVfq)aAl{cPn3*CWn#}&jto)r(n3aTeKldL^e0#X?&4t+!q=ASo*QRy z^_RjQM6P4khLmOKqR`dtWjg&|e7$3AZ%^Q_yKCFtwQbwBZQHiZUE8+ZUE9{KzO~)n z{*s$}|2aA5yqK)X%zCjhldNR$JRdI+5|C4I(03R+2WTm-;p8h&-7)1Zv;>fdQ|C@} zjg&S(4UMtD6e>Bkgcrn_R>z~1LU1G~sfAhNDV#h;QRG^}(x%bFpXrPcHk8R|bvR~x zfU~ovFm%#tfQU$6L8yU7JA4TYTEm4yD7PZhoOX=@NK*S#P%e`L%`7JKGSp2~uhNN_z$E4h&H&aqz2_3RUA7)0r_68^8V* zn;)Y?T1nMVY&@``T6i%Nvsxv^Bx?>?WRE9}>?A6YV@Ai^2ocVOIUB;uL54bJ_kUq@ zIKU^qmMGmcqQ{(oE&wYRYt^$tNt`(~i%%s2)TlQ{;I z-mWify6>GRUsQqrLsgyoIy*bOZp4JItmvARIsI_?h6;x6fLvLU2@A^ddnY76wiPa z&nPR{BNvom25^EFG&RnIXfsL`ygJKW5)oOwf`Kk2$Y5rRWI>N_48YHPlP?Gew5RjbGOF(q3M&KsC{Qp?TR zM@QzFCjx_uU0wReww544|ErJKH?rv=R{X_&k0fHxE4BEd$gJXn9ZJyOm$;9C9wf@t zM^7de_~!GM6OraMd3&?P+@ik=+Iu*tWDK8A?FPS5JRUy?FHLj6iPmFR4qq)L7@^sK zls?HORiJUX#2^;kha$4G@X3)dm{vvO3Q{~eBDVAY8W{)-`Wf)*>J&}$z<|9 zTgD2d;yqMi+m;BZ7Zs{rm!uy)@z+}F-E?OZj41rytKVO4UhP_1dTcP$?d5y(=p#&n z$!55I5@=Tga>dKFLiO6m`@AMuBlzgiFf<5HPErJa1F;9fzf)gtsHxY=!1Q7Gw)Ek9 z=Yv4x2>hg1>cqj+@n?=GND$(V!TRv2z`|ShxaaG{Mf&{cUmwd)SGDHrMn(GYF+Sgd zPge;<`*IOKy_g>HZ#OH1Zj$#_bk}OiTJ~ayJhRbw9q4pen68`AIj$|IasSC#xMez{ zdOB^~0}FL&K+=t0c&?kuqgc;0I|cF9wXZi3v`1Z3v{m#%K_6W+*WdfwXlUBPpTJ;cSGxldl6xu(;H0q64{R6HrO^CT+iG~Di>1llJ(S-)ms!Pxn>j{hPNdaz z@|x9{wi~Go3oZsZ0$DZ!xrjunS|o)-=*Lb>Cd~))hb4O2Cg5`mFZ4-@?W0Fppv3@W}C37x5v;Oe6~`^YT5KTn#8DZVF> zH18>P&MbEBEN&Y31HLf#4sYV9_b|^oXg`hXWq(3L1~SzjJMcnVT5+S4OMd888Z=yN z5#{iBhfOop1^c(75E3I_tOrUwIW|;`(7;A$`d8DGaaHIofGGe&Iuz5bv8IE+F=u?YC(HVrZr{O|Ws?q0TwA`N*-#$nsiCVY-pyuM zWtyQwGju~SMl&}7a|V4(%*h=|-vlT3cX0tOw23A~=Vpbt%5u1->=(V(mSQQQo}B94 z9b9C3Xgj%dgMWfXT+pIU1CK7&q$o+6Ibu^!WI+&*nD~&<>M8QksS;CXOblH*Q(%rr zXfTVqgd(GIRFq~^c?PZM2rebnPEw&!Eh^f5ad`&4=m_vnLt5d1zLH9Ju&eVW$~T#` zcEsjQYeFb{Fip~H)0T427IC9WA7nh@{jqJ3_}vt%Ytmr%8X3HlJdLNw0BQ9FjJh zl&wZdr%AJMS^{aiR}m-al7DHmN!qPkC@$zT9X{xCe3~2;CLN{;@+Gi$SXX3*jG|-E zT_LcA5qL?0eB_+_Foid4Wj6I))y%;@lErN(XLuyRD&tHNtl%Vi`WK0UjShpDIVEE# znk3{Vi4o~R+}lY*6Hb?M+1A@}8YMb7gz$4Q!sCAfm{9qMLc2g0F3E~Fi%7QhB3)S9 z!@KX3+XPU2Oi$Ppn`pZH8~tpC$F`Qaj)blO9fz#|^|ME8zQbuTB34h7>g zKL>~FFcYNBe&kpm6W9LfBWETcWnMk_g99~+ZDAD#ag#B6@wxB#b8U9a{Cl4D9cuN0 zeEgzp?1KL_A{Buzg4A&ed(U$9<3P}*aKy+7e{G1_15z+H++4T(BxT<%*FfhtCPs-v zm&o8HlVvYa=%y;=mY-V4ChN~5aqzMe)sDT`p(=UsvJ};>t=OT9Hgps3r)vZNItKh} zXM~0nn~C!VpD{m$>^ixH-+_~K)PgK75CLj29Ak#!=o>IAEAPd>=K+s~ST>QL-BO&y>m%_Vw z*C61m$_83Vx&y@@`pf^cKdAl>_NcWpYurTZgmN9^t0$UGporKBy z37Uo`%oi;+CF6&^pKCH7S<$kU(JinvM2`JQcv`^smrr1@s%+1jYQ4RQQlVHY7ms-z z`Tp{$QJx!Bq*}#k|6gydeCvNJ^)D{~*|g565nu2H;J5qbWOg{V_=;1-PXlZ7p~f~Y zmAlK0($ff1GGq}#q&tIa2BX`9OMuMA%Wc@L_^?k5h zunyLVw{5ZJ<#oMFxbix7YlFexn`nD~Ehhxe`Hgc`dKbU+tv-};{PCjOPc38alGd9@ zm&H6}H=a?L_iejYt{ZSIY;7)+fhwl1QTn*$mRRb%OWE={Uz0Q1o`m3g5LFkZ8lSUL z>7-Qgclisv?QOE1HAi<=lsM1-eo)@_?1nsDAGeV-vR2iWyUA7pTDRk9<)s!*S;r|e z@yC=xI_`3H0bkikM)|N+-|?pgM8=UFOR>nEj)M3u&6 zj3DJhz|}T7*rQ~DJpO6zE^e;OXxl*_y!K;^wcZrZ?nj!f!`Fbn|46sNbp}Ri%Gwq& zozNuumJd%!5FHO5*K-jVe5cz`0bd8T+5F}V7O+bR^BM2{B)4u|wj=?-dX+^}Pw>5V zTg}x=70h;=`_5{b10u&&waaiz3S!&4u3+}T+wJWbaM1s%j`z@c5M+%f&T%@mdM`|# zrap)8&g#g+x1Wzv)@+5*y({-w^z@)~wmnZ473M0L|@wqru7hnb!;(yYp$)oYUGm!`Ii`lk?SjMqNsK1kaT>-Oy%#nIGTle|R`->ZbL0<&_uN z0B`hdO68B9ceCHe-^O18eYfx(C!8TK~mJe?7YI zOum$!(x81!&-b)>=N6FhevkJjMKG)Ej1Px*ZT#qQp1+;0H-^f}3J5r&~vc z?DMurvU&F+`BZyU#<&rY2~YPAKqCj51YiVKDNFa ztNLvG{+6Vo*tO^8tQKCS-b>5r6-fnGcDmr)MvM2|QTgg&`=4%Px}8+`o+m#Y5xo5| zO7F(5v2>U*)+yo^WPMNn7Lb*m!mZC=*HnLpIky9I)gd1*E8gzp-XGVr@@}!+RC$M^ zvIf+HfJ^|&AvXIrA@u8<$Kl!+t%fFL0p%4VcyVA2u1i-dRG%Zkt}2dBL)Y@nRYhUF z^F&mn^>Nsmn??xt%6N|hA9~Ny(dQI>Ht!U!f_P{x_w3WwyW9D#1oZ7Klq!Rx4=|6~ z++Fy9_|vjwg5D-z{dK-^L4I#D;j-oO-1cI?>RrY)&6`cSTp)xH)oCP%`_7eyqF`UD zuZz!0{~wpE_%s*3_2Qr}Kk;4tJQMCy6s+S$<5YOBm#e?i2r3(A=_jJ^slF%q)7LW` z#w(j=MR87j?g<|o1fP8ruM>jq8k)A&YDQ((sIEG%I}{K-%X0&K{DXq*9pld*dxD%j zzw;{jQW&GJGIcu+w=$vMPRT|gc`L1zgwUrg@&4tD9|!2!f0&g~zt%k%<8Yi_>HE8! zDevX8SKGWe403S$o=*2qdycz#iRE4$Z-H{_s#NZ#v+v5CgtBTBUzwIIr^m{YrLUjK zvm^ZR`RL90m-DDjmkB8Nity9=M8YI!Eq>3uS8e(egGcyDT@gVTZJvjqs$TcT<~8>> z^tqBR&snSOb}G1%Ic^B#+=yQNRWUAUv5Q_EFLxVjDUMGjXQoxeZmlO}1JmnX{}Lmo zMC(%5Keei+V&j%>@yPK7$x46myR^EZMAS~XKAu?WH<*5;8gf{6UbV(oyyo$4|J>flTDpOO@F9q47uKbE z{TM?Ua;sb~-C5tDD=P<~R%55{c2D)Dyczu3)&irPSKm5Kp*G}85 zTW5LsK1bW!U1$Lq@viYdSw7KcrsMJykM>e^#}&rs>VH4Ke;cUgyZ`5H3b4kbrRvJb zEa(vM7}K%wDZ{^cAHQ`*pW?~+vUGSPSAX``NiHLF-5biLJ$lg}WBbl>WqGi}D~ENS zK@+&!tJ%9E2MB(9bbJkF+d_Rg^+n$UfcOVc`g(Wx?{4^IS~E-^&11lc3H<2qNgIB> z!2Fx99QKdF6;wIVv{=fDQ68f|jG0eJ;dt@qtMGm!r=p@FYxTGJ*$0-f5wh`rKqSCh z<}dJfv9U2Rk+G2x((%#J5pp3}x$C+lmcCUqTCs z4|ao{pTLvxS2-+BS=WF4wBAFpPj`C3pkKq2`QxYtP2XeF4B*Lp0ZOLSrn|hMj*-mn zR$)twoQHZw?#9&aGB;!!a`l2dLz=to+@xQ7x`*!~EPnK!!&aM{(lqe_X{!TL)uBJ% zINa3zq-x}Pbx4{f&M|qe!`{<9au;Ftx^r+`2T9++6Qrp((s?98Uq76=jwhq{6Pg;( za`8SpkgG=)FZ+0ZPCJ9YkRK0Ik+bRABi7D$O06@`@uBahQUwkTz@He zKva2k6!2F7F!669B5*_`phQ`8C}4g%$b+hqGV4rG-xEiUA7WWo*VTTz^ZEU%25N0LefjWW{NXWIJ z1Vbw+mb9XS6rzB3_^XY4l+tWok9d!UP=W$prAb9tUAq86MV=r(0GoIL?#y<2dw+xUNb2%+xlTw6;`6cvV3E<2Y zO(A?r4H{4yo)YB62SvT4E&#%8QLY>TfuusHq-qg@hOHb*25n&lK5fHk2k;Kuq~kiu z*b@5ZONVNTMLU&oJ?E! zc;(VQ*OOqZYLoO^dPNASS&2CHC?f>;L844)ZUc5h17K`fMXpZ1sb*z7z@w0lwtqPR zcIlJ0p;12vT?1A~B!tyWKIxw_MhG865YNq>FcX(X(a6vyO(D_)j|Ef?YJw<@ifD@h z8dG!N02PGvh$&dsknc?CF$Ig?CXzr|)B0gV-3$||fJ)NFtq-m{cEOnD)#=EEF8%j_ zCN}c9kzwc11z;+$DkH7S!?gn!jCvSp6jb6_LRhd?AOW2FTOE8mLZh%m)ReKRX^l$} z-9}A>g!@zuG?@YxNbp#g3G2$xQuU~Q#zdZIV{+kF^hA^@5*W8pDVL%>)5hq+X|AGK zC4C};;%v5ERgV>8Zp7nc22O2h-ejoesbf^57b|p8eoMVVGlCGbSVGXtY9A2OUFW~MBnksi75UJDlki!Ti_Xxki!T$3LQU7EZUN;P$X&spGA z-oqN{7;roI3PCf9O5FG&%>>d!B>9!1(h$S*D8S@12Ph()mWdSg;?g?Jq*5%`kxLeo zkxeHsBbAJ*MQ;7H3df+4N9?mjfEvxFpwb;qouvY3wWLxPijk0`vbSMHB1NPM6EW!( zPn;!rezH$HqN@=lRJr9vE%q7VoiN&ueCF*^(ZXp_;xPk3G=J1cuY!8qw1yT=7PO_y z_^4ZDF4kZ}kUE`(8Nas5DZ(i75ivwn5P-fwEEQAG@-qTQrR}230*sh5epfl>N*wZ3 zpz;`lCoglPmpl5Ujl6QQ&L}T&$g3WHbYq`bT4n1i9O_j5_4N8X$uh$%6_Eay6Jd1S z9Ne^%V^-zx!^lM;AEsakZh!{eVu5fp0f&o<*bEI-BmA%xX-X_jj|zw$V#F;DaJFS3 zjo0nnq06SiUx0S4Xpl=6wcYQ7Jpx7%mBTfEN~_XO#Ox7jLcV}JV3Z?A z0g|3J_!AEP{HSyjhSgmG?%sjD)u!Q~UOwQf4R^(D<*=(7+@%C}Ma9})wY*oWXxye2 zyUo=Z3U(U4jirYMF43>8hyoPU`&$mZ_euD>lnch<_1PDH|=65QWWrDgXE^iA~=205NWBJq` zw6vl67=pj6boZ*Z3|?sD6Z{M zLBPH#p-HF#n6?sZMw08idXz3+Cd4mv`Bw_KYUE;b#6iYOhL0ghVNpGWct^eDf)e1P zmWXnoVEe_uehk6Nxwr`1#5Hc*Tmf9$jWKaCV=OO;5jkY!Vyv(ISPSlInWQ}-%G$ib z^Z!OhptG~!XR-$5P3q}znhQL66+kgcgQLwHEWz7pl(&EP2IP(ZQ!kD$o(52055A*4 zVW@bV&5LbMjRkaq={+6{3a!3Ji>!Lc7B1?hQ*|$>(%&}pa|u$o7)^AEs1(f4Ux;}@ z5^~O0>#!E??q1s5pbBeI-`Jln9cUHTsFdJ6M3Av&>Y{94%`BW>RCHU#T}=RAAvy6 z2iewm4xz*I{u~%+Z$-ZXZx)~z!P*c65wPHs0$m4uAz$^!m9v6~CGW@y!UAGyjKXV9 z|I|R{fy0=*U;?I6C>!v}hXF#U#+VI8SPcg*!+?nhwAqN%{)pvRBIs6oio9p73Tgb9$XQb8a)%s9+0)FQK+?W!(EK?^708FgZv7q3+S7#f6xKxf>*$& zx2QdSCWMq;KTt%`394>Kl*l}e0+^SsA(d#nvH`XzJufJ)*ihhU7?0$TV7cfmuFMUt zP`AwL4X)J9)bE}@f*$a~m)!;*VJIJBh=R;R^wIn5O5wXGuzloMenpsm=wZ7SVft)C z*OXzqm@&J+QM($*t!1nqZxt_4CYFa+BeN4XlY}q(PI#yqo!n9o}WqV|tgo70(y0eFI2*q#< zVtFER-qX~|To&PBi!PEbea5>EZ5RE^Bar&}lqypFnY9qs;6G4=7~i~jH_n4468)mM z4_V|(^l+%AhDDhkE7Hu0ivB`XuZ+qG;&KOUwI^uxYq2*Fh&3UIbs~JVI;KW6)_+{Y ze?-LpheOQtdj$tRlG)i+ekcX1*xj)hPeEtH>^otfwn^lxpMrd{!8~vi27(NC4ezH8 z@2gJjFHh~Wa)qxlg|9M(?;6AR&Eo3K;(i5ieh@l)fH->)JA0tiR=iYDi>p-M5bNLw zkNq$af{JNda@-#XS24q?hvg4BE;K0DpDyXUxP_N@+77bAp+pE6P%RI07*d@CG{xJNXcPSoNUJof%~cl zuW`m}yHMT-6WObc3Siv}HUoWEuU|i7Wzm|(R*c;D?h|BTv-#W(nd~hiF~r&^1Sy7@ zf?{(0wiFW2f`5O&j(bWHwevV23 zZym<8lVdu{GaY>OM|ejeJ&TKNE&dP@n2xhd2f4I^3T*&1BnP!RTvD0OIn_Z zza1pw`%T08XH}OcOguzkj6?Zi)FmZ~i-A1c2|E7>Cfb+CcEkidP( zSNuOv1SYUQ0I_`e#UHG9ZU;h!AEEl@!2_N(t-Q zWc~J4)W^PaARU`pL<`_ay6_}P)m+)w0weOa{dN8$SNyyi*ufnxhyF{WB6?B&ut$$k z&nK?25->z#J$9ynK43``JxrX24KQwi#n~?~*cl!j_^T7ZF5BK;fR1OH^JEzaoRJ|5$vevjTrE5rSdn_JgZdWtCC6?7e`=ukV4NbB~;pTKvLNg zfM4WsJ-A?Yq<=DzoJx5&^cakyI_8oMKbyuZ)KB z5eEe=VKTOR8t!8bqt&pXuTj%-HK`yo7oLQ9>B=}Y zaNwT!!$lMc;B-b~b(bx0-)zVw@Vs}Z^-^=FVtt}*WEVwZr`|` zwsNB0$$OTY3iW$G{9tSnzN!YI{7OClSzB3iy+6CN*Yr7(t=LDrmE6a|(0(4NtMhNy z%O?2`OphycRBfHg|7uL9MWoaLxsIA<~bMU~?L617(Lwv#=B=p>s zRKZ4OZcP+JF81eK&h>m35$hf}`a8k7i+?rfuj(}CnVsk6^c;M`nH@jt%O2NhZgrOR z-A=!)gTz?+G@*!}9r?XWCObF#RrX|K z4>Iur?@FzQMwfbv;>9W@OZ#~19CzI{N(l8ZdpKX=XZ3}$J0@&nM2V1ahBkfZnT9wm$&)?## zQ3vpyco7%!?$doJNat79Df$atm0ri@1Q=goqrb*;`!{J?5$>G{#ysOtRy7_S{Gb|-T@cy1!k zyD9N2+XlwNO$GFKm~~N+vb6{7hcC}x9*>Mm9>2R4D4`flcDd5M$R~||yU}O*VMoL5 z@A@}tp9EeI6^1Ytrto>Ys0aJ5y2)(cyz7j349PRJ${?wAsqYj+~&=o zNqzL;uiw55J@X5m@aHs9Hhhas-#g>?e%h(7jXqh0a>=!z5%hizQ(fqL!*AwU*nJNS zwe9v$eM^4W({|z9-dvJzTj$A3teFlz;k5C7d=0hbeSF9G$3qPFwG~_iuZdgQ!)}xb zh`eo6xMS@)Kc^fY#--eL3>G>np8I^nnpEwBd6He%@n3BUTTi~MiMCx*BP(oUh<~KC zZ;$Fnm$Mn%ff4vSJ!Jpu(w_giH%iAIwJ-WSy9}Ym%JY29i7n09ebK|plJ+~ui4CeeMz7{Mja6=^d;P< zUt}}64wZgIvUd@A-(u~!*Y=;SCJOyznCwRhu)vkZ#fsObUn?B)34LH%rEky()C*o=Td4d_;qhL zDqZzCx<9&&lKXskw*7PM*3AZmcl{5}zM2@`(`%vZ-^Msgqy=a75OVx)-#*QEBBt}& zbhCs$YVQY2PJSlABj@v67^W-bQd{8S8dt@9W2mie*I%f9zg(I1X4CJ3ALQGD<1P@k zD7MV61cfN~Wxf2K%mvGx=at(BkEQO$*2nhcUl{cZwtDv&Xm6{=vOj%q+-v8psY{O2 z@m^e>jB9o9Q9}JtX+P1lbK4Rfm*1L-FBON&u*-1D$Lr(iLXY2K1sw!mwXV-n3a9)1 z+@U@`BNm&Jl*fW&@@>oqp!p@ZG`Fba@_dJuJrob8$9g)!Ud9{6?>_JIKL70MDJ2cY z!C$!|jrbtJY}Cp95m4BYm&JyDvNfjbw8r9jQ@nY?G_ibZow*efk||qMB3vr%YcBhf z!9r-;JydOWJ#&jccU3ea!^^clK2+*9I8}UvLnV`J$Hwa|W_F*`iFkgv!*AB9#Jh5& z=_GXiowqa1QX2UicBsYbHi#nXAT&zrsNSvZvK5kJqJSiyF(O0+Io<;eM-xpvj3J5B;-}f-tZm=pto6?) z@7;C0Jf3NNy}1ZS?@b^|@7P1YM#SZEfhhH^uQh~cZr~pb4{LeW?YvKV&n7c=EqKT>{_zcdJl9e19r2OpYNuVObI{z6(ir#pW32x+6@-xsl) zZ7g)W-)qEsEe2Ur&V_Ke7az?=k{YCpcK&_`z731sqPuUTI)w$DJb129i~c~)?1K|# zqU^V-TEXrzwK?%m9GXfd)DXtplT7!Ey?2#w&12hj+OPY!=BsfmtO1kfHD;nJ=md~4 zC(I{&N3&R!ahk$e1CsQ4%9GQw27`}E70v6=r8HA@yX|GM*PWgwUnh~VznRiM{qm;< zwNrhf?nGebir>c3k}vZ=ms_LWg)e?HzMfA)DzkE=Xc3-nH=DW#|Z8@5-#t%W-U6=!}bwW)m>Igb^L8pFAMh2v|3ReZek`6Irf zU=0xO^%D0`7kh%(`M8-X3(_VS!0+(~v2G6bgi@WBwSsvm%zZOevm=jK>3;MxOo{O9 zHrlO<_T}dvBw@;Ybt-18F#eG=O7+yj$zhzA$+k3*wXGspHTyYa&-uJH>;ALn?QQPA z^qJm$86nbd;aEF%;}gH3h1&v6^gnsbcCU=bC0Pntna5iX8s@?YcG1w|Ypq6rrLSu7qW0QZ(F>c;5 zwi?hs?Sa_(32Yk8pjBd2|n|8ie;dl=tL)WbUGGcxaPB8K&>vS!w$O;I`Hg)8;Fbu>OQ@ zC|iw1F36TTB?TZaYUF@yqY_G7T)})QA)-)^5n3pxSQt@m@D3GiMTQqOqFBEeA}SiG zv`?|7BB0po#Fc_AKJZEq9fe#npjfXOBFY*KKl0OwHdt6FV)@Rf-gKtW={JQ&N(CxD z;0B(V?gLV;Fl-BkgK42~EWm}Fm~K$_bGPI-7wS(<8#s$f3Y9US9w|^1O(vY^4w;5Z zQ3akfYD~HwUr2GH!-ax~egGM=XN6XZ3YF2XeoatP5*95IlTTwMznF^)nbE60;3w;^ zLPA6-e31Yh1ut^!te#=ONJ%j|<@PQ=7T!!Dm)C22!dsjdCzqs=0O736iq zK6R&QqEp9%x@Irg9pn&35v=8799p|rW>y@tZ%T4ZG(ztC7Kia@obeCIe-v%c?Ek+o z%K3*7u*t=D-uaIZ;Q7B00{`P=Y~rgpzmyIYNG$YB?PE`=z#yq|8h3c ztqG-pqP`kNL4~9vQ1c$NQK)Kygr?0+Or?Rew3npi3OhaqDm>W348)GA9SFL#(o^eN z)w*oe;%ZA>p;e=%9o2Ygr17TCIb@Bc|~&oe&N^StGJ#d+;L)5DB~ ztzJ}`uAalCx6D_qo}gZ+H@3&gY|es1TFY(TiYdC2V)wI?)b5FLc{9U_Rl8i6$sWMRNJT^o~5Y86PWw}gKaJu~q`34L91PueV zYyzm|K*e!~Grv{0?gtL&)??C|`pFh!xk+GuUktW1aty3QG39sSYzIx zSp5Z)?9a291b;l}ZW8qE3Ls}m5D2R;iCy9@d2A%w!UPYT7d9ANfF0`thb zY_un9B;ka8$A@)S7=d0Y$C8;W`{9to!RS_w&2r6x(mV!A8{Fb2@iQc)PUuxBW~m}` zR5RK*0o<`>OUFqrNqT!Cg6V&QSDcEGW0e&GHe4paZCUekT7lA5G?RHvf1@^p*qYHUtWl%JMP_g2CzF?y?OUWKadw3wcGfca5{- zT#1bfHpqs(6SE9h|L~X!Xau$5aB#C9TbJoGv<)e`lt`@opB*6qQA)YgVGfzd&~r6P*Qv4#5~Qj z)iPCPcm+!^EHNjGn!i{xrbRr)_EDZFMXbkYTCm<0`lvb=<2k(}o5CayN4ia>; zPLk+Qv3*6K$BN)+{2EA!xEmDYIg^20sZ^{R=SXD7N^Ay#ohL7oEw!WSq1bj&OR?l) zE_10gj!E_q2BtG@xH~m4MT8X<#7u)?84&XB5^yF|%zq%D1BHp0CT<*%fWstJSg`_| zm}?8E2Mu(xYZj1vY!%>S7R&#KNv-g26bjW?E?*)JgF>bA1!o5soRAXbG&mGUD272s zfRV-G7kxqo6bl&&D0Y|$vZhw*W10&d=%9XhVALG4$ftX!+(ACLmij2$s28nl$ zR1cduNHloyxE{#5b-4%agr0Qcs@nRw%lbL&`nhD|YO-lv*>JjIU71;s<)Sdb15tuhnicz}#~mhAaq412w|4H(kzcssDyJ2eIzdobryH3oIyYs>!k zHI&Z3p1``RK7n+`feF?q32qnam3GMKW`nN=$D&ZHr=d=l7@q$E0-eLX6@yqK9e$^Z z|Q6cQ)dG0L#w=p^9T6d z1loXM+x-H#051p=L~kPvS`{ zrom^h|89?a0A)nNA)?S@4Q}L`)IkI!P6^e23~ZETuN`HCVEoPXbSX9NoKtEd4_PG2 z#*pnr6C}#SM1E{6S0#a$l-OHK>?tPZLKADtZZ z#sANMPbuhC1O7PAi6L)nz=sN=NeTX#nKNV7lA!=Y%hZQ6lKesJ`Hw=VpQTD&QTZTt zc|{=We4V#V0m9Yq{m_;hP^F12BEfUU>e#<23X9lyLfB3NYNG%pWmnEDX?qNa)6@RuYGoqQ(j(Ez&ex^pgJl(L+N@=}wDmhf+0>Kvl8z0i zy{tgt+r(;v@~?sKB10N#`$#KEBuH;% z=V@US!I5r$R6RwOJza=*c~(DV5-5<4fzUNV>x5M~y(<+;7lhV5K<$K8J4LRWBNZ%| zL=2@9p0|YtuX{@drF< zN#A-lhtJwm^TjLr;8(ry$ld-?_Q9`vsaE!pQ}S7%=EKQ0{SC$YD1hHG$2o<;&jWLM zt6QG|>i~RZ6Ov~Nm^Z(7VXr0kPRPK`7Sbsy#DG>P6loGY-vtHly4~q(2_Dj<3lmJ|5K}rPf_$GV(5^ zHl^HX)SH9`lTxiwZZbkdN{w#3tMfjL+}MoZK(K?ZbYqqvMCC8FtaKe0pFnB_4nXbOjV8$zkfh6>TNYHm!Hs+7{KFW~vr8k=%Sa zk&(XvRH%N+(9g;Xc)yFePbE9AQ5WzAiT`!v@jwg?EkmM0^|>RRLXskKZFryb7qx#R zb`dy8A-ax2mc%9ReMI<0nkh0BN8glLWqS7ur61>Qvto3x#K^h3 zIfTp;s&NTM-&L)5E7Mjr{Fl|}urtZ7gk;-~blX&_b6a8IvQ)ftTWR64HOcPir$KSy zvNp*M?Wd-^aM_z=*GGBw^s7sGlon|3I-_J*m=2AP1W0A#AA!kbyiR^G_B1HwO9q2) z|Gy4(A@gUoqz8FY$-F)7OiuY*T3_kC6Q6MTZv;d`Q&yLvJ!b5aFJvFOqUdkJKs~B% zN@JHERXG<`s_MG1D4xv&8 zcQ%!-m?cuNj|e3HYWzNP>0lne>*%=nT6c>2T#(;m;Uy?JzQV2-#w(VX&x|N($r*~+y1ZjaK18Pl1#TArt7}7 zY*nE)hu6Dx7jSi6-vIpqy8^~}t2R@^oH{jd?zs`R9*(fbJEKnkzD-1J0r%!JAoQ(? ziLZI2&~0Sj^oNL&!`^P){k7EBd3AaOf;UhSTjxjIYKo)t#%gG+B)c+iu*Ho;KD53K z<8!V<6X~|}+MmQ=C#o$D_=Go?-Z{T|`8b;hjK&Xbs=MI5_s;4;g5N#7_uts;=nM9G zCZpEJyAN$MJ24Y&VnyfC-)M8EUwxr9UQ3~-erw;br=PM;VXiAP_Buj779gFxV!2p1 zwS#45^cyPFiCwF12>IkhVKu$afrCI^KS4 zPwiWa%Y2(gJB2`ja5}av#&4~YsZo7RD+JcrygdY6?+#g5!RVq5RcddZvezh+X*`lY zzj5Dn7H^y5iPPT@X2*?ZHfq`_0x$3KQx{*?8CXAS^S51;(h2gpwt8d7jvU&92{B)y zjdd72vO4V#H~e$Fn+Ijt>-CH24uX>J4yt%s_7(|?75@02A?Y7kwcA)fNi6c?n4^jR zk5{*Ej%z(?xW$ z_4ImZd!f>*HUuZi_EaywPG5p^LDSbh)V#OD#>S}geVfUCaq)FN6oX;6zRo#a)~kP- zJZjY4Ns~Q?)Wa4#Uz z_By(LcXGOyoo(wN3BfOO^xOWUXg|Q;l7ot@Q!h0r6fNB|$8?VL{ISJ-j`jTMPh`4d z*I_JH=*xz+|7TbG_j1cfNxLni^*2~z-Xc%5WCV1ldgC#4)8jfdD>SrpMtGG^UGXZF zDf|T|X1BKr(0x|E_k#JkC)xedVr|VVD&upmCU^@Zg{hUUeyV>o_kanWT%T66Jv;FU z(4h<4NxD4MXkN!iGgpiM@YY#q?QIO<_0^b#*V6LN+PkCJ*R;@a;Lyg~R!Nr3U|i|5 z_!6B{EW}%k%++0e#%p2CxIxZ0DRFtzoj-@_7_5H+Lzm~vwv`+2CR)s##p*%0=8??u z*FCYC+4Z$!IOn@~4WY5@s_y5|NLQ2Cm(m1i{GZ$97-DwaPsU`iHkI@|$MrL16)QTL z_I@2}eAWWJrBtgo>N1Gj6M6!3AKd#7S^8Z=cv`Mp4y8B6hQ%Tw{g)gE1BNuYOK|h75D9l414+J+4nNA z9V2wQ+9^8}pWLORznj5)oMTm-c@**VCn2o7WR8%7Gj30B3#Xj4 zPIv1OuF_i7n3l%r%NrgRtvi3;AXxP#wpyFY^y(tE1~=EjwdD|W&1Q{pzS0(5H=J;^ zUq-b1uFuj&-_#>=R(U5(gU_E{J}ww>-V^md@1n2Q*Sx;cnkbtts#)o7x-Jr5FOVr& zdOy-)r}cbIpIiU=Jna?jrZL6aUdTFg(u88Z`Yy2#hoH=KLH#%9LOXp@(1z*t$% zzn*Au@MGs>Ymv3$7J+G5mc8_j&T8no|Gu9p-K8!a#y6<5)zC*tK zv;EOQU%={g+zJJ(TVYSTDg&S>zFnMeCriL&hu8S(Dy%pP-!Ha$561MqA)x+qP}(v2ELXY}>YH_Sm*P zdu-db?K}VTzR9@ER1%deEv?&?7z*8#Mh3TH zG5W7OEZI|aOsxKnFkL@Brvr`)3~_#APyFK9kKHo+Zp__(N(o_HmSxt$v*xUCJTd62 z@&8W0i{3%idd_i(UV2#l=;u8?10Rm_6MM>NXl-^(@@Y)f;4M?qcuDuppr3dA`Fi!9 z{MDwZJn?SDce^_piMvd{KHXvY_};O0_!*%_(@^u(QHPJ>8!q)H3vxII7Z}>)7TgMq zk>R0xvwi2i4%VfBB?v6ow_0-xi7TiFlH%i-7m4qn@s^ikiyfPs48g{Ch4Dt-l)mJlj4KW4_MpA2SuSr2K-x#;2qzv7-Z!O)Jl$guFpqG`u%kAn-Z9IVrp8*;v37+ z->RE~o(F|RcRXm>ln;!&-g*hxvx}pNf|-%L0jiUPfR}l2ax~^I)Wpop%*w#Vz}&*z z%D~(J&PW{t6GI~dD?3wTEdx6{3kwrN69ZEt$g9JCNpDFmqc&_Uy#VkAgfCI-IG6rQ z=A1XeNi$(=6IcF@M?4{sU zZNU=(5(-5VbidI^0dr}J1cjmnX2^70sG&UVAAvd((UTOy)L&-2O8E;+Mtl2KV1p^? zxmqFI`2k1FCX%$wA}uiPFCde@QM61$Y%uTCAv30xDVip5Hkl4?5m|B+tkOZ-O-8zO zl*d6-D4Qmbe$H28X_^|~$vG{{T@+e{ae2LQHgY`CST4ioKme@JFe znzRw#LIH!o&VVmn4b?4RcZG&pG*$^bNczqQEdc*BS!es{99Xodoj_m#08|S7KRO2- z|I!xz*UrJecX|Gc#E|x*ME`UAw_N?FbD*xQ@*QH|kBE+f%8IB>h#HT(QC-@)0~B~# zdg`3k<^Ud&&Uw5ASZ4>DMmUM4)yOo?ME$;Cw|v)rUQ*OnNNcg0nqI6?mQ3sOyUlT0 zd+;s0Cw4mL*x$2xTVw0_^K**xIU!M}#716TTzm$X?=z4K{7_?1(cksXr9|gw-mPSu z4l)J_JZIwgCxi=_ARoF-2cB+}a35Q(XrD9sK3zF}TRFiO^$p&Giy zTfK^l;zqgSaS4r11uZ!U@RA5X1Q{Gi!_K9YC@m`D2k2WE;8-~vb%k(mT!4T%(qEv! zTVPxkkgHe+J~NF?66o5v$i1ju4Uv@RTaUxlhQxZQR88$CR5$s77%+$V{{Vol>vyQI z1;nW%n^6E4K(Zjs)*9A|dtSl{4z{oAn9QU-r~+mfgRA)!^ZroLi2^^F=Q1XVAKC(e zHmg}UtfoTX5>pt1KiiTQx{hbvI2lJ_D-gS;Hy{cLq|8LxZ&x&qpgOcNGqV`P#^@~I zwd6o>!->37*R4~^BOSce%Xrcm-d`&lIOuCx6w}9A#uXz{`Gd56;;3cZ?=T?pp({`b zCP1@CrmanbO1YNn3Q=rEKT>qSiwl~cEUzQ+#*DRpis3&}G*{xOE8%4vSg}lhu#SWQ zyW=k=#?(aD0>zFXh@?;JI7wp!qeJ(JDl6B!PQ+vkfeYgseb5sdAf5?Y@l-TRg)-1J zH+B@a*+Z%Xez`f%%(k-X?4?H{=i4Jy<3}XE$$h(%b-%5GNg2CK7IYW}X@ZdOqh?p- z+aec@1AA~#Bypn2gh}{~)rmcdrX-2Z#eg7VV9G)Rs-nK_xGKVE<&ri!~ zjG?>530Co-TOmnhE(@s(Ps}ZyRVlEGIgJ?nYDab&R!!{t;tl=(z>PYfa7J3_!6KLvN$kApu~pLqDvPH=#g+- z7S_Oq)S)*_oYE^%+Wrg%a!>9ZXynLggYM0_0~1=5lGabqM0ZY~iniJW8!aXdB@fj;5q} zBlw^z-F|M^9$~L90c=D9j4|?!n?!LTR8mH-lf+iWJyw7V{kA9CzCCJd`294ye|=Og}12s;S|Hs=%;az zsfxmv2l_)rW8y|g=p%~UY6KZt(TH4gVshI8(IZ?7s{*N4Hb{U;l}<(iveA$3m>{d4 z~bm^TADm#ULQPJYZ+mYAGgMV$?G< zx4}qY=&WmaqO5(Dc$en_w^eH0+XPlLt>Swd@RUv^enc$XNuMz|QKDfaDzq7}Vw)e} zSJ1Am+yeTJB$E|R(Xqe|Ok3!0w=SFw2*f)&ljjASkI+p;Y3$DF4!h*&B!Hw(Y9Bt? z4SDp2uZq7G8AcFRMISa;0ZTj$ORh3#W#CtazK*tJBlP($Nj!aJY@!cnKNyGD4;5{f zaDu@$AVFkeHXxC2x&lESo@+oMizL%O#35k}`zP0UqEv@us=t^*3GUA>5z=K%ff;=N z$Uue>GS0o&WwEtZT=Q<@<@OI1Ed@OXeQm!FA@U+R9l+$1(XZ$S-zT~Te6%agCe!~^%A@H*qTvEwZ`C8Lk23ZjQnUwPUo!(1{B)yXuWPZ}3Sz2Z zSFbF^APMj<@cl?pbRajihg36g7ojpL3?WG)V3Y>}ydNn#;L3{hg+-mF@2b?UQ8;)p z5Tb&dG0N!g*Q%(;*TF*~d&I%Vg?&<&zcEp|#}gQ6{V_r& zXubFp0=ZVL}MmkRIN?ZDmYPD2DI;&jr)hbZ8r7Xj^z_n`CIaKdpx+sb>_VnG(wv zSmn!&si~~x{w zNH0dJe*eTUC{_WzoWNM+Y_#0a*j-|hjb~3{ri4(pO6cIi%;oKv4Tc5uM;t8K%iv(I%0__7$>t@}m@A?F- zGlKR-r+LS>4z$J;TDKpqGllkrr+EkKKgj5Fd+EWJYA=E2URVw`utd*#8bJXh9XRZb zFz9~!1rEw=BtG2eXP@?QTc{K_x{B%olB1BoZQ3lH(mSatFW$tf&aQh+7$exK zeYAZ#6u%n3GrD?cw0zwGSAPfwFT8rFulZpcoTJIh+kq3b!F!vHkkpyWVNjEBU65V8 zg()eVzEgis`xb4TH!>_v4!8=y5?I#CtNYN!Pho!W2cS20CDh9OYxe2EvP=!nR&|Dc|@gSk>`;ol=uk$@<>F;G4A?|1-+-sw&pPJ`t${PW2uS7-$w=7hd0($vkXr@y%C#yAn!Vz9(cfF?3%)86~K52M)@AYc!_4TQyT8Q zj`HXGQKFf^z6@bD3}HYpS-=8dv4Ft>hxl=S4xLPGz1GZ^jmx~eYyoK4M}>NX$Sym+ z$47BmmXpkYpb#*yi&H|d&Z!WvCGL->qpul#60qnnfl|QGkRdQMtc;yH$a(aKygUT4 zfZ5=70D1AuuDDjFd(Dapb^RJxeWiu{OFiC|KY<*Jy=kHOVX(n?ABR9}%m~TBn6+aW z*iKZI1~bZNF_sgL3zl^tT6^XEn$6E}F=WRmz(+Zl?vzpVm64P1Y(^>Y@u&0*53#{~jD~B;#$ogtk z&o=&D!)9H>7F~Se9@nxS*9P)6vM=ut_S$`5o)(`Kwt7rDbfMpe&{6$l#tXtLQrN6p zx%c&3#!(%^(4Sg^~Q>shBKWsi@EL^8H6+AbFdkyCKxRA=SDM~l{G()@d2A0 ze`BV{?(K{+(q)Tt*C5lC75|Z<34pf}U=HR1aijs1fp-y@ZfT<94LcHRfkLrrCqk2W z?CE@bz#QicCL}`lNgmS?IV5aP^S;ds64Vc$N8t~eDp<7(t z9*oaKIM{Qp!V044*`jtcF;PH#A8kB#y!z!{eGgwBy_(eT{KYS5VCwkX4|RMTHb^xI z_4}NloefAtNJqEKV1MAFL|G4l4<$-pUkv<9vTJ;K@M3(zI z>EScTrZm&@Y624Oc$g&XEFfFymne5m&{#pYMLILWGJcO22@p@}<@Ova-lCqFr`;XG z!-K4Kn*jq=&Zp^MAsh#NkjUPhNnL!MkV2aSfw^eFozE2WZK7-;waj}5S65ti5yyd? z?U62aex?Wa=P8^cIMrP$H8WNBdFe#vlZAbjUI+zB@a`9R3-g`yz>z9t#J*j@qvey6 zYRok+Q|au4@ZsKM{2?*XUUH6$n6->BJK7Egmvo=1d@m$_ePZwsf zbdAN?3LC9VTdjk{M;PAmD+g=vuC=~kk<5B^;5^9~?NmzLz<(LY_ zlJ(*j9Zz_8;jS%L~;AV1PH_YMh?&;BbgU=+?>ab;WX*neaA12;k zHi~+O6YXL`x;~XPbA9SJeG?9iTM^5So$vEWImOaeSq@}E>D%}IGJN=Qs=7B^N7p$S z)ln;YUALpPykkz-zP!zw0Hn@)!2(@#$jO}edfrzJ*qD+g@6Vd4PjYH5-tG0{Pjr}L zNARz;Pn2b2qpq8&<&MlrmPeErPN?kVi}`Gatgc29E45n97B;<=4DUb{ zu^kWIDK#UylU?uq;KORV5BAnJh3Zt(@2#E1?=OR0U`p zYF^XVrAOI%Yk8i#`-|GjE*l}1CcJcIO@}*CqP#XDBTugL+fVkz6*;o6f@D$wJ zh6S-5Gvjtvp1}C*ccat1J+%wtxb)v18?%qPbnREfAhiKn%mK$Bu z;}WEU`}bYCbZ7l5`Eci*8?y$S$-hb(l6gTZ4j$4du@=vZ<%}47p30QdEZwp0MS2Rj^n~BFWlSPZ5e3#4tQ9YGk>~fpmw}}>*__50b13t|K zcHhHt)g~Vm4L2_)#Z#8o-!GI+RXID~>vKJt8{fA|w)iZ)0~*XWl;2vc!{IN#CRgcAdbVYF(p(Oi)D~rQ5Ybw_7&XRLtS*qS zn_9D?w_LZITbZ${HE45E<2~BHIzBtsYL&Ou4xERAYv0??qhFadc7X z5wjv!+lszx#h%$9*U$2@nlBzBBh7hf_w{AS*wWtFhic+?-VF=lFW29n29nQyvEJ_N z_OHw3d7qt>lMOe!x206I=-lOgQr|vUk`}0WwpH@5{?ZY?$riHpyeiP<2I<8T2SnxLv)QKJl%Iw#QGCe zCi@+6yRS$Uw##AnwI1a6`d9J_lh;-Vs=s4kad`L{==Y4qlq{e{XxU%B;Xaf44?%#&P@sR^Ah^|=04dDNr z(nWID2k@Z7z8NKb$js{a{>#q6SD~>;`BC|&<9W~5ll?xItePVRd{9_%fKsFnZU8fh zAVFMkWD)WsQ!`owyd1TBhK+wVC1Ot_K5sBmsG@4A054Z1)We|9Ls@IRDcTu>1RW40 zc$Q3?N?72MU(;-LpxD+Gx2iMG*VyNtt>fLj=h?;g{oQ+;uC420j$F0go4lkvqA?wo zys7XI#STmS7ZE`cw~fs5Z30~VCI0gXjd`s2lE%m-(74C&Q(;DiP|STcf^z?$c@<_A z8;@P;fQ!v*Ero>Z*prGfzRVuLshpKcDtCv-(D{mq4{9uW!0z!_@MQPh`dA2A;Y| z@bXpS{QW@rlImd%q%!c?+_1v+y40%M31$Mes#d=|SIrF#My>&M154j|pII}|sq;u< zDz;2zn}b-MssnvmY`U3t!$s4)v_#i+ZBhfPmcE2x=vX@y@h9GQB2)8Bhz7;0GgW7# zB5|% zoL>bpzTV@uHKQAl`fF)NZ$`!q1!4~-04jglNm^xLLxx5jDbedg^jBSk-jR#Wj4X)S zTZk;)d)&;{jPOTuXY$J|LVD@Sw#Wjt;_oxD2D)3%v7$3(Uxch2Txg52YO>-+Ur(r!6)}|>&#WA`A>JlYmv7y!Z-f(CWBPSQ62q@9$Ou-XWrrX12wCV z?8bB6-Z#m1x>TRW>Q~@)(&yGtHt^KH#n%Vut(_-+TMF&3{^sjZrs19PdI}xw{btB| z!v;6G8X8*DzkWSU%!FGFY03(6cQ-{y3rj8$^tL zbaB!5J>246Au4;{dsy^$p5!mqJcTG+it(b)d4tlIvix2Pa2Q9)S0^N@aRmtze{~YZ ztRN`sc$B$}TQ`yrrP35pkyp^&JPh5uJ51C^R5Gf}7{O$w3^gzAq@Wl<5kx8rb~37f z7{PtM$qIe$q@))aJrK*A=iw5}^_K?XUrKN2^+$cr>3WPyNsALg{sWbUW5gDWdLX3Mz zn)C_^M2L9*oJ0!qsA~9ICm(ey6p6d06D(#3gRi+HFX4kkgJkqD0~B(Ej2Di$Fnc3* zPYDz`5O!*2Ab1ZMI{`A3X{so{Rqvmltv%Vv z7&wW>&C70EIy1OCdx4%U>#2Lx_&IEk)6X}!>Jl;1zcOXy7W0|){ssG=|3KONbUQei zB0esEx*aw@`h&p#&+66xTJ0eCsdfBw{I|cLF11|UP*fV_fby&a;c+Pm1qCH)6iv!rUUSV#*M>X&y{dV5gL&r*WpyuPs+rlRx8ITYfojK0R7g^*3vpOs56N zLPOjlJj+QXl{~#5?3tFoBy0c&$oQ*czyb!s#G)*8QvjH-CZJi`ksiQL&cU5mhsvT5 zSR4Uamz&N&D4aQiBWFe^ZDtNywUFCHH&1htZMoDS%F8q!&^R6NkNQx?1XD~y{GORI zjmfPDx+XjUW_>TvVJE|oFqLN27PV^Hv{K3KMm4E7kHU^6eUd)9py`a$@2Ck4y%w1= z#vm;Qc7~t;z*R0lE@Q;4X*sSK1$)S4UW>1uhjwc{29$-HLBKteAH1f$G$x1_6o1Gk zGen4=#*NADLKv;XYCZaQf@~;26PBB3<_s&a^GAJfh6!wI!jgt55TvC-U<7oc2H0H8 z4K2n*EL%L3o)zRV@~7!fpXB5|kth_iz@mH$?xvVSuQC^CJWd~FD^TDB8-Q768R$x! zlxJg1k&c>gygf54#;C*z1U0@T$cB-cX}rfPW|f_l-N(#A<4nLKN${5dg01#XfEz9R zB{Kgd_oF^c$K`dg$lSp5;7id&`-y5)CF42G1 zV@So5AwLQJi9Z~yh`@wxLW8cr4%$AsO#oKZF<{w(qfcnqj zWn7B$5jQQ!M$R7r45C5;0pHZK0XQms{?gS~0XUe;i{4_O*z!2Y%&WTp3nT;$0)Cx{9I?YB`!T!T}3k`drdU zTA`5B?+zX0lX`YLisk?gc@ito!97*^fR+G718Rzf0C$TUswH9deJ@${WguDgAxrJN zETOqN-L|!|sKK!PzJWW{%mO`hN4{wy(1)nZEB26erf~R!`jO$m6Az_3z>Ntpq^>3k zrG2qQfLlsjKEL3Ro5;I9L(E&LFoj$fsQnCxt+z#Fx@V?Y;;H$($ zg0=mM)au1x*K4EwcY-(BSe?_6x=;Q)Ce%>9p%%4@5B0Ia^zZ6-^(y(PRcKuo$g|^1 zVY?m{8elDI<6AX6nr6O5cwc@#QM(W^mD^V2 z^BXke2?vnG1ePn!CyWc&A_?EO)P&1roVH~0?`_a@^dnDzv_#NfG@SsYi@k=ccen^6 z7zu;y(T8Qt-8pGkcEb1vZR1qe@A_OYhqn{D&>U%0G2FOU`Y1J| z0e*p^xsIxTOhI+41#SXGdP4Q-vaTE(+Vg4jpsDD%bL14{ei(4jGiYf5z?JsJ5;gFJ zPv9k`eQFp##f@Ch6E%d2n8t=p;Gw4TkTH33e`fG&tMGCVB6Pqadn&@pQr-=l#|P;3 zi%{RwIr=ki9ZiUM2_}wbpB{{w%rrn;YV&f?GoOIA2t-#>j*mftb)6W@2-`tovh+gH z?X&F0LN8GZ_G!{$JeQ+tD+Qhh*jqyN!LqiVGF{P5Sx;<3^;)yG5@X$0n@xBoFkVLv zedw?p;pa2rhYfjAVBN=?O$-Mx=HY9ayr~$#l`;2SCOY{j?Bg%2uQD#_pNPcUy6Ix1U$}|HO9Od<5JbbrFI!0 zfRVWP_kq*`LLw%@sT5M6DA>bd?+`killO(RLVB`T1znMpghYSZD{KT&ETecQLP3PG zSRGN4VurL9v8YL!3luhzC~D@}VjDm=i?)E)!kXJe9wV*k|MHSb3zXZ{LU*s_@ge%? zgL_GlhxoOp^_S=($QCl50c9JB&fvF~)Jc%Ri>m&ExfgZZf(TI=^%A2xMa$+WSjax$ z2`rFkT$76&1vN;Uluy8R_d%7sW zXt!~TeO&7LO-xbh4dG_%@oN?0$xc9WEYv7O9MTjH$;>&0JyJ0Vf|vlKZL{pZf@Jen z%%=wVA|`bb+LVN(U+Q&qg{A3>I{xlwf&g$MDcN2Nbu2WVE{iCgn zAU)$OC|i^F#@*RPc7}=^BJG~;sCW|{utRMH5a@YeRfzw<4`Bbm4~qZ|w|C;Z0FDf# z966@@A7S$iH>Ty{?XBE_q6>WJM@Z`#+D1a$!)NlO(NtJwWCgUwEd1NS2>G_U6iNiK zfc9>t!wIU2jmC+cv*CqMA4`$&bT>!vRV%!J2c;oRH!~r4;LkcO_gD3>YEq*UJ1mVgn{%sygGUcTiZSWcS961C{F3+nenQgA<^)Jd9PM zTOXzSBhg%wP6z0JQ+(cujMCrR%QYK*6V%}V>p2YHNt@726V-A5NwbCvu{dJ=h1PwM z{Tbol0+`N|IzgB8Z~6gULP3G%r%g>nC0SHTk(h6oje=Suslccg5dr$+J&=hEp8s4C ziVXfyXMg6ci=(kdwCP_LWZQzNmpGb!l74g$H7@gE%qBd#CJjnyNoq`zYV4FTQ#i0R zT;X_$S+A8!4$%>Xd8gAja)3F^%H7}fyjs1c{_V?eT0_vlmt>|PVfGrv;!87IFPU%B z6p$iEqPZmASeb05K|Xs|COrNdiT0dSlQ-3Dm0-4hHaWM!e(JEn{%&+gf^?lY(2LL{ zj^3UfGV)YVb%o3rLF6J1<_>if@=w^`KRW-sN$pG{XiOf|n4hep1gP~edjoz!kEFVo zB-rFpyQ>l0@iGpAY~gN{t8(>2unwA8nGYMOj@%D zF@8f1aZ+wfCE9iHXV6RTx=28^j7QjY5Cv;Bq1dSXfDmNC>&}!L7C+T_e^8GUV&70e87V%rTBMm zi{JrwZ_8gwf!cEzy%!$8Q24GD8UI+2U+|58hlx5~(2-8i{n$+=s(t+;H~j!;YE%K- zTcCT7;9%@fNr2#}{Oi?0l*xO6DcG=aYAjBB}&HV=V z9oe9bZ`I*cp#nD;rA6^GVrD-f$TX}nRWX3@h{Q-&Xw@X!9GzJ-Uhek9 z`@XJYUoIbRe?6sePL5^V(rcD|3Vojrvp+CmRjLw4s;SN#OP_N=V1Iu;j=jCG-W{s- zm7Xko3EY_&q;KMf{F)v7QdFUec7lg{YldtWwY|v9j9U9GqhWy`m2K%lTSu0AzEFn? z4kLIu9x&9+I{wS8eUtT|Kl#BISs(?;s;^%%3;Ty*PHkZil9zG3gzu-Mp}L>#Hf z1%0;lUA$wJ;5feYSFNKlx@h@R8|(I|72|kv1aq)BFG*>}hQix%ly0+?9;_|L@hPf8 zuRX7&;Xq(TUaJG};RLzEXlcu5Xfwx)vT1&PRig2H^}f6gro++8s$A{8AWDl`5Yx58 zQDY5#eqC*=n0u(r{x2_&Lc6Uhe93m}g!efb7$?s=JC5$Ln_YYA{mg6)eioffk?-3b zA-fQD74yS$qev5P>VuU|%S}T+K6yt@D(sa4QYP}r$K9)hQlgcCle@_!@SxaR>$1Y6 z8u(%Jxs)Z=cWtwlPnzS$1>esv`I7@LU-7QHa#U~go{`K{U+tc(GmhNO_F|tx=GMV@ z)-r~WFBljrl%>)h=BC4!#Iiu-&GYU~`<=3T?fQoCEKFB2Hd*`nzSwK0m6h@I4++>s zLaJ4F-_fVw(DMDP6=rN+Z&OJz6#kd%Yi{N~^64mvOZ@vD2#i>{Q3m9!ue?B?TBqqF zcxSkb^k)4|((W)gO`q4XHKoCagjQbbd=K>56p^m{M5)M=CilAJc6!{01e=PG4YB>B zP4Tz=Uy)fcbnE!t^$P-W?Jl(W^&0{u(eEa1pYAU0A%E^cOI>ENz{neOK5OIXroYYf zZw-I(^dovO?mGYCA@^{t)AM2MdKwhSf^J8fZ6JHl{3CBt(odhFu8S=%{fzZi*M2oA zsEK_a2i0v}g1*kyw5DlOQd33I!_7P@8){7%KGV6EyIu3@GkKIO;r_T*#MVEDGg&fIO$nj3c2Rj}y)2nom4C3g}%a!14^(DgYxZkDB~GI_P& z^(*>yEX{a5V0z+aTkn2xxu)6HD^GbZL4_H4Usfs9JHr2FdwUn`JtPvRD0^K&I8XTc z{hHh23FO9z=jODv^%7;s7V&L0hkWc-=33kPU0#K6&)1|U7@ZsNgM`y(HR_P>aKj^dhYxGo*$e1p)WORa zX5-12zz2J{NA82xj(OWpN%5WiZiBFPC(^$!jh1}IT`zF zhDU?@%Wh}V{zBR;voEtvY2lcxy~skg<3eZYr}@ubd7-qWty#_18tD=&Ym&!9F)1ze za$8Q0J8|2&umLab(qs((9p+!D?2f~{NLMkHr!F6;OHTOrY@d;@1=oKlQ8prn* z!XwM1>gOXdk*=qXL+AAN$7Bu8glyL6Zn~^=b;`$eJSSZC2M|(NrNGf<<5gF6{bCPp7 zg(}%f55nDOM`e9`s+P-j+tTaAU(T(ykA7Owl@kBL;2{qet#!F68!>#Hx;t8(q= zRh&|@T8eWM-afa&T~oRriATqqEh~n1bRxXbrc^nv$p!LwU1l21=ve>JGxwJ;%vAM^w&3oUEX#- zp$SzSFCWI2|9U)~s!6L=<2$}rloGY)x_%~FQeQrVaS1hPtr~jl73Z9oaB;{Z^`)(^HB# z893+k{djRioE+}_!)NS-sPl4!uJc=r^JMbVHJMfTQT-c{Pp_sNi+|s`w^giWAp4z& ziod}NHF-|2>Sg|Bx((lVh`O6{1nuc1uVQcO(*J^dY$MMT@`3YU(E~-roMmZeK3SrC zovGs7uy0vvqgUl)RevQR3U`o7cymcNIUG z@yo|Zs>Vj$#r3$o``Z__dgZH4NBa8v`6-tC$rjzk(PJ8Nn3}<6GHzM+!|`)wxW-?u zwbZME{&4j1{KwC5;gkKi(q2~Md%M0xMtx}-2P)}AxG&v1r+B%oTl^}Fp{cfENBoA~ z9Qv+>+rG#VzMvl|7rWbvj;kL2^88FheS>DwLi{jso|^o=zE%1H`G)N3IoS0@w`LnX zU*S?rKm`=bm#G~>*f`LCEGXvK9P`p=cq6y z<%s`{A3&b=QJSa<(6#cU7uW``%QJg0tmchkM!WV}sHxmYp7e1b0@+mEWh>8^Eq|AK z*$q`Hf$JS~rzqu9n?JyHL7kGSdnbfKno^t?t6rC_eiyW#ms+E4|Lxh$lE-T}=xI0# zFUb9Hhf3XtEah{uo2T|0zYd9~?f@JI&*L>B`5>H>Z~kV6dP1zySdQiux*X};uST8n z=ZR9OO(N=@9%09+I}ffAC7ZZJUmy;X^AA`X6st@&z54GWjNQ{KgU(j^*(qxHQ`80| zOGS7E-3RMBPgDe(&Q7tD)G&TX3HWNSpt}e|cZu^nl|oy2T>+YHY3|lL#4+lS!^3i> zT1}BpONu&G9oobLX=Mgy5E%#d+9;wf(nt$yV8Dv}!ZFFIy3x zzOJC(yBJ)xF$^WFnDkMf`2U%+NB=ZEKoOmPwW0rPDDnJ1HkAIarpN!>P-6a94Mgw1 zD9fBbw!}Zje+$|>HNcfo)SmTf=~q0XX+f}w#F3rBqjClK(W9}~TQ6cJ{?>O~Ptuge zhe`D6&@f^dg(D$J(J;CMVmS|Dd0uapGOF`QJwzUUk6ldA6@E;oI&L_+{MPMw-h6-G ze7~A_k}H*0C@R-fQMu|b)vQp2@nRd+R8JfvsgJK5v_Rr;Oi8~)nkZh2HqRUh0c!vn z#$1)>8W}pB1X^A#MSm1%hH=!kf?ka4`8;?7!xE-uxe=(;VMzyNg>F;s7_~AMnIcjr z{DYbgR^H36gW5<6IJr<_-y2G&&|}$DlF^2;Rsq({h0j8Xp**9)uNF87M@9C4EKmdp zSsvsEvh5|$*~i{Tj<0Nw1*6)K&KyQoAtFyML=4Wk-$52w3Z}AdF2IUd+SD5>X5o&X zEqMv_@fraMNK`6Yc<7G>$?Kk5oHz_^5uV$Ows5P70CYA7`dA41cpCubBp`&yG8Zru zqD6moTMykzTn- zw|0>akU{=(a<(Iz2->u&+-0_-;lI4jF(Y`V9AQr5Fr#&O$1g=|R#XxE`~1>e*HP=f z$|((;Xo0ws);u>_={S^p?Lj_gG#m$oU#tGJWt3akU!lP6H^8Zv*m9E9goS~1=`slc zfXFkBFcKmNmO7ddbp{Ph#XsXoHXLR-;9O<`VmdlRinb#oOG3n!fjHw@RhU30!iC_c z79P>_byLPP>dSuW^tA;SrVYm+wHXmMnI)yA4> z)f%tn9*)gEQN36XVdA0)vSgz27#ql_*hHgtN!5cnaGqrsqQ#=;7W-uZrRbbXVQG|* zXR@N6CugacD$0)}%q$B`4Jx)D_vSF^Xo)XXnoxX^pbaxNMxsMbH@NdUL6xv^*uq$tF2 zxfXq%vYzGqZOS(9s(qJXAobrqNnvwC0z{|MPzz-WHLFQ|=`v=`h$Pr^Rz{`ilJrpQ zUAQ6{TYss3ekjbFO{E1XX>_uz$zg(g#v_9r09X@j4~@>Ni|?OJJ>+TNw6lBWMU2}IlvrK-VwV+Fpevch~1%3dGE1X;04BI0K@ou z+49EV_Y&h{HTms_PrIEZDsVdsVcpV9j)7UG-~oM2Z}OMSpa@^O{*nF21tTkD^ZBRj&;RIp^W$=&kk&gL^*2$>Hv~uFQ<;(?Ha|`^8 zd6VStNBDVh`Mc6QUm!q`aam*}21tOBvT)&aNLzaR=g%o1KtUfU(nxSY6K@q5g7H9= zL~hd2r2A85czmYjy=9J1*-UScak&#>F2@#PO|4CF;L&6e>|?$J$3B5VVHoPO?WU#c zOOW7<9m4h%oxIEJ09>I325fl77Fx@$NSl!CfhPoGa7U1 z1i8M9;3!1*eXoVlROnoiPA=G#!R>=6PBPd+a7~s<>JB#6#S7O+?|#OTJ2dip<&Z#| z#ZgwM6aWHG-3@K`wFtu)2|(=Ghv-Z^F>xAq#sv9~QHJMIxIo#FPIy*gDyc>Re{nNx zNlg80Cn+23yy~N0M;l_S$S7>=`|s%@epG_KQ$)Oz2LNl@%|!1J*8B35x7XY8iV3)L zNaT*-jRK1`?W6@=*h8)&L)DX$8p}zHWT(XO5EFW;3O;29UARN8LWf6E+Mzr4l0sQQ zf<4uvY2S|FtC2!fgG%b6ZCn8@k9RO5??Ter_>v{0tmpL*ShG9y%D{(iCE!{d(U=p- z;l5d7hK2;LkK}a|y}*`0;%nEY%mqBcIbZ1@Zp;U~FSNmv1%zte;I}y9-_|qZAN!MQ z-Y7d9T_-T-P)>Q#ao+bNFn`Boz6v@VNsXr0ko@j0p8CO*#|K0Pa(*!7V}88{sV)y- zcT`n=8QRJ)A0BUL>Rq@!U3E-)g0hKzWET8>Dl=(&*2!6cLPAn2=T2T`rEUD}3Ii1u z?8^eI)zFNK{!81FP*kKIe&76BE=a^kz<)>T75kLgTd{tucIc)omEl(?iv60rtrXQV zQ5Atc5}-jsSWC3jq)1DNMGMaJC6zGIXkew^3oBbfS((oRC*bXqb)s*op@Aed?# zO!ql!%>`ETkq9F3M6$C$xulRkX@$OE2LLK81t4(?W}FK&Lf|E3<&(3*owib`Wc9^g z=N^_iDTMkbl#&fz%?dAXWrB(=R@rKanyrM24IAZ8^c727M79S(sAef#MF@Vj`@_qW z)}$y0lohWqz)r}IpkG(&!C=vf+>a^%1PH``d3p*CHapl6AMtO%m zu~;z`Vq6}ho8ls56Pn$_(5+6Kk{c%Q*h0@Ne3F!o-q-_myldR?Hj)%_gck28l||YX zE)XB0ny&>N zBvj_4(ig$-i1eTH;ns=M;Tp!44fgoFXmhs)ak5Ol9;?dN;bCIsIe}{8uby=Nht#&} zN0hY_

xmU}Dm>J-18ENzF;5Qlw)nqO&ERd@wlgP{)=p}W-Ri7L<&7q=;EX$Wg+ zNH@n()KjQyy{HL7~+Mg2{xfX2B%Qy!scus0yNaF4YKamjO> zGL`4XjpOi>VHBf6CDfyeH1dK>5o%TlF^!4;$swc%M~VO#>o=tLfQnfCf<)Ut=m`A-WRKkHVMoDIYUXZiT(uY ze-zaPC0YXGY)aTqT__9Jqs2xX`P|`xJjW0zV6KuOADBrIs4l(lc6t8|DGP4^3YG;9 zaE8v%$J2xj(E#Gnk#pkU#gpPcCV5Gxcxk721&H%XkmUSLsF+p57H6RbaBI(efN&E{ z@mW%Oa5+X-T$wpQPT9Ot$MN2wK-6-MFIMb+^dfHf?Nd^rF#!EJB%X4DEvM;fNQs7OG z^q)MP#X)yR>n){s`x7qb3!k7j^>8LSpr$nEJ^ZFAi0&&Cq}=N^FKYJj*16#PJn~TG zhIHDcdu21(YZDZ5@rO}nfuk(H&=OS;XQ# ztaJOwo&@9k*g{W^z59it-jDvCa6klzs+Rfw4abeR^e}jzm0h}Xw;x6oU!#IuD2lI(8i?|fUIYL>p4P4$2Hy2t29*05c`lT2*e zwr$(CJ+YmMZJQI@HYc|2iS6X{-uuHo-@#g4y}IA3)vLPx-tTi?{hp<$3;g*~i_0Hu z<{N?|c%P47<6m1wDt2Fh=A>USh@8}b<|Ml2MgI4f5I(a+iVV)qhEBX?Y<02ro2MGL zC1urp{SWFDP7XS?i#l2oM-y8Cp1^AD3b$CnWz~`br+yu(#fJWnvYV6Noqu%m$=e_C zv))Zfu%u*92ox%SySrlEi zEb-JE@wB?&gz+@uea&rVt|rhH3D;k*rUO*Cn5nOfo2_5ErLK?b^72#YAdt6Qp*7Fx zzSFF+ddV9NEo|Muki&2$UZGb_=<6gfu?57hH_2e9__$DL*viAxjBUR7gi#v^jzG8n z+>-gqgCh)$0U6yFFM#9m{^+c#c+TF=jq*htm-wIqpEog-n%9e}ID~U4xrAqZ!V<;X za^&1lJyV+eoD{a|D88WUn$9`h*%Pi{ALU%is$;d|P{Bn!bR4Q&P16z|LUG1u?^=At zNUhrPhx6A4PQU6|*S~o#o2n+A-dB4$J!^en5H&6&r+F>pOMGxyhIKLaVis&!`YQi@ zN_cf8X1LdicP4c3ILZf9NJn#@yqw$7!*&@*tAiKo?PDz&JYrZ4i>lDdq>8sP8 z-m@WiW1hLVEWZ^MzYdryRpIBs*w>mf9{cnea)`2;Yw*JVoRPxo$+0-;JTt4@Xby!t z{oGNYZ-dp}ik*{H9cFAGR%X{0FZedN-DB*!>^E{w=TO)8g=25ZXUy)(=`BKIFt?T7 z^MvdKl=#YVDmu~)I`LSbR^#_PH};$%9NWX8){BzL(z;nI4#Ye;ZL5=XWCF({ZL#u_ z4QwW`4aC?03O?_7ofj-ij62~U8DczpBPYgwKl+|JjVp87`}We%4d{l{$3^*=+a8O` zQ27I)cLXR2{lj;OaAATr(fXPjwhw2fJw6U!g5$t6a5X5yp~w?{ihO5v8Zj()#BDn{ zO{>P8J|C;46DjN%iGMMUvsU%9EYYEhSpV10)fu%bJB=NKj3(29H#uh}@M9T@$+!MK z7P(Khvl$NX1~-w-)lY6*nWZZEz=TAciOb#GWt#FGlcSZ6MjINg^l4uhBrO;x24dff z0~2blbkt}(_%-*{zREE#4UM|4Ku)JIyTO=1IJ1MT%~xhQ@IK~yBTE?H3Bc1FRxY%< zqrVX;`%~oyob<<&0q^QZRP%)uVD9Ln!{_B=+=sA0Rng;A9kGXp-dpua_5}WBX*ww6 za>n2TRtsPMh3q~wrA0sZA(O7ElzNFC)iS>)O1p3U*q4ndLJ)eI= z*slck_-L)enfRSqo*(^*>|Wgc#LkP+{I{bv-fWLyk^Q;sN$ma1RUj&RmkwkPZ0}d> zd(|G-my&~uj&(E>BqX%2=RppbZnbr(QbY~<0L#dAll8p(g8J~GPeD*Wq_+nA(v@NP$^=k0v9!j=fE0$hOCSS-{ipK9`qaNz>Me2%!1FYr; zHxgeh(8u?@6>Y0!*Q0Acxv^UAQqfBI0yi%$mSAEqHS zjk`B1@*4XrC&!kkm8FzwEQP>b7G<~GHIR90EN`QN)md!Tb9K=hJ4KX_M_Y>}<*R>i zX*(PHa(OE`^}KBfS{By)ejTv+5E_uKwz7&cvfaq`QfI!_S}Ad0jNnf=uHHz}KMdNq zydQ5A>#^w~YRTrsQm=$5uU$fEfaHi>@^Al)-!kCx{0T?^TUBC*Z6)6yaHo8DpsmW@R_z-krjFlZ5bdIrQ6qW3;A3a`tgp_ry_ADhW1y(6GOPtPb<(?!oPtOw}}?Y?NvCdUKrZRmuitPdsKwPS(3|VET?%mCnXy zPt+(=IlEtl2cFI<@lp&OnygKqJ5#eS_TjuAd|rx{Zl!yl^wyT~$&rnIsjh^?!;!IH zYAXHef-_s2z$v_<1XniKuq_-~$C=1lPc4apc4JacZJDaxm>Ub~UIS4m8{y&CRBEgw zEC(k)g5UJbS(5)%-F0f0=L_}nwIB~(UIpvdes|g|>O0cE+~nI{Wjsmybg6}J1HGlK z)XMpK|7G))F7vqk*8REJvwzT78915ySVH6OclmM|_%;o$Us2QbY1q>q*!l8R3IF)$ zuP>MV{yOv-_9m|UxCHqq-|9)0mFYL2H=rp??7d9=rdn1pL`<(+ub!)4*q+x1?HAc~<=r?q(=E0?)yj)KIr%{2Z6I+E!K{OSeBha#GPU zKkqXQ^ikXaGgepgpD-`$70db_4f^7@kmHq8sgF=H)4sa4J&j+JB~**B$9n7Je3{5z z^iN$MbNfCeY!5tj>}fej_OmYyJ|(YNzaGx2*VH#S+C>Qg4_z9{F$G?hfAwk=Z)se~ zaZyjqx#Hv|5Bw36mmErlqi3~2OfUCQaHsAAUNcc;D4=EMJZYz-XQ8f%Z@3j%(w|9*W=$ zlqC@Gr9u7#Zm${51Pz>h+qJ3OhM>f;LZS@BgJ5 zilvT$fvKgDfrWvYnTe5&p@E_0+e6vf*{kfz2b8r}ZCC>N|?KY#v zlQ9%*ixP=;*xm=&n_LG#0^BHPgC-PrS=~VGx}$;0rLI14g)?-*&K+^!IOmGzTzLXz zx)^xEc)CJOG(z!8;|$&*fPMK^tFRHY%t9?NZ5x7j=}5m)Fp=@i*(sVQKmh$th=eS; zl*(4=lz`%=0e%a#8K2&J^ zn<;had8R`1T(Hvbl>8*LQd&||$V}l3nCv8$Q!i%2dEbbYgd|)PK?La0g0OS%0$@aR zHbfB=1QehOf>EN1z1uERCVJt>oV#QDPhRq|%%@AAf0Bc(2^Q``snR|KJloRi<@K4h zIO9aJhyZE(R-6a~`qm9NO=mfKsBod`ksNX=A)!iw$O2u7JmY?Z6GZOystDF%`%VB! z-DY^u^Ugahx{ku$D9IQg?!aMM_-#lhcEA!HWtOxfpMh`-0%mqLg`trIQ&Q_r1VKnO zs?1itT6rSBORQH@C{BT<)Tk`L*CrSbA)}8v@6Qg5u-F|;HHgai4wI0SK-#pK6L)rj ztV64Ay#qpHq(Wl}mvsbUPz!S)1SrO2UVG4l6d_VJE8Iey5OW4~9$9)7hKN}ZN}$$X zBDIva+7TG!mc#gCEQJiq< zIm$Rj$s;A^*n$JD%7TSNGaPG5-zVu>c|Zf!3sF2#Fj>YR>K2Bw;n3UB9IGN^kxa(4 zC<2PDiX3qO=0ZjjHQ3~2UhprrlxELc=X%;-XlN7PLA4OKs(EI9o0r9aLRZab5l|5& z3I3>mClANj>uq&tZGb(s{$VMCcH{~fT)Iq1{4Mz>j8Qmnfla{AI#nDxL52c6VheV# zMtU&ZiM+yJcn3q}z^|Eo--H;EzFDEc+C~B4sR61u~el zic(SW6kGDrD4K!=&(X;g0!tHzVvwPHEf6aOzkHQ4STuy<8#4VV;w2doV@cB^g~uVe(WAw@F0b z`DtY3D*ug9m;H@V^TEmMlvxgWdSE@QV{aVuT+F0DdpyF?cM~Uh0_Z}a z*a4a}GeyENgy_yHViQypwPyd2)JG`(ZGv^#C7xXTYf8vhdbX*si}DsjUML!Qkw)m( zNYQ)a6cHC|f0NYIX0>7eCaGy4=QPWIE>HYJQX46lfsF&uX@$U!1kfkIChv-coocnf z4l&qEE!I}M@;)DF*h?-}SG$TjZTXhkovveC$SYaw2?JtWWb&QKz!)1M0D?~j9$b7S z9q}`Lz@JT`T4IRuCzaBz5*+(>@(6`d7cU*xYk#^E-99<2dtE`n?W%7*nBEd9yrtb> zDo-aWiM`?~5Bmg*@+jA3nCGfDYw9qMnSDU0HGx!mxr~1i)IQaFE*gIm)aX=t;;MgH z)m+7U56J+idXDWcl9~(z^pyjel=2(nTAcM@LcLd((#id(m_5;dlhld-CaDPpPr&46 z39{gz`dgr$r(mYs9eJH&8`myQzWs3?XX0Wan&0sva!tv?SUmWCEV1ojnZg2I((D;h zFcuk!`kW7vl-U<+!jJ&FHq(hm5s)j@3(DNef&2asNiA)1J3i-ns_TLRyZZt42JL$@ zR~X%%9uMRK-MKdu99;cW{+}eZ?~Rmt=imtN8A<+3IOP?RVz8KagFdqm!Wd&Plt`n0 znf5U(7{sNTrj2NN5Ph`IVug%1kS&qXHWhG7V3<#6C0>cF0OCgSJt`08yBOgMFGPYD z)bjw$XHoK7&FPLEGYf%XlluU|0$EZpjuq+v)yX=Humk-rU1daOPt2~|&tb-oD&wE*9 z^ntYonqYauutVbS?Eghl=l06>L#-gNQ!+9<3zDL+^$XJ?P+P@ae7E_aqBSsP=}+O+ z3!?LGAS^7bpqQaHhU*^9vsnQdBW}q=YRN=w`Htd0IZMhJVVqLRPN0F_+zV{jfhxHU96HstkSkLy)DC$G##YM z>!8HsNd)lbwFqKDLs8gg_Q13 z9$>|$Dcf&R!V6NhAUz#w@&ALQCI;CQ8*p|Oyeu;GTPSdcBXEZ!(KEAf=M%l2AnDQs zkkmpK?*{MnkiLQ8dFi{U1J~FE19lVxb{GS890Sw+*Vuj6*kjk&L)X~RH`<~%*uwpf zjUoE@sQmskHs>GDnwY+AApAPjfxyyF_!a|3Co|v|CZ3A1zd3lMz9)){p`G}Lg(jqk z!TDevCf3H1M>-kh4|y(vXylc(-;36SoA|+xV zC1N5eV<1g4kmQ(3pfHkr24=F?+_`6>S8KG_QxtrO2yDp+Y^egKk`$)$7fj_chQ>ICMh$cA zZ@>o!C+(q7z$jW`2d1&yjt+$Sj6e;EzppUX?#xc^&`$2uPVU$Mk-C~%U8T~VyE>ZifKM|%k3!nE1oso&O>DQ^ zL5GVld!pZqfAeq~cGmR;x0P&9C_XkaNXzr&JE^W9pd;!T4~yFWDv`q*fdtQ`r`7Xk zFn4%AT|sT8iYb5D$i2o5AzNevNl)5+0y3CX8`=SbKfc=TakRu_f<;e`062y}oSK?iNB?xAGk1}E%d})WF4-)XlBA|=fWAe!_(Qoq(xS1*19eK7 zKJp$O`Q|?ewQnKuCFS1&^)}OoGv2W(FYDzpXemmtz4K_1_{j zzhQrA(=D*zP63{OE+%j)RYmgVjwx+a&&hy$aL^5v=5mvil_^tab=haRdY7Pz9nzvU zjQ=`_KX=g^o$wBONbeOF$R#7lrvb$K*k361{}8Ez=Mf-=CT(7*C8sv^vC18_EK5T& zUeeBA-Ylxw7o`H}C?zZ*_U(&9omQqHV#38Y>G3FF#R?R%DHe9@ElbDh<`BYdkc;R~ zq=osaK{Q|5f#c*8cyo)G#^taljfcxDNYO?x{^P@aj&H#(q;*3xvKiX{@uO5E)Y+>jc*H0D{i3z;?ip zh!sD|BDQT}rdcN+XP5}ZJknRZHZchhsjDY9gfiPm)7wzf+gNir1Niu}*?L~@Nrx_6!SK3!F)D>B$$Fd2Q*V8+B+PzQb--NQe5@}8fenz>nphdSoQxVQ#a5s*U{{2raTyG zrUYz4!W;3QkMGik6W72QP(kKtLA=EWmsp0OaOi-brcD`UJZ18{>6}ko(n!8Fkl_Bz zWHATZBkPi`L3Ga-)1Mk+C2`J+t^8(`fG}L`R<`FvhI2CvT#Gml?*F zJzY&kP5gCQPMeweX?f_RjBEQwX^}%;LVI}f)xb6^DM$FW$+E?JI;-zxw;Iy_WDxrzB7hi%DsBR*PNF1WMuhF9{RlT+Z}E%*Qj@F?HhY^ z3m%;2>o>tgy^ULs%w1JkR*!AduB?AVX2`yLcI$6jDs3s8*=@L=o^<7yOv*K>TZhW6 zxPLm|msU7+G%vrF=w)SaS!=5Mt)?^`ALU+6ZbOZ|xV+JA_0X@nJ*ZgQ*>n%`b*$4+ zGrnlsO-8gfu!_!qxhUQ5sh;h>v%^11)2^+_CiYCzSM_KmgHOc*-6-AOu4;tpJjRG`*gcKsK&`_?n>En{oa6y_}%Qf1i7`g)km*;Dm33*Z~W#) zxhLG}VILR&Yk9qmp3Ehu55FrLfl#yS{U^M-w{w>rrM}*4pQ3S0YHrH1iP*>~->1aN z1M^`=*Lwl9_A>Um$ICWYHt)Vf#+Z3eJ1LJ#WvV>pN`7AA53t-HS=P|hPq*1mm+qcF z!ehT$U6W;v^+fL4#HW%_@cBs#o?8qpvXyf^IxJ1>=(inoJMA6?=yOqhnvQmtyE-vU zEgsyo@niS-UAJbxKYMV0@^E~1!93toeNsG}sel;VL~YyUY+F1zl#&~&7@B7cQSU-)T?FNSJ~duvIB8IojE zXYJVYmS2>KU-<=0`EecZ5Bh30-Fn1s`KC&p8+#e<#xT9Cn}|n4)j%4mkV4zn}Xa_x!{n!WZR}^f;Wyl$O!( z<1WS#-|wL;u?Y>9;t;&0wa$KDmL4B{leUNJ{+6D`-nMp;>X1D;Ya0M-B=Ogq4p!l2 zOTX9>C%}Aq^7$VBST{GHragrB^P((zIl=VKb^8s|@g?v!2TeNQIr#eAJ#9y?<1#VQ zL*YX6`sY~bQ$!_M)*~RbhlBfb5=~g@WnP{I*IDZJmS0zciSiv8P(U_|wOkR|>79sp z`RgIn^92-H_LF$u?3R{(nqNQYU`~!hc{?_@Z(6GLIL+zz_*I8_XT4cq>=)H-|CE#I zulIlejr~?dzR#84F5^#wskhBRVQ~Y6U7anUH1N6_{WUp<`ci?+v^882)3#y@pL&@M#@wmi zhE|Vii1s-h7Fdmkr;|G4@-iXJyaH8(){vcnsi^;R`y`^lNAHJe>U4<#RbY|gtO z#>IH`rn++H@!-!x@7NZcot6e$zVq_rw}0SRvK9}x9{em1?3$^I>1dfNdN^;K<}X)B zIh!8+Y`D}LFTWmaj|de-9B|kt3x5?2K&UItFS4kwudc3Q3j2^br+fS= z@DHBp3n4|p1Qml4)XV?}$-yl!RY5ZhO-__D$WVtaMrp24Q&LLH(8O$OFtXc99Q3jF zgZ<8Y=h5?&c4gPSo!+L^+68~!rE)-!=nG8BJK+BTYtzepA{L^X2?ugcll)g zOgd@BX5cfUacZUR#AztX=~5cZeXmB#+pth!g5$LdXrGGLm-|rpwN!H-P;pktZjWiI zzN#UllEEyXnPl89ia&1<8l~RnzgF!YP$qI$PS#jJHnu1qyE8-z$ct zBv)Ooy&3!tG?vJ>$m1Mj@2WsA7Z-cBEbiaC8ji8%&O-Zsr;@TljWM*IQDIKQVuBbC zA*NFEvL^*3q%aV}u%S`^z;v`6ai0$?IJ!3HHVH%8x

bpUU1&n;lm$c2}#@qCUIW`KYI5PaAnhe!aergukRHPk%N$v>}HqCj+{C z=}8Uc`CiwEpMDyPyx7{g^z3*E2X50dnSaT%zAwc)DlB|ihWl?Qw$r;x{rnPy%gn-^ zAN3V_34OwuBO(0}1FesEK6k!_Vf4%qqO6EBqLh?yQ=$nf?PL{E$eB^hA@<8?5h}w- zgY*Z+VY-(rJWO3(DudJj0JYi=YS<*OJ}~(b%%|ZfLuEIZ8d6zuc8%)av8NLNIb*>z ziwaOg!VwOajJFjbfgv@eHHCMZHJJtAXu zQpC2YE?!=Def76n#(tFb9U4~4w(9W3fkz1cCJoav_ER)rWmEJGj`^ms|0EgzBBWXWUxM_%lMD_(!_Z&H|B+-syTdH6Z^IC(At^bOQ3xW4 ziV(%OUO*%)Ak{&l*^HREC9Ik>WICC#Ne%rXS(Po-uA#`MrL9r|Of*nx(b9f4($Q9p z_e7K{r52pHO{K4La!WQ&KY8%z=a8o}A9r4POuv2l0u<>9l6j4qZaH+6qKXG*1@vFz z^-usLcx*M>HT-OnQ`a@#4TZZEgl@!Ue4mt#F9q3SYk26lu@0N_9j^`A7?QHej)ZLZ zLiAT^E~ZSQs-s{9!ay5ohwy?^+_b|wE}Pt5b^)~Gkb`GlWDFa-h%{1s5os(qP<@1N z*m(B-PQ!s7ufYV61O?gx^Rlq|k9h0_cDQ(f9g4!KmA=s=e=xCO3xV5Htw=B$!@<27!Kv#w9@zGNT5~AO5K{Ge~U^ zT#N+4>@Us&<&c+NiBL_!G6F?w5}bl$VG10p<)l-86-epF z2(M9OyC5bv26in>WF7TorV)MfSo&R$AzncIY~Bz z8!Bw0J1M~sN7}CCFSqoL4uDHyY(x7(2T=Z z2=?)%9yznqf^5O*snmJd(X=8W+_kw9mIF~ldiqEWBpNZ3p>e~{6=1VUu{1~^{|J^N z5Wbz7|IHSOO`0(@HY?|oF~^?e>MwKfT8d;4fyU(t z$_Z2tl76=;)cBKvryV7V(Z4Q0Mzw)p-Z5*2S~xD8O)oB;MKCV>wupdvk5Vw?%~B97 z1=-}842p>lZ66g#oUa5#qcAvMsGLDcz`r3KphgQoIuba7u0HV~$+e+q>kgqo1hxC5 ziXPM#%DbVq4+3wQA(D2mevduz$JLyq2Lkn#gF&6jFbDYp{g}wY zO^_jVINdpn@jwg@6|n{inl`<)3VfPa`cA6XyJv<@0Y?|Z2Ch##b=*>P2wCeI3>3{W zULB8L8Ju(>y$PN(r*l40wFQ(Km=&W+p>3dAg; z)p?TI^a2qt(XVDc*jo$FG+K=;&uhW}D{08*QTXyIC>8v19Q#(Xo49d+VaQf1i+nF~ z9gcG^{}Irw3%p2njj8BT_~(!EX1?OZch}$L9q%gqslDiAwc34W<4V`bkmrimYZW^W ztV3tmpfx^FdT*$ma1`zz;(cd%0n3D-HFl_--OAToG}cP43LOnA^fTfX6q27KrNRYx z(h=1g5_gplt9;JdB*2yzlT;>gcq z+50K%5|NW3a=CD+6a{xgOt5lDb;<{=r6l+qvaeGRPQe}=P08PrvN~JU{J&ECb7gmE zFkyZLsQ7u0I56hsvec4`~?_1WRQ=oDI;nh30Z zqxY~kj9-51AgH;iCy;j&eV!!NxkbQ-rk%DIg_H3hkA-64O8qc|5g-{sp*om-{AHb` zPD#+JZP)~AiqyRbuXZW6gKZCs_~r@Ox+{LpKlaCA?HQ#{R#3Y)v5yurVcj?Goz$P3 z%wC;t zEfU^3Jmr>86gYIux!62MnGoJ5(5e=pasVvJyceq1B7jew`fIQdDhK6a(2%W~pZyR#3wRDhY1Via+v*ZP>v@Rn=fHSe zH%=j8a#cL!+ouiJ(TcVMBXK;A9kdP|xDM;$2pHkEW0W{fA|8*u?X|pY^6C9MBfm6O zfS!z+dt*vPA-@*_Uo(r5BVA3M&>O;?2WTBKWZb(x>`(8nP`R!I;4MeoNY*L@Z7P_D zP|TNe=c~dOU_9dCXJAk}6^`NC_b61Ix#>3Ps8X|;) zvO-#Il1!2>&=WvA?iH%BQr$PqFaNVp-X3O!s0eye<6ZDG?i*o(Z8-35u>i`CpJ^ig z?jgX8>A;M^z&A9YMpU2$LBNc#?1!UrCGpw*nFr1g<_Rvmq1lrc#(olYXiegH~h(91;?*HzHoac3BNlHzL-MAm$3|q(uh1 zTBsuL+?D^~srutx(EhWOuE>Cv$e>H0UBAenOQg?J^ZODtN~JobMkJ+%G=+vVrACcn zZ3^I}Tw6||L2d7(e6b8!WSdBi>63CL1EK!g-!~iN71kziyU1S;b%&d$k_@g#6cA!? zb%t*%iV03E*x8NmKdm{oW;o-iVTx{N^E& z0x}vIFm4!#zc*#C!x{r29&C*1ew_OBObhe}?+cMGyj9^~pC~^G??pDDIB%zrY9nI! z_s$3j-1__Az>f&(Q1xd>#pfgnYjV-Hj3nb1g&WMjUQk)2>~+*Wrm&-PHNr$5%%_- z^wL3U139UcUuo}CB-|?j=?R7O(o$-JIjQyQm+6CfeRabVb;V~q;H}4?pQ7m&M{uVT z%^w=YU;nH;S@OC7Yk0fSuv4V}nOk%Dkn68JqoDjVU$sk0r3P{F48NIrT)GGGO2Nw2=4O(p^)OFK>@Wce=d+;v$q(= zP!l6i^AV!ZP?K5ARFeab5u7S^kjOeF^xXp)9a@`#Z>!P7+g!^z!Di?rX{j*kN@hKy zPbPvO-ZJnrAzgG#ji!IZYH|a*S=HS3nBS?0%gM{-2+rgv#^i{C$BFnQ87LzI8R%T) z$0NszW_DNP*ZJ(9bKS*NeDVf2;s!VKByjR~!ypnmyFlKBmt=N&O1jj6r#;YN15_hc zyhoL)VWte4Gg0e7MC_ZW4AU;mEK|rL?s&z9O87!EO{D!?Q^1yVQy)y$@mEHxln3(# z8tmRuO}03*bbm8U=>}_r$$FG?xj6i{T>~&XNthRlZ=rGDsDkY&G~LT1B=R>U75pt> zieF~NrgLMm=Tpfnxn$?a?Ukp}4O=Ps3&G#Ii^lX1MJy^oZR|L7;BFBhV-)g+wkZw4 z_Pr*s%7ii{`}rbh=0%LMgVC|eJ3mESX9!7R#Y5_UU`qWgG!@1r;TSeAMTsE=q6juy z9OW=F;bCL~l8@EbLlwmH^A!Z=c*q9xP5?4jrRN3mS6mbi!A<5h41vrLXyEG)J@jrrI>nQr1@I7iduY^0mL4xV+!Jnb)IDr&-_I#!oeG&a>Iu z&o`FJv>&R^sg&j`fv~IOqTDH+1 zOrgBUxyf@8H6DMJvtLwz71y7q#XoWNtgp22xn%R>cBVLT5=nMV1Nwwij-G0ky3kx( zkEc*v9~T)z^?8=marJwAt!IL+3d))7Y&5#aioG7Vom#Hu+PIc)*WLV{W-`L?>;?

~^W1aA##}OVOX|UA_oyDgBVJ+Y>R=cjK`m zqXbbuLlE3TbZh)#bwC|6#;(F^nY3QrG*@1M{sqjdNh-{|9a3gKKutmrz= zKJ?<(vw5iBI0%+Ud#ELWcmljj2)fTsoqS;pNo3i31v6L=N{W!~x z#dU&LuV+WTA3NqjJ>Ps;&LDTWuU8UMc-rtfJePG{Xh?qJwT4#zoox6g<458Huo zV7lz{quMI*!ZdaP+Kbdk0kqW4)k5PN=D?iYpu?VDM?) z39xxkS^WzXg85G;y>nS)rE3``kRZF#Qef+V@oD-#Kd0cLD+0`Oc z7xg@@1ZpX({lYNy5=e7cE&2QxlX%A`kY4ls{gNpyoL^Mao%Kcl_MGsD=3z5~+;)OddIN0PX)>o*^yr1Ss;0wO^d+IS1(WgG- z)$PE*6TjYm505#uQBQf_G&ElK$Ck^=$Rifms^6bpe(yI(mz5qK-1S`Rh_&nM3-YBy zK*CXF`jo-z{pMq}X1>4ghE@B4h6~sA1UUP5!twL@X*rlL43Kbm_V`fQ%UuV_lX`DIL`xKk@CE3e!c#_V_krU1T&{KtC0Pb}_Y`_t!mGbWeZcAnR<8 zi_zw?S_(y^;*#TTH4)=oIOVQ{`c10WFDIa%_78h%94EPir;d27RFltZJ!)Q>X4=5w zV4>`pX0m*`R=RJDeX9VUY zi6?X6cXJxr{WUoDHvM@V!M)n-HO6VGF>nnApY7+c_F;Y{;ItPKtGVZPpmkgyYR3=g za5C5mY2}_rM|hK64h{x9_O%c4Z`d)Zt_CIZ@<+FyKn)PtmkzU%W%rVx!?As5)`Lmd= zW%)dhuJh+Z!na6ISFduJ&rJ%kbT!%+YfXDw?^d@izr%W2 z%dmZrztdx75q400H`8J8j`zxQSNxI=DYC2y9`SV%${r!_Y27YPt)b`0`_piC#p+dO zRkOPnF@$?RAv%y;R0jQ6^DKtzXW06zvvgt{Lbhthn|iZsD6DbYuJ;`duj;Vnito)& z4UzxY>(Vstd4ZuMlR(R8x@w+aZJ5o7J*;}=?nmxNh30 zfeYL&#(^@QY6f?)!t@$8?>JiZoH=(<#9F_M#toztZ$X-+VI;45D_b;C8vKnry83@N zCEE6MS$!SP8Gcds*k0ulAukymPTJ>O;HDpo#Ba~1xS2&YpY>n6$@~}h5Hbz1h9w}RmU-KGUgagigJ?o3c0hx5~tJ`XCHn~je>|SQZ@x6sz`sb5e<|`=H z{B}JpwV!C~yYP=*Ik*PavYbs{p*eY_qF)0aS{*p*XbpZ%J$w!!S-xNcdj-`?Zy#RzQaW6UcN^W5k5?IoPTl9JKN_?%8qd;wb^?Q9bo1RF$zRCdoOHg7 z@D18M{XG8!e*ALC*NHHs@4ac-(J_MDeH{q#k4|_7Ml+V_J(=_Ref`p44zaRzV$&mR zvE|DkXv6;vR)@jqNWAvr1^NAGX2Vz6`mqLN-R`{lF6QVl({SZ$Cq5lzCQtpo(Unj3mb}+4G z{NDoWQnz;sYw*K51ex?x(%{kdfyF!5E9c)~aBr;vn0L>AVcuzke_`H`jbbDHgERg8 z8#6Pb0L*)%cVnf0VrFn|WoC3^uy?R`Wo2ezZey??_&};{TuWN8a2d-$J%9`n@lpiO zsCmGi)AX%&=0XUMQml#q_He8TnlC6Cz-#a1!bX@bk2b?Fp+wsv-`1j*%YsUwteS)Y zz6d1KM#w@@C9MYSQXbS%s8pCKSfGZ~#D(V0c>j`0R~0)^BaCIqe~A#Ph?=bu!c!4w zk`jt$s*stf5~BN>^%l)nEe2cykDC7?mCaN!0a(UE6}m$WR!dJrP{&HrG!+!mQM5=V zZC4!~z$`sX2qF_zGc=(-0D@7Gr=2O7Ad7N@^r!@;EpMc3fGX4x!b=f|q=KsIhY&dM zhhWh^RWL&pYYW;a6KS#{3RW{rk{c5r?h~3x~m{o!x+t>uT*#ZCS3DaAlvR$NEb;XMrXv^A~OQGQ~&1Ls-G=DbHoxnMm z@-P0|rrCJMG2ogou(i?}f!ERC0io$%sR$%szC(id0z?^rFMtE^1)={JzVNU4j{mb1 z>OW5Y@1;`7`*Ww zA9H@*?c*f0Y`9%@Jl(ZDb*00|U)SU-Eml{RKZ;kZRV2Xbpp9!_CN7dNAVw{k?P*FE z2cP0i6>&saR`!a7F!PLK@g~Z2ZuK`9igo3|HP3Vuib%>j}a zyvz_M>ySnxCT3983Com@h?NHgw*;C;dn2KgI}FV45N`H!GTz8AaYzwX;p{z5f#Z7l zrO+;L6Ud5W2Dp@yNH7^8DR1g~6Vd{tuLnV|DZ>g4+7dyUwVEC?43_377I+#=0N4WD zYycPVjD?^sT>9ZVhvE-6gwaI_2qXdC0$TJ1`Rj2|SREX_8HI1rjd^v`0T44HIx{L- zGcfcPbaWPkKT{ibOobz4?|BCRR;RZ`2Chdgm}ZVR_B=^ur6rncQI0%5ht7Ac5E;-a z$6o5bc7dYoIxZ%ROyOE?5WGgY>v`7UKr-q-%#@yey^K61Ee(0mC}k$|ASijwglz;8 z&}*kyMwVT~lj6<1%W|O}1OmMM>lt8NtRtJ6bce!A>6{9BtW-Y39{=V7bw|DOwS1u z8;8;itXiM)xJO&SFS1^zqaXK=>az?|e1Rm7Sr+synh1kuDO`=MzQ zF~$Um6Np-LLl6=6Fw>=^D`Ph{XMPtY7#n5+GAmMB0ud;#aFdUo9y8nIanu+)wk2}! zqDxHk8`{&~G%74Tp@7e%xu#k(S#wB*pGYt~N7LGKzhTIN=uvEM>Ko-prP_p0Xz<}L zJ6L9@KNqe`91yfKKQ#t>J~<5pYT*{4Q6X)z;B!i`PC$XI1bCLEoEOHqQp{y6)v(vT z8DhhO9r+7tF{CL(fe5fbhqU5Qc8>nCT5q|@5g>{CfJY6du)!fo=qobF28-5`IjtND zDvCBpBb>~kZ7+)|aGvK@C_b%qKRU_?*g zjQ=EVGk~jmnbf&XL5tcGS^C2fpKs^1naC0ERLB{xV$2cGY|I&tb7fC-d*w(p3`0)D z8H1cZM}7&h!vI0a*^4kSW6h9iFx@s`KAl%E#4O0-N z5g_u#Tb;;Z@e@*$_4ubVeGP5GGG@W@l8(VVaK`~_TxUqca_2!4fyYH$=TuVXR6-{n zy+<~!2QQ&R$H{{ir$NC<6Q);oA!wp^XAX@Auc*vF?NE`?$FqQTD0hbB2|3~s2*3tu z*u|<-6YCHS1=te`f@FBq1s$e%a=ARk30`lI0PhlwViKbS$nYt-lImjet=>Z?6p;Yn z8;UeaT+oJV#p`Z&lnJz6a4iOKFQUsEVY!WE9l4ER9=h!@!i$v}m=E`1+rAeAQxBl! zlh~rQ>4s4t;(ges5y=>oet#G=2c6&5UlcgK1Kt!j+ObrumsYRRU4YJxJH;LJgP*~A zz4Sza{;xE{4G7-1@Zhc9))u`+x$YwV@`^m~AhjSv#3Uk714=+N^8tr)bCeU{)QtwD z^mW$BqxFkzJIu7tI!3El;if-OQ>qd*9)kz#ACSh`Bxv50j8?_MO*2VSY~bWJNaw7j z;hv?6R)J+2sXztTHSnS5B-G<)(eo#4tAGuuejukFcLkH7Y};YOaQ>Dl$RRKQHuMJ6nbn7vJMYApGKMWzoB@ z+etdMjSf1txjMFOCmowBwr$%^$F^W+@H=|Sl;{FIz9hdF zA(%r&$`tSLp8oys=7yit^pOlUv2CG6;q2K%u zyFxz=&nmM*HqMY0|MVV8P~)LtzO#t_ouqFy9F4 zqMe>1pNa(N^aa3lVJws*Ng`w zw?|oBHJUU>vhN7w`)&y52_fYPy`qpw{E|QvK+AZ;(8PdEMN989513H~iSp$&_5r?t z;tBq^l*aSM=Z;F`oQ7Y`uZV79V*Lh01$!D>eRwp;3*k!t>&TrZOPVeOU=!p_lgZ>t zwj*joP&r~3OdHn-B>#fZMnP+ZQ$CI>`6dMBjxhw%^~ujg^pt;+@`v$?b4b_GD%WV@ zwL?T9fy|)%iSq=(F8y))+ZrW%ino9><)^ZZBL!UbV)D!MFW(mbB~4(Qb()oRrXwg@!>O z>eRP!!4Nx0RNTZLls<_%C2&83w5tMvgP^?7BIKJ8W%o^pTB;P`a<9Aj4B5mh?+|AoJ5Qp+t?xHOeTLjyhV!}Qxsn=(pQu&tBmrQJ6K@$P(6xUa5Hl1RDjMP zrPeZP{xkB8e^MF09SM6%tVpRe8i4$s$=B@)l2IR0sWZxsgheM*Jgbd_ed%rOI~ee- ztb+0cMT0Z*!HpN(rk5wV+X%HwdAW(PTgO)~Wd(D1wH|cKr5@43OOKrh9-t zXF^#%FiHAL;z70gAIYU=tqp0yH z?XHveHI4@zGXYPcg`v@$rW#(70AGnJ7T1tMD(~g1 z^?Tk&C?(<+lU^3b+dPQ1(okMh%Q8~3j3pn~Ya_A47wh;2eS+{DcbOy5D(D)- z^Avyd=h=!C^uZ_C7!)5FI-u&&%dG}088FKgrs!Al7A1EIM9DBbj{kcIZulF~yQGK{ zhaj0X`+D&`zv88!;stHy9d*V7$D)d)bM;RdHbHls`6{qC+xVi()#6Ke@iV>R18(M% zd&X-~w)JKEsg^WaorH({EO}FOXw?#93J7#1p#P-;?18OYP}p*86SLGJW*bd#Y?GR; z%z)FCpS>uHvqtATah4WuI+JMKkej_IinDG?a4c7vy=aWH_NF*-78P%5Th3h7f&Ou& zpM-*#3g?>rQ;sqoad?w(G{-2V;wRQKxxTyBrEQ zsyQqe*ky%P1{B125GA3xL?DYQl0o+R_?Cn8nC3<#PNA`X3&4a8T>sdfuU*eoVr!2@nf@igsG>i(OCo6;go3m6jAi8*BZ!9mNYB$bz7FtNMaN~k=!}ZueFwY~dIN!i52s%* zO-%`wz70Fs|Gxi3d_)s&O=k~~LSbNB(KKBRBwPGid;ZFHS(H`&YVkscgN=?}aJ7Oh z#alJd0(maKVENuGw9cyHHQw;)wCvo*8~P2GM;vwxB$ z=(bR+5V#CUhhx_q(K)?Onv6BJ^VMZ~FSGMLh9#CCW79?hDnFo|ypU+0NHo^fTIk5u z@e4=yD^l(5eS9S~BB=4i5T)A`vNX0Vbjf8oM+qT@Klr7qAZ4?V&#PFUP=NN=KipS* zn6&jaRO&XxWlzy8qr>ddtJ{^;Q+Ry%v}eaN{xwzuC8m_4lT;GqvOa#VWo01{VxJ(4 zzPVdf@6ufktW1c4Zx(Ohaw}(eF))vN>OF^|dgdFh*O)z7M1H)sPDQwP*xLPdak;>q zf93Z8G`;QzM#&$!w|w+T8G?FlHG6oL8QtI3F8}z)>g%pf z@?LS%0>ZyH2XN#Yn|1p|FD2n#j`=C-89N1td{t1VZ~fwk)O4B_I!6UNmxkfiP4qeB z^gQdIV3Xpa>=7Fb|7yc^wpX#cgC`4szc+NcybHoFJ6oBR>M&R-XETG&9RvB+39^=FK+_I8mX|mAZvL zesw5fG;sP&bs5i}SR4aKL;6%lZN2UL-m@vUXq84kVDUkR1`~9Pa2{(J{9^8Bk zuOqB(?IYU;NG5!;4{57iM=Lqy%8!~(TZ!{A_l?Oeo?MR4RhwKKIVp93O-~!U^t;>6wk^ckpY#Cn zL^PX>S8EaIM2$;;(YPoiMflszVB`TnKAH>DIj!vuvdr$FvG3q0tkgPprZK!MyX0eo zjlszrKbN>@2v^1mAOnxnX8Qnnsy*-A=v*yWi6EysD*NT-{2@=p#C!XkeYr&;J>olw z+~G~?#9r|Jk%DRa72>-1(RU-ng)}r}-s?o#Wq_?jMy|3nJ&KWQS9@Q-D0PI>iD}H( zKW?WwVmdrdPR(gKu1}DSl;&$Y&Uf%?b6+{m%1WL(x|N28XDK<)D#K{sR}zxL(Uk1u zA>!fHyArp$>^xUrb_lqFJVl3?ZU9u;*&Q6tJVnFe8avIzKHKYtSDa5u(!JWkL5mX) zwIr%evbN3TNE+7g%hrs?@U1>6Pv?5wUi$>8!d~$<7CT6Hh#wv#8l^vST}mj6CMURm zZxx;*I8Br>UMS6^5C9hm@PVBaIm#y?ciHQ-<}-uWFR^y!PXSAtS{bR1>2=oWD|)^A z66RiF6ID?%swKaARkf!cc(w1X)VDrqRaE4dPx&(387^6rk=2lbUXV$D~YpUjb?zUBj-7rwYZrzXCJO>e$Fa5a;8 zIsVmdeJ$DGd20^k)nOuim6#5EmkJhozAiS_wQ+*-7<4Kjkb9_zDJ4n~|K2PVh~H75 z{PPPZqMIb9P(TwFhIB$ZI-M8ykh8rZv{kr9oL(6aMq-D|ZT;(@c-C?hmPjC$EJ-wS zI*~7b?MU(A^cT6!@YT5{i)a&7{h_e@2Ti+*2_86U%NyaJi zMmC|cGV8|tMMC9CqjeHnMS($DQSND2oKi(W+ARE-SPPZr5~TA69P^}7E67!}Op0U_ z^l0f7h#t;3&JgFUj_SofiQ7;+20eQYEmL-BErA{ytfK1*t$<+#Mp(KOw_4Ngr3|or+vW-1TbVvvP){M|R;2(Vu@nV!^Z0h)!LCx$bY)JlNH0VP zLvBH?jR>uG%rFWSj=ND786vW}0KXvNc>S$XlHAnv+ik@$Lo8VW^L>jLW+Isqb9+KXMZZbLFJR0eQT0h_65cqUj6?0~sQWVV4wOND}Mc|4gFQX*^$PkR^in>kYW0WR!I;bofel>Dv6-K22lA4{6Dl&vQ z-Bg0J@F!Mtk@fluj)v~jmTD8G^(~n)GesQhbSo@SE5f9!Z{NHkXcZfqAXc=$Onqr6 zY}DZ5mWzMYB+D{3ReT#xEC*_2+~pW6&f-G^c)=Ih`yjXiv6f>ff0n~Uh6orIT_=F1 zr|2tcCZ%8SPFZ1Q+8fF6ZF(Z07-Q3qC!E+Ga17+CxOuGVp~9H~z}Tq$WJT4?cUHiY z$z^SU2_DhJeTOIfNe~5dJH1xF#>$pS`I9PMh~boZ6HihC@aOY98R3}wY$bV#tseG| zjWi{8FnYnj)fqRd@Rq-MMdCm2Du>g^sfgqBEn45%ln@+3$JD zaT&qo>?Zfn$m<12BSAHW^^rmSUN`i+80tkBsL986u45@s)UetkSBupi7R@7S+1N9F zhRfugRZwN09ORm-rI0ji1*#7}a%8$=WARQa1V@;($@>_TTDF{5pL5wMU_{trOMMA z8doAe_P6AG3A9Xa z#|%zi@F$r#MR6^rl)k|2Zrj#tfA}RljrXhJR-6WuiA>}*S0NyPlcxd;fxQnIWv@)w zpdF7=7%>5BPDDIHBA;w9fe|IYUoCX!BcneCoiu2RBNW7FfL+Jr-1hAmecZ1> z>>*u@3aM+#B?;!ZDYu<&(mdXm(bkSt#n9KJbkteNPg#w@^-~+NAA@Ww@so@t`fn{idaZ@PiEvsPMLbV^-CJj zdMZKWtQ2!!+4p?rc=?3{lGgU>`YyPNhx+ixPYA&|Jc~ic#;*;_PJ3P!s$WB8SV6o- zm0Z9}yo$OvOrBf9mM{lwqp>f)$wkGx4cU86e+JHS!j_V$bb^)I5z1ZhnCU_QO-xZHKfwPFlg|i2UQ>!tyE_SV%Yf@NdKD!9(^S0Zn z3x66tEopA$X0FneWCw3A4!4 zZF8YP%CsYS?R5%)2QkcvN5GzC^yyq6D-jjxk!glW9ek-JL;QyYChT)A_?__eemtD; z&7iSNZp_8M!x+gLXx3ZVD}DVSoOlErvtvqyLXSVvVu=c%tYpfgFlYj05m-OpebB=+4YC{t#IjQJCbT?j;_ zjzXo*LZ$XarAAg|o@A(UER;DrJW{5!BhFQEY5hmwb-r-+^K_*4O4w298#}{5Q7UkL zJN&YL>z~Z8>2?1@u$o<4td5|A&-a*9ST%~6@%~lCM~6j1{}>xRVRTv&u{U~#0-pUi zp5-*y0&dEbGV9}nZjO})QjUlD?^X8?9>^VcYa%ayN-x2ZoYcMKksI8yZ(7li4abnp z&!MT_8{F@efg9Y3Z&cCsThb1DQtJgLeAgzG7pV4XbMQ^Q{d*mS7pXQRM^YwWSfu~D z5OL+?K93;Ll4%q#UQkqL5e(U%lr{qIm0`5dnot(=TW4H-qHHLiRdhOA-dbwTryTf9 z-=<6?6dPmK)Ej$kOA6yCCbcalwJ(NdPx{-A^tS_Pku7N$2a2M2qMI|$)48KUA-fQZ zf0f1WlVu{S1PM=+j*GyAV#jXiNRt!4WBH}d9_tnUx^QUTDS?s{KUPh|MLdM0nCdH?DCycb=YumQLB;`L?bBFF}B9T%95I_ADD$u&wK0k3QU& zAjy{?_Sg)GtkycAfiSlCbu6Lmcy#E{x_#%>nJzI?h7p{hLe$w2-~Dsb;`bz?i-au+ zdt!047E<{!|`9=+3hhS|WFhZ7Lp8 z<8c%tv_&2FLB4`?89Kj*$Ea>QW3yVbV>+--a8(#}MY4BIG^!ukt}2%uJ^6Sw&JpYh z>q0-*hUnrBX(vW{myyY!UW7zqZswwKDVcysLfQJ9^zc8EAuoQ<`Hh}Svmn^UseoDI znrnQlXRCDO{9Mf^pVpDd@kB3NaQ&wX+XVJ%%PrP-A$eLUh+eSp(@{;4H&2yULb)ri zO!rQVS0oge;heqQ&pQ(O78%uC%wZ?%u>1> zDt1(=b_gne2`YOZLai4)MCp8dr4NC90bc&lkW63Xvc5Cq`bd z`|mtbiP86vDoeLlolOC9uqrW{m*!Gc9ir47dG#3ZDoMh(=H!&-ro{@mJCUrI!joVk zda12v<47sx{#GtACN0fvj4cl0>Sq&v4dY|okCS3xP)+_}Z&q_OD|aYw;cyiC-T+b- zZtC$o2I=j9R7#b?WQS3^H!s+!mE$flTK6i_TL&S6KZb+Dngkw?EQ$V5t0odTEJtKx zkSAxDpUp0d{gJHUk!WSWQqoW)@>R&zFP1VN;eKfLo;*oI{RoQ9O3&`V0<&OZjm+VY z!sgJ-<{-)CK+WY~!Q=q{xcWnub{MIFk{t+VUOur&XZr~CjVxL{$3H&DKRM5gIL$|W z;S&H;{g-e0?`5Hhe#E+I?&}s@7zN#&6~k$d>JPAruRE4zR+;?PV%MKK}vH%p;;+he}nnn&#IhR8EXNqzjDT=_9UXY7kV z(laAo-ktMrTMo$h2pC#Iu7KpXaKu;S%VCiSezj-*LhzVrVty&rsMcv-HD?*2Ghjbt zd3xGDXm^^yq@>a>=nWn{A9)^RU^ovOTRbu%PedgCWCXUtgETIh;Tg2JW8sfs35gkT z+|7f?y|)Lc-(MQ|#iPGsY%^V@VQ?Np0Y7PGzhvJ9yXwI^TSGjaY5O>&S$6eje2Nc; zg9&Xazb#pL7)5VPXxP`eqzXXoh`NwxLbdh15rr7|CSu59)$&dncZ-u#Ie*`G1h0LU zJ5s@+uLGC63ISoq_~@C^k&DmAzLwX$|A31~zrjUrxn|0i+3$SrPP}jr1bgLPMxJ5qPcI#)7WcJ0*_fP`| zn0ILtQed;8y{{XR`J+{&tJNuoO(5aa@GyaZf#li^zB77$wWEx1)FkKm)%@UgV9iLG zm&Tif!l`IWeSXgnE1R@M3`YLLmaBtAaeUIH#+p(F%XO&?wDCq*Q#9}Hj2V0nf zZ^PAvr}9vOPiW?5D{Ry2pF~)*a=}eHka8G--d7^RYuZh&mMDF?6wbp9V8_sN_h4CW zW}NI>ck=OTh&&mY-bid9_!`c0cg2wfD^}+eWK;#Oqs5~mA=2QYLv+R^_#|-jQ@*}^ zJ2)PBjy>zaugoR`Q2&{RXsNsP&sB?_Uas@}V)ESy;kcHk!}OGie%b4Adib&q(`S3C zvAy#z0mTGC(?iY}tl#1~M||=n_`+FJ_UdyOA}Op5W@pj`_~l|p5!~pVAIFXTOz))H zCNB4x4lVt zIK7mQ;dwHn_6b#@`=ua<6LRsnJ7vbfCg|#3fk)N5`AGAYE9spNXDTa*Ejo}~MtR2@ zv81|utftM(xV0)(oe@SI>gA){X~C<)Nk4C4`;4h$p1Q)~R+bx)wZo$i&E1S#U9_nj z7&h#EJpY#Nq~uoN)L-x$5?iFUrEKnF3&z^zbmR)4(&Wu%ifZmHybw;$=k}&eRU><@ zTz>gnRrtJrF{G^bb;x4SHNEcM7vDsvJM&rkbw&S{zZ2h-eO=>;g!^WOQoGfBFykb< z>9zaC{HaZd`b7}L6>Ufu+(Ai1n>8^y<$2JIDYj+YwP1IuHBDhJV$Q7w`bw9!>UI9a zS957O{6aY=(Je1~!gPV2e#KeYFP!CjJW;{s=9rIqovGc@p9Jwv~(*y z%qku7(0i#+93ZqYI9l3=gv`r&enG$6%GBkRq?a~7yHAXD>hR#0zv+L$%?EV#3JUs_ z;l!2dN>l>B5YLB>&*{sxSnAo-gf6^4x0{~HZFIf8z>)hVI?Dw*Rbsx}o+bNwqK&Z# zwH`*1L8sbw3u8s&ZLu-lCP5T};9hS~D0jG znH&F38FL3`!w#!sf!VBvp{t@aMDmj+FJ`k`)&s=Sz8g~SHjcTuZH%1NHNdy-FGm9^N~DAsRLs zYi>k*JaU)}%qi6HHcnDZU~Uvs)4v(FDZWXu7gBx9FU3K+rFrzE#cmul2nm*Z+~ zm#2<5&ZEtzN$w-ymW7n&9Mz|FfxT%OAzPTIiCY{Sbpf`cy6($%iW|~=*OmT=CQ#{J zw>I~=d3VjEHoEa#@d918y2+r6nO{V^AQ-*&E%xJvK4IX(q23qv1H6m=Wg0@|aOmWR zDOh!;`d>N>;d^GhPOp$c~3H_)py*k+4%ybT;DFI>}8hgI+Kh#YW?M^zvCc-3UJC8hcR`ruhF zE1C<^-$bqp;MP>z`csyqJGHUtU>;yM^d*JP!R?9A5W0tcXinemVJPQHccp=0n$rFm z7AG&1+(bgJAl>mpk%B7S9-+ynsj}&bXXlMwwNB7ZFLrbMV7;<nvhtQFf1R80c0o zztwsahJ(mj-rc(VWL)8FN{_3;9WJZ(hh72c0-wz}Li4gG^IN`uQ_BPiu*q>@t>>W6 zbSm@95ev-{H*ZL~W4mc)Hps~o;s-0e$;&aF+s68-4UC^Zjow}@c|&ZL^RnSJcu zE{$po!8}+K65|RRV&xI5YlWn))jlv=cdmu&dL*bolMKB>E3~fq>VA=s)18Rt7g?UD z+El!uXZmk%FX2%?vBf#$-(a78_Lp}Gw2PW|;B8f#+4@ppLAO>k2@vCT>gH(}=ucqPm-g{4EOJxJROmMPD!5DUr@sb(wv_N8|DrsX{w+^K_CG~xJLqe zR~ZRg!aq+>UMP%;Sjg;~v-E{rofMZ#fd($A9ikdQ@qJer#;!60c35h!26EmqWyJy$ zlhV&HzXJ?2Eh=I`{U&sp9I?zWgkL23cF>Cw1tkaqXc)Ox@>2xNl#s8F!K{_`Fh zx*p+Dv*7;tQ6;P?2(H2uP-0~C-FhJ^$OJ2tQX$x@{Iwk6=AfiJ<8 zlTp~LD$nngr}qDB;c+yu^loB^-HZMg+@4`Ydtr%xbfZf(s^dPh;u1D|Y5(1!-+MD@ z`1L>PHQ>4)YQXx;gT4PhC-rycz}m*l!TA5xFY&*LP5<}!0Zu%)$$!TGQL(uIy1Akb ze0k&K{P~;vhcmz)4JM%co{uBVDi@rFHU@OiK|H=tTs(f#QlSb%j*_&J2x{~JnTp6B z22@#zC{glKla|9y%*s{s>a=pEP{md%`Ju|wF(ZxG((bFHDeABVuz6^Iij>Sx z^Jr^B6w>>C0IWj|dr1t6qW{RXm`TT%MhA?I|UVw%_g4Qo^%7W zvo6{3=bernG2%mK)FUyN%dub{;Ee9UBDl&y=g-O@9xDIjLcqg8EpD4`H;efF5WH|K zME{9mJ&>c{s>}FypJ5xEUu9YiK1~cm$hTQiS{9y5vm7@lx|=D96^8iN`Zp$C z%~wHmZn;qi|E2k~;9Nq@{OMrSoB(eoBKE3YZGmb7}z#5tHH^(o|IKpP62YItGooQHwAF(|C zq?x1DL8wCIN;pQOFe*P^jL?2bPNpn}AOh;Kfw>d?H6G z>r0xj@7!`q{D10^U&&hm&y*8;m~fjP4mscsb{p>rr})ePL)V!%#8)X{nZ9_ zoDifWn5N$haw1m^5#f70-hE;e;j1W!Fhi4^=#&IX`!ndM+?QlW0g1dz(lHMboPl_L zotQy|v>`N^D>{Bv3y#J-W=ay?wKSKAbxhLgps?{q0rOfRdasaRN|M(%+1+5!RpwM& zI435a3sX|N03x1DL3XDwYZN?6rCKyNZlt&F7|+*@#x;GsQ}92QY7y8`&8xUtGcAFI z-2b*zhmZ6-T7yO!Z1sr}rRX2cqFtpJ`MH?3X)6vD+HA+*fzRXwaKlKv#@PZh47VR2e0|pjwff-NK56p(^GJDQOdZ~1 zYF-oeRJYKcT#4LnNqc2{=1Q+Q1zb65Pqy0buq3%M*1gtg6i;Ogb*P^Z)`LAVfvrhT zEbmF~pJ0M%d8rQTR$=)vAe$|*ugw)Q9TRmz62t><;6y!U{B4CGJ=swWt%`fqzI8qM zMtcRi=8CyI+18D)30}iEK;Afq6ebk`FF0RKypwMWPl))_UI?0N5_0v z$S)}S+;PH;=BLt_t{4t%H{)b%(*om~&MAB90M|($u}$Z)?Z(t@V_Ao^Z_G9;%dgku{q-aH{;I%>hDIx@h&%GzMaIE1l2=knN+zT=Tv#2 zd+3j82jql&8=3OtSQUNRdF8&p>R<=l>EujPRiu5z=|~L14ukgF_&7|3M)(!7me&r^wsIfiOU+ecaO* zM1)uJ2FYeQ0O*c`Xm4-@33zeL7ye3ist}75d zP=k(b9l3;3--GlnMgzIFEt-6zr@2n@=znod!uZQFnlKby|GS(J6o7wCQcHFoLKw=a zkL9&bvkDEc7dfTDDuqpA^r3Fr7=fT-1abHG(H_xhYY9yO)jD<%+Rzr_g_4(*5*ob% ztsDud{}maog%vf3Um%em%Rf#-6?&&f&k_<{p|V*^{2fRL=$956YJ65_9Ph=k%aiXG zo0QKB$rqBuClj$vP}`;8c$7}EgDa?~WveF`bi?659&CfpAY!#8X0){-b|s|YFfkEC z{}-;V%gD^>RT|;*vA!Nxu&xwttvQ&?D5X>bL9PgpQo?DlAjFBv;Osp}s;RO4N8aUo0wvaSj92x5{ zR1Xo!4N0sUTI>rtalJ^)i!%S=5-C#`-SB%5(OtUVxD7PZoZompcxr>+_=?{+7r#jd zHM~)N6Txr}L0RAFUa~2eyC=Jvlsg9mBE+6cYS4O{oA2Zyn?GFnB~{IgTFPCIKbhwwfhMhOC2~SN};WkU5c5TPuqnH7y(AF2s6* zZdFfK1HY_5UKorvfGb-L?4B1);C{m17{-W^2w`fSf=MDQNh^+BK)kHmrsH`R^jnr1 z_CP|e$|}VNYgNL{V79jzY3AC?-xxVuiQt-i+Wx3f@(~BGbXOkMT7QU2Q{@XF^PyW z7HeC_om4Dl=tdQK&=k75*&`?2p(5JjvQb_vuKI0?o*OyE7WiEgUt$}xgYt_?BCy0& z_S;V#Sdu~}H1H;%G>(yH5QT(J2qn*`8WQFvpyWXzG$5+R_Qc7UZD|EU&^IJN#|LLG zemM0Y^d(tUTMnCbwyI&|6inv`MVz)UTO*nWf&BHj3m zYk1^mO-!pA@=9udOOp9D=m&&2{){|01z(WR1xz>N7_AHjCI>P%;^h4REtDGp+cO%s z2#O`IV28>f>GBwhz8CN?ngq*x3c;Oph30sNR_*>1XURE*`IYZAkRCED z@NFI8U$z60((0RZFr@7(i7cd@7?N{vh$Q~+QBJ>3Xw$_qdc7Ta`boR%l=M40#v@eg zN%D;hS>#Co(j>rO@}Z?1zCX9Z@V_z? z$76PPot((-ik7KWldAgfP(lgxwIA+79?Lo%a>N$Ypz*KusuVUN?f##220c*T8GQ9FXy5E*1LjxQMJpQ0j6pREL zT}7{pj4-E#>ThTF&nc3Rw_@Gs*!&XvLh-w2^x7vrvUwlgK>68xdEK%FruN)F)*5koGrmnJ5H-VZ#T^D zW78K}pM*1NiHI|5G-)=y+V& z;6ITBHqAzKh2<2B!&tV43FAFpMML7a>-{;1 z?|1%!xxw{h*qgTd2%-L7m-nW6Tt%*I7tCXrn52A2W1yN0uUkE4pr=Y^X+_Kkf#>kzI-pzLQ;m^I#fhc&TlFV1jZ8&rsfb+;KF-Ak8VsaK?$Q$|Q8QYJEWMO*P zv%ocJS%ub|oA=OcD=GeuoPYaqYV)I)tRWqxaU)brQtIM=v(i$$s(8)It4CWE|9ahD zr004vZpQv>#58_wzhpnuM;j0aH2c|b_+U!i80G(Z}YCDH(*XcEsAB}}_vi#>+Y(P}%dxWD4vx#^id!A8J zY6Fi!-{-jmk`>=aXglwa(d+h#wCOhIf6_IGUT2<|teuA3vuZ6J>7RJeIdB%?@dFM? z9M_)6^xAmr&n-lxVR>rzkCDoHZJl4gDG~jAc3|-WGwjs4Lz#rdp{b9Lfg+ao; zboO6*I^DdCG$dkF{}8x)UPZ~t2{>7+HH38P2t;1Z(Xciw^G|RJ)>2+2D>PZ&C(I%v zNXVl!(Hi>u8!T3Q-hQ}3KDdsxvv$}d$~o3}ul0rFD=k-4T}7&0=Il0VwavU*Q!WU; z>GLNqIFGK-wjW*26e>ORb~Rma8yB;zT>(Od>2mXMQbentniTO7jCC$CjEmiyaGz#I z6A@0U2}n?i1i2etUyFvv%po6E3c)sTE9eSU9?P}V_$Fl%ClG8p3rhW0liMsNtD5lV zKT;X*V<*YxPhiV4)+!6<^ZrqGv32a{l!+{)m7N$Czgs<0d>xP}Eu@;ket8YaaHerS z24vSN3WAn<|7~03x58y6bqSf38R;}vr$#u@Kj@Hm=p|e!9kDFjacuC%+1olfPRc+z zT`*Ek=HI(es%-u(is@}q{7QM{oS@sJxa?)qcnU<+P1x@8Zp@ZgoELq|onIK9^TE41 zrF11ko!8gZ(|do)^JKZUBOH!B9VLBc%KNYAga+!fyn+W6mwPhI9{YD-RZx0c6gWoT z_dMu;pOV%cebZa5{x%eLJ$8)1e?W~#8ci#iqO61ZY)AZ#KAJ7rg_9-qepR`(wW)^s zd_(-swh?^>!37|C{^)C52ziGl_5i@GziQ!o;QDwIy8W_6CGez26IyR^tiD5-7whm`w)?GB_4r?I+f5)G zsjO$cRBcAPu6z7FBU`WLZI%SL9?Fd7_FdaeEIqPoBlR3}TL1n+FT~cWj-OjP>roVL z_DI!;A!RP=PTSiFwCuNxSzkD|`wIj0UJ8|MWqU=zd2?CWN3XVI^vC{T zo5kMrhU)m4QX~vHTJTG@WAz*HL$Q;j4(G+?6K|vOgw`7L!l}9Tx&L63687s8Vi2Z| z(^R!CYu#h)o#}?h++xktA)uucM!GW5GJ#OJA&sHb)@H}+#>=p^T{UXWLA%>b`F*#1 zi>}REDDi!7BGlgo&vp)6>($Nhr3JRxyM;959C%pq253Hd<5lkc_2W$6ynd(Y#;Zui zqG`{MsyY7AU$_*g+*CD+%jeaI8~w%bm%MiA24k#D>AYLDYvNe3@bukD_3p<*bs4?) zm}|ut-|pet%k;8u*Qj&L8!%e;jrrHUPuc5vNRG4Hkoe|Rs)lu??K=bk$K!P0S{rgPi5_icK>c2WOOUi|#x!ZEH2!H$EopwfjR*=frt9et;I|yYA zPM){hieyUF>WY(H%&-fxcg$lzLKis_*t@1Kd36c!)o*Cg;);CcfSNNrZJ`oA^Pu!6 z!2+56MU~$QnUWssn$=4__Iz)Fg<;Q_^Ql-lp!k9@$K7n~@c4z~Ldn2*ZUY3$v?tl5 zi#<9~p*qPrq+#uRe(+~A{jX{Mj=R^966bV0rJJ+h)-3Mx94=`lXJ7$+q0Sa-v*)7% zWCInY!(1w!5}>pwfOa}b4wvw|EZ*6ucxw^8;CZ=Ldb_!bP=tuDboCi58+mD2{1{lV ztOw8#7!etFIt<_};TmN_6eVzyyIQ>(Ch?ani5qn2Wj9$VPH z^5XKcoYQ2L0|@h0d%e=!N?S7dF2i4;dgNZ^p?KqP0Lj{obMuos1I9{qqzFP z7+pYt1`@mZ>7=_P?me3>t}^d)vju)96|wz?txS@-CF6<4q|!-|&CAab3zdb!+_l}s zciQr1!X~}NaUZIM8A$k|bFI%}hs_t{k+p**C11PDaK0AX6;7a2Q&5Sds#q|rMi%T@Kwf)~>PgjN~BB`kf0q_32u(0I>A5gY1+Gd*Pppwe; z?eJx5fVau4^DctfU17oht&FQBTTA!lM%i>OTJxyG<1Fqp zA4M{~oRGjp#fbyE%Xt<3=)JFcU8}sMe3g^vH)*L$;;F~>GFMkco765ngWE%ecT@J% z4dzGtbGo{+&ZF(sV8PecXA{QS)mcz+_Po!BL6&J(h;E~a_Nv2R=+jNlr%IA{b#|N) zNaqDuV)euNn=9aTdbQH&5(!}KwzE5m(;bbv2No2EMcjhFv@e3#mqi+zy0H|$9($*sDg}8LvUyK)_onXtV%RXi zSh$*ju?M9a!4ADDKcE+yOelOe-M1y?8vB#KXp|xLDgM)HzPOO|;|=`+77!a7JHvRx z7+M=T!x+kVQxcDaj*O3sofy)IB99x7giVNw?pv5J1%K*uRg)+r;ql$WP1%`T)K2>J z@a5~87kZTzPbL;v`Nf^~cDRNrT5x;n{L}d%v0N+EU!?J~0WBk5^zL zHnjQ{c0iW$>?{sb1pR?Ve0T(!nkmf_|jD%gn6pUl-SbgQUPuaEl$Cg~XZ zx7S0wNx8lRVNZLn)O#8+I!#G>wVKX~gqETt`2Qm99b;^X7BZ86DESXx#JMi{5|B=9$DW$zyF~5U^BuxCW#*t$RfuMnzMF9Ui{)+2pTHQ^ ztzN3X`bB_EJ>XPb#!nzfXbEYEHKcLhI2W%*cQuYa-?&`QO^xntaoTsL%akoo^2E#e zPA}gcqdb@6kFD>o9A6vhDG=PE?>_%G4pRTqZlp}6YzGfGRfXKC~^VS5GD)`vw7m@sX+BYC;_uM)L3{S zK4BGMUNmP=HNuS0TzvfYmYOj9Ts){8!fh>52S%$|2XDCJYj*LgjrT4Wx9nrD>#Q_5 z*-E8V-BRsMxvT0Nf}tO4-JMV`$xjKYa44FlbqEzl30sg5!Mw#ZA__rX#6NrWiO70j z+zh32lLm@K=I>#4w$>!Q3*HauV|}Y|thGY6-g36gx*znQczL%0I{YMlQ!W+J1|)bz zRg#&B1Vc@|Ug(#IRwVG|mQ+b9zeSX9eIP*rO>TS<;#;FzN3K0ZwJ!yc0&H2OLhEX- zIYkO43zpLTx}|lAVgor`vm^@->?I-cB`^-lAjIEh)&NW>x^#YNNLb1s@rLE9++-#8 z#v~x~ge@q3S94W@(*2YgK`qb)#(+1D5Z7P)IE2a}U@Lh9PNs#vWQ?YkkxL=6ViBTkaqgR;T6?r6w*Bxk{G^h=7uEVHLuF^ndKt zt^c)GV-lJ20&=NAaHR5!N`jounU9aw3$*X}{i8wH8BT=UW9k=X8|Ry*3M(Uue*9xp zQcoON?pQO-n~2dGzuWaf4Y=&2YM98Ei4WM3#d~lJM{}|0B^#J1ZH%3c)b5RXjfDm3 z05e>N0wrR(mmAGe_p<^>d@#ft;#~AjRyaQ`s0u*UY5d}U0RsQ6(_+i#;B&A zjAdGvcj_}{r#{MRs4K$qra+g^oFf}OE|WzDtHg*l!1Ke2#6~`_<^sS(Hs*Tj;m^R$ z-G~z$B;71qDY&UxJDzk4KU6EszEzuIh$jz>pvk0nF(2Dtg-#OEoH4zge?G z2vMYc(@_XH?cql$6%VUNmM)!+AY{QAT7k>1*Kxf%rd6N@Wh_Pz{?H(*MfAdzSVMn% zY#SaBKzpz$8*e3prJ)dyZ4-wf8cJ&@DE7^mpaN)AFo|JOMaBNIcV_%W;-4rKmr6A|XoA3H z#?aJSqDl};q0Pmd7dFf`kaeEyAk?m`g5Bh}p$`JbWbO<&0;$$GuK?@>SLw#I9XVA@@;3w`W zE@MM0W#gT;_2wURYYV@JK{-ZK&NTVUHmzVsqhyzz)s(uYsRZDOd~6w6zYP=a#FkRZ zmNFC!aOnrf3gUo1Qz#Hkgw~)eGB!5Z2sdm*lo}zD{s%xCs@J)zZO&&<-F$!Z2L;Ld zfUIHi)Lb=9qWv87z=5o4{F=P~_` zxRt>}7vR(&o$1dCzt)u9U_}=Y#grgh%YoyZi6qvwaqxSV2ESC3L87u%*P)Gd9ub2= z2^o?m&C&vDrB7AA(cW}56-ao28=G8BOXP+i|EvJ0hB@%B6@@aMA}rgc8zC~orgjRB z<+9%v&P8tSH4LCv3J{R-4W!*;{s*SxW~IX=;FH{PCqdOaa?LgjkMb*r90U(zKmdhK zPPr$Jz9){#>zlygxnxo`5rvMZa#xqaD@XC2ljh-h;MkA2+W4Qix;PNPE1q8*kuP(J z7^}AEBD=yhQ+pLc26PiAy#H(f#$DJpoko>A4@P8Edp6qM1Oh^QhYEC869VEG_sT8_!pt_}#zBtX*G zHjOE`UIgoG8CuxZh^56Gj2G30$RzL5)lYEwaE8f(lxd@Br)C5n>pz2YrcYh$>C5%k4pkS4@Oi%1URiKq{t!JU~ z{*MSmm5Q>JaNLi$8hD|c$rJD^AkeD>-~*NLVte$$ZUqFVS4%vn*TdfCcW1UEGg5Tkq)D>#oKj_N?J?3aqbwy`S4GU8<)MT^)0borjXTyG6#7a?Kr zqFvkx`cjWDp^@o;aK5oB5?WZU9 zmZ6klg}ovzVh;cXwsB%WEJMQiP%9v4Wps3}1mwspgVL386y|aN#MOVPbQu@=inE9W z(7IM*XJ?g>Pm<_?bqePgETb=G>oj7Gnu*8ZBtV)3QVz(XmGifJb#+!S;int=c0Gz| z=rEV{J9}742Z(_U`fj2`G=N@^PBKivn*@sp(Iw5-LVxhS-2P&VtOdrNvXF`W65 zP8m>CLqNq$*`&Js#Nr-x;pn_3)CE*~MU(lt;8^wsJK2@8(97W4W{ImUE6t0}RCR`-$FQtMJMgRKknpxW?V=4>G-KaW?A4M^J7ydXWVwRvoSkg&)NP<%**q_#h z@4ixN)1u+LbDB6VJ9=_qLr4fAEaSx4MX%i8V(|? zX3u$fBdCcHRzDkF z0r4M)e%`l!9eWUGg0jt25tR8yfiR`2!2bgDDn^^(C(WIvlqlcaC7|AbJMX`QMzq#J zY=~BhRq^O4NI(DMzHBh?$Y#ggOZaF(xRFr!hKzJ&M!Lbsv72qw$2}bAkq!BbMY;hk z)w0d7>pdL!&W&^hFV%vkrrs%Z+DB=iK4sVEbAIaZy82^sgCyJMF;j_ z0J~xY`_hNu(uF~1i}ItBG4S1I0;90Q1WvNEb9kaabTCpBM3Iq%RhFm6ir{_+h{(-kY|=(? zErSZeoz%&Cz#GVaGhS$F2+$U!iD+C)g+*7%rgD=p9eM?=kl-lG$oY#g-blH}!@a^` zGE&Z4DJ}CJHy9d@Rn(^|8?y_(@M>06F5A8G;5)MEiHHL9LnUjmmr+Gn;58n@Oh zI(fz2uld}sN6MOIUOpipHD}>C%|6Rzb>cUzO>TCfBl~F#7eH3juo|IDBI>FPBV8Fp zfJ%N?4>(aZ!m_I=Gx?=}`FyG{=%~8vOjM&zHtI2VhaUuFw}I?@{O4kfa9Wr!#&muK zZ$zD8N9GTG83N4KL1K5X2#75PpoFxG&v4s|Nz@95rUzPVt9lf0ckI1g)PM#(M?fVO zvPT$|R$?2sRkRG%C09UuY?!JZy=HBT4x08yH(i3-TMKf&b;oOI9<#9@@`7lDX%gfd zhHKUK5rGU797mu|h-qfT9+;%h)?jsRO4fGiNzDwf_bw$?D#uEpa6U>sQZ z0}%{or=bMXmJ2YRm=FWUW?*Mr)Dq%%tZtT=0E5Jjx|-(UpSpTf5q{>My87*euXO+d^%$6PKCeW0jaD^>5o^8f-Yy`ScB~caD%!S+AXp))V33r+JIj>sc(f7i(~S zX5U)znGSVXOVewx+c?;-`i?ttlf0Cbk&-hh5|B}XZP;X1UC4A^utU-@uJ?J zXQ$mPP1*8xm-hPaO+HJwTD!ZJ%u(`qi!FsO$IGDHQg(MFf8A2?ZaWm`B=C?O(Z0o) z_``aC>AH`;3;^?Zo1ErLFNKkO;A)g83P!)X>YN^z!T)`Av*;%k#r_@sTC90=X=Ww) zf?BW1cwg`Jk?+q~KIEe@z)Rcp=eaL}ho^ncuk=Fa)056~`|v!k}| z_`uTy(udIA?2FOi^CM4ZUD#RlX_)XFGMUBuOXj|5^q2%sr{FV?a$n{3rF!}~a1_Vu z!-T!P>0tfy<1i?qY2f(fWp-O@%DtMO9*@!1^oipj=2n)Yy9n5OE1DO6xVt*9muNRe zpj+Dwh)N>0dox|a5Vup-E87=*D5pC^^inh(98mz4B}Tr!S?YTJ_Nc1%>g)X&x2qL; zdjBHhal7|>SK%4&Nd7SH2!WT4eS$9eNja|7idSzh@36dHK4wIC+xUa_mWvF$-PHPr@#NLW_ZbvL%~s7|m%IhbC@i0o z!#Q}X`KE`th~7?H;rk)7Z!ev&l#`#(i`5=P?Jb9X`dvO_yEyHP5-%R{@khAV`xe{r zIhsz*q185*A3I-sc-y6gS_cj~@2{Y4#XWJOMIRcUOWLhzhsQ3FVsV$NBp*eW$DA7! zclY(QPb*wL0Wr;9-A$eQxiQz@yG1ToQMb3?%MBLa(L4Lc*BOoOIiQ!kVH!(ILhtkI z=N=d;l{dGdEYYp?jSlp@;Fjz5+v3kTmkG|Ew=2&%FX7KQC0*`z=l<;8_He7W^KXMO zT0XaLpC#6NVSG`azz}fT8yQ;fw=sCtSl=b*Pj<^25u-12S%JbaKAxXnNWtLN?s;RF zN#ET#YBB3QDU_CP&2yxR})ke^Z<8IDljaB4eh0Y2w3Yp$CO`6tfRUm-T>h<}&l zG+~?5-$dSZuvqre#d#YwimQg3d6vu1U5a-ew_ce|Y&4zc9~>4V2exQ6=g*ca;B4MM z_W}+-d?H`|@;BQpOX)~wxxh#nenwi>dOi-`NESne6j)nMwdF6ehHa+JoP{M6cj}#2 zX{EVJl;gGM4K2nHO{GR>r>QZPPa0WemN}@8C+JU^I*aOMS=&5t|J4Hb@p1FXw-7y$ zzHIw5*^x;8x-7l)94JXk%_eWW1=hbfKqH?VS0X)$x4;5N^#U~cEMzy$J7wlPj-og% z_(4B^xpm$@wa8^9!SP^kgzmHg(nM?OT=lLukG>w$$g}OUz;uEnxs*K@P@K1(UCB~8 zMPT(Ig5afqm6V)w+~*&v#b^MR_T&j&CAIz?b{g}r`+dG?DVPtjM@Lf(rp~Vbh4?46 z_yD@Ox!I*T4o<=}{zRGL$ymrtTf8gYNE{s$Q3X|9JB^k&Z3Y|?;ZwID&Zd2g<9Ow&%R}?khmaCza&!IGcVqDrTI`#Pz+S=zIzWjnT7+;p7&4CW9+e*?FZ; zYh?V&#mfkwT4URSh}ig-RjABeStSUGOw|6NInpv)Wvd3u%#hPlaw1x~C}2G&Z)4jD zxJ&3M+vdD6NIOHeI)&z=ZjJ7IYk<+brb|`Zc)=6a@9~`b?rbZdFkD#*9u_Zf?{XI* z_|HrLj4Atj=t}@r5#cGVu9JM$NwOq_%>bh~G7K*i6b7vVb`zJ*GDYB9hXaGs>_$^F^tAUe>ktlC|08 zGx_hgU!$knqdjQ35Zc@P#ST?ERq~lqglp`52HR@^@SX zDkKFB$t*Q`0Rvme9-SEwizRKBe5p_fpnf#eDtyTlp)f(x_}Ik%B-TI9Y3Ty=M%GlA zA2szwOsq4CL~x%YWD}nld@^EQ&1=9UT-|?3hb9pziQ-VPm5W|5u(Ghoz%ULfA2DeL zLd4?J8v6hEsN><*;pxf!ND1}A;nrb=NH0iNCt$<`d zeQa9$gxfXyHT!XP>ujP>j5=aibMwHdpd^R*oYERsRA254h9jwPVxR5;J*HQcHUf5` z7;8BrGpVp?tpBX(LijIdIfUv3#&o{Mxk8u`jFOm`$GFJ1;b}t`-lS0ot~Yy%)%iby13n~0kwz|}&}Ld~bSP0B?KXOEak;wDUVb1u2+z|3cW97*C( z9UdcmI7qQ$d|K50i*06VWxcuCO)C1VNTsrcD1KmJ*21}Y$`N>`hCoq9xnU=Mzh)A> zaxtFy96tD%#yqkv0#cZA+$n*TNl(#EX>a)QzISUTl*k<`0R0 zolfLIIL3UdI}oxfnM}fuoaf%gYVlHY5pS(VK(Z?D+FbRc5h?1KNns87(CR*_3~}xK zB4IXGqa2-6nn|D;F_GCz{|w^;NiRCYCZzi2NaXcwq&tEI352^B<_@FJ)I&VP1Nyir z5-OBorj!Jl3&SmtHHhr9fjyAU0Uy8$?_a3mV?f6@!_;fjW&`^t9Kzn11|vY> z!Vtg*kFB%8lHWO!Rsunf81kW~q0-nKb4iy?E6V61Q;^sbGl$|M5vgJR{e8zF6t`>I zI;{g2fxk_t_&$QQUlu8DHb=HF!pP2VlQ7$LcLZ0!r3914Uz>g!CGn^uTQJ(-Ee_t< za>IyTgO>h?8Qd;`LX^w!TeLui4_=I)>aP0>RM$5L`w$wE-YPRx_Q!=lij?T@YNeqf z56F>SJ!6C!X$m;7j!tLGAr|Y${{uV#s;bhhn9G=`>e#Dy#g(xL5vqOvT0&?I3Lu_K zyZD`wuia^#DN-$1zqo^=Y&->`BHe`+lra}cC~YQplQrkM=^Dj>QBvo(aq`RiS0O%8 zAu2#xPE)<|_>e$)veS2-IxqFzk)E(_3ryrpoi^oi9WI9gR%V5fmvv&Y)8B71wH%B} zqb!5SWTP)F>hW5EaRzpkafKo&j-8@G;zBflq#PC*tZL~+Lae_=d<7&d^K~qvGj+xz z#K}hWbz`aJfqd`F#=0dnQ4IL8CodkriO>c{r#$9C#5#|_y%W?4Cg294RO06+i!r~1^u5>|WU|1O#; z&S7DIT@uk{0A44|k{e?2>*X@$cQ7YCNE;yig|Y%AzRwF(7XmA;2QEc>$K3+ruMLt` z%ruAxoXVm`Z8d4^^iys?3G_#FrcCX%0%g2ap^MmrGS27$XSg9)3e?)irf#olRd>d? zZx}?Sj*osEQ;Gg_aAL=W&J+oGeDbP!Wx^x|*eD?OEINARxCgw7d9bhP=r!>UaAgb> zx+RfVW~aE8V?nYDrs@mRT?0Uc_^Ue=9hBl8c@f^9#HZeGy&0BVm7-1*{RL#Te-ea8 z_~~Reps}9;NtFgTM@`H?m|m3}xEy_IT%}&SsTiUn$#0sSR%6(0)qQK6#a6p1?!Hbs zGsWtt!xH+~NQ0ereaLMU&09P~m33Vkpv!R6lEAqhhoL5+MQx7t;^hyhE*j>y2@!?d zp}t7iRA#^#KCB(G<78&;$P6VTisAOUbYVLC@n%2!we}Qf*O8_!-Z9WSJ6ew+l*@%e zC2c-Bk^p-3(aI35O(qzotx<2TmqdZC#vIdznBjFVVBumfkgZtHEvsSmAhrVvST;PI z#UNF101;J$C@58OS`R%2+f#?`hH-oy0o+`|{j(W@5XqN;mUXuf!=`tbVtHX}VT`5$%( z1-TnB^$dzuG`8cbnX93)rln0Lh-S)I55EJQ+W|hP=uU9j`5!R_i^az=AJD)9M0|DP z_&23@Xk?JATXZ{zQ7fL+_El1QfH6u9quw>>UCDrB{zkp4TCg4^5Z)+(c^^l@A0{K=#*XEW$JihTWTAw|YtF2x~r0l7Kj1?VWcU7>74R4xF&kl3q! zqG76W62=+&!?%0?;0}HyE1obG1@v1BmeFwdurzv-;1u=sN#rluP@S`u4bdEiI0|*q z<{tfR=&>uHOGLhp;-TnDrG&zHq2U&)c(MrUyofA4S}O&5f{w$S%$5hi5qm>It)wRz zN_=dEdWxdt@-=B%5)QSAs6rBwgheQCEJ#hd7LFPq>#Wrz)=Lk)LVB`N&d z4(cwvb5C=izzV`PB5EFO7l}}x%uBK@B`}J(g zT)b@GEqq=0x~1$V@pMhilllaAW!bym3GVyig&$MOXHINb6t;2Dz;fM5N@h?(p?>@% zb}TSLS+3*e``DEJn;2kW8$vZ!(AP!eA|fG3QOIER(TD=rBuW=#dL0FUNTR*9)vQNm zxrCdhETwvZM7^_gb27GxsXL|uHyT2>JgRn76Oj}r=6W{JL9P2KWB4n}G}IzgQG|1* zgq3*LG_?a-ZwXg`9qj|RSBSLsDSjR8#5Y$Ap)M)CKWBGW41q2w#9LbNZZ2*iE-A>4 z89}2C-U;}l*&vxrlc{=Rvl$rVQbv$*2m*5hfiQ=V7{NASQ~hC6M4?mm+Q)GnQw2gC z-!aQ>Ox5@3+Q%+#StuP-25wnN9aEadEQK~26$T`t-F9m7P`xW;(1=N*TvcV@-6)rg8qE?eE`f!vudHc6Kr8XrMRm{ zE@~`Sb(V0`)q(lCv=Sp|*>RQZWGgOeogZuTvDs^Ui4nN$_|zFlR*(ZtLQsM%stko1 z9L519PryZAKQYzaYK3j=B!2!10fr&#D5OVdpGWyIi$kVH7JnCM{d3&vyEqs*is3IT zFyabY=P`UWHa%jKusb2tQAjo7z1JNTjCy8&O%H%$^^gP=o@Fpwc(Hl8I{5;aAX$lB z{-skmgpzlW3&#+*5>9{75f5HB_(0JtY%=WRRh$#_bH36BmkhX={Rh!2nVnetJsHFl z4&g`#OfRI-Nb#)k9!8mXq8+vN0|$Z8PX+2v$$3oV%Fv{euEv%AK)2YPG1xnp2Gy9zht_)Zv?I$@-uGl z1w4S_71G{PW3gA?P`^3HM5=tOyTcD7pZ}exW<2m z80!vF$b%X)y=tEemh+x}H|re?gw(Ir4Q+^x_FyS#tP{~0;aAxc*L%~&QA*6$Ih@(r(__QEeoD9#R&Z3_W!@QdLcJEm= zlv=Y+mL)3CeR4j-8Iy`{ySEW2CK`F?cXiyG{xdJMRx*)cf`7(WjyS<{fA)Dsd@Tbu zGe>#Ze&6BpaD(N0uh-Um{L+v*tWyu@*te{34lunkw|SOydw$<9e_LP2B?#s;I~8m8=+C2COJ=^gg0OH0s$6L`u98MFOYyPT&~&2Bw*J_Y-!8ZpxV*Qj=Kd3X3)R$|qV2s&RO1pW{q^2Q>N8XKJDP$xl&&}(8X&@ z1;zLAESrsUlx_k(JPg*!zJtw!mSUBv>eHG8m&G-BR$Cf4RdJ_7U#|!GsW;kDhg)9h z_@zkICXp)gSTkWxqW6i_mTic`g?h;^ICqpmt6sfpU%Cx0D>((BPm zaNW$S!tpDFggoYLGFR{%TchDFHEGGvq*B#=P9mmsEa!G^T_Pv)vsH2f06NQn+xD?* zL*QNe#K(G=1)BS~fcjEN9QExp2!04I+cb}LA!(vxeoH`6F_@Mr$0+YP>yG}oqGskD zev-q=%pgzn@|)t=LN^adr<3z~zaG_Hs61%-FPO&3LM6$2*O^Ton)HQt@h&p77Dk5K zm``(1*XAv@R;o==S>(^Ib`bSbuXFg#*lhA*MU7#!EgjvwZCv*|_fH(tt+18khZxki z^EtFb_lTe_UR{$<_&(~uX-K#Fs@-L&kLPDkh4?9&yD$ai5LSdS{ZAFKC6e+`Q*V-VT~{K%rKNX5 z*mswyzITB};k=e+qkC@15?^m7Pm z!DlN3*7keRjhlN z=j-kcN{7yg^Kr%GI7Xw_>#yS16@5WYWj1@Thbja zg5;)#Q@Z;87VG)w3HU-K&-wU0507AD)-}qn&cb+(2CCCX%W4Pb*T4I)tTH%GYg;m~ zs>;lIJ?wwShlmTBTjMdcZ7$x1hw7E4-x`q;jQ=QTSUe^c=5UHyb=VrW9hXa--AJy1 zKXJ=0o-40ZM-<1mHClF?-}de=^gmq@mBS<{y{z$bu9N08kMlASek>5i%rE?&?yA=O zlp!f{XMenJq#i4s6ot1rZ`lQGw6CNlb@>2&|A-u%El_E0HMD`>q{V1Ae&UI$7N<_0n8@ok*Wbk9cynUc-00 z0O;CWxew_mi=b72w;6;@)ln876msh@ojajwynbq=rsb_HTs!ha(r5~8&Z)hRD<6#e zaIf{qv1)e_fnAU#_%8Bhu&L-4_Ue&vQEPfWT9B1EF_Z89-6_=M9A%am^pv>8 z)K{98aXOX3ZTu)xECi=~71?=W+Hr(y(Ys z06wkF^Q@VX+EJ>UbDVUmZst@l;k-q^SnN5Mqx^bvZx8vax{nm=z~}VboKu~}{9bk<-22&r z_u(gJQ50^o&1wCknb~+u@#jK$X?_F6Idq2gVZmJJ^dEK6WL}EJXuOX1`rqYc5t1=5 zoU2EAr$Rgb+jL`7a#G9IZuHyF$w9)Af69oPqd zG_d9$)e6<3s14g3XCQ}YZ62A&;JosXoSRK*mX=&kY6ofe#$WnBto1&8k{Zr7dQ3fq zGu8M0$WrRJ&!@|u37u=;{s;@-?i!zd9iy|&NaFJ{g$^3wamDs+P2NGVv(_dWr7rjN zWfa>Bq07Fo<}e){Jf{;G*q_DlHlV;gEAytK_43Q9E$aIiw=h~et`L6cTcB+gYe{~* z(J7aqNj;f$bR0gzsaqZRO%(Fc#HfkzWxUh+ANOEt@{fBMAN=7SOn)t0=P5*bUd>PF zy>zU7;>9J9)P7^I`<70b=E{^UA13%9=FODt8yI}$P~zVHW@BSxqGF)p;NoDS;-Erg z&KK}65D@6^BN7lK5+E!fB$9Ix`?X{*Azc6Y0<#`t8V3L3#vOD#*N|{j^~L*3lxbJZ zffr%U!|Dfp_&iugkZ}b-C-S$Lonk%&3c#)n3{ljt*!I*MfO76b#AXZK?y$ts8njoC za&039OL%XCIx0-NArpsj9yF@?B}%8aWj9liP_CQzon22!6A;Ai4=Edn&2)Z2q1r-} zdh;^O&zRCnh}|1#!VQ=1WQRgEhalzJwV#_dt}7p{+t+MGuWJ71Pj5a>Z(z0mo+Zk` zF=47=Dx{7E_EeaATlisOWgv5tD!m3RT5qO+x+-f#E$Oo}!Z>+;5OW=Rxw3DBsth1? zMZlmc^v^?vo9JCq4xOQ&5GE_KL7!`acWVAj8t-oH_56wbxwDt5LVpmjX$F+4%GenM z2{mDqu!gkvhkU3WKS&g!rS*|%ixIWqRIms+CnfjrLY>f_b@(A4#8tW{n^y1LWNDw$ zmJE3~pTGZy*xBSKa9H^cva|XZIQ)MWJ2U>DDhv@lO9vwwTSsLjNC4p5pRnQI_+Np; z;lF03#b<9!339z!IX-7{i*bdvLM#+xvKgSFqQX+ZAOh(*nK`D@4RJE1F>?Yvlro7V zX*HBsVL7?N{yV*dq9P-CAyj^6vIvN=yPwp7m{I(fPmEjk*ZufC6Qe`Q{g%U)!xXmz zFh*J9hf!S*U)pj=Vq=Pt`6&>$Q8A2hKvH=A1S)VjYZSA59D-7^bV@<9!Yl%E`SN+G zX;*^8QmU$#Y}U|U$mwZjlHZLZ>+ zr=qS=L05ZZ3zx9|D_D|y$IdlTYoQY^?#(oqgI}&`CC7j&Hd;Y0Rz{fTP}0JaD4A&D z8q=>oTCbp*CtZ}?L}$jFxJetEQ*C4#oJ;rQo>!?+D$e_bdD2*;Uz`;VDG#1Ei>VR4 zzVhT4iuvk?Vv4_ggk2mUs09x@u?!%cz4aHIY94{M8wnR&3!f8<*?A~RYoYR{kII+= zuL)CCEO(I|j3X=wa!~$6!(fyZ7V2yMm$;R26aIz;&Mj3;q!f{zK~gCLfKY?LafGwR&Y z>E&~Hi%V>mNV+W|FxmIe- z(Kt68BTx$l>&NH_#%ZZWbuFnq-drOiSh)8Hzl$TU%9ap}H7~dbwPMb2a3&4Lrala_ zcythRyoju!*J5T#H^*cwVW~unHFWB9N|sav(J43}Lkf(kr;1PjRm90=%7e;aw)Gh@ zpU3oY#2TK)syv~=blqs4{yc-=Xvi`7z9E+G^0(P+%VaCh?HFD7ej=vWEt)*t#2#i+ zu}GYE*#y8jN4bHIAnGvm;8zRTw z^)u_Q0Vf$cun;pUeJf(6wEvP@$I_AhP`>^hXe9=}1-|(tl)46JrIr7Z0ca&ONaZl3 zdJI|vjg-cS#ium_qu=scXakJ`JeG1OI7cryYz z7#Q?~#=Pyiho`_|UnPJEPpVvm-LPX9a1-ISpawo;|5OQiFTGvJ&XjX|ThshjWWN<< zsIl>#MlY2fWolHDKl_IY-SXIkBKR)~1#&9(v*HtwC6fbi3RrTKSB}nZi?VGx45ZZNo*Hw|;ah9J6 zcBV_kt;sGL{iYl1reCeADk%4`&h-t#p5M*F6D%v=_CyLb_O-n~HA^8*vACR29Y>KR zb<*kBP}d&owMrQ~9ovvo@0~k9oFHf~{I-5s{?PagMV!tUI6&kgfx1Ik*baMl_K!W0 z6ws(witgb)3WoV1N?+Nlpp9!<0~c|&9cb5$p$2(V+q&xoX=HYLAsB+?qe^_;YvkOLdKw%xI2SZ5)pH zdHhz*rkOqtm`+GSom)d+we)9xe#g@vj<6{wu^#@0@EfFA%;efrI^YyR@+#GA?LaSO zblBObrL9vO!P#t_VN7VDI82*uyf=)e!iT5vDoqIXrusk)c@ksZ*XmAmgBW~Kqu+<= zPL%l5=Rgj5PGKR9>`=_38h8Nly6S)&-270qMyASg&^&*Ac|VfZP!~jCS32+kq7yys3M!`T`@n9`fyUifn+Fv8(2z5kaiUag9hmays4kHJ;2c&kLU( z_Wsd8i!GGPV;vMzsuM<}voHdM04yd_IB5nh`ipPvtuioO{^4#P-~pk891nB;N9Y{m zxA9rBqr4lKRE-bENPiPE=P=7Hc6m?|Q-!H7Xw1}b0mchAFu=>?oe`8S0zQh;lyU&Wt9G7gR=U`Tl5h8->U6ESm7)rZ5E|324W@dc z_S;IYT!bn_{bf42_b8Dfts)~ps2mg-io%Il_EJb7HzN8DbV zx<0|sBdypevDw~B(Nh?Q$Qyhi<)G9@WyXh-+(25Lt(o^vKD$w^sMODoJ6`0)u@e5mN6jvCfLHPp~@Xkocx zS~Fm@Qgjup6%6~1p?!1=F~-SH*=xUifpS&r3ihfpfxj-t z3Kt>3LwRw;7^-ox15<}{9C%&RxBZ_l>3jm8^X3Ha<|K{!x*DSKn-F7^AF^T%KF;F2 z`|)4U$=;}v-;1)Po-=M#(FUDxnGU09Qw0fBs0sHQxS67gbmm+wI|7|2xYpfzk&@{9 zb?DaJ`?l@=8P(yt&eAZ==hnNd zAe(n3AdgulFCW){RIX=()uC-C$3X2TXo&a;1=1L`=f7R*C{u{s8V_E6N|q+H zG_I(`T(MPw4o?W=d^do}D1a8?404@SevzU1hVyHI?Eis5ripb&FXRC;^BpK~X!6Vz zJ?^UUbj-^04YoIw%#2V~)jZAWcFFbIL17R7X;}&%JG%O zK)hkV3lya#D99ZmAp_aRy`{=Wm;Qt-Q@>hQkqhYwH7!<&bn<+j<;1$%6!OLJb=%zB zRVVXL{9JLX(h2%mn3RcpN6XvQ8#AignEqgDG}!55fdEiDyUkl&5%b@grARRu1+ipM z;pnst_MpJ!ELiPp=xQukQq)0PhSdU+YU3hXt}}d^*Ox$j=RZK;+w+nNl*onkUV~9k zYWt|Q8(F;0*OTTr7t|`c~qhaf0b!WT=Tz71J`&8=0LcNWcvUF(dhZyFEWVNjp-JH5p%HX0F!_ocK3A)4Kd3sOo2 zcL#3Cp-4&3+=DL9d&t|(0(E`Hjm_ylT@ockDk5UV~krXo=W&9awFj>WQv1+v8l-VK&)lX_?3e zD8GW%iDHs8Oyb$KQ1McY)5WW}7>eTF0@4iK6;96%gc(QK?;~8aK9*#HeQphAKBD04 z2cntbGqmaxj&hC9Ly%WT8xHX<*X?>_x{kNM4HvLEUzcBN=2%Q{4i-bRr)4x9V=rE; zb+o?vWr`zz8o2aY0$^MpJ>}$GD%rPl;G-F%Mj+-A$*>vT)WsFgja41qZ#oR(>iTq{O#0NKGu8ZL9V@ulq_ARMn z6W4wJAEdogkYz#Jrrl-Rwr$(4>auNj*|xfD+qThVyQ<5!&Ds6UH!*L_O#CN*L}tW} zTp8E~Z32>qhT0ljIcq{w%PR(IJ5_6O0=2!Pb& zBUk~#%a5Ie(+|D8aUW~k0T7aYjZhR8J|jZ*YQB%du>}xZR`MZ@2v}5SZ!Q6Vw5S5#9996IP@sTS=A?jMH}d;-3F&Ml`OQA*%Yw|w&Y5iprk|t1 zK@Z^0sXOmPJ8H*PwWxP9G66X6kXgU87H3I-i24yG>``uhePwcU%s$${I^ya=U?0S{6>#m(qI!f}}DJrwJcR zETOIV*rBXhSt%R3+HdWeSMsI5i2sf>tpQ@)fqOF}Vk zq}Up-af)8euV{9WnMX28;C*zQ8{Z$gEReeAsO6XK`tn)1aZQ(q;48Jacfap9+{<1_ z4r}q%k@qg9$9i7sOde9(?_jf6prcTu0~>LE|9!K&UaOCtkniWDo3k)O5Zz!(O%6X?%p;taqsPk$!g%-3TSSAqdnE@`Ir) zK&vOAV9DS(uY1;IxjXTGp47fcgwHC$3@4!y5dT`vxo|u!+!r0ByLYqaR+eh2N`8wq zM3~jboZ1FYeL#meR7=W;~PS;kSr$hP5#`eWyZKFwRkmO73SY zEmH~(blQn-mo<6u%kj-+)=?q&E5_pA0WR}X^VRp*eE^N`cAD#&-`}xm^`lAUc=)@{ zBxPghn)R`6<6JR2%N@cvI6WiE^1A&l<2YH4CBaXm$AY(g@rz@Y*liy@R~N^yeh?mh z2CvQ)hCgC!z8itb3t<`zFOIuBdtB5XZp6H#r$fz|E8|AL5+&kBKO$DqZ?2Ye-BeM) zjw5g=>~%=hB(`DjN_U@9WA6J6mtR-e2zC7;QZtP^UHWz=*4 z%87Q7q+#3c4=EcorP|l!<%my&l&Ngb*MsI4Y2ijcLZ5<_!%y;%)G>EK^>= zUm-TLy;XzTtuB4clTa>YzOpAPF;G(&S^bkPQ|7NaQq~C8KzJeJ1Hh^(7`DDNJ@8 z4^9iVFb!me=O2;~6a7QEU$9S1jIviioHV}LreVrEf7e0w#>71c`$ww)L=PafX(v4` zZm<1D=tGh7TwYy)vlOoUjy>XjeF#p(XMA)FvR(!lX*|WsZC{i9m z(j5k&t&V9@QjO*pklgA*_=7ww$@!oiAplS$8jU21G}dc(iz60|hi@M9slV^gB@dv9 zITru56it!#@G>CM2;UR<$1_lBhMJf49*cGwMb?vlH~$|5X^*eJ$iDE(;NAyZ<_0ve z$FtqMGLN1=&?6-Mfo)K?{#nH5&)vA%e8##+ea*k+rY5^5$=6Nqe~2T?n2Xq@uFg0O zJs`U3pv`*px5z`ZnfdhdJg8HrjO|4_klS7&mbyk^YbcCJ(02m3{%o6Q99>u2* z*~(0T(UK|s3VgZk*bl)36xN}c1KcB_K-T{x3@Apx)r@h?Q z`@?mhk1TX|UV*3oEcCkq3jKwwX`z7t#*vNi|A+GWzd1(#PlLk$s`LLR;|Kx3H~KgK zk1{`iaipQ5`c6=@$6U~s*Irr#l@U%zO+SeyrV?y0i^{!#K$G3?Ck888T^L zXK9x#(~_3lZk=g(m2R3*VrCI_*yEinlkz$93vh_Q|Iv@1AUEzf!0U?Z+H32+r+523 z$LX4kuUK3zC|@2=BsmvU1VW@ljv5s#b;p4VH9u_U7mvxD<__v!uuUAFgi7$Lg%G+} zC!)+qFw!iEYa;-1r1AxNyffq)I`@6Y5))NNWoMCC02;UFFfI-hxrQIMKu=j9<0_I* zI1CAkgp{&wA;p}~bQDe)UV#bG&bKDdl=y@VWDX@PJeL<*4BoMR$Cqa|P6!5SUWEB` zK)DSoPYpU1nH6O5Y3)BCNAUk3M?ts%$kCZ-hLA28=2Tuz@T9a5a&|KhWV}^LFijqR zdNnAkBosAJK0g!A+M-gNdVvWF>|}Qod3G9hT^rlTvXVlA0*Ai2)c_v=Il6H@^q25X@zh}4 zhOYJ~AQr}QCY-pIJV1mPjsvh96Q<=;)JzQRQh&#J;xPj&-cAsvP!bN1Lt<+G*+m8= zIb#YOtCoD$lABZf*=qznh@#M-hc@c)llehKu|cdXb>eC zq_RMe1e3P8AQoI>t01aU}} zA17Hshr@4a?ge66@x^mzS;TC3Qd@|)zrXIH$}+1kiUG=N=6{f*QYOtyoaqih?q7U` zm}w+M65hyD{<0AXzQL$OMDTI6KniI>DWdIG@stb_Qkx5;<4tqO#q*dE%!g7DKaHwH zZ+)c;Mxl}a*k_3XHI!VHk?qc!V}NJ}Cy~OXFq5Kkbz+4hMl^+;Spk2aIv|rky5G{wL&!l@|axGGS}Ql!d%mw=9n$4_g-2x)j>tlWtXGjP5I@ ziLCF7i8T5ReCMbn`iQtA@EcuB6DShPz3ZSP4;3||UsN;32`T2j2$^vWwxrCFUTvQ@ z`Om8*+w{UBr-J(aM>O`a`4Xo<>2SMf%zgCYFYM!aqn%>M=|7H7=|buB(=33C1W1S{ z-X{(sj8R}2&Z9~Pp|RuPnjRj@&KvB3Yoqg7MvNSUE%z!4`r&npBb}*#O+_A2v~Kai zj?N2KL1^=oyphD1m=nb9^MTRR`;8K^C1pTmoVgthsFpma1dmO@x{Salcv;&fWcr|4 zG|IuBRoU7qH5~MngWfx^H#in|ddk7wnz1)p7k6rug5F!OJKYvnyJcGI`nt`lgP%*D zCk^oMP+)G3ghrMV>*37m;??eeD;Eq$*a8+Osh%UaNaoRo z`^ny=66}HdzAj;Vb=*vW_()lK@Rbxv{bj2(>q=LoP{Q^%bZ`UZ{)FD8{?II(mt#+} zeDhYVg&<`MbQ33|;j%$2q8DDjj8qa4J_O%sR zz-=w`5K#LWSo*kFIQ60FM^!ld8Fq-%KN6b6)=Odwh`RI*{rXKA_}0rZv9J_d`)KrZ zG)_EgrJZ9|NVzv%P8-xa_Q?2;G%288L)hGd?NEAmeeBDHTL<(mGte#((s0 zQdd?}Cf-y#=6R+@+V|Oqul8aD;Q$TwNU6mw4A(;1NxebiBiJcRenI*l#Qp;v^;#VE zXhVOoi+~`jMRC9i+X`!r=Bq(f1$<=~%5BKB!2~%b&SESN$O!})+ImY!!k+Jt?=n?I zWd@JWhGzy()#2MA3lAc0kYYEW*1ElDmp8};_T7?y!4Y;!e=Ljv5r=C zm{QmjGtER?W2AncAwr)Of!T}N85=>1+=;2-5*u(YWrH9HjHxU4^F)wiS%r1`EgOWe zOTG(Y6Pp=~TnA={tk##(CKVCTk3gAUhlXzzr-3Bgg3$C>+2mh62dVu8iqvs42w<_* z6bN2Nr@xSgv>4)$#%eS!UMVs5fe+g6Iak1%u|PAl{^3mI0)32H@!;jx*D0Y27P16s1T~FXr3V(@rX&J zre1*gCwwUeiAGDJ>MB};DOv+S+T_Em-@~l;WHx#<8aj)~DEFdN-;)R}zH8N;CoRJfU4&>5BaOmzR_{jzMF zaDDE6h9JlcEZ=n4UL@`hapsR4gBMe#4_eq>CD`6M*j^^sUJ54PPAXqxsnwkM8=Sm7 zir>XCmC?D$yQy+FgNNLNb{MqBZaJtTqY(x_~Dq8mX-&zrAqWr z@1PBw6zEz7>M{nazbR*lltzE2jsB)Il9UAu zGf7!PNmLfnl0EX6iv`+-1&72e#9ilx8gqvcPs_rlF^c#Fy@`q>_|^Q~#tzbsm3oFv zIvxiXrh#J%=w!q?vy*;mQv;8&QHi(kXMcXWJ=3@F8B!#_oHj-n7@rFDn4m_sB`(jU zc2O66C7*x*wp{;af9KxOJ}tl~0Fm zrBvvmUx}qy#wFi6kov^|dSbwz8T_-gje@;M2Lgg!Qn-3@xV}+bU7=hbEz`ShX@l3y zVJpmG+s5#)wOF@H-YC3k=G+5on0;?Wl zU$j3T!P@n_pzEvsyJey`&YGAWwY^QUtT?PkaUKoTTPK7duO9101UW1YcxaG1suDuu zg-K~u9+jU+wLNPQ06JO?nA)Qqqa*4*2oCrO5J<}Iq^iF7kgT}~{ONHxfU8Vn7|cFl z=Q7;P$)sV|&k<`u?*z#Wvnlv~sE0}QkiqF<>=1(z=1)N~d3e|ljg%z--L)e*I6R>LK?*i9yCqUGiTD)lz$Yn>gba&inKP_8EqB&g*DcNAayG{ww6u;UhGEDT7=s zvXr!>cS9rhSf}?0sq~pqhHskg8IAaiGw+jy^bAUJC57kMYdXaDC;T0c#14$)%2IrT zCAw{!W$&vm{GEj4N};MMhwS|ARnL@-@eauJ5knTUSuKYIGHZpzA%MuCzC&vbM{j<> zU6Wv#hBR7ztfNL%nPz|2Meb3+b4fqq%wF`-Ewm3VuupE@CyK}+ljtT4K7a={KmZHa zj|udO8T88x5|a}yfKXlp06GG-a=->=13d66>IA1`84I(ZrrCv`jLN6;9Vvto^p`{nh2L3?vYCk8{6P7=&94&{O>3lA+4I49|s90 zGi2qH4a-N3#AAE(v_83OawIW2iFY&Ua%v62+Dr(tT0eflbfaYZBp=WyVbLgI z)#ygv=tk5iFkBud|MCtg1ppm&THROL>mzU4m3|+>L=R98SPc-OdrI>cN_t8cjH_@ICT52w*E;LjTl$vV#0WN~3n3AWM@=i1W z(M}-O^SA)7B0&6dm)RRF1>p<_Riq1+GT9Ac6TQQw4}_LIVw_5hH2X&U^d?9t646M2 z|09>dGU$-VUmW%#P&OCm#4KA5Y>U8|1@7@1SXtHL_EJZ3UH(359vHH7UoDyEV=0D{)K%EjaS7X z0>fD#d9O;KT+#s25J~Y7oVo8?B$$DEQq5dqsx_VO5^CxjJOJltw61p!;dL7Q<&3!E zPCL{w!$Q>bpXs`6Jg!x(>Ip;5x)fl#Mgy3xrM+}v8|z9QFHL;8HQqM#`aLvT z>FnlK%zoakLJ|3@`nFk}?0=a3Ompu-7`@f)_7Htt%B^<}=4&!O|Kwn-`+mtD-%UYG zV@mYuxn#@u_0~P6^GGGHOmSRee$J4;e-H)$SQ!oqE7Re>4*T&I|OnSd%$4cCihx>g; zr1M>erEfw|41f3~lJ7{AxkM?PX$`xbEL86Z zo3#5ZrI*yBT_=ahd<#*>JU$=ViQf)0%H!^%+x&NTx!oEmZ$i(^z5(tx6Y?`Dw>YWZyG@~QFRJ=- z+snQQsrDaJwLvNGUEa|Pj|=H=dcH$7mc2PACx>A^T{bIbCx_N(c7N~ochf&mrh!e% zr$%T9LVF*!Qj0$>qtHO61uhTaE_b)D5pM2ox7qCJW4&@x$)$2RV(;%b#Iv2QbT1B% zu}ew6llJ!UZdSE?M`XWBcXHk6!k_LYyryy7S(he^yO^E!Q9sOZzqfE|ze0E32VjyD z2nSCXBIRYIO6XzeP%Rd#m=pKH`YJ7TV7cziY!>-k$p32`cY%r`NJp^_9*qx*+U(CD@+oyVqTC zO9%fz*bCHPzpvyBKSfRAEep86^8xk_eLppH%Hel?>Lp*^*VpsNt@<|JkX-Y*zjuF+ zyo_v9d;vbZs}X)_FXKLbHcF@W(C3-Am&!qfV0wJ5;ntU%>gsWSW?#G0*vV|&@fX%!=78b&0`ZBwA+6VLL&6OW`~t}d$+5juaS6Mot8hCedRh=B@>5Cvh}yp3+74p3B@2MgV^;6ZdlxYwLHXr>*tFf_}84nst*XTCuxY8m>DpRGA)_RTTyKrQW3|Z$sWi*ld1ge0TuYuxt$U&;(e9W??vLY-zSQdE8e}n9?j~Y6>!p^~ z)=SNM3Gpy8Q*a~08Ur*}3(Pek3?i+$Lm>@uG3Oe}j;3%=D11#~mcKcH&gv$Gm4csWB>x(N+Wgcq=T})LnJq#FaZ}BI+UQANjrICdm2BpVt|MIP6 z#*&NHOVxU++EQzHb?MF6Za^_Z;%rw5G~DFUb3kydEz&Hyuwk{SKU& zGgKa|ygq&~_R(Bk#heXj?uYNu2kGuAl-lXjc^I(Ul}|8?g0>!EP4GDJ`0ts9m~X_AGxmyem9Ev| z*(A{?`@sRYc3K)e5;dQY|Pv8b-&@6fFWD%b?$R4CMqu7fi~h z{gG;p-B;PMoGb5-#u=jGje1Y4M~3<}1Tq+G2a{9Cn|%#z<{LeJvw80j?>IT!o)3v9 zrMNkM8<+fl6aADP($^x{Ti>+?YFjPBqI~E6N)2vYokQy1=|9JLE`ybvK%@HLl&DoYedJD-p86FnUvB)MMOJCTiB>pI z!$w;;SI!lafhNAzhb94Kd#>o1G_cu!@(gudj?Eghxopbi-onK1uoSS@@?=|iv_ znXu;oZD*e6K}8rHN!$aFQi8ypErN1KjLJCF0&7=9IHSg=?zDims3f5>1vroZM@b7w zAbCL1Q!7zNB@o{s>mcMO{es35J0NQV6&5&ul%|eM6KGo%Dt1Rp7L_H?STt0Y;5EUU${EJ&B&47tX1v!3x_YNev;*^xQaPScPEhKWKjoJl`gA<^S zo`Y09WL|P&Lt#-MS9b1pGx(O0r$FQ}LvY3AXpAlVbQwr=MXr?`*^VW@`jNiR7O?jk z>+}u$zx@tcy51Tu?~dnj|9|`r|817m|Ih5>Jr2z5-~7L`4`&qBX9b9HWN38CP8(Be zF==(m6m}69I%LYi1VkinC{?>KX z1Qq=)huZVJC3|FQG3WG!JKcLs>T$o-ecj{b*Xy;Nnf}D2R#vv4T3$eNpITsDJ~56K zdY={4$Yq0!F~OC^9^ZYeiUu^EP$SPKnIIXN;Dyb7aw$t`mILZ zEjD{5!RyhV34$o!La|>5=<}#!Bd;JnS@Y=ib`{g4CWOZp-g)X!qev$J1uQBG zLM}bO4WI7oau`<{|IE9Tt&?m(Dl-5oo4^Z>=y~Fy#{QOw6%p+iGnpC|lAF zj*oX13=~-}$~3)EH;KrxK@NqRL#P0HD@xn{VWi0v<+Tlpxz!C^Ma z*-Sj`v24W-XZWwJ z4dFVj51ZLw@{XAH&EW}j94K)o0dSgA48ViHSuL5B1!MW9Z93;d{mRg-TJ#Tn`BV{( zeG$?|zd?1{NDHhWZ!nT%3MhfbKsWGzDRDx4ma z8=*F&R~z7!hr0JmVvJKC;1z;$iad1=KQQ^%8vsW}o zArHUZvE>TC&Lb2TAMekk3viR$;Y-MrjDm1t<%Tk-sk-;|pXP<~HUPfg{AD9R3j`x; zkdORVOxgxxVU=FVs8c24l9{Y!B6+n&(db>Dtc8H8UblSc%9zYGcl2F-XSh+i4`;y& zMS~>nHpKn-_sDWWy$gAr9{qj@dgoLm4KP(ouBTLu6Qv3~($W%e9nnIzF+9uxl{9FW z0d*zzpO&n zKloFFzQhD)TV?vpf%TD9IVx&=m+-KcVhu{G-cnh)>VOBGyB}2^|oo; zFZ10m0V}0Dk3oSezF@T=(0T&Y?szIU+(kPs8vW-KhCvNM>$o960sO)uQg;H}wI+Ag1LB5>%#ootb|If&D|~nD;%noh{tZA| zH#-Bwwa*+vdi!!hIahAG9TIK3MEvL);tupYC0#)hVA$Ccc344(V@x5??oB>aILEfo zkXLFNwnFK_43C!Mstf?lgWyn)GUydJ;yb=3pFB=4aT|jLj621RVCccj4@rFK9R`#a zZTgKk@_-TYGLZr4%TS>*IZ7C;Md|>w*;XjEq3+;LMobPUA49T@2Nq1VVfEmlI%qp& z{K1MgsWRBdHmOzE%3X3IR@i>I0ZR=$4ybBFxW#D3tzlFA9{+VQM%3ruMt%@zUK)v2 zmFUZTWZN$gNY!$pgC=u+@Qh(s>WJE$tuQMTf$VQnIAd_l+IKgv>ImmA=57;G;OBer z3FiN@752WjHYrFT96;&^VGp7DV>us3yw~i@6!j6N;j)TnZ9SS%zU*;F|2(HoF0FGo_)y1fh4_T;xzE+Z*|0fUTl6ax`IgJkW;+l=dHZ?b%B$B7P|J@H)D)NmNs< zZdr?^SW0mvt>)6E$mLn;(0S^VdFt4C>YRD%fcXlLVpR$0O3_8uEi#o;QKP5GT-8_3 z&@>Gx6VvR^SsHVwG}(NGes1Wp0s;&RN{kNRE-tQ*)kk7=-y03AE_xH9a;=+r;Qq$@ zSYD~${pVWn?o4%=u=phX&RD$0h#di;SgX3;2ymT+DX_A}{p_SzPqw{2n3<7+ZwR zI_Wrt5`j3n`G~MnQ-x!lpsI_7YPRT-m&YPc{Q;vf|w2_+&4 z6=ESJqWw=oab?0tN@}ke(ozx5*s61ZHsk6#y_ZFr--&vI4bkwc*}Lv? z@O+n}DbA%?uNCXelA}u@pqF`}mmyHAUQw$M zs5D?GHDDZ_MAk;2itG}3uzXU!Wg#)f0`z2oyG3zMidknT0zbmS4u^KphIYSyh6xnF z*Di8?FuA;-oZV2L-B_L7V4vM+o!xMs-FTkelrHYVwH590)Zpqh^u@F#fyQf_NcneB za$c&vU|qous_ut7sBor4xcPd-ZKD=l-DWyU4TTUBU_cc=j4C9sf`JjJ?xM4@BiWHT zuA&p-KNYq8au3Nx31J8yaQ&I&Q-nJa7RgYrnML+WR+O5>pg7J(Ap!M~1TA1e>U9Nw z0V9H6eMgUUC7OywW!=->YB2a%Zem`)eq%|Q`kc$xGoZS)fyp4dmCrdHV#{@S(Zl%;SG|acS^D=Ch3iR?7f`4AwH=H zuSn!)bLowRq&9O($C;mdIfX-f3K3obDN9i%y1l&BRp8?Mc~yD?!Ae$#rh$EEbinFV zz`ZHzG$^}lY|$&thFwAfFF2YjQ@^ZCDPB4&JY_09Gt0h&RIU|4uaH5n;;-IO10YZX z1lS;Xiy%M5kX|y0Z^DUh(unhnRX_tV(SSmgkjnbXAXSE@;9jGoruj6n1F@P}RfMO! zWuHEDf7Ng($pn#8Nm`ao?#R{-7zk7{tMFb(Ir>@7qJ&t95(W1c=W~n5h``xo68dOr zW8XQFjZUth`gRm{1oi0sAwiFBUrPv1M~-=}Mq|fdzIG#zgG+ z(XQ~GvB?4xI#k1bW|omY^0P@f5Oced=?$H1uB^$8z{w5d868z$6aSJU(I48$>3rxd zYUcJRoL<4)UPavRGA2(gCQqqYhr>|4m_!j1+J-zXzK3Hn6$_Tn0`37y8X+1nL6@qP zbJ64zM&U6UMkKLHjkG!ankj-EJd9Q;E+&h#GA6){RVamKYc$arrc!{Jjm0*R0GK~5 zQm`yi>H(#oSz4JtS1M2j0T<;F|dT<;K(FF zHxDnn$uM^>k1*VQj&Z@pWb4A z#$(t7AkY`&cn>p*qQ$uXWZfy@IveZMhEaRRsbpK|LWzWSmq0G&rw--P_&`E->vSSb zi>fJj!=zDvWv|+=g~>c|epxs!XP5-^n||-Z+-JojUk};mFDMs&J;t;@&DbT9^2A|i zCV2M~^<=)k^-*zXrSA=Otkj zsX+ePqow-1yiY{uJ-qPAE7ghe>DhAvb}umrCvWs_f*-lfPBYevzubz?T5;87$+=KL z-syfe(evhYtf$l2++!~+dUrrK0o!wkkXWMoN~N1^Zf_E_SN|8;2i(l@n+$Ewr#sV+ zv!p!fM7zthx<>k1-Fw+8`~2A}t8X2{75mxl9CSM;`5inoSMKM^^*0^o0T@Cd89yi;k`Tr349NAb<7NOr9CWa9UcrP z;lsDA{9<@t3EQ^U%{ezkQIB~*Jy0W z%kO0zk*d3Cdi)U`hVSb39$OsencAT3%iCW6Zp^2_ac+&ocB7_b9HgVFpit`t}fTc`p&H6&tPOGxt`ngO`SHN zu)1b3jXdQ4_)%FyZI;JR9+ zT1@(-e z4yk&|&A;0GCbT}@`FBuZs>|7Hz53D}Px!99`9(lS_}dh(f2JvR^Ns za6QZY>ubdFBlWmS;ew&e+41PUl|Vn%$ENld+TT-$uh+i{lp|kL^{Zc!$`j2q+rD9U z9~8HKjNeL^$M(JZiBD^7ZP}#XXQ2c-pC6YLUjhKJH`eWSRpl-1lhXP*$hg5x(r=+$ zgxiMdnJTLe+KCA}V>6dA$;`>&#t2Q_?YoA_$Nn%s1sYQZy0)fQj3$MCgtIRXpXk)h zWHz?}YazXsB^atda!T3e+Zn6ATn`OsBm$XQdAS4Zl5Oi<2mXLQ@Z*E@Fb4Ov514D6 zSey>z8wE1TVVFC;@4nl!oUxbFh$1zKiTx?UiZ# zhlYhX*AxBYoUrVNiD{(}dIasyl9&0t_rz72Ua`iwVfWQFge!Wz^kc(ko3HxKBcQ~! z`{F{9nWd+O)^`nT3^;02^8?exPv(h1{J-tKJ9Q2WJYVjQN#GWnwp9qr0|+>*Q(DvB5m|pk}c5C-=J-6zyl&x*Mz6QK3y~CB19l|UmC}0yKv}~Mn`%cfcY+k6v zq8!6%*UIAS(7n_dE)@8xXR8Wu;7Ub!Hrc*ni+z_6QSEMeviyX{c{*50Z?=3v2vt?q z&Bf>E`FnGlz*Qag^w&82viWhZjBf-sfxcAe5&d1YGuNE8yxHQ^RAljEY#*DTt7;nK zZ=1_t%9=UsN)Hdy+1XewZ1V8++fm0?QNFw4OSxlVFI&}}tUX`vDYHvXI>91kgmve3 zou~KD+Zlk=nJPEQKLO|+F2*Y7i-g!Zch|2w(??d$5O;(w>5e_6emJIx-p+OAuaBjB zBvhwq>Opn7#zw|6o69Hrh1FJxmtD;< zufoEH`HKF`)tb}v@hjg9-}d%BpoiLX!dKqG@f;Lr7f)#T#nYfi{ef3V+j#%eE|u=8-3NCt|);d9?CuMcp-!iFLMWp{h-(HjMLn@o(q`X4H92r>`iEK9Ywq@ zua4{)xQ$*ueLHZXrTFwYzQ-NWb@l7e9QL|4YI+^N*TTAO{O<$9<=Hm{jc=x)6~BBK zo~<`9QRwfWsaLT-@H5|4oZrv-j0`jrv^S31 zP{74be&43da6i-+e9W|Ij=F&BuKNG@4#C|J;CYHahT0vm-NfqVBKNGlVeT(E8O;+* zaWhe&E&fSG6?wE6{jIGSZ(s|dIqJ~PH52<)FVu1xJ7>}5wO5>;^Y-FGu5K~12;-3Oajqms}?@>kE z$iQlRQflEOV6pxhc;sXTEa_JSYq9X3pMzRbV~SR&V1H4JbghU*3|rA`1jB>or-?`m zJ3zhQMt}a7OTre+q#W=@)F<~fc>e0lJF@g z|MU`3cn3AaB!&wRscc5`+>znIQ-COhkSQL}eI9m*5D@{8No2rdkW0#%kjes*5D{6e zDa3e2v9O8%@=L&>a!^9oU58)rUKYp1yx!5>6F(?~b zxS8tP>08=aI_v+(A&~!DFTwQxqCL(31~dCN|Boo-Qrp}eMWQtY43nhKpNfes4cUfq zxZB`{9^59p-`}6G;y2wI4T(&S=9odCWxob9TheSlsURdCFx2Bc_<~WUp72TjbA}*c zlD(O0wXPJ^v-tDw%F?|?Cy$fa7S}P~@#GrPgM5Xe68ZgILc6zxCX42_idm7ot;%_f zXCt?cE$gXfHqqZacqp|}jiyuXMs8Y-sWWFS7s%~$`B_X2U7qUUDyVU6L&vcwkgztwO=~mYY>V()XId%+FD*=)K3ey zXmU2mJsOB^@=6zh`zxS{b>jue(x%u-65WisqEWFjM+`J$!W3Ya2=J@WifLy%kFjb7 z6xIa$6lb{+nv?+}WJxq;Qzr3?Dt2dQo|{R0`YSv|z0|VJ zm@sio1tlBCah^-k^GpmVu`o-lcIJ!m%Q;xAc!juO1|l?qE8;8Dh8ce*4pu0$jm9?- zCP0ecvL?Yqa*HNjVX7tyT)O((D2N)XHrG*FFQ>U!(cQ@!7Qh#^7FN_K*FvI6kW%Wg z1W6TsIx%I0yPSE{1S0y1lRODW35`Ijhj{*2G%-B|kRE@4g-d*`aA7##s^@zXk(6AX z^-+w2V>e<^3j#0vR#yp(JzRQoE`*}UrnC@^3PvF6#@&%QU-5~qoG=4 zvrI!~u=a_pp&TOI^|ZOk%5rPikZA%OAPfVggnHk+Q1e)U5}EQ1T3%j#Jh z<15?!B8ZxMH^!B7-usTx89%U8D%(-*5T-J2%JY{b*lO1!)gmJ>gkdpX+a0hj2xk-NxWjjq_su7ND%MX&_^ zwGB~MKb+q`bB(Z|72MS7qwIv1_i+fp0H68eL#Iwq_(RwTwG6AAo&Xa4^AEV)Han_8 zeGuP3Ke7n|0*glE1rlQ4U&C}PROa5;xqTdXS@wM$ zQzo7q1xCg#gI*EjJft+s#wU?&Hd|CV*3%j^X@!*KR=U&G35u~w*Vc#M5B5dU_Jdrx z!x)3fH{3`T(y%MbSYBrf5|4FnJW%XCNq$Oz>TNHK^b7yPziB-aCXx*In#7ClNTZCg zj(2s2vjGzb{)QGmsm^^A)>kDEIp@#3w_*us*n}pp@N%t?syh4hF(qXjc_VM*$X%Z! z8Rvhqokst$omhYy1)wH#(9%9N4W6?4E<6I(VE7nrhjb^wQr(T7jf@y*`q9EZZleVI zRsH(MZQ9%W*wmfOi23vDH*)yKl-q@BNt$rlcS?rxS}(xaPNGlELOD)sDT2fUc}DYo zv9v`k1VhkkT;BqUw_9xplRNpyyn8Y4oCLf|fu)DTKEtaquh#f4a!@%i!`?)g_il4( z3}D7R$kDH129qm+Oxuvd-ZYr^c=ocCB9#{&@vA7FdG9)u65Cy>_JoxK&?ibzGzIqM zwt&<_a1w^^_D8}vY0Az$Q&sevsPa7*cQ`L3ly-dC*yQ5F@H6yDNYNa3#cvnNQSR|e zxJn~IBMgn0A8HgtMzUgPRM287I)i5#qE?@jCo!3I5`)+$^3GNo3m_POn!|tb|F>Jh z_E@vP5bvGV1<$-(!tB+5axbcfE|^Y(VDqRSyr`WBU&bOR=7tWSFdl9_Jm@EXiMW9f z82TrdK>a6|AowSjF#9K$$owalunDmD5ZB(*&MAr$P0%vLtGqpI;ad~*qx~nB_@QZS z-T9jy2ji_vG4ofxAn_tPO#B^>w(;z~P1&#*J*@C0`_JNXBqDM2HVNn8}OjT$hU8Rye? zFfmOjIM?HMFohkw3fyVca23pi(41dQ@QM=0Ze>gvFD!EDrXATMC&d%jxFfER6*h`l zEU$<=WYp($k#RT3l$=xT3Ob!JHV=*GbQ46~PKi2X6ac@(+)l|lWbBmWwRt1KnjIxi|prb^#Pxf}S@}xg2E)(%r;P5QLqz zH^P6V-yQGDFq8J?UGE8#+(t9LFhN&OZNQDJ5l5yMy2AmBCXYZke)6xmQ~?|79wiM1 z!waD5d13_vif3j&aVUirM2xbS3%n&5kk?eyXg5aJSyPu4D2Rf|h;T_mBD~Fb(l8f~_gEpT)V^2zv;gkI(sbpgP z)=d9de`2V%z4rlis#m<%LYji%+Odse$8xqaj|(Fu6yLt^2n}t*LeoXyS!mGC)c%~> ziLDAV=-X0kGf3gWX^h2k<q?4MkpQ|dEHq@ypF&uxkS#Q5 zW@;w_lJEZe#8a(k!M@f%6Hlumnw>OqQ`nrqT5QfI2LAaBQtXgPtqGg-(oLj+~ zL)#w`9t7N{`U6O|h}*#4Kr>I&XT0QnD4Fj=I#dYI%sSW(rG0-m#iuyZ(%7u(G~DM* z4kX;i_--7I#wbo%a_T3&!^gbS$GjmYwS)Or1eAVjeDZTl!kNF|=#l*3l%E*;>Z~t^;-@OyY@!FPR@SyS= z0|RL3N%>3R2~uqF8Z;#m&(jlS zubxnlu&t!4u4FHfXy_;kVqu_fBi8^!IvjgJP)@KTf!Yh5K%MiEZ{_|{@1pDVtRkU& zzw7;sj%=`;Wjm)HW#{dEdywOn>2TaM2%(C$YPaDTRJ8bbyBpat-mrb6UZUqD)+N@! zNWU&J)WZs`WFyG2h^cfQ@;5=Q|FJL!FtLHr37UQ8KlHEEurd=1)o+0w1*3At=Le&Q z$)`BDKgn^R35c-W;j(!-v9Cl}-8Ma*cGY&)5E`q2ziUpP-@R|(R|&FmFnJPBik?~9 zd!)NKUyI%%sJc9J%K6>HR`x6&h3`w8>9U`zK}WJ{p0lEM;xUPE5W;ZT>18LObzYvQ z5~HyA*0O!#oBH(aLCtbnNRD0!$KIm-S`_>NXEP5=1%^YdZnQJ+jj*?rp$JW9ZeO^ezKU1#{x@l zG+=`Kk!y{gxb5f^6BV(^wDy^SQ8JEMup@Ig4Z*{n=k%YaAXKQg9}cs(ai7N?(9~x1 z$HEHr#GRVz-~&j{oAz+fHq-50E%4k-63&i9ZP2+*PI+SvdGVW>P}PcQad7*mkySd6 zRLH`;bcUB=t|E3wykjPHxl2(WUJGsRjttIP-j)y4EYL}tqkwa!ZfqPmc&|T=P)FzBvuRYBSe&W_wR1G=ay93m%p=-bpygUx@EOe~0I9 zh!H7TP%DSRb%a@Vn@%ttzVeni8PE{7tv>zz3B{|2*1Nz5s6DhPC6i5tRBRK#??@XJ zPcQk;9Wm5#4A`~+n)*(AMpvQFyuMSh(Aw!yzI;SnC&`8Wwz#07_6JPWXGydEz?Pil zLpzpo7deI;p+UB`d(qEE0H}d;LUx9Jsgq4QoJ7YVsl|2vciWg6)pUCK3__cR8J6qo z8!eUE>9!eQXIW{(Oxqo!%7uo2A5$OmnX4ME58YI;okT#Cue{G2+Qk|B^IB0fwN_`6 z?dx{&=xb-)j9n$$T1$4_3>cJ*_tkY-62U32+;nAHROf!v1)%*&m3yvux|;f?+vcOnnU7N$PeuNpy8@pz8w9w9~3-MK6+?$)WJhk`nE?|QoI|cqvN|N?lOFom2!K`=~7|8v4~5D zS*n8Q_h6^H&ziJ)JIa4v)gqk4+vfe*TuzCGVC>1^yq6_$UVi4OWKrX|L{9mX_W-8B zVsY_30U;~b(k4EpdV*8jro6pXS?LbRW!E@t$N8SCY)w6zjv8T&@d=Wxr*1S1dwry2 zr=*5|@lfFhJa41rc+FYi+!Qx9O$Gt4n2$?*M*Wcl_WVvlh}z8;IYqh97+;^T_qg<{ z-C)F{QSlO(KDaEf$`pl3fCf@&Wer{X}Ql?;dLVQ1hqS7kR+fADuELqZlUvTf_ zlXiedP?P0x{MG8{IEh{r-X-5rww~U~QJeTol;?X?1{^E_YMHGzx^c^jX4Agsiyu0K zx6SNVx2iCqFAs4Z8LS~6D^8W3Kq$${cQQ@-{-WoW^l|_0IUdMsqC8~oDG{YpTr$y= zPB@|E{!6pAN(V3mEL3hdNVA!6r3S<=csfUBxI|ihJT5fu{XwC=+wZri0!UG6os{J+ z7APK%x1u9+vBku;XI4d?ToN4TO!+B&XiE-{q`o91nbH^2*|E67=x~?|3DL+4xTUeM z>mRz{>g31ZY*4oQci!LI0%T&Woin1W8#={pZm~RGUc;oVo&Cj6U{_~g4_QV%8Qxv% zK|J&0bV$7R3wLAQ;Nf$Gjk71w!C*U#@`MP4a%uK^OJIDaI{oeU5`Q)j+y*kP}8#pb%$s{zLcVpE9IhRRH z-rLGrTkU1i5p^rzMmcO#DdwBjnIT`NXvflhV}Wbi@3}Kg6*GoZ$4t)(?eq2W6R_|O z`4uDKbFkyy3@TK#?*#JvIx!xSCOpJf3pNt$ZA7a5BeYW)_yvO9wNK_ixG^Kw!(vBewF+?#`9}^qWd{1yw>nq`}~j-?IN$0 zemnGC5B`Ky{TKY+_K(V~7)@qiy6f`9H2mJZ>bj zRDqj8@6(EJPh39GzIni?$xSfdNSgN-&yV4O55`sF#JMg5m`HDL3r|09U78QAJ!6g6 zE-?g>vO4aPMXX!EFh8{XNdxqigRQMTF)^{hw!$_OGLykJ)3(APF*oZsW?B*w9JWx= zP!Tav5eRW8aYMS_1gUKZIor>ubXT97N{Vl_lU=9R)<2-OBguKK&i4FSL0+ABUCt|a zpCsbc4&K%=o1aEQpa=EIsTaRI8PeGNX>~dLBhCaOkxle`ti0bEa z!xm#23;44V^;ve?z?Pq&-6_{U)NszIqh$GoY_+7xc3K=Y+Fvv^p}rfAyJ3!!WfwM7 zq3Q|R8n*nbtDkV08P%apF;70Hll5+J0=+1;P^DZc-4RT>BrU6_px?L_ELHBZ3+m;D zxy3EM*esXPD*?vlOyVx9@&x_$?m_=Od|>QtdXi}=~!yu|+_aA0QCGjuUFGjY~qVbpUnF){uRjnMmVd5JF2=G5Qg{{Rl4p0(N{ zB2l;lrZQS#Ag-$MrdFCmU$79HX2$*eKrGxhB$RWRo#^lBxuOnLS~e98=bFD86-~6u z4A(#P==@&rSuCqK@w&*8G>vzD>OAmz1@)6+(^^KK)}4HTnO+BCFNL1-u@c_i-{=W7 zQE0JqXW-1xJcCuM*r*KAJcAa74nvO66p%qFWbOzis(ey3u@Hq-ZgeWCtT<>j8kv#m zc03)aCV}BWzZdKa?0jbITq)Ed#*BJyfb!=wxH{Eh)l{sG=}+i=`2_+fC?r+E$;Kk2 zNg8mMTSXFHSYz1Srk@t6A+jA2St-A`4|hzdB$+|7DSD;)yL~82TH2QZ(d1d?S>}yk zN=#Hr;kfYu%ogyTfLosuQzF25o=RFlcQF8Nj{S2f+}t#gdQ@mg0uJ=CrDXwRh5#=~ z#iu#BJIYV+jqUa2|r&DFGKKp)}jMxEN#$VU_%kC9uOO_ajgDHb5tOhZ0aJ2YYdM_nLtsO_aGs(Bmh(8s0pzP#d*8^B@)ao= zo5(A4^J4Qcs17;7+jTy14-1s6rTkJZa}1%$(~N_{4j#}SVhWrP@l0(G5awURlzk@I zlL@;Q6D`sxcESTAnccs0uq*l;PA(P~HhqmjuM@tlnIIMbVT>&ZhmApFcf|dp6t@!3 zLb)VyC~l4@8ZHuOUJZ}tEJ3s$cHRA#HvAwF-i+hu6^Y6VwiM>;+cz;du@Q7^XNGF0=kNt3&gyI$2 zqtui5W#Pqr8CDxGV;ra$n-9H(hMu>31!I%E;g2EEKK|8H=5vQhE}{dI;U@%)+lF+O z$V`S7+D-j>Y?7q%58esnr{EygnN$Ljlzi1J_RwjIX>&Gry`&_LcqJus(d{BufP~U9 zx)6X>^n0K}@vibh#D$OCA^2M?@-q-j<5y9XN?(8PCwSmH7#lIp-Z&wezaaBvUdWAR zOUTVs3~SsG&IFKe!Cv-`5FW@9#FpsYROraXn}fc#R!f4B!HfO zR4O@ryGR78S^G~TWms@#7+Z3(7$c85YG{~X75x;r0h(@w*i-$Q-7Mm+2|_bRwshqi zLbj;bZJ5}vU%>nn1h;pk38rgBX>POEz<#Bf;ZYHKrHKe^An38;jbHRsUU8Q_nSRoT zD|TUD;MRj2;5O%tUi9R_!$II?&E57ksu*kn$!V1h}kREOR6Sxw6tzG z?9?!}?FIGn?FBaJK02dD(=ySZ0;3McBvLdFZahTK0rOriPlTM8C^$ud3Zz+Hj`d7I zZ=w`Iz6tR~o*pRsI;P0O3XwD%eyO$_Ig7{7W8xutv50)A$X&6ZoOLLTHEK_O*HZV{ zY6u#wYqdC>df@4{!58T`UT)XD)5{iU+JpcG+P&@3QvDVsJ&}XvDxadCcQApaA$a7|7L>%G|jl_am&o0>$Ytag=|FO zkj@ypvm1rl1iW`~0~$CUg4Y5EVDAIKw)P4rGXvfT3@oV){t%Q z=)i09);Obk_0NgIXsDBMluuE(->(s;*Nyh6Zo1LDv zc*IX_f1E;KKlT&*Y41@rqxEVno3mgxpIT06|Dl*$JxvqKwuXE|&(j%(QWJ&jFy$zT zo;FKk%@nNqj?Id&CIETI8j!&$2!1}&%{$TjP#g%#nNOLniQ}{} z`O$oeG1-E)8!K*S^1}p0t?mAaTHk=8*0?uhc4KPoOnOcOKT+yO>c1Gu9R{4=SGqqEP0z+x*ZUwI`myAd82&IwxZF zC>VwTww4yLLs;bF3>Z)587I=VM6m2AID6u@M1<^76khSmIWbJd(CcNE+6)b{9}Ahk zxYkHZyC#YuTd>A0sMx32{?TboI3NgYyG2_g>+jL=gg&3qvQOayT|i$W13lpZ+)Wa7 zjZ6ph2*T|s)V7YvqtXuL+j4?`0s^CP`n9tu0PP<571Xna*fJ^7xf|LlHOuBM8h3oRAU(!U>vzX02C}A(^MN5RIo$%la&L zQ_SE!eHO8tEmOmA-!9vJ_?)kMrw%RF;bo?x$r{}20?-iISKy2OK@JU4_|fDnU=7;c zsV$(O_3q!G^?|;WJ>U4i6LPWH`S=|1KS68x542Qi>)%aLcTNPqg_2my%yR=GCrAOG zpR-|zpV=KvU|09gY{y~=;WH$l4^o&L)lE=RrB63+fIhv=XGJ9Bty9g7Z3IIW}jK0!}Z zR~);n6#PS+wTNgO5<1Jzl2j@Y(HbQ5I?y9Jk$ya4>H`=B zCd3odO^SnrLb`4ncdem;3_5OA2P4QH*wFxMcRs$B?3cKJeg z0-?$NwcZ-deEHkdb%nsFX+ZJ?xMm~hQUlqcb*59)GKe|s7Q~+=3N}Jx?|J=Y4rnRp zu1^1A4o?AlKhH$AQC8LYfi;bA*QD4TL6IAh%o3%PLDm0Z4!b|&vd>~EY9(cd$dMVq zn?_tAfXIf>+Y6ffL4#>qK2n>rwF{ANO1-Ee(uL@_{Rsm+;zHzSetqE_u40{o+ zUF-

7sEjBr8;6?P1wxg!NvKjI|l1G z7V2C(khFwLR1U;jIf>&+)c1bfg2p_$x-8InrmDG}bv-gWotZ>A0{*(aA@JT0nlzHc zW3hDaq0ixuIX7iIvFF=!DUG7L8=<7Hr=}@pILOc#Yue_PzYqCUK$Ww~+ZOWQb4RksXQVB}2h-zdi@E zJ|&jFxqYW<(c~qDd>7>D01o9#BhM!MB=R8#%~E-ficY|Kk7l9FHDql&bwOcmOJlmC zZqrOm@cy`Do1}ja2AATwDvDgbyR5G{ISSkN@Q8Q&E@y3PC#NOYyPb>9D-t~{~Q_7UA4RSL^2e%_j$yK_zN~n`9+wGq+=X1 zx?1Gf0|E~c`{kdjl_^y5!miz6wbaKk7U&JAv>~8&8g^`D(FhjYX_M4;keuX{GOF1p?&m;+7y&VT$ z2pz4Mup>cr?q(+v_4+zL$Y9AqM&=mpQ~lWDGsb* zBJ_PmAk)u{2Wl$5yV>ThEC%z=tpV$@>w1@xiJf|y8?u%lSnN2ooroiSR(^6ZS4cCz z_$)O~LfOgcPd>9>mn^eIj;CTYSEWiBOH)&@(|gTi_#khpp43rfo^7kNSk7ImvpWr? z#unBYEhki@UxcS_W=Ad0IQtE6aI)L!9u7g%kZRob3T-l0f!`0@k3BU^eyc9FB4}T* zbwblhGwuZ3M3^D+uz-S}h$c{I6RHgtiGMH>F@PEq{k195b&6#dWHn7ERoN4d;x%QX z%N038sLPXj@-nwg8wOR84@2oZWC1?z__u`Zw8`-JFKJ!9sY3+qi(Wccw-W57275DH zmf=xc)nisqX^gJBPh4I;6rpvn4P&D*{NV<|GdZE`$9O&O>FnxGg{CBJ?vp2%rRy68 zo2J}mh|+S#fXIC{MBOjYhA*M#F=<>K_eHHC`S0OD9>$X#1zp`WbEn z4!&po>h}$L6}hVo8zv_Rku9#p=rfzI2J&r~OxA&pKsc)e_r}d`fYHEhgg%4G1Q&n* z_b?rsDC)E)lH~b<gVCf$D)bs5U)@7^509 zdR!+TGpm2Ih5_Em$R9@-SCgN2Z@F+2h{6bTB{o~pm#-6CXAfP(BM8nb%?fYswI8`Y zW>{C&%9jK6x@ZgZUgq#N*}H`ZUn?n}4hUd%?2`6+9)YUQgGncZ{2r!xKn}{==fIk& zd4j7p@*){P$6IUG^R<8Q`GzCoVe5+2Z^eO4cQnhwlzQ`*`vG}ao!i+os}VdJoenp5 z3QdDNxhKjdw&w8X(c<&Nl^!X-ET2B_Yctrl>g`ipsg_{+$PKR&Q8${i`C{XnzK3$> z#oCTLj{AnG-pN-^e*4>Z3I$_QF}#d!i)sxpM-(o;oJ^_~c@(<8xKTAs3jr5* z-tTv_rMG!Xu)UMCbyFO*UV1w7BT|E1E`9^k?lg467fd&}`^q>sg&s6JaC|I%etzSa zWJl-ZdX!1>A6C7Yd~2HLF?}ZlR90=!40Av`79z6J-GkORzUI_XJOxn!sm!b0*M#LQnXSQy%TSK2(FsyvgNs*0m%3l# zbsG(@kEbp}+0i*vxn2yg z?{XX7nVyb^wiTp$c!2xOJKyTsWu}qzgD&c6Am_ZneneVC{79)J%{gJGu8NG$;;w+f zEVOvZZc|zQfjdzPrdtz%x_v4&mqV*dOy;ig^5bQ9Za!d6NhxKka~)A(mg41{R_42y z%Eywy`h6^nWvJ=zi%;tc$3M1anA+|MiurWj%jAI9Z*#f#X>FPfl||0+l@&X7stRh+ zSrJ#Bs)8OUA25E0W8xPsRPge-4X;p_P2MK% zKb#~g?T<{)pQ!B5E$_f@lZxRL<4nzsF4yGO`owrjLZ^iHF><5IFXb{Ta@$VUP zxj3vG=p^-{&jH8A#%RL!6ZfVom|V=16*$sayE5C4zM9|mqjg*p^ zHJR35aS8Uja9!sH0Ff;}en|#kTB#~hp^w`-n~q58>JqF>zQE7%D|{5af9@=;`T`g1 zJhO2{7ov(jqY?fj47Q+g6bdahY`n|r+2iDq(X7yHa+pfX;EOTL8h`T-_ zL8D<@n1ep<;qyoV+d;&AnIWPq2ltgg*=%H7Xr;{Z^=82m7Jrq+LfNq>NjJT&ZD&jA z7Ekv*=S_|F*69fV88)rz=``JvO<0G)y;lnPqs=PMtVbv86r!K9- z(cgYL?C`vDl`4Ku63SI%X39_&BXqY#?3P8kHZ(eOt=S*JJ{a8Xj|ztJKa&uygVqVM z8e@4>wO}W@0-h<3Kl0xd;FdKH9PSK`rbA3c@R_ePd^9%OOM(~*Ye9i}OtR?yi zF;^M9wO45OR$ER>LeW_4nhX_p^-9P$gug=p_2|~cEo9Wl)#P7RtqA-&b}`sI&vqtn zUca5z>7cXFzdK}8D=he7xW5}8bz+xkJ^+2?Og0{`W|;24j)NkC#56H3=+|ejWsA?0 z?3C*7d=KKvMhoRs3uDw%n-f}jP4=4ut8bLwl5fS*t`X*4#X88eHmPzuV-ff`Er)hy z#^vJmM4vYRx^}-|^y&`9j@~V8KHmE`6MYw|AI7s7s*)xmrYAK-Hj5@W+CCH>&bty_ zPyBx0Zm(>X7-_{c_^7?mu$}rpEI^w*$Rm0S>l+(*Ht2SNps&wT|-{({&|ewlnuSZo$_DjCaPZ#!G)K^Z}nQs z?SIt!%JCZN(roeJZtZuzD|~$b)`eiU;r|Ye@SG6C-vinVl)A$0mUHg+Q@Y^qyW}5Z zKNP*Qt4AYEVHkh@l(XphPUqNKunGbVq-D6+B`U}DfFTfQFrTMdk6&Nw{u{V3+A%^g zLIr?6?Mw>`DH|&rdK(*S8#)^r8)_RH)3(ZWYa*1l|Jh+#L&_)P+rqd-%Nd=aPj7qq zAj+KnV^*PqA3Fg4>c#ppT*Z?4_=#@$bESp!Ln3U9{QwH#JEx%y=zsY%GuYCvy|P^T z@HS`k$q|M-v)JN{A4G0F+N`LA{hWbx_PDeo*grDeGFf3=F=?OuCYHZ6{pAIJD5z!A z8yqlc7qEYLd+!vat?D~W=ip%!eU&&k@FG$Lm$A+7_U|-V;> zeUzyk?j>ZafHrmdE80q*tVfu?Od+%1r7}SmL@Gq;-0d88Xx3zCQ}i*^YGf@&;AuNe zT_I616OIt{qe$O*mvT~uL2qdn;PwllJY5<|+1y$y6C4*eNtKmODw+Y%3xx|E=UNS#5nAgeYbmXHoeLHD z`KoITwl04;12GG<&PJ8Tv7TOb=-8Of#6|Yacikvi*rgT2u!8 zh3Y5P24y}*UNQ|)Mr+y;ko21Vmxd90}>3t zbRr|Bx`AqmJ!HOnhf{~=-k|6|VoIduPt6Vf+iCVnF({y^QpgbGG)NWf2Uv#LdUOvK z^`Vw!2*N{@95E7u&+SmmVUW-DpdMeN9;XFVeVJPh~uPEL6Phiwj6^90Bu%*{Uj!7XM9mgkY3$^qRDUI3^$U$X*n5z;1%h!zc|u zpB9-GK>O%V3%bP*;YmOf$JSa9M~fOaJ~%D1jCQWf3_0MSmT5?Oj7R{WXka_;36LTa zr*oXLBzmMotXK7CAJG&k&}8{04&P8lJ5=IKK8u7=Pc{RV#E0; zBdEs#?(2D8d5G8S4|!&$pFiMK*eMqaN2W7esA-9^APkJvn`;%G3@nACJ+Xezl*Ex* z2!dl{4bRH6%g+&qJB!m~yc#rt8GA9aifMo$nv7er0A@%g(ZF4ZI%-C^Wjjce#X*#T zjU{z%g`WUK83-xx@6dWD&Hw0{GclIiC;fgFa#6(qoCT5(3F2LM$vFyo`=ji`Vl&xL zK=CPX%V&(sBcM3Ldw}ZI?(I@Jwyb#&NOneoK(vq@QM_Lg9p@~k5DnlwSv6rjEobUNt}MeVDA(pN&@3zgQr3wShoQ_X z1e_@@7@lnrSwz^_d200X;D_m}&fc^Ihwcv_%9y#r8*ASYZx#GIxi_qmxHp7fjsm?7 z-E&@k41QPf!@pnwb8dHno4XoAoB?H0op}<1TzFKD3rMrMe+XI9Pk|qDAE7vRID@nX z2z}>2rraCk7TgR4mD8v7k*0A*-tszX6 zPX;<#k{jHuQ8Ovq6?+TM*;&o={_$1ipe2~QRJ;n+V^{4LZ`eWUb_A6;Y?uSOrG zIHt>lXKHlQj`PZB!J?r^`_dhvvx)b7lLcYSRTNvz?yCz>2#1VgV_>J*q;Eg3jX|Px z%u_1uia$~GDxvs(s*BqO?gh4>>;RDg0xr>I)+CS5a_sgmjl@tl_@~H_7(`ypX+1*5 zF{%~r-)WhH`eCa7ND(B3*o;ABxN~F#ty|Z9c@(2D~Y*S}X*FYg&T%YWF3$Xv%Jd z?0!OuKqDCY_Q-xz<2OrX7n9w;jKLT&+3a4KfKt(a_Dh`^xanm0*t_^YDudjYIv|88 z!`CrgwA7Aoj9y$v;Bsq|En6^hr@B9^Yrbf^3=uAZ!&E|jRW%(QgOD1@U_rftjxxFb zK_X`Y*%2^hdl0CF67+7!Q4+vZl;P4YXa|A}UlK`zS~Gg^Gs)NNdpuJZKx)H4haDs$ z+7_|hnmB5Y5rn&<>6)I4s}bKg*d*Fki~bm9Ad_DBz^e@70ZmV8707Ntj(k#OJow&| z+I2JVlevDRVE0e<6#P0+$nADq>yEsZ`c@zYM*Xvov<)n;0~ixS`7erCW%3&EoEQVL zIg0f3$?1SSL5Zd7iQ};!F3g=N!VG^Q1G7eGW;tMNqZ$VCn`^)Zcr`UNG63@5km2_Z zs9i1IYxx&4+zPtoEA<`QHd9s#CjJiVl!W*xjI-dFi~?b`S~%yBsF)&ADqirIZwG4t zyCB}=QT^G%_7@OkMW57LIyETj$_Cq`8rqlSwWsJYS^n!A$aO$|2DuF1tV8UeQ>wW- z$((x;FX<)^Lm!hVN`oa7rON>vdbXC$)k$L%EIa zNK{>Q^N_p~A=KJ?y#jN#q$=q!$cDxWl9|w}d1CD*Y?OpF@$1--T+Ly|tH~Rq2{Ppb z6gXUP2l!+^Ii*SffS+N}D6)7EgPlEzeF7?F!-jLcU(Esm1_fDLl;VK^=$^B%9VT@L z+Oa_DLuLc~g5EWd)_@e*AboO38AUQWtJ0cSD(2L~fVaEO_+R$IdfKB|DF<^hQdrbH*#(=##m<3`C8npGfgA@3$2%YNf zPw&RHdX{(1MKg>-8enxbts?{Opm_CfMU!nNY7B@p=yVM}ogO^a38|e?^8n!*_0}(b z1;O#K8=l`(qN?xU`XVq~6Sw~`@VniPBIoNJ>}11*z6JepWW=+G*;k0J;Yt52vQsxa?tk56jnGAmo2PW#GFoml^ zEcDs_l1Hh?aBM^6F*JZ7#=>}~+!(oZM$qbIV{8CN5M2BdOXk1o)j1JnU!S}P^7gI7 zH4h@{X^HAN_pA(;;RuQ|p}^kQc(YUsSDO&IAu4u3j$I%h zFUScep`N2g|JQ4nCha4ZGmg!S6(j-WtD~qA;`UZ}ITyLJ>cd;uuHTnYLYkS$w3dbE0$Ni?=;qBm!!n!1g7m6d!$w@KegG3Hp*!)^G1z91lfgjcG@5 zFw8+V*N)_0UPEo>-dFb#`W6Z5r+S@h?6$o+k!!zSdrs<(Chz88VX8Hs-j!uNzwy|m z_zF_LAGK!1y@oC)OkG)vat_kv8IUeg{@T)gL0C zD7&X2k084zr7st?EZ*-ApD;S<25nir2Uoc^;QTO``nzHU(!OsbGPT*Bo1C_`8F>(# z@2yYZXROY4loM?`$U`Si5aouS%L)(+R>yz;Y-t*zd;Ue9pghuG?K*c2K{43h9W}O0 zorlkCG!TyOL3AJR1_z<)G}!p9<(Ftby3)ND6v0=YHHUUpw?T!Y6ZdO$O5VCawy9Xq zZwHZUB_%r>ocm0?y2!`e|3-e8N-%d87q&dOxI=W7Hd&daJSzK30xFFM?`TEq3&CHl z;X3F2UTjqJb#`+@F*FSu^<9*PxN>>(?Jz8_E$(GLBdd`4TEAr*^YufSW1~L-dnhF zm6W7~@ngL(Y0W0z_qG)L=J>-VwT|zDj(nqKJe@cK4#4@{hbm6gZXe99-?}Cq;7yNW zho95jd~BonXkQz(w9a)uT2|Ym&bT~=q91uJ(`VN++?>ML0%=x~eNTX>XIvM7k>qc_ z(0e-#KvP;DtWg!pBX`T?YphDErQf=2YSY>=$;VH@b)+p(2m4<4S>gMtEGpSGd~wd>3XzN24TkipA57CXjb;OeFxgg`|D>p zcoEyG=a%LkJ)#adYSk5b%(Uqz>Iz<#`8N*nt_K-JzDNR$fpu%24b`=8A%GBY1sz>6$A~_HwIM&9(8?~_CcS2okl)#^ z9_~%5Y)7y~6@+uX!cIMMrfE zI;zieJ?Tl-gsX@7o*OZN_gY6gS49*Yz6~>^jfhJhJ@7O>#*zs$TFC6JK=9F>w#7FfeOJtoMA#uTmxe9QK+=w#{xhB%Pn;$=niY> zgn>m}Jgv#9c}SuCG+Hsan(5sRc5~hn@sU{e(b@cv4`z4K#`WL@bhGX=)bl7~XJ<3^ z4Z&sK$ZnU~dtc!rYz!Kr8u_wBc6<~5!U^w|xWrY0*LdZ|r3B|ySE13uQzz7`1l4*o|X#RF$-vsx2b`8UL@OW<>`l&se%Up3SIa*|aJ)LAR5m`zFM`g*BHm`hEmi1Kb9a>F^-@YFBZX6O*(Ibh$|k1h7Ohyf zG2`Yb$0_xcb!BkC%J+k}`KHgTXMRFy71!5e^uZL_q&ab>>%L8TZj@!=E~6ezMXHya-?TE5D(_Db9+q67!|5*0~Dq+yMdeCzpS zcCG@ZA6GYfMf^ykgUj?zgZfyTl;I)(MM*|8zg}Za&y27FSrd0}dKz3UX zw^!ferE=6h#>Jv+gLfZ(Ha@N(vjk~I&s?cI150>J={aXdd=Tyi0{&U z*GJs=>~PB+t5R;b9{BZBd$X|T5SfnrW`!w`=W}LVbu%Y9^pnxgGhz&7@ox4bWHcSG zGr@Mtlh(KE32tu;o(HpP4Wuh~AZ4aEJBfU4&7g3T*5lr5PMm!O@U7ZukCX=AvF)}A z5~|{M?s9Isfr}<}NY#&YRU1|QPiB{wP?$@M+TEFfuPs>jC_y3A|l zn1zKrMe}I14(hhkWnHRCR~4XyQdaj$B71MIv~(EAewU;KTshpHRn;uE-akp9tMVAxVuiio!-&jgdZ^jZ1jm}rUmj0!aO;c$;y}6 z#Vgk()!VCpMmtw+2JIOUzPA(nE_t2(4ws6kN!Sx}SI?^3W^oAdNDj-0P4p1B%r$oU z3uv~|<67h@dhJ4lv#jTFHJu4G-{EAwjBUSw*4p>9hvN%GMdGA&DE5o1lIs4}PO!OE zsZuv9x5#LtbNPecq1m2yT5?Z(o1CGrw1wGkk3`d!%~C4dK8?Z-j?fJqki1T-Ks>f2 zY$i(24+6`r1>A(q32h~Jc=xq+uRXpbJcs+!;s-*-H@}<8&6ZX8~4$l`Sq3RkcDfS9asv2Vn zCk#27$P4>;S8X-3uk^1kkH`p*NhG}I#r2mv7}QHOqE*!K+_moejd729hpHbLF3qMN z5{7=d$(X}lF;-3Ow-+ab`?>rKvng8)mF_pAeuVtbh?kqQFVgJqZqwv|dSCb?qrLp# zqmC<&(ic4viebK|;-LT$m*fD?20{MJSCXn3E7YBm53l~MM`#2D1o#NJ2!t5W2_ZrR zQGvK8PHgRW+w8?#o&{#+6;Lol%R|U$2f?_8#CKY|Is4`9G^|!CtLC0Rlw}p3ECj`GY&d{-pJC+CE^A4v?@EN5tW%RG! z-r%Q-T(@sb9Ya z27a5LPKGltpp}MhPo>HS&!SQSjOlBN3Q%4N1qlhDOQI`}+>y((k*$Aie<}W|7^wIY zB3Cnwjky!rsx!fZ(f^u-ppp#7^9&jFK|{WHWTX8t*U!9)dU+btYltC5c+gl%-lGUSSDF zGG$H}6#3_C;GQDY!9vM`R3XV6$rt>PK*SqCai+s>rP9fX!{U*Ng2Bp&Ns39b9KD~o z6Xt`+*s`9}I_V1-L}vUY>|zl(ELbOu1kBUFeR@Dx6x{g3$jqcYvs59?t!SexdG$pD z5=k(`C5hFAkbE{WF^XAYpV?RfW-1CU(%8HjanJY{$@+Bs!NsX-U?j0{o3!O_9wcC_ zB#}DSq!#v$sDn`Qxb4Np`;1L)&tNJqNDpIr}Wi` zc7a-1Ammtq1}1Y^8Q97{z~ElBaB9A-iEMo5a#HsA15Yl3Q)QboU zmj2BDf;gs{U5GasI?ze%9G4qgG}ED3rIt|%s8K84RZ5$Bd_3e8=2a2-;v+^>@iS6p z=}9OsnF40l9vXAN$~hJWUxMcNi4+5rt~M^2F)m*D2bP0 z)Lykjrd|Gh2}3w6!_V8Ck@y!0-pea{I(!4Q;`=o<2&SFheQ-)lmviLtujtRT*;oc zh-}s0>DzNf4-FCdN1ZW62%&nd%$&*tTZ+@~%0fMm#O#w@VDiYJI&5p2W}T!RrZMV# zI3|#O%CSb&Bol%%dc1CX&PAR01C}Q5lE1hdn1aTDv*5%6@y^dzdd2bD4D;2GHgn0$_7=i!`JQ)4}3h*x;|+l6yVQdBqlPg-}mzcU_}w) z5=!)t(bL5ZW~k2w@M*YObh;y2>qsv~?buaD)wUEi=9S!ZZ;8zf%T=k>#?`hAHs)vF z`^@UBL#|A4LYDZ?W~`U;)d5%<+t;RAe)nsbSd_~GKJ#w-IG4WsDP)xYnTQ0<6ia#~ zBw@qSHcCiG*qCXnIA4CqX>in!bmnmUC8&ZqHlY|csCikJRa9dm6V6XSWo9nl8=aQB2*2K42EJU zXkD?voPkNSBgFy}vH2`gbU9)8P(sSw4?oMDsqeG}JQ*Pmt61r@%zJ<-FIbgR=~b+T z^%L(|>i=vs01n^N<|!}`ztX>Th5`jx-ZLR0_v62s(XB8vgw%T${=z`xN2e$WLR7jko90NqQK}0ZNbrk;O z^aC2tiO4Xz`;r3`?>_`ld5!Vj$Cx)T1zE|P_ob}k&F@VY>?G@gggt+FhXfLg83?+< ztNhy&%fnfk7x;sQvxI|0dg4(dS2QxO6qT%p9kaneE}Kj$(gc};deq1G2ZcyGcH1w^ zgLe>_ifxx*^W9RpVth@5_}Z7ztgn;#)L9OcoXZa-%#$iA+0D-GH%%9axx8&C7RJw`5sLIWN9rKt+Lzi_opZb_$^Ftw|2;->ZlN@ zCgu>hPB|z;pdT%vQP4zKvte-hP*{-#Q2~OKtVpD+vfKc{T{FUS@;hXOe~LVS2xr!S zk#YJ%Gs5rpeY2SZvosf0x^rV!~gPFNA(}t}wzWQy`C6tIpe+ca7;Gb+QBw!cyYAH!E)btTb|=B3N>%5E$gpC4fLe zM|vSieoul9mG)Yfsi&dKZQcXTizqhu~b8@i!piAwKlLCcigB8Nm z@SaTOZa0f%wOYlfX))x18SgK4=3Ur0vHhu`-X_Xt9)q-+2|cR&+-sPAhR(vpfFnR$#dQE-!E=?-T%?A6N?60i3NO{d4L)Eq=|(jyeJ%u^m6 z4-tzOy>m>vepswNdYm)ei2CUV0Z6aMl))*V4-Vi14A7ljWDZeaPGMy*Jbz0jIcZI$ zv*OrPb@)qrbiN_C$Q(>^QX(!*9-kqLN2kT{JL3R#Y>V%Lu+IH#wx6GLfhz33M>@v2 zCBJ3Dj)vjRhB7qnAqD;d82ZjQ00|!JAM{2y$i@^{U$I!du_B2z48ZG+g$7=ITcML; z!6bBARI|q@SxB-x`Hr@n_2ZJTY-Am)+}f=UP3)L|3a11G$AXGe`>Mivut~H=U~kvX zNVKN2b@=_SUZu93>A;QwFhnrkF>GX+i*8sT3I6BUqVYMZSv%QKaUAL)zG$AAd5ChN zP2lLZ#wDa8x5j5|@2&LcL>WF^9wl8K30>YW-5aYW-Y=_ikUkgtO38t{xMikm&E8|J zk0jHFNwWt!vxiCp+kYWfrjbN+7}uGmevbmY3T2CHR~Nf(-!TV?emumbteH#qXrnDk z?2_!x4~4A@M5~_3_D!Pf&TSXx*0=0Ve&E&x$&r0Lw};PEyDrX=z2f(1qJ1;(dued* z`5OO+z^H}{dCI>&=RzSyIgNzwUdM!_q7_sVpyAL0nxuRu4l6~t%R+i~a?e0~%+Y%+ z5kMDI@Qpy=&My$8$K8|*x5xNC+dkAm+x>=%T#0xng4JSG_Lrq5_0X9*%6H0vM5DEJ zE;oUhn;K$5g*4w<1S`J|Sxa9kFgRvTat!oP)DV6@6-gX10T+BIKJa<}@Pl9DR7ou%zH3Z}Roq)gERm6Qo4iKH8-?xkjY8`D?zvp=80QQ^X2>ZG?>O z{M-=bA3wcbpNo#Y4i46G0@~=(OQNVFj9TA2YW!Kjv_xI3RB=mF2jgs=xA+jP7_v=m zks^1$t0qMD3ao(M_f7>*qQbP#Y2{A4`{DBq&i>~m{OJh0UlOl>?}MsLE2*+$9G6Zq z4>$PJgd{&M6V|#uqc~l27Ghjv7g4P`e3$LzrFnI>OC+n*`^_Fo{{OdZDayG@}dEODP@ zcn+nA_|GVsNLo3W9^50C#*0s{oFsrxUhTHM)dp(GUOlcaji~`o$q(BcvH*Mhyz@U_ zo=@5bs$ZU)1Cu9roQl%LKK9*P{+^B=k^$>Cq5pbQRpMSe2AWPdt2$oc)k?8yBHgT%FptpKA!+})mo1iF?I+Q@BkT8~oe$l)TB&gkDZF>W z5tl7#{hvrzg7mHSnuxu4dVd!&wf-JF;iluxdBDv&ofP2RI^=P7R5fa9%qA=zdreFOU<3A?Wm4ltBbq%RKn zuIrlTqQ5_B-Aq0+WGIeajn&(rD@m}=oA+66w1xYayFaS(>_{NA`Sjc~RHs}l0>V20 z+7fb;dL2ioxTc#P=H?PoCNVb-Ty~w6$l&~FKJXr1Tf*+|_#1oi(QOMnAk=iLd@V}j zFSYKyb)&*o;s6E>{U)W&4kxn16c)Vq%OE=m4;J2E9z+h$1+jn?Y)r1|$4I~>36ht+ zzftLI=)V;&{v|mgJQYj%Q580ZPUoMdlZ0>#M4h)i&Vsa5zRiQZc>sYnMXnbNTr5m| zheMlbZhJiQBt2%UaV1fe(z0NOJ6N5;)zVLb+I#T$$#1+extk<14*JY_*C-#a_ zq>o(mm82!C*JNJLi$$;Jww~v7!A!{6Hkvrk%6z^|@LLC&Ts;VVJMmpuZE+EO^U_Wr zj3CfVdj5MTVNqwgl{=${sWnT|dXE#i8Znbt6+BSNJeSr-l^#+7t?FEkfM@G*3O$3r zZ)aC+d|YkEn2eJac2su)HAFtJ>8lFcT``k8GTwqCXU|?&uVAZOR2+Y!CmC0vwg)e$ z@Me2r&s^}rlH4L@PTa#_1y|orcjnFXyVz5y@atV>Cw2%Pz|g8-7w_U;8*i@!bOAoO6=k#DoIM99&4WI%7Ev!% zMFEd10tlq;dG1Rg9gjqJUAx+}7k^65(_iAtK|dKddqtcbyL)dEiu^l154)nX z;#5YHPJo%-5n09yUTe=q<`2gjV!9vfdZpTG8b)mM}5b-qG~ zAXIUn`{WgI<3Bw%{CVXxnH2X%@DO-bk{kdlAVjdeS?Tli8ecxQK+`H;>;Cig1CtA% zP1z$#r<3nzv-xI(T5}{FuN-2xLaO)GbG}t?#M0W^15?JVVPV{xm^p#xEtH#SSDxa+ zNS4}rbn7b8>Q9krTS2+yu3>pvCy#Va7e70-t^Ftvmw3Z6PKWA+OY#453PH7Pd4jy3qPk!A2&d|m>o6dnWz;fXrAF(4n`+j1`fNu-R}@YGD;MhBbR+?= zKAN8?3H3Jnvn80XPCIZMM~{rfco$kP$@P{b`t0T8ri!B?-Y->cIXPd{#nW&zvXXwm zx0Nr`6MM~=9sly~Y^0U=@7f&>+^@#(C1Q<^q|8Y^4(#H`J#e*t-KE$}UfDO!CE7>^ zcs}t=IJ}zytf+~6+(iFcC{e+EtRM0~xK!vI8@_qQwv5r=kGFoFm0of!(v@lcftul3 z)RmO;L~+5H-t6=W|N6m3(CMKELBY1jpBG4mz?osypMVwg@`t}t>Ti*FU|4uMB4V`g zi`+$5z4eO@+=pyq>a8-^^nJW(+`;+#f}G;dF00!{eTxqFBlc(KA^_qn_KR!7jL43c zZWGO2dzgYgou20XpM;vjO+J&L0g(XhKit;`rC|oVmm4dwc{<0hs!^r_JvIfFNHhPJ>X1nPST|MPW=(T*@{m1z$BCStyRSWMl9NPyT?>698(q(jwu{L*BNCcZbH;h)C zPSXs97pe;Is;bj9cRar4X=uycgezVAW_6#47ItEG9M@>?3AzgIrtmbT@&p9_E%e?U z)qjQcPJWdNY^mtgJNTV{rGHfqy?@|4?z&N2W8E-*iquxq8KU&9&3i|WJzl-2R`ty` z`sLp*kJiU`(}iQ`B`Fnz+E z=zKNASL9xRmDE2r`%2;atJHp!$Rr3xF$p$W5cf0HXz&)HUKefO(4BoSV0{^PC`?Z7 z42omFw1#Pe-S{Gw|&Ln%ZYJO7wD8K$5RHNlBm-q?U{UiL_Ky378Dfhq9mfs z>lY1@6_SxEQ_#|rlk>6D^U?FMvXiotetS*XS$Sb)PXg}qF?WoiP-t!-1gWUL3^Lu5 zw-&#^oW%EcIG$WZUK_>(Bwe*#`=bO{<2pXDn4bYh=Ut;lC^bg! zcqCZk0@vtOm2}u&ucP9u@%qv;O}zTd3Fn?65dv^s(s;i+KS% z67b*>r*R@o7~I~D^Tjc&U8h6z{mq!w&pgeIMpV4s(Et7|ymzhRRQ_p<(6T3-o=wcD z2MysLa(4OUPk0`~v8qbX74YwC{J;2Zx0M8EY=io8W)vCV0Q$g@g-;xfRZjAFyKom)r#Rf3u7KSA0sgP)N>ePe;sj%Hr18M4}0S*F~Ysald~smQxI#rbJ}YTX5O**R-T{tv7&t^&IOOuPG}0+{iUSH2Tvtddk1cE7#LsL_={m)RyT}{g zpiLd>F<%uvCYaz<#p0ZI=wnQ8t?34y8IJi}z&zveP46e5B)Wb~;TOujXRB?atT!Pn zgnicb#AL9jvxvV+AtCtiP{wH?jkMH)9(ABmMIq6#E7n?=??wd_uPQTY6jZZZ^W5T| za|p6%9K1Esx1P8Vp~EXbPS_GU7wXiT0kdqIQOhiAk>n&oxbg?YnNXJaO30iR*&iyf zZj>dgL~RzW;v&jGNR!_rI*`U@O)ALt(2NiW-J}}7$U{aX=ERB;pfgp%pv4PtG=3>y z=#1r;pfok0bDwNP=VwMNd;>RFUf!{2I_T6lNW0~C7kQ>W=x;T?REMl^{3XT~bTJ%ZA5}7X zJ9jK=v!A~&KU2WF+F*IF{}gdkBk}{w`%#8SGKDUAR}nOJQ5Xj&`GEGYhdKQd0tSR*(0nHq8=8 z!O&%h0l!N!2+L_KS`;}IQ$3?`P{v`sQTq+hA$y5+MQKki?V`FYTSv@@NHrXWl;z&{YH+KOV~8WBw1wTMKZ$p_ZSgKFOi zwV=RLqLU%!Tl6E>b_x~hRj~M#9e9KWO$kPzDg(I3C}KrT<-C`yTjkNuzgu`92I<3U z%3T3f7-3oKv82=IK3Q}q_Kg%iAnyU*kq)L@$?WyF-A>N z?bfI?OryVHM8zkXM#W`H>PgKf&`3*$xD)q2ilq_{#iMpS6G6>p{JL|0>l-S8(zEOl zv#8=TlFn+!3L!_9iJeBmkm~!A+)zyqV;+&Sf$w!=DT@Evo45QKowFd;Z=GhAWlnuL z4~EyEqXtR|S+!wb5dj(TZ=8KSWbdump2PS!NHiHeIS`Fv*a)Z1QAO+p87P=QDJ~7! zfYQca%vugF#~DnMw0Bg#G`lF%uPVW;f8!FlcZ4%F%Z4F1tMdNdw0E=~m3Kt#pIiCd zu6A?RwS}-WYfjN8`k@#4an2F!9GiDU=Wl?F2KWuH-#PSOkXq>enHE;A%w_X^<=@JFEG%H>cYm8?QWHWoQwq$ zk@F^|{m}U3s?sM@aVHWu6V0k+4qmyv)XtO|%<(O1*xPE3a#>|L>M7)YQXg)wrJ2#M z<@N~K+gimq>M7uUk{|ZKh`Rb)sH+A1d)M;&snjFJi0lH5a(6o9(T>#b3b)@PbDRor z!AUm*LvCn)ll1E-u@WCurg}Kv`A=sbI*ODUIt*f@tqtqigpx_Mijj661Oz)DK1n8l z_cdW~MFn^l(T`oO707MU1Y;v|vApYek6K4_E^zYy&8aE&2kwCW z{`d*tNBoZ*o5Y&U)4?%WN=y1LGj=MBc>!Xoj4{Lg zDzvC0-7aHQY;A)Slw}EwmQE-KGGIE^}`11?zjZvCd{ML zF`U%1$Z}WBKX_a3H&Va%+LT~dRydAr9G+BhKW3ZM^AFuU(gJYXu;@^XIu1q^6QgS| z6>8g%&J8jRcH&K_jaLkvK1n5{tVb~r-s{Qm(6|~1*2gO#9zEU~- zNW#k64@kFRjho=<;~YCw0R&9hIy`H9T4fVAsv++b>Jb)`2cw~=Rb;v#6uLwdx=0i{ z0190gN+lJU29rdUn%b0KmU9W0lMaz!ZB!i^OY>_|Isd;=10IDw_HaMJs-k?_m!P1N zr=c3`D@8;>&-UVflJP~y4^(L5bA?>Q$o!o^>J;&)zkNRW=|7;9?_F7^ijQ%E=WW=h zL@;hC5gL^v_jodZJfT$!`FjA->nVzAQLtU?v6~<hi(E6&0eDr{ook zg|Ksm-W7of#3YFp0Fqq`DY*q=761uL7zs-%2}>-=4Z5fauV`T?2@4ojxuqrR?970; zvq-37ve)zoLe|jq9%ikSf={)L3a?Q_xC^_mYpXd0~W$6?7_U-9M{-T2mu0 zsaf~x(c}Wn2haAE{kzCftON?akT7>Ebyc8o6&8+GCAL;2jusP3-3oJ^!N~ALW;!gnOL8lYU(c5w^s0(v zEm@qu_}Q^0pvVyJk%)LD8Nz`W&Oe9Y8KDKp#Q|4i1m_>k@NCxt^jHh#YJhMsf%8vi z@MP1y#%uw~WUJojY6cN!?P_G>g^c?&(2EG8=D5@OB5_Fot8Pj>Mc%Ec|Gu3t8e&vk z-d^oz|3(u>Q9zYBEx?k=3=NL2bi_N&f)XV5Un3+YRMywy=^H?b9FP<dF@BG?&h)Ed_0Cw2}}aot4*J_I?Z0$&@%ChwqRBw z41JDqx|~nUa(g56hdeX*>HS|3Ed>ws~&uks_X_HRh*(!pDGu= zLMZc(kUnseT&5SlfRkNn#VPvcgERi6(<~jxQ|B^R=(u5Z+Q(q6t$Z3w z?cQaoIOV85trdIN5qsbj1#$)VT@}ATiUKJ`fk+6Tt=OQgIG_Qn;Frwcm#pByuC$>2 zyci%k3gY=`3gX1zSai`Vi5Zo-xYlP&OG;1}(ah`7j|*DyYN{cW)Kb=eDhCq@D+bT1 z%1rlAmWMyeg%#0H(ZV2kvvCJp={Z78H^EPcn#&8mqf-Uqnczxd!QH<@S)+BL8?}s9 zA?rwR>tEB6++C2%sE_ZIG3yJLLVET3BzOlJMtG+hD0vYkGf&mh#DRpWc8ASc!PAbK z#15$Y#x6N|Tnw(Cq71wfElUtIB^tb61ue~pY~=_yQ)I_XINC|o$>eJ-BV#bkDg^EN zz%k|Tiv8upp9zUSlM;XKCZ5gPfU&bPfvmNU97i(i5Om7|k6BKS>rapM$B(zij-!5eThp-P4JR02&n_}Lm(w{#ke@4F?a*Evg==G@=)LaiZX!l~BtJum(Zt{h=Z|w;@=Y@^$ob3+#`Z)Gamae#65K^m;vg z*v~+?ef0WsF=f!gDZF#=vXQ-B31rg=x@XaELhyAiG&NSA62Cn^eKyL%Sw9mSKKKk- zS}Y-D+Bs*`${n zvr*JzleS9q6yS8?D0{Eh=%MS%cluTT@aYsXx5(DBz7q7uZ{>BN{=!lJ7k=dC>X5{U zMS=TH&w*8BPjjbWuJi4tt>M&zdVCIE-qgH0;y>7$!^;<6kg^rBIL7mUB2%vB%K*+y zLATv>QAkI+Ru26p^EBG4%15=$-jDl{8H4nqrPqsAM%`JBhg!zb?eF`)e^y0!m#~jc zw-h|mL*0xY>c9x5HN7hyVnibamo`zaWG@@ZY-SEY^xm&ucTrk>mNPt-g2(X73y~Uq zYR+8#CZ6; ze%N)MHngXSj73iRp!&t$Djt+dhQ4XEtJDzGnC(l!!Rv?Ucl(Zdgt5O>>2f};mrPx_ z2%cQQJuzI&zm&-_t{rY}?soSGtiEQStoDu_RM;Y5DqwT;rSsdpdSvCq`qgX#=rAcm zo!0ScYBDx@U{;OXN1RvdydN)NA}e}JX9@VIuik$kviUUb&Qan&KR?xG6a$u2{((v?l72E55r#i zS45aR_OW;N_lpjf2+KAxsm`*DI>skR57-u-(c4G{t3{(^zLkH-Tp(<<53%(^L8w_fG42hvGvn$usG zGrGnF{_MZ}oSY@m=~?xTU(cyrlWhu;LeXHdlWkm&OPp2yB{7{=HCYdj-b@}OuG*E# z+x%ObA)|3`*TLzXF1%;%Z<>>BNsn+6ZoG%Pac))oux*p=a|qEMTd!5wjqD5;?h7Y} zxEJ59s_yP=V0(am3YOvM-p zdj65ew-2IP0V=@PlMIH)4-U5(!6)Q%s@10Wi)Do7?;s!__!|w!g<($Qg(8Rh#y#@+ zQ`hKgv@7fQ`lucI%I|2TMB@=_)j?7JkhQ*o}R~D7}D4%5!bUmy- z?i<`lPi_G$7tK%ITWJfTolk_1&OSB-QZhtAuLP>Sj7~mnl^AGjm!zGtr^WWVgFbCx zq6+qW*;l*%`mL674Dk^*&hH6y(?^HAtz+fX*cZEu0^NSn>g5rYOnytp+cgF?w_7RU zc8H#B`dCMLrz>UIotFxy@HIZ|PO=EqnBQ(?xP~(ioS#Mlk;%z^Ay{8mPqjl|)?Xc| zkVm;*6K8{WyeTht8DIPXXdG{vPo|=@`l7FBy;UDde9zy<3UY(U{pQ{2>SuK1N8aC` z?Uy8LtLKLOYmQlq%{QyC?}A7HR&6ICI1+f8gQuZF`!hN??MwvxtHngUzjGVIuRd=0 zYG!TK+(89J!>`%{H@RLdvWFkw*y8c~XK8l2n(+kByiff@@{H{|jk~p1|NdzUJ&TX& zGKkDOOSZ`Czu2CgBDvU)C(xB0@oRj;I?1=&QQg&~KjHFL+|<^3_3KEQU1+5+%(ywf zg|ps9<~*2;nm=0V_+4{P-e-u{6bi(gdp^?t%Qd&ZW9T>~MY)?PB6w(3-o4Do+ zHlf5H5H^r8P9PbOW;ZWRL|vE^N<3#pGl4}O6Y=JjI)i{7yz2iIe7G^4^Z%ez)i$tjtL^r6 zZht#`f5PRyVElOgHCdNO2)qEVoZM^td(p6Jqwj2x55KxTxh_a<_*>2O#|PHkEdF9f z;Hg|#Q}wx~!rN{Il*vxdnl%$wsbqOF69<@Si%Dm^s$hOgIjxPT!-sv0?z6v94I+C< zn%n82&f7yB1sv*2kWO8Nl2StFIy|DUpPARE79RBab;rxdPsq7qCk?%EtNPV4(rhp) z${wiE=Axn{It%7fUbnfcX2=B8D?&qM)*oGksrPXk+3X65{;b1knNKl(Q9GKphNZ#2ttr>aSST)`dglHv zMHcX>@((n5$qk`aMqtCH%c{`Z@wRpKqk ziT$VF-1K`%&3a{Gd{Dwp=36+ZcaX89xNAxOkl3y3vI9B-8$=@LF4vhtkfxwaCG|Lt z$%D8o@dMF)y5MZ`FFppV+y3&1#gG8QKu=Sux9k$@$Exg)hj?u5FD+a?Wi8Lt!?FVh z`b@u`1I~BUG$z;w!VEuw*gGHZ6AXI8yX5%0^Scu6yr-|40CVWC8R-j`)kcR{-PWul zg?&2E!si^I=rsNMiDLEBADddcv{cyq*CCXAWRvK{_3g<*MVvO-$GNi@k zpao(waAK%5;<6MXK^zl}ICr5dc3@1LGG~|P)!fDod7aQC?}&&I{Td%S;N&h@EK@yR zfs00OOGE{3GualK@O{d4jS+2WRlA#pI%u}EZj zV{b^!$4glZ;*lE;xZyY*=FruJ#`ffx*5ZW+b>65+qLK@Zt{}|a5*xT_U>~)Wz8_Q} ziC&DTYYrY3pV%v=);cPrFF_)S{@x8Cya60k1d~dn(=jeEyYrEwZ7f)~yl{1O8HdEAeVcS0YN3VtsHe{&ryanr`v|`4Toca@aFeN03F{LeO z!yiqEMDaG`L~-yTW3;Z|ss`!?FJW{P@KpJ0b@eA#c`BgakrN}+5xKs;dccSJ&eKPL zx(5|owajk?EZmC9d()-nmDoC12)?6(bA`?!3KxZR{R4s*L`Ig}$=fw&&q&#*BeC38 z2;BC>xxvwO`Xq`K{&|)<2e|FE-Y*)9!s;YAoiME#vUP&Opb+1*(aSm~)GKuTgh@sA zANsCM?Yx;uzrA&wJ@eD$;Xm|q>7fKgYB+w3R7vLS31hfmxHfn26O&i>f>$6aE|!Rw zu$1KM;3k(cK`i@eR8H;!tCO}5(Gg}v@Q}Q813J}N1Lv^Oppygp!L6aks`If9l+lFb zNyr&wK%2*YQRKU4_JA+lz^vuhfS&?{{9=Vkq0{16YsQRJtX2QH_auE`kohJ*o-bL1AVzv;o{i>5i|kFyt(N89<5HpwFM zwKMGtWl8#>I|g>rcusK=wUszcsjHD6@_Y8RDr?h zAn(lJ&uv)N1pVw_m`fU!;6I8fjG=k)s?svob&>5N@VVuEZkj>`f(F-ApcaLQHsL`+)#&kH~8W=~RVQ_-0 zn(Bk;OPqqMQf`9jBaa{eS6JWxZH0uFEaEY6?FC_d5Yaq!=&JdFAIAC2dO~CCa)DUi zF{&7`7GfrW2q4XoNhRG5XwdcX@TC61kSX^0gi9ovSC)9n`3n4&$q)Afj#gwjc%9}! z{BX#KG*F)Pkgn1HKpyNs)&D>qI12G@@GWkdLSN`>B5EpeWzZxT&#nW?0A6b5AS`i+ zBWc!|WZ6Dh%Ik(;mx5pyl;IZ)h$9n-BO2J5T3T*Jb;sONpk5(RFY_|ZDc1nHi*=?Q z+cFO(#mX_a0LaJ23;m0$sgpQEJcts*guXpdSG>5Yi%*u45wFkoz)FF{lL30OeL}`C zo^y4eGkwxZ`~^+tnh@fjm69B!!x;69B=yM9*fB#wp9q?kSRtDB4NyK-9_u>I_Rc+F zT9EuY4yNlYvEQ#2oSrj?0=Dgn4#-#n^v@6Mlmqc-2fc)a*h+n0h;1nQoAavgJUU*D|u}xs}>KQg}-OU!dJ(M7dbVn3EL|3++ z2FqgQ)Bg9{sGv~FG0IFgxzAz(wHhO2GF&)8a&Ry)c-ZO(5m`kGdaz>fpN$HoX8D!C zLG?aS#>qpcjXrmTWBvXz*5jYSLSJ&A{%C8`a{Sc2ka8YYa-Bij4JWz8i`}TtTi}m0 zz86d&h8cam=si7Z&%8n}ynn7AQU_K^!fFR$4JFZg;%I#dRG)ckt{<8QR$+{j8zMJy zUWA46zS~~Fkt;bPA-7ar&1LN)B-sO1L0me`TDh4+2nn;(u&T0^Ain`mxn#QoG&BuR z7blcWnAn#psFBifCiyV&Nh*3ed?pVuow|^W%o$-O*&b^wQW-|TF>%R{X$tipa86@J z$@zNE4B}HCmJ!frzMz%GsWab3I2J-A(~p7n?8N&lonjYq4_fmdz{3fro1(E4`b5`d z;5bc3WS24>!%nRWWHQLsI(RX_I^g+>e6Z_}QR5Y6*ku;$m4?OB3MTt^yy!oKhima( z>Of1xi|@Y(4<-e;CS65KzSKZ#xc`JcZx2W>iSd^K^d~gxqaCXcJ|~E%qvV{$wVI=5 z>45<<#S_JK84Od?nffea%%%KNQ zjjb{2AvVN}jujbajLnEjBMQcd$}x!BCIyaQvqw75UKJKCwo=DQD^V6rK0C;JT#CFh ztWLi971zLDB}u74lm*PrV;uJzl-_}EzuFOH;-vyGsDA__*R z6E^aU$G4~>sUc2;%ezNPxDDv>#W-uN3q8(ce;s z;jY*`0JJRi#16BmH!!kMD@+QqyMGogVgTtPc$IAe+bmje#g?}=4MtxA@fn6|$+v9r z^~Ie+GnP~XkW37miiIu%ut!vkhn0lIgw6=()EOaBWGkTnI0H4}(H7Klhv zFD>^)B=apz-iRR<$P+)@GWukN2^f@?L_2f(XpDEfZU1XxV$U{CC5P3&+_ z?08P)N&dU?0^Nk84e&$%;MRu%p}P{Ci|Q;8C2C6C^ zQQmUMp({}u6wApK%E^_=b%_By0j$bW0O|p8oOMm=hT%3Th2M^_fYr)2lkPg3k~|wo~-xfztgQy@`RD| zq|=z;JT^)=E4hwj~ER`^6#@QS?!C~QC|Yzo4w#8axcXtW)a8~&D> zeS{=EAX1#^%dGgMR1K$8QM9#{-^xNHwT~~x7>fb*pm)-P3}ga7h2ZX4Veg&&fIq%} zgghyo7vQ_-*}BpKq(OUIA(Hj4WcEtQpdUM3uOP! z_19BEJ%kc&!+N+&5_0qg9(ihUiut3(g5sAg=P!mQX*@wJocfdIGc|#}Vqwgs$IDde z?}Bh_AzBY!(4pE#K_(j1!v143v;qLm& zhWUiUKK-QKYE)}G`r|mOx@(X>&@C1A#*cB?Yo*gI_RsCb$%+H}CBNwv3^OK)t`-^6 ze~I-P%^Y}I^sQ`p*ABbM#2hh%j#(Y=vgafHrap%1i&;90x=b}D`m ztd8ldj*5eQeiMCpDM3&vK@llIz0_d566Qj)f8d#8CWJ5~%)bc_*C8VXizW+)n)x^3 zLBMdSUvqhytpBSgkC2&9TwcH%LdKuce+F;*>*;sYHduMT5WF>VNG7}jLNj(IuC?h- z=n37%QbIJFutW_d$U&0PEJcA_(aPz>{)r8k7F4X(%_Nkc3vwkkwnRR?iD)@gKzpDY zPGN&LyoR=)L{WpIgt|7kJ&UF~VYK+#4=#u^N)|#+GtdsNa2R~+dF2+AB2}4KKwm)% zOh_uabb?jXXHug0cpB4yMXH2C;Pi5$E_nRbH|9y@bwuzKhWZqU=GWh>G} z+0VUf!&em90KP|-_$+0zL@jIKnBqpVtdGMn(ZOP!Qo`J?oHp@Cc_l}=s0yk`LR(QH zw!t6<*Sy;*1;;9-o_*qVj`}P*9SyhGe=WXW7gbb%pzjm>KX`{2jpz?+lBEZ#29OwX z(zma89c$mxy-b)l1M&n}FU8b?nsGw$gaeFd=^);PRIXCYjuoFWWXmx41h)B`?@Sqi z&T7wAELotgMI>8gLa&fro7vFS3MCS`LXYnz7A24mU=6t<$NYU zJ5Wk52q+-B{bsq#;x18rUtf& z3NHy2)4;2b89CXiy^k9P{XA-HYZiBRrbhj_EZqn`Yj>s_DW$vM0%8f*TDJpU9c$jX z&yx;-H|H|memiZrxAEV-8Jz|lyo$H#f{gw~JGlL-qN12Yow%bWfGpO+?}nrtk9$qX zLYv9-&fL?8GqUeLBYjU;x~t5EHaCVLrFhC8yI1Z?#k9$pOuP zc|+4iKSYnB&ffd7W%<>>&vOfl^ThJjw>K=TZqKFW?hwI~CkoJj{H#+dH)A=fmHEXy z=NWudti@P_B|1%a%^d#983RR)t2cc4wH%)6)~|R8JpF)DM5D%li|5m73jX)#$C#+R z)RobIvh8aqqA5F6?1<)4LdT{n-abQPJ@4)Ni?t*js+gn459R~VXe_EYo?O-L=@L^9qfvLDIWjKRPz4b@WGA7R9@Lo zN?+9XJVLb9@5v}P-fo-HUGDWwpyho4&KRjQt^Un{vA5oJPHtt)L7Rg9pUeUD#r$0RZl;K>7cjn(>q*ysc#UIw9BTmA#qW3i zn%23nqWM(+sloMzmCIl%8gc&Gz3%+oeLG07$lFA1Y6$4Qjb*v(vfcdmdhb?lZtMD) z63S}!wA=vpU7GJ}`-WF?#GCHDt}8y$c_{05&RX|u_T(baBAnRi*)}tI8Q*JTxH>HR zoXOYQMdi;H7eF+t>7YNBd=sV;si2wL`s*X_=n1dh(EjZ_zK-nu&BNbD0ANU-qNGm! zFlZT*^>{QmOQ{YJZ0Po907%Z1j4MA2nw@?|c=3JfZ*N`?^Zp;k?kPx;_FWtOw2f&S zGi}?pZQHhO+qP}nHl}S)+ugg~_y4U}Yp;WE?}*BXtf!(Pvrg(DujjsgasltPKd*Yd zp)X{ib$Yo>e((F*H%mI^27Dh*eAV}!=RY~he<=RlDQ@`2uGRZal(Jso1^d>3w-xP}c9+ z^Hg6OTuoi!JT_V5!1I0{(8Sg2I5 z;<$}HH@;`wZOnXZ)O=fyJwly>*E2Ah<5Ayals`N=LrM3BU^;8GxQT{xg~1TWy>_rW z8!Gd@mMYS^=JK7NYV2}mp}Vd)@}&@%&c!%7>cd#IzR_i^<1$^XJmT}a+e)fC=C|*q z-%~R9(KFh|z99m>mVO)a@+65yZn;agpwAQb{>&zu41t#C?Z;AqcCe%O2I%?N@ z*4|M*NWtHG|1>^t&T4CjefWN#tb@q*_nLgq8;-`GRP48YzW9QFt=PXLvWsBLW=D_; zaoNcq694XydSN+2>4DEV(Fv)26t#ZA)IES<$?jh*!GGF}tvx_s{(#bsq{Vx^Y>3C) zF`Mt%7>)K9_o=^faP@qlH@w_(O<}pmW5rtViDYB&2(>>5AK_F%%ca}aq)%l$mBE0p zN@sk!94(EeC#8kFasFdl)!M2(1F;B?rFnvD%i7t`Y$$a2Vk8SR2aPD2>_|&P3g&$=7Wt8*@QHWN7J(6QN^Cf$8jEAC(I(l3DBa%LxYEDG8 zj+h^PtMg_#1>b)*Ni=0wjS;j*BSAFWKnfI)Nw7?f;TIw9GX(o*auNku4gGQM!vujg zW)=3Tqk@kKO5SL&hqp<`RFmd$?ItYA@TiAoNxGU z-l6Tg&8l)9Dd>z_?iNQ^-3Ctx9+<=oA(1=TV#D3FbzX@b(|mV7o58Bu8C@-3i>_3q zsp|{f#?{=;m)d|8ISD?psyQ|8P0v_g{Y8qe3 zvZ1G2q>j+t8lHJgG^E~oXSz)U-&V!~zO>T^t+T7iQ3E0P$gR#iO#c3+GB4+?G91dj zBZk-RluFLFXl`9LK?x9GNAlLqc1**ieOHN^P9B!_O5ri4Qx@c$9@?xw>?M_c-WT$kk6>j!=DP`|I+{^`ss?x->o zG7O%-uSCGYzFC-TTxJb*f%Q{K*<{5tEpbH!zw%@?L6iyZS$e5^rN~~LbMb7f)>aTc zkWe>a)S`o~t*z*QSQ%WmWj?d$Gkq_7e00P*oA6Ty-#ddqtc{}HU}Tz_S~V4Yvd-}M zl)L8&JDx-v`Qx@Jqx7XgkFzyx_v#A~)amn0V0?vdg=-NRdBgaz^jp5T=43Lbuh!rt z!Fhmx_5O%$#~`_P>hG*`=u3M}ZQpCxx9fMA(QR(V7_P}MTFhpw2@`de>3BXaaTwst#7Q!&s(`ow$QPIx`z)?z}lS>9P z$~gv1XFO97N=7u!1%oaMPbBP7EjbN1Q4<=C+oM^~>34{!N0?8kA`(inN5B3uF1JIo zh^Z4?>e6FCJ9!lNL~NnDQUK&ULiyVeEZUQh>RE`vZ#ZjCfGox^OanWOhLA84xnPbf zd76UA*j@l7!w_InpaxONgbCbJJpH!)8DGyP7*--@wWJaOJB-QCq-jvd0VSUWB!v81 zDme{y80yxHY)I*60ozYJX?HqhBnA}HzQS}kY-183`+d%gi8|e?>IKX~5+u^BU=oyg zDwWYGby=BY0~QYtXi|<7Ufg}q+xiBNmcpu3b_q?+cUP~jXVMu7T(tVo(n7f0@|&`E z+W*dA;s8DgidY+i^#4~$*#C9T=KmCk{l6h}JpfMf&+)%Pm@7{JgueRh(+5}BR^VI- z-L|V>gkH_?o1y^Hz;1lBf-r6jR4~Dl6ezX83=C;bBb9bL>l_lVxB^9D zvC1h5R`y6)n9}J3mP+;X5`{nj3OR{##zq+=oI;&Nv&7FKX9|NP4V5kNVkQa@xWZ=o z`Dx<8uiL}}%s7jsK_wD7eFlrBDkY$0a*}A3V!8_FDm)4XK>&u1NKhM+GE@*h!NmM8 zhJGeZuHlUIUl=;1Qi7_$s!|}7Qp|-ds51D7`2NO_`>}t8eU3ezdtL@HvvTcng<>>T zcT6(89|f~grH8pfw8HMP40r{$zY@3w9c0R_9$R^T9*H7*P4rJUWb^hsl`K)3GHd<6 zK^!7-s^I7BQjkE8?NB@)LEvrzf;IyXB6DKkSfwSW>|2Zv6wN7cQj7#wi+LHw^8-cV zSlSsX++yP~*NJ3c(i_si(vAF$Kis8>wDTE_$sR}w`;CnHL+$bIJlf<71!p5ris_T( z>!KF&z^C0MiOoj{L7L4{VKgTEYP;Y87gMF_3R$7dW1ooPcr2 zMp+1znQ+QZMwB28odt@Gl_1vUL}o(pnKLwq#d2L3U!gUu1JZ9uF=aBkR*`6#e=&43 zR^X;tFkGX&!Y-bl68+wRA}r(1~otnoLm(r8KG! zFc7l{YJ)vR&+^R-Ak#4@Z1)$6#}+U!!O-GH0_Yg1w6>?5(-bm`aV9AglE(lcZ~*M( z)Vv-x*(suUN9S1Pw=sYzQDt~Abl2ueVy1|;2!?hip)JEcyP~n(U83X@j{k{Lnyyl- z^!Z4a0ekV&H#fGJFB{%7U%b-fwNi;5g#r$|nF38ZF2L;2m%EX__6Pd}I+DT0HoRAX zRUQ*oKFScCG`p}cfGH73ELkl4mnq5RLZk%o4v+i^Vpo@!JDfD&&~w<}#cjzbgrn~3 z`>%!WwvdKM0I<-jO`0?+`3sfx_$)H?sY*-cA_k<*1>#GV$b&@8qfDYSlKm9b@q1c( z=b(_{e}bEUsQ);y)o)W1Ghlnh9|ENt%Y--yAY`CR+Uru8vBP`gC1La5^RmGhWM3wQ zJz*TgoM9KC!@2*B8Dj`GNS{7sr$l~m<4%_rlw=i$)%Rdxkme8DkS_tg1Y3_exNd|p zm`)h2mNGd}{w75XI;ETGk3CRKAmf{PJ_N6j$6t1~4w&?@q^@-aFKwnlKQ->_vxIZ2 z&d+0>wEPc+4zNp1Z#81~n(-zrxoN&=N*`aW{*sqEbF6S^f|IbBamTN6gu>tyO9ZnJ zSLYbd^8hod(S<+AkZeLigGA#URnLlTqR`}*6jb{e@g&JPii_f{`fJqR>`@~gX0|g8 zbCR6xZmnCTsMfDztKqZ+$jS9tg9U%6(4hh-^tS3<$KPo*Ai8?eZOk)zZO+q1ZN^gu zchj&5F^>~+G4dEqYwE>R zNX8Z)WLM+7!v_#_=$JiZ*i*bWk_KdEx7(~|Mrz}%Hu zebikQ^xWVKI6LVE2qho2SpcB~e8ZMvOo&2;v`Z-cqq(bPq{0!fv)jnWu<{VS9J z6!glvu6c{H!DO(=C-UY1&jgaaP_}ElOA(D^<3R3TG0zFzXpU2=7;aoI@c;!K=f4#6 zpEybWf;f?(_x(RH*(?liG1PmJl(o~@atrgga{3&ma>l?&jdxLIFU@n-839g-yUFbB zRL)6ycORo?yy+9t?4@zeI^}IF+kTBnU@n`%BwhWg(Qb#}S6jcL9iv|>gC#&gKeCvC z1t{q2mzc5u1-(0Z&SwVPRS5iZ*ha2eBh|!+EpXeJ!3?x56!zR4^w~b+Zy(r2tyce= zALF&)Z#N>4qbz&;lR#32ze^^6Ory5aW*z%&8UAwGPVu@K_8>;RCa@mo0^sx85wB_N z#sJo1?CmsLc0&Ou4dV~Ne--q`e--pM#s-S2AUxwbhBvfLG?jk1p{_cL96p8gF(fUX zkY%}CJqsF9hy(~8eaUw+Muw#D7ai#CIkTpib0b`Z2KdZweK=ULOJGYRK3k+;9ZE{^ z1#&{e%nu`iLn}A1mU|?Z^K~p8$XQq`w|&C)+67wN57HC_I16<>jY(pshN%mE@)Cl- zjzE4D|9oTl*`jCYlnIj=cU4nZU1Qq>mjBR(DjlG z3-WnMv4i@&rOrog8EC~RtiGidRlxQyYEc`_&}z4qWug0lz-NnChpTSs`A*tKgG z-$LD!N^-6aC&Q$GF~u$cX`dt~Dhh>+L!8A19phciBMFB}?vW@1_B#ve8klnvD#nSZ z4Ti9dQvSp69|m0jz@V$K*3-#;C}4tiZWar(OB9ANWr$r6wn;MCr{)y3r|!~pgSm#= z|LdO*L0>PZ+NJsgbPfSbbX~htw|;FTP*+TXHpyQd(xZp%J>qdjvcNJKMfk={X3|wC z@Q42>=m|Jbm_&#S7&}PWp@`X`NZCgLGvCNgc*p~D_Kf9c9P+8HRYfo*uI zljBWoz>Tc^1C(GDqlqSp!!ZL8^tKA1l7A8On31GiuZ)=c-!ij*GRp-QhL13p3ck3~ z_^m1%qaW?@G9xI=mFD@lF|7#z2>MJYqIaWKItY-gUl7}@?i>)4BG+Ph0=*v_JxSDc>WiNFQ#`=ysgxig_Zk2qxU3zE$ zP=bf0PNUsV#WW+eFKHTJSf|#(;QAk+L`WH~_c3ZX^4K-Weu#FI^X;fKK-c6T$LT*n zNtRyhQFu&!mY{(<68)wTtn!Yr3*^TXk7vZer4|Ec?9>c8gMYeEa4-`(q`W+t(6CZy zSsIDZ@CiUfM=#iiA*3*Yl&4n=i%=I(nnEksmjpQIdRv$8Ls|_@zy5@@20L|O79~#Q zUT2*TM};YTa&ZOD?%YFB!{C^!lg-_s<_{CCntI1kByxn(L6yHAv(cH-k`Hf@-WlHzh$fRUwv0DKY-8J;t?@*+;-IQ$^k1~AveI*d`{Ubw@-pJ!s^2y@^2Q&%4ccD5D6Ai{oFB4=piE{}P zW|78E>JHEU%!4CU>j81y{OV@x(o6M`r}|9LelzZTpcHyj{`E>` zDj(4%Hpq?=ec-Ad(MC4N&XXc&lacUGj-hjx5YP|bK^+MuC8NHb@P7<+2=#XuPI62v zTte_3qMU8d%M;eX5*O5f2U$Wf-91ej2b}}W>!iGrE7;t3ZognunoAddTQ<&T)`3>8 z&KGnfvfH6dKs(sr;z&&?X^QH2V%Mg-furhe6Dko7Jca+l5@cCLDYBdxV%4#@-v&m0 zDk(w|Or;@4tQJ{w;1U4=GgKHWTI}pPtX%Z0xBROaub_W3S3R{jje)#L@$&byQZ4WVx?-PJeGJ>`2@sX$^)pWz@<0Xh+Jz}<0 zaXaDAxnrS)s(;YitE-+=l%m-S?E)PUo)VD~Z0u47SyvG0KCq0T@hjhQWla-ZJCNmF zRHWc3t4jEo=k)dV>vhD=374_Tcy_zY+df(!#$f)D@0xXwsY4_3^WrW4>#)E-*C(px zPS}#WK)y5oU}{tU)P0hmSDh)Pw#~>Q_Q)fu8&Pi>ljr+u1s{9yu08PBrhR8(!;E#a zSW+H2W3i<+xp%>^Me8<;i(=`ki^I*$XgyBl>0}%i@6vY{-<`eF!DW-^v3!--0FRKiDl=a^Y*o5dabotG4lj=p1QY2vxT>~ zsyF9F4fob|Rn_wH+x}nt-zs!IE5;x;Ued8qbv!cD8w$INL+9DuMy6Byc9hzp-+QJJ zDk?7a!Ot@r@hi@p2%g#aCzjT6pRfFnMRpelF?zD==PW9+wR^RrlfSB{*GWReGe4>V zDdCcBE>H}(3EMTO)EBoo+>MvE+Ixl7b+g*PIqaROxXTW&s|pbYt02}$m|IX^)E_-d zq_)+oE1mD_INSpx49^VtG(1URTd;DM@#eqo)!r%%gFQ(-gf)&xe}7&(hZUPXbt7av zMc2usr#X3xoxrc0AuKSkjJnpRc(WShr^-@Y?RaUM)v9V&cZfG0*z%qWE_P?kTC7xO zKK9iVNWaAzoX!1&@%Wr@X;zUjqJ5}66@*|evvEg#u7A7^Is#EX(Nr_snCVF}j;b@5 zjp@eS=Eejg_+S&LB;$<6T+)%EyJ=3cmW%R_(|}a(SC4FIZBOf5stQh|w)?EWjqd{Eo1in$ z7N7UE+5Ma1St#A2X+(^b{7Jf-aXiDOq@|xNgVbbRAX&hE0@A*>@garU8a2sa{;x z8^DfS#_ad?=IdB+H8YF(>MKTM7_%u;dHIXS7iQ$|Pww2Lp&e-+`3j3$8UgmIQRLf> zkqM3JQJCAeQCJCai?()}L$hTf_!lID)YM_y^In7xL{TpeCMSg8TkN-N8h8Ac{F*mO zx}M{V+&Ayt(DvP4-LFC6mrgw2+s8kHXbC4sRo$vg53R+n z{L>R&i%-kpg|6Ew{Lz3tOG!)CfA7GfR=3Mz^X(n<%GL8mRT*yA>r=+5{!ZfjVE~$) zZ5Iz+d&lA}Ecr|H!Cfl5g3B!JYjQE^s-4MwD9>>n{c1M%S##xOe+hZ%*P_W5%_qot z&+Mj)<#A@Vs9&sN)laLrYXY5LxF2Jg&fuBq#DcYcOjY{kp*>nwW)rP6L@P51IE zE7|7+Ty#3+3Hi>;;m1JnXc@o54)|<_P62!hDlR-ZyZ9p~rgOi?8$eE*N#cq}{~FqF zEw6;b$NpTUuP5#$S;FsPp9+TNV^YpUYNyN#OHC@thPl?58jHEvPQNFp_O*G=-Z&XH zbe_gbNtjP7@e0?IS~=~)kGx@O)+9p4p=C_Bl3;*B;4#C_0 zZFZ9mgMF;UJPx#u9#!MNMq%kX)Uh%)pUuHvUrTGnlBsH^$k^Y{n=3M4P6t1^6V6*G ziW)y?lg4#W7i_yvigg99==BLl13WcPPb^iOwm60bV#DvSH0#F%@Y%~Nd!hQ?A zdphn$oYYh@HuI!wLs41FI8^V~Tu!c2@xm!?KNp^Pnn@OaSKLfDE?rD7`Lj-xE-l7P zK0jZ2bT_>hhT8X9ZwW4IR()a=C1=}ib)`1zWH%=#mB?2Aj} zT59GhI46$wPyKbMSUARHmyf1Z06CCVsY6Ybp!W@ik;!&v^_5RhxxNjZs7kqs8Z}2? zrT#M9=_vd5{d85G+7ayxJ?w?}8u)_qEo60OduW*F-&dV|(Czkd{X`)yKilq*dp$SS zU7cwgxTl`kCVZFL8=;uq1_AWS@KV;!-<3OJYd)8b;^V(qqbi8@X4l_if6x|PbQ$6% zZ)rkM7+N=UiRt+4FFxIQ&haW#@sc+F?AO(LJi)vH>W!rQdiU_19Qvlyu0>wH$ybQ- zYGAXb-h z|0wIkqIB)Goq9AG;jsxg{WNWGX#21vGn_IRukA2sZ#V3nyDVyHjA<`IW8#dyuKfeq z{X?U?G8ye~XnO!z{lb2sD!EfH8?br;_zqz843@-a(*+cJZ9}_`2H>!(@{d}fqU@5S zNv4JCYG%#YZ*=~LS#K#cSb?_*Xb);?vvC=kcpU?X>0xNAV=-#cz*|EOP-gbHmto3> zG%1o?k(RomRVmqj0IR0<*%w8r0DFC&YFjhw1=;~%ucr+wR~h=Dw)+pSP64iHz|ap= zrSsPp;{Aewoxa9mIi~C3IGLy6H?vDeO}xV@djB8P>A~;bR3mfN>vYcCT7~4y#k7B* zb^QMhGHwAuhHFtZ=6}dX^QvpKb6Yfo6~`rRoNT z+e&cX@3Ju*b7UKJ@dzfk1?#`e)TW3zr_hio9aQ>5)}!R$6u`jGDdUKtgcjlk@_(2| zl%!6_TkRzj>A)#mfP~43Dv9WZG>LSABuC34%D401R=lX(o|Bh(b#d_ML09ZDU|FNV zn4(OxROVs&N#h(#bx(=N2U38a$r1lFcPe9$ow@_+Dq;pz-kTh0MUq#pphj1YL>LoZ z*5;BbBP__^P@1$AAgiF3F8eK827`Pqj;w<`f5D-&lV+9ZCEJ?L7KxZ;K)PM3CxbXP zz$9*B84>I`Nl-Klx0DCYk1RK6wL~VT8*FA;9pkiejCchv&li0F^3a^OXhr;N)xghm zSJj7oiQ^#{qm-rULtb|9oM7BkGk^wXGPsNn?Y{f+ZTHUx8>yosf{fND7#haAUGDbCGCZ zfJgyLds#Jn^Wtg=1>8uJ6bMVb%T#^WbP7q{lagm$PsdIh(~5tL^U`YJHq512=3RJ0L4epozLt->9GwDdW823q!9fpE zxQf5tOK@Bs|AJsPL$Qjb;l~6E(6BVWw^JEyF$SEU5!$08djeRvWO0fuGM9f?>4j`u z7#S@N@xsiPzX#k|bJ`+XWV%@9+z-PDZb=8lGvYH7yErLEHT7aeWJ|ok^fy83$V5@( zBz~qiP<=U;1+cqm?XDyk@h+k7))&5|_;YKW z{V7PDU!!6=0$;c~18GOZM54DyN+CnY#_m4|*|9M}Ubnrdc*98Pna&VL7t?MPq(tr& zFci#;iW{t&j4|l;zqw!&!juAtzPW1_+05e7)ug9I%z1P)jcON(YpdJGN(5cjEr~p% zQES20E#;p!2tBg_FLF|F#l~J^ zvd(uxoG;0M9A|?%4nai*F<)dh#j>~WCxlcL?H|WC&IEFZ1@-}Z_ReDp96uYvv^Q&l zWGowI?xRkjHvW6D-4oNoST8?azQ6#u^eWR|Z!fegb~i2g{4k&U^H9{kZXprp4Ul5< z2*}|jg_QCHC9@w))|DQ@;k_xws5EKnt%PYb$^|QpQmRwbpV9{!m>DK{Qq@~jOx8Kl zjW3K-tmD*M?U=g=e?hZYxJR;-$$qrSZn)g2t{;{;CX z5(5!M!v!{ys9c(c;|3PIZ8?|EQ?-fmp!kIB-b2v&7ThG z2Xf(k*7wE-ZBrlSvy>mIV23X)9P4}e3Ta$ zf}R=*)s*xqBv3Kh28^jFK)rmb=kXieN;->>dy5XShPmxmJjq3w&}x4E#or-pRq#M2 znl@-=wd9hpWigo|BGmCcELE_dJEyMw%Xu&u(Upmm!Aor-KX4)}6;3STbv=Lsl17>( zEyDYpyEM+-I$K#N!8}_ZC3#d_bEdSO=IQ@A-!^VLGK7;~> zm~U>mg|^4En@bJa@Ce$F&wnBiirv8kkHoAXgbCHck$Tg9NE$)ns{&?6Dm{QNLT;%7 zFLoR@bsfR|G}~{qe%G-C-vDJq!GeR24?~g&M>>{Gl7W^VAeSe2rf!jfZyaX{T@K<* z1!Y5Wy#>bEL$D(PYom1T!e)b?z&QlHl1DqLl!8M@#)~JZi-|!%F3eR^s z6YumScRH({BVLK6v4_@vBXc^doGZ>1NOcRQfr8dXLhX1^J!Rm_pX75$hu%&0Nyr<{ zA;}^cXVGZV#0daLNcb@?&zJB4Y8!DF@s*w8_F*}Ho8~XnG(XxLJIeZcy zF?$1eMj0_%h>{BzF*_bHD>7`FSPDy&Sc*3l&*=bP35qbw&h&iJqBEz$? z3u~vkgRw$#)l6jxfvG|uZZcRvkW85+ewfA7TLsSrzQkZ$tg2De*0*xtSP|cvL#5W# zRTx#=g>y1Hc?(V3s86(P`<>U06R`7meCPSqo+aq$4h**`M5o-jbScL*>3keW}Ia z37yTYTn;Lefl9NHU1}uUlYqMT(Xj7LOvxkHV9=3(%9vD@R&F#*1z1pOFlb3Ybp$NH z^teBVyE~cR=OeTPE3LsQm>|GJRR8h?uny#}ARzYa4NFDQ_Myh_JV+^{SpdVxl~6E(g9?F&|$%RN?BMk>tX@v z%HDj!_iLR{G!G>PgQAv3pRDP3O7PYlf-*8!c2u18>a7*JCD_dc3l0JX`pYDfnmmHDeunMr~#TV36+LAZklX@lE@O-F>o!jn%0zNT7Bo$xUc^;i!Nruv*7#PwI;3!RrnZ`Yb{=r(`sO6_aE#DXYHlS!KZ8I zGZ5Nqcg_WEH^tBLnzlwyb=~{h(*EqN=LH>@t8R_~YT;7aMRZhE{NE3MxA8B%Uo0*s z#GVIqNrH3r^tGFIxEr4WTwQR_j^ghpQq8uI;r6+3a4 ziprS+QZikM9PjoXF|As{yMem5QHp@SonigbX>~gbC%f_BcswnecH0OJCx`y}l&jQ{ zYF3WUEMwYD?XFwl>MJyKvx_A&S$a8>v@v0IoXyodc$0(MJ;r0%#uD1dqW*;DjCWDG z0eW-MTeX(s|8Q(UmvWE(Q#)bP`qOUm)#{scdi*mP8l8oH`;m)m@puAEncys!m&y!u zxbbc!xO&_znp;{`Xn-T5x&j^UK2^nR0q&9;it1G88pdNyWbP%Cg4eShJq?%HQ}95Z z2x#x!bqfAlbP7H^8tRo6sbj*VO6PuYEf_%#%qA^G;qzhMi>f)o&R^^ukt7gMzSb);T}Lw_UE| zx4CLK7Kmed_0D&0NSt${V>~TOD#!V8ryT8`uk=vG^twXXV-|}G1B&4(Jd~!*aVyWS z)BQH@p|AY*8fOwx$3Cta(MTMKW-*m4}4*D&WpdeR(V8JHSgE|Y=h+S|(-i`&`1M8y^3zMx7CkEh&fswp+YB5IIc z-!|IHU+W!^^Ar^ySCwX3KI$KySE_ovO+nryR93xcm#da87N3-L_)nx?m3=+04vg2R zYd1<8UzEeVe^z~V_ybE>crP6SKSi~BcerF$h|QpdW91UdcDD(I$t(U za=gp&xh%n(d6)Ff9-uEpUp5ea48qZ-2DSqI{T|!Fs`;FS6&)E-qE(Lz+YV44lrb0I zB(i#?H^YW)^M%o9`nc6zI7YF(2CB9=IMkID^gJ572+jYrpPt5^J0Hxk3VJ$xwAcys za&=XqZ@Tyn--IAGiS-PjG%#X%FcLEyOa-`&4in#)^|Keie4CHf>IAn$yzhaVbrztl=3hB-z2&)b z0~+&6n|UEV*&S1=@-KWFfV@TR@mHSMnvf6Rar~e7yPaM0vcHE}Ny+h>@aJB7!#G^R zS049*y%9dXXJzxh7hlgL8n~Tj*hvR<@w%I?I$x!>cA@I4T!40oy2e*D?AimWsY{FeFUBCz;>M&fd9d5ciT^;F;VVowh^8d z%S7~i%dX9uDuTJ6bj7+fqWEL;x_H|xV^VV4{&jhA=#@B?Rn119OTNK$x&8$2zWsC> zRbnUu4;bPbr9?n@pBY#3CTe;;JSmqlxtYlS{&hl=?|g?K3!Oa`F%|R9@YIG;-JvJ5 zB1Y=S`c`Hs8f-o_zc-S;H>oXEf!Nyo)Zk?@Tc2HiMSDcAP5p_4)jeehypxMozo&yY z$c&r7Z7qez=si^DV!hg)rtP^ptBrPU;3C61oK4`dsavMT6qc2{lINiO6*g5|cDFSk)@Mtz@dO)zRAgt(vs1c9Uvi+N zJ5qC--0>xQ)AItse9^)xSb0Nt0esn(i04nH(PvSO_no=*K=?9@cJlMpmVL)1>|`Ra z^|fYX%XE*EGNF`=7Oz+1cPbt0xA&%~XCya*?e>I+!R&n2z>Dcf z?I&h$aid0&?RBi`+P%+q%*Fc+6jY{<&h7NqVgw8PdZ_KC;ll!kBj)$UPUnZCd;W=` z=gG@-DTpHPS|@cZrH={wG|&6S9xuIW&qn3UONYJ7?*soD#V9G(6r845PV0%Ju7_D4 z%;0<7O&*^$UTXm@|Lp0L&&{nUm0p{dHr*lqD#?k@GoSo#Ql8e{ZByFv(XNJ-zxAJ! z*XH$J&#Ph7rUN1_b1{qN5>N9XQdKDZsoZss_e}$D}ow zbmh%ZMMy+yq~`V~{1__f?&usGQJD>=33e#V4ln>WDpE)rp)C+%v>DvZjv_XUQ zw$kyg>r%+*-eM%UcN+)4L;s^@@n17{;sV<%lkI$cL&VNE-4}-CbUq(s3Nc<$s(y5XrUXl{8c4o zSDAorIAa~%*h$aME8Z?@-+FGoD?2#X(_@ukpr8;C%I^k4o1n+*C6I;s$=9-7gu`Ny zvuHZ0I3jS`vQFxcA|v83NXH7yke5XC*&f2Qtz-;kS4WJ**d<= z-H-x;cC7>BzeJeaD_WYXGMgQ7Eo_d04Ib(kC-!U)-h5su?PxZ*CjtB+P(yU{IEuyM zX?m(eKnQWPfkX?`>mNePHfW=ulJ!(-)M8>RbIDGZBIb}zMwViP(_7Qi*PB(2>w!*J z<~5O>6A{8c!LSudIOY_Kn3wL zjTIC>x9<|!0yNh5VM zA9A6=)B5;+(F6Z*h5>)&a%WgpNIvU4{DGKm^c7f+xEvlxSi~@C#*(KI>fLo(SGdhW zx0G>1Q!;7PO!kKg55*j(px`8E#s&W#h(c=$9c*8@WJGvhaDqV@@PBnmi6s_P{TK%2 z(s58!0w+o96H*^;Puu4zN8vIBo~>j{Eo1wR-1`WKxHm#QL08TyEo18~+W%;Xa4e6w zUm_GGp8>cblJ+!xZ0}NeTA}w$s3&J51bJI?*8zIqAQ?(y^@kxX6JqMY!6{Z+EPm-@ zi&aQ}B@kO`D=4$AnMC)tCXbp5KA@=GVu5SVk~i#d(Kvn(+7@JvO}-jrP1H*sSM~;0&`7=zV@VOn^d;j zQSH~x59m}Fwn>fvY)l9SyWzGZ>SC>C_IGZQrW(kEuzvSz@-_p;pO?}xvR|`iBaQQF z-xe*<=96PLPSSws522FHVdqdQBSXq!SYJV@i?1KDINEK<=S7+SYu9FEkJU#av^+|@ zn@CW>qg=ir#k^}LjhG*Ia>r)9%ZLl8lsZ9qpbg;LDfrO~^^uw8A?E{^X0x2`0-~$1 zSQ{YKO0L%lbt(5+K?SCp*(Z}+t4*QXiK*+U)YBvX`lfPZSv#@nN};<`>Ghd+_gFZw zDodgJUIceeV&RGg2GpI)FAjJTyh4O+C%eupW6#Oe0E7dXwu#y~?2q{nwo9O0=OHl8 zH?A`u^O@ zdI>kxM6A)ytkRSWtBXzDA6cONI;|1sTZxdQ%ou`#~;+Q0M(9DVoRzfDU?6( zRPLo=3``supYfOxLNUZjWCd_iTO8j;LAw^p%V;R#FDrAZ-Pi%Peab|amc+ZY1p2z9 zjidU=bfBz6-rzUzHVP6pVDfkPU?0}Nt>(u)is087dXVQ;B(@k46G9Dfg2f6i7;lyV z%XZZBG_ZrhzjW1b(Lta?nE&Gbq=y|9E-+L>Mh{QQ2SN|e&#BFYGnZxb_u zv&=8BAG1$5(YJO|1isj%NVuxaw?eo`2wxr~VLL%!=YL-z^)58u@eNVqL!cI;4H%7P zMA^gEzK;yHhnM8x0maq}?E8y{^?w?r@Bvr*pN&%VZDPUx;|LP@bx<}I;YvTnTM(Ka zD;xY|^UzqXZhtxLhr(gvY76^sV>=>TRHg(TUDP5N7r(-C< zFr^YC8yL)R8AQ=&L{T*(q$Z0rs76vnBc*1~p+C&1PfVvz%%D$9uTRXV?`%A9J`$81 z5FscdU+qMi_Xv4f@gp#-csWt(s{F_Exjk?R!?mYAcCs=%AMB;K?ltu7*CA{iYtSFC zwF7cb?=hi;hs#GelUYKJd(`lT)f=A6;X$VfywX0{2^|wMkEoE(c{ulk5D>ZWJNCH0 z>|s?5iU*E#dybIh)6n~lWEAM2_o>n^*diPm7{2ULy?Y#AB6MFlCLhs8AL*FBg&4kt zn7)P>zJ~O^q~yM{h-*2^FBgS(q;cg(+mpG$x2d8pgJ&F&|DYk1*CFuUb0Q(=k5y5F z)hKCR3wIegVW4E@`bMjv@<9IHT)L6Evc_Zb0{JaIe; zEAK=O8G^BAy5>3O z{XH>N?om~2m>RYWbq7p!8>YrI`o=WI#+`kCD?lf`1Mn((lyPG+Ii_$N{*J%2rTujU z{e09A?-U{L>ZZEC?(o!Y?Hl)8)QGAbk#li~e8Ju)qfSZ2}hGhdJO(f&C@KJCkzMtuVu z31v|r*d@x!L%ZZ>NKm}AgL4a-%I%nXw(IjQrXO9@5dT~`8eTy#ED6@tC6TI}%!MCJHs%l_U8{IC&v?;!q>h1Y_MII31?TcF^#+koY?B0qZ)!>?IQ4ECZ) zmzOQg-+qy*+TOda*>XM|y2?hp)=PKjt z_wxJ=daajj2~D%}HRyd$hJK_+zW7PHRw&&v1S%Qz@=OGI27n$CW8G4`B-OxpM9E5^ zQBPUx4YR>kRE~drls_owDVgG`19BIFCkh=pD)ny%?IxL;OQ;(2EZ<#;FVbvf{~eam z7F}<*v!Zt(gIywnozzgbF9x_T6Qm6j z+flbD3-(4Xkvmlq`a1DV1ETGlJbyr^-#=wYWu?3^0AAmkd8bp4cPTSaeZD&`ur3U~ z81HQ#Y+*ZWyVd>bFHJ+dmIYBWAyG3aQF9N`Vi5p- zYAOIuf0eS3tlMSf=sZx2&7#nCO7v1(^io*#a;!X7_UZLIwDvS)E=U!$*;Ul49n(FE zju9ynwkW(La*H)|TtJ=kBu>DK#6dHXz>Z@wT+~KYDx$$A8(D`!%RbWPsF`%X)j)na z{9EAx1@r+WXwadVbZR7BQ;)p-A(!eKg+^55I~(1m2?q&7^!dQ@GQQ6a_cJkz!ko+Y z!kkSSq?e*kzR}otAfgq4tH&Yqi(oMY+Qsu0i$U_1O~*Pij#UX-BjHF3OsH=CNY;F7 zMKZPbKdj2fYFz&IU|^g<8sLnOV?}e1N`4cF@avh-fw?21(3m}dlacc3*soBLJ>iQy z%y>qkLjSP&jxih_9- zr7c%!d43(p_5UdhxvGp@^4w(Hv(Sltbb^TpXZB zbOi>sd2+kMG!(QiosD5_1G-bsMl*whwl>e8eAq6LXSP4z*0}-dKXslQokTTPKX10R z-|d%B2Y&4=Czs&aYU4f+0TFX_0CAd#_bNasZPAQh<`!;avdz&xPS+o&i*UTeL9tO! z6|`-G)>hdG0ViZ~ZbPg1>v|j4DG5Yh()jz(yB0ZVq%w&TfjP{_lN!Ak8ZEtYBSjPL9=JAz#t>eAKf7uDEfe%vtoNH!< zQH%6=_T9`fcYTZ7!PkC%%npH7e50yQkxfs!R_@04b?X^m@uiDhyr9YetoopfK6%c) z5jv#ju-wquyGw64Li_lrjU{{QyjZ*wJyAREor1qX9N)s_oaKDrYZm3y zEHKX7zB@gZa{LKh0O z$&J0c?qtmP)VRw^&AoD(Fw3FF2O9b|C-lp8zOf#T(>BhGJacKVMMaaoJOxV*%u*dD z-Qok@%U*7wc|WdRA~wK50cpe6m4;~hPfq~XbgB&Js~erK@_9;7pO7|nr_xPdZvg55l$F-mrQg)X|q!S%~ZHMG7S#GTX9@y9qL_7ev ziob{MAr{xHJR-U6M4xKUMOYZ)aY5PW#Nh63`eumr3r&DSmy7D-eeg635*5%|#~&8k zru*ECpaq0pnmfPYb2@*Fa=-BIaU(I#czj;Eq(gm9wFV)-)d)B9-H~5O!S%+*EQdV- z%6x9-#kTp%BYe*QqAl;@RM!emay2`jH?T{-wdLbbvAw>F6{loRo7!%rAB9(&GMBk> z(rzGvcb9NcQ-Z(bZ_9E?u+*m+Hk zKn4~}`J~$Yaa3Mr=C#zjIjaV|KJlSz6}HikAEs)tu1jL`MCaO{6!MlsdA)hww#M_= z6nLB3D~^*~B&Hrru^Vxl&v5Ob)e^ajxca?Cl@kqAO+sXb(Tx zb9kFyMd#2OICGbpT& z-3CWf`(-uW=Mm_D_9~kR-q@XQ=uk*pIGKn0u54X)4jxspe2b>4NlTSki0!A^TFqla zdU~sz)QphlzD$>69e3%ek5SxSA8s+JkLd3#glx-0pH1h_64q(wDSM_~`MLC(&ziKgz)*valJLVl^S<*2MA${B;J0K^qkAHNIVn(@*m?tpi? zZJ*c8A~rS;HoBYVYwiP2LPC6hALM{X;D+ZTJd5+Cklhk}wZmNDRql_yPgN6>m(~6H z@XumvNLvq-kHANO{8`IzJpEfE_~&FlA7{;DT+C;wt0SVtPS~#74~gK2rI`7cSoP6D zMLeO6Q1*{*DCOUnRWMQ3)dZ4-4NpZ7mhcM-3Q__Q5y}yW{1rP-ScEA!%KnnX+yycy z;DX2kQ3~b?rIIwM`fKfuCr7f+r)=%bp1E%i4?gX{%YzrR8iOck{`Ho?!a!c4+>XsP zvwhO@I@O9ZA4hKnVsxjin~#Pgw-6h;9M>A4avC7?mUohF9qpQ8il4APtpyHJzM^6KxXosiSc_M(wR{=-K|ukUvj47$gci6u>_nCHR81b6g2LA9*ChEBAR zm`-O_JuL`KAsE}suu6YtBS~QFpY|^ry8BXWvDKm7b?H>Reje;5YN#R=D(@Zbc{vGP zth@arTF+s*rfE&8h-=mrfCmfdZiy@}gPvbYx7vog1t* zR8boe@TH^Hi{-~ad*Sx&=Vxr6H)KAdkL(qmnTgUC|FKAgCQiEYAo`({ni2e{B2&%0 z(BCQEt%qU2uJKE&UuJMxEcUBz;N>f|+$2@asbec0bcPq#j>aqa6`o^U zxtq4Wv%ap-DC3JW7Ma+Dw&CYU9Od-r`Mu(cwruX|pZ)C{E3fR8k;=r(lTu1e0miaY zUiSW2g!S2qMk&k5p@Y@46KoCq{uV{DJ2UXcqFn&9L2$eB&zn085hfJWny7V@85mQe zd({DK9s0>x`J$+)?r<#wDAs0Y!JVh3Ko!qbaT}$dmp=U*;wsdtC6O{kx9qRS>#2Zl zS`*``=>i*bbd;)uR@Q(yZb!MAE+|#CAnLGoho6GNmMDW$z+W_Zq;168q-%DQ)$NTl zU!*t-rEO*quHYrtQPQ9NI$%12ITAT(Ch4s0?+XqAdP~pER~pZcVC-d56SR$Q2#u?} z8(+?Rg^xZ;FGJjymvzv51U;5w!MoAxpGDBR==)c>zj#3h`gY1NuJi)TuQ6G^cu@pS zVs5?D`QJd@AMyf(jqenP<^yy4@M4B8R$mZ{JHdBsGYA*yU-rhXmlweL8wUUL+qxUn$_(DMfz(X9f7b{ zK-|qUSS?WggbWo4sf&M$j^xR(T$nf>35>5R7s$77GMw*uSZ)*=jz(8LyI$&3UOQYou3qtVa)y}Nj zX;=;YP>DtGoW(^Syn##JIr_tGmF5R6elwIfjgWFd39T1J9#*Y-dol2$yVzF+BpC zIuM~+hSnFRy3s6g2r#=lc-iLwQkN7`p zbt1XX+hEcdmMGKx5!Wf(b?Sj(qzX=|Nk0+?u!NWm44;BW4I#XksTtpp4C>Uc@nk|U zd_c3d@nk%Xs#*=D(Wq7meSp8eH`fm$W|EWI_HL!C0-z!ydAot0?@!wXe_O)Eyl;&UMlwhp+^O6+E3#ln8 zJp=AG&?v;MX#{?4wG<5(OJtIA9Fm%7s8SH48e57aRp$l3kr7t=S*4qV{}oN@><{*u z-k2S!kI%vhh zmF6~R!Q@y~+~3`o0b2aYd* z!M=9nP@UwKeywhk1^Zc?=bTjYHDN+Mdmw83_zxE@j(H+(o@D^nxayqg%{@Y3__nkM z7wig53YHaKB-~t$B}dp?z(d=hThoR{UfOEUoNlQ5mY>I-t>xFFqlNh)Ug%OdCktfuvhE_0R#ap1T)9AU%=KTPy7&a^_8N9eKU3cu>; zL9UMoxY8Wu!x9nRhZHDym>ZwPnMkv0{e<#BI-YJ5jj;Ej4v88#$v{5mFfE!jRG9c` z-`W=9=2Qeff;N~K4Be*6D>O!4-`B7I6#_?`cs9YQ+Pn0artr>)eK1c0fhv9VUPRG-dW!p4&JzYv=X! ztT`hMk(m|IueU1IF~QQ{{UAAbCX|zNmj(7OG*f=k{lKo`tz&ZB5p+t!6(1O1P~Mg+ zdmf~5I-#40$&nkc5$&IqDah}u>{Gs!7OKFLF&}5jMH_jtb(=It znJ&X^qQiJ+__{9q@r{afV<;;8aqm0wC8hU7nkea6(3_%u7~?&YNjhYD9DKB9cShK_ zbcc*+h8q*gkI@^AWqqn?A$^Yq%qI#jn4SB_GTF1duckqo>M#+;np)qgBi*a3_q$6V zTm&o$%;8C@IRq?2^BR?=+JvV(lJf4&1x^_P7K|K4Z#847AOJU-=!Vr&jrtver_ccl z$j{x=*wIJwbxZ6B*+hTRz!U!-89PMf(bKQWZ%`6>D?J5S@QZyN_9D+uN%Sv&`*Jd{ z)+a`gcCa&>#4pWAFtxlt1L1xKzZOwQ@KyOrtjDrffgTqhg6XMn%NZ)I9HRaY=^LI| z=9Bk0!}V&KWyS&HuVZz9;k)^uP0zKy2?x}~^li}EWUcsDA|ltvH*GWCZGMJGc3R;@ zY$PZwk2-BSN*fS($eW2J;~G zaQ(&mif~0+C$?DAfs4k`ZAhcOLl|8=+s#O$J^@Xw)l+}g>2TGBR)wP8B&oj)g*3{G zqW%!yT{jOly6~O)Rb78s)8FLuTS|582#iMtX>ZVHyBy$|?bfAvn%EuiHvaTbc23^%iV1M=r5*Bo z;OAg3g9!aLiHF9mMdbbvwzXUsU=|CV!$a%#5VW;?2{D<2!u={V;kIF4D%lg;VbrWY zl(P`P6!B)SYS+_B-ZPRlq`lPv;E0Up%bz!z#F@FlxC0YxSBIe;hI>ggmxr~au}gCc z)O}zyXMDoMR6hR#++zOe2fIM!)#H)OcEJMGQUk%}w!4u9Y4f|sI@bO&P1tCI2I{e4 zORXpE5yI$$6!X@#C(ZZ!;F}WtfnERuKIlw~{y=CWO+MHqT}ko`8hpx;Lb(vwp+ZMk z*~>pS*UNbXSZkvH?)u}lx6SRVgbAb6)icI|kWoVUGY+l{mSFF~-6A^r>FrfLBxK>T zIsPwWe5^Xe*iHlQf00|*Kf1}p!_?u+2u1S4A}pW2*Be1X|liJ|Q;s#r6l&Bk>{8^*%hsvm?25$M&cGA@U)<2+F=9x}2VPig%q!Oh|0g z$Hl(dZ%VV$N6GQqAkbHX<_Bf;~xD8~(n0w{Hd#xxNEN9$m~eBzo& z87+WN>rh5a8^TbAjIUAcWhCOl*{2R6V?5OKrjcm2JH|e3u8$*^XxiEN7IPXQC%ek=7W2-QuJHASx;^z+IHh^)pVGbFxu;@Aw1=O? z#Z<)#!dNmzCgM)R#7@UjZMi8&Gjat|DFL+aByDhUM>kQzss2bhbtG-vRMRA41yVJ_ zsfWaEd-+3xXWW}?&$}lTT}q z9dk?1xVH!%qLXCnB{=zL%sit?n2FA~2Nl)p&T#0>EE61C-4*$J-7SD?%n3THfA?`` z&0lm)95)OivNuzDqn(ZOWuuF?@8lN%1#XE%7G^=p9S8@e_k&a=Wg50U(|gAG>4+F) zdMw?79XyKc-GpaIS#q%UKK;k(zDz2_I@&n`uvob;k>S?WIju6Chgk9?(Ue%`MD^#! zto**E0%0ExUg=;Qpunwq`kcFjg5tQn=m%nzt!7=&{}@EyqwWWV#7eA%2>~*T2RXg= z_m3Spz&OES@pP0WB{)?Q%b#zRm6m~zgSm}xMnjGm2oUM;ob0?hJqqSIk^QOj&|*fl zPPiYEuq5bScYiCewvR9a*=VxJjiBjTG(<)cb)QKt zfVQ6_m8yTZ^aok8AOP;BHA$eWyN{T9@J7P`Hdg?pR*3O$DlwTAF^uni`s$o#4OyTS zVLgzvo-Gkhkz|x$9wW}p+g}cnq92;5Sw5Z&MnaaH_$M7ibC2rSh{HJ>`=ZBsgJdhK zrg|P`R6_Jm6LK#lnC7dZRY~z?8HqMqk{RnEqP@=C4yayS-fxltTq0oSJJ3OruYTor zY_h(4zY!6j32o@|=Un^QU#Ij4(;YFS*jqNm92mI?BzehGy~LBe)RVM@i1B|V#2OC& zdYgwt8n^p`cN?Dr78^Mu_qW)WPLhv8^3z|OPo(G*gFu=0i8rO6!7`{+V#|BM_hu?kExGqR1I1>s z*slFo@Tv^OnvKM^ixx~XmtxEMz{$5!urC{Z)FfDp16OK>{1h@9WS*ZgPLj3Dk~rYL zWYU*x2ltM(vz`(HFQF(!zB^*P4j;X-Z!`knleoN}-d~{bpWN8sKgL(nUAjX9T(8-P z<%9ook*<+`6X?^yJtbTo|je!6xF?Frfj|)8{U5xuEDdc3tdB zd*u)++pdCrS5|?*wrr{#MM!KqQ z9D0qmeaHU%xSg0$fq#Edr6*;jQX*PKL*lr;O2)u)RL#UzsaAY{knP^5zQcUBxVi4S z8?hc*Y?s(9An7J|(+d>!zI9jd?X?04C#OKGR1YKuINWLP{{>_{hW z;;PPVve`J;*l?)OlH2w7#m(mmuOdNJ@fC;)hbULi~7*-M=*e-!L+R|cdN~hch$NZZ>I&FjxV$R$CChB zgiD=Hx>Hq$=DLr&9huS@Jnpa>Rp-nf+#VifMThGEp_gQrl1fLpW4fA=ftb1NLqW(X_rnlcj|VG5 z7E|4^CWCeRogQGUk*?Bb8hUE-@)8+Aj&=@1KCGHR=y8Y$JtE~1XXXLx+S-~7+Y8T| zv4a4_yMXj##>F*l+vbj%Vqj?dQJue~9FMr&G_ui&-m|ufz$T4n6C2lW&dxuK&Z4P} zw4A>?IwvahD388EoG0qYwHzY@sF#c{k4wyG;V3KX+K=nDgUSgySO%2a3OG#HLu7uX!##+_H^6x*!Koh~Zyai5n!?R#}yIAp?P zg!^$VF1pS-is9fo)dM0CJfBm`eb1ezmp-mJ1a7a^@jl+oUX?27`Oaenf$`1YZp&;S z9ck3Gi)N_*_2uq^!setm0pXky#g$4w0ucD>s&2hCM0I@}6jY5eXuGj}HPq;zyHyE< z+0B%5)wcxan}!0qyyp`4;jfakbec9~?&Vwai7iyazZqlt+)iAo40Q=3e~?_(@Kvy0 zDH%U`I%uEbVw?SZd)>^fDTjYk#`O6D8N-g-Ao!Or7Q$wS%uZ9)R0!}b2qHGNC2aEi zCz(?0?&Md;bE+$|#9!Doip_18bIRH+qVLPkYpa{<%Y<2%Y!v$oHj1f^uHSGl8s%^n zan{jm`3};SayIU~HkXcs46#$nXhJ8o9})@`Su#rGK28QX>~-IZ{g*>MvkDD&7J~ph z*G$fB_u;{tG+if+v6)FGGV1NZyO^~)s&}UVA{fN5b3W`bM;8Ojeiy^>q^5DcRT-JI ztbJ{UJ9Nq#pYbe`dLZ(UV@crT1v8ff1ld6c!`l+c2qQ^zhsCz@$uMh4VfzO=#Y8*M zw~2}Jo-?`0NEjG%&}Zm7MCIA?W!&9Dshw_8o3>p#`uGR8Ysm?Y3KIzich)O%V+S+& z^;1MNO6pB~zTPeN9MmkQi$0{cSf@JKb!->BjXYE4xJSEAeW5bPtQnP-ooW&nvzsY@ z!8>E79xkVwQb9GsJvTt#@*gbx!ebFwWa6W!S{2I zG_1v!%9FS^?758XmzookQ1K01b$7M`w3FB;v3Yy~>etKfS`h1&tNp4(G0IbB;8d@u zF;25ktZ?m*Ceje=pZ7+isR%C9__bqhy7sMw&WjUBNX1R7F#6wNtCBF06MrO5w5cn$>`f&%C6L(*A|Z^(Q8g8 z*lc}r_*Hq2aOU>Tr;RWk+wC%F!L;_%+e4q~-lDrD;ExNx>tAysKNyT(5Dzy5p3G7w zZLgX#yPn<#(GO!>X7>d$sectJtlvIwxY*KepT_cKiZql5zYu*W+z-7$0){SieLF4A zrna_PIv*>eP@MrA8_rRbqljxuO_Plk{$>v@Q5_DUFFX2UvVARD%H30zm8s|LLe5q! zRDQ2_(kf)l^+TPuCo0Vy&m#xrO{2;ds@Dnem!F#VedPr|S;rse*1;2R8DDS;RCRvG zI}}2Ufb)2|;5J_)IA0tR{fvy!E#>tx6cu>bCQ`Zmq^#s-xAdI7z+j1>LgeBRsawcPoemwqoP07Uoij zi^BXInEQEL0r_zbo~+li!^?X9SN*E%1#Wv==vCj9W{Yg}8qip+(eff-=RCHYWZTQH zqX;$FNS+b$8Em!dyDWzL`QSf7A6uoL0ApdYiIfr{yU6L5t0 zij0n)8kd%kmXeebmzI(ifK`57TMJDgFTSV8*aK1yQUdER8QQIy~-_)nr(7$SJd&T?%%^&4R%#xEn7S+g}zm&{z617-SoYBDy zwWejmF^XR_)YIA!G?5}jEoL#c2ILgr=(Uuh#>Bf+zV2DjF_VMMDJ}{w&QVic$rjP% zJ%kBs7@8M&p_TNWsBiDP4yoMN5wXN*zlxxRT9&hiuYKmfN?{9sUAflc%?Xh-ODs6L6y8@&L6Q0 znzLDhO$*J6MtJIZXwR!jkSj}bK5qIcXckPEu;3a~Dbzq*%usTAdqNv4IV`R+t+?QH znGEVV3;lR-nt7ui64V6LDuq2=jTq8c(O`|_{6p7yN_aqD6X$nOEC@W zPh0-+V@4BBG+;QBkCCALPWl$a+ZJm#myT*`J8y60C2#Vg{HUB4+_myP2Yp3*&8 zypmriXr#x&H7p@0V>>=CwAq`X61*oNBG+Xq)Ts1){%4&?b<&{?${h~UFlS+`vQse^ z6|z=EE5wc>CFkef(fibJTCSLBY4+Gg*T7Tlbp0n6#|JgzG-?xqk)+gIZ14simv=t3`z%jhOSMIqA7s zXsANfl&nOyOBxHrXET@$3kiGZ)2HG4%B|w2xRlSn4gdX4@910+2MGcDD|k>zOrKz8rb{jcsJs< z(hq=dBS<^}qt9P<+)5x+(5NuQnYg%;1T{t(M`8~DtvbJqh05IeXG0T=rWvfJ98yCL zwRtv?IS)yB)S`z4ZQ7t^c1+`hivO{!CXmYf9S@&fGaJELQj}@5!26qNoj&AownWoU z6i7&>5v|L_Cd8+pjEE+%glBodaU$IMCxp3(*3f4DIKA#t3jeesp*%+c|2CJ#PXF=W zRPq6WK2&KmRuGO?s`ROwFb5jlp_KIamc0Ob{~WzVK^|a~$jciT&p-3Toa}-zragBW z#zKJOo#C&?dN@V?()c#dVR}oLZGO_ge;izl5Xz1TTlQ|DKsp*9^*y+h`0YyyVISQn z1ilmwFbf01g6ww%Spe$iAAhbUcnI}l<5=@QFw1?TV5&r(PG^5t4Wc2cRQ#PG_O=tf z{^&xm{s9|hy`f%Bf?}=gM)Nn(f=IDO)@ahvUoHh3b@@)cO7X`kwY!ec-y|2BdKEWC zd1ChLR{IJ$8y_P1EiHerR|ps{GWjoDN^M4~_tkNcwL)P-mEL3FqoewH&}wMZ zE2S9lpG6qq_(;#5c3*xw)1!OQU0?sITQtOnMTNveL^#dv!~l=E(tQ)y87M@%Dult= z?R4YD&yx2~oY&!~LRFar>)+{C<+plk2rdj8;QQid4Gx*1~WfmrPOMpZw50JeW|FedyF zRAS*t8eeJ=xWR9Z+M1_mIF*SIh=r+?Au@@5ml7#p)MITd-wN>-F&l(VtM0oCyTH|9 zf2rC1sl0_}yH??K)ef@;V7PeFmHsVE%dX}a?$PHk~g&p+z zg>g#<0^_%XL5vL}MKB=uMDT@UK92XTYBj%VaN|@Vj7OnuxwV1y8vhuq_tm$A5#p4s zTZXE@7h$Da4$ds@@6cKd;+g5?GJ}sWN0Fk1&T}NE@^Q|}tJp6F{D$a9a$ip4e=rmm zM4$u{Qq~yoa0^?!U(kPHGZ5{2Ig%x2fxGJ!xwaH;g*?hs=4UT55K^Py(T@|7Br>Cj z4o@N#mQ=d4CfNuWpR&fYlFiWCYVEhC2ZwE9hS7YtXcTgOb8wX!r3jF<0)kVaSRO=B(k8}^F=#MSX^UKhcDoW zvdjsWn~EPJjF~LE!$rRmxfclkEFjMgN1{O@0sSh6x~93q{WDPN=ZgYsYY<`r*KrXp zw$HZMceZkbuXX>C@CeEy3xm$$-aQZr2Yav=KRu<6CY&)Rtj-lrbrVeeD024`PxU9% zLCQK}6fYXpkEaz9ZtM6pL!wYLDj!cPL8ucF?u1D@LxQqOcLOucq*`(ggh41g zn9D;00&k2HuEbUNC#@j7CQ%G26*#S(AoX9IXVaMmfFYr>&S;a14p=cz z9I9+#f((>s;R?0tQ4u7D%3eXvj%UbH!!s${tQ|B(yW<4e z#ve|L%5?W(#PhyUX5XJZIqXMa%$loE*6Si#1}H%C3CeAYP=iHwY!@1aGtRm(Ir=2e zfS8a|+8hDLMsO5yH)0q-`e6 zM=6sc&0e5#>`@9`CGvmOPk=NMfPT0emJ3n?hTusUsAsqx6fWYxGZGF1e1)gXlUjiq zqe`Z~&Bk{=jG)XvfwKg-UIYG}`+XZsVqCH)gJW5D!tA{uC!}jbyV49MOoO4DYAmgAC$13Sr1oSQ7kdl;vE7Uz{C-o;P+ z8AP(UAIlnN4iCE}!gjpAO%i%Hs(8%DRg}tJr1xva_ty--13DRzH`1>{sLKuy z*OnE#a`M~&Ge=O~v)dA3?sQ^XRhHi7M-&Xh8l^ucrgcU8 z5&Ugr&rJ!mbu~fFQ;l}$#$tH5JsT&py{V&(VB20EI%$xA0KXMhUin)U5gC6z+7T7H z-;KTw+-@=yD#8ywupK%66NBrgEE-tGr!^1`k6}W=AQ<|G!$k&AmEJv}@V=jp+IK*X z*ekO-^FG6*=4kXRPQ|f(h~hgm(DCp!OxD16c^{x?<7;H$%wBiHqpg4vTk->xS*M`J1c2 z+R7w?bTkg$m_6A6r6nKYP6xVcbyXunjMnN^SMDY(oh-wj@fxa>?=c%yZO9I5sWqFY z)uBpIgAUf{#%Ovl7v1xx73H0Lgc*srOS~pG!kCX+VJl|xM0Z}z5~(*+xn*Hg*;q~; zWqTW3_CO0#=lQOi-=jH^i>`ZM_|KQawn*JFA531uMMUSVkE)*TP;%s@EwPR-0K};M zh!8#Co&0oC;UhIJQXTLs*==r}m+UiIyWaA)A83KjW_iOX+w*Nc3%ldqezU>nm$u4& zZS4)qSM8qq7A4pT=3Qe3odQNPyuyyxixD4dW#&cZi$Y6FUbvkcX4&+GMXEF(TXU7v zH2Bmqc@6cU+U1|xTMw67M`@aC^$$FzvXf!kM`=c<-p!kNcqq*Y-cOiyuPxCuuiWk8 z%SucRse7W1KT8B?3Lri0J3VDbYmJ?Ez3^+op_f*}PUG3QT#tqia27ipYI1pR zTAF1C?dn8mHkX+mg5Ue`auY+?h+1x)Vb%7dtR~;*{O}4#Vh@gvh=kBLE&rlgvtzXz z=zJf-ozlTdJRety*2Y+5%1iwON^lzK3~Bh`a;74z`oYATU~*v~={R zClMR?lRK04($(=MMyLLGHD6&^tOjMOc1Sd7&_`FCkBjJ1mWL+qa==k~cu?>q5@FHf zz|!gtgbY#cwM+~7{-*M17t~D;R&tG)j?olRZAWB}Bf}mg3iP1kW!F#p+JLhq|+)=WDl)@bQ<9D(W30b9Ec0OIIIbzW=2xYUX?3^VyAwE&+_| znhU)P3BC!j$xsz36|~XlAghYUUUR(PZNXz(TL5IruocDe)h1@hWa_4qT@h+lxZ2`A z+%p)Mjvv7|?ccZl>hU#BPiLMT8%?epMQlQ4J>P{#^X59WEVtdWVy%8QY`0HdgIY~q zK^(p=1aAdD*vDH9>Z-1CW8n0!LgPCe_-hv|;-Wt&NckX$j>yHW?FA6R;*Y-{ zB}bLKse}QvKi$z`@dr39>V;gx7gwiebjP~%0W>~`#IJVQd^(JjTBG;!zwDQxfo`Je zndHFG^PDWe=;eEn50{hKa!uWOtf=rM&(omo*?fO|nqcwY6biTu_SZ^U*dX7_RRd$Y z`oT{7i?^HP>#tTz^caegDHi3{YvL|e{0}XXu7GIZW{I+-iC@7Ua)rmwN@*zg-Gfv` zCLqX7vwDd$A4)_0vL{W_X!~^NY{|wcirLqN&F685O6e)MK*Zh}l(AHq`k4f;RyI;! zE&DmO&y1Qy|50gzuUjm-ym-#dd<-V?xnw+Yk8YB*dDl4b@ldb)cc&-7wlE>09ICZXZDLbL5~PY!v*PuM zXABI{^J=_9KRIl;TDVJ1C4a4mMWl_PE<_=bBa$4QP;8|GizwvM&L>4!MoXu84Q})O zt#;ccCt0qdu|9K=Ls@bL@HNB$)(3f9$z&Klh9Oyqw-ygKascITAuqjaA1t7gF>o^8 z*#AUP>g2dG?>%!=tQRL$)O*&*2a+p|_VO;4lZ;?8!RXNyUc9~6oJ&7IobWdf3?cMX zDtC{&CLRLqV?5e`W0x*Ym@HT7P$F1L($0-+EqZfxQ7c@xS5-Twhvd1a?PO3jU6RRq zV6Y`>I(b+ZTI=PDRaA3b;8Mx)dMWa&1(1_)fyHw}uXu%$7dWVMrn(XNPn0T^B9ca{ zS+H}R)`^g>sRW&^$TF!q9AY|W%(AKOFEI1_R>ntDPe>N0Q*Xhap0AWF_acGsku9*q zmw4~_!+BU`d~V@i}-X0j*!~vcz7Gv4Aucrx=ydH4X_4)=l#lA zvu$nkhFUxz?NmuzZT-ag>UBxbLvS7cBrDgV5Ln*$j*+M#emUC#0kgPbUSBmylUNfM zvvz`;5M(!aoN~T)l^?D8jk)zv!s%D1`D!2UEaK{s4h>WWS)RvX{NuKgEzvWXuf;;A zLrnCB)1LmmjdHA;^Lc^vIC)Xv^3*c#GwVwC@knf5+d=Fg75GNBY2jp6*Se>Z<}Yua zWj5xPEC8QuSv}Vr|NGDTx|iLY&1uBJ`(?p%JT~W^8MDdr$oHAWPmTiCA=c4W7pphq z6C4pl{Ha64*MMq?(Y7;BaqDD;%Fxk^Rb9NprWlF z#?2JlV~)3NVkUa>O-}E(A%>Lpg?r2lqq z5`xF|wfQ+Dp|e%4`g`+g=mVRV^4+;e@28EmSoV|atLK>S{^%3m<3&zmY%I$C`{#Yc zxvh;QpzQ0b7;x-^kJi)Uq9+9Z%ng?Ys$=#Ft8C9bS{!$&b~|$SN$)S@$0%n94@lI|S7Gv;!2RSY zADVzQ1q@a8)iqTL04|go3czm-weBv?y^67Zgbte~7JVpF(%&r4|F(sO=%$Jb9p3q5 za$r};HpvsPWS0KL7j6mH7WCBh=_C9&B4=z|#MtKPq%EDr$VuDA<#F^A6%!lzXYdM9 zGEbYBn21y7i0Zgjq zYM^SbhUQ`0(LP0r7PHhN8oF=rzq`g}C>Kb`Jp4%J#%lCu3?0&t)dOulanJTm92^Q_ z<HpgCP5@&5|Hgmg{(BlA*|geXXc;O|K+C+2Hmu6B zh_;#V00}CCdh{tqg1^LfiHmILur75r0>6cdMu1dx6K%0drSqny;-YG`NbO3S@*L#? zYGpyoedpr@aiZV(D|hbXDW6^rS$gYZj^_l=;Um}KMmlks#oXP^&GmJ~Lw?7KC&@_* zTnYmdWW9hsAx8Z`cYTQyGV%oR@*PE0w{Qpp_vlGFy?s>pY=#@FG61#V*MhQ=wiPy@ z_VX>h=KBmZTG_53My5XfwIpPn1Z$wg5Xa6i7dEa<@Eab($NV1fT{;JPnBeQ^q>e7}`$@_Bh(|WmEZa*9T?UtQ8 zzv~OwT%P|>(Dy0%tSRw~DGWJtLRmMZFEaCa>9oUyFFB^u^`yMS0}&l^p#G7M2I?-N*(eQ!Ol`T4>eG?m$&aTN&`R-}Hs7iAAVRIDL+q zRIr*-@siBa0$(kVv*5wz^QudDQx;Yfxs6xtty$8bz~%@$#wqf=b4qB@8Z7czqfyoW z1|k?gVe=kK8iR44Q!0QGHHK?J57B$bIl2ax$S z*9(}j92NW)SGI62$J8t{tBNyWk0eQk(g}a~5e*z_VMmi3>}8e-d*n{EVoK*W;Riu< z#8^_Tyw(2ij0}pi9VizzI_Uvo+o})R4jEuG7%7Z+L^~AlN`{J*KhK{t=_^j!Dv}{y(F~pom zZO88*CHZH^8RhWtjmMu-5f)YBB28rIDW`IEgS<1BC6Gq`4{gG8b z00aW0;Rl-GzU`PJ_tAty)`HKFYwipb5HPAiguw=beseS!nPyV^f2dTnx|6qbq6e`vBlpqbXITeP` z(^MvX@VCBa5i1mXcZcFOs-YK&eKRdFU%&?ZAp3Xf=hXGpI&<=_QJlL-z!3%BHt;Lx z5HW{(^Z*o4m2!~WRq>q$oq7p)rALIZl}I9B3Ouh;ut21qo+QzSGRZner2YwMj7^;6 zU6N=`G{QLRXP7p@+A02=&tNT;rdGvx&2X~b#)=U}u=l6gof`YQiL$`Vf z5eEirF?{L$@fUZ)!}sR2?WJLToekZytMsDaw9i^oi;_PmbI~1Wr@_e=cZ-9qB7!d{ zF<0_PwdH>D&jkC=KmAW?kSh&H)h8s{GnG`oZPDv{?TD)08LH5i@)wKTwG$CqaY(>2 z+(e(tz;277;{%D2br-x0`|)hdSSJR zJMR>p{<=PmzGi1In za8~j6>b<7(H{t=h7W{QBfpZgrJ2Gy4dU>h5yrGi;%r6t#$46)5!`q}Ff-Tedlyrt9 zbvmEt3?F~SB>z_`8$t(s%o&qr!6aNdt$;~KnMr3m;fzW8uT-aW+8&b*(kuWmNbM6d zFxfNFeq2PVUY~W%ofL-}UqJD;J&8({dhNR#GcXYq~6i^nI6rJy;q9C ze^6>~v}M?x0%7h#2>X0X4LT0fgQ>K14*pLNf=zHXL)ELw(#S%6pwUX=)T){ZXtGz}e|GJXWy z!4r#EoSw5DLt@KZN)pU>bI6Y7b{QTF2OrEsY(Ue)TzE>*haK0m(Q)Id&A4L``!+&y zL^2F3J$7rUvEK^0MKiR{5HnOv!ZZMwK9xE>;P8^9XOfKrv$Di0@}^$ip-(GrBSUoE zlj*Lo1lI&=??_or_e<*71PzbT^AevVZD5Z@jjrb;V`4#qZic`hD?)~~ij5n1+HorP zijzBQa+n+&clWd?t zK2X06k_yX2qc>J-oouj1GH~AnGhvFEO3Or}Wuo5vGwOzBPF&sAiHMF*Oh+~R<7o)f z7IfV=NJLW_tf-Z%BEV}CrW&PFM0Q0Ed*Lv_R!MPDjqeh9!zTKqEhqS6i#iwpi?o=g zT1g|87)ico4JWj3Jwj3&soE%ZMda4l8Un9Q9m*D*V zljyrAUgt}<3%dvNJS3G0xVK^jwD;ZUfW8TmAaJdDo%9m7ek61hagc_$8wD%!C+^~i z^@uRjpDuX3DzQ$@)vlKO3+^~hqMIV6n<@=I&Wku+P#gDCazXQhm;^HzdBSBDlg_OBl=b)Rz35~lO8-gAw4 zs~Q2j2N_%K$hkvw9uV!Um$q;P&$(smUc4dma~eBy)sk)Dc5L5DSpaCuws<+Ve^Qoi z_;7CF>AQJvBK_hUq#?y)vZxINm8VOJ8(qim5RhO_hh(TtWbn~Heoq@XeoRh!jL0sQ zy91>)M zuE_=r=t%AH#D}}iB2t9>3Eh@gW<)^*x1tU&5CD3kAQ4`xg=0I&flZ+!c8NgLGc3ka zjoYcyAdK31Ej%c?fQ5Qg+Eq`~94s0@z4#~sffn)un_hW?%1)8t-SG@*?WqX_5;s28 zI~CqO>*(QzEU(A!1E-tIuPC&ohZL&dsp}1OUw2}eGL=HoCtie-kY9Sfn@L|fr1}Z< z+4tDXR-J}=1pq`?Il>f8g?e5zTzKxl#dzI_9HIzRM| z^!jFNTUXn!D+jo^`QaV&qkPswIIu!`Tdo6sIm2#j^N|hLbx*I}Np(E`{c|CVGm}e! zzr7KBmH!F^mO+>7;Y69wAPyR=x7O61X=6-TgKl4=rx4X`?`~Wa?cal@ZM;H_ zctPxF>_RsNwAjcvbgw)vV4ZWcIV~pzu$b+H7awF>zjkl<8EfvS8R>cG*z!GfH*-@L zuB$lXw&#d_r)rAsPg6j;Cv?>8;9ys-Y?kX(_;Ro^8GNQbcs$QbEvUt8>K5?H;2_s~ zc=Pa>y#aTJAly0UN}3+sqIkUXBabvi1bN#FyO|a5o51U@R=cT32M?#%Mn&jxp1E=@ zn7Ti0VZPkBx@&yR_BA=KueuX4ewk8wS$qxFUxM5|e>Xzhh!KciCkY`YU55{^8$Nmx);W~kH1d*Lmi;i&?8 zlaxQKR=IM15;Hdf)X=BY6X&ezSZQ)I4{fcq&bEA?EB}t)l`c?bgEnqX*DmexBEBsS zVj*8)@xQ)&KHfc9J)2&O7Hjlie<`^A`!UgHoWtoE4ZOa~UEKOKxeyS&x_hj@5IK~6 z9R3+t(dRFW^xEe@-d+5UY|k*6OY?@TY@WREz`xi{a3Mmoodpw-VZ~o zb*$g{*-;z}ghdnFQ@#bsMHi|(&(tN?@YEdqVs0u}^rh8|VWK~s){-j4ls!t5RIXE6 z46Cbly7rJM8B3=hLvy8NB|am#wtqP!W%v?Sc|8muRTMd9DqDWCcl(0Fj`1>Ljv(3dQn#nWg}O_bf!U?GoEt&|_Y@!2h&+$$%xm1T|&4klU@XfvLN|rOnLFhE&+KyGa;G|!>kKjA(l)SvSL?7GSI@VvO ztL^uiHO$Hi&d`Tnb9-c6X5A3ZTRw+azqTbza^-fUlE0S&j`mXcRkQf5y@%eyh2JZF zgyV^Jsd=0pM}d`}iE_OyAtbT5+~2_t>9*;F2t3PRpqu?Gy;m=)`xj@rb~(K({htRv z7n@m#y8ZPZ3JXd*16lTX_d;+_TKbP98szuIE=7>xkPXd(!=> zY>8fzMpnAm&e}rBUWTUAsqaoD?$*We!iX${cZZ89-si@wg&p`+-GZBws4UcWy4*~U ziLnpW<>Sf-gv_Qgb6hh|&IZL|uG~tLMsWpyssGo zyi@mQK$Gl2-B#bWuuN@DMmIR_6*QoAPWavel)xIDLsO z6-ym0`;)~U-0114GjZw-n%xfZ7mck9cvyR)Gv&``VY)rqC@5JvQrX>=7v0vH?P5w! z%2@Z_2khWFwH}@Sm>*0)U(2WVx{+_=dZth4EyCd))Ma&4F3!2mb0WIoS?wyw%%BiBG%bcgz zxw_p%5~&pUjE}q9iJ9BN27Mr3 zU~KF(pPST^+p+Box$RkC+?f8x`&K*hJCypz|MJ|N5(j5<{{E9`AK0DAJ3sMvWlcx{ zE1(R=h>zomi$tT&3(JqMaJb==o(c+OArOVDA}SyWVW2dbZ?Ki+|3MIm`ih`Hc!P#2 zlHmT_y-f^O&Mon=Sl7N{2HmKe;IX_Q2#R1 z_8>=%N}IO-t;j?&Rgi(ZFkm$-u1Z{u@!hoRm8yhvCuM`U+$er+?D>~dW+#9S%*vP z1kX0tGy4CIT zxv&1?+tn7`dSQue%TR_Y%vH2|YoSy!5~vD)8#c09+Q3K{BMJ3)CGAE^(u((jrB%2L z0j{mK(vX@gtVWQ-ZA_-5;)xg5n*lw$+_1?>o#~t02kw(iX7|7H`L6A3L%t5hwN&!f2-n6xce&eQ>f3;8f9lu9(2$^Z>aGNpP=fGFm$ z3)O)#xax*X5mRL#^E`}09e{-R>yCMeS>A?CkQKSAG2I1RwIX;OC2BffA+t$z88juM zK#>3vMtM$)4~Ipq zG@WB>K?VyD04qBsuwE)eJ5d-S5q26~4t^z%nzc-?0j23Y4*sQ)Z<61SEun@*dnTDQ zlx@J+uzLjx9|CW?h&k85^v6V%Su$0@#9WYiTT$2%0W?xK&<;{h6%^J?uf2dU8fU96) zYRd3r5`fi>es50z`v9X-=!u70ymQ?~)CY`PhbkhBygEfj&fd9~&@Gt@K@)5b2l}=V z=)%1rqZ5Pg>i7MsBn>(89y9qz);x{?Y5WPoDRzhsha_R7m@gEm3ER#B5DXKw>LqZo z9CnwLDL@U$8V@9rF;Rw!ndjKz0FXhJ*86(j2oxZb5EH{p1K$h$%}LoaH26Kx_AO5q zFSz~T3->Aj7s{BuLWMCc9L7v20dwL+j4@p<%9K6hJ!(JA7`d1J9lpn*%7l|tgc(bv zvX5g2p7hhqv=|_*M;41YNq}v6C@_JL#h5KK%#e+#7-7;@RmEmfL7@M&4zr0s2j=jm zRxW!<0LB$uF#~Kt1c2>aKFeMn#?em$#mjMgtadd>li|*Q7D0OMND1XOqXfjww1-Eerphz8Y8Ze3=Fmw? z5U2-rvRA85+Lk)q1iaxWAHk|0-~cxOu%a8FHNs=&jT4-&(><$j7>5Cgg(ym8J;343 zoB6Clqs&)`Jo{o9OBC`Z(24TSj;jU>gx@t~OFsn&Wnl43?i}PH-GQiw!}L%T)`x{*(uh5uE=DvAKrvG!2Bml$8Z0^2YQino zcBTQ2Ixin^YGvV;A9R}oUg~24(`@#;teJ02wA|@8#=X|qwkMi@rh;DTa{?kBIN0Bb zpfC3~tr1-twj682S~d2)tNrZIR>R)D*TN;0+tlR?8;tY0#HDb+bd*M@M=+GjCF^gl z$Q5ZjAMkc_UI=c0!`#t1COdl5I-&KMhPYiBk^L!#gAs-+Ihr9Sq!xsTtA8}5auz9@ zNzT80#doj!5Q~@QXzZm2@6?QIhHxJKquXPdV`)VFk#VBP|Iql_fbWdG6;x0opE#i;mnNylt}%1&R^cSyPUaqD@X|AQ{p?gEnz9F1jBJqYv^T-T`fvqy>OI2kLMqEhZ+a*pl(n(24>fu%7}dp9;WglODv@d z+7>ZysqeK(5WHE1wVcILnu0N#?T`hJ0qr3O^33ex%_ifq?|Y+GtKB|{e(#0B8}GBr zcEFc`Ky|!r9dDJx=wQq{_TM?$#k89|>13n^GwR-r`3GS)nbygucQ^cz#C~kGoo>fr zBm}8xvR^uwI}5U9CMfqfrSXJTPgNCwcl48iqivuq_rnf$*0SXaC}xZzY4e4;$mi)> z(uqMNLGT$!y_e4~sEobpLUqlWH^#Ob<0{t2Qh5z{V#Ke4c_R0|k%&ZFDkl`miwv<= z#f60fc5vBxWmby}gq_JbSgj8OqV|VHIPo84{wsN@jc7=wSY4zm$tXh6BRZlb8iM!H z`mI&V**+COE8f7LD`g%;dq-;k*BxV5IroOGUE_JD&RaK;eIhnIH!h8d5M=D9C z3XU9$5@rvFG?_y(y{IS{Iu=O~=Z9_YV^M`pp(Kk^03ptSj1Aqo0u|##!jbvyn?|t> zLLD2TArGcQT2-o^PVQTt+^6em!5BMFK@>wa?+Ja4B(-_OJHIJrpVl4NG052}#owp1 z59W48-#YQDPX`Iq4U?i(s&JQ1BCrDnY4ZneKWFr)wnHLWOY%>GV~k-m!=R}`fjj=4 zfGiZsfyAUtaOmSL<^1J;$`gHBh2V@wcgoW*c#;q}jSs~YhvfVvKC3}_%A+}}L3)}^ zbXtYxysTUjfbvuG(sT;BWZ-f)!5pN!gP9}asd(bNPch>;JIrGRDr}KWzJp^Y1zKpJMs=A%6~@r-J|3cF#n1VDXAxyRO$= zV7sr`=&J*XSj$FCn#TMQU&akN9{Tl9eX=!rRxWL&@+NN$5azn}$dR2dDb>!+GOq9W#S6USAZFhE=y05G=VSY+#EeQq;E!u z*CX6JM8pvS#~R**5{{7vRm73CVRIa#GIH8lfqkaWGkI)-)99r(lUI8@pd`*PGz^-G5ff@%^b2x@8sm3uC z%u#0kz@Fv%C2MTMO5$2x(%J>6<}$J-Tl{ehFl`p7CL8x`{hxf3N`VQsfYd8sT5eEH zSExpB!r6MMe3KS|iQEcMzzApB(!pWTh{T_pWWi@BOk@22xb}bvD6uw}eT4tGc5=?W zUkb1KORImUjVhT&37Os)Fh5Dh2!;w_3>5&dlxNWxoh!liuVq8~us$PzUg$dW=zbx6eenO(=sh7zSN z6KB>)^T-qF5ysH#4%;{9z>xsPK%Iv~KPmVe-6wh7W8T#~{YJ?T*j|Hlw!-r|4v{yK z$gym>>(b%<#ly91%%q&NF`&p3k}ulF#BJGT)Vf80!45f0OSoy9WZxm2c>}fg9ED)} zg42JcM!0zmz4t8eyCqDh^^+`kgP3rWD%jqOCU^sxa5LA?rBejxm*0r4B#ZLj0kc7W zp-gYtWnBw$QXMmr!Cv&ih4zt51)E_m=L7ZrpO#*P6L1V31)!lKINCz!g_# zpeR&9E1rtGK;Qp*4Cmu#8pVSL}Ul3ccVxi3m> zvA)Y)6KjOdTBt;cCOKCRmr0mwnW;3GIwjn&EcM+XoUK8B8rkOdt^5=BjIUc~pF^Kx{~{a0)6yX`4Cno-uw~l#ce{TcxJJCaY~?ZJ z@?6xp-r_7AfZlI&)A(dy_joDd)zY)^cAsINb(r;UgJ~~oUv+IQto;kcmO6$qy6wIU zQD@*Z?c?P2x7J6<&Vj0{wJ3Vm`Of955Wbedb4G!V0rFckX0jr?uytH+oL%Bqe_C#P zr{DPN#vWV7o8*H{!^fFX6dOYxxaca>!cr+$L(FOYoLl?hlF+lH-EPKXBPK3`+eK%W zT%on|jlUqSCsDVzD{U9k8!RcdGo|lN2kn`WfzouINxyXwKAtwU)`ir&PmA}&`RQ?U zNz{O*8s3fg4bOhd+hg)??B~g(u<5WyL7?F8uQBFA~3dgB^RvLj#TgBcvqigOTE{_-tFdw_fJ$(8s^bA*f3fv(^9q<4-d<)6{&ga@2X33S>wH*yQ<2_RoDGK7n^ypjuw2Z zLeQqRQpLjfn`m&^S0rnusynX}eiHagR^gg#gP+iHqvET!SJJP?Z8mdrS+$*rj<0df zg1XBa2!|uD$Q1|a~Nuf{140pPeoh@BujciHs z!j~?)wYoHA*xRPPi618~W3Q*@Br37t+MVgh`m{V;rY2F( zC=HL`&wXBR(xXJ1u^U+}K%OsmbDEyqliv<4-H*VVh>XRz0egvtwS{D zyCo~odjYu2DtuBhSvq!6c`Kt6eK#LgmYAoNc*#NOGF;f0_G8q}vkO#L_r1+&pK?w6hV`5xGh^1Z^Zf6fm<*w2|>Ckr79aWI_AU`zBs z5m#EVlcjPpwk+nYetfv-O%BazM0vTNON%8X8LI8~MIQVRAx8@vslDLX`YUjOPdag0MRwaA1_`Z`U zjjA4lo8%(7+$Kt~ZF(Fm?r^BP>eLiU*DX}n%~tjTL4#8M>K>gv%yDu1LgY?KHP4qo zyo2E<94_q8ax?llH>Yg#(H!z(R@_wUdAvzzAgX;Y%J`}3!am8Jev5uSkeqJ&e%A9x zy_n$|lXrP*cX~ff($Ao8QuA$!z4QTIsLKL3)f%J{ZPBo^+@5`!_Otd;D@Zl8VtP=> zU0C&Ir{80+*v}vKw&=Dfo~yNsGuQU`T+HVAhK_7!${m`nQJy1|1t>Z0 ze0laS8Rqp>XwXYhl>@o(J>o%4%U{Lu-F7Fuwzdj3o3`63{h<7A!{m17xkY?fx3!`* z;SkD~v*1a4eY`_bg@OA|ChQbX?eGvgJqm(P6B{m9@8e#{aSnxl_xFqTe@bSrNke() zeODRXW|Esr7Qc#&@bP;b!UnUx{12CV9?*%)&+z=pU=qbm`BrrvKL(%EzdIb&tiK=O zhFO>NzwNF4DoFn3`+od0jNUjKEm-*0^&1rL+#2|El$XY9ZIV_q@-lhwac$6{uEDx1 z7I`O?i`UuK`iEI-J>M4}F4{`^e73;t%ORfb!)8vL_p z@Xd+o^S7_Um6lTl5dS)APy4nX(;fdL8|I%UyL1%dJe}M(KE9f#53xfMiAz6GII@Zc z%`zoSXXH_SNcnRXhdM@ETYW-8LW8}nfg<9U5D2a%MPvAHbVnPIJ=UCkO_Fkhzpiez++QZf|u;4NBfrkV_l{b>jp_o7mf*I??vAS9UFE6bk`>~$i_~RVf z8YHbD%}uI}>sYTa;f_s%mWPJDxa_E$EWPV1ObS*xA$00i8+it@;UCseBpL_;SeVM$TE@JiAm{BKio^v|mK1#rM_= z2m&@n9 z`tu~(|0v2Ytu6nM9XsFBDO)8W1l0Gy&xgYl((=T$rP#pbvgc!ggk!?DCkKs0=T}lj z!&o$S;uRaoNe1;0YY9VHT$@C8 znl%iY*v&V^l`~#bjhSlf0>m@M1$-o|_>~qc0h20z^Ta9Dlh-U-H(N2pTj0hsP%8a1 zd%aZ78(>*bRtR)xe^XJY6v;3K4-8KMX(c0n3D{sX%nbvIZBQhMf+U9 zE{|AWn|7avtt;k`hc4x}n1v@Zm#_Z5XO|6;Kz%Yj9vU?XSlxrd!Ou(!s@;`^p{X zu;E7XRS6k>XogR1@Q!Hxmj0Do$J-D@@ABIwwY>}K;guCAy?2x!{qgI1&8>^T-i203 zO%Rmc4yCux&$W1!BAY^%k>jt~FwA5Upuq~6M4*@C>?Gr~En3W8#GW>p=8UU!!IodX z`OvDe6lWA`6Xy!o6hMSvI7I|F@W1wgVSHHvldiqlw}_`yskwaDa}tANqeode{>2tn*FX`taG2x ziiS;YPsf$UBh3Fl_p#0dln~Jf5*6Ak_+RUs9%do?rb0{RND^-=jIvFW1MFJ}r02Yn z-I`zj%YA&n4$3ZK1AYVc<39d^%Rfwo`C<&*U~$^XfVjpgfOAnzWQP$lA<`%lD{)kZ z{$%af*OqF8wmKk6psxyw@&y}ce~k^t2;C)`W2_2`5*eTMj}n=x<)iy?AA`4wv%uo_ z{UNf#h>V(TCP1sqm+DNPq%K z!jBg+px#`BD=!3EEM*!;FQte zl)>PX$>60R$Vzf#RVXUcnG(&jKLQt5D=LKGh*FjC8Jc?hFXIlt&qkXsBWzHWiFp|5 zo^scbKknn(q%@i9k4uN-E+Atf3(aS6@Om?J9I$%vHEd`2=k|fVWZo{A1xhas!Ba*o z(|qiE64EKEO-=pIC@uU!5A z#K$p)9~u82@iC^~0fXOBa?c%TGbEKC2+Ef`Q&Ys!KW65ydq_VH9f)62FZkwtTeAi5 znq#+tsVNezDZoksnyKay;{JtLqHH!8s7R|(qlcHR3yuH_A{td0Tumi%Mi)dS|F3x^ zu;5_G2<3@rNOmK``p^iZ;X#z)LFM5=mf^vq=!moE2*u$+m>>1=wDD-_9uX&Df9Yta z>0Tu4zUUoDjkv5At(7E~epqM&tDtj_kzTb@{obXazmxgz6y*A2%dyOn(fjz+xTbJ& z!J0_-5VSkVz*q13V6fH&IDiF{c3CDLfK^9C8XbAT3`0Zew}zC&Q$uvs35Lc5i!txa zsOI#jX5Xl1K*AFd@dc^qvZUxTmFV(Bbd{QYUB$5!SYq4QLNwuwA6;;bV!j@+K+Q7n zCl|$A*{x2mT{leTHs0@SWk(@?S^&VO)XfB8MJR2?JIBdF6__skM!91KM8Ux z)QR%PVha7JX)EN(Fu{E`O_Y4RNa+?7|6s}FdRkpr9?tj$BC>=uzit4hK zV~`*r+T4!mz4Vp0@1pe=*S%JAN$gdDQi0raHlp7zt~!2g=l8(%P3ks3>SM%@(zux) z^>I_*kNOy8ro$dzS4fUumO|=Bee4_i<{?hNv1yNvmkRy&f2)t}*X8HWZT&R2gh3xe zR0Z^yfUdgKG_Qbq6bG)XY+A2~_nu(|t-g8ZE#=$=SU***Jmb@S#iV=85O9(lV{`TrAW6)a}bPDNzq)c9%^E#T)zh+r_%2>T|Gkyl8 ze~n?hMlsmuDDUXQY#PIS01S4~jCS#ddu@h$aff?#hW%x0m=KE9FlR4Rz%9>IzylrX zDsEYkoR*Y?ZMrhC&hrfk(Y*HXur3lTEoVW0+{Z3Y9P`4D?m177RbK8-*I$eJ1mI5* zf*`!IuyLmvmcNI(As5ga%L@?90<(o=fGJ4&bj5`-{s4wFXlhr$YYDOH-qPToj|o&6 z84e3*4F8lv__g`RJc^jaJgJ+=JYuDUoT;RQ9H1%L9M!1@$~Y+H+am27Fn8&(Pd$AJ z({hcAp1MeuhivD(`3Di)LI=BiVsw9Q=Rjk0Isxamq%t5_7$|{DXe`OA_Ho zljT-3DOx)rsOpE}O?clfhLj$hyqF4d1%sW*YZo&nMMOPPb_IL~VSRFt_f=snlJ$*H zj=Hz2txJJDvnl;?$k^m7i}v)}9A) z8IpvAxY4e{5M+l@5YJ_-Gs<_=t+`hXy>4lTNm{u>RP6@qb`eMG^KLo%Km9SqR9eBR zKRjvO`GsM6T*r>!*4DK*rsk}UwXNz_d`MANau;U#!rsp2yDvL_e$+pHKlYhCDrngc zPA*=+g5N*;>W@{MdTaFC%klRd3k8TD{qaklj-Spch|f}7l$;zhs5C6BhU={&PMC@9 zWW@8CGrKNSdwguQ!h#nLX86SbJU!gVODXrF+@F>D4$KEdh0ibM>Ep3e&tmJPav1OL zs}H&$`S*#48rSH7l^45G7tyC`3#)A@;g^_s2xXF@%Imndu{1>&F1=>n8h*=@za}}+ zLMxRgE^!WJKKm(BS8s-X?~277wl|S8gN~h0+eCc^@mfFD~a7 zI3Bt$%yCM!ydq-T0#*8$W9DEwi?3O$uO zVB~@GESu6d-0o_MZnFA*K2YbNrsVAg8GFZ*@6NUc>mXI<%vGx8W%Jo@8Upuw*F3T2 zw&>1n8Wg_&^;6xK2lqwra4?&11sc?V|K@tNl!#xm{nJ%66h zYatcpI*^&!NfkYGW1Q9J+*R@hlfn0cO{sxT9iqHxz2WWoB(ulUMhHAC<;IJyvk(T1 z^6QiRD2zY-zN561wE{6Yt{RV_p+UJXBtx`i-%iaQbt-L;W5lcNnz?ZLY`fx6_3w&R zw_kpm*S>j?OIo(>wWiQcFsn`HW@GHaKs&gaW0dM7{)RUo?Lr>U+M%^;6ZH%roaE}& z_T-kvmdn)>h~0EvXmxQ5MOSKK_BDu)cij>gY?Xn_yLm%Qr`Ap4(b_OI?6-Lz9R>|> zIyvXx@#0V$I?c=PO3Y&Fm|?7IO}t24b2;8>dT7OD)7z?fcW;wR%7nXVnH)&zZHeBI zd9mjVvm@R98l322?3S_Tz^WOVtq|+cIy%xM<@M+)3rgeeetUb*>p4C88z)_jjlH)g zVzmF3#qZwsM$IK#p?SR*iu=0SbxsW5a;t1COrN#Ty@hbgKJoP%Cl zyH!y!Eejko<{Q-{tTKLmwsaX6uEE~jyYj?^`eoU? z@;hUFrg^}4_j-^NmzI3}NQ>Y9lNKX<_(I=!UmEPDVff?YG6%l;wN0-L!4LQ;!hWyn zZ6R!oJ@*gX=4lUnFJ-KLD;tf#{>;T?{*ScSgZ$Fq^``u3hLr4Ddgv>wiU0Nb=idA8 zNxvG1{hprczMy|%7Q`Yl*aS2eveaf^EAI{UsmMJwyHKwt$XZ=N&uu6Twz0}%Paa+8 z-tGSPel-s7+gx-V6E4u(Z-*gf zUD=gRLYNXr$Sr@q8(W@BX;+;VXX~Uil(BAt5b>PEj(La(_2IK zb+sb*or|0i&(@~eXKE);W&4wjYp!-@jIHl$Yn`1V!b?BMm-TNpo3Vdhl~Wf34;U{S zUvKb&J&*zKdY@ycrSCZxa`x`rH^Z@Ed;EmG-|HX{dRLz2KzDwP#xn^&WA@)r{jpj( zRWUuTyWbhFY9lguvyAJlLY0ue;CYIcG(J8U?$pN-y>aUYpSZf&Fx&3Xy<_OhxngSo zD{z}ZH3!`mu82AlviPgMKf8GP!#*M}WHy@ZhF6U8LUH<^Vr$s)`d<2kx!-S_4wd8V z?wUUQJlWs#DWW)Hck=TY*ogy$gozP;|HgBq-^2!&g3*p8(7#*8MiCT^)R$pdm*L1n zRS%B6N9!BH2y_w8Kjd5n;Q+w^g#gWs^!FchaQ_{`1ggX{GgmjmCmnq`sK@=zGjo07 z`}1k{^1kII8T2+*Pw|?VO7pU=3?F~QxzZ0_A3d6FMYhrK@8Txt|KRJLgCp%8h1=M+ zt%)_UZQHhO8xz~MlZl;7Y)@?4{`!5-J@@<7J-5yuyQ=d%yZY&_?&|9F+H39DG1bOS z&Z$0|sj|v}6)%C|mF(gh@58=_d(@yXXd}UA9=|` zbi9A|UA}}Fx%Rj+I(^~tM|_`~)^Sa}96D~NWH(l`{_M++JpIiadBQypoO08gt5g&7 zw)GV@9v$Oj)OF(+IQD^5@F>wZU#I%D?Fi}Ar3pE$^$n9d#d)bzbH)1mevK*BlD-Z8 ztNN8}Nz@m0aqN>o6~l$`4e(2W9yjYEcoZGEy3?79!C;27S9{f8555sjS84S^gs!$c z^(EaEXbwXyarrvOo87d)4P$wRUs*iPZ$!nB<8%75kX_c8#%vNM1UP@-0vJXB|acT($vxyA?%#g+|qO) zleSkLMriILN$(cTr7F4lmtxh3J1($=-qpTL*Z6+kYFb~$C&^>)B4HCA9@i27A*&qP zzf2NQQ8x9u@Z!At&j#_Yn1>|w{v+7!fAOF`_AEeHr_ny)h*XTO zZMi?8(SpCs-#V<)XqBT?sPmPc0p0=m7X6%s(_bU)O>!QHu*m8(Z^m#EXelJNe11kx zQahx#TTKXwQ)6%?o`kV?COA{SxrM(oOVCtVeV`B3di8|nU=34j!P6MbzCn)S?=kHD z(N9&bZerv1ZHJ}tn2tI)*WKqc>vo`BXBU6<*YvAL36x_|b-; z#R^WRPOnR58)=@WkNG@VZ^&so^j{d1aLPUX+zjMAZ~iL`mIvXyKOhSpqx*u$S}`w4 zTQNqvXTFKT8$r}$wgf}J_4Ya??eQeIgg)cQ=zj=e?1Wny+`ORJ=ZcUV-M$D_2uz~G zl9AN5N>C@>6%V{}=@Vf{i|Wv0llQCnh}d!46cv)WyJPN$JVSh7C+z!)htC_-eRJYS z?$tYGIz9sr_d`?>!mq>zve;1O)2_l5H!aGg(-Mpa!SlaG(R-6~>Tbj}$v)wVGL!vxD>aLEM2--{XE!~}^Pg~6g0GOqFolMIGMDqvlt z5GU9UhDPS?6b<_b10x#?h#YWM5GCDw4XF^c^T zipAujMa2DsCAMlKNOC_E%G8KXYkOL+y!y{cSFF1L2S>vg8}Mklw=dIO=o0aGRvzHE$ywk{^k7$3xFVlsNbr8Wl(!>6N=j2f}l z2vRvmdBXa&kW{JQ)Nl(}S78fN22+<6cl*uEvO~8nS3GbepCs1C*^*Hj3bO6R{Y=N0 zVOba9A%0h66vXnncJKc62l~I5$Q4jwg4XDh7{&qu8W;LsDop>Yp7P%rrvIcw^ne1> zKjVK;B4`g()fKrxwE+b1wX{@nsAPJy@ntx0t^DR>G;$RKiKK!gHd&`@neoC;Dx-BW za5_RNBI@WUsPYSH7!?%}U{S%fWYvVtJqWv(ZBu6Ug`n;44<17QT*|2&@0s@F-20y6 z4Q`exnJQK7GFZfwrI&OU#SYpm4&fc$uEE3jSHG@_%Xl#Jf~2_UUFWVR9l@i0V-HEB z3m-WA14@Cq*%iB#nz>vbb15fE-6wwks7JA^i!e5(%;#~0GLxNx_u6!V(K zg2D}MTE%cU32RaU`_1(zvqKC;msvr}6G; z1mh|W)y&+N3y0Oc=TuvxxJ$Hngvu{Msh}OPLCcrpl@>5(IWe+~DyiAhiM1(S9S-s& z5~dkgPk}A`NQKatH4X(pH;jC+j>JQEQl|)zg|hwWtKz2c5`4|{^ z@|M_uB~7!}JtY6km7|4?lx1icBVN{N7-58`7}MHZ!!$JGFiH|fIhrECPm*e2sjobF zmTj0re#=Hfl8FC683alNCn$I%OoB~daq2N*7&|5?;!dOQmAT#bXA-f98^N=16L1B z42!N@??l2v-0DVTsgHVM7D#ExZE+6iTqD;Vuq@O9IK&o3f)2cB(tx!93mWJr?4Orz zPLxBX*u%~e1ETrSw-F^0YFNnAVwOP*rhL6JQhf)m_-6c=rb90?MRrXFDfTK%)6Ave zCOP5CdG=J-K$(NL7ZL-uC`G>nGl5hINq#}rsE9$tlz{LlgF=aRE94&xl#vbCB&J%w zB`O{wBr=#ai<8qyl)B2I&@}amUu02#2rNHH8`OrsF)(o?`7odaOPERW%Yk9yGZGhYT(684fUo)KO16VZzYWGnBei18j(!Q*m z&C~hA1ZW#2E>+-zKsuR)Scn_tRAFO%!ON_XJ3^g?f!=6k|=9npT z%+&=U_!&Hs%z^pC@e4Eh9*=x_SStIN+R;xZ@}7r$TAs?0UU5$+(=Xvqdo|RHMfSZz z_NN)r>`*)4ypOR&KJV;qm;o>>>uG%97&v|kQWFeJbw-;lkUWXBJbGYM{3^W)&u8o0 zJk+(GT^jt7yv49LnnrP?5r2KU*sUena4xUqLvBJp3X6tRBdR5|L53J5FpsorYWN8c zW$^LT4mbn+RTN77Je=B(e%%_dXHkrewB((hYB2X$lnwRdom$nP_ezuvS1E@TnuhiK zp{t)Ap`lm2bimm|atemLN@jl(qr+HuaR@ed!0pI_EoqDHTq2dbQ7Lo_bMCmz zAQ9#5KXqK}=*rH%2j{S!bP5IaXhWo!-4G}~7QW%AY!)fv18tR65{8ufQ3ZFK-6RC9 z^q|}{VEYvAwCMMr0}Y<%2d$LB=;i(Jz*o9?)9F8l1+N=~(Gy4O>QT9QliDb?DMU~= zl*o^3R7eCtszH#1p#9NaHbGFP&KzJY=o)@e&&3p^2aH#SKtq{~rgCBWNaY>`MvR7HbmVbLVA--h_quSJO6FoBQreGA;+C3?m2v=jEV1qB82ExFXL|@I znuLi-b7TACWg3n?zWG zV7LYoI}du9X7%V-L>Ul-fNMam;;=)L8{lq`RR`EQCZT`^6DkQAa`P;LHxv^8%ZX+| zev6aZti6J`>}LHJC(^@iyb;B*9Yl~IXx~g|+l*|(FNV5^p&wJkYZ7S(?(e9FWt(X$ z9C?;uVnlg1>gl6eG)fNC9lVJZV@J8UI$_vFxCqoTot5zN4r4qKv_a>#5q-7^ePPug z>enl05iT4$lZ{vkrxIf^dNdu1nnR*VMW*?MOp}XTMM$a!LaOF3Rt+4Z5m}z$m*Zqg z1pPOksP4`IiKzu5sRSO{s1A>EM&hIN{_1@rf@{@AuJ)3 z%z6%&ASzec;c%Qp)P4wmHae8_hnE+2slvnjz^pB+j40-T9L_Hxx~OA|pku5()6A%2 zjUaQb(yXJa*&6#TxudR35jBDGYJoDlu#Tp zGe$BqW->D-GBXx30#gZ|Kk-yX5+AVamL|vbqZs=X08Z3yq8qpWOLh;#u}Yt;to!%R zANY;JgPxgW9qSDY3v`?gu1tND7BDGD^-?VcDNOY~CI`BWmTURyeu>IX`fmUtEuFa3^$r;DqYC z2&`%_+r*9@>>@mc9c@klq1=%J^o8{R-(E^7?zCZ#*9OB}QV;r+oDweCNq5u`gui-V z26wEs6Y{-~QeFc*4s2UItwIv_c}u7L@-KiAt>ObH5o%Cb9HK);t4km3Ka|M39*&`) zlaM``+=~>7U&M(o5sIE+Bf9L+YJqZ&s^3u)p3|1f^s&f5b5k7Zp&?Zuj}hduS6TBC z5wZN#O(DxJFVW>aM&x`JfBX#SrM>bkRPB*bkyl8i*CgW&o%l*|-ZuvMnS=BS8s1T! zdCzYu;vJs!>WA3ILQI=Emg5ZbUas*F?@Yuy9Zl7Cf~#(uwkCY6j|5=HWmK_>6ijH4 zv}PzBGbo*Q{Dms0kW*E#2QTzMEx`EO)=qaVn&`YFEsWo- zg>zPDMnK`c#g}=GzcimO38RE_cIsFfe%_Gx_$UK-4Ls^uu_A;(3uhJLq=iX&Y2afU zY=a&|Z7oL!Q;10Am4WUg8jllH%rvc?F`+IRfNg@zs=tncc{wB0(BL%7qgEA9`|ars zlz9y`S$NGfox8(K=^m&jNNyrZemQB=_NRJKC9^v+)u-@{W0OMXv=frSRF~36NayEB8qCcBVFgc=Pc2?(c_I+CglKlr0 zRWW(vUDQr*k-E4Qae2$TK0%IMbdO$iKaSU+`tb{)sMinjoqdciq?xp?WBa^-6g9v! z;2^$Nsu@3I(ngcB?M1|TbZ44=K5$H+NVxk0Zh}5orIMcsH`(OtO*IO7{hWX}U!uh= zRBy5=2OJv{<&~un(W_e zO=w%mA{EB%h?N@v!mV4g$e(d&~rqy)VZ=L%ww)wpf?R>cJ zGTNxzS4D&E)g!BGLg;_p(^1et@qDM&YcC_^6g*q9%jx}TS}nrgUr%_L7hN z7Q04}k{*F7`0!`wC_VUFwoqFL@v08G@raA>P2bIyx8BplY{jNO?RxF& zYO7M4M>CUqzgFmas8h`8?6|QC^!$*%Vi$9zIjvg5{zA=NJ?GD2R`_f*EiOHF&0lJZ!B%NvY z3(jF|WDCKC`)prIsR;5X9@0)n9*#-#!;fPwgefhrDr?S_u%?gY=u&=dc-lR9<3Gy! ziFn>7{=HxS{eUXB=SNyNGWNdX?^FKmGQB+XHQupJ*M<1>zSJCdxdiK;{R{0X_%kH) zsp`n!y=hXHeG~Hi^LmFr^4p$a!_JZL0Fx1&drYC)V-)3ETeuyrx5&(+qqsVp)=x(2KHXR@SL4TcAokZPy9IsWcjY4L(|%0* z6vqzJsyLez-;L1EhPiH%&yD4I@=z8Qkd4Kymx0zPp)%=BOn`X zEU6Ql@-JzBs}wiyo->%O2mK8Z}kbTvBNSD!J*gY>?0UmeGbdHDHp-es~^ zWeYf9ZHvjk+Z5hqA%Fi?)6ZKI-?ZfxfR`H^0A{MXnf;(qwDcv{if}?aW-88$)dCT{*L- z-?8?#S;Qde&9`m%Azb^S!2X^1gZyX|(b&t!hU(x5O<9A>ZJWn3w&BaK zY_8{yN%TdJZ^5da$+y(1^iL(YDJjG1GBdvV&kjS`pl2RQPts=U-L2}~zrHE_&G#PT zWUMjVI5(z!)*co+p2M!UT_zVRT({M$)u*%ml)%YZlx>@L)VobLF?~0QU(a3$&W?G( zgnu=6H$8KGLBj!MMZ0o+sa%IyoR?WGI+S~G}hUD zQTavghkhp4H&T?!BVN?B$$9$4cH8vQ$JqaQc!GUOnm7WZ^?86B>^=E##<6L~{oWW+ zP$%`RXfQw=*kZV!Ib^sJevvjGQ{hBBT6y}ULFYJE+FF~@%Dmys+Ryo1C+AhyGUpAb z^6t3Scn*)L+VM)Te>*o0d1r8gqev!~H|QJ+I6#O&qICaLh1&YeE$`_u-u<5FyYM7? zYF^6wcSf=CETN0yJ}~8|b~9EW$e2od2gy~O(-o)S6)5vOy z?(HtjovhiR-n^ZAHKnqf@K+4qQ+jPZ_O3q~uis|+%e24yb4jls0*$8Pp0fT<^ybxg z*faRcC`_k)lQ81bA#$qw4SyMrhPFep>8|Vd(D_CVY-_6 zhis3A%3t%(O~U^D4Wi49tbMBPBMrn|~TlP?>pn+ORvR}r0p$DMlBS#9}~1D{e3j)_|d zW7-AS^t00taQ;F}b?-K;rS)g%WIgt6G04%4%S?_{gD@CJ%*Z%8H&XfzL8D{gXr^On zX{6)g;ACjwWZ|acYNumoYNlskXJ~3=YGh(#YiD9%X=MiZv`_cBQdq2NgI-9_UXTpr zjT$dJVeyqY5Mp+ijtWm9P+@3}CfQ>9+~97q9qbBgqpjB_*yEZo;%-tOBrvE{!6Z{P zjUxg6=%?pKt3p$uYK9rq9tG7Zcbm#zq=)k-Ml>pTl}-`rjG4U@BB&R-$1HKfE?N)e z;Rv)!3&9C>%gA33)k(;IjaKTEnFCC55xP&~0vs*?%;^g`VvxFIrX=oRrD~oC3VA79 zq?e9q#I$q(Yl%plEuN&0xd`PU2s*0)*Fw_@RTLqP^UW7cV+(aNL{l8y$FrCdtjDN2vcatq%q>mwiX^#uFN((iinEt=pSp0Y1>p%I4IiT$I&-nl0Co<~q^0jnfs}&_rbjcB9WXn3lun7^7rV8B@ zEbQ$`GGrZ_O_o_rdCCOfq{muyg|iWWTP4+L5~U&uIIFgn&lO8;IM79 zV5o=P43$aysjU)H4kd^oT7@J!5`sKS?$(rv?(E@L%gfYbI=kxsoiQG&iALEhu^S}a zux1(zG%S-gWrvP7N6cNIfMf|IXlPh!Wg*F&X*rD{$cWAa=hVS`X#{ck6KEPOhC(zz zs#L1H`JhwaFxdrM1v?id86|Z<7E)EYEqUiOKXb&$@$Y4!XagsHqajvvltME?qb3?C z)Ts&B61o9n5Dp464Y;(5wY!3HWj0D_fI?vn*rF6PJ#Y&z3PyQRK!=!uJS7tJnQ|aR zrGPN)Yy=EXK>$F!8A?1Y_MUOp679sx&53i%gBR5l1K^WYyfrt+5Y1^>DfWQUoLtDM zsQx6w(oBiY9fw0`xwOPLZ?_rG}MbS9bT(PX2EocdsND)OeQxxxrmYZYM4 z%t6bgTpqvIQfji+#jI=xcog!ncP~dFYi~l964QmME#GQa6NH znqgiQ(8$VI)FA+ySU4!#ZgSwCi~S*yi5kwTrwg`Vt_eRK0~EQQgq#y-H6|k@)Mk@pVhR{vC>wc)5_k1VF%a>VGcIIH zpO}`0l}(4MBZAPGlvNkgVxCxj(z1`sJW{gkolIZIH9ht(cre=vRI;nU^M`{r&6;AG zwy;7K6>#$FH&c)TgGPvd;A+4@gk~u8K#|nS&h7$wUYd&ez+_TWCt{%0`>#QC>$@Vb zL^er+^Dgl=V8PJC5uoLoj6?h5$v@JlX}5p8<0l9$^CzPy>T*bs=U5t2fS$FiJBfC} zPyG%uOuhIZ>5yHH9+ z=+exB3V&L;UvBPMsUJNZgx%RO-i3J_vWKf*Zo!+UnMOd-eZdrTKkbplJaKpu1bzx~ z)7{BWy(yGVw$~m3cobI7#8?7qGwJJHC zQm$9)mCou_)4D}m&-hF3*OfiqaRi2e<}>dod|Asl{ajoW7(0W(ziKFr39{zz26ZzpfC`lci*vDF2E!dygqmD|}%qe&}^|s$B@>9{4J^T*Z5?8pG!i zQ7i1!dfSyA_$h1^;pJ0`NINISlPP4tA=bi;z%$7?no>QRHjhP7tyVS#S%WRdVVoi2T8d|6v1`Ce?46F%EG9 zu0uTTpTH)qy{O0AG|?F5F!BUQIc{1Xo2X6Ov;|uf#SRCevPNYCsF&Din;xPU z66qa5lNSNEm#B^Al*NO=Ax`K{9Ei-HdWV7ZCClvUy<~@w6iz5<=>R@hF-HNzsay?& zrl40$1N#}}YJj>;>IAlGuQIQ{9>)$DWe`b+oF{C_F+~9z$04O2d$~_l$O2Hfgf26r zszuQn)GEhGdNqME$zZ5CV4>0mbqLT8G>xAQo^n5Vn5R0hm=Z^&d=( zFTg+0*an4bkZG(E_d)SLsp)o-EJEnG)k6?I>WPc0s;FjDi9y+wEb!UH@8^DN$J@4& ztv@M){T7mSLLZ@3BowHpwTYc#sdaeC_yO{}zdN0wp01KmL) zq(uo!%!?bGv7F_16uXVsj1Bk;TRP=nnSyzyOyp81QWXs{ocYk%p9u5vEXauhh&e%Nrm$Xp*{ z%pYP*A7U&YGH|_2aJ>z1y^e6bWlVleRDS!B>$!6`xcPgO@g=fUaezh$0KN>Ka`iPl zd<0_f!0W!{r<7wpmYeJ-WHl7Yrg}3ph(N6!O?5@R(4f4z^7iKsk zD|{6}%kOU@s&+L(;ueOHWw23yw8W^ zZItSqJ8eg~p-2))O6Z@DvzSsHAwhLj4!Ag32>c{|s|1YQg0H7%;23@~U~Wi(Ib<~N z5`3jAQM#+NzY?=i=B4ehs1CG}CcykOL$}!zm7h_&Fn@I!qTido{IH^U)NehWPLx$Q zPbeC;8hj>>KI!%K30vr@;MBw7Ed(PboIy5uc-Rk#k|6}!wPT%;1?k@n@~+qFVfICh z1;Hy@3Rn-zRC|6~I6Dpz&_98GASazwN0h4K-Q}a(csd8jHYj$G5v>RSIp$BFaOR0& z_X#q|8$9x7s_2=b(1vmG`#=0dY$J(l&u=8cI|}*P`#<=}S6{?CDd`ob*v36=jULf? z$4h-P&dF;)^BXEfOeT##1(eP_sY3>_!=Ye1Gsah8`)%b`>`DY~U6Sjjhd}KGxV=?j z>qB8L%G`FuiEUWlHCP~b@jJcn4uS9vB0h*eGuW3A)W;N3+YHi6I5Ex(H3x1ZBpWzH-KzpgcyEg?tzj^~3})2eKw$HbbwT z_h84pi-lD-#`F?3KX*UU{+8mIDY5KjP|oQWYN}O(Gu!A1I=in5Eo?9=IMc{FTgy7* z7PiyPZ6H|J%IV`0Ed{|Dyu2@9a8v8Rv!EniU^NQ(v^V! zT5u$Daq0$EP&dCglgsRX%xDg>S}a7ahbNihMAos~7+F`;2_)dVK#yT06dd_lKX23$*lhKILAD`KPh=3<>LDCoxiO}IDfUNGmoZhML) z8f`AJSB&zH`-?r{^b%pGtl|ZUo~~rCvuq{F`^)og)j6ihyJF9od$dGM9da9MvD3gJ zf+P>Md&m=hz__dUJKFONIQXXShlm*p_3Jxy%3Be$??b)1(cH@-!#4LUP)bkZE%cPa z_o87M_e0-h4pSt)f+m~wwFhmh$>XQBqQ=*GOwe07E%)DVyx+>^*TA!c>rD3B!$n^o z{gyc%i+YVdOSrl>TyQl)+WCIl&Xmq)Z(B9rLMaW$^?n~L>AkLGS_J&u)5rEzCChei zRy6cn{7Tlrg4ZD-{@c>l^3)s7Rl1K`d|S)cakV}uc^EAhr;>ucAP*_NEX|~Pt#wD@ zGpxJYTHGIQ4u*~PIp*Jg6PRLWUp9ZwCxfvPs2Pt}hul5+YA@4TGyN>_40U~-_({yP zr{9F~diTlSYl*XZ)lHAMxpIxY|DA1dewh?Sq0P6px=JaV^&vBTmHRQ#bNXC6O*qub zgWUT1m{#a}wXMgk-%PpO>S?JJ(a7~TO||*F)AIV^elv%)qBL=9pw({YI*fhU{Xw;T z+kEgL)%WKoUw!DcW^c#uT>U5thUmJ4>Xz>IvN!y{Q}1%M{-0Ju@jX4-&&9i0XDE6P zR43c0`)z(g^nX5E&@qh@6<^&3>c=E|(ijj~=q=}S{i~e!wscFF%6pdxeSaYg`1zbp zvPbewei;7j(c(q_*R?rf_aQrSmyfB_W}=#bkKsVN%L>oyb+eWzQeoKkR;5wC_F%~S zxP85+%zL;y-t*UXd@oG9j(2|^ZB*3z8m-Ae#XRtQu~+Z$rZf&o)Au+EE`(M!yD5-f zsG*tC+w$Z#-0sxo>1q*O*B6s_ym!6A@2M{`*d+)_{*>v5-@nQdP6X2Z$k>_T!p5LZ~xV?Arr5VPkIHqa0%&I(*qqG;x5)o3jwATj`#7O z>5zjVKEdeP%c1j#ZLVsDFT@MVv}!nT$hh4rVD;*hi?^gVx7 zg?zDtPq;0djZaLgai9KwdRK~ew;8A&5-y7B>!#;-@?-D`ZMt`M>kxSr-&^|idd9@7 z_*Ec0UK{6ivGe#257nolx3?Y>)7%-j=+Ryj}&Jlv}FUXD1MUJ8sQuwXiP1{%A3l)O?;K_*#zp z!`!w#CExCISH@ZA@zMx;I?>DI`4XlahNpQ`meuQYVlrJbZMS8r|Dd?$(>z^D7=5eX z_L>p=PRLB=@j4e-6*Eygy`!C%`~DCxM$j{Hpj*VeoqxPiO$>|sbv;uz3Yn|F&GyFl zcp`?_4yntDi~QbuY+ro!;pC&+aXOz4JcQWZB39=O_e2VM$-ujGP<9r6gz}CpO$$#L z?7R7j9s|$Ltqks8)AxOUQx|s5pK0jgutYBpRQ>WCyt1d@=;qu))ArFLzG0|CaXhIP zG|@T#!JxqT=(4iFpKzON?bl~IkT=mYcv!KSr#p1m&|rG(;Ay{ID6 z7X2pc$um2*ife_WzPbAx8smiPtPNS`!~0KtnN~RX*3)y&_a^@K4_n;Tq|U3uwti>@ zV{3gQmuhr728;64o~t&c(#0k z1qT6!ytD3iGE+$cuRXaekPA1mnSx)h@~w(4rL{HJXC#|J#iV-8_AD%pNfhp_w|KyA zCpO=s+&&TAV3K|D2^jshh#y01N-(qSFwfE|beJz<_?N{7+|%#O4gUo_aGl03GY@Df zMnq;``rE5B&j#`$cKQ9{; zBfic}!L%bt-HdkvTE_Ufl693CbOoJMy&Zf`Fr_LKY3zRHn-6NKFa(+>m*UBk- zl8Rpr)Kyf3Q1v(5f^kC0zDw-j*Ocl#%YN}P+NGXVN~(p%!Yd-`{ziC!$SI)?nJegt zvUDIDYT(pe7ncP8$fnCx?rWy6v7{G)wc-anw!bk+{dYfT@S2ge(h;O?CPmO$Zto zHjI85MtydFUeHZorWM~$n4@!(UJ{X27NpO;2@dmKA?{`cuOKHyn~CZNv~tee4PlDk zCz7F(n|lXa&H(dG>b8rlz`Kgq;OzrL#*Vd?7efGf9Bp*x!~9->`l}tVMq+<6>c-{M zw#gB6wPB)JT(!XPE>`*% zV|w{%S+Qyc_CrPta-Xtm*8*zeqt&|=^hdl8G?=Y*ryqwLpSp7MiXLA=nX?DKXs&0a zK_C7e_Qr#H@Kup_GTv>+c{A`)Gkt;Gv&WO(Hu!Dr@c-SA|3nt5;3iIyf)b*-BOA&=JPg@XQ(fTV<6(|B&!6r{)IV4yNz)gKE;5ym1rlC8H*u zZawsf8Twvh2o)^b29oYmIxUK6{}D`9O8A76xaN&O(8=GhF_Z$@RaRk_-NqrntWhLg zgml_5mJJ4-)CcUV6*rkw$nl^l9Jh$^K@&$khSfO32^hB_5l11ki|%?&DIE+-k$yhd zEZ0%!q~TNHRV_il9dQuDOdGFNvu87DW+Qx?$-hK=ooP;t~5@!uT@@nDp}?xMkX zb}}kv6NEI8vyCSX2|IEj$;BY4rc=p9gbNcFj^iT^N8UJ$2bT|sID+tGuhlCuL;)&9 zF9>DKqiro-i%NSTjXNPE0mFaE^nO~us{HUE%)4%0Y-=fKA~12_c5)AYf2PP|~YH7y}fHvH?g?(1sw))t4rvNx6&} z8I@Pi(FoKi%t--6(X`aud?}RC7eUN0)cG$8ND)-ht1(bPd?F^^s!P1O3(zmqubgrG z$7H&?s;zcQ&%V!UuI`QVRiS4tySp9K{1Cb^^y?%tN1Tx6&Z}hf3CWCdMtI)_p&D@Ft2Ixie0pqm!}e@AU~<%S>BdYr7c3nM>Y>dIJ*=^b=jISvVo<_Q&j**{>p@KM5K%9y zz{XBePrvp|6Wr1!wbDE8jI20PV-R6e|srAqB+P6jeJjE|aE|MW_Fbv0W7#BW8pNhmX$<$LxHtv!Ph~JH|;28o-IFW^Io}4=azwgqtRY zwHM+835tn6;co&08~!WCCJ2bJu`x=cEC#C-WOU6EUAOU@PdIbTfkpfkVf4U|U%l_2(I}+8ma+o>7jz`u?!^!+o&cR zR6W5YBveX7V_QumZJA9(W7tdkmIAw%i z6f7ir3Cbgoh8p|ibF2-K2Hr4k3ARz6^A!kcA0XvzKVmNWdExvpv?v|T}Q<+qvAjh2(zU) zs{LtS1?^&3)jCYuw?dz^@N4p@@9kbu>WfTmUY0^0}yST*3IUh0+DSJSUd~X?3(tn1?~>j z2qWDP1P_rEt>}Q(nKwC0BMb@(B^)A5w3GVW#!+g}gD2klLy_+n2 z3YO+c?l_aa2<@H=AahuNg##LFDlczQcdi<{=hmTh{y%*16<^-O{tdDf!RU_w&MUug z(c35kf^0>MP$#HpQi(u&ixi4+-ci#!W2WxWUJlF#c1bp7+-9>=!OWihjBwWlSD?7BOZodGc%?{xGaMi^q zo*pf%%_%(gPmryR5mtvaNV>QI!L~WyaWft=%{(wKCchBtgLeG4pcg>2+3AH^56zLH zu>y!TdJJu6DgSSfEfbRx!?ULs<_@n8_RfJlq)Kl$Gp)Lzk0;c5t>FNlXMFa=;z`>J z3WYOO0XUA0(e(Ty2PLQqT{TOQY*+ge=#UHvC9yO8g)zvA|=Y;=fet=up`|Zy$=j#q>rJi z4jdI$*e*AT5f&hkHc}5kkMcv9mmcNkU}hWOL)&_nWRA5uHd+A8l7OmJSt zo}sl)^dpF|LZlSLZ|HtR99!s}%US(G{8fUKC_~zcOQhobU#m2F_1L^k=z9wWfl%B9 z<1&gEYUU)>^h1olV5(@))w;wqpj9*?0o#M9Xl$0(Csx#VC>uWJ51d6tVW*kYJdor+ zBAieY21k@U!ewaX0sUKKE7^D+1Qgkf0^H<0~KoB_FXxAF(4Jy2-Cu z0tQ*-cPsk4aiq%Lwaorzr%dF5C<)sKU6-ntG5kCa{5C2WFC8$UzL$jL}Dhv*pj1QrW z52=g~v5pVjCr4sZp%RP_x#%P(_$H9NqtGX!lK5myT0_*X(%K;Zegkv*Ypta^FUZa6 zBZ?{#$Ms~&DT+Hy@5P~vP(UKIOiQOZoJ`ALxmG0Fh5Jwuk!FF+KprteA)Ow~?OXX% zHmt6))VZbfKH}7R$c2yG8kpFcxI#~4%qu7H|M2zBL6(GFw`X_R)n(hZt4?*Uz3C@@Z1bSrf6+PuY5!4a-LPgT+$yNR(WUM> zXJ848IMfmU)ke;6rrPkOBC=g#C&)k^W4zs>yT0QR_(8Iv{Rhc*u>T(<+iPGPY3|NG z<;S1KNxybTICsA`5yh+jAlXC)b`11uk`0Y`M~Clf_mD17MZ~-iN`IPdknJmfnr*r( z`mTSPZQd!gp#`>T?r0IN1e*Qdt=>MC#;jD1V=8}=qGJMRJa*5EVY96TY#GU|Joc%; zcUwZYH#>f@grB6yq$E2T#IGNgceuYU_b%NOd566cER!{%;Y|p?5*Htcvjk;^fHH^f zho9EoU%g<*m7KIOE=Tt7CZ6tcsXZIf5&I`$cP#dN99cj zqiBlplFDd%sMlIY_8}B_TZTGh0WzdB+EK+ztn%c?rZ~UdP}Gey?~OE{p5V(10o*M5 zn4RFep5WWf46~67vr!1Mkq@&`48xrRbAq1e`$yQwM^6!~jDZq3x21)~8#c6ctaO(` zSPFJgjsY*6=i3MSoMvGm2NEg{vkx8nbQeb31UASyx@3bV2J>Mchm@gYaA!d_J-0L$ z?nx@;ODrAp&W?0sVwu>-1M%XOIKgZ|Jp+{6)9TlG9s-s3YmN=v9x|{e>lmSOg4<0p zv}!Z~rF&X0W%;zJ;*yV?5~{C;qzpy$AfaVQx}Q@#Iy@G`?@eZ7pkQ^!gh)e2@lr@6 z_61g>nz%Fs!@LTWznAr85Sc{Qr*qNCO z2;Ftce6l|0vy)4Ik!U56>zt^q*odvrh^bU%fG<*5dlA< zC&mw)ZI+yAhTOm`;jvgQ$uAiRx4?_tr&j|};3q2b1^G*Y07r-;Y$a-OqrSFPB}1g^TAiCP~Sa4d+$qlO(;# zV9_jO4etT?{g=l!6knO&0VL!#LqHrY5+$r`mjEL&8kI-#V%Z~yfGzcl+>dl|6n)Q- z+Kv$pK3Ye&6+fD{F9$f|O&K_f?C(!hl(!Fy_wo|L)dxweN){IrvCvf-2JJNr!m(g< z0{kG*RS(+T7E=C^w9C`uwkNdirvFP;C?Zq~oN_i{&!NSQ>xas}=UmjbW;qmQ7 z*jtv3JkAe~Ejmj}M{9HVwQQROz*1;@voI5r{O}(CaiaeGcDr>lMjD|VJ8|YXOJIeC zuDW{u$9pHusWZ*wsgeAt!z{JAm#*{kn%eyCD()Zfc~`ntOViR{-uEd@Ymc|VPBMe` zuN7*lrE0E*dMA;i=uRf^#f%h}*68m*t)Yqy*U^g(tm&zi+LqL94x^`BX9vVj?<@Kg znLsw<)i)!&D7Ju?=_=k9=7+yhOOGCW@!Q{+7u${s8Fn&s>)#mNVYW zN^W>w<*k?(D!P3G_^(f{LlzI}Lz03bZY>sa=jc#X*2{$GNU8KWQ&UBX_HWU~{nSU3 zdb4#vbEM9-5t}7>+xT&E9jNQ@xs>*$k-G_I@>fSifR(0pWnOY|z$eQ^Z^~D@=S88r zdj`j|=S5bavQ~X!{py>-GH!L}_BgGy#A?sueQmgL;iS!5QnVHh>&r`lM&U(aL+J5v z^G$947iO6TLq)=8tEHP^u_|7Bm#(MIxxfC?%4J_vGvKDyhKz&v)i^JihT=-yB)>j@ zCd>PF9UKABB=Pj>AE|Rwi}uv=bo+VAPMqyPa82e9e6KXFaAh{W07?oJn}98L#E0M#j&W_^ zeBGq@z@vTq8t3N6v%w85rSProgM?=zT+4O?I+uac?&dZozU{7i8(hxv`LNHLGv|S`k&gaPKJnR=yYyBz((L3=n`lW$L z_50CBBNK^bLZi<9<)g&PX}Xtn(=!U5Muy9m@JS`&Q|jfoF|oVn1<-Yyli8*C%D6-E z!ev8e!sVQ|!rXhK#7e3ZYeqMuhvn$kTNTIl`wAyhN@FfXq2;D^XGT%Ar{(m^h)aB+ z{1PheveD10mBxG9irr-P;KhVy(NvTiVCiu~XR>&5?F1v}*pklLxwh99 z{8XFWrHYH8+=si$cyv@%MI!hN>b}hUwtdYT3cu;iNGiD1UcW3dML>Www)S!Tm|U{z zy|ySG$-80ut2b9-_HtjQ$6LSVdw1SASZm{%^VWNcsp%5wrh3(zzh>XqBul;c=DRrx zi#yAp;dY^t`WN%n*sDPl<_rs`*`}U{73aqoi3jMpwIVvD%wBWAvBtz0CEJ8F&!we; z{mKqUOPSIq7YdleLTK20kya26j!4jUmiJIkRd)f_+W)~~20bML(4q#ynMZ7> zcl6ZUmG~P3)WTIW1V}yg7jlR^YJpZ|s=k8YB+a$h)T4KrH2n})_hC*hgH$%n=V93T ziyzb))5J4d_w^QLw2PjVa^Kq{!?cF4!Lx5;IBv7uFp(48lW9Fx%hyzitBSSFc~6y& zop-r_uK~+4?|X#`%S)r*c{12l8*=*UuPyBwC&#XjK(4LTr3|(dCO7T;3zdNFFUG2F z*J=3oha}9Z?!)>qhT1M{k5^*HTQO@suV8{}Vx9(C-0m;@CA?SFw|K_N;_FN)fI=;? zVNYLk@GLSL7i>EWQ`$1e6{>*MUr*Y_dfRmw;jaSG0(sj&o3Ujhg@ubf9F+&j57s0pYH=JqbVaDlW=WH;ZRL2( z?teXV$G0|cv^EXX9oXSzxtL4(i z&j=-LkpTwwZv}GnB2+lqDISY8XhBQ>5P{ZlpGL@QXRm^Qidoy{2{^J&sdDWl|2&UG&(JFkZ1kYRq4lz6yh z6M=sM13|ImJ(iw81E{HKR|59x%|+GZ6F{+~MK!AesG)vR4xnr$v9Y*ZYW7%syH+_&e|qV0jHokYQ8i^j?o`taN$ZDb6aALeG{PgF`O^VI34K?9gZk^&73cq<1Ll9k8~T2e zBdllXU_@){sH6z_3)njr)c8N^e~C8^)Ls8W5R(Bif|M99n*lPSgDAv$kIVfFolsXm z$>&QTRg_tsLl7Yr)5YTY8wtornopc8`L@FC^+ z(~%Zq;O9h^#!EISLO&O2i-zNsfx)gz0`;ol z6HBV#h?!VPQ(3r#!4;GH2t$f%S#6lr<+`(~(P92m-g$(BV@~2uG{g+XUMP@ag;?|} zYQyK&xR!iOUyraU>*xNHi@R$j0XnJGX8tcv0RB-~`XJ|*ddy`r@%(7cF)+w|4$hnvUx4jFpc$fbG7UGSovtSVG zAb)sK9+v%n2j~y{R-{2cu_uL2kT`6zLF0}R#ec^W3?;Y1CXSox)#GG2YfPDA4~Lv& z^&<#U6d`3%xDwhea>)~&Usr8az%jub#{l2gM5T*_iv#bKJqXZK`-?Ti=5ou78uw59 zo9&ezxAwKt@}>zD=9t35iQDJ|0$@)s!|@C!Blbuu!@(3+e1JZva3FGzY>-qe8nsKI z_%_i0n;VRP0~)>W#mfVmH$nu!Z2o%~Df@PTjDp)qfby1FY8=eH4HxL^)jCJP6e{n@ zU6Ra878q&HdQ{bD=hB;emSPKorDUze~D}Lkz`~aO#Pq!|= zgC+GrDi?CmCkiO;RhEB}6jH@l=>vHRfnTaRJ+ub@GkbdRds_dz)x@ELy(=-!y3t~^5e_OgJgf8{yQnO( zLoZ(eFmyGYmT#t&R7rN-!#~4?CvUPxzphaGLO0zKN^dLMq8*b%Y_FiZ7+JnsB}H{2~tdW*fjLViQX@+J_zz`W*5VVwmF5vo>zO4yH1&SBR`LMK5Z zjw{P1RZf6qTl7+f8tI5qYtYnNMh?_9B93<@s5VLKueyaAVG$=;{TxtG-;x)u9#Pi> z&RGJAR+VX9!e*#%(Q&>xp*{SEzPCcD%QQ2{XE|_6{_H`>$)=}`7O6gzQgb{vA(Tpo zc5I6uY|_f<1`GOTB7!=LA@}IMWvy6uQTU+)^Z$9))x@`|LLMgK(8ECUGUdN# zSLe-B);4Dc5aDyxulXCxlL{+s)@ug2dQDozPw<`}aG?gd$`83p2)XKjSjQ_i zk|o0}?b{v$>Q&Tp)!U=J;3whrx?cy(7QUQ|I+a9uw~$+CguMo-DFUYyx&tPSB(7QWB@VrmzR=n%c$1F0QS{S234CSV z%Muj><1plgeN}bLCkeudoV#3$Mj9babJ2c!AoKSh|hRMb0SsD(IWK{ z`G8D3;e;-mVS7AdIZ#sly=kr|^l*8uf$qZ<;;u*1;ZwkfCo|&#LzU04y2vaN9FIkz zSHAzKT<%5QrQA`{-q& zisYgv-2c~+Bpwrt!cI;z99;#=Gad>en7KkhSsYDunW`|WJK;@m%tCPN{A68h#t(MJ zh}BZG&Hq*%2n(h+8H8g6LdUJgKk^o*7oPR!m>)fi4l=BUqKo)%pYcnaE@b+?1Q3~} zFC9f5%`FLNX(Eigg{6U$y0v>eEKEPr3=)W?z|-0iSE$5rARzN0$V8rtv6LrEaO~Q# zb1TUpY?a*fbz<3r#+%_y;s#+j!eA(JSmSTt8I%%*>>H&P+?di z3b{N8xxCL%L?Wa@A_Ax=&u|`pf6KZ}Gp0Uiqu*ChT<*WG_TX(*fNa_H|G8MQqaF#O zG>E9e>*!H_$YT6)F>397oFLN87j{b~|7O$z z8*>zkIb{?rlE{-x?q$>p8gax3qn$+WxRd$s?0N;VRRzJ|L5h-HdZ>`3UvUTUBk7Fz zHoFq~_v-^ky936VmiG{=M=l?hUMfwl1(x^Zt4FhSlNFZt=&Qz^mJW56^?XgMCt|>( zC#B%_Cvb~;O1J$?(YV{L?Sl{swkBF1)C%!{pL>Zd&tmjIfi2W?6qGr)+6`yKvoHcJ zzmywv6e$_F6tiwdgSn4!2ah~^4>JtpRIzaeo}F8fOp%uuTC&NE-%v3>)otY@Q?vDI zE105Kei+!=s3mwR?2HRsz==kV5fpL_KX%p;`J6-;D$^*Ilvr7hOy|oWDBvSEOJ+v0Fj7@1Oorl8kE3N(6~Y!4kA>Qj?{N(f6eAZhXj+vn zSX#2g-uO{~L9J>Lp=V)W95leqqYU%(WON`sNjkwUAbJ1O^)+i^oSIwziHld1TBPr} zvzEJ;H;F7xHA`^g>9?{{@x~cB`)a#e*!P)bog1ZECXtDsCL#*$Tqc=_P!~4J{70UC z8#W5lK}iaQ$RITbrSZ5NgGf|ZtG(hxeEuGyhPLqFIs#)_ECLjq>& zDpjNTLQ@88mCyK^xZWieX76bfA5@iq&y%keMt=y>pk|d$CPhq|K0YMbe_glP9?9ib{I1S|&w}!NviRwwB_5syrhRU0a_UUox1GM1Vw0P-V@3ny#wuA7$ zSt=bjJRFN=4esm9U6PIf(@P*a_eSko_~vp!5r$biDY#}tMC z#Qw`7Z0qI&+xMT{b^Dgh*#65ZZ0qMAK?|;#w}wN>1L)V*Ff|Eko$`whyR39P{OA^* zD_?DzB?$UU3Vo-}Q3MsjeA_7Bn;_iW_nrhMhs-UBr-bb0JzVw|E`AU1cZH+Y?x!Prrn(scow0q;L}_ zQ4m$l%#xYWj8~Z) zExPd*E^flreYh;fV`0-RMO-4vBbT0yADB>cATTy^VMdRjW6Z!d*As=g5pn3XUyznc<4A6a6=@i-_N_I6a=vZ*wOQ_31Dbz_xs z?kbZjU#7G-&?P0$@X~{gzfxa>r%B`$e|5v1%#tj#-!#7m{p(H=G^q2aB+E zeR1G!qAkeSEr?te?nV!wmUPdoaGE-soaUbHCiXOq4&%PlQK=S1JMU zqns%etCtm|n zQ@*OgU2S!R##&?NQ|{;49?$1Fqf1?yi;VxO0`~t^tT4KZ(Z<*BAH}Cd%3MJ@hIOy zgHNxL=K^M{pdPpiTnEo39PyeHAJANggmLz%YG)Wh8G)@6Pq3l+s9tf?8Ev^#Qmfe_ zAH39!eDELE@ZQ=N-`ZhqbjRMvZ+wTpa_X)nd)`MDnpQ$k_NTWm^WVlVfe~`U`gFSe zUUqLyrq4QnKIM!am}Y0)Sya70jkl}4LErMT4Z4BO*|aQ&I)OeJ4j;SaKe*n`c;}qm zZql2zKvlgP>0rTie7~f--q;@7H=oSDw*tH`Nt@JPp&neG7U4A>xePVAf=^t)2e(~8 z-`Zc!RP+aX(SVgjUyp52mR&gpwf6Oa$4T*tUH1Hjf>}zmDqe3@7NoJAOCB)0vi~F~ zl-zFj;Yhs%8tuAYp_nOyRPZ+F?ORP2tgo*hsZcNd8`4>b+D^+TeLGrayfbVyau;SC zW>tphuHZo4AXb9;n4~iKma(A<7pFA`!(n&%r!y}v<#H6i?|i;!|IVBp*2R9X1Ha`Y zfm+Fdv3?tOG}FQR$S`bUPWhPT@pRi|@UNrNVc%{;`P_!!X_y9vooz+eQzTQG(G^cyy~>nn#Im?GEVifc{lOzHfdCZUp6k56ut(r>CkuTg zNb@LsyY|p$t;$AVT2|>1dFNq-OShWFIvie_!^W~s{g%Zof_3Hc%l$vkzDTc?uFxXB zh>8tq9kad%dp3AeO zbL+T{a{F$MhwWU%S{p>jywJnSbg98&YD49Z$>#+&SPht5iK{iF%3L6DX=KZDmwTq`4 zS+rX1J72I}$(LzeZvjsoM{+F=veptln!!bz%>=kG8q;64uTwci?*r;N8dj@GgC!+v zK$p`ccTi;#nsUflv`d=hNzIc48WM^e?2}vEum}5dxxnSOJ;PXs5SlUWlz}lGkF~bX zS@;=Jb2KIFv`JqSykkcF5yN12X5G@13&XKL-DWxiXYy+D)_CW8y=Ku(zR{2;^nEJH zW${8V8?1_y&duiU$p(=XNPLvW0+by0`N8?~?3ZXO+ zks)hWkEdKx(yMYD_&cxVi&VsKhnrrJtJ3j)IK_3Hv!O4%_av6P?9cDIk7zI2@8a#X zwwGOB*v_x+rj_p})GLecu5W{^^KV*ktU>1Wrj#4}c@%|@>0@shp~&PlaLVqov3J$D zP3Om5Gx0d^s}%{j@Ao&Z*Hb!kM@H|4_p0=DZr=O3`t0PXxeD_ z=VH@YDG&CjeM+4`?UORu*IVhC+E3{nDmpI;uQT?ssBJO%uIXt{Vx9@%G)A+VucLvk z0__5*Yv3L9CQNvU1r5rGNX(xhOHXsOv#7~Jv()JdD`L5d-K6>0(!2Vu(XxqcF?0HQ z1_9~ zIE$suq3rZ_wf27uKsW#Kr31Hbkyz1w{Q~Fu^^5-(_Ah%Q2PaEM2Ra11E_UxE0ezAn${$-|asZJ6$zFHww(Jg)}FHAml z!cEy^f~hQzP*F|Us%a`OEK0VPGgBF5(xps(L0B2BN=aiuuQfk{VwA>EK<71e2dk_$ zB{v9CL5;?Zh)RV|D@n;_p^Tan$s(w%gPbEKFo+U(CQg-EKC==S@PX_qQ_nJ9GJc?O z*rRgl4<_`Bw-B$*+`h7hH?hgb99Z6{v{K%a%>S&ks}vB==cAR+fECFZDP+T1d-o+< z?$2o@(>X*4&caFlR|-0Y>MspgFCzekjsDMG}NY9XZaP;SdXL+6=U)m%n@Nm3$2_p z{56V2Uet`T<8XmFi9iA5{x_zLEbB&=7NmlqpFKc}AvOxErUoiYeZUTjvYGkyC-~57 zafjV}Lj-8(9Efa9M3I+?Qlotnq)N2ri}fLY!wq2U5uqU#OB7Qy!nX?KN9Dq?9ns;o zs6lX0a^mZLBa|*hjHw7zQlgzHMyzs&fo6t2&>Lwbdv(!eJP46vAzrF2ik8owaDoXI zQ32asi!vohMi3(9CrWX*DZaWv=l22Csr?ZM8l+5jg;T(~Uo=on6pq9egWenI`IiMY z{8ug*WG=JB@^;P|ZG@PyA-oP3cT9Z&HB&sCTj9?_;zL)V$7H2Sz?rbHY=^OMz54r| z9y7)gkClYaA1_j*C;oD|VJMYWb9%+&qq7Fj84wOp;jiWVUk@-!c^(8rMVE(tWMg3F z;hg?$c{Nkqmw{AVD@2YdB+Lh{rPMBEH zjdvZ=*T;uxCJC=JCRZ-UD{iuoKdU~Y6Q9Wyq@rN?<5V~0i7KQk>gRI{@v0X~4VP9l z&#cQ_fMAS307Ll@CYTwRyr_;C{(Ht731IxeJ)`)!AnVfiyI02T@;E{urB2-d*b;U# z&$NP`Q}tl3vHLNyUZ2sk3{d)+IQ(`?<%<(R=u-kJfN~&>+!!b(eu1sZmjB`mT8Y@T zVvO6JjFF-qH!+Ijkb(VuZ_jii@Pa&Wel451!Uy*Nu_*gXSge5+O8xjoJsrwd?YBK> zrEd-ka-%V5C5_*bGT%8j-?<0h`K;fP0Z8R=k=0S7SNE@o{YF;ZN@(OAV6@6%Xmyz# zV3EqjJgHGrW3`6p9&A)e;!eCcE093nt%$oN;<+b;Y*`jlD*RMJTO(uvf@FX9gI#T~9_&D)xexe& zj^F}NYyPa}3xIFIg;VR+0(qDb!Rw&-l0elYpmbPbTpQ`2`Pm@rw(b~FfzWACxUEw5 zjw?fJKUMA+Z9{4|3E$K;`XyH)by#|uu0?mGTao|TzPDzDZ(kJ#eerEkTT|}xR`|UV z@up)Xh*G9mK|ZVIAcs>x6y;a7YM@9Id1i^S`YaxzSiaMt0do8)XX{((2W^R@soTB- zlp=W=r8O3SAI3lsU&(b6HuY1)?v8vaC)8Ri{lvKOrQg_}T7>5S}?i5N9LsvXXO zN(sY_d!etX76IDsib_RHeO2p|jjT&Iz1Kxpqw^b%U6osOsajY`1$XM0oE*Nep1VF6 zx)%YIos!;NQuoOuc6A@9o(#lT25KS;F@=YQ-d$MtsiNnC&TQRvZwkfdp1YQdz7`F6 zMsA`va*B^T6+@sgcpY2Ktx~1?}v+MpA1(5d-?>5-(nnBuNiw*=Hd55ky z#T$CagB+(*pETyW+;HMVXVPwAZrGE6Ya0g!=%O|R2}7m zKs3}zbXQ?h;G!)s16@TFS&SlSEl75>-mfzxsmbFL`a67LOII5w=_pR%?QuO=X3WZb zQnlcWiheyCRcK(u_MSD68aPOVcrc{`a%4F>*z+9%`SaoazZ(HOm`g$~c?$jbc$&z| zL}QC0+XX;<1h7~w5@J5R(iGLL;%1@+9nuBN{M*WXsFo4d(0WL78_3fn4ZX%MN%Y|9 z`x@Y`HK1OEAH7L0NwN?cyBd9jFulzIqAQr&h_rfG9ug-3R<|_MCs;2@WTly+^6nJ| zR$Uv;q*&--;5lp%E4~$vQpfmOxU>(id^kA%oUweFxA^j2$EA^O8L&z%BY}D)o6=>> zD1-w8d2#IELHWWRMl81xD5F;}M4B6dbygDBhZx4n(MXZl!go=KLf9m0i*ovcBB3Nv zeg@m7Q_FlxNvq~_ee5oNNO~RdJ0L24NRYi^g15A)cGMGLl%~NoRq}dWFNKUiU55!v zlS-IE1+#+cF0)#3q3sX&lRQE$rzC9>Vd3o$sGEh+C$yeIF0LAm$%?aDZ=qLlq-_#y zC$yhJE~9^&!6dVe(?P_Zh<6TBk7AQF^Ai()qBsI5e$gT-0$?G2uw9M3doID_UO|(> zlKVe$$GNne-Bf4Xk_)aaCHL-mQvwiCXpreOR->eGaB#&k?kCtd;q=5=4^~s6*y%qDi5lmsW2xTf*cFbx{veV!Z ziPe}nzv-fp;cStMK#moFr=r<;?rn-_|La_bypi=|>ubatSk}C&!j=`t2?0+UI?`hN zIM)aPC+Uad$RqlHv9p56=fuNMfk&|jh?RDlwLNTo*}P4ApKeJ+SlTpE)n^T^tu0)> zJOAxZni+O4oY_eg&O#c&PV}Eqrtdf2x>t%Ph7XshA{!c;>b*LG-p{gQh0?{NL=EYi zd4GvQlc04xik4yOon!c;(Oi%lgz9u%pC#`Lc2oE8fF*zW57-lb^k9tdYZR7R3J=0CP#TBH zF-Sy&MGuy|2u6gh>MMFs3J+peq&sA%NF=esH}6{Y`mg~5kA8qXE7BPW#+~u@GX;F` zWY65SQqkyyG*II=v>aIIub8&Xn5_s^1!+mbypyOMsi2QB@eTKoOsz_2na~l{cazq4 zBbcd4o2v1psf0CDAeyON@{czY(rkw|RQzmE&DBX47itu=Z#t!G;BTQW_=;=ql0oivZ$vKd zwxWOR-a{pD@P{};xG;`^Y*e)dtUEDmScmL7E5I~s=(lX5 z_FqP!TYHD?I;+4myZ;EoXA3bPzdU?86HH6Se7zBX!_4k%>Yjq7UcE|iprQ1qi zXa{t`IeU86a`WokSs#Z_YE3T7#oVx$fQ&5>F^`E83!CTCO@cg>i z^HG(QhY6@90f8+qU%-K>j+-0eQ4?P8+f;7ul>kXGh@Q0#b$<XOp)j zo~?Pt{T629h?&z-=K(w-v|Ex9yJ^>4`bKR-c9BoG+v}I=8rFT4&WC9C>vq9ZWT=iO zpQLOC_wj)O)g*R{!5yvl%HPD+9@Qoa>Z*CIA#6q1m^C$6&$IHJl}18XS4q7yEKO1> zw6f2oog2Ja=grNAQ-pu#(VQ1B2{$uwX8J!v=&*m%-7Z^KP} zNn4JB%UBS%S0@8JuJ(dH4#hO1)3Y-!8+nt`E3-G6pEScXTp!LC3FZx20e4(M#1bmj zQ`x6N*+tH~T^mkMyHmMZtyS$!wA*;Aks7QGx|i$YZMK(F>0zws@hw)z>eej)^3Zl7 z-nH|!3EGY9aj0o)YkoU|p*a_(`ZnA7ysnP5Rs~+Md$dcLgSl&GDRgg!H0bK@_QHs>vCeP1@xE!90XxU!TsyLE z>AeG~xZ%t0Kckm!Zvy#WhoKvMFOt~}otrIQ;KE+-PF_IE zodyFrGw}NC-}h4aV?busTQWg0_(J5%&w|^#lTWhPPaT^sSIhmO;NP8N_kNxC9U}<` z_|ZP9^Y*Kcv@N2RPQC(dTcmGNG+ z^GLz(2V_F4rz{4TDtK8Y`Nfv&3l3v~T%M()o<$fnv+KbNn~JRq(%+nt?_$CKxH8R7 zI!P;@k=v+EHv+F^R!*g~(z@>rJQ#3HjjpcKb@QJqWk9?q8)l21I*F|)J-O~6~@+c9=XfCSv%q*yOyqZ z7H{3KJn4?q1o<4ewA#9f9IgT)rRNtX$+h(Isitr(c;2wEtYo&?t?+B+H6$Kg@G7W^ zYCnmBb*@6EpKi8!*R$hTZsxwoAI{s5dd9n$aH|a>@-8`HH1`uRLgE(h>~TgAfDCD0 z81;iiptIRZpgzb|JUBVs$O3P%ngC5jlsEZ2kI8x-pMgtlWj)ciJ=N-81|%=*S(X2W zv#iNnPx|`+ib^}fVmI4s0Lyp}8ljJ9itZ=rA4%fo-aUXc3yZ;jC)@n)lTFDt8~4nNbQFiKs zL+ZNQwQL}Pw`%wMT1$5;dD%ud_c+H|;f3;H6Yu5|RGf08vmnxRQD6 zd9SjH6Xm|58=i%)!!StqtE=LzRHoAd_h`xzFD^Xm@QypCLaR0^F1sx`^Ly;4=1}-( zx$IdqD_Fzfl1&2OEKpv6C{4Pem!ZQLLs^HT*x5|{JNfu6&M>DjPOe{hbXMd2-0!p6 zGFQ>wi0o1XU+-v&TVBDz3-L2{DZ7j}`ySuh)McuYwzF)euGIQqeGO0s9x%Kj=YWx7 zbvm5F13ii81C^^HPSAz+F77Yn$Jrz$I}~6_(XGs5E?X;<1w1*ra$YxMYaJc?LFw%) zm$s{puEB=y)Ec7ju<%ovJBq8Dky6L?a&v)IP^?agq)IW@$`rtJm%baosCZv89oNfc zNSF;Q(5ZO+!ZC1r?cl?Tl`}usldt|me@%u9ikkoZOE%kIh0~g%NQURzT%=fw_3b8z z0A9Q={1l_Siy}+jI6rxta#H+dQ<(R1;qz=}lJ(8)<>yhLleMkCQ%4z2+a`Y+geRn} zc1n1=L*ZVvPY+AW{bDcXG4j%JdY~a=Vjf1&y@=AcU*{Y&EziMLja2Dmyzlw;3 zgW23Zw7|vsCO8c*b8Tu-jI_0alUj$j+mNByerSNzc@_xYK5S0r8jQtywoX~Xo2RYp z?eY59)0KS8OJU`CI_N%q`d&yyS6!uMzwdQwa=$E;3Fj@BR^M zE7x)RP?cAI-QDoHioRCB7~1V$%X0lYiGJ%QsJSn0Zr10QA~sfD=(HyD?CP>bTP8Hp zqOO+eQtBLw(;V@(57s!BVAj^{0Ph6w8f{~+J1#w21lyG6loj)Qa&uTO4N@t&+AlYe z$xu>OZ*6ov5}Vg4T=|N;24KEkKmAOP&(}Hnp0~Ev{CK`<-2Cd+uKk>}lBibn{WQ=v z{?6N!%ch!ALF7fpY`GilzW&}cZ0ywd@$Jpomum|O?5dr*vq@ZAv`h4*QGRYtw~y=4 zNa=6m9H1K6sr91LXFVy=DT6OMBVeE7oFD(Wuphh5Jcj$l#P+>jDaYGS(OcV?$gjKQ zEm2L$C;KE+N9h{R^#zYKT`KO4Nj)kyntB4482MNAU+H9$Z<7Q1JkD}|w?;YMqz~e% zISYlj_Rn{OO)B2l*w|TyTZYiu&{>91hT9VY0!l_wLRL;rP6k#&0!~U+R!#;&LO6?7 zi{%J*k01>3h5_)7ysS9~u{8k)n{PZHuC#}W+IHxD8npBsu zK#>*$F?a>dTH~f0M5;L)iMPHB^;7W1JJfLjhSSp!suk|)UT)@i+`tw;7N}cP>MpQsUMmD zr>4KX@FPlF-&L@oLD+fFd|_0AI@%{`skch?zGLt%#Ki3KJZ!P7K3Nram%7qn^Wo>8 z6QvcphhypJ{agi7-w@_B9;&Si$x_di(vTwQ^1B@GYr9ulo7AL0xq3@1W~;Bd9;vi; z{ZWI%+Y^0CXO{cp+tYA7?#1NGqgCC>OWJ@j3-A4ZIQBe0j(u!pZo2OeCwj_VFEw0M6^57LOcC;(wx4k}RDpq%u0o(F{r;Fo zD#7@IAi_X0vGEC&s)rG`Xp|?ZhihE!buAW(Yn~}fEG`vZ>1&iiT`e`OeHkBZ9c&SA zb{)BXoR8nfxL;ErXF6;-Ol5G*7QCo8k|65ngsy{&wu#C!HXyNx`^%fmq$VJU`^ycR zKe)OXiN-K)R!q&MB;@jmE9c8m3A9KXRftcU11(a-5zhS0OEs@l1XCHeSZ2>s z18h@@tOa0|9nKP}iN|(UW+u#6I-)MF)oRE@8Gx4*4SZ!a?5$E3;$fs$2~_03j0dZ& z!P4?_;e>1dYfNf(!H4yUY<1cf0cJ#Ny;z&_H{KJ*84>AWT9y*BnKYBHG+_^w;vyBw z9i?~6enn(9AxzOoGNpvy+=zCLyis@`1of$K4j36W^R2b(?;2WxDPN;WVybcHmMaX5 zv?4QpXR0u98ah8&5an8n69w6sq&f*g%pgAo4r-CX4i!N4;}M43$=G5r3jIdb(Js=vP?`L`h~YY)Q+w<=oA{F`5J+=$DU;l zV`>cYkomfOSlGoKevvb zMC>95XmCiT26^55VH%HZkeHot(aQa?7;3HY94vv#*w&OxQOt>a6wKlVPVvlVf^lPe zFIx!wWRiWtqO?Bt-c~B*N#TLWG{;}n=AVr#=^`k-%Lld%N%!i3N%u1hYdeB8#}3sN z9L1RBhaScmNe)qpG{YbNXohR$#~D(K4ol=oE}mx*5$B+FgSwqVSe4T&23ZxybA=_W ziViK~N)IPuglNW%4q*(kp!hDc(|xh}3h|}A+s_u@64c+@6?I-ERvSj1}uJT`lHg4F^QHSa z{r{az#R!Wb*=k52+9s(sF$=zRQQXHe-wY9Aiizq_xbW%-7Z1qAzg+aC^;qv;P^QwugE?9*z z3zpC4%6buuhI!=#=te*Th{tWd%xj0Ruz4n;?Bt*gL+S(~cQ=0Nq9y4{#DgxQpgBk1 z_V%j_;Auvt);Tai#q|NK_KiWM7<=EHjVy0NqSGbxVPd`1*5g#i_s!E?np$%*zShE3 zC-ud6FutZf)+<0Uz9v4_yLO~h9d_D4UYg>2GOqS}FPm+QH9t`j?5){Ye_b1FT~^*P zTithnb!`Y1(fKfkcA&iY@Ft~k+&3Bwo5+kfB}R6|@EK391kF{{C%SB|iWQ}M9LNULLaLS-RUVQYXG6m z7qhzUMv{L>=-HKIjLEwmE<3(&mbYQ^3gk=s)BBJ41Ks2qSN8JlXXzTGx0Tj8%;HVG z{ZnFXAHpMq^x?~ohjIcv=TX3-Cjt%FleWRlxflQ90MzS4611KTe>Y+`b;-1X3VkIJ zCuGl!RKAE)FRL|?tHuC>94cTK4m%!5E5q zHu$^L73WszyANHcf`{=t2>-Us0Hpu98pXfm*hro9Fy=pudgieQ32+*d!0Va*!=B3? zM%=_EwtpMfyoa?>Q$-XQRkD4fZ>IMOV^zE*0Wb#?Gsls)_(Yy;@%PT@M5E-Q`us~C zEOT;FmD|6TP zl7uJe?2tP+D@V6p>yOa@EbnKEh zNl&6aFCL?n>I+cYGO4&f7)L_~?J!qxO|rR?{i2R!B}*wbA>(he?R6*X$nc3|eq6r6 zgpzP+K`=K@xU56s&l4)Lhx@@Wyh2%ybYv@x3uyVDeaDB}jha2AU3$QYNh3Kk(_tdaj|8zWxf)o@7p5!l|(-71s0#UKLhj!V%p z=FqFV^SS!bA``g95Ej@T`FS?{rWyLp+9sJreaTl!IVQ7or+YK?@OUoay~J1yb74J9 zI9xHivWXg{ED4n?%)kj_s4RW=8PCexTTY=vRiVS{zzJ!gLslX4q+I8=T<1Tz4J+9& zGuPUvGh388q4VU7lUi>t)ods8R0*u?q?gRdE$+9xaW&>#Tuk(t5Col-iAZLIFtgnG ze=+j7ld?qy0TyZ zSOvM-z*HAHiUmcwxG)eRU%N92KV|HXV)Ua4qGd1tXiUPQUuLy;DJ~2A#CwAf>z_%{ z>tZ7xCCD{FCB@(d=Y-__XO7Fg2g(l?cE%Jk(OAI8c8^|4~JZA!pF zgHAua`o53NW*U2PNA2Uz+1R6%7TYz5DCQYIXiqdm9G#`PxYfnN)3MT2HnxiX4c8P_ z1-XTXbAA3`<{M>y!@LbC)gTrf5dJ~4ft7C*_YLzBPN^>S=wOL5d8&w>+}1>Um?A@n zef({?x=auBmukLS#_a03J#pqHn}vQ1N^7H}k#pbLYDuwi&2R}d6Iojvxjm4fXb3{V)%Tlmuw{8ql8l338@y{_2Xh=} z)_k4zoIS0S1oa<_Fjwe>)SSzuj(j@kGvB^(&eBcyvmYn6%cCP&^t;Yt+I<(5DYEzeDsgC@g& z9{Uz0asyPo!KHboi_=NrS?S47q7yw_D|{3=B%J9+m+9V$^_~6!MoaQdlkVOb_{G1+ zn><)29#ze~cY8R^Bwn!LYZp@;M~%S-q3fQj&Kq}9h-Og@AhQfYVXYu-yvHck~qrtk2_B`zy8=`vj_Ahpaav*R#u#PWQhT(ds!WA0h<GF5P}zrYvtY2R%>Ala;ywQT1_%s@zxa3t0`EpBqT=2P zG4W@g0lq$cfX)e%8SCNEaF{iV^PswsP5l}=t(l$zeQ3xaBjdt+Q^Pn?tzxm&E4J4y zd?rDXVTms$)wtg>Tsynv@~!HQtyVQ0%#Po=c!*FM zk@c&o)Sia|)Dk!v0fz7m%c{E3CCA3S+u_J^F#ISDovn;sO(w(NeqJtY!JeBvG$7}o z+pMr~qdZ;e7!Z}5n^ttY+QV?;A>wtU=;O@zmR608?0}`(VS)zYdB?E0 zsdqId{p)|gyR-K%6Nv$dIKYpR-D##^CRG-B0TM4Po2 zX4Lb$3auI`Q8LdG7bV!666l*}LHg!CKcQ4R-M;B8OL^?>**)0}-51YLY0rRwH-L!< z=r)CBK7BPD(*)kMru^9e&Z}kx*UmA-0-LXCmx~IrQP472wk%KMF|}f13@Q&f=!iED z9WH#^osz58CI1@A0Rw@d4VzC7ebGwIM=^uyZ&Nl@#M$SouHs^ND1tYS?%DLWpoSo0 zWEgk+mdsO_kU1Zw7O2~)|I;s#ms3slF>bB57Xp=EYUQ_%-7TS9a5vy`zs>e?O zSem_8C+>bNGeuB$OV?sUdPV9#8OZBjJP2u*TnZjasDmoErV5kwiIPDum$onfn-PYu zK&x)G3Z-qjsr-(G6E+IkVN~s>D1}8$gWlo97zR*NVCUqY%!K}A{~xxq9Q1K1{^bdp z948oBf+n3w922g;ZZ|S_B^#QvWlD- zUG!C5Rr{;;DgUEz9wriZt$NPwZ}_7J?^8ah$%p_F*M6{fnflKfy#(HQELxBKl8(yC z2lB1Hf}n#MNd_2LU=g1{+X6&mGDdgFc^zN6#;+lprodf=XtvEI9DNzc?J)C!#7QH| zlCeFugf}o-PGn(4sLzh-{B;i8e(i-E>1jIMWpf%`|D!z%6=~Vwc3bVIyv4aKQ>yE$ z`R+Z79RS#gL=_f6Wrpe&OR;C~f_qbR2a`kTyEjD~h;5CnUFp+(7?Ql{EqbZi`VCo; zK5e2TnRddxIeho63cbc72e+(oogdh{pLBTcsJACURYb@+Zx}ATNmo)T1BfFftPXza(-7MZB{pHc5}Pudu{A@X3)X>vO_f3!5Nz*Poqu4}*UR?zG+|>P&9?DbHyV^K;lQ z$h+Ddo?K1wQcJh}9-l8179ZBColCF?FbAAx+e_-`_Sx86-+?b~eNDVZRx&xA+QU74 zY8Hn!pcm6rw6SC>k5u}QUVKiYnRr`)N}?YI_HR;I2_OLJt>N+C%f4=H{zrJWZKtev%_ zTc(k|a%+3Vd%C_qiD?#Cr@qRK(G(-K_v>7&o0PL2F%-l!tTmps^>kz{TU}Q-%*!4x zvSiTdJNEqeWnS^-@~;FlvaxwT8140dnc(L> z(zjKJN8toYa*o)MXP#{CdVUJe`-8hd@use^w+J%y+$62BsWxoRI%CT>(t}O_T)#z% zxu+c5H~vwG*AoO|+~}WB-nON1ls{?<0Z|`x7=5~-Ixw#b@h%v?vYmCbJ@RPxE@UCc zgE>cX`5p68BoLi@ApfJ9S140!N{i(Zk2zYV_5z;}{nX?ue@E*%; zF7xpN>cl+VmVL$g^u*jiw{DP~dain~{?h#v#DZUEU>3dwwBH>wOAt`Q-^Sqm`QErP zcWAAcWfNQm6`7j26@?Y}Q??XQ8hB7i><0|0V^+8I|7hCsGbnc`!KKrb{zcV^6>N!0Tw}DWKL65|dApmXy579&_sY zXXGTeE5)xfdAx+xOQp{dI0hE7xzxu0pz>qD-K<`8##gDi?V{f7yq9^tUCJT#hGH-- zHcfho(!VwKoZk=%C<`hdMa{xVEztd)L*1R&VQ{h`19Eiv@R6DjF;gvB4TrDMH9pzk zP3PTxm;1v;B&Bia9lE?Ucy;2>ggOdN*VlHpA6UNm*KisMJ7i->ssq6&v^11Ux$uW%75U*IF|!c zz&;)-cOUv6>q%FO?jP0zjQ#@4-P<*~%S#D4oBXQs3z)84A1eZj3e)i~*;R-sHC@rJ z>#t{nbDFo+D&4-^c4tA@{s4wS`z%g6)2BJC=Bp^c5aDYfR#m|&?;2b78{)C}M{V>< zb`@kwottJCE90j9f%`S|5#Yx{PQr5dPRjFJ?H2TwMB8q6=_8Q$26Z+HMhQ$rLq1%R zY#v2&IKSZ+kx3QyJURST)m1&ng{G4s=RE_yf{W z3_XVp)kQdc<)OQPOTJ5YyC2qnTHb+v#j{w)AeJ`y$zc@tR1X%lE>O3w9~LDNJ-I5O zM8hsTOd%z+7bA-g+Id#iCvgwSZNs?s+pnLF8;;YJ5hVtBAK4AJL_s4uz5SHJ4C$`- z7<3>^S)aGv{Ins1?P`Oa1|sb7Hcy0w$asIqDs(^_^6I)Q%!d1iZy@@@0oMkWg}W{yqgJuT7IC*hE6lAQ$=ZSu zrv~PgMcGEJ0}U+?u7f8SN1s*Bh*KTxPTm2h2(#;roqxY#LpAJr>8g#gAZ^NEHGIa; z5GWWZ(6f5dp+4Me1sD?&c7FT3T+Q&zotz3j*AMJ-}6IAXn-y=D%}<}_~=ty<+|2C1(?qTiF& zp`kUQps-i!`Be+gDr`(Q)x!Tm*iWowtQD$lzGiQdZl-{;(pU~bTpwKK6IqdxmFl`h z2nfn*&lakLYSb{kXhSO+3FBn-zcn(}$sCep5m$rcT=8bP;-Zkup`%&q<7No(Yk+V% z?ge^tpHR0qK&6>7$70y@3ZafmkF~WnxuGWo}aL73;&A!L=^1)BJ?a zfr9MC@=VJ!^lMa|v2j0zqqN-9{;cffHs`gq1@l-tYiYKtxNwSnZIVHnV*4YM5Oue# z+G(++8L-I1c^7F@hB0emlxyC;m-nEBQE3e~=Vjgou!ZHaj^=K2arK|d z8}g#1bkY#aI(o<{aOsDgnKAeojN-P?l!xE2ssW431`j1TEh5q(Dw+qA3~eKp5M?@V zA{x~uE2w)rb*pt3SPgGl#$h5mV@!J5^B+hD7ve;`HS`$oXVdOWc5Xg4)4UN7$KffZMjSL3Wp@_ z!Y_62@6DhR8lf!8eA!6pKw__^Z1Bq8Kln3*%kk->RPY>~38Lv6gET=W9x7PovVU}v zi6>E1D5l|zg5$U(@kiZVu9fKuui{^0c0@dP#u@kXrnOlAq;#kc$q;cTU&euC_&K6e zGOc(SpFv|Cdj#kn@Xxw8uSj>?`m$2`W zXB1w?W zcq3ZIV%9R8;XXftn5&pycR4E)p-@h=V|i8KT4MnXZkk zB3sZQB7)~X!n^vD28~eYo!&uslD#92{WV4h(XXVZh>w9L<}~+TX(fDgqN{-z0Dr?D zktbD@X?SfX0ZxqY+())XPa#}Kgn%t?ju`<>z89NtKt5G?&^9*uij2|Qdu$&&MPpOLUJVPHC<4@|eX*x5t zHeq~~eW-8xtS5BZbe@^w3n%cWtexEwG%7hYglw5*G+MB@HWWt9ykT=$Af|n`IO+?X z9OFd~U1Ej*7DNlL+}W^?=PQ^Ww*r%#Tt_Efi_SXqM;NY}&LB328EWO+zJ8f~>}NO# zg75zVC$<*k7*^kjgtD4j|81CSpiWsK3nEMO7&i&6s`)UNkRRu=(45|Y?iY?=jNA1) z)q`+5ImY8t?i1tp^Z9&xa*S?T!AOXL|m?s5WieTj*Q0;O%T|0@%0R-FGjN>BEDzVoN2(}C0* zqPRR7IHXhBQO;EbrC@Wr#1i(v3)3lCs5>f@@*Bx(z*-T}Tn5{7B=;9BE-QjADGV~k zw5ufgSH;?mScHX)ct`uNqh_r2xJYl{7jU7NfOq68;w9TbC$DOr*rTcOI}QGAfDvfZ z#F#%Z>bczpB=EV4k9yW%KR_Qz)ev(_YYbaKhmAcXAdvJWuh1h=ADEZ?6;Y9(ufMv3 z_fv2@!ulUX`lt`xk9}jE2oQB~vXcwW`7pe8RxVKMo^(qyDhQ5kS;Dprm2EwAXrl!M zuF$R5wXDaBRn!JXIWAt2Y=EsAmhAIIzNpi_Zyv06g4q})gUFD%RCc0C*0R2q&`>|3 zGE@&{U^(ucEDt7&xb|lPoh+q%voPq_!g&w27hGD9D~oEko3A84L5r1P<56T2%c%5C2)d@SF+ei zqsx&T^w(Aq9RPm*ugd-fWjkO&M}q@0%*IenKYg=+D`nhh{X3_K>%-!_HJnjf{~d<9 zIbE2UZO#E*80rhO%R>hE@OC8iHPgR43?i8BQmSjFsXGi(;q7F!7tA`2Dc|>|k+#%+ zC*+P~!(?&~WK(d{$qPkENc&q*6H~wYM=F2ugeNMSC@7mSE}h^OJFE+z*nM*$syTYA z&%cEdJN$)_r73n87Czx6cBm?59+clOodBaeqK$eW88c_tw7s83S##O3Zq#r2a|vU^ z^hDO$Hs{>}YQwd6}RLVZU!pUig3_4P!9IqC)SppF@^J}ai zKbwee@Eu)e+6%cZ)nsi5=+Kz9scKwgwUN0hKy{`tzt>IrJa_Kz8Rv4bh&VGaKDD!OemBqPw#L^3q%J_rBy*{pS=wFOej|@B8ACbLv27f*&9hUA^r~jsOx*=)z-a7yOyQvnSyQHq|P%!+YbmnT~ zJn9x}W!{k66Y<<|*AJY&sj&$?_S1G${CcT*_pg4Bt$vpb*f&NPqv3uFgBUA8psN5I zXcqL`q(O{nK_|N+(4Asxuv69gRQJ>?1(+s4jF~9}vWrY$f?|C@v8tdX=3#7Um}Q7r zRyqn9O~dxY|4=$+u~n>;K2n(PqyO~}Q3mvpMmm*6|TZX;RaDo5V@)qz-NQ@KG3fvNd*_XEK)|g zMYnj{Ed)hh?caGin%yG!-}xUjJB}oW_M+fbTk^FFs?Q_>JTNEG;Z+kJsGsQ24toOi z4E3e@3u6&py}C~ho}yGj_e`h2RiF{uGS$x~j+5h{`a>PK9Ly@HV-a#mzfcKkr+0se z+R1 zL5O7J2e~4*1k^lIB^eb7!6HIops*+b1{LCHUz3omAd$Lv$XuTAiSmzof4;Q_uAi`v zU4=e(8+ltV0R}SZ5m3rHj@chP{@*y@f<0_;;FOkUJ`QL>j}r(SBxE8vv8bIZkDYw* z6)r^EgL*SlqlNrHD8r-;4F`0Mhre{r+$v&giFg0R$rcc_)X=~$a!zNx?*01jkAtPc z?~5NB$_-)@C?6IBLvBwBeqC`f17zV?C~nD*MxGVvqU=5&UgnJDcy9@Q(Cwi^LkrjI zScY6o9Ap7M&YQi!u(59I)4p7t(yHaK5!!_mvB{pirCLaq5|Oy?>Hy}P*Yk9+oRl`R z`@dXv6JGbH`&hjx#tTA#@JegLqpZKH%rCw!LikfZ$q}2+l7#W7kbEECs}j=iw4d&1 zqD|Rv+r?DSMP%Xnw_@9b#kK=&9wu5^tL*A5z~o-@$^CkVX)k%U=Y7}EY$fH3*DDi? zbwv=ZQ75>^l>9abRowHHqQmM1_A6L=Ccmq0zbj=cY1dK#d(C=nXGlnGaKE|GDFgrH z`0TY?i=R? z{7pA{_{}?mw=zWV*BY%9*r5b=>yHB6o}3!z1E{1AM}7$3yxEeNuRerDX<2y!*FLXb zw%qOZf-~rl0js;+a4pg~Hv!wP zK^Lv5(AGd0HU%e^X+YDW*ic(q@1{(Wfzk>(2Q)QA{1% zd9QE2yExK`g=_cnaEMa}G{ZPLgT>&)BygXh-`JiOQP=T zz^(s5VM?BY;_AZy{ zhNoIvvx71nn_?}!`&zi4#IVs*ZZ!Xa`NcpjaeXOker2)CT62ElR8B0r`tGKLNbW3# zuMeV&uj>O#g99NKZmnrArsGLiTp3?_w(4%bz* zbE9x=3_sv&M@HJ6&O*UoNCt|^$<#}Zke0Mo=Lb5(K{45dFu58%rgrLV>&KNp|Ewu| zKJX@S)JpvRP)ezte~{+JSGKDd>MyUzpBj>0f!lx2gUw$_qOl5ZQfl(uo^7vpDHMc! zMaiFKP4-ZbhwV4Er&dac1#T4Xeg*i^4qhA=6E&_d>tHOFU^egzXsQ@;>~2P>YTzf3OvjZ1iRIGPAFRdt`HyM*&k(r zEWeI?<|q&I2=88RRHm{wnr@jJhX%*#Lmw{Q7?0bg$Bj^gydS1QW~Yd#2%TV`=Bnz> z@$NVVfg44Zz=q3^d!ZCoCjtxRjnvEzwrZWezwGsxcNYpZ5k2k4HpYE^cuvOg; zLBJY%di~q033mG&9<6qL+Y5hS_fI=Ae{USKK!UB+hNmvxJVj(iO9IW*yGhY0O1VTE@~XV@BOjYKdkocnAoa za}V5q>N&Y=mekq}#96tlBU(;Y2l+;Y(pYtecoQl6EKUdrf+@OXkoMh8)uHZDU58p& zrn){n$1_-K6wW+IpFZ>j#NHI{Rdhz1k+m6HQ+fL2Sv5HO#~WX)=COCaD05&W7N2=+ zjQg+(Je=AsuE}nD4=tb7*_o~8ySc-z=*R}?W3kP}`4UKnYfGM#2(dT;)36fSELAI* zL{{2UH@%_sGt-oZU6xW}r|;c<+-@fhzEGkgq&a8FamYw@s$N!4xef$DP+VCoty7L6 zrqxdlsoR`im>nq0BqkP(8%5uFKdL&3exI$Oe_h$lkzB`oc)MO_VmLUvSp~3OsQ14q zb$lomD20GV1&SXCGtSNQ%5bhVeG@ZU-$fry*IyZmUEk(zUc~GUhCA`I>^ip1Dyu2M z{OMa}Mzg-_4?kDN8#8e++1T>_es{WMkc)I?(mdVVtWB8{8?Fc=+K5#<

SBM$HVYnnvQuV1bJq0DNM`JFA(NWEpc-w$noW%X+RMbCy8Wv>b(>Td&gIOqZp8 zfJ<(uc-60i`|+)c1k(-W#@{c5<4s3a5q@R>i_r`uo(hDr zDV!30FY@1)=yJRIdKdRj8Xwqo6#x?ig|wYlDz0E%cEKwg)JLvTsUN^ql> zz|t)l0^9SvK!d`!#g=sL=P}>o;4}&pZ{L;6Z&5pMbYO$-)`CA8kxwDu75C9DdcS6q zkBFviN)&wljc)FB&%vUwm`8W#IiFvx?$#dxzt`z#F>B5iExCNhOHWpP4|Gw@X!X3! zE@L~jNSANc-}^u|HD(-_Kww9m!47TO?CauDN!7{hQ(gDo*kd<(%K9p5cy}Q+;>Gu9 z0c{wy@Ez18kTO{&zqJN#8+%@8mifMm-d^xH za2CE?@Iz?*pcm{Rti))AT#7U`@qYjizkg8W%Zt8dDSv3^J^_r1p_bmEZ4@YHHhg91 z9%jov)M2zzvU5V$d^rvvmVF%cyr?WX(&1~yyO1XKRw`j1yd15R%#7sCFg?UXeayl$ z68MhkiBt_NEG%q{?2Ig}ENzS|jZpS9aBwy7(loVFQ*qOA(r~o!zEHCm$1)!N`g;QmkGaVKgN1?)+mrSt5_1VH-XW!cv(8gHpH=(*o@A}%N zHcYBgs{T8+07N6=QV|P_f2LT;ohz89hO;3|q#@iyD;y`>{_`z__iwP(BC_;3voH{` zwMXbS1ueu47agF_LDe({wa%~?K+Nh#tzwZv-KIMn#BcGQKgYo0(jE)zG4)m?oTqzm zGEUOI!`zGv+n+`7(oSzJbLzTSZ^4Np=!@$p=5z6{yk)^ zSp0tBtL=Xdj0}&Ps8-;)P&Xiul9B@dDpkc#eO_{;PvqK>Sd$e@h`OPP@tzw)xf;St zqGm{3MBL|wPFMaEO{8q3PFn6j(pOV;!#%^j0~)KrbfGS_sX?aC3yl)j$7l~m8fixK zcINEKh?0Y~Y9VM~SR;{Q^+Lc7em8{63wRM+ZNu@hA@&*m$xDkb8bhYRCrbb2fA;ww zKBobLIS6Io{{KP_Oh5l8<-n$IY~r`Q{UOt)I|UPng;y;V2J>q0f+y5|98ki zRYhVBwMAk86T7i`e&V1B6>`kGdg)p}Kx^?Lcz z!UEc2+oe{Ox?@#U?n0-pT34E943KQWw}dOSyDL-VVEiS++wXCs)jpp(EQy8|W4?lx zm%lMTibSmfP-RRP*=tnG^rJU&z{oo%dzW&OL(#fHxQhR8-6|4=`iP)z`nrL%xeYg>b%?BdDH;_7#Xgus8by^DgL{52yZLX5GWuLh zQ!rKu^EUzd`3n4{yK}qnuC}?^2y-cNaL~vt@W{{b?;gUWcEZN^7WoizO_u8NZVha@ z=0-ux4RLpAibM4l#-pl1XWrza?L1>TWm>IIH4GG^&BPK*?A=8mesq@3& zh9N^);lemuvuNOmhiN6|_Q6dt0bG;=CQe{Xq@viZ8)1rBmKBGNsut+5Tz*u*mYoiN z#v7^&88z1bN=`E4x+O^Q!vvF6fF11rU`kW`i4QY8`4Yom*4SHYeg%<62S?dQGB?f_ zYAIX5W~?b*A@nB^r7bQXjsSTdQVcg=(!H^5z`ETuc70@u=)}u|%6fLmMV7l>);BM^ zkVE|Za)DNOApScBW!gx&lppwKI?`!UV+qZrFBW36aJ$sdO;J291l#g*+nujPAljYy z!y+a29kObHM;>u`RKj&we!!qvko312`$2dbp`l$|k>Y`HcEQRhXm}MN0n8%>*+?q? zAm~f(b4xmN8KPBS{eHM4*{fd+i>U7|aTqHFYh({&dc{E4KU)@i|z9SOlB z!Of>4){`k`c$AH2&L#U8s0G;QS@p;uiZD_Wu9cRf#f8T?Fp$*qokzAF~ z5uDF2@E7IBP`s5IQmuw;GFtsa^ivPE{$bds76x&chl=PH>1_5;^C0{a{OXP&dl(al z`$UyGLI~YpW6+G?6G^`P@D!H4x=6qWjB0(cMB&pS95Y>rG_03f0?=_U{4u`c<$q}4 zCKKc)Eqa|b@y4tB+JLgmd8vCVhTk)8^DRz0h=i@0=Jl$HOy?_N><;!p;3Qc;BZ+iC z`n2q`XzddK#bJ9%F_4`3cW_XVB!Hj27U}p?C6Y%Svo#1osC0=PJ)G^K!F&qbuR4>N z7*#(EVxy@|27X2wlB%$)Y#{AT^{>^NbHL$Ttf_w-kRJNy=l-96V!x#^ND3G z@xy#U>yqVstp+v0yTA7;2MBI)VTe#4PWS1={UHe=1f(>0Eg(Zxw|)-o#93~YA`sA; zUvn;Z!|Y8@A5&-}Rud&T@!>`X|Szk}WUd8q;PS-K{?*+~c1r5%r=uT3Hh9Cu-52 zglH#&@sg3Nm@pAQw;dr z{LeW;f5|=6Ig04ws@f3N3Q1B_gQhL8FS9emToar_tk{7kOO#^m9Wd5Fz7H!uFmm}; zcJ);|Ete^SjrvIwzM^2-0>C!RpJ)QufBD14L_CdHDfY!09lOp#NjzNVJNaTQI}ype zFWFt6+)}^Z4zJZ7#rr$G7)G$rd2#wqnFsl|i zBY(&TM=(8OyET}7i9i7DU`zX{E|Yf&ViC@-mfV*Nr*Nz{>@(Y-@|-(iqKM7= zw{Y__xb{^-1ctKpCtMScRPrFPFddzA*my0iRlkI~e@Ts`yg3dX3;IYM{crVR4NXWC zR%x7nxYATii9cKOf+PN*2_Zqhc8zdr!KSfRtjD9Yx1tjulR^g;>_JJO2eza4dK>g! z8@VM4r7$k2w52lomLdIN`e#*lGf!2IC#QZ&o-Si=#PD0vS$|q*N7OkiB~2#Kf&nb* ze9V|W)}G%=7JhJ(ZNVV*xr#Vo7GzDqxU=Zr^X!s}HBcasPqP}YHO)AyL_^twQ~EBf zydR8&29FyF2m@WBr-gn?o6iRU{xYK`jqu7QdbUvA!YLiZ<%1%rZmE>aqNtqHsT$N1 zxjK1~3iE(Or6O#Jq4PbAlU{c()xh)_Ll`@ozni9SpVr@X{WY!p7?sxkbGL)Z)DuKIY--jd!vRgS+G^YlRaar@;%cf*q ztf>L3Q@<;*OlPPH0@RtgGa_M*yz%~{63zTaB|6I1MnGN7eAw$64Z@dJGH+lHje4687F1| z`cp)?y2MnDla*Cyb<9yus)A*7=*%aasu-6EbmJHuIjoOYd zSxoepRPMqx^M8S`I(LkxY5Bi*8bu#rI*Zk$QhE)PeAb8R;Sc*E>YGAZkO7F|J~%j1 zdmL?{Ueu)ViXXVyy+fB`!N~(oTFIX#5qabA4x5q@(3|xlA%suU5Ek9hYoN zLcc1eg&?NomI!M>8r@p?9Tq@}LL&}@yNSXP>Ka(@ryq1wGDO7|GU*Z_8FnY5I3t7b z)8}u_dtJJLyMe^*jEH=X6OAR2f^z(iNkkF@_c?~r@AMJM)_KF+1 zFc%Il3%s?fif_EVH6lyC%~5&C33f8L1M#7DDSBJT+g5FNK4GE*p8R$&L)$8V*}a{% zO*`z!t|-5K9ew@0>&OmO)csY(1sIy&&WE;s&V*Ic*8g=2-KI!Xcn~QE4LaQL26y?@ zIRK-fSNQLzQX~_D&vWW^6?u`L(Dg0r&hLu!kjrmZ;vzX${h5&e(fbRczE>?ivc992 zd|fYdw(c7uyz0}A2yVmauJ%NA4WnL7JfkP@cHpFXM>{*#fs+b)bnZuP`KL;h*IS#; z_!XhC_oRzgk-swFbN*qT@|Q9xL96evD;}OclgY@gPaD`pG5-h=?rsO#$m;%zjU-jS z{g+B~J@Brzi0y5P{yhFG_HN?8R3ds2fKwCWXBWVUC&u8np+)Yi;t0J^>?%H@jekj} z=q|`&19E?Hm)31;fOdaP0bOKJR7!mi06E7o)_R9OE1b<%&d7-tLN|TB1F!s zT~QGr6s2mSG*#myoQpnrbG=G^F6>E&}} zZZ-zwFq{C$=NR=hd7oDEn5yC1`r~{hUT46KEC(+tal#)l*D^piEo|C3_s8FAz!xv+ zQhg|Wq;)Bk>fKE%<;#bt0(_guU-mjP+DDcETjUpN4q_eNGc?qiKU2`H73v!J1^FI`i=@lQ)dNaN=pd z8;LzV@b2Y4UCJeIPVtPPhF$6|_Wi()d@&fRq>%SAy!*3c+jiVoeA!2;7K%M(%w6My z(B(X%W=B?u{M6yEmw-N1j_NWq@w^%Js3KcJiK-8-Ty&sZmteabS<#eVX};*H-VT6R z`3|VNyA+3J)wW;%*$@-7(QM=}mY_i3vYnadN>R*x^yinlf(NiiNW!HpU2ffdN_%Xn zUW6m-uCAd$uBjpGE&zsh=#uCxHzqXFm)kimXya$eZ38zS!V#_`GMG}$?xBf)7BxKJNE%1E+d~fXAD^VzHFXjt<95+Dv;yU zVRbi)%?2Fox`0(r1ibasn-VzL{^g&P0#aI?v0r?`l{xR`F;f>+uipmSG+Z7``pE%Zy_NY{~`d&uvxUm-iF( zzgu;2npMKRNy2ye&2%sfp%s6BOF6xeP=oSKgxZ9KRmDyKR8VthsEefpMbAtxE7cQ7eM%gGr( z)m+3Xl2+=^1Ij!i`!vory=8^D=hCtq40`IKi~Fo^RzxRgFPOh_*58aZzO%hxC27Q} z2xql7glkUgBzBulM*o1!ImbS^Z3c5y*IVW&<|wyT=gAHFBA6hn9$+Ubdo!j{WXf}k zJP&9s%7dE{`@t>peAcRh?-(y_Z)KeHt53(1C|Z7O#-=6kvz8yZQrDOM5N($o@Py*I z9cIpsJ@=@TSX1U_t2JfOJjedypm=^B#3Nd!UtO#G6H+-b&s}E=WR&YSrQ1s93D7Q9 zYY~{9DOqxt~NLI+w;{~l2f2)P!+9HWpM{gc+`(R*cb6Z#qQcO73VAhSIU8wAKCK0{f3@D4 z?CUrGjaI+wBNN8no+sqr>Z_*|ex2mEYSttA)(G$Oc5f^ZHyY9PBrfV~E<+xd9=mR{`_Udi$_nn3T6N$`Q}S zo@WHM>z1(Zm#Xum`-A+XO_eA{cO}Xny0#G&Z+E-ir(1QOCIa7@w_CruIJXD%sggE%H++iamqw)k$ zmhcGylqK+}G5YAw1jyl7+#Rmxof`9y{Leyi_Hk8}(Tb2+08F+PD@qLSJEvlq0>1m0ce< zae^+%SRbuB9NIZNX%v@jd=mh;bon#A23FJ8ztvaBCJ2~%LZj{nV3xiPNYhsIw*ZtS zHy+#gt}7b#Lax+D-w_$cn!bF%tlffRt^ZXgScEG?LihVvfr?~qiaxF_sgFz?jA~0k zi#qO{WV+G>7}wgE6g@&p^6`{oWlV>A*wNnH{2z_zCE`&jYJ`yF<37jAplw5s*9PX7 zB%`MDbCTo`E~y7?wl)BK>DKgYZGTv;7QkMbrk*l9f^VLMq2@IMyd!DC7-Q`_!4J;m zlZB(C;Zk|l+me>@IUzoa3l$q>+r0;*TE55Yj~7Mv<=9f&#~Yn!0RwM|4B`Ay9W#V3`D=H>P#mx?Ob{rPs3 zKDd-{$)fP}b`_hY_bo58_%vdj?i!DCJ-zIZ$w{5*o9qX!lT9WzPU@kOXzAk3NKK@4aO+r8tMr7|t2a9G2YEcK+LkuixN8s??-xSB{2qUDTD zy%9NtH)i#P*o_w66@T2Rx@L3jcMBc`)Lf#$7A4Niz`F;Oj47^^I?epsDYhwe8~AxL zG;J6$8Z8^Pjg60Lgrd+aS-u`E8~(hyDExgl#aH{~hZC^mapqq38=qf|tf&i<8Gjy{ zUq4(8$lQEk4=QRhw+^`TkeL99d5?@4m6Uk^IUVrVI8`7PxKX(z!+onk2CK>+Zemhk z-As^XB0p$s;c-|x(Dh$94x4`JpT=`WoW^Mo8^I(XaZaN~Vzhnzj0ME%KV^;o-1|quZmy;=cjsHnpqVl!=FI`eIo1%8#krx zB2>0x4A^I3N>xth!xc?1@KH1ah2Aq`Hl%CBXsonPVzLuZk&RYmLX?ROBtG=>dEoDn z2N=*94cEVAz4M)O88h(=wp@Q zocI7>mSg~!B?{v&A$KtpG8KTi2Y zjnp#S3IVny3V#I=>%txD*zBF@C~@-9Lj5|kE|~vy%v3MJo)ThBkB*BW1jj7VVkI}d zY<`o#EN8~by816^=vX3iIMAxRQWh$68??q|NX;1Z#%z3Z9)(#>wOLQab7tL(pF{t# z&Seet3vRH~{lX_@feQ=o-BM<(Mk-8c>WJsxsZ#-Gg>K+$qVgE0GURqg3C(HVf+_`7 ze~oz)geyYvULgH>}p^&{<;uXq*kAHCo0OF zRv$N0YRxa0TGCG}^z&C2n)$m4cQv++a-sP;-L>*C>1d#lK!u5PhiG59(XUE0id=(2 zXF6O<2{PW;DiK~o&{W)?Wpr_@Jey&vdMvcC2uuFnOwKZzZbLJU86S(2{3U6O> zK`om25E3QXdHJ6;C+p-5cJFp`r_+Wo>_g4&YX1${P3~%evG+H)JF~JE{~EcX_7z*M z5mZ@m&zw<^Plhw`FqK33>j+kgn94&z?A%(h+>iv^Z~|&N12d_MnA(-f z89{da!EGN#=E_IfED1!*breGd45lUo_8(@+-_p&=l!!NP&UQxm-k9wa0rE-{w=@pp zQ7l9NPSdDZV+8xLJwm{!56)QLj;VCWq8|pmlI<#Cat9hePMo~^z)%_TF0Ayx!5PU738lr$ zpOjUF6?}6vix4GxQ7X4gKz32Pf&Fg8Z-|b;{|h@7k)d2cBwX+}>PjsTE@6}JAJ{>e z)dB+{XEII}>%*Xk{bAv@lv%d2zxLt-q}7W121+4`rqhb)$P_f8iTIB;1lE1Yr_H~b z38$*=v<0!P1MQ%kZcV(c$Pk)`HxONIWsx}(`ca_w#TC%gK4 z=RVT4zlB>OOC(;mj}!d%sF`T3SDyP*9-VB205^pIs<|7s^!0UTQ z8Hj5p1diTD58JdMo&-oDBB?3E8VuYPLt83U0hFb+EQ8jB4Dn{0vo7z{u%!cGqLAO6 z1;R*TVUDhe=5q>$p#{6EY^kK?1PQkF7%cS5NxPsJ0Ls#K+AgS|Jwm>_TqQf2slb1f zCAu;3!-)@B!`sl!nU0Mp+AstbxZ-u?|?3OofxW|Dj26RxoQSZ5Re%mgWG0g#7;m z2_tkfoPa2T36q%~gpGCRhSC4CN0BOeky&oc=~EI|b3 z$-7tmwH(NF?Dvfk<}d^FPee&=Dqc?zaYg#_F;;-o~O!C6>y3X}&)x zBWK8eSp@6<#gL#8SD1uCqd{!5g0(zG;F@2pplOhGs-&Zt!joK%Ql%3Zp9klN~fXtu9B)|p1tj2~BwA9>>i z!bvdy4gCH0bYzQ33_})~rvczBMI8R4M!GY!uaH^Ol&GfZYg;}lRGDlt=m+({l4Qk7 z%)6DK*qq_gp>SvinU-~vcWxJI4e5MIc;RW)E;Ah&ccp z2_vCWIAp_b@IJ_6l^RUhK_<5GMte{phmK{3&>@Ej@M9&RhYP~@nAB~&evSUZOw@nP zwhBl3BtssZ6wd&#B|7#(J>vNuvUxfWs3Zj57!rrdrC!Pq?>%nLF=NKnnX5e$?x5V^ z0V?>#is?O78G2uQjFmzA3WB|7B8zU?;0-v2P0OtQt6~h>mO%h)DR@K9u=R^Ncms!F z^BSN?kb`acFl^=NJ%5z|{`DOIP$Y~R4}=W}T!xqaIKQGcC`+5&y$vODoLebv@S{}BW>*>+bNzV!6i zSbKUNzW44N>&RkbZK?T>vg9BSl~^Xi@6x67mvF#<#}0UL8}_RcEEiZEZ@?XuzYSzC z=PxK?EVw#1ui1pZruTKkxY!6LH0)yUk%#0psY~A(gbJ zDHwJEL10b+0>^qvcJ{xvOR-y$ky-hbym+Mm`V=;_RZuyv;9?ExGt*TRo*+1>vXhqX zE5{uz^Nj(&f$zr_HqGU?9Dv(5+iE%emQHg_;_Bymgzb^xbjmakt)>#cuH8iEU)0aH zw@FxiaP}wVDi1fum|i6SOQi^mcCDMXL{nwZks4b>wUeUUp@D741OCyw*iR6K%{!KhaZNh8 z!}CTcuAci5TkY$Q7P3mKxL&x{T&)LZr%a>mwoVhcRthNXlTJC&EG@K&tFmOC9cGH) z2t7X!s0P;H3T2l`YaWn2*!SFx!+*lp_)NA3sVn(ym<1(C*_5)!s&=&AZ*SqO+6D^t zQx00pFAM#gR>R9N2cmk}+8m|iiPLL*f^co2H|Q$PSqv+Yf;WrNp#;v2tHDF&~bo zW-G3=?(-qot@(iUTs-B`{VW=k{G_Ve3e23LK9I5IqwBz|7W_e;^2w}gn=2%B$h16Z z-{!Ivbt?9^Ngj`q*j$oDg<>EhYQ0F=XzVVH+4J?b{QR6x&)!^l06AXiK^oF|qUZ!+ zo!<-{N7YKtzY~)_s?cJI+#_A7_H;G!WoMf_ReKz&waxqqn=hzCKyG_uvzCBB^0FWQ z$v)1ef#18nq&l;+NgpI}?R=D7omZc}-`FBM?_khg8U1I;!R!a0RoKE@N=sA72mQx_<;%LL zMCR>WSy>Ca%g9S)HquCy^X&@cEh-spo`#qA`(cPhwY}onym#fw=J6;g>v<`=Wq$4r zLJ^*urtsy`w&bmOT~h9b=_bmtDGGl3=V>?(1o}))%ZB}T%bX+~c?b;blg%TS#P;vnvtVe{O>a$}8>yhTv~qlrp$Hvsu=xSrtD$Lgsc zSeH(KW9?i6k#Q|D-7vS4Y6Y%nw`SfG+Y}PuW8LW1`;E-xx^bF5X;F}3(=s2zI%S3U zn31HbR&O;BB+BaP;h=qG9uYO{-PEhEn-IK~y z6X4)F&QH)i9?{T?6z7lAx-<5e5>qyWNYAfNb&BurQLvO_w{Ws=^yR*b0RZfSY>3ad z@~-Lg6%(}Qr=Sc@_m^)c$_@-jOZEMb-NEZiSP!V}KBm2(qoU9X4lqpi{5w_mFb?q_Lw% zQ@6=D=6Q;CtFy!bH0Aa1_*}P5w0u|l^HS5nv^>d9Q0CXUBqqyAiCacwDfeEg+vVn0 z=aENY<3y(}tN;w0xGy$&^k; zPGK^hEz0xi4^jEeb~cVlFpRSPgK*JG5On!l_2pE?N21bA6n1vgw<}8%LAArrKr-Wb z6-COm+DmnwZIDmvf%quFc}235{@@`lDrSz4bFU{JHnE~?l$-gZgQ?mx>|H)Sy8kdRL-}%6-QCS8E$cKXE3jDS;C6lh6b=g`zA(zU7{MJ)#^yJM*C$P+@d8F?h z4@LNH{Ca%DKE8*e+uz0p4~2qCeb;>HN*gC75uoKNJcG|;_|l)O#3Mvl-^YR#oAm?iiNNNx!&-!E2`@IvsP5Gj*-oy@J|MW z6@u2;3oB^Kl%-)o&QWAccJz79=iKj~wVasqlng01zu-eW*eP?DA2RHz$5uZhp8hl6 z>$i<{yh`T}mAj}L(DlKr7BlZ-o5?AS#%r_V~ydK)~>yF=MVwWQpOQd zzbrq6hdRU3g~eh&%yZsSWtlT%r>gMyk(B{T+bO}al*v3b>Z_}b-F~_om8)4^uFU(a zu0YX5qi51y+@-Ynb#|Ego5hWy-RETKdV z%`D1O`^905HTo`6M{3q;!1*Mzq>jT?7^UGHr1`iSj&+ud|K7-m8wa zT2JM6sbJCms(bv#_tSsf#bny~s5g8+8$ruf2h}HKe0syWHBCW?<{Q80sgp&GqAj>O zq05OXEQJ-b%i7QGM>wrU>{-ktGR4|>?h2;@4`+S(?!C|Y%?#JBl$3^6* zqzB8X0ONocI_mZN<|0H`10=>?;Z0Rvo_tyh|j- zkA_#giFf(N9Yj$~SCRqiu4mTfk?rDk^4nqXyOWwP{^gq0=cvD@#;-HsyK@phpr`%1 zDd>mSw;yk}C-)`g?sZ2bBXa(oMKU^WLd~Sxe@3e72Z=*v`6Y)i7 zhiHw2Z2tERYTyGIAt50pCoLxdDFG!X0Vh1qpjhyT$cX5mh}iJ3u*lHx@bJ)J2wCjQ z0@_B*KGULnJ_)Zr+EZe7graS_*Xs|Wc3+`bRwMH4;qnj0q{HobI4MuJq_d#a_At>> zdq{_%gq&_>UUSnEEM-5c%ej*RIepK;bkB2#LM&>*1pP9+V+nTX_Vd6A|f8W%NXjgbf7}7RjZ`DatFd0FVUo@&{LxdVB zT~m};;e%Lzhc|z_Rv3cj)se~z0f{1)QV_H#h6;;a0rwY{r!hc7k`*KcYNin!cxyM`)+U8G%L_=72>_@F!q6VPNUldxnFih&gznU@}j^GOP?GxstV`-&2RHAQ_MC zqX{sfmT7+ogUUawI?nck=1;lU+eMJ+t+W)O*bH;Z=Yri`O+r+8AE&n#Yyi`UF zHj*yb3B40QMdHO!ZN!he9uFHE(w6LsHo0Q{E(LwZk0Zw)ri4t@8oEXSB`RkKSgp(T z;W*U?#+iB=jYYFE2m@OOWkUfQJEBY!AY=5*Y-_I0YIY&DxjP;iJ2x@t9NC+F0n(|T zYO=p@M~7vU#t#lV7EMb3htxp`<^I6X=ga#T)B8IR%xR8Fnb%^z+D z5-q$MH%syZGiQU8kLq(^EYM1NDWfngw-8mq9B1c4l7N&V z7stuwAV-r*PeY^_kmhfZkM#dRMJAT2P9ZHFk(Ws|3nx7n3KubJ=1XD{DM&;nk?+V! zBx(rce?}#c3nW*%S&3u3cQWcn#es{T&C^2>m@xq<5Q`Tc#%!TrG!a{)is(2cW&Po9{qIT9?Bhg#^nh9)1)Z}^3@7&OE^S$=@toO!7jk;J z5JM#Qu>4m>K?c_3{FiWGT^9cpac4rR-;*nA#DQRorUtQ77trd)z;^EBtq~J_NXY8t z0UyQC*NouTT9mDJ6}#O90Ug?4Zb8u39h9v%WxL%qhAoOiwy0bE0WaAI#GPzato|&G zGStHv5H?y;>&t1qp6O!|H1Hj8C!q0kY_H;tFp!ibk&0HoS5vRXn8d(gP(=ciNH@}f zSs9YmtNv)Sn)3gKLkN*VEBWmmTDq|C+}Ae)Fp8-ZlWGYaffs9R6 zO)C!W8e}xY0AelM)2uzdinI!|65e+gP?&Y0ZYZn=sMWAG)vm3#F_Z`r0c z-An^INX_}PmJZ>BUjSik0 zB4<7{sLppkiSLN7zvK|0%?iQHN9;Nrz>2(zu6!yQbPmMFAoPi<^U-&P1Y>&tQHvE1 zgtL!31!5f(A%wyRiV#Mv{Y|TApno5zNNydOksn9dKl<$7?5l!S3uS4rK#P)x%x67% zdP)iXIGq|{$7q4cD)Kc~pAl!mMm7O24%*D$a!4MnlF$2BUkf8@i>b=~HH`wdd9X>H zt`?P&VXSwV;8mai4ycpnU8)geBX3?BL&(x75M>e9YY3(}*R0vl8;d&L075y9M#;d1 zxc^)j_8_&ElgVJ{;@?sb6qP>|RU8ym7!*|+R0S2XDjR7fit^<9U&|scpeiwbH82ce z>Z)vG%P!!}iV>*P@z$Ft8)em_UkvnD-J7U;Ut^f~bP=0is|OTbKBL0#=W7?}li5Pf z$5corH9tP97r+qm&+K5uO3#c!vqsF5{A`C3Sn$Fm<0tt2C;Ii{(or)6|7NHaGjIk^ z~p8NMAc{RUhmn&05+!XVgn;?i=zI$!~{3bZz%cs{?TfL z0$^Y-g0xH!xAYn|nSUbbWlDDEgvGXo(6f4;<$q)82@))W81@XLQ7%%+4Wy;@r4t!R zNf}AAO{Jv`r4t!Qv-?ECFO7pQQM8H4gkP!CR+P2tR5j^rZY2&IS4!&(16|G8WzdBY zIG>MNjk%_2McK20Q^hVa=?H zogRzn5EszH&a_3BxdoS{3g1mcm#HEv)m)6bhDXz9Mm2j!HAhE1v*MaUkOcN)oI>+Sq<1( zp>ZeCx}|L1MOY0`YXGY;fYF^m>x`yx=h3P(1z^W~hf^C$o6Udvu~Ff9KU85GOYEV^X-zkrwudn(HeAU;BN zYnXFf4?DM~Uod>6;2U9m@bO7s#CQuvL$a4>ViE!zWh7*3t)C+w@{(Z2N@lOBq3V?dS&a#(d+{-rX z|4N2>VMe~jB;9O~Zaqo0?=kBC%7%L3ma6z6eR$s!uwcP>%WZroLW@kNqC*7tooBjb zGTPn$ax{VNEbMdFKsZ1JKAKL#}KdbRv*a%uuq`3k5Jw*h5eky zcoiDy&588y!2t1N0>7pQ2mIo}W)Aa1mN4`>V1cNxNBNhxw$|xmhP8Pq39-mZ!Nuem z0-<^J`C?s6H7@4DK*wX1JF(9Tadb{&{kZ_#f){>EdZfVgLHx*pGJ;H_vOKhBnbhuF zIXEzOWMlMGBz_>MXJDjpYRjr>!niKhzfE(YFTQX3cJTYkAV##~gsh34w<$2{Aw-lB zyS3xzj+{wL2h2$#`)E!qPj%|SPYz18wn+PX2ip8>aIL+0XgDU=j^IrwLtyYtQS6n4 z!=P!Tr*xPas@6k{)|wWFw6$rhjt{$WTINJzRzxktL@k6wEyF}BxixUJvjMRB8IlEL zz0K>VR{>%%bD}XzqIPnkH~gYEqh&F&uOE<5+H>Gs76%n}dhhMp(Y+Js7$GvDE5fT9 z4_Gs2`IRttas1xIj+%)i4(#Lc5-w`85%sp2XeJce_K7YhjpP@777EJ=3;C7kB_bM> zfMJJbVnIFbj%MPcp7=C00R^YXcR8|06GoH+ukQz(n+^#P8&+Z!iL#dU4>h&VAPs>$ z>r3bJfk!SCnshjJ^S>Nx~iyj6=*{gR<$>iDdE^ct%5r z#nK7#a$uBM2J}LkdrkA4O8yW6NAO+x~FFZS%)k{0;Wx*E{a@tcNiOlDAjN`p)H+i=h zGC6-$XY`Z=4W7ov@vAASbWGS+wiExIDG#BKp9{B#PDK&ty_&f*pN;Ppgp>1_fogL2 z-!s+RPamZtv3{Q)yK6;QmVA?HU(P`@e#JN^5 zZ}X+DrP+Q*j|{jw2r``8;5s8s)~}XG<#rC$zOs0mzK{1OaC0tW-&HmT3LinDIng+O ztG6AV*q+3ml#)|oY@=_6LQPwJC9E4(VU=(AVwo9$bPlwlBL4jSn4-F061oaLyMh^* zV>x;0bFlCgO6V^dlfb*C*V<=|WPZB4fx=5z zFo>i0&hr9y{nJt%((^Mo)8N~YdU(^*&r$rdbLSgapuai~mj(QA=tc6w*{e_!*fU3Q zw|<0n_Ib7m%>H2LsWB)W;Rc2hTi+z%&pn9+rsdh#R-K| z7=uc~!1zx^I0`2i7{&J&+0lw0`H`E%4YmRY`9>#G@pm!{udm6~5cUU6<*prVD2Drr zYF~*=bnQ(2`)fqXyid?GwC@t#MojhV+KAZJrDfV(V9ZQamt5*jTY#Rs!}Bu%4%O@a z_5)`l_W3~{;{+2|&)psNg^MTEr7_q~lA=1xXKqT4;?v>#=N<32@e^|PJ~v*DUb?Hn z02*BnS=CqoQ%mP(Sz8)W#I)LNPLyrKe*a(SZ_$O41fplJHxV`_U)Z;$5jXqIF^szD zi3ue$zYd#}6oj3}Gq#YNhkL3KUoN-34&Cq1ULOm>cG|bQ&5MR>&o>x=OuPKPJjHIO zNTUCe8PCuAqw1zcQujOPx@x5>?f6K~Vdq@AAt}aA=}CWaGE0}&Mr^#0)o7-86;4I+OIr-y^JOrH77E=S9qZ-`u&-5^ zAKs*%ozUUHU5KJumLzC%Ms};cstib4$l2iTOP$f@huj0CSE~m;L6GIcsw$BU0=Kh~ z*x_@eUTb@c+pdTA`a8+rAEA&EIp#|Rp()3Lf*1H_Ea^)xOL`i;c-aci|EKyzgRk%Bh3$IL3-r5V>mWx? zdNV8di*NgWVkxHAZ~TYpXA3X-M_K4q=uON(FR(~pX8d-kIg7XZX+LIKMoYO*POi-Z z)4FQ6X4J`2JL387f^7v0t)V#}X2WfOnY%JTuV>Ovlym>!q4?U$wnh0i*)7J|+ejGf zNBY>TLx2*86d2Q^3u;!F3~ z#^q5AZuYw)MSC*t_V-5WlWupEpL)V7Lf7ZnZh@(GOTQhWAtFMg*C7 z-iJfE+q)9Eb-qw%kGEqyzq=&%)rp?2uiG35_*@^`p_nP!=Eu7|wWH?=+nK|RM=aDQ zDlEIl`NMN3u8&s_KWQ6X#?1*krbyj}qLLWCjgA)*nDrh@)E|fTcK(-|m47Qp-!C6( z{XQ=>L@NWbi=%gMcij23be3;DD|P|4Kk0T==`H$_(0zM{_j5Viz&}uZ-v!=oMN%Eb zNIz}}q%DBEALwU&GOj<{4+Da$N4RHV~V>th&pnILO}@~5So8fs|>_~KXz>R z@4J$IH!%(px{=6ln+pzAWweO#DgwmAdUA2$L@MP{ zddW4=g_aI=Dm(MHPiOtiXy=b3>+}bvn%i*Wx%!EePHo z;h}`VWh_gM7E;j;Zt)_{VxDUYTPRee15w>V!qq{(!hKNtWzn0)IgZ;Y1TI4F$$N?!Fd3pQ8l;guz}GKYI;1Fv zz-%;}l+7Qwqm#OCS5P#6FUn42I;m~~lvamp^XIKo+ggq$>OR5^xRk#Op{M;+0xO5w9@6SJ;YJ$4@lA|C^}GyJrvE3iK4vXnL$gn z{)sDRxy7YUUPp+?SOnGjlP>n{DOjerlc$(EjLmwnQBzQGlY4C6k%0W5GJ;Qg6*B4K5Xx0mgx+n|L@6h6YK*@hWG*9Od5iD@f<(pU)ECj^|J&=Dp>8VM@)-WS&mp6xjvJH^hd7+;z76 z3XR$*V)R6S70%URNdz*@IZt-1fYiO9_2HATl6{&BtodjWeizA&HHq6|F?O+V4aC~z zUUW8vW&K_LiX;=4>~S-?D+@&VMQ%S4n-)>0tSFBXDfgc{Yz_2;EH^_noOsLsUPLxHgw1W zFNYv$nvtUkI7Y?hv}@!c^qCj{Wm~FRzbN@r@4UIOqu2VLcB;fz<6up-?O&3>$WLkWKW^HRgN9Y>vNA3?psm_>$&U&vXAh0SKq>ny^nw zgDV`TFF_=Q<6PX);^HqBw>P>&jXyKr@j8uXcIxfC%<#5*Z{cTpx1T zr7Z}RlMFq9M*Z>?zMOExbr)>Xd>*}pdC#Q~QL5{H>9&l2!vwRqIVBBZLiQOHN;O^> z;86L1kcT8;SPoCAE5cH#Rdq|D#LP$z>R+YNEL$1w9ea&`1720nYCza)051-g^QjrbS|{0X>b@cVVFd&JTk8VG!3|lFhLgdgKhgE zW55~Z6{jton0PTm=Yq0P2O~PUtzun1IA@ch}<4{S%Rrco;M@J?9h8U{2 zE8o-C_Y2oSbC+A6mMRExqf~SkC-CvH&w(a3gszd3E&ibVPV{V#@%#{ zZNN322sI12*w-V}xm=5RhmTJ8&HOU z0EjmP|9_NW0MXU|1myaEZNqc|nlS&3|0%G#(sENr)>uIZ4Xt~r4uzu@x1(hGn@<&h zL9MWY1A-}GD4xm%wOSh(wzX23mH zT{t}|+ao5~s+!G{QZk1cT4>r_6e6j3CSO;orYZZc5j}<7O_>ON-!siE@C_J)Vy5$t zThE_saDbgUH>lPWOE>zn08^3)Pmg|nB_-{~tFtR6NigAlqIkF!aOlXD+*LUaE1dyB zfH9yZqVBQ~ZA`k8xUeyxevpjCf5vmeVB8ST5XS%)$FmR(l&&mbl2*<2j(-5Nz?r+a z*#t{Wzk-sdj+2U3>zAA3R}KpeNWqzwTXx2ON=vg))8v$8AI8lr)>_W~OD8Q(4k){# zC-_%3AXiEyT^d5VctXZZ5BMxd7eqP})p#W<$Xu-TPFk1xkmhQj(Mo3*vlySBeN(y` z&nR;&uEgMqxF9ze*QXNMmA!FO}9?6u0S@ zF<)Vr=b{0>YPW3`cBjal5(lTp)C9DDm;tA*ed5|QQKkskd)2(#k8hBPSibRv1T?ZJX>>?UC zPto+iV}pp?49aV(MG@mm>C7ldv15z|?&_`eWf$~GylO1zMU2X`af{NRxENy#`q+Rb zOi)Kb(iETx1H=f9aFt&f6kJc56jr=A%LFE%<6y^T!c(IAn-VPs-+k&)H0eDiB+gx3 z`#u#*@D&qw6lTgZ9ktrg5>ej7Y~ftjJ|HtJP(5@D2i`M4(lZ=v3XCLKUJphrpBf*$4e+$2CZe2^z+K#lGcf2mSS_~D2d~q&|N-* zvZSb3d3v}7w=0eHmappK3%A!hdIvTB+uZm$n5FvQ7Jw#!}BA^K)9o$nKLaMMJ2VzsIO5X^+Aw95R z{4bbIh_snHCy6PNgRo5xlRAnG{IYBmFN$1nezzDdJ^KX zQ#N7QN_rAa^!tDs*G&>{a$(vL(Ze>1rnVICHkWw|PW{yan)as8J3fd8?gnWl+!4~k zXPW9Sq3C092-%%Ri>S|^+KyZ@pn^0he$eWJHK0ljYYx!(RHfe-oK&E9iUV|EPApzs zzN7}|jJi$JML>#(y6Tk+O09XqAwZ_m<)d}+4}Ft^p-U+!wqTSd&sHJo%});mooSGK zez7$8tJ|SYE}kl6<8zY~KpZ=nzYcj!B0=5h=@K@1Z9CGLVD6z{S-nUP$T9?O_!~Psq|*j-dsgAfOW@K-;p}F; zWWl5KuZ03V(^DbHW!!TEo(S}6dH16}RtLEi=zH#meK?~$iuJQMB0n;QZ70#+CZ)h0 z0HqhT00zB?VQ(Ug2fhG?9q3_iGK_~V{YiuX2EO}YP{!8rLvCxiU7(wXzhs}sRi5aY zsLFq1pm)J~ikiVt6+l?&ZbgypDHGHKW=3+6E|F!XPEPqh@rkWHrt@&m&O05DPdjh4Nr71-cO~0cYWn zSI~x`Qp5H=fQAfWEl^$UMA~Q#0 z3ePOm!g|l|^!V=$gl3ke81Xeq3E;D);3{&P_c z=R`nr!QIb&kXJa76Uhn*3;f!#oNbO$>`~^ z+`HlyrBDM-ByXhiNUN0P$2Jr9>RB9!e9%uZyFD>Jr@w4VAH@?6P#&wCDKMv5tk!Jcvyp178;0vL;_l7 zxKq717QcG!lIfIwMf_55KNqy8qSbU}w`&|M1MYgt4}@ua13|8jA>%F2YtYaSj^QzC z!SQQyd^yl??@_i=kVz7NF@YNuIH%0WYr6BI5iu!XK!FLIgp{VvsphBsW7cMV*>j;Q5WsYA^SMNy;$J1%4bgv5qS5kp1q4Pf$Tsn z*Ke8;nS0(k2@j-n*-*kcJw4I@b9-rFNLssFTDyOI1}MTH78n)_DNiC57&c>|It`Yl zQi%?6Gf;k$hz@x(P@;t@)BzeZBI0ZxymuVB>xPTL&0sj|u+Hoc7nw?3iN7Vv^#V*G z-#%cZCltjryHPAOf-M|IS5|BD^xL*-ss6xqAYr?r>K>~>jxnQ8dwtodXE93)ctH z3_#`r-$TSV{enq>%@=|q5;=(_b7m(ywI7V=%QBeECuw^|9B+~(t1_&HLm?eq(63h* zfDMwB>@(Oti`FmUIzM$D5ia41_Pq7t^F;vNX=9gXXE$)mP3Q#ao8K<@&)`?hjD-RlHPT{LC6jPK#9cUlP5+cshcyq_+Y%@^nK? z`Ex=ZB<*IY9>S#W0-bl2j>nwZM=SMr7lgcffs;D+XbYBBgA}?T}L~Gi%Pw zkMGA`RrOZwwf3)Cdq3BE-`5a^3*C@5{l=JG%cp<(b?C0ME^IR-{O*$+Y_ljJ@`BcX z8H!=k9k%y2cW)gu>@QoPoK;3poc#P9_C<|S-HuWD-n47JjO|u)nW(ODK7qXggZ+u_| z*#>e#XKpU*3wL({H&m|M_DoU+7h!+`wQs)%i7Y;tZ!1d%ARNz$BZatQ1jdsECc{$_ zp~HRUvwRT1S9%s|8fg6+G>B^b-U$i^#>^$(vGgD$ zzdCb!Kq&`^!dUd;ck%+%zyG2zU>ujCIZa+A-S={%-97|pWF(A~%J7|X^^UUDXFVM7p?&Dgirt zL)@*18yr#CsI7;9@SER8T#KPJp`<`dE;*!5I-2t?OMUd6?q%4X=a2M$M&o+T2s-h(AL8*M-C|k%p%ns&Fr>VvwmL{&{37`vMr*j^T-qsdRYT?;SpHQ+1TXGwVa28CY^bl$2;ToT@G!Q2pfZP=ReEI zy+wy!CvfP?{>1XRCaCS-s4BY?fwpfS-rwlb*+!n;c5GtFats~$JR-Y4J&+VC{kG|^ z0=(Wtx4WC0;1Rz4k|jk_K}B9Y4NZlV%_cqRgOB%uKwUpfDxxeBa&8e|I$I>!cG0De zRj4ztN=$JOlCp~42*dlx!f0%~t>wEdhF6}eoc z^!#KgllaGT(q!wYNAOGM_*WRHEedk3pzOjqiboV%+rCO+hwk)GL+&5P71UJ3TaWJ+ zmm6rup|tCsBdg2}DK|1_sSMTY&<2Z>KP>ao@Q8I(wOh)K^CbuT(A#^~(T_u2_Px27 z_1?`5bhatq!uSUVR^TaVwtQYd>CYdf+-4f$!O#yfIiB30bGhql3wCC}3EI|IjW$UK zg$ijEiybZG1%CN3S**_Un}(9tacdL4X%or3RTM0VWRtQmbG08qd4ne*CUH^_6k9+P(yh1wof zEZ$Cci<TS^3r)M*!2EHS4^zIz&4Xhw zgA~M&_44Fnd|LRLWr6C2t~DiPaqkl5JRed>uYUbID&EcBi+WOWuV{ndsN->7cT`Op z_PoAKZ*B`I?=!uRLez$rxDE0<>2v0tWVZU|6YX`~L+G%BGyo>TSQy6u-8oVo1zF4B z-J9g_HgD~D9`eBa3UL*Wb$jebyP(o_vgf^2WA~ zj%~DITM!oBX`Ck3v8}s##YtEQ(30iS%V*l`>J3JA^%H@GE`OKn_spB#X@zoaK35({WB5&}N^V?NVu$?ntFPLBx!IoU{ecDI-DYU|3bcKhz8hjE)wot?QJ&Az!WTqfJD zl#@Zd15v(jb9B0e@hbY^?mfQVG-xra1gl*#a6-R=>?XU_L(-q3g`SRidDv`u%Ga+f z*iZ0?7qgRBf9_X5ba7Vyy2;nR9Y6mhh5YQd%I8=34jx1NGkP}xeA2OWPaCUfbo-6L z{~0TR&ERc6T;ce^x*C2A_h6jf;qaf`EvLh=GfY zf{2WRjf@Dye3geQmxQDLaabb{)J9(Rw3X^KueJRfp$}8qeF^K>m#J!$SF}F+gQH&@ zVa5Pw=Zw`!wsnesN^PzmPqe~%a&zE^^H5w@4Klo1zt`sAA9*Qvj(w=5n!l!Fix>NPg zc$3Oa@Nh?n_^fC)^CK3u1jdvH>t)0W6>OK@yv*K$Y73dwvdnZn8hsM4O1m)?;46bx zBT93)%aRmZNY-900SygSPJ_ye6OtrJAxTRk(Td>Aje$SVX685-e;Ze{sghljmbzp- z!P`O)k*4N2mc?mZYPIsz+nU*K7gwdJlJ$~QYGusF;HYvqFl8Uw!ASMZWY zOU1XgaKKL|DIMzwng6ueB;7AB^M9aD34U*jH#Nb0Ph{JwJ{%o0_3gjbeuMunvM>u! zeehaCuHsIZKR1__cm6 z>>xEq0fUG`iC;a^K&bVej&T5_254Cv|B9N7h?GHh7LMbdDCnd+A&YvrbkYdYU zEQS3c9Tg0nNLO0)AQcn~9Zv@lM34!s&zTC=I+SygO2ax9@Ti^^=xr@*`2ED#&rCO2 z5@0^2H)Nz_4r>5Gp&(5m&0(ly2FEdS5E$l1MkjaVmt@38c$oTQr%wiP_*|+KOZdk+ z1w<7CI+Ga4hLO!lS`n+Cd+4o*oR4cBS*>B-`LCU2Mq)d$d zM+!52CIVzP2BZxKBn^GY9jnmS82pyxH{z0uwQZ*7bEsH?c15`6+0dvEpc*M`!iAFy zWFjMQ>Y%})ezr4GJIRnApvI9gjv|(0CXOWeTSiR2ETJ@YXf5)E!qZY^8!1IbuoA60 zz-xb!B;=*}AuP}n5Y{C5A=MeVG;GEiFxTZ*`GY@upbr1qT9cd@B{2_xKK>z_R>44> z0pf^1biv<`fiW`L8FCRf^qq*Nk}5WSC{Z@%1H-Z${!L2n-@_E#yMd*w5sa9RMh}$+ z)OHkLqcEd=BrG&GUq>>k&ygSUL!@-x7?TfyfLm>8p(y+ET>OoQ!8WhcZ882GZ7Xo#vEwZNG)(;+df5$Q9R31Z|-0uc8;G26Ju~r zjx%N%Kh1G?WbMnm8P157p`p2g9oK?xEb<2ljvEPS3BswD?9y0Oa!434A(;weq)gV= zalaWuh3%X&E6qoP#zhcGWM7Z=;Dj03@;T}$=qwohNyrB+4Z;BAm|WLS#QCS@U1Vd> z)>){*-~6Y;6cl^_--mLMN(dTX$0QKM1Tbi!-cNga10XasSeA4JxC&Z!DqYhOW+4Lg z@1IMEtw8}qb1A>bkn>d{DI{~{b7xQW%ruI4l9l6ag?C9`giae<32a#16)aVp;bltsg* z$DBI=?w6Z8$65jtM*#38A>BHVN!%8~D2sm|U!kCzC7OBqB*_9(18U4f2$BWQX-dCy zj>?6HD-4(e%nH*WJIU$JMr#hAO83%Mk5xTtpq~I>{yuCCet)Y{2S=8jj4Aa8x}C+&Og&k2EeJwJ?I^0;ag_&z8MP{^_e7G%K6zYRc}ZLke$ zhK6u=caV(`&OE`3fAAc4STPKI)n{F&2G>Pmm28`O zx*i4fkpJGsj^$4bhbHET$6e(A@n1b#W(WUzwz>!uq%-cX_A{O>r@*^*we)z8LAL+U z_>Q7ntr(BN|22?;((gzzgz4^}0$+^jg2k++zRlE@-@IV-gRe`+g;RhZL8PN%6 zKcax;!u_SPR)+x`RYT=Xs&`w0_$%IjpDLX6$Uk1;#)~?^%wZzsS)V7 zl`sz*A-E~K$+Z1M)^TcQ54}gM;Slp><*#~Gc9UxR2_4zCQ6v{agbkxmZr{*O zV(@0`yJ3W1;E8@2iExcaATGZqQzKsj*EBpxVv98bRPY-Gf1@Tdf#9)Yqks{J+`56#d@nN?D3|+yTyC+HTHQ_G$H|EN>>7K zS$0F7{fO5L)+3my6k2w}uDys?NYjbs4Ek-mAUg|AtwvU?3oB@cv-w{nxam=s3*DY<1)x zzIP=WL}bHpgH$7lV)jNl88^WvkHJJ z3<;YynnzXTcgz z5y>BD23JBf70fM9xK-zgV<=q1DHs}=4%KA6+v%DzmD3k00xPH{3QZh^WO7cn3#i=s zDcW0GJu9uiN1|*xSJW@&;cuvykho)}>2COSoWGNJDo^`wh`0lyLI4qy6D{lel+C8{+1QA;u+{zoQ-YXp=bBB^ke?o#^%| zZlZl$ZVOPfrO8Y(eJkxx2qKMu@*lV;l6a7^ z6<_*VWl*L4e??m+`Vw|L%KIOG=NaEkEaDyQ-5{7teBdWYniiL$tj&2@;Qpg%i-SQ! zRs{5c8(rYRT5lZ1tjCv)Yccm<(UwIVE64vz>;b||AR5%b<(HdE$NLvZ zoo-RNopfT+&KOooz+z%!uchI$M)2%DeFQLgDm)gPRhxhOObZB-t_E^O#NgcH3L$lZ z*3A-OiA75#DLQT1x_r^k?hK3b#MIuPrn#EXtJSyh$*FrJK*D?-oF5Dh>=lYUP{zPLseQ(w!}<)L`-b=%wIER z>Qb4$(PhWcvXUxU8m#usSEuF`0CySz#m1zv<36q$T|4HhgY$Li#fRf-V3kAc%Q-`% zqLJ}oYcv5DyNn|`|9ZB5q@%*uU=EM~dbVD}4g$S|cB?Iin;P&m)A_sb^Pl5qx8q(x5u;~+(Ti*qy9e^>1y7B<_oUQ)guc97qUAihroNnyh0JHxg!E#C_ zxErtF>g&`NFmMXL%CY&6GyG5QhYLJB3UIUr&I!gjUrB>YDrofngXopaPR#E;8KmU5 zER186H)4mMv3U%H15&e2e0b1?N+f zr?Lj9&+?ZSrYn2>ucEE5P=w}o&z4UsWSj1rUFT?o=6BB)?w$TiRh|vpyWJ;jJe%kj zJCBKw^=ma8S$?1%(iO?jU?~<)3D}Y1QB$MK>B<75@`gZ+7k^jx?~S0uxYy2?-(I7T zr<9)KB2#!TN!-L{N*=+}U$}a{)i^7Wezt0y!`Qo;QUA0bD6Mga5*iT|JcSt2bKwZ|PQ6<59gKvT=GC)I@#Kq8nt8=L`k}a4gQ6;t?IBIv zAFLO&J|5?kXLr>;9+H#H6pk;Xm4V(kU%P;&t=W8R zCgLtXv`wk}%LB}vl>?~yZMdRgv{y2_s%SVh{C<8jST$AEzfQ+vcy6@U9YC zXea9M-7bm=r4b)cq^o7f+X<-Zs;gU^Fm@#+lLyf%JTMGnA7tHr@y>>Hb#+=%v*Fp8 zKb>8vQhRApcV3%ZSUr*S8EnKB;?-5da2q@Z78QCKQtbzDaNnDwN*q&TR#zPdFTIaF zh)$tzIihRl&%Le_P5|Ed7sOc({h;)swPQ&B6>g<&x@ZmI{d{|xqj8Yj zt`}-Y&A3)TlH#@U+zY2R^iU5|{a_oatH6omt{?3SwOz{x>htkAPTieyGZ7Qu9p#0L z*gOWHR8{F#lOj+^nJ3%}-e{SWb)OHBZ-KT$mh`62jyHkbp4Ny|o^|CfSLJR2jXHx3+03^z^W{6k{OLhLc2UTzZig!0D2{rBrl06@ zc+pp0rCSpMZ3hy~Ds9%_j)SLV{Iu{WbJD)@33@t9|O!bM=aMsAg zL$4`Ac8U}uH?I5{ z=s{#B5%zRe61SJuWpM8Xplxu__sz2C?W5akY#*47KJlHDKyT>_B(ylq4#x@hAKL37 z)*raKULju!0o8i@wl1?yL!a7Sx5^C;PviG5?(eSW`7dvJl1`|~?QG4Tt=JMtO7bDk z2_!r`j=>6UnvOL1%nS6<*F`+pZb=L(IObhsSHR3yv#QOsI^3eDyvEMNU`h&@!r_v4 znkpKHExLc(F=Dj&an6h_oWJuaEKESV+lx~j76S3>v$k5~1(o|xlZL%bOLce*X@p;t z6$FbBG{`yJ!WfoybsmC5i5Kx1sp_Yb>XowL=1n5B?c(1aHsSlsO-nwvJK5J5yW$j@ z;eU9w9C%nvH?@~vKUL(a*y=EvmPcZ z$>DjzQ4}?o7bI;b6g<7}5;^s(pF4}V5-%2oXbQ|MAuqIq3?A#?y!lbgg)0=Za7jjT z=`ucGn-edd1PySW9hi?@=T=Q0wpd6olDI8JwCpdiiQivJJxiRKjp#+YgUTof24`WS zbTq}a;VfOfj6EKg`s#`kA^Mbau$mzU;kY2NSmd*2q9P$UD5KC&kg z(N1eO7f9kP@aBFOeYIwmsnWJ9@lo0uCpf0_MBAWpG2#QE%-!P2BBaR~7Y+Nz-y((| z&2F9_GCf=9h;3`O72Y2;gFifah+{c*rFNY;oD*`m;&zsX+6&(#o4AkY*&WiA*{0Y^ zhViJToQk)fZ!^KM&U1H&biMzHOpV$+(Zy}*k&1q$n_IbJW&GSZ{S)h+mumBIu`0{p zsGZ6Xj(HV)gMhQ@Qlf~TdDsPsB1Jdfg)C#7Wv(Xn{XyVq@PlqXQPZnRTIfbmeK zl3^ZH?ok%W^(qGnyvVyt~#u^tRN*J<+Fc@9_`g=I^e% zhOQAQ^w8n!3uoX(-N-0wHo7ILyOnYb#bb^VAAxV$GrZ0oFY7UG7lEttp{t~Tca-%t7H_Fn+|ag;xnmRc-YxRvsjc!{9&={; zoy76=y4h{@r~|XH(#h)OZOp}QW%a$O4t~g@PyWd5L=6Q;)_c_&uDwnIpIu+1sNSE`E9IG8ux;Muj zLJ9h&gZ%Zw=y9dxwF_Bu_tuJOtPBN7Qk(IO1ytkCbxX|;*Ee*F+s5(E18Ext z8{JH|`MG`7G7-hwsx4rn3^yHG|=&WtAayHS) zDYxSNsWV8TtvZ?n+Wi3~DbafiMr>89TT*j*Ur?@qGiNSQQgXVBIU7 z?&3H*!)$xYi|?`klgBDYWzH8Ivf%-vC8Oxxy~c94ungMc`|?PykW&Ci*#Xuf%AG6- zhO)arw;CyHO-xKo%}vQo%HNclo0J=}ho3lDNQj6yxM(z?ae=`?!hs@zA)>;1)aCcr z&O@dxenOI-J(K6;T*-wvOfFB~{44p;(;Dw*WLEX0t*)t(0u1`*F*R$Ja5BIhW~5wE3}I9bnAA$RQ|PW& z%$H`>YihpPO@(Cwg4P3|WtA?N&JU>60=ZLeUIt_si+c$!djn00oztA0(Wu67rCfOo zic{zHW}o!>nzc1+jbEc1D1+9EUohrl?uqof!6Py#UIMv6oEJJHr z`zueqrFrFcaUO7z9-2z6v-ub_Rh3B-Br;n3D2pC7(KqhJ^!R~(=+)IG`>FM&yIQR0 zMb-Mi#baw==Rb!BXDG{)N%rdf<=fT8FFb}UocHbj#THrsY@xw_n~nV+TL36L{-3FZ z|0>e5a<uRad;(hBs1RhJ? zviDpc_lIsH6C3}c3&$@1q6?|!U5!LYK0cwtAxRC1uyW@jmY9V3tY_3xOomLS>Xo@Y zJwn0^t18k_OMN4Rm@4Iqbn;rH@nsA(YgJf@!l?RFj0Ggkv9KP8rg2PC*7W?1H2$?3 z4-hIteAZ3Mwlj2Sm9fnHMZYy5i1hWxILySUuu8{fLGWzCs0NKDSIS1u;&E;Ec-6%h5pDv8XqXS|4|9^sz}vG-pk#_A1pt3aLf!#Ox52KR49V|?Q` zqdCfmQ1FUNqH~z}%-Qtn)F$OY;Fa^93DW;5@UTi0{wMGls{prlm4us#7KU)=?!X8n z<11kRidIo~&>Cu545FY%pppyvu$}J1F%~1zee$@y^tIOe5_l9JEmj`Tm+jxO6 z_f%XBqna=>n)oO1fYkV9_*0zfx6~xvN;6t#tiDl+FhrlIZ`5E;Jcy$T1X3)8)0P?P z0-Z^Tu_K8hE==3cQ^YY5noliV)j9N1E0G4%RC$$vUMJYUV{$A5o%vF7O!R3|36z@d zj`aDDXgC4j2Q|NgVZ)UnJm3{EMaqAui&P2-&H1u1&}BIU80O>%f}F}IHMyBL!x*tL zJWv}OawRmDP-%z+q>RFx$+&xvrMEr)kP|lo8#FheD`ld^1Nb|*=V`@WZy2%YVJac0 z$90V-ewHw#H>P!&`NI+semzovkn2k%&=L(abZUQu*7=PY_%XzkSq_p@2mrW(SJZ4E z8sEy0KaV1xF1TyDTm*)10QjfXl2y`v+%Q1j(f`xj==0i6=l~slPTWu=MIl%N?v1OG zKU&0+&&MgMHAYdBDm6EjynsSGcF(P&3$DdgHq^aLz*1WH|)53$J7bl$#} zj&FF@Cx=EVWQD5)16IWspChVK0%v$8x2*&gXSh|LBTDJ|jBN^b+5P|{cb|(_VfZy= z);`NqcDPj{(OajiUuJ{`}=;S?M=#`{*0O|lDS~!~{J*sD*-Voj6i6%wT zc^z*J7UZ`Tc((*ua4ATWTQ;l0$ABkMGFV{vW7$uG{`m$Cswm0MJd6|o6;8Dj@+vNC zSZi9X!2igNS%c5NRO)eT{V=i9?3IE^DFmvoCE36_K523CD%ek=S7mJ!3Mnw`yi+#B zsQ(XnhlX8L*6^{nN;t|4oq@~rbd3oz{DCFn@au`jZ#y8?dPE^x)UkyZ`K53liD3ji ze1?xoI=QS{I0*V=>&GR67nv(KmKe@Zr>Yp&(S$ z_z2@g{)azm{14Te;tW^I3~quzDvs2jh^OnALB_lG)SHq_S2?nbb?>P(r|Ydir|TlQ z##w?2bq~y%TA{3V&7SCsR#m`Xf@i2ck(R%$5m$oWbghLTlxb8FPAW{4AkCrWXPAp) zFpcF7>5R75WePOh?!H1uui>*mINg4D4lC-R%K~3W-9~9w!3fg~!QE z`~di02L%;-Wq;VM><(s+zA2ZSQS|&n3bC+B_ZPDUa=AdmuG6^3+?wRBPv~3qq^{Fh z2i)(J*Q)@dkH8|U!&v~+jRSm0EVscl0J>y?0m-S zNcPN0jVXuQAB+bifp#shs=u)Ai{^3xODTr4_7I&%CNpeyIG9Qnz~2CS2PDAW(c=F| zV!EM;W~T{!WjEBxiMpZa`wqYVRpo$n&rk1K0u=3P$a56=DnxgrxRv5_Gvqmhea~(? zf!;y?eGvJIZYo8_UoO=xbph=)#o$JaiVFZsfNw^^Im|L%ddbT zmbk?e_B@BDcR?#E>NkRCU(%!e;c+>`Z71v-z$1?1G|FLQgahxamWC8gW&l6`G3H_hAqXSCtmfD`k zD%>`dR-e;TaswJb8m97bZm5q7Siwj;L!Bz76W6DVK>Kfi9w31GM>RM(AkAUya;M?W z4lou{VbA;_WAEDejA!<(Ms^6$MRJ_6OXn~SeiDXD?0K%BGVf8B7n)Z5bvDH~6+oVC zqV7sIPE+HSVPe1-L?ll8DSRc7fZ<108%W0X1XnbxF=CWnAp#fS;9^G*;ljbzyVx5# z(7X9}&sKl1mX5M42dA$sJV^Xj(wOLCHzDbOB9dUvjG(fZFWE>SS*L(qI7iafkEWEy z@sx0;u$nKq8NBTQ`6P~H<`Bn^<`hJm0j%u-{Uk2b#-a9}Ynw={jue;_TMx^a9=oCf z*2ErO{$~iRozns1 zuTLaRGN>elL}<`4xg-T&Xiy5D!US5LUh{7lyMU4>k+?Z!!ZsnY=;5I5?S?Z#Q7z%s)fXwt*uWocw6T5T{7H1Pwh`ZtNFhW@DQ zvnKOv8H56>is&kjfV$FEZG0)z2 zxKKVfzvZa7c8F&9qX1ChITgfb;52#zrw^Zh;8TdqDFx%$^g)`a9!(k;CfZdjDnK+d zj&dL_nw=QWMWTu*&IdnER&Ug)H6M-$;1cIL#plNTI!o}Dp!#GjexN9Rm=}7(B>1dA z(0$#xS3?@sfa5w&n9dd@O_zcgZso2RQiQSq;kp*;+`+OzhaMvKw^xU1rX<>^iaW9t zXwgRRyX>afbrytbP8Vp|MC!Y2`f1}5XxB~|xXz8gaRsyc)c(^3p@!ot56BmR{vT7| z@f3n3D;OO%x|Ge38C}K>HQZ6`d)NwMq!p66CILIe6NusIZ`L~C8gAYo=l2@M{7;p* zt1Bzw*XU|$Q%|?+^`5R+j!+n@@r5>CX@9!{dD1Z@@wKyp)zzAUD1YB>4icGYxbH`S z4c}~RO;iFJySjEd!B9jU82y>LTtjQm$aYoj#+##pHY4o{9&qdxNF3g#@=Epk^Wo zwRG0@@Nr#&rEK{EH3Ep{<@@YpUGq1&2CVL z7ySN56zET=(f+wRT80PFy#En}&#^cnxK033ur#u$lWL$Mx(llJ+)o8EZ>a4aKt&;Z69_&v3fR_8#``_kR9(Wu$wn zY$dQn)(V>a<*rpp3diWNmVT}x)jCHXR27lc={f7do%xx{l4x!BgPNTkUv4CmVEz|=)l2=}F7sU^as6s`M9c*}J(ci{znc(fU*Pny(m^tijuDY`W+G(y_< zA;B8%UBqbi{HOS9wNgKQ_Al5*$uyxz?A3h#KP&w@o7R(Q9!PIG;bl0cNso1SFO4r< z*BeJqoaGeWHs~wkTlYta6+=F9GVg^aUndMo*;duv?52yOlH8Yy-X7)F#f!7f6CDvd zsMPj*y*||jIx#L2;S$pDH&A~gVn^rVa#`MbHaL|x=Za@7%c8Muv(rS-gK+P#aSZak zcsQR*l*bh~6c}}G^Qt3xFjX{XtCYz<-R&y!f1#c2aB>5o^YvheKOKj_`hxP z(ZQ6Z)OG=n6vrh`OU7%z zK9kl@+N{J5ExQxuuKW8|4#)zdFR~rO^>0^kzurCG73275FQWyzW7|R5R$WNV!3VnM3eoWiP z+v2zfxjmIBaU&gxFj)ot0fyLx0Ab@)^(4Xb-GS5+n%omwItsUm^fRvgAU}8bwhN6w z*9}8g^Qqe&T(@`3W4Uy9KU$|!voYciRo?edAG!$Qs5?eH}Zr$l9)72Mk9Cz$d1 zMyJzqY;nm1j*XhX?+$*{4sJB2%6GhvYUd zddgj>#|pdqH}LZ)Iqti>B%25}k;bR|C6=l-Vd}0Y?abwbZFYN7lkEagC7ePPu1HtT z4R8N5FSg@X>vwtz!?lZkI1ZiMu*rIsDeQ*WHG4BlA*o^Hg&o1{N`vmooZt?g7JY38 z;zjS(#F&!>iL>**q3cCV=diTL{rJ-`+Gs^MuXR?}KNnny4E866`+t8>G%k5;gB4^I zRLLq&rL%vtMf)VDEj)MYH!ol2BFSV^oV#6;I63UxrNkP~XpQZC-m`c`)-j zUKW8aWHr^x4oPu~b1Rq(erb8IF5;i8*w)FjmU_IWrExI5`cb@Uc_5mJ=%CBl{DLIm6zm{P7z^U{Uf{Cxa8P&+G!x@?iY#MU?*kc zR2Z3+*14Okm#y%2I#bcgXp+luK=po}TUFQ~zuM(9%uW128wq$6-nPnL%=UaxdAU$n zbD4VJUEN}FU-XR=|KyO$KY!3faX*5?&P-I&J4dw1zm|^RZuRtwl-ayHGCZZj{*+(s zv0tyZc9f9SeJw7RJjpte*Zs9+oUfYgU41pQht!sH^9~$4g!EQrZ5JI=ZS`>2og$mF4H*<_1wFfsoKT=7;U^>@g7?EP^`^5LGO|-@{NjKQFWpzF>Su-y>d#=@-}@h zm!#{1rTyGXy8bnGa2DyMRcuiaeDlH^0dnw_}mhq z4Jn1yWLmjwIBno16R~&n|yS*}PP2zf8x%H|5YZ_{^=NuSs87vBA) zCF1C3Td}uqcj9-O_q%asz3-Id)$dQ+a;fS`PTi>GS^2HI1>Wm#@wm6GO3*~zuPG<6 zj+=00*>)jU*H58kY-WJ{XJ{K!e%7}7QvEezb++_RdBlQWqX}-!o)13u4Z?ZJ;U&MBbYsP5%gL_A%@p zjIZi%Q?a;j=09NanTrCQV>x(|-bkhoX$uDhK0J9Iholq~61d2C@fhi3(c%%Iq6pBl zvGTFEp?4)MCnGPety)<>@v?skzo1sOL{8K7|4n-FRh?1vq!ITpySn@awG&0oW`1(y z$J##I&S#h9+1wp~loF@0oAmEgtgEtBAODa z%czB#j&nqvB-(_uykHaN^nf%$STN{RuQ_&oK%68h6mDsxT5*)wIdBJMW{-0b_E-zJ z$Xlu^FYQ`^Hh`HMacYlaS(?_imMc%aqq*gkvQLsKS`SUN)`oo+hN{ct$u6ZNWx@{F za_(_EptD12O6vapd_vN8e|<1?q3^OyW=K2R@vzDa_v@P{E^)KP^vbif36HfH9CJGI z6YqZw?Q;P``+)6%v{OtVATxj(_~zsVE(Xy9kqfj~eU4ac zo*r-^v=}=yh1zYjf{+jx`5fZU0x!iU=PRU}Z*IKl{^VnEXXhiY;t%ic=giejqe9hk zTok2p6}d#bp9?Hx>r|1*;O%;inyC0JA~l>9b!OO>$a@BXzmkbnl>%Qd_|b&Jb69@# z$4XvU(32OLnRZcAw>r&?_zC+7I%h^(N-QfD zrKrKZti)K7MF2F^a3R^7mGp=}tg!Xq!?Wna=a%&w4&;(MxN7-L!q&914+FL!?$rEc zVdq#5Rm~0;QXfK|H`TmsWwCD&pTdhN457{|>PZr&qncAgf2LW{2KVnTWYZ3V)hvX> z#*UXL$JvHqCmE!It~2@*R?`@!)a1kkaS~c$urdVH9|;%QIGTv9x32Po6$yuIB=EKA zxX&5a5Xazz3188#>>b9A||uy`h*fSmlM8l{o294UCMIWg@*_*ffNtkYB6` z|L+Y7mG<8!_K~3+A{9oaR>QCu35@F_z2FOqu1u495j)TGAAS^ zDLf`BN;oDa2}LIwdL|2{rxc6mo<$-YNw6D zh+WxpAkU%#{Erz*LrFaAaxvGR?ddb zkTHzjpA2i~ALbAnb}tllPlWnkc8uaOHd#gctg1b&iao73`|Xkv&3hB7w4nUP$iMcaHCqK+v%O z(@9VldwMMcUw{pRM!8pes8_qnzg+>q9yP~%;^|? z@EZ)OsK0g*K#jrrC&{()LU##)kKhUyE?LT%`vXj|u@uVYDc>SoVzF2Xlx3I=5txmm z7*^29Ioanri~w2`{kaST;Be5q3^1)#xxSe@j5c&P=ojkSDqi{Js;A(G^eKH7Dal-* zO8TFT0jCvPz8!-490Zb?AMxOlhx2qMNX(-M`WJUQMeez1-2LMobZT~TN*uR9%8bT} z_zgpqf#=Fd6xV?{58)Kkxkm~WF74`f$iz)-e`_0UhLa@Lx_tyhAr?eYgxDtiE__6k zk@Box&2Wf6tZ@5(G=_FMvRp;!sy|7{`srJl6V5)Q)K)2PGEkh(VIEPTAql#UPK7o$O}#v;~M!qTl~r=cR#47NslXvhIDKW<<@hmI%RlG z%D)jJl_EB4Wv>#Di{tY zk3LP`GzKMn)3xbQ*swH!&A{Cr1H!Rrik$tVJX}rJt^It_e&z49;Tx&%>bANoIt8^X zfj}?=EJdGP#qlt+JiM8tS%{Vlx5jQP@WOAzPXr3q8T?bS!H;WKjw&?2LDm!YR9J)z zj%~-dvF7`K5rlb@%}nw}y{Cc!52Oy^hrOqx0T^h6I~H@0z73X;RMUj?d5Y6=AnZ;$M$)mwj5a5{Bm|=<$Qgl1PJ5GbTSebNj=h66x)kIW?97a{XzZ(?RZ{9`EaWLWi01C#{L3|cFij~C3#1PpmzFl zKtO3QS_$$Gy=4lPm@r;!J!36ope!JOH%od_BzzOvK0*{CwTyRq0jQZVkFnczuHb`g zC|I}vYur1Kf{okV&oS8_mL4KF+3!5rFPRt7pHpqf(<#$wJ9Wr&HK>~QJ;^@e~xx@;eU<$&h zKxb9L-2?O+9D5k%keNw=CE@$pLoS+W2R4TcAQLs;*g-M;+WaG%i(&aB$kKRdUho_+ZbzkLj>_DBY2UhQkh zSoslo<;ddXX^~i5zZ1J!Vj|f@JT|yje}%xL24mG-+i}D)kXu7Db5MCQ6U9?&FadSuwie@%tEa zYOcNAL|D>DX#0j3E?Z(w8yN<4T(5zO_M^q1TZqo5c?_RgwL9;BS{qc#_jwF3J&K|< zNhfgCV?t`^B#_hlMWS!!qQIz_+>nK{^tss)z}w(FfUOV%6&)g}2y%q&Q zywXR^lVn*@u+F@OODdBMd9)8dCz$b75=~p4-OunNFx_?m-FC4fhZDY*TBz3Y7A+Y2 zkFZv)g32Whuq7%muenp)bf2_zA1_es<^i1M37qCWoaQl{<{=#DSyOoMGZsjDt_UBq z?{S*nOk|A{(K1?+6ZoxTu-g8L3g)$D4|`ZtGlPbPP5mioPdaOUGR$Ru*6d|YB1T+|l`V4F1W#I0J9?Mf+30kw+EoC^Rfx7vnUCyix9=-A=2`yl%~HHWeCCy@3Bp9m!ve{%}Vc1 z4sA28WWu6qC+vY{B`uRq#g;iF5@zBtFnwO8Q?+_HGl+%BRdq&y5WURyIvh{x#cgq#V@*1thyPmx?yF!9t(FyO?P9^ zefz;)4;PZPeY$rY?!cM;fXJUwt!8o{D}r7)Z{}{DqSYQ^S)yi1oSftrj^%kB7+B!&woGEIeSMDQbPD| zoewd_eYkbD9*ZxiOnCSz;nXZpWEPl7vJr=+B25|_#rXWAsAEB3N}kY^IQHg7ecS8H(Pd!lZ=(NEQt`KuDn*4w>`kmd~{ zD@)K$_}_0Hlp%{+Sb2DQo>*yZ8l139du_+%_t9!_OSEZqwGJ}KFB7iYPIsO_*{`Fk zL(YKEW5TSwxmfP^r2mYJiBd|onDNTyshdOAo9F$fo;o+`;!h!ug{z-&iRDjeKTqoJ zGQ=1dnJIjOA<5?kkyt2t9Ui zRa;mR8L0aMz1dPRofQGE*RsFXvzbOa7YEJ~ciT8`jQ15gf;Bf%^k+bpW`DD20>txW zhjjyIhWgmT+7ePV)g61=V1Qo*Yvh&5bUno~hTLLX-z;>6hu}nt_!(})-0Pd2qx_wJ zXU*m`OqN%SOeQ34<6cZtI?86pb(8i{75vkt7*>Fu^cq(9tY7c&)uzVBk(`@M(a~%3Zj{CF?QEyrxTeaZ!2Mj5 ztA%S*)oc?b6Ln(|Rtwc?sf$m;&Cpeh6~GjvxA0Qw^~ZX%%bsI82W5 zR6_6W28?HWP7;osjX$i4BGc5_+6VVf^ONH2K*IrH!^}PJ*e5kug`)@CeVRKG2ZJSk zJgxJO9qW;d{>S|avKJ3VFAu*|9#St32iD?0+wI~slYAcMM%qkKXqV4n20FKI)_28s z(RbZ~us0KV`X>F%Tl5@ld(KfO@_MBiYrsQUdnQlP7o?29FGIiw!9Rel>4?XdQ`@+o z*RqD0EPx@%k>{QAI_#-0>R~1Pr1+4)4lwWxqc}*h7cgH7u%zJ&y+JDr_cBGr>6)JY z+!Ouf*;IN~^t`2E-!zhk%}-_Hj!d8p+-ovj^DzHDa^oxOe~x;?HQ zRvK>a$+J`6^436y9g!b_Z+? zp2p?eKl5c9_F~)Th28a&?{!|wbL=#$i6DE4Ev@SE8#=arYw~GYlwfmxS4QMRNj{K% zoszHa2~h9xP1ZfJs;8e#JEurdI$vRJTc1fCJ8y$<46etVW-%AzZczeVwx4fXIVT83 z6OILPcuET<-!J8Gf+P0Nx5cJBYmXO9eEvHNy~8~0Ew3+FaIH9z+Y!Y}5VDlm4> zVV|2p#je;NVnaoKh>rtFBOOUv-Af}!i2DR2PxzC6RwXMpoYwOhD z*&&6NhVF6Xd*kGwMG$jG2h3pRzHLH85a>ui=J22+vo1;i0f8o9krC!>gUOi1Xp~f) zv4q;fK2bez(0@n}t8<$xVb6VG+PYssfA@#9NiNw`Do%SGjVH_SCm>KPGjS>G~QpVpL?u7SK{25tUut`kY@cwF(i<6r5WoP&}a>4 zWVbu&uTYqKR5v`4TCF0@3u#n}^0Sck>6{n8bM*lb)Cd zo`7|l?V4zvH(G?BoAw9EvX5S9Vf6UrGR_*f9a1bM3GoolZ>{-vt2`DmSURGsr0$pZ z9lN0<-dLqj;>PE?_!HO8E8t_r1(5>BD}`#osj&*g!Z|_R{OOdaeqSJ`wxoy0nu1sM zwX4~aWXfySc%MJ2yS`sOs!}ci;nO`N?PqPrN6~NkMcGlL4kT>WMME{w3`co2ng;Gc zR=w4%7lq#02IkN=0vWfZyYg3wv79@fz8ODmr{2sZ#4veZ1clvzJK1TiG$j}JWGrlE7@VU9hjQ1T^s)p+;g^2N1xY$v^)zO1`5*-cw){Tcax59*i zLF%Htyjqo^^q$gspb$Pv;pgwgPW}&)Qb(X*vX%ioq*=vp<}$czJG z3mS4=zm|-D;3XM1KXLJTe>Q}+Po*K)a9Rh70>^NcHM5*7*Kc7}+Ny%CYU@A|xkSmJ zo;Ql9?Fsqsq+@wbHiM^)Lt2t(d%&y=w>zlgMtK>X1wtgiImaoVOpzRJx^=C}>Sym73 z;_U1XKE%@`laBJ4yGi``bFT>); z8uesO0c+(bPY^dA95nT?#!P2WKDT{>^2fvY{GqNzKtZT>t zXp0IF+9>$HfFxmBWEf%C8Ub1q*O6mN5B`;!piRf^w7h&Lr>wz{ON8ft#1tQqh($SS zT?AqI&gRc7Mo%C^IiO`oa7$v|bruxcvy2xx$<;kIz(aPb{Q4;`iMya zM{KZ56Le#4#C30zu$lFecgY6NJ>-(Hj%GyjaUC0HqkB23aHywrR!05e2S${NG5rk7 zO7a<{8wCTf*l5$R2Tw#4EP9CVNCRwZG1OLTbyNEP+j+)#4ubN1>h-{@%=ROAM>ugG-C)y-n6>HblLziH`Vh{EEoX7r%L!r}+SpG;=@ zi~=NKRMRb@5;>Q7M7n=cm)c;fS%TN;3^&Y`CmDU|5L;i@EtGxH~zPX2}VUh@huByzz}@EW6P!wK^43wf`&R9dMBGL*Ad^S za@|MQXtKpgKq8h&$7q^ujz!|5-otjHoKI4&Zs4`Hetv$vc79!r)^6mA*6A{ZMrG^n z>E`8*$7b=#W6H&=>xuVhQ_@kyFOW+!j=YtNKhL4Vfm5kQcsrwg!!+qdc=M!I(l>9p zXXx4mpx#M%XFJnQ3eCos8hIn1EsCvIH=Ic6ltaQ=5Jdk(;5mvAqqG81p@)7IbAdQA zEcETQJ1sk_+DOn3V-5ZEp=X+1$EFh(WTtxoot8)0<4B^IBHEwUB4JE^Fp*<1(XsND zC|d*jY4j%+IJ^(9DY6AGL}n7NJ3OuIJIGhIa9{(iv~u}RV$dyH%G=j6eS;bo823p& ztpF*4WY9q4czk&pgn5M~Q z@j~nGr2nE^-40tt(fU;4xZ2UIn>yvPWf{j*5Ho~eHvMR&${elPio%=k@Patj1?8RC zac&%lt4hHodSuZD3{U%BJ0ALJ@!2u+%vky)5(6=_!Cm7Ho`~*{p$^A;GCahM`R$Br z8I$6i^0eCgf5d~?Axjz{n;y7?S6DdRwxc{?1HV*t1R$_YK>fxmLL|Z(s{27L+i_vr zkN;sv=rGOz46Sl-iu8qsRq9*tS33HqZaDB(ISI+f*72eyZF>lNREV*SFBOb~8EVFe zqcHX*btqFXtuN)AQU_%9s1lsZAW%jiQHT3=(F41M8-s+I(hV+^uBr$3ZI~SVTgWo0 zM=({lErU}+b7Ktb<6AE1^2X;)m`GAC$9HO%PqPMNO!b+jsB6#(fhk<40SZ}aSq_{Z7-Y=GAZ#@3~+t|OP5ouScbVWr`GN==<9#+m)@q?!VIqaHgvA+W46+nhqn?4Tn4HCpc~i-qAE23n?+3@<%lC_+*gBg*BpZP~ zg~pc|#oYdD#f~GBD?A8|mtYnijZYyi=%dh*L z$50p=_j{i+4F;O{CSHv9muP<46vb!$$nq(5qX6MzmkU<_IY7U)Y?urKmMx5QtwPng zANqXQ42VMszuM5Zj!qSnh?;r2Bwg{3zeHY(1EEaO3zA} zca0R6)Yq0$c;}KVxauqq^6jPP7PXtZ-eE$~www)!9^yljS)$)DkoXD6b)liOE6rBm zQN)r_2|h!9?r;9Ja}*>Ky3hCjI4dZ<1mXDdy7eNkf_#gLv62WG9Fg8?{QPr%SgfO(g+y)+wWF;BM3Qci#K-Cf7RIO@#j zcrqMlsiK_pP_(@?553Y-c7KB8?e(3?Y#`=K`wv{_B1hlr^NX}3)W?<5L7Uhgzzo_5 zv;!SUvVIk-zZ+JRhACJiuI3zzGqyteqks-09fD`h|k`U?S8j5nQFEnTD< z0{GyF<4gfv>V#4JDaE;MlK>q(W#^L4R;{>q4}J}Bczpv(Z;|_{p*?#CyUKVX1Y0cy zBZ6@kA5Rj_AQ@JVf%C~-zcz!q>w#ubJ6|YWzlQHTwD>N0p|^L+T(9PBKNR>bDZ^^H zVYEL|IeS!WKSVUEatdEe?h=Y#8CQM->=O8~;m(EosU&UqfDvd!@cxV1Z-5=z72GD; zN~I+yKi7bvOu5B61P!%^=+wUtD9u6aclBqxm|{w3N-S(5kUkqRi8P0Xgb5)s#&l>T zLKcS4#`iu09V;YiXIkk}hQ-;o7IaJb8=$`i!?BoztPrb$g54gHuvrD4RPd=oO+eU% zdk&%Gr|2Z>tO(B~a@X4V@V(P=ZIDG|X3EC%!D{EInSRhhKl#ZJqOdWw>r&<1Bl)Oe zkTTYWpL{C~VrXK|`bH)cB^F7gK$;Eqhm*_8(0kh|U;z_dyxA15te(jh>^(5pqXqsQ zAbie0d}_N44&H+yo7NN8orNA-28`B->v2G91~NZsD{dFUoK72%xd`!=qKyiIl`3{a z9#_G9FHiGtV+2=*@z5pFKw!jX8>AD|TmG)2bmcY36aqyw$SeXUY!Z-v2JTYtu)y~z z3YstDDUvXI5xW2y1tOTRBiqCpyTWUEEyOVwVZ*l5vs@x4Eu{_{bL_r02Y6gffbKiI zR_Qs=o5kY{2S*&VFVCnJ{+3{;ye#87-0MJMgvKGoUZ`adWf(FYQ4O+k?+lv+R4_k+ zctKrbo_Vwy;&?oQ<6~tLkhW};#&basuA9C<|H?|xzv_c{>%-X&%NP@)YnLL88yObd z0w~L1tRsqeb;2$`dTOgF*ruBGx~KlkFoHhmb#`kN4^o1;o!$6}5bR!e9y9D9o(GvJ zGREFNBI!;4u)wfh_RU-gy|Jto^97SNP81Gd4MsL+LlM%;`5Izgu~L=TNz?GBw3AQcvb|mTDcO<2EEUdB~ex;0kpe& z-?fIO_b86}hZeah#PlAetNyT{T>Wtbg2)sR$DLtJE_)^5UT@nuG!qzDg5Cko>lz+t zS`5t0*X1ge<)J|4p@`KqDt4A8cK&W-S5C-t1$^+;d9Q`>j2jU~>thJoVgK#7r4YEq z61dd?&(GXv&DF<{w8QSV!;ZG+l)1|u)_lQ-(e(?~lSg&+T>Ykw@%t!51@u(>b1tJS&`b5flaPH*5I$DkC+a6$5FQ-M08xL#}PL7>0w{Z%yHO zDPfRTNox#bR8rDeI%ZNH_z*n=R5JLG7Wfdk!2S>T5GZWaJ8V?L?+x1oS4X}(3(m?| zTw?d&@-yA_8sIu<=Ly<~WtF_91XRYfjSqRSmD9zTqqt+bX81MAPeBauatS&WMR-~U zoR!jtXPIMp-peJ|_EcwgqLp4^9@91@764yqC|@ClM|v^aXL%2^vy3YeVSXrRY11;DYC*&e5oSYJDpR(1uC;4ii$3#}z@-|5Dr4NuZnpLq zOJ}sT>okkLG+XI`znU(-7CzQH>)#(ABel>F;`W>tHgJ~~eszPMH{Q;Sa9JPs@m}gx znYUNl>A`5fL8MV7PIDOISzsWvm3P`q%27Ylo^{Qq@7bE9mFcUmd5N$2P?kk%|Iv*M z-K*Vzu!ZI2XZzmA;zGiR5W9n62AWByBMIWy#)|h? z6~|S=_w2;3DMZ)Yz-^>&S(xaWLTI}b(#IU$=M3Hl7yg@J2W7_r$IRb1GMbg^*G*(eu;G-Xt9%AXjxIK2p|pXZQaJ z>^?@qg!A6=LdiY_frra%z}I>V{YQWh*0#?aS= z9Yi)Izm-=IAv5I8OrSN)xN#WcxX`BTfnFg-;*A)R)#msUzpB$pTimJ-0i}nTrH9bD zEm$+_xaL+8+PH)Z?ch3DT&w!J+KO&nIu|jekDsNFAi{3~xgOC#7yI5VQL$}UHV&1A zs&)@?+ZH^ZBnC#oJAl%KL=Cv zs*lR94nxqk2%_3VLZWet9taNGs zsSHB0Kmv)}`K19yq`zNqQcupe1}48NiqFFbrP9wTkz8BSq=UTbtlu_xRN1e;o;P@^ z6fiZO_3h0hy1d?PD&INIYtNCdPpL&R&X=KAhPs-xH^elb@!b~)P%~0dOI3d^a$buV z&{4M|0mKjNx#;UxCKHRpJR#s6pd$M1pMlGx-#qZ5} zQanMUs~8<>BH1@9W8_n~J`#7M4X^0)SAhHdCn4Fl3o4W_&ho6UMdlx83rT?B@&+X` zB(MAZVYRD%bGq`mA=@szlQugoM-9uq;b#a*`Pk-OArj0gRz0_8W6&hC7mv-`Q_=>H zfgR7gn8#ll_d!SNVUdmq_e*T=`mGLH3pVsjPGKFT@@=b6X#<7jp%Ntxldt8@t~Zp5 zp1|QqBFV@61;>m9wglVvPmGrfl{n zXF*x#k~+8QPl==Sxi$M%{lbO5q1{i$yGK&mCwsY(9~zfx9av{!e94-4eRRwYYAnuz zTiFhar;UHY2RR0}wx~2UwSA4dA8MF~bTkU_J%8?DblP<5lpkTc6Y?NDOd(2P2YqO{ zofpAnRA+|i8#fGIz$uc%%C5B)_W9c@JSM+O`*F-~?X~qt4%sEIgwT0~5BWrSeMPRo zIQ5<<&&Mx+u^#(+PV%eZ+*HgBS&EOk?rgIVG#UInx>{VEim>KxygjWoyL6A|F`T>O zyS(9-k_;g8q-Q>TxCo+KSf7W%lE=Mzvc;cmts2g_+@l>jGLWeISntDlf#I9qJmO_! zjd=Q()QAXYsd;wvXuYnjhTR&?y>V{yzEbFUdGLTG$*|VGo{-PfS?I>PbQ~|~boWGk z*}z^-)|PqqF9WHlLgTG6NqseYIALXFYM`>p@`+ zL2GLC$Jct{R!&>j7steB$s}U`XQ@b0X7k%)(4s!Un)!OaCyU^BwRcwIWy;s6ZFbtR zbGi1n*!rvg%d7Ia>|vvc|6J)!Lt{PCz@kgDf78Kf%X9aO`BU5}_)ARlj=LtRcN04v zQwTF~(%HH(>dei;@(Ih%eSKs~+ngKa;DxOQ3MxQR(N1-%pTET;B54eu3})Z1_8TumNGWu*wlqvgQj5S%230-b^D`xWYU@i0I9NT z?jp;+&y-&!t!y;E%ct=Y`s|G!0*zM5=6q~$v82VlBtE9i@UeiIQcQTwRUn;tJc7@ z|8;mI7j@Q^={H$LqAZ!|MLIV%!N33EA}Y*^MXYQD){!;F`Mz0P;Nh+@(V9x8&8sj` z;Am}2e5J^sytmnW_UZPHhozT-w=_1DG0T3u$T2xZ#<7?T;5gnpbmDQ*$oex?wa&md z(RFsDeE#L=soAmVa$jZa(cr9k>-VO#?(jKiHdU3T+V{xAnnmKt`E(Nj{66#LgP*t1 zmvX{>6u}?YnTIJ(Tb+;Zv-*7*QGwf47|&jS&qbRwZl-X=%4;tF?$Ezyh@^SB?)#^3 z#kBlQUDq5|Pn^tNNc3n;P0hh?P?dbQU{-5-u9n1iSPs-f2X9BCPCNhPho2~HIQ0%L z=bGKwcZQIv*MEO;(JF6t79P@Get9H1@zbxhW4jWzkn(!GzD9411eG;uF0ylFt-m69 z*)ODajC@^eto!Ab?s9YjfuT7$&jPD>K%iEg(-wRb36)CCEj1nXd`^r>nh0D%^-PMT zOm@2M4>X`l`79-iFt#s>zA~ymcAkJRXsF`~#z^8W3S+z3l;-**?-KDN`~`m(r_)tO z*4EL_rMSBUXL&S=1#IeyItw;i|0;a#O@E#Nrw1l;XF zb6dAMwMnjoNc76@eb8SLE4H<#fhnN(Lx)c0>>NRgfh8p>YQpj<6Lu10f)&Zan&E=Q zddQMWak+ij_D4QurP)7vruRNiB(E+%>NfHINdCec3wsc z&AoNlJyfm+$iWP(HcLzX`iZAA8=7=+KC zcEhlDhiPi~qhwonCBw6x$#Qv`H73^?&&bjOR}*Gf{krkBsuH0WLkjC|$F)Cf%#$Bg zvVXc3IU6v6Z?Z1G!rRPV!*S;b4BtAeOs)(kyFJSi;XR?N&;VV{7i{&tt5*o)cB*qI zEX+KA}VF78fEcI@1L`R~CBsyyzS!n$;G*jn*1R zJ1+)s!4jUXb5LYlt@QZ}T%E4*k&&%2oB{<-6lYAA`XOC|vKB7=uR)?49PgfYF!r`(RwAF9DS5?| z=;^NPXJ-|MzK%^QakKfqxD>Tdcm%UFm1_H=Vdmzq&*zh`^>lQ0zJ7-)TLmXutQ9f;ZbtLzh-BLfLh zTf=jAyQpK_*+>b)aO&gz_Q;YH=dXUM#uq{0+~fi9Ig8NuMER_C@f{-TD|qFOU=L=Y zrNs~4%S%LM)Ax=nYB!0|eTJ>d2jWPQ^F6CUjv`QdZLh#1wf>%gLO!Qw){ zjoI!eJv%=i)S8dKW%}!y{*!QbPDzO7M^bX@m&7x~g5acf?6xuI+f&UUL+(KIx?J>+ zB*^X6gRW?d!DAJXN8NRPk9p*6LU^Dt?dra!zFS4XDN(ssvj1h3A7!7J< zIXNF;dNgrEQ0*EOax55+l=g#{5ytqBjTEikg@vxS5VJd<^vpPqp};zsf(%~UXnu35 zvGR{SMuqo#qO9)PCYNxPsw??qN`8 z)ySF&#v_a7NZ=eUh%m)Ni{|7)<0c3NqwZ0%eCcbo#N?22z$UaZRzb_k;RTecB`aSV zP_FO{h9Z+?4XIcX3%%@8Dp=_r72q+UA2kX>pf}2=@s5b4xBHusStvjeg~lbxsQO2^ zE`-4kzz|aV>u90ma=5_INvsr$Su>$iFyuU-;*udp=lnP6v%JLhSImF6ABoPfx-B%k zABI3}v0A+Bw=k*n{WK`#T}XJp_^HWjpF1jA3+N<(Mf|D4|LUiba)~QF7SWwCauy)!ouK-N%jSeyuR%lbiD(dX@~k z*@l%^qQ+!Z|HAJxeq_IR`+o&KE#H7S<^A@JI_CfXR$%}8qN4vJU>^C6m;X2Zm&j*d z)kOhq=?SS>S3yDJtP@TT#YRXyLl(ILdSKd2;}kpAM;u;!GYzJg4zof4w8Ek6tjSEV zesOiRby3Z)twHVgyx9iUx=mTGiR^pllMz##_r+6s*3t76!x1U-Rma<<$CD@DQzv`e zb27D}A~iq1VgXWxS&_uFohhxpZa8LMw?8vxS&Z>Si4i$Zym&ostg6omVDy^ST*o!o z?@#^Yc-;m(7Z6J?O(zntEW!G!&5zURg+8A;30c#e$3N({QacoF7=` zL(M3o4n`Lu8ry1s`5Y`iFRSF1n<9nmw$QD7=I1X6#IVG$UsyZOHiM`0HtW+@_+_c* zUMKK?y@j1cE7Mo9q&{J!#DHCasr5n6QXS1IjPv08^BKZaKnkRoH5)T%{2yO>F)RH1 zVd-E!iU58t9oqBIvw?4`G~(Z43h&pS$lI($cr3^iEb-(ly=Xtf&{CnZOCZ~G{?>n*fE51pSF zv9|zkpN<7YUV!d=u_%Hs-w#bDU^S%u0vjQ`{C11Qoa>8en@L&xAi zjgmPUuv^MiJ#h-e!;h;l05F>ZFlQ;d?qu586>UE+0eq;FQ?hrV$f< z%RJb;(2`GKUsPUZb%(&Duo+E|3^&y1f+WkA6Tx3!$XL>yMpm7O437kQ#xeQimdr+uinRutqu~Re)8HHW|qz zWvm-7?NDz^Y#!WKU{TG&^#=$1h7!7O&WAkO{A#~nLu_04wQC6CU+~10u`kL`##l7f zvIF73$lcyuju^W<&KL!Kju>Wr&KNub8{*qN4ix=Bv>3dp5SbIbqE|a?|9<;yq!=Z|*)=~?=?hYRK++Y=7;jfL#IF4c;6jjy&KNx^k z8d;URa1~ujcPR+lh&iDP0!Ed~h&k|fmEI+T+9iYBg;)00HFp1De;Iz}3~8=MGT~pFI#&^u+{i+rA*N`=s!C>3QSxP79^Ai;Ytckd_*2|1HPrA>L4ZV z#o0!&KD4$!9`r7E=#UM#`3&svs#ss6e!VJdB0Fz!_D>xB5h;EWD98i+{=W<{OW_G zx5hQQCa7}0|Dm1h{0yxF6DlebjU7|~s!|A|%_*ku!lse~LgNx*=t4ZEWazu_^SYtG zmKAlZQ&y!>NpGbIb!<_b_Dx!E<>`A%lJ*U#w-V)3lNHRe7LCXj%A)RV+9(djhL$=Y zjWyJi?^j{+zU4QZ9T$d=!PSP4;in>qZZDPDPnzN|@(qf3602RHx-^OJf2ihmJBEOE ziJ>nO$^P&=Y0+a z$4OU*!KC|l)vK+z@AkiJbi+-2dYgiO9P-GS)4P$zw5Ba!t{AE`5>QJ#pst#T#VHM> zaOv37xEP?TaO=V#4;bBa8szTRJ`E~7)ti`pZlXV&Shq8=*WXsQ84#Se5ifLB8F{d>T2XV1Jb;T?Cdwf3@-%qI@C@iw7M|iBNjLR#^@~ ztjJlh_+k$EnavRHVlSk)hK{9PHKI657$8GTmO8hOwcPJFPu>>p>mL2Mi3-*RRes=u z`*lt;pDq25k61KhY18HG+(hVQnZ?;*bxHl)0+^Xu^p8~-wQB_XG?=uQ%R^^+AB4v4 zCUDMW)UDPYzY3eMZ7B6UbxYp&oNIW{5Sgr_n$LLnQ?7d4X%l>0<%3S zI17G3Bf~{S_ES~kp7yIN)s437)^PX9Fr+$LtYto(I@4J92Z$i_3FrEw<=Q`yOCSYM z^1{g4T&7i+(i)&vIEH~dmO|SQEUQCu^x|&wfp2&A-!xV4sifd%WN(aaJ}MlB5l{>1 z@~f(QSzDsdh@e@5p|`@5+J}&C$iiwuQrU+*=8owE&T>E{{8EAq0han?Mu7YxTy>8dbY?s?RhX@K#lX059S#yt6^k-3XiLUCJ>C^VxA=DhKtba zT^^7JW7#=~@dHcuChB>Z_dg?{pW2NpQJxtXVm1-kQ)LY3U))W!RFPGRlAb*l2CEQW z70CyAod`t!xZV8)al1??{a2pjge(XXtpwLnyrlbY$MXQh=6jC!-kzk^LNyy;Ro2et z>)}lz$+GnhXN3Vl#JzB~Zng%pP|vA>?q;?ByYH8UO5l!ctZGho*a2)I5hv6LLm0kv zzu8IYE)AW+wTN=}tDegQo8_AS%eJfNj@BYeMhp}<&imHKQqZ|w#DNTAlx9CWy1{n?C!)WMKe0g$?!N8G( z(zrscUScdpT|#+sp25KP?oOFnJ;0;XCdFY~0bRF;vpVC>M7J3DJVQTo z{%c2YckME6;umGLySUCx=@M!6#ttfHwPXiaTR8Y5P3`!>vcduA23TdZan*fhhNw{$ zah)`F?Xz){MPF#`C2Vbm z*q30wQ#9=EZ871?G9l1Iwg6VI7lLGRUyV9=Wy-BTXcDI|YQN~~!p&E5@T_M(quza@(Ys`n!SdL9iB4Rc>6MrdFEG~x?o z`$FQ>wG&TxiijeCPu}x_|A9Kut|NA^6C;u8){|Fz2>N$o$Y3I%_Ha-54kq`7bWuaz zy}?I;naA5pVRjQYGqxe?asIQY{3E>lgZBH^yb1pmNrwCNn@a3ZIfz#DgXC2nlv;(+ zlbf3}%3mTSl5Mh;Q^PC!HbDywGPclo`!gTk-qaOu@0OjvCPcO&QO)fMfcZh7n}~{(g8rNgE<+Vp@ZI#wKt?Vc6BOQE zBAL%*Hhe56>rpZG-kMB&?7AYEbLzU{IWfEO4Zri*>jzSnvS1SuE!N&2xa$Ca`p+Qn zQumv}@R^4jhrF}1um{B(vMI+-j`aoi4JgzZL@rRWx?vfIE!e;bw`N;+7vf?8AJCA)xjq=bc{fr06`b{a5U%TwOj0*8 zDZ2xUF;EHQ`>x*A8>BZy)&QS_UwdBGU<&wOQ(qlq(x@slpOQ4LI%{7yN4T%a5xzP` zH=|5%nl5=d5?+6At+1yd0w_Lg_D zRq`?S#4_~_BZa_vuj_M&HZ1sIEae+SI?aQ}`>;Qz&+(oA)_6+i`Z`LHTPhw-P-N!$ zlcfp2W-OuO51W1^wyo$N-Cc-#(8x}5$5UI z#krD9=CUtXV`i6|t$C3aMUq)=LCQ@RL{(>Nrl)_Ol&)x->MBH;BJoccn!xzny8#v4 z{+;b~FEgL7GxjOh&o|Bw>My0tDI~2|{71LkX&q;vp)^LDg}QzowmUsrUiw;n%KBO= z_uZ}usJj+X(27s>c&*JGx*Zyw%wq3&2wa%0Wp$J z&A;p(avr2z-6xJHvJAOSo?sVYc=hq@OK8ju(AwfQUo1@T*KStqA6)N(GDjSy$qK|r z`Up71-%A#;hW4D?p5LZ!iWCsEetl9toGF0|%zJ$4w?~SnBJ%6>J-=C>6+eXj*N7|U zwzi7c*10vmz2~f&;L-?$M;Ok*`Yp(CJ zJnQXYjc+O?>Vkj=z4h2(`RYK|%T{Y4#{o$g_47r6)9d7W?!xpdBCu-<9TWL&bu&Y~ z9q|nX@aR#!bBI-XApUTqjR4i2yTdmF_CbSASne!wM+~bum=(eiP$hJ@e0$BR(;v8| z;E?6!U{|@BvpApWwtL;D8Q-rw>Sp>>Vibi~;%WKb#-p`tRPS|^ANTKVDDdEHY*3>h zuq1Q=0y8lmPPwv z4Qrmt#eG*|Y2z2Ol{}r~_=y`khOC#;*1A2wt3nPhkRH#5T+7MV_lkSWY*x8MRlD3B zVhXhF?+`xMgf#cT=86C}k(UNNq9 zBlp@Xy+h^b$y(k+P)TFSudpYJW&z7$*@(4(-O!Vz1%^|HbpBp#$Il-KdZTITcE_D= zOsNt4F+x|R)`e2y(yzyXvQeI=i9$t57Zz%<+u3ucTkeN-LqI1kuWGwI6??1Y#j=Zl z9_dw>Z6L$0lf0--KXul3()ar7Mjbi|X;q`8P3BaN7CW&@2gCZGP; zO@mn!y&sMNjOHQEpDJqBUr<#$?pInK=zJGc?dJGc$A2Ff%pG+%PjI4Ks7H?(dwddv@|9l+7k|nJ(#xcI4sO6KK-#)YstM5A|_POxH z*B=r(-}&l@2q8~bR6V%ZP~GMoj+2?=SUh*s$WU?rLg~5IZFdX_5bHWpmD{LrKOgNo zNTrrFJZ-T3a1WNot9Lmr4SKCM>tCDj`JD4sc`Yr7ML54y5@D-Y{JG}xlbf^cx?xwK zx@FQ;D>Y*^ucz@MbDnyGL`LVK&t>T*LVtq8i6=-J zWWx}$|F11go~k4-BRu#tC9vv#2~^B*BMf+D(M3bdc=UccPD2cHkc%HQGANL9$jE9$ z{bZ(0u*inl1T=IphP9VOrY2agpKP~xN`^Ecy`tdMr2VY$f!6?~MTuZ?CU`S+No?$5 zW2|w?xOyKNx^P1~cACB$lQe%ryAyI3+U6YS#8G%T7E!}cP#T#NwP|HiLvYbFmDy>I zf)oKs6Df2AzWU!c5aD!Xxp_z!ngAM^a`Pi^F*Vn)-wC7GFFJ*hqY35Mo7&U&u&A0! zUqvt@JxKgj@5ZqpCrrfs8pSL_lbc5x<|0t&vO6n?RY=@tZq`j11xO!ec_%%#hV9u+ zYSx||5{(G=_FvatKS2Jw$G{G}tPS(q|5F{SwjCmJ%;}=J^SC|Fa9?xD=i*i z_ILX~u4)fx!8oA}F3NF(_6dUh?jk>f|02wu5=JJg0TvX*WQZ+jLBW!B7c}y%ye)JA zI)Io3CYK+8sU{=@0GOa-LauO#si7x{DG+251yBXlq-0kgL)>NM+&($V%`T`uE>?MV zUv8FP$f+JfFL!Vax4Zoh`lE`*#_ z4+p!qhd}h(f>m8eCLz9%TpprVWpH|Jf6_i|<<}Ajry~m*4dj=_Nr-Zq9s{riG7~k} zl%q=f+%~j~O>xj2lP04t@dQ8U08K^e61+cr#=FmI_W`bjCQbDc0>xq?0@!nfQ! zwqY@EM!OZ!P`pI-u#Ty~e7b^Q?E3Rb-cZf~l31BBW zZE7c4W{V60YacMz)vP1E-*r@KQgP%mdC(*alBR2v1t4OLw2%|ua$8#|Qkq(Bo$Bce z56s5F0a{-+CwZr5t*S!+aP-qkL^LExLbUa7i=v2%L>!o|O~hF?6VW4mI&yhm?pIAA za(7dO$Q1o%XrQsQ2QSbdz)!yVxfBS!8cp{?aCUp%O;YC3>U3z zXq6KC8fhG0nn0&PA128@9SrSK#PkG6N80#pm%w%7-_M;Wk1}&{prxUrMi)V~{Doy9 zJe#C|Z`^q-npt^7lqA^Wz)sT|31cr@V=4-%fBw#q`zzcAqM}+2q#@@oG>nKQAIV-2 zCFD;JbpK6pmU2uXvR~d#Zuyo5if9%f-bF+6Ga#oLdbNjKx)z{8Hq4TlFfZwXzQSKo zKrAufui6| z9yFCs0#aGhMT#hINkL~#UXyy$ts!_!l$+X=(r?2(nrRq1X6Aiyev4>A_! zc4NN|x-23+szn_X0Ev1-tCtC+q?(EX1d}GB7nW*340%Ds?^93q3TF z3!&XCe%{*u?4(dAk917OIn*5T6T}p5hYMJtn=TZIA?9#W5g(_asx{eZ0v#7h(W3%m zgdBJvUzqd71~zaWfnA1DA7Qx7f}XK9DvAJg3$(+0swOLm0uFi5nv5PRGM@MpXuYLl zCrDd`tY=VeIJDYn2#nV6c9@uf#hsi zT_upNML6xMmS>wK+8W!NxBfwo#S0X*Au-{5*9StwiwSi|vIh87fA;7R+Yy!^lz?8A zR1+MPiX3+GB3Lc)T$XV)s-#L1j9j|473}hF1*1*}gS9wFF;+f263w5GwJ<`BtRv9M0)HVDWD5W6elz8!p zGm~N5o>!YvV0E{z13NSifTOq~#f~xvRAa5djo2X8#iVFkf)M(C4i&dKq+UpkC4tV&V(iLL9?9a~mlmlf5ZrBwJP7Ns%pmky={$UvY}_aBs*E zUh^;p8w7eCL9Ur~`yGjTSpx6OAh)@o8*jm{$4H(KC;})#^x6Au!w|dk;JXdsyLbNS zGC=g(lJ(o7^=I4vmJwU}8Q{wXf$UE?)i)Sw2XQ zez)i)EHyrCiiBZ&NI2(Im8YL8;{?uQ0W=SPK=)c z#qtkhtPV!27Jn zbaNezG3tk6fI^rl1UBD$OD@SAlu!%mh`S^S=Y}0-b_!CFZN6f_UOkT8s>0aG4`0m< z5$Ip)^10qog0F-sYVAGXT5b6AC2N>u8(m~G+;@KUtLQ#OWdw^A1jOYM;U`~H%7$U= z<>M}Duf2Kc5%PXP7RgoJ8p z;G~2KYQB9zidOq#0X^OK&^kuYIz9?jXOJ}(h8^Wddu%8}IU{Y=0=a4wy|@`Cj2;V_ zqW9|g9+AX$nM7B($6ftjp0bFqLJ|Gj;r#`$!5*=|9Ow z!|X`Q>!|v&3L;DY8>Fe4$&=T*U}BTq&ZU6elgH-a!Q=ta5)MGT_C0K-9GVP9Av#v{&?a2pB@8tvAQ2SGGd_+ zL1_;>y4G7OikZX5VWkkxFHN$uz!7Fyvc(m*I%J@eWdemSd?RXNAqf!4+EK79jRp&S zMnLc1Ap;qluEfR`ii#o=7vDL)hN~2&+Kpv8`pabb$tMaX2$L1;MIU`Wg#mUf5-N=) zrP-&u%OYUBh9LlC%-f82B3%uDuC_4h&lJN-3QIahoU3^}4c<;B@tA%^$@@p#nd!E4u*(!_{R@VKV7vn_lqm1tN z6%~FeiwSQgVglXF5q0rjV()Jqm$BDRJ_@@_RWr`kk&rbM`5$KHr)Kv9dlEGSmVVVT z57ysNBZnB-R|VRT+B}99Ho*^Ke@J_^p%8xKtCJ+I}ND2@&13luN1qa$>u(6wSxx?x^xKCwn4h<>2e% z()F32Y%@01PpIKL2!7zabkRN{-%$o1bKUsKR%LHz9blU>+w|JU$o*1D;dxsJNr8Ez zCe(@aQ1q1Yvporauf3~rY*gTKRqJ7=wH`THtw!!9E4q5gVrcTu&Bt|gAq!>Oy7b-_ zm!n-Th_S%O^SM-38%4==-H4}L^I2N)i0&J&`}>C3#xs5qOuGGTV&jLd@sG(D6V93m z?FQqy+ARIW1GrPJ9Narkdi}l2BFut)%Es%Hp}tXx#a~Nr47`}?4SU-(t6p=$muF)z z?d^>tvE~P&WTLOdfM*lsk*unZ>mooEzg5nxOlHme(`sNp?&HKyU%QFj93`fP)U1S(?SbKL-{G^#*nzW_t}^>i(^$`NQYk&3&A#P}lmzAvwV68HE;WKK&7luO`}l_EeM%fkU7GtFDNkB4o#X9& z%pZ>%*f*)60xmYWV+)N@Iju5W{0^m|pG6{bo}~F5Z|`f(n1zeiH=c}Bob817w&gd5 ziCO)fUOV~kk5LGnUI&cdcW&U3d@XiwFZmsdgFX+;X8q~i34F{Ir_={nk#aKK-}{TW zOucGmP5XV=J)Y!azi^4VzEW*^ zXP1Pf-roz!TIIoA8S!~-a$i73`t+7kiym3%eETVcS|LVwb%iw*O}QA`TVs5E*?;{F z(emEHEnjKjj1Y9==Uy9+i6?n|b(FgUgC6w9d%Lr$?=NTK{NV4W+`mY^D|b%&+UnLe zNv#*+3)l{xKHaPin_v4L&A){@2l20R@4gnl9Kw=*Y?aPE{4u@~AfEQ+HU~HImD-16 zd0p@a)y}`^GL?%m`(Aqcz$GJoNYvH(YbB5-apYZ?jtMwwpXh`>D3D zYtvT>*n~d1Kf2H4U_Nhx(6;i{HQ&rdNQ=9)cK2m|e9ZW$)TPFreCmlF77+O8*JycW z?k8Vc>0><~v>Fn8xJ@3ckv6$MHB(F-o|wBo`^RThEQr4@H4_4Ck#zs;H|t@yd#hS8 z>RY*3&!Crt>en%U{^^h-@cBHzD2bJQyvo$yYB%YTL_(5I&GGU4fh}OabY;pR&HJ{@ zUy^36gU8tYCv!9>z(;4M7{IdE%xjIy!l``)p?v}vEV4!anfvtl~e)6J~@VR@dd-t>A{j{jzVd9j+)7wo~ zrhUHVq5IIb45U3?eG#!HciPDTgP{(N{;i?M>XBSN7<0gRlgcql;WKJ z_nAYpxR1Kw7<%&C>L~sUhuxl;=5?#zeAUL(KIV+#-g=*7fYj0Z2K)D^`y`=|w#V1? zmZY1Fk?9xZ3+B$aK-YrR(6|56v!2Z0qGE;Bbg(E^u6vD5^Sm#M{I>E_WWm}*N(bcv zgd@H(8;b@9Vx$L|2FL*{4U7;(r&EgMATcAP^sceAm{2vo#u=;wGX8x1^9$*~;i6Rz z!rbDs)$@^`|CP`D@-qAK@$B4SF%qXa+H7e9^6lx*CA}>id^E6GwNDruZV@UnrEIy8 z%--VGFIM`^3aa_r%KNge0Lv?%{XAY4vNM8*7~iBV?-D{qQEFTEL>pXJV%HvtB1;-# z*OSb7x>iigWLV7N6&4&kl8Y^Y`vMq24&|v@=D1y#Vd*zhR+d*bLP=LSy30^S$3FvY zAp&Vrp_$3Ali|Kbi-fCIN2O0!7?R@fZ61o0PJK|OomLrX=b(x2P`~8gCyy8`EM}L| zWXB)6>i4dm3^1XiLv0_Tn?*9(Tl1s0CaitUriN253)PrT>+hmZises;7>Dgj)3-oE zPluYMHos^7NR6{L^avZ->5dqCVtK7$%1$^tw86)eGPmieHo-fWLsy^KuMsmzA0Br) z7JH|5Wkh`sHkUBHh#@>`<2mBj z`$ecFe76v}OL^=cRiV3t6eH-pbI|=%AFRqFkTmVo3DHMMOKe$Jv4zY? z+M(xNCMko|6FlU&iazU^t2L|UQF6mDFz`J8f{xg$;kz1s6$G>D=win^h*~uPU;>hv(5|CoZ;4Ojt zd3ASrKKBU8t=^3Jc2?fr40!~|DNn}VZdH-CQPBNEDCV|MNM5re_)e^sS%3jr4h*0M;6$V-^X+|tmhy_be&L4-?Vm2y8v|3!=ju-z0sk2?RFX%h^}7B&W&{K;n8MRK$q*e|I;3E_mLV$G z!x4cvf)ozn&oZ4np)iI7CQe_Srn!`+!_Q#IA88Ew!YN?pc{{L~$r7VVyL5Nr0mnC_ zCS`UQI2nd*lA4ylRVHZ+zQQSH81G`E`DQcvM#5@LlfwPDF5#o3CiV=Btg(h;vyfl% zImuxs^p-16fw)w-up_XR_hdoxVS-T#ZoBahC(G_j1tkj zu88-XfDpcN&|gZ8#()^vQWq_E0JXslWGu^T)2b`aj~>i$gYap{Eb05 zwBk>&SZ-YQQC+c+-FesfQG>^-)`H?F)Jb6kF|tTfQini@c$#x z`F9uL|30()zlsX~IsK#s*irbq{m(?l4OMkXu3rh-bPKK?EhwePDU>9xPEb=?MoP2q zAdX@V7+H^WvU~@v!f1?35_=XD6cPnMK!C2GlLuG__<_|^L;t3z@FJlJ_|&CvWTAh0 zpPIV8eso`Lb-d(yTzA}j{OM_4oc=wT=G1AS{Fvqq%h|?BQ>{+&-!>J*zig`M-r=FE z6$0&QqTB1QK%Ab>qPwhRz946mZm*dHR^z`euEg>jmpIH z3=x+RPHqJ7+{Xz%Ehe5JGLt|?_I(6h5L)%?oxOWKzA(l)8%QS?1k9kH^nvt|rL(8t z{x5#!Qf4yQRE+rI9Qg9$<2hl9K!u#Yfeu84wJR5Szkvm6uED8s^C+YO{U}8F7%%iM+NqgRtyQ$z=u);gF)o+!;2KQf@yRE65Y6s&5(x%Ss!VnuxP z7wyciNG29SZaj`cxH+wm7*5)`MS^hvD*q;vXL(d$Md2sj`bvyb69)$=oFkVz$4R3M zv>+D|u%ZwsNjqh1ZQ7oAh)_BVl0OuHSd?1;x5|KQd8Pn@%yahff-VbV#pK}V4E9fNX;8{s{vO9&X} zkSv9wfh!j-4^hs-|3;xc4vmN>*}bY&Vp@j~5Brx)Rfv^ItW+G0LY*ugnM7?YDji-& zGWg0BPcjyX)@@G$_?>W3LA^U|jtOp6xUBvSJv5BUfgSaWC<=AFIEh5zmDs;!s#fZsU=iO8ItmBe!J=;ml&(5pI&ueEHNtNr=_VPe z?HLqzd8NW{$E1JiEAGCPqZ}%cPbSWFvQ8>T_@@qWAq}sXgPCS<%B$}3(lb*{|FCcs zV#)$!I7!a@35k&o(*yugzOA$SVgH`B8r;6mU7l3oeOE7RrP%D$k)tionGSfQVv<1} zgkZ`PzeYeCETzKf<0=t!TA)yEDD4UUjuvN@@7UG68xoU!;yPMwbQijDy?`fB| z+fojA<%hd4B5#pN0!~5+c=bnFH~MK=vlCRxjXXJ<~* zs6kt2zY7G*AV5T?mjho8%3algZ-NlI4+mQ)NdiYyj|X|SP96RZuFWPS50*2N>q~%k zXTyAz*@G#%5O`uG=-e*eNV_bg+6}L?K^>?|QLEi+B~QI<1*T8!cCLK2I(7A;)qCm} zup|Vi5rNg)qH^=D*mlS_)i+esT@`7fhuCgsF})c>JgAH5{&qmqBnLiyx<11EcMrMp&1zIA913P}j+l{ABGD zYr#VHD99lp1xkbZ%-l-ZjKr{3_@@_Zroq@HNUc|%!JKwO5n%9j0oj|_^;fbuHvJ`W zybkRE`*w2szc3Z-MnVCnS@;b|fU}{Mb+)}&Y)-n70mQ|im%oidq#|S!LN5}Ie4<~o zh%bz*f#-Y%7^#%4%9lWHgNkKCI1e-0fs$S@7#GnjY!q@eO5w^$(XScE6>y|#Q6U<2 zQ5yD98rM-8FhGwgN<%72Ll?WQEzj%)el*zx<<|wM-u@1ektLr<$|9*>r^8P_KJ16i z+L(ZHG!RG}!U$c^&77a0SFRS=GAmccQ3pUof^xj`6a1_ zG-8bO5PahOK1U(EQvDyLV%Kg-;}TMM6bS4c47;;A&Q@O7`!pV_=tq%hVP^f zUg4Ja+G6$Fa`xLo^iPdn;f`G40uN4H;Rar3%3b5i^gaBH*lPGkrfRu)(!}yvfe_HC z4g`~VB(xYbI+}*EG4T+Jltv^d@*g25ZTKZPAfzZu+Rh$*Be{G#vWus2BwJ<4OQ)ck zrKO6g=!~lB_c5ys2!enKcN&9-K@!PV2oyyYG2#_50uwPJ5!rPQ8Gr+FRUreBEmEYf zD3R7MKN3($eu^&MGhb-XR--PxiaM;q)kq%a;^TDXOXW-8zcTA4;HzVTdnFeuP=i|4 zV^mWAOQwo;1G_6jW}FlEq6vRu-R*Ot`=wiv0c~DdUh`d5pR$}K{G=O8qXAl@LFy3~ zzLE>RvK6QH;A`!si9zRJPsUV_R!@&drhj9c|2^X0Nas}dKf@e!PRzbXi`*?rf4xeC zI~0tgB4U5=0jPl;(}A7+@g4N>9V9ly8WY4CBShZ;JiQ4#y?#8sF}#m7u8tV4j%=9RO9MXCiE3RI}>ra0ScO8x3s`b+M;vY5X*RZu;Fs#~nbFXMwyLb`Q!}`w+ zr?!k|=Jdf8XroQF*TMfcOXW(!#V7?)0(H?h=a77yX_2(uQVE&Go>bq)qoDTq1?+qQ z<%m{1*BgnpLdvc?kD1nk%d!Mb!f^B1YX!r?ozDJVjiEB{Hu;in? z_&RD-3nfsc8Vq~oBfUTZMmpH`Q%t)bzr#FJkRS0#FWkk~b2KzN1s9#K6;0VVZ-h+m zQ7EF6XcWw?|)no5y&)hX=yg$*F1zTMy|0&8Tr51nzF0!REkVkN69SEzT+UKXFd} z5_MJaOHUrVPHg&olxQ*6i;P){iFO`DPC1h!ops*iI+R1p;LOSX1aU0;{dVJy7 z3Y?XV5%kF^cxBIk$NAx*w?U%3L87d|m8ij$tig4bHdgNG6H>G2=b+3r>-8q;FFe@B zvS%tZ7e#8!H06P3J2g_UHB;>A#1Fa~^Ky-O3~W z73jR*r5$)vj&owNRPo5ne$YulZBtC7`?pBt%=Ux*3#KYo^jHnllK|UD+IxZLcu5ZA(Md=O;_Sz zOGQPIh>Pz5106aJv?~cLCqLya%bu@fafM%`j@(Eg`1y^5I8(?@9m&WuEw)s|pt_9% zcu}yQI9`aiRs!^OMN@spnB>r-fq~B5Jf7~q0v+QtgwfH$YQ51xyr3$&Ya z5#JO?Rm9oemPAc;N%g~?*TN-AVuli_+joo6gnspQyR?JiD9LWQ$JaIDROOk*1vsf; z&%F=GVKerg$HUcmrhKcBt@mOq#m~Ik>Y?!UaIG!?oagZC@d@M;W^lC(0SCwC=YHMQ z#>bO}?{%NCI;5@KH4mPzsB78$h=9}J{2xMbmup6X1OeNT1htQQo3*B8^y{eZem`#W z8uo7w99S=HRd*JZ-6pSRj5w~}Zl-FSK9lpfq&}>MUBno&<>f03$WF>rvjvpnmadD{##Cdc5Slt3KY}F;?*NyH4fFt?zoG`Vcx$ z3<_|by?VlVl09cqh|YiKVdvVWO!Xvxo-V3`IeE{e1!V1YTh?sW;4ShwWwp0|{@yG< zj9_@&ztCBqb6PZ|=suWR1DBU8%in~mjvvaAxm=Xq8GUqZc_=9IR3em%I`L)dL#;ls zzxC9S@g&ws#uX@B8Ii&@Ubqgnf|Qu@TzQtRQ?b{ti zYsE`D%C*tL9;f6-s26Wxv~qc%amrzh{>d6-tD$KW^+(Oe>XZ;x8JeyWO-V!uFASEUxK8!2?Fqfx@cu;6 z?Kw@Dk4y5L?mxvebxNeVx0Y{^zqYrJwS=5kI)-lDJLH(S@NOA)q~bO??yfLSKjls_ zjvpI7I5d^>$apkf+k49su78XXFuHnMOZ@gK@6t~&#Xus>mQ1Bu z{(?)6)LmC)xW>YMxMF2W6k5K=$4Kn8S+DPbGHtcm8|)#GU{@gglMs$@-WTkl zJ*8o4aQKqR=rx)IvBn2@;9X!Z=gsZuael#z?v?7iqQM(>Ufy$hTy@C#v!2XreTJDK zHHW8qg@(|zJyYzloHbdSz4AS-Nqe+KV5`NeXkm_@fx6M@c-e;k;#s}-5E;{JJ>|?p zU{hyrzf0?S4MFN~??Ttx&S6f9G1ZN_Y&JUBbp0p(lsRF??miy&o=sq9*WVwnBITgq@{B=DKyv8|z+P!=hfUG-| zb>f1j|2pNs*t_J>zogs0l$MXMwP$s^-}$(Gnid%CBfga2xMkgLq4V@c-Llu;Kh#4K z%f{zHRex1$Z!BGM#c@gamEbrj?nBYL+kUkOyrErHz%0r3QEuJgvDAKH_;l(~*M0U$ zPH%3p>eb68O4j+Z-T5QeelLVL&-oDV>&L-X`{iPXqN0FOo%z7Ht&C5;W_0_hG)`@lGy|kQ|$yMIl?a%S9s;kRT-*=8PxV5T^?koNJ zZqro<^S!9%2ir~lkuC<#HJ>_9vdG`Tjt{{rv?^Podi96qn(OAX4kOvmBTu_=&{v7}YZ;>s|A8K%EWH z$kyd+ERgE%SvVEHW@o?Uk~50eDq-i7dNnkyPFvIS%-gioy-?j|ne%e9RK+9s-k$EB z&y%X}H#XIILG9(y&j$bc1CAlbW8oO0MK5aQ)f02)BN9Il#&iUAnc*u@E4VSvo$f`& zZ0ft(2VFax%iBiI#B$k;ZI_v^*J{+sC6WeWsduV9hwi)0=kwswZsVaqvwrFa#i3YF z@Vho$m$;dB$7c7(!GXnLgv3LmBZVx#`)lj_UlF z%~_o<$F@zb4+sZ5DD$1!YmV8Ryuf>s3cOErmRyzT{pIn9wr-o< z(RjoyyM|<%>rMXb{`(xtm?3Tg^ROr|q)Eup*3J6C)Q)1wjIZ`FQaO?t)seZPXAEnX zMS1<_nG~VDFd#%6gCYb2;~*qNyf|g(0PRVD&l@H~SM&+h-FlUv{}AnBPq-Q0y>tb1kOa=Xo<0!Cr(i9F*UKW(XpQa0 zCJ^4NKs%WCCfHKdAH`G+J0G~SY#M5m8~_`vX6OdzE(o_!jh(a_yuq2_Ovxe@ zxlQ*sxSGXB{yc-RQ+pf~rNViFLcufxDsYDuh1FNlGL;&*Bv8)cL#bq%f#vU-wCOjo zGTj5RfmFy~7tOdY`bn#%;Xg2MwxKPN$@8Srb+K09+<74uwBdeH{emqB6DD{SjAXEZ zJ@2E+j~nKi&W}`TXX^XnZUH`_&yBVktSCzSsH}LNAz7x2C5Gle2)Doh?)|UTud1a| zAO4`GE`|`q4w$eD<PFdRXKp~P6`swBW}^foIg$zMMBU@C3=ev{H#e= zzjX;%^|6)B(8WPx*BgWyPyT)dNteHMvFD@E4TYOjr(;lop9>#WAFZ7s-WKzrTFH+C zHxqH*j>}B9Ma;qC4w3-6vmZ?u{NDfJ`m2fHp)|3`K&AB;J_m#QFOC0^!^8t8grsrm zw1i;-!6?4}#~AwmoDlLqnl=A5B1GKK+R>E3&Pi1T1_ay)I3MKi_J0I19hxveFlq_O zzCY|$s|xqWb^u*|?m|>)3sG1vxsfyc$ZrUdghw+lBQXnfDZwgLt17L=Ww=dX}hX#6JU8plATbx`YtT=}Jdpa6h4s>mgWk(`Yi z2PNbdn_D`Ntha;tr4BytK}d!MPjf_@yJ+F+7x6VvOyRpl6iu;Blm~YyUO~)tI%0WC zlz8S~AGTP-1dTvVMaia2&B9ekl!DG@{C0yyj+TiI#{Tq5jw6D(G@l8?IdO zD0#%vDaw}}V6GNWxX zPlV5pHwSDQ915~fE5RH{Q3+0fI|{<#Ta?uV7a_!$FlzdC6%pJD20;cjvtj>oIIb`h zY6rzp;))Lhvye^M3i(Y88$}q)vz9RF@O4VliMN0IGfr(~Rl|@s1 zCFH$@;5ODYKa|t7f6PmpXi+aG@dk)&CB6flZG-F=LCSnSN&PvipE1r9#=S3)1D^i) zm$e!lrh<|`yC4bhZ378VMMeggy!RYLtlKpIXjT$wg6M{6w0un57(vRk5sMf^LyD52 z9!W#AD`Kq&L?9uXa4Kz`*@kw0a{<2hU1MD-5sqjK8?l*m#3ryfjb!xZoXp340}159Gz5tF_{(pETG=1-t6_Ol<7y{`cB%DCnSA>`4?ztv2)P3P9vCFyhbHHt}2do z!i==-kpMDL%)P?|J&a^R{vl3_e8_YjgMXPQoZ4kt0^J@;Dgu5IjeuQbEXloE6^JpX zZDq<<>feAd=WP}s{&bjRO6$-gjjusocLo=3T7jNY6|eD7G*Dkdmf}rcXI(W=?+nzV z;x(H1iJF3Dtwku7CBHX;%vmQQT2rt_(iiP%Gxs)IyyXP`&QFX4N9PX3&kE!eMw_QF zuic{$F-bY|)DBk*72UJ}LQyh*c_@PtQGZ<&08?5vj|=e@icl0Naaj9T2Bj1Z|3Edb z`Y+S`gLQXuiGH?5M{LW{9jl2j4;}_@_$yXlhh|~G#{w_ifpvmjB(yCsRY^c|S0 z3%8*J9#=t`_{OpH?Fb3Mno-b3yG4`u63|XHid@}ZXEcRI=pWn6JAp`*9LN6m7zh_O z$m=uy?W`@24!z#^f03xm4!m&zhaM4pH!{F&9oo1+vH^d5z_SYMfxZ$`4iJiB0Ne(n zji<7vDFmxwd!`SdzT}>MD;4i%El*WcF$6eO1ftK>m|Gi?uM73T(eA+%Cb^3SEE;P7 zqN($W*|@{jB>~!VTX7kdVZ%!}N0Egy+~o<*7QeaIuR|7Ug-|e$B;eI3sg2n3QK_ZH zoLWK_>cY(L+Iw+Y;8<`LMb%-;4Cx8IsH$auRs{Ntz(AL#OUf_k z<0-)aMtesFjM6%qkviYziX3>KIhYd9Spgy6kFh~WNCVJ22vG15t+g>>9UT564;s2C)}m#l$I~6}A zH9szTj_d_K6Xpn{PSaIwKhPvxVSr zauAiJtgC*kc$k_-ACX1Nu}+H@o{Qq-Zs|bJ)Ck5>EA&9xdlUyH=s8$O5jz)nX zVRpClWD20!d(Hn*MKH(A1>bnMY`h2^9%J9gmU@s$#Lg*;`sq~Z|9x54Te*Y1ZK=Kz zf2@c^am)L9gG9N~=TDqGM(>ky*N(hV^4_Po=$kgBdx+nO{rew7_ z38okZ%KLCmm@PWl@9B?cQzK{SCJ)kp6jg{6M#)D@KnfcyoxVr!O)}Wn5-gn+y2c`P ztye)$Jy44Z1RE0vq{zb3Wo`ht0ZTf>0L&aT9TlZfl{k87z%#~QBuccRk3dVP!y!~9 zuBwp2niSF2b!?HA_OzOaC+tp+q%%?_PB<1;DF^{&9y6tah`PmKpvY|@98C#T=`Rvx z^Q*`Kh(vKzH=6!~L`i1kN&%6mpns64M}^sQ^1|22Os6f{!dKAPxI>>CJm|+{QU%U8 zMT%+pB*7z#OBVc#;r&=0{2pqdd3>@YZqWcoEFcmUU?GjV9q*9Q-UmdYjI%R*Q`j)a zGGb9Dy1z_N)*h7KEkG(wz=CAcm2~3rl3NL!m%RgpsLINB_563d8J|rF?&s~tIE(DMxt!%7i2b%)r75+P;gU$8kF$tIn8lbap9Wt|03-kgDYzrZc%q^ z+ntU(wr#s(+qP}n?ASIswr$(?Uj4iu-u>-;PMxarVVA>N6VBRNA&sQJcZ#gM~tjJxtcKB3( zQjpu836M(^_N1{2&I+6h%6}Pnq0R5F$VByNiC^%<%5fe;CXN#dCM_7j27&keG}kYw zg!Ed7&%;$k%eWcw!(I_%*6)wOER9~+d$M#M-g)XeekSi0^kbf9OD;r*I<_5X{krP$ zuPMSlvY=+392MEL>GJ2=QtMq6Gn|&ZC#-(t-pU3|T8YucQ)9}lC>hf5&t;IMs-ek# z#Fchnix*Nw*Z>ql=B!@7TP^-_DHSuB-H0vJk zN@-=M|(12d3}n`Qg5ZaTM6xKUaWX<8AFq%q2_KIsdkz% zS-N_6;;Cxq`qB?*o)GSZ3+G^w?qqV9Ex$;%IzlVk<2lBkfR(uP;k_}_nX>B!Zl`_h z{(J)A(EnoCoro*R+PI0Z>XbjTe1FHYG;xloyb3}az;CeOW+81L-|?=jOkforKHFGs z6%6nd)iUa2raV-kf{z_ayT8eOJmnQUc!R%_TusrwlD|KQC{4!7c`}H z-qK}YM;SOZ&bo+${yK7|#$vy|v0Dolp(G9c(>WbSp`m4aq}L`Sh|!xwdef(-R&d1? zt@CKjWMKco22IbHta?Mu2v>sd{e#Td<;7+kJ}esg^C^(>u2kc}G{d1;YkcawF-`}P z=Fsvi^59PNh@QtOyK>Pfr&kK^-Hq=J-c71gf^+B%e#9UhFc8pv74AuDC;Pi08A3~H zo~#T6hJ`)ts!geijg=>+Ri5zTl)OE`>{o}O#4rm+K^Nvmkz2WEQPrir;8vjL;dM!t z8uwh*_yo10h*Wv%5bovUio3B9e*M=*>Pg&^eVWUP(3Z|Gj^UV`1Yya16!jxlrY-&tU_)>~_E)Kqvv@&C`ObxpL(v zQFe(E^A{^$X{Cpns}@$<;?{Q1_7yJxweiu!_@R!Ll;VXJHkLp_d)fv6mbdwsR#Vfd zZrSbUbF;dn$6L|ySE=&s&GEo{Y)`EJa=$@%s?m8_YMYSFa*X46+S`RW(+T;g!hhtf zLKZY3z9)P~30X@2;5c`=tEu+btQD2~5bgJ#Kw*{84*7sYc;i{NO@K+dUiavDk8c-t{7XhYf+}CK5y8qYzxu2(kvtM47b(Xn(c(zjO1ayIq-o8J*n0ER22IQ zC-i-MaHVC#iSi9%-MU1{9jci-8NVfX%}T5Ka`5i*uyWw84u?kF5#)&%dygw@&7JaH z#tetxq|s{5ms_0zp<$C|Yc9IwI@)}^F=zkArVEebtMd9|wZfP_?g+)&f-**)d*MZE zL3@0?lc{bN%gq~OEy8XRLDu4~q8Qfz8Gh`p`n)VrLH1d>d`;K>~g)@G$i$hV$E{mO@{-iW42Wj2Q&!BVbGvBh-9kats5|XKw*LA#$ImS?E$(T@p%wj?uG?HDFQx8))g!R8 zU{A-xrbo?_2O{Pa`~`G^Hjh`zua8v`GH_~5M};tr1lEu!Y@e_E$04sRrIs7xQp=~9 zc{d^i&T0$UElUpFr!L9WD+p`zuBPom7P5Bl>x0Gak-PdSJRA+ohw`SE>*JrT!b6P- zd|60~yOgU4{_2e#y42JdL;Wdc-m*mSv29(3iwT_o+6ncwJ2U4e;Dk-5LuQ1T$IaOx zC9XO#z}f(b0z(r#)nC8ogUft z^s=H$%l5{_3G8ip^L>?Bcd12KwZ8{Q$r8MJzeda)wO3xRZTqrSZc{VH68s?$#!Z^G zN>LiPdq>taQ^-L z)_?`Jrmj+AM5+Ecq#q#BVpA2zpLnjT=tWq0IJtLpw@j!&{S$ z)*E&$R-;OKJ-$M+#9fs01wgQ(Ov#`vayjnzC&dt$7MqA1f7+;XbDlS^jN7ZIytH(i zm>)zN-0@twcy2v=lx~oD7`!nrTv;GoeAPcYTwm4|znOs2eI9AO&UyR1wH9|vZmm6? zH@7qI^E zETO-5`CJq;e_g3?t#%XM``(#^c|1yTQ{lIFyIJ~t_3pP?u&%FFH{B?g`}Xp@VenE( zs2p`{AcS|nW{?>`4dvhkKpWjaSQ=9xj>mM`G}AnLTLx^9OXI?Y^KJy_T{wpFqUi71 zeC<+ecotL#%qdUzugF3+=bU4@k~?73yo>_5s0`jns%uPkK`b$j`fXj#^?EZJzLn#3 zZ7MFkcIC8R-*akY`E1*=Vc1imGzh5s`f{q0J3!Q6!7xUEXwvwg%6Cgcd}(}tBhW7b zUH$~KlqH+(^_G-+J6Z6d1fiCini4ec#&Pho>|&$kMrG2T2wXDe1t+VsS_JXvWNRd6 zq$jI~?jZgF_&c2#iQY#?_({ja#Kc0+TFcbP#8S&d4`ELg2^-1Zv%bDOKL2NVetx`v zb{Lk6=Xw_v!4y^lJwW0x@=HGK|0%UEnmISMG;4tfbX<&*5h-N*tjWVpVP- z>VL}sZUJ44?sr6o-+&C@lI#Dai}CLn!2etc|IaGM{}ur}19}*LU;jr00O5)cHj4+ubYkz|4OPv+5$?D?tDthclStPIxP+-yh&WssCgQqKC zB?T;D7LL2W_DcmL^Wkt_1%(OpjoC)L{EQL77OkAxf0Ju3}=RW zn^0s5zIUvHAB#}bv9JhuQ|86{36r0hNH>aw46|sV@WOj0_jS&+206&i8?|xCzzf8 zJI-8TYNZ+arBOgEy=^Kum`i4O`hI>9;VZVG)1vv5p_McJ7!7oZT7FI!zGHgjezr7o zs7N-Ns~~zv`dGFEz3dNo^BH=A#`(NouN>d4YKmWmn+`Qj{2+d?Aa+!ml|(7(;DOe| zF2|09;iAiFw0$@$MceAWyB=H0a&jJLOh>I$SfZaW!+`S5Pz$R5$VVg^18&M+8Lh*f zE#Su+%9nX5sfTx(ftV$M8;rySVX0M1HE_$I7Ua`9In8@r<=LB6W#vFGvf-=5SiW(N z7*GKZB0IC_J$A);OvmIW6FCi_gso*ajx*8oV*(PEuQm0W3kO61(R%qk*l>Yjr1M$O zkFmP8Fh5!D3;R0nC%zn)MU8BJu8Qhl2!YLfx337JQ`9b@#k(l3*0ENSM-wDz06cBT z=)FJEhebQfX;2^ntOh|-HE6K$TQi@FSq_ixN(Bsq!iAH(M~n%B2;39vl$2QPd0i8q z^n4Fj4M2V-jnC6w<2xZd!UrRD$er$xNIH$wK#} z6gI}-lwtbhlvtmr6gp!!s1dsk&?G{$pkN3@1&28Lz?dVH=N+z_Jn;~=?AOQ?PloK2 z^;i*Z%HMz#V9gil>&QA*s1TKS<%%Wil|Nuy$ikgw^tx{Khej5Kn$?6T3*KmarAXa^ zYOQ)~CAv}hMcu+m-2!{v0*YZd+2}7>33VDZZgz}L8nrC=Uq`3Ys08I(v6YhvfT0hD zIK>ft6j1x-vx8W^a6i+EQ#FCq*tGk6Xl}z$#c_Ui03^y=(MoEclL0z0n7Ud zN+>eWpZH3dgb3=aT-fa2vqS%o&vQgY9TDw?WrH@B5bdQx>Z}y3??pC?ad0)$xw@WZ zIlKjf&#r)XHo_*YQ9}=fpd<^8EjSu!)!7aOB3;C}bV;}o8yl%d`-%i}Ze;2lgv7aBL-9wu%(;Eypv4+(7gaOfVv^jMGFi2Wl%Nve;xdf!#HuxE>#cyN84-+9y@?sG z3^9zcj8d%;*IU+)*HIISmlLO3O$g_RF=>2bS}PLFQAj80q>Zwf66PF!-r|fSNs7oK z=I}tL2xOB$oh_0!9MXU_qvC#Yo>jhJaB~^i|Bx!`u}%Z_FoZr&qWGrkp0hmQNeN8< zO#sQo;9Q|8rB{zq2_2ZdnC(~FTw#xNzGKuQQu*Ui{!}O3r+;>%Z>^@DFBeD8TRvu# z1TiXtd!DP9kk09}9@IcJ^fVG4$EnB-=0>-llGaM>l(-zeEIMEsV!uEEkAs59Lqg=y z0&K;`(6Box?n6eTNlN6=18ZfDq0Yq6aEi1t&i9QFBP+po1ED8-DG!F{Wlt2#KZ39> z1+2M!`gi!{II*jK`C5+n{p7{;n9y31rsKj`-Vzr)AO~QIOTm3rWDju`T?!Li_|XPr z_6GraF+-u(UD6(q18mIX<&Ye0QuB2mPjfdr_#ALgzEcx?R0;YFPj=99-udm0htx3= zj7)k`ao&ZUj~CZ5<^bN%u%na@(3>svJ&TQwW{zDl9qLf<+*)Z5SwHU7J1r^r@3u5V&{77pJU-UXF+9hin9 zu!TfZ0o9JOrI2rT(N?^+-)cMlPuSX@-+`u_Es1>Q)xPsUmKB6aYuSbYf_%PQ{M*rv z)mdBunk=ZV@p2~3au`47(E?3hvaD)S332M=_IKrGuL_m1CBM}|7ZOkQZ-|lMs`a87 z44gR*>hUn$a5QyRGxu5gVj z26?!CyF7uhrG>V&F?S`P!$q%8fL`rX)wvp&2Y?5`Z*IRLZnUH&RSb^JKhWCx725h| zE`gx6cG231pQnv-1yc9{yAhO*ZsK{8`2s0~&|2}RZN!w0SY^{jn5|L}*FSm0rwwI6 zWD@joXpPm*{lO7ZzRk$hYXji?**PQJgQNH#%xz&-gaEZr^dAf=L3FW!n-9Z zG&vcWCp!^v3#K;ogFrn)OuS(eDvh=A^iUG3-pZ z!_$o3&BFR*qk6tzG_lx~8gj7{=p#gSh$z}}JXv2OET5Akba5Kndsk)V+ZjeA7U~@G z1aexkx7%B%pp;g+_)7=xX;t_kMZlhDunpDGjB2*T1`C`L*soMPzpeXm?=cr=o-FRH zNg6o@zW|Y-1ZRw&^74g31F$(Isii^#R3tw)f92~pA)`4Ymbi+Bg||v5jVtEwYLWc> z1Z#4e41003fXPLO4sv9Joi#g@rOi3*M7oi9fYA%i?8cB+-MkC>8v!6gN62;+MMw2Z zaz|?&o-H5D*HRv{sQ8zKLvcV?-;R54xjdS4c)(?K{_-=z3pxN zA}gE=Rq#{i@H@(oH;e#Pupsxx)5GgJ`iuU?qKaUqw5Rn~Qi4qk=OC6%)blU2^r`LgbcKjLL zxPh7){k=((T7SFbcwlopy7gytp-=_K(CioAb31hTQ;$*hLidyQM}x@&WA?(- z?1E2jdCpw|C4nn}!_N(x8b9Ip_#at+ffQ8C|R8MwW7D_o@`%*p;5U=N;k01X`5%VG--8f)rJ%@mn&)brNw0WDewwW_msEAAFT+DA@j+(ph z|2QEek;4{6RhCatL`4cNFDuP|`~fdF7Jh5TEVq{u!;DX|P#TY|BrdIrzgcgj2fz6i ze>-yr54NNbKx;;WEN1sN1o+0UsR4JRE(#~VGW;~5Qu4$}shJYmai8gVT<&$%OqO>r zNu)VNgsq@Ge@4x3+De9w3ptk^Z|3Y#78)?DbzIY51~e7onQRw<>v4_unDW(0U-tNQ z=3`TeB0LHHWlGZD^eOAp6&Eu=6^;(?oV<0Vu=i7l)$_~Uz zRhrAGF)KJ*xg@+@sS-l+^G71zYI3y;ZA8Fl?$se^@5cbkLSx~C2Tu*3*Mceuq8rrf` zU;IX=R&Q7slnvRSo_TbVABYYY|h2O?e&FpSkY!g_5MN&BN@szJx7z zlc{XerPhe0=qeA?F(wbAv5MJ;53W6+)reIs)>GGWmnYT?uF)BTc9Sou&8Ym9rw|ba zxV9oxJZ;Wli*7f;D{bHotsRqruEI__&V9V=GyjPjvR&Oz$*pk<7ywuA)QxZUg0zsV z;34~9#PO%adu#O9zV{yp# zv2TB#u2S(nVp8$45ph0sKs8&Z=tNa#GMWU6W=qM+?6xo%xbIuvRx6wtZZ4h<7SmMw z9e)kow;9+ny#MxpIgOAxT&O6~3J76Ka}hkZ0uJ;1NFC|i%|@p$SbKl1cmp9zsC7tu zId>qNtL|O-NNPu8=zG)Z*6{4_jkI&7<1sxm%*N&Uyxe_&C#|2^zCdW^NJXobjwWaw z>y^@jVZ|Gz)(?JSXB)b$@J+ic!(a7tVi>|nN;c}=(G?L|*jO=*3kZ|mOt{?Y9SKd@ zeD8Wtf?(vb?;niz${MF6c?0IGc3~(@sG7(M!i{XyeNb3$3UwRIe=17FjV!oQX;P2K z8b8wT2p&*zrW1|<2`?i-qsPkzm3_N5AXD@*zf5P}fJw^@;0#|CLSwK1mX&R*tB6&r z;$@}G*CCv1U382i+w(P9z6~9?PKDa)9%K|*c+A-mS5O}Nf-EUKyTqxf4r^=5&?CD3 zX!lxpHhKFY1S9uysv_UbrTI#A4R#zuUYrva`JOqb7O+^%()H~l&PTr+`aWZ-`B2;U zvn2dghx~J9^HsLEq}^%d)Y0#=gtJxyaOZOK)nnf(M z5^Nl|tb4?-GfKzzGn?nNboVZ4$LiZV?`?B9$XD8~HdJln7#Xput3(22VYYk}=UhGP zD!)VhFF_5P{zZ-H-~&re7^^viU3h-ZL)QSD!ijcObx*X_X#N22VDFF3UA*b+6^v`@ zdySa$7SN~90M3@&iPq^4;)WNCU0S|&rkjMb)%SY5c1z8hu0^Z;xr+qv4fmekl}m4? zrulab&g*#;%4CI`^V(la%T3~fl7_AA_%GXU@+sz6}lO%U8G4~<7%KI=dl z1usVp)CtMduvOLfv=&`m6LGnW*>56;4iDB#yrt69kLxM`?96$Da&7dN68kxpC{{?w zvgp_GN^$$v*GS+FbrHeMa1^iCtM0~hx%$mq?@>7X=XvKAXJ-J#rUxfeE4&rob_d9G zm#5DmP56`G-P5#;xjTXFlw?dh|AkhFwYR7n!{NU0aSWqY0hep6{42^mfxFWby(uF$ zdUnX;M(8JosW$KXT5j9d*DJ-YPg#zf?ruG4q4(*Bu=aI}4Rjil^Jfbw0ehMUH(XhB z;E$rIQ>!IpHZsL^i}BhhmrN!l#6mCN{e;h1pDrOhg3_HF^v z!@bYFDqedAvZm-)FS61cM)bF-)FESZ;(+hCk{ees*eZ>w;J5VBPf!K%jhG>})wV zJ21e2z(_=+#dDZReWluEWscX6DQ;CYERLLet9k_SmZluaycuqkR0)s!PyM3V7Uf8d zFmbm0G>S^7J!0zQ5EndcR@lg=4hTE>6rcldMlY4QDrv{MvshdNhs>kNnc8tni~ zj%DU@`6;+n!mI|4L@+N6Px6eL`uSRMgTq~gm&pVb1&bwaUDuc&gH_bw`lnllHwC

^FP=u~6rKzFeVtL10VM_B}`SE`z`nGg38+~#nE%hMM+OoeCtQDf(ef{%elYsjPi zo3-qV{a)t>ezw~U1?T3<9j%(2(ZfMyXCI}>!>d~NQ-t|3P1e=R*k*m#D}USqng>ev z+bK|G&biNq(c<&_X~0JZ!S6%tCxT_0>GH?ZiBH4shq+Ol^RcVttrPk-Ph!tJukeMa zceR|nX;W)0BS1~6VF3NSE6Q5$-cSmpz1n1p*Ew}nlk#BGOH&6Bh z$?kt{OvZ90GBn*|14@0Ez-^qo)@0XO~f`_FK|<4Ys=`QO5UO>ar6$GOt4U*PH~ z**PKW-mHhfE1q`R?&PK&=}^@ZU9gk;s}&HB?sn$##`-eGXs*ISp2p!B3EU^t_)5B_ zrlywq*7|1VW|sP9`tbXzm{{r9>8Tl5m>HPq*cs~S8S2>S*nXaBcSn0B)0j=*%PF}) z-hsSvL%%QRJ!N)y5FN*2L(=i&sLJ9<*BG9ASOIxKpHCBcQ`m^yKC>5yV|lD-kzD2L zchNNE;NxF}2rSEas!n`<0_AiaL0mO~<|=_df}Hi9VcZa*7OHRnZGb>)412$TLs}B( z3kFi2E^9gS6sW&N1Aah@plF&#@o&iB^-qhy_}G~c;X3z4GDx6(oc>$@dyfAOkVm?*?ocb3Lu|_ITC&>eR%a|{ituqnk3$W0Ye})H#FEo|PQST}0p@}evSj}cU zh4Yj#(=b~Z_#-Gm0NsM&zTpYw`AFsf&+^=;zm*7_s4jA6D6DuZjK@RUfA0vyo8F_+ zKz{Tg{_O}fInm!Q^F7Dvx&C4a#TTz}4^{uSe82(F5#S04Ai(pqI@B1)Y?6Mz%e+S(dYD|6i~s_W#D3Sa^crZ~O8@kB#exlTBT^h^R>f zSWk0!2VTj`&;5VVr#}rlhZt_>#qQN|T6R#ekznk@?7{M3pcz$#B@+{hOICdoC<)i| z9ZT3ltTU?yI-VdDBL`PvR1xg{)eI463>(Y;poi5K>5TQNAV(yagQNg${#x-XrH@e6 zsC|qAE0{0i8`#>nc4Zdd8W~bV8jt{}dE_`XdZEJlLH|{KQxQ(AdBXZ6-!?2llH6?B ze&`3I=^jvQbJShFze74D|={p)R}AwM=l1)5gUXf#g~Tky08@~ZB~DR zz{Zbd6Anz7ajWAsVXhFW%m+1spvNVk?7gd8g45SMyYhPiK zl^-x2aP*34B?ws3EUc|2iD{>j#F6uiA!GXO-I${NuSVG__7toC;WfQ5UlZjHT%cQm z#Q6gie*b)+1qR6 zPfOq@3`ANXBjxDd`3;Y*?HVF898CQQC)q;lI_*Sjbc9i+hy;_YV8`D!bZ5YVac6zu zz7!IcS14xSh7pPPeFNl3cF!ls*O5?>h!iRlKMoGW=vgESrEo|Tenz2FC}yKkkPL>R zUL6UAQ_(8C4~CW!XFBHNKv-{!1lhU8Cs9#<_ z4eaOYb5sgR%@yod1)P#dyG#{t*=)Rn1X4p(?k4=5ci19?9Tzaav1j&oeuI3&{O|lm z5&0kpV19#;!FDe{^pvp2=R0+HJggzgwd;uRjuhP{D5;o4CfO{T>`$Ns>1`c}%!oub zSv|Y#vVY_zCX&f1O27g~zgsZ!5i-f-?=%y$OcQh<+9(J7==v$pQ6@VYm0d2%pa1fj zu7P2gPGa*%!0?g~>!G2k1nt(oX$vJAWB8@`>vM|UI9eyR!vDM1B>UHE$`QFSLLHFF zGQHQ1>n$g(@iioyMeeb{&Jw!jSa2f}Gs%P9Wb$j~VrsET@f9YmQH8oh21re6wqKQ? zPk1CPS3i5Tia=djk=M~AY*l{tddY@u{3LO{nxm$2y?Wv6_n0$BQ575t6zFi9u5C0t z7erANrF6?1&}-gF=Q}-cCm3shWsZbC| z8X+VCy%@++NY3gfFagAWflW)UK)38PF>~N77XF)fmU;YNROH7RM%%1@R{sq)K|E)c zT|z2+2!L}+>!$cGT_V>Efp#ffua<8=p!Heg_%C^bRf9llqd{xyP`ctNUB8O-p2hkv z>48<-4Ez_^1o>}ZlSSsavnw&kGfl2_$^|-v$TX2}!Mxr_RX9fO2blmrQ&>=I# zE(GX5P*|Bvegs_X_-&%D!+Z{qq0nflRu_LtM1YsG8@=SqGl;^Z9 z`@cnTj2MYPM$uhYB4A*&u55m2?V-iVxF~n*qtSYV#egM4b*%YOT%pPZiHO#3F$y4# z&<8<-HuzC!91*}Gu29oH|k|V+G63)L0o}qgXs@g-;j$@C#k`HweZhtNwEbCPD=~usDiM9 zzy>qlpb^slwV4bsqcZ|Wr+qU5Cu-2Gkp=ozTg2Jm&Hy7B%oP#o@@W`iSs@S8!blY{ z?P87H!DZZ*eCUga<=g2wZgHb5KkA}N&v`>qMOmpsX*-6ZC{cTHJ`QE}huEoFpAjv6 zrGe)>Ow)UdS^^BQJz@WXO^koRris5`Q&W$$1T3t#98?hXjhse*_^RO>pGLO;%rbOT zjnNUzbeIhbv7;lJtGm(gAO=P1>~-??~fX|Heyk`apVJV(6c{H%UpZm z_*;wvJNI9MI>%hh9CAcvQj^U`Z&815iq|& zybR`KES{(;i>&WN5}4sJ%|1nr~eV`Te2_hWDBlDZ*$diR>$2qH8rY%tjAo zBQG;ETq=^shw|W2%LIYxEK-gN0V^w0EI<8GW}Oc`t>a$i8~dFw&N_{7M^6mZJQj26 ze_P;q#t_U8C8P)Y*ZcobObY%*>2Eo3QbYl1ETALEd zy1(-qjHb{@h&2+8x)X-FZX?6SR{bcsvQe&SEV~E9yY-I*{8`DGj$<*&%{ zS%OMO3^j*HHHQh8DOjr2bT!%wzKy1f)z{E24FZD>r(6MQV&kR zDHHsR9hJT`hF$SIEe5W)*EoDATrP2g>Mn9;VJ4j}8myaO!^AX{Yt=X}KS$Mgbl>`V+a znWICu!bI!;5u2m|V$&CWRalGM{?3oQMEqAiaYeanJ2Y?qL5aU&QwBh6+M=w&)!jkQ zzs8bZmiP~`X*qaemuiHPuya4qPoavMU1~c-`Pq$V#i`jq=YAimd^>sj=$N7NDm{x8 z-Kymt_}|1Pc+JC!n7yDG>hPlt)!>xrjTo3wtPdo+c*Cn1{Nd`numyryK{cr=@{;uUWOAy!9wqA2IyUMf4~uRLWHdO(!t z9%p#a5LhQFeB%^ZFNkazV(RCf{6}np68XEs!ArNBt>6C<_s!W zl237^YI@Kr6j1Bf%M4gQ4Ol-3xW9`V){LN^#*piJkn2W}#{i{?&>an^Q(HJUU0e7& zDl6+gH3_?mjaf;RiliXi{CJkB=WhS8zX9xF`RyzZ>&ok{Hyc-o5<1vY zN!YUFew!7erYpOq!VkZ`B2T-oPQ*RQDC#D(was3Gs+ZK2JeAdszNVC?Cu8W|Pmr2p zlx6NL@t1?ww%nQkKx~Q^Dnk2Dv5BCX0U$P2PU0eQIp8=QHl2=N43A%ojN@I8@7&h{ zrKhKSi>zSqz&$US{1m!Amp?tvnqT2eSZR-4$(g5YmU;g8M{H^@yQ7r-m)NxPpJG$d z|6OeQ3pBYOFHoJ8B23o(<(oS1&66J%0b-LVKy3O;ICW8tMN*BiB^!OHCjZuT`QyKg z@OE~STu4?8zx@rF?>Aymd{A*|DC%++C|YXO5R+`y^X^HiTBm9}yrK9js`YFpR$#_G zO{epA5VjXPS#RXeY0{ovreP4n)Kb0(9hE;+UfVntq0kp{$d7u4X{5+!mL1D&qeTBq zaM*(xn!2J1hFzPPmt?`_Kw2k*mhc5RZu^92q${w+EqWO}?G-cY=dw!FMYD z?*vEQUKFD13t)l+GrnRfF43~cO92hQC(u_>wZ`Bh3&lV9P#>8BHqVb8)Ri@3!<7vP31~&^VB6m zT(T6cgNe%|rMuw$c!={r2s`AxTFXKdp{m+F4_npiA15Svg zwLWeZX6jIPnZ3-(hDyOlyULp&{6*Bg&sN(?+_eS0_msX?R9Ke#n@LsB+7jy1y^7LV zbBn#G%~$CSZIJhkH{7M}sDI^~$=11pA#pUOU51BPDdr>p5o&|*|kVn> zKW0-1z-&_gm)XP<^@s9@tmds9#7jNVFEO4=*N`wt9W1TEAf}Ga*?9|emyaVq+N&JJ zLGH(cb)u^dl|ki88gn)FjiCXWv#-;7&aqb6YU)x{n6Z`*0v4TFwymMxRGy!^pD@}q z%|jVJ=!Z!TM}1>4F|Fr&*mw@rp_}Ii={?kv1>s)nt?fHL&qQNvFC*{FZ=^3Rb}E%! zY{e-NyFB8{d)M~D8AB6VbhAST7}2K9)~PXb2bZoBUj0L8kKCE}E0KWe-M!e@ik7xa zsQN_%%Z*)UO7%Opg(Ma$<<)Dp+abfHlebsJ0SK*&oAOA`q&H8MOn{7JTi#0ob^|u% zOmU>gO%G4kU5s|sVO%B@gt|v74l{Z)Y|c4}EBm0&4YfBzbT`6ac{R=b&b7dAp<>Sa z_%qMV7fBJzr=FTeuUbMtp{2c%U=n~frAj1iDBE*YeC!1wzIEPJf&OxJrPScQrK zpiRneDnu2JJ+1Dj4fC*ABVdP1dal zT)0`yM)Nd`*6(R)jn>b1_AEWB52e^tC&kZ}GS%3BM{=TT&{aQuCpa>rJ6-Ql)V7xW zYN`r7Gg{vb6?r48ez3!6)7%8-y|;f&kCM9LIoqSjN#||Ru4JgPb<5zLEqiG}d#|X1 zDRymb;gp@)ioZDMYI9R)doGI6u<$k-PZ}C)In?=5pSPn}67_oeu6XSXzu1xKk3Jl5yu2-0 zcdz3ozPvsM%pM7*{@`S(RBVTC;eC?80e;wf{ucz+{g>YYrZr zTedzaBxIA@78;gsPC}nS=mUczvvs4Hf93{5r{_Wzmw2r-RqCFEBC<`QNGwz)x!ukT z_DHW1=jUs2lJ4Hn40_Vg(G;(wd`>!UE^P#@r#c-L**-D=b`c#O%j4F{hpy`4RM~rS zQayxnSL>Q9ee1BN*E7Ge&(tYCftBq0VrrZ9GI8sm{9f+V+S3-U+w$_~2SG-j?ctzZWiSFae;5D@^uWAkM8QRYf!3U$$ri-ulzf@ zneS%aMR}uo*)u18C;tSz_zDl}BTZy&_gw?mNXa9JwU_TeQGS(KPm#)a92PHW3U?8X<7um~JtaGRYtM7bt9&%rJ*I)bxY@(nsAnn{CobGsi;m}dRlA-h z<-Sigr`nz}6)UjqA!;$b!dZ2O*gEUIi=b&qvdZP8`p-4mRr6*V6Tg(rRZ0O_dT#&g z$vwuR$T{2Inh$}`pIE|r{ai;(DipJ>#R&8xZ>X3dw2PZ&zUFAN4bR~}A2cl0P|?{Q z-Yet^@m^e3XUHoZ5SSC=UHUm&I4i5sh5cD0m2`~{{j;fzUgwqT+kJ;(ORF>ccdZ)4 z0q655ZH=`%edo#dCS>sI~q&Gd%j)oCeZuAntx|xVGt9F>|M?>O~9nHyZ%jW zb$+*+AhF{i-~wz+CabOQ1#8Ga5`7E7D^#cVCAJ5XKri-P`Pr+y;EA5hZE){R*Q@@6dx4GLE9|JKd$*^_MJK4-<1c^y< zir_p73H-8O(+ujpC58>@3NsA~6Xc1|jZ}$wXc8VtCn{X3- z$vR?ShNeqigz@_;ROt?3yY1kj5c*tKqk=4=>2Qjt_akJDsz?jSGX?spqPWbBz7dj; z!@xB>FN!Nj%3)yZ>p5|YnvU4Pz*W?=iBW@D=k0)Q-D*ghQrN+K5cZz_XcPe4%c^N3 z4orq_p7w<*kC@Ltvg-ndqBh1qikMf}L&uK3)=1PrOe1-A&vS$9u3(@bBr%mYee}z! z(iM;d1TdK(oLYs++nD(?;J;KL^8h!GGe?{63zKt&^BB>$;yd|Fq0~`1RBm{0xfvKF zZ6Rln!es{(4HLH^^XHIr+Qh}6;JM4(mkn=}~Y7qWHBP2P#J{}HG{{6iG6b?Vj z@>z)!aOvupyzfhYfD%J$0$!#i2p*N5>umK;Wk`&KdQ`i|@Oi{rdzFL0=klY5&Cp|r z+|c49*PEATerNrX1RhdtM*AN7=lW-~*B8)#FQUH!IvZH(SFLe?Or}Ec+c&=dFN^5^ zPo0f_k7OVcz>NRC{?AC}?;v_MtOh19C?q844-53z3gSR&M_{$@#DP$!zI=>nX0r)W z#zkcw$o?XHaB1Rvpuz)Sal&8+K)|EM(dc>SH236g}Ni6JMuClMcQ{u!A96sAfq=rPl zUP$$@;R;ORSv3^C&}Al81@Oud%Y|HNYdL1qGqhTP64+HNevc@DAIE%Xe^7&>Rr64k zTO`4=qH-gC4~su(A2vWcQ#36HIqSfJi3`9mqORf&IP3YV{hXko^R1$H;B!SgMz@0I zufKzL!MbuBqiLq{yKLnzdTg0HbQ<2&QTz>W2h$s zaV5~n0tH0=W`rndh-kIr7FlWOaNPm)fXO$GI$e!@&+6|Xg*TjNENK>mrS67->N;~m zmDKZ%!K0$I(l|4EAg63BvfY za#VhPLR2gB7smAUoqsebZqONj#G@4=_VS~GcYj|K{E3Bc8E)#RYG4nEglBFZ0tjTN zU^>tMfs8387M}I#vTkCU1Q9aP@#MKoeWM+Hbm;FRYF6@{BU@ecwhRBlMDR~2B-rVJUgo*aM?uo@j6(z?~<^^IB+%52W}d*ePQT)%cOqit>1?r zMNq<#Amr+GBbT&@hR3(IrcD~0r9*SnbmMmFQqMfr4TY>FS4OB_A zNiRnHnK&1TGj_hNn2W8vk22EA{buFmiAE<9(b@SU%2yyR-rFCO`Um`$2HYj1|C{ir za)Ou}yS&se2E`m{SfT_%Xd*>|utXTej{%P|vA6@_@GYh|V8cm|mGAmX7|KAB4XG5y zVSB>+?Bz(1KW-AmxaqYDGiXUa3rCiBsaOTSvvj%r6hMg~98x<>lmIJNi?>Sl1Po}f z7&aC{CxV}^TAPQFh8#ktU+}L`D>SFm!S>{dhl}@w#p}nyYO+z}dqQ3lh$Z9`gRDTV z;1EGn~2`ap6ihpp`V{@5;B2ai&DZIba-Q7_QOx2_DPRYN&hwdJUqVe(;J-4dg z+^u&Q%}+5a_XvJy`hT1=`>RLgoT2hgMZnuOz^rB>j+;c2;wey>P0K^kmsgBy`ksMb zv(+`^LUSQ)uLX_DUGfAYvPMoMd)^q6T5=TK5TSAVEi_E@gc#SR?_8{TsB*=m~eQ)b;>Wta5 z23ba}tZVU2X$`fcNfCZI3k`TbBGWnqt)sxs9ojX8!%Nh6&SkX61hhT04Cg?l`_uz_ zfnS$Z*^8IYDF*ftySE#y<54*PA#z&~tU3m@gU8s_D}TFD=bsUcVrcCgiobyjN8$EE ztN#)Pw06kA#3-S;Lm~v*E9C#;>m7q*jo$Um9ox2@?AY3|ZQHgxww)c@-mz`lwrxy) zb56~fIaBrj@K*P`R#$)MuCD4@_j6yD6Pd)Xy8kk0IJI5Wx08XCK3A+8XRO8`Im7Hw zZq_;qjS7zHs@6E_AyX6v7%f2QfpP3wVR;*YZns5lJVdc*Rh%8dth}K0swiaiLIiG8k@|xEag+h~#qQN(anh>+Vvmv!4N5+M3zW~2M?X`j z!e$fjjjpBoj}Lv)K6wU%ZCK^UhYqtrOd8VLBIypRwMU)D#Ii$ez-%2{5j4X9ss>eJ zV5Lpg5Mm?G%4G*OdS6RtsoX2SAyWT|mUL;rU0?g84tD&}k*wSV5)W5U?|W^C&)Y!Q zbz_S$B@b$+jIMS!e|co+4aL?I^L4t(GNDhgAwUeb_4DoswanGXl0Cm$B-Mf1_o)A4 z&>#%%v*%ersUr@;B{(@zGY4wVLu$HE1me2s4+*NS2A0zb5!@Ila#_I}EwB-bv)Ptu zx-5ab@J~OWj?u{H@zd8?LCG-Fp4~Sqc3**cHtg!zDjlK(c?aEvi0&HLjX$B^g^vPI=mWH`uZIA|4?+!K~O5|%s= z#u|r2^B0LG7|9BeM3uVys8g_&DUHJ>EJuxOB|J@2Xml>*wN9-X#h#;|5AK|@R1%=S z6DC?m(|?@UkD&WARy(^->3M2Qgr)f)3Z8F2>TzWe;E-;Qa>4sFm5+F@N8t=k!lKbzJ@Q|My?wwpMBKz11@rsx=< zL7&n2G*FePGgrif3%@9aikw8w(qB9%A6t~o1~VD4IW=K$wQdHv$`YAYNd-?+g+jpv z=|2Jut^Y@$!7h}q9ViSYWC$*lpFqTzNOVgmv_}*oj3%^apk0)mHORw_qc#eMG(~jo z3F>Bz$R3f=LEL)j()o7|h=Rpi8AZ6c?Fo&83}@;$_zNano+a>-Hk~>vEG+_-huHb8 z|4fBvhwAw)*WRs%G0c74jF;x3@k>j2+nBzjL5R^GB18rN+Mfq3CaHd&go#`LGSM>v(p8HZV)m z81th&|CEgT2EL_Uy|FUxuUHLPu7&HaUcJGw?zdVE`5ydXcH6k~uHS$+?Xs_iu-C)c z8RBe3IZ*@7sF97Cy;umQL+iWJ(Oj@VTFryK3MSja#2$9kwW;Gv>o9@EQ?CU zAwgLd+9f|nit4Qsygsv{-IDdPoAv9qHe_!M482PV)fF)~iH5=V^~mXdbk?wO_dQPJW}9zo z*kWq|k3I@tf#pVf{>z~u^OMfVIJh{C zLL)oDDG4!m{><4GSVEC|jq+iiBUxI`fPzWD(LHd?1wOjx&^=1?ROYqtvtot`(&QuN z50~XNW*3F9t*{BaHL$TJ?MTPzgqiK*u6GM}fEqeP zFiE76@^ObAgF#tNeMvS}V@bEhKm($4te%0#(6X2ZU8W}yICOsW30pWDvjApmB2&N#_TL%@&3ZwCo8hl^fV;E+5tfvH_2cS`hyVEM403F>4Q;7W)q zmBphP;sM?8;zTyF{GRh~sB8uuu{oAvoCR4AV{foYLD!#{`vh5$mQV=AmMIDLx+sL$ z#64$Qp0y)~BNsbenFJ*~USePb#NG1)cpMDu0|{JbXQ3I}p2>k!LKwMi3-T*ARvl56 zsFy1_$k^;MqkN^uO*&4D*vK}Q;4X{qW>LhtzZrI3dG;hEA4qPp4vxTXQvH&?a!Kgk zgpzGXNTTws4HMnq_FR19@^?ZWSfe3~qZa70x6=Kx6%rTT7hTJt+dWd-*Z1)Ckw|j= z&HzEdKc@Vz+y2%&qvp|iot}ycd_cXFlQF^fmZwXiIBuZeITV0l7q*(}%9T-DQtiP|Mqi!28(Z~pb}BG^K^LI(ze z|3#@O_R6;5dgG=%B~OUSJCDq!eX9j=_x;s5c7$juFVo#}hMa?B6T&Mbw(>m`KaS$yE<>Ufv88XH(6vWr05N? ztZU70xo)pQAgsO8bt7877Q+(sb`>3_wtk+_ZPVJ}U3wiX^{#u389({?2AEeuF;c== zJ1$=^5Ik&11j}|O#qee+hTTW({+PMv zRR8_^KpAq#POh&8m-;-`M*28T`vm>IS6JDRV9*&e^<^9A@P>3PThmV7Z>%O6_9dG-kV$N~Z5+NEp9|=Y(u_*T$R};} zpXSnJzkjUncg!kPbN@w9Wz@ho>j^L*?&kp4Wyw(n{-#MYC{E0 zQ)BpXxde{S?#Xd)G~MihqGJQ3+J9X%_R%r2V6&5(@b!whKDB6gdOrn~VEgU{jehxd zpkjGys^SnllZn-!2u%=1Pr6+cq|h*&xTGg+Ll@ z`X`sNHyZ}HIlQ;y+bXy@Y0eoVJa61DH#Go;`>Qk>9*43cSvQh!QZaHY4-erf9wT0T zqC=%(=_EHa_jhIaJ{O$8`a89cOH-NKE}k84V$tl>;42PQ&3xjPoHg-MpB5_Vf5uK* z$2qN1Qe!Lj*KVwwRDJgj71zml)^Qu(E3>yB$582)`IX#mI-?u4ZH-3`V?hy?ySFXO z{^2xkOt236G~?gc57k(q{2U>3MwK{U4yb={p?_~-Pw%#MH3w5js_=ee&1}*g|9&x2 zg{ZGq2<=e16}-9LuBv#nse(bOBGXzv_?`Fk75mxZRCd{0Qx#m|w&#*rKRDh0orm&< zaBl9ZbuKOTz7o>N3RS&X@ahIU6O5h zypKk&baTM0*RVXT+8*RL1OXWF>!_#eNqzI&U) zzZrBg*6U*Xwh-gc1wuE@cTYaB@4hao=etS|BVNslgKHigy9?7@VkKNQW>hkKCy+Zf zrg{^fYq+lu&%Cx+orm1%pXHC%9%gfv9fy5&SoBBzJT~BbUaHxvy;|o(F+Lm{i&O3= zIe93D_YV0sIc~?6S~RC><(NWn@9Z0gU6AZ*?(NYsvdvQ)xBGSU@Y%iIKoQujRWF(# z*xXw_0;^(IuI}wOb9`)8+Ai^{A4STxBPv+6yS!{$aI*Bonul#RYS;Q@a;P`xXdmpk zwky-pzjSTYiamyl5IQ%8lL@+giJDT@b98f@R4ez-=qvdXyf?-Va{xyR0}?KoHOqbH z+qe&oI=-2&XKjow8)JEQ5Ro&lF1E)+D60Qae;DqTl8>YLUg?x}6z%U74v=sL4(>%X!e zb5CDhY?~hM;n?rTu0Kues%c`w?)z8&{i_b2%U84@xKQ79Sl_Ci@B5qPkbc{72Cf!C zJ7Uo6m))D|+50%#wYmjQ?IWI3&EWB;XPXbK)LXQS?mm;|MUv-T$2)&n{yfL0@M+gC zzwxx%OqiDK2BTwiZTxjKZ_HLM5)Zt}?Wf7xsi7 z=ULdR%f4=L%8%6*cRf6-zSf%V=-d2HjqbAPN-7}oQ-_fCwsA0E`^v8}^&Fy;Z;8+w zL|iIPR_udk@U8Q~`blCi2;aWm2snRxP7^RaeF0zTh~Lm)HSKocWsnb%bVa3Ril*dJ z&gIEr6tuSWAUR0+@^AF)t4LsC@d zl3VQptM)&2|FN-QpVIq3lUX&n`_;Ckr}@|8dLlN`>Rn%Jfn3Tc8W0;>S18#VZaKJ8u3ygO!Z5a2uLU?1nxjcVDj8?fpYW=9 zI__=eJ%5VNm-z-K>n(nhI)C{;=@j0djwh5yHcM~)y{XxF+6NCtNc7d31({$*Z41BkP$Ai>b(ai`05je`w? zaM;T7jVD>m)@w$@Gxk;p>4!*fKL{^N(}9m}G!D$?@$I<({r9f?`*vu$8rbOb%*c1( zY_zuDq9pz3=uf51jhs!4ld+(P9#K3e)KfeAu&xG6%|HAj>%Tf*Ux)px{wQ)v=?9;o z_O1<%$sasqywy&(sn%-VpCqQ$zS0o1G{3X+UM)NzBvmu%J#f1djv_xguPQlm^lw8& z!zNj}55cz`245A?bMCvVeNCjhCKWILkyHD|dv0`iVm*1{Hd&m0(>lt$G_Cvat#OGi zf4XiwiEE%( zpiZhP8w!ImK*57dQ`CP5kvNd7AFqyu_nSTLJ!!G9Xb)VZmsN-Xq%pe|{qe<;oMmKc z%Ib9Gcf|{O42&Vn(q9`AgQjA@LPkVp&*xE-hHz6O^Rq+VGyC;B`~O5i?mtmbUP=(g z`wza$mizyJ@A_{<*MC&%tp7U-V%D>@vv4xe`~Tp(P=3%||GEBG7IdlQp^l=q99mk6 z^b9u$=FlIF2a0AYJQy5+slCS)Vocf_XMasf*wkPPnRk`n*rch|_+#tXKIj>+VA;1z(IkydrQYAM8zxQnOF<^mkr>q9|*b0a>ya899 zW{MYLq3QHChzng_4eX3l8WQ|57z%Cx3hp*Yv@d7~eWwUmreV}n;F;xL46EZ8)v5S9 z>WCw*W*XC|z9eIPAnV3USGeR%L%;D(7F_yk8E?Gvkg<%>gsN2AVxRw!!_}E*%dEcO zv~>MY{vvaxZsJq`dVWy?EK0n;rt1h0AN7o+4n*vdx)Sct+nH8b9f5i-_^pj3P?gOY zw!3~CxaFKW3=HQ?2yHhVR;c%X8agX6EKO=&iNSwmq`53v?Zf^^y<#4aq3@KKU`|vG z>KkH`St z&Y1uol<2PmhCK!$$t4_jCGHZ*f8pwPAsXVUwmc}r89CI)#C7mDKNr7{U2=1#Of4XS zoP<(`fecyluYl#_vyi`(C6_LmV@_kknK!`0E)*AE38I>fX%k zzU6{;?l_S=1Smx>!0<6k(sK-(clLZ>cJ?q9V5H$zV3f`cOg}CdAt(IvUy|DvBd8!K z6k}T``&B`~HV?ywRe~`QCrC?EXi~)~1mgb{J4pzOq!^d$+j=$tFRA~hv}{$sL)xmK z)FnN)kqRPBg-~Y!J4{nZ!b)~wjo8u#t#JymaSF9Do6s_g%#xQ85R?1ZzPiIJaIReG z(gfXco%;xpzmyo%GkfA}uEvzk34ck#5Dk(jagACZoy)+Grm&SRxmFAXr4rc$obW0y zI7x&z9}K?`;qxD<2fLiOQod0F{A||!-Ga*4C7d<9mH^}kMpFR7V3j`j7PKj}Rxd3~ zS{1NUEqNdI@ma9TtEW&zB=#&n%H5FSa1mp}mP z-1)-)xik}JEl`PCshJ}vH7XJ!sTvXfV_iXyD4$JX-NQ#jL-|lOou4F6u$NT z>&hezI6OV7r7 zUU9(=Z$V}CpM>E#%79CRP30mdvg6ZuX_($SM=m(ySMQ1cVF;VjM@*zdOnf1B%M)HH zvbE46wiBRuiGWpe9mF!l2Ur2}lUp5~{Lj%Q|AxE-6+3zjC6+h_eud508V6=D1#Z>j zER3Wp(t7shHfOO~BBlVRUCnoqFjGx}mgL2b@Z?_(f(3A#Dw zz@eRJ)t3NhJ232*GGT^KLme z#}fXc3ssRfYmT=#%9Uh{clZ>-%0O5L^FZpyDiM~}P)aP47Zzcqj)D-c)lE|woKY** zzjh+)XsJGohcW09?)W&vUgl>n-v2hGNURUio)i+Jh>b)Q9ga%>U_)lzOMb!z!A3T@ z<^DSlj;+5rh;s#ZJ0%f9Ylotbz&hLxDtHEB8yVez##<^ZsOu%g9!hIpOOV1k&<^Zp zP)y6e&=pzYvMwfDu+>J6Z3Y41j~!wyh=!HeJ0Tkg##>*m>|r2RqJYi~p(4P{JPJG$ z4omw$(09R>LF{|>nAxl+Tofu0R1Atd z26^vbK2Cy7ONI;cO8Up{VM)Q!7r!GeXwRPS?pDcxZYqN2+**t8 zXaN18hy_`2F_({5q#%qfQ}~#)K^D>?ML)Np<(SHs&_2M`BBeK~^CodMr{|ao^s^vw zbr*B+`!=DSRQ!rb-Z53!IU{1U;)PrwfftIyB*7z-Y}5QO$v*B+SOj9if%u4icpTA7 z$_Y=>2~X5XWr{QY?^B*Wf#djusRGh0F)6OtcxO}6tYJwmE7Gh)vaH60skS?oxTq~p z!pLlSJXO4Ip7YuB_ATp$UWb-CAV6vvSx1K1h&S(GZ@9$nf!+0Ck~@PLd)gMhNDCyZ zc@#%_B`pgikP%urcv?iN7&HV2zEZ%w1@%-c2QQ3WF}wsus@Y7~TS-5Qp_bC?3iy;! zV`+jY8XQaF?5Q!!pY2w}d^=f6CBjf32lry|L{-Ix!i0|5h(hR(rAUO|H%AC`D}2z7 z%KgPoGZFajqIMh))~ieJ%%gxZ=ACrO@dy*RiJr6ajQvI^FS`E1eMZ>OfC*T-%(!ZO za_m&Gy>a7Ulv?$+di%oQ1eh&PW0mNZN8??QNbWJ=z0_;2w?nT4J=6Vc<lE`< z;@NtYd=o8?0NFv#RH?yH3N(sjoiRdgdv>VEa6#1-dSk@U6(THSiczR-%7&@!6z$ zhluk@k^07xsbR#h#hHhI+-QTH>g;;7KfpMiVqDHiUC#Lu&e0>Crfv?7LF5c4aG1K> zMCf$6w_`YzEey6A$}@u)Tcj*!jm ziY=RoeV1LRwg`pxUX;P>BtPpZeV28pwsM8`?_d>5GU&f<)GmsIg~;Lf$iS244w&no zj$IY!V95nxKcd||b1F5Kfz6x~10I4=3+ZpG5IJjZs_Z3qGyoy=ANW1J%V;anL3W_r z&rLnsB0u_S(is_lc9V(f_WVqpVBqnJLc(iTXzMHLh`Rvc9sx3W=tw^vRM|hh{yKq`zdpK(hY0v8Agvq1~V(PO7q=ZGSweH32J`Gx!&QCCt(F&s);499)1 zdq50qz+#rUpmCFRV<_~#?wD!RG2pOnxZmOmd|pq(yXOI*=c}P_oCwKT$S>TTbvc6JR~?Y?j8T8=5ftowQmhDP4sSHtWN zjM%j3B#~+5vI7&iYw4{y~g|0pp&YN2Q#N?beZ+xCF z7`7%aU+0EKeP)vZ^CgNbZRGGcEeybqa?~1b9YAe&PNL7ZM?&rMe9^1+o~ylIRnxe4 zN9VMglFe%x;d)ICFSe&W)E^E#zx<@UXaOa9Kt+lF?RZgy`Z6>D~vs*C0!^`fv%%zstZ6Z^}7IWZ)Hl~^qK`9fCVbSLZ=bJ7)e0Q0t3KzDV?%Q-=s!J z=dS%O^?1o6eod8hK_tzM6zxLfk?n+wjH<5lOU(~Qun_Q}Qn^N=Dvt`Ug z$uqo{3sIdM-=*3wF5#3_^CL-thoj-w2kOaFu~v|~Q#N`C(+1_$6Cv(Cgdod1ixdF; z&Vf2kXQ3#8scL5QSC_p)n*5QFbnHYG3XJ zQTnz0VK^M~f_9%TPgnk`XkCs25tkY^wzmEPh?-4=wUZj2t8{bo0y8-1zlxa#1xEQ9 zF8pk(qT8~^)faS)vui<-(H$iBr}Uw0CIqIdCqRM!ppWQkXQz*;OKM^pX8n^3hH4jW z9^Qnz@dv%`P8trSm|d9WN+ z90sHP%K5w7&3{a@R#k81ou{n$`+ODxlU-`jlmxu|DHV%$ZE~Jf`+#!sB1SR0mhsC@ zYJ0&K=A~Em#aVm=2zsCWj<@rRZYT7^|H~G0+kPFc( zfGU=&V!9jb!=CeN)d>hD(J>(GdveF)Qmf~hdUzY{;c89=2)Fm!T2r;X*^hu|*loCH zORapT+2P}TKo5A?4UU6}t+?qP-XDyg_tsA^wPL+|Yj9c0V1df}GL_2;tBY@$o>FvK zBDGz+OXRJ8Ice7_Csi*=vF3_)yZguIC+XDK-uPUpXgPaMAiKn>TMW*pv6qeE_V;Xb zqqAyNPqa)j0Kx1V5Y+Zs?zSRqg|%q9e6(s5!6v`$l3kvDrXwnomeXuF25Rp}PpW(zpv-s^F&96k@Y)Ot%*XtdhEI>_Uch5IhQ4x3mv zSunfjlQTTd$w#HTn^wVhjn-td3(GrADo`eyztH3b&=)A)ZJlD@B1X*ov6=PpPnM09eE4}h6yc~s*8N5(z6kiT=~}Tr?#-AQt=s$ zb+uBJl$LG7Z{2SOo-d?(vOat^CCPtIoSzeX9&k09OMQD)G~pNX{AREIqI{-ief^lp ztwBLhg1$Z~`gg2dKyJTH+ox?a>k zC!&1Zk%Jt9&?dh5a58Au>Jay4aa_A66mLdVgW*PQ?2e|;Ha+b&|CU`er7cCdTZ5*Q zC76h^T*E8kQCX3NsG%$kSI)`*ykSOB+CX;gR-x7y^d`P}toYE6f_h)5GaT9(S*E5- zuw2n~NBVfbY1u9|NuLLd+`LV#?wPh$xi5x zyMA|6DbqN6x^3k}hizJrv01Qug=<|v!nct(Wn%3tPq@Ddx=29Zm2-0uJ|uchUX@vA z6CQn)+3M)Lp@?&t%F51pBZaf6KQ$h*cxUs_$*$GZ`44uH^>0BsJ;F#y8N%?l<0T9Z zuOjQw{ZNMWL+Za8Rn23gg8NiUUfHFlS*MD_hLLZg*x&FtuXi_JJa19QH`m9h2eWlB z;LTbpNn#~Qw$!ri*&RN655r72I%6aJ>xK#<Dy~x7@J`NGLMFY}XkJeGc8-mWWPe3sY*9XnTi`eRw5R8%SjURJgii&m{VO)ZqtB}GY`itZ&u_Keh|8R%sfNAnW?#y> z?%j)0Sr=YgG{X%wnJeKBpHIX?)p;^)>-|vf9kKbV z>1@kw)a()Ly4Hulw!)aqXMxy|7rm3E*9?~y)9>nmuYgKR((BKST%XhK8sDJ6uK;QK zfbNO?p$%UG{yT)5?YVa;@GrMn)5Hz{oPyy&qLfm|&b{KG9`SJd<6Ftdm#|ArFnIJV zL;MHvG^TSM}3Q#aX_FTai0)ye_&fkkg#hsWK0+& zT=;-ydeV~03+!U7c{oBE8Smk7zB1UP&WA7Ll>S|T>>AJs@3k+iDZjgmXd;202xl&< z?b0UahW; z%V>0(Jgdys$z&eoFl=m1Y<de>5~4w)B&EwO4I8UaRz5&e06mYSh!?sQ zw7uCu6#gv$k96y27c(i_1h#$s>W<&VCzr7L_jTI$==uHgo4I}cBU3aQF672uD3E_^ zW?o)Du{bdMaD<#68fOJ^L|Hpa>D&(D~n#`BJVzc|5a_u;htFSm_B-o|=&& zh@vu4BpX}ACF*~yqW%5_<;mXW#up*IHF|X9I>>ebr$>!59;7+8c;s5Zr=qYSu-va( z+m)=Zc!|5K4! zOFgqjZxARBwqXEn;|w02OUD|f2)AYs__UB42+Wl(K%o_%QQbLVgOTN^T?bLsg&9#F zMQ%!K-x85!n5Xi4a*91*sxCI*1p4Qc-ojrLLwk#<_fmyNR|H(gsgKsizNkOAI+(b^ zFcDOv-cxu+Mx?xl5d~<0w+Z@zArMoF^vdQD{rv4I$&gkZ0=j{?OJ^`Qb$bri?Lj@S zQCeA;GH}cI%`E{S4y07FH`Xq0LLkNRH*KBFUA{_46qLG`Pn-mrVHFg}se(g0Kwp?u z6!D$#L<{WguYj+V7E|G(D%T|RWdKA(z=3P(*p4leXJ%1jbygD`q0Mc|lksCc!{WK~ z*}Gp5j}*-Y*CLz{EK0aRLXlG-xk0*6JSfkfI@kn>W>l%vkj=^|izTdj{eQ$ns{+)g z9C34q7b8n?qUrN=o3<;JPMl%Rc-E~TZpmmB#L%vV(k{6~g^&1B?VeQ=i}G_Mfz=bI zO>1@=(G;5@hv=We+&EU8}n6-XVmRA|xYq@1h#?XQCUZ?TQB68fN^ zD7NC4Y2v~-{VXJ9TE{BgGK$S9 z3{gErQa?46=mf(wV4nY~p7>Qhb@Ue5A9lE zM*=TmbcZTp4>`!tnpWi60*sb5&`jK!mwJ@Mx;tyF>#FBz0<(q7_!@_&vHtzF=?!aW7e- zjSSE=LyCG8t|ptQ5E2{$3|YlE1tyZYrL4E(kB0 zx46JJCFnymG-O)u@W4Xkoz;wU;*>MtinL5~P@1hu4~6MX+1PRu_03-_@9Mk9;IZY@ zsG1t(uBHlapQ^h?p+l>ZvE|>%HJj9WUdmlh<=&TxXA3e5yVM9kp8SCcq<$P#5^Q_f z^6j+;bI?|iR{vRb^gFP4dyJH%gg z9P0olKy}uWjntdyY}Yqzou92{oGxe$gc)TY`x{+0k+h3uvhcAiM|vBmsA@PI3VU(Hh36)~gr|{s#%xpWT9f z4)!ESMvk?@4ZCf=A@Qpl#s}rHhp&`{AYI(XIxOJLu*SVt)p=SqMlR5V=^~W+-k7@b zOIY0vrJsYm5=dJ8mdk*Q!GMg(fQ-?AjM<=w!JvrA0M58iQfP3aOqu2sv_U{XCs`k{ zt^qu;3=Yb%2A^VwH|jlaMbtzE9vOt$c0>njD@R-4&T&*FjyJoWJxZEXX?p8M{wXv6 z=l>X`7m00;E-

ON|k$m%$hQ1dJUj!qo6g*!d90-qQXpID;Gk-2vak@r!;S_dNE zaK57irSEG{w?kpU-&?hw7n+4z_Uv0Xg8gg2P)_hIyZD|EOY2Qb z8%=W$W4vWB$`4??h5W5(Y{qCz;}D;TxLaVX`_ExeYtHl3rdwFnD+@=G%Q!T|$yvbp zpOIddmg~-iVUUvrqzY2~(dlUV^vGR&3c^Deg-~~hdn5iWcEAg8eNb@AJb9-Dw07ZN zzG8Nt%(UO+#c~Yw*`&t2BIT(+PfU&J7ULgbNzOGymz<(1O`xM6f8Z-X9{EfIlGo*JnNci5lh%O@9DHnA&K7T0Gcy2Hgv5)6QZW zSoaiqM+n^uT+d(wESoa`$M+}`iuhz}Fb*~FY$zFRku|i#+o4qY8$@6FgsECFm z_vHt;`NSx2S%QiFPQCv3W|y%mZMN0!P>C@!vtgBZM6Skf}OrO{x{S=|3&R#_0aG5-{IUM3arVnGf0<_zua`_U<4gQp_G;Pjhxm;=gm( zCwEipZS+Qp4aOl`K~w_AS_QzCu2>KA^UWU_2(vj~13A6I;`fmbIa;3u>O?u`CbNET98f2bhwm zS65K*uSnIT4tf1Bcr7b--B$*}(*dC@D+78it)W;MM0cxSJg&kj9<#7tf)t7Rvzi8(YGRVMBTf*qQ`b^WK&4&DlZzhAo_>0whK!B5*2Zq(djT4m(c|9k3jl$I?fy1kc z(@BrjNr2VKU$Vuu`%FS=FT&lSg`?;5mT>CI9f+W(kIP1+o4E69(u$`fvL1AgB>qKw zuYpqP$Udnk?o3%Wvfe)v-NZuMJgLY@GYRfz3unSYfh}l0{{sJKGmmzRNj>S7b}Xch za(rMM3PFJ%cWRd|oa6@w1__*-4k?xpZe|gc#Hk&GmNsedH#W2G#QY;bfL8q4M1Tt# z!(t+8NW?RBi}*z_x+^o$Ko-O@aTgQXu?yHq#q#!CGHn>_`rnY{!V${KaJ)r2)DoC` zebs_k?g#=`5NhOzf&qval|7G(vEq)8fSAe+0Qe7DgZU4Ym;xm{5#r|N1tWb~SjH~wjv=lMJ+1okd#RK*8d;c$OKzWr-Km?H^eFc0y?MVkp!hdq7aOXC06YAs@Qxw2dtT6) zZkD?}cD{PgEdLo}bOld!^8DxUj%4-4b2*@kMBdA0E@BJh=DMw$`90Tj`lo$jntwi0 zX|PCjvV3E8pTvv>E=0Jj!GPL$&+G*M{HOa^4)g7{)Mu=fnNg^=`_kg>7vKF|K?(kL z4)O`+Kl?lzyI8*@cSo@xU-zCHqu%A&k`ORT>H=VYQLUT_ySe1=kE^Idq+lq`&ps?G zv!R0`(Clr+2hvg8P)#U0%h++PXQk$%#WX#fA7vw4=A&^F$Hn>hthD15u zd3U~?i6b{Ehl7{>C4c`2HVV~=7hcXAqo4B5cpC-8@NU*|efHW-6ct0>^c%sN2|0vi zj;vnucR|#j4vQ(~P%jbiLYnwtKKFc(?$I6oUrR+0nIwky(eH{NHKN)+3mKdowU(oe z9oG~h_l-K)TuSI++V6+ zPg#lWYWakNIlhX3g(@n3A{yp;0eL`9rm1+k>SPXQ;*7~%7VAfP4TjFf!rZhM8^Krp zo;e(WtE1x5PXY03VxmgL=O>y&C#kG^mwkOXjdqM_maN{Tuk?1^KAQw|rGDrhM92cZ z#!h2Pp1u~RV^`H5C)Jg=-Ier3YwobSE}M(VPKU6wwbaiwH~NN;)hHS)xW=Y^8x61M zKWX{8tVC|-NH_ssw;L^+@jjm)dlg=4VhEK-Uf@&Jvh{B-W3Kp8B~pN{!($z-;_B)n zvF*Cm`@qdN*dTMc!0)*2Q9h<74inqBJ~g(vT>$N10`oxkVD zhjdyv+*5C_$wHquCo24Q2wG_X>i+4}&xW0uynX=wJ^R7A$Ye!zv9?X>cIO_!hYfBhk4I2xcXeWyvbR!{{;Q6px3tY;{HHbct1`|U`u?l z*>-x)Xzur2`)2+=Z65fR7mC!nkDU{3Nn!i5o@q6EWeJ!`8JkjiQoSGSo1>bw`uG@R zsW7P4?neL=T?wazJ^g)y_#1E{Z?)#Yr@o^{b%pS(5C(@$K*tM?!4(0h;~M}Kcbwl-EYy}cl`$6z}%%uRP6 z@!-WjR>!)fv<=HpKiraJ)j&XR-`;XB$+di3`B{Dm-$P&JS@8iL(rWk`v!3paq~5M8 zbhIajuXlgPc2=Z!>vA%30bh<^oqG9xo<+p!xtfUOJUNWWO;pNqv%S3B@mpHw#E8A$ z<3HQ!@q{1QDQ0(K?jF-fX?d@lr^VV0)g;2eE`B7S#XkJj)9P>Lp?~%{eSY=cc8Pq- z6a>imypCT%jr(*MEyWC*V!XeaIH^vYUOe1ejnR9nvjt!I&WqBfJK;Qa58zz-YDC7$ zbv2C1O({s^sPEYY0#JmWyDO&Q=xyRBQE<^SWukw+F%+SXnvK?~+MBxJ@NdqHj8D01 zQ+(7xq(K9Dj4;J;NMdj_vR#IN-(K*y?z!5Mz~Vd%z|gap^6>tDlBk_1MNy*nf+=&8 z@^8f@lEWg%`wb-vwA&;1sM`mD19B-#<%uDoIwK3shUU!GUd|UErXJL4n%`Z%w=za? zA^Ey|v50G3SP@jXXSh*nKw}9(pKZ0T>&btexo5ff)MPWMYl@V`J+5M91YU%Jm1M*p zQe-KRa38Hy{$G5Z1x#a)!e<8^bZ~dq!QI{6T?Th|8{FO9ox!ER;0}YkyZhk2<-PxY z-`o8*oAf64mWG6;329Hy`E|v?)3y9wwCbuBW!R-{sv!_{N(pBdIq@ z4DPEWF7ksm5|XQ9-18nFQqmWZ)@PAh16#DepS&a#48DtC$E+>RxOa4Qtg-9XbBI7^ zLu18*NbXM^;VBtnc!4poS~ZjoQ4#{|0ZoC$xM$K_7@Kai7hct9ZBhkQZPiIaSDU|U zl-N*(M`r=H$Gi3KoBFr>RaD%P*T*hb_B!gSz=c&(^+?wvc zdynuS_S8btIWCoYeOrE1))?VDzchF?QqDRDz_^xo#jmrLf61I{j71350bzf*?+=lH zzjgOP_bG0M{M%tXJ5x2by}cV$AiE8NxWBnn-`9p}YZvwQ1nc{qvhTj%YNleVBPad5 zS044M$nj+Qah$#TLsOatO%*1RDe6Yund&Eup5tkq353RwHGnLv?d!#Ix&Mldqj_T{ zNA%$;RF5XFAT&;c=6kcy;6R~bKKyu=YqX_{N9Ks|RFIU-`n=oos?m; zYEDU5hNdovYPCu}=GlgZT0h4P_f5Y)_SDMuuESsD4OSXi{MFT{kaV-TKFdqiP2fy! zfH`XDT5#iE*9Ws~ND;W!B3ob9b>*u+=IP+Fog_+qnmm^F^R+j$*W=bjpMKo;f11p@ za&H}v$7AntyzsqjTw|jI@U$5TxsdJMs-$wtSBHEr9%-^lusqtk%D?BfUfpVu`8u?o zl+ekxLd|v}1gpzmJRz(?2>hJm*SK3udFkOkgw`WYC2-4L{7}l{D_%mXjuZa_R+-r( z6V9rZyjOIKEa$@$7yyr-jN4m@hBT_{agR5b`f9^OqgdBF5ucNHn#oZX-sd4f^qT%D zaLqBn<3zO7m@xXUzN;8j>3YZg3fY|M6hV4wHbC7MHcEIwzGK!$wp5+Rsh*`~vn%Yb z<;vvQYqiW}dNq>T<)-Wjaq`EP34Y?&*V)zWp4(e-F5f?{T~Q!gk*%*rT%A*^$NWd? zKRSa378j&87Ozd_Cki2d(wkhvLq5JrZ@&uqtPHseTC!8CBY41jabUGS9NCR(fSK~9 zz$}k&t#iTUW~9A*^8FKi%jzv0paBlH7|CQ~VeC>q67B#f}vmZUvEcDu6yI(Gn~%7p0SCCr6GlTd*CK`(Gk5A3Xl@BOlv+dgjc91tdWvA^ zWd@#+-k|a@Lt-|#O+}3y&bZ$GEX07~NsSz&K2mGU92z5!0|}VK&K^H8xWbJs{!9rN zCzHObIe2ERatXsMlaNv~jcH3{sz{VyS2}PsTAcB{9MdrJzZnd@!VR+w`xC#@PG(6O zW|;>|Ym?I^V-AL^0lpc@BJxp1q5QJ18_0SB0TseDLv=75&@w5aogmyHjsVsOkn1^& z!eGf$^owZax`+CR$LNuWB2g|K82kT5CJjJ80FDgn55l1}v&>`1a2NQ*x=&D4tud?F zUNLJ0MppaustZBAqocB(Jqg&CT60-qOvYsHQW8#VE}f2Oq`^oK-kSLQ$C+pNndRKN z-Z%XIW7(z+xNOt(L20Fk`Q^)*;Qyzh#`JGJ=96JD{@>~h<3H-me+0k*GsedyvWb7rO7?3(r7V;LL=~bjP@~=QQT?cWcE<*!tu47w?g_hF3f8soz}>8JxR<(B_rl2yJEc zS3T=Q;;D>m_)PR`Y;Ji7X69K48)MAF=(keiz5KX+7moAav;b1}y&IG$0SpwQ@_-W3 z{G0`AkfZYOy4cmseN-lkUr3m=o^w?ADzz#C>X}2Z)Fn<#Wh^YZsrbpZX&}_4I5T(5 z0%%?12d-QYnP>Lxq`L;@70#{5G#Es&<5SIxS{ZPQbnJa{pAI|H<;;|TWoNYR6Jr7Wh7`xA# zHxm|2nCg#8|EAuojA@$eI2(c~7`SASXGmm@YE&L0cBs3-vvHoxtEUMXJ$skJ1(?|u zdhkwBZ4UoUs>KUxUO?Mb=X%fs!uK^0@AAiFCe`V0v`p^D>A0Ax@(>THCKT9 zMwn$3p(~uZF8E5CptdwP){N!Y_gW$DOMg)Mw)IwIb8)fQh9IjI3vI6%bh~nKHTvt_ z4!P21gd>JwC6tyJw?4!^@E>XB778Wd^!CHVv0sN%1dxwYtSwZUGv_r8vMI0GXn^mr zok{Vy6|n>Gma(}K59m_J_t~s;T*-9XH7$3m6ReXVHe!;>;pVa%?`!A2&yVWdk@Cto zo5;1y>-nO=eJ_rNhRlx0HJK}A4U7mM7$<+P) zr!S+Ll8;mr0e>tc{CoHPH_7V07z&|fqQDB=)Fj+9N;uM&GHr(L%khM=lu?<@?-2eH z6gLDC!DTYI#&M&9jOZj4E?kZfrC@i>fj!OElOk<0YQTuviDBMMn42)tT*uHI0JVuaxq7|x<5-yxVXABZWQaDsfRME)gN@9^J z$VMWg4;t}IW06T+4#Z@`84e`@VfmBP;LVtIHEGn}(2yKmnGr<-k|g3W>1PJaP#1I~3!is^?mEVtIj&g=7Ep?|r@g5I!D zlKVfQ4nRN+ENMh+&@gG3L)HKbYHFYwMRk&pHHw*w!q(_xTky;pJH=ac-$BRzY{^2b zwV>LkGTWlXiP*8*{i2EEXNSaX2^oT8m=sMW3twA4;@9O5Be%HoV0pxB`N z{e)L+iOp`~2C(CMPyZWUT|P-R-T>iMkdhVt64!91NjMxDbp&{-NHZ&zwGnNd7FDCQ zOdu#W{x_6gbR_Nsig$uUOZY`tEIpt6kkE!B@Bp93mmBF z6(i$a!7Db)dLiRE_`TAs4^c_(@Rl<3qs+FI9}O~aC9CGznxVoH$D( zb1@MwZduKoGjTFf_k696YqRTL-oeFP8O4f#q?c3MTE*JpW(G)MTh)u_0FSIr-5?ltgd$RwLf79`OhI0ey z*xqe`z-Q+>fu>Q83+Sc8kCArpr2OgWvr=bXWX=?YoS$qACa16INCA~-s@jUA+uBBU z`*dKNKlS5kFVO<@g@b%@|KNwpP9eUMTQa+2TyYog#Ue_*C-vx?Ua^m_Aw=}>o0bV@ z1M_KjWiq6&@MS9CGT0E0|6a;t<16cvZPW9M8nEL#)VL6%r{j zRm(+(EZc_-kT8a~D)=EY$eD0x$k9d>)MSq~a{r*v9NIL}e`0`=b;CR{+lf?UM|%Ep ziFk9A4{;p);AtO1>|JPeFXH=qHX|CLM@)mA<9BKZR#hmCL{Sfgef$x~0uYcvKa$cR zt9C;IB!c}%5G3**K-xb0X)(Zl+GSMwBYAd;q0t<{ z_jYJaSLOP%JP`xbHZpOCD&&0-@YP<^CEnQx5Pl%NI{2hl%>qDrh52g;0sA}jmVADf z2zMc(MXm}^xq*tD6iHn~L0$Qu^eT1a5vOGP z(qeFvIjU+bk`1b*N)}j5_I1V&flhLRG&VWKS#z!$Kt&zs*AQ47*HeX zo_YJaV$be;6zIzIJI};~$F{iJI5EXTix@etEF@kSM~oBvUWbX3;-XQ)Dg)|9MLBih zA9~ews{%q~C`3HZWYjyBHoVB7*qxRJybY(N^{1u%NpnwOR2)uo|As+TuZ&r*{4aXN z!c=qUFktEs`Iw^*+_Ql+A;YT|?!#aaysb6bL~PmXULJrdokgT3HXff!Q2p~sud=oJ z6Z7;%e~Uyu$^<;p)(0&EX7PVnFnKGoW*4&hgr`lAmC!TS9{_<@;t51ll{%&-EUR(H z=&)A*uxE7q9T$leBgrYe*aEWHf|A$*lh{Hg0MK+M4Uy0~G8@In>`oVCr|cng!?ee)0m9Az);ILKJBjucdGpq|b znu+qqavJHlU-mc?(VqX*(^_V~Z4M>Jb69zk<)BZ(^?LEI@AFSpw!gY&p*R!__ecs! z^4SK2DnVJ~-|ULnF|C`v^6Evb z{`8{9iZPD8DnLBIYuZL@QZ{!(zxi%x+1RR%`^saO99*8@#Xh5jWe}xM8+Fplo5A}eH za^XR~1}g)=NVlhF-1Z$0@=Aw(fcZDus&9P8OJAH1{fAvWXaMVJul@}7vSZyYgWb`8 z9Zk&Km4mOvzelw|MYW9lzDP=>Y42{YIF{F%%?&@M@4v-2+EVZB_ECDnG1_t)>?Xj5 zd^d)B9l(6dWW4&xc!gjjV5WdIg`)xq2bt^-13V%C09G^3@Yb2~`~*Q+_+&-KuaUfF z{Ly#h|J2h|;W%dc;M}uS;j6rik1u-W3`im8!vtNxPeCzveM zkql+jk^SFOq(+VEz)yEd6xK=m`#eMPY;zdE5t@!DwKwyWrR1>#LRTOmtBOLv)5VVH zu{0CTpOCF}O!vOkhI2X2PQ@3_#b=h2uv3t<5R!=R##hcPLYkZFf!3O&Por!ppB()X z5|33Bk5v`7lNGZQ7PGTbz|X#VKtcut;591`sc&=^0oEtB#xO8LWy2Olmh|qh?WROD zzwVIuKT5EaDW({AE2hX)aAVI=o*F9@Ry7c@jJ9C{FD!u>1huAaMz!D$1vN)lX3Yi4QL+jZF|4D4Fm>oqZCx%Q>2qah# zDPLh8Nq_{a+>Uk2{F7iIb8SHN!~zKxu5f@0E%eCq3cIR9{$(0rY#ZF;*SEM!>--wJ z9jPu8%u?5As&&@Aa?a}8-Ct;#Z_(ogEc!r##h27yUK44Ee)0YVNU$1=&4t|@gl8&) z2C!Ym7Fec_uP)<4ec9QMS6&IaGRF1a{BK_nIE+9-%HV{&;({p)Z1T_Or|SU8ARzC;z~GSHLH`yO&V#T2!qY;LG74 zT4NV-VOhE17Vd&gvU!PyiJ8XIFrb0*Rcp*CF_g6Oy!cSFB1fCdx7|}FxZ2-A(N6u+ zjf0ipbaJn;3e5)K2|sZV)QU3qzI(4*=>@~4b}63i-BNh2qtp%1%bE7{u(K6-oPaf) zbfaPReH{;!*H&NHms&lXt1JvUtBUV$aK?W#FC$ zJ&WFzF`?gJ09O56#GTJe?KaCW4xIYObumlJn_7IaW1ojMm%F~yJS+u_Y*ILCc+h;q z!0R8ve|@nVt$d&yJ&nsyD$UB%sLylif3@?${q4mT$K^nN*g5n2ptnkMO>%L3ch>1` z?cuOjw+|=T{>pnXeY;c&KrW6y7p<{#NL{_57&rSn$m{+!@tlJ8)?p)l{niTM1t8Y3 zsp)526T2GMaat;jM7Fjjma5)tt=J6`~J2D`*8oO<}D$&a>IV_0Kd?CZLci(>%``j!`I)12e>mYfYoik*E_?E zTYcKXKlw& ze)7FABX>2yvuHKj%J)L>g9gR!j*{rnZk>S#t;-Jh5+3Rbt-c`Hjuq;1)dXD--|TQd zky2%fJ^kedHyCq$*)?7`NHPuvoll)9J{N3eCmP$;@zOu49PCzacRqLnU<*aL@&n90 z$9kxE7I}~sl^nSMLjZ=xZ*C;o*BPt6tDA?& zd;6&+UxXj({a_r+qe}{fS&urO+;5imakk0vy#ur~(4oP}ofY<+gc*T*r!gzn=;2@5 zy`t54iher?WI8QUimTX#Z-&YnfHez7vbFde?r(azm%Hc_v7UZ+iUjQ2-WryD2;Mqf z!lBruqN(;n%~61s4&HY3j(5fL1Q~({apUXq%qCMuvkYIo27~5U9Dtk0A)3wGTI!4b zWus9{ZhHb()yAutch=J~*Wtq3&aUNJycYe(uQVKo%Z8oftX!Int6+*7GXiyIi>I>& zUQ{3fs~`etUP=T$q0K{)GtIrgiQ znx3!9t%me%A5rD?WFK{x{LuTC{EE`64>fIT1l>1bx!%_oMvM9Zt{=VQ#4` zfzE}`sbvE*dF_4j`d3G~v>#oqIjt-{(dFlBxwCi#xy-EN+&Lb8FN`Rd3bh_FjSG#$ z6-O;5(-gAw{KYR-lBKI9l@wpYl$(94AKQ&8Q0j)?{=_Sv_=NtNJ*B8#G2(KYynBhL zSd`_R$z4(KxP@-&pw-F0bCZ#Wv&pZ#+E7&98NqmzY*0%N$v$bWYq-&#Z9B}U;mdgp zUYX?Vu*n5`n=F4Ar@vcwv7d)lR?pmRuWDtNjZUbNC&lHlrsjQbPc1=*+i+-=@wsXP z9A97fTe8c--*+zk1zd|ab>3ybE_Zmn8ANpGufKF{jPsI45XS75loO>Jiz!*=r>N(| zwy(FZR$5C?lM$3Z7R*pa)sl{@8;ahS2q}#TQ6J|A3!+0SQd?UHW@wP7Sa`T*WI1L` zvW%R#%3HdMA=?4opB;wId=A*1ZnB?z+!o2IQ8}vhQ@4*bVVTZiRWJ3{%dAp8YPhxf znh46dt3Zvse)E-p-+|JcZ{HWGp07P*1SZ=7G_K+29IizyO?ff2$BHIfF^~9i2}*;u zmgseOIGvDE?G6OgtSP!Kv#plweNUbPRs4(-PbQDd&)aJcEkYN1W&L#@y= z`0p6$otJb}z>Y9oIfl1Ua0onp_^srCER2>Ej7~p;Qhun|Gn14i&!cKjNH<@Afh0>- zqszAm)U=YAsIS9gMjySF0Lea%?M}O_L^%vgGFRQ5xS&FGDXq;-KC8V}c;eRfMa2xFi zIiOVx%y(F=YcajE{-wz_n_GG?xr$^WxvhN4Z}=uItM}0q)_jS{cz5{m6-Sh;Tx|2h zV*9=3rRVhN>5Yel6jUmTcAp%LbCqCCHend9qy&?DSCRe;U41CDoVs1c@CF9S@t2(_{jj2*-!Hc@f0R7eSBs|rzJ5=?b!YKC-EmJRKW*T{PI%c}g+s&3$2))_Bl5(IZsZ*;Dv zHRWlnTU4)slWo(N`w4@z4Hz-WT6i+jU=EacQc7sH3~%rvb~!e6ru<*#oBig&F>__BMPY2cZ8Zq1;FT+t4mzf z+OR@|#UC`Hi1b07@L>fhRESj+Aze#iVZ$*PHo_pu;3`5A!!~d)_+b^K0tVdx8wlfy zwGoA-FTG-*31~zxvt$5s)B>RaV@jFSt!Jp^YMv2=G`b!##QLvZDEYP5fsuH|s`2r) z+ks=)L{wTrBE4b=bOv!*!xCv_eSbsuNXdu|GrnPvOUN0M%FJy46_$W04U>m zrd@A__EC+)&g$vvJLG@+IFvviCqu0)EFXw6tcCtRDC0jWVE+SU{DV3E4;#lT9>VnV z_#Y?(Rs(f@HS`XWW(yvT75*ABV+~2gT3lrgkv?+4@}%)B=1*^Z`{AFeun6<;+B_2r zEj^`%NRyP3w3Ph{lZ^KcQJ4X?~t_x+&(`Fj!Lfrp}8vY6W@t0W}!zMm^ig@s^?Xqd=Ef1>WI$L{kDn#gB z(CeHCFH~3z8>`^wBn+qg7k?E|{-xuICRol!bp+=U0pKJIb{ckG<-k59d;{cI-gH{k z&#E%ob)G2>=~Ofh1eR+JTDf-J+F`a*;hER~H4vDE9=6D;uY!zXh_qlGNGxdanv4oX z4dg!&>kQSB;Dko3O$cdeYT;OJ!Zh+*;j}OXrGb+$=Avl5Xu%5_`S7adG=Weg=4K-< zN}I6RYCUuV2d!}r9tv@!jSN09__PZ|@V1$egXcwOUic4>SxGIu5eg0Z=nJNI7! zjM#E2bm#Q^R)shSO8;u(r}Z%0o$e$mKm+&jnc;~5`hoKm-JDe-;?H_{$T|_oY4n_- zrBSx8lc$QY`28}Vyki3NB<{m=5i%CdsHo}r$!>!F7}*n`(6{6bLm3$sgU1@USdr{a zL>tnts4eT&$so`qvWD)?5h%}8*KY%a6R*jwS zrXr*?N5bIBRB(KP2g2A`7gi@Km$ie&{KPApbD)t@^_uAJG(Sq16uGeQ3@ml6lF~iX zBrwa&TuYa=x-iB2Ax$rBtBP{_{56l7VG|tZZ(;=-#>g~6i6D4~1zvGfKq1956*`~@ z^YIFLP?EtKXoY!)QMnbw->Qr}Hc}4l3m_1iB@K>v82&(ophU!fQE1pgise=>(5Ywu zZoW%J?O3s9U5#+bMq&j|T1B8>9>`jAFO9U5R9TJ~4U`XWOn=pDZm{dw7vWQg4_!v~mnzwOHkmMa&i%^+Jg#R?a{?GC9rNc{=QUvpk!z{1IbF>Q$ebE(ue zACUj`o2;S?sGJHan8{YJDAK&}&kq4DR5Gk#v8`%CcI7d_cqEy`Bo2&O4taVo<|u;_ z12*cwH+Su@S8-btX!oX9`$gibmT*iWj*Y}zsL%LyIHZjXN&<-vRRc$ z9Fwv4u>abGp*8UT+=OY(FGT*KW|;56Vtd*%J3PZoW{|IMovX^@L$`t&;YMHD7jQzs zvdarYJ0rq~MO&iijV}t^gb}j~hfBfkfhFaRPx+!ZcU1J#M8&K%cLD~hiX|X*jRjCS z+HFaqQ3(EELI$wXwAWV*c&R{MTTI&OtOj?>MqXnFo|S{Ubs)F5BrVzGX{UW`+%$(h zl(+|?2HG-*bp0CV)vM8jg)4bv(Z#T7wOqv$5|gLmf<2({|e6YEaF~7y^vQ) zg8mk6ZN;=Ss_>&D!B3kX6~^3?Lyd*|c8eE$z8Sf89iid@t&A@Zx0X8+eE5)S{Tt0v zKC~yn6%qWixUb+Q6<7_@8F2DjMc=2QowcIe073O_N&XH63%H{r58Q-F_J0$*N)K2B zfv5?DtNEaG$5XoEs@if9={YS6SY(6%q7A)V8ux64t7oAqH8#aeqYC}JF5`Ss2tqZy zgj)?IN4a=i*OsEM`cHE<_2bq!l;$pxZdHMc{IF!N!@2#&h4}|~$Z^+VS$B=@N$VfB}ZiFo6 zfRBV81~zlSo3{Kfz8PPA*#h)OdpyEVp06QZrO3MoZP8k&3eBM|)tB_`>jLm?lE$Op zD7CkWVRV3-Fn%b<*j6H3>n7Jfg5Apl9yQE!2HVfl{(b{dHFsudPe2R%+=QXbgO^$6 zs$mkaZ|SvU9wqe#=I+)^c5$w_iSL_`uR;mxK1eHvEOwQ_7e#F#E@9sb;?}ML_w2CN zSOT;g9XIK|xgX|-JjETd9 z+$P^2|cIbRVC|hK?K7}>X#t_3Ytn7}^Igcf{^`!0tK%~lxRoO}( zy0PK+I+*IGWdh}8H4(O~(RZgXkK3t$$KG~bfDqE}Y1W#w+#l?R+rqW@&G)jGB^HpY zTi+5aYQNhe^L0b5ktY2-w@<7EZ61=9f`jYShYrTOQZVa}K&2b<+gTTFn*2M%=?14dIpA1wZUdn$Xa9`e=2*nql#*siz$QQMX?i87}TIwuy9zCndY9FeC%@=}7I zM?BxPli#-x+Se#7FMd0c?wi3;DUh$v;~xj`L2X(wWW_2w$bdxTWy^Hp7tRWTGHj_7$+1tj9)BwGH&*)x-tSjJKQkdQHw zKn0#NlbSM+nlh0}VInu!CyF&EiMq1cCZG^!2`}7{UM!K?qPMtFD0t)5+#DfiCcT*Vu=vf?=u`#e*}3A{xx)Fg&7R(O z%KEKq0C%Gs*Ef^%^_!DBiIY2u)1&UemFdA1`hh)b^(WI%j~lnAsgCyFLA-6OiX|QU3@!*A(asj5LHvf7WP#&KliJ(iiKPi~=snF?+-iw5zK35@)2k9p|Nh z#7+Yu75pJ@H6Oul?d9;~BocOb0XPYB1Du4(=HnYQ*_cD3kFx#0CShK|fnu15Jz!gp z=lfzDkZC~2j8$w}2vb$&K98R83FW;3Ct(Jmobx*CSH9w_%%1bFICG24oHK+k#Cwif zQu9<);LldzQ&i-l%%jjMcMU1ohMwWKk{0Hx1}C{;g&@o&_2QE> zNohhi%l~8=rsjhy`g}{7ff}NMxq%g7Xvz4m`=D%#NGVNIhAB;R78(murS-J9v1!;; zu>;7~(V?#%iI@nhN8Z0JDsS+BN)DS4MqcB}v3KaoJ=dS2Z=_5Jz<*<=lFf3-218uT z6y0(se-NH0amy#xG_yG){;UQ6Swow|mC?t^EBAozwskR;4_cpJ_IjMe|22VMII)rV zXCv~@dz_qybML0O#14$wl6JPf$4kPt^NJ6>z5%Toky@fbmL;oR=}0~J50dyLiJ5ZE zl&U_>6rr}4qd8jXdEjNq&18Ki7wg#b@f_`GjdBC6ZmH?=m+TA^ga%sXTH0NW6nJCJ z6#sSTzefDHMSa={G2UE110}yC3vmR|@+gZIO&t)Hb?0-*+z<`Oe<1RA5lGJ%(n^Nz zl8>j0+b8lBhd%O^O~*PR|EU18VZs#*fW@T`EVxS6;vK8^y{lnlt_8fS4ef{?XK;NMP zzuBK?!*j%gC!i8T%SjBuvx4CSlOkBImrZBJN%kkSjasLRvLm*^u4ZolMLH-RcS(GQiiJIhCeFT z;lgw{kLwKy42Fvo9!IBLU-vS3c=&8pJ#o$>ls28sW-&(OZi6Oz`PR`{{I1i93Af=U z`h5fK6b>bOKM+uzDI80Xo*px(ZU)fnzCJimaf{sgb6iu*>XSJh`3~NdH4U!2oB2m8 zcjA1bvw5pxackXiW0vUc7v|;hzK7BfRp0BvvF`Ko4xfAHLz_-NhsPmXl`rD@WX^7S z$*(Rb^(tMxoOLXdTHr#>G6+Uve;d@V1biEEHA@-*{`k7^9M9Et^1enK4!f*$yrx} z$BN^-JZl^9EnKd2zxI@1O|v6bFSffQ5U*p0scl#ND}JuuR03wYCn?6G|JrL>O@!du zstlYznY`W~#sI%lt=)3%?OPG>!u(glA(9N=R?AsoFb)Y#=YBlusFHn*Q@(RfJ$GyS zFYL^wbcbG#?&BO2W4#?22Ah>d+^s3u;<@wPfqxE+?f!l6#lkx8+Sz#S`m>D=#G)GZ zsRQ5s_S*F9-MJI*T5x^un_C18Aoh@S@;jf($S){5gmDa>H7bwgjdVk_wUEN-cKGLk zKA-H2+7kC4WMs#y!b>$+&}7nzCnzuco*k z8+bJ!Aki1l^eR%IVB|$M?o9UfQeW!4>ROxl*@FB&Mr@CGmqkUOO+}uz`t)@MC@fvs zI;L69A_rK5YA$ek^j-;=#y?E(9uEA7uX88RL|(gaH+JXV9{449`Lme=mh{&j*BTx+ zeI0r4>pI83dez0w=v}tRw{F7t47H8*x#-^Yq?b;;Gqaz^MsB@HT_oJf zT>0(;V(l% z)f^CXwGAUUReF5%J@q#ZFr9Jvk3KJb0e%BSK#Y z8UL;sH-6wQ^SrOz1EelA7Kb(+t*iDfZut_iL+^8ak=$Zjj-vM#qH=LRG$7bLR5aZ$ za1q=*6crmFpYW>#}lbrJvKV*YB1FX-WHRYYV{LgE?WE#_1K+7PA7OCPmS1*NEzdp|h7K z+)jEMy?3-uHr$2>o{P<+>^qW9etpj~%uYG(4#QYOZM}ly;(Z)=6W|KbIVU%qIQ_?l zjn4_Ru)WswU)V}H>=HY^TZxuveQZl^=H9oC&Yp#a5J(z?6jRY`r9mq=-4$G{&X0R4 z;g`PDNw4a6<0bC`^XL5S_POf?A5(8$w`0b>S=q_th*bof#{lL<+3iyJVzU>o<|PD7 zTT=WcS6S|Y(Zx>&6+M>Wx6Q z+0n&lBi|@ZYv_!|+dM<7l`lgpGq52`fGOMX958q9_gngRGYWz2^o6I3d>=yKXXIlu z^dzg=WgizH5*yBS6Vfjm%3dh^Vt6h5C7ZMNNBu5dL|E@D2d$U!^Of!oI^fgFGBp7^ z;oFs~tJ;KZcRhXWgl+d>SJH^*ro`{4)srWOnFy8>jd^##LZ$F%yrtNThVKsAR&@S<7ylgqn@Rd36@34PA zR#>g4zjvIDjQYXAmX=N2K3~YTW`geu=Vk%- zcuRfc?+~)7^@z3FVaVbIYEY`n3T*=H`5x3>`>P)!dQk9z# zVAtvP;(yFVYzXD(v~L+L9;FZ=yf5czxJFx#5eiQqu@NC?=dY;biNwo>d|s$57c3R! z38l+o<_O_O3N=|m2CEr9!gxGFkE>TCX_^GtqWzUX$*7@h9K~Fx-V=mSsU)4FY#0Rx zUi>8(S4Y({i2y!C0SOo(7ObXOpdHMU<*!92 zX|809Ce|#3-z{)A2l{=~x|cFFZisrSKq`}|l3|o=&k(w?tYC^d>Wc_|n7~c)Hxo6( zB4p{7xR+~duZCS91syB`}lOX`1RXfmf z79RN9p?LSc!DZw|QPx8jDu~m)PrZr&|5g(&$06`>`9aj>AjKjQH+}`1w$G=4jLXpt zj8#}PEzSz1(J9at%`vy<>p+FHv376=JIdDhU-DXFeL|4aMMXR||fJ=_0ydmk<&3`#hiATd9kXqG%06(V)Zo)eWw z(!Le1{)pBdCLCHQWif|@TF5bgFtR{bqA0qT@Ca>n`Bwz%M&hB2w2d|9^CbUnt48Kd z2pg5&74^4LYt+27PthZQxXqXy6(%978xhB}_LMk>-@lQ`vh0T=x65uMRe8W?rp)`FOd0%aA1$i`FjICBO#5G%G6_-YSw?J-IgVevLk*Qmdx5@ISwBlW|B{fZR8VRm0piiERh+K+A&YTxoNAZ%UtU^15$ zi)~R^Y?^I$E&AT0lv<^SUJn#KIuRt!?8-y58`c^68NxBiwF^dBph@r|NVh;7ji+*) zrR!KISI19qE%3--|1EkD;&CheTl9!Om`D*ruvut56Kdl#D)(<6jZj5`;8rT(Qc#~+ z8CNP*p>dkezGawB=3G~M>1)3((vFSt*`K>|YORIokW*Bv5i?|H^yDe%pl4zTP$}xl z3C>fmg4l-z^KWj6kxzoJiU5r!l#M8Xg};t@fsMgU#gYA4CJX6P^0U+QiZ&h-1*~Jq5KQm=7WiFF-)C`E< z-=2V`M{>p!8PN1VMtdWqQWL`V&4>DJ4jD)WG(ALII7Ot+=oO3MlgpCy63UeHQp!j} zB$F<;|J^Ag`RtVKhB_jL@Kqu%{V3j0%DDX<6xWdHPl;4S8X?jwf{`nHRn)q4Kvye7 z$m@OG2rJ7o*m>dIl0^Xw3-8q44Sg5e63}yk71SH9$WDQU=OynK*6FFHx4nYDq zOEO{s6qVqVe}1J9O+o;92eVL0Ld#3@Ulg~6DG7)-y^5&X;fdJcX@iPl3{+Rmf>q5L zV#*pS=ZIBz$jRH`Q5^6{h`2FEJ0wOsq{1RYJuDCI7I&B!;CLC&(+Is`K|4IIBNFh+ z>x1usgJda>+Z}ZwW^qUqhpN@XEb*z;(?Mu zJoJYbLuvsBLOv#j__UWW$j$hcEOPd+6u8~7wNf{zWdlS1fepm#7#z9>6(BOQ8<|-H z`1UFW=7I_G!Vzr^jT&%S0Te!Zbc+LCa$qiAr~#F#XA5OZKL1udgrP5G3DSGCR>;w} z#siywk=PJq_E4}SVrXQ4A1S+KoLIC8=i5FJ)A3DZ-8Siz@W{v2T0eT+{fL^O8pvfN{ecZAoSih5fk zB(PcV^Cyzc`f|k3 zadPyQ#MD|oN0r>4JausX8Hh@XB$lKzv~$V_6p(xr4k-AAFQNyepqHS+7a#5C7{W^%t7Q@e&S<+bczM$#R19l35;7`!q=R;9ovoGzCvv*m; zzq#qQ?Icz|4`bi_s)73NPMK{{=x3*FKk{FlvdsVLlx<>rRk&e2Laqt*cIra;A|T!4 zF?r##2@2b*&zN0JY~lY~^-$^dVSbD>z33_p_-bk{kUhT#-=~l+f$j)YJ^H5gnQ23< z!d(qLbV&aOquDc-8E7DFA|drb>5y`TB>z{ZOl=U@Dbs-X?3Af~cFN`z7}<>YM{a7l zfT~A4Aj0@%m2$NY+xQ1-9rVN#)>rdQts*;{m4{p+^eJK3naHiXEjZCfvBfM!q2}3) zz)#hK;@_%AMT~`Zm@4YqL;sIYY|aU@&DF>wN@hk?7RK_%{`xz=*43r^Noz$EB z43*J{14CsU_`Q0>XR@E6GO=Z81mW1}T7)(t`I*|!P+2aS_dQ9a%;KYW%vHLe zZcXTDu0RP}0gIp7pg#IGLg^`@!SOqo4FHGhR*)M@Z>}LB8dxghEHa772_vPnFx;C8 zRiyLhjg^@Q=k6$GmkoK!D3`W7AuqxI0W+olf0W8lkJ2!KfwEvm#t25n3`WKf#%pS$ zf;dJ-bfD|8i&?+>FV_Q{sU{roBl|Y|wp^dMw4WQmWNpu4 zFb#_bqt}gcY7A;>>`tJ@K%ff;okyUG6jo0Z{S{yP>czUJ(gjwp5B(KH`zqABhoBCk z#spSx2>q2&`-;N4C$}D=W{4Aw-^C<60?oF&l`)Ekf(9AYWaPZDFB(wKHh~>lOfnPu zcD*6*_B7essOyU|5ze8ww?mp=g8wWenW}K*faV*sQqoNi^(|AlZINzWm$$o@KDSOpuzYp8S8(` z9+2_`s}4ZfqaP@HBoVsX69|d{WskC9pzQIEb9WOjVDGX0jfYl0=D%eR*~(4u*%LY9 zJi9y4l>zX^7ek9Cuva@Q(~)VsVF?zW5~mkL&^Q87k8w zg?lY9+d?+lS`6JEmAk5k+(qM!QiqP}iucg{FM<=&Es$b`LP9X;v zDuYn@Gw@?_UDBu{{-02pC@@q89=fO*ZnSQddVwzkFZM$r9aCl7-tvF(b&gSv{C~oo zX}hQG?rGb$ZQHhO&a`dYwr$(CZ9Vzz?z8(pyLZ1#DP-{|pP4mmoZWAj)r_BAkD1LrS`a1u@D2j0GW%oQc)Qq4TWZtH_;Lq2WQerK zjL|gTL1NcNCP~B-_cz)IU?6+Kj;cde*z}*zs~{{ERwt!l8sh+%%A9u_$UH*;i0$~sK%tI#@tYixz!R1_YQ@EO7NZzZjuIKWx;FEe#%J)b&d|qGl|^c(DXrF^y{ZS z<1*hn^*zu~iM`kT_C^Jm${zZK-x73)U-%<^0H!hz42$*tW3rJBAVwvN+hd89Y-k=+ z*qIU>F+JZbsd=fIse$;)qAnm|9{>{(6^a5&v`+xi&51HVeleJlN5Gc)K^A~IIr?kQ zh|K;D5f`_+=Z+7-H;5mc?XnWgEA@vsVr)bdtHI4a;T6Q6SUnsWfx4_OX=SeyP7<;l znoryuyt_>5hectp(`FdLhN{}a_7E=S;bfnVMuYn98uf^)aXGJ%KXGoC&h!&~o8Kav zom*6(Yctv(Jr7|B<%+aCon^7Te!k#5ckS%9W}m*FHd)LP09Bc#lZM6k_tUP{r#%Pq zAo<=ZiZiETH_%-);J=r4n~<~Ud*yCz0S$}8nbKVRX{=^a@R`Q9MnUbFL|N<(wqRCt zgFcM~hTDbndjFlrdL-#+Esd3!=riW})hMt{;E%n{)ok2oh=Hoh)zg_Vm7m*M!+k$h zg5`QYI}OrWwpHt22{oZ0P;t>STjHf5!rdf&@j_iTtbo>@zY!6l?cvvgbRQ#7S3 z6_WDa!!D=NP{%hs-CFYS=nyACKkfgS@Fwv%w9IULGDP7;=5UgRobdM4>D}ny@hY`% zq-3RjE*v^mcwGuqd8;OQqL}w_UFK|MZh7;1xfwkl(gA4A8E%LCsh<@ao)9usJAcu4 zg&euQZM(uFyNDmqE_qq6&M|Mgm!8ggk~S|M3~#=*P5zwee0UqXsfoAjI3Jq9)bNaZ zTq@}s_kPK+ZMz@-y1+)SwKLp@N__JQlVkeKLi~Tdib}& z4%xBtd$lo1DalHC7P2>Z*+VbEkB=$#Zk)?F`bgXQj?pW1?c)2+;Z#&euLB9-o`^!H_$#`B3he@^y)re6%QgjR5VA; zSDV=`r7P34(GoZrDeRHYN@CGmP~IO|%JD2#o~zUFRtmR^#=bI#&~rfHTbjrF-OWsD z1m0YU+dci;!*@Pi<8jQUB+a+?yNZZ;oHTd0#FS6fZ}wL7+qd_fhbqxe_tfoOUcU`8 zv%kqc;}3_x2R~Rj7614yZ4KL&H|9OIq7HsEmWbb0%Anm_^P)e`2b$r1JMM=(Ygj+r zmv>*!b-jV%`EJKG^LXN=f%kO}MW$=szPo(9n|-unS@7b1kv$NO|FUxwKbk{%yhg<7 z@Ijpz1`KxaPUes80|qjo2L(4+G@M;l!k8_Q2 zZ*j%VDFhk{|H7?WwsC%dtl(vL`gEG`82FBZI@a+8`dUwZjQ&)Pu* z_2)C1fmQbz5N7Zr=GCyMIs5py*@o96&n`Oq`kduUy}V~SdMfHRs!DMp*^A-esmNu} zOX)@1Zks)F+B%lb=IWe>hiiR}F7E#Rc&la6(7Y(x^=spTF8JI~47&Hs$I ze*}g0_53b7C-|HVhlb~M^Ys4pBTMuEuh`)G`Au@}k~bW&y-S1O@i&_k#Qo<7$Omcg z8}GwXbvgF3Dbn}rI5;cd^>BWdMR&pWuKR#W{PuOyI==-NGmAaUf9BXHPvF*V2UXFJ zQaA#>>ohyDU6FW7*{s_ZT^l`n+Yskj>g$Ca3ZXUL zJ)tF{NJ;RPugX6;j1DB@r&7yQ(7)Gq<8-C+(<;7XuaB_igwTiW8x`iEpzCYY3QX3H z)-Ibi)mIfUC`8Q5C+>n_E7gR)@~H^xSRV;Mi5aEH3C)NRg=cu{=INXtz;UESAuV#J zm@EtH=1ML}o;L2)mT;FkS1wmJ?^mzgpS*mhuRYrnf<9>EwlhgCZ|A;`JU`Y{LWZ-a z#m>`SI$nZBZJhf=cdoE%t`#OF?fU6_k2|Mr`C8tDWQNVKHe}`l`QGina1%vIv!5$I zkDU~k@dn=VGo*Z@FpEUrwdws*#@&@wy(>4~bBC2%H*p(_dES|X{T=dcvd4 zpb|3p^o{CBqCCun)Vc?h%VHN-R=r}W2^BfuVwEqxjnM*gzB?9Gyx3# zCmJ>|z)k-3%KB2vrcCuZ&jz{}ugrGx689%$XsvRhqtnLcNz3j`Q zi`j5eT2YiaLbQx|BhAaHTAj#cwST6 zCKX4y^x^Nw-^%H@^*=ufrQg;ggwEGM-eVN@KItU*EuC>q>XYP? zt&Ae(AqI}fA%_J_mNfz?Inv=nK|`|?1XUdT5ovl4(*&n%!~9}9pbC8{zGqQW%p zJqn01CNL(KL(LG2X#w0XkpP-5BBoV9g#vgK0a4Xq5Y-Xh5Wooa)$bOJP)8t-YUKh! zJSH^KSA=$93ehj2#s9aD2KLc@Lqi*r#0 zizzuUAQ#=(jRpz{|Lp_8t+EO#xn#nCns;qU;0Nhrh)ncMH03s52YBphis+g;7);9Hxnwsp`vcITtGj-#rLbyHVoce_PuR#uXz#s=C z?cMwFKV8B)a0u2O7yrfa~UurX+kd zK34TNhv4sR*pdsZxL5$bJ_%NZN|~}e+PQZ^6Rw#ipz7YZ=Fx!IsG-Rvr+JHd=DnPc z`?!;ts$G{}Fnqn-f%oNc{~EKI>2A_>&$WH*ezY2DQm|A;9luyf>G(QR$G0li=+p6U zNq2u^0*v@oy;;or2q`tPc=N@R1}Y?kF|`H_SfpN8hEa-q;dRA)17-Zy{fN=fOQZbn zTgE{~vB|Vt4ZJ=z^}tyI(;Sw03pxvQ70bF5{CG4P@VcDeg{&HJpyFjt8auu4v81V$ zsUnwhUCMA=iMV29$O{Tf0;4W-+Pp+q4Qg;Vh_88|Yr; z0>w=i;1tz|3XMki&v%~ve0j`}3atE0Or@5vhpdbpD=R=7q#xzpc0>XpxfxR_R4PrW zJd~?Y8;}4}0XhVX5^uYoCpkZUj*+No7%#GqfSmy+tiY?42zaGC>hXirEi#OVtNki4L7$+uq@$0(ZXDsi zN#AJ%nvwq5MwuQ_Fphz^D>mcyZa~sKf;6u)LAtgiSh-e1B#=un<-~bzU2}D#R5eKAHzIO%>TJc_ zMuvQ3rBJRWRJpwNM@_dUNt4B{a)2E8>eIFCGw zTrE{Xpiw7pbqNU0I3rl*Xap)>$0U$~iQg9#onH$&{SSC(Foz{6=xa%uaaPST*jR}r z-oLIw`n$g)nTo*45C18;7c*nVG=RaVoI8--ScCzJ?n;27I|ULU%UC?DttIm*TVf$Z zXW)QLjDG|j5gu8<0}eYk9<<0-dIed}8iiTUGxBdWPTbdy`Hvln zow>)}CJ|Y#QLK+#_qM0*Q5f9Q2Ws51i4@j}TG8So6X0jRy?MRseE#CD%H{vi;I2*0 zY8Fq*VhxN^OPU_DaLj@N`qWMLN845WRqfjdD7rfUitekgdKp5N`8m#T)s3Y;Qc!-Y z&R{}l2+CWA=P*;6S)(*eAvNIAnte9qJc96_hnf#|EL|T2igzrns-1g*x-Ijcj|MC$ z0;(J4OlFDgqh*vpzzwFslYeI$iFji2g?VU% zmKA2f?dpkx0O|@r(|zn6O4c0EboYbNO@QY~`1OrI52X}oS_U2v{cQr=Y>VYkGtrA1 ztH)mV2OL`Ew#_50s#DpivYZKk$z{-wv6n)n9q95D8SS8;RuD@CE ztVBP*t%SgoX;czUDq2h6<`6~sRX0IM#!KetjkeeMO&X!5+7<1#v;&~MNLzZI1E4QZ zwSL5tZ`Mja{QPSm1)<-OW|-d6VXtAn5b9dK*Cn=>tCe8#XncgEu(}#c0^7Pou}ri zHwN8F0m4R0;l`(X4~o>oj34k++Gx`bwhUuEKd0_=0dbYETnTn=rbI*a;6dG} z(H$Z6rpO8#@*qXL7VAwM2hi(640(~G-zVx#P;8~hg{Y)HVbqd7@IAlIAbH=bbOuyF zS>}p@Rz!9cHU&G|0n^hy^Z|=e#H$5N56@sdh87(;IzhPqLTus^PK?hV>v?uiLle(; zULZV=iFa07$66Zp8=$HA`O`{FV4y$*g&ay)Nvl6ZO~~RCeFcJ5KRP6GHuGemCKQ|| zEFJOC>pzl=i&Rr*k^f^DGnEaxtwhY{EGu)HKw0X~4c-MCOi~Xuv*1@2$|Sj+#AJDvI*VVtgk}<^)o=Qb^y{(<&miY5s^6 zQ)*@O2~FsL_FLQ$(X94c=vCas4h?x;`vcmhvEK;|u&}E*%BFG535|JPdqMaWgPKz^ zj8l5#sH1oM@6jxf46hRQ@JVJ3Y6rF;QXzf_j5Dzjy4aZAf1>UICtPtS70AxG#AjS< z{Ku?9nZtiMy2;Nzl1dl}&$v_;)QQhB$j>TJotKn~{iPn3fE!OB7xf=+#_MrAyUr&? zU?=(_yI!=55Wa1)NN=4p1CiqU$OpD8V-5AgH}yO`k#`9eav1i^>MU~o;n68|nR~1{ zxD?oW>{AdkWb5sH22N9b8J)%2TDSs57I=Y=eyyjK8GC0MFaCSf9qCky0CwhC3Szc7 zm{u_d6nO7u0* zX^;*xebH30k2@UE?hK+gW1g!~mla+ysz1H1m$h80^hw1rYCh2^))XNYM+_X;GWF3} zS8Q#55n22q)5wgyREQ|p<}}Fp22JQF*PKFw*z^=fVe<5{5n-sn{4RX;@!Usy! zs6+-4=_!&7HZR@>z>Um>TVQRWSKSy#NK$#%*%u`-Vhi5jZz1q~eTB?kS7MWl38=>* zso`GJ&{=@0YOq`i^Yz0+^;KhytA^yNAUP0y4Ddcn_fMurPMD1zG-8vSgOaAj?>%CZ zXoWPooN|l<#l}Tqld6R@SV9}DP|a5f=I$EB##LgItO=;Ml}JylG90N_*tivc#@QFbag?_^r@m zYQAD6!A6H53AxwtWsTx9Vx$RlU~C%MMJY!cJ?*NTIm}TUt^d;T@gNgp!H&tj6d+$6 zX4>G{x8Y%D+H?cL?so9**QD3}H=<~O${hG5+yl}zpz8jd^3!+dGg`8wsPPnsgQ@u5&6oX40xD4@u^0i}w-IQ>rgMi%Rx-KNMF`&QPBO z=b3HPu1lz%774oQfL*&p-zBCw;8Of`So#ecj-YjFz)kqBizrNUyFtq)QvanZ{l-=J z?h_wO^B;p2?!L2yM35gJz@Fr@F{A#`p+5(iUV|z=OEVL&14DlSXCug7v+7^j=3Wnn z^S>3N7fjz$E9*Bif$QG7$QbA+_RxFmVbyBjzz-&W9wxnU~-oi;#>jL$=oaUpwnBIfX+E&7xoWA!Y3g$JBs4!g!BK-Ex$QexZU^L7=WY)ug*;3^(+Ohz zl}`kto(hXe@nzl-9%{qE*uy1cAO$kE)btp+E{HKr8cphE(71z*3OtrCY1_Bh`5``j7>HgmaQHpuu00l4&&fxlmA?x-^9!%b8)$! zi$K4XH(a>4oa`(xmx_>HGMO&K$xcx0(uL(vlf|)JcN$nT)08lL z+ex{)AE(^2%4|y3NkDV1t1aJ_%!-RRC{F;#qPUjFS+|+DY5iqA4iVmdz*&JZ>BD$B zgXS1${XmKXG!=Ozblh|^wbt{tax%Eg^~CBe*4dI3vs5pd6Nwu;dvcot_Aufh8%9$V z-jsN$r1Ds5A!W;RrpR+^7IAr56WqUYJb4aDQR)AX#=k@vvEy~D?V&s)#^!czaMrs(^#Y$HRg$Hm|iz6$Lyxd z*tWFb>FttAV4aR@(*3e)F3I6tIf1(JV|P%g6Q*lVaJtKzONfPVNM+-Sx{|&N0jJ4e zVh!KEwy9ZnX)EDjl*d6{3su+PIJsbMkl;lN6bDVX++#HAk)#P?^s zsYu&#s{Uwb?l&%-Om-+|UsO_Ua6{GkUEq8~u+vdK;2RShdCoHoxQvD7@MPHc0t%b4 zJ!XkW=LT=BB<73JaCyi)6}xk{oqe@m*5RQGy!zAL6HXLODl^I3luQ&4=ShCH@lU;( za{ustuS%3mN?RtU`oQcT=x;Bddkj(1Ax+fE>HU+bMv3br+?|tbr}Ca~n&5}QOa3et zBHfEp#sZszlf^*SwJvYwa34aB+PlMj^@pBy_wcvRiCHDP??RNH!4H|H;A5P*?%m$5 z>$$S$4iBl1H%T2hqS?B&A1)VCQ93T(4%OTk*qy|dt0(fi*qfykmX$J-vz(&1+F$oT zQClzl2d`hzY;To6og2>693D!7xm7042WC^M569ArZ&NY8-aWg>)OA^31xkb7zDXud z`>PM1N8L7&kTY5KgO@&sVZ=ty+FNZ7%(fW7y)bIm>rtxH7D0Sk2zhW2YNPvJeEdnf z${;@=R6mUYad&z-O{AAtniuAA-vp3uO?B2lTYAwuE#fUEsrDYZ0&CHkV z$|-0!a2=pG&$pkZQC76%D6 zx4mIwS8}J)m!WsmuB_<1sC~=ti`K!HH>K_aQm0IZ+~Hu$0$vYq;;tvpt~aVin~64C z*@||FN1ik8VOtOO#;V>5orynarCsaKu50n?{E=N8yZ)CV#E-KPn}5<}xn!i*Jl*}j zhZB{A3`9NFr?i#A6^m8iw4SgUpZCTywX-sEe9~UIu3gNC8^_O6yw?4Y77}g1ok=?= zORvTP3fS)q7wk8~N(Nj|77}o%y^jZQa`*Z*Hf4$_OI=YTSFq&mgD@SOT*rVv%`KGG zIzg$<0n@nczT`6kHYKcEoYMqCcL`F(NG&C@GL*c_(RY zX^P-zV5K5=HOFDv=?TC3dm1Y#X8$qm=xUSNjPR{i=8i^MNC@K33?RuY9xCrtEO8tr zfBOjSV&!QOBEGh;)M03jV8Ffb9aw0J!_-0kIDKh9X+8cCY^_SnOjY!8*NZw_BCYbB z9+IB8u!o;mE%B5fzwNnPj%3#rkai<^+TOmLAdgl3wBF@uPt3fgbz`=2xxbLd-Dwk9 zURJ>MR-}~HmYKr=pSFCpx*%auudBLQ`EIN0NIs70t$PtbKhkwwNCjN-$jHB* z3dgM3hY@|gR!w^_;1+$xQSxA~^UJr@!g9BKGK=!5J$mGr6m@X=5CyIh{rwuz^v4t} z*ZCk5d+VzrHGkTY?cbgyNfjKxqiIRxN)}eZVJMk%tE*0Wbcet^yX3alsY<) zS?)XN+jfC>9J5mDLHEASNNofA$>%E^@fytsUX#X6f#K?@liU>7rZ7Q~mj|ynaW%!$ zGyP)Ky*Vh;OJtb`eQ^KAIUbHwi>&niJ}p?&8?(Nj@C&N(JB;~bwwzQ6xr_u^ z6F#AX9{+QbG?P_+9L7~z`ra!m5k zm#Wp}t@Ab8ik$ql*7u+#=VMq&VcYJy?p3xVLXDSJo8NsDulF#!`^HmFpb_1P+G57IIQzgN#{|T-1&FMu8(wjd4lu3O4aRL`@-lF;W>4eJb&h- znxpSiD`MGxPfI%OkTxZ|YobpT=Vy$?;-u|p{K-4fR?`+{`seir@rXtHFdjvqQ60N$ z6ACYT4f*bO)d$KtZ*lnPJZ6nAQIqdJwP-PxXEs%D%ls6s_w)97G>AIzvz`2_>N0MV zdj4=KtEo1T4_b%xlO*hpsN^kaw}tEUwG*SJ&#|{O3-&Xy^U1@}w)?Ix{>Fjs(r(eG zVa?~Otacph^}7qVCh*&^#N+X?mH16~XBAb+&kr`$k2H_((f)6`b*s3AZUl!2FTiOp z8&CD)hvkd?JpOF%<|mD2#l)fH)+IIoboj!Vzl_9zj3D@S7*J11o&$REHq0Cm(q z+ka;cn(k^ys>|RTbTlpDoHS}RVHc7pCL4DAzYHs5*P{GENYLXpI3fR1=cxqk%q=CB zDSI_ORZ`kiE>i5TwJF zZB9(=A2xqn^&kllZ!}|QFd?Bz+5Lw& zP(0AzT5uO%mdd9u@)y~^y>#JlDSUk}j#B=fv{Ot0c;v1 z68krWdd#-hX`$1-gc$b#g7HBh4; zi3^c~*i9O^5vc=_tJt4osevh9r&^lvJOWIH9>$_UuKcuGl9AHSH3xBl88{em!4QG! zd=yGSkk7lxM1YjhjIRL2F9b3Rv}bs14>EEhrxuaPKIIow8Wd7mC{o%!By|uZ^<-G9PNPOGDw>Rj2XuGGM3_6nBg*MjTxPW1&B=$qZ~MY&>mSxjxx;MSqsUK z{uLQ3QIscOMkT^7Sd5%{Wo3AHrqWyvNy{YxI4At^XVU!`_=4j7p{jygW@W58x_p27 zTos%@5KQYsziH_M{g^^}H`1KJLgaH$K_R@rohW})Q}VkiD-tMVve1aaGDkpKU@Vzo z5BuW^dBltj=jxuEi@yaZQ~!O!j<=aT#~JpEAyO9HNVf^93(Yk$NGStAhxmNPdKlE0 zUVl3VDuKl%=M;_X{HenfCT0gB*uO-;`5>59z{EIt;;s>8pVuy&d|mt@PYTRKg6#Ec z6~;^>jKch#;#8u#!0W(*awdrce$FBmW@(9|B(q&g@QWF;)A z4Qc#l0YDGJU(2-c5AbriUc3BKoIU=s32+N0j7olPO_4bzFgVesP8eW>8b5AX;9xSq zz{q>`_6!Do08ybEG!{4n(6&--kF3JP@-6Wy*$LV2{*GlL0WCo;QLe@+UOb;MgGEnD z(jNo-lGBiHDWxWxTL2TWP#QQNvSb!#ONK@E{3DV18v{e%4Ob4VCbP2Q7c`+-@8`A= zu-d^SA#{(X1YH>j^2@ECMFlR;2M|(vNeLs{`lrd=8 zLa6StK0Rcq`)_JLoSvcjesv?kNJKMr14fk^aSqB9{6IAov2pVuD`t?3>vVo}f`Z@t zzUctw0F@(HlIpIPA!XTF;+&MznEL}gv4_Y4R;V(!fSJ_78nv7Uh|@POh7-9A}ax4p~SN4zo;J3vO`;>_(A57vj#?{TUvhdN#V$2O0c-kV7WI zVI{oSx)OeFQEsK55l?~~wAe6HE^wp4cE9FtGAhKmgcAUAa5~N-P^M1r^k3Acga97w zQ=!w|fHK*s)GMk%nP3QnPuwA_2(1~4R@c|ASJ%gIT$M_Q z>;BbglDMl|%uB9B=(MUlF+LZf)ui=MPdzcl5~5v4k7?1i ztCx1ytKTspUYTqn>rEb*MC?X*>mCEvl>g4giserX2PNhRheQ$3*dsehiybdGU&Me} zeeNPrpw7FuLCSP(KLOhQOH(80B7E_ARhsVAF=OV-Lu!sfZEL%2rOYIr*|ud z&sG5Vh>b548<3Wh>e!YjnCkzVDOYMa$z33%1BUeDr_U98uzm(5}O#;KqM=Lq#NMj1ozGP)xKw&H`zw z#>g!H0l(BrnEzP?l`}(^DxWPAW@SE&`k#P`eiK_Ycwof#o;Q%1pO6TGm}O$}WO;j- zEIon;OVI(a_DD}=CxKT)#UYVg_VODkWXmAl@lX(dn5@pIh%s5}CC#tM3PEG%;>2!+ zw^9F-H*A1S6F2lWy(H0rukUJNxz}+2!yDlK;SHZR)cOc60|4Hz0=bPyqi^aZc@m_3 zQ`u9(+l3-_MiKjfxm4RFXR{s;3)2HUg9QRZWO-?VS1>sg+(mcdyq(@?#)sa)XJo%nq zz48K~`9bU)5-kU02m|tV7w3`e8Y2am)L^g?P9$t#BCC*5E<|i0zdpd^JJ3|Hq3d&? z)<`OvwNlW0N?}53UCiyH=Ew*lN#@?fuI7?kBzfmHbRCj^#{|v=60-;kLp}%tP?KfX+@HgOrn!aywlRk z93O?pxxdCk&{<8}%VDZ;^>u}Owf|{LErw6BIax=#>9`kne{ZPxF0#?}A+j5tDJ!~V zG=Wyo*@6eQG%EE1>oKjj8JM3CPk$V5g= zl@2_TlHk_3rU#(2)K+Ah6Bq@x<*q9e+3;|<%^Soq2D%-8#{RtX9;LmvlP&81;x}Li zk!cuq=rW{|C8*^}O|gawu{dMkME23k=3caS`I*WcIYE=az>g3LMQKixlxNU}jKYYW zXHbNUQeRN=A{Q1G4Y*MY3tLrCnuyIas6s}$FDOZgcCNohf*zTZu*2FyWwPV9u8ZS#CLy;;f%LZ$G z!o7kz&`VI{`ggu-9E<|Z_-`&Sq6%84(ch_T`b5SY2cc4NNDSfwlD(NG6LVt@moyoc znKBxMT3AFfauIx^B7qFZJmkUcpvCKg88+4V^Ox}%HoRB(*PjR6zkJU3_z-S5+ktU@ zVM8Q7WzFyKXMBxp;$tHUZRqbQi2k+j65SSJFkLcoyeSDk5ObTLX%(P(ZPC7iY(Iz> zx+5oeWlGXgWn1IR!+`5yq?ya#QEz{6bT~b}oTFVS)FqmuML1379vlPD9*$#Mve-ds z_qye3tDY@s&Rp*=cZ1@O3|YP?Nd&NlOuY|2IvcTFmv{hPnDf$vYW@c74C8P3B=l8E zKsC23v~0%rU6PV-TuA~z161=Lg_cjM;B~@^sAj4kJBAm9LV_gFJosP8L8sf=rFst!IRdI5pHZ3hxbqJb!^L^h@)uAz^7zlth7@Hx;S<{l{%;>2NhyQS9 z5VWO5DEYB8NS6gFrw?8#%6Eno1E$8sW6H=+&GRgLnEwd_%JR_HMAZ6Ma4>P{$rBtG z!n?z+gXOMI{`B1K4uy!fJ|J>727mD%)^J!%$`4=-Rt9*OJ6uXG62A@}hJe!d%)4{V zQ6VkTL4L?_s>Y^gDHyp08;c7EJOI=8Mx+Qdiu;-qf z4J$|bz&6g`(`M)0-q+mJf1PkN#a{>5EnK-)x;x-~xwCIxwG)57DyE+kGe4d62g3Qx zGjZp;={E+xmh;fV+hU)LrmN6e(c4>7`7#ur-HmqWd=yn#;%2@u#MyE>>O2PKsdZT_ zc8CrWaNhe%hipxB^4)jCZJjArhVCe8ybEo$V-0SvJQ_+Ynnws+@2bm`z(GjhLg?%zlsieI*L?7 z?f`W8YG1p-a$Ebt{e|UMtjFU;QBvxd+ZnXtR$Vn?=>4ub+f`N`B4oUT-M`&OU-)=> zu5Z^Mx${p_E4#?NR%+p|ziUL}IbpYUvhSR6r+qlXWcRauKWdbzd`tgK&q@o&ezeTJ zQdd~N-) z3BTPhcfbm=TEQHuR!Eh7Z~3crJ=|44SK0dLbRG9#J?n0T?!<3*O~b9vghb&Yy;|Ct zVe^VqW+AoYBYA&3MnaD#$aiEvsXV0&Zk~#Msj|Ymms5Vri&@Ai%ZYk?apni2R$lbK zH>s6#cTlI;aE30zYi}jX;-;?okDE!o!=`P{H>+}MICFnI)Y|~R_zC4qb>ofsvYgcm z?#|M4JxSefF}M1V`Q2=1yPE`QW?>+1vmW?DpchAfkI_$d)6~yddL`MMHkYLM)YaVP zSV+@LAF?h@O4ZLL4?lVqKPgb+EGKp!Lx-KN+@UO(?y6`sUTDrKNy238t$65~7X5Y-Gj(VvLHYoeyjv+eLbIF5u= z@z|`~@^)y_JWWznOg+?5c^lmn&D5O$m}4lzjyz===L)|>uQ!DDU6dos?z4#~y`ASJ zH|s}LIC4v`E!3J*XsQ#__OMi>rZr7DZt3z;p4!^H1o;{hn}!xA@Vx5(4taH=x4$v* zF!uZUPN-&xNcMBIIo$LPeS-C#B_P=#jqSy!^t;PF|wxb@NV(AY*Y3`C+&M0?69a>>^9qf zybojpu{y)Ek#to@-^2MI$Y`v5{RplqTsXy>b)LJqw%P8PevO2h-m{$CeZSs$V({5> z{_rCGezEt$@wLCirMQvm<{5v~RRCtnx%L{@p?K=t{1gFKk!uo0oA`9QNJJ_wQS&}E zMPP<&vZj9m=(PWx9^*3atDS1C}qRB)j5;7Yimxa-maM}CK zpZqXM8U>uGa}{4h4hdpja~>1>2k|q7_l@_{<0(1!T5BrueH-~sk1v&*ihG?i_w=Ow z;);)~GHZot+qs;8HYQ7DJHxNv&YcvWVwtTd3sJY9w4%*ne7F=~F zIGqir;1@Kv&ci;YvkRT9>uIyestdx7E6?t)YBv}BSZOjcQyc;%@LJC0uJp1fR2#Le z7PRtFojVTQn+BOJ2H|H_r>RzjgzKGwv$VMRopG7$mHI11#dW4SwmVOW z=k+>Gk`u4$oZcJZd8mtuMR;4Gn^5MTTj0-mPRt5bMHcV39p@#Izn_GwOiID-~(SbW%A#QWd{@F>0ebmt~;Ax(@fCj?Uwt8 zh)aCrMf$bpc5GDEQYthpwT%irb=fzTBF<70 zKii~D8dcr`3=E^ClxTy$T5K+FtxI^IXqqOT$&2DEcwW&A!cS3X!Kl~mW+krE+s?-^ z48u?5MaqVomxJDnH^I&eeY<=hi66&Rn)bg!M5B^yaevkM-XGJB33kG1vwiP~yE|oNSy7&;FifmXDLo(o@Kb}X6C&`XDywu zKl^#uRT6MlEeldyz6O{UaW{GToK5Vn(HaBC||0CwEYoGf%ouPmMl4? zm|p&e+NP9O{p&U1EV`+xI&Y4)nZ_fQyV=Xe^?N&R`!mDi8%a+xgYi5LqvftJ_Kpz822@UCv*085&>d3+jV5GRXs?`LQ- z=|4iswXa&-#*I2&3PUb#1(U5!g*PW(^^I@cV9F+671jPQ->t9RfiM2tUgO})s$|0r zdP)V`Z|Bcn=d(T3T1s7uiFXCWrz+i$(C&M#y0`pW-&{Tgz+YV3;(<2l7PsO3uD;ji zrP603Eq}eWZ@Oo!AJES2roZF|z5=Lhq1i1_6%AX&2F8sPi z3EU?oA|e7{1uhZ-5-KhdE+}rHKVW}`K>p;W@0{$6jHJjdprQsea*cw02TaQW|4n-F zQ=e1xp^<1cyE^(7v;&LBbDo3ayKa76*Lw<7HD!9eRjvTm2ye0}U1D6pknp+NgHT@D z;x74RO7=SLk^#J+#><+kwGf9_!Df)@j5aPz`-&e%m{gXZt_ENecR~9F$?ab6B$-1wu z_*+5hItW~XDD08Oh@%dLs|gcq;h%G~`qXreIqVNf5+sqwB{fJZ12zI3APrtk_68Eu z(fneb_TH{fEllDV@1)mCRwK<5`NMBq)AngbKFGM{)bRjbm#>@wz z_|(#~ETNzpz-87!!l1~)paaByJVK}lXnB95mHA?XUDSm@)RB8(b*Z_v=OIrxd5>?8 zGYZPeuPQa2yRWy(&ZhoGSm5*X=jQS&`zQpgfk)Pf3FqznvVuz|7=VT=mPWk{cEKmn z7;({b6c+v)j%p;|07c)Rhz`4H0AH?7FWP!{Xx|ezl-s7%4+euWGXsfqf~w7-SB6kTNz)51 zQp!yQO2g_UOtK=?_Vk~pSUW7yLeuc3K{m#ao}e2$8fgsApLF=zK>+)O%Z}7Q1{N)n za+Wg-`lp@Gt_>wwGnRimLQj!!!^|OD&T5g_Q+A z$&nKT&4NWdf?t>}Sgg=b1XTW>6$+L=ztQEwe+tR`(3ap+B|I^)kP2baybGV0p=ik= z6O%FRf@zDN&6j#~{%oiQKMiMLCN577#s;)1l7G^x7kRX>4nqPX8-^NWkatKV%NZ#W z{e+L949pU$NIsn{`=?dmUPq-i@mEY`CLQ7_m+l306?0GAHK25nyWN=tDb!3?QILv) z(nJ`!;&191pLhZ5>Oz46E#qReSiwxk0P5vkA*A0{@x43j zzc(R5euW``%hl_ZEN4G&bko`Zf+X~RZGswunD0_nA3C8{arr(&z$DrgQ zdM@$dg9~#;AhHsad1Z@ye9Azi=mSd2vcmzphCW$UZ|)&y$Eju6rlmeU8jtr@i_^qq z*~dm#IX*PhV7O<;ToV^GtOPx?G-i$hbkXc-Oe69K{!e3n)j>gz5L#@0 z+{YGz4(_D>*ybs(dT51E)b8+P$3act&BeHu*UOy>=k6sq8yp-7kBwdMoTl6XB~%m0 zLoyHD5zdsMy+K@5$IQ8}1Ja=nP~BW;9hc~FAXLlc_x{7$3bzClK7FsJ*kE#`Yo|;bE-HtIZTAtpe`@H6~NZnH&!-CxJa(&Juw!Fk^ zvrTV>>o-gXx&8LX5cE(kKpm4r#LF7LS&uuKW%PlDg_HCWC)mh>WMD~Es(!D;g%m+X zK>R6f8UoT0le?+|&kz0EG6HrXBoWNjEDk!zx@64&g774B6W3cN>r?XgTr*8>A2dIC zxox8UM|sYP%G+-nm4~};3m34@ebAp(yNw0l=WPDEGMkLB(@WxI{?gI3+) zOCabqH7Xt7%YlBy&8ybnS#5*jhNw++-?yKH2Gu};Tk0$xK|{&)kcy#Qo8xx@z$L?w zb&c8I78$PqPEDT5f@+0!Rq+ycH_+WalV?ZtH>w=Xb|P}7t6BRAfkl(n%*)(mqKUJy zXmKw#iEDBu*1|T}R(zAwEUyK~cbf-zu>ed;T6@6vmS5nQl4g917D9V%dC*-MU<}y3 zPet2xGA>}xpa7x({WuQEj0L^-j@ah`el>z{~nbk1E%WIV%XDEODb z&I_a;$HZ22Oajy`|EvmRlYdr48gk@6tAZ#WL1u%;*YNkYY0`ozn&k5glc3M&0OGcp z=(+abcFH%yEufKOu%{00M&<)4|E*g|0s!}W-wop30Ni(W+@~4s-6T**LPK_w32Ony zggC^KycXxxDv-D($sSW}SXEeG15Et`KA8Omm6#cRP_@rU15O<#DGx*)Cd0r+hddBi zB$8y0vpA?KY^8yyO3LDvz>9cR5MsFejWf>L7tX|MIaa0;S*@2_zzH$QM#i|m^n9yb zLofwn0JHa^4?BE2YIOataP&rI1Y$4b@_;}`MliN@I4-c2$+Si4`CG;wO&+*6xH=5& zFRp4O!(Z26N>tW&USq?XolJf8Yb8?vDbWw>Ib1|Qa+e3yZ6 zgMo09fpDXN)4!n$5y%ysq-umf#6o4_J>HSrkBfe0s)F}iRHmxS*isU3zOV-_W4ip* z&qGsQM~Ic`D1Q!n{keY~kRqA=etd)0`Dsj2=6MeZo^OVU-x@ADgX@lXZ@5hqPEgXr z7Op4G)F$5Fd+fJfXuwmh$Qy#h8xpT-YVQ7>)ZH|GvGN;lNP(vw5r7mLKpFn+HRNBd3qFeq|aZwVk3&s5i>Pd?SyRR^UBNG+l0#lT#axSlQi~CH zI0#>Pgss7N+@sak;~D4ofC6Sk4R&S%xkw9NsgA8NhNUrvtwC$=VziI~OXifY7HN?9 zr30;B5UnSP_tCaFW5@Kf8~a>9utN+(M-HPSoZg*X`xt0+3xxwaYYenXMSuRe<1g|<0A;siOS6XjE&fuL&2T>jx{IUNC+?-HW z?e9=rl)IvFAqj!$p2u+Hy28D~sx1%?6Mlp!0^Vpu1ZtW(z*44|us%vr1KPi%=wyUI zt)gU^E;_ygWmZZKi)w#0z8@T*8MMV2ulvN>ff3zdfUe839!voBHpZ!!^|J}1 z0wE`aF8}NPhFk8gIN9CMIRXV7Ga1>~`OQICtQb?M1^=&y1QFNzH5@M;q41Vy!M9-x z_U(qe;sxKgxzq2$xmli|7lz=gJ~a)ixR*11HZ}a~U1U2=G457he6yB>?t;o6QZ=5j zr3579`t=fgGNE39Y=kBQd_$pcQPC~K*beecyV=GAd_ce=9r+2K^iorN-45Gf3dmTP zSv-KIEe*aEEeK9Q6)s#c8O-Z=gb`hL68Me5`w?Cy(eCY_PeiFd*uKZL+~A}wJ$UVp zg1d_-cb2Jtgq42EsMs-s1Bjv5DJI;oK)YGM0dvIHKSZ_>gL`t|{o4QIR|sVW3o?}u z16!Fu7q}4okaRXuo8t#`4(1iIfUQgjDii(2Q)@y-ok{<1E0Zj+m5B=2$`oO=YmhYf zQPwMnW{DaEjK3hba3|xO8mxhzNWad6$Nicj%^S~O+@(WWu>#W|jM4zs zS9m!g713ac=g}F8l|lKo{zl;Hs|&_6)g+Z0t{ci6s$*I=DzDayFj)q7gSe&Oz~MM} z@30tp`R8x1Wx^?0#n^v}*l>lU&HK9_D*mUHX;B!vsglJJ-nuAioyUHkV|B!)F>Wm` zZY?41$9A06%s+oy2(s2FX~xKa(&m2Bui&S7!B5KqH{t>}@&dQL`3lreeqm7chQ7CxcgGji42E_4zjZ5<1e`+$$u4e!Xm=J-dyqH)f zFqs%8VZi0XO+OmX1zfChP|*?xc6e+y|Q`zKX^g@G-Ia)rw9 z%8C^B{F5qr@rqw^|K4>+WoL}|vt>b?An6n_v!bb=(04|E1>k&eQT9jDOg{$pmnMWz zGbsoC)3-G#I>Sb-3<8J*_1FlLUe>;n$|G>tCSk=Ym zetZmednG0z+B`*z(H@$L_z|xwJ4{kF`_#bjuhul$wxV3eA~?DQV4I{=W5q)ViL6S9EaC2>LKqWQ5n6LuMK4e z^K`8?*i*=^os&eKSD~V9&Y5_d`o7jXR0CR05c(TG8?|j?HtOvZ2ED=#mye<>I%*mw z>+(}jXjX$#q3FmT!!nC=^v%vce>BF^=;_``Kfmw?yqxX4_&Tr6HX4p2FP%abysDYs zp12@dBfKyS)^Hk$T+hb@=d89FAGBu5^HAQbz~TJtSBEs;dwqE9a4dX?zD2VE$X?lU zE!)Xj4xSH)oBhCF8ajGJXw3hi@%T70%fP$Ue6rx*c z>h!^=p0n9WQu=W?QBm$z(*dZ;Tm$rGFL9PwOr@^$Q%v%Off0Uv;@5leA=MkrDj9u_ z<-$GxO-~*581i_*hpWMtAE(>f@>plNHB683eY>++I~;$y-D9MRp2&H7E)!#=7-E08 zbqmLh5#T6|ykZ-ivC^+g*-ET`*hSBYnW}Pi{k4dOXs6NopisFn_VvCNc-u(H_pWJH zubOjtdriza2VdWPcf;9-dDzji+5g4P_-AuhnLw{=ou190Ek*#-ri{NuTUsrdx z`t7;3ld_OO^7fV-*J%#^s7D2q#sW!T1hu%X4UfPjeo~#aO=I^TR{i_hvBFh zo6>i;goo}pK2Ed4&6?OQ)_2*Qr;G=)lws z@NLf(AWr~*AK`lyW7eJf9Wj_vO4a5QdAbqI*S-9pC`akKBmKSY95fsH^P}Bk!K2oL zI6uhp&QbPt|F#mC`zOp;D}SvU<80pbP)U4<==;i>+a^3jzWsFQCkIdE$?v?Nwq|g4H_cjobC;Ar zk0+}>|Ez(fOLBg^VX|i!5m@r!*yq96{V4O?tUUASINiBZc_Yn8*rn#~(St$gOY<@h zg`0hQOBsLmnH{h%Aorzp+}+d9sL#JIx@&_tmBr6#`bx(ru-M?Xwth$OX&YdhkYJT~ zlNHnUsK?~-EVe-Moo{JvY)uM-ADLxpxcQUqvnb)A(|dWR6kT8U=H{rT;mo6?I;A`F zX7U{sbM#sI#H#}T$DYtp41a_r(FdogZ#}@4l<;d{e!uLIi?N&2jH=r$e%F8K`NG-U?9D)MQJ;5lMbYBe;XQn|83xrq`m7pJ zlN9sPJFNfCtWJ02{eG=YSOUMcw{xbK(q7r2JnB~2G^jx~_i-lEEdclR^rn99+vxky zenO~zaibv^_GC=t{IBUsLmF6!q0v& z9e+++@0d|+bJmJ66S0nhz)ToR%k z=`Xv(HzDDiGa{;AQ>=O$klUc?uIBGNf^A0_&1}P_htEk{fn5*k^Z3CDv5an0RTm&* z941HSb-3RmROK9ZdI?jvzW5kTEPXbhK0Qr+KSS3beE*7FlT;zxkF`yn1OH1h4!^#H zD$#FW&JWF=N%(Bx>FS%Fm`_rDOId#_M|Z|i5iyeG`98JNnml~q%fa$6pU$(z=ww!X z*|d~NK0A#z+#ikN3Ihow}Yp-EFayxv~;*_)$T^g zQ&7?k`xg!Ep#VLiC~co6&pWPXPc~3fmQfZ zUX+UkHy6Q;I)3VS9Pol$Lof9QpVvf|1HmHThm35S@N2f0OIIlsYwN5J_aH?jmqqje zJ;P}^+6~^3$!+LWd@8dr`BOA*$AIiYR-aJ20{%;IJ@wHc>J@0{aodYo#lZYDCfVADK#ENIfaZRIxZ8(tbPTr zEn!>uwjie|r+Em#{-z-zG)8&+;)v)o7ldZUnFK8(FOO}aX*622&6wifJ;gjzaCb#uXA`5A7)pRtWZzf<)9X4KFHY_-k5+-Qqu|FaZFw%O$)1(K6#~X z4fn2nn0HjQE_0DogPj+$(eAmQ)wQ>b4}o6vy!zp{9$#1QDTX%}#ZEaTtP2Ur2Ztr5 z2JHraCX~O5&fRw1U25Nsui33OyPZkgXZ$Di!|AsU&@g)LFlP+mS{rGW>ezm1w!b6Z zif^{P#FJLux^?A^tfl8PkG*ons^99#hk`k|tuNye7Y=}nNx}$I#f24umW7f)p!ms1 zDFn(0#uvsSjYu#XD6ZJ+C0J>4K-ZNwR8CGF2X^}OC`P~FL?P{8_iGM*CTQ$mZ&fUDn?IB0EcZPo zLqxiWFN9H@F%=w#-7;}GXu_aJ=P!X^dRh)N)Ik`<+NVC#jXo0xVYKu5T1`!ZR604_ zzD3x4xD?<;`|L5(q9CI>E@Ifp-eo$!Zwdc4^#%d^9>TmBB``Z^upaG9ikTRJ_2e9* z+JPavY($w@#O1&(B*YM5(IhOy!LV!l(SXuEQ3p`o%#~V20u){}%C7!0coy3ULiQ}K zJUXttQ|p8W=B`y1-eZ15SJ@MgA{1`Tsoe`TxgD{=3pj#nI*eOP9C<+pPYr z|HHs1m>wRauZz?mEG!kN52L9Nh8RRdI5ig@n&Th9(PvDdx*;Npu#J+6jj^J-j)aLJ z7ALEiOhlD0FC+vaB#5kH97Y1$a($}y;4DiiwDrQBDR4yAxS`#A3iyw}v0kaw*xyG& z(pUab80dm92F$s|A7fb7EYALNs@0niaEGmDOD6$fIL!!A##sUFV;f}nI+{Mr>Wp8;S6 zhD7(rK>JUjl!lP*$!)tLcb}g>=y7CTRCjd)4J3T4V_tq5IjJe3!30y>(!nS3n=M`ZmU0%y9J)%Z(2 zWVjB76h+8~@jo2?GoXmy6=m|nJ1cD}5W&09 zU5p?6;kLv;EnSX14~o-zZr$h+u8~%1n5ByzY>G$u%bZD-s))0*HO-PpU`75y64~aO z3L+o>-MTz{f=a}8m;=WUe_zZjZ%qV4aHTkRJk7iXA}SZH;QKGr8C`%~S6_>Y%?knf zaVTBeXIy0HYM&y<#dug_x@655x27X^SOZv89cHqO$=ZG=*}AnJiUmjW$d8FMtu2Yo zAjiLpKU0XME7yS(74%2~_Q~+bAC{f#je}zXI2>BmYAutYF$c#fyfkK;j`33Gn(QwzG)h?GrWawL z3LJTnP0bOxpUycYsv#McpE0f{`}$*kkE4 zWSO=+mgznR*`VHtNW5W7xD*j}ym#m}qN${!Vw|Or9-)m)T96d|F+l9@PUY|jYIG(5 z<39HAh-G4Gt|h0$3Xq-m@?tPC6=MznsNCHdrRR*X3rvk?PrdV2z`vyCMD$Dn;9#-d zdA0BEGMABlru-^_DrjAwX%LajMc^n$GDXK+bIh|9zY8rI9-Dqg+G3b?ZDU$oMUrET zS(Ce^Z0f}h!ocoW#-%sa(-H2#!W`~sRuPlem`-5h2}=&lW;wnW8cUs=Bj%YZJ=TQt zoE*!1^XAsF6=F1>)Xn3561!YaVLR%oz#yfv;^RgsVZ3W9Y<&aX3qLE?1?7N#fc($H;Ys!058`6x`n4AC zqA@pyt%HB@`W12b3>Be9p1tGI!u?|D`c z;cj&y43Q9_(l}9>JM274iaK*azmQAGX+!Hbp_;(xS_CW3MELk~)eo8{bQfV3p3zKu zx&avdisKfDtm{Ls6>JM9t`Ug&14T4P6SCqNNt#E5PG-yAkgg)1lKs}(J*3rD{BQ_H zGTQqM8Ru31$?0Bk?`gTi2SXSi0o0FQLIRV*X6VJZeVF&CS?`%0ngDHFauIvIUcih8 zVf(6i2sZw7bPpWA1f6v!rb*L~RGRElz4s=i{`CwW#@Z#Ow2q)hvnUH&Yx+k#x}3y! zG@9fQkYfC-6fUCasz0mZBrxfRd0Mo%ILGS3=VKp45x!oFvpI#Vkw!H?T8q%9wne)^pf5PTR9q4bD{H;V74;ZS!RHgKFFw2D? z_dKWb$%c>>igpvR9wC@;*5UZ<88q+%wu+?ecyAj9G*%hpPR|0n>JiW%cO>u(8sB;ltj56ve`MQ6%k0 z*&HOehbp)nxFtfNG%nhJUc20OFWwv4^o)Y4f6w+j&L7&~yTAD0j2TV-$1w1n#LmPP z;`hP9`A6~VLFMDq(lsER+3r{(Q-!tpB!;CR|oI}62Ht@Q=xBy1n0JZ2XuIMfI=&eq?ZkXR& zC_tS2wCTHlIZI&qyW#$92)?uD-%u%#NS0x=**GzA zqWId%khnQ4ak2S}gp*wzqWeU}Mofl9)eUh~L{<8{^3ZpaX4pt-6xa(jZWO)f0W>g_ zBD}CSZWLv16hwP6f_(|zfjFu?37G(zyL0Eu6>NnfPSJNJ-6hiLo8k_k&Eu34n;IJz zsgTU+XGIaDjpNmnGsVnU-SKB;4B-Q`$~5s>Q-@k_XXA>Qz0N=SPwqX(H!JgdPRAIB z{?lIK3;gcDF?)-m3%{zapem+gErwz(#@r=2US$zpB_;eXzQ4Vm;Q@`20S|0YXV~8t z>+vd&xHP7mYLlI6^?sigpE9DG9~R5A9(HVZBGvqg2E_5ak{1-{A4UVndENaqBsn|6 zZQa?ou5r3{)Ddt2QFiY1Ti0ja{dU&~HG8;#1Uq-Wt!v1xoha}A45E@gW%aJY)oas{ zsGWOm&7?ntBLpd6-4Iz5#8!Cmh+{t;x6jQ=-q&_sskU<^f5Dbj&p zD-z2K5LD8f3n6vO?|ZW$t?Rvm$k5K4+T~0B7STdzwK2DFk!Pv`NWe5KN#w?G`SDj% zS3M4X&GcWO8nyUH_1h3WEPmq5S_-_URll|>K73Q# z+$A^wj(o!2*eNd^1@%#k1L46&-KM$dGj-Uh6oWc8Kc|a)=q6O)J%cj(p#xrh6XJKAp zxUeL+txyZxnDI4s(DNrBJC?tHaj+*v=sh&*d3{${1ZNEm(BCjEpZ-lFnoh*d#&$D5 z4Q;Jl=*v_bOfmw_Efx%}AR5pa8q5--)n1~pTZ3S4#A39-NccV>v-15xbE?|uEF8!c_T=g zmI~6Pn$05_UcnT=>-hZ7>$$`0x!d$g``pSEn?;P=wND^q^?uLI>-*|j3R}!Dy7!Ae zs$h`TMUdCA8meqC)tRIXnIdkdjA!Blhfv}}-iD7X@R0RHG1(e6s_v75Zxk~1o(k7| zw!k`8GN!HYj+OqnHf276xq~Kw#-l`d3r{f7|&~kRJJa0UWTzp4zpI znuco*oQ;sfWwSZV3G$i#zoYu$O z^G5p02=(fGp=nA`6Sh_B3=OSR{8Yv7@7v~l3f`Jf?r;10hmy;yk|TOYPD-?Y=nG_8 z2{4qDw~=Y+zHBSooT9v4d+jYCz(%x%)x7edu+EYoNRukdr6VU$$2xlT3yDayUXJ;h z_|WwARr4;<2qYx-ZS`9g#j?1amlpjs_8$6a^~6qTGWZ_WzZ&<6T`)Ox@AnuDz-BJY z6mXjoo!0Jpy@Kl${F>{ao{-@F8cS6EMQJ(3p9tz|t?dSj{x{(4Lhs8&-tWRT5J})- zba#&Mdo^@ZqmtbdzvI)+9+cPdMNKt40aGoBrrnctHtSXN0~ifI*J7vFo=}=_l1Cux zCHB$TPcxOX8q1?vyUnY+x%ShG#i>+MSb@<>8g=4jhNyRvf`Co^l^x5q;&v}}ysi$1 z*TNH+Ho%koF3V9ALGIZ_H-V3>ss&s==8ePw*hyhSGRKb$xgkp3v=?hoi<-8tkIsI{ zcI5!&t>J{nzns<8r^2g?jTEt^_Fw&#pRn70$x+dlaEC46@i=NmsH zUiRz4P=mqvqta!p;BIXqnjeE)cW2C(ry^Tsp{(MRT0SMEc$a-|!p3T^u+!oY*+E~1 z+Hr-j*{5&4Xex~^%Plr?znUN*n15RZ9Km{mO%97649Bz3Je=-`rZzvdHfN}Rs~clpE8kdlM)em6UKboH&@Imf=4I;O*4l_@~M}n+8H^z^}Knk{dyKJvn zg$h)*=uv}<@O3G_4Nqqq^E(1hn^Eor z<@(3HUkhLZv=3q~56)}z0S0&Oi&xjmUtYqdxwb-Q2Q}K70hrJ}rP~QAY3p3I_LG+Y z>^2UewnGI55w;GsSiS`fF1g-&Yh1%TU9Hs8zISo=B}=0Yvqfgm4}@M!@~#at>PXH1q zUV`qrms9Yy@sLnr0BTFl8TwdRUE)4I_JXC)Et1YJA%HvUR zbCb~_+Tekaf8}jgwYM>~bjMa6E{lJEbLUYF!Pgt^yNbHO!&|&4z9uW9dEElpX#Hgx zaLhV!-1gmsp9u~FwEL@}I+(MOLAc@7TX$vcQJ~KKp7Y15TeS=qvWqEHW&iC76YhJ6 z+zDK#lQuQhK5t^eiM(P1-T=fm5T$+AF3J$qw!$K?q`^CsX5=Ny=(A4AP5 z(gt$o2#>TS0Uu?_)pnbnZ$4J50uNscN1wK?XBSTumiosFR_Zj%b}KJExWH%L0(J8b zJCrXWgDBe}ei$z&2`!nawJyMzMYp}fe%z3JG&Ho$rtU{F4;j&TP9v)wCbzm8gVY+% zmyzH#pOZ!=4+D|4{$ieE=^7(jR|DOs)ao1V808-3YfuP@X|pBOOW+6YknZLzDN*8w z+KTUF#) z&YE%;O!x}MpsECLiK#|7xx8nIgk-26&Xd$YL8Q~wP)$S~o#|#*rHL&6__ox7Q88s> zbhOk1X1CaOaHKx_-K59>Hk?p-Kd|GzD=Hy5h0W9up&K7_# zd3-NGD{CCW4$mWV^~+nm;H-JG>*gI6s{cZ6-meDzS^)9OyyZm}n*vH*g+ zFFubGydIinAd<=mc{}YF13&bS_imi<9F6ZXcs0p?7h^fpFKqn1!O9J zi#CfvRJZ+o{M;0?eeYO34?SSVRgerCJ;hc3;MVitxWiY%*7w1v4XGR+&snQ$Ea3@t zK3q?DqNm?<#rT={a`XEt>!Tcl4=|)`ce#`$Q14ap67*4liynl~?7N)PGJRt-TkQ3A zG$~=Y+HumcW>6o8>R#f&a%;aWlSQ`P9==TQ=sy z*8O@meXgOR+fLL+ro~T)A8NKuOFSn9*4FmU-HA@uDzt|wZ*~khf6m6vSl^E9-N(I? z080Q}nzKm5qowW7phV?clD>x&ga91!8yGr@q|ngGL@u`roFx={o*&~)eIbVzgsAh6 zh*Y#wnJ>uJzMtUT+&~5$E#rf>_Yt@KtfQdy!xZaLz}=VvqUmefN7o^J%`VzN08DqS z8>94*Rp^cX+yM2mC5Go^fi{zofE*60WL?rVmJtR1HrbJW$5IX(h5Qi; z5$B9ps8wr4YfFW^aSC{O;+Ud5E%H36G*zrFVFG@UMoW+&HS9d?AYP^5Lt6IHN|qrB zbcq0xQniWPX&O}Lir6ta(Siuxs{2K{k zVD3w}@PGJpIqGs%zqUlpAVmh^rxb-M7N*>Bnn*{OR=B|QMQk904omtT`{RdsYdFh) zJLw65%wik{fdr0FkQ4HWKoPx^6w}unz>+sY5y^tzYUF=N`HyLzjcX??~`9YKCo{+72*Gip6eKGq{=ncd!3*A~=z3vrf59}4r($cFiT<#-2qt7>SG)G&*{wS)^aR?R9HAo(r-V=zS2o05Yg^9DZv4ISsD5Q#uEbm!&o;#xC*qt0<$@HCV-Q@VV7#^qRMvD^ zu+O##DJ*IgiX+joM4YA;8NUiGxF|TWZ-_q(E0DG}44Y#Cd!WyBoJg46Tn?V#7bGH0 zZ4h=-gGu|!D%`s34E4_=jNOGJ62;`30hzS#>OhjLZuEs3n_`*9lEYdTbWKzXXLkl7 zr9{x)D^WOxq`#?*IJvlRcBk)2xJmVoq(7Y|oY=A)P)onzf;`SERa)MJ|Dn+*X;(q1 zzSLYW-z)5#OGJCPmIU|Xg$_YtSeUjBBQ!kJcsWh38}1?mJvKpvrhRtQHhIz6&^ARO z)C!-4LILK!7f10|p)3+~b9g^FG=+|lzoN3ilhSfOyq8w`3~^P{I|*$aT#y7BS@RG5 z;LhXYndG51*TuyQ;7uk3x&NX$F>_^CU~h%meG*pDxDePwO8s2Y6*#iOaUyZxEP~bW zZ?m>H$sFwMnpt9thMKjSe`3#T2``p&W{{3YmSP1npfpxD{%9UK5?%0&%mz9qp;wT= zx>m}#*u6dC(s~14x@w%t0u{~D zKcixZ_U#ZO`o^Qqq__xY&TR~YNa^IA&{SB3^81{*iD%(ppjB41_smI8=Y@SGeRFLp zeZtbNeZ8lBp{6cq4O>yE3*sG3s$U9!HF!E{ksN=Rm|*dMXb!oUlt%xa^bR;{CKYp) z7FqF_q*9J^7`08^K;eDpKq8BnVSFkLa~EL}R=q!v{gp8U*PO%PNQAy$A)m&ej!KL# zazGKEv<=P|jf=-Z;YQK+W*AH;GR*KwM;AJoj4;y$4r`(uFKX`-|9k{v<)` zPp=I0n~Wdo3vrC0xAej5-!s|k(*)5@*eAhWjsZ<{?v|R;?Jl64o(_9?M$=-YV!s4r z)0KODLo^>6R(LhmC{~+R;G1h@_$N-CmnG$LUv6y( zqg+8m4&ogw>3^%9WLry%)+#svvGleo>TCQ-l$xY~v0^l`UEs$z#x7~w?WyHYQrn9} z<(oh({kKKW2?I)8?8RftxqZg>dw~59FrS}D$3M`IIs0|z=L7DndhSKCEPS7lSvT;F zU;ls#F!%G(=;tK&`DW~x0S_v?eR^MBl@88mlWIC?^!0(~1-Fgz2UgvqOCU)#0W|s{ zg)}axE-l3nAU>%glBj|Wi)0xR@+(;dXgqv%Fa$8EKhQ!JB(d&8uc@?aJ%uHQ{_4#{ zI}TGp!(4NL?Fa}Fc~|sYjb8^+uJu1kRBS}YXNDygD>qWfK`Tp)3qvbQR|Hck^cP2vjI+dHClRBuLJN+F zvxu1f>O zXneHPzB(C@ZyRcn&y9YAUG4c@eG45bsugfN4++(x9)oHQeZ(KPTv|B_VmLkh!%3LD`_ON-Pzuj>H z8%~gP!6tipjVDr$kpnklYB&7o7T4;`_{-S-yUEEj?$k?0&r8P0 zOTGOkg~KO>(Pua(aL>q1=q zd*UDX|Fm=wEo`RN29rP8p&DfWrKJ-D@#V-(O7e8(az=?j{6|YKE)JSU+wDGA@k25Z zE?$5&>+4TTEjG#zWo6D{RUA)q&t$y;H#3AZ%MWC|A){t7HghyKizDG9&R=oXlaC9R zoy9n@$q|+JsgF$cZ{Jo7)c9qxi}J||ik49qqlR+0LfUO-t9nQ%}A!mcYJi|J5@pDK><(Zh=qI6~@@Um*5hU#4r=*0TH#ZV$P7iNA6_H+gHbj`6JAPMY4 zOJe)m4Ug_MPTO{)ckkJ=|JF3UJD>h_hIg;s4TRk`+~x>oM;N0!tNt}c*LL%Ze}#k` zxxDU5zM^hckP69eiSlzzs>P&kfi@$MH|19!}InT=53d5IPXw#S-Id>dcIBz`J z{CvM2)d>nLcXj@g!y4W_$e@9?!c*w!#sV1KsvLr=LO>Q9?HjOBWEgl?KYdtyhmw}G zJQmkJ&vZYyho;yj=dA9NNF4^LPd^RD=rO5xMtsXn!j_tvZuZY+jP`)c5NpHj`#%|l z9@5$@%uQl2!o(+}BhM$pQD|v=(D^YA0*naYDNsJb!4Qor8RmG~WL1z^9P$+WN?IJ*q~OY$R%XQVmvc=59@no&00$Ou|EoOTyFXrv3ZZf z-YF+*XAcP|f`1pUuyce29KpZm8Ewl?_RvrE=x6$^W%_kEAePZ1MBcBO}TpSU0k1{5A3j9%QJ6M<*zP1PG73IHSBkFC8?ln?o?_CUl|O`fip+N6H z*&MRwHEj16|NJnR2b>U3D?OvgHmWlbp8+L82XSEN21hb+aPAcF?+~|{=O(8sP#M}7hJ%QN?>Xrz9Fu<9Ox-J!4B>bGZ(_lLQ=Z%Z9U>;}wQ4Ena zmK80rSV@{+J>S`Ao7<{V+y*kp2QRBQ%`i)n0vAZ84+5@<$nyp#f9|CfNI=L! zbJ2*QQhzHN$>&ZJ&5|jWBOvZiz|;tmAR{!m{uj>iD^?(~Ac?=Z5r_BoCc~dm!KsXy zjkV4LhI8=WqhVlL#!|a){tay2Pf6NdwS$Kns^b>gA^F&Us{3{{qSSYXYI<#?c)2au;m{|dhq?U&DO09wY}x$ z;Vv8VeX+0p!CIyPXqD}j^YDT;)0*LF6 z(S@3tW647U%dJxU>L&pJT}{~6X!yyA?~^$qWIG%krzPUU&!(_WVpBIz_^6prh?-`Z z5#}Z+MgW>X-pjW8R}iH(eBC_3rTSWLWz&_{qYn#;_u^d0%$WeQY ztusYe*4Ny*a?&uR^IbFFCOOU38u4c(Ip3CsIwc5Qjjxm2BT#AKl9zP|9Pr86?gs4K zH=E^1fKQxu+$`QW1iW|8ds>Ol2cr2_6|a7bo>6NK6X4`MtR=4VUnm50rGG6AxahN`Gl{l;bzL0BrG3*$8FLxayRKI3>g>!^xp}l&s-`4dR zUn|=hCHMWFk>f}Pi^C_k+fwhSRrAR3BTXLR9p?b?y2jGC*rJ~S?0s_J?-`2t!vpF; z2p?gm5yp3=+c){qm=?~1CrLTj8stP=0?&upRxR}HcBC`wr}~3mJ~e0CGUD9NCTHg^ zW5V9QMnfkt1-8__Gh|K)Uo|j7t*~}+DJ`9XZLT%1E_UvKK?NhYc6qhqdB5C;CfH~t z2W)s$q_b=8K?IK$6^$XP{vMs;^qCt~_+Cub<3m2=$aMU60yPUkm})?;cX>x!@4Kxk6$SDe;oPUFM)4G!XXFpf*(+;^VRx^2s9lx)axI3O?ZWrUEYhzC#4tLgF>I-|HBYsTOGvIYM zJUnF?6L@hFBe3(8_671Z;G3Vf)A=%NL8>B+3yjHIZ+Z?v0RYeDdqo9fLSu-FT@~=O zP;jHJCV=?@ypAp|3+)=XgP!*HPzByHymO7k$5HzfM7FLbx^@4ndb|0nFF+P-?TW>+ z8d-7xjAqpr)03Wy^hsp=+MMqx6jgc|2M~6F-K<`WoAMd*S=g`Eirh^40$f_vM_?u?zAa4- zWr*{7&i@Zz?;K-E`zYK_+qP}nw(V)#wrx&N+wN)Gnzn7*w(ox5-?`sCIr;8McI~8) zN@f4CtMWXx)-sWf&OzHb%e(Z|c&_>3`TpL7&$tOAZ(5}*Fn0zP`ZwuQLf|=Q;LDQU zeA8*>Bx=L=agtf_c;L^YQvn8UJQIBOT^a%=0zao7*9FK|T))ml#|2n)bl6+ZR60z% z_EYwD?L3T;IUSGEbqYon?G(bNZ7@dB(7SAt{NCs1&&9V2{f5u$-}I%&Tj8G!Px9~j zFJ)*Q5*lwOTvmf`?k(F8pF`l+{&Nv{Jh`=Sg`6L1XqzW~FY$19{&1>OuSXNaX9Cmd zt!IF?`c{u{#QqY+Swaoofj{}ztmC}5OAJSKtrajK$hX3^8UDfm+OT7Sj5cqH8jO$- zUQNU-?!k}W1O;M9u}jPmF%o>yRf}<)bygDENKtFVd3&K~@qq;_RS;G zM2HYY6Lic`MT>3S`a@dm@$h_Ao^rQ4H+`=gdY4fu*HS_#c=Imz`K(IxP6P$`Y-t`B z4;$&`K%3?+TRBY@=MN_Y_2_LWR4488rq4aRtxxWNIFRPA3ViFKSM(ngX8s&ljied6 z^HIHkljdd`dz=nAq>;FvVVu{yX^3<%ML$ytLujNB7)__ zvK(U(8m~1YJ+-5s;Cwth~B@(tY@HGOUpz+LpwFM{wfQ8j7RRzZb{Wz z*J|?^F1i8IRve1u1IqA%c_<+aL)(mlMucFqm?J$w8XqtFN?w~207KyyEcdRLMTKW6V!R@C3PYS=L zck&erFT5`u{{;LJe8`7j{v zKs2U*vurG>97K*$M}%TQjF zrPh(c_3Z(&*4Xgq3I;CyqLF7C@kWI<^KI5gsX>C4Q9ke2{RipowVS((dGtuTPF}n! zS6>?L*`K_&oT|8*)ys+l>^S($$T4&`41EKdJ8O;s%kbbnx%@Q_oO;b)&buefYNQJz zJ*%DtLYM;lZ8E-99Pb>~m4~V#kO+nkWS~4&HMUDP@W0l?e**Af`RLZLf7RQ z(4IszIk!O2V5qgbhMZM^;XDLI^J*^Wm-zMzjVm6ZRF7-Ef+Jsyy~4|UuYGH)Ox;RgG_tL>|Z-dYM*lPa?xDtHcmyq2W z8O4%^ANpg5=8Qh04~}$vy{U#Df2^?Lcde?O^C+y5LbMVJkNx{ppCgnF^i9Qe2+H*wtK%YZfXORVSI#h3G1sYIkAiblDdWrfHb zhSb!RLW37+fFpxpS9toWT|QQF4#vY9c5S*cq^M2eW%OPjG(vNVW?L@osc&@5GNaM~ zwSkb3!9zIm8}It>shSOcmw|1AGwPO4K2N)u)I3BeI)pZ=%CmCU(wc|>uwZ_%1|(^9EsOZ$%cxrkU*Ytv$Phb%Kxqwn_j<9GVC zjM-J!agOu!>*rLPJGlsQg{oRPFtF-x{>neGR%t?M1J3GGHf%CKQO%pu#kP_xciY39 z%iRhiQ(H$I=k9kw6GkZs}VXEkjS3zzy&=sqWQl`wJ40s?4m@hbj_E;jeSxyx20yQZ_<5Ccz zE+1`GOQw&>I;0Ty1PIg_(v~`k#reYGKoFK!nZhv{epDWwmY+9qI3=SY@Rd)ni`z^S zAxwi-8|;ELI#NKA4nP(M+y@@lzani$Gi<3C4;~T&g6AwqK?k`AhWy*Be?ZF1;((cO z8j5{g6v>5rt>4Afcj#0TgBiWrTDB9T4azNgPy^gWZg#P+5XEN3+6Xiqv)o$GG9uF? z1r(l!F_JJU!gan&T}#$Jy(pL137n5O3CV>bXciT`C2vmz7V9eC`Zc_tpWGXHRa_0R5B z&|3`b7AKd7jF1uCM57-j}!TRXEvHS-gbT=5M7 zBM5BZ2#L#}2)&a`ap??nY(`0V88y(!u2E2mO*}6Pi$>8Y28Hs@9tPD|K2I_ZgHrYF zAR7o6oQMquGbj{DxRX&;kcrg_h#?^jij^D%6dN2RlD29LT&DydcsIUZP|O0V;HyKI z!g1yYOMONP7`d0HZDN;vsh*B=fE*07NloAdn`$SzX${$`b;pHk$;Bh->iKB>vWiJf zDfzKVO{r<1d)cz($~i5a$MW#`Y5g*b#okG*cq5gTO+myHQW||wx`_+=s<87vD<8EWLG?4OJVwTM`Fh1gz z&9L*8iZJ{XtTJCQao1D8-ZL-y5Q!ob7(CQ2Pp~fVdr(R#rU2D6!*_1BkV+Lz3p z&!Y#M3@SSTvVjaH&>t!#Kfvd`hv|`n5CryPEEK#U;3e%=H2zBYMe+ zUCAF@U{_A)t-^tP5rK5GK^6Ru`x&r(j|N<01w21@#19=xNO^>z|C# zTMyA@a(RK8Zq$E~&ID{#BhFmyY*Xf0K_%}o?}j5(x}O9-(!*WxK_y%E`C@NCwC&`h ze_X&{k@TTV+~MA6!sLwf`A|dLrz1}sJ2Cnv0wn#o6Wa#{-3h_3%J4_mtZ9m&>ghiv z_n5b&X<{YU&OoG-o2|*t1Ix@` z$6d*CY3X%_vbkaNkx2-y+1KIls3o&Q`Y@pJks%DJx}_6B0^ijECbbE+hf1lX>n3yf z>-LgDyoSH6oUo-RcoyJWDHgw;V+0ah_rohFfXJi@k5AZ#oc}~6U*0ZXk$3?MCUM`r zWXou1F`+;FCuZ1N_CfLxkzge;H*#q|eHn_q)+Ehw)bbTg@yUFAf%KApp^XCY`VCTZkOW9P} zt%jkQ!kv)D%CpXh7qLXJ3ih-i7U(D9s{qFF_8w%T3A+wvlElx%5vkB#$}xIcdou-+ z9efrJ)ZlW7gn0nLyGel$7u(&g=TSdP)_s=WfvNFqj3^xy-gq^!S53I7fu@{DWa)6y z#B>@C$Vtu#AN6ayCY32w()yP9dqFMuH7c>4kFtHyDelSTA*u5|p09C#$7y+!jvD+E zeqKRuKOWqqS9j0AB_T&R;N|1-vc3|L8sEH(;ST=lUDbM@V&o4kr|y!+2!w~E83kT zj8*+=j4Iy3L8?*i!JSSxe})Dgm*}poR5j2NGq^2ZCaq0$XridK_5tZg0d>REI`Xpl z;}jSdgu~mw@iw(3YUVF%=3mfTpWb_iFxMp(Xj%b0&Q74&OjGOgr?*}tkW4?&v=mq- z5O|y(EM*#16BrbCK2PLTrMIkpDG!q1Ta!aP)>Y>4nK`#gJ<%Hf+ zaw`(JTh}SJno3K$Lhy*E3=t0kQeaefe+cPq67sqWl)!AQs^OXZ6aGZMs^^`z{A z>9U{yH0keY>+H%^C|ma*KxG(~tzasJ{S3bRC8&?k4Hu3iOLKQ(`uYy3zm5>K;zPm!aTk zP5)%Ue>wZXH|rB-!4G)e&#Pfiu#yM8`AG)gL^~zk!R#*uJR0>MEP6y6^~u+2UPdVh zKKN4(nCwHy@IUX~PyE<32S2y)^#)ZU7BM|0jUU_X+cyc>?T~QJsvi5Vqu=Rf7IZ*iaj#g{>&i(e5$tJ#K z|43E$>B;-PJH~m_oJca+y&ZFU+pti_TOHaK(AUye%kFusF-X06@x!h$CHgHTH}ve9 zZ@rf9{$59|_9eqe1+1$R?JnUe_<0eoF;&~j$9d-C?Si*maym!3u>Gc8NK{w-4k8`I zAQ0=`HDB#wc0Qh8ecL7S_h~}jVRo^5bO?_-wZ%(R-+leD(nYIp{)BQlaa(xl`i(P? zj$p#S@#NPN{_Ee%_mpt{^DE2epWO#XtuKL<9e8@}-hz?AkBytlZcWeAG?JJ4b=!y1 z{BG520ebkpf4oZYml=-GC$ZN&`tSooQ6Ob`eNE|czh?9(qXrmH)7 z_Lh#gW~&o}>HSzfbWQOb-^mF!_~-*|+bz4;-Ym_Ro7o;yHwZANzkJRVHTP#gn_Vnx zXp{<(HHjKuQe{AJJRaWdZQPIMep_gSy4LH*%B?kC%QCQQH?(%H26-$`&R$<4;P=)0 zE@NZ6+pOhgw>aJ4W%D;DZNFaTg6h+q9h@b4aHoDouJHJ)wEzBRD zbvoV5B5XUb{aiV15jo~=mmZh+NxzMAXp{?q%d5LG*|hMWb}|Y6iTeHQ!kggFykzQ` z@O5b-2OOQwrTc54o=uwYbE&2UdA@gh-q3a}1G8Wv6 zq~n@tRw+*#qZ9S-wVA#pUza1JPbT?hdwIwHT1*I>ZE9k>T||g=%952EZC{G_I7?^6 zm0|cNy}K9?XOt|P%@wk`9#BGJ4wy2Q&Y97;HhwoDrwiO>sft9tMlR2bS6#8xW%_B; zo;1BB%7d)n>@=4SPHCrlTNDP_8-YWUSTYv1Np$X2lzs>?^{O)ijv>Ad9>NK!8>>WT zGhjdSupa+7jKZ^Tj$7g4kXHc^^<9zWI|FXLXH?scY>)hxca8JvSB~Y_h@+ajx*B3f zK+xTEr=pf%*E%;ghIhc=J$5~7>z4XLLj6fU^fFUs?WX3hpDNjV4f?m8-qSVf8d5FY z*6H7;duuWg8I z)n^j0iXv>kjNxzlkK$c$|9OY_7e7~ORN5l2#b59}@D}g!~E_`)~s@6QymQ)&ej(lOvsG zagFO9s|*j6`)+iBjembhz;26Ajp=`w~A4MuTPp2(8`X^_d z*OmgO1kSBPNy-ZAzHq+fIP==AI{Es=XZL-VF+y@Z_Yc$W)Yq5f3^HkWRnXO2ojPN( z*=xiF&X#rNK8tmCN?#c&vL}t?I0l8xN9Xa%-lV^?O^WCoT`O!fmY6tz=dSb9Rcm!r%eX&&dF2tH&4Y7U==yGXr+|--v@l_C2FKzw$z0_0F zIo7S$V$T96Q;ol+(w+YL_7IAWFsK~14655WuwlwxnW^nBRl)Q72v2KlihJb__m@kX zSuBomRC*uA@$*LKBZS#k$!u=V=ehetlzwhw_T!_cxb)klRKP1PiuK7H=Z7E@ORe)< zJ;r2oI>MFP2>jM}v4p!h9{CB;HU)~1#_&VfMHMfi-qhB+J5yrF13wF=mvUhA0>-(xT1-IvSD@xld zByoPb2H_a4794-KNBK`tTU*aKA=cklQWgg3@GknYt=&v&9O{=r;8J1Mc~*aQi1Tf& zlf}n!dzcu{LPBJwI(>$h;M}iF48ufWM8iKRL2{~mhX!|HF1;Z1!)24n)I=V$ z@@sg>kI-%7hBkFEwYajKb7oXN$9Lml3YG$=B6iIGJLT784tm{PKaIs_h4UM(K+Kd9 zfzEb}t-`cGd~@>01N4*t(l^0zZe9q=Y7@#7#w46ohu`Bpg;;F+io1YZt4i`3sa=id zcxXYfC&}s=h;%e@M-6^k)6Gw%rpaJ0=i46CNB+4|c>QM}7Jpf{iK`Nh^aZnwo4M<; z^uR8&r#SCRqF+}C3J70FwI3tAc~2)>r=?FZ z>n)gX%?FnZ8TP0xj&mCC>EARxXt$f|x#j158`cvZJ4(?>8OwV$@6)(#Nf_l1%YO4i zHQ)MPTMhnvi>As~IVkT83KQBF&S$e@^yHDYGAPA;Uz?uHIH#XiL!&wUBdK*Bc9Z+j6?!0N3Aj8J(vy6DBc^i^GG4tkZASE{1nRntYA{OCP05c;)B{sN2h2-a zxmJ}l1F^8Nu`|;$H8Zra0bgk#TZVdQ2<;NWJ2`sx(e%Yj1< zY`xu}a1JCNk-YTh0lUu9&q8_YVZ2Lc5k@Mptgp>|5`ME)BKU$0B6aezVa(H~IC%iz z)24TPj;&h+Bqkgo!_< z1+j;w7@AlfxUu}vg>j*5gdve2X-QPNOqwa4p^t%$-GCQwvIYlS?t%G1GlRCzsgebT zxSP1$alvb}atEUmi_>Nhy8avLT4-W=qE4N~AS#%f(E0@6DmlnM6=_kJ3AdRqCJ%nt zv$NxsojOglJ7kj1o;3YLddpv}M1L zwQdOW#}6Cf|3%^T-v{FV=Q#ZTt=X~x=(YT_{EuPyf1530Ejg_3KZpcZFhqW!5Qf-< z)P`6JO9B%S!9t28!~Wy&N*a~@(@qhAtq#Hh1Up7-M=Nj@5kyN&977m{6I6B)R3AZz zC~9(xq4!U-h4h3czTcCJ;`Ho~_xV!Sw!1xm454U{*Uhdr6w!^%?Q6GdXHs{pH*m|i zl<%;SXXMd!lX6Jrny5SSX{l07n7m~&lR~s93yw)27H*jtHcOiLj8 zCa$W7(H^vD&}rr&?ZSe7wd?3NO93lA2s*0g@SZ#6G)*~*m*H%P6yBP+=d zwGm3Ows@_o9-sBX5H!{#k0WS^n3)DV(q&0=Mzk^$i$1u-q!}793riI!!&9shkvRBB zd`7qs5kgxzR8cG+P2EBcZEYm43aPQ-sH5zfj6QQGp#VlE@i|m6fwYnF6SJdXhA5^< za`2SPYo>b`b|xR!C5Dwvs39}^3b|pNF16hh%4H0exv4kXjiR07SjVKoB1KfD{0BEp zBDS$VFxW{xJ{sW!WP6%$ZR4PBSt1-pM;ar7iG`2{4~&pTO*s=fZIhr(Bq59<@aD)} z2MX5!K;pIW%6J;X=t>CV%r#kDwoFg-!?Xw1C=!;q%8X@5y_sjyjPGnV7#~Gw8hU0N zvOtz@G#TCc)&!<8T_)cEUANi@>VdpLA~VAhFb+?^jBact*O@v2W*06(17g%5*XPmL z3kHI^A7Rv4?i)*VZ_+`VaSIxXEJDVmq#r`5Bhr#M zqdYx!F+y+03o82gcyv3XtWCYboDhUxMEC_q2T`6#uFBv-+St$PQQ%~dXj%qgX#{a* z5#UN|-;BYnYXKBui!7N7#~fq8nU4wegAGR9WAhdHP&xLngVdjpdGt*}nV1>|LaT^{ zXRb)13*NhKrFNs)-Kr+y)@nRZ2ot5VkOK0;j%<3h;h-%BF!8u zCW-P~JQ|7Ez^(*!FDD5mgK~9-=-4=vkxjE?dJ2s)H6OKd2`{;_v@9ysmM5{;60PzF zc_b>K%+fA5F}$xISFLzd{{X9FV+64|3$PGrBC;hZwCI@+Q6vT3kh(@X7;v52aXBO% z?;ET}qzpB>4Z2jbPQz^k{z~Fvfs-QcPE-E^q#QimFUuwfvS-RotG*eK(h|xc5De-l zA!f=!F{DM2fNw!6_pyv9t8r-}BCeaGDda}8vJG~mj$4X7qG;dXgP~g$r2Rmj zrKA^Ex|j_Xw~vI);Rq@wb-)c)I&0g0jABm>Sa0qj! z=S-ce$%QY8rUH#;`k-OUZie95AvnXR8w>Pr72)D0P@4(2<4j?Y$SCGMszh5k@PDc; z8qhZ9^Z5ZK;l^UoDhT|7injC+1mJ>AVu5EQV?g27^*<(G>09Q(o2`O!Ao)v#w$TwE zE}3tz`Y1#fW6vt|UBktj2v(%j1mF}m$%3b23|fMgbD(e9kbLrY9*cvPHDGIM&^n)} zJ$wsyniTr1dxDl*p!Fb7JG)hG-t_v;Ash?qBiFJ`fdctQgr%Sb8@WkO>xvGj6J+r0 zq!8d=7cc8K_j)1Q!5q@KE3*{m6zftK3pbFELr5Kx!FM$U5#z>pBuF@Sr`+fH=8PKh zLCX5cq)tX6WP^W+UQjq0sU{(O$iB%(-Vah|t&L$tku%V%?>yKz3_(sM^%<~Rh~2m6 z|L{wPo5Jd=aqjhuJOjT0`$_ogo7@hWgQm~|@>*j>-(eO|Z5cBY2T!KENrJ#YZ{Hdx z)-`UDEbSoHdx%Ko9T-L)-#=1@DCtxYM8n3L^qYniCLKnlMw$UHvB}oJB5c($YyvYy z?p{PtyQ;r|djUau*XDX*30PSkJ#$=pLF8-&O}Yp0ZRBF3;xFL+irB)&H=f? z%!h}f8N>n4XV0fSP^!gA8h7dDcbD!K_ zh!u1WC^7?{4bsYhOUGZxAi)HR!iEsM3t)8xP^@!Ag?;bSln_$80|9|+0zBwFQBFj4k$Jjg^|%kbykf*&rXI z@y~6osG6&lnyzS5avAVLY7R9asib6)j+taD7P2WeDLD%%zPY5Vp(Jl#f=3e3EviUC z77-H~hownr-H80KoRe?}%SgM~UYzTmmt(L7X=&FJ8x>xyNWWGFY5Q^=!!X&Zqw8#c zsU@@;Vy&5VUq*lLT|#2jS_Fk)4fd_H?Z9zT#54*0#IP_=;~(E$mOoa%)m?#suv0P$*kmW&5kugf%D>k*qtzWC?|&0HDuv5I-3!(q z`m6;yx{dY+Urns*R&PB=s9zKaa`%|+&q6T7TPPdaP{-5A9v=3C>O~1acj-xwi6VDg z`loLieRSW*vB@~;D}YMjiYu?4QWyC`1aqyS9>7RuQk#A+ioX2Lu+z7mw`GuOv6pOo zT2xkl@B937)}p-#BKpWv@Wwa3Kmh58kaQ&y(;AND7;h2bos9I1Npj^VzCrN+=371^ zk)F-PH&kL;OSQE4M3!8i^i0_p?|@7nF=R1|)pJN7KmLgA@QUmR+cq&neU*gW7O=;p zN@6nna#dSarg`tD`Ez!yy;vdoh*01aOLRjaw8MSc-G|sZir88#w8IeA>yHKO&jM=4 z0(!*=>XH>Dh*(P8>qrgk%83@(-r81Yfi~LKAtS;kPY3s?)ByRb%K~BYamvbah7^nn z4vsVH6prh{oFpzXe2eai0nV#nCXJGH*TK?atZoi5#ulY$+EPXYsTy=uECX0kG^s11 zhOc??>J??}dT`S4f6!}8c z_(*d_EBiISH@$x$y`zjJ{8xHMQhLX3I?g}Q7OMtOgJu3UUqe$UY?t@_AWS9%eY$<>pD6U9I{XEBaqDU-KBs`Y0Q_zTdxS1@x?O}+ z=;M=`Sf+x@r)oFF7Jf#nMI9_p}SM)nA&F=)%A1xof*3k8SXg>UJa`t<*%zJn85>FT5v%_~oO($8+EyyZA%D zAx_{B zvRpg+=WT-6Qx1uhytT)}pVKcc!rPpB93GqD5=rmnPp<7TFkJ3)v!6ltg~Um|dke`o z4PAx6N4cs^LdYi$;Upx<#fu8SyiGg=RGaGH`jc;~KV80YPd8@XQ z#_({BxR<$-iNhM&Cc5^MvV-aDwp!0;C5G>#E#7Gi6q?ry4ZY8T*;?uDt=Kq2?|{^A z%FoomLpYk(IFxaMtIqy${VIE^ET4_>@yqK2ZgBNGy!z>%Id|3J*A4B5#i7=`J|lal zI_w4K1oITSX^!Ll2opd3=Dnx-IW+z1QC2qk#riG#t{0JPzQNxj6>~ay_5=))mSI`J0#X@;^meZ+0%9s|NiZS#A8I>ekyK>;>xB zzokCx={q*>`qr=ATYUNX{Yrt>?eY9}x&V7mzp=i*o+G~rb#FdbBrIzXR!ZfMQqk%7 zPj&}Uz-YYXvd3GZauU&j03j7I6>nMI%R^hCtu2ALii3(vo?)4Nn%A4OzHa*}y>3Pi zylK@~?Qi7u#o! zw{#A6`J!MB8O)+hGiP+>LEVj_53HEz~^>(9Er4Z?}ssI^?3>TK_xk20)X^)1U(OlPsv+AFp9t3HE> zbnP-U`W+0R_gC^OoH756!x}RaGfh5i#w7N^XwH-egH)1i4F1~#}{@_L+44h^mZ?0)Yp-v%SjZSxcMPcTzTdN))bcVEsB2-m2; zy_&XLgsigPr_JBpv9wM*a@MQ~{B~p4)5)HnQr)ZkKdskCKmC^3$L{dDI&+&0jDueD zOWzUJB93#V{EZ2?-#0co=n0mR%=;KM!!U_^~aCpUv2ys_EqkI-u0ip&71zXPx%^rFZ-M0OH4z& zrwxJ2sD(O#)O2ir6(FF@2cpQx>TXms5sWD6trQ+V_vsaTS1&JhJ3pbF z3)pz#Iaz(THIW&ncG<1IXI_r_8FZQUT(h^(O`mOD38>)A>D{`pwSM}Uc}uhIzV(L% z#wNW0qZ`Zio_Lk1<*i~EK*&?zR(^w7Z22(?*$VuC`^o5hBvJeMit=$Zv*D*=^Hc-6 zZhujIA9wVWX}I#elbDXW@)i8-I%sLv!xi=k>tZXQowqg(ddX88F82YGL2pWA(1J+j z29i1DVzU!xCl+~KJXo7HF|kQ~_j``|dgkszi`kX=736&`yHe6nk|ZWSOR zI#{iQc=B_!QZh4=H$(G46Y(*N%0#hnj0=s-4-IY0&rd8bu4@Zys|!ufFAt1tD^ITr z%`Z+2tSe5;53R#m`91w~9voKnuU!lNAG=nw&A)an!+-2r$}wxb|7F)=`^T>3|9|XS ztpC`x)XUh>D3wfp5dl^})S4;Tsmc`1P{K+Rh6@y{GKBK~m23G4-~XmI{f}I0F64*? zx{6u61=>>)XptJ~AJNu7>ZuQjdo;jXi3I@vmMY*7SHNZwz%isj2}T1+2BNsT6tfPb8}LziO>;fLd#&^#4<95nw8spozZ_xw|RaMlluv z)LA ziy{la|4pv70st9|RZW_V(SQ87;{IQv_y5Hz*tpr~GyTu`lmD7q(HA$gasEFIlo5be z>!0O+OrRWSd3(Svzi(H{80jJx+s#H<7qSvV?QxS(TS(3o(`p+x@Qm8nxH(TvkOv{I z{d7agP?iRC>{Z56m05L^>F9zUq7fI!u534CWO|O7I~p=A-gyNSKJV9$SF^c}JCA** z-~ITny{9IIh8N1pm0w@W|M+66=`1<~Crmk`Pnj@D{lqk{%MjhlGCOK_f?_vDW^mli z&lNNoN#c^lmZ`!LetdVh^Dh_MqfWLQ0S=O28)J zpw!R2qZUUp2NQ}^4}6gi#E|G@SvE|hnD0p*_Ckbiv1bbQT@cftw878Mh%U1RQ~qk% zD*ur~?vr8SZ^v*b9SvIh8KlPynMHY`;(1e#TfSQ&MJZUtZ5B*OXHf;yJLjd7lxl;M z-voaz7~~bwLI%~?bbe;kr^>fbdV`E0HWNqN!G7C_{>9v%?uvWm4~u1^#FuIPBhAv6}Y_-DQc5nEEjc>J$JnC=*RXsh@))b&5BJ zpL}%v2W0qTEerq|L^D?}=s?_U7c0dJVy(h$9G%HeKi{ zqE9DJ-(VA2PLL8~@_3LggbW>^sQE>cc5~WObGa`}>oJfntE?l$#H1K*vyhln)EB~I zo))__O;woy*?YzXmIgI@pLR@{fj0yl9SM1XXt0JEv!j1N25})@D{6b(7;+0i)V=Kh z*&DC_0vQAuc$K*R1sN=e61dL{@y4A?NTbpI1sQ^!dJX6>g(<~>1t)_zE}B6BAj8^S zRRIkmlgXzD71Cj7uwfAkyJo3Sm)Sh%mW}Y7IHI|v^kkkLHDCtifj!yM3#Qb?sz46K z)0no}H27dPe~d6fLdb&!#UePg*)8zsuS4LRe+VKiA$!knIB}~!%D{Sc3dOKE^!6d> zWM^Yg=|6SAhZF0-WWzkbVt!eIOh=$m2OfdK#RSbIaREY-BcNdhhIxo|om6?4*i8W# zk`tiVzeEGWh$2N(QcQ!{6fK&S$jh&$<2eH@bxlHYgEdXQ)~3T|wx0PI~iY-cXQM z9atj2bEV=Txyc`GY_J@?5|lUhEPcQTI|AfFfp0ETY)C$L7&xuo>oWlgU3*AC=mj1x zk~=J@@o{*Txad*_b?CICB(cS92I=Uu7^W#zd9*s|aO#$QR_UhJCMi{Fn$7ZNt6u4* zI3_8+^bIzJ0N8Wu1Y);52&g@&y?%6@f=VAm7$0R|5W1B3#OKR}DjL|;$COkE!%?WKNj46mFmYoe z_`eeA)Dk|W#GiVY>xWHE6h=mJk`w8ECGwGxczdlJqqg43I)njtK!A9w!%TTD#^;(s zPC<+-q3iAfWBdtc0o#S5s|Iw*=`;ixh^HVgLFcF>1E0^v&Qq3ZLY zgt$)xU<`)_Uqlf1b+{80PK@70eIeA1V}>}Ay!i<2(;-$5GKF4HVrWZ(a&(oEetgC- z*1Pb?dYU*2gvApi$c^|S`m7}Yf946|Tmnq9Spq+q7(x6s+YEwKq3Z(C`AmXq>mJ;> zQ7|ceEk2WGZc)TwfP4`p+Uzd&(lBw(>@^V14*i71N&FS6a{mtkA4)sPEN`ICBost> z%ayV}DUvHIRfl5sbaD|XC89Dc57s0WeMcwkIL(BcfgbdMaLofPpj-##;$`D{h8ny9{Cywj7HU^C7 zf(nSWBwFVcc7QZJ!9%$sMXI7X@dGwG@$fZk*J?-wQ8+>yBk1G$C{?UTfTpMmseGWW zBE?p{w(2xm{3ceI*X)4t`cvoffQ}f-f1wO9h-4d?lt}-DGH~e3dybH4+M<(O=%MZ8 z)Q02iNZJE9h5&D*VQ*sq%76nv8Im7wRsTU5@@s2!bOh}GgED-gwTmLP!xC=R(70fg z&5*4{(+B?tS`f`2$AQ*00<5T=Fw5r4R0^kZhSJ}juww;ld`bnOdM7z#>@JB?E!v>4 z-N->lr2Jssz)3)YCbZwx*}IqAyQkc1kiYe8+`GqQ$Q`EU@2PtEh}*m895e@P?c8h7 zyX6wPUCn#3arqqx`mg}vxgdO8%l)#eB^ZAlv}h9&+1kM5mtrZHJ2}tWnsXp#v#mU}2 z7Hi=xZkn)>wc{vzHMO-Mg{4bDpgM83bP>?oWGLci)I%Dum6)Sn{G$W}!)v<7l!HPM)MawCtQn6{gm68ro&8J5|H`swDP&SRS)?amEf}F&x1r%CEmFQoIyId8I#rc}1$X2~afoZ^I5%z*0g%(QCZqfN|gjoFUv2-3>5) z_0kiK|BRATW0*m5NcVnnx?ZIjx%h4JL=Bd*2%5rHz%?QkKT68j%~YR+qRPqI<{>) z9Xshb>Daby+jhscD#=v;zi-dpGkazqtm~@!tyL#gb+n%Mz8?h^2AVNdn$c7lh_%=- zX)U>YRYRnz?|N;bG^2>hXx$`;*Dn8lrStOd9pMjlBp>W$-^B?r1|twq7X_=;gcBV5 z!sVaJ(Z&iX$P%N~6E+Ow_xPb?Z1! zyH%xHkm7zH``zI~_f$ya+TD0_20Z-)%`50qK;-*1LDcpRQUb2@Oq#bSjG8HWl4^lT zZu|!tNqu3HAf=U^=yuU(vFbHV+nIU}fHnuvuWUo~Z3<9vCiyi|VR;Rr(At#qu&8&m z>^ENZ!8WJ&qcP_;UP0i+mu}2>8+xYwapJn5pi-^jyo;YNtOU6t{>Hpz>3;jvEnu}( z%0@?dzACVA!*t$PT)L$~rd{`5rsZ6weM@ovvN*x6hwAjHBEc>h_&|C75(qoEC%JS) zZQb5Wee*#256aN{{vVVU86!mW@Vy^k?S>O;<%mn z{DXpb0$7{e;X1M=DX}ox?MHto%-~O}%o-4@%;c%g9iI@7?uI~w_)$Fnd?e4N>(?i; z!f`)veTOJWb&N+3S!CtI2nde;1;PSL4(&j2yaF{TpXi#M*s(u#oU_egJ-7a^J9rgm zH~t^j^u(h3RWC$UC#4);4?vP3O+LZo=ytsRUC%-f4@fff=xORFe>DJTSIOCU%Bzp- ze~(uhuXNO=HLR~1l*l}8h~&`Do#3(+y+?ptghSl>S+u);-^i1QN%lT|cAr;;H$1H9 z(7j*xJ1((*bZJqKvvg$f@%47^xN4-BIwHJOa~0V5$U=a+1Ji7_>Z$fEnK&9#QoZDg za(-s>@VB;Z$tH4F$-MDgo#wvnooyU)CDB+aU87BX@&Bpfnq7Vx-;Ly^PwP~Cn0@VL z@O$G2pxBvs7%JY*w@4j~RHXa-C>SATcsdJ;q^CV%qk|IpUU*Kzj%4;WLQ0|JmxoDILCR z-Ij4@?CmS(@}uy&zNig?v~Bpr{N=j*iReOBfgYZsM)GZ>T{tUq7XOMS~+ zPhnO7X{Zb~?*|gD>FH+OdN2uh=UU%(H$om#fjIgK{NtQ=s<4q7p;AF-tN7kIu53TCN(#=r=()MnQQ0T3Oq5S=%^`k z-y;NofSsZMuV=B<^y0Rs&0eS*u!m&AxQ8b0t(|LpI{Qb@$3T!2)cf{rGgg%Ly5_{; z;X)R-+f&}HpO8&)R+dG%`^UTily_nN4u@@tPqT}ZMbNjtzEFr$^O!%T<}-is-&juS zg0l48{VwMUE-%KELU0V>E^L0-ch78wx^cWzePWTw-ml8bQwnnXuL$q=G)~Ba^lS|5 z^u+oLsvej}3Ceu@vihdDmAr7n!_s)!-5YxQe7&r6y|x?>zFTtF45Sb|Ue*#NX8Qaz zFy3uEUi2V0?B|bGhmQ>e0o0!rwZ02$902E9BBiZQ=b@gAk`KMMShbPvCf>S_W^&qQ zKB)IC{>xO}C(7uk6NQ%%*q|eRsCRz8%V)Tczk6Ah4G|w56|@kdXp4|`iCPhM3On3K;4ToC5889}g8?iO}*oxNO>9={S)vGgR=*|T2RW4l*r zeJL#@>(a~CsMi%JXVIp)@|;z%v~1R4K-uZxIVGGb&JVlSmjzOHXBnS*T-r2w(&xKX zT4nmBtZnpoChGG0yUy4*`O>8+RfuUmi!>c}ekoBL8@oPNKyW$=>NLNix0`*SaK7<5 zTWlQNayQAQ>u&6<>TlO4wSBqne(I9Sq;2rOH%7?PjoewwYy0=zQLg|Pcb}@YMY~5X zkA&A!9Xn?IU#&I;RM-LsyDZnclzm*~6v}&7I$KA0Z(X&O#8t0;n@HcfEcEPPAn14M1(0gCOqt)KnBD&wr@QSnH1l?;D3yKe z8TfF1a_x>hWWnon4_e%+v^~81`nZ=|{W(LFt2CJ?{IP(XYtfH3O$E5@r` zZ~D+hpo@PoJ8v2{9(r7-3l$GRrZA46~M+^Qh(>CrdEaWoyZ7To0S6FYsR z+Rx!_=>f&lX6skKjNpUY3&!@fP@Edi_dCs? zr2+h4QXc>hIy@W8UskssO0w-9d2zua=$2()CV@w8tYWSxvhsi}+tG*^}~**BIQmBUT$2FYf9k|MIm zy03c=XE7K8B&xQ#84l#)zYNbW%67Ks+gVz~6PHV7u;?e{by&|wKIEz-&ZrqVJ9OC| zE|LqF?#S*v7LusC8q%b5UZ?(6xHGUsIdNj~d)-2^UaFWpEQYj1Isi7ryIGHN4!$Y@ zNd7cm=G&X?FMZweAD=yXj)1DmNQRI1+c*y^z@>AH{MmP`OYlQu)k0>WNl-2tM90&@p((#>a@tr6CCJ&vux!&&Z1BzvLwWKU((KzEX?fTzkaS0xG^-# z=9y7&1k{Wij2(upbcp1-SNS|l`-S1z?vMD1F29fP=@fdZ*l47xJofO*eU&gi%zJ)N zlPrj&MwO|5>lI_`dzZTvOdi@wZ-J#wTmW&f~7+cgUAah~3Y3Z9~W0_CiwY}S7 ze6D_fkalNp=x9l`AkVRHp^GcAi#E8(F40YuUZhEj&&e&Yp-RX!C6pJPmm99qEUY|J zx`pDH(Omv&sYW|W2=pxUgZgF2WR-a=6q46$*tCnbV9?1nF$WnYJ--hV9ViVOM+0d= z1pyR^d&Y`-28ktohl3>Ls}M7VX*YoGZ}^KsLK^{W2mh|ClCu8xksujChtOc~JHSOr zlgF%Y7W5RCv|c$40t~h=YMg}$&J0y*g)83F2vg_RT_r2cL_b~Y>b`aorrUYdJMS=OXFDSn8Jdh2(hT-WWir(1oA~@wl2PMF)7lYzy|6LpaLwhg!&d&$uo1Z8@BvjkNE z0E^BWt^@Z?9NOcxoJXk=*6D6E(vHn(Za=q9=L^RFS@4YxyxlJ1l*M@gYI-h&{||J) z|LJ!7e^`3{*Me_#prGg9`hQ(+1L*(_71ej5;bg}F-2U52X?vO8P>l*nm`Qy_lN=4vjpFaX!CoD-l|{N0^i&-dIIMA&mC5dg%{Fre1OAzv zk>i>BUjB*q?B~qqXYU>7>8?Vu<+6$ecAL_QTUM17)l@T#dx-6^jV!nHxZNy<4l=Gg zd>Dbn^?^`eauhffS8m3r{k%pcBzyL6$ z5crlY!voVk>bCDWScc!)e3D=KDdG=9DTtWPsp$RH~&85NnurOi<%Uh-Q3)(5~E~!4n!#M7v21 z=mW=%7_B^^oL74PHEgss_#KChxBw%dIKodnH(hcN7KFvZ4wRk^7A2Ql)uM>1aTwON zR74x6ku~6H86Hd5J9dJCtFX3Gr}2%BN(%K)8u8TCq!aWhrlqn^B!~;NFNh1>w=9v# zn7NbAg{*;&O{Z$@lwRWqzaz}nxhpYOfdLY2aXnP}Hc)64zGO3hLhh5>SrTLu(7vN@ zbheJ>mL|MWeP)wjI9kZt({!^##&*FF&h)bhq@$ooLh39Zc4CN*-&xT(-6&)DCke&| zb$$q7HLa6_=UyZW)5QyiCm%C6*Kdv8LoOP9%R>{IA-M^Ld0=TF6V<9ryrZ8TJ{AeU z(yf+8^+<>&G_xx{E%EJ=)^BY?(CivCZzn*kfgT4#p{OvVy8n#@pwyKlaBwL0&zug@x$uHZd*Z&-%_d~fu1b7| z01Foe;HiBx5}l;dKMA)Ev%V2K?IoF*{kA_C>yjt1T*qfWuxWuKBrRQH_ZeNHsrcw+T_O{!bx zoOo`URGphVNy}MCvz~Wgb3C<_3N;gTZP}zYHg8aie5an65Z1yYjX^6DI1(RdA^!43l(R8Ef5R1t5A3CkZ2NI6;^eB zE4@nSvqO84>9bZqS+xdz2TuO0-K@{tyh-$(YeUq40bu})PDE-C0<{~S{9RX-zH?rP z8e^mMe^h{{K&-&Djx?%J+B!K-ZQ&}qJ}A^MqI%F1*^e|R?%NRMJWGDD zu<`u)$UX^^ilt5>%|(z~DmFl|~ed0hMDwqE!kS0q=lhkIg19DtNh;nkJOY zmc)~gLo^u_`&Wikb5B4$zs(PdWzdS<%$&yb@n*96s?rO=jx@6~Nz6 ze3OnSizxt(XJCDOV~1;2i(vOX9&%%!WDm?HEGihamY)GttrxLXDk7loSvs$l^a@s@ z(n78osqvAv@mH22QpOV*2+yslsEDL`Knc4jZZ+9Fix2!*fhkmiso;;4%OYra5mhJj z5jrJ??;2Q;#`n>*UOwiqVOfr}M{pN`GSMux6-3<@E~JRpv7e|{y@aJ~XraHC6^S4p z2i0x^=})Fp!W4qIQGGz!DP)s0x}fW_$s!G-HM(%>`q>B12EsA=!ZF6emr_w^BT*`d zNY(r$DnUxq9>@=6*c{h|7;Eb5(3qPbf0hPCP@`rZ>4 z;Pg<-Jm(ImycFUi1}*0Bj-s+f9n8i^#2K40AKrTR(D}RIfx8iYC%6a)N+Fwtd(bNR z9fO=O?TmU;6ss*^4H&9}c)+X0@$(YkkQF3l&y(} zaFh(iRZ;913IBbQkeHAX0%-?FbbZ7IW&9X-?JQ`O71^YcYL=ENmeLe?`L~av5?E*$ zR8-3p98|kdv24f)pU^>*&_VRj!F%|KFD9~L7BX0D)CGrlFE^fh8)Ak8G!mbH$#e0| zS}h&Y>c{ZEjVpN+MZRtpF9{Ukgf17dj?!+Ky6`uqxFX`+T$KA_@iB?P30@~~ zF!OK7l~!fyow&zF9yIC@GKQ2Abj7sBV8B9j zn7HM3Q&nGlN!MIk4w_EJ@YIOa19=tM{Z7iaT4W~5kA7DTnT;G1SM2ipUJP1p1F*BP zxAHv%uqJL$+do=!>E@!oZ-)Ghl>W}O<-kdpQX#t|R`6}aa_P7+uW+fR)6x}*cvg=- z;E5U3S65r>66(2P?70lj6DAYWlQ;c_`4ak*r~nLC|w9Lhc+pS8B+4@z>D6;|t2wsg49kAD@gV<9_&XTs6$FqI(A4UZI(l3PnlG<8_MJbQ zNn?_!t|WujnI~~bCw3@m*5Ur9r@Ez4Ls}S1DAKI%AAMg$z(1QcY(-rwEM^9X+MjfTJ0Jx%}OxI+k z{uM?%K3G|m#zn5BU95RqR6>=P38SHtv9rw{NZFT;oIk9v-l{DPb(QrAq2r>240~nb z{yEXKvWNsy0klnJ#CYa`e{JMJM=AQUaRcYnykn40#X5IswqwV{6}8nO&Wn!j;W0HB>jeXU8$V5kK3a3Jmbv^ z=Sj-mUC)Xe!g1+~@gBu!Z?Y08CfeEHL>0_VMA)UVfY$3#;j_`cv(c=vQQgr|-P_U6 zhb9o*KdIj?)QlgXS9J-x!mdw<&rb5rPWZ=86n@WnpC(tJeF}(xs@L{WF5M=#Ffucw z4!qw%gc@QPv7-%fb=y8seRj;MmV=fHBy zg%OY6k(dj5@R)WqP%`;J?DHgqSxBn!J9bbUmldW}%KkK_>%YJNisfI;jQHYNlVeQM z0hs>P!DC+6{ML=Iwy=)gH0i|=FFXhe&1HA6=xf4ZzkyBlGBt)&umbW35MvI}D~c%i zviOSNQ|0lFgQFfse{uNu`~s5UAOI+k1iOnZxHp>nQa~Z8OD9Sv5mDXuOrgL>Zq$U! zM*H%9pB-jl!U!@zjNhm`$BsId;nEI^oQk+;dDPqHpMi3HdwPva#!Ae>R~Gc6`#dr^ zUtM_C+81B4D>w7e*+?rx>Iei}%;&a@RyM_wfgWlkfSuhU|Ls36fcd|;03EqF{||?1 z0M|wR1@g^BWT=jXKGE7p4^sA)fo`v;&(bvkA=ZKhD?8iY(q^=5VF*q3V{ows>fE{mN%>MWXe zD)g(Wn{!UtMzepfmq_$vK3?509zdzAFf!`i>)zx(^xn<$Ho(H6#+3V5K51uEe<4@K zF0{A~C73a_cfK!u|*qooVEaO+= z=R17JH$2g7_tpve&NqGQne!w*v-!2jg%@_&(JlJ{HnV`nD;0LW5f=A~v7|m#(ZB&a zMqkC-zTogsiSPfpf^yvN# z5ZPwg0$U{VI*xU9qSnv8nbvGGgg2xE^#v1_dVTM}Pw*TF`Qy)2)8^;Wj$w;z z>sw9nPOSgt0755nt+~Ajv-^R>Qn`dQA;aI0l%Q#yoH2Q7zy*-L!g4+~AM#lc^uttd zIq7=(X~dtIx0ON-qk!d1S##m-wmAhrm`5@lZ z9V*G;vAM>!@w7P1d)m#)yC#J2O0FloBa9rW#qnZ4;80$swJ8S6gnjLNPm3$H``|>s z-!b&eeYRrn_)9Q-aj zqr{WVv()oD$xDN0qu1w!siD!*@-A%!CLhkzxf)-bJVM8hp|3sE8^-(6XT|&Q@Tl8` zm=a@lvh%Mh)wrK=ejo9F9v7H5P~M9k8K=D;p5B*@h&HTeXZ@NRZcZ*GmyEvFU4T>l zW#?y-_C5Nx=aip44JUpRz&HM`?>Z)b5lNeOitpo-&c<{_w19n<KPRJ|>G%g=_lY4Hv3YrNA>^;XuqxsABl)fp@-*R1wzg35khDQPAn=i?MD#CH5bq`Q0PTo3$x!fVfBLFD#k@e|LQDEB$qMib>6$x=Cw zRj_Myod%Pb(bw++c(u?N*DPb%Nb^3g)E@X|&-9$Hm|idr(+?P{#ie=wXxb3*bJd1$ zU!-!)(P!D+xQ>1vf7+3Gxy4eopGezXgrkhZLDI?SX572bJUB(cVFENUM)!e)I39|| zl{yJ>ysh7!wrvfsj7a#`e`JXcBayc2>Ux<_ULje%KXCvi*Ro?iYP_YN-y8w(uLUM( zW$IbP=dd?BzSx42L^^?&SXv+AEHgX#0>d-??Q_(H-TB^>B%JT-MeKuqBSWVz)N;I` z*OVbd`sr>UD+Co+b|mlHn<4yYpC6yMwXc-r%K+yO|E>*NDKta;Zw}PD#1tq2xvl+^ zgDq|y&K=U{t2FdUP2+bE^OLDs!Nn00&;jU0ezWK>%=E#dl}LzQ@l-?&bl|KpwR=KY zW;x^YR+xEB9SGifWR6Jp3(Tz@J0Ce4A34WgJJlRUpMFI+7YvI@Nr&>ZqsjMaM8InRxEyON>9hV#G=UMaT4rpk8%wEm_tYVMIskq49Znt%rA6+&}`t)>smz}VA+ zsyMUYM*E?VfG7Q2J}%N+I%f^0^mf1kj~_H=J)Hgt?z_0rWOj_D;_7XX%^UE&vKNKm`UrCQd9UdRYT(_&OG>UJ8e0VX4H!pyhy!)Pm zU`{;TV)CBcVTY{)zYC*Jf-!Tn{9?YV3TS`R5q&u>_(IxKNW0`C6 z3k0@i*=I`GX#`S!(r7eA zs@CqSjtAM!3^hBpx+L${IDb{6U0q(0RcBuDQ*8$FHl2V5?EI~6n&lm4Zsl$P>kHi+ z&a5)f3EWUDHG$7dz}5-Glh+c%T@|535$@|EOOin-DhMy#eL|6mQBs!44CEEpP|-f6 z!YtV>{wAYIMU7ap3y2fPkdPeON8dc_%g--V;ev2?tu1YyQ#v!e)#sglgC!xou?&hNu zA&Si@0XNS{ZQ3J&AMFHZ?5pF}5Ee%{fWqQk{V7g~G60pzUYaznC_RDA50@oI=F0V9 z$V^>EMp7~m0)vT`C06Lt3J@wXNs%F8;nA@(u~Y@d`Jah{8!&OexqpyJ z0{V!?fj%PP|G%>8|J6tIzZC=ge|1$O183>~t^Yl8@IW7G&SsPCM;5{aStI*}j4Yzo z{B40wlL^kSpT&kFF`a^c%)|`nBLZQg!%CbC7Drc76h?LwMrI1EhXz@Uq6!Rz&i|DQ zT2GrXerI)h`sYXQd-sUD=WBOiMa6X4&tt$Bx9V0_Wg{*-LTD|MXcJLlUKjBO1x7@m ztV1h)Br;>`CZeVbCQnG31@VaW36|;cM2q*MW&-1 z|6=EOjv)kF<)mFz{2yynLd+xQ4(kx&E@O67=)<@H#4Piuktt-P(!i=}kZ8{r3 z_r?6L7r>F^>OzW#S5qq?q)j&v>XF9;JQwx1Fqx7Nu&J7<`Cn7D#G)~H)<_To`K}}D ziIt^XapU@3ROKv#M=q6=oqIQYd;D4f9fHe&OC>K(R5Kg-_C&u}%|cl!2y=SMn}JXP~l$GdznyUGPtKs(}Gy;oMU70HyMtL+bZPdibcu$1Ey_K;+11H1uOj=ut zrgXzxDc+Ii#AHS3XlT{M(J#W&&%}JkECeS8R3*~OKE?XQ_?_FUo;sp##cYIz$9b&Y z+H}%F^h&5I)C-tkwfG6=vLn%>4ZiR1z2H_HB{QqyKs;o;tOheuo|kG+tsk=SM^-1HQ@c=}D<5 zlt&(d*ZhTm!b^iv`W4pn_AxJ|cFiuTg-V71SKObA-7d=z^}o*tA_KUUt*%ooAWdYG z9tMh}U>rTfV6ZJA3&_S*qr^P9wIauos1}B$l4Oj@$CDgUiv~R+_CE^6;xWh~cfcb+ zO{P3bVuM9q;AtWmPy)gSOL#B)xGbk3plOww?ZCy8|YY`&U-f^!Ij7ex* z-qA~c9OYI~$z_x7F$j%6YNf|KigdSbSr$YQwoIaRA>79=)1t-{-CIN(>DU_+X%r61 z*GiS|1${##6_sE4Z3wP-?3U)6A%M$Ekm=CP$!W=Vx( zcHZ!-zE!4qxuc&%jB^C;@qBcPW#&X=K-@4l(#V=U8eh=*=`($U!G^KL$)V$ zu_3_m-hrpFAZKjkA#l?{lj;KWD%9%=3}LW`pLBhl{85Be5eU&O41e@M%)4| zUSHm?c2P%-ENtN#XvCjkOK37IG*6LkN$=U;1Zf6{!?c!84e5v*>1Q7$BOBWV-`>-r( z)>XaBHl^+j3K3joB6u9y&u#L2*qB(eo|2I73C6G(;TB*&Tc!oEfi@!3CJdmBh#gV+ zn&k$eH89+_30nt_HSB9BGl-kvX>253z?(bTy zPD5xbv~dx{CK&^ShZT71eLZ080LKZ89c(w@nXp6mc}0w{Lo+|cFm`Bf zm4-$DN+cX`mj~oUtTl+(Nm;kaT?sjaIEQYCH^s|8R8=kwMeA!NG$2+0(W)xV%<;A& zreEBHpk5ZjJ}8}pI(g9BCx3V~(@q+X_C;$^1n|?gWf$N%h6u@*w0(w zHA~!$UUCdxN=9seMD{kHq~H%R%PvA8SHh9jdJbbC3bKk1xV2*4Lo`d$OW>>$g9}>l zNq}yne2{MjWGPm_OC)Sh3&WoURvCp=oupSG=Rm@lyl|aOCQ;Y5qZ+xAMk+;XY~j@9 zyADPg4n~>|Mj8)Bnh(kt44(CcFD0T>U|E@UO18~sfLAH}QupKvPt%GpvGfI7tr&q> zjWgUKIxj692QkP!tzO00{TRde#u&5-F?&Gm1vV-Ic{YcK9{pcc)#COyy0cLM(m)$g zz!-X0BQnrN^mF!~ji~IOjY#CiC(t9kcrX3O&E)s4bYTxf@ykvFKs~hIKzMHYUMetg zs2;EfAFwAIuty)5>A%7I9~lH**sZqwEuKREQ(@@NHw?i5I$IG{KqY(+ejpLUif};D zJ5r+|v&%`48%tNIq$vquQNS2E>HVY7pop|AX_!FFoz&|6*nXylMZ6w6BBn(-Lsxl5 zsaH6SkpF2Js7PS2TsuZ+a4NA?elb2>3|$UhU&zGbKwv#1e`-Tdee(u$>+LaeKqaY0xF_V1Vn$92LDnmhpLDU zI3y+2en5!}^P}HCd<&l;LCKNV0mcq)C#q<>I`WU2U1xmxZ}>{S+5d?hKu`60d%fZV z9up8NL*OgL|A&T1&(wh4%z)m|pli5SYrI!0G~>LYN9B8R2HzBR1K6<$@LKc13H5~Y zAwaJW^uz35RW+vLi|zgm@8n+cW~G#35_!u4qTpbbyu;GV4<0gt=|OM7twTZBIk(rCR*HR* zcwo$?^)GeM75&&&{OM{Nbnh;lGHyuE;@ zddgaNGPR4t1VT+fvv|EZii#H_1>XE5J0^|Tz3czyB9i}a7m;uYLQ~}@ucDee3aD>2 z<*c1VF5{J&ns+Z_lgmbdLQ9QQ`z5fd*FCj<9nG(*%&$OwOF;z*dC6Y0n2>DnBi4n; zXpnz0{Jkr-RTA6L-(c_~9_5*e^om(x^B}raka<7bV32<-{GFAyq6hdl@T#fD#edDM z_ku-}s7fw@0X2Vs1`H!}dADe-!DTP>d*~4j!;wW}jkPmu6{$Uirm`>XJeK<-FVTuU z(unTj{#mNDanGJF0t{6337inP`QK~Rj{^$X}<-IhI#Pa=sc>^+7*9Z}=Frd6B zHaYZdl|%Tgv77Dhp3Lv`654N`WNY_QG!2zaZE#{w>)$wpSk*r6`nK`K%3z0WRKzbz zA9fkBZir)RD~ZE2m%|t8Dw#{_Dq#rg7L|#jOkbD?VUOf(*lfljJQkwi@U7;Qnz_vt zwc{KDL~Y>^SH7F2Zm zWJJP{H+5DGBmJ@K7g>Skaf@cxl~daxlb>AaU9=?vJbTxqWRBvz7sXRG-M%;{ZoFWO z`UdD~#A*iy?o(E5B@^}7Mx-gL;lMG`sAGo|^50j4nJK3H-1HM(j(wWT84LB5NF}D4 zB*3NrW1`*oONSS}1p{X!h(EYTA3>UfxNCqa)PzhCA3|vzj?Se6 zj*-@H_=i$H+ndD8jVPtCj7~aimtrcN(jifxC=5!VbS}=BMXmzUmQSukn6R=J9zzSz zhHB;%a9o*(+o(x)F)+$9MYg==QTMo{o;!-j6Z|)FboRz76m=Kcsx%8!5)wYSSBdN! z`D{K8j(l`9vBdB{84(>4`mH3k%ddrPt6oR4--XCyCmy7b0s=^X#1|Ptnw`bNBnqvB;BQ{ena&H-G}s^b%2t zs8S}0p00FHV%(y{e`G`}GqbyX-DmD`KL|BAJ%GY1m<8VAo0Ik?y&aH~_9p`)N0FjF z2V?1mOmvq+d&;GgxN6OU(C$^Z7=@iKFMBkXs!UFSGE>tj+m(`6%%{KRIp;Tv0AHo# zcL_$Kb%13x3oQ=|;P0-R5BAPmt)u<YU;|UtQyGi!1U%l~EV`1#m zpN3cXdY=B)zhIJb`Te}b)3@vVQ#oc^bJOx=sQxvsP#Hw9GbOw9c1^~C?LE0xa98j! zSuH3osTxz@HZ*zBeDyH#-@>8bcOx^oyWX@f(|Pa@xi#zt_Z;QvV>hi%>*9vYnYIMa zhV$P}e(&?)yw_bP$BeE50^ciIGj|-#rf$`$CSI?mj0T^uJ^+_JcA6Bvrw8it6Y#V{9VMZ5pgrW|HMku(H-}V^QU?>-rVy>an!7$t54T- zWpx9(i2b$)_<#DDnR5HLtHvNOX3`z#YyRf<^&HUzoJU}IH+uj8#TY0CVt?Z7TMib( zg;#uhroe#4KW^_xeFPWMzxTer8Do03%XEV;*f^96zj_usvkFa`QzzxMr;nU{TK@L%!_nLi+uaC2O$-;uy%szdHGl%-%%n`F5 zsk?4lz)tkRj`zXRj^h32jYtu=EuZM~g@AFVNxvJXP1X}4N3j3bnBB|Anz$IuLU@8; zwUS3rwtCAD8*7GwYkN0Bjq!5p-p>BlZ_g9K$^(IqxS`L5946G~33(m@&u^b25bp}d zHa{*Ht3D>2k$U}nT;EsqEYc1%cyB>g6-w>C=7G+k`nG{~D1)96T>$0nm-E==F!10E z&C_dZYZlqppXQC8BaqeW$FFGr>-T}4oV;z{HAzeTu*;u84K3Y0w^)My%pVRxAr?EH zCilEVG+^Veo~9cO?gDz#jM#2<6Vb1SfP3D)(z33J*IRqS?#7Su93ltytxotQd(Ves zZ`gznz=<{#+_c;Cy>8->|NCDM!5;6cH4IkC*B4)mf*QLEy=g%LLw=APL^>Z1ISu2tAwPNIZP^KZ;MxC;hT z9ZuItty3TedXg-(nvU0RmpNywKNfmQOEaAgew95Q8p<}$0JIip*XL1Rr@2bt={r&wR~=02Edc&V>g#$Q1fGUGe0anAb~E|%T>E=*KL0Mx=Zc+~ zBv(>WGZEL~ptW5mbl>QT&z2fzx&$#~oF}P*pPPk-m7ga>e_)DYnJCt*%MBB@RMD$X zRYGCSFsrc?w?3uA6W3b3%9^|nA0$}*IIefR`nd3(ZtL;x-syNZnMkfy+jrlbz=?OU z5~u%d_Iid_Xq)SlpfKpMwFAE;2c#v2VpX;KoC$})N&WG5ehX@Wwj@3b89VMBXEbu;67@O zM6*}GshUi}>~Z==I(m+dFKxlYobF9V5-WWa)zy^NrFcsj zF12P0>jOvVot^73*@I$~4zZ5J-S51L>FRU(Br6X9a5cl**F-3&MCG?mNz9C6P6zLa z(vjpC`)|}_F1t)KHAL7ZAG=(6-L*FrqY8ptU%|((e|l~ZNc0KlTRgLChF*`%F0teN z-)&c^X6{|N^zZyv;^_LXS1t)42amrn2~(f0L-?a3FEx0OBxF_IDb~uwiz((f&Hsk_ z-<`Y}Hr(h*d*OEaDI%ed4)42)G}7~Vze92VKs{vO)=A1q&y26r0q+GPO$P@Bo$|{-WN5M~)N{Ojc6|GQ1u2>9}SRcMQh48MDaGH>k zD1M$jn?IfDO5GzN1G}Z36re_6TcK_J_L-0)VJOg3e+8y+%nqoyUPYAff=(1=@FBu< z zAC3xPPPFPEUu1$p4m-_VyO(c@;tMT_m&U+N9z~^8t01oV+DMl#)<+0uN?oi}4?O=) z<1(Is;X;=WX9yRPpF(r0D2CK;2P4j|m>-T99D`CUBOXQ~!>0qwteQlX-#?H7^|Pp0 z6a|9Njf1K@7FB%FeRVt+Ygu{NR;9(UU$ZtZTvVg!s^QZv8FM;O=5UJ==?2j`0JQew zPQCpR#j=Qgi2{auEmRT`Fq?Jp3}t_UnJq3z-4y8Hjn@d$NFfAt>JeLr?Pm z3Sj(iaR`&XoxP>A>HpI&gaj;S__zLFdB~x*rw5wqiUM2`svA`koslJ6A!yNi{wxk` zqmYJ7Ir0Qcq4av8eBw5*w5%s}?kWd0h7vL|I?51)xGBo-?)mV@aH=SwcvUKG6sihz zh>xo-*0D*l(re@Fk}n_MY!kC9C%@;+d*0*hwg5TPs1z1WgWSpUxRj8w`o>UHeq*y} zvx4LZ(@9Ly3QmlSv@{Efk>ZI3?etcQ(4})%IkuItLTkB7zuF(hOJW!pY4ZzS6ZKzD z_Srom)=ffH#F2HHF%)XkLcXz67W2$%UFl6i#awELCa@vx>{_T%5>|H)y>S^NM22;T zX%Ct@75Fa|AG5g@vm#rdx=rF7v&z!i!W9uR=`1Rz`XHc5pE5nLv+H?FT5=R!%T|qP zny=uc3!+_i!AjQ2LA_EK7RC@x7#7w-wUB~flmiRL&{ zo)jNHaht!B{zUW=6M*wd3DARezpbn}tT`$aC!P|dh;dSI6)b6O!x|?umPK@oQ`dr74{j;Z2RuDU393aZ-O+GUNtw-vgn(hI z5U1aECu677pd$x3#@TCuH!y1&zm-5F%6cvOneqjp;D)(uxalOxYnHS%QVEhwUX8g4 z@8F;gqz*hnv|_d7he5l>!X?go!EN=Fs7s1xBlG2rMFWbNSw#x?wzz)Q;Ki|OLhkUY zoq(s0LZeOmk}-sK$8J>|WpTLhAJ1j_&LXIbNKw{n0w$@|6hL~y8k%+s8SAtL8T<1m zJici$E|;Agt3zy#-LB3gHkafylSB4Al|6PXlGFGvHA8GmDNZ@I?`UT#LiXP&SRE+F z*d5eHNkb2v>X(%(1A1>9XdOgb!3WE(=QGoU5u>5md*IQ-lQ?c=u$*KvCIZAEB0Y*g zYS_?|%sh*#I_Ej`PL1?VQT0yKJLii!=Npn%BIduS!5X-W?(61FX_zGQA9VCiJ#`i> zTzDEu)9L7~WiqLu}L0}rnx3YJe>@Gjn z2WSr)i56}BDm3nbWoJz%L5qcK$ZpyP?+R7`R1#9>8V_#!nRe2LDdsB1VNZ`ND+hqK zrpJ){UzD9wkR?%+uDiNy+qP}nt}fd)PE~bTUAAr8wr$(CZ_S;DiI|zV5%(eEWbVE5 zDdU`zvDW_n&(lgFZ7BL~_hX*p<5v=4KRrEgb@)e@KL)6!6vnbh%^kg~-E$Tpc(vpO zBD}8n(@nj<>IhzJYzFRDjsGImO;ILKMJb3~Y0o;1d|e~X=oTgTby<%W&*;{%WRo1h zDmc$5-+^RP7Wu09&)JdW6Qq0{EYBz#RYEhwI9sCMB+ht}QIdDrZ!L)Ngl5U-C8io! zm+wwUOqGTeg}8duTqFs3bN?!jLYXSb_~97ZYi6Q_A!Rpi`Aw{>U*Ct2H=Oc+ae07h zs0Y(sDs$l-8C=0{o5%a*3_L(R&4)xmBbq39`*}y}Zvx`l!Sx6#U6cIiMe)AEt?O6% z0SEq#0-T z6+Jk($_~NxGQLMQ9@2+r%JVtmR<%oqUi6*MXV~*O<5u;sM|YOOm(+)A9ZbgFMjLYb zV*}I&jU=9#JA8)fjhLy<((lKuB^r^dK+|N-;aKuy-f6;ydy^giku3DhCzjJm4>e%3 zX=%JC84%c!tS>ct%wq_C?Xs`F#4>e`b=^jPE2^21)hAEVmfYX$I!&D>_Um`9oGtmk zyYHGh#@Fj4YYut0lDco3J~EpRy|E! zvs`O2E+eB{+DE}Qj0B9(*5bd`<9{()DN=_2MpRv_;l(3j^Fg}!o6eAJAnr`AfaQAE zzi4lkr;Yv~Yl)9DQ&V1?V2p8)G$%_%nj+y5C;=(B&9+Cgg1ed2k5|x2o+aG@=Jk`H z!|_7V2(SyoH^As3tqR ze>1{ecW`OqUK9Yu`>TtqPx9w{|4Q)9P`M7!o}K zsUHGpNY|)RUWiQ8py^Vth&*_44@q-Cd?B11A)Jpt#TSVh4s?C)+A67as74CEPx`-L z;mtXv!Z`|}NV2(LNM}i6XH2;?aR_Hj${A7}p=208x_pqfe`qI6VmT2?IpZ3Bbo)Qo z=qF5qInrcplfzT@J|P69Ss)oa<8fBBOsg8486%t`qBwD+E+fkR!dXMjBFB;<$D<-< zjXC$s{!^<+nZ*!X@~F;PWG6g2GpUHqS%fDQh|W`p&Pz~S=h_wo;GNe3yi*ZAXFoX% ztADrFVbS8@e*WDEKG5d9C+o043$_!TfP2p;!U){n%ava(g{RjAxDv=<6*V_ zt#x1po){h{HUXyTq3eBR!9Nzy$ZAzrlAIeX#AM0+g3^$%wsMVxy~8ONRb?qN%L)2N zN0blrRuzcgU9TPc`wvWLKQn()LtGqb_CX{ciR#X>4#3c_*~fhF>6YA#<&6k!ZN^CK zjJ*A->g(YQ+PHlI(?PUw@m>^`X6UR8f6!{bNi&$T&jaub z_%u>IZ%A&N=VVmS{bqf3@)R9;{ZHDD6e048qzZ?GQz8N?bYMwIlE@&mkO~b--kvBS z6&j@CZ5#qBJ+xx2Mrb&xkV*%U$e>b1x>vS_SW**C^R5jcz8%;r$^Efck!P*ANa>d< zPr&pxb*Pq_)aG5UzZM_b#$DJ>60E-#8Qf~kXbi?2x;GzrSo28$hl8zsn&&8W$w(B8+wKHywR z6#SAF^OY03CcrFEOAyS!h;jE0QU@CyhBN|nk;os!XT-=8=|b367Anw>HR|nXpF0dt z9jsCK`13%MV&je}yo91$9Y{Qr5^kz1&3V(t*rHz%UVj|WBKYi!=76-U%jVc|{>~lS zs{Vb|dm6pr9U|->x-L!e$jdkdf08a?TG?iZc6A?@0rA{UijN@8carpxuXRUA^vVzm z0Fk!s$F(Aug@M-3OnxA7U!eUYuYEAGJssYhlSeou4|`Dd0UQRv8%pAwyWBKr@%bWT zn%1UhgFoGv@dV|N^)uTp)X&+MlVco140#Z4(8budOQg|5?k*7ouA4}_!5HvbdxB;}yS786?K+ z0Mj9!!dK_KyLxg)9!T&h3o*BSON-9=d`s&lHZ$eoKK+T%(+94*CF|#F?^;xOBfr)D zQjAD(Kb+3=RF&O5-v0Hap$qMMXL}qyxjDA28e`8<0x~vFob$^UK4-;zWi9(xkfuQn zKfDUux;{p?BZXT)11Z5JgoGCjUUn_2ATQso=wbdl2&n7La1%*usOVtg)}t^u9t8K> zzWe1tbUu@a=NT#~U$uYcYz(7s9=~G|Js)@2-H$yP{aGRCSYeh>Fku)Q`XaoTS76?{ z35Oa+NM12@Y{?H-`UpH*d>!x0`0Fs*w%-`|`!U*?e0+pjhzXo~j(uK)_tdDfCRzS^<7XLjo0uJ> zyqv2Tek^$<220@4j5xPAEsTyb7x5e>*U@+bsW%r#`RBfRR|DnRpJ%aMYV~&*Cpt_f z@*QpZG~GE%hqJ|e;Ed^N+~$^H=32SqioJ}99Fk(`sZOXt_~*4kj%@wrb>FS5qqyrX zSIO5K5@m*rrlg9*VFuw7T&=F^pmK5dNT<*k_wwbhl@?)LAmnG590ZpjCn>n!o? zbC0i~%ZSExyg%E|y0h`l&dD`!O~SmhFGYc2|F9zZiXwj`|bd+8{7w(qzjuQW&)7!u7(cbPG0?-`$x8tQ?#+EA=h!Hr>*Sq!XQCojR?KZO52-@nxw`R(?Okd>_A5Og$wIZRZ zPbKlYI^HD`Gry)!FTnIG^5nA`4x9fq&ErOIjL-_T_^<+{qbj;`d02N|9m#TUwr&qm zMqkv;E7xuFrQHPwOnEtNhR;HQZ~6vWUoK#P^XmLKsbk1-S(>`{wvt?40^xnX*1i%d z%A=pebGK*bfif=6oo>0LejFV9om09vZ}vNH(wDy>i9YD&m5z+9?PGWz?k+04C;hT1`m!H% zb)veQId=74%(RkNzW6d`U(E3Uo$vBmS#@d|L(cMtR@Ht6)=X4&rT)FgmRw8Uq?HC_TF1_S8 z1>eGGUEi#puY&UK+#z_>a0Qs3$406ss}<;-C0eOOq)n+7gRv^PrRi?8A>rioO_G+X zhp;QHi+!YammM*fXzpnLsXfDb9Fc3SdhXy|=uv+wq2lkB(!qFEd+=h1QwW>x9V1>aK5RoSVahLh!d~7N*nx|4t5(mzTZMTP zzvw@UR$II*0`T<h zsJxZ}J{@3}*LHNcau(<+vpUqaxK)DhzH)Lm{!c$&sp-LnTw<%Tlc(j?;cd&Zf@eF= zqbak1@)NB2_Zg-h!E8(h z_1U_HB%Nv=%i?!invPewrpzV~yVn}exXi0NQHiybP^!06L~2_r=!*xE$6ows`iA*k zcuP;-<{>%xXL?!eP4i;mD2gdpF6(0U?zwJ9P)+r%4sL^NyM}pgIvCYW(P>IiWx3_Y zy)*cItyIKG1>XU_iZ6{-N|Tv}x6Q`H+iI1eRtR&5L*70dhWlVf0!X3@ayPBqQ zblvwh*ZaKCRPtin>dui4gd)$zs5!FesE^L?*R>6s_jxz|l6<=CwZoNk*TAyNItm8U zt;Dr-QYUPD?`N(?La!k^@8G3nQ&}0_C-GHzmGZYmAT6)4jAXOVET6`S?bY+m$B2dK zMTKqf>dm^!$;34@+=Ms1tE_6=wOohcy#unc_wd+Qvw$n*iB;&rfDqmMw57@H^%eUj zwi=zyw$^q#MWZN9HrGtYz%m{k9H+-)t%U;*(#OoQb4q*HC8VNn(z6}6=j15e=Bznd zTxL4%50FGu4t;?38za?6q@=^yLw?{lJNi?`{q)mw>bC>@{x`HbzmB)tGI{B@UiHdX zQs~A1{yFz$TlF&xm~{zSkDeQsBh^v8=OH@zqdWTXg+;!9KXHYw(T4 z5mhn!G2$>{n#Jl3Vdm5E(mwj~m3qDC$;ilP1C#(#LQ>iQDFBkEL{wx{q_l*jq(peM z+)w^xsJuj&yv%^+RcoeJbTkvHh%{h|`h=1%tDuwR`SBabJuhq;D?~EJn)jPG?fusR zvK*i%fyxj1>M-3gAfLB(AcRt>LbTI+ApCX!shdHX`du!!%g}?8j1M=~pY|DZLYnzj zG4w|bA6>0qo29?kv7ehdt?%&NJ}uM-0F8JCLC!XJH$P(5@F!1sdFd0bx7X`M;_V$U zL&Zybg-JSqB;(7znW5=ZuQ8TqxLq$nxggE%Rq80A_k4^qEtmWB2Dyh~zO@QgsTGCN ztL>;uY$|f7ZVLBX^(ae-y_5*Wmr*A4uRoj{jB|ztr;3Kwe&k6GYh$ z4u1y=hoZY62xTJvO$TAAkiKLBLl~zok&+4`B07sEVNdT;*}kWgtO;7)l;|+1<1kp+ zVz4>0)>hr^bq$j$Ik)69d4IEy$5Sh+`EhGBrh%4o_!EAFf_9 zml&9&V72H;6gYFX(4Z7wMEKzy1|n=2rz8*Zg4CdtDgjcLDbk=VOzn+y=-KLa<}T|j zqBV>D{W6N=MfJcXm3v19WbFrIVanAJLx29&l+2wmS@YlIEGFbE^YN2~#?YevQH#8y$t4RjTau*$ z?<7)^^ZEvH?u~4c`8Z_7!_U}6gj$}m#9AhCmAob{e$GNx5`@lR-erRF@ivAy#*zB@ zn$THcAg#K&MCOS+s*+8IG^o{C5HJf<)alOgaaT;}9GbQgbr|cqhjalxImHKm?QD=% zdr8WH!HdQyJ;If$3(V($X}gKBz_s-^I9665SzVJFg`npx!c=C;ih%sy${2ej9}0^7 z2?&}!8D)=sNi}OK{KW7Ji{gFOV+x z_5hQmIOvH*8r5g10hbnD6wnPpSiq&SdQaX&i{YJ}VfZ7=k+3xWC`E+mPDWmWes)P~ zWwPp#1q6*Z^-HjqoQ92i<)ODz7=apX(M@s}f`@h`L_ z9CId;nF{(<8a;7!Od_yKUxf3}jlNNh1&m|L_`hnAo*hd0VEM?Sz2OB5Flw@0sDbGl zzldb3NMFPx8$A7)7~zWQe6+6#y5ezu|6^c@Pw*_`N>Q<<87S9y#xJ^40kXr(l8uUq z%P!TZPAt`c|I|K2FE;ePf<`@8Ay%!J{-;zs#i%*R#4k4JC{v~Jc~8R& z5Vdp3)B|I+bK?@1qJ#{?+(#9;a~d}LgRn4t<+&4ctP5flC|Nn-&iPrk-+cm=z#iw) z3s0!&3aI+j0;%vM3)Ty<{jPu2PQ2`)Nt`0B)An$XUF@%{S;jKTE@aban*4t;MQ=ix z+H3X7w4hEHHv6x7Iz}aAK(iVwbI1=?om-){{{yI2DS{RVcPbqqhpNE-1Fwu*>c{zn zO@6l^RUn8(nB(Q2iuns55QPL}L{x!x!1tK0VV}~1r(*F*y*bL)-uP)lz;si@Yts<; zKr%s*%9EFo#ch90g4u4dksK&Ndx6A_|K-JbIB?X5r>9?nqkcG$qHG-t#Zx z9xI7fXWInP_9r%BBW#1lm1+Ikh^!U*;kzCYQlS+?Adk7gpGzVP2UI(rDbxYW1Ih2H zb5o+s&uNP{grA~=v>sGP&}M%s`O#jdz*0_tv<0ZxoYoJ9R z$B`Etj8dV==RS@LE+&HAb3%;GJFtvjq*~XhNM#+S@D4M2Wtlzw9=;HbT9wABH^He- z<}|IgpU^!_=|tlWBE9k?d!?4X@R6`?P3SO)<+pW2*|B{lMuCLKUWs%Jk07{$CtwVPPZVBG$>7{|QFHJfSRP{x*lojR9}f{c{a$rjg8akK+=?J+C`#;#^h8vj62Ckkc`v*`5MUIj^5P z4R@x=T7r$WfD0#d=$59PAG{j#SCsA$c1^ZKiuf0yCRV}#Wdgi;pq85Sx(4FKR6=!7 zIz*GrZl`f*lv=+CaqfqP_^o&plKv@$Uq?O&p}1dd^=u(EC6S=n44G2z5=e*=?*vGQ z1`NJ=Fu3Qb#};v3&kGEJ(vB5 zQY>KF+~B%E2}Hu%q(GQ`NK81p*eT+escgbbla8tEdoEvt+iG8!qII zDI<@}!;UG)jwvYJ+H76ct36O$nKm= zqRIV(BDZs*r}Yc4sP9!dqVN|j(X8^DHC9lvWYdSSUKAkhKg{j(!-A*udu72q5>Lrm zWmk?UfF5U-0)BHUK{3{{S=3NJuZ2>O3Tc--mA|M2t)$v=3 zib^w$VP8?G_B89d+XJ!tO2JhwIke{YSV^ z(8a`NXKkf3i9l%=!yinrDF1;ay{g2SVJdV_+A4=kpw=DsNdlS#z2y<249oU>^l!wG zYeLuvJ^7!vL*p%aW&`=U!7o3<)#xjA8LX7(OafO_4#GpO1S*#*panR)EUHe*T^nAJyG!$xCo__$xq# z=5Jf-b6AkMMT9=~23p*%Y0N(+-#V8vbS5H+$B11RI$wIh27s01xKdV! zfR-KGHK@=)ov6P`Xj~gM*BaTGpmB)DtMc&TH;Wf;T4z?V32>eXvE^4(u?e#LxGA(| z(@lf*p~dUZ1h)J*JP+-pw9vG2;E9# zpCM5?>Qg=D0FkQ#*lMI>A6~(YysD8-<*=GbO!n-F-polSMF0vk69h02;<>anlLV6t zhQlbD2NC9BpfSoVz@FNfQcAFD6WpnCQIa~_s0wnPWX2ix1aVI3DXqiO(e+d)KqEyu+p3SdW5Lu;U>w@Q;v57f)=t4b4 z>2Av;A6UumsDkga9#y^@9<;$)rdTTW@#Cp{VrMSd#a3PyU^&4O!Ub98Gum3 zZ@?J`^zXl(aGfX?5(NtMF+i^@uSk`jiKz)FjcAC%onl#diHz?_*ArQQn6BM`2igu3 z;$z$n6M4upwanf6Zt$4}FKk!gQuvBY@YB+0oc0@ZBq@^MbSC3vLG8gR4VXjD@Y(iE zjI?!0ZPl0>jvNpvrQbPehSUVUdSyu%KrVC2Q-*L9>1D76ICYd^m<4dZOCto%1aEIL zXcLwCob(j<(xw$k-VDv96C%tNv?`V0cpJ2z7kX6PysCw8VYHZfUnEEP zJux@CO#1gU(pU{f`rN!;Owh&V_?q-s!sEMde(~ioo6Bdm_-KK+yM|rGqk~lCnYsAm z84@8PmxsX*`}(Lj+k6G7xuUylO{ zukMA8mLT~TT~xv49UzcfWXeW#wjyp{J~H*x+6g``58;5}@lU5S)?#LsZ{qoxpL@oD z%GX7iIFmyW$J1XJnUa;C;Rl}eHK^fw(oyW(GTyt$mEFl5m1d3LkLHpRQu&SpFb=aK z`fSX4{P$XZ;_w*yGqgO`Jbm_KTCc zxg16p#%Y4pXM-@j9(=jMb{z3+2O9xiwHWnRuPIy8)#42Ay4zNQc9)|R5k-}wiVvn; zRG)ddttl_?Mo}B(G4Wy4&q&2MYCSvN|cucf*24>}-P}ja(%jF9XS^hbqP|CK2>}xleGn z5pSI-B{HW8;g`Ia&9n~FhCc2Ql}`V3JdFe}n3E_o?ss4A_wdylJbclk^@WJ4Q4i?t z&3M`?vhEx5uyq^p-s*E(Unk!gySb8QyZ}V0qm3tY+gtahW9h?JI*kMwvoN~PmiV|s zEuc|ZoxQp2^G4_QQ|p!?j5GE9={Pz(Xdnb1@8r{P4b%0XdBgyITzj=_D?h@#zYjbg zyTaW|MqE=p%^6vr!(X0cJdXmew-Z{-_90&RdHi;EefV-b37fB_h}GDX&u#A8q4M_s zN=xuLfWKQG7jx_8$pG#K>G^gCWIShUCpwq)_x8_}X@TVFk?vO4b+Y(vQv>WMMYI`z zEc$qZTTwT#%?GM00odz~Nr>HbZvXaUj`V%3W!<6!$Tr;GFJw*TWnW_Q;!x!}WjJ(d zig>Pw>cU**e+V3hO`>+R4QE3_uct4QpM6*xz z^Q!!*s!qqatsKsVvGt^e|A|V`=`GA9+nDCPu=*q*=5rhyp&j&+X>p%h|G>4AeaTr< zXQL^z>4UDD=HG%|ns?IN_e6j2XgThCLv2q#-u+x+gVaWVUIKjq>?<{3cbWEecON4o zyVS(X$CRykhby3+KbP9;U@Ar9?+9glGX@yuak*AOb7<1Wf_6cy9zNc|N0tL`ZzJKu zLvZ{R{3yDd_SVCF0cv`SAn0oA=&a+q0k*j*PyQwV^6BhsPvxzbrI=QA_HFLux(NV% zQ31$r(&KU5b(>Dn8N2K}BkZ`9)N)=t{B4EMLt9*K<+>3Dd4WS{QEf%$f&JSZ{XuL` z%WsV3pU>}#!RZ;@&k6S*)FS>&H}8v4xX|* ze!G*U&wQe?bZsv>H2NNMl|i!4_@9p5^~;s0^YW3rKH!zfKuI{Sv#Y_Qu{!qQgUunb z?5*a}nm3rDKi^ZOQz`oxXhkyE&EIrnF3@L-_RL|dHVz&0ygyQlQJ!SRa=oj*D4%_* z_CrUY`;_{3mwvHA40Jn<`N1Ga6K<#|apSe93$QqGj%evszIz2qpYfrb9ZgBUSXvcNTpA1cpDy=+E z5&J7?`tcS0CHX1)d#J3R_{sXxa*Zi<*4m6u6V2)>ljG#Ir6BvxT0SuEq-Cpe%cA5p zaP5F&S}G5J3p7&txJMesYI=@uv6a=SLJhlQxa)KNiUB!}!libfbC#t1ijDksdk|>t zlT0+`M-;XfRH&PlVsy454W_HG-MSrQ*37n*z8*beyP_71EMaRRP)-H(U9r_t=6&Da_bH*zpkjrxVakyd=^&c2$ zzMS=L*8}ks)plNW>_u2-f3`?oo+uutm;0;4J32}!#}@w&b@ zjU9@gJluOne<)I4roBi0^*=99&6qEjNLB;)1;B+mvk}C+z@H ziCwqhe=P-u3p4sT@CQ!0ZfJ_IkMZB#rP)F{)Kt`RJXBT7`Mtr6UDXkT?Ss$VK59{g zw5%qVMT0V4*Ua_M)!MyRrj?sX+1}XWX3h@Vg&VzXa@<_@8WHS+c#~~ee<=BsJ1hq# z%VFE_hccwVI5#Fev^y+ZSB0~e;cKHDFg2#KuwHh|wvyPa?!l3)FM+s# z8xUmt$#JZYRTmmxwcc~D)8X*;=~UG@_b1N@LJ|*$lZ5*ehevjPpZOb1G_BY5iS%b% zBVTPYrtITn{dC()z~jLTHI1=@uHb$~-QIe4@FOIg*FRYGgsb~(Kz_?{B=v9?&Mgu( z1-`q#tEVmv{aOn4>Lm)=Ap*d4D1yJ1>^%t-(F_i?dkbK#oO5DXE9c;mVmJm%<90gV zdE8fJWP?`WZEw4lZ%BT-bXlIhO@mjn{VYcu%}jx(wMX0xNb9zb4e|c)V;aAVzvU>e zGd~>fU+??=e!H%U>ArdwJ5U4m_4^L)W=w=_|ESu~hFL~sU32*=J}5g%I9pJKD zX@*l6QX6#?^LQ<_Ixb(#@eNh*RWo%8?5;B%?6B;CZyc^@Ea){ zj}Pv@x4h*i8V)1+VyFCyAqApYv&8*QxS*^RAlaKlJ4 z9;+)OKbF=-UvcJafL1>w{I7o6owTMVc}{#iE1KQi1k$Bzo6CW_`XMAd4t+F@f9ahX zY~hgd$uzyjS5N+{=43olbs*IB*c01WGAV8qELL{(8a>^|6%)2K0#*#WVKIrOxEG+% zbzsPN-x?OC>>4$!DK{0vQq1sPy`CBYkkdc+)ML{OV9C5R*+8;2ll zysA?9OaxAKbYyLj#uxlPrqZ8E|G-l6AoXR$Dx(q^6*8^o&Q$&>R+_=aj6x*=g=J^w zv8^FRP$XJd?)1{e!m^~qxtx*Ms(=+h!Hb|kSW;N#u#wJ#SPsE4`)h{P#;r`%&hhME zQb#_H05{T7Qi5g5Wg0GEJyck8sFdbvvMQt-Cirpb9OhIZjvVCt#5pgzn{!kN=5!UA zTALA@3$z^muGWawo|(z(g~r0{)pDF!F%V=h@1wmrLT4F-PIG|XV)H+8oFO7z?K#ad zP$+e=7SJS{RIpflxh8Ep98Q518xlbvTiBk(wRfbH387shm3EC6k3Mf1n z?tVjCo(a9gT&%$?RV7~TSOVC84^`KlZSj{VbiimIf~a&dC7G7h0z--*7PJuLpOm~r z>QtrV%8W54N~r@0s+kCcU?+wZ2vA(25$A%uNj*94&(PE-_@3>Ro_sMX*zu1J;z;kQK*qO3rRfcF8V-DGN?qJPVG>Sj%S?7L1T1 zoW$)s%-n!E66;7X6{mt#=p;*qtP*Pm=3J;rYek0Ph>}pws}-|7i8j>ULvyLz1p%-o z2`ewS44Osde&f_m!_x8Z0372xO<-Ls3iHW3O>B=!*^Z?@9ZRbnOUmw*#dn(W5%rFm z^3y=f9F_C6OPr1j^MdC;^YyJgie)M%3UinmXqm(e0iaGJ7ut^hau{Z?6t+B*|8)+N zE}%H0#7u-BS<Ka zzI5n6eW1^KZG!Di|E_&gNAFI*KH{}Xwl&UnuiF&#TvyqVY_s2aA>G{qoRMxb4aCjC zPCa1Tqzu63Y?7nSl(&k_s+i;pm?iHc&qsC#y?VM-M!e)Sn;L| z@u%XJejI!LKswLJE zaqLx%XRKTU&KBNS@`IUAT*w2SezV627K+&;7em5p*L)43Ry1?$|NJI$JK85RU;P*a z_2BSU{=F+0u^(Nx?!@AJ^kUyL+D(xAGkzaLz9z6AF>j?zdl+@?M83wcA5l-GDEPZ# zx`nPFd*&SDP>614s8$n{_hFS#hp^5zuQb)5;Sn4KWw`@Mnh;6bzhW$jIr*2i;@}E! z0s4|))l7{kkZ9?9latx=9K|pJMPVvqQ#RNcN1z!ITckWQHa7iohXo0+Ea& z#vuMCAigD@^+1{RV43|xc)}w#<53YXwf__F2hAmq;G9KqGVwzbAUUf+agjrF{g4Ia z))oYqosR;nXSrMzbUq$SIm>R5g2n!&?laikH1KnO=g; zujE6Z=V}(hiN{OAHfr~zw3GoA?O(?B0m%);>;TfD?uq55H_}Um=LQck7jj>DQUtBb zn`b?_=ZkWruvA%Q1WK48Sp~LV5%TPksFevKK=*EB;K}j}%q7V^@^K|mUnIxf8t5K|;nG$w5VkwG3p zsv~ZZ!QVgV0F>e_|9=t$T?nWVK_xE&|Cb=3LCrJ7T$=Qe14uNfg%01;#zesfILE!t zXDWAMAS#x9LsakFKRf;&AC9U)fvl+@sNp!U`cb5`%-OEU3-z(FMu?mLt zEj}$;;iy-WmM_OHX8$ZEudycR&xWo65cbr~0wu}*rb!3YXo9G*M%7;>>$xlZHSP+O zg!`L@8&spKj`YO&2On_G0L(ckNxF>@=s{x|qi2yBFh-6;6>5VygyO~ty2fYRz$&^& zM=q_WE+q**A)VPlKJvv0N{L_&go7e2B)1#KIA<{I&EwvQaE(HqlI#WT$T1q1h_<@& zPc_Q1R|-|Z?x2svDby+E!UWk#8BiWOgNM%JIy<)s`_JPJF&}hhcYya@YvWMB;MH=^ zC{Fn)SX{?~NAEs}XGQJD5M0P1By&mrbAs|lo~lqx9PFE?%kb)DcAYXC%$Q!~%x-r2 z%Cheg;7B%@a%nAx9Y`|(`sfYv;^;6x_K}?U6z6_+JZIdKjY|qmwpJg@Im(PxaG>?Kl}jVlTsdGJkU1*7|Ps%QQePe zPl2hy^Eb>#y!%^DDRlYo<7YJghf;)s=^J8n&golXH)(}!*O2K?yq-REx)ZU!rQ)ek>grz%8Vdn>-0cMJRJDp^a%MQEsKm)dl@ z=?kqWgzq=TqKKO~4X0QCy3IrdK*x66LwS!ZcD+22^qlheM_v~AOcms5`Tr_rM_U1* z_wq|hl^GQ~iSz`5tZh8UVSGrjhsO1Kq2IOh^*X0N{i)-7NoQHAVBg4VGR|J=-tS|- zoivE$%SE~+tv9y?k;g@TfBBewlIYrgQmrsEkZF>wp&5Fpx-txeuLVDkdFCgAQYw7k7Wy8UVQgP1JAsqlCPV>33*%fQ?7I6x^~t@ zFB7oXZrWGpkXoKT8l11^Hobt?lY-4B`mPtlT1HPir*fz8xo(>CzEO;DmG1vl7RD2w z4ccrs`&V))ZzYsXdy29z?W74`NL_iTcOETy`6=U_@f>iNdGV>kN)~ms-VVO$aIs|| zJS47Vvk$P61^rULIvAW0oox-v{ufmf~qsO8UyI@KT4LT%NtGOsu?)s#Zr6Uj8U4O(tzZ#x?Vt zYt{;|=l+h2^Bx-rzk2$*|3}YOZO8JrXt+|+lk);k6($?ro4cx5wboF2UNPVBL=hF< z=bn3M`|Q1JY>k*Bcx#PTRmaRJ!R0n1V^u|{Wl@=LKbEztdQqCT^9XZk*z-&dV^K&> z3%uZi%&UQMLv#InEMi^NyYeS7b3Rj5aH*u`h}DhsshKYv`qM&_$Yyd|k#7J4-y{;W z{us6!!KU)~QF$oXO2`y@3Q)$3_ySn!xE_t${$ z_rYrJm4~Z>jPuSp@YE?XKcl84o>6Wzz`nV|ZG*>iekEa`oDuY4C;%MpfVN>~zY62A z9S&`Vxz0=;#=b9?cUjY<+KY;GqDX2>1GS0^W;3(mnjT&;soE0!uSWx z^J=9;r(1@7-lMxagBaI5S5|a0iIIDvLH?4vw-I|XUl^|J5-+O>nk{7KVXv1qWT6&b^x9c3Br> z8m04QbYKY`dAsnmF)e7n89|Fg-lC5&83)kY7Tg8Y1orlY#_E^7To>}H$)vOFvO zT_{RTYmY!r=;H2pELnCd@Iznx)mdT$2x{DJ>FXnjosfS()fY0Uy3)Mb2l-A|=b-z~ zN59Bj`L-21>!vxOhpnk$-wHcnce2FSwP5>tHdVylN43{8u$_=ki^SIiGdg@z6oZq; zt|nkNJqDjJ?%EIZU1g!K8R+FX))!@CMYcDV@y9L?33*Ga%X7pNq`4i_Nba!i_+qE- z0KK%;?YFkt?DjBRX}sOU<6)n97PH6P-1|?j^riNi zQ93!^g@do8uT`f@zFBSUy)syyg`Y)j{k`0mg8kj*EfLG{dLEmxy5h+p{dTNfERsgL zTNqWl z*vU_u2ik6PDq1#~MI4}(E#{2ZX!POx5vw^{Gxb_>N?sCPuc&mm(4yC_Z5t*gk+_pBn*_tsMKGX z^$YUM)Zb38_4&`(ckZ&8vKdbP%F3&%Y&A|XQOc$8b~uNOY-1MR!^a*b)#*btvivo- zRArljTE2Vl^Fr2zx(mAj#8+qyFIv2VsJzefnDj(I!s~KgY zX7Sb_`FxYoL(}+C`qI9N+`5<_xYM|#uH7R*XBN98+y7hoQzbPcwd(<#xw2G3XVLqJ zr_G`#BZV&KFJ6nW=`v)u<_T>gZMo?LRnBy}t9DX$-3)CrgD7wpWsdLZ`}h^^JdLy9 z_IW#+99_=m=^*w}G<^>1b91Gf@Z)fv&$}}FyXMI;|0M+%XRi?U6HCS7UPr1NkNZb{ zK_=(};zJz{&zOv_Tl89H#91oa>eidtHrmX%hW&{TTv<8*?us+~$c>uJEz7oP23Ncx zrp5zJbC%g<_bv*p&C%;M?c(q6x38_*eSG?+8QlQ7=WNz3ne27!?MAWGRJx6C+1}k; zQ0M&e=6Rt9YxS*`f9I}+|b(!G|o9vnV1)1ZE3Mc(bR_06+T1|H`%iM7_ zOfi_3|uV+FG<| zTJ!V-KpIuKyVu5&3A{9%E5rj;s&}#R4hbkWeX7=6+iq^fGQNT+rjAl_m8XJZr^DQi^q*(S!;5Nl zbk}Zq9hO7yM14+q+{iDLPDeG-(VBK@W;ma&aLrmXfI$4$v(%F@tsQdgy^~~!Nz(3C z9r?oQ$F-Q-Q@0$inVK!WQ)8P*vxD0DhUjt0Jn)kswK+e`)e3EB&T;v8re#<*< z8Q_DvD8$B2@ySPi)(^t%+^@vge*(om=PLI(4U}Yo_0s?mwnxrn>uipYP{G@!9rU z*R*fImMM#m&*^4|?AyuvVhlo6RnCjlNm~<>a_3^rC7tsf08F3@Kz<~f18;qY7rP}x z-}Mpp6sAeWS0Q!P8}}i`E=5qW+GWA}fi)YYQx}tdaSoPL!=d9WsndD6QTT=cBqCYN z*;~mRW_oD9`M`@MI;Q=U>*zAx@lCavjyQQl#!2>aFtz)~^cJzgk`tJ47#`ZeVRL6A< zp(g-kKcUf5tTiQQs=hl$BccAd+Dt*o+_eDAE99AU7#Mj@de?s5U^I3w$o zv`>AOzt-!Y6%=+GonUglfq{8*$5Y8F%O_9kcEnRFh@cBBn5@+CeuG4^YQz{Lou58Er4}D{Ue@2e7=GHw!3j^zd=ULt ztpfI|rDH#hBjDi+o0k5c=!gF?>*zmU)cxPJX^}5!`rq>3N)`PYP)_K-*W{DG>6_<0 zM#ujNWGf3K0Hr|&)km`zvR^)vXU(v3al9O-1c6^5Z$rLPQV@zG4TYs8D6RcNs`d?} zlmop`k~kEce%p2IY(0w{$SrWRH^n`&-ty>e>z;k&eVLW^mvRXf78)86vE;!J8i*Nd zf&vG=*|UbvkruN{AfG|Le0%O?1fH`~YXZYcM9?GSuXoRWb39FPSCek+=%y3RPg1GW z9q9X!mIv1`EN_O==bmC!C*wf~i^G_)F*@ci;voD(KpF{DSVYcwCXw98d=gcV*m(xY zwUzZu7`I{pEQPAkp|FW#L+L;NFK6bh!Tq2Remw)56?*JemAPZl3@+kxl0e} zS|*~D0Hg*4wOYs;eJvq)2#ydx4lpXWGc*_jOl;MX+>99VD}6vnWLA^KED>J~9@kU! z44E{5QhZu@4;dm|G2}2@1nz7)o~J%u$db&=aKvu5;fSZgUmJF6RHS^$-=91$?0C*9 zn!YX8CfNa1=|0zbS$4V-x*mz0U9jU8QI+x1X~dmT9h4v*Ois?eaKi868NSX6#a)W+ z2qw3{Upd_x7PLf3jRj~0t}RQlym~e|yX=C|mRgNyWG3##TPw&JQ~HBr{JV%_B{7@= z=*Gw$5s8Z=Xj)qHjpDDBzWHd`@#F1Vg$g-g@X;T5Ubv>(B>`ElstMRf-st1qJ z<}Qj0Vor+T-HJ_1iXfl}KZw(lK)O&S^|uwDNjw^}be1wyS^8&~GR@BjWmGh=(Z}zk z#>rSTz6qkzvD^n^#3&*;is1Bl2JNZD(HJNWDrCr_{)v)t!~e8t=xjDD&CF*L=>Bx9 zpK@lqGgRD;S-+z7*kx)bSaF+xeh5+N+TadvD%P9}B3UAiAaW1Hk3UK^<*+@rr^yG* zwne2_6d>KXX!5=Ou@4FPyqiPx`O^wX;|vatPNK2Tp1VAAw()@_rDw=!V_G z;kz=>1J%son`5kQ1JIapKv5k~HNf>ToKE47s^@3=xSmt->UpsqZ58-rydGGHbxw3U zRaQGyn(fZgKW!Qyj|>p&O&E4tirv};f7(=fF0>)q9)XyLU61WdyB+3PD1c&X;UAlP zW^0hMjWf_3GqTyXjcWids#vd<6_NZb2?cKQFK422&qlsfl9ztzkI*aH<>W-X@IM)aE(z0Bno!g3Y_=>GRi#t@9N**}{|XGOWbK&Tf& z9JeSvCVS_+w-d*WV*7KX2c@N$H!XqW9uTr8(VbI07Ot=VJ6bRAqN{uEy;HcLsy+SLxiQi3BoDlrbGFwkJ#+_1e%KEz&R0k%=wysE(w7jY)#7+C`6 z@>0SQ;%sbB*{NAnRN@p*-AI2gB8!%9e;Esk5mULb~y?$*mvGxtFRK=j%UPI#w7!4A#@!7In$one;onH z=B}Lm4oz+oa*L^!jQh7|YBz`=#%4}zuk75&VfZrCs)Bf_wa%MHf!h{h3nu7NPbvbk zEiyfv+nnTQnqs-LKbMw`eGQpA>J3;XhdY+t?hp=pcOY^uMvH z#B;)n8k4G*h!h&A!M(Hh5^KrS!&&Z;#xVf`B7Q*hwLV3MVngnc&aePNB1A_OVj@H* zs(&z|3iUxYOR^#0?UKf^TLp&a(F{ptheXZDp}_b2CSThUULoWlL9-0kwwwIPEo7a6 zvUWKCkVA4fBLX)IcRAXJ6Mhv{sh;pG+r|w?FoHC8g9!Y&R7(l{ta z3bPD}6-KM4sr9$Fd;hgiVHuIrS*XlR@IRdC+CS%ta2YPq-w2-c|CclE`oiXwAUjqK zRx2@fA(^}pM!!advW&A4U%;gjcJs~OTOW&$Z!Y{F_|RS1jCl&-B0@olv&@hD1wIIW z^F@EBSoPyqC`AvBUu716xbH96g~*usuKD8!!l86ERvA8nqCUTP;6esh@OS-t{oe3t zXu5b9x^Nh}bQro|m~tjG6#+`XhpZZy0?PsU!l)qVDsfI_Ela4HMohvNG;RL{P3zI` z`9MD?swH{#3yMUoAMC!5?2s`9tv@gCQG1Gvi0;;(A>5iyF>=)!#np1QLq3q|^BMo7 z>H{CuaUk}H3=RVZ^GWrQE9ZMf6MIFIm&>X=&Kj81ezeGfoT>N>YtA*&S8yhjJE7JynnWAR

C zdstS9SW%wPe2Aj@=VMk0A}|mpOnEF42Fs9Cw@(zrFh7=sC5?q8h=nDI<(l3wKa_<9 z0gI+~4yX3JU0f#ejx1}Hdb2@5v()+-%7}TDwX@Xg`LtalW$1LA@}Np3{7#P>c`KL%ILR3?)YfnTbp$H6-f6T>G{6x$N{-W9bul2`lt8A`$=* z2nhQ7LLt65770*gGh#3>VlXl47#;SAifagryXPUZq#?7!A+zKrvxH!@DgF56XJm83 zR`ByS3vLGw9hd|T{H>9@Wh7XIct=3INebtaLH|V1xXu2BNwdTGq%b`3H*PWYf*@|?z5W$}-hw%NTRv0oOc ztxehg2R=lE{9~qNE&hX<#&SsQW-7mW6|X(L=(C)O;{*t2gFhCm^^=?;t?P6adJ(Q@ z064q|YoR;mh9tNqGI+MbW*EYcZmSWP@`{P55-DDYp#{WUY*3*QvH1S99ryz?%+R;F zDqgZU66;^Wm?{X#L3#dSOc%=v<}4(ty!K2j*u4tl*@Q?n6Ofn@RFG6Wgy@!44%Jr< zg`L9ezFV^cA28WWY9z1a)YYBU7PGj7wM;KF`5Ln{TpM@fgy4UI&x{Goz`MF z32X;p;a;`kRBrbL&%R>-yM!EmopQky8?5U)W~iXFXfQUJNL2+)+}MM31W!IP82fTFd-CTFbJz5Jstuhdk$)kKP3hy}kyZ?wf! z)|=-A;9*_tyoK(#1@E|p_o9W++xk!IA;mYM(nU{nFXi)&5Z}u@i%WfrOMa=#4#~@y z<3tInH}633O1+?tg^w?2+FrhR_#?6}(RVT+G3y(ypP%5Ao4=DgJ zm0lDp6#%;AqiR8TKB@6alnSh3zgC8vEfNtHOBFH8zXqTqSivG#qs3MfFpcfqxO;p~ zLi(KCWM<=jLfLVFC`bif8|}i-lC^L^=|fPSRFCgbO?{H&TOq^El7Cs#c5#E_l3IU% z9wT`DCp4{EzyTSZPyEF{D3%Q5(CwIIU2{t5Lxd0h2Q-b4 zsAHYmc?H9xV2Trci=YgjL;ek2B!Oqxjdd8Dby%s{^9_^^3HFH$o~OIimVJ8;{6D2> zuP zt^ORv5QeFor&six6>%J~w?`YCxih_RD>8LIay$K9oeX zqb=CYL$S2$9)Cvcfr2h>fk7w&SJhj2Izv*Td3xIztnVA!pD4BTXG@NvTM>sJ2wOba0*wclS&=tRveh8y`7TTC_Ed{;tsduzX1$ntV9Kb>i~4nM$b1 zB=Oc$)I=^{b=L8qy}oLfUpT4}6|m@!LfkB3;_P^ny6v~Pb?2FDYn%ubWo&;ps3}K2 z*jN~CqqKK#c0Fp@5DNI4x3E~3$E;_SqX^a!V#}fCGR~E0$?r5AxjipbrtOYoI4-9Y z`B;^}U-yb7?w-=c{{4Z^YV5bwu>86Jm(;mwn`Kvv(FC*Yn)%)yo;N|>3nyzkQH$P- z-u;i`qX=s}vM#ijnrwbs3I{6MWFMC%*0_k{yRoxu*MfGER~}JWbN2Pu^W=FPh+@>{ zTA+X{Q<&^NY@KcnQwZCAbk26`VdtnF?A!gLPJr%rx0;*D0~_i7A;P!UpQD!7f};_# zp_ae+spaW4c^h^dkg)g-lZzL3xz`)Aywsim+*+T3hFERwr#sA%5kh?)x>+%{V{w0|FNb#+t1~TrYU;=kY7mvl7l#v{T2_ z2Z_4i!8sp+Q7jCv-rqwq-Y25tPxlnM=*C<#kM|A_ZGKL@O#=^Ig4(+(Ud548?j8Ym zx_%jLZ*F%3#>X}nZg2THc#DsAhDE+tGR?q42gb|^YSG&)#j9%ojc&373PHfu#a(d7 z>0YO%diwsM&YD;L2Gl{}dT0Jl4wKcvVdd{4UtPZWzTe3oiI{au6IpEpH7z?@NRN_M z`YD+@got~-zzXYvt5=sdPv5r?a5i7&H$`BGGTC-a%xGqGpe7_=%v5?a*>(5RW6t5s zcF&<-ATnSwU~LsB9TA%0IhZ))Cam*ejg&nUdgRdgkCnb*x`p^R54UG0&+Oc^W%?yXzIf7ASgoa%*qxX}oNOMbdxkTo|fKjULLeI_umaiQK9R zzDIlW)%B=a5y9^AO}cIY0^i9$G+g*QFGwNFMvhYO^uIfee8f(kIHtX zQSX(Vw6{SSJ}tMP$G3cULK`8!9#6J9*#^2W>=qyF6VsE@m{+G!J4c7MCm`qR#DRsT z3In(Q%|{|HUi#F*#`>aq04dt^k2*g4{=ti5!i!Y^AunS4(cxJ%90}Iky4JyYgfWdF z|N1PG_*Xo8Tg%n)Ys`k=>rjH_qK!`ILrT0CkWK#UefeVH=jCHg3GQQ# zkLJD7WYk@(qvY8HhU3TMOT_G%!0qWIHLQ;T^+t&6CEp$LSJo|&ujO{qmKKx_`5qC! z`+i&qbEnBCOg~uDYvn@*?B^$!T^$r2wl!Z=IqbK4-o1tB5O`qgy4?+yQx?m^c45xd z`(5np9OTwE21&!Jm);Ls=uBVtnN>~Gq!(BbHPX~~gExR;f=?bEn^x1b)9i>mZTJztu4o@wrH0~y!8(l*Q6+0BpHMP3)q1B;5Qa9p(1xQdKl zZQ(Bwa#VhJ?vZeOUem3@r@q3hJR*K@zif92`109(9BV3lu=rkYZJsm!EoF`bd$RKR zQ%X!4JnvtxRn&M}b3SfdrD0*_Q!CBPB2}Xp%MnVQ-_AT7F-$F7ZART|GaGAD%R@;i z8oD4#Q5NQx&TXAegz;-_rTVdt^|7{{8+y&>$K?*+rTfS2@*Nd}O3mhs=0+?#mB}Xn ze@xdd6z7#8X{Jc%`q$>u(bX~tvO7z-wKX9|-CRbbaU|Hj)B0uTmFKtk^Flc>=kx8a z85AS!kCz_n8^x;*?&4aAGnzkcZWvcWxoscT$xA9;YmTx^6wfjrkv#3Ie*@wsaLM3x z1$VM6#_Vs=n>Sn-*o^HY*_sI2W>N28mV-|q0fRKv$KrPGe@l_p`LH+8)Czg3hv^g(stkUz%Y z(DQRB)YfZh+Fqbl?BVg`K=Q>ccFotgd!2v$BS%oQfJ~lKny>>nNg6`U0 zt-nGM*r>U9Yae$76UZC$+Iox3SQ0Z@?`Cuu$fkRKA2U7lYW>V{=3m;uf6RN&fpZzD zx>O;!VcYH+ZmIR&9F_X@Tr~Q9@J4CycD4&85r>X-u18?-ukV|_nx84S&=@m7W61os zhWvC1Hha0DGGEmWc$WB>1vbNFnZsV#Fth3zAhFEJyX~<}o(qrC7L+=tpqGW~L{5xY zzkF#~`YF=Vu06%yEMU{}f*GfHn;fnX_6vuGm+#SE&_aA*yW$ z9i%~4j~St9w;j09kTtt&2ZxBIV41qO`!$nXvbxnpLBnF*UxW<6s38-e`85S}0Yk#v ze96Akj6t8^NJh&1_-g~+X-sxmbCeXUOw(LrjDDqGMn^%laA!_48ln;Yj<9D==lS)a z?12R^Uwer#Q@p|t*9d}1#GJ|BPJ>(PjHhPE7wd7vXrwC#(i_0+puo1ZQqvef?3lw8 zhU4&rs|mU>_m<`V4aG!i5(dyFN0ajK@!jbC{$i{HLa6W(S?!$AXSQ|}AuX{tm9l$a znzsE&v1-EIswx&F3jH&?yTPYI==Q_tVnKNuLz-j!SG}D&r|$8ytVk!`+jw^opq;uT zNIEv(S9SlZ;NbDq9HIYej12m(IfCzh$dLNqtm*$VL+byED{y@^M*Lg;TgBl&GNe?O ze&&-xDyY|o(rttK4|kRri!0BQmIzsf-xGj^kt3rXQi5=zYpVOJJz7}*wy@N!aJJUA zV7ISmQ{&XovJ=um9$>sU_q@y^`7YJ!rF+Z&c7NbLJihLE@xI~cbCl_MC_Y5qU`B(? z#S3@#<8SxR*z~+{RRvt>LdD}!N$G;$$LJ?~>==a7g?RgEu>2S((#Pnq+!&)yzrt7> zK4LaHo(n&Uun=GjDQWqA~?c0ceD-ivLIruLWRjn9zo*85Xw5ushHs4 zlA3)K$GLy$m4x8ZNOtoyL|QfCM(e}PXqWDrPO4bI82Pp7A(YqbqW`N{RMtnAm}%FW$+!TE{KWK)_3)4!fn(ffL|p>CI|)E zNel^ZclCJL6`?j6>0J5?HKmg|J=rJbp+X<5Ili3EoKYGm$sZf z!oA~ZwTxFd@x%R%`y^X9c84P^U7UD83$y_g{`x(a=hq5_Im1t0UscZ^pNzv=Vf3EZ z`D-7_gE+v;lGWU|U>*YIaP$woM4sw5f(oxnXhtVM zNqS6*a|0$yrK7Q;;}UorCvt9rC$mKNA& zXf8onD#1^@UxG*ovAtgtF^dRH2B^j%Svv?rk4245#T5y8;)|8ja2d3|J#Y~4x2F=4 zXr9zmbBvbBp(8RvPu+bW6Gm(}+73!La#7Ma!YMk#z24k%1+#(rNVD&ds6WyWA zG@&zHQqERTEEWDG>JUyGKP}nIoe>Kfib5Bqfm$k-=?l1a8mDR%_kcx6l~X>IQ!ka% zQo!PzT17F{mVSM4#agVP=U`YYn5 zxE^*%J+o~`#M&V%3_F+&1lMg(phh5XcpBnzxEGSOpOX|#LXp}~KLLO@C0SaNeI_H+zGOgA7fCT1|BE7%;5dmaL;9dh zkLO&!;JOHv9CLaxLblBsI?8L1f{a`{&2--V^esCCZRhkl+4spR^@r0gwznV(V6o zudFym3b+R!K_v}LKz&h0izvQ+F|Ixt6?w*oJV-+)=(GC5h*X3>yIDP>jUV`i&LEOk zBIxT<)J)3NOni;f-ouO=(~Tb-9Kb}@lR#f+Mb1D(&)8wK4U+^a>(JMs^(p|CtXST2 zo0;6PKXc7%O<8>`Sb+?oDw={@hdA|<1_}+r38kv6kR67G(`GM>Y$cLEw?xc^Ltz(R zWqUNHupj-hZDOa9aQ1%C=HXG??NRQ10P3-CQ%GM_Vczj9$Eg4;ISzwfgSfZcmg86e z7TwxGuL0a!*Tqy{=0R}K=Fxqqa3jAKr*R+bi%cAxpndev8r_=dsU$ccHzNGLCkCII1uT&ao|@Hi+6nzxX6(iQ zYu!PfG3pLS4<@Cr^ut5l(G-BJ7n2(<5aM+k;$}Bv5*4fARhb0y*@J}+V!Nu(qQG=h zH#yehmyzHbvvk9>eX{Ph@{_-6gnrsSoHU~^LU0mlO68u$*x-nRYR#h>Cjv&>umt5o zwGPmO>N-V)f%^b49bw*P7>dyDM4R9XI;I&!e%VI<{UIr#TD0Csj)(jQd>aoOy5I8L z66M!^o^TEdtOY9UpzwPvGGo`Rkp;$RFgmS+5VRHNE^U;5pqW!O`6S4@KG|xm*O_5Z z6K{Y59p{eq+MDM!c7=QpY7}-LOEhXU0Y&esA}O~-SZ}nyfo{cw>Ni?#%jsdEdWIf* z;z40M0DF&p>NbelwV$LtTN;SUv|Iwh?oUc#rlh=W=P|46#X1x>4%8`*RL2zXGe!}+ zTZKvOVEc=BvjfIHZdaV1mNCgm?al3ViZI6%#mqKx$BX#hmNB!*udAtR^rG4C8Q}5{ zT-*Jnt28fK4MJ3qhB(6HsjTVfH)d2FD9wan+Ls%!L%;3MggqrHX7g=z;^_k$$DPf|weFjQD~a`QQh0 z^oc}@9wwWz74eL;Q-5$DumbR{H02TiDHT?bfWP`;VD)M|STb{0ZQ$RYDhx-_b4C78 z^VRj*(=lq)+HGD9l!A8L=Lf*mKZEht#y}G-P&+txefI;kVeLksx`gXS8lOf zVT!B4)>a@p^rZks*F z7Z?{{FzDLXTkV>yjL*~t6&TY=jB!LKpW`#=I@Wvj&D0jq2$|+(mX8UKP{C0o(PzNd zCX5lcDR3f29LuV3bsHgexzF0@d}n_b7PPkp(8^SAYq{GsuNZe;zcj%_Z&obO8Pw%Xf(Y>9KLs=kK3-Y0sI)hqrEraBhbYW``W(Gmx3-voqWA8)q!J zqpYtnZ!5mF<2u%9@2uJHu-Wfru@zp^?)1@yLHJsA<5SrZRAvn)MUI{d0l|9nYIYm` z9(_FPHU^QJ*agXOO>121Cw8|4jO!N;J1$eS>lcnYDpNsgV2tay&32x{L2G1R&syv} z$Ai|Uw3^@j=j{(6zCB^M(=5i0f^_MFIhfzT=)5Yy(QwluP(t$cW&f~R_2d*DgR&EP z$%LE50w#xLrT~+S0ru|f1&IbLqaCngx0tD1tW|+tYiG?#*4OFQu3{=U5 zZyS(zTzOGgUFj5+gnGBTQK^+c{4zy31b6as8=%3SkNxUGA!tFZkt3_2Lm7w}vHLLs zn)8llxHTCSavgQmerQZ82x_9#QcQ~RsAa$GO;N zjr1P%Jhrt73Fq;Cy0tl5e2k%Sm!M+ce<|?x1Sw#Vq*JAmn}pHfQPd@pT^34bN0qS4kcZ3UYknFifB$;`%f+*!$4?#$MRBb6aq!dQ!0M zY@rcD(x>McB(oYqHoiY$DbG+m7=NmDo9x}dG$`w`%mz}d!$kb_edT=;LV|M~i3a7!dK&nV_Np%)7sPO6Ho zDY^0{oDJ&DiO->YW~iOra|yf2ALP&;p^j<&J9z57uIu>d6Wg>FHYyQ&%`OHDP#irh(@5+vU)NBj zHN*8x3mAuwZ^3xxI3aQy0mq$@I)9SKH@W_i=MwyB!oqzZdhJn_6xecO+IK2=A$q9e zd-XE@hV7WQEpZFtOz!ap)2EI`_xWx+-3e2 z_N8_KU1!ZwcndhrXeL5PEdJ6U1FvS zkB2Qp@Nj(KT259lc-0(P3)~(C3B`!P8IHd~GIpl&kx$a+j#nT?+7es531qmQ6a2jS z*y%iXv5ovL17*#Saf)k@ji0zO;{Awb&~5vQ9;9tB0VmNgk+HicOgbeif0{lG28sOg z$~zV5H2%F06;1e)ThHo8=YR}hN2ZQArq-=?vwc_`A14{h+2IRoZC;J$b~x0*{xgQ> z*h0c#oDFD=*#Zk$+5!6M)^DzuH^DWU(>EHjm5o!wV(W~^zfCEOkL5S`8V&ZWV|O?jINcYcs3)9cV=GT z5`SliX1gJI7aFB4?k^kiEnN+BH}-K!@!#5s6PH@y&$yi1eHKn8!5<&#Ov23lLY;cF zBp*wGD_*G$eCbh({BpC7bQ&eMTPA|S+q>JXc?AuaYCCY%8`%{f8+F`QeHGUup`iib zy_Y~&8%M`&iRDu!!KX4ypYX%(nEF0josR;yrzTNe&HV+u3Fyz6_IK|LFoE=tF5V}0 zzsCc&Ghb)A9*GG*qP{-eCWeovSExfnuk+R(WykHIo2ww`=Gz7ppf%xp=XFyzAtLAy z1!aAMl$zNtuRkrQ4lGoK^`E8Q7nzl%8ON%&I=UJ52f?M7xG+O6P?##Y_pe$~lT#5*M(o;56JZCG2OB%c+CW*>6(OWdS;uGx>ay z2Chl#J!jGh0#T|Dr!X${v&T)WrAp+z5P*%Nuva$oLgVvauO6iS{+z8 zRg|rjX=)-KIh1eS9OuVGT4H=oYw7u1_F-no`%Bh3IwEIL9FyTBKT#~HI&^qNyaPg& zFPu7d9plw#UdgL%-q=asHsd!wI%va4<-ENi+#}35pDW^eKU6~zWSBfeySjwuVa*_4Ot&t|uoj_iR67Y**UZ-)JlMAxp`yjoN` zsE;ApTW-O?73Yer*8|S5I3gQDd!{!DgPybDp8=s$HVPj*( zssv{rXgj+H9Kem&B+0g0R;a8xJtAl26^wx=PVKpfah%u z-#e+jkGE+~yE%LRzWevTMD%6khaD|_bP831QsStfwDVDQ4Zsud>FH+*Q4yssXTw9W;61xp^M~QJwOUP{!Htg})-iOZauI+KM z+P?d;P3qzf$wRlvy2DXMIZX22oo<M{z zQNPo~Z|wWKf6(#Zj41U*hsMK3?!)zzUDzuiw!3s3`OS#msFk{IaU4bM8#~?|AO9ko zG>q7^P+R=>*?fw<-!^{LwIaP0G&)T_2<9YucqdJkcr&(uNsXIs zk6|=hGW;HZSCHMY039-Lgs7s2rm`VPq;%*{kkgk2OGPPi{Bbwtr?sr2kB^>r7js;> zqDm#3Gk7azf?;ef-`)0!qV46;?;AN;WTaSbmA=W2gZ29BB406XaQz44#~c2(s%f-2 zMFZ1i8KMVi5=>P&otC3I6lwd(J}F#^Wa&QjA)%42F4Va-F5E1;Ps7U(5=ZInm`pjF zZGbrJ^{tBg459m?AF3hzcUo_Oz;hMXn%b?OqPiO<=ig#941{lvvYC9AZKcakc-U&Z zqok^j<&Q%#7Qk1YdypR`e)l)^K|(Gtlu}IQNJhlHenjt`%byP&e2(k3=N1~i^{`43 zr+#i{gi}d0_J4jRwUE9Z-7R{p!au}_K2!m@H1{u@R|D!PKg7n5X9AhuRKh1b~>4!$+vNt8U8`W2tKk^dT5#Y|TcoDsP_0)dq6Ckb% zK(6FGbM3XS02hi|aqeOcPN&9?qps;PEwH`_i%kA6!s2s3&o{MV;p$`ynnn0W8;>I& zPZh>HgF`s+jxJe<-hwl1ZhYzQxoZKKT;-eoKsPAP>aSbqaO!Y#Npt9M^Dy=kG9Do& zAqFNU9w80^8YUhtCLSgsCV^i);P_s~)hQV7PlG7bdS>>Uqx!78qr@jsAOr3vz_}V@ zc6{=aaKhon5^hL9Frvx-ba{Z~;3Z_$;2OynP}At-h)g$*8S&XGucj||u|u7lW$-qK zW65l*E75btwnsFe_BBBen|>WLGRM`5?bZ>Ue%E($1vaFI)Vk-1ik2h&nHLt*7;aGD zN6pN*ezln<^M`s^lnFlpL_`h5UQW!i*&(wUfC2l{dI@*DBc(vkzEST@l|931VMvI% zjd`z2W!b1)P@?9n7NaY;Q3W6o8=&VxGEtRLQA6ub&|e#2<`ZQVb+9odMBn!3aH(fe zjShdHHp0~B+_FzKG(gQ|XS~YM`pyA;j4}P$GXg>P{c9sQ6CVuAgd+TI14|Q4W*?R` z1BgSEm&=}yc3HxN{eATD$;}Ee0k~gO8K z+?COm9+7$=3U-5KY8Xp?S7V|pYo(!JG=$1b<653$$NRwql3z}Mt7>&(!Za^MS!kCv z{I=A#(6$U}wcT25RTa}*w=Z1{uV;MkG-gdeUdz6{#s7G=muJ0g^xW#mnd-Ql3>3Ql zz+g-N^DSzMsNB3riVdc~eR5FU3NtKX-^wLoSo1qT%Se-P#9V%dg0YN&>ZW~#e|P|= z;>%>_kRG&?NM~nktU289eaiqKuuHj^q4shY8`XsKAl^vHM=Xp>wae9uFp~X(V#~`5 z9yOC8O*fa_@gvgCKQ(rxRDTv-EeF#GAWEOde8PL1S=hJphrI@X(;`MM^>aoo$%7^B zHeCD62_E1ljMy~XP>vWhg|+%kkBe-sj~T=}5fjTIl}QQ~L;Z&Mt8#@_GVhX`u0YaR z3SNe0u1?In$pkk)jB@3lj#^R()zE<{>d)gO6x*K5sG|_WSi2Z}#Ga_&Ovnj*9 z96NhgaIsiW2YqJHDGXCHJc-BveeMN*)^EF-CRd-*jelMNLF&kA(rNM{5no;kJZkqH zzQMkDAb3?PUXc|hlNy_V&y3!ac7_#^yF^M2)2s}NBskrzapo)4ttk!Jpqno?7iGs< z24un!{`a+L2nz0;dsqmmnNZ^=WLgMyY-S{{X8fc|aIle~3*M1n2PO&#b;}WPyd?2_ zzdg3*h`}Bg!O4tYbzH79rkfJdD)tF-oVe1e85x@+;;^ItMmD8?Q|O(Y2tt)T52n!I zD((r&sTkvmd4tCk$T=i7=(Slbfd_pgIa~pJ&kvDxvj}oKHNkM`)dX___^Ma=?-~+# zL*bcU$Aw0a1S9=sumPU=79v!v>nqbwg?&1-ZLy`=2n|=timcLnrsCs?5YA);8wk#0 zk!58D!|=fxCSR?`OgV$`=KQEGzmxl>O#0}ZG~5+CPnz|ofp%Aioo0TB{iGlFjbXV{Y}2YpFDT(CPznv(=lG`a zk}dmn-fch^QN9&lUujYIJz_s6L}+%PLe)b2peBP^BMv-UA=luq?+ZHUHsVy_Yz0wD zyw3K?EbZ>!&)ixzKg`1_#!t{gb0Y1kkXUpI=-1b}}a%;hG)%?VXBjmzgs z7aHmHmcT-+*kdb#%NFvj8pt)z6DuZTD+n1h`NbPO09N*J*m74Llf{S*1{D z9e`|dP5v~<=ou^VkoX10a3H?lF=?u>_@_uT3OXbv@2IAd)YjK8Eqz*1kd;WfMBPXa zmq4&1)$<7&e1$Q+$jBfbByEy0B`<$%i+JdiIux|!jyjc*2;%#dB69*EL>)P^Ceopv z43O{$)=8HL)(P)6&?@E2a&DWAGUN)u7z&8OqR1>b8f($rj{`B8M7w|h%ajTPJLcPM z#@B}Xjks;YlDTXxnug2i%zvN)YV${I_}V>fn+$0i3$0ji&up>-nf^HgOpBYtY1@#= zw;oC+PT-Ba8vdD#KJrDs1zZK#L&Z3&mWH5m+*6!CUb~QJoMY@ab*~pSUfaMrwpdF4 zDr~aik!GwrO20;2XH_s>E5|y}*pVr=HbmK#&q}L?tXKM_57Dw}^ z=Z>%DHV7G<1u~HMVz60oLkAbhy15p4hI_!VzEGw=%FP3*Q$*!_0{oSadGqI_Mr4ha z{`T|spY13ED7)Bvw_&h>K2)xJC<&VIq1qmM{--Lufy?q__610*of@Ut-b zs{+Ez#5jHmGEWV$hsek?b(|GP*4iUuBj5PA4q`G-Me&DUy@aq@`jpMGeqWd2JMy6f zJP%@YjiAGYjhP~BT>Z*BFJvX$gfrH1VE!Js0NMYZdr&s->`Z5bi&V5Th@qB<*e2PV zNqjYyE7SQIWKzD_^>~C}0$fWSFQv9S3E!l%UWxJe<^v>bjy@=o4`t5;2*iq2KHPyxkI^?~D{4(Y1lrBUqBT~=u<&niL2sSveM^sePz*W~ zk$Bj7-~@zYpmudzi0V6|l5(JxNh(t3@8YTqDe-j>A%NLqnXm!;E|h=urTa8fSBgcJ zM|Da;1d8A;($E#6!T{iSY@VF^zSV{v@aplMqp-s)f@;Yq{&I76FC)E%SDd`L;Vf6 zEQlsV7)LJZ)AWaGzqtm_z;DylMl*@zJ;2xMZ$qZ#(}4t5mL_4 zb!vPIy>R&WH5;sqqpNClyp$v6mo-Nj2URIYhH}Ql1zT)jwg+*HMhRlMKT$Ts-4o+- zg-L28Z@^WZEU`Do)YY~}I98a{4tix|I1it?GCn1>Vqk$cP9W0yb3zt{&|s zAq~DBZZP=KoN^|Sy+_f{A{{BL6w~SO!QvjLUCj(n8-vhpU1rdMv8u}z8XjvWRXR;x9FdjGHZ zCB8D;XCO`lCqoYP9yHUC=dFYP=$I^D{E4$qteOoJ6>SaQ2yZe~f?Brl`U4T5EPCoh zlIyqnyWispeXR$sdRB!AC{|cF{%lsc_<$2Tt%;l1fb7>psUnMJR5-VV^7!w9T@iNL z!><9|sBj1iW%~X`nxlTw+6KP$keDE4FYLeZ<2f1}3!bFAaVG-3bo*}|sok~xc*!xp z)sj)w+HctHtD+7{Kp_aXUUnCE98otiLZ>wB?%7s7Fhtg zUJ&^Gzz-a945~P@HwIu-rz62nOC0r zUUxV1a)vXGUiuIogO>Q`Y{&9aZ2c&FzI`3j@I+`V-0z<`}g+z;gl_jq1AJhM^DeE?>fTuF8*EA z8WHV%=d?5SQNZTP;57tP8&0W52KIHE@*R~k3>(*ZySDP+HGcN>i}Ia^a$Fmvc{|UN z;59P#bswd^b5|?*KN8=%hf)jsg&7Hwk)g`IY6&ZzrJ&NS1LA-De36SD33GpU@;6@6_8@Sd`iYTb{d(5b^n{rjD z#!SC?&;`b{uReNtqUs#-`8On$<{1}zv{OJc-S&f?ZW~X{uoi!p@)s(>_(9$2Ab1@- z(RP_{h~x|Ter{{a&u07fFMkP_?%L%oK}E}Ixa;jH_yd3`ReR%Rs=iUxsRDC6nA5eT z9!YtX3m>(KoBXZ!1`O6$AtLEGAd?fxb78}2#h zqw?Zqf+B4Q`14! z%lp2V9+>AA0xlM`$l(hS^Q(F@TKd#ugTbp>D#NV|V1V8H3u^vo` z1o#ks9!3X1A$__@L-k^`aEAXY45Vls0=^@G^!c$+EL;-cFepIus4EG4@Cnd&wYQJz zvW;5wEofp_e+Sr^5a}aS-@K6?S#th*l^AI?P;xr%SnX$qfu6|i?zlyP)YD+JxGp&V zHxTZiH+Ct-);!Cyt>f9BvbC6iWy0Jp&^zE6|E@2B`Qg7dKJ2T^%XQ7Ka_29aPV?3^?ft2{m6=SD za;xwzx})D7or91ta_aDpn*OAX7pW&^L*naN6dKokp?t8JxME>#@ z)1l%$Kilj2=}9j=^=XpNFK71wrmUo8q;fPZTh?SKF14%gJ=06yC$86|_?-W%Gs)FQ zy!=nqcCf`0p(lg>Y_`DVHtJ@X#Ytk`O(3VgD9@lzPfSVq$A2;FRWuEhmIXBw=U zd}8;;tESbtbAI*-P{$@{J@Simk!CcOAZS+F)G4;mL zv2<*Nm=LIe1B#Gx89;3<`Z8wX;wu7NMx%AzGw7_px81mEE@}Lvjn47h**8yZ-PT6b zX=4af2l7PjW-|7`veHF9QC^$gitV=^`~2LWY|Nt)b5be?*l_WqcW~c;_Uc= z+T}p1mg^cG6$fS!OgzK%ko|F8 zqp%wLHABnd{EW)*=|`8L*~9zzIP~pK$MChEcpr0SA}A`IqF9-?`ygnVR(A1baDQ0)2>`G* z9aW~kS7@I4dc?jSoo@o((_i}*@Y~KcEM}1jGTJbP0tCKISeNwe2rE8d6`zc(zkgXc zp6DfoBN?21i*TJ>pH%i4iYq^C;gG!^1m8=~mcJx(JFYboihd9_v3;8nV*KY(c$X>I zUtTvxiq+AFh5-}`@0HV#qTf-{E5~WUUo*XLQ|GTFVBaQ9TSrO(;-?Kqgz2Rp^{($8 zRzD;5LG)2rI5D84l%zj@VA|m_q@R-^it%(nkBn%6`)`Cc{0x zZ%d_i(-S2;45i=HUF%P%ErHM`Qp7shc`1qqbo-1uT-}%7G2EViJo)mz`G~eZ6sOEb z$}uOOlFD3skjADiiI*;0Vw|(GEo-yTD);vI)}pga+h;cYy!rY!{O4a~y6zhR$cp2# zr+CS6?c2KX-2@jNV=Q$0A38WJd>6QIs>Q6f(6oI_`wXk_%?n97hM)2hl{E>EjcE^l+V`ATFYzK9>u2t<2SaANm!`FA8!7w^!iQz5FKKizm-$Kr+)?BB zm|BPO6&_-+J`F5pNzv8kH9nWBmov01&@z#}Wcm_xALx8%WZ*e`tD)&5q+AKVw|Kvu-PF{>f&jhq%g9_v-0- z!u{V_vn?=dE?%JJRlo!Rx#9)^5dy&jaW->tvvqZ0P%*LeFf*_>u(G#uHTXZPbqtK% zOfAe@4OkcrT+GZ&4Osqf7Jv_6%>19@e@4w(9%6Dq-VPWBZbf3A_qv9*uxKEwh*H<+zQEgoz&3HG&xjdOwYo3T;R?FVp zuQg9+Yr{TgBz+{xP8)*3IWrtK$HcyFh|-}>9&8ESR<2zHbQ42<-dogg11Q%E5|PaD zi`IxOHOI-gr!moJoYe;)*B%rR6d@qbWD3b(B=Og)z5j3VjFsDtU@n`JE+n zAXp2Jj9kGG-O{Wsfn(zm;T}%;d!5PuZL$NnGG)g;_Y22MeHXzUaWRoN)sP?(RF#7g zZE+#+?JY`keOK}+(XM}G#e^UN@cX8XJUEQr51ZDKLr?^V!dVd{cKi+(jcYitgmiN) z#Fc$%g&3|`ON-}>4GvmxjwD!wno$@Es155c;!Hsu;?=mpT@+PcONB6!2e)m`3zH@= zl*@|x5HX0kU(Pi!Oe)HYkumPe8Z@j`8E3+zEsHGCe+|8xe~Juuj@mPjmF6!+X`M7U z2DvUZfE5hbAItL1fg?nSew?TMC<1+~(65lR?0VmgoD38~FN74mZ>=cSUvA{Jk#AcE zfI@G8157|qD!~YmqY^N+ZorAdYWg_Hidr0zWw;MOA8{6Kn97|o zS(4ZT`A1FPq0h?Zqv!wb3TB!&G)b3}uk+@xjO=MIi{M<=!UuVxhxB3#VvTdS-m+q$ zdP;vIy$TXE{xKjwsRsk|oYdx1z~)YHz#mhYLD;9NguG8-5%H9ci}#HB7~>tuIHXDv z@q$eya@V0E2u{fO5fkX^!Jlj%qR*N<^8)!vp|vuG>ETGISVMZ#E=Wo8USEYP3k>xKx5l-DjJ1!l~VGxFuVAr#yFhs_uv zfwY|%rZPGMq}>>@WjX`sD6&g|R;y^8qo=%jE62QgqwxMk$r9_023BEvHCsXV1Cubs zb#pAG>kN+LKhVw*o@+QIi1!3>!a3>9R%}DTI&`4P{c#mbN9E)=DlwvQcj3@qxUhF+ z7$(VmzKgbk&gNb2aipb&9#`*V{z{%_)qUvj+D*P&)ra^5X7*e*Bg<-3m8z>>d zu$4m+(as_w*s&=PND@D%lv^$&I==x1Wdo2>x`tZD4R}&0)hM}=O4LO|Y_#EG-NieI zX=8dBa}A)KDv}5^Z+jw6Wc#{i4>IzCoTWyogWBsuRZVHuz1mf1bk`_!Y=HXU0*D=n z_RI$I)4I@O)Kx=}Ub69l^_OKZf|_6*Z~;xM9aLx|K3^HdkjGoPAE@m8?cg%2$UCT< zMkKy6D0VRpRFZP&30cO3Q}YLHy0(VDFa;Hm86#?1GiirQ4}$qItgle4g+jEAiJU*= zIW(s`aGG8c;DVLw4yR&RgK4i>WhBpICQiFA(@rj2yY-U%9KH665wu6=^1jw8T(e{!4Kt|iL|%Y1m+?A3|2lI$lbizq*ES5Yu97=*{DKSS&b z&4x?MnT?0Xx)9<7Pyv%2m?D9K%7`Kwcw~iGkG^yxi$_eq}cw_-kENli?SV`Iecs;-Y+5r5WBYc;`E7nCv&E)tx= zzzh4AL)uq^$ca~N7PX+EUD2^yJ9e&0V#h~vQ&fs?M!I)MX`t_4bi)i5U-EK6NSdxQ zH0MkofBW~aku-fQvB57&PK}D02R$y`BH)OJCwV$g&|E>4IxyKZG}_gIN-MF}bc8g& zCZoo51f7hgj9QCHZX)VbTHUwKbOfG^#vFJ-W+G~BmBTClC5YAz@oH#87<(3+lsA4OB=E)dl9>Y!fLZsD0)AB!NfwRNYCxCSUI2xiQ4g1KabYtW&=m2)R zs9sMTz0K)KYg6MFhp@CX(6l>ASE;bH)##e)@BMeVpi?*yX|1rd=jfWtG!3?jhHDl5 zO;Uk}J`ibt^FVWhZ0Zt%q7`82si{J#$1zKTY)6Jj>BQ%&8nV@q^EZhwOmQn=HX?@v zJWZ=H(;ih5@I}2T61T;uBQ~mo{uu^?KmHM;mV~AM=|sivMjW~rf}V6M?Tl+@m7`Jn ziL$aT?4X^hjY}&BAT2O95ytpW$lL`CnUUu||H*Xrz@9$sq1U3h920ZxQ97if$PRSCWu&O3X3L(MFWH{veURKYZV66^vBa3o*xvQo1&gF1?>|2Q)fZ3ZHJ!O@ zrq%br&pmJ6?76)$#LFA1X@^ulu3rCEw!vn6z}@I1BGsNvx@DhuwISWPr8G~sL$L*$ z;@VYRvcX5TbwzXfBq-G$F5SteJb&4kWZwt;OyFGy*Qp>kROVE-339=+k|3$fuQ@fJ zq43gKYtp|18DJK=;xnAtOqHR?qyjXO9x!@KGw-4P6EaVFZ|^O9gMq(vLU*FvDBxI# zhXhES><35|NXK%R#nk1m2iIxGK4PdOTkxT!>6iZF{9&v%2Y-c zdJ{ir$b>N)S=o*5cF9ziy-zri;YRV;=|I`k2aFFN(zS4%-jb+ zVTNEVavIQ7IjJw-_}yb%e9pkC3lsNz2@~E~@4R11ITn6GtEY*9!9{!bkU`Tw41-qh zTGh|7hLb3Ts43BK@|UFqd}TfITou_N1sr@o^>K(V{ri5~(bj*1 zpyU-R_h{be8^`D5EdWQU{;^tAJR)USwX1&+0Q-&O%K46(32z@@8 z#crx6aLPe7>F|T5zAE=iA2$rUX`S8DQ>QF}K>PyDtM{Vz%6Ws@-ZycHiPXYng9XP% z7FX+f8m`v7_N4I%AU!_`c|vs9gO=mm)iRFyd~Y+$hr_yjyUInj`10iN zxFxpp0Dd{ZONcx8?XfmQV+-JB)5Xoxb>wS|v@+{3M`mHwSd!0!*ngX~P`N)PNlN(b zcbAA*ak+`U+Dg%hsJElGfTuTxc-g~-ckaSY-?1xx`RYaDg!eK~%i{CT@M!6@&lQuN z_vKw-y820d+6EtAtI|?f_Z-uC`a3c@v(-I2;Bl39bk6v?1&14p#a*&K;(bI<_7+7W z?=s$%sT6XRp3Wv~1bw>YFEfW56WqJ3Ga=%nF@eFx_t2C=j_iTTn|Cu5XQuzXJb0^3 z&PYu4=Rqf1aL#nIA4XWc!GUqy63t0pVO;bYFW;lIs5Lvu@TDiy5NG5>odd%T4P!P} zXWLGdEm8KK%r+H=_@Qao&RVZD%h<#X9L|I8R{sdR>boA|2VFMIX>^sbCuTDo!KRLX zWppD3uB(M=O|n;CYV}48x0bAKEiM%Vhwk*iferV^UH=3)xSuCY%Jkb+LaxPkMZg*Gh& zxT8A(+7fH+I#9$xeUmG_QNw$OK?2p?VDE*Y7c53bJ}s;MkB}70y|&RFDj!2Vm(Th8*W0c$VrPKhP>9-{z|Xerr>+hL_ty#~(!G3^4$&{3hMk!W z{^zPpz{!2)@K;hg!cOS-WzXfi(NHUWx+e8fegG@5YXx3HR6?Cn_jkj-#h>R}|J?RJ zsy}-5VOPF_>iJa4Xy=!^`u+_&zq?rp1XR>|9Xn+*(acU*I8-_Xwkr65n4S7tG~tM)BZFUe=Ud4^6-24C*Z~WMcCHC_IX;|%Tk9CzcPq18Ah>>K_`@B|;DFq|C#Jp&$DYJV^O@NFM`c%|PVf5TPQHNfj^|f| z^lGaS?s`4p1HXpR43yS%xa_8go&naqOL7^FyoS&USnXtWXV=gB5dNbWbFJx9c?oF^ z7+0p_qgdW-+?)|xA1w<};jm<`j`0A`J41@(E~niEyV|CVs8ssytn6J=_V{eR+w4Pn z^Qp5FNHXd_86Uxr_mL^K=A(>u^7iwjWk%`i3Tdt5=H568xwC}He^Y!YS#|D=iCGS& zPB4x!xiT-!;@#}l2S+wfdktJ3q}ZE=@N$D@_(bX--uNsdqp?Z-TOHj5TxT*c1d1ep zg=lE$dHMOr#`7NAUfTDG$$v0$nVtJ-u@3A#-StL%w5I1sY$USiEtncdHXzlZ3y$MY zXXz@xKVL~6VvplT`}CE?+IIH&|5!dLSqdBzth|;Y``jlj>pB%X>UEd)e7#9;Cy;q* zKkg=}P_%Jh5$M)S9TKJ+ED&^kT^{DE<9Rs5y}94FKJ{5;1|Jm`d-E^Foz?58*lN|i zYX+Kp75*+?hy0W8k@M)=?mmR1B-0+?rsvb3m#h|kCgAd{y?rUz!{g6ohloLl2SdU{k-e>(-A=F2+Y-LMCbjyvaR z9n)6n*@Ata`+N1OcIhndQg5E$d)?zf@l`I8gxlkk{@M0!_oeQ~J^p1nO%am5+|-ge zg9)k*d(SnZf!FrwPmkG8m}iy!E3)`4&aZZN0*068mp;1hq3!YSiRWH}2j87e`fr1J z&aXD{E8K6tkBx4oufg5}DD^l2DNp3Dy_?kU-fs`}RG(_Mf&&dJGyEOZxax1}2GU9} zo$r;dKV7WwVn*G>2=kQ@(oxE1VA?J5y!Kh5H$o6tR2`h}f{8_xl$;UCCr5xF5egLD z4>(=D4=CMpa_)#|`=DqqrCGlA#UznSXI#mo&NQvclSYkmJ_&4#A|HO1pcrTp_jlMOuEl|7^)4Pe6v4X^&#%nUel%h>xMkn z`dL|3#ge=hR?Cu|ITKV$m32SHjxI-~9Ya-S&%B`KwNb&o3D0s+=?b=Okjt2kE=&B# z0|FncbifP6jg5$41DT%zc~FWS$IdF8q^`&aOHRdGl~q!x8WZ&Qjy621Jc3dP6JFd? zJ$Dk-bpScUVxm|yElkUCP^hzlw&(twEFMJ*zS%G#&{atb(tH2_dWu8Q6qNx123rz6 z$;NJl_7_=c5FLV#HHl5i1SS>U zjydaJxg}*`LLteKl5<)Z>e`DpE;V1mmg+V<-QA>8Ylg=Y+q!5ef(K^Pgnh7IC$`7_%V>Wx zp3*;ma#6o-kajftrY zGM21xl9^d0CoEa5$#%+PG)}SQ3|c2klFZCb4h}jcM|*E5cK8^VOhYQQGv{sRPq8ae zt(B*_d$g_QfBqy0bN)%2uokWm^`B^xURh7E|N?c>1?*EPH| z-F%_8@|BxQq@A`@G%r-g4~Ax4iWibM1`Vu~COcX#VWCV_UL=O#BmbQTQIL-Jp#FlK zShW~~hoMT-Tp(V_hQsm`5H6WcrM>>>;^!x2kWhp$m!~Ape*XAmBIuY@1aXyX8&VWW zWNKVd6s(fYEMYNE-T0doG9=MxFG_x9Nc9a*jlNh`jYvW{7@Cs z%f21|-?^!!Kqv>nWQHnvta2=GGS*`L!ziUT&jYL?*f@05f~U;45Y&?!Iv+hYl3FXLR1ke?*Gs+ z3J-3j^|A&cFa^6~e~26far`r+bYe0FLBxb_&Qkd~lb)02V0wm-D7Y+;<09(6_bZW= z9D=%}LalxZaN=g!f)gVtH#K>FQ8`UHuL@ZTpp0FSo2y86q{bb5M#SP4VrCv3 z=lzAW2&vhsOB*xqd?lcbTOqdnVgAEY2621Y8K|{&25N0_b4m_@TH7?B)>ivJT3arl z*7h(Xzv7Tn-I|M>!iuX(HBw`QJzljD5kGJ+tO;IzgOv z%Ir|pqzxAI^8Rl>!k#j6c|gzEFrspSNX4nwFOibgycO=`g>^-c80Ul$8wAhV!UDD- z8@v5h_IquMd+p;JZH2wonZwq+EYHjZZ*78%8LQm5)|upBYPWm}M<;;O4k|c|X%DX_W%e@$lBz&G>eOFgB1hQwmrAg2PJA5_IAQX{bAe$d@0!5%7JqNWP@RUna)r6_ZNK3gA=UMqurBxXL$?xqO5D<>LhQIOxFGKuzCG1;qyHplXiRo&u5;hdKJz!th+~5uY6q1=rhD@7dK_%0@Cyr9DUuqC zMcVarnk(E* z=b>is{blh9vv{JNz2M4QGw?K@y`R?K&Fsn096|A*Li(6Q=^{jX&&Jbjn>ytc%*q`ttbrvhkRIMba8G$`eh9k_C1I@N5 z_CT|3>@9fGq-fgi9g}U)8@P%Km$o>;O9&l)(d4u$*?|w$gJAI-cUF>{N9fT1)6OoS3x<9;=}0Xa(5R2QwUfi+oFHePm)yyIZF!T zi&H31Y?9~K^`&u%OX(8l5&g1YbQ<_N>3-u&x=8S*10Z?!bO!M*VjRvVg7t3D_K?w$ou7-CDmOFG!D2 zKY?j2pItEPS_j}5^0A6=F&q9Y7z(Ji)hS)sv#l5wqfaSxr>UGzi9iMmmfn|L#bN`7 z-oY+45$+a0O^?Zo>~wq?F6CDzdJd?DYubm;zBc(AoLh&`ZP0m{43#unAokrE=rX|apX!yV6RXm zHqCxn5s-FF84l>}U;fu?>i~Vltm>F<6x2luJkxbd7ah<`0{6fs>X2dXlpFiU)hCr; zssJj_J;}M2Qp&-i%$Y8taC@2>8U@N=-3|Hq$#}`l31ai|GPU|j#EZp8n?t!o{l5ysj z9ehMAusoYvr@K0NK|WRl&`*=Lu`R|wy5VIWS6Erf9@P~vhMfYZzq{{lG zmI8|*Ye%UJwJ*o~I(-46Z+eLD)cqYQ^3afT2y=?%(u6C2_>O1TwwW;{e&GU{bA#O{ z?(+ha*@r$;kJ1zrE)tf;iXOGdI1d@){<6q85E%n5w8B#(B1%kHl@_zeI1(A-8b+y6 zH6rR6=(zzhNF=B;doA*%LOcPXo9kD?Fr`|7aDQ;touE zbZ@amwp^nGG)WbhH49E*2BtZHYq!C+*rHjk(F5-6{DS1ixYDJ@CMfVJQeDI0n?%Nc z?d;j3VygV9X~>`Y<=!a5JT9Mv=8h8TvGSjZm!kMNVa`Vik_yEP850b3S-^er z*D0qFY3ascq-zq|ywotjj^M4@RhaJ)J{W$-g+0soi$ zb8ycBK6=g-@)gnb=RZE=uakd*dp8Y#1O4~$pi`ghUG`9NfYG-S9;)aj)4uXpq?5mR zz9_SavIR@SqLec@ZL<_TmJGj9vhO_kD|(`DmLz{L8OLE9dx9ky*sd1EY2r9O5B!)l2E={y6-`C5v64Z6JrctK6*wELeq*8fYPb&B zK!-GA9n7A(*x)6%RUPKwWf-zO$K~K<8nQhbP-qLh;PHPNZa;}aHvEV-2@KtMMSp-0 z8Z(gLP_r1(F%=|`f0Nn-n39mGSo|999~)kPI;O9I&id{|E(w4UdIR5aPQ2&nJ(P#Z zTFT#Z*@0qgn2Fr7d`W3`X@cQDSR2_%5RBy6PGRb@zk0mr>@x8}*qrFf%!2&E=I#Lk3lk)_38Pp5m3bLn%+327?D)>g9z(ujxJu%}Hz+yib___+0PHgXmEM z;edKuVPKqySUEUzI0xoQaQRZ9mAOk4%v%wKma{Ng{Gi8MYqih@!I#|E*!8wC+1B^H zhXd6g@>rCBQVhc5X9k1bLc*RI(|MblNmyg*4G!^DV4buEyWsp_sRP72*N z!TDa$?2whKnFhG$d-0dll(_tLcwJN~?$*AdkNP#4#t`7%qxf^DxY75}%SC~9h3*+| zRFBQ(SpnrlIzY#r;^(8q$g~#^xE|8VvOCT&xldjtzxuD@c)jQQ6aFR+8f?E!ht*jP z8THgJfd3jf_QnV}4`Sn!iJxKoC6)gitwm|!g-X_KUJC96CciQFqYqt3ZEuw>Zy#q? z&syJF!|J3ZJLB8efP^e3?$hg_+;eHL=7dr{d%hoIhPnqI9^3tt<E z@}v0G3yeWT2ko2}jo_c|OZ$r`#n)4-auNc*7pE-0*@kty=SwvN+JxMSi$>b1-0d4| zbp`dIH>gjS=v7pOE@GjiV1#-OR!gDMw@>JbwSvdC8Jx4nnxu3-<7zumhTZM_NE_|E z2;Kb(ytP{b;BlQKNwQV>6Jld>TOWkgDBoWmF01~Cn5>;`j0G{{ww2jrEUlbBA)5CX zLv69@T8k*GyS-XjrMF$wHE8WPWCs45e>+br6soD0)#vipL&t5k#e7`9TG7h)9GYp9 zD_*M`z(>f{5NjX|)r!JW6>U^w;<($I4qi>v;CY(s5^G)q+gZ9=-_9S&3yA*<^-3vt zdmYWYv%)ru6c>N*3_Jch1n;`9P4oFYS=_Z!rfp?*m2|6RB-Uba`&TEMezw>xnuWsD zplu#O+~?AptSh1}hqc+*@QZid^=!YRm!igX@L7SpHNdiTLk*Me?L-&lQib_k>zE_z zgb1xGpsU&r<t&!{jP@)Y;+u3^3>IPb0X zZBY37kFT9-%8YdU>bqgFLTl)D;@lYLRYGW<-_`ZO4r}YYSJd^!)yKB9Lxh(Bm!4he zN}mR&r(0ZV9%scvIul&&Lx*zToC>a%RpxWdpjuDV$(YF2_Qp~8!^v**OEx^s)@fFH znM!D=f31W`yL~&Y>vXT&@d}paX|IkeT+2Bf>k)&~amwCH0r>Y9OF+u-nAn%%nf0mU zFN*go+3q-9uXehhs|TUjDM_B49fsXKX!72=;Xjale7RX4KtgXfUh+2| zm~TXKAJYwew;aB*(gO*PLcX0}k+Aj{UKe&8O99e%tAKe1NZ?b@DE{xSnV-C~qG;bf z&Bk)~z?-QC75rBH4Hu$apucj~zSJ1N1f)Z*!vcA}mZ9?@cdsS%p*JVRc28b9FcD#( z;p)q`AY*obS7RP@b7Nj9OS-; ze7Dw5tRZ@zbi7({3_QeicfyaisMbS{58L&6KgjLta}Bwo>w9goP&PgFyt#U3lrR5; zja_O!(DX`w5o}vhq;fpm_TNNRFG8Ku`;6`IiNprDl#@Sy?o>=v{nOiPVo}}Rs_&~Z zidVW1mY0C%){|bstv(-8LCWK!FIHBMS@TtNi%Rlu{vOTkEmhrmh1|SNrfBsjIc%>CL^|F~fl5;r@bjx~J5t78`dmBpLfWP+cSa<**zD5b zQoB$BtG%D9*v3EK6qn_dja}24qQ6uWmovnNHOno^8eJe1 zvC}gISd-ono#t*)QK--v6YG6UaW>cRRBQO)RQ1oO^x3M9urdzUdKvH3D+=~>ZAFg# zn3c`_hN7BHPc6j7IA)~IJV}g^=9iZ)rhS0%o17GrUgv|Bvv-HQQT_9JR4IP_ zJ9?RET58bKeVfWwT;qCp>sA}iCh;gc&d`&MHB+2C6T-0e-J9BLkh9Kvw?OfYs8Hq{JC*b;QbZ&?FR_9}@7l64WC)8Jw zT8il{crBi;VA}?;a;*=M-_bfz(Tj%+@cb)mqta7%yFz-D; zr*Xh^w<5Dur&ko-hf+}_U%eR|PJ_vIvQ2hJK3!(xjlQba<6#n@qq=$4`%}%i+U31& z0Lni7V>3YhHDo}Ii~!#saY3P`utH`dOI1yPQc4>8P>X3F=h1ZQ)9QWSx}w(KCrF-A zjqI~$T_ctKkHqWZDR~KRRbd>h98aptJALcJW(flpy{9PO+T&guhMskWflai{6r#Ga zo_!WOEp4wvuELw?dT8IXN>kbZn#Xlz*~5ynJmkuDSsp#E6FWd#O77uIu`9q=YvNd8 ztEaUcQMq&rQmgm8Bes*RUF`XWyjt3S+R_L3w~hL+&s`|b^ddH?xaoxODP51j%k?T< zt+&i?QBvluT*zRjpln80kOHx)%vWR8q2n%-2_EE_Hi5LuIiaRJ_7S0MX?m z!@1?jI=3RW4#QONzdfJcdOc5=H;Kr{W79U0muegXoe$KT;@n$<<#!$SuOZiBA=SQc zX(s2_s1}Y}uhw_C#AH&ZZ)`1bRSQXEr%-``fq_D!M5DRA zxk95kqr?*;YEn*0Zc=9Sc5sQJ@`T-r2*sMim9zM`U6u>MA~OD6tOxXbnFaZ*uD5_+ zahChdN18k(r}*DIS(pDdP~?64IrVh}9TM(B1}unT4w@ zFR9T|j0~5{JQ>0YXbj}Aq7jm<1?Re8(+;D^=6K+--g{usK77xtaitFo;tdQ_2qR`e zbiiU>L6h@;IRU248lWW@Z?)os&9`(RqFuq!@~=59kC-)6NHSjR)(LD)Jv0W2@CSzL z{h!vTxd38}<&0>3WvettOABhW<+fyXm6sSa=ZC}@Dw5IX<~}uIe!9cXQI<}zw((2y zN5p9=g3;%OJ~d`_##|#nYj5k=+4_nYZMh@n${$4UKT(J1fA@Pv0_%TaHRc3q>5i;J ztukXZykxDR$m+_HuLZWkeq9^RG9)LX;c#62mARfTh)1aG8FC9w>Eeeys3VtdUtC>g=!-R$SQ2aC=n)gHNA=v?$`pQMc9`9*!DeI5pPwOo7lyv-wGPVV~7rto>5eoVyTl77GKvh6a% z=Th9LbT~O>no2V@=}V~vr( z1+zjPK@}u3KE4W78#P$m8uhV1IJ@Y?zL6~B!AZ9obX<+VGC_c{oI1R29UYgF5W@}v z<4t0sNLyl}JSa-(jxxToAW5XUHAB|BcFB^7CCXp1JltE)qjIzPh3cjI|Uwc+mQ02=_-wr>8KVy|5pR zWdS%sD-5RmvuU)vgYgHdO2Sj5;w+=3Q50t}*IY^b3S+VN2z5VHZ+@`&X+dLr6?IY_ z`$p0dW;WZ1xmka>5=}5hnvEh0q+ApM>mD731zt4#;vxr_CyfwGDv-o9 zl1Y!UbFiuy2{Z^J>~G{iYjdiq-Snd)ERvqrdXN83GkvWc=llmoBC$WZT+$d@g`$-) zQX?-^D-MZbNrxZYJq+vHV$F86Y|UD|1iM%S(mT{Ifn89G+M=>VPRzEhWQ9|qQZCrD zZrB)OEa*eLrJyDj}nL|a>%4ep(>F|3xv@CPiyO|3xu9 zWkK80H!%MT#XLBzq5@IOB<;>%3J!5pjX>6aQA`-?)1wlVV)X>4v;%X|95+BAJA*k) zMa5EVudK09av@_$xVWg?sYQPU>$pkZ*P#UfW1NWcRt{DgUl=;nKO&qH=<@9u9RI`X z{x>|MA`B!pXT2IWClbCb^oh-`uYE+IOO*mb&MZ8e;|$YuKa;>_zf%pnqih_bqpFp$ zx;G9Z49uzExygyqiPedNAp0U!;I^Qx`K~SVe19fUqGp=Rl(tM2BEE+b*Q8Cc1&oZ^ zirZPT>t&8GYSC?=Nw12+e}Cbt{6Sod@TmcePMuAFyy!8fehW zO?AfH9a!_-+Fdw%nwNQ4bLs&NO*H#h1KbX`zs!zPFQbfq?FE4>W+H3u3>_^FsyVN5 zACE5^$YKbn3RI#`zuM~Q0$&812wph}^<4hTVhHDSf`Q}Fs;&q5#y4OY(eMHc_`Vdx z{`^d^izSsZ6hROHO@Tb^2oRc-jS`G61ZzV>u$V+XFK^E7nzj&H(hbA%1ZnbMQz?dA zLq2P;GgDvbKl~Mg2)!6|CVbYY2d32T_1nUpoANywbf$dPxVNX4ffoXwn_~Hys<}8e zQV`i)?AejJ)-cIdr8jBK5mx%>@NFeN1MEcEs&s8B=hl=JiApHqg6bko7D?o?MA_D! z-H|KVfgI*;rYqVbFy9Mn^>0@{!8QbA<~}~p1^`|u28Jjm-FX^z3b!=eTvvf5iUPfPTqRuWJIyzNY|i`(VsBG;k~!43a9E=vTVQZ9&9{h`OUDOq?SvK`vw* z)xfPX=Kc5^Tg9352(aPC%-Sn?R4H=Cf^i`Pn^XQ z@9YIc-dZ%14c9(>Y48+Y<2z%cM#?Uh)K|x(%1t$rgIU?4?yk0$3pojY;h)7krhk=- z1uH1)v969PrT`uJ;H-xu(ds}J%f@ISo}i8K0%w|z;DrzxfH#ngoS7saf{^WMPd%*iC@ z)I^Rv!)jv3il5(*o+~D}rL5zs5BwgcZ=n8BBQ88#BZff*E2gSDWbGNT`c~M$Z9hnW zU{k;|P+6D`MIU8@dT3qfp!{PmA*@xeG-QBVp31FM`3!~XF){`6WJ7#42w~0+^5Zuv z$Dj+0T?11u+4KO?tsRwD1B^fM$3WRjnj&=1Eu9ex`%rs`Ntt<#@r9Co)x^3L81e zOqjLqKVgBzt6yqaI4H*Eic&f7JO$$2W`2t7>Y`mN59u9|mK&HmG{$r>C=@sl@Vj_q z37paii%LNd5->z@z>*tNsRa?Ypb_)sfw(&G@F7Kt0VCz0A((;Oi2dBgea#F6H9+ZL zXGG}4txT!F#aRj_QK~qK6>H*C<1T&TuwG~4(N$2_1M+5R`LLch%+txA^xbmvBl*0w)#GEhrHX zOwD=yn)5=Q(;_?J)dYA&h#o6Iqc^2Tt~iZPfa$`QqS zF~gB{?Ex?j5ffXDV~DB`?77*cl!ciom*N!If0*vclun|jpDloSR0y3IZabQ9bkiNP zRw9d~#<3(SRvEYa-CHgc(TiOt8wd|MyqAeCsVy~^Bzh;#l0wQS4p~<5S&K^e6 zomql7oqvpVF%k=@`0e@zmJcvP(L@{fuR$U2&`T(i^hb#S?-!LDq))+Si^Ti`rFo*)0ReYL7r}xL6+l_XvD0!4y^oCGlv18D0~ zLKVj0S~tgXM^+Pb`%-LfUYPKhSSS-JA64)>N9hEST&w<>3j}EC#4ztccWxKNK%glu zB`CfV*R0{Rzk=m8BN~4HY3v5xFp=O89tpi`7A6IP56W1SYU+632U7y~0C4F6iqTlG z+!#f6f;`e#Y4`y#?P?JDA-+ z;>dPx!491iLE7PncF(~MUW7sG!awQ@)b5i8WGjVG8}IO$3Od*??~v{!i!!;8UL+7` z%R5fZcNhw)F)?N$@E+2fE#t}5-nW?(QqWTxA_@I{ZF(o)k=6|=!Nq^f-dE4Rz-QeXs) z&*@Ll4Ph10$n6=~h~lb%=iMO%Ut<94TpZu^Y$)z!q`drx&J&5o1n0ttYuQOs-^^h& z*wWwmt-PPmhXAxCL3!VL#boMrD?Cq61Y55ch9`M|SD|V3_fOgllyQ|O%$I1TxygO+ z`z@T0j9BGj(Fo1NiD$K;Xi)U`m$#{|DeK0oYF!{;W|Vr*lqi1gQCy_%BqNELFCk@Y zsTa)B|Kx0H17`!gdES{mx8&Bjcc-y>)>Y&l+&NIO^LeqOo?!n=nG6hWmi2sgdg-gI z_a(4!&@->nM`W{lQ}KO>fQipGBKMIsbLWY6o4lu6f!mA2-Gspocd}M1Gka+ggM)9Y&ViA-@jq%opxS-bH)v~gQS zi`C%pMxo{T!Fqx~AIiMco3~WU<(cqlx37L;@2tg~lLnyVSv<3?apP?7fvHV#B3y+?|di^?o>~gPu^`=Pq^iVrqn&+;6+#_FAfuU@f>e%cNQ^xc<`B&E0 zXja62KCiM_i1$uB@iG?9=WBCsir_XWe>%O?xp}5MGg)(iwO+}=jz$I@r)GKoCua#dhBF-zRMg%DX1mWrnrzz^bHcYH#@qwO=v5OZMB~k;9WrxJSRD2WV#!G;NLp zUy>|0^H-IV*zfm==+?^fCn)1?yO~}OJ9|*Cy_bc+saiKfv~(PZoYQwkFYYHRFVRj1 zbg!pkDcPfF0idw&ukl9Vjk^oKoOqyb!W|tx@y_kr>iQAET$-(hf%A76&;Gy5*CADt zMUONLPxog#d8O}vVW;#nB%jB7jWg7V5|=iB+mDDLe0D_MbvP&!`5HbfH{~kv9>N~R z?0eHs^7<3W;bVCqc%@klzm~-Rba-!N7CNq9x@4k9bT+fESc+e)MHARI%)esv==Jz? zXFr|i(550d%s#_gg?fgX|9h%q?YvuBIqmLLnYf>Gk9EaRQtrlkNFYM6bK5W~rQq6w zsqAT(pJ6xYuKF&_&O!DKlH#{4oq;>8=VSn?rfIe9e(UA7cw4XcF}tdp&riizo=b6? zeNKk3v4`h6Nh_#r?p|I!1=$a{KYwk2a7y#);ZdAnQj zEv?Y6o5#WbkzQV^vVCu=^kuKv|6t#1GJ3)X>oebO=iXxxS)bzPQmT4<)8*qX4MMr= z8Yj`Q?*yxSO~7IkRi15i#1*QhNZy(b(KBFlSAXK)fauNw!UGRD|2!ldT?6$NV)8T9e!pEO284h6=;8i8 z<=qL5xShfv_Km7$OW7e<<1hL884P9L%V0-LoqA{f*T?&)KJfA%1dc9q6S(D|paZ$A z?StRJHJU1Ox-Qu(-wL;IA3XimYntA4+x*Y)cKS0hy&<{RGL*DK%X1ij@&0A0Cn=2E z=Cx4U+ph3f^=hjAnP>=}^?+J_P}w)M*4B4dCQAaxw+AI+H`h+(g<0ZlX1Wag!W6eE zc7DfYGw7A`(l9<;MK#I&uWpVIKL?#F7apB1}>OU+)V z&e_<6V=D z9)uUR3a4N;)#RkA?o=Ul?|tBHrMyz`Em`S^J!L@=IY8nw?S|NX=pS%}gK#r5ocf_M z1b1&|yfICl3?GB%rgoydZnJG{W@mMn=cdE+!CZ*9)!0&{_{a!B;a)Nw1N=*cpH`L? z=f}`FNAmRxub%4->fw9*ma+Mgqm`M`_Cj2Q$3m61<+8OrzeM_9kEB*OhP)Va@Mui0 z?_aF9+_oWmmPn6#scM0?K9Q6PUw3@9{p)2f2&dA{pRo!H8|O#ubg_!&Hru+B%tuOj zirb&PQli(bdi+Pr>MrvzqtiG(;-b$mT~fzse{94N4O=vl34RM zlQqCrZ{*vn9nQfEe`;$F#+HD`pJSk6^qj)h&~q^!+CCJmp8D^fD{V&e-17Zqk{0tjN19vAl*APV!L5@ocOXO#XSr&~nuICI<4M;&p#)ut4~HRsS{LGK9d( zU>vxBb%*_$cTUoc-r5@k*U7SKqpn{n_I~ma8I^T;=roI`XVEKsGS< z@+3}DT))07)~0D5rb701HTtYnbnz|gttvXOXT{4qx2gKiTOObBO6T#Pj8}&_ZLEr3 zbi0au6cv+Fb=Y;ol``kq_7PNkApCR}y1|_&Y|cNPI{R6=Ue+8ZN2@I;`4+m8o%Up! zaU1r}W%@2~&a-HtFR@vXXXSpwAcbgUzEYm9gf^lz*TqLhdI^zRRIZCi;? z%Aq~6v6iaD#;$NyN;v5wtLyp;b(^{#?49n@VQ*`5wGD-j5n99k%MInXbIR&9+}WEy zs2s&uC%1-B8vJJ6r88>PYX!L%vP<5xfp$5!e|OSlNmZ8XY*DoNUF=U?TrXBos$MR) zTN>woFZSX!m-S@bb9^kHJUoXZ-WN`dwJB=#RfR;;mAn9tqy&eV+aWz3Zn2(C_r;28 zr_hgKBfWJJ{1Qq0=cZrHmllC6J}99PjPn_3dbjDx9a7qw(u;LJC2#2 zua5Jh@7r5>(@R!czRlON6_-6zqRKD~@$OysY;vE^AFj#GPLcd7W~}e;KX`T%>81OP zN2KrXx0#x>-X|{zx%cZ=m@n~>EWr#>PSuPwm)j|NW1BC6lEDE= zBr?R#z16Ee@yKY$r?v@y0aszDSo9c2%10z*v*I#y!W*F0y(rhu&o6IhcIPJUCU56v zN8tex9TO257Zn?Qi;?3uEhQ-r=@9>K!e7>_HuM>J-ba64OeLkGEWK#7-Vv%wo-l0MfNEvr^_({j+R#RvBE)**j8Gwi8yTrb zwZOxkKOR)yHk~Zw;pXsDMU9l!!l>Q|qDC3on;HtH0sQnDs%z6p24Cs|APi%S{Jg$^ zQ7k}}W=G%n(~%PwN44IFThBew=`vdYPPe8JZR1h&mz(JQ4d6Us>STb|dE^F-+Q}I^ z;dW!?8|44kSE7H^5(b!S=#l>*hyN%0%KuF*`G1H0|9>ViaVCJ7{ipr^$R)^Vj8{UN zqJq|fG~B@SlVK5*0hl^U)zLt}D6mk@6hKIp&1j^Gv`cE`i@pj!@OjsA*K+9s4O{C< z+f4;TAdHQCdNjJl>u$G@Nm5H|z|{TAGQ*7U)em-f#%so7hVywc`tJyfH9c~BM+7ck z(WrD%oQP>Ts#dU8A1S#Y$a=y3Xn=Tcr&?XzPcqAo`MOulx2KtYZsXg zGKe^}vT9RWHt~-~gw#p}>Oz@{E6)M?)%iAhqUNbZU8t>$n1ZZaweYJ0Zjq;? zH^(lr_;BvhXvo9GizJxbWp*CV)P*7w%D)MRyFWYg20CLlJ$qJE-Yt1gu$^ zBst_(cFJ$96c}x=FIW;~VNE64Dj^}Am0VBC?Kxfkd9>Keb7EcK6uO#7H=p^08P+r8 z3XLSiO$cF8%e_`Ypq=AJ6?!XmtaC6rww)djNA2%QrKG{t~7^_>UR*6)2 z<^U9KuzT>ZcIEL-0m&V-->PJeD)HMBGdwgpOm{T{hQAu!BVVx4s76^Zwfqw=--T~DIUr`(Wp2uP^jl$iIn!x zp7#elo)J+wQuTT?r5Xr6fzV-v8o8Nb^>VD)Mm|Tf5w0;j>gO1ucOOb0$l<&IOxf@6IalL<`j9f@&g4oD!2Lhl|SYCqXp2$`tOmHi_ zN$u-hbC{K&4A55KtXBh}sX@F~8wl_J0uMh70JF(D#)NZoxY1L_wf>-8ZJSo111wNF z(P;Nt0){PUC{0=e$UNVCuyli1442!IjFumPhUty5n(O=m{>AH^j;}_*!bLz-N6Z!C zeiEg=W1*=KY7!N_7jg&_Kl!_$Jvh>Lq}=oQ;WPJW*pp2zoTzanbViD@KR{c&?CHE2 z1KrZpEBY}>&#}aR9oVxuoVO4atFNw*r2Qa=iI(Pbg}h!~>i)ds=FrE_Swr7Utpz`D zVSQo^di)n__+Spl^x-%w)poDThWXl5n}cC}++!6ZE7fPrW0h@sqQzOSCGfr>d#gUT z&hAN-zB|&j(dfWTf8u0wWy(ms{?p6pw7c#*H@eI(ITDDROBlQ=P-R8YATy&{VvbT7 z&3$`4v>2P?_%poW@mcSmQ3@EACh14F&#nvo03m{n$;MsrTH8OO<-<21Vc zwC+AeuXy8@EaMj9C*_nj>2?<~=?f&03sWpz(?tFXPH3wxP8w#}qk>wF3}(DLs3zH` zs9`r*@NVe2n!|s~VBpow*eZqpO3tX3^4Kq6xzc)|;Hc;3TNN@83j-e6wClDEqrKGS zw-e`Fs~pcU{`HQXyvzRe5zvf&{T=H|S_n9fdQD?C1F@f^$oda{_#=?njKqABpVOFc zKk7A$l^n5O^>5AJCr~5zY#7p;%;qyz3)NZJt#JY87rG`o3x9lrdteL}pi(A(vK4Pw ztpUER1)G?sVRoH?lo-X5Qkwm_X4JNUIS0Jz8?GXEJlQAkY9zuI7>hul^*zKn^8Wt@ z4-0Cuh**3o%);{u;lr&B(rlFrLNHQCa2p+R_1o(J<;Q{B{V){#I#tJMT9mkqsdVd- zYHrs3R)IHnOz}@*2%ZI;8Ex|@I)*0w^-dbXd3OA-egpE@JBCmL_)d@;A$jJ=$dTRi zr6AotH;3I3xRInfM0f+;p^a)Obdi^@mT6C zXV2Yd30WXhx6`Gfu}y`+sGa;7yzPcmGk}o`ObsNn;6HYRn@nBZqR{%UU_Wd(MCxo| zYKw2V#G;X-Q9=ESCy3qKG-bJ_S7f_l z3UN!d*wX!bcf}OrmWugvc7KID(J764O^@R479N^v8OAfh4wZG`*e8kNLiGk4?H?Zy zPD{ijBN0GHl;|!*UMO94k6LxFQq`EX{0gUcY{|wYf9&d;xaoRZ~c&m(L6m$cFwtr=Sc%mA7=5oCz_16rr_D`geL}rLDGUL1z%a<4#kslR@s#^fBezR zdH$cTgm~*}>nux~zBb7JMILr>cLB$j9lQ?y)n^|A>^>XP6q{*$%l?Ca7JNa~L0jHci@A*$CWv>_m}>p$ z!%rrR$D-8>0*(}rzV#&y-x0Qy<6VuM*r_M_04L`(JjkuaX*~mOZiJcXkw%QW^t@l< z!ASgUw~F;)u@S>;ii)G+!^nKpTgQb3KZRm%74JUrVJ1FmzJtO&5Zkp?zd42W#_5#lRNh~L2hLJAwW`Bx@PTO7UCpqyBOnxGjhi?7n z;=bu($8vRS{tiukf<7mu))HtFr9XqBosoTw$D;T+6M;%`NfE&LwaLDT zlRhd{?<^}1m^JP+XHZv+2k_X)9lpBnPl4df4aSRo$X zcLe-k{=m5F37m%N)Qav0Jlvnd$K@p%{k=!z9mjVV_<-OtCMHMHVImK;#*)3;*bDJ{ zM;_wOU_bNH^#0=x=Lr2+AgHLR`g$UR{%$(j31n!L7yJ)+Sl``q&$^YsUv4RX>q!`_ z6tw4$J#8PH>^q7aGv?2JD!i@Nzavd9pO!OuW^Owzp7lOCTVado^4 ze^ww@?D*MP*oTObS-scD9ARz7%nLI```CQXf({Mq09y$9Sx%wV#TI{0@NZtbpjzKT z^SqobQ~)dJX!yDQN69vxe;6RA_|xM~!Pg6%k<=08LibBgAcG2d9MYP?{?thk9p69B9d;WbW_9W%>Uq6S(k#x7i=DeMmp!FA9jWY zePJyvD$P31{N>jv7<S4MH|$y#i;o`Sqc0;X!zs^Z z(^{qK31CFfiGFU%*@s^mQF$4=ir1c4H0%aS197Tvj`Af=9E+DcHYM4t^E{W-L=Hc@ zOr63A7mRfeeny!8xs;7S@8+zSA=sqrL~h&_=Dhpsk8Pu&f2rk2z(0EQB{`_q~oSH)!o7pfcvSqi}aJzdWN-AuZ z)yO$1^0k8%zxGuUk9+$X<7f+DOfc3ZUm*9w8GBjgm)Bp|5~=Clmdb*UGI?d1_=|K! z?xeleVJR<;HO7E3)h=Exb1u->u1k2-*Olzd&U!H7dxV#imaWsFDY z*tYuoge38}J9Xpke!>|WK(X(5Q$de=CBRUEgOM>YSw3s*uSAdUHGQO%Hh>;g8k^g! z{ZtZ=i}bPm_xuh$>SkJ*Q}Ur8F#W)O$ywj&m-W8u*F3ew(C3YhOQsp1$2_;aeJ>gI z#x+JEquQ-6LK&(04X>r%Qnd}=gt!ct%nK@OPCO5YSyD0yWElAKGZE(2AA^ZSq&zrS zGBRXubQ@(4dPDA&*T5$#3hodTqLcfWAZE zP}mA^{EhpbyM7K*{xcAD-}Bnelj>;=7-sMBK`mPk?8k4Q9!#L;whiEH@VssDFQm4_ z#J*j{_{$4qWaN+cnPCkp5jA-&zN_r+TS@A~a)nQKry0--Urz2ecnR|*!}*yfzUnH^_TgDX z(GVEdHNuqh84`fG|goBk|N63zi&EMj&Bg=@d#l}%ltFP-&!Zpun()yyvX)@mNMg&c`?kO*i`l~*LlsuX(|DUde zl`I0@XYXt10%CK8cUI=}u>5*A*!^O(Aqw6Yf2Le6p+))_VmTF*bN=_6H&|uPdRxOL zwZ+a5_m>=Z8NQ_D^4Ao6``8*nu{Tq3(zLTMaU=knw5Fo$%#|EL61jgu=&vvVna0@4|NYMg;`6+f_-1py$B$c$SdkEmr)Udhx28|{m^;C z<0=jfb@(m2+~1iq6+sznrOhLELo!8IFWtY^q4bEKOPkUkwu*T59t~?Ne_p|ifGNLU zKK^m-b&m$N;`?~`=XG|m>1Zm5S9LhGmN|W!?#0O4>Wqt7)FQ1d;^pX*kDf0ZRg^9u zwQAX8?RwNl+^i?nu^(4+Wi8u3+s1YO*imkJ7vu|(;V(DbrX-f4duMyxtHLi@0Q2~t zkSLl>Hx5Fl0x0c{ng{-Z?6M~P6-=T}IOKP;<9d%I&0-CofysCWH(y8G2Txoay+!v7}wqZY!iFKqvDUhJpPS{ zcr;KZ+l*xF?Y)z)IGTI=K`IV@OI)~Fgyjg${DoH6zj_z?Bn^ws8&W56}#xk zNT$Sl|HZ9D2s*!CX^mleW{x+v=h;%HW>3&omtEua@C)~L?hmNeoR2O&4}|n+i+h7Z=8G2l-|eRW(6S6mb)T5 zE^eGJtz{q6FCobj3$N+t0=7RcaqAJ%-TEhOdU+nIq*)!WPP^r#tz$A{r)RfPqPI=y zb2Ekcs2G@^drY^yJ>1~xn`4P9E+Zh08Yv<~bM&_vw<+-ePj1SE?ec8tk9Km&?Z*Xa zC>VajJi8+n&&MkMoD^@1>+ka0q$Rj&*OE8f#$Po)ow?gO`ho|D#u&ji9M& zx7dn3TgqJ8>ejx#4^~GPWtB(u=UaH36O~afcQ2!8$C=v{PTO9TS2y#z_Z8LRy_)(y z(GvOC#82QCWO9ok=kq;X@{_TX?{8yC+(#tQ^VeVA@;0j+bhPTXBgzFQC;f`LZajoT zh10sdjNeGNQlHn_3;77)f7doN7W>7Ao_5jr?K$WD_8m(@o6d$mM8n=VM+j&e-@1O| z!(997-o30mg?(>-b)w(0pWGw`Gx+!J00y`JJfCZl)h1=mb!23c^!PY_q7qr{Z*&;E zo>}RtORAlwJWBzFekD7kf2?iR7{4xorjIzp&%h4q2b}+5ugyD z;Su12*$w@He(2Uj`ThO=yM}_hME+kuUxtD4Dbh6vtA5jU-SsIae!0ua?)0);7FQSF zAob!XIjpgDf*I@ky@i_cJ(s&d5PnE(4%uE@rCGVXyeT&hGRXO5dx*hc0EAWdr`B_!Rz;KWu9%z z-4CyFl4vrhmaZit9GoNaFs?d?wWZo9#a}LfGpMP~u4OobeN>V*Gz@)dv_(VLCws^V z(!vnWCQ|i+Hbs~R##&#yiqBWhh%>0MZn}AXLX$Ss0{x2akM}Eg_#xut0QC}EUqp7_ zo|T!`Z$m~bbC4yfo!-Dt*K}Nh{k=Obip|1RLK-FZ&LMm>DW{z(9X7Wp3xC;wvt7~}tjS*SR<{$JQ+oFB8v zf7<^;K^fA5a+gtm&(*1qrh_C4YbLcIMIJ(KuOC7tB2Sr?n_BtYe@XF~uzMr`#HTT-S&$<8d zz4n^!X1AnL4S);5W`#}mMTWD5J5{qRpyUASoUegooC}yYO%)aI5k4U|@vvDfARZEf zX`oyS!sH^43c#L*l3SA@TG&3|GuArr5~th%TtX&=84!O!U3Z+^SFS|%nOrS z1q=SD5iif6oe*C{rVT2nGR)C;IOY^~+O!PG;f#nJ0j@f!s$l8rCEy^H0bCIy z!&oFzt0bh32sHF=ZYN2{TUuuI3WJ`={3v!`03~42jAs`EeDVvuPG$|#B zoXrZpkP@IwjH(bisRnvh0*dDE$p=LfM48tE#WG9f0tQzuR8}zyNyAbCEl-&j4#m=J z)_$^eufsM+ByQR{ch#6rIbmV=*QJwrohYVZv_0@&6d|n>xrG{JhIpEF;VM`ug}j3Y zYO8p>U}O@ZGsuReTR}^lsbW8Dx1%=qB;tN@(HHB->M zy;MO`MS&ny#NRCun^cfu3&x=FSb4@sCnmtzQ*dKR=+;*vFilW)l+Xzi$uxnbqZf)t zo*j-{OTkg1nK+4?69b^JgZZgIjDu6IwOh*ts!b}&r5!YBrLr*;u=m(njpMZ7Z4jTJTPDGLmQCsioU zgvC%Mqp72qlzqZQh&R&*P3w6zq9}|c%k?2DcaL3+&^>Vrs#3%ZX`9+M3v7QY{SNRA z1AM~t`#~~~V}S3i`E&6ovei*SvjmuwV{KaaxzaMMG-lH`{Tg7yg3SN{&+J1KV%+;> zg%11Z(O@!OEH&Xa<||$l_lQs^qJjahBzb%T*@*PWbL6x1flCOjIIlurRU@69tn-u_l9Te8N#_ z(jlDb(lT7>6f2y`v`d64o=CAPH<_{_K;(8JiBceCp*Dc*;;l-^AUY0QYV4l`l*hCg zaDfO9nWDT*+{sa6IKL%*OUE4@q6ESg4_bcs2**nDae}jGrl{J*(ysduq)hz4L(lpA+R5&(Ug z1#qIt;tbfrOk!joDoR?#Tf4&JJE1b7gvfoqAHk20I?JK8! z#9gnn7TwNOw!5Wc>-rkayMty*o@ER`@KEk=cLhh-66&924N#^IUeQB0L!5!fQZs!> z*T4>_OQV#m@mH8O5>4UZNU0-0OJ!nM!MytvjJll+en`9#7M>hZ4II%s!k~*wLUnY% zDq>a2T9n~AbWlVoP1BXOxjy%Qm0l|eq(gZNsqj+vzN!99#>ztTOq(j8Cr_DyW~ zJgrHTSLAZ~{VL|35pHs zO_dw1Lr}LnsICKUKyojwz&-5#^EtE#yd-pNI2`~korb8Is8JRU;i2gSoI-5x9o$oH z23`ibzWnN~e2bes6EONoAPm_ZWUQ(Q@M;S^1k`>6=e}6-n|YldTCFR{3$QOyqJ9cr zTuViv1!S(ed+$y}s-Eq_U=(bb_>L-r9IZ=tuarylGAY-l>uH2~#~GQ|WTOo72E2?% zX&4hrN5*Fga*#v}RT4!3hzu{6{ciz>)^RJC2+|FfCw1-eHXvW|p&m`hFG#{Cx&}{N zHeXQ(?SJMsIXm#d2X?QFw0tU8a(m}(<*LPi=O>QJ$UvN~icA!0gYHX+Rp$%xoXe^m!8H6YZXstt8j zq9y1kJn)xZv^X6P4N8RhQ|4Z*UB;+81tr*KHV0b=V{6@pqpRfnQ6z^qX+GPnUzqO=JK z)gn<_ARmF?dQ;WvJF*C(<5mxn`=}!-tgNJ*O<}}iSF*rs5xBEh-!)mM8*BMQ^{-Y) z)EQ=!UVuQXg3&g!_OGjH+s#+ghsq*&n8DH%GG3426Uj4~QJ8wf+ICQ*YWQBxPI(h#fS5Ua9_ z(%8hR_xx=%TxfO1IG&s{WZC`EHQp~bv~VVyvNRIg?V0Td1^eEkAug)=1OkFW5T=XI zmz(8#qa-;Ii!VPXO$>8dDqag9^n{;Fua-dV20{>E0EaI+afFsBIVlFsAF<$oWF1Xp z!Hu#)ogo69$u~)*!p<3l%qcH&bN`h?qQFam*vk^IEkSHy^is_HA$EF$Gj)SAc!M)> z!_55gm+>RV?1lH|GWPa8{mlu(aC|-94#~WOC z;qq_sUaW&&fi6)t9VxaCw)Gosn?Y+=9K8{?5Bl{R&z9Zil@RtiIC~Qu-&|XFl=U04 zmR&lI(!J6eTE+T41P41<;+e4sP*F`MPMaIy4XlX9zmBItE>0YL@26C~tPP3dXR;xE1fc%fMel6FJMh9gHSqrYSik^A&@VH{4P(d+b4W}MIDbMpVZVJA z&eAv6tFny1v7CFX=tl=S){}sMqL4G(m z=TdMI#WGP;D`yVYgP3hX! z%&kPstz^utgUriCwQ#cw0kAuX$!6Ek>(-5KkXgJ!nY>Dw-6ZMVDCynIrF>kw*CeD4 zqB!1CnfgBO|7H%IUi5qVD6533{tnP*ELzB6m@*EDsT+}`%ootc3@R=~ySN=UQJ(i( zDx%MTiD}BqMK7kR6x^x=|F`#e;rpLVK-S0U6PD3$qp@_y4Slds^^WucLbS4j?R`z zg>>}KEhq#5kG#O4_16ypiOyDGVTnia$VTx5mX0vgZ72*87|w#pc~t_9pok}kkQSat zpM1ZBff<5BEtyM7u+8x!s)PsEjni>aW(xm(3lytrHU zTLFxl)jAl26@dt(3YwOOf5Sv>3-95}rvO)%p(Up%b&E|9C}|~y;K07HU*>O5c?geu zpS#^x0gJlq*3ON6u6?&69G}Gw)=(6I2Y^h#hs|LLiD@3+cb@urk^q0_KqEd$m>_Sz~1ZgTFF z_0tC7g1y8R6E9@5_7q1w1^x_yzVjnRIQU?QHGszPi-J=S%B-=oW*IXSSwuNsg!UcejK7OvBjuVDB2~zH9tf1G7CU ztzJhbLhs&Sj;S~U%OSIHLehR^4Taq}TsId{je5Gr1&Vh2LBFrZm&6FZKHVrBmGtxm z_fzqW|JUmT+r_qL<5scz)K&yc_{7@GnGV~_>@AOcZH!iLulLh<(gOS^#0}c}JFZ2~ z`*oE62iN*&+x(#q_37*7>pBtll1C|=?CjcNMNLL7c1}_AvC8KA=eu$`0XfyX8vZKI z1ozc7-P-N0z}I0G!ISK?6lG+tH^YUej?3zmF#EW=+G(tJeZNO7Wrm%%@0wW;j~pJdy@&4D)=96d&t&H7Fd&RW}*T2YX95`wzr#J%uc-tYP&uw zF*|D0?6DM^UWF=+dYnh9O6?&$UhxE{><+hX?`pDwMbH2J?2K=*oS-LteG$x7!(@(4 zzG)0-VXwr|ncS9p=PfdcN}b7u_Q9&>)ARlu3Glj3x*43@&(T2MJ!ELrZUa2Y^L^Fq zsj#s=-(}CFn9J1e?*j;u>iS)#C+zjNcnhWZ8NS;ezXAsF;y>|Nhs(RWKN?mEqB+}! zCTew2@j6!a4`;xuH&#sYW985Bxf$73MgVWeB_Ora_qRXm!0s`8g6+TVZGCeS6El(= z@$lG5H~LXug+92)`xxZ9SDCBbK@4$R(eR-X`PIh5$;?DFgSA&{%`A7( zY|6gt!OKLo>DUq9!W0L+RU|KSOLMI4xqupMlka6=?krE(_gJ_M{_IAm`rd-I;o#VQ zUmer;6}?C7r+M3<5rbZB)A!{P<9D!uOx?_ za9XJV*NiK%-+;fp$A!DO24(jWPl_wcCYCocDcr}!`+9m@yoRQwmSo;tZffDxpPu#uj@RsmkBEY;8iX;C_{~5J7nFpj$>hDxwq+Hym5o) zYwM;mck4I$y>wsM|wh$W}}+t ztMWNFsY%|`OAbWHrRMQI&ej^r_OEJVr&Tkzd8LJ|=f=vp2|i!JeIV9Eaj#d&OmWqA z|LNM~V(|B2N&>*b*73>bG}+q6`MTMZZtO>(qjoy{#rOMt_%V` zA4b9V^T%Zccdrsg6K4AM@9>x1iR!{$FW9F}@U?Aw?pcwm_OKO#G|fMUj4R98`hqJC zjq|Fv%g`VE2;UZMyM~_xWa`eu+sZr|%f3}8RDk2RU1(H=&*}<>WkIME$ zwz20R!wz*M;2ZEj^G^%BfOf6o46ocArSGlx_tMwwcQZ zE7Kfu*U(xD_d;@$8e*BZ?zPdcQv%Zre=p^k$CYQYJrxibwl=G!x2l2CsRoVgr!dRT zD{zy#*TDC(ND8SVJpF2Z3u&sQ>GrPN(%Nv;m!S+Mv%Ll>2Hv^3+ zFO6byiTtcaOAf?iravU5-EP2N+t=N8kY%Te`{<f! z9Dd2kV)|kL_7u14EqQC7r)-U|etpc&V~iXddYPcpzt!sTH_ZXkh3y*BOL?J;2Cnj!N{HceD&Kv$n9uQhGRp0P@9b|WOYeEG%SN-EZy zICO3P$JT;apw`odjQw+!1&&GHxy+V=`_$I4Yp1=#n-5V7!32hE-)J3d!e#pJKwOIk z+jbLrG+!kdBO4bT8|X*&xUv-=?vFmsbTMM4Z?)e z=ldY0@;C-02~c7tsT?l56dg8)(gIR-bAll#uXjHr&LcMmO4yb&A4|g15Yxe^NC--} z7I7a7mc={Ekl|mbVB8)qDxu8y5wQgZjdn0_#SZ-E(TsW%lYJ@#c764Y z_xIIXnBjgUJjFM_{cP8e%b7i%eAn?|;J~+k=vi%z=fV%uukUYKt)FM7Rzv1b@GPE| z)G*AqT)Q4CeFOa3P6`PvU*G$U0|(~1eC6(y7IJSxO8lxncHR4MM#0OiN@effGV1jB z&l`WVVTEhL#;CNz5pM@B81EETC3pYPWk>2Xo8#5KQ-g>~ebOekA6w?HG6^ky4mz-P z-^!pqrPdExnNfRbK3o?nRM^kS-DZ5bb{`e?7$?O|szC?VknPV;l!2^@lQH)D%8#?b(0p-mFJd^wW!9fN_F8 zI)pLa1dLk^uct=BFZ_w95%`V+7(@;Asc%?~FJv{Y8)}S*YdYrn#XoGyD=jn_=xHjr zDrom!dnE_P$d=XU1{hTjpMIQI(q{VulGV_Zjbc4D7|;H|#w&@)N8oYMxGIY=4z7g$ z6BcuIBWMr>o|q{{kJL~|(NHf&!dkpX--(2i(Hpc_>OOUFI#7heBW+DgS|rWaP~fYx z)DsmAQ$%mea`*$tC;K;c2!KhsQ_p@`hkcz zqOqwO3=~mQKbeBN46}+HHfJ5>M?$I zK#Zr)J7O`Cb2SvEaM;hQx^!<+)$VJv-O2r7aE&J<&#Pp3akcw7?g7*O?G7pxbnlPlMCCjEn2pTh66>EPAu0PZDmuw z068|)a;p9Js=<)@i=7lt*_BnGMta@EBE`t<;ZPcCK11hg!^+@~10T7}Gx5mCA@4tSBbbNpA>gZxH zvb0BU?$cnPkZLJRYFv@iu@5kkW@dI8&8{k+U(jGktBMlCgt0`%SPB@Gr!2a#hc%_| zeGP^fQZ+2N!8*$r#y0TGvRj?g*^!MeR`~z#QPp22HHOLsmJb+TZ;Lz>2ic&rl3iYd zfz_Z5#@ga8hx8E)?VN1e&5Wk_y98)K`!X|+l(=H#lvJf!n&UpAl4Td8t3#JH= zhh-3m5xOX&Obwu*M+sAxX(*5=vxnRt<$|R_wQ63d0nQ zsWQhA;Al|a<6>QCFnR%C)Ygzs*kz@BX$=NagU+k! z4UADs#z+K=A>DkdbOJSKpXb-mV3;yW?yd3 zW$wAgCm1FH#Z=Ge(Y4r#OL(2ciUyH`GbKS*<8@Hu@JQEr`+S z7SQ5ST;+408|PZcDW9YQ4TU3Gm|7!I7Nam6F?nRQywZ=IlX>1n_%cUxLd4*v)l1%s z=7AV7c?rd9gC(>?hXraVOaUb&s-j_%qk`;80c4}urRr_UfE*O$t>zAZ&=OUGm_Ulj zicADWYGtpSGK=*kEnEX4`J$QwhcrY^M+C@GL zfidpMt{7YMg!*G$n!s64n`cqJNwF>Pz1;;K zE*@p%VVMQ4EZx@b-2Waj!!vuivW5dShdv$Hm0;PGK7i5S$3Az)vorgssRly~hYBgi z$dT1(2N;W7f+qQ~!}7431_Qf7<#+~Zz6yCr2Jrxp?q@%AO=qig_Am!PNKCnOw3IPU zLP}5nJ#E#$>{+mru^J3%SQLe6ST4%LQr=LbUUv7sY?Ze9)*3_a3QosBSqVKnwTiYIG_vh)qXfSX{Nj4OQ$rX$H2Btwc0w1&= zGlR`&i9a8jG)q!Ga#k( zoZyC!H591P2?b|Lglx)gpg0thb@muLD|yix3UOA{x?=KVfxNz50#Bk_eCmG!IGB@h zGc+7%R)`wP6B9;`Y)T|xj6d8owl{n7e0G8cLo$U;sE>?cT;nO};lDQ+5=u|E4E$b$ zA*rEE!3hnMJ&6a3A#XaDoXr+Tg)e+;hKrY}~*=mzAn_H5^C{ zy1J=wU^((+Q!0ZTsk3sAwJ}Bo-fwBXX7A?a!uKeu4T_;A)|An4^2oZt@-}kcAF*ky z94GxX941@rH^ypt!*ck^htwlLj%#-phKyqO9IpgxFhn`HaQMpM_!h$PbKjb-OWERZ z3)NttaFDg0o!D7&IE>p{HlKGbwH(x-x3~C5S!2*}sEnGzVUxpQEcWNx)$}S4;h-G# zCTTcuI7pGQA>Ai)7}xCY%vl)xF004+Tn!y1FeL)n67wz2HW0Z?O{PvWroY<0B%U4wzs zLF7==f-!c>YJ3V9z3#U^KLbp0fje2vP1>W7E8nEZ6sm^8FonZK<~Rwi)Ia~%@D<<+ z-3UhdYB*4Zky%k0#cJ%58!Q^sh+6I!{i~q{Syv`hv!(!l>`rvBYE+ih2m&?wZR>p9 z6^`EMrsGnGh69Z(QG<)%Bu`i|{ADqW*E19?F*JAw zyDvUCLW6s(q8DUSMt~_k z(V;b$vV-#DS`CFGreKOXvL{A~hBUZcqyt+Uzocp?kQUSvl~9u>jbt@)K#d6#j*Sa~ z7Nvu-_M(OZWrKL4))cIUuS`+Y*amJ|_FER4(2xJrP~a{n7^=NYYLu4uft$dUj+MHP z^<)#e)5FcO%(WO;jq!4c1_MRwmG@i!$*R$`qJ{!_;$Sx7Wmk;zwC5jB8Z^M*AWPep zcQqVhM%BQYT)8iE7iba}ZZpIZnuO7^fGql9t?T1Q_&CCGUg= zLo6_rQIjnerx)r0N3-Zcy(8I8@s^t!4pbe~7PYM4l&+8`_5@(uXzpCj4KV2C&?QCO z`5r|nr5Gw57-OeAvU4CD-XYtow_#l==!wrgXpnJ_H$gy6Lx zYYuXx!iLmGc4Y}*G|$Ps5Xe5Lai^^Y1BaBBhtdPvGFX;lC!{0k?|++*VfQ|ZBQ+eT zNhyX(hsl;Xay)i|9{Fv;Z;ocyx4Gl2IcQ2*UpB~k7>5b-#~%JN3iP1&`%GVK%|YU! zTrtUEahPD71alfyt#AQb>4U%1V2DG@c34T-78`5^oxfe(U@epfy;^?rpoT+^smceA zsi(Xd916C$h5Znc2JJyhti)9fhp3101!Dxt=~xZ6yc*kQZxUPS?eaAkCOPa6sg*4l zqmS&0F)X{|B7f@zYS6PjLwt(ym5zp1KP(vIJ6R3m#C7FjISFOhS?LgHjX`Ebt#77T z87o(5KfsuD`My^WTOGHWT4PW(lqn{TJei{baKt9Ze;vcFaLc-CIK&l>)nm2HkqjKy z2FAG>hYECsiyLaqL2O~=7%GP}7ee|^?J})gSvkhf)L`IQacnzSR+b|Jbr1|yV5W3zmgRUKFfs>u_o&6*!_ae&21ArXr324wimZllYBJ#OUH^IV ztOhw&@cLPUfz$vZyOV;f92O@hPe3}}8{k|glU?8j+_&bSL(8uB7vv7x4jh}uU#?S^ zEssNPFIiS8)q~|&Cv(ID$ArtNaW~k7c1$@92aX37gj?Z8%N&WoG1enDZy0dUb5L2q z8V*!qq&zr^Niv0T%VMR6nfE_tccrI0YAD17j!o$-d1#GO*KX-Ck2|yL{n{uE2Udej z3pcbD=dO*;rhs>-MR}tUP(OFjV{V+l-5@ z&|rvasBoC7^iw$<2EdU?gEwuS>07^zXU zs$dF>gV{=;MqH<)sqsKTPu6C9prJsf5DKonM#@``9iT<(9@hcJL#p&>p_iUw@Y8F!nRlcBMxBLx0f;n z56W=alLVkRRlD|HU$!<%T+>jTec|Ojk4je_=F+j z_VcV&H8=jq7Du431043QaM}1p&c=CAqi1NZEj`)u3!hcfP~hUQr%+XHnv`_e6mP&t ztlg><|5046LtX$wd4eO_S)Nzp!ds~a4_nR$OXwbOPq>D{q58rY79V930m&#=Z`j^t z?9Tk?ml_Q038@XW=`op7T+YT#kmLAUZ7Qt=Ip|*W+8hlBsxNxD#D?Q{IUGifK5P>) z{T-IWV~sTjome$0COs^A)E)H5UJ^7R7n*}EvOD%`I7DB#ls1u5Is)YAk>A_1uptLo zWGi3PV4##zTiAG5+|pslu_N+G#d|)$K`xgkJZSS&t~-SaqsO{SVj* z^L8)J7dqk~J217Zm@o=tj5x5RdGz2%KCsHtY5CP(gMrsKvgu%LiI%6f2FP)xec36S zARhF{ZD2hO2bx+U2kXjQSq|eStg6NCx*ueRc3B5&44Mws7K@v(=!@n6==lM4W|W3Q ev_+K%?sWF!4~jY)zbPL6IvZ-U Date: Fri, 29 Mar 2024 00:40:23 +0800 Subject: [PATCH 35/43] botorch matrix plot --- botorch_results_plots.ipynb | 134 ++++++++++++++++++++++++++++++++++++ src/visualization.py | 2 +- 2 files changed, 135 insertions(+), 1 deletion(-) create mode 100644 botorch_results_plots.ipynb diff --git a/botorch_results_plots.ipynb b/botorch_results_plots.ipynb new file mode 100644 index 0000000..07034b6 --- /dev/null +++ b/botorch_results_plots.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "d6c817ba-cab1-419e-97b0-d5d5f7fd4f01", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sn\n", + "import numpy as np\n", + "import torch\n", + "import pandas as pd\n", + "\n", + "from src import visualization\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "783c52b7-cf3b-44b7-b14f-445227c15754", + "metadata": {}, + "outputs": [], + "source": [ + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", + "\n", + "n_inits = n_inits[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a3411fe-7c63-47eb-b080-800019cd1458", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix[i,j] = best_y\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f0925240-1130-40f6-9835-51e6a4fcf89f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "62d7a546-5ca9-4836-b075-dfa5792c7907", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.savefig('BoTorch_heatmap.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce3eb3a5-5150-4b5b-bd41-636edd081feb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/visualization.py b/src/visualization.py index 2f75dbe..3b5cf48 100644 --- a/src/visualization.py +++ b/src/visualization.py @@ -20,6 +20,6 @@ def grid_search_heatmap(iterations_list, noise_list, performance_matrix): ax = sn.heatmap(df_heatmap, annot=True, fmt = '.3g', cmap = 'crest') ax.set_xlabel('Noise level') - ax.set_ylabel('Number of BO iterations') + ax.set_ylabel('Number of initial samples') return ax \ No newline at end of file From 5cd18156884753e20cff3efe64aad5c650ba958b Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Fri, 29 Mar 2024 03:00:57 +0800 Subject: [PATCH 36/43] final --- botorch_results_plots.ipynb | 123 ++++++++++++++++++++++++------------ line_plot.ipynb | 18 +++--- 2 files changed, 92 insertions(+), 49 deletions(-) diff --git a/botorch_results_plots.ipynb b/botorch_results_plots.ipynb index 07034b6..2fd5bed 100644 --- a/botorch_results_plots.ipynb +++ b/botorch_results_plots.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 73, "id": "d6c817ba-cab1-419e-97b0-d5d5f7fd4f01", "metadata": {}, "outputs": [], @@ -18,55 +18,76 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 74, "id": "783c52b7-cf3b-44b7-b14f-445227c15754", "metadata": {}, "outputs": [], "source": [ + "seeds = list(range(5))\n", "n_inits = [2, 4, 8, 10]\n", "noise_levels = [1, 5, 10, 20]\n", - "\n", - "n_inits = n_inits[::-1]" + "noise_bools = [True, False]\n", + "n_inits = n_inits[::-1]\n", + "budget = 30\n", + "iteration_cutoff = 20" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 75, "id": "9a3411fe-7c63-47eb-b080-800019cd1458", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "performance_matrix = np.zeros((len(n_inits), len(noise_levels)))\n", "\n", - "for i, init in enumerate(n_inits):\n", - " for j, noise in enumerate(noise_levels):\n", - " y_vals = torch.load(f'results/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", - " best_y = torch.min(y_vals)\n", - " performance_matrix[i,j] = best_y\n", - " \n", + "sm_list = {}\n", + "performance_matrix_homo = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "noise_bool = True\n", + "for i, n_init in enumerate(n_inits):\n", + " for j, noise_level in enumerate(noise_levels):\n", " \n", - " " + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for ii in range(Y_real.shape[0]):\n", + " sliding_min[ii] = Y_real[:ii+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " \n", + " sm_mean = sm_agg.mean(0)[:iteration_cutoff]\n", + " sm_std = sm_agg.std(0)\n", + " sm_list[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + " performance_matrix_homo[i,j] = sm_mean.min()\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_homo)\n", + "plt.title(f'BoTorch, Homoscedastic Noise Kernel')\n", + "plt.savefig(f'BoTorch_heatmap{noise_bool}.png', dpi=300)" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "f0925240-1130-40f6-9835-51e6a4fcf89f", + "execution_count": 76, + "id": "ce3eb3a5-5150-4b5b-bd41-636edd081feb", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -77,20 +98,45 @@ ], "source": [ "\n", + "sm_list = {}\n", + "performance_matrix_zn = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "noise_bool = False\n", + "for i, n_init in enumerate(n_inits):\n", + " for j, noise_level in enumerate(noise_levels):\n", + " \n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for ii in range(Y_real.shape[0]):\n", + " sliding_min[ii] = Y_real[:ii+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " \n", + " sm_mean = sm_agg.mean(0)[:iteration_cutoff]\n", + " sm_std = sm_agg.std(0)\n", + " sm_list[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + " performance_matrix_zn[i,j] = sm_mean.min()\n", "fig, ax = plt.subplots()\n", - "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix)" + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_zn)\n", + "plt.title(f'BoTorch, No Noise parameter')\n", + "plt.savefig(f'BoTorch_heatmap{noise_bool}.png', dpi=300)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "62d7a546-5ca9-4836-b075-dfa5792c7907", + "execution_count": 77, + "id": "6ea14e5b", "metadata": {}, "outputs": [ { "data": { + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -98,16 +144,13 @@ } ], "source": [ - "plt.savefig('BoTorch_heatmap.png', dpi=300)" + "delta_performance = performance_matrix_homo - performance_matrix_zn\n", + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, delta_performance)\n", + "plt.title(f'BoTorch, Delta: Homoscedastic vs. No Noise model')\n", + "plt.savefig(f'BoTorch_heatmap_delta.png', dpi=300)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce3eb3a5-5150-4b5b-bd41-636edd081feb", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/line_plot.ipynb b/line_plot.ipynb index b3e4256..a3bcecf 100644 --- a/line_plot.ipynb +++ b/line_plot.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 84, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -421,7 +421,7 @@ "0 10 20 4 False 791.936157" ] }, - "execution_count": 85, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -564,7 +564,7 @@ " 7.3395, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407]))}" ] }, - "execution_count": 86, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -582,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -662,7 +662,7 @@ " 20 767.079651 0.0" ] }, - "execution_count": 87, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -687,12 +687,12 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] From e3e973f073611b8dde92a469dea43612951cf489 Mon Sep 17 00:00:00 2001 From: Karim Ben Hicham Date: Fri, 29 Mar 2024 03:02:44 +0800 Subject: [PATCH 37/43] botorch final heatmaps after 30 --- BoTorch_heatmapFalse.png | Bin 0 -> 106601 bytes BoTorch_heatmapTrue.png | Bin 0 -> 101793 bytes BoTorch_heatmap_delta.png | Bin 0 -> 110282 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 BoTorch_heatmapFalse.png create mode 100644 BoTorch_heatmapTrue.png create mode 100644 BoTorch_heatmap_delta.png diff --git a/BoTorch_heatmapFalse.png b/BoTorch_heatmapFalse.png new file mode 100644 index 0000000000000000000000000000000000000000..191cd468a17ba66f18fcfe92592cf27bbdf4c528 GIT binary patch literal 106601 zcmeEuby$>X|L@o?2J5bXg21i~2&iUd!C;Qa-nyZN z!5j#{VD=>*+z;QWduh}Me+jut>$s{rn7g{0IGbSIv=ImnSV9&!P z$i;K%H%nJnM;BpkZo7Z|0GETa1$Wb!jyk-_PmZ^AT`(A~Gw8ov9%&M8m|Yl*?2T(0 z9qJvjfYj{jac z|Ghi@yL9|(Km7k!9#S63 zGMvU7{?~(Ij^4*y#$ZY&zPx=v&+#s6-O0&GRZ=qG^y$;CZ9?Ogjn7W5e|bO~79FiO z9I2ib&Y|7!Q^QBCm($WnE8K9J=tv@1ds8U{dOPv?_rLO8oej(?-0*DU@*Lw&z(Eh{OOa1k5BcTJ9n0byyvXemnN-;sw;A<7vz|Q zT{CJQ9Tbe!#A{8aC-`N}nFu~>7vE4AD7C}SnP38>UJGNhChxuGx9jg&d4b-T*AeYr z%d<2zG)n|+<4pNTuJzGZ$`o(GfO^k~q(>TRYHGvvjtAkoUfsqe_;QpMo+ zx9xm4|$4He#<>lphEl*pi z$My7RFbg@q4`VofJXI@4udnBM4*6bTsoelhS$yq}nfEpyzBL4uSQFTfoV=P^`)D`1 zPt5nc>(HSCgnP>zjB^Ys3Y(pUgoM@?+a;)9AF)z`xNBdSoJh+%8JIL+U!_?8h*o>5 zH^;`QxO8%$EU(nI?~Zz0i1_-4(=U8Ko{MxGx^2|)m*ZM~`L)D(^FtnF;tM&EudCgXgQ>lo~u{CjCE3-KqdDUHEW$trEH7i5j zRNm7+-X_4GwGCiT;>+(VE835aG8daR9aZ0+A8DdvVX=3w3N*2VrP5#Jo)`M+Rd{rC zRzLve;lqcSb_1o9?(~q@I3^;G>ArpY^xzr4jf{kcg{4kU+wxg;-7qmRq4byFlarGv zcTYW{W$hhnjloWI{2nLjsm8^{W!+z#d0r{jvVc;28h*dO+$jb9erRZNaWSu_hsTHE zVdIxCUsAf#f};6sv_?!NU_|+CKPO5(I?8t8g3Q~uZ_!)qKYA(yrx?@g;ZL2scYvkC zd#)xuEG(?6T42aSs%+TLyJR*eI^Uw5WoEEKT~=0BPFD71NJz-LckhlLJC-ytVHx!3 zI^%tdc+Z=|ej7Qk=H(X6Tcgw1)Nvo0n$m|F39rX0&&WKtgta5=wUlv{hbKHCfg4_H ztE(B(LG;#8D1==nR8lN7)6>&!>I09+DJW<-I~Oi2F4Fz-OLBhxW$F}Bxiv;0ub|et zr7cdR#H@w>+}X1kiHVXhL^QOt#5ec$kf)~fQ+(6X(yUfyNocgYy1K%`!(lPZbyZvy z7S@Bu}4nH$a<9Ubke6H7`;LXW>row}e+ z?B8O~EHBs5FSYH2hYWxHI-|N;6b+J02n*wtJ2!HckHC5pJPgIFC%U{`P%3oN;=_yI z-O7k{{{BJBP`jqBIr#Z|p%O)RX6y0<@wabUb)`PJV%2qaB(H9}(0edEO2j=^Izb($ zP=L=Nw?r~U8aO}s3L`hkS7JkKE8m!{3jX7dy;bX0q)P0=5zdPjFOPkSGIgCBs(H6P z^6eYDU0p`ThgB$M&t9I9VH_G73O5Qmn_}AZyb)~`!dymS0aKvKC7t{?g5$9+?9(kD zKYo1t{5d~8w~=jAoxv9SoL?;5p2>;8`Gpp4{c5+F5~3bynbm*OM0IDriM7lo7M_-G zwtVDhiee0Z`1a7&=H{~k(@*pdus70}f>#GaoM`6j#nD zOKkNdZx2?u%rIIQwqnVxW}2ia3$*0sJEUke*SsaDBeS#iH)Ld(XeBoD3p^Lis~Tfp zy)vy{>XN^?v%PhMfgw_EV+wZM74K!+GaMW(xwTm-g`OM|62l&AJhuYgp!idO=7NLFoUsAr=aITs-ds=_UZ`z zOuW@he@SDx)5u|3+7Ra^mCjSp2<~JX)%2I|Y;Od^PO2vmN}8vBqNNo;12EE&B;{r( z@446z_}b_zxsAR`EZzxjPJ7ki^v8+ey1D`{s&!Rv zN_KzsqvqPVnY9A)9Be73XEwPNjh6kzR^k5MjUlWK#PRm5p#xB@uG&|S>Carem@!z3 zUp&bOD{QswM{(uMn#4G2XoN|7A2@m{KQkDL(g-fbzL0WgxX{?#*q2JUX>FZF8sSz} z+#3K(+QGo_?LL10E0mED(@#*7FLH5}X4XBue+>51l}hZCmRVi+zK6AIV@xT&8_RC~ zeH&f!+68oC%kZtnKe-^bvxw!^igtWulP7y}KB zyQ0gVO||xIwQL)>c1pV)FrU_h9~BbngW_9ukn!xfbGigKxS=VpT)YxywN+p^d*N+( zmLJTM9pfBy2?t(>p}{e~Xns5EPYDvkxY{gyvZu{(tuLCh9F{6P9*@>bpXhkqIt5QZ zXQA(R|Ni}F4s~0-V^g^oF$O9sw+EeDI0Ik3Qcv585I6vhv+MEW$N3w228M>g0CIZ9 z{Og-{`=e@j?B}0vQ%`W!Z91QUr|I#Nm3=Y~n@8*9A7YE*{$o)Vw%c*BnWb|{l{6Su zQIC!j5`NW&Ga;-JL$~DRi`Iv;3s>GAVD(WU4@)4Zf(f89HnLa%(UxlpVI=}WQ1INf zYZt#=iw6{|Bdp>EaGRx1pFXvv3|~I%yWD5N$ZPrHD66=wItiw}`}~m;mn9{3Hj7%Z zMMXt?E@KLcv4W~=Yik6?Ca8D>s3UeT*179|Oe*JUH+W6n?F||(;)I%Qm-`AD%e4pl zC!1LPi!+Dd&+JU~^!`C(snM@rlhWh8n!~i!)RN(D^Oe@yKYr|lg}&6It_+=~X>2`+ zfm;i<7|cXZPRNC#6fL23^{%5Hwug&=;O#=g4O{j7cFJ)LdVNY5uzGfz~ zgr>s4s&{sF>gC_QXGmqZq?72fcy$7}`hk)cs7#^pQ#!M)MG=~nU%gH@;o9R!3xz%cV&dd`|s&Gl) ze}plWQ@^yQZ!M6nuf!%Y@F=sM{g<=s>~bTG!Ri~!Gvv9sypg65o2_-~I-t2r&Hjt6 z0?jQgMM4-aIVGj%Tot3gbe}AQI^Uh4nsWT)$;`yW-(cRYpo>LI`1@7Z8Mw6at#2;2 zxA^Ahm)Q&NY%bElPrZ2YGaWrWJ0G8>i2H00RF+~?NO$k*k}w_%VHW9Ks+}NJmEC-P zDiiLR?eb+6=(10rJn6i@@6e^%)C2*C_{QhV_@90~|C!(~wUDs0DZ^c}6bfikExOFI z^YqsC#vG%lhhq*^A6An*ca28f=0Z?|jh~+%BaivB9B0qoh>wq#Q&c3tf-AZE@fY>WgO?At#tNzb{`>Ehleie$y6@KPmoD|J&W}Jh z*NG-3NgW7gl`sNOwm_v4;N7XPjV$NCHNd>^5O$WjmFWP!6OFuEbGKnxWWeIz^|DI$xm-Z&pKA|A^1`Ox`d&|)Hu>et z?O;YeIhcQS9i7)u|L9m*^Uldf=up=Zb&Je}r?QKBDqJi|th#S*Z*MOwFKfBEm4=3f z7TXQ*a~f7^0Mx^I1<`YM)zeC3?`*Hsxf!=K1~XCN%_FzBXbvClk!CPVMQeOX4=qnX z7AubzoDe0(mzx~ZwWy3&KA z65rqKVHEQ!CeU^h$YMQd@(la-@0SaO%9RZmzKb|GI0)a2WN*^%>mzb#=k~yoy);W} zVPO&HJzK$e`F21*u(3C9-gK=HvNZ8a@UGFjX9@}mDkqYIik(Jou5D~+3*idUz;)Lj zV_{)oC?1Qz9JR2;bON9>{jbyG$Pq&!#cc18Zy9%X+BReK6?C!HuLS1)a z^`vrYiGP$RSstx3;9a?ORYwm6$&0J9#zDRabstw4Yk zO8TX?PB$PG6o)XAFbU&W&pMLZfE}IvxmcJa)X(Xuu;VP>zkh$Ct*tHaZ2iE%fXBv6 z8HW$|g|lZ*?Ad=f-*P;V$v&ULcsONyHVNp>Yf6bdsh@Hs^1|=GCk+<)<<%_rvxofp z8OU@+!oKqguT{+I$ThTWn}@M|W|1d>z~$!G=IE1<_^@M+B!Ud-<-GxiXlR~=g&jY0 z{(Pec;?75$<*p8Sjx*z%5R?mAhudkqkl=Gia|_7QNua6pc0N$bwL+3kFYgaX@EkiC z?|B0{40%W&Aa=1PvDl*^?!^FOPY2q}e94Ac3a*7BC ztPR9cL(d#=aN4~>b&0KwmAs|j9~2@jVcw64&pOP|v{YJpF(M%$L37uKZ{O~WWZ`oQ zGg#^9%m|`3Ep@88Es@-1f{)fo?!zTn_MW@GsLxG8;pxwvziV!;wPuw00WkP85I~|D zoe7%Sx|(IxYu|d>ZSrc&HJQl!nCsuYJ1iRrs>%F#g1=TDLa@ge8Lh4Bhlhu=5`BD! zr1+RIEN~;4Vhjunrl3q{5xHTZ=hiIi1r5t6C@7>+*}Yi@{WfL@mFH2dFpu2@N(8fh ziFG(gTkM-o?(W)9-&~>aP32b2cJ=1yD^lz0>#1Vy&ZIhkl z4+9zLtwn-ncb9s?S`9dD#rU%CQl|{1Gv$${o#^5xp33j<4k>y9QuCV1DVt_|1t_(4 z_1mEm%g%Q=FHobh8=@Y32BcE{OqqKeS%#T&BK$Rh^AU zXo=t~su&KWtc@p>eHNwyiphMr$kpNnKcoq}wV4S(C{jxBl$DtQZL|rcw_!iqQ*~dL zpjz23wi3(Me)*Z6hC<)N4kYb4S5d0!(fh2OCfnUBYUw; zMA?1FGo5Yj0j+p`m6&WL45=%yodHc^dwV-2sDoSJ<`s znx)m=i#@$PuqUJ2#5bnd2M*HEbcwADnl`t5`qa~3VuR0~y13GT@C)kPKjb3WSLby} zfa_l&%z&L8>#ol|!)2s9P(;F$?ByJWk(k9AOsj33FflZo+9$o?j`I zeGDKXQD{8Yw0Q%%h|Olwy+(c_=u2Iv^I$ai`kUAW${flLy220^rHu#m-|0$~rQZ=Z z+aXO|2hPOjxo{WX0);)2>4X1xoV&HN4w+q~4YV2#fc?3pfLfN`|Ek>cCS#fhq`HTH z{1JwOUU~f2Urn|3p={IBKGwh)*ZGmRb10t+QqV5L53tRBTF;twkg0qW@4xM-UI}cm z62{WbzNxiU5y2fchmWD>n%mpUDsmW%Jl=j=MEYp}ZJ&Q;Q{^{G&t>G@+}y?Sjd#?Jo2)vId22YAV8si~joCZa&4 zXvO-iPhJQbZiS_K(cj-cW+3aRzBY4ZdHF$5*fT`wqs?u{jV{VJ%;*zigcy0z)0nFl|`lg$-VI zY8DvwF_qg`TeE_`>sA6nm_&8P*fTm!4Qj%l|oDsiAXl$spGrRL#L&Nc_ksAMXM zRX^;=GORKyK>cndd@#dn>+8Mm^QXVTruy*VmikbF|F)I3KFm$LA}y<|8faLT7SK4y z3>^X-O7CxhUuPP-xT0cY6i?5ocegnP&;#i$F^H@3L$a^_Fs-VpD&X;a!!6XdGG5#G z(!yB#GqJL=mN*R8c3lOoCMPei4uzC$2Y@apjS;AO`L2F_1c=Txd@Dz{sPS}OHG#Xg z{M%Cw*PX2u?LI#{j)8O#5gXjvdMn|(m$aza@MBpyXO-s{77BH~S_UuFelKyu_ecM7 z@}!man*v)*qOg7d{|&h3=qsjyR(L?qAyec?<6cno_0IVAjMjKgrbRYGr7d2_5}$2- z8jFx2zzVkS`JflVl2q0<_QeJ-b$%YJ%*eR7@4$hWv9$v%9+FUZIsn#ydYGeE(i}Dm zqO7Qx*o=K1^i13mB9rzlMy1n#I6Vc@%vH0b)~&VU>FL>DXcnZYFPordV33WS>a~J- z1`rwpQoSpXTC|5KIp1i3doQk@v-+HG;zq!KTAmpwF{$6b1(gCM&lkX}u?wRgX|$bZ z2g=&RN>}uze_BVN;&glbMjE^+yrI5T<#fHEnNYJB+BNgu%z}=Iq#3w-j^BQJvvl(I z;#e!=)w_=gc44HqNY$5Y{S8Uu%u}Gz6F9`+c9sSl>L#J_U&%QPShnkPfhiRfGYwC3 z{XRlAYz9hSQF`l0#P-@dUZ^u5*wK|54-^&^m4N1}ZUi)I#;8VZC~~{hNIir_Y?|T^ z5FY0Wm8U_jpx@lV->hBKwHT9s7BL?X@(Qf*L$EEcfqa}|bx6ZId_^4Hi=kabRTUl> zZGXOrl-1C4W|1(EfoW-JtD1CPnYYL105y57H7O4A4q6e2?941P4H%&A@bT4!DKJtv zTjgbCJHZ;lZm*3`QSf=az(6!+R`i^l3L2hP*X?Xq4TgvHvTr!5foB5DnNzEk7!kp# zp9N-=8({_}R(c2o+-wkAB>W7DEZTb`jr}q~^sic%NXrT^m2PIL69d_q+Q{T!IXhU< zkN8o4;fh|C*NI$Fpr9pPN@o7`rzTn0=dMKn^{>!#aqNq`q7*A=qP`XFrHuB-~#8XFS6FmH!@X@JQtBGQ{V*m21_(dN8HYam`hixx*Svv-jRk4K`xTZ zP=Uk5b}Kor5-g%irNJP%7m;SLlpa|?fgo5_ zq&EA>+RbE@HJ*h@1a~5j#eZ|YtNPO?1u<%mx)qonP|8lK{+N+ORF8rox>=cXdk}86j!j4}8*!?1r(ZGVpph!zVA3ksfAR#s3S@XM?1;M!TctMqD>ZZpM# zbfiS@X*T|Cp&ITLX-#2KQC9B57KJNGC%t!uVy!?GioR<0ba`&rc&7C1M&7xL7e9;% zllR@SNsenS-Q}L@eNkL|=(GdyA-?`coK0c3_!i?)_~iBzo*(X;ebo#E_t1&o1V3u;Mst9ZSdM zrzu%@I`3ctf)4W!H-QlbD=Cfl>(t1*nwm9Mo~_bxXiY%Z&nWnkcXy|xr)M~!e_Lei z){2UuK~N4+m9)YO&mXXG47pLqJfQ-E!)Dj450ZJQ!?2M_98XzZ__h#tnQ$}SxHP0+ z!^oJ=SFzM61dpGI$qHk*^_@yBsbKEmg5usyb6b}E;>C_LA);zCrOY!!#W$ht*w<~j zsT;x6LT6sHE+gseh#?#F_3INgfYr2eY{mRG%Mp44oS}i%!&5pAc247N{BfrHludiP zt(sh;8a-Ok?~)ZW3emi>AklO-hjTzD5eMf6nNpxYT|HdCq^>@_=ItT}YZEzK^S?bn zrMLXcZkPray-L?ix9QJGzg|$f$iX4s(9l3nywkFl`~1zz*`z=ZPJ-u;eVB0q<&79$ zg58Eh0{S&5(D;0%K9^ajZFs)G1xlp*d1E`ayu9dITxEIuf>!liGc$r$T|NouF=w0H zHaHY`-}Biiq?leWux&4C5o?$GZG=JEDCxKRv=a}t6ZImuxuKk3CIJed-rju-1}fy{ za`@8v{o`X^pzyL^8X=R-UJ5{K4oWn_`hyOuPV(Q6*Bp1ZnVKu9p81%r6qmKS>VZ0x z0Lk@L;Ng?W(78bX0aZvI%mmy5wnDh_Dnj{_1~!ukE=}%mMYEQONzjISKD<0b0KEkH zh2rAk&Tgff&520{%7#v<-Wk<4APs5<8#RF{tuaMJd8?PgWhbog+SV0QN5AKHPljyR3VUyY@5ONDbTU9)zRE=!(y2NWb7Ml*T=s8dRX z%XoirgNM}G3Yr_m$;XcmZoxmuez_S6QWf!)-)^nEIAe%#dO-7!P4JS98{-Igil*gJ zU|@6?uvi2et9If<+@rh5zEL_c*RNl{q@@jnv}Jbrj;X08I5M3whL*kAQEWBP|Ai+R zN@FgY+y~uJ(Gw|=5!}Z1?lER_tzZgR$F=A#54pOz*}vFzTecF4CEc-OCe1M-?)D|K zW->8Tss4VCNLB7c6Kzf$;L!C6%6S50>OfH6>i!4G& zSW{C|PQ)adIrWFO#6Z%E*${=Q+lsaB2b=OZE2~3u)z(?LZYp=v%=P-VJS>Y5VTZvC0Jx5043;^Igz^5x?oS0Wd&7@Km*_ zyIWn%dzqI!(^n+(JS6RYH03iz-YfP&)rB0g?Yy_& zV1lY0YS$WbYscFY95gpda%>X-%%bXIXh#_5LsaSXT(`1B&Z}CJs^6sofwFO3CEE>E z>l*vaKQx>njMl7;gh=S^HKU9S6*uq^4N|~FiCLiMGROcowVx1wrVUFsPo;9Yw@eq( z?uvB=*rGqmsgs}B*rXX*3wIYY1%im>*LM$bqRPt3phe>c7tGv=`R>wNfbZ?JYoaxV zlzv|}Xb0%3`D?wED`kVl8H?phoAI5n158P82PuVHU`D%zAa7W*{>G1aKg-FfXw#cr z@L>+@(1%zMtW658ufn5fLYGtYWuT**1O!_4q3+B5L$=oG=aFuBdJZ6BK}8%G%Rz6P zZr!?7!NFrwKhc@OS^L;`h2+lJcJ^jHGDd|&VkE9hfN}@zA-^IA1pr)b0M?L!Woxf9zstCXA)reW~5qR~~8Pl_@<0qP5O!L}zdV z*haTq;*dIRYb<)_{lTEc&&r_5UPbms&a7Htzy3K}I17fpLkjC2lF|w?(G7%}R z(Tr*-YQrlpKrv#A3lBHb76CDvo;IPOscG^1Nf?ta0Y5Sxk3|6mk?m_dAgZFFlMYfE zqkw%Z8~2qfst}M#RrLMg_E}swdP=b44pUd5i0zWFCDwSm8-i_utA=5$fgQ?UMh15b)RUpG$b^JE zfDeE~kp~L@&jD8r;*hp5H}7%KmkXfo-)Yt_G7oNGSF{oQW}qDfrWY*M@!t}zI)0H1 zN^HNk=YT7SFFlPNl=mQ9*wW~M6GH(|QSzY<047Ja`tqEDy?rjY&9wN9PvF5F5g5FU zUF+9a);l{1f>&u~FtXPsCnx!MlN-}&=aRtlQ5O~yGX#|8RzjUf3DFxRfe>-zTedZf z9ft1_iem-8$LG~%A@%vR|LXUz*02N9uyd7DTtU?^jk3mmYiXLk++Z1mh%P*q_Ve9G z3wpilfL)pHsB*lhF8D;nS;0{}78Hn2T_t;Zc~Qn<9CBY?(VI$o#8P-r5U|Af4~8$U zUkc!A(1nEw?AiVgPCXXKP>-+CKQas=7=7IiRMaVu$@864*JtjhCGQL4^w6z}y$pE3?mmne9IfGXETrvusdp((Oxv&%%=*b!W!G)zvjP zIFBn9YI5{tlk367C=s;hz|jey7V3Dd99O6=HcDo^E;ht1AVOm{{TXh)587*=Ye-mL8edJ)wOu?5~@~)p5$;Za8q~@k_BP_#1=)a6cnEM zt$sa#l#ApVsLbU<_sz>lZZTj$IUIYh11th^O6lO%Ou+Wiu5z0>4mS0>ch}KXfrX*` z8S;Y`E5b73!57nE_4$4+*!GbZ=$h8R7S}TvHS%)6$AS7ALelOCU=) zx+K6Oxbw>K20RSLM?%DwUxP=;q5j4ZEO`p(;#}3=ucMI1*RNkuZteQ@`ydWxLiued zmfM20)eU$Jh9Yad{0}c};}sPcMC{jx4AEL!=g*(Fp8j0m4Bo_z8-IX5bV=)?KSWO{ zAk0X6qom`zhlltegx4)J2^h!=s3Y|gPrGGD;x%NhMhiIPLZ<-BnH_osSfJe?OQ#{b z{oJ_*+!X2*D(;L#0V#;51wdfOj#OEqX9qI4!d%@>?adGtJ1jHArclpAiX2){UYr1i zQN7cgafJ{aL}WEV#ZWZOJp$Og7igm^xFP_UKQ8qHsNw_Mf$u*F#srjli}?mC!}ZmX z5CTM6oVE9WqBtC$+i{%)PFj}~tyn6EdWrpZLLwqIfUKegosyBT>BV{Gj8uZ(dZwmD zT&ZQ}bx5S-I?JyHUWCX*ce--f)FpBuV{q@np06q^F9uYfr3 z_8Ua}dJdHaAX(t2pML6u^eP_q!NBoNv2&mnLA0&|ByP(^nrjy>T!4Kg3*b=|Dzoiu zRp}CVK)@5b!LG7!F$85m402~EATk+|Ol|1v(}m|suH9Uahb_~X%MW_caYjbHqCI#M zXhrlK10)g&^l44yGlj!5Wy87Ozkg@6Y8e>N2T)jp6eUnn`wVta8w3WyF1DvM$r~~@ zpwz3MvEvJ@$ORjMV3Y}P-v%rT732mHun1W~Si%NIiQreackkYb^{KovLH$xL2rp;> z7JVe_K5L6**4EY*gJc0<)ev^ldPO$yM<_T}{K4%&I2@|yZ&v766Vp%fm|M2R3c=!l zF?Pv4AY0}_A(p;*(|Yv7OGJK_09t^zVd@1t@i@f6+y{Vdd4We&;=AEy=uW~~_h##+ zksmyGfCa-6gi1x&ZSlY^r(y8H$5sNzlL+&geZ{m<7wyUn~TQT2t*Am;l!uXqa zC4h3E>{VmGi<48Qy0TarAQ%el(Y%Fg7-M5&6o^`fIRk?%bU*cWpcsI%EKpL?8~bf0 zTu6xTKr=x$F#L~bG~xuVy`WPfy8u}LGeb4{rj5@E@H>aNt5r~}HAG@N19W@+nyw1N zDz|8H-%139A<8=eTR5-mfF(qoQJe@-&77sJxyi{)_;DM6G0|e)rI5Fq30&q*f`B9n`1xTp?-Q+A zz-00Pa|T5}1@`tuh&@2xw}!0lbtx%Aj=lh-ypw7lS(d;qhWz}+-+#Y$K`FKaL`jq< ze)C2Wb#22+S5>flDc-NOVqkR8e0LQC1Oml04<4guB!e**jKG=C1yTT{<^D`B&MgemWsUbpQQ$3dj1^FiY4!fdq)KzI#-=&w*6|hikQ1lxzTA;`S zVZlK@1ZGYJF2Tni58^u*L^q+Qr~@Vy6&39PeGI%;UAU~hhJ_c(QWHr3(IeECBOn{;x}|!X}d286Xn1K`HSU zdyaq~yc`t}zQ?Fs(8|_PT_1+1eHExt6elF&!YjbkIcE;t@|WSSpAZd0uYmryG`D~J z>&GAe`4r{XyC(uwhadd&;}{KA4s^-CUjP3cEBgD+4}8@d-h;VacObIXXjlppAZI70 zp@#lJb1wa|%r48%3w1(p=m@^H+n7pQXStrzS<@~?75hW4egi=Fd96R^`N!J@JV1uo zzg~ZyK>GVj!Gk|?w|{;4<+mSm{?}Lkn_ndU+tJvgEt$0kP_o)&-(M4n-s3;N`4h)65l`hl@HPp2rJC`VFZsNO!C(&SP#O|Pfmq{RD>Z2(wb4UPm zNrmiqGl^7FYi@Mzy7{gP5IAAuiPzOS=0N9Dw=u4T= zX&>ReMfN0N63B5{#@!$==0Mx)4s(Ze8XG^q7CK=Bvl|v0n+=>oTFh(dBBwf-+Yog_ z=kc^w_Wf1Se%x|msi4qz;bB^ zCP)}V76G>jF~kX|quQWC$uFY7E>NuMW>`#I|197{Rn#!R^s;Y`;jwrQgoiyx0Khb0 zpw_SzG(I}DaC!(`lx?no!B_)e9(y(q9F#GHa}8;e$;qe1-~eIxbNNv5&F@m(G((UW zs99*{pdZjq_~qxH69LJ5F3$3VW!evtU%2nCzl!;fp^;31b!j8_=+PrQbSW)D&2(Xl z!X%85rj8pd6gb#+%c>G?r4rmE{Pf6^3-Oot*@Fn3_w3m-xv4Nx+dAlCp^)!P!&bUZ zCO`T0LIf@-D2UH}c96a4z?&<`+W6}gtcHRY%wRiUNhtN!1z3YF!m!*iiOm-*U#;@f zD+DRk9z5h9gpIF+nB2;tK zZ@(jCrP<G-NBmB;(iF^@+W;&7}vv4Bz{^$D+26l1D-! z2}j*O{TXU-dPwf>?&gDI0qTyfa9Rbj=n55NuxS*n^xIBGq;_bOef;Ox;G6<*I|8L88g`xdKbLxAj?|r3%HmeqB&2&N zi_YH&er^GhG2%_A9{kXdUwh$TsZN3#M}W8<`;l$Ah-*DOWiF)=k&)VIc3NQ;Hpab6 zd)~}D?f+M^v|K`r0%#Ql1|pkxl|vR)E99$zP&|RW@-(wWh#OczlZj~>10z#zd~8gs zFGS2&x0@Ezg#56-Uc7qXmw*s?-@bD4JaCU&p`iSrvoHwFLvuy0HzoU11X7O&=Z&c2VI0~Sh45!+)MMQCWO|1`AQMQ~gao{unf0%=1iEyv3Y zm>c{K&6BrlM?>Y8di5QwfCQ&`!owt>6V5>{t}Xjj!C<#zvFL+@g?UYNHGqdZ{k6g( zBKn}K@%6(9Hqm$dJ%Wjqf3};M0xL<2y5~*7zgMs4CMOWXRPaPRv9cDK{I#oh)Q3PevRBOoxfQnrUZvfM zG_c9;_Vf3XNQMA`ILd6X8|?bEaAAJJfiEZisRSzev8Ptk<58C2%$fQyBE+X+`Y-C& zPSfapy!yD(f5#6FSm=_^DdSZw2~`naOhmqq^~e9$FQoG;s?ohbjB3F z@xPR*bPzKj_0ry-BiBb)M;FIx;D(<7wMm~O(^nX7r*2r90LCG^h{!;eAE*K;Af|M= z!q6&MoZ7Rx@^3Je5uHW&VNYowktv7lTh+c^BqKC1`8H z(K}5Nyq6U~sqj+wX2>1D`DUYNagXnbC@^Hk3fuh;HWeR)CVu-Zu}c`F44yO#NOnOg zSZ-<>per02)U@gc%?QCmXCKU|U;kOT)&JO_)XOQ5#tZUWZ&>#&G+ugL0(A3H9bUfU1 z+9bk3kjBn(Y;ZC+M-po`C@fvoFdyi2zUK12|GBGn!PD{Ce!i?e1SbabkoSw?mO4jb zK^;Vv7xeNHLGD#>Wx|7kguT{l;qP(BON5A=C}_Ok$iA*mc}6zH%jvvU0)>d1hMcPHEz5sU}>k04YbEdjs* zm}m?s0coZK7^zgoCnXFC`g82g6;rb3J7D%}d`jQ899mKgq_v*X8%FQeM>WEF>4D?EOK=jD4bH~C za|Yy&w)7RJOWEgT6#ffTL{4Kcv$(F!@0<^d}*ucZ0MBD6IY({L&QwF`9I9L~8TQWq2wLWDd!7DC-*GWlbByE2OG!nsdyvTTaE zF~KoEGP{2j@5J-K&4>|kr(W9bw3;~*O!+#fh^ubO4 zhz1#)y{w#vFp|Ny|Idl+ztt@a=IFZ;#DxER1=Kq`XU&12vTddVrzrmQ`p+C(aL?bp zJfQ#Gu{ZGf6^;Mrn#kLeZ~nea&o3!INICJ}mgoPDl>a~bER08Y?Z@(B+@63{G?;Cc`xQADRLmg7zOj3k?0jpD#(yE3txm zPheEa{tGMOpDhjw+5f$qc=Eq6rs#7({(L2tI_|xUNqk#sL^qk0m3FwC2}qyOwg`10C6Tn&Eke;r|qe`#ERivAyZ784;M z!pcE70(TYBLTsa~lJ&`-ak$#I3r;JcL9dEvo*oK+8JU)v&3n!wy*Xp<+HT9s(HjRc zd~dzpT~kdSna#cb?T#6FeA3Y?dO6{inu(KBNxwVRP0QnUe)Rep$NMI2BYS&i_=eK8 zdsi11k4HLRm+4a1ymOgn*G1!&HyEt_kjC4gp#`d=wx4Q&(ioYI>zrmm(<=>?R?jTa zoCgn%6L>Wgw|vNxW4P*fr_;>GCmgM#m(QNl_O{=vA}}~;&{LrHmPua0>g_a zaUzT1(3WT%p-?`!I6NXIb6nf^?#p4W@-+L$wb?h*dT-F{g}5seLMn?zAea_@;y zdoip@x;@R}VqU(1iPOv8#u=jf06z!+WI18vL{Djr*TXWkNF3+!V<*mP!{=YLSdhnU z2(>!Cb-qR4y1Su$OOiZ%mTm=4c9z`i7mkQek{|QYF%vSTrB$3BkRS2FY8M4O9nZX< zd^%^aNO`e$Cylu&J0ZP!WM&rk#MgVOE1q1a2W}-{T zfVgRq-^SW01(cR9LS8|Ec>m!PwQszRgNKJW=2u-kh}|lw$2R=2i{|hWTbY7^LFo3% zyuHGLS;yb70c4 zvoC>v4WCS;4pa(`Uu)jF^%&arl=o}N61wo7R%=@Qb*E6?R72z7$Il{S^a&PO=nf3cXJ}%P_f`lTly&FC+^P4L$#QFCt$ z_?>2+T4epHOX6O9P}OqBzPL&XzLkmBp*ul~xUZx9IDMpsF3vdSpqP+(*t^ZF`1Cx_R5{RfZxohpmUTLX2GRUH56B{Sr1)W(~v*wSn{ z6|@6U4KA)W%?O8nUKIh7<2^1`|K}{e-{f<2nQXAesxvW@)W|$_506)JEhTnLxb*mP zwKLokX&Wq?8oIikS|!7!cCYO#zwXA)^=CDU!=R~19K_Ur1xLdY(hBH+3p&^hYB6Mi zzj3>O?Mi?FN5>Iryr^A3SJ5d);@8Lbq6)7;YHi!2!jgT=_ z`aClI*wkIW(uJoqqhBexzbQ*pRB3H4i3Bt{Y<(@4GoB+{y{>HJkvVAC%Bn1XSn8*! zN#UqSZdz>yz7^WA^nuB>cMW~IBVJ1{>(V2Q3<^Hw7LXa}*n~LL`}XaFPxDa=NRPbk zyEW?5C}e2Q#Z_53Cqx6M>+yKM0z50`$N;^k8(qL8V8SnGK+og z(}RVv>F1>r(j>XLTQ{9D6-}di@- z@aITT-&P5*34Yj2lyh^7{}zISgG!2=`g=dN&MX=5TS{K2*=lrEjF~T`p?R7$onkk2 zIhe7ps@WN%G@^16BWVcn)kiNt4~C$iDpFAa(@)M24s*okbHjNOz>lChrGwf!0S7SD zfp}p_SRztbG~u`d=krrLZup~{X{}A!{-GOT=ZEew60xt@9Mm`4oLkP{jqf%1i!wHi1L?yepFl%{G1HagMPMZd+K_$ZtHa6wKnzK3v_KDRqkj>p*|* z!4BVU#y=cNZF?NN-TMsk%p_mfi@BGxIe6NxxrCkdtf-hAGC8C8c6c-Sv~MljO?fxF z?vI<@c-7dr*q%I1eMA4Y-yR;=A2}8>(V-jj{8Z0VU(NQ+3x!3+Umash&z&O7zTeB- z{we=8)`uj&i&$}U@6NuGqVRSYX3{s`E3S5+Q1N6%`ZycIg$qY)hqc@fvu5`yDz&RKVCRxc z4ji?fe4e#OFx1tm-M(^TGYMDCxAAR>+}PUhG4Z&ejpp}SaSE|AJkaf#263!4o3cle zB(oU4iAjy!rl+A(tSUw)^ zS@>-x(a<24+U0*C=6>DeWZ(Ln{GpP&edcs@Tiqdbb&)s&u^ZjF%&L`)7256ml^f4H zqqp9z5Gs48r{UwEG<>E}2chiWb(_~KuXY`MfpHI!xYo~O0vyLnrd!Yt<=*G>J=wbPRa_$hpXB54JP}{$P3+ZVf}sR z9_~w?hSQ6RY@=3@EajHAw8>}Hms&~X`R}eUhK3e(hsDO>_#Txuw-D;b=67d!KI&G- z3X+zEvMgE-s(TqbhaSbnXXI~Yw2=s-&+HNo1qCgAP(D;VvU(+`@LXWhYpDZED+On~ zzJ|8Yo%}^>y+5_6E%z#B^KP?E5~Pq#8p!?KGh%rHO;R*lN;7ef>xWcY%nf z#y(5TH>Fhe**|6$HHLe~JgryeZ_LQ&df1;J;vZda|GZq0m~pGjZ!M?vl#sP*>F3K! z8$)~0M z!cSY&uY<$v(-wnios^#m$-HqdU#75i4|UxWT;x)UimLE3ZBv_(c#$!lP*cgf^Yo`Z zOKXvM?CLE-LikxWfilSg@8?zn&+GdOMjn^K6_b;H;WS)&tVsn_t>T>}?z-195m-1D zP9jgmd(WzZS$??w@;=`q^%u8**uMlNAH01~WO6}@29nn5{!i(Atevu155Y;|9GnUyIcOVkya%hn{wC<3T z1u@l&!|V5UTdKsK4SsyJowa2}WBdCDA-n#lE_wI4ivp|OxWRYVYt|mE9qRc0=61>9 zgW1LLd~b(&%;-j^9u7xDyGdAn8^Lq6>S?GQl9U=t_17ASpKPk*NJtNKeaxFyH(XgW z_+>82nTCcs*k!EfnG>@YujzO@+|=i8kLQA@sdujo16MBZs($ZBQE~ZQN2EPkAOXN9 zKo;8?yzl&=&O)!wB%xOSglRLAaJkJxvZ0}&Yd!B`+kWoa^%UvvD+)o8_Prqzc9#^Z zDk>(%PM+aljy!8O;HML5Of6ie^}9hd*a+O-_;!7(c$Ax2*ymkV+-KAaHx6P_I)8*z zkYf8LZ%MhM41^x^9r)miGe|CmFcrwfDquZ6XO+l>MmY%%M)QXcDu6p6wu|x|=wv(O z%e28vRd{>5{_+w;U2H&qrFvFKW-cr0v2{!>xur^Cd-xXT0|}Atsc-5 zP+>YHtK40CcQGOBDo{sOuR?OydtP=5H5#$UcO-34*MRgrJrpSIt z#8Fa)&%w{j?}LVB>0wVJuw~eyvT&kvNt9sk(Xmq6Ff`umo7fA8M> zEvPPCd*8WTysjxN+1p#D!@Igq){=kv&fBj3B&xSvy@}_lqSY|_Sf*1=RZQ31&U`w- zcbBEd?%majlOs@#b@Ghw2a>`=Vu+(mgWpsj255lRC@7Fpr1ILFAAcxllcAFi|Zi%g>ZoV$@+uXDa z?pQnPq5F>Ao&Cexef__=5OFi3E{zaE44HfRmM!W{U0_*E)q2^LcGVVByts&H)tl;l z$yGr%`_6HObH+qP+F%X!%flxxa3suV-fR5OL^7-wE)^hiu6xUbQwtYb7sDRmsqjZk zg{_x;!=gq;US69O5y#8yEh>9JNyq=${(!*oyAf;gE8iW9D&gc16h^uTX3-CuJB~Ix z#eR_)*el)&|AV-%ltvK|kdT%XmF_MHTTwxgkVZO(92y2i zK|vV08)0aW&fz^y_g4Hp_gc?;Klggx_0AtIfr;z7=Dd#c$j^851ko`m>NK1r%^vbH zV|yr_Lx)g&Lo=S+@EfGoL{wPaMb>A^3;UnY%23pJZY%_$jHD!c)Z)A`!x^WbMm_Sd zZ-g5Y&mZ!3NI5+C7Jj)iq+v~Yz|345LsM#ZD(3sBqpOch_1Bgs zxwqt_Tnxydv6pEoFJxtMms;B4rvx z-GBD{`I~vdm>z0Rgw`@#NADc~pP^dK{Pi%HrGy#)R&8w?i;|0O zC5xI0*E3urQmv(i#(4DNrAo>bJU_UYJDyT65a2(+VjQ*S@x#gW*tr%Dq0+w$W4=NR9_g6k?eF-6Ht)z z7L}bcNtb082sey=a7td(&rB&-GtAG|eJG=Vkx3#=O>>9_E zE!%S~YV0cePcf;esF*JtTVgyf_O5~DOU_dbh}B!1P|>b>-eBK(_JphTr*kd^s>PB6;1uzUre@1Fwy&(L_7TL) z=^UDC$Lnt9MYyd#dp_WIsHDVD^3@VW)Z*iv+mqEFxkRjf@Sco}^UQtT6J+liVaj+k znpmOBX0-$nw@pdm`06F1e|+}r?7~=j3d>}|MAbmd=q>1P-7#pT=|;_L~UojdkN^@OV%E=>1) zze0Zm25!G)G%4*=vtYjTsM+JslCB#| zdbpu;T3O_;>PM!)*;KzAy~q@q7?pGLhV=8P6+>Xat?uTX+cc^@*264QqJ@L~wFo!Z zW@GSu_-lEys)fV@-e_fiL0+Mflbq~KQcK_T67L)jASfS%-klu%W3HPatk*46Kf(9X zx=&b3M~kP?y_Xaf_TVGSI3|%cH1E@BJCSqFY`|;mAkeY0WdI;T?5S8JtJ5cXv|mpXH77Wio6NuK50aujG|SVV2x& z>Q8+&_@$5~>OX;bd6lSA*Lr^M=g+F{y6SQlvm?15eh}9gYiuYMTuhJQ#joZGUNR(b zUD7Ro9UsOU4-S~3N+SENOmA7C_^=1sE=6uhI*PIj=Jg+PhUV)W-2;My;&4wlvBS-n zMA=hiCf^}+?>@GU@ZQ6c@2sUlu)5so&%?FGYg`ybT__$TeXy5z0BF?=U zk~k}l(MWEO>WDeF_)}8y##2nJ^ED6NHRybWJeLK@GqX#$lTn*fE5SSzHalZ&__byG>vp;+isx};`s-m50P?Tr( zqt?Q4r)Szi<^7UX{Z7LNc=1y$>2i-kc7{bH?1loieWhh+r!?Vf^EbvosO=BD zbc9)Cbw>^5(8eCrxaoOt5E$cR6g!$^^u!ISBC3monB@f?yC84k;kPO5kmse>5pGJ1 zmdiaSm)&GEr=&~p{Xz!A;iTJ5HJ??9xoOV4`@EjSO^o%vldusCwpycW#!4qfFmhi~ z&~`R~M;3TA37$_BuUQQ5?P$t z#;!I0tU{MY$K^?z+nFHQQ^?kKs;9Nl$=Xws&G*2(dKYAc4cCP*@E(FKWK~#0X;fJ$vl%&11CKdIB^z-qRA{e%#7E3X4f~P zt}hMuyIQQPy56lPpA>m>caE1lkrAo{G%fYQ3LDFL7M)_U4PQ9u7nVof*gA=bpa3a_ zakJ;W&aUS&gqpWtEHroSG*MI$(y>wF-0r-=t)SCY@>p`5Y4T&f)%_sN3K`q7FLXggC4o9AN6N{~NCAFtGd~gww zooi^!UijQtM0V9jr|avY>_lNQ^@*dYf~74VySWnTrAqFjB~m1fDI zTs_5GB+m={{GIqIRxPGpAJtFD^jrkfa|89<&Lb)$G{XZx0`)>7XekUhqR6ulroN9H zJ!)uI1f%wd;euKm(P#v;jM&H7z!(-n$I##dVI1HL6H})uztBePJ#tn(7q5zRx9r8) zi+3C*zpk5qj<~21S@iUxxjdm)g^0|C;qQ=U!-Jws%OZ!jBK0NC3__6C^P$XN&SB@{O5#O&& zO3ODX2<46bcyp)eL~8KqBnRumBKAF!uGaN=&4m$A0t)|9+wgpX&En!cMLnzbD5+Y! z#+wtH_pmc_Gfm&#+_4{AC;00qu9UN5Xr3T96fL30fzkEe`J{(!!aO*tL?XjdC7aPVCAY?NTtF}?9 zoZ``&^!{Pd*yHwAf9$6c$Jz7r7hfk%d$!%G_mKBl;eKB9bW(3AK<7v3+iD^mvT!dk z=V{_#mdG7?tqPY1UIajQ98D}jM=d~8imV@+CE?I11j^rK7zhM_?Hd^WfQp7Hv^+y6 z0rVQPii+x@HaXaB96)lTL9})+$<@`>6kvI~_w7r&b!8DX$D%pXqOtw!gvT`Y3yCxg z2WdjGH61U+<7C{YbsA)tHrtWlj(2s zy1!#;egCk4>F1tqzM31g^74gtUtExJbTwPBQr+=J*)D5gxq)%Mc1WGItXj*jDcj&t ztp#)`iKJvF2FD-@L4HY4rm2F4S1i4YnpXYl;Gx^*Rd0-1Nnp~hF+g26 z37XadA{x!sH-iLr?x$0PM6bdktS+@YN2pZ}{L>gWCp5?JA7o-KN9DbZm`Q$gFucsX#9!*Yw~4 zjS$hWz+}Be4Sqr#ad>3SB9!-x>n)R>$KgfI#uyDhuDRLy_INRerMkw>rpVUJJ!H3{ z4jLan7S(fb*k!B|ZxNcX180@;tgo!WzU%psjhvc7KBa8C`Om9(`?BLMqkWr{!f**U3rt@#Z;TT0>c`hb4EiZ)?YsT;O*@Ap z!>>JfaL>MHhGEs@t6scqlF7|@`|Nl!K3{I0CT%9(g}abmX)9meoMs0ODr>DOH5m;S zb)o>e8i}Hy0YI8hB2;A}pU=PC?I|yi4ZVUe)zd;jDI5j;st493OGX>Bvcm|@oRvnW znJ=)$U0wSt`TgH^GV)J8yOgX*=8Et}sK*xvg#z3R##67_kK7K4flX!XRtWH|WYrQ4a)3staiHPkrA!HRmZrCV zi25+v9sQQMWHiwNCOmBdC0`5FLq_a`1XEUH(>Kmo9>VAL&+&X+RsNi8Mp`DAeU;Dl zhs%u)W#$rY)Q~*2hLn9GzhxwO+zy@v)Y0t1jH01#M9rO~>yDR7QPsR`e7<(ur5;h4 zs@o2G>t5?L)u6p}X@6*^@$p-_R8DN=xh~1(E(-koD=x0*PPN)bcniIW9^odhwdBZk z543gS-Q@JpJqVfh)$h~OXFSE`uveul_NE51GafZ8DCD1ta!FZ!Hggu-Lg;A9lx&~P zF{6cSjw^X=0%IMO}U#*j`|3d$>@HeVPD`I*9u24n?P~Zg#vQD@*R=oP>Vx-`E$~ zL;J~@nVEn-n^afl$&flXW3AlE(ciJUbRuPSq&X#xnH~~W@fZFL`}RKl4l{&+KtRCf zB}Gipf{DIvW)>^6e8J-tD*s%C&Zv-mT+=dwQQ4Xi3{JA1oeY-neR4#Gg0uBys&_o@0;%^MfGyxgWD_Nj8cPwlzBM$XN{kb7gxRgxoQ3a`p6jTkXy5D57b%7OW>!@xX_JuzmF~aoCZ0<`aKs|VzeLHq{T=J!VcJw^J&7-- zNc!REyIp(|Qb5g7xx;f46YZp%euuKT_yW*yBw>EF|TY)Hj!Xm-Xo~^t1u^pZ-w??_%p!R_LmoF+d+vgy{#pYIo zs`u<0Z!7J}GAOtxaMQor{vAO{+#SaG)s(Va_CKF8>K1XYYOfzAXiE)Li(!q1Smx-Q zfc%hZDkfa~d^N&hRlEP=$CDeG6(pVa7?rZG_dP*q@(|fAjImcCzXg=dmm2I1b#oWP zGNwi?%$l#hE`X&N>KmM=M#o^%Ewg^mpo;$5D|N?9HR%HsitZ0Pu72&@n5PQ}2~T?^ zwZEger6oq{`lw}%R+!`4PX|=y^bIk*23IQ2 ztvEx-D1X<$q?`265r^xq(^oEpZiPaev8Aexx{=WaaU%QMB)zcqrBP3|w)H$_k(IDg zweao2xlT@vW8YJY@1+S@*CpGKCyHDcUkYhxy-veMO+8-fGv1jH?z&*EY~q%XdNWU} z(l;R}zFdp1zD+g-V(w5Q?Z1iKxp8`=aXtfsOm|_h2X|1TKTT8?t6d)4t;^oI9lN`|$d0*-QDM%Z~799zK`jyu%L@&K1wc^`Dh1olkB#f0qD~RL$?<+Zruk_zfSYzg3 zu6ODrWhK#L9L(%}vGMbMlSQoB4q5>8xzwyv%vUT+uik5nT(y|CG@uIW3F(1;4%A6Amc5#ytUM>v4@Kb| zk$&UH?hCuD(yHASpzc4o>h77E%kZN0ea;au4jTUZDEBrLOh((AT?T0W%9C`a%qj&~^N*pmiZ z?f~W2tX59xwuGHNgli3M<9O9eHu|q#ZeK7PKT%yqLz9=Xdd18ZPX-w%RdJx^sjV`%ubx?8UQJ{hMM0QwNboFkWuIoZ2%bIB?-}T6Q&MD*+ z?rf_9hayjcy3^yly@8>jE>p`8De5?t(+v@+ztU{FO`lyDH|un%FtBLLS7_~?am00q zZ*(lS%EI9C)tEs^Ul;%|m75q~LjC?;wi)Ed54$8^YUw{FZcwGA2-q|yeO0c9X=|goDEla$vIQNfP&_W>eT%tnTASFOGVuq@2BW4e6rtR{VY9wG_{wT{ZH>391_Ht z+qmv=Wn@%v7;DSw5*#OW#kfsvwvF|1g8id`(KWAtz2%9sFBzf=HFxr^oCS#f0HUtH9*pF{AI)E07WDgAFYn$9M!bMngUmH2V|`KraH>!iG>#vsHNc7=Rq z?~jtf0{l2|GOaDI=xPVwY1HqOvNYX%rrXWL_^#B7vp;^f`l`|A?%YSt8~N@jEeu^& zs*HrqLJgxl%Y>f(w5AI=H{&K{H@_+DR8|_n&5sUmy7bfeF6*Pt>%5~NE{=eMk{0#MD^T*@o0d84iq zH5c>nPaIxyZGS*G5*di);Td9P8ay?N>Zd!vVgwcm+Vn#Xp+vW5X~%zhkyA-Xj#0nriRBXFI_lIe_gKrLzBOz z*{1Q;5Ff(>JY=^RB^pTI4?8%vSUO(1-$H*e(P8IUtPlpr#>AvY%Qw~fv$`F*rwCaBQ>Jrs8_?Ie+5c&ImHaiAGc%kPwpI$wmuK7A-@|;gBCU*&tevM8i-1o2$W_U;2asc z0xgTzb>0{-9kMKf)Xs1sh;k3J_kT-Q{R3=`0&ZI(NGx%*u0pQAs0j~kg&YL3GTpym zk^S?SIa1!by-f=+4j>DrQ;OtJ0vFL>ynMJ|K>KFt+LSVYeq*{}{Fz;^R2u*yfk0dG zKtq8rc^?P5A_5;fAyGXVY!p#XYgOEDn+2Wi<{E)3Ec*yg+ygMVFabMI4;;Lo-zM#3o1&X?1|sp>m#OH%k~3Aou`;78C=5X$ioIEz~`a$$z#rvEOtI(e-nr zpo9gCF8_9n&?G~bZ8Z+S7#}Ig5W2?=Oq?b=RH2oXs;3oT8?tLa3U+j@MOfcYI3To`GB|x%AZ7*$`c$Q*tCEt;f`T12Q~@@a0%ckbV9i^;(KX4#norM=JA&n3*(ho{0H{fd`HUZ3l z7ursDf@m(9Ssy6mCbNb)AS46`cXbMkf|~-m5-{8eV7x#G$QY?m0FDpM3kS!K=1+qu zQG)M_!DCrJL6jEbc5=&5=c?UXBLbY}92ZwZk{b*v-@HvVgXkJ(|pu5 zI?jC+y2j7+RM*yWIF*WRt`LAEsF$<=9a5kbc^z*4CQwcVVv-DkN(XX#Z?3lDO@X0- zFl)+@lwKRwTUd;YG|=~OpZ&@dXjW^vd9MZTWyFD%5`wl91TF&5kSLO41njmyQgi`; z8{$`j!roh0TI|xv>gf>%sn8Q{OGNmh#(sOm#4~T_;x(=-vw?37nO%7gE{x+Jj*@T% z>^ThFApN8R9h(7#{46Y0HO>AOP*$v}sIG(tb>IM)# zm0g=Ky@lbWB>PQ9VYuC~^jF`W2$K-RNF3@|A$~8E0I3exH>)SJLHS6wQ<=;fcHcO# zuEaMk813ahrn}o}XNRhh6_gS>ckUW&2@9tC5UCN0OZ1cApgZ5)V=Rp{wBzWhsD^&N zddswXO(LD=1|ajtf@Lb!EQj0K*rXSvRv1DNDIn1C2m5=nCb~Z?ni{}la&CPfuf_o= zz!mod%nPEP`Z&S?f?{Idn*xMNh3R_@td3*50YmVfvd6w#qs22vYB7t?0luUGj6WYo zpyYt#Ka5)e0YW5229muOv52SxJDz1ED~N+2A)FyiAmsusV05tnqeA(ImCxk@>Wi!! zKwftLmAD>~4~j5{Sk0^bfYAyw_@iJ}Y_!clUyi|TB6Ih23ACpJhUB}w4jQh3-3O_R zYGTE}4$1qHm3jPeT`W!mZx={Ufo7`RBmq!00pdb4ASD(+uww*T!OpCPU9`E3TpTD> zA+~Mh0_-P<^m^jN398z;4zu(m(Dy)sw}8`+fwuRcj%n!5LEZy$M-V5!hZab>um}e` zc5b`Hx@Fff=64n++PUN!>!(`MLXlX>kPTcO9N?)DK-I4aN6J7m8BF%q!mg)x7) zwNJNIfQT2{WHOe-E(=ndesAAa-xCA(52!#lf;e=Nc@$t!Vu-E3VHpqvRsPJYFvx}l z(N>n4ol&cI_89-WGhuG+0g2a~ux45i!-f2onoAehw4V^phPW5&>s7vL02@sO=udA4 zh~ZZEPlH~I0}vb#l|_ER7hnlMw$0Jlnknl|d*Xoj6N@7wk=-9K)= zGHGbsK-L13tVilyPM$i26u1|rdwXN%Fd2-0m?Q`S{s1mS+&)%{g;22^GJwCX83tqi zVC$RsuRr~94;^@W>i;lY*|NU>HI(^p|Lr+O?|1O-WpTWe1J0oFe|82(9!kI&^!(v< z{2v|He;kQU@XsR|{2v|(IN}(LDb0U`r*a2=$2gD#_+NhdWA~2uh+q_)cm52O+qP|) zb{Jy*|Kg`BUg>)i?iP@Lk#h9%3;6mwr0fHx4&&2k7RY87y?mf$7}+SDzTw?|Y-~ zGPG5Mva+$+ud96Gkt>;RkVUXv)Io)2jUe4*+LrMiu_qx%YOThGR07v}ih#CkofQhqv zREC5{jb8jp(G|@s2$Y~Oi+4X+h8*biRQ?b|ZmVX_x$J2;mDAGkCdrU$LxeDppr3Ze zBNR3CBhqzBgZ88*sKH0D0$k+Zhj`opOlK2#i)X09dplt`h-(ebAEdqob3K6#9ZCf( z@#tO{s$(aTTXp5vZ%A&PPvRl+TcOC~EXD$wJ9q=}E+HNg%=In>(KeaM)~HJqAkit< z0ZBRs<+xuTiNSO|hD!-VRr++u6I4y)!L_P1gYXM1FaBF8CJbJ$zegps=ny7|D44*U?qoR_Ox6z<+yth8*m+ zUw*8@_8o_9Lx@7qq$6JJbSm>A8}f016;nA5N!X$!rQe^1nYxTISBAF@RLRN8gBTn( zW}D}tK(|5yT6Dxs7?R5P_5;2i1pSdJf@HxI`_@h%9&ixTsRHwcu&1+1OLmHHE~I-l zgUd-b%?ORWs1$f-!3vHT6lau%oq<`TjgPGwXzomkNqh=j;ZC%~4vMrb% z26OB1mV=CbGAGk{NH2O%192e1?SQYj8FVNC&9qj?w)GwXS1{TSA@}l=87oR!zLN%b z7qGkoft^lrLrLaO?!a3M0BZIbX;{-p@Lqik)+Bm(jn&RW-q4HYDJK7k2E@gr`2;b9 z&i1<-(vnUO+HNk>kh+=&kp)?VM|1!ccD3DG*Ma))S`3zq!MTqNNtu8j>Bzh+Vuqwl zckOy(5(3n)X2fJv2-yA4=fS^GyTSTQp@b8V!B`*&(zgPf8zQ@c!;ZW1w{{4aZmnE* zU0|11{yI*0ouUhTdAShBpn#uiuf!AmbI|UEAeu<$!)0z5!3_Pvw6+Ta;9Xi$ONtXR zUZ9M?kVgOSXPbZcXAyonUd9J&%ok>$75$FDVgk#i7Nl(}9an*LFa{kA4dYSr4oTm# zJCOfAa%)O8xSv3)1IUe9m1Dh-uuu_%bRrTKg@l{WBIG-}Z=C)8d<_3dOl}g=YWx<= z?ZfS{xm+ox=JE{&`zpuL-Qxm{D!(36l5nR3H_cDQzSIh)S=sw744PBa2|ol+or}SF6w)_1 zBiXaqXz}**dtq}(k@@rJ#Ng;qRspgI-!(GVkU)`ZDs&;Et@fWU?Zk=uzwwx5$Dio; zGGfZMKZiXLErH;8=0+YA2*Ey*s5H1fs^HlGiARn`o-@+G11=J(-uC+(`p&|FSZu|U zRH1$Ejs7td$|EfTkb@F(--OI7FT^}0AQD5`9O?IUuGnmhb4OI^FKIWd6l%QOa)+!Ic5*od6x7y;70C zuM5VD`!6rEXArp}DdIxV>!*iMRd@m%I*0 zo-u$Qlt)d>P*ep@wr6qrDII z^>6?AE}7f^W%8qU{y*@^k*Do74Nij$q$^Os2>-pOvsHuik~xXN>Vpl5Lv{=j1|X#s z4xDdQ4UG}!ULg5FaS*99BhhJu!%Wu7!&0*Tap~FM(nHuN;knk?%L7Xix?g4et~avP zrvu#@+@5A|Z|8eAN$QlM%TY)s#3<5T4T8=d4%*)r$!pI=iu@fKFb4vZ@pR~;l}%8H zblirAOKHbxFInnNR0r0^(K-WImH7&0H{|Ng4+mvzu(7ww=PuF z5wID2231E;hUh^rWC_s$YZg0aqMl(O(x*19LA$U62$PzEsLU$p(;b0)m-95_L)ffsQZ(ibY=?D_`=o^9k=0F*lmd`FNV zTu@y2_f&6D8u<=UV9K;K$jp`Du2TltzB_w}FdSb%M~Xm{B_zt`V|SkrCJ+PeaACIg zuZx7iSc=1@0an|-ct6x%Ak&?~(*rqUWGb9BC+przsL~)r%s&sWHRKJ#!Nx{UN3D9EX%k-b)wKMc^+uRloc7Zs0sW1?L%Z z9OtCDgRb{Z96n}Yby8EKF-Lk0b0o*pCt1Gy4_UqdMerNyVt z{jojK!m;{;ts<=x#e_Q0qJ@Zug;x!<`%rXN4Z0-j30nqC+xXfK^9e|*AyH89R6$rO z9b|Gq6}UR?0_0$Lz$HMK4!@>!Ju%s1zk3oeeIGA@M+zx0fP|;>-2HL@f(~Hm$UwQT z5x==%1pwgyH!DydM$~E8QRTsN#GM|6?VuI-v}Yhz;8gi_iTQ4|E$_rwU)l{eY9y#L7A_3v3N(PCpaWD*kmfZg0mZ-#3pE|~aM*WHCJ~jPz`a52P98aFqzl~) z>Zm0kDkx9b1P9FRyWJmqOt^l8-103UNHcT2^z+pZ-`t$7DxOZQiV)aKEGeAj)eFL4 zhBPyS(vM>}wAo{RhWOLvzFF=N(ff~ z$2kCbqm2#xbA?cTE63`0+sgiZisFUx8HepZca*kaH;9Z`GaFhHKk-vG@~}EE zZoM7h6xb!OLt_oHoT`x7Jd-a%yG`A2+-U15)Ep2ntu;oHV!_3$I=}bl&JRJC5V#;R zaBo2ES_KTmJ)N^#ui-xdHyfBccYyl%^zkFN7GiCb1|M(OdY1EF;EACt*zYBr5a(dwcmmJ|BgJ;9N9-AOpC1PyrHzimE)=@KTPg zHRJUZeAX_Ix9r#0CG1JMhs1$E`3lwN(`A1h&>FY-It3>G@1J3M4+Mm%u)8C9R){!* zek#FxtW5$A;xRN4fo$PGgYy&p5hz#8DB`xR2g?gsHAsM27dX{uqj{qLg+~rd17v*> zrkUdv*qA|W{jFC}ABC&11_d?;5dRs0G_=o5Am(?L6O+M&76sUUd#E#=g9#Krx|HAF zUC#$wx;5myLJ8xi!xbBQ4#@i8v3vXasw2yJ?ASv!wKC#eJsq(}9^vT--b> zZ;w+grcfP=W#t@;uI_MDQJEApSgdCK(ppFXa-C~eCj}$4lR{2jHcN4McntU4`^NZ} zC#F=#)8P7Ww8Cbg7~POZU)7oGQr5vmA+xdM1dInSARZ zC^e^UUT>y{Rnk=RY2Fh*pYdq zZ`;0IwT%cRq!!q~q4uY1iT}4hB?N{t5nu{Lvg(Fh_cI75!d1}(PBdFyFUYs@fE$Cf z;*pYg2#?9YtTtu>CA454VUqT-^Msv7=c8U9`Fy#Xm@#pT-tu^g{9XF))sM@ES}B1n zM$lXT#%_15p;=if<*TIX+M!?=k=qnqdC#Sks_-yx-0clV(PS3Ot9^YR{ii>fc0YP& zxO!)^n(8KN>c`UiB(kyA=y2CcTs^-KpV{In6^s2q;tR(cuXtEWQ#7SMU*x6q+S9pj zmqc?(k{g4HfQ_I8fs=EkPOAFZ*}|e$2g_gyzn+^#;%*B5rM83pvP{g;YC3t}=BnRY ztQuD@Ssn+6d1y#|UB^^0A<%Uadt-Mxm69cXM)O4BhBpIEHc8i?v+OMuBTnCG%91P6D!D1+4-HqakhN8d#F^b(5YCr^vMDU4 zipmCmJ&nZpH~qrdE_>EE32x(%b5-+EYT$BUVN(BapUitdrLu~% zg=}QvOoyx6tg+k%fnPJjR5p%Cp3GirxRCgi!Sz~Rm+gz{CFIyZnl)ED^rFq};0+K# zhy^{U@+6fAkAsK@cW=cO1brnTf$;h?sJD$gp|z}o>xrt631jdM z(P!58ffEZ@^>m~vg)^m^oP7*s*PW{yb7MwKmRBQyAu&hlPFpIEjC_^8P z-|6!65MK}n;Yv%)$=8U8N*Q-6@|yQ|1vO=zo|bD#f8pt%EQt{wo$GBGuXM*wE7c{+ zi*&cUWejy>b$3=zFdjOPD7Jv_Iz$>CCYpUnH(S`ird53tS>nl#)K4^fJ6wqCJY#xD zx7TK)L2VMp%zOhzgzO7?va{xD&+%joOqOEW8W;U%8C7haDiSB)*BAtQ6jK z!T3*yTX{4jnB^AU)0=y8a8)WZAa_nZkUpe>|3ypA^K$n7>Ha4Bjlr0>Rm*aY@Ri;% z{qonYc`uq{`1h+_o;xP3bWJg;C*&TE!*fD}o^Wwe#a(EBoN;@)8*XYzq9cbhsc9{Q z_pjE44WUu2z6jMSKKuU0#`=8MBi0-ng8rm{YxE}(Xo&(9b#GrMclCFuXxsrghfHV& zQ8C7TNMQnIL!9#Em02SZ3X*_2~z!2Cc*WqBJpQ_Uuognlx z54tNCJ77o^30d*nD#OU)VVfPlzmq``bbXMFEI6~$en=D)B1RS1;ghX7{`uaB(?22u z0*119MOBlccm6uW85aUwoAu7sag^0`T6 z^$7bui_RMdi58V%>aWxBoFtV_Wj~pDb{@G6TjR)F29a2!Cprm757zrEVaJ=QM={Iu z!PEuF( znZmI$s}}hw>6E5hxs5+u*E-~DTKWEt!J?<_!~v-B z#9M{eN_&c?1^11#T~?}>>2s4>XdGnp$6MT!(G28RsTPvCE}b&NO(z_I^>oj@+>7l^ zqGT+*xj@RBy?F7wxF>_%bJFxAft8K|BaD)u|DD#pCXkzsi$A*IejeKUM3(y=BBAht z1(d80Tm(N$`{cSvFF5_l`1K)9RM4NwUj(^T@C;RlYAMP}Ay7!R>MeyZt^qpx=!lp{ zkNiD%?8QvKFs6I|tl}U?uaooI3E84c<`lffYO4hdCvXx?Y^67orURHJjYH0?2UP73 z4#$yF76fhb&s|T}!RKl&8K>2};8Qobj-fs((clFNNvwgB?P^mZA|be}9M84|t)%|x z72DZ4o{fY!*QqL6`qq#~t?Ds?3-;etG=rBmY^@{tnCR>J=MS?meBjw@M<@uz6f>Qf zd!)prqZUPN{EAxry8K}Wc2-H-rHQ?k)6&$uj!;p{_=KKa7Fr*(Sb z^Wt=F)a~v2XmG57*Ih^x%YM#i!Kzj5Rr6Z3hE!2hhT;-w?ZqK^tvA(Dm+xTH{)SxHG{VXAAX6at%{&?e#%Ibx#NBTZy4~1|_)?Rqq@Qba- zFv13qm=Lrd>6&hZ8YJX6+ql-i16cSF0Og+~D1cGb9;Tv-gZ?&IlyE*R4ZRv60(S8c zu474D(~w(w0S*Gu7;aE4q`qQ}MHZbCzn*pHQX6D*ks2+edrLpMk+u0c?SJ{G6+}IZ zn-Zg`Ji#AFt#iJteOsksf3fzivOyv_HYLymg#@Qnq5h z;;p~5(#RbflWwHaG+_sgiBv~a@>%k1x>o}iJ1E?$Moe(y%Qxa8YIC$-Bx$90x%qK7 zIwbnfP>a{-UmNRaTkReVDjyO{R65=rs+o6!jq@(ndk1bT$z`J=(2Qz(4Ft9ugP|!l zp9-Ef)PAQP$&RcdagWWu=+x+_&Yfru{PcY{QNV^p%P!EuHs>Mvx`X@Xqb#e$2L#U! zLxIg=$7`=?q-Oi*F}}{=v!(^HnGC2+{Bkk3O5P6 zM2lvRB{J_VFBNOP@oy%p8aJEk85j$yVj4e7S+1o6?$Do$%Hz;f3_fU94y+g31R{-~ zePUb*G!!LFZ@KJb6>!g6p(uoud{NgSs+%C4cka&~{k!`2S$xqV)LI~W_gD;ktY<$5mO`@-hJX}qJ4{*WW!>CSL zTU(pbTHsj-&2XmSodzeM2M=W`lhiPakhQ!6s>+bSZ#@&F9SzR6G};poeF}BOg1jbB zX&mP5789cJd&!(@ry`wLH@_8dzI^W>K^1g>*dowH&&3&9&~(GDTBmTDXXv#QQ|KdA7`%(c_`)==3W8oJ4t&&-PH zDQH|huLn)+Fejht=Iy&H$`JCb7oZ3#<_8+Rd&@Jl(}C*U+m=~XxMA0+YcWKe?h&i% zKF(#eW)r%9`XXDBG41H3!#k=Hu{%Fu&Z%Dyx5oKeniu5S=h@w_%o6DgaGvdQ39wn9 z9l1|dPG#F;Ixz4gXEbqKXu&ooyY{;&1Zo^=`=a7UTRyEuRaKlOGN{-*=D%#1bk2Bc zBrfW>7xA-XtFmuyi(Z?j%7;aQ(pM_{8;5W)E&gJ)j0fcF?5&#oj(Qk6y1sNK%hKzu z^CC+JW`4Y6+n7@>%_f@(ht9j39e~nM1)bC5B?PEt#KmoZQ!gY!1e>8WplNG6TBJ@z z83@7p*u1b2jC90yT_9`P%Qlo#Fh`;1^vYl-YB~fp(Vv3pGyUZVtPH^v_@Z&u97Web z@#sYC$L&la0&-GLjG3?ld9J~|to!_Rfz#0C;nWggL>O9_o22753=CmYu2|0^dnxO|&Pn=kZYjed|#I z)u%3TW2W=5ZDYOcYrdvL@a5)3iDjAgyxtd`_FEH=zRGb-u+wsPH0??Jgi9>M(ziCo z8Jv3jc$+{63cw)U5*|`%?40N5y<3c5S4K1bY_YIjQi6OC-w`iU7O2xguf;l1)dXd* z=;?KAr1&q$DQ6oIegadv)Zt8b7wb|eok-2 zPOe@s{XETV2G00ZxEU#S?9Fg^77TsIpr<8;`u)+o0TfwKbrdOJq3(6o^x~|hH3XT5 z{51m8$S zUx;9w2Mz26}bKU&0e3&ii69A*1cYhk+6eGfvOtvP2rO z{4j=s_b^nS|EVxKe}hh-gk6WvUX0)nD&i6VrFfLnLAswPX$)g3xb??NLHKn^&XRdQ z1Fea<@DgZe|9^;k@2DuV?OU|eHV4{(n83EhL{tQoi~$vqAW^cYWC@A|k}-h^P!v#! zisYPgFruJjC^9ILljKl@H@Dr0=lt$?_uX;-cr{LsKGIV4)&BOk_F8kzIaf|sdRKHA zvF$Ui;Wf2o4Nwm@Fl;mI%CECfJmi(a(Yx%KA@hG|b)Nz+_bW>UuQS4oWn$+{o%J9*?}ynz&Ch>W!DehuB7sjTCJ^{y_HP zEAyODv^w?fD&~@gKY*>5B6br`tcxJ`B5fSZZt=XvEtoMRx)wIG96bSl(O5n|BE#TA zTIYjnGw*?68!W=hz^=xaG`VlK%?a0|ab80dbM*@!?NF~ba7Rsy)O;4K(rWdw%?(hl zrgg^-LQrU!c__BeIc2L{>s@o1uJMIqVa74ZCcbYqwwX)ox+(z>&>4Ga&lL7aJlbCw ziOTNmslkb(bdP7=^2ht)`C=NyWHjufST+iU*Zoj-pFcTC)uD4u&_dOQLhQXFlmfbD zb}y1OPjtN$Jn;g7_Y?PLS}-eZt3ucvL|}=|UJ`#T*ELR$JAbix;@MJX8TN(l`lM>W zs^-<#&YJMc8!b<|Z13!>7u5)SD92K|a_Lo5MB)trHqqTtK}Gdp29KR0ES5A@%O2eb zNzYE6{riVM?eaex&7UzfsA8Jg@1!Gc0<`>R7a3b_0_rPJC-xAlBozO|X{sVjepgRJ zmYO@-mcj*RF(Edn@Qm&IJ{-^L;b-LT=BDvP+?l7~z>#+(=?V@uDW5}U5UxFAQ&JP= z^%6*6X1Z|(6zuv}h!U#32DQmi(dnIR&{iG!Hz~9q`?{F0AlA$mg{CAOjkk(msm(F= zN=>%!m0J(=m8);l?wuO2xR$4#a_cjDTa>KD@sh{4arRe?I@y~leHmOleNP`e`3%GPhPIHOmw zbk!q&?RKCz6+AquGPa{}JXfsZH;PHTzggY0hfWnJ8eXiJU&i(_Oqtw~nkqpA^CHBG zQ0@B2nO@bV?;}Ne!XvN$w(GCDcKZ*;+bLg@jtrH&Z611E>N0Whi&78&l_TQ|Z{KrS zMq|{d?Tr4KwN5jlz438}3jeYgALqUlN$f`~ZTYF14 zpv7Ja2{j2K;6rG+KY@S_k}s*4tP$0;Q9>CYlfSpn?k#?!$H?GxKW2%=aU>`@x4%%j zT__tW@(JD!4aSLwU7)Minq){3!5A^lf(8OWX=jD9Rs}LyJS?Dt1>d+aA9(2zkS{Q? zn^3SoDL8w6c{^!(7I<;f(1gDF{bUJ$sNT#PR7td~X{&h>`33xeS(}NVUrR&^78yzOR{egm^FPw!*Md^L;D?P&wPvMrj zGo`La^LVKr4o=XFmG|t;)dEkfDMoZfPc;6~Y_+Rh^{V}TQti->;RVbz9%-+-;BrP4 ze!V9i=Ev!eWjYi5lRMnUTCE5DA{4nJ+pk5BRq1Eh(w9hCQxAKz&VzxjtEAPP zo-}}V2)|P6B20QG?Sp{BH#zJGI}?hUPAErk%j|-nxb4Ja?xm2(GlmfqrFT|u&-ZCY z`w}{Yg7Ux<3?M?Fw*|>b-i3K)pm#QNTu}BEusTKU^vV(68J<(0(|yL?Bs-8<^`JuB z(#wd3%-aT^b?;Xdv3VBAT)R)n*e1ggR$EMd-)co;sxstn2Xz;oSugswxZcj`&>Jm@ z=hJ1SYrdLZ#_b%cbLSq4In%t3MNZPad|&XhiFZjQ&);bjI#4Ldv^~KtHx?A$W)HcN zkT_UnpHldh6tsngx|9ZUySN^IQMClAg|`bGyac)ivA)MX7-ZSDEfIHCp1eQJnA1}> zRmRjgF!_Ijuz z*xG`7RRJ2wFvuRAWR1{=Q#gB8zoRV0RIC1S9y6H{4eqO9_;9K#uoEu45K05_rOE z^S|5K7*%DGq01PPRUMnb0qVm822R)3|jWB z?(JRj%wgi1%Z23~jscXFsl#$9l}no5tYubGMj{)huGac(q)5Y?S2-|!cuUVwgI$TX zG3IK|g4=v5m1_?gbicP_rlBx6Nm-Hm(nj9sT>8FGNsASIzPdbAVO#cm?fJ+Yjv)nL z34`YY-&7^LU zOe;{K%S$U+Psd-Xdi+X;HmS?sHI_cT^p9LhrY^cEo)`WA!#w=fty@=mL(ifc&I=@( z$hE+>2D0@~+UL-VA3w~%+4em9HN)5&MfzxOP+xH|-~~xKAyAL7%krb{9V*cpHK8jof4E zUz|xkf~QXDUz`eRLq)Eh?lAS*&XY3sviX6(5=2B zNO$-4+VuD4-XEZDj-k6M8BE99CQMsOQtG0pkFQuS7`Xs`Im`Ly!DNnwFFn7E(Bi*^k3Zb7)m@|_2m9S?S^XUPlnSvsG&eCFOI)qV$h^v6SnWxAX7V|2ByJ zcz+pZPm`lq#j0hMVd}}4c$la$FwPLZ<&;ci$>~x1n3%pG?BTSjgpeDE9G8t7T|Gxm zuscw@#zNxFscadOjpOR&y!oZm)S1;Om1auk<2>G%d`%MOVW6x_LI0zt*0T3-GoD-* zX2Hq`zDw2d@8j9XydNEtdBz)@&@_3415qFvH0zoU$~EBIo)!QFFK!UJK`B3m1$+nD zbTt0agbheU>7ihQiEi!(4<4B3{AsZhY+<5YuZ=zl@i1AX^Y)g{g{O38%w!% zTbRg+pX%z&R-8Zp)_N??V{(s8?7i)1IG0jfR_rzSZ17kf1GBXXg)YF zTrJZu)p#`{_>AEit6q9tL*T9_Pu*KR(gv5Q(+d|{iE^H|zqhROMp<-y8oKTm~?j#KJ2`PLk7xSdD#3_dW*xOb8*PAT*`vIFv(ZP=@HMCsyE?QmUq8uU`Qq*;^q}lmW+32LSbaJ=s#tj=nk#Rk-DIzLR zFv|IBAcUtu)f^lfJ1~=Fi{$!?zi8u?9cU(; zM_1(9+*mNbd{<$*Mg>}*?0l7R=hZ}Zz2mJo!8O?%H@5Lj9?$-&Rg8|TVQOo;ZnD+N zjqDNpnG^p)mj{pBoKMvmweU0=R5sodn4-rUswTOXtE*Gr{}Xrd4G%Tp1C0;4D*Ie( z8I*b=F9g)|Pt`o%)B$Rb5_t82%ov4*MunB}G^j>WenQpp3Ex4kWL&#ezSjTUJ`ybw{}dsGS%K0KM)jq*0n~LIZbR1_mwQzdjtU47>b8 z?C9n>KMtX{b+?D()F8^HlLAb-4j6bZT|}Zn0n;sXs|D=1WfiT`tZD`eSPJ-LB0txs zxs^Y;hM$3sk>P{r+|e$;r5dM6sm!MWQu%<_`a?L~|DYj(-#8C)oxKIM|s z^6{`_hTYqphGhYh4lDc_o7zGi^)snO8}nPoK4_pAx{J+FsQ2QWR(3Hc_8#^axVHVG zXXa+!1wS&`Gg9Qw>ct$QJrFzjq**k23#3=F|iifyOy>#LD%JxCn5GBBKxtaL{e6KnDlwfK2`hNWd*9>phKZ;GX*rIo)X>5b8zXX)JH^U- zDXPiQe5Uo&$4{k&&GdQ!=M+--DP0o%`~lCwn~U8m2_fni}vkq z(-xQ^HTMyZN zZi>}@dlke#!5TF6(!bqfA3|XHd5U)-mRh%XDDz&b+S}6NOV{{DJjj8UM)GW3>nH`= zGAT$JiQ$%6q-_>70em34dVN@ecI z<)OU+I!SLFU#|!^TqDD;HhH(8pc+cd=a>_4IWK83%QZ2AP?7YFFqM`waVZ=-cFdsc zP;%>8qQ;uB$0m?Pz9CGXcYSc3*0Wj?(M_GZT^=H&qo00)`TTRVPNR;rKLi6myF$ z)m-*`-Zjm~AJaR_3Mcxa$Jry2r_9aHIeH2^gm_M;D{Dp9MtkJ+aK}AAm}zl`D<{>Z zm$_Q`a>+0=Ex~+{babD1YfYP&PCLEmhrNIBJm2BwO1p{9Hs%Q(+O{1mlI$f1iyLKp zRe8}L7HI!4&zEssQ{T0i*y8fFTWw?NGAG&WbSa@9yQ>p81=!nFhown^3X@~=g)uQ;}iMn)vGmv`oub7Rz*IpLl*8KDhgmv`}GgGAM zFNfkB+(lG8Y`gNTooq2j?o_1xBH2MA0g@4&`aX95!`>D3kji_obio&;)P08}FZh+d zLRNBa!XrzI{j@l!)8&Sf&4;86KI@5`2S+9MEYa(k-N zxw3NY(U?8MSwR^o9bYOKx1N}nOYgj0ex_gvj=r2N77ph+Q4hJVIj_pj+Ek|$=STKP zUXq<)eo-I5IjvFVl0IiQWh-?DQPGthr8So;O-@+{V+6Q@?AcJztirUj1eWbaN4YFb z?<#GxzhPO!cIl$E>zYiTwJBIcqZ7BM`Kl{QOW)#ie8zv8EVV0QNY8q8-3N~1eF8{=7b?PBNWPy0OD+}zBEei^l+^nFVlQDs3J(1~DB2|*Bc zWJ1C)q#7R9GO$}1f+5syShKW@Q>vEdbfl0nG_MO4!ZLr8{4bmi^w^xK9#i*w$;bWn z>q*}YxtAFPQYle8Y=0`exs*ldnk;k?B_CWibGB{X?DT@*O0cJ}?vBgbLc?IM;g8$h zc5mBu7+@h(e`i3cqe4?*gnxU|p$iB_{HXkSM+^A`g9hy_H!v~ku$*EK2Ywxe$y`Ofl zQHG*^Hk~g{B=pK|VUHmEceOWNth95rEAxON{WMDPPk!&L%|wmmSES* zD=X5(xAWay!M%EV+J5Y5wAIK0&c^hZQ-kG8S1J`eT|zaeKI^N-D_8LJj@;YEtP?Wr ztxRnlIqddPrM8r&2OjLos)d~^|FIN`5Hf*X5R2W=8QcMJd;69X>=1oc63KvZLuQ)X z*FF_UcF03Q;4&K9wMT{eFZ27x|}{JhD6(9bKjzrjPr$ZA`5hO*FVQ16Jn&wp7{IwH(YC8 z38tv5`&CQ}Fe2g=^w74vzHCsqH4p+1&xB1B)2g_dbROFF2#2gcc^1U_-+#F|;!M4@ ztA*y_Y^C1X4$n9#i9rXA2)?@+S6jqU5{s*$%;HLLIIR65dB@gD!t|_vEvz%LKz4AQ zDEFw>(OxUrps_7}tDXhD3`kRaWr>o@j}|B;&u4nggx*q@X=^tkUorC_|4RWaLMyUV z$5$=FE@Ah$*VbDA7PIvjUWy#MLQ7k(ea~z)K(4+1-I#Ps*J`TK#C+J>*G;RpxFGMDakkP=~ zpU93_gsoHgZjt+jIg_uT+0;kBp7@1e9y}`%#CF0=Bpo+J8#7XM2@`*E(p)d>3Dv?F z>F8B}+#8*~jx7%qLJ3H{q!(x2Chfm47p!rNQm$ZBR)0a_92z~GB_W{Az`%>>SM}1S zzdCR21TC)8VR~?D-r4uQQ&;xt7ym`H_`BMe16u{+*(Ema+31?A<>+5CWY{%gWf7gc z=s@K3!lnnzdnD3LMj}{zn`I}KN@-;r>xH%A=q!Hu=o&OX3$x#%Wa+u-q!91fb#` zlVLKm>*4ub!hIu`moJl=8Z2X+XbWLi*u{I6`TS-!u5`an=i6+%bG0ym2u$v1bRpV5 z-i0xdUVYzUunun`SllovOTGT>W!D}7q0>o6?^zh|d-z5?DC4g&XH^tj#VoIR_=TFt zc8a<_@$EBf63hFx-So%cpghuh0z1ZhS^5u)8#qs1*XPfl<>=)R$}+n!knLhUPddl# z+ils2AwfZkQDO&Ed(*qFVve~1#62}><{|&WL*&VWjB?MgAGX0%&dlh{RYJZezEY(@ zl3-HzUdw^Q9Z5!i{s$zhqm?J-ZP}D$5c#QZ%0I7d<7gcxi~rk=!ZE#Je#)amRA^fxAPRmSy z=tzYM;?5s9l-kX?F)IpBDN!a!j{3}8t%QCRr)k8;k0+tlunsu#OKXrTShcPp7zb?A zHQy&s6gt_O5}b>?gToM?*!JuR``k~&Xga#O0?8*#kSf?LA9;Zd$-Z-^(~mhpLIn%7 zB*eH4*}Cfp>CC?@B7Q`SzBj%7{bMkcChv;H4;7*uL|&lB&z&+>JtxDY*t$Wl3wBTU zHGunCUO&AZPMthymdM1V9%@pb-a(Rgbb}JX4I*=0J8P06Tv>#n_C{_1KhvhF^Wf?K z`^vq2bHAf;H+(Ki&@0^`K}zUEgEEeYYdbpNi(%$-+vs_4@}=sRMCV%CcIgMZ1X@Yt zLCZ6mRjdi_9${CZP=s6=a}QWlC@P0xiHUjw<_1bJni?;Q2+@S3t?+3m{#kb96;*cy z8?f!&D@%qlBWh$#ka=kTz79F#jn;43qTX4Z^B!AI19llvM0sXhllW<%q598MDAuey zg1m^rOXkU(x;=O0631;4gf$K;#{du^P`%pAK`L~*OaU%cAPgb>QXNk}Hv35w3gcfC zCvngr%FzbX@|sih1gs%GoWEZG@3uaa4eJ3nl>v^4M2mh8FRucEVI{GeYVp#VNdHZ? zijPZdg6A4!$eRODyB-#l)9Iw*0~jDrEV4e z+Et^_x@rHTb(?`okJv3BlYzq)ssuIN#`pJ`x**6ys_4!dSk>5o-gHBzRk(bJYlA)A z*sXHbeh`KLSD<~oAnD0*IWxN>*gL<6PBP741A!Y_WxR|4grGpoqe2#r;+%kPfz(;C z52PMj@Q4vxHdwy&>SIcB=I3mf_f-I@&i61ofmPbriJbV&T!c?byW6gUReB^i6F> zX5Yq<88(XZAe;fwmDIg*CE@j{sfWhtbgyblP-TCL-@&6iV+fh*T@jqo{>@aZ^8wR_ z?&+6vcU@sAo=hA(`TYVyLYbfKiL&z6dv{(W+j8_IgR~Wz$CQZnx!U5J&R5ux4o?vU68xaujTRlr&c}3t1aZIJZ9U((Dfc>D zP=?yip-Njyr_;&EBX09d!dg_K zq+F^ly^u6}Z^%9=!~)@*_}L1Dx)*FKV&es9%@9_U%V0=Kj6Mmlt2Co|`8^}5U+rhj zWV@BRy19+&PtAQ3B80@8=8>nwlI_6*7(k=)83r`2Ht{rg4_tSIr^lLGT!*1W6x0N7 zvgY!KS(CniElxxPS?v?2uXGOh-4Mo(wfy9+_vzSZVsAT z4Ks-GX%slL6q6Pqz5+Z%i3Y&1E;SB4U*B|5bPRAP#0Lt4!~fsr9Yzn+sa@ef1VGuw z_1Of>hVocZSGZta`^`D_f2Z{S%>#?&yxEff8=olR{xo}!VmbeB+_+fYtj?uSO#ctu zxG+BD-;ZwEvt>T4vUuh<8MfzMUy#jp7;MJh#j#@h{>ZJ61szLgL$=MO&wEz@jH6$& zpS^MlC1^V(`orn6q2Fy{xc;&y-ekXCO)Qa=Xs4h0&|eB$=^ypl_7Jk(gZlxI))6`H z?8|K4`8iX)|LKr6=P#Fgbj7N9xP=?%d~7L{+?{idhX3Sv$PA4py zPDx4<8N1|#4Nfl*Nnamj{P)f{x{w=AM8igRibQAB@lPfqwQ|s9U<%qlT>dqs!DXlZ z2@SFO;ltOF`f+`Ey6>nvUoIc43}b$F5$2+$`dP$=UXY<^B+Zs!TRrKwwbO6wh@WB! zefHZ^z$qZ>=a+yOmjzRRe|$&)RgyRY{`?DoC6b{Ta3@BkwHxd!awrm=DkftIPQ1&@ zBQiq>G!<|?F(SWK9FsSIpj4q8gO$;6G}P)u=psBdR7R488#iv88e&+sOb(|$S4KAv zv0FWL`gA$?TC5VVTB145TabAE{jwPQSw(ed;fl>OxY*O^pLjs|M3ydYVzf}OuN(%Z z>YKLXin%j?+#sg6z;i%BH~9Q?A3S$PWGm*rD-_RF!S)j!yCAYXlxzkI=nQt@N)5~U zAaDdU-AO$|f~HB>Z>NC`aRxYl{$(>2exbxE(qyKtuI@_XOl{-z&M_ckK%4 z6#w;zQOwxMp?Ue+EmnB>lTK~TuD|}u?N5IxmVzrI;9+1NrB9v^QF9;9NU%zpv|r{K~NZe9Xt5tIv0E+R1wTE`}gmc?2$+NBd=>P zEErC(QFuSE8Ww^jEI2K8m7`Pkek<%WpV3LxS z^ULOvLQ#az*9GXxEi$Jkx?&_V8&M%)c=4ec0-@pv8NyGEkVFs=kXQ~=;nNH|H4lU5 z*vmL{%`WYm%cq*&kXYEnPg6H{fw?ie#B))HY`_q@D(~4P2n!4@le>xe7EtJRJUS^u z{F^6F$-&bOf2p;}fJS(@IKd>E>+K;+vs*uJeoQR>4&UtG$<1%VNCZc()fD9m z!3OD=&oTRQ>~#6>>r!7S^>pv+sNeFAss;1R_=ven%CDpoE@2FRoa9H;UqS}%v7huV171iBr_SkSt;_TH_iDw4Wk&|!(Is?~Qnjd1D@@Hpq;3C-qsTawjvc#2@Nz^B zb!g))4K~kO$KiS!$c3Ow={fEPjPC+t-)N&l-;mYoxl0Y-C91_lDY`;!;r{hQ$Ikmm z(+@zA3BSu^li3qy>+JrktpfXtczgbOasPkM5Xhv6Ya6i_2|?d8m3&IbM4F_*;M{%r z!HH$pM@r9AIGf}sR~2dGKmToinEtOGgD*w2g#lNV{PISiP}b!wLoNk(cQYf=f3Mb2 zFLmaj2!)b=0wzs!VB194&||;_0%h z;-FcHiq!=#dfk^4wiHe^Eo`gv3GU5ajDl6B~-r7)p1P^-; zkQIN@A;!D;?|G@Qw^_c7kRDwG$1M_Rb9LT1d z{4_KxrV(S2!%OCjY^wgmgNiFSatdDW?=B{hL&z^NaF8t3cU{Ui55=!IdR|NyOU=M3w(F6;qE|4ij zqL=*oT*nZS*+6f@3+Pos4)>8*O>I@4J1KI@aAnMM z-PLn*8Cs%bxcDv4PM>

GqPdb zi=6)Uq)=N&N5@e2Vo&s#33D5B4{6#2ir9DQa9eihAti_8*X;0#M2o3DD7A`WN8d|f z$vs1mZ39oxq3Geio!Hhj*Z#uF%h0fgr_R=d&GOVv^*$*YQ25lcos)r{$J+Jlby<#- z>84V9NTzz!5~~vK(9f6Al9-UqTBg}mJojm%-<~5YU~$di3UIEOcBU`cG?%qkXj!^h ze&QJ7$r=gifahBV4?@rv4f-LNuG3?MlMl)ta9N6cHR13fgcT?Q7B;q=Gl{j1f#B~= zd=0DLzO~EcJ3Y}7V;mJ27)ZorNZ8s?BZ(3H-ZtJT6dnlvCpA2xh=>Sm!pL(pqYb4f zixeqIsp{#4loWs^BP$Y z@}CxNT5p1t`Zl_(y)E2r8p4HIa6-0Ezzp~_JbcV<34)}EZnlZm2{Q=ssjgr1kRp7m zNN;`a+&MmG+)pU@M|HcQ#P~P)F^L0YX|DGn`yfvCVKJ5V^>-d;daroJW+!Ir;yK$L zU~Lp}pSo61)n4F|k`d&j%LpG-l7JR%{X z9qu{voFt>PQFIwsskv`q>GboZ5@N5^I^jNc4N-1drT)Cb8K2G*8a>byOnDwa{_SrDTYQ1c3av@T2O@Qj9OBS9g zJD%DuObxONZk=0LV1lgY6H~#7=4%KLG#s7EZZ^dXgRpM4Xmt^P5^D+TVix4HE8L|} z$|Uu|c#$J71lHz}PVq~SC7}|C;eFT^O+mKgY!bxFGM7&D-C3;9hX{!5S-?{un83gQ z>bHl+3UG0jmX=P#mWVp3&R%*}KqoWqoZv~dob=W!k24+m&TY?pa}3*=HCIzLGqZ$( z!#{KJw2yc8=_d!3raB$#260%^a#fCS-giEf)8!m2&sGgC*#gyg6HRl*l{Zwk|1;UW zsRzV~Mg@!ITbVf8(}iKaqW&bADesb`?i^8&KjS_9C;i9fzFZ*>n0aaasp4ZqhaK6Q7(P`?y{q%ht;zCZ{ z$Px)tG19dVe48LcngwKgE;Nkm0MIyQ<F_e@cUcqHdZ&>x^)Y6>X0u(V=J>Yg`BBp(S4ski%SPu zK_TRGkFJATCvr*V$dR+W8c9bLE#dJ^-g#*eZ=25XrMS>>5#BAnhFuaae|ph7>-0U7 zOP$wKPK?N7hUF&AtBFNhP>(fj!Z1CgQ|KmVR=cafV+cC=AUd=Z`2`rHu7PYk3Q*{m z2|}Z1)PZ#5o5dg8y&IP%mLpg&Ead0=;J9gZTtlZuoEu58BCUGH^^0Oy5|G7L@3@`% z8ts=#;j%?KKEsZE#~jepv-b}mfxW+C&|PT->u+S3GOzkn5(36xb5jno}7n(jx zkiE?^FX|`xBv@%Sst9U%ouIM%9E6D(sUz(<2?NSK8hN9kpmWEMnF6p3fQ+4SludYGKeP zIzE+?ej+d@$A#8$&F|SW4tV7`k>G-gD>=NfR)Tt}(Pa8{51afWC zNm%|EUd6L06?8sEM`|o7Lo2wSxbU+V*KIjVsCIb;V182f}5?16jhH+?>H-8(R+HgXVcyh zpnUtdLp;N5rbK`>m)znX{Rw-M7uBgKR8FJ(VFo+|o}kx8LNOp#SL z*Q8hB^iz@KhfpPQ7A|})=d{h7-}4?dk>97}oT6eQG|tao--kjPKvSiVHN8CX^(*n~ zX%l+OO4ANZ5jN?sH{{0@b@P~Q;xHWedho!aqO*3_sZ*z# zAqP23It{ng?gKLD^U3LmyF1#)-;TQz6@xmTU~I0*FvZGx|_Mm zheqNoM8nahKC@LtI`X&Q*7`E2ps)Qt{@Q`8Ian27QuprQlXnM75~E1Ji8Ip1XAKH( zQ&DaM8VJ9z=p8$D5J0OW^9BA{GOOb^=@P3P>h$Nj?QLD$nz_(-FuA!tL-@3>P4VZ) zib&}Y4w#_Nv&{GVAw%oKyUo6;7yd@Asfgr+q$Iwjez}!;`1E&Gu7D*W1D>Yl+d?~1 z3PvUUdQ73X<>P&Jg}F!|%!mz_%z64wg!4kaT!FbhUHl-iSrDa1$=Z9eMr<0bFC&vK z0*@RmB_&0h0S=$qVVxs!T|fBQvpA9~uI%573D5OwPDGRJe@1ALWtaH-FW*i-*mVqs zDPtlGzu0xwhbXhec~wa=h{>ZK%q=_vQS=CTQ$z*3Pn-=LpxW8pi2Z7EHtsDl9e-sp zSS8+?K)lLnu%T?PEg<$d8j`?ZL7ehjFqO&6VI&-stZiD7o_CETzYVr6Hiz$OJ)0=Z zORDC}FbZ;@^5$pKRTj};By$IMyY+LCU$qDW%;-foED9a&++@ho zSYtTY!}g{hEIS<4XDvO*Vt&llEN3j6`a#5tLift<1~;P(9PhcE*HJlzFrsNh?Rh5W zU93cv%2x`r2CbniQ7i7AlsM=(iuGqbtDvU#)E+rw2LS>YX5+OsJ9qA$eJQJ$R#Pi< z@N<)}nYJ@mCSh>6k0YcOF3;sbw8J^MR*-d%$cV;pSaP6#-OQ({6arLae3)MJ{^FCViB*|4<1 zDGm1IXw_COF6(Mk!o!CTr^b(F&}70G?qMqD&kI9j<74b5|N87$>0v&;q@&*O!=^#i z?@g}~I39#Wa4AS+0s{M>L)7lTu`?U@gb7orpbC;##-4Wr&542I$ev_4CFe*_C5_h~ zo@<@VkJ!FM;gl?6zl>Tx+K~cQST7~jYJ0p_tkbLmmxL`_s$F5{$=zW;N{H_Bs zmrIQm8h`%sMMK9Mm^KYqqZhMNBJhLYlo@!<_nn(mmscNEs{1k^@siJ@ZrA5eQR_Mcvx*qK6s{NRPwUC;$-@U5MidgWA-NMlVLPu2-?|h8@UKUP6KLgi3 zH_(RsUyS+CI)Rjohxtl{NCrq}KX2X~iCl$Zqz4QiO}QqQ6H*m6>?Ek4)%83zq|oI1Nos4OyI++hG$y6#q&} z8_rPDCV72*@Nn1aIjxcSg0QW7_wC!ZV~6>;s2jY_d=I#QHk)s(T9bKwexe~02fqug zh&zrH^2*_|ii#>ot$Q%$}@M%a%?DETPsq@mL&6_XLe_cgB#ksvd?HSTYQJ91RW z;{xpqUwQLmS$R1;?J9)hg~4K)pn}kZ56DJM+cfnqJ~8|=8ZHba%z`2%h@cMM@%2pv zM?vIQQ1%I!h?`$rB(uXfEv*0~nTWW)FJFRaMncc000%X7S_#m@0$f9eF&*r=KIvZ! z|0?`nr|@Lz8KyPF$y-<`9V%}B3vyHo-h|KnuI3wB#aj$WS(4w9Nk!TuDih)OcFMSC zlF?CQR5G@WEZqRVS~XpXm>s^OtL7o5aWBs76bLmYgim!`djJPP0zA18;&dV-Bf%08 zV=_)_@-|+LYkie8c9{ogf_b@?3keqw9lP3n2^S%lBccG}FP=YtAC4T1Usg|e0`@U6 zg4flPD2)@*2v#wH3{eh;gd^t25Ehv@f*cnE6vK;AX4EG#i3`hY7UVTz&4_li)M)C> z%f}X;fiGWdk*jLvkJ<6@v}f*bxIuc6aSFmgiCQz_P-~cNX;CC9cJAaEY${H}U>ccL zKm_F+j)H|^0gT*;9VhcK?5Kk<&t(kU8M#Y{VifZ%j!@&?a)07`S(q6~B#vv)Bd~-w zfohosc(g#Tpy81v3(%|Fxt+fE? zI|Uz=&aQr>33SzwNJ1KU`h9?fEhzUQa0NqkPXq+O7SO#z+N(9SoI>ePq<{ps+PZ>LG62TY6tmlF^702#Z-?o6S62Fax4 zgAC3ho-c#`>|DSGOkj%^VLXMs#}mVSWIzI)R}zUF^xmsht->J5VVV9U=(ZT7x&+>- zi;5{?hg`|ktWE1$+;>U#Hb$IWL-M0@4l(J7BS|Hwxj+c8@+xg-e=tLlwMNvSwyM#J z8}Xpl?!oiIX0ycFYiogGvZUTEIBZ7_mv~Io61%({s|fU&ZEq;xOlkb?*~4Z+H_a31*8pHpDaa z3qG~E=C8L?m%}kCM~h(7I933NR`Bq6-$#iO2u$^@ z{Qh~w70H2Po*l1jIX|SscDYnMi!E?uvnI{DFY2MJP+?)=u4Oq?opPj&fv>anzh$^n zILqU?bGLcQdCbnD1g4+s$V4?Ae|IRnP2H?r$ZBxa@clYm$Z{W=PH@LO9-A2b>~xl< zC}F0K2AU)NNRY(8$d!X;rV~yET#A*CN{0roczoWMx$R8tzbTX_v z`VIaA9cWOaxxJg*+}c|o;f9q#;za>2NStOh^B{?g7=BJUYOVX>2PVE$6NsAwa&jdW z*Fs06rKP8UQ3o(mt%)pj$^_TR$SWvF<`^k!z^quG(S>=JK}S<{F|>i)GVTN5M=k*L zp=XIF&2e5Q&crnY49;~yLZK2|6{=|6&1oHKkHDT7Lfb3p`*<;{>K4u)Im;H93)G({ z!%uQ6--t0Q<kf4t>`mW=BCOg5u=eKqnq2#Iaft+CRUfDm@C`E6AnJG8K#>J6WNA(e z%e#c76qADQ66jQG^}_%Sl`O=h!6k|%Spe0sU>BlF%@W`IaE{`BT|T5&rkrg+J*E5~|; zYRtALFxJdj5q0j^l{a_Kc|3YkR<;9bQ8=vI7k!!W54c~M@g6>Wln|K&=EsO#mIAeP z{<%83WjT!c(cTW!a!}*cj(0d8#?mo1&Mk@hzcZ#lNGx7-l83z$)?9r&IwFRGj zycuRPB4Nh3F_Y}eMYTEIDdEWnYx6KiSKgWD=#Q4H{Tf>5!4NAN*U%3v9G3}M;q3pL zkrMQRap1WfAXE&ZEBM&h*rHGs$_W7jbN}0ayAObnj`c{18%jDbS>ccEeAbs->8#&a zQI6rJUJT+LBhnOA;Om4}52PGP;a_UZVFW1$33&N@knp>K)|Bvi+|04>@Zq=267q5?8PaN{ccPKo6r$T8ui(8FQKw9wGWG)rJ_|9*?9R^?`Ghx8KOuf2f*w@J* z8SZZ(m#Sdm5aWtK$MFbF@hQ>d#H+X&2L?g{U$G<9iO*+|gn5L8)j<7W_V7Bbhb85# zVByzWIDMD$F+vp!&Ru-!%TnHcetv@R)qzEz6Tw0df8z#a-TL*3z<(r(!a|iW<>O_2 zg~JC&U^fvD$z3fNSRi+`BxxVV#AVq>g9*C_=nQb>z&U&e@@AOJBh3q<0y2y`5)|F$ zO0xQs{!SDp94$o<+T>;UQNxWfXxrdxN`M4HKpu*9=%t=^T*~$RvXcV`j^M(Syu3Uz z)v2ZR41!!TkXV8H-u6p|O1t;rN|0n?!+^6&(rw};z85I}SojBdVMsHd4yjSECuYm!#Gb7N7qWKqv3!~_&dNeIuV!awiYl>`nKXGa;= z&1{;TqWi(28-e9JeC!y}Q7da}15k#MMo}N4A2EUkDJii!*ZWVGfwjd-v~? zi-|ye4}KkB>Bqe5{$%Y81-NOkqmR&o1KBnMGEKwZ0*L2mzDVx#!38@b_&Q)J2u_DP zM6u2d<^gpS@n$r87V&ZrC`b>5U8lj3hn|aUJO-n&AR2fm!qGW0UA4CAWrgpSy{?t0 znbHDV;!|kSaJQoCVHQr-CT3e#2f-_5fnR{vR2>5*8#Zp-h9}3RJ>&)%w1ItE>nS&$ zzzRWWv!46*Ri1Rd*o;UH= zYhLZR-?x^Ms)#i+|El?Tv~W`SR>@!g*tCVrkvnUrUp+fma{Jb;DfDmosUm;<^9QSc z{BypO`C!QyattP7bN~IHt5<=>|Le!*7Wb>a{tim`;EzB!fBoDD{yU7~|NODJMdnui z`pNxi^{c=B{{QqZesusa7pEKNEb4cyrNmt@Qj16oM)nb+rt~upQcm3A-d$?UNy%!> z>RKLQ9&Fk%`i;f5aY2*3`o^{??Ds!^{x4ktYe2X8=SSF7ea78%lv84^hW>;6De&f3?%k*8Fvq>ka1XpI=dK>{MX8{2y9RJ+)>aUlo zZ3mF1G#w?N`mdk=R>&3Vb^Y~wl+M6+&8+|P2eG(vblp)(girlHpAGN)U*F+-l+>ty z{50Q@z=;&O|35xTg8z2SaE^&kUj8~;keIb-dy|Hi5N zSDHORHQI2Qa)K3(gum9hvli-Rru(!7m!#7f+~G&=sw3Qotmgto80(sA*4)5c+h6C5 zlIuS|**^reqy^wVd0FQ0;WN6OUUP$4@vnmwQCn44*Gg^GkA-ecz1ui8Mx zJzvD~bOh$-m|zx4v(LZMOk4&r;egW*iY7KgcN9(l2S-UO_=G4yX&yHXp_gxh9ITeYR0gH5n#v>mi# z3;Zhgxt3AMCS(z{olf(=`8TbaoqP{ihS1aKN!IKMJ?Y;KsjKeEbIwmeYkQ=_T;!@Z@Zy==uLEV@1?T zK$K)DJ*+=7Got}TN8d!)rnAiou95a5)@vx5FaDM9QgsO!zI16=P7Z1|5`FiK@q$P?zm=yt5OPdA+c+zCzM#FwjkO^g}yB<@$zk@ zF!%mzXVkWq)^f+E>cg+l3b@G6paPAPC~1h&0j-j)aJmbc=M0bfb@(j}78igY(DVj)N^ zO1c;S^#Jp|^X=o^|NHG@?{&;L&Vu!<=egs$uQ<>1I^|XR`d<%LZ3W%8xcQ zG%)m21DUxh?I7z-5pIFRn)*3v+ z$T4@oiUUvaJqt*AsY3XlPrn37QiCElfPC}jo1(_p|20UWF^FIdWE?*v3jvZpbmIgL zlB5MsYW=z#Lzzs>{+#UQy4$#-RW z`FN)A2&5MYb{r>ihqeoZ!Yz1Pfuz?!%2D}A<3a4>PO?a z%c;G}QJ!EgTe-AwP&ZPV}dzLUQ%mr+a{3=)k zQNrKOZ1lhRO4U;=ry0|qpZpC=5L#xXr}s82cOXShSTm<^ZnN+Hu11kb{5fwsr6ry0 zo^*WCPBhlf`Mq&}V|(IWA_qOQ`rK3O~MLdr}$?JSj5oWlEeqyT-V` z`{N@gVTFxLU}1fGzh;uRy+SZi9(K6MHT+q+&HmV8;!A>E zr~MU&QyA~7VSf$8MR}q=SjjPngR8)QcvDb>`>iR4FMoj;8 zSXev~s4G^v@$sUd7=lp$AYTY61Vn@i3?&@8^pN}z7Ve`SK(+mvsY&8;F<{*u*dm2& zMz}Z8Y+AxSer(w*^R+!`41<6`l?IxWggkK*Qz?_J!BxN~=j8OsD#qz~o%Wz=d(~Pt zsYg{?id*5fBTBw9XIaL+`-3ocIK?;d#-CavlpG3rUn3p*e=A$zG?mxg9A*>i58{=R ztE`asSGBU$a6Pf-w)RsfTp(KzRWyq(cx#32zZdg_;x_-y5((`5V7aeOmC;|{PB1Pq z_b1q#tgs!q->O?Bt~h;=_-*{1nYp5vyw@VNuqag&r~K+`HfJZco}2Pl-42$YS*n#P z*NPV{9MIO%qAMNZigHI%+xCZM#?S0eUL70Y@DHK;%yqDjDRAumBdSkMx~V5pmpk3Q z->CN@;sA=a!nso%0`l(wRxpK{A9%4*2nhS#yA|sKsA_-?IR~Ov498a|kcok)l}ll9 zXIQ(QNGAKVkiVZFQ1+yfV|nR;YrK6|koi2Sn2WYV;ja~Pw{r(rgeDc?pzJA5IEY6yfT)PIjoN#3gRZAQd>V2;MTwUoW z>+;Mfz3_mLFAw-^9;x$fudFXWGEnDNa^Y(7v( z`tT<+p^D}PK0fy@v{u$%w+A3Lht(BkPipJ@wSZwkD%ocF*Ku(aJ?Fy0Qzd6H5)S6M zC~E6{*TrvU=Bi>(Zj)b;N>zIs=5Ys;yFn0CeIcPS6@Cpn?R-kDSjZXGzfL~f!5i2^-1E} zaJW%u2HDCO83lQ4nZ5*bl3{;!gPv~sVXz7@aY(6sV7K-GMb6@g#`_dU?7+izK|a|{ z5#BOTbTRK&VBYe`zNoU)wLvA>vXM|NsvB5R;B#w4SZjTbAS?yrx%At@NchJ|MvEnJ z)giF0MU8EZs*k;_CmB`pV|L#axGk_`50&d4%=3QiS+nrBhoLrGdo{vo^DwUK%X1+i zp|zhQ;pP)X_t1_rPNC=yuFCYBFIlNK(@RRkUWwPPtQIcaWkyXmx3S)KbTX3sYik=@ zXcHcZ?@63$IP+yGJd$vglIr3|fzz9PC9C}5DcS=Gg;w)J+6S7ZLo@VkKj$%cUiy0{ z+>-U)ojKt%jYk#0X-q4 zUome!iLPHA)V6fxRQ;vCY5Pv;gKk=l>$^hR8V}1IePfc!1os`SK5=IL+JW*)-T1j& zAlIOE^d#KL>ZB6|fo+uPzQ{q*#A3LHkR$&@b$32_?+!f^`;1QOWxDu=YRcY3Xd$AX*s zpfmdOZW{N3V3v#kk!g2ZjagTGrBM}Ur=caXVaP_;N{J|i6lP0(Lb}%bqG>hCpJf_V z@ZL@Mn(A!{f;n9y{ltAgr>mZoP>qhpx@=T3{`_H+QH(M6S24f8#BD~Yy&e4>6-~#; zs;e1v({wfA=DzJr8aK6YO5OUL=?1f^Kz5MtzH1~Qhx;$F(mKyyUmnRuRyn-gVh8oy=dWP2Fr~W+}5g-UMtKp z1395{j^;~WCrEFYbDEB!QN~st2{Khhuy|FGj7$T>Q#;A`XOojSADt<(_6GB z2pCyzJ$h%yNqaB1eMkA8OR-HF6KX@V)IC^b@j=h%1f7xtDjsH!FFSSz4_CUlmt$nA zSfy=oVNYm?pek3TN+l0FxToZG<2y!bqwl-c2&IeBmr)rP52M^A-?qMF~7YI)ahcaILdRq!^R|`YO?T0;t=VuSuq8+v~O2 zQ?E1&EJFPJ+XK{opIOS3Pzb{JMb~k4e9v>24v%v&e)~BhlQK<#K+eEK&d=~y{~*h3 zPoKq=Nv>TYv)<7aNjbiS7+TK==HS`Uud|jRA48nCccwe;yr>do?k?5LYW<4oMh;lx zzqZfOl^qDHnO2S`KH2BrY`Ih4kC6-?Vegn4WA!pKcs4i>(0+$rN;?5#A~S>PA2c01G1tgT4;gkfOpOas~n997vr75&s8E)DLALYm zJF}@fK$SI{xq70jlyJ8tY_6a9y)NH9hcDlD*cZ$Pt`}%v(-&YFmeL8(>C&;cxX$+y zpZQcqDMw5!?$vrAbZx=R98*xup5+!9u$UN)HGcE*($JLbFAHQ6wB z-QL%9rX>aM>AhAoH@jwAYm=g$hu$k^#7~(I819<1x;Mc*wTq7eO_JxZDL(?tN~0@+ z&lQ+y>JS$_1gx&ZgKa=0bwUj>$2iuxj_J2Opy}}@yH%4$MDUTC-o5L=V&ZEbLDxD& zc6nYGO!W!|Pjuv`rV*~9WnQqX`2%lJF1nb1{V6vGqiTEkW`!d9bo|DdU&|_+1GkJ! z86D}+(FeIgQ$nu@iUMsV@=+ysszi1D3(BMYB*_Oq*+mULOZCW(LEnP)+Cz29OJN!T z*Fmh$xUlaEqJQQK@XwN9tV2)lMaMQ(S6*g#o3RcbADc9E4qI%Gu-Z~WHt`sg4o2Y zgDFu14L+V1x;=A6*$Yhz1=Oze(JBCVxXkC(XB1Wfe?<$l6yHMX@WEWL|3sj>Qk9i< zYjJ*!r0jr@kCi>cQ04cN^jweREO)6;JwlFJ932+UKe{}XK(nn4-6!O}M6$9f#6`JZ z%DlT?OKcv~CyXkT(=sR-T!}T<)uEs-^UyL-3mrODU9o`i=&|^sBlh%_?HTGJPc7P7 z;~ZlOP66BDCccPN8AeAl&2Ks}_uJ%UjdI|SC(#m-2VehDS*(p&U(VaYCz;wHnb-A> zJ=B(6^_>*nR}Tof-sD!vB|9T=7sv;u*+Q{cX=e$FnOtT{6+HAvg%DUxh1M8hIJBvR z-AYy-dR+=A9T&`>B=NLhgeFD(AEioM?0ASzAPYp!-pG z{@T<$twX47tD#VDzJAut^!WJuiWs%A?Y`Svx4EwDwQ_xVe%8~qf zbk*ybg;vps#TVu96p|cIC%Mmq&rFnF-!$!R(%!*MU_y0`wr1u7vS{AXsvz;l(`j>80k%)qaV;#_9^Ml8!yeXWJC+^ zL}-niKvnZW4i+2z;)Nm)Gq{>SEz32qoc@iFurQ4}O?H2`NI7_sxuvU8v!qTuJI`)O zJG`N0qJ`;1*(PQ86f;4Lj-2HIOssGWnp5F%b;PwBm7_Xm&k-v5s07;URXK_G5Hsv+ zuK0$O?l1H4zt!9QeS?tZPrCz|ng`(ryS>zCMVTAdQE5WoPM&ND3pPaa{M=p6q9h?D zYaUuvS`q?N@#^#z<6zq}RPW}`-&|>1?J23GU zwV{`=(cO1-p;r`0=2N7v&bzb`oj%$7$Ib7$iqsWdObf-dQ5m$=*w;U5_|3OoX}5HZ zy6qCA_A4Sgu&_I1GeEwKltiSUrJ1#-*q>*nl~4M8m>wp2TyEI*%Zn3otyRUFkNrju z_8fhN&c-kP_UjI{A7^p=r)aL`a9uSe3Y4&N3J{_2tM`8zO`3N4G`Do2H%xNsn3EV= z!s9G)pLcMUl6Dpc%UzSdBoeR#siEMAG0hGNBg zf^>=@(9qbNBc&R)QRObBI8jP8K}Jj62)%NFkD&F9fUNM zTwGjcp8+5zBb%if=~ZN0mwESPM^`~h$QKvf%6L_o!Cdw?DnD@!Rn6DyP1p_Y!H! znj07y9=&mEe=mi)B1$gdo8Y{(m(g;`Sb}bu@K>k!r5)u+9#%Sx#kSgb#F%x}+YwHu zdFPd1YiydSo3$~8+wIzs2cy;uEc{%aPjwtBr>cs@c!WK~l`9{-qTuwG&$Md2++F&d z=Bf|<^#Qq9ooh|4O{yzjl@@T*@qMg=X51F`D_<`~?ye9=ve|!h+vD|s;=Iz~Mu7Uu zZ8kP}NP2^862k8SI70f&j336^{yRuw*aPjS!GOJy?L26lq=6W@Y%T%s0?w6H@xWt> zm0WOrz8@^1qs+^;ix5hAvR4mtq)3`Yc#F|xv|6Ozj%AgX|^wOXD;QA``W^I zpo?Bza8p~hn~TpUm**}&H~gi;tF{ea_E|j0stY@jCgWBv*cx`uUwWP;Y2K~MWAqdo z>cj42w@9Ju1YUkYvlV%o%a72_I_8FaVfMT2=LAe#0I?i3r#z;QAr?Vp}sI_)`%$L4^p)v=sRr zP-IbwC4X97II%NZH%AbW>@H~T>wz;&l1AHd>*}&raP1q_atqidLEn*FSLzSR=LK9l zlvB75h)`x#3XYYjMPxF}`*zgp0p4N)+eY%#viGk1z4Y|)~=m^ znGB-o;?ML|;l-)qk)CgISxTn%9;`ZGF%sbVw{-RG7xkvNEL<32N*o@uR>$4-D8*L# z_bit4=C2j{^DwTST1|xtqmuWv>a-A~c7$k#05$~L0(Jqi1`(P70TfXHx8);DFMx^- zYFj;e^bQg7K?IeW!&zAg_n^ra2$=z3rF9lV3v_y@vJp)(Gqk)7ck~L;Jf&LlGcm_y z25{)A%r9{%0AG5OwsygL&M@qR_WdP&XRY44TaoKG#!No9iqS|&I{UuGR zJD#4F1Db)%HZk+VCk&@!J8(DX3QT52u-%2PbXbepsRX*ZsY~)!|pPn;3$S%;05HxH) zB`>frVz4VDr&WK&2Rl#zj!xvhkGe+qb4&ZLT$7u_%+2Ue4OBY?Mb0HZt?r>2ZRR4j zy*8}{9(_e~)%?`!daX%diV3S}Y8K}DZm*!1SCoTJ8V$E|u$e=gdmcEJL!ioXC~6N# zK4tJdOy4+cuS^0_?#oR?0RxJfs(`u&YGon-c0jYI5rmB(ch}V1tOz(TIMY8O3ise` z0k(-Ldt!bp7jo#8zycSSKyRdE2qBV1NEw4@GSH$!vy+hgU1`qMj;feg=M@TPr|4O! z&s)WwoD1o!+5;IJvFc3;xc)QClP(s3Gv`7g9KGQriACu?={c+omnRAFMa z&h?RW+qUM~$endX_noHJmA!*&ZZBO572DeSE@)KXtEmSAfkkH`g%&_4Mv!P5a5G6@2Z?1Jc|-bOX$F>9z5$qh>!g$BnYFNmUd^`y4pVa}snJSpje_{6b_ca2?0*rj zD8lTtx=1b!+Py(4kD|1{p_1A_9q5e*Zrd2V9$BQ&QgMepK+#bK;Z-BxT83{evu^JU z*drditOC$<2XI=Djtl~u=TPsEEQG3Vxx%P1VgLHHuE5&ZdpV^?Pn6!|8woC3=KqNnx+3%^+sJ`k~ zZ*P%!$B~%B^-dq@-E}5Of^*jL?NfVS^`5URuX0C8^9pd(PvSk6@|)}U&V-6~xjAAV zh(~EKu>2~XG z9etWk$ggfBe~e(}IZv2}lzW3e8f&yLo*x^2X5#ox*mIJAgd~=a^m~C-d?uYPlN@XF zwj1N6&ViLLudKa-wV`~v`vKoej<{^z?2jK)tRn_-KkY^>VDw&ph^7_ zn3L2CDDR=yP*sEu2+6>umInDR^k+kVkXK3pUgB*q!0IsDau-!=e zIspG=gQzo<3nJZ7lmKM}=%&w#i?b%t@?U7yBK42LPMZ$B0_LxCgj|DXg9Jq6Ra9cn zX*x97_iyxThk&MP6qjqn=)uyH`6nC%`+J7@EmgeQj&}ZyJ zDSIQJ=B_#Fz*#$Mc}kv#Qsi9(7l{r!De|q3m5`|wKy!lXPG}h=Y1_Are>WU;Bf3TW7e#wSzOWGX}==w9m7{wvGr9BUAd$-yVCjdd|C6UF@8S$R~q3? z_os_3R3iIQ#W~!IZ8BxH+pYD4TlPLLgxLn2eCjz%PscSqh~LyE$nlpA1UxyO27jH% zywbC_vHVD@{ucU+ho>p}tvfvu&&h0uY*2?QJTv->liJf7zn?hkQB?FKWN(d{*T0c} zfpkDTQm~Hq785%)SF^b!|MGM~vZ<8RZ0TgsW@z-g*;4QQDu0&w#Pr~xP@npUABCj* zpJe4kHUkys4$~I4HDfxKEn$waB+){e<3E&WugMmc?Y&^XciU>vT`coJs-TP#RapOS z!4*{U^j+*e(W8bLStRc0B_ub~)8(&kCL~Q7qsGEyQw+^F@-g(Q-y_1?UeuhhuSp7L zyxGEs*IFbr4j&y-S?b&lw=1Gw3Olt*rWjddYT{r$!vG%qj_a%KW~WvGP8$VtF9xqYf&3t z1_cL|m62#=+Pmb#Z-LkPpv!4-RZ&?Q7Yge~jT_9F-{YuB+OW8mq+@d@bgIm@VcVz~ zlY?QR)6v08I|(H?&gn0&9!T@Aelr(+U4={wR`L~ZcI=i8^G|9!qk*ZH0zt?j9y%ou z5ta>!yNKk%rI9Yvg$zHDO3mC$^(~UJrrkn)T!9dArQJiYhP_PJt5uh(+@mxKEz}~z zA|jH8=-lJoci+Wms3v)@udi+fM7CsfoG+PK9a(dz-S9@|NewRlu7K3skl^+R z{ZS};?iW1qzh`;+=_2pjn9Oz+%bx6$?GHIt*V4{$;a;{k$DG?LdOn?QyvlKRyk%I> zz5Iz~vi%6(Pqz`S0=d~0Yz2Y+gru%Y!a7QeR-{-ouKd$|dY`uPUs$r4%(>J6d4mbI z)IKPlC3&dYb&ty(FvvtfxKe70zW+)>gP4%zI?QP|H#RstU(atqBhNeW}8n8}{K1gU84$gp?s(%Y-l z(yxt-&oeNxR>@yIkY(CjI*d#5pei{zZSFicP~?0v-;mmxH3rkWlXrt}{o@AZeH#sE zIV)(l@OqVVBhgCZi&e0PMxOdy@wJY-jI8BOuFe@ySuS-mF%Bdjl@T_s_yqu=MB~x@ zyW9BUq}!19g8KQpxZ+8h44Ier;p(ic{@YG+(rqnWvG4sPZ*H5^QUuPE<~EEe))dzL z^~!8A%h^5Y-V3|>u~7=g*zJx?jxgHWH+b4pmVUk_eXHkpHqY|*pj9hH zy1G-ExQmY7TW(6;@td+B+jk{5K2-fe@4jn;BvEEX6cJikrDp2w3h_$j)0kD3 z`LuQEV*2&_r8nt0xTL9NVp7lL%w}&?Xg19q=)T~1aGusmC`(s(Td{tFT13%Hio&M( zI+XU&y+uuwqcI<;g>`afjDDD5%91r4tTGyI>8-4A=cck%$-DAU_v!j`n;kf;>c91T z6US0snX5PiR%t8;W-W98ilo4?Hk5h?(E`A1evXqS=720xNCUrx|oFbOm;sVTD$MZ4LZ9{!}hSvbp)siwrs_>CZ9wLG^wcXCvElgcatNem z!kVe0>n|!&&y(`S>FY*gPeyVsCj?-am+8 zlkgZ#)rmLPs)`-0@c76MQWw0;_r7{mnyImh>BLTPu10dgR4WJn))6#b#P{-~LyPxrlz_s9M82k5vATHTus zHQo3A_;Gh-b@Od%xX-iv)zH{yk7!jS5?7Wt`?wCii0O5PQqO@!LVB6^0OlW<`Am@L zlLAFNXtqsyc_uZ!+RwG_{-g1PiO7wqKWX>!b5CpFSI1<@2xbWwd1m(Kk|%Va4!YJ{ za2^@c*85xePbavA_;bg{*ipRF1|e)RA6#BlIxW!UZ`+<*^?&cyv2&jQ@)4y!Q}D3d zH!D*j)8CC>Or}#64wak;>!fh+$3Nu>H#84-^y#hMmE^mRVZ~_B<*(X}oTN4tZSTYO ze))o~ZEek?=xLWpB4cmEDo)c-U zh^8_yxF2?<{}J!+Z{A)r9yGG(XnzS6|K}?O$U&WGy8vZEvq8>BGFQFn*_qUy<0{{M z4B>M|AUBmS>Cq7%vc4svZLj&vMU-}j{H8oTH<_FxbCsTj;o%=1l?+TQ3Xi6w_HBQC zAd0^4Xum37FC^@}xuIYWIi#fBNAK;fE31?9u{(dbd}Tg&r}Ga#(~;`g)qRzek~B4X zpYHdu(di+kd`<0)@=^-EZYSM-g2n#jonUt5s=~y=i~XF14Qo}Ngf=`O;_3SzSLd*6$mP58Jy)t-DgBn>Gx-u#V3e1-9z>z{_!8iA$iNB{P zeErN`@upO)PAnwR|0Ki(SL_mneCFRJ@M!-WI9j%LOPq~VN$GNyf__%s_kkyzJ9Xd1 zLgMPwLdwmaTnxDqc*~44?`q(P+SeC;_#`BFvoDm?cUa~Z_i$r|kG)=L4~z^_3CGPv z&bhR4O!L{cGc>bdrlY&FWm2S{YlLw6Q+a8Gh0H=y>+qF!?Zo4wzj-m~oiV{CLtUp2 zZ4MNmezZBPHh5KU{Ekv&1)_@3MPIR-A&nZ&6=*l$1t6~hl@pJ#g<>>sXOS^Lp%|tZ zAEL}6P#;`!xs9gIMjl-ujrde`k2HA6NFQBE=spZI(>jo@QP9s_2gqG55KABFPaFaL zMeQ%Zw=Lw=UqfVB#Ke$}4N1^fqZ2xYQ8;GxkI<5;O7v?snz=OBA)qHe2Ui^1a4=W5#AH#z!9>D(r-7|Ft z{t!c+5b4>(u65z)TMT01X+XJl!w>pLUbp~xEPteDo`g;+aJ)K!)Hnm(v=F|dgwEj; zG^j+3ruE;dMhQd?KO{p9VL?1F2%vhYXvjNot?+|vf1`M|y1sl242~x9E?m#B z^cbg^#=(SljBk5Gd#d{AS5fNUE_NLLRsP;P--oKG>Y%#wkB@%l#q4X?)=T;_}@Is!12$` z{DA9_W@z;9?*1Q-1h~Ec2&6_0ug@wp-T40}H+H`ZAk01N z|7m)TsT%Pq@I+o3M*m?#{`E+{$ih8*4Klt3Q!()M!~b;M+%!E0(-ZL@rsvx+0$W}Y z7Mv(E$A6fRf4k!T!<%AODZnp*mseKkeS^{yC_rJ&Jl^lH$tM>-Z2*=D2*|D}%_s-aFt2}OuKuw* z{$<1d@#cSRnd?`f%LWY)H?HY|DmBu(6x3WufQ1?5__u|7cPkxR-UBTuhpF|s!p7LU5uts0+rbQ{G z{UB&&*FI5AD#Z24Fhv@b){$OjSo*@{=Gz84e6A@#xcfq*52L7@3TV$Z4#e!7(HB@m zFyK3AI9vhI5s5v(0|g9g|`f5v`n%0#)L_f{Yv)pBTZaG*Vc&VL` z%mp#5*Pv{I^!A$oQQ%-8a`S*t+o~5)8FC_lG+M6@<+J>&UhZJ(qPEB9#Cl_{e3Jh@V zxJytAoP#(sxoF-z2_1p~n`5)$BFJjyN5r27pusV_ z8p#{N7F63aXnv7i-v=%XqC%4dGO(S{E%*wzh1%K@Mg;Pc)R39C$x_QpBxN|kn76p%MHPVJ^+#m<--d^if#xFg82Z#( z>i$g{kb40v>qN;!kUv5K^JavkJ=p)9WYC2I zmCRT04;c*&(mJ*iygd`>HUR^rR`OI2RNnOS`+-Q^whT($;vfiZ8Ft=)9`9)w10$n9 zROvWKrmjK9Dv}LmFaWx^m2lIq)&UCKIwk= z^rC=6sP+3G=)9t`8)H*PZUc9Zh#s3IFg%ugd}f9Tkvgmcu6vgeO7*7*^v^_hwSfkP z!^2oXzQw8o9CG7N;3hj^j+8{*Kuo32V)^jWgEZZ{Yv_|DD#K0_fi@s#F9GBeMNG1_ z6fB(yV9W)<)7HZFCjP-HpA9SI5iBh!DjuMjK;In&J&e7y2wg~{38=;GLA%tT!}bIi zkh-6MCeeP-s}y81fpu~Ty} z^}|p7rGLTL&(H>@^cY(is4w#G!rm)637N?7}qi zo`_>h)bou^a=`WAhrawlS1rA$Of%cUc7&65Lm~a)vk-aPcEA=$8m4`7`@1!qY8kE0LBk1*lIB!f~J#RcsvXSw8SXfw{Cz$FUTon+TFykNm z$yjBnpb>FNBm*RVkXMF33=XvYzDd*9HqXq;8jSpY)EdkR?h;aD*!KiUq3W~sjPN7q z(2|Mi=)HWYvC5mt^3ZJFNkA_e@7d}djgKM9yp4M?Viwg&j!TlQ;ZI{VcvhN$MeZRcpZ(jKWeACgFU?)SQ2f~OV-sj0LN?vm|ptNndbI0k| z7|3R7V7C?Jgt#ccrulewIZZ^VcGdduUgABZ4VF)O@k^niYd!WRs3oCd zM)PFhQ3uF4EQ8EcMZdQ^(&*r<74g-3cloO6syV~!=4`Wk^@*wGE$E-|zG%iZw z;7P_$k&u$s$$9Q{Z_UbiLW{tIUp^uMp!&tyi!1>9(8I}pGcq4~PL%l)Dqk7GQCm3f zk6sL4kHa?9e+yTb@G4G53As8TeNXZX8sFW9nT7V*VnD6s&?Y_l=%>I_)dFt-s9Ilz zThB$B-%>&AYF`M3Q-ltnHTrju?cn*c2nHkwHXLyD;LIEcQfqD~!WaUOd6~hX?{@<= zdRGue*#=BW9e6mvAd{&jR+kD{2XaOk=$i zUCd>;v&=`o%G&VBw0;L-7D?(FAmS^cADI0RMsx#ssV9 zwlws`C9{FPf%Swu!@pk$jly>WgrUTDbTa6YT%&>$kxN4Wxr(UD{llf#c&%D}-j6Zo zH7NAGehr-IU?^X*Y#<_=6y_k$ojw712xN2UL!~PxjQ@BKVf9WwJ+vl*0Paa2tb#U! zjody}7cPKY36io%K?AQkEr|X2*UfIimV*;OBTJ`(zp#FZ*yGpBg=_hsN0>eVPJq>> z)6tWOu3rRm!&dtdRuH3{BqEQzUnmg<)AFPaP4@ZrRl)Pfr{L>9vcK`W!Q3P zp=5&&?2oP!pYwLyFdamqQiD{p)D)QYt*$VD{$i;{-KnEr$Or}tk{z`k_3)NH2+$4* zjUuir{0(NAh9<*M%Jld`E-=(e0LD?Q2VKBG*B^zCI1Re9r&5EU7rN*P@s&@DtWt;~l|2qhM@AgzyAkHvY8$M8w ztSSMD`vf>4{;%)1&hJA`W*mZ%K+wRx-X^p;jO1_-38QrN?|@%;4dq`(5dP!XE;_oh z*I6XLVb%v`V&sFcyUQLZ2?xQWgQY~q!wC-b?j72YM&7xsd~UvCD+~yLkyq_6SWh5M zWb4Y&R^-u~Jb@%SM)CGm!m9!V7E5k`8U4UtY^AbP>gA~2P_}sCLb@&FNCUtp6=4AZ z$@3LQSuvzwSuOB7zm1;}L~p5KIa?uumu=RRTUJh$(61$Z4)Mk`a>G50#pzY@lOzWy96DZfPE$L{VM@AB_y(%?FNsoB1(J4 z%TW|?(g8p~;SK^D`Y`-Baj10Tc)n4ney0Di;(j0q6$QnTr-37Rzg?g z?R_zj>XI-&`a%>pEh@(c+^GLDornSISM(KI$i4c`xBqyZFtnc^b1{K<<(nGvIj@~+ z)i00^!FwtMPn7mB{P68u?(8n{0Mnu$nf|o%8!Z1 zsH!2>O9Bj?4G5Kg2lYV3uhukg(|%pZQ?ic&^(aDaD6C)Kt zrG=cTRtHtFSpOSAyfl>{(hEMd=?|-;Y^($rk|&^yfSipcuq3cwUKjph13!cKj6Dbr z4krlNIJO;*M%IfN*=T&WQ{N#Hq=fcB4_fB|NPOqn&jzIH0E`PcC|6%b9t)I}>LIO~ z1aJr>SO?);#{fz`b`lD@i@vwPnO2nLqXo&@klEtnY=DI~*Z}QkjkI}S8=K5@PH+^X#pe;+^TbUwzoM}UIMfFL=f~`JK^_wY?0(FIISOxMvs@NJU(O$ydsfn zIl-Cn=!Z4p7em{u*Wf29n?~0V2`~WzYkdHAMk3rJEIq-E0e$56O~7yx9ytMTsQN6M z0nb}|evAnO0n=`SY!vekH_)(F;@i_s1qCe|5UWq!z=w<4m#;K2_NpIe_T&j+)Xm^Z z=@XzSjRXb7=H=F&yF;DO0HbHb47BWO9Rd&I6o3ngK*Rwuk)~o<+BzTMXamW(qy>sx z*tw8&2<~ATvp-IfqwIaa)IFip|iD*wx&l?N)Eyx`iCBK8to zcU~A`Bur4IK>oCWi9s46lU=v~i^p2y%5e(3hYG$YEGp_M^pmio1Rn$T%LL-+fCgbL zMEIoOL~527!K~rH97}+kIUZpg3w^$!*$^kl_|BSuiD6bcJYC=5UsT^i#mhD!;dL}j zHa86o@=y?zJ%hmN1}wo#NDgCfMpDENG#bE z-hy=^CT6!ZDuhHMTQ$@;`Pu#|n0WJ_?nmAMDk}Vbj`UUoKcI!*-nVeM+UI@HSUkDi z1iS{H^m40I?QkfW6s47?E8p-c&HT|48o`41*neZop&9I#e8R5mzCwz08c!*1tT=)t zp{}KTI~9JztCj7f6G1_tyrb)%nAe4PD;q(_-=`FV;%$x?JFFSJxjWT_l5d8kHxJSl zjdlR;L0+QF;E3d%B;rLfQ%A4D+jNk5K6=Ru{oDNczkL%)su!9=1bqiW7?2S+LaZA! zPb6tH_}nz11)Yz%`w1>9-~~!`TVT;BAc~fB1MTBp_LQSk)Z@8}N0kf-RP^L6 zxme12Dn~6hAP_~htjSo^&;=W?f05}wen`jPKpl33qgYnV89^|oJpqr8ZuYt>;<`awC&Whs2@E7r2qyx^-^D8VZe-Fu4>mn- zyj3f!Y%mPhCI$bP9DoIfuQVyRA8ybDErG>)155*u-6n=GI0xVc8<#R2{ZhO)SdU@o z3K<%C29j*V_0Upgocdr(l1csZ&F!w67Y{#WBP#=%2YrW&*MH!*H#?dx*ys8{db$Ie z>I{LQ4{s^%o;^MS?IA{P7J_|fgEmdAMUKB8*{I`E#pe(>I4WS~mb0zh8NuXUy6`q~ z|8SB-7ucu>yntcV_5?C#2bawIvZU~<01a%@r*2#e+TK*4w}Iq0B%ut)p?ypA)kWXe zqg0ypURDc9hTRgKAK64yvUM(rGy3U|XW=goW^F5IsKm~)>-qHOyJueM_+n{iF~~_V zH7&l3?Na+>A^tFGQ`wE*^==jmbF-aqVWMrOQJ!@FMxJ8kRA<_C)&~!o$1@EX_0fB^ z%d_*_d7llSI(rJ6mlAkRKU_E_QQ9Z0v%p60=g*r@2K~lAlsz>Kh!dnEWzmV9z4)9z zLpkYIXTI{TN#`dU&Njx^pAFQr)U+?!hLhA5LNu6661tnuo8TJr}JHRQT=-Ai(Q z0=mw*=(#JanX8G?b5p(SeKpoW!EwW@7NI$f=5H>gF7n)z9rEl4aoVA?yP;vB^>q1D zwbImbfl;x}H<+4E*h}vA$>=}DwB7lLk``XE${nPWEg-hkN0q2Lx6zqo zUuN>Y80XmbVp4iSQf|0#BsV0y`#KReUz)PVHGF2cT=~^Lz{w|SSdG8iUJ`k|Y-?<_ zIcFs`J(c}rDaT!Zx+o^mHPfM!x9YL~esf6UmfdKUlg-N36@j@`iflsE^&#Y}hJAR0 z=Fu(=2RZIbxIcv)I{IC#FXD_ct>;HXJly;AI?I>)RBxMTeV|Sa7~l#TvXZe{6DrEYHj9|eZ=!N!Go>?0Ke%N})^Bx3NQ!Sd zFc+hFitoyvpB=s9ZMSr#cyBZN_TXJ3@q5~Zng=X48#Xmh?qk|ID`oN@hUcp@M{);X zwA?HxON=oO)~N@|^!HZ2S${7k!U%b|)!ewV+MH`v@@M?#oub(*l4}@dm*U0Wvnee1 zSGzZ`eTg!Ak1@H{ceb?Y;8of8Mba(XHrPpj=lj;s5NjRwa~DOrxmvM@&3t@9=we*R z{qONa+WPW7Im?a1>Nazl?e(gR+2D4{lOu-oFgONZWdc+!+a7aOZ}AOXS%N9;`wU?@48FHsZFg4O%WP=3eB)Aiot1}0z*&rwr6m|6v|?r% zPAKr+R$}pjDy7Ji5Ruf{%>M7?w9ONllbMNZtUPEg>xd#v`y zgN>;|H_C3)Jh3-bZ(-IT)n(QtaVC70D3f2;S$Xj;pM06a><}LjRi8+=p;DSn&5M#% zRgCZ53IWTN;lxCe<;icfZy)BwR1D`RcNa`M&UVw|oJYuQuFsVfFHKXO2z2NkQlNVfV64|EX7j4l-EnjocJ{>+(4Q=8Jf=|Pg7l}CYjm*9$RchzUgfdpwS=U-=*o5>ES{8d;oL(%aX zwDTv(a8wN+-eqE>Xwo;l*h%f)9@E*2yhs}hK7|F*Xbf#v4~?>j}vsg zo4jLhHo}D4%&O`7GdXcGa~wExL6W)$yp~HXFUn8beDA>73^o!-?1s%%t?hm7ta-8~ zsW>qrrK^-K5&ukW{|blc)GgQjBJ0tc-s^J|K7_NE${d!| zZgB1YYD-OItHM&725)IpZMFS^Y%adE>i6c*^IpU=7$Kb9hQiza$L?FZqdkMm{bY4@ znU_+mKlfYv*S*S}TK!A#RACWR&g-^J@Fczn#}4rYkqoZPBt4z=y{oKm?|UZo$DX#BqejrFixYzew7>aNdlA#pNx!B_(N@tD4#> z6mcyvBylZKB$voqeAUJMxg%_%L+*=J6~&CF;mYd!`8V&!SoeQ)j;?A?Sk4T`Y2PTs zLm6Cx$N&q;b*vLO%$hT2dJJ7Rmni6{OU%Wx_I-A#sa=IUJenGqb(LyHw+vjMvNihF zRXmCC+X6B*CFYC1^ik@SpA2Nw1vl#Im+R=dCqz`^uEh*xjT~^$(OnP`G{W%i^APvl z5BzjvZnQ{z5BqVQ(GQL<|G44(hl04pzs}}H6dO1Ta&L4yS4sI^jr8fjYTJDgk>F@`F-bOF(Z=hVumP_G_W8wvpX60h?B4IqRZ> z_v+^+Qh9_ax+~SLkhvT0Pk-Ncm?J4$ELU{<6>)VUK1c0K%FPsaE|Cb<#<wf>bZy`UYX!aSJ|VeBBzX%#RANa9@7Z%a+Bkx=va>7CCcJHf?zg_) zuU}oN^--*9d8*7c{brnLq>s$ z3%Wx5X8u2x>cU8JbL@2Er^@#>zB}#j)8AS5heU1)W-m|CCwpm5J4E9A!qqQ?&mJv^ zi!+XU3&diufYaR4ptP@M=`5Su`DOgG!{fx`w3DN8Ldm6u`wZNbBUzRq*dZUc z;24*b!urIe9id=gM%tezf~mZ2AVtfs=5iaY2#E=h?IPx-1#PBR^_0rS+Fr zluf52xr)Z|lu{KpufFT6oSo%iS8CjlzDW~wm7i^4xW+m#x2F&@DzZQQtKPUyjH)Hb z%OyuV#M^X%l}yQ_Za8{dx(h9Q>1&W-O@-$T8q#bnm05GL?E6bYysY`I3Dzc~D{B}_ znR65>9ha~ixt287~`+GDAp-B|$i*1Xwmh^P5>=sKN;ZQF(P z^q^GCFu%o)v$Tp9%$IH{E3!&!NwmupZ z%03jr3HQ|~OaK(H`AQv^t|7@$$O{X}LT?9vGtfPORvCFNh3+%)IbX)E?us4cEYQ$A zY4lUJSr=k8C`pVg2G{*zTruy$@K`JrqrGJ?QFh|?>3mJUuw1{{)uM7oHhE`)^Yd?u z$R?vMWjeB!pdG%A&Nhowae6#p(#czA<#k*dq}SloI4CokpVk#q|1w)8x*o-5Z>8b- zG_H+gZMbKwa@JiWlCw<1>;%?_+`qfHU}mt?Jvn!zWa+)QE#r*(u1n$Ko4(kM-o5k6 zCs6X9N;nl!Y|QDaBY%)z$a7i8mOzGQ0`e3iQ#O};#X!8=O3qXif5*;@N@Q#5lhSS} zfkf>KT1IxY&M7WSwclfgIQ@+R&*~dCV%5qml-;&_Ki|d#70SoC%yn;l<=K=eY!`Z# z>ogHEt68!5!g@ZY&tt1Mn5pq>ugU%5wWvG7F%`IT#m@x_{jCXyXg3zSY55DyNb$cA zKKb!E^o}KN+-berba_kQ(v!t^lZD#PUPWG>s{c{8AtkW+OOsj^i)P-ZEG@Pqrz5<2 zxrSivbsKY(_{6upEwjO<4}~UD<=lEMg3F`E$dv+pP7P$K*2(&jWU5F`cyC|0khfc% zlQ-8UP)iW!o>x0p>R>_QvoIvm%fh05>Hj0{y`!35w|C!Q-BxUXQeB{+AfVElSP%rH zNt3Ri^dchN5SA_xq^UGD6b0$MgbuM#lqxm!D4|F<(pxz51>C=V#<=^OJI4LvG8V(7 zBq8~h_nqZ=K2t3yrRUK6MHe@xp@E}Ko~!ltT%P-$CGwOq@sVs7ht^g-$63>vMHC5% zSP?<?UuA69&on?gTH56L>Pl{c;h{_8z2Th;%ceahbm*eag zBTJnpL{htB>I0Td7LVs%wW|^$O7gi(b_p;KycgumeyFWjD&_7XEj5?xHZvP+2JvH9 zRoUw6Y-;*eAGs1n8={-|y?c!dgU6m$5!O7;wTFf|G|Rs5U1-W)t}NJplgKkX{zMr( zBgsV1kjqBG?>(0S{9o^hkc)d%S~yhkxPJh<)1jFN2~<nVk>fxK06e*&_-0Ke0C@`6JwBXd)2XL?wbuYL}{Yf&DMH1_j}b zaopjI4ql22y^{03xpStd!70j}h{KMVXU~6Ucl?q?dl6IM zwLPhu-GhJf;duRCZTdUSC?tyDc<}InT@yDar;9yF!fP$Cg_3?O#I*mBpqUl5=1aZR zfeE~5aIe&S0Byom$;iO4nA`r?G+BSo%{}+0V;g;HrFaf$yuve0yb<HGx{Y@OoeeTF)CFlU7l z>D1r{%?i_8QZG!NqPE1)gaOl<*EtN;>CvApiE4&0%?DMF+XoE1Lj~;c)hBuOs}UUB zf7ieIeRs9DIv+|Cj$`z}f!)zwe6)F*E-$usr{++_3u2*Ah79G~(IT5EU*DfS3GRl! z0pWkAwM&JBcEvcSrT=0l=U8WH9*L;czRtK+mx}wDmA7;W#(EI!- zZ;pOHS!#!?(b|(ie295fF30s}dT<+i*+2f-ovgxIGVJ}c(`z-5_i=GvFcG`fAwF5< zIl-~lRs$L`%JvV1t5jYw$coQ?u91#8D_jtZfVt=3K@a{~{sgT#yL{AexG1*8R z|F$j8Uvpme7mgC?s}xHnS$58{AAw9YPQ&<8z<8N5eAk+1p62KCcP(F#W~cp3e^ru} z)NVcT?Y;ZT%3Df)mjmthNotH*pPz>2h2$qGpj3Vgiyt7jd0S4lzCy|jkTY2zr5C5^ zYEHY;P@Nx8f27%QE7Wy?CCn1^_t{4hWSCi_k=OiTT+O*Q9L4)#?;>>ADtGZj9)KRZ zV$Z$L`v}zVj*mvJbEG)OA6?G}r#qqZ9~}Jk-qn$3x|x2Y3(@Rx7a|YEG`!+y8OAaw z>xQGFW!anNINq1 G$qee~RW-GPC$H794te5(5)>uKj9r_9*Y>t~&4D5m}pcU%yl z`fJiZV^m?*>2SSE9W5MwGEbrDUN}Izyw9>d>-a?D1G)E?8#BW*1EzZCVva7mFGYu{ zmCx&~jC6`omNe=8&7SVR7A4znv)aEWKrh9QC)yW=#0o@XT_(;K!@Z=t0-bdgqudN~mFUAs)ZT}#TGhHcV``_W z?zel1%DO#E}a7yLW0Aw$Au4GYnNfsUBw;W+S-v zOn>rp{QNjBrF8zRdbT`EJP_t_)Rc7neI;>N9U#vlRL3!PV`EwMLc-J2#p7R^I(1bm;n+y1C0x5WsK`YZ z>+V9mhZY4tvb$AfxDpTXeV!o4;l|-R058K&6P=4crLXOpp9+?gs}FH8S16&G6HIF! zYi+3HbvmJY~lhr1PVye)T&AtZ1s9~7+CdI`k9En4qnsvrbM+EeQf6V(Jq1|YebCX z`8KaxtvjizY|A;5mGiQh56G@O*n>Q)OXY6kDJ^l%x$@A04hiv9Rz1p2UR~ufD5Ldo z`BcU81pDrgkbqFCIPVNQ7W+_ovHhQg2UVpvi*sO$8&V)fbyeN=oC(xa7-tULJFuW>X%qB-C4S zWvrt_EyB&_cbavkvofmq80m|t5tQ3Qt8jgbc}ixXX{UQn9N?J!+Ul0@2vSMXh2QsC ziwuaZ=bQPN#RB47#+c}d+Jpnl9gcZ6ZNif@a!Z_UM1Hc9rUidMNsJp`-_kw(I-^2i zD2Q2-Qmn?$W!Xf|1(umYeH}!g@K8sxL&Gx;qxpNK3+)!KJ@#Dm*=t##xpHSgqv;fG zb&i3fG3uD03Oh##Q+?pcNeWOP04zxZVOzV zky|mgf#5J2492aPp_UfIfT-oSkrt#G_5}q%%C<{$3tz*A^P$4_Yf*8(iphSBP&2>>%h0 zI~X2S);Xf<)|%y}zUZD} zDXlXgPw#{0eEKS8;h(aW2%HxbU^VidkQMN`v3KgZ>3(4y9%?1qL@;Fr@$^L?NJ)B1^GN zGizaV{7Ogm{ZvYb(L1~#xh_0M= zk839sySpdy#+thn_V!BbCp)bg+v~1PKY=yieV29ZdmQn?1yo4Sv+5J)*#H=RzMdG2o#*S)G!{O{0Eq$Y7F zy+6yVq)PK}Uuu&_K|)%o^d#H7{*i&9s*l)qheYZ`?_98J`Kz2ZvOfkRl@pon`gidU z=)ECTuR=xF@p+gkLO!u#!~?@KLGy{bnyO>7Mm-O0;A`Tq1q^Mt6e zwruAotHESkgk@jh**5mordBtbPj$PO9ez$r`1d!FT0Tc=%8%dLmxI(A9_^Teg}1Dh8sAw~rrazwZ7FNw z%b_;DbLL-T^dc8)Gs~_b*_G)*rKhY!;&A*aK0Y&ro(iE9{bSB z0{l)d(9Ik=1#PAVm|Ds|=K%7ANRQEEB)3m&h;nY%3-c;N3OcX3+?aS6j60*T_vqQT z&$O>pz7f&qaATh#^qr%aL}lW)%tL)e`@}XkH57?R`CwMN-{-lOIN0G zeXkGP9n_BD|Gn6AHCrLGA$cyRzfHT?t21SWMp$*ij6U`1?N&$~8q;N2u>SgN;d`zi zFLt%(i83YEYTESHR1iqK)cdfvNNyf^Cx`#)wky?|<(W3GgbtdLiLCux-YGS1uHm)( zno)>6@=nYHQA4sQ8--F7R$_H@^if573Fac<5N7Ju z1rSgj6*9jbz#=BVC}4OLgf$vBZV&|TL%C?9V}HAHs4*tSV-?)N@?-4`jogM7AV5&s z<2~PD7OX@xhAM3=7*)>QtqFyTG#jSUr!c9scf09Xv>W7p91romW^mcX%&05>noXm= zx>h_4`Nk>gmt3h$&@N`r>8z@i)NLCOWJ;Leij3)gs|1z5Xl#^rsu(fzp32YWV)yYj zsnyzVPy1MjDrPFQ^Q%r5wQ>8OxR0#bOb=r^6D|mh8R{8H=3TdGjMmi}JXijzK%4=i zAeZN0x8tmB@O44>u^s;Xnx0A4L#ypFVh)3E+OkcZ9fkQVUrCa3G(sc~!lUa9qG=B0 zjQi3PwqUYOZu!nuK?#;+2jdPl0PMqKQ3c0A%gXuUwP!m_vtA31sQCx9!C)uqH1)KB zsfui1t>_$>O>IHyXda-xi5hu!XwPdqiKt_{MAgWA)2VkEaE{N38fR}5Yq*t)#NWMZ zHL*itGX2SD4%Lfj_eLb+?eLIYX)Cp#p|aaZ^KKNl^}lp}pZ};JG*nd~v8TW)jmomh zsxQnNutUO6sgb*HVOU-3$za^yYzloYUfj=oPw}e|tA;r{&J7H{U!$E;)>)lb&~slB zkvO(QOFXYsn@Xc$FfJ#cc#Cw0X!2v?fISA0lJjO}$d0zo3`4`Z}|P?o+yA^_0xn z^s@9^C|6f`UEq7BllF`;kmSXF8am%mQiMRY@}!=Z%O$omP91J)x4eCQW8+8GysSvY zqp5H5$Uom?hA$XylX^6m+9Hbw#tZ>Jci)LwF67b7luS4SDT<}NOH_4Dz(SGj_3OPD zbzLY}^ju{!I^x>rZXs8}{c+0Y-K5X$DM2kQ;?tzx|JnTRZrvM$B9;|qvASTRB^OmG zv1w(o$PYocC?{i<3&_cjqVXi6(eRhvTZ~j#j25iho>?c`c@7Q=`(9#eb~qcYooAO? ze6i||m;FAB)GPi(i&h&@GX@bMUPZ!m2wq_l&UzLDzWOuI{QX-l_ks%c9Q>adl4T*6 z1JW_UWv4uQ3|xq4nS!`0&NePy$3}Tw$BvZVNcNRVk_MgE!R!llp#MwcTlGM{3B>-1 zU7+Dln;~Rz+1z~2q>CGVp67Jfyn)o&Nx=kLP0w$8V%`p7L0jakeH8fhPjla0!M!k5yx3zAc;<4UMfqkhI0Y-ZvbD-x+# zb{5{=UPh9-cPsd4Gj{d-&iK27)bT>aZtXqKqY@hRj+h8sBM<*Bsos}5Gi$+?t9E1{ zli2796$!NHr=(waJGta$yi|YJjz!GBtYj|v?R-KIdzON}XY(To4gKiXQ#jGN`oBHp zDJ!$NA^yswiiIMAljdKTi&&(7gCW#Lpc^~a4H8B-$RpQ{s^IIC_wu4~uTD=-$ALq^ z5g8vdK^qRvH+ir*oIH5&-0e^Q90gZoDcEBPOYEXEgiI_y_(9MKLUkvBqC5V|mykD% z3T!YyKKMpPMuNDGIxM>;!Fc9oRvSr)JH!#ZEQwtpdPEjLt%r22hlOa)dR=ByG^2u! zwUjAV)(L$rFNvXW73L~7S&6@PJg|vzq0tB1FSa-dC zbt%`zpN`pSZnKOgHtp%M%AVuBiM5Q&4yc2~N+rx3E1)MXI)+tuc1UTOSNl^E-*=_I z5q^emyuiCV|A3FHLpP_r^u(CeaI_cBwC?83yxv*N?C1B1nW~<9d*hcGmu0C+OS|{* z;IP5s<^#{u%Nu;(VJRyiPZOg&m)e5O0C)GVd)~0kExvqJ`l~k2$zn?4d~u#-Zkvr7 zdyd229!sBzRLz3Y`!&DVS1IM1xKNK3>d)N#4qT={nc1DpOHIc$2$`JpV^SxAjtL7- z^y~?_5Fm2wO(bYxOm1$GR7U=1m}(c_T1SF&B8F^Tyn!!bVmOG4^$xKx$=VSn4p-;g zt?h>`IuP~c!2d~{rAyI++l>_}bdF@~{8c!OAg>jc4>)?Tpy$5wcz(BivKQ>;&tz9e z)Eq%Yp3txgKAs>{x`V1~>cWs1@tB&-tX7_Nn{{rcrZMDrpqpJcV8xK3s2k@6^+H%5Y!Q(KW1V ztxzwcpb0~P%&Lpw!%s_s{7+}bk2XTaxUfnz@gMGceUwkR{e3hatnjGtE^y(t+stIE zq+Qyw3GKt7Z|e0gzxvv+yGu+i|4@7pk~=q5b$GrLb{I=$(wfDCfEWqEwgRi$UXi6! z-xt1WEME=zG%FUV@_JsoXQju0aAWF4$*h0EzCAZ4+?B(tlhhJ*YCG1Je= zWsH1s_nzOP5=5uq8Qj>Bf#IqwBQYZLx6-S%rZs8l0tSvs7^k-m@}UVf@%W<}`ZPxif=^Jz~WmHOM!DkdBM^ zF~Q`sv)yax77w*%hN&-L(=ve=b{ESBy zD+aK5N*>nf@WG};m1?ABINF@-*(EN-Bp4u;0K`mJ9SMFG5x*8!+2cVRrLLtiPWMaK zDqCOWyLo}&Fh`Nhz4+u@p;>|W`>F3gN%@*xPN@B@Aj@u$jzlY5AX))GLctAX5m5Ut zK(&}yZwABC-Kmh_RD<@A6*+pq0mx}jT%8-`B`A6Q4vDEiesvIvgTxxbF3~x#40c#0 zf+jLTAltevj>A_xTml}u8W6ARMdb7M)05ZaSB4_F zOsh&Ovmas7S@q08DeE>?6z3wn@R7x_hBT)f#aCDFe|YEA zVV?bcJVSyehm`$wuOfw8J}BHGO|rfo1<700lnM5H%5X?ag1)FN>(lvek^NHzHga-j zjEkhhGX;YBn?+ubj*GovJR(0Pk6)V~e8iwk(G^^kQc@z>&bcSm=I41F!nAIKOA^Le z$d49+z~USj1o&ryPMz+Y398*^A|(d04{!m1iLCruH%Z*oZlNrr9bLx)h2I}R{&Nme&%*JXTBPLY9uR2{%(kBz zfXn)9kSuNC4bDkjP1r`Our2;I(vQt0l7m<{+(ndXYxqR$-OnFmT3O6qs7&5jIr4No zdd|=oxFjNMWDWLFqKS9@1?jDj?<|Q@*`K4n! ze{j{zc7L1rDph`bRaW^(ZMW=7`<&of>APxj)B&5CntsG-DcE}GlF8v(jR>KR`1A1x-ZI5yOo2wPrSEp$x+O+(xWo6?O_xQjv>#u1$epM z->18)^{Sexo1jkGX}=a17WOAaBdM%Nao8ndW&DGn$yD!;ua;(-);*Ixa$Zwk)cO!V ziygN6omhezXw+EPYL$5VcJIY_&muW;YG%8@E=V9_ZAkS~)Yo`C{>@24L&MTaD#8PR z-#Hl0+Eo}Nk6iZhL9BXZS9O~qe_=xQbY!PvXwJyJeJT?CSYX)$CYFg<(+!oJLl|O6(wYz~FHI zBvYUNvFQ}{IVeZ_^G$kPV8^af#K*E^`oHxq%YUvvQ(st)vkF+cXT3sMbTz9>6yhD< zsTG3@@Q^jepOx{t-&*dtkb!M^^nG9b&6`JDOIPey#@gsjRZco=OWw~jE^~@--C3?; zGrvn+b&ftJcDgS{lt)0)lq~LIxu#;ioPkcw%GfMD2$IN(1~u%rZ2FcTm_MZ8?EMb! z@8yTC#bi^eayS263@T@?tSnPM2u;a1ou}J1{sk@Z3ziK5;(1fgJBSu*md1sPEUldF zeZEr8O+ah-tw61s@zV$gG@xBvq8BsVdzH^sdt*(yRSovWlJxXT&sR0PIh1*QQ5RJT9mWbP@#ZV-cL!C( zIQG9}L&_9(87T#jOvgdv_NGRTB%^@QF^UB!3#WPeVg$O63=LM<%Sc_FnoEy$kNb4W zSd-a5JUw7nE>p!nooyvzr`nLW$6#ukvm8EL1vMXHjZzbu(iCrl*WqSx-T_k zxFRClvq8!8VF^HDj_-M3*~M90V9NrUEBUz6NAOuHpS7LLwj9iPm2=NH?Pl(v_7CgJ zEsuPfiu7tNyWhJD^PdV>*-@08kak8VLMZ zq~=ifV3&w_je{U^1=;!>`=FQNK0WJtL%^hb5j&)xXL-<{DKqL_!H<_s45tN6a7>mO z8clNyH}|QE|M&;~J8TN>z;llM6{ztL3%6r7*cs)A8=}NKRqXBUSKwv~2CxG^T0JYl zQ+`M}JZ&xZJk&WHjxzSYbOtNF2gjT4KpJg4k2V=MRix0>@2qq$XSNi1bH!8A&}+4vV$QL z@v~>o`VnPAE-i*(1^jdGc5L8zFqnK$VJh1dk{!Swx0hKs=~Fp?PA)60e!f&7h8g76=JII$H_qWUUzjxX@)**0JkO9G3Tjt58 zNwEz)nek{bv4lly!<)C>bFwq}4z1_H{%3pub~*C{pg|Syp0exza1*jZX$-x!wUv!B)v3ptYmNmKxY+jhrqK8?eKND&8#AK2&fqj>l(WcGFC4EC?!gA z64q7g;M?nm0id8GTRJq(804Sn16I;2VXGU+vkCx)qsY4tH|$k#W1cr+SNDgDIt`|2 zuiuWy9C-5N$>h%RF<7H~eG8#FW(L7b+Pz4a1&>_ud}X>DOv~{v393=du+s&?)*@9E z#Kr>fB7f-J$4AS4_ugs>=cMhoK5)$Z5o?7VKv*}+JZ8BYAvYF>pZ6o~39QAswKiLv z@gPiQyZ_}cDy+g)jnJ?{2&mY+JmFcxm5PnA;x9a=5x z*FUd1S(;jL*s_qg^tfvTdWnJOf6yQ*KjC!M0a6Hr;!*nhgyEN?Ko1VB6?t)Mrvj#Qs}*=Q zp`-o2hGb{wvvElA7ST|dgrOrr4179T-EOGFjyib!D{MgBL2m(ZE8aJ#M2esDLcpWA z;0tdgU0YPdDq*eTKvrTx+tyZoi9e*x;|5T-;@vMbp0!XZ=yfnymge>l;Ad!K(;c%h zgiFin@84+ILI!yK1(t)zI*eQg{ZNKbky^`*Sv_M{d+m71A8cb*6he?xB3K`s%#HPi@ZAN z%IDmRh&^av3xVuD9Q8`bLY2_IqjlewTkF?{x5usi3O_0+juN}j0frkud$cQX8Pv z*8;1zf>LmUA@r90mAIp+ehJiXpxsHKu>|kD^Ry;&(><-F0_fA{DyX~I-UrcP$^$F} zlPw6h*D?9VyIA2rv5W;07j41)muExDc*9rY1v5z0Y!E|BiR93O1APlLEf0Y}qT7fC z!K~Ip5pUiSXo;hHs}t(P#iJ>OP{d3vc+2*oitgU++qc6q96GXom{%XcTye?XB+_1# zQ8zS#stGo8W?U338l(jq+xn`%m!WS0 z4~I@{RJ-V57O+HboC9kmqpmYz%4kJ#{PNe+JH-L|=)%sMLu7~tt5vIzVhvAZ>`L<7 z_F-0C8~2X%Pli#6dGQ8srQ@9~YX|D5!1;sa)_|4%3qYPcl3@DJi)(i#^Rpq9%AZfPQWXpDjtFKFTgNwUQf|TTH4ZuNCNR0p1=e&gNaYcbL^hu2csmN zQ`)<{i-ET_`|X%Tp$6!y>!%(|GWv7NS^t6u+s~~)=<=rB7_#ptSOBFSgVt0}|Jcf? zF7X+XLF;4~0%sb6T-CiC7o_crM-s$QCb9~|#3KMO&_f+AE9gqE&cfDNKq%PSs`>9< za1YL|RtgyaW?Hw&sTsw*8Bnz6Hzt?3P5~BCsvq!%D*W45fu~djD<`t%HvuK>l`PH+ zF}9M2NkzXt?V(g0R!h^0y107c--}DuuCpQou(Aa~gpO7hGuRK3rxCny`yNKyxi^Ru z2ogkctBxOlF(EP9_)3{XP(N5@#Z_T&D!;%8ic!!2O(Zl8{HNs5@v%QdYW01h#`Z;` zGjQBcF?Suo00o9%IGU)55ewYbVwVYKR2g^xtkd?fE$9EN4eE`E3m7Ec?|VIhu9Xh9 zuR5S7GJ;^n577@lJ=ufsGmyij2%~%_!q_9(hj$~e;?(Kh{x%#aM+eYWUy+VpjsBikkN+*Fca!bWo9vcRMtD#OWJLYY91HO6 zII+-swzapXYR15medBeK3=qCm!~!bP)Cl6rNn{^6+b+`i_ zMfY%1BE4z6`5J7CMF_#EYZU4;j%TB~Cv(aMi51hq4w*OL z7#yyG55s!bFk*56I1IKTCJ;xMqS9@tcm@QK*2#b3Vgx`Ffz#IN2%tGLxZe!MO>W%C zsa&VTLP8ul16KwMa0I(ePHY@hbwEg(_N^RfL3mg}P<+%w> zECl45Dq-tE?VZg3@vmtkgeq(wRP$v-0Vc@J057dTa!6HbB2+S3+_B$9XA_&DB?f2L z8q$GLH&|=%6D>fuY^C}r%j>=`EtPgQ)Y8hUhmObkm&g?en%nu8BLQ4dd&$8TL>8)S z(BJ4bU_}}LggEhAGa$vig;7!eJNuDU{H(LIMMGqL!%%GHq)PQ0vB z&`|Y9o$T;{NeJj>88<08w&<;m0$v1)3V*eE!Nx2$0y$%~oE&P=LJ19}Of7T!jV&Ng zqsDQpo)TFW<1~fdUpg&z<1KqnaKZ%y?!D`5veR`!cxfW=#i|5K#$Q^?0Oc-de1*qc!ryo_k;l6G?)Ut5|F7_gtR+0n?zj}c91T|Ph5rKH` zxu`$1FklpS5UPfAwhL^R`9BsCFy1NuKi3eB^V#K{;=;6w)18GDIaCtdfJ12Zd*CY- zuI$QE_i4npd%B+u_4ZM5Jx&+RU)lH};lzn^P_L!RI|%ECGFt%bnyp!Kah!6OY#p}o2rfe*$u2vZo1s%!B}MzBFP!A7JMjPQzpNk-{2f(MTp z!hHrDF~hxTlSAuSBf^lTNY549aBwd#nfwbGu_|zkXG$@5e$0$y;W!kyv5WW!KA>I@%Vr&18QTW-92}0#q;g z*oScY^>W}B3?7YUfF;%zl2^b_Z5*?ttN)K&Js2Od{_~{2cEJ^bo;LdpR_5ZbMJTIH zF>&tfK?8Nr*K36NL!|C&w)eVkZmd)3bEyIhWt*Gjb3F(!Vcw38jqJuV-g?!3eL!c+ z@G@X7>sE3e*v# zi3c=g-Mzc(50eU7TYkofu@1C)t2c@OvcA_QW)YWr4;oSgU&m|Vjkkg|>_#<&<cDOex|6#dMwdBusSK;1QiNLxZsI5{~(X!ZgwKk~=3 zjd$ez2I_{U5a3v>Ou7>NA<9KgaFBmqK$XCeob^Y!90gc9MqLb@5PICgXB4Ayf%hE+ zMhJ7=0~3z2q0k582R2?+D;EZ7T&zEa%JYQ~YoCK?9|^8dP2ch3b|yJsh@$}m>v$~W z%|>F|HlxiYE(-?3tlxMquBDmyn)MY$||%DwTMP`>MWaf{)`-@d5A zS{ZSjTgYhr-u|$^_8}|A8fr@ZVG$7)n^_&^EhAb2HosDyZl+hTG2PsR>01Y=!2A=V zKluFr2Kod*Cz^)c$UTf)^GdU+0(eiE^_OivX^X~t9EJy2plK4KORZT&7hRlIfpxu+ z%wa~^;eLg;TEP4pDi7A~>2jPJ$r~FO26O%j#!yXLv^72BS3z<0<~o%U1N$CC;Uy`5 zwzTJLXsLm8PXpW{=qu?jm)5Os!~e_2`;WmPe+H!ZbilxmuK(MBls`>?*607A1M`0! z46~W5|F@n2!lG*R!V39D5;3HO9r~nUWX8sq^Aq@xl92X6Zxs;h=-z|)tjAXN(z%TX zjNgRMtcB>x9~hEt)z^c;i}f8fq(Y4P#5kRynjJql2wq=rzjt!u2^Wq2f;AVUFq$8s ztu(^8tRs4A^D-R;?xajB>}{=7s2{xesI>76?_xQOAw5j+7IS_G70)|a8zY|bPpnT@ zN0!^u{ALE+^+^MV?Azc7b4+>C5J2bMW@ApB{D)hx=!nRjIFnj#~c5Gf~ieA;5?Yilh5$3HV-1Zj%G8qd4eesb*SaLP1 zWtTe}O$WZbAm}k}yaDyv{}|hZ1~Sa;+&IYIY(VVvF9YgcG6fjln+MaIO%sj6K|va$ z6x4uWQpo1(`2U&2gn51vh{2a&AXhbXdJaQ{J)qK`X+0$dWGsqE5W5&m(Cn~^yc+of zr{+C?nYsumZ$Gl&f_F>?aW}zik(%WIZCdnjN1&=!olcBA1ROlV&C}@jUUbRY4kL`} z4}FvJMdn@<(25U7Iw9IEM3>9}cl=b1$j_*W25x#OWUUv30p4u zoXZsCyWmv=*7%E#-&l*WXf@~@!Wt+JR6z=fNOKM(ZT2W^rIasP6dBkToy>q59VFP! zEKrz-lu`kCoDidUvj|)P**pbd>maWXgZ59DOFbYikD;yzN-&RkZpI52E79T!P)5`x zOng$vCi%b@ZZ%0%lSY8sCnV^2OQRlV;m{i!#5_lGGKj#|C1?i~SWzySo?fsKTi`mk zp@Bd!s=);=P_|5fWP8^uL4E1KPt_vA8_j%(_E$nX%oN2r zMqKNcO@4Q|gE`&XQG~68e%m#+OugI${i>dOWO5|y+StZBzi7l&S$hKozrMb(jX{sD zWAcC-tP&`YR;l_^N7;|kS!lAYXW9tKX-{%P9aL!C*yjEle+xEr<$Y|D!bofa{UkWl zh7g}4Y%dVrN1Eduzn+YEKX=d`%2a#VB=aO|w=b-X_qw`iY168yjqm4`w$NR^3{9lI zN_ovQ9rsq>jp?^fKC(0F?rP8I@VufC%2_5xm%L@yU(f$JaC!HLofq!-VSA5H*STMp znluVEhByuNcoH8UG9Pkkl-T%q`@i0~-?J0CvYZ$fb?y*FxKTqpZNp)NC#*NZ_Xn;) z*+P3vno6ZI%Xs9Onwp*&E{0Mx|Lx5{4qnB(_%WvA@A?>7lk)QNmkrxLNx4pHL2fCG zqdjDUCw($AGc}z7HE%L>371yq$^3t3!q-qe-eP*A@xK06v|{|r_IOYH^Ups&q5aB_|PVOI=8XjhGWL~=gRcUGZZ5u zqgkk0*uYIj&{@aw!&6&5Z4-B5lJ1_N>%~A}O93xxcLVDD{68|j-r;rSAn%D0!q<ey``_Q?VCbOzu8mN1y(%>``nV=L#Gfz z-q+wsLE{U%mN&ON^p=yhDPI&j3&Cs)WP~RaL4?r)=Ogsx%h-T`y^9MAr(oPQ#EVcq z%5sWFPuTc^0Hu(Q>*w}Af5))y`LmltR$-dsN{8{@@pLnMSbxxmOaEq{!{5ChAKx7E zv;M=KA7Pc>{OZ^ZNfpNW*uRIj!N32%fAYT!(34sI2KBqjAtycXjp%a`rebQDMb^JD-#h-)8{qfddjTMg_t2&^FetEZ8)-?LFxv0EBj8U^ z26II@(+!Jm0Il4=tQLdWa%=OwL#^@m+?_KTO9Zn2^Ct zJ4cB=_HQn@)BODW&g1J} zzW4UVd@}j-E^U(_shERw^@O78oB$^O2Zh3=2*+goEQ|bk6b56xRmhu73%1QN)3P3uK>+RCEK2JaWWUdJaCc;#7X!a#`2 zt4p&mZ~b#?yfV`hADCM4)WuM!E^nSg>s7WxjoH3RG+_gYquR)qV3od3%fl zMg1FONXBty!;LYSVE3pi-u~_~IB~Q<$N;F$%P3py8^mP&1&hWU^c1eW^r7d-fcZCk z=gw)s{{9VgR&9ljX0szL3NU0KG$tm-(8ws&c@p$Fow0W|e-lQ1dp%x^6na_{jVpi@ z?<%zI&{YW4+aPus7%`+X0tfcSg9mwo(By3R38GtQvg2FdUD+w6L;oQh9eLPmxkHlO z{yrG;^+ayx8AVC=-|ra(jSVSIhc~KQ@XOSfS5{ZiSPqqFX+2d{)%a-o{rh$9-OBIMr?4|Rj9LLz-5}1o9>a-o=*sLC5yg|j2 z3U?RjXUFsB2U6P{!J9Jz;YxPbVvVM5c>4amdt2dir3kmfVF#($t1Vl%LgCj{ZwY2w zyaPw8F(i8gjWEnO++QD~VyJ&kaS}F2I)+fmR64&R}C6 z{)XX$-`}DS4u@y_Pf!V~;a2Fa7+=AZ-XBzAICA7m<&&g;-4k9(cew>;rNDL9pF`f! z3N7lkBn>XSi3JokMTtKAuoZy4=WEXWpI}oCd;a|GuV441ba#B{`gb~oxmYIRGJZ3V zO{%@`ZmWA=9|(K*wnFTG1`7exwh!K0@1u3|&sUa=uCDGCDOp*vY8copQI?rfSKXqL z{`pZ`+6&_4t=NGa`^n zw<{?%wl-A=b>TP_-M2p>==l5RhUkm!zfqD9*!rbW`2xFrP2E>rCG&h34cr_LNm zd{Vl@B*deDl%;xbv^$|ly4SFl4IdsB_rze%=HoNAXA)tiAs+07rmrSniNAdiRdB%~ zWQ^{)v{6r6vLheC=E%GDk3W7pPM>kLy`=GucKun?(kmm$H-~g7HpP-fNw0B9pH^A; zdWe3Xwled5YiT??6qQasy>z`k)A8Kpvd6*+Dmdr8cBgfRbBNK>Az=yos$Hx!$2oZW zCy!#Xp26*#`u6RIW*0|C4H$bie)Ay4;4ipc>dbTV9mp9VtQ-NkiKNp|0JJN#w6wsv zI!_$!%uOs)-lp!;B-0JtC7UahizYJ8lz*(*4zn}A^-S2;;%w$Q)-)esIWi}kt`i(l zk{Vrp_hnoE$y!+9Gx!wcE@?IFe7kh{A+|N%>aeq2yId4rR3kAVaF8--?|$Keh)T#> z!S~oqO8>z7IhD)&q><({KmBdnYaq)Bcs)-!v^rBw^Do!Yo(;0hn9qpOpit6hEw;Wj zjVsA-cOnJrCK1)*&3cWDS~KkmIj?L@XUCKjiI3rN#_7iCZ*&%q;soh?&*nsAus5qT zu4Sn@2$Id)-bg-uI_f}K(w~jQ@Ve+#9~@=r(-Tl_&}N?MA4qEmG)Q%#w09kJz34xe zsbPQHI4sCnRAQ`I^)tgH`DP&Y(crk2n5!2{TS{hC-h?2JX65X5_eC3}>K~(>d2tsy z7jv8MpSMoFjJapHhw-Rv$bMFFbqEJsQg(QQ_)P2oqOAk89a4cxFUjkH2%Lwxw75#b0#yUHNHI!;_=23W>p5Zd^E-rAh~#({fQw;AqD3~y0Lo9y;G_F0v{8P1sv$F^+j%ke}-u9iV0+` zGQuEBRDFF){e8OBbFXx%z3%Ld}Ql~p#`0EI`R7hz91q@hJ^C# z3hAXp0izLbuKejUKYMN%y_RtQJ$Em4 zmU%zBYER!vv3_M;BjvR|yep6Jz2?E=@t=7@W}8x?n6RsBEMyZSec|vs#QD&RS0bWf z;*6+IADkUY3QDM8xoi}2-TGLomRa{+_1d@Q;WS1%72&e6o7nuZ2>)UiuiJk^8&yKr zkvoTfX3x|OKyN7ekpyuWy0)W=$}#>Sxy2$@seThXYe}m;fmI)kX~o6M+sblhm-W++ zVMb%2UpfXOA&a4FKX9XxB~drSFc~uJ`Nv@4Z?PPXn?KK5CRC8Ko<)E zTkUm|+)mqnJ>uK*w?ZIuJcMy$jk1mWlqDbw9>i=V+__lISZpmvuQ+_UQMcSA@u@{% z24O5lss7t9&%V~|ikkViQZGUiBFYU?)UmZLF?!wY1I5w>JJ5?S-*cFt?8`?X~0J9AIKh`C=RH$@Yd}WtwAo)=4D% zZNpc=q=yH*nf>?s&0ed1ppoEkKqEc7SL ze%V#_ds;rdmqT;%RpiPR{+=8YzInNk7F^xgZOvg7(4B--0XL4g3D@I`XAN$?FwfB-0iQBG~H>oJL zF*2!ncDMht&`@`Ip5{Z&()=3mN7y}4z4?75<*gS!5Gs2sRStLW6P=lmahHpkq7hnY z<=d}5S(2f>g-cV)6s*(jF-d-8FFw!h5v$9AF&vt(Qn7WX zX!F77o0Pg?Q0+k1L~I1+4oARrp$zCtYk>;yC6VaZR{H7FWjk`Y5tveZF09UILeNI` z{y8x49$FqzgE4eFbzEU?RvQf801dXHr1NO#-6{vjj8PVaL~}oW{P<0!W=re70NLXx zjGYNT!EaRDn&7-uyt=pTYGcp&uZKLm@*b4Xd{vX$m~)(_Q;*P$E!VO&JtkAzJeG$1 z!pmRCbbaY<(<@rMi|f>QjWd*xOULg}e%T+=GW&^SBb={0Q76Am{S23w?#@H1RwgTo zwQ*M7YgZ@y4Z=Bd^{N}+TThvgIyi7Q;Kw7gdb%lUcM7#LozJPhe{WUWbkj3}?qahL zDNrnJ@BQINF$A~j80`xe`1weqB7;mvtmoFs+iGN*+EOUtVvN@HAq6+hf9`Pev^33h zSu$Pa5Xl%^9ILLSWS&fjn3PO2ab<_t#ro@SE`tXH57KtmUk#&gFFL;P6r0-CDya2? z7RbMf#k-~mV;cC*eCswSZ}XectPxBz5R2Yt+f<)b;cPvphb^;kPyRK3`7|!Dh(Hbt zFL$)tX=!2_T9@+hsuU(G0#a`ss~+%QYD{(jcW4aCkg2~Gp!!<|^Qb&PhG3PPId z>d#Bd%EF?fGdenM-n|prC)<1H{K#O0NjrnCp+>wUU8@@8D(eXd%-lLdHwFLv4s6U? z(b3Uu7f5i&&%y}NT3gYv?;jsSaRUYM1mzv~{`O3y*Sr&QT(Q}&#meIcUSzbrX0G*-;B)7ER+^-&bMLU>^GPo+^*uPHp3kNmbzq(F zk=sXiplv4?&2dMeZ6BHHYRFN$ied3dg1XeyqY}}w^a#V33AUbUJ+^N}9um?MhEpC{ zV!HQtLp<_`CEXpm`E)`ciUla}m)_pu&IJhGU?~nCaV7FFiP$~=N&Db;!j#xebpp7V zes3g-SrZ^F2NhM@QXCF#r@jt@4bo%+F;^bV`M(6H7-SZ^xeE7Pw0q=)Je{to=~nr= z7kVzs(~YsTjDGi(T+YW%zhY(Pc&z8FcX(_>(mKnB-=OAo9?x2Rvqr42pkTF*ouGOx zkBK`kpg?ilqpC7ia4=~t!m_?;Dkmk``9|&Zc#jw#P6$Lb5|D@Iw1GL zyAkc}?Hz`?A}B~oY@>*`vgcz;4&b@QCTAYe@47W^;)G4#(0Q@Hp>43Hv-L}BPltsE z-C&;F+nq;~|5DiO&p~r?3U*lt?gJfjTbHRYpUKz4+tVQRDtzd2>%^^wX6%YcD%Fpy zclxb7rIDJd=@e4>TnwJHhpPRyB+3;?CX;$m~LSqaodf#c>e-fV-C$2x- zbhLpiKH*Y?A~gSg1L;Jgc{Nn`C7AhK(0{G7)Ds0sKTeJ`f5X|qrlzJvdo&uo{~TGj zdX(uKhfR}b7iP^WwNjD`HfUW(9^ELO_n$W(v*Qw=(s}z33?& zH>BXw-HhKSRP27{Y-)?Cxh`m=s!92P7Mpt2!_L_eKfQc`Ng_0{a5I1a3oT*B?#uST zW#NKA_Sg*k!pBO3NGYR`latBg^DJaf1p_%#v8l&FWs$OCbtt%w4Rk7!$=k!yyh z@2Q|<2oog=#xybbLnwUrg=)jABL;T-5h1a`NnLaiqER)kPsR&51X|sHP|u#y&@`+O z*v6QR@p0f5lq`Q&^@(BKQY-D&uv`DXc-E}Tb~d{X(454_sMglkC_$_#yqrCBhg8AO zHr;@!Ki@tz^tvqR6{qX^)EhL0T6*?JS9yMd`$f(=$roBz8|bQHv2%9(A{ps^-bH~? zW*L>LEJ#+-%MVGWL2{(2EltI6D`o@`?xG%BB`L#Dzw?(g({E3@NgbicF4qZd8Ijg| z@&PETsk#OC9?WcMKXd8&e%G}M9G&aRgJJa%&sjDOxj7r&!z)W_#bNg%+jE{>+wu?R z5(^DVV2In5Zj%Jx{ALa46lgC2UaKJ=#SF$u4D`$8 zL>}+g5FP35Qre9b#CPtPjx$?3ymf!)?enRtM_Tgpyu$&))|t*w8i|gFrCKA{YOuqa_ofq*~GVRXa7j|WdXu$C9!oC5Hh+R zR0j{LLMef_=(FgvpR|r2J#Au*BQ)Gt=Nu}iBDot-zd+wp`dm%{=CcE zFZdj~h@-@=!m@(s{Z^cdWsI32E=Z4K7LbsMiR`Au)@ zl6!B*Z|)_8Rj#;k!!B9vPqSUq5w72&G|)LGyk_6Cw#*0_kj2&tXBbaSS#)>{u5dTX zuW-$avG1w=OPQk7&OsCzmIciIwSKkZ#z%pO4<9n4F)_fhMu$vjg^3fQ6b$FAuzs__R3{Mx$>^l82kjYQZlRIBWkZ5-BL-Jul?)~<*$LWQ(6Rm}7o z?tdW@xm`71Kzz{ecHMOHJCS96H|uv674VokJ3Z@@$u?}3B%``@=pcrw*z^p?Xupk1 znYVJn?(B@HaPdcB90EQv>HHuh)NV3MEwpf@nC2~_RO1vaq;4=>J4@+)WHnn|N7={y za9$!0$BKn|l*i(l<#%kEe-w6JQ07PxkGiaZQ$&5iA*$XGs+z_ptv#=DF5j-YxceJQ zb@@!dEx!NW9jYOD_9w_Kha{(w-g| zVYWHEu!>&AD><%@wbJ*G4;?k2HRI41%=5(+AMWZ=@x*q&&XeknTV0!Zba+w1IaprJ z!Y6j;Ns6@r)ZJ8{Sm12d*DPnRI3+eu-O0-f4GPU3=J-bX=#!1J-8!g593EbL(uMMr z5Pwv#vp%a*sKGzhZnxBAaBAJ39kZGLj{*_|TdLwd_Ijwde63T_63MNS@#w_!Nhp?_VM zUQ<_>sY2f~THGqlFN?eQcNKqXY*#mL5|8`6$~O+ZZ!HUjTzyyK{LmJy+9FZWJ$=Hr zDxw_K<4j|Cs*>`T>?fvmwqlfQ?hW@9GeK??3-#8!zzgdD?IFPH(ZgZ^f|a*`*3k>3 z&m3TKYr0qg(#ybYRuIVPyLZs|Qf)5yZoPW;Z*UINY7CR%ob2p%d0B;p(NeuYH)vw2 z;qN%#HrD!1gSqUUp<+fQhwb~{J#AIwIx|9!N-W82ACVBSUkr%|PJ93P!)5J{k!6&+sVZ0VIxzn7NV>j+r! zHbImPvOelbMn~-VCuY9hS(ol>HluFKTWAyah4lEMP@Uf>bT-%>XK$g7ZcX}^nV}zBt!1~-4M0f&Qd|5p5pF_anr?xb z+w;BOuh&zO{alxLZrMbxd9=&1#_jY*?3Pn#5o=)HbfF$Kz3R)=W0Vk7y6uE%kz;n;$+O?+u zW~6Pp0)7D)7;O*9`K>i5gIhp1-^JzSEK;v3vn54-5tVDx5=grL08#0jjt@KfvYhd{ zwfUvDoh8IEF4I#qV$_xkVdPF(bvO^H<7q7T`JV23wa!i|4EXG3)Y|t~+uf{RG&@WC zKL>i`CKomSM*qAR8-t$wAQAVZ!zc8z;tTMP`ikyQE1utpk;>KRF%#dHuC>^IL6GWv zbx$5={Zi1Y@wjCgJ>cH& zw$2dC2hPFAJ2wlz_SrBgy$WW1TuN6wBi|~Yooxq|Cjy9i9*74Z=D-IdUw}U`^JwKt z17R?%A8XR$Dlz&e@&TmPwW08-R=PPp@C+~Qqp*L%PnflOq$Dw53(XJh%G+tb#f1&YaoDZr5x@MXLEG^j; zZ_UhpO{L5kx}8LHU;k0)v$9!b({mzj2c2Y3<|Sy&b17d`{Cu_K3l!Clhu8AXpjy9R zM_%U!jc-?2ObN8j9Frimht^QK&um`pHdWRL} zcx|A|hZ_*V0QwfEWqq?E5*?~`8U3Y9N*Hf7vPaLZN#Qz4+ zvCY{=7JP8b;?w3uZXIh_7p)h z`r$xsU~ZJZ{r)5cduKrp*49>TH^Y0Dk)rYWu_YF-6Xu&po3*zNCe#L@(U&tCZU^8-loNUZR6tMNcyw@t`n4W zA3_IqEMQk;kiUav@8O8#L%G2DRgVz-Rw+)hLbhJ{{c1!ghRlkis(oSpJ_X@ym}61Z zQ+|(K{Sq@yugF0L%c1$=cZMXJD@RrYM{Yz0-5X>OU|+FFLkb+hcNWCsnj`${xcP(Kh?7R)qVPjAw-s_^c|eR67m5a2DHINtL9(sf{M_yNvlR?nmd;0X8<12gKl zMv8>i$fa+^Jya-B+2@1~OFy&MtSP2Hu)?S4>OMy+evo&k#b}?Cy1wb4V;V6oxjyTh z-KV`hx!*9>E#n%m*5X1&n@5-Tk+8}D73G{25whx2Pu*X8tH0~dD#)@D(;&YRM&p*K zp`~uzPcD3xRhrCzS-vGx=j|X*;;RHE{rf6+E$KT_Qg)n|*nCz!tG17&h>h-+>TI40 zUmi|j^hoGVYqgdTF zD?eCn+>L%T$7-WXXrApr!I56EVtpGeJJ*_zM!4B*mPS3ccu^-NrSz3cEzh>)N!d~l zXA%i0)k}uXe9%$w@P3yKoed}qE{_zwkZWV zoT%{J3|(}vFI=8|>DQ%{ZAq%T~J#?X>*g8QiPrEc- z+yKjrD;R%xee~NiB2sNfNpF8TdiMuNrZj%}@)peW6L)uRsrZ0}pFi6HG93@t0|JoE z@IaDclY^$uw~K9tmw}2#1htAF|0BC)<2RoY>p?JVVTn0q&5nNQbOGdj&{}Dxp{g+(xBpwSw8oO=hB+kD#JjR;o7w*Y9>DC zCuO;J-4_GR7fBAq%d>Bkx=A@6n5?0#!#Vq_FO`Ut5mHCOGhW%|rtpbL-8@pX8=u^a z`{1skq&P1s3yyJKf%E6tUST&6%+J#-!bp*ZEA6lw(`KogrBe>4Px}+It*cCOyt~c_ ztHAHwOTml44(|b+cLMaKv?SuQMV)5e`|)b2;ejOtNH`={h`@9Jv#R6akrGaU@rM>9 zm{HGNeFzi_5T_1mCY-{;nh0P8*u~rx0LBJ+k+&61zY!?SID)YZgSrAYSK0@p5=l*A z>gg}_B-t*DIK&$gDEOEt%{N%eb9%xprE*( zu{Tt#vlkP%Ax@0>eC_{A0*qiI>juhnUNvdSNSByq#(O@>(x9XBvJ zy>|K;F7uIBPVBnJo0ZR}_-F{k=~T=C=PrqGs!KCb433Mh4K_{kHHe2vt9Uc#7S*>Z z3kGqJ(|5I;MmzdjseOEBV)V*1-Xn6@((e=bYtO0P+L@5zsI-CKe8cV4`@eN6bC&&H z{OQuwSdYVti+oEWv>fAvgDqRvCXKn=9!cx|mff<=KqSnaC=mPlc7DFY2Q1 zJr&>Xk;uySDJGnw4*xdVl_qe9)a6}>&H=nl^=+gsKRrF|3L+EBpc`>sOiVY(n0MT1 z4GLH?H*XRFBn3Fp90x$F5=gD=Gl2=usa7K@!v&>GjytUgzUSB(G9L|j z)@eoAFdMf`3X|_>$#dDFI)YSc!P4W06(B-(wQdcekKxp8a!R}aq=*k8ZqQGo^892Y52q0@Y24S0Skbg-8>1D&l=4SQVx8H&E z0uvIlf|ryD3NjY+{NAn&yArq&2YMqDcVzncPt(qu_eD@dVd>>77n-3a5C`T z5p+-fT31lFBk5D&!RIhxoM-c7)~z-=v#Kd~pS1g=F+b;*Cc~>-yAt1bK0knF)WQf3 z)5`+F@`nmx7dilk$dm5@l40;(*bc5NE&ZzQMMPu(TJO9>#adJ6()fwv$1gu#G)9C) zBCkA3)HhIYgh(DFY4mZxe|Lb85t!(Zd^isK0sa_)=yjIHm*rh#eDJa}-%fD6LFICu zn8jUK;Veiw!0w%fa%T}7-53>b1T;S9=g0|{SvvlID%1v(u`VCS3 ziknW<{asbxx3~7YhE?(F-n;!SfR&|p<9&>1G^4IR4LA{3sDewg9KF8&>)ZHvPsSaN zl(aP6eaaA`-Ac-Q`{tvoUOjoj=p7jvM%cKS7OI4g(T6lOT z4&QQ;j{dxqinGc0@6!&hVJn~6xkNvFoBZL8`xeRlm~Vgm#ME5&#ZlSA%ge#Om@>s6 zyTgkPqTCwwcT~92s&P$=HA1lw4*~3zF3euFnOC8Z8&PXNmj`G>FMg;F^BHYYyF)&qgRPLm5d+?s!^&( z4tjlk2{00w0~DFs@m2Jj8;yg*N&xn0Y;5Z4kJhVANi{col4y72AT3c8iV}`YWEZ_~ zFuw^1w6X9FTRfAxt1AxN_y>nP$;rtJ@6{YA1RjLW6rGeuIW)n|+1cLY78KMl&DRDD zCbI9=R-+*{4QtVv{Hk(y$ITsRk+|KRO`d#jwuZu>2ys}7gVc?MxHp#RWa3N@oPnRc z8b7QvqT-80`pZy#bx^(x1;v+$;SHdepvAT%a+IQkT)#({UD!M@1&{^cHQ+B4;~|dF zhy0_4yu9D!C5IoXZsB`~b-k02ENA~%m56<%f!rs4$ix@v^10c35Z7Yy4T^t ze{NbVfWMYkQ3+>-Ay#PBuLig=T1QEsH^L8{{72Td)**uabL(cBA)ASR`t<1qDK{4v zt;uNGSdNPq+qtHGAzyt>@6+BzNzdLtko%0#nc~liR49dn@LW^k;BHJ#fx3*&BuJV6 z+Om8BKu&r3qV$c{tTEm^j99n3pW0`1PXOm2J%pi z0G$QaO<;;UERdPGxw*1aT<6cX1GU5QN_g;zU+fd)LXMQ2vE*gIYY>D6TM;d$vwa_~ zYwA6E!7m_yaSUi~Rz#%5kkkOH`OmSBJ{SL_D56v)gUNwb+e_*C<&;chUMqm^Qiwxuz_2N$LY=J zqcZ2A1z$RT&6}Kp0O?D_69aSc$JiZb*;NH^6+wdUr=WoHrUIyFh`MhStjrB#KrrUK zl$0Tix1f`BbQxsi2Y!H*O%N*pE!zmcl$GEr)wP!*+Blx13r9ys8}s)(@qTk;H#poe zwlOwbeSG`*J^gpR;@0!WQSsNnHXtzJ5aRN}KDTfbiU#*xenf#Rz3kV_Of6saLuO{2 z&AKmwmJji!tEN;n`T9tvEvn_a|KYg3!O;g49*Nq|*2pV|Eq(L73OOnrqyk9@s_<`U zWR#caOQ1Q@OFk*t1K>maMS;wLz0J$<;xEMl6pG;|kPM2G|hIO>y8R=3jOEYhM)a0kVhWs#;p<#Uri? zAa2tRq?l|w<-eUheh%vA4}Gaw@Brh^fEYSFa@vwVBGX+mzh5tDeD73!%rIEQppuFB z(w{%yxc<;h@*uXMvu$q|0xyD#2WGcEbO{M*Fa{D+P`?!#6iXbZQ^GSLQ+k++Dl9xa z931Pvj=IZ+l#3-L4G@$*+ZqWX9Y|MY0495|3@P^@+k=aJx2d5}0id>SFeE$*;viX2 z0n}o^Vf@;>p4kH!JNM1nnoCogq@*OM3`Nao{VC7@J9!OAqh{iHqqhg4{tPgQfzq?T z4h0HZ-SKv+OaRh_#rgaB&1n6?jbGLSzAt?0X22MLSj}4p8}^?7`MDtQbZ>NAL7p0+ zaFFIl4TGQ=bt+hRWphBjtE(ReWBw|D)%WacBz|pNMGH1}qFfz%!li{P(WN z-&zrWkftQo#-1VGvrYMeEJV6mAS0b`Q#yGHF^FKA7JvRs$FBoo7M-PaFUex>sk%?X z$oUryO$w0fWjZ=KfNTP^6!-&q17IdN6t+Ssh-dTg9*rbj7e3Drl=#y@za9;fJM3o| z>QAyB+|NTr_|8@^KtZkC2XYZ?4g8zGeyb|>J^-0fe{fsuK`fOUK=l0My)7+D#lucf zNH(~(u8vDU;0`o$>hS>i&VlvNy?Z@>1XwTKFtiKpr<9wrMqj=>2;fsiNQ)LaoA135 ztLJppR2C2*1{wFSf?GDqwzapoFft!VMcd&2~OBQl~+*o%m^+B=ybrE@Rk5UK1hRyuu3eqUzb-@ zEU=gP+cP426xxm&#Ch(N0`~-1wrPli1{$h+d>Z6};D?lexT+$;ZZS4a23&PKBo&Cj zbL#~uAfOMYfew({FYOn&K(#z#;H~QW`%Bdg4GdyI3QPbLBFqFOa~h1*u3r6E8}jRY z!OjO@EWGcxD%!H*j{sJ`JZW#7JY>8;Hao_9)V)oLcjZrhKk@(sc2n?;K$zj!z@H-? z{*&?`Obp!$*TR3;Q~y1RM((jw=D$H2cLlX~bri)f89(H_D5|G>G%ys3@i(`>2r_ri z{hHo?KlmfmmxX|t6BXTS%x?~IQ9ua$<3z|`UXei3x(hU7AVLoBmxUOhr)}>;R*N3= zx_t(qw(AjNzXmsq4a9H_y&$tuil%mNFBOXgZv{G7nj<-dkHayf2#N*x0ClTR82MHuOZ%QhscBkscTCh z2Lj?<6?9V(TMeY8zOoyCJpgL*ca-c4Bm)9iVgycwWKRHzh=rJMpzOllekQPA3Xs0C z5$&}ghwsgFh%MP0lmv>u2IXlDezz4X7}Vet5g})+3W%Qyg}tw+x{vH)CM#SEbO(c2 zfv58TK#_ZAg0vT~Y0~px0yMP&dHru>GL*D7yipU#PKU+W+u6-%{+bf-=={e}N%9ar zIjsG>2QHqR8h|2Wxc_$5<8ajp&^rx;^9?%T^*=4@)gV_5vbnHvo7-V`6yZ5W5(mhzK}L zeEYPEmkfV63~@(k`S*M*&e#kuG8?KP(82; zQC6>4xM7yA%s=t60;(Rcet$r!JrrEr3WGnX<#2@8;fv(ZUHbHQ{KOS-Xy33%8GvGU z$MZucvb5>{IOiXKaU)}u<;l;hg@FJ%16%(V7-?tEo(07LHxZFbw;BK1HJuuUXdeV0 zvwv}`Eb*q3x2)+6ZCWU-Cg`#l%@z74D=;>-Qo;sbt1VW+Gl$os5>7_e8zj{f-z z4b4rv0Gfq_cQ=_kKu4G@=ExJs zF0idJiM!I-)`hru5ZV!eC!2qP^h9zE@Z=Q;_YwjJzz+TO-03&plMQ*@kdZM5YA2Gb z0}hkAMV&1y6d+1@N#AX@(9Z7L$gllNwAfb~_ru4AO3d%Pyvi!#3(pZwCQS5@!yYxC zJ}pBsfDoo@^A32l6-|4KuIhDIG%o=55lO_3cx+lve*YeJ*yGu=XCzRw`~iIIx4yo< ze?Q{kI~b>kd5QJhd0YXv1}UctG+P@R8w99fbbwa;IYUE3xA0#E7FoC8h+AlMm;vB{ z5nd@Mcm^ZZK;b8GdO8CF9etSkNa#1C2XpY(W(iXV1!`QbV2DYe6?YRDe<@E5X=!N@ z)^4EWMtbCB^ZQf3Jf`@s7ZDd<`WXnk1OjiE0A%)F7*QD|B_(Pfb3lv;Td|^pFyY_5 zd$+>#r?>`O%&P@-IgLQfK^2^;j4{n1AbO@EDn27Zt z0k9Vf#qU%6N(SpPhuhw;g84+X@9tmcLjBjkW^Yx8&vvz>Z!czI3~ex;2lGcs`8S^U z|0k09|9r5t#CMvwdw&FzbY@G4T?}c;t|$ga#Otv8%Jl%`A9fM~A4h1qS^i&*-9HKQ z|D8+n99;EZuci841o&B~_=J1^NksT7vWIZ;2og@7+jEC4l^dhrB z6;=&I%FEI%$K~`g)+wZh_B=m@65lf?<~a>8EB;UVc8pLQ7YPlf*$kiIDJc z%_L$aD9?bbMj-J1)lJv9F)0a}W|%y#Oi#ZB+AmL$z7=p8WnNy5Kxs)In9X^Nf42k zLlsgUaw>@4W}&5K%_R^o=>)Ygtw~F(p_1FHqn=d>w$R=I$=X2bq|klS39g9n?~$y+ zy&*^gVIVrhZ|&cgzVx>V!>a~e8kQj*`~hSUSAc>b-15gRE~o2Ca5#&`5Y7RdBL*ph z*VrRzd3Z+k6iejxxGr2MC`eO`KtMQ|p!n?b2-Hgu6&@setzZarU_?`fP$`Zg9o$ol zsFp#}xK8sU+3+4U%<0Uisj1yzQ3OfR(iA>Owalz z$!=Z%y&hK(oupx8w3^lhQ5xWA8cY|W5Dv^=uaEXC0D^*p?~u;Y+S<;mxh?SO;|e;* zLG(xB`X=CBHE-Rz^`aip$^h+Lnpg^iul~!4ua;G$+N!Q)5OqjTU}9j<{kjGMs%mEz zpj8??bSUF0AicQw_;Tdy_oW;D?H$hi4k^EOWd#My!!0CV84+P_UJS7m6cIIWXjuFP zimWZo<0RbBPQ3^!!cm`XzVPjINJ@ zBoCMPcgPE0E~u%g!8o=AjVr`bdl}u`GZxZ3ZV)`uFfm!1m%*n84SE`8dqOwo^)ld9 z^x#ZE+L=q71yx^t^zN^#s9Hm(05#a&1W>}9c<>-Slsi)j?8Y+qsq`C2Gdp?mVbB-3 zJ~%j7rqjzKQaQ1Xx&pmO_RfIN(wIHB7U-G8K;1=RrOyb@lOFq}v9S~MA01X^v=t12 zj0R1FU&#mZ(Dk{xxTq5cfZjxd-2UIc9VnO4f=eAK;Z_77Jd{vK0z^pY6jV#Pa<8|5+kMwayyzPS!&2N@zHG{pebl7 zfnH~X1QUoDgjF832Cn5oMO%mi|-S9nO}?^GnVk$-o7^2nm{@&~40tIJiX{ z*9ve|;GbjtUOFKwtv3OiU_upI7GvP8fFm<$ z5yuT@hrCSK3bf_TbbJC`#WG-+iSG@wzL&_F$v6tWC!%8D7JfSw;i-XPMnjD*#TL|a zbzlxF3a-T^L34hOcHxExsXD{TUd9?G8ZoRbb9$>*B zx82&l3X!(vbYIXoyW;zYxAWhTb?fiFR21zrl&|TZ3p+M;7o$G~ z6;h!BkTG|JCJ-7f&G3Df0iUSu!i);|)jI&j#n{=;V9+7KEzT@xCoRiRFJe?P;fAhI|QdaYYXGqI<#K6n%a>)U%Fcp zQf`PgCM;(>?yaq@e<0I9LKz&%be$;%e&d(o;^JJ4OE8cuK@=61;~>^;DCQ!^>_GPg z#Q29j5_6hCN)1Q_@3aMIgiK{>oPdS&Z}0IQMz7u(>1cZcX;*T z;36=)jFEv8t+O#cK2E7{J8lZpo1hu;RYbm?2YslT`EM>mJTndoAjmrTZdl^VZ*07L}{A?+@NvH{cW$U+*Kg{_pLfI(u*hHwtz6a`Rt)0kY43 z=Z61}nZf_{!T*281Ggjo99n-X8vRe-_b;tQ$n+u#i19`L!QlA&!S5Ak_CWze|37$% z{~UL;d-0!F692zr}tfc_aUfMuQth zXizA6`hRA({y7@|%V61)R?9tB1Vgf0=>PKIziJV`Toz(p`iv!VXL^9=J_UghF?q2Y$< z0_G8Za#xa`-BjV?<`^9Tj(|^8e!i&S5e;Jd-F3IprkYwAy3)t;tzSATOK#LgD;HY2 z0|J)Nl@|d*HYdj>6Uv`nt4&F^bhdhWyt8$n5D3IMAlu+*!JL?jGJK3H<>IK4mYa?c zbtSxBiMyp{;JrPfD-xkdIMtzLT|>{=*Qr<(Y)5E%_I_$b&ez0L})M@Nv?(< z%Wme~<*@+V?8a@v5LP>)&CGO?;LBD;Bn6_+3rb+nmiEAjcDzF|jWd7N!}__Z`O={@ zHBv({18I7dK|@tbebRn)c0`S(LOiXmur@}qqYo38v+^ilEz)bFxoz;e5rsp0yFs>K zuCBuVb+>RaA-*bAOfgI6P5+_?#7d(=trRH!tCgKfAdFN-;0mo%ZDu1gtX*D4$N1A$ zJSO?~Y>d)cW;rQo(sHygj_PW@na^H%_oj(SQ&lT@yW@~f#dfa2rX8hW$UdRY6m6s7 zD_OV#6XJEur(gp+940f2oJmsT^Un z6+V-^qes+v7yWf(Yu6NCQgp(s^YWI|Jbuju)iXXjm&$N+rBmZs8RuEn_@PHYLLFcV zj;|G?p56`>v&gRedDnAT2ph*c+jX04gn2SRp{=ziRmzCezcH*+SnOI=+-Tp@KIi&9 zav^1dLUJ)onKey!63Igc1i%O28Y2+0(&oz4?$oV7m$BmTGHj_`Jp1dJ%rfRpJ+Yma z8KE1I&)0~BX;_UkyQkAjpRSQR><-CAUdBbuxh&s_5!H8=x-9n1avvdo-7_kgZrrso z`|0nK*KDw@^uoDGb(Ayd^O|#=!M3}ZuV=)g@K>f9uhRDy$A}hF1&8}aDdjjOFwaI! zzi`8<-_TM`G18BYX?@XY9W^~`+bi6s-RYJ6Ua?b(<7iLxG501)G~dZnV;He|s?HvA z9C0Vdqh~?tf$ zj;d#zd_ArVmu@)t5SX@jd6X5uoP9m1S3mq{*iD*|-Sx*f%O`v=aIul9L0BDQ!xf;!fgCnIws9hvE58b7~vfHPTIfU zg;*1A!$}ijIyEuVsy0oRV%DxNlIbh?vmkH6v^a6hk1k*GrGrVF#%Z&6c~7SLs#v;nu7zqsgl@r+#A{Syu|4p1U~-8Fyx zNd*Z{8E)ya8772j>FUui+!}3$>%-715EHt3Gq^Q9_e)7)15Je(kwoMTg}i3|0IIuE zS9Y~ybJy7<13J3#8P*4>6)Zg{r;5IkGLc_cmKVZWdPQWKq|4j$O!Fuo%}%PxBtj~2 zVt&(`@Vu+Bv9N{GL#2(L1K?Xx(G0m9e*#^=n~7%>Zeh&qxGqY!QMvWVe`L!z1$fQw z3AX$Zmn|ONlyKNI7PLNoUT$&Gq04_M2X7)=k@&hX@YKruA679M_Dk?i?9*Y`I06WxO{%KT=s5yInuIv6(;G z?OO5Tg-j#&`H-|SNAe$MBy@>Ys|K8!v}OvUtG9Seq@jKUpB zr+5SHq7MVT`Xxqjm7^bW;5nC$WV%zLdIaoMHCy5&8{3QRY(pfq*2{#L{dfe&t3Ff( z9w@X;SdbXVylMQth3K}kaVWC0>{&_-Afnswv-sku6$?;c^7+6X+|70QR*hJ3H(SME z6MdPk-bis$os;D`IncZdcIFuYSz6TC16ZKrkM4NHZVVQlJL=9?nSW#6{R6%oI8TN5 zw>@S>T&~4cx?&=C1y5YKa9LVdktMNLQtw{y#sQLf(mnD4-GTM>{gL*&6e(e`t*s5c z^`kcJJ2eYBKeyi4Z4-ItZa)*tGz>%!;4rQV4lyrPI#NfIcyT*86GmO4Dy*p%ZSStk ziZJF3`+h6nFvUl@JABC5!vD4*6s84-HNVW}j0zM7{J`WC zZ6ySo)Ait-tn4C%m;if?HM|p;DhDVC$ z!mGlq&1wxga)MJSf7~~ZQexa>My$dTkGGxYT6jHTSUR3aJhHsfZ6qM8 zJFvTLUhzHjU=#Q-s*Z@lxJk)+=Do#*mE3~jSxFtPt%~-HuIb3tPuH%GlDZBwjka~M zlZGF6X+ zg7f0DLl-)e;&JJFMXAjN7ZfJRc3)^)uJ$88~QX^vRVZp$<2?8 zhigWI`17oa`G0Hw*9(eujJp=7wT!G*h0=i_(w*){?)i>^3APStcy!LZ?YT|5fu>bv zUIDmgSSgnAnLJ@dz8K6Lqu3WhA+#H4(-SaYwyDJg4Xy9A(fCxcrS$)lxDj8RcF)vK z2OW^QyXtROGn%Jo<*w4qn0bD0)K( z-+ig}>Izm$<-tel9>XviFH*tbOLH|bZzHi19Liw=0}mQvYkZt;BuQg>*yIdsu=IVY zFB0ZSG`fv+*2_BsOpUm^gRd0aoGuiyaCmJPfp`S{P=bs9>o_OI?r)>J1x7Nm&dT%; z4jHmdm$LM7v2zyUu~IloOZ}1gAt5@1e!bhp#>KB|blW_B?jj^`$WZA~$h?0Z8>4&v zO0J8Orj?#uXgJNzT<@&U%8xTlO?2A!Ne38K1NHJ;1M@O3Ht}$asVHd93oeHajrSQa zE(nrQ4zIY^AD#VOitlkr&-~B(-`9={Q@9tRp9ml7gTd^Z5cAfxsIk5-EtO%Qk{PdW zQ0Qzd&~&`3vou8yL$4gy($KS##Bsq|MOc$^zP&S(M3yd*@8cj`V7T-6Sqv9vOG~Zx zolWPWf)ic*%hLnHg--4))WQ<(jtlb2N6naXct#uID|QuAQtkh=gibh~}7k4W(pTHQ&b;Gj0bjZbt6gDovG+2^ihqoY_0 zEqgwb`Ei~66+Jc#ZDX)yJ=#_khs23f~TbDot8Z37rv>A{Ek_8(_{+>|uLMsGdUDU*ci z>p9FQ@h6!VWdYZ*KG|Az+x3ieqqJ`EEEg#p`dGrirdhOxh2_8%X@gjsFefz76|Qu0_oCph#cfS6AO`ZDmhMF(WQVOR)xF-t>a8c(adXnxRee4oyGI-FfUNIy6Pa8+&go{D(+fV zvhFL?+FHZO(ko)qt=i`th@sCWXRMA|e7d$pbLv}8Wz0E;dW{;=>|%`T_h%-JS)X>k zqcW%ZYb{B&yDcG0hThZq;}k@7*Hpy0Xl$$bN?0&OgQMy2b$OVOj zzO-exH+~_E^B~;$^LI}s1?B9{1KB>P>Uo=|FIV^EM zu*XpGzUDmQ$=8;xdVtsj8}SDF*@*$=&?J#9twFAkoSs*-Nt>?s=;U2wo9Fr6GT z3LR*TX&Q9Nw(#i+av(V1&Rn3RTE^Zj=y)LzrC`ByEE9h$0Y~Wc(5ZBaV|H7hNQudQ z!Y6@*7}tf%of$~P^U1{$P%tr$eRW`T||HnLrJctEg+ zC(sj{Y_U_eFaEyR{cp>C)ZEK!lSU9O2VwH_5uX&x)O}rHl^NfWEC9?NRai1zpZl82 zw*~m9It(YA9wznQ$Miau-%uSa)i#PyRMX)2=|ss_ku7u1Fo%nx`s>x5Ny*;SN4W!+ zsqlj2uCQ8NcU>-mF zqb;lDvEz3myt(D*L>^l(oXUE!s(;RtFHk?iu^2o^kg9S7&YM|ZgM z%zAoO=`@gBF#iGrgL{cOO$CVmo95=`2>RK-sdjfh20+5l$y;}z#)q=(hkc?=*+b6z za*eGmKp)Ptf6daU5W95WvB}7(_Umwj=qFAo~>LR9L%;Ygr$9L5>O7e#Oz{$45BY7OMO+rlVuRg1n zmWpDO{8jZu%P~4a+_CriGBn!f9gMX3K4J?R7`k&tmXGfFxaDXIpSw zt*x=CMFUYRbCfm{JoK}WQ6S4G*&tftwx`?FO$>f&N}GQ|koc_h4$oIxv`wr>pOdsM z6$@x#2kfhmxEybNM1LeFNqpFERkzCh6DmfrrQH|fnX?=TY(3W<+@Ub{cfvb#m>eM; z=R+Q_iUEL<3^?wxP$-NI%{y6#AJ$MJy*u8tRzQIPp*{owniVj> z`4!fxo*_pqmliX3N5wgMU0w^4-<>}`)tG(Nz4URNS5H&Y;s%m^Yx(|cplCBa;B&N^ zoxG2o{gH_&V#b%BcPRQ)v!6p zbr5$`se3F9HQd9FdS&*Xbkqy4TFjVZmeLZYnzcSe+ju-Y2)l~6>Pn2EusXgYuLgQ8 zLX48B@0fJ8H|FStwTL^fGaNX3-8hbZxM1#nhR?Z|UzR~8PBbZ$BC$|#qlAhh7HuaR zX191`VtzUy?2I@4kdT@t^^a85s&5(=k^48yKelKR8h71OCyttaCBuK4nzng=k6Uhd zN9XtkupYmclb#hZi`;YBNn@fQ5ptrIn+3-mWvZ0JcH}AsEG>3hH*6I8n+AKISA{Rs z^`mT6_HPwl`@~YOY|*NFgeJ}ofARY!&n6tnP&l@WRZsh#5VmtOHK09m`=;Zvy8MgY zXtByFQQx>D)%J@=mdDH#vKQ3gTdQ7Mk9+AY%FE5IfI##B7PSoh!ime%T~Kijy9p)4 zkta(^2#_BH_wPVW^$phks|er+hF=?IPEOIHHyPL0>rh=uhYhYreslh-^aM_{*S0MxRnmNrQ21 zzf!3kEWJ&{DsX2ZetEm9#KZ9`Z+;l*9bY%J-O37P3#-csY%KdQQ5oKN&~sM}Y`>Y5 z7B(lG&a9@n(!onSd(D>I%O1!&c3)6+PXhh#XtmY$&AzJ+Hm@yt#kx$ zB~Vay**43}O!=+-E9oECc7-#mc!@n3%~RrfO;x^|H{>HtE7n8nBzG%!mX<8C`&d%? zCN2a24U_vNOCX|u;Mj>A&2Zc9pS$IlHsM)*#!>MvhAMg@sRTYM>6G%JCXZcJxViou zcTKJ>2|jLN!`-zpv+RE7?sm`m&tzz9&dln$1+z{;VrY*1i>j%ZTgtr}eC{cq3%*6& zM_h&C58GVj22t!+H#U>K!&Y`Z`qj+%Bxb`!uOoPWZ|P*&R6?qW?`o z3B7+X_%r0u6_)=`d+!-lWwvdLF10Mngk?Y^Nr|E$2!bL>LMftxsGuZCN)RN9NRDPD zDj=wU2ndKI2}%ws83jR*oIxbB$YBAmPfE|;uf2QjIjz0>-u-h|YkOA}EY|wI`OP^- zAAO9`2X$}FW5`mvI4uUYXlFKmkqk8!voh71$$3e69T_{d!DqbSu4D@L?$8gqpD*{e z^Z7@Q+@xR8@Q(*GYW`3Uk>C2vkKtsEQ zinLwXiPz3RAi(PN(D$3cpO>g+G_>fV$dY^ z@$oUbZt}NQq()O#oQA@@&>NNhz242^4t}AU*ZAsvlF!9__}FZFs>D*u|YYBfl zaTtiGCrg~PZ;#_(;(v3C{h3hGxIAOy(K))u;%i%RZq5d*>uX6W)fG~ve!uS%xvoF# zxY^T-Kjwx~gY6f>TaB*gWN>otNvP&&O9)WTsR-Vsuh(;hn&FgTbi`avJC}FktaGdz&^hi6(#G943_XXc zFC=I=JUFaTk$vyUfv^O+J-hoe2OW$@_U@hhVN*t5&-vvIo5kFlKeKHoy4H9*bT#VR z+m``;&BUh;L8h9-x9mrk?{f;6=zA%?XH%R#>kA3ysvXdEs%1NT*)YK7 zYav5_%;fgJ{?W*aY!7yCtypi!9RK;=YwP6W`T~}6E@rZTcYL{QudAq%rjoCUE_=Ki zU>s=w{(AePOAU@`*PVpC2I3^*Dw?I$L5i#h*%xO3U|oQ`j%o0-oQ;`*~JQscxgzg?SfmnaL|L2E!RmZ*rTfJm)4x zj3t%kG_2H=t)FkXIMA3O6j@qi-ps7~hik>~j&%pGhctC+;|YffEyq{e1CtvSZWX4=bK#as2dmeQBW zH}p)cJFz`g+`zD>i{aIaH^pyw=IaME2F0}|0(lOxNti8Nqse;xmDOTloiQw*zHi-P zWOM4~T`PfaDgqRB@)bd{rFnCGNFW3J0wV{3}t z*k!cO$^(%22L=YB2s0WbZCa?c_(n<^(7~_f|KL#I=UJ3*9kl#@ie7c5jJ4VY9%yey z+sF8r826hioL*ab6?)Rv4IYSosvI2`Ybi)a1^hfBR~-=aOrgB^S)+!|Wh?jZ@4LOM z@@s!PxMe<5rmtpIWLZswZ8~j2Ed2V^+Ez7x6{{^#aYOH;)zYWpvb>d286S zjKe_hF_W*))3`H-hN}GnB&+ZjrN|o$G4MoOz{aI^9gL|}zING4FPsviy~42AM=n<8 zPhY>+*S7BGK6k!@@o46wgaE-(pIsag0SmGAYlIltlD#~ zqEC{;-|Kg#<^PcyP)O+d{@n<3Z~dQUlKL9#X@&MvEs%b@f_bJH7^jz_SF%a&4;pC% zKq=)c%2ZH7tESGk%u2&d>jp@HXf@vs{gDqx<{t+sPDt_d9L)RT0&*sKe zO8Ks4b<@I52Pr3b6*vs(pHJe`I>}KqXE)Y@wY_2LA$KC7iA}Q`R{ToMd&9SD)+dcm z7}sdHTPx?J+Q`pa&|VHLI3}Lj9eu?$x2xB2M20A}uN10Cxnh!4Kk%dX zKq;qg`ZxE-5B<#yGeyP9TE8B>QvWU3blt)8_s;B&v5=z57ch#{IBrnTq0Myr$F*Z% z!nLur>x2S>K1a65)_?uQ*TYYCg>H~l~i6s&l;%rhpYPWyGFwyO0E5Gm89>7ntt9d01sa; zV&|_PnSHpqCdIyBRwm)-650XzEmYU%8OlWPE}G}Fk)JMe@p5QdaP@Y3U19rH{N-IuPS(sF{j4I3 z%ZdBjvV0)nRoj01>s_bY&97$|KQtFiZ)X>)upi#x_6a?LkHhFbyGefh7D0%@vzBD- zNOjvxeQPUBv$E5oI;#1}SQ#?g1pCr-^$j7Dx)-F^5A)>K(8bSlXdXR!l%9#{Lb$GG zld443R=VvczkG2XDp*PMox;O}XZPqU*h?z-&Yu6o74yx8bM|G1&EDsO zRpFNId+$YvXG(8taFGd4xqM}-=S>go=jcG-5{w&Hyw@W_@*qlOm*3BhJm}xjrhfR= z#0e80vuBG}-Wn8q+cOuoY26@2`wV!eb;%QZHw~E6gv2IzawT2em6T5?s1`VJS8y{r zi;C3*w^`~HDc_xZcI&w5wctOsJBOnn{iv9%+Wy-c|LnxVKq00X^|h)js`{LCySB)GWVKG$keFTyoM|y^0-y8Zl?eF5jJuPG(ff zNna_|tvukV46%;x-+k{tq^z{H;}ncB`r9n^k4~E{l@Dqf!&3#y7KOulS7fR-XzqMH z6?~%he&Jo~67d@xy>~*s^?$6%Rmpq@zXd_H`S}p5*_%%8Wf@z~s)!`2@j^SRCb%*1 z#@^!3`<|o2u-rkJ-%KwkcQVq)SqPWp+ua;HR5-wU+R zq+`h!C;BU2-`wdYA6!@TMJxUNtV3~JgW=(x>2}TT?snz!A3C5_nIyy9Lo2pUoUD56 z>)}EdXd|N}+(MERhDVk=jZ@Fnau3`TPd)SebnV3Atm*@U@`l8u1_f2UZ?8=YNAziV zUJ#}J(02Ontw35erR449@j)X)bI}hyJMx}EhG7Mq{%-ngRQM~;_hE_4q%na!Y|=jA^N{g{kd|9!~5dIMSt zBzfvD{Rk9uI3H28{zuIPrwoRQ>f+*0l|D)iv0AO6KYV99rKtCe%=v7ZuTQ*@_0A;O zvr(Rw1k2hS)nly5L!;F`2lY>Fm>9mY?vqMvg0LTP;=-Fi6gf@jTe+oOd@$EleX4o? z@aEuG9oGiE+F~LKU6|va)g8aVz;j3Q`K9cJSL%%&jMhlPT*!FTy2Dk_SugamkQRPE3$nJz?=({qEX~Se4`JIXB+qYjnup zUAvqq=}hh5p%ioG#spc1!E&Z5O;0}6=)c6pRE#IpY(%C9vK*aqt#U($%O9+Wxjvxj zyNbCXkgj0wQ&S@I)%P1&M5sZe(t*ZGp{J*a#yaVC6?ceb zYYJVk9d>_Pnst${%vt?>W(i!IMPJDknDjRE9 zmCM+GFgmiOnZ1m)HOiq(aUyqiVRTIbzlENVCu2i;)dk*zSI!HGTZB+6+Vk3N=iixW zM-&nAjpasaRN&Ub)^QETwhXI;_&GJ0zn@Kfk5%T~)umvadH++vtaS(xnicGm&+%r(f)tq1tqQ-ZJ>{$ap@z z?)m!D{2Sjdv%m69?`SKS&;~o6oDZGf4j(+OP_g)R^hrb1=oJ0FoAWU&vQu6rucyqP z?zIW%XnyuwJxjUdF^{~{$*D=E+6dD-y0Xuz!f!1cFwF`z*VYL09~9S!8>75x@@|~$ z&wJ3ptfVfq8q-%iVn%F~yo>jXJ9htZw6)0Cy3d3|eI2Vyzw<|3smxLp8EC7nzpIw_ zb(MAHr@uA1g^y%7+isBW{rdjP)a<54h~v!&8mvtOqZBxa}@# z03jfZy4g#pG<4LVZx$Ww)fnt~9Rs;FOiErwDSk~JFE@y>nhNURD;P?*y7_@=)sMI{ z7|Jlyq<9XZo8iA(eK~5Q5f&-$7EmI4O7`nL?Sxb{r~XvdZ?mTN4!#^tkNAkr_wgZ1 zp}qo}fWF3%RIWW!P4{fhY*3L8Y*MkQVB}M&?a-bvKEzi2y_H+*LzvK(FCsi7HE6JvZkK2Y>TxGaywGn9Z7Y0oCUM>)sFtchZ!zLs*~ zqeHjYgt)&{$lZE<^<(Ls$?Yf4+F!1j%#5LvH1}3?7}i#k+}X=tka+?}@tH_f*zQ zdD&SjRB#x6eWbcR>*Cgar4<=5R)%l1G;&0g2IqHu|3XUAm>jT+wPHx|!b!WLO%tT>?Zus=H zrOkZc=gUtQGFYc*6?g8utG9`X6!mnhYvB++2*X?ZJVSZsD#j^W9!{R~w374sdf#8E zsXnC1@hma?lo=QZ`-vem(}0%?7O#@$UxX9n}(0h2^x+V7kVfN$*ERdCAuY zw*hDs$KL#9m&QC*=H)5(ttK@$!Y{?m;7hB7LyB4MrIgTwH*ZcnIEn1VCj~RJ9LFs2 z>e!PPqm54}Iy>JOavF^>`b=ze^_T9s3>Aq#tQy*njm25V8*g9u@~4k~HBYs6OlxM@ z7oiUS0D7CfaUaGe`!|K6IP3lQiC~+OH@x=kL)={hCK{)DPTBi+oQWu{DNt*dUoJPO z|2#M_Ay<6n&Y}5B%@NEBmd6IOx!G=uCLmbKNq6#xVzi$<@6J7E_9#~Q`D(wA;@fa% z)k@V50c*2UF9(7Sd?uUl_Is#^(f0FM0`KRw=U)%MQ=#Rwh05LS_isnK17TdSuy15C zM!Q+toYR#YR=kw5D^B#!xOBEf)rf4DVHU9yi`#^X(JXNOCF)C%e^dbB>?qz;bGW$T zP3njpCS1-I8Au+OOQkCnQ$IiRS2gpZP`q}uTDA{dd8jX!L3i)a>-#boktbMQEA0Cy z;whhV^eyV+gq&-%IH&{n@;Qfo)*mQS_rXJ*S{PpAIe0lstq~2pc zB0Ic+R+Q-bY@vgp6t#B~O$D4Y7k>X)uQF&VgFEV zSI0o?*3UOxJklr< zcDCWFoiBy9s3xj`0lW10L+dES=AzYJ zgoqwmuT*hA(&(!&rV$Dute1Bgv_!MT)~s}sn1zXM^N0mAp~)$Qsp9=_*BYM(DSS;p zEkJxq4WHqzpm(VMosDy~P@qQ)ov_GLOaOfas!IQEZkaXPyOY!Ii{`^Q;jyvhIm=pC z-4)o1uD9m)T=9Q=1af%Q;g}4 z<#*Y`?t1M%SQ(lcTH)xT*PVYLg)#Ddd&W1NeUlOuEfw?oOLl%TpKPH}j_Iy7q!WcC ztQl=0!NpUWeIq1_JQ0UhR3>QW>O9Sab^x{g9-9nU09|mICG>eL8ShM6H>Ng?1=yN5 z<#x=*WJoveWFDT(2>Ptv`PN4A!SRr}zmya9HPlz%p?23Ahfl5dE~v>CEv#wn-?qMQ za3S_nf=aAAgUo3U^1s2AG?@-DT}kgX^rb~yds5sE9Y5&y$?=kmbED0G#~A}2uK*c& zx2XkcLS3TeqB8f(&I8MOdx9S%>&ym0p1#{a+WUZ4Bge?RcY=!P{eYHv;pcXyI$b7I zPX$h{vRc#oG0$nnK@Lp{7ByG)6ms1YlCPH)$(;yH&3|>~a#3=pq}Ls8*~wEwQwxH8 zOf`pjL|N+(8!4AGby@|!GjmUs3}imz@swq)ax8DPc+2ed)8{$0d2^aG)KW|}Bn7yK zn8uyIP6ms0`X19R9PyU$#2;jq`BzJo*jW#xOkY)2ZgRX7B|P;qJWNMsrdcc1u%N+q zcKDobU0QEqw%-D4l4((S+Dv+?Zna-MHMb{ZYt;72>#4O+0Vo)c?WPttvK6cf8J^#^ zokA(SyYGd_k66vjLr^o?)|hJUot2ek2-P^uoZKTI;5OXDh)&1NJwcYIPX&t#rf;g- z`bAp$pt**amw7EuU#&RLUOm11yPko%?>p%E2gi;jn$CF%#O-@@BA@@bpjWuMkele+ zh3ts#*MfmMleBJr%a(v+7b>;lHP|l*#xTe;*SOuCFp{o${W$E1j7*nF3sb+4nO8(v zZGP@$KF_#Dx2cN1SVMZxk}b9J@!re%CrzIfw#)a)cp5!T)NhNh_|b*ZR?*N^w+TgWrZBsU0ly!lkBz!mOj(v-39n@IzUrGJ8lT|IX=&jLqNhV!6s z*x$j1=fmyK$DQZ5{{FqNPd>2IeL#s??~~xh(Zv;TBENmY=s3#m!!CLDDP&(Ocy%_% z(WC*NW!}Ohr<~wk;qOSVZZj=b{yuN(m)^Jw-ENibBfBe;oa2Ofk`>82mE$WOR0KG( zuFc_X+HlkF_Fci4f|7eeUXgE&r6bmK>uQ$!7i*<*MLH((D4P~@^^Xcu>Xv7;nJil` zb!9Wf@+_I_tvlLPQZo5)$I%XSIdXqlQtMOnx4 zyLRQ2Ae<`>T;f>e+vEKfPRS@Xzn1R$KhGpAzwT&Hwxh+JAPsaPC-rkFw%y>Hj~M zmbhlEYt{E$@qg}}a^(ohw3c2EcVm_Rd0qMNv&-u2kxdVh;wi32J?a1BcmKmb__rSl zl>Ss^Iid0(i5GwC^Gkhd^lx7f^z-)|`5W^neuucx7Pj=N zx{bP_rPp8bC0rGkn|3AGgY}=)`o%Oh8X{ig96Dsn+MNst7MK2&Um>7{2 z@d8;{cydoL1gOt*_6yuN$nnn}a^2MNN88aN6>TT_oyWaA(0#~P=*7z1Uz^a{`4-v< zmEhs0d(XHq?FqiF7&>$SoYRl0h%)EedNlo`A-Z9hns=jO>zAbqpd9;xj$A`b?4;q8 zcmZY%3er69*JasRW-o8|v3z>7c7p86+xXfwxEUY*^6 zCQg=#z8ZJvp5@mKq>23MGsk2XL!LfW z`{6)Y#qjf-_&vOR)O7=6(RK%ubxa32d2^;a#N)f-&W0Y*xL$dz62m3Z!0uvLbIS?m z$>N>f7`rhZF5LGje2rD>#|@H)5hE(UY`8Jr@$<~MvfIw~Ms+2QYS@x9MF3j|tn=O}Iq#dakYqG2;z$BkJRm^(j4S9Rz>+W*V{@5R_xh^c0 zpZMoAxvyTe_}Snu#pTfPV2{rGn=4DK@2sKkDCJJz89%J1rk1(hWheXPis!7*rZHr( zX|k|@=Hd`l^sj8yUx#Veqr!U>0|SF1H+t?#V-tK?nDhK>fkx%~PKb8hCl5Jd7#+(V z(4g@zp2s{09W+w&+l_XS{`GL^6>wZ*Ys>r9=C)=K+FG8N*)m*PhrLvwY16EfwUTw$ zwQJYH&`X?%8`sl@yoFBj!yR{?^}GuAa)XBb{&L|?58>}mbjf#&ph^n7#|7&NZ_T@CS& z{Nb-y@OdU7$LSGG?aS9><|%wiBX;^)`?x%f~_0?0atvc?rb^bdldj2e~x%zua587hMr!)SvEJ zzXQrh-jEtB!i;bqCxX}3u}K^*4dB!6n>hdK?4^JlG?oY0vr3gD|1@>7_nWOY$YTo2 zeGCCA{1&I~>l78@Y@Y@7Cxt|v0)gmWNc+oP*|X`vD}3sBcj&(vuI1INO$eby*k->1;4gkna^k@~ktnb>1woW+jX2ugjR@g5fWRG$Yhm{!U78oEf!f zG)*mNO1IR7Ov;^S!(y#7r_aZ0*rkSiel9Nd`-bf-$5||-iRg|+`yGZtj8bC5$R+u7 zGx!^RXWI01^RP1mJb>&>?6vMvYMrh(y|C2pbP~U8*}PwHFe^%_PV_dWTN;cHL%!y_ zdfL@X`!kV~` zM2PHc6SFos6(S*i$h;-1w*P>DKu5~g)9%b>X*=vE`sj%}kC45QwCm@=s5^se@IW}B zn63ilmJXrZ8F^`%Ltw9n<#*ytb$Y8J5@D?B-kGYOk5S$SFF_9>L$`CMML3|eEEjR8 zE$Ys`SH&)u)+y5dwi5Yj&{*N$Irqnz0M&UHzy#&weqXa91FaMv#@ zP5G}N(XRGCFH&Tm{m$FO%RCpKn-CLl;gv63fwB;8m7Oc=Fs0`bGB6rSjBP?sDhoFn_FU$Hl~_! z{R$x)_A0&Jf_#PBPVd74r$xH`UTLIXOKh=3-8Mh+8BfG)%wn~3C13@@!4*|Ph{muf z%_6a7EydMm=`PwHLTEYZ$zi)|r=A_;f|d;nT$CqN$aYDZW1O9yzCQho1oU6-14b30 zw2C?KkZHr9AY7m~#KjKSLpP~wZrQ2?IMaN{DsLUm8aqvcyqPz!#}Kt)4gKEyl?)nx zLYw;jfpX#S6H}TnzO+7-T({hXi3DN`H6_mM+GJ74XpoQ5@!>$8$1-rfIk+D|hg;>_ zG0NEt*?#6=%1R8TuqnV>rW_pM);sGBLiXj{bWJ&g+q8v8(nW+qg-qghtU# z#9c2TLP%tvn+0fOBsQ3=iw}pKPEUDoNd+_^XX>E32E2A8ZTYyRf;bjjaoZ-kCKs0l$55Mt=^4BTfS*Q>u5mLP+#ZE`{Qt{N#(CHesU>^PFDo zqMl;7F#+n2v0B-pK-OC^Qo#d~MrlJC{fdP|#_lG)a6TE=PVgw^&|MzAcMM}wm_nc! z9(&>KUSu*h6TnmY1oA+~bDihcIC}HtOtZ(Qe}CpTGIe|N>H8R=GyDji7y?fgJZee1 zPyt5YI@dC#SO^`?k5oX@ExnA(D4dL-H~s`^=1wZdtQaXLQSRiq`Lu9oUM)13FehbM zl@d-j(Sx!m#fY2esOL;kLDyt?G}88y(V{$Kk|TL5xFj8G5ngSRbf)nR$KsX>Q9rmH z*;#MsLy6Z*>J>jsL(xM%E;+W2 z$KN4VHIdzUajrFL96Q&gp?^axt%D)=K4L{7Xs|MD+z9nUcL4B0Y}kIC=+(j_&;%0B zuo>2BJCIivz{f_ytW$x9_TfoA8*u51j+&e?&tg1qIbVBWG(ayC28N2TZjidJ9lUbx zT=TFFj6FX9$^Gf6H64pniwoMck$aLnXR{F|(1F8ZVJFv$0roP)@Q{a2?tHwjh8wzu z3g04@QT$#5{2m8HvBqTTuK~JHH%gioX7{W;x|7 z6CmZi`&H92dnlB}Nr3&R6qk`(Y+DR9pfeZ-j94;B2oNIl1U-Mm(Yi+Ty$g!^F zCxGz^!?<`&rp@rH3vZ2*#Pv)4{04`x106^iK7~^Ts-&mPI%#ZBcC)@wxO>xsAGkN! zR#JLr@^)G86&r&)3F@h4-nA9rccHr$VxkgvK@bBaR9`rB=N^LSF)H;xA347k+R>W{ zCnv@K3`=JMR-oPt7jn>~?oD1NsW`5BF5zTv@%Z%hO2NA*v<|733k0y_k0MRq;G3Bi61h>OY=@So4CydEOO&o-HO4L10TosH{ydgZ)k}m<3;r}K_LOkhg3Hz8$yPS)03H<*{8{6n#5PkRMOP6IOj^qDC7ucX5Xvw^@Ms zS%jH~cynpjxNqE8A+x}`ZZ9Ss{1Aa(_p3s%W%|!A=x^`s5+|c<(WYu6KS|%jZD| z0k|lJU{Nbs#zjcN6=D$51(})^FH0A1?L}C{J6c ztIl%bvB+H+9y&GVRFdn%yBL%gN@U&$XdZA5Ds3Wev(FovjkJfkD6T8+flPJ1 zb-KIsaVOYMZx0U-J#A}i>nuU)Cd3~v&Vphj;Gga<0^8_hNoYxobi^4D{(q1;d{tV&RiTA2;kCd@ zUV52;Q)GsrPV{SB8c+JYb1VVNdYkQuhL4?IR^S~M0@7eLyTM8YD7bG*rX`iRl^z7 zzLUfTah|*9PK)-wBnK-DXMavG3QtSQo|urUUtYr!$T}c542B@jg%zNlYd8uHUp2?k z?ncp>C;N9qIDv!Os&XaoZw!+aA=D@augNDR0IpG?whB%{aMGcLHvE|DT;xlWec601 z=WuckEuEh@!jDA^(nQ*%&SE`&Ahwrn1=q0l;F3{vS)BLFap%o=RcEwWTJ~20lAXpHG2ONaUY^TIa{B!Sx47MyA$iZ`*mBC%v$Hc5 zF5BTs<=+92Z^KMqt$O<3M%GXC*Lt%pP8V@G)oMAI66JRX7E3e>7UHUW2AKqn@?ky{ zbp)5MdJbHlpWgGFnGhnA1kr(m%Z7_JO>F50b>fME8$9(bpb^GFxna&$Ze#x#NLDC5 z5@4X$3P-aYT{KhfZ$TNj@rK7j6}s(Z_177~`EMxKY-j%zs~IP{|w{HmetOR0pZsSFED*K(d?HPHP5FjZpqnLcW{DD=hHFjpUtVQyvEJw19Hz?u0-@NlV}gqhvVrX@vVE6wG-9^L|;(_=O z7ukcz(5FfptR+b%6Xb=ea~6zuI5m@nTmlv1_mL(QYhLDd&=zR`XNpIf#O=VW6XM;2 z*2*4Bf&}xH!z4%mPn7Ankh?gSOBQyEI*{EKHW);@9gAawUD9bSL18y`Tnq zfLKc6c#~Z4oT!k;9 z^y7LV>BoDOCT*KZhK!__;QRUER?oZ9FTa<;9iPUI0Mw-bmy}++4;MYef(_>hk~?oh zG>mN?GH_S_;EIc*Tee0QPJ+ZJ`G~}~Asg1B&s@ zS5E-amcnx!R9N^2=1%?U79d^ytn4U+szYev_$g3`?~qB|pM<4{Lp~1N7lw#XUlV-O zN|M89a|K%64B$heF=Cuax*F-gZ~!(1;3vb{cukT>QOI$eN#cdxAAH)0e-RSDa=ZTx z;_6?2^uLbU_}~AT7Zbp2F+7lFp8;tS6DueBq6s7dmx}}*^uB(dDj8*E<GcmY==KVDjVopgSOC&v_6}0pUFqNAHPvY=2}Azy9q~SAmlGUJVj8so z^4EDgi0TN1qYIAG3&gD-!dSdmjx4?T2#)$z%<)xK3sBIjkeD+eSjB~)hgiAgc-zGd zOK-bL7zaWX{QAtL+;c%JuDql*0ihwuha_&i+$GAeAA~`_i`qmE8}}Umz__k_R0(R&BrEO8YJpacvR#2eK(ia1H>vk!(t= zUJ-v0%jP{nSC2$jkFY@``TiM+nO!(un@J%OGBq+dVkC?vSuL_14;p^CnA_6|s+|@AGqQUet7f(CIR=Ety-BpY0nG>EHkFi<4Fki?si zhs9L zog92?V@G1qc6Uq^5CE7;Z#3S7-2~r6gtmbLinMyfphXT^fw5$gHZvBE#kxbp|K-UV zbk!3K>4n=rjyX?LqC@5`bEtI|J%|}9NFof>4GI!kgJc9zxV+)@Rl-%%?Izs4>!vUG z?zFzwF+PKUQ;+M`v>}C%21nG{<$J~pi|&LRp>#g(wu5X{!XN@iX|qod__4&UU_~=3 zMua+AZ;Y}njNIEM;b?o~doFRp;Eufvp4(l&BF8JR-fPLfF%ze`imv{E8|wSGI~)L|6p zU%xYtpY*B|JiibLUHk%)cp_;dSr_167!etgD@^P3YSe!_12oe~5=cmzZhd_S3v;pc zA7g_}NH0li-{3olvs#F{C%1|1F4G^`tkt^hP-0Moc}(nBY)JQaJ`u6JOgN{7@hHh- z@Yue!rY%__@mZs#)1zg4?h*dv6Y_4XK%OwY%jb!xl~JsEDlam5$jI_P~{N`e9F zko+JuIb=RoN?msYP69_zECPtE%8$30ZjujxDDyO*3ef-RDQi_aX&@Ox<2mUn~r zP93{%ODxm)P_q~kWI+RBaPXuo<35f?0Y*R(s+S)IgP;pS%)z2#HkelkERB-gG3Tt#Xko(Sm6I$~g4BAzy> zCm_em-n)6Hj0Yi6hOOV4gW>Fq zO)hsM^d8!9Iw5|(u)XKGu!Rn>NF~;QkGdWC52%=`I6T?Z29l?U5S#>@hHIp|EFy;h z3=RaerF)~h3Hf&f1yQ$iZ>`$k^n+~rp`AN-mcs)* z!9_YwWwy#sVi=0}cBk>m<+ZD@Xaj-7Ny(lC^VWe1j#KY!2I@BA-0t;H$0UwQzrU#D zoPpglN>JxcHK`XMLj?Sc5os#%gkE15bX>ccMuc)MBp%`YVBojg2IeXeAZWSO|sfsnsAX8^f^1C6k15CB;ha2MrKS9L9d;B0~Ai z-Jj~3MFvy8hT+<|ES(N~YSeCB7rv9do7NddAY`dp0k*OiD59;lG_tWqdjv4rTqoMt zg@i99t+)yf0u`2fi(?>77y^QlNpxE0G`6K69d1#tAHdN_h+?w&LdMP@=796|HL)3N z5XAJpj7DR`jglx16Z07)2&{{5ZYy9=qS=bzMV{Vl(?uGD zE;w9T@qr*@8zp)cBh4j_OV>Ne;~M{BB>?WlmL@Q-3b;_cIRv0*Reufx&*)Iw36`Wc5vrz-Ya>(0kk6Fesu3d3Cpg<_lNf{G~40Q;Xx> zHH_6|1=Dow^%WNnHc5%@oMTR2_fLr2nhGQ}SJy6&z@2x(XEt``6VJ9~*q)soOwhZs(hk&q@L&QWD@G_RRY$eh_s0>d82bG)8y(_z zNnnjo=WN<&SqYW+E>H!-i?@cNXcdHRKO$L#+}uNfqLu|7SRh~8Ji?gH0tW4-&bOGB zB2%hCNQv{&iu$Rw664y_;L*_WSDvqKgRv4$p&m5nTkuJI2uIo|Sk1%)Z-o@!o?@o9$Qw?MQ2){;9T}MDJO&>W3 z-4M(83vXW+TXo|X89E`B+Ol&Z@z^weO6@bLwhNIob?IT#xt{AY_P7G6j7{XQag~7P z6dOw08DZ|CvdkQTQbe%JG5x>0*pd;c&%f>t+^Z7Ll90P#PpWxNAExFvYxx2rKs(7B z9EcD}a;Kr5k(63G1X*GiAB*SC#UB6p{YY_aB!DMr9?^LLqIjEhGw_mJJv^yEwWl!~ zSiC%tSDV!1ols$%%Rz#flXnRUARqPU@niap8wrj@U~Cx>=QI@o&&)+&8GVL7O_xlz z_#tAw2esbxDjFLxA^X^;Adv$y-rHG8Op0+JMd0LZ(ibp3$peU}08l8su`Lm_E|>bD z-1*5yt{#SAZ$#l+uh15Xb0bCpyw=QHRSd&JwD$Lam+^$}-PH*31sd;r8YW-s0)CZP zV`Rys{qyS!J*fYbqm2gS@<`WY!YwL4DkK5UBB3!|70&~=q=Bst13zDqLl&8o1!mth zo9W*0Y0^Nwg?7p0ORey+s_Ysre)iHN3)PL14d-04u);e)2Fh) zM7hiHrthjJgQGeTQpmqE`1E8WNoyv}2M;?Mgv7Xg?d~UMyH}Qkff2DJw*`0Qm!m!3 zr;>>L_VNlU=zp;YaU2s=WN?p6lLAfs##W5u6Cvz>8?~Sv*XxZjWqc9CfiX81!@*T@ z4U!}(#|4#>TroX_<(8cbbE9Z045w&Bij9rT2zWCkT+Cf^9UehQ(Q|QMOioHt zT%PWf-hP*v9pjSgpCcM1iCu^0HTJ0dtV9L1pD~Ib7=em;^(Hbi;0bk65=oK<%QZt_ zSTM8kqO3MfnT^9B$tHD|&QFE#k&G##!WDshc@62YPhIFZjcp5uKy^WNUp1gvyH%3yo5##iFb3Kc~00QTnDVM-JEtSZT zX}E17>%3*X3FUW)8gHt-Dpg2$u?@kgvuEzlo)WH}Ro+PBofaERAw&l$ z${+uvPd6dUzIH&fH_l^@zcYNIpX7nxrc7nFi1BBX-zHf2NFuR^O#|7ZNb@9eb z4+jKN;kbB*ps9C_X6^$61GX5=?&CzPjfO=5J<0{r4S~5xR+pWfP$kIwdn3NlaeCKr z6}gfkJTRsJjcX?b(zKAjA+!XBA8kSyL*N>T(3-0rqSwqo<$UrNFr1~{VvX_Jo$$6Rs5U7p1yB@al&3pj-}Z;eQZ z&WXWcFw9UVnysB0ZCeE6j-oK-3@qq#%s+sMs; z_!y>+p&XXnc?VLyjXfHRQ!a}Pm_VvSLdryV96c9&3@Vk5DJy@|D#H4Ulc92XrE;fH z3ZMf$9ITbfKoZ9UaiSv3BDpAd065{U7U#& zKoyIQY+5FM^aOJ#C3>*hGtSNAzUstDSC15<7}?f{9a@H|#Hs=QJb|eoEC)a|SPqmC zu;3^gfb31E``Aj#kf@&yROE96Xlz0np{{3w@H^Pf5@h?n7@3kpB)u8Lx&>6^iWH8s zzOCp7Fs4T)&8;WIMS@^wVU*UcbxiOfUF)QB%L5q}!XmsBb$;c{Z;F)iO&fg7}iS!ucE_ z-0hnf>F4vQ0N8O|U0wMY@1+YPNcPPupu=k5OfQk|PZZZnGVp3_!<^>_KTDWMQ-}hp zpvajZM0I7nW@d3Q=WL`j*4rJqLuuswNCgq8IJn1Ztx6;#A#&E%`e zWvB&^Vdz)nh&d#$hr8)$Y_~{+iq{J%LYpkTuKZiG&;R)$mgIlof-FcziH21%GHVd3 PigNPUY3cYQ7jOPwnj#W} literal 0 HcmV?d00001 From 4d652810a3541ba3e57e286118bcf0230017a7ec Mon Sep 17 00:00:00 2001 From: Utkarsh Pratiush Date: Thu, 28 Mar 2024 16:05:42 -0400 Subject: [PATCH 38/43] add: trial notebooks --- botorch_version_grid_serach_try.ipynb | 7063 +++++++++++++++++++++++++ gpax_schwefel.ipynb | 5279 ++++++++++++++++++ gpax_schwefel_beta.ipynb | 242 + 3 files changed, 12584 insertions(+) create mode 100644 botorch_version_grid_serach_try.ipynb create mode 100644 gpax_schwefel.ipynb create mode 100644 gpax_schwefel_beta.ipynb diff --git a/botorch_version_grid_serach_try.ipynb b/botorch_version_grid_serach_try.ipynb new file mode 100644 index 0000000..5292950 --- /dev/null +++ b/botorch_version_grid_serach_try.ipynb @@ -0,0 +1,7063 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "a3263ed0", + "metadata": {}, + "outputs": [], + "source": [ + "# ! pip install botorch gpytorch\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8a718b29", + "metadata": {}, + "outputs": [], + "source": [ + "import numpyro\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d8b3bd8d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def schwefel_1d(x):\n", + "\n", + " return 418.9829 - x * np.sin(np.sqrt(np.abs(x)))\n", + "\n", + "def schwefel_nd(args):\n", + " output = 0\n", + " \n", + " for dim in range(args):\n", + " output += schwefel_1d(args[dim])\n", + "\n", + "def add_gaussian_noise(signal, noise_level):\n", + "\n", + " return signal + np.random.normal(0, noise_level, 1)[0]\n", + "\n", + "def schwefel_1d_with_noise(x, noise_level = 0.01):\n", + " # Calculate the Schwefel function value\n", + "\n", + " schwefel_value = schwefel_1d(x)\n", + "\n", + " # Add Gaussian noise to the Schwefel function value\n", + "\n", + " noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level)\n", + "\n", + " return noisy_schwefel_value\n", + "\n", + "def schwefel_nd_with_noise(args, noise_level = 0.01):\n", + " # Calculate the Schwefel function value\n", + "\n", + " schwefel_value = schwefel_nd(args)\n", + "\n", + " # Add Gaussian noise to the Schwefel function value\n", + "\n", + " noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level)\n", + "\n", + " return noisy_schwefel_value\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "c4f9bcae", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def create_data(seed, n_init, noise_level):\n", + "\n", + " np.random.seed(seed)\n", + " X_bounds = np.array([-500, 500])\n", + " X = np.random.uniform(X_bounds[0], X_bounds[1], size=( n_init,))\n", + " X = np.append(X, X_bounds)\n", + " X = np.sort(X)\n", + " y = schwefel_1d_with_noise(X, noise_level = noise_level)\n", + "\n", + " X_unmeasured = np.linspace(X_bounds[0], X_bounds[1], 200)\n", + " ground_truth = schwefel_1d_with_noise(X_unmeasured, noise_level = 0)\n", + " \n", + " return X.reshape(-1,1), y.reshape(-1,1), X_unmeasured.reshape(-1,1), ground_truth.reshape(-1,1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "6500496f", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from botorch.models import SingleTaskGP\n", + "from botorch.fit import fit_gpytorch_model\n", + "from gpytorch.mlls import ExactMarginalLogLikelihood\n", + "from botorch.acquisition import ExpectedImprovement\n", + "from gpytorch.distributions import MultivariateNormal\n", + "import gpytorch\n", + "\n", + "def step(X_measured, y_measured, X_unmeasured):\n", + " # Convert data to tensors\n", + " \n", + " \n", + " # Initialize GP model\n", + "# print(X_measured.shape, y_measured.unsqueeze(-1).shape)\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + " \n", + " # Fit GP model\n", + " mll = ExactMarginalLogLikelihood(gp_model.likelihood, gp_model)\n", + " fit_gpytorch_model(mll)\n", + " \n", + " # Predict on unmeasured data\n", + " gp_model.eval()\n", + " with torch.no_grad(), gpytorch.settings.fast_pred_var():\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + " # For visualization or further processing, you can obtain mean and variance\n", + " y_pred = posterior.mean\n", + " y_sampled = posterior.variance.sqrt()\n", + " \n", + " # Compute acquisition function (Expected Improvement here)\n", + " EI = ExpectedImprovement(model=gp_model, best_f=y_measured.max(), maximize=True)\n", + " acq_values = EI(X_unmeasured.unsqueeze(-2))\n", + " \n", + " return acq_values, (y_pred, y_sampled)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "2653595b", + "metadata": {}, + "outputs": [], + "source": [ + "# import numpy as np\n", + "\n", + "# def run_gp(num_steps, X, y, X_unmeasured, ground_truth, schwefel_1d_with_noise, noise_level):\n", + "# X = torch.tensor(X)\n", + "# y = torch.tensor(y)\n", + "# X_unmeasured = torch.tensor(X_unmeasured)\n", + "# ground_truth = torch.tensor(ground_truth)\n", + " \n", + "# for e in range(num_steps):\n", + "# print(f\"\\nStep {e+1}/{num_steps}\")\n", + "# # Compute acquisition function and get predictions\n", + "# acq, (y_pred, y_sampled) = step(X, y, X_unmeasured)\n", + " \n", + "# # Get the next point to evaluate\n", + "# idx = acq.argmax() # Use argmax since EI maximizes the acquisition\n", + "# next_point = X_unmeasured[idx]\n", + " \n", + "# # Measure the point\n", + "# next_point_value = schwefel_1d_with_noise(next_point.numpy(), noise_level)\n", + " \n", + "# # Update measured data\n", + "# X = torch.cat([X, next_point.unsqueeze(0)], dim=0)\n", + "# y = torch.cat([y, torch.tensor([next_point_value], dtype=torch.float)], dim=0)\n", + " \n", + "# # Calculate metrics after the loop\n", + "# mse = torch.mean((y_pred - ground_truth) ** 2).item()\n", + "# average_uncertainty = torch.mean(y_sampled).item()\n", + " \n", + "# return mse, average_uncertainty\n", + "import torch\n", + "import numpy as np\n", + "\n", + "def standardize_data(data):\n", + " \"\"\"Standardize data to have mean=0 and std=1.\"\"\"\n", + " mean = torch.mean(data, dim=0, keepdim=True)\n", + " std = torch.std(data, dim=0, keepdim=True)\n", + " return (data - mean) / std\n", + "\n", + "\n", + "def run_gp(num_steps, X, y, X_unmeasured, ground_truth, schwefel_1d_with_noise, noise_level):\n", + " # Convert to tensors and standardize\n", + " X = torch.tensor(X, dtype=torch.float32)\n", + " y = torch.tensor(y, dtype=torch.float32)\n", + " X_unmeasured = torch.tensor(X_unmeasured, dtype=torch.float32)\n", + " ground_truth = torch.tensor(ground_truth, dtype=torch.float32)\n", + " \n", + " # Standardize features and target\n", + " X = standardize_data(X)\n", + " X_unmeasured = standardize_data(X_unmeasured)\n", + " y= standardize_data(y)\n", + " ground_truth = standardize_data(ground_truth)\n", + " for e in range(num_steps):\n", + " print(f\"\\nStep {e+1}/{num_steps}\")\n", + " # Compute acquisition function and get predictions\n", + " acq, (y_pred, y_sampled) = step(X, y, X_unmeasured)\n", + " \n", + " # Rest of your loop...\n", + "\n", + " # Calculate metrics after the loop\n", + " mse = torch.mean((y_pred - ground_truth) ** 2).item()\n", + " average_uncertainty = torch.mean(y_sampled).item()\n", + " \n", + " return mse, average_uncertainty\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "6205bcb5", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "import pandas as pd\n", + "\n", + "def grid_search(seeds, n_inits, noise_levels):\n", + " results = [] # Initialize an empty list to store results\n", + "\n", + " # Iterate over all combinations of seeds, n_inits, and noise_levels\n", + " for seed, n_init, noise_level in itertools.product(seeds, n_inits, noise_levels):\n", + " print(f\"Seed: {seed}, n_init: {n_init}, Noise Level: {noise_level}\")\n", + " \n", + " # Create data for the current combination\n", + " X, y, X_unmeasured, ground_truth = create_data(seed, n_init, noise_level)\n", + " print(X.shape) # Should be [n, d] where d is the number of dimensions/features\n", + " print(y.shape) \n", + " # Run Gaussian Process (GP) optimization/modeling for the current combination\n", + " mse, average_uncertainty = run_gp(5, X, y, X_unmeasured, ground_truth, schwefel_1d_with_noise, noise_level)\n", + " print(mse)\n", + " # Collect the results\n", + " results.append({\n", + " \"Seed\": seed,\n", + " \"n_init\": n_init,\n", + " \"Noise_Level\": noise_level,\n", + " \"MSE\": mse,\n", + " \"Average_Uncertainty\": average_uncertainty\n", + " })\n", + "\n", + " # Convert the results to a pandas DataFrame for easy analysis and reporting\n", + " results_df = pd.DataFrame(results)\n", + " \n", + " return results_df\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe8f5ddb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "01f42404", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Seed: 1, n_init: 5, Noise Level: 0\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4834703207015991\n", + "Seed: 1, n_init: 5, Noise Level: 0.01\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.471142292022705\n", + "Seed: 1, n_init: 5, Noise Level: 0.1\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4834703207015991\n", + "Seed: 1, n_init: 5, Noise Level: 0.5\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.490060567855835\n", + "Seed: 1, n_init: 10, Noise Level: 0\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4958250522613525\n", + "Seed: 1, n_init: 10, Noise Level: 0.01\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4987499713897705\n", + "Seed: 1, n_init: 10, Noise Level: 0.1\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4684855937957764\n", + "Seed: 1, n_init: 10, Noise Level: 0.5\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.4980649948120117\n", + "Seed: 1, n_init: 15, Noise Level: 0\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0959746837615967\n", + "Seed: 1, n_init: 15, Noise Level: 0.01\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.8582366704940796\n", + "Seed: 1, n_init: 15, Noise Level: 0.1\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/gpytorch/distributions/multivariate_normal.py:319: NumericalWarning: Negative variance values detected. This is likely due to numerical instabilities. Rounding negative variances up to 1e-06.\n", + " warnings.warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n", + " warn(\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.8659234046936035\n", + "Seed: 1, n_init: 15, Noise Level: 0.5\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.850039005279541\n", + "Seed: 1, n_init: 20, Noise Level: 0\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.9220656156539917\n", + "Seed: 1, n_init: 20, Noise Level: 0.01\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n", + "1.7824245691299438\n", + "Seed: 1, n_init: 20, Noise Level: 0.1\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.952939510345459\n", + "Seed: 1, n_init: 20, Noise Level: 0.5\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.9188467264175415\n", + "Seed: 1, n_init: 25, Noise Level: 0\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "2.348071575164795\n", + "Seed: 1, n_init: 25, Noise Level: 0.01\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "2.3391432762145996\n", + "Seed: 1, n_init: 25, Noise Level: 0.1\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.3390276432037354\n", + "Seed: 1, n_init: 25, Noise Level: 0.5\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.3398637771606445\n", + "Seed: 2, n_init: 5, Noise Level: 0\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0889959335327148\n", + "Seed: 2, n_init: 5, Noise Level: 0.01\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0889965295791626\n", + "Seed: 2, n_init: 5, Noise Level: 0.1\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.0889968872070312\n", + "Seed: 2, n_init: 5, Noise Level: 0.5\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0889971256256104\n", + "Seed: 2, n_init: 10, Noise Level: 0\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.6680113077163696\n", + "Seed: 2, n_init: 10, Noise Level: 0.01\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.6696722507476807\n", + "Seed: 2, n_init: 10, Noise Level: 0.1\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.6700429916381836\n", + "Seed: 2, n_init: 10, Noise Level: 0.5\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6719379425048828\n", + "Seed: 2, n_init: 15, Noise Level: 0\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.7897077798843384\n", + "Seed: 2, n_init: 15, Noise Level: 0.01\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.7927340269088745\n", + "Seed: 2, n_init: 15, Noise Level: 0.1\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.7925740480422974\n", + "Seed: 2, n_init: 15, Noise Level: 0.5\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.7896960973739624\n", + "Seed: 2, n_init: 20, Noise Level: 0\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n", + "2.1042706966400146\n", + "Seed: 2, n_init: 20, Noise Level: 0.01\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.103878974914551\n", + "Seed: 2, n_init: 20, Noise Level: 0.1\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n", + "2.104372262954712\n", + "Seed: 2, n_init: 20, Noise Level: 0.5\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.103878974914551\n", + "Seed: 2, n_init: 25, Noise Level: 0\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.1595683097839355\n", + "Seed: 2, n_init: 25, Noise Level: 0.01\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "2.1565327644348145\n", + "Seed: 2, n_init: 25, Noise Level: 0.1\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.161508798599243\n", + "Seed: 2, n_init: 25, Noise Level: 0.5\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.159533977508545\n", + "Seed: 3, n_init: 5, Noise Level: 0\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6003402471542358\n", + "Seed: 3, n_init: 5, Noise Level: 0.01\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.60093355178833\n", + "Seed: 3, n_init: 5, Noise Level: 0.1\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6003402471542358\n", + "Seed: 3, n_init: 5, Noise Level: 0.5\n", + "(7, 1)\n", + "(7, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6003451347351074\n", + "Seed: 3, n_init: 10, Noise Level: 0\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.451256275177002\n", + "Seed: 3, n_init: 10, Noise Level: 0.01\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.4498707056045532\n", + "Seed: 3, n_init: 10, Noise Level: 0.1\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4499812126159668\n", + "Seed: 3, n_init: 10, Noise Level: 0.5\n", + "(12, 1)\n", + "(12, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.449458360671997\n", + "Seed: 3, n_init: 15, Noise Level: 0\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.7906279563903809\n", + "Seed: 3, n_init: 15, Noise Level: 0.01\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "1.7892225980758667\n", + "Seed: 3, n_init: 15, Noise Level: 0.1\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.7892377376556396\n", + "Seed: 3, n_init: 15, Noise Level: 0.5\n", + "(17, 1)\n", + "(17, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.789238691329956\n", + "Seed: 3, n_init: 20, Noise Level: 0\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "2.0686287879943848\n", + "Seed: 3, n_init: 20, Noise Level: 0.01\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.053746223449707\n", + "Seed: 3, n_init: 20, Noise Level: 0.1\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0540030002593994\n", + "Seed: 3, n_init: 20, Noise Level: 0.5\n", + "(22, 1)\n", + "(22, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0544493198394775\n", + "Seed: 3, n_init: 25, Noise Level: 0\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "2.0695652961730957\n", + "Seed: 3, n_init: 25, Noise Level: 0.01\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 5/5\n", + "2.0689313411712646\n", + "Seed: 3, n_init: 25, Noise Level: 0.1\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0327603816986084\n", + "Seed: 3, n_init: 25, Noise Level: 0.5\n", + "(27, 1)\n", + "(27, 1)\n", + "\n", + "Step 1/5\n", + "\n", + "Step 2/5\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n", + "/tmp/ipykernel_1292286/1166517645.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gp_model = SingleTaskGP(torch.tensor(X_measured), torch.tensor(y_measured))\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "/nfs/home/upratius/.conda/envs/gpax_hae/lib/python3.10/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n", + "/tmp/ipykernel_1292286/1166517645.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " posterior = gp_model(torch.tensor(X_unmeasured))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Step 4/5\n", + "\n", + "Step 5/5\n", + "2.0699245929718018\n", + " Seed n_init Noise_Level MSE Average_Uncertainty\n", + "0 1 5 0.00 1.483470 0.533026\n", + "1 1 5 0.01 1.471142 0.559593\n", + "2 1 5 0.10 1.483470 0.533026\n", + "3 1 5 0.50 1.490061 0.552982\n", + "4 1 10 0.00 1.495825 0.691920\n", + "5 1 10 0.01 1.498750 0.697778\n", + "6 1 10 0.10 1.468486 0.678819\n", + "7 1 10 0.50 1.498065 0.701203\n", + "8 1 15 0.00 3.095975 0.546266\n", + "9 1 15 0.01 1.858237 0.474034\n", + "10 1 15 0.10 1.865923 0.469591\n", + "11 1 15 0.50 1.850039 0.471573\n", + "12 1 20 0.00 1.922066 0.307850\n", + "13 1 20 0.01 1.782425 0.365753\n", + "14 1 20 0.10 1.952940 0.362849\n", + "15 1 20 0.50 1.918847 0.308808\n", + "16 1 25 0.00 2.348072 0.225434\n", + "17 1 25 0.01 2.339143 0.226166\n", + "18 1 25 0.10 2.339028 0.225726\n", + "19 1 25 0.50 2.339864 0.225640\n", + "20 2 5 0.00 1.088996 0.848628\n", + "21 2 5 0.01 1.088997 0.848630\n", + "22 2 5 0.10 1.088997 0.848629\n", + "23 2 5 0.50 1.088997 0.848631\n", + "24 2 10 0.00 1.668011 0.796792\n", + "25 2 10 0.01 1.669672 0.797943\n", + "26 2 10 0.10 1.670043 0.798097\n", + "27 2 10 0.50 1.671938 0.800202\n", + "28 2 15 0.00 1.789708 0.501271\n", + "29 2 15 0.01 1.792734 0.501560\n", + "30 2 15 0.10 1.792574 0.501509\n", + "31 2 15 0.50 1.789696 0.501207\n", + "32 2 20 0.00 2.104271 0.313216\n", + "33 2 20 0.01 2.103879 0.313138\n", + "34 2 20 0.10 2.104372 0.313260\n", + "35 2 20 0.50 2.103879 0.313138\n", + "36 2 25 0.00 2.159568 0.236000\n", + "37 2 25 0.01 2.156533 0.235369\n", + "38 2 25 0.10 2.161509 0.236499\n", + "39 2 25 0.50 2.159534 0.236013\n", + "40 3 5 0.00 1.600340 0.669741\n", + "41 3 5 0.01 1.600934 0.669584\n", + "42 3 5 0.10 1.600340 0.669741\n", + "43 3 5 0.50 1.600345 0.669737\n", + "44 3 10 0.00 1.451256 0.629858\n", + "45 3 10 0.01 1.449871 0.631171\n", + "46 3 10 0.10 1.449981 0.631360\n", + "47 3 10 0.50 1.449458 0.631218\n", + "48 3 15 0.00 1.790628 0.425344\n", + "49 3 15 0.01 1.789223 0.425260\n", + "50 3 15 0.10 1.789238 0.425436\n", + "51 3 15 0.50 1.789239 0.425482\n", + "52 3 20 0.00 2.068629 0.345784\n", + "53 3 20 0.01 2.053746 0.344664\n", + "54 3 20 0.10 2.054003 0.344776\n", + "55 3 20 0.50 2.054449 0.344390\n", + "56 3 25 0.00 2.069565 0.221183\n", + "57 3 25 0.01 2.068931 0.221628\n", + "58 3 25 0.10 2.032760 0.257487\n", + "59 3 25 0.50 2.069925 0.221043\n" + ] + } + ], + "source": [ + "# Define your parameter space\n", + "seeds = [1, 2, 3]\n", + "n_inits = [5, 10, 15, 20, 25]\n", + "noise_levels = [0, 0.01, 0.1, 0.5]\n", + "\n", + "# Assuming create_data and run_gp functions are defined elsewhere\n", + "\n", + "# Run the grid search\n", + "results_df = grid_search(seeds, n_inits, noise_levels)\n", + "\n", + "# Display the results DataFrame\n", + "print(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "44006b4d", + "metadata": {}, + "outputs": [], + "source": [ + "results_df.to_csv('grid_search_results_botorch_v3_normalized.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "af6ce5a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Seedn_initNoise_LevelMSEAverage_Uncertainty
0150.001.4834700.533026
1150.011.4711420.559593
2150.101.4834700.533026
3150.501.4900610.552982
41100.001.4958250.691920
51100.011.4987500.697778
61100.101.4684860.678819
71100.501.4980650.701203
81150.003.0959750.546266
91150.011.8582370.474034
101150.101.8659230.469591
111150.501.8500390.471573
121200.001.9220660.307850
131200.011.7824250.365753
141200.101.9529400.362849
151200.501.9188470.308808
161250.002.3480720.225434
171250.012.3391430.226166
181250.102.3390280.225726
191250.502.3398640.225640
20250.001.0889960.848628
21250.011.0889970.848630
22250.101.0889970.848629
23250.501.0889970.848631
242100.001.6680110.796792
252100.011.6696720.797943
262100.101.6700430.798097
272100.501.6719380.800202
282150.001.7897080.501271
292150.011.7927340.501560
302150.101.7925740.501509
312150.501.7896960.501207
322200.002.1042710.313216
332200.012.1038790.313138
342200.102.1043720.313260
352200.502.1038790.313138
362250.002.1595680.236000
372250.012.1565330.235369
382250.102.1615090.236499
392250.502.1595340.236013
40350.001.6003400.669741
41350.011.6009340.669584
42350.101.6003400.669741
43350.501.6003450.669737
443100.001.4512560.629858
453100.011.4498710.631171
463100.101.4499810.631360
473100.501.4494580.631218
483150.001.7906280.425344
493150.011.7892230.425260
503150.101.7892380.425436
513150.501.7892390.425482
523200.002.0686290.345784
533200.012.0537460.344664
543200.102.0540030.344776
553200.502.0544490.344390
563250.002.0695650.221183
573250.012.0689310.221628
583250.102.0327600.257487
593250.502.0699250.221043
\n", + "
" + ], + "text/plain": [ + " Seed n_init Noise_Level MSE Average_Uncertainty\n", + "0 1 5 0.00 1.483470 0.533026\n", + "1 1 5 0.01 1.471142 0.559593\n", + "2 1 5 0.10 1.483470 0.533026\n", + "3 1 5 0.50 1.490061 0.552982\n", + "4 1 10 0.00 1.495825 0.691920\n", + "5 1 10 0.01 1.498750 0.697778\n", + "6 1 10 0.10 1.468486 0.678819\n", + "7 1 10 0.50 1.498065 0.701203\n", + "8 1 15 0.00 3.095975 0.546266\n", + "9 1 15 0.01 1.858237 0.474034\n", + "10 1 15 0.10 1.865923 0.469591\n", + "11 1 15 0.50 1.850039 0.471573\n", + "12 1 20 0.00 1.922066 0.307850\n", + "13 1 20 0.01 1.782425 0.365753\n", + "14 1 20 0.10 1.952940 0.362849\n", + "15 1 20 0.50 1.918847 0.308808\n", + "16 1 25 0.00 2.348072 0.225434\n", + "17 1 25 0.01 2.339143 0.226166\n", + "18 1 25 0.10 2.339028 0.225726\n", + "19 1 25 0.50 2.339864 0.225640\n", + "20 2 5 0.00 1.088996 0.848628\n", + "21 2 5 0.01 1.088997 0.848630\n", + "22 2 5 0.10 1.088997 0.848629\n", + "23 2 5 0.50 1.088997 0.848631\n", + "24 2 10 0.00 1.668011 0.796792\n", + "25 2 10 0.01 1.669672 0.797943\n", + "26 2 10 0.10 1.670043 0.798097\n", + "27 2 10 0.50 1.671938 0.800202\n", + "28 2 15 0.00 1.789708 0.501271\n", + "29 2 15 0.01 1.792734 0.501560\n", + "30 2 15 0.10 1.792574 0.501509\n", + "31 2 15 0.50 1.789696 0.501207\n", + "32 2 20 0.00 2.104271 0.313216\n", + "33 2 20 0.01 2.103879 0.313138\n", + "34 2 20 0.10 2.104372 0.313260\n", + "35 2 20 0.50 2.103879 0.313138\n", + "36 2 25 0.00 2.159568 0.236000\n", + "37 2 25 0.01 2.156533 0.235369\n", + "38 2 25 0.10 2.161509 0.236499\n", + "39 2 25 0.50 2.159534 0.236013\n", + "40 3 5 0.00 1.600340 0.669741\n", + "41 3 5 0.01 1.600934 0.669584\n", + "42 3 5 0.10 1.600340 0.669741\n", + "43 3 5 0.50 1.600345 0.669737\n", + "44 3 10 0.00 1.451256 0.629858\n", + "45 3 10 0.01 1.449871 0.631171\n", + "46 3 10 0.10 1.449981 0.631360\n", + "47 3 10 0.50 1.449458 0.631218\n", + "48 3 15 0.00 1.790628 0.425344\n", + "49 3 15 0.01 1.789223 0.425260\n", + "50 3 15 0.10 1.789238 0.425436\n", + "51 3 15 0.50 1.789239 0.425482\n", + "52 3 20 0.00 2.068629 0.345784\n", + "53 3 20 0.01 2.053746 0.344664\n", + "54 3 20 0.10 2.054003 0.344776\n", + "55 3 20 0.50 2.054449 0.344390\n", + "56 3 25 0.00 2.069565 0.221183\n", + "57 3 25 0.01 2.068931 0.221628\n", + "58 3 25 0.10 2.032760 0.257487\n", + "59 3 25 0.50 2.069925 0.221043" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_df" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "8ae8d2ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Seedn_initNoise_LevelMSEAverage_Uncertainty
01100.0042108.11908817.544797
11100.0140813.52953015.307410
21100.1037841.94507715.802484
31100.5037550.8713663.830761
41150.0038389.63200014.470949
51150.0138349.54083712.016277
61150.1037489.49278011.868182
71150.5038289.5088833.746136
81200.0040191.9832643.811547
91200.0137858.78213222.654309
101200.1037773.53406217.449285
111200.5040106.16278320.949178
122100.0040301.27388119.379407
132100.0140308.00880419.379004
142100.1038778.94186610.257270
152100.5038225.9417094.929497
162150.0038749.7617024.129729
172150.0138660.03254314.906107
182150.1037840.0049334.291684
192150.5037846.2691254.292110
202200.0042369.54536319.049765
212200.0140294.00149817.056114
222200.1039115.47505010.273374
232200.5045580.41014518.010720
243100.0040642.4254856.167951
253100.0139174.92458014.297714
263100.1037548.82134515.840703
273100.5037905.8828493.912628
283150.0037822.9234113.523961
293150.0143567.76893016.288372
303150.1038178.29328723.374256
313150.5041235.7544447.550453
323200.0037889.52177223.649989
333200.0139445.48427414.958631
343200.1044042.98032618.591112
353200.5039537.8424234.317835
\n", + "
" + ], + "text/plain": [ + " Seed n_init Noise_Level MSE Average_Uncertainty\n", + "0 1 10 0.00 42108.119088 17.544797\n", + "1 1 10 0.01 40813.529530 15.307410\n", + "2 1 10 0.10 37841.945077 15.802484\n", + "3 1 10 0.50 37550.871366 3.830761\n", + "4 1 15 0.00 38389.632000 14.470949\n", + "5 1 15 0.01 38349.540837 12.016277\n", + "6 1 15 0.10 37489.492780 11.868182\n", + "7 1 15 0.50 38289.508883 3.746136\n", + "8 1 20 0.00 40191.983264 3.811547\n", + "9 1 20 0.01 37858.782132 22.654309\n", + "10 1 20 0.10 37773.534062 17.449285\n", + "11 1 20 0.50 40106.162783 20.949178\n", + "12 2 10 0.00 40301.273881 19.379407\n", + "13 2 10 0.01 40308.008804 19.379004\n", + "14 2 10 0.10 38778.941866 10.257270\n", + "15 2 10 0.50 38225.941709 4.929497\n", + "16 2 15 0.00 38749.761702 4.129729\n", + "17 2 15 0.01 38660.032543 14.906107\n", + "18 2 15 0.10 37840.004933 4.291684\n", + "19 2 15 0.50 37846.269125 4.292110\n", + "20 2 20 0.00 42369.545363 19.049765\n", + "21 2 20 0.01 40294.001498 17.056114\n", + "22 2 20 0.10 39115.475050 10.273374\n", + "23 2 20 0.50 45580.410145 18.010720\n", + "24 3 10 0.00 40642.425485 6.167951\n", + "25 3 10 0.01 39174.924580 14.297714\n", + "26 3 10 0.10 37548.821345 15.840703\n", + "27 3 10 0.50 37905.882849 3.912628\n", + "28 3 15 0.00 37822.923411 3.523961\n", + "29 3 15 0.01 43567.768930 16.288372\n", + "30 3 15 0.10 38178.293287 23.374256\n", + "31 3 15 0.50 41235.754444 7.550453\n", + "32 3 20 0.00 37889.521772 23.649989\n", + "33 3 20 0.01 39445.484274 14.958631\n", + "34 3 20 0.10 44042.980326 18.591112\n", + "35 3 20 0.50 39537.842423 4.317835" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_df" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "610cd775", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "# Convert the list of dictionaries into a DataFrame for easier manipulation\n", + "df = results_df\n", + "\n", + "# Plot settings\n", + "plt.figure(figsize=(14, 6))\n", + "\n", + "# Plot 1: MSE\n", + "plt.subplot(1, 2, 1) # 1 row, 2 columns, first plot\n", + "for key, grp in df.groupby(['Seed', 'n_init']):\n", + " plt.plot(grp['Noise_Level'], grp['MSE'], marker='o', linestyle='-', label=f'Seed {key[0]}, n_init {key[1]}')\n", + "plt.title('MSE vs. Noise Level')\n", + "plt.xlabel('Noise Level')\n", + "plt.ylabel('MSE')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot 2: Average Uncertainty\n", + "plt.subplot(1, 2, 2) # 1 row, 2 columns, second plot\n", + "for key, grp in df.groupby(['Seed', 'n_init']):\n", + " plt.plot(grp['Noise_Level'], grp['Average_Uncertainty'], marker='x', linestyle='--', label=f'Seed {key[0]}, n_init {key[1]}')\n", + "plt.title('Average Uncertainty vs. Noise Level')\n", + "plt.xlabel('Noise Level')\n", + "plt.ylabel('Average Uncertainty')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b3efada", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-gpax_hae]", + "language": "python", + "name": "conda-env-.conda-gpax_hae-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gpax_schwefel.ipynb b/gpax_schwefel.ipynb new file mode 100644 index 0000000..4c4b2a9 --- /dev/null +++ b/gpax_schwefel.ipynb @@ -0,0 +1,5279 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 23, + "id": "f64ae931", + "metadata": {}, + "outputs": [], + "source": [ + "from src.schwefel import SchwefelProblem\n", + "from time import time\n", + "import gpax\n", + "import numpyro" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "b01fe3e8", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def schwefel_1d(x):\n", + "\n", + " return 418.9829 - x * np.sin(np.sqrt(np.abs(x)))\n", + "\n", + "def schwefel_nd(args):\n", + " output = 0\n", + " \n", + " for dim in range(args):\n", + " output += schwefel_1d(args[dim])\n", + "\n", + "def add_gaussian_noise(signal, noise_level):\n", + "\n", + " return signal + np.random.normal(0, noise_level, 1)[0]\n", + "\n", + "def schwefel_1d_with_noise(x, noise_level = 0.01):\n", + " # Calculate the Schwefel function value\n", + "\n", + " schwefel_value = schwefel_1d(x)\n", + "\n", + " # Add Gaussian noise to the Schwefel function value\n", + "\n", + " noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level)\n", + "\n", + " return noisy_schwefel_value\n", + "\n", + "def schwefel_nd_with_noise(args, noise_level = 0.01):\n", + " # Calculate the Schwefel function value\n", + "\n", + " schwefel_value = schwefel_nd(args)\n", + "\n", + " # Add Gaussian noise to the Schwefel function value\n", + "\n", + " noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level)\n", + "\n", + " return noisy_schwefel_value\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "ecbf7466", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def create_data(seed, n_init, noise_level):\n", + "\n", + " np.random.seed(seed)\n", + " X_bounds = np.array([-500, 500])\n", + " X = np.random.uniform(X_bounds[0], X_bounds[1], size=( n_init,))\n", + " X = np.append(X, X_bounds)\n", + " X = np.sort(X)\n", + " y = schwefel_1d_with_noise(X, noise_level = noise_level)\n", + "\n", + " X_unmeasured = np.linspace(X_bounds[0], X_bounds[1], 200)\n", + " ground_truth = schwefel_1d_with_noise(X_unmeasured, noise_level = 0)\n", + " \n", + " return X, y, X_unmeasured, ground_truth\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "ae1578be", + "metadata": {}, + "outputs": [], + "source": [ + "noise_prior = lambda: numpyro.sample(\"noise\", numpyro.distributions.HalfNormal(0.01))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "5ba4e33f", + "metadata": {}, + "outputs": [], + "source": [ + "def gp_kernel_prior():\n", + " length = numpyro.sample(\"k_length\", numpyro.distributions.Uniform(0.1, 2)) #0.1 2\n", + " scale = numpyro.sample(\"k_scale\", numpyro.distributions.LogNormal(0, 1))\n", + " # the hyperparameters are returned as dictionary\n", + " return {\"k_length\": length, \"k_scale\": scale}" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e4efc886", + "metadata": {}, + "outputs": [], + "source": [ + "def step(X_measured, y_measured, X_unmeasured):\n", + " # Get random number generator keys for training and prediction\n", + " rng_key1, rng_key2 = gpax.utils.get_keys()\n", + " # Initialize GP model\n", + " gp_model = gpax.ExactGP(1, kernel='Matern', kernel_prior=gp_kernel_prior, noise_prior=noise_prior)\n", + " #gpax.ExactGP(1, kernel='Matern')\n", + " # Run HMC to obtain posterior samples for the GP model parameters\n", + " gp_model.fit(rng_key1, X_measured, y_measured)\n", + " # Get predictions (we don't need this step for optimization - only for visualization purposes)\n", + " y_pred, y_sampled = gp_model.predict(rng_key2, X_unmeasured, noiseless=True, n= 10)\n", + "\n", + " # Compute acquisition function\n", + "\n", + " #Upper confidence bound\n", + " #obj = gpax.acquisition.UCB(rng_key2, gp_model, X_unmeasured, beta= 10, maximize=False, noiseless=True)\n", + "\n", + " #Expected improvement\n", + " obj = gpax.acquisition.EI(rng_key2, gp_model, X_unmeasured, xi=0.01, maximize=False, n=10, noiseless=True) #xi = 0.01\n", + "\n", + " # pure uncertainty-based\n", + " #obj = gpax.acquisition.UE(rng_key2, gp_model, X_unmeasured, noiseless=True)\n", + "\n", + " return obj, (y_pred, y_sampled)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "23e5cdaf", + "metadata": {}, + "outputs": [], + "source": [ + "def run_gp(num_steps, X, y, X_unmeasured, ground_truth, schwefel_1d_with_noise, noise_level ):\n", + " for e in range(num_steps):\n", + " print(\"\\nStep {}/{}\".format(e+1, num_steps))\n", + " # Compute acquisition function\n", + " acq, (y_pred, y_sampled) = step(X, y, X_unmeasured)\n", + " # Get the next point to evaluate\n", + " idx = acq.argmin()\n", + " next_point = X_unmeasured[idx:idx+1]\n", + "\n", + " # Measure the point\n", + " next_point_value = schwefel_1d_with_noise(next_point, noise_level)\n", + " #Note that in experiment this is the step when you go to the lab. Strong insentive to learn how to make convergence faster!\n", + "\n", + " # Update measured data\n", + " X = np.append(X, X_unmeasured[idx:idx+1])\n", + " y = np.append(y, next_point_value)\n", + "\n", + "# # Plot observed points, mean prediction, and acqusition function\n", + "# lower_b = y_pred - y_sampled.std(axis=(0,1))\n", + "# upper_b = y_pred + y_sampled.std(axis=(0,1))\n", + "# fig, (ax1, ax2) = plt.subplots(1, 2, dpi=100, figsize=(14, 5.5))\n", + "# ax1.scatter(X[:-1], y[:-1], marker='x', c='k', label=\"Observations\", s=64)\n", + "# ax1.fill_between(X_unmeasured, lower_b, upper_b, color='r', alpha=0.3, label=\"Model uncertainty\")\n", + "# ax2.plot(X_unmeasured, acq, lw=2, c='orangered', label='Acquisition function')\n", + "# ax2.scatter(X_unmeasured[idx], acq[idx], s=90, c='orangered', label='Next point to measure')\n", + "# for ax in fig.axes:\n", + "# ax.plot(X_unmeasured, y_pred, lw=2, c='b', label='Posterior mean')\n", + "# ax.plot(X_unmeasured, ground_truth, label='Ground truth')\n", + "# #ax.set_ylim(3.0, 8)\n", + "# ax.set_xlabel(\"$X$\", fontsize=16)\n", + "# ax.set_ylabel(\"$y$\", fontsize=16)\n", + "# ax.legend(loc='best', fontsize=10)\n", + "# plt.show()\n", + " # Quantifying prediction accuracy\n", + " mse = np.mean((y_pred - ground_truth)**2)\n", + " average_uncertainty = np.mean(y_sampled.std(axis=(0,1))) \n", + " return mse, average_uncertainty" + ] + }, + { + "cell_type": "markdown", + "id": "e2771527", + "metadata": {}, + "source": [ + "# run experiments" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "c0a4327d", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "import pandas as pd\n", + "\n", + "def grid_search(seeds, n_inits, noise_levels):\n", + " results = [] # Initialize an empty list to store results\n", + "\n", + " # Iterate over all combinations of seeds, n_inits, and noise_levels\n", + " for seed, n_init, noise_level in itertools.product(seeds, n_inits, noise_levels):\n", + " print(f\"Seed: {seed}, n_init: {n_init}, Noise Level: {noise_level}\")\n", + " \n", + " # Create data for the current combination\n", + " X, y, X_unmeasured, ground_truth = create_data(seed, n_init, noise_level)\n", + " \n", + " # Run Gaussian Process (GP) optimization/modeling for the current combination\n", + " mse, average_uncertainty = run_gp(5, X, y, X_unmeasured, ground_truth, schwefel_1d_with_noise, noise_level)\n", + " \n", + " # Collect the results\n", + " results.append({\n", + " \"Seed\": seed,\n", + " \"n_init\": n_init,\n", + " \"Noise_Level\": noise_level,\n", + " \"MSE\": mse,\n", + " \"Average_Uncertainty\": average_uncertainty\n", + " })\n", + "\n", + " # Convert the results to a pandas DataFrame for easy analysis and reporting\n", + " results_df = pd.DataFrame(results)\n", + " \n", + " return results_df\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "63b0451c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Seed: 1, n_init: 10, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|█| 4000/4000 [00:18<00:00, 219.94it/s, 3 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 1.26 1.00\n", + " k_scale 453.36 0.00 453.36 453.36 453.36 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:21<00:00, 184.16it/s, 6 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 43.01 0.00 43.01 43.00 43.01 6.19 1.21\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:28<00:00, 140.09it/s, 9 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 66.33 1.00\n", + " k_scale 32.17 0.00 32.17 32.17 32.17 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:29<00:00, 134.14it/s, 19 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 103.83 1.00\n", + " k_scale 62.30 0.00 62.30 62.30 62.30 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 120.15it/s, 1 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 23.13 0.00 23.13 23.13 23.13 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 10, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:20<00:00, 194.76it/s, 10 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.85 1.00\n", + " k_scale 51.21 0.00 51.21 51.21 51.21 10.25 1.11\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:25<00:00, 155.65it/s, 24 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 111.50 1.00\n", + " k_scale 55.50 0.00 55.50 55.50 55.50 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 121.13it/s, 59 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 15.22 0.00 15.22 15.22 15.22 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:31<00:00, 128.46it/s, 4 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 39.38 0.00 39.38 39.38 39.38 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 113.39it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 nan nan\n", + " k_scale 1.44 0.00 1.44 1.44 1.44 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 10, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:27<00:00, 143.14it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 nan nan\n", + " k_scale 1.43 0.00 1.43 1.43 1.43 2.54 2.68\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:31<00:00, 126.65it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 2.29 0.00 2.29 2.29 2.29 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:27<00:00, 147.69it/s, 4 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.66 0.00 0.66 0.66 0.66 33.72 1.00\n", + " k_scale 110.68 0.00 110.68 110.68 110.68 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:32<00:00, 123.35it/s, 46 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.55 0.00 0.55 0.55 0.55 25.39 1.02\n", + " k_scale 61.08 0.00 61.08 61.08 61.08 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 110.28it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 2.29 0.00 2.29 2.29 2.29 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 1, n_init: 10, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:27<00:00, 143.49it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 nan nan\n", + " k_scale 1.43 0.00 1.43 1.43 1.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:29<00:00, 134.99it/s, 42 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 nan nan\n", + " k_scale 22.43 0.00 22.43 22.43 22.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:28<00:00, 140.47it/s, 10 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 nan nan\n", + " k_scale 19.19 0.00 19.19 19.19 19.19 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:34<00:00, 114.43it/s, 59 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 0.50 1.00\n", + " k_scale 19.17 0.00 19.17 19.17 19.17 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 109.37it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 nan nan\n", + " k_scale 1.43 0.00 1.43 1.43 1.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 15, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:42<00:00, 94.46it/s, 907 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 11.89 1.05\n", + " k_scale 21.37 0.00 21.37 21.37 21.37 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:42<00:00, 93.05it/s, 2 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 0.50 1.00\n", + " k_scale 19.05 0.00 19.05 19.05 19.05 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 76.33it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 4.72 0.00 4.72 4.72 4.72 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:45<00:00, 87.51it/s, 2 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.58 0.00 0.58 0.58 0.58 43.14 1.00\n", + " k_scale 213.11 0.00 213.11 213.11 213.11 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:59<00:00, 67.51it/s, 4 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 0.50 1.00\n", + " k_scale 19.44 0.00 19.44 19.44 19.44 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 15, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 109.75it/s, 8 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.64 0.00 0.64 0.64 0.64 0.50 1.00\n", + " k_scale 29.13 0.00 29.13 29.13 29.13 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:45<00:00, 87.38it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 nan nan\n", + " k_scale 1.43 0.00 1.43 1.43 1.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:51<00:00, 77.59it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 26.94 1.08\n", + " k_scale 31.46 0.00 31.46 31.46 31.46 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 75.96it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 1.44 0.00 1.44 1.44 1.44 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 64.91it/s, 764 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 52.69 1.00\n", + " k_scale 18.97 0.00 18.97 18.97 18.97 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 1, n_init: 15, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:38<00:00, 105.02it/s, 14 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 17.26 1.05\n", + " k_scale 26.21 0.00 26.21 26.21 26.21 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 107.60it/s, 21 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.63 0.00 0.63 0.63 0.63 0.52 1.00\n", + " k_scale 59.03 0.00 59.03 59.03 59.03 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:49<00:00, 80.36it/s, 42 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 20.04 0.00 20.04 20.04 20.04 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:54<00:00, 73.99it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 1.43 0.00 1.43 1.43 1.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:45<00:00, 88.47it/s, 2 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.71 0.00 0.71 0.71 0.71 0.51 1.00\n", + " k_scale 1767.44 0.00 1767.44 1767.44 1767.44 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 15, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:41<00:00, 95.97it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 nan nan\n", + " k_scale 3.31 0.00 3.31 3.31 3.31 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:46<00:00, 86.92it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 2.58 0.00 2.58 2.58 2.58 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 76.68it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 1.43 0.00 1.43 1.43 1.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:53<00:00, 74.14it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.70 0.70 0.70 10.83 1.00\n", + " k_scale 43.37 0.00 43.37 43.37 43.37 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:57<00:00, 70.15it/s, 7 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 23.98 0.00 23.98 23.98 23.98 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 1, n_init: 20, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:04<00:00, 61.87it/s, 222 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 22.21 0.00 22.21 22.21 22.21 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:12<00:00, 54.81it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 9.48 0.00 9.48 9.48 9.48 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:02<00:00, 63.88it/s, 526 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 44.27 1.01\n", + " k_scale 23.40 0.00 23.40 23.40 23.40 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:13<00:00, 54.58it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 nan nan\n", + " k_scale 9.27 0.00 9.27 9.27 9.27 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:07<00:00, 58.88it/s, 2 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.71 0.00 0.71 0.71 0.71 12.33 1.01\n", + " k_scale 158.38 0.00 158.38 158.38 158.38 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 20, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 65.36it/s, 2 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 nan nan\n", + " k_scale 22.11 0.00 22.11 22.11 22.11 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:04<00:00, 62.27it/s, 3 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 nan nan\n", + " k_scale 43.46 0.00 43.46 43.46 43.46 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:57<00:00, 70.05it/s, 8 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 9.87 1.96\n", + " k_scale 29.56 0.00 29.56 29.56 29.56 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:12<00:00, 55.42it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 10.78 0.00 10.78 10.78 10.78 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:15<00:00, 52.91it/s, 37 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 0.50 1.00\n", + " k_scale 13.89 0.00 13.89 13.89 13.89 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 20, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 108.08it/s, 2 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.75 0.00 0.75 0.75 0.75 0.55 1.00\n", + " k_scale 3627.27 0.00 3627.27 3627.27 3627.27 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:53<00:00, 75.01it/s, 65 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.70 0.70 0.70 129.84 1.00\n", + " k_scale 368.42 0.00 368.42 368.42 368.42 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:54<00:00, 73.63it/s, 20 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.71 0.00 0.71 0.71 0.71 0.50 1.00\n", + " k_scale 141.89 0.00 141.89 141.89 141.89 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:05<00:00, 60.87it/s, 15 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.64 0.00 0.64 0.64 0.64 nan nan\n", + " k_scale 29.60 0.00 29.60 29.60 29.60 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:59<00:00, 67.46it/s, 128 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 41.78 1.05\n", + " k_scale 95.80 0.00 95.80 95.80 95.80 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 1, n_init: 20, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:53<00:00, 75.17it/s, 3 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 0.50 1.00\n", + " k_scale 41.47 0.00 41.47 41.47 41.47 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 64.98it/s, 120 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.66 0.00 0.66 0.66 0.66 2.40 1.06\n", + " k_scale 55.54 0.00 55.54 55.54 55.54 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:57<00:00, 69.45it/s, 4 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 22.33 0.00 22.33 22.33 22.33 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:11<00:00, 55.70it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 0.50 1.00\n", + " k_scale 9.28 0.00 9.28 9.28 9.28 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:17<00:00, 51.53it/s, 710 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 252.71 1.01\n", + " k_scale 33.28 0.00 33.28 33.28 33.28 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 10, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1381.43it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.37 0.44 1.47 0.72 1.99 1667.02 1.00\n", + " k_scale 77684.52 19737.01 74845.76 47334.99 106374.62 1028.83 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1466.18 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:31<00:00, 125.74it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.78 0.00 0.78 0.78 0.78 2.52 3.55\n", + " k_scale 1322.64 0.07 1322.67 1322.67 1322.67 10.24 1.11\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 118.31it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.72 0.00 0.72 0.72 0.72 2.42 3.45\n", + " k_scale 730.23 0.00 730.23 730.23 730.23 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 105.32it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.80 0.02 0.80 0.78 0.83 2.63 2.48\n", + " k_scale 14139.49 1284.56 13997.53 12068.98 15933.91 2.57 2.74\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.42 1.99\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 109.84it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.73 0.00 0.73 0.73 0.73 2.50 2.75\n", + " k_scale 770.65 0.00 770.65 770.65 770.65 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 10, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1369.12it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.37 0.44 1.45 0.71 2.00 1405.14 1.00\n", + " k_scale 77769.19 19278.49 75023.91 46533.59 104351.18 1023.67 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1126.38 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:16<00:00, 240.32it/s, 299 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.33 0.00 1.33 1.33 1.33 4.48 1.24\n", + " k_scale 42100.00 5.49 42099.73 42090.94 42108.29 5.03 1.39\n", + " noise 0.01 0.00 0.01 0.01 0.01 6.31 1.02\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 118.59it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.76 0.00 0.76 0.75 0.76 2.42 3.21\n", + " k_scale 898.89 0.00 898.89 898.89 898.89 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 107.37it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.72 0.00 0.72 0.72 0.73 2.59 2.77\n", + " k_scale 1049.96 0.00 1049.96 1049.96 1049.96 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 111.18it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.80 0.00 0.80 0.80 0.81 2.56 3.11\n", + " k_scale 11041.06 377.98 11029.73 10460.64 11624.72 2.71 2.63\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.11 2.36\n", + "\n", + "Seed: 2, n_init: 10, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1359.35it/s, 15 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.37 0.44 1.46 0.72 2.00 1399.38 1.00\n", + " k_scale 76932.52 19242.04 73795.89 44063.46 104610.05 1136.90 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1497.74 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:28<00:00, 142.58it/s, 73 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.57 0.01 0.57 0.56 0.58 2.48 3.09\n", + " k_scale 34279.02 5444.43 33814.66 26670.43 43931.08 2.81 2.21\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.56 1.58\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 118.47it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.75 0.00 0.75 0.75 0.75 2.60 3.24\n", + " k_scale 875.17 0.00 875.17 875.17 875.17 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 109.10it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.98 0.00 0.98 0.98 0.99 3.88 1.72\n", + " k_scale 31298.88 6078.77 33533.25 21089.32 40153.30 2.86 2.38\n", + " noise 0.01 0.00 0.01 0.01 0.01 9.27 1.11\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 112.14it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.76 0.00 0.76 0.75 0.76 2.48 3.09\n", + " k_scale 781.51 0.00 781.51 781.51 781.51 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 10, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1364.35it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.37 0.45 1.44 0.72 2.00 1317.19 1.00\n", + " k_scale 78146.24 20451.55 75144.06 48157.69 107827.02 1008.43 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1466.19 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:32<00:00, 121.95it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.70 0.70 0.71 2.43 3.41\n", + " k_scale 415.63 0.00 415.63 415.63 415.63 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:34<00:00, 117.27it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.12 0.00 1.12 1.12 1.12 3.34 1.87\n", + " k_scale 10019.22 0.00 10019.22 10019.22 10019.22 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:38<00:00, 103.47it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.61 0.00 0.61 0.61 0.61 2.62 2.93\n", + " k_scale 145.20 0.00 145.20 145.20 145.20 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 112.26it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.71 0.00 0.71 0.71 0.71 2.81 3.08\n", + " k_scale 51.69 0.00 51.69 51.69 51.69 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 15, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1331.88it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.53 0.37 1.63 1.01 2.00 1339.42 1.00\n", + " k_scale 87519.71 20460.04 84588.73 53983.97 117041.66 1122.05 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1468.50 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:46<00:00, 86.38it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.02 0.02 1.01 0.99 1.05 2.51 2.63\n", + " k_scale 34436.52 359.54 34392.83 33947.11 35061.64 3.35 2.12\n", + " noise 0.01 0.00 0.01 0.01 0.01 5.20 1.84\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:51<00:00, 77.26it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.64 0.00 0.64 0.64 0.64 2.58 3.41\n", + " k_scale 3486.92 0.00 3486.92 3486.92 3486.92 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 9.53 1.30\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:44<00:00, 90.43it/s, 154 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.86 0.00 0.86 0.86 0.86 7.68 1.05\n", + " k_scale 33469.60 5.34 33469.00 33461.31 33478.26 7.82 1.04\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:56<00:00, 70.67it/s, 14 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.84 0.00 0.84 0.84 0.84 2.56 2.90\n", + " k_scale 32029.86 741.37 32171.88 30797.09 32993.38 5.79 1.54\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.43 2.11\n", + "\n", + "Seed: 2, n_init: 15, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1328.13it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.52 0.38 1.62 0.96 2.00 1356.37 1.00\n", + " k_scale 86537.73 21212.71 83274.28 55043.46 117774.72 1280.80 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 998.80 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:09<00:00, 428.27it/s, 4 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.06 0.00 1.06 1.06 1.06 0.66 1.00\n", + " k_scale 40269.16 0.00 40269.16 40269.16 40269.16 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 76.43it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.79 0.00 0.79 0.79 0.79 2.43 3.51\n", + " k_scale 1027.18 0.00 1027.18 1027.18 1027.18 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:51<00:00, 77.78it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.80 0.00 0.80 0.80 0.80 0.50 1.00\n", + " k_scale 33483.21 0.00 33483.21 33483.21 33483.21 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 65.37it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.80 0.00 0.80 0.80 0.80 2.70 2.33\n", + " k_scale 1012.96 0.00 1012.96 1012.96 1012.96 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 2, n_init: 15, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1283.64it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.54 0.39 1.63 0.97 2.00 1412.43 1.00\n", + " k_scale 88030.25 20978.13 84436.31 58139.70 121263.59 969.50 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1462.23 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 110.50it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.85 0.01 0.85 0.84 0.88 2.41 3.31\n", + " k_scale 59930.79 3737.75 60122.50 54468.01 65680.98 6.86 1.01\n", + " noise 0.01 0.00 0.01 0.01 0.01 2.96 2.56\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:51<00:00, 77.59it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.77 0.00 0.77 0.77 0.77 2.94 2.00\n", + " k_scale 1017.46 0.00 1017.46 1017.46 1017.46 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:28<00:00, 142.06it/s, 2 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.48 0.00 1.48 1.48 1.48 nan nan\n", + " k_scale 33536.13 0.00 33536.13 33536.13 33536.13 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:44<00:00, 89.27it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.98 0.00 0.98 0.97 0.98 2.56 4.84\n", + " k_scale 43486.80 31.29 43491.35 43437.86 43529.67 3.94 1.15\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.74 1.89\n", + "\n", + "Seed: 2, n_init: 15, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1301.86it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.53 0.38 1.61 0.98 2.00 1580.83 1.00\n", + " k_scale 86985.98 21150.47 84024.19 57585.51 119230.65 1301.04 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1223.35 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:46<00:00, 86.77it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.90 0.00 0.90 0.89 0.90 2.43 3.26\n", + " k_scale 873.22 0.00 873.22 873.22 873.22 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 75.72it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.71 0.00 0.71 0.71 0.71 3.35 2.19\n", + " k_scale 216.43 0.00 216.43 216.43 216.43 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:51<00:00, 77.57it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.06 0.00 1.06 1.06 1.07 2.39 3.61\n", + " k_scale 3997.68 294.03 4012.69 3541.05 4426.89 2.49 2.78\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.52 2.05\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 64.66it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.73 0.00 0.73 0.73 0.73 4.38 1.59\n", + " k_scale 66.19 0.00 66.19 66.19 66.19 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 20, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1269.91it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.49 0.42 1.59 0.88 2.00 1200.97 1.00\n", + " k_scale 118164.95 26672.58 114027.52 78118.53 157083.81 1027.62 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1473.49 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:14<00:00, 278.66it/s, 445 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.36 0.00 1.36 1.36 1.36 6.42 1.09\n", + " k_scale 38612.71 10.01 38610.38 38600.81 38630.97 4.92 1.52\n", + " noise 0.01 0.00 0.01 0.01 0.01 12.88 1.18\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 64.79it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.78 0.00 0.78 0.78 0.78 2.66 2.62\n", + " k_scale 2825.90 0.00 2825.90 2825.90 2825.90 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 76.91it/s, 4 steps of \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.80 0.00 0.80 0.80 0.80 4.62 1.27\n", + " k_scale 36894.27 2131.50 37918.45 33092.84 38763.09 3.80 1.48\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.11 1.83\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:18<00:00, 51.17it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.75 0.00 0.75 0.75 0.75 2.68 2.43\n", + " k_scale 1570.49 0.00 1570.49 1570.49 1570.49 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 20, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1271.76it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.50 0.40 1.60 0.87 2.00 1672.10 1.00\n", + " k_scale 116911.77 25761.19 113579.77 74720.73 153895.17 1332.34 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1418.84 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:26<00:00, 149.17it/s, 49 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.73 0.00 0.73 0.73 0.73 5.04 1.08\n", + " k_scale 45344.56 2409.35 46301.34 41283.48 48429.14 3.15 1.70\n", + " noise 0.01 0.00 0.01 0.01 0.01 6.74 1.43\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:03<00:00, 63.17it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.74 0.00 0.74 0.74 0.74 2.43 3.77\n", + " k_scale 1386.61 0.00 1386.61 1386.61 1386.61 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:11<00:00, 55.99it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.88 0.00 0.88 0.88 0.88 2.57 2.90\n", + " k_scale 1705.12 0.00 1705.12 1705.12 1705.12 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:19<00:00, 50.52it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.72 0.00 0.72 0.72 0.72 2.43 3.49\n", + " k_scale 1499.16 0.00 1499.16 1499.16 1499.16 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 20, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1253.16it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.48 0.41 1.57 0.85 2.00 1376.79 1.00\n", + " k_scale 118090.76 25711.47 115393.38 78558.57 155900.17 1266.75 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1253.46 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:13<00:00, 54.08it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.77 0.00 0.77 0.77 0.77 2.37 3.57\n", + " k_scale 1044.28 0.00 1044.28 1044.28 1044.28 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:02<00:00, 64.25it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.74 0.00 0.74 0.74 0.74 2.53 3.15\n", + " k_scale 1796.61 0.00 1796.61 1796.61 1796.61 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:11<00:00, 55.60it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.75 0.00 0.75 0.75 0.76 2.44 3.08\n", + " k_scale 1853.54 0.00 1853.54 1853.54 1853.54 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:18<00:00, 51.10it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.74 0.00 0.74 0.74 0.74 2.51 2.85\n", + " k_scale 1225.29 0.00 1225.29 1225.29 1225.29 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 2, n_init: 20, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1259.29it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.50 0.40 1.60 0.91 2.00 1529.65 1.00\n", + " k_scale 116887.55 26172.67 113047.02 75429.32 156277.38 1215.53 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1075.58 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:11<00:00, 56.16it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.02 0.69 0.67 0.71 2.41 3.26\n", + " k_scale 24016.94 296.13 24034.71 23626.46 24425.04 2.56 3.50\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.47 1.85\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:04<00:00, 62.37it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.62 2.93\n", + " k_scale 1000.69 0.00 1000.69 1000.69 1000.69 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:11<00:00, 55.71it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.93 0.00 0.93 0.93 0.94 2.71 2.50\n", + " k_scale 239.18 0.00 239.18 239.18 239.18 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:17<00:00, 51.35it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.78 2.91\n", + " k_scale 1985.26 0.00 1985.26 1985.26 1985.26 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 10, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1364.40it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.09 0.51 1.12 0.33 1.93 1670.82 1.00\n", + " k_scale 72827.19 18358.48 70312.22 45540.64 100423.82 978.72 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1387.74 1.01\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:31<00:00, 125.78it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.66 0.00 0.66 0.66 0.66 2.56 2.91\n", + " k_scale 1285.04 0.00 1285.04 1285.04 1285.04 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:30<00:00, 133.06it/s, 511 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.82 0.01 0.82 0.81 0.84 2.63 2.63\n", + " k_scale 29644.94 5260.62 31315.31 22760.34 36941.11 3.04 2.09\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.26 1.73\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:38<00:00, 102.98it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 2.43 3.25\n", + " k_scale 697.36 0.00 697.36 697.36 697.36 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 111.64it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 2.58 3.11\n", + " k_scale 680.96 0.00 680.96 680.96 680.96 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 10, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1382.69it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.08 0.52 1.10 0.29 1.92 1407.85 1.00\n", + " k_scale 72597.62 17751.08 69995.16 44860.73 99821.12 1096.96 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1354.13 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:31<00:00, 125.61it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.68 0.68 0.69 2.50 3.50\n", + " k_scale 2849.67 2.81 2850.45 2844.25 2852.92 4.00 1.54\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.62 1.67\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 119.68it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.76 0.01 0.76 0.75 0.78 3.01 1.96\n", + " k_scale 17294.53 181.36 17285.67 17008.40 17589.36 15.01 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 2.62 2.75\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 105.46it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.71 0.70 0.71 2.74 2.41\n", + " k_scale 21914.92 4980.35 22556.78 13193.37 28289.91 2.54 2.85\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.70 1.79\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:36<00:00, 110.02it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.66 0.00 0.66 0.66 0.66 2.94 2.45\n", + " k_scale 782.16 0.00 782.16 782.16 782.16 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 10, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1414.25it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.09 0.54 1.09 0.34 1.99 1345.22 1.00\n", + " k_scale 73442.73 18546.60 70887.00 44081.99 100563.94 1125.69 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1401.42 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:31<00:00, 127.01it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.72 0.04 0.72 0.67 0.78 2.58 2.54\n", + " k_scale 20545.85 303.07 20494.04 20097.95 21003.21 7.80 1.56\n", + " noise 0.01 0.00 0.01 0.01 0.01 8.85 1.19\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:33<00:00, 120.69it/s, 32 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.22 0.00 1.22 1.22 1.23 3.40 1.95\n", + " k_scale 46053.85 4719.94 46442.46 38450.46 52729.11 3.84 2.02\n", + " noise 0.01 0.00 0.01 0.01 0.01 4.81 1.69\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 107.03it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.66 0.00 0.66 0.66 0.66 3.10 2.49\n", + " k_scale 2318.61 0.00 2318.61 2318.61 2318.61 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 113.72it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.61 0.00 0.61 0.61 0.61 2.88 2.52\n", + " k_scale 392.86 0.00 392.86 392.86 392.86 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 10, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1367.00it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.11 0.53 1.15 0.36 1.99 1465.46 1.00\n", + " k_scale 72984.98 18601.47 69862.14 45510.64 101631.62 1006.12 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1488.59 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:32<00:00, 124.92it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.70 0.70 0.70 2.39 4.23\n", + " k_scale 7140.82 0.00 7140.82 7140.82 7140.82 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 113.60it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.64 0.00 0.64 0.64 0.64 3.38 2.02\n", + " k_scale 181.07 0.00 181.07 181.07 181.07 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:37<00:00, 105.85it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 0.50 1.00\n", + " k_scale 625.13 0.00 625.13 625.13 625.13 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 112.48it/s, 1023 steps\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 2.71 3.05\n", + " k_scale 78.94 0.00 78.94 78.94 78.94 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 15, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:02<00:00, 1337.31it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.10 0.53 1.10 0.36 2.00 1518.59 1.00\n", + " k_scale 92965.45 21841.80 89628.97 62175.77 127285.95 1193.38 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1338.88 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:46<00:00, 86.93it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.74 0.00 0.74 0.74 0.74 3.06 2.49\n", + " k_scale 1281.33 0.00 1281.33 1281.33 1281.33 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:51<00:00, 77.30it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.65 2.93\n", + " k_scale 1172.89 0.00 1172.89 1172.89 1172.89 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:53<00:00, 75.24it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.64 0.00 0.64 0.64 0.64 2.42 3.78\n", + " k_scale 1595.62 0.00 1595.62 1595.62 1595.62 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:02<00:00, 64.17it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 3.13 2.18\n", + " k_scale 993.19 0.00 993.19 993.19 993.19 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 15, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1314.98it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.10 0.52 1.10 0.37 1.99 1444.20 1.00\n", + " k_scale 93594.58 22037.80 90110.72 62150.04 128535.92 1150.32 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1532.01 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:21<00:00, 186.69it/s, 93 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.84 0.00 0.84 0.84 0.84 8.90 1.22\n", + " k_scale 35965.30 573.32 36036.41 35207.02 36779.65 7.67 1.05\n", + " noise 0.01 0.00 0.01 0.01 0.01 8.18 1.05\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:35<00:00, 111.49it/s, 29 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.72 0.00 0.72 0.72 0.72 6.12 1.11\n", + " k_scale 34234.30 1889.20 34463.96 32046.79 36858.13 5.48 1.44\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.34 1.63\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 76.06it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.57 3.06\n", + " k_scale 902.09 0.00 902.09 902.09 902.09 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 64.61it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 2.56 2.85\n", + " k_scale 307.71 0.00 307.71 307.71 307.71 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 15, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1305.07it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.13 0.53 1.15 0.37 2.00 1311.67 1.00\n", + " k_scale 93333.09 21738.07 90516.12 60132.22 126522.04 1211.89 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1079.81 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:46<00:00, 86.23it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.52 2.88\n", + " k_scale 1541.87 0.00 1541.87 1541.87 1541.87 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:44<00:00, 90.58it/s, 647 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.10 0.00 1.10 1.09 1.10 2.93 2.29\n", + " k_scale 32615.19 683.14 32782.82 31548.90 33491.86 18.84 1.11\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.49 1.69\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 75.79it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.75 0.01 0.75 0.75 0.76 2.38 3.80\n", + " k_scale 9405.91 124.95 9414.80 9194.33 9575.03 2.78 2.57\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.28 2.10\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 65.41it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 5.63 1.45\n", + " k_scale 1010.56 0.00 1010.56 1010.56 1010.56 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 3, n_init: 15, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1326.89it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.11 0.53 1.14 0.33 1.96 1604.20 1.00\n", + " k_scale 92860.34 21734.16 89477.55 57990.46 125428.90 1250.82 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1338.40 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:45<00:00, 88.13it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 3.73 1.39\n", + " k_scale 273.22 0.00 273.22 273.22 273.22 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:52<00:00, 75.98it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 4.60 1.48\n", + " k_scale 218.84 0.00 218.84 218.84 218.84 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:53<00:00, 74.19it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 2.87 2.23\n", + " k_scale 325.07 0.00 325.07 325.07 325.07 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:02<00:00, 64.51it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.00 0.65 0.65 0.65 2.47 2.86\n", + " k_scale 76.61 0.00 76.61 76.61 76.61 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 3, n_init: 20, Noise Level: 0\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1249.57it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.11 0.53 1.12 0.35 1.99 1416.08 1.00\n", + " k_scale 103793.10 22921.46 100951.85 68617.83 139373.88 1388.80 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 975.40 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:19<00:00, 208.22it/s, 2 steps of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.87 0.00 0.87 0.87 0.87 1.50 1.74\n", + " k_scale 81931.34 0.00 81931.34 81931.34 81931.34 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:01<00:00, 64.93it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.65 0.01 0.65 0.64 0.66 2.54 2.88\n", + " k_scale 16396.64 2730.67 16537.21 12003.95 20176.70 2.49 2.89\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.51 1.93\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:12<00:00, 55.37it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.68 0.00 0.68 0.68 0.68 2.60 3.29\n", + " k_scale 1287.73 0.00 1287.73 1287.73 1287.73 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:18<00:00, 51.28it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.70 0.70 0.70 2.49 3.48\n", + " k_scale 344.00 0.00 344.00 344.00 344.00 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 nan nan\n", + "\n", + "Seed: 3, n_init: 20, Noise Level: 0.01\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1268.59it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.10 0.53 1.11 0.34 1.97 1668.94 1.00\n", + " k_scale 105032.97 22799.59 102893.23 69152.84 139024.75 1271.63 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1200.58 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:12<00:00, 54.84it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.64 0.00 0.64 0.64 0.64 2.44 3.46\n", + " k_scale 4593.36 0.00 4593.36 4593.36 4593.36 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 4.45 1.48\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:03<00:00, 63.41it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 2.68 2.72\n", + " k_scale 343.26 0.00 343.26 343.26 343.26 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:10<00:00, 56.47it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.73 0.00 0.73 0.72 0.73 2.90 2.60\n", + " k_scale 15385.37 137.50 15365.96 15194.92 15600.57 2.95 2.60\n", + " noise 0.01 0.00 0.01 0.01 0.01 4.96 1.78\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:17<00:00, 51.49it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.66 0.00 0.66 0.66 0.66 3.40 2.21\n", + " k_scale 374.75 0.00 374.75 374.75 374.75 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 20, Noise Level: 0.1\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1241.89it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.11 0.53 1.12 0.35 1.98 1166.88 1.00\n", + " k_scale 104306.29 22616.40 101626.67 71035.95 141794.48 1016.80 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1335.59 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:13<00:00, 54.40it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.87 2.79\n", + " k_scale 430.68 0.00 430.68 430.68 430.68 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:02<00:00, 63.78it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.49 3.55\n", + " k_scale 4437.30 0.00 4437.30 4437.30 4437.30 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 8.50 1.02\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:09<00:00, 57.88it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.75 0.00 0.75 0.75 0.76 2.51 3.08\n", + " k_scale 16119.34 66.89 16109.67 16023.27 16241.33 7.75 1.30\n", + " noise 0.01 0.00 0.01 0.01 0.01 3.25 2.36\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:17<00:00, 51.66it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.54 0.00 0.54 0.54 0.54 2.68 2.73\n", + " k_scale 1269.00 0.00 1269.00 1269.00 1269.00 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "Seed: 3, n_init: 20, Noise Level: 0.5\n", + "\n", + "Step 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [00:03<00:00, 1271.50it/s, 7 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 1.10 0.52 1.11 0.33 1.95 1569.24 1.00\n", + " k_scale 104599.68 23676.24 101679.95 67753.70 138586.31 1137.48 1.00\n", + " noise 0.01 0.01 0.01 0.00 0.02 1359.68 1.00\n", + "\n", + "\n", + "Step 2/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:12<00:00, 55.34it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.53 3.18\n", + " k_scale 685.96 0.00 685.96 685.96 685.96 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 3/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:02<00:00, 64.02it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.69 0.00 0.69 0.69 0.69 2.88 2.15\n", + " k_scale 65.58 0.00 65.58 65.58 65.58 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 4/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:12<00:00, 55.18it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.70 0.00 0.70 0.70 0.70 3.08 2.58\n", + " k_scale 158.34 0.00 158.34 158.34 158.34 nan nan\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + "\n", + "Step 5/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:111: FutureWarning: `noise_prior` is deprecated and will be removed in a future version. Please use `noise_prior_dist` instead, which accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.HalfNormal(scale=0.1)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "/lustre/isaac/scratch/upratius/projects/gpax/gpax/models/gp.py:119: UserWarning: `kernel_prior` will remain available for complex priors. However, for modifying only the lengthscales, it is recommended to use `lengthscale_prior_dist` instead. `lengthscale_prior_dist` accepts an instance of a numpyro.distributions Distribution object, e.g., `dist.Gamma(2, 5)`, rather than a function that calls `numpyro.sample`.\n", + " warnings.warn(\n", + "sample: 100%|█| 4000/4000 [01:17<00:00, 51.64it/s, 1023 steps \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " k_length 0.67 0.00 0.67 0.67 0.67 0.50 1.00\n", + " k_scale 437.95 0.00 437.95 437.95 437.95 0.50 1.00\n", + " noise 0.01 0.00 0.01 0.01 0.01 0.50 1.00\n", + "\n", + " Seed n_init Noise_Level MSE Average_Uncertainty\n", + "0 1 10 0.00 206665.11 4.705776\n", + "1 1 10 0.01 206703.03 1.1732969\n", + "2 1 10 0.10 206704.39 1.4820279\n", + "3 1 10 0.50 206695.95 1.1728493\n", + "4 1 15 0.00 206429.17 4.312067\n", + "5 1 15 0.01 206703.86 nan\n", + "6 1 15 0.10 206194.23 41.119843\n", + "7 1 15 0.50 206438.97 nan\n", + "8 1 20 0.00 204810.6 nan\n", + "9 1 20 0.01 204980.75 3.6358469\n", + "10 1 20 0.10 205313.08 9.566991\n", + "11 1 20 0.50 205050.28 nan\n", + "12 2 10 0.00 206827.7 nan\n", + "13 2 10 0.01 206230.03 nan\n", + "14 2 10 0.10 206634.8 nan\n", + "15 2 10 0.50 206980.36 nan\n", + "16 2 15 0.00 204085.3 nan\n", + "17 2 15 0.01 204540.2 nan\n", + "18 2 15 0.10 202565.88 nan\n", + "19 2 15 0.50 205339.92 nan\n", + "20 2 20 0.00 200644.86 nan\n", + "21 2 20 0.01 201124.2 nan\n", + "22 2 20 0.10 200712.36 nan\n", + "23 2 20 0.50 201804.08 nan\n", + "24 3 10 0.00 205953.39 nan\n", + "25 3 10 0.01 205981.02 nan\n", + "26 3 10 0.10 206296.47 nan\n", + "27 3 10 0.50 205876.56 nan\n", + "28 3 15 0.00 202675.38 nan\n", + "29 3 15 0.01 202699.94 nan\n", + "30 3 15 0.10 202658.06 nan\n", + "31 3 15 0.50 202984.28 nan\n", + "32 3 20 0.00 201440.75 nan\n", + "33 3 20 0.01 201990.44 nan\n", + "34 3 20 0.10 203485.16 nan\n", + "35 3 20 0.50 201839.02 nan\n" + ] + } + ], + "source": [ + "# Define your parameter space\n", + "seeds = [1, 2, 3]\n", + "n_inits = [10, 15, 20]\n", + "noise_levels = [0, 0.01, 0.1, 0.5]\n", + "\n", + "# Assuming create_data and run_gp functions are defined elsewhere\n", + "\n", + "# Run the grid search\n", + "results_df = grid_search(seeds, n_inits, noise_levels)\n", + "\n", + "# Display the results DataFrame\n", + "print(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cd33131", + "metadata": {}, + "outputs": [], + "source": [ + "results_df.to_csv('grid_search_results.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "46f6f5b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Seedn_initNoise_LevelMSEAverage_Uncertainty
01100.00206665.114.705776
11100.01206703.031.1732969
21100.10206704.391.4820279
31100.50206695.951.1728493
41150.00206429.174.312067
51150.01206703.86nan
61150.10206194.2341.119843
71150.50206438.97nan
81200.00204810.6nan
91200.01204980.753.6358469
101200.10205313.089.566991
111200.50205050.28nan
122100.00206827.7nan
132100.01206230.03nan
142100.10206634.8nan
152100.50206980.36nan
162150.00204085.3nan
172150.01204540.2nan
182150.10202565.88nan
192150.50205339.92nan
202200.00200644.86nan
212200.01201124.2nan
222200.10200712.36nan
232200.50201804.08nan
243100.00205953.39nan
253100.01205981.02nan
263100.10206296.47nan
273100.50205876.56nan
283150.00202675.38nan
293150.01202699.94nan
303150.10202658.06nan
313150.50202984.28nan
323200.00201440.75nan
333200.01201990.44nan
343200.10203485.16nan
353200.50201839.02nan
\n", + "
" + ], + "text/plain": [ + " Seed n_init Noise_Level MSE Average_Uncertainty\n", + "0 1 10 0.00 206665.11 4.705776\n", + "1 1 10 0.01 206703.03 1.1732969\n", + "2 1 10 0.10 206704.39 1.4820279\n", + "3 1 10 0.50 206695.95 1.1728493\n", + "4 1 15 0.00 206429.17 4.312067\n", + "5 1 15 0.01 206703.86 nan\n", + "6 1 15 0.10 206194.23 41.119843\n", + "7 1 15 0.50 206438.97 nan\n", + "8 1 20 0.00 204810.6 nan\n", + "9 1 20 0.01 204980.75 3.6358469\n", + "10 1 20 0.10 205313.08 9.566991\n", + "11 1 20 0.50 205050.28 nan\n", + "12 2 10 0.00 206827.7 nan\n", + "13 2 10 0.01 206230.03 nan\n", + "14 2 10 0.10 206634.8 nan\n", + "15 2 10 0.50 206980.36 nan\n", + "16 2 15 0.00 204085.3 nan\n", + "17 2 15 0.01 204540.2 nan\n", + "18 2 15 0.10 202565.88 nan\n", + "19 2 15 0.50 205339.92 nan\n", + "20 2 20 0.00 200644.86 nan\n", + "21 2 20 0.01 201124.2 nan\n", + "22 2 20 0.10 200712.36 nan\n", + "23 2 20 0.50 201804.08 nan\n", + "24 3 10 0.00 205953.39 nan\n", + "25 3 10 0.01 205981.02 nan\n", + "26 3 10 0.10 206296.47 nan\n", + "27 3 10 0.50 205876.56 nan\n", + "28 3 15 0.00 202675.38 nan\n", + "29 3 15 0.01 202699.94 nan\n", + "30 3 15 0.10 202658.06 nan\n", + "31 3 15 0.50 202984.28 nan\n", + "32 3 20 0.00 201440.75 nan\n", + "33 3 20 0.01 201990.44 nan\n", + "34 3 20 0.10 203485.16 nan\n", + "35 3 20 0.50 201839.02 nan" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "373fb5c5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-gpax_hae]", + "language": "python", + "name": "conda-env-.conda-gpax_hae-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gpax_schwefel_beta.ipynb b/gpax_schwefel_beta.ipynb new file mode 100644 index 0000000..b5daffc --- /dev/null +++ b/gpax_schwefel_beta.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "id": "81b1b4f2", + "metadata": {}, + "outputs": [], + "source": [ + "from time import time\n", + "import gpax\n", + "import numpyro\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7845c44f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def schwefel_1d(x):\n", + "\n", + " return 418.9829 - x * np.sin(np.sqrt(np.abs(x)))\n", + "\n", + "def schwefel_nd(args):\n", + " output = 0\n", + " \n", + " for dim in range(args):\n", + " output += schwefel_1d(args[dim])\n", + "\n", + "def add_gaussian_noise(signal, noise_level):\n", + "\n", + " return signal + np.random.normal(0, noise_level, 1)[0]\n", + "\n", + "def schwefel_1d_with_noise(x, noise_level = 0.01):\n", + " # Calculate the Schwefel function value\n", + "\n", + " schwefel_value = schwefel_1d(x)\n", + "\n", + " # Add Gaussian noise to the Schwefel function value\n", + "\n", + " noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level)\n", + "\n", + " return noisy_schwefel_value\n", + "\n", + "def schwefel_nd_with_noise(args, noise_level = 0.01):\n", + " # Calculate the Schwefel function value\n", + "\n", + " schwefel_value = schwefel_nd(args)\n", + "\n", + " # Add Gaussian noise to the Schwefel function value\n", + "\n", + " noisy_schwefel_value = add_gaussian_noise(schwefel_value, noise_level)\n", + "\n", + " return noisy_schwefel_value\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7ed300b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def create_data(seed, n_init, noise_level):\n", + "\n", + " np.random.seed(seed)\n", + " X_bounds = np.array([-500, 500])\n", + " X = np.random.uniform(X_bounds[0], X_bounds[1], size=( n_init,))\n", + " X = np.append(X, X_bounds)\n", + " X = np.sort(X)\n", + " y = schwefel_1d_with_noise(X, noise_level = noise_level)\n", + "\n", + " X_unmeasured = np.linspace(X_bounds[0], X_bounds[1], 200)\n", + " ground_truth = schwefel_1d_with_noise(X_unmeasured, noise_level = 0)\n", + " \n", + " return X, y, X_unmeasured, ground_truth\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "53f74f4b", + "metadata": {}, + "outputs": [], + "source": [ + "X, y, X_unmeasured, ground_truth = create_data(5, 15, 0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7fac3cae", + "metadata": {}, + "outputs": [], + "source": [ + "# set variable names for gpax:\n", + "\n", + "X_test = X_unmeasured\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "defb4547", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|█| 4000/4000 [00:02<00:00, 1438.98it/s, 3 steps o\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + "k_length[0] 60.16 18.79 64.62 43.15 82.82 28.27 1.01\n", + " k_scale 72369.17 15777.58 70201.93 53413.12 99248.16 53.36 1.03\n", + " noise 1.62 1.59 1.15 0.09 3.63 70.02 1.01\n", + "\n" + ] + } + ], + "source": [ + "import gpax\n", + "# Get random number generator keys (see JAX documentation for why it is neccessary)\n", + "rng_key, rng_key_predict = gpax.utils.get_keys()\n", + "\n", + "# Initialize model\n", + "gp_model = gpax.ExactGP(1, kernel='Matern')\n", + "\n", + "# Run HMC to obtain posterior samples\n", + "gp_model.fit(rng_key, X, y, num_chains=1)\n", + "\n", + "# Get GP prediction\n", + "posterior_mean, f_samples = gp_model.predict(rng_key_predict, X_test, n=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a95987d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot results\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(6, 3))\n", + "ax.set_xlabel(\"$x$\")\n", + "ax.set_ylabel(\"$y$\")\n", + "ax.scatter(X, y, marker='x', c='k', zorder=1, label=\"Noisy observations\", alpha=0.7)\n", + "for y1 in f_samples:\n", + " ax.plot(X_test, y1.mean(0), lw=.1, zorder=0, c='r', alpha=.1)\n", + "l, = ax.plot(X_test, f_samples[0].mean(0), lw=1, c='r', alpha=1, label=\"Sampled predictions\")\n", + "ax.plot(X_test, posterior_mean, lw=1.5, zorder=1, c='b', label='Posterior mean')\n", + "ax.plot(X_test, ground_truth, c='k', linestyle='--', label='True function', alpha=0.5)\n", + "ax.legend(loc='upper left')\n", + "l.set_alpha(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "672fcbb2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-gpax_hae]", + "language": "python", + "name": "conda-env-.conda-gpax_hae-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f78df9afad079ffcf1638c40bc6d99fff157340b Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Fri, 29 Mar 2024 03:37:25 +0000 Subject: [PATCH 39/43] GitHub Classroom Autograding Workflow --- .github/workflows/classroom.yml | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 .github/workflows/classroom.yml diff --git a/.github/workflows/classroom.yml b/.github/workflows/classroom.yml new file mode 100644 index 0000000..b58bdff --- /dev/null +++ b/.github/workflows/classroom.yml @@ -0,0 +1,31 @@ +name: Autograding Tests +'on': +- push +- workflow_dispatch +- repository_dispatch +permissions: + checks: write + actions: read + contents: read +jobs: + run-autograding-tests: + runs-on: ubuntu-latest + if: github.actor != 'github-classroom[bot]' + steps: + - name: Checkout code + uses: actions/checkout@v4 + - name: Hello world test + id: hello-world-test + uses: education/autograding-command-grader@v1 + with: + test-name: Hello world test + setup-command: sudo -H pip3 install pytest + command: pytest + timeout: 5 + max-score: 5 + - name: Autograding Reporter + uses: education/autograding-grading-reporter@v1 + env: + HELLO-WORLD-TEST_RESULTS: "${{steps.hello-world-test.outputs.result}}" + with: + runners: hello-world-test From 57b8ea9909ff630615fd48c8f9323007e9f290da Mon Sep 17 00:00:00 2001 From: utkarshp1161 Date: Sat, 30 Mar 2024 02:27:07 +0000 Subject: [PATCH 40/43] refactor and combined_botorch_baybe... --- BoTorch_heatmapTrue.png | Bin 101793 -> 99068 bytes BoTorch_heatmap_delta.png | Bin 110282 -> 109999 bytes combined_botorch_baybe_final.ipynb | 369 ++++++++++++++++++ results.zip | Bin 1039332 -> 0 bytes run_experiment.py | 4 +- run_experiment_baybe.py | 4 +- run_grid_botorch.py | 16 + run_grid_experiments.py | 2 + .../botorch_version_grid_serach_try.ipynb | 0 .../gpax_schwefel.ipynb | 0 .../gpax_schwefel_beta.ipynb | 0 11 files changed, 391 insertions(+), 4 deletions(-) create mode 100644 combined_botorch_baybe_final.ipynb delete mode 100644 results.zip create mode 100644 run_grid_botorch.py rename botorch_version_grid_serach_try.ipynb => trial_notebooks/botorch_version_grid_serach_try.ipynb (100%) rename gpax_schwefel.ipynb => trial_notebooks/gpax_schwefel.ipynb (100%) rename gpax_schwefel_beta.ipynb => trial_notebooks/gpax_schwefel_beta.ipynb (100%) diff --git a/BoTorch_heatmapTrue.png b/BoTorch_heatmapTrue.png index 6fde24cac6bcce08341b4edd64768a6687fd7440..b7a364eee668bd32a582cc2040931096e61a9e7a 100644 GIT binary patch literal 99068 zcmeFZXH=Bgx-MGgZe!bKNp@oZ5m2IJZ51Sk0+DPY=PVhug%&{+K_sI{PAVW7v=LNt z&Ovf4P@?4XOuN@wd*3te9cS%3&bdGCuF=Vc`s%Cs&H27hn)9B5oW!=x2RBnFlx>n1 z&nrVvSa^~kl)15-Ub1>S4lO2CtaXazH^SCQ6V_uiv zc0MvXE5-7wmck+TGc32x9Qx_E-+pUdTKOVz<#osn`!vaqeanLSRaeI=*|~%AbIa|= z^BFy^?q7==`1|F5GrYLtAAi8V-JO1-knj5U>;56i)_?prg|hc7W&gkabmBJUyIB%i@m16pN5->%riK(>r$TU=Y6fGAl$^VABr9 zbb%`C!AkaVy=M<+##-v8`6pV^^ey>+WSRM9x`RSdnEanB`ra$%zstCC;uXNDqs?nn zuMjI2#^btl)8$*Yr=ZJ%jU|&@!LQ8BVWQgh`8Qh`=3mV@*fb3tacUB6I!y65=`Hao zo3WR;apT6V(($&;RM{Ywe5(gNCx&KT>Qx-vuxV49zuL3kHY96gR>~%mb@O>M8jymN zuC6ZYsZ)xlPMvCfdG(H7Rd{mlc#gD6;-v;z{*D*tJh$($-?fHv=Euj}KTS{9$?vm& zER`<7%Kuj?J)=3_EFGhjyY-_C0*^B*wg8 zUR*YWuOT+VRlU6ejgBRQ&#XOeqF^y4D`d@6=a~l8gQCt^N=iyH!?q*!HLV%O4bLy! zzh-m*mvO(JcRzOx#r@BnKm8QBFf%&1;Ogj@QyDIj*xY;>x8IWIwzA~lL&q-{6cm(0 zd(*Z#EnaTR)~(8kmy$zEtrw;Z83e4}`(MJhZ%9(Bi?<5xV4_eW4~5_C`O#t0AcHd$G%&U3J4*UC}GAh5?gA#FK}e)Z0J%6+4*g8Xx%C1GN2+M`XW zO&Eq3sRavC9#?M^>hJeBDj%NIneT*8RlnP;KA3LarQP<%)cSZ;d$zUd+7=9D1OBU{ z36S73ALbF?G6!q&%KRWxMYxE@u48g1#$$HhyPe|X{U`VJlC2_N-|ylZ4imJkl^0un zv3bw&-RBZa|{FsG3YNM*r>f za9x1EzjAxU8CKo>9*nNvuI9e$T3KE+Zh5V{-VNW%ooy=s$(c8|)@?Fcm>%XEo*Jym z6q4n^hqO=RjHYm#Mx3_%4PVgcRK~_=H9ftU@vPqMxlLW$_}e2d+~3Q3=uqT-buyI> zaT4@$wD*H=ps%klEA9QgU4nLFmrrHo;tOzgpzcnr$7+y8P{HZ-RDy zPFep!k7tFeD;ldyQ@(QH!tr&n@`=v#lbg3}p<=k|-MR)z^3!{Sp((NtrWc%Y0y zVmQvs&Mq6mZ+=Ne#=EDt*LI-%KmaRM{OsAY396~#!F;9($5j%t@@Wze=xbtRLu`hs ze<4$258EZ>{?dSk3}aPXt%Wx8uAssNkkYtv9kP*yyD{`Z5tMtv3nU%!5B&9aP|rM1PPvaqtST*T~D z@nsa*{qxUC1~gH-v8QA#>g$!sl~0_IF>Os}RVXx+$5MIz{CWEI58J~;UDWU0yEpme z%frIL!rOQ5sHEx1HY97PVlLVl=aX-N%VYVm7Jv9K`sdv($5m58@pn>*4_%^VZ;9EK zwVJ}QAAFgbK0Q8`w%Ax*t&m~Xp^i6LN{a-Rm6z9JaqeMY2wQ$jjg|@^!<>ON(Ajx+ zt}Djhe>bfoS2fpuqA{Kt>;LpAi@CYEN}R&kUw-+;s3Rw%Z8hA_X1rZsd3mm@HP=4% z=bwM}_xC@{#Kc5qI({iZCGp8Y9)n1HYG#vGyVs~t)dBo($G%4rJh+9i^cOD<%SMQ5 znHMhGoqCfrv?!vwN7XG`zlxF8k|tqkX(?zs;$P~|8iflq#3`O*5VA{GGwYWM2n|i@ zF7hO+f^R}(@*}h3*W!I0N>#+S0m7SgQ)~;Q<`{2O@)TKnMab8ZxFYB-^4^JK9|?LQ!`mT z!w(JQ7y7%lDI!=qS@+gZSiU}wirOX_ql)LL$&rc&%-3(){PJD&YfOjh?Uo-H!~>42 zn3U!%sl+QCxn3WqSkjoRp=D;TJy0HWojTb3>blnP(abJaZjbE@M(a0j9B`1a9c|=g zbpCRdnUAkA+R>;XA;8$FuC~_OzRRV_pyv67lo<&Li9t12adD5Y!?6(u*KOQ-vh)di z$l$AsT$pk<+VP9qmwhIUa&0*=t^p8oNw-u_D| zn!|5Kzm*4Zm(<5oeXtY;=I!UErnnd8Cd|u2_^YBF$6RJP3i<3OI!(P9MOwd1O_gPA zGi`gr`{ab0pT0<P%5tS((`ZZEfum_ciOh@r1H$=mlIBX_?}MuFKwBx}|*% zE5W?RdNLt=6{nqMO>T{7yt;PxlvUrwqOLBnuBwE?%!d!Z-;X|g3P0`b`}CNjA=_=$ z_4;JrC-FNQw@oIenzXzwd3$S}&knJr>WflRRyirt(>A?#c8H#IoEta&wy-durkd|K zWBT@dfs1q zeZ>y4va<5&wvGmdx-=xHl$klvW@4=e$}Q)cw~Pv$?$!XBlII zg>k*Bz-4=2N31+k$ZIVXQnJRpNc%x^jvpunV53KyRyYs_pRVud@rW z0c@&zg>EQsz2Q#dr;aPfUCA5|ZR$)nteX^eTb!kFqe2AgGf}gxbbEvJ!!3t%Yr~)C z%uX&6u;b3Q18wFd#eNfL`FuyX-@xS48bGUhPf0TqTpIn3}&1#Wm zDM7ipy0R>ezrzF>CB@I2aX$t4!_3WH7r$VYTl3m}LOaS~YGA%GMD=9P;zS|;NK@*> z^eav0{DqN3jtr4setEZ_>&72cTOS6Y#*6au_9N|kV*P%ixK35@Tj#d~H6`Lfl<2rE z_bY2TY$)w?7z}e>_hVq;cC z2%dHshlJp*mZm45)iqof>$8(tJZniU;D$H7KyAFBe7LZoHywZF@XBIOqGq<0T*Dk5 za85nbh7B7Ipg6su+G>p_rWSCacl24fU|n;$uFUHfoz&bxkq{Th(yO9*<;GG~_eRot`8 zPJ)sZhuXL9ZE=lGxS(xmR__BLE0=F9sie5PUah=P{D|_UCQ5>~QaL?2$tC*j^PRPs z-JjpxDamNk9kvG zf1#C`s2>WG{z=(+6&Tw9JYeyKrrEsBdk930!sThG+7jc>?S&s(VC4vJYeAH zuPQCAbz5E3(Q;d!;d9g1KYaA4VAwHHQSFW#+dAOI(9*e2$07^>&E=XlX#fo#6m!iR z&!5u{6R<8BUUFOd>X~CZ8iNMCJByuLue@4huD!QjzsHBavwD}j@T2@NU{y4OQD8yq zEN^e`tprtW*?YnwA>VJhjI%J#X>PnV-SBapgM64^G}hNp!Qwde(xoTo957^2Sac&O z95y+ks>?G?IwRH3?hI6hjoPfb)PN<>w^p6p3KT5MEbeU)FD4};QxCjJAil1io?w7I zYCWF5Kad7PL-Y3h8bBP+6W_*R*E-o%J75rU8l|mCI6y||)RZ-CfDnbU8R&X`6Zn+H$!qT-E zizK0mJnCRo1gC<)KG`6y3y+V=Dj69i6gg~X6pl0N$Qd50iIQv5FE&OEpp1Vw_?AL> zyifb)S&yqvYdv-_jek*BLDd^+NDS~;Ts9dP?hhkaKI`}Ojt zrxqs5IB5krmiLHh%YhzHzn=o957K7Du?UD;t zSDdJk0&Hy3q!v5Qj>S**mkHGmpxhgMeE5sq9W$`S#)rS0Q_i(BonKsJL zxMH;HGbyJ5K|#aS2g7%Wev9mI*DCVZ){tqg!)w-l8D&i=h)Wj`?#1uFuP4Y)$Zo9K zV;fzl>*pJzjmbG935oupp(az>3JMSL9HOzt+3@6V-MU4{L1WaxNtZTUq)FZ=~ z%#En@=8v^sUAsUY3LXRX^5v%hEQ#d$?b+1ArOEpQEw#GgpPRRDx4S7)%H;a(?5?9S zFNdr0^785vRXNF9(O{_n#aK_zi|_9vQ^jwER%TW(uO~8HeNK+S*bF@3xgD)FUh?tL z1g+dR;4aEMxy7iKSy?44<&fdY)A<)&8lhOd6D+9`!jSOb=AfWm=J4ryx&Zl;eSGmCA(<9qpws zqlv2tfBf--Ev;3>=OC}r-OYRaQOiJ(@d)L_R+rM^Q?M{bgN;*2A5)1E-!{0T-DhQG z(R^m2O9!ByKYG*p^#+(;Be<^}@{qTrRHP&5DR+d*i!$|jW=Cxw468#95C&mS!l6065X+pGC)YNf> z5No*%yZPPbgst!8)DAW7L;(&le5~U-ubWF7Zb^%M1!Cmfwrkg}>v^-S#^RW;K8AHM z0u8fevG0wWUp{+DTzV>nT{ee}vG=m9xLc*M+t~g<~U)y?+=i|6TgqWLaxGkfobMu!k zUrzS_xNRHnUJlJ6g^8y`LrNITCUIk(s3=(5zF8YSg z^A0gDX!$ZZS(;hkE4I>XR=BXTI5FrbvcIITOJ!=Hf*UN`WRg=oou$jMx2MPZ;lrOo z@<1CLT$|8!O5;>hwP#mm8~57Y*gPa)J+Pl}71Sw#>%fxpz2Zfo zta&bsSBWl`$0UO?*xnQw!?f-NPvl1{zHYyHDAcMkplj}e_GYdPHj1u;dz7`QXLRbb zt@T4(CP&;jtZX+A^$g8%SlMkJdOprcz9%S3D}5J0kB?TaU4IZiIMRL*$Mg=-j{)%= zVF7Kss|i-`kX!7H3 zh8K+IjC!LN3>l?GSwCAyNlSC1K=^E<qn`Yk4SKYn6 z*Qq6QU2YQ=bXL7alyA^5GM%`~Yv|@sjb44yiIF+LeDvtiuDn^ZT1i6Z{Po{fhC?)I z8$Wmz?<=h1r|0J4n#@ysqS_{8Gj!$shYy1q5?FT~GlPvuYQ4%D_lpAdQ$*2OI_Prk zJ8T0hf^%l+Pg}e-J9MS?h2;D|##~EoGe6iJXV;RUp&?(>ZLnIqpHk7nwkJPBEfWp# zlp#39ToecvnqpbZGLZ@lmh+O5PUP}yo;!EW2a3~R@C0Vmn@_tA92iz`vM%MkX}sTq zP`Z!>s4U~t!Brkt`{;h~dGO$kn*A$?L2}6wH=)}^p{&Gkfek*Hm~n11&1|99pBwL( zTGG(8S^8!fb@t9iyHx#8uP7F%x#uDToPgYKU^Ub!4+7qVnipX4b^0FzT|5n4K-$!z zPrcMZ+l!f^S>NTbq5mS~Ci*_#V!Bbo@vdG=UWz9Da_5E0jqdkz3ez*km+o^G3@MAu zuD#dgJmn*8YUh#Pr4Izl3!T8Ewy3Cx!Byd~*t$*Ic>-wt{kqO*{@VwEUSGGlnK!@u zrOMeMS+dN%x3*ek;5cPxRh-4xT zZCP_l7{p8a4u{prs(0<(;Q>MyUHO<9Z( zcBmcr#ocQU+d*hN+ncf>bQR${j%N&Aq+~^+Xq!xhJ?y%;^v1L;c(NTrrY*0F2W99#Ob(R)KvoFE7A4aO)%ONpzitB22zb-c)h7#^A; zCMeh%vjX|4cdR8%F0h})$<&_jhlh_IUH#p|Be-<`X^Sh}Jk36vdvAH?X+LOpH?EI+ zBr7TrbEe``XZirqf5RQ1-#{=78=I|4~zY4IFbIl=M%% zrQ4c5e!LiBrJio^XmmwDq6B%HA5?=KY>=Km-Y8=H3P@u zXmZhU96l1#>kLgeG{qWvjh)#23p**5<>l!?e=aoXxK(h=S5DM_V021SXuDipuxgdE z-u-?r#1Z;!l!@k7MFY*KJi0GpVg}UTu&w#rGlGK8N73|O?mxFf*e`O)g`R$-AKp_i``xVf7Y<)b`OF2B-T?V~W-P7hxTt7n zw=>(NBn3$4th7cAa}{VA)S1O^--c3h$6rgXVvU3z|G+TTAYj>ZKDI0#sxr}Or0)Cr zHUXuDag06?8n+Ov@;f4Jvcd->s{piIr@QC({6b@G0%=pUgs82+R_!e>l)67YV&)r6 zMrR_tvre}qcSUR71dYEozTjO~mkwazYf1arscKB%ko2V#R{NQCB97AvX?hhhh6L5( z`&_kO1xjlfbDAAfiygIn4^A!9227r_IG(#DIt4}p%aJ3+{&s_5_BooJk!SD7v^{$C zD0_Tys!A;6O&Qcx4fFhlVL{-@2^&WHuhY}Y5YuD~!6U2i{EP{GGBa-q=#!eSWZPO1 z%v%TPQq~X>SuNOPZ=<7EVwbgw;%p${ZH9~Jm&(~z{hG%UQCorO`J($9Jof~k$>t_A z#K*_0qZhK8g6;!bTgB%Yr(~DJEG;d(4JATxVoFYF^a3q=dA6;8c(^?|1IrNK+tt_K z<1b2j0a1+;#}6GkLomq{S&oEV9-a8w`+*mN6R&A&{XmSYnd|DZeMmZK7twFJZua_Y z57240YCHfh3Mx)(^y_v@nU$55k^1<@b+&D}&w!Tp$Ck1FLC7WGB}ad*=GBhs3@&?D zJ~Y}$KZ}L-Vgf#lsPi374;X}G=65iOhU(7?q(%b|kK8*Jt^o14?LoJc)e+~xJu&*>4&qob zG3f`m^|EM-uTB%)LB-7hx*?jyNB>3`B7tR7)6?nmW@N%>aa5@YEH0Lp%lI&gs1rzi ztE>~8nL{({iWJ*NfyB$Np36FgiRg%(>=*C zj_SEt0?3iU)lUcv6$`F9Ni9u=`3}4$!h&5EhGPMNl*4uV=qpBgd!t~`33QkZce$Sd zer9FqE;VSmeNj%X5h8%BA$Yu{!~5I$HBTxTsbx>twGvi=*G`&xdUzxNRLBKVe@bZ# zoI0u_xs5V6R8z#x%m!B$9FH@0vN^XB>eqx79sZd*G&D3u1NS$AkeL7oS)_Y9ZQ_E! zx=hTL%{zA3$A?0O)HNTpl_drR?5UXa+o8Gb3%86@^Ba%4z*bFA&q%m-{d!s*;hV&$ z$!xc}PXq*1Az4vTQI1TB*EFUVw>y+87K?ERr-q1JH#BOZ~sQ!pQ0=KBMQlge}L z{SO&%3(lBk`Dy;x<#0D1(>e#X?KD(ly;$NCf#y|1`QM`HYB}qaay((BR{Qfp_F#A& zAy8I9L>)GbfDYOJS~COAFxY0-w9C_=pm=-^3#11qzjoOsPJ<>r@9#~{%-Cj^0~Ch5 z!DA<2KikYnb696g_wVhnDr0vXP0qA|)UMmbf__nt zs+xjF+%K@eJScky-?7bG!BQN3Tb8-lYa!-5r0L2-yrN=h2z(f4IN zaiTmr2aH9gt=KZEtMj6@L|>koal=mIyPrC3FFiEQ2Dh^twKy850W&#vw8DWsOUWbO z3fho$M`m1QmIhYJIfyKHIToMcq<=OX)?g)_^Zzs?Cf} zW2aocyxSDBGsQgbs|UtGIdbQW($~M99+&>=kx!-(z4t@aT#L1S{q@&$lNK(>CRNjC zBvCuyL?i;f2gZ~Ijzy$Fg;NbN3QQX;Z|JJaw(RFm$@knjxgc__;d_O>d!IEDwlEN1 zBT;Rst(bZ|4wHStQ$&wVro)) z!XO|O+m5KN)8C+2t;lZ?mYb^U)a0Ai^javz^LbW`*J8Vt6D?z5X{yRvi*p_(s(OKQ zHVEr8Fa_4bhtESgZvt44gQt7C{;1Q;zDrDx{QRz^t(aeJc>FkD@%4tFf!2mRHq{hm ztkVw1UeVOZcNYM95@4Ra)9mtv^Z@s3`dhDWBBWL{p+=~DJo=UNb$`sWbp%XeM1hQE z@bi#bGxOephh6kF==5V7ud=6MrOSI&$qZC0C)gTUR`u64$7l=?&H8baUm0 zZl=4Xj z=!c#eFc|!KczAeWD9X1U3s<;_j8hZuljR_6SD7%HpHeo$`vb zv7E~=;WSs&EGzDo60%3dI|xyYkU|t}*&l!WaRCJw%YX-!r^f8%7ie+#9EFrRiGjRN zoO`sHcGAcv=CXk%qD51uPTOYNzf1#;Co zJkwM9a4L;;&zO4Ue2c%=%9NUN%Cz;q{rlqpszzY9HzF!w3t%JyC`jyCnmt5|?;2&< z#}=`RwZxsY3YqNb?!Ia*2jfotFm;RP-McL>fE7OUPr?bG#_U3t9n zk=Zv(b0DB|BD<7fSr?L@X)WXuWfmH{8NtT9KL}s_b3M>{*)uDtIsrDBP_hQINVxee zhf`F@WUy!|o_!1r;wb)I5uF2|n3R=unhKYJrg^+f1xp%|vyY4+LNHXYA?bdU$xla< z;~vDLC&tjwB8(%DDOD)^jK#_2~5><_Dr_p?mgL zGLuq865&?@^J`>h#l5shM-EAc?%f%kQ@A)T?aW$`op)Vz^Z41R2Y;Hby3>r#7229;+*Km=m+h zkUQipY&`2QuGY=L{N^dJk6P(7ukOzo40oC)u5nNy1YbVtDkt0$R8=$c#DIX3?+9Uo z1R0!O;726A&X1X4B1PBu&WbPY{-B@6lAXwO zIZe+8Oy{Z;5 zm=nQos8p)f=B=%Z4@%`x(Ph=O%Z@ice)ur*dyC51@+X9PJ=xDojN4fdoH7(*W&K2> zl}cma?*H*eH9C~J)O{~6t9eIOGIsPdP7z^Y9-AQrYAdGK47l!Y^|lnU%&0eRyyz%w zLwsdsAjEtim>=53z%%csPxqIJ`FEQ||3!V?kql`waRz8|r-G(Riu(U7D&hrTr?w)H zG+oYJby{T(AlxjC<9btyRs(2%Y^kLu(h6}%waOJ8ChVXDW6=Ily>E|wmB?K43)x^ENsp_iHA|59;I$vuq^e^a%%C#e=9}=c zBN9*2P66Y_6S0k-Dn5R!CYP?IMQ2MxTQO`aDq?Q3<%cDAH6(Hj_UIN}T0L5=A?kKa z`uq3iJM7v_<+OZLvcNl((fFvX+*QK8^*_LZv&-I1Up24wt0V~tBzR+T?~#lYjty<{ z%jdnusR}G!H;X6K-WikU-#C#6{@J{~Ai{OQF#5%d(^{7#X;`;1Zu8C8eOp2Yk(g8j zvt~)3)%p?b{y92mZrU2abQedUcIyhj8|9F6PbSx_kBc6DDe2!yhXn&NEbgB$oGDG( zP8CP<5oH3Ya}9T3y7_rcNIC&OsM#6kY*%3(JNDc=W>U-KIP{C>WNru|ksWjanVpUV z2=yx*kQbTpNABsG)D)awXhj{h;u53}@U0m{5l|v#YTemD(Bj-tHElzqrWAHSx;i#4 zuGd*1Dkn%5h`VD9{ez)kC&}cHFhnUlu;?yG$He$l-syise{z->#d{N+N=t!yA66=>w;Gk(9(h3ZdAh5WgoX9nWz#?87odDA0sx~SqM1K`Z1qVkqHCM(EiI6&^IYK!SvlbvU=(?#v!3o(Ef!HUH zN%P6T%8cm=-dQZWw&iWkM-1DuUV*3YVPrI$>Rh)&I1*2+!K{E$#Iads)eXr`qLV{! zwu)!Rmv&rTo|`FLU9O5=I-+P;FpE0=`5=Kz#U&*pkSmG0M&3*^u; z5pTYst4pS{BgQn~$rC+(9ke=aml+kLjQ`Y&z{+d(KEWZiLuC4)dZwAF6rt#VkwJs% z-hfTtAsY&RYu+5lb!)!ErKbn^%}nM>+93$Uh`KBoz*(DrvXhQ35wPbRM4Ng9kd^mW zod4y=ty}AX0rVfQAht2&L1$Kt3Sd>ShtgxV6 zMp`ox5xRi&2_A0L{POBT-(z``&JYo&Hx)nRmQ_TE=^&!2gx)ia0(F}HEiM;El5*S~ zrmklL`MaE{#Jr0}$d&{YNOqEz;^~l$*_*7Ht%Qrd3F0~(3)O1~h8=)9IyHiC$6%Z{ zKReWcRLpdLppMbEFWp2U>2zJBIj_s+Eco`k;$s08CbC09RcGGg?un63>&(~iLzFVY ze34@i78rNKELcf>tbBy9gbJgOoeauWEkbFp>tNZ_r|=`2k(xK@1w@^Qu^$2rv@mcP z1bqebXd1VD+?C;P?b1Xfn!b;ZI5s(IDykPwfko?0MczvEiXuwnT;S?r*QyDl&1uV? z-$_D(3^{P9ecTF|r7@5@xo;942yveeh*OLbSI@SJ?&x}j?8-}R;u6mz0c89bUdIXv z^TG@qRw%r&-vcN=b`#^xGf>hRfB$ipR0m5>tuqa&?6(e`^Al?+Ecf`#J56?Fm7@!) z;Ttx>Ua{`@fLO4O^Vhq`9*-#G6P8*LY>p-FI1r>yqG5*b|JZ4SzJ`FB4dyf?A8AaQ zG@;E2;D8GldxnI}mk2qIAitxG+c9J)0`wY*2{Eh1TnS@vCY=j9@H5OVgYG}?l4Oaj z1Co=+-wf|S6gCN3J(>Uf(W!X4pfU5h!Xw$eXHRrR2){8tn~(X7 zqE$O&(*>+V)5hPIAY^3R`1JEO`qK)aZmOV1Bs+^%&aRoI03epeTK3_C1cocSG>}v0 zd@?7=Q`f_ukAdwS5@YTA;K3!NK}pPe+X1fg7{4bG5fPBdG8|`&mQj4|*7+eJ<%_Jz zfIgutYCM-GqsAPXxv3lpG3kV zs#>-I@e?Geb=_Aa27M(?K0=hxH5lBxf#!+80|k2lLo$PW3i2V9&#wEeFa$7S1kh+| z{0k6TBKHZ#tDLHxU!S1DhFrHK0C5Ahgrxb}VgMKLR%@4+Ed0-&JtKyuSyuN(>ce@! zitodlYi~Y7Yf~k;73i&q%_TvHH$d2?WpUHSjU&%I8Lb@e-8mNFtVGO6!}VXGW0G!9 zD%wa#ZYBV93XnNU*KsZ`C43&Yk5>Uy@eCwm3A_i!;^yXcTX80<9kL>sKycQhNbDQ& zD557r;M%iqUo65EL<4w5cRsiOi3S|@LE%x-W_Zyu>-2R$`5jSzed8YTp^<>ZZQd00 zttQw!ef6jMV|iQ$Nw%JJHx0&0)=H9GY0%lYaibCD85z>yH3^8~5)MTAa0Bdn+lfvs z;B3`muRSQIF$nrFh`DNFj?|%K(I1p`If9Dy3{qdTcD`fFlB*lC;6&WO8m;$dWMoX) zuw`$YepPtPfi-@9emqEnh+xkE%0UPTvmm~bfUk$VmEv!|-G;s$gGpe7;gOSVF~1PK zO!Q^<$6G09?(Jh>FuiECmahsRFlw;M&d>f-Ue3elIP`3-4jmLpHorIW$TG*6N%WiCIeZ<= zizB1x06bS1GZxYlqhqxh-HEwNZXZ=iHK(ARlDL>706%z^-;{GN5R2HKn_LSEB%1&WWPI@03Q1|G^=aWDh2?S-@r*nM__gwK=3j@%~p zS7d)WDhvRQq{}jDQi57qOr7n%C&(R*l7)xf7*P4eGzCq6jKXIJvu*YtjQRRX*^Fl4L<>|*2A6u4gIyoi6KbGtN^a(YXELGw>cw*Pq z*v#$&2Mja2T!4$G+pViwUhrcl1c^~10K0i9Bn!DOq7mIHWr>$s%-Doy_yO$3 zU8%gOiL@0W0l*lIM7CBFq_+0e^$*ji^!tW9euGLnLmD5@U=C{9RK9>JB7z+VHnhIh zyfL1IQHd;-4hMo%W8@X)$5-F3+hKs`>^Kr1yLo0HHVyY1k6`l%kO~P) z)8|Nbz1Ec`(7ZKG-*+6Q#;*p^qYxFYac`K@vhUDM3%_b;ix>y7c!IIZ33x4ZumBzy z66q1H-?}#~fBnG3XIvG)2U(;6kB&rZaYimpqV?;F%3Rt}WHMHue!oxV{v9$(OWTr-fNq}J~U?q%W8VrVf9%O;%uw13k z*=$+N(O0ISDzO|tE=5oZb;ilj(Fp3MpyPBLP@X9XcmpA*Uc7kmmGBR`GZT5CCB96l zP<67C^PZ2)X{v-SN2jKy#>hvcU~&w@Wg>A(2FGtTn3-UN4ZwN$>nMRZ*Gs%Db5?pT zA$og3TpZX|9Xok2IqhPZcWmBV3rjj0>)!}X#t1azT;ZeT<>dx!o2UpCppyMNWKCIo zK9fl=A*tM&N;c*6RTZ7&&{*0`asTbweh-qZ#WH0@`V)|0^XAP;Sn{|U*^Q;6xJF6h zMRc?w`mvx@-ydWX7R*`FF(}s(l(3MMAfnY zBa)~#cs4vw68uEs?(W^Y@kpC*-yqLmi@X#Ko0+gPRT;g`@A2b)*r1V-hsf_>Cm&(5 z&!(fHC|y!k_D32b8v5s_qM~zXNYtA*voNodfTBtvaG1^zO_3;%zP_q((tr%m)&&sz zjK}+oz_O_CI+w5ub%<0^BB#^zZ#(qE%;1&mv7|43A0>n?bPagYa8e?~21S4uC>#8+%g$!IY(>yhz>`0oi!HieMu!!)T_buUhT371>IGCW>2h zj^eUtVVLA9xHW7cLv$z>zfUYNl8Ob8-JGn$dXHrFG5biI7iJr-T|WL8(}sZLcvK3r z`G6eY@G^AGx{U_Iwb3nYBs-^#<#x@4xHaUpdmG_}Z z63OV$_vo6*w!eS8oBgj(LHi0?o&eV)WwE&V;6E;|p0wsao8 z>_{>0oPX3~wwBWOsrNTMOa3(!s{qlHdDyp1aY$esS811}xNEZK4zBc={dTYBIqh6= z$M3&Q%{8jt&;5}?5!(Ns2K0Z+u^u<~*#l$#^?G^2x5o_7CH?C9|KgD$-#1DBeYML^ zJS8kMu`#Fg-yZ4e2`FGy@pJ&ORx#ZUW zdb#U5+}v%yg+htm%8c~FKVJWv4tkj&Q|a&5lYYf^R1tqZ?|(jC{N&#_wdQwwOa6ac zj8ZoFg=Ks%g(7v}zc{4-Z=T1`f6WEke`79?qWX{5%OC#pxO6MFvtwYm{*8g*{pYh| zIdK97g5}zO?ae$V*5b|T|HhmD(?LT2mq)@vB2ZFv|HUWr?@sdnxI%s|x^n?Ta^e2Y zxBnPrG_n5^XBqu}`zZhKpGG2=7dfj!$@71GL|&p?{{6aJToje1@jutQ|Jcv>k!|U) z-4r20fOoUuFaffU9^Q;17!?UiH|CJTHDDJ-M@N$lZUmJ<@#txEr1(3p|K}7wbC1WY z{j>|Hof#`Rv_cS@!wMFU96mghoY`56Y)_q2T3HN znTy!TlL|;t5ipLU2zsnUC`orn?B}l+%L&H4u4>+?spbWXCv*xlN#v6%FA|{tMU|Y- zFirRnOdUsTsP@NRGG!-(|01zF)fhr%_C~iO*lHw6Hq8>Y1hy=svWa5+>%R`%lFG+c zxxx7r>{}z-+Hia2{^Q-<&)|-*{rc-~Ak5^bgW+XpzWFN)qlBLonPR_oEwZvR#rt%g z*5>EsDaXn&kZ1rX5Pv?|>`k;uvj3xKhVt_0_Z=F4eVHS*%)Y|Uad$#t6A&nCz|KoG4PdB8J zxau%T8=o}IG^`VcFI{fC3W*V$_V}FIK;1CRDu|hy&d$uvHda47hvDSZvTaalg_1h! zg!D-jKZPFKPH}gg{(RO0G9t6h*JF4fd5}X=z$!^HWp<$$Xndh-btM+U$k8fC%H@63 zGygy_?n>A{Egj)98^p?9I2>gCpd=qT6xv2NPNElJQ>{}>ItX^K_=dKrT4~NtA0KlZmm>C4h*cR2dnW**9?30NDfd zk$|3k`UV?j)ICSQiKw&w75gC1b2crfy?#xyTGUp^+w8v1mG%*$E;4I|5jq%#@L?P0 zLrL<)Y>o6!P)vRv%$JhlwP&pg$6NV*wOsx;$Md$+dsFK>uS-dnv5X&G%hC@a;)R}U zLgKaJGaEK84F7p|>ydD2MVsj#FVIA##5P3Mf1UoKkY*I1B9`uVeB+aV%)r=6or`t% z-XEs<-fy}^jn&;>+#v-F!qx@7_F5|=jw%2(nk*-$)YRF5qtK_Mg4~ePQ)cb(la$32 z?B9d(UfzlSNPz<}tRQ0FZs`en|2SU+qHTxET*vmzUGBC-Fu69_UQfsCtl*UCZ_6{A zzEN}Ww}QohEz@;zbU7mn&vquLN8^&w(Ygln2@DI3-ZOHW~3a{l{*Avwc2JHwPSj6|2~OABXO z_9f2DJt*SHU_HTeu_B~NxojY#KF?trud&j(TekvdsN3HhIP*2gp@ZUM(ALhb%OGNX z$whm<`l!frIa?RU!Hb#ozer}fe)HiI|Ik<1tDB(mLoGvxkby#3$?=Jaz){yhB~F8) zTg(e~bvo+#{5g@b8x+p0(mHh31)nZF8ZGi+*Dl_LG+)zNjk~H%iOebX(hPpn)xKTE zi>C$`;zCPrIvLN(MyVt_zhqr*iqmp*D6U-_u#GbQ@afYyvg^(NN!N6hpK^h-L!Eyq zs?PSNAnUHY+ZEDJPMk8G8*Sq3swoVdaLR@$MK*#SI`sD4leI5#gwo5r^mHBsVcAfr z@PeHD2!6!cb?f%P9{>#pTiaS4@KMIasHxUk@8e5J5)q$@YN z;M=%;{$%4dUEO}M&=<00>Ia>sRE%npV$RJ_SVZVYVt+k2G(XIrX!Z2Ya=km>NLOY+ z!IGnowu9-TbIZ!3moj+mWOig2XqP=hjb|JVnxCVN#~&-}b`cSNk((ni6y6!8QgUo{ zwIFbcEvi6vbo4iI5YD&PxL$k3`C6S5$+R82wrgpFJKHA&u8cQ-^nlESy`rD^b)W!c zK!qBkK}(2-Cp$lYEt#P>Qs#B0h=@o-fy?-v->|nvuXP-8zXDs$-O%0P*Nwt1_FKv4 zFTh?`+o8|lp1EuM>DRko6(EjGPw{;O*7z=5xG;=D1oL|N=zYWtp;Jc_(*cg+K^)*f z7QyfA8#kLZOei`ObiHzM$zLorJx(q8;2#c^pIJhF)TGB@KH#gO&qzSug#GZXA2>PX zI~=L@Dl9seeC1bY@`nfp0v7jBITy7oV&7E{EyncRN{!|IW>|*@Wn_(hO+ODpm z35p`;S5^jGjmp_YcGJ^GvdX&#m6sbwojpD}dOdbG<XT4QHRy- zMA?I$^CVvcL3U^)r<#^IckGo`s*9&I{uA|n2J#`+Vgq7~Qb&S{)s5hLO z?qhBWuHAd0C3KYR@~J70HRIZ#qQv>axbZ{TbjQ**!H}DxLfD#0?GWN`Jok8Gyj*hi z*z_;!+6-w6`RcEy~e#NYTW1L{ts>O-ybTyEY6wZU(CFOpXA3n^X@iJJQYx!yXJeS%TnB@bQl`sNm^97F z?xvec>P^Epw~E`I`wzc0+2A+bp0OURp8h)M`Zja+n`<%?dS?rY`d9*<+tG-~9L7`641S789he6$MA1|uJDL#=?C?j$F$w$Nu_ z!6Zz8-vG0yny)%C3YjwBo zSjr8KHm4LAIyHqywdRV=#ipC@Z&bYc>}*S$q2YmrDiEi9PDqOn_r`OVEg%q z>RUs*<|m$>JAeLx>+$2_POG0>-nY#(*(o*a)|JGf6D^5M7>`E0Vbs#nI}qxTZuGp{G+5U> z;-YYD?m%T(hL6WD7a|0chp4eLElh65Kl*x(yR*IP=8m=f<~`-2JUTK!H@6zMti9SZRr%A+R3o|8yzLO3 z@7q;p2ko}u`Q7VjE$9VCWQRTWjbFRAqQ&Wg#4$NC4IP9#<*3AhXre5oV`_?Nf%_q~N@l zttjQ~|H8YqX!^o1jmGZg<_2>pQ6`9sgm#cr_xJOA4qKR-9T@lsAvTi1S%C8~1D}3S zC~T8LGjYfIM&87v=ZUXMDW#4N7+!O|oL*!0WzK4_N~b!Fb2*MvEYv^jQtTxp%6_PM z#yo2-e6cnmBRAcwNc#elpdt6#LdS;o*f(pgFkns|wDNIakNSSGiSP=Uwy6R0mvl?{ z?ekOnrrXOWW{Qu^>BeHY$Efq3vl+4&UsmoL(NdrLfJ=F&4aX+jt&?99eaq96Cp}|+ zuEu%Oy0psuHI40={d;Z{Xj-VbiHOGO&)Qv+N+@yulDs`a&LV64ef^g06I(u)_WTlW zUUU>2Y&PoA4phHy-!ZV~hTZvOuRXTZT`xBc zZnpRLtNRzQBUaWBB*!{|9A;pMmPiQ8G@KM9q@@v#sl|r)AmeaEmyMAB0a*zq2^;-+ zaz2Se7(?IgpMH9%A4HMbwDv8_E5xxlmcJAg*3skSqZ^xCM&Troy;jK27`3Ly!}ELw z`+QKn5+NQNDEFjIVm>tC>bv1YAGgO8$|0w`xE`WhNVfJ|NKg_u?Ia{3!rY~p9Vp*_ zLbB{v`_sxzIzl@}-+xL=aBhjq3vQU&PGJ%lS*oK^lA@^}HYO+?`Tm}BV#|K^Vk)^$ z=EauEE-ld%5GQlFe5|K3!KztzS=lFV-P=>AzHSfL_BJP{J~2AsU0}?RblJ!H=d|1o zb&0dL%Xc~2DHprHZfQ$he$yELnbX}b+0-X^bFRt38dTTh=FtIBsqt-GP8S`sRK?~U zmz_c^D^^_8_vJ@WPFe^I_ShdOCEH`bCQ737Al}yjsna?)_FskTA2S>PKNU$%me?{J zRM0?(69T+O*vQZlb&K{DM22h;fS=n(w-jJZ5*ew8(vnl*$l(`Ti#~F!B0p4p zd&3s5p&!;PdJODW$sCzuIimT7FGl3-SzXz-5AugkYwCrt2(pN4uJrzI( zII3!H#@3a6hD*1+?AD7HS#y~j?tZsWOkN;$bE!6(*9m0TfjDc3@tUy~BKk=}&YU?gGv3hd z|K`fo&boT(H_OTgjxEH8E`~pgSo_e^ovUD^tv*j~-uJ@E<+P}?1Fo{8Y*}N`o77wv zjD25Q$*Asln^U^*Ex|a8=vf!@$JFbicx`=-8DW`G~q;u}L>6r#Z0?_1Wj{jL`;UjSQ*^ znw_U`)SG5XFF>P9m7Yx)}_*hCYeIU))IUbb6a?yZi9R_NYhm zs|V<|SI4f2mh@fN);DM!?<;66sz$f{LT<)(`%OViLk+4ct6c+OuRf$(Y)acpiLQXi zVOz9~Kg*w=Cw(Z!i<~RvAL=~3CIB_c`19LaasfLJ9QeMO58Hui3#sgG@Qq>SKQt`- z{P_w_AYy?KgRRTeFaHN~Zyi?5M00wQ(9CZ#(JKv23{K)MAs9TrFkh@_+< z-MML0Qc_x4LAu%GhRuH04W8fgKI417@znRn#~9~~b71FvU)Q?UnrqIv^lE^DUVTPl z30k(mU`$lWRMT^`9R2)Rv0>Gr(&lgQkJ<2y7C3ZH<8GmxFTFLu zO(E%G)WisB@zKB7F{wIrg_ToIF;~BJ)!fsj>h4_TZtX5Ki)4skO$lgu6e}YmuVfL^ zJO8Fv)^kbT%*A`>^bygwRKf5%6RyJ|#9jg~S`J5ZDh|$aUBe5!StHO#lqS7FNrmU; z-iAKxM7051w18u_=}x$&)msc^{MTC`Q4WJSUH=zdNf%%lSUj)hd*$O}4xV60Um4o1 z*JegVSAnpW0vf;$BSOrV0f6R=+vR3bn-33fG#!!SzkQTbDyF$l8JLOQKo2J_s)8a5 zX`l9YNz;M!NN(=%e42b^$Ut$rfmC$$Rf4H9P;g~63mpkbZ|zCulzW=nM?S*I^ZWf2 zPLGD`SFv#a;8cjhT9c-KEV9x*iNBFD8@w(dQe=~O&zXX0Ak&kDYGp9tj&dcZ-J=nF zcTp_oUbY83sV!-`r;nVk-wc%`+3NSjOtl;+s5*L&2r*Y0D;mH2ZTFZ`wVVAqu0LtnBz<}H0b#&D>c7L_~h!Q;zICAr1CP-axC(2Uq+Ky4%aR; z98NHK8Nr$}`D;>mw(sWVraa$^mgbE`^sPp%JPUPv%EQh11#7}k=DieIiV}CEA)~gb zdm+yGLd>Q$?5=0DO z8fKTC#kZsz@e(D3+B7(FTPntXj@AiI^qdcJh$PT*_PAeXu6awMr{iK^hsaKlKqxR$ z6*q-&zLOOkca#~9KMcXGn|(lM%}u8y4X$lwUy*w7C|sA4t|yBo*`rb;-8Atb53NkR zWM^fN$H&Ax;~}&lD%1GKW9;;(w!FkzyKuu!nk(J=M81L>V6OC7QlVOyxhLCF8^4BC zS@_|6|1aZr%Ez$xj30adRUq!2| z8lB+&j+uCKrBmT4Q%TY8-%L%@DKzAD-&CvL+1t(;RN+gF(JA|NspkxF2O&KBo1-U6 zHGJ<)i+lG-$$39vnx$U;`J+)0;-%bpY^9J(#t`cf8!^u zq}gC`7Vt2CDN%wRi^uW|I1{DGmjbzjt@X(^oV|c=vdSyzQGgGXXkwT;!TEoIo!v+D0q2s zn;#LZz}wb(x?{)N1P~Xn3s9;%`RtNVzGk*_TgoTX6;{plMTzPerCq*=0g{BZi|o9x zqQ94VFkHW`!xM;$r*(FUJa*c|E-n6&gEI0Qt?y&kG}lJvllUF{JWh&^co%U8wK#Cz zo(p(>BkGWFPj8EyxeJ-4_3AN|8*fSC(eB`}@!EH`0nSgcOOF zW9QzjZR13Uoj6f`3f1+Kq`ODeBY1cgPqJ^Oj*1S?>S)aE7Bx;tRa;0Yp-MvFbpcN9 zJ}4u#W!iSWLwx*ZD7*7?F&~ubfgN2Scbt4?qrUOX`!ozhIcsA@T5JT@wt%o z9~`=KBMrhGE9d`mUW|@=n?^@r**!IyeQk^QZ(W)zmu^;=)y*#y7M^0>Hq7Xnh>?8I zJFZyW?S7`UV`Y5*DRF6ZQ`X%Sx>@^AiJk3LDmsbOnv|3p6+#R_)Y-Ql1eeBC^P#A< zdUd%Njy(*4qCERfPJ4$U*0bxYr91$C8pIf}nQwFvX3JD|MFPEo%s)MIs+u@sIlUM) zqi4C4N{0;Hm5jVq=7+~OT!u)WoMsqLksNk4vL>7>QXCAb{~bq!^6Q7X>CqQWU_C;u z)HL%HaU2*x8{r>Iu9jNh*ZtY;oSl*Zg-Tw4fAML_LaqR&h9>C%tS6TmqaLdt@sc02 z2h&R6ks)@hCMAL2+AM~IGM!qHxFNmr_Q=-XxsHT$Fdl4 zl612+f${aWW{jci(O4=vYu4eX20X2Vu{*6!W}glnUX4ee6`kR-w{h*-NarfI-^e7! zE7jPz3+`A|;&C+nig%{!8yX&BhK967JT`OV;ynru9iB-+{breJ9BLds9vU1n+sn`2 zn)N)IBOV`;tE2(X2FU+fBYaB$||&^Y;- z0ol8^z#YhPFGLapuz0W(yd|p22Gcx zal?KhBWP)m(EN~zZ;wko`Gx4H$ls{&apV;%Tg`7Us}+8mc?w%SkyWHpmS%t;5c?Q( z@8K+i*{+K_azZ;oS=^)q<%o>9@2v(peVP3a3l6u$#l>CXFR2uc=7{n*>7{Mx?%tha zdRTEF$g|87H$`gMn&=rz!mitxD{|=fgs#dhw)N-3n=Dei@gE6zGo4LGley~*uTz&HseZVvCldsh-vuBegasT<5$Y;sj6XeFp3&5b|>ybhA0$1VM zKItESZ5pmSlB*)zcCZBzChXdQyWxc5b9Zo){Eie|z^_h&zXD%NbYprH%$A@~HMJ7( z_6NFIAqWT~bSP)OSy`XSW;*PG0q^t(T7F(KPZ@K9&El+Pv0oHOswX@L^5!0(Dc9#zIx^bdpxf^d<%SaB zHKKOX6n!;zcC1pGOh&Wg05rrVc`#g)ez! zqXmu|8};{&7md+v$|^!*VXfwj$jw_bolLg?*69GKU_n70w2f?*IojnaK-P1-X|190 z9zYkug!;$?VtR0>r(F>Hs4CG5bPw5F<}5-N)SzoNj9AVTk$SB!^Fl=i4F$vx8_BQ1 z*0dny1>Bh;m7G0C+y~Iuq(Xd3hfEI&wQMgh)b{rFH)#BUX_S<-v@-Ayfqabs3c#6c zE)@P+CtvNY2ZIA-f!-W@_ujo>4<=+FL!9#@WyGqt%G}4?OWMOj^jebMBf@iYtn&54F%08d;8XCC3SdrAltPe{vqhZz_oNe5g9M zW}c+$Ik;F$ipnjExnf6Q9i(6sZlE~L*Fsa}zW#-RyUN59 zDLsg|s&tue>U3ASI>LYmMV~XeE@$`Yv2aW%fjyEIHYp`#4wS5YaYB)&}^? z`4Y1_Zs%sfV$&}Wy_?)TAuG3!26<+H`6x0j1H>_oOGK#cR9YSR1&to=BxakZxT%aX zPWjf^2x&^Y<(K4pg>@e)R*XZ|wt37QmR>%T71t5r1XZ?|MNfvZ(jd3PehKWRj7d&c zAJ6w-z`Z1AL1*Upi^P6vuhzMLB}m* zVPNLCY^-g1z_g z7%uNr0(=S9rdkLN1o?}!Ay7geK!aF0ZvEBEmnJ|Uh9X-EJplcUY-FYaVA^1%6S20m zDoISi(haRpVz!-#5W0i>M>xc#5&KziXZr;0ErA{1n?ckf>8tN)$H%H7 zjt7&}$N?1>gpZ#jXq{`S1PfP0 zl{hpHtgfxeg-1)YqHaccpR%%^O+ky%K&Brvm%=apdpf1}x6mq2igJb{)R|eD9kN6v zPn@EYSJx`mJYW1P=T6jk^*5ZcXX0h#G{n8#SgFE7?7yHjrYo*asQDLERL28iCufi2 zGGl0OUeuj`Bk*CmYsD%nQL6O$@usnr zKozLFA@3|BmJkzD1jhE6b%pZ-coO`e-QyKlfNp*l07w9V&^o;CI zXa+h!7lf=Sw4F~nhF6|v$mYdE=Nzh?>}bswjNgKvSxOILBY1gmsv+sGR5>jhs09|3 z_9E4=spmaKbdQE%Rllkak}{yA;y>^ur^Q!)FYTxfQOeS|*0mvTypxtV3B+%AvI|No zfBjWV=r|^aJ7sP_yUuMrcN){j_EDNcG1$4GW1Qt^RKclxu<+$;7$Wx0jJfu&Z3W(Z zXCPG*%Gt0ddnCbW&Ahv|aACfzB5->22lbN?#S?doP$lOkPUVw7SzDR_M|m0;)F1|$ z=@@W5&+h^j0yw}_31R_u)CKN=GT3yi4lU6`A_7a+5ph~t1K8a-#n{LQ2?=Pd60`!K zSyWkuHtZKoCeBEKN{u=#sPk|)1* zU{26>@&}H&r&0>y;>ptQ`wSEDs8F4S!5+o+Sw37^JO+78&i0gvQL<(AJBl6rg{};u zVES1l)i-`${$%sRrTuc$Z#f#Zf^Q`|()e>gWBDlmrfapnmvht9%TT3=!9xi)MJIEz z(J)y`~S72${qJ z0aZw-^l9-_(5iZf@9w9*;&AU?^HhF{yPn?uLn4zsIX-fjE;2Q)p?HJ7wAS? z2Rqj-;xBphyL@f^8e8t%m#Jacg+7xF941#Yum^ zLhNC~uUjoH-My5*?!!YT{K=Y7!C9az9&UH$=3%cyko|;Dg?KG~2L=$_GV?m|yH6B-w3AY!nhw6TYAJ2Rl6?t-R6p^j6-P(Z&}t%R`jtG$?>l(bloK)iil$pc4n8a&aumiAIN2s5a~(I)+o05 z7+;no5jR0}GZx2Vo(gz&|Df$uNa19>38q*6;+@IRwNuPPO5^wcJ9yP{HXeds(-sueS~@CC6a+VUzp%A68-JEpGR+BLP8Qi^H6 z=TRqm>Bg{WD5oEpifSJmRBRU(QUE)`oiwqB+*2WkMNLEOo-B@8mWKi6X^J9dY}{@0 z6A67E7gs4%VacIAnC%?rY_<49s;|)EiBxo@*}Z4THvUZ=RqnQAWmxK*-udwaJ8;2% zHr2t5Dx0He8rRfl20$I&$|`yr98eXlJ(n|#1RWI78tcn`IIBf>m!-tfLL{+tTI_5* zYt1U_FfXUOIn+OD^l3SLG_lZ7bB~8>urM=;-O$aVld9(ok7dLPWZ$PZQZju7qEZhZ zA79NP7VXH;D0Cb3L7&!TA+EOnr2*~B4YQDx}(v`6qmz)=pOo_ zUkLC*M0|Wux+*}WhRr`hN!_PEB|jq6f57QD;f$0sG}uP*cNxOEe605x7G0rJ)7w2& z<&Y|j5;LDppvHG+Um3sgcuh2@j>gk@%uUJf~M#*8k(t}{(NUkr7uEgY4o!u$b==~R0i#CF9QTTl% zFtK4>Rtx+oZ_fXXEobb@P%mnl?b#YVZUq&P{lgUQGQN*R`fV}FccuKEBiNrpuJj1x zO4DiE)uma?sG$tq#OteTCEXGWz5*X!qzn&AkSPZvQ{q%v4t*9CMr6DoOKut`5MGF* zF1|AT!Lzz;f%6WGVKZ7*(j;x;-d64m86q@&Tn_j;2dRd+475)nW!k2T_4?| z-#J2M5Vwjlv|ATF)YZL#2Gx_b!c$X@Ireh!X;NKJc5Y+KL;$MbS`q4-Jl7;Z9C%SmrK zzRh@hpMq)6a!X7?f2_#BF+f3~)2}=#7ZpUTDiiM;QU~I+^*Lpgnq$8h=#*55o*CNz zy6fE*yMu`zD4jcjzi^Vt{jt-Y0FmnJ+yv30Gz|ze!gpj zLvr-WXEvKW0Z_T#yjWUY5-q3CS!SeR%k$y;{Ps)~q96Kv8kqH>x4^VvuisOPaKUC+ z26dCkTW>nvy)n{V^+Y#w|Vv@^Et|keo`F~Ui|B)(Nq&;L&fPSBIpCl-?gelV%En`&TM`prKy_M+gdy?SDPk zs~kz!yQ|z?5%Z1^WO@fA7>FzmT${zk#Xp3Cg5}RoNi+y;j$K?$2Ku zU--Pz1&ZrA_&5)MuN-P`YB>VN5#+`&&iT&|^ox@U4aj7$zx^j11;RZ|5OY2-<7i=ikgF;gzvd0KnrK`-w@LMziZ)`{%GMW z{~P+Jf2W1}-za_lztqAxIA1`7kN+Jh++&i*e=aHNr0@pO5cFeffX)%eT6NNB6IUXq!}$S@|IpnoZ>&apUh3M1Pb-qJd}s z?U(RbkY|W;Nm3J?5+y14N7VVZ#EE~jKu=%(Gq(PJ_RISomVXW?K1OBufVmlY|H-@X z{<$UT51TH3Ms+hW{+B<>?>_W_x+pj!WPkypvY|LCIrhgbT){L9z7@X1pv;`h=NEhA95+K$!X z-RQqigGz-1)JNxs{QBOfpq}1kP#z>h<@*1ZFQw!dYzec-|KxJ~hu7#o{3U&LG4bOm zWV)(ap@}?V1dWDPL1X)7{KiaMp>%xT#_rY?`01QC}7d!_{ zQ|f{|))FImtaYwRhU}@A*_-Y{t0V}}G9H*#q;{|8kK~B#8mdhXj7lE!*TQJ0`|!@L z(eBoj>%C6XAAIN~Lmnh4yp?j_ThCHrAd?KK1v1n4LtUJLbm-}WZ=m!X#VY}0l4}7i zdVksuLjfphIkyK)XG__+E_n_-3jDid1egPP$6hzW7_Q@Afpy>yzc6`Z@`+{bp(t@FVAFe8^Q_(6oJ0-Hh|V&2#{HD+?Fv zzR_UAt-W8gV>ggBuzp0t7p58ARYz(+sHh}309ayWF z!RIh4|7BfUFQy5D2lc3JJ`=G)>SqylvRfW*S`oBD*XF<(%5npItF>?r;8QaeeD8F> z(I^--Ch4+*@fFCykeC15HvaOwOzMVlA;?(}i{FI}b+DPCcIcL00mYuxIx^|o08Crh zo-_utW8A^V4r2grdWP9gj-hEbh#KYLGrxKLMpF=mVwpta;oDP@QepgP!Sn`j%THS4 z*0i_xmvk<3+5pSnDcZJmWVXHP#>!h&;QG-3QpFfs&6uTP2~8(K4~c37pc;u~;h z$DoPUUV`orqo}jQ467Is;6pO*!Fs+Ay1X&4u#wTFgFR9KgIsNaVXabPm9SWF)IJa~ z2fA;e)vu|caS0)+V`j17-+TjcE)*7c?CR4N5guM2!)MpI04fV(%;WtfkxB%SX$2ar zLgNF0&-4S3L+&$#`9Y_o7Vh^LICBQQlNIKJS&$b&sVQ6SSx;$vA1r4ue7%uu2Aw%zC7KA8 zHP_04Ey--L(rH4sJP3+fOyX_6{)LPu&lDbq7tT>?ta$P*BmfP!vkBh<(axw4W>2Xs zx;`6%AfOka^;8mcCpYN1t&Ud0HgE#dpmA`DZ;srGhUyA>aXsh;@GtY}9EW*wCgpl; z``3Ct0N9+QL4vCf>Wkk0*ek8LYzJY6V<38|rSyvCMk_j%q?-n=sEV4}MWoBUHs8-t z+3EOhKP=w|-^MYxpT_p{KWwANk=O@7*u)w>jBg&9_J=~54T5E7FllHpY(E%@Q<5p1 z$Z#18zE80o$Qa6I{16aIuOwDN4=Z9v=WomjFFP%;=tSl^zmB#lyR`wfekLH9M2;CK zxyLj|aVizS(y@h>mbGKR=-viK9+nnd^pLBzU{cj9x4!?r!9tqZEkdvhu7Juqqdirs z(e3MBH}1nB(6ykd<9rRwJ$qfLLw%7SP{2!y>+f)R_UykcU&8)rU^4)2>|JYv)>7Ph3}kIYhcywCW*bmm`-(4SIOE0TP~}v(V;e z^z0Co`L@`n(J1ML5#p!xskEzO&8x_{GBc$(1K<&CULvyW@1W-|rl+19LkHcys{7 z;;ki50u=$j*aZ*oviL&58E!EoSlMuJF!AK6qW{i7=;$Fjal%{vl)U7*F&5^pX;yXe2rGQ@ar^;TRoZOJWyXMFYWj z8bl6^>?26bJ4Gk#ma!A(H0G-Yhxq;b$Vn5(HyL>;Z9lV4$bM*6j?#zYe+57rW37z9 zg?MQ8EC!$r?@a>YSx+iKO$s#Y2fA|*Al$&UGzfI#63`{Q!ph1D--Cj+a!p}BbwFC+ z^z_#sMV|GcbumE(uGs>WOLbx#;LYfuhx@F8u!U5lut+gtXrpDuU)5iONeeDVdx9^7XeSUtC!!Q)#Rx}q>;cRdF= z>(Yyi_U|Fu3igCQM716?r9k0C(-1gJbF5+usDhp_KB<~Xjvj-qxD3YU3>_`JiY`4X zt9A6|Cxx_`WQ!I!QML_D!Nc-MZzh&rHmXw0l2 z)rbiM)G{HD-7SmI12nW}48CsT31wnpVmNi%8fQ70PDU}7*V%)xOT3)jzZCr2*ay(q zm)h^(brHMKJjhwsFo9J&!%Hg8EkHK>r0WT3sWL4xxEh5@Zw9jnjse}>F^WX^MzHlk z2ozZqTncf3asjrUic#TRmQ6eXD?)ge_r@MMcfAguPPG>j9lloAEDs>9Of?GwLsL^^v`>?!mDoX{ zE#S|rItfW;R2aXaqigz=C&F`?1?&UJ7AfX8C}9c`+E5#^sUe~4tJ)~a5raA|L+Okh z52P~P$;#Cb`C$udOspVh0zv?X4fXnUrp2uTaJ(O^CR#2Epe^rjaKsHZAsP|1h10v1K!T z;DPblam4w?u`{$nMJ#~yJw~j%bvMlX2y$D!EXrXAKuKlq3WF0msyzvi zRFvJFgq>OsHXrAZFjbFkL+;EFl${-rDNO@i3_g6MfTI=6J+;nYLtYYMusp0>I;kvwK83S>21#*Tu$s4huVH#d#NcAiuuu%9Q*drr*+5{fBA7MS_ z0pt$v4(yBzFd$adZGur(auXR=opX-%;6M|^k3i@c7Z}}^*`3oR_`QBJC<3}?HpoWF zLGr@2qj$5*eQ8Kx@EXdZZ#ZY${8%oQLBlfe5ZcOFVCgG^m(xrq9D6VM6Pu4#XD^&f zZ%uL3k{`S_&kiGD3Y;MGGXTMKo<)B?d=3~rK?|lI`xrqPdO>6G+93f_W7JeGx8*=B zVYi)7i-Cd(?m`|=A+`qks>l)ll^D2aNYVEC$Hn<%xVJ*C=n8N&M(RYE2yDL#bp~>d ztu>9pv+BU~^V;q{{^oV58;EUNV-(eDgN7)Fmnhx_cWm@1QgYTJfg8EoS0#}`W(CR` zqqT=2SHYXTA}iaHf0IWtWUJNM<2%rqi>wX+^q3DDpt^_<&$5pFw%3xf~t>ZG2bWy~^ zjR^=|unow89PbAg?>>-g^dV6~DmoK5!7U^Hw6#Lt!Mh^!k$5LU6~{sqWzrTD4&K0v zDIXko_kaE!6!{+kWcygy{zIRtq!$yT9;;qKWxoI-vT8=VEZi-q(lxRU0uk!RdsNA3 zsgO^_4-Z}(g}&Q{%1%^Bdq2wai97C3KP)t!?6bpnAd|hy+kcqDGu??pay3Wbmp~#n zFW=w)DAeN{Pl*4tZT@pub{n9>B18>@ta>oFl|%-86*vF+Tnv(^3V-#|r-nl?NRtha z4?g8(z(Vva*hZ)h zK@>uky8SYe9&Mk`WBVJ(!VbIJ?Vf?a{6P{}Gpxb>2j~_`quwKWoN^V=JS5+s28S6W z`-fe1Wk?#f7PsXk1PX(vi9`AH5g1f6oAlh?wGi7uX}MpXLK%|IE%FCe%SCEEZpX!; z{e1>BL&=opq7&4`PmsAlPiK zz|;yc$V|JIps&f!Qa+#m9`~oQD0&pZla+#yiw|Kx0GM}hgFLKDlXnz69mr05 z^7I=735~)0gh%nj6>wHu5$#&bg)iDH;^+GN(OrU@eO)Nl3qh=R@t-y{w)TZv@^qt=gmRVf|?hEs|0;f zm6<-^&^E$>9@GVwJTu_W9(fW3c?t|;6rrbw;{z%eLqjl%FizGzbMf}yj0S(2(fwBd z972-7La=M0r-y0E)JPc-R*#GVG5{gsTdAav0Bs>)jZxyGgHQRlKl0YF)>dlh_HGYX zL5h|5>C-0#0}$F?W`T?=Aa8YbH|5Ww6`;DMbGjTJGoJ9`wBJ?9F|cV4RFvVn2JA)# zP6RfNdD?Vq1%M-o`?XM2#E>5Q=b2}SY)&_~w+G#bbD{@_GE!KLfP9g{M)9rQonS@I zg@uuF_%{;e5<$O!V0Gg6YvmqZ6rF_0in=dk#mO`o4D7nk=oY) z2Ai-!f`Xj+S}?|Y1u9b!1cd={co6tuNk9sv4*@n0hGcHR(4;Y7{RhCfM2V2Aq@*2? z{z36~c+9bSN4qrM6G>K@r~X{prf@0lYi_?|z$pAdQt{rU-bn&$PRq`OazBU9t?HeT z(j*9SKeEepv3;i=PbnWX239jO525v0wOu4FBjk+j@r%}QI+}6J^)@3>t?BX6Wfl{O z4_3-|M_<1odaB%Ta6?cGGN_nwrse&tBUP-o355hhJ`1oaDANA(apYbgyDGQlbjHG; zKEBtbP}M)ZCP*Ltsl8GEwNS}r4kiUFP|?*xRSbzQSsq}y4?;eD9oGWLq(|;glU|Mt ziegc4Nf-jebOlq5XNLJr_{@M?2Tigcc}nTUjQb}-n=K5g2xN7idAI^tyG2nakFsyI z^HoWOw4H-5=`{;LrXG@CA|aN|b8k1`CtC5WR#aB}L6G)}jX?4GxG%Ym`nL=3FhT7E zxg10|7>jZqR*?QFSokoOg$7Q&^ab`m&tW1(O{F5`HSFN<(;hIF{4AW#@#8UeZD|Xg zf8N;)LC;|>ptSW$j8qbGw_}(8w8Nfi=~z;Tt|Q9_=Kx^g7}5@4R{67nyN?jr1P-{Z zFTqSsQYfK{v<~1ql-#fdQ7keiG}1#GhI#vk?Z4Cf*MBvz?7E4LQ=cWJQa#ij!^crc z|EIs%s10$q2SUcySBk2$q@)O%3()t6aK=BkIUULqA3QW&0+h_g_vl5ozsY`tz4>o{ zhoZ;_@(!l#D%>nw92g?^h8OTJ11Ow}w88ij`H_=`wXz6(2g#fx9$+bB07#cD@R%FG zhR?iw|J?T)ifH;pheAss=Vs3mf|i~F7Xl;&iWGEy>V~9BFf>C1X8Mvs;q(SVpOQ%h zpn>fo1UD|_1ArN&6b=c-ZRkIhz_CSMJDf?4*4*C@fmkka zX45eba`|s)Yf8K$f0NF+HydVd&|B-AXJ#s&d5a;?WqbXj*kE#pX%M=j!l8@F{h*14 zgnwkwJHZ9)`eluvlYq>>f$-UbHvMe0hn<=l$5Y&Z*2<*i`}#9`Tk9u>IaU@ZS8!c) zwE?n|_Z7+(Xbx!~Y~ zy4~Yq<>rh^Hp*#uV11?0vc2vlPs&p6()>5<_}-`><>506G`fO>AB8x+_xX*#Hq`+~d6Ue#w7Pq9z1VJHZT^O=6!&Y4?XO>NEmqS8 zIEoB!2(CADRZ+hvw@+K!s&<-77`J{rSfw7+jXlF9OwAVmfB*+gVbXZ zt2T3vk5~2&#wG48_pmkyD@P`JY_D;wgz}w4B@Apcy9PqpE{}6SIy>M-S)@iHcfP6o zIr>Y=!!f#Q7bX=J<|el*WF3WK<0eBDdJs(16qTr(DY7${`+v>#W(Sq$5M*1Tt86kh z-(yTKy@}=tC=#p7v)}Uz$P;lgRmfCVrq(T=|IygY=ov}UO+qF0aJI#+ZJ3ad^;P%p z%`DbiSs$jlTij0k4kzK_l<4Um?SCBc?&-y5K= zT(1`3(AnD5WSeK)iI`It9bUl&&@7#hQ+}vQQTK8?UfDE2cwwO7+u_YNnLDDf>{bJX zW}fvSA3t6c$uYxT$Teq|AVSHJBC!f^jZn573lh zd^d&%=9jAoBf{6O>@GO|q_rJ8TJtgY&YdVc@hrvt&8pasuA?$+W9G+ez1If`+*@lw zQ?(K~GJu$Os~@j>*05BmJX+s+sXjMno=)4v;q{KS2fGwuiE3e-)|JQm)Dl&zMg$>K zFTq7{c~Ns^_tOelRgNMye1&kDD$aJH-`CK3oyT(J>sAMh^-ROD@VVW$H`&;xcqlIW z{$di@P|;(%ZD;3|8a)Z|O2Xi)$E`Srj@*RY&T*oir4gP{KV#u2xnDc{7z@$}I_LZK zMK?qkT;ty7Kh|?2JZoxI6ybNdZ*Q@;Ex10v^_RTD7w7bk?QvC3wy$u9d#0+8=hv&c z1(XT;Tcfd;yFgA} z56eOyjvwayM%GOOerHT${Ql81U?Q8 zGEen1`+s#AT-s^!9Q|gp*^iNk`n5E<@uS?F`f2A+r5Oaw3W4}m)iCUzqU_zSBQykQROWJmsZHo z<$JD~RIW60602qfP$WO{V_g#ZBxP`@$Gm>*8k>P_-Bj9TZH}olU+h#vx0qqOBBQ_m zc$Rtr!)^czv*wc{jHH97qcGqta}0f&Bb>>X@?pBLg!Digby0c=??_uGwQ^K}Gb7dl z(7X71Owlkla9j;{|a3SFa;8Y|t= zmB!5RFa?}%6Jg3QUdYxg_s9tQ<`XAaqP0ouG?Z{Nm9Xs?BJ5`PG?P^)zG%Sf{X4(c zr0yF{CER9rd!F~_BXKWaW8?1R>U3|uatycAsQr#C>zg-RbFKG+{S`=U?MGXj)7ihw z2U7Rt=!(0#Ugr3GJ?%BGSu~!E~Bq*izmfcYtVH% z5aa3L_-K+c?(mtJ*q$5pVIFTaSZQ2SMnb8{!DFH&!f!!ZY*h1JIjTC;-+P$f{ zx4kz8u8eCqWV#4sBOiH_lYuGVgX@zv`7pi+M~Pw_{UffIEhtoX_AZU%his7ij;QoO zMM8m?4-A2zFN~H4nqM%s13g6AShlItva7P;b<@%sWctTaP4 z`^_2Ms0-t|@#GK5nSy#4d+)!fUr#h`uJ5qS3C!#BD3`!g@`q;>ca!hzT}?_FoK`P} zhC;x`SbMkxGyqiEZyihT`!3`n25yD+t~0YAoeU<|7KY?)`%g!)Ld-0i3=)Z^3Uc4Q z6E=GoZ6&cb6U!#ce2c+PHO0_5F8xC=Wus6{b7Z1UMj&50d|T-FBFXwS$qPPR8_qjcovq8@a6EKVqv|^B z1K)NdCcnOT+}|#1OVkaX1}23gS#{|6u3Slci>*p#zj%ATWq4hVAA8BdYP(#-A?^A*Kf>l;YG4w-OH=f68y z|LRo-+(whm1dmL0jBt34_IYB}>?RyFad$Q|gy^2z63P;dF>#5reL-AveGRKz=K8-F z>@J{0En#z%?DxQ*Tw5FFRAITv!m{62)qz?wlEYXADA@NQ0Z9b76`G(Dwa<>zmh_m3 zr&?-e?+s^33_gu5D1WlI8i2-ozgp?^90}|oxKwJj$L@8^Zw>Yk0&}#{TJ{SCgWNks zyVZ}K-#K?th$+0k&yeFr(0tEhImaBY%B;(b^5*|&P%zoyiIJh~i;G&e?Ts%mY~0=b z^j8>eLO%n#1e(Wb-&=`nEo!pa&4e)Y9iOk@FEXKh!EfBADC-uAcQ$KAO+_&r<8^e$ zw@mjJOLq_ItA_T>_0}D?o2>sK`EGnhw!Q(!j8-I?4VPhl-|$%`%C7qzohpG^xrg=Z z?!uh0cssXYyF6W66cy_uZb%Qhk3n@$#cyvS;}eOLF<@6J1Z7fQBnW_+G>e&_8aR&t zYB8RB+qOft4Pnls(5D#(s3r(FKoQ$}&p-dNadj_jSlVo-4y| zKg%7hTFL0BknUoS?}C)NF(V>k#H!4xwJdUXq-9#DWaqTGRE>J{9GP8Qh>=e?>byPApW`z;lNGriX~pqW~`C;$B29E;L;LMzBOW zJG@gdcKW}*zF|DZll3SLs#QS{AR$wr>wRy;U4+s^7n!N*K8mpNN`Oz+g~#w0tP!$h z-*euo0<5LMFhZN0XV_j*+U@F(>>0Xt-wA(}q`~!7tp4XOmCX)I13h(=3++a3w0pEq zSASmIn1C0M*zz(sUMDery#cCqw@`~87pJqC83GBr&h{~OJ(7mPDG45T)~LVpU9aY0 zbhm7IVST^-nf6DWyr_J{E zs&d15tV+^nmY0R>T)6ED<5BGp@ODdIh7;wxj!l#~YF?i8 zkXR48A|voPdUIVQRW&p`wEhZ4D87n&{2kxQ%qQoLB*%WuJ9JLFBa}qT#vJ5ziI||k zcdY#S8Ntr)BKELow=Jpu(Ica|RyEvnhtagIj%w(gqAmeS!lM%rz~NtnRLs!7r7+z7@LNrFA@Ea{L3p% z5hl4`f`*(G^zD~!*TU6B+F4tp1pMeeh76r_=XxOk)B>SJFO0wje{u;YAQFs&y@s?; z6X1V9mZ7{6Yp<{d{R43kMvHu_1>8kKePMw43-3ks#T_hhv5xy7oB$VXanA%Q9Djar z9~;gaH>kf6Z+#rUfsMD|KrMDu+nwSqQ58?v`NjCoU@x=I7AdV3@%&=rMkEFcvkpj2 z?S!8>qmPrIdJ*BW?fe!-W1Vm(rn(TiV|S=%s!@pJMSq|1qoA-l^8#EzrGP^lFo!xW z`-nA*zqvxM4+rGdTnR;i%9UK~z-mjgZC7%GUr~Q-$}z`pGL5@Am=WJIlP$~zRa{9) zc}Cl-k>zgAf&w0QPtA9;WefZ&G3i^wUg2jET>I%{iCc@zx9L#XJS>m`UE1DmNPUC1 zb*-z<(=9#@e2;N+4tRh2@-h3_i+!~5d=lC=jrDwH=}3vIfn0T~(uwd$5l;{P#w;** z+=pdgJOdZ>&n^LK?+a>uA><)QR|tS_WP`(rgs^1j!=#W#?>KOf6p?O(W)^i7OjsC4 zxCRKT0%4qxl7i1kkjU<@Gb;c9<<*O@-oNj*I}oa-yX$;&pv1Xmy2!Eb>8P&C>j6T- z$i!*Y<7bv4NKx(7B8~!;S7dkz%WAv*tC0c@cW?vGWh0(sUS(;fsCIA?#BSPB64^kC z(yV+%&h^r7irUN3mMPSvZ0R9Wt5)sdgCeTepN!;~Zw4sWVyaj5GnrUMAgy9CQIk9C zv)&Yw61SUrsn~NdJ{C1Kk=BJCnjyH|Hnwux)IC$L+qR`qXweO#@KuGht=`MW8-i$^ zp?*1Lrc?9$`Ngt2YGs;15#u@k+c`>)193KOLtCC_1TSLZ_livNwy7PKzpRWCA`_wV zc!!GQpRo4a(R4AiLM|2HmH_}D8Lm+Rn-RHYwlF869zm=D$?^vaKQ^H7p7H=FeGJH^ zKPEX&3(V&to$+~S0p|gT3b@xIa5&9~y(pN}f`yw)K8CZZQ?_by4cki07H;Y`YW?E= z`Ez7&NT!tOsNs>y;T<&7CVqIcX};XzSCd-h(*9piT*te{fy%^FI1&W+S9WY^6Sv5=dg)cPG7p=7ChkR@HoV z;sH@Hc+2!>vF*9Sl^^AQO<}voK7K?NBs9{KA<(v{7Ykas{z}^F&yz^Wq4|;M%4sPl zqLRISjp9c?$z0_+^|}jJi#!)8dxT+I{)FwIKWm+A(6(D3|7RrZEd4^g*R3RnJ1MB)N z_|wNF@_XffExAZKUEJc^l~)GG>dj@{p@!b&RLN=CMLXAA zUr!u;Ryp>0{J2=gMymk;W%Bt|zpGgLH1ocZ!aD&J|j!=48_^ z@Z4GIO|&dr8YsJKP&m{5G{bzIiY&atZg7@|spW)MA9>hP^g!%}30;cEQsV=ucWPBm zh8MEb&O}v*>505SCp>*?Qo;TvoSV#BdD5|7$Kz z!8~6Egn$&u?_h>te?Z!+^Y>;zFmumaSgH;(Z9EleM{xkksb-SVvRSVNM?$&q{Of8g zU4nst zgaDG(UCG+!dbPPHmm`YL!-`3BdE3+RPZr1^C<6c=eRYD4__SuBn{?rfT9vhywVLiZ zbJd*YlHOd?jrPe{NTUNbAdhLrad^#N>Tt~o*x=~N#cjfwdT&y)=Pdi-(z!@x^s;ll zhDz^x)<$rtAa`h}Q6Uz)THJB|hq?uqgmO)UL}0)AWU2yEV@V@57F4Sx%U_Adv&$F# zUbmol`Yp=i^Y0GRRO)tJvf5bq4g+>W=i-=mfM+IFU^F43Xph~Y;{37UoT7{{e-PS3 z(xQ9yRw1B6nq~*WZj?X$ zExeoXO$)WN(wf}aHZ+5Z6)xJ3JT|d7q+8&=IVzOVj>9Q9FW zMukBIM4G*TzyQ*vI8sJX0i{V*x>Q5&;5bSzqaq@8Kmnym4Lv$ilqxm!D3MNxp+iFQ z?GtdG=lRxpfA9Clk2N!EjfN!meV?<>-q*gaYZn>=Rp)@YLDA8MD=D!{6OP>C292Cu z%FY8>niG@V7F#Kr|MJ8ul%{5Q5Y@Oz)?+m31KRS-b3?Lnu zpb+{P^=Wo1tzI00&CSkkyc3rgQhAx%7B@S&@w|7Q(e_4Tvy}ow`9HDuT_Ud~J>yQh z3MBW3d%3UYPat6okiol1z&6&+y_NmDhXcOxbb(fDxqbpERl47eFf6hCGsWdf)Q%V) z-F!c#k@RQ)mj=HumIbHl8P&;!zxsSWT5}xtNkHd~gnW^f%`epT$f?4j-y1b%KLkf7 z7ws2sH?2JvxJU>xSgi#FE&RB&e9&s%O$p+=l@Sdr*5u>%S(9i|JhyMPxRYxZL-K#yMq%CjX*xyC>|Y@n?z z9EWTNCIu6LaUON?eiPRry^xyabsASI^q8W7hgJ{saBGJBVA}MT(A5hqfj}W|;$y+V zibHe}cfXb4Jhd0&kwKRBxyDtB!_J-QIE&?Mah{FJ_$vl5pKtFwmYA7f`WBmHNBfUc8hD&efQ{Nb+?jejXf zU|$qO$5p5=eWcaf0VTMUr?;`HSSjyYz=l_I(((9Qk=NZa%B>FCu>$XXp=$3bES<(| zP_0XHj>E02k@pI317Ov}&+qBlPoDbw<`;l!$Uq5B=t$nSR!HO(hMk`3Uqa}N$~v7Gzl&+5Dw6(COHLJ{Qy)rjou z#KxEypiVV_8<%cI>TNe*a%puJm@7d;7E;W3Q_p@+?C-Tu3XFowPb7dNK0ZD`rD9T- z!OMN_+fB&xp}0?Uo`8c8f^lM0%(k)lkv$1H_}#oiLi5vlflc{@>|a=A@7!%DWzg2v z49o5g+8P&#nh7xI#B4+3#Wm3%o|ifoy>|_kPxGHzM>o>Swxat(LeY zImF|$q6PK}JjFOQ{ry193r05=>^zhD-q(fV^=kTAhW!~U5iLG~z*DHxP0N27Pe2hj zZj0Os%}{|&Mz6`;cRQKlnJ66)OCSm}v24&e;>2YOU>_kMqYf5i&Z}uYyc^_?neAe9 z$Zrro3!U&*i^jlx9s=G8CC?>5D1h`xQzR@j^rYs~Iv@@nglR_o<8B(7nnc8fhnh-u zBM1i%Lyg$budr~i%)0;YI5A6FQlx`kKi%i?M$F-#^W@8Z!y243;|Qu+|KiQ4{}Cad zi%T!ghfN8zM#~rYoUn;brLM8O3KdNW^6qXfzz$DTOHOB$k1;kBQWz5sVzy1RGyU3+ zD1nboi1{_qyk`q6iE}gb$Lzn5OBr)w-m7NDtUu0Lm6;^S`_BJyrga{}4ktW>`Eq)~ zLN`cl)Gt4scs2b)0#0f6o866jL0;!w`i?lut-Y88x8sH-T+O_F+%(H;JmmdK@<*v| zx*mVDFkJe+bX5NC)*W>^^`M`lta_9L;2yftNLvO(a$yY(sfQwP!)rH4E(X52K$`kR zEry>DNeR!HR-VtVoPJ#nGZu>U>>tp+g(yX$u0O0oSfS`xL>YMp&;lkQ0R@7?DKLQ- z0L#p&v~m#PU^bTUjgY7FfF9~*H55f11?IT&ua7iQS|7@<;Hv`N&X#0(Y4m|O&^iz^ zViYwB&R=_Z9Rsu)eA9$Aevz(u;J6eO%Wzg0P^%Kj%emkjcsnr3^P?CB@4*O@%;^Ol z%WLZ#qEZk)pVrWtMTsN+-1Hbe!g`NECV?>Ytsw9y^lbbGwF?Zd9~4zuzgA6~l-Che z0{xi-=f1<7YLtPcLf1Pw2Mf%wJqk6;bBE2>+d?B38t)}*Rh_zOc`K`p8?V;$cDcO= zJ&RWX#hHa#@ge{Z(qyO$=nk9n6=@}%w^Oc`3Q&toJ};~IOhQ`GxNs?2nDksK3;_gP z5BG*iT@`=-jO&zE7+2Pnyz6|*OLqwFGqTgKVv3XyC`yZaN39FnvAb@DA*~)D<2C5k z%^o1trjv&SFgWSLNi%Wv!aegjtjzAbIt=WV*r@bb7)Nwn(z58tJ0D*qYJdAEQX2qu z2!&@?|sTb8N*+9Ul?1ho#f=Ipqd`-j| zS^cOLxF4}1%6uC##ia7+3Lsk~0(~rx&bFH|0SCWTNpa!2*WTP>N5qd>khTE6hG;WKnz{e#psq@Fa4Ry3Eb1)9}V%S=Z2HNFugo4<6* zzxNztm0EMmrI=uL#R!3eH$9v=2nG~IFktdgw+$LiCKu0-F)UDk4xAna zbIwffU`{WGdZ3wEiG8=sfRxkqaUlQ7$>#@*e$4F#Qk&&^oS3?B+dQvc6!Z5F4LHOc zs$ApM(>sM@~ELz^QBZ6>yRo-qFo5? zM%C+eew6WIfqGc$&9er5X-C!r|FY%voDS?rcj|JD!A%5KHO{N%SPZBaOO$zyT)((M zoAi3i&qQpbl8y9N-B?O3>r#yKPkjvh#Td1!st|f_C7I-N^T5%CQ&$0#KeaUDg4Mcw z=gttLcM#LqOiX`KQergCQbc@Kn*h-ysTqE8;nY}i27Rq}kb*ZrMU|u~G~uNjS7{g8 zosvyKEYW2MDrHm&yt(y`wQ&}>;_Ea5jBx2SVnM8+f}jltL?&cL2avRu=%wCgl{@2H zIh82`5*&$q2BI3HJB5;t%*KJv%v;_N)iF?Gs8W|nIbUul_g4bYV&WFGH#qjebo<^76Uk2RmxuG|h)4i%pn!^8O>25!AY}jlw6MaMC5xk?4+EV-$(u7^E zU2m`PWwS%Z3plAt37aN?m)uPoi|dL2VHxbg(UI}V&~ zxJ0hya(gOFxE++{x$lf+^kGQ|(cK*ZP@+0MXu$<~>h7HzIJwJx_7JHVRx2#(cCDmC zY&hHK?JU`MURfD8A9>Y92d#7?5^}*lMVsFnJo&NHUBGmDrWoK0cYU=ZCh#C){bA+D zV~<`M1;Jkw-q*K?gYv$bB%uYej?HZzB(N$%+L;mDIxFI=$iO}pss zGAxKHS>eM{k-K*8)OmiQ(3Lb`SZj0jiqV^2#znZYzgAwbRV#YTe6a(1u@k&FPC>m@ zLVc_{UZe*2%-IsYW`S-9j?biz7Y&GP3l=xr1`EuInsnl_ul%Gi2?nho4sd9wH=Hk6 zc{5yjm`qlj`2X_eBgCE#8p0ao=c-B zVHj^4UN#>Pfa&yxrS^KheqYhus@jQ92gbM>$QS-Vj!*E@?StqY0N73f5xa5&q)kpe z6OgGygT(x^Gh%{PPNqi|-cE?`H`5{RBhHuoq#B^-Z1Y&d}Q$8b^-6t_b?!IXoSw6 zA>Yd_8sE($ET@hggWfJD5`3d&UEFV19EJdH9V>1!gtr z0P~ibZrv5_AKU&{Hxlt`N&^p*u?~vibigk=z`Ymg9u6Vdk5l~CTg?VTzg7Wqc`^6N zPmAqfR9ZI308MNFuluKql(#$B>Oh{I4$G0L+<-?)L~EhAO-=~1QHN|_^euxcMdO-= zfi-xI$r_x1d^Qp-IXI{hIn#G#uBNpXm1!m*RDfhmMdd}=YM1=wN9CaV%>Yt51w0oj zjApjJl3?RcKthLB!QxCo0LfvHL`8h$M<*`t22GL*3`R)5cz@T_3{(>^M2Z~_w4C0` zt<~3rXrJ(g^&oZWBmwu+GclkM;sY{$mZJ@W?Scg&1_MPNQNn59Ul0}AZm^Rv=Y`8@ zX!`yp06b*3wo`@AoCyU2p-VAYiLurWD`Og)mUaZGza}9SSs&39sW^c)13*X6Q$l2B zkQ&~o5nw*JBV3@?+Z~+z2+7JI{Q-!0fCO!R;4Zr)20(_{kCY~Gz95<+Fy9?}CP45M zxb6XRw`#a8{i26A7eMxY;F=nNL}A?nl-byR7x<8=H!{Z%a4J%4j+O=xF{hSc2=QyP z6Pv#is|R~n59Uk<*3|Ui(2(7q#Tj6_TW4u+e!C+M0B^hwV$i4Xkb9&20wN4_f}%i! zqas*J;jsfT%&KQ2ZML|*v%nZeRwe(s^=NX@^Mz=l0+R_)Z4X*AknGwd?J>E2eLwP> z2D5>aCU8$=rn583WJe&!7uRP&NMMh?{N|@?%R)ra0gH9CHyRPJ4Alg>)I%4%?;>bQ+AkgESqKn}!dPCWW+Bbr617B{w+q@cO($9dBfdE1rnM2Ef zIz0mzsIbS@vmjt#AR09wwK*vo1I`wW&|K@fN8!S));~Y9#pKRDVysNw4koKyh*^)+ z5ipOEAN7#Hl2HxdGZqT zs1E_rEW6S&`?qhc;s3#$=pQek%SB0 z_rSu|VlDbHc;?NC%l7})Egjp6+Sxlm9K;k%!2GfkoS?5n4L1<~sleH73G=YSL31ww zDn>6?;)??K@!9!LnQHmz2oQ+06CGEW8a(H3(%|t zl>i3Bfd-Q)D2H+hTu4OW))MqOgR^+J8f5Z*;h<(oHm zv;3|BQ3wGO7_c8!#b79`)B;!~X!1Ak6YsGc^1QwM7IQ`1iuVGO1zoBH+#@bT@u*nO z2Ms0C#CQYS9P3*E6=30@a}^-9tq2rB0u4iuHkCE+oIbN9G1ET%|8Ak;C=@WBV5=4w zxR#!Pu8;eR3smSP-d`Rt6{xW5d0ur-@I)e$5jfaJ6Kqq$&@Lk?+`=EOxELu0AkBj@q=-Me!1BZuPv{xU{f`?k80(Ib?CRsj?KY$WAY4J z!RYvmNX6Gj?p1JeD1Yw}t%F9G&YUe4HvCY)y`jhdXgRu{0JbJ#)1H#-%ysY}5*z`d zrF+tfUN0KR&QyLQVXyuf+RpDDVpWkysa>Ka^W_gaXjST`Qb1U0AZR55WF`2Xr-$bt z#Y4C2X5PLJ0cF7>^;e$#augCGE{K@U=ir+mH6g@kj>8aa>jCOAzq;wG^Yv$27P9XD zZ5^_J^tS+2UB=>&o)`{{B2)fYBGSk~6g}?@841$Yy*>|m^glJ$csA7n9_`=}@-xz! zVL=_u1<96)eyUDf>>3=YAMDsb(_n19t?Fku1z}Wo7y^92Kq5c%!`{1(Ph4IR66zlA z*p!lJ$2(Stue3&Vh|p9$-z3D1 zN9u@NCEMoMwcO%1L4yy~(7b$h{?7GEQ#gqx;n2U5C?(t`H9L1}cBoe>s}g4W5v`ftpgseP8IRc8CgGvEneo?g@m$ds&U zBef_4Lq=HOE7H6-2zwshRt7Rc<}vobewS+u2S|*N*c49Jn#9yp0hB>Tf?ZbP2B`H> ziqHW`J6LTUXPR0G6;z7{QU?hQa1>fX6+r<|z7Z+^ZwPGOx@*m2@0U>2`^RY{3ckH{ zMYsbE)sV@Edaue-)S!X5D%wj5H|)v3msKnE~gI8N)9^LuRj_Q83iq|=gY=Q)c+DPUjt7F+Kverx6q^!wfR&J0QBmId~JZ> z3Un2aVd|c>GK1%@b=k;pU?C}#Qw>A={rD>!E` z1z98$f~qzmA|kLvkh zVF zWJCWx%iLvVC>YvvFU(zPtryF{-!0SJ#pjR$^U2P_;FUy9q}beaWfDK?1{aB~?1hE$ zQ&a~G2#ECb;SAbV0VMPP3u>6c!%XpY&qfq0!GTPDnz1bO3c&5Bf}8U9Y2@SqZb?)B zb`feo^UC*&ufR@#T_KSVJ=NS_Egk!VU=?%Z@5rX2bRFJe3J>mc!fqVj{u@?Eu4<+5 zZ@~)dY)zZ60<%z7<~(_MKgf8s8cM+jMs%i~8=|k;hYPSTG#<1-|7p;^-UaHN2MtIt ztX2gLxqm(nwf7*XxKm;9Eg%^Xo_Lw68RRU(W$Lce(ElzGh1FWX;j;`FYAU?2r@j|W zMPE_V5YnEkL9i>pfaezUd8{9_==EFpg4r84X>DP`V3ID3^#@h2$4iH8(;|ph3GDXu zC9vzN=%GTkY~Fy4yTKyCnosBRL}E=>S(j2)ybdwkfZd&tRUq zkYtOh0Sp8FVbEk3%Q?CL=h?FkQ@ApClMw^~KjNzFeF^z=q|X@T<;X zg19*IACLxe;b}lkAlzE33zB3`DSn4HT)u1uf}OArko}LfNlsJKkW&TIFime?LJas2 zArVRC1_14KVCe#cPB2p9g;pN8xuN8+5}9U zHW#wQvo@&$JDw&FGT3URb|`{oHQltzQ}6Ze>_k{#xiI5_0$>x<2m#8!FH-HK8Wj29 zI6wu4Q>ljcji1r(YUnGpBtmJJAJ)kXO5nE{SEVFhyybQBC!pVu7a4r;hiGk;yPesQ zgcEvbP60dYX?y|1!QA-3`Nyfc6`X0#O>>Uw$&!#Vd&4=8umK8E8lukeUNuY$5{W=bY){RVdEo3E~_%_E>9Gy&vQJH6qjYj^7fMu6edcgumh?P|yJleu7H-}gWwP@ai zj{N8zTo0c(KF2^5!5D}jI$7Z9{F!Ds{@D&O#f0aW}db1M~rmBDj&{ez6oD076z?+Hct@ypCrnk4DUP zAYI;Mf*;V*P5ax@rBeD>XFKBYBtabK;NN-<*@4?{mEE~9naFJIf~5zuutTmI&2h{N z=-xMIR=b0x=MqcO4k`0j#&L>?n$UuPxQ47)7iaLI(Q?z-D9sfriQ2L}(JqEHVshus zloX*E+2M@xL2_NuzGn-UoX%vX=XxD+TTr?3u=UPOt%L8*H+hN7&$YjIDR^71^r8jJ>a)&b?-Rb%NHH*Z|9;{m`XA^u4tQxGWK*x8vLiBM1?^u5#s4LgBI zKx71}?qQK^5dXxx1c{WY%8T}OIP#4jY%MBOFU#7)sD~*$LKlFKP_-PswTkS)u1MW! zU>)2MRkN-o+gEJiYiXxscF3rEkOm|-d|`E$8Zym7-iow9)aeFT&8_?5ZoY%;Jnbwg zVwP&T#@PIS8wViYGY>eZP*Z}P9RYvH5$3=CJ_OCO)PX|^RNKJj<%D`B@1Na14dyeq zQM$xL(}C?mTct1w(rC_n)j4$0fPDyt1hMqVf(yzJgBrvTMQuTK$dWI>trCL{HKJ@- zK)P9Kx^+dd*34FdEmQ$hcw{tyOdi4LgvozFl>h`we)OQ21_VP8O;CLYKoa^qpy&Js z(Gr>UBK!yA=$p(86X=+Oq|m^w<-}$2hAlTCPkvZNu`J(xAHYYU@#JOXY=wSii*g&c zrDekRHU#wMJ21x5od%JB`2x7O+3#=*%hw-_l{XXLQVfg_kZk_x*5-@;2k(t9dtpBU zXw*MF0Em+*Ok2PH&tc^HFL;A1fG;LPKJe$jsLMXg++c3x+KVK)gko=4Mh|`m_FV-d-}{~5DzwufO7`elYHARJKp1p!n4RB)XDj-AlYLvU_TSvM)_Kl52K8pqzPdKVI5O~DSPU+MX ztq)~CJ_;pz3>tI>`AgH#vJd#MAbcUW=Mz-KxlvmS+bRU*8ZfjJ74gyVH$W!>Lns9H zS}zZ~>wg0SjkwYY@L=LgQP_&n-4~3N@fT5x0dOh_%a<;XP8e@E=I5^D%x`5d%(c{w zj4#0rB?>anAFMVbPH^a(o$kq=AC899LCiyCa3qZHlY|Q4Aar0N(B}g=3`}Vk1;3ys z1TxSw%4f#dTU?I^FkCDjPC;cg3D%}iWZLG;01kBpS34Rmgz9_Ao8OH|-hWRE;Xpyw z^liJpKdulIr@19gZTLbR^F6TTpB>wa9P{Dt_`o#gKg@8F{(UgzvW%$a0bVu&AmB4_ zd_twYe!dAea`Z+$Z|G`R07nB^m~?w?n(eVD??gQfKs*t9qaL*vp=Ia^(iz8M1TD{2 zk!vyUc5>BCWu@{XBRs>iXaOlZ-$lE%70$m<)ck@5E;meH4n!STWP|qGBdbIugzzH* zTP-CD(neQt8%Gi?iJ`$?F9-y&l==vM2oI3`eLXx+*A{R1>9~(xLd$0rU&-;c&2@Q$k%L zD zkQ@Wt6h$HKK<^r{w}=qMBY+x^|YsOcksKC|GX7Y*~K-TmN^i{1YF59I7x#zR+A zLG5=D_&s0O3-$gimO75r;N0;wCk1}0Hq`H z4m5F(9Yf0lx~0uF6*t2jN3={_XHJ#FjBVU>qAaoA2wt`VB z$Z`@+;AY;<$PR}ux)fkB2%u^a&PzzhQFacdo7vrl$a)hI?gFCk0%TzUB%SuHdmwvu ztF;dqEu?6pC0*Bm(h62fJo6}vRHtl${CuFa&!MKq@#&?=fiX z86nxTq`t)FqtnJf?tx%;rr|lbt;*HT#}y!o1GptQ(UFyOEwJ>MY#C_fngQye_#ZLj z&CW%Uao{~bq~{QuH;{dX>f>?MPz0SVvF z1bHE@p$*HqX^7Rg^aA?6+n6(9+`2(Oo&l2!rc%LT@l~1TR&0EofT$NhdxUBdAS-CE ziTSU78xUdU#w;w^Hn5_5pe-00inwX8D~$5D3cYNg*KE#K*vj~Vp?jQtTOYO=ntQ-> z-UGYElBYS}^Kvwe1S%g<)JJD$hoKiypunv8?|L7# zob{OkU~dA4jo{7gUW%q&w64~8=>;M@V_X6a3@NpR zxSq?~RSp*cfk~n4Fcn^vH{Yi`Y`gPr^npI+BkNAl$gls46Lbi@2@@t@$Bslw=X7^` zJ%+$p@Lv3$e|tdBBT1XYLl-^)oYdslR)KPVB*FY%e7|l5XJU5bzyE-o z2w)TP1LVe+l?Kd1+u}b)a-jJqouo}`aUp?LD3e5L0dhrsTWC9N#uGsW{#bx3nNLSK zxf{qJ{||#Spf6olc4>TmeM~fn8&Q!|RNSY#&mtG#)l|k>s$kTQUsL``y3;bD8PGrg zjXJRT_t`YNf{Nd^1j{8J--xhVFhMKmB%x6(;O+@#+QCECXwXqriA~gWm&A6iv{I@qk-l+SyJlj9z2?ZPi zWk6yISJ&Cuxe5&+ZiqAyp`pkK@*0dEM|`-~+9zXk&^>!HHNGc0rhwOI&E=HKT-Sr< z(O3bxPtMHl*?vj-Qn5twd}L^7S0KivTI#lb#X5hn3t493!%@uJlwn}fl(q&6w630` zi>(iPe}1r%@#6eP{a4DIL*5@hQeLwA*;?oJFT~e>bD;%>Y&@58{@I){N~iQ>gs3!n zBHGmO`aUuHp+CxJIt+;S!`uW-@u54GBO|l~4sWbX8je$Q^tl?3x8u05BP+k2GcBy_ zY~RMx7Z(W-K}G=TvB7x+P(Gw}iU8#!)B({sj~`113r@^T9#+3X?TFWp!Cqo;uEN9N zK+T&-813-%+|B*S1_Rw5l1mT|08Uy?@jp~OX$}$KP$%nABs7}lkQ>1W$YPVALBaJJzkObA6iQ8_!}HPB}B+>J~lN zwrY=t31KJ!Y<2Rf&QyXQHklrGXsA^4Wg}zlfL0^W^R7=(w-w!n<_6`%@fa(UoG?;q z&VF$%Xz(CKhdUK@pWSB-m%0A6?HKS0kX0v>yM?TvTapsi_T1Y{N;+R!GsIB?JrYVk z$eK`+54BN`nW#2~N5hMz+3aYT`d}hy<-z^3pMvfUNv+qM?V(3LXpR`IdF6&3%({I4 z@6|?*tomU7DyOB9bPiQ97YYctLs|2lt5tDcg_#fKRA)+iY6`n~#TNlFzmECbUe@l~ zLYFH8bJX3e)9hX*em32rEie@2&Yk_myc7r=b;?AqV&Y4|ubEP^*&XOF7bZMv=A;S8;+WlYoCn&KNjYvyS_BM*lL-fIS8cm45H%57tU4#|D|tv ze-ms8DF<;uU&mIx$7zu>e(sKZ1O-Z(%-Co8lmnl!bcA^Tv2S&~fN}HH^Lw-FGnWeL zf*PMF;2gEE(Rrhl(>C42(pbev8AGG-61J53(nHpCs(%l(*CS`HBoEfHGIy!P#KB)F@;)^f$N9(a!b(WS&L$?DaQhh(^e<25GRdZuE7U~V(SXo2= zU3xfl$9kQXsLUz3QU{nlA9eU(gRl}^gExrcRA}!(VNKo9%~x3d9S>KJVj(#ZnIx$vn{XQnr*T)B>_BI=pV6b2aN6ECL&)n#< z3tpH9Wch3tY~-@Kgkr>xsUqi!$>BW?#JCt+-BoS{xss+If{vH*iMw=G-pr73^B%`= z1qIC|m%DzIuwG6T_rlOVFE?5YRG-95#fM}Tu=ygrWF=X4_h|GP&^sa|CCRJ;56Qj|)&D+YKp12(8>+!4qFL$mD zt<1Qn#Ip(mAr4#CO61AnJrq>#HS3wWLl=j`(QPhlVCoq)_?OlZ<)f4AAd^~sGb=iF z)$GkLwJYr*f_en4XjyT&vQ-+vuian#sD|Jwxh7oEZy-IKF}(jsVkXQsK&I2*JKX)^ z%pnVc9OCUz8zZr*J2hM=KQwTo$gP?sb`Y>#M`$@C&RsnW3g{&KFb(OsiR;Icm~I_) zRYcW>w0&&7?9;2Pl*33D0gpSuwuyf7tTv|jsYuvO&3N6_+8TM=l#25{Uw(g9>#dUz zV8FrED33&=66kzjr>i;2TZt4%Je(C!Fmf^ZvG{IJ(;<25>;Bf{qk4xA^i{mL&Tc;7 zSnsG(cVCp`U23cvu=Z&iH;MG1C6Tvqh5t`;QPE+ul5{An7;AirGck;Slh-c1aao*R zT+^sMzTAI_Wv_=W6uFNd+NR`G0mYadrr5+*9A0^6J91WnHf+-I+ym2^ni2}qE81e| zusk+wj?cI;lH^}8P7@V?%%evOOIW?z06T1aT-1!)fc^z1HXlFnq9ECC{vM`1$h_S! zVQ+UecSL+$WIYFCWhB-)fiaS}+E05{^kz1QoOeznTH4k*NKsHT{;9C|--J-7ke0z4 z3AQ`BGnwu8-O$Y5v1@Oi8oBB0rfU;w9638Y!N`~wp;q-!33P5F zpUq0z&w%a(g6ui?XCPUPLM<*pCJ;3aagCr*X!HE2Ec@b^g$fzwj_FB06K?MCTWYm) z5d}rAbDmK>{^4w*GlNoJSg4*@7RS%lC(GAapziKCd~Vnb`_~D6J_CyRDQ7KE`wn|K zfB_QaKG;1DKZLPjC)nGoCB=@j%#GAq%jWF#l-fJo|9R>D~(8dhP3-EZA?+d)g>%( zbRMr;cAeRQync4La>hq&liuk!3%{w>6>f+#gqQ2o`{6EAc8D9qIEh;A>PtZ*%>I7B zLHP6JF+?y&YWi~qB~FmuY|euc1jiOl=zk+ZC1@-eF9s3)5K;3PsWsH-$3>MZyXnc+ zUQ;z*+6VWjs;U%n%?cNuxEQ2kdBydrM8hFB%X6S?iDceh4nJF#cnwVX*?8 z>Ia*r9;M7@1O;Dr7Nbh1&a(rVkU+^I=1{tw!%G+M+>t-EEc}CJbCX4P30sjI%35_* zhTNxP&*ZOk1bLbzQEo@3O2Xpy$)+_8>9_u^p<}(BcS*zwZ=9}H;`va;Z=St zcb`aVj02jEMg#^!Zf6b1SN$M#@bwGZu>qx?!)?NuP>}}ejEAgz&wIp*IK&Y*7hwiA z1E|tW0)H#25&s*3vwhT)-Ku09gDGG=&_|zCS9+!C7OmFc00|9>W z@mN6-d9ex)2h~gC9z4>g2lj;v(M><>s{e@B$>}A@p4)Dp)v#}-F_!qq+;wnpxgA`F zG@DulmW0HDx<#dfW{)~5T7SM6w%qK}mitX+tbnh(K>d8K`O-?Ta;r>%*8Ijuvp`#< zBu_Cl(B)Q)vn*S}Hv5cuAvb9J&~nMHvcQDM{i6j5#}Lwc>=@!{Av!{@{lAvkwtv2` z1cbmH{b-A7o792*blvtVV$iEpsp4bIZ;Ly zA5_)QKkbk^G2l|GRPZZNMdh4ng~+(DS==2iE)KNp1X<1>$1;B^#b{;9irR|bmzMAb;z0aq=P%xmq|G;=l)AS*5gv4Q{ zxjViw>{F`kZeLh4TQ=CSz4X!=DU~4`==#J-)7B$0;Ku}W@0s^!`b$n8mOcZG)q6En z4LUV)jXVM(eZ+v_!P3a$-OFty(oScif@&Xauvy+4qJHI8KQW*c=>8%#HqtUG+rKY7XP(Wq)!HmjGA2>L9^Wi7~yq(1uN(llPM(cVD0k)^%d4^%?eD zrDq?#et1V!S(Mkx&oVnoo4G@Hle}@*q8BF`+CR)wYwUkYxVO;=>>~j%E4Udq2T^Uk zHg_4MuIiURF7Nyibx43`e>%q!=G>XVI6nU=P4lJ_V0q2vy4=_|EAX$kKK5O#LQB*5 zg48(Me9(_COaN9+`qETMO!g|F3@opB+9f5Bhh{$KW~b0njNw0F9UAuU?fG_wcD1VXA&G?iCC+B{Gr zlZ-c~s`%4W)QQPAtIRqEb8gIde}1T$L1*38HXIWf!Qm$>dN4bn(!-Hs2P^sMq@VlO z#HtFpDGJv}G{fsl+9ZS(6_v%g4#l~(<(HUd1ud124Z>B`6E|$xLPdkdzh3b({g}Br zMNS z5s_iJVAJs;aHG%A=&*y7bL7jJUfYctL2Y?x4T-=GH0ZLP)mw|JFfhTW9^|xPvLyYR zg|Sv;vCgpGXwwF`Ci|$jbf9TudD51$!J#W}HSU$_W)-4b&Qyb$JYd>MxFIY#l86QStNg+QvU zWw)_FLj<`se61O;_M)q!RIb zBIXj=BHsHs5#$2LogvSIzR;&+@71RpKoYKuIk|#8zT*ACK&qa!X zS6vtRYFKAckzmZWHw6eS7Oh>iNH35BLtDoW7ogdw^4Sm3-C-n2kuKjG9Eh!6Me`}G zzd4$Gtr28^7C`DiBHWsxQa@A-jt*)9Yw;U@e!g+%l>4cFJsg;A8Tok2bD;C2n8R3j zZge5svW*4&HAUg)Qg$%fV8NguTnQ)g%RT?)j|bLI48-V)Kg$aRKdmYEI(*QQ z)s^RpO11Q`1L;?b8*~f{ju!ps~I-+^!+ZZK= z{>!;kthb&Ct&~s~6!>LUhVeoX*a{a*>_=qsD<}JgM~ZKEJzWKI#%K^j?$pyfBV6Nz zw6kH7oy&lU(W`YBSLm!U(v>|Vac^K|sP^NEpTIe@{Srmr9?!(#=bjj9Sm@+% zAU=nF7>Gke)=Bn8C~A%$*S?^4n>M>a8LGsAq8*SQN%C4L-!%aTc6hmwgQ@tZ zi<~d^#tXlC_uYK?)ID~}c;o}U!(7?2sWKa(Dt?P~cM5$?$0lA~Vw?P#;WOMXmTewW zV`i*tD5~i9C9Qm(W;yz?gIu=~wBephc;kb`<3+F4Du@c_(c&k{o>XOB+&M0jHqJL( zz+dAh4C}_2BJn%_6Gyx zy&{i>@bACRHp{(J-hYbTbWy$Wc@z&(>wwu{7H)U9YHw}-XoGA2Ep_Oan z5o!9MrJ~Q`aGE$AX0wR|nho+&gV5UYTv8~%d^8-*Ur*1@9u?S*#;IXoT&K#-n>Sx{ zO(C@mLfAv}$o(+GB(~TVO-$|h%qYtd7KL^ofak7KQnRfIHILuT{?T7-K^nh$laCnM zW(sCejK;9j>dic{uJ2e_emA|Wa#2@JAXyq~f9W!9d<=6lhdn#saF1wsam-xuWX^0w zHHS_hb>j8I7Lw8VFx>Eb!emdy)lBmH2STGU?KPal)9qoZIlc8|PE&UH^vk?mRSqT! zR?3U=pEEKfeLb8Hun~N%O&GYCMz_a~w}0?Z&Dd2hW_5SIwA(Un2wVb3s~=PAv~b{z z$`!CEO2SJFJ?uv##oFW$B#yfm>0(p6>b z>>n*beooh`6@tEq%nX)089SO3pW@8Fd7%B;+L3jyxr0jOlht zuHL;lXTZNhMa4qsYiKCwR*t~vh`1gxpr*yg#a4j46A>>o2-48iTJ1`uKe>|%hQMKY z8A6YDv#h~^+K77VGBEAb5@dl$YK)95p+2iegX&~}d9~tAM#Bn&7%M@@^}5qC(^=J7ikv8xZLuUuFKhN&kZhF58T@ zHkf+t4_pw>hQ7Z`U$Kr+nJ@-?EpseKih3xMn&gJ7@hJz*YbIAAYf>dJOoDWhR%(iMggTQ2y$CZ6N-V*m&vJ z_=~-9j^SRJ!lCG2oYICbZQ3t2x^{Pv`^fmk;lcUUb!|VSy0p7C4+;YO-u+!pFJNoj!60{a z%riV+2>teQRP!{SfV9GTyGG1D?VBf0o|NZ{dKM8^fqaV6O~Eh`P#&a9&|R;Ersdg= zIUu#irR@01we4<-A8bJjV7owPUEQ`9aRV_(<_Pi~@rl*Ut|@px6fn}1p;K`Tc}3c{ z_sB|P)C*$i`Z9?J2Rb8%S#nW^N4Il*wAYqG*!DKfU6km zvW%A%>WxPslk_FJDJMVH$6&m`jE^DjYvNC?E|v&@LtEmxq+RTiUK`7p0yt$C0^#aP z6{t5bFgTXH`AghsMGzbFp*o;Izr6t_;~qqonx|S!!EF)|787P5+lk~?kXx9D3Gm;5 z^O+lDyjEbe{bqo{TJ@N8OA3FHJcmXKBK4sD)JV!i636bO_+c*LZbjRt#tGEFOV})o zpE7D}j$6@bnA4CAk3a&w62%wSy8oV0w!oSk5ciHg`Qz`yj9;!nZX=KPf19ed7m=9w70l7nKl^is3T`nS2T+9qd)f79Fh z8GczLAsL$%W6l`dH_r!i(A_%0Pn*cW5N)yKx9)5G>LhU%vr^AXj)PC5fB!wE_L-51 zt|)2|3hBq|miI2_C7v9JtKiD!o`@Tyul1iLtKWP|AcGB5^=U1f^vPQPrpq0#De7Qx z%5ks`aE#FhY?*q-``*%D0geL4%CVpJ6!V1Xs(;vv28V6DMQl_Us>S5d1_IZ;5in4R z98<5-=_O*VyBr^)XaEIB3TXj{Q(}w{zzs)aA!jfXk%7mSLyPRXCNI3OvH?R8`4P<5 zY^q%&C*{ByAn$Q)QDG%;85=Z$-?SyYm`d8#`Yo)W?vaN>vA%$|ys7K2saLe2r9GFc zD&`T`<5}95obQxqd(uj=#JI|CI9DgL#)9&?^!=Ypc#Th$H-t4VO~w^+0!3_W8_MV4<3ABVO$|v@Oo*|BtTtg>*QkRz)d)=~MKcOX z8{7QuqWJ}IbR@mhI4b{eQSsbQo~!8%B`-BCdYfibWc&Y`stsw0n!#&zuhkQ^!bSOm zuD^7Uo%!^BH1`rVq+>?fB|0K#B}YSPcQ$`~(x=lMf!-#N5SqFEN>K4$Xlr@>pvAQL z!Lfz-f>(ets-+RYzT z*WSLLZa4DccCJy??JNh|HuIDpg8e)kc*xhx4Zjq!P7a*v;AFW(@^nrGOpT3GG>p5s zn2H;WZcWtF{7@~ei9h9a^-lj?g5vvaeTg7fIIT$K|K*l4;wiFu>6&>wXkpPi$}-Z9 zR6L;V$!CuDgq4X{35JChs<~!0a%hGw5+A_*R0HY$3jNLDZ)h4Gz;TCx0TKW@!Xr&h zO-y?TXx?841cMKzmF$+fq}!|m_bp{Q1iZIg}U+mME(BTq8|N zCetNmvhtF5)l10wB{S#Ue#6|ZQSoy)IT6#DCvT1;av6IrzH6z+bOw_q-1&X%@ISdo z50_a#b`2sFt=@I^^s;6tnn_ZG<;ZAyS5ZB&Bzu7pNZwG5DCQS8sOxpLEg*a6%PEWO z1$lNyo9Zvdd`Bge^?nRqu>?VTC?pv@?%Z*nh$@Mcd&qyB27-AYrGRrvyTd3?3230tA#v6OOPi?<7{Q>}^dPF!x`ehTaTS#D}DJ_1w#I#vsPLTz=r9-(! zpY$bGCJx)1Yl!u6({G%_zjD+6b$q9Teq$@czoc_z-&BCz-u^GNFZhYJu#gddg)R|> zFec5@&JcH|jjvC>!ywMhXJx$e0QKYm;FR%%`3lX%@s2#xh-X#qFqg+=C{1R4Y4+J< zVsVM<;M?szS+_URWoB02?+=JkVCcuCl-~L{twQY!R3LH%ao_ktZ4N2l^E5xYXt=t; zhq&QW=z8^Xi9KCMyED%+=MaFkSG;YP%^E8fTQc^OxK3TkyBnpvB#(7*jrP6M&|+A1 zw#qzHzUcX{Z6K8EKz0g4%$n$P||$u|e;1#t)aQCC{9Te9U^`OhDuf)D_5sq;l)G$zfJW6ZFO~JKS}7Cg1Dj&ZKT~+ zZsUU)rDxCX)9Wd|OB;PfrH-l7+gv9O*z*2qrzAN(Je*%SoQOZ&9<_7KbI&R{Q8X;t zDUrPXkZ}Xhn?V2fsu*RwR2aP`d5Ze>xM<`0zCm29G*rrUWuL}MU53vGu9ogO5b&P9 zW}j>1km#G8cTHh6&CVoK$HH=i z=U;EB3E*dKU3P7*C&%C**gyl-IUN{y9bf?k-JozF#@2(7lZfVfufj_MFwKId4U8l12+#I!QQpFt4DohNc)*fPzf45A(`FK|t zfumO_^uX@LaNGvX4b$=JW8SQ~RuX4{R!VUrML%!&XPnPuy$a!~K`J}eJjkrXp>*TI z7vT5W^7{Qo+n966a0nqyycE4zD@FgdDipUazrAOsx;EtW0IEEjifDnNTt^qAuPL1z zIDe;lWe-DZ;o-i~iPUNtR(rfu2ji_Knowurw(DCUvYcQtW0?+a4N(FC-zsw4eunc6 zP0U7O1*WWvKpU4PBF?wK#-w^j^;!zcb@LPkab{C);#b!uAKHFu+Z`9<#T2gQk_$w%fg8&)TdXyeAW8PoG7K#)X1Ge z({1rzZw0wg0JUA{uIl1ySd`UVnM%-#^p%g6`9JKvXH-<#+AdmZtKG&{L<|IT009vZ z0SPLKq9i3qMnFjdA~~bB0T8fNR5F4f86{^72uN%sBS9zu$&xdiXBN=*zW4ji-sg;S z?yt)jyLY!zRjbyT^PO*a!pn231dTO9W3LRJ?$6}(+e+*0823k9ig(aD9HzbjDcpnH z)vlH{xkclJ)GzP=y^}lHgSGwVJqx$;Wrj^FW?crC<-RIm{(a@M70|$Zh zcBUn@!_MU%XA)SN?p0c^w_#c z%bGhHQYZ=RdiJi(Q*BDwS0ZdZnW2nugCR&_p{;y$?eFT-pHBAYP7bmjUM9_5(qVe3 zWx8?Fbz@#pYps3uv%-g|4wow*R~X30Ly^5CJJig<^gw4_eIFj zZWl>zE%J?Qhs3<@TwB z@e#Vrjr=hmL-BIGfpRsn*9sNm9jNG<5 zj_6KJhU_<)ekJAdVNG{RVVC$3Bj>$pR%|ovv07RfV~Be!k&QK-+@;XwZpAs-HyVLT z_tN8u;o!5=6Sf(l*S;i;b>*Dx?_yAo8L;lAP-NXHM?Kh8QVt41>OBuVF78D-{C@7F z%p(ZEqpeRa27z}d(7;PXZ^$A?=7|c$Gu0Ak|9-DhyFCPpsPK3e16kF0d6 zS}y6P>x4lr%4_TL0825+yyslTiuE&vjvtvuJ?`I5OLS`#{31r$i6+4lZ>|(pT~dzQ z-^QUt-zTBIY3mWg+V+0`t+NiKA^E-PjoeUQ17BUT(Z*AEhBYyzBX4%z`w%A{Lh<{i zRr!fCegdbe0f6aYS5D3~y%;y^&{zNAu{vuB{Kp5z zv+okDv_C6zISzM5RS;p(m!Ch!X7E6&Zw&f_{=e}N4JLv%0}eX=eW?}tB2;M3xBy>& zMZUnH^J0Jt=`is{9kV287*gaM>(8NcmOh3aFfnoiK_s$y1zYDkrXFkn_?cd^C%5CAiE# zea0a#A@S_2d*(#XR)e%Z_^BDod!swn-?*A%&Rc=E>b`{mfj&8uATXr!z6qq9+(OQ5 zMj%o&Li|l3Iq?x%^&3FgEr{~9hixS>!!Cw)_N!JxT2ktL>N&|!)0~Xkv+5wcKQiRF2gUmq5ydGs|f>fyi2B_q};#Z7O+vjO4 ziTAjj28nIz+#AdLr&3LolCF7q>d+we-x8l(1eHMzREBWC2|-oD9)lkG)mAT%i!fg@ zZpu=$-ZOWR4+VjmFo*icX;5x}pCjm8ur7Pc2Z=-&p$s9-Ak{C(yBu4u&^~}dQ3N0O zB5B|dl63hDfrMk6D`!)M9uJia99k5YE}iL^`v?)kogWLeNXDQA*$Pa%v_8K)+wUx8 zw*%5Ztiun`lNig{E3}R@i|*eiA|>mV(Z1SJfDf6 z(pa&v16`y-Y$G)j|8xgrvYPy{6;vMqsUs?3;B$%M!9~2VnY)<4xD^Iz6xKeX!x<7I zjD(OF@e+R1rIhQJRUD^>`@uUBBpeUu`8wnKh(ThBkk*M?5kb1s)Qf@i#;LGw9idnd zK}ISVzR+?cVjW$MbH3#iji#5MS@zmhY=)A{pW<@%x80a#)w3eoyL(30$+Qms@hOJe z!P;2j8RfNDxq8DTvXA7aJ$($)esL1iH@^_?&9O)$Sq=7WJgO)BOy2eNMq*#Ig_k;J zhMuEnFo62Fwuk*#V|ibB$B@vS()pX+3t@(RE0bxY6@B3@JxZ9-;;;O=Sgky&^G{ED zC6O5*8aQ~PxgY%(x4eInonJ4WW6S1_ixF@fj|CMXOx0i*ny2~wVunnK;4Y)=JQ=xr<)R-Bfl*Nhi0FGB4PXyedHlfn1=?|i z5SAk^?)(_!&WOQOtzL*Rt7pW}AW{L(-uVr1LkfcrVq&&8!0yHY#kgJT>Kzz=QMl5j zGyjlB$dZYL3&tC(exe>vtm*wE#X#4k64*Fo08fL$5DfS@kLtok*o{w41R$3va2|SR zjEF)@rBuu-<#^^8>u()CfI2P9i}w8%FjcO)wqtJqapUo0o>)Gha$JHz#OMG{xWRU! zu+uIynz|)wo^unnhI|6Gvx%s|pd?wXFwai^=FJ{BC=0ItA;-ofL*>_29gV+cQZ41idex?$W&-nGAU9-4@f|*C8 zxe)ilJ3i0dbf*JsQ5}o(Gs*l=lE33+3dX%;Z<+KYR_7uY9Vf>%FO^3Rm5Wvw5eR4f5;YqE~FCc z2O~~`G+_q{m@y=(0-+~B7{^5sO`w40K^upG zID1Ph=q-P{PY$h?9OrfGyi6UT61}K-J|=wQW5byM01UUI5#F7IGoDK{V%OW5{jI-i zP$Im@Kpgzrb?dNbi2)9e$~^H)nh7I|2+)STgr7ZmQlT+VUKcSZ5Dv&|i$P@z*$<&X zU#m+94_)BM-8c{24RNF=8rGk{@8DKDUF$c@yPeJtMEx0aGf?$|VG3FA%zbVl^t zFU4t}{nzC(3<>O+iR$drSihw2Nnkq9LAR24HqSf!cAs*zRMo8P6j4MYJ_1DNm`mfW z(^$8T@K!QHAumh1R(qj*LH%0s!lm-14+U_XdO8&4hi z;8O5e8)4xRb)~(f^BX@x65)y@6ydTKyqnIxDpzz|2s%uQHAeRNN$SiT7@NPuI*_y5 zEmmERY@znGVa7*D&N5#Fjwigd#Y{WQuAow;=h@i)?Fg{b?)oDjrrB-tS+!9gr`jEJc0orG9G16frq%jY2u&S4};!nGw1nN z82##08+>!g^4xjq}vEzn+Vc~M|J(&r%Ah0p0Ch@ZY|^Ww~G+W(C+z{VD@A$CG5DFK=aQAUeeD*a-2W;yL9~XnH)t+bLp}cRoM}%Dn)Ufd-#;caB&} zp%g$QX*+S&!+xQi<3z=!^7^T{YZ0-=chV&}C$WzujgYJIvqvzRl0+7~C1yVS?u&hm zM9~tBU$v0QBS!U`7LGuuaBbeoYXq7bz9<$jVJ`1GFeEj92I_C!ug08?(8L{S5!p#) z4TeXX*X~{>aLwTdYC*3yRaRpHd!l z3nCzlXZe2F)4cDqs!b$$ce z?xDm`5%ZBF&HYrYM=rxr&E{0zVWzq2CXHb&@=S7Ix0b;I48dzi$EPAc!>IAhX z0PwjMZ`Sz`Tyq;~l2aX(jNG75p40kFotfaR_xzA}RZjBK{D<4QOj>fRhRVbYQZA4W zcjDjSnI}xC5SORW5s~&jhtYImV1IvX-w%n93tnGGQP_BpHkt`6?klf{%w6$Y47x_O zXl$&}^d>h#N!*dNJe%cSHnb76X|6{^F8c1@eDC)g|C)ROG042VaLCz&Blg(r)Sy?i zzwjCd93o6>1&5^1dmlc#pc7?|BWm(bNDSokBsVb=HUv5C1fs`!5VL8Wc^s;zkKgQ@ zyFuNNPvUPZB6RBMw**c?(&uAjza*fX0PKuBD%*GWW$bSblRF{0n%x7UT@pG2>1*L3(%Yo ziJ0zTR3v|YMw}?cSBywTk|Ah4#s?%5qU~z0ugRcz^n;PhM~rfbDA;O+dF?1|Xd{u#QKCJ@X|agVl@yKaos^glWZJjZaqJ{* zf(i~r_lBvBkeDFCuLQi0Ts!!x>s&mi+>oTG++%`u9Z`0`*SLQl_3B}wXqmVD9^-|_ z3#VY^8T?)`^z{*~c8m$4fk%b$$z>v$fp7?(pYH>*b9|4@=bB;si>@={EFo*@Xiq}< z4Sq?SJ0PyKnb882lifoi#DjjJ@7@cuqpU4d&$shSYX*v1|<$8in)J%xc8U0^M7c4;^!ZdN{jZB zuMjRP0foGo+!Y#gfATL;<7X&oUN!gk`Ri!m=BMV+e;01<@<$n4?!8Y*{BpTr3kg=~ za-3J(7wad-6lb(A$zQ&|oR3oZQ;X=IvFhii{v&|h;ZdEtz;DsA5)A)0u9&u;oc`Yd z@*g?If0cMRzh9`&2ILkN;@s8@y91LBtKGg|(O#BLnG`ze&F@bq5XgeY~jd`Xsv6p7k0sCk-P zftH913cKEsI^s@CmOH7qse!lG6ezigG_)CPr`^1_4zVdIDW~&lGptP+p;Th>cG2=2 z_ysaT7^q$gd=#=l#|TfJl-Lj|5I~IQ5MS20X};3jCU#-Xq8M(Xv+3(WhQ~+So@-f* zrz2B`jLdru$7+!JzXMGLG0Z(k)M?Hz&}h@bwdx3%H%^bQUC;4|3zFK06Ccaz0nQTDfKtOS@;pw zMVepV-h|Z7lko<$N2rkabR%gGCL@nKpiwYMg+8lWEWB3bA{g@5&abdQNq*>{w!HqZ zCUVFg%>?lT56H@Wua0fvh&4pULy7=TW-Y+r1^o7Bq{H$RC<42B5Y~ve^qHQkzgeUG zKrLX4eLxg;X&U3GsSagNLjIZoxF$FGXeGI{tJ{PZycTMV(RyXa87I7Zf^~h7d=+Z7 z=em9SwcW?QU;9d^s5C(;oMNe8eprg(&OIQ`CR=!od^?oULp=ugja4rGG zyN>83kw+G(l>yaPkb5l6V8*$p4mC}2?In?(1(nN@m86A0uoPtq@bd=srBMcj0bLIO z*59rj2pm~Z2A#ZfkJ)A)lCS4_$>1WCfVCFoE~C1A&>hO-4|S0Z@V}xiO7xju_%)nQ z)V*v;2M$4AK9A_uW)R)pG9esgru@v(=~pb}=W?Y(7s9<`oILDN?vku{?*!4YiSoE8 zq@4vtF90O^XbB>A#;%DQOkkq;(9hi~kwyp@T?(?FM~BDr=VE@PVmv!V{B1zr3D8T% z9iT03_undvxQ29EqQ-T-qo+MLYz0Q+B2=?Rf`%Le1X#R4)sZh-7d?8w^jq zAtExbV7t1HjjeEKHT1d3Y5eWl`9P!j2Zu6eNs7h3>_ej@>BIqnDJ}AY_G8*JH3$Jz zx_~iJq7PLLP;2>Em-_4$RB?r$o{KarMDlIM(XfXgdPSn@9P(}CMmt+@oM^j$pX6aA zgbM&P|rxumpn{d9@EBC zSlhF`*jZ;n{Q!f7BNGiyyO+Ai++!i`B@mS*2=C~pJ;=n=Wt5P1-@ot9-3c2-F;TmA zfhK3trgYAl1@+v;ASQKBm)0V4u&`;n;6~26w>$>e>!d-tXCgvtvK{;wqzVf4$h4ia zNb`>fe`%XD2KJiHn~OYrM&k>Z@s|t1ttN=HUYWEIQ?d97l`@x<*`iJ0-w%^~G@gIW zLokqNA1BgCPIusbapRK2nX#yLXcO#`{K+;fi9C$?Dn*+}gDC}iN^wV)Jyieug`3d^ zB3SQoVgm6uB)nE-dXi+gBc=@DPgdvNR9)Cn);g#Wokx~3sQeCnbx^{H zRF-1?6nd#Sn6L+{TXGzLcZiyaSE-{jT?9b$s-L~v;7g{rd@=$?zj$F!iun%p( zg%j!4xd|{rwIG2y*QA(eh4vB3E_#25&`Nq;TDp87VEz&1v$ZO;f$xvN{k%a!2BX?J|aMFMhiTD zhzIB&qpa~ZA_u}YodH#<;DSVo`9fSFE2GjF%4aA6xO<3o5zbt2PqEJ7Qb0-(I(xu_ ziTf6-pmE;pR>~y9yow~nn&@j`OUM)U#42>bG-ywM8)wEpKt?GAY`UY`_yDqZt~y1; zEl55y5YGht+6gN(+7unzz--q1a&;Tq-*FqjnqKq-*C`b1Ak?(9&v&x( zjObWQsp(tTh?M^($y-p0;-l$4{@Yw@Aujxf8Q%QeK$R(jCK2t*M6$#~^wSnoMtB!u zC*^;&SF>;|{mTm%^e(@hAx1^A-2blYJ-_5A+oU#BnbFoi<)yZ`m``FW4U`Ie&XL=yudum6|U)PI&XY7rr!|H*6rS8v0- z=h8MY#zm_{%;V=L2e>Q$7!drtRX_hw^fK~m+Ru2zHo0p;3WZ0PZH16}h+BmL_F{iOeTb@my#C4?&EA2U6eHx$hp$x~ zRI)UrGsGD9Nr(qjy(KBDq;CtAJDy=IABJs{LF?Vb{@XH+_2IK`tNf^Jd8*!?TswBm zjCzV`=DI9RpSF*@TO8z=r&gA3nEbsq;^KyCwz{6gt><|2x>gF^H;Hd_Y26(eFTe94 zlL8~xu4PyB+?C#3Hmo0iUt%g7i+41*9nUK^tvR)`zgevW} zFvgb7Zp$sPVSDNo94J4aRA8__toZb7^PT4-1i)(x2b_gO*E77?9=Dh!$POIGm zb06NE`bt;J@pPN;TMZ5xsUg=4qV_4$naXpzf9#UasfvqsZeplnd*Hf8fQzHUR6Ob2 zGJWSy=WYm#MK@$@Z|lD5&RSE*n3$n0QKXo7bYss|_q4Gebkswp8@}@Z(zgrfgGnuKDYyzZtXYXE%6^<*xy}j~EzAQ+qFihczN>9372|V9=_a0If{V=nK z)+v%iT&oC_;okG{_C%T4$FBn+Laf zjl5jSl9OciQPNvkCT@?K&#c3FRUzKC7`63ZK7Ub2-#}mC>cC|CF*S3!Smf#AL(_YQ zzO$UGihc0xYWBqto15J>%^%M8*G(0a%JghWzF#=GyFKi;bg$rzQL4fd?}j(m$55mA9@<4jylgKvDaI-R6QQdi7#N;kX@9}I>;(``b#eR zdX`J4yJv2fF%_$%9`yG)vOSa|ApMJhc(NyM{Y9;U&$ay@k28pkZe%)?Eztgz+Tw;k zJ=xJFUcE}Yx@DM6%Y?oy?Dm&k?P1mi=s4;>54K=HVUSyJr@uSlxBho=f~7LCoX@vB zepce%wOp04LD?uExTMQo<@_PBg+6ka*%9scU)-j&xu8@owt7-I-^&Xdppg*B9u1_Eb)p>LcT#y(K=6 zhE0}P@Es^?EMFeHj>WHU{B4e7?(73cD-V`Sy}DW*ZDSt{W?hPvleH(d(DUBMGLnnd z4a^PMp{bF|r+;EiTi9{c$EFqj$ml)V!qlb4bm~V3KG?2uv`lzoW_s+kD_=x5--^z| zWLq4_WM?W@_j~$X!0Jci?b-rwexdO@PXzB23D`^r(5>&;S;D9P3iYU;jivj}5+T%) zb#!zVraTl%Ell;owAL7vvnw6B=B;PkAivT0z;lRUL=3#sRkTq)ugIVNSu46hWqoY1 zl*8EdGhccM)mC80D1DHTXa90jS?I9Xdj%K7)(BKEE;aW?*V;JGqRrX?8?~cv)PYe*fLE>QCFu*x6dK)0d}ve17NfdUI5sJz}bNwe+)) zg~Y1HrO9{#OH{Rb&PeggJ2K>2dad3%LgMj94fUokPou|T9z4< za=c%ykIhJ$arvqp5$I6U7IVZAOD z(S^_CG5%{;83#^L6yB*O+ONJbCRz;MWi`}E+gnMGk3EHDa~mt-09g^&a=Tb^tJsyf zd+thXJ)N8d4T8*w?WU-DJqHzbP1Lc}ym(Kw`0klu@o}mZ$u*muFoog?TfeZA?j1Il zf&}&6Eq}vaXzU$Hy*TiX^`X5R?F5@ZOM8+ai=W7{A03@ab23?hKzt5dQtdriSlyN#Fw7N!C&M(XTgxTc zYb_gO{Ow-ssg9)R^wcF`(eC?s9nU|1Hq95of1-_P_!@pka2ubPpulwEK6Y-gxsb z_(t>N$XW$rAb{%FNX34*(gZUZg^pS>Qt<_cmNsA4?2>1BDb2k`zkx@INB^mwG5eP- zk2jug_`oE0k%{GjL)2N54`baj?V_2tdVFMx6f=*i`3{dT_b%2CzWt^9m{_L&ZQ8|q z@a#9*q_BD)uUNDaBeh6IdM_c_3M4s&mejqzd{V_>yp<-?NBXM73J-(9qEgJQs07u!3PSkAe;U4J$31gv z`@N(uE_2XK{5FX#>S=^HkM8Gfky;0) zG~Nl_DY_DJ#o6L`ChIS6?|iZ?NP5!KCY~=GE;}HU77&0depw}x%%Jbp&2L4U4ET-?4m^*!x4SNNOap2@^y?IuP2`8_ zo9!~p%A#QUF+KV3_FThj8`Z=5WoCU07hW-y^#_Kj zU-H$9ZIE5x$LRxdEeV2{@l5(}6f@)BSKjzkGVf$x2KHk~!6$qVrdqvr1cJ}CicG<2 zqzzU$f!}=U6Ff6hva-K%z3GMuIkj_(5^Eov<<{(K>&cVgK|PT++~H5-?J`tyN+uM> z{u*;X?(?@dN^*Q+nnIo73B{kZn!;zlXzcQ_e$L#S@zimr4f&bJTie2_O%IBJG0M2kqU zb5vyb2Z_F+p2}Xfe+AwS4pg(??Jk6!o)CDx$<-$x%MNvdG2uddO4)J42tX zjsYN*h(M5`8F3GUY(KH;qM=F1#DX-l%K&aHI#LE$2#vJxN@zH|Ce6Hy`SdOC>NJ`7 zqk(emWUY-k#qtYn5}bY;Onsn+ld@FO=XWigOs`54r_T6DK30aydN?d}hWPpKGCuJQ z_V?o7o?CPnVW)4*0^W)cN-@GDRTFf3&eLxj8Ng z^9@dC+_F?4N;V+b=IjBb@QS&4jbA;cg`}dCmQ;)sRNQCEV*4Db{6)7R#9Th_Qu;MRaLhI`DebE3 zQizTm;;xESQSbF)XPd@2k2QDWUlsd_i20MJ4hdJJF;gTkJ^H%bRE$=*G;{8!bcFh} z0PgtdkGI6`-PgQIk^Hf4UBcCp&zbPkrp6U=M1Ic9wi@g|pm5TV`?cwSn>9z$=++vZ zWR?fa4dV~Tv;!`#w~sCHW0X3-`B@^DlQ-AvbD)s%h($VD`UbaMwRYc9)A}&wB@WPn z`cM5e+Ni9SkW-V@PttJ%r7v)_-xFeFJ3=!DjvvzQRZ9fdzAIGblFBCKWV?jNbyda6 zS`99VIML%*G-MlntSiEDx-+~o_-y1J&lsTm$(DIVupxu(WR!*HbE(-DK#Zws$7uyK zxINPvxhK97D>Ryf^Jw-g6WZUSK1uXsAVs{Y(5XM%HBP?b4UMjnvNV&y^R@=HRaRQ> zDnK&hNuAP2P`{_v-!XG?J&Q|L>UE`KL++00($&NIfO|jj3EtDH>CVbe^v=>UuUpKk4}wI z%TJ9L>+DS5+p>tK`S}gg3fpsW!eXtE#7s~fO^Lu0YkZ{0RnNH8`U7qKDfOx}_DzPK^)$v8!&dDL|pwwq5G1n(~a|!@d;@sdOmWHIvJapf4=vP(mnOZ}OO3fyUJ- zq4NjMj6^fyh+KHOXhfEn6qmK1x}4dwRpPw5 z5GS+m;T=gK$vuOEH@)RlVh_51$L8MiO~buFF26p~ygenUo{`6JJY(!bSKGdP1`>P~ zJK8=!=g~VI6dS>2I=bu2cGJ^@03MQ2&|OxQ4%#TS@kI&KNt z=Mz|cisp!Y*~G3Gr(Md>_mOa5iIW7vtyqp&q>B!Kj2N_O!qd`F{oEb<&#@bb}c1eF)%IX?zO9A3hlpsiruGSXp9 zY_JLAg6;s!XP(28hlU#KP4DY1kH4(Wc+rUG(_AaUf_Jj;7Rx-eq@|~j{j){dw4(FM z=*~2iJ4IsZX6J*ZtGJDG-s`Qjw4EmXsc6USKfiCa>JPLhyIPQ~esZ_8Eip(QY>$hJ zv+DoIKwKmnU+Jtt?gY&%CIF|d5`j>lEC_Iz47pPPm>W&h4oi5t+9&sBbo#D|Jtd{@ zsv;C;G!hi2PxpV+5?%)xgR2uqj2qSmUp?<)#9pT`VO{=IVd~iaz=?!wHHU3bVcd8f zt)FzV?dtH>kHbi<{AaIUFyj$BuGX*AxwCD5m>)VETUWkfKQknk2Nu{0uk_N%a z-hAbj${^rOH15^SY>#6RTFrjk&(;4%K~uSQB)ThoxCgK3Nh>7_ zPKPWwW zUGR?4v*%%|>f@(nLCtt%l}jRyh}rk-NA4o8cFsNr9Yw#;%tJhy_dGV$nHg`5=1(r^ z+?0wVDs$w4Rm)zF+_Y8$sH#UL9LSxehCCk|i!4i!hzDG*ibH+tm5@({T|SA18(*oI z+EjHqrR_=_JxAFVd2Nw|pD84)2~wFL2yyUhK~_uA+K9lq2{eHa^hJMl2Ie46P6(sfdqjWi~EsE04hjv^ZrFdLP-fp&r$$ zeDC>2zbwMtcS*yQW8(&rGq?ORNB?d@C8=luDwLN2>JJt@lPa9Dj&AL z9Hp9cq?}{lf%|H=*OW{gEmu2RVvD`ulVr3pV668F*Y`)%AQ{)(^voFilk59uY75=0 zwccU9%EMh`MHqx9>~^oiC+|;XH*Xabba2Bp9kU`$-(0Q7Pr8@+=Wcj;@2OA z!>7C`S(pMN>r)7x`$Oidnn25fKU8k`6BgoTdR%k-@kvi zc*`lD7(>!!`jhoX-%<4jtIyT?gtwCy>>yLV`&*Hte_PWc5yNk*G&+3|4~dobR#-|E z4G|+gAlCGCw0gub%qP}JG@B(hWwm_`Z< zBB{`3_kcj^fvqwXnsf-j`NIDX2RSrs*8dhTDN;5f zl#Qa{_#Y2)?#o@mv}CqtZbn70>kid+CwO~Q4bKFpQbjq3_B6Ad^I2v3D)@ZvN;HducC@g)6F_L2R<}llZkuhJ3 zZdHBSc$*KwA4Lt>!{p7QD>hrG%n}YreFJrb2?A(XcJ=Es9HAA6F0HL`W5?j5aJh&c|6tbIHN-d2|I))Z0&nZ16(oHj}sqWJbrxs zt5lqY``aZ#U-rx*;U6{7CtoQCt%q~ z<-R$6(qa@d2J$nqlGr+-(3R@|6YI-qV1?pT%6id)+ypPjpxB^y*RFI!SO~C6(R8@( zc`|2D3AP6OMr4C&Zy*xV@KX_t5T=OYMEAyo<-q;95DsP?8W;oz0^QWu?WW_elOUtx z8S?`0I9t0tuN_}606VG+Vv7WU;0^{*8f70K#giKj8DpoE0n-y>wi@0jQVCc?mmr{I z9xx9#c(_yka%BdwO@aT?)9-~JM@rYywCHLdrcSvuVZX?x-7WUv?d$h?JF~FE{zHTn zX?K7EQO1$IBV|WH>=^s(d5u}pu^~UKV6Iw_y4agTD;8It3G>IG*9X)4&m1|?HgLJe zy)-gxVDwj~%d^^}bA*CzoW;I;+-h)X5>+?9Hml?JJ@*|moAj^7t;Mrn-Omk*W-3V%kRnIzgD7!1ze75met>iTx(zOe#Q;pgrtuvHyai%$Libn(lKHF3akQ zN!Nrr0u%+HMcEIv%EM)&Ks{a$=%RFTEZhKI0O;t@xjVa12NCJ*qk~1<+&zHz9_@;X zrx5^Q!>T=jkU&$_BF5#Cue>|piRgYGxJWSo6yxQWOGB@4 z{?dLB<5$`t2!0_^gR08gOd^S&>%&y^WC&Me4E*vffX>$Sm=gePz;w3`X-0oXXsg1F z^Y8YznoD=3PZF?KPC>nj7qlQt^1vW@3rw01HPpw*1yYkA5pS4C{JMXi*aE?=W1Se2 zx$K4wm-?1pTY%PveO$3(1$o}YL(!XKY1k6G-SeEDZBh$ZFTO|rm$6>}V@NmrJ9Pj2 za{GT^zbb*X>bCrMul?_dNdI%}*MF8P|JPUcALh;Y?qu1DxWC*%xD~Gh{$tqFEbM9@BpZ$@_^j+u@0`nn(2)3XcJdw@qOAsZ&Ql?|>J_EMr?-Q^L5k!lG?|U=p_! z?kWM0=3rR|V-I_R_3u$$|ES7-Lcjjc z{UNPjtH{7nos0a#Uw28x#2{w)>c z=R;n|Xa2j==|BI?d3s4VC*scr926cnp0|j-6Ga>0cAwcq|GD{btdIvMZB4e^Q zon-n%+@vB_T^LFan+e-^74X`?zdo$ws>axjoHxjV*{fnyw<2~vAiQLFjs{Z`U2Jgxqe_H6DmVL zm=PbJvMXCm*kh!CzvJr#Y2awb%f};n2hdkxTUe=;k!eETe+dgC&~@@FtWA!DdqEfs znYWxWMgYdobDXYr{P@RjbehfY@7QJ>*&6Fg*tIZxsAWFs%Va+iAK*qDYg}AiNA*+! zd&dDRXUv9|rp4yBy~X5+qd77X9jcH%d+=aeh;c3MTK8}r`Z{E4MiU{IWtHoAL!smg z0tz}p=Q$ooN7)ttb5H}KMWRr6iP-=7!nyqoGC@&CB1wkx=8b-31nG=E@$|gnKxCT< zn3;?|-_zHW!e8j}vu0y---FPgD}xTfK6ZAR?L7!@>#J9>2puB`c|wdQ7E$!fgM@8Q z#L(ZMA`x}O`9e~XH)I%z2WgjamyG&|s8$VzP4&po<;Ij3Qyv;DB|N4|Ds?UYoox6< zDtP^+ZA{i#z(mEX_07hlUxk^+eNQ$8kcBhW5w;BB5;J6veay*(X3BGor6Z<9<&o5S zryp$kb385||H@3pR#9-DA+1!>$!_r{q5&AtsWx*Fnm+NP*)i4Al%P&jl!xRLK%)Oi=J(;PxsB>Wki^H55KW7J1t4FyRLck;Czz)`;H zFiIfvIQZEPKeO+%Z93axRgTqk@MgbLsoe(-fz= zoJek`>GLtd-+#%!ORgX9exdvd#k%v>N{;))gOp^fP)9a;06axh)Ppww$J3UkXcJPB z*Lj>#G)@93Vu>2dx^?(18ehs`D469+$ezHFbdUxJ>6Bh*Nd{ykNQBQL50s!BLT3Sn zXi#gSN&o{-1Yosk#Rn9O8~nHXf+GPzL04ogdC+6r*%C~jIbjS3386++m*Hh5B1HjAGgj( zT*D<0Cx}dWKW|(Z|j^kZW6`@0G!3BMq zZc{@MhY2L@4|M|&*=#d}z6{Ea!#G|#o@5mj{Df1dg%tjcUU{gfBWP5sHXYnXWY1DC z`3**p;*s6{wwE2|Ol7_3Ife9s@nTPe0ddGcUI+mU$Q+mNFr$KH(?DLilyGKwDit2! z!_0lP)z%SHb_v)7;tgvlB1XU}Qk;}@kYuCTvThwVv)gZUdvcg8>(@1;TY3<_4Vbw4 zfhU|Alo4cu8d6NC;tc_i&6q2vMMQ~88`V{PN;9YroE>-E*sC6+JzdA-W+qTuVsF%$OJn-0{&GMpz$jmq)GnO73JRj6Y#4YcX49M1@MQFuboy?w9xph@p@T#)2*b4dp1h2~-e`%*oi>9X*?zX)>Ec zT+Y55vHT3{>?u#uAeh+PntUO;fK_vw<8%u{_I=`FDLK>twUd4bNk^O*?dv+07}?`r ze33#)yoUwuw1pRI|0s?vHZw!g$HdgrgQ!K7%@WERY4^AeP*_87o;i=ilpJpI8DQ%u z5T>hv))E}viA)}?nV5$bp6Lm_zF0%!86v*pM>z#mdKCQSqo1T44uNambPDo8KTJo={aGFDFJFE^ zS|y41$#wIGLoCAtN9~@8U*3us{)P61sO`+uxM!NOwadtMV^4Q(uvR}dAKkbq}B)}0Set_e4T@W3~B z+SNwl;CCKDZl)i$-l?Zx`F5xyg;z{Neqe?1PdjfhGiZ5)T*gzBsoU;hj1%cF@m<^Ggb{~1~)d`!C?#+ZA++KZ*n4dEOlH_H9 zhYNe!_3yRJ3)k7Z%|iNa0LG{gz;8aHs5 z!87*MgMYUWLfezdI;T2gD8`!Lx#Ufa4|3b10q9PYTKFw{eLcA%_@$;{xp3y17$|6B zV$frXyFdr*F-iXeC6(rRvCdf}X5EcgbY=qlTEC+{rntjI>_)KhF~m8LH=iF;H7(|m zk^UMj&@Ck&qq6zr-Br#|TF{=TOETPwFZlq2%BzOHrD%9V(P%Qiq)2CJ46`D#s}T1cU;vvHIB|d!#@fcUCg= zj`))ti!_zarz7j?k2bbv(}9U!DVnV87k1%^)nEUxnhPZD^G$Gr`8{B=zCBI}tewdi8cj@N)&ebk8ZG**(<1sb z2OvTTXwLSP;fm~`5YAKJ{F%z^cz90@utpH~sFKqpe+PAG`8cI&3b3<_msa`8{?0EB9$4w;YBJbJ0V}}~jbV|sNszO@EEM|*kt4PjB z#_f<`$|Hgx1OZ9heZfJTifJKA79Fnw+(}=D%#n6GSbn{^JQvkV5F$+FB|hnA!{5tx z;z*5`DoyE(h-`VcoTZ-1L$pHTOZeGPDL(m5mMu{Ml!wy=$kJ}WHsthiqQ_?i#Y4I; z@r%bfeP(@FFv!@E)8I$e6X`MUn_YLAR17#cNyPx__KalqEZo?(;Oydzljp?Z#uV|b z!#5CN!~R0aOf= zeBuDnf2SEO_m2o|7S^Q_ybT*jk{J;xioWtELX@lEhZ3VZ(JG;(w!o$pA$B}#?ppxT zF`E5#oJV@M$VXyk&2^>V$(YCNmt z*0KubGkN{h3afv42U+RWyTsnw8>oC<#`CHoVOgP>%JbYCU5w{F3ZGoP>DGFP-z~(; zrv8SnOy2HiT!wd_TyMH~U93!6WD)&_E928;9WyCWO`NWWliR6`rOo!)0j2F(oJ}(w z8Dn-aS+g^?tC^3b{I%0B-MlOG$B*mq-0=~cJJL=`03MGH8r&f z9D&h5=|p6FSwd+@H5G$@HNWFbMsH(gByyohNCS0#4sp%M$S6oh(o3mMd z{HARWG&MChFfhDAYZ9_&=MZrekTP!g{r5nO^23n0wKlNg*YWcs9)6~C|4RP3@twe2 z%ais;Z+HCN?7@Hk|Nlk1DgRq{Vgu{ZqnD2O6pt~`(Oqn0pN=AOYN1V1R#sNl$=aK_ zxVS=_46k3mZko} zHQYe|S$`Or@_@nIK#7vQZ|<7*`*3O9y>`S$2BSkU*lV_VLz*N9N>a7vY<`Qhp78#Od!mYS9p0mm+~s zrhV%4X;u!7lCi#6Y;yhbP`Aa47tifO3gy6@O2NJ*FJ8Q;OEC#P#Ld0?Y=GT>^NABD z#5Y)MY=tYlw~&3BMQ?pLe6PO=E}z4! z1jRj%R_-bUX^FKgLI=#EJUl!=6c5Iqe_{GPHz#M`fdgc8{u!>QgG|)U&IsZvvtspX zPyC{4myi)&RRQ*TqE)}9AD`)-CN3-?eD!kNakL_{PgEN;W~5362pLu|aHu?tjg37i z7!n#swG%I^_ zEqSJIXV<$sk;h-)1lM-tothQl;o(=3mK+rjP=35uN*-rfA`YO%AJosLIZUTP61L*y zSvox5CufCO?1%nDX&s2VY2)S@ap7eJD>oh>GE&`2j%W_*LlBXyY>hFt=@dG2dMXD7 z2g^~Q_Vmu0@Lm7vG&MDK7+DkEyCbh}6tknwrtEB-dao)fDi-IhVG*jAja5ng+?<7a zM&pm{M?-Z6vGiMJa`W=o4jz<7%3Y46ZpA9c5Ee^5jPcb_N>u<>%hZbm9zPr0<~I`X z6VB%YWj?g6Vp%(aPbw0oGI@wUL`GU~I*tdQAado@?JNi{odJobaXR}aVN3l4Jy^yszDPwRF~CYbA?_XKvLJb*++HEp1qz7>n>CwI>-WcU zatDTkN*PvfIz~+qBr~T+zKqAH#;OTIj>hz~J*IM71lrPPd)waLK2f)9$29t@3U^ir&lpRcK7ALBLe)oN2i`n*u!^+HLw}5- zeg7Av(ITNo{jD~<2TK<+ZYRau8D-EPe$a*9`DZwq3}lM+GNq?l4sE;&skYFDFL!!o z{0?5eg_8Bb1n`v=s9uP}Hwn<8Z#-G$J`a`($L8ms$($M}%pFFm+J#YZd^$*i_>n$5 zcSfy38Y6{W!qbWCkq8jVl&zXUDRln0ChFP*?N5(Rc*u{8j8vgSIsx`cWwG$u)vI}KeOf`!s*9bYkB-)#U2Cu+==PjH*onxbj~1N8s9YW7ePU6M zH-_Phg`>|Mn@@e6nURrj`NOiEMNeM>|IsEe0;KANME%P1qX)V(|Ke+{mr#3gVrjW; zl&OoGTjitO=N_aX8CHT^iGQ%asi}oU6*hlsPmiI>B=mJ6ii*ysPs~XaodORMU$da* z$^MJqdV7s3h>D*X7R35!FF(I%l-1rb#6+xAKx?o({Ni4$;lC2(OAf}S+_+oz=zO0r zhcj}=@^qKwYZ$|@N52RV>TS_((7eOMLZ4>q)FZTmEz=La^QK za%7WT==<|@K6ygivBL9**D@Xr#L>IO9M-}W=-Jriq5trzAV2?LCyv)bggO7bhj_b= zU%S{8V!k8BS`^PodMtdCOBgF1c9^mXE-5JZj59_&;i{QgLJO7ZmPQiSvfz6SgQhIW z85pyyAKrNuKo?#-+gOf%??(w*d222TqPKdltvW*P91bb&PDq=k*TS zI{L~5_ni!c+A9?(M~>XN>CTUHBYe%ORR;z(HT&XIuGZ1pcGqwT+Ks)gnLcv?i$V@{ zQm{X-u|JNWV2tKHbsOrN0|mu|+pEH4c2e8Ff7iXDqw_56RSU4S2OE-KmTvzvw5HJ) z0}M?YogdhZW~c)fSKE*Mc!8qaprW_8H`H>t4F(k2{JCj3CPIV5doK5x(k_&eHy0H= zj$`-m&|(1KX2~^Os;M_V$|hf{{rqCQq@)BVA9p7*orVnScygWC`-9yF)Kad$u(P+X z%(AzY@H%t~e7$O91x!l3mPb*iGu2)@a&HD5>XPRHUS8n>Ff*M!?z(@}6q=Hsn%lpZ zzw*#?d++cJ@5SvBvOwuW|z?l4yBjxh|2_x5%b>rOGv-^j6TnADDw7&oKB2=|G)x)Y} z|LD51J500JJp1PMQ6$LaxDIAI1_lNRKmJOy=#+G)zJ_$22-Ci>1hHN|40+h)Ir-#b zR21>#tB_Pw;Az|CY3t~8jrCK#`AkE-d5vQ*&Fe(6v^2)c*H;A}URY4TI``pJ_@h-A zI}C0O7P20AiId4+M@3UJs6((&NJyv?K#!YUO0AQ$d+|1cm0pnK@*;YhxitTP0Auv? z=bO%6K*U9{{j^=TDJUg1)vL3jLiV2RC&9dihK6RI0j>Dj!?1{gpz9eZ*%Khv24?1% z`}cp{y=PC`=4^;=_teG{bV?zwm6^FY4^wRh_DlJstLumI@_5lf*PWw(7GR~Bj@gz* zW14ZtLC9|G`ZR{SJe`;kw*A&t9i@o581v>~pz2oG2blG?|GH{b1x7hkk!tHo=!=OC z(oUwgvvX02c_$J-GO|Ewb0h}0RSvt6pxy*F3ZyJR?Dedf2?Gd*!L4OoZVJF%`e-brA^Y^d{soI*10@+PR#@}xA66?=>fM>#RT=pT zdDqhp!P~cP9k%Gcw6j}1{;C@i{3|Q|GG$(E7+-t7Z%v6*Z+nJ2a9ISJm^EMg+$ z(R4OlS}Y_aB>WiZ-(e@`WOhX09#Qr5I{7Hs6+mUwhLM&;j&*J>N9!U~WD%yNq~jLRrNv^fWUw8?%wFekoFORWqI0w=c&TISuqdFMwk{5z5yyu>_^12y)+vQ7# z`f8DBMiuH3sv78xQDwnp80|0+DSgZk#$UiZlR$~3j*iZ4Bs#KU!vg%#e4Q5ug5d}c z&=)T`ZTcByUq64lMN9!n7fYJr@1bl?b#w)@MDCyvPg46FP$@L-$}8yl-%%{%YTo21 zm}tBGy3UbtW27lQH|haM6I;3dCK_bYQHW`u2r^BQYQ04Tki|-|itIn%%t?L0vpRMS$Mlj&RMRoiRZu*+#mGO<40&yBe8v5+&3^<)Xl+{^w$kJIDMR7{b>= zE0C(nN%c*$(v}MNBLY>TDt3H`+4v4c#*@9e>ptf2UIj4HCDpa<`9lQOwnd^aZzYsk;ZDFuWmMPVmzu@>yajhnO*Tp8av*)MR@-pAxaLo$wg6GYT@kmP&w zm*DSQZEepZx)#Hdw8Y-t-X_QlC5TBPFkdP{npsq6ql!{d4|w(1=O-BqeSLk$^e+sb zgt1mVmzS1;dN^i|PEOwpreqyJ(zX`32|T^K?k*I3lduslLY@Gk=JTQ#o;!uY#RJV| zWM^k5r=)0VY6dJO<{Agz1d`Eti?~;Uf{rh1@hVaalfBhwQz&F!iuLj$k(y`qUZyT- zVX>N7pM;|X6ddx!(`acXm&=7bQV3ctDO^=1i*>e*rXNRo)19rF2WXsvqVw8~^MLUT@u%SpxH6m(!hsBjCya;9PJ$nEoygWDDN%P^n}LCc^=-(PnJ zL5Pis;X7vPA5L`PCPxaud^NIM<0w6y5W3H4cqzUBI~Qa;kE zr5=%O~@evInHe82(0X#|B zKT>CLtGGBAWS0;etPNAXmRMh3-vylmeuC&6L~9rc-9{v5EU^e%JcG^V17voJL^OQ4 zY$!{R%J9!$&YwX_4nL0+pl$tkC8hMGWx1Bc$0vAr@a?+H@bM3RoBw~NSc?C~O2}2! Wnom#fx^TspT$O{Jvu(MxU-Cb}FFC{j literal 101793 zcmeFZXH=Ehwk^8MRmN=@$WWFMP*9PajHm=9BUwd2vZO^0CMcCqqJU%-$w?$<6#*qm z&Y%(&8Oa%LpH|gA;l6j?zNfukx1H5mg{838_su!x7=85K$M-tK>z@C#9)D8sSgRTT5wtpY#Y(|U&&uYSg)T+rn$<01Gb>|*>%UTUEi4Vp zOnJBjxOfi#deh44mZcCkx5+<$fXmE6pSz^@iUL05=UW$4Eh!YPz2u)&wh`h~$|?%w z;@MM*w!uT~HdZ^8x{Am2YcKqC@8rh38LfMt<92SBef{F6JHzL;5ABlMxhpzj? zD?GXxU-b7&QiRQH+uv{S-!BYn-~QX%tK0wQ-Jh%RKP&NfRQ%6M{Le~IDE|`$|2IUz z+pdASn7u(2eiJ-;<@9FTex``%^_B-j8U_cz9>rbG%^ z54=j&DH$l-CSLdH$-%s(TN_i2n~(dmB}F;UrVDTU);C(MkmAprH*F`g`s61*z4B)w z;x>oX)8l1LW`?v%r_(J*z3MzZ|gn$7DHAm94{t z{Vh7)qP@d=C|j2nC#o2aUUj_`CT7BsBp>lmIsVEEF3oIPgB#@j_TdXoa`pH3&ri3S z)yJ#yI88U}cd%}yP)^3AnYIf#tt`!Su&%#4KGmpg)c5&?q>K!&Z)1YGN}O_ne3{>o zyc5UC$I#~N94Hjmm)n_{D+ciE%o#J`v#k;U5`lPEkwI z^Awx!+v~J6)p&gKUYd>DwVUtn{X9?`e;_vQ z1frs93qH|SIz-4?I!sD3&SiP-Jc8+Srg>Mc=+oj*CGOT7=H!&h=yc90Z0y8tC51W9UwrrOofbUsBR02Vf z{nd?W^3%bvrlpZe;n6z&PRDRpR|>ZWS@4smn%2-$uKx1NFE1rL84a+Mk>ffgUNLxM znqfrmmTuY}OIB`dY)r^*{1t~{^z)A&FVrJ+5W`QrFMpcXlDD_dZb(p%LSS=pab03( zXWzJKlfsP~uLAka65X~i?Jg78$|@3FuskodVJq|J03QAB!NIsvUrwXh?_W7^j1){> zMMW7Ex@{41oPCWeu^Omj_hna**t2I(tad?uAg@s*hf=JqtN5@?3)voQ^Ul`WM#$bClZrsQ* zek>TjXw;ZkpQxEbzGrT3E=JOq^U{qkWd>c>R{Dd&T#+Uy=eUU zKyyme`@+IU#%<@lS(Wg~H8nLm@yS?MK*8y_U%8CD|P5*Mtf-l$Nv52 zkR0k?o_jRpJcCagi161MzaUOq@qBGFs#R1{!ikkfpg;WmcUc_Sx)*2Mf6H4hwUWQV zxFc6pm^$g z$(T=lyU51DTv7?X>{(BF{^8Ms2gmjWINMJT^-~=}gl*D|%y7S^X=z4{TwO?={IQBL z*Bg6_#IIeuR{Z<+y&f__d|9F+i;m-;4pN=GSVdbB(w$e9Y`S9O5|1N-{YQ0TRgyx- zzTDl$h#YA;rI14%7AXk|4K>YO7|D@9YMmGoKXBkc=QIxwk7t$GLODIMxLmq%vm@K1 zec|R^#l=oD?X(Xa9>ecb8DUK@z8`iI9Tw0jn*Q*L0Ru=bKp5}5k8EMO^r5Ch#wVj^+8I2x0 zqks6y>t8#aQ8lh2DGOG-*A{k2h>>N|b*OL=@PbZ0ts>eRaB1j*wF3eR_UH!vZk9{twU)jnW3+L2d$ z@8=yJSTXy$xlE1^f4m|XPN1;$k@Hd zfBf;Kp)30e4%V@2@7M1A?YG~mmQh7+ihO^&MnXeF<5J!ot;V@em6d$T32M3`^L^nG zii%Ia(r7AQCe*_vwshq$jPRg>%0`z21q2w7I?&gilkv5ytEI6Quts$3c$B%9q!nf-1m&4_NkgHIHJep<>ftMmErT)cR&Yqncen4d&mystm^1QbVe!er!J|h%lN}ywH7m%f zzkE%QF>LS8KmRO&)662~=C5V{X|IlIfpgw5!@5J^7CohNV){L0{``qqy^I;lN51vV zr&{&b1b4Ct+z^g=Hf6tbgbBe;mIg(B!0O#rAlnD!69OA z(Nqg(Uy3NgoJ$ma@zqtKTZ5U~hdMLN!lX=Iy?RA&)9I9`!>ntdc@;>i_x^IkXR`jv7)FP%J*EDsgBd_&l^~-a&mwM?W$^&`J zZhn4#yUe?1vC1eZueYIT$BrGId(OCJ);0E3hkF7n_=gIcas0gMySiDPFRh|-(OJl5 z#CUOOrju*RS6g5|L zEd+e^G)l}WPFN9d)Ufn4M-_@%DCMRs137G3JpMc9s_(+1xlcy@n=MxAViZc6@*FZ+ zt8~l@rhjq;K_VIIlh$y6^86!>Z68L zB+NHAH`{kVK5x}--PpY3LOmxh?^9-t>Rgwg&c8HSdzq_#0skgw(0}1>UVQvvPZnY2 znGS~}B;p>6CGnL-1@g_v8yiLaP=5zP9Y)Oi&AwIech&(A23Q7%h9)A*58!$&vpvhp zr9ByVBrIu_)&{vglY>bGu?{mMYB(!p)|YW-IV0IpGwn8EK+z$wmRPf?fqE(N<=ODE zrL4ixf)!P)4^%!oc4n`H zhwSOocWTL9ow)Jw9~EKZo%Jtze}C|x8rRj97#0u^gZ!(dqV1F>wc>Aa9IlG){TI;{Y8{oJ{82Gc{$ruAW>4(aGj6r&6D zFWUF`s#uk<*z^Gp-#4V?KG<>4aB+4_QcB7)yAt69IL6N+Xz`(EIexYUh})Hxyx}P2 zZFdo!_S0+hk#BG}LU}4mhK8~BGb6)CpKso_t?~7>!n#-`&T|qH94Nfi)z$M~-PtUr zHAKvis|Fepg80lj;(?5^R$N7X6%tZIVnaoYmuL2HFJicx>0r_uluTd&+c2bJJB4=)H-hznEUs-l=H}PQP zmDF~c@-6I4yIFl2#YX}E5UfHP4-q?gQuk3M0EAtd^Z0^&u_L&dKrK4NXua#IHHt}E zqUg8=u+eHc)CAPPgxaXfK?5#8>x@EH(a6g}8S^Ct%M)QMeaH>zoZB{UuG^)e@dEo3 z2V_S86IPJdW<(?B?ICvdi)%J)twYS#0aRubAJea7R`^3b%R+YxtH>2V1qEB%%*tTF z@FVK!eL17~wZJ4Y*4mC^=eDp2Day&oWg}@%BFA(uCIZPZirBu!ay^>}L_6e*Y@@p| zFeN3$8gU$lb~RS^#T^A5t!%3px6KUSj#?SjMtb7^USiu19XfOYfI=+ZNj5eg3w5C#r z)%*)?y)mdgFx6Lm2C$r3u<7TY zh=9)EOj3;T!Rkkqapg!qe*BnV^Cz#3QCXq6$gsAYh#H#stz zjR)GYQgEswalxGX_MOeo&j$#X$N8oXHNC)o-2@n$(^@F=<%~kNLSr^-ep58L01!u) z8DZZ|1TSoYXt>O!`>E(q{ zBix!`3D7yCz^&&(R(;QZ|NWoezB8{8j!n zF43~zyt}A<|MS|K8V{^|YkdO3Y7D(LO?4FKw)ojG9cHea>0%}`0p8v=sRYg359){m zkGTmNO_iUAr`EK&TSd#R=&P;$)PQchdPd^gF=jExCR;E9p7>4~DJ>prvnf@w&B`W()fnuq55!q`e(fM|LUsCHj}h?Z@H61L z)=kggH-vK&kv|vg@>w@`bAU6-#G&~@2?p)7;R)*u8MY!|Q zydX`uMZGRy|8w!V?j7M;dG^H>Z0YKxO(TMik_Kp?N-sswdUzR}({OQ1 zqNZ@vOre*jCvS(#ic>fe?^b`;$|E+~>+-a_qmD|Qq@y`#UF*qMJ84HcD&d0#gH?7T zT&spX2RrN%xmIbmHI|{p3`cFCdYgU!@(wjvcszlLiOI-=S+M3S@5hjcc1_>LUp|9p zh$>9jgI@mzTARqvOU1>-LT!UHglW2EHbFr6qJ}L}%r0zHl?1K4(VVyQpmnH8>p+n1_yz{CK1OsBs6l6D{}V*yt}9hXH+O;R zNo8_D3JGZu=N1-j{ko+Qk=F$XVb|b53$Ym*0EJl|GuEptYYU?1nB(lNoMpqh=&$3U zRc_aI(%&oo@S$lqrwM1ue|oQo?f2($=(H4=Q0ck9PdN@IWVDD@qKeWMR=(weGGf@m z#Ah{Y?-#tu^@)#9@O+SEHximef?AriQ-@Wp%(@0ZxIt-a0_c&%GkMqRYe{8=(V4A% zTeK9aa;0+ZTTbQp*TPZOgAMD%SZ&r^HPe`>wB#iiJ0jePrfuK9-+$hG=ROp~3{%hM zU0z~gM>H}&|Fm^$I%kq{LSU`-PWF%ZN*R-d`FTd<;E38OR9hCU0{};k$1zq==a-RRfrUeDkU6!!9SQ#)k zYB?lm3U3peeJ*8^JNxYs)x>I~tvP~^+58~o(&fwivC0XCDpk!4`)QhJv8g5^V;^>` zYp}F>Qdw11)s$-Rgdp;3ZCDDbi6Bo|k)~HMDSv~LW*C$_md;GTyEtPwU5zurJerSinz!H*#G^{R6xQEb0`cyMWE2@QOY zU4q55TO1l0O`CRI6&IxSxrPVc-ux9;#a+7QlM0qCvO58;!gb61s7}Z%3w8~5=u2d1 ze8x?m`{luUsnfA?){e5+TlmRhoF8X=#Z7zJqXA-E%Vq==g zg_E2Zg4VUy&UvKS2G!Fd>#}}3wbg{G6U1kt_IPp^deIEWah@uXE~-OTVZt$;;%>VU z04d`cflkK>8UKagqu1^f78HH^$u;H8n<2l~%QzI{2~QS8r%-UXYmV)nZgDwgv-+x8 zE^U`z%Bt^xm_w}UKo*{H>0)vJ9OEoxa7 z5$zKxE-TL4yqgXu?NZ}fjzJyY&crmBEN1C)Disyb^nQ}twSrepSFc{pPg==c{RPBG zPJKZ*B@?BJE9vkb3sReWJi1D}Y3e1dNuoHS6jz^;^Aw84jrv$6Mt;*^G?;>*R0TB6 zKKbk~EiFCn+ip>IWaHMY)EKbkF6{_JY7Z%@}V zyDKvhv|q}Z$Ph#_^-tUauFSA4Gg&h$&Se`yIE|CDkGCgg@FWgpJ=iscW*l+r#aVNA zn*06x%^?o+lLr0^*2R~_mu?7gsU!xEfY$o0-{{?)bw31~cJmSz(+vKbpMR|#1414b znE}W;#^MjQHKWbqQJE78YeSDvDCoFwoSUH1hIrK{W+81!5n!P095wuXq8&F9M^aO>pCLg$J|i+4t%!w5&^U=Ql9bO~WMO8vP~w9 zfRf_JpkRT_f=ak zdM>#!ncj+Bdg+%_h2JCD)ikZ=LCMaYJ70lp$ZE?7XA%mR@U+MZc^?hI0F>;Aa^9m| z^;ZdTdjtgs;z>3d|on{<;oqr9BLIenrqyduL)r!@=aV~ zw&X4j#qT%8X-Bm4+pIKls3sYSTWCGbq9dIJonKdKhlY$3vE*r(a8eHk?d>3wV2j@>HjrMnbz*ER z*}-f9#7diKm1b7bjH;?CBRJyx`GL688F}ay0pBVWm7me#$$oTMPYDjz$N9G|3D+E4Htf(%5+e*G|~ z$nAS2x?3ein&X$3mkB9f*5$XnWi82qFYXDa=x*AJJ`Wu$pD%=zhGB+HUV7@;^Uu&P z_b9IrK-ez;{ewUYZ0@a$f;Rp+Nkd8-uE@JQ{z|%W0AV!bSkYC7@&Jb9r!T-X^^r ze5vmIO9K0+(47!%6(USw9Zvaa&#&?p?8vzh2$w!2gjy-?yr&9&fBf;BPTNrq%_XPR zLPsuXOcVXR?hF#b$}D%xt<_|tIiZLJBL?_wu5$UKQ|)scPAZS0280TpTIu9(`RpM2ukyzw8`MUcW;DLK zxMOa1_K>i!a8!AH$xa4_e&tT!iAWu@wmzE^wQUSpW({iA#cFaElR}*3Mp;MX+{IYDR*ZD|hty?%1y4$!&MrNq+`8#*+Oak=wWe+CuXg-2co`CC88O>ivEcH7Q z2W~SCD0QGIIXv5DbYy(u+fDX{m$?$8ll*b6K@r%ddf&UZHm6Z%WbpYu_p*n=aUgL_ z8<#*Fov@$0ISi_Nn2$^OGo219MYzLgo{CxiTx5*n%JNd6h~1k5mqX*B2;%-_ScI%( z{kb&jl6B5Q3@hH-#cAsQ-a$*wVR5Wv>fHvmXL_0W2q{pKjxY4;q&o8c+`p_OZ6A?o z8^=)2)+Ad=@CzW%UZHgh$d~mA2}!69KhFsypU^YDzobgl$|^PIjP=KQT6ud_t(`fI zCo!CRZ}-33D?iuulxNb^30abIca$ zBHFyqbF}GUT;tAP15SOG3t_;~B3983Mq+km}X|H+iSfjI`A={S= z&edNw_0s#aTQ9)KW)PgTA~dX-KF@{4(|0Gd9EN#C!E)Ixiy@PMNo?40S>y$RljFn* zMI6u1P;9OT9&?GwQAGYzLxl>p{r0nhDn1!|pwX9}=hSYgVy?mcXz#C_UPiC>0>CyB z<#E|XS6_dSoG#p1yWxzyW2HzQo2|JcYu3&%KnaS*JmJ`>ATpwH_5w@&xHJtjM!ytU zbpBd7xIQ?Axt_Z+=(E@S_@s`-)Y$A?R{pK?AjUOSvn+ZvOgHRT8EFit&`ViZpx)0| z;d}HXDrwD#hz)xC&){rMn6?Sz=)SfrtE_AQX*4z4f2nm}~~OYxCy%1s73? z^Uw?vehn0&{FjA`YHESIxiq3%O><+`4s=BX zx~3`!>fzOu@0TYB>dO+tzRkL*{(+EdStm`l8RvWo;#)sJE5UVj$UXP(-)9#PxFRMt zy`sTcemG$(ixA4MosdQM@5EVxG$@ZVTai%-g^Abv+gZFxc)R45mvADFMx<Qvbl5~AVr)UA;fD=0#GkT0@;Ou!edQO9O{%vSpnD`gGw~370_`T5w!u%Bs4S0#U>ETA3S(4 z5B`?dZv2KdP|LL|wnI(FwlJPJRMoTBva{R&;Rxe)Mn*&aiP%GacPipW+cvfc^NWwv zErBbMF5xw5sKZ%d&hP9k*-@nmABSnqFrU-%91Vv#3=lICiMSuyScRnsF;Z;dZ#D!ZQm$vb^s0je%zvwzfEX-Ya_rbK2#LY1OQTK6g;qm-#pmVV zRq(IS1JLLULE_7-03I2yG2Uixm^e`wmBF%dJlzu8gK+Y77AQ zSoG9om}!enl=Bd45k%wX2SaU7-@bi2a)R6(;VrK*WhHI%nS|DtBO18~(k_QK38uN| zP3kQyhh{U*a+|3c_-C#VHh4Zj`vjGazzgj^^GoH&kFSkNb`U!UBvNHkAE=`p320^q zKyaoCkK#icxeFG*jV+7DAihum`ykV#a=Jh$YB!ybFBr)x+tFACUb>Y4^ye3f2Tc;x zGb(x3<_{+3oN$`AifUn(sZy2uX15j{kTA(tTLlB#pLUMhAp&_=z!(w>jOmoq_b;ai zZC=WiGju$o#X^NRnb2n{H1}2n`TF|yC_4-`CXsz9se>R;YYzDx*Q$Bgsc?a;Tn&W$ zHdF2yBSB2O>_A0kt4)ft-o_$yL#6j--2FR?kamcUl@%HXFct-VLXA83 z`kYaLjo znwkiy;ryLf>D`c9qln`DmQS+PQd#joh84eHAUjnHwRxx%f=rKcXQ#_bp29F)TAR~> zg9iuE+*`*TF%vs*e&L!kV!o(`Key_MOAr&ksiL&BM@&528Pt@Xg!gC!q4z|-0cbXa z{l+v+MWgXH=rE&?f9#2Tljkra)V7cXphM+(jU3?0kSeZMJiPpGi8<;)|65f0B$ zJMO$mAI;_u4|cTMk4O#Rge7WltEU<2W(2;4UtnD0c*{n$)SE9Nr2Jb%O$(1&Rf*2@ zh1hg_%5aAEGwingQ?)#Zw@`NcL2I_xQtyvrYh4Tr-;V zpvkEYtLY|tT*rILg1nwQc_J#7xcIbqCKrwx(osgf>Fc|hq0PO2Dx1copXARMB%`8f z?He-$a@2mJg8z}RvHu+$5+j_Ltt$mGfKX<6Q_7*%XHK6U%71D;`~6y(3wvR$ll{H z^hnu+Gvo-uev~b+U3zx(ovC*jWTVo~&VtW6RuXVF@m~M%lY%NvCgB3E_p^1_U^kn5 z;h)rUyC%?H48@D6-U_O>ZoM|mn`(H!b<_v6Pe$|bc0uF{|LIQ^<>hs-ZD_Bt+%yVdSaDdg|&_6PE^uGgiV@Ot8Wlc5^`y_H@x2g>c zXH;F3&|AscOp@6(^FOrh1(NX&kQPVFWyv~c9F~V@&_{C(PG4?s7%C;$(FuEaX=(|* z+W>5j$gO=Z+=F9B4bXyaHB;;WDMZ>Y1rrhY7>K7c=J3&@vV^h!NFOrwVaMgBb(KVv zW_A32%KwZ%P?r0Gs+QrOj%2-gRUFt1_nuwgP}0>+so11Toxh9U`r=B&X5h=9QnP|3 zDpYBE!)N}lzJI?(Sg6Q1U?Vlb>rr3a-s^U)&r5jZGk}A14|E%J;Wt93LhX7PGRy{8 zg{&?RTSH7dl82ph|0~=Z^|mU|Tw?CHWKOd~WfS_i`~o8N4Bu%IwCwE@Uzzg;?4en> z&a9aC7_Dqj?!hf8{D9GfzE+p=$B}C=)vibHrU0sy8oT_IU11W=rtCH| zG_S07-P>72Y(Mv8`<#Fz{wIBa7;_wiCA!8c7gCbH${P0gn%Ph|2+j~nE%FT(;)iye z(UU-!hY5Q)oSbsF#h|PVq7R@BX8A2Ud6G@pD!xWrGVp}KOR&!|=<%rq4%oCwcwhzN ziKi;3XXP?%e4L4yrvaEuJ@+3uA_Kcc{xVpqY^8#I92Gy(Ri-VTcNU)A&JNy=IGl6FSC&Ry2z>;GZ%ky6sA4JFly@*#G?OQ)g zG*&`yA@ioqPxk9%w3(cGQ3ehn0$4hrbSs-!JRk~jSXyMV_5QGoTJ_75cHi{Vmo{8Q z9I-JaE+Pde!Z&%sW!W+6bT&wlKIpnNpI@A*e|~CB-bAJFy7gJ4*tD1DlMTBZCN{Tz zJF$#sn6{_FVtP?tR#x_QSI)D7f`WGWR(wx z20(2nL76@QOAfJ9Um6|_Mpp|%u#jW>&Plb>yn;bky?8;MX53}9dD<|O3q;=PRQ*=- zZ{?k=>2QAEEs&;AL8QDzr=J0|^(wBQidMh6_@oXO-cA&j+j9}o032#CR}@wyBZs`! zvg_I9KBZR~Y@h;;WLXZ~HIhzmo%N@qo8i@0+a~he6-PWAjBi|B#cy1Hg<56O7fi+N zuSNkIx>MGMT4*)ieG%T2<5iZ6i;Lvwpv382np!{O&kY;mV@PWd4;*wPfZvNv zJP}-_A!%4Fdp$HApec{HjJxkRApIE!a}wb3St;O5EV`O#)SZFGq%dHmDC8a88#nsE zC_VJ@YQUAGUMpOm!|?o&plRmU^-D0jQXNiL2J%X6+I^VVbL#UPY?NZ;&lwmPJcI!% z*KQ&ep8A~h#igYwd`C2fC5leik3t{z5nEZboEq)yObvDbBm#jOg{o!%+!L!1a_?oMY|sHYIB(ePeJ8mXY&W<~uVxD!}Ci<}6MF20;9m!d9*HGm=^W}$vGt+E;# z!3y3N=WUejL6wk$tT2b7&I_j(=shVwG-Wh;XxAdq>JNY~6!f-65rE^wC{+7!snprC z_aZNcC8COS>^!~#zXI$+zBq2-`A!S5IB2CPtRQC7uD)B1^2tYAge9~8g*&T--J`ov zq4tu**k!POj2OYvP1Ff%{bOjMF8T#ZK~ZrrsdvC>j}4S3O)}aeaXp4GiE&MZ`1(d5 zt>1*t2vj2((Ev_T=QXG~i$ztuzj4=7_-WwKFai4vQ$3R3Fd8>yv?CM4gMR9!C=N(4!!V(cL zbwK_};@iOztCCua_A8!PrjS!f2-MWp66-h!H3Re-Et%%w3Mnz%=SU(1=Bg(och#u3 z*nBkXdQ(k0-T|d?N^y}{mNanlT9La}`8;_-I_CX8m~GYn5>0+AvC+uLC|$q) z5?LtOi#dn^n_0ea%#DC>Fv#{+bRjrBI*Lp@>P zvK^5)!)yjuC=g0z6Tz6FAP+;j!NRoSbF0zy}+bTAm?W zC3`r*YD;wz=A5D5#~|g;Wo^Wqcx!T|i7?Koc_=QY^Sp?c8v=cb?8+z*p$F&^!S*Ov zN1i>S_h(yx6;{Ti4xiq`w>kV)QpLJWyXj$Uzi+>MK>FF$8@jr?np6;Yo0e=iKEamk zmJIncimeNM$nbZwl6p3Ml6fVN7S6NA?bCtQd1*=*P_9rfC95qzEG+DpS%*l5b|WCM z%|h}N%+wd8qeR8S?$DP%|)B!%3U@@nb7}`vLeyDlOaPD#_i{@qG`-m#ON?$Ys zxjie%%*O*HeOA3Pa@y@M6%D!I#=Hp)J5MIJnMD5$+gMLPgy>5yNCp5AY|qGH*6eIg z)30JBuGy_@Wt+Hjzr3zQr|pT5up1K0f%9#gB^U`on+IcxJX zcJASBsgod#?zy`U!f4n4yuxey{i^>U3^h05YX@nY1GiGk^&J4?gf9a|d_jhY+HB+u zI`i{TJ}*yU*s3QROKA^0It7M;`gz!YLu;5wAvCNy3FIY1XV;rd9B)7UWj3d zA-(hsYuE0~JdsdOj9pNz2*M|0KK_F?19ca1yhzc~S{t4-scF~atCoEIKG8HR+F!pu z3Tvu^J)92RT?*gNe@DD!@|=AF;rb>(A2M=Qt+iUZF(JGSx<5X?IlXUkp%}ieDSTPWuxCgZS~Ww+>5Rv3{GL--NB#yjXpUcheH&q) z(M}CQ^QoT`kg{$J^BXWOOIce?%*-_2-6j?d60p9%Co0T-fSsHlF7Zp3IggCm(Q%?$ zgyIjq(^P`dBHInxqb|vSEkAu(bV~7s%7>dRsWEQb*tDYg9ujL_8Z6N6jqTu4<>8LW z)wFxG`$($ul0ABh{Lf(zgy9Xt*6)h-^odO^_%TDl4EFy}5!CseaA+d$8@+X1W&iEL zLBJI8euO>oscy7B^8%hCd(^*F1UAv2*L4KPA!nFo_a6WkAV-`T>R%P*V1W!J!xS)a z$ewZAEO+bHt=lwlIR0Bw^u0sTH2;=2*B=XaveC>)yFDzn^*Do3rZpLu{vcBWC^``i zBUw@O68-t$S6Ys|bAqj4yISHJ|~3=(+H=Cs?^?L86ksN z4Psq&Lbnag&FuDVjTF6#N!*MP`eYK2!6P)(l6utbN#IyRc$cUP4ZRU4g^Wlme%#uL zb`!l>Q-R(d9(BMJiD>Nlp+G1iIzk3!0*w-@@#`iT+E%BKoJd5#=Oa&4lUj#Y$m=p7 zQ)-AtMvyFkCD;8b;)|q?w8g+zN@MyCWA`c4d0CV%0{){~tID9YB#^v_(J2&%s)<}o za&2Kaeiqi5&w!xa7PFj7IU0TV{WN@^Ot65QsQsiYDoseNIz&lP@Tmk35c|IT3Yg_U z0rQu{|7p`J3St18JL1cIT6Ia3I|I#VELaOxqohnI#N)kF$KhB8A6kvkj>&J2 zxMRWfU2>YM2KpxG6?VQfM9^?k@|hoctcw}hWb`?a=<`~aMq;8uEi#$y00;?JIow&G z4OlIY`IC@&4`!&j#Mp@pUW*kcJF|sZ;PYPZ@O>P^f3g(idh~QA&g&>Aw;$Vlh9Evj z0z2vGqR{(eQ0owcLL50Iurb^*u5}E6{RC|Q7rmODwT*c7>JX--V0<*JetDlrufuzU zjzD|_hpB|Nm}q!Fz=uE-zj*P&5OWTgJ5%mcy;0N;_)`Y~;;|(&J$*D`c32Mk@{v(C zl*(OfY)R-H4q;i)$)TMO1R&|Qx~j;CFC=N=p{J)3fCvgEu{HlWFxC+By-pPwwyq=3 zAq}R8{Ra*ZyDQpL*g54uF_Nr;0nDAB(yUO{$%cXPCzDmp#(ciq+FHOb3Rt;1c(ahy z?Bff-!E*`;siEVOyl_EQLc$IDebOI){DTbY$poG-XCFFpK~FD|WKm3x9pd9tcDr^6 zu=KCtv575)KZIE9Rs+xtLAdxPrC7y_P+k>@ex9fy=sgnvY)fDYjt3dl^YQU{8Wv6RVaM3ui|>(3ah#Q}ILFVZsn!C(nL(YXJi4`Tb3F zz09bbOZGTFc>=$o(2&<5)*nA2PlobfpoM}YId3C44wps@K$;dSnWZGJoq0cIDJUmb z0SyH0SeC32ooPEWyXnureO~t~{6Q4IH#a`{1g{W72CLJg9Jd#l=_Rj+bY!~t^-~aS zy^Axb6>$LF|NgHXAd&q2ay@qxgO>k%9lk>xNPoZnuYdQxs&x(JOTG~Q<*Dg^P=3|! z3Ez<`x|Z@$q4=#~2kUA|cbo*Tw%N8-l=0NQP)v8<`z2gA$30i{-ft<^wMOJ8-mcR7 z535NAVgG))p8Nmj-7Fb@{_AzcuL74$dCFH#V&L!BqaP9RFTMLdL2Blouh)lzDq8pA z(~ke^pLTWI)xUrJ;YM}b?L!70GXL|}>+Nqprh)!n-jSi+KVPmd-?P4@xLz;beD$wC z!GB)=gi8L;*MI%)htK1$pB;SjV?bQqjp6M-fBXOAi3>xk$e1yCA!|3Srl-i3Kl|q& z;ZJv;0(tt+*MEBV4wwGdYv-8KZ4-v;kLj%c&rf>z`}6;N z?!&)(_it%g_D0iML}c#29+CesTEEgrLHhG`Rh93DBdX>9AkT~b^FSZsAP41;-hVGY z{C-Vm2Nq%M`oF#i|J#Ex{=Sd@ZMpu7pS%~ALAvrEFIU}W;|?ge&!1lV*RSH=ZH40h zX>#~4wnE8`T-PD%e|=y7L#FwEPS#id?Y3N*rbpW2fL=I2tb=w^Mteb?dI3T)7EQp$ z&6_80YNN-mMfc*1F-P=zv9J;i)I>ZwzpzSnoOmbxTD4yc%AXx0M6YO#GWapT!R6Ie zQqt0OFfk;+Y{|4Zs5?uFh*h?^?f= zIT|yhwf)ZcGD02v`EvcGzxq5|J=A#3#2K`$H`_AJBi}5pZUJ_`2|Ns*O5>6$wgU~M z!4ge;MefR#E0^qmeMv(tI{tAlh7WBn&A~hOg2)W$#w>4lIiPVRdWS+YbzeW^LGmmO(%0s=(x2@AcM&N03baTJ8g8ye^*1*j^k*Urz!xfo z(yB0Nicvnnl2xl#sb$`J0r#1yO(J-l#OwhMGOL9KjdT*0&iuNiPgk$`0t*`H>mxC_ z7fEa^{Jt;=+-!P%jXb0R%)7-jw;0ej(L@HC^}{MM*eDG}zkL2{fhf=GFj(DycGQ%E z^QK*ITZ;K;X4h8tucJkzCjg;QrI_w8V_F-3^}H6W^O%)AK+6QRYCn`*GA>d;+&B>O zWpp9MX9I|j4irE?KA5Gj=)3SnNCg z+u0*(b@Fk$)1WEI@S3S9V7;Nab>wI+HUVoy|Mh%u*;? z>{=Tfj zoLRN-WU0^n`-V&ukv*8Y{ZsLL-V?Sz2JA}YeqRLzg^&@RnXI|{6(F*YvS9cj>1}fX zU6?~Mq$qZD%isYodF^yn`UjxL zO+{Rb>s+Ni!L_2l@y6c0d##a>c3(*X)1#JaYhF19?y4SY&+0+)>=sC)f+gT?3_J6k zLTP3lxy@E>=s{O62qb|qB_ho9XeW1nr$ZOUr)Ax@hR#{=yZYRv(EFvpy@a=sfn>_PJWG;+eesMm$=w@WybX7 zpE`1BzSCW;c;v)c-8G9WsjJ^|Gz@Xib6W=s1-%R@@dlk){MeT_UWut{^G~n;kw55c z5Flt7**^;j#V<_zo!lpk|H!QR4u5kDw4}05@s+A;Bo!jWEWJ>RV=?yBW4By%EqNcW zQE!XkFGv?w6&P2cdMDXQg@xv#npVgtva^?~T#U z;?1WU&6MZedj@%DIax+^uKe}HRC0fagL}aH&hmI;r^zxQr`a;KqJxp$PgN2WwQRKO z&+X+lJIEI1;-8!~H%@KIrA6mgbtO{{X|cIvD^9p{^jYMorM;i3J3AqN&(cLLj5dcO zR$WIkJ$}EyzeDK)IEMT#t|=tPV=7{OU{G1dYt&Hi?YU=TLze(^X@5{g|!xr+(}UWeIH8V74~)BA1J%a09<>>T~x_A13-UT4ACe<4~u!iRm)cYJF=RZgV3l(n%u zCryA;SbaxFK)80paH=r>bT@;j=mfvXBf&`j6`n&;i`8HAPPmw)%2(-TBK?P^arb6U z7cq0se$aUNlJS|B%~`gF5ts4MF={(+$W8|Huf00<_L0RM6Zw2gy5hCa$JEP|jT^JK zIGmU$bf`DKlj95D*jR{@bCG$1usm;gW);}6#cvDR#g1fpd z>RZvuo9(=+kG^`g#5@tQEuAt_3}WnMnpyc!!hYc0 zSf|T)mSv08>>p)Lib?-4+~^XKdqz*+GxM;db>VV*?QyX=55M8&guzw~A09j_x-li?6_;+!+bCz(x7fbXaAQ^XQLJ_Nea^@vnX6g_%1pz@$&la z;(KO&_63C*S85I2Z>F884O z*XZ5E7KV5fn(@>C4MQ^4PKNf#=oC>2`7t(3tFJ!4H&B=SB3AL|3PBi%>#7QteQnlV zv^ES{c~@}rlXBGjyu;ur7H{E%(#LdqNp-OSE)0q7>)zP+_sVUi2BIb3=~{YeuQ>TiedTubNzklNdzgEZq1q$<@R8=>uSQQrn2xvZ z6K%^0TkW`#SCMHk%9`NSEJfL@QJ%J#c-9Wj0ht;~smfNefh2C<8=x;D{M_-d>YER# zxfYP8B|PZAS_orb<{<=~!1bUX=F;bTz1P{|!E=gOMeT3m!3{*SBTpG1jyZyE^1mkSgLy#}<20X-WVF46 zZAlwX1tKn~I<#>NZjWo1;ZTTyv0!)Y-nHXY6C2B;Io(O3LcYNR)neIvNnI8n+md7K z*ozX5B|nt18d;-Pp8VvYAoZF-DkGi@C>Ql`)86&BVlwS-ZKxd^_Z5C>TfN-rb?@F& zJd{zGTH3#C5vHE+D9ft9`fUN1mYS-M-*8LRhm_Ez!}VFA4tr7xl9Jrg^ZvnH5^}L2 zV^E}^W|OOw%F2OWZtwVJw(wV}C%mVt)7|vX^kOtasg`5Kjgg&J*&nh?YLlo0<WklmZ7<@s#j9!VOo}yN7qVpGV*8~GT@1krjXG&gRI24RjU$5BeWtt z?1cZQQ>=1sSSNe@AC&O3_9I!eeVR=vGxzDVK0}tNgAcvBi zjo6n&i&Lu@Kj$aM#6NekYd3FS;Vy9u<=Hb)bUS(9IW{X3m&bi~dz~Z}G#frg%u{l? z1u{M6Crz($1PPm+xjp6cq+;dm5ryKpBXkyF4zaGU8BX@^);Ms|DJWoueI=RR&xOi& zW27674W>-Jid>U%wajvbc)nx6;vg{o`*%oka^Yd<{`O8}Qln z(D&r_IT5{-=yDzmJo_$!S*d{-hDQwL{no$j(8uRqGP|tgnzjQ8OYx76-DDeaE8k^V zku7Cqt~?&Y(>oDVU+FT9!dK<$Y_fBw)z{WfKmV+sI_}-KdT_YK>1>D)U#XyJb*>+E zWrzCJJnT=jR{N&Lhw<^oCY|OVi{#cEl3AZ_{J&^>3#h8sZEbv^+r-!yAiV`K06{=H zRYE|zrBqT@KJMI;mjBo~5oNjD2cLTYWgLy(Ro-SEv9-RGY3ANT*=^WFP>Ym7a{ zIk@6i?|kQc=JPx=Eyjx!7cYMy(X?vLgKjqN+Ic%d^%&M9t-pr*_+>d)9@8YTUDD1Z z6%&=*6)``l%^&u)TVllWqZ_*%)`7w)F1?z0n9G)k1dpgo>BVGZRRyYBuyiutz9rRH zOx7vN25@CUnHqV{00HG~a4^&4V0;KDquoFyQfsdVf-1D_ojn^5WH?!5f5l_5wp!ne z+?YV_5yK=K!q?YZ0QNx%BYj*?qF%dIThH;3ygdP1oekUzmdQbDIdIl|2&s)k&!CEe za6E8dRz7%{v05JZ!@AP; z>aGyhF1JQh_UuJNE3L}X1JxM@yBSHt206**l|58F`9gXD){t1c3MG2et^8`_qbVw8q;N;4tr<_2?*Jcij!KxJSHQ*W|u zl?tG_(z4@K+LocCL`&6qJ=jD4SiLC0hSN~~4hNQzgS9g{`oNKsM|fyE{SK>n;WA|{ zF+ZZixhvZ;EI|St=`tOeBDk7(d}BP85`Uapy}iCwG8vd4E*(X4;qp{&Ciut^E)(VD zo@vn{J~K?bbh-?f{K`A1bkD?U?(l2WZoPSP-mZv~B;~X^$2cDsi?-BhLXu?F(T#;B z>g7)p=G;kYr`!FLK4g>^e4TBmC|n>$V)OV zm)s_wT{l;qd*#wR7iHz$5L_41jxDw)@bAoJ9v67DVEDe~5cJ}V`*El4!H$5O1d34tSV3Rtd_1J6a8FL70a zt*oN7GQM7WGTU)^X^}l5*K+ebQ?$HN80SukMnU$5?tn~Vct>qAc0o0J&SU5_Eq|Sy z6vl zg6sMQOZy+3I=i}s*%gZv=$FE~QNtW>% zyayu3NJk;bTRM#b4nl(iB$*QTZ;rhZTTD3w*3lbRD{Vgy=1fwCH+P}h>DHju-AT&$!ViWXS!0M;RW%@`<|iwFu(8-lFn44oDJz+Iqh+#&e^ zN9UV+f| zM4z{)uPOP<8DWk|mj2bhE(tiYatzvWab_=#L^^CNOshS`V~(~Z>s?jLbpBRvJQ)@S z7e@d`HFpKJXOUVO%=3`r%8fGbgFaYF1w8zV8aFY?k2fC}GfnTJjdO{JTW$*^!CE)BmeQ5#r0jtn}d%Ynrp`;Fo)tNsDgu&nM#~oO6e7zHk>!w$6fB089AaKS72WM zy3$1hgJFjZtLRGU@xzDz0?%C?E7X2SFwj^z8FFFw<%+&2J4+xt0jlL{`%>3sv&798 z{{G0bPi~tB?3jp0F|02kkCz7y^cfl&XtZAh$rG^8rqFhGc=x;Fj78CHI?Ol^In9Ut5w5UUo+*_Z^Zrk zHIw#TK9WJzHvkHV(G%c^-ZAP(#ha24XwsMNG*BpPa%G|v45FsSB8sPLi1Zz#Dptxx zwg=b)kH#$333EAhH+jh1ky|vSr=4zRRjt+Gsn|(~xlna6y4jMRpgi-iM&FS?=-men z_&+7n!l2SRr5L`?QI`jbgIu_<9< zTU6cI!VjZ(X0NQBe|7$rYO->?P*amO5C9MEN_0!Ip{Jz-LtNc#c}j0~XI zeqZ8-_kcb&VW`gQQ1I1}OcsIbJi+`W?3pYX)6I4EGxD`tmA>hBsVxUgzR#r=Pn0A| zJ`~%mK1zB8LGlt$51KzpOb8boRrjCil@GEPs4CCQ8Sk>AE^LERA<3G4F;sPICwE}f z@!$#5)J%~Q1=^n7lE{0Y>>y|BiFKRXR4m&Nj>}gHOp*<}yO^uYs zajQ&%XjqKz`%m6jF$`;>g%^H<+uW3SbDo`$`GGSn%4H=p_55Pdof*A8pq3{7 zt$vpmCMwH=gAJQIdazH`74mT%5Su4mTGc4nIDW?nfA-S?VSo5L{NWM#+P?Sdw96%z z(>$%)k4Eu_?k*p}lpctY=I}#%9;T%qE*~W?cCDFLV`nB2gS-i89DqZ zz{5T3O^th(W~9C1ntKw$S@*N|M~Ds0P7402QOp$Oa+K{KjH7ehnXW*odQuLTd6K?( zKsEa9=f>ZLlzC(H&4aJHeXCcxB!A&yJH4Q;S=_eKGWT*(1Tc45q=C8Jc`MK(Avm>x zYrN?~;=|sRTRr*PugW8q69-(k)$>gCWP{pbJkOSGv|KIrm~r!V7ssTH`I7seac|B) z1>kM&1E)J8+&begUaeacZ&M7OA|#l7Tfs)A>@IfbhVvykHAR1V3^pQaalRcylZWa< z${jhLw+^i97fMD6MZ`KBT5*H3dfISfXQvpifr}i0znFg&jn+?~!U#=1r}tE}UGP>@ zMuwBn`}>@pk3^1uv0oi`hE^St14A*Pdar+J$$onkznf>y&JHj;6_>Fe0{SAek*)*u zD|K4QEw7=EYCc*=s(+LfXtyA#c($+RP(}D*FC_+Vpg1FMP+-r#4G9T3-I4(mTo(1M zk}XAWWBVIa;5KVh1kE0jOZ@(O4E!RPFrw!NL|Gk3RRCJM)}6x?&+*X8*ns;CDv59N zH46Y3qNblis+Bjn$C(~x;z`nv&-%FR#?uBe(k#Y9JZwTP-5M+WVt71iD|vtlo9CLF zrZKdBNj`Dn+v>Ra=8l0QURGeb+lYX19%#QM@GgL2D_dF%x)q(P^I4PKiPp78uuPNd z4@JAY@kLd*+bwIGA}upSdZl>ZT~A8plw13cPjueJZKVbs3z2c+ zghTIZ zKNpho)ZR?;*?`VmPXjOJ$1@NFg$Dqg+c=${N-49pE~d? zS7-KPA{Dt9RB5RqOq$4R76{(Q!%n|Zaj1Vem?TwSOx0ZwCBT~*w{T-@v^6kFD2^X% z(fV98v-QP?Gu&xbJ7TU24O&c5MhCAwx{|6?7SxlMZq97yus%-LAD*n<_%bBlmmg{z zUOqM6Z-&|BYi8baW#e^`+2+q37%&H4#X+iFoaT63r8_Xf*K^&(>E_r|!_BhRRUF@_ zGBw8~`LKOMGNX0Hs`O&>DO9IoL)ud!>c}PCJlDrRKWk8{dv1FBCF!p5mPJ2K!1Ll) zgOU9*`x$Xs5tpfe(fU!Fr@J4Ta94oNiu7-ifpk$S`M^*G1(G}jmJaB<19iSCauBpd z2(Uqf13=77%*-KOgcv1Nz73DRIoQzwvG{>}H}dU3GZ~-|MBLa9*a8KZ3K0!Yv4{$v zZu(trVUXZiiRxjP=t-{00Oz;(At3Iq&>&t0BK3E7M&?!JbCM#vANjF!RV3N>&w-m) z2W;$U9zpm(DI}DZt04>XML%2zs7|NqKJU}!6k_LQU_PT&Tl&D3N z2Coyg8ZFm-X%jGoa?=6}^I2DVD|0q6Z|~gd<>B%O>GAwt=twX6tyv^Rv-%YMaf72%b4L4sm&zi@*n5z z9))ox*aXb>+%hN`qu1f%jCsg)+|>2FpS*~Z{4G(#%@Y_2WQ@+$f4Y1oyB?V{+{|;S zzgjQ>Fh?7D*y~`g*h_gJPH4a*4ck8x8jt}Hhm5pzmU$dFHzJBj)YRVkKvH__&Q@eL z)TavI->(R~6Sem@49iJNP7_9SE9I{jQOiGz60(OaKi_0l|CDGRn(S_Y+1Kr4)))cYlyAuHU#pa`ecR)Gy+jSkwKNJu$XtowBaoomEjJj6?7lCl{Ap9e7HD4kp@t8oI3dKw?k@hDrH& z)Ht}VfmT%ysiqMc5V-)tVQ?fN+>Zh!36%1(z>s%HIf?0zz#OYT3>Mt*9CLeaA!fQM zaMls94604UXaCj52J!1Z^|66Ejzn|7=2im96`-O;4``i$D-K45q$15EgB8QHEWR3v z33;k43!i5ee-#R{I0slK6x>( z^$}BOQpzrKam^A|m+!l9W0Y0<^&7H zjPkb3r!gknX_eMKI#RFL_Tsz8)zTHu!zWl&WF6ghsDcFz^)!Dw&xO%3gP=EmNq zar9@VFVvC`CI|?g2cSw16!szII~(RqOrvG2(+=#^M)=rlmTg=^an+yi`!+d-i+A+P%f_lC>%B1 z9pOwHaYmt5=aoP(ONvo{3V+Qm4gY)pl9Lphd>B6B)cLV)JhZz&WtdVw8`;FI!pjhQ zlg>ndobsWFsGy+si^SR0=HC=Ij#-)yggQ+&c?isY6U)6tOL*2G>p0i`*UOdDR(g$j z5l)S%8a%W$UrO>*_IVArX1?Hmr+fNz99L0@4SSk;dy3lyLlIj6rNE}`Pou8eRJ>~V zmn-xGkA8TSZ<^m~vN3kdueaRi#RYX_2r+f14AGc`WAFykL+jjI;TL4+79XnWC9M(@Zx}z9zA|s4YU<#TF!xk+1rP_NP$2+ zbn1+1wf_FN@OO2-4~*v=gndN}9jhyOm#))dbL{Q10!h3UpX}S;MqE%&$Sx8sD&NYv zK>LFG`DwtOCi*Aw{JMA*)myFe&0Q*Wb;HICiaiI`Hy?_$vUO5}V>e{Py(inSve?*2faQto4xOBW4+3n4}`z`@!Zd1_>r)^)WI$ z^oWOf&-&i;YGw-6i{pHAs@WlK&eGq9kA{(Q#&|FupR@l~{bk4C(sEU-f0f7yOw+WR zi|m0z`fJAEffBT%SyrE(-;u#Zd~L7Q>uT1X9irl}z2)vu-#Etp>buoRvB@^P3*&*% zBenbif^X7-cfWvfSnT026)sM0YrEX!nK`{E0NFB}Oj_rQJ)YZhRxJlpg4SSenL5Ry z!rNT+ri^a~eIKz|#Fxbo$mJFBFF*{ycB6Co8X+wr$2B55Te!qtPRYB$Ayw6OqmN{k zdNSSZS$N$=kA>7Gqr&_{m%WA&C+JQMsb}~1m9$>}h?Pmx46O=wIof1qwVR9SDF8qR<;)|1pU^cm z?+IV%Y3R((ok8}#J2l^|IRU#w_;xxWf>MMhLVzbdUTK@HHMq$55A53F-SnP(u(!Y{ z+Cxf^bRx8!H@K(-o+~53&Z3;K?QP?d-`}<;(IpU}@zQF-+O~z`Wu9#CrnK<%6qy2K zW2E|oVrs~k`6%>ja&216_ib9V76HU7OKtf;?GGZsCoNbrnx7>tRtsRip>`n|GjNa;tByeRBAiFSR&h*D%; z%ibfK3%9p|Jrxg@H^P8L$~`?qXxeFmXw`y=C!}g_l=GuLitYFU@{TVx}o9L>Nqr zuK%lkkgfF+zDV@X-^J#O%zRuRi$$9FKzhJcfnz%^FsaMTcn&NPq};4 zLDSl1iB5Q-pCAws{q4spbi7s@`eF|ozhwNmMEx=69tGV$$AA8sqG}Is%6kEd+4pX2 zj4E~-ZaWF1+!E(F33ulU_HDVhCqKN}N>^DFC19s+x6_reN|Dx_^~PjD=wwBFoMl9D zirV653W1hZuDqd8Il70v*N(~IvJ^?NgRv3TQwfjv_kfrxMuJ*NqmMN_pWa8iyPCye4T2Q1jU*VVye%$8)gs0!ko+Bd+fyqL$JnrZfq`FV(n;yo`;t{i)YD-tCqs-UzS znYp2M^lhMV0!g1iPB&EgM%-qaa%XSWF&FAkMhS){LtnvR;?WcI6t}h6Tc145qyze& z1GwLG7#MEVC0jP8q>xj^Vz8o2@d;OU?)dW1^2I`n<^ZqPsbop+!6yp)Fzj0PgzW*k zn=hKz>R{yF&DY(_i#h+H5Aqeyzi(Rem~WQv_wm{Hn9Gj=niX{mfUMrTSN|L)je+fo z7T$8VrneFvMyqR@z#dd=a4$uUrhmV@O#W5e0X%A%572)(tE~^-2vr>vf4>o-srIrkSNUg`1*3vW`Y5&EaNZ1f|q+2E8zcy7hnO3BUj81q;88D zU^Es{`4340hV2MC!+&PccQFF1Nfin=WN{Q^cb{t=JEV`Nj!tI#Z>7;P3x?DdIl~RyZy8fnY`N-xsX>t90&PIVuyFEOy-;HZ8L>Si3SUE0u;`KIN z8(qeP#@Cz5oP%aen)tsT{#(%^7VwV^;J^3B+RR@8_z#nKH5JKn5hAI6bG9hTqT>;LDOG5PuLV%*X+ z|96l0d#5AGOeA1)RG`W61+Rr_Kv(J3*LPHYZpvO!QD-aoGhoonb`GO-&=;@{qXDhG zFL)2s+&Dyg20ZCBP?w3VY^eqGS}Bix_daKPoB25w({<&DmtB|s&O#_mB=Z4>ZSdFk zggGS-OSTmRXLHw@13k|G8ujvmbc-k?BmRScmSq7*Q4N0M=1HzUuSiVnZx#GAz{$At zyb!EieBhU@bBI8(raCFSq)Sve=rZ@`BQaEhC*QWV_D?|bCXRFmafrm~F1`PFeTnr3AY(EV> zGUuWVk*yzLel@Ll9D6-bZch*HtQ5dyT9f=f{?hZFh6(8e4Vr-F*$*=#ELS!G zkbeL^K9iz+0YDbv+(af`FdZ%2N&)(7vcY>9sn{$FD{J4w=p5(yVLo6rkF=>M_``8n z@NW5c6`D?eCS~{Ae}eCiggaqD>B^4JS%i=S@K#In;_I&S5 zFQBDM(ju@&SQrN4@rUG7}%$^W`bF5WR=<8;)xmF zSec~;U@-lM2bl6TnG)go;CM7t!MO=Th&h47tMttTE$GzX!rr4{yfF6UMjs47q3mQU zo`@}+&F`(7!Gm>V6?&d0K-!`MozTf?v_^mz$bAAP^d9Xz-sX|-)ue>3laD`(o2;zt z2nq!19%$_17a%&em>jYo{KwZKxEq4S;d>^^eSD`rbN%I zq?_CM#fxC!=3Jc>076QCP~*5fCv0>RJjqeGs?MvSb4%N*+Q%;ePY9~bfI{czNi2romb88JP|KhTT7JGLwNfsu?6upBt7V9c1 z{%#U!BdLy4>VD+{?JLSzfN%tWKAu~-xv;P>{8mN&YatH^G-)Z0e!oES=!76Z&=5{< z$17*u`}E`-+;1A}*1QdjcBlb2Jp=HGn_MOEjrf6Z^BYeM^pNjtH~=wv33O~1MSiY^ z{*bzFH~}-Tj-s4BEzfrzG1=Qqgy4&VkA{V^Y6UgV!R;)eMhX#$ITp%YgDU7WfCl(1Yoj(?sFOH4+rLmb|QcTdr4BGYFA~ zUrW(_EmS|H{_8(EI$eBQse^@&*`Gf&PZEok|{sG87r6ICWd@WL$P>OtMy zBHSjZVGMJDiMne$Ncx?e!OAKj<52Yq2Rum^xk z`)t5`@8Av(SVg0Y9&|w-rfX59{4^2BHmMITf>mG)Gz61nO0^?l3Vh$5B4u##HjHWd z0`6eNm75S$PQgQoZ=D68*l=6NAZG4&3~?VI$G-!G+@%zpIP3%1EstMQ=d_!JTNmS_Z`Qy;f-9SD9)Ig;$1BbE`T9&10q9*mP*3)vD zn4N0-pjEdvselc)nT1H#KPNMURykZ63ebkj>X(3)I2M{hqwG|*SGu?FlJY-=xq*fr*$xu#!dZR#H|zg={!SV3;$JhbRbh8b-h@ z>m_3VU#xQ8{vu?Cj9#i;(3>X*4S;U^Tp7&L9EO%eqt%UG4%jT;fwfYxF0hl9xomSN zITMwW*P5KlPhX0%SJts_mY9dh2-XF=kTKA}@Q8;fMk^Ec+rGp&Q}CEHMQUanN(1Xc z2p$Gx4~-&W58Kb&Hrosyg-el-ZX)}rRKht}b{#jzfXrM7!)S+LGYtfL`H8d)W%X>?@%>mL~v9!B+%ygb>-~7zmv$hw3a1hm;VNnZG@;<&{ zAI1kTeqqwwV`J$B5xyVB?){hmzT5ZF(JDBPVPGumr4I9OtWx4%z_@uBJZk-+?Rrff z@|P5MnwS%(F+>I!p89@mV6~hA)UiH3{@O|wY{4>?ivwuAWPqQjj}OY!8el4~%zbn> zymb4(#)?LNy-tJK!5a3#9G%8|G-Ct#pjs6`{udy;(v>E{sC|AB*5pW1TtMAnEc86L z)>?5pXJCW^r3ZxO)h-xUKLV7|02q114BM5yxcC>a2^fZ5DG)rwE&(?`(-(U#rU?#m zA6wD)ptVZ+omVG3cu%^iB)6&G{}I9l(?Zrp*|C%$rg||vJiOM4-#Lr+y1b6P2dAP5 z+%z4VVRP?cGkBh_6XpiW6G=}`$C1|--VI=ors57h-;*n#(;Q*PuFJxevs{c;Wj+gI zt8T%Jau&3iNPVgPW4%R|-5=cTvOjZU90v1Ej@f=jDPtiJMIg(x{{a`Fmv1(=br|%R zgZI~{$(kX=`dQ+f?7M`}CBw`d13w3HFu+x=&I|9_DdIeD2VaZQ+jtN7>1 z2QZg?(h)=o!(RJOTwdJwea>qhpzP|Sf6Otd98U+Ql?$Te$cP9->lHFOR+ zCqzSmIAK_NM+37)eFx!|tEZ61SfQf@ksmxJiykn2Dw=FST^(5(WNN^K>d|?0B``4p zpn#~lbyE1PO?ozqUBNh<2^<`gA-VvPZXgBZ0KZ9~m4o?)GsyF}GM2$0LOBoP5P{*7 zvh-AR2gYAE=NK%HmdwS4t;D?>ThTNLxsel!5&d(Jv(eZ!aKP#3-x`B&l>)anu8e;7 z#m3Qcn0_^m<;;I=1^X_|qY9GXj6+E!ADsD9pf;K8gyyyurQM>UOMUv~y`#Nnk~Lb_ z2lM@Mluf3r6day5m_#|KOK!pvsaH2S7CH*dsF&1Mh37|lI?Gq`P;>~MBAL1fn{ZF= z6t8s98Z6;dp#j(vhM}17hXqCUI{0e*-sSzH7eo$}0O~gGB@QNxhULS)ES&kTnaALi7sY`cQI zavt`}FCeLdhXh4hpRTp%a(a4t?%zoDlWhI>&gzvVS^*cNgRD+ca$eY2CE_5d(5UT$ zmFf(`>4#C6>S|htI}-quJ3)wC6flq>01h6E3H`z6#FASQzOoNOS2Gkn%$@t&&5NL%fp7A|n`~uLMP5`rKTM1wmktsSx?lsl`xi*R zwUceiSIdZ1;M3cJeHR6Ol-;bS_f9u^7~DYvP+8dv5f>JQiQzWCc65Ej?5p)xw@$nO zsrWU|)sn#~l(>SjwZth9-BJ;#rTAcmeD+;Z#nlf%jriD`@>;p3N=q}{TtK=w3%?GV z*Dx?Ad^6ogStdnLm8|yp*M^ojs9kLT(0yER@C9~zS|19a&2h-_s)2DG`|;yPW6@(q z#l$FWvy$JuW{Wquv5goI7P_>$x(^J|C_JstY62jt++^@OK^I=2LIx2${X8Lbf}?#w zd!F3*&PC}{`1MBX;abMPw66<$%V3H}PynM?8A(z~y0%Wh(qT7y2V?J7KASX;XgWA> z=7-f$7v61g@r}DsQjb6Z8c4r2bSQ5L+26v;3q}qT8BmV621(h-6H+!G*cU!zT2roB zd6q#3XL3^>FcqrIU+#oS=d{I$Hz8XhD;V^2+nUnA`+^;3HH?2d16tHTAfVR3GKd9k zS2J8ZV0lYHqQnXn9T_xQQfIUG4jJQRdkRB*0_<(d#7JA2NZYzAdAdh`9?&a^e6Rlm zi~COjy4U)>d;0@MtVyQbkpwei_FIL6B5=*jme=7bLY1zV%Kh_eG1{jwoj0U|+8lqK zc!hi|Wrc$(v)bb3v$krkU&pnOv#;<3m4JQHHFEU17`7j&IvBHe82F>0qlIPYuP>W< zYJui8qt}0ldm@N4W(7=EInh%OOsN-eVLBZ7e|}ty>4=vT2Z)6H02&a5DbzsvoR@5f zs{hPqF>ga5{{-@*?;5H@VT_0dARiYQ z7=kWzr|3gsfT{+B;OuU6;)hZ00mkRNdnpn;^ybADKaV12A9{S8zj;$U$Shm&1p|ny z@Tf_*82`nuj_rw7dhCYVMr|XtI*SGb)RKMZ_NR8K84;2XWP=>UR)%?5zn0!?00g;ay zt+NMEc})hF!3u!H$^dFo4!f}i7_g{?P79?oFl_cNOpIm)yon6hw0@8ZC*B%?)d=DM ze*{0lH);3M|GHO~|HSY;*M-4{=z{&swNPq+!sg3N2s6!KSi(u%EB_$o1VYup2HwTV zrjV3vl!4&78s?CxtI+>C(j>2~9yYHj^j#?Iw|uh(m}~?l1ECUC-82%_>R@Ixy7rl( z{C9+)U64j)?h9ZjibFx1lDvmRdi8yr)D*!aT|qklEG0=$=)01RM_3gnfBr_i2yt7g z&w0du((Z95j9!Kq@zuEK*P}yz*vqaB%!0x_`+?RCWj$BQ^#Lf6(a`HVSZzy~Tc$<@ ztRwWQ6@fh!Y@{W`E&B96K$6r9B1>j}7)~FD7OCsm9|`ln)|WCPL%33HksU=9?qez7rkr$^C#|^Fjm#yNAIDr|Lex<3(}>a@3yJCk|+){ki4q_HuhDK=Vt=3 z@~n>!A5;}wpYQ$p<%c( z0h{#_!s_5#rjp{|ebGJu@3f2kL`wPp__+d5dm)7YvS4~LGF1ECi5B8V2@2ZtVTe6n zIaHg z5MxZ4t?T?eda-WhuL(~BBj1FEQY5!^=vVe^?T!Y!;7gg^#;%Pz4GI>ZAlK`#YQ7Ur zGE?fS9uemir5zotRv|wVFUy`hduqxrV{lszc1KI&epeTWYGRaWn1KVcm)p{1CaAMG z#4@ISwJyY}AX$h!FtGQ_d9`;bQ846t4pNeYeA%CegE4yrf1!2kx}T@JS8t*nZo%wV zuLXn2{=4@6H$$Z{cmatHKt+e@@ifgW1_*bjrGr$RBN1dmmGY~jk#ZA?;>%aAyZ|g( z7*Jq*6$DURFvo$4y~7d=fgRz%-j2X;1x^9;@$4Q~5Z0cqzLCH@&l`9_n%RN==NTw& zBlrsCmng)-q;MQrpViSPEIG?uKi}|i5fc87L=U3dJjfhS?F119f*Vf6V;G(wKX~xP z0X#vB<~-etKR@$|Y2OeHH{|%vU}4ts$r^CTVW{-%BJG;^^K|#X8n{q$@XKa1U0IV? zqg9es(cM11^YbE#1=Od`?n|PB$43rt1ypr+z`2vE%zl2mSNI>8CZ&kXoqmHrVJM+u zLRfxX`;>^QF?OoR$|VyZPxGuj+{Q~+GX)xL_cRO+JPHsP^4ibvU_@pJBn=4~zx}$N zMY0IBT$!QNhj_)n@Bq-4eCm&JM<+Y4upZ@ntqU+MIHvj40(KUj+ zqY7+>Xdz<5Mn;D4uQc6~{QUaAkM0GAuSOU84wJzZ;3IhN7z_YJL!>mz-QhXh+xK5> zD)VpcuaLij_RWoLKuZIlu&Ra>hyuv|0Vq|n0lNSQVZyP15H&;SJL(@Q6F&^YFftQ< z0?Zh%I!G|~q5c!7aAnbM0pSLkAaGnTjQ~wbJxj$R9kf*R`RCbHGy!Nk0#3@!eA8(; z4`6jq{!sV&#hWRThAjz3IOVNDxc=BgR-WV856gx->*cR?+&k{{afO zbEr;@gqYHd$L^Yf!Uo36e9W)^&2gW(db&Tq3i+3y1N8=$N;Uu#Nc>s7493F7&E@9P zrY*5b!PWX@ww4XyeC+C1uljOJR4K9J^>R&nuM5mJvxOH}g~u!WjKnDX!$VIGX{(jM z)+}tV2$^q&t-6Qpcvl0k>D^0rhrd8;rBtqU?Grs-@4ef_a$!b2?8cw(=D$YXmMJK^ zC04)E_#p5`|M06fD=&=G!{54XeI+*fh0?hQ8Y@KO8)**>i!M+wb@~FLs}dTNUZA zJB!KGE)k(Cnk{M=8n32Ddx^oS#eVjZWNg z?g=$`1iU4>T?z%^(+Zwj((%MY+2e^)^k-{~Z2~r?cCCb^X8Vimwm4qewXN$Pj6FW(sLN<3=U#*Xv67|kg~f>9O38xfSCY?r}XLEhkZjw6;lmBj3(i^gAUYxdGx zBN~BKb@38m)*l$c^G#{|BY6E#y5j3=u>Lvttcaj{f6ubb>hkUjmsSd^WxYinxj37} zXIc44d?8KxuQ^r@vCH{#r}~bVhtg8JxQw36d0c$|)Oz{ANOUye>RpBz9C3GW2-Draxlgi?wcwIm0;90fIyI0niWyq(>}2?EW+{Q)xgv@y2ix|QE?R^9qu5mu3&J_qx>u&%?OKGDZ0UwTjT42;{(!QR+@GBHq~Vo&V7BI!Qs)%DSHbMPQ;XFhUmqWubG zXMn(T4BwBnG8n*6s8xAxC3rhdHuHqdZli+axA^_xZEct z}PH^;Q+8!nw@cGn*dxjMYS7+71l@?FY^ zprS&~>PHk+EOMEWk_z+*XcWaIM$?L(mtp2pP+RJ5rC`rc#Kvs&t)kDb`4W_L@8KF}o$|*Zl37l{x%>j%eFgp0BV*Whg`}y-{O+cFO z;6#K$_(2WU8%eX-UaYre-mDG9zo6_~f%^+rb%6BrgB~dyZnVwq(dt@@lhj+clF?FE z7bQ*WO%MHbq9En0$mYsrE@1M~xjFgYlBc3!7uix57q`&VKV`7HZ5-+R>7#A?0acuq zZ0MJ?cv)um!GS1Ff_FseqZ@bhOOqPPQ+;2K^mpUb*C?aH3u+Hz8S`m5LpvvLtpw7g z^LmCEe@h9T|NK^Zezfel>ccxRu8o?d7mQgm)`Yk}n2!l@+w5$;D^8kmF*CBUDTPeJ z8`m6f^wRGh(}Ujgiz}*Q-4#vutAm(045Zt~lcamY;?&Pl)bwhF$1&USSQ|wj$A~T4 z)jkM7cBv?ilaXZstd3^#m=!Vnp`GBd^e|0xVV!vZPMTvVMz6Ol(eReEkkfAJq~rE8 zZ=28S&yAMT`W?(Wt@Ak7|9W#Mbb4TaAoZC8%c8sTEzdLW1T0rg=crRF+9{K9#1ix< zJ0<0k?q1UlL2<8nK(AJZuK#YnZ|gx~gw^h3d{&lmnag2YC1!?zxd?s*wTtiiUyp1o z*PB|i8y@VsaF$HUl3z-V0sd-G@GdT&0!ZsyudxVGQ_xHrvNhco;?~ zl=Px`n5yM)cfF53j_UU8FP7Xi4qI$XI?|0xG-EJ4XiF$Ze@h6nYqM*=CeArJPs2EnK44PASDmoUjfTmr2eUnCA_lmo z?&D$j>!!G_)XHl1_h;ZKNM{d7w)L-Qn(p$1BP~pBeXt%r)1Uf0eOy9hu_7aZQ;C`_A zJ~Q>WC!hI}qzHcIgpP_80`wCFubY#P&tMgcJ;u#5zXY{Q^cL+;s;4?jUj3Jzo1so> zfEo;LLWdkJ>Fv#7AK1X3!a2S$SF5i7H2E12-@ST*@gr<)YLM*oPQie&>0xM#`a@^5 zpk*XMIT;?B$^gNATB`#u!v^c<9^EF^rWR}y7uym_!S z-TL6`lhG{Ks&Lc43O%n|cJmd_wsWlXSg%gxYt-1bhx9xc#C^!Mrxvb<(N00ldRyHK z&;V$h;;#~0rYe@oGzB|mD|TJoGu91}}aG&AaUu5D;Y1d}n&LM@)79$3&%JGlD{mF!kS|GA}OF+?yb=|_n zU0^;3o)r50ky+gM9o<*WR?Qh&;^OXWb>E$bn`&rm?>;B2|}6J<-T={e+?$bcF<6TfSUw zTo@G0rsSm0(ZF{l+I9ML6)t}x)(C|LQ$3FA3`rf>3)tC03TjfdeT5&d3BYZXqiPG} zp6;kw$TNTNtxin~XYjZuZxli_)mYNh;jmUa)$NTe#IVoc@E0)7eGxyzy*R^1E9sn2Q!Qc?lvCIv#f8&mn@rVXV^K0 zHt0t;XAFM4rnf=p<=D{A44p1&6%D7wmTU~p=hX+i%iF8^2<4tM>%GG51`JxR%Zy5d&r z?4dyq7GpMD<Aj7%J*aBuz#SM3?!Y}fwc zwR=r{=L8?x3T=N~)5KR$+kW;JPp}g^Nqska&mp*lw8UPMG1Js^qsD z@%{?wfmaXIM)zTgSmWgHc2LIH0N1Go$|Q0a5%dDIClb(}+5^;J?3^gbuM0Ve)X=BV z0jMSmI6$0(c<2|#0i$O=RezgVo`3Z1)Yf4|D<~Zx_i8BQF*U{6_G|0BE&A*42*bDsGg;T8^uuY@Jp2qtL}t4q;>YcM)?r#%eYw%N^!@kWGYPF1$T}75IY{aL8v6@_o?x@7NqhYwT2eo}A z9wnTayv(eA`(5n=(SM64MJ{`ay58{%yIv$i*k?xI@ZcIxda;;l641kG=$Ybs-j3+l z8y#2vSDc&sd#Tm6u^We~h&6sMve3n)VuXteO_%i9Qz?zm**QHPX(!LBQ)p8)lo0$C zfgLdM>H1zJ1?b8M)OIhS*OWlqM65hyY+k&pZW6LHxD2j5Re-gO7J__uI3dC+MAEtja6SHR8_k9hj z^}wLx)cgDMB&z0dqJaT+83M@-<7e8~eWLdZ)O=qxj?eUW`Qbmo|53KBesJT?LZkmq z=d{t>wLdV=iVehZWt=?(`Gy@W+wP-b&sS#79wI{P!>Pc}V<6>B*8!Rw35*V{iC%|z ztxgD!997NnOVPitmaVIAO?ro>Y`ci9w11&dJAK|x>n`s@8qwY1BR0Dc)>W45;c*y@ z*%Qn=fpI|OUc6Bd;rD&X`|NH6&bm}O=)yd-+jz^D;@PXfhYVa45OBLS#*3F8%t;;$ z;lUEY`8NQNvpvMo5_hO>WczA9c22m^?Ydb{jLxbRi+DdVX~*Wv!$TH}R9Kcm1dg-2`P)SoYo61*nxnP`d2n1 zhNwF%yNn^S8W81mV?A041&{SLvd>E@gcm{(zC?ZAu65)1u;6pLXe&|Soe^j3+)PTy z!I|&R>}OBWhFkI|Sa$tR2*758L$o)Dk6!%csxpaxApcSlI$TyDrBX zK}!HXV#V=*AYwB#C(K}-gC&j18Z6IpZ|qsCp|)0^w8LLPtF-+u2ebm*te$q;({WUEupi)9xf2adB4IAhEQo z4?4_#bJWw0cHy_`S<=knDOqeAV(UHj0bsDauZBSYaVGmR~ zeesKzJncx&2m}by6Wx4La@?rIWkFXRtS?V3juR9P6+r^=au1f7N=`)mGXp^F=fRP- z2E7G!g#h@51?~$w5@N}GP)m zx^;dB0HBVwac>`=K>dhQ(=SC&N>KA!pDSCLyF0g8e^hCczOAJ1+tkpvZO(l|VI$*K ziB4aR0F>}jRGZE&k?pp=_K(RRzu+%M>%#>n0!?;A*cdTZkfIcs{br`{=hhQG!<^Z- zj0}=$3pxX{-L{+Kbx#{M9cH7meAiYq^!$RWsvxbZJ#d`#=wZmJQ6Wko-~|{0Zi8h#|6RuY2|V=d|Pyr2PRA zX9p0K3>F3WO=(+_{D4+^iwUJh+X53yoJ(qg;N0z~l^xPBmcD~DeQpuGS3=`s{9+2+ z1_MUR@tb;K9-&utC{r~u`p3xIypJCp?n_k&sF^#L)0iGrFoSL2fBq%ry<4@x?&i&E z@M%2ab>D>7K2WduW6ZUFz3k%41Mj#QQwg<_wS6CB{y>#+>nyKHlMlz69K0O&$3@DH zM2+jrTkg?!byUy50~y_J(gsdcA^i0rHJ0q9XG8^8CYJK{yV(aO9G|MG%ParBPq5^E zSwVE1Ugk6Yl#4gVvKzGWg{<7>C3?69Agv zy~4`shXoE*Gqh?op}UN_Yyb-)Qb6x+GxB za@yPhpAR0OG&w?%u36U4sF4<(uX5TcdO@*slE1pwH|SZCTQTM z>nvI-6jCNR(6)Be!Fy89q7@&6(ql4CYk~?r_7nxZqp9oJM`fj@U76Yi=Y(yP{o66B zOloIEeo!CZ_nXS2EWCV6Q%1&YsplT0l&= z-q}G|qp06B44%ev`p`UHtlLJ61NMxAzC|)ouI3MgD6_7*d zR1)P8qXkHN8P3g$2VjkEvKx6a7RRPy85w#)$S~Gj?(vrV7Ml3;ak`3{<%@svN{`0J zO7t2x$Ij+!EU~=<1;5iwbN)>N#%$oV$A+Zd)S|x89ldL!#;jlGaG|u}>cWOrs+yv4 zO{6ENs3>Tz=YGzk$uk+`Gt0^G(STAnbZ2G!XyE>~NP~^p;CcM+5nN;7!wQ-BD=(lpIM-Wcg;fp_`XxL)gShF8It*ehqivzfBN{e?1R}U2g*(= zp2nT-Qb?o2ngFVn}8%+?laIRxuz)XY$gB@Hzem~Nb+&~v`u2hP!CuPKKv zMp&+@s3(=gt(xDhY%HfOh7miq{W(T>;F&>Ze#^wy$`kwN!Cnz!E;C~OAlgG)8IeLX zbkwXX4B_$ri?sKEYBK%SN2889>Ih7xRo zKpYT3s`O4Q5Tr{-T9nYM2qZuVx%&+`zd8SP?*E*-u4~OK!PM`2-~I0W?B{uQh}+D& zzC`oKK}`4SI!}d4*+=;w_b(~mh*QFw-|b1WES@G{T`nI=A~bFNbLWnPTXm#;nU>x` z`dPs>Grp!~s9P|zS_+(EHMgCUT?%gYt4p2(6X#D=KCRi4vl=UYdU(9cg7>2q{q7il zoT0;diq^EMjM7xkJg45HiRJ0BOq-2%)%|fwn$@uCFYih93rh`cCvtFP@Wr6a^%3-G z7+W(|!G+g6ouavnxx?PmrQRAC<<-#FxG%W-wXQSO zMr{aOQd%LKo+)1>6w2rwWmkUbKFGCvaJ!ogfHuF_m=UB@^z^=6e<;B7Df_D4Z^OlP z(N*EVXaS`IAZWNjn-O;6NqZaR#eSnPYLu9Pe_UNQL;7^M{wuBUNc&t^)P2QQVNnlB zy}3HfnKh@M-m9T~!}bjn+L_M)CjU96&hjm%OIwuDZ>Jm682KkLlNKv(DgkKl(QGo7 zaZF&4larGKY$!oQV?$sM5Va$GXApnjG@HVqsN)8^g6CvHrr|5Us;jQvmK6_Z_k#d zrKrXbxicI-mbrac_Hd^r@2kjSI>opQ0zb7!Sq825W0a9LRPzwdpzz>^UwOB*A)5(>5-JzJGe;7DB|WL5-K9MLNoN> z8l!iIMA3dEp7~n}i6wx-?Ai;Hu7Mem4(uZYWS|9kQn#Gqh4ROF1ro|{5I+l@@FBj4 z`wYC3W1tOkPRcU5QzHVXH;kD@#6gRiM>4ofi-KjTJDhF8|l zx?qL&@0+cfV-NRQS}35$7HZ)47OoJ9)pJZA@!^XxLLz+UrTeuX9kS7m5ejFxI`rcE z{G>Myms80qTiD6=bqDUn)klfBXllprN-|KDNI1+2^PO0icrTFJ9Ig4TtSfL}jus#w z=qUPaCTw^oU!~R&!rt=k{Dz{{LVeSWD*|c@-H`X&9LaW~bW&whifmH}A6pqC}l##V^x+2^QxqRmiS?XAe2&Mb6kgZ=ly25Fv zsSh7-*%{dRswpu0@!{&NF2f{Sv4Z~WB$L*|EP$Xp;z0AOb2i*_?+1^~4m^3So4P4Q zb?P}ZVO16`Q%4XeN|g2>$m72FdoY;8DeNOo-eHOfO(ccX1&H9mkCQ7e07~$yZ~F#T zcRmGi9n9olyaUj6iG8b8O7hw>wphA>GVp?6gP`!tDmPI;k2DmsfU)XG8d-&CVRz3y zIdskkh+{5;P0I(uz^qRV4Nw3<8NVXUY z^s#p4*Dp*s`2PrTJEg~TYAJ9@lc$Y89x!9h-95fEIVWD&!Q~vdnGjVjT><$HzkB~D zZsjuPn3@E4zO_MQ%!q=GuzK&2UqP7-@M8Cv{}PCUf-%9jQIR=UCuiaPE)T z;y|w8nQKEZ;5>o>Q)R4RuAflALia(oiG1K}rHe|0Riy=4w3iHgY^n>^3+2`9d8Jib zMk|M;IA2>iCQ{LV9Cwi@J)BZy5TBST#d`i}?n)XL$W}jC+c^G{CxxsQS9C}aHkP%se1PTdECOJ(m z5trMuEWp}j7WR_3K#Nn3=h0YTH35y*o*%R-92~Py1S=t4u?MiDTGPDia+aXaG9L)P zN%q3-aInHoW;CJjgN(aas!4Rm?9}s1!TJOb$$lSdB3kP)jO~1T)yb0c zAs;UFNgC!k8cxsBdYx(4D{ksuq~CNSQ5%g!QwTx~wyuLL#OBr2y6t{!FWf@9TZS8dqAUkkxCgV0g%mfeSHdC@k<1z5^kW< zaECu)1ubOxg(5N`iVlk)X)PFc`2>(G*pBW63C?ZO0-_qTt`tf-=C2!d@BM`xqB_(V z6*s}2{-n?Lqeh)7?!orcS*zmJP=s2J9;1`~KC%;$3`i>m!Itgc+yAP)aE9_~XQDOT zra8$yKp>uG6b1M{>Hz?A(W1FGDf>3H<$AW2Zn{yfFJ-ZTSL695@x-f5Zjrn~DJl%b zyCIhvj9&JvXQBo8(;}gxk3Bg(AWcwn;_Z5EdDrQ)aGz4oFS!2_O{Fo)=;V*h7he-;ylbBnGvzn z*%TJ^&ZM-`KTIGIuK=qxEoflr;U3CYTK#1V=A?L}gFVk7*KVYhW(m1qpTg!B0z1T2 zJv?GMfp9b&t~z$PIeY*i(QeiJ(pG27K*9#O!@vVRr=u?P!R`~Fih2s_ZjoZ{7MB z&xke{rL2T@2u-F9yt0AQ8AfMNC41(>gJu9F&p$q-2Tk|7&h&@pc5sq zb-RKd?1Uct3Lc!8Gm_#toDxfGNs0!lpQ}bwopUCF-swZC~Iw%}2+xuK#mn9cIFa>Y~EFU!y zrwzDeyrMs0Fey)gQ%gdT8vg5WJBBeb$aZt|k+*g7;YKka?y@PU%oavFv&@zkKs#m$ zjdP=g&v3O4T3DW4frO#Dx438Z0{vjP@@`j0`z>7D%j7xpa^$ z*c(6DyL6aa8V+LwD}|Vg)gCbBhk|~AevtC`_65xxfNmOS zXHkwh&)NEe8TNq(*nt9d`2d%0_P)@{{=}wN zU%;OcZl_bp*ekm%0rWcixg2K23@|Bf!18?qniN%-PRkFHgHVugR~F$Yu%&@oKtkd~ z4!*+QA&|nMv~1hDuqKcK8AVtZNVM$dUDiGoW(b&kY-xZE#@I5Q#*juGlseTwk>v%K zc~g@2^WVN~Vpl%l1g4*;^Ou%*ZS9Hscl`7g)N#<;9RR^MS_akjS>>Dn}?2aDE%y7l)LefN*Cjl%zn2gt<-a{V3vmNVu@0ig#1 z+zA*G%#Bid3dnxardrls%3lB!5N|-0aq0n^z_ZmD*;_4cbfk$5zt5(46ozQsL-K4` zj@3xH0k_l&tp!|%f$bT{MtAD62+lgRq$|brO>Z05ScAQg&(6Ywmh*@}=txCFgLydk z^ckFc_f~@XYcFWaFOt1$M557V*b7Jt^pp^p`PoVPRN5xiXBqq*)OwgVS5ch+=?_4} zx~0HF2nO!5V=hwM^Fc&J_$Q(%Lgt#W>;r-)Z6e6s!r-!C>1Wnn2cs(tTvL!JTqJ@r zd+8J%;C(RxPEB=mAFQbpur$!{;lB72=D)QEL#RN1a*$0zP;hW-R$<|?J6s`_f+sK# zX;pJ)hQ;oKQdU$fv*9`&~tx>I$(kb6fQy_*RPd|kM(_q;ZqE*h-H9;FD z;v~Ai5KV4=&asBt9@yHMptBBfxp(3Gd8Ba%vw^ff;F@r03b?kNs(udCBhvOSfCQFz z+pUi!7p;bfB8tUg(cVD7GLl9kFcD0t!w|X;!}kX6Z50ee6+rni4UYUUaQ}98DFf8e zIuC4ljWQP2@6Em=(2mtNan(f`ERrGw26AJ4eUQmO<0!;`B;f@@Zh2q!D#)Xg&5+mf zn9Kv#HS7wzhJ0``Nt~Y)(jgelzTL#G4&p~pwwX?Wc;gTU2M*@fRRe*(umOj(QrapD z*U*;`}5jt zUUx+MAMKU`LHn`xA@1n?pjQ9ipL=_KQv0<%nwU*Gt?B=rDdN zAq5g_Z9T{6U7$mT-uv2n`-}hVKk;I^uT)c1*V~K_0O)LBU>ka?uKlylsSURUq7H!$ zZtZ6ba%)-ptE(D_gOFfiw&n<-5^3(C;nob|KcxGf1P738P`@CRqGR;7Hfea7yCA9j zA)b12dP-op&|V&(79`3s;7J3C5868!ncUH^8U&1m%wDf&QAkGu886xsZT~4N~DPaw)q#m8SpPu z21uk3X{Zn=f&?1Cmu_?`Rud3eza!%>|MwOuj!8|*bM+saB+X>!;QPrOTxoy`eTQV( z38X+p5YxfPWuN=A$YezJ))jcH4BBN@Wm_k`)^CN5*5K{-G-q-J(ntZ63EH|4D)P30 zaVE!ek*3HJBr5xSNpN!t6t8-K7Cs69Ex$^j5YXh&sF4gkP2EGq_InJSTJ<3~=YJT?Up7(j9=wTwyF z@KL3b5#Il3IsBh}hcB}BS@dDB1O3U9++{F2*^yxO}IYUA;0G}@u zeKMpbEGjDc%@X1cg1XXP|At=LpC1I8akc-ab%-y-Ap}*8QCWIoI4}z8HrUHBXsk^1IaHR4~!H%*{JAQ); zPC=ZAhzJe=J|^yc`#54YI6+F_YyD@5lGmD)Wc-o1{&2kKaLiu>LSM>D(F1c}5(9P_ zV8o*CuwueQhc1$-!Mcz`ZaV0E2bM<{WHtkE)k9k^DT$-$pyliv(PrSMp@b3@b%B8y zzmpqI_ceUz*K(U*+um*a5TQxm)Dxf;$akU_MjUvg39cOmLtxYb%}cO~I$wxRB6R43 ziBOe+ZQMRI)+L5^F-BTM5G+nuqzCXn2|+N2?&>dUeNp;7n|1JL6|gK8)-nXxg(T3m z@u0?6zI~_7A0=?_j6v9O3JLX)VwV8yQsjU?j9A?Gm+yK*2Nh zAJ2dsz&xFp%CIUz$USn9fgy1YH~5Oge4U9xFALX%<&~4E)1Wq;y-_}ZfyAb8y1LKJ z&7ll380@kfa{b`If)wEZ($s45Nw;w;qT9<9N zihYWZs{B7rBk0o~heQV28c;6*j9K>1;S5;Cn1b3+LkSLOw1(?|O%WKV5z1S|V;jo> z*>WfxOw~x+2E#1_k@tyZ-582gKu+IFruWlQspkHQNLdpU1jXqHLED+)^KLU2cuR?2 zfDgU`s?0D@3zAS0heV)Au!e-wsBCwA!jdhj3E&`LRYChn|Fw5k@$fT*VCNcrazkAP zXg)>CDuGEy5!0cR!g^XJdE^qlblEpg`jb=3cY4xJfzLil|#c5T-+c+J7O zj&|<>dS&_Az#7vdAlom|KkFf15}oq`*8oe9xPf}$IqH<4jgLITmJLDR*o({rd{41_ zgY1AWG}hVhTrM0mh0cY3INYoonTgb_9DKxgXo3U@a)30`L#%hamJ1%QRnRSo#bqcK z_yN7FRHl31&KN$}V#5n$;7}pMjH!Nrw4S3@q)a9Q@}8zHUfwR(u8oSC;!wp4pNi*r zli|D>Mk8v%DX3n_wy6)a_ZpB`S+1@uA)+_8oRje{LaNH8YdP@X(`#%(8-2jNA=y1V zJq5O`P4P2FZ}1381W*4yC%9YupwtA*gj2r_LrLf0ngJgZ2Yi^QiB+_zk@zpU=@(P* z@hO8(h3hg8&`nu4sDO(0rI{Q!y40(JB}FJ?;u=u4Y~H~+dAlAA)v8yP<_-#5fc?{2 zPz5a)1Sw?ElW#&|b1ard*>1?n$|ULFXXs#U_XirtTQJ5u+R1>^F%Zn-u&`hwE2z+` z2Yv<@WLy00(7&IbaE#;o1ns%`iH5?E{wO%2<|oA26In^Be_R8IGm-I2Ez4X%ALdq) zD;Rp6XrcTq1x8Yjn&;4L#Ti8n7!!`LL{YZ-$7|B~425-n(L42`F6$e4OD04+#nckxG4t>xgYQ63X2wkN`w<}p~J{%K>@$1fq_7KkY6vF)S?ibNea@QyQMH_qY=(Z=<}pU6pI>NWCydiL={kHZSNxvMKyr~ zMCM&_wLTF@CCInksbK}072oax&>^* z=q=F|Pkb2wY}h#bnq31Xh|hfE{sn1-feR060u9I!m$Z;1voCoa-WV6>XI^98l|ZR=D>$&4=ls8g;)UEk)=!VGy{y3ETNUh(}|M9^PS+q0Y{x1 zkn^g+cOwyHLXhgf4(!##vw`z6t3m?HyH9W!p|ZlEB>vF3y-)`Wxc72^{>?AcmyPuy zQdr(d*&x}tTZ)j3dZ}uw1)g>eX?Q7lWQiAIkL-Agr^~@sOQst%tJm}3wx3_XxIP96 zYkQU%wjaPImJtF(nBL*yauoTLALc%`6^1MX0 zR?FRR$_SyCS{c>E27yua%d94-*P-QVR)y4?AyTO7L|x}JW0n_4%_m5J5x(OX&00N} z$UAVlTTQwtSbdBbB@_L?ey;Dp9BON!m7@%&6V&pBjmR_*olIwt5Yu&{197K8j^C|E zV}k{b5UX^sS5f)l=5U7uW13{T+q0JoOHL0127yd$_9F5y2|zByW19ueYZT9FJirl1 zd8!WiOO76Rhpao&z9@NlXVLb^JVFwIc+ffp-C|Y>x*7}h`;kNw0T^n&yg&ieI}FAy zi?Tt&??^?Q97MiB1}GlTEc=t_FaCn{J^g!ce>@Oim0q7A0lLyms;$EvSTL1n!6N!R z^4~z!0WuPROc)una3ZuFtr*hy1~=c0H_+_>;L|wyQrtkD+3Fp*fNdL~gh9oHkqNR9 zC|N)g4#tx5jPGn-ODQkp*In*XQBR4nv53j@mjNti@BmS0iedI>=+^0!#~V->LPt?MT!FCMr1uKh;MX&0DVAP%t}ef?2j__XE_(S8Vwb+?1D zuKW)}?f+s&3u56e2-gH(H1=E*hAxV!bU^t{fdaz21nfuj^^;`R-sA-@(i0(X$k$Az z8w23{^7;yfmA}pYC@SB~WpBP4cHxm#vfvNf0YR`G=*hEKLv}GA@f&Z{CLz~XMad?= zQ)@5$M&{0z6GJ&ZOW)@wU_f-Ig$V2St^r!S8&YmX-MJ!x*4?rzG)nB&h6ZmhLr=NiCX&`Z5pjc!Fgb{w3x;b?%rKUU1po4vV>_wb;5q1ml%+;zw4j zpcJmUtSPV$rMZ6={Iza*Vi$!e3KJ6-?;G}J+2NEyaxF^pe7F7)`<|fmyj-;8`}ze< zDNSF~I%s8K3S2>s1kp(R_Rl9UCoh6#8KQ&{^aIxZocy?M9)!XC8?pUiWYT_U2<(no zK!X?LIfW&sAgjK!P#2XGzYbfqNgx;iU~Wyj+^o=~HH8ox-MX&ezdQo)hJRL}s}X7n z4E^l4wkrL_4K^5WMe=WhDb|asEqq)(fXBSS!>&2IzF;v=s{ZdEwCVsLI{^Hxuk?SL zS9fjn;>w3Bi{#|U2j$VB*WVZjFnN%=b`ktjk=;#{gb6R8{@8)n$?-wRAdXYQ`1)8j zV4*;=JrLY}*}%QRaQ2(vi^E#Jj^LAkd84%uy|)v&Dzw|(%*ekauT|eUf95Gk2vR=7txf>sy{BqC_e%aNdB3286Bm zUocQF%WH}o7oA{~z;?(6K0>Fd)Y|n^3q`KST1HrKM;wJHEUv#Eq1t}3g@1q=>XliR zVSXy780#W*{i(2t1*?JU!03zn;5xwb7G}Ez!g105UyKD(;GjZH2_X3(@;GvY`7i(e z2DIi<(0}0K;6Q9%knZLGXSc_ph7Y7mEHoYZCLuytRYY*PUVO*eF`A{*e^YmM=o>pm z=?H`)x*v0fS%uny$UYh_l_Kg;BgzI8O?d?u*1u8Cah3rAssb1#U^IZp#7`AmY!Lqi z2Rj4>%S3deSOx^o*9-`n+_aYF$gCIepMwxnSYZMkb0{g?9vXA#9O>!WHGSK7 z8kW)SFRbGoqvizbqUg{$aJEAK>~9jaAP+z$+n;4_!Xa2ev=2Do{OFK@r=Dn}Xy@Ge2kAb5$xN)}8@CZ#X`o(T`=uHcy&7P6 zVrh)^hxP8qWZp+mSvr+<*i%^KQNBoyB_us&yb^|{{)Oxn>?fS$29ol{z zOBjWJoY}Rmpv&_A*UFw{=`?^&pZN)}$T@?8e)hVh#|>Nf=J!!%3n)1U2cr0KtNM7v zFn+39`-pGsIWV1<&pmp1`itW+XC2llt+vqhE(^C|{{-9*&8ed+eE`ARWv;hUx(pD= zyRx!!_f08aQBzd$^=Dx)Z76W2!0|TL37q!Ks`G1a;UD)9r5fPGw!8pf#?|0R*6-h_ zyyjosaAV{N*ktx^TzF}V4#shUAZQj{~Np|<7@A?i=)6lvz1L@FalhVz)%b5z@YbsQWBZcnu_?KCIsYZdUg9)i)qs0iqiB(%;!Ux#tpFPkU6n~D;{vD zu}*zs+!@%}Wru$llh{37PYW@aJAZ&RedE&GwfBEBf!dP*Zof2R2QmDQz;-f;r)ffUZ*q#@G=sBfIQ&%6olwuwUJ_pP&Fcy0vTdW0#=41NVmkEwf9TG zpq>VFr4V}qwHF7b3dvzWr*)(vXc>HQ~g`*BE*r4q?_sC(+7P8}6Ah1=*Rm_Htg3%=M0x!U>g&oYBO&hi9%ldNS2PG;VDiMujuG0MTCTVDabJ9Q*bqm7i(XI!cDlmCGHs3^ z97f*?l}tCGLoW-(7ocAVAD#uWZZ+x>0n@OiXaw#fvYf9*L^?zIn!PFJZ2=0LaCjrP zvWA~=N`UJDAmtbf9-+;ENjUbGLV<@Q;Yf}V3~q|Zj|ZxJ5DS^}Llg%FQs=YfAO1xO zpEoK7m_Qt`@&g>v%p9S;1m-^FGz#~}hV6a>MB;A6@j!Glct_5yu5Tr>$=&p?) zD^K3vsUXVb|I7v~fl>h|UtR#RVXpM>@W|@$(319g(&u*SN*RAUoa^wKK*DT{H5l6E z0Sk%M?IHbmkRT);P(fzRPat#-+6Q_`v#8=iCvXg9{XkXP3SXrgSPY49{3CWQw9g?s zw*=G8NM3vRKO)>EHE}orWHAXOo%L(5|Hyi4A6$46QM=l$`xY!sXa}Iv7_nCYK)m`l z4q?U!07ePEN(Xq+#k1Z2k%@t$SpZ5q(ECBTF@ zMTHiJ2Sllsd(NN}scWV;82=zvn_3l_zoV26Sz*4^pWy^U)}3;_yAZZ;ccMca zWfK5vKZIIF6P(D9*BMxeGSH_58^#JK77*{52m(c9k!ZMTwYEUi3?Pk}2FbA)ptzHT9^DNh=ryd_ z3Tf-DQ1m5E)^7(|2n0h=7y{h`mUzdVwQe^?O$+7aQ2CsJJ~|}PF!TZ`Fv06%{iCql zyHKx{vHZ+F^YuTO0-|o3qtl!p768hfc~6Q;fKPI_1gmdzBl=P8*L5onaD*&JW@f1L zM{H(rJvKqj6;_p*^y542XJyUCFkl(j0|0YZ4c0tHsQN52y+^}P5ET@iV#q@ja(!g1 zzde+NG-%eWf7pHhAPsFJe7H51g|IO6L~{WfIwGo4@MBfDsiFl4517}scGw=l`V21$ z(6kF4A}zC48&dQ-A2cu|mkKURk&%|X5arSg>Q429eUUKkfXDA%!r9{x*2$u|DDp)Mnn3%3z@afUpzKTe4t%n15~c zT6jMfI>JDLiO6{9k6L~)I1>+m|9JTfsuet?wjf&y*S8T{8s4j>9?l;GE_#fK7O zAWl{RO)8W17H zR0JacgxG?)5#5n~nZaE|cWO>@48GZs;yVa*ggaWedZQd^)u*Qi6 zN)&9vxC;j3wi$IF>*H0F`Xr!$15gG@OyS!Z8uFqBkR^m0SbJauSqa<*e#D2{m-^G4 zNVsPH(tSSfW@jT$i!mDp8}KE!4fwx*z&g7!{8dUxL|-=>y?A(Lx8!ta=iz!$eX=EW zN??Rv;SgI_uF>F7Qo-k$L>@vFR)fgLUiQuR$&h6DUm?M#KXeq0e`@t1R33oWNa5n&8E~jmOQ7olay5 z3e@@dwT};Y3@*_II_Rc>Nn(2X%RmP!eFPAJO&+Vq23H%Pd`RnTPg)^1j}Xu~z0}oZ z?K_S;l`nLNcK$pl0mT%l{+(wNfAvbv2UYQBvLR#>kKcYFdxzf%{Y>-@D4OM~Ch-K!2#^`A ziBc}_T2iC7P5#m0+%>y``|?6?c0wqA_f(H;k2(4M?j2k?~mYSZxNt5k<4 zdS5Oa+Pt(d5RH{Tc8RxuAvqv>>l(IVwMw!Q7eEc4VSK;mF+qtL^4+3So~bxJD(|aC zg!|yU^j&_4N_2F^-;Qp?_3)TApkYE16*KodMp_-2-ZKTQ#NGEx-CoZxV*LijJO&cA zMe$g*Q>eKi=sEaTi2ccIhKufR!>GXn(08O06Lp`%1nO3Q->>!u#v>Y8{jo&B$O<|} zM#k?z1S=`IUXnLyW`i2&sPqdAL`goXjk+f`i#nYi3-h}bvvVT%TkKrL?N54NB_nz? zh;m8YgzVd%m&@c_r`_nXw{*CfMFV-E&3@lbzP&HWS^AQajY>;zZ?>mjHeT(~EZ{G=2q%!!(yYEkqW)=S<{u_nc0GJ- zmve2k_+(hk$zWaz`N;D~6Vq^TPvh&1blDlaz08?FZvgL`Og_(;l!_ zh0mSxr6@1>`Au}S^~a;$=_)kI!9f;s3ko3B;gVp*-mZT_=U7(5Q&aGUD}hm*_0071 zmh-ndRWI=rxw~!OnA&b8=qE1dIYik`?o2#gw^&tNZDs8JEs4E$l3tU(8Al4G>8Dma zukduZd#gvd&-ZR`wNh`wQc#v)h3+DKvgh#tExe3hs413!!-t1uP)d&o?oH^>Wk?5(hj%;0HTHGG#OqG>h@199=5o+>)|Q!I0l^jEg`NRz%ttgMet*&_9chr?umQu@rw zRrYStmGYT~aujHEbMJ^shiO90S9~}%!s6KGYV~9{abe+v%lF?G4ON>xq)1kX>t#sv ze|7OMp+vrJg~RCra1I_9>(Xh0Y9?>~=}qPGMOw+gplv3Dj4Z3b4_IRv=pXjWG&-gS zaaAz>1~V>1g$FaS2{Yzy%;~6=iN3;trE?T9A2Wyx z6nX%AQxUcPkDS8hXC)ry#$?vcl}-(+*q-SnWPQhP#{W5LrL3&TVezlapwJTr zhgUN$Fj#=2M5p|4lL`Oh5sA!i_^k_5QO&D2Nw!j7TmQ7Cnq+%9mVKWM*^W)r4AA5G z>Rbs%V_JrC_IqvFnv|ypW}3?P=!f~ds$ac2EDIRQZr!0up6}WaX zB_q60)urrZo~#B@Rmzlk!>iZ3)w8*9WW-Eysc|e|>IBBAInUhqZuci>FOnI?y|Z8H zcsikwyqt9@ko|DOL%J^`bhPDfYjyR>jsi~~%9BkLc3N29w2S{Zjn+vHNycU_5@{uQ z4jlUYD#DcRJwj28q_P?i0MJ&KbdtvDHBy=y)tm%~S~1#()z58amg_g$Y8zdfB^qtC zYDKdRfSmJBhui1@Ysf;r+%0>y@lw1d5np zu0-Zi>z>&!%veH2f%nL%=5ou4r`d6mow3ZJ`-$fgm&d4lLXBObU{SbIB}S>muE=Su zevh5cchOcoF}*Z1!6Wwg-ZMGN*+);aiw2ned@bU0WbZxL=R#LGqZl*g;0yv*H&acT z)?+O>XEYZ1vZugAL3GZ+zk99y4$W-KxBeS?MokpfU7L{99hn8Ft3xV{tR~VeT;FDb zndJurpdf;v;un>nCszkRlc0$p`}dY8S)GLz7eXcwH4eB&sC@n`!E-d=ntI4+Im{hP z-I8mq;FVRuGc^S|(Pp5YEOtlWTc@xU@~5abTMJq!8NGLa&> zT30#s8q~gYrwr+CN#7G-_t^911O3sVftbR>Fb%q|r|`IK{@cdk7v=8Zecir-Ug<_= zyTgfZ7z!^nlk5HH%1W6&V$1ymd8J`VL&)lVR3+Uey6m2BxD9y2RZXkuYu~Err0H)8 zSru57{*{|HlbwDPwqLY(+rur{r?_a?CB5SDpWFmr2g(p<2H8MGT>bFKD=y1Z4@KG!O+Rzktz{7u;Si+4Yw55bib6MaHLn?0 ze{=5X8TQT{O_&e$w`ZQz(HUM16IBXx%1uj+5(;-l=Ag-4Zq}^B2W=P#ypH1$$t#B~L z_evMq$M|e>s(H5aq@c^}_+Y-H;o_&ATJ2l4g`#d^87=kKOU+&KX?c!uvDm20^re+- ztD$9eOmE3h|42AT04*$R%Fw2JM^xrRF7i|TMBFrfCGsMMp94pd3fSiV_cq@4Ea$t9 zk5|fQT|N0^jS|I$ebSqhhURb?$Oj#eSh%bmZZl#-b5sd>T%{9jDfGXJnkQW01 zG^+_3H53;D-S`zM>#Cv2Rze)wur=a$yxDJ8WdVsrap&+G253CYM!tbo2KEHbrWW=eY**pAM#vR@~dVL)!z+s8QN-@(f}< z9lnbc;vJd%9NDkaYgd}nIp;LNby4%A`P{KYwsUy|Z`Vtq)vvv#M*n6c9sg27lhyQTeL096Pw`Bzs5 zkpof)H5i7zy$2JRlF>kqdV*2D-9DJ>NAkty;vwZ0sPLP{=nxI}Mpw=<*e}hin6md^ zXS6@|mIH;&ZXOpyoiX*0&~ujP$y)X&js4ynaOC}i$cZ|O_YIrhr1>>7}-`8wndpKO0 zK5SN6eB3k^E0<7RH$ z?3oH6_{xa7Pj-#qnX&VF{BkQVF48Gnp=-b0{uJc+Ct{A{pVrmuZK z=JprF-=qLel+juqr_@QVvqSu#Cy`Ue5uX6MMx^>&MZ_ZnH4MpCbuQ*29X#V}mQS^> zt)YVHoGV$Nc`)!CMkDn6oFHF2seekqxhz{IkoUw5HQ~Vrq|r%G;bTv_XiT29l{2of z`RFtKRkJeC6fZnb?g(Q@$ByJ)IrwRDeyT-PW9MYtXsP!Vs@Y-+uQYbC*XSf$jGTi+ zxS6V~LT2gYlZ)gmRpz_JHb+_&p9`%l!Cfsp3^&4sSVljSxI!-V?mGcg~dYlkg$7<5=h|R`kGJ*vi#&TiBs>6_T)H*(|iVF|C$vN zQc`H`zg}?CcfPcmrl^toC}Fg@*fGI*EOo)V*sCcuUaOa=?^Y=L)18Tx12vJw;pL1= z*KC&NOeKCBjuH`RE_Zwmg^Yp2>y*F+qk_ty(C0a;2K#QtmfRiN4J!LvqtevZVlzxI1AxVD6D! zrjsH7cY>^rI-)0`0d2fR!d<0;;L!I)ux4tD(DQhPTz=YvdhJG^iCZnr-iD_z2~bX&hyM(of%P z-$~7&+~dOvQp~*N;yvx1jvMA55RMDW6fmsD-CrgtXzmPol<>ZuLGd)2eK?k=?_A2@|Eaxox!PAZ#Rybu@<~g?tZ{L0;hFTC*d%Zl$>OL$Y;=wdz8 zGZ0#JVPGsKcYxqrF0d~$(p7NN4ZnsMnIniRuWp8SU>f&CctX$V;aeJVK7`uZ`nBR0 zHSn6e+E+@X)jhbYD1bDIKmyh5DR89WI{V8(Q~Izl8ZiXn1{t=D-97x%1JY#ry_y?t z!$Ct*?hWJVVcE$MOS5CHJ*!J|M+lr5Kr#*tJTuWjBA9YMRa2#Z+ID}>l(#)keTO=1 zxVwr2JKUA`UbPhulJxoZ$7tIt>%IFpD-8~^hvsTbGstq14cEB~`9^laoFW=zvH`U5M@j05pLu4H1M z&n?pXqPgZ{Ql2H>xSpk21}ucfn{-=u_lOo$f6(sqWix=R#%HR7PU)UuoP@l_*Lyf< zDiSwC$s!d6nD*MDF^LJD(&^$^U8WPu1x{(ZF>+j((^qlR95qcA;bk$5cH#nu^vpTf z-2+>>%(w|ELoZ{)H|yvs#)W~VPGlKPgG&#Wfor1fp#hZgzDUhw~@>^~tNdJ1vu}K_5CI40KrPfAG!t@_EAURF?)azb_WUNf$0vu5?_J^g_*E8~RhTP{ncSTfALkIz0m!xb7|d3`BV=jyO3P7EDu(wg?bL*(ZdblBO~>pF=Be8g!~B$ zOHa=ydpR}e(1<+SG>sen8c>DXN(=wp#I%HHF@HJRw=Qat5-d5@*|b6HuZ1H!ZJ}E) z;{@A8mEGsV^N4kSo2XR-6S_49&FLz zqYDW+InuDZq4!dWn2H}#)7(L$JT&c3uN5~u9apejT@9kHQ1 z>32HbTAiQ|KpOeCkG);X;HJq-;A{w@#3pVz zylm$!;jDG7Z?!n{fJ?Mn#(-p}-|;Q;lTXIH-D}1dVZLY8;H^2!vF^}pOHIX+Jo|l` z?2`ieaSGB81n2vkc{h2v1$kGvo-w8eZylI@U2x)$R|#k49?8!Y4HA>4vxcZmkTVgK z<$dRqf)igZmWMYhxxJ*!*0fSqkC+7)_M7aUIOP!5GAz;-4+?MsAaV;-HIrv@n5g_(O9Q?@D(`MQll)Q(W;dGtTEX zWvs1JS*bKlEPR8oaQ41C>XjgH;RZVL$8m1BVYTN!DuV_Vbz|)CWF*&ufxMQl@F2<- zDG1Bd62uI$!~HD<4m@S>v3A(QJx7qHAwbaUD5$ChSXL>}Ys{kdqk(Z~!Z>Sm-E%o2 zZ3kKmX!bG8L@(<0cDV(^pko{}a)%hUFqFw5tF!Cyvj=TuMe>*AJ=79rviTv?q?*?h zzMI%B9X{SF+1M+RUP1YME<4+2M$E+j)yzsFdvH{#gH(w{-z$&hg~=Q@-0u|8LPrV}U!2g>*1u|#TuJhR6y z&^_9U#_zUYu2I2UXTYZqf_c@;XmUooOqgJcr0@4g_A{5idM6)s{A|c!Rx~v2v79tm zx1X=B0JJDv)#yJn=F*+s*{N_+#A&jU^ISxQ$$%}nNES|q>{1o%K>6J7?(OMCRH4r2 z(TDts3q9)!%8j?lawY*Oq4sa6L5jOpmsSpd+bhA-{ZeQa9zP4FR!y|ZI3~6ECeu%; zW#7vyvoVju!n68mG{f+nI!4yk)};cWPlF?i5l1+|q%)!z$y1S4W=C~zHX_oR^^|tMR!I<_eu{{h6kseum)j9^;0+!LiDmd&a{TS3V;5~L% z5y_2^r3>$*?PuMBjh{{K50@5RCvZ`0t^J0msgD5JkQxstTHcsS(}kTyA&iS&{HXLq^xjzvFACQkA3|Zu06xOvt-MQ zRs3iO#LW*76Ke&Os6MlTd-uK^pc9yChgU)ai^iyalbox1`4$bN#rX!mFij`oDaAaz zv-=av2h?!TIf22U8|`*-`uXcd(NoVaJlEgmU%1`>?`);1H>VTN)HM2yr1xLrY-)-z zY7ofRH#;yFf84N8!dJ@tiVuCoMH*i%*oN(vb&g-qZN%t;z!9Gf?S+e_AfCkm2kx4S z=2W}>$6s`eHhxj=1X4e%*$PS%7DaW>Fi<3AS7E}VP3DEOGw5yg?iQL+%nF->R;z8< zN304W`mbE(z&@8(eJ-*wRA;7fYGCxV_mSm0ISd!h--ds`CYq^iX_YY3;eBLEW(7~y zo{r(>5^`7H%UQ;SiBleYG1~vP>v5CZY&}=ZLYjLa71zdr--FKwr}8J412o8jO8U3! zO^c*aMZnuv>SbckyFlX_s1qU=Z)&w33F)%LfbHCHSN1`WM9!2yHN0ZXEzpx6(mqjZ zXKddY=D2&RyLX3d$XuEObET`NHBq{bKpQ`3mD$L$)uB9joN&61rniBMb8lseFMi;) zyG@7D&bRmeo%cU%(}ZiwweicQw+90FcO&ynq*k3RB{L*oL~6XjR7tXK)(kDFb?hh@ z#UgzMk~d5(`1C%AxRjiTb_`U`aop(H99h6MzT^M0ts|roBKO!$*G6SIe7W!n6$@Xf%=>hAu?<| zo-Z{CY*Q1CJ~w~im(XV-yDJzheK#RX6%;6E&}Fqr*rX129KDb_&LSZ+8ZpUIXgfn^ z(p0~k4W`8-p64P+jrKC=G&1Gn&aVfBmd+xZ<6sE+5TjUT17LKqvPjHf=rgPD0YI-Z)!%IJ0It=5e3lj2}$iHON(uW5U#?*_mp>wF$#U zRUe&L{9@w?udAM(#w$Hab`1U(zQ%Xne^IB$pZjda@UGc*diU1`K9_M$X{Z+Dfk`h+`g*rpgd%G;`cYFjwE2=2zI)Q}8-E!qZI8 z;q3IYmWsPf-+)bTBq^lO3oFy!z)VC&GAGZw!OX2c+TpdjUtf^Hrh?4>u|vU%C*$qwsUgy%m>w zb9{n!bll%(J*%i5-RRp8z4*-e;WEg#)tlPR##sZVp@A!c9DHQpu02|l2zYNHeJCIjoFo7{? zi+oSEWabP5R&Zs+>q5M*a9#VSr@u@ziq?pC> zOr+IsKAG)g`8_^Uz-VTuRwd1g5hgHpz0kbL_2N@$8CCq)wiqhz1AFSM{E-lO`DWkQ z(Bf>*i2}oG+uM>k%DvAtxlKt=IrbD*Q=Dy?bEfT37#8vl&zV+7iSL=pe{PQz+SJyD z6y^Y>Hs<}vxP@$iPWZ5(ov3ChjCpm=4$vYrXy73b_$q<@_IQbX8BQVrn5pA|Ohdv@ z+}mj^GD(nOzu>FmXFlEAb=@?+UFrdKG$t~_b9UJk*GHa7Tfp10DJwVj4y}BR=Bf`b zHNz{kW-dp`f0BQ4N-S(nW9P;BcEIQHOWz*C!OhqfljgM4n|8Hlv^B}(`1R~e4O$_f zbEoO<+Vc%&mrEUQ+*`idoi5*~>Z_A51Bq~2Yc8jxS@caInLNLhCZ?(hg`W1HC|Ex| zxVC(SZzOVThb2H2qo1hdFJ5ZX*rdE}5=U1C2Q$^OTI?GiPuu?*a}j z-^pjpWsdVoJ7=Uo2o+CX=xo`^x4X%}L@#P_mG1)QOlQ;9K~gaQIeJMA(o_2;PE}RP z+2}(8V69{oU+SQB+#oL2MHO$_;QrfETd}6*+@rXeOHLAG&a4WfITwX{%dXVf`w6>PtH*yT$50L)F4T4c#L~A-2CNJEI1>-ty#k)6sU?g}b zkiFz?{Hf!8M9}AK-qB+TcQaZG#Pj@0g6oTYur+1;kp_jZFRSeP`QE#&TEI%?=inF`8e*+DG~^g&mF_e?&}Q5oS*+dQbHvTcbK)5s#`jiQ zb1k3C55K8+Sm7P-d2oU;~ZM`xXzGpYSyfi<2 zs~>e{)|pFCz|ruewog{doBfyg751-wf2>`3Ci!q-4&j8gO_{QzZ$2fX?~Q^lo+k6K zBFofOqt|0F2hrxd8sZZepoa zrOPoxP|kpr=iTLF&98Oq@MepC-BG8)2|l9(@-Hups59f5&>WRJ4LonR1|sJ-#D)Va zuGL*e1!c70CY8ARItB(jNUw=pQIVV0BEl4q91EFQg%>!c`F$vtLbHHbGwD~pvR5OI zP7e_99290z>mOfIJeCPUU6zCvc)Pd0oHw6=N?727*Wto_K4pSyDb66_!X@tAn>Q=5 zzwxqcY6CTQ}2<%{c2i90KnuCfL*U zn)C4!eesuzEj4{ssR0(VSpTR+;_rt@VezekO``2731OzgLh&NZR)a`)DH3(YC6H8gYjc*i(2sr9xAbF)jOB;6jDm80o+9q;&;cL`HL(>wj=Wv*$ zb-P!Q<4TL8dvjUnMFYdKY~#Nyuxm0hw)Y3lH;Zj#t#l_*!lO#9UXG4lwYVOsT6kgg z&ADsH=H<+Sk-%J!6yk;ug$l4W4S!ZZ#{A~`N{Ae!*#Pi>MNT)=q=MM`0~+7t_qx#e zf`JBUM28WL74aqla&mG&u_;H~rBKFTYusatm9u!{iKE6 z3eRP%&qx|T1x=hlTbFS-T0*SV{kEjq_Uz=C=+^PnP79yy<T5*#77;6wIY_^jKt>&ESRe99|LwqSJHH`BQ?}ad$_DG9FY$ z!_d_hKPEpbL6Fxg;TTJ+huWd`)5a#y=De0BuRizeke8R2{OD-5iL%D)pb$BwOPBI2 zdw5^~=3`y-bc)z4TvuW=l6M0MYQN`vKhm9}q!>>s^k!Fi+xGSPZ~dSmM`R5oK(4Q^ zM+Q1?^B8=kJBWZ$r(7(kZI404+~V;DM}83a)O__e^pT5=xO*WB0|0@cPNEHs(TSaP zt6eI!d(>$UEu!O-_MTiwo$g*8hl7V~Wrk?samcbV9hQjMq-JLpIqAe{2{N1zBTk>( z?Rkf}7q6bxj3yomBy;KBa#BC2p5a?5vFW^VeS!ee4}ShIZDqrS9X;EmGla zB%`8)&p?iuYm0}Q$!%$7Wrh4Qb}#GUfX)jMghl8{AUgC;aJ(jFi~DS}<5rYrU}3tx zi|Z75C!IUaN^!!BJbko`dSO{*@bY{kH3;t^SB<=FalyGVUtj(=0Hi zf#h}|#XT*9_n-3TI@8XgaC4K1fO3)WUp$Jd%x>c*db8z^9;2?U%}M8W`BERcw)CM} zEnEDc=XGjFkM~{p``_4?{Ttke2?S4#0_~N6E zmr}zWX7WzbA($KKhgfGs?>>IolHfEyfzOt zsk85Hu07>^#o+Q~O3z~ANVwlTVdK7Z$aCI6?fIIg-~krT+9d@2ud+@e#pCsrK0qHi z0^4K=zE4JnO*81#k!=E{(!zpr(YlA+#l7vH&2hB}DCYx!GA!8hs1Uayt#6c~9U53Y zWk-g5yIWk3NoLk}UU9=wOPGOG6HPzlL^Gf$j_On$Ic;^icLi zr^K-ImxbniG~i~cGb~=5g(9;*+804N!>b3wQD1t;4U567L%ZulB=B3c0rR2(C^yy@ z%TW6L>a}aUeJp@THm>~P>b?s;X5wX@ItBKd1)wIvzh>*A8jfEDSa7F|4L(_3g66J$iRnT_PXR`w)I(gO-=GN7P`Y{27=R+>eUa!a^DMtMI(H1?58RMI zYViLc`V2@qE|aFX{^zQPb0Cov0r9W;OgR85ef0oe#r@!O)e4d1Nr?c#fHFJ{Wx`vB zJ4kmgU$wnu?+q9HYO{lh-6kNe=#gs>akD|gJp|T0em&N)ykM}t#_fS0!><$ zDFo~{JyxTmb2ABYGEq1xmeS^xQAPTaJ)R~_&UdcC#K}@`>U=GTCe-AEn-MVlZ)fVR ztcB2*{K+!>0l$^@=2~6YGs(p#q=~VD$;`?`4`X2%@j*-wcPRHCsUUM-({ zXB*}QeovKr3&>{ZN=RncSW2kfT|&op_{^O@e7~b%Px{kyhlFSx@d6i6lBF7kbniy? zlQ4G}sp#K+{?G`+d>eIgLY64hln0q1p^V^dE21+sj&6x?(hfWZ)6ER2K`BT>Z6KvL z$`>F9jnQb>uG>4GZR`qrgax?$=H483V;cl2=78E77_2lUxuO)hzDn%;)`?}blYq<4 z3iM^z@1qS=?`PI8J zm|VUP@a(*4-4V#kYeMfdv5jvZ!#)>+vJ(<*4%MLh>}lDt-Y{H@Od^+Z1IJM^vb5*c z*g(MrNJ#Q4q%QVO8L@8;o)Cl{B`;TP0MRu>6|*9STq>H zQ^XBEABZn1-Q6HG%>Mbd@|Z|0%rj=0^NWM~PUBZ<1)!L4D=}{FL z$y^bw4795Pwi?9SLLQ*Pj@plJ{e>Q6If}B=KN#GKwOWWFuJ}TDzR={p4H+JKRU3F30D}mcyV4ZFB!Gn`@ zrO`-&3aJL{dCfzItR^rZAGG1b3c>($aI3RPW16t zrTZ>R<84LC@i^i-A=Gk9{qJ$hC~Dun1_~+;M7c)dHdi>Z2RwSTyyf#NlmT>|MuX4+ z4^qz*kWdFOiQTS!K(etVL+Q zXYvaaX99W|tu~01h1M+T5YoP+auSnp86p{Gaz@)5FiP4|@P5CEPT#zXx;5o~rh` zLg>TuM`9aVW=eDGuPaI-(F6*o8nwTVhEh%Qqpk!&GfF8j`Qc5AQ%?bdQ#L^ESKq(gE zNmtwfBM>)3H`X~?0r;ir9-|n>E^QlmkTweUa1cS4kWK^8Fw;7ucWz9%K&XifvyhLW zLY#cWXT0?A$~ewA{|%_D7?ClpE4FbwAVpFiB9Clr4$ zu&mFF{&pKP8Ae@2`gTqq4t!=YgIfxL5NgO_X14PWw{Z}lAl~As4$S&9(5iM<| zriJxBNj;#DP^glY_8b0>o^pED4!6oN`y;f_y$~H5m{7Pq74QHd$cO~F9rwNZ*9f0L zS9Fv3^;k{?Vf8S4SJg07NQ#3Rxgu<%mm}9V`FA(NJ}<8f2L>XZu+^t4to#q&k-o9r zo+|Pz&}j%)tTtKJLVx4ql%kLE%OCLwqfoJ2PN;#|+E*1s3M2UM9GuwQ0`~hMY38*X z2wfTt^!P?K9D%$NK*xRnj1UF0?fDJjHi+?LsXp*|cOC4l!$@w9e2V}B4;WC}K-dM; zSOIob_PE%p(w#fM&OVx^4G$Evg2{pvoY?OJX&qpmx)pvdbXlIAOSg1mmsm_zlm-5% zbO`AQw!eS?J{^b*C={ti6kIkgF8qa+8CalPH>dOhaqdgGtw76s%1K%PyA@$$&x6o< zfd&?;4+BLvcbs9zKFr7)4stSg zAK{VHHV}6DB9NrU1GSJs`@+s0Ml>g}EZA{~5D-MS0CI#iTnzXQi22IS!U}4z?wEcy zA>M;S49QP9F`l~vFh%>3d==rL=LQJwf;3#ZWm?~~Zru0{38wl1qkxhx)NOQ zzUXWQIxwmfJh~gJ3a<`D=gR4@SIB|7Qm#QUvjq zVb`xx;<>v>B$a=$qW<%n+0n8W^Pq~76s++54o74J3;{f(CW2d_0uo~21u%-%LgEsU zOqFij&Q(8gg~Wj4yMi+KsJ{+7ZW6jDHp1*F+(PFXbRv_=da)LZ!-#lISQuyKTV#sI!k7{Riy-~uRcGi&;bZHV?d1rze{AB^^v zGe8!%1G}C&D6>H1Ujw*@h>6n4l+-218Pv4Jn>9M=nv+!%f7h9UpbC&c1Ay4D_4@YK z<}hkq%!?D_MH@8~qQVH~JvnC)MTp1*L8r3?JRDlB08N91WP6WD$H5VD(im$w1CUm> zeH3&mQ|k~K#~3q%2^u9D#C1_x3vo70#B0HV>maJ^XnArzBS3O%1s%{J z2It)XM}paB)TNmSy9H!u;<3x1H(n!6w&$ml#kNdR z(58YquO#R|VGTyyUz4=pKtZiJz&4+`cnkFoLt$R)GT_!b)`X?De&h;;- zCFt&SF4aH`?2p2S!yN43p0o5qYp%MUTT;WUB>35(U+4wQyb#K@Rh_v{avU0hslsWC zY6b?*xEV+692IHslmCgfHk47m?*Lu!nxUU6v7R`e0qb(4G5Afm6NkzCBS=At&pBJYXMLV&yO2Fa7Mue%Q=$-@V%+7N&`DupdayiKL<^WprL&OY9 zg$fw=IZQkRL@TlaPc_S$HKWk(zds`>AGJ>a($JKxC8XBkp_Z$Wk>rffmw9r2w3L7w zhWqAnAk3zGbAB22@^0|;Bn*HP0%<3#MOa#j_V3UaJvB?Ag~TvO_A}bbfi-1-YMMP4 zlIURBhrlyrqCxhe7BdVNv^ABYubAe(=ak` zY#YxqRl9q$;NE4DmF>}CR%bqGBU6pQU8s-@cQJE&Ewc!Kkhu~xEw(=>f3PWie##Ho z2}eMP9C2F&gaLSDv@D9xS`?24%is~ENN?>^NRG&%mW3ZL8hHl_cv(30{ll||Lwb`_ z-3DkKc;F)~&#YB^d%{)SB#4jfu=3nEL7D^K6(G$>l+RmdtA2<13~jUFES*co69@@> ziREG#lM-{@iq+88Dmb6pbK?g&@Ov|=pn%;Y%pUmbNUoG>Fn#WB^VlRF1~V6W&6!bR_RCosO2;N%Gyj^|t3C45cC9Udk`rTh|`+^4fX4YIS0uTR@dHeY8qfKy{Ls zhCuB`2;;j~o7hhgN2-)=F~rXLkW>h>hZt`NS+gc-iw}h9xv!2Ef!^5wj-O!_1_8n> z2!{>nq*ZrG<~omEw}~%5k@<6a+Y8Dz$<`r)t`K05^^uC80@@>C0nb7QyJK&r5d_9R z8rV1y89I>pXI+~#SRdb{2m@~+9m*mj>}XpT;J}JzOQwck#AGbaBi^`3*DgMD(#O_O z%3-=H|9;K^49B6>5SZnl_HNV|N_j1qrJraiU1Xm<GzH>fFSl2Rju4JYJwLcpF~qyxMcs%|Y%>7APhEB-s zoU8xidmLna04}BB-(1Q++ymJkx&4t;6^O>G$guzBwf}c-11V+p-&0~gcK&~H|NbG; zR=t$+hW-C!s0~r3RP)id2jZ@!E`gu_nejlHEFb%MZ}T8(r2_M796ebg!RFf3ka##E zbekG}&lLUMqKl2neB4Vme1%PbCN!`&F>Xjm&t4gz&Ni%B>(*3bLhWYC?&TWe-^ts~ zv~g?Zn?e~6UuI>>5t_{>kbc4U!o5+YTPn0OB836byGvyH0@8CAlrin{u+FO8l5Pmz`&VeZD(7&53f~ zpEb=+G1CEGPR{W6FGjT!98F58f|gzpF}-d9bS>FNu3JUZO-kFmtAmaX%r@=cn060k zHM4Ml+Hzy42$$|g z7d{Huhy@lhIT!*m7cj}GBw9aW!$N@s%* zXcVTiE3;xRO1Mm)ZOwFBrjty~E7FMUw!FpAx9;V5w|0JRAyPd@m8GRlMc^BQs>%)8 z7|9HK8x`LnL#vzHcdWEyP7g5?#F&Shl%97T@HKJ&{ZlRfy}8es(LTbKoGGn~rXiMI zAxs{;hs1L%{davf*J2-f*capHpc`I_Gdg#6m!!dj_p{n3F(PMuuDUipb5*%@ zOER&Dy8S_WFtM|)JbUV> zEHk^8xqeyhs{P)SeS08B+&%N6YF5|ndmymwF3RT+P&M;5d{WbzEZfI+zJE0)Hts`Q zk#UQ6Yqo4e5g{GUULq}32^Vmi4Eb+dZ5r_n460dy!X_U(bJ+R{&R+HLiJxa0OY#kL zv<;U?eG2LE_xe7e*#dXx53zNBBg5KY`O&%7%xL<(dl#vWk}W3UTHJ-1 zEWYo~vX+jkOnS?v>0m+y5wJi;?{7iWqIwu@evf-%u2YY# zS{FlP?oWM6>pJ8g<=+D3%U!oU_wl(ewTG4zEonWnGwI>vWVF_dv#kH{)uqh-ucxY3 z*n`Vm$rTMxqnQ}B@|yAzIo(<9+rNc`rZ#k9ulXim{ZtR?zb_fhK3#63s%v%W&aX2P zCz|OUwQY@CS=u~)ue_7(+7|9;KS*zva;z>RaWIOG>q5#VL&v*&rA21O)&b5|TsG}P z6sbeUa9!pBYu#DrZQA3)aZ0K6o!Hm1Z3eaVd$~Oh+q7rR?jG9s7~mUF+Ni|3#7xs5 zknU~NsMKQ2;PK@J_rjmjrf&GjdsD+Pl`*d;=-oe6xeXS&FV+rxzzZ;H*6xQ{^D$tO zIlZs&y0)H@E~T$grdL>IbD&^Mltz3n(^k`;@S9&)nQ2(v9^B0%l-M-0_hp?;5L+@7 zTW(=jD&8y?HtS5CHyCo`!X}3>>BMJy_S3mHzp$#~&X0-j%)G^n+agrU;H!oY_YLPW zy4d;6S1vph=@7i%|7UlRvq6IR?S(h|nq*>M4lADhfd4S`fp+ta!EhE8xvW+0azIxS zTc+37R|z$*$#v`Yyq{7}3-mU?VufSGD-{-ZVQ$!<2bBkR@>a!t`E&w(OSVH?x$&mY ztr9DomZf%x4w&0%o?T@A6*0_v~3N(@hw2AgYAHB4TVW9&UA32ePIG>t1^T+$qH9AFeeUB7c3V{j1}VY^==__@MP$Zx8Nrp; zfB&!C52cG!M|IZid&k`lZ<_pmYD0Xen{4i(Vhr8cre)V?N2}bS3Be?z1^Ra0=9LNV z?DDv^nQ!9?S`3!r+ZJ(zH1+^r0~~WjU;c%@*R^ol8OslUrST|UjLDAwUjCco_m7#i zb#41(BLWIEo;>ZWkb7o~k6(h}8>6FLAh6syKS{>9t`}U_^L;tSowx2;LQTO}1o)g39=Y<+$HFGIgb@Z+ojQiOL59WDW7|xU zF0;{)2GT(fR&@EI90jl3rB}g&F9HOk!(PDX{$3#+*u_#oWi!}&yq-ZMPS15B*Jzr5 zBRwTH>vl|+#PW^rX>$+d7V!ovrKw~e*Q@s*^RLS*R9C5OTP|0L5BIEPEkDhetNX_+ z?Sl!c_2_sL)7p^{Uz?66jit`31TL1|k2uC`EqF20ZkO6`HkZ$=={kR_Dc7dJNEJYo z79J`sy6C<1E}eH~M)dq}Pz`TNas1Nc5gzpfhw47|u@2`niqU&>pW;Tim=8_$7k1*v z=$<`{a)^mf7`q*iVqRA!X`|9HaQKiQT$jLj`jhNCP0gxhd!Ov(9x0MeDNnR^xhJ+7 zGR`|=`0lLL`$UE2NsZFJ0=4M^p4Dn!x#`i#w+1+Rnwlnii*Ji#Ho0+eWv5K@$rg=T z7oI2;*efUK+_?4I?tqL7|YJKHjnZO=f*&)FwVnAFR7VZ<&4{(`nMkJ=1DY*w8K2W^J1} z0)2K@``P+}2ZT)&xjq0LWUyoUyp4(rZ2I>z6|XiQdloQ3f1&ranhQ>p3+JD+sKqlw znRvK&rVPKCf4lZmBTX_TWqNPES2VdpdTV);mVmFs?oy-PKCnt@WOkSp?vAwz6JCvy zDTM`JskdZJIHiE5GB-)#6de=|ki+rCSJ(bUePgV)bU zA*!(QEZL%2U$8lM=G8ipi^l-{5fa&ELdS#iu+fbhFuf5&7pSoqIkalo*f$R)$h%K4 zw6Xh-4{knZJJ;`I-w&HIcljZ$aJ9q=_=5T7gXdd0`wH%Hsqgadc_9u4il^V3?0W5s z5e|ruq7x)+k}G^5JncSL89m%9T%K0JIe+PZG7SR`FUF}t+SSxm5`)QC&LdpI7;^;7inFywm#jo}zZgB-1j1BxyBp70#3O59$|OVn2Zp`!ou;o|YNZ|${?fw6mbO(sac^GzY=Plyr^9%Ce-nLS zZ3M2#?<-fcH$QtPc8o$R&Z)t$wZyGq!o@LA$hG}a>+&}y{*()l|kV?&n&ng&s^7o$)p*<{i+0FVERvs3Oi#wzb5dDX_+P0MwH~NAE>v zm%F-{Y33%Y;XD%>m7sUAAQ~bl;t@)c{(j6$**a@7-NbX=`p~XO9exP*?+m;ezcecH zU|&wAvTuRagCOC_Ct{)T;m3lizcKwKJUKHuZ!N&|T+}o;J@5%{@Y%JeF=Yf*OW0Ze z$m_GaPen;-DIv(tw7OZ%hA+D?w+Z7v&TKRImVl*d*~$cctH*;(p=~NwS?ugg3S(lP zsT8cTD%)TO3{qbslq8Hb&*puG0S{%<;2IQ)X`Bnu5fog_dsxAL-D9rW;tL_V(alt8WA^E~{(HZ79lBjx+mA+K2#q{$jTTWR zQuNkmt*o~xy(?G;-Oio2H^VnDN*UPM2CWX)JlPqXgR0w01HiYPga|%C1 zjDwCW$1{Evy?-0*J6zYEmdz(}dBb?U*URXV=X(;%cqGcj&g(ryziUBUy`?Sw& zrx_VFr?shb%B@|l>xjjNi%upG_1Ndn`3D9H2_A3|4zbuOxi{C?-<4W?(Ae+LOx`-f zqV=J%ByhgqyHeirWiV*m3(zd(U+lI9!%L7iDfcX$W)KHF(kF39=H+_`kebtvgz zoZHuC>Wf-%wpGmBVE^>R!?dMcg~AYD|DM6F4vs#Mb)%$|-TP!NS?QR}Gl=1D!I09( zRPWhZZK2$ZlbsslFLAIuIhmBZ5Brr_*f7(`=#a$h^OdSZMfrs$7Z)pz$l~Sn*g$a) z$GCE{!IYKt%vvM0y@bEeToa6m$j1`Q@nd_{j1K_sY(_LskIzyyS2!ghxGdMP^7pZN zpL&_n*sMcj4}wB8&KIiAj$W;k{99Uc|0SyULHW)Zm2JWv2!J$dX>n^A>85FU?*4&# zms9A7v}<0uWA*Ed{ew8dJBCOnCtJcLZ+l2!dwHqIXmf8&PgFJtM-!#{x^5h5=DL;3 z^bap5zs(YQYNyz&q-=dr&Od`kZuAt?kSqh%3Qyw{BA@Ak<;D+1Z~5k?ve#pHSX%|B z--c&8v-f^HU-(I_X~-{-u@vX>EL!N|8pYLU9Z(3A<6dl%FZ;m2dO6`TU#u}VW!cYY zDaLzRAg$+UP#=4|*~j2eZaZDS^(|@cElaM;)h?;wzE@wSPF)S}a`#n>byqbTps~@5 zikcARBkz;Mg+{tkCKQ~s>7cF@J#Wf9sAi!!s2rkN4gFjEF2vq{i8AFisA{OyQp!s3 z@PMNJ91^-$fY$?y#K_JWjs&%|Ta?ftHm$8ZqRZTmW@gFtDebC(vwGGkq|Ip6O9@GnsBtn} zOvT&q)YF_R?dP(cd)998xX310)_mZftC~?%b-_RBiaZjyX=Sb7VL2b%WTJAq<$DhW*{#S?pefZ&T8NwIv7aRs2-m^(PVu+R|ZNY@tXd5DTzQ4WJ)UoHRm$v|*e zz%~aMU?U?&eT>agpz*NXoFBvx7yg<$eK){MXgh0osHj5vh}+@v&Fj}SUPqiDuPbj= z9ZL$axhU5m2eluoFL$st`!`|5erzd8#Wluewr>(UMfD1W7F#Xl7JX%`H7>t6r)Es1 zD^z}b)s>-D%q?KqU($6U>00e0V_R1o1{+M21ZUJft( z4I^zJm|I`pFMYYoA|S;ZFc_*IdrKnmT8q)AN{>Q{*AElQGM*^8d0$4xuy5RBd)Xrp zY1EW%WXHR0cKV|HcgZFz8$$y#YTDh-8#_1I9VgGw z{>BJRvp?U#mKEdvP06a(Q^~sm9WklKE5f17vzD6({Xy>B2XD2}uR>t{IwLG6E;3x> z=o|pSUrv5Q7k13f0WDraQZ`wfgAr_!8*`w)?t~Lf4ASK8~5-PH=Lb z^EvFc)JW^r+~WA9RZzb<;-JBN?MT7$yY%oHN-Wb6Y_ueYh^;w295K$QkGwn8>u{e+ zYB>MS=-Mq2c40Y2=l(A)#U-;qQwk~Il+SDE4DKiiXqtMttm4KplKWKe(XOh8m%q!t zx~Z$n6e#xn55v(}D3sHp$DhycgoB!^6%~$lt&n^+A#Al;~k8(AFRxIS9sg#`g=6Fo=hFaEKW^8_p}^F zo|tY+W7pVvhDAul4J*N#>y#4|$9fRosz2X%o z@b|~X4VCLff^rRMqLky!BSB8VjP4Q977qgP3sk$HmOpF-RuwF=F{En=HfofhmOI+e z7YcZv9=dKPz!l%^HyO5ZJK8t>rQBk(xu=Z^?&Fa3W5p{AdQSMxr!J4XJQ%Sj*hQ@LH zm8XqvQ#3qa60}-lyke;Xz!9weRqkIR$OMD-4bprYO!}i#--|8ZxmsF zd*(ad5NH`AI-c|h-r?d}3sCT|&sU2|o-~5fKxAQ>mVJy9w{XG3LBoId=X(UsYk8te zlPdAY*2pkP5xtBnWE5s#ZKs?YBo74XN9Wg%ABap2P)O5)uO{p_$I13&Vs{0oIWBEt zl33%NDX!)vriao;u$vU<+?HqTKk0CC{!Y|-Z6%|V{n1%#iDiiKA#jnP=@BocGX6zQ zJuH4uGkR0_e8|c9FFI?rK`=lHgK_>=V{CQwKaTFl-lZ?O&(01W1xVo~Y3H+R!Hqf^ z!|Ne9J?gKFqDH|lKMffVK*&Coq_8?>&OOx;MfL!88vT}5@_Tl_o+vd`5e_LNv}{4i zV`e;Q67mDH5Vt_uZtVjdzI0+{BwsaV?`p=0ph}QGD!_*G`uq2&%3IJ4dZ6HbDIN4Q z>j#!;D-V9b!I#F zqtvtg#`>KpPprDy<@Dv8u}}pzzIFDAEO(h4Bwe}5ZB#OTAA=E5%bMD##H)V64_^rf zf7fgXQ_41nre*YW3OwBLcjV4~Z2zik2*8K|`P7?oF%Jg-16Imh!zwPspZ&Yj#QtZO zmAok`W3y8rfmy8l%0^m8eN2tD@1za>%jS_8VJ=*NPkJAqjyMa`Lr@~?J#zUF<+GHuyG_YK$|KI zu32D!qwg8kVjxy)(#hp>=dEGbyLxx+bq~7sgn<+tU)qalicv9w#tZM2dQHX) znhqLq_!hU(5auiVO&f+f_WxBp)Lda^2X|FGp8&k?{W+1eFXtBWQBd2H|?43s6qldQ+#Psg5a{Lmsz{?J!*X0UO34# zkI;Ij64aEj&S*5~mfKsD7ubIN#Kz9vmpL8=_(W~$vFp)Q% z1*QYzQ_~uwh>3~j*@U-njivdMzr`@QS@CfVt{(g(JEdNospopJKvhk}hPCCjA6$KT zTbY zw6O0PUyV1)YrKA(uA0k$P>83tHbW0zPJQ|5y58!NIX9F(CfYJg(_;i}RGVq~;@4F(rRCi^$xXyYaq!+%CM?4r0D zmj<*3rB}1`!4Ev}h@8td7=jUUw?JEg@CUZ6@ECU3Bb=dOEVFmjQ>7Xevkv$kNM#%! zWgYankE^7YR%D=k#@HcQvN-j+h!u{+Lp|&;&f13IztiOn{f;ZGbfZZ zRKe<(VSfpyAo>yX-!^)Hhy1p~*mXQ3_?2TM-PkUBOmquPS6@=;n#oFM9;^s%A)!DN8zC7F+qau0NczY=0>!Ey?dM z4;O(1v2VZU&9<`u8J8keK<1w8cSnDLsJ`lR>M_yzBC+S@^o&5D3+F3q)LLZ6NHL5K zcMRp1KBWgta1sG4oqKc$j#|U5ffTX}H*f+I=|8DB2sk z*Nc7nnpZfU?6BFl>;+XRaA>m^V%dF-rZ=C8qy#=cUMERz*XjKu&V|)-K`n=UJkzVg zR}C9X5pnudT%Nq|1R%g{C) z=)ZNG!n-&d+@aQDqi1Q7^d};2?oFQfRA~x@>HIg>9$s!>c}z?+%YQ;*=E%U-6xGzw zi+6r+#u7zb7*8{+KYk;smj0`4mLxm-e$9pUp|7{#e=JPeQI7qe z(Pw2JIOw@HRw=%7r~Zo};HLoWN0VWYBCANNW2zq3{u?FJ|KC(l|KkJw(LR?h8%&-E zZ&U{0&}{u0@Z3$nwuSn1UIuOPIT!!kr6lE*RDjp0^;Ak~h7TwV4M#PQ&kHU~W!idnML7j}fc>j8$y zcNZ$|VxKSoZ(lSTtThDf8oj|W2aa2i5fh;}+WosoRehbYv^2W!-ojP@j3GM(|NYro__Jz~@ zP^prGWy8K2y4PT3^yDA)^ujGDJu)*1MMXCt`zYi=!@4!ldFVueOn(l9%;K%fV9QD! zd$P+(jwHusxcJXB{1jn$2(&|a?p;k7fMe%MZ&yVDZXI0SCC1PNIzb!^YoX92x9Q-% z?>;}2^tX|Jq_qMh>v2GJ&|n;f5tX7`w}DLAtrgQb4ZtAjTrbOnzYz$LWsM2!>fzJ} zM;J-U$p=y>aVGYyYPWaATK86Q!pV6E&R!bF*mY{~Gr_IEUC-NwoLl>f?up8y;4ddG z56?3}Vu{%giEaloFkrOJ0?;JVVbDo~F@C4aiFz`(0#Y2?;n+VMC@InE4!J302RlD% zdGe=d^w2M=>SMZ4P*GBOJ&^QVhNuDc0Qw9dd-CppZWk2`= z2iwCyUfyy2K3_&~UPL50;s&GFt`nO~$Aely@gG2{-xtQv9kaC4K)pP1lK0{6$3OJd zF8|#JVCr1^4d%g-8E9PF;k*GRZ?*O7pQ}FqEl72haJPd7zS-8J^^hC^Lk?;q70ljS z3Tpv^TP}3)g{J!h1yInu_qA~$+?USb|I6nl<@Z@uid$5yj)@GoAkHe__g)V?NLx6EBQ@WF}2Q_%A9R(2D zIwYusE_RcI%GPIM)bIQQLbEwa)TlVTyc94?abSFIn}sD0kv5?p4*f|)TR1Y4!v4rK zZqf%26}3aZl2pC1o&Q6ViVV8W*|oCp6z4z;?V;NZ`cy4ApqPFklkzS4{OLE4c zUZwx#eu(}saFh}~2F-tq`=^fbK#^Y%VPa;$))Xq~;qFg}fxpe5=GA5alW_r!D!ZTe z*7{6PJwCJ6VuuCaf4%hW+cO~4f7QXgbd;yd&i5;Xu&^x2FUUuW71u6Hf??cy3C=#J zJC1HK@4XxK;}QKc-M^aj80u-;>W)CVj^{_zJIdc zu1m*zX$zVOtyGa8nSm$7DgXj}+&U^yA**^E0f5UXHjGkjEI(Wh^)QPx1raDLSOFa>BAP zMp6>_^ob2K5Mc?-2j02OBJ6F*Pz>ZxQ825<)PU=rcN5kF;x>txyFi$zTZn-r$ZG9t zA{^|c+RpyCwpvtQ0boJzX}JJDAo3VNGUr(%Oj+Np&2=Mal4IeI=_ zstV+nNQHje*P}%E5j5i#n2msR2QDqt?d9}kKd(o-rZzpJeuQ}bw=P|}gc>A+!>*&{ z9Y)?${dZDC_>U_3pKHX94e~z@fd88*{ofcAz&`N5YfwOj0HM!6%#8oa-0-*A70iyH z>Tmov@$BEqe9F`;fK_evk^Bk&pIRrb$Rb+CXUuF4dx$>C^F}x6cXu6 ze!{FDKeVj2)vy9Rlbr z%eY(j8FMr$5wC_$bp9wDWX=Ww)`3EY#I(Sy<7is`$CJ=M9k>7Q{m_T51bw{F4aAB; z&O*?jD5U9>3ZPc)(BK;(hlD0#AJdhH#lE-#tbCw8BX+L=Y?9D!y2Kb5PI;Dii9^C8 z&(R`|I1{M`uv2nJ!-fzlY-5N(ZLq~5_=pt*YO;X!%Mk&;d@=FJ_SrKZ7y~F6En6A# zMLY#Zki8hd;uJ+_&+rU+tShzZOBy8sl=QvoCctn4wJJUp2Fx==pv zJvp-kxKnwn_5dsYZw*Usopy&newN#F zAOt#o49~%_3n4m`r(D1lH;W932EdbBF9U@~4Q#c?BmpIGI_PSLGY90Xa0hOC?v^ok zZ{3f1j?;zWB7%}n-b+IrD3C7}K}q5&bxQ3~N)DhK=IG!CYCB#$y`Y7*S#RFCS?J~c z0o5gOE!eyNa1+;<^4hy}Z%+OwD=AOJ%gCQK4DVLMJ4Fj`p-`TOOU z?m@+bD9@nW0``v}Iy9he$7SE^*`e1>e<*~d8|#(ZE<=M5PwHjuA}@lp6!LM?qq54_ zy%z|YLELW2oq_y)@g$jaqM`!= z7Cn<#cvD?2x%pLbcx#=2&y1nOc=l%j@yEC$MHTK-@|y-sUD0BvmI3U${1p|jHwvfi zt5Zi7ScC;ZKH7=+zyL(gK`wMS-!|3HdwhQtb4Isf3cB&3yl82YM!EB%58~FLzP~E~ z4G;Ue4Mk)sbeKT%hkX$vFJ0-rIi!VOQ$sFJm@>WJOh7$eI%FT_M&==jmCv-$z0~51rNAZ} zj^(GbtO2=F19MV$MjR?}7G-m_GI;cz8;c{PwnVo#=1m#4Dl)UOE~1mL3?4v;a4JS} z7}f%Jq=lh-#_aBxzl;{R3^;nr;4MAY`!rP!Vx)l9wVUC@=&)RZNLj~?B413bVdF&xDhoOL@g)ne{@e9J0~-V|f+HFnkuoK)NmtW)N%d<9!q<9h<6| znQLO6Da0;xrLfp{M4vT-J-p76-y{Dp1~aJym`#8lu&QP1G9tD&aQ=xl7mNR!SER!t*T;7Y8O))#7cd4V%|eH$IRZts#4RL|(pTANK|9r|awu z=g4^)(%i4*w(o2Dy(C=2xtv1t>rLL=2*YN*UC=o3l+E?TyCY7g^SK=R!r237jF(wpMPB>h{?DQZBr9UOuI6kFcGHLU(Yzh z17Oo4My*9((7GhzzEQ-(!*hMu!ouPJ1%)bDhZo=!(tyKd`iboEe028Anb1jCL`C^3 zA+)u&u~D|SFF12fP*D9Cr}hmgDbF_NJ)cFOGh)zq`S6!7U!F9d`|4J|OdwPh`gw6%da<+K4WonVkx69}HhM`q*N@L^oXPxjlQeTN1 zaFzTyad91(ayLMOz5g<%|AraiN=;7*c*LcY;Olo(dU(L&I&L5#ArTH~p%*sSoMUpz zg>{(D=&2TweoRYo7g#CcNO>g$we>j~^GY8S?{XWCnC99a|+EeM$l7 z7DeXhJ^LPf%o=!KH83#ng4G6nE{~HS+cAxx=pnw9S5Foln|ZN7m--x?rKR6xw|tn>U=w@H}23B5QR z?#(KC2s%5H;=bKP%X<}OK$7^0ka=$3Z;5@FEMYhKT6*3*FjGB~NwJfxun0EG6yQcB zq=$d}@FDN+Uq^~dOGV3~a|y~PPo6xq3~%T8iI_L`j8KE>?R7hSef{HhyD-}Z^nXs{ zEAekuVOL|b6~`0?LyVFF+MyEbQ6KJ3KtY1L|0<{yIXc6MLqwZ?=c+XU)Ebdo;k;-< zd>1wGk zSB>agHs-KVo%Eg&*uP$}c0iy6>jJF-oOv}N7W3YgRQ%%)uSbs``-|}NtG2eb+BeC| z%Xb7{(DLim&rX7Dz}ZQ1_`GJ>ow1>&qVhVqVBiQHrv55q8s!&3NHZN?=)UP3C1mv> z$+SJ`4FpK|>n(FM+ofCdC45yKE;;RGk}Vn{}In-x4&JgS788m?aJ8 z#K~ZA3llS350tMeD0D(gba?~d%do{;V5-s-V>wr(4o zc%t>_N}}ik_F`CTpmEMy*eH%0xVyW<^`$?0^eECk8YXCiem3nQ5JTq6`nuFEUw#U( z;k$G$z)_Ftq=L(feC#2+3#LHA0)WZbgoS&OU_I!7dO?oqFaN8(EB~iD?cbWEnwm6b zCR5R>9!nW5Wb0^=%D(Rhl_@!PW$7Sll4mR}Vno@(!Ql`(B^=B&S_mhOh=e5jQg*^~ z-RIQz^?Lq==k+~5%yf>kd_MQ*zTfZby58^4bqnPf0@HDTf*E<6)Gzly?v`;UUKebS z&ci7aA3nSQ^3q}*5mHP*w?scIgBs9b6`ltx7ANSEw5Ges#HE+$cQLPCOre{BMVp&}t6QLB1x0Rh2^#>+6g(7hU=&M{$C#L^~1jt>xjCMQJ$X5q;$NQMDX{I4L%gd`-SSF#EauE^Y z%0>{WH%u5^VozPnZE}Mq-KZV6KTguDf#Ir@J1n;dcR;>edT4 zG5P95dB;fC;=w&3GZ^6gy#*F5Gbc;MMM8yY%VrOBgPI=aDbRRa!2D60l#C5$zY0o0 z8R9sCsT+>e7>R)t`z(rKP2<*tBR6#BAt6x73yk3zM8=iq+TOq#Sh1 z^4PH?y@b7%cNE}zl&XB@{m=C0U=HI?C9XdO}iHdU{T4966#LiS%NEMK?V) zmEk}AvBv^x*0=pK8B3Vg8P)C`4gb})_mcnmm(0$cJKL1qNh(D6pvb@kyY{}u!NM&40JZ}cDNDr_cK_NQE+K`% z)LfSEqH+=ZMgzQRGcY-NF}u&*KfuNW+UnI?rvdY3CgPP}JY1r?kLv52pFMj|SX?RT z-Milql(%i%xPP)Ou#D{FobS3)hzNMty|x!tG)4Z?`7$OMNoX2iqRg{w=~Aiw37qD! zC4!i^1MYkG0vFC#Jbo;rre2 z7k;i4d`w8_*Na~iD)P$z@DJ`!{`=9RlX({5`;Y)nw$`oaf{MR;ZVr?TSwhs>yEh3X2r|6wox256aMXQ)>oX2fY#UNmn>{dsyzj`di1idJ zVAp)7){znS>Uh=04E@_m$>rtc2fGJeKQso!wR-58hBDPM${s1HqgN>tHv#5P`RrdU zCYFWDLl16As)JATDCKcgm9{=*JPMU>eZ|1^~R6@fUOV8m3{iCyQ+pi5#4tm=Q}1P{Vea?WPsg66#IBQo{`^IO*A$Q zyED*U+zEzVu+_1tZJO}+dV!kCS)^c)VW!DUEb=B%#$+w?Q=g8Q&!`&Aqk)1z_~MsW z!^2I$b>%UHUp=zx9hOW+g3i5m?FZnEs-gMZ7{FYaEn5zu+99>$Q}q1&ylY#j4Q%oZ zfZ3ws;;h>9@bO&@Q1#9f=3=bGI&!zbhclf!qPS_(0cc^ycq8!5WB%8t-#&6hJQEwz zfZo^(PYTRCLl5(7`%#e}I-m64Z*kq-7rWb`55Pi?64F33_4O?zIr{Z$YP~~gX(>6? zwR@a1z;17H%A9+S+Aa2zG)u58;cyyKHGiU@HeZCAt4X@vx_ZCJ z!Rt3}q+lw*3Hn$4yHu8yf`Uu43Cd0;;IO>~;7=>o>^cOFAp1rm`eIjA4EC@0R`Gay z$?PdK_syJh&Z0LFSQ zELqRRoYLt8nI!AxReo9=E=RZHD5_go*p@-Xf9kF}haXge?tBh5TQC_>sk~_DaO#xI z=FNX+c2D7gjqdGw#n2VQ&4ZR$Y0Y`|ArsK{{`uz+yz0%FR&1sRl};~$bd?TdTP4l; z?bIn6_7-8}p&n1eMAXvMe1VC{K3OFSO~RUB7A>t%G!Xp4fw!!;OT;=6k>krG-GH!G zY*3@(6ZP0yg6&YdzT%&M;B%%2nXmzcSKUpc5II(@TQ?ZxB>M+C3^IL&-)QOUD?Wy% zlLFo%g-Ekx<$4~#lvjKCBAXIlL%5%%^$t5gnWhi28$a3B{@Ll zF3ZlSA3R8yeHZS7wu*yDe|%Zq?-wu1pLwriQ|Nv`Qt!5bj!yW*P-_-S#4KBm6>I`` zT+!9hF&TctL40!b3FJq}q|=|{#adPG?7b991RehSDxUBDZTu1>u^uL7I`){fb#7&I z3}|D#yw-RbgduY3D`{M6CptB?~kka?$DmW-&kSpbyyulff)f zdwbr%fHRiKlb`2H7v^VYQ1TqZeY0KXj9OY+tjV><)|JW7yz5sd?zF!Qg*bXx**UR^ z9dPHt4z#*N^!5(^oiHDO&JZ%hDd$1W*gnIy926XwMronF7K)0B6m%#duD(Pcx??zo z$QrBUVe8y~&$SF(2|A?za1}6u&L>xWDx1mMq>q+h-HhQr=3k1~ov!Hwixw@iD)G$|Ii+0(jDLGL4}DLU{QdoPy0;H-xQ#^gBe^QTSuRSud$!;v%~=#&&XUsR$gQ0ckY}cS{J-)L9hNGbCw4tU#+Ob zGDyCN?z59T&9)R0p&16%p+N=&@{^2=Ly=RXeJxZv$784WW zxV#^`C6XoUT`jP?)_}}`bJDUz{YT#O-O-eq>T2-7POW?I!APU8T|34Dd^N=`2?Ggk z#jPJfQ?z+kw-7#T0@<leK^m~;Rt6AluGgkx^Oce z@wBU~R`yIqIpq`?0)^lUSDK5wvNBzPA+EP9wada!Fw#%>3^u1_XJ?<7l+Z+*nfdg` zt_V6J0Uc@73vFmWvYf<(n+wJZSit0K?KbT7H89I!AEK-}0=b~KWGhElli@RBn}yS5 z_${H8}8^xMubnPdH$RSF)6ea!w4Ov_C^J;TrlK~1_%wD z>G!t?cLb^G;TGNRI0ixvEGj-Tiq7iQqrgNU8EJ3rPVanWOBCUCrKDl*`Mtx(17l#? zC|_Fh?AZ$(gLQizgw7qTarBBkt*YAAX1@|!1T4$PXm^cxnPgkM7{ZM>*s<1*6<1>@lnHiOm%Y))A3Sss$W4(05gVAto$fCaC{8aLKzk!|8 zGc!zx^Yq!l2W%+Wn;T2qI}ZLan;#t+`F2bp%#)K#wxVRD|LyX?xAf0J(HFrk3j;E3 zP`UA-uHr8x_jq4Aorayw7W*rP(W>s`H6Hcv3 zh$~%w;SIC~^!q4yygfwV5RovUk1wb0R#txaPr$3n>T0iOgr;+s2ah#K?UC}WCAN7 z%m@M9I6f|}&t2~R8DN2Rwi5f0fRU?AK|0xBbS%sqIP@A|R@Ef#OYELPNr;9DE%}Dh zzf#C5ta?v0+c$PpaiE^9adzVOt!1u4^XwQdW|3PmW~R}@1E+U-N&;$n3CGT3-<2Zz zIMZ;YxYAwjq-Kll5p-^kL48o=dfA#XO{8BCEnG-wWFJ32m3zG#8p=UgOCOT?<0b&2 z21JE}>WbF)FIalq?_#g8*Os^h9qjsA1zIYUkSZFx56l-LD(UGBtU0$N{ z@bOX$;1L7#AxxABMYP^e*5$snYh$*hF=|oU8_u8%V2sY&P>~5ai@W;~V)lI7)9AXw zCC@@tB;4tsl(uVPQ3f*mJx`qqLq#GyyOx}CY_^*YA92-^R zIifDp?9$S*bL`Ro%9v@EJfAzKQf5DD3{9mK1`LM-AhO4UVO+BLia#0-g5_6PUa!B8 z1y&_Cj<`D-FvH#mRJenZ=lAiQW__`3*e<^bXX81p`pQ?7##k^M7v`wnkr6ny(wIQ= z+Qz;C1xuGMHNZrRn85|Nf*hEc_y8!AA^A8))|vsuqU-&8+We>K$A#(X=|r_IetB;$ z!5=B4#t{{KJfc<$^4emIG{iGq`{A;M($>Ei6(?SR0J*IdQ=moJ=!?KdTo(mld6vR& zPk$(`>35ze2~m=l=Z%dG0G=ygn2m;6@@QA8G{LMsh%}W(*9)t-v*_!^tG%FW&d&`8 zI$^9R&26fxEjdKXRGXi& zueUgT`gG|Ny_-7@bXCp`C(2>Qn&H~3x7a?ZsBi@Pgs7P3ccp*=v8)J<4-qriAh)kB zEe!kUVEZ>3IvHn19<3Zq*bM+@OhFxja6tJ}wG*`qrm$?Om)n?{o}L9|x;g)3SgX^~ zK)2uL-iFDk$P=AfNa*J)d@V&veS`nVfxK=~9wCb+f}k#1TPf%K!kkaNFO9}Ts7r&M zSwCw97{NqR&u*en`yp0wk#B$g;fJK-k1qEDom@qxz1Cd_CIkH`6HNvL7=(L(RztFt zYh&X`;=6}wNP|TP3v2{v$oE~bH^5quHF}_W^Uz>?Cn;&8ZtO0N%*@QK#oQmI>`F_} z><)2%gS|8G83BC>W2AW1<_E-hy0i@1&Rob(SAX@?Ar-KOe*N>h*_Q6o8k z9kM)nO8A47LgkT z=Qad>$^*||MxBK3w8_DP7tsMh3&+MB>LkoR^z1SLj|Rcgjf}c|I~%71g;{yL`F1ik zl*LwQf6yle9KUIV9)UX$uw&09{07K*E;_g{g|t-y=SzP4=_hUfH+Z|Wib`|F?%lg1 za%U%p-ROTeuZKiVtmccn2blnr>XE29v7iv22GL+z~y+8l~v1LIZ5S{&ej&fnqa}UVr z#zg-4Cj&_r#XK!wlbDc@5T*{Qs2y%7-MMU-*CZSs+XahU$MqU;nhv_{Zk)J9~VS$XRJ> M=pD*Ac1EdMdseHi`VQ`@7qJbDFqwao4$X8`oXE`fK|2wj`DGTf3LC zHJG(_r?$WR`2gd&b!VTxd)y--`%Cm6J@reQd2Ux-c@k80NaFs4VT!6}rchz+r|I4f z=aB8?yc_W4|9HhuiE4EJ^#}a-mhImtC`C6&P^d&POyj+9jC*<8-2Q1T9%4(j<;9kR5v9HVC^sp;n$HNHx&&C5GI zcxsW;U`+V6-iZXamBq`lvS~^kT3TAu^P6$KCX{v8pWlBP5WvpCA?3lqa_zU@?ugI7 zUoRbe>e3EoftL1k-E8+YH)X*eteg)YK3rW@byZC**nYe#;mq2ye1zL#Lio*(TXwLC zYy9xT55qmhg<39O&$EcTwA1QRsI7N~G7ai%sF@TBwf6PHT@CrSn1*9Ne!L+z+pe2x zaliBJ+qdtghnh9Jx09t)LhzWV>gc=}s11s;AEOWX$%oBS1T7<=335qnR z3m#e=No&KNXmoSpms>u(jX^YY4Z zsb$4~S@V4K=xI<;LevG{#H_5Ak88HJw*1(5|EEtsS1?;9SH;q~jvc!gCgPC8(eSme zlFMRwq%AG&h;(qfqEGS2`fL45eGhjXB_B~+du8{YJ<2MQSFb)cXb4Xs7ttIi+vYmg ztt^qU5fFnzZ1U|vwG6$7Ng*E?C#R-nKKaR>ctmd0D&QB&_S5kfd|A~B?2P?K`UeI~ z`m6mHKS%jazuBYxme(s|?c{cexh`AZO*{5Q$4ZB^UN;?Uj=HeZy#3{$siIiZjPLb) znfUH(UKhVRXWX}{s!A=}#9t{zE#|x@=j(WTcsY{rYtnCqR`hduAp2%<6pkd9Ls99dfLQ^nh;rPV>pxSpg|2 z8JX6{2QO%}9H0bhyXE6BN4|vH^8+S}>?C|{8zC`7;@W{x>mSokF)R9(cR#xgrnD47szon>UUnXbp^5sW1Ha0)8 zvZltzAIrAv)#6gmN%ptjf7n)B>PVB}-rs%qof&)7%a?Md zt;vlqBs_4QW2dL>78V!T4;?yJ7b2)!Xg}8S{OtO;+?Dr#-c>7h%ATF*P3!8?xOeZ~ zaeT-g4XfLcQBh%9+M0}e_Qdg&E?>hPreD5%xw>iU@3A|K)0m*N5I5T><~)0hpI_0~ z*m&#q?FxE&(H}p4Y{@i~%?-6|=iNTmTdLieYe`P%Q-A*$+}6GO_Y;wMOnQp)3#cOl z0lh0`fgvGDzyH30Mabq{LPA1yLxVCthAc_8X`4xy-)OpV^AYo|ysMWkUBaTvAqCQ2 zNNk=Nrb(5QlnD4=)zC;L@0=Q_6Sg0HCMqiW6AMcUw`M`Se4LDoWhO^`{^p&mEfwC( z)cG$u`uh3_&!7Lgy1GiP_sSJ7a=nR)9G(eB5)TRrDkmwYwKhgwppLfl=^GeS_x5VP zc=5udBO`{MZE8rJ-f$srm?__|fi<&tro}N|IZaDu$)YuHxLxJiwMflEd*kL<>C4j6 zib_fWSg)V{_P6JM{PDLp6%~o9nFhFL$FM?+tT(Beg<0mE0xTl-S1Ns26%?Y)J9CW5UAZmiWn^UNod~L)ok9LNCN8dJZEc-uVUnF~ z-l-(mb2CbO`Ad057OK+cGiwXJtS(>9?#a1Qjf9%0nDq4N)998K1?073?Ch7XUcEZp zkr_t|{7G{R2dMS@9?j@px1|(Lu3vsR+v_srUE#I2?^VF19FUcj zJ%9c@2@cdOrHdCINOd|?Bl~4xYsR|r<@fI0TU}otkei!}==_2?y&}oHM9~Kd>5X+Jdqj%45pARy0dsSZUJ(5~juY&qhK9*Wr z8+4Luxah-u=KbuZjgjXo9{h07>-qENmrLbcT#EP{C-wS1H%2kgnz2e-JJnTGjEa`V z3nj7T9LeszC9eJf+8P>WE|XP9E8e}3@V<5H*5y*qLs7^A6{V{)JRz%+{@LBU+jYmg z@+BvFiaC;N^iJ#@408zo+AMoU=u1t0K;~TK4m|t8}T68e(Eh*+Rr=L1_@?`T1 zC-%-GQYew&Lt7>61{y_ zvR&)NT;^Z$=~w@BkejliM;Yx#| zr8?!C`eH*Y5~pw44)5Bx&x9=J{A3M}_nkZ6d5k2##j==O9dzh*RZclU*5KVcxy%#J z)1R;Em%Q=Z!%WighlvTx=3e##2VUhZPu6(v6rV48xT-Olifl!H**%Cge0} zif-azkJitaW9hS zN%6(c2cz=x+FukT+HwQ?3gFP_Yux;B@4knJhxC#*poQ0#T}PODUFX6c2L=wxh3w%{ z)AQPUvf|^-tE;-BFv>AvdHcV`=Syp;%QGGA)`z&_oz)fL*ROlnudV11bBG^*LF31jT8xa%TQulriD zYDr=^e|utl{M&hydDHFtPQ_Q7R%3O(?R`^I(`afXeI+Gx{7(uce&l<~dI2l@vIL|R z#{RMH!nV{CsGD9Q<9Sg{ArX(bCxNgn_n&d=`Y@~B=SBGvF6Qjz$y3svI&rY-`|aCL zg*y)Hjf#$bow~?gklvAR8%Js<`$)=Y>__APKD8_(ojC`g8OO2gw*4l~l9KoNT;{C` zhYN80N1ZPAneekvdI}xVXdzRGvF218;C*it5|QaSHZi9x&7B8!^o?$z%put{c~~gA zE=~{0>%4ry-paa9XtC_o4~EW^n}}V>hH$a5)sS=N&M{4tu9bApC}v4pbQjQ!Xo>!H zGr5+%J;Uqs@ROeJw{6SJ5B*T?A+rCS^&6qNvE1I&$@B4v;AJLXM*+xTL2pzcWi^}Y&k$vBy?#O$@=xgKZx z?3oDnRX#vtJqNMy@bC)sQ%XrZEDAqSxVgCaNk0Sn*cGG}$e+qW-A=m&Xu z->GC7JudI9R9td2F-f$3vs<}caZpiNQ~%SaPfYhegi^lXP!GPF`h#Q%tZhyb_jhF! zih>t~_xgInO`$%lVkTdIPQ+T-&s;&l=SNGYn8I9%3MSO=-725ROwr0TkoGTjPh2D? zCY4#iR8XKZ$2=+PsH&piZnRGocXx`bq)hyxxbn9@)jEAyL=5EL)9Hp?dDa4J7O6_j z>#mpcdI6QHweab^{aeV?8qSfVf`USJ&h6F|b!LFLjC>jO+!w(N6y;pAOOkeWS?S1E zA*D;lH{9ZwooYD4eEaro+G1gs^F*02BJ3Y%-w{@2zk}<=^tkj~( z%Y|-&sL zo1}5t7@e_x-kI)f#(C@*@1~us8e>i)Hz+s1etaXK-Jm#aouzE3DCxzdqvQ*0@c7A- zk5-Nn_qX$LG&D4%z1nlghwY5k*n2;Px%Fj$5R9E3rKP2HJgN$U6d-T5&XwO9gV@kI z6d`lk(^NJ6H{O~X?a*y3Ognpblcc?Ujc*Wewb zdxegr)&O6zb?+CZ29=i<(;OyxByDUmG)sM>*tb$-gHKhx$Z2@S7nM8;fEVIN4+ zbux0;_{7J=m1T6i3~Yl-a}$>6!4)_t8ft1LSHgs|>75HHxjiPfJ6-=C6`zkPhr zU<5~8(RFE-hC}-kBjdpQ9-BJ^{Jk%`4Oo>kUmC!rZqgEe)ub`<-fzGCMi3ofqVekT z0^^ALRGlC&a~nv6_i`dOXkt;*V`5_bW2m3soh`q=Lmm)PvDD2qCOX;(iFOekb4mc8 z&Z`ZsIKfJ5X%Zd``%dUR2o|(V1<9~`_wFH_W;)6!L0mH#-98X|Z z6I9kmzP`#vM)BCcR6zH-FcD=yID>^V=nK8~3pMpmQNy?p4#|#F13@t<8u{1m>nP-> z0eUHrD{^xyQ7u4`S)3V(L$)VyZ0n94idZoQ7Gct+NtCXw7It)%i`b98iwV?9#9p4J zG9VrOWv`uO~g5SGU8 zo}UHme2m*e3E)TB1DJf)-(BKbNH`0J9>)t(f!ws{OMVJ!YV4Ot`$u|R99L~b1{$1q zh>TN)_4^MWITGwYMM4B9qlu=hZ7v_8X>o zcwD8?NNb8cZ7@++&&jEP#4d}l-P@0ASl$z;ej*V~&0>LMArkaP@gZf@0eb)+M*pXf^BLC!(pKpW?D+P!n#U3vrU!tneX1Ylc-Utg)oSc^x1Db^K1_q3Qz8Wi(*dGa%c^g zmzSSDe*6O6j!|7OKf<}x4jph@?$8y%ZaY8l1KK$n=$_E=E8u!cyY;`_hNY;@r?(lU zQjUH{Wm~~l=|1j3+ox&Qc;2Kr)*H~ie`F+Sbks~=PmdfEdXZ!1k3as%S-*{{SnM=2 z4YI>@@>7+*v7@77urM`I98s|^#TjHt!BA`nXOr|1zxp$4E}+S-ASfp%Cz}vN*+z{A zxKuM1*9p3EB(On~^AaFX!OCnWV#(4!2DM-)yG^UPB|+XaYuqrxwbg_L zoLm7Y6>8)8J){*P=MTF!1K{tk*&K0AMJ3+4uc8X*abRS<^X{L1GFqPhihhq*>dcJ8 zL}zXqIn5|tgcbmohwhvlXaS3^cr;FQ*NAzX1=Cl*Z_#g)ikqLGM}u>An3fcXFj53K zCOsA(-&Jx3SC^+`GiQ`jH4+d3rd3Z4gD+ zJSeec%NCMl{Qdp)1LjJW#+wfZYDA*{HXUtGHyEUicNdO4k>b7zeEHM<{mD3uTv|mc zQ-ckHOLbT{yBe-sBhZ$UlNn<_Bqq&H$+y|H`b>H6<8Sg;)=I$XU`GQ;XfAXJK!eKm zt6Di`L9935uL~JYe9Pj{^ba~R#ezaXD zXLU21%X2{X;^`(Snh990LD4;%B5n(p?-gT^l9EcPU+VAc(>3eJcr##QQ8*^xc|uf7 z%p1)~@~d$1H*ell)bNy=QA;YZK^-$<>>rJGzF%H;iLxE6XLKxo^~$Rw(@^=@1i?n& zJ5x%y7QrjFw+u{L=7cZII%;TyreFW_&ekcAEb7}sma93^RLmR7Zm&0YGMz_5Sb@N{ zcwJf9*}c9U)pE2K(E5ZMP{7uy*yv~`r0r}Y!w>=UM60liIw#y(wL{GMV9Fd-SDUeS;D1sl2_2L+={y!rEasUdtZQkYVnK ziCWutXrMU4PTCV5y!Z%NCLHt-*9bt5+*jci=;$l3F*=Pt;3Yd++z8faQ}eCc%zCrz zZFP0x2}^*9bc4EmeJ?y%PrQ2d%4~R@OPi*H>WYXjTCVmIS7-kPhcUXo=v3`VARRmZ z%ALxr2QBkHZ1OAu6GVEMsI61cv9ZBB%uUQw71W(6ZL{fo05Lbd|9a~drLWKTOisd4 zcjUXTSl4}RTFxnrrS5C&Z9HpBO1X2%QDV2q=VyCp0!MxAah^~V>OW834b;UPsO6OK z0~p?v3Ax8;tUOu2gb7n^;*$r%o$Egqt|Hcz+Qh8h-+FWG+~dcOE0!0g%o908L^|ik z3n#qDTIG!wjO;hDG*3+4{8A@$SzWzwPkWK0t-rnqef`b0R81w1*$HfU%v6Xa!2aj% z!rb%XAn$c?YXUS)P5&8+0J_AQ_wTP~Qo$9O4|k6v*pS7VhqJf<98#u(SJFyXPY_)9 znCH}~mVrj$QL#O>;bNT!WOFUL&FDSb`#vq$g4~hB*O?EmD{`DNIQa9=Wy#_^JUnF8 z6DKV*+*X{D#Pr!fABE7>SXxv2Wi@3tZrH$tenRVB@o#=lo_KBJ&MP~xM!-+eEZtPU z2AQnFvUEjBhnbKM)TckHJ3lP6Ig|Ez&iU(`Jsiv38JU^h?kgkO`XeD%j(D1?s87Tp zcj|gBk`sc1s^BE822@oWCek)Mv0A?KjDjkU7<9yVrghiLHu-D_-MVd?cUIP^gJ806 zuFSNhY!W)$!JqaKu;2J`Z+Ut7Jh*hT>eB0feplYv*%`Urd0;f@LDahUh}NEA=~u`* z=81FZm8_Xgezrg^>{Cjo>~v7+yvsdyb}FnL#}Ud{q8+fA+XOJPg81#`O zuFRkMX>d@Th1+RivfnCxY|oz1I^x4c8f(K%EFMlAULfp*WKh=pVC1hSu+lIFyp(Po#m{1ny!S zd;%_xuAuFhpP#QN!}AqjLOrViY*AXtVq*79KS&e^3@LUj$}9)hfMz^J&z?P7fgamj zo-|RG_c}9E*1e~T3vjEvEQ?GbI!*kJxPcAHwH-fFas)J-(S#)xrDnt)VPlmI7ww1) z19r+T3LBDR7L!u0&fk+1cT)tmf7={RH$NoGqUsEm9TasD zwwce3<*|Jd0DCk?4bvN`CZ>s7Da`k0UObz?9PcL4apFe$S zPqA3CW{uR*yQLs2l>HY_Gr8_OfR3SGex3Bwu^*BHd9*DWH>t<1x1qU}IL}#lj>wQh zQ8-bOnb1v&97j=WdcT_qI6FojmMjxmb*lLQf`n0?ngwE}dPMb~y`kyZC&O<01TD;_ z-pn6Y)^dzFF9g7mlk|-qi}b?I^roIjw#Gez4?Jbo{_a3Tir5b_`JbLhaaKP&p?}$b zA$g-hQBjc>+T;$!QXM7(vEOA}O)?yQ&(7(bYAw)VssOUlyIQeJR`jZ_xqSfrd#s*L zb|Bq-ZK-!_n>IwAH;X>CoeKB@(9EsL`_-Lx(3gI$<~2D&(O?07ku)<)R&_SpG8twg zu;~_>Qd2u{jC3~B+piNz3iW3AIn0Gpf@}~{Dh~kJ2r0<3pdnN~)b;8>>0w|Q(Xg&x!KveH%{{C#7Ef+9vV>Gi$zhv*KAY*!jf_;~)~Vd0 zrlxCAvJv7+x#!i_m(f1FP5+8~azecquh>02`>6sOZtdaLF#0<8knHh(p=&8QEx)6l zsAtgve(eZK-gt~%B*%ZJa4t%7fp%ZAffOYV%+0V=K}`TxQkL0;@5;!bjz4P6}%H*DIp53Oi+l3V%9)$axXVe$Td2AJWdTR4rTBwvnuSV1Am$P@B;^eAKG zNE@$=_-U?Q^0 z)kC#~ZSP)VJp%)}WThYR<+Rk zWFM;x2>-s7z6{lot~dudo3r}M^mNvU{+@DPPN!W8)KqJlki(ltyBSC5lRwjcz^-P- zC*eZ*84uaf_$JYbGL}DdKn}0&InMeWsz=W?M)adi=LUC!yBI z_}$|h_d9MeAc&@aJDYgdxXq}(6d|?xkTWxldch5FaCLrr;U0N*-Ex`>;$xXw0~nTfSRC z?aMkAm1rEMksUDzMovdZ$6%!L_3L)g{@2H$ZKMm|yQJ7ag(SnN6?=0yA#OBr=}Lc` zM1skw{omrFR)S(eLtRV|nJAJ+IZ8%rXI7tk^Xm+;V><)Jv+Vy?HcJZpHd%&O$%09*t_FswsHpjP^E0PF|^y)7FuIgDi`Y}2o6^chb-UYRc6ro zhQ!QF^lAg~>>fB?Ds4v3x`g5@kkjg_X;XEW$l|JW)YO!G+d<*%@X1P7ZEbCjaghoi zmL!B-X4YY;0F}^8jecAd5r%7vubiq%aOf2Iui` zm#)!T^2PZ08b9Wi;5YYkPh`|^XC8A0cg_$v_<&qys;xcrCiHKHk1Zxv4aW9mC5~KB z9veg(Mx;YkI_{z$w3c!)F>#<(8#7Io5#M>-Jz}(W%mVU1Lm)gW`92ih_s>y5!LZ~v z$)XY9;_f;O8Sqkr=#&>xKfnH(bpWvA@|CP ztDu-dBg`qZFcmzs>&TUtg`vYn5GZhCpX5qTe*O7N7Sg|G)+1K&Lx&E2)H6Hj~#6elRfZ z=;)y5S?gxgKXW3!87n!;d8gkV^gW&81AuV+%$Z(J^G|OdGZdG#yws8K9C-v)xqT)w zhpT$R9a3f-tNXHboVjk?)16*YG?88b)7IJpDLE6D7QtycgirEp-LzqYO3}QA|74rb zwf;6=s4<$ZDe`WGrbK{hI3jbrv>Qy6lH(ZU(q!9Jj@2@uNGyp!>BF1Qxk%?ugo=*N z5`@Y=s0GAeqKO6Z54c()n1sa?b{IEvD2(yj^9j8XsPdGT%fSJDeo=7N1jk@QX&*LI z;UdVom^mi`ezfYv;@awxWoAEdV1x_v*EKFypZWDD#U(g9r1nuyWDa93DJAI4`>a-g zcMWRL@!IBbD$k=+BQ?$F!>fDj?Cdsqp}_Kl$~boH?ZcELF;vaWMM@Qs&h2ONki9*i z57jm$Han+B>L!crd`M_ShvGl~{FDEhjAEs*a+s}{sHh!UjuCIco?`&^Qt~);{2=4K zL0gla5UlQglbYio1{rr+6@oZ~hHK)l{sUUmnz`S>*gl4jf+exJ$D^{fr8PYr#g9nd z9leg=H%uVuq?Rn2XaY-+B{S=FQ3-osQ^dOxj<*fEu9|Fbj}PtN{>6nt_nXaVgYpvEB^U>>b#{6d~Jz zr!iyg=@M+N(~m)CzYghXxHToDTwl1EkSr*PAsb^rHaLLze0QVe z$c2{I6OIF}#V}uib>S}fe88iI)AHl%hsad9dQ6h4bXd1$kdQ~P17ND7U>~Y9Y118Z z5drOH66hRDFF9&mLDhyoV$AJq8J*uq|68!tXpcaT6msbvew!_`FgA{TA%v#H3=n&F zKe0%_f58yA1qtdAd_mi=288-DhD@^9;JzC5t+f<%$N3>VTnlpVWoHBb(#A)go|&Kd zuls_83@T1g?G&^D31%Qz_M|1pT-~F_6r3NNBDGHD^rbM+L;Dj&4j+DF*0EP`V%FwU z-Hl6(I~PJ%7@af&!ow{dD>c6CAxP^YuyTleXWwY}_xy*!s{t!EDQmm-XMqPkIbud> zS4?JEOSJVe2eT_sgqs?&ef(?W!@5O^kg`Ipy9bj^ z-&JdM+4S41uBycWi!*5F=9qO{gYsv;gG1Y`u%@ai;qh4g<3QN-Pq*=HI3-s@3=(3~ z?<5S2aZU?B*|66si!&*9=d#uGZUA+vG98<6sr8O2Z9#^Wm6h>NA!m;)TfO?8T}Ne9 zy1p;?c(xq=AFN>2fNRkJpponN-WG@_KV``%Z;r@LH6v*e6MxQPUl> zRQ4O>Cb`3R;JUfiuj)d+M@n}$?Py8W6p@A? zRqe>oAdp=wEm8v_j_W=z2=Bp9!L6n4?w*dKf@bZ{3Am)| zx6yj12yy+X%8z4t?dj8>hzh@~+B#$l8wQGU(x_x*chdLdypN6JuZ z#j- zb5bR#rlYs2XQJy&QA z_8t3A%i(agJUJ}Iu34JXpqVNvDLiF4WTd6c%FKgQNRldUJKgy67pQhaK>3PD6#U>@ ziM8m3lW9n{c|pOFx%rnbU*7Ey?7AKR5?jDis7sb_y9Ao{wb~|FGxgi7+CX`Rftr2+ z0H0ayu?$LxC|0bl(^vV)u5{UWc391h8^C_ZSow;u3-sc%4K&+H9I#_McTQc9kriKv z24R+@XrZ|RQc7$Fv0_Y1RNN=-a?^h<9N=UTK$hApEqEMYmj@b;|G{TgvLA(0ySPpJ z3bGHFVLT_a8)gb_IVb@HG#zk7`N1x%-HKiA@|n#D=nWSXOR7=RtY4MM{_2`MvoA-$ zaK|vCsqL2gzyBVO7DA~lSHR;V?va!Az4D;EqVLbj#FI&Azs#)X&wqA>-;|E}yNdW6 zNlZ$;2Gau#=;hw;zpI3;`Q;Xdm3LICC`X($>FltopP8Ng=90!*nnLe!!g;nsW2-Er z_6E$^sheM}i@_+iJ5|2(+u;dY$hA8((P3q_ecQHX6eTK9$_gwyPGA1qOzm+TR21Hv zsl&85+i6LQkqW$=CfmT+nlm&!o|~gj}QR$~I+R)g}%k{MOlp2yEj^AQAeDtU( zfN=pFT(o>!LkKHM&*I|aFOp&^48PL{(E+Nag2Gecz01r>PUaMY#e&8@ry+LK0Xl4( zwtKOv8vNlsFa-l5w7KJ+olT(N3c~_LoHzti;P#SE5wr13yRI+EhK$8x4SHiPA#mTc zOpVFQiYeASNgvB6Wb+ZMZccACt?1rbEAFACvDnfIgf#Xfv`rvLC1zz z-6)VFM5;tsp9h++uC8tZyq*DhU<4gp$vFzv%^`>+EpUG&X}U`%j+xVgKKAu-4O|X8 zY3KthGFL0tTIAihxVZDT>R2J(l4fdkDq_tDBnB}r%LOihjBEMx&h&-Pum%5jBIKi23*icN3xbP10P|^J^yd?%o{(`t}kjpGeB+siTS0 z8w6?^&2ZzBr+@tM97=7$s}uQ0tyz>U0_%%f`L>s*vVH-AVQky^?PQ6OlXYY4wz+<} zCrpsRToCJIQI{CnN~t4CnfmJYH`>KQhWFeYffQn!R|EtUPqxa34vlVoWmHg_B`y2LN zoAF5}{~3!AG->W|sh|1sMGo+p3R-y>vWODQ#>7GaY+8Mz`vR-u>tU;5N8sMfFL>OXnfAZuBsV1tT7wfC48etV4 znqCY`Sk8W?klC4)=|Ax%1oAB~Og$%;h=@9IpQ_TKe!v0Smu0_Uobw*Cex2Eo)QX&=ZS zCS8SV*KSNLf&lzj$R_TRSN)-S9p%@tpDEk(9%Sd9;9#~-QO~t7r~y|-f2ng98>|8CLC;-?ZBq4F;Fi zkuNhdH29Acb;61NDjJ<>?U1=h2#e^=cV-=Q-npEY0?PuUZN#bUxV~iRkWhu zH%;Ne#l^*8q8bQ%Qo%rz0D6k5I^JfWN=eJ~f8n7!2?uwt#%V<(#u_!S9#LYj?> zQis}^fKNQw+*CM}Q?r<4YydQ9PFo^l{Yx4BqIYvBj}p*uerGLOmfuJhA73#;fc`F(9jtAY~yeJNiA;K9`^7N zIUx$AY~kD4N!jx-yJuS*GJEBLG3=>z{5X^^dhDdB+JZ1x-5In&rxgM?O8v4Ij4%eE z$w4j8XXH|g{w50hG7HuZt9Qo{n#;=6494rmALPkqYC0yiWL}yY_{K!#=}M?9Vfo z%zyM$v-F69h1#x;zwKEJ90HcYcAw=sY!)1EL+OIE=Yg8BHkt%Z$=@I@`+}D;jaj9` zB&+{}9i~KlMJFFf^q;?QfkRxphtnK#J;M$^HJAlJk%hz{%_P7Q&ybxW(2}ODO$@YB zHFb5=o7^3s$5f{;M+jPmODs7fKmwqM1hm4SpJ{S&jO2kE&z2t5kHNZ?e$n<**T$uM zn==kG*$ka56pA4a#+$Z9$Hl3S`4jKT)~W5RqU|5+mT-lg-Rg09c`5{may20^ZWLBq z-v=yIi!Mf!Zy%Ux*(>f@W(~VzN^!m`oHXj9cAR3UYILg8{X%_PwnhNkILBu@9)gMy%`NCM!!hhR|$tLBy$_DT=+WC7Dh}%YXO8Yc>#0wp%eJwmjF(-Zk0OY%;j z@nIBe;-JqFf-Zd=cKv3X)*{V; za^12Ae#Pu_JtkMJZWHIp@SB_ZmR~_StB58`%gCgj;8pCw!PVhg>)yCW<2s+2o;ee$ zD!Oo^fE?mp7(A8u7Enjmy3eiCJh!T|vyg6{bshp!YT>wWW)=nyx~(f&vag*0MUvM88)Ri!-;fdU^i(>&DrD-auj$^}Oz+PXGymXU#U= zLd5~7RdxCgM_3@ng7;*C;Jq;}eo3jh>yoAzT0Ezg11XV>+nnacQ_%W`#K7wD#)RjF zp%sk}yLq3nO__+6BR6%K?w5DEXo?BYGr2BhF!k`mu`)C?Bx9+ppFC4vgmRwZ7MOW8 zLfn-z_JR=qcDscS&trnwR`ZJ{OQWjvgiQy-sEnpl`_G`-*_Zx$;1Xa?+JK6vl_pVRya| zphZu1Ued4Zt7LQUIzWhP>-eMWqkV}hU2Sd3=tWOSfk}DrW>(W5{o}hAL#)U>iLhrs ziy^oOtc%^}n_Gb0J;0LG^xW**Q4C)K;}CkpYbbQ=bX87P0svG#177!s>z)a z^@jmR3UFt2O~|)Yn0o2cP&T)B+thF|+CBh@U<@aejQL2%+ueg*NPDKl?PynL2G(dmIBr5LE81O;qcCg05rAUq@edY8AycF&6Wf_b2N10T+#1 z$-V)k_q?SGS`O>3rV|e7SuJ{Hx>=TiHW9y#r|8|sIdM!MaU;iNzIbZx|Loc4=zxw? z7dZzA7(nBys`_J?xZap~>f}lJC`s>(gi05*uyAweTCa|`B=`|RuCTKY>aTk-d1t`D z=XZ64p*){94UUKKu>#(Dk1>1$^Y*I)*G1u7_hikY`G;_Rhk=MuIsXK|NNZYuLvAty$ z2tX>rbv8(PMC~McB8;2`$3VB0CCH$>E&JiahlGFOlsteLJbPY>0_I3u?;px+0AFBM z@BZoS54tFelH}x7Ki0eH?98m5W0szf3=e<#fl{;wd_eC}kPJ-1iA@8fCO=eY&Pktr z{M7>!SA&918mc{Yg`Y_wC=KuW3?A`(4`}2*M0+x?Hsa43W?A(WF%a|8A#}K*45;KpdBZ3zvqdU@mO3hDNkN;-JtNJV5aT3%gnT9SDA>th^mA5;`xScAk5 z3JcT!(Cb2KP#oaUHdO3S<#T1_r zmr(^Ipp>+?nu?&9o`55aYZ9q%FJ`BF zFsY-gzk)wipxyNX&$t&see%%WG`Rfz7ZRg{=VA?QruSb9FCKI$w;FS^7@M1#nc>%R z?DyX~VQPz!88WbiEcAySD7tcRu*uuu*&?$+)QE8$sjtMNb+g+x78$V*RqteK-p5B7 zD}{*@mSd_4=3y9tJ>41PyYzjT0k%A0vTh0U!7NB8O&)q(>|!?{@Fq#AGmkeJq4ozVfh9iO9UP! z=6BEtyJ0d$D?q*&VL@geq5SvBHApFxoEiA^=@?J~)LsZni5baINeRKNV|Z2rH0~2YR}2;~X)8e-Y~vG(kD)6M;{Y^c z?z6Uj0gmK?v7FB7*Slnmh%Fz!;{yi|_74tz+jh~La%9`#&QKM|HP9CeHzkn+zRP4* zBw*r~2^fv}W*&&aa!z<8u#B=O{7Mi@$Q(YHBKA|K6q>4Bb#WJjVj<>?tCG0OnKbv6 znhydkhtcFT5&NK%jz}apSSJi-aY+uggP4HxB>}e0UjsF49>U?>kCqnJ#}QPT=8tbY zQ)W%V#9a&I=OXA<9r}T)`**R>;$+#-0N33()EXs>(5CjmD}f_))7kQDiDV+=aOC$0pbg&A(;Y~yB05Xs8Otr%a5hEDL83!A1@ zPSi@shT6uOaOuB83ld944a`MQTcP_Ge)HXz-C9SaC&+XPX1yCFeRyQoO}jMR3MQ9ngl8sM#2NaY+F+`ldwJss6XQcpoBJR zu-x7>{GF*7L)cf0f7~V+vb{STH`)hZO_EYKzC3$jhoSmu`uh2&v?OI_OnT53WuVFA z44Dtehr;2_D^rrNQnr4lf^+Bd%~o$rOx&k2&vZm4)M!7}o^DP?w;I9z#AT-(aVCbp zE1{ zHav1nmG?8t&QMavkHTb(!*#}N(G@nl8*|pNkt_KH#ku61XCHkj#-{E}748JxA#Yqr zM#|;#(5Cg zBLE6nHR*P1M}2v@1hL?jz+MlQP)Ol^mEJYj>TH<@^uD!)o#5y~#SN9DPX=8f!%L(m zgxi9sW+qU$vunoQy|_!}un@0>^iDm%Au0~3VxhD@X@kk(mttk56>=IiML%TZ(Mm>M z==4v6GX$Gjis_s=6PoWMg5c4D1WAb9!<@?u%?HU)ZCVmf6 z$e{VU3JiWlSs-@*wdKJxj~+d;iPwT7_MV4FXI5&G&tqWvHiXv;Vg^qY9xs}bA(9Sb zv9fvpBVApzoTK&fTEO-swZbyh8#{x^o;c#l!auas!UEFWK#o%!S>DUSGWMFW!(#r+ z2mf&?GQcCzFRUBF1in=XQzxL9l?#&#FfDb2NH1YZJ<@{R(Iksv8X`l)5<~;$TSJ7! z)Gi}6*r6X3PALM*H4H^e4hgo~QI;haF+7+x`_!;uA^iYa-C$A248XxXkek{=)8M2c z&yyh!DZ$jZ$B1Ci6{SJd3vXb;yKwHD(btb}NHeRs04_a6+il^^4OZ<71hK1?xa4D& zRz1aLd1Zwk5IA!a|O>t7DNu%d~WMi}J!qZzSkQiAKDM54NZ3@4`&#RdLl?mlVjb32)l|shj?{OO8%FI_7 zT4$z7b*4mIBQF1-la+a&BR$#2>JC*I!6m_mHZf!%A+us{=ckT$>*udzTh~j=$_j!D z-PVVmU4V|pSm3C-Vr2n&0ss+aH4o~j_=Sa;lcCTGz(D1Gz8bUzqyhU1yVyP$HEiNb z=)#iWMC1B4I&Ozz(KI1|yCL>)1IMIHO)?#VmfpGDW{gC|a8R8O$&-mUKiURmYk_1l^U zSx=^y)}S|;!CnZM?;?oY0<$Y2&UYfJ)Euel$Z<>5^Se-}tq7d|wnEkGfRAh# zc~Qc$x!|&lj1MrA9YkK@6+#5cAq``ykcPGmft*UWyx$g!M^jt{-F;kr7HJKzf4|ez zs>TzrA)u>{9X@;>)*#4@*RUdJrHB$u9<_z;pXMa7W_E+=J`un0xTUj@%H#oW$(z6( zP|@an(3-UJL4QL=T;YmSgn$P5o%9lTS`Tq6qIF5N_^6sB&_E_NfcgVx$Gh=NmY?8E zOM({%OH4*z;Rhu^Z{wy-!Z9T$NfUw?!ObZF-D4*1Ck%JO9|>LVGD0~s>F5)#8QLtL z4MbNWJ;5U%35xL8>C-B>YGMFFAq!P%2-2XMiNZKW1}{Map$XFv5)zt(=m)A5Zz0wr zptD$d@w*14xYDtNgJ6Jrj?hbj_{D9mjpGL*C<>PYgPR1|8dx7Ygwjw2hTGGl&?hP!oH+_b9=0{| z%pdripMuFkTZWttVqcPsbfPJDl403tdKV*vz`Le!K?sA{K}StSW%Ke*hv9Vd=Xk4T z+78MfUr8ehU^^uP59H2&{E<9gjF@!xkKi#~Cy2wka0iR90{U*#j!DS)abVlJ?|plo z6nW|sGV&+Cy#fC!GxqA=KzS*JDQ?v-%1aX+;l3FMSZP#R?940JLXk`8nqN${_>Q7*EKmMcQV50O?=){Ct1P&V z!fvOT>z-e*g?eA)OnGP4I6wKz>l^=HSij!`lbHY7qFbFO^3hG)vfc6D{$j0Ei@!dN zVo{@KL*0g%RwgF|)nBh$A&{^C`U8bxapzwjR5(ES>q)rJV|h37Ql1Ai{%cJAS5L#` zB{=%;FaCQ%2)=AD*0R6;hC+FNgoN*3um8=3@Zh-)f4%1P@#Mb${*Bycy0UINC0>8} zKkf}5{9ixiw-&$f_t&q|ItU$1ItJN|lanR(EaREu?# z_xmMwUjOSa{~vpB?ziy&$G5!C!58CFHU8UQ{GTE z{~9g-%+j~^l^-gj+@hcV??%Ia+LV7kef-Of;H3YzZ{)_aXB+VAh5yDn{2STeUl;IS zfAPz=&g-3TqFmd z7ynGFc(Ut%nR>1xSN-EVsgzQ9MJlnqb04`?s9P7N_e)~@E z$y4e9*{Z6&^gQFzjEqT*?sF9Cl|M>-KQf<>zp5v8p0@KzXf?Pi^8xR@`Gv&zXoV`(qgg{%PZ$iXYS&$dtx-QZT(0`|DSeiP zt8NyzMTo)?Pf_z9VmHOwH6c!Z$Fr=w)WTY1_Fl^;6f(?2P;! z8IBl!4mr%szZ6KTuB~@)3R$H3v?U)+%e+`>K{q>W)-f@?NO|VDmEqdbvrWV8dAEgM zpOPzlX0&l3TwI2w>TOGmrR%4s1;d-?M{-sNP0t0|Z~gK$y`Vk-k7b$2*ud4DU-^Hq z_7+f4c5U16fC{$-7NSy$fl8^Qq(O*CcMC|jbfeyq0*V6C2-4k14N8M_=Ma*^fOOA% z=LO#H^So=l?|Qy({r{|mG0a@Cuf5NG)^QxSYzJ=F_ln|lFC{;Y|A>C1)zke`{?~ zIU@OQExgQlPu0Dr{>OUL=xE~3M@6JAQfN?|IWIrv6fp3+&d!y9*YR56GNX$3krU;~ zdI-aA4s2ONn!w@v$3KVS2!I0{T3e-{b?Vr$W3oa*f4kgvcz&oWk!WvF<*@s34Mmmn z+?R%{@>$Vl7N49Q9Fpbg66d;dQZlIqr2+-;VPw>Nid7khpG$1?_}i{{kIW6xi=6!N zSbdn9tzG-OlYYmY(&cIquQ`0qA@;GL1*c`SAh!2!8c7o=kC>up^xnOsVA4uX4IN$f zFZTAfrd`r9S6axGl{@2&bj$gsUcNn|=w{X_koaokg7aIqzG7i1LiUrI1da_^HHYUO z3#G|119j}u>O!CSE*PKTW`tIQdl_1}3fF52afF2H2?G`SF@;(ENiBP-CVkaz%O@@8 z!)kmc?uT#S7Y9mb-%DVV7z~WovLd=tO*$_fMRt+c^;0O%L9r)K+Mtr04_!w{Bm#|` zN=DboRRN&_@CZ31fexs*cE|@o;FP6mdiU~?BU`f#x0Ha}m?Kemg@l5{y|Y8|CT{a6 z1x4`9d-sH7KCJ!3qjkM*g!5x*CCVmgaD$<;RLw^$Jp-6^*z=XBl(?!`!ulPbinS`* zX})qPFF&Us_~i7LDe}8lAH7*!|Ha5LS$<^OZdOn-W#fz0o37-cyC)tro6w!j_->}w zqv{GnxEg~MX}}B)GVwFAYI(8UqZpq~nsNHRy!`v-4tgwVunAKqO76Dz*uQ|0{-$&l zZ&6mxG7-?ct^iGBY!C+^wwx@OFS?=~wtHul>g!qu z@jv8 zkT`3AjhKVfv?7!vR7#k+?m((_0D8$FmzacP4x#J)HQ=(4CivO!q;&FzhK45#skvug z?XW1V638{eBh+F&*gWMk%s!gCV={qIMRLBHi4Fn&M8|( z{gZjw*dzav@v_|f?Cpn61e{pOcw=aAjMfV!SCp|N4DRb)4f`@4y*m84y&Sh9MGM|! z|I)yl%xrZ!QMEE(E?H|E4Bv~h?;om+j;3`2a72OIv^I`ENHK%c#$s4`!fXBgHRv@? zO)eI4t61kxJ~HZZ|H#{8tF|MCg5TXLoVFU9nqQ+u@7-HlW)*MtbPPB~*74P?+(sd= zMbC(`z$|5{ymYoR7#rC`t9F0K9yNLaETrvBr))+Er@}|hlap9?Co~hZu)V^G7W~30 z@MP~9s%($(-wNLtSuEMci<@RLu;Km|{>^obN!nG^PAP)6`JB37o9SNMYkHcIk8x4h z4U^6o-n6BYxX$Ti7UPjyJyUCY#2W6y(2pdobP6RBwE^uEGhqhNGWz<7DwVx2?lrua z&NfI|W&r9V$S<}*dOM9F+TjsUCD<89yn^h{hhZ%aFQnn&IjP3fOv zjs6;0-?JjPm-#jQY>9z1CUxmRw0UZ>$TzgwmgFFk#-NmH;=OV`WxAW-+nqg1j< z&V!=kSA=E%a5f|Mfyb3u9@ zpa2-oZWIJOHb03QJ+-0u;8+3$5{)g}vBZT6-Ou6G(I+lZ-*-|cI8GZ-7A5PaW3pQ_ zM;C9=wW^wOdfD69B&)JpjQ&g{O8)Wcmkmzf#l_u>Cbv!Nyr)kk>C}k#T+f{yQ+}Up zrk;Ppe@3H_4_6fv{839;j11LccavYvIpeT*L7d14Vw_o7nAO zWZ(;0ON>|Uz&wh_8ZCYBrGh4&U61qVU z?AqH}G~0HSXg(27Ub#k6Xl`D}XR&u9VmU*SFZuE8MZKmE-yhC(6@OuiAgMgbVRlxx zFcO@vxYuz{S4_3?r0$0~>NO3M6y<$hqy*P#nuNGA`D3)}x{6rR^gP1USYR_Ns{vi> zlDFIe39=3Q+bWPvgdKSmS1{7 zWp~Pg!ydd#%v=ObMaha`y`g*KF1Cgv*-V5^y1PRBqnW$q<=uM(FDrq`MgASLmYD1f zkN%g0kR?eYO4P25C^WtF~i9FPr?|bf-=>aFyiczFhmkxS~E7o2M-Qbt^ml zV%R$Q^^+(AeurRHj!4+ozC^YGP#8RAjVw)y#)4cNm!+8jY5uE-K6X>#y~^+$ph|w>9A4TK4B^ z#b#Y%_fP&%2+4@i`Dul<+m-#-0rJ<;FoK38GXf?t&aXgb7a+T2`uV;>Roypm+TWF% zr9vhD#;}V_|1#EM4=_1rSxi49#9kJxVNcH*eeOOvb^hj6HYJFIZZl^Li#?#K`TG{n z03XU_v@+*d4+&~;$W~7l*u zC>zO5tMw~O6k!wdao2BVjZ2RLh0b2u{d^W|q7R2um|Ff@GicrFNmQGG8a zsj&=+jeuvX>pm3CZtvJbHC0E)c>P8Kl0t`yGCWx5?)?hcbET-G$sP?aI@mfhLs|*s z{3Uk7w61LS34UsN3u&zkX$noA%d6~k*MQ}1T5XiN1a0I zOBB$GpRW_@w!2AA@rg=+hk*@QUr(R%CH@^lkjW$Sx9ykMqZql9zwKYtzU*kSwn7L= zyVL4ot_ydYd_&uJ@j}`P%R+Kn{iv$RS!vyWLh&o8cRWeizkf;iK^-5Ah)F^Vv=jMl z6N$bnEzIJv_x*ScV8Ynh=?W{eH|d6hi66RGD<251;W|ZJcknT^ z8+xE+CGXhqp~Opj7JKoysmFEp7uym$`075gHHWzFx#qi!s4_=?d@dKY!%5d6Du?dw zBV|?i*mDwRlKm!tK~GhJ>%`-Roz72Xf)Yms3Gtbfh23pODeDKl>ORB#DI9M+YTJ?$ zo27kW04k*!U5@(v1|)BYoY{SkcJkYkF{q{vUvkX~^mf7z8K*r!%5t$b!GDkcr5M8<^TVT=?+hMzW+;&(oew4RBSxoon?OvJ1hmh;V7^VMoGO za*wapFbdADQ)Vfad*{XXni+|u^xzAHN$sh-H*Zx@fCt|dz(7NgFoRkbXn z>;*+&7NAqSl(vs|fR^9>m(W#YlFm|GWF)1Y;6Wh2G(Q}gPH8a77u*YJk%Xw|ZzgH{y=R0**c9R^rLF*O& zH(h8g-n{&2&Ch9NE>5F|lA~Y8>Fg~|cW=LJcPf2@R$9fz9xpBWbS*gKl9NuY)NLmH zO~p}lX#te_Xk~?#w0!zyOy|{epIFbL-pM|;#%xKb)LA!nxEyk^X{fGHm}+iizN04j zl#gZ?$D13$LTw$**C?^l>8xyEhBpbFD&Hu5>MPYk7a1cL>$u3=b5crECMrudg+$_N zSFV~|O`&mYy@>X)zt%q{g?cUL9{=?6jzlTpezL7!QTR!nBqDia1>C3w z!5pVPLS3+xgzj&o+6ncui=JPg$fbnvBG}kWT2%3jLru5@`b72ag}u1o2{5THXjDt; zJ+#3!=euI#wx}5N6|yw^fS9-~ApV*p#--#LmTN&yKT+@U*WzMnFEYB`FI9DG6Rp^; z0&%wTaS4MW z>+Q`|&e*~0RBOBG!i_m&lR>}Vqxh{RYV?}4SVv+N)X2MD$Pq;fyOjO3i1MTUTw1j2 zg09dU3OICU18d-Jv_Z0h^=q=Y80$2quu;qWvq<3H!&X0D!`~d`j6Zw7NQnoBe3~n@ zmqN!z7O!QKE%2rGDYnoYE74Z}+dRrG_!6gI{x;zuM9s%eO1so4ua(9{OtmV<;Y=h0 z>+Ni`QbSzIpFU<#AMLV((Ja0in5sn7!UD9&2uD2(7(~s$NNfuo1%^QSBbi|Uf6n!0 z#en*YmSQh7YG1~%jSP^QLXSc^=^~wGA)meG>z<6PsIexq92g0R+D&>a`jV(DT$SoN zr9c;AZ0w8k!X-s9mO#nrUi%8eG)h9EwZ|{7HCl!m z)m8RmTqkdm&Ff`hu+%rccb8G4&yJ#Zx9c%8aB8s zg(+NDR>Al{&F6%SJ=ZjewSlW8MnZ4DW0rT{?AomDg(l-1m-J^%&YLOrSWQWR^i}e) zEb4ekCxDr(0;=E^Gc&Y#i2-i58`u=Of%ah7Wu>_U;CT=X>DhlnxMzVr9zktGD{4B^ zZ<7n2z*&l5i<8_z@8ldv`|x}!D!!?tIYK09emWAj1dzO6@@ zqIb`vvwua$mu>PF zLIgJTYfyoSS3}j;v5zFin2@T**8BIXZLMT{3Nwe|M?&rFte}*RYB7hnl0jHVXa*s+ z)(h201QbW^WgLig04>-(Fm6TR}O06z2%9C@j zZFZ!s*wB|Qke-}kRgT{w*oOV6k|YxbwC9%<=YozO!c=MXMjG*-?=(AXaK7~<2}!uV zAk~etca1gW+AK#Yh4Gt3He)&IR@6@x{+iDjk8v%Te9Ou)-A~mhLUp05JOOzijQE@r zuSt(=&V`93iQZQ(F)uowZs`_yVN{(%_s4~ZUfl9ev`O~$U`9IB`bMEUzG3;XKq%|k z%6_BuRXS5m`vFS(0S$bMtq&w(bQ-v$de+MvPNHpNVI4P5I;b684O#3~3o9<1!sK#Q-Xo>22NcL2b&1E|}RbF$Cp`?lIJCKX4<0 z*pBF9)&<#+^Sxa;l-KC5K3c_Wn9JG7m*?iki*|C%25bb^wrQe_E4QP(*7la0FY*6g zJ6yeeD)Qmb8@HZ|jT#xaSFnfGwMgGFKE@>l_n*SEBN?bgF>9(a;4se>r z7J-XQw+SKhVc&uuxmC_PRlz@PQY!wr{jG;CrQ z+J5uKPkPMfoGha{$zHOC;7^g-h3aUx_rrRBdy>qd7cq%_ecrkG5H5t<)a`&*mdt$> zCExkuZ=62SsCmXEH4WBlqpR7gukZq@csxH@_r_M|*`FZ>EKl03^UmLwKkqS6B#0~S ze_7?Ue#C>S=lESUf$*?|A7>JaQ7w92b`01iC3o4Si=D&Ou;H5AApe+jsOqOEd#;Q5 z7}cn*(!~nA0Gvs#iltQbN%2;>e#=qKSlAwtkAd6o2+RcG(EJT#Dd_;MmxWNc92ip) zD#wszI8zS;n@7n^4+skF2D{q#Kk zbs9E7{c6#_2ZPZnLZACz>OikdWI6T4o^Q7sb^>SfC@DW5QKPCBo+%M!$#ZNOS*Fi8 z?9qCHWiYNdxzDtJur8;Ndoaa_SMnCKJu9NpCJtF(PUvm2fd+UiJUm6{5Ce*!ILNCT zLI2iMF)G~8W z$x)4L>2Ozwx8+JS>+EbHdGGw*_u+_9vle~n+d24GtU0&cHI2L^!t9YQEhW})lc+E&goV5fJFZg7~bP$BICKf9w)|8kD5O89d1v_6)a zduE$9SEH1ZO^rqHnvHN-Ise)e+CsjFZFzc{a(1MFZLKx&P>U%&w9DM+aFHy_e};V+ zDMgvtJzh|XV>Qct?RD0m}&b@eYJ0sP3ZOn19??_WMI-4g$?_QPbO#uYMWCEj4= zx?(x7^X|n;>Y0?P54Cd?GY2NqZ`>CSG+#1)SzA^f^y&FyDqzG}9T4xu?v7+Mc0i}r zTIE$jXKMF4sWPfk2J(aKY9keb{c}z}qJfM{GgfH?rLfm8AKx?|iTL_A^A4jj)dP*+ zbuHzvpgw5uLQZetB4t|b+T*5>K)q?h&MP8(L@gXpsaS1T!7ySsXG2?FR}tIpO&%JNU_7{0D8`{jn}(0ZB5+ zN2EK5ySEtslnf=Ew7Ii2)9xi2mH>{bkQbQ}?d7{+&zu%!b4o0qd*$XsMN-0X+?rX< zTy60p%F=Vn|A+15+bQzL?rPs=t*{TK6sSE-Xgdzo7nH>2IpK5#GP-|8b-{ zyukUkvYOMc?~JQU>DQm~hbWBk7(fGL7bB~rG*)q{%Da38|Ks+G0Di_$!_8@8+-DD>Fwc&ym&Tzoab;X`WM#Gs`VzlKszxSS;&Q&IdN#Y+->2TM7Vc2v%jF<0iZ z9CrESZy5|@iBY?OduT$l^P>GQx7pW6S2IY%{fV7_~S&aG&?biUx{QMGp*rd6- zU5SDDG(Ll4Ip8}*Cfcc8Uzq(wLE6RwM$o)1$U-qJpGS5zh$fT=1V14CqyU)S z_mJd8WL%J9AgD(Hjc`m_1SyKc6Q+J-Rjn4Fvostl@aj1ler^O&EZ2PJhxpm^Scq_p z``TDa+=fuXA(nV{X< z-Qdg^c6&k^#2lzSqDec~$0u@i$G%J9O-X&-&g#&7LV{;B-tfz@V}8ubM9I7*)wT7# zP0Qsd~w~3Fep5Lfuq! zII}F|E#Kc*!zQFTeGcUOJ+`dS*w|V$%X^<*fIdClOkws6ReWEzwBPFMaUC;mhw29A zLoFIw726*J=Ed%cV_l65CZp>+MGYE8VSl_}W=QCRf7P!^GfgR(hac9>1=7)!rfraYsiP-c7S!MyBTgzj?y&{$se)iX?LWFP1Pwg;wXjWDc3He;| z_cuIGgCryCJ9BgyZ0LEVpgkFMT@-EP`O_SGGY8{EX$kp_JT2IEAQc$D5@ow|YH%u^ zr?Zsp{9O4(^gtB3#{_;a>w_^hh3cf!hqX8C&I8R#+`Y-|8!FpaTP z%8I-NrC+ui?{c=?SGY{uyS9P@je~+?vP3KPWEKNTW!(sZK@Xl8@|WMY`v zEH5(WlS>fnj7@V+5vIhlHgZ2+##fO}U-z(Q9y7C??Yk@u}vf#EP28tn_Z9-)$H z4tu=1O%2#jh;6{h4!TBMY3cm^JN_8idX`NTnC?A`b=ke?;TQ#VQ%7quROT7#(X>$% z=63I({WS@xpPOQgj2Cn+p>F3cRB8{e)Yv=e{hT~@lH0ZU-1uVBDR(UYirYZGMCa5B zF_p)_X{h3zP15{wpMbA1#Sg{3)=8kw7wC8ei5Qmh9CXP^VJl$ufS1 z{uMj)(1@H0nnbB4ncqYZPf(=oC@W8TdaWH&@C97l&C)&DonLn6Der>`?)G}{wY*C zFU+GHDYv*+!{KT6Ufc!#ew-1#z2QIN&SZ?(J-{&aCu`wH5`T;bsTwn*?DKe*Vnxs! zg2N-|B2RmBR(Hm#U*a8w@eSLH%ZHgqDoR7t{u))Mq@c? zg>WEEBWvoCrJmGV5t3eIXQvahm!bYm)8{7NK-!VMzRdqIv?vKFghD1JS+6&dFOcZ@ zN8liBf@wvnp3 z{D}v?z84Bm27fJ_8=bpRd2-XqF~!>SF^At@O4%`4zT!_m1qEkFczv|F!(Y|i{Fr9r z_hd_y7sd3A_^!5g*OaFM?-S>Ls)zACJ9c7SX>aBfyB?M`S~gtS3^g-tm8t!uZtnx7VNSq$l* zhdB<4Og_2!KxX_fP+&W`dySkphOiR%bzUQt;+E@LOCz*Z|aErNKIT25L$U15jJxVz#_rui|HvflEYTZxXJbizfy zmD~!tCdDenVR-2aWFFOr&&Bp!Sk=k0La4%2eiy+4bOL^7Agza*1_B~SB$a{_5ONeG zl?b>_&3NFp9~d2jX7PVs+;LDa0Z2BxxHF`LQmF;pevf{KRGC))G6-!TDWdKtR7A0b zr&0g)9or5q_q~L!uCCn4%}oac4W3wrkUjM;17}ZU-#wHiji-l5)HymjEfZS+1^23I z{ByQJL`hq}3CNXFayJ&ku+c`4m_x+X0m;%iK#IzI0C`ba;0}bq|3p=C@V0Rt%b)Lh zm~oo+Jue)BJZQv9ZFM!GU=J)CL0^GT9H?9rznXNt@0u#m)6~cn&4ohA`tyBj{ec2tLQpgb zkk|nv*)*>@<%7hd9!BpW;dUBI;JyHZ0t}ls6zFn$MzDai@&j^@vYshKzaAC44Mbm` z3Lo4Aup+{O$sq~=>W%>*qEG%XfVwA!diEwCc;{*%`*D?C2mGpbXs0s9^eZR;hfcvg zR^%8D9wWR)AcH$*=MU)1#|2db5`Za_ageB!Q zD^@*z(BNFCc0Pv19>|btpcIYP&;_y90Yv}DZf1|`q{;DP$5Q!4fjGF3hvC8FPakKA zpF7W^3HZ}FW2PgJnbFVP9}d)0tMt}`Lj?6 z7g4Bexo?oqoamC18qUlC=yl)N2I$B-sycU4d{@Ys^~Fd`A2#$pT*dEt{ouKKBApS*MoIs+{D|gIpf)y5y^d?IR&PaGhW$o?JWg;n@v%Mu2ug zM6zZ8fE+yinaw~_mG|Q-&Ib~wQJv2LjKdB9`taF%$oWzA9?&`=1Y$=zfHzJtf#sX7 zh~k4hR=%Ct{Jo>ny)W(VQF(cq?Xrv+1M)$@}|%KAC*IL1-E^uTkT-pJ)GX4zLA4eIwUKpV+Uj1WE zE`5!RjO5;#&+E)YIu{1jk09b;)sWnbHZB55xr!x!i!Ko#l&$0+EC;)?;NGlMXQ4SX zKG+P|bV>xT$G})c0>H_r*X{Qky0016NDS)_SMFblwAvY!-DC!+*3k)qwlbUq7+9=; zQne0di(D^IT#RD~c>5DDyopjV(Iq25s$D#v+ccM|?xD)?&o$=9;8&BZhOFumG)iGQ z#sigKJ1{Er!4yLnOsqP@7-|6SDd!3v#2Iq_Rq1>nnG0YfN#_5rp$4*MAHW`ROA79R z`UkXz0vfdIf}ooVlUgAYt^laJYk*#Xij>Ainnv-M^v<8_fAB_0TKW`#uHE^T>#yd3 zl$XK)h@a{2-vX$K=m3`3R*~+3Mnkg=WV+z>zI?7-`x~#1oaFuMlJ_S+!OUkJheVbS zaH3ZOiEJDIrlDWKx%wd~%{fR2g|VEC?fL6qIeVn%4VstM0AYFvOtiU3W+Z!tymJ3W z{pm5kEdJ-S$eU~jUyBX7b~Y>%@L>KFmHQt@p;R9I1HE~F{m-qyn?Eid{9alulIX+( znmm9L?7#loLH~6o?mvy~#{Q2zh?3P5|I9Abi=#w;&^Hf4PW?}B*8kUFWCZymxPsa%m8dRe z#)EPAU&rZRxAaTDs5F>y8r3%z~v){dthb-U$p)C zA0EiR-O|6F(tmpMTi73GAy<+9!|X9sUxV`h?Ss5F``7>5c>QZu|Jxw`k8l2KWHa_h z*5LnoWJCV`|1j}kEIgtD{=)?L*NQx||M~x)SMa~tihr$v|L~@eIZVGoOs*@he#v2v zj7GBsO}1FPD&EK{5hRIHDYrRVZH}UXd8`+oDYbcAJF{sA5-c;j&6e7ee>}=G#KYtj{V?Cr9HTUWELXJ z^Uj-NUUF9!0A))Hzjsb>wJ2ZfLDXRA!=8!IfAAL4sluuG2+>2p?gGB36=2wVg=Y^A zP-%AxWsm~UYzGj)kkwx3c|8cU$D1{*ZXf1A=nxT;wi+->s?qkMIS)ALlm`yLEntOl z-RSPFJ2(O=^&u)U0l@L6fEr}q+Zl)OBr6}H%)Q0@j3^*&8T!oU_!8lMSOPlb8sGy! z*mY7)ZGzjGH9Wq*59JCcw$9p;+^Lilg`OMLJ5UvE zcE;e(fr{$Wa)B51?~8#5Qf^Qz@FLp{;0;lCt~u~hP=ms77@!|Hdm7uaI1D@&jJVKe zH*`T_CIuJM%02N8m_{1(_bOJR7QaRv=YtljaMnu)H`I59c$7&2h~u0_pp^S`#}&pa z$7Z&pOR*jH*7@^01wd2>^id(kQlMDidHcx0*NlBp+~S}Wn-C3{bEN?b7^jqJxK3Z7 zIW6v}0EXw3X<(tld_Q)u@p^=ZM9dN=(;#7hx2+l_hANxJB@&Q ze<>B|ZG>0yEx02Nhu@>#%0;@3U>;8GWY3hPy!Va_(h+tLLQ;gnvG>7WtZ>2H-ya|cBTT5y8kC=!v} zcY)MW4!36g=V7dQ!Bj_J>d5r?@T3|bB03O>TFvLw)q+y{g*PN8eF46opeYDUqw#=_ z4{ia#p`oEZP$I*QU){eJc2gfFH4V2gzJ9%i5Oz2~ zl&jO9tC!*i{S5D*D({qL%@4j+iX&)vmvv|VaY^b=z@lktZ4HFD{tED{Mybx0YQj2! zm<(X@{vhUh8N`0nXZeto4{{?{IowRtg#X-2*i}@^OTg>l+I<|fq@EOzf)Kr2!v52L!DTQ>EM0fF(o_!0+4nU5SVZX~g&u!m z_kVYJw2#k3^}&z1pJ`*^Q?}g!YMd?2z+sp&S>#1IKYY8DCB=&>o%^=~A2eW7NJJ=x(?HQH2;TpfNz`OA8eK7~AYahbvVUDAAk3(0$7cGf7U{0@^*t5o3vr zT*Q$dY8_x7^37s^Q)yN;4e39ISDzb9{&i6Mu7fz14$R;gAE9&S&Q-?%C(vhpXFJ3$ zArv;kLfwi)F*Y4yr*q7)(4i$N3Rr-Q9x~(@?W$)bWtn26m(g z;QR{Aa$8FqtC!N?iIc~Yjq3US;s0E;o_ zC>cONx>(@#!1ZcPlPuDl&(8|HfFJI2kp&|X=2xx%%rXwdCii4*LpKpZe@PQ<0bF&+ zzaYLneVQXTf&Jh>2PX>TUw^`{m7^nE2He=Z>=+=3=r=d=Gfb3eCA#MtbU`UsPm8r zfE^7{DqMm%$h?;ih~dPZg|pv+cZGzIppir6#{Pw|I{+b@o$;0Kbv_`yDtYF97>P>q z`apPT1sVTqXMB(q0vCE8)Xm3k2ZC^hQ6$X#rCv0iqaeruh!=g3bzPuv5Tm2^Cu2H9 z!Ak@@h9Dj_SPIEyZs5_K0NSkpAUS12aunX)U5z8KgVn*dOa;;m4j$=3NH-p-UjOp0FkkmZatW zw5grhUe8$=2e+L;Ozmk=YMIc<{i(rj8qhdw1Zu?uUohp0-`s&z$~foJa|3#0(T!80 zy@8;l2IS1ozzhuVKAGm$Yk@0UDp>37B}F4P5ZSwteZSjmY<~pX9?;mdJ%mLO_$-uY z{%=#F@{iopk=EnTz8?USS`a}&_V!vVQs+2cKq$$(xX1RhQ1F!96;X>qz}Z$%H4Bxi?`CL+~fy7YUgYa~`HQ zQ05*8Q)v5!)U`7a!#Vrutjd89R^YpIn?5|*1`LlAh>3+{EE^mG z&-&83hO;tYMWO?B{bR$k(EWQrnbLD{ae0!{(biF0KZ6vP0I)t`3}L&14tC@+5x%S? z5M`-(OwRwQux;)YDXlkft<^!TC;I!`E7K2<$m9 zW)S;J*c>BwzW_ocelW2_fb|gBuG^vR$36K;ZX71PKd_)*2CmN-PJ96Gf;TXl-hSMD zsK5HyD2XAkIQ~#ZF4V*GvY&e}qO4t)k`!H{q05U9i`lBf@vWMei@N$;g8O?4yG5z3 zgo39BV1?^~>aG>AeAhx^>O+Ye@T1iN9XrDzl$|%KmVCaJTHXIApQ{aoHVk8MA&;kgz-avv<~ej8%En!No_};c6(geoAvZD9rwo zz%a}X047vJI8p|>6)Hx?dLvb2yi6moYsl}w)XxguAhM(VGlyco^Nf36O|`|FyLJMS zB!3--oK61dduCIjllzzV{t9d=MmYGLe)5^iYRW{<($&A+12K6DsJ{e4*7rrpLEf~_ z$OU!@BODnG&kN*}e|gy718s|UU_k|ey^;d-7yUsALI^yIxiv_$%>e7rJdjse)nDo+H(Woq;4XKgmYhCbbyWZx!0%A&^pK=?%f1B zQw?Cd(x7(v{rzJ4v3e5N3OArWS9iCL`Yfa(5uv&B3&84&tVWx~k^SQ#UKgT_q3Neg zc?N(UpTTdYcAn2!g1td+d18MI9+X=nlLq3e)FF4^q*}sRxbp%{VNLTC0J*bfmp!81 zfs+c#UtCBU4=F%o%p?09XjK#gkr*UWFXn(w^JgAgV;3+9NQzGx=+F_vvH*JD;4KN= zW|zEnMn+clj6tN82nZW39XjV!Z= zPn5=19wAoCf!!W4<;am>rO^Ur(i@uadSnm0-G1%89G7_)2!nMXbQqks?@=+R0btc7 znD|+5Qaz$xF~{a1ii=RQVlQ~R0{n>bxZn2W!*+wr2yt82w@Pq0e1Jl_=f$`EN80ur zX=k=t?>DG}38$FQhU{EcAghJQ&rg7Rm-L%5Sw<0p7YLLRqCuyk8lb!19* z2(Sa}ke*%#&~&`)2d>f!KX^9SB`_KM^gXz?``SPLhG& zd_y7(j(9p5sj)veVZftFPv2w4+5w_Uh6DMtWX-*>ijdvx+2=b~z~eM_+rQ@LZ(vAj ziAbqWky2YeNE9cslP&aUk%c2l@UwzMJosMGNpKq+_Zap+kCL`V#vYhsPoD;t7op7N zxHk=&1G25ab+7#2Jv1`zQ4ge}SV)LTvy2z;sL2gdW9;C6eUEDlLi0Qj}YBoo+ZkxY5VRAa-EJI%qBBwKuSP(fTZKS*te+zWdL zWk*&?p4qQA!DsBD21B%;D~Fj*dHOVx>sGyRkVr*!(ZMPXiHM*A1>R@W;9$X>Ou(b? zfepV_3%?u+3#nhRjs>#i3&j%wNL0rMRfE%~tst&I*d5P87@hiNZr}LiKA`ubLv@Km zKG0*p8c6bmgsS1DE+9@f7{qGG{ZD{97(ll>N_y9@E%w5l=MVBtdRd?w4!+LoDEYYsi`~ne*V!)& z<9CX}Sibc9EL*D!V3)Hu>UD_8EmT_WXZJ$i8xH%0 zAtVbR``rTkw;_;Su>w1zSMn!7i46g_A2BkrYJ3c5e2~Y@x_gkn@n8xC-zS*{WR4{G zkrHAgDA-&~|KolpABTe%EX`pMa|GG4d+MhT&MG}4HcnO<>&n{&fFsJDq%D zk_LE@DZ!QC_*jCV5jTFT)x8#+Nj8oBsOrJG_=}}O9!|HoI*&;2xUJ{K)PR#S9%<-ir^o5J=V$ zvMCUmKql`Dkke=MsKbA3Gyg>Y(r9=NLuaMI;K_S+}UZSce%^H&q9y#mTS@s zRMSQskC($vwO?apecBUJ6Q>}2*KDlgGq-_u`YqO08z0*^@raPS&wB&f6C|1p!(%8- z8k=;NQoHZEBA>F?PS?!i?7U8VQrR!jU)OT6rM)2AHs7>|8|;6&y2d3f7R`%vl3`SS zA(VP0Lb1`tgI^t2O$YC)Qn33aZu!TT#qlX-G5c>ZhSsbm9FP0T#3mS@Z#F>B!)ZZ* z3orE6-#XG>EMpGW;x}GkqJvNU33%MLHpqr`LFVnkL|vfNTQuSfA=O(D>i9U@;eoTk zIua-t=8HrJ3RL_;>e^xm35uAFFHMqxhj#1+7nTG|u>B638B(bniMnZ1OT$$gJ{Biv zF5pzzGxD`$8p0iPhN?HQ;)qQRHMRGDu*P8$Nohb+W*=!+Svk{_X`8V3w5f4CK1{1N zk51Cpy#nv%-@CM}fbr89EKL3IjrQV>*zV@5=`@ZTSu&E5R->cb6O|9!EkenH*BKeJ zyrrg@yg!^xs-@GSKFg{1YCB#x;{08QqL!Rq^C;cl@bf%NBTl%p7Ltvu%ICGpf3;pr zTTI^DuC)&s;3AvB@V|U{TT5`YA)j@kB6K9sOT_KeBt^xG*b3v$uPUp?gR_Ex0quEa z+UyO3zoYW94TaV5KQ(xF1sw1D>o_dFx*NAqIWv@NmavD^i{n`hs>MGFdP_~U4vlRL zhqEvb*1Bof2M!O~2P~K!i&t@*$XL&Mazl6gwaU`mr@)EfeyuKJGZd;{1a?wTaG*N< zfoEd_oQSmFl$WxrNY_S~cAFLJ$Y&|hd@J>a!|Bf>Tq7uc)rD!%P$yAm8|*GLaE-51 z*7iJnTj`B2AFs8brl3*f9Ms3ue*1R!Yj0+IylaH&Q4f=j0pm>hk+dC_r5y{}v-|@^ z{rZ?8k^6Ux)sij@WylTT8+mgQO{bM9POfzexZ3-#1PltU4`hhj7!T5SPMXSKrzFPh zF@gdj^W|#6+vGF-x7=*;cdfRywR{NQCYL_nbvI&G`b%CR#;&xw-E|{i#Ee%sBC*+I zZ?B}loQ{&_cR>aT8Vdk{YRxF;TI5HKO(4*{Z099R$F$%QvY8&PfJL-c@XqwLGdbXq zAwPGu@#&k`K1yA&rBv0Flc>vsi1`IKQ??ZXzEeoLm?VwJPRQ@Z)>4`UZP@I+x#|Ye z)j&*)>%4xNT4N)l&cN%0oZMpeaEf1|tv@(F#0=hCbDAg{+T6NT6|XX}TfEi4HCJNV zmlDZq8dJQyITo?U&#Y3)ImOEAn;={x^FEXchSSfknNjPz7F{`ywsG(dEpM^KF4}_j zaOhd{eAi6ym;$qMGp{?G?hEH%(P`&@)F;4Wt(}#%K1n^-A77<**2%+fa8D zIWb?*q@TsCs(EhZd={9vIJv>|GJQApc=eGFSC~{#V2T%_Kj&FGY6PD!6>cr0(Zy(_ z*|F6H3=}AoZq8mw5?ZN^z(|+EL`=V_Li6puf867|kanAo6vd==i&V_V!)q5c3HG0q zx|(HpDH>idF>6w!Jr&n66INkwXbvQ6Ruy(L-_By%v7f)BM7_PXxt1)d+b|bwD@)at z{I?uZONX7RyAW4W%6HaB2nms@x>3C2jdEoE8UyKSVY2eMH()dk@}t?@2+s?)rNT=M z2cziYtOw68Vc{8^zs<~eI{>r0v*xghlhU=F^~{U8G|lO&yMdz;6m)d3F)bepjSi)@ ze*P}{4s)&R-6T~GVucm|J<{rK2HJ2tvtQLIORc?_z#@j$f%$aIkC0{^!x(d`UmgN? zlhu+`Q)WBd`OhA4>?V9zH+f?pXhAm2*sYnLds9R*T@}TbZV|Y-R}|n=CPMWCvys&5 z&np_>*DDxP(XWEeh&W$ORBG#AwK2q8!D+Z*+R^AAjD zt%R>2-?Hkn&jNxtr$rXrI9iJ5rsVEiWmiE9POUZzZU4+=T4|$Kth{oaQM2?$_^Q;vt zqQki}MT`f!!k2SD-*H!brhbR<26H%7UMTB`X^oX6{pr%8MQSZK$u?xh8G71M7zG~v@4j)H8_s7ZFpCoJLJH17 zx1Z~Ro&U~QJ841zVQ-}Rc2rn@s?o#Ug`ZgoZ_#KM9r<0e)zaVqmBNy1(FDVne1!I2 z#rP#9Q^JY0`o`{rrG|XRrS?ybOJkJQ6Y;mKb$fsGEL9d@T9hSls+|68fqlHV0Ydqv zruxbTbBv6DUd#CAScVkDcjDusNi;zv`0nrJdIp0H+vh4&MUY9nngZ3T?2`cbc=iR0&3j6Zd-FzRxjy#L}Fsx!&C21|6 z&lT_=v-f`(RZ=Ia)zg9&Cqm`Eg@=W-YC+at7XoLb*Xt%E_+Gy@i%B}#MR&yU+tT17 zb(HF9=iao%ItC9))1NTgL%eDy+_^a2w^BE| zpid`>T9{J_4S7mfpZz1l9`OC$+%(oeAvYk#>*B8!y z^M5Ie6K?VwS0ZdIbJh)SZ&EF3k*);_l$!tLPBxenh`KQMihN4a5B9!yj*KDIiA52% zhCR8pBL<5s<($qK=C^1nMk&Q=jm3&HX#Vy2`eqgTH)zL`|mcr7uQw1Ox9w6wZaTz$x&VbD46Hi9sRP6Fu zvH;q*W=JePKDji=pxJ6ET#$5`m8-zlzI?h)iCNicsftwxCo%t~ z!a$N*>fTS%7$wHt3=7GNxdy5`ANyA(@*D?8GTj1OXvVwtNWKnC82t1{2c$;fPQM!9 zu*Ta~y>qZK-L|(5lr%HW=X}1ybW@eqy66H1*=wHnXN={LDfk3mZNH^wKxX6fI)f)YtT?UkAtKr zgs-4JRK2^kQgoL%nU%zT@f?QV6eGWrx}(`LN0E^cVOH_@`_OfTQr@x`5-Ln83Xygh zg@xg7qq=8F$s|oM%RfG!P{^G)W&m^g$8TY)mUwl;W9B* z=LIklpCj@d;4<{&N*{c0`Ksc!joG@MrljaNKQ6J&*_89kSD?%c10jbZjvB!yaaip{#p7}}Keq`c|6Ut4}`N^N93$x<6LL;6)L zL?(@m6FwV^faxx;b^K71;|7XKY|Nf% zWwYZ3MrljsjZDGCXg7}UW^VHJ)!qJ)pmvc+zV6DN)7L2Btscs}d&488DxvJi-dko}86jjG<5)$qooo(=`~5le``!QlegE(ExIM1A$Qj@B{eD01&wIU| z@4c3Tk0&O?geWHm-oK2EVyciII-K$)>qVsRliNS@|F)Phv=P&Ex6Toq@J``f%a^rG z-(T71tYD=A=Iv@226Ks7qnh^lz7(jBL>cH6+*9g@WQsk^^0r;6fRL-PvDkYbt*W=T ziv>OGztZWXse0M^e`v2UWSTeWUTO#d!+$L!IVdsJ$>yAa2J=FAcHlFXo`Xwiu2KPt z!6N5zRp0zh%+=K}^f9lHa%SSXe8!tqtQ!spn5%X#8kH(Vmw%@ZAeA4jOIxS(BY6@S zWAsV_H~MAs+;EextL|G=EUzuPIc%8mgBgPnm^Yi5#_C_}A-*&4bkfJU$o1mmu7)gU zjZDF_Z+m;nj0y52x{qvVS6DiKG0Ybqe0_-{cJtf9>z{Q;q_TdiJh+KhLbrxb%*`bUgT=*V-!JvV2rdQkd}Nn-gl za|VdbTNKU&dYec=m7)~BH*6Opc+!hRGZNt@=l4o%JxV=Sx9HE1z7G@s9hzM7e#q{> z4smG>mg??x1=)B*1x|LK^_axOIc)JoAG7w%Qo_>Vi~6{qvE`GQJto~$ZKqFp)A(zg zv>G1ZyH{U8PVPB z6?@UgtL|-@Rms-)E8(r5Phpo{C>Bn3jc}RkZcF5K{(&WHKHm!>(FipJfsoM4P0yDY>dJ7Hi)n$#HWAbWf#YBsFKXS;3k11SNg1 zZ`}!NvSyoAk~UXvq{(H3?%H0misYtfE6zm&DJ{X%JUl_rRdWv2x=>#2Z+(?OO{AO+Q z*SR=Ei)^SVsK#04X1e+29fhVb&VAKGfz=Wu<>W?+3**5haUpS(QYXubZP}=)y~UjT zhnN>)+{}KydtGR5n0d~CCCGvCTK&(g;j=NG*Q)<9xMiZ))KzCR=>E24cURqe_qOtx zCsEU=DdNY^YwP!TOhTy2>pmEi$<4}+ZB7=o-A^$T*ybZ?J#85KAzJBki8Wz65AR|f9^u`g%*MfXi%K_yh z^k%HAPbsHasHqL`#eoFujITmHc*OqehFj-17pL{|(re!TR1H1i@7%{_H;oUKefQ7M zvuCCiqPGH+mA>_ z(PG_Ji;gWcES8<6OV!fMI^)k|=zd-q^61QXE9oBsjX_-QK_)0;QL*x~yhJ8bZ)trt znr}EyWiWUxBOIrb#S&HWERIUbJ&Ht^smrRO!c3o-(%Z#~iBCuQW5{}<^fT%dLn+bP zfBv3o8Pco1;108d=U~Chd#uPxH?e(MNd{+NLOtVp?Ee7 zEd?HE2r25A!PUy$IpsQ15~2{ZAm~0gT)Na-azS6M?4^L!$ddoXJ3mg{8tKqEc!vlv zd_w7^c}J#e>vT#c1Ve-2$E0OH>OCIUlv|!uYtge78J*+d4(^N1As%?86A)3~i}kov z#qT7o?>+gptxP9WVLhAIfMzAoLu%HO#POZ4^C8{dppH#tIT`Ln{d`MjeYb8Jep_AQ zn^&z~zufzY?R|4iw?h1ARwm-gHj6Ho*Eg&$htv8BGj|@Jep@u66`0 zO?k4W#%&U6SB8^ZPt|V4t`rGd$4TnVh|L!0%$1}J`Ikt>7z)+yTIzz>u+(*994$y; z*P%}diWx3?Gg~d0@^ka_Y+V+sdKwE_yaA_91qZp_Q)+ZN0}6|0nAiKp04M7QmxNzI zvD;>$Y;LV?3d;O$(26($j!iI30Oe(82Q^OiDikuK7z?U~k_CNcVA51DRE%q2*pZ=C z90hDY#)qbcIM}0 zohWzqm%mmhPLbgO)%=O(m zH|Ay6Qq^Jq7WCR!2S&ZF<<( zhCeO0YGb_Gt4JOAkl>8L1269}#jsT5u??*E-}_GRxC@oaj1*b9Ca$27!aah~T0XP6 zl_PeEgF(XrEqnHHcfYeY+!$wiS|bDzWu*E%OJUv#Qd!+|AA^wqk$~a)5-D1?8?ky6 z8oQU&z^aXWseDqaCA#t~ntS??t7?eEa6ITFC>&F2Q9RS6FxhHP%c55pOqg*sjTRjL zR=V}5`_7jy>1-GOSW;NFZU0kR;v^>dvJPqt@?P#$)733&{tITT)j`MFF)Xy-48t_Z z5Y&feVd4VqvBFU8O@yR5>Iwwyjz1_dPeOlFmU?P56u;6lvY*i?3!fk^_7YiEawQJv z2?S(R`Ix7BZ!xje_VC>^%S~vmT>MV2F)KD3ATk#eMF_!eP3yH~U+eJm``jR3gA8W$ z`dEFj$mToo2$fcqLX_5$#VG}hN#d6+ zUu}8cQTMXBKV-fZr%{s?CFpOmk6<|S7h{k6(P9hM6+=>UPnP0(XVDo)x>|$Qpnwv| zH>?ZcnSo%j(F3j$or4O3S&uyOAV9BKxw8e^VS|;J)lC9hK*V6t9V!<=lIS#}l<;_| zd1UqNm6s}Gt${CYNd}(@s16)W%HZHTB_w*uieZnKa|j3VP;jwQ_rQm8E$~?es8i~5 z{> zdgS+FjP*cNcDkhBK@mpJNzwigwCp@xwLU@VMlOkf+7eYvqioC0 zKo!RXx-y}lg&L6N#-z4HP>)5kCe_gWPn4*sV)ds`H3RB(@biN{kveowp`K^;vA75# z;hRL6)_VdIjhVH}41|O1xx-ltmedr+C+ z-0@Jm*keJXW-6u-M8%Vxl($zCXs_9~#IXe&PHtA5DzSE!uej*$g8pnw6&)U)RaEw$ zWx+#bbyvA-kWq}g>Be|mYuB%ohfk*BB?r>`SDG6xcHpmtWvY6An+eIdG?aO=X{1^H zV!I~Lvpl;+5BsY0nvw&hfytG&aHxxLMcU@?COjySN^kGcNU|E9I4l`(@i23ASuvK7 zz1z4)nSyd!ZM4eHN4&CYGL>h|J!m-gZ0q%dST?u2UXUsp6Y4v8uj}Dmeyfl9Zqr2} zBl#Uv{#%l3-(Wd3*&M}geDt^PZ{1|Q_48bI2k}JTBHy&`XcY}z=S-_`)MRIZ^Tw}l zThAU=IG2~ARw$_`Uk<6h1urX4(O1iN9pQ8o|0@WZxXH zQj339HehfD>W^)a?XUBHG@(CkKJDxFlOG-pTY0Z2FNsub`S6?c{^REAg0oZKhv{Je zS*7ac=1O&n=1)QBeFZu~ukhm6s~tpCghN-H+%Scz;Hc#$NNDgF_0%jElH3hL4LoB} zf=cAa^JCE1bco>sr)H)$HPp{$K_?3GO!{-$&qbR`%Nhf;t|f9cTa~h>k8HBVYK~T+ zVzlmhk2kTddwEa-S2g=U>kb%zAY8b7NCioLW8>A;1n;JM)YK5$oT3^`^b{uzhL=oP zwsA`N*w=lx=;ke3Xn+)|lG2)kd^9OVZe%T^HMq`wYBSVj^6M`9*3^}eM-zHRF@K5$ zU=0?7xL0P@5~DisNmfk>u#q@0*=Vno<-fZm8FR6^(Xr~{T^C5{j!T)1b%iCbx!W(s zFB*umvaHJ&jh*2(V4Bf;XXI(7fOEBWGi%H&#JC~%+VQY;Z*?~DUfBAw_)J8Rw){-# zug7oON~~_DlYZ8B43A;G1&ed%)FOu@ghfS5>=;fR)+-tHy6j`9zc%w}q@Rf`v)SRq z6v=6|L1VUj&a&%b~oxOhYk{>Hb}r@E1;j61ErPShW+&@vE87h9T*1FuRt=nvo=4< zfBVWzw2m86*!u3;Q#_St`OqeWG5@RzL#)T?Y&z*yNTgZ1wqoZ-UqYsS(a17-4)-x^abLO>R3t5C7y`m=EHJ%< z9X}H(l;xkFKK=TmcSp+w!_tJ)vfhGO_=R)2Z+z$m^KrF#g{h^DBPqPvkzKY%^;jzD zPtQZI*FcHw)C``$%4{%77>Hs!MmtK&q`9B%LaFO?dv>j+ET%WXDq}gXUN$34D=b`H z`@9DX7;}d z4W*$saB9;iIr@wXJiLwpzm(OWJ)V5+`lO zmf)-oe+jxWL|R|Hi|KsD_meL;I7@I^FfZLysi@8lU#$^Z7q*zml5(i`Viu`pod#;M z#EffI+PR#|6TK&K*_&}!iByMqqK>M!)AYPqnld~#vsv&+L8fMy_8Q7waEHW|xZS-G zop*fSqJBx=70ALv*Ns3d<1I0vg567UTfq}s`yxw?HBr@Ue)aH}QUFN79X1`+HB;!a z5;ZP&XdC8xFmYHJ&EW-5`K-sRd*Eg_v9Rx?WQ>&K*~EAsdF%3nn9kF?ekuQi{>}h? zle!w{cQS!~IMknZnkQ>8yx?C2`dkMPjbE=s{l5vIX6<7xfG(|+y&s?}%r0yw2l|ds z5Blp1cNL%qTDzjBSI646!zQNJ*v6gcuI3)ZJ>-;NwXSI~1GQtVsmgmiy5U{3o%pWt z+`;5NQLS$J1^W8S<#wg=98x7}MRh`h@33?D^euJ=xfy)1#&kFIKw2HPt6y%VrZ)0= zv*{>Zw8cE%j-BS!DIZ-3dthVb2d|<5B?60?-h&w{7u9OY93OO3e4wU|!RziiB*xx> zeh>az2mWAyrn=R}Bow}vB43&hmda1+MsYPyT?5&AUi@j&dalNU0d?T`$|6Am;ID^a zRzn~t(QV5YG?Dm%^MYe5{uwRP=G@INum%axysc+rrwDB%cVMDzR3%mkkui6acE; z&3v%v-OsTv3ffja$`w=v${X?Gv)@bh{^%dkR63ZTrkv22;V(2m^srECaKY^|Em5-L zO`C0%p{BkY)&DpmbmYgW9)=&I@32^P&XSS!M0W2R%1?Z@ZqeTDn_I(RT)3dNr)6l! zO0R$aG8*1X{etxymyqanOU1Xd#fCXnN|Dy32a8HeEvv-`p?a^T1|Z7(mxFq2Myje} zdIHcrD0}-h00C?|Yq;y5Ojq==hV*A;J{x$sSx#4nOR*JRhq)&jbC0m%p0F9z* zf`}j~r!1=c-F(z(LCVLXnvQ!3$!C2^=mhuRE;+bLDZEY6^lCIu(eXEFP zg<=-ZpH(^0`x!$`-6XiikZck)nKL+3*zo$~*~$7ziFlVR-O$Yfohhzn?uqwzQQg#i zWO2wd^IVqHR<>pLC6cSi*~x}RQ;aWB0TVsSTe;W{nwm{e`|JEFw@)p%?yDq4CWeyXYzip~-#G+J>pzkw^U7+5^?o#9}F zhWoi(XO!(;NpAKhdDsbS&5l3t9{%VmIry_jR>WV}@|&$i_uyIramc&RWNWZow!|gt z)=YbTzS}U#zfZ!*{n`aXl9Oe0McbQyjF>u88h8Eb-nXJ;)s2hW^EKDHY}7G=Dl0*+ zzKb7<8{9#5n$OuDdAKT<3p)CcN=5!kH3$&woquoi)Zi?fIFhK7e}9lBbq+Lpu^vQhM{24Zh%cef)^CJz_@{P z&^y_FWdJ%;Mc}(TD2afyeY9Ho)JW+jNm^eCnH#B>gSNm|cKJ!#2E8UB5;t2xKgq%e zmqxH&90DZjyc1u1e!ej)?viMRCV3MeS){pusY(~8bGfH9zc5Vfpi(Rsv|oPs`hA$A z_fQWfv!qL0Ra=q`PIIcnR`;(V=3*_l08_V5Q&IMM+j+n0lHB@w;>D18pV_xqpAYe; zt>54Nd-K_FyW7lyg&u>Nqw=6@YvFQ6oO0HD`e)ukSBXo3 z9|r_8AM-+I%9vzi;ngBcuLZv1B)_Yghu=cw2vzF*DuJ~Gd#PpUe%#9BO95o`88Z&2 zR&#w(seYg7T@apU)GevOinXrx?!brAHwWcE`?9fVcB_7dpEGjQPCG_lw^&fp;QYl$ z(CXERg$2C~ITcX|(O=s@jz)Z`^OikyHP%2)hIMUiEo!M1TngnFQL+NFdSjFibt0n~ z%yKg1dI$8>&w*B`d%~=zN2_W+%p5xW!ImSf8Il<}pB;xjwo{-jrH2a$aB|e1i?pSP z3?evm7JdRd5@9(8ogL7|tha_SKYnjuEwr#6T>k;fttn+DY8E{WZ?T_(%~3T{Ral2b zLS$9e+Lq^hb5^h?D$8j!M5>oVv2N4h`~J3ci;=8gE4(nDZS}Xu<{e&FF$K_J5qqqJ zl(FI4EFt1^$4k)l6IRNTwm8Tr)-x$daiuf-=#SC5?tO$zB z%3|Cz_yRT`){FF7KDQ1%H%OLnyNfs3+8h93?U3C&Lhk*a-#zOaKJBwz@TTf3SJ_yR zr${pJwEXf|`ul^ssu?P&jbE0{fWTv`hSoiD&KsycwWxLbU8wRi6)Dms7z%g2mQ*0o)KYw5 z@SU4$PSBnnzV77cYFM^-9(80hsHmNhQTgLO6LuM1{5U>25XTP?eA|;!0 zHcwI971ujgerm{XtWM<_F3IXv4lP{1+mAfzM^7;`mpfZV2zGntV9gJjr`~Qbt>KG3 z#<1o3G3`ZY!EAMq(U6_AlO;~v*5l{Ws_F8E`VigTwK-@msY{Y$IofZcG^T(H^ zGovL_?*<{0z{+z#uwOdgxHkP5NvAneylQTu8}*w&M8n6(tiRq`*bc{JF5jX5=H}#B8=F*xTBzE@kySotAoe4c$ zEh6`>drf`zzByJTm#IfuPm#Omx`*$1wo17HdrsTWA&~*y6pBJ8}au4X={rCCX)#&*WVe`K9Nk9r#f zTQlzl3}-RKY0QOcD1l2f5@jU9sf=InJtpSq5Eyq+;NCgCzphy+2u<~!t3JNZWs>zN zLGU!OP-Upg=VSWl9F*$?beS;Hg3xoD1`-mnA`M5l#SZdS+%F$1`}Xi4xxNa`TEgMH z28>!p-p>6nu3kXHvS?#mPU;zw%R&PxxL7d7-5N!!E}&T&hY{g0s+}ySf`)$dfFu4$ z%S%>5Avpx<>_rKZJ0K7bB;T(9VYvp=wK+h-^s^;~35LyEZh+dzPepQ#v#4Ddgu@`t ziGI;~aOv`8LtAE2*ucnmPNtqx2_%>4&Kc1{HHV`_Ephy7f`yZ1$DII^p*NS#@~-uD zXn4d)NvvxEL;Otev5M~%U$bZQYS~&O1by^;oS^=DPLnP|w&3B$_~2XTqd#mm<8t$i zz65W1yZvzSQZUx5yAjJ!?Y_LU+H3#mW;d!7pg(k(+O!MdS84~BBEj%=dKf}7ZX_03N z)zt#0Yd=N>pV`{z$#vAZTbKlNX9lrtOijG5_QHiH!{|h|&GpAUMY&6Y>(1r_;+xJm zp^{ZRN1|jP+mK;VoO8P6H8pwt)pEJv*tobi#W4$MpD60lpM<*^cLraEy|^LkWZfYy zT0F2ZmOKCKS?QvRN=@{&sG(8{)4IJB%q%RCHtVmWx|AYoRi4IG)OH)j37*&#%$^^~ zl{6AQ#k#rLENt`j36mAm*w5NCd#D0$?PET6P>UyoHmUYSRL5z&kQGDoywH%n#kGdX z($VIWgnK>?czy8JhCfSs*1qswh9TccwHXQKO7gEV4Vc`69H315(7ey7X{$j)LZfKTWvpysRuKgu?E5ko!xP7iO6L5Dlp$S?IeDDXLjUT}>6hx;tCH%G7}l zIv+QDasA&sQ7JJ?<#}fyd~yf=Pcomb<|zRe+W~f}ejaa6oLrr^ZX#i>bF~$vReT(Y zZ5A2PeS&t3Hc>(Y!>`wxv}*r$C5cJCUb`FAwc9i$kH)MP2$O@WB)Th;%UMx!>s?`N zZF%>5(^ng2RO(X?C#&NrJ{z|BEE{GT-eNSH6*$K@mOK>L_0~OO{A-0ql1U2B8#Yi8 zd7V3#ULGZfvR^il*6TfQOYV8GuK1i6)eP74ISTrDCe*?C^5j(U9}qFNW%Y%(z6OGz zQq~dPGiQ1yy^x3rgn@O?^^yppd~j80IAjA;dlNFoK}ze4bSdPd*cJSeAl2WF4i3Yh zXAJ`N9e6WP@i>O$&B!xM!&U0#&oKk{EfCz76c?$#I!&bBkF5!(8m;uWmhiSE%@h+f z5`EGE&d=%Z1+A2z);$CC7T~)bs$h09Q5DY*fWP>pAslwJ+7x zWQj3D!#CY?Wue!8F=UHz=or6ni?|n|C#$KebuPNdSVw$ylER*#NT|=(*nOnuO;99J z-fLqtP}r;Fqy7C5iG;f!!|hZ9v|Tx73r{YL;LfnsqK4af)0vI_W#Z!gyo5sGea@4t z&|z4CJMK|Qdop8WK;+h)#ZHww#P+>KH|a6b#R{Bf6+fDPMe?at@Zwp)14JqpH*gGu zrhu1Kt_|f3SOujp?g|kHdM2ZR-sg{mJ&?BQhjN8I_#-&YH4Tuhh19@kjlbeR)p0Ke z^}!+bf$(_zqxS5Gu&`Qm@gPwx#m?UT&&X2ORv1clj(|tm^x3^$1Gls>1X>jr_N*vc znK%RdwTR-Z1cLxBs1+E#(&98(n|!IApDrvJBO-N=SJyC+@TTwdDI!7l80vL_s8Xij zVZ~{~TXanU3U?+qPR`AJS<9;C1C2*{t}6x!uD8q0afSA;GI;_{W;}ylv4bYV{h$^N$$C?;WN<%T z(gLcphh_&v4)h6zgex6{olyII*(Ew2zGopRUjp6-*sMecJ&DPm344*t10`4J3m26M zs8dRiHyk;gb20Q#w5TJ1em0CK2@b+&}!SA=@WGi`W@xXaU^0&7cBjNl1w zXl@Pyv7p^tmYw(6O}JnPB51NUPx$}`Ji8-E&gnI!H}s4nL_9xR^1!7(p-OnCpWg9~ z{+G0Zm2(glh1~OEU~A63%iSrLq0n7p6{i%^ea9nNu_3j+=W?)kgP|bb*N9VKNx}XxDO*4Z@u;z=?y#=}^pmdJKGkO4L5(;vR0ND$u zsAw#S+)?xpTdD$eEWy3FArU9$I!mXu3#s8CUCZe+5+npMYa^iyX*tPp=gbRNfT~3D z2tNXLK240M^O4PM70uUgAh1vmVU6z`cmyxSeQ+h z-DGFl+#|M)<1-cgF!*KrC@>dPpld=l@YoBFO-L}Ghl{{WMsrQetkmK z_@u1iAU|^Q)a*M5WLH5aH3A{I(o})uY7i7jngXEwl-6ZMrZ0Tn7qy{4hp~*I1sM_O zQiEXI6hqqFW{@F6O3}J&EW?Oo+3(V412~Mc;}HKt{b5NPD*;sym@kJsz&sd1h?^MO z7VTit>kfQ7*Hvmi!V^m>Pdmt4dK{HOk&18_065n@ZM^^VLIQ5+CtwDb(b(n{nsNYN zotRzcp;r|fUwt2U2z37yq3#OiMkM3|>ySDca-l&8vpCigGg7@dRXq$c^hPI(?N|CB2%B6F zD(r&JT~PuaSBTpL+zi_tP*=`@=&Ba_3Xx2e?81WnbOf3w0`0t~^dRG=CWqTbFn~&G zSrU^PhY(>!V^GDDLn>LQ`=N`U2MJg${0h|RgOTbI92prIPQkStSEv#q5ir7p!ddXl z>e^NiW31WE!Z88TCej0Lk`%ilLWs{|B7#GGgDNy+^=<>xUeX>Zji6hi*ZDdqA}3O6` zB2giu-oA#;Rq^gpNW~Rtifw<`T<>V0N;x<>js#w8@dHjI5A6SeY*LcT07XCqPYxi_ zvI3U_1#AF&3K5-59S4?a(gHvTxUvSSv0J)XJG7ZpNuVnsNX`hNK_EFPIZ-uYCbh1k z8wUp`0zHc$EVXvdmSgk2wB!Xj48<6JvR3$mCTK`8pdr1L3uDTH1^;^)WH&PHIIdR+W&DJ8EgVdY)MF?yoVY>DaxE6z=q!< z226A^(dJC&K-X{N5(x~LLxiD+1vE#3{VR@3&kw^7V8bq)v zq#a&*v3;6-vrsoQYQY5ay<`iv7wvw}m~2Z=pe_4zYwJ)Z7L1$IWak+wvFThg{sY9z ze(*-(?Cf01(PaKhw-L5dS!(-qUkxV{4M9&p#$3UWhnIl40td7nB;tlyBB*d6RK2Ez z{0W3H7;OwA!vY{ov`VwwJ}hH76k0<2C<~aaG@fX%nE%YgPC;1WcS}V=fSvt!_9>|I z&+TV)p4(0l$WFQg!Qu`;67*(_pfL;Aol`O22=+H-5tcd=oEtGi&h4kDCZfF%`fmWI z03A30`_-Ecjf#r$sLGOdg?Yqn;2@|Efdka33Z5x^_5NEbu?_bVvg)!9@J4g<@a!Id z!Rm{4gd0;yVhboGJ39f~1i<<(P=|wpiU(vbY9#@XqbxDx)->J--6!v2`Sgmd%dK{< zvn9~+6tjkwMc^hWel(~KSc#jhL*QU#wV+xba)@fM%S$M+PpR?SX?oRf6g)l!yvf%`oCek>M9DGYWMb7T$)$kQuo*H}Rx-Kj7P_Yi=q6=!G!t7DY$K4h+bTce?*lY+PZC`DVq^Idb%fXu0@Pc z1Ix%{V|7kVUq9M|^!zH7KPVt@pFLrJF#0|4ktCrJS(AgX zztavJ6G2320U|oY483M?1LrWT;M5#q7CtYz_9N8K53x+h!Vqwc_=w9hOo$|czL10h~u=IKhQ^$TuE_x6Nqt@FfCx zvzAfFmKiRGEsCz@0KKQ>VN2Lcum2ZH@1XuCT4HvI)XN@Q0NCxJPlQ1tW=!x5m;>mp#71qoeOq$op-HH&WS zd}y2uaPES-{^(>Y_T^${r;=Fw>b6B%LFCFgXj88T;{#ZC*%aj%iZdb!PGWys1h-;{46o5%4877dv97Z0J7vLh544|KMlMD0Y+}(rb z~WKCvBU7eHZRRHwU2>D!W4JUR7!byBlB3nZJ5DW*tn?2!t=!i=qUq z=pcFmy@=8}#DvJ1v8<3izVOEf^2=f?Ka+d zV`G?rVKD<+iI&wyGD!&iPNSOVu?IrYL>Wv0JS>ced&N_^?ft2GN`fIQbO8iIhJa7U zW>gD>DFtlaoq&eA&MC;8`y8xpW~Ox%$x`7ifqccK%N~DnNV|;sRICWl(2Qdof=mS& zo#k4}6~9T15i)Kt&6iy#uMuet9_?J?@;x=s<}3qI52e;C65zAKx@@~Lbx;cr7P0_n zQ3Npw4SvV_60h#S%GFW#6&-9^B$ptVWMDM{Z*rCiA!$LQLmp=QY9v!fOF5GN21$BI`UX1jvX%1v4w7%etTmOL(Q<(W(;c26E$< zhF}|~L)*_y*Q1oj&L7_a!$^}B0P1BoM}+wR?Rdoq-mF49)Y}EYoSt_a88C>5dz#Q@ zK}`*GzJS}Gp46h4{1^a!)6p$Xb`O!_%4P#KokBR@M--Oca}s0w4U$x9Nj|{3)PeL{ za^nJ647Ags2o&&Yugbz<_f1U<#%Jr2ug38x7W*1%4QTL{W7EK|08}psR)cNE2pSr) z3ZW6JS*-g#*joMkR)GjTf@n2p)uPcBh#%^?Jl#1pW#G_o(jJICnrX`Mw~t_i?t?$7 z&^Lv>HC5dsMI2AGY^|Kb9_^||G)RQx#Kmr7Vp2t*r0oH(zB=n#%>>o&w5fyiynz5e zv$g*Mc@@3X7CO1fTgA0Fi93tJ%~F^w0rA41A7bi&zm*ste;o3-bO@6LWa*@qsofXk zF#R6@z^qQxRaRD>RE`B>lGZr3cYF0yDne>z1T<9*P7Kl##s|mGMfjHok#B5_?-#(! z22jHB7VYloY9{|9!?#im7!d&aUWk0T0YtU^<@}lM>&~}2{~g-?cSu^qiDo6h(@J-F z%oKGCmcvw@T<)ceGkP9U+fO@Q0P)*bD3EdFwb_`!ii5>k+?$iEzP!yh!${xT@5_`6 z3p|_~{M(FSdXDTwa*!VW z?Qsfukr+l0{cdbpyZs~o%XAJ{o!CL;l* z2ms@a6UZY$D@CTpC2Msj?K>$2!AUu&t;o1P?nNLWLd5a-!}eKt{x>qg(Bng9#j>;0 z5@E5K1Kt!ltuXQjrJWeEk8D4fjPG`Vl!;8ulV7d~oQ+~nz`at7h_sk)+ZkVLls^Il zPzXE|B}DxE5P=?X{(yPfaDO{XDi*S}@r({L8F)8dXA;t~0Dg>F@&xkx0qpjof0|@$dGL?sGzeT>n|WP_{B+w{2;L)89FIQ!@~KV zC6l$m^$P*wz8gfRCJ^!2Lq@9(`1(6MEDUBR@n;4~__Q(PcOz9iBrAs#0(hUH#u<=k zpdL3hRaMn2cVMhQta(ro% zhabNQM776%ly3h--j#XZ+)G_lUB&K=D5K>)E_XN{3zuDxox>Sp>d4G zM?c%98NsxoZEGY=;(+bqWy24_tAnuAIS8<7(ii__0si!5N=<)KCP2H{wWz;+sM5_e zkl8`35!osi?-^1Jjuo4OsEtPx_xuW%$={lK;D`mF~;?fBt_M zPJ+0Dldvwjfxrks5k&Wm#sWLX>wN-D8;S?PFtW2FS}ua9)2;8{SwhBR;9AB*+{pb$ z_6?0kcgRn(tQYhNI*1WyVfaAVS^Uq?68xS=Yv&H{q=#(UX=sR{oeqZ*Q<2^8XMv_2 zol)p!m6wPEiut~?zgH;@T)09A&{JZEAUm+*hxlg6Bg`9-jCu2hlDr?{2*WV~BvKd( zNFy+6IRHGIi@L?u?`v)1#-RFwCQs!t!n)Qp`HeypxQgk=7yvbuJn90LqKr%+v&aS6 zVCMI=Ra318HoHoyG)xF_*Ex&bmw&U~R;+goV4SCW_&4W5u=DJ`7US)mR4fI}M@lsT^Yx!Fj3PU8jete1 zy{jS=re^zt%nMDBGJ$;_O&&w!TJW#9%3KMoaGC6&Zev<@WBWMb84;7ey}cbJGln5G zLWXQY^6u;mB&nFt!%=u^4>c?q2u5L=6nfrs7G@Z^WKghoQF$22Mxj<#59uR%bjpC_ z2}PnX$*ybzCW!J}5Y-Sl&?r-QfwTiL^tC}~N1wR>qZ-871q5Ocm@R5*|C%m>nBJBL zSXN{N!Ovqs9jM@?ZXd4kdhc%0CO{(E*u#R5mGMaJ0}m4oL@*5S1MT@I5fU zhM|BH2)`17*$^Fd`VeEZK}Mq#cF2p%?%S{D+X%Z)51gwJz>F#$v=@Vn6C~FRqTvI> zSqo68&xQ$ha0#nQVeX*`KpJ67MRx$Ig$e(BZGsYd&P!-wGkp1S=kIl*D+iV*s@36G zfzay(2F)G}zn#EFke@k%Ji^(WgM>>h+D*YUgP-^+JRG&eYBhzC4V9@sdH;xj+aMjd z;N&D);{aMJXsa@Z5=7BY3phA&fWM={7^EGatwJs&0FpcS)8tujFoR$h+$jS0zP#3k zWRvCSEP}g3MYb;+C5nduvS>hdlhJQsr%C2&+PVELePPQ*X|Sb=r7bf;)kq@myi{&d8s{V_Nlr^bMzgM%DXKq;P11-!aeH2K|xtK zx9t;&{cQ6ba>M*O|2wn`|rPzvhdjWsuKZY@4#Ae%F8CI0eG}Xc{er9JzNPYK-r zCjIx5CymDEKbv2&gnU`!=g+Ba+6)W~kSTh4W9-kTj~|;u6}I@$@2w{K<;QOod>Cnx z%Z7=^7FMr{z;!|LbDk)$jLW{NJw=eQ>|=?_W>Xd>e=FgQ(1QyczTh z-_YZ~r-`8-l^_1GWB<`l!uxk7ufg{p456?DNZx-9A43oL&ksNH0sZUt*Ei1Jqwe5m zvG0ExW26oKH6#o@M0o#yeE9YM#bNzJUW8yk;j-IsdwO~{4GauCpx(K8q(@B73ybc? zNSk~*V0>x`%=H;q%8U5i+m8~j?l=_ihQ?-`zA>FJ4AJxw7A- z;SnENu4BZ`NJ&Y_zx-&d3X_l?CI0*VRHYZ*(<3ingYRrRfsP-2p^u2p$@IH2rwMlX z3yeXc-o(QH_$+wIG$<6Nfr)GOEj?hf5Pn#2I4YE?vvoQG=!1` z)Xwz*B$lp~Q%}D#Mtsb~M4_ojlDFVaNzPEH!Cq z-~NGtEXH+6WtYf(CO@70m*s;$a%rNZuvLeivuJj2hg1`8x(u7~sY>_E7| zh)-b^IJ#dc*hM-HDGezGd|ujBt~1fH8sHb7cIle?PYK1e_yl zh)ja1{(V8DSsd)0{{0xhko~878BM~VTUeM^UH_~qaF|k6CO*zfeBh)!gNVCA%jYgT z`>~_g6Q4e+!3lq5$wZyW+sx5<>9f(`tSwNj+Sk4?*K!$reOu7LeVRMFK)YXGzq450 zPgM2^_m?3pA4As07Cu#7msqM8rLGr`O&Nlm>8LOi=NW?<Hb5h+@PcBlTydi#jo*HJh*b3TSW&BrWd!SEm#mO zJM4(&OT0;=KYGQJsR!`m9qTC(8F9P5cNh!Bw8q^XpY%6GN|ON1H}`C0`(0z$E$xHwpRbq3?`bNTn%)GzF?01YbKGB8 z=Zf?QJmu60dM!bISfIK4_J5PDgH5eIm<6qC$fv!7wAX0~2?NMc6RWq@rIsc->f282 zPW><*JJWa)J9Wi0K5o-{K|6z3c;I0B)o!Ye%nagWTwL~7mGTh$IaW3mSBaIU_JT}@ zeV6_rZFCk{yekt3Q14EDF?pm{rYbfzd4;CE!$iI^Hc9#3IipAF#iZ&wl@xnyCeJYZ~&2xp3Z7TXv+D- zZ1jaAyq~piN|hWHUhSY_S+y@u@0&k9b|rcF96mL)FT{MDh2{s%u#p@!-1nEfIDMJ? zVcIlQ7o?%kl@9cVFM}5ugDxC91lvtkE&=8Vm}Tj|zxfi}*(8{n)gxhuN!qBCSp34X zbavuRcudN>#dCK@9loX=#bC1jl9#!xPW!oIK!f^$&jG%LlU8oNIfE7Jx)K|04&IgS z)k@jxpD8&y*)M(NGTD`@-}FFQocpF}R+j7@*>WXG?MgIUT^>~J=6kB=S@Xz6!7V|aDg#j1Z8iy1@;wtZG+nf##} z>gf_u9ibJYf6&+Q4S(rV*6I`ZOaPu}d*}M36f=t%%w`VxVT>qejwR(u^kryeB)fzV zI@d0F7m$1fSw+$+?ftpA&nW%rURFLaX~-gAHXLT@-uub6glZ~r#QzM@{L`$i(blZm z9$eNn8u^ad@PI{OB5)- zl&nQjbjU>D{5NywFqzZbyqbA!=EYZ9+I;%4s!drv4@Ij}Q;D(|OHS@{*Hx5R?sC%~ zrL}5Fk+N*a{uH(>-}t#}n1!Oqt^V>}H&0D`cCk;8NTL>YFoE)^iYA_`I`?{jD|2W1 z9)6nmxKF`D6= z?2+{hjp|^BrPS4ez>l@ps97SEtKr7Q|(O}F)X zx(kzcMw9Q!zkC@IhkG+nk4sQl7Y%@@G5&43)KL7#pHLTef~J78XU;S%9=!{$7^~a2 zT|qF@3tG%x#l^)UHX#{r;k_o~Pb>@SsSPzJ&dxY+}p1ZGRl@z;k>{FOo_F?2(2ZXz<&7N0#SEx(Pidfoj1@?PI)vIgkXG&GQ zzn*cpPM5YmhH$P6j~%$}QOfnTUXjMbxszUR87CY*X2;0Kw-Qjjxm7`U?SuDx^~ZTt zxiSL@RVC4qR{>FX`*N7TBXK4EnG`FT(|JoinMk8Yr5S~BS#aw3Ty2o^E&RK~LZ(sq zNtU79A?pAu!f`3e(!1*Ke-K8!uI{jQYs)w;#e>LDrapEnslU7FYV(ynPQ&`OWPTQ9yub&}?EBAwtqO&TZVOh@`J?Ks#79Tu{$4Gi`V3OCyWy z;`zSagfAGN;1J3!&|=yf!jxGv0!m#(36e%JB}gTs=Fp2BTYy&~PXLg-wN< zFfX)O^8lN;`I^g9OlEh6^E+F%fgyDt>+cGqKj=lzvrn0h(w#2Nh>L%5i{ZmVV)si8;xQ)>OW&0 zB4VOxOUJC5Xdk(EeToqJB&v~bGxxH;g-+2kO6h?=?aX`x-7mYuK0CnJLU54Q-e)t}T$sDky(tNV=#=s`E*b^_;gZ=_6N*Sp zi4<@g=UTP!oKT1U#vJ(v(ZX%4o%5du7ss5=@z*|}?v?Fb@F7)Ny2#EIT&!y(tkAlY zD@Wt%nOwU?1u@Q_2KqAe2S)Wy3vs@m=)DmB>UAUUZ1#M>pZTR&sfdx&Gh(yUzbyXD;`4ZqtCYQa)9%XQ%bZSg}+Ku=|;RxTp{e9!)g%2OPMa*7PkIq|-4vtW@ zk%emx|3+%S92({Rz>@yS`#4}Pf37`XDzv^9Vlx~O9IPxZgW|!)o2SlI5|1da_laAE zMz#M4idmJb_>fWYaNq`In8vFAH@{-}S7G(F(MOBUTu)Hwnax!N)T%~m*^1I!O@t=a zh*%0Kq#RwB?vwsvm8B7s3*?;cCIRvnQ!KVdgNdFK=b|xZ-z%LVHM8_AGPY7mSKYd` z<-WXjO#N+xZT7aRBt^>r+&c6(ciG39eaxTq*{jd^ceaJdt10~#HHJUdyFZu6!2>kDVw;gQ=}cRLc)vW zhn$uRsBC(tM^ZDsO^v6QDoa>2P~zm!6I^)&uVLE)izsc$mG-y*HYTZcAMuEdFABW= zCs0;tng(%?Jv_c!a9+B=p>SEVui^XY`*HHiFF>RcD;Rqb^2O#@ip*2$%U61;jLqV- zo*l5znCN!>fsc2!ot3ZL{C1I2^_7m@Nl^}RU0ivvCNAPhceFEb!0sMdA-rLXz9WtG z^0HW)IKS7w`FW;qQ-fT&gutbs{-qy0J9_D7ne~}!YwaHtUWt#6)%2u(`tmklANUj| znKzSg6j%p*S@gCL`3Q;KX{d8qgoFl{^VzoJ0ru&h>P#9)gRYxud)eqDuDp0kswdy&XkFYp;+{1(q}oX&sKpgKm#)eu zCz?qPnzQv+sx!B6`I4+qP*Pc`@zN$`?>GgQ6)Vn$Z=q)l2o{uSqSdR>c}quU zDU(&_=-qt!Ofy3=PG|O?x&L4XBKCBINVMFTmuKnCb@5tW|Kr{sOsDzNmg-w0u!Rg= zr9s_S@$qHx3LKdN_Oh)zn|{|8`7vZUOiyksE-iCnB1L)n zLpIqiU$wwKqRAAX!8|RHU4K}D(e}Goc27)Q{J?r7k#u@cYsYyBTebltM(*tw%a)z8 z&N|rPjJd+t;!D{%pWkh7>oK^x_M0iMPg`#kuJ>M!tUFXwbtG@8V95_{f^K##x_f6D zf5s{;TL*_b*}-!2GBbr7I>nhG{}PE6H!a$=C)b`JPWT>AkIH$Lot@q1JX6FCVmYX=PbZlUpEk=ozYRq2QXS!4qT#JGo7i0z`MYc5eXK0YL zd6&|V(qc;!%6R>|_Ue0Kf-e)6Mj7JHEv_zx`1!4yt~0Q4*c|pa$13Suxe@K@@6YGv zH8pp^b#{Wyb)_!1EXzw>dF$yAJ^G`!4RNJs?+SSZMZBkot93VHX=zzFe&rL>WCEd_ zjp1AFboMo-;#O0W>8V;vD+XrN#??18g|pv68{}ApV+U~qw>*-`Fz?hqwpEzY*WWKWYzRa>o%%gIhPk3R>!gzvOWq_k=SaQkq<+af2 z!2$*Cz?iIe2T}YIkQQ0Lc#fUxG(G7iBAzB7(7S$y<0(hp0a1%J;ovb!#RX^8 z39{Jk7+XHIHN9M)>laXj{nHAxfpBWDGVsT;fx9v>sfgmXhZM9Ilx78@IX}?x%)mlFB;j|M@)FRPlva@m7v$IA_SjZ`aVSUcG?8qrQi*<2Iul1eUHy)uE zy*RGYCP`LnrJ;AqZe_7ks}C00Ql!71)wY;kDDyxy8@$q6|9G2VJ20rCuA=h}N(k8p z6P;IRKQ`jzpsZ+7(Q}@BN;S`KHmyj~+n!tKhIVY$%P*5p*hn?5CL??CxoEIb7&~1S z-@jg~rL0TrJGr$vGl3UM4~!sKvBnyRhMo+)Bz#W%!2tR6r#H8*y`hoYI_j__=YbPq z+p#%{!Yct^?KJ%!)E&qRyFd$=c>IC{a-uFELt1%8&kr1pBA6yxSPPP2bq&;7@Uj@Fx)b(74@n;5RX_5BUWIP>3$-JssGMu!L8?kbu!~iiWbNY_ur61@7Hwc zU)`db-Kbi;|4NH|?%juqJ+%eKH%wH>^t5XwRR&&Jg9WIU{#&U>r&0@_SQ914u;yvc0A&6HBo z(n?^7*7@hGz}-)X@# z&7Ca?u*@~-d=b$uJYcC2*4=M6X;iwhJ;dNTKAxMrh#xjA_}+61P02p8eBgFrUdb%f z!Rfa9Fq^i`uDX3$nOe{GMdIt6r*H0|S;Kv(mM<=`1`IhObm^#{ii(b*lZlOqUKLqX z?&f??(YKUliW*^Up4^gbwoQ)?PMp25ZGKrwwRS>w@oaX*jv) zDTxCtx8vHicZd{z1M+UO448V+@}0yYuRQ6p8H_K05a~Njn5e~j%T-(wpZU7+ z=M2-(Nj@!GoHW)J-N~CTc{9g9Q)6aj3DBHJIl(*m*wnrBNZtaNj3d`2$Q8fOn`#+P zP)-6`or>XnBr#e@m!dW3^kZo!_dcV)a&CKSw$og9)YbOnvKyIf`U{8REG}ybSWsvX z#f)_9BQn-C&P0Y-sF4vcYp12%)*2@tm6(azPsxUGPcT6mbeY43~uL*LgXy@)xcozm`s&C{2p*R9kxU5!;)aap2Q z(;3w1D$uq>)3hs((zDwH>MM@lIamFoM$iwM%W>zR6?q8wZ#RHW*9DBbREY2j(63BR zxx?xjv8)u`ZWe;UU~GVzQ3-}Pp)rBehoJEc$w(NRV4a)UoNne#S2E zv0|=CS$;kSlW%(Y`c97P%QyEPjS>Cb7WdK%MXOA&Sxl$sO2Vw!>gUPM(e+lC8vHsv z7N6O=lO)lzK^%Q+GUn{n7tdU!qneP5s(I6pm0@%0^!IQv6tkny)!Hj0QEyFgHY~;i z2Z>$H^Fjz_SsE-Ej;$Kb z;8!dIJs7wFm#$ye2C54d6dfr*nwAz3DRk4ShJz<)fhj^b_NXxg*L^DKYfxuX!*P|V*>dcrK$ETw>IE=rKsdnr&6Kb%Tw#vN!{4#BSnxkrEN+i1at7}?LmKJt8e6WBTO3cm=T$0U z6EnwKt>-a8Il`LF;;G*x|3z`Zcq!jm5j#r~cU3Fb&<9B5$xoj?wVyAv!D+!s+FhVI zNn*{i%UT*EQ3-$^W9K{Zqd-q_a0PlFa9NV!IhUB2#6kDGxUg^+SOeg1kTBlNs+Trl z2Ftn>Bq6c`pD{w3e9xfPG0>K8sxe{&_^n=v`w0jF^3G_=d)Vu))a$-Mx z4Z_or{?-{$^P>ZJPBEv)P2eYemSg4MB+>51#W7x%Q*nrm-K~O+%M7`&hE% ziVk{9Nvj1oeX6rm+<22*cDPb-(vPiLb)W}Z8}~r?dN|fvz$Uhp0>GTM$YOoVB5}fa z!UJOC#xN!|(WyLf<~hc_P|{fI>An!mfMbEDXY#%%aU0j7?#h))`_4RB^w5ne+Jzp= zG5Sv*aiLN|G$|n`o+sNIz6Zg+WESfs;o4eYvTtSWf=Y4BV?<@m0=j*k#Y^wq?@sND3}*Tyf0@55zOT$l@a za-Jlr^Fy;+bAz)ud5CYMW#N_$ZAYUO-)jKQGesRgj4u$(`fktT&`&CarCSMu=&u4qyV@u#wI2#tgJGyZ7r^@Iy>^KEiEpB`AB6V(#r$Jbq@&0n4~~0 zrVIvegjTO@WsvIqH8{97OwYg7BHGl@si&hcm9D9;K)&VBaHz7#9Ydka#a1PeVsei- zS!KTH$gy?Zndn~jW|m(mKaM1hgl@Rdk0@TqjnVdMB3jRrsLX9d`#P?s-oSSV=>&CU zN&OY`_Np2=hmPJDz7p-XF8fvWSm&zABSTF$SC3j}8e;8yKP!4{ zhNUg8vIzGq)x33xAdK@hT)vdCsD+BUbx-e6H^;oH_TRsbvyjK)TLoL>8awKW-&|%Dz^U}4dmZGN8q9m4 zhdO-^TApwoxt2finXm7_l>l)3&_|li&2<=AkG>*7etwPMi*k9XnUEQP4@&mPhye~1 z3duy6Vt80>SP*8;oMb{tp1;uj*~r8M4`z}*P&O6<;D{(;zUQ&k>*(kpooYBK+XDC^ z;MEj)@;_~yF%tBg2HtRNauR5BB#^EVG?<`|l)b(*F%9}|M1P%m?$$GYLD{(XcIc~< z-M|&7yH{B-S96$cz(wPFKs_tFK&FnivpO}w3a6rcz`64!t6M~7+2zYu@c?tPWap%k zB^>ll$6Z}Mf_pGVnY@^#oWirQl+9`_S|-CxNl9%vd$%#T_xjlhCOm4UJWwg5z-~ z*e3~;+Sat{x4pAPqJ`z4i;!G4v$>~oz0rwaX1=wOWYtc}EgLzseK#$oskLj41}C$w z=_2UdTV_4+MBAtFV?CyUS97AXS&dddtE6e(C@m$(=t|Z@I#)MFEsiMZj&5>KHH;@c z_MNk~Pmwi?GshBFQ+`kkf%?<}#UsoWt(#a~(rIHH` z>hL71yZNtg$qqLSx-*#?v0h+S03ze|P>jWKT8&XPKPD!Lt*zkL<;JS;GBP@Ee}dIe z)67hRjNt8X(cIZld@{-1OqSCZ1XyL5mDyF+hM3~3sx0WJv_{>w^~Uud79BpCxvb^Z z`Uw-)>!gh-B0JnpudWy0Nv%|f|8dv)<#X)9_wV-P#U-UBsp@{?5$)B1&zw0$j|yk! z+~LU3FwPH0h4ecvhs5ZY4Oywq=d@(+Q1H7?AR%R{49 zz3-yU#&LR`ZH)N0*$o?qq5AyBO%9J>q00}>sW>hY=1;?T2?Ia?!2Keb;=~C# zDg7t!&)(vR6$17bl#|O~JZ`q|wU!8EO^EjDSnKJ~CXs?e84=>h1zQtl#VuMaIB0 z(hloe2hvIsZ%e(^Y-ngz_q_XhQ%LyassCK!SY14md(_X5$7?E3(2n>0_*IgU$!7f` zR$!3?>N$t@JuwFgi;3x$-rQR0N!<2Iv7qLAGG^d1a!CtNUki1JU%cQOK`_hQ=1Lfw zn$Y!qL}KpY@y?yUelc4SYSXto4jGO@g*$iJ;CRPEv-b0H;hgW!ts1gH z&j<;eNLh;PteINq2eooHZ$3sCuCSD(%fctK*iVshOPnfQk05?9ElZcYer&2sT31^l zJbGID_2YMj*4JIN^sTq$MCXLwJw88>Sp^b_I&7`D++NJEDkJ;YV}_kkNeECX*4n7T zrBNcT-;Q5?Re|Q+LZzTu$*t#%(SotbtKf)l8o8316Ev$pf-0iy5>lDXn#p?lcxM)@Zfw%iON1g&t zG8d?hc$h^hHa1z8-$O-2Nly=Twuw3D5GdtmfRxbzvW#UQc$yRN*yJh@aUucU@yEyw zU?yeA&nNLBNj@}4@E|_yM4Xdg!-jS>kOrD&%$eiP^+KwqHMH*Tzo9+gJdn%P?=JJ0)ed`e|&5cc8K)7FJNTKA&FH+i4b(?gMv9<2=v{<-U(9Z)GW?ydxfP zENxibi?gr3g( z^A3)Oj@5l~)_dT1$SHrOWrLpn=92~RPQeFyZ6>4%LHUxYTV5o8AAApjii?Y1uy1(q zT)ZfO_&{~?qk6#{ELufE;u#f(o|W`A^nEQsY-Z7u6|MU1B^{qU9iL5C+>Ix)tftf) zdKoWYlJ4K9QayIhtl|E<}8bnAvfNrVSlu2siMby z_yNkF!M8QOs<#Y{(Om2S&riVJ$X*`ut}NjfMD^A~`Vkr%YpaDLg8P7W^kyX-t+#^I zpt9Ob)F)4ZBO^}{X>kjP8RVGdqZA<>p5n3U<&^aO&C>SPtpIGqWQ;Ydb7*KN**ds2NJ>x{(dD`(D@@v1<)DJfre5+ z&|-Ik)n(k*uZGIHFpg;O*&DcU8%02w0)_e2tN(Pig5??SsinmSkDolTzIO4~+wk-3 zO+oQptlnId@gaaVjGHwjIav)%w+D(WfW_uBUt3>4t>8rrMm7L*SfCL?E_tA+$JEq_ zsw*0CK$2I1%^`Uea=2w+p5$+FjeJ7r(_ob*69k(K*ufhIU;Xy=tE}ki zWc$a9_kUBRN3K9{xqr7DhF-Pb3G^)yqF$b%Tkxd`!0(z2Ro8pX2yz6SbLY-2Lg;}R z18}O1w@%dGLJ-2p6Y_`#I1%mY z!0<3F0AUearU88#?ChJrZI6KZ{30Nr8!*H8*B2xkhKH;BSJ;7eI#8TFgMlufL9y%W>-*JzZfWTTWGL0R@r_haR>h(; z1W}2stgMwIbZTZU6H5q-I=!o+vOM7y%3GT0Q_l>E6e^X~tE8?Ixb0#|F$`|83AfBR zFIH}(C)~=3JTtcz3IE>(A8lx8=(xG|AX>yj3y>91Xaz%O5vBt+&sgTzX?{f#5|RKi zN-_HTN6nbWp^kz}KyEn^@;f+_;~E;@B?Sgd)!g^%NWpzVwV#iTO-|o^!3QiL&6yY( z?*!Ea&||WzwIm(?uHg9+qxn7zar%d zel6CX`oBLBg|hHvi=#UW?WI$s&uCYxH$`2S!<90gL2RLg%9u*eITZ|tE# z{{H(BwQpArf>R;caaJkag$HGgO*y7oha#V=y3 zLD~~mUS9s>!tY1~G80=XlG#LnD@}-{iHV;c{c;~eiG8a*UIU+82O1Tghd&IYsMYt> z*VevO+xKfaz}4C5LGM!qj^^PuYlEJWt7{qf%uI~@_4Cqy{;QV`zT-+0SL}jN zlpcXqr&Z}n=s=)FP)bSq?oQmAy1~=*s2N-E-Jun6FWCnFTs)XpAtf2Qp58eXcDEzs z`=giD&S& zy}M$%P(36*BRzGVoSYn1D$*VQd1HSof{;r?(g)b;kbRc%G6#pku=|q#KU37Zu6~#B z_bJ8|f7!*&O(uxJ<@<4fSz7)jO=5;=o&kJ7trEL|rw<#SsPL}{;hhQH-_Dgn)44;3A0Ax>r7>s3U+2hB1 z;FLg9BAAw12xWot3fyF^Z|+V8gM+Xt!hxQ>Wd%)SPMU_loo0UymmLGFw(sl;L_Jrm z)0N#3B%FZMuovK5ss7!0=#K=5BBxm`N#EUP#*zJO`ThJsjeKw7aZqpmAzeeE{%h0( zMB|@V*GT`e za7+RgYjgeXy*8j@^uEgk^d;ikPyg(OD8*`cjeQuio#j`hjvh1aZr9&m+ssNseJ|_) zKZBRDl9C~T70PWydF0~6(#3`RX z-QG=cp&z&hzDg$GZ&M%<4rbIXvAY3*A8mL0*JrnJ(vY@n>_K%YFnZs64JF@~+FEhw z2O^CcV0O{d|FQ&ur+Z(!gZg+LJ_?e{Oh!1>2Oa1_6OAV0Uj8(}ZuB1T_W@`f5bT_8=l{ZEkWyu76N+H1`1XXg%oUEKza;I;19lfrk50l^V_4(Bz`)(& z-Bl5H1g0oVC<+TxRSIxhRaAUMO4cj-$$&Ez9xd6ww?3GS7LbSWC$vxgOJjHf`Jr1j zikH^kr047?>93)GgV_*1O@i{LKRj|E1MHKW6i|pz8=8Y;eHMS4g(1)MMnl480E?|) zpcj#(!D-dYV2|B9yZaQBmES(j22HTg1U3BwghH=0#oHNHXe; ze70{Kua1IPS@!ub*y7-aceabcr(AOERE?T^Ic2w1T{v2N;ddp=z6k<7L~mZ7Y*NDsBIK z7|MVxFdl3J>sNKyD+T_1Ng=%Ca8gRjG+-lLVEnCbY-$=Nvq{|8m4DRPB0smBoDDO;&+IwIZ zbOjHiW!ULh1O!TTz9M*n^N>plgWJqiaR~`Qeyv}lU?xNgDJZIK@)gRa287Rq?Tz_q zu&Y&acXu!Sl9mFByG0a=Sf<%dZ)Yjf{H7`k|I{I<=b>|JaqOw>E2wNe=k({t`yIPMa6Zr4*y<8u<8~dbul7!@{D@9Z#UCd z^m+~7P^-|wc=hS-Cg=U^349AD@Eh3Os#EvK0L2~MT-&}odBLFJ$-gD*e-tFB5ypRu z%IzE+{~0HK)+g`VVKivzc1tK&#sAxSc==xe?SCbo|M#^(Qejq8al(NfNOdYxI%>H; ztM60ig%OsW%2el>rJujSPvr&##Et$M$^R1a{$0QQI}>?@FqeKmm`BGafB!eiPU`?t zHbQ>zKf2oAum7K`_eV$#e!qC;(?99%?;jzh=l|-`|6Q{_jZFBxwV`S>PeC1mifg~O z|6e`@{~qXn-}L`?*Rp%K0;>10Boy07dA~dVYNT>@_oSLAs)@*Yrn9}aFmJACDj6@T2Z0kl0Ss4Dy>_-;!G~8BtiBSa zfq09-4_8+sPr3(Bf@p;FZV1yo3Wy7>f7af(dw<*yCqu-Hq00wqLe+3@w6n8197J^z zxO5d2LJ*Qzn3?OBJ*A|cgH03@y|Al_9~E0rWaOB6x5 zI|gCbaDpcAR^fy-%LN`@+o6nVAWfN|5h@Y*U?Q-=Y;W!@#OVYI;dCIoh8IQxMh6?M z-V)tD#rfST0hO8r8QU~`LN{Qu%O8$%ToM%20&LX88j2(y^A1L&F6_~9@b7a&h*@aR z()nc(-~urvCA}&!z_9S#UNi{ftwqOVFKRNuY*1AE)N|5(nc zJ_vgbjQ6n+%h~$t`WhQ$5YPu%q$hmiiKSsK2+%) z`uOn{)X`Ot&&r{vd$N1Lh7nUq<^AmWZrns}+5FKYgwRo1q7i1lKp?O=Q_X|zl#X>Z zII8qyxWj2OmxP2W8blD|67by${f4q*_+t^DHh=>jOva#ARP7TH6|L3@T0Q!d^WG5xg{Lbb#bgFpXXrDFbe zD(vZb_dXuTw+H(GjFlH0k&$l?HBe@mUf+sw{oaw&e*{LwdStr=$BRg;F7R$uR|M?8 z|D{<9K&F~*zSTzFHxfItKfXA=7fCdFdo`5%a5!DWJ1kE@NhwIP6tXjT0T4%20}j>* z{I;os7?`64zYhuyw(Q~)64HV3Q-YBGYKg_ z02{wA0txE_!_Z@;q@preohCgaQX#QL#jc}a6FLuGr_0bl&=7{hJf0ffzb`iiH&_EJ zDk4%f_z)FEzIz3225=&X1XzwgaHOFm!k8dUwa^ft(k9EkeTOXH15XHn2jOf1DKLWJ z2~~hrhU!ku&^oIAFd=utON~6~ zfeDXzLx3GSU>HJ!gYTzLBk`VH7j(b#TK>Y&W>_Z@BO2gz3ssO>iF+u-2&F-x{I+3x zdz)6k-YD}c^cnvMkU5V4TDJh>e&CMrVL=6;i})Yy*|Wz&xC%f*!1yiIR$-8|yNa#K zq0sEH;JJoCzW;3MVr&So1;g161RXgv0$D)ZqNJs@$e$kc+?X{P14u*n%a<>zD?n!N z0Kszl>4V-&e~g9VQ6!YZ(KSe9SJs7d0tzg!ERbXOFKZ#ql9BgM7-+ll^2E_C9WQ6v6k81O#Q43ScCnqJpg~0uK&fJkE1E$TkLJQ)p4a znq8p;%T2Y&1rY;7`x2(WognbUKZr2)5u520aEFWNpB*e!hGtMZw6mKG-9~+=GhQ4P z`v~a7?^${MD$-rlg-Ahmn%_W8T}LMay0JZG6>#mT=;?0NqEaY72vA7i;fN*QG#fD1 z!uMpyA~adpK9TYiGLTC>t6+@BseOaAW_|aDqs$DDu4f|_tBM?52kJSibWzxA`_#I^ zO3KRcNnv^&TdTwRzIXT7?c240LYaAkDOUmj{AS>dZvP;Se+*i2?<})QC14gat{>$F ztm~f?G<e>qawf!5JcS3cA5*6v0ES2TSe(;M>sB@&@`w&^t|mX$-^PbZ>zq)onMd z=A78;i%JN1>f6|NE)$AIkyEx)2ks-_vU(gY@0;(Ehu78i?KhOV#=ByYOEe)(|xC*TXyC9v1yC8?)a( z{O@X||F%B)W7+@TXX@@oZoqtijN|~=-~N$TLp5vwcSs}N*eJ)<$tvq}8wwB?mJ6&3 zxuz|aJ@K6^m#)}5KE!utrIi)>3S5t@1UfbL(4-2v0FXog%qQ; z#V=j^q)#HE2YPuWSEm^Hi-{JtGn$kqsjds&_BCUmikq{JNO#fjcCQdpQPfBbd3ico zRu&sy;?VGg!_o0Cv4k#OAr0LK$xGMT6owe){&wiHioPR{jbAgOJbB%w#r`TySAy{g zv>8RZqSu+y(vnLlaf*k{-wJxpJvIzdGUD!bcYkujg8F!RR`W;7uhVQmx*emWy3EF` z0Aly)AALrYp&`9<&rn@=2L3WYn^VPqKI`RDUcM!wFgAI6(TB*TApVS{t5w}5iybaX z@ut?BfLNI{f)K*w*g)YAsTd=*u8f5MJOu6)`l0W@tSFo$fS zg*1xg7&1QRcpn*HWK5Y(VA3WmkPoP+SgnV-?=3IxPH7!d`_|hMC8NAdpRIvwJvD#W z?DHP?O70>rN&iW#n!2N9W*aJ2dVmTXVVdW@5e?R}`<*6L=p|ehjyA}%eZ-b~a1kte zWGnmmlzpl0*`qJxmd8VFc_*>rbXHDsz+lC3rw7{5VkP0OsPt#0mfSulf6=0Oj%I6h z;F$Z}BfW3IC(Pv7G=}crsv@Q>PI_RBn$mne?Ka!6o64DtVO7(G!b697!+uZMBlaI}0DONX3%m6=?tq-j+pM{GLPduuI z+w0wbIXcBPT~$LdX%_g~1$^vx8&?_9He6U-y)UlL8ukelx@4;^si>NeO(mHo2ePSq zjRo6IHTjT6>Hr{6(+Bwm0Hx)n__rHb*g|zeMqYGmV9GY9`Kv63ZrxRp)ljF?e_?{x z&VSsqTz8)@O}jiF%J(z%=0w%X?^8B? z^%CTK>i%?XQr`|ZcDy5OaPNGiY-nK1v3%(j!>y4(bW$wtmizu~>|T$hZkF?giR*gI z7Yq7RO4izZ>S2CTQ%=pxbwSPkPNCe2)bGlRuuEKZmxXGI`-@_1Y-oFneEOg6?~yXB zHF&j6A*|>{$M@--3s22}@_jc)MbCS=wa-JPJOrnv((b3r_Ue3{o{EnI1~A|?FTUT!RGJF8!Lea8g10;DE~?=N+~ z9X-5^W!|ZhyW8A!g1mIyM=&~^h5E@2c)^#(PTaCyAX)SB0$1@=U3Ink1ZybU=*!i8 z+>S8qFh3frkq<~Ro^p3B0ytjcS$9-Wo(EwI%Sa~GR10V1PAKSOEm+X_r zk~x+K^~Y|386EZZ8ivZfR8Oipo0kXl@WQFV+b$Cic>)gwF|I#!nZ$a@c7Cd6SA>Xx z`|7s6J>5i+ZlViD7buraKTcy~Z zO&fxqN3m;m?d;N#44F0lZ^9%kjKZwyGQ4NH@VI)y^k`>83#)G5v>~UN*u)!GyMDW4 zGaU^o!{r_hmCIpL5nBz#Xa?H{QqPYZ@f*{9!YHl!Y}G0uZ2s+))y5#^@_?B30hwa+ zk5#?)U*^t&tYWTTI$;Cd_Vm$Mwc>sQy3T{>p6aaf2Cxcyui;U0FYKpP7-=(N6*ko- zjZErnwbx)3woX_7mdOiPw}o~~B5Xv0_e!)px1}ZL9qW&`xGNki*jMjAF+%%%jZI?0~j(0z6uug&)50~ zS-hoX&vf0|DHB0#%aCjFG^Jcp38`B#%htQAI{(V(XzMWd(3N$!2QmE^@c~R^il8>9 zjH<2uFgq=-vJ$AET^s%9-b41-rU5&r_mO?;Zqpo{zLD6<<#rC&jYxkJG$7!t#SGn5 z2@6_lA7Z=mq;eAd22Sium9Z`l`&iCW47Z8Qa=-IBs9YMCqu~{?YF?U|;;?j-FuJOZ zK6~-T86g*p&>+V}R*7Ty#2dm|mIJoTnPqcRpH!@e=(MNTSiizD;`i&6GA9myl_Pp~ zl8jMPsrDv+GV3b3mEzc`96OaJ>fh7U?jbubN7JSGq+G&$`o@M$pw~qKxeY^#0uJ6A zZUM2_?Gw)VS0unGgPpk^hPKl!ru zZJ)|{lA3c?*yZCTxb##FayFeTrgC!ie&)*;w1;Pf66c*5@;0CVBcj_wNN}=ap9*Ag zD=X*e<@xpRxh33vAypO!p`B>3#qRgDoYmF4FMYINMn`Uaeu6S)Hd0?DqxL$PMQ1xd zTh)#U4>{Q@#?lfR$Kl@hWrR%e?xFME7Po!&Pkp&;Jdt*$&`2G<)OEw7c*%giNm;kC zRoRxUWAy3wBLh7~G3nA^S}=}Nj5xj1x+pisfQPi!drSvGhPx!42RT(dMUtv(Y=*t^ zbQV|bC)kbfxjXDvG3B%UtS7(ETZgU9RrhUDj}}u~XDkkm>aW&MMT?Iw4138qGG)?# zg#Fg;K(>3nUIK$VXxebv=I|zvC=vX@4~6$n#ZITO z>P4@{hMaH1%?A3m`HT`Ef;6evHct97{6d3bIDA;WSvSuByK*GBI!p%q-W)_TxG;2s zRiW-eR^<+RypN2hDZEj8yUEr#x70;29#7lxxNE*xe7?}%cKcX+GF$d)v~(BvdUjg~ z!SK`tKp7NrkCTRbEHBhDXgOj(t4s0B^?3c&u!~o=+=X~X8*EgUQ{M*MJ&wnw`J_cf zYsaltWo~B_IalKnX9hcr?o?-Gc4di&H-Ez%HmzW;(ah+hPx$ba0z(X;@ps+u%``!O zbSo@0)-Z88w8z+`=; zBt6aR(&~UyMos|}M;m!r=G-IXvPY-pOtfmu{PlNUR1zSDg|JiYEQT4nFYAy+`Q+QO|e>9U6=>lgI5Gbj?+cta21$)CJ; z(FZSD1oI4OFP4$3HJcY?@YZ$evobGQdQ)>)APx!P0lnA2@}m^FA{~w z)zxur)yt1+5?@|n8?5LV?>W4W%r`;ffFElT0QeuzhYA&A8mssUu6@F5z)P z=)Ue&si~E>DJlZ5@{2B~CgOFghyg;t~>SCUQ8IyS@Lu0Ei+klf| zVE5um!7P>Qg@Yl_ZJl4%dNkJY*^5a=ep1m!Qkwf=ucOtoT82|bFP;>_;kaGys4ELF zU67m>5It>Ia`BNT?NC}_n)^tT0KJUt106Brm(zWE@2ip|A8+Ptv=-l0)_FlI7x!|qm}{)gHYYTt8q3Cz zB*IDV=4K-{$(VQW>CNf@wv&_U#Ieb2UjTVkWn|0H_%Is9t zmuBaSPvq+aAvE(SoxxKuckDkLMsX@mSW4}S;GO* zg;G|{7ak+YAB*V()unR+xUbu$!JVPO>ISqQdj@XFecYQ#dn-tb_psWXSJ^tcq|&WT zs`eIYKA5d5$V?KWZ7WUET$iN5Mkz39YiRSMp%vIsNL9M7;W2#)w7m-J?85$zFlU zq*9)}<9PwqY4X^vAe=9nUAVDop)iAA1Twde*9*HLa(5MqKaDs*1m&p4WzAB-|HLMu zedQ;8alV<-Pp{w{ZhO&l^{P}^en(jgYI&TW;7B+S5n4Uu={-A3LdU|Vpj%Y=pm_Xv zgkWm+X4Kt9!akPlp}_#u5NOBiCXWY%Hi4%+$i{hRSXmepn~Xw~wTXFAQkp zR&!X&rO13aYZyyfXnov-H#zA`$Fu~YgRO^D#m-xybk$Iv*>bw-1aYQbAH5keCglJD z3e~avdfe1LtMC4L_Z;DFYti`Ok{`NFGazIA;R#x9 zUOs~BUTTbf(GBqFESPki&%nh`Jt<>7U!xGj{Z58XSim?WhG-+_Lq0-7+A&;5*1mir z$xD7A?Ubu^E!DIwl~__kRvllnGT&{>x{;W%esz8Ywqcdkr7pG;tl=ptf(nGG3;}ih zw282+;)gM1gL}XErdxliwM;RYz<;T=wD1HN3w6mCVv5d1{+jybeR+&fi?cGZ4WwUf zNeXB7zO~oGnj(_X=fK;*P>qXyR?i(SkAG3J{95jA?4UWT^$eriXousz=j}PKnFAAXW>yL4#C4t5+KQ>p3=zq%D+fT;+>90Hl3cx; zy&^}bpQu*J3*E^=rRu1JuMam>F%0-YZk=GF<25FI#wkbXGLuSz|Fy*i;CRQ4H?%5usILws z&aq`ir3~$4&5fsRm}-eA;I7@JzwmK+vx7jTU+e~kUiXc>l-J)fS%wCM-q-yBqnZkw zTK-Vzb^@)0m75#HOocnc*TJb88hAXRzT{{0rmxxjJ!hFE4Q2JY&Xp##^gJetVX^r^ys{ya~w3;)0!eG1c+1Dt+)Q3yo10 z$xybebnM)>HTIE-*PjFGoeVGd(K%03MX{U=9&-1^_7=E6zb7F!rrrQ!+tx3g6JQwO z#ahh58moH*rJZgU1J*1dn>@|R2k&#VD96m#1m@L(l63Pfa?gf$3mO(Szb_pSHC#UQVirM1inY)G{ASs&$n)~3z0iL-ZLVU zP_bu&=|ba0nWdu{fj}a6Hx3@6d@k&uXXz!JKD+VFaYiMGJ<*hvqxuesr=CTpkjgDs z94{BPP99exGKnw!b+(dg-bBuAGMYvG?scf!z;fkmrF*Lp&(UL?SYxA!spsVC{G2QU zKUR5m9xp5XG`S|PsJbm`Eo!*3y*S(Pc%FB9o-d#)x+f&&OJB+@hvq7qRJ{$SgsmqB zqN1w9Qu-Qrr0@$HZ`!Ey?3X9Vt9r6?7fj|kAkJrJ_ND`{(vIFLi@-B4<5TH~&e&p} z-nxQo#wS>BR1F(Qy@!#jGWo!6TCuM9m<+|kZeogeg!F5?d5#O^qB9Lf}R zS7HuQ##GL|Jvv3~ygqEvUo+?GxOk|OhczfzKQ)kVuA5rh>%#ic-W!EWT^qxpu_;bx z+AUd&Cf`ooN9*ofTk87T+>O^Sdxk|n-vzJoZq9S3{KZ6CsP=NpY)@pXaQq* z=mJuI`(s6I8Ir(zo-fa*}Dbq&OUd7eXIPt&gi-#Gu^rPMnjMsRI9B@W^~i{L z?V+_Y9i_1#uC!AT>vcWS4WF-J5%295%oVp(!iyEQLaUZD%U|WbVPv=UUbgG522|d( z+;%BvD0fN}($NJJ3DwwT6QiYea7m>$RucAWeN_X zL40W*t0xYZu|v`H5m%ugr*>RDc?PfHR!-R>tR9vS9R|JRCT&+k8a0t9CHK}5hE%fI3Nfp!5Kjm1SBT` zMGz!Pkeo(GNdhV$q6i9-5hQ1DlptAKyaBDvI#^kfA zo#{`eiysGAT2yA_#|=xR zdm>-1VO`Pku9Ra-sN3XdfaWfv@|gpDx2IM5W0&o!&sOWp3iOkhs$1;R=P<_Dt)J@- zozBTf&m20qX1C8_qp^DlbB!;rRP^U-<)~D<yu*mSJin3-sAJ@!Jg*~naJ!!O%P*-iKXAcYxS51*<$dv$8uEg^l^ z38ixbW9hp*k4aIqPn_OvFepMVxALNN<@mSFf_%s;w86! z#b%Dcs-D!_gEz-QC9Izc8JWM?Ul6V8Aj4{C8`Th*&BDnc6F=i%_qmiUyrO?`u4Y4h z!e2h%gGBSjUphO^DBERg&f8xqgO#`KG97vudGx)i<8i8|UH{^?%Ch4@Q9hwu$7G@P zW_BZ)Lx4{0w`1p$dwVkDc)b#@EOHI+Pj7JxDhoAf57#_o_Xt(BuS3vzx2_85_I>9f z846q$$3xRFuuuBw2_=mEyzl1bhNi=CROTq5X09H|=daB)$7{gd?RNa~Cnl0T58FpI zsa(^0@A3Wc2M=oNKd}ratX`LF8gRq!Jtf6LQaxQ-C(eN6X(ZK^6D~UTrF)G$fHF;W ztfwKP{51`-@=4oU`6Lr*E_uxjLp!I6`ppuZq8>0$Dbij4OYTY_{TnT8DsXucLwZ~V1|4MgdDgxdd2P)ZYq0J$Q+H2{ zBZuhePXV)QKbquJZ)B)eNcSBX%i`mCr%exCdJ=G7D|K{K^hrSI#a#7~M5-DT;gB@m zwdxY@;ey4vxYcWphRC73c>;yg>Z#tA5G%ksM!K=spFLVIpNu6#%^pUr%V6LUYNZPD z-X^~PM=tE?#A~jBROu5|S8B%EMzfM5w2)Yn@>dpJ&61Atk1fX6NK+5c z?H!`kY@byfj8ge99(MKPpI7x&uYy(h`%Qks;Uny!^X8UuwjT)N_Dm$EG`*_6ua*7c z?uw^-3(E5=s4n~DTv=fF%t=N$W)u=&K?XJvy++Y{aH0`Fukhbgf&;u zpred$x%Gipe?{fk(R2Tx4sBFKqq~@V>Rvl zm!tra*NbLujAD-W zZ0JGz%Hqh|%(W7Au!w8JSoHB~^ZCBukgJV{2~ zdCH&f&8^A;-Xm<>?403a0m(oOt|^98Ua`=5%gCGak~Y->);+mbh3U6>US4FN-WwFX zERyl_oLy;s(X-CY^5d>jz8YjfWbdpX;S7%2cKg(JGgZp1bP7 z5Z5)%Pm768o+&$M?D?|Q<9YOX2bDD}$tx}eE!VFQJ*}EkpWD_ObF8IaR1JfFH zHU?(n4r(b2?$TFsUT6osFt#v^^ps*>+E+^m?K71~f|iCKtE@^CzQLvBVte&Dm*Vw1 z!N+DNzpzEdd-9}6|qoIfkv%Ow*dpw zi&MLgM)ayabp2ctRza&gFi@2rUsbkH!l!pCqdcLpFm|!KGwzddRVvS_=LeM&Iyk8! zMa8QQKI7)#m%XhfD4WI`%3l_erJ=zwU1Cu7dN`sn#X2N;PyW(;=DFP_O(r*tLn}i} zl^3apq$;a8(zA~uk@eAuxa*74YY~u{n(olU%WT670qM~r<@ITZ_ zJ;UVpK9tF(|H`ngDdBX~jlFpj6*Oh()4b6VMqYC=yFN6==Ndj4^1B~ben6mP!;oS6 z3JX2QJYimE1OAk3?ZJ(q2c&m>D2dP28}VSLl9;UOpwK&&P>~Y~m48T8x5}I?eP6kx z6>S-#w*GFs>DIvQY4(lgDcP%*pVVk|d9!Ccow|P1ZuzU*5I`Ri*kq>6pD(e}Yc4x` zJ8y!%LrV5b?kE$+CkMcQ$=j`npgy<0#`3)3^6!k3LmPL*uD{=Na;u53mbs%om+qs0 z#Lw?*V+Gu-e{*?1gfTmDbIYSI-X2$!<>-mwu~gR^KP8Qi341v1KfCEHLy?WZUbQDa z#cB)eM!g=kDHvbH^D;*bXYkBlKmW$$kJ;3rXt|<1-T35;GIhi=#Dw>mLtnFun&7N@{-~>Wcur=~XnuHv z_MVwp+kPLm({V#K$aQuPXg*eKbDBxjG0qiEJ0Q){tZ>UAojZ=MH}H|2{w*>2zEO(Y zOm&GnkNT^k^anOnJ0={n7O>38pL0x`Eei+=rytI&n2KlEQ4{ylKF#PV;nECdkN0eR zt;}fJuCJVr+>+46IMKIbk$(#_Y`Ty=#&~Nq3>HzPK2qr6nvJ~fI!Q0a2#rF>!J>da$Fre6r$06> z6&-vEI_S##41q<`Dv83v40UhsJ3~*!pZEtTWOsrnYq(1NBe4EJp|&iIzd3#w#UBBW zNVq&Qn__CpyOHLkwP5V6Hk-hv66=x?EvYWGJX@l#imhj~V63#CJ(1g7)y6w7 zOe!tu(d3SH&87Ut`Rb3C&fiov7E}aXMr_})FOy^MB`nPpqaH@+zm3rE?r+c+Rk`b( z!EMUI7`-KQ>BC^X(2@6%jnUq_B&rPR6Rt#BxEJ_pEexcMT~C}}(;9v7Z~MjSEtUPw zt=4Cw9@Gvp9DM)zNVC&fx2lIClm@PSV%6t&hsVlz^|i6fTORTT5oPs(MNiq%s-!e< zH;=$;-AI4*UeY=OHh`-nAC_nes~ z$v@=^$L0F?y5%rdJbrPBR_ms=+S4;lcU!0>MMFI~?xvk#aZ_w>Omrkeg+fkTXq1*+ z(;#1F-iq-$S){%|fScSi>1Bftuj8zuoVisSF0tW5HPIHqu()F1-P&>7DdyJL3u=Mu{m%_@_AT_zfT^|c4>zcFU)`Or-086VZR|6QqN zS>;h)qu$}3SZ7U*zJ_zRc%2vkf=ho<-LMHx5y3s&sP?22%zbeNEaT^d_PcNoF?Vy(Who_IhmO4 z;@Bsd)vJG3VVY5TqNji8s>Y+^YsMM}Yi{w5uO4T}qKyc>n3RxFw)X~an3vb1A}sLS zYGr`d%YBnwr!)FR%lZ4FoJ#yH)htRrv`L3&r`HFsI$fXOF?;+&s9AHA0-LwK&B|w7 z+r4WQyi^806r4XQD!cG1e_n_$Qy;{jq(hYg^~Tant>CBG9bI4$-L+gc3m-gUc(%WID1EcL`Jw!0rz7uY+w`1G$?imPO{DH&RPWf$ijPfCDvrB9vuM2c;^rG2;h9c; z<(K|9iX99GS1-hhmhYIPdQ>l6OGz?y9Q(jtoo@mo;GNiZMcJl1FOg%>vEIY|cTM#^ zuM)E)2gktkOtnf!X~)zOxO$%SU3^y|=V+1L+IF#?eP48|S`S_#yQ?TMZT5AkddX_) zZ)bQU1pRWtR~n*Trn}#dC)xkrLskq^!B|TFs3)_Qgmbj|a5K^Bz(>clMuFL}(^tE9 zE=@;>_x_TDY9#v90B^R&$&|SVo5N+a&#D+&C){+(V^I1#>q)^=nYz16PS+m3PU~TM zKqh8oj$bRQs0>UDbPN0A)~7zLMY}KBSE@fhY}X&)R$0csII_m8YTfb5y=|4%XOJ4PAM!HnWitxM3*-oO zjB5?*y&j*u7oYh6)n1xmEeDx8T z#fCEoG8ey8Z@6*zcujme^@HZF@O^d9mBg|R^!)wk=Y@|o8#r4YXYWf$;4);{^rFgS zUww|{nlz&wT9?Ckrz-3!ZD%y&wnV_>Q-Z5EJlW9Su2P_&oG`0@Z==iZNdu}P%KQAK z7DJ>e>nfr|2>V*|Bre0KJtI!>!a=(on>wO!#p?I+q+XO~?YZb_YJ20k__=_V!`w`K z^joZRSLx0v_87Vt_U2~Y$=GYR&jkb&?8-Pi>(IwE?U*UH+pJY{SlC8Ih2&sFUOtJr zqN{qll!g+IeqS>esj+RZ!ueH98gs=9{6nLDTBwUtQn=y1HVzBkonkK}yDJ61f{T~- zmhrv8by(;`qO1#q46=(TY2BcjWHjpzV#u*c)2rV}BLGw%r8!({Y>}qw`#X@aFRF4S zpTpes8zzmZZ>*cMR?L}BvgA$8p3pp-*w$RvAn+tOA;z-3Ccg9*pV8PJzE$g@U$tfV zuXUPPPFa44uM#tXYu8+;too*a+7#m>%^ zH0OMv7wN<*{n@FvElxsO({0xy5_Yw`y!dANfn`jPdZuAUyJohu;H)F}>L#khPHUsC z`vPsErtZa)gL^h)cvgQF33w1W-8clbyy@6iK8sFA4|6q~4qbY&+j@6{cFEcX*K|&H zV-?=)Z5x`K1B+|RuHuf*g;SF1ks zbn;`Ykw5*ucB|;)fS8!wdV2GgCY=#y6`URGFE2~sm8Fq!*PPY{p@RAEMAt2JChduKuK^c!`N$bCx6LhF0%P7+>ye@M@`#6JKmFNwPTYJa5#ujoYQ@u~pzU?}0i`1$BO}V0$<5 zG(5fOc!O$^^Of_OU)17TGF?tS*&3h}BcED+ZMy_t!tNV}ioQ%rafJ_uPU>hbK73T5 zBRy|ax-DslLO;1)eCDC_8EUt1nu$Yv>#IK6NKEeXL186Uj)rbC4~7B$qPi93WrM;+ ze^)ZTFVcr575ih#qg%BQ{@M)G>d+ofD zvV0}avfr-19_QTIK{-g_%6BN%HKok^CH!>Bb@_eHXM%?prZNgRryc5Il{lcTrZ7DH zt$V;VS)Q$pq|eCDh2uZJ{`wCT%8oaX!6H8wWH$G4QCv^^{8JRa;}6RBevEs2|MmO- z_J_%}n%_P}H&$$48w2Hf^gsO?drtrQqu-Y2FUt4+m_4U|D72J$pa4JG;Ge2>UA*15 zk2-Y+@A&;wfBjzQeoF8h#+bXVzWSr_;J^IRfBG-o>v*ACzAMZB) zbm`j{xUStx`QDuJZsYfk8CMG(21W`)rHjM2-=X~bwZ?n}V3{i~PY z|JJf)uc36@`=>YLzj?18?Iiwwf1Sp%UHV@yn>h*v7%Er)r`P?DYoLY`@b!mBY2W?f z)f9oce>yDxnl{X z-^Q!@`VyE`)3s7`=6CWRvu0OA-zpsBF*p)iNytq8kG+l^hFecLGJ(hybWPyBo z+ek6=S2e@vEaruqYS7vxU%y_L@n82#_sh}60rZgHg>?FF_S3_f@g~7gmSjO1#E}<5 zW6c>5X|(ML(l1Ln-?8z>wK;rSE6-j$LXy?%qG3b)4q~Si=GyHCUM3$|BOtcwy9KE2 zxO=SX?6sp>xdPGwlpwM1R^{F=P=l4amZ*`zMgCZqwzKBj#iz4=e06b3r#V@VZ3rhM zDrWN@zaKZRwgX8vscXBT)JW%(g=0FR`f3kUZFE4ynWOhz0zKu{YM0kCGySkw4957Y zQFPozPCRv4oMjE^MKaCuUUo zZxQy`Z-`wfwh)q(YMpjyGtH!a;oql&f%l^jb1OvK@$Nu$#q&NO$ln8>SSwI)KF@{AAj4)_2cO#eC7 zf&p$^)63Sc{l4AL3L*7%e>;!vJ7@1*LblgiA)~FQ@&5OHID6QyU68vxI|TtKb|@04 zhuQgv%ze)M@@7pJH0SPK@fALf@7@YgvZSk(j~}61|1OL038cCdU4alQGb$;Z z&p+m6W8ycxc(wXxX4QWdkjQ3YAGSfVKz2$`Pp`g$y9Pi=?3+633N{MS2P zpFOa-dok{a@{y&_5~7~a(Tto}=vOK5#0PD2@Igo72=_c^EVgU(bGUEu>pzz(%%PFK z=+TKAhDjrV3^z7`7JF#Uw=y51hx{08@V9?HplFHfvgKt9?b9?D zm)xlqBW|Ss{btsm&GQ=89mdM}5A&>F4fz_?`A>&L&-KyK zhN*qIcaJ{S0#euuGx+z=K?e{=9G}`x6>a(Mz*fFo!t_HHS zu#6JPBdwOz-cU&p>qcX4ZV1lwU@f`Ys}q+WcKPmCP6;Mu4L`s3=C4&XFac1|HA)`A z4R*%~XNy&S`S{^uk)`=@f9rks-O2TgV0`AN_T3F!ID;-$#%&j`|Ng1B_YfVUBeoMO zq^qwV^)dk+!%?Ydb!JjiYgqNi!Oh>7$^TEh$$=^ifl>UpZL6qLnYz-)Gpi&Zv0!SS(=dJ%opMzBx%qF z3Vx{WI;L+T`t_`Rb0w=nou<^RM%sl%=z}S`+Qlq=CX%#shn{Yw%@p&O#dCGM-uE3iG|3{IG_MluC4Ruq3BtTSNMG_1 zCxAv_wi-umdP`#%PHxVEF*b->_uV~#v3L8slFPZgFpT6k8G(;~MME^B>;}w`7gnew z(Rnh?7K6hSMB-pv=Z9?yIILaQGCTgbj&w)^9z`bfer*P%fwup4>9uoZ#-+b+V#wR1 zR$>spnbv&gwgPu;{J&g`d<3yBbuTselT%Wz;H)KG|M;c}1Tt4wtYXY&wC!JXoQ%_M zOKfqyb?ZA!>(~PUk&PIu;)8mSk;!ZfDmnql&rQ&>ihfq+YPIS6zA^vIl{1*!ly0JN zch|||kTGY08*Lw$ga(57TwhcPM2R=T(fHtuDPD*;b>g3^C7{PQj&VRIaN;>WX`xBn zmptsI(GinKa5cNed#imTqymd^wum2=gCCDpNz@_{2xL*1V8j^YcV6%K0TY>Z*;Nvl z$){j^QdG{?4vND62z1di`RwM8GfVj&q0Nr}7TWx9OaBEW>PF;zJ#UnEiXMQWulGV$ z^4lN$7r^;11orW}w>f-hl^bRGg};B8Gs^!nQ1xH%``@2TESrPUap8XtJ;k<0=KM@q z|F2Hd{}HnvsMTCXar>9>&c9>A|G01ma3a=x(EORw7Z4b@4I@|1CJVzW8`MS1F>(Af zb9Cfz$EjZ770jP=qbQX1oLE2|ggEA{<|V9DB2U~2UoCw_oU+ut>+3JME`K_`mGb&| z(f1Gs(ayg`FF!8S|A&9Lj{_mtJ!j%0;f$hEm#yF0F8BJ+Du>kq@9m(*&#b-~LxXSk zasBlDyws+w;QjWG|4nG{;}%fv_;yO{YK!Lh2@R@dS+bB@O<#W4crd4>C&V3!e|tc!+C3u?DS zsSWq#H-8Z%0wga2;}i~!z8dJD7~mU%+SgEA$G(Ehj(cgw&B>31>?{a2IVc;#jB-G4 zEZ_lAGsp(v>t5AOt0n1(uie3O0BY2IP0%GVP3(h+368}s;=tr-8w6}2)O-*ECEYUv z+Dl9>bSg~;4$vVtT{3{({O=Xp3V|sIR60`cwC#xMMp1fmbtlIDSQ^6s1y*nI5DT{J z2_nY(Gm27ThF4Hd%}Q=%WMquY`IW-1EbbJ(C;n=@40R4~RsZXpwIrwU`@4|TK5;H`Kq~Tyk##_YpZMyZ#I!}k;tV5eU#5_jGlcoH$ zy%0j*Iv3Lh30{AMIYb2V1A=dF)U?qo zYa-K3U;X5ki5Dscy6c>5)RtX*^$yfj7bZ#E#jz@S#C@fcgdjVP*^PbrOe=6n8X4`2 z3N41Rrr+kp5Zg|V(TsuxPJ|JiP!4p5_H7~NE_6Mi&0j5RLh;C9Er3mVVbtDtzlA9e zQ+}zQgmBI(aM?Wwg}xHNPI);wVI|6vnD!{tCup8LVfA?F3;_SOc9J>1UoGn(ycPy_UZ5)`j`y73MOg=!>2=;*n5C10Z$WSz@iuyl01 z$Gc@hovaI@Hg^XH2ivq1vMjm<2I5n6Djso@z{M82-+rOq2#^Wq6K~VJ%ck|d1=lQb zm?JosT-39>Ei1UWEk3==#Hc)lVLnJeeugxOuHG!;e#+}V3zwcQW7a97?|e$pgSN+d z^9D8jh@7>1PHS4#h!cDxhMN#BA5rI>1kL{o2d5F#h|k03%Jg9pwo5_dZq?aRS?A4W zd6UYR;lQYML`nIklj50^aNk?5;Qjl z$46|BU9E5zUFef7wZ@)zAy8}X+VRFiY#%U@HrYlUNLd3YFccC^mTfeQar9I=ilYNM zn&Rf}#g%;+$Y>K5+M7frkaXQf5D4}wB8L~rfO?=)16uw9*GDroGJ{CLS{1-t%4(p& zB=HLF&sr8Cfrt?-nq)}MD&2g*_h(HXM_d%~xEP%4Upw8PVK!vhsvs*IkUTjK0ZkU@ zw@?QjA(kfr&*R!@yodiYbhZffgg^42_II7~#&Wlr)J7_0j%v6V8BvqkhbA4o<-Rz zHAe8WUAQPme@~-OzGG_t=NNug2n^*ngz-%VwJ**kmDmio3PuP+TQZq_-Mb68!_hPr z4C@UO^;bE-%ju?fE*b2mP!yj5)E5IcU%_S*-X|KQ4le|l5GCFpuBA*FLP&~jh>=s> z-@vhEU>m3UR*iIH4=i?I_VGQ(;7t_RhA>OaC9yGZ(joSmQvgN;s;Z%N5-1|DD8&f(`fmyY}Mo|;ygLfezl9jXdjYR)C!iK%@AUl$JvNzb0&?Us{&-BTW6(olxYvOAH~j{&qssTxQ1-riqk56YN1`|1x=oh2H~6akZ^am`2QSHM`Cp zOJTgF5`O2XB%SRP<|1!*>#DX=`ZlHhgg|J zVGr`ERx+!DkO~)oFVaC)qcq=7RzwgD9^Qz3dz>r#gaM12zdaV>(g(Hez!~2j3)EHj zLMozG)qF?0q!QUjzwM21sy%loKz@ztuY>GLV=6WR{_$FQCkTxaib*cX ziKxOOG|IcmO3oCzeIL35}P+y(#2@xcLT#@xJFFOZdrrT2mdDgrrRMWCb7 z02|y(Z%o`eNf=fCQ3B$aAs)wS&(+b<0IkGFJbIFnv<)sZZ&iWYSZLx7wZK>)HQKcj zv*^P)T}NGJrGV!p#0oJgGYp?L`u_BWB9N*9xu%%sH|iHUe}G_6|55TfilBAGZeZsZ ze?*I}o=F*IR%E`1Ogg4#F~4=&KpSGF&z(7R>fF;C4Qiu^E`-T52WncY0qt;N%+pRa zwHVR3Ow+b(Ttog&l(u?dDo2!K1B})q-fQx8iP{A}ajaZ_m2kYc+X03?WZ^%^ism&M zAxPNP8{%M!bz+Suz-Qcl@?y`E&?1jYRXSRiAR>6?ml+)yrg^1 zA(BzJ2s|V=q45_9gmI?M;DQ~|%(e=wUZO40T}C0f8mN|s3$zRQr1g83#zmJD;?1m_ zk>?<{z0FxU7{Se;FO+hN5tQ!V7Y3tLgTzuaoE`yd0DKG>h;|oTsb(=zb|s^jJ|4Y( zsvq|~7XX-W#Kiy-ehcKGNgqPaE=$akE(Ww?onOITNU$BQ%jl4q%gOGQcU0n4H(`Eo z)6gTx_LZ2!b0{f#-~sQEG@w9-aa%SYA@#rF*&rTPOhGL`?;p5zUy@JpInf`@tGMcd zkbbC>H8G3k&|nt>TA#9QL(YYnq>HecTmT))e=_{!mW`oe`^ea>BIFyg8abclSoLiN zL+y9E!noZ-XCg(c4)U2A|46vdE4j4m2+= z&1}UB0TjI}keTH+?<{;IXnFN(aIXB4iR8ip5*=B6gvQ=k1+Lr(tyy0}LqgT`3V@Ov z+?xS1F;+l=kWj4EWT8-s_7EiEN)SV+2%+&B0T&b{{np01>jEJV z{6#~NgN)31ahEW){-RX$DMjUwB%(mak9^v9vQaVyK{Q+3R)JRxZ6hJs{H*3`Wtbc&an!V zGsIjsD{veKT?kj(#<~pu^{bVHv^*?<@jSA|t(n|ydgL>&yAu}b)DD<)g|TT|6L%6b zI~9HXyR(EYKD8qUW{$ipm4t){X5~=?lKGVd*( z(f8=Ry}czfqg_`@AFw9r6nPR7iFg(t;7t;IbG*2-1aNbD9^A-X1hr(kAHmIXxLAZb zPQ?q5;|P@BJ~E4-W(OgW0HnoXc*xylLgESmc@0WSqhULCh)B<>=h~W+&@_Lh(~H0h zyxSqrA)5hAeUK}7k&~;V0e1)Y&yB+$GT%#lr#bGn1Y(~LOyGnP&v4a+F?@Ce%n2VAQ&xcyzk>B40CR|CawBrl$dtZJ-EV#ruC(y=UvnPhU2 zC)QK1243DF3WmixCISm*4!WmArC_d}oPI*FvGpGK>UYCQsDja6gPV_lgA^s%rO()j zPoF-$a3qr*Gr$ zxJrmiZ^48LdoXHIVrj&R&;+(MM85{T^7Vy1nZv@eZW1KC)P8Gp| z$CMMbcKM1f+Lnb@e=7AOB`SZL_L`!~jE8C$K4ZCnTBhCp2W*5ORq}GMh4bbKqGNuHkfy27+`@o+#%1}GL z3m;&R(fdmVToVhbHD*X6HGqAAo+y0MdeD8RY36fYUsXf$&luw;#5%tV^x!S( zOMw9};f+2cj=d*Gaa;@KBz6T1x?nnIS19O2(-AqVBGVd%Jcn^N(fPf4lT+==ON*p* z0Ifv_soONIKxzUu+1>;ca<=#)93Zv!1A6&K;D_p)o=oQrnA%7TPIi2D z;LGHzk`|K*TaD?X_l-lZzgabk*mE$s-ck;Xn%Bp-ZW4G_KPMQ9L_$Ip#s1q7Ng;V^ zGQz@m34Dwvj%uc&B~lv2Ff~E7{NS}UtM0Zv1>P414e7_z82C1KVY2(T6Z_<=M7g}N z(viVRt#~VPijqob7zf1W!Ud1Z?w37Pa(aWX|GX0*ZfA(YFxylUD!xR>!8R_yisY=5 z0q4a+f|2Ar5`Z95ofcezOVL3qur`+Dp^}}T z`#qejG?pKX>ey16S!-s}`HVlg>>p0Fr@J^KmU>WN2KE#s22P>lF3o|V`}a9%Z^1*p z6C)29O(B(L;kbjPQ!ro;HvjUl4sR~s0{Wt@_xKy_1|+}zm6FI@4U+$+Ys!ihB5>Y? z+5(w$v_bz(Zf8ZPXo2E52-JaKW1AH zk3s=9{a<`h^Ft~(gsXC3acseV`ysqGiVCi$>%nlHYc8JiSCBc^{5V^{lg^9>K2`el zTKvRp-E`T!oHOIaM4L6$gf2`&IgfnHq2TL(coeI|DU)dOiLn`Q6ah;M35WLK?-_IA zk1XN>ZZl~~_3+tsvX~SEndOe20pXA>MxJ~pDv~_J!o&)+Y(L)qGy`BoQvTCRzfUXF z^lpc_^$6{{xrXa(&=oV6hbU2zUUVjvTqs}}8f`&I2B%&EP!(`sbQ0AFp8M%$-Gb5L zovhA2q&$xA3(C%~9yP<7T(h6QGHXWuA` z{Su}jWHM4v4Zx;p-aSL$4W{q4n4K1HBBVRGKAzMgfD?lzjzVrL@JhqtFnLrOF28~UUH8~%UHUhaf*JKNLtzx93b`!Md z?oOg<3`fZ?Z^)<1!}thBNl7PJ|J0wAMq+{fBbbOKnq&c<5vE}ZqK_9dinS7^b3Z+h z>crIh^LG}=^CG@E%6e*Ptl>S?cuF@&byV3Sr>`jH>}_2!_(78=)3SM1?`WtdYcr zJON)9PZinSD=>6yPCrr=MIl{r+6#t~4lumLo(ztH`_?K;FCdVEq^O&^%HQnJ58gz$ zC`nqN%8DfkJfK1gNvoHoC5$~t==}`zDM>Hqa4wjqw`t~_BsoqH`8>@bCv8lRs3pJf zS0apno;V={uD!<`Bmo>KzaivChO8$CLYR@PWaF?#%E#ZLVIZHNdA1=0K`BjF%H?4N z6hPJQC%JrkNji!zWmhF`U<8x#trUb%nYpOq?RKVNU}j|eb{CXWU4f~SLtPW7G1)17 zTt;x^>hXeJ&eG0^9{i<=kie-Ja##1PLlD%8+yI*srqi%lzY4;+2Z3rWNrQj57_U@F zYBYh*Mf4KD>8Gnsa~c&1x@7`Qvc>5KBAJc>Mh;nx$}Fb&vpxP(C@mnBSKap2NY)cO zk(`GxdxqrkL6gcj0%DggnxW#16udDQRqdYdSjpD=D=BF4K`;`aS%XJ=?M$i)Pdz3H zd^V0t+qf=1+Wc;)oYYvMAgXi<3_vHcCA0zM_oQ+QV?&}Yet?rTK&-YZj#du_brW9Q z7h@o{4$L71VT&Gr`8lwnKo=tz))1}OSK#851$eB3)4;@@s(JUTp%{q3qDtHG$@WH6A zAp9nwF7N~a!@o;ChixUrtOS!KkHCidcKMv-A|HcvNApl7NztGSF^y!=kViZ;H$Ac& zGiZ)XIr)MMB#0?l7@4r`Ff7SW{rt8d8+NoNMF`iFjr!;)0_toqex%BSB#4`aHvB}` z7Gh@!w@jil02xM-Y4R9egXJ64Y{)-~(5kE)Df1n|_7m>lNki?Y}=fKEC{=90Lt3Db(pnov`)9oa7AUgH(3FDQZ)5kj_x6x*V&5+^A9_glZn zfm%dyjRL8@Kq51K=*nc7JWNV^V(e}cu6b_SrZR<@vk+2Fi?c_k*8(g zg9x>t>i|?+O4Ghkuf)a???pNYNF_fp`dy^6z}+3Eyb!>37oo7~;?<5};?|}c{dFD~ zEJ?VWBO2-PVsYtMFxLm)uGIp-7HxzgaQ|!}tjrej_2lhVuU`Ei6eHYpeE+_TtZpYz zzk8z7kP-a`?E%S0f%L0fF?s^1gyf1u#nY1+6>n)IDjcm-kS!tw#)K~Gog@^`Met1AIN3V4 z3zkR|gKFmW%dMCfY>rT7WPA?5UYxXHkc&_9YcePLUg*%`gwhcEk&-R+2pW~g-x3;+8D)2x%$NBDVQ4tcF0+ie(o)kd|DGhx2{);7a zoVYPDOBKr-kH!OVB8+$_Puz-PQp8WnNaN#c%_LJv)fv(@pSrt?uu|kK6Z0$%>q0mh zgz*ZUL*5_uB3>r~Uxg1?rF7uPNo`d$SbQ=F-bb+K5%JvQ0JiB3D*&vWe{!6Ylm?Rr zL#QHw_$UnVPtCWo6eluuxJ?g^@l^4T;%JJl`|_od4jfhTbC{7Zisa}2NIBsDzHspW e*T3n$w839_-N2C=nM$(M=fq{i68^Y!^Zx=^+uA7r literal 110282 zcmeFZcTm*XwmsgaaYjL|IwFeTH7ik(9CaKpAfS@7h=AnSL;=GHj);z-LCJ_@XmU=D z2q;l9G)YASnj|?1eAn^5x%a;MRlTbBe)aw1TlJ{ArFU$)`*Y4dd#}CL+UNHR%BNX3 z?A$=1P*~5N`SlWovi=5zvM%DsAMjsF9vQUb4+$rEEhkl5QzsWA2NQ~tk&~U3t&^4c zmA^ZiI5?Wy+8h@U7dX!M_iIj0c8-#Qg4X|hfq<=pnP9~iEmge9Pj+Xt9VrxnedK?u zT%+ZjDXS=yv%j8Ha}62pa&b~??k*oUYmnMQWB)PIw|niN{2QMJwVyX$Dbk|9&@5X$ zLvP!bvSGVg(%l=|)~&txi>5nU=>Fe+Zs>4Z`EcOq^Rq(7pI?3aaZ%f{<(cYb@^Za z&u;7g{^I{G&OhVfeC2KdoouV; zT|KSZ;f!VHBYJgVOZ8P%iYHH=ym$BRgxizqb$etcUK?hdb+fI^HB7Cnk{oyH`)L(JI4;(m9SzX-}dCK?cAAdx!$2^=tDQXpG z-&=0Jvb;pM+52JUo}V8rG&H%XNo}MvPu2U|xUcN|=dCg`ZN@u~UHNVA-n~sl9xg>+ zUvHGWK9`yptlOKkuFxJQ%f6Nle{Lb%g!rpI_F7N_Eisu{-Qy%hN-xnvF3^ zBRmz|)2re?EscME_oVXe+Y1T`KKJh3tK^SLO}*gwrMkY=&^sAVqCDH}VJniwjt{3# zy+6ffKG+aVxAD<^TUFKA`%td-un_*`1pg@thuwvT+%lO~?dj)QtKPkvz8=n_M5|1f+ZpASH*44 z9({QB>{*>6cd`h{Wq#Xv_w4!eN@!=NUYARcC;9JKMt(s-Wg3Ojv|qP&@Ps;_ZuWV( zZ(nZ<*Dmpo3D%|IYj1BkFNz_PgU}RZRyQr=2t_o=?F!sWbW z*>+R54|Eq5e5rmOwQeK(VQJ~kPh*b#wau0(MPF_O;MZPh<6Ke8wrM_#wq|6W#V*)i z^kZh{=898C7|nbmzPr8NkCm*9zF;SxaoCS?^g^jk$&$01hevQUB7X4gqkRGvn{QMJ z6r8tgrK?}P`m(Ht+jHvTNKZ-6y5!YZfeh)DZ<7;KQ^JbDlGKEW$w~dD7x`6Jg$D0z zH%L9>JrL=u_kJhdoYHbMZO?_$Uw-+eDW7U--JV{?x73zqY|-%Y_tL(dK9NenqUNo1 z{c~r}*8jfskQ#CU4ne%z18xgnra6{CpEn;bLb_h|3ucd~Srx!0+tXsRbO-7{lrtgn~sQx zsGFOcPfSm%pE+|^C-Yk5+?=b(wfCs+5Mj(Eo@Fu*64xKhKGP?KRfkscU=Qb{` zk*Nh{dRv>8=kk1iK8~@vo}OM%SYTjm#cprC46{1=rTN&|dKr(I!v_w?$H&KSU|~_i zqFpgIZq6`M$qBJ-JIaE!GwjH*ZN0f>{nO{q<8R$seaFWq!E0&3qW;C5eCp7bfS#gk z1pLKce!2Bvm&A*wPk-*~>pLPObP3POMCj0J&(5@Enm<`yUbN+say~ylKTpz@q|-oz zL6Q5r*4EY-A)LHSn=VP~?n3Q~hr0uPeSKd*}5txws7(Kqtf0{{gp5LT|-G-hKN{k=L=Z zsw0wr-o4wFI<@w6E|VkAtd2XQXL`ylPcy|pdC{gNm)WMJrBV3S!!*DuJl?hxgY^U}jN);xRm_glAbH^*Jvmu20dk*u4O z{o~*A3tqqe#kwO~DOk+51yO?KJM!e6T7HU{HABtb-X4{TN>xoHADKTG6Fh|q5)i{B zZg;}AXgYw~^~0*|+2_xxsI)Sh6RE>($D&`pjLXf{!m47mF4cv}Bp1INu-Iv@GL_ zO}$@&ZAEtqU%PzqOMCd&uXeo*%M=kj&zM~SuaM)H^L~=Hh2sjol{$s4Y4-Ddft;w- z+q`0tcZ?@KzbmhgI?a_3tDRxmmz@(4@yd0!%fa#i=a_A`bQGXMUyF&*^ZS#vQgw6K?pW{7!S zzyAFRU4aSivvP`xtC`u@(8pVJZFj8sf#vA+>(_ab>aGgy2$gaXjXD4P%5Z01W8{Jb zlfy0h@#8JMs3^k7D;hSQVPRqAUq6=m_f-W3_hn6MS^D!n^haVBu_>N6jY9GewN~7- zXHWUM?RuBbhs$<}BNa9aC36K1FY)Q;H(9isJd9PBsI%?Nt$!wNf8|a^aAWxU_ZP@= zmLo)LKKJ)a>6y71xbE7KFW8TnV7QE&CP97v%0#;>?}LK4u_8s$52c1@$(0twD;)b z*jWF}&d$(tZXX*RSPpyg#JF&(K~Z6Lw2PnNG*eMkRW;C4QZf#Vu>-XS??Q7tdGX?- zW0oy^#$P}D>4PMyv}ic_rN)0KrKEdqro+}B5W<&H=sJGIc(^T%y}~d3`)Tmuv^kn> z-SOpujQTMInBPo@^)$1&yf4tu#?{S@*0GEr+6la>d1snWEA`a?szF}I?++h7w4ZL) z{BlTv+8fB!N^uxAe-9^r{N1>tC--ZlV z625+kE%f^~L@%fnxBv88=}1obj#VF!A2>(~)bBa{crypbHR@tsoZI;qbDw#G~Q? z*9bjB2!CXVKhU!;+osMlnNcU3ac!|+tXOw+;gE93Q3d~<>d`AXi7CbT=d9^J^{ut9a>S2-(C=CG6^j7)RmS=*jWb5&y`V&_&(^?$sUp7&*ELH3{NA)6`7 z-zMw)%l$b!mOQd;d%EYAW_w2HDOM8MVH*h;h(dxxw_3ecGtNb2k$JZ#Ie#o8W`Zvws;P)gaTtL1;!WS%>daO^?P?!IHZndUHE#F7nd_<#} zsJ7#!ayYu`j4)C?>SPzI>5^3eMpb#2feC0c#aVsEDiqg0Z>>`=ULFE`dGz$@)z-1z zxQW%TNMn1-I{cWif_XcT2}zJ60;N4YJz7BDT|H&$i}T~X6HYd=?uLtn!^6cOLE6!mfuwB;WKUe!rmn<(>0ymp~e0^tA);<9FKtr#*ORB1h zjRYv8eb(aF$-IEvLeLy4ClXk3x=#6Yt6>UREoKjSnNg`vyu2Sf;TepKA_EN__ERum3KI2CISrMpmK^+ip5A4&Xc?F3z1f^S;n+^68^TMgc3NHAFL| zFKei2jd-|F6E4@oXWg8jj#jq8liHqcl0Wy!j}{nc7BKqpW90JEVxmg8ED%}VFQ-n? zf`duVGKk)?)wX2GRCM+$;F8n31A*7DnaZKJdHDFw;}HU!ENqxKOV5{N4jznYF#e?~ zfZsZysMMX7$|0Uh0;=ERtT$YfLD(mXRbZ2YCdVs;xy1f>y zeHo8HA&{mK;q!REt*tHV4xwL>i!K6re#YaydGjm3R_Y1hNmI8R+aCH6)tG@$m+ty> zlPag7(WweMJ#9>(l+C3GEi&ur}LN`&jSKt{=B_@=zZiVgkO7lw8BGf zYv<%~pdTihDNSeR?EQqHV4^SA@5z!Dp;}!>uQ-jYsr|Cz&6|PO8wJ&{6X?eJ`uk%; zLUy6|vcL&r6uM@GYCT~iWoX4De{mx=&382#qa1HVP1$e*wZJ%mBJkq=PH~<0aHl(h83ht9%>IsB;1`2s983{nFbT z1zIN#{p`Kpf4_*JdWv;G>;l-@E`Y15ZZXWVpdkX1@i3Jg9UYNlhT90tSzhc}2@nuN zyGM?X?hA$JT@AQx~!a0H|$LgR6?$-Df)b)6&w)U&#Aa*66CKT|H(sfo{wyOXl_utm%Wv zACU)lG{&lkefsoicy!fpf4}C!7?=u9bQ`*PZcm;(@&8!rtD8w61uZ*GH12oqS7>mu z7QFKiP8%k|1NO7aJlns+rQ0o5pW3*ltpPPKdDHJ8VpMw(^n7AH$P508f<;YCucO|} z*Mpe?!DGXY?~y5ZLykKWMzZK28XJG?Zm?%Kz(;Inm<`&M@{;9gK|g2EHH?@UP9MwK zycTyWpxh+|Q&ZDuH^p=3?gKLIT$!))zMq}f2LfbR6WygMV#bb_- zSM2U+dLkz%ST~1sdf$?sSI%+t@`>o7z$MQ_hDS?1_ba@AI-^uTQj+01Ry4~KInphC*C&7qV_sI?rglQtgL_nz=Vsd>E?M8H;&+Tm4_ zte~J^uiVPKO7zMhPEo5=hU(1C_1vlP$?8TUI?GejYGn0rjA_W&|TcQ?C9+i^2Pw_U)6i^CZjwp&QY z5X|%hr;ybBd6%S9Tti_WtA1OI5@#$=!c{V)T;bWwS;%z$Q;qJaO+-o7eW;y_U zhgg@>6ZxWTGJN7S-Rm#GQx4-T`uarRglZKy#BC^w;nUMBXKY*V#Y4>-^h08t2{TE z|0yIG$je^fIn)cKX&UXx6V<_~Ed>6{FM3`uYSz6?8e>fY zuBf2wBYHVDpj_X& zQbRfyD@9jo_w+!0X-w}ae5YnNy+mWWg(c5M3a8I>B-A^N#0=lvADOP8)tZJJ4% zA;??U+rlED*<+9H@(OF|^3Z6SaYaz_?#-KxcFB2l#^hHfSgr612xQOM+`M_S{P6)r zEBZb;xt=Q?)8pgz*96=>vec6b|Fl6~Q7Y9=(Mo&$ zf;C%t{pWp&^&t)ZyE<c#S>M^t%g%=jZd_4Sz~8J2X@7tloy?QvRA4K3Q2l+_iM z(}Mp19I)O`h}&a0&B8Jxqv=-)==Q{-rtjsrU>U6ee~Zi2?ilg`;D}B8+8Qk1{}qP` zZ)<8qu3WiN%`jH{V=R-wL9uD2ht}sg_Vairt=q!;FV zG6#s3J-0%b5QqbpqO;X}QBZUsq_&qFxzKM-Bzzz<6q=4%TO;d((+z4363T4DBHi&53i8$k0WWA z&7R5yP|)t&by{A&o%+O*BbGZPV1)ng5-md?tRAer0e0TbslOai_IMn+l@Yb`}U2;^TUU?GZ#8mzw$Af|N1G&l5TJAF>J(7 z(F+Z)B-$Cf8_vYl*;%4-%S&WW{hHeHMMZZ04Lv^voVA}2Aj61zd)ZMIzwWiPP)j{( zt$vADS7(jK5K`TP!Wmrl?z-rZSPvwROR>S~2rnb=t!|9()r2`h;%#<~{DSqa*iI zE?OceL|KOR?NOU?NZPmRvZd+<@rfT9b)GDJ!Pq&2HA2AEYRp@tW( z7T@yu`lsIy@m1y_0(fi8#_}SDvtAL6(?!=>-FfZ{zLA7p3)*9A4Gk)*zjsg;#SY(l zMA8gprh+-o=#E%~OnL31f=Q!fo`qZ;I5|)O)h)J7agSzoF$)DDwOV_uux9SORtSkSVBkvYd;S2S}>1}iD87;zI%LtM?gSS zvEZW*=g;82G?!2(^QT*M`5U$i8dzyc)Lu@m1+mkvuk6RpFAO;%BNIJgGV;%8I-$%9 zCa*vA2!3$l1dJezK&qGafh)c?kfvjd))n7SF-&8AEzG;{E1te0u5%fOv1H(Lr z(7S8uQ)&8c`OYI2kDzuQ6+x4Fu20Q5$+1~N3?lH}k%w~2sfo(Eq;uG7jjA*;G2!4& zQjLH?A?cY+&2u^({7?n;sm?zo zzug=RwW&Gi?m(Q({wDWI?paSyPp=~c5K+ZN^c_UL zjBUPn;lky#60vLVPk=f|FGwQn(kDGNuGun^*%GE?&Y^<`b-uNbrz!2zcOQ&q?;C?O z{c&QKJ=_ZI)Y=DreyV8P_!>sKdXn^SYd9|rfZE)%=Om$K6$?i5hdzT^(<}ll+;n&I zzG2HGWf`_39^u2)BQ_vqHU+Muyvln(IrJ2O6(NTA~< zGNpMn>6vFZrJR#aS?A19x^?E8ldVEgcJl{b`0e7U+R^i_r^gW4E4#qPQfxm6-=eNQ zHDELTrd)et--%HF^*7D1_F6A#vtQAno`kAvYVNr>L(Lrp>i1i}9T3zmS6cI&l2Y@x z`N?VYYD19Sn#;BuCP6%oN0-BFi1sV%2O>IN<^A=;$?aK%HmLrjUDwtDv&p|YFFT$6 z*R)xVjsh23X#5>CBEqqoLxs#qWEoWZ`+yY!avOat!O;fc$Vcbob|2W23=9yN&wFX6 zvu|W18HZbjZ+MYssmU{-{;!*cFsJ@zf!irZ+&m8 zfk$$w;Etx>&{imTd3l+dA2+MbJTgYiAWsgd1Vz(W4+p9{2fwYYZNV?{-TI;b1@yN1 zPdmpRo#sw+b$fG>uzKA^9yJX=uyI>W%ccnx7Z>M)wXSJMPfwo$X48!KuO+y^zEFte z6r0S{AB{|{60afzeRYGvgI$B~H=f7#X6DkdW4`)=79B5!muCTz2cYy&8KTZQ?XPmx zvL;E3$KPPF5O(#|jaAP5M;2dP2EZ@#tD_z97&)wdeQBU_sk6CN(j=#Vs8CZ@pEA=s z^nnF)*pJ7UrR{qu8k;(Ff7$`9Hn2u~|ewasgB zM63wx(gMU|buFy{I|)MnRaRcQaN&=wx|)t)0nMgYJuZUWIKs3Ch};BsMR2rw^z0S4 z3M~)rEm=h7ald{0c4Bl$Z;>$0<2bmpAI=v> z=Ng@d+ae#zE}Q|n^`KSdI9I1ICGv(vQ{k%u>r(SkDAXKn8PWQ+FEH_i#&D-A)ToAFl%xI zlyIWnXgh-L&5l-2w22vu>sDE|Mr_lU%Xg3y^hoXnh5eP9xbJ(|!G4j5mE6=U7`nkZ zkos-T9yFn(-a!hbpJ(-}6RcSONa!Rf*mba|-3=uxOJfbeC7(WBQ3?^i2!fcNYNpZ7 znT%}iXRv1G?!$*-9;z8Jwx7X+)!5uP5D!qiDX=x$TG!3h_1QEdb5;s*BBAEu;E>*) ze%z*er0;ET*<(KL35iAiseC?u{+hkXg+@heT#{EGLGTof&8}#d*_@z=x?xi^t>FoU zLz(yZzI{=(KitqSV~nTpRJ4KI3}(jN%`$4{HRk`@$NLUEQZ0ECA&YIs56wXjuos;n z>>x~9FcHNg|1le5AL0ze6G|M|)j6vlk`Q~*fyBwqzDnb(?T(p6CC%+zM;r_?F@&AnM&Q+;8zZ%IiBA+`}aEyFvdVSxdGO7F%;!5=Vy<_Q#G zQ4`*IdaS1>(%43S3N#|IQ{O_|4(Gwe%s)9Hh64;Gu;TJ-}L0;SONP zto7EWj?WGweUIk#a~F-5bD9qR{`>DiqSis^!DHg$4#Q-pLY^R8){}hB{K5i59RZY_ z;o;jlCG-1n`eF!`Rj1xhm)JnxY))%{jZPy4QlA)}4xpS8lmwsXnjF&CSjJ zr-X-ZrsLJxZe(tq&L8tXoovr*(~Xh@524kiV0`6D65Vhs+b@;D-npXhV3q`p5W4fj zA3R=dtA|27?`JvdWz2F6CH1N)?Hbc_t7TkH|Uq7ZI%Q9*ffDqLah(xB_b1B@VV zP-!ryL?F_XLM5{r+%{PT2X)7Unt?HVjLhia_8=PR9LlXN1;OITnqJivE-|&_f@*BHA@R*vD{0^I)Mqj#RuS8xxiKr{w zb@1mf0cDdN+NsmC*CFPMxJ`U^SP4V=ht5gG)V31?2w(Sp!+fWBQFwW5!qUY& z`($#{Xj$K~yR{^5XXS@1g$^&tKB(rTJ%4WgNI|76-xeyVhR2WPWo6p>Jq*^BylVi2 z8vPOI5NTlWWXT~VwmM=pyMLbryIysaCF5c8Uq|}NtU-|%NA15 zI+~29QA&f38T zS1_`JgR$WnAQOqo1Xx=a>24X6Wu2d}Xsz`1>(}@GV%^sQNFMQ%=u|n*a%u%MdUXTw zZM5385+p$~H1N3k!XG7&h|nf9qLWC1(PJP$*X9^!ZFkrCP|4;`T65rxHS@#<^4yiZ zaKJu)G+KT;2>7pw4v$uuoLBlWE%WKT*Yv z8#Xi?3cvmWp1y&sR)a>Gra?RwmmCDR!_p*I4%xa$`DQ#!qsy8#Wisq)r)OqbMsj-S zSW4pba{uz&%n6UHA*oXamzoG zBjBJ)c`fk__)VFVQkYVn

GqPdb zi=6)Uq)=N&N5@e2Vo&s#33D5B4{6#2ir9DQa9eihAti_8*X;0#M2o3DD7A`WN8d|f z$vs1mZ39oxq3Geio!Hhj*Z#uF%h0fgr_R=d&GOVv^*$*YQ25lcos)r{$J+Jlby<#- z>84V9NTzz!5~~vK(9f6Al9-UqTBg}mJojm%-<~5YU~$di3UIEOcBU`cG?%qkXj!^h ze&QJ7$r=gifahBV4?@rv4f-LNuG3?MlMl)ta9N6cHR13fgcT?Q7B;q=Gl{j1f#B~= zd=0DLzO~EcJ3Y}7V;mJ27)ZorNZ8s?BZ(3H-ZtJT6dnlvCpA2xh=>Sm!pL(pqYb4f zixeqIsp{#4loWs^BP$Y z@}CxNT5p1t`Zl_(y)E2r8p4HIa6-0Ezzp~_JbcV<34)}EZnlZm2{Q=ssjgr1kRp7m zNN;`a+&MmG+)pU@M|HcQ#P~P)F^L0YX|DGn`yfvCVKJ5V^>-d;daroJW+!Ir;yK$L zU~Lp}pSo61)n4F|k`d&j%LpG-l7JR%{X z9qu{voFt>PQFIwsskv`q>GboZ5@N5^I^jNc4N-1drT)Cb8K2G*8a>byOnDwa{_SrDTYQ1c3av@T2O@Qj9OBS9g zJD%DuObxONZk=0LV1lgY6H~#7=4%KLG#s7EZZ^dXgRpM4Xmt^P5^D+TVix4HE8L|} z$|Uu|c#$J71lHz}PVq~SC7}|C;eFT^O+mKgY!bxFGM7&D-C3;9hX{!5S-?{un83gQ z>bHl+3UG0jmX=P#mWVp3&R%*}KqoWqoZv~dob=W!k24+m&TY?pa}3*=HCIzLGqZ$( z!#{KJw2yc8=_d!3raB$#260%^a#fCS-giEf)8!m2&sGgC*#gyg6HRl*l{Zwk|1;UW zsRzV~Mg@!ITbVf8(}iKaqW&bADesb`?i^8&KjS_9C;i9fzFZ*>n0aaasp4ZqhaK6Q7(P`?y{q%ht;zCZ{ z$Px)tG19dVe48LcngwKgE;Nkm0MIyQ<F_e@cUcqHdZ&>x^)Y6>X0u(V=J>Yg`BBp(S4ski%SPu zK_TRGkFJATCvr*V$dR+W8c9bLE#dJ^-g#*eZ=25XrMS>>5#BAnhFuaae|ph7>-0U7 zOP$wKPK?N7hUF&AtBFNhP>(fj!Z1CgQ|KmVR=cafV+cC=AUd=Z`2`rHu7PYk3Q*{m z2|}Z1)PZ#5o5dg8y&IP%mLpg&Ead0=;J9gZTtlZuoEu58BCUGH^^0Oy5|G7L@3@`% z8ts=#;j%?KKEsZE#~jepv-b}mfxW+C&|PT->u+S3GOzkn5(36xb5jno}7n(jx zkiE?^FX|`xBv@%Sst9U%ouIM%9E6D(sUz(<2?NSK8hN9kpmWEMnF6p3fQ+4SludYGKeP zIzE+?ej+d@$A#8$&F|SW4tV7`k>G-gD>=NfR)Tt}(Pa8{51afWC zNm%|EUd6L06?8sEM`|o7Lo2wSxbU+V*KIjVsCIb;V182f}5?16jhH+?>H-8(R+HgXVcyh zpnUtdLp;N5rbK`>m)znX{Rw-M7uBgKR8FJ(VFo+|o}kx8LNOp#SL z*Q8hB^iz@KhfpPQ7A|})=d{h7-}4?dk>97}oT6eQG|tao--kjPKvSiVHN8CX^(*n~ zX%l+OO4ANZ5jN?sH{{0@b@P~Q;xHWedho!aqO*3_sZ*z# zAqP23It{ng?gKLD^U3LmyF1#)-;TQz6@xmTU~I0*FvZGx|_Mm zheqNoM8nahKC@LtI`X&Q*7`E2ps)Qt{@Q`8Ian27QuprQlXnM75~E1Ji8Ip1XAKH( zQ&DaM8VJ9z=p8$D5J0OW^9BA{GOOb^=@P3P>h$Nj?QLD$nz_(-FuA!tL-@3>P4VZ) zib&}Y4w#_Nv&{GVAw%oKyUo6;7yd@Asfgr+q$Iwjez}!;`1E&Gu7D*W1D>Yl+d?~1 z3PvUUdQ73X<>P&Jg}F!|%!mz_%z64wg!4kaT!FbhUHl-iSrDa1$=Z9eMr<0bFC&vK z0*@RmB_&0h0S=$qVVxs!T|fBQvpA9~uI%573D5OwPDGRJe@1ALWtaH-FW*i-*mVqs zDPtlGzu0xwhbXhec~wa=h{>ZK%q=_vQS=CTQ$z*3Pn-=LpxW8pi2Z7EHtsDl9e-sp zSS8+?K)lLnu%T?PEg<$d8j`?ZL7ehjFqO&6VI&-stZiD7o_CETzYVr6Hiz$OJ)0=Z zORDC}FbZ;@^5$pKRTj};By$IMyY+LCU$qDW%;-foED9a&++@ho zSYtTY!}g{hEIS<4XDvO*Vt&llEN3j6`a#5tLift<1~;P(9PhcE*HJlzFrsNh?Rh5W zU93cv%2x`r2CbniQ7i7AlsM=(iuGqbtDvU#)E+rw2LS>YX5+OsJ9qA$eJQJ$R#Pi< z@N<)}nYJ@mCSh>6k0YcOF3;sbw8J^MR*-d%$cV;pSaP6#-OQ({6arLae3)MJ{^FCViB*|4<1 zDGm1IXw_COF6(Mk!o!CTr^b(F&}70G?qMqD&kI9j<74b5|N87$>0v&;q@&*O!=^#i z?@g}~I39#Wa4AS+0s{M>L)7lTu`?U@gb7orpbC;##-4Wr&542I$ev_4CFe*_C5_h~ zo@<@VkJ!FM;gl?6zl>Tx+K~cQST7~jYJ0p_tkbLmmxL`_s$F5{$=zW;N{H_Bs zmrIQm8h`%sMMK9Mm^KYqqZhMNBJhLYlo@!<_nn(mmscNEs{1k^@siJ@ZrA5eQR_Mcvx*qK6s{NRPwUC;$-@U5MidgWA-NMlVLPu2-?|h8@UKUP6KLgi3 zH_(RsUyS+CI)Rjohxtl{NCrq}KX2X~iCl$Zqz4QiO}QqQ6H*m6>?Ek4)%83zq|oI1Nos4OyI++hG$y6#q&} z8_rPDCV72*@Nn1aIjxcSg0QW7_wC!ZV~6>;s2jY_d=I#QHk)s(T9bKwexe~02fqug zh&zrH^2*_|ii#>ot$Q%$}@M%a%?DETPsq@mL&6_XLe_cgB#ksvd?HSTYQJ91RW z;{xpqUwQLmS$R1;?J9)hg~4K)pn}kZ56DJM+cfnqJ~8|=8ZHba%z`2%h@cMM@%2pv zM?vIQQ1%I!h?`$rB(uXfEv*0~nTWW)FJFRaMncc000%X7S_#m@0$f9eF&*r=KIvZ! z|0?`nr|@Lz8KyPF$y-<`9V%}B3vyHo-h|KnuI3wB#aj$WS(4w9Nk!TuDih)OcFMSC zlF?CQR5G@WEZqRVS~XpXm>s^OtL7o5aWBs76bLmYgim!`djJPP0zA18;&dV-Bf%08 zV=_)_@-|+LYkie8c9{ogf_b@?3keqw9lP3n2^S%lBccG}FP=YtAC4T1Usg|e0`@U6 zg4flPD2)@*2v#wH3{eh;gd^t25Ehv@f*cnE6vK;AX4EG#i3`hY7UVTz&4_li)M)C> z%f}X;fiGWdk*jLvkJ<6@v}f*bxIuc6aSFmgiCQz_P-~cNX;CC9cJAaEY${H}U>ccL zKm_F+j)H|^0gT*;9VhcK?5Kk<&t(kU8M#Y{VifZ%j!@&?a)07`S(q6~B#vv)Bd~-w zfohosc(g#Tpy81v3(%|Fxt+fE? zI|Uz=&aQr>33SzwNJ1KU`h9?fEhzUQa0NqkPXq+O7SO#z+N(9SoI>ePq<{ps+PZ>LG62TY6tmlF^702#Z-?o6S62Fax4 zgAC3ho-c#`>|DSGOkj%^VLXMs#}mVSWIzI)R}zUF^xmsht->J5VVV9U=(ZT7x&+>- zi;5{?hg`|ktWE1$+;>U#Hb$IWL-M0@4l(J7BS|Hwxj+c8@+xg-e=tLlwMNvSwyM#J z8}Xpl?!oiIX0ycFYiogGvZUTEIBZ7_mv~Io61%({s|fU&ZEq;xOlkb?*~4Z+H_a31*8pHpDaa z3qG~E=C8L?m%}kCM~h(7I933NR`Bq6-$#iO2u$^@ z{Qh~w70H2Po*l1jIX|SscDYnMi!E?uvnI{DFY2MJP+?)=u4Oq?opPj&fv>anzh$^n zILqU?bGLcQdCbnD1g4+s$V4?Ae|IRnP2H?r$ZBxa@clYm$Z{W=PH@LO9-A2b>~xl< zC}F0K2AU)NNRY(8$d!X;rV~yET#A*CN{0roczoWMx$R8tzbTX_v z`VIaA9cWOaxxJg*+}c|o;f9q#;za>2NStOh^B{?g7=BJUYOVX>2PVE$6NsAwa&jdW z*Fs06rKP8UQ3o(mt%)pj$^_TR$SWvF<`^k!z^quG(S>=JK}S<{F|>i)GVTN5M=k*L zp=XIF&2e5Q&crnY49;~yLZK2|6{=|6&1oHKkHDT7Lfb3p`*<;{>K4u)Im;H93)G({ z!%uQ6--t0Q<kf4t>`mW=BCOg5u=eKqnq2#Iaft+CRUfDm@C`E6AnJG8K#>J6WNA(e z%e#c76qADQ66jQG^}_%Sl`O=h!6k|%Spe0sU>BlF%@W`IaE{`BT|T5&rkrg+J*E5~|; zYRtALFxJdj5q0j^l{a_Kc|3YkR<;9bQ8=vI7k!!W54c~M@g6>Wln|K&=EsO#mIAeP z{<%83WjT!c(cTW!a!}*cj(0d8#?mo1&Mk@hzcZ#lNGx7-l83z$)?9r&IwFRGj zycuRPB4Nh3F_Y}eMYTEIDdEWnYx6KiSKgWD=#Q4H{Tf>5!4NAN*U%3v9G3}M;q3pL zkrMQRap1WfAXE&ZEBM&h*rHGs$_W7jbN}0ayAObnj`c{18%jDbS>ccEeAbs->8#&a zQI6rJUJT+LBhnOA;Om4}52PGP;a_UZVFW1$33&N@knp>K)|Bvi+|04>@Zq=267q5?8PaN{ccPKo6r$T8ui(8FQKw9wGWG)rJ_|9*?9R^?`Ghx8KOuf2f*w@J* z8SZZ(m#Sdm5aWtK$MFbF@hQ>d#H+X&2L?g{U$G<9iO*+|gn5L8)j<7W_V7Bbhb85# zVByzWIDMD$F+vp!&Ru-!%TnHcetv@R)qzEz6Tw0df8z#a-TL*3z<(r(!a|iW<>O_2 zg~JC&U^fvD$z3fNSRi+`BxxVV#AVq>g9*C_=nQb>z&U&e@@AOJBh3q<0y2y`5)|F$ zO0xQs{!SDp94$o<+T>;UQNxWfXxrdxN`M4HKpu*9=%t=^T*~$RvXcV`j^M(Syu3Uz z)v2ZR41!!TkXV8H-u6p|O1t;rN|0n?!+^6&(rw};z85I}SojBdVMsHd4yjSECuYm!#Gb7N7qWKqv3!~_&dNeIuV!awiYl>`nKXGa;= z&1{;TqWi(28-e9JeC!y}Q7da}15k#MMo}N4A2EUkDJii!*ZWVGfwjd-v~? zi-|ye4}KkB>Bqe5{$%Y81-NOkqmR&o1KBnMGEKwZ0*L2mzDVx#!38@b_&Q)J2u_DP zM6u2d<^gpS@n$r87V&ZrC`b>5U8lj3hn|aUJO-n&AR2fm!qGW0UA4CAWrgpSy{?t0 znbHDV;!|kSaJQoCVHQr-CT3e#2f-_5fnR{vR2>5*8#Zp-h9}3RJ>&)%w1ItE>nS&$ zzzRWWv!46*Ri1Rd*o;UH= zYhLZR-?x^Ms)#i+|El?Tv~W`SR>@!g*tCVrkvnUrUp+fma{Jb;DfDmosUm;<^9QSc z{BypO`C!QyattP7bN~IHt5<=>|Le!*7Wb>a{tim`;EzB!fBoDD{yU7~|NODJMdnui z`pNxi^{c=B{{QqZesusa7pEKNEb4cyrNmt@Qj16oM)nb+rt~upQcm3A-d$?UNy%!> z>RKLQ9&Fk%`i;f5aY2*3`o^{??Ds!^{x4ktYe2X8=SSF7ea78%lv84^hW>;6De&f3?%k*8Fvq>ka1XpI=dK>{MX8{2y9RJ+)>aUlo zZ3mF1G#w?N`mdk=R>&3Vb^Y~wl+M6+&8+|P2eG(vblp)(girlHpAGN)U*F+-l+>ty z{50Q@z=;&O|35xTg8z2SaE^&kUj8~;keIb-dy|Hi5N zSDHORHQI2Qa)K3(gum9hvli-Rru(!7m!#7f+~G&=sw3Qotmgto80(sA*4)5c+h6C5 zlIuS|**^reqy^wVd0FQ0;WN6OUUP$4@vnmwQCn44*Gg^GkA-ecz1ui8Mx zJzvD~bOh$-m|zx4v(LZMOk4&r;egW*iY7KgcN9(l2S-UO_=G4yX&yHXp_gxh9ITeYR0gH5n#v>mi# z3;Zhgxt3AMCS(z{olf(=`8TbaoqP{ihS1aKN!IKMJ?Y;KsjKeEbIwmeYkQ=_T;!@Z@Zy==uLEV@1?T zK$K)DJ*+=7Got}TN8d!)rnAiou95a5)@vx5FaDM9QgsO!zI16=P7Z1|5`FiK@q$P?zm=yt5OPdA+c+zCzM#FwjkO^g}yB<@$zk@ zF!%mzXVkWq)^f+E>cg+l3b@G6paPAPC~1h&0j-j)aJmbc=M0bfb@(j}78igY(DVj)N^ zO1c;S^#Jp|^X=o^|NHG@?{&;L&Vu!<=egs$uQ<>1I^|XR`d<%LZ3W%8xcQ zG%)m21DUxh?I7z-5pIFRn)*3v+ z$T4@oiUUvaJqt*AsY3XlPrn37QiCElfPC}jo1(_p|20UWF^FIdWE?*v3jvZpbmIgL zlB5MsYW=z#Lzzs>{+#UQy4$#-RW z`FN)A2&5MYb{r>ihqeoZ!Yz1Pfuz?!%2D}A<3a4>PO?a z%c;G}QJ!EgTe-AwP&ZPV}dzLUQ%mr+a{3=)k zQNrKOZ1lhRO4U;=ry0|qpZpC=5L#xXr}s82cOXShSTm<^ZnN+Hu11kb{5fwsr6ry0 zo^*WCPBhlf`Mq&}V|(IWA_qOQ`rK3O~MLdr}$?JSj5oWlEeqyT-V` z`{N@gVTFxLU}1fGzh;uRy+SZi9(K6MHT+q+&HmV8;!A>E zr~MU&QyA~7VSf$8MR}q=SjjPngR8)QcvDb>`>iR4FMoj;8 zSXev~s4G^v@$sUd7=lp$AYTY61Vn@i3?&@8^pN}z7Ve`SK(+mvsY&8;F<{*u*dm2& zMz}Z8Y+AxSer(w*^R+!`41<6`l?IxWggkK*Qz?_J!BxN~=j8OsD#qz~o%Wz=d(~Pt zsYg{?id*5fBTBw9XIaL+`-3ocIK?;d#-CavlpG3rUn3p*e=A$zG?mxg9A*>i58{=R ztE`asSGBU$a6Pf-w)RsfTp(KzRWyq(cx#32zZdg_;x_-y5((`5V7aeOmC;|{PB1Pq z_b1q#tgs!q->O?Bt~h;=_-*{1nYp5vyw@VNuqag&r~K+`HfJZco}2Pl-42$YS*n#P z*NPV{9MIO%qAMNZigHI%+xCZM#?S0eUL70Y@DHK;%yqDjDRAumBdSkMx~V5pmpk3Q z->CN@;sA=a!nso%0`l(wRxpK{A9%4*2nhS#yA|sKsA_-?IR~Ov498a|kcok)l}ll9 zXIQ(QNGAKVkiVZFQ1+yfV|nR;YrK6|koi2Sn2WYV;ja~Pw{r(rgeDc?pzJA5IEY6yfT)PIjoN#3gRZAQd>V2;MTwUoW z>+;Mfz3_mLFAw-^9;x$fudFXWGEnDNa^Y(7v( z`tT<+p^D}PK0fy@v{u$%w+A3Lht(BkPipJ@wSZwkD%ocF*Ku(aJ?Fy0Qzd6H5)S6M zC~E6{*TrvU=Bi>(Zj)b;N>zIs=5Ys;yFn0CeIcPS6@Cpn?R-kDSjZXGzfL~f!5i2^-1E} zaJW%u2HDCO83lQ4nZ5*bl3{;!gPv~sVXz7@aY(6sV7K-GMb6@g#`_dU?7+izK|a|{ z5#BOTbTRK&VBYe`zNoU)wLvA>vXM|NsvB5R;B#w4SZjTbAS?yrx%At@NchJ|MvEnJ z)giF0MU8EZs*k;_CmB`pV|L#axGk_`50&d4%=3QiS+nrBhoLrGdo{vo^DwUK%X1+i zp|zhQ;pP)X_t1_rPNC=yuFCYBFIlNK(@RRkUWwPPtQIcaWkyXmx3S)KbTX3sYik=@ zXcHcZ?@63$IP+yGJd$vglIr3|fzz9PC9C}5DcS=Gg;w)J+6S7ZLo@VkKj$%cUiy0{ z+>-U)ojKt%jYk#0X-q4 zUome!iLPHA)V6fxRQ;vCY5Pv;gKk=l>$^hR8V}1IePfc!1os`SK5=IL+JW*)-T1j& zAlIOE^d#KL>ZB6|fo+uPzQ{q*#A3LHkR$&@b$32_?+!f^`;1QOWxDu=YRcY3Xd$AX*s zpfmdOZW{N3V3v#kk!g2ZjagTGrBM}Ur=caXVaP_;N{J|i6lP0(Lb}%bqG>hCpJf_V z@ZL@Mn(A!{f;n9y{ltAgr>mZoP>qhpx@=T3{`_H+QH(M6S24f8#BD~Yy&e4>6-~#; zs;e1v({wfA=DzJr8aK6YO5OUL=?1f^Kz5MtzH1~Qhx;$F(mKyyUmnRuRyn-gVh8oy=dWP2Fr~W+}5g-UMtKp z1395{j^;~WCrEFYbDEB!QN~st2{Khhuy|FGj7$T>Q#;A`XOojSADt<(_6GB z2pCyzJ$h%yNqaB1eMkA8OR-HF6KX@V)IC^b@j=h%1f7xtDjsH!FFSSz4_CUlmt$nA zSfy=oVNYm?pek3TN+l0FxToZG<2y!bqwl-c2&IeBmr)rP52M^A-?qMF~7YI)ahcaILdRq!^R|`YO?T0;t=VuSuq8+v~O2 zQ?E1&EJFPJ+XK{opIOS3Pzb{JMb~k4e9v>24v%v&e)~BhlQK<#K+eEK&d=~y{~*h3 zPoKq=Nv>TYv)<7aNjbiS7+TK==HS`Uud|jRA48nCccwe;yr>do?k?5LYW<4oMh;lx zzqZfOl^qDHnO2S`KH2BrY`Ih4kC6-?Vegn4WA!pKcs4i>(0+$rN;?5#A~S>PA2c01G1tgT4;gkfOpOas~n997vr75&s8E)DLALYm zJF}@fK$SI{xq70jlyJ8tY_6a9y)NH9hcDlD*cZ$Pt`}%v(-&YFmeL8(>C&;cxX$+y zpZQcqDMw5!?$vrAbZx=R98*xup5+!9u$UN)HGcE*($JLbFAHQ6wB z-QL%9rX>aM>AhAoH@jwAYm=g$hu$k^#7~(I819<1x;Mc*wTq7eO_JxZDL(?tN~0@+ z&lQ+y>JS$_1gx&ZgKa=0bwUj>$2iuxj_J2Opy}}@yH%4$MDUTC-o5L=V&ZEbLDxD& zc6nYGO!W!|Pjuv`rV*~9WnQqX`2%lJF1nb1{V6vGqiTEkW`!d9bo|DdU&|_+1GkJ! z86D}+(FeIgQ$nu@iUMsV@=+ysszi1D3(BMYB*_Oq*+mULOZCW(LEnP)+Cz29OJN!T z*Fmh$xUlaEqJQQK@XwN9tV2)lMaMQ(S6*g#o3RcbADc9E4qI%Gu-Z~WHt`sg4o2Y zgDFu14L+V1x;=A6*$Yhz1=Oze(JBCVxXkC(XB1Wfe?<$l6yHMX@WEWL|3sj>Qk9i< zYjJ*!r0jr@kCi>cQ04cN^jweREO)6;JwlFJ932+UKe{}XK(nn4-6!O}M6$9f#6`JZ z%DlT?OKcv~CyXkT(=sR-T!}T<)uEs-^UyL-3mrODU9o`i=&|^sBlh%_?HTGJPc7P7 z;~ZlOP66BDCccPN8AeAl&2Ks}_uJ%UjdI|SC(#m-2VehDS*(p&U(VaYCz;wHnb-A> zJ=B(6^_>*nR}Tof-sD!vB|9T=7sv;u*+Q{cX=e$FnOtT{6+HAvg%DUxh1M8hIJBvR z-AYy-dR+=A9T&`>B=NLhgeFD(AEioM?0ASzAPYp!-pG z{@T<$twX47tD#VDzJAut^!WJuiWs%A?Y`Svx4EwDwQ_xVe%8~qf zbk*ybg;vps#TVu96p|cIC%Mmq&rFnF-!$!R(%!*MU_y0`wr1u7vS{AXsvz;l(`j>80k%)qaV;#_9^Ml8!yeXWJC+^ zL}-niKvnZW4i+2z;)Nm)Gq{>SEz32qoc@iFurQ4}O?H2`NI7_sxuvU8v!qTuJI`)O zJG`N0qJ`;1*(PQ86f;4Lj-2HIOssGWnp5F%b;PwBm7_Xm&k-v5s07;URXK_G5Hsv+ zuK0$O?l1H4zt!9QeS?tZPrCz|ng`(ryS>zCMVTAdQE5WoPM&ND3pPaa{M=p6q9h?D zYaUuvS`q?N@#^#z<6zq}RPW}`-&|>1?J23GU zwV{`=(cO1-p;r`0=2N7v&bzb`oj%$7$Ib7$iqsWdObf-dQ5m$=*w;U5_|3OoX}5HZ zy6qCA_A4Sgu&_I1GeEwKltiSUrJ1#-*q>*nl~4M8m>wp2TyEI*%Zn3otyRUFkNrju z_8fhN&c-kP_UjI{A7^p=r)aL`a9uSe3Y4&N3J{_2tM`8zO`3N4G`Do2H%xNsn3EV= z!s9G)pLcMUl6Dpc%UzSdBoeR#siEMAG0hGNBg zf^>=@(9qbNBc&R)QRObBI8jP8K}Jj62)%NFkD&F9fUNM zTwGjcp8+5zBb%if=~ZN0mwESPM^`~h$QKvf%6L_o!Cdw?DnD@!Rn6DyP1p_Y!H! znj07y9=&mEe=mi)B1$gdo8Y{(m(g;`Sb}bu@K>k!r5)u+9#%Sx#kSgb#F%x}+YwHu zdFPd1YiydSo3$~8+wIzs2cy;uEc{%aPjwtBr>cs@c!WK~l`9{-qTuwG&$Md2++F&d z=Bf|<^#Qq9ooh|4O{yzjl@@T*@qMg=X51F`D_<`~?ye9=ve|!h+vD|s;=Iz~Mu7Uu zZ8kP}NP2^862k8SI70f&j336^{yRuw*aPjS!GOJy?L26lq=6W@Y%T%s0?w6H@xWt> zm0WOrz8@^1qs+^;ix5hAvR4mtq)3`Yc#F|xv|6Ozj%AgX|^wOXD;QA``W^I zpo?Bza8p~hn~TpUm**}&H~gi;tF{ea_E|j0stY@jCgWBv*cx`uUwWP;Y2K~MWAqdo z>cj42w@9Ju1YUkYvlV%o%a72_I_8FaVfMT2=LAe#0I?i3r#z;QAr?Vp}sI_)`%$L4^p)v=sRr zP-IbwC4X97II%NZH%AbW>@H~T>wz;&l1AHd>*}&raP1q_atqidLEn*FSLzSR=LK9l zlvB75h)`x#3XYYjMPxF}`*zgp0p4N)+eY%#viGk1z4Y|)~=m^ znGB-o;?ML|;l-)qk)CgISxTn%9;`ZGF%sbVw{-RG7xkvNEL<32N*o@uR>$4-D8*L# z_bit4=C2j{^DwTST1|xtqmuWv>a-A~c7$k#05$~L0(Jqi1`(P70TfXHx8);DFMx^- zYFj;e^bQg7K?IeW!&zAg_n^ra2$=z3rF9lV3v_y@vJp)(Gqk)7ck~L;Jf&LlGcm_y z25{)A%r9{%0AG5OwsygL&M@qR_WdP&XRY44TaoKG#!No9iqS|&I{UuGR zJD#4F1Db)%HZk+VCk&@!J8(DX3QT52u-%2PbXbepsRX*ZsY~)!|pPn;3$S%;05HxH) zB`>frVz4VDr&WK&2Rl#zj!xvhkGe+qb4&ZLT$7u_%+2Ue4OBY?Mb0HZt?r>2ZRR4j zy*8}{9(_e~)%?`!daX%diV3S}Y8K}DZm*!1SCoTJ8V$E|u$e=gdmcEJL!ioXC~6N# zK4tJdOy4+cuS^0_?#oR?0RxJfs(`u&YGon-c0jYI5rmB(ch}V1tOz(TIMY8O3ise` z0k(-Ldt!bp7jo#8zycSSKyRdE2qBV1NEw4@GSH$!vy+hgU1`qMj;feg=M@TPr|4O! z&s)WwoD1o!+5;IJvFc3;xc)QClP(s3Gv`7g9KGQriACu?={c+omnRAFMa z&h?RW+qUM~$endX_noHJmA!*&ZZBO572DeSE@)KXtEmSAfkkH`g%&_4Mv!P5a5G6@2Z?1Jc|-bOX$F>9z5$qh>!g$BnYFNmUd^`y4pVa}snJSpje_{6b_ca2?0*rj zD8lTtx=1b!+Py(4kD|1{p_1A_9q5e*Zrd2V9$BQ&QgMepK+#bK;Z-BxT83{evu^JU z*drditOC$<2XI=Djtl~u=TPsEEQG3Vxx%P1VgLHHuE5&ZdpV^?Pn6!|8woC3=KqNnx+3%^+sJ`k~ zZ*P%!$B~%B^-dq@-E}5Of^*jL?NfVS^`5URuX0C8^9pd(PvSk6@|)}U&V-6~xjAAV zh(~EKu>2~XG z9etWk$ggfBe~e(}IZv2}lzW3e8f&yLo*x^2X5#ox*mIJAgd~=a^m~C-d?uYPlN@XF zwj1N6&ViLLudKa-wV`~v`vKoej<{^z?2jK)tRn_-KkY^>VDw&ph^7_ zn3L2CDDR=yP*sEu2+6>umInDR^k+kVkXK3pUgB*q!0IsDau-!=e zIspG=gQzo<3nJZ7lmKM}=%&w#i?b%t@?U7yBK42LPMZ$B0_LxCgj|DXg9Jq6Ra9cn zX*x97_iyxThk&MP6qjqn=)uyH`6nC%`+J7@EmgeQj&}ZyJ zDSIQJ=B_#Fz*#$Mc}kv#Qsi9(7l{r!De|q3m5`|wKy!lXPG}h=Y1_Are>WU;Bf3TW7e#wSzOWGX}==w9m7{wvGr9BUAd$-yVCjdd|C6UF@8S$R~q3? z_os_3R3iIQ#W~!IZ8BxH+pYD4TlPLLgxLn2eCjz%PscSqh~LyE$nlpA1UxyO27jH% zywbC_vHVD@{ucU+ho>p}tvfvu&&h0uY*2?QJTv->liJf7zn?hkQB?FKWN(d{*T0c} zfpkDTQm~Hq785%)SF^b!|MGM~vZ<8RZ0TgsW@z-g*;4QQDu0&w#Pr~xP@npUABCj* zpJe4kHUkys4$~I4HDfxKEn$waB+){e<3E&WugMmc?Y&^XciU>vT`coJs-TP#RapOS z!4*{U^j+*e(W8bLStRc0B_ub~)8(&kCL~Q7qsGEyQw+^F@-g(Q-y_1?UeuhhuSp7L zyxGEs*IFbr4j&y-S?b&lw=1Gw3Olt*rWjddYT{r$!vG%qj_a%KW~WvGP8$VtF9xqYf&3t z1_cL|m62#=+Pmb#Z-LkPpv!4-RZ&?Q7Yge~jT_9F-{YuB+OW8mq+@d@bgIm@VcVz~ zlY?QR)6v08I|(H?&gn0&9!T@Aelr(+U4={wR`L~ZcI=i8^G|9!qk*ZH0zt?j9y%ou z5ta>!yNKk%rI9Yvg$zHDO3mC$^(~UJrrkn)T!9dArQJiYhP_PJt5uh(+@mxKEz}~z zA|jH8=-lJoci+Wms3v)@udi+fM7CsfoG+PK9a(dz-S9@|NewRlu7K3skl^+R z{ZS};?iW1qzh`;+=_2pjn9Oz+%bx6$?GHIt*V4{$;a;{k$DG?LdOn?QyvlKRyk%I> zz5Iz~vi%6(Pqz`S0=d~0Yz2Y+gru%Y!a7QeR-{-ouKd$|dY`uPUs$r4%(>J6d4mbI z)IKPlC3&dYb&ty(FvvtfxKe70zW+)>gP4%zI?QP|H#RstU(atqBhNeW}8n8}{K1gU84$gp?s(%Y-l z(yxt-&oeNxR>@yIkY(CjI*d#5pei{zZSFicP~?0v-;mmxH3rkWlXrt}{o@AZeH#sE zIV)(l@OqVVBhgCZi&e0PMxOdy@wJY-jI8BOuFe@ySuS-mF%Bdjl@T_s_yqu=MB~x@ zyW9BUq}!19g8KQpxZ+8h44Ier;p(ic{@YG+(rqnWvG4sPZ*H5^QUuPE<~EEe))dzL z^~!8A%h^5Y-V3|>u~7=g*zJx?jxgHWH+b4pmVUk_eXHkpHqY|*pj9hH zy1G-ExQmY7TW(6;@td+B+jk{5K2-fe@4jn;BvEEX6cJikrDp2w3h_$j)0kD3 z`LuQEV*2&_r8nt0xTL9NVp7lL%w}&?Xg19q=)T~1aGusmC`(s(Td{tFT13%Hio&M( zI+XU&y+uuwqcI<;g>`afjDDD5%91r4tTGyI>8-4A=cck%$-DAU_v!j`n;kf;>c91T z6US0snX5PiR%t8;W-W98ilo4?Hk5h?(E`A1evXqS=720xNCUrx|oFbOm;sVTD$MZ4LZ9{!}hSvbp)siwrs_>CZ9wLG^wcXCvElgcatNem z!kVe0>n|!&&y(`S>FY*gPeyVsCj?-am+8 zlkgZ#)rmLPs)`-0@c76MQWw0;_r7{mnyImh>BLTPu10dgR4WJn))6#b#P{-~LyPxrlz_s9M82k5vATHTus zHQo3A_;Gh-b@Od%xX-iv)zH{yk7!jS5?7Wt`?wCii0O5PQqO@!LVB6^0OlW<`Am@L zlLAFNXtqsyc_uZ!+RwG_{-g1PiO7wqKWX>!b5CpFSI1<@2xbWwd1m(Kk|%Va4!YJ{ za2^@c*85xePbavA_;bg{*ipRF1|e)RA6#BlIxW!UZ`+<*^?&cyv2&jQ@)4y!Q}D3d zH!D*j)8CC>Or}#64wak;>!fh+$3Nu>H#84-^y#hMmE^mRVZ~_B<*(X}oTN4tZSTYO ze))o~ZEek?=xLWpB4cmEDo)c-U zh^8_yxF2?<{}J!+Z{A)r9yGG(XnzS6|K}?O$U&WGy8vZEvq8>BGFQFn*_qUy<0{{M z4B>M|AUBmS>Cq7%vc4svZLj&vMU-}j{H8oTH<_FxbCsTj;o%=1l?+TQ3Xi6w_HBQC zAd0^4Xum37FC^@}xuIYWIi#fBNAK;fE31?9u{(dbd}Tg&r}Ga#(~;`g)qRzek~B4X zpYHdu(di+kd`<0)@=^-EZYSM-g2n#jonUt5s=~y=i~XF14Qo}Ngf=`O;_3SzSLd*6$mP58Jy)t-DgBn>Gx-u#V3e1-9z>z{_!8iA$iNB{P zeErN`@upO)PAnwR|0Ki(SL_mneCFRJ@M!-WI9j%LOPq~VN$GNyf__%s_kkyzJ9Xd1 zLgMPwLdwmaTnxDqc*~44?`q(P+SeC;_#`BFvoDm?cUa~Z_i$r|kG)=L4~z^_3CGPv z&bhR4O!L{cGc>bdrlY&FWm2S{YlLw6Q+a8Gh0H=y>+qF!?Zo4wzj-m~oiV{CLtUp2 zZ4MNmezZBPHh5KU{Ekv&1)_@3MPIR-A&nZ&6=*l$1t6~hl@pJ#g<>>sXOS^Lp%|tZ zAEL}6P#;`!xs9gIMjl-ujrde`k2HA6NFQBE=spZI(>jo@QP9s_2gqG55KABFPaFaL zMeQ%Zw=Lw=UqfVB#Ke$}4N1^fqZ2xYQ8;GxkI<5;O7v?snz=OBA)qHe2Ui^1a4=W5#AH#z!9>D(r-7|Ft z{t!c+5b4>(u65z)TMT01X+XJl!w>pLUbp~xEPteDo`g;+aJ)K!)Hnm(v=F|dgwEj; zG^j+3ruE;dMhQd?KO{p9VL?1F2%vhYXvjNot?+|vf1`M|y1sl242~x9E?m#B z^cbg^#=(SljBk5Gd#d{AS5fNUE_NLLRsP;P--oKG>Y%#wkB@%l#q4X?)=T;_}@Is!12$` z{DA9_W@z;9?*1Q-1h~Ec2&6_0ug@wp-T40}H+H`ZAk01N z|7m)TsT%Pq@I+o3M*m?#{`E+{$ih8*4Klt3Q!()M!~b;M+%!E0(-ZL@rsvx+0$W}Y z7Mv(E$A6fRf4k!T!<%AODZnp*mseKkeS^{yC_rJ&Jl^lH$tM>-Z2*=D2*|D}%_s-aFt2}OuKuw* z{$<1d@#cSRnd?`f%LWY)H?HY|DmBu(6x3WufQ1?5__u|7cPkxR-UBTuhpF|s!p7LU5uts0+rbQ{G z{UB&&*FI5AD#Z24Fhv@b){$OjSo*@{=Gz84e6A@#xcfq*52L7@3TV$Z4#e!7(HB@m zFyK3AI9vhI5s5v(0|g9g|`f5v`n%0#)L_f{Yv)pBTZaG*Vc&VL` z%mp#5*Pv{I^!A$oQQ%-8a`S*t+o~5)8FC_lG+M6@<+J>&UhZJ(qPEB9#Cl_{e3Jh@V zxJytAoP#(sxoF-z2_1p~n`5)$BFJjyN5r27pusV_ z8p#{N7F63aXnv7i-v=%XqC%4dGO(S{E%*wzh1%K@Mg;Pc)R39C$x_QpBxN|kn76p%MHPVJ^+#m<--d^if#xFg82Z#( z>i$g{kb40v>qN;!kUv5K^JavkJ=p)9WYC2I zmCRT04;c*&(mJ*iygd`>HUR^rR`OI2RNnOS`+-Q^whT($;vfiZ8Ft=)9`9)w10$n9 zROvWKrmjK9Dv}LmFaWx^m2lIq)&UCKIwk= z^rC=6sP+3G=)9t`8)H*PZUc9Zh#s3IFg%ugd}f9Tkvgmcu6vgeO7*7*^v^_hwSfkP z!^2oXzQw8o9CG7N;3hj^j+8{*Kuo32V)^jWgEZZ{Yv_|DD#K0_fi@s#F9GBeMNG1_ z6fB(yV9W)<)7HZFCjP-HpA9SI5iBh!DjuMjK;In&J&e7y2wg~{38=;GLA%tT!}bIi zkh-6MCeeP-s}y81fpu~Ty} z^}|p7rGLTL&(H>@^cY(is4w#G!rm)637N?7}qi zo`_>h)bou^a=`WAhrawlS1rA$Of%cUc7&65Lm~a)vk-aPcEA=$8m4`7`@1!qY8kE0LBk1*lIB!f~J#RcsvXSw8SXfw{Cz$FUTon+TFykNm z$yjBnpb>FNBm*RVkXMF33=XvYzDd*9HqXq;8jSpY)EdkR?h;aD*!KiUq3W~sjPN7q z(2|Mi=)HWYvC5mt^3ZJFNkA_e@7d}djgKM9yp4M?Viwg&j!TlQ;ZI{VcvhN$MeZRcpZ(jKWeACgFU?)SQ2f~OV-sj0LN?vm|ptNndbI0k| z7|3R7V7C?Jgt#ccrulewIZZ^VcGdduUgABZ4VF)O@k^niYd!WRs3oCd zM)PFhQ3uF4EQ8EcMZdQ^(&*r<74g-3cloO6syV~!=4`Wk^@*wGE$E-|zG%iZw z;7P_$k&u$s$$9Q{Z_UbiLW{tIUp^uMp!&tyi!1>9(8I}pGcq4~PL%l)Dqk7GQCm3f zk6sL4kHa?9e+yTb@G4G53As8TeNXZX8sFW9nT7V*VnD6s&?Y_l=%>I_)dFt-s9Ilz zThB$B-%>&AYF`M3Q-ltnHTrju?cn*c2nHkwHXLyD;LIEcQfqD~!WaUOd6~hX?{@<= zdRGue*#=BW9e6mvAd{&jR+kD{2XaOk=$i zUCd>;v&=`o%G&VBw0;L-7D?(FAmS^cADI0RMsx#ssV9 zwlws`C9{FPf%Swu!@pk$jly>WgrUTDbTa6YT%&>$kxN4Wxr(UD{llf#c&%D}-j6Zo zH7NAGehr-IU?^X*Y#<_=6y_k$ojw712xN2UL!~PxjQ@BKVf9WwJ+vl*0Paa2tb#U! zjody}7cPKY36io%K?AQkEr|X2*UfIimV*;OBTJ`(zp#FZ*yGpBg=_hsN0>eVPJq>> z)6tWOu3rRm!&dtdRuH3{BqEQzUnmg<)AFPaP4@ZrRl)Pfr{L>9vcK`W!Q3P zp=5&&?2oP!pYwLyFdamqQiD{p)D)QYt*$VD{$i;{-KnEr$Or}tk{z`k_3)NH2+$4* zjUuir{0(NAh9<*M%Jld`E-=(e0LD?Q2VKBG*B^zCI1Re9r&5EU7rN*P@s&@DtWt;~l|2qhM@AgzyAkHvY8$M8w ztSSMD`vf>4{;%)1&hJA`W*mZ%K+wRx-X^p;jO1_-38QrN?|@%;4dq`(5dP!XE;_oh z*I6XLVb%v`V&sFcyUQLZ2?xQWgQY~q!wC-b?j72YM&7xsd~UvCD+~yLkyq_6SWh5M zWb4Y&R^-u~Jb@%SM)CGm!m9!V7E5k`8U4UtY^AbP>gA~2P_}sCLb@&FNCUtp6=4AZ z$@3LQSuvzwSuOB7zm1;}L~p5KIa?uumu=RRTUJh$(61$Z4)Mk`a>G50#pzY@lOzWy96DZfPE$L{VM@AB_y(%?FNsoB1(J4 z%TW|?(g8p~;SK^D`Y`-Baj10Tc)n4ney0Di;(j0q6$QnTr-37Rzg?g z?R_zj>XI-&`a%>pEh@(c+^GLDornSISM(KI$i4c`xBqyZFtnc^b1{K<<(nGvIj@~+ z)i00^!FwtMPn7mB{P68u?(8n{0Mnu$nf|o%8!Z1 zsH!2>O9Bj?4G5Kg2lYV3uhukg(|%pZQ?ic&^(aDaD6C)Kt zrG=cTRtHtFSpOSAyfl>{(hEMd=?|-;Y^($rk|&^yfSipcuq3cwUKjph13!cKj6Dbr z4krlNIJO;*M%IfN*=T&WQ{N#Hq=fcB4_fB|NPOqn&jzIH0E`PcC|6%b9t)I}>LIO~ z1aJr>SO?);#{fz`b`lD@i@vwPnO2nLqXo&@klEtnY=DI~*Z}QkjkI}S8=K5@PH+^X#pe;+^TbUwzoM}UIMfFL=f~`JK^_wY?0(FIISOxMvs@NJU(O$ydsfn zIl-Cn=!Z4p7em{u*Wf29n?~0V2`~WzYkdHAMk3rJEIq-E0e$56O~7yx9ytMTsQN6M z0nb}|evAnO0n=`SY!vekH_)(F;@i_s1qCe|5UWq!z=w<4m#;K2_NpIe_T&j+)Xm^Z z=@XzSjRXb7=H=F&yF;DO0HbHb47BWO9Rd&I6o3ngK*Rwuk)~o<+BzTMXamW(qy>sx z*tw8&2<~ATvp-IfqwIaa)IFip|iD*wx&l?N)Eyx`iCBK8to zcU~A`Bur4IK>oCWi9s46lU=v~i^p2y%5e(3hYG$YEGp_M^pmio1Rn$T%LL-+fCgbL zMEIoOL~527!K~rH97}+kIUZpg3w^$!*$^kl_|BSuiD6bcJYC=5UsT^i#mhD!;dL}j zHa86o@=y?zJ%hmN1}wo#NDgCfMpDENG#bE z-hy=^CT6!ZDuhHMTQ$@;`Pu#|n0WJ_?nmAMDk}Vbj`UUoKcI!*-nVeM+UI@HSUkDi z1iS{H^m40I?QkfW6s47?E8p-c&HT|48o`41*neZop&9I#e8R5mzCwz08c!*1tT=)t zp{}KTI~9JztCj7f6G1_tyrb)%nAe4PD;q(_-=`FV;%$x?JFFSJxjWT_l5d8kHxJSl zjdlR;L0+QF;E3d%B;rLfQ%A4D+jNk5K6=Ru{oDNczkL%)su!9=1bqiW7?2S+LaZA! zPb6tH_}nz11)Yz%`w1>9-~~!`TVT;BAc~fB1MTBp_LQSk)Z@8}N0kf-RP^L6 zxme12Dn~6hAP_~htjSo^&;=W?f05}wen`jPKpl33qgYnV89^|oJpqr8ZuYt>;<`awC&Whs2@E7r2qyx^-^D8VZe-Fu4>mn- zyj3f!Y%mPhCI$bP9DoIfuQVyRA8ybDErG>)155*u-6n=GI0xVc8<#R2{ZhO)SdU@o z3K<%C29j*V_0Upgocdr(l1csZ&F!w67Y{#WBP#=%2YrW&*MH!*H#?dx*ys8{db$Ie z>I{LQ4{s^%o;^MS?IA{P7J_|fgEmdAMUKB8*{I`E#pe(>I4WS~mb0zh8NuXUy6`q~ z|8SB-7ucu>yntcV_5?C#2bawIvZU~<01a%@r*2#e+TK*4w}Iq0B%ut)p?ypA)kWXe zqg0ypURDc9hTRgKAK64yvUM(rGy3U|XW=goW^F5IsKm~)>-qHOyJueM_+n{iF~~_V zH7&l3?Na+>A^tFGQ`wE*^==jmbF-aqVWMrOQJ!@FMxJ8kRA<_C)&~!o$1@EX_0fB^ z%d_*_d7llSI(rJ6mlAkRKU_E_QQ9Z0v%p60=g*r@2K~lAlsz>Kh!dnEWzmV9z4)9z zLpkYIXTI{TN#`dU&Njx^pAFQr)U+?!hLhA5LNu6661tnuo8TJr}JHRQT=-Ai(Q z0=mw*=(#JanX8G?b5p(SeKpoW!EwW@7NI$f=5H>gF7n)z9rEl4aoVA?yP;vB^>q1D zwbImbfl;x}H<+4E*h}vA$>=}DwB7lLk``XE${nPWEg-hkN0q2Lx6zqo zUuN>Y80XmbVp4iSQf|0#BsV0y`#KReUz)PVHGF2cT=~^Lz{w|SSdG8iUJ`k|Y-?<_ zIcFs`J(c}rDaT!Zx+o^mHPfM!x9YL~esf6UmfdKUlg-N36@j@`iflsE^&#Y}hJAR0 z=Fu(=2RZIbxIcv)I{IC#FXD_ct>;HXJly;AI?I>)RBxMTeV|Sa7~l#TvXZe{6DrEYHj9|eZ=!N!Go>?0Ke%N})^Bx3NQ!Sd zFc+hFitoyvpB=s9ZMSr#cyBZN_TXJ3@q5~Zng=X48#Xmh?qk|ID`oN@hUcp@M{);X zwA?HxON=oO)~N@|^!HZ2S${7k!U%b|)!ewV+MH`v@@M?#oub(*l4}@dm*U0Wvnee1 zSGzZ`eTg!Ak1@H{ceb?Y;8of8Mba(XHrPpj=lj;s5NjRwa~DOrxmvM@&3t@9=we*R z{qONa+WPW7Im?a1>Nazl?e(gR+2D4{lOu-oFgONZWdc+!+a7aOZ}AOXS%N9;`wU?@48FHsZFg4O%WP=3eB)Aiot1}0z*&rwr6m|6v|?r% zPAKr+R$}pjDy7Ji5Ruf{%>M7?w9ONllbMNZtUPEg>xd#v`y zgN>;|H_C3)Jh3-bZ(-IT)n(QtaVC70D3f2;S$Xj;pM06a><}LjRi8+=p;DSn&5M#% zRgCZ53IWTN;lxCe<;icfZy)BwR1D`RcNa`M&UVw|oJYuQuFsVfFHKXO2z2NkQlNVfV64|EX7j4l-EnjocJ{>+(4Q=8Jf=|Pg7l}CYjm*9$RchzUgfdpwS=U-=*o5>ES{8d;oL(%aX zwDTv(a8wN+-eqE>Xwo;l*h%f)9@E*2yhs}hK7|F*Xbf#v4~?>j}vsg zo4jLhHo}D4%&O`7GdXcGa~wExL6W)$yp~HXFUn8beDA>73^o!-?1s%%t?hm7ta-8~ zsW>qrrK^-K5&ukW{|blc)GgQjBJ0tc-s^J|K7_NE${d!| zZgB1YYD-OItHM&725)IpZMFS^Y%adE>i6c*^IpU=7$Kb9hQiza$L?FZqdkMm{bY4@ znU_+mKlfYv*S*S}TK!A#RACWR&g-^J@Fczn#}4rYkqoZPBt4z=y{oKm?|UZo$DX#BqejrFixYzew7>aNdlA#pNx!B_(N@tD4#> z6mcyvBylZKB$voqeAUJMxg%_%L+*=J6~&CF;mYd!`8V&!SoeQ)j;?A?Sk4T`Y2PTs zLm6Cx$N&q;b*vLO%$hT2dJJ7Rmni6{OU%Wx_I-A#sa=IUJenGqb(LyHw+vjMvNihF zRXmCC+X6B*CFYC1^ik@SpA2Nw1vl#Im+R=dCqz`^uEh*xjT~^$(OnP`G{W%i^APvl z5BzjvZnQ{z5BqVQ(GQL<|G44(hl04pzs}}H6dO1Ta&L4yS4sI^jr8fjYTJDgk>F@`F-bOF(Z=hVumP_G_W8wvpX60h?B4IqRZ> z_v+^+Qh9_ax+~SLkhvT0Pk-Ncm?J4$ELU{<6>)VUK1c0K%FPsaE|Cb<#<wf>bZy`UYX!aSJ|VeBBzX%#RANa9@7Z%a+Bkx=va>7CCcJHf?zg_) zuU}oN^--*9d8*7c{brnLq>s$ z3%Wx5X8u2x>cU8JbL@2Er^@#>zB}#j)8AS5heU1)W-m|CCwpm5J4E9A!qqQ?&mJv^ zi!+XU3&diufYaR4ptP@M=`5Su`DOgG!{fx`w3DN8Ldm6u`wZNbBUzRq*dZUc z;24*b!urIe9id=gM%tezf~mZ2AVtfs=5iaY2#E=h?IPx-1#PBR^_0rS+Fr zluf52xr)Z|lu{KpufFT6oSo%iS8CjlzDW~wm7i^4xW+m#x2F&@DzZQQtKPUyjH)Hb z%OyuV#M^X%l}yQ_Za8{dx(h9Q>1&W-O@-$T8q#bnm05GL?E6bYysY`I3Dzc~D{B}_ znR65>9ha~ixt287~`+GDAp-B|$i*1Xwmh^P5>=sKN;ZQF(P z^q^GCFu%o)v$Tp9%$IH{E3!&!NwmupZ z%03jr3HQ|~OaK(H`AQv^t|7@$$O{X}LT?9vGtfPORvCFNh3+%)IbX)E?us4cEYQ$A zY4lUJSr=k8C`pVg2G{*zTruy$@K`JrqrGJ?QFh|?>3mJUuw1{{)uM7oHhE`)^Yd?u z$R?vMWjeB!pdG%A&Nhowae6#p(#czA<#k*dq}SloI4CokpVk#q|1w)8x*o-5Z>8b- zG_H+gZMbKwa@JiWlCw<1>;%?_+`qfHU}mt?Jvn!zWa+)QE#r*(u1n$Ko4(kM-o5k6 zCs6X9N;nl!Y|QDaBY%)z$a7i8mOzGQ0`e3iQ#O};#X!8=O3qXif5*;@N@Q#5lhSS} zfkf>KT1IxY&M7WSwclfgIQ@+R&*~dCV%5qml-;&_Ki|d#70SoC%yn;l<=K=eY!`Z# z>ogHEt68!5!g@ZY&tt1Mn5pq>ugU%5wWvG7F%`IT#m@x_{jCXyXg3zSY55DyNb$cA zKKb!E^o}KN+-berba_kQ(v!t^lZD#PUPWG>s{c{8AtkW+OOsj^i)P-ZEG@Pqrz5<2 zxrSivbsKY(_{6upEwjO<4}~UD<=lEMg3F`E$dv+pP7P$K*2(&jWU5F`cyC|0khfc% zlQ-8UP)iW!o>x0p>R>_QvoIvm%fh05>Hj0{y`!35w|C!Q-BxUXQeB{+AfVElSP%rH zNt3Ri^dchN5SA_xq^UGD6b0$MgbuM#lqxm!D4|F<(pxz51>C=V#<=^OJI4LvG8V(7 zBq8~h_nqZ=K2t3yrRUK6MHe@xp@E}Ko~!ltT%P-$CGwOq@sVs7ht^g-$63>vMHC5% zSP?<?UuA69&on?gTH56L>Pl{c;h{_8z2Th;%ceahbm*eag zBTJnpL{htB>I0Td7LVs%wW|^$O7gi(b_p;KycgumeyFWjD&_7XEj5?xHZvP+2JvH9 zRoUw6Y-;*eAGs1n8={-|y?c!dgU6m$5!O7;wTFf|G|Rs5U1-W)t}NJplgKkX{zMr( zBgsV1kjqBG?>(0S{9o^hkc)d%S~yhkxPJh<)1jFN2~<nVk>fxK06e*&_-0Ke0C@`6JwBXd)2XL?wbuYL}{Yf&DMH1_j}b zaopjI4ql22y^{03xpStd!70j}h{KMVXU~6Ucl?q?dl6IM zwLPhu-GhJf;duRCZTdUSC?tyDc<}InT@yDar;9yF!fP$Cg_3?O#I*mBpqUl5=1aZR zfeE~5aIe&S0Byom$;iO4nA`r?G+BSo%{}+0V;g;HrFaf$yuve0yb<HGx{Y@OoeeTF)CFlU7l z>D1r{%?i_8QZG!NqPE1)gaOl<*EtN;>CvApiE4&0%?DMF+XoE1Lj~;c)hBuOs}UUB zf7ieIeRs9DIv+|Cj$`z}f!)zwe6)F*E-$usr{++_3u2*Ah79G~(IT5EU*DfS3GRl! z0pWkAwM&JBcEvcSrT=0l=U8WH9*L;czRtK+mx}wDmA7;W#(EI!- zZ;pOHS!#!?(b|(ie295fF30s}dT<+i*+2f-ovgxIGVJ}c(`z-5_i=GvFcG`fAwF5< zIl-~lRs$L`%JvV1t5jYw$coQ?u91#8D_jtZfVt=3K@a{~{sgT#yL{AexG1*8R z|F$j8Uvpme7mgC?s}xHnS$58{AAw9YPQ&<8z<8N5eAk+1p62KCcP(F#W~cp3e^ru} z)NVcT?Y;ZT%3Df)mjmthNotH*pPz>2h2$qGpj3Vgiyt7jd0S4lzCy|jkTY2zr5C5^ zYEHY;P@Nx8f27%QE7Wy?CCn1^_t{4hWSCi_k=OiTT+O*Q9L4)#?;>>ADtGZj9)KRZ zV$Z$L`v}zVj*mvJbEG)OA6?G}r#qqZ9~}Jk-qn$3x|x2Y3(@Rx7a|YEG`!+y8OAaw z>xQGFW!anNINq1 G$qee~RW-GPC$H794te5(5)>uKj9r_9*Y>t~&4D5m}pcU%yl z`fJiZV^m?*>2SSE9W5MwGEbrDUN}Izyw9>d>-a?D1G)E?8#BW*1EzZCVva7mFGYu{ zmCx&~jC6`omNe=8&7SVR7A4znv)aEWKrh9QC)yW=#0o@XT_(;K!@Z=t0-bdgqudN~mFUAs)ZT}#TGhHcV``_W z?zel1%DO#E}a7yLW0Aw$Au4GYnNfsUBw;W+S-v zOn>rp{QNjBrF8zRdbT`EJP_t_)Rc7neI;>N9U#vlRL3!PV`EwMLc-J2#p7R^I(1bm;n+y1C0x5WsK`YZ z>+V9mhZY4tvb$AfxDpTXeV!o4;l|-R058K&6P=4crLXOpp9+?gs}FH8S16&G6HIF! zYi+3HbvmJY~lhr1PVye)T&AtZ1s9~7+CdI`k9En4qnsvrbM+EeQf6V(Jq1|YebCX z`8KaxtvjizY|A;5mGiQh56G@O*n>Q)OXY6kDJ^l%x$@A04hiv9Rz1p2UR~ufD5Ldo z`BcU81pDrgkbqFCIPVNQ7W+_ovHhQg2UVpvi*sO$8&V)fbyeN=oC(xa7-tULJFuW>X%qB-C4S zWvrt_EyB&_cbavkvofmq80m|t5tQ3Qt8jgbc}ixXX{UQn9N?J!+Ul0@2vSMXh2QsC ziwuaZ=bQPN#RB47#+c}d+Jpnl9gcZ6ZNif@a!Z_UM1Hc9rUidMNsJp`-_kw(I-^2i zD2Q2-Qmn?$W!Xf|1(umYeH}!g@K8sxL&Gx;qxpNK3+)!KJ@#Dm*=t##xpHSgqv;fG zb&i3fG3uD03Oh##Q+?pcNeWOP04zxZVOzV zky|mgf#5J2492aPp_UfIfT-oSkrt#G_5}q%%C<{$3tz*A^P$4_Yf*8(iphSBP&2>>%h0 zI~X2S);Xf<)|%y}zUZD} zDXlXgPw#{0eEKS8;h(aW2%HxbU^VidkQMN`v3KgZ>3(4y9%?1qL@;Fr@$^L?NJ)B1^GN zGizaV{7Ogm{ZvYb(L1~#xh_0M= zk839sySpdy#+thn_V!BbCp)bg+v~1PKY=yieV29ZdmQn?1yo4Sv+5J)*#H=RzMdG2o#*S)G!{O{0Eq$Y7F zy+6yVq)PK}Uuu&_K|)%o^d#H7{*i&9s*l)qheYZ`?_98J`Kz2ZvOfkRl@pon`gidU z=)ECTuR=xF@p+gkLO!u#!~?@KLGy{bnyO>7Mm-O0;A`Tq1q^Mt6e zwruAotHESkgk@jh**5mordBtbPj$PO9ez$r`1d!FT0Tc=%8%dLmxI(A9_^Teg}1Dh8sAw~rrazwZ7FNw z%b_;DbLL-T^dc8)Gs~_b*_G)*rKhY!;&A*aK0Y&ro(iE9{bSB z0{l)d(9Ik=1#PAVm|Ds|=K%7ANRQEEB)3m&h;nY%3-c;N3OcX3+?aS6j60*T_vqQT z&$O>pz7f&qaATh#^qr%aL}lW)%tL)e`@}XkH57?R`CwMN-{-lOIN0G zeXkGP9n_BD|Gn6AHCrLGA$cyRzfHT?t21SWMp$*ij6U`1?N&$~8q;N2u>SgN;d`zi zFLt%(i83YEYTESHR1iqK)cdfvNNyf^Cx`#)wky?|<(W3GgbtdLiLCux-YGS1uHm)( zno)>6@=nYHQA4sQ8--F7R$_H@^if573Fac<5N7Ju z1rSgj6*9jbz#=BVC}4OLgf$vBZV&|TL%C?9V}HAHs4*tSV-?)N@?-4`jogM7AV5&s z<2~PD7OX@xhAM3=7*)>QtqFyTG#jSUr!c9scf09Xv>W7p91romW^mcX%&05>noXm= zx>h_4`Nk>gmt3h$&@N`r>8z@i)NLCOWJ;Leij3)gs|1z5Xl#^rsu(fzp32YWV)yYj zsnyzVPy1MjDrPFQ^Q%r5wQ>8OxR0#bOb=r^6D|mh8R{8H=3TdGjMmi}JXijzK%4=i zAeZN0x8tmB@O44>u^s;Xnx0A4L#ypFVh)3E+OkcZ9fkQVUrCa3G(sc~!lUa9qG=B0 zjQi3PwqUYOZu!nuK?#;+2jdPl0PMqKQ3c0A%gXuUwP!m_vtA31sQCx9!C)uqH1)KB zsfui1t>_$>O>IHyXda-xi5hu!XwPdqiKt_{MAgWA)2VkEaE{N38fR}5Yq*t)#NWMZ zHL*itGX2SD4%Lfj_eLb+?eLIYX)Cp#p|aaZ^KKNl^}lp}pZ};JG*nd~v8TW)jmomh zsxQnNutUO6sgb*HVOU-3$za^yYzloYUfj=oPw}e|tA;r{&J7H{U!$E;)>)lb&~slB zkvO(QOFXYsn@Xc$FfJ#cc#Cw0X!2v?fISA0lJjO}$d0zo3`4`Z}|P?o+yA^_0xn z^s@9^C|6f`UEq7BllF`;kmSXF8am%mQiMRY@}!=Z%O$omP91J)x4eCQW8+8GysSvY zqp5H5$Uom?hA$XylX^6m+9Hbw#tZ>Jci)LwF67b7luS4SDT<}NOH_4Dz(SGj_3OPD zbzLY}^ju{!I^x>rZXs8}{c+0Y-K5X$DM2kQ;?tzx|JnTRZrvM$B9;|qvASTRB^OmG zv1w(o$PYocC?{i<3&_cjqVXi6(eRhvTZ~j#j25iho>?c`c@7Q=`(9#eb~qcYooAO? ze6i||m;FAB)GPi(i&h&@GX@bMUPZ!m2wq_l&UzLDzWOuI{QX-l_ks%c9Q>adl4T*6 z1JW_UWv4uQ3|xq4nS!`0&NePy$3}Tw$BvZVNcNRVk_MgE!R!llp#MwcTlGM{3B>-1 zU7+Dln;~Rz+1z~2q>CGVp67Jfyn)o&Nx=kLP0w$8V%`p7L0jakeH8fhPjla0!M!k5yx3zAc;<4UMfqkhI0Y-ZvbD-x+# zb{5{=UPh9-cPsd4Gj{d-&iK27)bT>aZtXqKqY@hRj+h8sBM<*Bsos}5Gi$+?t9E1{ zli2796$!NHr=(waJGta$yi|YJjz!GBtYj|v?R-KIdzON}XY(To4gKiXQ#jGN`oBHp zDJ!$NA^yswiiIMAljdKTi&&(7gCW#Lpc^~a4H8B-$RpQ{s^IIC_wu4~uTD=-$ALq^ z5g8vdK^qRvH+ir*oIH5&-0e^Q90gZoDcEBPOYEXEgiI_y_(9MKLUkvBqC5V|mykD% z3T!YyKKMpPMuNDGIxM>;!Fc9oRvSr)JH!#ZEQwtpdPEjLt%r22hlOa)dR=ByG^2u! zwUjAV)(L$rFNvXW73L~7S&6@PJg|vzq0tB1FSa-dC zbt%`zpN`pSZnKOgHtp%M%AVuBiM5Q&4yc2~N+rx3E1)MXI)+tuc1UTOSNl^E-*=_I z5q^emyuiCV|A3FHLpP_r^u(CeaI_cBwC?83yxv*N?C1B1nW~<9d*hcGmu0C+OS|{* z;IP5s<^#{u%Nu;(VJRyiPZOg&m)e5O0C)GVd)~0kExvqJ`l~k2$zn?4d~u#-Zkvr7 zdyd229!sBzRLz3Y`!&DVS1IM1xKNK3>d)N#4qT={nc1DpOHIc$2$`JpV^SxAjtL7- z^y~?_5Fm2wO(bYxOm1$GR7U=1m}(c_T1SF&B8F^Tyn!!bVmOG4^$xKx$=VSn4p-;g zt?h>`IuP~c!2d~{rAyI++l>_}bdF@~{8c!OAg>jc4>)?Tpy$5wcz(BivKQ>;&tz9e z)Eq%Yp3txgKAs>{x`V1~>cWs1@tB&-tX7_Nn{{rcrZMDrpqpJcV8xK3s2k@6^+H%5Y!Q(KW1V ztxzwcpb0~P%&Lpw!%s_s{7+}bk2XTaxUfnz@gMGceUwkR{e3hatnjGtE^y(t+stIE zq+Qyw3GKt7Z|e0gzxvv+yGu+i|4@7pk~=q5b$GrLb{I=$(wfDCfEWqEwgRi$UXi6! z-xt1WEME=zG%FUV@_JsoXQju0aAWF4$*h0EzCAZ4+?B(tlhhJ*YCG1Je= zWsH1s_nzOP5=5uq8Qj>Bf#IqwBQYZLx6-S%rZs8l0tSvs7^k-m@}UVf@%W<}`ZPxif=^Jz~WmHOM!DkdBM^ zF~Q`sv)yax77w*%hN&-L(=ve=b{ESBy zD+aK5N*>nf@WG};m1?ABINF@-*(EN-Bp4u;0K`mJ9SMFG5x*8!+2cVRrLLtiPWMaK zDqCOWyLo}&Fh`Nhz4+u@p;>|W`>F3gN%@*xPN@B@Aj@u$jzlY5AX))GLctAX5m5Ut zK(&}yZwABC-Kmh_RD<@A6*+pq0mx}jT%8-`B`A6Q4vDEiesvIvgTxxbF3~x#40c#0 zf+jLTAltevj>A_xTml}u8W6ARMdb7M)05ZaSB4_F zOsh&Ovmas7S@q08DeE>?6z3wn@R7x_hBT)f#aCDFe|YEA zVV?bcJVSyehm`$wuOfw8J}BHGO|rfo1<700lnM5H%5X?ag1)FN>(lvek^NHzHga-j zjEkhhGX;YBn?+ubj*GovJR(0Pk6)V~e8iwk(G^^kQc@z>&bcSm=I41F!nAIKOA^Le z$d49+z~USj1o&ryPMz+Y398*^A|(d04{!m1iLCruH%Z*oZlNrr9bLx)h2I}R{&Nme&%*JXTBPLY9uR2{%(kBz zfXn)9kSuNC4bDkjP1r`Our2;I(vQt0l7m<{+(ndXYxqR$-OnFmT3O6qs7&5jIr4No zdd|=oxFjNMWDWLFqKS9@1?jDj?<|Q@*`K4n! ze{j{zc7L1rDph`bRaW^(ZMW=7`<&of>APxj)B&5CntsG-DcE}GlF8v(jR>KR`1A1x-ZI5yOo2wPrSEp$x+O+(xWo6?O_xQjv>#u1$epM z->18)^{Sexo1jkGX}=a17WOAaBdM%Nao8ndW&DGn$yD!;ua;(-);*Ixa$Zwk)cO!V ziygN6omhezXw+EPYL$5VcJIY_&muW;YG%8@E=V9_ZAkS~)Yo`C{>@24L&MTaD#8PR z-#Hl0+Eo}Nk6iZhL9BXZS9O~qe_=xQbY!PvXwJyJeJT?CSYX)$CYFg<(+!oJLl|O6(wYz~FHI zBvYUNvFQ}{IVeZ_^G$kPV8^af#K*E^`oHxq%YUvvQ(st)vkF+cXT3sMbTz9>6yhD< zsTG3@@Q^jepOx{t-&*dtkb!M^^nG9b&6`JDOIPey#@gsjRZco=OWw~jE^~@--C3?; zGrvn+b&ftJcDgS{lt)0)lq~LIxu#;ioPkcw%GfMD2$IN(1~u%rZ2FcTm_MZ8?EMb! z@8yTC#bi^eayS263@T@?tSnPM2u;a1ou}J1{sk@Z3ziK5;(1fgJBSu*md1sPEUldF zeZEr8O+ah-tw61s@zV$gG@xBvq8BsVdzH^sdt*(yRSovWlJxXT&sR0PIh1*QQ5RJT9mWbP@#ZV-cL!C( zIQG9}L&_9(87T#jOvgdv_NGRTB%^@QF^UB!3#WPeVg$O63=LM<%Sc_FnoEy$kNb4W zSd-a5JUw7nE>p!nooyvzr`nLW$6#ukvm8EL1vMXHjZzbu(iCrl*WqSx-T_k zxFRClvq8!8VF^HDj_-M3*~M90V9NrUEBUz6NAOuHpS7LLwj9iPm2=NH?Pl(v_7CgJ zEsuPfiu7tNyWhJD^PdV>*-@08kak8VLMZ zq~=ifV3&w_je{U^1=;!>`=FQNK0WJtL%^hb5j&)xXL-<{DKqL_!H<_s45tN6a7>mO z8clNyH}|QE|M&;~J8TN>z;llM6{ztL3%6r7*cs)A8=}NKRqXBUSKwv~2CxG^T0JYl zQ+`M}JZ&xZJk&WHjxzSYbOtNF2gjT4KpJg4k2V=MRix0>@2qq$XSNi1bH!8A&}+4vV$QL z@v~>o`VnPAE-i*(1^jdGc5L8zFqnK$VJh1dk{!Swx0hKs=~Fp?PA)60e!f&7h8g76=JII$H_qWUUzjxX@)**0JkO9G3Tjt58 zNwEz)nek{bv4lly!<)C>bFwq}4z1_H{%3pub~*C{pg|Syp0exza1*jZX$-x!wUv!B)v3ptYmNmKxY+jhrqK8?eKND&8#AK2&fqj>l(WcGFC4EC?!gA z64q7g;M?nm0id8GTRJq(804Sn16I;2VXGU+vkCx)qsY4tH|$k#W1cr+SNDgDIt`|2 zuiuWy9C-5N$>h%RF<7H~eG8#FW(L7b+Pz4a1&>_ud}X>DOv~{v393=du+s&?)*@9E z#Kr>fB7f-J$4AS4_ugs>=cMhoK5)$Z5o?7VKv*}+JZ8BYAvYF>pZ6o~39QAswKiLv z@gPiQyZ_}cDy+g)jnJ?{2&mY+JmFcxm5PnA;x9a=5x z*FUd1S(;jL*s_qg^tfvTdWnJOf6yQ*KjC!M0a6Hr;!*nhgyEN?Ko1VB6?t)Mrvj#Qs}*=Q zp`-o2hGb{wvvElA7ST|dgrOrr4179T-EOGFjyib!D{MgBL2m(ZE8aJ#M2esDLcpWA z;0tdgU0YPdDq*eTKvrTx+tyZoi9e*x;|5T-;@vMbp0!XZ=yfnymge>l;Ad!K(;c%h zgiFin@84+ILI!yK1(t)zI*eQg{ZNKbky^`*Sv_M{d+m71A8cb*6he?xB3K`s%#HPi@ZAN z%IDmRh&^av3xVuD9Q8`bLY2_IqjlewTkF?{x5usi3O_0+juN}j0frkud$cQX8Pv z*8;1zf>LmUA@r90mAIp+ehJiXpxsHKu>|kD^Ry;&(><-F0_fA{DyX~I-UrcP$^$F} zlPw6h*D?9VyIA2rv5W;07j41)muExDc*9rY1v5z0Y!E|BiR93O1APlLEf0Y}qT7fC z!K~Ip5pUiSXo;hHs}t(P#iJ>OP{d3vc+2*oitgU++qc6q96GXom{%XcTye?XB+_1# zQ8zS#stGo8W?U338l(jq+xn`%m!WS0 z4~I@{RJ-V57O+HboC9kmqpmYz%4kJ#{PNe+JH-L|=)%sMLu7~tt5vIzVhvAZ>`L<7 z_F-0C8~2X%Pli#6dGQ8srQ@9~YX|D5!1;sa)_|4%3qYPcl3@DJi)(i#^Rpq9%AZfPQWXpDjtFKFTgNwUQf|TTH4ZuNCNR0p1=e&gNaYcbL^hu2csmN zQ`)<{i-ET_`|X%Tp$6!y>!%(|GWv7NS^t6u+s~~)=<=rB7_#ptSOBFSgVt0}|Jcf? zF7X+XLF;4~0%sb6T-CiC7o_crM-s$QCb9~|#3KMO&_f+AE9gqE&cfDNKq%PSs`>9< za1YL|RtgyaW?Hw&sTsw*8Bnz6Hzt?3P5~BCsvq!%D*W45fu~djD<`t%HvuK>l`PH+ zF}9M2NkzXt?V(g0R!h^0y107c--}DuuCpQou(Aa~gpO7hGuRK3rxCny`yNKyxi^Ru z2ogkctBxOlF(EP9_)3{XP(N5@#Z_T&D!;%8ic!!2O(Zl8{HNs5@v%QdYW01h#`Z;` zGjQBcF?Suo00o9%IGU)55ewYbVwVYKR2g^xtkd?fE$9EN4eE`E3m7Ec?|VIhu9Xh9 zuR5S7GJ;^n577@lJ=ufsGmyij2%~%_!q_9(hj$~e;?(Kh{x%#aM+eYWUy+VpjsBikkN+*Fca!bWo9vcRMtD#OWJLYY91HO6 zII+-swzapXYR15medBeK3=qCm!~!bP)Cl6rNn{^6+b+`i_ zMfY%1BE4z6`5J7CMF_#EYZU4;j%TB~Cv(aMi51hq4w*OL z7#yyG55s!bFk*56I1IKTCJ;xMqS9@tcm@QK*2#b3Vgx`Ffz#IN2%tGLxZe!MO>W%C zsa&VTLP8ul16KwMa0I(ePHY@hbwEg(_N^RfL3mg}P<+%w> zECl45Dq-tE?VZg3@vmtkgeq(wRP$v-0Vc@J057dTa!6HbB2+S3+_B$9XA_&DB?f2L z8q$GLH&|=%6D>fuY^C}r%j>=`EtPgQ)Y8hUhmObkm&g?en%nu8BLQ4dd&$8TL>8)S z(BJ4bU_}}LggEhAGa$vig;7!eJNuDU{H(LIMMGqL!%%GHq)PQ0vB z&`|Y9o$T;{NeJj>88<08w&<;m0$v1)3V*eE!Nx2$0y$%~oE&P=LJ19}Of7T!jV&Ng zqsDQpo)TFW<1~fdUpg&z<1KqnaKZ%y?!D`5veR`!cxfW=#i|5K#$Q^?0Oc-de1*qc!ryo_k;l6G?)Ut5|F7_gtR+0n?zj}c91T|Ph5rKH` zxu`$1FklpS5UPfAwhL^R`9BsCFy1NuKi3eB^V#K{;=;6w)18GDIaCtdfJ12Zd*CY- zuI$QE_i4npd%B+u_4ZM5Jx&+RU)lH};lzn^P_L!RI|%ECGFt%bnyp!Kah!6OY#p}o2rfe*$u2vZo1s%!B}MzBFP!A7JMjPQzpNk-{2f(MTp z!hHrDF~hxTlSAuSBf^lTNY549aBwd#nfwbGu_|zkXG$@5e$0$y;W!kyv5WW!KA>I@%Vr&18QTW-92}0#q;g z*oScY^>W}B3?7YUfF;%zl2^b_Z5*?ttN)K&Js2Od{_~{2cEJ^bo;LdpR_5ZbMJTIH zF>&tfK?8Nr*K36NL!|C&w)eVkZmd)3bEyIhWt*Gjb3F(!Vcw38jqJuV-g?!3eL!c+ z@G@X7>sE3e*v# zi3c=g-Mzc(50eU7TYkofu@1C)t2c@OvcA_QW)YWr4;oSgU&m|Vjkkg|>_#<&<cDOex|6#dMwdBusSK;1QiNLxZsI5{~(X!ZgwKk~=3 zjd$ez2I_{U5a3v>Ou7>NA<9KgaFBmqK$XCeob^Y!90gc9MqLb@5PICgXB4Ayf%hE+ zMhJ7=0~3z2q0k582R2?+D;EZ7T&zEa%JYQ~YoCK?9|^8dP2ch3b|yJsh@$}m>v$~W z%|>F|HlxiYE(-?3tlxMquBDmyn)MY$||%DwTMP`>MWaf{)`-@d5A zS{ZSjTgYhr-u|$^_8}|A8fr@ZVG$7)n^_&^EhAb2HosDyZl+hTG2PsR>01Y=!2A=V zKluFr2Kod*Cz^)c$UTf)^GdU+0(eiE^_OivX^X~t9EJy2plK4KORZT&7hRlIfpxu+ z%wa~^;eLg;TEP4pDi7A~>2jPJ$r~FO26O%j#!yXLv^72BS3z<0<~o%U1N$CC;Uy`5 zwzTJLXsLm8PXpW{=qu?jm)5Os!~e_2`;WmPe+H!ZbilxmuK(MBls`>?*607A1M`0! z46~W5|F@n2!lG*R!V39D5;3HO9r~nUWX8sq^Aq@xl92X6Zxs;h=-z|)tjAXN(z%TX zjNgRMtcB>x9~hEt)z^c;i}f8fq(Y4P#5kRynjJql2wq=rzjt!u2^Wq2f;AVUFq$8s ztu(^8tRs4A^D-R;?xajB>}{=7s2{xesI>76?_xQOAw5j+7IS_G70)|a8zY|bPpnT@ zN0!^u{ALE+^+^MV?Azc7b4+>C5J2bMW@ApB{D)hx=!nRjIFnj#~c5Gf~ieA;5?Yilh5$3HV-1Zj%G8qd4eesb*SaLP1 zWtTe}O$WZbAm}k}yaDyv{}|hZ1~Sa;+&IYIY(VVvF9YgcG6fjln+MaIO%sj6K|va$ z6x4uWQpo1(`2U&2gn51vh{2a&AXhbXdJaQ{J)qK`X+0$dWGsqE5W5&m(Cn~^yc+of zr{+C?nYsumZ$Gl&f_F>?aW}zik(%WIZCdnjN1&=!olcBA1ROlV&C}@jUUbRY4kL`} z4}FvJMdn@<(25U7Iw9IEM3>9}cl=b1$j_*W25x#OWUUv30p4u zoXZsCyWmv=*7%E#-&l*WXf@~@!Wt+JR6z=fNOKM(ZT2W^rIasP6dBkToy>q59VFP! zEKrz-lu`kCoDidUvj|)P**pbd>maWXgZ59DOFbYikD;yzN-&RkZpI52E79T!P)5`x zOng$vCi%b@ZZ%0%lSY8sCnV^2OQRlV;m{i!#5_lGGKj#|C1?i~SWzySo?fsKTi`mk zp@Bd!s=);=P_|5fWP8^uL4E1KPt_vA8_j%(_E$nX%oN2r zMqKNcO@4Q|gE`&XQG~68e%m#+OugI${i>dOWO5|y+StZBzi7l&S$hKozrMb(jX{sD zWAcC-tP&`YR;l_^N7;|kS!lAYXW9tKX-{%P9aL!C*yjEle+xEr<$Y|D!bofa{UkWl zh7g}4Y%dVrN1Eduzn+YEKX=d`%2a#VB=aO|w=b-X_qw`iY168yjqm4`w$NR^3{9lI zN_ovQ9rsq>jp?^fKC(0F?rP8I@VufC%2_5xm%L@yU(f$JaC!HLofq!-VSA5H*STMp znluVEhByuNcoH8UG9Pkkl-T%q`@i0~-?J0CvYZ$fb?y*FxKTqpZNp)NC#*NZ_Xn;) z*+P3vno6ZI%Xs9Onwp*&E{0Mx|Lx5{4qnB(_%WvA@A?>7lk)QNmkrxLNx4pHL2fCG zqdjDUCw($AGc}z7HE%L>371yq$^3t3!q-qe-eP*A@xK06v|{|r_IOYH^Ups&q5aB_|PVOI=8XjhGWL~=gRcUGZZ5u zqgkk0*uYIj&{@aw!&6&5Z4-B5lJ1_N>%~A}O93xxcLVDD{68|j-r;rSAn%D0!q<ey``_Q?VCbOzu8mN1y(%>``nV=L#Gfz z-q+wsLE{U%mN&ON^p=yhDPI&j3&Cs)WP~RaL4?r)=Ogsx%h-T`y^9MAr(oPQ#EVcq z%5sWFPuTc^0Hu(Q>*w}Af5))y`LmltR$-dsN{8{@@pLnMSbxxmOaEq{!{5ChAKx7E zv;M=KA7Pc>{OZ^ZNfpNW*uRIj!N32%fAYT!(34sI2KBqjAtycXjp%a`rebQDMb^JD-#h-)8{qfddjTMg_t2&^FetEZ8)-?LFxv0EBj8U^ z26II@(+!Jm0Il4=tQLdWa%=OwL#^@m+?_KTO9Zn2^Ct zJ4cB=_HQn@)BODW&g1J} zzW4UVd@}j-E^U(_shERw^@O78oB$^O2Zh3=2*+goEQ|bk6b56xRmhu73%1QN)3P3uK>+RCEK2JaWWUdJaCc;#7X!a#`2 zt4p&mZ~b#?yfV`hADCM4)WuM!E^nSg>s7WxjoH3RG+_gYquR)qV3od3%fl zMg1FONXBty!;LYSVE3pi-u~_~IB~Q<$N;F$%P3py8^mP&1&hWU^c1eW^r7d-fcZCk z=gw)s{{9VgR&9ljX0szL3NU0KG$tm-(8ws&c@p$Fow0W|e-lQ1dp%x^6na_{jVpi@ z?<%zI&{YW4+aPus7%`+X0tfcSg9mwo(By3R38GtQvg2FdUD+w6L;oQh9eLPmxkHlO z{yrG;^+ayx8AVC=-|ra(jSVSIhc~KQ@XOSfS5{ZiSPqqFX+2d{)%a-o{rh$9-OBIMr?4|Rj9LLz-5}1o9>a-o=*sLC5yg|j2 z3U?RjXUFsB2U6P{!J9Jz;YxPbVvVM5c>4amdt2dir3kmfVF#($t1Vl%LgCj{ZwY2w zyaPw8F(i8gjWEnO++QD~VyJ&kaS}F2I)+fmR64&R}C6 z{)XX$-`}DS4u@y_Pf!V~;a2Fa7+=AZ-XBzAICA7m<&&g;-4k9(cew>;rNDL9pF`f! z3N7lkBn>XSi3JokMTtKAuoZy4=WEXWpI}oCd;a|GuV441ba#B{`gb~oxmYIRGJZ3V zO{%@`ZmWA=9|(K*wnFTG1`7exwh!K0@1u3|&sUa=uCDGCDOp*vY8copQI?rfSKXqL z{`pZ`+6&_4t=NGa`^n zw<{?%wl-A=b>TP_-M2p>==l5RhUkm!zfqD9*!rbW`2xFrP2E>rCG&h34cr_LNm zd{Vl@B*deDl%;xbv^$|ly4SFl4IdsB_rze%=HoNAXA)tiAs+07rmrSniNAdiRdB%~ zWQ^{)v{6r6vLheC=E%GDk3W7pPM>kLy`=GucKun?(kmm$H-~g7HpP-fNw0B9pH^A; zdWe3Xwled5YiT??6qQasy>z`k)A8Kpvd6*+Dmdr8cBgfRbBNK>Az=yos$Hx!$2oZW zCy!#Xp26*#`u6RIW*0|C4H$bie)Ay4;4ipc>dbTV9mp9VtQ-NkiKNp|0JJN#w6wsv zI!_$!%uOs)-lp!;B-0JtC7UahizYJ8lz*(*4zn}A^-S2;;%w$Q)-)esIWi}kt`i(l zk{Vrp_hnoE$y!+9Gx!wcE@?IFe7kh{A+|N%>aeq2yId4rR3kAVaF8--?|$Keh)T#> z!S~oqO8>z7IhD)&q><({KmBdnYaq)Bcs)-!v^rBw^Do!Yo(;0hn9qpOpit6hEw;Wj zjVsA-cOnJrCK1)*&3cWDS~KkmIj?L@XUCKjiI3rN#_7iCZ*&%q;soh?&*nsAus5qT zu4Sn@2$Id)-bg-uI_f}K(w~jQ@Ve+#9~@=r(-Tl_&}N?MA4qEmG)Q%#w09kJz34xe zsbPQHI4sCnRAQ`I^)tgH`DP&Y(crk2n5!2{TS{hC-h?2JX65X5_eC3}>K~(>d2tsy z7jv8MpSMoFjJapHhw-Rv$bMFFbqEJsQg(QQ_)P2oqOAk89a4cxFUjkH2%Lwxw75#b0#yUHNHI!;_=23W>p5Zd^E-rAh~#({fQw;AqD3~y0Lo9y;G_F0v{8P1sv$F^+j%ke}-u9iV0+` zGQuEBRDFF){e8OBbFXx%z3%Ld}Ql~p#`0EI`R7hz91q@hJ^C# z3hAXp0izLbuKejUKYMN%y_RtQJ$Em4 zmU%zBYER!vv3_M;BjvR|yep6Jz2?E=@t=7@W}8x?n6RsBEMyZSec|vs#QD&RS0bWf z;*6+IADkUY3QDM8xoi}2-TGLomRa{+_1d@Q;WS1%72&e6o7nuZ2>)UiuiJk^8&yKr zkvoTfX3x|OKyN7ekpyuWy0)W=$}#>Sxy2$@seThXYe}m;fmI)kX~o6M+sblhm-W++ zVMb%2UpfXOA&a4FKX9XxB~drSFc~uJ`Nv@4Z?PPXn?KK5CRC8Ko<)E zTkUm|+)mqnJ>uK*w?ZIuJcMy$jk1mWlqDbw9>i=V+__lISZpmvuQ+_UQMcSA@u@{% z24O5lss7t9&%V~|ikkViQZGUiBFYU?)UmZLF?!wY1I5w>JJ5?S-*cFt?8`?X~0J9AIKh`C=RH$@Yd}WtwAo)=4D% zZNpc=q=yH*nf>?s&0ed1ppoEkKqEc7SL ze%V#_ds;rdmqT;%RpiPR{+=8YzInNk7F^xgZOvg7(4B--0XL4g3D@I`XAN$?FwfB-0iQBG~H>oJL zF*2!ncDMht&`@`Ip5{Z&()=3mN7y}4z4?75<*gS!5Gs2sRStLW6P=lmahHpkq7hnY z<=d}5S(2f>g-cV)6s*(jF-d-8FFw!h5v$9AF&vt(Qn7WX zX!F77o0Pg?Q0+k1L~I1+4oARrp$zCtYk>;yC6VaZR{H7FWjk`Y5tveZF09UILeNI` z{y8x49$FqzgE4eFbzEU?RvQf801dXHr1NO#-6{vjj8PVaL~}oW{P<0!W=re70NLXx zjGYNT!EaRDn&7-uyt=pTYGcp&uZKLm@*b4Xd{vX$m~)(_Q;*P$E!VO&JtkAzJeG$1 z!pmRCbbaY<(<@rMi|f>QjWd*xOULg}e%T+=GW&^SBb={0Q76Am{S23w?#@H1RwgTo zwQ*M7YgZ@y4Z=Bd^{N}+TThvgIyi7Q;Kw7gdb%lUcM7#LozJPhe{WUWbkj3}?qahL zDNrnJ@BQINF$A~j80`xe`1weqB7;mvtmoFs+iGN*+EOUtVvN@HAq6+hf9`Pev^33h zSu$Pa5Xl%^9ILLSWS&fjn3PO2ab<_t#ro@SE`tXH57KtmUk#&gFFL;P6r0-CDya2? z7RbMf#k-~mV;cC*eCswSZ}XectPxBz5R2Yt+f<)b;cPvphb^;kPyRK3`7|!Dh(Hbt zFL$)tX=!2_T9@+hsuU(G0#a`ss~+%QYD{(jcW4aCkg2~Gp!!<|^Qb&PhG3PPId z>d#Bd%EF?fGdenM-n|prC)<1H{K#O0NjrnCp+>wUU8@@8D(eXd%-lLdHwFLv4s6U? z(b3Uu7f5i&&%y}NT3gYv?;jsSaRUYM1mzv~{`O3y*Sr&QT(Q}&#meIcUSzbrX0G*-;B)7ER+^-&bMLU>^GPo+^*uPHp3kNmbzq(F zk=sXiplv4?&2dMeZ6BHHYRFN$ied3dg1XeyqY}}w^a#V33AUbUJ+^N}9um?MhEpC{ zV!HQtLp<_`CEXpm`E)`ciUla}m)_pu&IJhGU?~nCaV7FFiP$~=N&Db;!j#xebpp7V zes3g-SrZ^F2NhM@QXCF#r@jt@4bo%+F;^bV`M(6H7-SZ^xeE7Pw0q=)Je{to=~nr= z7kVzs(~YsTjDGi(T+YW%zhY(Pc&z8FcX(_>(mKnB-=OAo9?x2Rvqr42pkTF*ouGOx zkBK`kpg?ilqpC7ia4=~t!m_?;Dkmk``9|&Zc#jw#P6$Lb5|D@Iw1GL zyAkc}?Hz`?A}B~oY@>*`vgcz;4&b@QCTAYe@47W^;)G4#(0Q@Hp>43Hv-L}BPltsE z-C&;F+nq;~|5DiO&p~r?3U*lt?gJfjTbHRYpUKz4+tVQRDtzd2>%^^wX6%YcD%Fpy zclxb7rIDJd=@e4>TnwJHhpPRyB+3;?CX;$m~LSqaodf#c>e-fV-C$2x- zbhLpiKH*Y?A~gSg1L;Jgc{Nn`C7AhK(0{G7)Ds0sKTeJ`f5X|qrlzJvdo&uo{~TGj zdX(uKhfR}b7iP^WwNjD`HfUW(9^ELO_n$W(v*Qw=(s}z33?& zH>BXw-HhKSRP27{Y-)?Cxh`m=s!92P7Mpt2!_L_eKfQc`Ng_0{a5I1a3oT*B?#uST zW#NKA_Sg*k!pBO3NGYR`latBg^DJaf1p_%#v8l&FWs$OCbtt%w4Rk7!$=k!yyh z@2Q|<2oog=#xybbLnwUrg=)jABL;T-5h1a`NnLaiqER)kPsR&51X|sHP|u#y&@`+O z*v6QR@p0f5lq`Q&^@(BKQY-D&uv`DXc-E}Tb~d{X(454_sMglkC_$_#yqrCBhg8AO zHr;@!Ki@tz^tvqR6{qX^)EhL0T6*?JS9yMd`$f(=$roBz8|bQHv2%9(A{ps^-bH~? zW*L>LEJ#+-%MVGWL2{(2EltI6D`o@`?xG%BB`L#Dzw?(g({E3@NgbicF4qZd8Ijg| z@&PETsk#OC9?WcMKXd8&e%G}M9G&aRgJJa%&sjDOxj7r&!z)W_#bNg%+jE{>+wu?R z5(^DVV2In5Zj%Jx{ALa46lgC2UaKJ=#SF$u4D`$8 zL>}+g5FP35Qre9b#CPtPjx$?3ymf!)?enRtM_Tgpyu$&))|t*w8i|gFrCKA{YOuqa_ofq*~GVRXa7j|WdXu$C9!oC5Hh+R zR0j{LLMef_=(FgvpR|r2J#Au*BQ)Gt=Nu}iBDot-zd+wp`dm%{=CcE zFZdj~h@-@=!m@(s{Z^cdWsI32E=Z4K7LbsMiR`Au)@ zl6!B*Z|)_8Rj#;k!!B9vPqSUq5w72&G|)LGyk_6Cw#*0_kj2&tXBbaSS#)>{u5dTX zuW-$avG1w=OPQk7&OsCzmIciIwSKkZ#z%pO4<9n4F)_fhMu$vjg^3fQ6b$FAuzs__R3{Mx$>^l82kjYQZlRIBWkZ5-BL-Jul?)~<*$LWQ(6Rm}7o z?tdW@xm`71Kzz{ecHMOHJCS96H|uv674VokJ3Z@@$u?}3B%``@=pcrw*z^p?Xupk1 znYVJn?(B@HaPdcB90EQv>HHuh)NV3MEwpf@nC2~_RO1vaq;4=>J4@+)WHnn|N7={y za9$!0$BKn|l*i(l<#%kEe-w6JQ07PxkGiaZQ$&5iA*$XGs+z_ptv#=DF5j-YxceJQ zb@@!dEx!NW9jYOD_9w_Kha{(w-g| zVYWHEu!>&AD><%@wbJ*G4;?k2HRI41%=5(+AMWZ=@x*q&&XeknTV0!Zba+w1IaprJ z!Y6j;Ns6@r)ZJ8{Sm12d*DPnRI3+eu-O0-f4GPU3=J-bX=#!1J-8!g593EbL(uMMr z5Pwv#vp%a*sKGzhZnxBAaBAJ39kZGLj{*_|TdLwd_Ijwde63T_63MNS@#w_!Nhp?_VM zUQ<_>sY2f~THGqlFN?eQcNKqXY*#mL5|8`6$~O+ZZ!HUjTzyyK{LmJy+9FZWJ$=Hr zDxw_K<4j|Cs*>`T>?fvmwqlfQ?hW@9GeK??3-#8!zzgdD?IFPH(ZgZ^f|a*`*3k>3 z&m3TKYr0qg(#ybYRuIVPyLZs|Qf)5yZoPW;Z*UINY7CR%ob2p%d0B;p(NeuYH)vw2 z;qN%#HrD!1gSqUUp<+fQhwb~{J#AIwIx|9!N-W82ACVBSUkr%|PJ93P!)5J{k!6&+sVZ0VIxzn7NV>j+r! zHbImPvOelbMn~-VCuY9hS(ol>HluFKTWAyah4lEMP@Uf>bT-%>XK$g7ZcX}^nV}zBt!1~-4M0f&Qd|5p5pF_anr?xb z+w;BOuh&zO{alxLZrMbxd9=&1#_jY*?3Pn#5o=)HbfF$Kz3R)=W0Vk7y6uE%kz;n;$+O?+u zW~6Pp0)7D)7;O*9`K>i5gIhp1-^JzSEK;v3vn54-5tVDx5=grL08#0jjt@KfvYhd{ zwfUvDoh8IEF4I#qV$_xkVdPF(bvO^H<7q7T`JV23wa!i|4EXG3)Y|t~+uf{RG&@WC zKL>i`CKomSM*qAR8-t$wAQAVZ!zc8z;tTMP`ikyQE1utpk;>KRF%#dHuC>^IL6GWv zbx$5={Zi1Y@wjCgJ>cH& zw$2dC2hPFAJ2wlz_SrBgy$WW1TuN6wBi|~Yooxq|Cjy9i9*74Z=D-IdUw}U`^JwKt z17R?%A8XR$Dlz&e@&TmPwW08-R=PPp@C+~Qqp*L%PnflOq$Dw53(XJh%G+tb#f1&YaoDZr5x@MXLEG^j; zZ_UhpO{L5kx}8LHU;k0)v$9!b({mzj2c2Y3<|Sy&b17d`{Cu_K3l!Clhu8AXpjy9R zM_%U!jc-?2ObN8j9Frimht^QK&um`pHdWRL} zcx|A|hZ_*V0QwfEWqq?E5*?~`8U3Y9N*Hf7vPaLZN#Qz4+ zvCY{=7JP8b;?w3uZXIh_7p)h z`r$xsU~ZJZ{r)5cduKrp*49>TH^Y0Dk)rYWu_YF-6Xu&po3*zNCe#L@(U&tCZU^8-loNUZR6tMNcyw@t`n4W zA3_IqEMQk;kiUav@8O8#L%G2DRgVz-Rw+)hLbhJ{{c1!ghRlkis(oSpJ_X@ym}61Z zQ+|(K{Sq@yugF0L%c1$=cZMXJD@RrYM{Yz0-5X>OU|+FFLkb+hcNWCsnj`${xcP(Kh?7R)qVPjAw-s_^c|eR67m5a2DHINtL9(sf{M_yNvlR?nmd;0X8<12gKl zMv8>i$fa+^Jya-B+2@1~OFy&MtSP2Hu)?S4>OMy+evo&k#b}?Cy1wb4V;V6oxjyTh z-KV`hx!*9>E#n%m*5X1&n@5-Tk+8}D73G{25whx2Pu*X8tH0~dD#)@D(;&YRM&p*K zp`~uzPcD3xRhrCzS-vGx=j|X*;;RHE{rf6+E$KT_Qg)n|*nCz!tG17&h>h-+>TI40 zUmi|j^hoGVYqgdTF zD?eCn+>L%T$7-WXXrApr!I56EVtpGeJJ*_zM!4B*mPS3ccu^-NrSz3cEzh>)N!d~l zXA%i0)k}uXe9%$w@P3yKoed}qE{_zwkZWV zoT%{J3|(}vFI=8|>DQ%{ZAq%T~J#?X>*g8QiPrEc- z+yKjrD;R%xee~NiB2sNfNpF8TdiMuNrZj%}@)peW6L)uRsrZ0}pFi6HG93@t0|JoE z@IaDclY^$uw~K9tmw}2#1htAF|0BC)<2RoY>p?JVVTn0q&5nNQbOGdj&{}Dxp{g+(xBpwSw8oO=hB+kD#JjR;o7w*Y9>DC zCuO;J-4_GR7fBAq%d>Bkx=A@6n5?0#!#Vq_FO`Ut5mHCOGhW%|rtpbL-8@pX8=u^a z`{1skq&P1s3yyJKf%E6tUST&6%+J#-!bp*ZEA6lw(`KogrBe>4Px}+It*cCOyt~c_ ztHAHwOTml44(|b+cLMaKv?SuQMV)5e`|)b2;ejOtNH`={h`@9Jv#R6akrGaU@rM>9 zm{HGNeFzi_5T_1mCY-{;nh0P8*u~rx0LBJ+k+&61zY!?SID)YZgSrAYSK0@p5=l*A z>gg}_B-t*DIK&$gDEOEt%{N%eb9%xprE*( zu{Tt#vlkP%Ax@0>eC_{A0*qiI>juhnUNvdSNSByq#(O@>(x9XBvJ zy>|K;F7uIBPVBnJo0ZR}_-F{k=~T=C=PrqGs!KCb433Mh4K_{kHHe2vt9Uc#7S*>Z z3kGqJ(|5I;MmzdjseOEBV)V*1-Xn6@((e=bYtO0P+L@5zsI-CKe8cV4`@eN6bC&&H z{OQuwSdYVti+oEWv>fAvgDqRvCXKn=9!cx|mff<=KqSnaC=mPlc7DFY2Q1 zJr&>Xk;uySDJGnw4*xdVl_qe9)a6}>&H=nl^=+gsKRrF|3L+EBpc`>sOiVY(n0MT1 z4GLH?H*XRFBn3Fp90x$F5=gD=Gl2=usa7K@!v&>GjytUgzUSB(G9L|j z)@eoAFdMf`3X|_>$#dDFI)YSc!P4W06(B-(wQdcekKxp8a!R}aq=*k8ZqQGo^892Y52q0@Y24S0Skbg-8>1D&l=4SQVx8H&E z0uvIlf|ryD3NjY+{NAn&yArq&2YMqDcVzncPt(qu_eD@dVd>>77n-3a5C`T z5p+-fT31lFBk5D&!RIhxoM-c7)~z-=v#Kd~pS1g=F+b;*Cc~>-yAt1bK0knF)WQf3 z)5`+F@`nmx7dilk$dm5@l40;(*bc5NE&ZzQMMPu(TJO9>#adJ6()fwv$1gu#G)9C) zBCkA3)HhIYgh(DFY4mZxe|Lb85t!(Zd^isK0sa_)=yjIHm*rh#eDJa}-%fD6LFICu zn8jUK;Veiw!0w%fa%T}7-53>b1T;S9=g0|{SvvlID%1v(u`VCS3 ziknW<{asbxx3~7YhE?(F-n;!SfR&|p<9&>1G^4IR4LA{3sDewg9KF8&>)ZHvPsSaN zl(aP6eaaA`-Ac-Q`{tvoUOjoj=p7jvM%cKS7OI4g(T6lOT z4&QQ;j{dxqinGc0@6!&hVJn~6xkNvFoBZL8`xeRlm~Vgm#ME5&#ZlSA%ge#Om@>s6 zyTgkPqTCwwcT~92s&P$=HA1lw4*~3zF3euFnOC8Z8&PXNmj`G>FMg;F^BHYYyF)&qgRPLm5d+?s!^&( z4tjlk2{00w0~DFs@m2Jj8;yg*N&xn0Y;5Z4kJhVANi{col4y72AT3c8iV}`YWEZ_~ zFuw^1w6X9FTRfAxt1AxN_y>nP$;rtJ@6{YA1RjLW6rGeuIW)n|+1cLY78KMl&DRDD zCbI9=R-+*{4QtVv{Hk(y$ITsRk+|KRO`d#jwuZu>2ys}7gVc?MxHp#RWa3N@oPnRc z8b7QvqT-80`pZy#bx^(x1;v+$;SHdepvAT%a+IQkT)#({UD!M@1&{^cHQ+B4;~|dF zhy0_4yu9D!C5IoXZsB`~b-k02ENA~%m56<%f!rs4$ix@v^10c35Z7Yy4T^t ze{NbVfWMYkQ3+>-Ay#PBuLig=T1QEsH^L8{{72Td)**uabL(cBA)ASR`t<1qDK{4v zt;uNGSdNPq+qtHGAzyt>@6+BzNzdLtko%0#nc~liR49dn@LW^k;BHJ#fx3*&BuJV6 z+Om8BKu&r3qV$c{tTEm^j99n3pW0`1PXOm2J%pi z0G$QaO<;;UERdPGxw*1aT<6cX1GU5QN_g;zU+fd)LXMQ2vE*gIYY>D6TM;d$vwa_~ zYwA6E!7m_yaSUi~Rz#%5kkkOH`OmSBJ{SL_D56v)gUNwb+e_*C<&;chUMqm^Qiwxuz_2N$LY=J zqcZ2A1z$RT&6}Kp0O?D_69aSc$JiZb*;NH^6+wdUr=WoHrUIyFh`MhStjrB#KrrUK zl$0Tix1f`BbQxsi2Y!H*O%N*pE!zmcl$GEr)wP!*+Blx13r9ys8}s)(@qTk;H#poe zwlOwbeSG`*J^gpR;@0!WQSsNnHXtzJ5aRN}KDTfbiU#*xenf#Rz3kV_Of6saLuO{2 z&AKmwmJji!tEN;n`T9tvEvn_a|KYg3!O;g49*Nq|*2pV|Eq(L73OOnrqyk9@s_<`U zWR#caOQ1Q@OFk*t1K>maMS;wLz0J$<;xEMl6pG;|kPM2G|hIO>y8R=3jOEYhM)a0kVhWs#;p<#Uri? zAa2tRq?l|w<-eUheh%vA4}Gaw@Brh^fEYSFa@vwVBGX+mzh5tDeD73!%rIEQppuFB z(w{%yxc<;h@*uXMvu$q|0xyD#2WGcEbO{M*Fa{D+P`?!#6iXbZQ^GSLQ+k++Dl9xa z931Pvj=IZ+l#3-L4G@$*+ZqWX9Y|MY0495|3@P^@+k=aJx2d5}0id>SFeE$*;viX2 z0n}o^Vf@;>p4kH!JNM1nnoCogq@*OM3`Nao{VC7@J9!OAqh{iHqqhg4{tPgQfzq?T z4h0HZ-SKv+OaRh_#rgaB&1n6?jbGLSzAt?0X22MLSj}4p8}^?7`MDtQbZ>NAL7p0+ zaFFIl4TGQ=bt+hRWphBjtE(ReWBw|D)%WacBz|pNMGH1}qFfz%!li{P(WN z-&zrWkftQo#-1VGvrYMeEJV6mAS0b`Q#yGHF^FKA7JvRs$FBoo7M-PaFUex>sk%?X z$oUryO$w0fWjZ=KfNTP^6!-&q17IdN6t+Ssh-dTg9*rbj7e3Drl=#y@za9;fJM3o| z>QAyB+|NTr_|8@^KtZkC2XYZ?4g8zGeyb|>J^-0fe{fsuK`fOUK=l0My)7+D#lucf zNH(~(u8vDU;0`o$>hS>i&VlvNy?Z@>1XwTKFtiKpr<9wrMqj=>2;fsiNQ)LaoA135 ztLJppR2C2*1{wFSf?GDqwzapoFft!VMcd&2~OBQl~+*o%m^+B=ybrE@Rk5UK1hRyuu3eqUzb-@ zEU=gP+cP426xxm&#Ch(N0`~-1wrPli1{$h+d>Z6};D?lexT+$;ZZS4a23&PKBo&Cj zbL#~uAfOMYfew({FYOn&K(#z#;H~QW`%Bdg4GdyI3QPbLBFqFOa~h1*u3r6E8}jRY z!OjO@EWGcxD%!H*j{sJ`JZW#7JY>8;Hao_9)V)oLcjZrhKk@(sc2n?;K$zj!z@H-? z{*&?`Obp!$*TR3;Q~y1RM((jw=D$H2cLlX~bri)f89(H_D5|G>G%ys3@i(`>2r_ri z{hHo?KlmfmmxX|t6BXTS%x?~IQ9ua$<3z|`UXei3x(hU7AVLoBmxUOhr)}>;R*N3= zx_t(qw(AjNzXmsq4a9H_y&$tuil%mNFBOXgZv{G7nj<-dkHayf2#N*x0ClTR82MHuOZ%QhscBkscTCh z2Lj?<6?9V(TMeY8zOoyCJpgL*ca-c4Bm)9iVgycwWKRHzh=rJMpzOllekQPA3Xs0C z5$&}ghwsgFh%MP0lmv>u2IXlDezz4X7}Vet5g})+3W%Qyg}tw+x{vH)CM#SEbO(c2 zfv58TK#_ZAg0vT~Y0~px0yMP&dHru>GL*D7yipU#PKU+W+u6-%{+bf-=={e}N%9ar zIjsG>2QHqR8h|2Wxc_$5<8ajp&^rx;^9?%T^*=4@)gV_5vbnHvo7-V`6yZ5W5(mhzK}L zeEYPEmkfV63~@(k`S*M*&e#kuG8?KP(82; zQC6>4xM7yA%s=t60;(Rcet$r!JrrEr3WGnX<#2@8;fv(ZUHbHQ{KOS-Xy33%8GvGU z$MZucvb5>{IOiXKaU)}u<;l;hg@FJ%16%(V7-?tEo(07LHxZFbw;BK1HJuuUXdeV0 zvwv}`Eb*q3x2)+6ZCWU-Cg`#l%@z74D=;>-Qo;sbt1VW+Gl$os5>7_e8zj{f-z z4b4rv0Gfq_cQ=_kKu4G@=ExJs zF0idJiM!I-)`hru5ZV!eC!2qP^h9zE@Z=Q;_YwjJzz+TO-03&plMQ*@kdZM5YA2Gb z0}hkAMV&1y6d+1@N#AX@(9Z7L$gllNwAfb~_ru4AO3d%Pyvi!#3(pZwCQS5@!yYxC zJ}pBsfDoo@^A32l6-|4KuIhDIG%o=55lO_3cx+lve*YeJ*yGu=XCzRw`~iIIx4yo< ze?Q{kI~b>kd5QJhd0YXv1}UctG+P@R8w99fbbwa;IYUE3xA0#E7FoC8h+AlMm;vB{ z5nd@Mcm^ZZK;b8GdO8CF9etSkNa#1C2XpY(W(iXV1!`QbV2DYe6?YRDe<@E5X=!N@ z)^4EWMtbCB^ZQf3Jf`@s7ZDd<`WXnk1OjiE0A%)F7*QD|B_(Pfb3lv;Td|^pFyY_5 zd$+>#r?>`O%&P@-IgLQfK^2^;j4{n1AbO@EDn27Zt z0k9Vf#qU%6N(SpPhuhw;g84+X@9tmcLjBjkW^Yx8&vvz>Z!czI3~ex;2lGcs`8S^U z|0k09|9r5t#CMvwdw&FzbY@G4T?}c;t|$ga#Otv8%Jl%`A9fM~A4h1qS^i&*-9HKQ z|D8+n99;EZuci841o&B~_=J1^NksT7vWIZ;2og@7+jEC4l^dhrB z6;=&I%FEI%$K~`g)+wZh_B=m@65lf?<~a>8EB;UVc8pLQ7YPlf*$kiIDJc z%_L$aD9?bbMj-J1)lJv9F)0a}W|%y#Oi#ZB+AmL$z7=p8WnNy5Kxs)In9X^Nf42k zLlsgUaw>@4W}&5K%_R^o=>)Ygtw~F(p_1FHqn=d>w$R=I$=X2bq|klS39g9n?~$y+ zy&*^gVIVrhZ|&cgzVx>V!>a~e8kQj*`~hSUSAc>b-15gRE~o2Ca5#&`5Y7RdBL*ph z*VrRzd3Z+k6iejxxGr2MC`eO`KtMQ|p!n?b2-Hgu6&@setzZarU_?`fP$`Zg9o$ol zsFp#}xK8sU+3+4U%<0Uisj1yzQ3OfR(iA>Owalz z$!=Z%y&hK(oupx8w3^lhQ5xWA8cY|W5Dv^=uaEXC0D^*p?~u;Y+S<;mxh?SO;|e;* zLG(xB`X=CBHE-Rz^`aip$^h+Lnpg^iul~!4ua;G$+N!Q)5OqjTU}9j<{kjGMs%mEz zpj8??bSUF0AicQw_;Tdy_oW;D?H$hi4k^EOWd#My!!0CV84+P_UJS7m6cIIWXjuFP zimWZo<0RbBPQ3^!!cm`XzVPjINJ@ zBoCMPcgPE0E~u%g!8o=AjVr`bdl}u`GZxZ3ZV)`uFfm!1m%*n84SE`8dqOwo^)ld9 z^x#ZE+L=q71yx^t^zN^#s9Hm(05#a&1W>}9c<>-Slsi)j?8Y+qsq`C2Gdp?mVbB-3 zJ~%j7rqjzKQaQ1Xx&pmO_RfIN(wIHB7U-G8K;1=RrOyb@lOFq}v9S~MA01X^v=t12 zj0R1FU&#mZ(Dk{xxTq5cfZjxd-2UIc9VnO4f=eAK;Z_77Jd{vK0z^pY6jV#Pa<8|5+kMwayyzPS!&2N@zHG{pebl7 zfnH~X1QUoDgjF832Cn5oMO%mi|-S9nO}?^GnVk$-o7^2nm{@&~40tIJiX{ z*9ve|;GbjtUOFKwtv3OiU_upI7GvP8fFm<$ z5yuT@hrCSK3bf_TbbJC`#WG-+iSG@wzL&_F$v6tWC!%8D7JfSw;i-XPMnjD*#TL|a zbzlxF3a-T^L34hOcHxExsXD{TUd9?G8ZoRbb9$>*B zx82&l3X!(vbYIXoyW;zYxAWhTb?fiFR21zrl&|TZ3p+M;7o$G~ z6;h!BkTG|JCJ-7f&G3Df0iUSu!i);|)jI&j#n{=;V9+7KEzT@xCoRiRFJe?P;fAhI|QdaYYXGqI<#K6n%a>)U%Fcp zQf`PgCM;(>?yaq@e<0I9LKz&%be$;%e&d(o;^JJ4OE8cuK@=61;~>^;DCQ!^>_GPg z#Q29j5_6hCN)1Q_@3aMIgiK{>oPdS&Z}0IQMz7u(>1cZcX;*T z;36=)jFEv8t+O#cK2E7{J8lZpo1hu;RYbm?2YslT`EM>mJTndoAjmrTZdl^VZ*07L}{A?+@NvH{cW$U+*Kg{_pLfI(u*hHwtz6a`Rt)0kY43 z=Z61}nZf_{!T*281Ggjo99n-X8vRe-_b;tQ$n+u#i19`L!QlA&!S5Ak_CWze|37$% z{~UL;d-0!F692zr}tfc_aUfMuQth zXizA6`hRA({y7@|%V61)R?9tB1Vgf0=>PKIziJV`Toz(p`iv!VXL^9=J_UghF?q2Y$< z0_G8Za#xa`-BjV?<`^9Tj(|^8e!i&S5e;Jd-F3IprkYwAy3)t;tzSATOK#LgD;HY2 z0|J)Nl@|d*HYdj>6Uv`nt4&F^bhdhWyt8$n5D3IMAlu+*!JL?jGJK3H<>IK4mYa?c zbtSxBiMyp{;JrPfD-xkdIMtzLT|>{=*Qr<(Y)5E%_I_$b&ez0L})M@Nv?(< z%Wme~<*@+V?8a@v5LP>)&CGO?;LBD;Bn6_+3rb+nmiEAjcDzF|jWd7N!}__Z`O={@ zHBv({18I7dK|@tbebRn)c0`S(LOiXmur@}qqYo38v+^ilEz)bFxoz;e5rsp0yFs>K zuCBuVb+>RaA-*bAOfgI6P5+_?#7d(=trRH!tCgKfAdFN-;0mo%ZDu1gtX*D4$N1A$ zJSO?~Y>d)cW;rQo(sHygj_PW@na^H%_oj(SQ&lT@yW@~f#dfa2rX8hW$UdRY6m6s7 zD_OV#6XJEur(gp+940f2oJmsT^Un z6+V-^qes+v7yWf(Yu6NCQgp(s^YWI|Jbuju)iXXjm&$N+rBmZs8RuEn_@PHYLLFcV zj;|G?p56`>v&gRedDnAT2ph*c+jX04gn2SRp{=ziRmzCezcH*+SnOI=+-Tp@KIi&9 zav^1dLUJ)onKey!63Igc1i%O28Y2+0(&oz4?$oV7m$BmTGHj_`Jp1dJ%rfRpJ+Yma z8KE1I&)0~BX;_UkyQkAjpRSQR><-CAUdBbuxh&s_5!H8=x-9n1avvdo-7_kgZrrso z`|0nK*KDw@^uoDGb(Ayd^O|#=!M3}ZuV=)g@K>f9uhRDy$A}hF1&8}aDdjjOFwaI! zzi`8<-_TM`G18BYX?@XY9W^~`+bi6s-RYJ6Ua?b(<7iLxG501)G~dZnV;He|s?HvA z9C0Vdqh~?tf$ zj;d#zd_ArVmu@)t5SX@jd6X5uoP9m1S3mq{*iD*|-Sx*f%O`v=aIul9L0BDQ!xf;!fgCnIws9hvE58b7~vfHPTIfU zg;*1A!$}ijIyEuVsy0oRV%DxNlIbh?vmkH6v^a6hk1k*GrGrVF#%Z&6c~7SLs#v;nu7zqsgl@r+#A{Syu|4p1U~-8Fyx zNd*Z{8E)ya8772j>FUui+!}3$>%-715EHt3Gq^Q9_e)7)15Je(kwoMTg}i3|0IIuE zS9Y~ybJy7<13J3#8P*4>6)Zg{r;5IkGLc_cmKVZWdPQWKq|4j$O!Fuo%}%PxBtj~2 zVt&(`@Vu+Bv9N{GL#2(L1K?Xx(G0m9e*#^=n~7%>Zeh&qxGqY!QMvWVe`L!z1$fQw z3AX$Zmn|ONlyKNI7PLNoUT$&Gq04_M2X7)=k@&hX@YKruA679M_Dk?i?9*Y`I06WxO{%KT=s5yInuIv6(;G z?OO5Tg-j#&`H-|SNAe$MBy@>Ys|K8!v}OvUtG9Seq@jKUpB zr+5SHq7MVT`Xxqjm7^bW;5nC$WV%zLdIaoMHCy5&8{3QRY(pfq*2{#L{dfe&t3Ff( z9w@X;SdbXVylMQth3K}kaVWC0>{&_-Afnswv-sku6$?;c^7+6X+|70QR*hJ3H(SME z6MdPk-bis$os;D`IncZdcIFuYSz6TC16ZKrkM4NHZVVQlJL=9?nSW#6{R6%oI8TN5 zw>@S>T&~4cx?&=C1y5YKa9LVdktMNLQtw{y#sQLf(mnD4-GTM>{gL*&6e(e`t*s5c z^`kcJJ2eYBKeyi4Z4-ItZa)*tGz>%!;4rQV4lyrPI#NfIcyT*86GmO4Dy*p%ZSStk ziZJF3`+h6nFvUl@JABC5!vD4*6s84-HNVW}j0zM7{J`WC zZ6ySo)Ait-tn4C%m;if?HM|p;DhDVC$ z!mGlq&1wxga)MJSf7~~ZQexa>My$dTkGGxYT6jHTSUR3aJhHsfZ6qM8 zJFvTLUhzHjU=#Q-s*Z@lxJk)+=Do#*mE3~jSxFtPt%~-HuIb3tPuH%GlDZBwjka~M zlZGF6X+ zg7f0DLl-)e;&JJFMXAjN7ZfJRc3)^)uJ$88~QX^vRVZp$<2?8 zhigWI`17oa`G0Hw*9(eujJp=7wT!G*h0=i_(w*){?)i>^3APStcy!LZ?YT|5fu>bv zUIDmgSSgnAnLJ@dz8K6Lqu3WhA+#H4(-SaYwyDJg4Xy9A(fCxcrS$)lxDj8RcF)vK z2OW^QyXtROGn%Jo<*w4qn0bD0)K( z-+ig}>Izm$<-tel9>XviFH*tbOLH|bZzHi19Liw=0}mQvYkZt;BuQg>*yIdsu=IVY zFB0ZSG`fv+*2_BsOpUm^gRd0aoGuiyaCmJPfp`S{P=bs9>o_OI?r)>J1x7Nm&dT%; z4jHmdm$LM7v2zyUu~IloOZ}1gAt5@1e!bhp#>KB|blW_B?jj^`$WZA~$h?0Z8>4&v zO0J8Orj?#uXgJNzT<@&U%8xTlO?2A!Ne38K1NHJ;1M@O3Ht}$asVHd93oeHajrSQa zE(nrQ4zIY^AD#VOitlkr&-~B(-`9={Q@9tRp9ml7gTd^Z5cAfxsIk5-EtO%Qk{PdW zQ0Qzd&~&`3vou8yL$4gy($KS##Bsq|MOc$^zP&S(M3yd*@8cj`V7T-6Sqv9vOG~Zx zolWPWf)ic*%hLnHg--4))WQ<(jtlb2N6naXct#uID|QuAQtkh=gibh~}7k4W(pTHQ&b;Gj0bjZbt6gDovG+2^ihqoY_0 zEqgwb`Ei~66+Jc#ZDX)yJ=#_khs23f~TbDot8Z37rv>A{Ek_8(_{+>|uLMsGdUDU*ci z>p9FQ@h6!VWdYZ*KG|Az+x3ieqqJ`EEEg#p`dGrirdhOxh2_8%X@gjsFefz76|Qu0_oCph#cfS6AO`ZDmhMF(WQVOR)xF-t>a8c(adXnxRee4oyGI-FfUNIy6Pa8+&go{D(+fV zvhFL?+FHZO(ko)qt=i`th@sCWXRMA|e7d$pbLv}8Wz0E;dW{;=>|%`T_h%-JS)X>k zqcW%ZYb{B&yDcG0hThZq;}k@7*Hpy0Xl$$bN?0&OgQMy2b$OVOj zzO-exH+~_E^B~;$^LI}s1?B9{1KB>P>Uo=|FIV^EM zu*XpGzUDmQ$=8;xdVtsj8}SDF*@*$=&?J#9twFAkoSs*-Nt>?s=;U2wo9Fr6GT z3LR*TX&Q9Nw(#i+av(V1&Rn3RTE^Zj=y)LzrC`ByEE9h$0Y~Wc(5ZBaV|H7hNQudQ z!Y6@*7}tf%of$~P^U1{$P%tr$eRW`T||HnLrJctEg+ zC(sj{Y_U_eFaEyR{cp>C)ZEK!lSU9O2VwH_5uX&x)O}rHl^NfWEC9?NRai1zpZl82 zw*~m9It(YA9wznQ$Miau-%uSa)i#PyRMX)2=|ss_ku7u1Fo%nx`s>x5Ny*;SN4W!+ zsqlj2uCQ8NcU>-mF zqb;lDvEz3myt(D*L>^l(oXUE!s(;RtFHk?iu^2o^kg9S7&YM|ZgM z%zAoO=`@gBF#iGrgL{cOO$CVmo95=`2>RK-sdjfh20+5l$y;}z#)q=(hkc?=*+b6z za*eGmKp)Ptf6daU5W95WvB}7(_Umwj=qFAo~>LR9L%;Ygr$9L5>O7e#Oz{$45BY7OMO+rlVuRg1n zmWpDO{8jZu%P~4a+_CriGBn!f9gMX3K4J?R7`k&tmXGfFxaDXIpSw zt*x=CMFUYRbCfm{JoK}WQ6S4G*&tftwx`?FO$>f&N}GQ|koc_h4$oIxv`wr>pOdsM z6$@x#2kfhmxEybNM1LeFNqpFERkzCh6DmfrrQH|fnX?=TY(3W<+@Ub{cfvb#m>eM; z=R+Q_iUEL<3^?wxP$-NI%{y6#AJ$MJy*u8tRzQIPp*{owniVj> z`4!fxo*_pqmliX3N5wgMU0w^4-<>}`)tG(Nz4URNS5H&Y;s%m^Yx(|cplCBa;B&N^ zoxG2o{gH_&V#b%BcPRQ)v!6p zbr5$`se3F9HQd9FdS&*Xbkqy4TFjVZmeLZYnzcSe+ju-Y2)l~6>Pn2EusXgYuLgQ8 zLX48B@0fJ8H|FStwTL^fGaNX3-8hbZxM1#nhR?Z|UzR~8PBbZ$BC$|#qlAhh7HuaR zX191`VtzUy?2I@4kdT@t^^a85s&5(=k^48yKelKR8h71OCyttaCBuK4nzng=k6Uhd zN9XtkupYmclb#hZi`;YBNn@fQ5ptrIn+3-mWvZ0JcH}AsEG>3hH*6I8n+AKISA{Rs z^`mT6_HPwl`@~YOY|*NFgeJ}ofARY!&n6tnP&l@WRZsh#5VmtOHK09m`=;Zvy8MgY zXtByFQQx>D)%J@=mdDH#vKQ3gTdQ7Mk9+AY%FE5IfI##B7PSoh!ime%T~Kijy9p)4 zkta(^2#_BH_wPVW^$phks|er+hF=?IPEOIHHyPL0>rh=uhYhYreslh-^aM_{*S0MxRnmNrQ21 zzf!3kEWJ&{DsX2ZetEm9#KZ9`Z+;l*9bY%J-O37P3#-csY%KdQQ5oKN&~sM}Y`>Y5 z7B(lG&a9@n(!onSd(D>I%O1!&c3)6+PXhh#XtmY$&AzJ+Hm@yt#kx$ zB~Vay**43}O!=+-E9oECc7-#mc!@n3%~RrfO;x^|H{>HtE7n8nBzG%!mX<8C`&d%? zCN2a24U_vNOCX|u;Mj>A&2Zc9pS$IlHsM)*#!>MvhAMg@sRTYM>6G%JCXZcJxViou zcTKJ>2|jLN!`-zpv+RE7?sm`m&tzz9&dln$1+z{;VrY*1i>j%ZTgtr}eC{cq3%*6& zM_h&C58GVj22t!+H#U>K!&Y`Z`qj+%Bxb`!uOoPWZ|P*&R6?qW?`o z3B7+X_%r0u6_)=`d+!-lWwvdLF10Mngk?Y^Nr|E$2!bL>LMftxsGuZCN)RN9NRDPD zDj=wU2ndKI2}%ws83jR*oIxbB$YBAmPfE|;uf2QjIjz0>-u-h|YkOA}EY|wI`OP^- zAAO9`2X$}FW5`mvI4uUYXlFKmkqk8!voh71$$3e69T_{d!DqbSu4D@L?$8gqpD*{e z^Z7@Q+@xR8@Q(*GYW`3Uk>C2vkKtsEQ zinLwXiPz3RAi(PN(D$3cpO>g+G_>fV$dY^ z@$oUbZt}NQq()O#oQA@@&>NNhz242^4t}AU*ZAsvlF!9__}FZFs>D*u|YYBfl zaTtiGCrg~PZ;#_(;(v3C{h3hGxIAOy(K))u;%i%RZq5d*>uX6W)fG~ve!uS%xvoF# zxY^T-Kjwx~gY6f>TaB*gWN>otNvP&&O9)WTsR-Vsuh(;hn&FgTbi`avJC}FktaGdz&^hi6(#G943_XXc zFC=I=JUFaTk$vyUfv^O+J-hoe2OW$@_U@hhVN*t5&-vvIo5kFlKeKHoy4H9*bT#VR z+m``;&BUh;L8h9-x9mrk?{f;6=zA%?XH%R#>kA3ysvXdEs%1NT*)YK7 zYav5_%;fgJ{?W*aY!7yCtypi!9RK;=YwP6W`T~}6E@rZTcYL{QudAq%rjoCUE_=Ki zU>s=w{(AePOAU@`*PVpC2I3^*Dw?I$L5i#h*%xO3U|oQ`j%o0-oQ;`*~JQscxgzg?SfmnaL|L2E!RmZ*rTfJm)4x zj3t%kG_2H=t)FkXIMA3O6j@qi-ps7~hik>~j&%pGhctC+;|YffEyq{e1CtvSZWX4=bK#as2dmeQBW zH}p)cJFz`g+`zD>i{aIaH^pyw=IaME2F0}|0(lOxNti8Nqse;xmDOTloiQw*zHi-P zWOM4~T`PfaDgqRB@)bd{rFnCGNFW3J0wV{3}t z*k!cO$^(%22L=YB2s0WbZCa?c_(n<^(7~_f|KL#I=UJ3*9kl#@ie7c5jJ4VY9%yey z+sF8r826hioL*ab6?)Rv4IYSosvI2`Ybi)a1^hfBR~-=aOrgB^S)+!|Wh?jZ@4LOM z@@s!PxMe<5rmtpIWLZswZ8~j2Ed2V^+Ez7x6{{^#aYOH;)zYWpvb>d286S zjKe_hF_W*))3`H-hN}GnB&+ZjrN|o$G4MoOz{aI^9gL|}zING4FPsviy~42AM=n<8 zPhY>+*S7BGK6k!@@o46wgaE-(pIsag0SmGAYlIltlD#~ zqEC{;-|Kg#<^PcyP)O+d{@n<3Z~dQUlKL9#X@&MvEs%b@f_bJH7^jz_SF%a&4;pC% zKq=)c%2ZH7tESGk%u2&d>jp@HXf@vs{gDqx<{t+sPDt_d9L)RT0&*sKe zO8Ks4b<@I52Pr3b6*vs(pHJe`I>}KqXE)Y@wY_2LA$KC7iA}Q`R{ToMd&9SD)+dcm z7}sdHTPx?J+Q`pa&|VHLI3}Lj9eu?$x2xB2M20A}uN10Cxnh!4Kk%dX zKq;qg`ZxE-5B<#yGeyP9TE8B>QvWU3blt)8_s;B&v5=z57ch#{IBrnTq0Myr$F*Z% z!nLur>x2S>K1a65)_?uQ*TYYCg>H~l~i6s&l;%rhpYPWyGFwyO0E5Gm89>7ntt9d01sa; zV&|_PnSHpqCdIyBRwm)-650XzEmYU%8OlWPE}G}Fk)JMe@p5QdaP@Y3U19rH{N-IuPS(sF{j4I3 z%ZdBjvV0)nRoj01>s_bY&97$|KQtFiZ)X>)upi#x_6a?LkHhFbyGefh7D0%@vzBD- zNOjvxeQPUBv$E5oI;#1}SQ#?g1pCr-^$j7Dx)-F^5A)>K(8bSlXdXR!l%9#{Lb$GG zld443R=VvczkG2XDp*PMox;O}XZPqU*h?z-&Yu6o74yx8bM|G1&EDsO zRpFNId+$YvXG(8taFGd4xqM}-=S>go=jcG-5{w&Hyw@W_@*qlOm*3BhJm}xjrhfR= z#0e80vuBG}-Wn8q+cOuoY26@2`wV!eb;%QZHw~E6gv2IzawT2em6T5?s1`VJS8y{r zi;C3*w^`~HDc_xZcI&w5wctOsJBOnn{iv9%+Wy-c|LnxVKq00X^|h)js`{LCySB)GWVKG$keFTyoM|y^0-y8Zl?eF5jJuPG(ff zNna_|tvukV46%;x-+k{tq^z{H;}ncB`r9n^k4~E{l@Dqf!&3#y7KOulS7fR-XzqMH z6?~%he&Jo~67d@xy>~*s^?$6%Rmpq@zXd_H`S}p5*_%%8Wf@z~s)!`2@j^SRCb%*1 z#@^!3`<|o2u-rkJ-%KwkcQVq)SqPWp+ua;HR5-wU+R zq+`h!C;BU2-`wdYA6!@TMJxUNtV3~JgW=(x>2}TT?snz!A3C5_nIyy9Lo2pUoUD56 z>)}EdXd|N}+(MERhDVk=jZ@Fnau3`TPd)SebnV3Atm*@U@`l8u1_f2UZ?8=YNAziV zUJ#}J(02Ontw35erR449@j)X)bI}hyJMx}EhG7Mq{%-ngRQM~;_hE_4q%na!Y|=jA^N{g{kd|9!~5dIMSt zBzfvD{Rk9uI3H28{zuIPrwoRQ>f+*0l|D)iv0AO6KYV99rKtCe%=v7ZuTQ*@_0A;O zvr(Rw1k2hS)nly5L!;F`2lY>Fm>9mY?vqMvg0LTP;=-Fi6gf@jTe+oOd@$EleX4o? z@aEuG9oGiE+F~LKU6|va)g8aVz;j3Q`K9cJSL%%&jMhlPT*!FTy2Dk_SugamkQRPE3$nJz?=({qEX~Se4`JIXB+qYjnup zUAvqq=}hh5p%ioG#spc1!E&Z5O;0}6=)c6pRE#IpY(%C9vK*aqt#U($%O9+Wxjvxj zyNbCXkgj0wQ&S@I)%P1&M5sZe(t*ZGp{J*a#yaVC6?ceb zYYJVk9d>_Pnst${%vt?>W(i!IMPJDknDjRE9 zmCM+GFgmiOnZ1m)HOiq(aUyqiVRTIbzlENVCu2i;)dk*zSI!HGTZB+6+Vk3N=iixW zM-&nAjpasaRN&Ub)^QETwhXI;_&GJ0zn@Kfk5%T~)umvadH++vtaS(xnicGm&+%r(f)tq1tqQ-ZJ>{$ap@z z?)m!D{2Sjdv%m69?`SKS&;~o6oDZGf4j(+OP_g)R^hrb1=oJ0FoAWU&vQu6rucyqP z?zIW%XnyuwJxjUdF^{~{$*D=E+6dD-y0Xuz!f!1cFwF`z*VYL09~9S!8>75x@@|~$ z&wJ3ptfVfq8q-%iVn%F~yo>jXJ9htZw6)0Cy3d3|eI2Vyzw<|3smxLp8EC7nzpIw_ zb(MAHr@uA1g^y%7+isBW{rdjP)a<54h~v!&8mvtOqZBxa}@# z03jfZy4g#pG<4LVZx$Ww)fnt~9Rs;FOiErwDSk~JFE@y>nhNURD;P?*y7_@=)sMI{ z7|Jlyq<9XZo8iA(eK~5Q5f&-$7EmI4O7`nL?Sxb{r~XvdZ?mTN4!#^tkNAkr_wgZ1 zp}qo}fWF3%RIWW!P4{fhY*3L8Y*MkQVB}M&?a-bvKEzi2y_H+*LzvK(FCsi7HE6JvZkK2Y>TxGaywGn9Z7Y0oCUM>)sFtchZ!zLs*~ zqeHjYgt)&{$lZE<^<(Ls$?Yf4+F!1j%#5LvH1}3?7}i#k+}X=tka+?}@tH_f*zQ zdD&SjRB#x6eWbcR>*Cgar4<=5R)%l1G;&0g2IqHu|3XUAm>jT+wPHx|!b!WLO%tT>?Zus=H zrOkZc=gUtQGFYc*6?g8utG9`X6!mnhYvB++2*X?ZJVSZsD#j^W9!{R~w374sdf#8E zsXnC1@hma?lo=QZ`-vem(}0%?7O#@$UxX9n}(0h2^x+V7kVfN$*ERdCAuY zw*hDs$KL#9m&QC*=H)5(ttK@$!Y{?m;7hB7LyB4MrIgTwH*ZcnIEn1VCj~RJ9LFs2 z>e!PPqm54}Iy>JOavF^>`b=ze^_T9s3>Aq#tQy*njm25V8*g9u@~4k~HBYs6OlxM@ z7oiUS0D7CfaUaGe`!|K6IP3lQiC~+OH@x=kL)={hCK{)DPTBi+oQWu{DNt*dUoJPO z|2#M_Ay<6n&Y}5B%@NEBmd6IOx!G=uCLmbKNq6#xVzi$<@6J7E_9#~Q`D(wA;@fa% z)k@V50c*2UF9(7Sd?uUl_Is#^(f0FM0`KRw=U)%MQ=#Rwh05LS_isnK17TdSuy15C zM!Q+toYR#YR=kw5D^B#!xOBEf)rf4DVHU9yi`#^X(JXNOCF)C%e^dbB>?qz;bGW$T zP3njpCS1-I8Au+OOQkCnQ$IiRS2gpZP`q}uTDA{dd8jX!L3i)a>-#boktbMQEA0Cy z;whhV^eyV+gq&-%IH&{n@;Qfo)*mQS_rXJ*S{PpAIe0lstq~2pc zB0Ic+R+Q-bY@vgp6t#B~O$D4Y7k>X)uQF&VgFEV zSI0o?*3UOxJklr< zcDCWFoiBy9s3xj`0lW10L+dES=AzYJ zgoqwmuT*hA(&(!&rV$Dute1Bgv_!MT)~s}sn1zXM^N0mAp~)$Qsp9=_*BYM(DSS;p zEkJxq4WHqzpm(VMosDy~P@qQ)ov_GLOaOfas!IQEZkaXPyOY!Ii{`^Q;jyvhIm=pC z-4)o1uD9m)T=9Q=1af%Q;g}4 z<#*Y`?t1M%SQ(lcTH)xT*PVYLg)#Ddd&W1NeUlOuEfw?oOLl%TpKPH}j_Iy7q!WcC ztQl=0!NpUWeIq1_JQ0UhR3>QW>O9Sab^x{g9-9nU09|mICG>eL8ShM6H>Ng?1=yN5 z<#x=*WJoveWFDT(2>Ptv`PN4A!SRr}zmya9HPlz%p?23Ahfl5dE~v>CEv#wn-?qMQ za3S_nf=aAAgUo3U^1s2AG?@-DT}kgX^rb~yds5sE9Y5&y$?=kmbED0G#~A}2uK*c& zx2XkcLS3TeqB8f(&I8MOdx9S%>&ym0p1#{a+WUZ4Bge?RcY=!P{eYHv;pcXyI$b7I zPX$h{vRc#oG0$nnK@Lp{7ByG)6ms1YlCPH)$(;yH&3|>~a#3=pq}Ls8*~wEwQwxH8 zOf`pjL|N+(8!4AGby@|!GjmUs3}imz@swq)ax8DPc+2ed)8{$0d2^aG)KW|}Bn7yK zn8uyIP6ms0`X19R9PyU$#2;jq`BzJo*jW#xOkY)2ZgRX7B|P;qJWNMsrdcc1u%N+q zcKDobU0QEqw%-D4l4((S+Dv+?Zna-MHMb{ZYt;72>#4O+0Vo)c?WPttvK6cf8J^#^ zokA(SyYGd_k66vjLr^o?)|hJUot2ek2-P^uoZKTI;5OXDh)&1NJwcYIPX&t#rf;g- z`bAp$pt**amw7EuU#&RLUOm11yPko%?>p%E2gi;jn$CF%#O-@@BA@@bpjWuMkele+ zh3ts#*MfmMleBJr%a(v+7b>;lHP|l*#xTe;*SOuCFp{o${W$E1j7*nF3sb+4nO8(v zZGP@$KF_#Dx2cN1SVMZxk}b9J@!re%CrzIfw#)a)cp5!T)NhNh_|b*ZR?*N^w+TgWrZBsU0ly!lkBz!mOj(v-39n@IzUrGJ8lT|IX=&jLqNhV!6s z*x$j1=fmyK$DQZ5{{FqNPd>2IeL#s??~~xh(Zv;TBENmY=s3#m!!CLDDP&(Ocy%_% z(WC*NW!}Ohr<~wk;qOSVZZj=b{yuN(m)^Jw-ENibBfBe;oa2Ofk`>82mE$WOR0KG( zuFc_X+HlkF_Fci4f|7eeUXgE&r6bmK>uQ$!7i*<*MLH((D4P~@^^Xcu>Xv7;nJil` zb!9Wf@+_I_tvlLPQZo5)$I%XSIdXqlQtMOnx4 zyLRQ2Ae<`>T;f>e+vEKfPRS@Xzn1R$KhGpAzwT&Hwxh+JAPsaPC-rkFw%y>Hj~M zmbhlEYt{E$@qg}}a^(ohw3c2EcVm_Rd0qMNv&-u2kxdVh;wi32J?a1BcmKmb__rSl zl>Ss^Iid0(i5GwC^Gkhd^lx7f^z-)|`5W^neuucx7Pj=N zx{bP_rPp8bC0rGkn|3AGgY}=)`o%Oh8X{ig96Dsn+MNst7MK2&Um>7{2 z@d8;{cydoL1gOt*_6yuN$nnn}a^2MNN88aN6>TT_oyWaA(0#~P=*7z1Uz^a{`4-v< zmEhs0d(XHq?FqiF7&>$SoYRl0h%)EedNlo`A-Z9hns=jO>zAbqpd9;xj$A`b?4;q8 zcmZY%3er69*JasRW-o8|v3z>7c7p86+xXfwxEUY*^6 zCQg=#z8ZJvp5@mKq>23MGsk2XL!LfW z`{6)Y#qjf-_&vOR)O7=6(RK%ubxa32d2^;a#N)f-&W0Y*xL$dz62m3Z!0uvLbIS?m z$>N>f7`rhZF5LGje2rD>#|@H)5hE(UY`8Jr@$<~MvfIw~Ms+2QYS@x9MF3j|tn=O}Iq#dakYqG2;z$BkJRm^(j4S9Rz>+W*V{@5R_xh^c0 zpZMoAxvyTe_}Snu#pTfPV2{rGn=4DK@2sKkDCJJz89%J1rk1(hWheXPis!7*rZHr( zX|k|@=Hd`l^sj8yUx#Veqr!U>0|SF1H+t?#V-tK?nDhK>fkx%~PKb8hCl5Jd7#+(V z(4g@zp2s{09W+w&+l_XS{`GL^6>wZ*Ys>r9=C)=K+FG8N*)m*PhrLvwY16EfwUTw$ zwQJYH&`X?%8`sl@yoFBj!yR{?^}GuAa)XBb{&L|?58>}mbjf#&ph^n7#|7&NZ_T@CS& z{Nb-y@OdU7$LSGG?aS9><|%wiBX;^)`?x%f~_0?0atvc?rb^bdldj2e~x%zua587hMr!)SvEJ zzXQrh-jEtB!i;bqCxX}3u}K^*4dB!6n>hdK?4^JlG?oY0vr3gD|1@>7_nWOY$YTo2 zeGCCA{1&I~>l78@Y@Y@7Cxt|v0)gmWNc+oP*|X`vD}3sBcj&(vuI1INO$eby*k->1;4gkna^k@~ktnb>1woW+jX2ugjR@g5fWRG$Yhm{!U78oEf!f zG)*mNO1IR7Ov;^S!(y#7r_aZ0*rkSiel9Nd`-bf-$5||-iRg|+`yGZtj8bC5$R+u7 zGx!^RXWI01^RP1mJb>&>?6vMvYMrh(y|C2pbP~U8*}PwHFe^%_PV_dWTN;cHL%!y_ zdfL@X`!kV~` zM2PHc6SFos6(S*i$h;-1w*P>DKu5~g)9%b>X*=vE`sj%}kC45QwCm@=s5^se@IW}B zn63ilmJXrZ8F^`%Ltw9n<#*ytb$Y8J5@D?B-kGYOk5S$SFF_9>L$`CMML3|eEEjR8 zE$Ys`SH&)u)+y5dwi5Yj&{*N$Irqnz0M&UHzy#&weqXa91FaMv#@ zP5G}N(XRGCFH&Tm{m$FO%RCpKn-CLl;gv63fwB;8m7Oc=Fs0`bGB6rSjBP?sDhoFn_FU$Hl~_! z{R$x)_A0&Jf_#PBPVd74r$xH`UTLIXOKh=3-8Mh+8BfG)%wn~3C13@@!4*|Ph{muf z%_6a7EydMm=`PwHLTEYZ$zi)|r=A_;f|d;nT$CqN$aYDZW1O9yzCQho1oU6-14b30 zw2C?KkZHr9AY7m~#KjKSLpP~wZrQ2?IMaN{DsLUm8aqvcyqPz!#}Kt)4gKEyl?)nx zLYw;jfpX#S6H}TnzO+7-T({hXi3DN`H6_mM+GJ74XpoQ5@!>$8$1-rfIk+D|hg;>_ zG0NEt*?#6=%1R8TuqnV>rW_pM);sGBLiXj{bWJ&g+q8v8(nW+qg-qghtU# z#9c2TLP%tvn+0fOBsQ3=iw}pKPEUDoNd+_^XX>E32E2A8ZTYyRf;bjjaoZ-kCKs0l$55Mt=^4BTfS*Q>u5mLP+#ZE`{Qt{N#(CHesU>^PFDo zqMl;7F#+n2v0B-pK-OC^Qo#d~MrlJC{fdP|#_lG)a6TE=PVgw^&|MzAcMM}wm_nc! z9(&>KUSu*h6TnmY1oA+~bDihcIC}HtOtZ(Qe}CpTGIe|N>H8R=GyDji7y?fgJZee1 zPyt5YI@dC#SO^`?k5oX@ExnA(D4dL-H~s`^=1wZdtQaXLQSRiq`Lu9oUM)13FehbM zl@d-j(Sx!m#fY2esOL;kLDyt?G}88y(V{$Kk|TL5xFj8G5ngSRbf)nR$KsX>Q9rmH z*;#MsLy6Z*>J>jsL(xM%E;+W2 z$KN4VHIdzUajrFL96Q&gp?^axt%D)=K4L{7Xs|MD+z9nUcL4B0Y}kIC=+(j_&;%0B zuo>2BJCIivz{f_ytW$x9_TfoA8*u51j+&e?&tg1qIbVBWG(ayC28N2TZjidJ9lUbx zT=TFFj6FX9$^Gf6H64pniwoMck$aLnXR{F|(1F8ZVJFv$0roP)@Q{a2?tHwjh8wzu z3g04@QT$#5{2m8HvBqTTuK~JHH%gioX7{W;x|7 z6CmZi`&H92dnlB}Nr3&R6qk`(Y+DR9pfeZ-j94;B2oNIl1U-Mm(Yi+Ty$g!^F zCxGz^!?<`&rp@rH3vZ2*#Pv)4{04`x106^iK7~^Ts-&mPI%#ZBcC)@wxO>xsAGkN! zR#JLr@^)G86&r&)3F@h4-nA9rccHr$VxkgvK@bBaR9`rB=N^LSF)H;xA347k+R>W{ zCnv@K3`=JMR-oPt7jn>~?oD1NsW`5BF5zTv@%Z%hO2NA*v<|733k0y_k0MRq;G3Bi61h>OY=@So4CydEOO&o-HO4L10TosH{ydgZ)k}m<3;r}K_LOkhg3Hz8$yPS)03H<*{8{6n#5PkRMOP6IOj^qDC7ucX5Xvw^@Ms zS%jH~cynpjxNqE8A+x}`ZZ9Ss{1Aa(_p3s%W%|!A=x^`s5+|c<(WYu6KS|%jZD| z0k|lJU{Nbs#zjcN6=D$51(})^FH0A1?L}C{J6c ztIl%bvB+H+9y&GVRFdn%yBL%gN@U&$XdZA5Ds3Wev(FovjkJfkD6T8+flPJ1 zb-KIsaVOYMZx0U-J#A}i>nuU)Cd3~v&Vphj;Gga<0^8_hNoYxobi^4D{(q1;d{tV&RiTA2;kCd@ zUV52;Q)GsrPV{SB8c+JYb1VVNdYkQuhL4?IR^S~M0@7eLyTM8YD7bG*rX`iRl^z7 zzLUfTah|*9PK)-wBnK-DXMavG3QtSQo|urUUtYr!$T}c542B@jg%zNlYd8uHUp2?k z?ncp>C;N9qIDv!Os&XaoZw!+aA=D@augNDR0IpG?whB%{aMGcLHvE|DT;xlWec601 z=WuckEuEh@!jDA^(nQ*%&SE`&Ahwrn1=q0l;F3{vS)BLFap%o=RcEwWTJ~20lAXpHG2ONaUY^TIa{B!Sx47MyA$iZ`*mBC%v$Hc5 zF5BTs<=+92Z^KMqt$O<3M%GXC*Lt%pP8V@G)oMAI66JRX7E3e>7UHUW2AKqn@?ky{ zbp)5MdJbHlpWgGFnGhnA1kr(m%Z7_JO>F50b>fME8$9(bpb^GFxna&$Ze#x#NLDC5 z5@4X$3P-aYT{KhfZ$TNj@rK7j6}s(Z_177~`EMxKY-j%zs~IP{|w{HmetOR0pZsSFED*K(d?HPHP5FjZpqnLcW{DD=hHFjpUtVQyvEJw19Hz?u0-@NlV}gqhvVrX@vVE6wG-9^L|;(_=O z7ukcz(5FfptR+b%6Xb=ea~6zuI5m@nTmlv1_mL(QYhLDd&=zR`XNpIf#O=VW6XM;2 z*2*4Bf&}xH!z4%mPn7Ankh?gSOBQyEI*{EKHW);@9gAawUD9bSL18y`Tnq zfLKc6c#~Z4oT!k;9 z^y7LV>BoDOCT*KZhK!__;QRUER?oZ9FTa<;9iPUI0Mw-bmy}++4;MYef(_>hk~?oh zG>mN?GH_S_;EIc*Tee0QPJ+ZJ`G~}~Asg1B&s@ zS5E-amcnx!R9N^2=1%?U79d^ytn4U+szYev_$g3`?~qB|pM<4{Lp~1N7lw#XUlV-O zN|M89a|K%64B$heF=Cuax*F-gZ~!(1;3vb{cukT>QOI$eN#cdxAAH)0e-RSDa=ZTx z;_6?2^uLbU_}~AT7Zbp2F+7lFp8;tS6DueBq6s7dmx}}*^uB(dDj8*E<GcmY==KVDjVopgSOC&v_6}0pUFqNAHPvY=2}Azy9q~SAmlGUJVj8so z^4EDgi0TN1qYIAG3&gD-!dSdmjx4?T2#)$z%<)xK3sBIjkeD+eSjB~)hgiAgc-zGd zOK-bL7zaWX{QAtL+;c%JuDql*0ihwuha_&i+$GAeAA~`_i`qmE8}}Umz__k_R0(R&BrEO8YJpacvR#2eK(ia1H>vk!(t= zUJ-v0%jP{nSC2$jkFY@``TiM+nO!(un@J%OGBq+dVkC?vSuL_14;p^CnA_6|s+|@AGqQUet7f(CIR=Ety-BpY0nG>EHkFi<4Fki?si zhs9L zog92?V@G1qc6Uq^5CE7;Z#3S7-2~r6gtmbLinMyfphXT^fw5$gHZvBE#kxbp|K-UV zbk!3K>4n=rjyX?LqC@5`bEtI|J%|}9NFof>4GI!kgJc9zxV+)@Rl-%%?Izs4>!vUG z?zFzwF+PKUQ;+M`v>}C%21nG{<$J~pi|&LRp>#g(wu5X{!XN@iX|qod__4&UU_~=3 zMua+AZ;Y}njNIEM;b?o~doFRp;Eufvp4(l&BF8JR-fPLfF%ze`imv{E8|wSGI~)L|6p zU%xYtpY*B|JiibLUHk%)cp_;dSr_167!etgD@^P3YSe!_12oe~5=cmzZhd_S3v;pc zA7g_}NH0li-{3olvs#F{C%1|1F4G^`tkt^hP-0Moc}(nBY)JQaJ`u6JOgN{7@hHh- z@Yue!rY%__@mZs#)1zg4?h*dv6Y_4XK%OwY%jb!xl~JsEDlam5$jI_P~{N`e9F zko+JuIb=RoN?msYP69_zECPtE%8$30ZjujxDDyO*3ef-RDQi_aX&@Ox<2mUn~r zP93{%ODxm)P_q~kWI+RBaPXuo<35f?0Y*R(s+S)IgP;pS%)z2#HkelkERB-gG3Tt#Xko(Sm6I$~g4BAzy> zCm_em-n)6Hj0Yi6hOOV4gW>Fq zO)hsM^d8!9Iw5|(u)XKGu!Rn>NF~;QkGdWC52%=`I6T?Z29l?U5S#>@hHIp|EFy;h z3=RaerF)~h3Hf&f1yQ$iZ>`$k^n+~rp`AN-mcs)* z!9_YwWwy#sVi=0}cBk>m<+ZD@Xaj-7Ny(lC^VWe1j#KY!2I@BA-0t;H$0UwQzrU#D zoPpglN>JxcHK`XMLj?Sc5os#%gkE15bX>ccMuc)MBp%`YVBojg2IeXeAZWSO|sfsnsAX8^f^1C6k15CB;ha2MrKS9L9d;B0~Ai z-Jj~3MFvy8hT+<|ES(N~YSeCB7rv9do7NddAY`dp0k*OiD59;lG_tWqdjv4rTqoMt zg@i99t+)yf0u`2fi(?>77y^QlNpxE0G`6K69d1#tAHdN_h+?w&LdMP@=796|HL)3N z5XAJpj7DR`jglx16Z07)2&{{5ZYy9=qS=bzMV{Vl(?uGD zE;w9T@qr*@8zp)cBh4j_OV>Ne;~M{BB>?WlmL@Q-3b;_cIRv0*Reufx&*)Iw36`Wc5vrz-Ya>(0kk6Fesu3d3Cpg<_lNf{G~40Q;Xx> zHH_6|1=Dow^%WNnHc5%@oMTR2_fLr2nhGQ}SJy6&z@2x(XEt``6VJ9~*q)soOwhZs(hk&q@L&QWD@G_RRY$eh_s0>d82bG)8y(_z zNnnjo=WN<&SqYW+E>H!-i?@cNXcdHRKO$L#+}uNfqLu|7SRh~8Ji?gH0tW4-&bOGB zB2%hCNQv{&iu$Rw664y_;L*_WSDvqKgRv4$p&m5nTkuJI2uIo|Sk1%)Z-o@!o?@o9$Qw?MQ2){;9T}MDJO&>W3 z-4M(83vXW+TXo|X89E`B+Ol&Z@z^weO6@bLwhNIob?IT#xt{AY_P7G6j7{XQag~7P z6dOw08DZ|CvdkQTQbe%JG5x>0*pd;c&%f>t+^Z7Ll90P#PpWxNAExFvYxx2rKs(7B z9EcD}a;Kr5k(63G1X*GiAB*SC#UB6p{YY_aB!DMr9?^LLqIjEhGw_mJJv^yEwWl!~ zSiC%tSDV!1ols$%%Rz#flXnRUARqPU@niap8wrj@U~Cx>=QI@o&&)+&8GVL7O_xlz z_#tAw2esbxDjFLxA^X^;Adv$y-rHG8Op0+JMd0LZ(ibp3$peU}08l8su`Lm_E|>bD z-1*5yt{#SAZ$#l+uh15Xb0bCpyw=QHRSd&JwD$Lam+^$}-PH*31sd;r8YW-s0)CZP zV`Rys{qyS!J*fYbqm2gS@<`WY!YwL4DkK5UBB3!|70&~=q=Bst13zDqLl&8o1!mth zo9W*0Y0^Nwg?7p0ORey+s_Ysre)iHN3)PL14d-04u);e)2Fh) zM7hiHrthjJgQGeTQpmqE`1E8WNoyv}2M;?Mgv7Xg?d~UMyH}Qkff2DJw*`0Qm!m!3 zr;>>L_VNlU=zp;YaU2s=WN?p6lLAfs##W5u6Cvz>8?~Sv*XxZjWqc9CfiX81!@*T@ z4U!}(#|4#>TroX_<(8cbbE9Z045w&Bij9rT2zWCkT+Cf^9UehQ(Q|QMOioHt zT%PWf-hP*v9pjSgpCcM1iCu^0HTJ0dtV9L1pD~Ib7=em;^(Hbi;0bk65=oK<%QZt_ zSTM8kqO3MfnT^9B$tHD|&QFE#k&G##!WDshc@62YPhIFZjcp5uKy^WNUp1gvyH%3yo5##iFb3Kc~00QTnDVM-JEtSZT zX}E17>%3*X3FUW)8gHt-Dpg2$u?@kgvuEzlo)WH}Ro+PBofaERAw&l$ z${+uvPd6dUzIH&fH_l^@zcYNIpX7nxrc7nFi1BBX-zHf2NFuR^O#|7ZNb@9eb z4+jKN;kbB*ps9C_X6^$61GX5=?&CzPjfO=5J<0{r4S~5xR+pWfP$kIwdn3NlaeCKr z6}gfkJTRsJjcX?b(zKAjA+!XBA8kSyL*N>T(3-0rqSwqo<$UrNFr1~{VvX_Jo$$6Rs5U7p1yB@al&3pj-}Z;eQZ z&WXWcFw9UVnysB0ZCeE6j-oK-3@qq#%s+sMs; z_!y>+p&XXnc?VLyjXfHRQ!a}Pm_VvSLdryV96c9&3@Vk5DJy@|D#H4Ulc92XrE;fH z3ZMf$9ITbfKoZ9UaiSv3BDpAd065{U7U#& zKoyIQY+5FM^aOJ#C3>*hGtSNAzUstDSC15<7}?f{9a@H|#Hs=QJb|eoEC)a|SPqmC zu;3^gfb31E``Aj#kf@&yROE96Xlz0np{{3w@H^Pf5@h?n7@3kpB)u8Lx&>6^iWH8s zzOCp7Fs4T)&8;WIMS@^wVU*UcbxiOfUF)QB%L5q}!XmsBb$;c{Z;F)iO&fg7}iS!ucE_ z-0hnf>F4vQ0N8O|U0wMY@1+YPNcPPupu=k5OfQk|PZZZnGVp3_!<^>_KTDWMQ-}hp zpvajZM0I7nW@d3Q=WL`j*4rJqLuuswNCgq8IJn1Ztx6;#A#&E%`e zWvB&^Vdz)nh&d#$hr8)$Y_~{+iq{J%LYpkTuKZiG&;R)$mgIlof-FcziH21%GHVd3 PigNPUY3cYQ7jOPwnj#W} diff --git a/combined_botorch_baybe_final.ipynb b/combined_botorch_baybe_final.ipynb new file mode 100644 index 0000000..f6b613a --- /dev/null +++ b/combined_botorch_baybe_final.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Projects 23 : Noisy Nerds | Reliable Surrogate Models of Noisy Data |\n", + "\n", + "Description:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Botorch implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from run_grid_experiments import run_grid_experiments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1a) Select data generation parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "seeds = list(range(5))\n", + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", + "noise_bools = [True, False]\n", + "budget = 30" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1b) Training gp" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget) -------> module not found error: unable to resolve using sys.path.append\n", + "# run run_grid_botorch.py --> populates results in results_botorch/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1c) Analysing results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sn\n", + "import numpy as np\n", + "import torch\n", + "import pandas as pd\n", + "\n", + "from src import visualization\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "seeds = list(range(5))\n", + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", + "noise_bools = [True, False]\n", + "n_inits = n_inits[::-1]\n", + "budget = 30\n", + "iteration_cutoff = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "sm_list = {}\n", + "performance_matrix_homo = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "noise_bool = True\n", + "for i, n_init in enumerate(n_inits):\n", + " for j, noise_level in enumerate(noise_levels):\n", + " \n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_botorch/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for ii in range(Y_real.shape[0]):\n", + " sliding_min[ii] = Y_real[:ii+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " \n", + " sm_mean = sm_agg.mean(0)[:iteration_cutoff]\n", + " sm_std = sm_agg.std(0)\n", + " sm_list[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + " performance_matrix_homo[i,j] = sm_mean.min()\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_homo)\n", + "plt.title(f'BoTorch, Homoscedastic Noise Kernel')\n", + "plt.savefig(f'BoTorch_heatmap{noise_bool}.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "sm_list = {}\n", + "performance_matrix_zn = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "noise_bool = False\n", + "for i, n_init in enumerate(n_inits):\n", + " for j, noise_level in enumerate(noise_levels):\n", + " \n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_botorch/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for ii in range(Y_real.shape[0]):\n", + " sliding_min[ii] = Y_real[:ii+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " \n", + " sm_mean = sm_agg.mean(0)[:iteration_cutoff]\n", + " sm_std = sm_agg.std(0)\n", + " sm_list[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + " performance_matrix_zn[i,j] = sm_mean.min()\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_zn)\n", + "plt.title(f'BoTorch, No Noise parameter')\n", + "plt.savefig(f'BoTorch_heatmap{noise_bool}.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "delta_performance = performance_matrix_homo - performance_matrix_zn\n", + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, delta_performance)\n", + "plt.title(f'BoTorch, Delta: Homoscedastic vs. No Noise model')\n", + "plt.savefig(f'BoTorch_heatmap_delta.png', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Baybe implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from run_grid_experiments_baybe import run_grid_experiments\n", + "import run_experiment_baybe\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1a) Select data generation parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seeds = list(range(5))\n", + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", + "noise_bools = [True, False]\n", + "budget = 30" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1b) Training gp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1c) analysing results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sn\n", + "import numpy as np\n", + "import torch\n", + "import pandas as pd\n", + "\n", + "from src import visualization\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_inits = [2, 4, 8, 10]\n", + "noise_levels = [1, 5, 10, 20]\n", + "\n", + "n_inits = n_inits[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix[i,j] = best_y\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## combined final conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/results.zip b/results.zip deleted file mode 100644 index 2516abfa24e85e0c1f8c6e517c997d57b5cba060..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1039332 zcmaI7b8s&~*Y6wKwr%a$wv!#(wr$(CZJRr`lV5BnJ5J7i&VB2i_rAC4O!b;sy;fKE zUo%s^zMoc<0R@8r`p*TW@1ya*o&UN)0pS5TnL4}LxHvPasX_yR5+#G15&tt+4_F{z zuxDT(ATYFlV-^3+@Si6Xkg=YRMtT$3EGiZdkhCxmkkEgdLB-g@-BjOB-_p*~MW03A z&fe15)W*$5pP5PD$koK$)J30_N#EJj)I^{4|4HhrI=PxMIJo@pr7se}&HiKmW9bns zXb)7e4~1bh5)SY`z9LgfUP{v7O?bA2rL864L&r%JTXxb}E@m9ZO#za`mdGI>Bz{B@ zP(?mzDuM}K0TD2%xadMiz~Dy-BEVlK>sKb)ciwpdbL0JU$tG@QyPoEL{d*s?S9VKD z)v7D!AfT?P-!WZ(yIiU{l>oUtJC^BhlDp?kGC`z!gAyT+nReQ3M2|}QJ!DYq{88)= z*z@#H4Vor08oOW;LjkK@3qPM6B7{b%y7M$)_jVgM32?kzyQiqJ^McudVcLY;s%?M*3n>&;ARtgZ#6|5!ffD0zfUP3c zlD8%b*s*JomZcD=z+8Jt*efW5-znw=jgg>nl!J*z3y|3@WZ|#H%YL?W;U zTbb~i-ZOK6I8O|VNpurn=>pJ9KZ!*|DI!|f-)Sg((17(jOFd zpa6;W4<arDAO%y1WDJntP7ql= zk4lkN6`ODR5hzLKv_BPk5sPDjFpY}HG=%D9g8~ws+wvAfUbES$T-lUKE7dlg9hxNu z5Mch+XPOD0+6z7fl^69 zk0w!%DZ)%EV0wxU42lkUV-9(B0A7*7H!<+jOqf|lYR)-j2L@G#yj*2z$2=7vUbIuZ znC3OGC^k+^TFyE6dr)q~OQ6JWvtGES`=k|JqIOY;`m6zEl-vnvpn6LiuOQRq6IX$3 z|4D;R=o$wVhdnAVS~-JMkeh6nj%w@aN(TTzDm16%=7j z6!;;Np?wL$%~CKPxSRTn@GY=U0>YHyj+xoD`16AGOVoH1)<;@S;E{EQHrSWqu5)PZ zTlg*%8c*@{U3veKc5H18MjsNr4}sbzmDb0%_|8>h|1x4|O)0eA7)F15wDu2$mHWvD zuy2r%6pCQRCKDc5!qaL!ZamTC8BAhruE08O{SNmxp7QcpeJX%7X-XwV%k z+*M6hlta@Xg-coi#Z^Ya4kPa3R`*c!Q(rnMUSK-XjJ zxG0psHDx7*|AXN%6O0O*duvaUd+3^o;4ad0fn4u7f#Xb{{K^ZN)}tbbje|d9M*&Hc zXbgP}V*#qtHmexGcLrp8)*9;^{+0AHy%5`h=ani+1)Mx zQF=1pFJ(J@5ERuzlJ!O3d0>yCLL@i!2p&{1QxfAssS-(DXpcezeGTP$$f`r~2)6pL zNMW!6qK||mEU-h;1P8Ie-BCDqc(q+=Kc~u^E`FWQhPr+*;@da}m^g0IogH zr_Ij~e!8D67Gj(J=IDD9T_c%48_YXei2T=wdPieK=$#8k!aZ zU8PN__KBik^vxwTSZHL?D^0dGKHmG76ZnJW=ED#-RBciMj*UO5Tc<~W%lD`>ftJu4 z&$M13a{`Fo8}w_#8EV!>v*Zfi3(OmtDX*zK-4Mi-kqc-?d|1>)=%!@fK()vRf!K#4 zQH`S72Z7wDEMZesfIna47|;KE5z2p9QUdjmG50>baNnNu&>rm2p6qaL`W1Kd6?f(p zclZ@I`fW@44OhazTkp8Oe~3__jJ=PGTEO7^8??3qm-|Tw7h7+>DJfK& znrWk?R8&Tk3~hy}$$FS7e1Jbtb~ccwx9nGJP-j)8iuDCandb;vfxTB%KmrqSqE#mI zzM1Tibw-jr!IV8TD_4JdZE6#2#03D8SXh_Y5R?`Vn6K{!WI>x=lnHnOy$7H6U3V6E|2RfZApPr}&ERb_czP4Ky$S5@2u4pPy$7uJoqCSSz3%Ta zB5eSB4ld|~Zxfxc5Q;7W?kCW1i2WKzVP5Jy=}~Wio#S@MitbN@98|}mi9so0DZZz` zB#J`7ATXZp5dBSoMDg;bt z+YwVBq5A%5tjXFJSiP7+UIUCL$|ssG#b4STclYDfYR2T+QM2(=;%WeouTR)gPbYaE z)=rTJKjOJ6f7hk1s>9LxUqM2L4B^b0TQjRIY*9?|3bo1^6lp=W=K z7c#8)WNJDTXnHH$=MdKGvr;DuhMtmPPgUqWPT)NUOZ{d1L~8Fo{K`|=>JtX@S8m3x z8QeB8+&1~j3l^w96U6s2lS4l9T>$f40`ncFF~EK-K)#*@so*#E{G}R1)!9EgI9SbD zAv>Wd3$yb2^-EP^6p!w2&KKLNc4egyDjM0Z<@rNN#N|WR)#c_JA@uGK<={XpUJ*E} zb4RO+TJy?6guikbeI;`6T%4#SCl)Az2+)7=Q2x+a)z;|*^05cFE<~X3`7}^M8b$>Y z&%QLT$S#5e7F z{rYq8FThv*ySOG^FSe=gU*NrwinWRAP$T8pT!|fsk#b()%b|e4Udrp?0tkTwh$fOL zPnK86lx*#WtY$!zFU|Aw$n$jQbgYpGe>%toieWKBYDmm0U2p0|IJPe**}#e1s(4=! zhJ|t%EmZ5X3#IJB_eFEAa=8q7Jv`Ae3u^V;qscWzqF@4%JH!NbLP?xlf^Xm9q9PVf z;uQ~lC=o0`*g`!whD2T*mD2d`?iE;_0PRi&%OyZ1-)f){Ofo5g%%um}hmp`xf-?^6 z@{x=zxK*KQ=rf;Lh`?3q9rr8o#pXMnVTgv`6sz71zH)u#wvMaj;JOw(n{UL-md!qF z=uS~W zA8syF!IHnXW8Ej$RnryH-4)D7p1l_S+j#!?TwQ(LO>)o5Epi?c0Q;x1ka&FVEM=l_ zFW&0qJl;Y44q-N`RiRbt4bFDVhpE0j$c?qY!_v@3nczN_< z-?f~&!Is#iU4Ek{-F|;}3A3d@r9Q`wOAdJFvR0fLy*X|`Z@06W#>Ty$)avIbXeYvT zGK;!INX2#Ep4W@CTf4TFO6|}2WeIL$Y*$LF#{K^A4#&I0p+EzB?)PIZAFZ^zFSDd+46@Gwswx2|YsWe$cS?T7?@^O-;mBlVlgN5_{?B{=-rGFm3GK z*MW8K1>JS=DrCPGkzH~$t+iXFXL$+vy^i>JeRm&oUhCc;ZEM1JE1QkQsg1afp)u2! zXSm%0tf#r^)2QEe$#LV9UHq|XxA)8RTK&m*+PONz&#%iX#IcxE z##j$KC0tj6;AZ*snVV(VYk&VG$}f_%k%~1|eyABAKfo{J!;%i#sjvbtreG#A75E(&WHgZ;D0b?Mn7Zrr{g6u_muHv8S1%N0$oMsGE%Sgzby8g3 z(w#RAO^tdoJm-0i4?kyK@?Yd#?4RuEu<*Vmq-*nb^?cM2_UO23*B@*`^6B^*Z)SGo zdv^5p`!rjb|7LV==Ka2z%`me2h%anx?|X5m*7t8X$eNwX?6ddfVKiiTIXoao464R( zWbEZ~|Ek0u)fviA%R990)d|1g*`ZzH)j2@%^qLEI?l{js;m6}29k$h2z8yX@V*Kr7 zvN()f6n^RBBq2_D!uS07m;LfT>3>PS-~0Xh zBl5Gjp?-1^Q1BGbe%%2$_QUn6x>+O^+lG`nv@3@|cb*xkN5GcHaPID_WH+A1U^Ji5 z(f8^LOrNscwep#lMRb4t6ROHp5P^%kQ(dyj#mKR-eWtg4OK`JQk&ftoeRC|%9pCHp zL5X;3x39bY;f4HC>FI6}4~O{M%V?vu8j)9LQq)+}b5*C#=dogo#|zC-%DN{MmVn4# z<+8^X@A%kD2To?MZFX3$7im@v`*MHN+x#}tG(ap=A6P{zu6BoQQVxWcJbX$d>k%+rg6*taxE1Q?_KVL z^{e0hRU1zK!QK*IZ20{xAjgw7mjBc{EGXDNmfujvse;PQY9@c>y~*9(j*ETV-FF8I zwV$ZLd8zs0`D#7McJLNoseZ5nUuGN&qiW5(IzDMtlz<~Hf2G)^^EAp)dboDS3W2k- z?ZYpW#}qVfm%CPPu6ys}J)xB{##or&ZruuCdZ<(BK7$=Qi^XcDn_=1U>Pr4G9ZIO6 zZ!AzgtNr2LtNiu(c3FqX(P`1s#z5=m6n;3yt;xz5I>&9b?fx7yzv@%7_)0o!8~bPC zJ!yrKd>pQ~Y0cwtj(k$$Pd$#ie~04>0KSIrpn7NWJ@9yp(MY=EcgSsQKY`}DywARb z@oqz0;6cgb*zlaQIYxfJ&GJr<4okx*|H_TuUDOJXcpB~=&XHep!=vV5yNLGBs>d}~ ztM71Mmy-EZ|A)e@8{wiT);O>aoo>;a@^@ol5nFSb{FYn$bxFDe>Q#KkYf{mV>VZtm za-=D<^$)lij3$~%iX@gML}>^Kl(|U+GAFg1)HH{SRvGIbF`UjVjXAz}(;Gu*Cp?>v zyY5H7xv#!QosWm{w|3a758V&QtU5AmSK|S79aPW0s(9^}ARMj1u}4V9AEo*=|D!hwy}$GYaFwC{&%2T}>z=<_AsjZjr` za;@&pUK{nth;|VHnYP_sNy?m@plNbbvE`x71^gm8s|(z4C6OFkV$keuS0;d-d=H!! z*?5TGadDu*w{bGJBYL_?Pm6601%|6=GzqgiT87D9CZJ0jGgt9|1748_Ob5ftK41P# zi1(@+{p}YwWUy18AIlkUFln3+m+ugCjWn;r5bv3qW4Ys$=M9icYb~g4LS{&~Sm3Ai zaXm#;ip(T9O~L7bji~J# z$1p92PAe4Q;=E8jkWly8Phj$u;NxBp9$JK6eo17+C_5yLmMfQ8m4BlZiWOquG2GP>D@Tjddk_m^7#3Kb zkUOYQ9o-N#H?V!nNCi{cNvbQLp*w|4}1UsK}M66mOz*q`9=b2n9Dutlg706a=?(K1cQX z{P|{XW_g}{k2#N5U`{Ncm(*y3gE0AsrDru0D~-i)F`>*_G3*q1jT*5zgF04bMj1Zc zee(v!2SwPk2J_vg8F00ktWS&9wwo16B+lZSs8Rd=Uzr6l^BXmaE?bxuo+#Nx-|tN4b|eY&B9SZ%H`1MAz6XStrTS12+>*0GD4wZv<$#% zJ-}*h2wMNLD4uG6Xu{NfhG4~1(+DbI41tIf!BsiNp$|11cGjx1Fo=U`xn@Uhp#|MX z&JvH{F>b_URqw7Hu{#_%%mmId^O&~~@}XcmBXCfxQzRw%-+s2Bj5PZF&A z>w03W2BQot7Kh73FgqK92v({iL0)2+`H+t!%bDU9V@~mMvA7b*$PlJTj^J#?6yi+Ta*NHM{T8?;4&v)# z%P1+X>{T;^rwLYYaoV#3ceBj`CYP6{KMXe+46U(s_x>t-SJ34^WsAnAE#K4ZETjRlB^ zc1HsM8j~bE1S%#e48U*@shltW(09`OLw&Y3LbmD0+(QD59ieH$z%4{PRD5QPBzPmD z;u)Z+YdDFeC+mn7n!OrFB4=$lBBU1P*dml4>%1Xo_!rv|<%ozJjvn*^s;xQs*qAu| zc;|fmXIwI^{Zod{-Z<=- zK6Ct`Y|)HMM8hWJlcgZq0I~u5N)h8+u^s{#RB zvxU$FbI`JJGhWX!V|t)t&oTv_YbH()NwQZD(K8soLyH$5cj~lyoX9B@-N}F`3r{2- zDHU9ddP=(mxi{N(ib_buPDF@Vbcw$JdB=n)cC=a~8{1v=EzAh(J|J82 zs%dJ_Hge}2p*pLWHr$_C*G9H}bNA9ud1eoT-=>5$En9}|mNA0mu z6;VU8JHyWiTJ6b^9lB!^i{m4-O=nFb@MDVo3N!Q5ih`}$xvo^yoL(S2A&3Xw8fu&Y z(%hCne7dVOw8Fr7%RT712FuVOSfP$FSW{7+)a-rRm$D`5;(12^hVzoonDy-vyoc z-w=#;{$|O!+Id!}Ya8r;BE`HjL1-3ynlSDZ?Nd*>Fscx}a>XJ91Aq-V0CqLnXV5G1 z_l+rTM-jwt&ttu6ptTi1n06<{p-e{LDcaMMyOo)AOvQ#Oy z!BgXPpqa?36K5B+b0}!-Uy9l~H8X>AG*~ee|Mosfz7-Sv+wWoVhJrk&X<2}q4+ ziegd1$mnZKV9AoL`Czz*#8(Rq&|WCHIAIWgq7G{0I_!4}bR-1J_3dmK7Pv22?+?wvgO=QfA7lAVSY*{zyVkQa*B=O4_tWWPLmDB>0#CurqiNH&XBG8)BA} z1L1tii+zd7Fwx=n*A{tL)lhxP-~}O?t14Gg|;!#u2ka z)Aj)M$Utu-z_$UAk%_N1Z)fIPI-T-b%7Rno)$yF{TnxpL<>0O5lFoL(@HOesb;bZH z>OPs!IVHHD3lVw4qT!NGT-0q6b@LE&#-RDni@tfNIb+~DTL_6g{h$y^;)CW?t7{!l zH)?aH;Y{I($igD~ZI^H=8VolaFM4tsIn9Wa`KxI92icT>A^A;A=>)lGnp7-PAH~#6 zH1kn3v$bgYt!SEC$*h+L{;0T}z(SBuHT>5I;~brruX(K*AKgH(;j3;L^N*b#&Hb}* zCTj8!^&i%$N+#x0C9KOx5}gp7Rx>;~H&C_*VB^zj2#cXD(oj&kA&SAb<&Z~G`MJ;R zO1krd2e_<-Pc#i#7i-6*Fwdn6MeDqn>44N4Zi~C{uX4(E}@j zaqq)9tuX3AHn{MhX&76){5k-Y=qb^?Y2)xh2EA^4|Dy0DI3ZtBl{lx@7(fn|@{h(r zI^)!rQ_fkssmB-@kANiI_`q-8MqWLFDO5>11v0Dbkix8wnz+zs!B1kNevB8gg zQN9Xw>)@h|x5>5)BZ_^{+yo2{HF|XVy&*Mv((3+1S;rzqY4HXuLaRwR4RKICyK=ZEa+kA2xKPh)A@8_JLRIA0BcZ^NE2pm}NT~8y z8x)#(Oo=%LB*=^q?Xy}o2}hf;oNHb?Em589(i#PS#3se#S};4%Q38DS1CbKhD^ySFZZn!zu4kg0kTJ{}$vWg>P8~IffbJ zpbuYTqS&2I+<7u@Ta7w&6T!Ae8+GWS3|}{4+5H}I=%xwV`o+9m7rpN$0@t2x)bSNH z3Y`W9^a<}xotQu!){hDvW%+=y@nIXU6cvkMgrypPIKrXZ$pO%HL7&n$U+Ai?i+ zvoGlMO<6=Oy1h7#`$$&q$_NI16lHVbGuqL=z6rEGx*R>M2M-N)!tv31t?4yCGcw zy!TP`u;3gHf=k=TAjNQ?=xFNjvl|F)h)N!}rbr(5XZ0bwp6Wao-6l#hj8~MO7n_9zf7x7Kw zMO)&uf0Fo>m~pc3GXBkJ^WV!YOU0q>`xvgfZl~!N>Y_rKxJ{?rM zb9PF&o9nI}=DD&@iosSsDglqvHC3wN^vF`&qlxNCq)d-M!UBU!CM#JvPUsX*jQqne*RPeRFqLq_>j! z2*&)j^02<|fY8L-IbN*WymHKbkJ~XwTW_!TnoR<)&o{KSA>wdC`E0&`xILJyFOBRg zm>t)X8MT)oK9}^Y7Y|DQE9!K!{ikDrld}02hCBu;C)a0yN4u_k|GM$RHb>62Npo;l%6`tLw1o;lGbB7i^C3dB;G5Qt# z?T{A=R66T@kgz36RYR7#qns^GpWW3{vpi=5LDvEb*_Q<}&H2%ptX?#|jT>ER!8bgX&x6sH+SOHOQmgjn z_`iA1yX+8y=OEM2to!ETuluoL5?M@m)Hmy68&w4w5g? zM)szV&eY!JVt;=)dq1lzKhEhJ6US9I5m2)^FdJK(;BN8^#NA5wv+kht%B zLe3BZ`_*9b<72+ihu&+uZN6-f4#aB-C?{tygji9`-Lpp`o*P8wZ26sPW z;|S8K)%Y(qCwSVV3S#0gM3aviDF30gU!$l`(cG{Z4 zhFtIak=Ck%52aeO3tC6NHDqg*h{xrEf?c^?yatHY+`0(?ikmM`IQU9KC=`uD7cGg))9hF=Jgg#M{n%(nNH`5(E7i(FX@`~e^NetzLK zh|H^oUZZ)Mvoa}Gt4fau7ZQjFe6yX!$0vNspYwcWsU58`uw2q({nG-W+7g4w)ec)O zFcNo7FZ}*tN5{{<-06P&zED2mFEHnMABA{S`=54s3vG7;rMnU`GuP}ollu~D)MQVp z&J-Be{G3gU$$g1AB+-*^O3K$h*#Nv&zZqP86&n|X1iqu)#v2uKo~)NR+Tiu~3ov6R zJB>B33;L)0sjYab^xK%4yZctUlMr4lv(H82X}Aq1y4DBPA^BWyX4GO|NZ)6WS4le& z-XC&f5*mr+-$O}R5st*T&mrvh>U~jG7^CQ|$9(awOGN4%cQAMA3c_t)EA$VzAo1^K z>vDb5IM1+)sn%|SDqE<{Y*fd2^6&SHB!8Z2s_kEm z({L?&PsUeCV&&1R?t3>37BZ(zS2heDH-{wX)%7*`9<^QQOs^g%-|?n!epIR|vstNI zbq%kS0=Bo9sA+9(Sdxt^}2KpBSwxAR%3Q zyVCTl?V7&q>y9eWR-8*@^ z+1k1;Pc%C>4b7_K-d5#RX1ny^T>czJ98L7E`BxS7>Ay>u$_5y{TBonkb+np__5awv zVO;jVQcoTD>;K$~{&2XX`}(qfA~k*l6yWcD`FSZA@cR|P<-1*+)UfH-`wek7?JUjh5qW9sydJ@PGP;QTm=S)T3q+pfpQ6hi^%JbN zp6sOyXi^BwHUApaM{@Z)<4-7XjCe=5@F7F}?Y^KM-QpLkVtB;&2phitp?s`QVov@3 zqjE$=!fhTPUc14W_DM2_^^9$~{L^cc;BRDPba0S-pn{@&aG-LKtVqel&dp8E%tg%2 z&c#K|K+VO>&rM8?EYoT7d>ea@Zv~c!qR$lVBz-I98_g~7Lm0%7@l=(Z1{`bs5&f(`8g_mH}g zPj?O;p04w4MDA%Ub<5Usba6nb-o%mi1g;j>I<8-X_HV#cDz8|o%QNB>Wlza1vQs0s z%OmO^N--&GvrY}zu8;6z)X7B_O@x)ga(_ksK)Tw9W5wprkcUWP%LUqSEBtf96a_4q zOC3h-H|S&3(Z!@ynJTKE0-qoauMCfkKMGX&SZQh14+B;^mxLnFqm3MCEf&*BhH^ir z8vZ&6rzQ~K&uAI#h%rwY!|~XZ`Nn;LZ4aeA$jFrjQ^*) zPX4dDekpi1V)>8t|MP!i{r`J){r|`M|JOl){}1c$_K)-bkNuD8`hQq|w9z(s3g96m zD4q%Vb(9gLBtanPB|!~iVHRL~qIFrbtS7SM%*u;A(GVaQVpeFHh$cM@Xp(d>3{%tu zB_ts*a3OUuA+W%P%8r0ntMl_L`GJqVad*$RzLM(d*-Dku&mS(eo!qCCg!zJkhK2$f zfv^HMp-hZMMF4)!jA8N{*zSIdR8a50tb6ov^Uf-s(8-~(rxa3+4<7!JgkbypidEVS za~E0Cqvx znvvt?b&yya7k-i-XA6vC1H~3y6G4a=u8G>SS36K;)@1{=#Q3NG}Pmk@5L>D)fUzD)U+#Z-0tx`~e8;rq{B*9d$bH+BN*}hy8aTuY|zdTn(eSYd-fYEgwRHF0z={$es1ieZuDTMe=`VCJ7GO0wN$`~YR0cmw7 zx-vjFrHBX-Ia65-yxPcXKjp}|00Ol|#^eEkWd!!`-IWBY&}5pvoK2%jucF(degjPu zUj2b1FKTi~kmg=0lJ#1)Za;>0!%b=n+E2q<1G}KO6gxL_E0$&D@mQ@Sta;)a?h@p7 z;wy&2DjspuOEeQKlavw=jzK{Vm%s=%j|FfgI<1f?>&2mTmqw;o7K=%jGLx7}r%6&X ztCx5PFe;mPCLVCemjGp|AgZD|o;61YkW1rHA)v5|qH$FtLy<(LNRl$^mCT+dcr0q! zIAUZFBN1Nlq8Elr2#jRjraXXEYbM#IvGEo`vYY&BhdKiHu;o}0O%`z`rvFRyLtLs& zkL3lJM;rA3h>9_5KrrZ_De-~5BL0)gk^*hO><~MtsenVp?}AE3JV9a~qel&jkY$w* zbpCZ&q94)7XH`-;`%6aNOvz`LQaBZUAL!JKc#2>6;hZLs016#vM;-s-2vt(dno8%G zi-Uu0g4@bN=@|x6;48y*78auzsr&VD;kQ2n)J)D>NtkGf+M3Z7^y67p#=6s&427Oi zv+fDNPb`WufOZR%{%L*8+*#uGb#N!xgFZ>Qk}^QBY}`A8!d1;wf~Wc{8a7}u+iV>& z(St0c9x~83PUIcObRD!QfWBHV_fnK?_Y^>#a?po8c}I2XzY_pP?dp*`++CsIH+;8I z9`;)HV7O-)iUAym+--?5wk$y}7UYafTekn;0LfmTJ(;Fli9#pIik6e$8Os|Rm0`i6 zB3TDX6YbQ>aiU!V$p#ez*CE(3QAn1ngEE`0R{ z71SpJ15Eh$aA2W5deD4iA5qnvioSO>fz=9%AU;)B`+y5dmUVNmd>W5yMhM6l04sQX zFpOS4YYzgo2fq54 zO?KW`a)ig&ePt(wDF41<+caY{4%G$j5XD`SqXT$LpzA-%! z&;+jc0vHlr^A;(w?x9{hw~It4xbF&o&9L_ibl+e;#s{gIKeKS-A;(I@Q=!rq#LZ}O z{hkII+<~c{p-R51^$T!HkFp1C`Hrn2D38@g{$#MC^Pa#faierHFVv6V#Qh=a1R+_ zg5!XWH^~a!=O_LwR@Ju8c1l*aS9M=AYgw`xGl4U zz&%7rvTT61L*XAN>yV-X&L5B)f?0>-pk`)z1tv#h9~ZGjrX8C68`$O-iqQa$V>mU7 zS^z3wBYAd4bN3{lA8D^_f!ikfFmbIua=kiYDmn0(kU{U zdx=cCh9N{l0eKWlyrwQjqY=l*)p#W8Uo#((HW;~>jaUgrrcFeyrnD*}DAPF?3tHWp ztJc92m8~tGTzr3R{2hnN6lu7Jc}Z0v?PpN1AGm&e_%*o?$(XwRws1)0^)(^O4H>||NfzRB~pDsR>e>RGq3+2-k*p#(JqsC-%JwCB7w?8vS%P6 zV<5pJk(l{kizHSWNyv!FW@}Q>FeyKv;36JMpXkyX!^a(c8F}d?;ky1+EqxwW#J12u z1FK+>AupQ*fPQz`xX*R7Mv3B%I*qjK)p#S@N^i)qJx!!+p|J_hi4`{wG&O*8c zNTFBgq{jqNhmL)IUV2RFJ<(#YanoObmco=3g)dWA0pfXJ`aphTFz(larcF@4y0R@| z+b(>H^S|JctLcbT)sc4*RbHyq@90W=;;Qk@vR_$9FJR~VlM&ug$S*Bn9p+e$b1Vmb z!x10!q&HmR+qYPb{pKTla}nNw$S;uU>WoRQU#|^KI9P81O#Yq$Q?sS z9q$EN8>o6J!y1L~hps4NxF)(Bgo?D@gW6RWwzU^?qR*H`@3bNS1R(&&;unty05NPJ z9|!D)1?+|y?Ar)x+Z^h11_^4h5(?~670iVjJrF53N|4IVZkC>s>vt!c>QZHCA-C0u zQPb&7dFQS)J$zOPQE6cqg1J`)!iT2H_z12##!ET`pHhVw+K_7p+wyATq9WuLg{1y! zT9}Rw^rF)*aH4cFZw4vTv{nrBz5zYl0Vp=(V@ATO8KIb>f;myOktk`zZztf(3#h4! zbEc__OEx;|0u{|wr1*>~>iAI zR`NuPp#S7_k_~-=!4wUvdvNV?rZ(j4ws>x5Ew?jSW+#4TCtqGCg|BTu*$Fuik7_0# zf}5)OEgGkH3b%I)x3@XVN3zLZ2JYhpXn#WDNLr13m5a|mTj}5OT>iNi%XF8Ou+vR?+`p#0E|=-NwaU#j33Ibd zrTzupo9I{@=ngf~o{cqE3A<4-2K+b$!`sR5-&}x_rGQd}ITPr75~3vgW`q<2gIzOD zKq-5?_}$Fx(@I1%k`PAavRH>5lKD%+KLjck;+;`sD?w~Qe8M2OG{Y)kT3(&WWtZOa zo5QN-%aR++6HIZWYFO`$E~)AT6Y*W3CXo{gk=5W_!$~U(C9(N0IaJ~DNr9=tC{`w@ zj7->=Ss)bR^$j`;0tAK%Ky%*JLCq2clZ43%4`Ts8uMyz;8c8*C$tgBfzAEUbuW`^1 zk|yhWXW>5Pus*KHDsS||oN8=UO%?+x&a%hj+N|myu+^;#KG;#vuZk$eeGFk68lU_q z?%mC%7DDdK=hVN3_*)p->#fkyLj0Eq-*<9gFFyUh?DF`8Fmhh4p9;S5f0n|XkH_tj zO}hhr|ITsQkGz=R|Mq>oND3k$IIi|vTNnDW24{emdy`Fe^z}W<>Na=sXP~ke_Ep3R zud^+&F6J)uv%6^@=kgKY-8X@B!N=Ti(F3`#fAlSW^5gHSD&)6b{qu4CcjWzQ3gsj8 z#rF30w256^`?n8i(&L`M)#mGV#)&N@nR8ch87txT%?}foekpCc?c=qzHBb8;IKEEP z^Wx5~+EJMq8$vf$*&IPt zFFN>AGQw@N;iaCNU*GdL^V1p2y`lE~BU2I8+MB+!YlXav2OE1egi-IaB%$2- zUpvjVo`m_&(^2ROaM&nF=}$8!mKuHLQ*PD}uj0#DHo9mfo6n&)yf0GeZ#lP*#sYh{ zTDw~>R?QQ=^s6J)kM%YW&l#7DP8VNKsrdxacNC6%h%OnuTfFn(nH#UGw3Z&IBmF-k z(>L)W({{n{?jQOTaszXXrPdUydMC#R<$YK1nu>ppKUYlOrX!T?)X#Ii7VNz{fhDx$ zbMn;xT+i{VTAvdmx;GiTJ{*WLx)+zVa39T})b-pcPSwKlZZGCAMAvUWIBdONuX`HF zQgf_&JL)vcl;%J+iQB+49zv8M&~mKhP14U*-9PyC#Z<4qt(ML{_H55CeVEn%p+BzM zu3L8%{_BJvcAd}X+mSA_*xzicWQhG@nVp&K#{S?iCDV^(#O~24{=XP|r{LPwK;1UB zZD)ovp3#hL+qP}nwr$(CZQC|Z*4k&^y$|lKTa{jw>ecfyMjl4;{jIgzcQ8fnvt2Ky z4)?eBCOoNYu6eqN{lg)xwALN$ss7CHa!$up6xi=VTjWakEA2x8h)Ro0Ivv zm}RlnMOA&U3(l+ECD!(g$+NS=v+Oktt|uPI=F*Dy%S#Q+;9D%r%~PrKZSOklFB2z3**=B53M=O?nUN0n_U$wls4L1Z zPZq7=0eMTJ8}E^YPxx~ef$#mA?+3DoflsA=DPuhU?_1xmdA4+<_wh3Aj8BXUGKI$I z%SDL#%y1Mu|IaJM%k2!mOKG~M?H7!TXBVGuk}q%S*6kho73m6t?C|Jq{<~`11Lbpk z|Ie)N>6d3WrjK|(GwwLggr`5NT!&if?wyasKmW%*jk+%WO>XrcAv8HwiU7|}bb?dKKhMp-N9Xik2= z>y+)bx>hUGD&7)NH>p$|nxtTQ>B8ZTH~0QJ>QIC<+<0}{f6v@JKBP&ko(yYDwhPYh zRF`IYvIB=-^=3?TWz)u=f4v7a+(^tfmUtd=X;CMubfhVAX^juw+GH?H4qgY{hUh-N zvw3#Ayj-Z-7%vQFQhT*63U1$9sc-T~Nch-f@gV@+WKgB)v_{-Y_ya^%k^BS zGd!v($GfUm+eXRCI@moDR${uMo9{1FtPow#5P8OTaZfw%{`uXH{WR8}bAMK!x{I?H z3ucM>{@6a&&M^9>SjAj)H8Azwa~|T&eblePM>pYSvt8dSN8ToRsxMl`e8jjgf6CUe zXnAcld$HvH%J%hU+Sr+e$&VzRep1!p8?jGLOP@L`)tKn1(Aqh<*7 z@k@8v>u{Tfk-41CW5)S(qmOwRk4X9TxQNjHvqy+RzvMNBL85Xteny^6#rJ#5L+OH@WO4kqvzYz^-H50yjCZk zm(_+>zp-{g5~(zs{!BL_*5m!_1B7eaz3t5Eh{Q zx@vt*BK$Y*&U9B16j_{MmTIwzhWU!6x&|hDV<#i$VKHqI%w68#RbFtx*tW!2M8u<6 z!=!|wSlLlD!9%42KIaJDI9$0SxzJQJY}};HB-Mqf3XAKQ0Z)tf=^8r5?9VCpYnJY7 zXD2XWZ9ri;m;I0;v-NA3gDp6&&PE^wp+Fq%`#Pus_G_a$By)@5WW~Yhi7sW`cdqgM z7&GHB^Bt{aJ62uyQV;D5UFt>WEA2j1j?IUL2U3Y-*ojN=V{8tF$h|F(;vvXaYe9S4 zd68e0QXMYYEC*moVT_!Ms@(OHdQVk-BfgeKI$akQ3b5sX&F|EJllN;ruSNk3l{>H@ zTO*j(Zd|}&zPo?6)wDxiyXUJGi@cHCr6{&~4LuQ^54)%+WE`oKQ;J$69L-SGr`Uhb z52J@M4lNtPOnOsTv6kL$$9n=Qyk*QLv1|Z}O1gfGl;G!i_mkHEJo`*{rEyG zvMI!(6~{a;O4;T#8E%_U7QN#L{cVQ5`|{;0{02S_2@4MAk?=a&S{sZrz3kA#O?uy2 z+B+F_Y;NndZ(kWb(Y-}y6|?`!o>BeM{h9A`leyN(|NPRSJ-08HEVZJgU?G0K0sUJx zxbvxVo3lep%UQHII?LANyGF~cn#A?b2Di->Qfr$@Ad02<^wt+|l&rvtI9%*uF|~Gj zi-F;u(5p2}IC8R}6qu%T#teKpnu`*G19kaQfE~NgFr-=#B3~nQ|G7hi(j-)pCR0Sg zoj_%KHHv$5tfH7S`uABty=k3ljz=_faIfq=2?)u{8f<;wHY;otd_Te(|GViD9ky`q zD0-OMzHqRfEeeiPAaTiVKDlfFHR}wYYG4C3A3BbnZrcZis#yjoxKav@8d`Aujt!8Z zE4WOZO9tq+2%TmFHSClhKbgd`iC9vS%BW0Fz-gZ0X&arEKDjC%`cQZhYS_<@fc%!- z@^zrrbM1A7pd3dK2qh9j$?(+a|C1Cam_W{Y+!GX@B&fm9y4K?WDqk0Z1p2O!kSIL< z^VCJH3|fAk0U{~Xlj2E&wZVzX0BOK)l(4k%x!e#ch;~wx#}p(%4W1PY-LeG-^1Olm zLIioiP$EhW3s*Wt9NaMSo0@uF*9h)Ui`vLVj{D}TFcq`LqE0mJa zcJ|O#+^uu28L$NdO~)J+>)}}EOLiF#c-cg`pQ@#Qh@2ej@$wU%o3Gx%|7+to`G+8I zOB3+I1OR{%`2QdXSpOSA@PBL^|8oHOUp9_|A9~=Q>;K$198dKoC(~8Xp`a$#SkgGKRD5u4h%!S;;H+VK!n=*QHL?-f?J{l@kg(08p%@EUO`4hG&BVb ze|;Hap<60y%uo(O;SFBJL;|7Qb2ERsQ z9sC{OP%{U~U;}Xs18$sXoLM}3@>x^mN0l*FNg^z_M^gqoGqP1BM9s;XHgjad$y2Ot zdaG(pZa!8T~76hgq#2YN)+Ve+MeY)#}NVK?J2Ay}L`tMCajXt0|hrz*fB>-0l@%< z6M>>{g419UIxmi@3kk~niC6x4AZLzcZlaY>>4V8aQ!x>qK%aa3d@q+oc!3U00 zuy>bkX)p{@cwmqxf15Kwk~#?@-2i|xoba}hrm8Pq7}+MMh#B^p@$%LAW)&#i$tTeF$9n6wR^H; zIfzfvSmXs|&xb0W*oM@({edFR9lS)d^pEvLEFk=Zr3UuTnLBj-?$Bb#9I@CYou&m|GNI5MiwP2iU`vM#LE;t56NyMZ*@K42VnSEf97KaCSf|Z$$_*W6T~R!U zBMV^;uUM&MYZ-*N7%|tyOhITc3vT7Y!ch}5sRI{u7THwmqXR0Yq4))aE*3@zWwT!p za!1(XO&}Ro2ZVxr<1#9FD_9)2Z*^aG}+?x-2E5C;WH;l9A9%b+qVlV|G zYzqjqLkEa9?nFR|$VJpR9^^~VQVXumRS@t;bHoP^`rK$&GXkQe+^bpP*B;>e9t3qI zhq_@%*)pzZqg=AnRqFSi1a-Ae=|rV?p)~uO+T~jH_im?YBxFUQr5^njhsiqoT+#T5|<_{HRn4lKkQ-mYk#YUu3LXMQdu)2g&lUh4qcDPeVj~r&= z!m3fn8o7fju$aVfV}+pjtyJn3ZPv3(iI^L6eaC94Qx5l#rGpLX>5g#n7iEXV_c^o_ z{BYUlR4UVy_6G3JCaOW3I-tA64u4{G=iv@9@{7vhIdWpPG^X~uLKm0(>ss}lBhTTv zePXpgrM7m7hiB=XqsZZTO@rvBz~$5)K~6LsxhPP`VwwT*6-E0Hl~u4XvtK@-eY;*W zE1f?cB2GTEk~BFc`6|_#>=hLdpS4Yf-;E;z#5VbkIx_3-=<^KcoL+tIeQ`hO_{m6! zhCk)pM5BA&{xKA)$)du%SmDL=SDisni~L z#@=X#M%X#%mrxiltu<;(S#??Dt?C=M-f1nj23Z!yxg0BB^bXeAnb$9(SvIYN6Px6M z?9Mq*T2=knL&)CCo;c?)a{m1M)x!z|5vZfr7zUCp{Gfn0$ zK;@X4>LlN7zW~q33A|v*9)u5?Q$`njn@qVoVFXfd${kwd7GB2Z$<&=BNGuuSKd$`< z3cun4aVQh})lRA*mxdYz+H2rwkY4-RS}Qa*A}poned-3!9N4BmOvyD6bppc-pTr4e$Gxh+P11`eo)sgz*rIupz$ zq0ia6^%%ou(s4KukS6}-1M(=vJT4y{^%cB!W3}BIMtPhIAM;u*9bpB%INwaZ8~-7H zkejI!+F7^=zQRU{Gax8W5SfJ%NGMA0uRfh6$`9QL} z%jkl%klw7C z;DF-K_$Gs!Gr90*o}%k&k%6x{%yhv*Hw5ryUcnunHU!Y zs;km>^qfi^(VKhZ6x3vm2LZl(Rh(=RJIRVKp2Vw;&U=kE2e9QQNxR@D%=SHNoXcpI zZ?15wY8Fl&cy(dE^TRY5N0XJe11D&t5LO$)F|+g0(3A|G2m$S~+_aD&#o$-H{dbM0 z=_V*Utd(Gy%nJ=~x&)P1q(E;Xv^mkKK3uJuYL}4Qi+z^aTpL`(yG_|Sjyuezjp!d5 zjGQGbpMEJ`q7v}6BAgNd?g0w;hJBnv0q==YEyJlc-9~+%K|gk*UmM60Es7E?+w{9x z27R1^0q$vqi;MXFg6)OJin#%bC_wYC^h)>s@%*<`b|r7X(-Y3YSFI>Yqx~NVcT*DN^|jo zi8&Mc&nu>DuMij8!R7p8F#-_%!EmHtVcC+fuynTrPVL_oWI`BurE+NiQQ~xHNpdIh zmd+@VR?UG|Fd|hx$HCq05z4VJ?q$$wa~J%(HF|8KN-7|O6`k;lj z!wp@yt)P}9`EV?TKpo~YpmF}$H$aVL#Nlmx`7Ooas3_tH=Ak}GiO0h#Re~zbBXHo? zO^BOe@me#P?c6O7;0z9;3=X7R4&ELb0kPC#06l7kP9SUQncWm_PG;9m=+{m(M^4~l zXWQ-0@{zx|_<)ofdac%8&eoHelE)EUUV-#$A#c+l+Re50Sv3;|k?|o1go%m;RpH@; zRgp@sMondx!sqiUSkQ3{Jz0N`#j*V!d$UtZc-(IwKc6<0W%dzNg$ifL5>%xOuVd=S zm?3!V_r=+YW4$~5M`lzON|!0Y!LMI1@{i0&0&bk6^Wik!wmx1p2F{3=9R%HUf+HgQ zfufUl#~_i$D5KXdZZky8w+Sg< zV?8Y3sDIur`Au5s)H}7&^4Id>i|I8k57OZ~cZasQbG$+|6z`6Q8*)w@H4)kKO!haZFz38M zJ(W>j=F?Zc>PYm&&0)=4G4uzHaUHv_~jcMm#P+IQOp^^W?Ln>aKzwOt( z?>p^rrWp28UAgx@>ib5tvcr9Hl$oQG+oI0*!GqZJ=%wZdtr~9O-Y&@ZVR|Ri};9On& zCCee~{conisnLUAe0%H6Z!P7@@1ukMOeHwMmzHIwbUG_fF&!`8rT8YtmWe6>E|em&1vq3d2TrEm&$CV?Z#W$t3=JW7iH3D)?0g*I_9Y_ZHucoec+E# zcw7s4V-t37CbPld^(nK-K6F)EZ?pXFeVpRv}X~Kd~ER?Tf zNB4Rf`ko#i2~Nha*3*0FMhn(IM&)xnYwuP+j;KRZzGi#~CEvYGAGJ@6icfp34rc=x7n71r?D4;3uBT1j+PF z<6l6?54YwO;Mtk8)p6Wi2Fk~uu$>=O=hXttmFN4|uuoR2UBlza*AK@>aODTzP}3If zo}tOsbVWq^XC$=TBYytlR7;d>R+0(Yh7Mv zrtCLM6B9l9g@W0wRCW#}jf)$wR1`+LT~0DryNzBTvhlT@*KW^MKHQDDz%kf2akI%4 z8{dWb;%RYA5#n?R?xEaY#UQdEzCPPs|FpEb66OY)-QRW6uw-?9?M3O%PgiLnxtoG# z37-!7x8Hi71bp{%m~C%zATi7V=?BI=YKMied%-VdV{~}28$s`R%Wg8-U;BRJ{rS}Q ze&o9(V-I=m@s^YPMlVL%lFVG%1pel*5j*&oNz^6j3J4?l4yyw+*Iz1=Ap%*Hs{Co8TN!@V;k&F=Ktx4!J)@;d6hSPrLEzO$!k z*0LeDVD|RX-v%U}?J}yukS&>?3a~3Lv}W>gr7@%}>j*6)N<2aYDVhD~|3!`oq*W z46ls|Dqa0_yVp9B-cIcK(Q;+%o*jC7+gLH(vSu*3SAJL2;Z!*?jK4y6cGkW*%cbh2 zR%S!=e$1`8uHTZZ&0hO8A3EOY(OF5{#qk{*9jvI$?RZX0yM2?lVK{ot`1;Gdn=_tm zY~0!6W%lG+rkz9oa%IiDqvM=M=z)xew)?PsVolW2yX|6Aqx_yI8-Ln->qxi%`}s|B=2F|g z89senau<>Y*%CDU=Pe7+`JPu6oM5*p&_^8}3 zaz4he)QBvH27>1xy@T=c!f@_*mi55pnC?W;zIJ!$clC&*T7CG_)#PTsqve5H-@O`r z!hU&h{C8^4Tw-8H+osd?J?i(1+sEfjf6LbEX_EX)*Ha8RcUO|aBS%Q};rLwnjic_; z5Wd_}Bt0!gtFiW2sbuhSC8i5Uq3#q_SM=uJ@A>xDVFB9#+m9IIkfz%`|2Z2JSM{ka z`{ydapb(?e+Jo+f!H8$M2cyH7E^_*pVzv__&4VQPrVS&M6zf z243^Y@AC>g7bCQ#PLW{FE!zmt#aF&VbrOT{X*4$;3ltSXw&)T;%Nop;m*B|ilyY8d zd1@ENSPdQVt;4PP@m0!~u=Yg9U_9&Kx!N9|b!Zv3C-%*i*dU&JUzyFV-oRg;^#*y4 zuP<{|<=v`yX#l0LjJEzqW^W%!Px!}M7FKRwAy(ZD!d#t^*0MDdlG#rf%1_O+kNC9Y zl9ZqAr*8FX?u~M@ka)iyz=9W}fF}t@l{-N>MY=EM2*-?fQ%>;-WOPwCYK$Ycvi|65 z+cuQKvdReviP}-vk3b+yPqEk`YA%!ctl<1 z&t1@u5!UrK59%k4A%)66U7CTLD$Ailv!Is%F9*YQW99>KKP$UQ3bFu9j&q|Hi-Gx| zd(-?Pa!+bQRY@#3l`d{^6&z)bCM)$hfoPw5p14}-E}Zx=3qnRjs&Q>#mln!>8g9dn zCFH!-8)v5rYk?EnvOPTo&hw<|k!D?Bg7Ib{C#QQW%Lm5#roU`R)#$I>=a|m}+b};p zb{Ti5p6PrjRNrm7?QCmhCo{1KXpXQPf)APS`qhaDeK3KzNoi5ugu4KZ#1<>5$K%U4 zdB-|+8%Y9cl~LAKr~#j)lMHT>9r2310;B(Jr|MZh9f`-jCnlg^Ib5+^g<-|ZlU&Dr zLs96uIhPTXl%XNTU(2kSjnuwmdoJ^pH6tH*PBXS`*5yDY&|e?BrlpNyTyi;^E9f8V zH|^_ANn+DBXRVHQZgZ;m{t)g<`95K%o|s(3c!9)x?^gHjdO-a!2kUal9$t96hfJu( zBeH<~NCW(OdOVIN&%Xbj@-~F|3KG23cO|&T|Fo-Lg}RtjvDjSjM!ruyf#T}Ob+eD= zecoQ{u=RZ0R{eBlgS@@@5VWWyb4D3BO9p!|PyXhG+k2@>>=;Mz(wiW&jV0Tk!u$fe zlhtV0wyJR(j(kN9GeF9f5P}LMD%FsH5~+Hdz58f`}u%6 z4rUcZ{#af=ZriuTr6U;iii-o~U+IPqCSufWgwg5ZGpF;u5Y8)KnQ5c}!y{W$KP-@V z-NO$li(;|l$|zGRl|rthQbRawu*#}mr()at3nyMNWE?4%dc}r+&#u=XR2I#micXcS zg&NYlBL^K4DVge*cvzjzJWMEbvS1GN3c=4~{RXk+7_=Hm7O4A&QKNDB)GHmjMI|^A zXy> zSW$^&MQ>y(VBp2^NGK&xsCxUyf<8+EEt z27;)lxKKMyF&@qn;K?OfGbO3@shma4dxMWsJpNgVL~Cpfyv4}M?X11*{;%Oj^Jn;> z9bfo?gA}O|T*$1Q z*9^ImoZBwqc1E0U=sl8#lZZz=G?-{1{UW3D^*>xr5Z!*z64Eiw&DaEtqVbR9ZX(YB zMJL{Jj6rvUc7BE%nUT8{TxKn_j5z4K*IKZg@DRFXLGiel0#acBUWGxrjl)qp2+W3M zfQKUj5)@d!$=;LYpUp?DGy}#gT70oEsPOFApS|>LA?G!x>fg=BPqC>zBTg5l0aB(0 zwprZ}7$TI(*NuTLg~t;y71dvW-5kb*U`Y%%o|m6No!%q?O92M!U7%t{08COcCsl|L zjj0$aH;fOuVkVBVA&%D^*+hTRPImiVgSnSb@USKJnN22!q=AkoF zBz8m(!3ZdGLb9e|Fg`yP7X5E3J-mSlughP19^>p*E;I-A98&2RP;y@ygICQ^oIM^V z=Bo=K_V)Dk*ud6|4(!J@QjnaBWM1l6-hjko`lhejVW3-qis)8PNBZ#m9tQcJgoOYJwg`crCk2cb?-4&Y!^ZYT?T~Kd zD4>8tPAdgkqCq(I4fBA6VIHr6Lrtq-qO6e_L@b@$G!9L8r4fBp2(09~qOvG=F>?LP zJ_5<%a*y6!<_MLqi(WosvP9=)KXcFSaFG6vlY-0f>qH}AVm)N$3<_^8!#Yq?X2-h{0Ut z2r9$_hFc7CDAR=M4Qy- zZ$kiAViz~~=s0`KgIn>^aMYko8o^zkDK^b|=>Uq0$cF&YDZ_XfZ1(d)&Io(7W6%Ue z!RjDgIu3}sK~Pe9U_?XjC9sZF2X`QmQz{2qWkT=csi*7{SkWnLv&sivDM+WJscf@~ z2U=A^?>`!FnWjhsd&KuM!uKy2edQ!G(8+DJFc1+;SQ^?1T!KK;97JRf{G!A{R9#(@ zYFjJ;!ea^t19e1sERrd+xUhAtLY!%n=flp(8g@9LsOJUf@u-VsHGK0hFh%p9Ghmd` zdhSX7#wGzMnL51rpqEUA1G3YO?ieDw4D$QGo6)OS0##G=dTK&mVWDj_irH+-_`Me) zZ8ZO2E%JWtT98-kKlfzyo~w}BVI82MH{7?7Z?_s|L6~Rh|EX9wSTJI`Z-X?=h-F#- ztUfKjV%%C6h5yA`#D1_AU(3@hYI_p`S=nE#q;-{}I)C{?AH{qv`591V9&9>HY_WR4 zAyiju7M8JY2cl1H4rNep=kzW*g&wt34e3+kDg;q9|3$F4FrSi?&k5~&GN(&WA zOPkWgvvB9Ju|;zAXZ88;;mOv?B>^Q6!;OPck+U-#|5<$)eB!+v+bw=pA3r=m4}M%F zDRR-0mGLv#D-=jR?wjAReWyMI8L_?2zigJ`4|ClM295Z^WrD@yW?~Uj!Hz>#Tz^)d zICu>LPxL|KfkD>BL`D=zEsctv=lfn#{?!`v}IsW8$+%ZGoWR$9>>r#up7`1 zo?qXx8#GrkLbC{4)ivEaWVD=Xf^S4l7N${o9;Tt})# zH&)<9KNSnqpNfT-6f7UfFv=M63|NsxW;Y#=O>>_a^cabUDMpb-;V!fdHsZS!;maSN zmG0QZ{f1jAzxKQ)zi04nd66${fKhygmwAw{yu^1c$d`2xM-df@EjoxsNW)yb#xPyF zH|AcomSh|Br6EZ^Z542qPnaObTU0`Nm|n>~eHC~X$i!Sg7RYp6&kd4*AXuAt8!YAl zNj~!xh(E-0PVwyU*!kgnDA+#OC zkyI$X*q=ue2lco~I35tpJ!oI2oJ^BC;v2jSuuXlEec&rtERdu+*c&7jK9;ueNWlL1 zGJLv!ITj#m^8o*?Sj+%52@_ka&V&B9iiN>WxWZZxZVZQAJF)FM;$BlW5gm+XTt2H& zs2#AMqb`|6maSm)b!wh2>(!vgPqHFh7R(8t0nkh9aF0OG43j^v@Eta=Mtk5}Z1EdxVGv1}ZkV8L7g#T346kVVk6#QQ6ENMP zFx?VC+Xg|~`xxDwDBTn!7V?T8L6k4I2B!06d&WPs3*v`OD;xy%rgsi%crHJFL+mN& zZ^~7d1h8Mh5!CZQh|q$6A}`jM#z=LurO=Lh_{tl~Z0V#G9nBOq1r{Y00N;v=p&%fU zkWP~@k??{9y1+t#f(BrM1|)(8sDlQOVM147LivIQ;#)+C{{{;-L9mQMB2JN>yFocy zpf!i6d;UNzjg?aWKrLLAqIvJQ??Y`Qf2(M~Sfh(%=zk7@pj9A&B_&5(%nEVfy)A?! zn-O>U2zU_M?qX6q$ds>v)5s~Jn=2+GEEWnps)8*s04>?gb^7@};sBc$f>j)2s+41> z(9+dt8R&El^th$^K0*QiG!2%_z?RHgS}Wi9LFHOUHzNr9y=elf=Xt6LeYMJAAF!!s ztifL45v&n|mw&z(gSU;~y7O&cG>>k;j&9ullV_RyL0VG#)l>S_Q~TF#BnO=}RsAe{ z*KI9fh@MC5AV5VO*eq^hahhYUL@K7rCNwZHV>Wm{)=jX|DRL~4HGl|)i2u> zWCA=jMGdx|zD@yMeAyN0GCR!D;Rh94fb>DtICTlPQAprI4Kllw9{BQ`d!f2>yn(Kh zQDmjw-@Wj&hIcWN!QSefm&L{-OM3PD_c4k$*<4(GhFcqev=LUa9M?k(9RXKf8Zt>I zkpvt82OT7c8F(%j(&LMH(s{Ce`*m10YcgYJ!(Vl8kc2acKU9k$$Xi8GT^c}i*{PF6 zx>a_(!(XX9MJ;~SOX%1#Ens#S5|TS6S0V#jZpkIVj&ymM+mHlh)wxV#KIzvjM}_xnA*H(xu&93 z%TSdZkA7L{pNItzJm}poW~`=#ch`BbYwmv{7UrE&rj4`j@Qs#_DWaDm25b4&4D}@I zccd%1YZLS}`Rw<^zKp@8jLve#aAb_mzZji+8JF{;VP_UPKy;SP>WBNR&Mr2AjiY6Z zqZN$36DGF&C${9PH`sTdiAiihu`ddzsyRGG9a?d|f@$fX$`Q(KYnsjes8x^FVdxPj zt%S^%tD;p8t1bmQkew`2pO-7wS7dY1jlZ2QQe)>R*IVRhP1fFR8HvxQP0D{pEXV*ApmrhZZb(pLIojiMfZ`uhj);wD+7LQz0L14|k_w1z z)18YzA)Ui? ztO$(}%NT&;^4WnHn0Wl*_uK(7BbNw@euYKt4tR6dg4O-qRpkp%Nj^>t`HYj`BSmT1Nx_85gPFy6${^Jn!GYD!<3>h zAq7;}7W1ewH+S=1GjH8Sy2qhW8Lwh$Sq)Aus^@mO|0%{Uxjb(PD_AhTnP=SS=J8Q5tW zN00jF)~skKER#6DycE@0(nRjICoDLuV0U^Kp2|w`PsHL~ti0lY!B(`UW_KP9r;#fT zd&bKp6S>tTLyM~W%<)xS8eZi^MPN0svT^`SGuZAqetmXVL{~6*rVewEiq<)OCfxlw zo)g@ss@=Q8Vtx#lZ8F%25_)}(iq=DdZ_$-i-6B54zIXTRaqTX7T&)vLxrw;8KcmGp zQ}J(f6{~06By|)tW zL!oghzl&&>n@7&tcgOyy-Et*!8Mm?)KJzEgiF3D0!l_qA-T6flo}syWcO#Ro#C6>Y z8g6~N6rP$=)4N)yqGyYlm!mk|AIJA$(Z#p;ql)VdLZ-m>bs~3VAPZGAC2r@XiBT{P zE5s@|;isgE$!{HiWTj!3t~w8%RW|EG&Hm??miC)__s+Dp7VSxI6sR9eMaN#Qq_#df9dq_1xx1U@kV8_;qUpLd$(-e3fjQvo7}8uKSO67eo0u(ADQN<^1cc77eS{(H&kNAIn)%?h(Q{h}GGM{R7w+E2r1;ME4=j<{_^M-^oJV{1=~nKoyitnr2JOjI+YF~u zQVrV{oh@EZ2YGUjZfXZ8uKXF%;*_a~ssT^g{P$KB;#mzoRNlp_m5srk8lq2&v6ir0 zR_^!3S@h^`tySCIPEMxK_|3g?=b?%w->S7=S?|@iBU7Uh6x-Q1w!QI`dj8SwtwO|E zT^jVWYA{?5j$@r@ zYrDD(w&qsS-|ij9t!CDex}aTmqJ7z2U%ukzU9oAeZ`_u-`4;Zag*U*k~kqKd~fxRc;o9_qN$x*ss3zz z{X~|+621#n|7vsp?6T`_WQ*~9yfNKR(?*^;u>RU=u~8pOdtS~^I9#&}-1MlI!mO{} zfYF|N6-#ubjFjYKJwCigUoc;v&B#xzg4#aA^(%Y?d0ebxzI`wv&* zP`@WMTmenDY~^=RjWkT{HdEN?yjll&7V{L>t=;iBEF_I!yZ36CSGZJ_mdj#`M8@3b zM*DEYkb$o4y!`g=*Ls&cORYw?(X{?|*b~cN!HcIC?f(o}Y&cK$6(nf!AiK}nrLAvX zS($Pb8XO2HOaz2A))~ZBi;939a-)9EOOP;9o}P8dz8)$?as& zvKHiBGGDYFV}`n`c;ogpO@Cf>Y}?03 zP0Czq^93sY4Q%bx?m|Xs%T32_-yQ?=zL&j_&e!~M8*k8~+0Xfju-wKDXCYuI8(Or! zcd%|f=<2z?2TMaa?b#mTE9H1{_~%IQLCVF1=C|sMBe}iKWhbbLY3MSO(6ldkN>1D( z*ps3*JY}83Qs2qZCM=^Watw>IfxwMZ4VOipu6-)31Q#5mVRt5Y(6)O$7d}0&W&P`J zd=;n*$JN**^S4({)CI*t!;}w8^?WKVpwg|EdN+b{x7~Mf#y7_F{Wi}jt23A2uP=O( zjFPd(*SBf0W$$6|Eo;--P_II+>dE7-x%h3;w+Y?VhH1~J8%ZOp@{?!mc$U|VW6166 z?yYY~Iqn|MYkakhm+@Ce!x^Y2Fsyakb8KHtVA{pSFlBN6J;xTo>7_Tj&+Y?{iu^jh zx-Z6EDc6F|i;{N0Z~iGq9Ng;zl-iGQV{?Z!=(h2_Y6zybxeJNoXnhbR8i)>!Dw#42 zu0&%HsVRDxd<0gd)Uh%bt-%tM40~Gb3GPNV{e1yj+RW{#&rNBj^+|%Kj4L+s4Ze7_ zQ$H$;`z5uhC|elV<2lllUh@f5(0wEo_aCwvO;j%SBVG3KQNVl3YatgTO83pii zO5doxpX1Hs6uPL#l(A8AyC_)FBJXqLxPn?rA^?QHDZSw{1s+J5%zLCzNd0v0c3A1HqGS(c z^*B)>P82&(K(c+90R$@#l_E-GU;C;_Ls3AQouBOm`R9%rJG+-iAyJSR5j#7(m5ybf z`6%bc4hlQtfmjLOlKR*^Q+zcXWNa(>6j_KEe^zBOjs%}j} z3!9L0&f2|m&FW9!|5_Y;G(A*4Rebkqe{@$Lv&#ewcWFqHp=G~tc` zHTviJzbp>_kR~wdDF_e%iOxK9U zr9y^~kL>BoGUNXcCSe4K34^F0pb4JtK0X9_In>^Yj?&8xNDm*}!S}}*KRT_eO7&;2 z?am*amIQy}8J46?P0^e*x8z^Rq`>TfTDYa7notYprQHU|jLw;GEPY`Jdng%6rG+oR1MIY6}S&W!O+vG*7uIufuBL70W}7=sXUFpPuW&Q;vz zz-ezHQ25FGj1DE{5R_n#9UMv!$h@A!COL8<0I;NiHmbRZN+#44Oo+Vl#3n|gwqjlA ztd$v>A(M`T*Y<>D5GJ~9)ak^_Q;|&2ul@{*$L89dotPeE?8C8A3>j zLYiu*hm;!FRFs0<90-hXP849+v3b$u`56jVW%op`w5YMe&6sNd?f#YW^k3n9`^oM!s#|KrxiCu9;1RunyfPnKP zOEgSG48Y5F{!wX#$$-zjqQzwc4%Kp)k2|u)dh#&?!cYSGN9sW6V%%yoKxf^z*@J|N zB`RH`eAv>(9Kt8MkkjJ85NBe}{=z+LT)$&$hZR-twGpo}4?FmEDY*Kh(h^|&tFl&# zP4Wy|s7Zj_%9Zz{(t0JQr6uJ-qqX%S!Jj0Cilzk1kJN#om0cVVZ@?KJHoifct#b_( zoxVqs)!HEyir6P@8NNrVp{DI+@lC#=$F{m>y<-#XL7Id=n14m&fXY~eV9f#vO9rBk zP4<{3-Axdy9aJ?5@~K{P78RiU+txb;R2(y{I_YOpZ?J=iTSHjxDoDWHW7=NIoQ9J| zCfbOH!jWr3b;t*fQx=y3kx@bd7TOaiPE-~I@a-!niXuRO<-5*r1ts1~LYzW9qMJn& z_&TU}Lrd%;3TGdSb5zPSF@HcOo!rmC4|gxYHbFbCn?)Mjssd*px{C&BZ;E&$qC3^E z`-J5yje5kuHc_7sym#TpOa@|ak|7$5AZ}G43wT?W(~}Xp&VB<`$J7kbfrYT^ z{*BfvN33#`q}SeoQ9d)SFI@{*FRsr+!IDptQ;De4ECu=pk(cFM$Af3&;U zO4M)WUF!y7$%^FIO4(xclbpj)4snJvz3^ms@;|V)NqaAfb3mhZN zX;TAeGHS7x*!ATQ4BbH($@=AIi9)(Y1=8f`PY_%bOzc|E58gyv!bJ^)56aWXA9SW4 z3hZ=D0$0Z-)!Yx|ROXij;v~xZO+s+eE+}IKl8vOuQdZ@)$Vs`FrBhw-rs{KcO!Q;Z zdN=HVL-F?)x(SR}PG->3mhI?Dx_uKGI#gTr|)Ul&MUjY4#=-w8OV@!${@F4R((vgM8= zAo0&GAjp7$=%YZ)VD@9Y!f9^cO{-LLcx(6z3Ydu=Yig4el1V%)$o8*I%Qw4!da(32 zeon!`U?Y?UHDGeY4g+R|IQVZ9EtZ2GZ}A>f;ubZiD#$o10PUV>gS+z&<}ZF%6xiKO zsCQ^9mje69j>K3%SKyA*-atXMYub*6A@vTXP0O#K;OS~dYZpQ9sc~FSefHq ze?SQ1^r28~Oy1*Irv9QJZ2YcYi(r7W}>mnuz>TMBqfid6zmCLZO`%9PcXNKT2`I#jki*?DZkktCyN-!;3{Kq*e zg4YD;V4h?2ywOqJRkAC}0hfA6w{Nf!Eky+SNoTv^>B6&=5!6+hp_j@0nA?t|bjj8x zTVFoP{Ubl>+Y3qjBW=M)8CO79AhC7if2NI}Zg#v45c9BPC>q9_4-l04PA1f3lKID`#jB(4ATG6=LVQ;b zz7&YK!eJPO#!WNWE3y&33Y0Gsjs(^O-8YSKDcbQjr#pbHhS)5z&twi8xhGrdpV=Ye ze=zorF}6j2x8^CIvTfV8ZQD9!+dgI6_9@%8ZQI`Eu7CG^`}Tc1ebdRF$y_6ACo6k@ zS^14Q#)G#4bthRX;GeB%9!fA*0iCa^UVtHOu@pT|B_~?xE>#ICRVgA>d7Y%jOHrLb zQSGUuMOs+0=O&blg4Cert9G=9VPy0~MtyM*rpG7U+l}((t04}qdV2u;q$;HD&rTkO z-(h$-4Ub10u#!tdq6(|0Md0o_9Z!9aHi;?r&w|ivTp5Fa+Np z(gi0=1+slbO1d_aLl;&!$T;1aFKrz8mufmR{0Y80c;z+A4P5+uRN;E0akm9bI2JWv{vYE3lK@XoQHQUyUsq%gxu7-^<;YPfrdW8W^gnf4oj-M2)94gO zM5BtpJ;nX>SYtUI5bp(~HENuxzg`M91V7oX-i?Qrv7JVzjhglr;zAyBdHDuZw-=%4 zW!CdGmP1T6fOuay@+l!`YLI{MtKWp ze$tag=}^}ofc~pMY!yQUv{h)O!_-h4cILw!$RZ8vkG9*YDpD)&+K4`>H=W}aykRSL zNh1QX32q_Iy2TM&P;lto#1hDX|^2SkyMU3C(;s#4s%f^;M^uSVo&RrSyLdDNlLVnJ~fp}#`7 zv~;lfT3T4=MjN1K)aUbYVbp?RITgPS<4tCW%Vipt_i1)dsX;X0r!?*oA$+V5sHsou z6;SI6=7ab(dl_gAHGOE!G(oB(Okta#Ci?IGmWO^w(DaJAQ^c`DI@H!J&t#HmeW#;k zn`BzDI##AH`<2-I8FW^xK<`f1bRjF{Yn zo6*tsH1#hr1N*h1p1}*`s$h15!Qs`&IEIr2zQ@Ejuf2v;PQqKO@yI}^Jout9rAr@%Q|+Y2C|#bPE7WLdE;XuxEW&5~BqlNYuxW zQ?99ercklnej=H~k2=N9!N2yR0rrWS_qjtMBmOvx&iRKOuSUqXm{mO^R5q#lAVdGsX{q5J`jJ}w46kK+O+D@L$&&*%e+drEvx5tcP7bw8CI;2zGCqwol5>@A^U~M z$wz;V&>x@UcgUh#;pmw->_AUh7MSktkki-4l3h}EpSwj$%U2g_5#}^J44gVT-^uVM zPyb<&$&Q;jFSOchE;}??)W$7npA|^Fp7h#vdGCa|DrcquSi7-H7i`npS5cE3^DW(O4eJUkA1<#}%Yqk*<^H_7M+uJzc zlW}+@^WK>_zdkk%ly#_vr`zgm;vw@=r5x{O{7`qU5F z{-K(mIwG&wczXR3w_G!Mn45%PEN{|63-lT zA$)(~GAHJQrr({FLw-+e_VKfqq=^G~cE*;k74Io7IN&=y6mrIvTt6_NA7{I_nz&*vxNMg~pU^!q_3&ZL?Y`+3PZpFP zJLtdMGoAIf=ds~>%ZPm6HK6qz8&8Ln(4-Byr^Bw5V~e@_$lRkIZ>bjYPS(8*`LvV1 zr3GE^d^9KOl{kDWdqqr{PUda*O(SdK9Y5;G(q=B?ez5491J>D& zEY9j`mF|lhK57p7!ar*B{|LC+H(o@r$_NPN0 zf736JyN?yP6?n@Sj-9FU^5~UpjE!8W%}^S*%DR4OWu)#VH#SUgeYoO$^I_I>XJE;P zpi_|wW?W{F^J6%f`1lyLOyA-A_J-cx?5x51P<8yJXrfef)gC`@)^o9N6R}=oho;wG zi(YX6n6a{d!Kc^yX^i}I>@3i zugJgo4|4C5055ZkK3MM*S1v&BD$#2$IXgdQfip)(-Bbn*z0=;sQ*;jW6XPBvd3$h5 z3ZuG~DeD^YnX&bbI3`#26Qi&#ja2(%c$meU&03dmnJu)j5#Axq15>@KZGPdkDIV z`z=ja+q@^mXMI9PC%(RwEAsMd)#Y{A8s6#8!)gb<%L8M!r)$jEW%M&MK>RX60sg_l z?62#lc z=!A@}NSfOHF`I@^t#`%8{NkHlwN?et__$9Z7EX}TTZPMxd_i4>XZX-yUfkQv#fKr@2AEWr}xg+d805O zqqB6IzP0CJqm=Hz%gTGvjEKMV)%o)^#I5-n!%6Pj>FnbS`$T6}AK;u)Y1&o!)|I*9 z_d0eI`=r15-t=;i5ZoRQ*yAy)2SM-u+pWoqQ{BHJsZPO=llIqH~h_KnlVuGG3LR%jNs{Q{9ec9wvu zTos{j@B%(Myz(xBXj}0BOL&prl~=)4c(P$_=yX^&PKCBWDNcA_khd0tV4a;507slp z4_Kk6&-TcDdG9*2^X<8rw0^7ZxnMn52|tdcv!dpRVCCl(Qrnt&ps&Y#eaR*6@Ya_ku9sHR zrk?Xi(cQ>QdNSZhUxlGOXkG`(10tD(1YC;c9SwOB+WoE;pT{oHHMlZ&XIR&E)|1cC5RP zsi6WE!xPhP*ro>Pp}*#13$O~dp!|UR)w=YnAysTEnz|C4|4^r+=iHh%n6urKJ;9sc zPSR6k4}qP=hWgKXSH&;5rlajC?j>OCsC@Fsm3M*Fy#)D1FYv!STE zcvcO_z!6yO;(1_Gq1YdY#vKq-R70PHMD4vW!l!r#%%ySPqZ{jvwWm*Fwc_J7dOs{^ zMatI!Wn-*z`)t+rJzhP2jIp;q{=M{rksXSUOOT$dONZ`9)-SLS`swBFYlrUeJ-;2m z1dRTZ?u9t>82s5O?i<~>mAJg@yK^7jPq6%fajy8 zo2kCi|$<=Nx1VkFJBCuGl|f-i10%k95MRIwLh6Pw^3Jlu0v9 z1Q?|#iVM>@>-h=B`ZW^C0?jy+#60%V0>tWjrGPgArJBMd)q=2K{pE1!N#^wA2cIy} z2K(Z)&N^u0AX68qRTdcZZzIUnpgcn$$_%o-dQ?d*?tn;=SrVPHdW0eetN=_9jw&w_*o=Q~en4i{zN@ox!mmPjg%;*8RLRD6sF&t)e zk3=V}pgk0B1pF5oF-cvE4<#}6wj@F1$e|#uGx$ZAM2bHMe&~?26O_EhB{m0oXb|4c zRU$!lmxyt1U;``R@R-SzY$~N;Pslt(9VfXgbWNx9F75DM-udk98?j zEPNCQk!=~5eT*0TvYLks=^p9E##N&{q3@9wX%)& zTd(Q1)XmMo^_`2vmB=1nkFL=+^X{34&fF8u8QwWSE0wBfK?bWRTrM6FyrNMAmpJK6 zk`)uHr-y+*k8-J@%DOeJuN)i2f{Leyy?>TsdD1;Wvoc=cEEAPqu~5zJ1Xg}@xFsRh zTe*DBDz|7&5Tzx5n_$fx6B4HiDK?8%u};mniL*iuE(s8kot-9ZBPn>aN>RB#G`A>H zH8Y8oYPfYJ=wDO1KMKFfh$;ielW0K7u+VEDmXP*E9JU?y$MQV0IHpaR$_!;B=Pa5k zP`boY$0Wc7ilHi!<}y&aKo=k51GPK};se($K`UJq8!EvAxy~ybwmo;H)FvQY4s`AX zc?+@R%W5ECi+W`l2!FRWAY(ZqW9>mkuR-RX2_jSe$w;U&Hl3NJRMi@$r-hcx1y_X$ zVk47WG%2xA1wozsyXp@4=a?CfWdvAmNF`GenRS3G77X+{itJv_; ziv1&ClJ&qacSW0bQlf;>;xZzzH{yq%>yJ&0+hsh^ESDJm3iu3Rm=JsJW)r0v+dgH; zwCMzDk@TKe%@X1}Ck4texz!gDnTo^!Qypn4kg8ue6JzX3tGy&HAwM?wd==3PET>7# zj4H!wRolb-zND{=^ip=2H8Vm;$Dpw~o+X``J6&?@6u<%z8m(1WJBb-US;rZo5X&hL z2rU9NG%?_i79JAN3;jKhOKJ5wXA5Lv&eRxL1%x+#it3rH6Hb$;T}FL&MP_BPG9m0j zPE4W3R4teP0bt00xtOz)rJep{Vy;8QYNQIKCcW=dh)*t7dKN<_A%I%bpV2|Mq7}Xq z5>eTZ?j?r;hw8$U5{PQ&q%MQy!lPd)GCiLK|J%e!;L3DL3#N~NOblgUD2QK3m~Ln@ zX86ukB82E*^6jH3e8>j(7C-#8%>-$=WGPnGh{d7>30_oW8f`Rms2$%y2{XEYlJ3~8 zh*XGKuJk+-Pm^+SHQ^TMUKAXxL-3^5!dRiHFQL+K%&%}&Ew=n&%7;M$7PM5gG`?6B z0;dS^tlq%Jt}=zSq;9!v7Fw=(0MMFZ;@6pSq)~5>AuHFcK>1W#Q8GqKa1xAYX;0-6 z%gz(yQjrRfl65XIb4jQ$TZ$87nE*dR(QO6>ei~Wog)}6D&JxGYF1e z)RH@gQ9GaX&PxoWk+bZTg!L%Ll(Xa|Zwg4{%1q=6OZ5B+Uog7$CvT;zUOotx!G1#sCu+WMHTh?`zpqWI)9STUag3 zQ-Cj#?t`I$>=;S6bk*M}U$dE1lBZNCT&mN-jEKLdp?waMK`k6%^khSS$3#UEplm7j zlJgU0stbTYt#_F2IKF4|&&1>>Y5^9t)Jc9kwEVOOn-q@7&4uo6A@l`Ogi<*ir6vmu z`F8Zzj)jHPAej~atr7LOR{+6J-~#~EdTJCvh$_`K`m3bK0!}8fPkvhAj!A783VF;X`79EmFks0Rg@@%@q8V9PUfiP<;M4AIj>8?1 z%mF(b$bf9-fUPV@111}J>xOj@#VAO^D0O@5y%u&MFlGm{zC0%>Qgf>W5uI@3+&(;# zrEQ(snb3pJ!>S=1CrofWc(}3WaT*GwUZ1`!3KFydlC!B^853@*4GkW_;y~DJwK}N51<7cRmOVyY)tIy?z zLERZJb`7H!BO^PhK3c(#)+t5%k<0}q<$OAY{jzD8LGsC}S#y4+N`+J1U{`wd%@)N| ze61@%Y4=Hz<}~F4NTm#gP{-v9^BlRe z;7prBRJUgJwy;)+s)8z1Wr3acR=;IyKpDE)2(ko4!yG7ftRd{;NM%O!oNrM-n6+dT z5wS30Y%Xan8CLYOV&tW;(6eH`odz}tQdch*=xlPUgc;PhL_BrDAy)f7Ve>kCn2Q5) z^B)a(-a9eai;mr*Rv^S_Sf50g-yJN@E&8OuY>ofsC0eD7>&MMalF3dt1XcrO=ZyKR z#WFGv+Je~toW3NJeQ1EXK%P~kmEV4~sJ0EY&8({ec09kj1O@QxSKyWfE3C6l8-logs8=H3uls@|pVMl8 zSUIr99TK2F6*}TgEF487$byviVNPSQ?&6mw=hUs`%rOWUkTZ=po;8tbj9EBlB@76x zCe_U(0~r1cHr3Vk&^^ z)@i733=RHZzJIwk{9D#-#S&F39JAzgcM2yuo=;>Ee>k3xWKYEiXdApgp3h`YrFVBa z8_1N#v7__xa5@{wl=j=!0lz&)ip5)C^oX%TB3VTaOC&j0deYPaAr63~Ayg8QD2R&- z;33rC7bkikAbKz$YKThp_)Y(y5HP8K#{_?1`jwID!9m}UmC6~Os@XS@H93*hKT%cn z8)WRX8lpl0_Q`PG$=a2Y2BD*YoR&HHj7(96qzfh*q_v$Ti5hRuX(T&h`S>J zj^*=-gW&Egac>VE#SKuHqiY84-*S!9IV4%x@@Dd64W+m7dH(Jp@hq9{HGq_th;rbX zv|%q%!W+~DkD9bTuOXRj>h6OsBeWaTh96G$yqOKZWzU!p7a#nL6R+|kQq9@@&^w3U(%SUD+8JhT^}jUST>^*N=cAo3GpIVrL{v^}?t*2flW z{R{QUrKZ}V{&FLXsgfgoNaHBdb-;@duL+zP;Avn=RoWwtzdQpxA*hQFYsf|3F{^ zSVo}x<$(+%I12pbeg?W%1tYL>S%Gr|1ocjyAM8RPmG@2Ly!Zt!R2B74xT)^1;8=3S z{tFxr!ZNb4Up4)(-kON}!;!g{IT&^xp$}gkeA{|U7w=sIcWhp!OIgdOLZ&t_e+N6- zX-sLC_ySC*39t2EOW!*OR!hCk2HRI+=gYkwfZyj8t<>RD&-Zf*k5tY4-cF-YftKIf z2>aul<)ddc)D2CKw&cS3e^}q|(-iTQPCZa>5Q8F1`z;|Um+zWNZqQT3d6ud4rqW8UazaEN9GxO?CZ>L>>%D!jJ zM!K&<#siDQHVPEIw#PqewD#I71f@6i4j1Oh(VDblf}`MsOa(HFLc4BTH8I897U?q< z4TGtv4)kB{3TeKBvpx#GlU*OLhsBN5a*AjR%pM}-Jo!qFU5fSj+_7!yIx|%8w*IA% z8mfCt?{#m{pgHsh?Dun}EK|=!b-2dYf3%stW-=B$KV~V@$5?o|wE#fQLY=UMXd?CS zS@GKNWyxuuu$A3hi%6{_)l1n^J?H#Zp0|1ml*^8!gu<}CN{vse70+BcuJUQzItHKF zZBIqbS+}yijlpdsT{k!yYFR?@xTdRP++7<*YwV#{mpDUHwpq|wE9SQx-7`+wy|#=_ zS?R&~;CbaABl+}a$89r9J*%&cs&5iY#r)Vw=C!_?InMC@7d7Au3Z0dzmjM@x5zQeA$TFzCZi|$5-85QT@*?vNuw2y6 z#EPvi7;ol0;gPMkNcQEughfBDZu9SGeho{A({r^Rj(QRMY_rpSRY!(T!{}(bHU`Dh z`7UV{#QCDE>PnTZfaxeS(IX*eM0Nfy5OPDmY1rhVarqH zv4^;=pXxRE(>dho`KgNlWh?JL*lQaB>AJaTManirJ!S(v$;yXj{(1XKfi`WDbsoFj zYWW3WuDoL>+wwRMZfIBOk7P9257I*YqJ%YSG|J1zSns46QB zUp}GvpjC-nZrgHs`RS(AJM8iE+Y=+kU68n7_Na~@{vWq)fHfWZl~p-t3-ZY6e}wkKy)0OC_%P?1WEc zg|gHcD7K1>ao5{kV7-uX_u#L!(5a)g+w_qx z84cU$cq(=mMQJVo7>cpl9UQY&Nt1;523a4Ud@a+|`~D5hYcC!j_oU$Gc8AP&)BGH_ z-*Pz@W_39as}nCb+u&w>`gr0ZgwD@vayInAHyA^th1ZJg5M6FyI_gVmi@ zA3(pThheWYw(bYu<&;zAb za~Nm%1oNxK3dPGtK0mBe*PkIT_NI$!wugxFmLttEU3r}nxnQ#5Lp&Z?T-y#e*#ii7 z9X&w5zjkm<_63^~i1-NE@Via{IIVuc1znFs^u?JHAL}zy)51Kd)p35Mit9udbA${; z88t{Vt`GMOQ&VexRNMAD(-WbOSFGUEcv(zNW}w<5zwl6227h(2NqGFzR%N`D;5Ey5BlPlu3c|2#ggXt$wcIdOPc(~Y#Lpu_7 zI6OnNI88Tz>w$fjA+hz(z4{N#8>m0IhzlR)r%10|`@ z%Fw4Jc3O{Xja4- zw*GA9cZtVfpYc2D_26Qe7jf&H@qZ2JAmg?*jRaAnN3pE(x&HiX$hzGkbF@ZH@i`1W z*D_iBn&bMo_mjpCrEQ3nJ(d60kBMB!Z{gha*|7F9xLT~M*U3b^9ecq|_0rv@7ADwJ zd}70#o8`jr-XIM>31$~_lDx@f^;MgfO6oI)+hA&T?u(&h^qediCbgZCrV8%~bLQC` zJkHT@dj@`F0h$aC>Vzs)_zU0$)A*7K3iq9F8YY`*e@k97gtcKX`$ zImhFsPa~IpCM84OTJBWR949Y>Dk^)6&pN+z^Iyup?gxLR8!Ny2mNIFTpxU3A38dZR zO?UfAPsO&e(;`E*$>o{3S@EWg<=zsrijXs)QSjm={iU_(nZn0_&G-vyuk+*kc*f%! z`s?qvEcKSw@$5j@0B%Ond5%I=CMp{+`|1fse7)hlb2$nXCH)+;=v=V!^uH zHAry{E7db&?N~BM4H^cduZc#to9S-Y+U@`fe@{uy5>55Hw}(D`rSSbJ`@=c!;I~lw zm)~V!Y2v$p1f00l4O~+8K2p+-tT@Ud);v6RGB1L1y+~JPODC>AyneDWuf`Ng7 zy@b1j$sdy+LahWxg$O7Zs3<5HI7Fy8C`1StCOL`pCiwp!Ix-+hL-mYTqiv(o!8Ssn%2kLA$^M zz@HF4-cj%Jyx*hc;wq=il!N>3_diV(f*%t_ufI;267AQoYp(wd+T?%OB>(YHSpR=n zC{{oA$$!>=vrrB-J=~F1SLC5XkWzqYI|wp05TwwXg$zT1t2LlVLsDzuESRQ>iX^YI zC9Nt$EGccI0>%seT0^M~R?rYd8VGBepa&D()+dWTiwX)Ly5BI3kBN*+#GnEm5UHDSl_ z2#@@IB$C74I^2qpt$Emi<(xn_$To(9{8?`~`GZXJ33^IEJr6;crc0kJ$O!UI*fL?W zN3uh#j+2H~2T`@JZ6;e!_-l3^#dDi4y$DkMOv0Qi;kDGOei1|#5@@+@S+=xsndgZ^ z4b-#eP|>>V(7hlK&9TFgYmrwXT+@;<)v3p%Nn7BF!EzhN4#RRr%6fZK2Gm57x9qOz%8K2`8*BSq3dexe$-_lmpLs9IVDdz}$uav3x;ZYDEylJcx%}qdHM! zHbeq`4%|I@q3)jvjmk!v2EF5$Yr~os+;8lP>M-%d>RDq$4o!@F8$Hv%((IDJVPF`d zv(Yja|9r~NS+Y5GQwbeCafuRu9tn`WlrVIVQL#Pa_atm8OX0lAFJ()f=h%9cMJ37a zvVa(s(NILi-Yv0Ju~b!_h?y}g)7L7Pd3>;>OP*qP4#>lDhXOVKus;-RRl??)~M$tDqG{fnrjAk5o%@R~LDr>+1N;b)@aish~g z0*)Mk2%*rl3me8OS)@}@?^Ai|D1x*0jUXlBaX^sc3P9Re~Ms=KtezcGXtDqke@A_R~JX2@D_^@2sU^e z-?qz-#UPbewFH%vUl>xG4}7$V5s+Y6CBVrnmv@LkuW(5I8^u^IPeKlbLZuzB zNC|x!6e1`T#h@d=$MWYFJwh(zBKa7wOHjOfN{ML~t1Lvotuy;9B@3A1yKV_Ge^~&e zh3pt`B2N|Dgj%UmC2d=81rXbsC4n|9YNhC!rDXFuopv^vb~c^%XNk2-sI`mP4-ETB zTXWaO(gmuSOB#gCOOsc$nJW`KX4*iYG+~V@;uI&KoX9;as#`h+5e1x#dR&{c$c{?3 z%+nbqZZZtXk`6OX3_^ka7>^HFP>)YmnTc`CU*_b)yE=Dbr+3cE|s3(Z1tbYR%X^z)Inc=B+~Sp)6pjV{pc@{ z6Udj&{AZH>02Mq3otS?wsBpWsjKQnb!s%E$6%eCtcK~p2e;j^(Xzx>?58!>2GU*c? z=sxioj^3@2P%N6qdvW&K->(DWz>)F%T&_jF8bkrbSho&*ZLcKoGz86z?Db*>O&14+D^u=rIG`FzDYu+*q zE?Bp;`I;YmT-|)o)&d89HzIJVbgoIKmuxD(?}91HSk3Im%(CN&PqxK~mgo}Q5@oh; zOrWe&C~HMadS95p7}9dcUCU>|1*3on|8_9AQf*`o3Pb;+5pkBg!|75c-_PXmgl#6O z9Ix+XgO6%SY(O)nQO9uSTP1WaT*bXCqWIbk*r$WMT=wTh8S3&d$OXT2%Xjy`)DNrp z$C5RpsPM(1QZ$M;2^Pk(OOClVz*H}eVlrWB5<5QnMMLZ*Blg@HbqR^N_B}qD6U8qO zz?}>xr))66P;1VzTc!S07Dvc|PUEHN+ys5=SV3NYWH9C+`(wS;RP^(jo?qklnFgZRA`@ z*##1y1^YQ?-59H7f^S(5pW0(+QxyjQTPB3MNI4uyY7z) z1v-@?WUaahirfbuYz;k4RT1DQGBhxy>@tYcmLj&Jj1EsC6OmGWv?Bl0XLibj&PKjP zd#eSE?G34qbZ8*$eFhBQ-0ud;4`AvhLHO}V67qg;>`DWZSVz)Brqtqgli2q^0}_bd zpt_3)ToeR6Q8zTgQZ}wlK!WZ-A3=e*6S`a-<@Xot^X19Ajx!e1WzGZ1 zNB4-(L-Twg%I7&i|B8x`I#69kaQ%2Bot9PZSYO*;QZ?Ho?#j499#LFJ5DG)p=hNmmD@tEYa-q>(>?4W+w} z(hj3|ic~XeS|*fg2&IcOYYGKkbDjt)`Ao9A7`|*WX4V{O$&m$IKBt@TouESTdyDO|8e^syUV7 z$t)Fr@{%W;_oFcU1|+Dn zcFG33I%99xEls~muWY3WPD=+tuBdqG(ODT_v8it$-9+a!;g}CNaUg=Fqe>CUsx#nD zO_1N2)(x=NX*6^B6o({0^Lg@AV7;D>^=78XC&~0rv~+sBHuq1^@FeWJ_{M3#1to6P z@eFaNG)U6)2yZ^qsiA6^x^PT7)a0CZaivz}h^d`WrW53fgakRGQz$a(hDQk1l%?g1 z49SpEnNcb#7KsdbNvTY#6&S@Jp*kg(rIm^dy|1#paNYr}t$S~V#fG4GV-&?r<|=V6 zc@gc$pYsn=AAWTC_uj{|(?FwYpdo5>-m^MbgX&zmULx6z(2>S`rddBAS5;UBUW2E$ zsnfsceNt-b%BcC3^wtOV-gBPmmG|C<0859X)96#xdKdTJHvvyjfu$@#(O98qEdNM_ zz4dYdN80Lva($fYGJRv@VC2b3QkW)P34(09hDd1XEYwMJF#D+7Bth0BoQBAZ0PdB| zym2R8OcUnxu8_&E9FsVuSeX4mD9Cf@3X?dLEQZh$_oHx>1Y{+NLF&HhmXdqCHdpHD zrsVBLsx~$ey*xRQv0;BE6klbi35v63#Ce++vMsm}XYRY-9@peE0k>gPpyzOIZIF$b zNWYVx`4)EZvc5)_^l*?wHu;3gKLX6fT;q(WH`zcfT0;oWCxU*k#-b2k&f z6DGnB3pE^R-z-7Gk41j@{Uhok=Q&RC-Bf;qYImq$D+^*J1e=!qqDn8e=2 zZ1H~eac*3)bnR*hGrvPBXhN^Dx8gm)wLJ-sffgANCEfHT+;a_g)>rGlia@ee@9MwG zK(bv)w(rOf-jF5Tw58m0)rM@*Pqu%Tg>2!LYTX(-26%z~`oL{S(iko7_Zke4%IK7T z{_2I5su&aU5AZ`MfgexvplhscK`Qd25`06@pC5gN)}LT)ck^rQ= zr6=zqW9ojuL#n*)3u6{u99#&kte{U+9!uw3dDnG(dh3EgE(J*u5XYh{-BuS-@SrR| zl16Ar&+e$JB|%#mvhuYlT&XE#Qj`lPEe`G9d9h(1-g)V(@U`#c_j-1(rjnqCm$mNe ze!K04GxVa5So!GVB+GW~c!#)mHw41YCb8$_!Fx2_ygri@&3k}ix0iye=bYcA5ERWL zL&FSC;N_dTdL@7c&FV@B>BxSkuZNVc%+-bUK+b&JM60?0dcIy#k_hkAK4R5moHzX5 z9$nTH;<>u5}cqwA^}iWWRs)sYFr{aYngp@mJ#3yDtsXdx>;hSB_S1 z;;%C?Qh-Sjw$ld!dw z&TCxweN6teHoLB!VK3XCbt6N;t;NQ(Ok4BdL&@1qGmcrTKezcT3Ge3jsfPU=Ll5uP ztVax6`;0NVjl)z{R0LFbd5~Plhg$%WjsEtpE9H1+*uzDya7nOK^W-V@8pn$P<)-Dd zda+-xOt$QYaV@cErVl2Iz2|Cq6_OpFE%$!FU&pUUFR1`4^f?_gRufj>Y9ZvUiGCTT ztH_T}FAA`cH}N#?GLJ^rE^yrkT5ZO*T;sht-1SnW`3cuYrms97v~4-;_wDo;>lL0D z>1oI~2UP&wN!_)IVXJelCis(HfP1U-im^AtNAm6&P|H=nn(b3Sa9i)UK^KGISQ{>M zlfJ|r*Oep+r`=S;xmvo29W8XlsqR=Mkpoa`x4Jd35|x~|D*5J0s=Vd+yb8A$!>|iV z8Xm%CwrhEfxi%hCmr|z#yj_hOCgajSaW2Qn)@?_Ka4x@IA;d+a?Q5mm(POVzXZmb) z|9D}PQ5(1@matp^yxIJ&n)>AwUoVriWq%U=F0}*3*fQx4u6&u>){bJnvB1s4&EfE| zAzT_=#=L$#9J##s^f{+ln!VR=?GxD9#~iD*qA|Xh<~WzG`TIG65) z48?nm!292I@eu6$ZeD+ct3Fy7p6#nY7<;9to;UQpZOp(US|RTzHXw)ieNcKCmP ze^J?eQtgNFqw&Le1HBDUpP3UAH8X1X9W;ma_yWBV=NR=sU9e|cO!WNv+^RVD%Lll< zEcQ^EAGks0a%O4xz4O)hJ_A3#+%Ce`{KEmf8vZ0DK6y*kI)8og@;$pxH9}L3u?VZNxDD^Ev^zQeAnWZ@tV|j)>6W&pHk@N!_EgC40TauCnvF z8GUSCZFBAGwKOz4 zf%4z8B&u5PFmv95J3Y^Kar=;cj|N)_J;T#-^y}a=wDWy?aUOhFu2`1b$WLky3%6Yj zt1=wW0UCGE6s8}SEE@Q(zUs8~{9}!|vD;HEm`XG^tFnZzzc!ty#Ynzf)8?G_$!I!U zvMFt`-MIhF(7m`zE-b3){>wJGR>S$|ZoLIfRAZ>Q-4NbLw;bVjb#9Y;p4h_oMrh%l zx#1O9$Cj4cf8MO)9Gsq_T|x8{ft>_^w+O~NO)%OHv5$`e4ve*@cb`ofJD?LuN4IEuSXl^Fa3JHe*0N|8q$})HyKt2+4LNCL}ZDAUG_B<`t7VX z`P05pIv+lZ!8J>sB$L|7yVbhbMP{e)jv7~@;%wDBR|RD!PUrPk&hqkG3L8#Ouaq=I zgY=zcYra~CJ@4Gy)=(IvqRZlM>`$H1JgA*Cf<->8O(v#{j`T_BJKsd8I>50}S_$Yc zNkDAu#+6+qUmBd`#K3v*wE2G)b^2WisQA%Pz+?fyQ)ke6{Cs^l)<-hhe0h-xDwzBG zF2#_cRw-+eeXYX&N0N}$A9`hGb@Z7>J!rG?@h$xuGwhOhyG;-@yy)6TNPa@?F>FI!JnDeOqnAeIX4dryk_Iq zn0)BQElm0Cdsea6fN035^Wu(=q5_o;zvc5=d^yG)J;1Dc`H+p-;MkgQKa^Xm8Vb20k2&$pYG=U4ett<6Mc*l+k@TM7%n%!!}A>ek6R1XYhE zr?3FkQ3B`Hx}L0mQ*4VEN9vl-@HIT!zGPQb*c;3M!apoJ!@u+xXcG zsVuTo5B$HNf)o$%fO+Z2L!$%brGoC}*$Qa?)fO#bqktu%M4cadPzWm4YHkabSqFqO zne-WeHoWPin3|Dv|QqhNOoWl6jiLpZAC_joSG$p0Xs33|c6du{J z#*!yVabWgef(HX9728M>n5;2$-SI`Z=E2qNUA(F4e$a_|C9y(GlsqP{@6r>;ru^q} z;<@#i3odafZI?XVx}M!T?EjomNB+#Hf2~0ujQ&s|{h#&UX4IXUklr%t=($m}n9?e<>yXg^1QH=4lYeob@e*Vy`R-)Q^d8J)%xy1S zkcz_X!Rd8LN}`lov`vkc>LnCSHN~!qR+JU4<8C)TcH5bf>%KcT7dOm3JCi;)+fFk- z^Xc!C&G&7S64kOYB`}D}ai7MD;|gnH_6Sq!s5LXRG>&02_J^vb+3`UpPB>*eY6iwD z%$)y&v2%{`Bx>9Aw2f(F+O}N0y*_P(2rCapR7D{ntt9mIrC1rm4DDickdG0n*L;TsUdI97 zus8@=8QnruOs@f=93THi=_bLQ%zRKsFP@oyYVLZVwrW+U3bOcyPrZPv0#NO(s_O-! zD*j<<7gaJVYY3-%09R%f%fO`ns%VWC?N}Vg0&CbDwtOQ%j#N{WZIB1_?x=8L5b{UE zs~py<|D=XBx%e%%MwtHwu9Rs?>cuh_AfaEFiictWdr5#_f=)$*8yJLYDuQwz4Qk&D zN&$O`fL~%dS`pd4za(867du4NyS_)9xOdY}w`!o78GmBCAyQ;wsr9K~(*q&1!YHc= zX<}j(<27`J1cUbvjh+j5Xc8}34Y^MH6R3B9kpjrWy(;FxC}{7A~RD-Z;*ES zj%bSBE)!f3@)sj|Nl*-PBaDD5y=I0e@Mkl<3jG^hlG-Nt=GjpBB=G%Fd2k~GXv7IS zmv?d8=AyNNhdiMDJvA=QB^})qj~S*m_zD&|aF$qy+JCIKR{)i1&4b0UE#mw3E|P$@pGSo;M-lMahi24Dq4 z0)UqmU}OQk#FIU^GAm39vFE`NA*>v~WVPU71N@ljgDvqn&rGvMnU1(KaOnK%qeIJ( z_2_Ueq$xx}@Y936H)8(i94NFMuDk2v&z-!zM+pBzgP%7y6)n^W^sG9HuUw2OlCI5R zsL2&~Qal|xHL=@~Vp_o{QcN_!ol0jEr;$t&eWwhhM}{DnfO3x4a%3Yr5@aQt>)}Hm z(`Q8>_XlydLnNzLnF)u*nRFiA5uS$I6;|jP4$s6K4?n;d4j)B07LLM{5q1D26Eqgr z)eR=XKVZ*eNRT{IXr+c90Tc#Oiy01wGmIS$(jUx@Mi{S}(J|wXe!61_|E>cK|9aOd zu?O%oHCx#C$NT7LlGNOb)L4@Mssq6imgVlSD0J=h!&m?)|_h-7nuumWQM5ZQnw z7FOvou0j`wY4#}rbr%HAhmq}Jl{oITZygRAPwtSmvsY%lB-vt2p{PmluBhqYvv?FM zy}P4v=^1?N`p^x;3vtBF7{o}fvPka!Iz1{V-3Hex8>=b~bxM)3*?DKbX~?pR!MkxZ zmy6R!+0TIFdx?b8+G1t4)8Xgl?0;p)6#)J~94SzyVt|JMZIy%IbC}JDCF_@Ux>3|I zST!Y_;HC4|J=q})H;yDprjb``Rx(&kL7GUGpw^_VvkD%pPePjLPEdPtHhL3{H>t?H z!Hzd+fSOV{-@*FZ@F2bSqCut=I`YjM2Fu&VZkOkXvJ;3X86L3SNl3bEF=}|y-I8tL zEPk+wAMaWq>@ST3)}2INtDgT0Xx?{ZtDj{65t*NSk2k7o{7$rfR8$6yd#glEv~%7l zF@yOQ%O|vJ?JQ{hgMLfqStFp%sUQFD#*-FtNUc>!*uDfWf)COz1^x<6euL@v)ncDF zFUs@$Blb;MMcjg8=06BVx8JLQBsX#JxXE!mgan>S0#9O4tA&k zj=XEj={ADCvCYAe>MFUzgVRX|cJGfe8TywVPomF@8tgtCWwJzp{yQ$brCI4?>UUdq+pe|pmnL7 zCZTZbb})EFnvzh+9|w7qE7y5NtU#c;+ZXZ7*tK1mdgDd994emQ5WE8K|C}FHi%AiZF6G(OH4SoEX%@jto3Eg?6Uena09V;R71Z`ZPymT?o+Mo` zZ5L|$gZxQCF`%eMFvTgD786?g=Pzk&Fa-@*=amdfE$@`d|nYuPeNPADZWf}$60^~)qVZ>*dbD>?5mIWIP9 zj&wI;Twsm|V&wG=2XzoPx8uugR)eNAd5Y5#=?o*wpy|CJU-dCqTq?%^ zUS_7q0ip$~nglC{nh_fEFhMp}TrhPk?jL|SFjQFyngX7LJ6etuNCYiMHUSlGj^CZR zl{N3`nz3>83vsk8^QcS_OeynV5AyH+o=!I@0d5BIxp^dM&(pji!E;e24a!kPx*x*~ zR$t<{m(tS;tY$wkc(07~R)>+m;0oU?!Dq?Dw9K-oN~cPJ;6ZI~qIy>Q8sk)8!hsw1 zu?z8?A%|d5eGc>~x(twLQVRi*+axqvTBGw@u0?T8+287ojRjG8onAa~DTb&zoMT}m zdf&Ni8$i*Cj_zK1wCM9%K7k>Z(gu{38y98|$=vUky{jZnPoSKh`3g{4%v2kV>jg$b z#3WRt5Jp2NBvi$e>bEfosPq3SQ>%@JI{vCm%GDbbC!k(as53;qkX|ES&lgRRdK+oh z#!dAKQuP#Bl|73M)-22rFcrS>r1akj;8WkDs2wS)0iUtJS;4-^!CwUQ_R<#me}>WC z8yb{*`Codf zps01L=&AoiuA_k`EWnakx`C|;SW;Tz_6!L`h7^heDwQcmIT@wq4WiUgn${=r!~gn97O~Ak*ALYMxP=Quylpt34hlkxhLGgSsF_LE0-{tC z*3D*2KP@g@3zw4J=gSw?$jt*tvC*-StK|-)Db^?xY_Lf-pZMpzYvODmF!N?UA2<*% zsl{^D9s;FuwF&)iW_itk*QCesy4Hjg0?OAVDVk!zP5Hix(%t)%Mhid=nOy}TjHg0B zSn!fr@ZwqUiqd@x(_?>65$sjKRi^I}aO%i-nm!~}eg^eCCDuPF)<4ZFyop7fKRt@JVteyq;I=!c@wTfG7Da>6mO1DrozLzK1 zEdO(QC&AyWraE;IlK2TtJ9U&5uG5fg+EAT7WhB_R{tRnNv~&z+>Ggo}KSN5Eg-Y?Z z%Yu~t8-sAVL@<`Sn_`8A^$_;PN9S6^c>-YgJs?q7aKkA*+<^dKDT`a_fC>zu*HNY*8*z zZBAowe*pFTy(bVdUyfyLIo%>5sRp$BHR8_KG+3vf&^vn{!Mo}D`-!-G(kIAbEI-~@ zaX3P4DSW_*3&54J>9n_NO{=zV^2#ap?_1N zZ=*s&|Mz^3?TaA`Oh6I|)zABK>>6KTef@eIZ!Z)49_tCXTq_|~e$+)%;mBtvMBQ<5 ze5~HI@3>P7U|OnB;Q6>Y%v9P4Fdaihvd`cs-L7Qk*6H+I^*W!su}n+T@VzD7t?TQucaWiq-08Rs9V5u2O?H|x!t_e8uG6*Es`cG&IMK-!BkD0P zb1f~dwJdT)H}5ggEQH=1W9H4d_AcaB)u(^NddCg0b^G)Tf9yLLKzFrqYI(qw8b8aI zg#+umzV?25shFUgX#&OFTUpo`Vze0DH5Rlg?uY_ooKwcn6R4p|rS_yzD3yFjV- ze?Db>{7}y!am)Ea)W^J$$aEcOj}j@q*Ea4b2pwjNzFd;&w&i>`Gs&vk?3Cua_VGC# zW8JE-gwWBnZ%k~_Xb7(D=Gi)LclP{90>SBAWNk4Hq$TkWx&3SzVM9n+R{eo$NCZFX z_oB|JpxTrD?nJ|8u8>z^vz6|iE)sC|U&eGDd?at|U~6#m5)}?ZYFV7TEA^q!uESaK z!Q7W4$>7^KJg?rWMnLb&l2B;V$;USyO>t+VpJ`J;c-Q2Gzh5L(3($ z14{f85qG)ZGjIw0R|x+t(x3E7pM)21(2NUcm+X%|t8wo=5|EWOBK71ykXoVaUVw|? z-Hy{Zu7sf0VmO=EF~y3qLmgEd}@H?L8|E78C=uJuzxSlc8 z&NUmU-&>cU<-e)Kd%JSGOO@#MjW*3MAL1K8PAM<`{w+7Vba`u}Ez*vr8y_5ZyY`=#CgZoDH4n*YW#2VSEyhnq;M^(`EY@jX>AqJCglS{r!IAxO)LpyIt4$ zi5}rw+4Ct@v>#Zo-Wj-`z|nO7wDKYy8i3#dvqHM{@pZWUok;&0*yHT<*P44V+9G^iMZ$6`(=uRr`Kp>j)bS@FgT~J zUe+K{A&=`pzxf>QTlF(deu+lW)bi*j8^vSKa&ykV)nidxi&&|}Y#5*AHm_US*RQL$ zs*K^{i$yHk=kL9x%Z#<&xQ(UhbRMc|#o1to&#dxgY95VGvdjM4nnt^;O|o+ss|jBn zo#pSz0o_wQShoR1{ zwP+THps1k0wU80c=~a|^v883|(YD9@zv^OFDw3&jjhgER6DM=H>#^o=_NqZz`vjNe zcsX>NU)Z!=V{|EWqxZ2r9VaV~m~NX8K^yI=)#lrF z-W6)s@pdn+p!6 z5%;kV8wEIXUZq_3lQfs*{sQ{XbU!yY9k?aev={MkekMU!!oHhp`|^74K74<_QoQ+c z@;y#&eLTKN+SpZGhXYo-cyhOfhb)7yaOti0TvyjMJbTfZ5?GtMj)1D&`4~h3Z(l8J z3f6h7Y!KhxH_|=4wy)DHipMU1XL8DPbCS=2^(B<&kE)#f11rF{HMgfhICa|w9P1MK z?Nhh{32k@pLkCRo0J@kt41dpgQn@fY9j{WjHzCMREnRY!Z0ro`TOPPXV>|KSwwrHJBD_q#t{-@+YU#2S08IX!W8hyWhvzXzAOhYFo`;yj-puD15d4P-N%4v2CuMfBX7GeAP_L7PNR$ z_AH+6B^!g7US>4yttHzkhRS|E6Aoryejqq{eHmWEk5y^h8-M$XW_#W_CeNJ1NGB$6vTjAAso$Ni;)Lc?^=_=`Fd71lGc2>OB zn5tgp6zOc9xg4+iOr=F(A|LnEZjb9+8EHMO_{6*w7WH}ZuGf6eV36nEk99x3qAcx0 z<6ZI{oP0mAe!))19#WZ3ll<{*hAl@K!mCXy5@+Xo>fUVMd9Qj$0%|gA=&V)GTta0(CXbMA@9-_KT0*=RrP?$EI;p)9Gcno zU`BjOxGVQ?pfmnyMcUeVFzA!DWABV(kpr!YL&M*JSBcmFvKnS&R>=0J&L@a6B5{8lyb9vGPWaK zHsPsVj#P|fM0RAX=%pguV^ZA6ypXWH%>@<`5;8<~M3z*PG(?s}cElg)9|f!|^iKjd zdRHd}+21k1*Q4S_JkIH6q8LZ%H=>$MNAbkmAB@J1tDK;PiY%uTS8bBgMSmum0 z!SP;1Tj1PZX3&OM889O{i*5aEQ13=j%9ZgB$Q#BHHz^PLz`0N=;K}98P(xbcAd=-S zBjgGJw6MMe2!wfS&VdRYQGiQ9T06i;k?>DK+u+DD+}}$^ffg8de8?%?ag;UV2pjaf zT!i!<@}>#cP1*w>TxQ=gIkOawcI{C^PP4E41zH%VjySHBQWvmt1+x^ApZ+Yq1;TOu zK*kRGdUOXqutiN+4HS)Fd0~Q`@M%bBg@L3`#6$E*hU;j%&(EE`P_M$7bbuoYasucQvU={v134!7NmgcVH;A~85 z=cKFz^$X>wDQ2TtAC3?uL1_&TSFixJk)Sx_|8Z8A| zY3dJkpoI{VbJ``-V^OY0F)vdg%EFBXwM>V0?I>%B5F@O=3J~;?Q-%nu9{_`>lmaC1 z_X5&vtwET$r}VF;q4Km#V?pZ`|bKNa?BisZ;T$=ph+J0bd;80OligKqUq@u622| z$1%>8;MQrfwv9UQ6qAtDqg~OI|JAEjgVk?$3FZz^#4>!ANmfjt1;S#`M&&$+mjbeA>o$E1MGySNdBZQvR<*gfg6g@X)hTJnc|qe49NU$!K~_S$Ys~j0PgvxFS1= z7#ab|QSlp+P~dN&coh2S-aTYCGp1&yhzaE2IJ&H~*&YL|9U5m5aGyo8Q3eTi3x*$D zbh<7qbbyLA+oEum&^>7SrNAzTLQ6V5Y;T@KghX$6fq&Y0w@ z|A9Dr0t-U3iwdygg7zaYvg?DAZMNC8i~R?t@T-Z71PTXp7O*2`;1ZFEk+cI+BC#pE zC%H%cXKfra0-R1pWk-sF-4gUz+>qU}G1q5P?iXK5z`f|r1gxDvez$-J5W2*1%3#wT zXAvsP?k98&y4Dk*Ai6#+9^4qFbFVKvc=}m2e)_gtyrY9u=P68;+bBBtD76cE&ycI92D1J8_kf%hF=q&ieY`j(mR)=)=2E{1p_v*sdU&xu1}&2MfSpp* z^qSBecV(bWgg$6B;p!Lo_){%yni|hVfTPdS@LqDPa}TUjSc#=T#&8P zRK{RzOUC^T!OAae@OIaE`oL&AuyE!T4D@hZJ!CE71i#o_LU}N&h)5-j)Q?;g=#1TB z6>-$2u_ym#9}%=0`-T2uZG3(RpOrYktn$ujmOe)JSfTOCpVb0mu>|85{0Vq*&}QM5 z1M(Q0Zx;m{Sxzg0~4FeZXfM}kmmeyac@s}LfqAR((TBCDX2 zR#r=?ZKl>L$T3*bE&g={)`<11zGDbe(~ylTdxW*C`9r2hH|%YDQBX?>7Uo};jfrtbiT?*PjO&)AD+#4TIE zt*OCBw9!ZO&(Y+AC;Ih0wwSe=(AQ*Gmv$GS z#suI6fx;t7js~P8fYBMke8thc5KRT}nZW2wqkTYW-at3+Bw6)UtOW2G!{|(*b%xNi z{yA3qSV*E8HEBgV9!9*&q>hw}6E4|=<{LGN?~vzam^;pL1a7QMnQoIwYT< zD`@e9aSqm^`oTEURouM)V4Q9GEN2thDgz_~G-s{9x2RgSHPn1wN$Q)_tY5f|egl`p z*4U-9v5X)WXrjz?)WXr_mEe*6BYDGzY$4!6fC3H0wi(%UP(IBtL)l@k0?%Uo*Vs-J zr{anPbU6oY#)b^Y9-p&NeQCn7*)|)nrh{tn2Ce!`DqOU=(Oi7=TYBW$^N4H3HHh~} zg?#2Ke1k;3k|*7`X6fe{4)V-|dWIm~K$dDHOSAJe>gO2>@=S+%CL`ToqOQs&x#xV& zZN!T5lGFIagb^J}I)(!2F~e-_#b{Miua@#pQNp0J)bAA3`$(ERw;*Y&=jtqO|0;Iw zlF{-dt7gLhVS@r`5r4EE?!}Ap=fVR2GJ^dW!g$GGuuEaEi)F~2uYe6YQ3ikTNb;xr zDdnwd**#%{T0;wR(lx~)XQipJL)krv!ybj{Sj++f3PNx^F%NP)I;PS*Otx~f+;Ld2 zgL!E7rBx$k1X42!LhQI1^xirdSa zS_JQp@#dq&zehlY{8NxlbbYo5%ecqSh38ZG)JYF%6gL%7u95ViO?VUf%x;Jbw0e`|1Faz**3 zCFt*YEfRP2pzriBO(+)jW|HVN@!Y1d@)a_K^zi-`n-i-c3?#y->iFl|0|#5>dtnAjASQ&u zu?^}S5wn6EiPOW86s!+_l2*35;UXO+N)&hoAiVyp@3SA8f@X+)xw(_bd~V4PO(C|$ z3V#j@;`5e54pk66W98`iPzRom#w<#-$raAn}gXc?rj{P%87JPZE&i2J6)y}Ww= z`?kaVidfxlFLoBwT>krQSL?NI-Ds6|XEo{J?X>R9%Uw`c<=}*q(E9yhBfru)X;oZFH32AR~R+*$jX|5uZ$Mb1`{?7ePR z1L0LmJk%GCd!p!8_Qob(kDF3eq-Dq3#IsE!@$~r_0@P=r?M63>?@X73s}&LFE73ah z#`cFt#CFQU{>~rSh_xJtL+I=*wv!qJ&ll4i=pK!K3({2xT0O0Xot-}FoT_zO8c6Q1 z1@ND(L)8?AeCSR}pI?9BbGXKH5Nj3B(YM3hX{-eQocCz1Ve6j47lB`>{FS^{95LX# z5@e4(zBPQHJI}v$HhWaz0X#kpie%8+@L^rQY3%wS*>+)uUXXQfS|VoWP+6RsrnV$W z7Ry`(uW6f$gxd4470I|>%tU_q}!d~7}9z}PkDPiw#-d$(z zGo1Lmbu3AJZJVd;dK2s$^ES$_?#Bz1*%Wh)jx%9M5B(wTdUKsLS zS4V>2eSam^cko8lBQwq+eJ|(2ysaks5EM^~(z!l(y}tjx_u5#``Es{iV8kP%=HewL zoSsM;GUDmF=D}O`-3EKlSoF$#HLcyWUzxCeZ@zb7?;nx2R$~t4i}^tKI{VNIOqtkg zTf*DAw|Cw0q0O_Pp=-I@SgIOnc4yC{_PNb<(V>0kIvp)`KyYRFjJ06nd}+nyp!>J* zGC44$l=bEy-4)uAt?EH>92A;yi>2z?ON&6(mEdkO0))`wvD$FCWJR~Hvk*P??$H&x z?rmUHQvgoa^5nIx>d2O4pTWIXUCg0#wd9wm4Ds}6Z@j8W6xHCp(J54LjO#r#a$1<( zAo=Fe)+e3qu< zwV8t!S{C&CYreA|*Fs0!jAgY`w3EfiSgKc_Ey6Z#`$!zoKvXIci8}i>8ttZb^yTBY%Fa!u@uu_?7c|{T27| zyb#Pc`9gjHCtyDNX}<1!iO}NvIEsC~58$0w)53{-s;R9|2o-5DwaW=c zNauNp3T4WnN}UJ0)cw7@^t|c0Fre*Dl>hLIW4z%yV*1tmd({*cE55lXJ7qnC%kgg_ zjj@*c>g`^KP}j!gMRd;*{EfY#Mpuga4h^1b`Bx(^#l5%I-IydgTahQndRJ`r>Dt7| zW~vVpk3(_n#Fl|=od46|B27%Qr2D``*;@*!sbQ}q#&AVk5?eqr(`C2BRB^H ztr)jEta?85irW`$TQ)M)9#ppie$xkNx#mIk6KY#G)n(^bw+wf;7ghJt%>sK&js6su zLC@(AmHgJ&>-!~QWNACa8vPQ4rcq~YT({QOs;*@CaFgYI$J)1g)n2Q%XVYm{BxVN8a{>QT z&zB>Xt?l7T{k@@NdF)cL!wdmAbe1B==Fe!IS0ur-mz2t<p5$hO`F|MdxIID=smB>!`3_5E2PF5l%6*vpl>D<(M_yGB&quM=O$jz>?LQ{KkD85Mm?akq)y%iF7XcWpWA zmgkI3uaZ8^e6?PRc9i>5pU+=-=~ELQt6!g8?#SLBU+YY5l(?Q~UwuBa5YKUoVti{8 z%ybr2@-JrJJHnq#KaUC$eI3Nh*YPm6wIW5UtanTGzEjU#lFtPTfx_$9UEbhfhL`i) zSZsRr`B@jS`WdUOdzqsfr{d&!Rq@%Ln1LYa<7T8gNNlQ{?dIBJJsp$8Xm0xi@sQ#3 z`h5!Uag9{xW)NU|?og&og(CIZ-rnOQun2$Q70F0*;g*AEx#_NEpIYZ5Exp9c^@lN;{(x-e^9|VklfG^n zG4++C_a43JyfwSw!vy<&iv4wj@x%mZ`n2@7;6rY_9>8V2r88?!3-;R-{I>#U^>&|k z82;g^ijbw!205LwIxERGU;n89zLuPr6FTR^a0I;SWv|0&X7P*JHGISbPwynov;lYJ zeW*+%Eu14wc}E%lHjTnYCU;00jWh%e?IfUm%>>*8T=XazEd+dcn6wz2 zbeL2W+yq}$=hB4|9PZuHPa7&-Tcj1)-D(Sd;PBt2q0oT-a;W?N>(5T9rAdB7*xLV3tC(svDGcrpQPSy^$)H5{fo?>>#@%_h3xKfp6V!Yx3om5uHk!tM%z$E&a6dmQwh5m*5I zFJb5ChnEdr?1Ovtvo{3d2Ld7Z{{?~gugVU?{{UwHADctIe`wkNDgQ;KX(fx~-xIeC%~O@Z(f~)0J8RKN{tB&j%m^{4@b^x&TNzvq>7a8PN1mensPQ|9PH)T!r(M zUS^^>Ubu2~G;68!%Egk?1qx^vFRcU=W|&m?bkjhzuRDz}t^zhwdhmuZGuFCX$ zUfhK<`5X!5)PGnZi{w!jfQS~QdKV-{xpTii08#L@@pA)ciK%`) z3HtQSq*dAag=WIgz@mZE9J!IOPDyO|)TraP>d>VIQF2peupzh9C!(zopWQ^^K%(ftZDL_)&3>aV>UaiYN6b z$m*hH{4s^VyFs;N5P?~80D=H1v2o2V=QRl9wf9fZVs(qM_H(m2z*sJFOPa(AK> zbpa*jyj=DvC-C62OCx52^9;M;Pz1g%T+cl95|dYzaoAAk0{JQ|KQwUx*t;HtdI3IP zj&XD(gS9Uiw|uKy`m|h>L5p<9h=@W%57CTbwp=(wTSI{jYI=Tfa3pjh@SVW`0ToBe ziPwbnC%DQ@#9O29Kn}43Hh82_z5E`n2vO&?TZ=3-`HEgd<8*t%l4AOZerbKt_+kK4 ze=f5Mmq69u!7}QY92{z?0Aiz{yjA|A8gtu4ySli_-3>mG{ThL-4}EwHQjh_b%;j;G z%+MGBD47L&4tCjY<{1&(F;f9^reoBS-DvEr9R?E?{8$|pJf%p#IUXE10Sh+yC(9>G zU3wmtkx&wBDa)V<)->rpb0+3om~mTWC9@GFKA>|G_77A_0Q=`j=2Buq-$>@t zsoJ>}X6jzyqms_Kxz-N35~UOQDJ*s5f9MYZr$#Q|9HMd4p7SBKMCKj za)MI?$PKMf%n{x{^auRq{vwJkhB46VX|%}B^3AQ@Gbfaw0PZXD(c{%sYroE#^rX_njh`Loz-2kg83#++FXd-;u9@ z`R-|NV6K5Z-OzjWB4jUsr8Pkrz=QB@Mpj6zY&)^e?2Y^K9sf?*@t8AS4LZ)~(aoJL zTh+=2-TF8z8AQE8gUknlV40$L`UU=p2o)yBW%VawwEHJ?t47OC3qp25o=yC&np;kV zeC&wwuLMaQezpIPod)>y%_!RKXf|rxXS7)JkHLG8*wdfb3wPXA818y>cOyCy6vy7r zY&crS-meJ%E<%tZnb&I?JowphanhoN@VqbiIG~YbY!Wi!xR3wUF2qFnfUlT z@%|YFH5~Z@?*;t#xCHN&OE^md|3TW?A8VG3pfH_qN(GdN(${f?xKR6sMEDlF=sn$-hR%U^1lK?cU2wX=!CdQ3F zH6*n^%!hb<2+ch)F1K3LtKT1bmLKByLDn}!0>6JC51`^J@O((r`ko)+J3%-%M4@J4 zAEW*Y62DHpD-CI?H(ZFZPhz0}*r2fZo|hN+3Z{F4IYeg$WM_ZF+Vu$)d`u~wG&12Z zwGR6EnQcgvu>yb1Hk1-i`)$tR^VX-gjYOO{g$T&l5OOvT&CC&lR1AfSLjQctpa^1+ z&MwIH0}3ASV*(6yO-B`kNEh{%D)d^ZdgE(LKz1Na{fMA?yTbOm>h@C8FqHu=Cf-96oN|_oI*#ChDi~f>u0Vfvubs+R1 z?u0Aqge&8e48s|V=!AP(crO+(T>zRT_S63DWCEHs0?kPUnxzPql@u^d>4_ySV#^~J z?eXJkrw?3Fu`RJa`9X>Q zcUvQWIcz8v{_QrzeA{FyMf^yxy&EY6f`URb2{QKr8VS^wDs)|~odLY`TJO#=<><9h zL8h}|5nGNIufv%7)q_qYb9?C`rF&zz$-%R6&Al#rQR;$$`5lV$`zE zF^A^F$tB1QQ@#0EtyS>;IqWJkPC8jaDl>!?j=@rT1oHYM1MY%K2Qc!tWr|9ZX!(0g z1eCn!KW0XVsE)ye%E*i(tLk)6=DuGM)& z-=8%ZJ#yB+Spp?Lf+oWmyfRGQOf9F-vmyy%}NrY5yl-&LgzWFLYb!{cKb5qLG~Z)!|r6dQW`b);V{P^G*0#$G)Lb39j${MuVlNgXgk{B zOiMde>@b!AV70yBA6KK}9Uq;^&w#*p5r}8Wp!e&bcVoKlyT0c-;+REBx1qkAp4b=$ z45uegPql~?#IoNvwT<8`)aUMG>H1IRvvw|P;sk9W-l=aMxL!iONXDkOTC;n^7#2|tt1m7)s(c#E_+<{1l2~c zM=Z5%e_V|+ZM%VoTvcju2{9kQy+;?jU!I5ohui^=IE7!;h54ER{3&c`E5GPGqm$F7 z%1BobAAuA1DwMD1cWwgTPgVweCB9$QHFYIMzCYGA&x8}#{CwvkqRrpw_`36AWh@y_ z7u-xaq7)7!+ok5hpa~KJ!=?mdePlf;BkZULtTCh3AGV z3_+lq1onBU&jU5~hdF-trP{oeSEE>zc*FNV3-1ThHOrjFzBO|qH@gU5dWhile3@-D z=MmOmTAV)eEVYPSjw3~~pv`N46v zTa~$4Ll;2E)pT>;8@guPx}~f?`@%Q#nCY+{pA~I2_tk59qkTxP^DoZgJYB&zNt>xW zEH6@eTX&a*5(D9htiw~N(8|EZ*7KA%d3IJKP7LMvGjFeDqj-BWlTr@5e4RfFwii9r z)71Wp<#nw{xisIr>gtMlbOOlwe)MwX{w=hY6rYHDN_?NEN_U~Tw7$3;pXqB+6!Az& z&G;SjO7UzFla=}B>?FsU@8BZtsj=YN`f5rg@u8Vyssov|TGn*rB}jb;0YvhB4u=Hw z4NO8l=2o#I-zJm}&h>$bj+Dd)d272aHlC>PPBZImmXAIbxPEvTV2w2tgVAd5XXrE0 z@BHd*GuU>Oan(XRp|(~OnV|#6g1Jm9VR&lO7L9c{9n(0o?gCfk`}JtL`;Z(e?VYd$ zfd53-rpHO|sf`Yto<1ILDt+4|eQXAkU#e&J#(uLiJ=}l~UA9YX>{H3;qplU2>JZkze5$`_w!^-dX!t4Bl2>}x9U^xZATwIFyb z(K2Pd4)|-k$&%o~GAH=W@$vQOnRR!Pc6=~_5Yr4ufyes{Y*IVHRMZsfnXCiopIb^_ z;+Phh{x`<%F*wpNTo?6BII*osCg#Mp*|BZgoY=N)+qP|VFtP1qXV%7AXYD$t&X2e2 z>$kr~RabRY_ube1Sej~hgp5f5k6G+&%4^YIUP87fx`Q;1;+*%tv%k_fO5E0re3!!C z+<&Jj_jpx3y4vQPB;iMYS!dNKGYg}+!!gr*oC|Kf+r{yaf8eT_5vF@6<#CG#Omiz# zg!04yfREfx9BxuP=NNcf=FxKk(cQ-MBumrz^xo5{i9UPb$ZwC+PjYb(_`AXKDtId2 z*tgIWu6anuI4lSKu7T|ja`XLOKSj$(?c(SNou3?@fpC<9-XZm#988V`f%~l1Dh;g+ ze}mgj>3VM(Rn|(W1ie$z-^K>JgT=g8%gpSRiW2|U0tKv_s$idYh@S7^WkNn~)4K*f z8_vpR#$td9#6HWbu@+K{2Pn_=yK(tn4;~=ag@L~DCZY5nvl!fpw_os~qaq@&9mM%U zF2~${g~@gjnW3~jfLW)*wYtDG>z?;>mSie7b<$arXqbFBp*I(brsEEs`$w|cY%Alx zm{M6!Yd&&DALm=4jN6V=n|r}U6C7&^t<_F@Soc`{{re}}b?ZTR(tbmyM*rGq!<)5H zfe>GZzQ&ls8g1~ox#Yzn5Ar$VGjgny`Eq8o(S*WE=$Xughf5}riJjZvAmM%Cvq;8pKF1c`6>j}!U zJET2kt}d%)Ydr4ec~0r8Y-`FTk}T1Z=jf>07@mxpJ}ajz&1SluhQcm_mzqgy-J(PE zJ~9s8lJ~g8z;mf4n(D?L!1)6C+6b@4-aRo(w|3?6dTFy&Tr|Y3V|Tsmkx-DS^Re@5 zKkB)}=$OBZf8Y9*)ibd!M@c_)-SvU4tnuXlq2wW*ARrsZaV*#5s^3a*>+a1T#DOT8 zR@-Hn-rKz1NbF?^CaNnFaF|HJ?=_8I++_)#*i~L7*K|RMRtB7o?dU!_S>sNW|O5D<1J6ju8xIH4eeJHPsS8iK1 zHa>=Izdah4OZM5|8I`~geZM<6aHQINX?d_a@8jZ_{FLuYL#66GS%2sCdKnXd+_whC zuPjM?F0mS@H8Y~Ws7kEvr?po)%JL7~GGu$cR2vjVhp}oLZJSWwz}YJjyjxqjRrn@O zb;j0-uuKSGSmRvZ4PUt3>0yOvX6fe|bZda+7ipO8rPH5h<+9v}LDcM-Y@FHINki4e&GfY7Eqn^OLmC0m-Vu&9r8coI(=!*b$OC9l=1I>%GGIDj7z z21ETdFR0b~j-US%iN@SWnnP*k<6{cJOM{noMXhzp^=`~D$TR^j zHxcb>tvNwwEsXXI&R}u|6YK$P>--CY~$|MsjIw)T6FSZUuLv> zByG3vQu@Lua@K+69eqRDUZMT_=9?4i!QY4P2FJh+b??W9G5N|!3emb7#o{Li-d+z~nFSohwWx#Bl zde%8;V)Y{4YfCr%%SIwyb)3WBLq?8li5LRfq--9hqvvBpx^?y^w$rr`i~)FoWU)TTy{1aF^s}Gd!mm>RTU%RG z)#;iM{McN*SUqkzG9qF&@(NK^q3D?_eEwcNZoj@hK|v3F8WWzaO*Ek1D+Wn;b+aq_a@SPdn&YEvk_KD&sU}7zSK>bU zxG0XAx*-32iYU_33-Bm`{Tttz3X!?oppGm{Q7a@V~g593{8aHhz@c%(b znEq#&@E_Br|9jBzzi!j{uY{!gtAOg?`foYI7a?&(SzHG6D-%ij*8LdiLY=voNi-&J z_|sTeK>gj9P`W5Hf$8`wIguO7Ym)YZCjT))Agl%!i-)TA0R|>)sKCc3>n!x0A63}s zd5a-6Iaj#*){aeQ`l|!OP>vN29eSE_9Lhe*or;)_mm#_PlOz_v!$Go4ev zM*Y#Y6SEC*JYn(xt*B59d1%v;!Ftg#_aOP35sm6cl1Ad_qbBZH+IBu~7}W^$618S1 zMQ*HDFJfT1P;WM!gCmo{i~st)t*C;|2n(fHGhK`hfh+Ot384B%R`_~!2L z133(0abj_1QGFL;vHoQwXQ+o-OSzrei@hYZa2lEdvav(Ce0&G7qHc3P0Twhz`j1~_ z^d=<^pD6}P2@R+}6c;H`{g)vziTv&~aH4i@D4RT?3~^!2&D&@6Rmz(Mj(ZR#G{Y?68s=8-duX&iWdR2fna0KWt10O)r^wgTN)JXXL5>Ari>Cw9waCAVxwH;fB#I`9IJPKO7j5DHyV{1(vP6iQS!D%rIFeOp< zCMK!iuJ_LBM)shfNe}@8ZRr}y==E$vYD1D8i+&-7pxhz4Ac2HGM?A%d`^68GmEbL; zNKs}{9YComN53XUScRmH36&Z)6t`Y`SOopaC?V0+f*@ijnsTBb)P`?5I%kdr zV18Q0^8xK=romoN!eYP4)^D3tbL!-2mNj@Lqy7*=SNmj42pQ|+XE~k-{f#4A-OnRp zyAs;~2U~-l6j5wlr=4Jp&N{{v9&eHv)FlCAtbqATv<#U9q7bmMMKuRIf@pZxA$_ciGFJHx)8>IkDbZ&O}3P~iA zCAxT%hQn3J_m$YiJo(?GBNoA}ENQsvL$l@xVFa>mi++0FWTfRHKxq|G1r25W!ima) zzkT{PkV`PcNcWr*vKmnJu%Woh$nSDx4Lv_IT=_vs$?j^F4Y(17-x8Bd#V4`>>kqo08kD~B{FnEe?U7zHMIY9O$1MK6pQ^E z6eSU&HaM81xxw;hIi}F8P}g17(u6##3tz7)+=VWAKJ0|7VVfJeBR5o^D@8n?4Mw#7H45mZkX$TH2 zrS)*Aj}EKjI^$G96=+vI##tW9m1H1ORu1Y4MsWgh@ud_KpkzWVEf}6catGWCx0`b5 zP-pIJV@&O^dxn3MMHX(y|14plkhLnpwQU)KikWt4&19(&M!Cb+rGk6$@%QVJhVVvN zkx|?%>4H#nF;ztADOPkci8-Og@-zm?rgA-}1=UIE!3VFVL+f~8=y;%TJy*Hn$k}$1 z?LDOeudYVxfTwW9Q@-NJ+rE!%;a?RxpQ`2e>ALO`ZmUJmunQ zGWZfu^#G6;;dWtKq)5Aoyos$9MP?DVs%yHp%YyN(5+);H$u+j8@ikSC+^a(!BbS9a zHtBYiqMz!6pHwZ~qUh{&$2h?ib7yDIM3jFJu@}kK{$crTbo!R~-LD-@Swog|M?=tN zpB4xB2Z&f}ljH~V4|#`FBx4c=nn$3a|y zYJ=&w7EmV}t!`9<{bJ#_Xh*k0Tj84?&{acf4S@-3vBN2#j~SL8FkHi8jZ7&BYK_d& zKWrP1h`rP+-DjqX!3tHW$Gs3SqAhd6RS|71p@(@vwD>_<_E7U(O;xbM&%*m5Sg{Ey z7OuGN6ZbzTN!Qf{vS#j&ts`}|EIM0N=<9!!1hU9wp2=U@7IKtn1(g+wK$mfYIN?w9 z9;qI|BH|8|4NrOfq9*+rxRKSC8MLN&A`wKLA1BovH>y{DWAS4gT7wXR{|+ryY0$b;cZu^t|{ z62&)ICgo5q`mOAY?^kuBTHm;%@|MG?iM7JDFLm#5{pw}WDcrrv_~;;vW$*47CJK&> z0&|8kX$jnv{8p*TXp|SudboGi{*@Ujf@mm=`xgc|#<$im&f_3`}%?wIYXG2UqR~R}h*C*9t0KKQ*>RTUdf(4h_`;{D|9dS)S-GV273VN?fJ5vi$!+5*yer zNCL2x9tix3a-a(DW-uhu{QRQJZBekPg44xuo4`VYfpq^MiE`eRPF_uW5~E994*x87w|&iiW68&qB$S2<`rd`MW1$mVr6 zV6^oHrj5Co=h_7}eueD*V#lwRhu~+65qBFSWZSl5;;LT7;e`^3jGMfeGan?c%!`n) zxEa8wV+z#}jC3?!HfD~{q9WbwwdS}Em^=R#y3|WKW)AZ8)6=_pxqCG;Z0Ny041c^8@^9M&^;iY<;05()1@&+T{gJ6ALd;V^nmJVhD?9#L zdj~;zfy9Rwba2df=4K_KX~HRI!tG{N&GM4|q!eOiC56MO_+`W1OG}K`SFeMhg}r>J z_`jf??pl}@7VGC^5!Ok?bmo$Rh%}+>!!@*~Q!Kt(sE{oqg8kf3aNyF9Ks(IJ z5OB?B<(g8piyQElA>kV*?^DES#0=H*x($uRn!|8$ck60JSDTy4#*Q_ zU{+k7K@hFcu=QxB*M|}*`3PJlaI(3RBzgp5NoGW~Q~lwU#T_8R&NGAsvV1`hN(>8l z@I^suqqbr_vRJqhAhHmXWwCk#Bewc~)wsSOi4q9-8xjQG_ChnZEf^Frg%C2w)(_w4 zXjQ~0;!%#U0z=00^sz6wM!RlLTNs-IPGQ#0lbU*D^Fj!qxao9R+LG=3c*jp~S7|8u4NlK0;?l{s4b1BFO z+HeQ;($ejT-Z(ZZp9zy0W*a91@Q2;^KCbc^`>x+tAEL2loo+h2&_ot|t}@EaT&$fk z6V3D-cHbSH-ro<;Z_fOz=s%XHt(bK*2+pgQT_y_@tmfV|E@Wl^-mT$4*}L7d{aE^s z!mf38FK6ak4g1O3aQJ~&jBuDXR?e0F&(XQP9J^@6&ySmMD<9mXGXx7q@2xiA>(jLM zXQhijkJ*P753q}+9r-;uyx33+Ct>$8p~lc*p~2ASs37YhrKX^Vqov1P8*kHn^r~?5 zgXGdk#;Z&`jMw_GbIM{1aGibR+H^cyp{S&-&9UG`#l_zV#9_!cQ!OUH?;1*cTktJH z_33YWpFhDL+g6@zUC*<$j`Nw?UHw(5(4V?F7dT&}ZS$h)4M0sh3E^s)V5&~l;n;V^K+YlHsN#aW z`+7`FaK|HC?@A+nexNXVaKSPfFw7f#HjAU&#h->|y~K?VD{(q%u)efJOa4x_w&`9O zH*#Fl#%A%RBuhbvQ0rP5zL&z4MNB)}Z<5)t;%MI*$J5Ao885r+-0)3ct9dF3_HaK} zzHHjZr)@Z>v)J+I(A;BGu}-eOwl$MBe>7{G>VLkhsyiHhX#qk2*EB!6f+eR2YTXZv z5tiTjwi>@as$4FHx}Z3DZf!lPBRurp>+vt{UN;{AOKz>^n_-_EtCQpB=AD1Vf%V!` zKhde5j;En%k23>{1$gdk0hOsv56g-8Q&$0p10^^-7lYO~?cS_|!;KY*>BsdiElM~C zlkK&m!^|m24yOU*UUG4Nj!ebQ^w*7 zSLt?dueu%XC+1wJ+9e4AM?s?Z&cG?UA0*Czj^<`qoWAZ)!{!UYRbiph$q?i&2EC+@ zPJ-si60A z4g#^Fx4b>k9%?()a~~AW14M1QJ+r1~gL~Q+_tGerTRdkw z$|k@xJEzWI&gQ}Ywz z^&mrYXKzTTS!U~@Bl)x!uEKx;us;Cea4)A(Y+q@<)S1DaQHikkJJ082K(y;~VDA0! zIxO`Tq2u`8puMvWDCYC`Eqw8U7ytH9;~-)`1lYY5>2^K49=*8oNnd+T1ycnedw6Yb zo%z~ZzThQXu?<&XzFPNw;|2XRWqZqo&5tCjn1ezBaMcci|7wfiiFJk5dF_;|!_>in zS91Z^$se|23$3O}zTb+h{sqtU45dYu{cgT4yf@@FuaW(HVqu^g+wVaG_-b)kNjau_ zvk;v^+MAFY)ct#QKE&&Mmd^M%BDa^+y9dBu#-sOMx*CJXeGoU`AH7fbPSwrgEYbbe zALCaY@o2ALY{THeedIc0YPG&< zSGdNr_w1!&f-UfXQ+=o5K)HAG-3V`a@!nEgTEqV6j%V)_Z%}O`$7|@ateLi*)1~^_ zIg^{@vtD!<(7#xW>tb(L*Y@KO*l5Rb(r*Vh)%`9hF&Ft>@h)U>*kuI-uBBI0rplXm zZE5SMG*)=8Og7Qyar}GwHu4us8MVq4!a(%cwwCiNKac!O?aT~Z7 zOo28h|9n;vS+Dl%TQ4yfW<>1nCB35!aehAlsck-Wh7V~{aC3zf0#-=2z~+7O+PnQg z2=;$9E~HZAYBKR~E6A=rqtN71xodQNtewZ)f!OnpE1pjB7YKNzYl1C8B?0KR55(NT zOBKZK_Z@AkyfSnCUCPe-aQiH{U%OtmsoJt>@fyBv*7kYi&E|X^9oIdv*iaO z{e8U854~GQXLr#1eRY(rS)ZhM*1I#@{=7F1gWA4s%FlLW+rI1d_k0f0ID=rxe4Ys_nVN>T ziu9@WyUi35#6+Q4M=;8f5Q4p`MVM z60ZiXJ&2f{x_bboo?)P;Z>pzfZE9+0Zf0q%Yi+4#Y^rZ&ZEkICtg8nBmP87Xgg&%w zEHh(>HB-|Fv67gx${)ZPJDha>TT^D0hx)6&e7lf0en8 z#vfKVe?5T9GSFPX7g?AEEAQ)ytWmgrydU(JW^jdeqlW$uhY`3#^>>GiNvBlaI1x$2 zTol_(;1=zRZRCHkjgmj+i2>L~%BeXTftnxr&tU@PQvVbNrNC_>$Jh2h+Cdp`Km}dN zKt@>gPi*-8huDyGERP9hlpQC5k`DXo%2FNy#VpBE&k;_Q#@t7~Q3Y35G*kF89IXLd z)V}CSUp-q$e?~x&OuOcCC;kbJR)tXWMIq?QKJw(4SHTixtfT*V_6E3s^eF-?c^&kx zw-`>&5*A@hdBgv0HQM;U9z8dB!8CFvU zk;f1*IT0>T6utsK5Bs=eoSV|f;Nk|EJ(Hyu$Ra!APf;A*IN z0$diTB1F|In|cbC^d!Pn5ep+R^q)_Ro2W)ATqCrDKePvr<0e6nZ#!g?1!aYr&-nWW zTp3|Q!|Ij>6yjWDHSCE4*6?P`}UscT5s{#^31iipnYI=g<;IC^49YSVsv5|s@CUQeT($#etJ+gu; zJ6HA)wd|$%p|rieBO74>Cb;^JX>CJ)S=q}OM`5u#wO8QZ+N(f<=5;FI(gM%(`wBa_@_B1WM46Gf{{9b8_UsDf- zM>XvCY=_bHSX58POj?PlR8@N`(&QSvz^$6m&00tzX=t3lf}tNMRVv;+9fcpj06iY>wX-7Nt}T!D773 zm_?=3s*ZZaN>vPgB_5lU9vdXc6K=GjHJSOQYBGq>Lt&n031UD|*)oirt%jj7>Sb=p zdr2fw{aNG7dnj}#t=v+T@eaKq6$KKWe~mv@k^Vaz zwZEC1at56MdBZUTj~zn|1vEoHY$>>g>CDl9wk>)Kw=0OE6~jaZ9HiQ|E+swmHym_$ zxlT8RdHKX+kH6m#)f_(rQxO|O`z_VksmIKR8fi^pkL>j=AaSkf$WtehMlI}GRy)|{ zEUkT2(ij`tayy_8whIrF_$3n-v=ddqB4SdGL?z^qm#*^nZ4oo9uzfEBjWWu?{(7~ubnEj>dHp@7A0)$Nu7^bpt=>4U z4&xGKSxCLyzw1$8&8?S`yB>pzk^@OLC<~qxQxI}XNbG|s8CC~^26OjvG&#Utvj9Ri ziaiyNHKj1NgM$845af^=bgLM3%LI3Lj6DS~W1F6_%_`mDlI(BQ?00j1kO;cXU^p^h z!$8hZf~oku&85=srmcuLLo#yAe`N;GR2gG%1ZR<0TNf5?#a>_;bbBoZGq%mhze2m9 z+(tg8o_wZ_-;8$%F#P3#s%gnbk3_3S&J!*d5oLTtqqNNc;~6DF_JF+e^)Dm*EAWF` z{g`Pzb-6>-&*uIRrfMOdG}~Og135?^h8tJ zb`LH4+Pltxd{$oH*A6X{WmF4d>jX1*@hm>QNbQ}53@yt=R!=i`b zWnP5x^AQ}%&VeE3vyK|;Q+V7YA}6b`^rL)tDc)^p{o${TiDzab7S zz`!-g_r&odzy{TI4P@H&w!<>IfgShh>G)=Zfrnu?cSp_KIVuaYaoZm_*LFKMKL>)m zwuTi3PA9H>)xLV=2d-TZxzjNl^?FZ)e4Ny;yI^8pk+{N6(_q(6z3AoCb*Zs zdS4++ZBLY|k7T2}w)9Lz7NBTR*0du=)O_H(rp40g+pVVuB==|a5AX_~$_kL~LE1`+ z!0Nzw%apqj{c7Z*yy8WF!WiFhIq2!8AqT^;7{v+PWk{rE*pX*13;I--b&{8C3^$J< zN~CiSK?Q{tVIIXpqlf+_TxOscj#`=!n~GYRCTAsI;3wiF7Hf{ZNFq#Wff^Mqw_0wN zC3=4)ja3FHGi~bjt>-r9BYXI5N660W0atG&X7wxOoL7)~I~ctJqgO+K0d*fo2Zj{p znO5UFJ6O70KHsA@x^W48X-;fVS`5)5sJTR#B60~9g}hSELBU7>{|JIaZV9-VdAO2X zur-0}>9V1(`Yt-pIT2FpXn#;dbX8u@#l)`>X>_W5#uZRWExct4q1 zp3Uf{&*-kr=pKghvB2`#Be&dh`hYNfgK{)Sp}aNOd)t@$5Nm@6Njm2pk(s#4MPN65 z%!^I*oE!T?kwX(6DKzj)9N||neFQYcnXscht^QrdEc17?(<1jpm?E9lF?W=77=1v3Y zu4TIYtu~9+6;$i0+_M+w+3(Xx^~%DtH~qRs`|3^3_%WxAdIK9e^MbIeAqX zi(@FzU`-A8l%I6vBd;QDrxzp^^s}rL`k@g#Jfu^ z`)_D7{P}-ACc*EnH|NX&ccJ*!!4W&oDM9PSsFl+famw*CNSpU1v-b`sFW^q^aiS;m zlpA{uFJKrKDw-{1a9{mffv?OM7p$5s#jUo}wmVt-ecZ!=uQV7JZnPVhuIin9hfdep zX3Ut+ISnt|ND)a?baD{hzZtH6nQRxg?~fTess&$%Aq|zI4p}&A^8!g!9t66Ns+J?G zy5*OFC8uyxJRv0?~}u}(`2cDEs82fhos zMevQKf_Nz6a8A-(%mMMW6!XaurMWSNetLmHV6tNVoiRZS(dzBRs(-8DWVtsC_UQ=T zCix={bn$)bLqLo1Z}veJS>+O$eG5^2PRdUNoZ zq4Lon70rj597UjVZYH25m=Q4mvbjFjSKK1@)#2=Nn`zL}NySEUNjg*`5&PfA0E_T2IZFVEeI7G8pfgj z8X<|9XUC*EJRf6e?x}!=@)iXJwPH8|dmUq|H$dSeVS zt$%#9!u~dwzpjhU8vABatmPaC{4)lbwGuxJg^)7z#c z@h;tSM_`T*+wMtE@=?2)%Un%z`abyCKuKrgz;Tqscx$N{-Sts;c)6hN&bgXBcNn|u zvypksYv1Jlsf^+NN}~Z(bIR9_^SL=%Nitv|ee#%ACuq7>Ciq<47(ZtG2)P41U6?z% z8|DE&rLs{ti=6I#T>onLj;hkH(pZAK>tn3zT&ybbWt{2e_Y@d@~W4wn6 zWbQ5}pFf%!t*}@20M**utay&CEpkUrh);+8ah=2F4_7)VNCZ5mo^hW8XXY3W@2F65 zZSc>>?!vshrd_X(XM~IVI!=ovXKuA04TsT>(Je_(AvZdBOrN`sxCMV7vl9sHe8!^3 z#VU13$WhP{@EaV;pyjRn_l#*vzo~hg=Pz+8B6gdvW^|N5*NAnZFB`-{w{fILtEp7^ z5vpW$+qP+eA2rK?3mXKEUM_V}4s*JyOIU`1MVuZ)q-j;gbWlm=5Od z*>g_sfZo~~`yz>elt7T8jc1A5#hBhOv&JolHDYa4->tRTbeg+~|1xRGZr zCC2%VUh+=nKX^@n!ju~8;!8(eTynX65?s=BE!<3ngcEu*Aj|`YS>irtNk&?SyUL%zLW+;+n?v8jsiH-isgiHY#hSo2_f}|6IP63?AAQ;oo9E3TryEUM z;(g=cz(5{E7d=JwVg(-Aw{tepU`sUWHb(b}!|sxwKz5tKULv#(r|(tO&7+V+*ovH8 zj=>1HA8l8=+x%$Tih8qUlO|rab+5z#OB)fxz074eG=$aHVE0k2ps>x=?0q=`IKEf1pb}?^3+dJm z`~WBxI?bsF$es-w0fEv^I-(31=%IqmIWcaX_}86{6&^Q6%_^N z{D%djF?$!=#snv9M&CBt+;^cV(6aNF=Xj8@4+%?p*n zjM`XVN~il+n}74*6bF3;Y}+2r_zRr$K8ckz$O|I3e#4|XZ_bi4H&d?xf;azgJyp#+ zj9Ix(M`>~Pi+t~z$2dnJxwm`g}fe(*W;5fk^RK`jo>;8R#lAQ9rxZrY` zc?OVlJ&Lr;F##7(y>tHw106XKh~12RG{GoDvsj*M_c``0J;LI52RjXER&@~G_??Tf zVcAamTVJhzGVtP>ibV(MV5cn5D_OodROr5k!px&lQwJuI=KJg~-gYRtdzB|)jl~{9 zRHfH73*U~GuBme=X4rAnmfp=75Z)y^t>)(yP*VqIe{}|K{6cZ;fYi7ml`spNWkE z{Z#Fb_7p$pw^7OO$4-z>6JbW#94}lLe4ga+JV$fPsf)?sW93%eM}w z(w}etT48uF7f5MFlBZ0zt?M@PHwxvI<~Eq?4SvSXy3?jmJ1)zRZv%F@8b8N^H{S#F zru2)`MT1L51_1diuxyHO;$I=v@`N>Edm65EX}MC(fvJI-kjgK%s@;nz=QfCBn6a~) zw92;QjDzWz=4zdlmvq<6kEtz=&JK#aA$tOYn}}O?z0Y3)kGu!)jPvvB)Nd=DH#+XV zbjG)6=sffqGixxOQ2y17Vj^l!$rtVjm*8Cqz0Jq_@71vT>&_Mg7kz(|62 zN~2Xtk)TNeB5oMiC>8h<@wjNT|z*&1ilyVT&I;d^BX8fT9GG zs=k}CF+)tL%jnk482-#GeEAW-KW>-{#rqOvQ_V>mzK@xmCw^w@9Q36G%xW4!~+4fB1mY;5o$r>`fx;) zc}Eq(V5H&9K_$BSPl4n5;GRs>3~$JKb*k6+Xo9faU(aP59fr?AB@{8LFYKXs@Oz0K zR=K%EzfZQ2u(9S@CZcuRX`le4LB<5iLl0E_?st#RzXj*|Jm@~hwft8whVLLiy1 zvax^b|H`i*T~UQ!0ZHglGlG}g-(e}h1SY=`eismE`G(K0N$Vo{(6>*^*p3kN5mgA00tr!0wsx*Sr7%PS<$E`U2WK|EdIkBEkQ4SSTax2u?4pbldf$8UfBMCbMT0qe%xR>Ns*pKLXmis zR-~5S4)aDV^GtaWJC$`Rb)i{J_JH?O3aQVk3}^MP*la#WMwqyeeA4hvLwMO=`3>~RLWGvqK{V9NAq|W1Rxwr-%{7Sl zB_mipnL(G!)l;wW5XvF<=y9poYoty};4mK;61`io`} zZqKp;1uwjAp>T_8S2cRxEWvlyEXmx^u>uF2d`NasRkEK!aMv}MGThM565P2+-!5s$ zXs}}HdMPx_v|gWRRS$!5C9!uK<1f#^{El@}(IXy9%9)I?hpIWUoN!}Voayky0_lmk zsE67~0``89yFw_~LF&9!BTxrcww9-~7>wp!leSMFT0y>{T-*~^?e>QF^;?CmpBMe- z3tG>BEld>#KU2dyoa?T+u9A|WpSb<61JgfFM06Z=TGk!k##q2f0sf#9wm%Co;l44# zKt6M+LGQ_@?zf}){FFa>fn+2yhY2oiV5Hg5@9UHqW*a^3nR8|vNXlf5Z5{QnYg6N9 zo3EgabxDhB?CR}+DKc0NM(1;A*%%|MhaHK*Iu0J)VK}(D5@73tB}m3np<&qD7h~s; zHkKMsz)BPuji3OGL?GuI1wF_Id*f-=8&$O+R;g$i0Bp#?4Rw4Q(Ptf7JBcQR{eJB=h_np-%?;bMOMdMEkS?X@8%FB3$rim@x!{jhG%Zw(+go=oe) zxRl9$k`}ZZF$be{0pQ4JCVrB+>uJ!uf61@4NouSlM3A%a;o>W4Q8q$y*A&0;LjN(Z zhgckr0F`-kg$h-(jvj|^#`N0)w>IJjq9fj&Q&Q9S14_yumsPq?;Qh^2TH;GDpk?0` z1KTIB7~!h91Fswy-vs(Cs)Lq!qL!1fwROx~y()Jab^1?n;HqtyJG$ntUbOp9A>pbC z_Tkn@Pi*%g!Ct;0kkEXKeTmU0iVOQ6guP>oEm6a^dD^yZ+tz8@wr$(CZQHg^+qP}H zd-{3anat$Nmv4SlQhTjrrz-o$u3GoHugkM4`M{0miVk|Q?~oB(AU?Z^FQK|!) zGQPBgu2MYn7qm$7C=Lk&B!Tp}p;9_CMRI&NGexF6%6w6fNPdwxM|4F3X)#MhNakL7 zsd={O(w#Iwg+PZ{b8k!~PlY&|N@Q7FMy^N5*m6mOzSMKBAoYGI>Ll7+q9`TuFqUp3 zS%hKsvXuikYd+`q84vZ8oc6o`6U-8k^f;JOE;9v*9MF7G6+eu4B0``ap4?w~BF06) zM#7~3=2vF^mJS}DFQXX62YQbnRl)4bjNWPVOt~Q*sr3&_2fJa zlG&|>XRO{1nbRusVB46a^@q;I!k^HUqYj78hr&^e@l}|~RG`UJsL51JWvbX^s%W#+ zv^nbc?U|mi4V+n=(6|Ok{~lUd@!(k^+7*F96v?uK5CU^XiJ^Cr~C~@t}+*?i(psN;cOU2zndN{ z742F@zdK{zXDWkY3ZI27w7||NuI$lM@{X_Q5mMGY)|)EP>;BuRd!#qDr`K)r=fW9w zSqpZV8+O?Xb{Px1ij6}Jid_wgLoJeB%{Klxq=62_?5ISRsRa6`9iGO-U*|7S_ageK zG5735;1^gJ%Hm#Y`5;yM;Ei3VDh9UB6pl|SS62vER|?lBuIsD!^)2W1t;h8(*ybKg zQAt8+4Yhc40i3usbji$EMHI2H3)hty(HS(6@`0b19B-1yvro%}E#$K6TdbHgaWG*h zGDP)b9hyW)z~7ikGbY^35Z)5|RYZJT6%8$}zJh8ceJtfwnMwzU>XA#eI|v5J2IA7V z=XoShnRe7idI24k_?Kv>l`R1;gi|aP3RwZJJ8OrV7ndEaroDTqj^csIuKsJT?WCXZ$eUVF#TIa9X%^=@O8mwM?_D-J&YcEY_xm_w?SX z>wMt%O|d*WcK_$?+A0@;k4LAE`7|5dIj4^d5z8O%aSPSQ=$iLx`6sT5cOK<0f7A2W zq&6_K7Xg_~!K}7Wm?55l@DC)iYk8SX!>qOu4trU<{hY%gp5buM45Sxmx=Pctj%l8< zDo_#Lys8|spgD^pkchq+5n!J^aIG%g>L}6FM|vIBkUL12gIXtT)f+{s&ioyxrJd*s zU%4eqr6*3&T~Og&(yN|fgx5qOyA8iSU2K0YZ2wMde@|@xUJgKkfEfO|JR*E>PQW8z z9RK^Gb^=p~srLEe{D8dFkchT)!O@(?&?67UZf5j^fRqpkz_CYo|5tVO-F^My;jiL; zA(RTaU^)M!kP1LLn58i5h({1Z^HG9ie3}utKMrOMC$mb&>Xwct5}(Zg6)~pNUq^wx z9AS&dQW$L!tM+FEy0mgI{tZO@+Pjx~@0SSgmse+(IH#95AXjtYUU>Q8RBLmF z@1N@%sXaEW3qSWD!iXpqEU3=&)gllf)R85u24&G|0y7QMfMX5p>s!LJja8L-rs`er zX~tW?JQE#I9w|@okJJ|wr|Qd+zmse;4Zu0Zex~YpImX`MTm%4@qF=xbBoL1a01`z2 zi;()h9k;|d@misg1px^le;>Y)OtT|0=CaAo{^S$^i6P%F7u+{rwkgsLkBfWY(wXXmmEBNhRMYxtM{~ z11${dMIOGWi&jWY=mQt&$*oS=u-7^Im?S~aNH>M^>9UZAf1ml?cXNzi_Vx4B>Aoza z?2uSHC+@xRy%FkoHF74WTq>lZx_aRh7_rLx=6!qa0ix`iR{Ne^knpYop@o^=IJk9- z=yk{I$PA(PBUpJ^5nd~g*Afvs@}>BX?veE1*JC`TcyR0d*W|(H%l@Z;GW`K;_abM2 z#Qu-2Bx+N|8e(j$yZ7k=&+Sr!owS|qX70+}5mWZvSBXB2DG+0?+v-BjBo_Cbp4(Xe zdNkYTyL~R=@fLrc_R~jU`39{r*G}W%bdPNdUS=jZsx`*xGz`m`0|8iy%crLfa$VJ` z@dfb4G0smYm4Vc*j#p@y;Vy@dsJ6x-+erHe#BSt)@_=^O(m^k9N3dM$yaMO(vXp zCxHmD|I&uUzRiw#?M$!z@N(*L(~-}lrrtVxH|*_Bs@i~p4BX}_Oc5Vff&KnUwIpU~ zOYM@4uqHm5c2-71iD3HEkS*NL+3u*2-HBagu`=6m@?GIZPO)I3gJ-fA*25f{v(^}X z?M2mOY@z45L*zhU!f(q4=un(vV$t)f&z5Y(R+GNjb8Mj|aB99qBzZGK)1hpGiAU1M zYUS$PME_5i+uldpOvMu|!K6%vCr>Uh@7oud$p{Z}V`1XE$WN*`+O1;e{Wq&9-A7;R ze(;DYC9hr4(-4WH2uk3thU+$iYc4^UmDD|2_(W;oBo4~G z=H-LGs|$)t#!n8~<+)b<=jEe;UC9;_O{uzZH zxC%R_bsik{YwgpwLu9%(`dWut|CR1Mt>C0#aT;%Ym*j>Yp}!2PE#I{Og1qE_M^>IM zE__PBI_{p^`=`k|ab3KhiIy|&{ap}(ozdct{^6E0*Bo2*=$n7IUP7{{0h7O9iHheQ zbn1#5)LzE3&-7N69iNM$hxKwf3(R(ZpV7|OQ_Zgez4MMXSKra7YT&l7^EdPY0`^kZ6Q-S8_N!YrQ@8&!2OS zVQX71d=0tOMcqEzrVja>EIubV$N4rn4HDkpPayN#K2eviM~QCvSZ94%O}#CA<%b*T zcmO}LC)UZHDTCQ#T9a`7zuFp}Yh_5E$ucy1f50zY+hc#2zkO-jR6h__Z#2ev!=ksu zA8KtTZkBlgKkj~JUR^!eKRrQ|y~#h=uc#gx)bvG8%c>2%@~*;Jrcz8_`_AZ8moSu2 zZd4gUIaO#fVCQ|JJl#hsd2dGyG?!{@Q3G46v20(avS3YeGPI6LR&q;=|i zzeX?coGC${wxg_3lg@##cvv)a!*gK&f-P1*?Q0JAIf_&4SOuXGW6x-{uo`pP;iw*0 zrF8A4c5IN{klzLn3yXWu{R8vhs1W^{Z9!M+u4vMAs04?;w5?#)Pe-e&X|`T>UxJG1 zcBtu3?e5B3wx5~4O7zYn_Hf;dyW^GfJr9~zAMD<(PwoK^dKu2f`?~%2M##xW&0`M0!2rs^G|H; zxxLkB7d2j;Pn}4$*}Xr|9mny~!`gT9s-E)7&(}A~gYK2?TjqxrE{RbsjGUI*>{DVC9qDmaVzts`!z3=c4BI zh}qmI)T}POXTp*pSg}qmxg=eALYdAYT)~BU(ZOGt(w_GH`{+ZXBl%den928pHt%cL z^zx@Yga(c$4W1R>YIM4t3$ckaMbGKY;_q}`!CH3U3#r#!+t*l;Oezv@?U{D#u#-aS z#p_o&ir~TwDRuA1hpz|QOI}$8dkh_BWU>fA$>?nZu~6Succ&{|8nzOBGAa>EkpU|- zkjWn$3dO1-L~=5ah*4v?RDb4B3Uq&mv|9;jdj8QobXj+*Sdb(FbkNcD?un|Cy`esg zP2cOR5BA&-o|Ekzj^3L!OJ)5v=$_LL+ROXXTp{mVQC~|uokid(&5jqGimIWQ9WdF@ z?p?&!erd%u#ZS-Iv=57J(R+%*?N(WgKJ4wa2 zWIl9Iigt4iW#8trwi^R^r%&1Jye2U%9ZeqaeQm@Z+omp9Ufiy#@KqluXpbLDgd8oH z9CH@9Q>bpRODwkPQP~RQe@9{Y*I11wy1axOTL;y-XyV6Ne#ze^npZb!GrD1A9?eWe z;B77;VN-+Gw*u-dPo_|lHE%qXEE}D*fplAW*}NTHp}yNMQyqn>rLRO7!Y1LJ6|Yx2 zk4r6D9Z;Ojn;NpYxIR^ldneiFeEd2oBRN7}?oa8xrc`h-x00XpHyvji%|(3kd~L1) zvYYaE>PZNx8i|C({KGNYuIoHhX!Z!Bsn5IbL$M^K{S0?7EMVOw4g`|W3EmmS!N zO3<{{CaqLGPfW>94*0}GROi0a3GP6AB-xmjp3@TC9Yt6n* z=nuMbfEnz)K|Lh-Yq`=6)G_O+b$;SCNHiJu7|9CwY<>&M>(9yG4eanQ%I|5i1-5h9 zoXS!j94f>pN)$5&qZKBRW5gtna!`zrV=PNj8e*uF%aL;gjvIDwe6vzxwLwzCR5rpn z4{Y<|Cn@@Xg%0XfZleL%$2^3jqr%x?TIJdZnfXrTnrK1y$=5Z87C@>j zD-A>@k^e=t8qKdtwj@^%qFoIVRM*f*fi~?A!77;uP8i;$Ueyv%H#Jjt$O3^QPOC{J zSBK03$uvo$)j*AG)C+`OIFcAsn9lDtQ{TjXx1=GNfHLhrhQTbDmTL%GfqFKSQ}4MT zmlp~P=tfIg5}P3Wi+tIbSJi!;L|p)*Zway>{GXw$lY%OII0;mQf?aHAV{zgnYC@rY zca-artc`)v*9EmZSdpW5e}-2y*^U zKsToUojC^nm*)1L<9`Wqy3`@ukd~fp2UP}olBJO;^(GV`X)61mXF~owa5P-jP=p@0j2RP~&VPP*dPRHa z($hU>cusO2KC&HeHi?H-%h2KBk&)%D2Z6hA20qnrM zf|X<7)c{Nm83G#$9kL`19KXwW?HkLB*f48Sr|yzx+^8yWp=Z8}V-wDdPCx|?KI2!0 zFmuk!@SBA62V7c5P(q{p7b=zqI+p@w@el0SU&96fu8hW?lT&WRmY+;67PgEI&dd)C zcmV*0WL69e6HC#0@r61wyftj^%;nIV^H3N;S}N13r9|^JAC!TKRvaoLC>l&UdpB9m z+qo#ak+Xq4Q?P-H7=bwwZgFKe+JwsESOF$Mepjep@1(!dp(tU>P~J}mY657=IWM?} zHHDRZ{>w-(2-RZ}2m%v+d)eu>LsJ(9Y8_@aZdj5VSwNEUOXZj7hED^-)dzzUL zzE+im)0`m;oOhV~OLXHei$9=mNmv`V*H|n7(Wl6h#x<)1x_W#HlL%yRYy!w&=Q`x* zH<>^d@GRib(w(TyeRjt8MtIlT?;b7Yphoq@m}QXHMS3Y&%bV$wG14pQ5pSopX;MdI zufo>`mKUmO{7$|%0G%)sj2Xp%2@@lo4F-29rgsBT#_Nc`zJ6EzY1)`Ef+Kt{>48N> zAqgCEBps4cmzSO2kle|Wsq|Fw|wWubZ2 za8xBC4M?#gK@gT$C#e#gG89KRI)D5f;fW+Esd<8hwWE2}`+rz%Unv1%a{Q?Q6!!}B zD0CnTrwa7ifOw~U(3cR|2U7mimMQ$DclmKQh;c5Lsww)=rmeePb@v59mXD$iS7`2s ze&G+no-PGM$#(dWqS&d8mNDZ1o75ply)b182cp(|ZxjH0e(mJ94_SIWaJNuma(Vj9 zCph4!-@|}4)L0sIqjb3PdJGYwdBD6Pci>Qk_OC*Kl1_T%k=$MxaJ&otzLAU*yrtA1 zgmBag{&dDEG^y&($>R+uj1wy3)S4uXR?U+QEQ}M7#wpdw>MgX)`aH#J9%QTh=B#iF zR)w%$(q<`j{?E1!0aIl^H-V%1*^xjfoS`_@L2iGMoup^>=BY!RVyZkf!&E}0wyPQ4 zTgQ-BX%+QCrTnuQLFm(Zs9fS_0R^G}2*NxWp34>{4)alNsD+wj?y$Y*Ne@EZJm6di zYF)Vcaq*&F5}HvBsef@z+^-_HqV-IxqVk(Jf%^auUX?cIZ|fr6ZNtC9N%jJ`nY z&c+8GF$wo(1!D0u(?BCmyKPaI5EyGW%0{x|qd5ujJTyd}A|lUKQI~Lx4Sc^tlCK$n zuGLI%lW!T1ZShRM{UhIwVIkV@{rhxAEppTdhD*M32ya3%b zz>5Q#3nc!ak0R-QE&y!u{49uV!4pFG+jZL|Sj$|@ZLTD@iLjf6{oT26xTk%Bwsv?B z67+Z{{$!lDjo6dAhkCrqfp1c<$F&ahxfA`L(y+%+9BIA3XMvzdpn2+~R>XHf{-@gz zET^d=&A`GaD*UmWY6xHN|4>y1V2Hcw7;*U}(q)OwctVuraxpD53J1pmwd$JfL#n6+ z5qnk!@QI_3<&!4`C6D)_*{VWd659IChxA|)2vvyI;|r98GA-4BB40&%sj9*==?nE~ zoJtcgmtlu8_c@2!J5EzC{p=(LJ&c>30-sQ5U_P6Hk!WDTQ$$Wz1ZD%G=d5w9{$^zE z*8{V@LiUi1_M5yeL*aU%@F9HeTE3?6gJ&LS1`t~L+d{w^4EJh-b zDB_bZP%3aD73y!dt&ct?NVRZOldh9g_v;+2f$ZD*1h|&@b1i`Fi^LU^%rOhQURhOI z%LwCB9_81KY~B6U%pim%+4LlTHlI3MjAtIBa)wwqPqG|KYfq{DUg?C9H*cILk?NLA z8>rMlT;?RMbjGMwEQ#1L)wi4Mn}|Q0L!8CBmO{El11k>|D#z~x9B1UqA5!55J=i+* z`$BetFl5?&{-9YgL-gh4qxtlPvvh*IbmFR<%dvDKUoiu^bfUCmT6o?#rU|MrrwMmB zhqNB5JWMwO=;4iKg^DCK82xnDDJA`GlXErro?c?Y_022c-JD#b&Cxlefcaw8fr@Lop4Y?nqG+_hFr}1SvDoY_Q z^QJ<7RzhpC`9ELBf?+qeGFxVhTVU5|&m;uBQkY6BC$n_@U*j*5|9j9#nph*P-+d@0&L zdwgk660~q-2Ur0zD^zWq$k^pEc*cC4a4~q&qo!4<{3DHmGnvuK)ER`QBFcT#8KkEo z&MZ{C@=S)IS*T1Z)EOkFBH)&)Ud5&&)+|&e`+F!ZhO`))-P>U3pfKDRM)8yR3a!dt zgojEOXGVw)-@E<#8fNiQgej>bA*g+xGJshTeX4+;k$(DU3H-GsO5P|~m3#O_`)88o zexC8ZI556IFuJ9Jk0b#n9OgfhAtxYFQj&e@suc9qs|OlHf{W#XjjJIiI8jobD5MZ^HKzNQk*@*&tE-}ZLv|FJ@4V(Z_922?R`4{pF_iu1HY#r zf?Hn!kqrj6ea3g**h#=Uhk_TwChlSj^a?eSr#A?@HDw*s)~U^TO{zZF3qL>zKfDM% zh!?yeFKZ>^TH`Nef$yF>C=`-zRYXP?|WTGrE>V zk!zS#0fhDvNyANK`jQx0#I;EHt{^>g^pa#cVe^udg{bhHxjp*C z_$DZf{2}lM!0aM7bOK`0abm%&stpL~+Az0kqcZQpLbL$0_Mdk*Dr+c-%9lZ?iwHr7 zjMs}3VNC+7G+^>~m#aiC7l~3L6S2^Hdd-@Gb@#fCY>6GmfUGL-uP1I66UwC8{_q7u z4Qgg)*R`~?Q(?X5dxQLCO$9i|jrQ@)hGJiRkYS3o|!WmCaBHxTUk9s=>i| z`iF2}mm^li<_!GWVSu|imp)HnHJ)$T&ggzT4S_U%T7SBWkHxxRehN`G*baZ*A7H$x zO~e~+-AvrOYglWdtPXB;XR1{Bt9|cl^+Ll|dzQRPgOy^&t#L*QE~YVgKJqO0%Gt8B zc>n-p`E1fKLu)Q?7*IS4E$74f9Xs;BHv=nUmPJ)(qLR`|a5*ahF26`tn$GZ847+FE z-}lE1sXp+=CaEyu2@wo5MFxJ#;hglWx28t8U#@%mfb zzvZ(Js5;y(qiJ_^Mw{=Y%wM;&_DaxY733gSzC8Ry(Yg0T?#Ah72vs^{ncxUgCO3%oL?#TV<*}S_&<}{zXHOC=;Cf!n-N8k$Z*u@jOa+Ocr`g}L62sdXI zv0nRzuV5`<@Az>QY%AG*dZIFI5YL5{?WXjmhC+?D%%f|C)!?r+Z5y9w@BNH8kT29z zR0qSyXU=(ZGW(2JmJK&k5fzuB87j_3!jl!w@6zxk^>9wN#Z2uK-nJx^-O znwz;+zpLJZ^=3RB3cXyby5^0~$zh$Uwr*eZpiFNkrcR%0X*Mo0o`=nUtRR;JgD_L4 zP34!hk)ghN))ywSm*sJI&J)Aa5WG#V4QhQ6H++*kP1{rJNGg`0)>L|E(JUq`vE+I@ zZ;>M`=ocm|)!5Qc={C=a|DGNHQGi_9nQpe5QrNV}-xjTs25MO~<2c*)ex*cu*M5yx z>2soONP|andQA@du#h?S&Fj;$T3PI+@2c3eoPW$jq${Cu8I2^@@_NS4TsM{8WU$W- zbJmVHtP#+Ar`?4t4MtMJm1|s z=>&Yq7+*HV$9tk%@v$EcWu)ry-OGN$^*v9a3BLY*sOb7ijqo+q{n$#yy>xp4Vk=i{ zePPRbM!a;rYv!}Q3d1;%X$-L0I=4xXBlwymKxJn11c&Cy|RQvX5xH}zb`x#b>p_A-nIh8dJ?{0@WjSf)J9 zX`(Bu!u|aQVxmzk$hM5bKGifD)&8v$cpx=e?v5qWLaFiy&6)iwkz5uy{^0Lvs@YZM zEfgK-ID@stipnHim1&7f{wZ#*tMz_#t}GZsUmST4*Q4J%A!f=AVBM=Yaxw zMZtCRv>+JSAo{6P^YwXxcJR++({Q@lOl_Uu7P|4#WIX8Ui;n6$_u_o@Ugg=@L`q@* zQ@-+YRb_o1E0hMr$|yDqLzrr->f35b zaY6-!{D2mPHFlX}>oS0sz#q%nrsZzWbLn}KMlZJ%$9LVTEi-ykwQ^@FpO&@QdahZ~ zOjiM>@2!`#a}_BwNS&?MzCMt0%iczG=1G@319xS6N`>xD$tUaDz;Y99!|RzjcgpQ# z!9J958={8I zVz)qhxQY)4OU>@oRI(m8N1bZ9HqFsmr!N$Z6`ib}3Aif~be-r$t<$lm=+SVu)yg%UVXv?cta#hXVd^k=wIa0izmk{}qc4W=Wv{CIl zAl1ose%E;*i;6?}HLr2j^zR{;X3IN&j{CX(3+ZTJB4PRF<-=!YpNZ#3kB|KGsCCnl z6#Kgcx(Yd%uMT5y6n^Q$#GnpiM8Pqrc*I*Q4C58wHY1rZ+~`ShG6Z<Dd7LNlD;dLyDq?D1xDg(;1=y3AF@IlAp&{bILHRg!9Vbr zPU4D@l(&a4iM(k7c#HNwqI6$*i&TmZ?a@G7vu}lxSz4xFx)>Py?5A+K_VK9;L9BV+ zn`H3cPU_jKzt?#M>(22Coz!!`=UVxw@*w9)Yr*l|aUTd}+X}Nqzan-*$o8qI^J)-{ z|4bytXO5MrP@Mk9+%6(wgHWi!68L+G_Y4k&XiJn3+P|{)(SM^?T224)y+>(9Z59bX zB;y*Z=o=Xvjsw%wkT*dR!4a{k$}^<|@vf-|V2%7<5F4hq{AGqvkM?Tj;Q#2=2mhm2 zM-?xaQ*6U`3I+%QJqNO;GT5yTxg{8!t+ImWNzi+OZ~p(#x(a{G4x=2!vo-&JWe0}; zyU_3->j}&M3hVmcl^y(liw^%e{+BiK|5#6uhnHnCNQ}hrs{r+3!jMwplD#PagZRb3 z6aNgZOPORm5+|mYpXUh8j+*F^>LX~$s{`lu391{R$PW+}aOZ-T;TKSk_fT*J^i-VZ z)SLl(oqT4Eqzn>v3a*|I_qC#{fwsKuD;-O7& zX}gHqA#=VG^Ntu!pcwHIr2J}6ex-$8toPvAR;X);2h*-a#I4|%UoU)$9EHj*1eNH6 zo<^O5jJIN=cdIz8I;dF*POO4PYtN;Xkvmkg6#TnGEa6r?bF@gexyZF+!I%CiiKW_mkAM$Pz;$Fjg4`I6Fh$)#Vh( z=_;G?BV$rR%8u2=L$J0SwwOIuSMvOThY@HAaB-{=_tGk%i2#&Uhy#X=7)QLYb zqpL$_VovG1xon?N>}un#UB#2EQq!zzr8=!=w@sa>8C8x-77Ww$9aoe#Tqf2BY6zwF zrI4WY%=g-LKNZ<-YCb2~F>TZKVphk*Npf$irDYoS|zZ zz`7j`{OdfqCs~c3#x<;|#!~&qIajadDA?f{o_lJ~{>VsAJE#>ICF7L;XV|SGP0vQb zJdCbkf>4%pG+B=jMUs)Nn%cx3S)Ek;*Lf0d6qsGEhXw-Li@zbL%DFkU`I$e+nYdMM z2Iq)F_&?H4`1noakEmpm9Bq)^FLN=L~I8EyA7?0*VJ#KtU3S z4^e&7ravVD1)3h?p;njS(w6za3qeSmi$9cX8WdGs1T>9Mgc1v;PUg_vvt}U}jem`_ z-=9gp0s^)Gxe!)va-g4AHm1m~eEwcLVF$t7=}QoqumURdtchvRjCEHhlte-%q5_nnDjfTP_&^F&ms{PzSw5OtJ3;K89EH zyImxt0m7rJNuC`FHGQ`+;rv8AB9|<+??md>eQ9R6B3D<>C#T@uz2f}Dydt->(kCbX z{Y`jjhNL3bv?N#Yle-e#1BLGdf$u}APU+-?acQP88X(4ns1vH6V{)!ZFd`quSZQFA zlDy6+p#xxgIOP~+Fcr{?OLyxGu#h7wONX`;PdW?R+!#or+aB!|aR$bq$*rT>>sCnp9AXKclT^Dzs| zMxNd^yI;2yo{|H7JW*>WjJih{H(D)}O7k z6!=i7>zpdn47FKZb`2r-B^BZ+ed~x)>rHYCh4iT~Y}p&J(u=O6TkiIyIecM>QVT?> zvs31VqjbkvwC}7oe4&X_dov_`PGI4R3d;WOc`T9f$DoN|jGvffMQH8nRt(f_sMaR+ z3Ki_P@6cB`9Uj2YYKK+{Qu0geH zzu1$yj(egLC+J=Vz|_o(<%5R?JuyqJjK2>o&BIB@)Gy8^c?lI;yvY2tv}Il#XuEHy zM;+Q01pb3nn+F1`m#~ZIoXL&NP1XNU=MS;(=xq-KuN>n`ZsV>lDRNNo(qVLfeYOIs zeVa-^#cnUDQu3q1b%8~Z#93rTVnuF0c~BF1xIBzPG52r7L83egx=3Oz$|8Z1gc*`Z zdE6XXrXp#%47CPxfo`5V*5W{fAp&2Ud1Gg24RHvf;A8Hx9}j5mLF0SD1zw`p+tcvN2E}k#2+o5+yKZz zpd5@2{ON}rK{zHyH5ozg;k8?+TMS|mA-dY|0CGqShlSYF0ogUrXcRB9H?gx>a1A)&8SBJU(Ly6&L8ZIE8mwXM-IKV7~wm_1?WzH#qwT=8y zxXv@Rvqoi{-#+~V+DxP}7DDryULCgy3Prq#>uMqfSyNS2)MNnI}6>jEMO?hmkK|iV7KS;(X8(3u08-8> zWqT;2DLX4Fhy75tLI#l|B3}KPPjEwo@?paXLxvim!^NS)$DqScek~|W#1R;Xba55? zYZQ}>isH%J@N>)^HD^VJ-E9J!r$}ekwc^GSU|F+fA+%w*&Q}vo;w}l=(5J#AK@#L@ zOiy}K272yiqb}VeNb#}{Zspqt#)}7yXQ;>U`Pv>!#LTLS*`=!9;T6-R6;SAEbc%I4 z`CGM&bvlN6-8DcLFrdqSL60A?N z^>0rCjS_Yb4(G>C57D1Mj`wc89}okOty_tXJ!PxGA0sTC>W$kEt3kXvm|A@-oe9>i z7;874joVPGL9Epfwgwnm!v8Y6z#BlUN!uCHc*!OR5sBxH>hfud8hOVscOpfH3Na2Z zRb}3)``0Oc5XQoZGn$T&=N?F1mL|xj=GrPvv8YIrJyP( zZ42!E5}PpXHslu0PrjQvVi8ZFc>H^efY*QOn$|JCB%xYsANQVZF>Q1eZE@VhG_C@A z=9J*hmEq)<29=&PQOkGa_kCnLW9j#CjfcJjL^lni+V~jvaZQK5!Vz9fMK+P5+Q=~M zWf}H!OoqOKiMN;``?Rtd5|pSf6+x11ldOv1ZfO13B6@h>k7J z4eNt2)-Z9}tCPRWo>So0m*{N$p$;Sv{LrJ-IkN zJvlwWWW1dScSOy0Az2sYGj+W`5-uG$KIwGjpyl&bkmSgwET~H(8`v#~a@NImo5&>( z#ga(ouBWq5jkVcn#ycGR<(H!u3aYqDh1KC?A{%mkC%S(pJQ~R_1`D9;$S)=esMD=V zMgDxq^Tl9`V}CjMy(@xMgxmemc8drR^WPGe7Xae;`v8!5oD-3~l?`G}2{kWJR53KE zr=XlOKlq8anDlEtk*EZ=Cd4dY=THf;c3C?&&kP zWtmkw7t07E_UcCX#KQ~6#}o~8=7t#)d1q5*@V`lej}->}3HCj8ZRt}*V?fzql$sTa zMiBS%D6 zgMXXyI&^h}U%dW#-{Lk+pl%yoIZZlj`MDZwe?O?3Xb{A&q&jo%uy?k~{p9s3~R^ zi?h|R^JlZS>G-r&!M?rL$Q`lI_-%jLx#0FSh%JO?2PpNJ#4;e!zPEEQKo`;8)6!*e z!f$fsXV+Zx7$(OlXjPl@>wu2)0MUxqjs-Th)3ckM5jXY2X3Ju|_tVE^K8dQA7f2`L zmq+8M9Cftk*vPYx@Sa4qjSg(08S}V$EVHyuTLHRbw~r-A#}Xka~i4~V&a(vzXFF{5d29cfpVsQbfzSw*s%r{$;xLmJMNiqH9R zsI^+P)X&xB1%m6*Th-djCUM6@%W_B9()Vt}G7(kev5Fn<)$VRPEc;m_{-wRZ)PLE&FC7te@7~T= z;yJhPfmmu7FLp64L*>MnWv|&gP^78Mdf%HuP4q$a+Hr{Y>)d+$eOtR^rW?L_ZR{)@ z)s(#dF#GfXTk_@pv^$R|1I}}s-}SM-x#Bg#!P<$)7J*LO>D3fnJ*WmkDi3Li8D81HRTx zZ$xPBZF;YL$o2ag8h;M&j>CM;Ees;l<126YvQ!NF5-(xB_??@ETjP8B*&6m;_5^(H zRI2+0YPl@DTB19fT_M%&JpEw&*u;kV*5=)gG{g?_CKdNvG!FjrV(bT7&rw_0@219M z|5`c+4IGpd_0)^Hfzy zhl%RW-v1j4H>pnVp znaUCI;14Q`1GmOF0A8k(bvg-R`+$u_7evusZz>~Z2dv*Wh)w)D2&pCrtdSdKS6$?o+Gu^J24)LVJk z?^iEeEB^sA655&L=e_lYA_Xn_trhK6jsNp-+ZuAglcVotw^Sqchw-jGkI37Oq4}hN zsx8ao?Bg@B=Je@Xvr8c4Wz9w)<5;(&6q(+@(Vg+PQM&Q{O*o3&fMI*UUOfL#3d5=gl? z-i4d%IOtL2#)p~Bb+v7jSZk1F=KOJ|$TTa0O!@BCk+y=82pQM+XsW@ct) zW@ct)=2W=CNrjo2vBIe^Gcz+g%*@>Rrl)o9bZh3%NItUl*vGPD$+F&k)?RLQSii_R z_*!mwC?TY+JyT1G4CR;6A^C5(m!U&Dw`Ame7mF>G)h8=Q-uq&PkH!hjWE|Ire##HC z4Om#Dmr^t|pkH#PHj;SS1MFN@Qw~Hgi_4@<6PKBwU?lu~1S#4v>FQX09XyjE(j|S@ zjMg4FSAW~*b-Rz6-W257k@3``Yw4~pRUUx$&VN+d>|ZfE1H`QCuG#sAwvD2eWf@>l zHe1QdYfngmn#IOL4v5M$pv-nCZU6W_W#J;y{{s7s(ve#>M$F{cUD33m4sM5Dua0c- z+3T!WEWFZBFn<^O&Cu@|$QB{sUBYC96+OmERt@QT&zW#P|F)KA*!sxIZ2Di5l9Mogf{-php@B!Vl)oF$C1Vi_rr8KA0rT%~vruFrX+I-32xWB>~c;zqx>Wy4N#$-m!Z)7C=~mf9U3cq)sbD!qk@TO^>E>J}F&Sh(@a_-)jOm2c8#RPx6wWhiS8e=| z6zTOJ_{_>xXyD$AavG=%Q7MH+c_CwOtI$b9IHIdM!ar}MMGf=MG@}YJ`)3Op_=?_W zQ(vE1oeG+sCNtyfAs-)xEz~XwOj`$&ausrLB{_u+c|e#6B9R&vamCLF4Y5>244n{h zX#B=;EU2tstPX`wZ;eU$2!s!fwrAik+}&m-uT8t7h_<8f?u1bW%<*Jqo7&xunbirK zuXAO{G}|al#7c~tlUiAn>Q2#i(Bi~ndM@-OGpOj6dsXA7o9{R9f58-GKS2?oPG1J~ zCnz%F2LclPe{041??KW3sdf2ZNs)xHor^hxqpP|aG!SUiPfYZm^}j_$m)@RQsw2;S z;of@4O;@ls(CP{LR44e#PR7VZ)pHfh@lt`rW0UeiYG~DT5wiL^w${qFnkzOq+i%Ys8#rSSf%F@ZRU@IjKD;jfHyH$w=EJj#VEBs|Df2;zgo(CaUB1;)R z-zBf}C}z=y=WnG>41jPcRNzmvO%bZw1Vy|HYdUDUVIQ*!Z8AlXtDO$VQjTVm$Ob(h z4magG}4L*3r)fS!nL*; zk&>@vPDzE&8uTz5D8Ycwn4JXUH?M752o^dSlDFzpw34Q1+@t6;53Dw?l~*YYSm0mE zS2@?}F(X|Tz%JLuV3cD~sZ?Apm!w`B3qmEUt-hvPWk79C8^<+1h3Vo}H)hI;wm^mD z9rPH@N|LB15Tr#(DItpqT%wAxSxsnJl)6;2%U9@Lt8IY$oxn+2lyYQNTDP6a;JgP( zQ9S5#o@A^U)k%zPrAA_2v0f}ecOj9slM_2Yz+{bL?UTjvHo7qvg8cwP`fI5^T}bs2 z>w=VmN?wHus)eH3pdcQA0sVlFxHA^YZ&q8n85OBUA+MlGlF}c(2ouxClo8TZ<|^o^ zTI%<&hV$}1t)+0iAyV2RXWA1EE6)Yyb52aC)>w@5bOAGTra=WWJM5UC9iKI%O5>G9nr0AoNa2V4%KEw6P_%F&)_tROZHcIUA%w&otkr4hQ zOf+fl_i^3!ho5Gl6R6MyIZLGq1Ae$!9KGB2*`Q&g0HrgN zMIbynJPT&Z`fSD}t?qzo4ly`=f4bxFkA7o3`{=ZB@>ccIoT_E(TPt+D++@2+z#^KC zQ!Dip*L<>9$ShUFvVwBuo-_sS* zowU9=oN3!boN3V!E08_*?3tJ)M}^0L(EAK0oN4Z1TdpHYE3Q~{oY=7loLCz3P4&9a z`1o|_q~c8BOsgp<_~rr`h$S2wR(Q)KX`E@evBi2l6$O@JYUIVvb+}C=25_f0%}Rw! zq5z%f%tUBPUaRW;CWXVsWlPZ@I2_Mv;u?08Mhnks`OakvgL5{6b610N>ds~C&Slm8 ze~vne6Tu7I)vDpk87y1_(&rhS%i4MXDs`Gp%2PPHsAuHl@qX<_caALyM=UFNE(c$! zjUz{Bc4!B1w)@@SOnF}CR)kF%pE$b!CeAon)lSoSgzcRBy92k1OL%$(U16j~XWc$M zKiYK3=D^oYjUVB?U}9g080xQ>D7MXZ4BJ+7y19cR>sr@B%?jtp{xZ_re9Z=m#3I2X z7s1M5m@>3jSAP@-g<^sDieZ6i$rY78NGaBH$w!SnIe7wd(3euKJ0QWKP{UMcK^G!{ zJRET91@tA<-Q(~2fQ=S-=#Ty}g1%ayje!BMG*j=FH|O74#(}ivHxqnswKSN1cy$O@wPAf3`gE8|1M+H&igAsjyvTwNY4#JZIpY!RwpbH3%>h>h&-koGjTAB!Bm~R*0ve@eA0N}(0j5@ zAHTFjVi?vH!BwBmhY_=-nzb4bo3g7h~d@l!pVfzvplrYDTd-_DdZ8<)QN%p1|xlb}fX8w_bwf?}htDfB9LuKjKHJ{at( zgJ^F?%n) zIP4Unc-1?Q?8NeeMarr^lw9mKN5YXNiWPb;AM&(BDD$He>M~e1#%c5O+A_2>hx9am z*}phco0V{VvSQZfkA9mm{Dp6#Y)R%`4A|623gbzn949;z`$Zj6#@hCmu8`p%6(yo` zjQ$YCjl3mUVKHXYIr=wc3!bzCEZHj(KCaVYGyfV9HTIt!qIgFAbKWQ6Gd7hImmsg7 z%h=M8k0e1>uQ?@)*3ukm@g$h9yT4(p=h4!U_@iX^)E03$6X`4Mr?i|_a}s)zvbgLv zVxJSiKr6{3BmzOWB%zCAQ4TJs`T~oBl_ZJlYFLXa6G-NamMfLYX!~}{;&K2?mVpD* zeb*{Oq~UvF>DZdHOZ_DHArWJ;e+AEM)0StnaYX+D(^U5W7?c7w#!XW?V!z-iMQ*Ds-2&+Pnr58-3zz-bua8cS0(!hzGZE;qQfe~6#D~eQ3?6cQ zm0V){s~O?)#pbuOI(oekid`8TO}0@XN!(Mb@E#ZB{vA5a5~5U~#2Y#2;_5~#=>pFp zSb6k=>NGsP_$k7SaNog)G6{RL1Z+Wss}`s4{S?8i9K~nZl+;BvX`AjUVQVpcPsnSru%BJwGcMn9G3BoEucf43e3t#cgX&xu6 zr~SC~ZzkmCllB_@XSEQ7STE}M7PwOztmtM6EwbeEJ8dQr#0b|dXPk0#n!sU%^_&@*u}Gxd3%{>_}ETTk9i5$K3~(NjXqa$6PO_M;K>-JKJxIm!KoYY9sO&y zwB^P<#S?ns`8E_MMh_{zPboYlOkgc?7XX~dUQXfI+alv%_ zkgV3IX6vQ%4V_B2kZP~EA82hFm20mc|_O|Nii^%RL&}qPtFR@v~KOb(+ba70M~bE zMBkrd)r?X17x3b9V93yYMaPsDw5?+vAYg58SLfrTCSa; z2THE}46nsS2dclvhJKHgmLIUH-Cc`0gI-yIMrqU{zeh(UhEnlTavbLU|H4``_3P3{E6Gc)f18+G@$8)i z1`qU+E7jwGE}?e-fxi`bnZS^-7rdDeP~ox@F%hgunLt`g3F9co4~y|}Te6a{b3YFq z7Jb5jvm>MG&g%{C?aAnWdP8D|XGThQa-b%a*3D0@@aE&oGrcl(aD};{AI5xJQofD1wl^}N7ij3bR z?Y`AaYdVhwK3@>xbpR*RbxHDb74Pi=BO004;*YHqw zQ-P1TW~3bik=h+@ME%CTas|yH47Za^l~^Zv&WnMXlj@5p(hs}d#IVLq=VmcEwIc*% zCEhLsIRx1|PsCz(S8fD!CNd^rzC1T=QE#{2UIbm`lOV(C9uQjFem^#A+k3wPrE@mk z6)?;ert}PVZdwNLbf4c;@HGPO!Zgho`N4|VWZJg4KeE@v7iJZiSGH8dPHo9m| ztFO#=^L3Bft8z+feT#Di1hGX$_h=05EP?-dVnu6~Rj z$sb+Q_g$M0AAe5DO8x<_kz0;+Rg`PQd$vvVyUEh!d+4|dt=Z6)weHNhuWfl6y>DZL zMA>N|Wp9o@g^j|bDsa7PKLoW2wfj6|@^HXHU3!=fFK1!I-#>n|Fmk5DVpQut)jvYe zYGu8()_t4N-n66V^30e3qx-TnWpPe{OU+;JuBBnr&Z8k)_wR3?Ej>8Z9_s<&PV(s$ zHv=t5l4qgBY4>V)xY{os^3@JChZd&9 zfJbl5-MuGV>jpT)zZ6##fe$WyRvl(Dd;aO-=xj920QUiC*YP;~1%EjEhe&o2spssH78X0sZr?R2 zfKUc~=mVqa&mJLDimNy2i!n$yB=9fyHUw^XqMo=;jMW8wz|Be7@HBKdR5vw^@eMhx zvpRUU#TGa3D~;#X*YPCk0Zwy^HM#Y5-PxYoT@}npdq!;D4o8%p`_9<9;=io(6Wt_2i@j9^JNc9PAeRU2b1qb;%B%6PiKq<7zoKrG zS7@xf-)pJ@R_pd9>sHGJ-m4eYr5QWXPPPa>t{JyxxMsnwJOg%CHwRfmvYO1t_UAF6 z9D3%{_*EUPHJ5lU2Z4DulO>hW&LSQ+f#W;r8StfFk7CX#85#m*OIP;q>64b)TQaCJ z87}*B6CaTX%vqHsO!Yn{x3$~%Ye*+GCLoU~`Dsl#&@cE>XYZ(rHdsF^y-df`B|*D5 z)F?2STg+1?nzyM;{g6uO`Bq!Ptyc?=!%Mv<(#vG77waGunFU{)p)-+n#t#h3pKXt8 z5Lg@BW4ZOt2UsA~JiGK=E5kFgHC%a*N?C>{%4{;!l?J09VU_u!X-zf$&^uxGT$?z5 zdVf&}7!3Q&+5rAd#UQZk>DQm`*Q7FA4MjpH@J6QV>;=q3Ied+M?77hHnDXW3rTDoR zesM!^Ol`*|My7viI;Xo8vRJ&dcbE%k#xgl+4l4R%IVZIl<~k4Q_=IQ7dswZ+8vLEi zLA}O2GI@`-e&RZ8u1^j)Ow>g=LYjJ~l${yriFl*FL4Q=u@r)5zWuF7t?yfIbXd$J~ zzc^V>9l+5Qj_MWk;+W2X-SbD(?tDgt^=PFjcgh2yeKBae)LvX|Gx~a*`g~8->UGbH zlI(d$TcNHD#C_ng0*@2K{%D*l)i4_Mq|RmE0I_Joqu^add(KmwiPI9m9EAAh?)2yY zuR_3AUZWd;S$E6h*&5k^A<*aD_7RT-KVY|mFzv4WqulcQ)PY=A=zE8cK9kwpYJlAYAb4~)62GV?4+Kj-5axJe_Y?H zAd_C}^gN=~Q`=aj!+)$LG&k$3K7pb2=Qv^sU$Ln!&`DsRKIWVbP!mbnK$Ob*`wnv- zv8gGLa>chNJt^uqtZLMYdkuunc%%CDv)U-9*x4kC86!XH(T9sCr83VCuk~m#t1J)1 zP+72xi9FgG+q1j&rJ8lh-MzJ6i(c8lw40iiZq86H%dFbc*1&uJDTh#>@x#kvC}jVK zM{ACCP=7k#!VDRii2IAj<;QHVtvIIrxE3 zw^hI6rnf7(a*}+(TQn5kLj!BW<~de3i-Xy^0RuZtG8xlvwN7;=#rhcrvuPzVy6GS5 z%|+PhLVK>6#H8WlQ~Wgn3oQsA0wjdzA71?hdoFFR2E)T~R%7xITZkV@ z_rg0@TZh)|7O+nVdv7RUy}^~AXkANuV$7^MbTQ^h=`P;~Y+~Qh? zUGu$>a`WtxB$(R+{S|1~XL!&A{j6$>i9XLVT$=yrzoHmjR*-exduaP}4omDpA$5?E zp~#Pq^lJE&Gl1~ykAI8bmqQd}Q*#C(-UjcLHouOftY!vAf?Q2+7O{lzTj~1-a3jzsHbF{k2h0tcrb0i42o{(dd%XT>YjySXP3jcl!dDgR z9GJO14bSyH*G8MWk4(HDyi(DXb@uq=4(`G?;|yWDJf>d@q{%cQ3JU=HUD&W;#IEMT zSLiH}n85P$KgCzowZ@*t2c)?6RGFwLP$S}K6SgaiES_D-ZIy(KZj>XmV>Gp zxTxQVvwQTiykDi$;Jyh_nb3-7>uR~A@ZgA9En&xtu{Ui5>)i&cLE4C4`OC6)+3zZF zDco*kejVE`;pFYZHMl~&KAvE@Q_>1tr@)%%4AFV5Mdzy;>U*OLF-F#*E-z_C5N#GX$S^rxa^M8Rm z;yAf*z=^>9=+i(bZ4|hMM;^64Q zVnP^TfuqiF<9{omNCFE3^Z$(Q27pA?x;?ncQv$kMI?eA5=9knTR}3G0cfK{<+?^ZF zg3nxyjoWnu%i9&&`6{(2ZWOk!8Yg^aJho5kWdn;A`zY_h33L;P?jvqH$)Gp{BARbs zXGtGwwasL+y$eZ6FM+qxd%nwOajWZLb%waMNjLBl1HwcCMy_ga>jBAn@r?3JKsj?( z|Kdu>3UX6PfP4x6_%X`cagn z{hfiyfIfS#(nuR1)-vyypvEQ$;Q)qdCjMP%2V~4lfy4kB1kHw&pvx>)T=WD6KQv2M z1c#F_MD+5t2pC8lIE52f|R}YOq z{ckiz!w?KyS_GB#U@b=+fp~{ResYriPc!p^uo}0OB^#$$(Y=Bq^!457uQxpqq2{0jVg$82Mm4NvH0_pYdQYuukThDr-}C;uJ^DcfZtZ(~tt3^AS-Z;G#8h zM>fZQQ_kQj#Q+aRL)N9_cR=%*{Za^R)S^t)&W~{y4Zzg@7lGoD9!+5RtIE36o?lA0 zvyEK4YY@{555b;rmURR#Ko?e@X_P4diz0Jl*=BFzqJDHAIFc-0tere-W9iDmDOsCB z_6ObxHUsUF8re<`6_RWYMVsFo+1!nt!b$U$Uhz9BX{{vFH4A7VHm3o7q^t&=q_=5g z(FB!_Q)_1ln=t8c;f!^~P5x91M)6ai)F>DsmKtB`6@;Mw)nbGj2?s|6f9l}Kjb*}C zc2OLo}xHKs_G4z21eW2I6!B#KpV zXjT43qn48mMz1Ts(ev|Clkp&JwPFg8Y&@FNigeAB1wirac4n8kqFBaOQA)& z-im`MW=2*2Q-KBB%fVFam>o35I-_xtM*W}Y4&J5-(C>q$Ry;X4ou&c2SctGxmdp#` zF@2SmOeVPAa_Pv3-jGC-aJWq_>Ovo=8$zk1LNd@bFg^okRUX*nj9xP7K2Me*PZ@HL zey|c6t~6RM!7<0&5`bO-fKR{gl^XoG6g}ILic6D*Yi9CaKGzHlq(P($^YEG_h&oQT zB`wzsGX(Hu=x;G^& zhl*Bx-e_9I!pzWovdTUP1z4z}bKV9JQStzAl$=o+AVwRz#6H{VI~3$k9aR0wE(m&tY%LSc@3f5Bwuirrv{3knE~_)k~U%u zy1}0R48luw!W$&CLk`*leL+z|kZcHAa&0Z=9rRp&noG3M#j@UxRwt{lm-`*;90DHb`BGgwa&`nAb zYTPR23OC@CAcc+Nm{5kz(JM}3wTgrpQDRK0M^+z-TxK=tmwDj1inWzIK+zg$_P(Rk z940?Dm}!ifecxa?+dU<{Beni~O?)&b3Of&b?X(Xk{vxqbIObC@pc0JO2eC@R)EO3v zZ5Kf+Roti1D)j)QyHAn;W)~DChF(k0pki!v7o8Q{n(+HW$4i8~ioBIr#HA5&2NY;;%=UYxv2bKY z?)FAlFX|D5w*Vyx?gB56kgXl5x1+E}jJFn-W-3rZ1sAnAU+V&$aWyb^P3G3RRxEH! z**sP(z!7P=A=Rijfvi$9|lCA}6Uqc$9zWK7XctT^vTMWZYbhAcoxTsf)oil^{0 zNzueD>?064KGgSdi1H;7A4@T8NU+anIItr%yhAm-V;U{Uy8lCFI>7Ba(Cs_m9n~b> zapv|uwT|x)g$U-#*^9`3(M9)w^b;zq2=~r^Lh3kix}F4auyp2||3zw1F^c@DKcz>8 zR#F=n{RdxU0`e}#!VP)#P`J#7oLW|*P+cCC-X1|MaLn9Y1k#-L(W%4|mKn2?RM>&jA7>NvEqN8!|>PA;*GBC)I@u{;r7 zrHQ3o!&0BhTyHQibhgfjE_Fnq$Qs(&+lItYDpW%x)bkJAiyZVVCcYQg1YP?k%dD?* zEpVk4yk?2+g}xc!X4m&^1ZyyZ(HTJhV&4pivFpoP4diS9*WAyJ%xi}MTLWnUY+{Mz zCYd9{B%V000(e_lP%L9i_ndB{@H(Ft^!;qZ+b4RWTu2nqhPb4u%EP*q=E=}}^g=e2 zHsoFu0|%b+uM*fhXd$&)gRm~hAUdQpo|1gz;?O(lbn(cCQi@ZvnUqGwlLo;2v_p0{ z6ZN0@J2Ch@2WVQ!Uj*8x@LG*`n#`BdjuNYfEJlurE0MT-FZ!&vHwhG@e<|}_5BqvR z^MAJH)6GE&w_-ltCki=w0R;HyFlBbcihxF}*-sh^ikChE8omBNoq$&V5ikRJTgjQX zVSGuz`9)|ua7hH+q6noEb``JTJupylTB@@^I{3h9;*~6Zb}ZI9BK7D*vB{Y3*s}=r z42F88Dc5Sqy6ZO(?41bxjD~vEBG+1);W)#z>o+og_H1Tgd7Y{elz=Exd0cChtmm1_ zbQ6>s02>`Zc%4kE3J^yhmes*xM8&F!_xyAX(_4f5s>=K-WdBS_Hy|(RVTSOdgwrEk zTEPb8!~*rV!g@(!c1&Wv!88S6m;yLVg-SKCq2?;#P9AAM-5zK_3tF}GI|B#xrWTbn zYf3`B%Cw?~41E3!dqJ|ZTLlIdhrW5?of>;}PCN3S6KFSh6R~533xk@>D#fXalJd*K zBd>BAzjbo3qwUMZt5!(^4Uw%zNmDhHHMPrtJ*@+ANRTdn-1Tka4N^md@+FJgQ9S|} zF>51o%c{x=DyuRKit3NN)XfuGhx9Z(=~9hgH_@uHf<0DM2xsMKeE~{QvGvuDJbxD@ z2)P!UmrTjUkloGDqHE%}_eeC_{) z9+3leEaLD3|5lg0#^7sL#^;y7=TD8^&WZktH}G`r-;tF10d`($dAjeO$fpjS!3?^4 zx$HzbDf$U2Ry?{W#=TGop$8=k4PxraetwzERJLSmB(xp6aLlRS095BinnhJLKhODj zv5&uX114E z6|Dewxp+x^g0>jHQTJwmCZ-en53!@vfn)m{-peMo^3}t(&%)ohAvTjy)Jd37X78s* zw-(5~C#2n*51gg3u%mUe>dp9Gg0s@2AxI(2-{tykD|=z+t7T^jmSQ3h)<0%i>ijKF zAe&YYp@WVWjIU!H*m8YQ^y~I|%aT)Q<;9@;>^Xc!$`*-_oy}}0fzaDg>!<{;$gs5I z%X#z_&h?V3Y2~)BRp4&5U&_b7T{TF}wYoKjn-+bEkZ0c}CHmh8tpR^Z#I;}!Dlb?Q zqpaR`8)Wyd587(n3U@{ACSv$4&M_>tpVzfl!YBI6EuDU>#dZdixjVxug_1@5a~KF z2;OSHD+MilJTKe+eci8g!^=;_RC6OTTnvUR;a2EYJO`X--Yo8U-CFBSz5RW6AY0wE&ClZT=xEiY++*P$!9 zI$>cWFlO_jDC}HOpK1zMjWt(rFRw+iTq&zxS6^xsY(A|F^;` z{gw19KkIzvGQxP=@YRo)r=s;Aa+<-Of7u?zqisU2!RbCmE}*#awV^GFz;FG+htRU@ ztcl;dD-LzJYMZ5}CGOqjzQOj*pla`B)C*t0U~iviCMqMTr+;MZe5Z}ge;seSX@$)% zt~`>q%7^9Gy*8t!gQVId(bz=Jr}O^Y2ZO<{qrH^oBT3)Qe-ts61iteVhXV7BId$6T zD{ZZ%)(24yZTDUNTj?1WKQ(BA$%1<~bXOiGL0Omt&rUt=+&=YoDQWMcuV3@=Z3b5# z?(m_FUAz0CKF%zz4$*PA-j8vwmYQDN8*??9OZZ$_-tJmiCcE^E&yPbc^XdogS9ev2 zvC&V?-SkB)EPGv>)9&=A`p<8zQJni((H{L1DMu|khRUUp-sVn4i47}0n^W0qzAM#F zPQ1(VJ)gpE=qnx7jr2}2X<12kcM&~&R@>o%TSEeIyDh0N&sQJti{jh3{#0)4<>G*c zFV*+aB-}(#JU+uMWZvN(f6lnjXAVTNVkS3&>P$v`AM>F$eRONs?MVLL=9sx7mjtUrk)4?bpVHEoF2|{$-8` zY41bba|06UP7}BeP7XY0hY}WC(;o~TZK3$QbpF&c6WS&JVqkZ!_r3T(Ee3u^b#1zz zM!{v|y|xYrGLAB2^JHZ$vI}mdob2O#k`4W96^P2>|9sfP-}lk$3QCw^^M=3l|40Dd zclT(M#?=&aH9XqK2%pev*~tGyxZuxL0~`{p8h^~|MnBaY$q(z(J~>bHA79kQh`yIu z9+#3h1Q?Y?mi z9ge2^A@z6-ppTo@9s zIxUX1zu1C*<%RXQZGLRy6~$CK$pZrd@}l1e_z_PjvZc6VYeX^IH1}*9l(QK2Y<;wU zwCU@=9s!^)JXcAnn6piL54-w5O=%R)C+K<(+ikYA-MOpyPu&09{Td(Px)Jf0r{ZeN zOTBy}Yf>DF^WNOAj|kAz(MOyhz&MPc7sDm{4r-xb;NG{@_x_}(O7V#iDExr~F(Cpg z!ro7oPh1NY>L(NF$ONbjgtJB>th-`jVI;Ay=D@2hYcNG0gNOto(p>4UPxGgdaErdr zI9%Pk_&;yH^B%wRdyi4Uc5n#|QYis3bBUEo5-qdGHcMwKJ^G+*|8<=AWYj znm2t&mZTvg5!a5EaoF-HoPF0ZXdgGfOpV%W#f%g_jC7{Gc&|bH^?A!+6V_Ia-~vns zsz4bfUDKQWo15>8*xE4owz%HfEKY8xa1dVH? zSpvWYTl){$Muc^rS%I=czqu%qsv8wm**cD%hDH!Nb>1}Y1w@{;T)tHn!=5u-}oL^sa1Cyl-wE#>P!yQuDHR1epEp0M*v=t`|ytd^xtIqApxR12ENW-Ui4cFlR z)E{Yhzm);|dMGZx*T}d$sii++i$Ys>X8kTxcuDiu<~Joc3n(RE&8qG9cAzmQbWAM6 zAvOV3T3ar@IK?%(s;<>l$i-AUk!LVCKou{l)6f(}PQ+iz(t)qDz zS$XV0LS_qU?T@5|vrjg!-R*v}fE8joZx&z_no8Rdt^Geatur)&_8^Wgf^T-`s>tL9 zZQ|D8|Ru_F5wNw7`KAM53 z_^dZI`@`{-!M|G`8R-<6r+R0xG=Cp* zf74U&@-fvN`u8|W>1)d%fp*AY@x80{!H0RA^WA@>2L)%xN8w#-By~D|VQK=yh}Wdf zGVIIljkor@PEP-aNX%}tU4XbDw7>HIaPmX})+9l8Y2zzMW<)@`Cocp#y+Bc<+Lisw zmzzv^03HrHO2^&i)z?nkM&HL`2TiY^&)d={ppY$753Zz3MdeGrmWbz4F*Ydy7 z#4NXYWIC|`iT5cG;4js|{7-5JQ%p3)!=w<)x-S$j=Ww#mjQK85o{Q%kjUnm>ycvKW zn}AIklGLtk6S2(cHHTKh*i0butR6OlR>rW)B=+2`7yA#j6W`g%ti5Mx#i*BhfW1Be znM5wGU8^n1s7%SE9sM8w$%(c2zacSZ8MKS8S#3@y?PwJ6cRE*%PDY9-C&V8C${1|g z5S4Om?B9mMkDo0l;I<40Yf!yrKRc=_PvSa35#xqhLu!LXwi)Em_7VDRB4VA7_6x8$ zkuW5rG}5UgBEn>e6H|Z?hsthh4}|Oc#h79Es_ah6QbhRBin<3>AWmB_;xbVcs9-VB zcG;Gl=L)s&W&e&c(bDd;C3Wb&{G1Dx;&ZT!ULY~e=8yeAn7=qz+OMB0Y;I&B*jKo8 z*nC#}uaM~Y$AHqYvgZqn1q5`(_y1u)`EM%w|D)XPe`Q4f4`gTe$A0pk^}mHgKgfd3o)FKm%IT4y!ktx#vY?(EsWvV0PZee~)}94VuqA7+Pwghr-X3S|4K)?>wM!)t!o z3u(q?=)JodGIDQDqq17PhqWQ=;@8Qet-)i>kIA*ZgLURdGIadOH^av}R~#&pf(}DS zC?10N*Qy*Vx!>Xpi?oRIj9!k-lH>8qt_5M`1$j##b(Txc8LD znjJ&F*0YhA;mJP|+5dCw7D1*i-3SJEQj6XTwA3u0VXcDG3Oj0gG=qqhLJk^DSoqAw zN*XV5vH0{4rl8o`gqb|Y3B+x+450c+M1(j$u^pwoB)vM{S;QZ;LsI-CXsHdH!xDM1A66MuUbVHb2LrFRqS7k?+jLHuksCrf3A(v&Re1gLx|xGTbv61mdGR zv23Am>10&b>W*;1AqlAJ5xfrX?-wSq#zQLBDAWk@Yn;xPoY%SeEtAO|rKxHszFSgP zD7AovU8js)N?5so(A!35aB|7`JCMvQIABc(eXzN)!G{jc#PgYO5~6N2E9@Y!fQBjh zuuEg=j({f{9>4cafHUwg=L|pfIU|AY8t=NE@BX@uZv<|Fl{@mVe4eb^Flva5%rYpM zsF@RY1i>r;zKJH^G-66?o1&b~A(YKTTY$baeb|b~=zA5e6Dlvz<1?oMm6s}jprFJu zXbz}SYW`#EAfCbb?V1rt zjT_H0k1jaHcw;V^Rn7optVE@FM`GWglNDhAHl9b`g960{6bxt=^kFrk)CQVBK}mk~ zA2WeuB^T)twQkTRlbr!wqS1z1YZ=EMBT8$-RLQ@+M$X!a=0L~z83%Ciehnsg2k?d0 zYNJr_NwolNEmXgl25mom7cD<*mo+}zD2ovULYR7n~<^9+O(7l8#0xNi%DYx|5>HvW7f?=*ww*pb9wwq!$0Ee5%K^bq3)rsnDTj8_qjR%UpC&%vQB?sMzg82V< zQL2Ai2Jxf(uoqnzE_7;GS_BuShjbEwpL|g}(pr2*z>w+y@7y6#&wMd(1YT`Ek>wg; z^%bn_8D%8`{NCG1f(h^ubwF_Mg>=B0DoH~rxWx?AWG;F-KP8j5fzeZ7?}d24nrwus z=N^M)5G`L_2XmdKIv|tI|AR=$kzcBo<(Lh#Re3HOJ6zGqb)7YVj~m_#wP0%;;x9_j z_HXRPeAW_et?hjGO*kCz@p$1bdH{n0szIw!*9d0SVW-ozZZ9ah$0+WzGQo>7xSuo5 zv*oh+%QD*(z1}*A3gdt%V_dAkl7zI`c;JKNVYG%#=}$uT*usmp^oxF zKHQuY&m5O$NJ^&h>Y)55o5jiQ5wB{9lg(AE8m(u!*yj|8|PJhqr3Wc#-jGO`H77&JB= z3Jcf{N>13%4LN72aC$&IM{?BFPjb<-xa_{8aQZrehqZ9JNGylFaC)q8n!0dW#}Y?M z!hv5YELRC%1HXr_;p8!g9lMbUo%3zqJwxZC;}t8SrgSVj9ZjL{pMgR|2zOeamL}RR ziCPUaz^eW%KNk^;){wo|wUbYUt9LFPC0lX9$#3v9!=E*UTwgC&81yU;hBe$iMnT)( zeGN@bx+oGNGrTcF3iKfdlKAcDg$3Lh3n|Rf3(J~ZT$X1H7nn>P&|=`@7{KXo)%SEu zA<53e3R!)^-2EdXRZK(M%2RlfttMDeqvk273NWp0KjXYzf9Xm!p41B6Rf^BZ57}Qp zpZsZ|L@|`0#fvKaOM5S=L)8YTmQ-1S(pAJTXsj)YCiUYsxJpA6>zN$pKdt`^8g!=0 zK8ZY(zSEi}uKN&R-Y|nZ)HHU*`sii%iu7@1z~)bho3bbRc8NU*3UZ+GG~HHM`s{IWEE_Sf&0g&>a|Pvz4B}~(tsL#S1nO-Pqqh#({UzCR$z<9= z7_63pZtZNa>m&=&Y)7|w8MgDp57`i;-^2~ydoDz`;RQT3qT9sQbMPem@{_GgMTAL* zeubjXOC!xqXy$c{PsnFQG}x^jTJSWTZo_4$jzll&4d;Ky-nox|;Ose)g|Oh5y?xsO zW_vC8-AR8-Vtew9`YNG^_9O5cW!p`0w*Nu0KHaL)YH(=Zoa{)Wh1CZQQ3ZRh$XM{3RsimLTk7mlCLvc+a|)>o$u$AEaSQK zTUrhKdg|}%QPxNLQ)<*e*jlRAjc5^N1eWiQhh%5P#pgrSei;J$dgh`@0mb92u9ff+ zo=QykLpkys>cp*%qPlTxWVhOTXf7}LMx8!k+s7I zCrAtHPXb?(1H6_?5W1}0Vb+1`(+4UxVZyUL_T2YGbINyZB8S`(`F6yZV9uq3$0I{B zVMAZ93oCPM75L37I_rCGOH2-0D<-hISIK2hJI{I9UjxY{`>K*^Ni}F*?uB<0K(+s=aq#)C(VQm;30QCMR7ed1Tf1ODH3uLv41Mq;Ma`UJ9b4 zwb{#{$(PagO*n_nmb&JuqJw1g!Q2PF6(I{qkh31sH_O8$z1QQ*%z-Rn2SgFipWCIn zWri8%XJf*y9`lYqu_dij=htcT`b!Oc6!lK-SN-)jIdG2Swh&eoSM!-Jez~1pt!-72 zhBK;IH`LvzSEs!fZM_W5frbldQ4%Y-BWwW&vLsEk5cVK%($9ajagH!~+pk7#HI)c%#U7Fw8_KV&8vVy`y(YTbElkR$uO-qdn&&J)eT zMn}SYfdid9Q@ES{%>0Uz1z+ya9ZNb|Fzj5u*y3D~ne7pCtY?Heb@ax7{s zt7Tj2Y^mbQ+}N1y({nV)tUd8yt~~g~ef^I5FWJ-G8(q$mrj!B#rWypWdZ{I*UaE1M=TSd>w7!%VX|X-|CN^0DFba5i;oyp${jozvsTtPsmh zIkqP;QxZ^Ar79~n5TEr-QRpW2%BQB5iovyb@6=E3LH zGqsI9(X|Kb$9n0Cvgk~!$FqqqToCN~IoF0snwc(>LP6BWzg2;D+n$rJ72`)<9xG$P zjDR`5g|yiR@Ac==+Vt0(*|*CYvm=aLa%JWpO#$~R(Zt)12Bm72{ovi+UJX_0+2vsD zX;FevRrt=&j?>~D%RV^A!P3FO7diA^pUA`uU0|PAywlpg*Y`?lr&=K10>W?0BVL7w zUged`jS{FU;#nN)p4Df}+rpyPcv}lvyb`m0UwWTR=31cMM&uu!)WsS)Q`CN6kJI~$ z+aEKE9yBl;hh|6~n7-cFPm;P^(>{9p`sOVp#2hz+M9s1s`KS(bZ&+PGe>&~xN{1k8 z>Vc)0$sCE7bw|eukQa`7)3>(Mbh}Si8?x@k`-^+n#u8^_Z~sbUr`OD`i{Usc>#gHa zk2UsdhCB5+NY8Q2sW*%+8i^G}A&cOS=tF|5ybOu&=A!ElL9h4XlqYA`a%;C(G45B@ zvdt5>giVC#;v&}4Y!X{fY1S-o3(QWJ!@l@=@LNT>@;1f9oS<*n_nTtPH>kER=MK@? zpJYb{8ha%2(jK@AdE}mtGE1G>SMa|k)Et7!s)rYt$~+Rvx{TeRTdhGB41l}-qn1<2 zJ8weBcs^(p6T4=8>sptESJ;-Sn%P}PZwflN$T2z%ucgtpH7CS8vpYt$W?5Y>v+V3X z)hU_nZLWZ_Rpd6;?v%lOy!w`Mc@D?53~LP+-%Gqjs%*aFFuUHQ0?TLN6rF3*mO!5* z%ZI^z%TmgD_Y-Oz^w-DdYlrVfawGIcLrcr*WoLTWx(l;B>gg`V%Phgmpezl+Z8>~n z&r6v|ue{ynNqR$BSSz`_c$E=1IXY(_2N#n@^qX>j=+Ez)uck(gP0K0~a9nsO*aeoD zpl@6gIi#C05ma6D_U#dPA2#B(YcC7Z?%LIJSvn;_zn8m@TqCp>_s`&>E<>gldot{D zl)aMFaa@vxLjR$c~W$vB7o9jLQz-txYBl^j*(V`1uP`AmrAz4KaWvqa39eZFv8 znl$O+VJ-h+6Y607=$B5Gdby!jh*pwI)8xeGymYiY5y*V?xFXINXmZ&+ci<`#I@@@% z!0YVxI=5Ku^|ctx*?8Ssn>+9=|8bzm}6*?(JC9!|^>%G;wP(>k)svprm;LK#elEi%qyK?HRd! zft{b<>w26QaM5zdI_O@sZmG{FmyeNfR8r7+GNky`+&b= z%LCpUb7`=&C6>}2gMFSS9EYEW-Bn{qcS?0Awl#j--n=bTW#*3q?O7vLsc>`=y*aRd z|0*DneX%|L2F$8lM!a3LS(b@f1}d0vh@X<^+n)ZHXhCQ@Rf~#vr_+acB{UKcGaQEznXg}!hSTN z=W|?9Ni5D(4P`*`J%>Qh-=;(uiG2Mszlh5~QJtFh!6V z`Wd=H09!IrhkcO?@F|1pn`V*)nQsm;l!D1qQkrZu-2W&OgcWCoW2qA4@GFPuo3~TK zRk{D*N|46@E-!3Y09|Qw+B@=o;J`+aZ?ObBa5zor>;%N@(CJflSUW55_7^ksM>Ex6 zUGtOC7h1QNlD7mQ{>1nP?tiBc{eTprEP7YP8{n{CbNs)g5Dfogt^Yp~Gyjh%L^>dW z_;3B+Qivl>cQ+*E71==*fqcGvc=X~bSdOefe=8}gnK=2q!<|?_CL}@zu(@u#-}!q zKCjt(9cQ>5fsq(8nN`aJ0+oGylPYp4N5_)uqLS$~r(sEB>er{ZEGb&(!lh~!l4d#5 zUeQBL)sYhMAL%i`pk_rfpu4d& zjMwmLj}{*HGcY(4eoysVUasa27+e!pUVfN!T^xA4(c9NJ+aa*Kd)uAQJR9FmHr14g z@nzZ46wz-SX~VcwHoxUDx-`wuwFx@R#R4GUT#&$temxxY0yrWes1?_T3j>KM*qHv` z_wI3gAtdKF4|pbZN$3*pBW8RtM!&_4^cwgC7x=_Ok>|53Zrn$0Ko8{@9{Ih&xsqJb zv8fCNaws4@!X9B4T%XU9^XA9?Nc@{JU`)H_iXt5hF|D?s4#{1L=>BJ}?|xUIj~fiu znav!{kHn-0a^D+ote2z`dmkK;#-iO%CIJo>3T(j#wxTVk6cRJacWP$r8t2KCGz%|wi2YmFfVJY#Lu&>rSeBd5Kx=jh)8G`Yjuftz6sj%_*GP@f zke$^aF#B@CNFy-Q_G_kD@Ha))m_=^RAp!YMW$#QF9(e$S5W-w;DUUp&EeOb9CRh`i zvxzNh0s_nh{}Vz?ii-yS^Vg`q*`tP^vWGC2Z~}x79gUO4wsS_dyDCz<-5kH7PvSD90zak-9Iv=O$Gmb)NlCjV*;}D-pV_00zR(oJluX+gb;J ze6VY@xHQ3X+7;Y4QNMxu!kX+8E&I6^F_Jsl7Yd&-7Y&A�J%VkWA-_B*-QB*d`@h zqTsk0EqL%D?C3L@18oVTxh+5aohJ1bqdob=k8CLL=Rg|Uv*-w=;a|jGDBLSWDk3g( zhg-oCJ2)Oe4Qp!P4I^w{nP-iudNuvsJ3Y=)BYK2Oc(qS~k$oBP)nZ^>NUG}`;GTK^ z4pP*YxF;;RCFQ6GcF2j^MtA?U?jyZi*h{o@ZhQt$IfLi8u}fCM#+`^MT-5Y08KY;< z_%)CCwI362eE1hZ3^qdeP86h#JYZ3jw}UjXe*0=OX)f~#-9Pu3-)}yFLAnSckowraGF0O z-E82W1;D8pQJ(1!m=7xscEaE_5hGosm=7IBQ&}gbwh-$( z`1Yqnea_7wZp@GZ&hc{n4}2|TSNsWQp`D`N0|YRGUd2UyGCCC5$qEDENi;$dAnz`K z7(&%a6Sgz{_}YyoA7(q$7Q(R(yvLdl?s-Tyh<6og2N6==)=N??$osz-;u#QANUp;D z7ef&DprBvH+JYurr;1_vIYT2%Slc%EYjFJ+L--q22X+On8fcwD1%k1{U*b zcdqKuA6Sj;5~7LZyReqeqYH&4i;~zQM2SSZO`ad){cYI77$M&jYO$WYVMem*!XU$h zi9*39iD8rInwLi;78NIs2{7C-_^VHlXz8dSO*fvptCV(0!X_+kM@isDF8|42#fEw; zgyNJ~7kW6b_*%>W2Kyi(sVGqx#*`t3Mbkd{XP<^h{DFL2d!EE~T&``>Y+QRp?3zKv zA=N&i(@5g_zV47l9dIOh&7k9u3US)oV&`zREYsQO!7A43KWO%|IWt--&$Fp%S>bayk) zkD|sDzItz=>3w@8%+w2tj+nz!Rv@S&yriMA;C5~?pQVfSQxOMygj3#FLhfb%LD;3c zK66g2-6X6{j$ispHdWy_RS{2D5^HQHnz`%pPg;bhz!7UKi#08ZHB}{>y~PSmasb-H z5UH_?ZQFNMIZiZAuRqbi|>wKw=0Xa1sYv5wh19nfdeBh>po55gRi# zpJ?PJs!*(mgd;Jrldq-8g$$4t+g0v5ZGK!_Z-Mkth;2-i9)~bj>H-&pAO>hq_PYc!k5SIz?>`r(YC%Ji%;Mq;~ ziWB1zBK8d=X+@1;i8BiUzt!YD8^5hneRp*@+wm-3@htvH75niQ<3%J^;d$js8KSog zfYEu|cYpkoIX&-W0~fnb0m2N5`%r+Z8lp+m&&E$DBeCZa5wu}G|5=D^V_Ia#S^1~c zQK9XNIC#UIV$&yO;3@#w2CdlcLm0AEQlU*7a6+|8rjF{@F3M%GkYLFl9TKpl`9tQ0 z=L;Q$h$$5T=q`RP?(WCo1A1k_V*G9j&@*bUzd|#%uPSULf6O@s(R|=`_pEtc6!&fd zvdzIi1&#iTA(Sn;C=6W!7o{&Nmeh0@ncZ6z4INwMSW$?7!&3U+tAi{mTmd7a0c(~+ z$k~EJt3>i0)iH-aP=;dCieddm6yRGoxciT2R|gR76QVa5kXEH5EyV3%qQfW~kDlPT ze*~AWzH*vwdX72o~*BDq4LYm*3PzrBu*8~PeC$$!Hw?MYsfd1 zN$B9fesVIL<>fk}{=sZ+tyLN&Q=Benp&^4vQ#>tHc$V|77E`phN^12}nbL|b+&ew6 zw&$!E2Uo(k^TJ*R4c7b_;5HiF#%hl?QMF+;jV_Pe*eZ9KYVM44@9j(dp7PlxkKE>b zO^an$srzhM-OW3%ACl5}vz=#oUS#Qan|?c8?AfGp=U$vkytCxS)|-?#Nme3m=9yD8 zysI>^*>g_&!$lvMq#Leek1?R*#p?B!6WY zljXR3A=!_ryHH3|*XNjrsvP?~(@D4WW?CyBcXtME&#bd**AZcNlTDwmOVg7=ZZtCO z8JMs7*il?x7eirvEZKFuPKkk(Qg;(B=g%v2$|XgHde^1zL$_JkzaLpSKb{$?vFGzu z2Op^}`LVc@Wy`WwXXSA7zk7Vh&sS<|zPWI;xe*Qo?_MEbDyPyy0n9MDwXmzSjZP_E z+VNWFVIc*Htp&GzXD_#}@w=U@vdeY+1;*XB^N&|~-N!VV=)!YT?T32Iu%}3sbZoAL z)ScdJr~MM!FEZTE_RJG!*s%u#MiHl5xUkxs)jM-MIq{vH>(9!mbP12k@R>l=l6Lcr zLnRvx6Ot{1Q5BDsKWN<_ja9+y7g~?08Mvt{vCebKQ%F##Ev|Fas!TgP9-W!E>U$o~ zk#jcd^(F^z?u?bE)w*5u?b@8IU)w4Dw7C{#MW$S+-Na7!S{^53p(WhBW;EQ?&zsMQ zhFXuc(Ug-1*4mF3k-6AEYnC=0`yY*1x@Ws3)T)FkS!q0}*SC@$)0g5l?}^=pI^2xy z2{M>0_GhoP+D*OMbK#3%Qe zsf9Z(3%r%OEV4bl2|4$Vf=i2fXx!{o8;+q+e>TX@1pCY43&^swU z)oc1S*_{t}GtUuSPmH>rFPbi=jLr+vQt^+B^ilYOl6K>P9-W$q1v*l{oOsd}+BO4;!WO7_#Iy zBdr(Ncr+*O+wJO2+0QwY>4=v{gasu75V%`4XXsAe5DJ%?kat@mluk*Kx{rCqS&O^+kWsgS@H};;|awv;RR!K~YQ7U)t z7YxcgsV`dR1AKFhS6 z!;vx=ph=T65iW8w;5SZi&EBwyR5Vt2`@f<;*ZN&rx#krN&hmus&KD+{j|{ zdXBwcg$Y~~p2@o8{dG*Dpvc$5ljtVS1sr_Jo2RQL3HRfJ`c?^UMUi4ak$@B{?~W=M8ZlmO~;k6&%cHX$5Y#PF2<8*5hdR#&2Ee4 z1!-FHd@j$2ic{k!97ohVj{QfogZ@NM%CoIeQ<-%h6PmBai=9H3vy*xDz22!xyz>Gm zvR)5!b6+%^&OH6qxzbH1uj@AFNnhu=Ugi{77>aDkN?u;Y?`ddO>#H?R^M@e`j)Oyk zt+i|#4b4a4(kfj<+=> z*6tS7`3@XK$}>TA%Xi+a>jKNOcFz=_zl(rRA&Btf^@ZR%`fjLW)0mMwDzkzbs-)tq z_e0$3x8Y3rbLc;)C(W$=i zzA*wM{9a4poln-Zc7)XIB~beU371=-QkoT|#k|KPd}S+jmfIeCUPr0FBmMFFJf9IK zN?R7Mx_j0Ab}H%OqWsVne1`XBeSdxU#D!W1cT=u1yZLlEm`R6I($~Ll!`)ziEcQ59-SB}nWe;!bg?1ppMl>74i z^vx@^#FFQncVF4u6tm#%!JVD!67ag0G@al2@*jsW6oKAHFcc#lQS-JQah zScA`-1Zx%dFO1s$v5)PsTw>H;uKY6!(Vv}T@?|atEHpeMq!bvI7;j#8lwKj_B!Ind z9dX&W_9ERo^wS^UThK&2=6T3GjIAG*w3polG-;nMCMkbZA6>o*;n7!taan~VIoc;^ zXv$?YiSLH25D-UpO9 zR=z3_U8*v|4q1|OX^wtJ`gzY z9?HmFxGE?D9T7cA7ke^NheXNQPtHf5s@5@Ct{d+U^aO3h5lMBd3Xs4XXuTKX-5w<+ z9eFQ$N@^vK`|XWj_;5XKN|)J`f}V5K_lBA)O(>aBixs;N@^<4IuufhL&q^@no(H!^ zTY>v)?^9Vt2;}Sly+dTmKvs=W7v1yE|H)R*0_=NJhNiCf{C}!wV*H;?@&8LjlPX}( z`@i*n+xH&Q0C!UwewIz?3DPMdnrDF*fb=I$lKM>nk2nz0;ODk>xqWD}dTDaJEgjbL zC&GWLSWFZYq26y*E(6~f1ztEek34`}*h&lm0h#KyWV5@G6=>(>>kaqEOPAp)i=FZ0 zh12wSJG1K&&>Ayvz5-Wymx#E`OtnLr92p>K*M=Pdk44nB39Igu!X6Bg-)BgRh(f?4 z5+^)IGt@}04}BlzeWfytsm-yJ9>2_Pwa@!AMVDq3fwfj-)?dh$)gaGj;6J+81Ql_b zuq8$w$v8llIIaLkD?XC2Oo$M|h;l&JTG~f(+^9puq&8452VG@c9xSO@4!%4yqo|iX zx2C^VVl_ym#<~%Q!vnSaux4dbs@OyU+bYQtBjAD%{;Ut1X%O^8G+pQaJKAJbZos72 zE_@mjil}IPjTDrnpi#MhWNlBmE?kxnQW|A-3Oe%wd0!8zIn44 zI^?F+4T(+XX;NXN#NBmd9QmZ1{)ks-7_yL)SlysfsSQmJ}nBhLq5y^TREz=sgj;Ji4$2I9|?K94i81L z&Z&&T#5qOtuTj6ruu#L`x02njKmziympHM|M5=5X_A177DPeGp3BK(i@3HoVx*r}c)U-PQ374)s7k-R37d|ARa{SA=Mpv<0grXdxcr+M8sfGXU%*ARFXd}I0JEcl{|wGTw3d->5wB6 zHffbFt^UtXJru7o=%3>Ec7>;h!OhtPW!_o2ug|da6H&mC(${C&`3WTeQFlP46DSUUYp%|Jg9@`VK!*yl4iYqh^jGjnSCm~tpQeYi;Tb(~hw&M@8 zMXTV{HfmrZEKS`o9(+SmAIAViyXablqtVyVis(ki81HJ8@}Hc(h4_zF50X81Lflo9 zG8GP+kpShf4yojtYM)i=Uov^EI^SfjaLhfJ@z!y^hN^JXRm$#EalYnQYNsuq(XQoq zFkWweN%+0&v|HrB`qSVeRcg2Wh zE(tE~Q9{{2qHQKK0SQ?OHpGifTniL0p2bJsn#pbDvsK9iY`YB0xaP^C6c=cVvP|1gI$8Iu>#~H3<)k2U0S$>z~5C*31G>vJf zVFD6la@gxH#n>5LcKjku<7O05M03p0ifZh6KUKQ=T9ttvfv_)5fUKUGN7zMr#^{FF zHG}^~1d_mu@|ueAJ?rI5v;Gz>|SW6RhlFWrd27>e5=b?A?-ooDvzU3{3z@! z&OD~46#Jj7?){&v4kMPVfPn$9&M}yYl_kuO`74qZ$`HyGGzv%BEGE>Y9=I$5u#(Ft z&I;DPDQBL`FtpVi3j`c|W)J}*P7^Ig4L_F#K)g+QJ%4f(67tEn+o&qwA+whW0kV35 zO8*%*)s&p&JUM43zSqExZ1Tj4gHj9V^ogr_>2MXL4#h)+O4rv)@a zTqhC!09ifnLyUG0>O8_Si=j-D&Hdl049+(2Pw^%{x4ktB!`${5JlGQ zV3J0QSgywtQEKu@YTjiAOk^s$GF9j}^#<(q`)u|5?Dg;V1|)U{PbWi|ec{PrmF$z~ zstIKa34vXdq%y>dm}03cAK<*xq{VUVw>shs_H$YWsWCmT=S-arDL~ zU`HWj$LPEToas|R`4gn0iDK!l(cu%4+=o~@lr1HjcT8mHwis^h?6CkhS%6^zTTw_9 z72v81CZLMIdZVK#w#pVqIwIziJyqP52`M_CDQ_t?Cshc1rfyNf7l@5DY3_-+u^}3B z5KpipF0dh@V26XVBf{Aee-Fo(9g1;DRZKN5w40j7QFap)h@N)flHk~iVHHdBgl|8u zFBLhBK}4IJ2b{XB^l-Ia;;#*d;TjSs3HN6dgIltIPfCiqoN{l&y)ngpJmKaFa`U9x z=;I=IsMcoq9a~n!UMQo&Ty+T8hW>pk~XC{)+E`~;>)iB9F#bdB&NKaM>~)!GH=$zWC=p@QVBsB9;;9!+5!YC z%1t=2(*m}NyqA9fvmx4A-F*ewO8RKZtI~}Qe#eF`)o!6_B=QPc!k%X^z@b`b9_jeB zm17?w9_Kxh&jxw6YQqW`SN+pd1X$Z2-cQF+52temu5ll6($L#H&k0PnXJV)$G35N@ zBn$yjxjvg>@oRzj>!JU->RPyuxap{iCjZM-clgg$|JKv+&P6;0=JtoR`|a&k*|d)5 zT@<|0`?U8=57QdBs2OS#uL7PY%d{l>R1sQ!ZmRU;h+8g7Zr_`?CCY9eYrpSPPIPTA zvsue#-+Mg7-5>6mfb@b)c5N=RY0G9mYq#$+31}8~(%#vjsSferA+G-B`%?%&A}M~G zKK=O|LSS>P)JhMVv9#&Rb3G6~AIUxIpxGi+CC%4mTAK7&(k(wnsqjc9y2TRSbzS$4 zCbG*Wx($W(=VkxxYY*~${AY9b&*tHuAPgpu0T|5RV1Xfg)PQBJu%-2h9o>Fjo*%<6 z3FBWzAl-L#ko1(|_)jv-dG!W!qzLT7iEvNRl6-Y2>0gT zpMgm}nSt?VG6Tzr%2NNI+Hyja+Id;*FoT6~h=4m1TA&72Kv(-HeAnWBr4^Q7nuew2 zA03Mgj6aF%inC6A09(DmfHqNn6q9kZKxTa^ThO#eHM2dE<-yzOxW?(&#^@OAvKc5x zBaYx)$MBhLQ82UH^z@R{`dQ5SS;y&l+v%A+@cmS%C-z?-%2in`OYb|Ns_DuFOt&Y` ztU#c8q_8Jr2|bWz!k|1Fz^iZP39Iu0hc|$%Z|&p>ul)){H4YE?M=r@=CZA&UufQsg zA5hE$Mm+(B)kKapP)HRlim@x7GEqnsZB^>?=VML|m^}gdaRCTZ1gDI2z^UUF8YUhw zG_oLo0Ve494CSFWJUw$NkSP-U%ud0Wo-sgaP0>T>HW2fJjjZqz^WU=|QF`J7a#Epy zB#&x=mf$VBWfGV*uJeakhZN(JGmpBJ;S&mZOCT+QFd++5Nk~2jQwjzHg_+hrITTD; z9JyesjChE}F(C^h#Ppo5K1yN2w?BcjcI%ojfE_tvX(5K5?XdpXSRr`nlK%H!Fykx$ zTzxM8CN0iSSY&gE_mNZcv@-Ja+!mw6tf1t)fIxP7ZVAt}TIkBQz@_$jQqE~CG{Nl3 znVu4VpS7-SR51@9%SQObc%^dZ_MyJE>H-;6#OJ~0V+%Xz>Aly7m9|Vyi1iIKJAV0~ z*PBh%Hw#wSKElXZIdHbAI`S%UXQS4}PA!r1tzw6jl&D(&bpAF;(d2Oqq-}G_OpcO< z6&ph>9sUngXUlm`fRt7M)>(7NE`#l7nQ^$A*NDF=T+5KLUjO0abDQr-S*{0h-19XM z2De?i-uCynknNKsE6oVfSM0;2#;!wuJb`IgcF70a0Krg<^C;)9msHru%uY&Of0HjhR%J-duUIf5p<9{*n_Z$dZKhgO51QPm zoJ(nS->tFe2-!)z?g51P>F~8_Ejt)!(V6r1yOliZtIa|og7aT6i+A8eNky{KVza9^ zU)ihRFSo1KY5u40A#WTOr?>V%7T;`T-7XWJ=@%3#S7@f($!MvSb)4@{&y%e7bd65) zqa9UJUQCR(1kYlf(MWOs3WPxK!u>?xn#^>xL1_s8Y)8d)sW)zle;(ia;&2_{VB3*9 z?+tAFa=W9M2QAcLQIN#A7#;l{O}=^`V>F!0UUBe#D-p9yYaDEKZzsiYy~=Ue_*1jC zJI>G0|1s;iX*=gJX?U6O%T*?Fkj}OKk32sAINf*OL=r#sR$hB!j*370%^{+sIIe(m zw>ERu&V%r(e7Zm1s;qcV(|NtVk`3_McHf-u7k_76)%(cZ9%0619f8Vr$H8}-*P%_} zK1pqbaCh|~?(F_s_vWE%ws=VYWa71>qHd^>d8yanoCeayV?XEC%K-Lp zo$P5hEmyU-=ugLMRoM_ukwRhJ2;28%+At#AtZ;oGr} z3%IcMEKif2N)Q~A6hUIyADVxwMky>?dr`m_Q{>6dz*%sa6DYM z(9ckrg`b9)b=Q`dwy36EttSfD-lj|UPnt_!9;Y9x8J5NG^Nt$XTXn=`> z!h1Gzi-)~@8gqG2XWO^1TlcALYx6M!?t|-IHI6j+m3Os4YptyCrPqJydjInH9M}vf z9D8KH0N+j)4UT|#_42GZD9)$BGrPY~l%{UewrlS9yK+AEW6jp*oDk+!d$G=yVMn@k zZ*)~V;DNsIA8x7Ly5qFWXCiV-! ztFvvr9ohI?haO}rmg8yYPFFjlU5*B?_dZW;I$$yA%f0S;4esnzi%|f^FZGe5# z;Qv0^!-HvUV;p?lwNS|ZWwgHot1Z<(Tu%Ko6MugZlq4&juFU1jOi?Q?$=i1nh2$;s z$v65@%PT$c$h(3~cBK08>?1otOG?8gI3XF?s@QE`9fD1IG(NcVS?cN-8&ME(@Cp{7 zXBL5-1E!{D$+cC^nwtmN+3)FOLayiXAAOsCB7 z&eDAj?rCbk;i7VZDBMS1sxh-Cjq9Rf?!tz7+R9aQmM8P#w-^<bXXRaO?>GA$EZ{{S>B`2Z!2^ak2hoR*do572?u3J@t$%EuI3Jr> z1)G-TT;JO@q+lm6c$S7&tQ3q|chRDp&YzR!WJlA(M^!!)b_pr9-xaYDa6X==vDEL4 zE6K>X*JqTLASFb9+4e0c=z7|@E**-H(KXDjsMzJe8=`Vg-%}*~<%itjE3SahyLqh~ zI`R-t?PPA0Wybuzt|4n|_^3}4Le|`>%76*9F0S+U9O&#$l8)jEldxO=3VwPYujr0F z$ug;aajrYt%^z8?>Krm>7*wx?-bjvCxP^M$Ip(hNvPf8sJXj?Jezgz2Zfm(qhD87F zMZtQ#t43q6s~t2St=jOsjqlkkeZq^{cMlx|mMlefyOf(*_X9y$J?bzqqAEw8eTtNY zKu8UI46Tgz=~)AFilN2eXx^j0PHz`v@DhTk$(h4)V#Wf&q^#~LM@O+ckWDGlVA(fT zCFK2_EvH}apxuKBb8Ii6XJ;Q; zweP{&=4rQ(@i0Pp>#Q}4VHil6=UC8S@9Tso=Hq>vHr#+)#~4D?dADEN*Fos$=#_}g z=6%D@5sz)4YaMOY{9HDwg>pl}q<95X_%| zm$IAF^6E8*{b`jzyK1u0`|1@nZfiX%CGI00nLW4vFf7G$@{|1|VdfQ$anhaPvav*1p{xH z`Xk_S&_$C6IO8!iSSu=;(PDQ?K1 zpU&dWPaGhvn_^10*kyRo`vnJNadA`}lQ1FYh6hY{NHZw~pb$nRqgsv4sgEp&QK{(E zL5P|f7c^j}QLWk;9yY_qlj!|NtplV?V~BJrH+=oWCrCocA*22h3?f=3)Dfe8Fu(x~ zq2wp@3sf?Rd&oyKZv4?0VB;C4Tc{YnK`j>WRd#{$g=V@CAh!EV-&p z(2Z0F7n%eiC4xdhvSLc&Y0_ERlpo%;w6t7JjZZUQk;kzYc9)deh|)WAdSl<=5|d2| z<5W^xP+LFIA0NIf?R-J~?|}&tKyb8_eW5xA7?`l({C^pk_+LX2{|!vg>l!#4nHoFk zGScZf8XFtw{;y#Q-TzB%lXE;EH2QD3LHz;(c>?6yL6G+q7VC0JYL^q233h>7WRPKHuQxFaVn;#+q z`AnVPV!Z4DiKrN@3gO`a@F%}g=Qy5>0MWb?U*biZRt+trQS2%2+P zI^4HGP#GFesWkQx)2U>pn^GT8OHn0W(4o}*M#3AsrLr{GA7UrnGTU1-OO&S*SsPWK zSHxS2dV82)fweuSz3cD#}M#M~o$p2BrFg zn0h*I6{7`}NfdMLw+tT?X28cS$h~;HhLh(blJN^OYV4npkdfIW-ypQLh#ZF5;lyO4 zI+az9UW>Z>mYtaafu#iF2?PiHrXU<96kjR0G#khys6F9|8vMZ0Y#2Nr`JM`7h6zY= zfC&vOtr?A9=wn{61wXbK1&tXAwHrFE88wX=kzb3)KN{K<(g__S#(GqqioTd4T0g4U z!ss)Hspa2LA4TzatZbU#>o0CVLCJow@geX-+4UR|=$V3boFKSd2?$1J{@~Lpi>40I z0=T2MS#vW1F$pCOQ;Bf_xL5L!15bI0`RpYu9FR&)2wG&o&%i*K@!i#+d5x3*K# z|EUc@QXTT6HGI_s!M^eN@ZesGfOTRGjEz{SjKqjcb1~%Y=JV?i`i*gg^tYf)sf5hs zBjB9gQJtzoeiwsxLsLUY8g92ih?p5%#u>9^T4Td`AYIFcm{hpi(_~ByqiZ0Yxs&eb z*O!^|S*7Gtkj&x+`naBt?h%AeDo_o&Z6N zI6yoHjVquYV#JVbaQfQ0Ksh%0$6R+%Ff9o!>i1+?oT)_Ad;HpWNT|1oaT!oh03u1_ zYE;P%Ss}#Q!T&+oI|j+pM(vty+qP}nwtKg2+uUv2wr%XTZQHh|-)~ODi8(WWrXn)x z$rY9Lt1_cvt>?P#^jt9MJ+o#*dS?v{T}ziq`2aHy4iQ+!v()E&aO{co9wVoJi5q zin8u3Kqmo9LP=QDQa3>>uE~#s3Xcm)@{k{_B}Fzq z2+0!R1CJk&w@Fz-ImS2^d^jyvnjav((^t#|wJ#Xrjv|v96Nvdrkw%FJS_`qO%9|EK zium%1MSgwZBDXngU9&kxc&$-GbRhD-U;BBJ`6ytRrkYu|}^ds@Q$EJ4u2SxBvfa0I`D~(O0Nb0uyd2Wa$q5sgh zZ$M2*^&#?DlNy_-AfeYL^;j#7Pp=eZ*VX~zL*BZSy)?CY%*$xgJT}|^Tt~X)%=mF4 zWh!%Y$P+oKD-;SNjR{;2G$h+Gq|qO3e*xV)kIQ&#AGO+8pMsc&-@A|*_;nzt+MKl%xP60T?_@h z5Hyvw9^uo6HW#r&Rn?{nuyfYWHW#rm(GSP_x@|O+ZTusq;u1abh68$3L!;Ytd99>S z3C=H(vPG=vT5G-LlVI{9ox)3?`J89GCY)dbFP-wG*l-y?X*p*vF5SmCSBG6^5PQ)N zsrp@(C}Yp0G|zOSh|Y0TJ56LPZ~k=3ocPEC#Tz|4o#gLFjpF$m(G6`yi@>3nf8vdj z0pTkIwFI@xBZn;bD{Ue`t3nq&(nabs;cX+Lda>keD*<2O9q;b0mBQg0VqkPCIn*1nW$_Kk*a)O;BhY^|~Rfy~ntR1>^ocqJtI z1N|sVMSwG3S4^L3ygEK{WWbub94w9fubB4T6~U_C^|T(Pg?zxkot6>%(jPzkXqWhP z&WL9f@g?kQx9~MS4I%VE+)#8Ck{?O#EW_MNA0@+>)vj@KfO1#>K5^j7P@s`1oQj}V+LJQV!hY&F#_G<5jeiEV zXxm)y#z8}85t19GMCgLC8zxDobjP?3*u-zJx@#)=Z-J^)I`oomz}RgpX^T|aHFd9{ z)3|U%$eQPe9WvokvhU2%`Ymv4f7iu6*c^eEQ6grYKzFo|GxCpeTO+3;=E#C1MUTMJ3sJcFQ5zRr@4m)Y zv%4dyijKH);z(wfL~)O30M$`NCiU0WL`7vfu6P%LgIYVT$S55Xv2s}Et`ifn=BP3m zU$_s;Np)E`@pv2usJlAoZGKWVBVDa`~EQT;ssGU9nH@d~=VE4XQFC?DRBVUQJ zue#KaSlw%Jh?m0Pk9vag`~H`Dk{C^7p6es}E%DNIYqXheUY>wdghgYG6H(rBxK?G5 z1D1Xc;=dccl5N?f11E>}AChpb4;A*E1b;U~IXCIX_FOgLTB#k|bdm?IGH`5Hj_f-L z|8B_A$kh=6?9%?y5<(>q+hkBl3x~K3FP*xDDCyOM47XmR<59d84J`c%b>>r5!dUL{saD( zmSElbzP7V#9|E!(=ru1c75iL@wL3LpgboZD;-&5Zm@Uo}7XM+94@}es)x;k$*zq!`9UDLzLyl#^VIp78t?tl%}lVqs5K0$l^4f!%D zeZ?KZ8sk=b)^2D1yYFPHUWc{4S!|p)U622^UOb`Ox&N4${@Ji_GgqBdWu!ZCzsHTG zB9@kF2>1JJL@(4g42EQWF4L2aiRWPpz%CbW%le}u5J5|O#d$C?Na?Jc{T=qrvn-Xq z+rwjM-bZJ?`)hH0KPyb9e%qJdNav%Wt!KMO{vs#mR&Ed3M|qR}FN_QZ7S|y6&tCT- z!mDzD8?KI*_{-DN_O@@g!%K~9_wveCt+csjw^Pum;#qcW_p8-7{@(1r(3J!HuNQ&E z(o~oENAatE=HhyDOplK>D<~Gl| zXy-Y9M}w>{=$6+RHGc6?=c(8R_YC!%Y-#0nl^ySUTw+eIr^|~Ncb2nCZVh6wI`t`M z|S#d|UGt8uCe7R+k= zw0GZ;acsHwltS_IV(p_xs*Pn#NiOuQ0)e!r>tv|+aO2yBZ9&w>ZT4QrMN;mw&CJq~ zC?>U8l{%kqxhohh>)Bh<#2lwN6?el~H7n3=Renf3MZV z&sE+4uXnw~ylVZrIgv zJnR>R^GD<)9hEB6N#a^8e4mM}Gx8q3+x1~w6;J-7f6;3?1Zdq)!fS(qx3)bLT(~F$ zwf3&~ef9>zx6m)2o=>7YDBo=#U$%QXmdu|~T=`e_8g*0*ca&#n*>&!w)Ux#~kBQc1_!x%kl;vrwPW#C!9~OwG4pczGmM)}A z!#?koW<5>{8^I-Ay39LW=zY6xWJA5Tx@^R-X`B_UJ*mD;T(qA?V!P_$2cM@B0_S*J zo}EHK-Uucx;__;=J3(uVOo@vI{hL!6u9qP>1>E$|`t@NHDb(=UtTD`feLqOkZpg4JR zVW&Q=BU{rEgR9Nkz~fjBkQ4sE{t$br+Jt&fr_Q9dTgnyGqsVP4bWDq!M&CYC(yPk5 zS=n%qfIz)SWm{@-{Y5R1#LrXTCb2;qCt8-8IB)NoF8|gnJ>FM&EzhqYVoM$Z!N}N%O_QTxpw%WhS z0F8`^rYW3Jb|V+SoI3NenpqMNh5t0Z{k%X)(=>tG>TEOB z%^d9)_iGao{q$6|!gI;SvTt*qx8l#P7;9f4bDxW+PO^7;-jWorRi3~vB=R!NE}VR( zmm;U@kSmtaW$kf2l$7HB`ZiN{-74mjT$AEmyyj^38Y?As72U2$?=&*8`Ce^gHF%Nj zcjIf9WKDOjnhTIUvD&oR_hp%>IEks!xumPv6uAnYPD@pFt|#lF_&zo{3CF7Xq22n` z1zLT*tgt{x{FFsO&PSztSjZk_)2af0_o8Z7{S??PzkGXqkt2My>Nvhmq_aOGceZAE z1AV%D&FUdzA<$9nU`=>52;V=VqGyICw9Q~j$bO&PA*;Sp_v$b{nUA+;m_*KWe*wA# zoaP@qV!6OMz&}sKUsCb5wFnEVT9jEH~U3om1DlT#TSO1@<30CMS38;iRLh7&MTMynuqef z2Zx7;$w^3y9wo!&B_t(;vJcx<4_5~V4-a1t2M5~@57S;>iw;R>Ovb{VD{pZvN#c?8 z7$zL4ZbT{h$ACZmAbRx2E9JC6SvW84f;{MTeddx@)W1~CtGPi)w49^plkSg2VO5tH z8Ok*DD2^tc0Z~`he~}A^DjLrv1sgB$-|Fx<*UHF$$c1QqL(x#K^r6W|6d+MUZFI z&qG$HV6a21R}kG2AA0}0h5qr|LeFfq7|ZxgKG5d{01yVi0dO>Na)%Pq~>e2{^SZ5y>mJ`(dyNk zZnsbwvij#2?|Yulrvs1C?9QH-td}gOnN0ylLtwLjzA0J#7i8L7q@t2aG71x|bgjze zi9jF|t?Udn%X^kn&Z)fLs9}@-04A++l~JKIInAy=!qmVhj@_I5?Aplq3z7`)KKvm< zJI>v3MU|f>VBK26k;Ve)rV3UlOx;S-7ydNzGzB0%Jxc0rq*yUYW>l2qrV$i!W)ce& z%IRu|q)A-;f&nVP7Qh-MCs-6GgT}l|45W&2rhi4pm8goc$k`EPrevrY zvw!59;}mE;kOx($O@*vn|ACteVVeqR^DfB=HOtE{FgBc=qDaAB<)%Xy&wxl}gkmEX zS2U+%9ochcXUEYLaJ~8nd7voZ`V>d+oikMvikC2-I>CBooF|=<*o0#(X=$Vuk3F4H zVOA%cP?b+tl_)JG<^^`VqAKPfjZ3fNM5kagc^Gw8bQO)lJqp0REt@KBlUWe6FsQ>F za!Pe0%_hNb`?B-EX$gpdSzg9qdr9!#4m)dU(pjr6#0mIpVVDzpj;%GLnpQL&o4B< z1pOL%g+WOrGYht1aO*NChLMAcaW6ZJkrzFaVQAwdl?mN7^en_b3>>FGJosp(Q-y_< z6!P8mF@%M;1mr^PH*pECKBsN*jQM$3l-7Y@i=Q03l}h|m#^@IDU*O_=^%o`tMfVUv zh||T$)w{ef!vBiGPg(YN=PgWPZaDKNb4492F)NVX=S0YeknV)iAn}YSbIN`ddXFYb-?pUxEU$YSfaHYE(#! z5-4doMkaQ(VU%T5M16Wa)i4L28slWFG9%7}3QXdGDvauV{y7?zh>#Xw@^vLvS%i9V zSsolJl2rLWE@eipF$G4;(ZLLpK*tU`c{qxt&ZRyW10e)rzwA>Dcp@oS#YU(yYwB{= zszuJI#dRVn@_a|la!NJJK1ta|r3=k+*s4X3szqdta`FbHlSU`mu;!}#WDI8Fz z$HhOtU3tlWmkvD8x9k z2{0C`V2(+%!BS-0B<0gTO#KZ zew=-@BcWkh!wcdN%{b=+lJeF5@3#*|kGIJ=REY|D*bXKpj8D!2SywIZ04e_$A9F&z!CiUvc0CMLZ#L&b6^2TrzQ-Bp3Dy7=pd zKbWn6dh5vQGFbz7s~`!Os^u>olhKPX5Cbu54^}5_=(Qo6C>rzjIE#}QSrw@&NSJ08 zbt#s5fGm4aM|$@g(HtgCG0b^f1cuD&>0ROs?&44*|2{1uLb@aw+-Wen{1N(68`+2K z^5dNBxL4r$E9b!!Zkkq2Q9wnyNSU&>K+|Z(VLH<=nPZgBJHwEaS58T7D>~X z8fuBT|4orc$2@o~Jg6J5pzd*ZfF<4UqNg_DoZV4|{d_(d;0g0GhcB%=oxoh4A@74v6xGkRDBS;Jq_5_4w)JL`|+H}T198v?3Xf+E0Tfgj! zVIGkYCmLjW8_sMwy9r4;L|bQ1yGK6lA1588)>0W9w49>Kw_7_8Ry&VYI}hiJ#h{(X z>rDfMYu^Z-5orO8J4b8^=PVTKau_gO7WxLPGfP~8;9ej;Npvd|n!k%o`JhlR19Ps*UdE2M2oC%2 z%q{0nq1c>4Xu_Xe_zhVO=|n-HCKj1+CjkI!5pgweFTHxMY(at_JE{-gPC5@1R;bXfsm3% z$V^$p#vq=9On$*8c;O}~j!d!p#&}bS9x&t&8gj?6oarMDOcNaGBTh^coaqZ3=?k3c zs4fNsusu%zq*eVqlepbHmAe<6Ql*OiFpH~L-4iS(+fxr!*1U;kgB%X`t&j6WENLwi zGA4IKNx>CISckG;qzcS%M*0n4aUm^#k-_7lDFp9Wpohd!a?;tAb!SD-FlYO|(+R*Y z)U?@Kny*%!(8k44a1qHwa%_g@MwqXoAbcje29o{{JXI1kL4H)qwSUIwqcJDxV~|N=N>1E^4i2M44~+rtxmGZC_y%HMC98Iz~AHiZ@>PupmAYlgnJ#(Z~aNQZ<`ubyV_1bKqm z$WcpiKA{$q zVuM4w!KvkH-*kCs{?mLS!%vH8&U?IilUa8S2>8;~Pz2sMDvJ;T+ezHY3Q7jnwl^Pr(bl zSo?;~^w#y=<`v(GEj)*t*J%3&@$?p+)Ae((Q>*3lR*vKKbCgr-i3AQiH8=o$T(Tjg z3Fj_-l>nX0@@{u;Q(QvfvY}xgwe=bB!^t0bnyV4cGplLIAFj{KIgCg7Z^8Ahw0Ck|H?qX@icQLD zuM{e^f&4Pr(Pp7!D?=7wLRomtVXeN$!fR~#Yp8hc0Pws{+28bfy&@4`KW}!ri;pgn zxgN9CyrWJ&dcA}Av6^*j6$`RaVcX`mK@AIc{B|%2&G%PZ0|5i>7_T>5b`wHMZ@}Y5 zO-UttOape09838R49+-7o2by5Tlx5VSu6BjY(@GSZgYbTfm+lzXPd8}Co2Bc){y1B zm(S-o)b-N17s~@swJr@m`d_stF589&JRRT7k@^@=x^I`=_y`d&z%Lbv_4_(1?W4+T z@U;`4ivIh)i@o-G5_c#6cLv~iML$v6!4t)zw^hddvq!WG5i$CU=@R#P8%CTJOgU`D(p=0Z(gsl z(pV!LxfTEDL5H7K8{hEzGxqDawbE$ptYon*sn};qRox3Tz^-6wOp&-I7k6sv$F}Y7 z@ranAlZwhO8C|@}Q>PPPCu<$ne%bMYE;+@WQM%KX#jJp1Fdde=S^BQZqRHy(F}1lM zcwAONmfI79WBk4JU?F4?yh`)tNv`S3JiAFvak{sIPCV6d(7%$n9-R62bvPz01J$I) z-C@}0y#>&CICY>igeQi1hJw$D=tnNjV|O~*iwvq@$}D9X_Yd~U)TJAjbebd4dY4V) z$N1_8qN&f(LAFgy!Z9vCjV7lkj`=j-<7Famx8y?$O!rOh$-&sQ&2ZP89wc*b*}9|1 z1-hZ{*VND?yUE<~Ht!FfZ-=(u!wh7(q&nLRJMjd)?(C%;p|ir{2j7I|q%66#M2%`e^C? z_kMG>%pplGWwj^w4ABWKE$)JAclaZnqudIX%wcFW{5!~}C6Qlb+e6dBD$;M#Vxl_Q zHcLXk-VK!umXgr5FAM*pk{2s z)~e%$1o5v}{GwoXNJMu=X6i0wz$5$5BP*zwqx1@rXeS=oypz*UrS6B!;prUnNX@n> zNv2D0wGtgeJS<5@xlW+W;Y)S}@%t1FVeE}AktPWp;YaWLaJ6zsy!X{MfzWemy!9U! zil>Yfng)ln?4;k{YlVv}vD>NaVdMa^_omP;7aseue3vPapDi~P%@HuNhd5%(nXx$X zTe|#}$Bh3CJVW+9o1}m1a`3^bWBuiTXFvSWt#s$JmRGShNlR}m32(|0v@gbA`VST(0qj=8sGpZS zeD>cG$KRV^f8H7vkygOdkN6j$R(li2XW0AyEPA(N4(`oD^t&&HWvbu?YNn!eb{zSZEoVpsFLN1X~D4z(Mci#5DG7ho0K z@Ye=nN5Pf(r3#u&rn{;tH4>W+=*g5eEqdZd71tAR1&;2LSBxyZ^{ZEC^_&-TBO;Fu z`z;DO9UO;9j4x})zB9ZnNcruGGoRj5gPDavi3w)vNX&m06@9#^AeVC?dd_nYI^xB- zUO0V6=jqqHE!w#r*!MfRq4{E?yhO6L{jB#rO9xWEukNR(8TW%yJZ~P6;yo4+rN6wV zx}q+#$;0PTd$~H#YrzNOIE%rFvgEYhjr8a@nptx;^aga?N}c=7@8iWR3Fn;5TJd4k;HU5rOgiF=KE21Nu$IU4_BgcLxlXaCWMfhg{_H; zJ%7{vsPC&4DkG1-}uv-~>84hy3g$SH!iMMV^>gE^R{73{VgX5uyn4=!O>^R{-Kh4Y=OBnV$@Wk3mTSlyr7tpM zi-*MS`|J3hP0TRH&XzueK?kCZ$H)1GtU@zKgTekS{T=!DoVBLfZLocG)nza>W{4~a zH-}-EnK^o{r0(!HM9k4QbqqcWD}B_PelOaVl0^Ir5!sPt2de+iwOnc>F4YB z|Grr53|p+pd))75a8KZ1r+Tn+{ag#)8TQgSSR6e?4c^j9qVZ{d_IMohy`C1I+^i-F z#iHIRWz{r$%6_^AP6B)A)O*i67%eAnjwEwvhyP+!P9T5luGrgb$`3D0$X%q$)3!NdL2`+CY2&#OP*WSUrua0RUtdjjt#LWBCD8|U|)g^}bi zvS9UKQm_S@%ej|46Q-=wAUa&VBr?7bmUcK|7Iq{g*knqtB$lom(3(qfA7J`t2X;>{uFfIHx=zI1-?b-IZ3~f-ujNUcdq98Y+@9k z$z(h?;ni6QXH*=~a=`M~FCh6_f>^<7Z(c86+9tJ5H&E>wFMXuzP zLvz)5O!eGrmS^_&=Xl292io`V&y;UlW7Y9)O4*Ozw;gE@vgNy%jn6ImJ=`}+B#5%! z1$}a%Pn7B(r?;EWa0C`ddwd2Tm+B4hc=ddkPDkhs2mTd)((Hb>kR;m#u#jxfPRAjA7I8_ zNN}WC+&20YBp<=PF$0Xfq#8+<~Bs{XKxJqw`I)nfHjeAKUqo!k~Rn zko7>#9kaXs3^xZ9no+dL_xZrX09AJ`7*q0$Csb5?+4CcYjBM*=bS3X5s*$;4j9vu)#Wz^{7L3MMF#HP7CGNHeO0)i zwf5#G#R2d{Fs|RrmX^n8$5JN|8Q79r77A4pIHUfOK{^6lZJjwe7DP(hxxP)qzH8*I zZzO?>%3#u&N^!t{5vT9k_?1lm`DAxCIBps_NU#3^|KDSg7aebnxI=(*um3+|5vKpw zScLI^IjR01Yyr_PSMcBQe~v}8-84`}KKSeW|E~4{v57?M_ZJ4)s3XvX?IS)}YreJK@Y8XX|&7H0!P=t|MM%7zct9Y#pA{F|YnZDTC!5jI`yyT4Z z`trcDb2Hy`<$7Fnaf3rTuv$uy)YK$c@^_Qkkc5wAKw65;m_KO|BU2bXg3(qko1CT* zU?v(X6_-WJ)I}LAS)wA3$}-hU;Z|N(C}AZ7o*lTr}C68R{fKlNqnGG8fn(kSQIPe=gRrj8V)|lBm!E0 zoM(BEn{JgCMK>|KDWe(FqRz;Op|yrNDY5nhtMQ)FNs1XY2kNj)IqZGm~tK|W0rl8nm(rSXoS(+7LHD_b2dwKJP@fd7z z@IJ#}ToqWzv6)ZPomnbO;sK-4unvjEc2g%AR$IiXa}0{iMW8-M{A1Sg5^+!0d7 z5ATqZ6o!g~Sqm94h8y#s_we${=rc}^I}$k43n1fjOor2)xfo#~aEi+QnvG!QP5mQi zV9?mpv582}W3|MXBnSZ|45m?;E8t33Vi)8LH%UkyjaMOvLI`y-twVrf5{WyL5Uq5R z`|WWc&R<7_rX{gbMO%u6@Fbco22DPzu(V{N=6NDQMI_3YDO7CBiXk^_-;G$_?Uk+= z(X*T_((Fj3?CkejVoVJ1`aiz@fn~WF}}#r&ieP^l70Wia+zmIf?il5ahtX-78DX zmC14IRxc1)qGDuaCGdqKD`Wg-LbK#cD4$~*C}34)m#M-2ORu8m=T=Aqfs}w0W(wGp zUs$f%GbB_TX#W|(;)lII&R^3{!+}|`*R7nj$Kw)0=8zb8+mwJkQ7L4}nu%JrpJo&r z(ELV*H8c8scdQC!PGRt)k zqg=LY5wmL%*}a_nUP*qxI0wfsIMGvz>@_Ko3spLs5?onSN&cXCdLKxIaZXWy316yg&6wzKra9M0lgqsXg zf2WgeO+kw2P9F^yRG=W98_MB>ct4Fd%H(lOAbnJx0Nk;@WE-z<`Q9_D`hbm5j;U!4 zn8S!;F(oB*P-p~;FQFtnQC)lZ;|bQi$Fdhfv3(Szg-0N)5GVoN%{ z*hU?F>~+@$j7Bs>KMoRU1JfH!0ZK*AkP@Jq31gsLy+2g6&TKA$Qvl-IZ#y-+k$|J;C;% z+Y<89AhbQva@$Jvcbu&9lT}z7w7YDo93s3t*;r`YRK{LECfuIjx6a^ z=Bx}x30YjwW}L({OD_FioRb~TG$rf6Q&D&u@(6FxmqbN^jD6qmF!-2eq|Kx{Mj9T@d{{LG z6r7XV=h|?Dc+_qg4$eW<>x7K`A2{D;jXRLFUL;i=Ope^byiXg13C(9?HV#toQjdw8 zJz6idx=YUOh7*qo-CbI*_}0stE))8@lN zs!w;?Ml_dz@$+*T;pbq_NWKHfnSxoWU{_w`%{s+X*-9Z7OP>hE0m{deY@vZZn<51> z;pNe{_hgj>|QXHOxN#>}p_%kqOdm(B?wBShtGpVVWs%&L zj*#YC07d$$X!JN0(?Uo(d|`AJ^0JJHIl!U;a%&4c6Riv;Kz`|b>H@KkGT~=J(vP}R z><$Bg0(6a3fR03jIr72?ln^4SntX1u`XyhEs=t5h#0TC_CG9O&`ykPWV8iXBXUWU` z?Incaz332U7siY0)1;CA6;tlb!}w%`HRqvwPKIt9w{hx9Pl9RhiW_pSBJpcQ{qB0l ztH-yF#t(3%0o>W+ZtWiXBX;qxB^@4!*i2Hk*5Xjc1-04~*y1$GmVnYGGXy z;Fs~=CdlJovl6F{=K@B5$5AAQRx}e@1VxU&=N2ws*#DnU2CzH_=HE~TV+7tvseP7J zRM>suiB8X|)vRd+GDqZ0qm5_H41N7(jyVYff)fhCLB+tK7$hPNDGCP^oDZ%65LV*^ z7_x4t&^2rhq)=^G96~mG5=MJ=B6l|$4pUPh)TR(kcx}_^*J33wFRk_`B&R|M_6++A z+BRtvhg5T>O>z5FjD8(M(d&72`&8e+&R=<&iT^I0LtrNi$|k9{`LGn{~#c$7yiPLpypi)1v5X0)oID1hWQm0_vu?|B{9>yc)V?hY$hHdM?& zP|mAP3Eqosj={kNB@sDUh_b&+O0uEhas@*)YlJ;wh3dIIOZuY11ZY%vC6+$b|8NHG z|8WM&{}T@F#feqL5GdbgKH|kl`k$q;1x<6It)6Zucz(V5gKuj`-HN8R5-GI@ z6WGbVeJZW}ZU-~kfg&TUP*Fisf3Xy4b$g(K3DUb__Wu7sgT&7N8#G{3h4p)s`ImVt zpENc|UmEsxuiQ`1d?!TF@J}Z6A-HT5y}&Sqg31(H@h$`b6;w!N60u-knvm)kN|8}MEaFW_g$AKu zpPDA~DN945r3E5(&xjBeAFQ0@p>(~}Q#*3C=zYi?G`BYuq0yU`Z9=J^W(T1$?4DK8 zn&FSB>ZN17p*q4)4avrzko*erQ-rqlj(6?5b5`Rgtp?9*{gkIc8tl6`tlS( zC5L|)NmS#=IsR;= ze2`MVOsHL}L%dW5f3TAG{NJF#5|xJK(aesxI3^6&L^m(ZfHZ_92u*8&TG&8!g1!o3 z)P+#n7QvotBFqlbzpEAmTQ`wCS9OS1uirBIzpDTQTlf%rFOuL5Bf`xbh`nbOh*thO zjv6*VfS*BKDH7xPUFHG-LRmg?FFylTsSGuF7+^gl`PlSF(GY7jc0!3Cq`*g1-$SAk zyU)=rq{ZgVJ*OS;x?3043$cIfSvkm2x3qRTpRkZg90$Uf+ACvghB{_uleMAD9<5Hp z=QT&xloA{`DnP|$hzd4l+<63;&JJV(zGA26lfWheTo}WY*izL5wjf1xfDR*1eZ^J! zY-}U~bUr|^H6*;RC9ZEGfvk4d3&3|>vTB(!kz(ePpM#PNwzs8R-L&lDaXsJK{p0t2 zK_j(z>GgX_VHqo9+r4QrEcD*~Il_LQvxpYU#=4>DG%HrO36A#r@i0po+^V+&?Ho5O zJcrm2qOp^J2SYDx`7V6eQji9R)5DXJCgGF2;-y#|ABOGT0d79SJ`n*i>^P(5RxX>O z^49~;yGx{Rk&71m+J8rGZ4tlnG>P?OH1GNiYvi$8oWBrWX%)PRfNnq5ka6>{i&ZP* zWvYBL^Od>0Z~{oc+;yZ(54QQzwNoUe`ogQdDw2A#cb9Vb0NHunf+l-pVg zjXiJR^}(4;dnNwiUC4jcblHxYYnxo;-q9G?@aX1)n0><7kzTixCGFFS@<}p_eu1Y- zQ$cb6eAb@oyAhl6nRAzS`xa6Vx0Jg4rbhU+wHfzq?}MrO@ZiHMw~{nh(~2b3TKiDK zu7k}(>nRIEyWwZQU4vAf?5E~7wAg-9qxEK%&5Pziz2}J7T}rCzmyf=+`xzgA-UFK# z`{hVDZzIB=fu~xf{!*J)(eR`DS>T1QrMBMX;{N-UZKftcoWlQ3X>yqhWyGn<8t$*7 z<;0!~p)=byv*stGYnX~#t%{=xCQ1}~lY=`T?|qycj+17kEccu5>Fnxxz>zo5WxA9W zD^|SMMyb2c8cKNiwk$2hYV9zdj+UEzozXsAx7UE|u)MxwYYLMzDFp6rK6rKp1ZUu* zqATq1v>f~x_73Bc9-5<)$zq{uiPDy?hw8`K2MHiyW-_a zrs7z05DHtA_%L5@>nUVN*CuYAa~VtGRlBIuI^e}gr@~Cf^)hCj!EHw#-|U!_cxB;c zJW=q(@+oNa*l*)7XEW?i!lMr#&sPo`zw@pemsp9#Xx5)-GgqIEU-G0FmKe$VvFo%f z%XHFSvUXYLrRSincztcvdptI$pf;dyo35i=BG#YT(&4t$h#ODbnRb-Qzx&^&ntHx9 zlFXpWJDugGs4V*?*p+PXJ=*!WyW>^gY|_y>n$czN_!v(l4Dw`9J!kL59M%*hZmYPm zFKj52%5w*Jdv-{#mz`>w8uk{F(iYr`LVkaleat0@#jL{@*Lb>u9lG;pguXCJZrf6W zx%$|F6_YB7zXafvw+y1`2|eCAu3^W*2jY|bD-8;Q|BaLmze%UX2jOqa_(Y1TrF~NM zL-<7S`h>UsQ2^{+X64lB>i-jUrw0mW{v>!i0P<;-%HL zI}wuc2J%ClTJ6RM;T!CK6C9A4y8N#d@b$k*2)i>12NT=4PL@By4yu!QR{u$l{B4Gw z!v*i_HSA^}Yph)%+r{_uc3ZM__T7r+W(uyAD@ZFmn{MsDYNFRMwauKGsu%8(8&#e& zKHGHBYDr#)rVq^d7$3YIz}9%>Px^zm{n|~u{qN7C$l5*Eho)G${kx;Dt#SKGo?O46 zjHhFZ#zAmZ6`7!sb9(laho>>(^QRYaX0975)c4ewojYFVt@Rt%=fRPo?^Fr~<0~L# zb4Zs}(p&qJDzD|Y?k2CqNwTBG6P1&0w_(0KxM)s?8jl$zsa)R{Gqpt@s&~*$R}&l5 zVhDv+8yw$Qs7wcw(Y!@R%ga&qLg)y5rOJaY>aTOi*X_GbRkzL*&5zQ)WN1|R;4j^5 zZho&7pMajpPL}i02&D@7+xCz(JA9>}6D)OKZ6zs}oHahX=m+5QGqzGZEAA9UJ|nde zv5!Bcp3zy~Yfo&bDusjxYF2bxr9#>)QjQiRx3!NNevWVOKtBh)lwVj^l*Q{F&2RY* zTTl_tPn4J5jS(3PhTl`q(0EFIe>euRU?>XVmh7_;ZXfYj<(Dn`n$%E}+)M<&_O5J| ztjORrS2WbbK)KJdKTG+Gj8P-e)3H*OC)qohjNmtmubmB4sWTy7ZQx?ocR1^!JhS4M z&u9}$yFM*{Qn;ndHDcTQgC5<==VKno`9BM@uyh^z{3a{@E*`JyaQYGd^j-TspL7TD ziF~0v#=Mu)Q1ZI;hy2k}=CU;@Eq`-9@IR}^6~Nmv`&Rj3R5kUKPIntBUARAGF1b3o zM>bvef^KB-gs+hI)ZOUzX-MbWc^GM3Da|}v9%+`}l0Tkn-B90xlNu}ZiyRxnT?VPv zi)$~x24dcF{)o~0qn%+98(pclBj$b5bNV!wncDt%e0i$=u#$Gx{Pk`N5jj?v3Tu_T z{C{!wPC=FhZJTy=*>;z0+qP}nwv8^^HoNSqU1pbU+xFBu@lAX&Gx5*CAF*>suFPCJ z_Q5(>k=K*=#mW$8rd=v42`lEonaAR>(I>Or$xQb&n%Dji|8y}ZnQQhoO($$#k(qhQ znnx<^9^yRUKi;0sYvv{S<<5(N`QvV!t^>EFWtqQ6?${a=y`RqX(!Gq)>SfR6o|ygW z@oyjQ}zMKA+Ni^rw_!DdqF{{n23IYL%tr zt718eK@X0=t>P4;wN14*`j0;s!!+-dk)UG-OZDAsaDogXJKB6Y^YeA~n7usfvV-Lb zx3OoGbe{UlMGljqIGx^?EFKC{3K7 zitDJ^WhB*mn)1FSgU#tt!-JyjJRWvhV6cqC22(PC?iI ze$#b#IpANHM-iD7{4Q`<6>W--l&03P{Mr`v;^=ga*2o9ymk!rsm+l;GmcSb2j!Si` zME^kQ+BD5(h5Q9`3jW;?X94jXIIeM~Pk-`t7``4&J+>dm%dPW;_!eD1i$Btc;xIVf5+lbx= z;!3=DP#+P}X>H5;xt8pIQV#@kZ%tg%ptq)W5Qn8IXK$W$Y9XnR<>JubJ=c`g;MFbj zNC0+|8!F8V-sIC<5PD$FgCk~jrbUaoiV=(G@9^2mF*9#MQnAhx4y}0JOxw4=NmI*) zO*`M6n{-^U=tgm7-pwZD+S=i>LYK2P^obWH&N#G_cvDYnqjD{7@I|5PMXOtAt7AtT zS|PldmsRbUwi4by*yCrhKFLrdHDFq?$!gc^eTf*oMo>H)P)SO+j;%|xHYOKas2Xj% zE~Ep?Ru)}kuqd?Na)utv3gduUt!1*Fx@1%@3LmTrW3%q10edvRs9101Xsi7Z1)$@C zRimx8jl*V47doQQXfAJ|wX^(d5*u(^()9EH6F4`I~kA2O5yjerNiBhWV4VC@jO04Rpm~A|%f0@XtBn=9&s-^}TQ*~fa zCQR01G^_gtMHqP|urn|n|41%rd$i;z(w`7Ji)ke$VONEH+?#iCZ4j*F2S%G*1da7u zV~(1w$)Xbz*Q;%TXKYx&DTIcf1J5RUkW@??1?F*x&JUPPH`QYq)q$70`bkhgznp)} zF2)-}q$pYZ0xd2pSIR6dCl69rTA=hp5zL=AsMn;a7Gzkh?mB|AFd?+QFl2(nR^SwZ zse}|L0u#hxns{|ATzVo%uS%X10U{L9{#Bm2rw&xf$;ngD0i?GWgw2OiC@AYFRL)X> zo~xjptsoBM+MJ(DI8XMLYs=h#&c{Rz;h6`fnJW%6Z<1cEiT)(Z%io~x4_A(Vi)Rs! zfE{)ol;AdOHEm|&MmR!uv(t>quM4+f*-*%qRDl7DGvNG&gU*sjlwyb-47|Wm2*ffQ zRN8T-p=CdPmtZ^0x@d?8sStP5VitHLr;CU_IVA7TrP>NXuIAx2|lVl09ZdS-2P( zL<6jOW6Xh|b|J5l`joj2j^+7avE(At@JHpV09#VNeEpi!8kP`k7#6k{7GkAca;d3SK%5{iM<32HF2#9c> z>kSol`^*R+VujMovD-7%FAw)Bi(e6j|XMRQ%AW#RzkWo|;yR zZHBmTd{95UWf25ULipu64Z+**TCP_vVd4F@6FDBJhnNpe;tVGu-d%0%Ih<_TTmXjH za+|*_GNTRih8UFq6k#T)U&fn+(XL|n%C?0Pf!grpM}$-o8muyxfnGbGuNQ4eV$Dcs zN$fEYpTex=qI@cPTH0D9Gp~(RqMT%dJ%l~)KzmLa^P~(e=#Lum;FAw`oXO3}h{42> z@MZ6`i(_;j0L-8O1MWyjI~nYb))hfSq-pST=!(5!pMrGCdHf9I8a z@Mtl1&RCHbrPrH;FdU_q^>_JI>!vntd7q?iBs#K)9ecLcU0`gFy$LH2GtGdQYeECbe{-bq~#YR@>*+faynx&_AvnAt=}>m0iFp#ZBH( z@N`!|o^TNGqvs!UqV#w6-)&vC7JWQ<9xg!liaZ~n^*}?!L_&WCgnrS?1r5Q^z4#z& z6oN^6Mw(8Dq|(v@>01DxBTe(lpwne{Grv8(5JimZWI3$^Y8;dYtJb znEk#3h$Wci8D-ke1xrno4Pqh&wLuuSHgqM>H0>pU#XV7` zB57yBZb%?q1D;2#@b3U}^nd(GylICuxC{+E>!a^Q8PQQADXyM`9OHnVtN`*vQSW4d zdCfZ*2vP52Z;vMnDbeSgQsW?&T@e=mU~~yLY*k4YbcC14aV$M$e$>sLFSYu zo?qP|)o91T6MDb8i69pGwHiU?U{4mEe#L++D+nwCx5Z9`;Pcg|MnEhnF}AUTIw-@8 z5AJMbg4$UFaOEAocMc4;Xn~%ac6v}JA(R;KsQ@3!&_~3S>2W0ndcBwppcci;8H! zOHf)3giZ9mx8lxUxzYx~aJRIA@I7JnygLA~Hqo2lm|NOFL^ePxsOSpnHZp-BvyXIr z$kfjuN=siWQDNP_Hc~MISaIsgh{0_eEyi!mSg;us&_4v$wxlqB+h6kOp_`u|n+b(% z{~~hw9iP#X>A+$6jD{wZZ%fKq!UF#UDG@ue*@Sc5rmFTD)p}h1V;B!7(_3$O?k??( zVc!)h3cM686hSE*d_gV=7E1nkAhX}mweeswWU@A`LL3i&U4ibK25`RtZ$tuR3*cP; z!5taDI03y>TpH5Oz~)yG1JpLO*!8SY6vmLPA)|GWAa%HqZ}OscJgA%`UI?e@LF;-^ zyPOuyl++2Op@!0ZqIMKhxkxIX+=U6HeVr`*0aU$^2uATra>$0+hQu5Hy~j?c11=;D zFnIyKD+L|}@{#tzlkmY4@mZqeLs0X<+uOfa7&#cd2q~JqP_gSLmGpFMF zjhYve%M>4(`xB$2b%LV?#?Rw?a+}kr`NskD3U+9kNp;BlUWdNc{53uuC!6R{Q7<^u zlhYtOTl59dYEIjMZB^3(8D*F*7dsz|J`M+GM*M^w$W!8<;N65(cu~DpN8*bV3>rq)qeqTaag8~PUQ<)iAHpu5k+d{pG`*vJ zq{itlL8o?JAN7BSjz3Ymxd(UNBsSN^%B<9rv0*3&tN1Vf=m;9g6IYXxrFIB3cS$zs zNU6!FH5rG+SFO7_(?Dl{dm$ z!I-yTCyVd0Cepm@MY^Ou%{{_YdXEt`j^6s2aM1ggH=nzfoopF0(WprmxehCGdf(0H*&wZuOqiL{F)Z3^StQPk*tSaYEwH@7=>G({xm8Var#@i=DH-I4|HDMB~h2jrF?k9F8TFqze{SqQ)<3j7JkCc`Yp+G zz3%*^D8*?K^N^pXZHbew{-h{*dwbx?6RL6dZ<|(O_{?7t{dJ67p-`KAXs^BbDk%p*nRFmTb-X=~14#NlUh_OLc52&RrsqZeAI=Q+bmDb%kq6@t8F1 z`XURI$nX(Z`%?rb+BP5??Bs=<@`}rMBC?VSO3sN(CGrio8XfTfTKBYr45qn#Zoh%> zzEpp0%H338nrj72lepaSlPHu7XEBK_%U=qr*9^aXEFP@N#SGx)KjBD-aGyXX4-pC1 zl}}(r#Rdgh8I?x@QCE=)gVjT`?W+T^HY5-jk)&fn0nTo{TX07Ao_kJSD0+nhRTnu@ zi_vdS9EQBUuKR-OGqI1YU0MH)cl6lz5Or^B0@M>Ew#`q3^)9`8c_V3B@d4Nk79O68 zb*-QYhq)RCvOJgcEK_#Vs0I{PqEGTr)fet97w1Y%m%H}gy`R@HYN4O{-`*2AwDoKs zacWLVjsEZVaK1ETg7x(treM1COWt6s!`r?3n(C^}7_aK}x++de@y`wMZ({Q`V7&R|w1)a%0|FBe*HOdvj;WM|WL(+Uj^#DEQ>9 zw6pdm!&9eZttC|(xtiWv*ZcoS+tw!|GPa9;i8_~DL$E*>Z<=pVzo&-}t7o^C%-UU= zUv9#!-oq$slg{9G7Ve?fS<%%^yYX1)mWT|+?Vx=jaAAmcVyIwozt7I)KAbUzH9me6 zZ7O#ByDrT-?+BM|^4*^!Q`J~@*Z%VOyn%??J{;bRj`C~5-P91j(yu>g?tg>pXkV7o z*yz4GGic`@;lC}q%EF%Ms7iXasYE%v!q76cZ|a2deN8%1Ff$0!ZZ0>Ox_U6X+C^bA zH=~Ov^0!K{bjV7rZ4kx zo-Rf~0lMzuuU3~tTjfr4u$dM_vwEtvyElQzKmCq!qND$DoCNW@o^5Mi=f5v$@^N|g zME_gg+^>3^#&Nl5at;RJ>Nt4=E8AY>*bqdxz*c%cHGtr4m~R=W!RJ1OdIm2K7UMjj zqUO_B3UHiNw7yfSqIa2z{e4~2>$4)-=~nq{$9r5jQtNc7byE=RR&!o|E}##A%iNoont5*~qn{~&*B79ibyQWq&v4th)wbtBQOXkZLYNzTYoh6J-z9f_9{zyZ6!Tl0HCv|6`#kHgr9tu`LkxG#hiyTSd})=1 zWagCMR^ADRcIf))=J|SmTNdrj=vKT!zZ)2+@Jf6ChD*#^yLuq(_Bn3}>BHz;K%~6- zuDOoL@x>W1pGgn!bK!h)bdUej0$+l=-RR>X$D-7HUWKHjYM6ow-rPz{}b?k1bNl zHi7Xo24@;YWSPp_1Hd$zJ}-H_y>qUR|Kv4zLrwNa?JTn5b~dIiZU&3#q{j|M3Au4xTuS;@x>D#mteU-AKR$9E^7AEom!#ci{Wo**ET>&&=J!Xw za569R5$rI1?5(*mHm|exi`2VL^Pfw?J{=nxqql72Wb}9k1?6F+@SR@sqwOAN|12(# z>#%H>u*|k>2yXw`s`o1y5Y(>1r-nW|FV177?{Rw&POG-J8O>Sya?W{}zPu>tZtHnAyDvBQYP^&f^8NLJ^to{#Ub@;o$Zj-^zV;~IB0{c1g){fQ zG(~L~UP1mF*zOsfPWg&n?nxs-L8vA=^U-c0F;Qs7(oyYg4n}2EUJ>NDEQg-PYAq%7 z*1f#2=~38xx-9-yU`@Cc zdb$OEgKfjJ^G3t#tuh(gbfJ_EL>?@Nza0Jjal)DHqLi=Ftf;aC1LKZ6cbGf`mv)j< zz1>>mitB8v6NFk(CZN-0J4z!ip=-ey`o%?<}E9S1!R&=#$Z6}G{&?6xZAx2;4P zxVD?}^Bh0ENZJ;yO$PVQY(WJ=#o<8ZQ!eip{Nk<4(nb!cX~=iV^P%1A^Gh$qw{h;o zcLh5et(QVm%lh{+b!=~qsNtkHjP^B7s)}A}AAJ&?xW3_saDtLB2F<_ab zdloQ3dicBc>nJhXE<6J39GL7!N0F;)NeF~a_nmWsBv04u4r+E0;QTwZRL8P>oE z4L?B*O_e}c$fC)Bsi(|IfNnuklUN`tChM0FHV2KxWrSvmZb4BKTX3qXXiFqTZG7Uas!}Q;Y^e%M zORFR*VkrviFEi6kHLIWnC)^qJX5gY?5z7dZIo#2oNg;0b*fOxL9i|8=pNmK9CP>+| z^=IO-ie~wrBfy&X?>T8Zt?@w{VVq>-=V1^3e{WVB{1oZ0r=3~p{uJp;3H?9f|NkrE z|9{5+|6euw|CMX>zj>oU;=#=R)Bcam>On0i4;0bgc4^$S3i%t5hji+vqU~dILNK6G zl7V6ZsVu8*^QDqzZo+~zJBpc2ko@@pQ9%JZR6zoYh>0t%)%6*yT*5#Fg)9>`Q#7m(~L8SXr~QaqDSkEzeHOluMzi>98(}!pPG(+gKZJ{yCbX(&AHr zBzXFuQm}l@VoKDQf1Deh+4HpR8;Ov)jG$R(ds0~-y3QG9V?+*x25z_c?LjIXm!6;s z(eR-J{g7Ij{SNkx+}iW4DKm2+t^|V2d+P~sTHdmRZABBXQ=VOe!bnYo^Y+qjgit1? zyW?)f4c!%#8R_$Yc7+;Q5m2cwJ_!=!h$aV|Bee!j4!LkEm;3?ed}G2B6VM#l$I*B* zX}caQEl!LBPQd9Tnji*~S8_#5sjl6*)LNk|+~}Do-Gptc^?N|NiZbm+=3&UMu)l*0 z{}zgcP}1y^K;aSMX@flGYO*RchwRL}dpsLPH76w`)ZzuW+|n#>+>5PG1T(!eGx-}d z_Zu)y*Va`a+|fuI2c6Z5WlZz=iks^y{MyydD)CPX($vk|Gi&0mh7;{R;d-fOj~xt< zIxf4)KhD}Ws)R{F0z(W6n!O0=5eP&&DqsS2x7G-i?L^kXL7qv7h}Z4b9-Zyx5}fTR z$C%cdDLQ5*IYi54Agt#MEnwGIcv*^V;b8GEO__JpTV_vfJ`|pVg_04TLs1GgH2|he zBpL44SCEqwDk%Y0;JPp!8G4`(aXJYVgM6x?Uk{=TV-N;vIt*%hnNoTgC4;=WfuCaF zD=FAf8d|!(q5++XL9QVMF#`q~P#ml?eAucr=n!_gG-|rJ82HXb5F0sYol>@BD5fag zff~{&75#K}*aQ~?jAy1U&=(reC5~xqOn zOuRx+b{lEdITghWESdVdn`DU-G(}j%lNG^6%GErxAH+2Z7(kfFD+^qbOsYih?`6labA=4S0({Ne~G{1@bG6(rY;d4m~s*eTW3$3d|>v>Y{=}~-nQ`Ruv3JHeVejP zoU$2#?eKJv(W`gjkrb=YQ<`=hfr8}igTe~Q0~X+*49tt!N#>rXwU0NeLc`^|lv5Nr z7CpeuCfocMzh8*#vqMwCP2NQ{Ib2YKlYg)?)DE4U_lph_AUdZJ&89WeW;Yrv&D>{7 z{89q>K{ej;%j=i==#F}*(k@B~8td*RbRCAy{pc~aDK# z2Hb9@c-s}B^(3?Wo{k%hCqx`F3s>5w9i$l12=e&;>{E$NHajI@o0eiTmMC)10x6~X zzztYiXozno&;}IZlP;4t4x68_gZvEHP3{gtMj}55#j7;%e`@@*+rH_55n>o1UDce} zVZ}^|G)ME52&+SSV(OV{N!LPE9TG+{)P_WRgA7@HL`7h$_K9X0s{^A&#udV&MWz@) zF(UH~Dd>=82Qu3vOyH~wjEZ;3ZIdqyahd8mR)EWV259G+@lLVn z+&|3&I9+;!7W&(DIrx!UX6>I$=qBv`4Ik^{R$t?9(n4U_1W-$41H#%Q7Jz>55+Q-w z{D~GpsfVH)Z|GZqAW>LBWp5VAge1-S5x<}*_L-H3%5kTK!~5hB7AjLukEwUK_!!1d z++Z!>({^%dy1_Kq^a>b&c94`gR3ja3fvoqZuJEct2ivC=8dB>-F9K^9A)~?Cp!~UJ zTtn_!FR(AfS;0$*G9anAM9j~NU7^;i$J(rw9V7?vAh`*pR20%w{S#5sjW9$_QB|j^ zvWTfRDz7%GsNO5D-m9qYP}C$<&~z2XT; z^(4Rft%wn-KHLL8#ud{c*vrB5yAO<}>{(eMY<)&jN@oN;)9OOPsEAak1^Jz*N=7{3fC`ICm`n~OGU+H;2uVAZFJ%&-osL6G0-jw zp`Sop9v{ytEd7(FI3cXXH&29z&`q^Bq%|lFxyL+)tT&Y7W+B@N(5yVQsX6V9IK?sI zN=syoi+{wf@XJYL%}Z<+5MC0JTKb(_%}%Lluh@XD*sxV<(k(ouB{QZaH0FhY!LI6) zEpW%yo-|wXJ{{sv0sFuN{NO}8bFG-K4*HIOKs^?uo&Z)?3Zo~C@zG-jz@Gv0m%!*s zrF$Xn*l~3MoSz2qm%?lbV00zWd7#Kuyu?x`rA^kOmL(cEMbkjVV}O-yLbLCWJ7lxQ zLW+kPS9lz!J-^5YE%JUTXCpfnjshfwBzf~YDUBdUO%OY6G`V*9fN1Vp7C?TNi;t?*2S2)Be?&2*&K7*oN$*Z)HS2*~W_VbE{ zdS^mE!=hbjQf)Y^v>jV&bqgH1-a1;cBEAMRc|pO5jisbPf%IBny`(TX94fXqKy+31 zd&t7tj{kUtUo_MRjEG_xK{kxVt?Q zn?+DiNthK}+{+cbt`S_gGd%73N*<2O2vG=D2rC=giiw4Fp}z-eKCQWe5W+lY0Y?^i zln{_BER-QuGn6qhFAiSMhW+=08sTk$K#Fmuq=?2yv;v~HA;1t;Q4<%us+z7yQIl6p zRg=SxQB~0{Qd9$<9qf#Z4V%s2qr-CaWxDBvWIX~(Nh${GJEH|EIAtN3U>?>tDM56M zOEF|x1(Oq;Oe~|;{f}P7=(p|9C)Rr|Kx9d4wu(^-z@Rs zEO&X4J4ul{QIWe@@^A7FqYzL!`%vA5+Z1y8NV&4j?;S8PLzF#McsJEy{1_(Q7^YM) zO@HhkgE8kK$O1wVW)~^rLCG&10U#CRT>++BlavIWtl)ZJWlWm;z$EjWnB3K9@KXZp z;$TO`*bKrpitVECx9#FF`$G|nasgW&9ID_+DTj0+T0eOxWvp-8C5MzX7T|YCCRt(t z7p{Cu>fH;wCd8M@mF0cM((%H&VLT%K2VmMe!O`V+ z)0kxCy2KOiaBY^cRQ~SP!)6HenjuFy3Mzfvi4>2DjFW?#-(s7aS29pqGpZ_?1zL~1 zyPI`g>pT>Y{&RTm{n>C)ulD-s=5nbXH5Mg3WmcHGaz{2ZQl_SbL1zyb%ut_|pqXV>Cg8EGFu>55INSI>Zt58w;3uf76rv zZFmOs;K7X0Q+B5BME7oeDAerJ`Xc$eR@_Nh{$6r--mDFKQ?rQ+tPMp!)~exvi7XWA zCU%Zyvp4$Iq(y7XxpLMCvS#HGZe6Z(rM+^gv&Zr1N2?r}$j3_qYth(w(!Wc>&h{Hw z$Q3R6c6V=c38s!0lXLp&4~EJsq29eOM0_lvu8UJ^9PgW;PdyC1|1Pt?sPHbb9LvmQ zt^0HNIg7_Ch$+#I*mqy^vgk17kDqn@Qq0xKp6?`8<=VE_){|;Bi_a5nxxL=&?`pG0 z$4AQWKVHcX7yXG2he*hmDlmUPTKOzL9k9}S$9HD`)p}?UexKT4x0i#;x}D!EU-B3YN}xeDjn+3MLIl?HZA ztB=cH_Y)sMX0)-&i|6)md3kx3d*3is4Cb!$C(V|F<{~S>ocAIw z?+EwhA13z^vT_e`syR0f#9g0}W%XZeeUB5yY(!saD%}jnlrM30C;0DGYMXAAXy2$G z?o}tfHrP-6t1D)o$~Sp85sxSz-yX-;Uh)9{XQ!{#wzeFSFYKnb-J-Ic-jVN&?_Bv| z|Ly!t`2+k=U${*0s!3SfPTQa~TX!$pXtYy3w}X=D_hOQidd_EkkN2YqJvKuBSLx3V z)JAG-I_|eOBzo`Op}ka$nJt%FcB>u9N! z$K_OfIyv!osnPu5S?aEfn$6v2_RahHl<7NVc)u&d_W4d>sb$ng)``K`i7T|fmX28F z`jL+f*Y`0|N#I_ZNByyhcA9uC&hMLzHUPb+vegN$?hU-lxFqJKnv6@stbbefdE3qD zw07&?3$x97Yy%H_fbE$!q(|<&Q_iN4Ztl`y?am^p74Z9O$@iHrReuGEH+^Ovo6Qg7 zX*+>?E~LgL7ys<8?~U{BmJ9#h??FDN?~KG=mwWEtT?aTy588K$-`!tEtnc#sAkF7> z*K>J0^@@Mo_`tsZ27YC2X16@cGNirqX1kZaP`)f*@B>yH=ap5rlwZEZM;>!08P`0l z(lCJVUYuqWdEYX&%uih(xIfPlHke;JF6rlc=JPT+eIC1O)E~bjO}f}l#*hA7Kax+1 z$M&+lw0-ULa}lmu9zX0$>30n1a#n3`dMgPBU)K9QRd&<_^{!Ror#U^PVf@5iTzd1s zei~gxCcT|L{dKO>;GZqqIz}&{zjSSAn|IaV>RJ6^ zdgST~9otR7);_1ZY5!(O|GcIf{`l+VChF^}_FcEsma`ZFz`XTNXy41%v(&IbY5aV8 zTny6^bNNH!U|@{5S0B#-PJSoX&j`nGI&;UBeuMe-VG!S4N(H`a;kMe@I{v!dd`n-IFC01^gKE_uyLR{EzBfBp!&uOU41E@u>MKyY zsbrb-`&rzc_pX?@OEZ!!8!I$WPo=CBw7^r<7Q#uWibzqD=_R#H^s8aL6x)Zr7Llnb zQzluKbHtU|P8%W`FW zE`nN}VsAExp^SRF-gl9n{mwUHZgLe2-ap1)K|)j~W&x_Jev|}bG`skqE9IxzBbU$K z{-Byt8u*9qAYNipe&RPX&rh=DdH|>W{3Q9QRd&T6@hQ%h@$C|mJeK>;&KyKOg6*T% z3zo699Ch8@%9zrMoY!6Z*3aWv(P7_iVCG>969%@(MKH5Xdk4S6iuc@+lE73HF1hwq zB$#j;#;Il7j)Y|nDvM{yU$brdhqcVF(fo?wW+QW($$SjP%y{&XM>iCSMaKfpZfg}m zukzSeubiEE$w^ktW}UvCtJmTvLKJ~*0iI9A)fcEpFiwekabRbGM*B2&g|I$f0c$mR z9lV8?j)Knw3uxLF?d|H7cCu8?*yqxeSlMz>PiLStsBwLdD}C0G>_%s?j1-xg_>mP= z4~jhypA;h0%#^;(*TH!bWYhj;e0ps^!`fhZy&GysSoxjb@|!%P>HGb#XfJJX`EPG9 z$Om)hrv7(H_M828e^iyaf5Qikty0DNYlGy=B8@&WzL^t0Kk(r>98wR>x25|F(ewQw zMc%XjvOh~Wzp>3FKlkMv{)n#h7wSd3h|T-laH_wPO zF6XMlXPO7s==Jz>X)M+a|7c{FhWc2Q#%p+^6KlphE7(OZl$+&~O~kKxY3W-G($W%C z*$GN18fA$CqV|y3{9>oj$*%+rk$LD02^wT&rIvr$SInGq$7t13BeFeEISwMQ42L1p ziUkZDXE8?s6#i+3`&cYe!Ta45@RvpBEavHd3U^4Nqm{tp3n);*!d0y~%a;yAC}hxa zBpfwQ14C$JFmQw%HNBKlF@MtcQ3|Oe;zT2=BW6eWgF^*s+uCh{b7i9 z1@TqUh@mkA8?=s!r>I!azXjSDg;bCV8KT2$5EV(ugbm>lT6rRJQhj`QaJ@|6fjq|CiSIe-+`Y zIJuhAIk>2*KmmbJCW4v$r~ThpLqlb89bZfZ2aSEhVE~ui1Vizl5|S#2Lqo1qH#~#d z-64H9+Z&?cizM}AGQP9KG_&M;u6lBuRe8LjA!Y&gnOAatvSdN2w{fH0oX)^^`gs5T zmd9cGuIHHd`15n?$H_pidFl%6+L2G78<&_f2L#ut8nxt$?>Xw5Wr76Gt$pVc#L4*I`R&W3B7kT*pjbxF6Zl7b+F z;-pR49hw3NISKVKZGk#&9F%=oNTDP%oxu{nOe3h#*NF#EBnK^SVAds{$3bV=jai;uXm!6+%fF)>JmYJ!=Y(ijX z6)L)iSLeHS83Uu!1jd~Mk(qKVnhHGgiPdb0=pxC40i992RcX1TJjj>pmJ&=ya$#c~ znln?c6>EuJSqVddOJU!Fm%~Icg4#Uz)RE_>tS=X&o4P3-DGsy8;h5SMJ4_Opr=?u26Pl7DE zCyzias8OUwc7?AjV`KLykM16=nFtAW1bGk$1uUOnOBm=J@65vACA*k05hAY91=wh* z22FGiYNJx78H2ej6D!S{vYLiX2em%Yk?N9Ro zx=h)GNe#uuBps@y8N@U;oTbz{<`QVb%-jeXh;iG>BJmvLC_3foNc1kz-VJfdjv{e7 zfl_Uz$oRy_V7W{JlWn0y#Y3A!29soQaypGtR~{l|Lzl#D7LiiGBo;j@>fdwcs6bL3 z9KzD5eUSssDkMmv+hhq|M%CizY0|Iav6Vd<7GZ*=9uF!ZlsLm;=50z@lxj1{7ELC{ zxL+&ri(N(xQ}&B)${;KpEEuyk2zS>>9g87`K=86ke}2g&qk@=8r<=CZqDpm-+csqVm{+ek*?HRKL7p9UWsuLRPpf=xQRIqw?|)o}FEOHE|NAPvFR67egu)bR!$7`SozZ zq`?{BHL&zz(%jr%3YnHf_shoIUi}AaeEnbz@xUQ$`r#nbg=l&L`lmC|?&m_-RBQkf za*l*F5WS_#Uf+a@d3)YJY9k@BU>_G??Wb`cS zwrbfZ5-ZMs zSt~*M>?+?f^3IN#R*XUQDLi~Np?jEux}o*-sXcrLZr;=Y=iFd**Jxc-s9k;vH)~}( z&H8}zfS@0+p$9P}J5FZhjQYm;Mt&rf2t>3%Hx@ujdaTmkca;d6KUZd#WNXGPHbWX9 z>;f2vB1I$h*5n<>C6ki?5-&%IedF9shmHLWfKrc*zDoEH*bumslN1scGr`U#F)AC4 zRD|Ml$ZX0%I*z(`w4gSp!0zJE3~X!!a1y^{xPUjx!C)_gfjRiGl`{qD9|gc5FFx+W zv`K(}t8UQzRGleeY}MU(0Z7?-*C?{EP8C}EChW2`PJH}PPI2lYT-vsWr0s4SvZ@)k zKS=ZqMv~njDTIrPRu> zScjx33{Z}l`#1Z*a|Kj2%20Vjq~Q}T`O=V?>F<#S*zw07^>P~+qTfQM->XAB9Zdv1 zH?_do1&~`O8QeK}R_w=n!u7ve9^kGE4Z*c{!I3TNS?Q4Z`qY3T5+X_eAOC?soT8(=8YRd;HKsv#8qQ%_zZtza%ci}H0 zrJM~oEH4j7^0!u~{)07Ep>Hi2^n-jAj7vo#7t;`{@D=|U44zF#pj8m6p%SaH5Uarw ztI-gv;Sj6w5HA6VRfDQbYDl&-#dFZkj#Tknh{)6knpg$4dkibV}3z4034! zKgAXy%wt`-LvX)`5C#!=i8wDqmx`OV;oeKqCA!WyH0{VOC?k?PB=0keh& znUM8zv*5*p;jJ9$){09*isP40Wx#;(tyGN(zE@d%)mj7-aV1pL9qiMR>C+O*)Hg8b z9_`br{AG;=a!v}qGyz+E2wSa=sX_ho!P2N>s#jyI*Bcm;W!0yOpOm#@4zt;AN9>Wq z?at)zidmZdHjVWo?sE_DNTTte)x6fY}?s_{GtB5NqGO z+W_$E!Td+jyP%F$e9p&_Oqe(RI2!}ClTQ$#lFVAR_S3d8f||$V4NBcAkZrx6()F?m zY#z4mW=Eii;>jUPofXh0vp|92tM9rku>bZG`KTX|=PB-Ja|#H>M+walF~k@fKP17Q zHy$Eip@~iGQks*J!=yOiN*Vz5(+u5aPt<)@?n3AF9H2s#aA2=2h4ZwZ&Z}b6&6I9I zuY$`9rwO`#xW$sYFNOCoa*5u11^!9oIeFR-X%L|gx^X5sCJWqg0PuQf(8c#eiv5b6 zuiUm2maNi$nLA4h5zx1UYDz;oIbWNz;`tDRYqf9QeZqxmy^d_9vXZO<*+Hm0{DT??a8r=sL);F}~WD5MD>bg%_^P3)p{Y&h~wIo%C z6Y;CIQg5+RkGOI<@(eDl4<9s;U-DiytdBOV&lV2YpAq!S406K|a>EpI!x-}9feQGS z7uv6GJ<OzVtntc1vQnrsh!nWmj%oVt|GTskqe`v;*grMBY8J$os%40Xi{0|sh z5B{Bv>W+@;j*V{KjKZB*|6mRJUn||iC$sh8Twe0uvjdB>-W5g9WJS+}MbC@W>f|53 zA)vJep`Slkqm_OjTdwo^5av%G)u0vZMV(g6f^{KH)Jg;yo6&<+^3a)M@=(H&M>n=9 z$j>F|$!C#GzFtg&$j?CV&j8#~m11hKdXWuebb|@HDNb}l0nK>W?jh*wUwruGeOgI* zKHNUkJpodrSOOGzRiv?2PDm;S**Hr+qqna4glX~y0^G467gV~rbgY=@ht#dnXTcc1 z>?8v(I1A%l%(DYjEHjFk|H0Qg23Hn+{kq*@$F^;EY#TdC$F^;DY}?k3ZQC|Gw#}RW zdvDcyaL=h)^TQr^GS*hz($eVlFNcvX8o*-^PKc?ZhlMg2dEukT;^wvJ{xa7mu+tj?mD zriI7tXoX;}3v((clK#xmMo;O(wmqjwWhT=du1}z~jP;T% z@wSE_YArr&QCI6KoVU?-y`PY3vB8bZCedZR!1X)2S&lu2m++BNo5!&~NKJc5&@A4S zMg00iVc8=HOaqkl=YKT|?mao0xa~g)F7s}{;530Zj$J#1*#@T}hq$-K5#;cCwsfYn zyZFpl?f^5S9p5t@PU|u}ZRCuav#;D*nqL=mf4#Z2kS9RcazTWEKfFSZB2zc3h{27pG@lFJQ*~c#lN&V|7mzPr9;nJ^ER`s!?4#x(7otPb9Goe z+2LY1b-}qY)d>1Zka~2ua}UhuW#Pf)al6>wvJ_d3+yjoYJYqe?;QSr*?HfZNo6yP2$$WfNLKQRvxjhK7*f$#P-?nev`m^6TasF>TkyS*G5$BQjyDIKY?OAOl z-_@X_+7hV9zAvKrd)>nuy|Wndvl2t_xQ8$Nxx0~mUEfHP52p#Xflr{v5jvmOTW#aY z@Z%}us+Rm_ur@cx60RIB53^iuTIsjoao zE48&bGei9kvItolz@QLKtfZM&H32Nun_ffqriUj77f<0m@R*RdGClK%&VsQPDPd#9 zSrnV9U7hK0%Y+TB1LhIkwev$2*;0-BM?wUQ?lz-|jV+x^y*1Idcvj<$=g17)Wm!I$cZW z|JIi;`G2n1wWB!Stoyw4gMOKD?EAqMM3tCFp%CLg$AphR^VvB#endRG)6%;W_(V@+ zG(+>-hBJ5tWs?oR;qlAA?rn1g<&b=Oe1tm0w8Z0Tm1vKpUz9AoQb@zhrw6M7A2z40 z+Oj)MDmD1qIqhw`!H46k#V3yr8oSA#>fd=gyzc((pGM$Y@@X4z`${a9v*>NDzN=6y zzCJuwdX9DD%WbuPo=SsubLh%`N!o(;nB94Ws<)1L)Sb7un%6n)y*%$0i)EPO4- zD^Z4rd@Gk?v@Kc=mU^z@H&=t+>Uy^ zcpt5vAMI|>Yp^sYUV;15%k<}O4!7>knO`TH8^Y<~yBvJKRii3siD_8Ii<8@$HZN>I zm;I26jL+!OI|+h~G}27a0fD}WVDtXSW6YHE*&_1cHstjfD#e}sGOA`Vev*PD=d{b;G>$_n~NOwdgBKl2tUO& zBbb`kc&7_{+!5j(t#(}aGWh3=f~M;!NMt57)#W5fO1}`(o=%MD)YKe2l$7}GvrwCp z!_Jy_9>YxZI}GN--)Ialex23OP4Lu+Vv^Kt`yzKO^RPy^CmnEF3~Y=NDdT{81#pPML|(XrFGUp=%4dU=^w zTBg#`3AoqlO@$tp?^wFeyoTXm|3oPquNl*ECN|@ecgpXNVb8hN1nCsD`IM7laT_RJAXw{y3Rct~5U7oc@ zpr2v;q(9y^)`zzdTt;IrdYv;JJqJi1(&49ECpu=pd?`4d><12A3D$f6p1f=yPC5J? zHZ_FF(zNU;&ucZuOIT|?G%4~e-}vc};E`1F-e7+S%rX_@YxeILZ%=B^)NL7Mg4`AA z+jfk;Nsg{LC9m?8QM~9Ebsg|(1fx`J`}zotlQ6#>OkpD6)2{q^`*H}QPV9OLD40C^ z2!OaCct*jer+%frWuZL;)j6{K8e%|snMkB;Uw)zOwKX#eiFglRo)zw#T1N63^}Qf? zpxiEz=DwGse5T@|zgUFxM1;$*&3ZE=16B`~G@PHa0@yVfK928AzGCS*RN~{J?-3$p z2$xZ-kt?kKt*-Nr+`ON|3CpaEK- zbc)GaZnP++GK{GfFqM16uSQpZZsKmY$0b%%Niv`gEf4uMf%2G4fy`3ki2!5TE623P zcr5Nd=HR)^0n7GffVlGgijJNJo1(V~O-h41IJ~e=vIbx*B0*$$A|awsgAtVfsFWXC zs`myJAte{0xJS8S-j|;*!o&g1hNhsbhlwW@P_XkFBPtp;w@bM~-IottB3#P>?GjW| zN(pGg->3G6FS-(}q69$Tg0EwlX+z_R2NB!Qtn>&3aPeeEi!6|>l=mp2Nrn)gB3nu8 zmF0^d!TiEOQd0&vQRdV4<|B)uDddN?ft?Yd7G(rH83cZ5RnWyfRq9jkRYMqCF2~! zC6(SJE|A%Lk2J#CMOq+<0r;x2u#`v^0kn_cAER6x{cCG$ax5oi+r&OO{>gHOP3*6^ zq_y}cE;IG+epY$={`_wqV*j1Q01j6Q!C@BZFmR03Bf zsYm;JdRPOZ(9fVBlnz~L{Ik&+x;7X7PpAUgsX>s3@;@l>h33BykFW?2yu*8l+78Oa z816YxqUvHWEXACgY4l8+#uGfWO%mnm^Jt1XwI(X|3;!PmMC9Co57Rp&N)gHq&y0 zwl%T->l)KfInRd@Tfs<;k|faw4r4SGPPKI#HuwMz^jM z???%<=Sz`h05wpoSiCvrK!62Al7-tZ1s4Se-pGj}UxTGFAIl8vR>~)%*S8L>-x~GW z2#YxUr5CB82_m8#@j4a@Zr996z7(4w-6Bd*sw&$G$!*v zj7)x-Hg4#cNH>I1yLHIFc<$KF1m#T+P^lLChh=u>Au7%s&+g9`>J#>oYg>qx1Opmo z#gCU+ioJpgjx89!5c-N`fIAK|CDRPKN@0{B_*F)@3Ic%`jPzBe&Ny>59!r%ZRk=k1 zxu-`yP9Wl5N{LUQWNSudJ`BFWu@=vmAj(>Ce>Xj=86oVBv?bH;uv_YP3Q zkPxjxn1oE0C=1ffhl(W!OqEE-A?IJ$fG}==WXDndsYEV1_(etjJrx9p3P8?F1IUkz zrz8=RM!jT023Lut^fDw1kC}WA2_(shLpYC962I#xty=7$z}|fX}GlxS(lgNr>Tm(_ZKp0fDzoyUQ%Qcafd%PSw#J3IQTR4sO;r$#w`%?5sTD2vs4Q7zelt? z?yM6p7O51qjPfe|Jfd)SFczu0%Kh*0*oP^Lj2NZ**%n=5eLQposB?^_{}|usawRYh z4Ol1UNrZRLgX9pvSky8lLvTfHj!Prmk`fH12dzm`!)QC>5ln&tAO9@QE)!QSUN0Tk zuLEuZ*}mf)%g%!5uv@u;UMS$T)|%KItI%%aAY0_EVGSbJ(Q*{{9n#K+fyWbndOXn? zr^OB5e14|d{)Aarp;tC+4;^%=#a<_~uu1{eeh5TegjiVdD;mCi*W?#gyn#cPEN#9) zHaz2`9ZYo1f2U7UQzKmAxtjbVz4d?U&M{|OI&$K*5XTy{&dEDy3*{;r>q?S%y0&3_o4&aSVbN*!L925UQp|BL-)wR zJj)H9d7@PlVCwJ#-7XYw)+%=!#Rn~`!#?u^wXSbtbcBG}3%9(5!Vlx!}1q=EQZt;Oqo%vaNKE#5@5Ye-Q@3_c; zd(a&EKPkMi_}T>5aN(U^o!pJEvM}Pz?ZQK|$cIH)UwW|STvgJEI{lS2c?D+o2OFW^ znb9)q|LTtFIkTCG-@WGknvw^{pxtRU-^rJ87Jdftp-9k;SCO_{0Q?Sgr8#G4(UO>E zJyj8dSb)2hBBG~t>Qp6b8?r>sx;cGz?I`1AiwO<26>$R5D?P(9-MvdxpicgMR5-><5W%CeUYb5v*S-O9{idK>hb%Mi+)Q#uLg_AAE!KJ_ya8WkgR6 zYWtgm*lm1skR;6o>r5L*C%UVTkop( z?@>M84Z&MjKEI4WxEekiWT7627D7YA;Z+)7RvKVc?qXJQv1kmkXgKRPoQ|0-!m&;^|N7IG zkjjf^^ankfh+G9kT|I{I{!|_0(P-wWkBhq00TIKD8l?@lgR?bY1&-WR@Y)Sp$F?q( z&*K_dxYsf4Qbsg?=YbBi19K(Df0&9f%-14M?i(GiPVb?LKnghA>}l-Wv&x)trnB(S`ac#XCZ(YK4z}PC_Zla zfnz{OQkuAgFRV^tp?-8%OYIo1(*k#5R(aFo4+7u;`On|aITY|fuwQ9TG_b!2K#5f$ z2izbAz#v5MK@EsOcR3*j?qEZ`AO=V`3gfaSh`GVpM^6yD1eP9%FV%>=sVeWK_Y0S6 zeisP4m_EnThv2*HOFxXrX=z4Y8>b6`gH@Dy)D$z-K#z`ac@J<4ccb4Bdk;iN4wRr^ znCRtAdMvteC@Fc1R&zy^c1bDg8tQe*^>_q0b&vL>Pxg37LoThuR=8rST`|`jGS(b2 z*K8T;O@22`_oPqvX#CA6m+F=ybk5)z#hmvy6>5ov@IZpPH}K9)sS#j+gTcq=BZT*r z!2Jy6d}nfeM#<{WVg5dC1<&w(nZo-{;p+6_e)@C1lR3J5Uug7Y&tWve%2sx)^kn+` zNa}I9l+ySqpguv|BzB7&w0LpR#XDI!mp7Ju4ZNS0TFCZ!BYmPmbiDVn@m0Ay1=Q~l zQL;gnlDJDjq2h_CstDC~ynqz}RJQnhT*8A?d}=oRdJgG2XbA>R-Q2|QS>MRdbZ9=a z5|0~`D;Uuo8fcx&8~!$LPp@9pDmWCaSZNgCg7~{W~ zJ#KFVM2ivpt~E(jC6IeB0@kk^yxd=*vB@~-%ZW_k8Y^rNk{9s;1-Ku;UZatY-|EIJ zFrEXFZTL^;tuZ8#B4KR8)jX2ozPO4f{$PZ$Sii z*m*%Put2&~{?N4dN^4*;ob|f}1>R7rFE=t<7`r})6u*|}Ja7Rz1(j`?!EBJgK8ZJ* zu|QlgL43~;TgDMvh7nt)5uZbduKyD0?feGoPJak_w*|PAp#`h*J5$&b)M}c1{6r zom@h9CMB3w3rexh37jy3)DvCOIJwb9K6*q4s|VX`C_aa0MKgf%Pj6~4FcdTi|9uo> zY5&)_g?*}VBPZVYsU7w>@pWjU|EmOTf14X+B75y@4L-*HB#PFxOoUwB182aRMGyFN z103f>p%^K%e15l)iMsv%SdD#InxP(w^Ui79V6I&E6&q6j&P3uP&qsK?1NBGRwok0G`*a0-WngX`&*TbTi;XUXsH$Q+{ zkXlqg*6;G!O!{$y&?q$nlIZ!m3YL(1+H5D+e+{%}W9I5hQ81Hrmzl}N@{StGWOf?K z4?Eu*qu(15#%W>3SkrYC?BjKOD_PfwkNqO>wvxCnW45 zoHBPl?kv+H^1kV0=FHM_KU*XnO$cq~Oz(lc4CeRcnXr>ZxfigIvxIlhxD`SX-Aa9^ z^v|iRA^~hkf!>tr#X$pHa12dtudgVp-{B4!BrGWgV@iDZg5Y#&JGU8ypI*UWxj!^m zNk~HD;s!?Q-8|lJ$6!R?U%>Hp8eFmMP@zbvM3}j@LVK&D)exh}N4bIvjl4fJfu%D~ z(h35^pai)Dw|;c@gQCi#uCRuzvm^2OLdX2lO- z>2Wv7VmorZBg+I4x(eUp!(ACL)35yyLS(alw;MR=&7@7^A^ph@xPyqFw)SYg*hKep zU%#AGd-1yJ>0tqVQf~8Kx~Q9MjjvroOh?urt`m374_GO`q_EuHh7#HcYFrUPLjlJB`$)NQ{DW5)TRPvyeja0& zc0JoL$E2mGlF*~%_*`FgGgWUDFfx#r$zL!#?A;cYP5L^#y;QfA&~kEZOm`zbtOko6 z8f>MTw!Ljy@%wJxp4J8#hCfqRzSydEoF7d_V29-R&P^}dvKQGMq#10rIEB15n(=qf zaFsN6OiwqbaXW5#>SEluf-F|aX6_t~qwyzo`ARV9w!4-iSXe#YJr=nmge!%=7$;xD z;Dlh@LcUVG6d=65gdgz)LpB%RT&Y6i%n~-ob~gvCH=P^^eO751d%i9acim(WpnsZE z9x}*iwY-0(3i&AZh0Qnm(Cwm_PJh@u1`GMDUi+;3*qk0!Z(g)`E&V0#vYx!MZQxZJ z81BOPx-nhNh6L66%&%qfIm|`PGsk6+@EV9EHrLu0_T?+m!1?(65i|cHe^eoVK3(m0xj1aw z?TGbQ+bQ*LDq(VP*p|Nw_HCIwfy>Ceb@yidN-_D1^wah5L+R-IgT@>m0Y>v+;Kj0K zm+#A7^Y+CT*4JfBs4s%>gLrMs+!m%H1dUAf>-lS~PuD}4u{@pZ-CcXi0>KJ@ZK^HH z+m8R9S-0fZ`rJ+gk!Xjft3^yo@!LyL2=Pm=^M}{zV;6%qkLE;4TmDl7laEiQyLYuq z`-j(7arT|VGU|;sTaNEt==k)N+2+leGibQ`K{iSA7CApbFWk#nj^E(>=JTp3(n*k4 z5%?g%!^+7~xpu-jT^n!I^eFD;iFIqNuY7n1YkNyq0%++Uk z6}9}`uj*hD{87g5a7QxnkL+jvf4}&)-D2<*h#KUUZZ~G#6cP+kEp9y>>9n zLp?v(=5exYuqtri)Atv`8p$hoOQW7F9q(kyImKmlp;_uM$gP<0rqA20W{RbYol8t* zwvc_K61WwLHVFcnF6f!QSm*uGBZhzPPN$qsTQr-gcIVR+y;58f+yfKc292y;T_m=b z8B)7CKFjWRcS(v>A6+|?_nyfob+)A=wo=nRg-djIdE}aXbpAQ* z{`s_(+ib^Y^bT$QE<3!=rN3?f41VEUdF^<>1agu-N)+^)5jgS6^TfX=#p z;*oZTv9jcEY13QBU7{PDA9!jnbY{5Pf?S_@fGq#K)_I^f_w+El#O8M6zbSnXc*41^ z**7{!jMriMilXDxy@AEFR?X>>wZx#HljpUR^MN{_;_gmfHOeb^&Ln|ex#_9 z&FbOOwx?|TL|xgid-X=yCUXhWoGujY0O6NLb||Rm+B zFYl6RUs!^PL@&{Q(nvXO!BwytJ^mR}wY8u%%PnARB-+t`ukqUkso3W1m6ZHZYu&B> zf{R0SjqxLTg<=SgvJ)xQ1TA3xQU|EMNO7Rc2P`!(`!KEe;<$RqY2S->U9&UCeOkwm zyL)@B?p^kE4M{>DHx2vN`}k~Kd2Qizi|sm&*5&ctww>Nj?4e%-`V03)+{u@@@b_V=>-I2samxC7q< z{`~ogz`2-(w>=V3crk7q`uLv7*ct$x}fw1dv{Q8l83LHK(*1TBt{IFw4xf%(rFH;pIfYs2QhC=tBF_$X`=hpl4tSy!A- z4F3%)ioaonQD2hl7RHYski0*B2>qYJ8{a1lZEVdQjsI&Z)&GfB{7(vn&~H@npY^}s ziVIJ7H6`&UUjf0W(o!?+(_m?PVV7Py3^Xmo@LnqDT@eomVhtrMbxHUX$Owi-#vqEErIn&J8sCFL zEI5A6sK`Sz`EeOFdMU7vQV=ZETigT+pp-H|!ik^|3kbUiA+qNoWzVE$T}MOxjteLT zI+4(LCIc#GS@N(}C0=1m^)VZm%+sf0^Oj+im{A}Kr3CAv=LPSyu5Chze^kohOsF@^ zT8*(~V9pSaiqHOFKZPxoE1{94rkgZNTE(7;pI1(|fmT!~kABH*#BBh8?aoR$2Af&c zG(~}|`$177Z3n~7ECEZSAWdlUn57Np1Rxqn)@|j7h)^*3@!4GnqpqmZ6QZ!-oY*WD znL-5;9ATM}QGNkte;+OtvdlD~HcAK%*XAnm9n(T;qr#2GODzV5NtG7Y=LIieN1vdFqvz(r2`xZ8H8z1qLmuF44Z$|L1L{b0SFL`Wuy{< zRY=D7(N9G^JZZ37A8r-AM}A?^q-~Wdce`VX1`kqf?|M-!==G?WEm>k#CC-uLF>`Ac z;D?e+2~kFQ_7N4f`_dgR^);>@nkOY-y& zLI#9}A%WM_=+!J{PSrH+*LsueDT^SlejzR)wnhaQ&7~se$L6ba+W3oA3l}Wzc>fW5 zG*Xf2LTk;Ki{z0t&wUXy&$o#e%kmdq{ZZxmYa2>24mtP`#67gB^3V9IP-%Q;Qlgc>r7XjTkQ>AelZi#W3 zBKg;4!60ej5y+oVHo=TiJfJ9-^0=Rvv{SLMGqcV(kSM?`IckjBFd7-;B4e=!USAlI zrd!D=AChW@XkkK&QIN1PJAo@AX)P8Z(jj(I5{{#ch=H|V(t5X$%~A2lPEmfpFnd6f zH|<*+eJiVS`gQ1!pkBsOyU^;u_=oi2x7;bul6|5DD_7#ZSg_#HuL*-5r#bEbT#^wu zCo$A!K4NQ@)xSU?@tKlNr9 z$#DxX>}zvo34bm$#9j_k9Xc6+s$(1NWxCS+v;M)(lJ~8S4qs@Yz8GGNw+Hv~NA3QN z;YCg8g9U(AmMhFc>Amgd?9u*KM|+XaiK9N_p7alcsStWQosXRA7eS$p_F|wbQqlh? z-`8X9S6{_m8e2LHU^vjjGd0$=S82*_u7k@=@}5**6`LAk`!1x_*U^0!zFV7#Mw``H z*3)6S3@5Fq*y^nvYO+$*Z}FPG-n6bkPkq~w%@bQw0l6%u1w0bMu;58p2`4um#t^bo zo!{?*Sxh_l9fsNySiX%fC@X_$JvLKdjA?x+>s8hPltY1tLs_`o&lj>Y0-#g0m`-x$ zW=-U>{Yj*cSnrXA)T7`j`#TuPg8|?> zz7BRHqr25cUs8$A2Rm-jnYVV({mh!#2+e0G$pG&y697B1z+J_F0rthb+!ED>Tm_up>SQ`SSP$GW(IQxWQWH^64yE zMZ%Mz(d$V=Qr6ccmOZOOTjtN25j2kQ6&ex9dH1hj#vX%1Aog$#h{94Q#u2NEjIve) zg#2pU##C6Krf{zzt3vwJqj{I046M8%(MM|PjR};%j3j_c z`Hwc*sn6v(&bWcs$4K(DC*dAHndz@=vvw~;@XpU;I1m?7&x?KR!qMI^KJLjAo2#BHq^%@+D0dW-;d0nK;DMlG1a(_e3XIfcS zW-eT_LdNgmJiiPm;|zqucHzjp=gprQjdwZVqNp`uB^o(TP) z2K@I1Cny74=$E8hQW<>8U;{d}%O8-c$w|rqMvZP;TCd2CC*tXEcZcyk8PfS9S`gQV z%(3h#f*q}}hm&Ms>?uSYt)$<(eHr6#9j%4=!!k12M;TyHj|2Y-&4+G^tvb`<`toRr z9E2p&Q)Bgci283x;@rXF+)3lqd&hagQ`{RQ4w*V82(Fo94vaq$7+5C9d7@L)N5?a# z#w&J>Rk-^FxIE7Mu$6_~)#%=DD@3cWGzxU!hWCaD-|3Z7yxFIbRy=}7LM3-mPfv{t z-5B>2Qvuovna?PhR*{`9nSS$W+>xOE|F@0{)?t*9KpSP zVvsj2|1RL!fg|(u_Q2dgcZ#HoC#%#NBG}@Xi5tXcKb=>JhdXB?Ths*OI<7~kqfaW` zxma>TgU1sttuuMzGrq(aFYkb6t|Pm|7*u|InOKAEw!v!Ge05^JE~w-H zCp+2JU8Cu?!E1QFE~>=1okhTOH+9@-cPAk%K&n_1>?J#fkblQ!7hP$kRH07{G43k% zS6#qBAVF9Uca~wm#xNJjh`iA~J+lXU7@>ulO)fF{xroIivX0SV^92 zSDPdzX_C-J{ynubm-(^bH3b|0-*DhKtla|)P zkn|<_I#zluG~P1R-P+gPLUlOMJz9X$$+G;&zE|ty<~h*#Xs~-=sal+FQ{fj^;g?tG z-sv~yd*(}it`&o0Xj?kEEO^6?@;K_`?FcMETu^gc4e}5@w^4-}V)X~~wy!(y+OSL9 z$!=}>9Bp1Ck8Il9UAlz0Hn(@QZ|-eg@Lky;xVd_cw6BwFZQ|cuK7vIkDee5YrfO0o zCdvZOK?1+kzvWzgBcT7r77#+P6{N}8jP937HLJXC6yCkuC(`dD9#c8bah+tp?6&^Z zcSAB>CqcH`Uo)somytiG8)AG3fsl1QD-4~#K69-;JPe-wxg)!B=~CPD>6!`*92BHd z2!{Ci^49iKG_V)D3yg9A%<|Pi;H3sV_+~Uj5ktZn1X)1fbm!pP8qKz_$EdjU0iQsAZnn?YiSvlg;qBETDUyB>QcR&g=|VQ;qSf>^cMJSPG%51^A&3y66AFkvNsR*ECHpT{WUv5s5&`(hL?fM4{2V|d+3uZ z?t~CY3y-&N|4RAWpC;mvN;kgFSKB}EM14GgyknOadPM{xm8TBp0k4)2ZKomJSE=?v zCkHEuTQ)wc(@@GEEhWVtk`C=RV`-dAdRo?1-x4R+vbk4qT+z1fM2S*-Q-kZ(MxSJ}U;WJCI?K@W^cRbJ` z1Z>=W3)kcm7L6)h1IfaTb)0cz<#-+Zi}!b%$$w$7RquR{87|f|RSne%a;ls0kZp%2 zUmZkIv}f{k=5uc^x8(X&H{1;TvRKS5DbibCi(e|N#3kI$Mk~M?uAB!4Fjlx1;zLCW zNavAm`T9y#$WT0=K_fI<+jB->s0a4zv8Qcr46W0UcQ9Xmcd5+(Zi2+ z&sn!3!=}r@>-^x+W83udvVb6J5z=p?MAI6?Mf(t2=rGCTZ~a*EL!h6(D0j zWIPy{6FIXl{I)u?pkeJ+>=LP85hDxn(RPqimw+R1;;$W&q$%R*I-h;!uUHVzz~T8# z&;VGXIih(jr(Fp5dAdkmW*fT#lCH{OD24~$ z@oh_Mmi83kXi6`jpN>j(XCHB`m+E@8MR#ahSEe9BYZ8M_B>yxexJXEe#iXTr9YCkjXoVs z@wn``H-PrXZ(IAX?mlCs2BD_J^|aS~dkgioA^0;NX3L8xzZcG5Ujla;J5UC{J><6~ zXBYvue;M&g%*y)@)LkKC9o!TYT0;+y-SRZU##WM@Z!MjUH!o)`Rq(d$Ns&48v`(7hmmDoi?-D z>qPbzwlsC|TpnCGs=U}7-{DqW{@m1hbmzozCY5z~w5+D(OpP!n(9AUrex71?w=U+5 z9ej4Tp56)7Iecz-sjyuw-S=fIZPpor&%?g)(>XuzFT!*9I?s@;)>&Nr=|12%`uE?{kG_bQuXa@)RaF`(TPn<-h`KyZJiijo_Eoc zozqr-gYrxa>E^w(;J-e#I9UaS)IOu1uv{)1io{`_lxIK~qC^-Q`Y7qY(BG3EYg>4> zC?wjAKVK%^OggnQS1NBbS2;b+gNuS#t_27pm9#HVQ5Gu7iTG;Otw{0Gt1O;;wnk}`ynl*r z&ju}Io@wrR=F}ifORSF zH7YJf@6@2z+mTCI2HiDE_tlLzxM6g~pjX58=tA{UR~2s-H`Xy*DIJ=#m=m)CaxWqV zJ1^rre;*n+EcOwxMfV;jjR*0ctcL$qN>Uu?7OE$2smZ4s6%I7Tc{gV##k9HfUg)25 zgP^AgcmWZ&CAET+!zsM=3IvH)jp=-~ilEWenfTmmLNAq*+$>|ZY!7=YHdi^(;x7bR zmaPDTf@npw=6)cCBGj^m%+v(E=4IYRTDANCyP?)nzFu2e9$FBOItX5?cNJFSrh^9o0pk~+49(SP`0st6 z26I7i-_P-pD)1+@PM+VaXVHY2#swd1`I8FOlFJp0 z71fEb?OPF-&uFr`**3#Rl!R)k$`>?dg%Et4kL{8=fPG20CXiqaFFg%LRolzt%AU_u zuy2u~Zqu@4Jf*^&`0b&{#=JUj_(E^5KO))#!C2LbOIfITyR8_iaL{V^ARoTdBFC4mTW%r{mvDq~m|ptS(b8 zsvEe?;51IAsKf7ibZ_q?jkXBcebdHs0gykulmkL~)?GAX?Bk(_K|Qa#3izE$Rfx{E zcg#xGJPFo4DXdynzXCEI0lJqXU2C2ZA0KR<*$|~t7sX1e%gwrF4Thg%ASMKFrk@>; z-6QNT+nMd_;n^wHCwE`tzN!YFE(^RLKa|6JK}J`)tyj8~uhaE>koF;l~qTb3An`Lp;+UEFn<$Iq|@bK_313i_s zoV1LTwUk|z!=eOane1yz(b4fx@Nt%S>FKHAr8(!xTLZ3YghLab{c}>3+!$otB`z<% zK<&j*wpfQFV)?8-uV%H_w=eDbV7mB-TZXPYD72axU1|RrCt}jnB&DeRyHx9gfxN+y zaJRIrJ+4|Kg zxwNENW9G{E?;$3`NGD*hB>?KmV}sQ4h)&J*yZRCRU)8n%sO{oGJ>hjv81y4}a<2yz z@$v;khF+cxndfw3blRGPW;Og-sZ^y1aGI-gveXc1m^%}urS~cw19!IuMsajQR;UN$ z@&3FZ_Zm#gvhU_k+6nJLwaRw)u!Db7eLTyu^xt2-L*1>Zwf_Y0~#=Hu&UeJu+&RgE!(@<3ULvLf-Z>mVPH5GfFTpt_|=v3!^|`|^X)|Cewz)d z&h~|aN}%TKX?f)z;OQKYQbXfZWudY37X7MqVro_L_>DK`=Lb)97eK^cu0E zv-ATqV)`8Sp;;FygV;&zy=ikLLHhUGUo!uQt-^8E3MqRFI5O)1e+`O<|24pbAIEPh zjKr{l2#W+3F{j5zdT7*(A_~byj@rlYRW27+CFX(z92EqsH!@pk(cd)_1#weEm{Y|7#>HHGwo-XGY|7j`tlxFU zZ6|Avn=Z3d;>JgLi>AVWC0v^%ihBYJuqcuUfdrwM-+hX3QS>c5t^Vr; z`|hY%zop@77kZQ6G1@SgE5l`xjYiWc{12%Wv7#{$1?Y$)1Y58g8W#FGE3>V%w^bCS zVV3+rDno5m*~~8Ll?US7PoXhN)S$N(oGVqRyd|fv^~}=3~3t=0z>`rsFd(Z zDaLtjD}-W{5*Nz>m{67O?)lz7m>K=~ zBz(PL1RVmw4`4w3nsTD$6X7btL81snErc+z_wk-Ea98Qs(|-UmU|;1yK_~PG<1l~z zZ37SR%9cs9DBHN-boddf%>31>9C|t+h+QTVQ-cYpoM#er;!?2X#xxk8qRAaMYNXt# zz(+M#N$H`E`dDo1t$>CVl_pU6HG!mPnIl3%D4d^9KrbpG(N7ZI>uAELn6J?&p`21s znCcWpZQUSJ^jj=n%3UWv9+L*pH7uE^E|~uwl|m`F)Nn;Dh50%mq!$tUDoHiR2t{Pd z4hkTakVFlm4WsAF*pYOsoy@Wg67aa+HOEMIiv-lPmb1%?D6Y3MD>Rf zEO*fW=Jw_}6U#mlJ~5SPN@I%c%#jUp?1)J+iTrfupvw39bxk0VkYDj*5SB;BA%P!H z=68T(UAW`V!IvO|>wK(t;lWmg!56aNJ0r9sD(Wdw>M4eD4*5U!KB9xIYJ=_?Yl7i- z_d+{G97lSY-X;q9{qDixcN3w6xp%q!aJ{q;zsq6^M&PWI;_4h6D~0V>f0>ZTJ3{wv z5Lz9T(PR-@0QZ(%HLetLVt$=q(0klFDA|nfr=@fe`O|H4ZRT59Uv~D(KeE(TgsK~zRLXHvY{_6&^Dg_ z*e#cS3oi_slWp{SJ*T;#u4K<9^+2sr?rshR*V*Em5GM6dCih=dO8TQ6K}J(Dyh+!< z!IGrK%2+Y1B%h2l`uv0bAHMD}NU}9d7j~D8E_T^gmu+>~wr$(CyKLLGZFaf3Y-jn) zy=UgkocSWoj}?*mtW-qipY^_Z-&Z7YKUtEE3Zs(Ll9HrrDeF*ZJ8S`|GB;D=rL9f(T)!x*=Wm~ zfW@M#XcnoAAYm$?JE*O zIjB`X8Q*(jr<-Z8R^69@Y9Ltp9Y4Una z)xWQzTnG1qDPp;woa49@uf*p(vB#k4dPkTxFASpyvp*?} z8Ep?orx7lgwv-izATXv*SKl28fmxkEE&f*!uz>0W1lT=A3e4`wLYp zzXlHUE-K2zT9BF^D;xY}6yP$Sz`u9g42DL>))WX_M`HoVQ&J6>|BrWZ;V+`k(LpBe zP=)dc0{*Xeu?QQMoBpqN@%WE-kp%QE0tr|)Q2PGl_lj`W@lwl#eJ^5T`Xz{0r`DxU z<7qlMTH3FTbdI&F%PZTb8~sLVUD!Yk6&_F`?wA(!OrhrA#o~ym zA6I(uNRE11XjW_Y(@SVHlWS4L#v49MRF;K0wIe^U^K)T-3NWevj<9;m9(enVdtgU;FgpOi83y1? z18@cbUikOVyoYwQ0XA79dUh%N0n#?!8lP3sz0v~&va5o93m;G$_Uz6lL9R?)WoBe> zt%~{$5|g03-|OWU2FCJ{3UNW-z?nEHt{y_SSrCsbOX$1HGk+g_R{{nilpunGVbYap zqG6DXi1kK?L`H^4M?^r3_8~@wqM{@Gev-ptp)%|di@GuiyTZ~Zq7u^*-+N-IStqke zWcr|RUb50NoaW(S%PvFAAH@0yZ#96KVUl=ksF&9qfY3yWHJ>ilGo?Q7r!{hI0P5 z7^+wP4@+Cj?u~`~b6vyMG8pZ9^bc=Y_?D!q3yPQD_%yEitsIh$krH&A26^#T5{Re( z6LjC5U#?pWH<}!Gp(;~Jd$P`d_$>ynxTRXP(?we_I>BfuT~q?maaJ49iG z@UKY5PEJP=(9`*#2N)Fi8DPNEpsq#+@6b;(|AV{8W?O1{RUxdtA_9HYWXS857I?!? zQSBV~&}_q-3;V-Py4gc!j^_>JE+BgsrIAi$q#{lY3}1PpmPka}#3ddX$u|jp+xsoS zJaeF4!H{p}0`Hg(@Qw$2MZ!E|l5Z|bx0wM~V;Y(~W2esF4;Czc-U(Vfs?q*nRnsMe z*Ir__k6^s{QwVH^>Z(+_Ul|H)mL!oLgtXcMmZGKSwY zf!|zYys;bU(~Ayxiw=N_4hV||0V`!h9kI@J(}KyC`cncLL$QiTpN=3_4kExf zL{$S}XtCUzOx>pZ;W(p3FyI2*o{m$dq#^xwx^WWSB^+PiVwq`ttwU+IJ`kE|Gx6jSV z_l=W;jT5}F6TI=0S;wifh^_2>;;FD*192ev}>1v(S{(+U+F z^+46Fxf_K2!9g*&N`h7swEN|n&yOtBrY#rjRw%}Q((lE_(mCuF+EPXZ_MJr1c?-f?zEHRONFs3_lXlT+r=2jLeG+-1`NmPM7%*bumxUTb@rei{m!@GT(gkND^@}F&eFnGK&MhM`UxJF~`bfHI$NblF@2vD+GU~ z)^wqP+98_`wiCI}A6IE-$L{9Oe^Ch`Y6QFgF)qGsdp8}W;9GvXj;+D4e1}uQ#<2C) zK5zF@Kb)n(oAd!zSW0jl=lY0a+1k12rKaOI9Ui`at7Y`D8q({jZM_zC%}?aBXBTGp zK~WUR8b@&PcXpP)Gy02gIJYT(&Vz(8-LAt<-5_d__XE9#{(C51?6X#H+k^NY9Es0@ zr((Uh%gxbVx~JBM8~&vCR*WewuSyB=uaERet`(iNEj=`s9wEgq-A|CkL}JwuvR66= zJMErV&*apx!$Wwr0B8DHXTvQu)}IdpOOJN_<8bsk|id-Vu+ z$6l3hy&yoz&1Ab)6YQ`VnWU9uAH0*LJ?&VaZ7urc*M%%JOW}RRlG(({vbjF@zA?6* zPGZ!5YOVIra2WsO0dQrsy#n`o`PY7?NL8~OWen`Hr?Lz zvaGl+-oZhdr!Q6-W!g=OF7xykT=VLyXTsa%7}k4rtO`Y|-SHvGEDw(>=fWrfb}Jyv zR>$XE$oKAL{L#nz)!jn%{8IOGG?YC}*G6NYmo{Y9ZkKNFaL$hS>f+(vTbL@9J^YPRYm&@s>w)9S=>ucGmeC(Q(wu5zl-|+`V*VhB%-QzL# z^!+xq{%TveJGl1DL!Zi*Io6M0RxL4qKj}6^F{0nz&&_ZvO^%l(0lwvaw{}h8WXH6Q zRJfVqJ}MZc0;ZU>{#O?S;=xyZ`6IAlD)>BZ_`H-qAN(1| z;4+Rsw72iW0R_fN_Y8ty1ven>c2ppGGl<}R>iAw zcm3RMgF1kBTA4my{^c~zTB&t_^ajN{|NYL#IN;i(>{H}ZVNK28ZK-^!D=S32yUUr$ zN@Fz5^yaZ3QgI^)h3{oKjd(QY@pjAea&Ni^#aF-ldP~#I=Y7v>u`+B!(*2-ya3!VZ z>c9=@iq;NaWgw38d+XDNJwdOf8(;`MQ_8(&rAyZB$@7Kmt!4AQtu}5)kHe)8VX5c( z^WNgE)#HVECvMDqy>FCYljZD*B3lZ8heW}~TIgdt+_y#+^{(49eF=H*c6oc3(t@Bq zm3x0DT8LET^A(sDSCO3SRG$>be@v^>b6Xe3;EvpIMb^~&Vm7f8))o0w2zR?TS>yf^ zI47*O)DIhDd%ha;pmPP$14{w-c%#rJ$!GiC3C!*v)fbW zYXc#nX~cWvDjGbepBECuzcNdr zBx{UJ%&+2k)jTvhnsD9$DP53yvh8JcXqM4{t|3^H>k}8(mE^wQZT?IN*XPEa^sSwl zXYSUFrdVQVUM7qv!mdFphFOFOt zh*d_}l_`HZ30>_g)%rTP;}zh%M>jpg)s$VS7c=`UAH>$K(zwlz-^2i=Ndz8e%@zHc zQh!uxdG(rVvB()doCyPWAWNTXH8MPF5l6yS;08nJZp0!-H1(~6V={*rbw*s7-+gLy^}BYAEZnv*IM!bt1iM;$4DHiFVt1lxz2xT+YioH*c6jH$ z$r>f8ZTpl%sm2r^=KkmbYuInQ>jEHhsH~x!Cd}Kl_jJkjcpnENB3_vnFFLxU-skcW zN@Gy8sBg&p`7HZfF5X&@y(zT|@nSc8jhVZ*xO9Ug2r8c5%*j3G>EdYhgE<->x1CM* zh%nxUJi5i~2KtyREYjx}xMftH-USQ!pW6I7%}0)n-T7HPiSD&|k{n`_p}j6hQKo{cg)Z^7F~Nt}CtNlIn(@XCXC zZhaN2+(EiZRrNH-v8!%>TMkEqwBErj-8D~nWzM;e_}I0&XW`i@qSv=cdffsDjtadvX1#9@AvR zPdFizhB#PN9;TSp@Nl`=k5B0R+lUdXl4!a;Q8F)4if+0t)BO%S>}rkom!K?GKm9W` zgT45O+^pWi&Ha1y*=14=#7m)WJoB(1D*qpB&c@KvUVv(n{q|253{s59|z+}sNonekY)p2ctT zObxF$q&rjHF`@BK@|U;gB68n4cE-U6_Rwe41EJNycT|ceIclZJV#{Vp>J%zasuU5? z@)X5lA))dmrebO8Qpym+5;7y@ktLS2pY^R{MTr_Z?`jkoGGOAIdv4ks@G%*y4FZPJ1j)zhesL{zk=Y97}{II=&*qbOvfrA~6V|iDO`!*K3&l(!kX* z4-Yx@42dTj)G*f%IVCnx;DAE_7WtN{!Du*Uh4lRTsZ&G?F(V{QA-!rTl!{`O4}uD$ zIf^qz-6Nni&OD+qsc4YR^7o)SML0c@>N(;tGs5ajvY&}W93;^)eDntorN;J?DOryD zLZgz0PlZ`F`kY_BH*k=_K9*arNGM{Iisa)*O(%jQ|2tE;(9)$KGIHoZZkD%JE=q9QuH}Ta{N&UT<>?o02=fWt4tC9(s&TZ60<# zX8|3L*-dlB0h}79^v=$zjj+U*u|(EaMy?bIlb=|$74|9P*80vPw1w0nDVED6r?eG> ze&~gZ7AgVSxzw^Y#?Cs;mQfoFPECyTCC%1jdVb`SKCBqbOD7-7s!{!8l*;mHmaIRT zsiS6we&SmQq0@g>TQXlJb+F zXp-_;ar%$+p0bwTfJ~x{xgCg0L=&z&Lq8dm``^>s;8%<}0T^saa_~U0&ES#uU~>0? z%+25uj5%qT3@gRytScX|V>u?g|zVqUDl+yL4*q*8_gyTEjGtem1h1|I*nob|k7 zJ4rlJ9g_yrvME)mlH~z^RTz3I1c$=@;2f25%P6s0!-q3zyqkTx6zd;x@Jk&=h&QT% zY*fHMaW%8bFtt?o$iO~bPHd6|W?(JUU&Pl*>Hb2FM%^RU=ai{jQ@HZgXHy5lJANCzBnQY5IGgFsTY6vA4@H#+L>0sCkU+UggJ@$?hz)iJUz-N zACJ;idfde(I_q(w?<1hcd+a3qkfnsM?8L_`JpA?^gz|!-o?vvHii@z>k3C_h^pPth=PRVN)%3`QN zPiHlyVF~@_2NI(126UrL8KCR zxm^EV)J=iHrs%iF@OY=fsZ#bI!(W@wa5B6RLscmTE_bE*jC-xC1*Z8-d#z({Pp;hQ zw!}U+Ja(m_-|DsmJU7JjMCdu$wou`C54NupUzld5>}u_sMp(6d(yi;$oPAl5RLCD1 zibl+2M8Fc^IiNYq;FMyUD3_Lx^|Z$PYQCH4%y!$x>w>%ZtD}#34Z3qn?>~-qy*B*H z7H%jFZO|2Iq^iDcj>ob$5h(bUEIZ6gm3S)*>W1V>QvGBc)J+`QOW{R#^t<-wjr&cI z?t~#U<(~Q`g`q<)L^5?hNbGP>`>r?{hw|{gL4z-AMMr}@pSYk$5yK#(7YkG}RX=0S z`Tg)wp&UzzH<1xyr!%+4tBqoR9GcXcD-9MZ7?hsMB z+rVIFQZk{PSJ1)9jGQ~CY%(iyr))Au1AS@9)<({B;?gM?!=;oVI*auX8YC#(FJmm< z4zW34F#v&C)!H8Th-kLiiD54XbLBMR6Ay<^-Csz3z_;=z=7kiBMLHULu*vlk-)`Ysj90=}-7@Cg$1AmMh09ZV(>If@@B+ zthLm^*pcsr*m(kqm=eg^yy2}1`1+T$W1mQnya!Z=Dw&yqs=t|2zqH&}yGJ1#zh$oJm8v^auTV$u4HYwr5k`BgTBqf8tRrE=MLJhfEp5 zs-v+S(o&5&t0&&b&hH(P5&6>s&`mHlh3A;3)cL{mNe3BFw)3CRasCC=A0gbe3T*O^XCLTohEe*@q#(= zC0@UdVR+{Q;WRIVTLk(hne0COZD8jE%4TuvF8wv^RU%=V6hXfZ3WVD!O`8;dzfR(u z4HtRsYbLnF6X`D6_>n@YbYY4Da0!%1Qs`Ghay$qq5!fx=ya)393vYo_WZu2L;4u+O zb{H%d5~?#k{RxlIoCetmkK&vL-ANYpN!54f71iPZ<%bn8mlK#}gXWvbM*Qxc^Jy_U z+z=d`1GjX~%}$ThvDHOVS!q}belLc?z96j5{)Z>Z0nsu+o1uB*WkE2^pQ<&M{-^F9 zMfU!MjAR_SB?s@}i_Fia)Dj(SJV9bh{0OGVe=;gY*j#Zd1yfim%=6NtXGjY{US~p( zeVDb=L9yV&2bp=}n-UAivj4`gk*e&r>U!G;u=|+shQs-{l&?#-XCS-UM9*Rk5feaRZ@4PyD|rhP-Xm< zQ$x-RpZcfxi!{}Dg#M#S=arNAgD1haKcv?C#)CG(C>uk!Y4Uis&}hokXt|yD9ZQa= zZ0{x!@3DbplNx#iJlRSE(Gr_#>4BnIW_sY zND@N$Ap^azb4!QGIeSazCMjQf2A})J>mR^SQI7+n=jeK66j1B#ddom6Pd=K-WV$M< zJ6xk3d)Q4xaqa48eT98j5g0ruNG6|w2+}@|=MVZ}Aj1h_X%}$ih))T%GVqh0Q{Dio zTt?swIle=khtF&>n9KVrYC`-C3$}`4w1u=iTyQw0=Ij}f2Uw5q(6y3wP-ace`*Mex ztI-c>KGs<~7lwNoB`5ce`$V!f$G5cVPidTMQ5a=^&O`YJ%jTztCr^%f>5>^EdQyP0q-pw8$dvVMO) zrin`Q;JB~USNm%Fy|HgXl;D#Q8*Mtz>T^BxLzA4w6J`XwppxCm%pPh$CU=yCyC!epF3Mf=th4#0Ev}1l9&N z|2ek%-qS!VaJ^cATML_qFJ6XzF~;j>8TVVrQR+AuXR6O!`}Z|Fn_1oN2KNy`C%Grt z>0{<@K4_y-Uy)gzVi-d;Buo3rM_X|2{5n64mRh89y!NXX|F)k`Q{fP!K8V5f#YwIz zfX^S}h`h{(Gg;N&FCsu|U*)2w5Hzp605o6Kf4=<1GAeghMsIFh}W+p1DmB% z#TxETQz3FwL+WR9J%>y)h}XCg2y$j$cmlK4>wqh^1;1Ldsh?H^Tg_KGKQVP4;E2~T zklZM_-OfgGbfzF161LV1)X1;#Qn5$GFdrwWl0@r-b#k@gDB}zZ2aa~!&pV-AAG63x zy|l0TuWZ~ZPiJJd)?$tx7JC4B*aKv-TLdlkv7+lMee2NY>dAA3EL0 zQ%pPUD)@bUo^3DYBLigi?>ifx7kmp3!d%Sw)n9BD`Qn7sU#<_Yn=+6me0|*a1Ln-W zTW{WYmOdW_H)N92XYJUF{`9iwsHjH9G3`4?;gV7~W&A+J)AwHOlpX$;OC) zgnxZbGzw?kUHG^C_V+`!N6dBAgSTp^ri{})sJC{Cle;_BgT;REES@ecmq37LX7PBq zbC|q4xpU=83!%HWkSWb^ z>#c5@r>7!AO%A&^KY7{)3}mx;e((I)>!4!WaLS+k_TqpuUG(r%g)W1DgRI4a}1cET_#-QH~Jh~G_F zC}UfF%GUAVem3JW%;q_Fe}t!*)jWSG$D4D}Iwf`-Ti&&VKi7#Db42g$rx7zhcEeOAI^w-NIyH9Q_Jo)GIH2JkJa3&qS<7PI{ zTV0M14U4+f`B`^7j16G<_QiLX))$in78@RQYcKul*VSLpT18kLxN@b| zc^Aj85B8ePqz0vaTROSfxoex`k76`sT6r7732GC9_sjms;OrC22eae`hRAq(%B zt@KYLDwp1a#B`}M+VveFu(G(p>#aAFDZ#VV+hGPdJDy)2CJ#4W z&k|V8huhp+JMT@=nm_k0Pw6|6l;Bh`qq5gb5>=LpT2_L2E z#62l7mj&wb?IbHs>pt?a6pu5WMR-JI9$zp;-l``%Uq7RqSy&IY3#Z~0huU!PH6`i6 zkmYtC7IaiaFTDTl^?fWvT@}+w#aT778Y`w^*_57c8t1BnQc_@ zY_PUE?18c{kUuzEKMR-DC`7Ase?-Pyy%*Un7F4r&zPx{4RZ3Yq_r=~9*Oy&!-7c{{ zxVFdmE?FjJa%1mo+LIK{!;n5NPee{svUL=WhNL9bte@x|9%SNkTNiE#k@LoGn-wy6 z4IizFKWe-V*u=?VIz2A-k+5^WKR+t%@>k@(PDgHOHhsz7R?27@Y-n(5JqMbdLF!>~ z7n1JOo$ROYXG_V}`tm7`FhPuGa=W-I(#A#Av~{05+&=CZWwNGx>C&DjvYQDkOz$ro zVNrnqRh!Xl=SRLn&*Y8_P4`yo-NAhTH?z~~Re7Fvt@Dk}&N(0Y&YMQ-rxb&Hs#^6` zuANV&=5~$Nei1{hVzp&!_rpgx^U~fU`KSMR@HE5J%Gn)tnsV+a}VW%GkqG`H~-J_`WLV75W#M5$#X01`}M*!xx)v3 zQQw$})FR~mz2%D@F~x-EyOuG30asxVWb^#bocAc`6hRzJ;g1~}{8I`-LPAOoS`K1T zVoDBT4tU;Rs8^`aFepfH2uM+INC5Cg7ywF4pAPB`{CdP}tw%)4yN}_7iZ89C!s6;L zF#H0JO2E2^k_VIXg+1kaa~V#~_ZgL#*S)?#wcPQ_EfgVFQY4Sml@$bQAB;l4CL)!m zZby+{QBKCEo@iLyp^j*sPW=%YT1yRGfagpaoD5*&C(I`IIjYtp^Z3Ii1pqHu|g9! zDoU4Xi5U1PL+aaRQiMF*T!2%wC@D>->a5@jjG_JMp#T~`;IoG7+O$(3R|@=)hA~Hh zZ(qQu5V-tvSI_9ffio+4_h=;@^XgFylUv>??3@R}?Ss|7v%mw;>oQ*AcAV301i-f4 z%sMjWVSn=b?~N4TWw*LIXMzO|a3e+X{}q1up9aYPFZ}YK$(a8u{1OQS(f>LA*G9^a zCX6%M@Jf!e`aqqq0O42<_)vTZnSg!7U{DzO`C#8PXtyx)f|9G8K_9mecvcLy~O$17(p8@qaQ8(U>i2zuTw&Bt?T8pxZmKymuFDR@Mbs76+zZ{uDVj3dShY zvs$y2owlxT%9fe~BPnUtxG9bXxY^d+<<$`P0s*SQtu#VfHTS_@215NCeo5&+Fn;ifBcg@(%18EqZLMBsE} zxG_|BxV{~%uzdVW{IAeAbx{Ko=W zv}xYail#6GTbwwnI6ZbW!0^m2sM3uYP&a>c7kQH}(gz<8_me{l5S+(QW`HMc3UYBT zu+&#UGY6+qL~~&o;!4HD<)mlV{AQ2|A36moJgxsD{|C%BR=Ci&ZVjeG#gxO&Lw!QU z@mFzq;tDt@iy~&;4MUM`*-_>BQ-KB7p9`r6R7JK8y2-Yx3==GsQu^5=M3y_yD-cZa z=Y^5}oSK%`Y37i|lF1K+#pW;$fN_+cBk&`V$(0xpcaB+TY4i#uz>G@@`LARuR5Hm3 z36x>429X4G(#RdO2vB30`_&Ztlg3DYI!$ca;$MZNXq=r`;V8iF29g9ig=42*JQj2< z?f2Elzn)!lBIgE-aL;C(CY3}xRa9|XoOukv5sg_}p`{USH!R9S$ir4ZH7^9WeI#2| ze@6Ee(?r7b#Y7tZ2IXm^Eb#m%`4aVS@}-N=c50VOBgTm+=DrE#28sE1%}D#N zn0t7P<8;fcsAY~>#e?@I>|?VfjxVJcrwHuhbqhZ1;|1c~LWh}Qho?-zbo!Y?%dBIO zFz^j9JX`3a27x3*&d|*{1uRAyhS`jOKVm2E(RL?bS&EEh20uT zklNq{W_(P&oZp8cIEY6nYIS`UbCLS z^vm_{en>9zql4+7SQ}EK>x_PK&GGwc*)Y6Sa7Bil`y^{j`7&)L>S_;trw;Z>6f8;Q zWElE%Yb)5b0eQo2NAo`%;F0E@9GVRs(fTxjXCK6Mbl?@2)XD_%h#XrG#Apqhx@mac zWpJJhH{TInM}ZnhzP~@#1!;w@vR+U{?uoKjkX-p$4f669?3xi|Zn?XUF6gHTq+Xf( zrONHb=*>p${$p|EDhPUQ5Vfuz@O1UQ%dg?{y2#b<=(U9lrY@*1ZFj<;KG7kGX#PKJ zez6%!tY=rdXX&m($3iXRMGl?~L3@hY2Xa;A$Sca%t2GsFN}vRb*hfO{WyTYFSNboS z+K*%&7JC&f+ln8{m1?EVCwxzbG>cf{bTm{>Ldv4>!7OTD@Xp#e!-^zlpjX}PT;DCg z@|J@$c77%sm8Tj?16Yy@7Wny8; zxAs-?0f_}qztj`#S_$Wl^9A%==Vfv4sYX6B_pIM{^xsv?omt+};TA{SbsyE{ZfJu<3si{xd{z9|prT}Xf!p*t^VQa{Sw zSgdymn&&OWn=~x!_&UulgCCZt6GDj1lKLF4R-w!G414rdv6;a=b&$4@SnyW++(L#w zfI!D+9fmCmwJ?b-3ftgDn*=n%`7Ze$OI1{6@Jb!NrC4c75*N~WVJ7j~H@5^!KR6Yy zwRqa9@2dT*{5FUwHqyo<Hgx6I!YxD(aM1cW9auv zZW7h1SapYJ_1kE5m}qt0AN8J0hL46rXVc*+;Tab9lxnZ>d5;Kp)R<7;l`07|G!>D( z9yvhXm@lC8@z7N`q2XD!P&;|L_}c?_ixWBtJ$Xd!2sM5+;CE99%{xWb;EEQUAap=^ zI@tddjZ@Nx6=h(B=M@_yItk^G7|bsdy~P!~!IkZjUcSK_G#HdADkmd~tK@<}P@l2ptx z*I^*n=@M?iVW~&`P>*uar!&x}Gx^6z_lyX5WC8;+=~kRzRuEw4=#VRwZSC!6QV@#m zjP~ogYd(A_8?7s)ke-GN7;rd2#zOy;G^Ev_W zj&2~2Zp4mmI;ZwzruLqH4`w>Z49n?g6c~nR+1tRA%#KHbi)uJ>U)s}LMGvVQ{C3vn zN{D^-?U?K(D7n1*V=prlLX3$C)BG@pDcKPi7*}b}jh%_WPv`*nDZxlK5 z(_6~4+EHTl@KxV2QRWXGpNj#L?K?`{A6o_B{Xr(+SiB!?`NWG*f^9hCFTaS}y6lm1 zQftL_L<+%2O<4an<)<#s2AVuN3FY~n!aP8b%x!z6EbH3mrrN$eo<5ODw1SapLR~AU zdJWdHEZ06(goU3O6nF-koGA!x8Wp{9kX*rtZ6(BbB7l{7Ei4 zo#NFOV6|2iy(cPm$jg}`j~K$cJKzC-ir&;h`|RKYc3hy}`;prQkpW3W_Gv^nn8JO1 z7SO>C*r2$jM94E1(8`0;)9sbNW>n}AmN~0gmIS7?rJpuuEoxd9B!lRwep!@_?@7n) z*5x*HkmGA;@Tg-)i{!H@lnw4J$fp?<=i~pQq60k;ZEctZCl?69G*U16e>@O2x12Gc zivDUoGV!m9{sZWNL{x<10X-0)ioOdyiS78?B!&ZT659#rf!u}{cf6|84wC=lf!x;D zQ2{*=673uLsMv;S4xk4z1A){`$1znziISdr!jP_^dhx>K-83=fP?y5ybUv5<$DFx! z+0<6t)K<>)%HOO;R1YVw90IE^R>d?fEW2r)J8TBOdU{(lvzsRUgOSOLUDn5WU`PDd zexxg)4d{V9k*#04LhAMO{iqVEBJaD(SXP%!GjSLaWm_G8*Y zBXumqRD(cvvY`iPf_!k$O(p`5SD+1ops!(Ss4>z=i!;_hi`9wxeZUt09@a`4>&@jK z^y}NQ5XWy?9#LV^**)U2J|f(bm)!x6W255-6A7+lveTcNMV!pI?59ee*}G7zuMV=I z}g>HV4BRuM!u6AL?plfJrK}IFwyt#kU$Tl72BTp|9BvAaq7sj#N%A) zMaIk@nH9h1+@ygX$l#X{|6gvclgg;y6Lwf7<^}U9qQW^DM%4VeVqV(=X9)zU;>cI$yOr(~Yp`^D(_* zrKE1Jb_6ra1f{|XS;V?J5?^rBR)lYW=g-P>yRt7ry~obc64F&f9qfz-z?|HchqRNE zr*oe^z(qq{6t<%!efu1M>fN{Ues_c*{9^er`j^g*k1~FNr}Mc|#v`Z3R9&F^?mcCI zn*?J8{^@?0*Hnl51b0I3W4AL`7q@8KYIS*B@3Wb@Yl0_04+>IjWqU<6snWF@Lpreg zWxqJex6Ayr@DT|>_jvfYe8AETx${civZ)?qXU}~tqtkyr7KwQm=PFyVuyf!+ z)~@wR=({tjBPS{Mh$vEp3*_ELe)l!^tKl)T4c*bTW@nCFU*S_y=6DyLe&z&N9_>>5x>1DS?Ez!dz_=pIrz z-4_thz277(Zc1)`$4C*|JlmOGFz$hP5e3nQ_G72!p7nUcH^8*l&AZ##nq6j_SASZI zx#z0-h2MLBG>M7-3d`Q*F+6}QB4aa5Ep=;6hg-K(Zfe70+qNAK>I`x&p|rT@31uSp>SSW@WQ3+7uj*W+ZiqOUZC#r(=Ig6rJg1F=VQ{@nVf zOcN=$)Z3V&+#VX~hkM>yA4XRpMNYley%~OTo!fhwQKa*4e*iD{X|M_*Pq*l`zBoJ9 zxp8tmzGYUf%>2C^KzC0g%KQE4o|%4U`|Nmh!xp3DU1j20hrhgYtt4v)7bYpEhVl@T z{+w%_5CBNSz>+1`2BUb&FWK7g7FZdg8#m4B9r5MLd6DmRAM~=#+US`B4W(K#l zH}sR&)}SBC*G}q=%{%hNbB%GX+%LdV@FlyA+evnqKL0Hc{NO_h_4557F-2WMQ*7CYT;P1`rL*w|(8)j!e)of@RoZG8pG|~QgKJhY!c2KWF$ zNO>%SCvKy$xHaq7+bN&UI5WE1P41@iXho%$_im`Je|rx>Gb78*))qah)pM~@O zG4}8cP-KVgr5pZu|8_7b8&?Ma_>7mwd`wONd{6%%8S|bVNG0K*vFYs|=&a5jca|)E zggvuv-1qMI?O(TlS*PQ{KP}>8h3*dpANX6x2L(J%ESJ&;EBaS2>n5Q8AI{v@1avpTOhktze==Dq zV)WMK1a#I4uwgz`GFg(iaP9+j^|fwXr7PvYx@D@Fn##TJct)1wYq2VAQ%cvX;`~uX z1>_?VY}ViOKGs<*|GJ@LjFMIJyZiXQCaQMIq#QH;7<$Z^SBuSB{mT@^AHJ^n9#rn4 zU4_(AHTPQVCPl#>!Bub?l(MoUfo>*qwKCXtZ|AEuyv=Rb`p5TeWt;k=kQuKQug7cs z?>C%Iy04EsDnefu+oJ<&XpL%a!_x-}6f*azp-8WX`zqC64G$ONGhzI! zmlku@Qn`u3nqP^CCOE#EeSeQ?w;SE0cA1q5+4*B?TAg!G1x$ywz8-9Ah-5@TaM_t5 zy!?N|n8KE@IX2q3xjoB{HA+wX6lIngl$W3y7w1d-#dH~x8cGbU`j9+@6d<%7wo=pm zAhl#_ijqD%<-JcMLFM%SG4>WPbp?#NWpQ`+;#$18ySux)ySr;~_u}sEP~6>%I~?3$ zPX9ZZx%1wexY@%{#*EuJPv7zOm)^mN)T{clTw#V&67={~Fp) zsouMEF7tZrs!kJ?li8P0A-S~vE$6h&8cMsUh&fC4?Kt|(=1`@`GEe_W=01%+mp|eo zTyNChu69x$=C^lydEay4TB4%e(5KpRY$n_6`ALz4FCb}xI(r_nxg~gX1xdI0yrfS; z^9eXERl*Y0MkNQ4z1U1hJc=)4DO8I;PX)jp!^qyuFS#b%D(7ku&?8k|&9 zl8$!HpShh{5VBO?Q!We^$XlU@oJIrm$yIcU&g`Y&G!hWj%N%AUi;PJPLa@XP?=_YuhR2oOfyuLe24d^C;|<7JQHpTWAv^ZgDs3>K4d z^uNyX9CsRDn^Vr1kYA}NL)2oNaY}j5cn3!UokJ+JlzG0*hi70pt`qvbJG0mMM!)e} zeimU)`nysO2NxcVygyl}9P78dzt=>_Od-IeF;8BW}# zQ1uo`pAOvRwMb|Gm@!xAS^Cobst4_PcwgVK(?3NToJnDet|N$8b(Y>)8n6!VR^RvP z-9^juc2&^~5Iz2~8T2KvEC;;Hui=fpo`;hjBRn%4AXi=1WR+{D=lhBMRP^G~cW^S6#XZCpYO(}50 zyrDtpz4};&aUWbQ47lf`L5EcDwgQQ8Wydq4$y3DDxwgfa)tW5DnJ__zVAm85EFs>gkZ%Za(*zi|KYBcJW%*3$cFRIk&*>}@1MNAzI;*Kt8P4w}g2?*lKl;mOo zO@K(y$t{J+92FdWEHZHJK|US?M0>?PAGyw z49nudu?DT^P0fscbnTEV2tVkv=;I)y>5vbNQujzTY4!s@Wgy7bcKD9yszt{Lw4Hb( zQ}xdeX}nhFYnDxM^QG3QHv1|M^;O)R{XazZJdaraFO`S}Qi-~S770C|k_%Ske>U#` zc0Y_Uza|Md7%6 zD)-Z*tVQqf^~?Jm&)C}bQ`d1<@6KDU^R=v+S4xGTAThCEil0)@I=HF!@5sWx$To;s zBxEp%rLvfp5N^cuYNL+Y&Z43c5f~=mfSEf&Z9^ixr~Mt%+d(W zt?!sA!hiqWjS@oBX8`__+(ZpF^`y!%KUL)?uM|l0n9Xp$oRF%YiV8_5AnVp*fLr&F{aK$s0YCLpNFs#OL_Xo43}g~9 zMDg5366V~>$_Kh zZ!{ecVQGS;r-Dsf;1q<+8M|OAVf@D=g#H-G#*SW@8g^~itxN>8$tV->aBahYq3=f; z1yMK5|6-ggkpRv8t`4aisZn^EFm7b_CUQrnS(lEIP?e8|av^63fNS6-OV}kWIeNlh z&bW{i@y<9iRMcdmEi@3FRH5{WiFsrTRmV0hNsJVM-OAXqY}x(?C48CH!YX!;9Skr# zE(@ztAq~k`J9P?Ozbi2Y^hEf7zzTj@pp9b*4mE?hcof5ysh!>txCZ zwZ^Jdx(tt@Z#JV=dc^6-b=IBK=(31YXqF97FHe$NS_6#V!s)3gzEMg=^}{;s{*JgM z?(_dH8`S`cPU_$-QIroZ!x~g-vNV%amPd&dOfAN!`ruq}c3hf~m#6rl^ZV5^^X%AM zm7l-#)wAR5*l1~ng*jE3KMfre>A{)sv2g`j%GEEo{MA#fA+VXp0ca^Vhd$kA1_V<%w+xooIFK?n(1iW{)xrt+V2c4 z_VV3N6`)5r{XVEU64Kzcr!J`dpcQvs0nC{@MAMj(7VC#zh{ zkAWyY5-gs9Yj%x$*#ftRUJQ|LAnoJCMT?n8Y$hGfG6-OiKq(h-lCdlZ9*$I%I${;x ze11ep_#k=X;5RnnGB&KBA0P_0ss-y}oCfZ4=mo#_UAU_ZI;T&!`otGcxk0mgltK7N zS4CB}sCr*j`IpPxpgPOXC_yUnX#TR0KI;ry%QLN7gTJYC^(fq|&j4PPhR*|{R@8xA za@8)xat{Kv8?M5=hvujib{akLVuUkdD`#|Yj#q4Fpp=Vg{g+K&VuN$nUPGrT7{`@* zE3h*{Kzf4N57S}z5SAE~*j=c5*h4NS0ZLhpCI`V$AOlhw`!|MrM0Q4^aVhF5&#jcM zh?bNYp@-DSd@NQGflop1by~8q7|X$;(wz#oi$gQ;+C1P+3;>2xH3<2`LXRE2KNRP0 zA8@%w@{6rmu=gk3xe;+*Y<&*Ef6#Hw>QJt}CqR-qqyRwJfG-TQ3a$=A zYd}_`WMlvUQ=+no3fCf5TO=8Q;(Arp>Jy&@QVDPru76Ih8laO)JIrQNqSSR2eyqrr zopQrYu;sE8+9opdfF@3}kT1YMcNI3vTxS=+qD*HK>&>KZfTv=d9B3PS6a1%WXjk!s zVHeRR+=`qr^S%YiKmp1I!+td;cMTR`%cvhlplDtq6S)+LT*;ysV>om+5P?xis)kRh z#zU$GN~)$VRwW`VZVsdosS;D<_~E=W$F! zTvYWD3{3edQkRb}58n^KEHM#JC_i^Dj5)6!uh%PZe^E%Poz zbbaQX2H^Dr@WucvOz&dA50>{ojPJR9&($T*vE+8%N`tlWQF#KXY&9f)W?}k>{Ya(f z-$v%|ptS0+JMIN(fsyheX2N)-k%Pp9_Ww|c&;(f`G0&k(w9&l_jgz0|03nuz(_gFzeqU@yKtdE_>f50kTQIvBz)u;d?Yn|WI24KP}tByn-uRG{GAO- zO(Gsiw%GCm-IWHVFIDxE)IkfDX2v`SC997dmdI1*qba8;-ZVWVM-I@*1!5gWScM9i z8XjY_;@w4iZ$-$k!Z)7kJr= zJ>HjD3TDj*cK#D`85DB)9KI?PzDg9nY8bwX9a}>hTf=_uENq+sL*j@+o+ZrYs~fpT zl;DHS@m{IBD2++H7ww`~pi7#qE8E5cVgn%6viszMr#H;jm1*mNumO0p8MIytTd7Cz z?_+x>-T-*D>>_ZK?v>WyD>4nnaU@T8DTuM;?OU7`LJ)i#XSsep|Lq86wK_9`l%u3%SO#1KkxRkEQW{=nF)dNp)v zk7|sTqWd5$P^bo@UFL6^>a#B?rz`&-@7+;6wK?*^t+I^NuE$3HG@EBrHcqW7HXovf zh|i4?N{*?l%-zsAQYmB|4Y6Ny3-M?)%)ZDWc_f@jFyP^Meqp7DmGkU#RDF(G2yM1y zmJiPUT3f=gg+AJn zy~b}s@*!Taj(C>6T!WzxHSx`)n6^@E#|dmldEmDUhd$y^p6E#JI7zQK#Wt@swRq*1 z9|k|>t@4k-=FcEx{yDkGpx?{H|5Aqa%{+~#NCFa%{^;M^b;)c#(`v4ycpr=t0xC*g zv-BR^3%z2?K7kNI4hSJ!b_}35O`tdD|DlM#Y{Gx-!}Y#%|O*Ye;8!Q6Y9gN!V3oYS3h6Yo@hjHc#eMCA`YL@KRU+Q zj)E4o(5C&%V-FaUGErxEf<6;7Ooz3;51OTz#RX3_XJ~^@Zz^TA0|A9KvojvEvo?=2 zm9Oo8GUY_otlm5^^Jex~9NtZA-rly4h$fG5CXaI2uO}hB@xS^os_Pp6VTt0}1 zQR0|>1V+KDPsm+K2{#`|Cjqrf6PwZBCY3jD{lZ^17w^n0QwwgZ&Jn`p83D?oVsUdU zl|J^8*BE9sYlhN_L^{EWs&BqOysW4nNWy)Bnn+6`JV=Wp6%wy3tO$$0%dP@1jtYe? zoMdK#&cvLFjT2lt!ceE9FhpcH3nAxS36l3cjx16_Yz1%b>jnjGpq@-4kBoRtCxit^2mK@M5gjzWt*WoMb;5p5Q=H@wQ`MPHmKm}dplqHg*y zPK|d&RQFD2bPK`i^s0MY;v}pT2CZ!#D(Ek>t*1^-qQ%{>*DW6NL=vu>m6OFD#jnR* zosaqr^kMqFC5$J&mET+q@&4Dn9ovG2NVlrJ(l%B*nSs*6%5k(&Qu~VSMUTe53hj>; zwF4sh3bV1pD4dpM2iNdri813<3BvlaLi?g`YW_>eDLtIVP^_XKqQd@BQbU(HTgpWW zoaT?m#`W8Y81D3x1|MK+5C{sK#}>DoHl=Z8g|1Vc#rVC`JPz2duP1<¬XImKt@nKQrw<5v%w&&hb83FdaIT2rU3(fEdNQzU zi(9F2`n&u0M%`Img8cy#uW&uR!t;N;8hD?w?5f=wHATDgdaVc}+So*JyZ6P)<@KEN zWBY15{aG=SfjdCdbkyazydjl!y__!UI*l)N*egk;$<-^*F3ZQj?5TY^sn6UiVQr`*Yl<&B>gzZ3^Q&o^9YCpLLL=6ani&y|ts ztE|)>r!dGJGNjgcT+a=7y~i%j4HD^QA8@B{o9tvZpv&@2t=wnc%h*@*`Y$#&ANH{N zEl)S-^;~WZWZ#*tVzFQ zTW7R#BhVbW|FCp*pQ_Z$ZKT9+_ianwd3P)#BK1nUb++oLo^a}Z|HL~*(OpluUAt@5 zoVlbr97)#iNWnSY-CwV=?f&(23qjeFhx$>Tw$ovCKG|)l$2XbbOWjkMCck5nrGD5I z-kWHY%e8W}D;&=t<6@G0o3o?k;n7fuS(lNi9Az_Id{*0& zsT>c2xICfmXMUHn&k3#yzE!S0<+s;kSmHJv+tsTB;Z=X3zn`bsv5_C-OEbOC$QSO_ zHbfhhC`Q~1T0j_rM!V5_3*W*&w(7PeT(@&c_(lmVf;`Iwha2>IL%jJ24cMXj$7&II zei5(Azxw~_9SPpu;VydwTN5`V8lN}%x*#C3;k-WdCvYOM;p1w}w9&4YJPn>MPt;xO zdUZu0Z|<5C?40+ z;IZz7Bj6Apsh97Elfu^F{dfVQIG~m)*5L*hQ+TQo}Nn3t4fc>IutrehEN7=Ti)cBSZ;A zW8UB@315;51#?!p!qPz7dZdJR2c=l&n~xC~d5)8IZw#86{quEQ@n?-)PMZ6(boORk zyW5q@=6k76^t1D`IP_j;)0w!O=IFfKv@71~rqHxz(Uu-wc<(GUgLyobhksSHBOP!K z+otf(taujzIjaP}Khf5V#d~dvF8R8z56?G>JZ|^smFB>G%V7Nrcp}hTW6l)%0W$?9 z&-#3oZ3MXwhaOG9xzF2&Dc8(-{8gZ;)E8Rh6l6{Jwz{!sZ#GNt8NnyPi1kL>xo-$s z`*n`>?l?wLmiN1QtMlRZRT|$zyF*r6?Mu-r(`cpC3e;w;pY^eh{lOWp`z_u`dYxKL zc75yK>SClCGM27Z3*QDjD?16L@Z)`8?)Af4Q-#`J_7pFZ?yA5}J*B68Qn96>6nk?q z`*vzVP_T|@(X|!aHBV3b%)ykRjh&PjF)Av6Gix|Mh1Q;gk~KFUtbFcjGt z0Q&H!-M`m?ACoe?E(pEJUTr4e%ibYTIZ3gAP=2`KSFo2^lh1>fBBa;d=W{to$z^^A z`8R9D4bbpFd+W`qeh27f6ASuDTy8^~a{+mRMUehz;{8y;)&RZIS?rdlZ|29DM=@)H z`P)v$%Eh<#Cxvkp1jsRgTf5Zed-lHY``&v<#cij%%96#}{o^PO4j#kyN42p6s(yUL zhU?#+h}@=njBKD(MYX0Z)0om#44JPTh`x!FTsGpa$MlgZH5ryngNwF`78 z)EKihCDLkztnyC#5{yH!(V^kYM&Rf5?B%*4b!B^F|Fw07#Ag33+Il8K=euQ@FK*ymN2*+-E#xn%#kE1jH2o4`CNkKc=PKAVD|x8HaBx2l z98%T4kw;@;Sq<#c{rDk03t! z(CJ|nqDFaiQYm&3iY%eV-J&iOco0Oul9JF!p)(8@mHA(?il^-#G*R#YtXfFV?6 z;}}<#3g0_)R~bjXTb@^R9*8t42qJ+r_8d)}^lDIv+B!PH=2~g1^~Hv|C7({qYtErG zX=yCZec+Z592V1FGgIW|SU-DiSlOug_-|mR7zlP;bg}8wfO8WAV8vbde^hb*|0O&B zA9I%(5bONA{V!7?Rn!>$E4TVYtjCQ2zt0+*CAnd9VEj+MD8zG5RNl-l> zX=@bBc(0XCE$!-ct)}&gV#t~0^<^*-bR<9-7L8Wzdc{u0nyedp{o|9*zl{67=P2+` z|DE3v@9`GQZ?X>dM2XCwNgN;W3D1&B3*|bo^ZARoO=hMJzvc}VGg)8pdA;Ns%UD>= zOf7!R|DG#LDY*E}ELY5K`C6%JrfT3;!bkr{ocU9g|3&G`r4i+~a-dYSW+nSrvOtpA z+}V;;;~xWNP*b^xnAGPt+RT9;8Q+U(goQinrA-E7QdzqxQV3G!ltS^yU<H=OPnDzC=#xIwT8O=WH>zk|pKY&mJ;e8ka2?$HYLIAuSdMn@-Jew&(=p z*x47DIk$p>>XPw>oe)a2m10pprd((zs{io>dwx|xF;^;EG)!SU1*9mi`m0Yj)u*fe~TRhXQz-}!ezeCWTH_v^NEVNLIv<$ z=7_sLs5gadra(%k&RZ~RP)#errgJEI4XV$f@RvW&WcYq`FAKq+E_2+CQISqC(MDMowCE8@HutV78011 zOU+EGf;2PZStKXVrCS!h8D0fOEf;#a)$r%SDY^8`XC){7@aj$vLzGkV$X^>1`>uf< zgcU~9!68fS+-xQliEWa8!YG4Tb8;qAGJjlAl9_x;b0T5I1&Sut06ZId%Pg{cY9ZR1 zCKvr3%3wY)iY;Q%L@nc|)X#gFxVbD3w_k54Fva`k$9@PfC2nDI)(Kb-ZK+-hgjGl0eXJM!W z#GRz9!v~|-ee=$WjLQ>6N?U+g9j*h_(C`8`mSm~#IcV}N8Y_tq>H12jB6Y$mO#&D+ z^a??WKdj5d>cN#S-Iv|RT$ER(zf-}pr$C5hj^`ghnL4ghI%1;wn1E9m2=(Iu*ne@`0~v!Tz5app`($~r57=~IsaoVBbIr~EjF&ij;wZmc>r*UW0?5qc z$x?X41<;{=@a2AdBO+ylZxTyyHQTF4S_{cUj0eqrF%bIEht{|X!x80!L=fm=h>}&p zF*1af&5eaYjk|F#%TieMJ(=DtK4@)Ikr9dj!g;O8AUESvLT*MpvCdmZp#DuuqJBd| z1Orp?7k479^iR=@YVjL`+4M;&afTzOUP&+r1=`Rfy+j+yLB3zbfqc`VD@xl?vvt(S z6V*W(xUQzc?RuzZvqZwf3=>6ZaPYmLv=C)ZDJ`M*3j;I&br4-}%k73s4_}@;lJir8$F`$m~Yi z7Qg35CJ5FI+p^K`x2BbYK&oh_|K%)JY~agYIF>D%UC}`>a!Lr&s#Q%~zq<-0U8E0ux*`# z?Ji|afPzG@Tbxc|Kw6HpH&Ys7y0rLYG1_~Rfpy?Cp#Gan|KPU84>r7$D8l-~03@#_ z93&TEUK@S@uGbI+c+UxnHp+8>xQ)at0k4sE5a9_*7TUc?e3huaczjiY*Nxa&tclH7 z>c*o9*gFb{Pw+Pok6QcHNLJ3WS@X!()reQ4Lca*;v>i^GZcI~QavCSW@A&2pDJ3D0 zOz8%a*$}UTf-WYmj&T_Mr|>}{b|f4vp)(EWMUv5|vGCxHai~EA!I1ibkVJw=%`i0tH`2SQO(;YeQUncth5{E<4ku-gI?2(@S=BlHFY~ zN4ck=KbHyrarR5<8zY3urd#4z%dT!sH!)zfM&@Lbs961O>=E1*N?1Q>TtB&AHwmkE z)ascr5^a*(H}Q_ha3`X7w?BmVashlKJ^sB*sVGMVrYMdtvOH+lhyO67c!MT12^Dgri~ z_Rntw>xA_90u;R*Ja*(y@6UU6pol?D9kc8-dzh~0rcDsWS*#KOoC}VKMvLWRy`NTKtz87vDbvXN?h3%7!3n=_mi3YSjBRg=gDX4|gh&9%mHpxhGelB)V02>Ax)E$2l|qkm3Wf(hJTB!$s{6;O+xJzn z-J!r7-Xh?>1bu}`RN)9K%tQSVMQ#IjGgZPV=l%+~s|OeyVC?QLPS z!kua)!xQ5X$uV+-Az$KVzIV>DB1T)S$+4&l}Kdu`jK7uVDRuM}18 z-?!-P2w4xyEP(?$q^7@{Ea*aINOmsbDHQG;gecBKw0Zi6F82G%l`v4@RgyB(9JmRp zNh8B%m0mC_OT9Y7wFPqF0yQk>9^u|GXaU*#wErXkC`tkg4bHnjbO~|&uCd-Z12ub=*bR&ClIfac7WvBXK?Fd69#&S7`#BQ*N*JIA>_#~I?t2iCbpBs;57 z9q(83yh!kZ+$O1DO3oV&@l&JQ)soxQG2_!+`Wpm$&8^^5JxAP6D~Gx1yw3PICftV> zZ*PU*RD(q-1#9tcGQ`#i?0wijZR6n^*BqNQySwK;ZCif_FH5L4?QV9}M#492`8KZr zyKZSntzN*Rn_ag&q*nfC2k(F(OA6?30Ax?;>bEek;Ql5-SJ*Waq>9%2ww zYOdr2*R9`d=TgNHd7bFJ8vT^Fe3v z``a+SWf1&^fV7Mh)FOU7$^C(6G26{RM|9~<)h8U~66u@(gQ~1A@sM0t zc0tzj0*k(_8OZj_WC-p#xb`u$l`;_V7{$Vx^1+=9V-ppetE)c1&0>wEZPG}R1&m=8 zPDavZeG)s5u>_IO`-J_FsMk9>;r@N6*IPQvY8iXZUCWWFr}py};q#=~tWhu6O^xxy z>6ii;@8k1s8d{Qer3d4J2pwx7dk52mle$-(2WF!ixcCO$NE{FJ+%-iVOZrK$uxBXE z7w<|0zme=%lq%N$@}Aerbp)}|@PcOCz3cH=meoC=xPyzc0@reP{K&oHn_=8xixdZsA%3yGHqk=hn8oCjU`V zcT&g3o8bP$n1*hAgT9(?iB=}2JfyGdjnQJuvWaClt#TLMNa6#Z{sqCECDxmt^>kCZ z-punaa^K$37w0hrr}-0W?HRjkiPyzmW48@#rRCF;L;D&s&%`V^viTnw#HO98bXC0y zm!e)f*0$5W#s+Hu&(hqfjThYZqG=BcEuXvwNo`0(x$RRqHd_2=GFy@O2FdFm<(KKt zv-Cvm#@Z*f@wIDjiBC|QxKD??g@Y)0EUz^;CT?$U!7bjmx6AR5_)NqTTRl7b{51=o zFFnb0=QTc^DL-@H$)8_OK)T;A?vnMly-2*dx(9`V4&_Zu2QO?)&ew{RYQ1d}x z=Yp>yGi`6E=%3sFtCvUeVzt+useRu2Ba-UWCNX{9cM-ZU-KL?v?)B|K^tazw?CJTo z$>__$i(pE|@Ig>r{rH15w$pSwv$TVM*agSVe9l$WU^ z%fzaBcu6I&<9mJ$k)xT+(#(x0;z}W|v7~BvPg~8a*Dc7f`>N3=Yjh&C@$PDJ23=L3c)`<8$To@wbd`7l+(h4K{U4?t_yr+Ql2)$ z0)RjBM`CAJqS=H@iI8X1s&M`l9{ZK=&%`7?%&hm{0s-7E3|}3B0O6d2wC_Hb!&jM* z{YcJ3_6Bd^chANnc2+$0gu!>KAUouxc;Ng&BQp$_`0X`Z@Nao9TX^G;{qYAk4}54d zc`%uAH|F@b3Jc$#6?uB_Kp%Tnv9}=euf_FXHvg#pJ!wl<+XD~5=I8Q*%BlUi!`2k}jsc;D>2WFw;(?G|4pD z#^-ICJdyff&euc8%~5J8Os1B1uibL?RxsBJa#C4!D_oW9P;R0$1`ZVc`f+E+N~p&} zP0Q1JS zs_q>wAEn1hQuBx?YWI?J!X4s2CNCDS;Y+A9sNz0O1h?MxN?94_?qhPjcB@xNL|x92 zWtAF@Cwhtmh6+9#PnVU=B1D>!Syv zcNpsfu0QS+3|~cRd<*_r-l-a^o{FWj?$@Z>+*cy@`pVKeo2RLrZjzji&T~`2mK)ZQ z)p!+%R;ene=iT9tUT#gPZCcI+cDprb9M_$}UKPY=rN7b^~?%ph_B;#7QAq( zvbg+vPr2`qH7GHzqL3wN}6_JjT$UeQ=HB=h7Yze_#T7}KjvTKF>+y7<-J#V zoaFb{4&1tbv#Kl05NbOES{Huc+!n_{KdU^8wja1omY6?3w}~P?nd|4ZZ>-9;LNhzZeuOqZtit1Z8KC+@}HyUC~DdB zt}RuCA=L;TrpnKp!dv*p@V`=hdg^JlZR74oS{YvFBBTqJhyqYXIDh^+LB zWQY^zYqD~0ZhO2->#X8tuf_jx9Uv#6D;SPY^5nfy9NGlUdpoW+^nQ}B%Dhcq9gU%e zr{)p$S*9?%>5AcxKp4zx#Sl@ISeiJRt$XgcUjyacNJs|HzVI8PxZT&7pM1@q9B@Ky1f0BeXg0#^C0(QB{C%F zDCus19xYG44cqZDT3f9)^8h2(O=a&Hujk}o(Spj z;ls^4+jAND5zagEBQu2F_XaMkRlcSSzrcWCrmz@fGB2H&`z$^(a65AK`e z)5YXoSYJHyUwR(w-+HdD{So@`NV*t901wx%AMKMK z?N_M-#YEySZl~{L(-O*NXl@w z>HF4drwMLC)I&m`YlYRZhR=x(peed$P=h^tj%YOMNYerHLFj=5Pp&wVlB~NJDcG{R z$IO{YHUn_fOj;@D7Q}HD1NZyiwcXR^gubrbDxC;^OrpqoG#`A|MoOC-Aealml00MXU8J2xl zJ6?lzhFu5JSxRRBSC?-6hOefObGW4r87p||`j{qN*#fT8j!C0bK)pYkL|a2Ex<9oY z{s?__z)J)EXk$l}v9t{nv)8DOCaphvK+9t>q90wbCWuqCxvq_TII$h{Ps%*-jgUAe zL(39gfUZwW948M^$i6yVbhbY=3cI>lAWf{Y(S+FUa^1(9Bp%y0_&)CSI-X8R{virWKWvqR0E*hEqn0JAAkujuAS4(AayBq1i*S={ zLT5RRVfam%DaN6#!N!Qf`lmoQ^*1=teygSQ|!S*)+`D68&#r3-^{F za0bHiZY_gmXpI<^gY%dKhQwmSWPiU}?!wwg ziPMJmTY?nVWr&l&PGPJLKx@BJ5A~_Z5R0f<$fB<^hPJ@kn1>*lm~*bK-w|NWQHURL z-JCfmP)&Fkg~?R%u29IaPBe{NN|l>p24Er6V_w+1pyQy~cYO+q zYGiK)(I~+h3%`TOq;S=RzG_h32K`*Q>O;%7N!lHSa7adpD?u+pG%(x6j;ED2)2U$b z4Zm;|!Oi*75EF6TW6WVQ3(10STQbY}v0kOI4mx6b%muf`>`9Br-Uuh2xke8V82CC1NTmB5#~3&~u2HN@SC;QwS5W zl}n^!P+7>ro@644X(ix$HBrGvvwLa-MRf91!5asQ(WzB4zZ=eI#waA0m69Dr;z$1A zOM6W!-m?)8#1TB5jy5NuyTKOYyd}7`nKMeouy%tyWfGum$y5h7f2&$IvkHL{KjNeV z_NO|3+x;-a&fj97{v!k>7XMKNap5;uwKG9OBpe)UZR^%N!^Yd4{wre~%M*_F%D*XI4uP-o5GsP8oSii4L{q!`zwF(~F+s}?gclqMnNGielDcg%IgYhMaG-_iqQu6Ex<bjHdv{JiLq&~8)SLeWSisNxl0hfC-aLJ;2+*8cu9(%S4MA`P0)4G+fb~$IA z?yHzboljk~3Eh_6D0T+EMj&fE8ara^tz*!%niLF-8&#NzXcj$Hx3d;lb(I&)a@8*m z&SA6@=zG#C_%Lo6cGj?)1B!dq4fb1%sF5BXtcuJV2mpTUifU1ZEF_idas^v+{5sB~ z2#HarwhPSXp1cL9KBV*o{(Hw)La{SC!#&mbm}hp_NzZL7;T=Dl~j9jRCC4>xlz5ElM|BG`4E&ik01cBXc8d zw3eU|iybCl3CRSUQ42?j2mtHa4%PZhTX4ZN=w3rOHF>IXjy2kK#VZb1As(LoY(RB9 znj8%v870@vw0)J(vPA=MvvBmw)W>9~WEY3#k${(}q91BzzU8`@vUr%RX^90LPA{?Q zZ#x8_3*}RQJJ{2ljc_GY#KREeeGZy|K7@ZvW+j{ffJ*RgZW-OAdO56X(o1q4nznUe zQDkS#_iwEd?lAzN9 zRAMjsb1TZ{AoEjoD4+%9v<^60QT$e;JWj$}lTzsQT#K?qS)JTj22!r{S}36Oz>NKq zOg?#tDnf-iUXfI(SVas{sn|v=Rx$+@_b0LHxaDH93U#$2V@+78ztkO%RTxo}g7dsc z;|D_ar6L?xeX2<4!TbG8x(zK2-hj#1)-Y&KlR*zOJ>{4Jy1mX3CM+kLGd(9bo`Ur? zfF9GV48bx#A#6R7<`B$EydoL(1VpL0O5jp51tmD(I!QrJQdi8iT3+NF)syRi)lK z8d2t4wo`z9XZ)Jump%5}J@!CYTu#3X{N6~vXW6}H*`sIK-RBGrKV?q81r9$X$M+Jn zcSOc_GlkZvMF3vO9_4KLR7GrF#(tXU4e9CnNWIOM;F<_L(d(~JM26R*L}9NCV<*cQ z=TCv5?~-t&mHya=imfvxNsTB4Y457WiajM(PwK7;s+6js&kWqE-$fFBSvC$NI5?8Q zzW+)eOh}(dpgNG4+?U{)Oz;RHRUAr4m#>&?jCY#BOrqf>E=1+39g!FmD_5$xmAG$O z>7g(7b+fo{(?{OATxH)T)|PZ*%BR$TPEo4Ed=75@6?|C2d;R9kgXqZ`pZ4P2mF?w& zqC1czVo};_<$s@C(y^-lU|I1Buj-o`{D2Q(4P&b}-tUzg{18N>!QfPV3l2Fe-&%9gt3YtP3il z^(1Iz7D62W(L06JwQS#i{v5Qk3+I={;K8d8fb97j>(ih097OvPRQVjlo|G+R_s^y! zrY&IypZ{bcDpW|vX@hxBG>wAk!*2sP> z(`X7LbOuYzV4V2M8_q`lED|Y}BS((g8HHz{MjI8y@${YBG|xSrk7Gj}eR3st2k@Z@ zumwP!{=|zvf7?gdT9NAFf8gZ3o?yC8+!?llj< z@mr>MYGLxg`pj>f{a|1lV_jQIPGONKMOW&RU9UycZQ@*<(w>@63}JA8Q<#tlKWxpZ zEQ1)aXG$x)`-3Yxl6cE>K~$W3BRJhBk*i0U7Y_s4+0Ao8#Rs_MX*uI`(6+NsR-|mLcD>oiL}o*}+?z{uRLNb6+vYB{q85 z%?$XnnM$0MzTP1%Yn_8vZ@CGJ3`5M`JeeB3PvWFEcDFz{&OZ{Bn})y3OLA2b!0BV9 zh}4E1%{yaWj88z=eh4~@z2DPyzZe|BjZCAl{=D2shs@tE!FSYJjL;I2jOI zO1WInItBz1LU(0l?tvy*7^wKD?t-#8K)!EgbTm?Qy9_glFP#uw_m@?uS%IWz;n`L# zOyY|h5)F;8-evN8e_9bmUD^?)iXnF1E_>xx@sVAxMpvjzUDOeEw**X(`^*J%j`$q0 zeKOtRc*op^ic3kqY@M^XeCj#C=5uMoAG_^(KQS#1`&dpm&wq^d+)k|{zuGySp?e?( zRF<@dKlXcnaz9uQQ9i&&ZE$WPzk;7s=e&GQsn|5O5kEk`tU=e+5zjqY&D~$Uyy^iy zfV*FT9(_Kg)uWv&!)D}SlK3X|nQoFXt$vQ&hmD)J;{vB6@$=cW2$8BR0R5qF$f`cY z?<=B2jNlYTL4%atScAzxzE_$TIy)B~1s;$*1wf6qO#k?QazV?lfBonoybqE+_Vq*6CwFXWHmA&e7SH)s`+KBY}Int+s0yRy^$?dV|@o= zF`k3mw7d&7H7Q=sm)HQqWgWe#uX5Ezpvvkx$;NrK{uVSAFwVOjSm!;VqQ$$~F(ig- zrEvY0^3kC?RDEo1TgSB%_7b)7QIbgD{?2#i!|by9S+C)}cC?~IQVxe*5wQUo5 z`Ju9WnN_WlLahJ@3?>o-{yYpk&mLb39E?kvljUD0Jtn-Ky_X+1y1_zskV^1lYY6}N zP<<@ej(f00a#nAvL-g!bdpe)!dFsqMg=u;DCY}##z4#^C-Zw_HFm!3MM<%vK$*l7T zjJ8LnRYUDH&^vK zEH6^_ax6)_=zia%a6UH|%p!Sm0di?7x6>62&3|qDuHuJUmAjTl2e4ECm~#Ko#kK-K zY0J=m8jJRjaJ^;}>>L!nl5iRSo||aG)^t>BaVBYk{8^M(3Vj$U|E@myC&~GJL&EMj z2Nq@RD(ituQo8ZeaGN*BpiL^b%bmyb;smEuyt@0?wAnMO`;6}+-^84n|J6->v#Iv2 zqpphRtZ|(c4eUdesF|*LzV(CI*7NicHVZOp%uNF$2cxmgyMM~ZAM=LT;J`ULM3+F8|jN5@8KD8K% zoiAL^Pqk`RQ<#EM2Nothc$DE(AwQ*9@L>y1-*Ry-MYqA(gU%Zt)bcMvt9W!D*_tu_N@Zmu~ub{5|A%1i|= z)UP#AD_j)lViQ~2=5jas{i151Dh#5b<3^PK_R;clQrnybJWIht+Sb^&PKMw1yamkC z)$+pmxdCbW{yGlDY0f&9xxMVlJR0F_{kv9X$5*7~!D!2x<`aH7&)!~q5zeH?%a=bz zQ591<%P$$t`5(WN%4L+AX=?hXzgBw?E&@DV-y4>9g4Wl?DFJkIho?^4@TqV5lsS*- z{r$k(sNOo=(~6@tCq@~S;5vaBt5Tke*Mc)_nr zh?dhtoN2uR@aw`eKV9C@M7x&JonX|vud=B}rQCM@EP}N@J`)=fCl9BOMvq+?^y`xq z#-YNFea1nE26mn7gq!Kww&?STGGN(pY5K9s{Gr$l3z8=23T66ZKloceJC@!SEeyhw zWlV?to^I3%8s8}K&&7p(=GCSzKDqt+wfU&Ouqugfk4{r*jR zeXcYJ9BN0kNR~LnLA|VSXe&sXsUShU54dQ!96v+*bZZd8CZMpX+=D%W5zzttybXgv z-wM7ix8<2sAZg<4F$bLEQfRE~)#I$dJ?O_;?)$P6}WPGV88aD0i3 ztI{=-3^CH{2R?04Zv=S&7JFz!xpq1K^X9!J_3IE#Umu*?n5|l*K%g*27Un>|A@dLl z)=x3b76y^NkU26mY~qB$k;hsb!QDP1=2>0__ag=$P^yTybG@{H2?Lx;O1Ypu-NqfE z0P#SJkgCdpw~k-G2$J17RZJ8p911O`m6G)1$~AdnJjdgU_RGpE5Gxm%lwf29b7-!EiLX_tEF+yBDaj*WmbMlF7U#!owo0f7n$FC1Q) zh%PM4ZzgX^l8+%EK)VCJlTqL7kOAS94M3g(OeLJ1O=uWzZe4nrICGkfDwUr&_V%{H z0j}F%wAz$1$*!a~FMf@CvRyi^Q1-?hZnxj!T5BYe+%j*tilbP1B6APL1X|}+Opg9<(!`WT=3=~bxYEKq?n*lyG=#BwD&V!KAjp^cRp4}aq}1C<*Q$+pC=5FcAe zDG4H=IQVsW|FAN60p@u=xQlvAH31cdV= z)-0UZb3s&Cx_qPTw>3r2)y21L9)m(zAB8X<>(K>k{P9N_0j+G|BDYDE{hKC~Q)r{0 zEa$gUl=<6mRni|&_foljBBXe1g>ZS{g5;GbBA9sNxnZzujm9lUs}7nRy`;iM`}qqd z0s`^V0|>{?CdooLW^q*1#kl+p(hdB#VAEu?n)qsmSr-JG`^gR)^$_Y*&}sPF((ULQ zx42j4AXlR$2r}l4JAj7!L*0$YfPO8D z$A+I5YT69!NE0TT#qY~O@Ic`pw_7;m>XRnTRsJ};8kh|Fsoa5kx*hjoQlt}WNZbO@ zI9l789`n5@J7{xdn}Y<$C8$WdgrGsu+u3UZ6e&$ULJfut*$BlS21%i}D#f z?&kp@5=}0RON=zDEY2AG#A%rt9_f30li$u_<+^9v&&3EJH71f~Yp8INps|dm+3vK;g!gfp9|1}z|-6d(cQ?$9Zr_&@L^szXKsx~Yd z*3I=c*C1nb`IV|RNcn~*qTjlo-EV%pN<03X2QEoLu!tSnQqsvJRS39b#?nh0XC(!- z_IJq)&JrIj&v&9fE`QJrgqWn%8N(MDJoiP+@5EpXG{hntegQoQ0W-F6DyaZ>F) zrH8Dk!R&~paV1o`;x68H((FAA^k3qH)21G@8ly6IIe>(C!GNcv1(7?6HSCFxr&svx z)Itf)b*|Dp?i>LQTr%syZPVQkjQ0)itRw<1nF$<0b`=Ob@sYp)Mz%%KxlVuE&hgIb zH{^kr_EU-#;EbcA9 z5ANYpGIkfCjssMm$5{3Q)$Bb7!qgjS(0_mx)^9L>ffZ4H{M34$4Y&$MnuMz= zy;%Dc|9=51klW56b(gU#d%v#f{aNHRdOZT#G2%a=`0ZtYh|ac+dMhzTKnPW-P@lwg zut2F44g7tn5==#IHpBicoud5icFe8paIPH!z#7db_6Z;QAqkXoV2V`1B-(qpX* z$_SjU<*-JR?&oe1H$^jk^twr<1jCFT-#( zm@$q=-I>1rY|(JVJG{r%Q8Icrm2wOmW--XB)?)~h*W366YHe400(%*$5ywdP2a5N! zsSMaA=sV@QAE8_Yj!68bG$_JJWaloV#x$!25g+1#*cI)38l9ZJ6X}3^I*kakzLU|w zNinIii%6xZNF|O)B~hemUF2VHg%`KZJx_!cICf%y~3;>D?$PWw#X zcie|-RZ#(@8*uQbB>j|tN9q#d4h;K4(U^iGL=tq|Qehie5_hEDG|C8!&fcWlB|@aU zqhq5~{uxOmLEw}b>P9TJS!$p<##A1}l*tHPf)Y;%DvyGgjX6kmzufolDBW!STQFp% z`cFlyZzASb_=8vYlUMltSFEgW*+x%Mzz>6`sNtu{q9-X5y^PXbPX68)YK06%A^?0p zQ^0n}xD&tb@dtR7Dgxn4Zd5AvuTsNZIlNQ`mU>zEA8Z&4Z1om%G#P~ta^F}I(m3lh(j8;5KW4G1eX*|5q;XTRsbjIJL$R*eq;fs6 zsnHp1^{lFfCc9MMNc+?bwLg0aGX@!Vm21SLJtpboxphN=nM~u4uj^jU-FgjDf78=O26pp zGE(R=l5H>np%vEJWYZq?u^#oI9`)Zn?m>R{bbnE62H&e}Y#)$jfwR>acx%FI~lzZFki zR<1f*eOXbrboL~whx?ctHffp2(AW)|VGKWXs6$}NDtEp0mTF??D*WP*X)3Up^%B+H!boh@B6=`^U`o=$nD$jU-jD^*3zY7A zf=*kymoPW7aTQW}SEzWwmg^K(v|)y@Mu)hFy+8v#!GAQLXJw^ol7ml5)1pSRy?hVc=^NQh?k+uc8H6p;&1Ur)#Ke zY_o@WSOr-W!W#E^*0n*($AA>#E+^wqbiYc0$H}K=sHhocu*hAit<2LlQllYaR#6~| zpj?JQhPSY1kC3bSfY1Io3SsvA~#OnrHW6A8>;7a57bkv-B2vo`|n@W zCYiT7 zMrgbJ3s`~Uc=~0oI^n1XgG&1v`d%s>_$G-KCP9sQL24VsJXsjd<_4#`VQr zGPNh`v)$Rp-H*W=Dw}~9e87vBlLdy;5|xI^Zr~lR$wnf{Mg-&apbXR7YX)&d1$sfG6^9LQ6UsDI-@su!3xvcnfPTYM z>y=|55GD*7xBayxM@k?BCJQrJo?sG|P&Y8jWl7ZCGZqB)0TX~^d!m8Jl?@S(K?X}` z1MxX5$^b(hYnUY_Umx}$rKGt1ooa+U&V(qR_$G_-YC%}>xfvlrac1NuA5>;iZlBnO zQT5!W607=|1q1Uck3zyr3xT!v37q1>*<@ro{33rg-U8xs%VGDZO_`hUL(1cii$lRh z+2@|iZKS7~?M}=LEco}2(-4RKVZ9Wi5ML9O<)(j9gehxl#WrwqLy zX{NPqwbtTd<2H~zM|h)lT_+%SHMV+moP4gIy%o$oyt~M#nN=*n4?EsB(hxa1t_RR; zy)Q^^=C~P%1wE&nZ^+Yo*y!Z6@D(h@-|F#cZ3a64XCGG-1|2^?th$=HW3&Zoc$%&C z1KYYF;l~)hJ0tB@6B67E{W{~?YK^2iPxc;p-qf`7a?pHL)mh%0UdDZWc}{m!WidQ_ zSLW-7)Hxnp1XuJpTyt6VZhrx>o_#>*+PEt><>PxdQebuDwdbI6{;G1myn5O0c(QMC z>e13Ds>UPV!QN6Umj`Z;3#>``nit6R-?VXkg)Q$l%aGvZSh!^R$ zjT*sHR$RW&tu3i*n{KC^pt~l+u{baT>akG+b$?%a5#5-HS7)qI0EHg@CxZNVm31K4Q4)y$a9DkbrrAe{poRt5V?}Phe~sV{?$sK+y~^B{ z-m2d6|0o?jK;=B?Dc;>tc^oiCuCk{&dLiP8sYtGz1R5-YA4WtAL>5HAzm)(-gR&~! z`Vag(!fub7)0mmcoK;J~x0>>g@i*$rZc)mQx^BGQhX$)4>PtWq+UJsq?&ouH=%mc1 zkEYQn^>ugqjX*!}i6(BJnm;E>M9u(}=hsodM{7P!-aG(QAMcJ%#KQmy4_tqeV$S(3Uhn9*G{--ltChTk%p7PfvqKkH;E?gVxWP67KY#(OJl#mJs zt&@)8%G3(LL+thAPbQbv(IQ4_=_K1QPQdxfrsf#q)~G0P+QI%J6D(h&VNpl|(@*tZ z80Ub^-N;_fkQ7@5fV?8&Hp3k2^ z_=t;ajh}Bink@Ej36C`!KK`|P2PPF&N>`tv!+A0LI95EQm@vMGGBzI(s9IEBOQ&pI zRE!f^5jBXwDJgSJMbCmM-+mt)fyVTD)-$2K2=3$Gvok7@t7Tf85AO~u5A*psksq(! z>CWqzbZ>f=bCv~~R@az~TRx1g4`Q9z>PgK3MD=?C&fOoAZc5SgX|e@>mc{S8j*2t6 zx~`dqAYHoAOsKJG4S-c8p%>podp&+rY-&ue0k%pGT4cjs^|49wrPdoO}xJ4%^K2v~@s z+Be-7jxM7p=@0NYa=0vbtO+@C9d?$=34BhAmUrfUmwKB=7;s#udo4W4+1+S6s>Y?f$I2buHY z;`S}uWa{zpt;^-m;bfcMcJurxRP}~HktsAWgyjeNCY;XqqfJvD7* zQNR8@dv51)I7a27{eur>CS%cwVG3rfM#U(jnP<-%aGqzjzq*#~R?~9(?qBWfFg%+( zhQ@a}Sa`0^>h>;B4j6;I7qwYlIdd*fn0$VlZI&_7tEyl!=2QdtN``%CjF0=&dIg6P z*u~@Wbfm=(EFuPMkK^Wqcb@iKq%C4MI8l;3nBESzUuu~oyQ?_%V&BJl0Wb~PzFj$%#XE}3wrcS;b*IQdD zlV9mCd37dQgW~fhvd1L__`)%%ZX38y6&G%}vNf2XsG^5M<41%n4^!*^sxF(G~^qc zN#lvK=i%ep_YL;Ogj0atyQq}rXHYhqNs^Xo?%USZ_Z;FB+onc>UjGS{?o*@6JmZfW2YcXIeQD zkcV9FT&~6CiA&EB>}-)O7q1XChI@PqmK(tlp-dP}GMAuKl0#rrsz@|%%``Sm6Qooa zr=^r~=!{&q-9a(I54r06ll}YsnELEoM4y-X6$#F$SGvj;ycdFd4DT^o4#)C0BYMv! z7ea$ZFAi*dUSQ$V^A8GK3IC|#eXw{tP+VAexQF<_(nT5FYCp*3AQSQuY2cdNV@qM=*uwp_2r0@34QUnt6PTpo&mpaF zq(WtK7?v_HPGwODL5%dvz9>+b-GPyT$7pBlBnrZzeU7kAn6YK*62W~?C>&^iKTYbF zUf}|iBoa~~y*7v?QL#2S^1ruI_{ETt(ei4q10o0wmEz)RcLK)oNX;4|Lj$9TG#=8c>WTVTS;Kf1F4q&klZwMY1O3NB+PU|Vw3cu*jhn>+El?=P7d;weD7dHQq zY1{!zFThQwB@1BbrBdkslWB1M%V4o|w$x$%@0rH`R(Zh()?NOd|B-3HswyhI5?A9# zhvT=#Q5}GNp~>|Rx7XrF7a(90)u`^D2>Q-7G4bsYDKtnu?6+@ma(XiEiHVu=L6;(T zQc_x)WR8b*Sy7OrS(?tlKST>;<>mA1)0-O+*V=3LW9H+d$CkrIr(y9zsoB@SV7&4- zgs;Ci4b_SSaI3Lf_?^(VmzodY&o1elwj23Z5016Q(YkpF0{0eUO;rqN=P;dD@~JDB_4EB?$v# zAq3lch;m7RD`Y@z#R7+9LIq<@2 zBJixnD_N~bcE5zPx!C4l)P(`iWZ}L2atAM_&b`N@*qTIx=W>SA+>8V35H71(rHqCe z7!?#F@q>*19OWhTX9;luL}6b*t)xh1*I^_zcv`tvoaGB~foMEdU^6Q=&j~9KhqZ?P z^(P`6#%Ma5p&L9-h+t&2H;Yi*gxUc@Ep?o>1sH0GfY5;gLk-9uYLK7?70X%vx<7bD zP~I_)G{M?Z`BfYgSc(R@>!!i1{PBTkyuDPS04Iw_WNV&vX0#Kv0q?{l5NIJC-m^V2 z;e|#1kZg2!vzhRicQ1DWIT)R@Q(3yu2=&X35Veyz33OrCXG|G2VS_we{ zA_XuaPQN;|{c^SZ4g%^yW3fcDJxYlpYKpN$YM>flLJ|HrhbF8XOXH1B0T)a8u8IiT z&ex8kT2KgVyWmlhPObdpPYf-gI03d@toc%&5~vI+Dtf^D6yFcZV5E44^r}5mc;tSm z84g-c_K=I{iYniR_YUN0eV=fe`7;6QKl<(ennrvgQWFSq_0smztMn z$c3pnB|u84cXph2;)|Ox^OZc{P!I6>GgtBOkY!=2A>W_iJlGiiv)iwEQ3N;yQb3x+`+m}*>9~f z?nfzL!v%N?Sa$&yUz(K;tL3~Nqk&x)pwHs@&_TV^U{(d&Au~Jx=IJ_J=VWNv32T{~ z*3q+DyJjm*@@!ua^P}BYb3aked=ff?g=gj9N;f}W%y#ULszQ%(FxqKS!0Aw)?2ozr znfO672n`fPjfmbRlG3IMSug%?+qRhDvLnuJnH*PnzmGe6|N1Qce|;8j^rzHPcUf=5 zQtx~PDUclb1ucQ{9NKLM^e4eV3m4d@S%`;HSFfVo#+04E5(^(@jg4Z*1F-HQd(Bpq|PUa{JDcw**`Ex`*kwgJHU@BCPDmhk7FnE47B4W#yYprsI1uZg7?r-4O ze}YJfN$DZnK1N6_Ee1hdiY^WHZRa~=CX3%(wk0wzIdD#@&6iuVKC(J;gpEsR;CDQJ z0iNeJ!SSnhI^$$-Ue#uv>D8_wgd5lr>~2c8cw4S0#oKZNX7rjhUYq zLzlY~7WQgkY5XYivTucu>ET~#6{Gli+scdlkPXg;>g&NUR z!Qa_?(_2z(u^0Q41a(yLRlpH~oopjwae{a0c9<(8Dgwu*Ln{I&Yl*7S1p8lFB)Aaq zx9RfO5rh^>@6aix`h!d55b$@t#Gl*bTO#8p1hMzhaUGWA6xK_HS#l-4dtk#KeVcsoGxyU(q9m`+1qC2bHmdgl1Ozts0FFNX1LgfNJd0=|NfsRV3oz zyyarRkzdJD=nH$vsfSd>@vJ)I*Pn}y zG=XFIS;-}64l+y5L!(r}lh?ToVIl!o?|sj0nGWDRF&2QK#w*HO?u;K-2l`;}QAv+txVAcsdt|VX zTmTS_*DDpd_Ke*AmU+e^)$ah&%bIm(46*YKZQ<7cdYs}Zip-xXSO=@$<`+^oGeY+; zuD1a$P+!^N3f^)J-a<#{re^lul6%W0v-VK$x5@p-XUSvn#tPQq`0KNf{NuCK{q)yIwD;?u~zyl z|54+yJ>?X#3RJQr6%SDtQgBq6&}$D8E+GHGSX3N!8TEA;jsIFL?oob?fqwVg;1;yt zr-)F>Ksz3{+PeHj-p|nbhRt9CZXJHd{^SUAVCHl7yE~=BT?VNi{D6jGf5k|!N&{l| zBu9rx^N!1M@D`8+jj?iN{D){cT@GGqK(x`t-yGrSfIU=vn@?h-!)n1j974IttQ3z6CR${G`*c=P^0tU_p12k4Kqigh#wA`_7SbgXjdGOIxefYu_9E=gef~DNSXd1)3DQZ>!39_jcmFLD56_l?Jx~wGvk>2xks_P;NAhTajga?d ziwdR*S1lwl)h|KSlBU<)q#!>WkccRKHO^zu7b=7HY4wfb>Z>0jFjhmC9jSnDfT=@<;MN~BTSB_7c*bJK3 zOj01ux_U%KYsp7+o;y(4>O!z@CGbmO`U+KnQgL0|Zrli65>cvMt5jV zh1)zF)DtOffycgAZZTvozmlm$SPcMlS}1_$18B8?t0q!5(Cn!tA{xqZz}Qf;g}&Q< z4T<@OjTZ_9U-*$Kh?QMV8gB#4G`3}t=6H&Z1|4{f?~MewBO#9sS|kjNxvFPN?q2+m z&F}Uy5$4MI*U+PLrHS%-95L13TG5Pc{SGVR@P4HYoEMC+NDVBhxz_0cJ1&OMTELEr zp{XOLQ0F#|d1(O{F)|LFpFtS7M6MbuOEeCyOeBxrU!P_DKlv;a|Mpp!um1WhC1SOF zQiAnCA5uy`uf9{!ki_k{%pgDi#(Fy;E56YPhD|qBG@8jRIY=FgX~wE_hOTH_@B;cQ zFaPpcR#e}ZC@!uI>8HchnbXXi1XG5r*ILS>dVPIZFP}GfR;KqqpEh{f#Za}L3>;5# zr9Yp5K8xKl<{;_rLekx5mywkr@#m(8lX$Sm_Im7w0AIUK3kqH;E%^nTrQ9!8+f*yKVAUZS?Z6Gy5p}e3nr*Rh%mq zU7c&EHN5xTlYTrfHa|oXWuJ8qYN@JWto;7GTS#DLQXY6cH*txvcr*q``Ro;sv(Ex& z^|uyV1)kUD;@R6jfU@)`hi{FUbmQIHa`I=BU5cOh@ahd{C;#*4K{VoR2x8eRKxJ9` zlRqJG>^)tp#cX=q3h7~HwIMts=nb^Z?3|TeCb33myuf))hPBUKubVN(lYYepYOh&T zwcy>u0^4LMO01NJX~^bepw7znGdrlHzb@Oc!I(JG9=FS&jTU3?+#Pwh;`k?yxA=E=qEO8)8oh@`+@zGgv>U@ zYIC#S_Z|-5+sPW=lVi%=MgZ)|!xMaL9)AP~o`9ly>c*>gDq#jU&4mwQTW(GfJX*$Y zz~%nV{ocoXKFh}o+1eYNDDCH7P)?J#UElEM%S9u_Yot7%^S%E}3g6YW3mnv2T1$Ik z4E@>d^J#dDO;#t*^O+p*_ok`cC3%WzA9-nIhx_d2#^jR2!pSi$#pnFDEM-$#j18@q zt>F6U9-#K}w0)nEILFSjFQJ)95U z&V=f+o_gs^>&d=Nx0CM30J%1wiH_Q4<7UyD(rt%SkwNEM++r9Zeb@EP6jvcJ)WpLL zlaw^~{oa)JhlqBw096>A_5ct*0j? zuNZZgn(J%U=&WTr(WGlwov!c8EE~g9S#_OM&4a@=Za%NNOQG@~56CsVcGtqhy$(+! zXLV0b3?H4G+3$#i7w|HPV zeEvSY|;0LquLTR!jeH08tWEcG$s8%rLG)x$lFD$HbrP3^Iq6Cd-np;5lg zR*S2!%6>lY&zeI{2940)jYf=-=T_PuY8J1FmG{~oPoAG8IgMA>5h8>WX@S4t_s0)t z=~G^w{5!P{MDlMB*UJ|2O0=)9o7JPf)ozR6b{C!=#ELn3v$s&WXxmv(<-DwXpKP#p zdj>uKl0)=sYW#BR>%~FSMbVp!=p)mY(VH9UVZ_L_wzct;Ta9)rx6yGK9DL?e&!7}Q z<7Z*nWwiXqKCs>Xk*W3;{ta*lNK+k);r)oRc&=pWI&?KST@CT{{Cxqc5mJ47ed`iv zeYVjVDT8hEUN?8RXG4*=70Q3O6fPYjjXjijGww-S)@U+!dwM@^dfz z;RWPg{1HVntAo0gw?EyUH2QZQjc^DxqsmdhCN(~0TZBeSE%Cc=EHs<&p67%${galM z^6b?Ihx}UDDl@!aH8itV=JYwg)Ks?G5~UnG=`8NtY(MG^*S>-H$U%My5{}92f7f~V z{$mWbo4RtyD90pUX4~$w&s`-j=V zcQ=NqjwI5j&E)|Rh7#uRHD#00E-DmP{G4SAZN*-7T}*pBg}btat8m~v&bPt@KN$5N zu?zCQA{AyzBNK%flmpTuP-{RZD&tUJkmMoE_Qj zlP(Z#bJXPiENna5X&wdN0)l3=CA0PIrJ~^teXxjG82vq0Eq3=&v2|sXk)R5hV^IrQ z9H73HoD${x&}bm2cOTlY&Wk^N-Cnco9N^%sviw~N^D@TZ@bdiwWNEG-#Z|hI`x9X5 z#a#J$o2>?TOao)>)7ZY&8XV4=j+Qb>?{)E%nF`&}?%~ABw2{Oayny$4X@IR^y=Op4 zMBuL&bHO!uVLe5K|3$ji<##@{wvqk`{_FJN(kg(1=eQHk6!M!=v^U{5SFlxn4Ib7n z@;~oYa;w$|#-B9W8scy{qnKo`+`U{(E-haRovhcN<96s+nGS=9rn0|(uJ#m!1~+?Y zQDpdl9UP$P5|uw8hK_f%3Ge&NrL8cXx|r2WC#`jA)GJ{+`ZhimImj&?_0L?2V0p89xYnUehlE3?waj%;aRN>poaEblH%QssK}WC9?%@z+g=U zhAFlXaIPnRN8HBRy2$3l5vhM-B%a{YdUQ%m>G5vRtryq(b@WOBS(J${r;WHfaEHE+ z0zKllirrH`dHFsc1>U|pvEAIKLZz9r@qg@95YNfNkvwPTMm=gErKkRVmyn}0XdEtY<6?$d{ zj@etvCxwPm^oy5HH~9(A)=-e!WePU3ag6z3;)d^G(&g022?x@Aixx~`nW*4rNdyzk z%ZI^N;t!SIrD6Lp&<+D`gG37!eVL#aOoS87E5|xmFlR}0C-M>wot-GCS&Yr|x5J=o zuHBOd#SOnz%>#p5qdiw=L8JbXrw)t1AR4+mK&gnOSFlTANFxoNF9>r042IBCrVA6J zR>>%Z>~_)8nd;;O1b~lf3M!C1s2+}n$!0zXB@mvKk&)Y_BTHEQye zPY^KV2}N=6Vh0viE_gsd1pZ&emH!m-u>bdv=j*=$Af5k0wMfK58vi~2E#>)#YEfB2 z?u8_WyI9oH3=bdaD&^dPD^dTe~a5RDk$@v zh9Rq3=FK-wy67*u9yikrjRl))+)i|KvP0HePcO4?zaOr%a;`FMHu(QM*r%j_%rj&( z1dn%2i(D;T#J@LoN?bLaZHOf8&qY$9R{upCsYZxbXpAdTfwcQFZ-)my?l^5ek=}DB z7u>R3T%YIjF&g(uohJ2F9J{nB^&Uz&FZgG^u+4P&WMmw>fRjil*vQ+vQqoMaDw}*l z9|*E_cz(ucys0jfZ514eid(LLK##(H*jS+>d@0J-8Uo!zOv+$$bczf_((e8>5xRhp zhJ5^}u5SE`CY2{1M2x+^b>pZlQaP@N>?V@nSs%4<5?Ccj<1x2 z;yGZr$-?*w2-Cnb!H@X8ToeFlI&BPQnu>_`T+uOeVqQQgWmTU#$7qbj7X2u@Fc9TV zUS5IDen63xzF;@L0a^U*L@G z>?Aw3ZQD+E?CjXKZQHhO+sTe?+s5?of6g;!X3kv9TvS)p>eY4C-A_IBuJ`*Pn!EfR zSTzLqnYd^%RjpyGT!~gD3cL;nY3(S;O$w#3L?msIAZBclr<#p`**s%}H5~A4^dyiR zTa>U1B#kFR@ATjE<%U5yU#=Zt7iYh!dq@WV$pmQ$Mtx-AxTU1971qZ9f zO2sc@IJRh1e@+6=L_!-z07}e>g#!wRtB`PQsu;gWzA#Ai}feYdeGV799vSzdEW{EKm*cMKO{mUf7&x`$x(HoxEf& zVpG;oFsFpEB3Mi`3(y676=Hg(XoOoD@~c3Dj)pv)i3RD@M-P4zi4M&Eo?C?6 zLjj0aNOl^cP~;D`TloSPq+VAfP8nL{oG>K0xvumB!vj1sp&65K9Mt?G&h1aHvHLkcMHw@pQb zK78=RTSbO+ZkUp^er3|M5YoM84xP~}%aE9j5gDBvl~C1DhhlTF$_4mU{3tlf5k?Pm z{0|KX=scCkv+u50()d*YtU#-*ml}F7?7nF$f_SY!BGwwJj{9aXZQvi#h}>-WHEGvr0OI0 zsx4WK4QTDV)THX;T5D9>6RNPPEg`K9weHj(fL(J}a`i#=rPlVu@`GwiKmQ)OsR?#t zKiSNbi6Qew6_{37I_c)|d#g#Hb=l8sa+z3qBse&iG?!G+Zv%=lnejj5*esM54%gn| zs?E0}o!RW$H}0sb{h57o+h9JsvOtNHeD$!-)5ybVNCRzwhMjfYif}CZQvsqUzY32n zB7oaaW_bg3Bq{*uD_TQ$cn-@3F>i37v5@@dMKmFS-8hjSA~e_RfuK@$afWvacpOz= z!wBfHabK1S%gO!+oDLI(-s&-HhV8p;C`q?dIlJk+lnfuJvzL@PYkvT@{5*}OZYHz$ zQ#l9eUz~e%;+t<)x3JbZ-xuCb;Jysgsq=@ORMB2;O9I8N&H$ zNb5^hcCtltJ6SNS3?si9W0X#vF@nYXuqE;xnA^e!gCOP9ZSA2itmWEWr*>;lHV=|K z`4M>(0zwgv_~tp2-#9RM6az}Tm?qtf`B0PIveb@Yb<^qHjeGW!-!jyWv31jJIgN!d z*Nq2o3*=Aza~kt;yd5>WBbs1%6;+_A^K9#F{)%o2EW%RnL6anfv-A%es$)JuR~#ES z5tQEoYyA^WOer}F%|)AojTZYT6MD`k_^><0Rvq>ep`XhHQYEG{kP*V9bP!F_9<*=^ zQ~!vs8F%=9i;V8_LItKwrvm3y%io(Y|>?dmzdkvJmBhE)+8;lJSUB%%?AO_d1gLuHyAXs`REK@9!kt*Qzm(em56FCeM zIW&_c?q34r?k53=B?UZH@Lryaxr@#jqqza2O?B+vN#&F6X=ag@93n=81P*X6PYrOq zDK4}UM-OBsHswkqI~{j2yP;Fy7=UAK&g17d{P&CGI=)BQ&{EQKGD zG%+Vjr>N>HE;eZ6d5S`RGA6Fpcd3tc*I{L~C8k$gr0|Da4sD3pa>aJMh5^HzGa9Jo()b zgFYrO6*x9;Sn@!#fb$o(4T*Td2Jzjwf^-6CT+`B&85L)cA7xcdrq|)n@n~Bez<7?e z-`d=cP);Vuv#4`PsDDIM`GVDI26U|RikE2oijP!dgG#e2bCGL(mfPPsPtN%hmu3@} zeD8Ld@xAb+KhypVz|K6qeVnQ%j_i4ln)09X3!9O{+^PQ%Gj0&^PdebcAd>|pfvj<-Y=;KCHIcP?~wBV!|%`vwjIOm zb^QW_-a|KP!{Rk~*4~QQbF?t(;!t%R;UF$Fu$T9X zB3Mu$`H)V4P_V8c3#ge*n4S|c3AR%IcdT}qU4Swv!8TYCmM-2-W3e#A&PT!HKlf0O z%E@{LULp5?gm>>^MSH-7Wz4g#dPl~<}w?0tH)bCPbo{4&6^;lqHiapN`^CMBYIX^sG zKmh0qMrVm>DqeG`f3AQ0OFDV&BT8m6CbmvxGe2Ii9(UND{XaIksjzX3dpSBE zTA7t7JKgnMZ?oy9X0~6ub{YzGt~L;C%y*s~@_5~s3gtmFhSptqeA}%K^36UrD@OT# zY?On%oy>x*M7_#DSL8~U^i(uoZ1M|x2(7MgRb+y?<)YcP2KXae_}RKWI!=wnxxdDb zUo#Aeu0kJjHZQYf);>J@xxcQmFJre;qORy;Tj=3ySAA`Qp*n9yk9#J+xYq^)a>fux zo$FosEY`8wM`ynm5->1U{No;5H&#{4U}Q&k^YTupSJppCOX^3~+3buB9K zPpfR(Ql3nu21Zz7CNu?ID zVqC1&E<9f@*-@qE$_&f2ZK3 z$gAl%JS=xaf;ahitlvgOvaLJj-8`3IzC9krAJ98bQ|3*R`+k=Rhez`{Ifs}-x7n}Xg%M@RVvd&BKtil{0ViPb?WIko25Fmp1rX>Zef zUGt~I7zg#&-Y@r5X$b@qpNYD#*(#P}W|NmR)>l^4pD)H-9V6PU1JvLO6JJS+P-kDB zZMBq!BabfRTK?!f9i#2*D_G2oRGA2RVh{J8TiI$?Km7dw`OuK-5(XcgUet?iV4u^Q zG5W#RcUYe(?mk57*dMt!M2rKBX7lQcjK9S8HKwJ)#Wz6wLQ%QiM7*iilE@#hKK12? zxVz#JceT1zrCf8SF#SJyBLy#jYbE{O0R?66Qb68;*xeET{?(S^+4s-Bp05TNn%+yt zB|}J5?u67;G2OA#X|c9#`+Vfpuq}$}zCCnfgO<}5ex?+eVZ^i!bbL`)32n&uxl)AT9>vKHr@4l$QV&%fJPG?Z~(XtKxCHWc7y4Vinjfvi` zFO-SW^-cPKQrl9Z`SUlQh1Xxry?G~fZNZ&ose=5TF5d1zSY9jqj>i!P%9G>C4?MkG zf}CU8c6KCQecE=*roR7*F9Uf;%g5GkJ}z;+l3=E+`RT|C)83t>i>VWH@eA=D>ufxH zjPO}arm((F8AHoMigiNWy7{Ei<;u2a+I+IGsC#SG=i1XWS&k1L=sR%ot}nIHy7#9s zEo!@Da|@Fr%F{b@On16kb2I9Fo>*O0+@*ey&|k8>2Ff98QF6m1iRCyi*g4Nm zEvz{IuxAFBrR`ah9E^G+IO5JdaPQZ!jQ6ZOx+cQ@v?+@} z_;F@}%^P*jKWzO+60Gbp_nu2rprDT9}Pv}eSyWY=&)lB~?uy>FD9L=|4M7(78)9-iNbP+87PA943Ftae@u@u(x z??8JmX6nuzHR=U+w5402dB7%}SAP_#7EmvPEv45s+GRASOQT$gwKTsZgP}=;~ zRAe{YomP<3!o66e+lilq>-%Tb+qqUMJ6pi{k@x;iwI&^6jG0HDZ zP`MwAII%8Io+g|5HS2*jTh*SiuLt2l38uc&dQnx$TA%qWm2Y|<2Z!957^zoZEt5Z~ zzO_T2%{#0dH#;Zvm$r8&zNV$!dsBL_cRi}10aCu%{KI1v?alN_J*vphtg>sSCv(Rb zVLGl)RXSfgqp@+jsWuwG5NvK*uZJDpLfc;S3F5xZj_#(P^hMr32KZ=O>yT20yhfiN zff(VFh!(xR*E^~FQbhc%Cf)(PZSzZKKYTuqH@m%sFRgH&H}kS$_TP9FePg2-C2#{T zOZWVua&gXIrDOggPAUE#(SrOTpOH|_y4V=fUpvfrXXG3l94s_UG+aDfEHqp+C@KX) z0fU1D#KOWv!hwN-1%m;?M1w-Y;l9CetC2T#U*Sow0eW#tUi7lbiJQ|ukoz!XJr*UT z0tZ;%h72det<)K>@AP5;s;%)_1rS6s1W`}l&r_70uURwEQG~;VF|Mjpf z3pyfU1Poerde=AV>Eq0<_f;wxLYl;`42`~ri}gO4W>orS=t3?i`!faVR{tGhDwAO?V}(iAEjM;jU?Wt9 z6Z!t0_b`)&D@Zb>@Q3Xb;Sn&ESfU86FErStauyWn&x>h7uvln-&$AI~z-uce2}Air z)iwow0b2n})yT`Cq?IlGN3rl|k)48!7z{(IOa)Q4@MxgGF93pd>aPxhRS~D292s$h z1;V53jQaW1muNQ$stZl$o)<3}jNx`6Xb62@g<!}u!BX_I3-4$7Tr=Im_`f) zS#hEgJN6`rmJWxBzcn&&I1@lbPzET_Az%q>g;~;L;@`XN*TszFh1R9leTw6!}@}OZR)1Ui3{!L7_fT1e%N^2L|xBeoj0x_qX@{_kLQ1=JY={L!?+%mSRvMvTd`Pl8JfPCrr0xy zs;tki;LJ@wBH5bcyTE<7Cy`%hJ7mkH0RZ8A@2HwZQXcst!$~5cgKFl!2gz*1=S=@HG69TBX;5cM>f$qP=Xhe&1HRuCycvVv9L z=u6?aLjuu7CdUDHAl0l{%NkY7>pXdhepYgTvOBzcHKv>;Z)vmtf(J;cGeYT;h}?x_ z)-pb6hDJ6240xhOHFN!PI|{?F*hP8P^1^CgJoT?rh56Ip5ZS~Gk-!V%mv9bIc?=2{ z3fmcyBYb~REwBJJi(_GE)>xS-1M)eF=l^OJUc~>Q@+1IM9y|IEdt|r&_%Q_(DEOr! zLnb%O@ou3$o#(Hqg}^tIIv~q|Yy13&tra4)&gkI^uR<96Q4iyB@`(mk4!kBQu~^4P z6uK8?sUklivLH|M1j6K3ffN+LLvEc(H~hXq8G?UWF-)x25g(4-BAdXI@q22A0rKE| zwIpKx_c-asgz8U-kZn(^NP)9fm&gdJ#GY-Z{a8k^en86zmQQ&KAbjizGaX)ax*%)} zS*g}W-ItL(b&?=Ghh0|8C&pTC^dAuq4b@qx-qZifk$2Q>34CbC&Pu(ym**MlSi9D) z3w3TpTb+KE>SG_8MnSB9_;3(J&hYI-HZF5u%o8yk6LXGB;DE7UCL5n;y@$i(e0fH# zftYdcJ<59Fo&oPU(cXoe0rxr71`4F)sgG2-LLT~!6wn54$lTj~Csj6_3?4W`SsZm0 z9=az7@&SiHP%Y)qF9MHr7uP*O z=ou~C!c(6Fe(gc`nhxFq2I?scVzYi2tAzvB`o&4jjPB^KhclfXd>f=0$%`sEYcK~x zQq6?%NyTuATPesYWgVhBjP+QPElAwYYowsYTQ*=m0FF`hvKH_JY`JN{>qY_GTR&1g z4%sW+hmU^1_eUf8jRTP<-jAQtknbq!4a0WIG>b9Uam1@1^MTZKs+ZHSXFuvKgO%)8 zz7824*>e!xDJMopf&6ks@>oS3!nu^b&Ld?Ltc0Pd6^%QfZ(;B~4C|^8cr7&Ta?rqb6$zHa#UD>3j~p7=8mmG=k)mK9%fkRA z|EVw!18)Ei9y_0WyF9-YmM#Jt^~h_Eyxh-DVvy`vyCAALZ9y&rb=zAlzHxeiWCX zSTMO&X#R#UmyuW+iJN-@=YB4ax}z4>H3454zik42pfyW1>Uakbx<`!y8}x$fpfX4@ zqN5kEcCqNjd^ik1$s#&aBAHCs&kR!;#k8M*B`mf|HKv}3Bl~Y%bS6}c^W)sYBb3{A1s0I)mYgjut;5Kr_E(}#1 zX68JY^?|C^xO8fta<~xhs?GOxhe9a!47-cbPH7tZ)NdmJ195lF zl;)ZWaOO#FukZ10=)wU!%@tdWbV zx*~t&$7Rsuve5H7h}R>HVBH-Ru4s^w13|q{x`W}KOnIbNkC158!~x1VuE{V)Mnl%L z2`tezD3wW8jtn@dG852|K2>-IRNL5J2zERnU?&SQS@`zr^WL9sq4Hli2+%MW&F%!VHsSyJEr=0j(?`x__8&U=`NT?__=}+w=B?+v8z=I113nBtAvkl_os4bB~dT?@(knD^WA+n{^MkdP66;?)RFQO*R) z>zz{pqxSE_^5k|BNG{~z(qAG`jxgRRGf}#X{{E%79O47asxzj+S+g3PsT!QQ5~5SL332DFAiU59t(MQ{O+e6Y}i)L<q@aC8Ta#t1HZAnB>@~yY6=Yt zT3`r1;v777^X;o$PTn^H?-|H5*v~Ps8R^flZ4@{4>)W*((BH4EpdF02XqG2Nyk67! zk-dceL~33XhE6)#w5J^%20qT(mY^|A<#RPwk@=z#*!@=4+*P&tHL!Pw534D30 zZYH270^#d^r}9v=z?e$}xginer1AEcPKHQ%-9%1^U*N(#It;gvw1)`}C(qw|Ly`qW zYuk5ubncf~{q=l6qv&c3h(;cdTR9EYzJj%t2f3OdJ~bqLY9;>624dl}R@#QNw%1!*MU2IOcm;}+;0eqYhaz=V5 z`<~A0)sW&!8^K1N9R}sQ6~Xq>d#B!8Z4=dAGdb9aM(>gCD$WZ3>Lrubt|275SD%P% zLaFj#7wekz_MTCxRayGNgzw!@H2Q$-&tiGiZ_=64gQHfo!?d=N)p*b;2Tm}sX}J{v z_BCea*q>ZF%*v1w}5MYeJAxd ze#fPUye0qjcl>=q-fzpD9&4BPFaKUytX&2^OT$4r{O9n-XL4NCFCHNri=&6)0{38B zK}S#TsRKdoh_HaxGVIKS46Jk54`8l-S(5@ z-=moegG%ptF2e`4J)>?If6gQLe)ryT<3sP;?Q0WMyTBzwPI!z;f10^zbWY2Bog1b1p2-${D> zmv(CEj=|3XNNsySAub#9LcMC7m=)z;5$U~`#o;;Vinl?XFSK%ukGFC6{wwk0DfaFm zx)?NP8~wO&qh`^w%98nftDSOhC;+VEDDSjC2v64GrMV$I+WPxU?an&k9s%o&+7}Lq zG>3H_7Og_bQ2h59>1=2auMFdSbf*-$YwB#z`dsbf6UO0xuF=QN(lVvje>RBY^)82eO89@O-L!0y zNfw9yY=K1n0!)@7*a1AXCQ2oS4g0hwPIaz=fBOgWMyGCffo=232IR|suiDQi2NM$> z`_v#U5O&j)>@!M%%=b#SEq>LMROm`=?_1&eZM?ZGOIbZ|z&)kp)BjabeloM?V(_*C zX|FY_UW?P3W8Jamyzsr+(2Vu6gL!8}Z^Iru-4;FCxsxL~GOE*77jyen@!`Jd!#Qh; zXj9GxYqQsax)j)Sri0tls0HK)@)c6rO_9nek2I;u^SQ@?dOkk+u_eR4UepMFaND_W`L6N=K>m{R#8^r@uS{KvH6 zm-J)^BDdw0>(i9%#@(9ya*wW3)4RvGIpAmBz1;aPA+ovxcSI`*-E&K8CYGIwjrX*c z-}Z`MkJnR8qt*ekJ0hiv0s-gI59`{Ydb9gJD!E5T5@RJ{;$Ye50e|^Kg#82Uz^$np z{UeZ~y|4nQH6lI{YU@pY5^CE$6k~JHN%w${{M&9%p1*bASXnskfA}5=UU6Yqma$~uberiNo7)zxA{%`g_RigloDD=g&C8jr|u~7U!#oE3el_* z`y-jhMoBv2m%`U!4xUFsd(3&UoFRSGdf<6}5dZp;uUQdjYs)qR%#!>{kM^!uZMtmk z8t?h|cOJ;QTkW-QXx_W|e02NO>@~|gBeGU2E}v1+MmF12i5#~1Eb~NzzhTs)>eu`- zb_j9LmNcYC;c{chS;u#+SI2v;^a#Pw L<2w%9{NuWbCkO`iSqrt=*D0z=0mg~K zG4q+@X=JsM&32Y;LF`H1QrmTac9L z0;dnT-i`26VwLFMPh01;cXQ*DyF|-;9~f_?uV1{tRf*k>ZEi20)UaSp_><`dWH)c~6n42TS5_Db5a9?a>roOr& zGB&B!6*?*dxHiVS?fA+H48-NbH}I&LYIW5bnS>lW)mw%(tTj5bhatJLvR{<%{qC_p z-7o5jJQq*9U$p3TJww-jZzG-@ObiYZOo;4Td0`mUW89|l|$sb zg0S~?ht$V@lfFb+yn&`&uM>dFI$Q+8L zd&w^TczG|c9UE--&bx2!amLr(5>l(}c+1ElUSpd`aMl$3!Xq0=n5`NA?SE6LvqQxx`RgN>6cS|#lT>+<^*nYgS7dB4;&rv> z$4B&IIX5mYuI{$%HZ3fz?l$d~Vlf;69y%H>It&gP0Tux^HYx@#CMp7IM3e1t+B)#R zssrG8f9{Hs8@(*I+4bd*|Ee_VHtS1cJWKnNZ>mZ6TMGtx*Y1TSam^a{3?;@dD|%@+ zOk?WF&g#Slp-W?EdK{}AxS1wL)s#Skfelivzw^&CFphIFZplW}mkkR8LRZ1i4>8#$ zxLIiE&%UGo4GjQ>Y=bUYE>5fJpgjK$T;Ow0XQ88;)62N67?x^~n+A)n!J=;8tePJ; z)tLb%w3=)ErOeJoLpP+Cc|AAHtWNmK{&kFGGO+SXYq+bEi+Yk!3)<2`Yk0=V@lTqt z1nh;0mIkZ`2jwJTK`?7Wbrrh5+=K&^*Ukh&*eD^IAvf-a?sD4L!6Bg2(>113FB?x) zlJ;u7cG&7e4Qivn`Sqt;1lzOG``KrEI$LKUVXm?12lW4x9-08H5}&IU4fy|m7$5&v ztHl4eJgg!>m&AYD|3~3rNE6!W*YI+VjCv^0U&Ie8Xe|P4LaPNZl$1CmG8#~*X`_Gsbk501Wg0pX& zS^Os?Iyx$?HcKzwFRIS2^GX5KXHH8MXA@tKOU(<$>cb%ZuJ?dAtm8s=KpYlyj29&S zsHun5TENt((*dbq-JN1nG(Iq|kXoi_i%5qf?s#~$li|;vX$ao{RcD?`)FE%f79pk^ zg^|Z40yaIY+&K8#ry1y+s7P960i|SFDXA8a;NlQN-_f`e6lT-%f5yjRIW`m#z&4oqddoC0a{#J zpn=efNby>X5=Dh#{z&zIq^L0$Nea>YH7Nx}^IGJGAo7{Y3_+Y(A^zpzv6j{TX6b`~ zFBc@n3`!R;v@D6N;hi-`#rb2@f?dsr4U-T=W-?aC-H1B?6`91%umzOib3pPzO zMQG-sRcy)tBC1R2}yvK`e2 zczaeC_R>13NZ#d5pfXV)6!om3jd5q<34GZI5LXl=ZlnM?>Zw(Ycu14FBP`9_NN0Xu zB%N9fSV!DH;^}7AtEOsd^9vIj303+#4ZKj{4M>sVbj!VE8VopwD6$a7W3r=+SCCGc zdpAET=%EA~`(u-%P7NIJlyMZ>W&*-B0oQrPocZ0GvE{iy7V!X`_GSrSssgDfv$(-8gTEkl$^VNFiEB#WhoZ0HZc%cN8YM}gB znwvKLY*3iL1&HSLeuNkZ2BPkRV+1mb_E3xe7xICgIsYH>@#XH>vJgxrut?_b-`G!p z`dfxX3@Fd0A1RkysX&v8-8=OVk|=n}mjbPT$v#esYJq49LiwWZ1O|co+!}PSYVCU5 zDZL6kICD6VXnOxpqxf&$ANC{_f2XF$z2LrIge_0uEFerGRPV?JL18RB4Ny@`{{M)U zDm02ZaEQfljN*;2HzvmS!nz|=CvMrE+ZYa`)LT7wT5AMSvBnuzk zw?ig0Wd;@`kqG}4P7_bZm$@sMSih8P>n~8{cGmB(}WB>O)Q+zM+S1g*bwYr*eCf zth<8&e2;Qg#7n6Mql^nk7&OrUC8CWCI2Yy=_3B^hY;Zv!Sa>l=v~r-YK#I+!NSE+= z=fc4i-pZf|YW_fPGO45Hp!ntjHwb(sdA=w>?>?BnGW&YN%OQ`{0N7--IS+?~EI&NM zmkdOx%q~BSUT9<=GU}`L-UV!MZDCZcA&lNAT8|usSGMx&`=7mwBtSBMaBc0co*BRp znk!{y*;YC}&?6@VsU%RmMWRt3FR5C}rHdO0D8~)^|M(uvr^tQa|Koc^CH#-?K}PTh zgh2_F3L@|-29YYe8$rGx{2~!K9fFXDkV=wIOzZ&PL%P*NOfE+H+NbizRaTpTZXmxp z>whI-_2j}!$?S5OR}EuTn`tBd>Wg06ErH(50QHG0v-Z>Nvu1k5wQp?`Hy4$4>m9fN zckO;OE3`5x5?llEecT@sI`Nb*a-=~!v*QnU?cR^oZ5ZL1B-y^|OK^pu5H2iS`${4p zaiGcP0k+nkSbk$B22-Q1XvvcpXbt(E(4p!9?j`v9qY32$i}Vc2& zFWMXMSH(+Q1g>(A=pSQMOr*%foL{8KbR7g8ir^rUHc3VZD_|zpve0nYVk966YyGqY z2@so=aOEqyhPO+Q$1LUH9KBV}TTlTSowfIS>2azy}`y z_>inat&mA9tK&lb>r5CDFQcxLUe{qBgf<+6HXVdE7=$(%gf<$qlnqUt16>J8Riay> zDM^s`^msu985mZ)6e&~Pn(KMQ0sO&m{b7I|tTHYS$D%r>gSVZV<#YdMJUf>+reP&k zhD31^5cu(o6Tqy6PU#6srB4q-ruh8GBvW!(43rKHRk zio&NPX{`yAJE*`(9@h(q@3))(H}pEG{~0}Z%bsw@9&yK>ac64s8U6P&+W0ft@RK+8 z?IYx#J*SUN@TfL-0AId@t)9jgeMFCJ4-ka~^gq5w8#Wgp3JX(rt|1{nlpG>YQ0NiWKlXnVefFC09M%-(PyF9a9%)dgVX6YT%)s7?ikeplu@!qRGT1hw zl5N=pX4zE))gy!SDho=Q3kyS!)CL|QqURb!n~p{AJgC>;WSh0D1Kndmp5f4dw4KdK znKrdFyI$jguNdeTD%5LMvdtR}Om)eb5^D0&Kf~mu zB}-*h`PzyqJ{v|g)&G1CAa?L`fbU^d_Ht1YzwKKlD!#_hEl0_^4DdZaq{VDP#cSjMSy=h*(0wNp(RlD1m4%^@ zF?jhg+k&{=#F*WvnB9&4Tlm<8VOtf&1qdGrrw*K-^m_UvY=pXo`f5v-?A>G&1!1vf z`@~|kVj5L=;u<*R*EMQ|G`fIOL~nfn_`XDOQLa{4m8u)vuD}UL)SVt5YN^xr%94WzLqqGWzNPe{emhK-;%QPu#E`M(h0Pvxo z?N6>H?G7ICnI|mK;)fGWaYBI*Qxu~)ZkM#9h>Rl(L=j-JH2!>6|+Stmz8h*{#TgogPV1ASjM88>SYS+<&3QH zOgkvjY$>u<0{}kajRC;N>ZQEZvebhy1=FsuWYK%aZF5r*pV6D+^hD$SV16-j@{pf{{}9%=i8q1OXwjep8BKwGiwtN%EWnpYMA=wpAd@1K+DU z-uNcub^ikX;-!|k1i$q+9;XTyt+q8Hz6wYw8(`fR|5-=pzbW1n#; zf8a_sGgRSJIXwLqH7#@+**AG32N^7~QS{s~=Or1SHPH7*&K*8i%w2Ij+?g6GA{lk`Qup`qfhwuhb*FIJ zsE$S*t@+07n|AlGtxFBd;%IkHPC8Lc+N80D7=*kRLn^iAlGliIsrTkh{s!N=%hn|C zb{gyL%a^XrLIf)M0(RsJAwz)7ws6^d1EKdRWF;_EICg^qp73k-rkR6dT_oP?~B)vR#Y$T z{6!`wt)kQo4mL`gD@FQLH;?K5O4DP9FKnl)%=y8|;9)tqCHQBD-#=OGZ1Tf58+SK} zcQ=KWx5}@DbG1r2evdXMp&}!YiS8_p0fMWH-(Q{Xi(VD(1cb__59$q+f8UPWi$B=2 zzrVNmKCZ7vA2{5t%zk|%rq21Mi650_h&wqL#M#i`R$^)V$UhCz^L;#xnk~zZ!V!P3 zs8VX&Uw)0M?&5z`YEJtUN8bX@RCarvxSsjf7HvKnUv|0+o=`vWd%o4WO>g*ns(k6l zj`NT|Y*&vTJ!iLlV}DG)QF9W0wmUTC5AcJ#2?^kRGS6n*-?CCsYmK_s%-4BrH2mvd zjh4NxCj!ksyS?z)DQIg>AIjP{=fhw4oQEs{e%(cWY1oDL;7snb~uw3{}p+x<1%_h$N@w zx;}yt!$H{Ka-vf+cAojSD)rEw$UXAe(R!gx_oF?Z*JR9B1FL>Nklw>rgPz9U0e-J% z!D)dRy$pFn+uPXgM)xwV@DQxW+_gEZC)L|!Rq`=AsHwEU`|w5HvHwNRcKCNP*Ys_S z)5v#1<%M}gugT@d-23&b`2WGzT?WO`DB8ltgS$g;cZcBa1b26LcXyZI?(XhRaCe8` z?hbeI?tRYL-@RX*D!LeEda9;se$lJfdXP#`px|A3Yc%gx-(r`ZG3Hg3-p<<42EU3B z=6T&7KbziU5nVR!B@f`BzkJVHKNWw7;>eo)0M4~vru*)=7FJzlz=k`pOg3PnJEB^< zTcSB!pGeWBTyuu~SLh>LR~&1 zX<75-)S14!a!WVUrM&g%5~Up9QsZrrb~%gPrPJGd+hN>#|E51l)SF2bi;KU^B;2vY z0>k%mEu?9*WZ{iIJ({=UFe7o;=SnM;vvkyY|2Pu7$)nRw#~J~Psj7nfA=Trn&@yFh z-NJRE(=hYH;qpL;ena_eSrxv+$74%%m=_17o$cZ-hEvy^l=ICW$9VYWD+!yNryf78 zt!A*b_UGJy&x=?zSD=pjU4qwC>&HNJLqKef+{NJZ*K)^dC=1fCy;nD_riR+H!a{`C zXbNS=rZpF(w^^8^q()Mj1acuO%A_Q59Ws~{sDy~Hr##a{1mssKLqG;)b#F$9`5`|MG+81hr?Cnx+r#7F4c=jB^hM*->O=}~SSvjY=X6U=X&FObV({cER- zIupJbKG%)a&UEu(N1yH@<}`g79*^!To_J3sx=TLs2eG!?Pwv8WP4>2K(5ywMBc~$1 zHhf-I#^CWRr@2=#tnD(FxVOq7LRo{I+hs73KN}f4^;*+TxK^`{S=l&i3iqJB%h$yO z7EX{0+fmh@^t#gP@`i?V*-*bM@s%XtYT?Ww6zp8Rhg zM3jLqiE+u_Joxff1WIa7EUq%J4(A;4Qhs-)^UsKX#5@Jo_+hZ8yK`INx}oOaEh_nf zz?IyBWWkdCTrI@(abE`W;>8rIZo;X$`?~P^+Iy8f{yGl+$Z>QRt|%@E!w+p0b{M(* z$U%Day#vRwu$1BEJGz~!a5Z5u+JCYB$lG!`s$lFs?&1Ba z6YSnUZ}_S&jMz?I{xua@tD0Cl8QV=A@9FoNxSMKkUyMLLJw^YFpR_D&-j@0}Lq$Deak&!UZMlW>5(UPP|9O)#UAxNrXw?;`3_D zcb>(=unB}I(kqYd86g`c394 z+W?>3Gz=rxg}FH&-Gw>a-k{FvaZRCQ=0^0vJw;! zs2QLQ@%Q+Df{AZVsEf<8A?V37LiETHP(z3bh#oFxP?(Ul!0PPz)82NwZI_g5g^bMGU#&Ul8Jm>&;XJU98Bq1CoQW*D8T1v->y3|aU$dC()(%i z?QQdIYr@FRqV1NDP+y<0$R|NiBcxEXNbA3t4zPci4&2Zg`QO(oOd=v-v1kCM1Ipe> z8WWnwz%xUnZm@OaM}j9S4Au)hUt*hC#a@64rRX(jVez~U=*~o*I0bqVbBd5^#X9I%$)h}fFku;C*`#6{ zksRYMDL7h?UtTH|))o86e4>nDrEnGgLzXd!J)$;@_{ zRr~%j9Y+5!9SJl&XaJ@IlMCIISBkS>BKmi~*3Z6Zj|(OcIjG+R$rJ=@RFIgOLzk#v zpr?#}O9cWMf-_5CYiX8=l(b9sZRlnw8*->5DVCYA*~ASasTF6#Cu*Kd=$6$|ohiX} zfLwwel(R6cVt2NV4zmzb40V4JP6`gH%|!(H8Z3=ToWS7wWVBSII&NDsw|cDxVmx4R z2ht^<0Sqh+A#2nQ!*QM|)S0XUdDiAIT}?@aCW2xGk$Ew^J#iNAkTW)lNl`+$U!2FW zm8Q9V$c<>pai>Jz;`K*XDj=4is89`Lzy-t-vr7n+nEdpEJzL<-RbW%ElVpRAFvb$8sFxLj+GGoK1`-&5 zCIszAsbR63Xa-Rzm2@vuWCmsLrbg)22NoigRACZ+>kza+D3dRZLn%)coj{?elSDx( zDusHLLK0C)Apbrrj!H1z%c_qI?=1>kD=Ot(WMm!|Ld4QKw|gRXg2d-z(`4*`GPF+mAv{wOvQoZ7;3?26X!c&k7$;0qTK zn|r;G9>R8+oR!&f3G#KSjRhM}$U##(Byjy@xdfRiy;uNyeDr$ezwPmHfITk#*Bf60N75;porH5!=A>Qqbx6u< zqB#m<176y_B#erMGyNkOJ3>5$GKn@HPI{Bm*VJ#v=GP*6lEm}UjAj>3m~Ahu@cBCU z&QHX;FM^5@a}+r+S5j~AE09Zh@k;>HQ3CSGbiU2@1aR_DjJz4s1Ho@tC!oxDDr~MZnUm5`5<7)VS@o~_<_&A*XKlr$kpgu7- zYPcHG&%fD3MWbKzETrZm2NLJaKuA7*6+EHRv|W{ z^0*Dc-avir49FR#TD#ZGiZoU+o{YMBFbf7r{bem=?_{=hoMyvt9%o!M$S9b|C|Jngu;D58iJ~sZk>^GmfN+8%xYCm9QjOS~ ztm;AHPvdeeOHrVU8HY@Ya4gp&oXe<-mNx7)TDo8`SS1mU+7K)aJ=SVvtQ*L^ITHP> zm=|aGGwfc!G)<#+%^Fg7d0BU{YL?m37)&)7EVW2XwX5C3_Bnbq@dGkh=8*f|HblB&e;q-PE+zcqC-q!D zNH|;qUJ`g;VO*VIT%9RgojgZ3Qpab*!)x-1UFC`0#fe?kiCx*WfgHxrWv0;O-Smi@ zb|&E=kmda~Ov&s-G^mLBKMpH9N3F~ts?h~=j3;i`TW=?HUm~T=u5NEi_`S|kf| zLb?Ir}e!-ey{VBrw)MaBn3b7}31~_by4I~wD_X#Kv zl5OG+slD_S*KeZrXEr0lBBb%bc9+Gcq>%eg{J8)~hdTi2AmyN|5Ga9bs=S0wTHFlc_q+#N z8;5MHsjN?j?3tar(DZKi7Mru?3t#eQ8nYmGlT+j=ROOLY>LD)|Q!D;VL42Y(;T;b1 z%tU$$ifJXsvX^Dt%QhM083^-CMtZ{ihaHCjNaPHAI!4A1$m+|nvD6iP@d#8EDUA9H z8ZRgUJElA!s+b_E#K2$7slwWi;G_~bRJyuJt9lDOdvN1 zA>Resub99t0Vu~A;&US5HHOfRZAdR@NUzX8KnLQOIiwxH>iF?fD3{x&dR#-$;c|9P z&VOD~NMvE6-)MRbpe&ZDU^__&ND7g{n|fe|epF5{-h_z_ffn`gqn0BE+rg)Vl;cvv zE!J8G+{?bs#RXFfXe3ty8O0k-6O~QWFP)B|uUdkteV=?j{x77Xuz<=ysOT@!!3sb+ z(g8>ZEdc3Q10Wrc0Hh-qfOOQt<3S(FXwg)TgS#ol!r)rW$~W_3l5HPn4GOHHXz|AZK!0J^fG>r6_ zHmw!}n8q%d*aDD_kcqA2iLID_Asq<-q=UC)vZmYnK2=Lx-Agv0p91)G@c$ zlU$4>H1hq0$MOEc<8C{F*dH9^0}Bc>WWB&Fzr9g8^?b^0lRodmaob*g+0_KG?<3#TsLR{-eVje$rdn8t-2Icw6 zpL9=N3vA>7-*0@69c_$-7hg9W?xNX5?bjp6e{|%(UjIkwKzn@76A%F?9lSoyn?e@c zeX1Xib)#MVf0YjSQg*V@*lKkyoy(!nph_Pghy9tRG%ssm)YGGF4#%L8r$FSGCc$OK z$+KxG{*TG2Wvq>P-Qy^&dP=5MU-z3NNBS?bdv@-5!OZ&M*SfV5*tWgwT3npYgk`?&t5npT`Hsssb*t48o2EBMdpgHF4+;)^-i<9w`nMRh zKXB-&Bd2?k`|-xplZNSF37tUrv@2T-@Zre=o~9TMAL zuO7MdGdN1{@e}Tb!e2_(Ug&eK$gFUB&o^v&HyG;`WUaMhbuw5pTTiFYQ9noM>8CF; z6BI1o*V3DB)85Ujd0Uv}kKa5^zt%FB-(YJNQ|LVoww8HdIP-5nO@GQ}G_fMaj%dB? z9&HrZ=t$ohnhY=5q3_8cetn`aqvQmoeNXTl`mjwTJ1$xLM!&RLyEal&*=?tN)gtxG zWxII(G^5gwL%!HJ-T3-^bi3yL z+glsT_syi$dBvTcZ}+FfPc1n79;=T46jl1hc zsp&T6u0Zd!Sl8_opV*1(+h3;Sw&6L42QTkv`?eWf?;*B(7~_$ho&B~x#DB75`8rn` zPY=R>U*;Y3G|6?ocMD1E8wz>9M@U&>929)6VXV9~cOf%zh;vF5x#$qRKSN}@E zUwX|zlVNjvA^rxzgZu07Aoik)$;q)Zc00+}SaEdj4k?z7o>X#r|ew5GEOt;TSBRr~4M_xHkciZgfGm;InGrz&Rn+hka2qU`pUTh^D; z)a&49eng51pZ6P-@yf^}Rq*OG8%9p|M~v}G4I-Tei5@&RA?R~I;f1>nRi zye0suaYgW=+VZliEW7P<1YpTKxCuWuh01xty^|h8a=phI>QAZG+3>d=c8l-kw`g~D zPrQyeSJe8yhi+XTgKO3O2Y z@5w6i=hZh?jOwzT{mAv4)cL*cl%CqFl{uSkWS^ho<}IGnx3?K$oAf^gZymeoF&WA? zoOrqC+>6QUHFwWP4O3=5ne(&$J3KpTd{3QxY^rB{_0C~GA5SlEAX-m8nQ=JQ z`5r#VV=nnFWa+M1CvM^$$F5Cd)^a$%_U_-NL-u~a{poSrV=vWdy*^owonQTWiHrYe{aVBn(pPpj&UOzFxxMaKMe{y%z9Uu=Mi<%HhRBO zcFN*5dXi1K{wTyA8o6$IkIPcE`?B~N=i7OsPK5v7zkE}|yQRNzGh6=+L-%!M<*NoQ zN7t-j{P+*u)Q^n1Z8taZ_J_VB^5`c%npJnRccg;)aZlgL@crSbkqYPPwOdWYUdp%( z)9Xy1<<_3-X-6@!9QGKS`~*Ym2PoZ_BtK1??xxHrXM9yT&CG&TnB^7 z`(@eg#pN%$mizwhIK1NS`u#(?QLaZ~wCa2h?Mu_T&xhmpEj~+s$aP;D6(;%u*SueD zACYi;pH%>)cE^4WI06D@gJDe*&%}Nx zCw|;8vzxWY`Ytp634RsKb2qtyj6z*tmz-`2)LfZWR-1!MbAOq0JCm8YPa!86bbpDnarD*2g;H$f0h021O0}*73(@>E zb1mI&vTpD7d`5*vjPs}_?ey2L3@%br1P=W6Brl;UdvE93`-f@5d5`>Ny+~D`PWp!@ z+!ItaQBM?X-jfub^u~+qQK%Qw@n8+>W!hB(S&OL(9RJfq+CZ+aHfZ~ns`e`Qp7*i3 z$`0#56uBR2cSAL{Iw|zo^!SU}pNcL#9CmD?%alho7j6N85)?WId>3|GK(;cXL)lV^ zJ3b3bKGe>*Yt~O5if=ygcS{?JtEfkD)19e44+BK_tsXJSWJ?JAo7)oV>nfaoV%EV! zw`Zq%tHX=Krb^8*vndy%JxltuFr7;u%PebGzkB+%$O=T4U2Qy+@XXX67&%F{6<3)b z#^`#9t~c8f``$On@H%%-m1NOsygt|Ipo}u<`m;0>F=$uwiFUKyt5#eBPY~#%()GJ^qQy8R#j-l-t2>zkJk1i zTsOC-GS&4FY6Tzl)6Ah5`!hR*`kc#kbz82+fTF&qkx;dbW5j4jnv8hl#-Lem0S7#B z5DU&jg25y*voDujoZA-lhcP{0b;LEb^_laj4wdOQj7O9UT0V)3AM4&~g~2WnH=ucx zluDM}t}+H@ceXqs+E7?9&G`~g)|!B;%f!;iVv=SBRNoR4mTM)H zi1H=%3)~U}xnzWuqLTO(%oKr3_Ll4AArb`d_>jQw?WQBx^KCcVfpd8PzptLinnKZ1-ps*BJ z7zasM>BI{4SISI2a!Cw>f|8El8DZv}X`MXakrcsu3vZ=qe(8-+qJo2ObRUCc@>t(#*{}JMCK1b7 zR5#*KHJDQwPaS?Q>@7waD;gQSZn%mbu-U7M zLhvP)cJn=N`TobAR1=`ov6)V_YzNqry5#&{{EL6*BmckI$$ynP{y+Xj5unlW_xQiU zlLJk6Hx%V%St)-ut0IE>uzCvtnoW$ttU7*}BteNtFmR#tLYX<1ldi-@RLYD-5?ZcO zNN5r0upJmkqTh4C#%Ra=@E}Dr3NBvisOsIGSJF(0U^m_q#9vy49V;o(SUk*E=636j52XuK*#t$&JP;MVVX9XgO)k-97Ha{zVfc4y zE)lyDG>O0eZHSo!EOyz%0ua4TL}Sfpg3U~!LzTh~?UP_&fB=s>kCXC&nx8DPHN-&^~j2axz;Tbx!S;Pq;gxB5~`TA ztI&x6K#h?(*E=DF8;Jch>R3I#nx&!2()Xd3ehRfKgPkATf=ts&X5RU&&osAG~`S{0)u0cd;Nfh*}g;x{HHyZy7m8#s5adWJ;# z_&^P3SclwIUWmFuSP*4s@W(23QzeSRQW5C(Xpl{qz6_=rp{;;0rSkgQB)O{J{@B4w zMq>uOn!+A&ooR}E%gc%P;NzM|7g!=8Lwa_2pn?$N8GBo>D5x^9q$mS_tT)?H-8man z{K1Ww2r^+K<9haZrOkgU9x_A!LPnB+BO2|cn1Xxs4}hO7&)FMWLNo2kg5Sf69YIHCJ>1VALsfkSdTv)nh@}PM2SuEhH~|vE0h>< zsT>_xPCv~8PzQ-WA1HymK@TW&#ly_se!Ku$9!0CLMFaxtF^Wn-F(cGYX`(2AI0Xk3 zIg;NOPz#|Dh3)MN@Mn|a9Izj?ejyvS2_vch6u?D#2@W)x7aGi*i&a+U62J2BZ89DFow3Be%XM&&n8J`PqKAL zXwWREgQ{5ZbyT{%@);F=CVa`#;KG8iGybYnPd6Aa#VW1zy>JOmH%`r}+gBG)(2RK% zM2LvCs@$M$=;GZfJ$@+mengJj<_z`brKX>?j&N zzku&B$M0hx7VsNq_wypryhp-q)LD!79sPWS{H9Cb>6X?MKaCf5EDkbi2cmk8KE zCPjhAu1VqYVDc*jZugdJ)Ri$sJses83vTdQ<{>VZwq{}nMi-5#G=6^QM&%uVDX?Ky3|EdQrd4OpK?n&emnv$I)J& zsYpIq@NY&txto!pzh8fqo{kN@;DQ?u>4n@`855Q!_*-l>gt|}TA#r;qJ)JXdHH^4V zXYcTMCOn-}ZZ(v+Pk$zOMLBMyf!HNGp8q^wab%L)PvV=pqbr5`Q#G+N2~O9V!Hn~Q zf_?EQO(yFSs3UBa0^MsIFW~Epxsc0flHQalP}7RZg1kT&=#kN7o>1Qwq-`NdW(zwwbmv7AMSIAC%j_kSU(D z)e9CUpSN#8D+Znj!K*)+jH;pG7v-}KwD+u8BRpR{Jf(W*=2k5d1YlDt8cGiZnMf4v z-msnC?+gcA(vnZLca7`sO;op-~a>%csDMDR;x&OAQ_&L{)d#gNMWaR zVWa3a`X22H#3p)wb79-}Y^e&?$P=>`T)9JAv3kYY)4f=LXf`9Uzk;RM2cb+!x#=D9?hMvUe9g%yE@dPxI)vs3Uf zgz%RlsJ=MVF~RC`;MN#RnluwQJxUM*YN3rBrsk;eqA2E9kWS&`PA5X?Z_rLBgEyHB3s} zu^+;sxCy7{aV&j=1J|^(FWUKccKHojGwwmTcb-2vd+3k36=vL%a__zcOhF4A%?3-TWjg0b^5tK!(>r%hAbZKdQ#LPJcHv_Jn@m7;UNbg665mkQTU zi8J;I&)Yz8;?O!x)js6*#7gVRoX{a`J)qu8{#E#5*+*Le)>7%>FwP#J`AOVr9KDm= z6ri~TW%Ht9H}26#iv!l;Mc!&$z$0*GKV^K+@p5dhFxz1icNGj<59OLzi#cu_oTUX} zAMrgwfGsie8dTmb@}IBi5w`wU)Rw5IEjd=9R+4}ktaxX4FKytleLrTU>llhh0?Iwf zUhs}eT0LvNRtM;jMoOXz;WESxN?{qrYN>okKY5WIxmY_s)QOJ+*(RO8Q-?i*`xW_> zzg;YjKhciPH~&X_Fu|fORiusBdHl9FtWYo{S3kK^0>&xAdl?dD6K6V5>J1XHe#y{3 zRCRwWLjMfFkBWu;DGDtWI_D(< z*}_S(Tr&08f5n-1KIKgz3YNeu}=*R$(a?Okm(P+ z-rfuCoK{r|K5=WYy`6&UJS~$K|JH3=&*;Dt{(c z$Ws3n7&$Wrt792I&eXgbb_6BJ4~8gJ!X!?9Y^1nhutF&G*y&Y=T!@^v{M%Xk5ReQH zL(K%O!Q4Gb!>=w-grLbz`_5O-{Zgw7uUj+{-iCn5**JgM*-+ezNLkqctw&ek9*+CKhRCpqox&Q#^VTwDlXwBJ@TB6Ei}=UY>YH|c!qb;eM0TP1jp#*0!1Im>dkbz<2Y>=$&AMkhf6#J-Pb zqEEV-9TQBv5mlY)*!Mrx6;-}3e=Ye&?yI#=chaerdz+v2X)0f+H0kFkm4j@Z;SYqo zGfmZ#7GbP=ZwKZy7e5-8IN@v>dqys=(w$s|s4NW6KR#IFUgvs_rt4H1E3SK&m#2y7+Rv`8JNv+xP!oh=bRTw zC)28|gg#xKqFQO&PMo~OQ0T2>udLc^;(oH#Q8a6{dIKfqok+BbbpX>0w}LY?#>-gL zgZr~#D-_~<7URLV;|s5${MLemE269Y+T01O!^xqg5so3#8#Asaw^<*ygCS$fr;8%y zdovM5U2ZM=+h|xoGpQ-@%JhT27jAgdo?p_NWNb+1gDFkZ7E(Ln)mQPK@r(JYHmJG-d4ivf1b5 za>Jw(l9LueLnTDI@OWJm$BUEYnzhXR*A9c*h!D3t6XO@k}`q% z=n%abqOPcNa#;(+%~%j+$5vNK;@p;;zFw7IBJK+6mb(gX)P=Li;kI4$KQ(9{UsE2S zj@3AE&vU%2e&3SR9LZrAQe8P7dAY4BGy9A^86Bndt(yzPd1xfnZJGNh{vA5GYEgtj zZg~iC-ZZxj&V%P>UGnQE8_nn87CJT0Fx#otf(Z3yx#i}z3KSjm+0*Yq9fK_InD`t{%87{NMLPA2Y!J$a z&SQQ9axJ8NeNtPCPZ_mcKEKOK55#wl^(w{CeW^u03Db+1YTf<0dMz$)B zx3*!aT7lt*PdR+`>sL>gmlf`=?peYmCE$&cQQN0m7DI_4^H86!%9|-YK<{RZ-53xy zuGODRykFcHt-+?fj8Hd|S}2GKnw+MC`q0{3L9|?9HP)e89Z@xUt67{DhoRKXuUVf7 z_4EeY{z&)}4`q2FR<=pm990RN>YQw)TOG;^y#m&}o7eO9RJ;0HGxNbEWp8jEBafeM zpo_dtFH%P-cm*JA-=gnxv_vFiqdYB}mqjcL_7b_)uT&LKB1FBc(_BK*T?cB+)@wY= zB9VHAgL%vHK-hmO({{V1pU!$3v{{&gACzQr9Ze?*EqoMS@0wQo%7Q~kc?N2dmpW%m zZlY*vyH?Uy{j9gWxd~Ld?)+VzVyY~k*HVa51^iaVqxq0y%c&rvUFXhTGg(%+^};}9 ztCnFg(s`icGvLGR<)09gEuH%K30{6tpqf%dTDyRaN#BCHB!6hK>nQ2{Qvv@l#@nsc zm9&Y^$7m~vRvC4%s@~;2Tk>?eP_)Fd40Ow@>j7Ct8Pc4b}Ua$^0ywgdFb_4&9uut!Nj{gZFIbFDkw;(abK(J@CQ zuCwFN2<4?m*Efi_;j>g?de+x#L(+2En8w}F3n~lITXJ*W6dSJU?8pnq`o3l#B)JmT zr~nihL#0zuLAqz#sO!|HHhSs4?CS0Ch|MGFbI&${3Fo8sEqL;*Kk{U5p@9~=vThDR zA!X|(g1UH>xoSEoUCL~{tGv-m>DzONanb{~VNq$h*mlE<-B?L~#l z)Wzf78(BA!%8XeGOvJ`}F4SFbHz9>h7?t-YclDi&QdW&$UGlZl#?)0cv|BHBvA?Fm zu$wCA%h~a~AI?H7#bf&x4|@1G^@>nDj*p^R15Pmx9~3KGHBV}ii(WjG)lOE4K{x?o!V`_E7J&c2qE(g9V6w=c#skOMBK_ODE0XQiVx1cLx0)l$~zpM`MV9>KRf z&h^?O?VMERizC;GbCF8BU3cmav75QD>r39VT_3FdzN^WRZ(m!V*|&V>Y415c`yF%N zI+lk&e7g+ZGTM`LU!N}UKc(NsVIQ`15WVVfYxcG3uJC<(zD6X|-5TMQd|sf=#FNQf zcw0(CuCGtZ8uNYe`R1U$F3u4;*$X}DzOTuA?Ly*r|;Llkkz@Ge0R6SFMvRC%C8+O_Df3G;*BYww5#zn)%M*ohE zg@S~Kj*EqYi;Nr4G?7Ku7BI~K3RrRSQ=L)tqLXMdzuf%-wF5`NXPx`WzrXfj$S~4Z z0+twb&%7Mlai6fGR+7+q6xcRWE3FoyP7XRwRA zw9~m8`D$8o?PdprNt%d}>?jzz;O-Cy=mUEWi=+sjjuhP-ES34bN-)n1c$)tftKCSq zOqC4Xnb^X0!IU$`rR^eNm&EaG#Jg=sjcCM6>7N4DH|$zq=!JI=dBYZI74C@fgK3`F zx!`lxw4QhPpZ%u&fA2T_@%-=orUYTonW4edvpiA>@i_$oKZ0@t%$?gmTtQlEmOvhk zh{%#aO%qKsi>Hr%Yr}xKCFYG%W*4T@l7M#6E=Dt|(xV0qZTL{VUl(*GMwk%RAgETL z=PIi}rz7e7pjepF1Zu)2Lj>_tYiKN}mN;CaxkzbMxGZcCzXHj4k*I@y=axaS%u|pA z94Zki$(#A3^_L1tVKp-OFD-dtJan!6oo~|e$8pyM0+$ekVg9q01rAR*O@szF!RW_U zHyv=zG{ZK$fjBwE#WU17PTav$54MICI5BBgvKfB);uUFy<}T6L3XYg3Ots7l41S!q z;(%xlB7cZTV|{~aX0#qDZ!+8w0rV`?bSHvP5g+ z8>o^08gDWvW|FsM0*p89#mJkK)5nV!Wd9v+nl!*gI!j^A3s3O*Np6xiLMT4A*c%&! z-lACxCkPD@7xTQ_0}C6*Sm2RIoHseM6F%lIlAH%@6tTN73v{NI-GafPZ3Y}AK9J2xVf%9A>U?8T@S}(P! zmnXY@)I+{qott8w=4ZAuHUyQokUGY1focSL&|=KGK-og9IBJEtqi{x|y=%h*y&$M4 zR7%xJqJx8dgJr9DChH{evil(MG$xJ0BsYaZH$GAYBlq|{cF{s0_4;9UB>UsLaX(hk z2}I_hU80E0NieW@VadXHOlpNA7;$cM#^!cO8pJSGE?E(?T}D_3w2nfeK9hK(4py94 zOuz5yv^1b1z<`nj+{~XD1<48?if*wJ6&OHCf>neO>TgLR!T&!>5=I0Wej`#gl2rU< zP*I6V9I++#*>dpNZ2sYrnd3z2gqqja)qTOuWCqq>h7z#pdaUZXEFg-RyS>^uf9Nep2x04}wa43j1 z;))eKNRl(Bg~+Jqc{!Zn&(D{(lQhyy?BjwZU7`kWh6By>D-LzfA2J8JA}VD|@qYrz z_0aV{1swwptx)pw>K@;3^rubFE%B>jIbH_|=+=f%Cc7q-f5?43Ro%>!zkqj9Sd0Zx zavjxcf%2-{X)3|>G62b;a`P_UX-e6BXbV{81+Sq;?d(>%ewFOA>sE>L+nSqpnnjQ5FYV& z!6_zzpfJFY1#$#5;5r(%o7EpB+Y4cnn3fHP(~natAt)y5Hw%Zv;6DpaFTIp=t{Rzi zQ-fuH7o2YQ48Xf{pnVwpnT={0aT@d8dE|ghO9Jp^cC^4b=;b$oy8U2`ZX@@&_DqaX z;dVcu-(cw6Pi93|Cw}|YgX=u*^$RY)C5kV*i4@L%(CFMoq;w6A7>siO7Mzk?p-Tk; z3r_UI1i}u|c?SPlaH0kUbEU3q&XxYxf)hGm!HLk|O9$c;lHf7N=z+ufJz}T4v}%W^ zR~I$G5;#sPr4J6JRf77d3fgWR%voHGa*F}73CcJRuW3x5_I?@6a$l!kTkRV=l()Y2 z#yvCU(tvBwU@fl=GTs10t3)X*T&u(iY@mH2A~xHgOuzXGBs)}DoZCkv^bLQT?KhQfB2ygd=JE#XHFDVJ6_U%VVRJ2f1~u!H|uAR*SkL zR14gEEqj$`4l!W&ZIopNnH>sW4{)h3$*;;DF!#~iCnE_9>%$Kfyz52*U6)U|$~&`E zGXv5rLTt4v0^+n63J3LF8$5dp%i$(EzFD6OP?Crtu;CNlcvJ)v&DV559ifrOOaI%y?;)l1a2PbN~Ny_wO}0UJtlb1os9?UM0u5J1?4JG0goc=ssn~S4X82*r9Doo z0?UDjBz*7ZzFac_3&HbJc15-+g-s3 zpc4@vTxdCah98zKWQYEnNW_*O?$|jP3zeq}mYLFHnBX20?pr?2EeYJWJoF()hCWBg zo4L}xA4xlDA|4H(*SH~T!T!$&i0>gNe>)N>yD24oSBxRskRjXRAvtNgPaOXgB%ngJ z&7yR%hOQ)#n@g15Un!q&03C_T!M_~|^1mGk#Q*F_z-audBLM>FNWcgap)Ru2Th5op z1$BkdO!)`v%$1Y%87ME7DLW%6v&(}Y)AK0vi^s)@w@k!cGmuEKh$k_T3^S4pGn0@p zkYt;POB;zNF_Mt&l0~00)6^&0CuSnkaqjj4zP{+UOq^s_g;9K0gH|A3TOvk~bE!rNkyx(O*ql<1S&UY`?vEl*h{gjs1Z zO=D4A*qKc}YoIq>syAJ%{fN0%M^BG-#_xd$^o$f@Nf2Ub9JW#wQ>})n7M-ycow+ud zp;l*LQpUAMiQrcT_avsn$B|%b5`+g9)cx5SKfS5H5AknbKet4V&H#?igdd#|KRPw` zuY1x4EK>(8(*|q-y$HY$bLh4SysaL*tr5I!KPwua^8rjfcz2+sA)@=wWDyGS{Bixj ztGy{XE!J-IiXq}vx2uwl_d^2b6kmj8VJ!-1`*=A?1n;~;N{Z)J=xs4W#occt@R}fl zLRYA&@U?c-@++dK&57k&BBzWzDqdledo+|Z^Bi>9Zp_c_y4osL^B|Dd@=%V9L@{bul?_mM8{2|mi50&62K*J zMU_71^c9|v{=Bou$BLr4(-u?}ly)`gHaV8_*7y=F^x}=(m3fsep}J+|Z%yUj!cGP9 zJ;IDn^#wO-BwSgEFOfty3b6O^3+)4pDHUq z`Inpzb&OdVZh?&-Xvm@zsC`KwGv|ryvI*_-iZ#-p>C5#y3*q*Sk%x1Bw^uL7QG530 zC@<1oAV1g{OKx&ynZsS3cgt)Yh=K^oXBe_IkfJX80t9_OZ@JSYfV ziW|Dvu)>6?6f6t-b2H-8l0t@783Z2%%`B*Ul5yIllt95Gt1uE&4V6vp5#Wz&Kx_ic zTQ3KJT|dGUQDNIkBUa>2`gG~^!m?{C%kUa1({u*v4?TVv=eZ4PiMi7x>%nlN{>~T# z!z_NfD=zxiev^tv)|^Gv6TZdzF=JxCgwa}lkCBN)^Dpt*O>Jh^V5HLt1n?=`vMJo6 zsg3N34gU$;Xj2=0zwl|rES}lwr$&XW~FW0wryKGZQJ(E z|3=?9eNOj7NA$yt*n7;mo_4I*Yt1poH=_LVCjJ*1PJNm!A>qKLcFjW-|IC*s9j z!*!x*jZ&AD1fBmqdRA-DG2P>IwE_<4?8ZB16|R@LIg5 z<$W4)*`J`qZ{mLv1e5sCGfIdQ&0LU_cDc9<+=d^KijXtUM&g{IB&T{Cg&a&E{|QP6 zeuJ~T*~|D3cjR7zh0No_C+Q~x(R^t^HavbESL#zXau8n(j4?_RFSWVVMJ1``j>2&U zqCkwwZD|L`?-~N+$S|XCuqZ=_#D9(Fsk7X%Gcbl@xC6=lcY;$isULCB{~7rf*Jpmh`TEow&J6fIG*_K4a;nPid^z~*)${#I3q1Q0 zUsyDE`||cQ^)Ow!&9gh@e0su5+@NdB*nZ{%Ez=!ayr68%;qdO@F0%Pr|Mgi>>KafH zy>yDO?(ny#9KGXl%e~ayP5C6WpA@e%{BrKLGp^=w6&Y2RWzBiF?Ql%{!((G8aFwPg zU~}=ky$0oz{>&_=JaE+;jd?qLdaAXS%zQ6O=giG1hHeSBV@tegqDpJISUILzdBNAh zfOW7?#ak`?!?~}Nx4iRP!*g}13D%#nmFs<7bb0!XnGg_tz~wq?5^V-a$3e$4_nvkde$!T{lIX024=t|ImhSm$wf)%&V?B-cU+c&%EQo} zkGjXy@P-jPX7u&2!mt82Y^CXPiU86=i@re{TE_>BjQ8u4%cNujzM;dUnCz=X9Q~%@ zIc#%1|1ZgM*=Du_JDKPWNus>djPSUzaPySd5;Jw<7 z^>-X-_h`!=+786oy4?4_ua06CQFU0t<@XL!pmVKo8a%)L-rJXW_cE}fyF=t&o(y9T z)*ac~_S|0;UB2zU5h|iQ%lX~Zsq|<(5R#tY+@)IoMb(M$@$PzV;iP*mAY|FQKU<(t!SyLn2mto1#_wDRqw@c0a9$x%r7CjRoJXY_z$Z) zuI9b;q4^m5KkL@lc*ZcY@Z5YxX^~kCuM)HK6`T%Cl^$e$y zxAyAKT8~pujxFZ(zD@A7^Ge2BD-p%ILt&$Y&9waW8SJ{(#(0FOqxG$hjPKtK%z!5r zeZg@61xM1xt0bmpkLTn>Qx}?gR{hFIl^M!br3d$BkIap`HQvit(#!Uxu8ZdJbx6&V z@~$(ghUcZn&d1M-o^H;WF77nnU|egcA?rF`=ciEACsh^irBlH*wZZo?_d*`Ht!{@e zsQ$n%+1XAF>WeS#*OJ|dH>L=d>Zo>LFS0Du_irBf(_w7Ms^MjR$IXe`rqxhb^jnrZ zrv-a2>yG+wTrZqY;T3nMEU&YQOVb+`R;O9n*6mK~cM~t3C;iWX*6lXedvh;cPrGAE zFB+b-dOY2U?NRoynutiOnW8k(&GxSSvMO^QvSVv8ADgGGEb}AD46KjVGqtsU*)gb_ z7Opxt&)TTHXWtJtU;Ofk`q<#`Gr4#Plb}CV$SVP)>t@X+mw>nU?7vYs@P2cMS?+r! z0I*?vW1A5|T^hR)z11k(IKkJrI4FTd5D0g$xD1HCUNoM#IG8^;Qsmzj&&ICzYUDmH zr=M!4j_b=G(uL{s>ho)NN3}J+fzg6>ho|7&+>G(DsvgC7^y9gfn`k)_bLpf3kjy9;-A$o-6*kW!f^w^e)1!&FOf zF1Yua@@a>xTg@GGoE>X>k@y2G;K}#imPfcvxEZg}mln$;Rh&&7t99?7%M_nR2EcLv zhmW9n{=VspxN~02){`Zy>C2Qa9H8#=`2n`-G_qjdj6BpcBudXBPjEe6XI%^PXR`S#NGH3q*h*uc`+67QBn8$WE1d7a%ERqZVPM zPdI?|JYI|s2h4-+*Z^nKbD!w$Rdo>&3!w0-ZG9}|jX%Y{VA0Jj-NIo}%Oh3;KAPCA zaJ3x#x48~((vC|`_{u5M(^;NvSts)R70)Gbu7j)O!;~16vTJ3x+d^~&pw|#w^Fwij zKebmyq5aS^Tx!W`0!5ADbGDUWYXS^pfM8;Ql3Gb6-?@XKCNk{!b}m!**vF^J@oprn zgh#si3x1c{<8Y_9-ie5*`e|NpFn`83apN`SGq9M+R0QMe_kMD|@G)}C&+bUR8;zOS z+$(X+_JSzy#EoV9Pg|in_xXqF)FEA3m3MMi5}Pm`TgPOd%ZUDZ$(Jm_bD(MF zGz&p)P}w_hOjuw9hp%`E-vmc(4wJ8Z>zvWV>hnp_Z;#76-O)LhYwAd9{Ylw{;R3;+H%IwNLea?;0A6qu47sWb$Ea z;eAD&7-J#`wDma$aR41|m?=tSk-a?C$Bi6!h2Cpq1fzcb0XdY)I6}q8tsE2zXau2_ z5&@}1SpLpOj9L>Iyl^hL+L=IQzfj!IswRXApc}bz_dzJ+qHNJhoqj>k0&+E*-J>D_ zZj|e0e^8Wa`8+>2VjUg-CM4Q&9OR)1$y(}uVQc7q!CPzyXo67XRPvw62nAY7k;ss} zLy}gVz5GH2ZWKDYJ`#jy4cBh{uw$OqfBaTIQaoqo z7eYp&8nKQ{2sH8_DK`>b(ZC^i3Nc}=SZ5?$3f8EjMPK3S9++?+iy$-jp$sj`d}fz=%{2+^tT(+Cyw_w(0`$@UVh>n zelLaaY(FS0y`Oq;!C%jdg8w&27w`bQ$P%ZEVdQjsKJ9`1x$$ zY-DQeq{~FF>u79jr2GFG=pgdzpoC1S-8j-{~5?}`mX z&_)P@jeHeDF(AlbVkS1kxji^iwG@qYizXZGjprhj3b`h;Dvb^MGRr!f*5x9bp0u`< zjg7&zXRn`z@UBe3qv=Ve8Pja%Y%agX1B*qH_;eN$8@^5ADUwe>J9gE4DFEvc%~-8; z-fX@>V}KmkU8co?u?fvss5E}jf@PfEMj@?~v34!`Pm(aI0j{}Nq%9@pU2zWNyk65# zAfO$gXfdCymXTz>So0AqYCfN(p0p4fl{O%LM=uC|3n_v^IZfkah$illUVQ;}&1a(& zMgmnQl^jJyQE8xR%_?XyVS&0?pXHl&+QmX0zPLc}6w5D3j znlhg^1~vZc=7|t~hZQaP7`ckdG{QBs_9Li0aJ0hyCsvdGWS1a10_KWXN=?f-LbT@m zbzAAvT5uzy*gjUL+NylW&D60{=6!jd2w1bS!aiODzdnni_?4x6Hp(PMZo+`e;J)WO zeNPTA-CbGrasZK|l2`#VQI&!zW0a9T_&AS{yejj|lyjkDUbu*ohf1103&tQbJ+-oY z$fQYX>_Wd?JG}tn32V{hoTigRKYl_`Tq3o^Z8TTfcx=JAagqh93o-145M%)dlM)gX z0z-x|K)lGE2Q$}b zQ=$3&=dcSi5?$k;M(XYH#DLgwt0fDMJy1rBn9mwd+zZ6Y-d6zuN;*kMH8+95+GX9Onf%>z8p(9|xbXm;5Q; z_c|iNImRAv$~+7?XUC#)&Lj~%VOK0u@OhX+Bq}`Scdn)7PbodG7~ll_5Pw9Si}#)4 z^Y#I;;w%#+#!cplQ2d+ci@kAn6rl+{3QpzVm;ggSB{%!LxCJnsGb>pW177;U?4%^D zds!Sl%Y>x$Zc+1{Ld;&#^llOElw{U~BoC=E7TMzp_X`7=Gg=&{EZj&~A?}PMj~_I4 zl1RV|aX^^yEr)--I(yu-O#TQ29B>!pan+cU6;z7c;tP3h8ax@mi&MT=dPjcG^8g}H z(fTLKfs-I}b7j>)VWD(}wI(de4?=wq?Qi}ee*?(@RqQC*2Lqi+5z-J!yn)NNX#bvG zwcmW3W>YR5(uUktuUwF10vrZjklYlD%@Z>7S&0UnE6^lD5N@{epV0z_X$JgV}qY|Wc*N7q7X|esMLZu{+{@K#m;JE=O>02cVk%z zPY10P`Z!mOQxYPK)~W`VvSMa>s`L_t?K5$w^MrG3ynQ@i?9iI|T#wqBQialKg?epV z;ZCPH;JK=?HL-N7)13WW58Ihy4I{JK*S1>B-J0gu&^9>LJi4oLWiEW>biWEUeeN; zp@-o45GHFjK%w5PY@Hbkf2X-o<>2mBzilbI$;HMkZ`{*w_eHI47m4$V4!C<(N^Y<+ z=jA305p|!y-KX)?s=a{RswZ=w$ljyT9M*hdwq0fFG@iLj=1HlKVA{ZQzu`7r*&M7z zKe)*!_tn~yw&O@$X4EXBbsN&oT3yK=RV$l8S*uVw$-$`NhSzacIMIa*`qMZp6%nm? z!h#;;;=f!cP=w|dPzXts+=;=z?Nfz=!k&o zcQ5HRRrLs&KTX!%_-lab9K?U3iny;sYE}F(tn6%YVn#A)yH||Y9S;7*pEySz4zK=1ywd7wIrAO0APWP_ zmMBd-Jm#ioQFk2O??Mm$CW>4$H%b|SW;-na~&mW z!nTmWrHTqkkHc-k^GM-VK+ea%6RQeMPY|^b1Nd_*Xyzv$#HAxQ5yoT+YbJ<_K*p5X+lEY^ovl$U$<(7i#>#En++oXH|+PL=W1r z=c<}dT)`JXOBq}(CBh{ZRmt_NWY3x=S2`e-8EL+6pq?5$6VH#!qo0=VFDnoV#UW9! zOC&!dCw_u0#->py&ZrcDJv*@2QA4?;w~JiqtpoICmF?gm^g%LFRp(NrIoq3%lF%jP zl~x33W~O2Zw*4Nj%Gja31!%V~?Be=&5JOzWlRKmk_i1}TUCm3{rND!_ilc25wd~MR z_;(ckXrgvM;Hp&N$m1_k{sbOac6C0*;32EpdPYDBQ)uR&!kiWSPtX7u{1~MpKcypn zC6gfhdp-Um0>ZQqC`Mo6QEsVN_g4G{g;@7M{050w4((WVn$gNKDd5wWDsXdq_+|a3 z(TN7Qp6+ugFxYYY0NAk(DV(cKuf&8J)9}nxG#P?ubrq3N^^kt2XmUc~(x29YIip2F z9&~z@YKy<>MxKp!e~H4x#dxO(U*if*ucnj|oy6UPVcfcZ87pr7p)$v6pD(v3iK6;r zln*eGfW19d3cMkkNTLWO`A5v%(0@u!qfu|RWhSEt>V8gw{&T~KqUGaJbd}Ggs~Zh% z7PdZ;#Gu=jrOM1mMr8w(ENFC>a^;1zgca-At}2x&rvgRB_VAeqpeKr`5(7@Hn7boY znA2-P7u%?4GPOqM_LUZIL}h7LnSJvk^L#i|E>iHIebPit>V=NMqYmkrrx7Rihw8Zf zhVO*Ro5exKx2>Jo(=XI`ggZHNs%dj8N}$quMGE?Vo@awfQ%Qw}$nr~5X@!Ph2q;aU zC#a=g9Eiu+yH)r$rtsq(zO+0i4tE- zBa z!5X6H?-D#27VNa#K{3kZQKk*Nb91R6 z9nh0s;=T{3Y)D9noBU@AA*Bv*`?BL{O=co>C$4PMS#o6o_lTy`qBxl`9PD@{nX+=z zWRS-i8;f8jRYnAyhr>L7k~ms(T#kH(`#viJ-Z{oU;m1Ea(RE*A-_`z%Xu!7b?96P7 zj$}v$k9&GL#Zl*#GMvN%Ki0EvRLe#3QHZo5*r=MfehP=wK<>I|L$qM!+jUR`uG0`~ zQ2vBHXad)92sW;MavZV{E$;%YJP7@l1c)WYiobGu!cr6@i3* zZ3~0>Wy?y)4yIeN-^5J`#J&;zA28hy2#-134{RVURJV2=x4~;qf-;;9=Okrj%+Vgg zTWDW{LBtCW!c#ZNo35=%RR8|(3R?d%{E%ZC1LDWK=x$Q=+B{p~|j zQXB_9EYJWMrotP;#_vWcWCgt?x)|fhz%(E0qNBybaV0N}PCSV}B1HQss~f-Of*>fT zBDvL&mB?rO>vfO1=VcmnW*85zyNl=1bnWGdwC8}E2L?Ban*>5q`YhISyE+|@f# zx#R?G5$Z``KE}&ZBGj|FkH-5nYP;6s^NdDk{@CODoSbbrmplC3Z4(bukZK|IA@dOAH(#3DX?*-1V6atmk0}^p&cz^)Vri ze{|JVBlIhlR#o5b=EoP)@pi82vjvuc?&=LyKgu66p}RSZf8?JzPWZIj_c}jet7Ns6 z(vfdG=VpJ&{Sf+iREfSF?KbroS%e05$N_eTjHTGx){_#6sqtVx3GnEqqi+}c5~BN9r(8?u9tJYcBM-lNvG3gz7>%d@Gqd-fBL{Y{|c0^jvsGv3gxvh8v+t7BveDD^`Uu15{~gsDdY6Ti6Lx&zL|I#cTkl7snb zY^M!eyL^c<)G)R(HaiVX{vaRZgOnLF`@NXit>3>(4#%`?y{E%Xwn(=8a1zVnO1|fE z@D&f!uN7)>S2>l+$(e9#P~wGdzBxv&!k5y|v84l(Fns>(Z+mxfuiIf=xU33qnz6c` z`cGgRY#hv^Ugc+rhZ=Syc`ur$W(H;^NF_^$GLM?7-JuHblF3XZK1k}>wS;Wxi-TM~ z-*&T73k_3IUxC5Hnj#iFhQWLIVI}JHf53?!-DCHkJP#kWgD>sXztcFmbu6%~z~A{g z9Ge}-nQKi#+piY~1!_I6vw1+7F7(XWhFYy{30~Mu`)@);TyBVGd{0gT&2y*J3<1~L zmwMhJPg8OCmpjWqmg+mQL3-}Pkt_i#0<)DWCjXpTVFNX523wTZddE1Dfy6%rZUhTb zqXO7g|30XeeUjQX{|L&f`!pl(zsw7W1ah06OrPzV+dX_@b<01)Q)iBb+cZTqp{vs*!;Dfl+)z5 zN@6&iIa%w+5R+05?`WNyowMiEnEo`hdoMI7NlFB`?g}`OCIQJ%tQ5=8B33j^aa(B z?M(^NhwQg*7X5*HLtP!_RwD$!zkI^Y}OrIS=JywZT;dtJhF#1@AkDhZG+&@7rjKW#J zqE4gx>VJkiol$4L<~CoBwpElEMt{8P64O{5GgnP}@L;2Z*R@Mqjq|Y(?YK|D;MGZO z$jFEH>b@m(ESf>F_S)qgX;m|JO@o#cfq~#uI%D)r8KV$qzo&m}oP~GixGHSQEaRx0 zie4XjPnGXd*-Bfh>u2z-4Y`1Hyytdw8?T^XNxm>eT5sl5#BdKe{VpA#e*b511X|AK zTKgCrz{}(u2gr5_Q}-TTvpNb|mx|rq|IoxlPm4W%Lsoq3X}c^rc}?&v2$<%4>ru6e zz%gVWDy>>r3>R!k`%0J=y*}3(?)@i@C~Nr4ev+Ni-FQrfWBlc2v-Rb4ZEdnJel-Ez zk*QZ7<730ab_nNq`xXFtS0akR{+Uv}Foh~LVXav`)Z8&7RHOYu-Zg=0GJ|KGeCWbA z#>~k&;?sR;T}*Gin*4Yio;n&|n@;AjZNCHaR?=a7e!3s+W0RExzf^={ctzu3_I8r9 z9yxY>oiw*Kl16`B+yVWxVUm5(?%49ZLFCV!7yr3ziS6=`;)UvSl>16J^mF_OH=uimu7Y5;Eauij*UV#D6W&Z3CrOuS!E zuJu-4Y8Bq9pY4wQgppQ{vF_|-16{4(?%{n5FRmdlpEWbgy2ln+uuZhOqf-v7nCeCI z`$LOg_kbpu$3*KJg?AW5CFNPk%`!mNbdNaU7}QrjQPc7??D|=i%7)=V;6D0$gU+J$ zMDDrA5}U@N@g8S$mT4x!!xihnd>|)=p}QS8VI-zP#fzNM$ohIPYw|t4HRueCEJ9Cr zn?sw6n=XB>xq*lJSbpwdMA94F43ECu`1SL!3_jJfX(_36L>Xm@oyoJO<%u)l_8xvl zkyG1L!wp@R(UqE_{hG~uF6{Djo8Q2f1#j+RzGcrp`*?O^78 zG6bE>g%iU*ilMkm?Wz75PfFIZx2T22c=GElp?mS7RaI&C^U;5GC?pjwhH3ohMN40X zt!n#excFUafoB5z`;UFhtCibXZy>L0+H}j6Nlovq&&KiLuzf-ey(HW7iR;U;H1ijd zg|7MI_R~H07oiTa?*dgbwU1Uk@6~t9x2EL9vtiZ(U3tGxTuoih(y`b6&8=y~=ntPm z#`*pFEiCTEqZMAO`%US}a4|S~-J#z`fv=k7u%@@X_xd0(XT4@Q>B%x1Rhdf7{_1A3 zy)l}X;`5F875dcUyDsJm_X;?rZqiq0_HzWjB_*9XX4`V*$x8WzZdlwGd*)Yo&q~L~ zLV0hD^FU4gbC3K1-MX~MPh2k+@qh@L2|V`Q<`gULU9A1{dXzxPNhRWeGQbDsGXttf zCjn!7kqI6O8ag60GCDFUJSsFYDsnh0^`=&4H*T(OcQ;Rej$v26E^arj4pB&A)~P?` z0FFCVLcALv;lQbT`Ra(YZRA^Y59-)U9f!{Utu+kXF1o@|mHU`V;Fe3Gz?B=N#;}ws z_1-%fqjIYw+drtypF>yU7owVZ?1-LCK8LP+*c{3fkBDQ!)T@9*q{v$uctj$cg&lq3P{^tq^Q%;n)gM;aZ`uQ;i@dg$e2+Am8tN6SBo9??(Vzy8a!`1q;s5ZPpu#`(#m{D1Z= z82=xh#s5jB{5J>{^iM41KjVLT7Mku#$ji_0JN}5&SI~g)e1F3C@x>T|<$I}2S*`pyhL!QEs0?DHYzbz+{e$^Fl8?+FYi6Ef~FMkJ?7 z!OS8PqP|qHa8?lmQ=C3b#aw9`d3S=i(y6+1sfMviP%7yOGO|u`)N4r&&~vX;1EPtA zAV!3GiRmE>z(~!>gT-(OLygGDf#~2b8b+yAWb(nPv+63mw{VJp4Zl`L~b zc;r&dlYwXOLCp-n96-&=g=;m~XnHKzZ{}IWpqPPe>oo|>k`GiMbm}c`LGYN&s(;ZC zBNP6W^bw%a7qH~cgXPPs!faIh_mqYyqVt4Zpiy8dST7~^MqR3}-pJsdJ{?=M+*xfv z1|yRc0LQQR&zAQFTFE1C5i@`!gh6AHG$IX@j;xJBb4MiW_hK4M5{KeMtwxeKFeg;H z#DcU4lm|&Ef|J58-y(S2s(K^wVV9*hsydw1Jg8giQ6TNg@nW}JaZqKBKMi2@`2VbW zs4zpQ^*-D-R-%|)RNvyGFkvGho6gjPa@%ZR>JgbBCXFyAe-Ko97#tRH0lD_Mq68;p zzxu|Ljl`9Y0=ZF@m`F?Y8%6d(&>jdYLdbRFAMSsSq0&jM28+!w?$da5Il(~4%Cio1 z?MKb(8-S!?lwHWxM2sz@HNoiMM}W#3{oH{yU-U`5vMgc7jLM_20Q_K>$V2l|Eb?&# z(f0i$oSbzUzYFQ$CwbNpLG_05N`1g6V^EL|Ntbt0RhEZvsgDuJkYq7iIba4)Btic( zAundf2MoAbAVOyVswmSPxLk@f$^#-D9~^S@3+y1k&rbq{&Pb>!fY>WvFPAYR51ZJ+ z+npb0_SQ`##h5-H7Qxc6Z-Yjuof!F+Hr*+UVjL_&KKug81BK}5V?l?J`nJ-M}GiECm>irPw7_zJ_|x& za6X_CzezoIjZiF$N-T>^j7L7!BOd?pzzCV{as71%N9W9n+PNF3lPd0whyM&{%U;QX zg)`wnBw6?vVp6{kWu7Yun`ji)Nd%>(IIj8m^~EL_}zen znfL>t%56xM`%_2|>_1*9KIzs)89{Ko#adr3Ouu#Me|v=>rB|W{v<*#KD@p%bs(3$4 zyZliU#ZUlZ-x=F+?}^kb_qQn;HD?JkjBY~7t_0FIj0;iO(?LKN9QOC$X^qLcSpA(_ zy&RQs17`Al^^NP^z28jirN2Vl(BNgk`6zqiIMHI_KP%*xk|Opw9L5bPsAsI)3D~QD zk%5@VLQQ5PrSZ_vdy42ik@>Ac`K{)Fnoxn7%=}D9P9KJG<-q&whHVCfb>;r*k=Prt z1p-9}2Bm3mJ_AZQt6&1Zg@ko}lOk>64AK!XQ-0kE4-lwXLoHv%sGro($y=0xiu%1A zE3i4W!wH9M!>(Voi@b!@>0q1QM+4eAMfE)GWT*Pu;ym%aW2t)5GiV1M^hHtb$eTY6 zUdWgiCGk~E?#S@RyZ|xgMNNE_k~`AvWXyxPWLy<^fZUvQ=$tZf;5Ip-`QudihL7NzGvVJR0{k@gI75|CSSB?%JCpnFvpu7>peQg zd-edEVemH}{Y3azS6BoFqr6DC79z>=EEc>yz=zr@IHOj0AZ0ee3@|$aOExqWW=Ati zHukSpav*uSVz5u;Nm)Zu)m%`;TvEk6gqJ3eW(AiAtsky%6@7-Jk)Q8FEH!{`e}wZ+ zPgINOqfhmQC=bDknmZ%w+i|rGqh2g(ci7#RZMF5Ua z*u%yM|8pC+nAcsyo;gh~Wk3oy(!Afmy*PSimm4==wn4#-tZD+L>w}j4CI;A|GCe9e~6kqpjz`nd1PqgR?-;b!cw;?$B8Ap zQY2yVE#fHt@1*fBb6oQD3H&)NB$6XWHRcq8{I*Wg^a;5+t#~0vOwx48dNW#z{5BZ$ zDH2p36T`c+9`RT`S!|hX6Kq@7Q<_Tb=p9IT1cCX&z)o5uMkGF@G*QH~ZG1*5;WW_* z#+bO#k7DwBHTfg^{7Ks|MyCA9)BH(F`6C$lBPn?kXKjqpzqUMb0T$%&mj1fgO(&1q zmVk=gpMN>1o(bmt?I}kbD;`OML1KHTy61Wa?$jnKDa&WV%)m1xj|*uuSimiu;cf$1 zYE+9T5;*&BMgM(s*60{^?t9zf_}qvErc%*QVfv7hB{{a{r27@AMDc=1us>sF$e6IR z$4(G)WD_M6u|xk>cp8YrsKhnu#W!xq^8SOMih%Zd5oB75;c6?NOI0)DU+nu_O4+1a z^>QTTpj+h3R5Z2-Yy>+puur1f@47Xm8A!ds2o{zx2{cKDSEYxKq9Xk=uI&wTQ*Zz6 zTNLCE&Fg_(|7Z6kEs%{SlRP0zyPKwI!d0kaa)f6!{yyNvf!%BQYT{blyY1i09ZG(4 z8AiEr*D9i2y7$xj{A!Sq3t}3Nb|lEqAXR1IDyhd1%p4VOtlN;(9F<9!4uj@HI7hy! z7rDofXuj$sYL`Lw3F^IY%?rka2_G3+Y!%I*^^4l*a0owZ*vIO0-c}r9h+GfE48q>C zfxrZrf<|&^qnCtP+@NLtidZY4dS#|CoaU!-Po%jGTW}G6R{qBE{Q`A&7{*~Gx>JuWrvLm;I z3B}3;L3Zi!^}X%8AmXEdMI@SuWsDM1W2L!uoUWt{qlXEWu7>NDPK%Wzn+Wl-%11xW@ z59r>a`2PX-Z79%5Y+onww@=ek3cGR%YF$^J*P>}(XPMiqmfWZkar&_?IyO-@FOyGf zT7+FYh}ze+=Qb^bUEj5xT0GUTWx>J!`RY-TATe^>$maiLklG^i^wlFH5jDjh>^!@=dA$Z!duhb&xV^!wIaXr&kkmu>6$CQA zdXXExXm4AtJHLuvzPBU0cDYj9gxx$xLc#?v`m6p21TeY@NlpmE>O%I{&*bEk_7`eD zDaLRW!uTb=G-N3H@7v*teNR?oz>cSGBJbL@oF0>Fr6f{>TB4TuS@(4>@UG<5imvxY zooaK!mRAt_mU`cj+30M~EPUIVYbOt+J!gy_^!x1J<2<+)5CR!n>ROWQClu)hHO9$W z)bgN0QkEycLH`unY+Z=2;`dWW3y6BJ@AnmrVZ6HG;I=slsngpr-S%vZjw$Ooi<@bX zH(ir$VD0Vo_H|^ud&g6io|&3~0J6QN*_eL0QI#BlxO1I-NzuZz1A#5*OS_U9>3*O}6!pi9ONl(#$RumNGumbEOwY zz&mqFHmeh4%6b%ZM8+XUs5!qYX4mD>alP2p0!_EiS&P|~n0-ZW%+;VhSF;exjKIAV zkN)m%bryS$?l@EB?dFNaf`8=VtKUXZYe2;`Z`7gDRJL&UK&$G!v6)sg4J4^eNQN3ChS==6oXn8%j< zHbvzSQj`+>PV2ryJZ|fL91=xjUOV*%9obmF)QjMWeQ?JedFw#5MfyN;&pcs=)<0~N@2`Zz7N@zpfuFZv2bWPtdHecNZY*s@3IH%;?}E$EPYy6N1_@)6SLE2|<)(rc;mu>|t9$oi( zx5*B(jrW?(Q^1AaHhffnhp zuLE-iZT3pDueLmDk1@6O5)&5#P1ErjK4j)Khy$u|e@W%$oyv!FJcDibt|(>+Z#+!P|yA-8QOXCG(N( zNSo#HtuW-*r{|ZMI<)GmU|vNUvfsy=>(;<~Y9YkRKffQ+i2poeTgCg|V1U?BC>@c9 zTkC`R1iC*SL0<_G(X#?ZkMwcE_D^_Z)Z=dAhnyu~ItASq<1p;o_aPeb^5C|;Yx%4$ zj$IH;`*I(&GZOkT;}6cgGTQ`S*E6y#U7-u%QyGdk=ezL)994q+K~OG^^CM-3UI+Vm z(=iXW{C;_l^98ZFqO|ie@NZ%MwHvhkSS9cpN&RW11xgz-$x=0F93hWoyTeFrd(WZu z=FPG5i*^Lp(h(;K6g?(Ir_Wfff}q8|!$I}bwUxVL{T{}K?c-&WM9cO@!xb*Yy=OSH z*Lb&_zh}?Ee7@MS6?@KPUs$X9q`43#rDi=Fclf@Gkh`K3Yv2D07m|8W18$fG3m)cps4_I)_GW4$E!|M?EC?{zUsk zcJ94S(gt0UR+=5&%&~%9lVw03e`4`$%lwn6YmU3QPyA?fbig%3Swa3;Fp!`z$tMQ- zy-{X*PeN8kD%!Ye<40CHypG=EM0-m(G+e2MMQ=yvd?;{f8q$1Kppm5FK%6>w>9Hus_mtLN zJ}m$=OFUja4j-|M?>1k3knbVS`a;v^)onaWEx9~&n=*O%LFpdNyn6=V0rj|9$3irG zGTQ2RF5ot=euLf%%Pn}Fp&t}`KhSQow1>%MvjWd=xmkkG#;9onw8Ho5hWRFY%GLJ^ znD&|RDOF?^m3C&Ii#b~x^;4+tW^dBy+WBi3jpgqqMOIFyCC?t2EaM_zv_h*Z?+uBT zOkWN?ig8QYXgFRp%7%GW|7D@06tsoA((4wwy44dM4$yV}%BUx^5{u{D{M2c3zE0f6 zhPi|&5{@!*I{h~gH`ZBobK4nE3L@}aZnBBw`*9|!uJgv*_rt2Hhbh_OWSV5n(~r&V zuR6XdksehJc~A013E6wvbc77O0vc3rbn4yk)97*MI9)HpqLK{deIT&a6WwGj>h!sB z2`b>`8qb%YB;JkZfth`*m6M~ICpzq=S&^IoqEm5itB0G&Hro*Rrn!!6=Gbe9ra48t zY@N?5QmU@EDVp2+{Hh1uALgx&kILH2FI?f)`&Y%3^^Pwu#8<^Z$Geh9(Iy*nzhy~R zcw5vb)yDya^o(`hMsL_5pVVgw>nL#3)Tu$*`?BI@QZl$gMO#G{VBBS=uvWn#SDWjr z`wMyVhNF>s37;&9L$_KM%z_&Ehl|k4#k8trPFR#Al7N;w&v&`{PoT8{tqK{23ox}X?>Kbv4j<)n6?}z(w?tp5Uy=!*#wj@r`m9y)*x5w`0{JCzoC%3J!^r^q_ z&TVu>g~ai)jeC>Zo%Gq&=VdXiWlmzYOxlK>$9hTPGl}nhpLgy-HGjlaNNG*DtG9CFm(8g|^;xotdymuW#Q~OJF-kJ)JbdX- z&29!Kj;@z|<}$^euOE?)&-?bP+$!oAFG5pYKAGUHEJR@n+3Gsme{xNPn z{EVCV-qm;eQ*?+W&`yJFc({o|?FXH{LIrU==y3_aF!1YkV;B7}$dyY5a zTZH2*UXKZvzwUhL*#Np9o-X`%P}le2UXK;qpTQ44h3L2#x~V^5*BzVRi0-YG-@c-7 z1o;hrN)=C?6UMfc=sBGY1az0F$+@|?87WyQc{zC*DS0W;l=lex_X!R94fgj4_lXGm z3HuEV2ln>s5?|jBI9}kmW5vRH@DUE3I+w3>WgSw!V+Jv&Z|gbvfgYo4pzWS298I}L zs01YZmxpJBZv_T2`>?MY3iWtvTA|RcV2<3(`6YYDx!PbN#}8 z#OElrL?i4(9AWrm>YZIMY3_WFtNNWA$xWF@DoDsy+gpDun8+iyxC7#hb2El%v*=|J znR?J4_54Rw{oktnk6Lr2(1@t5EfV1>;^-BZ2v!aFt2%eqma(rHqr{wpOB=8VtrgAD zuGDy!U7f#pZ#^QU1;tvDQla;#wa##!?B4`!k6M{+%2m9iP#g5K+R|B4uMozTvaFFQ-7ua*%fA@H_4noLsiTEL zfkK$A406mC2#k1I>So8|S+uK`O~ zibn)HiYF9huu5|_WS|XN$U}?&ZX1N>Kt&NsR75}}$R`X1-}@a(-jcfkMGoZSXysBb z_u%=(&Hc?Q+tI*u&2azabIsMzF*Q|=TDTAtcyjfIS$bYL(GY$JbbW9s(=kbQCzGO$ zgl!DR>qlp|4h5PP6R}IeUwg~o6gd-Olb13@sF^Prfpbdnla>5BESX0zPST#G6uGxq zy^4+GMuFOJ35iMpEjjS_Qm;fj$0rZiyZf1b$}TSl9%!*ndqunEF#mZp5;w9UHEXa$Q?y>;<9`Qm&TAs%2aR zVSW+hY`oVD=uzlE1rPzSutlH*GZr4lk5sDwdTe-5#^0ba2m)g?GXsJ*M={_hDo4m8 zXC8)|_AQwYQPi2_7qIQCKMJr<`T87YY$^z0* z`KKa(BxjW6+AisqqDkvKfn=6d#KsrMbt&UCgRW*&F)Eo~r1a|tId!;-C;HoBy$Pz3 z$?xe1Ol@tC7BK39u%5vHZ)C>r#s$qUTGNJ$M&0yRG7KMD4k&B%feV>q&<_&X&XoA{ z!oWtH^vL0^sC1c@;Foa^j6oPoekFHIUDBmYHd{U}(FrG-Qw7kbbZ$r+B7=DyT8d_F z6kQMyUAxv0(usJbXvP4`zEdSSW&{HpK}E_7q)mt*6|l!~>iPa0B<=s=>#d^Vi2DFb ztZ{dD2yVgM-Q67$+=IKjYjAf95F8q};O-LK-C;Z5H?y;|vu7WwPyMUv)Js2fb>IHo zi<^%thq*Vwc!9q=e%?4@1{sPT@x>JGK+_#mRo_pOL3d~-C0`LHAzx1K+r}}sk*4gI zIpE|#wgEYv3*Y}BHI!58nFszRv!n}48a)DY+2fPUtO^u2MK#Fmw`f>k7(#0RuJq8f zVV=91Vz(0o73;^onN6$e(k-(k2B8&`ew1higcnVe8y-o2pJVqZbT&@3_zrb!D%U;@ zcKJ^j#%u5$0wfF@$$)25ForF_g#&X#2=m$ipzqHb%(=^a2%9H9qZEm&QzJLZd{azW ziSQzNFa`drs$WGk*Sn${P@hU1TRIiV|Kj#sw3<+U32R_Vh)xsigvclyh%-#I6wZ~F z$zzs`jOeGR>@A|c%8+ie#-?SKit2I^A8S$}FPZ(G$Yv2EP0OlT>=BPa+t?|6O~9-g zz_9Y|o*da%1W+#-X=U2ZtuLPVD>HyJNjj1p9YehRN%X%}bv6Vj(?8K=^Bh@J6V&9}<2xTIH`pQOn9^f0^MHG6Ycn?8T7HrU&`IMt9#*&<|y)Ce=WT znt^A$|ER-iRd@M0UJ+w&o8Y$#oS6EY9XX3gL9x6r2VA!fT65zbi7WD&AY3){y=^BCQ^4k_5TUhFLtufG70`8(p)=`TN zXix(6$N{p2rEYiELb=DHcW|XFcgg&&@9p|l6Y*H;MqQs92WR1Nn{P%ivMhpKz$V%v~)pNKMWheErVXI}^_o+cket^0SUJFE~C)J_DVAA4ZlI>5eNO4pXt70j)b5{?Kj(Z(?)f{5aaB-L}T z)2zE_Htl5J|7{FYR0NdCZ?of7PeIp=)DpmNA69I~z-8w^`z4m!gBkVK4LtEUMYn3; zNr-v$^qm3@1@68V*;<+luRt(+IGPmPcG1l87@}9SjqiHKdjQ22wM{ce(s^0-b08|G zkG}t)l8Xmr)z!)Kk>po+{mnpG_0a!j)iWyBE%pem_{bj?;2jwQmX|=D!qq1%AW30M z(3e0VT1qYm(q}nnE`@FEn)TmhfjtV!;UTHJNcZImGzduL8UUMu9%|eWZ#Z`o7JKxU zzSY*_Z!Fcpm#~p0u>XW%?f-;fvHyf&r#sYS?1+OPVHj@M&ydA>L2F5iPuUZGL@6$; zdhiSKg%7&QhkCNiWif5Q(%YGfdJ9@&Y<}Y>p((LI6KVI=h3bEWVcd~<+x3{cV)ba8 z4?o@K*n<`RBMig*Ck*@gPZ;*+Kf3(3}3MhbM@XCSA{WGLcBr z;Du^>PZ>26quqtuV9Gvl@+SgoJOXPt0&6NFwKrlR9Ic9(LX+Iu(kNeFhFs+I@K6H{ znY>ifpRL(k=wZwW0@-*OWq=pHGKmD2yIk^2_UhyBU2wX>#oLh+W*5+$98||pg`9DL zofOq6J`&J}^y)IfmzW~$ca1VkBNmYCM|2g-EjFPBRn@~$`NNYmEGs;aDLgEbH?zU` zNkq;Ih2Nav{Q4vFBX`n=uf~_Qxq@~cY5GClFpx2fD`|%-YKJRphYNboJ$IKYEyON6 zYRecPlq+xdmh+^8>j!`m(yxVtkb5NgK4^M04P!UZnJ-}hL0l3@OG$CtBs?IhqCnBX z85#H9g0{jfml6=SWSgkU4mF`fmof6tIfQ3 zaL{2ymoTP~YZ>GD;XEn zsP(RNTejV7fu}1$T=f74bA-(S>^E$^D@%Q~Yc;JvQoU_#eS-VHJUZ9~q>)VFNZqk~ z6pjnrq{Nsg``cZ47dLwMCrqEnwUMpLpx#PB3h|4mjGFS56IOThP-)+zG+_%MLTa71 zme6?TRosM6Wm%-MF@E06tNID7V^Bv`UBYV=4zfhw`+s%SF9vK_6S%5HE1-nLf8#+e zUABMB9^8H%T{Qi*cblMbl_x0JXR*1CuM}gi!fzC1;Q=S`-kQ%a10(Xpnsl2W?%2M= zWSfRi=uoHHi+|@it2~Jyo%$8HqMQ1(&GoJ$i;5_ zF>9lz)XH|FsuvEG_poxmakhIt(RIk;S5k@#GRgHzjsby*h*!w}2*gP6ocv7(1jZt~ zW6&PCDK6k7*ORNObIEUced`+Wai0SkU%vixhE1VDO-g6bc@Tqi-p|(n*wko!N8pFPgqP7-@Y%_=TDuneihV|No z^==`8=ZHuMJ1C>E;HpDe*8A%S)x&U&)8?3+Re}A%fPGR zpWr%{hT~rGp5kYC4CL&7%oJql)ciYT3^?`x))dk?c&d7qpEMgJq#>uZD zc(ng1!_59E!RZye%B1n_D2rvNB6iFLyUTjJ6scSiSA{k?xke4 z`80LS4VMPt_*Z45r6^3SrSq!HNJ_G0^PS70VyGY#Q z=+f>a!C4l*NY-go_z=GsM)NIogg^)0{~aKhXCN?E{$)!#-YD`PVOX$ETFLf)2vj{u zDgITOIcXm9cK<(Nm^=lBk`@|m14tOg(jQ=z%X!?k$NKf9f$|T?83x5=H3|AWc}XEY z`OKFTnv`KVW%|Ci!-D_Ugl$4Ie=v{UBo6qQeIEgD#{~E4$fkf1=|-Gu>${Ida}$hA z77F2<;-6jw#T~>Om9kUhC#7-4iXK4sT{9h%DZL?iUGe8vCN?>FH_T|6w(ZbrQGm<_`H!$_e65w1+#CXvZC-(WYKC# zX~9SONvv*4WNk;4CZ*%iiQa012@JcSAMonSOHq~QimosG3G+GYqd9;3R@RmYarpV| zR!4_P$BPV1k&hjzH0_=rkCFZI;uQX)-F`<# zhwI*3j9+(|RP~}ANa!kYzV7+%XDxg1lk9l?bl3&aTS%cJJ)1XPXwiqC-Cifg%l}H+ zYE0e0`~G8jscxueIq)Kr?tD0pO|$BxIXO~l0? zH~UpLyKe5~twPu8`YH%5L2?ykJw^3>Q`&dSrPQn$7@S}YdUBd?W+lxngkN4MTs+L!#8-GBT| z7m4kDaC~Sy#3{WiKOm1@+_e6^I?gzG>T-S}kMfx5R#5xuy`Ufdl-Y1IeXKv5Qq;CL zR_0=Kew4-T78s5YYj=@@db!hg+7Ta;^e$_SmEuR2REUc35hsaLC* z8%}Np2iPBn_m|&av^-ib)wToQ(F~dFovqUy_!rg_f&Seek_gO)I*q3-7&UvAb#|91 zdiy&dG1jl`<3vNI+duD_Vsqad2g7esaPhr(jt6$mqRb5K6`#{9fJR=e(MTr)=jVqz zyokg^M#Ybg3lz!l$=;eSTuWt|SuI|zg(!W{gHsQv{9N~=Z6W`)hw%t6J^!*`;#aL9EHIO{xf90=n&{5*$;U-EC)9t>6k51lY# z*1|u<%78r+6i?j;Xg_lcmtqTBL`PnjrCfR5wwFlCbcC8$VxIgf9x8ZHHvN5&J{o#> z$sY1}VL>ZVqz`5RiIeP#xiFZ^9wmj-FV~+g$LEruQ(JuRE>2D)$bBC_U7x%*f*n;gUkh)>Ws?Re8~ z4f@t|ZoAeU#%oM`)74$-bT+#a*{pfFZtqHgkjP$bO~se?14P32LMFyVm%5rRe7w!A zJU>FyUw!@#v?|^5uhSjI`*;6p&8qS@zwE`CEp<6 zDs^JD+H|`$x^r|32N-p9&i0?LvEQ8Vbyf0sYkZcdR0*tCoZECWo*eDBcM%aNqGD_NUnzXUyU*GcOn%e-}9S+RtqsF?>#lZm8*8zt99_ zM)Vw@fA7a`d0l+wjr#I+DAJ=RGKuW1Qhz0*JVul3~eIPfa7t(}4{@#g(I4|(WN{o~X6 z2R{|};{uS9iRf|Q8uWHl5A{46|Gu)h@ErIYGC=rB(&>NxQ8FB-_kkJHqq+U*`6M-r z_PCF0r;l_&4#nvs>5eK?r%uh>6HuFah>S_n|Clo+*oxQ?`hS4W%KLPi+x_6P_GwG)sjiqSZOr&d(dlL>^))cQbcM2 z3{2P7U$4L3J(u|NkE07-J8U;wXFXj%z2z~b2!~OSX#fnQEi~xD#e~y*ZF`$&xRshQ z`VrF6>MHEt$4B?TQWs^>tVIzQluLA3tdIMP#iQ|6fUpSIsFZ{dhcX@`d*aHQS*V6c zT`(JyldGy3+wWg9*M`6OeeLMUxpe<=PVBrFKY3DyX6(i1qji8s)oPGrNW!&_QmAG0 zJbElf)#iKn1Ztm9=LId^kh}!1>GqoasbN_xRSfu%>pk4|w4I`vP&}Ps5U(7B+0t|& zd&J^N+(g;fH9J8RCR~uHrn){3^k2EW+=8udG2(IJ*+pyfP4$mKVXg|Te*D^nUc15( zfW#S|jn5DU)+6j&5LyC}-TE^R*;`2+jGSfMuh<< z(hE4?Q{jDlo|fu=7kQ7I`<1{@v)fdScvCe6-o#>;1Z3e;JP_8))xrz9rv$ z185I@)oiRTPAIRhhM!2$2=J-$8ZP~{A%0>mQTL6bh@tV_#G*-$Vn<+*v&ZTq=*j6< z1F7dAyNb(>|56oW2pleW|#M|bHcuhkhuK4s5chd2v=&)aNq;K!?(6CYPx`a@y!L&|WS(U?r{&d(PreZW z)@^$5G0j-R$BuO_3+972DxF82IPzRJ{~f)f5hNU;B}Sy9MW)YShPj8yz7d0+PDA-DGQpOg@};^YU{J|U{z8T1Eo;`*#wBUmy3auRSYx^1~)pIqUk zvE}0V+iJL<3!GDyD;N`oFGk{}K zGWCp7R0b}C5>wR{v;?8(eN!(<>mbHDJW4vIc$`4pg|rI z31BHKR%&o0`ZSXp%_oIQ!k&+IDKgEOCEHpA{!mOJAT3Hnn zu*NvwnGzgcxS?4eD2hb44atwr$P6@u!&-=mEZnDB#x5xATk)XLh3S)oMaQC!N=r9Z zk(CH_yi#uJggT|7iy9vko4G+}2rOUX?T0~-0L_Y>k-B3MM=tt;zT70G-8M)!Z3Y7n z(J_c4)8=>X!UrgO$!DB-8RQc(uB4k%kjGKztTIuf#mA58*M)P{5UBq|5nuvrpz#3B z>P^b?rwbElq+h>2oVDd-5GL(~q?FYlS|pArTgT7&R*WonZYpzSjFei2nPuO-5XXKIZY7nq67#z z2eFORw`G;AzGYlJ3b}A68(a%_U^07PmC13!D^J|>8X$|HZ8XBb4@OEFvt(Ag&7_!{ zTa9Cg;JUGvdUdujv4$_=gMUL3lZ4ApYsPM0jqc2E;n4$kll+NfnVhKyM$F=JP!Gtj z-Lijd)h{$~@>*|n!gP&Lb}i;Tk3tI>))5u};4Q}l!okz-9SLu4HhqKl}VOv}4{k0a7m4o`!RXbgZkZzFDh5RDge9liFH_3wbz39#m9dPr7mRGZj=MNWs@$(Almn?YXXe5C91Nl2o%#h2y! z5oo*6LUK_&SIW6d@v{V|QicB192pB78yhy(cs&}?W<@--awxk5X-wlr1c(Dor*;4^ z!a9c3p6y5%x?M-u-9~M;ijEqN^ zjrB~-3KqYSGQ$_Hb#H>Q=J)#uGyI z$sY(M!K>tOk5H!SP*a%0rlv7YZGJd8!nztV2|>Tb8>~=wOXV z%8?krGf-Cq8x^7@P^iqrC(5JGUQt*WR|8^_%Qb7z7xq0WzNLw$a*7nHX3?mCW>(87 zspV2GY0)h-6KMR^sALluZ#UY}!vue0E-R>Jr7*>&p*kZYpi%{;h$m)H&kP=-@mn#q zct_cxO1m5Kg919k+!Ja?i3sB13Yysu4C|&)itN^Yv^20f&EvW#Zpe0XR*f*B?lK#? zeJl{anP{oOQDmb`;A#3~(Dwh)pe#%a01fu`ed5pEP~&#MM1n@P9L;2WT3{yjRYsIc zMbs?=s3VSb$U`?7U&TG8;gnmp%WpK;CO_yYa=;mN+o!r+#C>R%{f@0%+utTS_zb58 zJ1sGN9Jnn5&0dkPH;QDJn%D@BV7Jn03#C+$h@BcVNVY6kQ|lTziXZ6C9KRNK^QCD= z0G_`{tO3U(t*{ld02fDU##<45O0Sm`Tx~W1 zAvk9_8*m+no6Z?$A6fvmg_)hp2hO~XilomsL_(22z(w}cRIC}He*WrgTeK_?l}Ox9 z8*M2KV2q*Cqz_Mv#ZwmF=x0s(V+W;&rm-dq>>yLO)2i@_x9;7~WD+G?+H>RUMJ75L zHU0wd)q*|hz(TDL5Exj9zOk8htD9^_S(Y+*3~wb?7ET2asz39pLvQ{LKvC~TuJZ96 zd3urFKQ&9OQ&R7aEBEoOym3?CKg}3ha!Re6Qtwuj{<)wqf6Rmo{^9~nB?ES?n`}Ff z*7z+;_3zD?q$AtWg?ct8j^#9c5c5&c0){NF>_gO#l-HzyX&fI35pFDqz9smlAqYip zmoC@%CLY40x8s`;HeR;E0#LmC43USm5(qPy)b28yJWEcvdV&`P&8(=o{RZI(gkVY< z3ag%pd$|`y^Z5;?-xzR-69oEg6ln(GDbV$!s7YeAw55=`(`e^Dh+fdENrmg{Y`%S< z(6ggy!R@c_On97)~&JnAXK6OYol;jz;Sv@ru3lxs#}E5w}C z7&bu$j+0aCB6dH~*DU8BF3GT`#Smtrw;B8JqOM|VJ*oQG(-~1nf@14E>%EzgzAi&( zB}fM|Iwc-~OYV>-LiwQLMA8}4>lgR+{tYiyUdHDdB-dakU4vPD(a`DTEDu%a5Du|_ zt0yZes-l_ZGehoXony0&KAUMQ8f{!lw1A<4Fq>xNi84zqMaViiQf9;+v)oJgvu zZ^tkZ5GUNrtoLFup*jj#NQR?NgrkpytHQz1bfB&_r>+*1*Y;Imb-`JX6@t+H%&)y+ zjnLLbNv=SGb*>}+vMoIHPI^|My1MmJa z_9Y3grNM!FtKIgH#Ct@tQytZ=l*D^Nic?P_opD^9aXcMnmPSpM#t-0#?&yeDVq$Yt zq6TOO4u@;Wr$U&G%QK7h0BS1q$^!n*8sZLLVA=+6x-JBOf?AjK9}pDx9}wh!g58WUf-Ue8goK88{>5XWqLA!?ZLBf;U+B>>Ek^g8duv)7@h0)} zBC>-&l|QRsv|E$8 z&&ZKGWHgZzeB|S?yszrHWpBo%8F@_d6QZfY;ochIdmJb`E(|-d<2p^SqRm@THY^cW zweO|&59pPWY6f=(4#_%j`F+oZtu|M&|AC+&k5cN-E|uZldymjb*t$rgppY9JR}OP3sUbqz=@oW5Y1YX zJXIWcsb6}m?9+4J2wTz>_{a6#XHq2ohR|5HN0juqPP3*wtdW!+b3dlu-Ac~-|{)+-#d?X8$V zy_mqY7;wlkR@5OYM91tQVMax>d`>3&Nh1Z*S}*JJeyfTdaP^5{(eXhKCKG(tHfr0eC7W4V|4g!r!_n%zv`f0K=; zz7$LpE?&@LuGl~>!`^GZkHdJopw{u?x+r5i<mV>$!L9fp_a@kNWZ5oWceOYymmecc65PO_w{|`9|?QaoPQ5 z>fL7S-DZv&NzT%3nQS9cS6mcvBMsXv}K@cUn z(U35%akH2=bFQ!&rA$J{h>3md#Ys2i{;-M0J7ccU7OYHSMHqCQ2*VikFVKW*3X+LR zT2ML$aiJe~O1~bu6sR1d4w0V)J2x^g#~}%yW8Viqv0XngC!5Rr^Ge(wySQIF0eM&g z|0{)KqF@OZte}lc@}n%8*;9# z*gX~vJ`9oQjX3*s20r9{Sp0OLJUk%*VL4(P1z3tm73YWt+Xxifi1@mIU!Vq*#2Xqk z(RR~sd`EQfBua8TB8P$PVJTbKFUiI^qM~fFpQUtycSIPN38N8fN8vu-5PaRxjNTdh z)&I<^Z?ONZ;OTzZt3m2`C+HYF>P8$4_o0bgGQ{cExAjBF^zLedZz}3|vUSG3`aP;E;Zl9}H7(x8T!7EU5>;LLl`_Y;mwTpgzHU^#ReFe@A3dg*j zLF40ZjBC9U!yVJ!ieb$BS0i@2t1BF!l(G;?nbCQ%zHifO1u?+ z;?{`Ku4qSc@58K4;!^7QfV%GYHkL@^TEpMedGk+18ZL zhnX{O=swkH#tE-n@XD`F3S)+&pnu-dYWX5l)L(7hK4Yty6vE(_Lreq-p|mQzb)Jk2 zGDCEft}`hGN4@XHp8sDGMYs&vLL=P09muWPRTlHX(}uvkxzkIo;hpF0yq&#F@9Rs(v!$w5zz9?b~VU%#a_Z0J6qQ2MPG zEJPY5L+lG2Fxzah%bY58-5f?s0hnUWNnn+tg%-Vb0)#~$35F#@%5H!U+ioHL)lU75 zS@&PU3D5k#+x!;3f0-}iqY{scUpj$gO+Fw&)L*q@A>@HwLhk1lYEF|on5xORFV?mR zuyps?->IHIe7%h>F`_f-jPQ(EU#S#07*?LWe+_4;T`S9t8fwjBTbCAo+zD$jDl78- zNKKNSaL*tAkeFHiFh6a7MN=d|U@m(A`bZI-M7tT+bD}d9dSZ6QO z)Y;A<{Vv-V-y3J1yz5bmrAFTU&T~HHeZieW6rUGmEJCeGoXJzoRjR6NhwJ6EE=U7E z!)LZtgXY<(DGmlZ`|7J(J>HdSBj4>qt$<7zz8CtZCJBl?#bWiVW0y}}m79Y2_>Hf; zqF!^u4*NKRm*0;L3B>uiytMRrK9}WICgWS4o~Ce?+B2QUhrRjm|CZqR`e@7*RB?W^ zCB#QRaSL8zkbGJ`a8EWMtRP(oR;b7ck@Rq}yuP);cb~+Ld07?t_|()H8L5q?-|Boj zMX^oZJ^b};XruKbDlhL*JE`flFz?c1aegaXd$Om2?Z@O*AH3hz%DQiDiC_1PrR+5d zqwkxzz199zte@Z)>ssH|o@|oKOb+D-^UA?abW41aDo_Z#R_R_U^yWL2Jl)C$0;6AtvI~VRi=gL-}d{k!z(ft`$ zN!A=mhAv+}C;WT7_x$t8c5{cMOpM}7L@r+3H~PkKbNnsSoK}mpE8E( zm)6yJTTL}P-{`D)} zMn?4#@`e|O9hjK4EA0JKdgxWN*!k}^_nQ?Ix(pTDrxWz&Eyl`#t@D%ON|C%5kE7G~ zEAl4)nj^y>SBboSA0wsLOPl8^lkx9%2dU@ZKUMvp7kA42%#tOB1z#tJrQXQh4Sapc zdVUP;6rWAa_&osS(n;3$_O!~>B|otD54{O>Q~gg>0X?>pXY-JnBzZG0X18`OJVIPH z!;KAM?>;6Mwqg^!O(f^JH+O)h&y3{sZdcNp>^hKdD&55ZtMhnyGi^}r?JeGo&W}y< z>4{9p--X?-E6+&KVnKUv*V*seYVVxKXV%j8`Mq7-^t;VMt3&eVOyI(c|^4%>FipQ2uH5OgNK4@8d5D zoZNw>)uJk`tt~0K~rKKe!;G^BgTt^#> zgvOu~A*!Z|8ZkX8z_Ks^yx|gHhSHb6M>z=%*=LKvG7kkGc?t<9g_D4*26;}#qGz>7 zBaNJ49Ky4N2_-TD5K+jD?%YyU#9K0F|o!AK(1GIZPLk zf1iR+YM#C5fGw$*?C!KElg*z z`Z$(l>Zrwudhdh>_#wXPT1!L&xv~_-C@1CzZn)6s`t4Trs`^)PJhCnqK28v&{pHC= zlV+EHpoPjY0c-R27$_e)1(dC45+tqVZGfg^QUPf#mjdK7x2BhDPJ5GPmN(o|-Ck~8 zdrYQjI%?}TwINrTv_mt_svwFIDO8vj9)iEU=B+IKL8c^fB`|)Dr+-5ZuWcQ_d6dnC za<{jgb{Hn%*WLa(5Aj<`OH7~>s#vmm7rrD$l3h*sn6TF1s(pxEEMpe{Ks@zsZz|;0_W~A zWxZe+Si2#0d^&CWy>`K+E%Mus5*0Dyd7mi*CC@LF;UosgZdDA39VUF0+L>9zTz+~a zTw-e{xw4mshyy1Ff5wHnj09aiBnUsn=LUs7EwYCi-ze!gY_Eo}2T1|QuM7^Xgr#&9 zsh1fOHZa&lg9?N6VKn15l^?mUZu431$+hFEe#N}@tGISV-qIy5B-&Jl)@43J6X!9i zr@*8J(h6}uthWT5Gc#;8I3~*Pn10j%bs6qd?~g&D3P7~vvqscbh4J&?RJR);^kjNG z4Z*q}AluA9BdVUs_{G3zYHdX!pEH!6jsPCtjy=`66_zDBeAW6S>((#oc-UY^(C)DCSnL*;FsWy|w+B^C^qxQ&iSA^|o!6?AW&A0o}rb=52lQ32}?ZeeOc{x1A>351GP?Uf{s> zqY=h0{ntCB>j$|u>}XcqX1E-rCJ-G%ZVv;r6P*hKV9o zFvyDsnYtq!VLB>`;4Gi^hfWk*4o5e{tWhzk@rfIOvDKtq6QdCdv{%cblUd9_HYsXE znZYC_ng;u=K;o-B4y|0wuW1Op((%XyD$lrAiLoTX;df_hbI)ytChdMimCO)^#l6r) zvy8^Sb!dg!WbidzEINm_GL#*LuuF# z`-Z~!$fB_dr#qu~{{DF~XVAncq{V!21>HsAQ<65Fa+P1Wv4WLw-$%^yL8?QKzyvd@Cvallz@K zS6kotwFE|pe*vQ1e&|>KQac1*k|H_#xUIFLH^WJ}#|ij$UM%?5|FGQj{N~rQ^*VWG|5aY25*iR10;Tct6Tx3E=m35lLz2##A8{WF!S@c*tQ(sl&SMmD1EE zikqQ(b%IpNw3P={0q?8u{P~rUTEfi*rKDuf{7OX$uEUKv_z#N`>k3pE_IIBS6o?|245~ANe|tu036JZ)9QnE!&06? z%$>#?2z;3*)#va)Ct%NqM#7pNLCkHmXgglL-S{y>JNy?jf8kJAC2eK^F%wxUPL0qq z_4ef(uJGOwj&L&hH2WBOm?VyVt`G2B1)e_&F)K5OQE6K?9KirVM{|%GuF;$kRm26W zLVj2rN;&$-bb1z1SLbARQ&TCf)*)nmEXXZin0|LH6_UIwO?kbc7ry5CA6aUmo^ud; zA%ipjwl~&N+?Z1Wmzl9fg_*A#9s{fX<_jWAs1_Jd6E(Yza6#Y;xWae1)7`kPEWu@r z_oE4#dWHp9tDz7y@JVYqUxMSz?{MXa{*fi|RZ~VJ5LqHy+jBj64VXMmTW#f7LxM!y zi!kXdaVnuMm@bfpASRW9Hjw^Zn^v0Ip_3E32-j%*i=1plglo2euLQW2SVI=b{zW%S zs9xugEz)4Y)r|dXp0oy#ir5- zY0hcDQRHcWytS%Av>NzyWZP$!q_R*nkr0WDGtGyn+*Z^rZNoZb3B;X0RzpjP`oY}0 z^(HaSHFcbpXPu(Kxf7Pw$rEtuzn7Iz;}hy4SXIJ2qsXl5wzGhxGf@pea>+&l;c0ql z(JaIQdPo%_8zC^iUZo^PJC)>5BMyzzO<>SWU_=8czj_#kwn>M2vIToa!QZN59pI>E z7^$21$%H;zU>%rZ9iY!)LAY3<(G;;9nr3uaD%S>kX2IXym4js?15yQS#B$$l= zKwzo%=C%f})fTLdROxGUZ#CHWZ&e7s9Al~o4hHt=NK{JS2MI7UiXa;hJYs{Mcp+GM zvw|MOAP%yCKPfpAQ(+H|ZQFyM=MQSYegfb<))2A1Xj-D9fP9u$wNdA$SZh>>Yme%- z>6Ob&Y7M?dgFo7$&f&4vP}FU;Dwk_i8(tGGi~3@2Nxr%9o^ewon72*JnHmTyx0$DC-2z`+mC~sAmCI;Q_1RYDFzW*w2CVmc8w-Rm zXRaJG%-!&M%OWbLqIwU()$P8MnWvzA0;=&0QXtjDKZ0KGX?M)M6G7ETF zvYjn&2YClEnG9r>CMceo*PIV=r|&d$Y>3IVnXidqFDKY8VawJ&@?n;9jnks!UDr4^ z&2+7Zsb<~cHKcJg9qkZL$)9OE%c8}NB$T5u5?@PiclrRr0v9SPYiN?LN($n=IB<;u zgc9rPZ~_Uy`?_T;PghH|8oJ<^ID(@&EYTBWg5)D1jwt4sc!aw+AVF^Ny;q{p5;rPZe3}Ij zC$iLpQJ=CgtXYM1><&ipwjP-d2iV>kM)`V5v(oQnZm498kbR@zpHzDC>10P?4xe2z?v1uUE!bZu8V@;lwh=7^(1%Qc}2v_(Uk}^#ACY-jZ8E7I#0sb z!K3p7q0zQS*jUb0MsY`*;-m|vCA6mAyA%_7i)pYARzG?HfZrcGGT# zO9k0BT(*QIYF-lNs7&A?3ddIBx6R&_42w}1k+Ma*yv03xqK1?yZom0L$M)wcxS=w%rht+Ppk|T?cMo%%MbJbk;Y0#7<=L5uqjB>) zq`4Wc?3qWzA_u6VslFqE(IbMvBk!~sF8sPf-U0`Z@s209dy~!=B6DRq>P`pGS0_X; zyAsl|#2ck)$Ns1n=G@#vMM;*^zNGbULK=@3@os)GAW@7&ybbD}pN;3s1-p_1KFTeJ zTKeX^f;>V5t?<^JI5-)ZDD?t7MPGjeZW%>k0vc;B8fzdLYa$wJB-%AUMPURQD;^GQ zqZ)3bnp0vn@(zNzA@gn&$E>bZju!csuAx*cqI$mmbXV59dJ3a3y&v1_rUA@zuyoYs zsYj!97UroS_1S%dd!QlGwX?wP9PIAgD7xjg;?|KuN1qn^T&CBY&VVZ?t8Z!|M|i?L zeuZC7;ypBlO>JB?7oN^7uFfr z`O8#@BP+lQ4*W$xU|J4udOYYoJp6GqC_NcaXN>R>!01t853E`a5;8~lh+up&YuR3~ z1=@htCJ2V(*qw2V9s-ILPjU2e>eY^rid4YA*gA>%G;;Q3M1E29{Z5PAq(muxio<+D z8+$?siw{pzPO<}$bfv z852y3J1*F@Im4Ecv1hHIIz+&9t{G8*5Gv_I!5__iMKVN?BCd%7u&oC%y*}!8y&v$M zGEb87MQ;QuiE+;OKP*T6>MX&_#oZ`?Dgb*#Gj~1Q3yYF>fY{-juZw{S8i)M7XYyQm zhsF-?n5_vpiEpjDv7=PAg${l`k8s3CoqtJ^qS*l?X>xo`7hM05==jPdV)F<&YwiA2 zWciZ3>59U-At(RXAibd!;vJw)V&3Hx$hW8|n166RPM4AXlBiDV8<9WS2HNuA5erZlaH0$*g0R z|7_J>4!AunB|8+FD@?YfV3KAO7<`^;ish*;riRKaJ^C5LK>y&xW~*uSce1{X(>b~~ zQO}y<_pD?bDTP#0qTXJj&8!sk_wORm^$I03d&YWpcRvD)q^ydhY>Iw=DRL(*68clY zrT8=m3#UJW%xAqwL5Z;wI`j} zXJcDzfR^a3y)<%vn`q>An`lmfRw|3hbA=55pc76s8$~o*u7r*Qwgs&R4;lDLxG#4n zmDAr6@H06ubqo_Bwrlm+M4#~JdWcsS?SpsfPG`CwUg!w3;0)3*yX3K%?E1xzM&7Ap(as~ zOZC(7oWO^xNikwQ#cZjt5{iJ)gkk>~mEJYSbh=?6CKl?X3n2G<4?=ca{ffk$1tx=@ z7!$`;_*p}70Xv*v>>C$iPVuf*nbCVFrz~tRdr>5G6U2B0l#n0%hG^DYtAf=5z0N+Y z-r3P?`pbK{y5u59S-zJq3HMxub)s+MQ*uKfmjSRktsx&)AvqFvb#b_5M~oRFgoom5 zG701M(bLT`m6ac1|Ax=YQL^XnZC3l~!izabyDNkJ zg0>*=>wwvhR-An?A&cbr(i_;Y`stecX@|40kr1e@8<|nSsp8{K#baQ%Jq8ZbWck_x z*Ld?Nwo`Z4WnYQ`-U3SVzEZCqg+pwsiGJ$5srJPzjnP<6w^W*v>~*sZ!Mv}iYtod% z)=j^Jf+k7r?x}@R_8j1*J4E+Smbt;v8A6R-|2lbhIZxrrZ|UXgl1jY9V5?8d_K~N`|8*}7OGfv7rN~V0 zkHe-8frEybegBF^;_5xb^i|Q8;PFM3uU-A=oQ|)j(yhyqt}~o&hqD=we0VZ<^}e&X z+pWWn)q084?z6j!wafRX@;j(MrFSjPcg7k+p!O0K9%*@PG_V_>NNO$JW)x69@lp-RtO3Fwnl~HT2Z|VB(1awv(C766X zrV2%cb{`yyLDZB#?=jI|>#(r4rFd}lSeTseiM&s|99GDg&{iu>6R!#6*%6g% z)>u6ZHcy4izV~+^f&&#I?Zx-UhFsNDbTr2q_%m1w+*1=d7(DeefZM+>T@?eKY#4`! z*z_B0ZD9?WJASI=tN8qmBROIF+td^vb2nmndM!vmeN)Xn?dMmN#MvWFax=a#pB>6iF@VLN4mdFg_(5OQNdUdHK!6-Wi|#$f zhnrXLw?yMd`_b`fbhF(R=*P$B3mwq|j_Aer{Y~W0oL4z-md}JBOWt@tlqXgVtV%ZC zi`AN8&(~&hKFd0qzY~6aQ>U9fGc0^=e=oVxOzHJr_7mAZWxu^S%3lkNE-fkBP1dyz zHXfW`M_wRTC^ipwJy$A;gO<9VN0B})$RqBr#WP}=PAoTfP1U29DH~LWHU-mNJO#xm z@LKIOL;g?f>(g-e$U-Ie+;<5`p50o*HJW@c7goEvzSgS;`z16cA4h2pPRtPQue=hl zw3J5UFQ>1t;CgB5!{m6of8ppU_Y>%S{~jT!P1`Z(mbupKvTV7u?{*1Ub$*oTxPd=z z>rBs3vb?#P$sOBu&%fN?)}-8bgmJ6d4@|u5=cO>uZ|V9zuf;9%Ukg9y zRBrT_e9J6y8z9d)g`+}d?7Fva;h$b`evf`NJsg{#PRw>*b=n>DeR&UO;kGe-e%iTy z^nM5SBTM(;UkO64FyE`|@-D~Vx^dv%R7Calb<+A-z$uC?mlX-z3z7rNWiHIG|3JC5$lGe*YfH!Zo8jl)$P&J-Kzf}w`bilYkI8rIrfs|qImXM2_mZXWJ%}#Nd?{1CQk?t?Mp{pYINnnR}9Falc3>9Z<>!$QF0QVg6ijI(keL&i&!Vb)sImZLGtSpcLCip8K4wDOQq53YdXnk6%?Rz)ewxw#7wE%w8EqckX%8 z(yJsB{NGjoQo6l@6BJYt;q?np8}lbb8qUb$kuYQv(R9_C8IgkRno*|drg7pKZ4J#? zoMK4NwU;>t`li`FGZ!8`_gxJjz&Rd~RP*msBlPZ@otC=QTOU5K@YhWbs#a6)etaHf z5$riAZvN^j9+~xbe_p*6{)SJj;ldv6Q!~5F?TV!fT$J|J9NALqC%OGC1z;U`3VClf zH0kZ}`h5Mm;GZx17^!P$6;hk;bGg!_PjkD2y^((MK2&BM&vL~7IJxZ#n<(dC&sXz1 z^frCunn#_Hiujj#Y6Gdfg`u*28iWBq&_C(CVI?sV_yfZ)*U(b3ph zf^P&=>*CpM1u!m>PTj16Gia*SERuZfDes%%rM&urJ9t38YJu1}ln`T8l#BL&_$()% zD$BL%y)CA`t;ISSMPSehn0BBqgTb0Cb>A~ziK{}XpWd*@RZxZ!`tVq z;hvsLbCyE#s;<0wXRfz!o!V;<@Bt>}o=p0pwddA+UwLwBhxv z_m|g6G3!T90XdCd$a1S+ur%{z3NBM0*ZLIqMJbQxJ;L%|3pS4A95mX4)L zcJ%gN_eyhro!^^K^i*gy#~v@cf_tgw(2KpYeYpWfpkA62to%l%hQRlgCRYE}Zbqyi zOKo%ZbKX?h+4($!;PS&`$8>~OYJNHWzWxqf?|am{uqI*8k&{Yg+3|}tvLT`+4ZFE> zD?Tc1HY>e*^+UE+Dyoe7!7L?p3qMthlx}j!G9`{`$VrOcz$DpM&y=1=ib9&K2$hy- zqFyjgkL^SC`_$=2&HPL3b&V_s(QqW*sh2VZ%pcDIP=d7)!&J^7Q49Od7EuAKx=d53 z1O=2#V@QSZB4Y|Hkv;>J(jSWlV&VP9eF2HFNfboJ2JJftlA~utCX5u&aG3}?6F)T! z6T!lu1y!*rr2^}B1Ord*$c-fw&~UH_+r>4Q62TyZ!>bR2X@F|b3 zVF-`*(?DId#onO`{;O9JTP__}S4$YIMe)t_&aXub?jR+kiX#&;0Qt!-8crpMVtQMP zN>q-)gmQFaKM_<)By0c<@5Uy$K*op(b??@i^b2Odalv>$xPB*;NVPXIMSU{8T}5%2 zg9B7XcVnGy1?En)S*5y|mYUUue44pi((Es`?HapC=M!A9Q|Ifx%*ZGBf7xQDztsy1 z3^%fcZ}npQ|D|61Cz|GeRxcR!Z0#(ZO#TmB%)+;F@lX37)C-iM=4=?C{!uER)s$LN z8bWHXRI*^upO6N?9F{LoFT#Eu z=Rm&AO0PmP57}>#u3y9lgb9XDn6}*2@5E-t&O|Z&&@nGsq|uO(s}Jtu-Mv( zi)28+95q*8rq*N}90A1?SjAYMApl+2SQQ50v@$NY93vHnD9F;dnMj_OGxz7hO}Khq zF^pweZxmJtg?t7TC^eKzt)Nc%O+3t5ngC!DhWwFt!~*p~(Os49jt?3tJ{ZkZyoIy= zqF+CXg^qIS2Uf5QC@l1^0(mDD7Vc~N!yDO@%=38CSq{$L52C9^&RO0ZgE(dpT_oBz zVDo%os?8@VDEYs&M6;8i=^0jr7E#nv#Mne^>_&K_vw=6R#sO$}_t5GL%fPgpfiwb; zjU*FxA_oXy!+(adswB*?orLNcN`huYy4WCrDBg_`B#aT#$L=w-R*!Z(K+YKbYZY>B z5Y5eiFQ=Nu5|bw#UeU}Co$`94WRE!@oe3JkGOWypPu0bk&}cJT&XsG z6)E|~iIW;a*Fiw32J*$!cyI)@)GglkV-NAm6Fo3kp1B;I3u%oHi${UAc9e<5U$~CY zWnqRZ`PfDSj$HGHK?Z0%SwJ`0Y>a#~sDC;bfVUbZ!m(`QE-RiMVayl+LV=hR5`;3J zhGof?tJ`6d`EBK~pvFi|OVSe9H149o$AA24I6+o$My^Cl8 zUKo}55Huu@=-@7nh+zp#AC*{P8h7WIijhX8SPY9!nIsyXNJ%Ur8KFQl$Q#VKo%sox}CPs@xNgW(UWF+~Tu|Iyi;QIR{AN1B7?6?>mJUxfw(Vs9R4$YP(L zk3kTmt&O6<3;F_ID54M_U=2*i-Zn-J{z`TeSvc6{Km58#bdwGlt+uCKI{4~>cFdm0 zAt%4bqY!bQA(^?YxcAYFbWB7#ouKGC&w-8(%>LVn?N7r9s3eO6oyy*aY^h$ZQ{EJ0 zpD~*GJYh%zHWwA4>EVI;y#4x*Vge~8=)Ml$Iv=^)2Y|d2Yo`|{Zd$3YAoNy2FN+L{ zQ7;5bmC95@k)N-g0HNP|lT{QVL`oq}3(k z+Vs3ZPtvbuLP#icPY(4)4j6sdz_Sj)+9mjM7bxXzCAgKWSKtHYOu8`C%4N_l%wbOe`?7W8jG;e>&X7ACF6x|lB>9H)6=H43BtLa}C9r~(e1CD!2I)!fL{krlJc~yy zMo#CrmXPET6Ene1Bh@(_43?7MbI85Xg(nP^aWo|{qsZuLSN0A`?8HNAlLalV>XW4} zgMrzbAhlWoFGu#(AT2)bL$}J3eaUFk`j$lJ6L#tu`1QfE^R7{3U}DNQ_h#5?tDkt< zML7R%5#idzy$#jsJSF2hQrF#ex=g);15z<}W_jo3{1JzvL?I@Com%hm83h#BfvTz{ zPqwQiWOqmhw(ajnY`9Ge`uq_NfY9KT#m*sWrMIMZSG(~Syeq*c@f+K*Lv|OZel9S) zSpjtsSEt-!_|XJul84t6sSozB3<}~vze`sco)O$#i)8zT0YkmdEog`loDn=y3uTK$ zB?MxN#5RC#pMZd+`VS*b9hM!k%D`m-M&>nc!9yKn6Qw`BHA?xtqjYUR)7XIC5LNqL z=}CTdO_(ip{L?Ai<6$=NrN6_`A%w`iz)BT}f5~>NKTIE_azA5BYA;hq5cg(lEOMXES*?Yne-oL460_YsCh80SLP4g7Xu+dF|~ja zw@_#A1nKo!#&|JXc0yZrQrmY5;EX&iN0bp71w4UTI_p1E%+)8i%u1jzeO;;awP2Qv zFMWQtTm`e^nV`NO}%@U@V|%4Z;1xNWj5HpMG#rdH9gH*>j5HZM8w_7eL#%+dEWRkvK9vkyni{FF#u1jTx-z!l z^$gH#LnNX}=|9k3BNjrZG~F{+p3TV>E(@4nH~g4uHu|+_5V1BwNn+=vpLTwCjRD)4o zOjG$BgP4koxk{EWYf35bLrOLk0g1SHp_Zw5D+ZDW7RenZQVjzM6b4ca6N#>oc(j=~ z%6Df(25XgF-I#objFYfG!dS;{@6X{LltYmiNl6#@A?0O@&_D}|#6zQzUb<5K?wO&# z-Cq&^a;q(g;jt8cd}ehI$TDEuWFT&vbA4P;dHFZ+cKh#dwc0 zK~e_K6vn*Iu^>kZxF;Uu1Nxf4j9I`ZQ}6fIP5?(&9OpBVvooCYqiJ||XmXcya@Q_( zKq_ruB6VOQZD5-zOpPH-?fY{#Ju;`A33L@$ZGR0zAUnYX5i0(qQEL}#12ei}Sm8F+ zod@+A;GFVBC$M?Ayq6vhEy6~JBzc-gpU46RhOf5E#li;ZE4Np>CCAfR+3M&Yf*sy3 znqM2CY(if)@}ORanK7ghuOs|#LYmWm5z=h?x=+TiRSDGswdQOTXH3?=QoGKEjq97# zZC-d1DPLsq`FEJ?Y@^XdTPU(;hL}2ovwbt=lTU&OHbM_JNDy)8T5;tjz~Vyffim0f^5; z)Dq%Fh>7bCt&nI;=sWrJd;WN;#}DVW)w@jEfQ z937oa5*i<&86PFPA5ncH($dpYepsv6K2fY_W_A7ky2v}bFgUxg8N0X~y|{cFFF^j} z6GT$0?T0)2_-3TJm;Al*dIcV+gQ~-V@(VrtRn1a4HpO{J#Uo$OquG)x0hv0Bk^8Oav7GvV)nLA<|A2i_Wo~1 zS{qIyqx}z9$3Pe*6!Y6-$<*>^UNczbJQ-pGeBopZqB_};sH*BtU=i0@LJ}!~Q1BFH z6zDK{AwnjcGiGH_Ju)DQK-0xh218>GMkZjn7(M+%K_I{2Ul1IRRp2-hz!Fe#!GG8T zulI@3f(yj!|Bz8?$h{X+ZG7P(AH<1&jr?Pcx%YEH)P15Iz))dntSsGDa&liC(Sla> z3SUgO=!OY-_o0AP(?cJCq51q7(V>mWG%4)1YcZA-#HM+?hR!y3dSkby)RW=V-u$(E zeS`^CzcZ*;fV{8uRRVuJ9rd%kR0Xv^m#kmb7svxtneKPAACXoNC_?O zw0hX|#Xk+L);^eMFyzdt))1&z6LtQL!F?bK?>q%~+oX$Ax82Kp5_QgEK=nnM zo>O_e$3wpIb7>Q@x>_WLSJlQx-<6qodwQGHRoIXMgZP7j6Z6QGd>KOOhTJN_u7o`k z75%gnUC)kj=fzqrmDSm2>Lye3JeyjKytKXMY&8)BtEqQ((6#OKl1~^l(8|Z%&JJM7 z?{GUl?S0>sIrlKL>v_ofeO|E!3!gFHwGAoTrBd!qLB<73f=a%l)YE#J{jHH}FT*ovewM*-QX?#tK zi=-;6{Vu=l6-U-8iOiZD==cLEC8_v@cf67P4Rq5-#cQACyrK1Ztt$zN^_uGY-~;KC z4=p>pRo|Cc_vCfFYg!FA2h($)2<`L4-%(U#1Yfd~HeWSa+`swS8m5L}G+XN^f0f&r8Fc037$DyE;P~ zLDyk3>pB-lM5;KppA(?$%N8GNLRz>*8`~;1_^WCSiRWxS3W5`uL)wm$cf2uc$@W`N z1v2QHwpS%;Y+pJHeFFhdPQY?@RJ&{&TAV}|l90=7u2X-2AT~B?G-&NS>NhslE(@+` zI?s;S(7@$ZXKNEX+^pSWl4CZt_vqi>!azIP^0_e0Oo{oAY0OcVNoQnk-i&T5Jj?0sJ ztf!q2B83m)UFS3B_WI5GW#qJ+H^C?INo9r4g^ifq>rH{3JR6g{Wb?d8JL~2(QL1CxRek`7RQnxGR!EZ`-VOH2HL>$_SN6~K{maY)lI>Wp z?$(5!JBRsp`|W-ip6!gC#I`oBW`9;rJ1M!R7^T}1N0qU7bH@3r*$hZB0{ruMkw>d# zOHuRv6}r>Y2kU!AwyP6=4Th_m%?Q#J@l4jf5_pPZ@k1a!nvSE>r5Jw2+IwCGw`ZQd zu7VA`n)|)$!RsaIM%!Wx`qjj(ebCGz)`LcS1~wkmf!zE2K$W{$ALS|Gre88!hRZ=u z&zEoyvoYdy>sQNn>6mjFULgRy6$(`SS>lg z$Q%x^stSDmUhQ-M`44Pc_hKU7^_VZS9emhjU$z1B{W688;P9g?z-MjH%f~v6=clf> z4a3cwi8t8&wYJbQB)jTUzuM+q%NI{@jhWjr@$q4Xo?g581Fh{zQVZNsms8)%W#s{9 zNJ!Xk*7Rj<{?`-I{Rc*y>PC1TeUwPNp%2>*1D=<)tsP5&Qe)Q&&dceSUqvo3f+A z!QK@6d*5w+&o8|l-prXfTzj+TcPkEj(HYopf9u5;nhehF)n|h=j@;MfAObv6&(_EL zr|&!M+ilxX_4S1sEpMg==WD!=$J_(4+Y5g0REDv9<-+}Kb(dz6<9NDH&CVYFYrzsW zr?p_KU5c@t-#+I%>RS>Jozm8t7!UKSYO&wuCn3Jj^WKM;y-V(a$JtvsdGI^ED-2m8 zshobtp#K5fC2d$XQ}o+-H*zEgRU!&$ua<>)u<`PRBFH{>Ly{5NeTI zFq&Zu%=~J;HhQ!v7z_TU6vl70@x2eU2=If&zHc%Ozwhbz`{60%^)6Gl(t~8(Q`a%g zy|!)_YWo!=W~!+F4!^aGTJl}4bn?VJ+X65TFyu#Wyza$gyQMd8PYm(j3`ALlwdT6d zM^k7ZTNLei>Vdvuck03s%r~qs|!iX%S(%>i;D;- zB9tFqnH*eLnZ-DeYWTBVAuzCorLPxgijMdxjF-_oa>E(&-a1PwglFk0P&X==Z9TCQ zw0+V{2#>#(zvBAadz~O?^+c60#yd|_C0962CG>j$@4A|&UX&}Cr-HF1h&LfdDt498M2oFb~rm%#$qA9xQlOP`aug4`| zTBxdiiu~9SzS(@K48abDNQwgoxW<$G1-fVkVZ1eg`xLkkR|6Du1g{xFW(9n5eY{~XXWJA<5g4hlg=28k zd4*~JH#11lH)ZT-^0Nx(n=+=)`{Reu51b#4CQdHa&QA17M&@oNdbWBNwieEM|2t*u z|8|2g|3^;|HL!Lvp|f{ZQHJ~hVj2%-`cM0Bg36_qhdQ#_GJLMT0s@$uf_8Z;;d}lb zOn5F)cpoVp2RM=h1IeTeu%T3xO0xc@rOsBnLQ{3qo?=QniRI=Qu%!x>2-q{OTh+SP zbrxZQL?s}beP%!pe@5tPGRte`I{Tjcc!QZe7U5i8j=7R}u&Dr+a6#lDb4MRc7}>3c(~ zQ?Hmj`T%9&RfmgnAjKVytDg~{S8zT=W`6oMU<6jTRrmDTXjJJ_sl#_!C?}ynom7uf zC0I58GYqHy(ru^DZbLwSx!yLMDn+sCHMj#>L?3V zD}zD4U{ZPF?^tuP5t5&$OPj@WHjXFFTYIANTt)LmN|sq{LM4l{*>$da#idGlfzQhI z!Wl#*ERGVP(Y7O+R0@o!SfxHKJZnQ4Hd1hx~ZBXCcU~L;7FxaxF9~lQeGwUD0`|E z@(PIvPd`s>C7hpm+QY)HC|yU0NGTHK%9q$=*mis?L(RBAcU((dZ1a*k|oyIc(gER2b2&O5@06)hkHmH z`YPxz!yP+9E)9-Uh4%B*+yh%wf!UcQTd}Zw!5(O3oyA$2k5^n6ewZ#4wzqd)=%L*h zc3(<$Le0t&7BOm>KhZ(hb=Mi^D*71>+Mq^}QKMM7cTUk$UD8sNt$=J9dO#*WD2_}) zLA!)OlTn21w^$lYs1H}7mpHiqG0aS!$kv^#Cnqv=gyS$kY_#ieHbd-au0Z&~oO8d) zkaIuJ=)m{Zark(69J2(A+|1jwGt=P@3Fp8EDCYnpsaa|!g;{C*po~)zF;cwK35wmT zfJdWuE6$ocRlbR%H8ZDCf`sEROxa4kw4$tnndA@JuU5=dLTM<&hZ+^!^QzmZOI)hw(yHe)s^^rIORJSjO8btORs1t~ zJ^GE`@6yNNC-fegenXa76QF;DsY1Ns6|3fYeLxGXPApzdm))@g407cMI$3d_r}U95 zp*EoGkAfgs;rU0+@OpI*yv(5sp`p@PhPcRld)u81@>+eXx-59@sDI{0rB>;ab)b3& zH3w=;sxoLv%^=P>#gR>79gn5C_)wcAGwSKeQ-saO>7K?@Du*Hs1xXgq<>LTwGu~O5 zO8pa=O1yq`(qX;}w8Ok{VCz70;tOnI;a9eRnp!$j&onbSP z*TQVujM27APvi;V+R%zQd7s7kK=<e{QL zP8s*Sv!{N#Bg`eBs}lOcNJpRl95`J19(SGK`UjPA>2AYOrbhA-Q6K3HRlX>*agLuM&x`=aX9iN zGY(f#=Hf@4kT(tcP_bj)Yv2L2g?sdWzs9p3QBSAVXc&D?Vco-8O}2bHOzMWcXRz); zjHC(tE21X`ogV?Tx)WRT^kV_%SwIZ|+E!gG4+Hv`L%Vl{9i$x@0jXwJm14TM8Wy=nrruV3gRCY%Q5euslqkPr5I-MHU&NU_4=iN? z$?uX%I}4?I6tL9V0h=CW0b(;pY6|C`KcRUi!0jCN3UWC|T8!YHCpAfQ;!npImrL9} zs2wPKg7g=Xt3WDrZf2HLND=){VYT>(sC1i+BwH^wtiK&XF1D_f+g}0=GOCBPU%ZwO zkAp#;dB0jerV}YpDXDSEyEJa+`_;Xr^sICL1cT*v=1Lk0ZH2s*4 z@w>OTVZv}sC&b|vakybZaZKls*)iyJ#T4z70_vU?I@;iy&^v|)k;$OIE0K}6U=gg( zAr$@-IewJf7*uRj-1$40A;bmmw+osRSIn+6-o#%IoRWKY*<%vfW5~>Dl#+X4Su@BI zC#Di7sFH?8HdvB@7CeF=w^}$Wf8FfEn@62$Rmnb*h0}m76YVs&due7pxkFAyx=e3r zIaWaybt9(K4HS{mpDN;s6zR@Px%$AOBg(J@u(a^#aQaO7pof2167dy0O}C^yB+!%0 z1R!&cgPg6kR4&!wC+a9`<3mwl<@e_>-MhztSKQK1oSO1}>h zbv6!_!rMSwm@|tHr14KNEoLwqCZBx;nby9(dr40q@zF|gY97M5RvR*VOIgYwKdO{1FE-?3^)f;z>@X9cofIMuq4K`1Xl(LUCpU1;Uhcb|LHVEXh= zF8NCreh)-ofhrsd=$|y46OMg>+Qya%h@OInuo~Ii*xo%^!xXNN-%eP*hGSS&EJ`4f zRU{BWObG|fNAWZ1Bst6ypCD3vF{F9|@z@Mv*zznzd3_?(r-Zp1Bmg<5(-i4>YU%l} z{(0)UhY45f!=Py+2b_t*ok$5jFFf2bRcfo5%RUtru!7J)Dr8Gm{*7vb=yQyHaCBRC zht|#h&6YO%j)9PM9o^Q={r*cnbX)jVd#}-ubz-^=%kSP~$T~9JMz-Vb6HM8@HQJ9I z&O60Q!X!9deGo^BJ5t?uYGle&C{!}=&f#o61~1p9@9geY0zd}zJo`y}L}trLJSVBS zvRCMgH-0YAb>#{y$TcQ6xp^N%vd#l6B*IF83}&5`nchTo7jOKre$4jeMO@qY`Z5{` z3>-~>q6xz2+!pZYHaf?u5rG}8eY07>od5`^Q3^ybS!^gqAtJcLue~`=E%4YpCp~q( z^l5qj3f_9sX%Q@S?J>)qYXOj7MqVg2-)ak$)}36p_vB2a05XU1D8x%p%+r*doHxxA z@$3T2!n$MWDrG>-5H_^E*x#)_3oe~{H1}TaoM3hGxd`{OQn1^Pdu z`))VI-Ss*xFYB|-aRup9Tx+*E9u+l_3ka@HWlCn&;{CX-*WEgOOK(?4QAZ%?uLQE~ z-Cqlbegh3BOAVw*L!U_u(_ei~eUf$$w{23R(P7U=D|d=tGghlZ*^GlqWy4 zbJQRjjFmZ3n9`lLwC27}_-d+nj&?9Hp4Vl!4a+FY<5WwuIg_Oi1q0Kufx7 zDScye`75>qZ~PotgQjOI*}8z^u#1v%q4ivGHK6s+)Yxx{z*u_rzT^3>3?eeGSV5H; zqcGD@Ogp5^+5%%`HxQUy&Bk8$OnS6u? z$9_bHr&r9m5sI_YqmaE^t-n8*%81?^`+IErNVLmCaba0K9)Q-_u`im<#?tugi*GBO z-7{EV<4Ol3|L{&Z9C zy}o=ncqyHBle?fLy57J8a&wmW6#mzY%<0pEfbnB)kl9P!kw_Ps} zpKN5d4WFPVN$P+*PQIoh>Un6LzqxG@If!CC=rReBpRRI&NSF6?>}{x)3RmY{<)y1K ztDxXb<9^dzt}PUtuPP{sB{GXI>CrjM)7BEN_SCYlF6pcI{CY*Y!qc zyvlMyeC^!KL-I(=zTov)j?wc~6OMdJB5R+dBPprgY-I-M5^Ain-c+i25WW&VL9!lt z195h^jie}zLyb9vlB87fh8X8MeFvFI;CcgyP7|(g;0A>&P+jxu+|QDBK`;1$s4G7;Qa7<^Bi_z|8-C^W_8Mmo)w|`V`xu^balQ81;mUL36 z8TqaKF66aavR<4M5c_K<(Y+n5T__9&9C8E zx%SRfS0`oivIAi<_}&+$dezb)yWq8blTv_cGWh5~4#%dWD?+24r;YDfAAQ1XS71Gu za6VX$Cilm;tvoPX99HK2NgoyQWo5`q@|bxrd9ef~BUzgeX=3b}uBCz?)AVHwB3K2H zaLN<~Uq@ZkTG!j`cC=b$FkKXuzER61Lj3M7=F;r~3sDIXP^-t0xc$ETkPfdFt|D1) z^A&_D94N5XGLfSqe3+N=ENb)Z7J|dLJgBZAvh}?p##(xSanLyI-UNhE37#am(Gu+- zF`o3Z8Eog2hv!M%b4$z65}zrclhbtRib(rVwK*M2gWZmRZ^p~$lt+vS@r>~KD2qdx#;FX|%dAu?S`~@bSqYVD zD#@MzL|GVK_b7D(4sW*Ce2atUDG?zCq$a|1!Fl^AefBPF{IZo-U4B~uvzGmo z4#5TqS1@(e3qyY+Dv1OEGo75xUnGov2`yiRbt=IFQCI`_q$3VndH`{Pim11-FXBff z<-O0U4!e^vF`+N(PIr66Rm+*TthI0=N87%DK_TAA(WZOi=cjfhVKh40lKK&pxc8pt zO3d8(gx@*7*JPp3V>8&nADVt-Z7P&28Kl%iS=S*`6UgUdoS_NGd%GWe=jBUxX3-eGbqV%om%mX(9R`POC%+oi=K?NjL{c-sG`O#iyoxV z1@3R4&G5LwRZpA_4f9x{XdmAaLddz$5rw_D@RWtPs04*-Z3>18zEaz5$ob!s(_!bG zK^?2uzT9n^s?V^_MpUFnJsjkw_BJASMT&b&ZEOJE>NSXFx0A9UJ- z-+c?@e~^P+$Kpx7Ij+MRD1b+nn&w!>*g~8e;QHMuV4Q{d=4%(#6P`DW9^`xFkaptr zqw(QaUs4E_E+ zN6XY))&AgKcl*nj?n)=|ocj{Rx`20iU>mbeP_?ld*>GW}x!s`~pCa3^3`?99sMW z5)Ae>^*9rE-m>|UI4}qd|H55`sHlzu3K1?#jr=MOR1_~D9-kw}twG$qMi?qMbtr&CEVJUPDw(U;pqUNwz-< zTa4o=rC(e5VaCPPGRVwNMB?r~wZ2xWYo{m0aDPw3m#XEZ)%QWuHwjNGj;EfMGNrFt zo`$b*J2!}HE!r?Y^&t=$qiV~Y-b0%1@m4omRWM}^^yRiSZX*WWD9p^SSwCBqFlGKL z+58AX0W$!i34 z-?}PGg*~(1`EJG0(xxdx#h{69!MzIgxdC;$iggD1o_B3qmo-=el+As42K>_eh$=(H zC>wp%tA3OXcmtI6y=N-?(c=2|Y%3eR@2mbHTaZRb)2r$L1oHbDlqu@s(Gcy}@+2BV zb7HgNROKknun_X=s{hmN9Vg+^Tpc-eca{)6vNCnlQ+TX}5Fib0TIfrh%zZni%?$4I zoSVr>bN6;~KPH&}!{_q*Tax*w{Wn==P!sY$l&;_m z$L$udriWa4gi%FF%EaV&B2a{6mQ~lk#S&(&e-(=Y#VqC>!*K#}0(VM?C`M_2K7yh) z{!~Ob0FRr<|5lNDIN7*Yn@{dPJiZH^vPl}4uNm(@eXqG2I%H%^6_^PKC@6Bra~N~2 z1R_<&QNc>|-PTYE1?}4}>Q6=;ej*wOmaiw%Q3)ES5=7<8oCraL)Xma9HEWEQ}(E zEypm_xl#^G74(AjXNDlkV~`)v1_5X`Z04E**Z6B1Aps+Ck=i#1T!PXO+M|{3H)-m? zg_)~d(3MzYz}gU@*wndJ82FWxt6-JmLpGe8K1k zFL{tI*p33S`y`IEv}R>m8=5(CeIA84omw#r9{G~c)Nki~=WkL7G;<3 zU|0fKODXiTW84YoC+8Aq8UI=t@By;`cVvuT6r#Wan}Kn1*f-gsoCA-UpBmi- zPzs-&C7FX4iYGo86(K-6yo>=eOoI+fecN0j?i>SAvPl$*WfCcp$RrXeus2D+%WrC}pvT^F~*t1ro&zNw6YVBIx+CzHKgs71SfZ z0m^{AvY)g(q3&wQQqw3lXc~1|2{++DD{;%w-m#}GmSJslOmMVqjVnQw?}@fm`WXI7 zYAD13(5OQKV8;9T;b(-sda-CC8bFbdt-J?BB?t%Uy(}8Po$!9&{{`E@L{6<9=G6Or z-#|0XyudM|y#JAjb$q>Pyq;vg8E8@@`{*zz<-?F$Pv>-k@Ce-f*^#sAKQ0$w z9h7(f5SGX&c@(dw!a1!G`+$T8VM(!e6FEQ%YdMUA7925lf}Y5k%M@aHDr+Hp|T ziasA5*efiwjV4R`ZTX;geAZUG@;)Ca*sFC*`{lAeAFN%f?A;wl&5J#A`A};BU^~#rFN#Cw)TlKe zFf~C`x_TAvJ_UO!<$EsTL+4tkH3L+-V#=M}O1E58HY)529n2&2vnDi&g+GEVh3bK% z#;aLK*LRa)cT(jxt5;~@qI0!CM^67qfWe{qR{~5nC?Qs6?k|p0^k94x`*Bd&4yDcS z1eg`;?*th8vF`*JqU9YJL~beBXv@0*r^4$_7P0g&B`sc|xr3o0DD6}*y7XRBrj=M& z%{U!)l)jjTUK8Y(G6e0ovTA(2UKPWS^hTMryg&cgT)qQf&hh>g0K@bh0K?AHT08st zNi)ZpEVZy9bJ{M}zDsB|Suc%TkDGcYi}){_3n?MOKQrYPbXq;^6fyGghQaDcPE>1B?6@ZgJ-ejW7nu$RBK ze55>>3-k~h*0y+`XP)fHxyR=3*>=cb#gN9(0H4WPfQt4JJ(cR5F8UScszxD5}hUFTtp2o zK-Yxz*tf$Fj#bpd5vC_Z*cKVwKMmLtA9PzPaEl>uiy^6$n!CjjzLg^B7WZ+B5xIi# z{Y-%LG5l}MW#AUOa_~kxWS2c;*X_SG7yo^>_^pWo>l zOB1i^7(M`dyjIQE@Bav3hW}G@u^#Fw6D5U>UFa)Hj0g1~Aj&H$j@Ql=!6dm%H*$cZ zX;P}if^7VwT&lAytkf7z1>kfk0TJ>GkZnT=4ot_5%@f8?#-)wKjTwslosM%0A!N)Z zjGv8jLt`^HGOZh%8IlGFU}odpxnsFl#WRa(b;7frHkk7gLxh2xP(l+e1t_Q5vgZ36`-g>m`!2)bA z4WPZFXkGENZpfOqA6EUD>jA9*0n9K9>aKHfLPqk zp1C3Ie4Nqs63cD=bNz461&j{K;w&FN$?3m67jQExv@PL-x=qV%#O85wUfeS)`btMX6Z~aud#E00B%L6z_ioFr#j?IPL>fCX=_wP8?phBeT>h z*4;dzRdu>(k*{n+q9kZ$`FUY^W^U3E@qxf`{RaHI)fHgXBmr6HRNw#|k=c%eos-6Qw z|18@+RfLA0BNn{lPA(M&wHX(EU=Uy1i)`IE4{`U0c_tvffQxJ?Mzxh<+Rri^|vjf%*ir3Oar#h_bY(48q$ z?Y)JCv9Ft=;+JskJGFebu#zn^hz~J@Qrz_}=5JpHknbhJTXexaIKe$S!96^|J=+kz zU!{ZyW9ATcSs{F|GJ<(zHa630f(|tf=4Hd?#rdojCVCB~m!<7H{M4`=MFhq9aG}iH zVF3TjB;%b)^00StKOf3DV(>c-9V{%n3Ko{ZF38#B`$C*QHNQeg%`c-kqgjITsfLyF z38ZyHfd56k>hm~|n*%}_6^61bVomO>-BuWo!zsyn7__Wx04TLn42Dc6YpKI`; z4sMbMW0k-dlE2H)2K0>%?6HW{UC8-C;Z7K)L;p>4qqBPcl3Z|0DI&H@Jf0HUDsnx0 z@}^%po#enIQ}S_UG#1NcugS4F<>PQDwlQRYthcg|nn zA|uOBcv*}bj`u6O-K88A6~x5(`fjLy(~suw$}E21VNdARwe++&8~*jR0{lOds|_We zb$RD#xB4EOScxT;bbXHf;jd+$c39qPBt_sfnw_-%&N|~V`D*G)_cY{`;B$969qv;G zzAn%v_TXii&mF~TM|WnBKONzwAJ3uv`)c}D5AZ(OaNN(xuH($7R=l@($l*6`9PPoX zUa!79@#$^FzM=c}dH*F%XSO}Y%|gT7bV2K=c)bj@x8632nbm8RX7ZEQWH`x5DS@r4 z)6jF0^Su3-Avi+D^J2{!Gq|iNSr12<=_cQ~B~KG<`@PZ>5&teZJKdlCtXT z@g~$&V(S@C@YnItS%O~~&YeBpeC~Z&$n|x9d(MZoR337Br9ZiBv$xY=+$}8pXI?%x zh@Z@g)9YVzWouf5g695uy1L?f?*X8EJuNA2`7^-l#=G|{*Xu&P1(=&=(Ekih)|#8@ zcpsF6f1?5abu8(ro0`A7}2e{~%VO*B7vu%nW92kQL`!t z{re`3qB4Jjo&U?MUo^wslTh0hy zZ#1Xh#Xo=4-05EdC#vJT_?T~y-si`_;%tmwR2H(+O;*u{*_xIE`DViTmPq&9YU7N;z z8uYyct7-}vteLrUX9Ja?JTLdSQnkI@H?r`&pW6+mg42<2&NFtSkHUC9Kc5GVsvjs1 zmc2T>b`yzsJx&inn_Qmgcd!%xbS!=7k79+Q!#+ue2l2%_KcyvSCNp!{Ia?1K@q7#i zPIYkE++Wk~z-PKzD#C7B=s0}kq3L%$3^t}&;)Opui*<`!MDDp}SI&~Yno?6`gME9) zm4@i4cbASE_2_@5nzvM434fSncCpT~_V;Z^Tx&TTm^VyalSQVQ++R;3GiB7jLql(S zHgjmJ|AXEVYW|pod{}P>KXDu9FcP+3SpVVvA#(bdZ7&6mat_<`{K znY8tG(#%; zVE7cyW&2h3M9)+C$IEmEaazac=yODyE}PNiV~MGEJZ|CRm6~nm#&szflRVe*so8~_ zmbK;Zxo~vNgVlS1YN<}PlV{yoFj6!|YxR=1!*10Rl~#A_C`b9B&HgC|_mK0)d3mj=AebTl z0r7Aei=%Ruj>GDY+jmFe@>d91Z?Ee13_dFEN)5CG{GZsG*Kswsij}bll3w{+#id9Y}y9V_F zD+wigI9qp@lmF#G*W*6!s?2?Z?Zp5G*72LWifz|F9V+sqv_-$?PU5t>Iy5>@=DSRP z?;V6!nZrurw9b$8YZd*Cm)6V1=CBg*{O0qQ-frmU)RL#Cs)Xfi<9Aujy^Bi{2r-QR}@ zzS$2OyIh6Wp&Q{5eLeoU=3Kc$y1&PgbP7rYmS zsi7G$MO@gox<=U)Gs@~zNxB+t0=ceIx`dxm1fsfm+u+}fWM7AY@s3a-hf6TTsupU^ zQqvAp=>o;^_EA^q5z3ZEE#yq?-4ii+^U2O2Q!;4!gc{u!07SP5=IHi|7RnsI35Wq^cSQ3CVCxJF}o=VaswmXt=Pt4i`m@ za~2$WjY!SGJE+p8KW(6)xMq=9#Z6(0&@;?)4G92HUV(7`I;cyV87*MI0T>Wb(i-3o z%!8<)R|_{$xWNfmqd*q_XLLM}LQnbMQO$sgF=n>#rGQ(|zzm&4#9H8OQ;Z0+PT}% z{ok7nUI3_;&!!U@FkrL6hU@=B3G#n~YBBzws`>xR2=f2vfN%kTTK_rzkIe=^2gD6o zc~y3BCB!?qAqB0DCgmFXH>QL+w7-C2->-u>RzY%7X^$HzjTQ?~iA=u6PDjc^OW(f*4fFuk{16W@PV45A1y-(H(-V$4l@n95 z0Iq43r4r_7FEmNVD#lo#D%HF!5OAur8F z{;)wgblE9p0Ykb$<~U&7@k3XlhdB5s>`*SQ#2Iwh>hZw!Dkr>i7RNBY9TsqRNO*xp zGc3o+%<4bY{M=Y`#xSQEFdb+7u7Zk03u!DpeGt-4ct&O`CfKw7sbP-{f`Y?s3@d?A z!PpuIR~#N$+XnJ8K#1lcU~W!XuoGw^3BmFqDmsEr{^_RXGz6Kz^F|s``Oz|#GB*X7pk>ZKUYet%uc*3;M)6dla^-Ii*c~Vf_Wq7`caNe(onG}5I`a8~;#VjG zDuza_xAFzerohH<$FtyL+SHg+1dBOYJhrNY-A6V(~FB?az}0YhNcoVN@6J0&gg&el#^0*$=_(7L1y;(gy{{ znGh{z2%3sL48_BefvR<^ta2O8SjG2gS;faV4J8y~3|bP^u}O1G(1>6Z-0-z-#F{Nk z_U8;}5Gk=fKzyV=0JR}pKe#B%c#=(^k7pBV0EG|;^1Sm8xyjrwW`Su+kV9h$O`&2( z(BkUhz7lZvf(1S$EC~t6f`zWKCT%57U8N2_C69ozMlK7r80oG}Ok6+@R6q#s= z{+I*5&WCeCUwGvdsE#P+X~+Fer;3Y1H0T_GLk;l5e#_|Nr%_%5H{SBxrqscAO$WL@ z>anf}xs7mC-D)|i?=v3PlNQq?#5_%~$mBiHuwzxhj2?n;>q>p%1fnp^JGAUdH<}`$ zj~T~>M%Fj;IRI@<2P1YzQ%E0{l;o~qMPcvhj@?2-_j@w!pd%LY83J8tKzx6JceZ-Y z&iM^Pc?t!-#B+bT)q$)RN}~6}NxI9GfKN(@9|j`TX~C=56(o97u&+-T+{WWoA!T10 z&X21|%Cv|$HOLB)e<(RMG#4aRBWG7>IW;^1-ilVyDcNNuNVrDfwk~jKocXxtcwpS& zY4jQ?P=^Wo(++8_)U+m_(0G!M6-H=yRv5fbX@cznss)$`W0C*Wg;9%=d&dn-!(P5WpEe>tIE4(D&1rypEu?8f2cjpQ zu`AH3F-^a|t?z*!wepAo>=K22i}dhT6Yp{*@CF3#CCTsRX%8Gd)N}j+TfRRz){z5h z+}k{>@8a9Qjt|}k#hsht{ylN%VwR{jR7eLtvI`H3)mnAnld(qFf$Iz93WTyzg; zp$?x&FpHcbyk&uHySYB?7Jn7A&Vu`h(dj?3yGs zB>K~iWh;$h6~sGn104L1)j1_89;>6Ic}>znfS9|#n0a{5RS*qEm`$WU^};q*EooIh z>uU-E_~wBox@#S-4dT0=r%<^VCwNNbP;Mu073W*u=4D#TTgEocI# zBuRIea~1j0`n3|U;2p^0`LE76)2s8Gp`$}6-L$1?mU6E2N2DB~^J}ld;V4JK=t@nD zkayf7oaaYz=)`QQqCzeaQwk1l%PBZB#Ug2H3k5&_m=O^_krF?l5jRbwhBYd9!5!P{IyHsp zvz?~#ur;+tL&gc^%G_^F0~?TZ@0nU{M3s?>C}DV^DD6+=Xzf>XMcMzoWYx51*1$YR z0}`cOhN%bV^H+hj?=B4~OV-iOXXs4bjZ~RLTPuSf8Y>sbG2FVkrhWSFiAg>c)-v;) zh{;L(`Y-QPzOWvSd@+9e1_gQf@dO#Vf5(kqO zp~!V=6(x2l!3oei9!4zDZBJ4|@U+(yiD9}jD)0YL#_6H |D72eX@|VsG{^cg`QgStIdaN%X2NKe zdqY*j-aHZW|Lsa>ph-4$$(qR@SsJWSv<+FW6-x}JAP&+I$G!oD+{?_(+qHW>LH#?2 zA3skvdJ;vVt%RV(PrP16qO~N^;E|uZFBh8B2v2cFqWy!NJ2vfsjlA{$B$Ak$TWtQfZRg@^)t%Sntr0C z3brm`;wx<8E6pSpOcSVNz&G$tR11?3h%BNJLnM0yfUXJ>=|El6Xwnf+HgDS1Jh2-j zJ6NZ+@g@2vL_-==cnLwZLd(3s+IQeelvXBW*^uKA-h3X>B3$1)t^cE=q3x2jkewK3Z3O&F+M^7tm;ED*cRkG+qm2!(ta^FQ5(MGk{u9GZy z)0uM1Mr_|j5vo;Fp=~>5;3@{i#uacWbonk7^jBAGcaoXd$pH9}pF)P0SWfqbv_#Fi zM3{eUSm{gd>vQ|9I(IRFHx1~S?_D(lNAJGoepFu0#jVaOpp+D*pyd5GBnkAQVivi!w+#2KcXK zA~{BwJ_es*xkIpWNxpN$xOcU`Jcg5jFg$m{{|Y}qKw0GsH50Uk@(&{HzvleO2SO}x zYNMCfsWho%y9bU=l`uS@rR7?eB9{wlhP!#4~$5{F(kPz~K9kRyBMcS9Cmf zU@bjeCk=)gRafHTKZZn{T2-ceXCEdB+ug1G9(txmeF?UYLmW^ z=*I#sYibjJDLi>`ZT)JzVv8^(Z`-s6!>QS>74)50)^VuSJ2a(#Dc(?Ow)WN+LPEX5 z|88Puo8aEzzCP_d_bgYMp5CkliGg>VO#d=_FQb&)R2ebp(ek-m?pZ1)$1YV{6=@s& z**nLog~8_}b5@Sq;Kti}9u+kF&b+h{O`?OtsVha#`RUX1*2-6i{%X?JN5JcS_Ke+a z3QJv)g>x3Xn!w+!j_H5?Y16K?^RjNJvqd_m-)uXS0Wpr$XtBEi8`7+m2I_=bCEyT@ zwTZn69trf3Ia@*2o|S5I!rrXy8hm$U$gRN5<^IMTr>BDQ-Nb+L#b#k)!PI`pu=S1; z06fgA+H#=<9YFkirJIht9;G9Wha>)cwAp*t^zsnDrY-Y($HzVI&m+Q}ZzCo(YCDoI z_qz9D#-h&@v5ty{V*Jit>R+wt;kY6uSN@qP`G*OOv-B2M;d#o)EES1jIIRWS%^d}ciNWqn zO&Mo}he<)DLsrf*TX4ARO&p$;=;!I=RUH?V)&@9~L8eY4O2@wDPXq(j+^tc_QWz0rxg zSlya$H=WX*p&yN1?)po>-1uE8UCeqs)1>&VBKDN-7tZbBzgHijyT#^PNsjO216lvf zcd)FuthG7E(7pg)WnBJ`)QYlIaJE|>m#f>?#S%(mW5aB}*C7D^%` z;PfArN+N3=^$~OD%Ek>I)!S!!mGHsExHGn+F{=l0E9 zFRewN?$?ga>`(lU14`!_3*wMnP3szW?T-ukSYU|*x{gum(Uy!kYXiJqV9s%OkqPCs=Gw5D5!w~x))g{<3lt(i7G zbMBg>+-==sb{{A$u7-#Aj&0tI-mK~X-^T3WMYm5Y+}=glaislr{=36!pyg{u=G_yh zb(4A1SLW^4Mz>=kohbL;=znUp%k5F4*sAlr2N#d1mM(|ibb<5+-H{#Ot%UdRtx$b! z73aBdRsE}(+@f0~?i`mA-JiZaKH;C5Pm8YlU%%Z7{B0&1{R(op3WKYu`bIhL`u*<_Z-5Fx>Ab+1IMp~wA(rPzCuT=c;)>__{B(;g zYpLd!BZAhyyk}Thd|!7@N@s)_Q&DGEIgZ_wH@0W{MPOGF zUko(*WGsBG`+T@1Nm!Zgr&1ef3RD#*3tyfcoaLM7C-fcCT;8S9u!j>E{m* z*-M9kftl9Rj&iOhqB}I2+==(ATX67jjtcrOCS=_imqk7GKA;|WDCen{GLO^$xj`gP zlUoRrxL-|z8#jWXUhV|9MJ*N`-q2RQZ3k*gdsmG_%E`jb7sJJ+c;#;W8!eyj6Pc*G zbU5-!e?3xJ3s=jRx~eCeqYL0;td?KAhOhcDUddJQ_?xt(=iUv+<#J1U_ae%EEj8)sywV6m0eAuT_l$4k(wV`RJ?uo zX1Rb)nw?LydJ5n6?1Tev>qZ|$%FAkFm9zC^vuf0uyISmHukArE+gKdL9hzN+#+s^q z=y{g#c%R}bJ)g~lzqX}m3TJi7!ZKWJuhOv5n?bkfe!BElqa!36eG4^|zRIk%Q|{Wo z!YY`)z3kQ|fJ>?1cym0OEBCF2k8qo3jFvOx{jX+Kei+_ruXZ(+C&@nwnMPz;&Z{pn zC-AOmZXSMA_am-yH`hB3a$9}>)c-tP;<+AYRS|U6n)Nu2>8`?eD?fP;2>;XcOCpaX zyVhztEyoZ4a4rwT`g(Y+hx#nHUByh*ac(W>TE5S~p4}#N5yM{laQ>us!u}d|d^P!D zH}K_8;hq0+X+)3xd3YbCendBFA$c`!rk{E{xkJwJ|H<0Xy}#>?Y*GLBWSIm;jIWl! zjf0z@Z`0K_QXB0WlHr;9+513bm`Z?T~1BzL2EX49yuOFWwW?x7U|1M2_Bl zp^OBOk?mJ&|JF%uj@kWTtfaBbJXqw5#YPMh9mBV z67W6TPEiktRjSBSKK%v->2>Q5x*ij<&D~Cy=S8dBWNF@@E08YyYSkbCA4{b+j+pSA zXQQdTP3RS+ScW=3AWl+^NLZbcXrAbJ5BY~We$S*1iMq7SMp4U`qB;9RWOs2%Eo%lmv^oCLk9rM zh|V846}{=N54Bmk(9m;>)oVH^Th&8SHo3e}=N^$Z83^~-+-~#3GC~wCR3M6sAb`UXjcyr~5`>3iR&s`~ zp~(2f+%_`QW;}1^ARPsbh|D4qj#bM@Rr^QEG|x=Oz1ugJ;IS zrsn-eqvz)1r{bE7wp~OzkdR?c?g|`(6MLch;E%uSy-TU~G4X(V=_DOw%wKRy(>81M zfGN@7hXjJPSBT%+f0Df;-8E&$w)Js)az#MZ@M9>&NyY5!|jG`LQHoOqqZwJ zYIyDe-kfc)wG$*zn`4Op&S~hp!!1YsYYRinXL#>05tQ}mbtU~|`O4rrEM3bq> z4h=>C6)aeSRMD?U!NuzXZ)g(2NWf79!FChbn~@BTkgW-wAV6kP!A&cL9uMUQAIvF) zR7=%0vkVqjagQ1!<0z=9(CK)iA(24JPNmtnns5X$qM2JLWFUft&IDTD>u(}BdT(*v zIGxbJ( zuWpb^m+hVo<+Pe|XuJJZvPP1k3K*2K zV^78a<-QiBUyZlRSGJT!6})WSVCw-T;;q}1nl-{ZthW7s$9bu zr&pncW)4RaPahnq7c+`iV~3A|Q8#vr3s zX9x?Y9u9{o$;)qm$j2`T$;(eT77&Gt7Yw`@$?K<}qkfl!LGh;~$y!8$yhnMN!6G6L z3E4M=#W$ws5hxT1frd-Tt9zjhD~b=RsOEt3-|a`LN+k^$;v7%diz|X=3MX8xC@mNS zx%{J0;i{Ln?`I7ChlwU{VpqxEw2g zYv|4q>YzCpyS9RTPX6BCMuZur{&lLEH>w#&j2TDeENG>ylXCW1ReK+u;Wtp!69Lqd zf+PpflLN0)dAk`hc2DU#q0ools3*aMK71!cozVTlFlHKr!SmR~$^#P<e%vAM(6erD}fA~wQJ92Z4(9e(0#4{{jS41_cC6$1;U$OW0R%pUGw2w^R)*w z{FIk_QOfk!DBwrwjZ+JPm${#qN;kLC3)k4oJOBQvcw&_?x%QAsmt3hwrot;*;f=HU z{)s-hmiYkA8M&G3;nR<2GOq}dK*};9YDmGsc%mJy)hOu1>)K`A#vTD|`}f~k8a3{G zk^>`pOVM^{lpu`PGNRD&|O!A;lk-Ru?0+{67px)F>3pWww|Y0jx>vJ zTHx_rihY<^_|rZTaD7DNNE3*+U?n!$u9$>H8p_R}qDbv5aLP9dW3Zp_XwThVZo9H; zyo>9Jb$1*C-~~K^FQL5@NuTGU}~f~7qx{IZVp1qT3D3)SnWvm-Kf%0iblJo-*h z%Q1rIHsp9g0fL|eP|aI_)`A0|wJ0O6kxE9pC371}ygZ`&4lTUC=1INtOLIf7lBS>0^LK11 zcP})Dwm65gQ?J;wubF+XnPab>F}E+}1G^+pn?})qz`yU`?2V*~Pjw7m%p~4~nlC@) zXM7`4lh?UWt0qtWSqU<8Wxw$PTKco_Frfl85f^U^W5u=7g>jCsd8%$pl!_67ieF(TlS`{`Zd=AS{p!Z4Wa3bp?z{` z-MBREFg0)C5){W3RYM3g=fFwWz~)Yj)P({_I&fGY3CpcS8b;LbvfOx*FTW<}dYSnE zdH?t^t-;Zi!F4uv#@Bh-G>0ajqZXSm&iQlcU;p3eD<7{?76nA z{OaP;lP-nzzU)LGta|GixoADOha8GumPd5q(7$D~E z`yVkkEI`anMtMD^BWqY=VF3t?mFKh4nIPAj;;R^RcmG$+ttf~aBjiCKx}u(HcsyN^ z@u3hH&$(0x_r#Hag=I&>0uoodlAb9>nmIum$`>5Jn^!%q_jVmct50M! zX0y#_W#@A`<8m&jYGLhGyFlbuExJVuY&34RzdYgRJ>Zu;ZumWK#653@%VTAZJ|JMV z`a^P7-c@&dhT65@b|=wMM6^!J2(E^$bm6v}25%;LM zJlf!!sJgFAWCIYu)IDpbkpv5{bYl;h%d@5ltJ6d>^%Yn_L^iNDWbyGn73SltC$PVt z{Tf#MwJemzN1jv0m|*CtpdyDPYLvrZc*nL64PHlpI}+f;PD#bc9DtHs`uN@hZ!s~- z0ZFP_*aDAL?w3VYh?1;v-;Px5&~s8HkJW6B@;vX9K^Ec6nQj&L4uujM2v4Z~8_!It z*}q_3%{UJMllua@3?aJk7Yb1$>zEto7zpQ>&;qx+e{v+qJt7$XUQ;X9DF+mZv=9={ z4n$9E>=1%P zM%X6)P<7$;q+Hr=Xad8PGvfrov9Ez+E0>^%iWhoHiWa%u)6zR4_9iyW5!+S#?sO=I zz|ZXNG_%QAK%`fUOw`4@E>Bh^FI*=L7YI}50RO7S>d0Z@uIQ{y9pPzx4MQgXvdGV6cTf-=}wqG1qKP(-UGEQ;iW;+n$*+B81TCe&I2EVA+q< zLbi4z!!fmZh_Ss>`MU21dH~e(y>p8E}Vc6}7Vlsk@r<ziq}`{T3$N_ypYAm_B8x|h z3%p5mC23OkuRpxG**ADwooznjNtPcKO@V@$xxCJIn`0F|c@C(mFWZ#BeIkzQW#fWi z=2tH_tx+d*BxWwHsXX}a?!swSfR;H zTU5NLZP4kzW4mwy{dlQ`)N!E1`$X$(o=mq9T;p`H<*4`E;j_*Pt6jw%e>E0+n+#PY z?ZR5_-S7yd$A24j6%J(iXb53ewSv7UX+}?@=hp7wfSmfN0dSV*t+WZHH-)acR}>nF zC-8d<&Y(fr#l~-TDSOusDS3$)Yvh~rq_S~F-A6q8_fvg7E%+vW*SQBcKSmEB-R|@Q zgGZow_77b&IG*$)F?ee9AC;l$+!FMj4Lv|Y^R=qaoEipp>1mqGRF_+J?KKj;wC{0~ z>OG}{$%;ox_Q7LX^1r_f>)Dxoeo(u5^if|gKdAn_0wwRXVVlnB{1vpW*=rK>><3+a zUDLf?4zhIWZgduejAv&rIoN>JiykO~9@y6E>gGp%^y&B3*qt}?NnQDVb?29v{ksLX z{U1>0O^fAZxAt$Fr(LhVj@jT&ZS4=&rv~tLG`b%%@vx+N_tm!zJe%Bo3V%xsrq$`& z-XE~b(l0@y=}KdKo;$#+c0J2))0UxKT-mPaP7i>xA2dLkzw$t$D%OygYP!LjFxaT6 zd@8^JP1xtCP|aUb+C5cS+#A3#wAo4-F3Z3fF`9BGUTvxOsqR)DK<<{}KKRqRRr!D_ zd8nf5Uf9o|>4m*F#$SN3~0)%jQ3SIMH+YSR;vC&9-y0q?<0|0efnx? zsq1?u9<^J#@oLn?>Hkcf9Zb&Xc;v6SXM~)k~%Lf7gKc*x{`gJPp^^e+~@vj8#8zBP36UYcks+Y zbK*4bTHAGj1m)_?8>(C&-4fJq#%80t^Qj-|#%joV^ev>G?1*EZf%>neTyt(21F_q+ zw>+tD`FMT2dUkrG`n7#!Z_wDDfx=Q1<6YpR0NbyhbvEDnxOmupWJbz*_InP>*lO_( z17o#R>^^Tl;mN&RPi&0dOY+?|bPSHhy!Z*no9@=OuL7G0#U}5rDf~X(VSuy#ECpAh zn-%+`;sykv>vemzuOmlotbJF~qB95liQ0DRPJgPPQTsd+eNQ=*aZEs}AoS$aC*l z=#?+qNfO<}b4BqkugxjV4Gv*t|!u==zBD;Z9sX*%a8)+ zH~(T2rL2M1&lj~H41nTAd*2-RIXkoqw{^q_K7y?(hYGakvr32U3&l&1$tlwP1lkT3 z+uhtgC(dN3}Ny{FG{J=y>D$3=6m1WB8FHzI=hlD-Sm0bBw2rDKFUOq%c(H}C5*@Qo& zkcEYatz$-1PY~mBA_|Q|#9|t3EIQE>!rX_LM9G8}H_Oh?RpsH#Ppj#U&zC;Wt}ZXF zCm-CMU|&X_4_wv}_pi+IKIgNOr8>q#k1<8tY)2;k`jwuMJEv4 z@b2`nM@@e{uEDQ*sn3JM(5bXIvNO$oPYlnd^{r=Pmx}Lf>zj8_6L}e;?Ks?SaUyBFC z_h#`OSsTxbVq81E&uDF9fquEP)-`OA`903W_LMqZlwYyZ;uQ{tzfi>7`0v_=(ys(y zeq;}mn-R#c19^bhkFY`M1E(n;M_i6N!;a(UeQr3B~l@5)c0xj>9VImu`ZHO+9 zZ}u{+TF;KW!}9(>u8jLSA3hTo;-UIkjy`i$bPFkAl~+Vx;Sw9*+L-COl#_)>6sD$D zoP^2Bo|g*QT>L`QC@dM_r9qk`yIWT@md@KR>k?zvlw}_|GV`L&AZ7x#?r0V;4clfi z?WG=^?Q@ztX~hZ*@e}l^ZHyEG%h|D_N;RuoI*N5Xu7Nb^Hw+x;T~h@QZ)lil?ED1h zo8QdY!21W_6;+njx7$OkIr)%6hjohBW!=v#(ZdAz)h+4+A3HEqYQJl^-yjR1TqWrm zb3wILnESx?9}>KTJevILCKsBn+rzn7)}LFfHvDR|XR9xy*|*APPZm3~=sn`32fq@x z8Al}L_3uhU-w$7LBzT^M$UWI^9ID}IOt@I!n5)2gX;xiDVW3D=`A7aO?Z!!KBs4tp zYmko?b~}pacCw7Wp0QHd{tIR~x@Yb6E4aGX{o?HANL@CUb5&y4-RJ@Z-lqRE6n@rM z1uiW$nWb zc{axIO!CO#=^1uy!|gJ;{6}VknW2~aU*R`G`v9ArE^E`9?+HEPQ7m^p;oN8N3zWD| zoMV8N2Ei)NPauta{pO!HTH)7yAASFwet-Fh8?mn6-d9&vjIc0ps#7On!$feQ)O82p zG{S%D=MsofZr@b?O8TSb&YnQg1Zy#37LV^cMjkZ;mJ>-hIJ?ryxLNNJ5Ib!BoXTNc zKH(R{4}oQt%2&cuz_F@Xo43KiWI1pE8TXqIWRo%v8QIIBS-sAi`k8y$t4HP-`+aW4u-s_;F+|}8W`+Ru>*S?(8%df^s@c~2Jf)p zusGzRhIQ(pqNWirDBVMBgQ4O2e=@EHLp1Xl*E;geD(Q7F514|kut>G9MopL+&@P7w z*C%i>M#j|Y?u5g12SS-bk=V84#%S#gqpZPXw7F^VaQy zX?`O=l0gIJI(`b*m=|nNGj)XmW)(XrYg(lJL*1jxQapkN?Za~6MWv7zU+E6)|w12;8 zyr6rIerOny%hsx<4DyaCkTyb%D2E-|C{O#+f{o_bT)l^nuUcg8rS~g+LQD&lqDgWd zV1-&Y0a&4MTc>T&Q0Iww@)a=6fP~`XOAuMfupU`YA_&tmFu}RDxm=ktSFnN1pvAL) z3BoDEIXAa$<(ZB^f-PZZBPAil_a_H#VCjh4Q$gNo(f;hzZK7l^6=9DGR6~kb5n_-0 z1(dHSt|0LPoTQ9~s8y*>R3aMeu_yxz3A#*PoaP4{bD)f%NNq42HdS6d66-wHP(aBN zp)8wuC|rhe5IVHE1!y%b*Uye8;8NA5$@zBrrZ*kR3FeG6qRr#6=18L{$FDeA5OX{> zxF#xa8xpz4xzoTwT|&<7h{@`)JYDg+a;O?W{JcBPNR2juP{uSBa-&kxWHUz`$mKWn zkA3mE`6b9__D&}kA08<@ISmMCR+1jP_PoYb6%04$^!#=~6;a9{dgHg)IA2NNM2dq6 zNTt`Za=7%gMWQx^YB_OG>MwptQt01Jkif@k^<1F+{+6(Wmc*x8z#UjZZpzBcMe7Ly z!?4wXFcE==&c~B^5LkR0OA4z$H$4+tFqNGeJT=oUTQ(nk1TQ6(LAJQpp+H~{ej9d` zyEKyYbk<(y@&UVVciZ- zkpZNb2^*Rem@m#4B^d<-&_eC&xy8sftJu8#Tt^TEPydFb7IpbUn5YsH#fqg+y`93a zVN=$M7S(Xr5dqLb(Lr-G;)rFe6V*u%A~xHCRsw69pA|+1Fe#m1CYysA%c%S{Dw2e< zc&SIeZ9|GmE>n|3UOwWaaxD@~X4flF@)RzRPA629l1-xAm6uM^6e{?TOP~}wmbLSN z63JDDrHx-Opwh*rBRZ@SCzJ-I3MXdLEQp*Xb6?Q0c0kV{io5Q1-v}YcA5<_4P@ci4 zHr1@x6#s(`Qb$zlF-$06uSO_ z04vo5v_e$h6%j{>i9rMsFIgoghGJGO{f}A9{zmGVVd|N015Sa`KfJ2L9vKl257aXZ zWt_cbe{>4|=wzJbbEcw!APjRM99*&mI^xZsQO_)rfcU!#WugRP!I~=(NhIQIP?MOT zp{6z3uLGG9%Qr>{nEtv2D8jpZ3gu0p zZvO}4TbKnci|mbvj9cH3RTz5D(|PgjDaoJ?FunV|A>otvNYN8_&4NMQczVww8T^J3 z;0NWv$r!R5M8!~XiAH^v!1%HHU}({%fO72p&;pW9N-lsxut%_b;{~U{7-) zPoypStbEAb(+q7=*_KSFJEAq9%-1ltMaH0M2e5F~HDHWzd;=LB(ggo%`}i{8mJu;( zz(0FI=nxxwWr?B)Pvdt%?Otk_HT32>i<8K?00MSnXJ<4pPqVQR4y-2F&HOL(^{ep) zo;Z_tGT^~^I<5$#vT2Fk113To_(zW zu?0$4QKZd^KcLSey8nP>&a-F}@x!5y)!|g&QYjm{Pz;<4!4{_1c`_Rgp9Z5%2BVDz zqs;~<_XQ`91usWJRdS%HAyb>q6ltA_2wq&Qs^Wtq$y5@gYxxO0pF04<8*EV+;UufB z%frgHQ@DY-`#HXhPm;R%KDkfmQKw604s( z;TP^BI*a5K?JKAhc*B!;!&6jF&As3Xy-bldlYqS7hD>+_e3&48i6_R0kLa@>*hR(k zj$-=7vV4s&f5}GmIz;u}MfFl(`k^rTu~7P}JL zrCbZl1$O3(5Huz1i}BGUI?#ZTr#4Nkpddkz(M4ImF;0-wog|L0JCv^eExD@`WN|r_ z%N|-1ll}1~BaeoNOm3`H&G-)x3FDzCW1`42kw*YtS;%t?WqBvlC{1NQKrpBpW|@pj z{}2Ph=N86#vj-8$3)B|P>cpkpE6Zefbs~cp&O*+;M*1nrE&G?h{23Mysfmn}&&R2a zj6EbL#rgg~%>f8ejYL-XcZDCDQV(1Bs!Ohk=y0 zy8`kF58~-i_YHJMuX&5yu=l(cmp-l73FZuTSkd4@E6LrCyxXfw{|0?8{)uQ7 z<-L6ZV1mjgzetAir43p@$fnpHq~Fj;@VTyHdl`iGBMH-*7(7!-?J3?{HWp30P8*lZ zgRCe8V1ycTAq5zr8lZds8KKm=&||s`(R7)<$rHYu7xC`ZFR~##Vkc??1$e{mtcX zIbgN345I*)&xP#w`2(Z=mCxhP0Oj+5HxEfd&MkWi{FGX$UC~0IQ46-cru>p+-LIxM zETlQxZhw9=;0>qRrVSY1{E*GmH~aU9;7z^2l^(C6%(MwIJT*|y!YV6R}PXG)aoXW6#VG zmW1~|EFNLeqcUjx$YHaVS?pt&?7#HdnW6ON47$p~?vMlSFqms6pU0AY_RyyPs&}8M z{_>1Y(=&tp5XWp2s<2}Qw`BtNU1hS*{BJshH`1p&B8Uh8L{t9@Afm4s*B1TtKL8Q+ zv0?p*kGRo)0U{+4$S@MVB!CfWu>RUmwewv$C;)Sb4E&KZ3;W!OmW{Qq2Wn9O0EqBs zKuI2)p$KC^z3`z-(3{cC0><=k`a;czD)jl+GK0*Q`{JS-exnuRe;fg`|M@+E?WAEM z$C*8m;g~ToU>{lj4-gHSwoxf?I55?5ovg~i%=ndwNvx;n#A#U$zalhOb5#|wgQmBf zGGJ+{RfRBHtJ@rEtm)yji|mc5gGxL7g{+^Q-$yV}BjiI?yqD!3(fiJd z=rZ0zNCbBP2n2;2(;%v}9SJ~19HnBK$QYO=KAdzDZ;o0?j|Tu~Gz(xu{)+{)>0+AD zk<2+_n$(dEtX)}iK#zj~xH}1)_h&%x#6T6{_KfN79V3zf(?S{nBAm1j{bDS!qp~-X zfzFgL3x;iyMy8zf3#ETOzYr|{9%R9?R48TwNhrigP%{ENA4uSM|L~AK@Q(yiVk|s6(eF`-Eu^2M{cogD<0)?(s`CLiX?gw~u)UGKV{R>d z>KOB?yUel+9pY+N*tbi#x{r4&QPW>Tru!WXBJ)28qzjr?hZ!NA|dNrRvZD$}{K6`yRW~r1!DZa3?bI|YH2xgWEI^`5EiF0(v{ZXnd zNxom60Zu5yrCT8Y9*UBZuf*YGV?8V?M-p*&-d5@Lqwc7ADbaT*V4yrb#-Vcz&7<)% zvyV1*p2p@mKO$Y?e|$iA4$ZHub^No(UgmpV=7bdf;_Gzt7t*;ptKN#ZMf-LqTjp-< ztMYQ70}OQl+%M*5($b(i=_pWzZw&SKvnTKE~E| zm)WpCpkgf1JGS0VH!wP@wOMMT!?T;PS^ls=$P+Qb*ppMZ9%aS=clhoU`Gn?neptJ@ zff3_zbz2{&akaBL-NX1gtGDslcI<|YD}Qmslfll;_ZjWHqklsnq1oB_`H`T}bKZ6! zz{aq-Or+YG>t1@@+Dr@LO~>Hs{B+3@EZ|ahH$`{+L-nLk_jSUB&$hN^+|73Bv29-` z(C&i9=1AG1+s81hB$~g8dIE#V%`$qTQL3kK8{u%eePdLe#&>yR)^gbDt1T$Z55u(d zRW=M8XC~@e*JWqJW$5+6u&@Mf;kt3;u*K^{GPw3Ny`xs(PvnE#n?-H$6NWlFLNDIM zIA7+(cMH+U$!*3Rzdp@T2qb4*ZnwQxkO|1!o4CtqkImmB-=1NRlaZS%oEw>s?vnsd zkVK!{WiM-5jQPRVORem6eT;{mVRdxdv{mAH3?qiKyOp^w{;ErtS7a#fk9YNnpWcKf zqa~%oU#UVEw~bwcJSVeDWqYw;nWbE2$qxj7+eUIHs89TK1#Hf`3>X9q6;hqN*i#HnwePK?ca*oCxW5*y-fxe z`uTW2qQsm|^tNyI1!g82h7j)WTL%!1bvDoUcXe65xLW5YdwblqGHVf1?!0b%F`o*D zC;i;+Mjl7JCSDh5I`n9^uaDZbR@rZ{^uET4jY>rv@N+VEm>m)-qf#aOk_4P%!x%bx zOw!sb!$!Ti|40}hxWN#WY4;qUy#Q@dluhyG)v)SP1inxtDt zvdeiA1jEm}fQwS27Sb;azz=Uh=Bt3yfiMzsAMR<7MTYx{#mM z>m~E9`zY2M-}fmAb+>1H@%0Jq0r})_#h#2FW@9zgM(fjb>;_)o6J4su_)8f9pZ8A> zp=dD)KJTe529d~~zkt(wle!q&v3UxS)eMRHHIyg0&`ekE zp6#i-ug8uw8b|w9=)K>%{i+DP>|`I(PW_Xchf6>1^yfb>oEuq6zCXXvp7pQv=U1vL z1#*wcPKt0uoLoO|9&t)HdrNu*Nc_~-3<4)h;I2>r;;S~nZ>P{+oi_Iq??1glMsBbF z#8c?KM`C{uMMAz~L)TWyG318ydrFbLi`*FH| zQ1)E!GGAiw{_NJuocZ&xXJz&*g z@FKb@Mi|Y@r(8O?F>N%to^@D&DXPA=lD543KCcftPu}5MtybsDIuTh6=wo4-1?@xz zmp{?@aQC~knDw`TQ~#)*!Kk+i=j|VHz&%tLa^CcJ9ORN6t3CxX(T!UD&^C+1Twf+-Wo(n5?0#<#4u3Vi4F$eOeUfL_%v2kZFW?EQoPynnFep6IOq1gy^yL=5% z~z{sHLY{Gx#s&F7@o?QV{wl;9zp& zr(mfObQN$D;$<6FEgcg$eX~9eZjSBnTB*~V$|#C39!T<{Tfg{2LBKknEM7pU@#n!<@Lz;aDS8O=_4b!u0woxw@kGe6@Fa-98`sezQ0Sgv}?ePK?hW z`y2F+{*)cv@2fzmx9Y6b9mdVs6X4q8%0hwOLt24?2d*S?9|?i%oY<-X0v3i%zf{Wx z7lS{e0>T?FX69j~4-W(zpG^z}^0HkFo}v22&tUQWCE?G*ZMznTv(4|xctlR*JE?p8 z7vFf_(iUHb`x~P!)AhYJi_^;D^=2sRA|ieqZ+!iU$&Dwn>}wZapUcHvPRO1zTu1uneQHp8J}WsJ_iw! zp%TKl~fUW?;U0u}F>5HXn|ImflZT#$VgqDa00!qB*!NgkQ3*4H%LR z$u?h(g_d4PCQ92p2sScO#$0N|zDJVo40A@EFrRFYuE|40UU3wPZsaQD$>`KD1OiDZ z3N_?O?^F$@d{|P^JU7I)8jidPa}?30+Q1=R3$VG;?rdcOng4GWPQ2BfS2!}hV6G#= zcf3(}oOXmrREF~Mnz#s40KCqH6XQSuV2KJ}E<{5`m^mVTe?9>sG9vC5 zse4mHzA`>BU}<+=3EayzIv1Nh%N~Y~5|wpQm)G`Uy;$iI8Y6XYKif+H<4;54PFBrNEtBt#7|$z?cfGK@_*=x!B3o8y|Y z3Id_Q1jLX&P!$Bhi2NalA%&6R-qDdj4?hq;j@K@YwQs$01ZGEjXA_NGO?N!Ze0z7_ zXD)477KJO%oSQArDn77W<~wLBHAruN?p!xbdIR4$uaosFp6;RidBKgPQ;&Nc;?zS1 z#mSY#dEK8Qac|jnCK;=6JjLE;%=oZHrKrGBTd`R>!KV zHOczvtUw}{i_VO<^jEW*K1`%;!ZlTynHRnLAC;1`AV`P*&7-7%@Hu z8)0DB(t^%JAuLJ__`D<}$={P731=x)2n5Bvj7bs*X^BvY$s9n6l!M5be=`SARB-`n zA;76}_LBAOw*R=0Br<>tO`Xdh_ys?k!o!Lo7WH~o8$%qSIk%mJm-NXC+pY`tT^HeEQ-U8jymGJ9Ceo>lERCD|=5 zT!yR=HjdKc`U9CZNK_VqbGBUtQed?wp0`78u~Wln3DrM6Ib|; z#gnztD|?E<;%lb%8`ki*qDi*jihHq2f^!$cRZkrt6Yj0{B!nV2>DIuBKtLqL-!AlF zm3~c8>YqY3fv|cPSR2Hfm;;snV(FM8ylE4#n|0}!1O2^0%4pAxX$%|`J_HH0M2Gq3 z;ZRaVMinqX1T0t-|B6s7tU?aIAY~#RweD|;_vGDgDA8@Rr)gE=MXXC+DYkEVmnYfC z>7izUS+)dcqDqKD=cB>IIQ)B_KVDogMN&*aCkiP(R3gbgo}5v-NQ*5}C3QCZ4vScI z!#rO4%p;!KBubKuNv+5`7KN&@3vh-Hok}R>FUNo=tj|xTx{==?Ln;nwzvCLy{Rxqa zL?edW&7xELzKqbQ(Bj3AAh#bJm8v`Ymgq}g6 znn{%I6Oc7%2z06sd_WC-fPtP-M$0Ot0sx`CkA%R7N$43e=$XYu45chHR1k*$;6f|` zN^mnK&@(JXf&Q-lVj-IUxX^F{%my_PfD36bI{>&4iM&%p{}!S32^EbG*@zCrjUIg| z+L63@zZYs+tx!FTdvd`Gws5@&M8wVovxWgMjFdAm&5y49dMRK#rOehqYxHr0dZ;8X z$~GD4J|z@)z!Xs~blVN1{jO@CZi)YU4)FCmblW7Fy?{cWZoYq~QvbCGSf}4S9j*J# ztK^W^;wX7tL<>}i`$L{hrPL-i%1f~6y>R59?I2gc;iS9|>4pbrva)b_Gf>s^?O_^8 zP)G!EKUKVq9QXyMOqCimJr20=4>*LdNc7SlRT3&bYOwqW1?Mm!6>usb@apkEuehnB z79comd2V3(N^=}w0X>@Fz9?(LvV4@i;IgfjvYi1oOMfc~7i&#!qoKSjZ=1V8z0H7m zPty$~;b}ce{h3+0GjwHOgosCxsS)SEORTf~7=`Vd z3oKzGNWCpiYjuhbVC?ar-o=^P05u0~<0npQ;LyF6vgtj(K0N91-XO|$njitWJ^bmd zXEXm^S%olensJ=aJmz@Xr9verSg~Dl4Y*b0tAX=2$wL^cyuz7#G%dh@u!$8 zBVxoR=6zxXrkJ6TqH~P67~v)VU{OP;iz2{iN|$hA$mgsGdvQRWaAlWgjervu&Nf8O zc~Z(y;6oz0E4lG}Lv%bZ2eSZk<8S~g_9EWfj`Dg3FGK$?6;jxDi;rzu$5cZY51zS4 zJP#5zAVv)J0!ojp+E>yh84@s#Ae~=_WosFtg7ouWDukxNU%mjH{mT0X_uX`8a7+!b zd@Zcz3vqPHzH*s>ma9|4CA#tYkMO>f(*)e%dg&w_L~9sr!)I9j`%?@KVY6VI*l2MhNCIq<0rccbZd;mK{TEqX0e( z;eUZ4;D2B!;y=Jp{5}{R&B1?xp;hE~AThS6%Z>j6LtOTy98D$Rb%JazmU6m7OL71( zR8k5J359}Oo`QvfKO&kR8csYi1UfP#Ix<8$G9-YBl8A*e92HL0CK>M&K*WQFF%E_F zljOk@NxceP07>96{IF@YsiG*z)%+!a{5QVq<)pKeYnCqTtqHa;S#Z^vR)Z;HLsyYW zA^IKtTq&_$g;-Ya6#3s3L5nlm@ z(}cm&v}0`8F*fcV9@82c)0!HiHZ!I(G^R5#?v9SMs^nK8OwZh!#%usS5q^<}{>uY; z>eejCU?kX!fy^n`C6C^fMf(EMvWI5fPs0ZD6-W1?4`4&}V185RT@iHdY}&WjEqn57 zfdVEldXY}lJr@&5#xOczPDYUJ@+o4Jk_D57K^G1tq%+tMLqUJ?VY{9eb-x-BT~fbs z{~HV`A^7AKN&N#uy@EE2`{03tBYD^H$(=M18ZTr_Yof61iB*>ruW9%+Ee5Te()ZGm zbXT39k^?5%N(sKHQlU{|CI^}={q~1$vU2v z-w&Usi#XYCLtx17B)~`_^CW@n760raMa;r~fx}O!6wm!nGIZLMU$kroV&TGrH0P%E zX{Vfnu>gDQMS!YLyJ%5*yY;Lf(`REugMc1 zMtnTE7ZIG^9P1^S$^K5Ly@BULF61r&ePj}JWOux)hL=$N$zNS{(MoGk;P;u!FV7$* z`$R_jNR?ev*eyfY5A8t!4C>2`3ivlt6cx~m3BuhyA^2O*M6gyJ3v#&AGu)ADvwrZ_|r(Du73IWuRM1fv(1HC7%iBwTXCE%m2C)-5FMNnZ?7~m!c zaN~KlOnFhQURV_iSm>dhh@zd~L^t`=NFqBpT7p&*xU3!3PRQxv_NM|BBtwEHM3Gm; zqN?M8qOs4zpY-k^_Be|$w1KY&=^6fuE@P-3u>Q#z2OtT2Tc7t3A+ zSON)1I#1K(9@l|6gVO^aA(p_PJ>Oq{AP*$#O$8)|NH9T^H2)wP(f#!|&p=)bG`}f&l!jEal?LP5fM{)8;&@U-pupA z*I~ZupG%dTbMoErPhN%F?>lf;hXV0!R&t7?;yQU>W9N%dY-VV& zhPxV6)zN*Gdls7iD$7;IhJ&@6u(^(3AfDP7ya|SbrKuhd@Nl9*zd=05pr}|)huAb# zzqBsb5)73F)1Z1GxD%uLj?hu_0Clb${f0DIXXuTYcBea2#%)utU_i z^fq;Fr0`ha4)%Uai{7Bx&dFTZT?y=@MON`YCd*HDmbc07EVXyxosPrRYV>e58)kjm zJ}{p28-;&S>YqQl)ryrpltXql|LJ6(_q}nGnXuFOdGvG{rQga?RJehU$#9F4-g zR&Vox(dk-^=F?_&Kvz>1E|6tfMcWrgw$FK07js|MALm z#O6tQG+au~QHG65(5m5j=WUMVMmxkj%~qPkQiA5iE|KcM=9l4UuO>;warE~hvPLDz z;UpvZ($f0LA$Rx8i-NW?2}dEm@t0P%l5J+>d&My%!Rpy&lkY{N_ZItRdPGEP*Om+f zvL5Yil0$E_J|pjYTWlm8U+eLdCc$$6B3sckEW@+jx2aQje?6Ei-}JK4{f( z8%I#&74Kis$~h(5*=n;C(+Is8>fAFI=;&TD(^>GF2&Eb_jb)Pp7V}Bv9q8FG&K9^NWkd6xb&RybF4(d2Sza4%TbiinEMV?Y}!srtiQ&Rm_*X#SD$k?dz zh5hQ}H~0ko<~e#M!4LYjP5y8S58sSh^E+61=l{3+4S+NU^Dj4rl@F3%ifgN;7K~(8 ziy5=?J-6?3B9G|s^hf-F!n3xxF;?qkw%p>13-v(@MU_j2u&E*^?5M25DX zy(?9#Q>X16zrqAuWDU#Ffl zxHjysw=`&m*2mLZm}>o7$14gn@p~KZt%)BfGfAIk2N5$|J)GX#lWZ=nR<63Y4rT&& z_aBFp1g(YpYYdSj((N19f1j>zWZHwFuoh23rVmSao$TlOMG4%TUgxiG3U=x6W;GdP zB_7r|n+}^B^=y6P=X=dQWNrQUPI8dI_-kg+$`5#2&Fn=^TCqB=@=`NBYv;3CB)UYO zQ;$?h{$)o7K%R4N)l*DxH?K%e;!cZJd){|17=#+XF(XoN+fsLP_vT#g5MTgbZm_}k zGyV2@2AkiHJHH;G`>@#lUej-={+LX>+8){cZ#z5*-VCFryQNB6Al}<)zSQW`lcA0b zjJun8(gVE@9!k3>C8NIftJS0X&v}Tbui?*5IV~Th>Re|x_1feuu*bKOv)T0&af#@w z3JyRg=iukTawykcxY%fvP3w$}i`5oVVecePI!6s26FZ;NFn7Lvw}YhbBl2W1%E&bh zf|*@L$6{MhF564yd}*{1MoMn3(WV*hnzK}}?I_ei$vyDu0Jv8)O-I+#{!$*y2-|x0 zYB*Y0RAmO;IJLF&ggec(>YUMr?LqwW$~ zxILt3$OtX8rpc$ZmA*1}aUoPOSWFSodOdeERM;JCUPw`-93|t>o1`rj&Qg}vu~4syo+eE9(q}NFwJ8vQ+TD%CWs6)FPuX zDUg^auz6?GUv%wR|H5t3b+fRDqCPn0YxVA~cl#Eq5?ILjE1`eG@2+1Ta0RKA%J*l6 zhrN{L{3~_a=CYX9XQOHe!aA9~iShgc;vB3xrp~KY}8`@G}DACnpw8Kxb6CV&h zc6C-Dj-z^~Ofl0rzxW2r^9F3j(e_x|kdiEIJAL(wn5UZWqOaKN`EL*E+(SRV5C2Yt zEu$qLJb`Z+_XodKuvufXR)>$5zjL^w55;`94EElhG5VDUugppM&uZ*h2dU>DVcaVLAQwal~|5C-Rqy{t*2trX*Qy)x=eZfGfv)9O4L<=<$NP%2b;}A?sH4!L6 zQbezm0&CL$14(JsT{z`~;4Ly~_(Q}m98wcH3E(pF4qrfQsM`lS!TM+zAV)4>3=eFC zDe9EeOcMo^OBQq!AvHB{0Ob-T3@VsJ%C)e2&BzbPf15cl={@8q4S?r;(3oi=$2Bxi z2GF6gbb~3Yzzkc5B*c`ADXV@M8U?F}{T?92PZ2Jv0AfZhxve`Asvs6M1V-S&;kSg6 z3my22*TVeUU@i8X{pZ>?JXQoEzE%B@t=g;_m35E8>8g(dgP9-B7|c3)yJl^*+A@}l!a58EFXB(2%IKQXQN?H68XOk#Q=n9wssay zCjaND80P=n)c=3QOU41{LyzktIC^hw@_JR z>vul8oY7wXoUo}W-I<%52cDBH7LK?Ko!Xwv%+yrv>|9@4yzK?%HQ?-xvK5?1Qx}c0 z4f6#jZjl}#Jp78Et>%r+rY@po8H;C4=l1!^*+oqD9eP^PnkWgWMv@VB7f;6mpQz)g zSf(fw1|m!7jXQng%aWNs@mmWjhzW=u|XHVvH)gwgYeR8iboRSR1-r8$c_BAK>p7QfUb`#o6B3JxaoKXPcH>T&+N47yuB#so zINBh-G-j2hw3sEcBQ&dI<%|e7n-%kseH}$Ie!;!VDaM=Ue2;=E&~lElnHDVgC=;Xg z?+T`~X;z}n@O|fB!4)pq^ezEAQx)OQOL!$rsOBRM=df_Dq;!`e5aLj* zOYne_Ny#=-g)76QtG{lZzG*f1n&OV?P=45uen{?0f7BOXQsQP$)p6 z*P{*@!)-_?EyI3}R6c5oqv!!WvctotfkRO5Un~um{Q%YA{M}mQ6v(iw*%MyLCd#b) zZP1T295RTy!6}-baG(~%FJU%!Ft5eQN$Q7rC};9K>RC)l{* z$FCcMa+H!^B(OBW(qK4ajl#4=^?~mQW@xle#~cy+kYu>y(0Y}>z!30;L*Wd;a=jAK zvMgC?UZ%9G_HgYQfgf(_fJZIW3>;x;6pE!Z>CK>^&O#VP`pFxlE*a4`6)J`CK2({yS$3Fcm*@T1#>4rL~rfRaOh*My>RTE?(_*pjGWRx)%pdEe_ zPluS21Z^ShEW1I};bc)hQMj@b=e7n-Y>-}uCX3M;h-FNZMr94wIA!00JVTTLX_Ykz zeUUK=J()I}lGLEVJx`;~qLyt&cxSNWV46_4i{T_tItXpP{N&7l`Ae#lKdy3+pI&@b zcpO5K1w;68WlvTP{|M@jGPH+(WZgan#sHb?ke^_5AS>AUM)>Vu?|PrjW+<-jp+T(V z3sXy2Cl>wi)9H?G-5`Z&n=Djn>OvSAnSC*`??kxXQ~I7sWyHQEnGT8Py4L(8e`=R? z_NmE*7{ex^=Q>6~stszlb>gW>CSajJQj@A}eMR`Cc+WI49B>R_;A&}Fi68B)mpWMg z;LDG_09&0NJ?oU#qYZXJQKEmf%Hdo#h+IbQxf@TVLd>V#;QG9qo!k!2#YFe*t_K~R)&$kXT6W3-5T81sN@T=uf-dEKh=}r zM5lpe#`hiNZdk)ngQUG~HL72LMWEal3K$k4yY(Xb$WIv_bpFKFaXCu#jtROSCCci% zlpntRjf{_$&PUGdrSt14cb z7+IC$7{M|zSSo5ka=RAbc$dp`dgQCx>j*EarC*UZv%Pw%|U@@c{#?2biEI3;C$numP&^Oh+8v@ z)N;{KOpa}&Gh*HLBjEdjVArA=(BQFbT%%m5k-oq0kO zyle>xkq^y4#IM6(W-tfPxL`9^PMcye8TM%$Jc|lKyY9oiKYODNEbjU$p;u8|RqY0L!&=qKhcQgHu%M~nDL~dl;Rn9D^ueUL zkWsjXf>rdfg&K8kUIP2&Q0Um8QZUC1mk zYtkg~Fi|c+xJG*EW zq5R;ucq9ud7nPV7Xz8X*wry_Vq7Ma^F4$TA&F5b}#H-o^!EVLk=>T|0_+S~YXNyxp zDZjthh)__}*IwsJ0tyGXBe^QfW-G@|r=#2n?|He_39)N3{FaH{Q{=Cl$=-p|{8FS| zFcLT#O&LL_DtKp#r%iHiSYBrWn?1?JS|(h64`ZEdH;#}4 zYds07^SVLH)r6V2HYciQbNqKZpK&u-xPOpL9xWcYC7}~AG=f+TGjtIn2}3%hN>mqA zlowONrXaRboH*)G*l&sxYY$0@86SsNa~=_)Xp^B7Rt$%>wuNQiT_JE{eXMYG0j%(e zlo$$qq9-%8GAMlJQ?9RkiT9^c3ZkFyNcXL9s+4)<4wJ{tdV5fQ5pNC zaZASXwXJS?mtHt_UWXiycD-JaZWkZDe$OfVGgU0{H%$qDcs?Fd?OsOnI5Oe58yfzs zO;%$YSl_=t(->`e>%6PgshcZxwKmbxI@(7vIjDJ0@9;3;x3$}Ng2N2lHG{Z$sc>w; zqw8`07<-r-ThzUEku6P$BzEu$J3lV}l>ENS$kqCD(J*1x5&Gu~ry8L<`*&@zQHI@3 z>}I4IMy#!Tj*Hwz{FBM{rVD}pW;F!yXxM?3`Cgi7xbfD9P7l?!TPAkWK~QbEJa`DB zn*Fm!Y%)YGi}E6ROTF6P*5hi(qiQ|fjFBtNLj>$p>+PN<3H?fK zidkj92?7=;8tH_BM~4-r%Fav4-+mJxEhV((@fdYlJT^K{-oYHV0xMR!rWc%6s(;!j z?20`!5`n!|tN_u%jee*1Ce>AwJTsGD{|{sT032Btfa}5&+fF8)*mg3pHL-2m#>BR5 z+qN;WZ9C~Cx4-lMx6Zle$Gug(s#dMt)wQd3byxLz*YmuKtrG`T_yjjk+o=~|74EV7BY*5T=dX5EQ?9ub~`q`OxR6%Uu)U{e**JyJ^>(^>SeEn!>Y; z+KID2$}HROv%~h~^3Fx<6(Y-H(;x?<7tLBC8$)qvj%6(8kexN=PORowmfsV1;_4=+ zz55f{?!_i6uq*c^eEzwqkc2_Io2hy9d-}6)7_+Q74@lJ=0e-XW>iGHeyn=& zer&B@{#Tw@DCMN7B^=o`0ecMvR%yNR6n)X%x96dSMfhM=MT#g6FXJA#()-KfxWgCL z{&7Mnc9%YU*Mp}WaB=)ut?zLdT|Q5+TvipxankwJj5;eX?Jcbp43-4=N<86knSU5e z*!Rf>V>bM{0Be;$6zn>Fs-wC|4LIb(Ayu{VK<@EYlVIFhtD!#b z&&wcFV{oPh(`ofv;Xefy6OfTGXG(?I9lJpY6-x?uxt2}Swl74l z;(fZx!7(M6T6?z9S0=iC$?7JiPKH`p_j$6Pz$Ku)dN#QORQ#DMGzlQ9WkD(^B`h0f zpa&D*z{l^N$%P^mc%sT`YCwN&tqJ?KQl!Gp<+h&H++n`;37+>fL`L!+jXmqrr3AZS zSmh8qR)j5hY5sCVI}8|Y8gv*P<;Wy$rl7r&w&Q#`oje$a2$$X8>gZEB)tudJ( z1#EgWc*$TOLHm({%_heMp#s0#01ys8wYS0Y%6hbbz)POQG42s69y{_r-S0HPfXvM$ z7kzQdUDMbbv8!9-v&Iqz2!TxyLJ^Tm6KLPxrAByqu)ZSrpIgv9UxpN0(*3c{BYxw6 zmy}z(CV_SuPcr!j^`on8I$c;_LKr{mkIsnjWS_EcIvy8`Q{bBuZvD2_-HrlJNh17i z9L4T|ODVToF!q^|1c$8t-U9creX`mRF_IB44l=*hZReq`5E)-PqPX7RdN2_xyBv~v zrCD$Xl>Q611duF1LtIBg+fgq$Q#xFnPIlsJSZjKrt|$J-+{ufkMYk)z_ur#B8D zV7pj9o>`J9U{FI>_7gzkY+T}i30Rtn8hxnM`QAeYqZw_SR@*Twz@zBuJ>4TSeO76G zNH?m)&W&RsK${0KU0hNatTA1ZQ5IFWJY9lHFGA8x`ZcV=Qi%1quGzBW z0qX|xJ@-2X$7RDWfA)a-_{$cok@y1n;7CoCd#p8MO{+nFbRh#Q%5^XlpQ49nvTAmt z?QZCvW@>6RUnTcjWBBxY(}WvWX}rpU=XA5ssWnaN;V%eF1p^CPWoQ#?$}%HW#>rl~ zAWvFw{>m1M5|4Hc)V^27OJin~j5`kSKoQ(I#iVC$^!gU>DPY{MqSRX(vP4Xv zDqtrmK(Bpm+l}X5|6(QjFBN-gK-_09&0h{`FfBDrh6ZtuIDQX5ukI3f^JM}AUG4&S z8JV0RxjlQG1C7Nx7F!1Qw8r8WcmmpVwo;!?6kg8ft9?Z(H21t-*VD2MS^}9m#N0ZH zOej8&9TuIPlK?CyUjHs#r?_dlR?-)Bbx3pJs8}{DB zJG4gX(;S1_HY=H4B)hUu7p__OoM)pK=}vxnLZ?kP{apV}8M_^VEy!U#BTTl3Nm+ik z-c3872g%rxO=I7y&(EmAQ(_i5MMl=y`>-{SdEWP)m!tc`Ho=5iIEi#GXX^%N_b$<$u<)~yvhU#f4z zlX&xQvXjYgO#;AQsuK~eJv)&ByMXVR+>1M`yTI2>jYgZb!U$Q1Z@>Lpyctdm-lc7tJ8gM`?qtA&Y1ql9`2=O$M9=XQt|PQZg>E{ z>#IB>k_@Gxrg}451bxVttOb7dI*AqA8=OV2pN4o~fH%A-yi^=ZjzkWZ98^5iX^!&kwk_8ggLxKZ>eqBwq?I>m-km*viIkA#?1Wz( z%aZX~AD?KOts@rcDAp)IQD`afWRFoTXT5>7AEWQ&*#WCc(R5X5D_)mETobqyZvC;aj$&Ou zsqX^1(L>-0ela(xQpfFJqX*y)%yz}8l2hNXV~f$RP*CeX7iso!fcwej1R+CP47|n2 zseDB92}Z0rU2T5gp$>Co+6X(f+ERpyxXY-0HKCse?#kbot`;&%bbHS_V&eZ%##ZCs>UY0C%>g;xQ1 ztiW6p-&kfWkiNchsX)B| z?%`@&k`BS>>&=WBE4`4@AO@YT4Lwtb9adq=b?s7B5Cy*h4OY}aOg}7&bwx-zA-;%A z4O~!pm~-rGq9roDX$6|o2^lp4WNB(iAzkSdbV-Q?UBO7Cf|!jtnFfqasr_Fbv%+Lz z;z85K@Xz66=dqE!IclMK?y;0YY(e`Zn18DN^Wf(aV^!$C<~582b;><0i$I0Otc!_Q z*~-xWJxHh%%YekD|Mtli`f2_f1GfS;x=zTb;$e~GXa zV^Kt+!SJO8DbP->ZGV?E9UjUz{jOAi78P{lelVE}nIf>MDa9)_(ivBeYG+#Orj@Z@ zyYj?A&a_1wE=0>>{LK_YIg&Y82@iv26Y*;o#?fRhI?RQGqmtURUcL!^QYZ!jWgONM zxjzRTvX^K>7!>E)#MWWQAbWgUq$&1|iI$oMQxv@_Ed5->XWYD?U9nivw1P1HRNl?h zO|$9+>mYp9zo~F(>DGyp5l+WdQB}H-5mplxPV@q~NFQ7>%nwYuFHeGH0t<3~y`P&` zk-d%*mNn3rD$tg-k2~dm*vd427-Yi#ndn1C!A)Ua2y+F5|X&=tb|S6!J*-w}Cqfrfqoj7)-x0G5(~fEH{HOTdYWKx&z9&J=)G z^yxu)X?9UrKwj;WSNQ2cW@)y#B6o5DQAuEy8pQVeg!j@c7RKGhmtO4?AY|-c2$w$% z({Fujz5o=KLE`7*yHEMP zi^A|lT*Qh2MlC6|uDD89Ou0M0+TFX<@I`QwD4&Q;Ou-NFT;rNwFhrVo3DN3*T;&vC zujtpHd!8#d&@+c)P@Kbe(YNY6g(bztb?0jwcaSN5`7i#1*@5KTK2~g9Uh0(rUb*1-9^QLHwof0 z*Tq8bk2TqCfYDBe@J%SU{h`;V!u-Z|=5r?Of{w8F0DFU_+;+Ak#y2(Q?+N~OIT9XH z^MVy#bJrnK+DWX^R-C(K91D>mDU^?|v`t(9A6;Y0p%jEX&J+SG;_P28eo_mMyjk6_ z9!LVi5e>Gaifik?gIw>&0OALZ{IO&F#AywP*i9*#-R64(*g^`NlW#dd1dI_o)Ip z%hnyJH~h!giyFQ!4MF&th60(9i>FGDt`PqL2Y--ha6D1@v6}bMv(epzOk=)@yGh&bxmRaCCG; z16rce+v24z>bMfbH*5u4wi2v2tNK4T_RH500$ z7h%dksiYxQjfzpPk5YGtQooB*-@>l%U@?5sA37foOZKm@prBOqL@9lGexOF;K~bti z($Lfscs_RlC1<|;Fu=!9Swn)q^wYb9y?r~p4N4W$^nG-X*#6NF_lwUH3~atJGDcgp z;1r<)$+O;`Q8ZRbpIMZF4Vq7E5d9yK{DM7ng(B}?RNlYwIwh9w{F1jaq}^vA?ug<~ zUB3a$(7vQ$dEtAhD2QL`Ic0V09+!1;~ zsC_{-)>)G88W{l%Fut5RP@E|_0*p|R`O-jV*_kLGHQFtuiwr%EhX#_!&!i^ChOd5m z$vF2I;yYg9f?#4M9JO+@QpQ=iVHHQ=;Ki|yl7pD&1#*imgmy-t*ER^ zjIIm4Vz#)lo}qrXOkak42Nq+Uu7N(?zknxdunRWG zYU56~am(GZ_hdbYw;IaPfY3Y2-W6`+&a!cfnWc26q}EcVzWc$+0U3W{EE-rq%XOvt z3UX~7);J=0W_6~_&Hr&m^=%O6I$<}=g+dX*oAvA8KViN9YHfOI25ii$;B6^J2bCfzxAOMf861J-e%5SnKA_c&uM4(vGFg#H2$%n0yI{(teD;)b9xoAdnK`Z zi8FgqFngKF`8W^kh)e87sIH1;>iK*m96GOmGU(~EsuHUn7^qEK!UJ1v)D1~vR>Mw! zRcSe({OJH{m)va5J2^_856REVBx7@~1t2tBT)>xuRRunz6{o&Ax z+TZc`&M@N`@%dHMp&f(q{}hTqbMNt}Lkh#dr1CXbm@}|(grlN{7Z0%1LllMx4dx)^ zyedI*$zy(nONg!D&41sazzsM6!IZ=}>jFO|4E)y^7+4A8)t%G7-ez##E+{&0bb~Az z=Ef%T{-wvK6S1wBRjyc-%?my_5s0q}sMX!{p=;^@0#vv528^^I&3T;i=0L5lv~_P4 zxFC>kQ{LN7F1yoLzfap7ovJW702?<~`KRB9ea+XZO@txV-MJLnEJtk-+(wmTv9g^s{HUb6RPS!>4wq;O@PE5dsrv+2NCL{Ay5HVX$a z=A^9Fdbw5>s&tyQwb){yZoD1zJ__bdSp?L zkBL@l(JqdOi@m^1Pfzhpt4FRp=vA688pD7MGVgA1CLhjWoG=?Nq3jF`W2cS9`Vsl^ zMvS6*YwmR|HJW3iklEUx-URvi<7nM%fBv;vGCLHW=oQ|24sv6|eQp`*;3r#E0T%U% z>*@;p!B0%r&vXLpleMeByRR*j9e~8`yOA4Xj+-T*{dT>l1Zx8;mNV^Ck3)9{j}XG> zYbu-H)&n%<`|}XWkBAomLQx)3@IbnPkme%d>Ep$EA#il+MtjrjO&PuD^(8lL=QKZ! zFf;2>eJRh|Nl+;04QrFP>n@t1y)CQvuENDPWNT8d$NgzgUe4=PZ$&%Iu?RIy;%)eb3r0?_m<}<=e$A^z$RixbAJcwU}bjhcJEfWoTduaztwH$lZ=uhxXS3h ziAk;A0PrDyQTAbf@mmCTc{!e6E6b7O=zivS*ljdc&%=g7SNv+4C-&fb4(5a5qU_85 zB1~HDeilRK*{{&x?_#X}_g%n8sS`W#bLzh?&Ks1~8`UNL7B!kx*OKU0=u5X+%kOt| z>)zGJp%HQY-cg@td4S)?v=;(Fz-LbK<-^^b*Lx!G-0|wWxW2&NH_o>%(qF#<;=}w) zpvY``VR!9vpbLUxtIOGRU_mZ%@=a`hF@fg?gMj(hVVMIKFW|j#Pk>ioc#_b`hTn|) zbZ>A4E)vrA;eLn0x7X#Nk>E>Y|GAez?%|fMx9$_yadpx!r@>V6zz|8mV%X3_&IjrH zWcU>@=1bta-tpGjSkwOYqP9Z&QQh))Cza{OwO{%OVCKhu-ptsPHQT$yZ-~V0*YGEF z%5U`im}QpkVwH1wkZ_yz{iRx`+k4EV{h&ds@ip_PG)i2F&wK4QqV2`C?QN1r$oQxt zvzBV(gb?@YxRt&_pk>Pt$&Rz3WF{U*m)|Cq>EOzqqu06I=mNZ{rh4=_8^~=R3D|BN ziEP3$=v}4Ob&{?mKhU`5X83|sTWwUko#3c-yvZ*5#ku7_Tx?_Qn3~bu-y0pDeCTQ@ z%=j%ju1@umOuT{gczjrIoV_jGDp(ACe*q#oZ{AdFOc$Ltp1r(*{}hG9hX6Nkc%?kO zx`c=Ln;u~DfLn);ya?cW0 zz~Rn0c)DAmmj1(hn~WivKlEm_dVl8&HFa;WR(F8R{bK|}S+07O_eW}IG|M+{%w%IO z8<+oN?uvGozqAuzI=YS5lm8Jt<>rK@rmy%mcYB(TvVuD1APII@8bwhbe9Bn`bRFuW zAfS;n^wWMtm6pqs~nG)Px*$B6<6wf1H-- zAW}gu%+o>FbTE)iqTj)!;YM6OU=OJF)+m0`kIv^icqhio7iOzF-M+?eF8sU1(D?a_ znxOa0YGnb!R4v>@{Qmag-T&LDbeLbWIbX+~=6F$OnxkKmu2+a>9f97n=EV7uKH#G> zmX9`+$$0r4aququS&M!=cK#r}3ilOfMC7fVhfxaEu_s8;`GwA1=RkZrSN7s-RookE z;;P^ra?r`vwR}6gK-oZTb0s$C$pi+vKL>vwD1rW_<^v9SQVWNp0vn z>*z}kq@$6Sa9j2}&?p_$U``a29Sqa9f=+0Ze;!`j<2Ac+npkWvE#}!PrOT|WN>^7X zX%rtX1)k{4=vmTLgU)B93be|!i_>Lz2|UP|Vx(bNG`zcr@|?nHo6}7%{m?0Yb4vcp z147{=_eaE#X4RgYzuM;*+NFhd*3stN=lnuyxDxRU$v^t9MHrMH^rf-`ev)*F1!O*6N4kFK(l&Jf<9O0~qwD?8PW zwZvM3(ip_IC8f`x9I~^A9qz>}FQz|19?+VU4T^31@(qGCl6;C7Tz@&4L-Z5=LR#>i1T{I<-Y>%&vL)pSdImZ@J(rwKUoE+RdTpKz4ySq zF{ZJqT+gz7d!SFOGDM$Qzejhpv1A1Fq8_5ZcHU}(`~6(Io|*2{cmpS=Stty&Q0hlZ zzA&e}c5#ZM^^COXbY8SVGFW9U07rbUx^l|DxcyMlgdC@__UtWw)aZP{c?8A3D39AV z0r(7~b|M(fg?IEN5WXOz;B%W`YjRlt65sW;X5Q6I{lXCS(2vZu%a)3!=t%#O?MhJmJ z2J3GeS)WE(`+%`YT*IaV<~vcpGaachj02`+hCzpf2FdbO2)1!r%~3Gzosu@gpOizY zniW`~6m+wEQv?=K6XXWQt~#8fjI(v4k+K;1#VP z7DXq37n@>C8UWfb8dX5Ynf9t(ko4Gpi#3K{hbOn=>Q(=5vzbV}7|?8%MyUmY6T#+h zZV+5fB2xc{Hv^VQ<=U81HB?-hKn8<2B6)XyA~ZaM;+Js!J&)#u{%m!5;RqZ>RtP3K z9(GlG0aYV8eX{oo4qj)J+coFl(htX>eU9UIPTGah`iEVwII)wiC!20jzDaez7*e0E zuhrfk|1&~b(D7FP_a0IBh56%$Ezkd-${W`I3HSW}OpyLBY|lm)SU z5fXTR7+Kl6sI{Posw8c@nn)lt)BH*kLpM2#+Vzkz`N<}ZR)|7)IwhSUmCC>OW!tET@~PZnu)jj|=bf+8uBHbf;OaS&yC*jajGJxOmm#3MrMs z(1{D-^7S(2r%m+~CKj>A`H3M$)5v%;StA*lUx_9x#*M1v+<|)<%l?pKKNAaB?yS{< zW-8!j@oc0dEsH&`W(6R=@;xgd&fD!rOxZHr*}&FlGv|%=pEh?JgSieb-C|LcbL+p3-&FW)A1$R_g6spQ& z-&2J?FM7iW>_9|hC^Lf)#8uF%CxT@~#$-ju>I%kUMaAs0hKOd%3+JCC8OyO^sw3y; z8aAD7)bN-inU>cg zM8cgen>9YG@{HNz(jkD;jL4W^mHQ2Axm1q~ZuQ&BhEJxt(S*iaxNTYU5=ym9_?g`- zh#k>}PA081s6%^sLWrP=Y{O3cG!7{WHw0=Uu^(~W)Yu4 ze2|S5TLz9;wAjDA1L(POWanjnMjNn!Kc$3xs!#|r1jaZshB(tf^ubS2z-=aXvQ>Y(QWWtu38p2ro>$Qt{m!9cK9@B0ve3Iv zOU^@ufgqDGZ8(MRl@~*6*go4)K^dI29?>;tsqb2?NC&EuQ%7;%^C2wbf&sPi{1S*u z1>X526<3vY6jD&!l^7L*uq!gi%Aql5I7TgNf$YD#qM^z~^Ku~{Dd@$}iTVOT5VbHF zUK3WH>AIv*ER4UwAjHu*5EsUG^AxIaqcfrkH4;%2@%r?AT({i?Wh~G`GG{_bWX(*A z1YIH=ep+D%J84vK@+f40<4C{=-v^KVE;zPB_loNYwOX6 z;0+Mt;B=iK69j!F}^M@Tbbm8tw$yq>QOhN@JJGj!}7xjG7ADo{3SiXs8tg_z=Gy z@`E)c$RmPbSR;Ip2m|yxbR?7tj1pkxv#(rj*iw4}gdNaDf(-X4(})Qm?Dh)vOYG67 z8UkC{cJXP$>qghI>T0>N>Z02>oq7JY8MQmQ9HjB0#Xzx4YB|Kvqc{o7l7PTRZEik5 zt~G=q9X5=O4xeT)y!{|J9t_{)PAYd$+Y-MlK0s9WNAG1M?4n@-kQAVo;Z3$-1`hk# z`*+47u(`|U$70ukpAF8!Zi~O4t}4zA{-IGX0hzoidgynq69QAcFG#IM?Xq?uRxbpx z5@q;^JvAYe(ruA@Y&L3(GtN zKbDSu>M3ZaC|2J{QtoQ8ww1A=HL{^YYIOF(vsmL?6b&2@2&E-`{FmOANX{F?5U1>Q z->;h<{Sx1ZxuIUg$jJAaprpx!d5Kl!{@ZjoNcA=u@+WH5>yxv+I@pA^DzDIUt+2`p zcGj$-0s;OITne8mbc|IW%zOid=wwa;?MJlv+G2spY*-2(jpnn~e8Wz73Lk~$v-i7b zzdhD{zi5A*SDgWaMW2c2*Q+jTf3kw&WS6wgZbd7%Q#@b(TJfy&f)mvvu25$VlI`Er)c5qQY|ROKFf4!s~qT zW-ezdGa-6lF57N?84%}TCX}A=CZT@7QcUlSm;lHsA1f)Q&x)AvVTvwk6q)Jw3s7f4z3RYBEdvHBXq~?=T9SX@H zHq>Q4@$g?=4k3L+P?-JGJV&{|y~H47j`qHRFh^=?k~S_1Jp&qj^n7cyIvl%TY)Bmy zj&0})YGeQLYrHyQOJ6MgL4Qh{_$8Fqp17e9b`)VahJh780v<;Amb9MCI*2fw%Rmc2 zsosR|iyrk)m-!DAtx=wWZD%3^{4n?_0;uaRud73H!_i$KJ)-kV5?E3Z9Yz$Y-qBMT zu`C1}?ZW|k)@vda+#quiX}yPEaYe)M!rfI4jgf}9@rKnY$kpk^J{ZOsn_c(f0JN}xPPBe6{eC7Eyox-X zDbI=Yw}aFP=+((-I>qs5l1=l0>efSg$swKhxIN=>op-70gaZ3iW#CNmdI5;{fFyJl zNnSH6IHo#}>p%i$K&}V=F)b+i#!Y@bLg96BjcW&YJ0;!oFScN9&F^tTd)V4iouZz zg)_m0<0C?a$c*gymi}DsUPbo)h4qs5{L}q!D5Yk5)5`JA4jz!Q<~`8Is9PC2O$dAz zPh|;W7zj3c8x<@Z)DaWn|SUIPBE%AC{JvtX^=m@LIp*I2L35SKkp-(9=3`NJVqbe{g zLC0t;Dsu<2JZ?p0X>_8)KdGpXr4$uOM2C5SGo!W5lOfQC<`8#yLO8Bh#xde_fl-#F zNjehgr<;8Y!!KQtsRnh6l$T$Pw8W;0hgJov=C3_k_0q`^I%4RZS=J9=@JHEsM!T>t z=gAXGm1E~i#!tB8v{vC7U&5gRNmC7S%#)rW8tf9LYn16GiN-0@2^uXDrf(9=ld9vi zgwm#Kn?1`1XEz2xXP8-C@L{$GyQVX9Dgf^1*Q#5Ccn2Y$YNEiC1r!+myWV7C` z_JzYD_0hV$LjX4#86Mb#!fOfoHA+IucUw-u1wd;SrrB#imv*6gxaM2H-V{}=3kBu~T3 zsBPgP?(`7jEzI3x&w-gvxO-(MdQq>0;5&^kI>`wKfN~QRyyN;dWCsjbBrw?lbDN%b zVoc*ruZi_71gi4&sxY$r^x&qkvP1Y#xi7zc1*q?+eCvco1%XQby+#WPd;9VuBEo;^ z6GkBnWBC><0M?C{8ya+zmdy3J}m;{yKV+*!Ywz-x)_0FvT;#f>KgTx z8~iln<)_g*%~?Wm6QiSPwKl*EEF^IP$n5-I?W;*Ud7xX4RDDMl_gs>?lMMRB-6#Oz zwxQHKeLBS?1VNF3m$ce5+T;W4^8=Cl`Bra)@atKua{YU2XS-oUO9Sppl!lDzu-4aG zm@skPlC9p|ETp@J9p?Ay>rJfNvb4%dsvbnASkbSg1UtC;uKaDRD-oNmkJ8S>W6}4ec*nYfGLTYGz9Gbh=5` z)*4^_Mr#E_@i@(N>ndAZozd>ZDL!9kV4Dd#5r&xp4t zVqJZgbUH1)F~vm3-_5Xqg+713%2ha}eD?^?u84Cou)8}=WC{?sb|+elmH zZSZdm{iK1f`>L?EXzq*{3qbEGZ%?j+-LFg#?UvKc1Spk!HrJd}^)tKcyR8a3Da_`v zQ*5Dk*2}QJtJkXA|E^tzwW7w?tL&8-q{np z?wN3Q9u-2q7Co^X#rL#Z5cVQ`U*AytguJ=BV7pn*ReC7%o!a+g=7+-)=~;woo=N1o zyY}H^>1fe*zYt9G`ux;zSuPVe3pr+JH6nNttNy&L;_y0nOAG})?5p_5x04}pJU5x9fPahI~)#{VXJOnwoj} zn0#6ySUf-c7@+ru0)m|L^j5KP`>v7aw4LlN{bn{tP>(0mz1F)e`?|_fqbY~FBr`j$ zuvg_UPhQ1$!Scr6klT9wAd&`?^VIb1Ww!QhQ&2!h3){YTK+ec3c@Av-fQJR}GWsl~ ztnxIiOPSo}zLgv0Flx1K+yDFZX4W3eXHYce&)^j$i4qC$_b`{u9pqJ!;MLjrQ5W`r z1D31(3}RROI|e`=xj=*=3qIuedX+JC<8 zxL7Gm*d-&aEV(N9LA&$k`<%VPd<@W6fVQJ}bkgYdoc+PZR=3!Rc7x9;m|W^g`F+1? zac=5NfKBKUq4_3tRd@WiRZ-kfucOVv1&7VWb%VaS1iui|XW3mc+y6~@-YRHt#@JUF zC-Q>N>s|+k%|OyCdMo&O#bZF%>T^k1L(#R~cS)yWx`R4&iIA|kpe!7(*W4q@)AR76 zIKSy4OSPO_k1ld^>HF7`kMDMh{C0KTHGEI1vfC<-Gj=(_v_-X*3I7CE5SdyO@1_Vj?;7b^&~mjSOg(N|Y`%HOR^`o+dw59vh&4aUySSn@v*+B z6u3Fnlp(xkFM5?J3Yxj}y?cC3N7RH0{h-TW3F1$1R}+}fF9mBW&L8!{T1-A)ySpgm zJ}r2=QY|(?UlcXcW5(ACey%0JTr-Fbs$shz?X5-%ekW7$EB+GD>lKNAKia#nUhNR( zD9aZ0nbei@()BEvZDA|5-KO_>-8(3J%tI??>b=^UbGx2inD1;At=ekJ^t?FUgJBaV zCq`2IcjZqb@gPERhzsK=5|=IfMofn~4eUs z#1HQ^Q`LKMlhZ+dT|b^UKwmirv~+^`((F|!!6SoiNn$-EfDeCYwFeep`LM{84$3{4R^SQ|e}KG~8$q|KOnb z@}p@||8BpUrup{x97X<2ed7HGI>oOM;UMtDv$0=$@mIHEoqw<3zPcodVv-)A{I$r- z1@+|e6*kj2SH^-v5vO;EQ%Fn}{@$wwKOt{yYz!nFWQ44RoMnV$ex3uZQbXiG zaq{r+km%5;=&-13q-VX51pgkEhbC1qWxow}|8=`VtObegup zSOr}wDP0ztVa?@n4%%9Yl+9Ep^K!!P*qkdn&q!Yk)a%1%A z%as+6iHuCnY^PBiiB}dbu2q1!?tmf`xxDwSS_FGZ{1{?&Y6&usK_0A|_sQ@-H{MD9 zV>h@D^~`~tA`?RY&orN_5M$**8kcyZwKTy{u}?>#`}Nl z2KsPI4m`*J@^jr7?z2C3LxV*cd2n5DFry&0u?0DctY^abl(LKbDtum?F}Z&PG&E)8 zJ_ZCt5Hzd=da`VsPb$73YuKeOrXC%i&LWKa&dV!3!%ix&X+GQOJnLJaw4yUH=MR;7 zDICna$}7{k<^^-rK4*a2y=$q?@qv5kBt1+FFBBo#u$iZ|M&Q(_*j)nA>MJ()7*YU1 ze&aNW7ReS@%;6AbM=B8AsNf%=?bKuN|L#Zms2!#fm&1#x}xY* zDG*jrC3orEkrTYq+Kr2BkjNY@PhYx0%LEMiOdoW~Sf4TAoP?PMGF;h;6&fQ*Npn08 za$fa^WeGTjza~El+`kk-e6+GklrWH|vfpVW3WRX1d7ua;b7SB+^`;$VH|_M9yKy9q zn`0lez(NttG~&VQDMx5w+COqT&S2)7dl+-S2w9^U|B_sFEhbKO@-}RcO6O@v#kJF4 zH}TB#>uMUX z%=H&!5%B3c{73y{d|490DLY6c}HaPN# zsCTq&wqP=uG~gucS;C2KniKy*W~5*W zA?yWJU>R5_a^io=>Lkm{y$k^cZWN|p)$R~N?3<;7=}|=_4eDU5+0||_Y=Jq-ISC%# zs)-zLod=h`nmn+ZJLY{V7TTGaDd4DX)Bu(K-qntnLp_eEv1=2S9#McbW**q?+YUrG zDAEmza=5fHHwsIEpH}-XJ30B$iJJ+I4^i+>4Wuu2Q`=TxH6bdazsOMU_8Sq^K-76i zOmIsqq`Wc$Z38?4k%qMpK0;m0$-^^kDIA@Doh+yioS*>OsXV0^C=lHM;g>HFX;(IQ zqaP;>LvQ#BND7-65TrhniDbl6s2$89<-9a+L4a>CZKqJ+&|r|_h=el97#?qw6V@xX zBf0=R8++=1>q94Qc@Aduq#n1L}!4*-WBFRw<=JPQ}n02H8w4wL?zHkVgmV2`Jf2Nco0ER^)v^>IfI!=$aYm za0chB!agqx3DKObxtkzkBt(X@MEqfBj8vFLdwYuWCcFPiT=B|7KORVZcD4JvX>dE_ zcDJ@1O$CtL09MV5qYZQ3Vn*+4qYoqEn?D|p>-WHP$u^)#!srKxkrU7sF-L+LZ^!p{ zlGm*Y2Xz+xpk)g%k+R=W3jE-QyLKdNBc$yAOhw&9O5Uwe2>ggg-HZj!lnq;@!nY`# z;ovsC_%ZIbo7TaX=a`6*>(2O(k}jp3HA$`= z`zRE`?9)3=8-sZA6Wty&?1yTQfG#ocLh>6pq{p1MGmK9)k~K8z!n|#;Fkgm10vUt! zfaNFPnto7MYWGW}o445AM(tj!=zwK;Xgx2qjwdSjOZ6M>oE;a1-t(A%WkZL|e^L<- zAb|esmz;5a+*aTYa1w#fWF;w@5?K-s;reb6hI98^qj* z(jjDnJCVK6p2@^h)^Z~m@kxM^VAXQ77>QYgp-qON^@pL2hoKFJp-qP^2LGqg!LcB- zDbhNZ30fJNt-i_`mZCKsU)Z@btj8tS0~+EZPn(}Z{p#;OBw>^s`Aktv(4pdZED@c5 z@<WgUA=UGZ7~ip*Ke|6B*(|>FJ3@uKb`BFk{Sm5otRT&w3H6g*yEOIt?^1 zj*2$z4?3+fmyZ7Xm~deVet;o!ha*LXmFa^S(^rJ`rE}y4XY2-N=!S*mBZTEc&*&w} z;3aC{<+A7{ir5gRa&H;HqM(q*QN|a5>1T-F2_1J4G&ubNsdUHYeh|V@Wca0s6)9n1 zP&YDyONYp$pfW#FC7W-J{DfP_36a$xNP`XIq^ek~wjd_DDT> zA2Tvf$n=+pDT|27pNJ`zh$)=tmRW>28&KTfurMjE8Ic>5brcS&j&bRX;NeGEj>vu& zbzHO3iJb%DVa+I#${WUepVN)QQ^x?wkW()C{advJtUS#Bkc=d~0Vz6=pKWh&Eo_<) zDO*Y>C78me94#uex-_raN^F}juRi=F9kzlCw4zz^SqFTH7ktSZPQ#9+2A#16ow){` zsbY8NCQoThUZL=DUPf!#Ac~*p083APvpJ-D8%ORR+4}Tf) z8I0fQbcahjW znCNCb-_n%hr5$^S*#@&Zj`2%q$>kEM5nvfnM(eCX>l6~TL!Q-!^FH*J+%SgRFo)zdfaEoS{Fp?XIaLJ>c%TOM?2Hg30}Mvns&U`!1D)471%p*^yM%G_(0SO^xo*Ab^o)_k9eChaVzHQyCm|=o0#E50Xg}EVFi6Ln#>_TsK zO{^ySKVyp!-6anH;-Pw{Jk1@tQ#MHq z1&3)syh3lXj+1Yy&V6oEtlCUdy)(^p^TA(pNf$6d08FOO7iqnO0FwZ~g;})Ye`v=! zlT14`lE{qQgn?HOyv}cv3MjJS4UnO7lpx<@f+@Ew;@20Qf4iCj+Uq zXfg}GjRG!~9~`I3o+aOb*q@vfy$R4WL%sg2BT~#VsyTz%G=^zg-x+L@!R(nqK8%`0 z;RDn?M{p<7U#k$H7dTwp&AR&%@al!!M@;p##~$bSJM-Z zcbj77%SBNNu!M@9J&9cEFJU7{4evIqT3N-{BY$dLm-IT1tleA73N8;l7N)O1P!5piWhxePOlI(Q1GsMtq zh(SD?$SPwkwlsp!xBlrR`D<0~Pv>8mij2zg~zPZ@>14gvx`s1YC+h? zdL`IXcyQDf4dWRlPXb3;T32r?Uxl;fzK&JV$5q!Ba|P!ihJEj}GJSZE7F5tIC&tUM`pKFl#Qfoi!Js!Sl?s@Gd(pXwjI4rSh# z#+v(m!_Aa%t@dKyQ(sSo=4o~~^sa5)h29xy_~W>Wj>@|F78`pIB9&aTWm)LcuqUnG zo^Fr653L>+x4BY<%T3LAlYTQv=TE-ZU#A$__9>~lFl+Jy>4aaO6lOyLNG$pBDEZ&X zQYbelJjH(94CL=EqSD_d7ItV(RNGpizQ67rn8uGVOvO>1Amr7s{LE{jtyr0>3epb_ zC!p>peFW%^o*F~&Y-YtaFw((02r=GT*p&I9&$?HJHIGjqf^AbrKRa7gQ&=p)IjpVX zpN+O39CBhS84ZP*ime{K`pNV?TQj^+te_V=Hck$ci{Sar59`#IQAG~Pl_jr#-!bIhNxZe--|E|SG;NG0PS8!XrSI%l zEta%ZN_a6Ob6U(U9iZ#kdiMs|dbkoamtV~FVr3c83(#IS`kI^B2Y$Kji+d< zb@UFWex-Cd?d%a>$ye`aY~-ixBr-jV;(VCle)Hp3XSMB6^ur_~XVO!D+~igkIf+|8 zKOOPK*AM}U5w0TH6ZFJC!+s|8bG6IToNaCyVdvo3Th3O}x#RI@hj`ukjVyd2oVyEu z8`pe4Py`KpPS-q9WCeWJ`hO{BMumMAs?tySLO<67s26S?LEofbessPX*HkOlT2uTOIPHlC`hjW?Y64*In_t&;kzGAkBuI=%`&0bot zTP+G@xhHq1`I-wk>>eE%d=*aDZSs<}*G+8#YcV=>WadFS}D{+1L?qR0n&g6`AxvGRuXLrrtT*m`u-(JjW;Gv&khq!KvskIveC_%hRZYF{*+8>&;C`QV zDpP2`x4?tQ+8m3|d=`tm^yhzneo};72$=8ROJ`+X(hRzykQ8scUf(l_*MH-}R@$A~ z$`Si^E1$0178?kV&wP26X6xN%XbE0SuP1o!Qmrgs2nem%$ImC;uKRunfPDXI*^0*! z$C0nRqYy1{JL@~*`E$~?;QqqloXPF}ei-@U`|~m{{{@@(C6uPcPxkcS{^e^d;q(30 z-hH5;5#xs(C)*C_79f8^*|{N+JpDQ##C%uzj>4$EFWsy*Gm`l6E0pEgS+_x z{A1T#XIz#(9gh*O-_zgv{RRCfL#DvL*Y*k6+T>U8b_w1qCvQ}~3Gc)8Lqnl;WVu&R z#@6)@*W}Zms)^l>ZWfol_o%S*ql95wXn# z3~(E}4cZrMs%LNdy7sHHc+IS}+)QtW-qjah+c_q9^WOoz4gRKJ#=rZaw)a!wMoFcz zC8?=RZkM`Z&nd~_H5h+r(f>?Hi#!=6$n3Ety%-x28yjhp5JM6L$&%V`O{S#Unwe!L zBhi}mFXiOA>7IEYjlUJVO&`v+zdyY5x^=(%bT3PH?&~$D!a%$@F9kLzz7jAipZ(x) zF?xsBCSj6e0+NpYwEyN0fzr@e-ElW_At40&~>z3_hL#rT!3-YgLsNP zDvl^;7*M!8Nliby2KvScPd^50{(UjXVI};bHIO0e5K3bWPiX%>Mx3qvdyM_&L~Pta zaaO|XoiLx0#Z1nJZ~Z~_V=lItt7?-pO%?|xzHBRk>q&F<^~|j1L)OB;;{jvFG2HGZ z`yQEBK{K9Ul}FU{{_v)(Ei}iv-EQi+(k9QrZm&A+$j)FicD|!97p+1WTU(W^S@1!Z zg=6Igz5+ht&4Z{F`k=JpcKoW@-`RLAeqzLRCmYeR$UOqmsG5pfxo#_B8%Jb66wWnl zw;v~9zu6lV$4VYm%~=O!Q6S|mfzP#OT@feVvrg)28wEY{ZSnK7f8<4BGP(ZGqft`D zmrgv-L!v6byqOwo=fUTfzsMWJF%TUXm<_XlkKcyk^}UPVZg$-3#YLF>*73Q6?>vo9 z%tW_sI&0s%H~ulpce5WVqVF~R^-eSAG!Y-Ys&oHSu@R5wF(Z?aX(`X+n-UX z@Sa(mxdc&aJFEdcR;*f?{)daaG$(AHeAD+8{v0Qn`ZXPL)2F0~?Q!tDF-F&nnuQO} zx6yK(>E!9)W6v%2n7C~6B7%Ltq7AQZaEg0xbCGfvJ>{H&S2>~Ox)fA~Fu4=JC3+3j zQ3If{q{E3#di~MIxMjwPoqLJUi>z?Tq$BHhdZ6dU#*G}%apiI<1kuPM`!#Q(Kmkj0 zyR@9nea=zECZ~XtjD~H$G`$gyX#T{JJs5QT(ge={3nc+OfegW6VtNKn1R+z+J}N~~ zfHwrex{X|^E({s$7$6RstqWrc%7>Ft2PKQ7RIHH~)AVSd%@gg#hcPXsQ2uw-JM}|? zozi@57*P-%YQ)dEncOg>piSh6vV2h_5J(&nnZzV`u>|)PRHHIVMbY3;I)tK#LJ=e| zd{=hLv7h0@XFS%XGcYva_n3>O8TRPbltxHujmH(;+J!-_U0q1J5b95<2jEn-hp+xs zrlnJj&Q}zj!^0Jqo@!?iVLslv@05p^y6a!y|107+0Yn^SdHEv**gt+ii~fH^9Gw5F zTkwBI9RFQB(Er~fj)OQbvw!3NCE`F?e3B0ZCKIG;x=?WaB_yrB223{(3`QFqmlq-y z%8JEpF;DtDRY0p$Vz6EyhA89!f`m`}f+*U97x0^qx}0{bh&F(@gc3U9<%&5q-Ej8# z^A-QwTR)!XIm>C9@A#SLcq`Sy*EUljkcf7|e!jRnw?4OrS{4>wPgb%hu#fTt$!nE1f^156Z)DWg4)Y!$N~RwwYspcKK;ezp zB*FosL5|KVA01maVyG3i%^TZr;b+TMbmdk#f!9PS*TB~*Bangy z(*!}lFlBRcgC>3wA!o7yvBn8IwNvFVrB)&b9fzm{kn=;)h*IX1LT%(LOM=F#5dBpB z6@r$g9E=YA%L2HvBAEWnt8|>4`=EuGz%qz*IZHl)@tKloZg zU=!1k_%GMqQz|qa{J~2_lNoxB0dItVAxERaFU5*DLTU77$h<{Kw3XtVAC1wKpm;Jl z`CP!#+Pq@it8z*b|Ba_IM>@G+;TQ<8g=t8Lh=2qjrD(fpVnt9>7=heeNisJlZASWZ zH(JjS=qyIwLP(US3 zE*AxVj$PfKkaga7uE4;GL8ob5U)(oj*O zji6jXWLXScyeEtMgj&GtO9*R*a%-w)SN!~df&KYcnt`@kux`N;;2@K2e( z7D5O{G9vzgqW}*+l%m+l7&8Muw;NvOFQ%A-bRcO{k}tGW+hM@q*fs+Uxo*PP0u9UL zpBrRE`eT8%W&4;afhyTnt4kUllsLJ<50fmf%|1qgJ9IcDs$|)a0SV8Q)CPp4LhNdE zL3Sx}ZgNj3)yx69VxCju5@)DQkl)eAgo|-dGSdfn@;7jJoNodu2@$-20u(;0e<&#+ z;t=)V()x)>uUHfnOPM(IGl6QK^k;Y|S;RBRf6=M-$)ID%5JV+8AA%2iuu0(J91CKR zBQhpIMZ-mZ=EbHEDjr8l@>{3A1al2Nz*C;V)RUNU+k5>Ijt(RTyj;CJQ3$!XF6B9tIoyo3Gpq=5 z270v}XEaV@;O&@Rd-h*q_ntYBQ)>QBTM@v{7G8mP___79m=9;*m#rlharS< z#?Xlozd)3mr?RmKTeM9Z!Az06Sm70_?{2_ez>wY@xn9`9R=Q&s_Zy%n18Cy90*V96 zQRB7)313BkmSuM2wOXsy1G{Breg`e?!HFoOE1)};s6w6R*rC7~m6?lu(KQ@)r3M|eZIT5RMP?c;*0p~WH9XzZ5Be_NW8W>05sJ}^RGI_UPCHC!a-t< zp0Xt}56eD`M>=l+vq9?po6aF#0l2B}U&4X?pM*n*47I6O1P_S@JN6R9=98MvK%2EM zNtaldqH8r~c2*VjB(e5~{=po(b?8Ct`s~2v)4=5qF#`x^secK_864zNFy6m}1Ad!j zuwSkP;~vaaoK75Tg$#82mopjAbx0SKn}ORxSqM`B2YZ+hUybTT4cKOV;_kdbDXdrC z)GjmGQfH9HGtwX@;O!rPNX2bL#%M&wY(&OnM8;xN$Y}IvHt^GQ;HSuB2lH5>Gjanz zkRIATp^@byne;hEj~<^~4{V4J`I4eyB1liqSMxv>>b3mT4|k6P>fiYCv*W}tqf7Z5 zgqVDxC$vhW5nDalImtgD6dyRjjuajoMP_VRasSYdWU}H000NF&(CPQ^$xH>J7X%|&8JykIjT~Jhicb%333kL+rw4sFrg%T+ii5L-! z7$J)oF^d>Mi|i?e45);Na)}sW+s=*1na5W4{0lguV_hbD@a!b*ex7?Hb6qxerOpWf z0*+GYymh?yJKZ`!z(L}jQKUcyRQ?k|jSXa6T6}9+f*0$pA#C}CL{FNSKeFWp6#Y%1 zIt|#|s?6!3Odff$RrHYxkZS-hdCz^af}CT4F9G9eOf%PH85{6T_jvmIKN5pj5rJ4C zft-_pFByR^jkmOZ9fd*{IL5bQ84K;`Kqk)!GLZRyCG*dWYvkxdLE#c6p@iy_!B-o= zSDV0h7dgFbPwc8r>@KJFm#6kyrS#h|hAuOQZkxbY8^c!*v!QLfpaI+=wmpqZK^qh_ z$f%~Hhc&&;_U5Qn*r5gea~S=%Tk>wN%_0rDUwAqw4!M*=V#0KM5B_mgIXnB*-WXA; zz0Z=kTL{R}ZHhX4J$(paed2jF;VPyArNa*zwOHwcno2rSo}*A8g*pJlAv>3uSsQ6IiH2zn@PD=%NCi_BN8=r!j8zQhz1;vblzrbacvjS@R&Xbx3G{M?5A%x)ab=!wzt$mUDv5DAs}+4ZVGd&7>iD zBOu?1>>8b@Gopz%Y)?0n`#rlhaT#cSl{2}anBIz#+RBpJ3X|0eS;6Q>naccr}X8<>wGhkOtAnXm3I8C zo_1d|$+VVseCR3&td;rUM3dRYO=I=*g0*vWkyZxk^1VmJ;O5X+4^e3 z#8*Sbu={MYDL7(*S84mU9i5E^jRLh=5S!__Ejbuu4r}{H8exOgBo5KV3jS)&Xvx{y z0@mCd*OBV^aNr-we(TRi9l68*jOn{3V$J|#I@jvv*XdyEo5Kp)Akp5Us#%7mD#Gfp zU$ee$NbYE6$Ko|wcKjxzyTz}uh|goM+mxfCT_)ea-LbwHC$5v`{)Mejh@Gzz5vTl{ z$I&Uc_x$aMZgBNyiBB)NR;_}Z?S3={A2v+=>-0rqLTvqvh5~7ZkM^hOi>s1tKGWAU zYiGRJc-8m-U$|~o8LsVZgPUV6Xy#W74?mq&bJsM13OObBp?11Y7H~8bj?C?8`A)nu zH+{YLRh->Y?;X#m#l>^WX!@^%;Se^Q8bY=B^tXw@iI`2zuf`l^LlyVDZST_4&fz7q zR{_3tN*tla zai18`t~@++EaTh1PTH=PwH_ZRs6*wu?1}sJAI7@g%zp+P`8nHaF57na4d($6(OFEt z^?TVSd7q4m{G<>6wyJ9tytEdh47S!ofPWlCuaZo6x><*HZ>pMGii_oapG-||`>?Pj z3OrwdLvX}D>yJxndN9*Tr+)`4Y8T&+*bYANH8Cwg`h5*tx^#UMrm}Az)Q-Eccl5m5 zL`@cRPxiZqkF}H?@owJ;o8(@5ejC#)Mfr5n4qjh%QF`v@Li+YX4*!*dd3)iJNjfe_ zUfRdA%f@d?y*{j7`52DNie9=+?rgMlb$oaJmXYs&gAjpuhqFm$t?oXz_nWwgyGhlx z-`nr*Hg+AuW8xvA;Mwh#Y0H_OJzy1?8^Hehcm?cObFMs8zs&ME?H}Ub>O35vIHa(8 zjd9v{6nDG3JubE`zxawzwqu<^_PWt#5Ga+qO1(MI@@=xWJv*u7_qDS*)Hw}{Ca85- zlkbkKf0<7BQ}gWmbc^@B#&+d+t>rtPDjMU%soK4TnsO@b=QgT6rq9~Ey{d9K^ z!JqE1qCffW%F6t4s|_dMI@rXN3*+;lbX>5EUbJ+VI_ntSz{aol(pYmb`^EM#MNyt* z$zGn;&G>80_nMuc+w}3IKyxJfLkthY&U<+{Tkf*UYNpZBcY35Gu^7YaHDB3M`wCm5 z|NE*FJ+-kpq#D#K$WdY3m{(hkcJnX6@Lu=&eyYWZqq%#Y(&8=w5Z;{2Ue_?oUz9Ak z%;yRq2GR>VnqNh^vze7JNOUsV>h(0S=jOC+O{e|npY%%>ex@jT!&Pw86g%HD;V+>s zL0hZb$WC=2PQgezz7c1>2lOx5s22j6^<=f(EnBIrZoR&DlTWpsZ-!4OnFH+x0*7Yr z0&>Go6zOdp4j32DUBQ6JMjb;px&!7(*{)`qg3N~Kb-82yYY}kg+BeJBsauF&NbW7( zs)WV%??f@gRIkpc@{sHL#=OTD5H|1F8UyciRsJufw85Jeo$+VgrY~#wHFfsF==H|8 zyl8=qTCr^@5}%I+q4K&AU%h_6wEA*t-@nn@&26gJF&-yxFMVCht(qSrl~I1%XiEJO zLlwvjy29^0jb{Uul6E59jr;TXrHAXzcTr#PmmKQHgM1qAMakC_1gW~3XNTH$FCbkn z^~rXxj?YvZ>N} zP}_>nd;R>rt)`=EflO@P(+KFFyfrl;&7$UJwo=cx5N8eU$2lj5&y!edJDu;A?oxly zIRvf`Lq(lC1@>eK2->_<2IKJwvfJ9L9*rBOwnON!Pq)LKZd*6y$R-|cMV6W+$2|ZK z-l*oYo&jTe%gQfp#joiw5bi3#XY&g!0Ir!!5u>}Z=llMqIfUeTf^n@SZ9@{0_qKuE zeC9L8@FTXN|HW;D_nGE4X9}5(R>g}533Q-}4O;$V zlO!E>b%bh7VMJnS9vH(fu+m2(6-zV;?MmsMcoAXPF(gt%Fhj%xOtA4;YvK9kb~!Bf zz}I7rc8}HRkFE93&gajr3Y(omZ0frvoh1ba(YgnEG>k*|@}=RhJKnm02wM^F^OE~I zoC8&AbecE5f~oQP>a!AKr|wz{m5n^K@{*SGgy^J*@UK!;M10$1jl=SwA}Q$uTXyWQ zzm@W_p(efEQUuOYomHA@7TirYVT91nsC_>e#)DcmB#Q1MFD{L7x+FtsG|7e;>=Z;+ zeyB>E2RpBnyDp8Wh!we}o!yY|`?Vbw~fCGhT@P#w=6{9 z_pL>72p`qh?>O*U`QGAaLeHb7YjCrTKsVFZa49p7qVsXLLUcTCC!8~Zk;ZIHJrJi5 zh)GrI>%J){BItWav><_6hdKuzTT8o69wH}a8$|2wmy;bIbvDpRFU@mIO{gx{Vw=eC z4zP@a0;B6PSmk#p^A^KcPg}Y7h8~>ppMp;}5a;y~%=6C{z15cLnVnE;udUjFSx13_ zhbB5HWd=7Lt?T+DV}d7M$3I8Mac|PwR$mvC=`J=iuQRq{ z5xUS$3l2ku6|C@F90i5w7eLDzvR^ZQL|QeeS&%e_c(fK0Rn=L2vZay?w0Az96YDjD zY3!VOH-9QSle%qZNkOSanHV1ag|J=aF2Q-TY2Qv@pW80{HCVX9Y%h=XNv)y|q|Y|Q zk(^l_bVpm)?Y#Xw$9F7cYWBKkeNP_7-Q;38%*J*T@)S#(QV`;_pGXzS zw#cY7cX2{u6HpU58o9x2>(6C(d7d<2&e(lOF3|B|x6;{ak#{u%bLaYQ1|XLK6KgL) z^3bM9hGw&>!C?DAh&4E}zq%lKbQ3ufUVHN3Mso|?zV?w7{O0A|vljv@v9sgGOnQUq z*uvx`KGI=1`2G4P<P5N?TTU2PeJ8 zekntIejEN2+SanoRRWHS@5P*jYiIWt(%h238w~m2@G{QJx$nozuj_j;yB!LgmjJA* z#?EOBme)1M+jsD;yo7i3H~qc?r>(2Eul5)S32l4gKZav-ZAf@ZWiun9<41YGUDQVcECh(HZ@oyps`vN|)XpVwzs$ctO zhgNEKEFW+tr)Sf}^1)X;whsu~frxid!hOeHVZ30-S)zhrugJBc!Z%`B#NjE&qZC1) z`hupKYfba9YwY%HCQ_GEU&;TdS2Ru&&pRoM3uVs$#X~G#NGdx*3hM&@W8N(OQ-;|F ztU;cFdGz15ouJS?7R3@xj?|C{`ytWVIbk?1?(r?K_`xw4w%wBoQk^7-_+V_?KOI8` zb)YinmQ^#F4lT4q@xgMWfHk8!QNUUn(_Nowcq;7zq4B|C#AuweafiL8D96w^T>Rmv zN$wF`r|6(zoklbOdo?qEhaVbllnB6H<<>*nYgQl!ztK7;P2j~3o{21I86E_W15T zMI(<;-J4Y!8TlC&YNYC}a>rQLCt3^5TV{PyLPX4qTHckf6vAZph`UIYkwUAY06L@5 zw1;26MVn~$uC3MPrnixcung*GFaUFnX-WXhX%K;zqG}QF{ zzfrFs^}RKO?3&~zF@F4j066W1|Nl7c{}1fd|463(Z`doscrden>WRM3Z(>05L&6~RpjKdGoBL=Do(r8&rCx$C8JB^z-IrMPHw!oqMJX&{LS z0Vu;T%1~TrF@-DXD`hbM?THKW_H;sDf_vVN$MbvEdY1dC_tfVMHwqgw&!{x$;O_3e z^H3#FnmX7E%#fxT^2kx~@_viR#Q~-xVHUvtmWv^sA#sV2dE9=?Q%c{zaB@0y)FFKL#iCe7HJZ8paD|ub@u|wQF{8EV6XI(_8k{r*FeEC1NXU8RflUKM zKYi#yK3>Sm_|?4rrjG7Os0xjXZpakEPG)>aA_F4x)w)LT4y$Z zNqy)|IN@z%5>4w4(o%&yZJjNPpc~G3fg0$+ZG@d4k=)jdri&|Gwf#4T{4wS~on>!w zMg!iQ|8AKXSo~eYV9cq^DW#p~EXZ-qlopCXZ&(I1u5cd= z`$;SbmJlq_pTo%=VpP*GlKI=VCi%=HA#%hy{I;Q#g!)q(SR2tSD#m}^=3~`EDdP105U~|PVkqK_=##aRh%IWK*~_6G zWKCi++fhlPp6H^M3y>)Y@*fn$>2tNO^QJYa?RZBp645xA43zFgq{AG$(Z*DgxosQ`o`JV^tdPo|U(R(kJI zN>|uZ1_lz%FFjPUwDM|nj4{ntJ8lc@oLz2ZB?ss4d^vLQDA>-NV~HygLGwuPu|izR`<^D3yXhh$|f^!AC#P6+gR* zL6;1H;*LlGf&h-j?}yn|mg>-8_8LhTbRNm$eC8Tam?X!5WV-!MYY!@oTre6i25mC; z)#1EkA7b}a%ii%NNWG`h9aAg7I)(eHc|dX{!fAJqTm5uoFZkBvkrBsw30Qhq`lJ5X zw4Wj@d;Y6AO{{ogBpw;J7f@Ls=0E5aU7kehV=F7ya~G-3Wx%~HQifak4zNBH{Y~Qz z@H*%}=vCHAqP5rmfL@gkTt=DYfT^S7au;K!U62v9 z7#euq(9tv-v91hi-|~c`xvlp+Yu$j@4f2^9RHI)wX*vk3cH^t+26JZT=X@{)?$deY zpyDnBhy<7-z$AT2HC#*wm_|b;@lk6uY1LdX_11L*CiEIlqTh!dyS{Yb-M1l|{%~GO zfPUlMuthJZxiBc56TORn{npup#8VJ@GXHygmUQkh;@Yc`y-?mvq-}5-mULCrA)4o? zNG{V3vr@?%xbLt<9|A^&`b)qgb@hf7yS*-q?af%HCb+FduT8S!b&aj*79H4g!%kPu zgaD}N7ZvqAgHS30s3|{c!fSKRghT){0nC_p9}Noi4E;L;$y<=`Nh>DxEJgL=y<~X} z_?e}l>;u&zT}3cNn5Usl$L|8>aPk*#*z+7N#YOMG@ML&IhUH3+udi=19~A*#Vc3`X z^sAu}7yX5H8VDfBojq@$H9$~EkwB^il&Fdha5x6YrBB9&BD_I-*zCgcdCL7i1awe2 z$otrYvI~Iwh~Uya9m50tZRsZubkb1h=^&GnPUht*!1M*93v1A@Y@_}`uh{=Vue!nr zbcX_M9HLhcn);&$f*2tMkZJm!USctk!S^MMq*s6hke&LHZ%FKBlW$4v3jXHBavNfu z-yb&dto9(v{N5roFt1ytw$Ur>h4p-$v^0oXZ$XIUOQ>zRUENXvdXOPTf|iyd7HGrtnQqcn}@1%@>gEFN!S&$ zpmVa!LrUoXgkGs8xg=EfSRbp{6WSrtF-azE5C)0{!VX1?9ubKi<%*hRQr=tTP4t^E zchisY>LhpsQQk``nrS6)X(wnhjAf9FStFXVx6KK{d!GBlp5<~^(EECr&7SjEvEl4e z@?rv*SJ-#gN5RPY(}`S6R2jmX^%>A8PPW0dSCoBXB~o@nvnJ*_;Ru*K`fNSC9ehfh zJ+mvMoY_aaA7S$>-)7X(Z7sZkW~>5WM<|@tM!T3LN;m#$bsw`80;nx&7*c>*i1ki0d*fTSd z#ZjtCVgD#B^$62eU5RyYd{q>l0?UXvQI1s14BehW{}pnEN7hH@!Hry1Vtb{bOVGKQ zB`LKG$}fhr)8bSdLhAMwxk}y_>!uxI@u<5G9H_(YI{katd{C<+Y~%r~xGoC2xM1Z1 znsK1Vr`gq(P?069mK>hiBb3)C6*4|pl0q#$$f~I#gOP6(f{A7auv8I=56Uo6n}q{p z_J1PW21?x3|3q+Uus?I%;;*jh@PRj-$E1lO_P|t3fd;U z=4tYin#CRhm{*c5J(VlLj)U~X5xO!>uSg+xax;qd;a-nQuVDL71wba31BNY5(OPXV0m8eDu zRWV~Ue6rNRNBf~jNaT|!?j+?#NRSx7n|sryB^@taGpzV*wFY%?sA&~Ub4`pzv7omT z`tUmDeMB;Bi#JZY&@yc?vWTuf5BLy$j}HZB?VH#AyN>Z76Q6vQcMv!~V~hFzF|RrY z3ZIY;FC?;qSj<__9d9HUrkHMir{4=v9gEmbg>Gi}5oh?3XG9xJxxALc4Wu3Y;5{4U zLsx5WC5U(Y9-RDaNq>jx`&94ywT^z{fkAydwRsMAwi*8X|~+S#~YlCxWMVY z7Ue09Z)*-U#1-J62ipuox{9M}J^GKGG}$Dm#e~ z{1k*ql3-7Q-|=fG`h!l$XBm(G+vf)afrlmsKL62UhDD$}==q+VWR7q+f~mExK;d}X zPqHI*5vi@q1dYwcgsOm`VIeXF`oQ0oG$Imr&_4*43o~QTA%oFb$Nk8+# z0}apXh2x%}+sjMr_RjC?imrHMNaI#ri%RP9`*57&!I*v3+6?@qi7eMwaE#_~ zwnTNK62nI{Ow$^ zDiqz~rTf^H1qPLyfZ_91*1}R&m9OgJ0)V>ppwb3_E#YjCTvo4?g+tjyhVox19`;t3 z?TI({Oen?bFG_HYU5lKz*l%7g4!v^B%`0~f$(O=MoiLXmJl=LS9J<+wwZ3xx-LQtv zc6NM@ZWv$X*q5|cO@BL{A}{YRv*l;Eni?i+I|dPl-|VQ6D+uboqIqMU9fy);q1A`% z-1jA5)w_FIrD!Y_8IAVy(7;r0Jfw^CsfUiX%ThCx6?azYo3slkr|kv@O~^c#&76tu zWMk$>zXyUr*!z6h<{3BY;*h`ctA#jfrZqO@Yzn;;-p?w5JG0c?nCy&>&>M?kXxnum z<4}_+iCX`JQ3Gt`?Lu^T@Fc6d|Ertqnakgfi96)o??jBUr|5N>$;alI&tAVjdu6$x zmYVO{+-u`I6vvMG^x=6ZMI){7dvDv^!#Bq+t6EM9`UZMLM&e~bYRR!|Xtx_}b$F~) zL-+Oxemq0Parkl&NKCo3YHZ-;+>>H3i_S~lV@Rq~53!N#QkCM-Hmp-bS=7!JHF43U zD1Pm!<}~fCJu{(JySMJ_w}r~-0o2gMGu}c`1;5bgD!k?Lm&@4BMd?B|%YEPpEJPyj z*+GR={a9)E^%QPxMP0_5`&&R({W*83KVMh6g|CiyCT_)N?KvStBJyU1-2ORync+zS z(X}?NO8vo=EY8Pn&Ys=VrfWV@pYF?QBF{eVLhfjKF@o+@fs$TTYUa^(!I7O#i;6>! zRHui5w5x7;{95ATLcOko)V zzej@Wun?FPT6|O57}e8KTL+0#<^oCoMW_otXUR4<{Z$$l#3_mS=Gpx0;efA0Ciuf3 z1{uJca#^g%`Z()L>B4r$N;v&~r0q^1wwBD_-}CaU`Cf9o&mTjws5o+iCW_!k=f3ga zu_G~ZTRJjg$BNnQakJYv4I(h7?F%-u{3@pa$m*8u9LzMNPbDd47N_%Ysw zH=GUO`v=tTqoXz2vN=EOo2olM0B~^ysKA?R_uo%s8c5%((+@?!cgY1@hx%)0R|MOE z+&q&^gWB$q*n9s}5{U*4!s=dq<1I5sT^-&pb69tC?FF#3u8@ z)^`QizwsK$%;DAadTfq+x;SoHY_$)~Ht@Hv{6M3%pKSRf-`VE5Rd-z{N>fBy=ybEZ zp_^gmICO?CdycZRdoSny-n;F(D!k_Nx%+%Ve&45c@qj;+qWMXYs1M4@9vcV#{qyNq zJhb?2DErgZ@&lh^^LU9R8$J2^D2qV=cRQzl3?xmeGTE&iyKOlzTiBst^APKq-TS=A zuAY)_&iyi>{n`?uqB4#5cFv{xw%l+(l3z`4exYND?xL|ZbYuUE?SVtfvV8M1Il8PG zZrfE*c{8Hcw#-A6rCQIrUN?2S{kS`;_O{V#l0ot{dcE0y%nruJEux)2qs*79aqwkFn@7LpB z>pFt*BMFVIIGRm?v9*l4 zXx^D)CFc9&Z6+V)s7<~p`g~>ude#*ThEJ9FTDCIm_S-4?naq@Dd*1}ab9Z@twa}A8 zJPyUxy7VV=mWoNYAFAhZo4>co3v_B+7HGM)bML{IeFt1Mcr!umz9RheDu*a5-V2@7 znR_)OhOS8FujBz>tBlC(p70TwRX zp10x}!QrzVZAtYfJ5FpK*2s$D%whD5VE7yLp7rBGEoy9XbR&btTl$nk3-7MmAz|*9 z&belnPSb3!<3ra@FRk~fOSZu2Yw{7RsZH&3manF#-$GRA8)BSwkZdr;Vnn-syG^rk z{)m4k1%-o|w8&N1MC$1#?7cg6wL*-NE5nCM21ToTCDr4Bs;F(hwsLL&DbpdF zlhN8DcCrU~?X>R5<*|l?w}}x@0>t5Va_M!>(t3>OaD{Vcb5_j$QKjQsbDHQQmAQG| zWnuVqmT9@rTUE2V&QJ1j4Vtw`+`jJD>C^6X5R6`|Ns0GoL-ylI_s6pXTWy&2vw@V4 z(yNUT>>$OF-(k&eIIeXSqYa0+IU?6>f9Qn%gt2JH>Tj#xX!xGJwRc4nPJh-36?&gj zkLp3uHFaDC)gv!gCb=x9d)H@=0mgNm5Bc8TKj#TMm&w2U-?w#adl_*FydUr`Fdy08 zyFEMyXOdu@BM4LJJ^c53R}>?o-g+)UQ|rI3d{5r-W)*FhgANK`jG?RZK>cFS1n-XX z3R^UC>vDRFrmo7{#2#+`{!PJsVJrdtGACsBU5BJG)dwW#AFUv~pNNm0vv;G?hsU13 z$zGyu^7CKuZuYxx^H<(=iN6M2s>M}$C99b3`oY_SZ+%YRy7;OzBetF1cJpqgQOio_{h$8XtkOtGalYXMOpIuP5Amt-1!NTx({fzY5-He zf2VtZsl$R(Lm=Zj48}Dyh3{jsW%U#iVSm?v+*=k2CS5^vy_Uh6jE?FgIPct&BvXAt z+VZqhP1KcuiKlpTvqYL{3)~~}ByB;dYXjboI>6#vx?0~uEn#E8g3zYsna6}lO}cuk zv^;u(Czxlz%8XL=8os`xw*d0RsXU`UfZdM}%VM9~{dooymSrn-oANtbza`u`|G*Ml z;-GJTRUYX!BYgL!n`kTc%x=%FR87}=2F~3#e?$If#U%=$&tSM$@r(KY8QA}y6&I%e z*?;*j6&E#tI^&~I5sVM zv2A6SOn6NB`k%z8%)$av0Q1<^+9TL|vqil>w?9%Fj9jMUA&rv8b_~U&q|6NG*wJ(;lJ@6EKn^t$ta!gonazvM z&vxU-W@45aW;%KzdcIiNK=y^U<~|gy$sfPCF5UV`)^iZXnb5Ss$+RF>=^BBTudG5` z&8(OPI!HKZA)+Q~=9#2w@-P?%jd*mKYtk{a;Lg3pi{^yF;A0yS6^J1?G|)uULMg-w zD~smj!lq+ypk;rn-tx~{qhF|)#ld>#2ZKouz_OaH;c>eew`pf3Bv}X0^Yj4eVhm7{ z$_ab8ZSBtN=i09wrjf7G(9bPKxctR?$(!Yv#7ImRYmyD9U6L|iqu2*(In{y_jb)lz z43Ns+e7U#jkKG9F$!4$&~&Nzx9M_klM}tj)mt#~D$YK`Sot%r zZlql+)>-;ZntY}-bgp=aJAG*FM~Ha`S9UMDDxV z$wFo*Sc_}{Yf~ymMZ~1$iT-p+8*{8gW^ktM*EfLE#M(W#an!8p*B=ggM`UkvsOa}q zg}>4T%&Pl+b>XjY*xRlve(6?^TT+GC!V*H4MME`Cxy8N`8|62JV{;M;mj)T6H$^ zxK!SuU;Y>WN=eQAtVHGzi<4Jk zA~=s)oaJ+acF9XWgJwukS({gwazH)|lSdtB8GD0=Uo#3p?_t1+vNKD5(}nz{0>da3 zRYz#JBhK?mehd0DnkOoJ%uoCyG@XB9USf0VB%lM_;5K2PsOGtk>jJsn zqaxn(NV;oMf$AE`F?2+-$#4OLc^o?Q4y-G%Qui1h0_s#l+ioON%pU%8snf*?)WO8$ zTLa4B75Ux0!z+nHK-@!jQS*l2CJtnr&@bUbH_RVrSHIM0n>O@0mI?^AdKedckE4`s z&#@Xyeb7I)g6Fh!L!4bI`48q2nLJ^zGQE?ebc{_h#UXQ*XjaO^m{3;AbOnR8ibyfh zpOPGj{t{{H*t1;=u{Ud-V`X0-)d-vf2Axa)!6gFr64)PNYEsjRg2iJTWsSs97lz>( zqlokwj9u&EG-$)PhR!fAw{Fe|++es$sM`K+bpOb{1pVE`D2B5klpSDB%c}&Dv zO@^6FhFMI8nN5aSO}Jb5-cAQfN$215u}e9niqb_K@N?<`ur$D#dMB37C(zHLyqD2 zlpno)H1CLwNX$Mbq8dEi1(N1`*yBuPiawlGN0P`e zS`7-nRw*1EBL1ru;Vsv}5{W@sya#ve3*6a&97(f&Jx;s@;9=!l-BNXlTg{aL^ala> zIr?Az{Qv1-$Jc_z(t^eQUk-LzEG=1Vtw}7cNo*}zEUo(FqoXQDbgRQMMbcIh1r>x$VJ9Za2U%KwtK?Qu}VLKfXszr(6E19kwAo zCXU|W^}Qf7-&%~KlUpnD*b+DMfEN7ZN~EJk3_5Y9J1&geaV*w1(^1m*M2$twNm1`z z2-DGUf1fxbkSXv#g?<1hol8fgsotE^)akffDqmk=+w3Wve|rKpZ{hu0Ug=k^xv8j* zsI+vgUG^mu>7C$4Xfnh<72z!=xltU`Cdjf!U^>J<6X6|%{7goAr3h%cVEt5N-ODpJ zf27l2X%A0T{~mi!UCM_xUexi-Be{tt2!w?Tj7F?Vg+58b7?}k0T?&o6WL8`+fm<3O zJL)RGsx-TW)g4rpwgLWiDv;iVt97_QUVxO{1?nT4_=Zw+2SE5D5Zw_FB??$Tf;wOW z<5CbUzyZkEbJ;kL>x#HF+u0U(n*S)Faymn=IX_diZ~fbMDJU((kYwrRko>3_WWG(9 z*z5UYQ~={zDS{*^Bg!-i=*O63llU4K;y6i@kBu#%2k2#8w6mpZFYDxZf_U2nobCTF z5j%-GxF|*)iH4rnSUT)tKr!8qG2}G%J%EUP%9;+An6wP%%=A}CRsymA){t0XSx!9X zG}m?^2BA5i`eJhqJ>y2J7pwDwwe>ciTP>SgF1wvR zw;eyXoj%-eXhs(NFTz&NLsD= z1wh|rJ)Nw4;mZ3ph$IrG1t+@0Mx&TZKTTW}mw8cw4p3^rlWe5Rb93KsraPap)=ukt zwbVug^jz`)9hYt{`bjTmoiuVMowP?|?d5cHY$NUEcyn!(pXI*^->EH zfT}_riS+*wvFA+-D+^QO6~6WbX_psSJeWymO(eM>S<`T{X0WA}KM-w0vLEi}g#4)z z!VM3j5+)!ic0t#^ZI}#A(iCyBIuSiDg^XHv9Fss9CUzAP{l@wK{i06SgA?iiRw(0^z%atc?=k*cU@Zz5>! zW8JuKi@I5Hl>3sFC0yYdZ{v+jsS95%aT{QlZjhzk`Mj63_haF%6S;&GuJ)I6Sgr1M z^DmB1rOjL^-WzD%zD`4Xfb7-ET&@lTI8wH(GA_vdVD=pCt# zD$iF#GOM>H!EK*g17ng3TlbrpRn>lwia+`O`^8}cv4KX9-o~beJ?k|lvq{*jAZaZt zBcii@z1xY)gc&gD2+}$kAVajG>0+s6L0iEcK9(E!2F$Nt-Km4uPgPp&yN8&gkMh9u zc?NAJCz`&rGrcV?)N)s_8V^}nZ=anlH1;x*^%t?%I>gU-J~!di{LQb|ly2Rrc;kDV z?k=C~gUgh`OU9#$mt6fH5Qg8YKI+}P>psur5La!O)Q<}{q*DZ@-lyV8F9*-{8Tj8) zQ*jCkQD2P7GuMjr`@y9D15-uxQ zsqHWhlg@Dw+j{LB_=k!}co^eDkYHK{M!moRaA*Cqq78XUV06D_@79Mf_6cDd>)x>e z!Hq#~GQ6s8TCndA!J2vND9AR6UTk+;e`qE<{Y3WH78b{k;_OBkwEPO$OHl*azUhz&Up!+}Z%+~9zrk?=E`tuRzXB+|l`u<`+{)5eE zrEY`u7iby?Y&Hqg;+nMH6!53Hy+ED&n!gz;rFn1wzFh76-q6Nmcp^XNasE-COBeF- zwbT(rV5gs9)zae1Uj00AL;Y@V``-MqQM)6}*_!Bz_DRD={9U-PxRI|nZ($l23Z`E(m zv|;-a_-o;1TidPv7I4v>C9qK;fOIhHg0n zne8q4rvZ|mlYTa5Q`gSJmU|W)0s|;9b9g6MdRlM{;oh-*{xvwKhE7l?BRy#KG~rhw zTNU0EU+XW!Jz~byEq+c4s%AJm{U_VSjuWSw@yvxsoZ82L9d@84>l1KxO#XRG&os!G zj@2S7{a=`PtLm3eRonzOmm`V2zKbnzdbNq%K9BZ{tIl*__W1k2ywTa0(Ql=2T)Z_~ zP4!ur8+Hp{xyWvaH#2YThwWo5NNOio~jM%r5u@h-#8CbGp5b&(Vwd3*ss(tNE|Nvmt(O0AfQ(tY?ER!F@gKRo%gc z(~S=nNAJm&bb*moXtQyc`zr`!5DGE!*3bkuSRxok&O?#Xf~BQpH$ z_Z{_G|5Sf`J&M`Fbx?X-0>$7o{him#-0?Z-Yq8v;pR8h~_ghbLH@@F5ruS2EbflZu z#Xt8i;ZF;%^HC$iR)27mL3+hSB177-Z@!Px-H~_;EC;1XB zeDVv$e zrJ$h?+Zk09_?{#XMJ$K}c{X&o034&0EJ+f#GkV2&*41nW?o!Zw#y4lyXSVy;-M{yG z4OR>{TF-OpQ5n9%wSQ)jWcB;!$iNE z_2}XP5|90%!RvJ)-;TpTtJZws#{6r{ZW;Ipz|NJs>4xkb-k(`c?U6XPSsQv?Q$4tH zX8K4FnZJX0^EDALxDb;*pL1ne39Nkh{IDhUa~Mjj`y64BTs6MMuHq}G+#^<8lkMyS zJkqa9iB(#^Zl5%`HD>hvUzyuH;-BRVhO*p&EuKIVn|eBeBe?1DyBaSk#xUhVS82VW zwf%3JOEQ&*6H8WU{gsgsf4jZpcPdMp8LYcVGv9H}=W$bPqSgZ$tbJ9RyjMp)*Vbs7amF9D^hz(t-trAq@0gHR4Q;48qf6vj8(@X@{!&zW11(=XfX!559x{hZ`#z>+oTkk*Ox%ckw>J27Zj9uTc*_&_i5c}wSF zxJ4k|vRtnm*9;6wT6y;T85Zfh!jNtrs)qcFK!L!{K^`=F20!OF8GT#sIFBKUAP89* zAz_>h4Ov>;22HYb8Qi0|yRp;!#x=m`FLp39Z~#BQ3>bBd?DPtN_^Vz;O*MU4BGK) zK)Z^PGe2^H5&mR_AzuW+5{y3O!Z;xx>pjn9cKr|8;I;H@x7}bO9N~-y_Z~iq9O09uO%F8j_PX<>h8&K}6^r#V0?W|e?*V0a z_q<$a5W0_FXbFzfLB+zw{4-Aqc7Qvv3 z!`?WWi;DA)4vNn2@#;+wUDt*dTfkDLfMcTVqNTz6_t#LS%On;a9oaYKvdV8tBcZO* z)Wod&zl>Z?xxQT!VGj$B{GKo3VC6IzWXo~jdlvl97)TyKI>VAxhD-&-Kvn?K*?+H3 z^53$#3;;|n{~Z5U4g~7~r@6fyBWp5DAJjHnE2T%0)P{kL3>}Gtn=bBZ>qeOwJ8eIc zxklDZES|Jk(CpP)u!K=d<7!=6N~=?lqaWRYw%tB8vnGA=&YypLd+3?O z?L70E|IOXIef=$HX_1s^P+r%Mgkn>F0yt{T zzH^X*sz}vMxR7ZE3Qk%1$cT*``?2{TiYOurD_Q@_HatW8M{EEqWTC>Zg1B~s4jtHy z0(-AA5I{A7vqZAg`LMu`C!IhIA*frUdU6-3Hfr-QC3e3+KBS0#&0al*i0m&I4ppBO z2t4_L229dPg|Bdh8YjODaO{|sdBIOFC2Xs`)COTya5mOJrd(k>D+Txg^+A+8(@*mcA~g{uq^28bDu8u1KBT8m z2(rtCgfH59(3}fACJC1}G*`L&Zgf5$%wT9nLWIK)L3JTk<|Ma@SiKHjqX#nm1^bln^$)UvP3Pf#WDk zRoX=zIV6wxw92LOsTC<@(rVQ4Qfep*P9-6!`jDsEcTaHx5~*ND z9)#)2@!2Q4cytJqt%-J11x@F!>Zq9#m`ZVz1#@*y(^icINPl*EDudqB0CsNl4R)Hg zAL@?V8vVYK!}hsRo1Op4T$W`$9K2@1IOaHhAvq8<+mTv(Z-a5n%NbbxT(#q(Oz>%6 zlP%I0QsOpG(YfzW`*mgO>`|bkOvYv2SW9h0YZ|#U5om5ogEoKT7-aSnrynw+3Q@N2 zH&9iTT9rT%j$>Pt7?oM)h63)37NnPdLvBzHDbRx=+74@A?$BcFUG;XejwKJ!MNwHk zM225kz$V2_`@~8fOT!9SkJ9U#%HcWf$jSMD>%8z^)EPVgbv6@>3Y&ZF898$sBo^LDmHQ}l%x4z+nr2ei7f61M zrW__A*(7@^EOnqI@=EfzZ?X1XQ39$?L)}g|F~lL3bg>i82b6)-*pM1n6Ex%#Gm{?y zx0kq$;*|A;$T3&Ni8LT78|Gyl%zl2#w-aQ8Ss3)JhQa|WWJ0VlL9~wk%hzzW+TnP;{Qh!&fei-;DRZXjkr5gt_1Bg+blbxa(?;gA@v zh>4fV1E9|2p>3n8rGEDYHS*g^P+)|~JI>6$WIWj^yoD|Fe8c?;AkaxL0CNYPQ3`Qz zna(9Y;G%1PL9z{@{}}AF&H$*>gT!!bL!-qInLscj$&7yJkyQoQIVQq@CKD!z80&5< zLRXTc*b-WuYnlVE7h-T+W|Fwx&0&VK?2#^Ux1IRS4?1k!ia@Hrn=z8A<+Q zbJ6-I{P`W4+u&V>jv&Z7JTTLSaSrY#OiqFQq6DzHoX7xOhkc=3_FYw|0T)Zzxda4x z8P$9AV?0c0L?{NovRp*cj83R&bw@OKp$=kE)CkCF{o*lxG#`jE9f-0VkTDuK8w~-x zv;F~ux-6tNwJO}g5)r_$T3^ivN0PQYXl5h&u~9t=&6V;~5N^GqX2Q?JXivX=a`ZiU zLD2xB{k~~b^(ibllI(O17kMmC)a8f<#hme#;o>orl-fH7M3u_>RQQA$r>qF~jtpM1 z5arOBZI?B+hDCqh8E%&~crgxs#~F|282m0>`UO{!CkxA8JEj+!=Zk{ni-P%!g7s@E zrk6UVw=$;JI;K~W$$y>Ne*xMy-sS^d^A&Y$(PHsncHm`_;$znZABb+#zX07`G+e-n zbTHaW!^x2roFcuMZUGe;lAJ!~;*Dv7v|*Mk*?C8S@l1Z-xX0pbF}owe8kiaUgppre zNGc;ix@924k(C0*RSu1rA_^O|2@ACe8#SJpg1}NPg^A*5heX_sLG(8o<~TI6fbbDJ zqNZ&Mhje!LKdiG#`qKb>oS7By`R+L1Oop|%rf5K}O{^v{MzfNv-amGikQVC#8%*#M>88!xh z8r5cGZZxnk>mC^MN=9^PtzgS-0)KKEddUqkqx=z4HjT_yz7@1iLkc@gbmlL-BiO+O}WU zQDIn7f33(QQ1_=j9Lel>6qvZS6SwURs2+A)(+KftgNqs6*83^Lw{gPnNryphWXdSM z9J0klQA4Uz6c~ZJ0h&b(1VIumAG|sKE(`ZZAlO#aVEmyS)TC5jGQtJZVTB6}NofP@ zN`TD;%_(>6f7@JiV^;{E@(Mlyu z)#nR_{g2IsWC37v!9F`C4%~4W-1N}uVemzb1;Hy{3Rnos(hz(uU*Z=N@_qz)3xYnG z>?~dSx}l}t=vfY5=Oo+cA-B5hl-0op>Rpg~K9}#MEQ5?V6~?F(MA{`FSqjf|jAz>Y zszAL`lxvgBbo93v_zZ=9#zMX7&2Wro-OV!^;2#fpr*F*@yWM7c=S`dspH8=4JWp+$u zy1}gM8Nq6s!D=fs-JvkuaTw{NAL+9j>BAoh2wcE|+GB&@QZOw*C}&o}#B4pNC1KWN zWnbKHUQ{APzi(K*f11;ZUsMTZppvqII&v%tcl5}G=UV>f7WmsRp#+Bw5v2@P79x+T zh?-txGx_S|t?U}cxf0(p|H-b+z6RkU>6i34md9Kxi_A9wv*ctaHsBXlX^wp5Qh zjF=77l4{E_gEX-FE;K)8tZ86J>5ses;SZedPMOf=(C|Q7jv;3)*LNWXzBwsZu#V02 zD`JZsS53)puuQiTMr>^j+fWu;!ygAdOe(nTqB~>wEs4C=r0x`??!=^mA(Hk&hPVZV z;Mje2EfVp;o6Yl`kZ}a1u>`fTTfs3~$uV2es)V^W&&VikMet9{BbwViWbJwg-;D6)`6{xT9zo z(S(}|I4vhrnqb0P2=hdNpIYgaFeisaKTz{8ehbI?(c7TzV8J+*fv~FC+#btt zrQHi!#x7J$lQqOsF0dijG~JltR@Vz961qT*qbC+ax8OYnqgEDEfh*kc(gY_%1DVg& zaA%6-$dN0Q?e5;f)__p$#WS4%m3J=%#3K<&lO)aGN<8@c4B_v(pwz@CCB)42Rz^Yl zjDmqLl3yac5pS=780rr#`b;v3p5v%D{peG5mYW{ch2`iBo#r{~fg1+%p^BV0z#O#Q z_~badaefWOu^n9WNu+C=ZsipP5C&Qf| zM*%?d@CZvyjt8^zw=u!5?&pgzY@F>C%};?})Y2mXm4(Ek%~{M-;dP#!M`X7UgJJ6h zR0#UK6aI3iac8f`e%xdG??0i#JB!X^W{_to3*O>Jbscy{kHgi=Gzj zdU6C%^!ZtJY~>{$ORFc#T>G9)nxyo40l-!s^riPFim5o!l~o4T0m(hT$-C}pVdB=Vh`Et(0;2Z=56@bn3h}N>SXJi z=kx%CJpV_gVo9O7GyeyO{Qv^f%pjs|qBASH$7^{9Ibz4w?BsLAX5+zPzCn+Zcgt?# zIr>LW6?eD-n%9fN2;A7I|DQoa8X81x|J6xBUvE8^ms^;vt{t1dC)*fzBI(GX*>&^> z(Tw!aC?no4u$G2Dxi6*}6O$onT9+`ew*_NWjMj{zPlxaYh1Ue3QOgwyAHLuQ96 ziRp?RJR}#f$gr=3qutFyz`$(Ps)fCE1 zcW|yX{+7h_g7RNaox6OgCI9N?^p6ZXdLAc~(U-SDfvWb0pV0TBM4lyWt7Jss9qIjM ziw}F|MkFgS`sU+Ue)s!Cti%);C-@>=)%1Nu<%{<_p1JA+H_{Go%dU)VKbv zlN;~hGvpyR9?V_uiijY+FF+qB8OTH56K~~tUPxc4Hm{>!L82F9Ux+@Q>%0Aez8?=;vjw5PK_9+eUtaXd{B+&EzPcfI{dc}uzk{!$zm3D@-tXi8 zNJOM@Y@7p#)$6B+ggUqLx3BdYcd!r!{IFFtug-T#5SYB4ukwT(1cWWWlHy-KqO3)% z$a}3*uQQsrCoN~+yMi}W#&wsku3LOm$OS2V>(~sR?ieLyILrL@54l%L0DG7i4rY&U zB@JA?J()dTvrIa}-DB9yBT@N2YQ~X~_2&evKFNKbTjwS_%L$`9{l)~=n7uuWS&-FV`Ta`whO-RAyy64xZ^m7QDd}}Ry=zk9uesFcU zPZ09m2K2qVz)R=Beqg=VE1%4{4!A{u@0v(%8&6vNA1$EYvqOSB2%UY~h6w#6SN`03E94J6J7;U)jjVL+yXW^m;`%r%?7g!(KVOfo>=j>M4XhX` z-YEOnE%s3UrtN-s>S%iD*&L$Py>WH@=t6DlS&4it@f+z6u225Ced-EixWb418`}_d zoss`bushpZNlhJZ)N?E=KuTDPjEQKnSj+7B%9#c6R>Me?N=TlgNrD=}5=kCuFekFG z5hyu;0yzapm;jy)7-0)MqyR-bB*CMNiJAxzla85!&JC0CI8T=lSl05i7sjoAlAC_@WJ(F0_Ay-G6qsSlY<91^FcqyG)@u(kBS|#~T}2PBA0y8id~WVa9A13K3;mQD=oh-iwWePevQ(&xh3%*P=Rrd>#D!3b6O+l@SA4JYebL z=w$s*i9{}pqyGN=2vhR`pfTKa(qFmS5wxePN@DW+>_T}jbJpq?t_{?Vz9YI8`bMH5 zYYYBh?kc0cdUYfsb#hi)>+}Mqx3cG)A0L!jWZ#>=+vrv$7L$HV#GvO%s+*lEt2$Q` zrkb51mgUxVxE`!5)qJ&oCge6dc4c~*_H(M5 zSX30O0Cn-pd-k!AU~G3CERasj?#bETo1T@QhPeGIk1<>F4@!yJ5@qg3B`Ri=oaf?& zmYB7C`}**OhBEcXeQpo~Hn}V2GvGKQhFwt1v#8I8C}7_)5kJvaaye5v#XJq}3s~`g zdTB?v6RSQeTKfgL^GA|KctnEV6Kj$pU}`S;dXt^SO`03J#37y9>_?ffvuvtMxhLk6$?zP$f)nT1Q8H@YEB8)~S$JZiDTCCCw40Sw7O}(HQSMJpYzlfsjJK=5 z8n82CVT2F|Zb_F{1Hgk>%5&E{hy0(~G_MkKS8WY|Qr)L6uC;38!#k|5KWg;pb@#Qm zL`J5j^?5HNB#LIi*Y4os>;|}VV>7>nNd@x-#Od};D-OCkO@n;B#L%89pG--%-++qbu? zLDoi&o+bjCbQnL|`2s_b+9RQUCD87miiixMLW!Y7CXAxC|0F-73gunJKeL0RlutR^ z+zjQ~?FM$2IB46RFJxOi_fM5TS*OdFxD+VhS~c1f8T^b2T(GAspj(A2Y5IG?q^*|% zj_daTL%BqdJ`4rt_IT28yxPVjVb*{y8GdLq=s^Ydw0Y9t2&sn^IAD|iM4x|1%KE0rY}E=E4TOuz-M! z0Dv*k{}y2E{|bcuqZssG2xA_AR?t7k|B(n?dV8v=O1}FG0Y_G}UBfbd_M(d+68qA3c3tqbsF+PMV6m1_|<2XY8hz4BjX)y8Ouor?!HcZ zpsMl^8C3`-QxvgrsVqvVCd(%&G18@$@CoB^sUXru1sjldk>V*)F6cKqX;P-}M`*b; zk+`WKitUgjFc9nq>`K`-Y{6N#krX@ZECwiqSl8T;9;4nP-c!H=%ZqBk3gt5F)^J`L zJRmgcN~JMNC0Qi}8kpEO?7)?9iyEX_E#X=FI3OR3?E9ed1qi!ouxBIQ2SHvM>;^jy zAdlt+fmXs)R%7&5LyT52m~6WkZ0vy+EOUJ9!+qm<#!6K#wn`ny()kGLbkQ6X@_$Yi zT&t8}-0pV7sIBJBDXcQ#b-*V+k=!L5W{lu0lILwrgwH(?Jwf>@=8FZAi({r6OH;A1;Lm(`0|_>rLK&GNPw%ERmjnd z1;v@Y=R)D%1vInqy%1nu&^btRSj@3XuvHl+0?VhejY-N8F{K(e*vaGY4@%8Ppt)oM zbsPoRv&`vlB{Zm&DC89d$c#X#$NV+QmIu*NeB zlavyOqvRwf*SlPGz}B)8o)T9^OKPNzRhVTCGWm*o%zo#0{hF)+TFO^a$}!^!7I}4J z$x497@~ea(^z-6>5@}KxzN<~a21S=Bmf;4YPYA*~@CR)Ywk^OrgoReVBueC0W>LzJ zQ-axBq~{jSonz)NmR`Y~gNSJTWpsnbBnAXotYoyK0NK97K0U5g`&G1b$`{F7T^%@bo62BCFIYFfakJ?0ljJ!+P zkxNq9(PDxE+a&p+nrIma$m9A_U!o&*L~^f+OA#dX9FdwGN5%zNOI$inaI#K5EF6o` zK`BJnqEbTEVJXM0O15Vut7m2Tjf&!7Y2JuzE_+u-vX>V}M!$6Ju!`kmUpPsn;9lv> zE{J5IS`n+XrI9v(H3o+?rAq@VNL# zafjU(v@?^-$3gYxMxfW`xSovf5;u+V zUFmj5-50uBlRW?w8SJ&Wt|#OAi1*r4-)=fL=XxT1&+&#Wwp&v!OG9+q+70c7k1@v! z@N}Htduz5M`~3{yab~N#3-)4P-$?KpMDUIw zAokS9WPK5MY-p8eXfKuHH|%Pwk{7toKXZ+Hq`sW9DvbM_X&x(YC#gM~`DGWqr@ z&M@7VMRQP&kkpF{U|-P-A&?AO*G}N){0i;Q@&$SjG+c)QW1xuUJIbX_gw^;HK1r}& zDS_o(Ecv$+J`7a%;Pq3z9_C-uwD;uoQ<_~YhW8UbgVd-pa}43>NWLP74>|Ds^PJ^N z+5ghUCXD1CE^Bm8{-KQ(avBJ!WF}Lz`M~+ko*>2!~AoB|J18YElM)swtl2WEB+QV7yC!6^jAL4q8^kk)l$PcXm|H<#D ztd|e90_2+v0~O9@don=|!{Ov;O?UJs#L-%&jZJjh!i0VmkN;=@!>oVO-7L52*mBWWVFN+5`$JAm+#n1Sjx6rUr`yDK>(bqy_X z7GwUfKVc`?7#NrV)Gge{U{FIFX|cj7FzNfX(DT11c$k+Pi$lM7)f3W3?q1Qn|* zojO7ke4?#}V%e8xg+!=LUo{f7a0(YJZolVZ%PT003SR?3i9-#yi$|WqC6lqLJOD0v zDvk#}+B3r>4xWh?tX&_1Tn7;smWTi<83~Uk54VvGwFL0AuOndOK})Fun}Gd<9ZOT#f;IH+?da>Kj$w_9x8@Ms^2JtllY;ztdoeR%@#AYsqx0K#U$X8$T-HR`iY8fNfTc|N$#;o zn)DJpYlxg1rH+0EP3?ncQbMsPqM2EMWl}=3(1K;E0zTlGsj1dP;N6cx=m6f>6lO19 z_569~-vZSEHojG;-pR$ot!dU=AAbFVp%ORC2xhWpBGB+_~1`&1`{5Xmx7@M{L zW@mNK#l6eK0bqp@ivCx%=zCIy*^TTiQD91DWh78f&$AA<#k& z6q|sadm^!II?Xa64A8xs#jqr0m1ZmHEhwT&klx3=Xjq>YQPkyl%JwQ{Y;7xv&Hm3; z0#A*G`!63S0~+?WG8y$-bNH#Dy$Rj@?w^+QgILV)gGEHFLa39av|Qlx+EPBJ(~dM2 zvMUF5%}P%FlJTnQgM|DGH)XG=sGBP7Gh?5?q36aonC=!e#_%C!wr zu}b&P>Ye*%XrtsLqk12@)I%5}bj2>RNVuT#pOLz>YBoTM7ua=^jhJOtf$(AaTx!)Q z)nAbndh%-iw5g^bjN<^WE5;yMHc*o-YT~ZaAo&`!-Uex+PL*Mttrs8})L$S@+;tix zX9a4ysghaHPPJB`ljGCMQE8>XDp8pH!M|aFilU*T{&~O@P66nRt;#y};St?zNv(-h zH6&q~;OA}0m-v%l4ICv3Ui|Bgi6+734?+Uf)kJ zg9Oz*?E&F#QczatuPXssl(2QzK*u114b;9%bf{+jksDjWjVs9ACw;}1O|-ttY(%@~ z5C>|EzDgm;W(c7cUchm2h<33d4&H%-rH80LZsactMTE#e_(-8q=MI?bo{!-ROe`J- z7;eCB9|Y(5C}FafwIS!ag9$!y_?_bJk@=mPLH&~4UpB6RXna%$+h%WAi;qQ6eEPL9 zzJ-8EvtJa3mL6{^8mfxPkJS4UI@Udn0RP*mp_BZCrwW);g&bL*2cuxYeDgrg6+o#t z90fTP(uduTgDHgnvj#d4`)i^1;+8i9sqChco(dlqP!6dgUm_h9rJ<@)N$Pet5Ym`N z87}W8|2<~5mr+e{%uTI?mM#Bb$==OXWSDMheHBHS2D{EDo|%j!$7#>0!_<| ztvJ#p<%0Yr#JDh@2LOvjD;B zu)O;|TyTHrD#|{%;0vK(jXsaj|0uqd`skdo5_2Ejr{h@z)aq1M6phegYsLO4Rbc_o z_pL#-;#2E#U6${gHhRnIqsIKE$j)uFb-Ul(*3%~8x_t3%xDLg#HI-7e>cd>OyBbn_^z8M@w;u{!;9ZMP*?Pgg~64mKj9 z@H>kRqW1>(#+B_ozmvOq>Zfrml43PcEzb*c;#P%x+t=^}hxoVY#A#~HlP~V)3|Bi~ zsxR{8$#ggJ?fueL-0!cY!a^XJE3tKBUhWWCrlHy6sAbEgpOKCe(sOjOI{HmHkq8&- zQ~mD0c%AzS-k0yeSMH}VsEFi-XBp)YXz6U{)&=?ZMGMZowIDs%leJQ;*N0b(aRw4U zXKf)jtriO7-Z7x>NoCKLAGfI=m=8bGJ?)~K_^C9o_|CL`cD|)e$2mwgY+|~a-QE`q zpJdNxxO!c#FINpBJ)1%LdKbwn`EOfzkDVAxOEd4CR283|I-fr_+EaX*-3go;yw$Jx z>7zGZ+0w(``?BOK{>1fRPL_quyS$F|)JdLa`F-_WYB`b&!uX`zf6U5ff+<3-Io?ri zgYgu^>wjgxn3n032*`U$i1@{`aNWm^ldJ&u zJRLWw!1!sPy^><;E3fIP(!T6gy)4>%>j8I_7Me_p#w`B6J@$1ewr$)IhkW5a^T)(@ z4k7z;zgSphUJS&i+?noJYjpV76$R~n=PJ|(90m1)huLCUiqQqv(;fTofAaR8Sw}|K z0Vqi3E?UYWg@k!`TTUzIE8Wx&?ko4UdqpI>0{Er!p8d_Gv*0^GVd-QGh8pH99@E1&@B9>~aOXLF=(- z@AgJ*eIe?O)SjFZ*6fWtAztRkwaq17k*A80au_9I%V8Ac;)zPD?e^}RCND8z9@CnRDR=%^P4`MOoMXxFWcRImKNs`okIU9IMeu&6pspk<&v;CUiGN=goOgJ4 zaqXT^N5xsT$Pzlc-nX6?VPTq zUTq`2ppUi?d_phoZtWURMewK$)@U~$g1(uZS@h90(vU4fSL_xLy7d(!uf%0MUb^g0 zQ~VK%<_fFh6N&BNCF7_jz?ro4E_Zw0DqbSao?|9%VZ7v_f{yRC;g)>x z+>D;qWgM)>dc+3C*F1gMV>?ETYuP$K)~F(+EA zo2I#|(1|PzZ*7K2-|eQ_+t%wrkm$AM>A*>@bQB^lhr2iu+n7-Er>N`Y1q#e1bCcu= zHVa?HcH{AD$z=GtHScFah#tyOA>4P5P^{JcGCunuI@jmkTc2YgdK)C&7Lv=15P70K zb;T{@u9s~VD$cL7lJNX^-1ABot?MMd91_RJd6R2kZPx|)Jp0ldXuReRx8o5dZBLDD zW_I<;#+WWXW3AC6@229hgj#+1n>v1*+qTyHk7V1r#`|Mw@2$ox=}JzkeVB;O?Mbv* ze=@xqxUZ*+>R!HO8}^3L2zPh>#!6ms-oI3MYi*j{=T7@uyxL2pD^P_t-dE9WwS4V`;WuCe^uU(#myey7u<|}20x!3tCSn3MP+>B^#!xqeX3>JyA$(6 z;bS$scc30+Zs!p%=P(x1eG9q$R-LoFlCMww2|A;J<=aHJ@Lzc!%NEP;mMzpt(5pVjEU zXkQ%rzew&xb%3yS#8#!%6@2UQE5@(cT>&y^d`a(^jidGN=i;UVvF?}Lt({yq#F;nR zZUsbmb(a^B)&6vxbiOhV47nss5Z^GC-~Ow^0_@KLV;X1r^e113{g;L4qx%Rru3b8~ zZ&6Nj_`~&Bz4SL-ttDJH1?4;H>+kwxUn!U1oeJM!052&`^|avDxA~YmPFw=~<9b9; z#6=B^6*`M7>LUYMtROn-^f((LBse%YL_AddIT9Ww7Cu%at7K;9Bz&Z1XQrfPr$%o! zPcaWkQE^veOy0SF{07&9AP)IkfMDd*rD|#X(2k3wbmE3AhdQkHPVW#SZu~ro)z61D#c5kjX9&y4WNB#OTg12at?-+Mc)Ho zTOU6j@UXwW;(2Fd_2S4)Z*LI)tAm3!)@XtI6>Ac|?AyxcetUg5S!pSE!nyVh^558F z0)Rbu^ePt70UnHv(2pMi|8LmizecCnxY_8l>KnP5n47xjGc)Kro0^*FGyWfHXfpub z@o)Wa%;W#Aq1C{>!vitWq}O29AxlUUK@rjr63S?VHl|ImoI)j}skq7mOKAiLOCmZ5 z0s#Rb0ga*{DJaAVG7u0EjnL&1L_tGS1m@o?&&{*S27Vnoa?l@>=<2%KZ?-+ZUsax6 znG~tQKtqUeD+vt$qA6FQfR^jMYM>U1-f>wqomJdHGzzh4rWIESK7kZO7tKJF z91hN#p>?a*M3wgV5)rp@lISV%e@&YT(L}>&klGE@uH`lf`Y~wJylIGnI!oD}tAJ?w z1IWfmZHoC8_I?h*@W7z%J@qwOD4Ao7?w;yT*$0YAYuv|>P$+| z@)AuUc0vvItQZVE2)qzk&7$O&7}<7<+D_Pq|< zEMeI30r9nSF2%ToVJJ;I^DrTN;|R*TDYk$^F;=cCk_C!cRg@V<<`qxxq1xSMBrKjZ z3=8{EmK%8+MjdUWDzeFFJbhr3)!(`c`GOz3Tcw!6sRG>2!Zfyy$0sK1(w7sd5YP<- z9a)^Y!lkY7=o8HX(%B{93gEb-cR2)3;UFkvJ5%J6<|VDHnyKTI2L*cV(LYptn8Hb+ zA?u-mhtz7h!Fqpr!RF3u%(P;DuT<$t@cmCx$L`C z;9x~Du#<0%_klL|=cNF}VI94BxCFwyQHmiAS~xot66VcRNdRXPh=VhV&@62f)uj$9 zTBOkcEEuAAI2ZCjN7R2Qv^)r`D@#C~7LY>YL6T))@5`?9jl&)8%i zlOaWdB7U6Mty?KW-;dC^eh?P4dgIJW4Y3QOC|3_+0M@#@7uELvYw%+<+}}4+Kq?h$ zG3p2MS{E0OB4-T+G|MlQQdZZ6X|8093r;xZRDGxj`{cBNyO` z{Z{H5q=7LaQiVVXT*Oc+Tq?p$Af5jgpDIK$yuUxP*8v5SP_ZhVbZk6M$s|-HjZG?F z1)El}C_0XUbVv&I+7Ve4;K2CplS2O?{VXSd3gc^s)ad`)rBucuA{1Aj_KPA;LXrg; zh2+c!K7yQfNKHKe47k+lxD=9>C-kd|Cxu6`LDOW=ZnB3M+(2B-IjTp5zZ&;w&BDoo z^6w(v0dKC0HCX?T%(i??IGqv-a4G_P3{e#Xpf3r#}H^yi6foTK2Or1XFB%rsa2+ViKCxd%)JcjRQ&=6M!6%MB)`NyPdlWCMW(Z3 zrnGFX48}31Rb~#%J&X$sBSo$G(Y|%H-mt=Qg znpK6l)2GZwo{=}~@IrU|jWUAO5GZ(IiZm*gWAUn{Fvx?VAzF=S5UJ59MuE{Q!?sFLyBOS+7HiA-!cJEqxO*pdyA!M1 zcb<0Y_tt$=@N*Fy*$q4{3e3%s;4oW!t-GuNO636qgF^{Ggz0&QEUOeX$iP>@FH!K7;M{RQ zi&PkKYt3T2iK9E&>~+I_i2V%g5(d&kS&>xPEa`ev;h!xX3eZ(?ZO%C>hAv?U;HA>@ z24ow^YUBscozbd4VXNs?I^at@d`tIS6$a0#(W-^1^ukm-y3}sD^7mX-*7EK0-OK~@ zb7N~2;(-yX;6}k|DBH;>Ug4WY zr9%?*xSESLc^*pCq5@2p(SuOm8_Tr#xDWFZA>$|NCBIhoJD;#a-_XBT6@hoHBm)ae zp}8lAx20z0@r!7dRWIShWx22%@6;UOmT{U%a>U(Wj9XMCZ+7-VOpAbsvq&L3oQ=Wt z{38h{uw7M6TY+p>TNwY54tM+4He#(kdcgK)xNlSiUU7^((nj(FS_k<{kkDN*K8ZW( zO$yY9EcNr|>P|KkIDuiKN58-VSz^dfE3|&AlWHjNUA;bC4P168UqiT!2S&`L0r%j6 z8gx5ky#BQo$r4z&7ReRZ^BpommWrs1;Q1OnJ7lf?w-T&mztjcKl^>g^y`e2pD(@L3 zYyDy-hB39!Rj;S|%By4|O_WKW$A~Z|d0@Aq4#)e@qBmm8nWKH6VWbfFfp9fx_Fn9= z%qu_L|HuT&>Wd0I^_{7=R0M8#IJMvLm109n8&u+Gy6#FtsknHG5MWOUfp( zxaHG3+#@iuBK=eNcxPCRM{YpW&j)p0QZmM8kXtKtad-1|@DPw9cja?(i`uzmLOAO6 z@b*%0jF`n4DKaZv3fw8Nl9E46$&i$vf*y`nV94(xoc-6(e<9xkp1=d1M6bxw1D?pE zC}9hhp9iAAt%~0-4YZq3WM<%I+~@^vahE-MmpyZrJ#g3D>?H

TdKB1Gv-sf}8ge zLu!aqyt}MDC{G}bd0blVi&I2K>8Yd{7 zRy3K#rBlo0eDWZDJ0HF`t_v;>><%r$z32DA$Sjo|@}ssD;-)=(hUlpxkvAm^;$ zOHSZR(BMl_8yiZmg}|j72bM#B4!JS|%5FV`1PuM|xA;MV)b{hwFT_<*% zQ~S?d;ZvByzWaarWpH)`aDKo$dk{On=pNmW9o?86-Jt6z-zuv&m1yjEx3Gc7ADf5< z=F_zPWP9Pe0(fFWjHfLwL?G9nE-BmCC6{;W4zdFwL>L%QjSn;bB4~lJ}JWRe*gZD>O&Tv+dh-MA1r;eonqjhSkwQ>gQ1MS z|0CkiW(by@jF4}iuNb&rcjmsPFn)%ouem7=`H+?^@QxY4S6x=&7WUrg@-CEP2O-hs zE1Br2;9^O!aAH{@-BbJa3nV|x#a>4|V?9B&NM4ayamL*e`g?ZpsM@DtWSB0%x zYSeITe+dfn$ikRA(ZfC~CK+#wo}=ESf&wTO2%%j$b+FGID_9nWo1o@W9t#OE)B~e+ zbb&@lN3*4s(o8EClgR6q!E1ohZ@=jAFD3l-2hBAMf z%5ub*%HD>zw!LZ4^qYB%o~%fbM{XQJY($vVH*tOj4<8+(76DOn?@v7OMT z0X!HmCZCGAiMX0dw)_1r<}eH9=6N$ad9y14v#W6PDp`OBQws24$f+jrz;YJM5FT@Q zWimPw*u2e|yfK)()#ZF$`nJU-_9EF{64|=no=8^@onG|1dK@Z*D#ZHj(ib7+QcU9p z#87LICM`A5`*&%gNH;e*X~*~eHP0NrG?xciBFvYggVgb09!Q1|NCn1FzwSl_E{>-?As6`FG4;roe3~2MR)oXAOKqI;2NJ6T8wyhHK-fbKRjEv=h@cHN3=Bt{n@7jNyK@SD4UH@ML z?c)pJ!Kl6Cl!u&j!G^s1Qa~;L^I&NFFe2JFSD_p7+ixZs(i^Fkr}GKuUS6G1uAl<{IuG)Wpop=(3Il0&#Kkd+DOAWha2_X zH+bg5?O2Ec+G@<$%JDuCv-dl1=N@3))bhZnd@qvRpUmpm+R&7y7sKil#L8oqstWZg zo*46>?h*}Tzh5voJyiF+em%p_IddQu%qTXo#@D{E=j|j$HsYf=*y|KFXQP;=x<1_O zuK$Xu72oTKkmpb0_EYI#FS?QN_w&qr-rFfOu=mMlcagk`roVoAKo_gFbTaRr)n=?& zd`n2qK4Z?cDpR7&(ul&zj|(SS;Dtle`mJk*xQoE zifJ^_!n}*N-m!?v_Tol7`I1wmA4XITh#ay;<1p!bZFqnKAIw z9U<%k0W(ulS3eC?vC`6iK7Pw$cjtuci+8bCn^Ja{9KZdQ-TS!}@3eo9Z9MiOsjL14 z-;{JWDLrCANC3egB+t#~aDkk-%;X-@W^=N?N0h<^P-p5be@(dN`G}?Fn!sn=?&tS5 zRIpqiM4;bL9-^^g52J5E*&Rz)A9B}=cd7Ep31bhlXZUQ|=*KY4?&NZ|!P0m0y-llt z!TWM|Jg!|U=IMAoRCISEO}snpP07iAjt@gf!?4$6o9qAnX&qs^KG~mhH8=Y)265u$ zA+*EUb z2D5NIaNBjQjc)u$SUyr*c{0Bh2mHCzX^{b%rm#G(*LQ*I*F2{-@;kMaj>qoodTePQ zUz2)$@~4gWEnus%@2k;r<;z5QI3{+>@44)Q{Puglv0WCg^Rwms@_u%87zE9d==5rIA{tEO&zfzzk{2CFnLH(Fkkp>clY zTH5ov_7(zMEag_xzHlwjdU6+|3tp~*j`9#Ax*fDzmn}^Gt70evXD#LSTyMQ`1VrjJ z^mv$@rh9MK+P<7^oVh!?o2ib5;o26#zawpq^?+DqyZ*YZa1zh&{=|UeLm1)Pec|CG znVjdN)0bM!PBN{Z-NN{?-qQ??`x+hj=ee0*DlTYHIR>+ z_oEKR9YVKU;Qs1iz;`7-5rGLkyCe7_I9pSJ(K|CN2i@0T%vqS%XZYl?evbd~=CNut zOMNqYGNl8neP*U1kspJjyxTh|OrQHAWZ>==?`yhW^J7n`?VDrRRrhV=IftU{8u+x0=!39Lz1>^;e7fn6cfP#czrk$dL}f&}2Ekg#{N5ga`uls$81}SJl)Iuh zx?Sm8OWpci%6Kp_`b^mv9;;wqce$m2Jetou&_2h@zCST1I!pdSKbh?YXMYH1ng&PT zal&GS(eLFd)H~)Gd&SO=cdwliTI>A8zxkBp+9y1xLo=U#~l0W(f{TZ_wRhR zyMFTQx~}(q_UXRfT23JmU25px@iJT_i@~EK-+n|Wh;g{zs{32FcxeFtci#Vuy|3xo zcWRP<3*6AMeA@MM2LAfv;i|2D*^-n+ZfeQ#)axVrTj<(rfETP^SFYVrN~VG+!X)PuW$&m_f)Q$-J#{)tQ$0ig#Ma>L5K6 zu7i`YlOp{V3`BD%=NX_zw6w!RA5gvzi%clTO181zH%?iTZT`)o1tK1Rkm=B73Jl8ykOjvMD)B)gemw)KKk2kkhMQ^ z58CVRhB;LFn1tDXtyx+95zfRc`L%cBt7t*~d>x_4ciPnVzV!Z0e?x}oG_xEk7Q5!7 z7}F}@%drYz_UmpsVYZlIi+-!7YJt-v$19JTa_^xZ1c7U$7Tuai=3mhk&VfC4(@wh{9>$+3>8$A8-jzoV$nnrJqeeWcv z9y0dfptQlav{)vLedP0r*ssga@LJyD5&Y;J+u>+6<+~d60lieLhqijWk#g@d{>z8* zoIR}|=y*f@#;5nY%}>S2x4Lw#*Isv)#JP3B9Gp9Rz>O{ozgG#hldSn7Vb-q64!*p2 z{@CgB&-8{`{5z2OrvR;i$-KIzjCDrq(-VH(kf$3@LXLU=cJiy@QxWYd_o_dm&BWG6 zWcsV~Zzi#P6)Fwju_K5hE0Uol#PBsv;7%cgR`@j+D1jOpz%m zGsxlZn$6)66kD&tQMgr!WQ6e^#R#<4ElTWZ1Ki#gKtY2fr12h-xU&8lGA79M#RVZy z=qREjw@O@Eeh!6lOc5!wvra+pAyQGdD(O^q4$zfYEI-Tojn8@)Wt{S>t1Zqtg*yv2 zhKbtmdH<0#~AGmjD>LEE28=QJ*IFD zL|hD;u(VJ-OtX?RJX8=x<6l^N*s}SODtFOhIb=g3F=D>Rvf-N1@A0VvyjPKLMl9IA z|1q8B*C=QA@Vzs<3+?k#W!tv9y{c=1MFo1vJQ&0|)#t#;+If6MpGvl~lUuRw9uX%X zNp|=9C&Y4;wCnnuXGGLlZv>D1{iEaP%%pp>CW*9%TM;o`Q=?rU|Hp($Uqw8OR*1tC z?J915c92im`c;i1@-?dNpYezV5M7PbR`B%ra1z>69!S9Hy0Ffb3PcV`^BIsaynqm| z0IM_WLxWknU~XKJz$2qrR&8S(C8(g-5T*j8l}DvoN`J35Y5O?knYb_xg6BG7yXsuf z&J>w~GO!m)2`NFn&@X~mY#=L`fT1O1NWucNUtB}KfEAXh4MG&47MKIxutOwf1OQ-$ zf~5~S#x&%ZWM%*~T}xT_ZmnL2ZHq9p*t}q|SXd8rcHlwM&Acs0z9~GdANes?^H?+2 zlg|RltSUzAIP;2T>M*%(GafRV9X$1XU#uNvu~j9?1dT(n#^gS?aIci)-Q^%xEel4# z8pXmGesB)my-lpCa%L#nlRyxrZ#@lu&2zG`#-IsItIi`LjL1U1Xggqp5dJTqSNi*J zZ9*$&TI%?yX@OdM_>aVYECv~}-s$I%S{*m1T&&hZt~2zB(vPvT}@8766M$0z!g1;%Z@^1p7oKjxb7W)hwj5 zE7L{h;1Otp^g^!HJ3>zyr1@Q%tP@wSg&1d(%Z_wG+`ohJ*ZM@d?LAFfZMBkdH76XN zVYxp)-z3gs3IJnZQDwa#(s5Kz3X6=wg9Y`{`b}$9X@`(&1q6Eefrkpqj$4&6-k>xWvGDNbF1*bihEE-N9;JT66 zf)rGquD0GWSyKO3k!*BU?31C8Hk&O#q~U?_wFi}~;U3xvlmv?DG$Dim$nj+mm|+QY z2#QI4GH_!2R!Ag}fY~;XK-m&-iLq%sfijWkpWAGjkrAiB=xtO%@DZjngWO-bIq3)$ zhJ6Zza+t&f)2lMSFv5#LS5YhjJ6>G& zMk4?D*Cb2IBw|_br~g5pCXl^%>@zq`ugpKK@WG>edrvbsT}|PaUHS5= zIyem$nFAw^`{z?95rL;Jhq)%FlT4xF_TD-&=Q#M4lM81}7jdHSHyNA}N9jl>k&&8O zGAUBZqXAwW3qL;1&Hbg2X<>C(n()olp_7*4S1@{qcz`H2{S#=EDScN!$4YDcMJGGy zO)?Y>;cP^MM~zCsJWL1edB=Y7eHCbOT8~rn#ReK!?f~ePa^^ezU@LX0^R{|syOiUV z)nF?=#guLVhkNDThClU`Zt;s<=UzL)A?<|!^{YgW_dGbti^kQy^UV?Avn`2lPHIoF z6WMev#|2v^ibU7O0qN`%p*p8%qE>y+5vY4vYaYKMpiCxOS1D-#BO1{Z`DOaaFjBB< zSZCmP)@u#`GOz!DOb)ezZ3+O$$Q}}=oH=pZ8iWmrqC|{{GcZh4*u(|wjZ4p7j#fr0Z zNDt2Cv2XlS`$Z7|yHA%sbc_9EHxL2gQ)tVV~0sbJLn_u^S1B3fYN=(xC%Z z7tvgfrQe~#`be)4T`7CWr|Zz)eGELud$lOAG5agL3T*G;U=(-kt^DU$gKngM6S>Me z7zeLzh-RFHZaskV`rtp(Wa~peM4V*uXz*+e#+E$8fCDqo07Qmlx6H3b2`7Ylr#pP5 z`#7lGZ{RCv0x#?zkwIy|>cDTI7yd_N5J+xjK)>Rdp9_d?RzO`PG$gkeek4Je5(Y_u z)nLC_2L`kxIbf&_iwfyu21oIM_hYw+Nx%j<#@ztejHr-l$*^djnFb67RGy)&4oPMx zVT*(X+gwYPUOM7#(dm zeI_@KIYl75SJAPPCA>5jALzLu@%D`!=Y)arh5!*9UszNyLJbrhvQ$q|i-ZPH#%{h0 zENquPbjYNmI?h1kn#sF>cDE4B3h?8MrTN`V1U(KBSKD2t&OG^Uq#|AaaU749n{?fU zn#n3(WY1EG)bG7i(lId5^FFo}o>`ROWiB*nki@WrQRYCvve1xKJ9^<-H6lYB=Y(ie6~Q31`j zM?g4f#dP;`^c2J%we)qy*z^HBE@HrH7Uv>1JIK9AIxu?c57kW6FJaRMOsJvJ6D*J! zR&01-G^d2nenrV4XV6|}63s%zLFVio&d3I`mt9WmB<7D>X_A*1!a%Y}edIxU&7kcl z)b3i;Zbwu=j?JED&>ncuo@me>dFZ+%S|8eR-2|;$5Zdo&+FD}dizbC%DJXwt)#p#@ zYyO$Y!I?aSwUL_+^dy!IYHqTu2bwuxBq%V}=!-RmF#@}PX)=SnLajxL)WR~Z#xlD6 zbXgUkH|iWJ0uqVvznb|HEtJTd*d#?tBt?oOPL#;=l*sgoBzF|Zuc#3zj*3qguxG1j zEfR9!4(Msi)GdYuH>EYB)PLsn>cLG{hR{Mgug( z8lJ!AVN3hGdFqcIwM+ZeNU_s|(y37)Zq};ay`E)fK9y&{`E7qny5xGbM0&Jj(zkOB zIwgC(q(PRDp_i1Pm)21$+$l7!m1>d|Ym${~)D&u@0GqpeM`QG85=UgsSibSsI*{L_ z++L)P5ARyy)%m~maCf)`d<6cpO{RO(eHyrIiQbV-_afZ36MGWKJ_F`2gV7O3_kz;4 zBYzsmJ_}|)#EG`ursO#a+`O}aDR4C!|HD(!0njF6-dI62kJ;~o+8qY%cph*6So-0b zvK{J7B!`N{AxTjx?w9vhlH#QWnqS;E#DV*^AD;g<6m2jGgl0n$^-CDlEv`aG>aNzO z;sqEGEsa+c&>DpT0hl08sXg@NH}7I~=T}44h9fv?eA>a@3a%Ox!>0a;HeGxvHneQq zd@kBVaaBN?9g}pDS2=7SbXBN^cEUAt9#Fk?U$LaY7 zuYig3X7WN+|LOO_#i&#Usj3bU#Fh%t9ca-VE7jII`i`Qu+qR_`_HZ2gc+VvDIO_La z?Xol5$}`;jH~suKZqXe&!5xG-pI9P?9HN_I@Bj$ZfH2g6ZVKR7z>N}^OH7pDmm-n@ zCR%`1bff?Qo!yj%q*0ZTRmoIIDIp{pGs7zL(^&16k}O<$E z%zRrs!!PiSClu?!vy_EJ(bo#=(Y-QOay;|EpQOZ5@wDQ>)9Toqz)dRRc8B~<)~*L^ zXCt}FYl00 zI$PlFrTf(O`bN5RVb_N+u)|aXR;U+MT9>^R`80tm;UsMNn5<)7OdMlgaYtLx*rvXK z%{Qhm#idxqf(rfuvDF`9F?AMz6#&@D$T9}YGNyrT8f1|;xOWKpUW*TJc9T5tM;^if z(lIw4GAuq~xHQ~kH47AtBf6IbKb`y9CYJNl@(+BmAQv#kVJcQc)E#YS({)h#hofBZ zkkrgm@NHd!Ce&}G+`$}bL*V^(^BA=}SrR*Z;UY_-F1fyh%7PAHQMVaFk}UDicxtRO zF#SZh#^fE1oIGHT-yFF=(p8D~BjTV2Mnrgc`~s5TJU@`Yad#G)u^o^dNhC#xxVCP( zVq-NBi~bm7$q3a&JjtrId~=hH5XQyD&LX@{9etk>b?j+}QkGe0h$?n#J9`YCSAvy) zhAe7X{}+={*T?8rq4|OYFqu;1hI}oL{93V_wiRjm3d_>@-R%K#KX&$$wlDndjN&7y z4jBLF-%*zjvG`L-_+h&7KY6lsdwZN^gZ}t_ydSKJo(tvdmHR#8Io39^Dhmq_@w1Z_ zq4F|6b^IJZbHV2aRwkaUZ}hPBDOOrN3!jtr|5=*N*MN6UDQVa?VZUc>&fD46>>v|s z==*3n+#PwcM3=dl(Ry+8llO2c zSSR&rS}RMVwC~G2-TW_V7$K%mwL13ul$dG+hbM)@cYaL7787avMmk(Re15vR0ZNSpCUj?)J%b%NwOGlh?$t zXjA9;YwkGi7kp20{;GVJy{Xg?{^5$}2C>`WJfOVN&iEK`0@G;$iv4GMU-$TETZ{xxuy&1{=!?9^&Ix-nEzjwd+MFdUT#k%E z%;=Rjb>8acwz8$F6X=79)jUb;VU>6)bzK($VXPM~&Pql-Ki%f;etCGT_OodrWud#z z^y7JPmPlw^7;fv%8SHhUBH+{<>=af=(4RB0wGYBR)PM;ERr=J0#lU5+koe_<>Ar52 zi_=d%)HpwHSZgJ#OgLSt>+5|4hF!XJ+Igi7_E){V+D(U5c3gC=O2C8o+qO6K_Ii7g_@MMCAY36~%J|E^-QM>7?W#&V2mRvP zYolM+1MmJkhr0KB7x@k0zT`{gK0G=KZbtUcFfdNqw73jKr;~`*6Pj!VpC*blpea+tI>B(EY(QohjeH@?Fyk%Ey;I3wm9L0@P2tStIdt!xq_3ojL^lDpZaOH zqf|tDMvwJrAh8%_CBZ6_wozn7aTjks&7)@_cpfO zi%RJ15Q}w;a~p*=9Ds_xz*OQN)xIa#uas zajurWN|Wi}84UTt+Yo603`FO1mezKw_r{kR_cP8Odv>-m&qpYscYoNT+Eo7AB-wUG zSG|AQuFh*{yXHtNR8}y;6XsO;r}ffrT293-`O0MW^>f+-p7za-O)BY*$scF<>uc8j_q_$U5_kP{+joEL(0p|Gj;I%`uy%b z&4%v}QLHU9L7{+?^QVrs8-~^Azjc3bzJ0zu);{LT$3Es=6%<~z8Qa}B`E!~`T6c!3 zvc1cD>o(vy$+FQ4mLEn^YD){D zYhF&!xY5P8;IGd!Ffe!DXZ3${zkZv8ciugf*8jZU>w8Gbf49XS{HS8y*jvArSY|nF zRN+UBBJM{ZS?WZwmh13A)ko$uu(nC}ojVn)LbDf{c6jNzi6S2NxrSZauc)qHGmG;b z`TQ_@Bs$ZUQCp4jDLI$6U1%C*OkDO}wj6zl49&dE`C092;iaB_nPHAz_(dnD%-un8 zsrOtRMb5kT$mHFp(}!I4s&GUVexE0!JvhB}0)<+hbzZ{3PfVA!Iag=zqCjI4VeOlF zf#$tXHJ*ST%i%A)-;REbw9TU4`YtYZ_=*twI@{Rw1@ejC-1Ob8Df(JvQV^BLQ9yTJP=QEw5cGCSVa-uW}=R^jQ}dCndet~lar zdQif|-1{@{*N^WYowJ;*osm|j*QWiLr8VUxm^*b)@yg*RbP4?78*tp|3|FpB#hnQ) z=HUz%uYh^$NAM|%RM6fLn7~zn7$)+^du2+tsbq1=&a8KCIiB@Nb20dLgh;bpN!rzy z*98O3@>PI^-`~0kI)TO_6A@^Mj3*rT_s#`yef~l)n5-wLB?*X_QDk2|39@P{Zm3aN z-##(&A$a}+3$%P>Ng0zN#c#3QydM$R5c#ky(98a1Wi*&$5(PIUVP(+aOtTa`i+N>h zK*DM4HEIH=-2shbp?RR#A;Tzt>05KR;hxeZCgQf?{&w5SLoXGwX~a+9Ut zgc(u7A)r)L1yBp5TP#Tw(XcTA`!?_uFR3s>ja;Cb1ym500u?4ccFn@t$_+@&-W;@r zj-4V6G98tH zodxsA_vioQb#wwa!f3$$(lmf0Kmc-9|NrKj{O@81^M8pQ|4Yp(DIU!1-}>K3!lk#T zhO+pxe-B&%5uNLD+hRUZI|1E+6j}r1paQF1XDEq75TW4_*^XG0eH2M=NSB+UR?<>i zqt#-YHQdssRpHfppmt?s3%&2+^~6TThBu0}zx+MV$K&|@pH^n)qfE|Y=czP6%_?4_ zph)KEnEd`lq3(B`3S2^YK}^F|3H!WZUc>BPbJx(HAh=@1nhjW7%(zHW^;}uWzjuyK zJQ@QQLo61}TpgY~gne|lS4)=t?tDR5%}^E?dK(5S*lV1y&*o^nf|A+Jqbs21ICq^0 zU|>}i1+|Tk3b}+Kd|F8FfXJlN1yjai#saFxAdo@=vCFVa7EQDTCI?WNOITSHlQC0I zxZxJ$9%LWVz|U4#uoHT7=Tjfah^hS%tXvg}adVeb4|}T1kr!A&T9Frw|7Z+_?Y4vl zyva-s2ryd+w4nX?7RdpgnHkyv12UVDfWnqW4-O*R2rT*tWWNbC+Xy@|n-PN2vYM00 zywW7AoazBLHzB@c&i!pVJ9JR$WgIH_+?9@-^M?qcvP}-G@?IcAK-G63i%qM}B4IGo zma#t9HvfO{^-e*eMBSS0F59-b3%hLFwr$(CZQHhO+qUhhuD|a+x8rtnpNAEhxiVu# zK4q?mG3WSV%IRDrg?FW7u;VmkFm_8M|G`>JCz`JugS7cHBHk*{lgRrFd zUwFUNZz4l6^L&3Ur#25?iPUfzm0u1Hp`<^uVPKvcFUF{2J)I_1A${gY&kQjAXhsN% z@1FQS|GiG-AzNJbcnAq&)*P%MTWO~BJx^ok0k#pfOi1;H&Shh53#b|=*cvCAnkQiGi?Gh+g!p88 zb(v`(1=cEtx@9-<+dRRG7}u(Ywq7}lrc+t^R8`b0qJI$}3S*Z(qjdiLyf*x97Y6jt z5Gk@u|F6jkke)8;zf4w;RsWyKs;S(Z?Fz;+z5pL^wXGmGln1i-pgv^|VSj4b$c5=C+G?6F`S>U}g-+L8vzczJS+>=1LR1`#l3_)5&tQ zZw1jq(BEwom)rOs-dcf$$}}w0WUeAacU}B+OOS3JqUXrHc5~=M{Sb(57$VhCmnFuH z(GHaM^RLqC*K(`T=dmWfJ>h-WWr=cQRPj53l==vCV$5^o*OZwt)o0x-4Y7>GBB4G# zWitwdHTQWDC0Z~(wGZQo;WVJG=+gA4uGGHf-O<@W))y>1fw8+W%gb zeu6+l`;WJZ{{Uybh^t`?j~qv*Ratf7|3oO zBIkb8TPn*jQeUc!n*sMh)Ek=3u(=QP#>h|Oe5CS4x%6}=fq9oZw_jN2@lmxFRw zcqODoZZ+bv;Ew)Azq+e$D%$celqe;Vn*YQ|Eo?fH!TK0BEY9<(KJcOh~QBK=7%UtvUK` zK*YYVVB7F%h9WO(p&pV8O_#0$S_(^{W;z1R6hsQ@=?4F-KlPjefTd8d#z7M>^C~D0 z;-Rj_=P58;^8lAWo+lCyBDkK%r>JZo&MgrD;;**~SYj1|2a#3J|~%0!5F*A}H5%Fe8n<-apKi zILattU-bVNtoq;?Lt`qHv+D@iv-C;_r36QsU)8ep^`BVhM)lB7$oB^22n(YTDOkm2 z_Q_eFVTv_z3Kt|%3s8mIfUtoIwEn!r17ZvOxt2n{2|yW{q$>4yroZ@Wd>o-eDWJ!L z)}l(m0f0E4Ow8)ENE4uM}L&%2HG2O}6SYw@o^c*sWjxgw*B=e zjj!e=ax6FK_uoe0opN`wJ>?+SolRh;FaH~(W~|0WYODonpeYv^A=|a?TQHU%;7+4U zrMdKn;qF;<@6U*Vs4e%@;2T1T$@P?CyrZ2PD2%B$<|JV=6T4YP`+~VFNfaffDdFN; zAZG3jKlmKWFIkTr0*>R~g24&iNHWelADQ(%kOBO|5cwAC?QJOz}gVlMYs| zf3-SpU11z{E}q*-70vy}U)6VBzP|q-f0dMp0cN@opRvDjvV_V%v^g~~PYh~XKx2ue z|MFLrmhZ)6hq*3%hS7(;9vPPDNF#jG!_63qPs1Tj=-mpaQ27u?9eE;7D1@zj2M96U zsp7!$I9GiOwzvCMW(zB$L8kSJ=l2T-U-2s?<10E9Rv!Nnj(B8xnf6w4kZW+O-t?1bOMR7jRiv}ifdyl`HiJY3iH_Txk$!J;0Q zeJw`*?q}PA-FM_Dl5UZ(ZQpVY?tDpj;cXom@N@6)q5)eUVS}f<@+$88`pq0I(XkVS zcLocUz#$%fgYiO|u2jny=oi4vEYNL>c(~D@4$;btX=TMD$&5jm9-%vF|Bt;&s^L%e z?Jh2iOnYk%q65#>zW4CZ7uBA3v@OqXD)|3nuabtMZGC-snX@BJ==-%-(e9H7n$xpRTBn3X{uLZnBG{~sv+a-${(G#W}rp2h&jU7CfN% z-X(tpR<}fGY(E1ro78+>Cpg?M9)F;6JaVysbZnia=>}3b+h5TT$sdM7=!I8CR>G=l zXn*BZDV-Z{`VK4iPN>A0*ouHgr?I)~-PZp70(v+)gygf(U!HD!Ahk582MT|VXb7G{ zW8VcufhKWx8jJ*Rx?hBj3NBzl-aqs;;xz~J_r*^>yMU1TgS%O^fjsRLn`5^?0x zD=-B-#i*TIQ=J}qlO4xv-J$X%h-&Vx(`#SQ3-`8n>g}o@-S#GzweP;BV4uXe-Tebg zi4$iICTHi)5!RjLww3@=pAK9aixX}cuN3E+)|ml(G%elz+(X0Swuqd1yPkl8j%SJu z*OsI0V>FLMDKCBM=bR0!i?p@|P|@C*vpMedCv&lf;qWP+t5{NUZcN|@wr)Gnl{`vJs_!%Og~xMAwW zW3SQZH@$;v-K|d?-Gx>^^p|fO@uj<-$hogKPT4z)95ZkcF;KVQqZ2oQxc45q1>8S& z2|5kL97`Tz9!fW?989Mte^sPg>J(BAa6Mmc)3eUwpAJV`2Cf$4Jfg=))R@m#_m5%} zc3Kp4v{V`FG^+^9`&cN}hs-&irc!T9iXN;M=VzkHJ1{C2uOEm8XqDDV&tlfeTT0Z* z+D}HbsZ@=NtZ%BtC45vxQDW2;cvx4&VN$ZTnaUHg$}{Vn_v{ZG%Zb-xcW6pO-;MX) zTj@9|6%Qx!ly+SH=2OxRc1g->ltG@4TRVtgEqPzJ)ef&%DNghD43SYWmwO)79ED>z zkE1e`ISS%&IJsUNOoVi(mIEf2xt8qnpFsx?tr|@VYx3-<*TDA zNZ<-?l=UG6Bga70D?l}Kax#AuCb7FbO5Ro_5>ob9*Qh&KtCeBcjER#DZj^~x2qbK{ z)XAaq1QpMQ|3^Hvb5Ih?bce{3*pAr_G9Xuf9#!b`w-#{-tU|!MQPjI4H(DNLb5){`?#}Mp`qw?>t*BkbJ2mv=WWE2aDKn(E2|J#t7QR6? zdGaBf;nO|xc45eU{vpwd^SaVvV(a0&GHubyMOXexZ(U(*LwXV1gWv(&f)=fqIdpZi z0k+fUdA_pl4D_-x+_E0D5gS4DJ$MkJ;pGhSa?%J*Jv>m*(Gc)R+bk zpfouB@S*t?H54+Q@l`XpRHOQ#dwvjWucGs|*^^v#Z38KV;AL{$AimPtfP=))d;9kh z`-PF`r7UZ@+O1c)bxMVT)GGDMawPen`pyk18DpMC)BX;tiDvXuAtxaGB`53h%jwxp z=yUeUD-8_n?z0o7*r62%&Grr5dgC$68=xicJ$J{>gQaH4ZIh-ZwMkiS{_d>&9ma;z ze|>fZ3zzkHpNA}r8-p8lL#5b?4*ep>M^4#IOwRV|6k%tshZ57ft716O$m-$uhRyzLWZ^ury zD$J9@YsGm9K-tE!WxIL7H8k6j?X)h(lKn(I%<;E#d8@6*dGxij`PgM^6_@oY#l;HQ zSs2mPVsm`6(ha(m;=l(90Og{LhboIZL%F~*x}k=HlXH2L(ZRIzgr#ER({raW`(Gxj z>!EKEn^p1$AJRrE_DS(4i*N1+(()Wk#kwt2Zi+%chcl1oG3D~lk~lb)pw+Uqw($D< z$Fs%ui*4rcRx zxR7aNDPgDeB037limdZByRFmtZfEoBqM$aJfJ(&@py*i7qNw$|tCEtJ;MLO2@c<{o zn9Ie5k^p7rTiSeB7cYln1Hl^w^e%m$)~xi^MxBoF3}thfkMU)SazaY2pZ%FGxXQ)W z+=L3UCxK_#=Ls0^jEWpo>MXk!%=404l_i-5dWq;gmg7llJD(o?{^ez?l|}c$Qu444kH5d2x}qvq*^6v3$TQt7H|qahv-ek* zYr3)dxl|O76|c-0&+`ba=f1LLqVb#^%eqcGP`WIVDKe`6%CO(}-qkvX$J>2YzH zPGS8nQ*VxH?moz^Ks@pIVe-;l`synE7!FsgtqiRpyW;a0@`61u{YJ92vhA>Fl}P@; z=Fop9=a`j__lo17$=+>sAC{05o=%kFSp8TJ!Vw;>Y&@Yd$rxVv^{Cf!tov%jB!x7i< zn(CJp>`%A_DPg%UT!N8!pPU>z=pB`hZe{y=s%sqySkm$a_EHtB$AnRyu-vEh*Va{^ z?tHH+GAb%6!rzRsViQS0LrFu%^G8THs2h~ujBH#SBm@MUAaH(x0{z-#v~|+K1CC4H ze3I^6v`6F}2?gAAkB=YT&7!cI^bZMQDYLJeqBiU9t)Vd5SACNG^rk!Mwpdvgwfzb5 zvPxf;u0)h$8VS!me6Iouo0}>sxS_!o33h7KeD0PZdcG(@`i42XTB*P8MlKTv?PK8t?F+m?SD3 za%xaiwu7TJauaN96;2*EN4PAait$BV>62G;=uA37nAu-fp{(!hp=GwvRXUrZy&O2(I5yivQJ;u=T5mauCzUpZi4#*PMU; z@crKuQLO(>OTvFCqW*s*;r`bT^`G&-k%Ue)2u~y-G#MaO@S-E`aDKBqJvz{;eK>#VLvfl zDrKUXbf`&5IMNN{Q6msi=I}kWM)S04?^aLMcM!_=E%lm+@%BM&jEwQQyN96vvb`n$vJ-p38IP zFG7Oyb*ZhWH_soeYMqDi0hTe6^G3mEWRv?%=o2x#i)MH7Ry3xJm1)tdP_>zWF;oXJ zR#GHkK8){E!h^3tW0+29*mU0 zDD~}157jy$crx>2`8F}2YTz0)LA){hh0GYenqehK7l7ea%!9>9^&F(nTw>1-(JFfuwThi-WnGSn7Bb6FxN##LF=qZ4?>+5X=cP|K5Yk36 zI2}~AvW+4ro+Y=1mW!PjxK^wTd0tD`XLrc6CY(8SN)-h~7XdjCLf`bShl6lrmD@O$ z3GtGAF3j(0j0z>EJq1pt(=`?kcVC#}UN>G6Hd62^3sircv|PgY)`XU=^G^G?5Ir-g zgGXJTq@r!2G$x)uI%x=Y*~Y%0%x_TB+O)&;ni3TrHiET@*J^zz#kbg-;3? z?Ra-k6MZ`D(Z?@h_g{t%l^+qKEiqZW@6zTkA^v7mkAp5Wn# zJ>f7k86jIVQr^7Lb;SSze0@YNx4*=me48G2bRHj&TJ&%vl0p1HnBHhn9LhxfwtB8W z?8n2uP(pnO#4nc)>4RKzXc}4cehBp^4KxbS%a)pNW1nh zntCNHHnsBX>tl$M ztrAtA?F5ej;*2xGca7tWp&+KX=X&kM8zyZA?_?r&Ok`RZ zr?N&5Q=QtNw_ciMtC+(&HAwEgY2u+E>@#gfcdg3-ySVFpGHqEv49`OZj1FjJ(uQ;GS|bc`X_H^0 z>~h0w(&oGBf_@4E!L`eMCRgi($Y_BSW*-!CGNz)%acKg^nC`kD){BrB#*B^Pro?g) z5O~P*KYKu|;Sn3kh>d0^$8sgSRe^7=5xPnPJA(l{cYp`sLv(*?J5b|q*_397NuCLWY*rvxg~2~#qU(ds4c?P4vV4{!m(Y-O(%Nb4_Mj< z$|ywT?nyXQ77Djg3V?7I?ac{EtQ9y%4z4p+C=By8n%9cpR899?M1~(fUn=HA4N+Pq zBEg_TJyn*Bswg4xX;EOBr~FzE46<;&Y)BIX!zDpOVSmbIe+CNIMBswVvzV+Y4=xAZ zR!E-jd>9WH#y>_fgcuYj0ZC>qLn9=}5i5aEXp2!8Jvj2`uJww7ncN?ImkfL{th%W| zB$OH-%uDXntwk1%axn>f?!d)w+LCxFmLNnEIqU=HF>zRzmx_}c@g&c>HNjG?kI7-k z!bMn)oAGYFxvNG=psF(t851QOH4IT84DmuD@tR)FT{b^W zss$3EVu^A%t$D-t!U;zV`~{<2$rR``ngGLg!*NFpss*D^$&|d;dBNU_H)6gBE-BU} zTZ`yeo8~)8MoC~n0RW6^s8J|-eA;-4^9S(r2hsBe;aW?D@4s&I#b~nPU)b z&6y_xIxbcrcVRaagkvs%15PzT*gWB^G{b}_Az?Z_%l$Hdw9)(1K7c5)*jRUS!l$@I zle@9SlGFGD4ElpR`9u)|3%d#CDw>KBGBeM7K4r=*nIeMkEI;_~qag#x{V=}x>|*qF z2{>v}b(ZxxdA@A-u?VR8HzL155iy;m+BeSny^Y3hyese8&xCL_#~#Mt=qK?R@exc# z-pwU$k!8~BCg=niLv&oSG!%$V)oNLSjOFYJuf;mMQ8kvWwqcg^o2(wjf(&F^RRGa|qyGidQ7iWkp zEpd~X*uQ>R#z6tj97mO@6edDxGRl+j4f@TQC=Q2}u2R$CE5Gx9O@=tx{vJ=L-34c& zK!w_5d+kfLHwG2PSVEE3W2BlMu_)0#ZHKxhy|;`)mwD6X*HS$P8HGVlY#^iNxJv>d8kj%uoTbLkK{A5@MnZ z4MUFXBh6vg%V+8nWYCtj?qZ60DsS2ltEze~R;q4_n+IO9)wG0e<&R@2G7S-J&q{RE z`G~hpk!;^G4CH=UIN@#lZ3J-7?ZO4TLV^g4dG;lWjE=H(c_WbWM4B7!!GY|aGmbz0{>$FfBu zhm}$m_1C^#tLV%*%2_96;fjoH%lfr&L+bZ1cBV{FqEVD--E!nu5x;O1ooVfIE|4PWX3eFH#a2li~`9-CzA4lg6z!3KK@&p zW@z#)Kr`G10&w%@cIXGl^|2JRJ?yj&{e%zDoiI=0n}83JWGxY*-CLZcU7Tc2MM5m+ zDv@~8De)R8%oGgI&tA>)6vK!~fKF~yM+_zisG{|xz~?89D4`zV>e;Hl_2UB1hg?Yfwk|7HUte^SQ6qZeqFjoAit!5U zb6Vkb(?S-v^h~t4N`xt=wRl0zXV^rBx)>Tjxdvz+enS{y{JJ6)j@xm7y8S%r za;o$F0*bu%bKzsNh6J)K{yiry1^HFNvvWIQi44pjp{zWAB41%rYjyv1k@O@7{FUJY z(jinty7Z!rs_drpH?$YyFse9iNDFr()DKj!QGQ0Z`qAlwrwJA>*v|dU+8; zxbos0HNo08-DC52g{FkTPRf;5#hqay^sq{JxOr0_b_#mnIi zp;4{f#FO8y56|M}#g_W=d$@WIAdAcXX1jc)Z;cevGTNhnI8e+!=~_~CgQ<`wv`y2( z%nh%y=PAET1M0)q$;6u33NI6O{H2j&o_?cX1xZIpbTUpk=(}2jXc@b`rx1;|d5+_y zq}X2$Zl}}3O0#ofzB9skG-V8*AIs_Vs#u6}P0Svqs zWLWaLr^}8f2B-gWsbFs3jul|+U-~q438YN*xz}HoTmROP@&qalEekS{oa%(w^G6%lMs3d`UWzi&+upUSqYJ;?F%` zG#6`&Px5|P=f?{)Th3L0NsnG$e8?_v^AgVze-NC!-0_vIU zYNDOkdb8{#m_a*r!JaYLTtcc_0%nDh=QTV`0R2ypkz6Hl8OB~}?k+1#e{OCEizpBA z08p%yq=+ukOU-)Xr~*BIthE4sN2;k4O7}AgBMn>%xAi^p<%nVfoW6ledDz!+o(Kyk z$VZ*eevkhuOE#QQj{ny+(-n`>r-$oCjjFbG$4dsR`bo11UZ^|d^FilTK?9D?!oM%_ zAL|+#AI#5)m*yV(IH8V{Nlo*g1%kHX5?!9Yjcz=;&38|nduHEHp8J;Ij8Be%ure{e zB`Cz@4o~Z4IVNiX{T&X`^<4s%nt0T1mlLv0>6+Een8xs^ns4XGMzrUaO^NmKCKZ|l zWh@UDBD0sP5HkKk!TiNtohCyUiH+a;yRCI?}j7Q1)zr5vNJ5m0i-W76(ca)&z7z4 zx$R$1kC&B^2jO3UMx)OT=eb%y+<&ia#k-D@=D@0hamtEo#J3!LCc~J+Ow3oy zXilt0nk-knB7BP2*J#3bauxhvE1cfD@?f~)tSl8X)K4!45#A75s(IEQn+Mx558m=QK8Y@G z@o&xSRC)p>91+$k8YTR74+*<&$;~H=M4tj#QPP?!w9PjnAMZ-#C7{uso$cEQPtF9M zhh2$~PLI76N+s@IH+4E5N{{=|c-V?Nv^_4zvH=_CZ4LUEDO#&V+^6PRiQ0Ap1|ioi zL}1e2KRG%2-z5(VL6@EjSY%he961g9Z)FU84Ws*u(X$ZoCo)%z$Mry#t?-_9O&Qq(;!9NamNX+()ORwsV|9=Inol8=G= zh!T<#MArJ!zec8)Bbo)^yb*wM4X4c+WRe0ML8N%_9S zebCssen#SQIRLm#tGWNF`%@n_eIq2qz1A`+M%&f-mhUz5l3g&xV{_RvMNa9nd0xTQ zQ0Q)Cu?pp(vNg4Elj*6~Q!W;5HZk+^ysWV@^ilP0{3AAVW$1Sy^zqVpxlWUbcF6}=6vV)#v ziGF)G>kW0pf4=}j27q?OLvO9eyC+X;ph|qEq?%;OOVWLdPq<|&K7ps|{M%jeJ180;Th zfEGEY$6K?t|o5bHiHFw2-{h5a<2a1`v78KVN`$18-URv7)-kogpydt;F}kVVxI)cvK&@hm z$~IXM3Tup%RWdAdR5-8>a3pQ@pIKxSgDP-!z!*Lz|J7J>>uGU0!q0bFDJ*k1v*DqD zT8L>gI3}tc1Z>05V0fldEdeu=CXiBJ-+Q>L7+^9ZvHd^Z^!%_oHbg4T&czBpbFV+B zkO4=JN4HC+F1@_e@Op0q>PgLM%3LtYv*k?y3%WunbEXZdtOcgcY9LeihWqd);dEF+ zFqO#p0ZBrobA6PY%oAX@@ypTwLg5yfk@S?xsiBk?Kn?rOX|g3t3HsC7RVQrtSu3ff zN{M7jB}f+&NIOaU-`Q7tQ*{$OW$MycBjD3bN4CibrQvV;8SjiO@`JF<;p0D`mwST= z5_AhBU)^iD!7WUxqLG)6;jduje50BG-qi~iEQhR^`|vT{ly_mAVVUEIP;8_}0L(Gt zK{C#C9lT%&t!#dm;4fZ4L zQdp?t!U9o_k1-V6qJJ_G&ZzXin7>U4np{TQlBAJn+mjaRb$=5{#mUmFc21s zY*Yn?0~+Jfd3q6T!a|p^Sm#C(9jS8Uh+l{Z>jI3d8WdDc5EFfbCI0h~PWn*IA%7|O zN}(R2c7}0P1?)pESnh|Up|)ot4qsG}vcWxLH!L8T=Eo{5_CDvS+vO($2)n- z!3V^Tko!JP(gzN;TP+;yVTr<}2SmP*2OO|_#3SKb#FFg;lJU+NiB0q|iQbrfl2A2m zKzl}70;AdOBM4B4v|04Yk$byOc-)ZL-ySF#fnf;(i~$Kb(L%qZ@cOxAbKY1VJ7i*n zvOi+i9-Jk5v%Cz=>4^{tUYX6K(j}tO8V$KpU|01Evd)+k(oppa`In6X&g@Fg>{8C| zm6wmKYiEvEIOmUGY5xT5*~3_~$^s6)GPChHxa7t$1I7Ydh$E!nZ%O^-fmy?%rHehl zrvVyZ(5fgJZORp;qFnKEjo?%ksM!BWVfJ%yc(ihfchuuE-taf#I1cTB%Mu?ETf<%k>f}`!Z1_p}SsZ#Zmu)h{1Fegig zOgP^DSB*zblTTNs@4Bk!H9QKFX@p3Ycn3mv%lv9M-Tx=4Xb&G?d%D8hIfHvSL7AJz}I8VW{boVE3gp z{1g~@A%?M{u+BqaUBa-30kNC~yxhR?Z2yi|UmZ>?hISEd_fbM4Sq5tj?2ab1{$i^8!mu{Jai;c zRN{#(k@--+>dZh?j1HRg1yd11;=oC<_+oa}$+bVK96o@NiaZpvQXFZ}d{9I#GxSw4 zsvHqvXV*n|Nfd35p*;EqS-b!f;E+)>fn5daXTAYW%v7@biPqUi~| zseu0~44%uS1e3~G9!kx=T_~@0M`*@`3UrWix4{3a{N2024=`AaK?Z+zg!W(bvU~R!1X?ytqPnlkFu z=5<7lb@ky5ru6L;ur8aqkPqd42oL-hcQApCS6apx|x@pD94efeD|1 z2%ixiGD#?bCf+^48H{U3&#jTnPIvpmz7L{0DnB=^Oux5$XU)kVHkfr2Z#HIE!1XIgKyCsNcOa zBM%y0K98$XP+ty}nS137uvA=^{trfohW|z>6o5nw{ly4kdyOf$YVoF9rqU81Zkr@F zRHMr=f{1ubULpf)AcwtE++FrAKTU=MQ)!MJjA>(GODv=o3C-%sO-LBZ zy<)}!`S?kH6N8U38G9;W8Iwyv{N_8TG&M(;4;+f)TC(j7=kBB9rQP2AcxV}rwTl?* zu!AJupr|hYt^LgHt>^PIDk$M4i%b7Y9Jik!7%nbf23apwhR+kPmyUWSue3^`1mCln zhhoT31))(oputt|BtGFzIm%4=g_N`{{Z}+w2E-2O7bAceQHml`paUg*o(m#YL5MLP zBpouZ%d_UFcLXf1%h#wTsbf*f%nlyt8}y@ucM=M5kjx(`%wOG;Y5DR0PmJ)eP{RA@ zfB`6!&!r7`l>{Cr^9-;sOI!Fk*WpD>QriBNC=X_83;T-^)ZnGc0NL1mCDvUuw!^cf;BE28Xf3{RUAZnfyw697^pBEHA(*C?1 z3TO|`a~?~cR;u)T60)xwH@IrA7C0C}Hm!r8cvNM-$h07u9{@Jl$%(Z{FmBN-T>lks z*HW6lO4n)EQklOBj<;q>a_lH7+^{0qx+Xt!RuOCQ5O4p=E8OtfmnO*p=7j^7C=0QK zd`|$CrAY9ZYvOtIkJoRO*PSIAe_XX|m<~B|REU49i!YQJtx9B*8LN7X#vgsS*M2AB z<((PN-9Y)G9>NGcZHuINvj8qzG*lox1uf+|EngDUVPo=YR!Fc-QSpE!j>qu?7C)@b zgFKfH?jMj9Vq#j96iiyI&!bXIONAKa$E=B?Toi?8l20lnyIH*qC9?6nyPNv}1yNE+ zP@PvN-W@l4V;bE{U2!((1n!AtfYlCMj<5NZd zXQm>s!v}rkNwX`>rcK9&YiIlIq8ir?KC|;G_Dn1hE9gmy3w>+tYEI!qT=vegxk5vy zha!B!;$j+4iMBL@Ydq`WbZ$kUzrS}-q04(bV*`n5qmifUpjvf?^M-vLdF1kR4ak=Q zgKNX9_V4_b@#noc+_|F*UL>{Iqb=#54;N_nF1M!Vq7%0oy_Sr}SzpzfZg~~kgJYKK zG@i!$@tEtiqYZyJ4v(X)n2McG(knCV;YH`@gR?DwQzPhhwjR{hfBIX2ep4Tbbt(rR zkXOsNe{Yt0D2wiP-=N&xHmb#oPk(*o2SA`y-rHCGXWOkX)5gHVyB2$UpeX~JCn?uu zU7lt(z1bG+T-GQD-5r((3yV4(Z?A2{CU3puo7T*BV+qNx?C$5PD&XD0UN3t9?Y>-SZr8SPQe?Dj{Si9Od8pFUVb+bPbJXfO zpc>DRnu_*07W~H_vo~8o#zAwM&(Un=S^X?F zw(9&svUUrm(9$Qh(t+B!o%>*6GoGK*1cKj5(#8 z{O?oNX{OybKnYGt^t)G|0S;avJ>i_PxRGuWgN!3ZA6+{j9_Y8-x0#^?|bbTR4d)D0sNQS-P@6 zx~`(jvk&rJ5$g4@hPwH;cBicJOC{W=u;%Mb=;=WE#=b4=i+zSLxCihWgv%j*j4Xy#*}&7P*or>3H4?{|fv74JWEo2QmkcD222&$A7L##-Bx zE-ac{1C!&@+7#d-Y2C@NaP}6*3=P~ln?+8GC~DuZ4X>!{b%j3!#v^x16UmC)hf4}y zUxgnP4cVB%J;GD1q{s~xcvQnZ z-=)oL3Zgt|9{uuRzGBewZNCU2{B?i&Hg+YAkKN<-uJ6ojo31PaVXTSY(%*JFV_@t% ztaI4n&fNzxuVHNy@1YNBs$cp~cSf2FyrPqcn3M!dv5Tlt?b(*Sbl!X6{x{XTZ^SIJ z!&VX&ea=qKM!^BibftK`unSBp_XI4VRd~R}7q7#q)SM#~M%q>=P3RYJ?cH5^xd>?P zj*JGOU6io;nHOHSunlDP{tw3PDyR*{U-v!k?(XhZoZ?QA7I!Z$#oY-6clT0Uio3g( z7PsK;?(Fp4?>cLp^PjV4U!)hI7n#Wn!{n3S^ChbFUf2p}Q>`%hmE&>w(kLKb56|ip zbQwD~_yr)?Tkf;%zy1`2>of`bhl}wvT=~&rZLN zy4siDI^JZ*L1soYpwH#nUh*x<1D^JVSvarRYA&P=*74M|k1as0AnX!;Z#iOVt=iWu zjBzA@ou~G2u!C1%fjSFKq~9FJjvRgLHG^+^X$q^$nEZOJR8b3XqtW`crsZc$#kUm3 zjO2i3g_y*SW9Os=b%MKifj{d!Hj_VWCpy?2V~~(hUGp3thpy5SuP3&<@1u#D%=`@1 zuQLDCueay%eFN={AgTbbNvM&gcH%mQcl6*MV1=e~;1r0u zYL?~g9wTsx%B}8dU~znZ%5u_uTvHnE^cZ$vZ|y%p=&s7edj8;YY*O(RHcEv9er?-uW#PR&|t=Uca-{EPkj;Tnn|7D6G1L+ED-O3VVB zLRwmj<+h45m=qk>vV27~11biIh~PrBTXrc%967+jFOdWC2tn6smj*r+w^C(Nq~)9x z-)ouAzUgVs79IiDr%#sMkM4Y~UrOIE7sklmx1VjPJs$_&;qU$XOMPF-og3nxOqOf* zK8ya?KmUdfa`=eGk{jNAG_8X4vQk_RsAx?i28s82p89PMZ*E%^%y~=9e<>hW{w%6Q zJio#41fr{c@9aD;dwX&d@9tD#-#2POYJIxaA$${^6uqn5Pi*EbcvoM;d%X1ZtG0r8 zqnPHWv!Ru3y~$}jofwm`%mWb^O;sw##%biuro1x%3vC!L7T&ooBWCLQUn4eM9oYW;?#W}<#|$+6Lm1M&U}y)8I4~KKc!eR zpsQm|Dm7e(V??a2v=YX$_ZQUR5oKLsIxIBO_oy)i6TAtU_-c%VVl%w#Kwu?F${+`_ za?=3i*dJLl`CZt3+`>w`^oYc8hJ%8HN`E57a&xPs846J`N%=4o(w`AmGFA*_u?2^? zYF<z~8E$Q6<}fL%qvt(k{_Dzj&q)Dx0(? zE3e!=-N$x;I38z?BrmX!syC8Qwt#(_qG#zUtd3;HS+)dd^;OF zyMvEP(JchdDOXDVALjf2Pv4dQuhkv(Z*CTBPOcj2h!D`);K7~0kN?ld+XK4D+Bh1| zfJ-=HCZoD7OUzXvEyewENt!B-1Vin#g7H;$1%Uho+en@%PiKf zKR4L?_(5o?>z|P}@muN7@84`sIpZhUqyta6-Eq@-`#~-~pzGZ0H~({=sfqY3jS6=; zIc@C`Qqd3z!LS{jV(Y>UoD=1CsvFE~wssal1yYH``12-QATWMWFRQqWxP|IrKeP)7H9S=zCqLzfeE><{8=llfq6t*j z!kl(t0PCXhEJx~vkV&@)qJ%$iFF@!HO+i(SiP;tjKuxmouMGx|S0%UL5;lw?ec=0> zQ?@~V#2aKntTSiC6xW;k;&mq*AF@1{8e)ZQIMY42rCrFYoF!bbbey@*e#r<1TPR{?QrVIsM@JHg%g@rV$Sj-7F z788NP5JGfigIg!`U)HD@CIM8$z6r@%-z&T~>hycm49@Qek-6)K$WmnQ% z22yPtSR%3TS)c>Y$lsiD*uObtuKhq0I*x4BX#T)VSn7^5-;8gEvAgX%fz)*)PqLr? z=9HJda}Qhb<{JY1$`5L(t%(UNSXnZ}o54Bd7I04a_dhvhuDPy+O5~lwsyH@ix}w<` z<{5;MB!VYn@3VH z%i(l0V!@6ic79aqtDa_u)+|b9vydyI-4Vz_jKI1~mJ(5;n3=@K3ixeq=a!;N5pT0D zj9U;n%rT*Hl2`)djjCL%67U>=I2o}~BTOf1umuQ^d_rUWW7Pyd=pnzR+s6S>!p2Ak z1)lFOKw{|qcfLOp#{!mtZsRUq=1`ww66VKvdEQr5QPIz6WFLZt)kV|F{Qa{Z9}(xK zxiv(mKfk_wKiXd^&wB^2F6iH0XPp{~!++=dePx0M1=;W56ie~* z#0{fkir0NKA*VBXK4ay`DZ)%2yC(J^tM7xv1;=qN3K3a6zU+6Yqk(hE}>Vo`@~2FK?$A)bT;OHV_?M zuqij%o+BuRCn-&r#GQBwk;cg3D$xo*z9O4Tp=!|BB~}PTf~y1`sZh=!u|-@ z&IVsl&foEVTQRpZnkm%Jh_6p!HKz{%VZc4>tcY!}t!{!J7Lj1%1+qAv_`JbHf zScFo@AE^_RR?hNX_%2Gnuf?#xsYsTONP%$d%9B5e;PHOqgs=}o;PL*ij)irfBJ6Nd zUVZTYttqE%pnz-27MmyqJtm9;X~xiYMCt3F`cZiMNojs>gtRC;KxAz*6~Nd9RfOW! z(pLYiDTjP!unPk$Q)^1aZ$p#5GO-%l^7N&OXoM=i)KV5zR51KXr9|hevm$L0zp`4f zA8lGmu!m=WDpE_*ALLY=g~zBwUNO6lW+w5sJc1S4Fr2_&#GE6rFfLJj$)CyK7lwPK zT@5l&wP)i=TwkPsa~ssW3}-#asKNokMOiGOy6uf5Yp)^x^u`$Em#NH+*6ws20Dc`% zFdk4aA2^zfNEwJonTSZ~ii{}yXmJFV8iI|d*E<8Z3g6#B6SDx9%Nf)0P zN0N@4;lTbdlEgz2Zi6$u2{Vmpo~A>TOO2Sj^qr3$VOp)!5GUG*t=EwUlZW%=iooB@ z^&!UML1f^9c=&>w`(=pxg^uHe%<|#Wz=Qpdhfh=vS(Tf7+J9@xS)WNok^9;6HUNLz z;>`{pp{uk}44(31l-Q5d7<-ClIm67&f09MOFj$+IF96gqouAd~_~G%q4QnzHqCRMp z7|uznD@HJh1sqmENd$)~wc&<`W>Kn@OPUf%nhHvq!bxsnh7S~>L=F>RS%Cj_PVv4& z+yig-D>#6+`*jwQTFg2d)RuQrM(k^LbCS^n!Q1@?7;C4?X~&7+?S6udZYzWo?ApD9 z-puiytN4Wb8?tlM&dgyy@3?7dvOJZhS_tIR-xYMdVVOr%oi?y)e!$X?li=b09$ksH zJQHJ)@g8qw`1#Lp^VcYq_ypSTmd31R#+@TQx>G&gkwJ~YLAPAh4qey9P^16kl<`~e z`?N}nw6J8m%RO#vCuV8@aBLWk@u=PMyd6M$&krjXL~G$7Q)2%tdr#Jt3n9DyR63Ee1v=`p%v3Ac<;`R;7(JjR!w!0XL7$PNyBz3T>+XwbBd$t2No~szqOG zNC90tcVF^4V9?4ifPXS;Wg~2cS^-0$HkzWSgdAVp{D}hLo)Mtn?t2KnqDqLH+rUN3A5kBGnG?weZ zLFKvz@7T)Y_V(gA<7xQfxpPjFS$(fu3McWn9(0tTs1LhiA1`DlW72LMI+Ru)uyqzv zh`w1Ed1`!l)v0+xR_l~eui=LAr$u;?X>;U&aRA4a7bq{_B{x*UdyG(m^iYCUP=YYP z73JhV7#sYkLAv0mvc3MAenaAb{_U(P;Lm4C19jFubM|Ffv(BniB3E(o&ypf&vfc!M zd%`Ks%hIE*pjl%IBy@~01hkUyClW?P7VqRLTqZ`pVj(EO}paNl&3QBBoS#|oprs_~{v>kzFgYhyfbVxW=BlRUaY z0I=Z8BRO(dge4`vFP_nq_9&tK5%wGEbM&4(9}adUK>oeiqF`nTQ~kT-CZY%a!;`ae zxQ>U|3UTarWwNp!yMKbpdrm?7m1;x{tple;qg1-<5h;C*{v|caggXiA* zY-ZZnC7s2|6}B1{206&1KoVOH9$ipXrMPKX8tl>BkLcY?yfiTHd+WFDUN&;g@7o^N zFNDB@KJp^&-)%430GHcg6D=G-gqGIF3%jVck&kbk&qr{fQtSO%ueqIEzo}dmCBG-tBrybYCa&Ov~J6YtiZ=izdW3(6saE8q$jT< z-AngnzcEeZe%In?V zw`CDyxSk#l&%&?G&?>#g_yr_wpGCCDblf{i=9TSVD})zvDjWI8RC3W4iXV`dNluuF zxV$b$t{R8VIs%p^;W!a534O(;gzt8Lza9R}T%Q1-HRf4;c*r@nB=K)}H}0D5gZ-3N zT<6?)qe8fRw$1H=)#z_M|5jSAjBc|%H|~$z{js*uCpaLl%m6&&Cet*P1HWDyeOS@B zP5s$+m=OMP(x-&PfLBmj@2hccF>8^jF7L0Ci&sPI>+Lc8hoj?dX2Ek2rWbl@xq#zE z5f=bu>mNl+T9fWQcYhxXF7QI(qKIhNb8f%!O+}6=U!grf!1@EZobDET~0M<0W2OL*u46+jF_*k(pI}!$oif=yG1)r zr;9{hmdLMG=-FV(8`-9ry=_g|NEHJ+8{r_E*kyeRe;f2T(W9J?BX7UGmB12w13OOpB)E z`oHzqu?M`}p3m>FTwOd0eMWYGL?gwB3{!6y_cOXq%Zg@65K=h?O^{B|6(kTfVq-s|H1&6MAtG%DaccE2Zn>OPcS zLF4qD@K+-(t)B8?bFZx9$DT zXTg|!r2^klbWXhE{VvI-HeeUgE+D+@&(pJ`XzPzx?01m9f5qr)bKd8*k9p}WLbxlA z^P3|=_aOhgzfpm8>NjW!qSYuroV)P+27aEJZ~aX!hHoVa!^CW)XWM!w+PBqQi^EzI zYZ=Y$S#beo&dWX`Y!9B(b?jk;+cS@M5?RcW*@j)4?CesRYCaZm_Z7T5UJvv8XaSrr z7VH79OSl13pqBuXch9|1jk*sH4H;cw(=}DkCz1EBFA5K!shZ+1?xNp(V;15T8Y|yoU+;6e z5udJYHT}adX1Pb z-i2SPHcNfDqS2~Hgb;%0-x`m;F)=ASmU&>N8PON}hmV^`BWrD3h2Kp0t4|3pZSUNh z3wzxX>i>AZ9MftM{<+_O?({eq3aoGGsPhz>@hLsbr*08;+sVRi5$iaFo8)4c;me>gX;ucEXFp%C-8iS)zE(q{f#*F*2iu*jRApVrc>ezF8 zc<(OqBJ?Pn4cf2oG88)f-f271rr}^Q&3PhnemGj;w@s;^t#Rexs=e8HN z%;pJTspgpcK`2$P&k7f2+uz#iZhUS34W9&JCg9_&J;V&^YKO6(-5kkq@8x@=bJc4_ z2Y7O~cD=#q*^lacD=qHCW51S$T>lnt$u~0kc;f=y=;ewoqN~8!n0h3)vcJ$6{7Gj5 zKirnlvcolsKKp%})+^2Gdz}SGF@qQMigfsQj~u?x7kMi>?KVLL-dMZvYpxa;+o-+mvR>omGg%ffGDWHA+R znx|_WoOzDuJU>!2(WuNji39#zB=rWr@>j|=m2>yW#R2Zl1T_+MV%4{N~v2;jINeb<;XKyniE0 znpJeyx+gFD^W|aqamIhFJdVAuj%-;m(`mic^4>3?ab{2Pp4lPz8NX~U`!@8Lofz(e z!WOo+5)&q7PP@fKAv2G6?;(rnX_skcbAXcSil`Vg{zUd8rd_}rOt%0}GYBW^mAUth zxc#`JsP)y7;7-)joDR0>eZ$YdIdjD^)gtDR0vM3kXRltr9Qtd~xhNt{KflTJiP zNRX~gq?budl$8-Y&Z@PWm2VP&TFWe6UxDC36*swJ(vn^0X9qqBh-)a~rD53_ZAAWf z7w15DwNZi!v{)EvLg7k|bHKi;B`N`}%c%uzmm}W-4UYPz{(W62RSB28w z=Z_?=alua)=!FTFhwU+=j#YBeQ&uxHj0K0b{Qf?{r07}^571K`FD6XZ2}`JohgVe< z+0Fly@rhs;>q;B3T!RmM=uo6DL53&Z$Rq*as^Eaa@S=d3)WXpXQ)8p_P*AHvnk}AS zivEV!+8t_>5(H_XX_BlsVn8w5Fl8*2ph2(+SX~ky*GOIp#q=FnBT~k+yiP)t<~2z^93jQ#5mmFqmPv?ReHCkzA4sOk;!tqiIfK&`k#73* zYYJ6nUHXXoAzz!;T&7xM(T5vy-Of%>wSG6y-^cDESvv zj{$tHTsykOSZYp4;OsKymMJW_yBrm33SYh7YX4YBMKgd@$FLjmL>S?Xo00s*Z`xD#aA0+0~3P85kmYu-edo)kZ8rosU}qGG^2cH(%aT6&B%E zL7P$u^3$jM3}s3(o)V;OBgXrN-o3l27X>P(+q8jRb$_1r*IZhGFKrzOIK%h5xh66)=#2I`LnePL8U;-sF zIXRk)cf14EA4F+Y9GW!SIu!4OC9{cwHfO>4tmwf^y~MTgJ{oX$IStZzX^+m+H4zz( zVF-H=#$`oUQX=vnwdq2^iquYQHL&N5DjN9Agsh27m56#n8SKZ1;xFC=Fy~sGCh=M@ z$r*i zvS5XB%*GmLNL*1Z!HPH*Cz=BF(ov33E{?0n*My>)w7egi7CNd>jCv%V>Ock>oL%nC zJ$EgD#b_EeUR2^(g^|GkVuHW5`Zv2w|AlQ%w@dL(h$^Wv0Ef1)%_&}5u)b(QX7;#N z1@GVN@}qRIYgQ$GSk_<+)#MhAc8PQPG9S<_h@|0`)1n85*6mJ#2kING!qd;s$)Wr& zax2`(WCWrw%UTn8a`e!rugl zUQ?D(g;K(_YAQ;J2Yc6%qSj1JZ9rBGISv5GzUqNRQTc1I zy>@$6zbS{~;Yd?3>yeOI1zH?n*eMiU#(GvEKwYJ0u7=62oF6;&m6^x#eg#i6a2V|m zO4SB>U()Uj9)N50VMv}!Fn9?EQo2;0(j`eShQ)$4um>!fJy_V$DKo$E@`aLAS0- zEJFZzQlGeg)dpYhKh-A8*riEmnuJX5|E)I0Yy2ocD{p?yI{ugqKbSdq3u9Xdb@Fmm zKPhl?IVD1M#HBTdxAxQi&BqP1qnlGmWl#Y`9^wg2xkIwiYkA&*`g5>>`t2WxxizFk zGpM{W&(kW1ysVy&xHatR9e3OvUFw%l3WLXizy)#K8p(g;Cbra*RO9kVcknp9K}SUD zbZQGimi%iq4I+gMabk@2XZKH^fg}z~2+9w0r@*zX9)vb&r$h(s&I0p%^BOCyR>uhJ z#9qdu&_9sM51Npmj2=Rd+^pOLyew1$QBpP?87&!eqHJ1IP!e$wk?#h{p%D9I>fXjL z&2v>APv<%)e9Pb{I;Th!UXu__o*H7{1|VZY7Q00su^Oy4f$SUfT#wVg@eZNga8Ng| zkJpAXhw)M;i$$U>kn^HC`E~BQCLJF-ejWWLFv`#vtA_7LaZ{uR*xB5mb(&0 zk&fXa2yd5b>JF+Cu~WdCN2%Dz&TEVuX{wI)}&rG0_^p=|a%Xu1OGL`ta& z(%c&Qb+KXrkw3yDFBq&g7NQ&+x7CZdTi2t9@a$0yF9(-6oxSFi*4{VaFopXVIgdc8 z8O3g7Z)Yb4>9{Y(aMJAD2qH55CZ*!J-#4Aqi#%f~JY#9vCuT25q%S6@I+q|lV`XlG z#a_N4_?t!KM{cH#Ul3RHpJRrDicq?pQAD}>9fkWHq597`!$I2w-IF-okpuQBRu8C} zm)Ik}b1NhB!Zy=nFZvG!dw$-$1ZlE_v-$m!`GJ31s?-N&n@4l=F0u&Ef7u4o`Puv* zwvn#Mgh2k6ZKD2@Z7L-TLn%4KDAmT|J%N;$Jd&nNl7-QfmnhYrrgNrwIz zZOa`bJ(njg{$v=rdbd3P!?gzy_3J_Kf#E_INt)g8(jY=!5v96#Pi7f>MPkq{CH2Y; znM$bPiY*8`&e`Mkw?OC^S)5?P`_WXplvCkk1a^G zqX@KXIBLNW?9QPc-H9IEz8>AN9`Br>#^|71cJN2|&;P1T`f)dY-!Ay;1QA7`U*Pbv z%S}1OJFMx4rHd@{e%r-=xJIvev+*CR0hti{r`daotX%lm^%F0K^VOj`ni8)^yD<45 zjbY;=uLirAVR$KP(lCrAj+=y>Z%;X8e~4A2|DLq;a8gq80x!PI*p0g{mCbOOyryzt$bS{C!IHSiHcOIP=RZAZkySHACmS^xdoHOt6Y%_iQ{v^~ z)4|~_g(!iUifMjxvmFdnB8S;@pxLJk+Hma`@zm{N^TUpTA}N^*TnW$A6umB)>kJjk zJ4S4X!#s!r)5=GuZafF0qyAH7EC=|AX0&zMs=F`%&s@LvKe=VK*G%anPKnj+@6RyQ zXYw+uj;;eDgAqQNnD=@ztLFsH(;Qp576Y%jnD->qXI?U^$ShTcG{MIw47K>f;6V4o;vmOh2GK8$SJ!UP>q%6;rEd2?lSL3^Q#l9eO1sd2) zlA*WT)O#RjizsA?=w@{aWpu)4bkdi66x=+grg4%cJ}I88>GG3zX(f3}W9NXYz^Fj1 zZMOW7`bS!auza4GhQA)@rO`$#}4_MVinULYt$bi)* z_pjO{B`s0#TfOsjcN1E3Y=E+T{Q1c3BHly;_@P9K|VEXgTHpVx{mH+C*`=|SWrJbnuGtw7iMbaBjYDXIgUeRP`n~UX-;h# zBMw*S^0vysQQwOu?&KhhNjul`pdd-q&jR=2p#2$mvqmt= zxDk9TI?sucd=zS4F!15519a!bq58c)>r!P$UN9>E@W=c7B z&KAvgn1I&MbY7hmjR0{aET{YeF)HMZbq5b8;=ZV@nZ?si{)U4o#B*hz-_T-tt%O2=(-rZJORrbNrlFr$TJuB zB8t9>f;x8e9EJJVLqKgWv#p~6#Sc`mXTEz=%R!l|H-iatGrgm_&o%L}CWO_|y3g8p zb?29eDV&+Bg;8tY%HbPs+|GVhY-KIJb*cFiNOArE7JfMHH_G+EWXYzNTZnn4RN&^Kj*K3?RXuQ~BGNaMD z)IZEJ=E%e;Rc);}p6w*os9({~=P*@gZtWI*rvkxZ04UJECVHm7!yW)cG z23c_{ygj{~H-b{XqI*Bkt#o|(!FqR55AVX)IkKMHkFVS5ygE*w0?1w=_%g`8g0=K` zL&t7(e{fsEY(VyQ*ipLJUHA1e9Cc04UXMQtpwg~&dUz|`?Rxf`5dPlt5!TwY{ii0n zC~bhuY#g-k%S#G6pX)0LdS0XZ4%vb*yXeVo%VxMJvlsu-!|V+k*~dns z#d;G-;TLjxA4fZVgVFA_7PU0DG?9+&J?jFGR=|0>-&Itz=+)IWzB8uAA7SC4^P&93 z9}iWuZ=HPpW#N1Gk>m;n1s@=NMUI9Vn%LhnfeQ(XR6p@~qXXWpNn_4jNF(G>UycbZ z$QDhGQnG}q1Ae-F8o0_5x)4O`)_=l$L(i#wuZyL;Z;gG3ZkT;vV{|zuZ%Z`}-mckP zQx-vZr-gZln_9|TG}3(EibC)6H)VZZGz>9*&QRV8sm)U#f4j9_^e;QyyeA8N{n&V~ z^76j_`)CjRAB{TwK2nsoL{F4f<@o>h}+yc|7RuXr0&Z;3>yAQW#`Je<}vb$=W<+?5RQ z`7jZ^?8QfP_gcHo;vdpqTP0Pw#a@MZwYTH_5ZuDOb;i&Bem7MAB{j_JF~sFdh;G%@ zMUDGn*4(xf?z{H1nZT{A*W*v@y)fnO?al7>kKE0Uw(Lgq)3|Rd(M)d}LzjF_chy^` z@ONx^AKQT{c^9XVGwq$e+sEJTahsVQC9}NIxX`i(hh=>;8n%XYUS1N_LA~KGICg|^r9hl@#EQW7= zCA(RA8Z+1D13$xL$$K901DdbT>1&S;f-?-i#R)nyKJ-CvIQnipdfe@|7+6%mVT;~4 z2-wer)RL1MgM1jh{$w*=N51wQp{0P|u8l|BP*mCMGC;Tc#(TztZ2#oh9}dBm%vlPE z+=sZ3h0kiv?usFKPLKAVk4*H@*gZ`M*&OBB{bI+i&b|Hap$X5g=kHrcynP-S^>f!> z4{7zu@;L5=*=nZW;}1lZsdf?Arbz@mh|&P=u@A7**q31Pdcb!XIKRj>MC-Z}hYA05&kD%)btKEk1O7s$V2Og`M1h=Ul z4WuPaM`C}3j`nve;H_W7E&Vo27X_e>hEkM`3|gTVflRx9;D;03twxQrqim|C%YFq0 ztDy57es)Y6o>6gNV843}#k}~L!{cw-My#F8SC5@FYaT$B>yuQ-wPKyU^8x<8{fdkX zM0#hXr#pHwm%B0G7(t}Se)^LwsWZ#){fWh$y~G}Qjr{gJ7Crqo z5at!G(335evEmQb2T_$b3+*MSOrc?!?(H=S7-y}1qgSiU2*+;Q!C70x1#jue&C_m3 zz}t?-BYb%;;?Mou#1mfChRte?p0rT)Z3s4%l5uYDpmwyJv}@p zDR{wYc+G$+E{v<#yQDF`xzGUP(<9rHtN~zPQES>)c1)Fq4*EfBG#Yqfhx47mGT?MWjFb)Zas=GXWv(r>nAO! zYb_Ql@`{TWfo=E3UFyXq&y&Yu(X1;J9KFi1nq*t0uAq*0&#Cuze|he%#iV3}Dx(MH zn4Mf;bBrGHQ0MeJ1%TrvRmpz_$N%UoIrP8}k?_ZRLPrV){y{VBpPr2?8mMY1s=aSs z5{yPCtb2-5>%x#KG`DzBQl`0L3@H&X5cu=`Ql!2uwjpU<&%bXhI$}Bna6PMe7+9O@Swlt+487EkWFhl{OMVBQanhS=AOjc}7$Jb7{@R zlDFE~TG}1Eib=PVxj4D0nsv^`TT4S)l~tOt{AfP4i2>9)F|{Ob)xCfI_;M{g3eIKw zP4C`6cb*GyCz&iY2BU_@V@Ss2sY}XAR%>L^ASw5rH84xmZMZI3%&47UY?*}#$5n}` zhlXm4<4I>>$P$NP{-SoT*TGSY_9>+1uBzHh2zbjFl-9w;Z&2V2mi;MYUIZ~H9JgkI zjWfg8p07&c2oqk);9|f-OQ@rZB!wYm5Pis|k}Ek;+$htN5fZP)Qfg5hR;}3#5AzjW zQ|xqeDg0!_&Vj8oY0?6I64uS{zpr;cbJyuY+v^;+u$Z%T> znvq}VGbF0KV=)DfP$>@2mHcACPpHiF9zQ{VCx#4Zp9bK^VgaFBi_!nS-kFwOFx>fj zz4N0dM$-sFT1N((dv7@h7R@dbWMz1_zNXZtISL%PNP| zybE1xIp@G|q@F=jKAtv855`{o`?u3Cm}2%tisC}x>m7&aD%M=69DDe(PQqicAiRO* zdI;B>jAJv&W>{UI6xe|dq<(-h7AX;T_P596K0gMw(q@3KcUsh{3}j%K{E2C>V5M|m zAzl8t-jVtsgCT!-c_N(xVq**mEo0IJR_#MyZZDF;IvhUYv?kk*m4f+R|C6PALJl z0v2%;t_{1NEE%qwQcsf0`pnwFWyNwWzB9tw(FNO%i5X8C$4)N8M%GWx7T9XtsL88t zWSXF$wdbT`*UYyasneb9*K6}2kfR+&24JYrD&j!WU3HjA4I=?N&|ysg;=c~`%ktns zYN@A?;THl|ajI(pWRaOqui=Jk0U}1@sBw>eyag0=`CREoHR4@;9kJF~48PzwrC7xjEj8yrXU#yCFqP&Nr z;bxQeuLIr4|8*dyCrq#d@d(%92-`sw(Ufb{OSuW?0*R^Aibbf@7-JGjKMyIWV{Oxn z-FrrIG0P=)BVy5xB-Zn3_x;*Wfv`pZ&E{2TRX+k;6NG6d2qKNq)rO*jt6Vmz1_%OD<$0Du3WdHqu$mAA zeM!T?4n(FMq=0R4{w(!~u^}2yC#?esMD*;~`O$)+#1%AJ2fW4sUeA91d&yJ5pH|90 zZOlKd?wnh^C9F62sD`^gte(AJ%AZ!wKRvl5>CDCqX$W+o99sGcwJVs-4E~ukEW~NJ zJ3dStcfNcWiKK&7MRc-;x;bI^xXc9%k+nafCl|yD?2$qqkc)4iJ#E}t`Vm|Af(#b# zNL2|YiUAmf37nb1=`6v$h(=_goQdp^x~QC@M--Oz%SFHN5{G2Z>`^2`IvX!GE<@}n z3~pC~^$>u**T7xHWolv8a5DPRYfv2At_yqbhP(QK$=z1tWIbO`dt?3jN9g^JW+h`% zd+eyoJ@J+Kq$X6l+62oh)2M!jeu1E!@lPMIPN9Ow7>j0gVR+FmcQX2+!+=!1j7j#N zARW0=wW~<9dC>x?j>ux;S(K?g;nf<8VeC-zj|@`E?bX4DJ2aR`UTkrH4|jsrNjp@; zL1Yk5@JOXRLFkbc@Vi>w$FhP8WMCS4k`FvCL3)^g`Ka}{tcKLZ5tPGZWK`~ZYU=A( zeATG8du*FhBf=655|aF&AVGxK^noS$5M-7sjJ`(A z%1bO5AgEs#9UETNDyAH5a}k97{p|Zq3lBM<$veETH!2DD!I_QU0;)vbjKQ{{ z&|gFq=IdT?p3;UL;e*bPE=X_4eYyQ^IY&jkSQ5~t80quLK`$EoU{=(i_s)T`gk9qTeFy{!8}j zEN}E;_?MM{aBVSkRyp?I;TUe5O=1J&9|(_VijW^+h)uo(+>;Sm))H1zB*2{yOBcc~ zlHfL->bUFG*>y`93%M%BZfVmVK_lFSonBrj{XN_PhLJk4&Lf{hC@68& ze!=knb)WooPfw2iC7_ot%*)oX|6sYAlLXiDR6$gra}URDpk{wtMQ}% z+1xgalF91*;76v4x;%n(Z@n|%#oNwJQkvx1%gJSOr@uLswfEfxp8V1ZIgiruI_ z&Kx0i!;BnBb!nubWJQ}0u9CUmP4Jl1KEh*tgip}0wDm=!^WBSIriAoGliuwUdke>W z3&)nk-eym`fdA83nhd=~FAngedOIa4?}V>mf; zIMw>n(}SXLmNHP5bXw$;Bb<0M7K&O-|GC>q8nI?C{P7*y-RdEoE#l1egm~|tyPZuE zB8lSA%2w~XF#;XK-&eV0mr@6RFxVHU4aG6?WK%Erd7tFYGhy<6>F*yKY9`NquZBGtVtpm&#F>Yhf-jJmQ_3a0^9!EG;vy7+sl0sG~@zQ?s$Pm&uMR zw@0VKJ*5BMiosSS#!fHRT@LRJ3+-(Y{|<3Uq;tVkw=rkeTT>5PGsEi1s&_$b*XvjZ z+dYWa5zFc+qIUt;v=L+1o3$9sx19~lYrB#fgaQq2L8-bk^E{nTQmhf$etwR(^M~Q)fm@m8{)PzwxYD+hxh{`lW zKUF`U#1&^viz+waWs&9qDhuClE)kWl_vSrKU8AsI_?rHQ0F{hPDMIxg2IXx!JQ_dI z5yA2+S3s7aeyH+4Q>hS&2<-ZWY(0(cRa#9@n^pX#Ctuu)pOTAk%>^BP{ey5y4513mf>`#IPT52o=~4) z#Csa-vpANjT&in9zmf)`2wyQ>kr3FTy|Fw5=&!dZFB1g(9_{)WC;>CZ!Zmdi%j_3I-!?;YV+Y}Um zYs<&Z%cu0k;rH%1E$*edxjH%cSTG`oy!HdeLQ2BIfB}#^FT4IL1z-TwpQa~~NB<2q zUXOt^Z#;kTlp1-(5@Z?jLH%P4!ovZvfE&|60sT+jd_dRF?m=7y4HWqDbAH*9T1HtV z4NhyOB~=1IE)bOi?3A1zoqg}UwI%0{z+y(Bk;_zGijMOukKMmd(&9&~dC+Hkw()=} zZQmlBdvw6B`s_9E$937WmX1XSh*<~mSqF-B2fo);ASb$EAm^$X58lXmcOSx!l$ajCkU>G87LnDB0rkdZDGx?)3p@Vl(8M37huC07%aAL&U0c>2aXEp;)9_S zhX0uWIk^CaJox`L0on&lfCkJJ7Bc{`9t1#!2O6++|7!yDp$?b;9Rz}w5VXJP9IVxUB!I`&~R=3x?}Sf*x=ci=HN(RU)7ERmmPCWZ6pz4xMj(kYJZKawwHN2|su5adlq1r48Nx?k(gmZ}sU7E!O|+Fc(5dn5b0S4}lL`6N_q?+ARHcB|dl=Ho__x86{n+NJ=StW1+c$}*P z1o+SPAWIvtt95&a4UK>ppR*^YyYmh6Zsdx)vFpq!vbCT`xZ;<3s0k_*uPI zXTeiEJ@-REipj5@ZaJLEJ?#ynVi2TSoe!jKxw>xKmT+9w_6~BMuM>)yqM7mEXQ>}1 z==i-&&6$RI4-ay<)Y=}yCyYINEOpmIJqt6dhmTQT?^>Wc&09U|O+~>78Xb0mf03bi z4wuu2zsFx5aEzq{4w`*dIjvy9VY|O*G-PM-{hx!UHpm4d6=R+!e~6Nq4B@Fy}OLzybs<#NMRU-tI|F`IP0D~Ami}l zJ-mz0v_S-e@o&=1KRbmW8B+QPI6V;- za}b-oS4|z5KWDj1(ANWQP%NWkhRSga1QrF`A@(R1aKC1yc|TE=N*=qG0&YB%adPA&o>TjE>)=&v02dOza$c_7T;5ACu{Rfl}YsbNR3y952c! zdA&av2XMqlUEf$vJsgstd%Q3NxgEt*B;R?kM|ajXXmR;u@i^#d4A}$VTJl3S8PE1a zJG-9Ua-B}OHjp|wYwDvM3tqRglB0IdJ}LW@1E1s@l}RP-sp_X2B_B9!jUxpH8}rWY zTJ8JWGZYT7br+X_*p)CpI`Yq8~Lbw4y)|> zX^t#9R?G4Zhz`}OgO_N9BF>mT>wt!Psg2 z1>FnM#H)(qQ`!A>cO3%~va!)^wr8QNjaa%>Bo|fHd-UWCMbp{46qs-=-YH|EN!6GWGxg6n~#GabcSJEr)E=7@~T(czW^FzX7da`VJ+|WbG1vlPWISfN-DiQ9S zwNuaMGzja8kIQ~Z?zg)HeG&WC&HtJLN%f?|3K8~`i0b>+6Fv=GZeZ>B@}$0eAN9-p zp|_s|S!)i9#pxRjZj>(tU#;(3`y+O|2%8nBBe%cbjGUXEPVAf>v!<42gd}#T|B`vh zVX^^}Q}}Hv%Rx8ijqfOXi>vEu1?T)lMUn$^a)M7Oqs;I!1!Zlu(C)%ylz}l`)N}8beVV=_3v~QY!oFGWG&k|H!@zQ}{F8dW6e9*CNlKr0yq9iq$}(!+!9Q+!q8+!4Wn*4-i0L_s?V6O$W{?*xwsY3@t?rkG2ch!2)q2~|TX&5($S-vkF zj#DHH7w8ubwqBoc@kFiRlh>G+ZE^xNFDGT)MR1N(a9OP|G3FG|!)P>7-2;5wI|iGi z9Uafhx$6%uxmx`8FNdRIh%*(=NosJu9RUpL52VqvCp#$x6=!VSV?7jA&j)Syx8uHa zzfQPqUC;UoGCF_dh72X1vYPXBT-M!8bU7Y>*K^HO8NEkg?6^;UAh=?m)o)w6pbK5} z-|xk1q+;>`@hUo&U~LMSe0&$sF<(Q!wQ1IVtkUBu44a>L4`H z%{yN&O;%lMIOo>p23=RHE`6Sv0m^5$uXg0e22u@ySQRO`oGE_fCAEFZ_IQWheMW9L zl1{x(Z{tD}9DrW>&_8^lJ?(wtK==715jqrL;i#aec^ex~$$_p`_w?Rod2TT2yoU4H z+r1m$)Ga{qNPUsf{#F$+m9p(Q`Ni{9g!t)D*D@7PIgkiYAl%^_Nsv5DPPld3}&p(}#a0U8N<1OIxOV32qL?(IvjOYJ5V3LBzY1 zkejA|fg@*R*4TlhcI^d!J_Aekmrz3GFhC|klktYdBHxK1|4z&=KMa(etNySxTLdl0zQv%|Pth9LNTz6`7{bpRw2e62(fQl`N+-IxhT zyk`pQ6VkTq(20*^b$>A#rI6G43kuP#biv=9&oH;OE@Aa=9oufV}(1j>V`mCs&XH} zLpbbwNhpnqORzVl(u&w6NQDYSFb@XGGY&;`8j&7;n9$6~d}2l{>_FLfPNrUEq^cxw1z~QgpYg>svsVhYYRQxWs`D^;N&lNZU9kuYBeEn)(vXrdY{JbJ=v+e0uWOO51>yt=O)f0R^`h_3|ZG zPO#b?a;S)ESf(Y$nOHqX_p39}SQ&<7AJCWG6Je+JN8_9v7n2OI%ABFLy_gW2!OZ5G z#9k^7GuhwrF1F~C&c{o8wp}FBtniN({Tbe8z*a|WigF%05k4;{qn?_FtP}4`7G;im zpHO`Kx{ODqO0RLcAXwRh03+HB9Sr?!G3xmJtDm137Bjx|2dvEeq(2%;Lzs7+9$CtW zQIjTDg4v%n&<#SPLqyX{L#8lp%w<&snh5!l5J7-E@^%xy2wwwzm4iv$Tc)f^;joaTYp`bsd%1 zOn)7)&G^0^3YX$Yav0}#nYF`S6ZhqH`2YzVJaYhI?&$!s;};ro=B8yk!mii^QnMS8`*AaO(iT~0gI^$+|Hsk2(2}md-u!g_& z#G$}ra!-XRxYt*v_x~#9&UyLTaBN|vgxI3dY=$PI;jJ?>m%*#XVk$~}rJy8kCJi_f zKJ?(F>QjZ4e)g{`R)InAGyr=9j|Ufvg@wxjI6F6>gYnD#sGDCO{3in|YrU7(b^k&) zWw->y-g9t#fzDzWujP-rl^bBnHBd|e81iF%0ZAropb^^hEBiu7H>i<6?Vw3a=2C=m-z7%;iWxf@2!;@L>|iI{hi_O-+EEjf zb)bc@qbih>WFBcr{NHmz1d-z;WB0p;oB&${^?KzeT(+4hOR6hpup^dQ^z~=@#pi5^ z9b#om{^t#p9v874lkpw+3?6)fkK9_1?gq0aBWFzx+oU6Bj=N6L__B{T?&0ST%2ald zCM=tY70!a)Z;+#i+pOH&S=J<+aJ0@pn@pxV(-Z~Sb}D^U`G3kgoOIKg13e*M#?c`j zauqR?p(MZ$J34d2eam(gPMK0g1iLG$)N5=<8z<80;h9xsR13Vjx8=VP(cEimudjM? zHOGQ^=KV(>VU9#umZlkjz@Z2&6zgJG^7iY4Pt~w##(;M$l!rI6P2ID1rc>nl-Wp_k z+JvNLF+2Z|to5Vt8^&HY!-$_$hp(9*pwhH8FXi{aoulKLu{$1j_Oj?5a~^AW;d5H)aNE?ziMM>PZA|x-;)kQjOz)e zeo(~lW(dQk)^nKhkP6E0K7)gMYrTiQ7B{v-jaxO@=u}0ZWX4aNS8c z*d2fz9?~xVt{S*?vZD_*K;m{?ijRMnt1hu*(_d0Wt2kw+h)+N%F`PXdv~{K*7kQ4r zTq{sEoShiQM@I6N5P5EnwZa0pG&Y=_6vvlRFARL?3Eximtt|yet0Dx$>vV_=%YRJY zJ`qf=c{}NLXS~~$ID;blNBsfRv5M7&CVcxP+BA;j_sUE16~y24+#_*MSFf9z5@nJ@I@+WT8(4re|>N%(5V{YCXF~NSq zD3~%KIMpI51C|{WDp^s_W-*npP?vk2e2c;eMr@TK7wX>%a02#}RX?)IgM3b)LJL`; z>C1~ypcU%p9~TruEPhKh%=dM{hohIp*f=PFtZo24j@0Cd?I#n5U?*rHxRyehE4<4q z*-H_LObpYBBC%04p-P}iT2ldpiIyVU{N#OAp?~WmfWueM>Z07%Gy~v4NZ;N;{Z?li zI*TFgH1=~DpgJy*+ykH+NQCa6vyAICCD@Ech%kcoyfOHS{$L_z_{Q{OmGWJZYN-a? zw(K_W6hIQByZ&0+d}{4+?h%5Hf_I7yMg9?nd?1x*mPE;vNJ;us`FMkCpmYwOfi;i} z*35ER3ba0gu|0{pO+SBA)o+(S?k6{mZ@kf-WIiHn40>8+cD7Ugk3O=fR3ydlS08DM zkxc2N*536zMy{SE6D*PvNTUBxX(cUl#LAr|`%yIZJ&8U*xfOP>=>)X>Rt~v-c`k5{ zY)xrEx?FfVj|iF29a^H6flyfGdvKiA>0QSB(ayYS001*zIEv%vk5Tl(uYBbddw8K+ zFjakUt2nJwc;FI!SSft9dQ9{~eV-5DT<<-q8QtGW8;ZGV->?w-UC0v1JEokFJyF-* zoP8*4;BE`8CI*Iwgr4YoBZ(d-TsmMzwz9t+D=LW@;%M>x#K`S_+jkJ@FFr!?*Dyc% zw_#qpd!s=aFt~Ua2S-I?+53-S?o6Ha1AvcA2t$5B&oY5Q^%MS>x|jHCn4i>Jvz8S3 z^FG95U>Mzr6Nkklcb4ri|HVh1@iP$kwd+1Mc>spF)^OTr|Hu$_qUbssD*zvP)J&cr zWv=i|7>^baq*gQIO?H@lujTq{m_JKZ*W20=X~iu47Ta{M9mzkyGbTPbSnF)hEWBk7 zi7b5kYnb!oj!4qW-Ao+(W=$!Xsga>5_8*&w7GX(TVev9Onq8?oKId&f<}_DrHgoAr zN2^%?+RtH}qb{A&7M+O1-2$iS@+3ZC@;o6l{D+u)od_2+(M;@;`7Fyt6I3wkdKvhu_rG zhcdCyXkHJVW;)KD%a1zvS|y6*%d@7*22PE)9NUC`{VZay`hDV@RlEkb>ZK&pq-D{% z@%W>O2k=d0?jk$g`f0(DMq%#4FWnkp!O^3zc#XlLm5=-q)SV3IHK``ieb^+Ig4Nd| z%~NveQ~DcOpFu%)?iS_To5@rME>lTFdg0Up=||SOWSWwsbx{+j3h&i*(H98leLik$ z){GR@q?gEj2ml`m0X47xi;ujT^Br3>i0FdRc{R?(+d`P}K_HL7^8**zEz67k!V-={ zDveLZhDKA!Rb@|%phjWwZjmU#gvu8ylP6ILbHB}|L$TZMVBqJczLJ+`qYERe6b0u0 zX_x)Q6G~WCMfRd8ELB^Z4Is>I{}Sfp=qFzgpVMk@n>KR2W9Q|OZQ$09^ax& z?xZ;Rk##emT*T{pKJ;dS5`60+M02czsttwD=%NQdCg>ON*XKIUSJGpImmWY}Y}8`h z-1Bfk-^)gBC$xB4#GBsjjzUkEzH2%OOqb}~(GhgvpPIK(XivG@+}(f{!C9L`(^kND z`%&nMZssgZT%@@Wbvbq)g&|3{X--E~)Al5tgFa@uY z&+mES57SUpqr(2-{JI8JAwLMVm)m_u;K?7!uY41QK7Jg02yl+jNox&kI9MfrXd4Je zwLt{)xnSq2!q>a)+uyqZ5p69n5nbNAR$92F9bUVyM8BCvUBPj1AKoVDt?d-p-)dBJ zYfOKhX=P+?tpBORm%Cx1@CEu(I+~SiYt4bEG1wn~cD9;$ z3gKuc0C_q8-q&)oO=og3QMgQhIYNZ?r59t^#mFMQ$)`IDOM&?EiN{j#g~h_t{}BFs z1*LR|(vYj$8$CilJ+@rk7x{E2xT`alfiq5z*y3!bBdN~MSlzjo->@4DV`27LFhInICczm7qhATnZSh@kANtZ{(6sd|?fEiZ)BuOnH}BUw z;$#_1HO`pMSY_oET|{1XejR)0365LUEU^{{FW}S5 z3y`rr2^XNuho{YcEmdph>P=n!w9}<9Q+ieCZ7`RO^)VGad&$b$&i6>1K|TPTYIW91 zDY5e_a}uYODZc<^!FZe=bae11aO~3MeKN#{Tw~np-;0vH{;@Ik6M!`_nrM#1b}m=r zWB7Nq%n~BtesHmx-Dpq@Se9~9#<#POY;_Gr%-ny5xb7(SzUO?SxrY3w{e zua&?3Xmf)2=yNj6SDAfYUYc(HXgoE(F0Uatu_O=?y)X>@;La1Uaz}eC7=5cIdZZi3 zTL;|Kp8PBy5EvZuHannTgE?(p@AvMj+wk!~do=7gzw%r;m0wr={?ha&G8Ur~^bxJ| zSl)#BY83mRZPB&HIWBS%F|yaaW`zcw_^RpNm;e+7Tn zZrwc*ykWznzhy}-cW&#C^`Kc^<(E>;!rYank$;o2$-%&aZwiP^apT$l7BOddbHDFy zxoJw{$XTS-Bga!XyqVMvlG)<vLTi}UyKUgF|6n7weWAeS^Qtw)tH zo2U<{-%LIa!*k*5E2j~6CSUduzguf$og>J$Xg;P==(}27yS+@FCHg8?b#+Yh4>2xQ zoeyi6vw8X57ov;Blbp+lxLlM@kp??m#5OG5p3;ya4BB+06_=EdoeZ4rw~P!^AOX(i}=I!jTVO5|^~^R`HD8b<5^UMLXW4C-wEu7al~%+mj#+06`9Y zFK2u%F1+`@K;@;FzkJxHW$Qv7QZnMX`*CN-OGhq&eYlnetP0 z>~$SZ2p!Vve%yv{BNtyDXrjav2$?{tBVsk&^H+}10k?^C`QORyf7yT9Syi--v=4!4 zR&9Ej4~}{=*3Bh&*sAe2GmMwN*R;SG;bc~)y-k9fN>XrK%}^3JuJV-6kz~P;zU$OA zE^`rhB5Ku|{<^W6cGaJbFDl5)HSWL;+dY!4**BcneW=UamM9a*VVsA$3}r)?`{;$j z59++(Jt^>Hv|Bi0ddo#oZ_M#9Tw0^Qb{HdThry!*1mvFL5U82jz7B!ra8`YO}Y1r zqFL9e?{;!3D|cpMgN)CGTW~5EBzuZ;8&pk;&P{dd5Ok~)`yALKlMv|;1IjY9+ehW$ zX_sz>vMgn-pME4rExI? z*c7mT33};J^H2betX&%2CzH09Z?N*HSI$!Fu=5 zEFp(-@IYXUZy>C|nHcXoH(Iyf{=hjGum*w$`BZ6cB6A0JK~cVQpr2WL3A`}K>26X+bWP)NFqa@8fJ8RhxYCTmwEQu6JQ-;EYX0C9aHYOK# zOH`yqN+5lZv%ZnpzB6N)HVb6~42b|qlh@3yRE$s?V=aq455`}}JSZ+B?hL*DdA4Ml zK5{&AJzSKgn6d_D)kLfb&{2O9VvrX5?Il^?h8)7R?$610nH0mtpf%DkvFZc=X#rS5 zR(S%mE|fk+OGpCd&WTa*84@T}1O=G?MuRV3Foo=1GEcLbC4g9n4g)RYWIUI}K! zVm6;6eVQz!Rs3EQn<&~7B#MNpfrg4maQ%JsG#Dng78=mi#gy8#mPW@cWf!b#*M1C8 z)%D_?vPMLiFa9W$$2kHCOP;`nO!q@9p1lN;xHSAv^-|6iI$!{VWV;ASng~>;UYASt)cSB7X-?2++WM(=BVXIjjjz^@(J@rs@!n|#jU@C`?;9Jc zUcGNRGBb^+gyW)!yi}P5J5??_pss$NNj%ZNRS=41Ua4Cc9kEhL0{G##P-NLO5DjY~ zbQJ3JGI09xU5De9s7}an@?I8yy$1!VF&5o;>d`Id#;$zE&t-un$aH5{?EYk5&V&Z^ zbnWZs2+(TXn-?@JRu*yf0=ts2fK{PRnLkqgv@4#(W>xOgQ*20FrJ^B<)3d;u8fW_E z8C$;-e$h458sJk-2VXw|js)d4kfYzP3i%mwMj!xjTkH+_X}+`tOmmpgHU zf(H`ok7Pvr`9Sr}OGY)30k*&4!_hV0N>2&Z3{*prcEc>yiI$-`6Q4m7NIwJiRSp7# zR*x_b(+-FY1|R12&Umy)a_ni8NIZ4q6++>^bQL4Ee4VCpdBA_MdJxz~9xs$*b`1t- zjtm0P^a+w0Swo)&8^kl<7PB*9N#6m|1}H#J*Da~!YhL^hAzbfvR9^jWeWar*R87=@_<)_sX`Wj*EWv3gp5K4@lZ__XhMmT&%Me{j8SAqNqsOf^Dgk5V` z(*%)$|FvgU3nBXNnywf?O&17*WH=J^^-f{vEm5}*p-gx!Fe=P7ghYWWIHu{hJ>!Kb zl+qlEWd9Ocz4%i=JT24u+cXrw9i}}LT6riI^_2G_nVXCMsr^J+@$U9pB7KJA zbaZ0y4&==GOi^70j|Nb$i63^Qh9mH}<*zy_d|%LOd_^c`k$Z%)WU19foOGb(+*H0r z&~38`I9JN>Qc`s3?zlByp0Z(OCu7l5l^tgX{S{KZu|F-T|V3Dw+;5;Y^bi*bEA*~l|X zDUo@ZxfW5c`GvzCMt7VHeJJx!cH~D6nf!f-O4(C<>Gb7)spP$K9QdyB$OZ~^E?wFA z{^Bli`L_0ZP5{#36&@^6t6MhzRQ!dluvMyL6%C-Pc=ReiQS0S^d6w=zC{R5w1M@4~ zyebb^3iO?^pjA()w0Fwiyej^;wl3Y@u3nY94^j0$_(Zg`cNxVAO#b$C>HO2v^%$+< zY29T7DlT+NKVa-&2>LDPJpJt5#pW&a@k7z0K*=8^%cCS?-&FZabQDRbpB= zvGX0E(yArTBq61NZz27;8i`VXd>mMH8o#*u6(+n+(mUxoS8Mj3gkZNUmJIC@|Vr49MDh z)yP?w94Ct3TX6|JXltV|JgueOVnUf&Thq0f#0ldKq0z#Qfs|q<%V81MD{EJSiJ>^6 zyyjKet$i&+m%CDY1$slhvMTUHWF(N*WACwe;r{9>Sl}bN(9Oo*eCEczJiwa`{lG~p z$ZatFP6KOWg&P#r!9E(g%bHVcF;x3hgml&M)P!M!L_YZdHQre$uff*?2@Lj7X?+69+~5h^tIbEI+^l4$3-H7d{oxi$Dk%K?u;xC6?sTw49NSUh=eQ=yzcx3Y$q$*_o?!jhB%5NFl z)cRmn;pK^#nC~^H(1thiH2vY4`_JFSyCh*SL&TPAVMLF65OA1VTf>2WVcpsGNuSkV z#fz_5%QkNKTk(h>EhDpysNmL#cmega)}XOZHTUkA0^Sp1KN)m(vzHE1f~<*cLqs|E z?_A92_ToJ=mQDDQ&#nLJE*7RuscCR-#4RF6R(a22bVj8uo> zHXvg*AY(EhV=*XVG$>*=aMJHT9SvQ8Q>jyvuPQ_;aYDE$1L8qaqRFDI6o>S9;Qr>p zeD0x-ho-^>3On9R<1G5@c^lY0MqY{N&MR8Yp&?O>-@(6g+ybcPilcP*y7Jc{J{SM1 ztBB69i02NOEff5stM~$R6+h6ap<((7?Cb!@e{>btsh(0D#OS}e3M5iJ<6m7x*y)*X z^4i0Hha+;w6u_*ET;oh$2G)NOWMpSeTQ6Uo4jaL$5~H&q zrR*j0H>9EZjYv?4d;?mzcPf^0k%%dph$)+h$&ZLBj_3we*Z}#T;w~(;-=-yuQ?dhp zYr5nTocIEG7Nu~Cp8SzpF4!uiPij!G`O9JQJ+$BKJ8EG}F-S7Sgi8~B*|ZTBVZl{> zBdz8CqYK(HZD<^me-^`8A~aJz#@aLV;LPft)%0+)#e`ubM85R*YVb(t;Eu@vh%4 z_x6X!Xh?Z5{gSmiDn@|eMF+W;G8p&!2Cd zZFnuEShuof;L^ckbRw(_OkWrx$lfiQxa!}BB7+kAtEMaHpPDWmQVc@B?VCO?Kus6* zziYZ`{;BDLEn0|y^y6PdIYtr7mAgt-ZR`HfZ1yTfXqx)b>?PR#aG*VFq2~G>aBe`2 zPei$kxJyN9!vHqy8Sf>y7 zl%HwfK6A>|$12sx3zi}eF2UUdpnhkCZ{Wh)tZ)S3zO(*$xI@TCul2~bra zWrcnBSV7Bdv4K_WoRoT)ksZ*KgxO@J;1(1bfD$^kNwCi*8s>9hpyRQLxo}Sua5qii zqW?Yx2u0u|3Z*|#E$lb}u?}^M(tuwmg$&0^!rvxAxxiXM<-hxP_y#gZXtp&;7*)gR zX|oy*Fc6$i2)38$jSFk^`za!P+J8H<3p7TsOEsR!j5dDYiZW&bykV5_qD|HZ;YFRy zC`D&*LB=u`UZ5U4K|PTPnPJJ&1ew!P z#O^@z>u}NNklpC8(e)7f-?)mEn)xH?vQEaw&h?7S>N#Hh3QznBPuvQ@&b&CqyH_A+ z)v<4k#a+3LE^~*D%kcqB4OaXwPh_mzCI3DFB_R0j_+OG98)>Dp*rtu z+%$C`K5w>FpUv9gyXZHkk{;RSig+tST~9jNa>5&LfRF|k5lx-R^Xzn@5`y;mxM8}M zz4QTiU(^5+My>pcyv!oR1jz>DO&!lL9q$jI=hOG=80* zv)oXZwUhNFsxiy1W#?)};9evh#i_^49D~ z<+lYvJ3Ti(wNGrcZ1~zz`FhKtvZIK(#S6W|W21u)S)$DsBiYRr4&NwBbF>ROC-P{< z)aLqLBuVAF$f#da5{o&JSEuVt5JAvVo5h7Gx!C1n{}W~Pozu`c2HnT=f)fw_8UOJ9 zUN}x;TOGgzhfnHDaZH`JQqb)*_$thIy0ad9+>3F#x>4Tyjy(6)$BBJ!o!b!O$^WTZ z*_(LlHkpX+fMH7f;UX>$b7JP%^XIhW&GJpgmF^-zqr9x(#@#e2QbajAP3536@fN_O z3vf(l^&E0YZ?JK7l3AoowbT;BS06RrEMW3h+Z>GGzc-`~E%A&Y_b$F21oGhyR<9*P zX<#1V1Zb)CwVUyt8>ry#I?FgBwzL$hb#n~AdE0B37mV;2zkeQ9L*u9>-CP*3S9K_m z3^zSHP9<843r*gYHyF?H*1)$UIK6LmLLYuG-RSjAq&zP)Si$oj@) zxV}ytXRtia+%?5;+Y)u>usnydEEJb3}HY#XEM>aWS$`J58)J`Tk~P=V`$rD zpYYT~#$avaKkaO?xS3|m6f}M!?aNeOzpx{2H@WcrF}p4>_FU-6scdboj*Mw@9>mwc z+;_2Av&mYd9QG05t#)`w66|~^jT2lkdK#=wAv*lL7XDqXLk7fmf<&P!8%1WyE5@gPDj0or4e)xte>)f8 z@&qs~gZ-bgr$>kUKyOq@7ls~j3ROy#0}y%w4YeP-C32y4Jo}KFyy(eB^{L-fCldJHe#_ayws~X}P;vuhLc> z$Cr%Tl~M)B0lS4=vlAw%NA|G-vt`nrR`%40KPYG9+le?9F90s+;kCqR$ynX?c;`IJk#%@Lqd*+0KAHS*o5c>nT9q#inwzs|)O%j0)_kbMGt{SBIkS zLt$?le3E$R+n55H8lLYxuRVQZq`xkjjeUG=++SCaX6mL}I9Ad+>9~doV96^amLnED zem^TxNMUtZOIcTEQL(&awT(-miznU;i8cM=?c(fa_`Tsh0jGo|aPbz^=YRapeWKBP zVD%$b?kfIfb~st^b8#PGBDqM=yF}Tg7B0(&0pODmE+`G^MrCdkj3zPj+jq+;J!9X$ zOH$LBzjq0A#b3I6JGCLDoNr-~KILkksiuEDz01sI>2_d?Xt5lcAPT_Z5^cHzJtp*C zT)Fe}?>c83g?=#D;99-`ou5rsLt1e@Dzm*e0Dn#O31CPy|*h~BpEhUq+Lj`TLzn`)NE1RTww-&1{hflMLkGJ8g$F3IMv*@O7$9Ex=fbJ^rw zW5_}ua~1gt{^ap!!ymAw@-^4i0wJn0VCQ*2R}lFKljhDA;CJ^KB6@8IU%D` zlsQR!OAvFu0VjK2KdGR3@A7p`l%6ngEtI`DEG7dFJZ)`YGvtjmkvx7K4==O~>FxT! z!#ZGpiKYJQ{gC$P($xDX^Q{fg*R?MHWPurTM#haPhtW|H!X#IW?ayR!aKMiVvw_;c z*!enAkFL`-zGMwF?#dHI;A)7-iyz2_p$Oce_6ZxG_<`jq4wstRj(hSd58}EkF3;~c zE2#jXY_3-R(RAAVgimOGbVo<1{ammi)%Wnd3Jv#0+WgxOnZC0&xlPbTRSOMMwY~GJ zNdJoHdqaD#gcdL3AtaSDL6~_l9OGG0kY$}^6dm5r!moH6RObYm)rpHCyPEb%_RX>G zm(2?omuvIzZ?Kg8GuTUOhgVY_h-vKvH< zHs{?+*2lu-e(deOEnHve4!~jR<3b^JJH?$-6{S7SGw_YOLzZoTEP*h5S-`XCdgV|s zx~(*y62&a}Ji@YU-L_X*V~SgtXr>(jK59mdzC2*qeV|OKe6c27wk>k4=Pg|=aq=if z&5(J~7ig-W4Oh@RYi=-!E)@sAmV~W+S6ZH(3|3mG-%&5UJ*%~@p}#oRslH`$U+2Di z9zby-%s&blUk-n(bnH6`Lf>g~Co6Ud+D1SRLee=-)hN0%UZCPj1ORxZNhqWFIC&=OS zs2@1XK`G5wL!2cjKq;c@3+d0JfNjUoVKmeGb0l8)z){4}w$Ipg5D_pS1VYBCyG5Zf zalO#x&foUzDUdn!=JV8r8ezj!?q#Jrl^HxVp?X(?7q%SJ_&K)n^xZ+iR_(FK{gB`D zrU_{$|56uu$Ajx8Wcnjw8b99VpaUcZe0$}fE4I(A2Zqc>e^zeUhn1b2{?Y4j>wCW8 z)+RpaXTiir3;}_zBfCMw2GyE&2@Sx_Q+0Nl(S+Cm zDuvR+-o;=JqQ5_g0>U zS0!dfE$J9f{TFtpU&#p1Q$X;yuEbXJT)%D-B7k5AqBKMLG*Q`jJaGyXWr2Rh4?hi#mL^eVGj*2)#<3EZ9wt|F2iWFB4=}-LD2^uRS zk6umLUUr&Y1vvhB<>8r<0ytLJdYZ}YH2IaIw6HNEQK_O;00lWc|IB2TJ!7Q#EEMfj z!yY1I0q0|(#@N+=@orpjgHY^*A3(5JO^~#SYPGS~rm&WD2%&S{zLFO**!oU&|VO;QfUe*gG@0 z6!%JzWZ`zj>}QMaL5<+>;~WS=p-DC1v1M@1;!AmL; zV&|rIx@jHPxCA>-SD2>H?d=xzB?J+I2|wZJz@c`?l^Iw`>%*vd71$Ufi01=HJ1h4~ zbDU_WR_*a*>w$XNzmcZ+>x;z-QHlW*rh)Z!Tv`e4Dn=dffaww{j=YLXh$Ldb&q|oO zH~)xnzi1rE+z2kL)GH)gQXy^m;i1r)wm~^J4xj9NnsCsGR1afBxFn7SV#xUwnUcax zn2;ioGZ1nY+E*ae?I6Y&oYHKuOQsWjU^wd*X1?bdCb{PpMup-UhK%9{b#>$hrIDO+ zozX7^ls2~rcFlReGSH1YNpbt*of2Pk(gGA$j(9*S?*C!z9D^f^8ZbS{#I`j_IyNV^ zZQHh;Ol;e>ZQHhOJK21T+FI1sR`u!IeeV5nf8Fjn?|GkRWEXJkAjVm!ZfS>>@@EJC zvV)iq74MVIq)Q=XLT%FDw7y^;k*nrUx!a&_UG}oGf)^7T6T0{y-T_aMBPFN+JWg32 z2KY}fVlK4PCxo~t;QP&6N(M!U5ZQD2hZVG*Hy5#kV8o6lZtp`MGDRPRcGQk0Y5#*E z@?Mc`YX0D_O!EFm7|t=KeTK=H9Zl@MW?GYS*nNfmPV8Pr@csp}KZ8=n_~GAaxM%1F z=(P-liCjMtTz-_+*q?RUiVsf*FzfSPO~cbgeY$Ddrs~nGE<9_fAJ*5r9^WH!+ASXF zQF-19G*wJq3xW_mA%EBwHleB^h)vvupgN9f#oQ_ER-EJ3)BI~N#1_3jz?!_}I?1rr*;5`>-1J&M^CobT<7H1>T{z7@Q_FZtT@8daZk#j43 z38PotEP}EpJ(#H}Dx#9rA66bfOSf*Ntcfhzxp4wmoywDC15s4J?=5n4X(C_AE+Iq4 z0bX4aydf6~eS-S3@?tocd7ukt<#^C#^gpZDuNj0dn>H$G6340GfMy#p=GcUzTq%Nb z$G-_gdARiEt481}MD!F>+$`yOQ*4fVhBYsE>ou?z+R3cs^-M&c= zS-=lkR7Wg7GibjXc07%{eiH0I=0_|8VOD1wcEF9f;_k7Irk700?Vz9ZGbI%K5+oP! z$CH4X!kIO4fp)hYD7HqnGUgDTne@w#>%W5pK%#ocv5nyp@r8g77ZPOL{3v`8GZ_5m zuPmDzMA9mi9lshlK!I>EW6f-XLNWoQ!fdZSY;!JI+5O#N3L4x3Vx!^Vs4cQkWf41s zn3jUz)TY|32R()IwyO}{S)f%AaaH&_JxUC(%wGy$jBY>1mrtm7=qitb{K$^fSRgm( zj@_RP2kO_f9SuY29ZZ|lUv0tNyMMTk(sgvToF(1BgDIHU(>~M6gouYx#*SyeNNh5@ z<#=tIC(Iegh&{{kDYgrDv2C#t-=*nW&P6UfevB_xo?$yK+FHHuem)X0AG_eAUWgxN zAU#QofUx-6R)hC2SY~SnY$7f=X_jTd(fppPTSx5uz7qj_?AJC>(zM{wYlO^>O_j&?3i)5bq z3s#8oSKtZKhar`d0+buVROu5`YZFxM5>z|MYTbW~Fu4O~5}~NkX=d5tKh78brS0Kz zPMrVde3^c$NsdOgB^dNZSc;WH_3Yv8C#oGEevj`5*T!nS&m9&$fhB>)%oDaL;c<7K zilw|ma)*rOr3E7qKRFdlA%xc?yW}gA*}F`mH!f%`;toLZ9wk5Pv*T(?u|_6olg8g5?8}j zy~C_8;dOrhf$z z1*18hjQ)aRSJAltRS?fb0#+`?s!&6rg2!AfigKkrQ6kj-IkJVmSnl=VP&Eitu3=9p zDe5SdPZ5<>BQ7qJtKLJZLW@50LoH#WR0vVjY!&EI7wL8i^}46~Kf*&TaiCNHP%2!= zRVBz($88-HCX=BG?Bk0uy^5|iA-3azKe-_vz|VMTi~YWemt1^bV*Ymc|NTI8dNDY= z5l`$dFX_*g4OUSIUX~4BmJeQ*3tmJ zOv-S!w6%Ep2I?RLUJB;=D;|o=M;96lCa+LU#dRRfNl0Um9kR#vgLtb2ZnMT|xqv?j ziEP!_(*St~TE~M|ZaUeV7M(kcEa^Ay-p2&qXL395G1x%$LdQ3eB{}I~D)Y+lNRvU$ z1rU5-jk<{v{@t)e&q^w#?hF-%j2bW7HWQ#J|GrCHw1M#JQbIOm1v$nBy_Lp(B&os2`@775h~-7S4!*t@Xtm`@k%ckuo20zAa`hkMi+g&>RxZQ_0Ahn^vs(e=OA7Ht zAh5?P?8kHE*G&ct|MMaT#>({fRW2sTnJ0%>nEM$+lTvQi@fqdXSZ5~*bF%2n_jIfG zU9t9^o$^?UVtnEPT)0yYzi^-GyQ0FtRnVUk!MNm1c~P*=Y|S7RX6I%g)=5Nll#_!v zH6iEj6+sE2i9PVdjM5s>Ot?jKuzQ9~ht71kPtpU()cCSP%R*2S@jecL!A>E@(@yBd z)6NvBEQ%CV(IP}7oeqZefmntGzB)o9gQy+3w;C2+;TliK*2RlsH7cFD1gx-ejpa3v zQk_p|Q`HbEL4jdP=^bD!t0y*keU75WCNdLJ1x6>PV^aHXCLRly!A#ASf7Lidt{Ui} z8$DHh)dy47i;&g35Y@Yo){C4S{MRsvn8qZKE8+F9nN}b}y8ZemOzQgylW;CdRU?;5 z>!^ZOLI5m#j^as=bGuaMQf==i2~_9h3UyT4$Js`o|CwJVS7YHVHe9V|2aJQAB~W1t z)=%)(N93xpq!_OvTuA%kzwgT8{`?X7iIXG=fD)kf2-RHU{6)Ja`DFb@*(L7>@>-Ac zOrDFy*ds{HpsdB&7;#t&iywlz;LYcT*?@|ce4Eg5{(!Ra{WD|uPn;yV{F+rSOfh2| zQxi=%&J?F=@}qr;`>)y;gt+lP0&2`Kk3snWfm}k*DU%YAs6Rktek6kMci_k|LxWs{PxihmYSjD6!G8^(^@1fK+Z zn%w(1Au4@R_Y(^-b+naiD>x`V4r?$fzeAThDSBas-Mz~q6?V`Bt*U+iA-S~I3z_n{ z$YGW~0!K+&++Bt}VZnY)x?Z?A2+v=Bzio21iymaWSUAqb(0rfvw|*EjhvsGWms4W8 zb~$Mo;(v2{JN;&k%swdho<1q~1N)I2RFuI~DZvm>wf;LBH=@b{9+vD%t916GtH8*a zp^X6^$pV&tIf9;OJ49rE-r1*)7DJ2jSW41TBbE8GPx)^x{5;F^y~t+f*ved43`rtib5ZaBdLUgDA0sx)!kn;eJ@i-#7Pv^u!We`@A+jW!dV zj)QP?W`f^pYiew_8fA-QMkd$7p^;Jw1*$ewUsODdJZH|Lz^gq|WJ6P1?@z+_Z%IOY zzN1n*vqMtp#RT-flEwC)>x=sD%9G6#8H*c^PN!7HQ@NCaN`xd_Izv#xaTm zg)6T$hc+}^YPKe;j3j3}XgA0|$5Rr*D%rm{XdP%r>WbYm?Te;>xObn@4!{dixCn*{Yl{&W(aT=yeCX+KeWSfc{KdU1|*dv~{{Xj|+k!$F`E08|V5J zC%l@kw25`jMdfxsjI@vVL<16x#PEt96Rx$fynFf;xioL$Es~OF(HGz+tkkC_cn<#3 zp-p<+H#;eyvD`6WDyU3tIk#t!PqIHwZiLwvjgAN5m9u)Jrt+;BU87CwlUPmLY?Q6^ zKSVO4WOE(pIa8&7xk;O2I((bED?2bv2UR=~K}maHL@a-1Pe%{h4BcMu-(nS%ej2*w z%ID3ZWPNr#_V@B&*KGPx#<%GTygXlDuy``GhmZk5$z^b0McNL3tH#cC3mNZ4ZByK^ zd#74%`+f_MobXiaLw-gNeo62E5nna?x8wOheGR|9(@w62t>~yXpEVZicB@GJSXVlK zt)I$Xk*{29&3{dnu6W1NTW%`UY#$?j#lL)ej0PCX^m=cbKc%*`REvH!LU7d_k*`F5 z`wVq&$M5pUBEJYe$*nCHzbo<|Jw0labxhC_MX`7@7DkwLKITvE?JyW`RnRhapJ@+u z)JM;PV2`J;w$!>T$xs-;RsD2ZnOY-OyY((Iskz2e;xcE$)tRYGsw`nGcA6PN=xolP041?N6wX%m)>!ZEtIy-?{})BVE6bn9nB*>?kd!m zWAUZ_3XXMm-tHl7;q~`rfFAC6N(g;*gf_>5#NAF$N_F)2^K-Up-8a+LQfKeNrMA7a zTEp8;);IUJzS6r))7Q#gkPYL&HHXH!X}15Ts?R&=RMxtuO^?OE*P2l#a<;$M;#dA) z(0W!?!1mMrH|9?jX)@7Eb_Tcis`!z1BVoO7Me;+NTFnR}0k1`G&M)IaQ{YS7tP=->ai83sk z*~}20b*s})t5>xB_gvf?N3$iTVah_C`TbZDD@?4yp2l{0dv#i7I#^80(MnfR zF>0rF68Q~VzuV!Lyra^}2c`+^zB9PHKzuc}v(t=AhQoabL)MqaLniIq6vZ?Av<73g zj#lBuawjroYW-VB?t~I|@n!^ekc)|@(^|`OL+E?t$UdZ0xE@g^J z)I?nU#?-`8v&##5M&UQ6`L7Z*4k|`TcB)H;wps|vFq$!{vG5wYg)p)}Iw)-kAw3^V zGm9VTle+amt0y0^k}^C6&okV-N9m9R;RRP5U1yM%F> zZl5h3(^}x_mRmP<>mfVV^#^wM`QTZ9KFbR5`JN0B7_ zX?hN%J6HhvL%F+s4MZVwSS8wP0qm)={clO(6v*_Dk41JvEgG1~kT9webC2{Qyg+3u z$Wd}Ks`7SCU-Q_>LFOu#?fu9K05e>hcLe)>Yyd9j)LasNMIV?w+-9nbTFKV@G?~ms z>o=$3yvOkRIPhA99e%Xt6)^HKl^5r{GiO5tw_V#VkSS*T&FM*_wX7S1+ttk>KJQCx zXK_cgihoFOruY1lPhe^8wB#Bwa!KgP)&f&IY6Z_yk{0*GK$8O~qWboSY zv0s>jk&47pix5^7_QzzXvM$@pft!3es9}{QH2cRU%=vJqr)t+ENM6vC>vu($03NMT z{;uHnb+m0D$Gq$6SV}r!?Qu=*U+!+{BV};+*LLr(oc<&DCTO&yBk!cwQ?(%A<@-mU zo#ZjkOaB;}<$Jd{8|x!4qpn(d$By$?;8B&2?brN}TkuCW@M+XzAJOqbfj7Nqef{;K zss3A}#Ehz2N(xu%_oMY^5#Hn}{3r44!~z?UV|evK!K$`8v9PAX#%7-wbjrL;z;xW< zwfJgkle2|-&IU*U4QEMxe3 zF_bjV-4fg{OR^}zTs;+#pu6;;kUYa&KeVYaJtMic8HDf|=FHf_KWe7BLMQr0W@2bq z(mPbyzl4*b40CCb_|YjPWa?&SLT8u*Vh7`?m~;spYwP|2MW_%EN#OLj*4FijqG3ht z0LDl|B89lLP4WE4DPW8Rlt{LqvfJFDHaV2nhR`Vcm%`6LK#nV<4S-{$FDQo_fwl=FOA zncO-zSVVMtP$@tFmjHEk3)9$!^5JR8IqLy8o3PQQasdyq-D721ZghA(`V;bh@~=vN z%qFX*Ob{7A{Hv=U{?-4F+2sFd&HVR9&HswOiu$pd{Ac`cvBwYo%2QGWU52C%qZS-h zk$O=TE0!QnFYBKwVebILk_p58fs~}N{k2E@3csmWIYcR`$cw%ByGY!ZBVmr(vYQ&&;+p}WfDS#s5%BAlZP#im+X-o=Ps$K z#6!bs)R;yrO^GORDjgy44}K=R8SE*GBDHr;h-dJC$6#})%A~OOZJF!!>d3Y=o$93V z6hT@A8Xp$~HroWIM>=~}U_G{Q*l zf*6TJSSofII>{4mg>dhW&X7c{#4Lm+#@2+oiD}6kdEP3|H*L`4{H(WNlz+&wPMhhb zqUU^`?V--Hlz1LRKj+BGVTMwDfnNo@EZB7zy-v*=Ph- zBX0QGVa}8K0-s8D-2yg=@mX!G)G*GtMkO-n1N$LfS@~rxO{G(Tgx;a|Dbz?9Uf^dZ2soiD=hNUeV>gAE_9) zyWD^|$}npzic>t?Lk0PuJ^(ytizKJ3UXF+QOhzjLix(*zY{hES|3E@1$_6zfD5Urp zpNuWf(>8&Z6j{L0oWX)c>R^I|-%2n5hWs=hz?`UtWbKX}%G4RCM6zgCB$h#sXs@Q@ z=9E(x0)|WkGs?e)(3O~(tp6u8dEv1%Ny#xd(-N|$$lkKIzxeIZ82;UOz5Mltkg+sfAdD(z*$1Rc z?f@7IWXHvS@Pv~y1_R%Xn8Zlrpki+vO2y8yM98seX@MkdW>!g!((w&V2L$D4h2!c( zY8Dly)FbNQbxSO2=P+vLl4|E-bxZL^mBqvfqUth}{PV<>3Vi2O)M;0_k3Z)v$!#Bm ziCPmUl=%prA*7LKB-_}-wTXyfB$eZuw+b9=#2^=WtANv9MfoRlq3a7l8KAqsRS44J zDGSM0=}-MHrdnIsb&5M(%@TjF`%BuG5K~z^@ zUmGiEB0(C6Ac7m%KpwveY3v0haox$4P%h=X)meA%P14@Oy$bp?PIJ?G0sO1zJ<`k9 zhK~S@muycuB0OPV>YxUc0@`mw0f#bjL5waW`RxQcPst9A{Uqr2kNYz4kbNM~!->Qe z@*vGg&A&ZFR+Q+llN2=EWLZvnaIZok(oC|fyyNBgGRog~Y>;C<04SuoVuu+_YIaJZ z_r{0|R;aq|v5~C!ST0&34?f{14$ReXhPv(X5p8j@c;EJnz$(!mwBI!6 zf)7LmkTzthK5_Mf{rTT6vX>m=>gtLc{&+C0=D*uR|`1z93glk1MaxU*L3}f z;X3;4ks(hC?0c8hlxahQuRqxLGNUOg;Ta6M32Vq6@!n|4u{xt=g#%?P!7z<2Sg%ga z)K7ts=GqtF*rpT?_5Tv0YkAZZB&iQiWZ7eoo7$?msik-@-pp}$OGeqs<{e5-*)pDn zn0W~rq2UUEE#?qvF+_#L`D0O*TS#JBG=SB63a!r%|Jo^ zM4qc0&B21VwlLis%*EK73AT0!dRw7TZ=^Un*wSTM1ACBLwvaoL+qYmkX721J>j~cW zPv%Pe8K~w>&I{4tO$H9t;7`^M)!<9+3)K)%;eSv%5hrMr<8_C1gh?2)QmO0x!^@jR zV5{N2U6mVk}c|ML*p>{@vUI8PFtKGe&`S|o8djqm&?XdO% zS(nhl`A$@q)BF~RjNtYMrH#VuJ(^Ec7tEX%iH<#*->5EPvKs(7yJWtwb{LXP#b4ED z@gP!9Ogjr=OOX0)t0X122#^TWpv0rdF}Y9#{W%g&c+yUId`?T`?9qyjc!#<7rb5T` zVN(XEnZxAl^FIpI*w~~dC7Zkyy zX)J8PO3E$3OyaN9lcd10lnMBZ2lEIDDKo@Q?9DvYEM1XmjLO8S8$|8>%7-ho}f`fzr3bdaI z!O*b6ob>Z7RpIC+oTD9e(ZF$nMB}f8vC=JE#Ad~Bnv0nGXDBF%E>bE=R8?g;Rn9wR zW=jz5ONI+9nSM0c{;I-ZP4K)jl9MUdPK#%-=}qj@tLKxeXH-=dIaThQ`E*oOZbjYI zJ)m(BC@Kw9)h6;9OBLN$Rlh@I&?J7)BpFmyXR?|LN0Jl8kyd^R;%r4xiqb@e5gI_i zMUW8|nnFQ^ER_*L^p7ph5PlNUqqHxZ%`AXoo}F=oTf8${-bb2o1gIHC9t4>DjIzuy zMo|HaGYlkrGY`H20Z(jzQe-*tsna7H+uH=qG7jQCVjlxny>+NQbZw z`6++X351*OGTW;#nfAZgcE_GegRd6o$2MfgHsr?+J*{mI<^eUvp#P>-cJV@KTUvaM zr#|z--=Ar&lF6RO6W&p6TJQXxYnh_hh`P@9XuG4MYSkyFo}XGknq*5iZ{TT4mpS_{ z*%R#o#>kZVDkFl{rP#eBAe-04TRu+^t$E0Im8<@(;{rCWD0Y=||E+TYHoTSlF42D` zD0iPIOOSWae!T#>k}t+c^~09}B{IE+WPP%tlbMk~gM#rCWo?<ZJ1SA^9Ovgv zJ5$g#SC6U1h}w%nCiEfld6$X%_x0Cwhe4SW?fpumA_G?er{7{zy+o1^fPyUGHwX4` zolc5kbG?=r6Itd!(UbsfDrglY7(_k(=>831XN5Xofr~15D3N8;#lN|&w%hj4uqXRK zKrJ(@`lTj{Xl7rm6J-!`<;M)S2(Vh2Qr>KfeZ81qZuJ^{*@{KgN1v0%dK9>G zY60%qNvZ=u&h9mmWY1ONF}rK=DiXC3p$A;%@;AQo>M<|(I&%}fvzpR@&H1SAPG<+_ z=5#tmC2OL}b#0wFtG#Ox(#{T~K1Za|#0i|4@~$p0v)o~^`^5g7m7%L;q5Y&7!0LVt z5#>BPZY4L;A&?p=(tVIYt53KjJ-vo9s+i}{_`9fTB>N0`7zW3oeQSl=D@)`!PGK@5 zS;c@CT*$iH+ami(wa_kVkuv&NNcx&@Db?r`I=`1@O=O%S{`O@@N~^!eI$7l`FMNxd z?4&E#VLC4YzMHaM*oC^S$rz1BZ6)7SK+)H#z$QHJA||!7%4c=$66do+L-F@J3Lr!3 zmuC2w__7P}J>YGPk4rRt(_UDQHs<`^H-Kf`=Gxt~IT`iESJT)ffUZWM_teshc4yuC zeh%aB_IkIp#V^<5&Zb7%nQn~_F{&HNYaN)%P;`;SwJ-O^__FOTEhc zeX(DC&p*zUah|&iX*>GNuW;Y;hgL=}=kahfj(SQ8yM34b%Za$%i2Xw_xFVT=l~tLf zV|o|8baA`&4EP^q_4l8xz~7gi;QhzbQGa8n|3sJm!2hlLzEDoZ^8UsHncjA z-X*yCY6~`lrA}`>03EZvsP)y58gLA|G9dd6oxWs#1092Z zO4Gfwnpc8Jw3T}|ZGB#I3_5asM6-J&+-0Mh- znXcNS0cz7{=gwEqi*j4b0I$`^yL+F1ufYZm(*oEJ+B=}I>I-?taZ1HZv1wxER*~b( z8gNvrOb3+?X$H}r(^66GrK{Zb43Gc^ z6Ic(O!s`ws>lL%8li>IX)|hK{!9zBBhL!!^I;|&}LlGO8nF% z3hDuW?3SW}T$nAgU!$N^osQWYr>40ktES^wk3a3F8o%65Hsf~7kd{|kd>mG-C+#rs zZDwy-8fGC)rufs_Tqm1nni{UzbIn)^2ddX^4ALgMVWGKwP9ls=ZYM~*bhxV2vafY& z?Y4v_`*kmuB-YNaj#o@PEZ`eP7xT!fba*PYpsd9A1u0BETC4_Vth@C&M2Fv}=Wh>B z2q~JkHbNKRD4STmA=msn9W_(wSRwmj=5NRvo!#PVw@sLw#qTk#4v87fX#U9bE%fQ_o3xEa`GcN?rQ8&t)B?@#0;p z#{2dF&PQMD0+S<3aK`c8%ke34ZWA4KecD_!E2-@=!RKgH^^A7#l;Q?S94UI?DH#{J ztrLY-N6=nDtEnSJtpJOvzbO$}K}N?W>?yQEmYDNihBWo8=AL_K3V8G4)$6QFHZ%(N zSu@t3HV!E}#eCJ*s+XR?|ky$s8OCZ#rXlHE#ov<-Ym(l`MrZ&l>rx$Jtdo{ zQ3skuE*EuHhn=_+Uwt7**$a(~@Z5hJjgfODHdWiNE8Uz)YUuJ@+-fr2T$|@pM5J&d z%*_X@=MC;s{@@axUx_s`e&tVBWSTTGvR&T$oHG(fx8l{&l)hVn-xH{X;9O)63vNF@ zr8;d)35sl$FmtJuWk(fXxxRc?XZL-Vzm*zV4-S>&qci_j;ru$0&qk7(7mkNPf5Ydo zy%3vLb3*54qhod0l~I9`G2M#VsIeT_pZl!`ewHOa$jz<`$H~A`vn(EIlWHSvdCZ6< zC08$&@0^7Da*?l*ZKeKjvRx1FkYd*%e4FLvtD%x>^7;OWJ4T8s`OLL7-E1OJo3!Tg z-gLiL)hE++4R2ejNbWvvo73TWwP0D?{j{d`wXNOi)3kkOQNDYA5dGBt3X^@$mbqDt zlDmtw{kp|1aX$6DI5>T&#^UwrA++EA8G1bCx$U#PI?bs!PI#M5due0+U2{!74gL;4 zd=+|9-EFyTdbsv_x~=e@4zu3X-RWlcU3*y;6#>wBrj#~cBOY^D=k~r2$886`UZGsJ zNnJ#{arzDz$?K~MkAAjT`G{7#x!CtM(s~Ro*F;};L7xZ>GjwagNAXc1QG7v;;?V$8 z3fqHUnUE*W!1vg;*1Gvz0c)2_`-_F*(+1wZW`^%WA~=}6>3Dd@f$SVGuQXNd#R}1! zZHehhZjWB`Iu>fXoUFEng#YN)dpP%5l|621(~TMS**Z?kiK5`z-K{ql3fVos?ccw9 z;xQSYgE|Kcve7FZ%i%kzDhy`&?f3iQJDTvU+3eZ}8s%GHBH`D1x9{bt;M%%P=%6?I zlqX)VKwUo!ADL%R_`OzMoGtQmz_qu=Ggr*)Gxj>Bxy901`l`IO3xt@+=UJ@dhybFtE_=c|aF6VAN661LXw&G?&VC zI`O9eoUmuGXT)4A1JfF-=o=Xsq1IO{Cw?HSha$o!Vs)3ROykQ{!dS&<%?vkB^yfBG z);rKYAfZmHCToBqLMBonF|{+|O;y5h!)R>{ORF`Gu|aLHi?Bww&!0sfr1>LIciFdv z19xL=Fz<&&Gh6}PEQ%DF)d!mEgTZf_!#&eP6NHETL(_tp{=d<*j(%c+h7}0Rte-Xn zIM@F}i}CmWrN#Ik!t?({i}CA6iSeKDzvTj#9&V}%!_VHlLJ?uu>I1ONXqqufJrpj2 zXq5gK4N{*a%e;h;|Nf<>`>NKip%IH#DmN&X@2QtpzH3vLwpU7^p39?9TRQ|S zP0jo(QED1D{~@ddW$#c+no5u~?2~rr`g23AaC1$LoM%}nT0Cg#3d1dN|2F#zM=P0C z#%OrLoUf><%pZpH1|}Jd3#BXKW5h_6$(&NJmcN={z+zXUke6P^e7b;n!?-XC_*9L;Z&jWfsCXchA;pR{)Q8B#D0^^nA2>RdR8N8!+0n(oMXS0v;gl?#RycI9Y=h#6!+I+I zP2`Go1|G&PI5#XeHs$lS0%!jV_pPoD6>}_Akp#AUD2p5dOEeo!zRAgzf?sGJ5vDqI zt3>|$m5w(UR(kFD&%f+m-j11yWm|qq6CHuF44g=JvMhpN`jzh>lpa+dz!W-z`PyF& zZaWs)R7p{|{YG3@A;5_tXZbiC#w~D3vmIS71e{WjN=dlf@0%Lil);Rl+pdEKPa+kY z5W%*u-3}rs=wPu1##J5i;ZAq0B;o4*{6$+qcVTJn>Y|nKekr4&*dm4+{bEjSRT8_+2G0BaM$X}iBoll8q+;>Jhr-~bm>A_>^Pi1&Yp$1~)J?hS zK+NjqjMO&qq3T;OmqALCu#Qb%ojR)zlj&7B@>RnNkvr>@;#Y9ELcs%q7-Mm?NDjQE15jn_<=c({>GLJ< zh88u^(6h`s4?uI1pz@tce-+|2UJ-U_LN|KgYj)95df)*Av<1R^Wt>gkgqjcN*ezD; zG5ygO7l>f!1|BV`J4lw%zQ4BMB&2FIfs#?@(+?SUpg=W8A+?*rp6eC^Zwx>+iCkAn z{gW$EJ1sMJjIyD$pHg>>&OvF{30zl&d&gORE?wm@snnXijT`%%oFHHyF z?qpw+`EuOece4_PQ=&6UJ09U^f}54(@30VMXO^fK)i`di3l*k;9&b|ry*~DbzChH} zt=<9FfuP|XNw``;U8_pb=Z7%r$TBEz>7asTh#B+me@_THygn#BAc^e`($TMcLhboP zih`f%OtPg?#4zOKENL{M$HCiEZx``>&^m-Cf~VrN0XU(EVo@ul(=Jq z8;c*snZ95Ryh#AgLQAPcN#P=5c(3faQ1e@L0IojTm=(dg$DgfB8@Gc}X*?x%jVAEm=mWm7j3%53WkVUlcpL$h7!B^5-H1v~xn7SdBd@ zc?$VS)&*qNLWvfU-DaSI{8){42}q$GR`P3W5@sTW9lV7vJ2s3)(2c{%fg{PzE(8wb zRwYEO1rxc_2X$GT3#l7&U$W@-V&sC}2XS>FZE+If#Am`ZI#;%Pf0QP|l*bXZM3h(mTIE!e!1eBMz?DZR;*GW#7Ap&)`gOWS$Xb=^- zy&S<+^9)7g;6IGNnn))3-YxN#YF$&cgsYiyF`4P2O;)FkoP#5$qIt3Tx?6JmCHeo> zIK)#<$aWXxL@&-G-LM9VFeky7&#pynQNn5vam-|FQ2?&Lls=5|{vsL=p*nWf_{r;C z9TYM^z}AU{Hzg0rn)!-jQ?*IJI3@pS;81o<{yVF+cew%Jb4gGqLK1Sp zq-m3gJE0lPYmY$K1c2%oBi&_e6Pwl*1Ch!wOaJIFCx4#W2as~I{PLHOm;fY(G)?(T zokI3*VJGD<_HLRp&LQdJ?@;z=R3|*rGfwS=R8*%dg0l)FCuw9SX>=#$nzIKM;H#$qCkb5OL9`u0Jikrh9)vXp%Upr z(pkeL0$hAL<1TaFR71B0TVAz)NO3ZZV&253sXmO25^V*X0`4Cj5Jn2?SZBlT98prk ziA+_dF|koox%q#-viuOfj$Rl*?LcDwnHWP{779s_Ok!)9l@Zu<9_;vAevJrT>`OAy<7t7ZusxUn;@Yk-tR&R-xrG+8}yJk>*=UJ$Z-1f@WRnJKE8x`bk&G7grIHHL?| z43?w-ga<3djMquIb0jo)-$t5Z#8?6((_R^I z6*?0T#mc^8CJ^qP`NhWxJ=D38%+w^!YR8usYI03OCu<}wVKt;-+aqWk*!=9JXY@=1 zt)9f+SIbW>79F3438^i}sIzCT8VRY-1=YD5^A8zglO74Fu?5vx!Wt}*%vP!9>Vyi6 z%0(G-=|AvnAsc8E_d!;OGM`h@;I^C6DU+xVZyX2H_?N+D)Zbq)@*) zyepdbSKeUif}^%1M11%dMFWWgR3QnBymA%5Mu{Pb*yd?u4CgXp#E7(Et{e@TgySr_ ziCdR;3zQ}r{rW)MD5P1;Gjd17$k&EBHoear!7nnea!wqBN08sPWVV0L>XCv&iHKOj zIVQLeXs)%R=2nZo<*Vq#$K>oq{iFxdkq*E<3Zoffnd7v8o0! zZUKOg1Rxs_mwqFHt;#gc=Qt(i4#I8|@6AW?$&vm*k^T^__y(ZpT$3vC9C4$HG>F8~ zY8pkK$~TA*_WEq)YU3}GT@Zcfgl!z+-l&p~ut3>w!?0#GZP_f?EH-0pE`M3tcao-!TMsXio zcWZ&7p@6d!(@aMRz#j_zZmw?tqUm`91ucoHi zna?%BZ#ifeq-yF-F~zr)Y)??}=?Ym&PQw+(k6e-Wx%qhB;7^FgCfG+8911o7grlng zpVdeMscYJ5hB5!v3k@Bhw)Pm&VB5~+wn^km{_C!}xwr0zXwcl;;2A$8>OB`0efkE^ z)3qC|ZNhNMY-ki1P}TI@vvP1&FhM66i*~6L{`6xIgS?HN2?A@<^>#;c!KHctjRqyeAoNM$Vkwv{Qh2Sc|* zdx-3L*C~1L);&;**6l2#l_Mt^s~Z!2#tjhHkzyd8H6rxYa1hm!_8Bk<)%FYpF$2QO z!+(pY!!b*To`H~bH0AA5R_65rZ~l(vJh7NR{z^zOif(dK4{(pb!&_|ZUL>_p zw6}2pHf?=dS=JGJsC%Noe|6ZlLF#$&^}p=$01rrrJ*-S7ZdVKTj;VR!f5(vJZfshd zD2%M+u~N4nyZ+2`GM&zq*yn zY6_(0Sl967?m3_m9zV$GbtL6i7T61jYGm(HxX*(Y4>1c6!nE`V^*Iv|IKk^X$26S7 zWPQNCf&UlV4i0yA_f@B@eIn=!eCC3+>ToK7xA4Y_60U^r^K{letFXH7@FjAzk0k^0 z!wA0iqOm9aUycu3kNwiro|^m9Aq@uT8*5rxXxBwxxDF=+i}nd5+12uVx-qpo^p*$*^8j>YZf20(>6r%Y<%!p#oNwtKN=DUuEPBwOu4 z+%P+pz%Ys7RC^kU&TKPyzoMUt_Z(Mq+02H17xV4SV;mnfU^xr!rnF5H#(IF}=huD{I0=r1s}p&=J2k%R?Z8)`?z8Zs zjg?)4EW0(BO;nq4bX(_sbfm3)gj8Bd>nl_i@BAy>Q0p}eFMWic^P44rp{|Ig3YtUR zAQiVOV(qXQAhXU1|AFA)iGbCX{paPfP0897?nzolUF>;Hc?8NBI=Y836{fqO4i1;NNRVBOh)e&rvhh@)m z{n!tmyw`=O2bYGGuo&pzIkp?r`jXIA(K8}hfWOaveA}lpuD4R>q-?+75!db*%xt#| zyxEqc()nC>Zj7b#{sqRY+VjxiUxU+ml@3uQ!^1AgTvpealV3!1)6sHVlGaRN&;!0# zYg`kucjU{z200rL#n|JeSE*}y|8K_ra`S5JC`HEcwDxKvxCMsjuJ(tW%sl)QHSlQs zmLsQl%#(M6(CK&Pi592QXZ^J$2E0UTk30GD)bVy;izv%d!<4+4RnTx7#?yeN_jB+i zdZ%&dXpgU3u6a+D!NqfvYleCn?~eC?IjkdnwV8JvFOSGoZM0R-*4DM4?%iSqktL92 zx^|sr?kok(@e38nPnP_9z>((6wRPo-0+4Tq351Ag_h=I2Mg!XDe3(cot;5(!9T|eV=x}+_k)$m`-w<) z>(f0Ar-|O{AuDh9Td4a@Xx4fZnKMgQk!uhoKt#3i6Ad2Qv0}Z4q}@eQj5V%BN>ud~ zxt5Yka%$UC48wAHkQ!E8VD$ zOks`plE_CjT!jSJe$s~xOyhy4VuOw9#n}#R?<%_yhmvgmFNse_05z$@R;zwaC#Rj- zbtxm?de7-CZ7&jn1`D$;PL&Od=51x_9Z6OAghsbYM$NBn*b5N>=VaeAzQy(?=_X&u zV3o_$f9J3(9|xB_M+v^gTmh0C(TTqMIt-ML-hdr!r`4_Z&HqB#I|f%0{_VQ4ZQFJx z#srg0Y}>YNJ1d&lwr$%^CY;#DS^syRQ+uEH?AldlRdw~Ve%)2;TUW2X`o3;y;WX6C zgU2>I&1-k}!_yMVy-U*4JEFAIfE@weXWp3ev@%xmj_%buh|>WlZzLiEQ2hWia^K!2 zkm(5|YHpR3fT|YrNvHQcUip%xxD7Rg9a~sCfO}(rXU|kX3vp_|vF?elfR!C=y7%a( z*HpZefV*LI^n_8*Pj-M!`o^T?6_u`jiKZCwq`#CkmEV z>z!#CsKiyzF-VL$j`?pBly^ynf2DLjLr#%RG7I zTkM~ue%u^gwcx+ndcL_3y2oZb-^cTBo4R`+_3&wR(|)ZD=aTsTe%fsg>Fv?);XUBF z!G5xT!58v-8}+mQQ%-Vj)#PV);9rwlU^=a%jm@MlV0(^t`fT~pN6B5TPd$2l_DOK_ z{`gf#pap7wu!sCP+7oBh~!(e<4CBgFyH#T&(x-yK|V8$*rQkFk>DXd zrzt8{9)I|r+s3wDeE^bPeR3v zWrAGss%*(OMbL8jfpNiiEj)GxSyi@zjh9Xg{|dp=)VzE0@g$};T*+-{*sN^K70dG z$RLG4C77#=Y`dRGoW{Z+t#{RXw(by|+fWkG4;D8KH6iu-KN!h5OzXk4ogHw;MHvq? z<3tx-lLn-51`AyWd79kn{i5Fj<|n#P@Xip)IWRmHE8D7d4w5YYZsUW^n1UbOfz!ty z7Au3Qbuhr`YutaP;=DmpyDgXf5>GCe3=^m_Z*&&9)j=P+5YBMy|E#0CjT-c7jCf4x zEn{tK)0@o8FM7Rdedx{g}-uya_tZMXD#JN?~T8a|7T!A7U+pu zRpdY?1NMykAIZr7+B3rW-+M;>w}s*TKq~T|?Z1-|ZBG^S)n{ayC{#>LMl01VlvH2I zKwL`^AQ-7DQR9O`OCL&LVTP@tVX_cC-|1K_SkbLqsc2TiZ6>=4&{EWX_ARv9-l*7p z-a*KaGTPj=oVl$fK1s;%oc!$a7JA-sKY?6GI$uitwY5!&Bve#XNFfkSR9@hcqE*VJ zMB1MsSIbq_^r}S{G{HgZtRg|jkj_@7B2AZaUYLZT6-@W5oP`eTUcw?7Z9^*T<3K*Y zQXim*+;A#9VWMJuQ%_0V;XlYVw+Nmh=X%?>=7a@=pcMmfW4yk?w9IpYmb#n7!*Il)%( z$Q$eeqEHba{o2DjNn^UzIi3zBAwg%1w}1k0%jYR4je*wXUY{M~7+8o(9~oy7mqCY* zrSibbC}Atfm<~}W;oU`!NQ@H*c2_-V$g6pXIN!$) zsc={kLuX*XdvN65e~<@nVqPer54_{vfnt`aExa?R+yH^)ScwaiGcx&Tpn-F=Ryn7U zhSM&RX*HC2CMyY#xlQ99CBxJtm!JzsngFMZS~-`*Ne5vNP}mtN$vuZ3@a?|?Xn2FC z+Adhxj7@i~-Vx zf)YIvTEdZI2?@g`Vq}~3&dvU!CGa9L{3(fB{3oJBsG>M+zWH5{k)!>eu29`t5X^W< zls{%#-J-9@h7?3Ae5UOw$U@mjp7~OhY&Yt(oH#UhcPbM-4nZkW0bkF0p!Yqw1jq2O zT($*>xh(iJF%l%ENvB+Y3dmeIa+FR^9~?l}HW6fU%VqR&??K&IZ9tF!|IArU@qXw# zY4q$USVx$2eSQ5Qzk@edaxkvQ)q&G)K0~Fe#*0&918;D;lW?|zt7h54pu(|)Bc$Yz zPF^ZhnNUfYc)RKT43(oo#4|JiCBHA^)3jj1TCkk?WVaB)^SaL?JN}8wo_^e=Cg)7_ zhDw+u?JUS0a{TAaIP*RupW}pJ&?4?t-XhL8>NuT1&RK<42_R)DCTa$nd?ZP|AoU=O zx1m65tisWPJH1Ykbv%_2%s6R!#NgWl#`2Ig-G{iZgi`8XeYS?AB!Q^N2{{FmxT2+Z zt|Moyi&~h*>e;n|P|JzZ#C4;E?^!L|wes4vLhe~Dd8-wF+)R|LG+-&v5`a5uSuLW4 zJ(hmUr6s@D%ybAQnXfV+D^M6?hO>5m>4(}4?gm^5aEU<9 zt~@d&n(1KuN7nf~^QZG<`fqvmMFrjkdUe*QiNyksxpD-FltQ5s=Ux#=Oa&#Qj^1CK zrbfhLsv^WEh1NNH;AQJ55szwpGOMdoaJRFq=Z2m}$eOU;p6N4%o8E zidm1_W;n90@s|X>7r6K~Cs(R-V(k|*p9pqMfa8PfI*uu6XpSO)yY}X*S3$q-61th` zjCKzs_QYPB?(8GlfkespOW2um1wtzkVlZOOV-|W8s{l$^!GPe9tE9M<%t0tX;ru+M zSGL>(X4V^Vkb9#S{o|M(fg^`Uc(VbgKgE%O2%}!Z&ID;ZeUKnpvV>LAbgEtpEB@e- z-e5U3{*DJ>0=*n6Zas>uL}^6jb}HvELjZ_DGR>dNbJp4U8<+VT%{|R#fd@`9J}FHx zCAVrJry%3jd?VJP*^ZNBzuKmh9fspu8}e6lrc@=Jk3|IoYgTe-U&>eVR!KGYOBkGx}8CrIuTYY~>W zP&;m(=@dXdu1-=;zc3$6-&#Em^^y;Ig9zp_Xnko%STrFSD#%?Xc}e2WPXmHDaOB=2 zS}C!fAS%uT6LN{AQ>+M#4}t=Q+DTxz3K%P!jjJNu%u#F(s0wVgeVAnz@t0`ke~G}H z(B!Wv4%iS<>NuppT+q-wHNPd&37ekD@=``LQU8Dv(}ay{q5J`bzJ*S|15XkOnDjHUa@hs(Biy-xuW}OXO*M7C zl}SGvtg0eIaqtHZ7b2qRzFA9atTgVKoqmJu%MFq4pw#SrZp`ca%?^V%h*Go z#ohuY4(w=#qt8DX0}Qm^D@Son22LW{@nseJtvT}+^B-|+Q13??+PwtSFQ%Yp^J^Ex zKL9`bJggby>tc^Y2`16&*QrR$j*%LF_M4nv(6%&2-&~Y?JaLesr#q@>I5^j%VCKRp z-e$CG)3_pArhl&2;fGtAyVh^DK{Un6A zZlKd@)N4JydYVM&?|kR&slbomlqfcsXb{%(XacqfD9RX>SlOJpy+3*VsLT-V06}%i ziDqxbnr1#rOeb3sA(2d^o1WaiS=zldi$|UDF^f zXqoB*_ggTQ@;iotdoPYF&tjr4fE{U}P`Fw3O|$;NS?%9R^@4x@g8%RWbK6ZSGH?8_ zW0g`+wA$;3r%RAtZM8rvblMz73?3qrwqosC*rsZXBcibm_P`|+QfpS*D*(l=HPoTg z7~RetxT6l)^d;RQg4tIi581RAZ4d{Vo75`+eYU-YX0YuaivsuZ~H; zaB9WVI}!&9hokuI*}tfQh0Ui!dIcXd$jkxKPrX56pC)EO0obO^cwzQ)1)=x+0_7m# zPHwC@0$Fd^(hQjli9N_%u*IbFPUQDnqYHty%lzG@-p{M*)}l%OuPa99CdFb&eM?G8 zmzOl=-Mx41~ek$x#yrTK=uv9D`{c=LVLMt>hL z+C&?_;TVKab>hM9_>KWLr=8G63NIoQy=_RnaQBBCVx&!9rq*BQv2XI*wKrj_Nq59P z!G_<}E0ST91C^d0(U;eQPXo*1#I2IE6(yGn-cqe@+i!htPYN&T3Lc1899#6Lu~}Kx zmPqfE${q0*5Ve-fZ;)tJvlSn>l9OK)W#UaE`U_c)E2}Zi$akjhdX`(ocUeEwoT|_K zyfoAs=vqpaAE0h+ZWfNrDb{yPnm3Ccp-+bEZC_bFcqUltE0T?m)*mTTq>PZsTv%nb%Y*F~cZj;5p&C*Gp#)&~`% z4Lzh6R|VQab{W{iPeKR1JmWZ%IlH?NUe}5c?-RZL(y~4rSu1^oX_DJGPfSNGCD-Px zHe>sp&frPp>t3mY5|A;M<2!dH$DA5C{WU7kF1m@RGj^Qm=%XfL4fxLRE3yO_vwGVs z503kf7H+i@uDbfQ8Z8ep#I1?Ql%u>TidSh2xs!O&bvf%X|E@sBK#rqcm?!IU)B}?9RO;t2ta%dqCI2t-JL}c-^P_ zXZ_n;?fgX$=`>c%Hs(-A98;=l4}!UX-n+my2T3~NIesHW9dDd?j?m0>Lk$WP&D$sS z?6cF%{m&#cenPN@$%=eNH@lVu{7j$b%kJUf!$on~_Z~H@N$*;H0-Y$d&q{i+#vl(rxqJI9& z5nuatp&#RH9eamp@c)DyollGnu@)|M(h!q@Vdw0(YCdL8%;a7wwvn?!GpF@XsMxn2 zwq$2n!(Qa?1sS#Mi0IsoWf?$|@BEYUOIOolek^yV_Sofp*i;NHd$KFla!4mLS2A<& zH&3HeMg~7RU+5OtsJ;Ijt;L-oJI_v|aauL~l!>y_M|;k}lu5UK5dN4yTe3b^l!8p) zJs&VKd{YaB)o>kP)=KZkp1l2>EqAswF6x)xDfH%05!&^y(EiIwnDy1_XpWD4Lwih; z(vdJ&K4biAiFTTxEv`)w*q#I1k`U309PAU9AB^_!{``TObS!rVt*nmj4aT$HD=U1C z!+72W3EKH>3`I$Sn5_ftQuKy~?*rR17+qYO*^Bt0pzXuPU?UWGqwU7u{?B$Voac;L zXbbfF@{X8vz`tvE@L970@`Tm*g7V3BPcrIeRVFIt^*{>esdqNIZ=yAj zm~uc}jr!x`VuU8Fc(Ld`tfMS)G)+Lg#ntc$@Fbzs1S=t;es@IoIhJv)z39dAAi)mrEIl zJ?n)Iz>(O?i{)p5nGTtn=lRLm=!aN>wUB<(r37bv>(1ZhWL+fmU1%ljH``um26{fX zz)Zi0wjC$+GRCLYwl7EF3hZ{rMRE$`&@)#`YmxDkr5qF<4|yS8FyPGoT40ryaoT(Y+C*e zIf;E(%fcK&loD>4J&}yath#!K{?L>1Ve#I$%`m2q@V@tdueggx7aQLOmwfz`NAV;a zALRkQbs9uoqgSa-Obw+jO>G5M0kdmgS-7pQhB~Q_B+qGANl|0|hwtCHe?+>^ciT1k zEZkoU3BdLFh-BoSzuV~FGL;l+wLcUO{wa#P^y+w&KS*Gb66drxd+9*3dM-9KmA1yA zYSI@j%9|6AM;*9d-)efNztE)v+@)zL5%in*2oiPV#2T{Ph}v6n$3Da^TlNhg>zDa& zXH_A7iyfWtm;30*g)}R*bX?ZQ{X%Hls^!?HNz7JjtMUHw`};2?2)@!imkhvQ$?@V9 z4fi*DUGZ~7HJ8fFhtK(aM^jZy!dv-xDx)+qN4?ZN)As>o=G_(J`pN_TQTa6MjCwIl zA}ghC=ikS2&17;f168S*Q1|SIf{Y-`l+8_DaOl>X??IrScGP%Zu)GN_(eU94=7o62 zgs7Pu@0wmU4Hc$!LI zrE+fgxdywZ!S$BrwkX3P->T9(f9U7iu)0#unmN_|CdKZ%NpeQjg}UB~WoyRAyzxL_ z$LiE!P@m)YKJ~Q8%2=P9-sV)%X!H^F=s?GW|5Iv#V5%A0;Yjp_Zq~HP;BGKc$Rxwz zy=0EO^oHh`rr_vCFG-#su6a|m7H0B6Qk*{V=A0-}ii*zHXJiJ2*$AT8<0A1!08x)^ z+Z!$arOt&mkZJ9z2xWgaFHM_%EiVKFTcaUYrPOuU_wWdueB>4bzDm>LXia`UKD;++ zPv{rFx7bzxLse7O;%S()&shpyxbHm6?qa>Q{1>|!x{3y#1E3P$u>rm%$ZY$RMzQxi z$}NqZs{7~I^+K)OeY&v3d5{?(Sex0{WxEbKpmO>H=+=~&UomyZ3|=1G^# z<0zvq<}E$~knHX*|8YvH_~Yp%oyXQ1##egvd^PGC<+@tq^R#*QXMz71kC5vo^-zM| zi65cQ4O9L%ux$FZ-#rrX>a|~reij{EtINmrV3l4>%IF`7n;K>P(bq;4DmOIR)qbbD z-((LB;u9RNf}~T@i-v%Cg6BLVncWV1Wb`-d-`8{KNSqg0Y{ac zu7rig_iA)r7lVF(PVt{k8G+a5TjtoSCeyc2iHj;Z!CYqU)nQi`U8!Tu2keC$U{6!W zOf@*5zPVw`k;w0K`SC-0PDrathgQIUhwy)1*=2sn$z+EU z#XP}tp3Ub%WTnz*U+hjKYTJ2WR-tMFP~TDz#cEWPL*MVHmqK45z)z@T=&0CGNa$#Y zWPkYST*6*@LPXhI`e}MX!d%^0#N1e!LD5k~K7HXlZVT}W8sQDRSJZ5=6!diVrx1!a z$E~KbJ${j+?H6C>z0PWv92#u`=`Uhqa*c8` zLE>w{k@5Z)9L{(Q3q&$LE5@ao=H~qc7yUygd~TUMh*-ABRDM}4koarEnsqqdEVq@p zwk^{4&S+DlQ$w}1Em59s?pTwg+L6{)+QW2SKVXkh7XEguV_B;OBx@j{GS(#8)^~wt z4>`eDI1<`KH}`2WgnMAE_OxgSipUx9gfzCzG%rp9hZb1ZjBY-Ve33^eQ-A4Jxcabu z1P-rGJsJEpVq1o)(j4=L_IA$r1JJ3>5n}Gv%?6=YqVGJHpmp~p8qnS6fB4o*O0C~w zcNe@?i_cnwi$59p`d{eG36RcQB>?yY|9|9GfS|_6)x_M?MW3BT-`UjEMF0P3f&YKy zR`!7v9{<_?JDmaMR)BQI4<-T!m0L#zX_tOxzss=8Cvaj=_??yna8Tlez}1-sP7hW0VEEb&a~7U>E?(6E2dggB+P4OH@P3b#`t zttdB#QNW?`QNWcdTtyCurO~R`ChOEJTBJC2Yn2v@6N84L(O}+Mwd;v_LPVDHdP!51 zoK=uiO^6TwLM-N2wTmQtP!V9LPPmhHp)ESgQn3&P7g$!1;cUo2k>E6=@@PcQ%!8H9 zfSrC&ab{f>%_%W5 z;vxD^shae61^a0uqF1I>_9>ZV2;Q>R3xjO(zau)@>LgQoTKyHJ@-xRdVcQ@$S|QdW zX3GGHNrZn~#^5SnXot{VB*8w`i{&j;Qo0;0+E6B%(%ootDtuiZPaXs=fvm;~#?V`9 zihqN#OBSUY4J$LlAkG+#^O8@?h1*r4+LkMumC9jSBFggQ%jH5<%vqPk7jU9e#@U@s zg-NHOq|2pCCyUB!G`#AHdSrzbR}r5>e`Ok7TK>ZcmN4cEgOde#>o;KXCY;e|GF*s zIy&+trDa6ef>(wft*Y>>3FPBQw=ae4g%?j54u6mV$Kt6;R4$(q%Gc z4vQ2HZ%ip6sD=xhVk3}0Bn3wK-@`}Bw}EKs%n)l$pEfTc>%t|g;Has2cZL{8`GvPi zt{rO7*a6+KC2#WiP!Z*UYr}P*z$Z!x;|UV%J%@EZgtn|=WP0BQBdF`4%2I8tJPKq% z)p)KYN`+yCm`jp(4;TCrvNf1tSyLe7vL&fbu>}y18pUL|#SLFJ*9JAo(t#26J>OqYW9ScS0k%}*p+0BYWVcNL|P07<%LrV zyeo2&=n}!v?(e}(7zFTUq~q%Gwo7=b{E2S^ z8ueNN9~;ns4{Cvjj13l4My9!rl(TTTv!b_bqSH5`wizt(7JC_UH;~@H*CUiP+HnjD z*i8m`e?<~P>Mh5OaOb`|`Ep{@b*Iaq|uSDbFeT{s50 zD9Zs>-1OERTdHt09X#HZvNY{Ig|s+wZna<;D~a#N(+Nq zDH1ya(SH3)V-7D}NgOM~my#GEwsNnQ27llxbFiv9(!>c1jXc#NLSrdh`J&Mo!sLY} zVBMLZ@kGAxOmTu z#M*Sji0KSa$kRxYD{NhFj^@{Z^S~w6nJmA3g!_=QoQGG;rCV!+@Yb0v;;rzUFB0l? z1}crtRA%5ihEatX<4F9keAMU+)_7}z?5sS!q5!}Q`-?gXkq*4!u}Z0&LHw&Y=1 z^&LZAS%Arz^8aRe#aRmT{ZpPnKo~>37Q%SOJUNk6oac>t6HvI%d$#8qal^~-1m%iK z(gJ@o6!7Q}VR2MP7Mc$eZLNTexDS+FdE`?a&gThs9sI%La1ar9{8^<#{t`=1g0YZS z-c&$An?Oy(awYHVuWCHY(&nCwgj)9V>Cl*l4!-c3t;vd_z2`W z#Zrd(01i=K3bkb@V1!%Kf6rlU8}W+pxlFQb5N)$jxbFQri*R``nXt8P01dPN@d*YI z@u<$O3QplEn=^-UZ9zB-f_4=Tc6iMuUo2DPN+}@2h8T21uURNcKw(7u{g+`5@jq;) z*tqHj$YzWVsJd*k{F$Za=dDsK#7I&cQ&=u&_FLl6`gx??|HEcDJ9H=$O8y~eL7p1Q zJ8(oap%Iw=cHp3DqgLy}Rw969?}OVQtK!yAN9k4x3lLPZWNlf1VTc`Ccyqc!iw3@d z!9D7HwGi)|27&MXZ?=6BWJVXt>547FIZX_>a=uC$YX8l4!bI$Nl>}zbj^Y_-kM?8z zAOOAiY~-|&6G{srj3p$3m9)Gi1mOq>0u?xVFx73k$k|!Gzz>xs8>lJ_|*w0&qzv`YUR|sOPP{{3-c5AdV z?d;K$V|Osr<^b37v_Qs@-hwA%`d@6u6c0ldic*0c)YzZ`Dj^UM4iB9W31l-I;75`v zIT_r_Sw+zk?0JEo%EDMJ)hlc(n|@UUY72D*puGIN`0*us?x-L)S!NR$!sNmjIk<*x znAnWCKkQ!)R04Or=KCQxJ_3o>B_rakQr0cbGA)RFub4Z8X81 z5;~GrENrLr=u^!KS(WghGLTL!sdNK%e#}H1{I&)$PSjY4C>k_{U8@2|+Bc8M@)5+hl)+US5du zd&A}E4w-_H$HAf7F&!5e(P3jQ+7uY^Vq>zmmAxcIN7c5KrH_jbGceJaIV;|BVq@+) zDBdzIOn1mn_5aR_-k57i2;>Sz`18tsp)8P#s#o!iG;MJI{G1N(Y-#%Fo4+G38K%-V z9t;b9RU2fg~}qS9k>?}4DJdoq)m8Ab0A#Tk3p(lQQ@Cx1e#V&kEfu0l}d1Z)wlg>3$0+M;W7@E!`?G-le$aoATI58YH|+NuD~MnX58 zi{hY}LHghii#2{)VtGU&junrqdtBC57nzhdr{ml}KRx()vfG8poR35P!)N;SE!HY? z*d^XNMH^Upgpm6x5CFA|bRiM!9+M4y-tP_2eSn8WftfFfS#w3zTEm1( zUPaS_LxX|_2>+9YIb>=Ahl2-g4#g=8Ch~yM%oA0B?r*~YOT;W-hEo^ryrJ=6&fpchxKIkpY=CcBV@?S*f+6lFh+OYO6JOrT z%LiRPk0b}u{&m>+_fWV|_~uPAZ5MrS*c8G$Tz9H{Eqlg2M8v%f>me zrPuSH=6p*etL=2DUv$-#`cD5vgRq~_mCv8{f^74n5q!|&b>FaDy@m2tp4-lhY@g-% zqqob5{`3Do#i#Cdg1gYZ*}fMn|_GvhtA-2tuJz@ zX6~f-^=6_Ds)e1a>7$L*IRu-10m`9VEX(_BDt@HV)by3Jo->^o2A9v|L@mCrUA2$xza!Kg-ys(GuO_{-$*`WO zPdKq8wzxtw9+PXnncw92ag|SmRp%QNoEvYG`h!Q@x!vLAU)_B4id=gn&;G?wM&qTc zOH`lh_;Jo5NWAO7zZKQudba$_cHFhV2{^sZ<>wpVnMIO~Kzw!RTlWQO>iNpab!E5! zF*k{7*vZ4wX3+lxITQ2IKd<0cWGGLlmn_}rX2g)_t?S{*J1PKh2`LL*&%bU$`Jo;kKv)9dnyrKF(zF4F?*rO0NqoP{Sp)mQ0)!>}A0UA@ zSx|qrfwdOcB#D zqwWhdG&KJC6QnA0h(cybU9nZr#>TXK>%#pkx;ah?_eo3_+h)2ea~DF_hJWB5jBTDB z4@@0Up2!|S8*NXp6Ml5Un8@w(RWUs zo-^j&C|J$@jK(xiGo$ZLp7`ER))b5BoSWtZM(K))wSN~q6hB8qWu&q`o-tG4W$j>L z>a~es5WH25ce-N3u5Zhk8b9zw=JXDYlj-Y_RRAifCM*0%x6oU9AU&xgiIYkMYq;+i z_Pgt^t>4ZEhZ5hClN*HuBq8K8J#(LSC3GUQiE`Ty85~<~D|w)E3CY(yDD?i-40pxx zN-S-x8N3$pP{VewlC*kn`ca*Q%+GUD~&w==&p{=N$Sa zrtA1VD%y;W4($bg~Mcde!V@@zjG_L+He;`1{HMHi2jj{)w7 zB*NwQb|u(9awGGRhiXZ#T=JY87yKgXSrq;0+f@FqgA~`V7P{O3^1)g(YxkB(Uz(0z zKHG2eBX9cbp1ylYOf{SeLZP2&Z;@7iUS=fKWuEvo=A0g0jo5T~&6{RR>KTWpkHLPfJQ8};q=!5{7uSbUIoM{nQrlJDB}4aI`n7{}wZe9tl1PB*w(FjTx}vL`5||MmYcjaK9$ncbbNFDt zN%?=lgMCke>~rMFLPMFS}1n{ znfYtBZu11JD|)2iDspSy*UWE%<+NZf6~OV%4}LHAPVQp*Pr!=*0er-LwP~{);3*Hlb*FJ<_&EL8~a@|$JZ|I;$8Szz(reYZPRW< z#lCfUX|e3*>a#6XmFbGl!bjz~&=wOT&V%2|V_f`i?kWwv`r_g1p}+ZIg+YSx5tc)p z>%qP4YIupS$BMPE$dT5;yb(y9+mtEh8_b(@Z6ZZd>iix6*Z7y0v65rbdge&8^D~|0 z=XUXwBby1Bm|mGK_$Xb^qh-KC%4_BCG%)~bKDBkUmuIePIBh?W9a)!m^sJv&H*JyczyA1L$&44tppFqi>_LFpF~We zW2Gmh$Uje(2mHn4u!g?O4-Kk#t0Am1Bm|JYlVQrW5>fE)zjbcc{6v5L93&?rFX)sE zmlTr}8wf9}OE^7c*&-k$#6&~G!o)Q@k^3EICh*!4en1Q|vew6Gd-TU?TlAv-C0w~hBeh4)D0IX47MTDBpP@0g zQ3EOJRzA(9pWwYudd(7Rk3r+5_vE5r(E#iS8#1~D`k)tX!>lqv zn7W@}Am)3+s4_h-8TiBNQToQ4bY@o<6zM;I-JhFjR|VAoxr7);uSCnR710F+;HU8Y zxq)_7`-~CK5PR=a^YGpIF>w0h{#=Kz`_HgbgxS}Q(WrU^5O?%X)f(A?+ti?I*i73X zk1$gu+xFpwe!186G9@dcZEtthE%@qoY9(r|>8?hmpPwrn!=2=RUsGzVWO;Qc=oas1!W5gRuf{r^u9$NyHiRR%2K_|Nv= zz{Qcarw6*~s=P8BWlDDgyeX;{&Gtg7?T9G6mh^8pG!eoDs!NI@sjKWF$R;=|OC>lm z5@tIXva*OVGNmm%4EhN&zA-IflSH!0i<@n4^P~)^glFF=@^2sC922uEC%@-EyZ?a~ zfm>C3P;lZFWdqmoN-pY$n03WaBLg^$5`4*h8dVZA228B%w=z9H^-b#7&whll<2;lF zKCou+vYSUNpI>y~Y(hAjd0`K)L4V6CN?@&3*9F8H61Wv+D6`HbT(e|8#%vqxS0&I& zDS(Qb6t$pgp@mDFMIn$x>P2Ks53! zza@#WO~5pSmW^z3nq#0ZBj9ka_Yj5WvnU6xHbF-dw)PWkW7$XL&qox>uO@6-40wWk zRnvPms@yR+Hj1+O^-gB7_oInLHD_MQgezvj5k2gS6r&vR=K^kYVR}99 z1y^`Bc)S^;rS`r=A*2BNE3Y@`q(jw=8Avwd?;~7la&Xs3I%rh9In_S7Ad^y9+5iZ%e+UP))`d#AZAm zvAk_|D9=bjF)S8@;}yFmmKeC^GU27hJa?YcLeTk*6|tfk1JQD%GhBQQma1jmKycs4 z2OL{xO`%=v-*11LY$Kisluuc*j*NvrU~Jj9v@JrDB4JGmv^M)|qiH!eG@;;W{tA=f z0*WpVg=EZ`mSz8@gAqDW2>hY~SN6c;2Nx$)l5)>%x%cffP52y}``Q3_(3r2TBgPPU zmXKJ0{39&$9V%51!4s2E=rS{_l=J^Kl@tK(HWNzhVN!hw}eNv}HpP-R0@R z;~@<`QrER=rMdGn*X%&Du4^{d)Uy|VUlmOkzw}3nPHZ7@J=Sq+_S=Ey%(x6 zJ>-K;0cG5*F5}`C*1AG1C=_5V=EzoQ?9Pma3~;pQ6bQUBgL?b3-~4>R7&^aJ%w7|L zjDcBJfuj1=#09T~eXpfG5vU4o6SN{chZm*g9JJylVjZ668ky%>p7$vBU!tw##^-x2 zA$=&ly=E@M>Pe10@r)H_SS|T|aLLluLfvtjqaRK2eMU$!l-@ow`=%qCfb2`DtFrbE{61F z1Hc%GAAPzdbgg9{LltcA$f~BFZlk6juJalkb##$&ca$qSZWiun@NnO{V_XjXq2EjZ z#vHp+$1cwP7`32+jj$k-*ed@#v;#Z#&xe(KnOI`g;LuW=50I~51fgDpE?UwrMk9rMdpHu@%ws_gzNv_STC;Cph zvZhDvTGYUMNbCT<5!d?aP!)>b!Leg`6T^XM*@Izlq~MMiE{vH&7@4l(2dTpH@2=J!8iJ+hFDS`XBDxNQ- z(Y?_432M8kf!CA-_7vWX#~NiUHy$_O^yaLvOLq)kG=`4JgdatqLiY@IH6Yp?29@E% zwkDsvDz2$}K5;>JX{bQS){|4+PjEN5sVlVfZAP|ENdvdqz6-&?^-r+IUbrbDlym_a z7H^t&82PPzHir=ECq9`AMZ~h?qj+O5$Wd~jWQ)i-5dG_zLBdBgdNgPKaNKxq7iD9d z?WS`mue1w_6Q4oc4ZwYBNgPZS%s!SU&G#dEQ4EDi<9iQ&#ktGz{Bo}g>SG-LSroFZ z3^a{@qI4<4dWhV3L-Hl+%*@$&)ss@ei*C4>rI{R3_)>1+uia}J1XfPBV5wrdR6K@N&rNi?f)zR7Sf;T$%X^pu!k-o^Z6X$xV$EPod@{oF+wz_N+A zn>2)8*g~D9Z0eVL0kA+554RHC8JK^Fy#(vtFy^C3>}d~>T}0W#Cf4EiQ0fd^y#VH5 zz4x?p=0|TB<(1?Ui}W-`S$ejfsPJ(jq4T(5aKu+OHu=UBg5ZIQE#lcc3$`+5BC&~U zwCweWM$+JD9h~3|-6CmY6jCgt)S+P(EY81-mBId1zue4msGZTrTNu05kFZAfD}7F!Ne416eTRC66~I zX)iDrkfT7jg44_2c>0*#ocUOX!X0+to?-UU=@~h+8f_vc16PV;&s;hX%v0O1VUMv- zwp7M`VBv(5mxzo5tmum>RnJ5x&ahuG2Ucvr?$3OmhwF6OPY-^fTi+T5n? zdJRlPsxS&lYuo}K7je!VGUPPtU=m}@P?#X61Y$ut3JZN4UstN4&%XO*56u2+F7C3B zLXr~-ADZyg@iZ^Bc@?XsI#=ZBcQ@*)3M5p1RUoB??q#8e?T6kZ3-IsizCwa#e)hp4 zCWH9FABr9Zq#vl!#rQXpS~Tr@)sjlfFxs;eT^5Ft88RPtp6&WQByXm;SFGbt$Y_(d zH{$jTy%h0Bv=o3+w8rk3e69Csxqw;^^Ip1KT{$y(@<#e0nLP@{eX{9PBVcr-us|9M zlYOWxomOJl8mQMoEBx!qN@oVtYe~jLwHv8)kxC5nS7o{9z94IE_-uu>LZa^=$WYv8 z`TP&PmOuI|P_LyO0%$#I>LOpbW6Bo?)J7UEI|Q!QU5_JnCg8ZCY97DiObCfk4-D_D z6OWlQlQyO$trJHz`G{G(rgJc(TfT}FnYPCOT6II1EAoFa_Kq>SMBln^+r8S> z>eaTb)wXTh{kCn}wr$(Ct<|>Or~l_>=j?lSa&s#+t7hh?N`0wRQsWup_ZZa)GLE{( zqjVF)`254dx#C(*;O{0a=piiVA;BsXiRa(MjII4j)`ei$3uNdgo0I!V&9o`lJoQG*sz!f~efGY?hw^y< zIjf{Usvj(5Q8b$}L3c zPNwEiTW72z?2t$3PKUaU!KcwrhzZyC{iCp?_fzMzN1}+}MuP7o$vaexPpXgr8kv&d zN6^?`%?%Olx(m8LajaOn;AjbPa)sfS4jSn!9r?HC?63DBLN61(_rwL=qFDIah-qUw zXvakb+rsyAA`8~+!n8RavU^vAYk@Ur)kYn-jK{P|8M5o}UxH2o1L_R#N;Dfq9q0)K z9%6Qjs{CwENi--3LIkx%l$}XszIWA9=3@}E4d-`<-MNbc;OyL$fazy5d44?u{o1}Y zWP{)ScPX{8$K$8{0Sm1}ep?*52Cv}0s!UnesYl}0qG$jJA?E>yjtP*+{R0YOQThNZ z5gHJc2u9xjOE3vUj>9Ta-+w%B9Ckj?HxMbdSdE+4w1FR#_gVN~!805$|Zg!@GXxq>#$AG^zx{ zi1%BZz@^}lhL5|sq^D!(4NF#74)mmyRF(#>P2=**O|OJWpRT06#iyvVwv2V@b1dVu z>reE9OA6`p8T9PEs=C)5-+BBI?exn*i#N*{ewUCI2!tH$w`N^lHwCVIi`oOUJv8`tJ<4aMH`Svl% zgh7R3H{MCab=T1|Jb}uG0EE}`>m$l^)D+@7m$I<4*3Jgkb?F*`}X8pP=f~NJl8{T z-fpzrK%J$N!>qBLK?7=Ij9Q(b6z%^_0bF(7P5sKOYAX!{UItLn%yh@v}au01%jiaks--(&bxhoep|wQ z{YG+#FSzyO*V^;Aph(-vLB+eAF?f<=L9r}>J}dj_iiek&E#7%y&+Dbh9QLw!h_}sI z=eG_xk7w4E#8DKl&a^22Bf6^aRMj+!59cu7=d!1Pwabkp ztFh_kX}gUUdn4yk$-3XvFk;!a;JEsdIle)`;GUFioW~SjBVg2_%PyA`avds ztIB3}x7^vHMT`JnK3eJn^)jaLQG8X0rkOlD_+5OlxZ?`-LZR@{mGjSOX0B7_rsOmr znFs7ciNYHZ;>wlTC@j_oA8M16ez$S*;iwxLYD}B`{Z!jkH%g9fExZQL;p+uYw=X`z zwDh{hSXU0uGv|(a|7JO|tT~p`!tpR^V4Pv2ER)4}t-;xewVboLQqm} z*yXTAV80py9)Kj_{vO3T8%&&-lPtL?DmG=B``s0dUCYJ&6*lrDzk&XI;q}l)vpBsK zL(sPV0=LBNt~#dvNn3v16b0Cx-jXRZ@tJeukGWJDs1sa{!EFk{&h(-3pmYD~eBCps zy~dDOyX(EI)(i1<>5J&Fb(rbz@t0LxI`&|DaT-|J_I9cuv!&QBzvmqhF0E;zc#BFm z%VOAH>E|mY(R;hO*dFQp%)%aWvaoZDc~0Ztuo%32(e0e^vMeWRS^ho@pwp3URn-}_ zTTwiZzNlR5s@&e_&Of>7{Itt?ZgbutyksKuAfKqdLm1iO7M#LnwdfmCbV7DhTo!^m zIyOyGJve{0^cwpRR>ydb?R+=L1T6LF`Veone3eeLq;0Pr7p@fqMZ9&gXW=&(egNv= z%TMrY-5jXx=9ZR+#fw0-8Uf&0xwUO4;Lu6@%72r~9_(v;q|g65Q`Y&it+uINiRoC`6aO*ver`j9F*HV;J! z+ucW}73YTXJPED>E}wQJT|E=BY*wXTt@O9UYxW$p?p_USfQHE=M-}kYM1{*j+aM0s znF+vr&dEExZl24$G@V@+hHg!6--9wJTNPk_N`uzz-+qfs``M=9Tajqi1JP%+&iacK z$<|Run)t*u`{}9E!_CxQ(u3uds#f3q>s!GFWKz`k=Nr*p_v6SH55#jyh^#_wQois{ zdnXH?E#`4EBh{Aj*`+dFuvI?qOJnWS{6t6D&`eTd@sUjtddQN<2J*S(bYivIgYtC* zi(-KZP0#B;t8#G#fc2_U5!YLj(=+dScLS;QVf^DnG(q9A(^}k;&A?dtQ}R{9JXW#YSrp_vp;C&@*p`XznoJkU z(#7_hO^Ib@0zsF;<`wnhUDoxr3bA%^G(}C!dcRgazO^f*L+{c21N*J1oBnI+)!xDqS5k_fnHr_~OkuZ~WVDVM~vt zFPC@7v+uL_x9`ZeCfJ&#YTSqK$M8$rOL@TEPK(dJ*QG9Zi7c1mK>&*@o_K}BSI8S6 zH{bm4`QLx4hW2>P`dD5I``B^Zxfu!!7ds=e$$P45oOl)%+^YBYTuw_Z&Dy`6SJPJC z*uOC9X}%^n1wb?)DF^&f1wL^k9(pjh&v$!je9EPKbWFJXdw%#+E8kupjqkgBh0ig_ zAJ_3!=;ohgKQ;<@u;C)G;Ju)+4QK)16b^9mSxkQmrmZriypc>J-zFW;eAx3nc6B{H zJ2@Fh7-%S@vr>~&{qYr0_V@P{6Od67el~?Aa|-A&=(5|xKwE$YKJP#zvBo~A&VSHL z#Q#d%e7_NTF{IoV#U^}pPF`aAIw5nq&E^k^{1_Wqvp^JRSf?>0+~fa*q%U#Mm%8eb zpHAHU#!`w11ukY15~HrRaZ!t>rlA@ zIwWyF6yI$%rl=I5KncTFkCb{AF11*~oMGR#t@OvS(q5bf^EO5#l|cp?TVHH=X})CY?z z9&T+}%PKkD5RYWm#~YoEd~rVXo7PeE+MF{EJ`&(Z&jo9bJ#``Zj0JN5*c-DctZgkO zOHH2cYL_mP5`q}}5D+6{38}l{1E@A|xobpO+-D6LPt;-X8T@sMO^n1EigH)ssybm| zI!_$Y#d6uOc-9xi7GyRI#560*z}343kJ~SutqBlQ^esX)0a^6D^x2rCsq2eo`w`al zqX)ou#Ia)SBx&nQ7beXZ871PKLy>_-;9g=Q-H8xBqRqkHg>5}$13Vclw2-mYpwiNI zs^Y8@AhyEeN)XsW8ju<3s+Pvp!Lj%d?Tj4@R$VS^(T+Ij-38Ie-oGGK@-}G2#%r>D z;L^2e`#6!YPWTz;cWRMik&Ojj|AhYK(i$KehcHnOc8{fOE`o)>iGIIt<_;e=7028OKxM>u`0uR-Dys>lYe0wm4sRGp+3lf?NcoEfBEnDWiuATgAgyBn3; z0xTv>se&}_%)uWsg;1eL8nrBuG)##iCPE20L3sQbHI8v2BC)6J56NiempUGjV2&aP zwfw#vgYqOYl7k8nqL6>0SllSu>Ha-bHZz81rmzXrpa)&Hb6%Jz-@N)sA`DoCdc1Wq z+-5!`>mf@I7%Euhx=ncqM97gr`UU@_Jchvre5rykGR52BM3OPI9lwEe-F1KReD;D^%DO#o}1v_8K zu2%UjH`kV^JuMY5emH=4c+C>*A9f}cYUZ&JIMAg(D;XF7Yqn4zoD`!`Szw%!n%Q)} z9-ljgdx{E}4or_rSN$|XzL>t#g98sekuMMHT7H)VK0HhhSoqdB&>#jMwM*6LrW}cy zcrL6GtX9l00SsL+g9MnhW=yXPi5j42my+U-CQ_>a{HzPpTEk?wBiHMt2>eU~zS)9i zr%JKgUEup(4RYlH<9toE>h-s!wztc)DfGFRMWi;l8Cux&KG*tOY$HUZHrwRxPs5hU z5QpF4L`)~~x*J%Mpg>s@qSdI~acVzkQ23ud!Z>Sb2plZQN);IDbJ4%R@CYMeCL#D% z0!r4jz?vYX>*aDyQ5L;d)abu+4jj2EcMaiByn4w{A7nxQ?$CnfAbE;NZISo9NxPUy zz58b^I;#4f+F*IwfO(*HKPCHniQEMBucW}}zGLa?Qh4IYy}W7co#*ziu)*ktpmujF zzi?LGI0@~YbJ+*fhpqp(3I8Q9mWu~NtYQ^+-&Axs8mH~hoDVSc24q+Ab0OOXx zs>qO+k*`gh$zOSZ2=LkY``a-B1u8PKTZ5Tya@e$srdO#rel>EDK;vP>S>Ax2Iwxmh zChdYAi*3d1YuV%fvi=7r9DqSaW#`S>st1N4ZYa2H#(&VB2jra&PZ_m)%xEW`-wf;y zoHDknIvBNYQiR5`_X$&jq2zWlC$csv7EllVwci&OH2tg^H+@?!7Schg;x?$uZ4?!B zMCO`;C%H|S3l|k_(o+OZ6lVyH24e=Q*gU<1McAq#--IQM+`WjPSUrCW-Wna~RRH#Z z^!p;i=!xCxBX~EtbZYmrFDEg6132-6B)|u!4==s#n-wK?_xbD5iYd*orf(-OR zjP1*QtniyC@B8B0HfI1nFW9+0;QPh~+oTq}`ga_J=Kh~KP^+K_A>>*XT4d#3)>g3~ zzcB={yjmKYaG`Ruzs(4Zk5u)(mvXQf&VE36FD8NlqN;u+s&7`FaUv77JZCYHUaf`SnAghr$K;N~g~3!{AgS0IU`_?14FsZ9AgSP@sBojG zfS{=SK~X_QQ6V6$?3Pmdlw}ZR{rSY{jPtBU1pE*lNK|OW*bRy52dC~r;_VSaYsdIWppjfkWo*OtegGxfKH?bU} zG&FJ(n;6j;M9EVX?$nBK1?TttrQWZ#X&$o74E@t2J-v|L{a5N3WdQ?AoerrUJ@zaG zOTC7^US}z`DK@StG_EN%t|>6?5u3=8g2)n!$dZG|5}e4Agx03?RnE`I#(}M1<|zku zuZMiL1|@fA7UYu@^sP$o8+hNkeplJ3@46PeX9E2r+obQh0or~$vZ4dadP$v1EeY~6kf>&;#IQdH}OiOqN=B$ozmiv z#^N31g@M5bsEEbuP`jA;GefD?kuzKw4v=#lYMRM4P&mRPNWn5~{QT5AY8^7@tY{V%Y7x2lXa z70;tdpL;Az4{TMZ*Mkq73|)PgF9Gy+2}(N#a35ostyTKFbi=(pSfDPLpgv3xSBwz; zj+CH%oJqi0GRC>dGRD8bFxU=zWDKg^ElR5t6$SB>CPxfrb|ekE0@AUW`S}$Dweg}K zdF4fM+5a@9si?}=zfy{yQ(Itv@8^&ysT6rug;WJUT?Q)P+ql( zE?LWA1G6aYNo|#Pe_mWP?Ncf&x~9_pyJ%74m~XLpPM@TSWb{W#G%^xzMi;O3ii$hX zgLFEK_9GH4B@!(sa`{8#LQ3S)N3>E913x?438uG#T`)3Wb9V9rzKBr}z0Ql?PKf-1 z!s<$qX>;no78TotQCZc*({+1`zi?dtM9|e^W+l`?(ThA|Q7H+lXWu8vei553lTR)k zkWUsWe?M6yKd(?MsLY|IAAdVvB*)2BEU?OziK^%4WEqF}&q9CxWN&_?;ZKj0usWlT zjG6D-9zEW6yyM{#uoNK>Wtbg%nrr#ESl$%Bnjev)+R;D>j}NW$G!S|rk$P-&zc4ma zfqmR|5!+3VTmjuNajHtdx)6J?|Ku3Gb{=|9bs!e!x7V^-(z+Cg2YOm@oL*KG*@m1O zSolY-pjfsqZdAn{IdpVM^dF;_(j9pU&qyFS80qo^=)p0eeIsOie7;@>e&C)!en|Gm zG6>I1AaUflz-TuA?|(#BP-F>uSpov}Nl)U+j=M~xqXo1D*ZF*R*)%tE!j6w6aEeRS z@=F~Mn=GQLJzXq@4P2|#0{155oV&u}jc=`(N4r-(1y?pM(Qq^;l$=aeHf9fgEc_Lm zU~#fXUOO28exC1|>nT9-m_JHSl#KM%(~eFim5*}<^#(Y1$9x}Ob9KPS!OW*A8+++o zK<83Sn6z{?0XqxxVQR8s$jhwl*K|ojTW)TXPr$zG0}i2f+U%BSSM(iT0&H`8Fx%e8 z$~fKgc)njp|MAe9%@Rk7)1xD9%hUSuBUO&g9#@O|HwM}fD(%5^cEbn}~ki}6Ad*6%>5a$Jz32l7!MzZ z%%iG$=N)?s+4Qr(PUWzz6~VBwbH-}Z!Y`-H_pe|Plj`yl%Tk@|434U$tMS@};Is3) zi?q9=SJ)W4OzY_=Hxy*{80%-D>ljd__q2wyt*uvae_0pT!${Ja!o{0oCg&WcLK%-8 zYdib9@CSBXA#s_3>lVic`v}`lWpl|r-hWTJX+Y=Q(_C;y_EwU*vSZ(yo_kTcV-@DpuEd(!s^CseHkkUeFl6t_SB8yki* zG6Dx%n1(obSO1QPKhgPKic`+z+mFvWXSg>Xz?Jn&Al+JBo^^X_n_5C7wK@n1@2wHo zPFLPaWPZv%idnZ*rg*i6FV{TX+zS%WHcxi{ZM~7Wx4LYCzHvMy0x-W3FrKvoa$0`g z)LQ~%)Gxi4f>~Rw0AJyZNen=wDZDE_nR5%bAKt+X-&8?gz!WGnp;DG;MW1&5BxH>> zT^Lq7vEy~qr7M0gEI9860d3Ux1h)Xb75;76*jJqZG{Q=M2VdkP8CZ2r^ji3fuKaQT zN60Q;U9rzajZNvL6xkl(i){Jn_Zi%UPkP-%d&q9j49~Lc%{O3eXA0l{yL;&a01eG0 z{?T;uaGCxS-T7wv{v?R}?&H@-I3x)2;z@?sx(=zT^FI1%McZ+9UY>WT-YCDiGU=jf z4LB#;Tt!x#w#IZRr{pcltVlWPSRilD#7SRxzhyY++$<`Ouh9KPbsTPptnh^_uk-!l zA}M3c0XfogmDaI1;Pm-%RD@69&hZ%=8#xftrM0SDc6{D4-m2e$XKYU3wsh-Jow%Gy zpx*bH$jW5eJvWiIAtOQQ-R#emmdO6Pu84ni&Q_8rRrO$3a)hBfb9VVJ9reNr+XLt_ zAVmkeCF0pZk;_UJDusI!mayrXH|26qMARw$-7Ga_BZ~~_rn+(Ng@&Rtq~oOzxrAm!(vi<neZ=(1U*E=b%d5)V4pKl^lr2 z=%-QWSFqM#sA#uzUbVi%G&-L-IA@ake_e*arqjIEu9SAQRUFcWdNR6c#>hfm&@4hu zZrmfWCLd*LIjliUE3bLEc_Bp7EOc=cdBRjU)JSb! zhfu?(l%$kx0k!EVtDz`?^*!6%cQW7VrN7*NGQ6ef^&WU~lZ?Y!_S&JwXx!;#?-?vu z!T<0LZoUYHQ)esH#Th9yat>YDcpc)k6uJ6tyeHtg_&BzVQ(esVq4EBuqD{$j_WGEU zQ28BTtEMD3OYs)WQ~|?tT@idMDU_p)zIEi5GxCYR$}9FTb!`ap6R_Vo|JM12M)rA_ zZR(jxZvc(1q3RN?{VcoaliUfL2jm*RYW84DH@V{9DNa9lIDXDJY_909hE;!GxQ1xC z5Nfk6`hNeedC)klrpB1q=~^q@NzL1#`oiHa_gf( z%PE|LRuv7>Fx&N?rAwRFwFU*lDojXN+P4PL602$JCnm$R%+5kpL@e*y!@`Hww)a!e zJo4evX-K|=9H8-=5ACC4JBw*1Ce8nmp<|wjO)d&6EDS{tGQn8g%WwLb9HFl6h37Yh zdWDLhsA)H%&*jf@C|QsQDQXBs2$<4Zy~h9V*)gS+vdXObAgz!n`Nc(kVJJ_ijHCi( zT3$gYUg$3oHRVLqx&1knQ01b+g3w&A3JT`|E^DT{nYONXZ#O+ z0@4vx`B^qNzBX{%1B&K%Ffk%jAY-GPfWWGp3Nc*9MeLeUL&x=$6%dQRK>QXMDnBBB zxUYY>uR>v{5Ncs)KDl%e)So|70?IcY&r=tjSzsU2uK?aJ4l=jPZI_*no}I4iDJg{n zN+mcL7$;2q)mdT<1%4iEfDP#kL#bsTjgX;caa?Is=Wq-$|S|F9BB$VZfki_}Lbxn#V_7IuSIwmMGf(e9%>4|5tKm>`S zCHz5CikepOEhhuV$i?P`Mzx?UO~zVcB_GV^EEmw0&ci2PR7JxkFU`YZ8K1N(^O$j3 z6AHmi`bXL^)1ObB5R^85F-;x9K2LcJC&7|8e3>ICeJ8&lCW)D*ZRhR)o+4`Rfr`Z< z3Ddk7EYp$D7z9@lPXS$TCCK5u%yxCNUGi-V|O_JcbgUmqkHBwzHym8rs@di zT%DPuSG$LL>WMvjBqKTLkOH0vS-YLD0k!glmARIUSgMgKF)-<9ygUXax~aK_&g5CD zQPv;WG!+42oHGgVjxzce@HPDXQ#%*Q`}bcsQct=0(8(IVDXCP1QLO^g)`fUZ&FCb% z7z-wqY$@kNx6%fTo3?Z#OANdcd=49L0Gxh7_#xz$MdFwNRW>bVI8vxmr38Dr-=56B z=LPPB5uGmof@TxrsG)&p$mK5zZJGHx#bR8j%V4Mj8D~U7pyl_OFb66`a{gtB2!7+n zsvj?Mm~a{M7A2m0L{JPXrNJdxm`)Un2z%hX;K(14DYQ0oHA(geZii=a`y#)6RZqrFv>S0_1t` z%Dtx{_}(W=S9gz*r)HRlKyfdJoiRijQVYyOaD{QsN~KbhGAyNo+M&DtQg@}sm_WWf zkyw`i{?IJ#J~(hNiGK~Muoui~1z>dKrYO@*6X{s(=sa$Ff_E8+z?wNC(;#(O%!UcV zDrX4;bJkFp)nU=xmQHJH_$H^#ou>m*ET^^mGCLFiS2*c_oyE)!3BZ-bRGY2&w3jB} ziYxtk*{s_`m8TzMCc6naQ~G?;tc#QMsBbSXx=VCqo2>xef^??gr&VSY`iI!|FlUAl zT9L^pUe#u%7)~~zz!qCdjGW2Hvy9r9mN)4BueaSC5q<2zwa~~ZeHId|oaWWc5=d!- zxwN?g*RGW`Ol{Q8EtB;jukTcQ?$^>O0XVx?iND0uiDCH};pO2@?5yBX0Y@#*_TFU@Y#wo^P(G(8vI5nxhA0`uI;Mx=QgDK|xTmZn z6LC5LGfNk87FR;`JV4M1855^0WJN=t6k`o!#L2OMV_&2Di|0Z;0f$_Ye}nojyH0$>h54guv>c$Au|NuMqlu!ng;H^Miqgo5&&;%O|x zsVB82eI%=a=dUHtrTW3=f{;2(5%l6eM;;r3xy$!*a@Dz`Kxza+_em8ptYv za|Z#qpV%~Zij4iqSwse8+*YH9XOu7&6NwN}#7)r|NN)9)M~)jbC(WOzeS3xCHT%8D zm%w0-_LB57t^IvWip)Xv|C>^O)X5@GnsYFHt5hQTi|HnBKDV zAH$3v*+wr>pda1}w~{3`I5h_=sspmQk{NR7d{hH;0sD}roV|5tuK`T_ky>xHQ8IMc z@@Y^7<%abqqgq6Wbw9pkqc(5_R%cJR?d*_HT^350kpL&<68R-z%XlZ#2=bhTDglrWz4v@5QPxhpNiUzxQsARvh#xFfWa$fp;VASCnQCAncX{KpHA`V zSlv4V-91US`l%7ujg_JX=uXP%u$4^}mr_d4a3PkpV3sEuJ@7#-c|p(7K-FL|)oK`P z(SLS^S{*|@8WTMleLWguJsLy3u3t;i|8yy5C#7>tq|CoO2z9DC-NBt6Bnk?4^rF3) zR(<@`gxNaNZC+@a_gdD1cNnZ(;WutNtOhLCf>-L`YfN#xW2|4in)l$X25{Db+4yCG zN~+=H>-MK&aRN>O^?&r`leK=~d;xKu3oNr!;w40v^sd!p-)Z_kDtc2L3W=ev#S%9z z7Ie!o5~H?O?^_#HBHEUCztI2fv{ARe1c`8w3yLZ2mq|~OH0M>RK_duI4~@S7S(miM zBtF!bp8)Hu^nZ?eSlj~l*gL^dnWH(76$8j!KYG~EZaBK3^JrK(w;~vae{GDE#2(R1 z>FJ*#kwDqRA{pI19Q21*@wQ0dsKaa^j2Di0|;V^ez6L@yI1w~v{> zGK_2;!Lpad41LEVx_XXm<)h!vHX7s^3iU`wdWIvqiivDB#j>BN`%xyeS6(5Dmj2E_ z7B1!Z(Q8z9{zZBj3hfmR?yY&ANV6BP;y5w!E=lYUX{oE>jgH--Z zDAzHC+RTO8EZl0r1Zlwnae;&S7(#r>Ahb&%w2L9UK@j{Mx_|_A8wln@W{|O=~Fn`9#vsNTHRbV1HUKI3#8ZOsx?D_~OXY2t^didaOE zIxmJ^7qq&Vt}<0koR|cP~_k(GlvbH{ktex z%NWOjU!w}?xMz{|$X8st)HFT5$I(Q+?%!CedRv;I9l+}d-H$2coT+8eG&+~5C7-D! zIIW_z27YwWKhiG4pM8D+#Q~n0-H6`5;A+Mfvb{ffxNW{@{vX zWS0`fTe>JH-TeM##%nhY%O^5((dX|-4?9mNr)+3KuC!G0K>rQN@_3LLnJ~`30gj-U zm07-P1h4U;6{g7k4Oq6rTUR=JLUK$qIXh=ovvRsbBd-&rB~<^zz+9i1$IdOmf?Bpa z=Pd~C3g`3}xcNjRJ6B{_fk?Lh;Ra+PO85mzkk;-^6P`PoGoJKMnwG85_lPLs;3>1( z$U>}@nWrKe+DkMvtdP+O-(iTiDXgaxlJXPH0GKv&eWh8iB0yg)HbtEBgS86_7LK+4-E2?Bel??&Le`gWWo9#G&^Fy~M!&FLM2*Wc< z=3?ck$BTW%8`oLb{^u9nU&j6r^%mvY0_uVQFS#*5NaNr$*_;aE>TAMjF(F0ax@V1az zd%j!+3@fKrZ;%PPu#s9DXEWxZgKL^&C8)6_GBR~y;G-W}7BZF1b)2ELG(l?Rfvuc{HcS>iuLO zaQ-C=zURHd{0~9Hc{T|;iw$aY{5i5U9e<59Kl*eBnM5JlM4SeJZ3*W4o9 z_@>-D#|`z?zrv)u7L#rR+_e1hmRU?nKONHf>R-2;Yu=flDl3_y>OjA5s(+qpZJ(al zaB<1*{T)#+zTrQg^D zSTgWYmq=&j3()zPRlA(-YZ3IAOiZ8!4w0bt>>TrLz;A2xUkBWt<0(Dw-4K;km~a49 zjUaUX7;$U2N3#qhtoZA{e6~0R@@#TK#R)t*F?V{??7h@RaAkPQ>#H0Ln7Z_HmQR`A zV10zhO#L9t!x37dR|mbn+5}F&d@44sIz(%~r~TelkFCTm=_)=JBx8ELQ|_Mnbi_(O zl>%R&E=H1Ce5@%~a|~=gw~1z+~q{r}i++?{6R-oNG8OFF&swnlB z;D%*1SZ2q38@ib^*+fLVE-fQmJ>Y2e&v5qS+142HdN2L4k&4;i^&Y#URmR`ZoODTF zaoGb8JA8$g)!JDvOZE9QB(XRZquZf&vj05uaepmO8hOA_e6>* zNx8A+Q#J_fAo)o9)nL^m>h@}}a`=nN2JJTI$F{|12bpf?<0iW6(xt&I;}JgH!6G3= zSH6rN+*Xs|+fm>gW2#P{bbn#a!l#HRJzyGQp^Qa`8r z!{)}b&&-b6z-yArq15Uek5?Ii2E>*#FUWfx#QWv?N;qwn71=cgv1qP4GE)roMyS61gN$>Wjc^9dBXkGT;?WL8ilz`53NoCG8f4w(N6x0y@k*2&B^kdJ=wEX+pAVe7YQ&1)b|XXtx_00f z&gpi6+_D!_nzfH&n*{>28077_q_`yq32{Ix*DLE1I5RNF33|%Jalt|;iHKPU;f&cZ5i@Ipn1b;ZH-sq!BNPW$UKC3LyO;Rin0tnTj zVYTJcW|!rdMjoeH^vy@{F$QORAoD0z(J8uZ5Dwm;Arx>nevRl}zJ3+%v!THlw80D$C^}Mx9 z{R7qen^?ycprOsF@t!yFxq+@Y-}zA!Uj^!5C;_XdPfhO?WW9SZKOboryC$SIC$&%8 z*13N*=ZWh=c6fg)>Xkki;*_oTDXJ?yyqiGk$p$2es%EfSwg#e_$Tlf4CwL=Ga2U;t zo)$0`@R`W#+4pXX+0(HSJUo=$bhLHiUTh$tw|Rvg^me=ccxt=r9U`%4)~Q4x^IOJt zapB}G+(B3H@-7k@bVRg$LDJl17Vy4=w2=Gyh~e~^Fs=MuEYjnE_bvvXu4=Ss>eT4p zJpinOUq#t`8-NzHl@ry_MRpF_<UHhatOQEwC5vbHobo2B z1g~XcD`J}c>X@HmeaY#=;qysvN6wPr`EJH!={`(#nRyR|xLQI@wXz8z`hZ}?=xH0v z^WMFiKvyNQwnQtb!q&WPdazCefVV1HbKr9dJ$po=x91+C+4e+k)!f-mYLx}uZk0=y zb1owoZKzo}EGb``8?GAL$PfOzc!W1xu;&AO8>Qi#F*Io}vEf&Eqr+M#Qgd>X3=v!P zO0iz;0Y#kEBGt2`16i5P#)0?&zk=Nqwlgq4G?~#|+%@Ks_qDuapU1h1j*GkJ>oI$5 zaFzM&;?41&n1Z;kr78PK_W3Yf@gTCd`h_W$ z&3c*r`E{bU_$mqGNk_&3ozoL4{k!c;iCYWX*D~c>;Fk#qhJ>@|>2=gbLM;=6KHnSG z=u_aTDoC$SV;jLc87c*_K#%|}YIvcJL0gGdWh^v_Lzz-OB1{m|_u-!~u@ee^b!>7) zl|Exoj6@QxEU!eetIPYJ3gqj)IQq@<=L28VC)nUqbObSMo0vBTv$LRu#t2|y7?-=& z()QyMIRWIL3@1yDQY%OaR63f?*n@g2TnhEhPCJz735EH3A!1mz&Ct$Fa&?3FpbRQ4 z6gQv%1xZ)O0_$uxuhH zNsjGr>9i6INK-k&lZ310+g%4uSmYVsN}ygf*r{u7lB;*J4pyQ4q0>OG5!&NSo>(W& zHTsEGw}sa2iI43kk!z{ihja#oMh{sEXbB*>V335b__u28=UWXrlIwu(_lJ&#BM(vC zx>69=$Is@uqT#1-3#lZM>-dc%!DBRONrdb#xoC|e&Qt{zm;|AW^@9u3hZvJ+(p%b; zp|)Pm=+n77*{x_V{|7=(JFFEM{Wkdn_ zqXqqE{9k&|fAO3^wV{ly*T7(kej8J1=LzctgIE_?K_vN3#Ii2PtTUf>B{ZT^qjr!O z#|^Ed2!q85yBaU}!wO6+s|tY$!N81mhsmL@fjxBm$B!Z*w>!oy`|JLnTY6gC^X7ZU z@ekDLc@jQS+Im*52nI^d&C^U7W2x1VHph4}a?YxeaV~Pr;80yLJ1i{PXx=JVYI%H6 zh>?2&J5?+%L3}>LwYi4hc1iTmBqJVKPzmMxPOS2-Cr00Dr3gxm{_hMErgXT- zB$j*h(L-TNUrKJR4EN~_v%Lb#^pmMJOTNDN8ndG?%o?+) zg0-0lWRuw7AZ@{ASyj2vX;rAq(vKA2Q@FVJvzss)X8e*vzGjHTa8T%wjs2h+u%PQ| z{F*doDcL4Sjx%+bYA6J8X%USxfz&evLFU6)Q)#46!aO-y+$x~irwpKtbYOV>PJU+2 z!&Vde)U1}(Q{w^AWxValt#f7~Gg9f>>C+6{B{RnSp=M?F!xW=`v)mK?r!^4EEu)Jb zo27!RFld!R-1LIn4{ZUYP=FI`!#*vZ#p7BP^t)Utb#JymA>^@femFTKQCm)zWBt@7 zq!>-uZijy>6d~dUVfq)aAl{cPn3*CWn#}&jto)r(n3aTeKldL^e0#X?&4t+!q=ASo*QRy z^_RjQM6P4khLmOKqR`dtWjg&|e7$3AZ%^Q_yKCFtwQbwBZQHiZUE8+ZUE9{KzO~)n z{*s$}|2aA5yqK)X%zCjhldNR$JRdI+5|C4I(03R+2WTm-;p8h&-7)1Zv;>fdQ|C@} zjg&S(4UMtD6e>Bkgcrn_R>z~1LU1G~sfAhNDV#h;QRG^}(x%bFpXrPcHk8R|bvR~x zfU~ovFm%#tfQU$6L8yU7JA4TYTEm4yD7PZhoOX=@NK*S#P%e`L%`7JKGSp2~uhNN_z$E4h&H&aqz2_3RUA7)0r_68^8V* zn;)Y?T1nMVY&@``T6i%Nvsxv^Bx?>?WRE9}>?A6YV@Ai^2ocVOIUB;uL54bJ_kUq@ zIKU^qmMGmcqQ{(oE&wYRYt^$tNt`(~i%%s2)TlQ{;I z-mWify6>GRUsQqrLsgyoIy*bOZp4JItmvARIsI_?h6;x6fLvLU2@A^ddnY76wiPa z&nPR{BNvom25^EFG&RnIXfsL`ygJKW5)oOwf`Kk2$Y5rRWI>N_48YHPlP?Gew5RjbGOF(q3M&KsC{Qp?TR zM@QzFCjx_uU0wReww544|ErJKH?rv=R{X_&k0fHxE4BEd$gJXn9ZJyOm$;9C9wf@t zM^7de_~!GM6OraMd3&?P+@ik=+Iu*tWDK8A?FPS5JRUy?FHLj6iPmFR4qq)L7@^sK zls?HORiJUX#2^;kha$4G@X3)dm{vvO3Q{~eBDVAY8W{)-`Wf)*>J&}$z<|9 zTgD2d;yqMi+m;BZ7Zs{rm!uy)@z+}F-E?OZj41rytKVO4UhP_1dTcP$?d5y(=p#&n z$!55I5@=Tga>dKFLiO6m`@AMuBlzgiFf<5HPErJa1F;9fzf)gtsHxY=!1Q7Gw)Ek9 z=Yv4x2>hg1>cqj+@n?=GND$(V!TRv2z`|ShxaaG{Mf&{cUmwd)SGDHrMn(GYF+Sgd zPge;<`*IOKy_g>HZ#OH1Zj$#_bk}OiTJ~ayJhRbw9q4pen68`AIj$|IasSC#xMez{ zdOB^~0}FL&K+=t0c&?kuqgc;0I|cF9wXZi3v`1Z3v{m#%K_6W+*WdfwXlUBPpTJ;cSGxldl6xu(;H0q64{R6HrO^CT+iG~Di>1llJ(S-)ms!Pxn>j{hPNdaz z@|x9{wi~Go3oZsZ0$DZ!xrjunS|o)-=*Lb>Cd~))hb4O2Cg5`mFZ4-@?W0Fppv3@W}C37x5v;Oe6~`^YT5KTn#8DZVF> zH18>P&MbEBEN&Y31HLf#4sYV9_b|^oXg`hXWq(3L1~SzjJMcnVT5+S4OMd888Z=yN z5#{iBhfOop1^c(75E3I_tOrUwIW|;`(7;A$`d8DGaaHIofGGe&Iuz5bv8IE+F=u?YC(HVrZr{O|Ws?q0TwA`N*-#$nsiCVY-pyuM zWtyQwGju~SMl&}7a|V4(%*h=|-vlT3cX0tOw23A~=Vpbt%5u1->=(V(mSQQQo}B94 z9b9C3Xgj%dgMWfXT+pIU1CK7&q$o+6Ibu^!WI+&*nD~&<>M8QksS;CXOblH*Q(%rr zXfTVqgd(GIRFq~^c?PZM2rebnPEw&!Eh^f5ad`&4=m_vnLt5d1zLH9Ju&eVW$~T#` zcEsjQYeFb{Fip~H)0T427IC9WA7nh@{jqJ3_}vt%Ytmr%8X3HlJdLNw0BQ9FjJh zl&wZdr%AJMS^{aiR}m-al7DHmN!qPkC@$zT9X{xCe3~2;CLN{;@+Gi$SXX3*jG|-E zT_LcA5qL?0eB_+_Foid4Wj6I))y%;@lErN(XLuyRD&tHNtl%Vi`WK0UjShpDIVEE# znk3{Vi4o~R+}lY*6Hb?M+1A@}8YMb7gz$4Q!sCAfm{9qMLc2g0F3E~Fi%7QhB3)S9 z!@KX3+XPU2Oi$Ppn`pZH8~tpC$F`Qaj)blO9fz#|^|ME8zQbuTB34h7>g zKL>~FFcYNBe&kpm6W9LfBWETcWnMk_g99~+ZDAD#ag#B6@wxB#b8U9a{Cl4D9cuN0 zeEgzp?1KL_A{Buzg4A&ed(U$9<3P}*aKy+7e{G1_15z+H++4T(BxT<%*FfhtCPs-v zm&o8HlVvYa=%y;=mY-V4ChN~5aqzMe)sDT`p(=UsvJ};>t=OT9Hgps3r)vZNItKh} zXM~0nn~C!VpD{m$>^ixH-+_~K)PgK75CLj29Ak#!=o>IAEAPd>=K+s~ST>QL-BO&y>m%_Vw z*C61m$_83Vx&y@@`pf^cKdAl>_NcWpYurTZgmN9^t0$UGporKBy z37Uo`%oi;+CF6&^pKCH7S<$kU(JinvM2`JQcv`^smrr1@s%+1jYQ4RQQlVHY7ms-z z`Tp{$QJx!Bq*}#k|6gydeCvNJ^)D{~*|g565nu2H;J5qbWOg{V_=;1-PXlZ7p~f~Y zmAlK0($ff1GGq}#q&tIa2BX`9OMuMA%Wc@L_^?k5h zunyLVw{5ZJ<#oMFxbix7YlFexn`nD~Ehhxe`Hgc`dKbU+tv-};{PCjOPc38alGd9@ zm&H6}H=a?L_iejYt{ZSIY;7)+fhwl1QTn*$mRRb%OWE={Uz0Q1o`m3g5LFkZ8lSUL z>7-Qgclisv?QOE1HAi<=lsM1-eo)@_?1nsDAGeV-vR2iWyUA7pTDRk9<)s!*S;r|e z@yC=xI_`3H0bkikM)|N+-|?pgM8=UFOR>nEj)M3u&6 zj3DJhz|}T7*rQ~DJpO6zE^e;OXxl*_y!K;^wcZrZ?nj!f!`Fbn|46sNbp}Ri%Gwq& zozNuumJd%!5FHO5*K-jVe5cz`0bd8T+5F}V7O+bR^BM2{B)4u|wj=?-dX+^}Pw>5V zTg}x=70h;=`_5{b10u&&waaiz3S!&4u3+}T+wJWbaM1s%j`z@c5M+%f&T%@mdM`|# zrap)8&g#g+x1Wzv)@+5*y({-w^z@)~wmnZ473M0L|@wqru7hnb!;(yYp$)oYUGm!`Ii`lk?SjMqNsK1kaT>-Oy%#nIGTle|R`->ZbL0<&_uN z0B`hdO68B9ceCHe-^O18eYfx(C!8TK~mJe?7YI zOum$!(x81!&-b)>=N6FhevkJjMKG)Ej1Px*ZT#qQp1+;0H-^f}3J5r&~vc z?DMurvU&F+`BZyU#<&rY2~YPAKqCj51YiVKDNFa ztNLvG{+6Vo*tO^8tQKCS-b>5r6-fnGcDmr)MvM2|QTgg&`=4%Px}8+`o+m#Y5xo5| zO7F(5v2>U*)+yo^WPMNn7Lb*m!mZC=*HnLpIky9I)gd1*E8gzp-XGVr@@}!+RC$M^ zvIf+HfJ^|&AvXIrA@u8<$Kl!+t%fFL0p%4VcyVA2u1i-dRG%Zkt}2dBL)Y@nRYhUF z^F&mn^>Nsmn??xt%6N|hA9~Ny(dQI>Ht!U!f_P{x_w3WwyW9D#1oZ7Klq!Rx4=|6~ z++Fy9_|vjwg5D-z{dK-^L4I#D;j-oO-1cI?>RrY)&6`cSTp)xH)oCP%`_7eyqF`UD zuZz!0{~wpE_%s*3_2Qr}Kk;4tJQMCy6s+S$<5YOBm#e?i2r3(A=_jJ^slF%q)7LW` z#w(j=MR87j?g<|o1fP8ruM>jq8k)A&YDQ((sIEG%I}{K-%X0&K{DXq*9pld*dxD%j zzw;{jQW&GJGIcu+w=$vMPRT|gc`L1zgwUrg@&4tD9|!2!f0&g~zt%k%<8Yi_>HE8! zDevX8SKGWe403S$o=*2qdycz#iRE4$Z-H{_s#NZ#v+v5CgtBTBUzwIIr^m{YrLUjK zvm^ZR`RL90m-DDjmkB8Nity9=M8YI!Eq>3uS8e(egGcyDT@gVTZJvjqs$TcT<~8>> z^tqBR&snSOb}G1%Ic^B#+=yQNRWUAUv5Q_EFLxVjDUMGjXQoxeZmlO}1JmnX{}Lmo zMC(%5Keei+V&j%>@yPK7$x46myR^EZMAS~XKAu?WH<*5;8gf{6UbV(oyyo$4|J>flTDpOO@F9q47uKbE z{TM?Ua;sb~-C5tDD=P<~R%55{c2D)Dyczu3)&irPSKm5Kp*G}85 zTW5LsK1bW!U1$Lq@viYdSw7KcrsMJykM>e^#}&rs>VH4Ke;cUgyZ`5H3b4kbrRvJb zEa(vM7}K%wDZ{^cAHQ`*pW?~+vUGSPSAX``NiHLF-5biLJ$lg}WBbl>WqGi}D~ENS zK@+&!tJ%9E2MB(9bbJkF+d_Rg^+n$UfcOVc`g(Wx?{4^IS~E-^&11lc3H<2qNgIB> z!2Fx99QKdF6;wIVv{=fDQ68f|jG0eJ;dt@qtMGm!r=p@FYxTGJ*$0-f5wh`rKqSCh z<}dJfv9U2Rk+G2x((%#J5pp3}x$C+lmcCUqTCs z4|ao{pTLvxS2-+BS=WF4wBAFpPj`C3pkKq2`QxYtP2XeF4B*Lp0ZOLSrn|hMj*-mn zR$)twoQHZw?#9&aGB;!!a`l2dLz=to+@xQ7x`*!~EPnK!!&aM{(lqe_X{!TL)uBJ% zINa3zq-x}Pbx4{f&M|qe!`{<9au;Ftx^r+`2T9++6Qrp((s?98Uq76=jwhq{6Pg;( za`8SpkgG=)FZ+0ZPCJ9YkRK0Ik+bRABi7D$O06@`@uBahQUwkTz@He zKva2k6!2F7F!669B5*_`phQ`8C}4g%$b+hqGV4rG-xEiUA7WWo*VTTz^ZEU%25N0LefjWW{NXWIJ z1Vbw+mb9XS6rzB3_^XY4l+tWok9d!UP=W$prAb9tUAq86MV=r(0GoIL?#y<2dw+xUNb2%+xlTw6;`6cvV3E<2Y zO(A?r4H{4yo)YB62SvT4E&#%8QLY>TfuusHq-qg@hOHb*25n&lK5fHk2k;Kuq~kiu z*b@5ZONVNTMLU&oJ?E! zc;(VQ*OOqZYLoO^dPNASS&2CHC?f>;L844)ZUc5h17K`fMXpZ1sb*z7z@w0lwtqPR zcIlJ0p;12vT?1A~B!tyWKIxw_MhG865YNq>FcX(X(a6vyO(D_)j|Ef?YJw<@ifD@h z8dG!N02PGvh$&dsknc?CF$Ig?CXzr|)B0gV-3$||fJ)NFtq-m{cEOnD)#=EEF8%j_ zCN}c9kzwc11z;+$DkH7S!?gn!jCvSp6jb6_LRhd?AOW2FTOE8mLZh%m)ReKRX^l$} z-9}A>g!@zuG?@YxNbp#g3G2$xQuU~Q#zdZIV{+kF^hA^@5*W8pDVL%>)5hq+X|AGK zC4C};;%v5ERgV>8Zp7nc22O2h-ejoesbf^57b|p8eoMVVGlCGbSVGXtY9A2OUFW~MBnksi75UJDlki!Ti_Xxki!T$3LQU7EZUN;P$X&spGA z-oqN{7;roI3PCf9O5FG&%>>d!B>9!1(h$S*D8S@12Ph()mWdSg;?g?Jq*5%`kxLeo zkxeHsBbAJ*MQ;7H3df+4N9?mjfEvxFpwb;qouvY3wWLxPijk0`vbSMHB1NPM6EW!( zPn;!rezH$HqN@=lRJr9vE%q7VoiN&ueCF*^(ZXp_;xPk3G=J1cuY!8qw1yT=7PO_y z_^4ZDF4kZ}kUE`(8Nas5DZ(i75ivwn5P-fwEEQAG@-qTQrR}230*sh5epfl>N*wZ3 zpz;`lCoglPmpl5Ujl6QQ&L}T&$g3WHbYq`bT4n1i9O_j5_4N8X$uh$%6_Eay6Jd1S z9Ne^%V^-zx!^lM;AEsakZh!{eVu5fp0f&o<*bEI-BmA%xX-X_jj|zw$V#F;DaJFS3 zjo0nnq06SiUx0S4Xpl=6wcYQ7Jpx7%mBTfEN~_XO#Ox7jLcV}JV3Z?A z0g|3J_!AEP{HSyjhSgmG?%sjD)u!Q~UOwQf4R^(D<*=(7+@%C}Ma9})wY*oWXxye2 zyUo=Z3U(U4jirYMF43>8hyoPU`&$mZ_euD>lnch<_1PDH|=65QWWrDgXE^iA~=205NWBJq` zw6vl67=pj6boZ*Z3|?sD6Z{M zLBPH#p-HF#n6?sZMw08idXz3+Cd4mv`Bw_KYUE;b#6iYOhL0ghVNpGWct^eDf)e1P zmWXnoVEe_uehk6Nxwr`1#5Hc*Tmf9$jWKaCV=OO;5jkY!Vyv(ISPSlInWQ}-%G$ib z^Z!OhptG~!XR-$5P3q}znhQL66+kgcgQLwHEWz7pl(&EP2IP(ZQ!kD$o(52055A*4 zVW@bV&5LbMjRkaq={+6{3a!3Ji>!Lc7B1?hQ*|$>(%&}pa|u$o7)^AEs1(f4Ux;}@ z5^~O0>#!E??q1s5pbBeI-`Jln9cUHTsFdJ6M3Av&>Y{94%`BW>RCHU#T}=RAAvy6 z2iewm4xz*I{u~%+Z$-ZXZx)~z!P*c65wPHs0$m4uAz$^!m9v6~CGW@y!UAGyjKXV9 z|I|R{fy0=*U;?I6C>!v}hXF#U#+VI8SPcg*!+?nhwAqN%{)pvRBIs6oio9p73Tgb9$XQb8a)%s9+0)FQK+?W!(EK?^708FgZv7q3+S7#f6xKxf>*$& zx2QdSCWMq;KTt%`394>Kl*l}e0+^SsA(d#nvH`XzJufJ)*ihhU7?0$TV7cfmuFMUt zP`AwL4X)J9)bE}@f*$a~m)!;*VJIJBh=R;R^wIn5O5wXGuzloMenpsm=wZ7SVft)C z*OXzqm@&J+QM($*t!1nqZxt_4CYFa+BeN4XlY}q(PI#yqo!n9o}WqV|tgo70(y0eFI2*q#< zVtFER-qX~|To&PBi!PEbea5>EZ5RE^Bar&}lqypFnY9qs;6G4=7~i~jH_n4468)mM z4_V|(^l+%AhDDhkE7Hu0ivB`XuZ+qG;&KOUwI^uxYq2*Fh&3UIbs~JVI;KW6)_+{Y ze?-LpheOQtdj$tRlG)i+ekcX1*xj)hPeEtH>^otfwn^lxpMrd{!8~vi27(NC4ezH8 z@2gJjFHh~Wa)qxlg|9M(?;6AR&Eo3K;(i5ieh@l)fH->)JA0tiR=iYDi>p-M5bNLw zkNq$af{JNda@-#XS24q?hvg4BE;K0DpDyXUxP_N@+77bAp+pE6P%RI07*d@CG{xJNXcPSoNUJof%~cl zuW`m}yHMT-6WObc3Siv}HUoWEuU|i7Wzm|(R*c;D?h|BTv-#W(nd~hiF~r&^1Sy7@ zf?{(0wiFW2f`5O&j(bWHwevV23 zZym<8lVdu{GaY>OM|ejeJ&TKNE&dP@n2xhd2f4I^3T*&1BnP!RTvD0OIn_Z zza1pw`%T08XH}OcOguzkj6?Zi)FmZ~i-A1c2|E7>Cfb+CcEkidP( zSNuOv1SYUQ0I_`e#UHG9ZU;h!AEEl@!2_N(t-Q zWc~J4)W^PaARU`pL<`_ay6_}P)m+)w0weOa{dN8$SNyyi*ufnxhyF{WB6?B&ut$$k z&nK?25->z#J$9ynK43``JxrX24KQwi#n~?~*cl!j_^T7ZF5BK;fR1OH^JEzaoRJ|5$vevjTrE5rSdn_JgZdWtCC6?7e`=ukV4NbB~;pTKvLNg zfM4WsJ-A?Yq<=DzoJx5&^cakyI_8oMKbyuZ)KB z5eEe=VKTOR8t!8bqt&pXuTj%-HK`yo7oLQ9>B=}Y zaNwT!!$lMc;B-b~b(bx0-)zVw@Vs}Z^-^=FVtt}*WEVwZr`|` zwsNB0$$OTY3iW$G{9tSnzN!YI{7OClSzB3iy+6CN*Yr7(t=LDrmE6a|(0(4NtMhNy z%O?2`OphycRBfHg|7uL9MWoaLxsIA<~bMU~?L617(Lwv#=B=p>s zRKZ4OZcP+JF81eK&h>m35$hf}`a8k7i+?rfuj(}CnVsk6^c;M`nH@jt%O2NhZgrOR z-A=!)gTz?+G@*!}9r?XWCObF#RrX|K z4>Iur?@FzQMwfbv;>9W@OZ#~19CzI{N(l8ZdpKX=XZ3}$J0@&nM2V1ahBkfZnT9wm$&)?## zQ3vpyco7%!?$doJNat79Df$atm0ri@1Q=goqrb*;`!{J?5$>G{#ysOtRy7_S{Gb|-T@cy1!k zyD9N2+XlwNO$GFKm~~N+vb6{7hcC}x9*>Mm9>2R4D4`flcDd5M$R~||yU}O*VMoL5 z@A@}tp9EeI6^1Ytrto>Ys0aJ5y2)(cyz7j349PRJ${?wAsqYj+~&=o zNqzL;uiw55J@X5m@aHs9Hhhas-#g>?e%h(7jXqh0a>=!z5%hizQ(fqL!*AwU*nJNS zwe9v$eM^4W({|z9-dvJzTj$A3teFlz;k5C7d=0hbeSF9G$3qPFwG~_iuZdgQ!)}xb zh`eo6xMS@)Kc^fY#--eL3>G>np8I^nnpEwBd6He%@n3BUTTi~MiMCx*BP(oUh<~KC zZ;$Fnm$Mn%ff4vSJ!Jpu(w_giH%iAIwJ-WSy9}Ym%JY29i7n09ebK|plJ+~ui4CeeMz7{Mja6=^d;P< zUt}}64wZgIvUd@A-(u~!*Y=;SCJOyznCwRhu)vkZ#fsObUn?B)34LH%rEky()C*o=Td4d_;qhL zDqZzCx<9&&lKXskw*7PM*3AZmcl{5}zM2@`(`%vZ-^Msgqy=a75OVx)-#*QEBBt}& zbhCs$YVQY2PJSlABj@v67^W-bQd{8S8dt@9W2mie*I%f9zg(I1X4CJ3ALQGD<1P@k zD7MV61cfN~Wxf2K%mvGx=at(BkEQO$*2nhcUl{cZwtDv&Xm6{=vOj%q+-v8psY{O2 z@m^e>jB9o9Q9}JtX+P1lbK4Rfm*1L-FBON&u*-1D$Lr(iLXY2K1sw!mwXV-n3a9)1 z+@U@`BNm&Jl*fW&@@>oqp!p@ZG`Fba@_dJuJrob8$9g)!Ud9{6?>_JIKL70MDJ2cY z!C$!|jrbtJY}Cp95m4BYm&JyDvNfjbw8r9jQ@nY?G_ibZow*efk||qMB3vr%YcBhf z!9r-;JydOWJ#&jccU3ea!^^clK2+*9I8}UvLnV`J$Hwa|W_F*`iFkgv!*AB9#Jh5& z=_GXiowqa1QX2UicBsYbHi#nXAT&zrsNSvZvK5kJqJSiyF(O0+Io<;eM-xpvj3J5B;-}f-tZm=pto6?) z@7;C0Jf3NNy}1ZS?@b^|@7P1YM#SZEfhhH^uQh~cZr~pb4{LeW?YvKV&n7c=EqKT>{_zcdJl9e19r2OpYNuVObI{z6(ir#pW32x+6@-xsl) zZ7g)W-)qEsEe2Ur&V_Ke7az?=k{YCpcK&_`z731sqPuUTI)w$DJb129i~c~)?1K|# zqU^V-TEXrzwK?%m9GXfd)DXtplT7!Ey?2#w&12hj+OPY!=BsfmtO1kfHD;nJ=md~4 zC(I{&N3&R!ahk$e1CsQ4%9GQw27`}E70v6=r8HA@yX|GM*PWgwUnh~VznRiM{qm;< zwNrhf?nGebir>c3k}vZ=ms_LWg)e?HzMfA)DzkE=Xc3-nH=DW#|Z8@5-#t%W-U6=!}bwW)m>Igb^L8pFAMh2v|3ReZek`6Irf zU=0xO^%D0`7kh%(`M8-X3(_VS!0+(~v2G6bgi@WBwSsvm%zZOevm=jK>3;MxOo{O9 zHrlO<_T}dvBw@;Ybt-18F#eG=O7+yj$zhzA$+k3*wXGspHTyYa&-uJH>;ALn?QQPA z^qJm$86nbd;aEF%;}gH3h1&v6^gnsbcCU=bC0Pntna5iX8s@?YcG1w|Ypq6rrLSu7qW0QZ(F>c;5 zwi?hs?Sa_(32Yk8pjBd2|n|8ie;dl=tL)WbUGGcxaPB8K&>vS!w$O;I`Hg)8;Fbu>OQ@ zC|iw1F36TTB?TZaYUF@yqY_G7T)})QA)-)^5n3pxSQt@m@D3GiMTQqOqFBEeA}SiG zv`?|7BB0po#Fc_AKJZEq9fe#npjfXOBFY*KKl0OwHdt6FV)@Rf-gKtW={JQ&N(CxD z;0B(V?gLV;Fl-BkgK42~EWm}Fm~K$_bGPI-7wS(<8#s$f3Y9US9w|^1O(vY^4w;5Z zQ3akfYD~HwUr2GH!-ax~egGM=XN6XZ3YF2XeoatP5*95IlTTwMznF^)nbE60;3w;^ zLPA6-e31Yh1ut^!te#=ONJ%j|<@PQ=7T!!Dm)C22!dsjdCzqs=0O736iq zK6R&QqEp9%x@Irg9pn&35v=8799p|rW>y@tZ%T4ZG(ztC7Kia@obeCIe-v%c?Ek+o z%K3*7u*t=D-uaIZ;Q7B00{`P=Y~rgpzmyIYNG$YB?PE`=z#yq|8h3c ztqG-pqP`kNL4~9vQ1c$NQK)Kygr?0+Or?Rew3npi3OhaqDm>W348)GA9SFL#(o^eN z)w*oe;%ZA>p;e=%9o2Ygr17TCIb@Bc|~&oe&N^StGJ#d+;L)5DB~ ztzJ}`uAalCx6D_qo}gZ+H@3&gY|es1TFY(TiYdC2V)wI?)b5FLc{9U_Rl8i6$sWMRNJT^o~5Y86PWw}gKaJu~q`34L91PueV zYyzm|K*e!~Grv{0?gtL&)??C|`pFh!xk+GuUktW1aty3QG39sSYzIx zSp5Z)?9a291b;l}ZW8qE3Ls}m5D2R;iCy9@d2A%w!UPYT7d9ANfF0`thb zY_un9B;ka8$A@)S7=d0Y$C8;W`{9to!RS_w&2r6x(mV!A8{Fb2@iQc)PUuxBW~m}` zR5RK*0o<`>OUFqrNqT!Cg6V&QSDcEGW0e&GHe4paZCUekT7lA5G?RHvf1@^p*qYHUtWl%JMP_g2CzF?y?OUWKadw3wcGfca5{- zT#1bfHpqs(6SE9h|L~X!Xau$5aB#C9TbJoGv<)e`lt`@opB*6qQA)YgVGfzd&~r6P*Qv4#5~Qj z)iPCPcm+!^EHNjGn!i{xrbRr)_EDZFMXbkYTCm<0`lvb=<2k(}o5CayN4ia>; zPLk+Qv3*6K$BN)+{2EA!xEmDYIg^20sZ^{R=SXD7N^Ay#ohL7oEw!WSq1bj&OR?l) zE_10gj!E_q2BtG@xH~m4MT8X<#7u)?84&XB5^yF|%zq%D1BHp0CT<*%fWstJSg`_| zm}?8E2Mu(xYZj1vY!%>S7R&#KNv-g26bjW?E?*)JgF>bA1!o5soRAXbG&mGUD272s zfRV-G7kxqo6bl&&D0Y|$vZhw*W10&d=%9XhVALG4$ftX!+(ACLmij2$s28nl$ zR1cduNHloyxE{#5b-4%agr0Qcs@nRw%lbL&`nhD|YO-lv*>JjIU71;s<)Sdb15tuhnicz}#~mhAaq412w|4H(kzcssDyJ2eIzdobryH3oIyYs>!k zHI&Z3p1``RK7n+`feF?q32qnam3GMKW`nN=$D&ZHr=d=l7@q$E0-eLX6@yqK9e$^Z z|Q6cQ)dG0L#w=p^9T6d z1loXM+x-H#051p=L~kPvS`{ zrom^h|89?a0A)nNA)?S@4Q}L`)IkI!P6^e23~ZETuN`HCVEoPXbSX9NoKtEd4_PG2 z#*pnr6C}#SM1E{6S0#a$l-OHK>?tPZLKADtZZ z#sANMPbuhC1O7PAi6L)nz=sN=NeTX#nKNV7lA!=Y%hZQ6lKesJ`Hw=VpQTD&QTZTt zc|{=We4V#V0m9Yq{m_;hP^F12BEfUU>e#<23X9lyLfB3NYNG%pWmnEDX?qNa)6@RuYGoqQ(j(Ez&ex^pgJl(L+N@=}wDmhf+0>Kvl8z0i zy{tgt+r(;v@~?sKB10N#`$#KEBuH;% z=V@US!I5r$R6RwOJza=*c~(DV5-5<4fzUNV>x5M~y(<+;7lhV5K<$K8J4LRWBNZ%| zL=2@9p0|YtuX{@drF< zN#A-lhtJwm^TjLr;8(ry$ld-?_Q9`vsaE!pQ}S7%=EKQ0{SC$YD1hHG$2o<;&jWLM zt6QG|>i~RZ6Ov~Nm^Z(7VXr0kPRPK`7Sbsy#DG>P6loGY-vtHly4~q(2_Dj<3lmJ|5K}rPf_$GV(5^ zHl^HX)SH9`lTxiwZZbkdN{w#3tMfjL+}MoZK(K?ZbYqqvMCC8FtaKe0pFnB_4nXbOjV8$zkfh6>TNYHm!Hs+7{KFW~vr8k=%Sa zk&(XvRH%N+(9g;Xc)yFePbE9AQ5WzAiT`!v@jwg?EkmM0^|>RRLXskKZFryb7qx#R zb`dy8A-ax2mc%9ReMI<0nkh0BN8glLWqS7ur61>Qvto3x#K^h3 zIfTp;s&NTM-&L)5E7Mjr{Fl|}urtZ7gk;-~blX&_b6a8IvQ)ftTWR64HOcPir$KSy zvNp*M?Wd-^aM_z=*GGBw^s7sGlon|3I-_J*m=2AP1W0A#AA!kbyiR^G_B1HwO9q2) z|Gy4(A@gUoqz8FY$-F)7OiuY*T3_kC6Q6MTZv;d`Q&yLvJ!b5aFJvFOqUdkJKs~B% zN@JHERXG<`s_MG1D4xv&8 zcQ%!-m?cuNj|e3HYWzNP>0lne>*%=nT6c>2T#(;m;Uy?JzQV2-#w(VX&x|N($r*~+y1ZjaK18Pl1#TArt7}7 zY*nE)hu6Dx7jSi6-vIpqy8^~}t2R@^oH{jd?zs`R9*(fbJEKnkzD-1J0r%!JAoQ(? ziLZI2&~0Sj^oNL&!`^P){k7EBd3AaOf;UhSTjxjIYKo)t#%gG+B)c+iu*Ho;KD53K z<8!V<6X~|}+MmQ=C#o$D_=Go?-Z{T|`8b;hjK&Xbs=MI5_s;4;g5N#7_uts;=nM9G zCZpEJyAN$MJ24Y&VnyfC-)M8EUwxr9UQ3~-erw;br=PM;VXiAP_Buj779gFxV!2p1 zwS#45^cyPFiCwF12>IkhVKu$afrCI^KS4 zPwiWa%Y2(gJB2`ja5}av#&4~YsZo7RD+JcrygdY6?+#g5!RVq5RcddZvezh+X*`lY zzj5Dn7H^y5iPPT@X2*?ZHfq`_0x$3KQx{*?8CXAS^S51;(h2gpwt8d7jvU&92{B)y zjdd72vO4V#H~e$Fn+Ijt>-CH24uX>J4yt%s_7(|?75@02A?Y7kwcA)fNi6c?n4^jR zk5{*Ej%z(?xW$ z_4ImZd!f>*HUuZi_EaywPG5p^LDSbh)V#OD#>S}geVfUCaq)FN6oX;6zRo#a)~kP- zJZjY4Ns~Q?)Wa4#Uz z_By(LcXGOyoo(wN3BfOO^xOWUXg|Q;l7ot@Q!h0r6fNB|$8?VL{ISJ-j`jTMPh`4d z*I_JH=*xz+|7TbG_j1cfNxLni^*2~z-Xc%5WCV1ldgC#4)8jfdD>SrpMtGG^UGXZF zDf|T|X1BKr(0x|E_k#JkC)xedVr|VVD&upmCU^@Zg{hUUeyV>o_kanWT%T66Jv;FU z(4h<4NxD4MXkN!iGgpiM@YY#q?QIO<_0^b#*V6LN+PkCJ*R;@a;Lyg~R!Nr3U|i|5 z_!6B{EW}%k%++0e#%p2CxIxZ0DRFtzoj-@_7_5H+Lzm~vwv`+2CR)s##p*%0=8??u z*FCYC+4Z$!IOn@~4WY5@s_y5|NLQ2Cm(m1i{GZ$97-DwaPsU`iHkI@|$MrL16)QTL z_I@2}eAWWJrBtgo>N1Gj6M6!3AKd#7S^8Z=cv`Mp4y8B6hQ%Tw{g)gE1BNuYOK|h75D9l414+J+4nNA z9V2wQ+9^8}pWLORznj5)oMTm-c@**VCn2o7WR8%7Gj30B3#Xj4 zPIv1OuF_i7n3l%r%NrgRtvi3;AXxP#wpyFY^y(tE1~=EjwdD|W&1Q{pzS0(5H=J;^ zUq-b1uFuj&-_#>=R(U5(gU_E{J}ww>-V^md@1n2Q*Sx;cnkbtts#)o7x-Jr5FOVr& zdOy-)r}cbIpIiU=Jna?jrZL6aUdTFg(u88Z`Yy2#hoH=KLH#%9LOXp@(1z*t$% zzn*Au@MGs>Ymv3$7J+G5mc8_j&T8no|Gu9p-K8!a#y6<5)zC*tK zv;EOQU%={g+zJJ(TVYSTDg&S>zFnMeCriL&hu8S(Dy%pP-!Ha$561MqA)x+qP}(v2ELXY}>YH_Sm*P zdu-db?K}VTzR9@ER1%deEv?&?7z*8#Mh3TH zG5W7OEZI|aOsxKnFkL@Brvr`)3~_#APyFK9kKHo+Zp__(N(o_HmSxt$v*xUCJTd62 z@&8W0i{3%idd_i(UV2#l=;u8?10Rm_6MM>NXl-^(@@Y)f;4M?qcuDuppr3dA`Fi!9 z{MDwZJn?SDce^_piMvd{KHXvY_};O0_!*%_(@^u(QHPJ>8!q)H3vxII7Z}>)7TgMq zk>R0xvwi2i4%VfBB?v6ow_0-xi7TiFlH%i-7m4qn@s^ikiyfPs48g{Ch4Dt-l)mJlj4KW4_MpA2SuSr2K-x#;2qzv7-Z!O)Jl$guFpqG`u%kAn-Z9IVrp8*;v37+ z->RE~o(F|RcRXm>ln;!&-g*hxvx}pNf|-%L0jiUPfR}l2ax~^I)Wpop%*w#Vz}&*z z%D~(J&PW{t6GI~dD?3wTEdx6{3kwrN69ZEt$g9JCNpDFmqc&_Uy#VkAgfCI-IG6rQ z=A1XeNi$(=6IcF@M?4{sU zZNU=(5(-5VbidI^0dr}J1cjmnX2^70sG&UVAAvd((UTOy)L&-2O8E;+Mtl2KV1p^? zxmqFI`2k1FCX%$wA}uiPFCde@QM61$Y%uTCAv30xDVip5Hkl4?5m|B+tkOZ-O-8zO zl*d6-D4Qmbe$H28X_^|~$vG{{T@+e{ae2LQHgY`CST4ioKme@JFe znzRw#LIH!o&VVmn4b?4RcZG&pG*$^bNczqQEdc*BS!es{99Xodoj_m#08|S7KRO2- z|I!xz*UrJecX|Gc#E|x*ME`UAw_N?FbD*xQ@*QH|kBE+f%8IB>h#HT(QC-@)0~B~# zdg`3k<^Ud&&Uw5ASZ4>DMmUM4)yOo?ME$;Cw|v)rUQ*OnNNcg0nqI6?mQ3sOyUlT0 zd+;s0Cw4mL*x$2xTVw0_^K**xIU!M}#716TTzm$X?=z4K{7_?1(cksXr9|gw-mPSu z4l)J_JZIwgCxi=_ARoF-2cB+}a35Q(XrD9sK3zF}TRFiO^$p&Giy zTfK^l;zqgSaS4r11uZ!U@RA5X1Q{Gi!_K9YC@m`D2k2WE;8-~vb%k(mT!4T%(qEv! zTVPxkkgHe+J~NF?66o5v$i1ju4Uv@RTaUxlhQxZQR88$CR5$s77%+$V{{Vol>vyQI z1;nW%n^6E4K(Zjs)*9A|dtSl{4z{oAn9QU-r~+mfgRA)!^ZroLi2^^F=Q1XVAKC(e zHmg}UtfoTX5>pt1KiiTQx{hbvI2lJ_D-gS;Hy{cLq|8LxZ&x&qpgOcNGqV`P#^@~I zwd6o>!->37*R4~^BOSce%Xrcm-d`&lIOuCx6w}9A#uXz{`Gd56;;3cZ?=T?pp({`b zCP1@CrmanbO1YNn3Q=rEKT>qSiwl~cEUzQ+#*DRpis3&}G*{xOE8%4vSg}lhu#SWQ zyW=k=#?(aD0>zFXh@?;JI7wp!qeJ(JDl6B!PQ+vkfeYgseb5sdAf5?Y@l-TRg)-1J zH+B@a*+Z%Xez`f%%(k-X?4?H{=i4Jy<3}XE$$h(%b-%5GNg2CK7IYW}X@ZdOqh?p- z+aec@1AA~#Bypn2gh}{~)rmcdrX-2Z#eg7VV9G)Rs-nK_xGKVE<&ri!~ zjG?>530Co-TOmnhE(@s(Ps}ZyRVlEGIgJ?nYDab&R!!{t;tl=(z>PYfa7J3_!6KLvN$kApu~pLqDvPH=#g+- z7S_Oq)S)*_oYE^%+Wrg%a!>9ZXynLggYM0_0~1=5lGabqM0ZY~iniJW8!aXdB@fj;5q} zBlw^z-F|M^9$~L90c=D9j4|?!n?!LTR8mH-lf+iWJyw7V{kA9CzCCJd`294ye|=Og}12s;S|Hs=%;az zsfxmv2l_)rW8y|g=p%~UY6KZt(TH4gVshI8(IZ?7s{*N4Hb{U;l}<(iveA$3m>{d4 z~bm^TADm#ULQPJYZ+mYAGgMV$?G< zx4}qY=&WmaqO5(Dc$en_w^eH0+XPlLt>Swd@RUv^enc$XNuMz|QKDfaDzq7}Vw)e} zSJ1Am+yeTJB$E|R(Xqe|Ok3!0w=SFw2*f)&ljjASkI+p;Y3$DF4!h*&B!Hw(Y9Bt? z4SDp2uZq7G8AcFRMISa;0ZTj$ORh3#W#CtazK*tJBlP($Nj!aJY@!cnKNyGD4;5{f zaDu@$AVFkeHXxC2x&lESo@+oMizL%O#35k}`zP0UqEv@us=t^*3GUA>5z=K%ff;=N z$Uue>GS0o&WwEtZT=Q<@<@OI1Ed@OXeQm!FA@U+R9l+$1(XZ$S-zT~Te6%agCe!~^%A@H*qTvEwZ`C8Lk23ZjQnUwPUo!(1{B)yXuWPZ}3Sz2Z zSFbF^APMj<@cl?pbRajihg36g7ojpL3?WG)V3Y>}ydNn#;L3{hg+-mF@2b?UQ8;)p z5Tb&dG0N!g*Q%(;*TF*~d&I%Vg?&<&zcEp|#}gQ6{V_r& zXubFp0=ZVL}MmkRIN?ZDmYPD2DI;&jr)hbZ8r7Xj^z_n`CIaKdpx+sb>_VnG(wv zSmn!&si~~x{w zNH0dJe*eTUC{_WzoWNM+Y_#0a*j-|hjb~3{ri4(pO6cIi%;oKv4Tc5uM;t8K%iv(I%0__7$>t@}m@A?F- zGlKR-r+LS>4z$J;TDKpqGllkrr+EkKKgj5Fd+EWJYA=E2URVw`utd*#8bJXh9XRZb zFz9~!1rEw=BtG2eXP@?QTc{K_x{B%olB1BoZQ3lH(mSatFW$tf&aQh+7$exK zeYAZ#6u%n3GrD?cw0zwGSAPfwFT8rFulZpcoTJIh+kq3b!F!vHkkpyWVNjEBU65V8 zg()eVzEgis`xb4TH!>_v4!8=y5?I#CtNYN!Pho!W2cS20CDh9OYxe2EvP=!nR&|Dc|@gSk>`;ol=uk$@<>F;G4A?|1-+-sw&pPJ`t${PW2uS7-$w=7hd0($vkXr@y%C#yAn!Vz9(cfF?3%)86~K52M)@AYc!_4TQyT8Q zj`HXGQKFf^z6@bD3}HYpS-=8dv4Ft>hxl=S4xLPGz1GZ^jmx~eYyoK4M}>NX$Sym+ z$47BmmXpkYpb#*yi&H|d&Z!WvCGL->qpul#60qnnfl|QGkRdQMtc;yH$a(aKygUT4 zfZ5=70D1AuuDDjFd(Dapb^RJxeWiu{OFiC|KY<*Jy=kHOVX(n?ABR9}%m~TBn6+aW z*iKZI1~bZNF_sgL3zl^tT6^XEn$6E}F=WRmz(+Zl?vzpVm64P1Y(^>Y@u&0*53#{~jD~B;#$ogtk z&o=&D!)9H>7F~Se9@nxS*9P)6vM=ut_S$`5o)(`Kwt7rDbfMpe&{6$l#tXtLQrN6p zx%c&3#!(%^(4Sg^~Q>shBKWsi@EL^8H6+AbFdkyCKxRA=SDM~l{G()@d2A0 ze`BV{?(K{+(q)Tt*C5lC75|Z<34pf}U=HR1aijs1fp-y@ZfT<94LcHRfkLrrCqk2W z?CE@bz#QicCL}`lNgmS?IV5aP^S;ds64Vc$N8t~eDp<7(t z9*oaKIM{Qp!V044*`jtcF;PH#A8kB#y!z!{eGgwBy_(eT{KYS5VCwkX4|RMTHb^xI z_4}NloefAtNJqEKV1MAFL|G4l4<$-pUkv<9vTJ;K@M3(zI z>EScTrZm&@Y624Oc$g&XEFfFymne5m&{#pYMLILWGJcO22@p@}<@Ova-lCqFr`;XG z!-K4Kn*jq=&Zp^MAsh#NkjUPhNnL!MkV2aSfw^eFozE2WZK7-;waj}5S65ti5yyd? z?U62aex?Wa=P8^cIMrP$H8WNBdFe#vlZAbjUI+zB@a`9R3-g`yz>z9t#J*j@qvey6 zYRok+Q|au4@ZsKM{2?*XUUH6$n6->BJK7Egmvo=1d@m$_ePZwsf zbdAN?3LC9VTdjk{M;PAmD+g=vuC=~kk<5B^;5^9~?NmzLz<(LY_ zlJ(*j9Zz_8;jS%L~;AV1PH_YMh?&;BbgU=+?>ab;WX*neaA12;k zHi~+O6YXL`x;~XPbA9SJeG?9iTM^5So$vEWImOaeSq@}E>D%}IGJN=Qs=7B^N7p$S z)ln;YUALpPykkz-zP!zw0Hn@)!2(@#$jO}edfrzJ*qD+g@6Vd4PjYH5-tG0{Pjr}L zNARz;Pn2b2qpq8&<&MlrmPeErPN?kVi}`Gatgc29E45n97B;<=4DUb{ zu^kWIDK#UylU?uq;KORV5BAnJh3Zt(@2#E1?=OR0U`p zYF^XVrAOI%Yk8i#`-|GjE*l}1CcJcIO@}*CqP#XDBTugL+fVkz6*;o6f@D$wJ zh6S-5Gvjtvp1}C*ccat1J+%wtxb)v18?%qPbnREfAhiKn%mK$Bu z;}WEU`}bYCbZ7l5`Eci*8?y$S$-hb(l6gTZ4j$4du@=vZ<%}47p30QdEZwp0MS2Rj^n~BFWlSPZ5e3#4tQ9YGk>~fpmw}}>*__50b13t|K zcHhHt)g~Vm4L2_)#Z#8o-!GI+RXID~>vKJt8{fA|w)iZ)0~*XWl;2vc!{IN#CRgcAdbVYF(p(Oi)D~rQ5Ybw_7&XRLtS*qS zn_9D?w_LZITbZ${HE45E<2~BHIzBtsYL&Ou4xERAYv0??qhFadc7X z5wjv!+lszx#h%$9*U$2@nlBzBBh7hf_w{AS*wWtFhic+?-VF=lFW29n29nQyvEJ_N z_OHw3d7qt>lMOe!x206I=-lOgQr|vUk`}0WwpH@5{?ZY?$riHpyeiP<2I<8T2SnxLv)QKJl%Iw#QGCe zCi@+6yRS$Uw##AnwI1a6`d9J_lh;-Vs=s4kad`L{==Y4qlq{e{XxU%B;Xaf44?%#&P@sR^Ah^|=04dDNr z(nWID2k@Z7z8NKb$js{a{>#q6SD~>;`BC|&<9W~5ll?xItePVRd{9_%fKsFnZU8fh zAVFMkWD)WsQ!`owyd1TBhK+wVC1Ot_K5sBmsG@4A054Z1)We|9Ls@IRDcTu>1RW40 zc$Q3?N?72MU(;-LpxD+Gx2iMG*VyNtt>fLj=h?;g{oQ+;uC420j$F0go4lkvqA?wo zys7XI#STmS7ZE`cw~fs5Z30~VCI0gXjd`s2lE%m-(74C&Q(;DiP|STcf^z?$c@<_A z8;@P;fQ!v*Ero>Z*prGfzRVuLshpKcDtCv-(D{mq4{9uW!0z!_@MQPh`dA2A;Y| z@bXpS{QW@rlImd%q%!c?+_1v+y40%M31$Mes#d=|SIrF#My>&M154j|pII}|sq;u< zDz;2zn}b-MssnvmY`U3t!$s4)v_#i+ZBhfPmcE2x=vX@y@h9GQB2)8Bhz7;0GgW7# zB5|% zoL>bpzTV@uHKQAl`fF)NZ$`!q1!4~-04jglNm^xLLxx5jDbedg^jBSk-jR#Wj4X)S zTZk;)d)&;{jPOTuXY$J|LVD@Sw#Wjt;_oxD2D)3%v7$3(Uxch2Txg52YO>-+Ur(r!6)}|>&#WA`A>JlYmv7y!Z-f(CWBPSQ62q@9$Ou-XWrrX12wCV z?8bB6-Z#m1x>TRW>Q~@)(&yGtHt^KH#n%Vut(_-+TMF&3{^sjZrs19PdI}xw{btB| z!v;6G8X8*DzkWSU%!FGFY03(6cQ-{y3rj8$^tL zbaB!5J>246Au4;{dsy^$p5!mqJcTG+it(b)d4tlIvix2Pa2Q9)S0^N@aRmtze{~YZ ztRN`sc$B$}TQ`yrrP35pkyp^&JPh5uJ51C^R5Gf}7{O$w3^gzAq@Wl<5kx8rb~37f z7{PtM$qIe$q@))aJrK*A=iw5}^_K?XUrKN2^+$cr>3WPyNsALg{sWbUW5gDWdLX3Mz zn)C_^M2L9*oJ0!qsA~9ICm(ey6p6d06D(#3gRi+HFX4kkgJkqD0~B(Ej2Di$Fnc3* zPYDz`5O!*2Ab1ZMI{`A3X{so{Rqvmltv%Vv z7&wW>&C70EIy1OCdx4%U>#2Lx_&IEk)6X}!>Jl;1zcOXy7W0|){ssG=|3KONbUQei zB0esEx*aw@`h&p#&+66xTJ0eCsdfBw{I|cLF11|UP*fV_fby&a;c+Pm1qCH)6iv!rUUSV#*M>X&y{dV5gL&r*WpyuPs+rlRx8ITYfojK0R7g^*3vpOs56N zLPOjlJj+QXl{~#5?3tFoBy0c&$oQ*czyb!s#G)*8QvjH-CZJi`ksiQL&cU5mhsvT5 zSR4Uamz&N&D4aQiBWFe^ZDtNywUFCHH&1htZMoDS%F8q!&^R6NkNQx?1XD~y{GORI zjmfPDx+XjUW_>TvVJE|oFqLN27PV^Hv{K3KMm4E7kHU^6eUd)9py`a$@2Ck4y%w1= z#vm;Qc7~t;z*R0lE@Q;4X*sSK1$)S4UW>1uhjwc{29$-HLBKteAH1f$G$x1_6o1Gk zGen4=#*NADLKv;XYCZaQf@~;26PBB3<_s&a^GAJfh6!wI!jgt55TvC-U<7oc2H0H8 z4K2n*EL%L3o)zRV@~7!fpXB5|kth_iz@mH$?xvVSuQC^CJWd~FD^TDB8-Q768R$x! zlxJg1k&c>gygf54#;C*z1U0@T$cB-cX}rfPW|f_l-N(#A<4nLKN${5dg01#XfEz9R zB{Kgd_oF^c$K`dg$lSp5;7id&`-y5)CF42G1 zV@So5AwLQJi9Z~yh`@wxLW8cr4%$AsO#oKZF<{w(qfcnqj zWn7B$5jQQ!M$R7r45C5;0pHZK0XQms{?gS~0XUe;i{4_O*z!2Y%&WTp3nT;$0)Cx{9I?YB`!T!T}3k`drdU zTA`5B?+zX0lX`YLisk?gc@ito!97*^fR+G718Rzf0C$TUswH9deJ@${WguDgAxrJN zETOqN-L|!|sKK!PzJWW{%mO`hN4{wy(1)nZEB26erf~R!`jO$m6Az_3z>Ntpq^>3k zrG2qQfLlsjKEL3Ro5;I9L(E&LFoj$fsQnCxt+z#Fx@V?Y;;H$($ zg0=mM)au1x*K4EwcY-(BSe?_6x=;Q)Ce%>9p%%4@5B0Ia^zZ6-^(y(PRcKuo$g|^1 zVY?m{8elDI<6AX6nr6O5cwc@#QM(W^mD^V2 z^BXke2?vnG1ePn!CyWc&A_?EO)P&1roVH~0?`_a@^dnDzv_#NfG@SsYi@k=ccen^6 z7zu;y(T8Qt-8pGkcEb1vZR1qe@A_OYhqn{D&>U%0G2FOU`Y1J| z0e*p^xsIxTOhI+41#SXGdP4Q-vaTE(+Vg4jpsDD%bL14{ei(4jGiYf5z?JsJ5;gFJ zPv9k`eQFp##f@Ch6E%d2n8t=p;Gw4TkTH33e`fG&tMGCVB6Pqadn&@pQr-=l#|P;3 zi%{RwIr=ki9ZiUM2_}wbpB{{w%rrn;YV&f?GoOIA2t-#>j*mftb)6W@2-`tovh+gH z?X&F0LN8GZ_G!{$JeQ+tD+Qhh*jqyN!LqiVGF{P5Sx;<3^;)yG5@X$0n@xBoFkVLv zedw?p;pa2rhYfjAVBN=?O$-Mx=HY9ayr~$#l`;2SCOY{j?Bg%2uQD#_pNPcUy6Ix1U$}|HO9Od<5JbbrFI!0 zfRVWP_kq*`LLw%@sT5M6DA>bd?+`killO(RLVB`T1znMpghYSZD{KT&ETecQLP3PG zSRGN4VurL9v8YL!3luhzC~D@}VjDm=i?)E)!kXJe9wV*k|MHSb3zXZ{LU*s_@ge%? zgL_GlhxoOp^_S=($QCl50c9JB&fvF~)Jc%Ri>m&ExfgZZf(TI=^%A2xMa$+WSjax$ z2`rFkT$76&1vN;Uluy8R_d%7sW zXt!~TeO&7LO-xbh4dG_%@oN?0$xc9WEYv7O9MTjH$;>&0JyJ0Vf|vlKZL{pZf@Jen z%%=wVA|`bb+LVN(U+Q&qg{A3>I{xlwf&g$MDcN2Nbu2WVE{iCgn zAU)$OC|i^F#@*RPc7}=^BJG~;sCW|{utRMH5a@YeRfzw<4`Bbm4~qZ|w|C;Z0FDf# z966@@A7S$iH>Ty{?XBE_q6>WJM@Z`#+D1a$!)NlO(NtJwWCgUwEd1NS2>G_U6iNiK zfc9>t!wIU2jmC+cv*CqMA4`$&bT>!vRV%!J2c;oRH!~r4;LkcO_gD3>YEq*UJ1mVgn{%sygGUcTiZSWcS961C{F3+nenQgA<^)Jd9PM zTOXzSBhg%wP6z0JQ+(cujMCrR%QYK*6V%}V>p2YHNt@726V-A5NwbCvu{dJ=h1PwM z{Tbol0+`N|IzgB8Z~6gULP3G%r%g>nC0SHTk(h6oje=Suslccg5dr$+J&=hEp8s4C ziVXfyXMg6ci=(kdwCP_LWZQzNmpGb!l74g$H7@gE%qBd#CJjnyNoq`zYV4FTQ#i0R zT;X_$S+A8!4$%>Xd8gAja)3F^%H7}fyjs1c{_V?eT0_vlmt>|PVfGrv;!87IFPU%B z6p$iEqPZmASeb05K|Xs|COrNdiT0dSlQ-3Dm0-4hHaWM!e(JEn{%&+gf^?lY(2LL{ zj^3UfGV)YVb%o3rLF6J1<_>if@=w^`KRW-sN$pG{XiOf|n4hep1gP~edjoz!kEFVo zB-rFpyQ>l0@iGpAY~gN{t8(>2unwA8nGYMOj@%D zF@8f1aZ+wfCE9iHXV6RTx=28^j7QjY5Cv;Bq1dSXfDmNC>&}!L7C+T_e^8GUV&70e87V%rTBMm zi{JrwZ_8gwf!cEzy%!$8Q24GD8UI+2U+|58hlx5~(2-8i{n$+=s(t+;H~j!;YE%K- zTcCT7;9%@fNr2#}{Oi?0l*xO6DcG=aYAjBB}&HV=V z9oe9bZ`I*cp#nD;rA6^GVrD-f$TX}nRWX3@h{Q-&Xw@X!9GzJ-Uhek9 z`@XJYUoIbRe?6sePL5^V(rcD|3Vojrvp+CmRjLw4s;SN#OP_N=V1Iu;j=jCG-W{s- zm7Xko3EY_&q;KMf{F)v7QdFUec7lg{YldtWwY|v9j9U9GqhWy`m2K%lTSu0AzEFn? z4kLIu9x&9+I{wS8eUtT|Kl#BISs(?;s;^%%3;Ty*PHkZil9zG3gzu-Mp}L>#Hf z1%0;lUA$wJ;5feYSFNKlx@h@R8|(I|72|kv1aq)BFG*>}hQix%ly0+?9;_|L@hPf8 zuRX7&;Xq(TUaJG};RLzEXlcu5Xfwx)vT1&PRig2H^}f6gro++8s$A{8AWDl`5Yx58 zQDY5#eqC*=n0u(r{x2_&Lc6Uhe93m}g!efb7$?s=JC5$Ln_YYA{mg6)eioffk?-3b zA-fQD74yS$qev5P>VuU|%S}T+K6yt@D(sa4QYP}r$K9)hQlgcCle@_!@SxaR>$1Y6 z8u(%Jxs)Z=cWtwlPnzS$1>esv`I7@LU-7QHa#U~go{`K{U+tc(GmhNO_F|tx=GMV@ z)-r~WFBljrl%>)h=BC4!#Iiu-&GYU~`<=3T?fQoCEKFB2Hd*`nzSwK0m6h@I4++>s zLaJ4F-_fVw(DMDP6=rN+Z&OJz6#kd%Yi{N~^64mvOZ@vD2#i>{Q3m9!ue?B?TBqqF zcxSkb^k)4|((W)gO`q4XHKoCagjQbbd=K>56p^m{M5)M=CilAJc6!{01e=PG4YB>B zP4Tz=Uy)fcbnE!t^$P-W?Jl(W^&0{u(eEa1pYAU0A%E^cOI>ENz{neOK5OIXroYYf zZw-I(^dovO?mGYCA@^{t)AM2MdKwhSf^J8fZ6JHl{3CBt(odhFu8S=%{fzZi*M2oA zsEK_a2i0v}g1*kyw5DlOQd33I!_7P@8){7%KGV6EyIu3@GkKIO;r_T*#MVEDGg&fIO$nj3c2Rj}y)2nom4C3g}%a!14^(DgYxZkDB~GI_P& z^(*>yEX{a5V0z+aTkn2xxu)6HD^GbZL4_H4Usfs9JHr2FdwUn`JtPvRD0^K&I8XTc z{hHh23FO9z=jODv^%7;s7V&L0hkWc-=33kPU0#K6&)1|U7@ZsNgM`y(HR_P>aKj^dhYxGo*$e1p)WORa zX5-12zz2J{NA82xj(OWpN%5WiZiBFPC(^$!jh1}IT`zF zhDU?@%Wh}V{zBR;voEtvY2lcxy~skg<3eZYr}@ubd7-qWty#_18tD=&Ym&!9F)1ze za$8Q0J8|2&umLab(qs((9p+!D?2f~{NLMkHr!F6;OHTOrY@d;@1=oKlQ8prn* z!XwM1>gOXdk*=qXL+AAN$7Bu8glyL6Zn~^=b;`$eJSSZC2M|(NrNGf<<5gF6{bCPp7 zg(}%f55nDOM`e9`s+P-j+tTaAU(T(ykA7Owl@kBL;2{qet#!F68!>#Hx;t8(q= zRh&|@T8eWM-afa&T~oRriATqqEh~n1bRxXbrc^nv$p!LwU1l21=ve>JGxwJ;%vAM^w&3oUEX#- zp$SzSFCWI2|9U)~s!6L=<2$}rloGY)x_%~FQeQrVaS1hPtr~jl73Z9oaB;{Z^`)(^HB# z893+k{djRioE+}_!)NS-sPl4!uJc=r^JMbVHJMfTQT-c{Pp_sNi+|s`w^giWAp4z& ziod}NHF-|2>Sg|Bx((lVh`O6{1nuc1uVQcO(*J^dY$MMT@`3YU(E~-roMmZeK3SrC zovGs7uy0vvqgUl)RevQR3U`o7cymcNIUG z@yo|Zs>Vj$#r3$o``Z__dgZH4NBa8v`6-tC$rjzk(PJ8Nn3}<6GHzM+!|`)wxW-?u zwbZME{&4j1{KwC5;gkKi(q2~Md%M0xMtx}-2P)}AxG&v1r+B%oTl^}Fp{cfENBoA~ z9Qv+>+rG#VzMvl|7rWbvj;kL2^88FheS>DwLi{jso|^o=zE%1H`G)N3IoS0@w`LnX zU*S?rKm`=bm#G~>*f`LCEGXvK9P`p=cq6y z<%s`{A3&b=QJSa<(6#cU7uW``%QJg0tmchkM!WV}sHxmYp7e1b0@+mEWh>8^Eq|AK z*$q`Hf$JS~rzqu9n?JyHL7kGSdnbfKno^t?t6rC_eiyW#ms+E4|Lxh$lE-T}=xI0# zFUb9Hhf3XtEah{uo2T|0zYd9~?f@JI&*L>B`5>H>Z~kV6dP1zySdQiux*X};uST8n z=ZR9OO(N=@9%09+I}ffAC7ZZJUmy;X^AA`X6st@&z54GWjNQ{KgU(j^*(qxHQ`80| zOGS7E-3RMBPgDe(&Q7tD)G&TX3HWNSpt}e|cZu^nl|oy2T>+YHY3|lL#4+lS!^3i> zT1}BpONu&G9oobLX=Mgy5E%#d+9;wf(nt$yV8Dv}!ZFFIy3x zzOJC(yBJ)xF$^WFnDkMf`2U%+NB=ZEKoOmPwW0rPDDnJ1HkAIarpN!>P-6a94Mgw1 zD9fBbw!}Zje+$|>HNcfo)SmTf=~q0XX+f}w#F3rBqjClK(W9}~TQ6cJ{?>O~Ptuge zhe`D6&@f^dg(D$J(J;CMVmS|Dd0uapGOF`QJwzUUk6ldA6@E;oI&L_+{MPMw-h6-G ze7~A_k}H*0C@R-fQMu|b)vQp2@nRd+R8JfvsgJK5v_Rr;Oi8~)nkZh2HqRUh0c!vn z#$1)>8W}pB1X^A#MSm1%hH=!kf?ka4`8;?7!xE-uxe=(;VMzyNg>F;s7_~AMnIcjr z{DYbgR^H36gW5<6IJr<_-y2G&&|}$DlF^2;Rsq({h0j8Xp**9)uNF87M@9C4EKmdp zSsvsEvh5|$*~i{Tj<0Nw1*6)K&KyQoAtFyML=4Wk-$52w3Z}AdF2IUd+SD5>X5o&X zEqMv_@fraMNK`6Yc<7G>$?Kk5oHz_^5uV$Ows5P70CYA7`dA41cpCubBp`&yG8Zru zqD6moTMykzTn- zw|0>akU{=(a<(Iz2->u&+-0_-;lI4jF(Y`V9AQr5Fr#&O$1g=|R#XxE`~1>e*HP=f z$|((;Xo0ws);u>_={S^p?Lj_gG#m$oU#tGJWt3akU!lP6H^8Zv*m9E9goS~1=`slc zfXFkBFcKmNmO7ddbp{Ph#XsXoHXLR-;9O<`VmdlRinb#oOG3n!fjHw@RhU30!iC_c z79P>_byLPP>dSuW^tA;SrVYm+wHXmMnI)yA4> z)f%tn9*)gEQN36XVdA0)vSgz27#ql_*hHgtN!5cnaGqrsqQ#=;7W-uZrRbbXVQG|* zXR@N6CugacD$0)}%q$B`4Jx)D_vSF^Xo)XXnoxX^pbaxNMxsMbH@NdUL6xv^*uq$tF2 zxfXq%vYzGqZOS(9s(qJXAobrqNnvwC0z{|MPzz-WHLFQ|=`v=`h$Pr^Rz{`ilJrpQ zUAQ6{TYss3ekjbFO{E1XX>_uz$zg(g#v_9r09X@j4~@>Ni|?OJJ>+TNw6lBWMU2}IlvrK-VwV+Fpevch~1%3dGE1X;04BI0K@ou z+49EV_Y&h{HTms_PrIEZDsVdsVcpV9j)7UG-~oM2Z}OMSpa@^O{*nF21tTkD^ZBRj&;RIp^W$=&kk&gL^*2$>Hv~uFQ<;(?Ha|`^8 zd6VStNBDVh`Mc6QUm!q`aam*}21tOBvT)&aNLzaR=g%o1KtUfU(nxSY6K@q5g7H9= zL~hd2r2A85czmYjy=9J1*-UScak&#>F2@#PO|4CF;L&6e>|?$J$3B5VVHoPO?WU#c zOOW7<9m4h%oxIEJ09>I325fl77Fx@$NSl!CfhPoGa7U1 z1i8M9;3!1*eXoVlROnoiPA=G#!R>=6PBPd+a7~s<>JB#6#S7O+?|#OTJ2dip<&Z#| z#ZgwM6aWHG-3@K`wFtu)2|(=Ghv-Z^F>xAq#sv9~QHJMIxIo#FPIy*gDyc>Re{nNx zNlg80Cn+23yy~N0M;l_S$S7>=`|s%@epG_KQ$)Oz2LNl@%|!1J*8B35x7XY8iV3)L zNaT*-jRK1`?W6@=*h8)&L)DX$8p}zHWT(XO5EFW;3O;29UARN8LWf6E+Mzr4l0sQQ zf<4uvY2S|FtC2!fgG%b6ZCn8@k9RO5??Ter_>v{0tmpL*ShG9y%D{(iCE!{d(U=p- z;l5d7hK2;LkK}a|y}*`0;%nEY%mqBcIbZ1@Zp;U~FSNmv1%zte;I}y9-_|qZAN!MQ z-Y7d9T_-T-P)>Q#ao+bNFn`Boz6v@VNsXr0ko@j0p8CO*#|K0Pa(*!7V}88{sV)y- zcT`n=8QRJ)A0BUL>Rq@!U3E-)g0hKzWET8>Dl=(&*2!6cLPAn2=T2T`rEUD}3Ii1u z?8^eI)zFNK{!81FP*kKIe&76BE=a^kz<)>T75kLgTd{tucIc)omEl(?iv60rtrXQV zQ5Atc5}-jsSWC3jq)1DNMGMaJC6zGIXkew^3oBbfS((oRC*bXqb)s*op@Aed?# zO!ql!%>`ETkq9F3M6$C$xulRkX@$OE2LLK81t4(?W}FK&Lf|E3<&(3*owib`Wc9^g z=N^_iDTMkbl#&fz%?dAXWrB(=R@rKanyrM24IAZ8^c727M79S(sAef#MF@Vj`@_qW z)}$y0lohWqz)r}IpkG(&!C=vf+>a^%1PH``d3p*CHapl6AMtO%m zu~;z`Vq6}ho8ls56Pn$_(5+6Kk{c%Q*h0@Ne3F!o-q-_myldR?Hj)%_gck28l||YX zE)XB0ny&>N zBvj_4(ig$-i1eTH;ns=M;Tp!44fgoFXmhs)ak5Ol9;?dN;bCIsIe}{8uby=Nht#&} zN0hY_

xmU}Dm>J-18ENzF;5Qlw)nqO&ERd@wlgP{)=p}W-Ri7L<&7q=;EX$Wg+ zNH@n()KjQyy{HL7~+Mg2{xfX2B%Qy!scus0yNaF4YKamjO> zGL`4XjpOi>VHBf6CDfyeH1dK>5o%TlF^!4;$swc%M~VO#>o=tLfQnfCf<)Ut=m`A-WRKkHVMoDIYUXZiT(uY ze-zaPC0YXGY)aTqT__9Jqs2xX`P|`xJjW0zV6KuOADBrIs4l(lc6t8|DGP4^3YG;9 zaE8v%$J2xj(E#Gnk#pkU#gpPcCV5Gxcxk721&H%XkmUSLsF+p57H6RbaBI(efN&E{ z@mW%Oa5+X-T$wpQPT9Ot$MN2wK-6-MFIMb+^dfHf?Nd^rF#!EJB%X4DEvM;fNQs7OG z^q)MP#X)yR>n){s`x7qb3!k7j^>8LSpr$nEJ^ZFAi0&&Cq}=N^FKYJj*16#PJn~TG zhIHDcdu21(YZDZ5@rO}nfuk(H&=OS;XQ# ztaJOwo&@9k*g{W^z59it-jDvCa6klzs+Rfw4abeR^e}jzm0h}Xw;x6oU!#IuD2lI(8i?|fUIYL>p4P4$2Hy2t29*05c`lT2*e zwr$(CJ+YmMZJQI@HYc|2iS6X{-uuHo-@#g4y}IA3)vLPx-tTi?{hp<$3;g*~i_0Hu z<{N?|c%P47<6m1wDt2Fh=A>USh@8}b<|Ml2MgI4f5I(a+iVV)qhEBX?Y<02ro2MGL zC1urp{SWFDP7XS?i#l2oM-y8Cp1^AD3b$CnWz~`br+yu(#fJWnvYV6Noqu%m$=e_C zv))Zfu%u*92ox%SySrlEi zEb-JE@wB?&gz+@uea&rVt|rhH3D;k*rUO*Cn5nOfo2_5ErLK?b^72#YAdt6Qp*7Fx zzSFF+ddV9NEo|Muki&2$UZGb_=<6gfu?57hH_2e9__$DL*viAxjBUR7gi#v^jzG8n z+>-gqgCh)$0U6yFFM#9m{^+c#c+TF=jq*htm-wIqpEog-n%9e}ID~U4xrAqZ!V<;X za^&1lJyV+eoD{a|D88WUn$9`h*%Pi{ALU%is$;d|P{Bn!bR4Q&P16z|LUG1u?^=At zNUhrPhx6A4PQU6|*S~o#o2n+A-dB4$J!^en5H&6&r+F>pOMGxyhIKLaVis&!`YQi@ zN_cf8X1LdicP4c3ILZf9NJn#@yqw$7!*&@*tAiKo?PDz&JYrZ4i>lDdq>8sP8 z-m@WiW1hLVEWZ^MzYdryRpIBs*w>mf9{cnea)`2;Yw*JVoRPxo$+0-;JTt4@Xby!t z{oGNYZ-dp}ik*{H9cFAGR%X{0FZedN-DB*!>^E{w=TO)8g=25ZXUy)(=`BKIFt?T7 z^MvdKl=#YVDmu~)I`LSbR^#_PH};$%9NWX8){BzL(z;nI4#Ye;ZL5=XWCF({ZL#u_ z4QwW`4aC?03O?_7ofj-ij62~U8DczpBPYgwKl+|JjVp87`}We%4d{l{$3^*=+a8O` zQ27I)cLXR2{lj;OaAATr(fXPjwhw2fJw6U!g5$t6a5X5yp~w?{ihO5v8Zj()#BDn{ zO{>P8J|C;46DjN%iGMMUvsU%9EYYEhSpV10)fu%bJB=NKj3(29H#uh}@M9T@$+!MK z7P(Khvl$NX1~-w-)lY6*nWZZEz=TAciOb#GWt#FGlcSZ6MjINg^l4uhBrO;x24dff z0~2blbkt}(_%-*{zREE#4UM|4Ku)JIyTO=1IJ1MT%~xhQ@IK~yBTE?H3Bc1FRxY%< zqrVX;`%~oyob<<&0q^QZRP%)uVD9Ln!{_B=+=sA0Rng;A9kGXp-dpua_5}WBX*ww6 za>n2TRtsPMh3q~wrA0sZA(O7ElzNFC)iS>)O1p3U*q4ndLJ)eI= z*slck_-L)enfRSqo*(^*>|Wgc#LkP+{I{bv-fWLyk^Q;sN$ma1RUj&RmkwkPZ0}d> zd(|G-my&~uj&(E>BqX%2=RppbZnbr(QbY~<0L#dAll8p(g8J~GPeD*Wq_+nA(v@NP$^=k0v9!j=fE0$hOCSS-{ipK9`qaNz>Me2%!1FYr; zHxgeh(8u?@6>Y0!*Q0Acxv^UAQqfBI0yi%$mSAEqHS zjk`B1@*4XrC&!kkm8FzwEQP>b7G<~GHIR90EN`QN)md!Tb9K=hJ4KX_M_Y>}<*R>i zX*(PHa(OE`^}KBfS{By)ejTv+5E_uKwz7&cvfaq`QfI!_S}Ad0jNnf=uHHz}KMdNq zydQ5A>#^w~YRTrsQm=$5uU$fEfaHi>@^Al)-!kCx{0T?^TUBC*Z6)6yaHo8DpsmW@R_z-krjFlZ5bdIrQ6qW3;A3a`tgp_ry_ADhW1y(6GOPtPb<(?!oPtOw}}?Y?NvCdUKrZRmuitPdsKwPS(3|VET?%mCnXy zPt+(=IlEtl2cFI<@lp&OnygKqJ5#eS_TjuAd|rx{Zl!yl^wyT~$&rnIsjh^?!;!IH zYAXHef-_s2z$v_<1XniKuq_-~$C=1lPc4apc4JacZJDaxm>Ub~UIS4m8{y&CRBEgw zEC(k)g5UJbS(5)%-F0f0=L_}nwIB~(UIpvdes|g|>O0cE+~nI{Wjsmybg6}J1HGlK z)XMpK|7G))F7vqk*8REJvwzT78915ySVH6OclmM|_%;o$Us2QbY1q>q*!l8R3IF)$ zuP>MV{yOv-_9m|UxCHqq-|9)0mFYL2H=rp??7d9=rdn1pL`<(+ub!)4*q+x1?HAc~<=r?q(=E0?)yj)KIr%{2Z6I+E!K{OSeBha#GPU zKkqXQ^ikXaGgepgpD-`$70db_4f^7@kmHq8sgF=H)4sa4J&j+JB~**B$9n7Je3{5z z^iN$MbNfCeY!5tj>}fej_OmYyJ|(YNzaGx2*VH#S+C>Qg4_z9{F$G?hfAwk=Z)se~ zaZyjqx#Hv|5Bw36mmErlqi3~2OfUCQaHsAAUNcc;D4=EMJZYz-XQ8f%Z@3j%(w|9*W=$ zlqC@Gr9u7#Zm${51Pz>h+qJ3OhM>f;LZS@BgJ5 zilvT$fvKgDfrWvYnTe5&p@E_0+e6vf*{kfz2b8r}ZCC>N|?KY#v zlQ9%*ixP=;*xm=&n_LG#0^BHPgC-PrS=~VGx}$;0rLI14g)?-*&K+^!IOmGzTzLXz zx)^xEc)CJOG(z!8;|$&*fPMK^tFRHY%t9?NZ5x7j=}5m)Fp=@i*(sVQKmh$th=eS; zl*(4=lz`%=0e%a#8K2&J^ zn<;had8R`1T(Hvbl>8*LQd&||$V}l3nCv8$Q!i%2dEbbYgd|)PK?La0g0OS%0$@aR zHbfB=1QehOf>EN1z1uERCVJt>oV#QDPhRq|%%@AAf0Bc(2^Q``snR|KJloRi<@K4h zIO9aJhyZE(R-6a~`qm9NO=mfKsBod`ksNX=A)!iw$O2u7JmY?Z6GZOystDF%`%VB! z-DY^u^Ugahx{ku$D9IQg?!aMM_-#lhcEA!HWtOxfpMh`-0%mqLg`trIQ&Q_r1VKnO zs?1itT6rSBORQH@C{BT<)Tk`L*CrSbA)}8v@6Qg5u-F|;HHgai4wI0SK-#pK6L)rj ztV64Ay#qpHq(Wl}mvsbUPz!S)1SrO2UVG4l6d_VJE8Iey5OW4~9$9)7hKN}ZN}$$X zBDIva+7TG!mc#gCEQJiq< zIm$Rj$s;A^*n$JD%7TSNGaPG5-zVu>c|Zf!3sF2#Fj>YR>K2Bw;n3UB9IGN^kxa(4 zC<2PDiX3qO=0ZjjHQ3~2UhprrlxELc=X%;-XlN7PLA4OKs(EI9o0r9aLRZab5l|5& z3I3>mClANj>uq&tZGb(s{$VMCcH{~fT)Iq1{4Mz>j8Qmnfla{AI#nDxL52c6VheV# zMtU&ZiM+yJcn3q}z^|Eo--H;EzFDEc+C~B4sR61u~el zic(SW6kGDrD4K!=&(X;g0!tHzVvwPHEf6aOzkHQ4STuy<8#4VV;w2doV@cB^g~uVe(WAw@F0b z`DtY3D*ug9m;H@V^TEmMlvxgWdSE@QV{aVuT+F0DdpyF?cM~Uh0_Z}a z*a4a}GeyENgy_yHViQypwPyd2)JG`(ZGv^#C7xXTYf8vhdbX*si}DsjUML!Qkw)m( zNYQ)a6cHC|f0NYIX0>7eCaGy4=QPWIE>HYJQX46lfsF&uX@$U!1kfkIChv-coocnf z4l&qEE!I}M@;)DF*h?-}SG$TjZTXhkovveC$SYaw2?JtWWb&QKz!)1M0D?~j9$b7S z9q}`Lz@JT`T4IRuCzaBz5*+(>@(6`d7cU*xYk#^E-99<2dtE`n?W%7*nBEd9yrtb> zDo-aWiM`?~5Bmg*@+jA3nCGfDYw9qMnSDU0HGx!mxr~1i)IQaFE*gIm)aX=t;;MgH z)m+7U56J+idXDWcl9~(z^pyjel=2(nTAcM@LcLd((#id(m_5;dlhld-CaDPpPr&46 z39{gz`dgr$r(mYs9eJH&8`myQzWs3?XX0Wan&0sva!tv?SUmWCEV1ojnZg2I((D;h zFcuk!`kW7vl-U<+!jJ&FHq(hm5s)j@3(DNef&2asNiA)1J3i-ns_TLRyZZt42JL$@ zR~X%%9uMRK-MKdu99;cW{+}eZ?~Rmt=imtN8A<+3IOP?RVz8KagFdqm!Wd&Plt`n0 znf5U(7{sNTrj2NN5Ph`IVug%1kS&qXHWhG7V3<#6C0>cF0OCgSJt`08yBOgMFGPYD z)bjw$XHoK7&FPLEGYf%XlluU|0$EZpjuq+v)yX=Humk-rU1daOPt2~|&tb-oD&wE*9 z^ntYonqYauutVbS?Eghl=l06>L#-gNQ!+9<3zDL+^$XJ?P+P@ae7E_aqBSsP=}+O+ z3!?LGAS^7bpqQaHhU*^9vsnQdBW}q=YRN=w`Htd0IZMhJVVqLRPN0F_+zV{jfhxHU96HstkSkLy)DC$G##YM z>!8HsNd)lbwFqKDLs8gg_Q13 z9$>|$Dcf&R!V6NhAUz#w@&ALQCI;CQ8*p|Oyeu;GTPSdcBXEZ!(KEAf=M%l2AnDQs zkkmpK?*{MnkiLQ8dFi{U1J~FE19lVxb{GS890Sw+*Vuj6*kjk&L)X~RH`<~%*uwpf zjUoE@sQmskHs>GDnwY+AApAPjfxyyF_!a|3Co|v|CZ3A1zd3lMz9)){p`G}Lg(jqk z!TDevCf3H1M>-kh4|y(vXylc(-;36SoA|+xV zC1N5eV<1g4kmQ(3pfHkr24=F?+_`6>S8KG_QxtrO2yDp+Y^egKk`$)$7fj_chQ>ICMh$cA zZ@>o!C+(q7z$jW`2d1&yjt+$Sj6e;EzppUX?#xc^&`$2uPVU$Mk-C~%U8T~VyE>ZifKM|%k3!nE1oso&O>DQ^ zL5GVld!pZqfAeq~cGmR;x0P&9C_XkaNXzr&JE^W9pd;!T4~yFWDv`q*fdtQ`r`7Xk zFn4%AT|sT8iYb5D$i2o5AzNevNl)5+0y3CX8`=SbKfc=TakRu_f<;e`062y}oSK?iNB?xAGk1}E%d})WF4-)XlBA|=fWAe!_(Qoq(xS1*19eK7 zKJp$O`Q|?ewQnKuCFS1&^)}OoGv2W(FYDzpXemmtz4K_1_{j zzhQrA(=D*zP63{OE+%j)RYmgVjwx+a&&hy$aL^5v=5mvil_^tab=haRdY7Pz9nzvU zjQ=`_KX=g^o$wBONbeOF$R#7lrvb$K*k361{}8Ez=Mf-=CT(7*C8sv^vC18_EK5T& zUeeBA-Ylxw7o`H}C?zZ*_U(&9omQqHV#38Y>G3FF#R?R%DHe9@ElbDh<`BYdkc;R~ zq=osaK{Q|5f#c*8cyo)G#^taljfcxDNYO?x{^P@aj&H#(q;*3xvKiX{@uO5E)Y+>jc*H0D{i3z;?ip zh!sD|BDQT}rdcN+XP5}ZJknRZHZchhsjDY9gfiPm)7wzf+gNir1Niu}*?L~@Nrx_6!SK3!F)D>B$$Fd2Q*V8+B+PzQb--NQe5@}8fenz>nphdSoQxVQ#a5s*U{{2raTyG zrUYz4!W;3QkMGik6W72QP(kKtLA=EWmsp0OaOi-brcD`UJZ18{>6}ko(n!8Fkl_Bz zWHATZBkPi`L3Ga-)1Mk+C2`J+t^8(`fG}L`R<`FvhI2CvT#Gml?*F zJzY&kP5gCQPMeweX?f_RjBEQwX^}%;LVI}f)xb6^DM$FW$+E?JI;-zxw;Iy_WDxrzB7hi%DsBR*PNF1WMuhF9{RlT+Z}E%*Qj@F?HhY^ z3m%;2>o>tgy^ULs%w1JkR*!AduB?AVX2`yLcI$6jDs3s8*=@L=o^<7yOv*K>TZhW6 zxPLm|msU7+G%vrF=w)SaS!=5Mt)?^`ALU+6ZbOZ|xV+JA_0X@nJ*ZgQ*>n%`b*$4+ zGrnlsO-8gfu!_!qxhUQ5sh;h>v%^11)2^+_CiYCzSM_KmgHOc*-6-AOu4;tpJjRG`*gcKsK&`_?n>En{oa6y_}%Qf1i7`g)km*;Dm33*Z~W#) zxhLG}VILR&Yk9qmp3Ehu55FrLfl#yS{U^M-w{w>rrM}*4pQ3S0YHrH1iP*>~->1aN z1M^`=*Lwl9_A>Um$ICWYHt)Vf#+Z3eJ1LJ#WvV>pN`7AA53t-HS=P|hPq*1mm+qcF z!ehT$U6W;v^+fL4#HW%_@cBs#o?8qpvXyf^IxJ1>=(inoJMA6?=yOqhnvQmtyE-vU zEgsyo@niS-UAJbxKYMV0@^E~1!93toeNsG}sel;VL~YyUY+F1zl#&~&7@B7cQSU-)T?FNSJ~duvIB8IojE zXYJVYmS2>KU-<=0`EecZ5Bh30-Fn1s`KC&p8+#e<#xT9Cn}|n4)j%4mkV4zn}Xa_x!{n!WZR}^f;Wyl$O!( z<1WS#-|wL;u?Y>9;t;&0wa$KDmL4B{leUNJ{+6D`-nMp;>X1D;Ya0M-B=Ogq4p!l2 zOTX9>C%}Aq^7$VBST{GHragrB^P((zIl=VKb^8s|@g?v!2TeNQIr#eAJ#9y?<1#VQ zL*YX6`sY~bQ$!_M)*~RbhlBfb5=~g@WnP{I*IDZJmS0zciSiv8P(U_|wOkR|>79sp z`RgIn^92-H_LF$u?3R{(nqNQYU`~!hc{?_@Z(6GLIL+zz_*I8_XT4cq>=)H-|CE#I zulIlejr~?dzR#84F5^#wskhBRVQ~Y6U7anUH1N6_{WUp<`ci?+v^882)3#y@pL&@M#@wmi zhE|Vii1s-h7Fdmkr;|G4@-iXJyaH8(){vcnsi^;R`y`^lNAHJe>U4<#RbY|gtO z#>IH`rn++H@!-!x@7NZcot6e$zVq_rw}0SRvK9}x9{em1?3$^I>1dfNdN^;K<}X)B zIh!8+Y`D}LFTWmaj|de-9B|kt3x5?2K&UItFS4kwudc3Q3j2^br+fS= z@DHBp3n4|p1Qml4)XV?}$-yl!RY5ZhO-__D$WVtaMrp24Q&LLH(8O$OFtXc99Q3jF zgZ<8Y=h5?&c4gPSo!+L^+68~!rE)-!=nG8BJK+BTYtzepA{L^X2?ugcll)g zOgd@BX5cfUacZUR#AztX=~5cZeXmB#+pth!g5$LdXrGGLm-|rpwN!H-P;pktZjWiI zzN#UllEEyXnPl89ia&1<8l~RnzgF!YP$qI$PS#jJHnu1qyE8-z$ct zBv)Ooy&3!tG?vJ>$m1Mj@2WsA7Z-cBEbiaC8ji8%&O-Zsr;@TljWM*IQDIKQVuBbC zA*NFEvL^*3q%aV}u%S`^z;v`6ai0$?IJ!3HHVH%8x

bpUU1&n;lm$c2}#@qCUIW`KYI5PaAnhe!aergukRHPk%N$v>}HqCj+{C z=}8Uc`CiwEpMDyPyx7{g^z3*E2X50dnSaT%zAwc)DlB|ihWl?Qw$r;x{rnPy%gn-^ zAN3V_34OwuBO(0}1FesEK6k!_Vf4%qqO6EBqLh?yQ=$nf?PL{E$eB^hA@<8?5h}w- zgY*Z+VY-(rJWO3(DudJj0JYi=YS<*OJ}~(b%%|ZfLuEIZ8d6zuc8%)av8NLNIb*>z ziwaOg!VwOajJFjbfgv@eHHCMZHJJtAXu zQpC2YE?!=Def76n#(tFb9U4~4w(9W3fkz1cCJoav_ER)rWmEJGj`^ms|0EgzBBWXWUxM_%lMD_(!_Z&H|B+-syTdH6Z^IC(At^bOQ3xW4 ziV(%OUO*%)Ak{&l*^HREC9Ik>WICC#Ne%rXS(Po-uA#`MrL9r|Of*nx(b9f4($Q9p z_e7K{r52pHO{K4La!WQ&KY8%z=a8o}A9r4POuv2l0u<>9l6j4qZaH+6qKXG*1@vFz z^-usLcx*M>HT-OnQ`a@#4TZZEgl@!Ue4mt#F9q3SYk26lu@0N_9j^`A7?QHej)ZLZ zLiAT^E~ZSQs-s{9!ay5ohwy?^+_b|wE}Pt5b^)~Gkb`GlWDFa-h%{1s5os(qP<@1N z*m(B-PQ!s7ufYV61O?gx^Rlq|k9h0_cDQ(f9g4!KmA=s=e=xCO3xV5Htw=B$!@<27!Kv#w9@zGNT5~AO5K{Ge~U^ zT#N+4>@Us&<&c+NiBL_!G6F?w5}bl$VG10p<)l-86-epF z2(M9OyC5bv26in>WF7TorV)MfSo&R$AzncIY~Bz z8!Bw0J1M~sN7}CCFSqoL4uDHyY(x7(2T=Z z2=?)%9yznqf^5O*snmJd(X=8W+_kw9mIF~ldiqEWBpNZ3p>e~{6=1VUu{1~^{|J^N z5Wbz7|IHSOO`0(@HY?|oF~^?e>MwKfT8d;4fyU(t z$_Z2tl76=;)cBKvryV7V(Z4Q0Mzw)p-Z5*2S~xD8O)oB;MKCV>wupdvk5Vw?%~B97 z1=-}842p>lZ66g#oUa5#qcAvMsGLDcz`r3KphgQoIuba7u0HV~$+e+q>kgqo1hxC5 ziXPM#%DbVq4+3wQA(D2mevduz$JLyq2Lkn#gF&6jFbDYp{g}wY zO^_jVINdpn@jwg@6|n{inl`<)3VfPa`cA6XyJv<@0Y?|Z2Ch##b=*>P2wCeI3>3{W zULB8L8Ju(>y$PN(r*l40wFQ(Km=&W+p>3dAg; z)p?TI^a2qt(XVDc*jo$FG+K=;&uhW}D{08*QTXyIC>8v19Q#(Xo49d+VaQf1i+nF~ z9gcG^{}Irw3%p2njj8BT_~(!EX1?OZch}$L9q%gqslDiAwc34W<4V`bkmrimYZW^W ztV3tmpfx^FdT*$ma1`zz;(cd%0n3D-HFl_--OAToG}cP43LOnA^fTfX6q27KrNRYx z(h=1g5_gplt9;JdB*2yzlT;>gcq z+50K%5|NW3a=CD+6a{xgOt5lDb;<{=r6l+qvaeGRPQe}=P08PrvN~JU{J&ECb7gmE zFkyZLsQ7u0I56hsvec4`~?_1WRQ=oDI;nh30Z zqxY~kj9-51AgH;iCy;j&eV!!NxkbQ-rk%DIg_H3hkA-64O8qc|5g-{sp*om-{AHb` zPD#+JZP)~AiqyRbuXZW6gKZCs_~r@Ox+{LpKlaCA?HQ#{R#3Y)v5yurVcj?Goz$P3 z%wC;t zEfU^3Jmr>86gYIux!62MnGoJ5(5e=pasVvJyceq1B7jew`fIQdDhK6a(2%W~pZyR#3wRDhY1Via+v*ZP>v@Rn=fHSe zH%=j8a#cL!+ouiJ(TcVMBXK;A9kdP|xDM;$2pHkEW0W{fA|8*u?X|pY^6C9MBfm6O zfS!z+dt*vPA-@*_Uo(r5BVA3M&>O;?2WTBKWZb(x>`(8nP`R!I;4MeoNY*L@Z7P_D zP|TNe=c~dOU_9dCXJAk}6^`NC_b61Ix#>3Ps8X|;) zvO-#Il1!2>&=WvA?iH%BQr$PqFaNVp-X3O!s0eye<6ZDG?i*o(Z8-35u>i`CpJ^ig z?jgX8>A;M^z&A9YMpU2$LBNc#?1!UrCGpw*nFr1g<_Rvmq1lrc#(olYXiegH~h(91;?*HzHoac3BNlHzL-MAm$3|q(uh1 zTBsuL+?D^~srutx(EhWOuE>Cv$e>H0UBAenOQg?J^ZODtN~JobMkJ+%G=+vVrACcn zZ3^I}Tw6||L2d7(e6b8!WSdBi>63CL1EK!g-!~iN71kziyU1S;b%&d$k_@g#6cA!? zb%t*%iV03E*x8NmKdm{oW;o-iVTx{N^E& z0x}vIFm4!#zc*#C!x{r29&C*1ew_OBObhe}?+cMGyj9^~pC~^G??pDDIB%zrY9nI! z_s$3j-1__Az>f&(Q1xd>#pfgnYjV-Hj3nb1g&WMjUQk)2>~+*Wrm&-PHNr$5%%_- z^wL3U139UcUuo}CB-|?j=?R7O(o$-JIjQyQm+6CfeRabVb;V~q;H}4?pQ7m&M{uVT z%^w=YU;nH;S@OC7Yk0fSuv4V}nOk%Dkn68JqoDjVU$sk0r3P{F48NIrT)GGGO2Nw2=4O(p^)OFK>@Wce=d+;v$q(= zP!l6i^AV!ZP?K5ARFeab5u7S^kjOeF^xXp)9a@`#Z>!P7+g!^z!Di?rX{j*kN@hKy zPbPvO-ZJnrAzgG#ji!IZYH|a*S=HS3nBS?0%gM{-2+rgv#^i{C$BFnQ87LzI8R%T) z$0NszW_DNP*ZJ(9bKS*NeDVf2;s!VKByjR~!ypnmyFlKBmt=N&O1jj6r#;YN15_hc zyhoL)VWte4Gg0e7MC_ZW4AU;mEK|rL?s&z9O87!EO{D!?Q^1yVQy)y$@mEHxln3(# z8tmRuO}03*bbm8U=>}_r$$FG?xj6i{T>~&XNthRlZ=rGDsDkY&G~LT1B=R>U75pt> zieF~NrgLMm=Tpfnxn$?a?Ukp}4O=Ps3&G#Ii^lX1MJy^oZR|L7;BFBhV-)g+wkZw4 z_Pr*s%7ii{`}rbh=0%LMgVC|eJ3mESX9!7R#Y5_UU`qWgG!@1r;TSeAMTsE=q6juy z9OW=F;bCL~l8@EbLlwmH^A!Z=c*q9xP5?4jrRN3mS6mbi!A<5h41vrLXyEG)J@jrrI>nQr1@I7iduY^0mL4xV+!Jnb)IDr&-_I#!oeG&a>Iu z&o`FJv>&R^sg&j`fv~IOqTDH+1 zOrgBUxyf@8H6DMJvtLwz71y7q#XoWNtgp22xn%R>cBVLT5=nMV1Nwwij-G0ky3kx( zkEc*v9~T)z^?8=marJwAt!IL+3d))7Y&5#aioG7Vom#Hu+PIc)*WLV{W-`L?>;?

~^W1aA##}OVOX|UA_oyDgBVJ+Y>R=cjK`m zqXbbuLlE3TbZh)#bwC|6#;(F^nY3QrG*@1M{sqjdNh-{|9a3gKKutmrz= zKJ?<(vw5iBI0%+Ud#ELWcmljj2)fTsoqS;pNo3i31v6L=N{W!~x z#dU&LuV+WTA3NqjJ>Ps;&LDTWuU8UMc-rtfJePG{Xh?qJwT4#zoox6g<458Huo zV7lz{quMI*!ZdaP+Kbdk0kqW4)k5PN=D?iYpu?VDM?) z39xxkS^WzXg85G;y>nS)rE3``kRZF#Qef+V@oD-#Kd0cLD+0`Oc z7xg@@1ZpX({lYNy5=e7cE&2QxlX%A`kY4ls{gNpyoL^Mao%Kcl_MGsD=3z5~+;)OddIN0PX)>o*^yr1Ss;0wO^d+IS1(WgG- z)$PE*6TjYm505#uQBQf_G&ElK$Ck^=$Rifms^6bpe(yI(mz5qK-1S`Rh_&nM3-YBy zK*CXF`jo-z{pMq}X1>4ghE@B4h6~sA1UUP5!twL@X*rlL43Kbm_V`fQ%UuV_lX`DIL`xKk@CE3e!c#_V_krU1T&{KtC0Pb}_Y`_t!mGbWeZcAnR<8 zi_zw?S_(y^;*#TTH4)=oIOVQ{`c10WFDIa%_78h%94EPir;d27RFltZJ!)Q>X4=5w zV4>`pX0m*`R=RJDeX9VUY zi6?X6cXJxr{WUoDHvM@V!M)n-HO6VGF>nnApY7+c_F;Y{;ItPKtGVZPpmkgyYR3=g za5C5mY2}_rM|hK64h{x9_O%c4Z`d)Zt_CIZ@<+FyKn)PtmkzU%W%rVx!?As5)`Lmd= zW%)dhuJh+Z!na6ISFduJ&rJ%kbT!%+YfXDw?^d@izr%W2 z%dmZrztdx75q400H`8J8j`zxQSNxI=DYC2y9`SV%${r!_Y27YPt)b`0`_piC#p+dO zRkOPnF@$?RAv%y;R0jQ6^DKtzXW06zvvgt{Lbhthn|iZsD6DbYuJ;`duj;Vnito)& z4UzxY>(Vstd4ZuMlR(R8x@w+aZJ5o7J*;}=?nmxNh30 zfeYL&#(^@QY6f?)!t@$8?>JiZoH=(<#9F_M#toztZ$X-+VI;45D_b;C8vKnry83@N zCEE6MS$!SP8Gcds*k0ulAukymPTJ>O;HDpo#Ba~1xS2&YpY>n6$@~}h5Hbz1h9w}RmU-KGUgagigJ?o3c0hx5~tJ`XCHn~je>|SQZ@x6sz`sb5e<|`=H z{B}JpwV!C~yYP=*Ik*PavYbs{p*eY_qF)0aS{*p*XbpZ%J$w!!S-xNcdj-`?Zy#RzQaW6UcN^W5k5?IoPTl9JKN_?%8qd;wb^?Q9bo1RF$zRCdoOHg7 z@D18M{XG8!e*ALC*NHHs@4ac-(J_MDeH{q#k4|_7Ml+V_J(=_Ref`p44zaRzV$&mR zvE|DkXv6;vR)@jqNWAvr1^NAGX2Vz6`mqLN-R`{lF6QVl({SZ$Cq5lzCQtpo(Unj3mb}+4G z{NDoWQnz;sYw*K51ex?x(%{kdfyF!5E9c)~aBr;vn0L>AVcuzke_`H`jbbDHgERg8 z8#6Pb0L*)%cVnf0VrFn|WoC3^uy?R`Wo2ezZey??_&};{TuWN8a2d-$J%9`n@lpiO zsCmGi)AX%&=0XUMQml#q_He8TnlC6Cz-#a1!bX@bk2b?Fp+wsv-`1j*%YsUwteS)Y zz6d1KM#w@@C9MYSQXbS%s8pCKSfGZ~#D(V0c>j`0R~0)^BaCIqe~A#Ph?=bu!c!4w zk`jt$s*stf5~BN>^%l)nEe2cykDC7?mCaN!0a(UE6}m$WR!dJrP{&HrG!+!mQM5=V zZC4!~z$`sX2qF_zGc=(-0D@7Gr=2O7Ad7N@^r!@;EpMc3fGX4x!b=f|q=KsIhY&dM zhhWh^RWL&pYYW;a6KS#{3RW{rk{c5r?h~3x~m{o!x+t>uT*#ZCS3DaAlvR$NEb;XMrXv^A~OQGQ~&1Ls-G=DbHoxnMm z@-P0|rrCJMG2ogou(i?}f!ERC0io$%sR$%szC(id0z?^rFMtE^1)={JzVNU4j{mb1 z>OW5Y@1;`7`*Ww zA9H@*?c*f0Y`9%@Jl(ZDb*00|U)SU-Eml{RKZ;kZRV2Xbpp9!_CN7dNAVw{k?P*FE z2cP0i6>&saR`!a7F!PLK@g~Z2ZuK`9igo3|HP3Vuib%>j}a zyvz_M>ySnxCT3983Com@h?NHgw*;C;dn2KgI}FV45N`H!GTz8AaYzwX;p{z5f#Z7l zrO+;L6Ud5W2Dp@yNH7^8DR1g~6Vd{tuLnV|DZ>g4+7dyUwVEC?43_377I+#=0N4WD zYycPVjD?^sT>9ZVhvE-6gwaI_2qXdC0$TJ1`Rj2|SREX_8HI1rjd^v`0T44HIx{L- zGcfcPbaWPkKT{ibOobz4?|BCRR;RZ`2Chdgm}ZVR_B=^ur6rncQI0%5ht7Ac5E;-a z$6o5bc7dYoIxZ%ROyOE?5WGgY>v`7UKr-q-%#@yey^K61Ee(0mC}k$|ASijwglz;8 z&}*kyMwVT~lj6<1%W|O}1OmMM>lt8NtRtJ6bce!A>6{9BtW-Y39{=V7bw|DOwS1u z8;8;itXiM)xJO&SFS1^zqaXK=>az?|e1Rm7Sr+synh1kuDO`=MzQ zF~$Um6Np-LLl6=6Fw>=^D`Ph{XMPtY7#n5+GAmMB0ud;#aFdUo9y8nIanu+)wk2}! zqDxHk8`{&~G%74Tp@7e%xu#k(S#wB*pGYt~N7LGKzhTIN=uvEM>Ko-prP_p0Xz<}L zJ6L9@KNqe`91yfKKQ#t>J~<5pYT*{4Q6X)z;B!i`PC$XI1bCLEoEOHqQp{y6)v(vT z8DhhO9r+7tF{CL(fe5fbhqU5Qc8>nCT5q|@5g>{CfJY6du)!fo=qobF28-5`IjtND zDvCBpBb>~kZ7+)|aGvK@C_b%qKRU_?*g zjQ=EVGk~jmnbf&XL5tcGS^C2fpKs^1naC0ERLB{xV$2cGY|I&tb7fC-d*w(p3`0)D z8H1cZM}7&h!vI0a*^4kSW6h9iFx@s`KAl%E#4O0-N z5g_u#Tb;;Z@e@*$_4ubVeGP5GGG@W@l8(VVaK`~_TxUqca_2!4fyYH$=TuVXR6-{n zy+<~!2QQ&R$H{{ir$NC<6Q);oA!wp^XAX@Auc*vF?NE`?$FqQTD0hbB2|3~s2*3tu z*u|<-6YCHS1=te`f@FBq1s$e%a=ARk30`lI0PhlwViKbS$nYt-lImjet=>Z?6p;Yn z8;UeaT+oJV#p`Z&lnJz6a4iOKFQUsEVY!WE9l4ER9=h!@!i$v}m=E`1+rAeAQxBl! zlh~rQ>4s4t;(ges5y=>oet#G=2c6&5UlcgK1Kt!j+ObrumsYRRU4YJxJH;LJgP*~A zz4Sza{;xE{4G7-1@Zhc9))u`+x$YwV@`^m~AhjSv#3Uk714=+N^8tr)bCeU{)QtwD z^mW$BqxFkzJIu7tI!3El;if-OQ>qd*9)kz#ACSh`Bxv50j8?_MO*2VSY~bWJNaw7j z;hv?6R)J+2sXztTHSnS5B-G<)(eo#4tAGuuejukFcLkH7Y};YOaQ>Dl$RRKQHuMJ6nbn7vJMYApGKMWzoB@ z+etdMjSf1txjMFOCmowBwr$%^$F^W+@H=|Sl;{FIz9hdF zA(%r&$`tSLp8oys=7yit^pOlUv2CG6;q2K%u zyFxz=&nmM*HqMY0|MVV8P~)LtzO#t_ouqFy9F4 zqMe>1pNa(N^aa3lVJws*Ng`w zw?|oBHJUU>vhN7w`)&y52_fYPy`qpw{E|QvK+AZ;(8PdEMN989513H~iSp$&_5r?t z;tBq^l*aSM=Z;F`oQ7Y`uZV79V*Lh01$!D>eRwp;3*k!t>&TrZOPVeOU=!p_lgZ>t zwj*joP&r~3OdHn-B>#fZMnP+ZQ$CI>`6dMBjxhw%^~ujg^pt;+@`v$?b4b_GD%WV@ zwL?T9fy|)%iSq=(F8y))+ZrW%ino9><)^ZZBL!UbV)D!MFW(mbB~4(Qb()oRrXwg@!>O z>eRP!!4Nx0RNTZLls<_%C2&83w5tMvgP^?7BIKJ8W%o^pTB;P`a<9Aj4B5mh?+|AoJ5Qp+t?xHOeTLjyhV!}Qxsn=(pQu&tBmrQJ6K@$P(6xUa5Hl1RDjMP zrPeZP{xkB8e^MF09SM6%tVpRe8i4$s$=B@)l2IR0sWZxsgheM*Jgbd_ed%rOI~ee- ztb+0cMT0Z*!HpN(rk5wV+X%HwdAW(PTgO)~Wd(D1wH|cKr5@43OOKrh9-t zXF^#%FiHAL;z70gAIYU=tqp0yH z?XHveHI4@zGXYPcg`v@$rW#(70AGnJ7T1tMD(~g1 z^?Tk&C?(<+lU^3b+dPQ1(okMh%Q8~3j3pn~Ya_A47wh;2eS+{DcbOy5D(D)- z^Avyd=h=!C^uZ_C7!)5FI-u&&%dG}088FKgrs!Al7A1EIM9DBbj{kcIZulF~yQGK{ zhaj0X`+D&`zv88!;stHy9d*V7$D)d)bM;RdHbHls`6{qC+xVi()#6Ke@iV>R18(M% zd&X-~w)JKEsg^WaorH({EO}FOXw?#93J7#1p#P-;?18OYP}p*86SLGJW*bd#Y?GR; z%z)FCpS>uHvqtATah4WuI+JMKkej_IinDG?a4c7vy=aWH_NF*-78P%5Th3h7f&Ou& zpM-*#3g?>rQ;sqoad?w(G{-2V;wRQKxxTyBrEQ zsyQqe*ky%P1{B125GA3xL?DYQl0o+R_?Cn8nC3<#PNA`X3&4a8T>sdfuU*eoVr!2@nf@igsG>i(OCo6;go3m6jAi8*BZ!9mNYB$bz7FtNMaN~k=!}ZueFwY~dIN!i52s%* zO-%`wz70Fs|Gxi3d_)s&O=k~~LSbNB(KKBRBwPGid;ZFHS(H`&YVkscgN=?}aJ7Oh z#alJd0(maKVENuGw9cyHHQw;)wCvo*8~P2GM;vwxB$ z=(bR+5V#CUhhx_q(K)?Onv6BJ^VMZ~FSGMLh9#CCW79?hDnFo|ypU+0NHo^fTIk5u z@e4=yD^l(5eS9S~BB=4i5T)A`vNX0Vbjf8oM+qT@Klr7qAZ4?V&#PFUP=NN=KipS* zn6&jaRO&XxWlzy8qr>ddtJ{^;Q+Ry%v}eaN{xwzuC8m_4lT;GqvOa#VWo01{VxJ(4 zzPVdf@6ufktW1c4Zx(Ohaw}(eF))vN>OF^|dgdFh*O)z7M1H)sPDQwP*xLPdak;>q zf93Z8G`;QzM#&$!w|w+T8G?FlHG6oL8QtI3F8}z)>g%pf z@?LS%0>ZyH2XN#Yn|1p|FD2n#j`=C-89N1td{t1VZ~fwk)O4B_I!6UNmxkfiP4qeB z^gQdIV3Xpa>=7Fb|7yc^wpX#cgC`4szc+NcybHoFJ6oBR>M&R-XETG&9RvB+39^=FK+_I8mX|mAZvL zesw5fG;sP&bs5i}SR4aKL;6%lZN2UL-m@vUXq84kVDUkR1`~9Pa2{(J{9^8Bk zuOqB(?IYU;NG5!;4{57iM=Lqy%8!~(TZ!{A_l?Oeo?MR4RhwKKIVp93O-~!U^t;>6wk^ckpY#Cn zL^PX>S8EaIM2$;;(YPoiMflszVB`TnKAH>DIj!vuvdr$FvG3q0tkgPprZK!MyX0eo zjlszrKbN>@2v^1mAOnxnX8Qnnsy*-A=v*yWi6EysD*NT-{2@=p#C!XkeYr&;J>olw z+~G~?#9r|Jk%DRa72>-1(RU-ng)}r}-s?o#Wq_?jMy|3nJ&KWQS9@Q-D0PI>iD}H( zKW?WwVmdrdPR(gKu1}DSl;&$Y&Uf%?b6+{m%1WL(x|N28XDK<)D#K{sR}zxL(Uk1u zA>!fHyArp$>^xUrb_lqFJVl3?ZU9u;*&Q6tJVnFe8avIzKHKYtSDa5u(!JWkL5mX) zwIr%evbN3TNE+7g%hrs?@U1>6Pv?5wUi$>8!d~$<7CT6Hh#wv#8l^vST}mj6CMURm zZxx;*I8Br>UMS6^5C9hm@PVBaIm#y?ciHQ-<}-uWFR^y!PXSAtS{bR1>2=oWD|)^A z66RiF6ID?%swKaARkf!cc(w1X)VDrqRaE4dPx&(387^6rk=2lbUXV$D~YpUjb?zUBj-7rwYZrzXCJO>e$Fa5a;8 zIsVmdeJ$DGd20^k)nOuim6#5EmkJhozAiS_wQ+*-7<4Kjkb9_zDJ4n~|K2PVh~H75 z{PPPZqMIb9P(TwFhIB$ZI-M8ykh8rZv{kr9oL(6aMq-D|ZT;(@c-C?hmPjC$EJ-wS zI*~7b?MU(A^cT6!@YT5{i)a&7{h_e@2Ti+*2_86U%NyaJi zMmC|cGV8|tMMC9CqjeHnMS($DQSND2oKi(W+ARE-SPPZr5~TA69P^}7E67!}Op0U_ z^l0f7h#t;3&JgFUj_SofiQ7;+20eQYEmL-BErA{ytfK1*t$<+#Mp(KOw_4Ngr3|or+vW-1TbVvvP){M|R;2(Vu@nV!^Z0h)!LCx$bY)JlNH0VP zLvBH?jR>uG%rFWSj=ND786vW}0KXvNc>S$XlHAnv+ik@$Lo8VW^L>jLW+Isqb9+KXMZZbLFJR0eQT0h_65cqUj6?0~sQWVV4wOND}Mc|4gFQX*^$PkR^in>kYW0WR!I;bofel>Dv6-K22lA4{6Dl&vQ z-Bg0J@F!Mtk@fluj)v~jmTD8G^(~n)GesQhbSo@SE5f9!Z{NHkXcZfqAXc=$Onqr6 zY}DZ5mWzMYB+D{3ReT#xEC*_2+~pW6&f-G^c)=Ih`yjXiv6f>ff0n~Uh6orIT_=F1 zr|2tcCZ%8SPFZ1Q+8fF6ZF(Z07-Q3qC!E+Ga17+CxOuGVp~9H~z}Tq$WJT4?cUHiY z$z^SU2_DhJeTOIfNe~5dJH1xF#>$pS`I9PMh~boZ6HihC@aOY98R3}wY$bV#tseG| zjWi{8FnYnj)fqRd@Rq-MMdCm2Du>g^sfgqBEn45%ln@+3$JD zaT&qo>?Zfn$m<12BSAHW^^rmSUN`i+80tkBsL986u45@s)UetkSBupi7R@7S+1N9F zhRfugRZwN09ORm-rI0ji1*#7}a%8$=WARQa1V@;($@>_TTDF{5pL5wMU_{trOMMA z8doAe_P6AG3A9Xa z#|%zi@F$r#MR6^rl)k|2Zrj#tfA}RljrXhJR-6WuiA>}*S0NyPlcxd;fxQnIWv@)w zpdF7=7%>5BPDDIHBA;w9fe|IYUoCX!BcneCoiu2RBNW7FfL+Jr-1hAmecZ1> z>>*u@3aM+#B?;!ZDYu<&(mdXm(bkSt#n9KJbkteNPg#w@^-~+NAA@Ww@so@t`fn{idaZ@PiEvsPMLbV^-CJj zdMZKWtQ2!!+4p?rc=?3{lGgU>`YyPNhx+ixPYA&|Jc~ic#;*;_PJ3P!s$WB8SV6o- zm0Z9}yo$OvOrBf9mM{lwqp>f)$wkGx4cU86e+JHS!j_V$bb^)I5z1ZhnCU_QO-xZHKfwPFlg|i2UQ>!tyE_SV%Yf@NdKD!9(^S0Zn z3x66tEopA$X0FneWCw3A4!4 zZF8YP%CsYS?R5%)2QkcvN5GzC^yyq6D-jjxk!glW9ek-JL;QyYChT)A_?__eemtD; z&7iSNZp_8M!x+gLXx3ZVD}DVSoOlErvtvqyLXSVvVu=c%tYpfgFlYj05m-OpebB=+4YC{t#IjQJCbT?j;_ zjzXo*LZ$XarAAg|o@A(UER;DrJW{5!BhFQEY5hmwb-r-+^K_*4O4w298#}{5Q7UkL zJN&YL>z~Z8>2?1@u$o<4td5|A&-a*9ST%~6@%~lCM~6j1{}>xRVRTv&u{U~#0-pUi zp5-*y0&dEbGV9}nZjO})QjUlD?^X8?9>^VcYa%ayN-x2ZoYcMKksI8yZ(7li4abnp z&!MT_8{F@efg9Y3Z&cCsThb1DQtJgLeAgzG7pV4XbMQ^Q{d*mS7pXQRM^YwWSfu~D z5OL+?K93;Ll4%q#UQkqL5e(U%lr{qIm0`5dnot(=TW4H-qHHLiRdhOA-dbwTryTf9 z-=<6?6dPmK)Ej$kOA6yCCbcalwJ(NdPx{-A^tS_Pku7N$2a2M2qMI|$)48KUA-fQZ zf0f1WlVu{S1PM=+j*GyAV#jXiNRt!4WBH}d9_tnUx^QUTDS?s{KUPh|MLdM0nCdH?DCycb=YumQLB;`L?bBFF}B9T%95I_ADD$u&wK0k3QU& zAjy{?_Sg)GtkycAfiSlCbu6Lmcy#E{x_#%>nJzI?h7p{hLe$w2-~Dsb;`bz?i-au+ zdt!047E<{!|`9=+3hhS|WFhZ7Lp8 z<8c%tv_&2FLB4`?89Kj*$Ea>QW3yVbV>+--a8(#}MY4BIG^!ukt}2%uJ^6Sw&JpYh z>q0-*hUnrBX(vW{myyY!UW7zqZswwKDVcysLfQJ9^zc8EAuoQ<`Hh}Svmn^UseoDI znrnQlXRCDO{9Mf^pVpDd@kB3NaQ&wX+XVJ%%PrP-A$eLUh+eSp(@{;4H&2yULb)ri zO!rQVS0oge;heqQ&pQ(O78%uC%wZ?%u>1> zDt1(=b_gne2`YOZLai4)MCp8dr4NC90bc&lkW63Xvc5Cq`bd z`|mtbiP86vDoeLlolOC9uqrW{m*!Gc9ir47dG#3ZDoMh(=H!&-ro{@mJCUrI!joVk zda12v<47sx{#GtACN0fvj4cl0>Sq&v4dY|okCS3xP)+_}Z&q_OD|aYw;cyiC-T+b- zZtC$o2I=j9R7#b?WQS3^H!s+!mE$flTK6i_TL&S6KZb+Dngkw?EQ$V5t0odTEJtKx zkSAxDpUp0d{gJHUk!WSWQqoW)@>R&zFP1VN;eKfLo;*oI{RoQ9O3&`V0<&OZjm+VY z!sgJ-<{-)CK+WY~!Q=q{xcWnub{MIFk{t+VUOur&XZr~CjVxL{$3H&DKRM5gIL$|W z;S&H;{g-e0?`5Hhe#E+I?&}s@7zN#&6~k$d>JPAruRE4zR+;?PV%MKK}vH%p;;+he}nnn&#IhR8EXNqzjDT=_9UXY7kV z(laAo-ktMrTMo$h2pC#Iu7KpXaKu;S%VCiSezj-*LhzVrVty&rsMcv-HD?*2Ghjbt zd3xGDXm^^yq@>a>=nWn{A9)^RU^ovOTRbu%PedgCWCXUtgETIh;Tg2JW8sfs35gkT z+|7f?y|)Lc-(MQ|#iPGsY%^V@VQ?Np0Y7PGzhvJ9yXwI^TSGjaY5O>&S$6eje2Nc; zg9&Xazb#pL7)5VPXxP`eqzXXoh`NwxLbdh15rr7|CSu59)$&dncZ-u#Ie*`G1h0LU zJ5s@+uLGC63ISoq_~@C^k&DmAzLwX$|A31~zrjUrxn|0i+3$SrPP}jr1bgLPMxJ5qPcI#)7WcJ0*_fP`| zn0ILtQed;8y{{XR`J+{&tJNuoO(5aa@GyaZf#li^zB77$wWEx1)FkKm)%@UgV9iLG zm&Tif!l`IWeSXgnE1R@M3`YLLmaBtAaeUIH#+p(F%XO&?wDCq*Q#9}Hj2V0nf zZ^PAvr}9vOPiW?5D{Ry2pF~)*a=}eHka8G--d7^RYuZh&mMDF?6wbp9V8_sN_h4CW zW}NI>ck=OTh&&mY-bid9_!`c0cg2wfD^}+eWK;#Oqs5~mA=2QYLv+R^_#|-jQ@*}^ zJ2)PBjy>zaugoR`Q2&{RXsNsP&sB?_Uas@}V)ESy;kcHk!}OGie%b4Adib&q(`S3C zvAy#z0mTGC(?iY}tl#1~M||=n_`+FJ_UdyOA}Op5W@pj`_~l|p5!~pVAIFXTOz))H zCNB4x4lVt zIK7mQ;dwHn_6b#@`=ua<6LRsnJ7vbfCg|#3fk)N5`AGAYE9spNXDTa*Ejo}~MtR2@ zv81|utftM(xV0)(oe@SI>gA){X~C<)Nk4C4`;4h$p1Q)~R+bx)wZo$i&E1S#U9_nj z7&h#EJpY#Nq~uoN)L-x$5?iFUrEKnF3&z^zbmR)4(&Wu%ifZmHybw;$=k}&eRU><@ zTz>gnRrtJrF{G^bb;x4SHNEcM7vDsvJM&rkbw&S{zZ2h-eO=>;g!^WOQoGfBFykb< z>9zaC{HaZd`b7}L6>Ufu+(Ai1n>8^y<$2JIDYj+YwP1IuHBDhJV$Q7w`bw9!>UI9a zS957O{6aY=(Je1~!gPV2e#KeYFP!CjJW;{s=9rIqovGc@p9Jwv~(*y z%qku7(0i#+93ZqYI9l3=gv`r&enG$6%GBkRq?a~7yHAXD>hR#0zv+L$%?EV#3JUs_ z;l!2dN>l>B5YLB>&*{sxSnAo-gf6^4x0{~HZFIf8z>)hVI?Dw*Rbsx}o+bNwqK&Z# zwH`*1L8sbw3u8s&ZLu-lCP5T};9hS~D0jG znH&F38FL3`!w#!sf!VBvp{t@aMDmj+FJ`k`)&s=Sz8g~SHjcTuZH%1NHNdy-FGm9^N~DAsRLs zYi>k*JaU)}%qi6HHcnDZU~Uvs)4v(FDZWXu7gBx9FU3K+rFrzE#cmul2nm*Z+~ zm#2<5&ZEtzN$w-ymW7n&9Mz|FfxT%OAzPTIiCY{Sbpf`cy6($%iW|~=*OmT=CQ#{J zw>I~=d3VjEHoEa#@d918y2+r6nO{V^AQ-*&E%xJvK4IX(q23qv1H6m=Wg0@|aOmWR zDOh!;`d>N>;d^GhPOp$c~3H_)py*k+4%ybT;DFI>}8hgI+Kh#YW?M^zvCc-3UJC8hcR`ruhF zE1C<^-$bqp;MP>z`csyqJGHUtU>;yM^d*JP!R?9A5W0tcXinemVJPQHccp=0n$rFm z7AG&1+(bgJAl>mpk%B7S9-+ynsj}&bXXlMwwNB7ZFLrbMV7;<nvhtQFf1R80c0o zztwsahJ(mj-rc(VWL)8FN{_3;9WJZ(hh72c0-wz}Li4gG^IN`uQ_BPiu*q>@t>>W6 zbSm@95ev-{H*ZL~W4mc)Hps~o;s-0e$;&aF+s68-4UC^Zjow}@c|&ZL^RnSJcu zE{$po!8}+K65|RRV&xI5YlWn))jlv=cdmu&dL*bolMKB>E3~fq>VA=s)18Rt7g?UD z+El!uXZmk%FX2%?vBf#$-(a78_Lp}Gw2PW|;B8f#+4@ppLAO>k2@vCT>gH(}=ucqPm-g{4EOJxJROmMPD!5DUr@sb(wv_N8|DrsX{w+^K_CG~xJLqe zR~ZRg!aq+>UMP%;Sjg;~v-E{rofMZ#fd($A9ikdQ@qJer#;!60c35h!26EmqWyJy$ zlhV&HzXJ?2Eh=I`{U&sp9I?zWgkL23cF>Cw1tkaqXc)Ox@>2xNl#s8F!K{_`Fh zx*p+Dv*7;tQ6;P?2(H2uP-0~C-FhJ^$OJ2tQX$x@{Iwk6=AfiJ<8 zlTp~LD$nngr}qDB;c+yu^loB^-HZMg+@4`Ydtr%xbfZf(s^dPh;u1D|Y5(1!-+MD@ z`1L>PHQ>4)YQXx;gT4PhC-rycz}m*l!TA5xFY&*LP5<}!0Zu%)$$!TGQL(uIy1Akb ze0k&K{P~;vhcmz)4JM%co{uBVDi@rFHU@OiK|H=tTs(f#QlSb%j*_&J2x{~JnTp6B z22@#zC{glKla|9y%*s{s>a=pEP{md%`Ju|wF(ZxG((bFHDeABVuz6^Iij>Sx z^Jr^B6w>>C0IWj|dr1t6qW{RXm`TT%MhA?I|UVw%_g4Qo^%7W zvo6{3=bernG2%mK)FUyN%dub{;Ee9UBDl&y=g-O@9xDIjLcqg8EpD4`H;efF5WH|K zME{9mJ&>c{s>}FypJ5xEUu9YiK1~cm$hTQiS{9y5vm7@lx|=D96^8iN`Zp$C z%~wHmZn;qi|E2k~;9Nq@{OMrSoB(eoBKE3YZGmb7}z#5tHH^(o|IKpP62YItGooQHwAF(|C zq?x1DL8wCIN;pQOFe*P^jL?2bPNpn}AOh;Kfw>d?H6G z>r0xj@7!`q{D10^U&&hm&y*8;m~fjP4mscsb{p>rr})ePL)V!%#8)X{nZ9_ zoDifWn5N$haw1m^5#f70-hE;e;j1W!Fhi4^=#&IX`!ndM+?QlW0g1dz(lHMboPl_L zotQy|v>`N^D>{Bv3y#J-W=ay?wKSKAbxhLgps?{q0rOfRdasaRN|M(%+1+5!RpwM& zI435a3sX|N03x1DL3XDwYZN?6rCKyNZlt&F7|+*@#x;GsQ}92QY7y8`&8xUtGcAFI z-2b*zhmZ6-T7yO!Z1sr}rRX2cqFtpJ`MH?3X)6vD+HA+*fzRXwaKlKv#@PZh47VR2e0|pjwff-NK56p(^GJDQOdZ~1 zYF-oeRJYKcT#4LnNqc2{=1Q+Q1zb65Pqy0buq3%M*1gtg6i;Ogb*P^Z)`LAVfvrhT zEbmF~pJ0M%d8rQTR$=)vAe$|*ugw)Q9TRmz62t><;6y!U{B4CGJ=swWt%`fqzI8qM zMtcRi=8CyI+18D)30}iEK;Afq6ebk`FF0RKypwMWPl))_UI?0N5_0v z$S)}S+;PH;=BLt_t{4t%H{)b%(*om~&MAB90M|($u}$Z)?Z(t@V_Ao^Z_G9;%dgku{q-aH{;I%>hDIx@h&%GzMaIE1l2=knN+zT=Tv#2 zd+3j82jql&8=3OtSQUNRdF8&p>R<=l>EujPRiu5z=|~L14ukgF_&7|3M)(!7me&r^wsIfiOU+ecaO* zM1)uJ2FYeQ0O*c`Xm4-@33zeL7ye3ist}75d zP=k(b9l3;3--GlnMgzIFEt-6zr@2n@=znod!uZQFnlKby|GS(J6o7wCQcHFoLKw=a zkL9&bvkDEc7dfTDDuqpA^r3Fr7=fT-1abHG(H_xhYY9yO)jD<%+Rzr_g_4(*5*ob% ztsDud{}maog%vf3Um%em%Rf#-6?&&f&k_<{p|V*^{2fRL=$956YJ65_9Ph=k%aiXG zo0QKB$rqBuClj$vP}`;8c$7}EgDa?~WveF`bi?659&CfpAY!#8X0){-b|s|YFfkEC z{}-;V%gD^>RT|;*vA!Nxu&xwttvQ&?D5X>bL9PgpQo?DlAjFBv;Osp}s;RO4N8aUo0wvaSj92x5{ zR1Xo!4N0sUTI>rtalJ^)i!%S=5-C#`-SB%5(OtUVxD7PZoZompcxr>+_=?{+7r#jd zHM~)N6Txr}L0RAFUa~2eyC=Jvlsg9mBE+6cYS4O{oA2Zyn?GFnB~{IgTFPCIKbhwwfhMhOC2~SN};WkU5c5TPuqnH7y(AF2s6* zZdFfK1HY_5UKorvfGb-L?4B1);C{m17{-W^2w`fSf=MDQNh^+BK)kHmrsH`R^jnr1 z_CP|e$|}VNYgNL{V79jzY3AC?-xxVuiQt-i+Wx3f@(~BGbXOkMT7QU2Q{@XF^PyW z7HeC_om4Dl=tdQK&=k75*&`?2p(5JjvQb_vuKI0?o*OyE7WiEgUt$}xgYt_?BCy0& z_S;V#Sdu~}H1H;%G>(yH5QT(J2qn*`8WQFvpyWXzG$5+R_Qc7UZD|EU&^IJN#|LLG zemM0Y^d(tUTMnCbwyI&|6inv`MVz)UTO*nWf&BHj3m zYk1^mO-!pA@=9udOOp9D=m&&2{){|01z(WR1xz>N7_AHjCI>P%;^h4REtDGp+cO%s z2#O`IV28>f>GBwhz8CN?ngq*x3c;Oph30sNR_*>1XURE*`IYZAkRCED z@NFI8U$z60((0RZFr@7(i7cd@7?N{vh$Q~+QBJ>3Xw$_qdc7Ta`boR%l=M40#v@eg zN%D;hS>#Co(j>rO@}Z?1zCX9Z@V_z? z$76PPot((-ik7KWldAgfP(lgxwIA+79?Lo%a>N$Ypz*KusuVUN?f##220c*T8GQ9FXy5E*1LjxQMJpQ0j6pREL zT}7{pj4-E#>ThTF&nc3Rw_@Gs*!&XvLh-w2^x7vrvUwlgK>68xdEK%FruN)F)*5koGrmnJ5H-VZ#T^D zW78K}pM*1NiHI|5G-)=y+V& z;6ITBHqAzKh2<2B!&tV43FAFpMML7a>-{;1 z?|1%!xxw{h*qgTd2%-L7m-nW6Tt%*I7tCXrn52A2W1yN0uUkE4pr=Y^X+_Kkf#>kzI-pzLQ;m^I#fhc&TlFV1jZ8&rsfb+;KF-Ak8VsaK?$Q$|Q8QYJEWMO*P zv%ocJS%ub|oA=OcD=GeuoPYaqYV)I)tRWqxaU)brQtIM=v(i$$s(8)It4CWE|9ahD zr004vZpQv>#58_wzhpnuM;j0aH2c|b_+U!i80G(Z}YCDH(*XcEsAB}}_vi#>+Y(P}%dxWD4vx#^id!A8J zY6Fi!-{-jmk`>=aXglwa(d+h#wCOhIf6_IGUT2<|teuA3vuZ6J>7RJeIdB%?@dFM? z9M_)6^xAmr&n-lxVR>rzkCDoHZJl4gDG~jAc3|-WGwjs4Lz#rdp{b9Lfg+ao; zboO6*I^DdCG$dkF{}8x)UPZ~t2{>7+HH38P2t;1Z(Xciw^G|RJ)>2+2D>PZ&C(I%v zNXVl!(Hi>u8!T3Q-hQ}3KDdsxvv$}d$~o3}ul0rFD=k-4T}7&0=Il0VwavU*Q!WU; z>GLNqIFGK-wjW*26e>ORb~Rma8yB;zT>(Od>2mXMQbentniTO7jCC$CjEmiyaGz#I z6A@0U2}n?i1i2etUyFvv%po6E3c)sTE9eSU9?P}V_$Fl%ClG8p3rhW0liMsNtD5lV zKT;X*V<*YxPhiV4)+!6<^ZrqGv32a{l!+{)m7N$Czgs<0d>xP}Eu@;ket8YaaHerS z24vSN3WAn<|7~03x58y6bqSf38R;}vr$#u@Kj@Hm=p|e!9kDFjacuC%+1olfPRc+z zT`*Ek=HI(es%-u(is@}q{7QM{oS@sJxa?)qcnU<+P1x@8Zp@ZgoELq|onIK9^TE41 zrF11ko!8gZ(|do)^JKZUBOH!B9VLBc%KNYAga+!fyn+W6mwPhI9{YD-RZx0c6gWoT z_dMu;pOV%cebZa5{x%eLJ$8)1e?W~#8ci#iqO61ZY)AZ#KAJ7rg_9-qepR`(wW)^s zd_(-swh?^>!37|C{^)C52ziGl_5i@GziQ!o;QDwIy8W_6CGez26IyR^tiD5-7whm`w)?GB_4r?I+f5)G zsjO$cRBcAPu6z7FBU`WLZI%SL9?Fd7_FdaeEIqPoBlR3}TL1n+FT~cWj-OjP>roVL z_DI!;A!RP=PTSiFwCuNxSzkD|`wIj0UJ8|MWqU=zd2?CWN3XVI^vC{T zo5kMrhU)m4QX~vHTJTG@WAz*HL$Q;j4(G+?6K|vOgw`7L!l}9Tx&L63687s8Vi2Z| z(^R!CYu#h)o#}?h++xktA)uucM!GW5GJ#OJA&sHb)@H}+#>=p^T{UXWLA%>b`F*#1 zi>}REDDi!7BGlgo&vp)6>($Nhr3JRxyM;959C%pq253Hd<5lkc_2W$6ynd(Y#;Zui zqG`{MsyY7AU$_*g+*CD+%jeaI8~w%bm%MiA24k#D>AYLDYvNe3@bukD_3p<*bs4?) zm}|ut-|pet%k;8u*Qj&L8!%e;jrrHUPuc5vNRG4Hkoe|Rs)lu??K=bk$K!P0S{rgPi5_icK>c2WOOUi|#x!ZEH2!H$EopwfjR*=frt9et;I|yYA zPM){hieyUF>WY(H%&-fxcg$lzLKis_*t@1Kd36c!)o*Cg;);CcfSNNrZJ`oA^Pu!6 z!2+56MU~$QnUWssn$=4__Iz)Fg<;Q_^Ql-lp!k9@$K7n~@c4z~Ldn2*ZUY3$v?tl5 zi#<9~p*qPrq+#uRe(+~A{jX{Mj=R^966bV0rJJ+h)-3Mx94=`lXJ7$+q0Sa-v*)7% zWCInY!(1w!5}>pwfOa}b4wvw|EZ*6ucxw^8;CZ=Ldb_!bP=tuDboCi58+mD2{1{lV ztOw8#7!etFIt<_};TmN_6eVzyyIQ>(Ch?ani5qn2Wj9$VPH z^5XKcoYQ2L0|@h0d%e=!N?S7dF2i4;dgNZ^p?KqP0Lj{obMuos1I9{qqzFP z7+pYt1`@mZ>7=_P?me3>t}^d)vju)96|wz?txS@-CF6<4q|!-|&CAab3zdb!+_l}s zciQr1!X~}NaUZIM8A$k|bFI%}hs_t{k+p**C11PDaK0AX6;7a2Q&5Sds#q|rMi%T@Kwf)~>PgjN~BB`kf0q_32u(0I>A5gY1+Gd*Pppwe; z?eJx5fVau4^DctfU17oht&FQBTTA!lM%i>OTJxyG<1Fqp zA4M{~oRGjp#fbyE%Xt<3=)JFcU8}sMe3g^vH)*L$;;F~>GFMkco765ngWE%ecT@J% z4dzGtbGo{+&ZF(sV8PecXA{QS)mcz+_Po!BL6&J(h;E~a_Nv2R=+jNlr%IA{b#|N) zNaqDuV)euNn=9aTdbQH&5(!}KwzE5m(;bbv2No2EMcjhFv@e3#mqi+zy0H|$9($*sDg}8LvUyK)_onXtV%RXi zSh$*ju?M9a!4ADDKcE+yOelOe-M1y?8vB#KXp|xLDgM)HzPOO|;|=`+77!a7JHvRx z7+M=T!x+kVQxcDaj*O3sofy)IB99x7giVNw?pv5J1%K*uRg)+r;ql$WP1%`T)K2>J z@a5~87kZTzPbL;v`Nf^~cDRNrT5x;n{L}d%v0N+EU!?J~0WBk5^zL zHnjQ{c0iW$>?{sb1pR?Ve0T(!nkmf_|jD%gn6pUl-SbgQUPuaEl$Cg~XZ zx7S0wNx8lRVNZLn)O#8+I!#G>wVKX~gqETt`2Qm99b;^X7BZ86DESXx#JMi{5|B=9$DW$zyF~5U^BuxCW#*t$RfuMnzMF9Ui{)+2pTHQ^ ztzN3X`bB_EJ>XPb#!nzfXbEYEHKcLhI2W%*cQuYa-?&`QO^xntaoTsL%akoo^2E#e zPA}gcqdb@6kFD>o9A6vhDG=PE?>_%G4pRTqZlp}6YzGfGRfXKC~^VS5GD)`vw7m@sX+BYC;_uM)L3{S zK4BGMUNmP=HNuS0TzvfYmYOj9Ts){8!fh>52S%$|2XDCJYj*LgjrT4Wx9nrD>#Q_5 z*-E8V-BRsMxvT0Nf}tO4-JMV`$xjKYa44FlbqEzl30sg5!Mw#ZA__rX#6NrWiO70j z+zh32lLm@K=I>#4w$>!Q3*HauV|}Y|thGY6-g36gx*znQczL%0I{YMlQ!W+J1|)bz zRg#&B1Vc@|Ug(#IRwVG|mQ+b9zeSX9eIP*rO>TS<;#;FzN3K0ZwJ!yc0&H2OLhEX- zIYkO43zpLTx}|lAVgor`vm^@->?I-cB`^-lAjIEh)&NW>x^#YNNLb1s@rLE9++-#8 z#v~x~ge@q3S94W@(*2YgK`qb)#(+1D5Z7P)IE2a}U@Lh9PNs#vWQ?YkkxL=6ViBTkaqgR;T6?r6w*Bxk{G^h=7uEVHLuF^ndKt zt^c)GV-lJ20&=NAaHR5!N`jounU9aw3$*X}{i8wH8BT=UW9k=X8|Ry*3M(Uue*9xp zQcoON?pQO-n~2dGzuWaf4Y=&2YM98Ei4WM3#d~lJM{}|0B^#J1ZH%3c)b5RXjfDm3 z05e>N0wrR(mmAGe_p<^>d@#ft;#~AjRyaQ`s0u*UY5d}U0RsQ6(_+i#;B&A zjAdGvcj_}{r#{MRs4K$qra+g^oFf}OE|WzDtHg*l!1Ke2#6~`_<^sS(Hs*Tj;m^R$ z-G~z$B;71qDY&UxJDzk4KU6EszEzuIh$jz>pvk0nF(2Dtg-#OEoH4zge?G z2vMYc(@_XH?cql$6%VUNmM)!+AY{QAT7k>1*Kxf%rd6N@Wh_Pz{?H(*MfAdzSVMn% zY#SaBKzpz$8*e3prJ)dyZ4-wf8cJ&@DE7^mpaN)AFo|JOMaBNIcV_%W;-4rKmr6A|XoA3H z#?aJSqDl};q0Pmd7dFf`kaeEyAk?m`g5Bh}p$`JbWbO<&0;$$GuK?@>SLw#I9XVA@@;3w`W zE@MM0W#gT;_2wURYYV@JK{-ZK&NTVUHmzVsqhyzz)s(uYsRZDOd~6w6zYP=a#FkRZ zmNFC!aOnrf3gUo1Qz#Hkgw~)eGB!5Z2sdm*lo}zD{s%xCs@J)zZO&&<-F$!Z2L;Ld zfUIHi)Lb=9qWv87z=5o4{F=P~_` zxRt>}7vR(&o$1dCzt)u9U_}=Y#grgh%YoyZi6qvwaqxSV2ESC3L87u%*P)Gd9ub2= z2^o?m&C&vDrB7AA(cW}56-ao28=G8BOXP+i|EvJ0hB@%B6@@aMA}rgc8zC~orgjRB z<+9%v&P8tSH4LCv3J{R-4W!*;{s*SxW~IX=;FH{PCqdOaa?LgjkMb*r90U(zKmdhK zPPr$Jz9){#>zlygxnxo`5rvMZa#xqaD@XC2ljh-h;MkA2+W4Qix;PNPE1q8*kuP(J z7^}AEBD=yhQ+pLc26PiAy#H(f#$DJpoko>A4@P8Edp6qM1Oh^QhYEC869VEG_sT8_!pt_}#zBtX*G zHjOE`UIgoG8CuxZh^56Gj2G30$RzL5)lYEwaE8f(lxd@Br)C5n>pz2YrcYh$>C5%k4pkS4@Oi%1URiKq{t!JU~ z{*MSmm5Q>JaNLi$8hD|c$rJD^AkeD>-~*NLVte$$ZUqFVS4%vn*TdfCcW1UEGg5Tkq)D>#oKj_N?J?3aqbwy`S4GU8<)MT^)0borjXTyG6#7a?Kr zqFvkx`cjWDp^@o;aK5oB5?WZU9 zmZ6klg}ovzVh;cXwsB%WEJMQiP%9v4Wps3}1mwspgVL386y|aN#MOVPbQu@=inE9W z(7IM*XJ?g>Pm<_?bqePgETb=G>oj7Gnu*8ZBtV)3QVz(XmGifJb#+!S;int=c0Gz| z=rEV{J9}742Z(_U`fj2`G=N@^PBKivn*@sp(Iw5-LVxhS-2P&VtOdrNvXF`W65 zP8m>CLqNq$*`&Js#Nr-x;pn_3)CE*~MU(lt;8^wsJK2@8(97W4W{ImUE6t0}RCR`-$FQtMJMgRKknpxW?V=4>G-KaW?A4M^J7ydXWVwRvoSkg&)NP<%**q_#h z@4ixN)1u+LbDB6VJ9=_qLr4fAEaSx4MX%i8V(|? zX3u$fBdCcHRzDkF z0r4M)e%`l!9eWUGg0jt25tR8yfiR`2!2bgDDn^^(C(WIvlqlcaC7|AbJMX`QMzq#J zY=~BhRq^O4NI(DMzHBh?$Y#ggOZaF(xRFr!hKzJ&M!Lbsv72qw$2}bAkq!BbMY;hk z)w0d7>pdL!&W&^hFV%vkrrs%Z+DB=iK4sVEbAIaZy82^sgCyJMF;j_ z0J~xY`_hNu(uF~1i}ItBG4S1I0;90Q1WvNEb9kaabTCpBM3Iq%RhFm6ir{_+h{(-kY|=(? zErSZeoz%&Cz#GVaGhS$F2+$U!iD+C)g+*7%rgD=p9eM?=kl-lG$oY#g-blH}!@a^` zGE&Z4DJ}CJHy9d@Rn(^|8?y_(@M>06F5A8G;5)MEiHHL9LnUjmmr+Gn;58n@Oh zI(fz2uld}sN6MOIUOpipHD}>C%|6Rzb>cUzO>TCfBl~F#7eH3juo|IDBI>FPBV8Fp zfJ%N?4>(aZ!m_I=Gx?=}`FyG{=%~8vOjM&zHtI2VhaUuFw}I?@{O4kfa9Wr!#&muK zZ$zD8N9GTG83N4KL1K5X2#75PpoFxG&v4s|Nz@95rUzPVt9lf0ckI1g)PM#(M?fVO zvPT$|R$?2sRkRG%C09UuY?!JZy=HBT4x08yH(i3-TMKf&b;oOI9<#9@@`7lDX%gfd zhHKUK5rGU797mu|h-qfT9+;%h)?jsRO4fGiNzDwf_bw$?D#uEpa6U>sQZ z0}%{or=bMXmJ2YRm=FWUW?*Mr)Dq%%tZtT=0E5Jjx|-(UpSpTf5q{>My87*euXO+d^%$6PKCeW0jaD^>5o^8f-Yy`ScB~caD%!S+AXp))V33r+JIj>sc(f7i(~S zX5U)znGSVXOVewx+c?;-`i?ttlf0Cbk&-hh5|B}XZP;X1UC4A^utU-@uJ?J zXQ$mPP1*8xm-hPaO+HJwTD!ZJ%u(`qi!FsO$IGDHQg(MFf8A2?ZaWm`B=C?O(Z0o) z_``aC>AH`;3;^?Zo1ErLFNKkO;A)g83P!)X>YN^z!T)`Av*;%k#r_@sTC90=X=Ww) zf?BW1cwg`Jk?+q~KIEe@z)Rcp=eaL}ho^ncuk=Fa)056~`|v!k}| z_`uTy(udIA?2FOi^CM4ZUD#RlX_)XFGMUBuOXj|5^q2%sr{FV?a$n{3rF!}~a1_Vu z!-T!P>0tfy<1i?qY2f(fWp-O@%DtMO9*@!1^oipj=2n)Yy9n5OE1DO6xVt*9muNRe zpj+Dwh)N>0dox|a5Vup-E87=*D5pC^^inh(98mz4B}Tr!S?YTJ_Nc1%>g)X&x2qL; zdjBHhal7|>SK%4&Nd7SH2!WT4eS$9eNja|7idSzh@36dHK4wIC+xUa_mWvF$-PHPr@#NLW_ZbvL%~s7|m%IhbC@i0o z!#Q}X`KE`th~7?H;rk)7Z!ev&l#`#(i`5=P?Jb9X`dvO_yEyHP5-%R{@khAV`xe{r zIhsz*q185*A3I-sc-y6gS_cj~@2{Y4#XWJOMIRcUOWLhzhsQ3FVsV$NBp*eW$DA7! zclY(QPb*wL0Wr;9-A$eQxiQz@yG1ToQMb3?%MBLa(L4Lc*BOoOIiQ!kVH!(ILhtkI z=N=d;l{dGdEYYp?jSlp@;Fjz5+v3kTmkG|Ew=2&%FX7KQC0*`z=l<;8_He7W^KXMO zT0XaLpC#6NVSG`azz}fT8yQ;fw=sCtSl=b*Pj<^25u-12S%JbaKAxXnNWtLN?s;RF zN#ET#YBB3QDU_CP&2yxR})ke^Z<8IDljaB4eh0Y2w3Yp$CO`6tfRUm-T>h<}&l zG+~?5-$dSZuvqre#d#YwimQg3d6vu1U5a-ew_ce|Y&4zc9~>4V2exQ6=g*ca;B4MM z_W}+-d?H`|@;BQpOX)~wxxh#nenwi>dOi-`NESne6j)nMwdF6ehHa+JoP{M6cj}#2 zX{EVJl;gGM4K2nHO{GR>r>QZPPa0WemN}@8C+JU^I*aOMS=&5t|J4Hb@p1FXw-7y$ zzHIw5*^x;8x-7l)94JXk%_eWW1=hbfKqH?VS0X)$x4;5N^#U~cEMzy$J7wlPj-og% z_(4B^xpm$@wa8^9!SP^kgzmHg(nM?OT=lLukG>w$$g}OUz;uEnxs*K@P@K1(UCB~8 zMPT(Ig5afqm6V)w+~*&v#b^MR_T&j&CAIz?b{g}r`+dG?DVPtjM@Lf(rp~Vbh4?46 z_yD@Ox!I*T4o<=}{zRGL$ymrtTf8gYNE{s$Q3X|9JB^k&Z3Y|?;ZwID&Zd2g<9Ow&%R}?khmaCza&!IGcVqDrTI`#Pz+S=zIzWjnT7+;p7&4CW9+e*?FZ; zYh?V&#mfkwT4URSh}ig-RjABeStSUGOw|6NInpv)Wvd3u%#hPlaw1x~C}2G&Z)4jD zxJ&3M+vdD6NIOHeI)&z=ZjJ7IYk<+brb|`Zc)=6a@9~`b?rbZdFkD#*9u_Zf?{XI* z_|HrLj4Atj=t}@r5#cGVu9JM$NwOq_%>bh~G7K*i6b7vVb`zJ*GDYB9hXaGs>_$^F^tAUe>ktlC|08 zGx_hgU!$knqdjQ35Zc@P#ST?ERq~lqglp`52HR@^@SX zDkKFB$t*Q`0Rvme9-SEwizRKBe5p_fpnf#eDtyTlp)f(x_}Ik%B-TI9Y3Ty=M%GlA zA2szwOsq4CL~x%YWD}nld@^EQ&1=9UT-|?3hb9pziQ-VPm5W|5u(Ghoz%ULfA2DeL zLd4?J8v6hEsN><*;pxf!ND1}A;nrb=NH0iNCt$<`d zeQa9$gxfXyHT!XP>ujP>j5=aibMwHdpd^R*oYERsRA254h9jwPVxR5;J*HQcHUf5` z7;8BrGpVp?tpBX(LijIdIfUv3#&o{Mxk8u`jFOm`$GFJ1;b}t`-lS0ot~Yy%)%iby13n~0kwz|}&}Ld~bSP0B?KXOEak;wDUVb1u2+z|3cW97*C( z9UdcmI7qQ$d|K50i*06VWxcuCO)C1VNTsrcD1KmJ*21}Y$`N>`hCoq9xnU=Mzh)A> zaxtFy96tD%#yqkv0#cZA+$n*TNl(#EX>a)QzISUTl*k<`0R0 zolfLIIL3UdI}oxfnM}fuoaf%gYVlHY5pS(VK(Z?D+FbRc5h?1KNns87(CR*_3~}xK zB4IXGqa2-6nn|D;F_GCz{|w^;NiRCYCZzi2NaXcwq&tEI352^B<_@FJ)I&VP1Nyir z5-OBorj!Jl3&SmtHHhr9fjyAU0Uy8$?_a3mV?f6@!_;fjW&`^t9Kzn11|vY> z!Vtg*kFB%8lHWO!Rsunf81kW~q0-nKb4iy?E6V61Q;^sbGl$|M5vgJR{e8zF6t`>I zI;{g2fxk_t_&$QQUlu8DHb=HF!pP2VlQ7$LcLZ0!r3914Uz>g!CGn^uTQJ(-Ee_t< za>IyTgO>h?8Qd;`LX^w!TeLui4_=I)>aP0>RM$5L`w$wE-YPRx_Q!=lij?T@YNeqf z56F>SJ!6C!X$m;7j!tLGAr|Y${{uV#s;bhhn9G=`>e#Dy#g(xL5vqOvT0&?I3Lu_K zyZD`wuia^#DN-$1zqo^=Y&->`BHe`+lra}cC~YQplQrkM=^Dj>QBvo(aq`RiS0O%8 zAu2#xPE)<|_>e$)veS2-IxqFzk)E(_3ryrpoi^oi9WI9gR%V5fmvv&Y)8B71wH%B} zqb!5SWTP)F>hW5EaRzpkafKo&j-8@G;zBflq#PC*tZL~+Lae_=d<7&d^K~qvGj+xz z#K}hWbz`aJfqd`F#=0dnQ4IL8CodkriO>c{r#$9C#5#|_y%W?4Cg294RO06+i!r~1^u5>|WU|1O#; z&S7DIT@uk{0A44|k{e?2>*X@$cQ7YCNE;yig|Y%AzRwF(7XmA;2QEc>$K3+ruMLt` z%ruAxoXVm`Z8d4^^iys?3G_#FrcCX%0%g2ap^MmrGS27$XSg9)3e?)irf#olRd>d? zZx}?Sj*osEQ;Gg_aAL=W&J+oGeDbP!Wx^x|*eD?OEINARxCgw7d9bhP=r!>UaAgb> zx+RfVW~aE8V?nYDrs@mRT?0Uc_^Ue=9hBl8c@f^9#HZeGy&0BVm7-1*{RL#Te-ea8 z_~~Reps}9;NtFgTM@`H?m|m3}xEy_IT%}&SsTiUn$#0sSR%6(0)qQK6#a6p1?!Hbs zGsWtt!xH+~NQ0ereaLMU&09P~m33Vkpv!R6lEAqhhoL5+MQx7t;^hyhE*j>y2@!?d zp}t7iRA#^#KCB(G<78&;$P6VTisAOUbYVLC@n%2!we}Qf*O8_!-Z9WSJ6ew+l*@%e zC2c-Bk^p-3(aI35O(qzotx<2TmqdZC#vIdznBjFVVBumfkgZtHEvsSmAhrVvST;PI z#UNF101;J$C@58OS`R%2+f#?`hH-oy0o+`|{j(W@5XqN;mUXuf!=`tbVtHX}VT`5$%( z1-TnB^$dzuG`8cbnX93)rln0Lh-S)I55EJQ+W|hP=uU9j`5!R_i^az=AJD)9M0|DP z_&23@Xk?JATXZ{zQ7fL+_El1QfH6u9quw>>UCDrB{zkp4TCg4^5Z)+(c^^l@A0{K=#*XEW$JihTWTAw|YtF2x~r0l7Kj1?VWcU7>74R4xF&kl3q! zqG76W62=+&!?%0?;0}HyE1obG1@v1BmeFwdurzv-;1u=sN#rluP@S`u4bdEiI0|*q z<{tfR=&>uHOGLhp;-TnDrG&zHq2U&)c(MrUyofA4S}O&5f{w$S%$5hi5qm>It)wRz zN_=dEdWxdt@-=B%5)QSAs6rBwgheQCEJ#hd7LFPq>#Wrz)=Lk)LVB`N&d z4(cwvb5C=izzV`PB5EFO7l}}x%uBK@B`}J(g zT)b@GEqq=0x~1$V@pMhilllaAW!bym3GVyig&$MOXHINb6t;2Dz;fM5N@h?(p?>@% zb}TSLS+3*e``DEJn;2kW8$vZ!(AP!eA|fG3QOIER(TD=rBuW=#dL0FUNTR*9)vQNm zxrCdhETwvZM7^_gb27GxsXL|uHyT2>JgRn76Oj}r=6W{JL9P2KWB4n}G}IzgQG|1* zgq3*LG_?a-ZwXg`9qj|RSBSLsDSjR8#5Y$Ap)M)CKWBGW41q2w#9LbNZZ2*iE-A>4 z89}2C-U;}l*&vxrlc{=Rvl$rVQbv$*2m*5hfiQ=V7{NASQ~hC6M4?mm+Q)GnQw2gC z-!aQ>Ox5@3+Q%+#StuP-25wnN9aEadEQK~26$T`t-F9m7P`xW;(1=N*TvcV@-6)rg8qE?eE`f!vudHc6Kr8XrMRm{ zE@~`Sb(V0`)q(lCv=Sp|*>RQZWGgOeogZuTvDs^Ui4nN$_|zFlR*(ZtLQsM%stko1 z9L519PryZAKQYzaYK3j=B!2!10fr&#D5OVdpGWyIi$kVH7JnCM{d3&vyEqs*is3IT zFyabY=P`UWHa%jKusb2tQAjo7z1JNTjCy8&O%H%$^^gP=o@Fpwc(Hl8I{5;aAX$lB z{-skmgpzlW3&#+*5>9{75f5HB_(0JtY%=WRRh$#_bH36BmkhX={Rh!2nVnetJsHFl z4&g`#OfRI-Nb#)k9!8mXq8+vN0|$Z8PX+2v$$3oV%Fv{euEv%AK)2YPG1xnp2Gy9zht_)Zv?I$@-uGl z1w4S_71G{PW3gA?P`^3HM5=tOyTcD7pZ}exW<2m z80!vF$b%X)y=tEemh+x}H|re?gw(Ir4Q+^x_FyS#tP{~0;aAxc*L%~&QA*6$Ih@(r(__QEeoD9#R&Z3_W!@QdLcJEm= zlv=Y+mL)3CeR4j-8Iy`{ySEW2CK`F?cXiyG{xdJMRx*)cf`7(WjyS<{fA)Dsd@Tbu zGe>#Ze&6BpaD(N0uh-Um{L+v*tWyu@*te{34lunkw|SOydw$<9e_LP2B?#s;I~8m8=+C2COJ=^gg0OH0s$6L`u98MFOYyPT&~&2Bw*J_Y-!8ZpxV*Qj=Kd3X3)R$|qV2s&RO1pW{q^2Q>N8XKJDP$xl&&}(8X&@ z1;zLAESrsUlx_k(JPg*!zJtw!mSUBv>eHG8m&G-BR$Cf4RdJ_7U#|!GsW;kDhg)9h z_@zkICXp)gSTkWxqW6i_mTic`g?h;^ICqpmt6sfpU%Cx0D>((BPm zaNW$S!tpDFggoYLGFR{%TchDFHEGGvq*B#=P9mmsEa!G^T_Pv)vsH2f06NQn+xD?* zL*QNe#K(G=1)BS~fcjEN9QExp2!04I+cb}LA!(vxeoH`6F_@Mr$0+YP>yG}oqGskD zev-q=%pgzn@|)t=LN^adr<3z~zaG_Hs61%-FPO&3LM6$2*O^Ton)HQt@h&p77Dk5K zm``(1*XAv@R;o==S>(^Ib`bSbuXFg#*lhA*MU7#!EgjvwZCv*|_fH(tt+18khZxki z^EtFb_lTe_UR{$<_&(~uX-K#Fs@-L&kLPDkh4?9&yD$ai5LSdS{ZAFKC6e+`Q*V-VT~{K%rKNX5 z*mswyzITB};k=e+qkC@15?^m7Pm z!DlN3*7keRjhlN z=j-kcN{7yg^Kr%GI7Xw_>#yS16@5WYWj1@Thbja zg5;)#Q@Z;87VG)w3HU-K&-wU0507AD)-}qn&cb+(2CCCX%W4Pb*T4I)tTH%GYg;m~ zs>;lIJ?wwShlmTBTjMdcZ7$x1hw7E4-x`q;jQ=QTSUe^c=5UHyb=VrW9hXa--AJy1 zKXJ=0o-40ZM-<1mHClF?-}de=^gmq@mBS<{y{z$bu9N08kMlASek>5i%rE?&?yA=O zlp!f{XMenJq#i4s6ot1rZ`lQGw6CNlb@>2&|A-u%El_E0HMD`>q{V1Ae&UI$7N<_0n8@ok*Wbk9cynUc-00 z0O;CWxew_mi=b72w;6;@)ln876msh@ojajwynbq=rsb_HTs!ha(r5~8&Z)hRD<6#e zaIf{qv1)e_fnAU#_%8Bhu&L-4_Ue&vQEPfWT9B1EF_Z89-6_=M9A%am^pv>8 z)K{98aXOX3ZTu)xECi=~71?=W+Hr(y(Ys z06wkF^Q@VX+EJ>UbDVUmZst@l;k-q^SnN5Mqx^bvZx8vax{nm=z~}VboKu~}{9bk<-22&r z_u(gJQ50^o&1wCknb~+u@#jK$X?_F6Idq2gVZmJJ^dEK6WL}EJXuOX1`rqYc5t1=5 zoU2EAr$Rgb+jL`7a#G9IZuHyF$w9)Af69oPqd zG_d9$)e6<3s14g3XCQ}YZ62A&;JosXoSRK*mX=&kY6ofe#$WnBto1&8k{Zr7dQ3fq zGu8M0$WrRJ&!@|u37u=;{s;@-?i!zd9iy|&NaFJ{g$^3wamDs+P2NGVv(_dWr7rjN zWfa>Bq07Fo<}e){Jf{;G*q_DlHlV;gEAytK_43Q9E$aIiw=h~et`L6cTcB+gYe{~* z(J7aqNj;f$bR0gzsaqZRO%(Fc#HfkzWxUh+ANOEt@{fBMAN=7SOn)t0=P5*bUd>PF zy>zU7;>9J9)P7^I`<70b=E{^UA13%9=FODt8yI}$P~zVHW@BSxqGF)p;NoDS;-Erg z&KK}65D@6^BN7lK5+E!fB$9Ix`?X{*Azc6Y0<#`t8V3L3#vOD#*N|{j^~L*3lxbJZ zffr%U!|Dfp_&iugkZ}b-C-S$Lonk%&3c#)n3{ljt*!I*MfO76b#AXZK?y$ts8njoC za&039OL%XCIx0-NArpsj9yF@?B}%8aWj9liP_CQzon22!6A;Ai4=Edn&2)Z2q1r-} zdh;^O&zRCnh}|1#!VQ=1WQRgEhalzJwV#_dt}7p{+t+MGuWJ71Pj5a>Z(z0mo+Zk` zF=47=Dx{7E_EeaATlisOWgv5tD!m3RT5qO+x+-f#E$Oo}!Z>+;5OW=Rxw3DBsth1? zMZlmc^v^?vo9JCq4xOQ&5GE_KL7!`acWVAj8t-oH_56wbxwDt5LVpmjX$F+4%GenM z2{mDqu!gkvhkU3WKS&g!rS*|%ixIWqRIms+CnfjrLY>f_b@(A4#8tW{n^y1LWNDw$ zmJE3~pTGZy*xBSKa9H^cva|XZIQ)MWJ2U>DDhv@lO9vwwTSsLjNC4p5pRnQI_+Np; z;lF03#b<9!339z!IX-7{i*bdvLM#+xvKgSFqQX+ZAOh(*nK`D@4RJE1F>?Yvlro7V zX*HBsVL7?N{yV*dq9P-CAyj^6vIvN=yPwp7m{I(fPmEjk*ZufC6Qe`Q{g%U)!xXmz zFh*J9hf!S*U)pj=Vq=Pt`6&>$Q8A2hKvH=A1S)VjYZSA59D-7^bV@<9!Yl%E`SN+G zX;*^8QmU$#Y}U|U$mwZjlHZLZ>+ zr=qS=L05ZZ3zx9|D_D|y$IdlTYoQY^?#(oqgI}&`CC7j&Hd;Y0Rz{fTP}0JaD4A&D z8q=>oTCbp*CtZ}?L}$jFxJetEQ*C4#oJ;rQo>!?+D$e_bdD2*;Uz`;VDG#1Ei>VR4 zzVhT4iuvk?Vv4_ggk2mUs09x@u?!%cz4aHIY94{M8wnR&3!f8<*?A~RYoYR{kII+= zuL)CCEO(I|j3X=wa!~$6!(fyZ7V2yMm$;R26aIz;&Mj3;q!f{zK~gCLfKY?LafGwR&Y z>E&~Hi%V>mNV+W|FxmIe- z(Kt68BTx$l>&NH_#%ZZWbuFnq-drOiSh)8Hzl$TU%9ap}H7~dbwPMb2a3&4Lrala_ zcythRyoju!*J5T#H^*cwVW~unHFWB9N|sav(J43}Lkf(kr;1PjRm90=%7e;aw)Gh@ zpU3oY#2TK)syv~=blqs4{yc-=Xvi`7z9E+G^0(P+%VaCh?HFD7ej=vWEt)*t#2#i+ zu}GYE*#y8jN4bHIAnGvm;8zRTw z^)u_Q0Vf$cun;pUeJf(6wEvP@$I_AhP`>^hXe9=}1-|(tl)46JrIr7Z0ca&ONaZl3 zdJI|vjg-cS#ium_qu=scXakJ`JeG1OI7cryYz z7#Q?~#=Pyiho`_|UnPJEPpVvm-LPX9a1-ISpawo;|5OQiFTGvJ&XjX|ThshjWWN<< zsIl>#MlY2fWolHDKl_IY-SXIkBKR)~1#&9(v*HtwC6fbi3RrTKSB}nZi?VGx45ZZNo*Hw|;ah9J6 zcBV_kt;sGL{iYl1reCeADk%4`&h-t#p5M*F6D%v=_CyLb_O-n~HA^8*vACR29Y>KR zb<*kBP}d&owMrQ~9ovvo@0~k9oFHf~{I-5s{?PagMV!tUI6&kgfx1Ik*baMl_K!W0 z6ws(witgb)3WoV1N?+Nlpp9!<0~c|&9cb5$p$2(V+q&xoX=HYLAsB+?qe^_;YvkOLdKw%xI2SZ5)pH zdHhz*rkOqtm`+GSom)d+we)9xe#g@vj<6{wu^#@0@EfFA%;efrI^YyR@+#GA?LaSO zblBObrL9vO!P#t_VN7VDI82*uyf=)e!iT5vDoqIXrusk)c@ksZ*XmAmgBW~Kqu+<= zPL%l5=Rgj5PGKR9>`=_38h8Nly6S)&-270qMyASg&^&*Ac|VfZP!~jCS32+kq7yys3M!`T`@n9`fyUifn+Fv8(2z5kaiUag9hmays4kHJ;2c&kLU( z_Wsd8i!GGPV;vMzsuM<}voHdM04yd_IB5nh`ipPvtuioO{^4#P-~pk891nB;N9Y{m zxA9rBqr4lKRE-bENPiPE=P=7Hc6m?|Q-!H7Xw1}b0mchAFu=>?oe`8S0zQh;lyU&Wt9G7gR=U`Tl5h8->U6ESm7)rZ5E|324W@dc z_S;IYT!bn_{bf42_b8Dfts)~ps2mg-io%Il_EJb7HzN8DbV zx<0|sBdypevDw~B(Nh?Q$Qyhi<)G9@WyXh-+(25Lt(o^vKD$w^sMODoJ6`0)u@e5mN6jvCfLHPp~@Xkocx zS~Fm@Qgjup6%6~1p?!1=F~-SH*=xUifpS&r3ihfpfxj-t z3Kt>3LwRw;7^-ox15<}{9C%&RxBZ_l>3jm8^X3Ha<|K{!x*DSKn-F7^AF^T%KF;F2 z`|)4U$=;}v-;1)Po-=M#(FUDxnGU09Qw0fBs0sHQxS67gbmm+wI|7|2xYpfzk&@{9 zb?DaJ`?l@=8P(yt&eAZ==hnNd zAe(n3AdgulFCW){RIX=()uC-C$3X2TXo&a;1=1L`=f7R*C{u{s8V_E6N|q+H zG_I(`T(MPw4o?W=d^do}D1a8?404@SevzU1hVyHI?Eis5ripb&FXRC;^BpK~X!6Vz zJ?^UUbj-^04YoIw%#2V~)jZAWcFFbIL17R7X;}&%JG%O zK)hkV3lya#D99ZmAp_aRy`{=Wm;Qt-Q@>hQkqhYwH7!<&bn<+j<;1$%6!OLJb=%zB zRVVXL{9JLX(h2%mn3RcpN6XvQ8#AignEqgDG}!55fdEiDyUkl&5%b@grARRu1+ipM z;pnst_MpJ!ELiPp=xQukQq)0PhSdU+YU3hXt}}d^*Ox$j=RZK;+w+nNl*onkUV~9k zYWt|Q8(F;0*OTTr7t|`c~qhaf0b!WT=Tz71J`&8=0LcNWcvUF(dhZyFEWVNjp-JH5p%HX0F!_ocK3A)4Kd3sOo2 zcL#3Cp-4&3+=DL9d&t|(0(E`Hjm_ylT@ockDk5UV~krXo=W&9awFj>WQv1+v8l-VK&)lX_?3e zD8GW%iDHs8Oyb$KQ1McY)5WW}7>eTF0@4iK6;96%gc(QK?;~8aK9*#HeQphAKBD04 z2cntbGqmaxj&hC9Ly%WT8xHX<*X?>_x{kNM4HvLEUzcBN=2%Q{4i-bRr)4x9V=rE; zb+o?vWr`zz8o2aY0$^MpJ>}$GD%rPl;G-F%Mj+-A$*>vT)WsFgja41qZ#oR(>iTq{O#0NKGu8ZL9V@ulq_ARMn z6W4wJAEdogkYz#Jrrl-Rwr$(4>auNj*|xfD+qThVyQ<5!&Ds6UH!*L_O#CN*L}tW} zTp8E~Z32>qhT0ljIcq{w%PR(IJ5_6O0=2!Pb& zBUk~#%a5Ie(+|D8aUW~k0T7aYjZhR8J|jZ*YQB%du>}xZR`MZ@2v}5SZ!Q6Vw5S5#9996IP@sTS=A?jMH}d;-3F&Ml`OQA*%Yw|w&Y5iprk|t1 zK@Z^0sXOmPJ8H*PwWxP9G66X6kXgU87H3I-i24yG>``uhePwcU%s$${I^ya=U?0S{6>#m(qI!f}}DJrwJcR zETOIV*rBXhSt%R3+HdWeSMsI5i2sf>tpQ@)fqOF}Vk zq}Up-af)8euV{9WnMX28;C*zQ8{Z$gEReeAsO6XK`tn)1aZQ(q;48Jacfap9+{<1_ z4r}q%k@qg9$9i7sOde9(?_jf6prcTu0~>LE|9!K&UaOCtkniWDo3k)O5Zz!(O%6X?%p;taqsPk$!g%-3TSSAqdnE@`Ir) zK&vOAV9DS(uY1;IxjXTGp47fcgwHC$3@4!y5dT`vxo|u!+!r0ByLYqaR+eh2N`8wq zM3~jboZ1FYeL#meR7=W;~PS;kSr$hP5#`eWyZKFwRkmO73SY zEmH~(blQn-mo<6u%kj-+)=?q&E5_pA0WR}X^VRp*eE^N`cAD#&-`}xm^`lAUc=)@{ zBxPghn)R`6<6JR2%N@cvI6WiE^1A&l<2YH4CBaXm$AY(g@rz@Y*liy@R~N^yeh?mh z2CvQ)hCgC!z8itb3t<`zFOIuBdtB5XZp6H#r$fz|E8|AL5+&kBKO$DqZ?2Ye-BeM) zjw5g=>~%=hB(`DjN_U@9WA6J6mtR-e2zC7;QZtP^UHWz=*4 z%87Q7q+#3c4=EcorP|l!<%my&l&Ngb*MsI4Y2ijcLZ5<_!%y;%)G>EK^>= zUm-TLy;XzTtuB4clTa>YzOpAPF;G(&S^bkPQ|7NaQq~C8KzJeJ1Hh^(7`DDNJ@8 z4^9iVFb!me=O2;~6a7QEU$9S1jIviioHV}LreVrEf7e0w#>71c`$ww)L=PafX(v4` zZm<1D=tGh7TwYy)vlOoUjy>XjeF#p(XMA)FvR(!lX*|WsZC{i9m z(j5k&t&V9@QjO*pklgA*_=7ww$@!oiAplS$8jU21G}dc(iz60|hi@M9slV^gB@dv9 zITru56it!#@G>CM2;UR<$1_lBhMJf49*cGwMb?vlH~$|5X^*eJ$iDE(;NAyZ<_0ve z$FtqMGLN1=&?6-Mfo)K?{#nH5&)vA%e8##+ea*k+rY5^5$=6Nqe~2T?n2Xq@uFg0O zJs`U3pv`*px5z`ZnfdhdJg8HrjO|4_klS7&mbyk^YbcCJ(02m3{%o6Q99>u2* z*~(0T(UK|s3VgZk*bl)36xN}c1KcB_K-T{x3@Apx)r@h?Q z`@?mhk1TX|UV*3oEcCkq3jKwwX`z7t#*vNi|A+GWzd1(#PlLk$s`LLR;|Kx3H~KgK zk1{`iaipQ5`c6=@$6U~s*Irr#l@U%zO+SeyrV?y0i^{!#K$G3?Ck888T^L zXK9x#(~_3lZk=g(m2R3*VrCI_*yEinlkz$93vh_Q|Iv@1AUEzf!0U?Z+H32+r+523 z$LX4kuUK3zC|@2=BsmvU1VW@ljv5s#b;p4VH9u_U7mvxD<__v!uuUAFgi7$Lg%G+} zC!)+qFw!iEYa;-1r1AxNyffq)I`@6Y5))NNWoMCC02;UFFfI-hxrQIMKu=j9<0_I* zI1CAkgp{&wA;p}~bQDe)UV#bG&bKDdl=y@VWDX@PJeL<*4BoMR$Cqa|P6!5SUWEB` zK)DSoPYpU1nH6O5Y3)BCNAUk3M?ts%$kCZ-hLA28=2Tuz@T9a5a&|KhWV}^LFijqR zdNnAkBosAJK0g!A+M-gNdVvWF>|}Qod3G9hT^rlTvXVlA0*Ai2)c_v=Il6H@^q25X@zh}4 zhOYJ~AQr}QCY-pIJV1mPjsvh96Q<=;)JzQRQh&#J;xPj&-cAsvP!bN1Lt<+G*+m8= zIb#YOtCoD$lABZf*=qznh@#M-hc@c)llehKu|cdXb>eC zq_RMe1e3P8AQoI>t01aU}} zA17Hshr@4a?ge66@x^mzS;TC3Qd@|)zrXIH$}+1kiUG=N=6{f*QYOtyoaqih?q7U` zm}w+M65hyD{<0AXzQL$OMDTI6KniI>DWdIG@stb_Qkx5;<4tqO#q*dE%!g7DKaHwH zZ+)c;Mxl}a*k_3XHI!VHk?qc!V}NJ}Cy~OXFq5Kkbz+4hMl^+;Spk2aIv|rky5G{wL&!l@|axGGS}Ql!d%mw=9n$4_g-2x)j>tlWtXGjP5I@ ziLCF7i8T5ReCMbn`iQtA@EcuB6DShPz3ZSP4;3||UsN;32`T2j2$^vWwxrCFUTvQ@ z`Om8*+w{UBr-J(aM>O`a`4Xo<>2SMf%zgCYFYM!aqn%>M=|7H7=|buB(=33C1W1S{ z-X{(sj8R}2&Z9~Pp|RuPnjRj@&KvB3Yoqg7MvNSUE%z!4`r&npBb}*#O+_A2v~Kai zj?N2KL1^=oyphD1m=nb9^MTRR`;8K^C1pTmoVgthsFpma1dmO@x{Salcv;&fWcr|4 zG|IuBRoU7qH5~MngWfx^H#in|ddk7wnz1)p7k6rug5F!OJKYvnyJcGI`nt`lgP%*D zCk^oMP+)G3ghrMV>*37m;??eeD;Eq$*a8+Osh%UaNaoRo z`^ny=66}HdzAj;Vb=*vW_()lK@Rbxv{bj2(>q=LoP{Q^%bZ`UZ{)FD8{?II(mt#+} zeDhYVg&<`MbQ33|;j%$2q8DDjj8qa4J_O%sR zz-=w`5K#LWSo*kFIQ60FM^!ld8Fq-%KN6b6)=Odwh`RI*{rXKA_}0rZv9J_d`)KrZ zG)_EgrJZ9|NVzv%P8-xa_Q?2;G%288L)hGd?NEAmeeBDHTL<(mGte#((s0 zQdd?}Cf-y#=6R+@+V|Oqul8aD;Q$TwNU6mw4A(;1NxebiBiJcRenI*l#Qp;v^;#VE zXhVOoi+~`jMRC9i+X`!r=Bq(f1$<=~%5BKB!2~%b&SESN$O!})+ImY!!k+Jt?=n?I zWd@JWhGzy()#2MA3lAc0kYYEW*1ElDmp8};_T7?y!4Y;!e=Ljv5r=C zm{QmjGtER?W2AncAwr)Of!T}N85=>1+=;2-5*u(YWrH9HjHxU4^F)wiS%r1`EgOWe zOTG(Y6Pp=~TnA={tk##(CKVCTk3gAUhlXzzr-3Bgg3$C>+2mh62dVu8iqvs42w<_* z6bN2Nr@xSgv>4)$#%eS!UMVs5fe+g6Iak1%u|PAl{^3mI0)32H@!;jx*D0Y27P16s1T~FXr3V(@rX&J zre1*gCwwUeiAGDJ>MB};DOv+S+T_Em-@~l;WHx#<8aj)~DEFdN-;)R}zH8N;CoRJfU4&>5BaOmzR_{jzMF zaDDE6h9JlcEZ=n4UL@`hapsR4gBMe#4_eq>CD`6M*j^^sUJ54PPAXqxsnwkM8=Sm7 zir>XCmC?D$yQy+FgNNLNb{MqBZaJtTqY(x_~Dq8mX-&zrAqWr z@1PBw6zEz7>M{nazbR*lltzE2jsB)Il9UAu zGf7!PNmLfnl0EX6iv`+-1&72e#9ilx8gqvcPs_rlF^c#Fy@`q>_|^Q~#tzbsm3oFv zIvxiXrh#J%=w!q?vy*;mQv;8&QHi(kXMcXWJ=3@F8B!#_oHj-n7@rFDn4m_sB`(jU zc2O66C7*x*wp{;af9KxOJ}tl~0Fm zrBvvmUx}qy#wFi6kov^|dSbwz8T_-gje@;M2Lgg!Qn-3@xV}+bU7=hbEz`ShX@l3y zVJpmG+s5#)wOF@H-YC3k=G+5on0;?Wl zU$j3T!P@n_pzEvsyJey`&YGAWwY^QUtT?PkaUKoTTPK7duO9101UW1YcxaG1suDuu zg-K~u9+jU+wLNPQ06JO?nA)Qqqa*4*2oCrO5J<}Iq^iF7kgT}~{ONHxfU8Vn7|cFl z=Q7;P$)sV|&k<`u?*z#Wvnlv~sE0}QkiqF<>=1(z=1)N~d3e|ljg%z--L)e*I6R>LK?*i9yCqUGiTD)lz$Yn>gba&inKP_8EqB&g*DcNAayG{ww6u;UhGEDT7=s zvXr!>cS9rhSf}?0sq~pqhHskg8IAaiGw+jy^bAUJC57kMYdXaDC;T0c#14$)%2IrT zCAw{!W$&vm{GEj4N};MMhwS|ARnL@-@eauJ5knTUSuKYIGHZpzA%MuCzC&vbM{j<> zU6Wv#hBR7ztfNL%nPz|2Meb3+b4fqq%wF`-Ewm3VuupE@CyK}+ljtT4K7a={KmZHa zj|udO8T88x5|a}yfKXlp06GG-a=->=13d66>IA1`84I(ZrrCv`jLN6;9Vvto^p`{nh2L3?vYCk8{6P7=&94&{O>3lA+4I49|s90 zGi2qH4a-N3#AAE(v_83OawIW2iFY&Ua%v62+Dr(tT0eflbfaYZBp=WyVbLgI z)#ygv=tk5iFkBud|MCtg1ppm&THROL>mzU4m3|+>L=R98SPc-OdrI>cN_t8cjH_@ICT52w*E;LjTl$vV#0WN~3n3AWM@=i1W z(M}-O^SA)7B0&6dm)RRF1>p<_Riq1+GT9Ac6TQQw4}_LIVw_5hH2X&U^d?9t646M2 z|09>dGU$-VUmW%#P&OCm#4KA5Y>U8|1@7@1SXtHL_EJZ3UH(359vHH7UoDyEV=0D{)K%EjaS7X z0>fD#d9O;KT+#s25J~Y7oVo8?B$$DEQq5dqsx_VO5^CxjJOJltw61p!;dL7Q<&3!E zPCL{w!$Q>bpXs`6Jg!x(>Ip;5x)fl#Mgy3xrM+}v8|z9QFHL;8HQqM#`aLvT z>FnlK%zoakLJ|3@`nFk}?0=a3Ompu-7`@f)_7Htt%B^<}=4&!O|Kwn-`+mtD-%UYG zV@mYuxn#@u_0~P6^GGGHOmSRee$J4;e-H)$SQ!oqE7Re>4*T&I|OnSd%$4cCihx>g; zr1M>erEfw|41f3~lJ7{AxkM?PX$`xbEL86Z zo3#5ZrI*yBT_=ahd<#*>JU$=ViQf)0%H!^%+x&NTx!oEmZ$i(^z5(tx6Y?`Dw>YWZyG@~QFRJ=- z+snQQsrDaJwLvNGUEa|Pj|=H=dcH$7mc2PACx>A^T{bIbCx_N(c7N~ochf&mrh!e% zr$%T9LVF*!Qj0$>qtHO61uhTaE_b)D5pM2ox7qCJW4&@x$)$2RV(;%b#Iv2QbT1B% zu}ew6llJ!UZdSE?M`XWBcXHk6!k_LYyryy7S(he^yO^E!Q9sOZzqfE|ze0E32VjyD z2nSCXBIRYIO6XzeP%Rd#m=pKH`YJ7TV7cziY!>-k$p32`cY%r`NJp^_9*qx*+U(CD@+oyVqTC zO9%fz*bCHPzpvyBKSfRAEep86^8xk_eLppH%Hel?>Lp*^*VpsNt@<|JkX-Y*zjuF+ zyo_v9d;vbZs}X)_FXKLbHcF@W(C3-Am&!qfV0wJ5;ntU%>gsWSW?#G0*vV|&@fX%!=78b&0`ZBwA+6VLL&6OW`~t}d$+5juaS6Mot8hCedRh=B@>5Cvh}yp3+74p3B@2MgV^;6ZdlxYwLHXr>*tFf_}84nst*XTCuxY8m>DpRGA)_RTTyKrQW3|Z$sWi*ld1ge0TuYuxt$U&;(e9W??vLY-zSQdE8e}n9?j~Y6>!p^~ z)=SNM3Gpy8Q*a~08Ur*}3(Pek3?i+$Lm>@uG3Oe}j;3%=D11#~mcKcH&gv$Gm4csWB>x(N+Wgcq=T})LnJq#FaZ}BI+UQANjrICdm2BpVt|MIP6 z#*&NHOVxU++EQzHb?MF6Za^_Z;%rw5G~DFUb3kydEz&Hyuwk{SKU& zGgKa|ygq&~_R(Bk#heXj?uYNu2kGuAl-lXjc^I(Ul}|8?g0>!EP4GDJ`0ts9m~X_AGxmyem9Ev| z*(A{?`@sRYc3K)e5;dQY|Pv8b-&@6fFWD%b?$R4CMqu7fi~h z{gG;p-B;PMoGb5-#u=jGje1Y4M~3<}1Tq+G2a{9Cn|%#z<{LeJvw80j?>IT!o)3v9 zrMNkM8<+fl6aADP($^x{Ti>+?YFjPBqI~E6N)2vYokQy1=|9JLE`ybvK%@HLl&DoYedJD-p86FnUvB)MMOJCTiB>pI z!$w;;SI!lafhNAzhb94Kd#>o1G_cu!@(gudj?Eghxopbi-onK1uoSS@@?=|iv_ znXu;oZD*e6K}8rHN!$aFQi8ypErN1KjLJCF0&7=9IHSg=?zDims3f5>1vroZM@b7w zAbCL1Q!7zNB@o{s>mcMO{es35J0NQV6&5&ul%|eM6KGo%Dt1Rp7L_H?STt0Y;5EUU${EJ&B&47tX1v!3x_YNev;*^xQaPScPEhKWKjoJl`gA<^S zo`Y09WL|P&Lt#-MS9b1pGx(O0r$FQ}LvY3AXpAlVbQwr=MXr?`*^VW@`jNiR7O?jk z>+}u$zx@tcy51Tu?~dnj|9|`r|817m|Ih5>Jr2z5-~7L`4`&qBX9b9HWN38CP8(Be zF==(m6m}69I%LYi1VkinC{?>KX z1Qq=)huZVJC3|FQG3WG!JKcLs>T$o-ecj{b*Xy;Nnf}D2R#vv4T3$eNpITsDJ~56K zdY={4$Yq0!F~OC^9^ZYeiUu^EP$SPKnIIXN;Dyb7aw$t`mILZ zEjD{5!RyhV34$o!La|>5=<}#!Bd;JnS@Y=ib`{g4CWOZp-g)X!qev$J1uQBG zLM}bO4WI7oau`<{|IE9Tt&?m(Dl-5oo4^Z>=y~Fy#{QOw6%p+iGnpC|lAF zj*oX13=~-}$~3)EH;KrxK@NqRL#P0HD@xn{VWi0v<+Tlpxz!C^Ma z*-Sj`v24W-XZWwJ z4dFVj51ZLw@{XAH&EW}j94K)o0dSgA48ViHSuL5B1!MW9Z93;d{mRg-TJ#Tn`BV{( zeG$?|zd?1{NDHhWZ!nT%3MhfbKsWGzDRDx4ma z8=*F&R~z7!hr0JmVvJKC;1z;$iad1=KQQ^%8vsW}o zArHUZvE>TC&Lb2TAMekk3viR$;Y-MrjDm1t<%Tk-sk-;|pXP<~HUPfg{AD9R3j`x; zkdORVOxgxxVU=FVs8c24l9{Y!B6+n&(db>Dtc8H8UblSc%9zYGcl2F-XSh+i4`;y& zMS~>nHpKn-_sDWWy$gAr9{qj@dgoLm4KP(ouBTLu6Qv3~($W%e9nnIzF+9uxl{9FW z0d*zzpO&n zKloFFzQhD)TV?vpf%TD9IVx&=m+-KcVhu{G-cnh)>VOBGyB}2^|oo; zFZ10m0V}0Dk3oSezF@T=(0T&Y?szIU+(kPs8vW-KhCvNM>$o960sO)uQg;H}wI+Ag1LB5>%#ootb|If&D|~nD;%noh{tZA| zH#-Bwwa*+vdi!!hIahAG9TIK3MEvL);tupYC0#)hVA$Ccc344(V@x5??oB>aILEfo zkXLFNwnFK_43C!Mstf?lgWyn)GUydJ;yb=3pFB=4aT|jLj621RVCccj4@rFK9R`#a zZTgKk@_-TYGLZr4%TS>*IZ7C;Md|>w*;XjEq3+;LMobPUA49T@2Nq1VVfEmlI%qp& z{K1MgsWRBdHmOzE%3X3IR@i>I0ZR=$4ybBFxW#D3tzlFA9{+VQM%3ruMt%@zUK)v2 zmFUZTWZN$gNY!$pgC=u+@Qh(s>WJE$tuQMTf$VQnIAd_l+IKgv>ImmA=57;G;OBer z3FiN@752WjHYrFT96;&^VGp7DV>us3yw~i@6!j6N;j)TnZ9SS%zU*;F|2(HoF0FGo_)y1fh4_T;xzE+Z*|0fUTl6ax`IgJkW;+l=dHZ?b%B$B7P|J@H)D)NmNs< zZdr?^SW0mvt>)6E$mLn;(0S^VdFt4C>YRD%fcXlLVpR$0O3_8uEi#o;QKP5GT-8_3 z&@>Gx6VvR^SsHVwG}(NGes1Wp0s;&RN{kNRE-tQ*)kk7=-y03AE_xH9a;=+r;Qq$@ zSYD~${pVWn?o4%=u=phX&RD$0h#di;SgX3;2ymT+DX_A}{p_SzPqw{2n3<7+ZwR zI_Wrt5`j3n`G~MnQ-x!lpsI_7YPRT-m&YPc{Q;vf|w2_+&4 z6=ESJqWw=oab?0tN@}ke(ozx5*s61ZHsk6#y_ZFr--&vI4bkwc*}Lv? z@O+n}DbA%?uNCXelA}u@pqF`}mmyHAUQw$M zs5D?GHDDZ_MAk;2itG}3uzXU!Wg#)f0`z2oyG3zMidknT0zbmS4u^KphIYSyh6xnF z*Di8?FuA;-oZV2L-B_L7V4vM+o!xMs-FTkelrHYVwH590)Zpqh^u@F#fyQf_NcneB za$c&vU|qous_ut7sBor4xcPd-ZKD=l-DWyU4TTUBU_cc=j4C9sf`JjJ?xM4@BiWHT zuA&p-KNYq8au3Nx31J8yaQ&I&Q-nJa7RgYrnML+WR+O5>pg7J(Ap!M~1TA1e>U9Nw z0V9H6eMgUUC7OywW!=->YB2a%Zem`)eq%|Q`kc$xGoZS)fyp4dmCrdHV#{@S(Zl%;SG|acS^D=Ch3iR?7f`4AwH=H zuSn!)bLowRq&9O($C;mdIfX-f3K3obDN9i%y1l&BRp8?Mc~yD?!Ae$#rh$EEbinFV zz`ZHzG$^}lY|$&thFwAfFF2YjQ@^ZCDPB4&JY_09Gt0h&RIU|4uaH5n;;-IO10YZX z1lS;Xiy%M5kX|y0Z^DUh(unhnRX_tV(SSmgkjnbXAXSE@;9jGoruj6n1F@P}RfMO! zWuHEDf7Ng($pn#8Nm`ao?#R{-7zk7{tMFb(Ir>@7qJ&t95(W1c=W~n5h``xo68dOr zW8XQFjZUth`gRm{1oi0sAwiFBUrPv1M~-=}Mq|fdzIG#zgG+ z(XQ~GvB?4xI#k1bW|omY^0P@f5Oced=?$H1uB^$8z{w5d868z$6aSJU(I48$>3rxd zYUcJRoL<4)UPavRGA2(gCQqqYhr>|4m_!j1+J-zXzK3Hn6$_Tn0`37y8X+1nL6@qP zbJ64zM&U6UMkKLHjkG!ankj-EJd9Q;E+&h#GA6){RVamKYc$arrc!{Jjm0*R0GK~5 zQm`yi>H(#oSz4JtS1M2j0T<;F|dT<;K(FF zHxDnn$uM^>k1*VQj&Z@pWb4A z#$(t7AkY`&cn>p*qQ$uXWZfy@IveZMhEaRRsbpK|LWzWSmq0G&rw--P_&`E->vSSb zi>fJj!=zDvWv|+=g~>c|epxs!XP5-^n||-Z+-JojUk};mFDMs&J;t;@&DbT9^2A|i zCV2M~^<=)k^-*zXrSA=Otkj zsX+ePqow-1yiY{uJ-qPAE7ghe>DhAvb}umrCvWs_f*-lfPBYevzubz?T5;87$+=KL z-syfe(evhYtf$l2++!~+dUrrK0o!wkkXWMoN~N1^Zf_E_SN|8;2i(l@n+$Ewr#sV+ zv!p!fM7zthx<>k1-Fw+8`~2A}t8X2{75mxl9CSM;`5inoSMKM^^*0^o0T@Cd89yi;k`Tr349NAb<7NOr9CWa9UcrP z;lsDA{9<@t3EQ^U%{ezkQIB~*Jy0W z%kO0zk*d3Cdi)U`hVSb39$OsencAT3%iCW6Zp^2_ac+&ocB7_b9HgVFpit`t}fTc`p&H6&tPOGxt`ngO`SHN zu)1b3jXdQ4_)%FyZI;JR9+ zT1@(-e z4yk&|&A;0GCbT}@`FBuZs>|7Hz53D}Px!99`9(lS_}dh(f2JvR^Ns za6QZY>ubdFBlWmS;ew&e+41PUl|Vn%$ENld+TT-$uh+i{lp|kL^{Zc!$`j2q+rD9U z9~8HKjNeL^$M(JZiBD^7ZP}#XXQ2c-pC6YLUjhKJH`eWSRpl-1lhXP*$hg5x(r=+$ zgxiMdnJTLe+KCA}V>6dA$;`>&#t2Q_?YoA_$Nn%s1sYQZy0)fQj3$MCgtIRXpXk)h zWHz?}YazXsB^atda!T3e+Zn6ATn`OsBm$XQdAS4Zl5Oi<2mXLQ@Z*E@Fb4Ov514D6 zSey>z8wE1TVVFC;@4nl!oUxbFh$1zKiTx?UiZ# zhlYhX*AxBYoUrVNiD{(}dIasyl9&0t_rz72Ua`iwVfWQFge!Wz^kc(ko3HxKBcQ~! z`{F{9nWd+O)^`nT3^;02^8?exPv(h1{J-tKJ9Q2WJYVjQN#GWnwp9qr0|+>*Q(DvB5m|pk}c5C-=J-6zyl&x*Mz6QK3y~CB19l|UmC}0yKv}~Mn`%cfcY+k6v zq8!6%*UIAS(7n_dE)@8xXR8Wu;7Ub!Hrc*ni+z_6QSEMeviyX{c{*50Z?=3v2vt?q z&Bf>E`FnGlz*Qag^w&82viWhZjBf-sfxcAe5&d1YGuNE8yxHQ^RAljEY#*DTt7;nK zZ=1_t%9=UsN)Hdy+1XewZ1V8++fm0?QNFw4OSxlVFI&}}tUX`vDYHvXI>91kgmve3 zou~KD+Zlk=nJPEQKLO|+F2*Y7i-g!Zch|2w(??d$5O;(w>5e_6emJIx-p+OAuaBjB zBvhwq>Opn7#zw|6o69Hrh1FJxmtD;< zufoEH`HKF`)tb}v@hjg9-}d%BpoiLX!dKqG@f;Lr7f)#T#nYfi{ef3V+j#%eE|u=8-3NCt|);d9?CuMcp-!iFLMWp{h-(HjMLn@o(q`X4H92r>`iEK9Ywq@ zua4{)xQ$*ueLHZXrTFwYzQ-NWb@l7e9QL|4YI+^N*TTAO{O<$9<=Hm{jc=x)6~BBK zo~<`9QRwfWsaLT-@H5|4oZrv-j0`jrv^S31 zP{74be&43da6i-+e9W|Ij=F&BuKNG@4#C|J;CYHahT0vm-NfqVBKNGlVeT(E8O;+* zaWhe&E&fSG6?wE6{jIGSZ(s|dIqJ~PH52<)FVu1xJ7>}5wO5>;^Y-FGu5K~12;-3Oajqms}?@>kE z$iQlRQflEOV6pxhc;sXTEa_JSYq9X3pMzRbV~SR&V1H4JbghU*3|rA`1jB>or-?`m zJ3zhQMt}a7OTre+q#W=@)F<~fc>e0lJF@g z|MU`3cn3AaB!&wRscc5`+>znIQ-COhkSQL}eI9m*5D@{8No2rdkW0#%kjes*5D{6e zDa3e2v9O8%@=L&>a!^9oU58)rUKYp1yx!5>6F(?~b zxS8tP>08=aI_v+(A&~!DFTwQxqCL(31~dCN|Boo-Qrp}eMWQtY43nhKpNfes4cUfq zxZB`{9^59p-`}6G;y2wI4T(&S=9odCWxob9TheSlsURdCFx2Bc_<~WUp72TjbA}*c zlD(O0wXPJ^v-tDw%F?|?Cy$fa7S}P~@#GrPgM5Xe68ZgILc6zxCX42_idm7ot;%_f zXCt?cE$gXfHqqZacqp|}jiyuXMs8Y-sWWFS7s%~$`B_X2U7qUUDyVU6L&vcwkgztwO=~mYY>V()XId%+FD*=)K3ey zXmU2mJsOB^@=6zh`zxS{b>jue(x%u-65WisqEWFjM+`J$!W3Ya2=J@WifLy%kFjb7 z6xIa$6lb{+nv?+}WJxq;Qzr3?Dt2dQo|{R0`YSv|z0|VJ zm@sio1tlBCah^-k^GpmVu`o-lcIJ!m%Q;xAc!juO1|l?qE8;8Dh8ce*4pu0$jm9?- zCP0ecvL?Yqa*HNjVX7tyT)O((D2N)XHrG*FFQ>U!(cQ@!7Qh#^7FN_K*FvI6kW%Wg z1W6TsIx%I0yPSE{1S0y1lRODW35`Ijhj{*2G%-B|kRE@4g-d*`aA7##s^@zXk(6AX z^-+w2V>e<^3j#0vR#yp(JzRQoE`*}UrnC@^3PvF6#@&%QU-5~qoG=4 zvrI!~u=a_pp&TOI^|ZOk%5rPikZA%OAPfVggnHk+Q1e)U5}EQ1T3%j#Jh z<15?!B8ZxMH^!B7-usTx89%U8D%(-*5T-J2%JY{b*lO1!)gmJ>gkdpX+a0hj2xk-NxWjjq_su7ND%MX&_^ zwGB~MKb+q`bB(Z|72MS7qwIv1_i+fp0H68eL#Iwq_(RwTwG6AAo&Xa4^AEV)Han_8 zeGuP3Ke7n|0*glE1rlQ4U&C}PROa5;xqTdXS@wM$ zQzo7q1xCg#gI*EjJft+s#wU?&Hd|CV*3%j^X@!*KR=U&G35u~w*Vc#M5B5dU_Jdrx z!x)3fH{3`T(y%MbSYBrf5|4FnJW%XCNq$Oz>TNHK^b7yPziB-aCXx*In#7ClNTZCg zj(2s2vjGzb{)QGmsm^^A)>kDEIp@#3w_*us*n}pp@N%t?syh4hF(qXjc_VM*$X%Z! z8Rvhqokst$omhYy1)wH#(9%9N4W6?4E<6I(VE7nrhjb^wQr(T7jf@y*`q9EZZleVI zRsH(MZQ9%W*wmfOi23vDH*)yKl-q@BNt$rlcS?rxS}(xaPNGlELOD)sDT2fUc}DYo zv9v`k1VhkkT;BqUw_9xplRNpyyn8Y4oCLf|fu)DTKEtaquh#f4a!@%i!`?)g_il4( z3}D7R$kDH129qm+Oxuvd-ZYr^c=ocCB9#{&@vA7FdG9)u65Cy>_JoxK&?ibzGzIqM zwt&<_a1w^^_D8}vY0Az$Q&sevsPa7*cQ`L3ly-dC*yQ5F@H6yDNYNa3#cvnNQSR|e zxJn~IBMgn0A8HgtMzUgPRM287I)i5#qE?@jCo!3I5`)+$^3GNo3m_POn!|tb|F>Jh z_E@vP5bvGV1<$-(!tB+5axbcfE|^Y(VDqRSyr`WBU&bOR=7tWSFdl9_Jm@EXiMW9f z82TrdK>a6|AowSjF#9K$$owalunDmD5ZB(*&MAr$P0%vLtGqpI;ad~*qx~nB_@QZS z-T9jy2ji_vG4ofxAn_tPO#B^>w(;z~P1&#*J*@C0`_JNXBqDM2HVNn8}OjT$hU8Rye? zFfmOjIM?HMFohkw3fyVca23pi(41dQ@QM=0Ze>gvFD!EDrXATMC&d%jxFfER6*h`l zEU$<=WYp($k#RT3l$=xT3Ob!JHV=*GbQ46~PKi2X6ac@(+)l|lWbBmWwRt1KnjIxi|prb^#Pxf}S@}xg2E)(%r;P5QLqz zH^P6V-yQGDFq8J?UGE8#+(t9LFhN&OZNQDJ5l5yMy2AmBCXYZke)6xmQ~?|79wiM1 z!waD5d13_vif3j&aVUirM2xbS3%n&5kk?eyXg5aJSyPu4D2Rf|h;T_mBD~Fb(l8f~_gEpT)V^2zv;gkI(sbpgP z)=d9de`2V%z4rlis#m<%LYji%+Odse$8xqaj|(Fu6yLt^2n}t*LeoXyS!mGC)c%~> ziLDAV=-X0kGf3gWX^h2k<q?4MkpQ|dEHq@ypF&uxkS#Q5 zW@;w_lJEZe#8a(k!M@f%6Hlumnw>OqQ`nrqT5QfI2LAaBQtXgPtqGg-(oLj+~ zL)#w`9t7N{`U6O|h}*#4Kr>I&XT0QnD4Fj=I#dYI%sSW(rG0-m#iuyZ(%7u(G~DM* z4kX;i_--7I#wbo%a_T3&!^gbS$GjmYwS)Or1eAVjeDZTl!kNF|=#l*3l%E*;>Z~t^;-@OyY@!FPR@SyS= z0|RL3N%>3R2~uqF8Z;#m&(jlS zubxnlu&t!4u4FHfXy_;kVqu_fBi8^!IvjgJP)@KTf!Yh5K%MiEZ{_|{@1pDVtRkU& zzw7;sj%=`;Wjm)HW#{dEdywOn>2TaM2%(C$YPaDTRJ8bbyBpat-mrb6UZUqD)+N@! zNWU&J)WZs`WFyG2h^cfQ@;5=Q|FJL!FtLHr37UQ8KlHEEurd=1)o+0w1*3At=Le&Q z$)`BDKgn^R35c-W;j(!-v9Cl}-8Ma*cGY&)5E`q2ziUpP-@R|(R|&FmFnJPBik?~9 zd!)NKUyI%%sJc9J%K6>HR`x6&h3`w8>9U`zK}WJ{p0lEM;xUPE5W;ZT>18LObzYvQ z5~HyA*0O!#oBH(aLCtbnNRD0!$KIm-S`_>NXEP5=1%^YdZnQJ+jj*?rp$JW9ZeO^ezKU1#{x@l zG+=`Kk!y{gxb5f^6BV(^wDy^SQ8JEMup@Ig4Z*{n=k%YaAXKQg9}cs(ai7N?(9~x1 z$HEHr#GRVz-~&j{oAz+fHq-50E%4k-63&i9ZP2+*PI+SvdGVW>P}PcQad7*mkySd6 zRLH`;bcUB=t|E3wykjPHxl2(WUJGsRjttIP-j)y4EYL}tqkwa!ZfqPmc&|T=P)FzBvuRYBSe&W_wR1G=ay93m%p=-bpygUx@EOe~0I9 zh!H7TP%DSRb%a@Vn@%ttzVeni8PE{7tv>zz3B{|2*1Nz5s6DhPC6i5tRBRK#??@XJ zPcQk;9Wm5#4A`~+n)*(AMpvQFyuMSh(Aw!yzI;SnC&`8Wwz#07_6JPWXGydEz?Pil zLpzpo7deI;p+UB`d(qEE0H}d;LUx9Jsgq4QoJ7YVsl|2vciWg6)pUCK3__cR8J6qo z8!eUE>9!eQXIW{(Oxqo!%7uo2A5$OmnX4ME58YI;okT#Cue{G2+Qk|B^IB0fwN_`6 z?dx{&=xb-)j9n$$T1$4_3>cJ*_tkY-62U32+;nAHROf!v1)%*&m3yvux|;f?+vcOnnU7N$PeuNpy8@pz8w9w9~3-MK6+?$)WJhk`nE?|QoI|cqvN|N?lOFom2!K`=~7|8v4~5D zS*n8Q_h6^H&ziJ)JIa4v)gqk4+vfe*TuzCGVC>1^yq6_$UVi4OWKrX|L{9mX_W-8B zVsY_30U;~b(k4EpdV*8jro6pXS?LbRW!E@t$N8SCY)w6zjv8T&@d=Wxr*1S1dwry2 zr=*5|@lfFhJa41rc+FYi+!Qx9O$Gt4n2$?*M*Wcl_WVvlh}z8;IYqh97+;^T_qg<{ z-C)F{QSlO(KDaEf$`pl3fCf@&Wer{X}Ql?;dLVQ1hqS7kR+fADuELqZlUvTf_ zlXiedP?P0x{MG8{IEh{r-X-5rww~U~QJeTol;?X?1{^E_YMHGzx^c^jX4Agsiyu0K zx6SNVx2iCqFAs4Z8LS~6D^8W3Kq$${cQQ@-{-WoW^l|_0IUdMsqC8~oDG{YpTr$y= zPB@|E{!6pAN(V3mEL3hdNVA!6r3S<=csfUBxI|ihJT5fu{XwC=+wZri0!UG6os{J+ z7APK%x1u9+vBku;XI4d?ToN4TO!+B&XiE-{q`o91nbH^2*|E67=x~?|3DL+4xTUeM z>mRz{>g31ZY*4oQci!LI0%T&Woin1W8#={pZm~RGUc;oVo&Cj6U{_~g4_QV%8Qxv% zK|J&0bV$7R3wLAQ;Nf$Gjk71w!C*U#@`MP4a%uK^OJIDaI{oeU5`Q)j+y*kP}8#pb%$s{zLcVpE9IhRRH z-rLGrTkU1i5p^rzMmcO#DdwBjnIT`NXvflhV}Wbi@3}Kg6*GoZ$4t)(?eq2W6R_|O z`4uDKbFkyy3@TK#?*#JvIx!xSCOpJf3pNt$ZA7a5BeYW)_yvO9wNK_ixG^Kw!(vBewF+?#`9}^qWd{1yw>nq`}~j-?IN$0 zemnGC5B`Ky{TKY+_K(V~7)@qiy6f`9H2mJZ>bj zRDqj8@6(EJPh39GzIni?$xSfdNSgN-&yV4O55`sF#JMg5m`HDL3r|09U78QAJ!6g6 zE-?g>vO4aPMXX!EFh8{XNdxqigRQMTF)^{hw!$_OGLykJ)3(APF*oZsW?B*w9JWx= zP!Tav5eRW8aYMS_1gUKZIor>ubXT97N{Vl_lU=9R)<2-OBguKK&i4FSL0+ABUCt|a zpCsbc4&K%=o1aEQpa=EIsTaRI8PeGNX>~dLBhCaOkxle`ti0bEa z!xm#23;44V^;ve?z?Pq&-6_{U)NszIqh$GoY_+7xc3K=Y+Fvv^p}rfAyJ3!!WfwM7 zq3Q|R8n*nbtDkV08P%apF;70Hll5+J0=+1;P^DZc-4RT>BrU6_px?L_ELHBZ3+m;D zxy3EM*esXPD*?vlOyVx9@&x_$?m_=Od|>QtdXi}=~!yu|+_aA0QCGjuUFGjY~qVbpUnF){uRjnMmVd5JF2=G5Qg{{Rl4p0(N{ zB2l;lrZQS#Ag-$MrdFCmU$79HX2$*eKrGxhB$RWRo#^lBxuOnLS~e98=bFD86-~6u z4A(#P==@&rSuCqK@w&*8G>vzD>OAmz1@)6+(^^KK)}4HTnO+BCFNL1-u@c_i-{=W7 zQE0JqXW-1xJcCuM*r*KAJcAa74nvO66p%qFWbOzis(ey3u@Hq-ZgeWCtT<>j8kv#m zc03)aCV}BWzZdKa?0jbITq)Ed#*BJyfb!=wxH{Eh)l{sG=}+i=`2_+fC?r+E$;Kk2 zNg8mMTSXFHSYz1Srk@t6A+jA2St-A`4|hzdB$+|7DSD;)yL~82TH2QZ(d1d?S>}yk zN=#Hr;kfYu%ogyTfLosuQzF25o=RFlcQF8Nj{S2f+}t#gdQ@mg0uJ=CrDXwRh5#=~ z#iu#BJIYV+jqUa2|r&DFGKKp)}jMxEN#$VU_%kC9uOO_ajgDHb5tOhZ0aJ2YYdM_nLtsO_aGs(Bmh(8s0pzP#d*8^B@)ao= zo5(A4^J4Qcs17;7+jTy14-1s6rTkJZa}1%$(~N_{4j#}SVhWrP@l0(G5awURlzk@I zlL@;Q6D`sxcESTAnccs0uq*l;PA(P~HhqmjuM@tlnIIMbVT>&ZhmApFcf|dp6t@!3 zLb)VyC~l4@8ZHuOUJZ}tEJ3s$cHRA#HvAwF-i+hu6^Y6VwiM>;+cz;du@Q7^XNGF0=kNt3&gyI$2 zqtui5W#Pqr8CDxGV;ra$n-9H(hMu>31!I%E;g2EEKK|8H=5vQhE}{dI;U@%)+lF+O z$V`S7+D-j>Y?7q%58esnr{EygnN$Ljlzi1J_RwjIX>&Gry`&_LcqJus(d{BufP~U9 zx)6X>^n0K}@vibh#D$OCA^2M?@-q-j<5y9XN?(8PCwSmH7#lIp-Z&wezaaBvUdWAR zOUTVs3~SsG&IFKe!Cv-`5FW@9#FpsYROraXn}fc#R!f4B!HfO zR4O@ryGR78S^G~TWms@#7+Z3(7$c85YG{~X75x;r0h(@w*i-$Q-7Mm+2|_bRwshqi zLbj;bZJ5}vU%>nn1h;pk38rgBX>POEz<#Bf;ZYHKrHKe^An38;jbHRsUU8Q_nSRoT zD|TUD;MRj2;5O%tUi9R_!$II?&E57ksu*kn$!V1h}kREOR6Sxw6tzG z?9?!}?FIGn?FBaJK02dD(=ySZ0;3McBvLdFZahTK0rOriPlTM8C^$ud3Zz+Hj`d7I zZ=w`Iz6tR~o*pRsI;P0O3XwD%eyO$_Ig7{7W8xutv50)A$X&6ZoOLLTHEK_O*HZV{ zY6u#wYqdC>df@4{!58T`UT)XD)5{iU+JpcG+P&@3QvDVsJ&}XvDxadCcQApaA$a7|7L>%G|jl_am&o0>$Ytag=|FO zkj@ypvm1rl1iW`~0~$CUg4Y5EVDAIKw)P4rGXvfT3@oV){t%Q z=)i09);Obk_0NgIXsDBMluuE(->(s;*Nyh6Zo1LDv zc*IX_f1E;KKlT&*Y41@rqxEVno3mgxpIT06|Dl*$JxvqKwuXE|&(j%(QWJ&jFy$zT zo;FKk%@nNqj?Id&CIETI8j!&$2!1}&%{$TjP#g%#nNOLniQ}{} z`O$oeG1-E)8!K*S^1}p0t?mAaTHk=8*0?uhc4KPoOnOcOKT+yO>c1Gu9R{4=SGqqEP0z+x*ZUwI`myAd82&IwxZF zC>VwTww4yLLs;bF3>Z)587I=VM6m2AID6u@M1<^76khSmIWbJd(CcNE+6)b{9}Ahk zxYkHZyC#YuTd>A0sMx32{?TboI3NgYyG2_g>+jL=gg&3qvQOayT|i$W13lpZ+)Wa7 zjZ6ph2*T|s)V7YvqtXuL+j4?`0s^CP`n9tu0PP<571Xna*fJ^7xf|LlHOuBM8h3oRAU(!U>vzX02C}A(^MN5RIo$%la&L zQ_SE!eHO8tEmOmA-!9vJ_?)kMrw%RF;bo?x$r{}20?-iISKy2OK@JU4_|fDnU=7;c zsV$(O_3q!G^?|;WJ>U4i6LPWH`S=|1KS68x542Qi>)%aLcTNPqg_2my%yR=GCrAOG zpR-|zpV=KvU|09gY{y~=;WH$l4^o&L)lE=RrB63+fIhv=XGJ9Bty9g7Z3IIW}jK0!}Z zR~);n6#PS+wTNgO5<1Jzl2j@Y(HbQ5I?y9Jk$ya4>H`=B zCd3odO^SnrLb`4ncdem;3_5OA2P4QH*wFxMcRs$B?3cKJeg z0-?$NwcZ-deEHkdb%nsFX+ZJ?xMm~hQUlqcb*59)GKe|s7Q~+=3N}Jx?|J=Y4rnRp zu1^1A4o?AlKhH$AQC8LYfi;bA*QD4TL6IAh%o3%PLDm0Z4!b|&vd>~EY9(cd$dMVq zn?_tAfXIf>+Y6ffL4#>qK2n>rwF{ANO1-Ee(uL@_{Rsm+;zHzSetqE_u40{o+ zUF-

7sEjBr8;6?P1wxg!NvKjI|l1G z7V2C(khFwLR1U;jIf>&+)c1bfg2p_$x-8InrmDG}bv-gWotZ>A0{*(aA@JT0nlzHc zW3hDaq0ixuIX7iIvFF=!DUG7L8=<7Hr=}@pILOc#Yue_PzYqCUK$Ww~+ZOWQb4RksXQVB}2h-zdi@E zJ|&jFxqYW<(c~qDd>7>D01o9#BhM!MB=R8#%~E-ficY|Kk7l9FHDql&bwOcmOJlmC zZqrOm@cy`Do1}ja2AATwDvDgbyR5G{ISSkN@Q8Q&E@y3PC#NOYyPb>9D-t~{~Q_7UA4RSL^2e%_j$yK_zN~n`9+wGq+=X1 zx?1Gf0|E~c`{kdjl_^y5!miz6wbaKk7U&JAv>~8&8g^`D(FhjYX_M4;keuX{GOF1p?&m;+7y&VT$ z2pz4Mup>cr?q(+v_4+zL$Y9AqM&=mpQ~lWDGsb* zBJ_PmAk)u{2Wl$5yV>ThEC%z=tpV$@>w1@xiJf|y8?u%lSnN2ooroiSR(^6ZS4cCz z_$)O~LfOgcPd>9>mn^eIj;CTYSEWiBOH)&@(|gTi_#khpp43rfo^7kNSk7ImvpWr? z#unBYEhki@UxcS_W=Ad0IQtE6aI)L!9u7g%kZRob3T-l0f!`0@k3BU^eyc9FB4}T* zbwblhGwuZ3M3^D+uz-S}h$c{I6RHgtiGMH>F@PEq{k195b&6#dWHn7ERoN4d;x%QX z%N038sLPXj@-nwg8wOR84@2oZWC1?z__u`Zw8`-JFKJ!9sY3+qi(Wccw-W57275DH zmf=xc)nisqX^gJBPh4I;6rpvn4P&D*{NV<|GdZE`$9O&O>FnxGg{CBJ?vp2%rRy68 zo2J}mh|+S#fXIC{MBOjYhA*M#F=<>K_eHHC`S0OD9>$X#1zp`WbEn z4!&po>h}$L6}hVo8zv_Rku9#p=rfzI2J&r~OxA&pKsc)e_r}d`fYHEhgg%4G1Q&n* z_b?rsDC)E)lH~b<gVCf$D)bs5U)@7^509 zdR!+TGpm2Ih5_Em$R9@-SCgN2Z@F+2h{6bTB{o~pm#-6CXAfP(BM8nb%?fYswI8`Y zW>{C&%9jK6x@ZgZUgq#N*}H`ZUn?n}4hUd%?2`6+9)YUQgGncZ{2r!xKn}{==fIk& zd4j7p@*){P$6IUG^R<8Q`GzCoVe5+2Z^eO4cQnhwlzQ`*`vG}ao!i+os}VdJoenp5 z3QdDNxhKjdw&w8X(c<&Nl^!X-ET2B_Yctrl>g`ipsg_{+$PKR&Q8${i`C{XnzK3$> z#oCTLj{AnG-pN-^e*4>Z3I$_QF}#d!i)sxpM-(o;oJ^_~c@(<8xKTAs3jr5* z-tTv_rMG!Xu)UMCbyFO*UV1w7BT|E1E`9^k?lg467fd&}`^q>sg&s6JaC|I%etzSa zWJl-ZdX!1>A6C7Yd~2HLF?}ZlR90=!40Av`79z6J-GkORzUI_XJOxn!sm!b0*M#LQnXSQy%TSK2(FsyvgNs*0m%3l# zbsG(@kEbp}+0i*vxn2yg z?{XX7nVyb^wiTp$c!2xOJKyTsWu}qzgD&c6Am_ZneneVC{79)J%{gJGu8NG$;;w+f zEVOvZZc|zQfjdzPrdtz%x_v4&mqV*dOy;ig^5bQ9Za!d6NhxKka~)A(mg41{R_42y z%Eywy`h6^nWvJ=zi%;tc$3M1anA+|MiurWj%jAI9Z*#f#X>FPfl||0+l@&X7stRh+ zSrJ#Bs)8OUA25E0W8xPsRPge-4X;p_P2MK% zKb#~g?T<{)pQ!B5E$_f@lZxRL<4nzsF4yGO`owrjLZ^iHF><5IFXb{Ta@$VUP zxj3vG=p^-{&jH8A#%RL!6ZfVom|V=16*$sayE5C4zM9|mqjg*p^ zHJR35aS8Uja9!sH0Ff;}en|#kTB#~hp^w`-n~q58>JqF>zQE7%D|{5af9@=;`T`g1 zJhO2{7ov(jqY?fj47Q+g6bdahY`n|r+2iDq(X7yHa+pfX;EOTL8h`T-_ zL8D<@n1ep<;qyoV+d;&AnIWPq2ltgg*=%H7Xr;{Z^=82m7Jrq+LfNq>NjJT&ZD&jA z7Ekv*=S_|F*69fV88)rz=``JvO<0G)y;lnPqs=PMtVbv86r!K9- z(cgYL?C`vDl`4Ku63SI%X39_&BXqY#?3P8kHZ(eOt=S*JJ{a8Xj|ztJKa&uygVqVM z8e@4>wO}W@0-h<3Kl0xd;FdKH9PSK`rbA3c@R_ePd^9%OOM(~*Ye9i}OtR?yi zF;^M9wO45OR$ER>LeW_4nhX_p^-9P$gug=p_2|~cEo9Wl)#P7RtqA-&b}`sI&vqtn zUca5z>7cXFzdK}8D=he7xW5}8bz+xkJ^+2?Og0{`W|;24j)NkC#56H3=+|ejWsA?0 z?3C*7d=KKvMhoRs3uDw%n-f}jP4=4ut8bLwl5fS*t`X*4#X88eHmPzuV-ff`Er)hy z#^vJmM4vYRx^}-|^y&`9j@~V8KHmE`6MYw|AI7s7s*)xmrYAK-Hj5@W+CCH>&bty_ zPyBx0Zm(>X7-_{c_^7?mu$}rpEI^w*$Rm0S>l+(*Ht2SNps&wT|-{({&|ewlnuSZo$_DjCaPZ#!G)K^Z}nQs z?SIt!%JCZN(roeJZtZuzD|~$b)`eiU;r|Ye@SG6C-vinVl)A$0mUHg+Q@Y^qyW}5Z zKNP*Qt4AYEVHkh@l(XphPUqNKunGbVq-D6+B`U}DfFTfQFrTMdk6&Nw{u{V3+A%^g zLIr?6?Mw>`DH|&rdK(*S8#)^r8)_RH)3(ZWYa*1l|Jh+#L&_)P+rqd-%Nd=aPj7qq zAj+KnV^*PqA3Fg4>c#ppT*Z?4_=#@$bESp!Ln3U9{QwH#JEx%y=zsY%GuYCvy|P^T z@HS`k$q|M-v)JN{A4G0F+N`LA{hWbx_PDeo*grDeGFf3=F=?OuCYHZ6{pAIJD5z!A z8yqlc7qEYLd+!vat?D~W=ip%!eU&&k@FG$Lm$A+7_U|-V;> zeUzyk?j>ZafHrmdE80q*tVfu?Od+%1r7}SmL@Gq;-0d88Xx3zCQ}i*^YGf@&;AuNe zT_I616OIt{qe$O*mvT~uL2qdn;PwllJY5<|+1y$y6C4*eNtKmODw+Y%3xx|E=UNS#5nAgeYbmXHoeLHD z`KoITwl04;12GG<&PJ8Tv7TOb=-8Of#6|Yacikvi*rgT2u!8 zh3Y5P24y}*UNQ|)Mr+y;ko21Vmxd90}>3t zbRr|Bx`AqmJ!HOnhf{~=-k|6|VoIduPt6Vf+iCVnF({y^QpgbGG)NWf2Uv#LdUOvK z^`Vw!2*N{@95E7u&+SmmVUW-DpdMeN9;XFVeVJPh~uPEL6Phiwj6^90Bu%*{Uj!7XM9mgkY3$^qRDUI3^$U$X*n5z;1%h!zc|u zpB9-GK>O%V3%bP*;YmOf$JSa9M~fOaJ~%D1jCQWf3_0MSmT5?Oj7R{WXka_;36LTa zr*oXLBzmMotXK7CAJG&k&}8{04&P8lJ5=IKK8u7=Pc{RV#E0; zBdEs#?(2D8d5G8S4|!&$pFiMK*eMqaN2W7esA-9^APkJvn`;%G3@nACJ+Xezl*Ex* z2!dl{4bRH6%g+&qJB!m~yc#rt8GA9aifMo$nv7er0A@%g(ZF4ZI%-C^Wjjce#X*#T zjU{z%g`WUK83-xx@6dWD&Hw0{GclIiC;fgFa#6(qoCT5(3F2LM$vFyo`=ji`Vl&xL zK=CPX%V&(sBcM3Ldw}ZI?(I@Jwyb#&NOneoK(vq@QM_Lg9p@~k5DnlwSv6rjEobUNt}MeVDA(pN&@3zgQr3wShoQ_X z1e_@@7@lnrSwz^_d200X;D_m}&fc^Ihwcv_%9y#r8*ASYZx#GIxi_qmxHp7fjsm?7 z-E&@k41QPf!@pnwb8dHno4XoAoB?H0op}<1TzFKD3rMrMe+XI9Pk|qDAE7vRID@nX z2z}>2rraCk7TgR4mD8v7k*0A*-tszX6 zPX;<#k{jHuQ8Ovq6?+TM*;&o={_$1ipe2~QRJ;n+V^{4LZ`eWUb_A6;Y?uSOrG zIHt>lXKHlQj`PZB!J?r^`_dhvvx)b7lLcYSRTNvz?yCz>2#1VgV_>J*q;Eg3jX|Px z%u_1uia$~GDxvs(s*BqO?gh4>>;RDg0xr>I)+CS5a_sgmjl@tl_@~H_7(`ypX+1*5 zF{%~r-)WhH`eCa7ND(B3*o;ABxN~F#ty|Z9c@(2D~Y*S}X*FYg&T%YWF3$Xv%Jd z?0!OuKqDCY_Q-xz<2OrX7n9w;jKLT&+3a4KfKt(a_Dh`^xanm0*t_^YDudjYIv|88 z!`CrgwA7Aoj9y$v;Bsq|En6^hr@B9^Yrbf^3=uAZ!&E|jRW%(QgOD1@U_rftjxxFb zK_X`Y*%2^hdl0CF67+7!Q4+vZl;P4YXa|A}UlK`zS~Gg^Gs)NNdpuJZKx)H4haDs$ z+7_|hnmB5Y5rn&<>6)I4s}bKg*d*Fki~bm9Ad_DBz^e@70ZmV8707Ntj(k#OJow&| z+I2JVlevDRVE0e<6#P0+$nADq>yEsZ`c@zYM*Xvov<)n;0~ixS`7erCW%3&EoEQVL zIg0f3$?1SSL5Zd7iQ};!F3g=N!VG^Q1G7eGW;tMNqZ$VCn`^)Zcr`UNG63@5km2_Z zs9i1IYxx&4+zPtoEA<`QHd9s#CjJiVl!W*xjI-dFi~?b`S~%yBsF)&ADqirIZwG4t zyCB}=QT^G%_7@OkMW57LIyETj$_Cq`8rqlSwWsJYS^n!A$aO$|2DuF1tV8UeQ>wW- z$((x;FX<)^Lm!hVN`oa7rON>vdbXC$)k$L%EIa zNK{>Q^N_p~A=KJ?y#jN#q$=q!$cDxWl9|w}d1CD*Y?OpF@$1--T+Ly|tH~Rq2{Ppb z6gXUP2l!+^Ii*SffS+N}D6)7EgPlEzeF7?F!-jLcU(Esm1_fDLl;VK^=$^B%9VT@L z+Oa_DLuLc~g5EWd)_@e*AboO38AUQWtJ0cSD(2L~fVaEO_+R$IdfKB|DF<^hQdrbH*#(=##m<3`C8npGfgA@3$2%YNf zPw&RHdX{(1MKg>-8enxbts?{Opm_CfMU!nNY7B@p=yVM}ogO^a38|e?^8n!*_0}(b z1;O#K8=l`(qN?xU`XVq~6Sw~`@VniPBIoNJ>}11*z6JepWW=+G*;k0J;Yt52vQsxa?tk56jnGAmo2PW#GFoml^ zEcDs_l1Hh?aBM^6F*JZ7#=>}~+!(oZM$qbIV{8CN5M2BdOXk1o)j1JnU!S}P^7gI7 zH4h@{X^HAN_pA(;;RuQ|p}^kQc(YUsSDO&IAu4u3j$I%h zFUScep`N2g|JQ4nCha4ZGmg!S6(j-WtD~qA;`UZ}ITyLJ>cd;uuHTnYLYkS$w3dbE0$Ni?=;qBm!!n!1g7m6d!$w@KegG3Hp*!)^G1z91lfgjcG@5 zFw8+V*N)_0UPEo>-dFb#`W6Z5r+S@h?6$o+k!!zSdrs<(Chz88VX8Hs-j!uNzwy|m z_zF_LAGK!1y@oC)OkG)vat_kv8IUeg{@T)gL0C zD7&X2k084zr7st?EZ*-ApD;S<25nir2Uoc^;QTO``nzHU(!OsbGPT*Bo1C_`8F>(# z@2yYZXROY4loM?`$U`Si5aouS%L)(+R>yz;Y-t*zd;Ue9pghuG?K*c2K{43h9W}O0 zorlkCG!TyOL3AJR1_z<)G}!p9<(Ftby3)ND6v0=YHHUUpw?T!Y6ZdO$O5VCawy9Xq zZwHZUB_%r>ocm0?y2!`e|3-e8N-%d87q&dOxI=W7Hd&daJSzK30xFFM?`TEq3&CHl z;X3F2UTjqJb#`+@F*FSu^<9*PxN>>(?Jz8_E$(GLBdd`4TEAr*^YufSW1~L-dnhF zm6W7~@ngL(Y0W0z_qG)L=J>-VwT|zDj(nqKJe@cK4#4@{hbm6gZXe99-?}Cq;7yNW zho95jd~BonXkQz(w9a)uT2|Ym&bT~=q91uJ(`VN++?>ML0%=x~eNTX>XIvM7k>qc_ z(0e-#KvP;DtWg!pBX`T?YphDErQf=2YSY>=$;VH@b)+p(2m4<4S>gMtEGpSGd~wd>3XzN24TkipA57CXjb;OeFxgg`|D>p zcoEyG=a%LkJ)#adYSk5b%(Uqz>Iz<#`8N*nt_K-JzDNR$fpu%24b`=8A%GBY1sz>6$A~_HwIM&9(8?~_CcS2okl)#^ z9_~%5Y)7y~6@+uX!cIMMrfE zI;zieJ?Tl-gsX@7o*OZN_gY6gS49*Yz6~>^jfhJhJ@7O>#*zs$TFC6JK=9F>w#7FfeOJtoMA#uTmxe9QK+=w#{xhB%Pn;$=niY> zgn>m}Jgv#9c}SuCG+Hsan(5sRc5~hn@sU{e(b@cv4`z4K#`WL@bhGX=)bl7~XJ<3^ z4Z&sK$ZnU~dtc!rYz!Kr8u_wBc6<~5!U^w|xWrY0*LdZ|r3B|ySE13uQzz7`1l4*o|X#RF$-vsx2b`8UL@OW<>`l&se%Up3SIa*|aJ)LAR5m`zFM`g*BHm`hEmi1Kb9a>F^-@YFBZX6O*(Ibh$|k1h7Ohyf zG2`Yb$0_xcb!BkC%J+k}`KHgTXMRFy71!5e^uZL_q&ab>>%L8TZj@!=E~6ezMXHya-?TE5D(_Db9+q67!|5*0~Dq+yMdeCzpS zcCG@ZA6GYfMf^ykgUj?zgZfyTl;I)(MM*|8zg}Za&y27FSrd0}dKz3UX zw^!ferE=6h#>Jv+gLfZ(Ha@N(vjk~I&s?cI150>J={aXdd=Tyi0{&U z*GJs=>~PB+t5R;b9{BZBd$X|T5SfnrW`!w`=W}LVbu%Y9^pnxgGhz&7@ox4bWHcSG zGr@Mtlh(KE32tu;o(HpP4Wuh~AZ4aEJBfU4&7g3T*5lr5PMm!O@U7ZukCX=AvF)}A z5~|{M?s9Isfr}<}NY#&YRU1|QPiB{wP?$@M+TEFfuPs>jC_y3A|l zn1zKrMe}I14(hhkWnHRCR~4XyQdaj$B71MIv~(EAewU;KTshpHRn;uE-akp9tMVAxVuiio!-&jgdZ^jZ1jm}rUmj0!aO;c$;y}6 z#Vgk()!VCpMmtw+2JIOUzPA(nE_t2(4ws6kN!Sx}SI?^3W^oAdNDj-0P4p1B%r$oU z3uv~|<67h@dhJ4lv#jTFHJu4G-{EAwjBUSw*4p>9hvN%GMdGA&DE5o1lIs4}PO!OE zsZuv9x5#LtbNPecq1m2yT5?Z(o1CGrw1wGkk3`d!%~C4dK8?Z-j?fJqki1T-Ks>f2 zY$i(24+6`r1>A(q32h~Jc=xq+uRXpbJcs+!;s-*-H@}<8&6ZX8~4$l`Sq3RkcDfS9asv2Vn zCk#27$P4>;S8X-3uk^1kkH`p*NhG}I#r2mv7}QHOqE*!K+_moejd729hpHbLF3qMN z5{7=d$(X}lF;-3Ow-+ab`?>rKvng8)mF_pAeuVtbh?kqQFVgJqZqwv|dSCb?qrLp# zqmC<&(ic4viebK|;-LT$m*fD?20{MJSCXn3E7YBm53l~MM`#2D1o#NJ2!t5W2_ZrR zQGvK8PHgRW+w8?#o&{#+6;Lol%R|U$2f?_8#CKY|Is4`9G^|!CtLC0Rlw}p3ECj`GY&d{-pJC+CE^A4v?@EN5tW%RG! z-r%Q-T(@sb9Ya z27a5LPKGltpp}MhPo>HS&!SQSjOlBN3Q%4N1qlhDOQI`}+>y((k*$Aie<}W|7^wIY zB3Cnwjky!rsx!fZ(f^u-ppp#7^9&jFK|{WHWTX8t*U!9)dU+btYltC5c+gl%-lGUSSDF zGG$H}6#3_C;GQDY!9vM`R3XV6$rt>PK*SqCai+s>rP9fX!{U*Ng2Bp&Ns39b9KD~o z6Xt`+*s`9}I_V1-L}vUY>|zl(ELbOu1kBUFeR@Dx6x{g3$jqcYvs59?t!SexdG$pD z5=k(`C5hFAkbE{WF^XAYpV?RfW-1CU(%8HjanJY{$@+Bs!NsX-U?j0{o3!O_9wcC_ zB#}DSq!#v$sDn`Qxb4Np`;1L)&tNJqNDpIr}Wi` zc7a-1Ammtq1}1Y^8Q97{z~ElBaB9A-iEMo5a#HsA15Yl3Q)QboU zmj2BDf;gs{U5GasI?ze%9G4qgG}ED3rIt|%s8K84RZ5$Bd_3e8=2a2-;v+^>@iS6p z=}9OsnF40l9vXAN$~hJWUxMcNi4+5rt~M^2F)m*D2bP0 z)Lykjrd|Gh2}3w6!_V8Ck@y!0-pea{I(!4Q;`=o<2&SFheQ-)lmviLtujtRT*;oc zh-}s0>DzNf4-FCdN1ZW62%&nd%$&*tTZ+@~%0fMm#O#w@VDiYJI&5p2W}T!RrZMV# zI3|#O%CSb&Bol%%dc1CX&PAR01C}Q5lE1hdn1aTDv*5%6@y^dzdd2bD4D;2GHgn0$_7=i!`JQ)4}3h*x;|+l6yVQdBqlPg-}mzcU_}w) z5=!)t(bL5ZW~k2w@M*YObh;y2>qsv~?buaD)wUEi=9S!ZZ;8zf%T=k>#?`hAHs)vF z`^@UBL#|A4LYDZ?W~`U;)d5%<+t;RAe)nsbSd_~GKJ#w-IG4WsDP)xYnTQ0<6ia#~ zBw@qSHcCiG*qCXnIA4CqX>in!bmnmUC8&ZqHlY|csCikJRa9dm6V6XSWo9nl8=aQB2*2K42EJU zXkD?voPkNSBgFy}vH2`gbU9)8P(sSw4?oMDsqeG}JQ*Pmt61r@%zJ<-FIbgR=~b+T z^%L(|>i=vs01n^N<|!}`ztX>Th5`jx-ZLR0_v62s(XB8vgw%T${=z`xN2e$WLR7jko90NqQK}0ZNbrk;O z^aC2tiO4Xz`;r3`?>_`ld5!Vj$Cx)T1zE|P_ob}k&F@VY>?G@gggt+FhXfLg83?+< ztNhy&%fnfk7x;sQvxI|0dg4(dS2QxO6qT%p9kaneE}Kj$(gc};deq1G2ZcyGcH1w^ zgLe>_ifxx*^W9RpVth@5_}Z7ztgn;#)L9OcoXZa-%#$iA+0D-GH%%9axx8&C7RJw`5sLIWN9rKt+Lzi_opZb_$^Ftw|2;->ZlN@ zCgu>hPB|z;pdT%vQP4zKvte-hP*{-#Q2~OKtVpD+vfKc{T{FUS@;hXOe~LVS2xr!S zk#YJ%Gs5rpeY2SZvosf0x^rV!~gPFNA(}t}wzWQy`C6tIpe+ca7;Gb+QBw!cyYAH!E)btTb|=B3N>%5E$gpC4fLe zM|vSieoul9mG)Yfsi&dKZQcXTizqhu~b8@i!piAwKlLCcigB8Nm z@SaTOZa0f%wOYlfX))x18SgK4=3Ur0vHhu`-X_Xt9)q-+2|cR&+-sPAhR(vpfFnR$#dQE-!E=?-T%?A6N?60i3NO{d4L)Eq=|(jyeJ%u^m6 z4-tzOy>m>vepswNdYm)ei2CUV0Z6aMl))*V4-Vi14A7ljWDZeaPGMy*Jbz0jIcZI$ zv*OrPb@)qrbiN_C$Q(>^QX(!*9-kqLN2kT{JL3R#Y>V%Lu+IH#wx6GLfhz33M>@v2 zCBJ3Dj)vjRhB7qnAqD;d82ZjQ00|!JAM{2y$i@^{U$I!du_B2z48ZG+g$7=ITcML; z!6bBARI|q@SxB-x`Hr@n_2ZJTY-Am)+}f=UP3)L|3a11G$AXGe`>Mivut~H=U~kvX zNVKN2b@=_SUZu93>A;QwFhnrkF>GX+i*8sT3I6BUqVYMZSv%QKaUAL)zG$AAd5ChN zP2lLZ#wDa8x5j5|@2&LcL>WF^9wl8K30>YW-5aYW-Y=_ikUkgtO38t{xMikm&E8|J zk0jHFNwWt!vxiCp+kYWfrjbN+7}uGmevbmY3T2CHR~Nf(-!TV?emumbteH#qXrnDk z?2_!x4~4A@M5~_3_D!Pf&TSXx*0=0Ve&E&x$&r0Lw};PEyDrX=z2f(1qJ1;(dued* z`5OO+z^H}{dCI>&=RzSyIgNzwUdM!_q7_sVpyAL0nxuRu4l6~t%R+i~a?e0~%+Y%+ z5kMDI@Qpy=&My$8$K8|*x5xNC+dkAm+x>=%T#0xng4JSG_Lrq5_0X9*%6H0vM5DEJ zE;oUhn;K$5g*4w<1S`J|Sxa9kFgRvTat!oP)DV6@6-gX10T+BIKJa<}@Pl9DR7ou%zH3Z}Roq)gERm6Qo4iKH8-?xkjY8`D?zvp=80QQ^X2>ZG?>O z{M-=bA3wcbpNo#Y4i46G0@~=(OQNVFj9TA2YW!Kjv_xI3RB=mF2jgs=xA+jP7_v=m zks^1$t0qMD3ao(M_f7>*qQbP#Y2{A4`{DBq&i>~m{OJh0UlOl>?}MsLE2*+$9G6Zq z4>$PJgd{&M6V|#uqc~l27Ghjv7g4P`e3$LzrFnI>OC+n*`^_Fo{{OdZDayG@}dEODP@ zcn+nA_|GVsNLo3W9^50C#*0s{oFsrxUhTHM)dp(GUOlcaji~`o$q(BcvH*Mhyz@U_ zo=@5bs$ZU)1Cu9roQl%LKK9*P{+^B=k^$>Cq5pbQRpMSe2AWPdt2$oc)k?8yBHgT%FptpKA!+})mo1iF?I+Q@BkT8~oe$l)TB&gkDZF>W z5tl7#{hvrzg7mHSnuxu4dVd!&wf-JF;iluxdBDv&ofP2RI^=P7R5fa9%qA=zdreFOU<3A?Wm4ltBbq%RKn zuIrlTqQ5_B-Aq0+WGIeajn&(rD@m}=oA+66w1xYayFaS(>_{NA`Sjc~RHs}l0>V20 z+7fb;dL2ioxTc#P=H?PoCNVb-Ty~w6$l&~FKJXr1Tf*+|_#1oi(QOMnAk=iLd@V}j zFSYKyb)&*o;s6E>{U)W&4kxn16c)Vq%OE=m4;J2E9z+h$1+jn?Y)r1|$4I~>36ht+ zzftLI=)V;&{v|mgJQYj%Q580ZPUoMdlZ0>#M4h)i&Vsa5zRiQZc>sYnMXnbNTr5m| zheMlbZhJiQBt2%UaV1fe(z0NOJ6N5;)zVLb+I#T$$#1+extk<14*JY_*C-#a_ zq>o(mm82!C*JNJLi$$;Jww~v7!A!{6Hkvrk%6z^|@LLC&Ts;VVJMmpuZE+EO^U_Wr zj3CfVdj5MTVNqwgl{=${sWnT|dXE#i8Znbt6+BSNJeSr-l^#+7t?FEkfM@G*3O$3r zZ)aC+d|YkEn2eJac2su)HAFtJ>8lFcT``k8GTwqCXU|?&uVAZOR2+Y!CmC0vwg)e$ z@Me2r&s^}rlH4L@PTa#_1y|orcjnFXyVz5y@atV>Cw2%Pz|g8-7w_U;8*i@!bOAoO6=k#DoIM99&4WI%7Ev!% zMFEd10tlq;dG1Rg9gjqJUAx+}7k^65(_iAtK|dKddqtcbyL)dEiu^l154)nX z;#5YHPJo%-5n09yUTe=q<`2gjV!9vfdZpTG8b)mM}5b-qG~ zAXIUn`{WgI<3Bw%{CVXxnH2X%@DO-bk{kdlAVjdeS?Tli8ecxQK+`H;>;Cig1CtA% zP1z$#r<3nzv-xI(T5}{FuN-2xLaO)GbG}t?#M0W^15?JVVPV{xm^p#xEtH#SSDxa+ zNS4}rbn7b8>Q9krTS2+yu3>pvCy#Va7e70-t^Ftvmw3Z6PKWA+OY#453PH7Pd4jy3qPk!A2&d|m>o6dnWz;fXrAF(4n`+j1`fNu-R}@YGD;MhBbR+?= zKAN8?3H3Jnvn80XPCIZMM~{rfco$kP$@P{b`t0T8ri!B?-Y->cIXPd{#nW&zvXXwm zx0Nr`6MM~=9sly~Y^0U=@7f&>+^@#(C1Q<^q|8Y^4(#H`J#e*t-KE$}UfDO!CE7>^ zcs}t=IJ}zytf+~6+(iFcC{e+EtRM0~xK!vI8@_qQwv5r=kGFoFm0of!(v@lcftul3 z)RmO;L~+5H-t6=W|N6m3(CMKELBY1jpBG4mz?osypMVwg@`t}t>Ti*FU|4uMB4V`g zi`+$5z4eO@+=pyq>a8-^^nJW(+`;+#f}G;dF00!{eTxqFBlc(KA^_qn_KR!7jL43c zZWGO2dzgYgou20XpM;vjO+J&L0g(XhKit;`rC|oVmm4dwc{<0hs!^r_JvIfFNHhPJ>X1nPST|MPW=(T*@{m1z$BCStyRSWMl9NPyT?>698(q(jwu{L*BNCcZbH;h)C zPSXs97pe;Is;bj9cRar4X=uycgezVAW_6#47ItEG9M@>?3AzgIrtmbT@&p9_E%e?U z)qjQcPJWdNY^mtgJNTV{rGHfqy?@|4?z&N2W8E-*iquxq8KU&9&3i|WJzl-2R`ty` z`sLp*kJiU`(}iQ`B`Fnz+E z=zKNASL9xRmDE2r`%2;atJHp!$Rr3xF$p$W5cf0HXz&)HUKefO(4BoSV0{^PC`?Z7 z42omFw1#Pe-S{Gw|&Ln%ZYJO7wD8K$5RHNlBm-q?U{UiL_Ky378Dfhq9mfs z>lY1@6_SxEQ_#|rlk>6D^U?FMvXiotetS*XS$Sb)PXg}qF?WoiP-t!-1gWUL3^Lu5 zw-&#^oW%EcIG$WZUK_>(Bwe*#`=bO{<2pXDn4bYh=Ut;lC^bg! zcqCZk0@vtOm2}u&ucP9u@%qv;O}zTd3Fn?65dv^s(s;i+KS% z67b*>r*R@o7~I~D^Tjc&U8h6z{mq!w&pgeIMpV4s(Et7|ymzhRRQ_p<(6T3-o=wcD z2MysLa(4OUPk0`~v8qbX74YwC{J;2Zx0M8EY=io8W)vCV0Q$g@g-;xfRZjAFyKom)r#Rf3u7KSA0sgP)N>ePe;sj%Hr18M4}0S*F~Ysald~smQxI#rbJ}YTX5O**R-T{tv7&t^&IOOuPG}0+{iUSH2Tvtddk1cE7#LsL_={m)RyT}{g zpiLd>F<%uvCYaz<#p0ZI=wnQ8t?34y8IJi}z&zveP46e5B)Wb~;TOujXRB?atT!Pn zgnicb#AL9jvxvV+AtCtiP{wH?jkMH)9(ABmMIq6#E7n?=??wd_uPQTY6jZZZ^W5T| za|p6%9K1Esx1P8Vp~EXbPS_GU7wXiT0kdqIQOhiAk>n&oxbg?YnNXJaO30iR*&iyf zZj>dgL~RzW;v&jGNR!_rI*`U@O)ALt(2NiW-J}}7$U{aX=ERB;pfgp%pv4PtG=3>y z=#1r;pfok0bDwNP=VwMNd;>RFUf!{2I_T6lNW0~C7kQ>W=x;T?REMl^{3XT~bTJ%ZA5}7X zJ9jK=v!A~&KU2WF+F*IF{}gdkBk}{w`%#8SGKDUAR}nOJQ5Xj&`GEGYhdKQd0tSR*(0nHq8=8 z!O&%h0l!N!2+L_KS`;}IQ$3?`P{v`sQTq+hA$y5+MQKki?V`FYTSv@@NHrXWl;z&{YH+KOV~8WBw1wTMKZ$p_ZSgKFOi zwV=RLqLU%!Tl6E>b_x~hRj~M#9e9KWO$kPzDg(I3C}KrT<-C`yTjkNuzgu`92I<3U z%3T3f7-3oKv82=IK3Q}q_Kg%iAnyU*kq)L@$?WyF-A>N z?bfI?OryVHM8zkXM#W`H>PgKf&`3*$xD)q2ilq_{#iMpS6G6>p{JL|0>l-S8(zEOl zv#8=TlFn+!3L!_9iJeBmkm~!A+)zyqV;+&Sf$w!=DT@Evo45QKowFd;Z=GhAWlnuL z4~EyEqXtR|S+!wb5dj(TZ=8KSWbdump2PS!NHiHeIS`Fv*a)Z1QAO+p87P=QDJ~7! zfYQca%vugF#~DnMw0Bg#G`lF%uPVW;f8!FlcZ4%F%Z4F1tMdNdw0E=~m3Kt#pIiCd zu6A?RwS}-WYfjN8`k@#4an2F!9GiDU=Wl?F2KWuH-#PSOkXq>enHE;A%w_X^<=@JFEG%H>cYm8?QWHWoQwq$ zk@F^|{m}U3s?sM@aVHWu6V0k+4qmyv)XtO|%<(O1*xPE3a#>|L>M7)YQXg)wrJ2#M z<@N~K+gimq>M7uUk{|ZKh`Rb)sH+A1d)M;&snjFJi0lH5a(6o9(T>#b3b)@PbDRor z!AUm*LvCn)ll1E-u@WCurg}Kv`A=sbI*ODUIt*f@tqtqigpx_Mijj661Oz)DK1n8l z_cdW~MFn^l(T`oO707MU1Y;v|vApYek6K4_E^zYy&8aE&2kwCW z{`d*tNBoZ*o5Y&U)4?%WN=y1LGj=MBc>!Xoj4{Lg zDzvC0-7aHQY;A)Slw}EwmQE-KGGIE^}`11?zjZvCd{ML zF`U%1$Z}WBKX_a3H&Va%+LT~dRydAr9G+BhKW3ZM^AFuU(gJYXu;@^XIu1q^6QgS| z6>8g%&J8jRcH&K_jaLkvK1n5{tVb~r-s{Qm(6|~1*2gO#9zEU~- zNW#k64@kFRjho=<;~YCw0R&9hIy`H9T4fVAsv++b>Jb)`2cw~=Rb;v#6uLwdx=0i{ z0190gN+lJU29rdUn%b0KmU9W0lMaz!ZB!i^OY>_|Isd;=10IDw_HaMJs-k?_m!P1N zr=c3`D@8;>&-UVflJP~y4^(L5bA?>Q$o!o^>J;&)zkNRW=|7;9?_F7^ijQ%E=WW=h zL@;hC5gL^v_jodZJfT$!`FjA->nVzAQLtU?v6~<hi(E6&0eDr{ook zg|Ksm-W7of#3YFp0Fqq`DY*q=761uL7zs-%2}>-=4Z5fauV`T?2@4ojxuqrR?970; zvq-37ve)zoLe|jq9%ikSf={)L3a?Q_xC^_mYpXd0~W$6?7_U-9M{-T2mu0 zsaf~x(c}Wn2haAE{kzCftON?akT7>Ebyc8o6&8+GCAL;2jusP3-3oJ^!N~ALW;!gnOL8lYU(c5w^s0(v zEm@qu_}Q^0pvVyJk%)LD8Nz`W&Oe9Y8KDKp#Q|4i1m_>k@NCxt^jHh#YJhMsf%8vi z@MP1y#%uw~WUJojY6cN!?P_G>g^c?&(2EG8=D5@OB5_Fot8Pj>Mc%Ec|Gu3t8e&vk z-d^oz|3(u>Q9zYBEx?k=3=NL2bi_N&f)XV5Un3+YRMywy=^H?b9FP<dF@BG?&h)Ed_0Cw2}}aot4*J_I?Z0$&@%ChwqRBw z41JDqx|~nUa(g56hdeX*>HS|3Ed>ws~&uks_X_HRh*(!pDGu= zLMZc(kUnseT&5SlfRkNn#VPvcgERi6(<~jxQ|B^R=(u5Z+Q(q6t$Z3w z?cQaoIOV85trdIN5qsbj1#$)VT@}ATiUKJ`fk+6Tt=OQgIG_Qn;Frwcm#pByuC$>2 zyci%k3gY=`3gX1zSai`Vi5Zo-xYlP&OG;1}(ah`7j|*DyYN{cW)Kb=eDhCq@D+bT1 z%1rlAmWMyeg%#0H(ZV2kvvCJp={Z78H^EPcn#&8mqf-Uqnczxd!QH<@S)+BL8?}s9 zA?rwR>tEB6++C2%sE_ZIG3yJLLVET3BzOlJMtG+hD0vYkGf&mh#DRpWc8ASc!PAbK z#15$Y#x6N|Tnw(Cq71wfElUtIB^tb61ue~pY~=_yQ)I_XINC|o$>eJ-BV#bkDg^EN zz%k|Tiv8upp9zUSlM;XKCZ5gPfU&bPfvmNU97i(i5Om7|k6BKS>rapM$B(zij-!5eThp-P4JR02&n_}Lm(w{#ke@4F?a*Evg==G@=)LaiZX!l~BtJum(Zt{h=Z|w;@=Y@^$ob3+#`Z)Gamae#65K^m;vg z*v~+?ef0WsF=f!gDZF#=vXQ-B31rg=x@XaELhyAiG&NSA62Cn^eKyL%Sw9mSKKKk- zS}Y-D+Bs*`${n zvr*JzleS9q6yS8?D0{Eh=%MS%cluTT@aYsXx5(DBz7q7uZ{>BN{=!lJ7k=dC>X5{U zMS=TH&w*8BPjjbWuJi4tt>M&zdVCIE-qgH0;y>7$!^;<6kg^rBIL7mUB2%vB%K*+y zLATv>QAkI+Ru26p^EBG4%15=$-jDl{8H4nqrPqsAM%`JBhg!zb?eF`)e^y0!m#~jc zw-h|mL*0xY>c9x5HN7hyVnibamo`zaWG@@ZY-SEY^xm&ucTrk>mNPt-g2(X73y~Uq zYR+8#CZ6; ze%N)MHngXSj73iRp!&t$Djt+dhQ4XEtJDzGnC(l!!Rv?Ucl(Zdgt5O>>2f};mrPx_ z2%cQQJuzI&zm&-_t{rY}?soSGtiEQStoDu_RM;Y5DqwT;rSsdpdSvCq`qgX#=rAcm zo!0ScYBDx@U{;OXN1RvdydN)NA}e}JX9@VIuik$kviUUb&Qan&KR?xG6a$u2{((v?l72E55r#i zS45aR_OW;N_lpjf2+KAxsm`*DI>skR57-u-(c4G{t3{(^zLkH-Tp(<<53%(^L8w_fG42hvGvn$usG zGrGnF{_MZ}oSY@m=~?xTU(cyrlWhu;LeXHdlWkm&OPp2yB{7{=HCYdj-b@}OuG*E# z+x%ObA)|3`*TLzXF1%;%Z<>>BNsn+6ZoG%Pac))oux*p=a|qEMTd!5wjqD5;?h7Y} zxEJ59s_yP=V0(am3YOvM-p zdj65ew-2IP0V=@PlMIH)4-U5(!6)Q%s@10Wi)Do7?;s!__!|w!g<($Qg(8Rh#y#@+ zQ`hKgv@7fQ`lucI%I|2TMB@=_)j?7JkhQ*o}R~D7}D4%5!bUmy- z?i<`lPi_G$7tK%ITWJfTolk_1&OSB-QZhtAuLP>Sj7~mnl^AGjm!zGtr^WWVgFbCx zq6+qW*;l*%`mL674Dk^*&hH6y(?^HAtz+fX*cZEu0^NSn>g5rYOnytp+cgF?w_7RU zc8H#B`dCMLrz>UIotFxy@HIZ|PO=EqnBQ(?xP~(ioS#Mlk;%z^Ay{8mPqjl|)?Xc| zkVm;*6K8{WyeTht8DIPXXdG{vPo|=@`l7FBy;UDde9zy<3UY(U{pQ{2>SuK1N8aC` z?Uy8LtLKLOYmQlq%{QyC?}A7HR&6ICI1+f8gQuZF`!hN??MwvxtHngUzjGVIuRd=0 zYG!TK+(89J!>`%{H@RLdvWFkw*y8c~XK8l2n(+kByiff@@{H{|jk~p1|NdzUJ&TX& zGKkDOOSZ`Czu2CgBDvU)C(xB0@oRj;I?1=&QQg&~KjHFL+|<^3_3KEQU1+5+%(ywf zg|ps9<~*2;nm=0V_+4{P-e-u{6bi(gdp^?t%Qd&ZW9T>~MY)?PB6w(3-o4Do+ zHlf5H5H^r8P9PbOW;ZWRL|vE^N<3#pGl4}O6Y=JjI)i{7yz2iIe7G^4^Z%ez)i$tjtL^r6 zZht#`f5PRyVElOgHCdNO2)qEVoZM^td(p6Jqwj2x55KxTxh_a<_*>2O#|PHkEdF9f z;Hg|#Q}wx~!rN{Il*vxdnl%$wsbqOF69<@Si%Dm^s$hOgIjxPT!-sv0?z6v94I+C< zn%n82&f7yB1sv*2kWO8Nl2StFIy|DUpPARE79RBab;rxdPsq7qCk?%EtNPV4(rhp) z${wiE=Axn{It%7fUbnfcX2=B8D?&qM)*oGksrPXk+3X65{;b1knNKl(Q9GKphNZ#2ttr>aSST)`dglHv zMHcX>@((n5$qk`aMqtCH%c{`Z@wRpKqk ziT$VF-1K`%&3a{Gd{Dwp=36+ZcaX89xNAxOkl3y3vI9B-8$=@LF4vhtkfxwaCG|Lt z$%D8o@dMF)y5MZ`FFppV+y3&1#gG8QKu=Sux9k$@$Exg)hj?u5FD+a?Wi8Lt!?FVh z`b@u`1I~BUG$z;w!VEuw*gGHZ6AXI8yX5%0^Scu6yr-|40CVWC8R-j`)kcR{-PWul zg?&2E!si^I=rsNMiDLEBADddcv{cyq*CCXAWRvK{_3g<*MVvO-$GNi@k zpao(waAK%5;<6MXK^zl}ICr5dc3@1LGG~|P)!fDod7aQC?}&&I{Td%S;N&h@EK@yR zfs00OOGE{3GualK@O{d4jS+2WRlA#pI%u}EZj zV{b^!$4glZ;*lE;xZyY*=FruJ#`ffx*5ZW+b>65+qLK@Zt{}|a5*xT_U>~)Wz8_Q} ziC&DTYYrY3pV%v=);cPrFF_)S{@x8Cya60k1d~dn(=jeEyYrEwZ7f)~yl{1O8HdEAeVcS0YN3VtsHe{&ryanr`v|`4Toca@aFeN03F{LeO z!yiqEMDaG`L~-yTW3;Z|ss`!?FJW{P@KpJ0b@eA#c`BgakrN}+5xKs;dccSJ&eKPL zx(5|owajk?EZmC9d()-nmDoC12)?6(bA`?!3KxZR{R4s*L`Ig}$=fw&&q&#*BeC38 z2;BC>xxvwO`Xq`K{&|)<2e|FE-Y*)9!s;YAoiME#vUP&Opb+1*(aSm~)GKuTgh@sA zANsCM?Yx;uzrA&wJ@eD$;Xm|q>7fKgYB+w3R7vLS31hfmxHfn26O&i>f>$6aE|!Rw zu$1KM;3k(cK`i@eR8H;!tCO}5(Gg}v@Q}Q813J}N1Lv^Oppygp!L6aks`If9l+lFb zNyr&wK%2*YQRKU4_JA+lz^vuhfS&?{{9=Vkq0{16YsQRJtX2QH_auE`kohJ*o-bL1AVzv;o{i>5i|kFyt(N89<5HpwFM zwKMGtWl8#>I|g>rcusK=wUszcsjHD6@_Y8RDr?h zAn(lJ&uv)N1pVw_m`fU!;6I8fjG=k)s?svob&>5N@VVuEZkj>`f(F-ApcaLQHsL`+)#&kH~8W=~RVQ_-0 zn(Bk;OPqqMQf`9jBaa{eS6JWxZH0uFEaEY6?FC_d5Yaq!=&JdFAIAC2dO~CCa)DUi zF{&7`7GfrW2q4XoNhRG5XwdcX@TC61kSX^0gi9ovSC)9n`3n4&$q)Afj#gwjc%9}! z{BX#KG*F)Pkgn1HKpyNs)&D>qI12G@@GWkdLSN`>B5EpeWzZxT&#nW?0A6b5AS`i+ zBWc!|WZ6Dh%Ik(;mx5pyl;IZ)h$9n-BO2J5T3T*Jb;sONpk5(RFY_|ZDc1nHi*=?Q z+cFO(#mX_a0LaJ23;m0$sgpQEJcts*guXpdSG>5Yi%*u45wFkoz)FF{lL30OeL}`C zo^y4eGkwxZ`~^+tnh@fjm69B!!x;69B=yM9*fB#wp9q?kSRtDB4NyK-9_u>I_Rc+F zT9EuY4yNlYvEQ#2oSrj?0=Dgn4#-#n^v@6Mlmqc-2fc)a*h+n0h;1nQoAavgJUU*D|u}xs}>KQg}-OU!dJ(M7dbVn3EL|3++ z2FqgQ)Bg9{sGv~FG0IFgxzAz(wHhO2GF&)8a&Ry)c-ZO(5m`kGdaz>fpN$HoX8D!C zLG?aS#>qpcjXrmTWBvXz*5jYSLSJ&A{%C8`a{Sc2ka8YYa-Bij4JWz8i`}TtTi}m0 zz86d&h8cam=si7Z&%8n}ynn7AQU_K^!fFR$4JFZg;%I#dRG)ckt{<8QR$+{j8zMJy zUWA46zS~~Fkt;bPA-7ar&1LN)B-sO1L0me`TDh4+2nn;(u&T0^Ain`mxn#QoG&BuR z7blcWnAn#psFBifCiyV&Nh*3ed?pVuow|^W%o$-O*&b^wQW-|TF>%R{X$tipa86@J z$@zNE4B}HCmJ!frzMz%GsWab3I2J-A(~p7n?8N&lonjYq4_fmdz{3fro1(E4`b5`d z;5bc3WS24>!%nRWWHQLsI(RX_I^g+>e6Z_}QR5Y6*ku;$m4?OB3MTt^yy!oKhima( z>Of1xi|@Y(4<-e;CS65KzSKZ#xc`JcZx2W>iSd^K^d~gxqaCXcJ|~E%qvV{$wVI=5 z>45<<#S_JK84Od?nffea%%%KNQ zjjb{2AvVN}jujbajLnEjBMQcd$}x!BCIyaQvqw75UKJKCwo=DQD^V6rK0C;JT#CFh ztWLi971zLDB}u74lm*PrV;uJzl-_}EzuFOH;-vyGsDA__*R z6E^aU$G4~>sUc2;%ezNPxDDv>#W-uN3q8(ce;s z;jY*`0JJRi#16BmH!!kMD@+QqyMGogVgTtPc$IAe+bmje#g?}=4MtxA@fn6|$+v9r z^~Ie+GnP~XkW37miiIu%ut!vkhn0lIgw6=()EOaBWGkTnI0H4}(H7Klhv zFD>^)B=apz-iRR<$P+)@GWukN2^f@?L_2f(XpDEfZU1XxV$U{CC5P3&+_ z?08P)N&dU?0^Nk84e&$%;MRu%p}P{Ci|Q;8C2C6C^ zQQmUMp({}u6wApK%E^_=b%_By0j$bW0O|p8oOMm=hT%3Th2M^_fYr)2lkPg3k~|wo~-xfztgQy@`RD| zq|=z;JT^)=E4hwj~ER`^6#@QS?!C~QC|Yzo4w#8axcXtW)a8~&D> zeS{=EAX1#^%dGgMR1K$8QM9#{-^xNHwT~~x7>fb*pm)-P3}ga7h2ZX4Veg&&fIq%} zgghyo7vQ_-*}BpKq(OUIA(Hj4WcEtQpdUM3uOP! z_19BEJ%kc&!+N+&5_0qg9(ihUiut3(g5sAg=P!mQX*@wJocfdIGc|#}Vqwgs$IDde z?}Bh_AzBY!(4pE#K_(j1!v143v;qLm& zhWUiUKK-QKYE)}G`r|mOx@(X>&@C1A#*cB?Yo*gI_RsCb$%+H}CBNwv3^OK)t`-^6 ze~I-P%^Y}I^sQ`p*ABbM#2hh%j#(Y=vgafHrap%1i&;90x=b}D`m ztd8ldj*5eQeiMCpDM3&vK@llIz0_d566Qj)f8d#8CWJ5~%)bc_*C8VXizW+)n)x^3 zLBMdSUvqhytpBSgkC2&9TwcH%LdKuce+F;*>*;sYHduMT5WF>VNG7}jLNj(IuC?h- z=n37%QbIJFutW_d$U&0PEJcA_(aPz>{)r8k7F4X(%_Nkc3vwkkwnRR?iD)@gKzpDY zPGN&LyoR=)L{WpIgt|7kJ&UF~VYK+#4=#u^N)|#+GtdsNa2R~+dF2+AB2}4KKwm)% zOh_uabb?jXXHug0cpB4yMXH2C;Pi5$E_nRbH|9y@bwuzKhWZqU=GWh>G} z+0VUf!&em90KP|-_$+0zL@jIKnBqpVtdGMn(ZOP!Qo`J?oHp@Cc_l}=s0yk`LR(QH zw!t6<*Sy;*1;;9-o_*qVj`}P*9SyhGe=WXW7gbb%pzjm>KX`{2jpz?+lBEZ#29OwX z(zma89c$mxy-b)l1M&n}FU8b?nsGw$gaeFd=^);PRIXCYjuoFWWXmx41h)B`?@Sqi z&T7wAELotgMI>8gLa&fro7vFS3MCS`LXYnz7A24mU=6t<$NYU zJ5Wk52q+-B{bsq#;x18rUtf& z3NHy2)4;2b89CXiy^k9P{XA-HYZiBRrbhj_EZqn`Yj>s_DW$vM0%8f*TDJpU9c$jX z&yx;-H|H|memiZrxAEV-8Jz|lyo$H#f{gw~JGlL-qN12Yow%bWfGpO+?}nrtk9$qX zLYv9-&fL?8GqUeLBYjU;x~t5EHaCVLrFhC8yI1Z?#k9$pOuP zc|+4iKSYnB&ffd7W%<>>&vOfl^ThJjw>K=TZqKFW?hwI~CkoJj{H#+dH)A=fmHEXy z=NWudti@P_B|1%a%^d#983RR)t2cc4wH%)6)~|R8JpF)DM5D%li|5m73jX)#$C#+R z)RobIvh8aqqA5F6?1<)4LdT{n-abQPJ@4)Ni?t*js+gn459R~VXe_EYo?O-L=@L^9qfvLDIWjKRPz4b@WGA7R9@Lo zN?+9XJVLb9@5v}P-fo-HUGDWwpyho4&KRjQt^Un{vA5oJPHtt)L7Rg9pUeUD#r$0RZl;K>7cjn(>q*ysc#UIw9BTmA#qW3i zn%23nqWM(+sloMzmCIl%8gc&Gz3%+oeLG07$lFA1Y6$4Qjb*v(vfcdmdhb?lZtMD) z63S}!wA=vpU7GJ}`-WF?#GCHDt}8y$c_{05&RX|u_T(baBAnRi*)}tI8Q*JTxH>HR zoXOYQMdi;H7eF+t>7YNBd=sV;si2wL`s*X_=n1dh(EjZ_zK-nu&BNbD0ANU-qNGm! zFlZT*^>{QmOQ{YJZ0Po907%Z1j4MA2nw@?|c=3JfZ*N`?^Zp;k?kPx;_FWtOw2f&S zGi}?pZQHhO+qP}nHl}S)+ugg~_y4U}Yp;WE?}*BXtf!(Pvrg(DujjsgasltPKd*Yd zp)X{ib$Yo>e((F*H%mI^27Dh*eAV}!=RY~he<=RlDQ@`2uGRZal(Jso1^d>3w-xP}c9+ z^Hg6OTuoi!JT_V5!1I0{(8Sg2I5 z;<$}HH@;`wZOnXZ)O=fyJwly>*E2Ah<5Ayals`N=LrM3BU^;8GxQT{xg~1TWy>_rW z8!Gd@mMYS^=JK7NYV2}mp}Vd)@}&@%&c!%7>cd#IzR_i^<1$^XJmT}a+e)fC=C|*q z-%~R9(KFh|z99m>mVO)a@+65yZn;agpwAQb{>&zu41t#C?Z;AqcCe%O2I%?N@ z*4|M*NWtHG|1>^t&T4CjefWN#tb@q*_nLgq8;-`GRP48YzW9QFt=PXLvWsBLW=D_; zaoNcq694XydSN+2>4DEV(Fv)26t#ZA)IES<$?jh*!GGF}tvx_s{(#bsq{Vx^Y>3C) zF`Mt%7>)K9_o=^faP@qlH@w_(O<}pmW5rtViDYB&2(>>5AK_F%%ca}aq)%l$mBE0p zN@sk!94(EeC#8kFasFdl)!M2(1F;B?rFnvD%i7t`Y$$a2Vk8SR2aPD2>_|&P3g&$=7Wt8*@QHWN7J(6QN^Cf$8jEAC(I(l3DBa%LxYEDG8 zj+h^PtMg_#1>b)*Ni=0wjS;j*BSAFWKnfI)Nw7?f;TIw9GX(o*auNku4gGQM!vujg zW)=3Tqk@kKO5SL&hqp<`RFmd$?ItYA@TiAoNxGU z-l6Tg&8l)9Dd>z_?iNQ^-3Ctx9+<=oA(1=TV#D3FbzX@b(|mV7o58Bu8C@-3i>_3q zsp|{f#?{=;m)d|8ISD?psyQ|8P0v_g{Y8qe3 zvZ1G2q>j+t8lHJgG^E~oXSz)U-&V!~zO>T^t+T7iQ3E0P$gR#iO#c3+GB4+?G91dj zBZk-RluFLFXl`9LK?x9GNAlLqc1**ieOHN^P9B!_O5ri4Qx@c$9@?xw>?M_c-WT$kk6>j!=DP`|I+{^`ss?x->o zG7O%-uSCGYzFC-TTxJb*f%Q{K*<{5tEpbH!zw%@?L6iyZS$e5^rN~~LbMb7f)>aTc zkWe>a)S`o~t*z*QSQ%WmWj?d$Gkq_7e00P*oA6Ty-#ddqtc{}HU}Tz_S~V4Yvd-}M zl)L8&JDx-v`Qx@Jqx7XgkFzyx_v#A~)amn0V0?vdg=-NRdBgaz^jp5T=43Lbuh!rt z!Fhmx_5O%$#~`_P>hG*`=u3M}ZQpCxx9fMA(QR(V7_P}MTFhpw2@`de>3BXaaTwst#7Q!&s(`ow$QPIx`z)?z}lS>9P z$~gv1XFO97N=7u!1%oaMPbBP7EjbN1Q4<=C+oM^~>34{!N0?8kA`(inN5B3uF1JIo zh^Z4?>e6FCJ9!lNL~NnDQUK&ULiyVeEZUQh>RE`vZ#ZjCfGox^OanWOhLA84xnPbf zd76UA*j@l7!w_InpaxONgbCbJJpH!)8DGyP7*--@wWJaOJB-QCq-jvd0VSUWB!v81 zDme{y80yxHY)I*60ozYJX?HqhBnA}HzQS}kY-183`+d%gi8|e?>IKX~5+u^BU=oyg zDwWYGby=BY0~QYtXi|<7Ufg}q+xiBNmcpu3b_q?+cUP~jXVMu7T(tVo(n7f0@|&`E z+W*dA;s8DgidY+i^#4~$*#C9T=KmCk{l6h}JpfMf&+)%Pm@7{JgueRh(+5}BR^VI- z-L|V>gkH_?o1y^Hz;1lBf-r6jR4~Dl6ezX83=C;bBb9bL>l_lVxB^9D zvC1h5R`y6)n9}J3mP+;X5`{nj3OR{##zq+=oI;&Nv&7FKX9|NP4V5kNVkQa@xWZ=o z`Dx<8uiL}}%s7jsK_wD7eFlrBDkY$0a*}A3V!8_FDm)4XK>&u1NKhM+GE@*h!NmM8 zhJGeZuHlUIUl=;1Qi7_$s!|}7Qp|-ds51D7`2NO_`>}t8eU3ezdtL@HvvTcng<>>T zcT6(89|f~grH8pfw8HMP40r{$zY@3w9c0R_9$R^T9*H7*P4rJUWb^hsl`K)3GHd<6 zK^!7-s^I7BQjkE8?NB@)LEvrzf;IyXB6DKkSfwSW>|2Zv6wN7cQj7#wi+LHw^8-cV zSlSsX++yP~*NJ3c(i_si(vAF$Kis8>wDTE_$sR}w`;CnHL+$bIJlf<71!p5ris_T( z>!KF&z^C0MiOoj{L7L4{VKgTEYP;Y87gMF_3R$7dW1ooPcr2 zMp+1znQ+QZMwB28odt@Gl_1vUL}o(pnKLwq#d2L3U!gUu1JZ9uF=aBkR*`6#e=&43 zR^X;tFkGX&!Y-bl68+wRA}r(1~otnoLm(r8KG! zFc7l{YJ)vR&+^R-Ak#4@Z1)$6#}+U!!O-GH0_Yg1w6>?5(-bm`aV9AglE(lcZ~*M( z)Vv-x*(suUN9S1Pw=sYzQDt~Abl2ueVy1|;2!?hip)JEcyP~n(U83X@j{k{Lnyyl- z^!Z4a0ekV&H#fGJFB{%7U%b-fwNi;5g#r$|nF38ZF2L;2m%EX__6Pd}I+DT0HoRAX zRUQ*oKFScCG`p}cfGH73ELkl4mnq5RLZk%o4v+i^Vpo@!JDfD&&~w<}#cjzbgrn~3 z`>%!WwvdKM0I<-jO`0?+`3sfx_$)H?sY*-cA_k<*1>#GV$b&@8qfDYSlKm9b@q1c( z=b(_{e}bEUsQ);y)o)W1Ghlnh9|ENt%Y--yAY`CR+Uru8vBP`gC1La5^RmGhWM3wQ zJz*TgoM9KC!@2*B8Dj`GNS{7sr$l~m<4%_rlw=i$)%Rdxkme8DkS_tg1Y3_exNd|p zm`)h2mNGd}{w75XI;ETGk3CRKAmf{PJ_N6j$6t1~4w&?@q^@-aFKwnlKQ->_vxIZ2 z&d+0>wEPc+4zNp1Z#81~n(-zrxoN&=N*`aW{*sqEbF6S^f|IbBamTN6gu>tyO9ZnJ zSLYbd^8hod(S<+AkZeLigGA#URnLlTqR`}*6jb{e@g&JPii_f{`fJqR>`@~gX0|g8 zbCR6xZmnCTsMfDztKqZ+$jS9tg9U%6(4hh-^tS3<$KPo*Ai8?eZOk)zZO+q1ZN^gu zchj&5F^>~+G4dEqYwE>R zNX8Z)WLM+7!v_#_=$JiZ*i*bWk_KdEx7(~|Mrz}%Hu zebikQ^xWVKI6LVE2qho2SpcB~e8ZMvOo&2;v`Z-cqq(bPq{0!fv)jnWu<{VS9J z6!glvu6c{H!DO(=C-UY1&jgaaP_}ElOA(D^<3R3TG0zFzXpU2=7;aoI@c;!K=f4#6 zpEybWf;f?(_x(RH*(?liG1PmJl(o~@atrgga{3&ma>l?&jdxLIFU@n-839g-yUFbB zRL)6ycORo?yy+9t?4@zeI^}IF+kTBnU@n`%BwhWg(Qb#}S6jcL9iv|>gC#&gKeCvC z1t{q2mzc5u1-(0Z&SwVPRS5iZ*ha2eBh|!+EpXeJ!3?x56!zR4^w~b+Zy(r2tyce= zALF&)Z#N>4qbz&;lR#32ze^^6Ory5aW*z%&8UAwGPVu@K_8>;RCa@mo0^sx85wB_N z#sJo1?CmsLc0&Ou4dV~Ne--q`e--pM#s-S2AUxwbhBvfLG?jk1p{_cL96p8gF(fUX zkY%}CJqsF9hy(~8eaUw+Muw#D7ai#CIkTpib0b`Z2KdZweK=ULOJGYRK3k+;9ZE{^ z1#&{e%nu`iLn}A1mU|?Z^K~p8$XQq`w|&C)+67wN57HC_I16<>jY(pshN%mE@)Cl- zjzE4D|9oTl*`jCYlnIj=cU4nZU1Qq>mjBR(DjlG z3-WnMv4i@&rOrog8EC~RtiGidRlxQyYEc`_&}z4qWug0lz-NnChpTSs`A*tKgG z-$LD!N^-6aC&Q$GF~u$cX`dt~Dhh>+L!8A19phciBMFB}?vW@1_B#ve8klnvD#nSZ z4Ti9dQvSp69|m0jz@V$K*3-#;C}4tiZWar(OB9ANWr$r6wn;MCr{)y3r|!~pgSm#= z|LdO*L0>PZ+NJsgbPfSbbX~htw|;FTP*+TXHpyQd(xZp%J>qdjvcNJKMfk={X3|wC z@Q42>=m|Jbm_&#S7&}PWp@`X`NZCgLGvCNgc*p~D_Kf9c9P+8HRYfo*uI zljBWoz>Tc^1C(GDqlqSp!!ZL8^tKA1l7A8On31GiuZ)=c-!ij*GRp-QhL13p3ck3~ z_^m1%qaW?@G9xI=mFD@lF|7#z2>MJYqIaWKItY-gUl7}@?i>)4BG+Ph0=*v_JxSDc>WiNFQ#`=ysgxig_Zk2qxU3zE$ zP=bf0PNUsV#WW+eFKHTJSf|#(;QAk+L`WH~_c3ZX^4K-Weu#FI^X;fKK-c6T$LT*n zNtRyhQFu&!mY{(<68)wTtn!Yr3*^TXk7vZer4|Ec?9>c8gMYeEa4-`(q`W+t(6CZy zSsIDZ@CiUfM=#iiA*3*Yl&4n=i%=I(nnEksmjpQIdRv$8Ls|_@zy5@@20L|O79~#Q zUT2*TM};YTa&ZOD?%YFB!{C^!lg-_s<_{CCntI1kByxn(L6yHAv(cH-k`Hf@-WlHzh$fRUwv0DKY-8J;t?@*+;-IQ$^k1~AveI*d`{Ubw@-pJ!s^2y@^2Q&%4ccD5D6Ai{oFB4=piE{}P zW|78E>JHEU%!4CU>j81y{OV@x(o6M`r}|9LelzZTpcHyj{`E>` zDj(4%Hpq?=ec-Ad(MC4N&XXc&lacUGj-hjx5YP|bK^+MuC8NHb@P7<+2=#XuPI62v zTte_3qMU8d%M;eX5*O5f2U$Wf-91ej2b}}W>!iGrE7;t3ZognunoAddTQ<&T)`3>8 z&KGnfvfH6dKs(sr;z&&?X^QH2V%Mg-furhe6Dko7Jca+l5@cCLDYBdxV%4#@-v&m0 zDk(w|Or;@4tQJ{w;1U4=GgKHWTI}pPtX%Z0xBROaub_W3S3R{jje)#L@$&byQZ4WVx?-PJeGJ>`2@sX$^)pWz@<0Xh+Jz}<0 zaXaDAxnrS)s(;YitE-+=l%m-S?E)PUo)VD~Z0u47SyvG0KCq0T@hjhQWla-ZJCNmF zRHWc3t4jEo=k)dV>vhD=374_Tcy_zY+df(!#$f)D@0xXwsY4_3^WrW4>#)E-*C(px zPS}#WK)y5oU}{tU)P0hmSDh)Pw#~>Q_Q)fu8&Pi>ljr+u1s{9yu08PBrhR8(!;E#a zSW+H2W3i<+xp%>^Me8<;i(=`ki^I*$XgyBl>0}%i@6vY{-<`eF!DW-^v3!--0FRKiDl=a^Y*o5dabotG4lj=p1QY2vxT>~ zsyF9F4fob|Rn_wH+x}nt-zs!IE5;x;Ued8qbv!cD8w$INL+9DuMy6Byc9hzp-+QJJ zDk?7a!Ot@r@hi@p2%g#aCzjT6pRfFnMRpelF?zD==PW9+wR^RrlfSB{*GWReGe4>V zDdCcBE>H}(3EMTO)EBoo+>MvE+Ixl7b+g*PIqaROxXTW&s|pbYt02}$m|IX^)E_-d zq_)+oE1mD_INSpx49^VtG(1URTd;DM@#eqo)!r%%gFQ(-gf)&xe}7&(hZUPXbt7av zMc2usr#X3xoxrc0AuKSkjJnpRc(WShr^-@Y?RaUM)v9V&cZfG0*z%qWE_P?kTC7xO zKK9iVNWaAzoX!1&@%Wr@X;zUjqJ5}66@*|evvEg#u7A7^Is#EX(Nr_snCVF}j;b@5 zjp@eS=Eejg_+S&LB;$<6T+)%EyJ=3cmW%R_(|}a(SC4FIZBOf5stQh|w)?EWjqd{Eo1in$ z7N7UE+5Ma1St#A2X+(^b{7Jf-aXiDOq@|xNgVbbRAX&hE0@A*>@garU8a2sa{;x z8^DfS#_ad?=IdB+H8YF(>MKTM7_%u;dHIXS7iQ$|Pww2Lp&e-+`3j3$8UgmIQRLf> zkqM3JQJCAeQCJCai?()}L$hTf_!lID)YM_y^In7xL{TpeCMSg8TkN-N8h8Ac{F*mO zx}M{V+&Ayt(DvP4-LFC6mrgw2+s8kHXbC4sRo$vg53R+n z{L>R&i%-kpg|6Ew{Lz3tOG!)CfA7GfR=3Mz^X(n<%GL8mRT*yA>r=+5{!ZfjVE~$) zZ5Iz+d&lA}Ecr|H!Cfl5g3B!JYjQE^s-4MwD9>>n{c1M%S##xOe+hZ%*P_W5%_qot z&+Mj)<#A@Vs9&sN)laLrYXY5LxF2Jg&fuBq#DcYcOjY{kp*>nwW)rP6L@P51IE zE7|7+Ty#3+3Hi>;;m1JnXc@o54)|<_P62!hDlR-ZyZ9p~rgOi?8$eE*N#cq}{~FqF zEw6;b$NpTUuP5#$S;FsPp9+TNV^YpUYNyN#OHC@thPl?58jHEvPQNFp_O*G=-Z&XH zbe_gbNtjP7@e0?IS~=~)kGx@O)+9p4p=C_Bl3;*B;4#C_0 zZFZ9mgMF;UJPx#u9#!MNMq%kX)Uh%)pUuHvUrTGnlBsH^$k^Y{n=3M4P6t1^6V6*G ziW)y?lg4#W7i_yvigg99==BLl13WcPPb^iOwm60bV#DvSH0#F%@Y%~Nd!hQ?A zdphn$oYYh@HuI!wLs41FI8^V~Tu!c2@xm!?KNp^Pnn@OaSKLfDE?rD7`Lj-xE-l7P zK0jZ2bT_>hhT8X9ZwW4IR()a=C1=}ib)`1zWH%=#mB?2Aj} zT59GhI46$wPyKbMSUARHmyf1Z06CCVsY6Ybp!W@ik;!&v^_5RhxxNjZs7kqs8Z}2? zrT#M9=_vd5{d85G+7ayxJ?w?}8u)_qEo60OduW*F-&dV|(Czkd{X`)yKilq*dp$SS zU7cwgxTl`kCVZFL8=;uq1_AWS@KV;!-<3OJYd)8b;^V(qqbi8@X4l_if6x|PbQ$6% zZ)rkM7+N=UiRt+4FFxIQ&haW#@sc+F?AO(LJi)vH>W!rQdiU_19Qvlyu0>wH$ybQ- zYGAXb-h z|0wIkqIB)Goq9AG;jsxg{WNWGX#21vGn_IRukA2sZ#V3nyDVyHjA<`IW8#dyuKfeq z{X?U?G8ye~XnO!z{lb2sD!EfH8?br;_zqz843@-a(*+cJZ9}_`2H>!(@{d}fqU@5S zNv4JCYG%#YZ*=~LS#K#cSb?_*Xb);?vvC=kcpU?X>0xNAV=-#cz*|EOP-gbHmto3> zG%1o?k(RomRVmqj0IR0<*%w8r0DFC&YFjhw1=;~%ucr+wR~h=Dw)+pSP64iHz|ap= zrSsPp;{Aewoxa9mIi~C3IGLy6H?vDeO}xV@djB8P>A~;bR3mfN>vYcCT7~4y#k7B* zb^QMhGHwAuhHFtZ=6}dX^QvpKb6Yfo6~`rRoNT z+e&cX@3Ju*b7UKJ@dzfk1?#`e)TW3zr_hio9aQ>5)}!R$6u`jGDdUKtgcjlk@_(2| zl%!6_TkRzj>A)#mfP~43Dv9WZG>LSABuC34%D401R=lX(o|Bh(b#d_ML09ZDU|FNV zn4(OxROVs&N#h(#bx(=N2U38a$r1lFcPe9$ow@_+Dq;pz-kTh0MUq#pphj1YL>LoZ z*5;BbBP__^P@1$AAgiF3F8eK827`Pqj;w<`f5D-&lV+9ZCEJ?L7KxZ;K)PM3CxbXP zz$9*B84>I`Nl-Klx0DCYk1RK6wL~VT8*FA;9pkiejCchv&li0F^3a^OXhr;N)xghm zSJj7oiQ^#{qm-rULtb|9oM7BkGk^wXGPsNn?Y{f+ZTHUx8>yosf{fND7#haAUGDbCGCZ zfJgyLds#Jn^Wtg=1>8uJ6bMVb%T#^WbP7q{lagm$PsdIh(~5tL^U`YJHq512=3RJ0L4epozLt->9GwDdW823q!9fpE zxQf5tOK@Bs|AJsPL$Qjb;l~6E(6BVWw^JEyF$SEU5!$08djeRvWO0fuGM9f?>4j`u z7#S@N@xsiPzX#k|bJ`+XWV%@9+z-PDZb=8lGvYH7yErLEHT7aeWJ|ok^fy83$V5@( zBz~qiP<=U;1+cqm?XDyk@h+k7))&5|_;YKW z{V7PDU!!6=0$;c~18GOZM54DyN+CnY#_m4|*|9M}Ubnrdc*98Pna&VL7t?MPq(tr& zFci#;iW{t&j4|l;zqw!&!juAtzPW1_+05e7)ug9I%z1P)jcON(YpdJGN(5cjEr~p% zQES20E#;p!2tBg_FLF|F#l~J^ zvd(uxoG;0M9A|?%4nai*F<)dh#j>~WCxlcL?H|WC&IEFZ1@-}Z_ReDp96uYvv^Q&l zWGowI?xRkjHvW6D-4oNoST8?azQ6#u^eWR|Z!fegb~i2g{4k&U^H9{kZXprp4Ul5< z2*}|jg_QCHC9@w))|DQ@;k_xws5EKnt%PYb$^|QpQmRwbpV9{!m>DK{Qq@~jOx8Kl zjW3K-tmD*M?U=g=e?hZYxJR;-$$qrSZn)g2t{;{;CX z5(5!M!v!{ys9c(c;|3PIZ8?|EQ?-fmp!kIB-b2v&7ThG z2Xf(k*7wE-ZBrlSvy>mIV23X)9P4}e3Ta$ zf}R=*)s*xqBv3Kh28^jFK)rmb=kXieN;->>dy5XShPmxmJjq3w&}x4E#or-pRq#M2 znl@-=wd9hpWigo|BGmCcELE_dJEyMw%Xu&u(Upmm!Aor-KX4)}6;3STbv=Lsl17>( zEyDYpyEM+-I$K#N!8}_ZC3#d_bEdSO=IQ@A-!^VLGK7;~> zm~U>mg|^4En@bJa@Ce$F&wnBiirv8kkHoAXgbCHck$Tg9NE$)ns{&?6Dm{QNLT;%7 zFLoR@bsfR|G}~{qe%G-C-vDJq!GeR24?~g&M>>{Gl7W^VAeSe2rf!jfZyaX{T@K<* z1!Y5Wy#>bEL$D(PYom1T!e)b?z&QlHl1DqLl!8M@#)~JZi-|!%F3eR^s z6YumScRH({BVLK6v4_@vBXc^doGZ>1NOcRQfr8dXLhX1^J!Rm_pX75$hu%&0Nyr<{ zA;}^cXVGZV#0daLNcb@?&zJB4Y8!DF@s*w8_F*}Ho8~XnG(XxLJIeZcy zF?$1eMj0_%h>{BzF*_bHD>7`FSPDy&Sc*3l&*=bP35qbw&h&iJqBEz$? z3u~vkgRw$#)l6jxfvG|uZZcRvkW85+ewfA7TLsSrzQkZ$tg2De*0*xtSP|cvL#5W# zRTx#=g>y1Hc?(V3s86(P`<>U06R`7meCPSqo+aq$4h**`M5o-jbScL*>3keW}Ia z37yTYTn;Lefl9NHU1}uUlYqMT(Xj7LOvxkHV9=3(%9vD@R&F#*1z1pOFlb3Ybp$NH z^teBVyE~cR=OeTPE3LsQm>|GJRR8h?uny#}ARzYa4NFDQ_Myh_JV+^{SpdVxl~6E(g9?F&|$%RN?BMk>tX@v z%HDj!_iLR{G!G>PgQAv3pRDP3O7PYlf-*8!c2u18>a7*JCD_dc3l0JX`pYDfnmmHDeunMr~#TV36+LAZklX@lE@O-F>o!jn%0zNT7Bo$xUc^;i!Nruv*7#PwI;3!RrnZ`Yb{=r(`sO6_aE#DXYHlS!KZ8I zGZ5Nqcg_WEH^tBLnzlwyb=~{h(*EqN=LH>@t8R_~YT;7aMRZhE{NE3MxA8B%Uo0*s z#GVIqNrH3r^tGFIxEr4WTwQR_j^ghpQq8uI;r6+3a4 ziprS+QZikM9PjoXF|As{yMem5QHp@SonigbX>~gbC%f_BcswnecH0OJCx`y}l&jQ{ zYF3WUEMwYD?XFwl>MJyKvx_A&S$a8>v@v0IoXyodc$0(MJ;r0%#uD1dqW*;DjCWDG z0eW-MTeX(s|8Q(UmvWE(Q#)bP`qOUm)#{scdi*mP8l8oH`;m)m@puAEncys!m&y!u zxbbc!xO&_znp;{`Xn-T5x&j^UK2^nR0q&9;it1G88pdNyWbP%Cg4eShJq?%HQ}95Z z2x#x!bqfAlbP7H^8tRo6sbj*VO6PuYEf_%#%qA^G;qzhMi>f)o&R^^ukt7gMzSb);T}Lw_UE| zx4CLK7Kmed_0D&0NSt${V>~TOD#!V8ryT8`uk=vG^twXXV-|}G1B&4(Jd~!*aVyWS z)BQH@p|AY*8fOwx$3Cta(MTMKW-*m4}4*D&WpdeR(V8JHSgE|Y=h+S|(-i`&`1M8y^3zMx7CkEh&fswp+YB5IIc z-!|IHU+W!^^Ar^ySCwX3KI$KySE_ovO+nryR93xcm#da87N3-L_)nx?m3=+04vg2R zYd1<8UzEeVe^z~V_ybE>crP6SKSi~BcerF$h|QpdW91UdcDD(I$t(U za=gp&xh%n(d6)Ff9-uEpUp5ea48qZ-2DSqI{T|!Fs`;FS6&)E-qE(Lz+YV44lrb0I zB(i#?H^YW)^M%o9`nc6zI7YF(2CB9=IMkID^gJ572+jYrpPt5^J0Hxk3VJ$xwAcys za&=XqZ@Tyn--IAGiS-PjG%#X%FcLEyOa-`&4in#)^|Keie4CHf>IAn$yzhaVbrztl=3hB-z2&)b z0~+&6n|UEV*&S1=@-KWFfV@TR@mHSMnvf6Rar~e7yPaM0vcHE}Ny+h>@aJB7!#G^R zS049*y%9dXXJzxh7hlgL8n~Tj*hvR<@w%I?I$x!>cA@I4T!40oy2e*D?AimWsY{FeFUBCz;>M&fd9d5ciT^;F;VVowh^8d z%S7~i%dX9uDuTJ6bj7+fqWEL;x_H|xV^VV4{&jhA=#@B?Rn119OTNK$x&8$2zWsC> zRbnUu4;bPbr9?n@pBY#3CTe;;JSmqlxtYlS{&hl=?|g?K3!Oa`F%|R9@YIG;-JvJ5 zB1Y=S`c`Hs8f-o_zc-S;H>oXEf!Nyo)Zk?@Tc2HiMSDcAP5p_4)jeehypxMozo&yY z$c&r7Z7qez=si^DV!hg)rtP^ptBrPU;3C61oK4`dsavMT6qc2{lINiO6*g5|cDFSk)@Mtz@dO)zRAgt(vs1c9Uvi+N zJ5qC--0>xQ)AItse9^)xSb0Nt0esn(i04nH(PvSO_no=*K=?9@cJlMpmVL)1>|`Ra z^|fYX%XE*EGNF`=7Oz+1cPbt0xA&%~XCya*?e>I+!R&n2z>Dcf z?I&h$aid0&?RBi`+P%+q%*Fc+6jY{<&h7NqVgw8PdZ_KC;ll!kBj)$UPUnZCd;W=` z=gG@-DTpHPS|@cZrH={wG|&6S9xuIW&qn3UONYJ7?*soD#V9G(6r845PV0%Ju7_D4 z%;0<7O&*^$UTXm@|Lp0L&&{nUm0p{dHr*lqD#?k@GoSo#Ql8e{ZByFv(XNJ-zxAJ! z*XH$J&#Ph7rUN1_b1{qN5>N9XQdKDZsoZss_e}$D}ow zbmh%ZMMy+yq~`V~{1__f?&usGQJD>=33e#V4ln>WDpE)rp)C+%v>DvZjv_XUQ zw$kyg>r%+*-eM%UcN+)4L;s^@@n17{;sV<%lkI$cL&VNE-4}-CbUq(s3Nc<$s(y5XrUXl{8c4o zSDAorIAa~%*h$aME8Z?@-+FGoD?2#X(_@ukpr8;C%I^k4o1n+*C6I;s$=9-7gu`Ny zvuHZ0I3jS`vQFxcA|v83NXH7yke5XC*&f2Qtz-;kS4WJ**d<= z-H-x;cC7>BzeJeaD_WYXGMgQ7Eo_d04Ib(kC-!U)-h5su?PxZ*CjtB+P(yU{IEuyM zX?m(eKnQWPfkX?`>mNePHfW=ulJ!(-)M8>RbIDGZBIb}zMwViP(_7Qi*PB(2>w!*J z<~5O>6A{8c!LSudIOY_Kn3wL zjTIC>x9<|!0yNh5VM zA9A6=)B5;+(F6Z*h5>)&a%WgpNIvU4{DGKm^c7f+xEvlxSi~@C#*(KI>fLo(SGdhW zx0G>1Q!;7PO!kKg55*j(px`8E#s&W#h(c=$9c*8@WJGvhaDqV@@PBnmi6s_P{TK%2 z(s58!0w+o96H*^;Puu4zN8vIBo~>j{Eo1wR-1`WKxHm#QL08TyEo18~+W%;Xa4e6w zUm_GGp8>cblJ+!xZ0}NeTA}w$s3&J51bJI?*8zIqAQ?(y^@kxX6JqMY!6{Z+EPm-@ zi&aQ}B@kO`D=4$AnMC)tCXbp5KA@=GVu5SVk~i#d(Kvn(+7@JvO}-jrP1H*sSM~;0&`7=zV@VOn^d;j zQSH~x59m}Fwn>fvY)l9SyWzGZ>SC>C_IGZQrW(kEuzvSz@-_p;pO?}xvR|`iBaQQF z-xe*<=96PLPSSws522FHVdqdQBSXq!SYJV@i?1KDINEK<=S7+SYu9FEkJU#av^+|@ zn@CW>qg=ir#k^}LjhG*Ia>r)9%ZLl8lsZ9qpbg;LDfrO~^^uw8A?E{^X0x2`0-~$1 zSQ{YKO0L%lbt(5+K?SCp*(Z}+t4*QXiK*+U)YBvX`lfPZSv#@nN};<`>Ghd+_gFZw zDodgJUIceeV&RGg2GpI)FAjJTyh4O+C%eupW6#Oe0E7dXwu#y~?2q{nwo9O0=OHl8 zH?A`u^O@ zdI>kxM6A)ytkRSWtBXzDA6cONI;|1sTZxdQ%ou`#~;+Q0M(9DVoRzfDU?6( zRPLo=3``supYfOxLNUZjWCd_iTO8j;LAw^p%V;R#FDrAZ-Pi%Peab|amc+ZY1p2z9 zjidU=bfBz6-rzUzHVP6pVDfkPU?0}Nt>(u)is087dXVQ;B(@k46G9Dfg2f6i7;lyV z%XZZBG_ZrhzjW1b(Lta?nE&Gbq=y|9E-+L>Mh{QQ2SN|e&#BFYGnZxb_u zv&=8BAG1$5(YJO|1isj%NVuxaw?eo`2wxr~VLL%!=YL-z^)58u@eNVqL!cI;4H%7P zMA^gEzK;yHhnM8x0maq}?E8y{^?w?r@Bvr*pN&%VZDPUx;|LP@bx<}I;YvTnTM(Ka zD;xY|^UzqXZhtxLhr(gvY76^sV>=>TRHg(TUDP5N7r(-C< zFr^YC8yL)R8AQ=&L{T*(q$Z0rs76vnBc*1~p+C&1PfVvz%%D$9uTRXV?`%A9J`$81 z5FscdU+qMi_Xv4f@gp#-csWt(s{F_Exjk?R!?mYAcCs=%AMB;K?ltu7*CA{iYtSFC zwF7cb?=hi;hs#GelUYKJd(`lT)f=A6;X$VfywX0{2^|wMkEoE(c{ulk5D>ZWJNCH0 z>|s?5iU*E#dybIh)6n~lWEAM2_o>n^*diPm7{2ULy?Y#AB6MFlCLhs8AL*FBg&4kt zn7)P>zJ~O^q~yM{h-*2^FBgS(q;cg(+mpG$x2d8pgJ&F&|DYk1*CFuUb0Q(=k5y5F z)hKCR3wIegVW4E@`bMjv@<9IHT)L6Evc_Zb0{JaIe; zEAK=O8G^BAy5>3O z{XH>N?om~2m>RYWbq7p!8>YrI`o=WI#+`kCD?lf`1Mn((lyPG+Ii_$N{*J%2rTujU z{e09A?-U{L>ZZEC?(o!Y?Hl)8)QGAbk#li~e8Ju)qfSZ2}hGhdJO(f&C@KJCkzMtuVu z31v|r*d@x!L%ZZ>NKm}AgL4a-%I%nXw(IjQrXO9@5dT~`8eTy#ED6@tC6TI}%!MCJHs%l_U8{IC&v?;!q>h1Y_MII31?TcF^#+koY?B0qZ)!>?IQ4ECZ) zmzOQg-+qy*+TOda*>XM|y2?hp)=PKjt z_wxJ=daajj2~D%}HRyd$hJK_+zW7PHRw&&v1S%Qz@=OGI27n$CW8G4`B-OxpM9E5^ zQBPUx4YR>kRE~drls_owDVgG`19BIFCkh=pD)ny%?IxL;OQ;(2EZ<#;FVbvf{~eam z7F}<*v!Zt(gIywnozzgbF9x_T6Qm6j z+flbD3-(4Xkvmlq`a1DV1ETGlJbyr^-#=wYWu?3^0AAmkd8bp4cPTSaeZD&`ur3U~ z81HQ#Y+*ZWyVd>bFHJ+dmIYBWAyG3aQF9N`Vi5p- zYAOIuf0eS3tlMSf=sZx2&7#nCO7v1(^io*#a;!X7_UZLIwDvS)E=U!$*;Ul49n(FE zju9ynwkW(La*H)|TtJ=kBu>DK#6dHXz>Z@wT+~KYDx$$A8(D`!%RbWPsF`%X)j)na z{9EAx1@r+WXwadVbZR7BQ;)p-A(!eKg+^55I~(1m2?q&7^!dQ@GQQ6a_cJkz!ko+Y z!kkSSq?e*kzR}otAfgq4tH&Yqi(oMY+Qsu0i$U_1O~*Pij#UX-BjHF3OsH=CNY;F7 zMKZPbKdj2fYFz&IU|^g<8sLnOV?}e1N`4cF@avh-fw?21(3m}dlacc3*soBLJ>iQy z%y>qkLjSP&jxih_9- zr7c%!d43(p_5UdhxvGp@^4w(Hv(Sltbb^TpXZB zbOi>sd2+kMG!(QiosD5_1G-bsMl*whwl>e8eAq6LXSP4z*0}-dKXslQokTTPKX10R z-|d%B2Y&4=Czs&aYU4f+0TFX_0CAd#_bNasZPAQh<`!;avdz&xPS+o&i*UTeL9tO! z6|`-G)>hdG0ViZ~ZbPg1>v|j4DG5Yh()jz(yB0ZVq%w&TfjP{_lN!Ak8ZEtYBSjPL9=JAz#t>eAKf7uDEfe%vtoNH!< zQH%6=_T9`fcYTZ7!PkC%%npH7e50yQkxfs!R_@04b?X^m@uiDhyr9YetoopfK6%c) z5jv#ju-wquyGw64Li_lrjU{{QyjZ*wJyAREor1qX9N)s_oaKDrYZm3y zEHKX7zB@gZa{LKh0O z$&J0c?qtmP)VRw^&AoD(Fw3FF2O9b|C-lp8zOf#T(>BhGJacKVMMaaoJOxV*%u*dD z-Qok@%U*7wc|WdRA~wK50cpe6m4;~hPfq~XbgB&Js~erK@_9;7pO7|nr_xPdZvg55l$F-mrQg)X|q!S%~ZHMG7S#GTX9@y9qL_7ev ziob{MAr{xHJR-U6M4xKUMOYZ)aY5PW#Nh63`eumr3r&DSmy7D-eeg635*5%|#~&8k zru*ECpaq0pnmfPYb2@*Fa=-BIaU(I#czj;Eq(gm9wFV)-)d)B9-H~5O!S%+*EQdV- z%6x9-#kTp%BYe*QqAl;@RM!emay2`jH?T{-wdLbbvAw>F6{loRo7!%rAB9(&GMBk> z(rzGvcb9NcQ-Z(bZ_9E?u+*m+Hk zKn4~}`J~$Yaa3Mr=C#zjIjaV|KJlSz6}HikAEs)tu1jL`MCaO{6!MlsdA)hww#M_= z6nLB3D~^*~B&Hrru^Vxl&v5Ob)e^ajxca?Cl@kqAO+sXb(Tx zb9kFyMd#2OICGbpT& z-3CWf`(-uW=Mm_D_9~kR-q@XQ=uk*pIGKn0u54X)4jxspe2b>4NlTSki0!A^TFqla zdU~sz)QphlzD$>69e3%ek5SxSA8s+JkLd3#glx-0pH1h_64q(wDSM_~`MLC(&ziKgz)*valJLVl^S<*2MA${B;J0K^qkAHNIVn(@*m?tpi? zZJ*c8A~rS;HoBYVYwiP2LPC6hALM{X;D+ZTJd5+Cklhk}wZmNDRql_yPgN6>m(~6H z@XumvNLvq-kHANO{8`IzJpEfE_~&FlA7{;DT+C;wt0SVtPS~#74~gK2rI`7cSoP6D zMLeO6Q1*{*DCOUnRWMQ3)dZ4-4NpZ7mhcM-3Q__Q5y}yW{1rP-ScEA!%KnnX+yycy z;DX2kQ3~b?rIIwM`fKfuCr7f+r)=%bp1E%i4?gX{%YzrR8iOck{`Ho?!a!c4+>XsP zvwhO@I@O9ZA4hKnVsxjin~#Pgw-6h;9M>A4avC7?mUohF9qpQ8il4APtpyHJzM^6KxXosiSc_M(wR{=-K|ukUvj47$gci6u>_nCHR81b6g2LA9*ChEBAR zm`-O_JuL`KAsE}suu6YtBS~QFpY|^ry8BXWvDKm7b?H>Reje;5YN#R=D(@Zbc{vGP zth@arTF+s*rfE&8h-=mrfCmfdZiy@}gPvbYx7vog1t* zR8boe@TH^Hi{-~ad*Sx&=Vxr6H)KAdkL(qmnTgUC|FKAgCQiEYAo`({ni2e{B2&%0 z(BCQEt%qU2uJKE&UuJMxEcUBz;N>f|+$2@asbec0bcPq#j>aqa6`o^U zxtq4Wv%ap-DC3JW7Ma+Dw&CYU9Od-r`Mu(cwruX|pZ)C{E3fR8k;=r(lTu1e0miaY zUiSW2g!S2qMk&k5p@Y@46KoCq{uV{DJ2UXcqFn&9L2$eB&zn085hfJWny7V@85mQe zd({DK9s0>x`J$+)?r<#wDAs0Y!JVh3Ko!qbaT}$dmp=U*;wsdtC6O{kx9qRS>#2Zl zS`*``=>i*bbd;)uR@Q(yZb!MAE+|#CAnLGoho6GNmMDW$z+W_Zq;168q-%DQ)$NTl zU!*t-rEO*quHYrtQPQ9NI$%12ITAT(Ch4s0?+XqAdP~pER~pZcVC-d56SR$Q2#u?} z8(+?Rg^xZ;FGJjymvzv51U;5w!MoAxpGDBR==)c>zj#3h`gY1NuJi)TuQ6G^cu@pS zVs5?D`QJd@AMyf(jqenP<^yy4@M4B8R$mZ{JHdBsGYA*yU-rhXmlweL8wUUL+qxUn$_(DMfz(X9f7b{ zK-|qUSS?WggbWo4sf&M$j^xR(T$nf>35>5R7s$77GMw*uSZ)*=jz(8LyI$&3UOQYou3qtVa)y}Nj zX;=;YP>DtGoW(^Syn##JIr_tGmF5R6elwIfjgWFd39T1J9#*Y-dol2$yVzF+BpC zIuM~+hSnFRy3s6g2r#=lc-iLwQkN7`p zbt1XX+hEcdmMGKx5!Wf(b?Sj(qzX=|Nk0+?u!NWm44;BW4I#XksTtpp4C>Uc@nk|U zd_c3d@nk%Xs#*=D(Wq7meSp8eH`fm$W|EWI_HL!C0-z!ydAot0?@!wXe_O)Eyl;&UMlwhp+^O6+E3#ln8 zJp=AG&?v;MX#{?4wG<5(OJtIA9Fm%7s8SH48e57aRp$l3kr7t=S*4qV{}oN@><{*u z-k2S!kI%vhh zmF6~R!Q@y~+~3`o0b2aYd* z!M=9nP@UwKeywhk1^Zc?=bTjYHDN+Mdmw83_zxE@j(H+(o@D^nxayqg%{@Y3__nkM z7wig53YHaKB-~t$B}dp?z(d=hThoR{UfOEUoNlQ5mY>I-t>xFFqlNh)Ug%OdCktfuvhE_0R#ap1T)9AU%=KTPy7&a^_8N9eKU3cu>; zL9UMoxY8Wu!x9nRhZHDym>ZwPnMkv0{e<#BI-YJ5jj;Ej4v88#$v{5mFfE!jRG9c` z-`W=9=2Qeff;N~K4Be*6D>O!4-`B7I6#_?`cs9YQ+Pn0artr>)eK1c0fhv9VUPRG-dW!p4&JzYv=X! ztT`hMk(m|IueU1IF~QQ{{UAAbCX|zNmj(7OG*f=k{lKo`tz&ZB5p+t!6(1O1P~Mg+ zdmf~5I-#40$&nkc5$&IqDah}u>{Gs!7OKFLF&}5jMH_jtb(=It znJ&X^qQiJ+__{9q@r{afV<;;8aqm0wC8hU7nkea6(3_%u7~?&YNjhYD9DKB9cShK_ zbcc*+h8q*gkI@^AWqqn?A$^Yq%qI#jn4SB_GTF1duckqo>M#+;np)qgBi*a3_q$6V zTm&o$%;8C@IRq?2^BR?=+JvV(lJf4&1x^_P7K|K4Z#847AOJU-=!Vr&jrtver_ccl z$j{x=*wIJwbxZ6B*+hTRz!U!-89PMf(bKQWZ%`6>D?J5S@QZyN_9D+uN%Sv&`*Jd{ z)+a`gcCa&>#4pWAFtxlt1L1xKzZOwQ@KyOrtjDrffgTqhg6XMn%NZ)I9HRaY=^LI| z=9Bk0!}V&KWyS&HuVZz9;k)^uP0zKy2?x}~^li}EWUcsDA|ltvH*GWCZGMJGc3R;@ zY$PZwk2-BSN*fS($eW2J;~G zaQ(&mif~0+C$?DAfs4k`ZAhcOLl|8=+s#O$J^@Xw)l+}g>2TGBR)wP8B&oj)g*3{G zqW%!yT{jOly6~O)Rb78s)8FLuTS|582#iMtX>ZVHyBy$|?bfAvn%EuiHvaTbc23^%iV1M=r5*Bo z;OAg3g9!aLiHF9mMdbbvwzXUsU=|CV!$a%#5VW;?2{D<2!u={V;kIF4D%lg;VbrWY zl(P`P6!B)SYS+_B-ZPRlq`lPv;E0Up%bz!z#F@FlxC0YxSBIe;hI>ggmxr~au}gCc z)O}zyXMDoMR6hR#++zOe2fIM!)#H)OcEJMGQUk%}w!4u9Y4f|sI@bO&P1tCI2I{e4 zORXpE5yI$$6!X@#C(ZZ!;F}WtfnERuKIlw~{y=CWO+MHqT}ko`8hpx;Lb(vwp+ZMk z*~>pS*UNbXSZkvH?)u}lx6SRVgbAb6)icI|kWoVUGY+l{mSFF~-6A^r>FrfLBxK>T zIsPwWe5^Xe*iHlQf00|*Kf1}p!_?u+2u1S4A}pW2*Be1X|liJ|Q;s#r6l&Bk>{8^*%hsvm?25$M&cGA@U)<2+F=9x}2VPig%q!Oh|0g z$Hl(dZ%VV$N6GQqAkbHX<_Bf;~xD8~(n0w{Hd#xxNEN9$m~eBzo& z87+WN>rh5a8^TbAjIUAcWhCOl*{2R6V?5OKrjcm2JH|e3u8$*^XxiEN7IPXQC%ek=7W2-QuJHASx;^z+IHh^)pVGbFxu;@Aw1=O? z#Z<)#!dNmzCgM)R#7@UjZMi8&Gjat|DFL+aByDhUM>kQzss2bhbtG-vRMRA41yVJ_ zsfWaEd-+3xXWW}?&$}lTT}q z9dk?1xVH!%qLXCnB{=zL%sit?n2FA~2Nl)p&T#0>EE61C-4*$J-7SD?%n3THfA?`` z&0lm)95)OivNuzDqn(ZOWuuF?@8lN%1#XE%7G^=p9S8@e_k&a=Wg50U(|gAG>4+F) zdMw?79XyKc-GpaIS#q%UKK;k(zDz2_I@&n`uvob;k>S?WIju6Chgk9?(Ue%`MD^#! zto**E0%0ExUg=;Qpunwq`kcFjg5tQn=m%nzt!7=&{}@EyqwWWV#7eA%2>~*T2RXg= z_m3Spz&OES@pP0WB{)?Q%b#zRm6m~zgSm}xMnjGm2oUM;ob0?hJqqSIk^QOj&|*fl zPPiYEuq5bScYiCewvR9a*=VxJjiBjTG(<)cb)QKt zfVQ6_m8yTZ^aok8AOP;BHA$eWyN{T9@J7P`Hdg?pR*3O$DlwTAF^uni`s$o#4OyTS zVLgzvo-Gkhkz|x$9wW}p+g}cnq92;5Sw5Z&MnaaH_$M7ibC2rSh{HJ>`=ZBsgJdhK zrg|P`R6_Jm6LK#lnC7dZRY~z?8HqMqk{RnEqP@=C4yayS-fxltTq0oSJJ3OruYTor zY_h(4zY!6j32o@|=Un^QU#Ij4(;YFS*jqNm92mI?BzehGy~LBe)RVM@i1B|V#2OC& zdYgwt8n^p`cN?Dr78^Mu_qW)WPLhv8^3z|OPo(G*gFu=0i8rO6!7`{+V#|BM_hu?kExGqR1I1>s z*slFo@Tv^OnvKM^ixx~XmtxEMz{$5!urC{Z)FfDp16OK>{1h@9WS*ZgPLj3Dk~rYL zWYU*x2ltM(vz`(HFQF(!zB^*P4j;X-Z!`knleoN}-d~{bpWN8sKgL(nUAjX9T(8-P z<%9ook*<+`6X?^yJtbTo|je!6xF?Frfj|)8{U5xuEDdc3tdB zd*u)++pdCrS5|?*wrr{#MM!KqQ z9D0qmeaHU%xSg0$fq#Edr6*;jQX*PKL*lr;O2)u)RL#UzsaAY{knP^5zQcUBxVi4S z8?hc*Y?s(9An7J|(+d>!zI9jd?X?04C#OKGR1YKuINWLP{{>_{hW z;;PPVve`J;*l?)OlH2w7#m(mmuOdNJ@fC;)hbULi~7*-M=*e-!L+R|cdN~hch$NZZ>I&FjxV$R$CChB zgiD=Hx>Hq$=DLr&9huS@Jnpa>Rp-nf+#VifMThGEp_gQrl1fLpW4fA=ftb1NLqW(X_rnlcj|VG5 z7E|4^CWCeRogQGUk*?Bb8hUE-@)8+Aj&=@1KCGHR=y8Y$JtE~1XXXLx+S-~7+Y8T| zv4a4_yMXj##>F*l+vbj%Vqj?dQJue~9FMr&G_ui&-m|ufz$T4n6C2lW&dxuK&Z4P} zw4A>?IwvahD388EoG0qYwHzY@sF#c{k4wyG;V3KX+K=nDgUSgySO%2a3OG#HLu7uX!##+_H^6x*!Koh~Zyai5n!?R#}yIAp?P zg!^$VF1pS-is9fo)dM0CJfBm`eb1ezmp-mJ1a7a^@jl+oUX?27`Oaenf$`1YZp&;S z9ck3Gi)N_*_2uq^!setm0pXky#g$4w0ucD>s&2hCM0I@}6jY5eXuGj}HPq;zyHyE< z+0B%5)wcxan}!0qyyp`4;jfakbec9~?&Vwai7iyazZqlt+)iAo40Q=3e~?_(@Kvy0 zDH%U`I%uEbVw?SZd)>^fDTjYk#`O6D8N-g-Ao!Or7Q$wS%uZ9)R0!}b2qHGNC2aEi zCz(?0?&Md;bE+$|#9!Doip_18bIRH+qVLPkYpa{<%Y<2%Y!v$oHj1f^uHSGl8s%^n zan{jm`3};SayIU~HkXcs46#$nXhJ8o9})@`Su#rGK28QX>~-IZ{g*>MvkDD&7J~ph z*G$fB_u;{tG+if+v6)FGGV1NZyO^~)s&}UVA{fN5b3W`bM;8Ojeiy^>q^5DcRT-JI ztbJ{UJ9Nq#pYbe`dLZ(UV@crT1v8ff1ld6c!`l+c2qQ^zhsCz@$uMh4VfzO=#Y8*M zw~2}Jo-?`0NEjG%&}Zm7MCIA?W!&9Dshw_8o3>p#`uGR8Ysm?Y3KIzich)O%V+S+& z^;1MNO6pB~zTPeN9MmkQi$0{cSf@JKb!->BjXYE4xJSEAeW5bPtQnP-ooW&nvzsY@ z!8>E79xkVwQb9GsJvTt#@*gbx!ebFwWa6W!S{2I zG_1v!%9FS^?758XmzookQ1K01b$7M`w3FB;v3Yy~>etKfS`h1&tNp4(G0IbB;8d@u zF;25ktZ?m*Ceje=pZ7+isR%C9__bqhy7sMw&WjUBNX1R7F#6wNtCBF06MrO5w5cn$>`f&%C6L(*A|Z^(Q8g8 z*lc}r_*Hq2aOU>Tr;RWk+wC%F!L;_%+e4q~-lDrD;ExNx>tAysKNyT(5Dzy5p3G7w zZLgX#yPn<#(GO!>X7>d$sectJtlvIwxY*KepT_cKiZql5zYu*W+z-7$0){SieLF4A zrna_PIv*>eP@MrA8_rRbqljxuO_Plk{$>v@Q5_DUFFX2UvVARD%H30zm8s|LLe5q! zRDQ2_(kf)l^+TPuCo0Vy&m#xrO{2;ds@Dnem!F#VedPr|S;rse*1;2R8DDS;RCRvG zI}}2Ufb)2|;5J_)IA0tR{fvy!E#>tx6cu>bCQ`Zmq^#s-xAdI7z+j1>LgeBRsawcPoemwqoP07Uoij zi^BXInEQEL0r_zbo~+li!^?X9SN*E%1#Wv==vCj9W{Yg}8qip+(eff-=RCHYWZTQH zqX;$FNS+b$8Em!dyDWzL`QSf7A6uoL0ApdYiIfr{yU6L5t0 zij0n)8kd%kmXeebmzI(ifK`57TMJDgFTSV8*aK1yQUdER8QQIy~-_)nr(7$SJd&T?%%^&4R%#xEn7S+g}zm&{z617-SoYBDy zwWejmF^XR_)YIA!G?5}jEoL#c2ILgr=(Uuh#>Bf+zV2DjF_VMMDJ}{w&QVic$rjP% zJ%kBs7@8M&p_TNWsBiDP4yoMN5wXN*zlxxRT9&hiuYKmfN?{9sUAflc%?Xh-ODs6L6y8@&L6Q0 znzLDhO$*J6MtJIZXwR!jkSj}bK5qIcXckPEu;3a~Dbzq*%usTAdqNv4IV`R+t+?QH znGEVV3;lR-nt7ui64V6LDuq2=jTq8c(O`|_{6p7yN_aqD6X$nOEC@W zPh0-+V@4BBG+;QBkCCALPWl$a+ZJm#myT*`J8y60C2#Vg{HUB4+_myP2Yp3*&8 zypmriXr#x&H7p@0V>>=CwAq`X61*oNBG+Xq)Ts1){%4&?b<&{?${h~UFlS+`vQse^ z6|z=EE5wc>CFkef(fibJTCSLBY4+Gg*T7Tlbp0n6#|JgzG-?xqk)+gIZ14simv=t3`z%jhOSMIqA7s zXsANfl&nOyOBxHrXET@$3kiGZ)2HG4%B|w2xRlSn4gdX4@910+2MGcDD|k>zOrKz8rb{jcsJs< z(hq=dBS<^}qt9P<+)5x+(5NuQnYg%;1T{t(M`8~DtvbJqh05IeXG0T=rWvfJ98yCL zwRtv?IS)yB)S`z4ZQ7t^c1+`hivO{!CXmYf9S@&fGaJELQj}@5!26qNoj&AownWoU z6i7&>5v|L_Cd8+pjEE+%glBodaU$IMCxp3(*3f4DIKA#t3jeesp*%+c|2CJ#PXF=W zRPq6WK2&KmRuGO?s`ROwFb5jlp_KIamc0Ob{~WzVK^|a~$jciT&p-3Toa}-zragBW z#zKJOo#C&?dN@V?()c#dVR}oLZGO_ge;izl5Xz1TTlQ|DKsp*9^*y+h`0YyyVISQn z1ilmwFbf01g6ww%Spe$iAAhbUcnI}l<5=@QFw1?TV5&r(PG^5t4Wc2cRQ#PG_O=tf z{^&xm{s9|hy`f%Bf?}=gM)Nn(f=IDO)@ahvUoHh3b@@)cO7X`kwY!ec-y|2BdKEWC zd1ChLR{IJ$8y_P1EiHerR|ps{GWjoDN^M4~_tkNcwL)P-mEL3FqoewH&}wMZ zE2S9lpG6qq_(;#5c3*xw)1!OQU0?sITQtOnMTNveL^#dv!~l=E(tQ)y87M@%Dult= z?R4YD&yx2~oY&!~LRFar>)+{C<+plk2rdj8;QQid4Gx*1~WfmrPOMpZw50JeW|FedyF zRAS*t8eeJ=xWR9Z+M1_mIF*SIh=r+?Au@@5ml7#p)MITd-wN>-F&l(VtM0oCyTH|9 zf2rC1sl0_}yH??K)ef@;V7PeFmHsVE%dX}a?$PHk~g&p+z zg>g#<0^_%XL5vL}MKB=uMDT@UK92XTYBj%VaN|@Vj7OnuxwV1y8vhuq_tm$A5#p4s zTZXE@7h$Da4$ds@@6cKd;+g5?GJ}sWN0Fk1&T}NE@^Q|}tJp6F{D$a9a$ip4e=rmm zM4$u{Qq~yoa0^?!U(kPHGZ5{2Ig%x2fxGJ!xwaH;g*?hs=4UT55K^Py(T@|7Br>Cj z4o@N#mQ=d4CfNuWpR&fYlFiWCYVEhC2ZwE9hS7YtXcTgOb8wX!r3jF<0)kVaSRO=B(k8}^F=#MSX^UKhcDoW zvdjsWn~EPJjF~LE!$rRmxfclkEFjMgN1{O@0sSh6x~93q{WDPN=ZgYsYY<`r*KrXp zw$HZMceZkbuXX>C@CeEy3xm$$-aQZr2Yav=KRu<6CY&)Rtj-lrbrVeeD024`PxU9% zLCQK}6fYXpkEaz9ZtM6pL!wYLDj!cPL8ucF?u1D@LxQqOcLOucq*`(ggh41g zn9D;00&k2HuEbUNC#@j7CQ%G26*#S(AoX9IXVaMmfFYr>&S;a14p=cz z9I9+#f((>s;R?0tQ4u7D%3eXvj%UbH!!s${tQ|B(yW<4e z#ve|L%5?W(#PhyUX5XJZIqXMa%$loE*6Si#1}H%C3CeAYP=iHwY!@1aGtRm(Ir=2e zfS8a|+8hDLMsO5yH)0q-`e6 zM=6sc&0e5#>`@9`CGvmOPk=NMfPT0emJ3n?hTusUsAsqx6fWYxGZGF1e1)gXlUjiq zqe`Z~&Bk{=jG)XvfwKg-UIYG}`+XZsVqCH)gJW5D!tA{uC!}jbyV49MOoO4DYAmgAC$13Sr1oSQ7kdl;vE7Uz{C-o;P+ z8AP(UAIlnN4iCE}!gjpAO%i%Hs(8%DRg}tJr1xva_ty--13DRzH`1>{sLKuy z*OnE#a`M~&Ge=O~v)dA3?sQ^XRhHi7M-&Xh8l^ucrgcU8 z5&Ugr&rJ!mbu~fFQ;l}$#$tH5JsT&py{V&(VB20EI%$xA0KXMhUin)U5gC6z+7T7H z-;KTw+-@=yD#8ywupK%66NBrgEE-tGr!^1`k6}W=AQ<|G!$k&AmEJv}@V=jp+IK*X z*ekO-^FG6*=4kXRPQ|f(h~hgm(DCp!OxD16c^{x?<7;H$%wBiHqpg4vTk->xS*M`J1c2 z+R7w?bTkg$m_6A6r6nKYP6xVcbyXunjMnN^SMDY(oh-wj@fxa>?=c%yZO9I5sWqFY z)uBpIgAUf{#%Ovl7v1xx73H0Lgc*srOS~pG!kCX+VJl|xM0Z}z5~(*+xn*Hg*;q~; zWqTW3_CO0#=lQOi-=jH^i>`ZM_|KQawn*JFA531uMMUSVkE)*TP;%s@EwPR-0K};M zh!8#Co&0oC;UhIJQXTLs*==r}m+UiIyWaA)A83KjW_iOX+w*Nc3%ldqezU>nm$u4& zZS4)qSM8qq7A4pT=3Qe3odQNPyuyyxixD4dW#&cZi$Y6FUbvkcX4&+GMXEF(TXU7v zH2Bmqc@6cU+U1|xTMw67M`@aC^$$FzvXf!kM`=c<-p!kNcqq*Y-cOiyuPxCuuiWk8 z%SucRse7W1KT8B?3Lri0J3VDbYmJ?Ez3^+op_f*}PUG3QT#tqia27ipYI1pR zTAF1C?dn8mHkX+mg5Ue`auY+?h+1x)Vb%7dtR~;*{O}4#Vh@gvh=kBLE&rlgvtzXz z=zJf-ozlTdJRety*2Y+5%1iwON^lzK3~Bh`a;74z`oYATU~*v~={R zClMR?lRK04($(=MMyLLGHD6&^tOjMOc1Sd7&_`FCkBjJ1mWL+qa==k~cu?>q5@FHf zz|!gtgbY#cwM+~7{-*M17t~D;R&tG)j?olRZAWB}Bf}mg3iP1kW!F#p+JLhq|+)=WDl)@bQ<9D(W30b9Ec0OIIIbzW=2xYUX?3^VyAwE&+_| znhU)P3BC!j$xsz36|~XlAghYUUUR(PZNXz(TL5IruocDe)h1@hWa_4qT@h+lxZ2`A z+%p)Mjvv7|?ccZl>hU#BPiLMT8%?epMQlQ4J>P{#^X59WEVtdWVy%8QY`0HdgIY~q zK^(p=1aAdD*vDH9>Z-1CW8n0!LgPCe_-hv|;-Wt&NckX$j>yHW?FA6R;*Y-{ zB}bLKse}QvKi$z`@dr39>V;gx7gwiebjP~%0W>~`#IJVQd^(JjTBG;!zwDQxfo`Je zndHFG^PDWe=;eEn50{hKa!uWOtf=rM&(omo*?fO|nqcwY6biTu_SZ^U*dX7_RRd$Y z`oT{7i?^HP>#tTz^caegDHi3{YvL|e{0}XXu7GIZW{I+-iC@7Ua)rmwN@*zg-Gfv` zCLqX7vwDd$A4)_0vL{W_X!~^NY{|wcirLqN&F685O6e)MK*Zh}l(AHq`k4f;RyI;! zE&DmO&y1Qy|50gzuUjm-ym-#dd<-V?xnw+Yk8YB*dDl4b@ldb)cc&-7wlE>09ICZXZDLbL5~PY!v*PuM zXABI{^J=_9KRIl;TDVJ1C4a4mMWl_PE<_=bBa$4QP;8|GizwvM&L>4!MoXu84Q})O zt#;ccCt0qdu|9K=Ls@bL@HNB$)(3f9$z&Klh9Oyqw-ygKascITAuqjaA1t7gF>o^8 z*#AUP>g2dG?>%!=tQRL$)O*&*2a+p|_VO;4lZ;?8!RXNyUc9~6oJ&7IobWdf3?cMX zDtC{&CLRLqV?5e`W0x*Ym@HT7P$F1L($0-+EqZfxQ7c@xS5-Twhvd1a?PO3jU6RRq zV6Y`>I(b+ZTI=PDRaA3b;8Mx)dMWa&1(1_)fyHw}uXu%$7dWVMrn(XNPn0T^B9ca{ zS+H}R)`^g>sRW&^$TF!q9AY|W%(AKOFEI1_R>ntDPe>N0Q*Xhap0AWF_acGsku9*q zmw4~_!+BU`d~V@i}-X0j*!~vcz7Gv4Aucrx=ydH4X_4)=l#lA zvu$nkhFUxz?NmuzZT-ag>UBxbLvS7cBrDgV5Ln*$j*+M#emUC#0kgPbUSBmylUNfM zvvz`;5M(!aoN~T)l^?D8jk)zv!s%D1`D!2UEaK{s4h>WWS)RvX{NuKgEzvWXuf;;A zLrnCB)1LmmjdHA;^Lc^vIC)Xv^3*c#GwVwC@knf5+d=Fg75GNBY2jp6*Se>Z<}Yua zWj5xPEC8QuSv}Vr|NGDTx|iLY&1uBJ`(?p%JT~W^8MDdr$oHAWPmTiCA=c4W7pphq z6C4pl{Ha64*MMq?(Y7;BaqDD;%Fxk^Rb9NprWlF z#?2JlV~)3NVkUa>O-}E(A%>Lpg?r2lqq z5`xF|wfQ+Dp|e%4`g`+g=mVRV^4+;e@28EmSoV|atLK>S{^%3m<3&zmY%I$C`{#Yc zxvh;QpzQ0b7;x-^kJi)Uq9+9Z%ng?Ys$=#Ft8C9bS{!$&b~|$SN$)S@$0%n94@lI|S7Gv;!2RSY zADVzQ1q@a8)iqTL04|go3czm-weBv?y^67Zgbte~7JVpF(%&r4|F(sO=%$Jb9p3q5 za$r};HpvsPWS0KL7j6mH7WCBh=_C9&B4=z|#MtKPq%EDr$VuDA<#F^A6%!lzXYdM9 zGEbYBn21y7i0Zgjq zYM^SbhUQ`0(LP0r7PHhN8oF=rzq`g}C>Kb`Jp4%J#%lCu3?0&t)dOulanJTm92^Q_ z<HpgCP5@&5|Hgmg{(BlA*|geXXc;O|K+C+2Hmu6B zh_;#V00}CCdh{tqg1^LfiHmILur75r0>6cdMu1dx6K%0drSqny;-YG`NbO3S@*L#? zYGpyoedpr@aiZV(D|hbXDW6^rS$gYZj^_l=;Um}KMmlks#oXP^&GmJ~Lw?7KC&@_* zTnYmdWW9hsAx8Z`cYTQyGV%oR@*PE0w{Qpp_vlGFy?s>pY=#@FG61#V*MhQ=wiPy@ z_VX>h=KBmZTG_53My5XfwIpPn1Z$wg5Xa6i7dEa<@Eab($NV1fT{;JPnBeQ^q>e7}`$@_Bh(|WmEZa*9T?UtQ8 zzv~OwT%P|>(Dy0%tSRw~DGWJtLRmMZFEaCa>9oUyFFB^u^`yMS0}&l^p#G7M2I?-N*(eQ!Ol`T4>eG?m$&aTN&`R-}Hs7iAAVRIDL+q zRIr*-@siBa0$(kVv*5wz^QudDQx;Yfxs6xtty$8bz~%@$#wqf=b4qB@8Z7czqfyoW z1|k?gVe=kK8iR44Q!0QGHHK?J57B$bIl2ax$S z*9(}j92NW)SGI62$J8t{tBNyWk0eQk(g}a~5e*z_VMmi3>}8e-d*n{EVoK*W;Riu< z#8^_Tyw(2ij0}pi9VizzI_Uvo+o})R4jEuG7%7Z+L^~AlN`{J*KhK{t=_^j!Dv}{y(F~pom zZO88*CHZH^8RhWtjmMu-5f)YBB28rIDW`IEgS<1BC6Gq`4{gG8b z00aW0;Rl-GzU`PJ_tAty)`HKFYwipb5HPAiguw=beseS!nPyV^f2dTnx|6qbq6e`vBlpqbXITeP` z(^MvX@VCBa5i1mXcZcFOs-YK&eKRdFU%&?ZAp3Xf=hXGpI&<=_QJlL-z!3%BHt;Lx z5HW{(^Z*o4m2!~WRq>q$oq7p)rALIZl}I9B3Ouh;ut21qo+QzSGRZner2YwMj7^;6 zU6N=`G{QLRXP7p@+A02=&tNT;rdGvx&2X~b#)=U}u=l6gof`YQiL$`Vf z5eEirF?{L$@fUZ)!}sR2?WJLToekZytMsDaw9i^oi;_PmbI~1Wr@_e=cZ-9qB7!d{ zF<0_PwdH>D&jkC=KmAW?kSh&H)h8s{GnG`oZPDv{?TD)08LH5i@)wKTwG$CqaY(>2 z+(e(tz;277;{%D2br-x0`|)hdSSJR zJMR>p{<=PmzGi1In za8~j6>b<7(H{t=h7W{QBfpZgrJ2Gy4dU>h5yrGi;%r6t#$46)5!`q}Ff-Tedlyrt9 zbvmEt3?F~SB>z_`8$t(s%o&qr!6aNdt$;~KnMr3m;fzW8uT-aW+8&b*(kuWmNbM6d zFxfNFeq2PVUY~W%ofL-}UqJD;J&8({dhNR#GcXYq~6i^nI6rJy;q9C ze^6>~v}M?x0%7h#2>X0X4LT0fgQ>K14*pLNf=zHXL)ELw(#S%6pwUX=)T){ZXtGz}e|GJXWy z!4r#EoSw5DLt@KZN)pU>bI6Y7b{QTF2OrEsY(Ue)TzE>*haK0m(Q)Id&A4L``!+&y zL^2F3J$7rUvEK^0MKiR{5HnOv!ZZMwK9xE>;P8^9XOfKrv$Di0@}^$ip-(GrBSUoE zlj*Lo1lI&=??_or_e<*71PzbT^AevVZD5Z@jjrb;V`4#qZic`hD?)~~ij5n1+HorP zijzBQa+n+&clWd?t zK2X06k_yX2qc>J-oouj1GH~AnGhvFEO3Or}Wuo5vGwOzBPF&sAiHMF*Oh+~R<7o)f z7IfV=NJLW_tf-Z%BEV}CrW&PFM0Q0Ed*Lv_R!MPDjqeh9!zTKqEhqS6i#iwpi?o=g zT1g|87)ico4JWj3Jwj3&soE%ZMda4l8Un9Q9m*D*V zljyrAUgt}<3%dvNJS3G0xVK^jwD;ZUfW8TmAaJdDo%9m7ek61hagc_$8wD%!C+^~i z^@uRjpDuX3DzQ$@)vlKO3+^~hqMIV6n<@=I&Wku+P#gDCazXQhm;^HzdBSBDlg_OBl=b)Rz35~lO8-gAw4 zs~Q2j2N_%K$hkvw9uV!Um$q;P&$(smUc4dma~eBy)sk)Dc5L5DSpaCuws<+Ve^Qoi z_;7CF>AQJvBK_hUq#?y)vZxINm8VOJ8(qim5RhO_hh(TtWbn~Heoq@XeoRh!jL0sQ zy91>)M zuE_=r=t%AH#D}}iB2t9>3Eh@gW<)^*x1tU&5CD3kAQ4`xg=0I&flZ+!c8NgLGc3ka zjoYcyAdK31Ej%c?fQ5Qg+Eq`~94s0@z4#~sffn)un_hW?%1)8t-SG@*?WqX_5;s28 zI~CqO>*(QzEU(A!1E-tIuPC&ohZL&dsp}1OUw2}eGL=HoCtie-kY9Sfn@L|fr1}Z< z+4tDXR-J}=1pq`?Il>f8g?e5zTzKxl#dzI_9HIzRM| z^!jFNTUXn!D+jo^`QaV&qkPswIIu!`Tdo6sIm2#j^N|hLbx*I}Np(E`{c|CVGm}e! zzr7KBmH!F^mO+>7;Y69wAPyR=x7O61X=6-TgKl4=rx4X`?`~Wa?cal@ZM;H_ zctPxF>_RsNwAjcvbgw)vV4ZWcIV~pzu$b+H7awF>zjkl<8EfvS8R>cG*z!GfH*-@L zuB$lXw&#d_r)rAsPg6j;Cv?>8;9ys-Y?kX(_;Ro^8GNQbcs$QbEvUt8>K5?H;2_s~ zc=Pa>y#aTJAly0UN}3+sqIkUXBabvi1bN#FyO|a5o51U@R=cT32M?#%Mn&jxp1E=@ zn7Ti0VZPkBx@&yR_BA=KueuX4ewk8wS$qxFUxM5|e>Xzhh!KciCkY`YU55{^8$Nmx);W~kH1d*Lmi;i&?8 zlaxQKR=IM15;Hdf)X=BY6X&ezSZQ)I4{fcq&bEA?EB}t)l`c?bgEnqX*DmexBEBsS zVj*8)@xQ)&KHfc9J)2&O7Hjlie<`^A`!UgHoWtoE4ZOa~UEKOKxeyS&x_hj@5IK~6 z9R3+t(dRFW^xEe@-d+5UY|k*6OY?@TY@WREz`xi{a3Mmoodpw-VZ~o zb*$g{*-;z}ghdnFQ@#bsMHi|(&(tN?@YEdqVs0u}^rh8|VWK~s){-j4ls!t5RIXE6 z46Cbly7rJM8B3=hLvy8NB|am#wtqP!W%v?Sc|8muRTMd9DqDWCcl(0Fj`1>Ljv(3dQn#nWg}O_bf!U?GoEt&|_Y@!2h&+$$%xm1T|&4klU@XfvLN|rOnLFhE&+KyGa;G|!>kKjA(l)SvSL?7GSI@VvO ztL^uiHO$Hi&d`Tnb9-c6X5A3ZTRw+azqTbza^-fUlE0S&j`mXcRkQf5y@%eyh2JZF zgyV^Jsd=0pM}d`}iE_OyAtbT5+~2_t>9*;F2t3PRpqu?Gy;m=)`xj@rb~(K({htRv z7n@m#y8ZPZ3JXd*16lTX_d;+_TKbP98szuIE=7>xkPXd(!=> zY>8fzMpnAm&e}rBUWTUAsqaoD?$*We!iX${cZZ89-si@wg&p`+-GZBws4UcWy4*~U ziLnpW<>Sf-gv_Qgb6hh|&IZL|uG~tLMsWpyssGo zyi@mQK$Gl2-B#bWuuN@DMmIR_6*QoAPWavel)xIDLsO z6-ym0`;)~U-0114GjZw-n%xfZ7mck9cvyR)Gv&``VY)rqC@5JvQrX>=7v0vH?P5w! z%2@Z_2khWFwH}@Sm>*0)U(2WVx{+_=dZth4EyCd))Ma&4F3!2mb0WIoS?wyw%%BiBG%bcgz zxw_p%5~&pUjE}q9iJ9BN27Mr3 zU~KF(pPST^+p+Box$RkC+?f8x`&K*hJCypz|MJ|N5(j5<{{E9`AK0DAJ3sMvWlcx{ zE1(R=h>zomi$tT&3(JqMaJb==o(c+OArOVDA}SyWVW2dbZ?Ki+|3MIm`ih`Hc!P#2 zlHmT_y-f^O&Mon=Sl7N{2HmKe;IX_Q2#R1 z_8>=%N}IO-t;j?&Rgi(ZFkm$-u1Z{u@!hoRm8yhvCuM`U+$er+?D>~dW+#9S%*vP z1kX0tGy4CIT zxv&1?+tn7`dSQue%TR_Y%vH2|YoSy!5~vD)8#c09+Q3K{BMJ3)CGAE^(u((jrB%2L z0j{mK(vX@gtVWQ-ZA_-5;)xg5n*lw$+_1?>o#~t02kw(iX7|7H`L6A3L%t5hwN&!f2-n6xce&eQ>f3;8f9lu9(2$^Z>aGNpP=fGFm$ z3)O)#xax*X5mRL#^E`}09e{-R>yCMeS>A?CkQKSAG2I1RwIX;OC2BffA+t$z88juM zK#>3vMtM$)4~Ipq zG@WB>K?VyD04qBsuwE)eJ5d-S5q26~4t^z%nzc-?0j23Y4*sQ)Z<61SEun@*dnTDQ zlx@J+uzLjx9|CW?h&k85^v6V%Su$0@#9WYiTT$2%0W?xK&<;{h6%^J?uf2dU8fU96) zYRd3r5`fi>es50z`v9X-=!u70ymQ?~)CY`PhbkhBygEfj&fd9~&@Gt@K@)5b2l}=V z=)%1rqZ5Pg>i7MsBn>(89y9qz);x{?Y5WPoDRzhsha_R7m@gEm3ER#B5DXKw>LqZo z9CnwLDL@U$8V@9rF;Rw!ndjKz0FXhJ*86(j2oxZb5EH{p1K$h$%}LoaH26Kx_AO5q zFSz~T3->Aj7s{BuLWMCc9L7v20dwL+j4@p<%9K6hJ!(JA7`d1J9lpn*%7l|tgc(bv zvX5g2p7hhqv=|_*M;41YNq}v6C@_JL#h5KK%#e+#7-7;@RmEmfL7@M&4zr0s2j=jm zRxW!<0LB$uF#~Kt1c2>aKFeMn#?em$#mjMgtadd>li|*Q7D0OMND1XOqXfjww1-Eerphz8Y8Ze3=Fmw? z5U2-rvRA85+Lk)q1iaxWAHk|0-~cxOu%a8FHNs=&jT4-&(><$j7>5Cgg(ym8J;343 zoB6Clqs&)`Jo{o9OBC`Z(24TSj;jU>gx@t~OFsn&Wnl43?i}PH-GQiw!}L%T)`x{*(uh5uE=DvAKrvG!2Bml$8Z0^2YQino zcBTQ2Ixin^YGvV;A9R}oUg~24(`@#;teJ02wA|@8#=X|qwkMi@rh;DTa{?kBIN0Bb zpfC3~tr1-twj682S~d2)tNrZIR>R)D*TN;0+tlR?8;tY0#HDb+bd*M@M=+GjCF^gl z$Q5ZjAMkc_UI=c0!`#t1COdl5I-&KMhPYiBk^L!#gAs-+Ihr9Sq!xsTtA8}5auz9@ zNzT80#doj!5Q~@QXzZm2@6?QIhHxJKquXPdV`)VFk#VBP|Iql_fbWdG6;x0opE#i;mnNylt}%1&R^cSyPUaqD@X|AQ{p?gEnz9F1jBJqYv^T-T`fvqy>OI2kLMqEhZ+a*pl(n(24>fu%7}dp9;WglODv@d z+7>ZysqeK(5WHE1wVcILnu0N#?T`hJ0qr3O^33ex%_ifq?|Y+GtKB|{e(#0B8}GBr zcEFc`Ky|!r9dDJx=wQq{_TM?$#k89|>13n^GwR-r`3GS)nbygucQ^cz#C~kGoo>fr zBm}8xvR^uwI}5U9CMfqfrSXJTPgNCwcl48iqivuq_rnf$*0SXaC}xZzY4e4;$mi)> z(uqMNLGT$!y_e4~sEobpLUqlWH^#Ob<0{t2Qh5z{V#Ke4c_R0|k%&ZFDkl`miwv<= z#f60fc5vBxWmby}gq_JbSgj8OqV|VHIPo84{wsN@jc7=wSY4zm$tXh6BRZlb8iM!H z`mI&V**+COE8f7LD`g%;dq-;k*BxV5IroOGUE_JD&RaK;eIhnIH!h8d5M=D9C z3XU9$5@rvFG?_y(y{IS{Iu=O~=Z9_YV^M`pp(Kk^03ptSj1Aqo0u|##!jbvyn?|t> zLLD2TArGcQT2-o^PVQTt+^6em!5BMFK@>wa?+Ja4B(-_OJHIJrpVl4NG052}#owp1 z59W48-#YQDPX`Iq4U?i(s&JQ1BCrDnY4ZneKWFr)wnHLWOY%>GV~k-m!=R}`fjj=4 zfGiZsfyAUtaOmSL<^1J;$`gHBh2V@wcgoW*c#;q}jSs~YhvfVvKC3}_%A+}}L3)}^ zbXtYxysTUjfbvuG(sT;BWZ-f)!5pN!gP9}asd(bNPch>;JIrGRDr}KWzJp^Y1zKpJMs=A%6~@r-J|3cF#n1VDXAxyRO$= zV7sr`=&J*XSj$FCn#TMQU&akN9{Tl9eX=!rRxWL&@+NN$5azn}$dR2dDb>!+GOq9W#S6USAZFhE=y05G=VSY+#EeQq;E!u z*CX6JM8pvS#~R**5{{7vRm73CVRIa#GIH8lfqkaWGkI)-)99r(lUI8@pd`*PGz^-G5ff@%^b2x@8sm3uC z%u#0kz@Fv%C2MTMO5$2x(%J>6<}$J-Tl{ehFl`p7CL8x`{hxf3N`VQsfYd8sT5eEH zSExpB!r6MMe3KS|iQEcMzzApB(!pWTh{T_pWWi@BOk@22xb}bvD6uw}eT4tGc5=?W zUkb1KORImUjVhT&37Os)Fh5Dh2!;w_3>5&dlxNWxoh!liuVq8~us$PzUg$dW=zbx6eenO(=sh7zSN z6KB>)^T-qF5ysH#4%;{9z>xsPK%Iv~KPmVe-6wh7W8T#~{YJ?T*j|Hlw!-r|4v{yK z$gym>>(b%<#ly91%%q&NF`&p3k}ulF#BJGT)Vf80!45f0OSoy9WZxm2c>}fg9ED)} zg42JcM!0zmz4t8eyCqDh^^+`kgP3rWD%jqOCU^sxa5LA?rBejxm*0r4B#ZLj0kc7W zp-gYtWnBw$QXMmr!Cv&ih4zt51)E_m=L7ZrpO#*P6L1V31)!lKINCz!g_# zpeR&9E1rtGK;Qp*4Cmu#8pVSL}Ul3ccVxi3m> zvA)Y)6KjOdTBt;cCOKCRmr0mwnW;3GIwjn&EcM+XoUK8B8rkOdt^5=BjIUc~pF^Kx{~{a0)6yX`4Cno-uw~l#ce{TcxJJCaY~?ZJ z@?6xp-r_7AfZlI&)A(dy_joDd)zY)^cAsINb(r;UgJ~~oUv+IQto;kcmO6$qy6wIU zQD@*Z?c?P2x7J6<&Vj0{wJ3Vm`Of955Wbedb4G!V0rFckX0jr?uytH+oL%Bqe_C#P zr{DPN#vWV7o8*H{!^fFX6dOYxxaca>!cr+$L(FOYoLl?hlF+lH-EPKXBPK3`+eK%W zT%on|jlUqSCsDVzD{U9k8!RcdGo|lN2kn`WfzouINxyXwKAtwU)`ir&PmA}&`RQ?U zNz{O*8s3fg4bOhd+hg)??B~g(u<5WyL7?F8uQBFA~3dgB^RvLj#TgBcvqigOTE{_-tFdw_fJ$(8s^bA*f3fv(^9q<4-d<)6{&ga@2X33S>wH*yQ<2_RoDGK7n^ypjuw2Z zLeQqRQpLjfn`m&^S0rnusynX}eiHagR^gg#gP+iHqvET!SJJP?Z8mdrS+$*rj<0df zg1XBa2!|uD$Q1|a~Nuf{140pPeoh@BujciHs z!j~?)wYoHA*xRPPi618~W3Q*@Br37t+MVgh`m{V;rY2F( zC=HL`&wXBR(xXJ1u^U+}K%OsmbDEyqliv<4-H*VVh>XRz0egvtwS{D zyCo~odjYu2DtuBhSvq!6c`Kt6eK#LgmYAoNc*#NOGF;f0_G8q}vkO#L_r1+&pK?w6hV`5xGh^1Z^Zf6fm<*w2|>Ckr79aWI_AU`zBs z5m#EVlcjPpwk+nYetfv-O%BazM0vTNON%8X8LI8~MIQVRAx8@vslDLX`YUjOPdag0MRwaA1_`Z`U zjjA4lo8%(7+$Kt~ZF(Fm?r^BP>eLiU*DX}n%~tjTL4#8M>K>gv%yDu1LgY?KHP4qo zyo2E<94_q8ax?llH>Yg#(H!z(R@_wUdAvzzAgX;Y%J`}3!am8Jev5uSkeqJ&e%A9x zy_n$|lXrP*cX~ff($Ao8QuA$!z4QTIsLKL3)f%J{ZPBo^+@5`!_Otd;D@Zl8VtP=> zU0C&Ir{80+*v}vKw&=Dfo~yNsGuQU`T+HVAhK_7!${m`nQJy1|1t>Z0 ze0laS8Rqp>XwXYhl>@o(J>o%4%U{Lu-F7Fuwzdj3o3`63{h<7A!{m17xkY?fx3!`* z;SkD~v*1a4eY`_bg@OA|ChQbX?eGvgJqm(P6B{m9@8e#{aSnxl_xFqTe@bSrNke() zeODRXW|Esr7Qc#&@bP;b!UnUx{12CV9?*%)&+z=pU=qbm`BrrvKL(%EzdIb&tiK=O zhFO>NzwNF4DoFn3`+od0jNUjKEm-*0^&1rL+#2|El$XY9ZIV_q@-lhwac$6{uEDx1 z7I`O?i`UuK`iEI-J>M4}F4{`^e73;t%ORfb!)8vL_p z@Xd+o^S7_Um6lTl5dS)APy4nX(;fdL8|I%UyL1%dJe}M(KE9f#53xfMiAz6GII@Zc z%`zoSXXH_SNcnRXhdM@ETYW-8LW8}nfg<9U5D2a%MPvAHbVnPIJ=UCkO_Fkhzpiez++QZf|u;4NBfrkV_l{b>jp_o7mf*I??vAS9UFE6bk`>~$i_~RVf z8YHbD%}uI}>sYTa;f_s%mWPJDxa_E$EWPV1ObS*xA$00i8+it@;UCseBpL_;SeVM$TE@JiAm{BKio^v|mK1#rM_= z2m&@n9 z`tu~(|0v2Ytu6nM9XsFBDO)8W1l0Gy&xgYl((=T$rP#pbvgc!ggk!?DCkKs0=T}lj z!&o$S;uRaoNe1;0YY9VHT$@C8 znl%iY*v&V^l`~#bjhSlf0>m@M1$-o|_>~qc0h20z^Ta9Dlh-U-H(N2pTj0hsP%8a1 zd%aZ78(>*bRtR)xe^XJY6v;3K4-8KMX(c0n3D{sX%nbvIZBQhMf+U9 zE{|AWn|7avtt;k`hc4x}n1v@Zm#_Z5XO|6;Kz%Yj9vU?XSlxrd!Ou(!s@;`^p{X zu;E7XRS6k>XogR1@Q!Hxmj0Do$J-D@@ABIwwY>}K;guCAy?2x!{qgI1&8>^T-i203 zO%Rmc4yCux&$W1!BAY^%k>jt~FwA5Upuq~6M4*@C>?Gr~En3W8#GW>p=8UU!!IodX z`OvDe6lWA`6Xy!o6hMSvI7I|F@W1wgVSHHvldiqlw}_`yskwaDa}tANqeode{>2tn*FX`taG2x ziiS;YPsf$UBh3Fl_p#0dln~Jf5*6Ak_+RUs9%do?rb0{RND^-=jIvFW1MFJ}r02Yn z-I`zj%YA&n4$3ZK1AYVc<39d^%Rfwo`C<&*U~$^XfVjpgfOAnzWQP$lA<`%lD{)kZ z{$%af*OqF8wmKk6psxyw@&y}ce~k^t2;C)`W2_2`5*eTMj}n=x<)iy?AA`4wv%uo_ z{UNf#h>V(TCP1sqm+DNPq%K z!jBg+px#`BD=!3EEM*!;FQte zl)>PX$>60R$Vzf#RVXUcnG(&jKLQt5D=LKGh*FjC8Jc?hFXIlt&qkXsBWzHWiFp|5 zo^scbKknn(q%@i9k4uN-E+Atf3(aS6@Om?J9I$%vHEd`2=k|fVWZo{A1xhas!Ba*o z(|qiE64EKEO-=pIC@uU!5A z#K$p)9~u82@iC^~0fXOBa?c%TGbEKC2+Ef`Q&Ys!KW65ydq_VH9f)62FZkwtTeAi5 znq#+tsVNezDZoksnyKay;{JtLqHH!8s7R|(qlcHR3yuH_A{td0Tumi%Mi)dS|F3x^ zu;5_G2<3@rNOmK``p^iZ;X#z)LFM5=mf^vq=!moE2*u$+m>>1=wDD-_9uX&Df9Yta z>0Tu4zUUoDjkv5At(7E~epqM&tDtj_kzTb@{obXazmxgz6y*A2%dyOn(fjz+xTbJ& z!J0_-5VSkVz*q13V6fH&IDiF{c3CDLfK^9C8XbAT3`0Zew}zC&Q$uvs35Lc5i!txa zsOI#jX5Xl1K*AFd@dc^qvZUxTmFV(Bbd{QYUB$5!SYq4QLNwuwA6;;bV!j@+K+Q7n zCl|$A*{x2mT{leTHs0@SWk(@?S^&VO)XfB8MJR2?JIBdF6__skM!91KM8Ux z)QR%PVha7JX)EN(Fu{E`O_Y4RNa+?7|6s}FdRkpr9?tj$BC>=uzit4hK zV~`*r+T4!mz4Vp0@1pe=*S%JAN$gdDQi0raHlp7zt~!2g=l8(%P3ks3>SM%@(zux) z^>I_*kNOy8ro$dzS4fUumO|=Bee4_i<{?hNv1yNvmkRy&f2)t}*X8HWZT&R2gh3xe zR0Z^yfUdgKG_Qbq6bG)XY+A2~_nu(|t-g8ZE#=$=SU***Jmb@S#iV=85O9(lV{`TrAW6)a}bPDNzq)c9%^E#T)zh+r_%2>T|Gkyl8 ze~n?hMlsmuDDUXQY#PIS01S4~jCS#ddu@h$aff?#hW%x0m=KE9FlR4Rz%9>IzylrX zDsEYkoR*Y?ZMrhC&hrfk(Y*HXur3lTEoVW0+{Z3Y9P`4D?m177RbK8-*I$eJ1mI5* zf*`!IuyLmvmcNI(As5ga%L@?90<(o=fGJ4&bj5`-{s4wFXlhr$YYDOH-qPToj|o&6 z84e3*4F8lv__g`RJc^jaJgJ+=JYuDUoT;RQ9H1%L9M!1@$~Y+H+am27Fn8&(Pd$AJ z({hcAp1MeuhivD(`3Di)LI=BiVsw9Q=Rjk0Isxamq%t5_7$|{DXe`OA_Ho zljT-3DOx)rsOpE}O?clfhLj$hyqF4d1%sW*YZo&nMMOPPb_IL~VSRFt_f=snlJ$*H zj=Hz2txJJDvnl;?$k^m7i}v)}9A) z8IpvAxY4e{5M+l@5YJ_-Gs<_=t+`hXy>4lTNm{u>RP6@qb`eMG^KLo%Km9SqR9eBR zKRjvO`GsM6T*r>!*4DK*rsk}UwXNz_d`MANau;U#!rsp2yDvL_e$+pHKlYhCDrngc zPA*=+g5N*;>W@{MdTaFC%klRd3k8TD{qaklj-Spch|f}7l$;zhs5C6BhU={&PMC@9 zWW@8CGrKNSdwguQ!h#nLX86SbJU!gVODXrF+@F>D4$KEdh0ibM>Ep3e&tmJPav1OL zs}H&$`S*#48rSH7l^45G7tyC`3#)A@;g^_s2xXF@%Imndu{1>&F1=>n8h*=@za}}+ zLMxRgE^!WJKKm(BS8s-X?~277wl|S8gN~h0+eCc^@mfFD~a7 zI3Bt$%yCM!ydq-T0#*8$W9DEwi?3O$uO zVB~@GESu6d-0o_MZnFA*K2YbNrsVAg8GFZ*@6NUc>mXI<%vGx8W%Jo@8Upuw*F3T2 zw&>1n8Wg_&^;6xK2lqwra4?&11sc?V|K@tNl!#xm{nJ%66h zYatcpI*^&!NfkYGW1Q9J+*R@hlfn0cO{sxT9iqHxz2WWoB(ulUMhHAC<;IJyvk(T1 z^6QiRD2zY-zN561wE{6Yt{RV_p+UJXBtx`i-%iaQbt-L;W5lcNnz?ZLY`fx6_3w&R zw_kpm*S>j?OIo(>wWiQcFsn`HW@GHaKs&gaW0dM7{)RUo?Lr>U+M%^;6ZH%roaE}& z_T-kvmdn)>h~0EvXmxQ5MOSKK_BDu)cij>gY?Xn_yLm%Qr`Ap4(b_OI?6-Lz9R>|> zIyvXx@#0V$I?c=PO3Y&Fm|?7IO}t24b2;8>dT7OD)7z?fcW;wR%7nXVnH)&zZHeBI zd9mjVvm@R98l322?3S_Tz^WOVtq|+cIy%xM<@M+)3rgeeetUb*>p4C88z)_jjlH)g zVzmF3#qZwsM$IK#p?SR*iu=0SbxsW5a;t1COrN#Ty@hbgKJoP%Cl zyH!y!Eejko<{Q-{tTKLmwsaX6uEE~jyYj?^`eoU? z@;hUFrg^}4_j-^NmzI3}NQ>Y9lNKX<_(I=!UmEPDVff?YG6%l;wN0-L!4LQ;!hWyn zZ6R!oJ@*gX=4lUnFJ-KLD;tf#{>;T?{*ScSgZ$Fq^``u3hLr4Ddgv>wiU0Nb=idA8 zNxvG1{hprczMy|%7Q`Yl*aS2eveaf^EAI{UsmMJwyHKwt$XZ=N&uu6Twz0}%Paa+8 z-tGSPel-s7+gx-V6E4u(Z-*gf zUD=gRLYNXr$Sr@q8(W@BX;+;VXX~Uil(BAt5b>PEj(La(_2IK zb+sb*or|0i&(@~eXKE);W&4wjYp!-@jIHl$Yn`1V!b?BMm-TNpo3Vdhl~Wf34;U{S zUvKb&J&*zKdY@ycrSCZxa`x`rH^Z@Ed;EmG-|HX{dRLz2KzDwP#xn^&WA@)r{jpj( zRWUuTyWbhFY9lguvyAJlLY0ue;CYIcG(J8U?$pN-y>aUYpSZf&Fx&3Xy<_OhxngSo zD{z}ZH3!`mu82AlviPgMKf8GP!#*M}WHy@ZhF6U8LUH<^Vr$s)`d<2kx!-S_4wd8V z?wUUQJlWs#DWW)Hck=TY*ogy$gozP;|HgBq-^2!&g3*p8(7#*8MiCT^)R$pdm*L1n zRS%B6N9!BH2y_w8Kjd5n;Q+w^g#gWs^!FchaQ_{`1ggX{GgmjmCmnq`sK@=zGjo07 z`}1k{^1kII8T2+*Pw|?VO7pU=3?F~QxzZ0_A3d6FMYhrK@8Txt|KRJLgCp%8h1=M+ zt%)_UZQHhO8xz~MlZl;7Y)@?4{`!5-J@@<7J-5yuyQ=d%yZY&_?&|9F+H39DG1bOS z&Z$0|sj|v}6)%C|mF(gh@58=_d(@yXXd}UA9=|` zbi9A|UA}}Fx%Rj+I(^~tM|_`~)^Sa}96D~NWH(l`{_M++JpIiadBQypoO08gt5g&7 zw)GV@9v$Oj)OF(+IQD^5@F>wZU#I%D?Fi}Ar3pE$^$n9d#d)bzbH)1mevK*BlD-Z8 ztNN8}Nz@m0aqN>o6~l$`4e(2W9yjYEcoZGEy3?79!C;27S9{f8555sjS84S^gs!$c z^(EaEXbwXyarrvOo87d)4P$wRUs*iPZ$!nB<8%75kX_c8#%vNM1UP@-0vJXB|acT($vxyA?%#g+|qO) zleSkLMriILN$(cTr7F4lmtxh3J1($=-qpTL*Z6+kYFb~$C&^>)B4HCA9@i27A*&qP zzf2NQQ8x9u@Z!At&j#_Yn1>|w{v+7!fAOF`_AEeHr_ny)h*XTO zZMi?8(SpCs-#V<)XqBT?sPmPc0p0=m7X6%s(_bU)O>!QHu*m8(Z^m#EXelJNe11kx zQahx#TTKXwQ)6%?o`kV?COA{SxrM(oOVCtVeV`B3di8|nU=34j!P6MbzCn)S?=kHD z(N9&bZerv1ZHJ}tn2tI)*WKqc>vo`BXBU6<*YvAL36x_|b-; z#R^WRPOnR58)=@WkNG@VZ^&so^j{d1aLPUX+zjMAZ~iL`mIvXyKOhSpqx*u$S}`w4 zTQNqvXTFKT8$r}$wgf}J_4Ya??eQeIgg)cQ=zj=e?1Wny+`ORJ=ZcUV-M$D_2uz~G zl9AN5N>C@>6%V{}=@Vf{i|Wv0llQCnh}d!46cv)WyJPN$JVSh7C+z!)htC_-eRJYS z?$tYGIz9sr_d`?>!mq>zve;1O)2_l5H!aGg(-Mpa!SlaG(R-6~>Tbj}$v)wVGL!vxD>aLEM2--{XE!~}^Pg~6g0GOqFolMIGMDqvlt z5GU9UhDPS?6b<_b10x#?h#YWM5GCDw4XF^c^T zipAujMa2DsCAMlKNOC_E%G8KXYkOL+y!y{cSFF1L2S>vg8}Mklw=dIO=o0aGRvzHE$ywk{^k7$3xFVlsNbr8Wl(!>6N=j2f}l z2vRvmdBXa&kW{JQ)Nl(}S78fN22+<6cl*uEvO~8nS3GbepCs1C*^*Hj3bO6R{Y=N0 zVOba9A%0h66vXnncJKc62l~I5$Q4jwg4XDh7{&qu8W;LsDop>Yp7P%rrvIcw^ne1> zKjVK;B4`g()fKrxwE+b1wX{@nsAPJy@ntx0t^DR>G;$RKiKK!gHd&`@neoC;Dx-BW za5_RNBI@WUsPYSH7!?%}U{S%fWYvVtJqWv(ZBu6Ug`n;44<17QT*|2&@0s@F-20y6 z4Q`exnJQK7GFZfwrI&OU#SYpm4&fc$uEE3jSHG@_%Xl#Jf~2_UUFWVR9l@i0V-HEB z3m-WA14@Cq*%iB#nz>vbb15fE-6wwks7JA^i!e5(%;#~0GLxNx_u6!V(K zg2D}MTE%cU32RaU`_1(zvqKC;msvr}6G; z1mh|W)y&+N3y0Oc=TuvxxJ$Hngvu{Msh}OPLCcrpl@>5(IWe+~DyiAhiM1(S9S-s& z5~dkgPk}A`NQKatH4X(pH;jC+j>JQEQl|)zg|hwWtKz2c5`4|{^ z@|M_uB~7!}JtY6km7|4?lx1icBVN{N7-58`7}MHZ!!$JGFiH|fIhrECPm*e2sjobF zmTj0re#=Hfl8FC683alNCn$I%OoB~daq2N*7&|5?;!dOQmAT#bXA-f98^N=16L1B z42!N@??l2v-0DVTsgHVM7D#ExZE+6iTqD;Vuq@O9IK&o3f)2cB(tx!93mWJr?4Orz zPLxBX*u%~e1ETrSw-F^0YFNnAVwOP*rhL6JQhf)m_-6c=rb90?MRrXFDfTK%)6Ave zCOP5CdG=J-K$(NL7ZL-uC`G>nGl5hINq#}rsE9$tlz{LlgF=aRE94&xl#vbCB&J%w zB`O{wBr=#ai<8qyl)B2I&@}amUu02#2rNHH8`OrsF)(o?`7odaOPERW%Yk9yGZGhYT(684fUo)KO16VZzYWGnBei18j(!Q*m z&C~hA1ZW#2E>+-zKsuR)Scn_tRAFO%!ON_XJ3^g?f!=6k|=9npT z%+&=U_!&Hs%z^pC@e4Eh9*=x_SStIN+R;xZ@}7r$TAs?0UU5$+(=Xvqdo|RHMfSZz z_NN)r>`*)4ypOR&KJV;qm;o>>>uG%97&v|kQWFeJbw-;lkUWXBJbGYM{3^W)&u8o0 zJk+(GT^jt7yv49LnnrP?5r2KU*sUena4xUqLvBJp3X6tRBdR5|L53J5FpsorYWN8c zW$^LT4mbn+RTN77Je=B(e%%_dXHkrewB((hYB2X$lnwRdom$nP_ezuvS1E@TnuhiK zp{t)Ap`lm2bimm|atemLN@jl(qr+HuaR@ed!0pI_EoqDHTq2dbQ7Lo_bMCmz zAQ9#5KXqK}=*rH%2j{S!bP5IaXhWo!-4G}~7QW%AY!)fv18tR65{8ufQ3ZFK-6RC9 z^q|}{VEYvAwCMMr0}Y<%2d$LB=;i(Jz*o9?)9F8l1+N=~(Gy4O>QT9QliDb?DMU~= zl*o^3R7eCtszH#1p#9NaHbGFP&KzJY=o)@e&&3p^2aH#SKtq{~rgCBWNaY>`MvR7HbmVbLVA--h_quSJO6FoBQreGA;+C3?m2v=jEV1qB82ExFXL|@I znuLi-b7TACWg3n?zWG zV7LYoI}du9X7%V-L>Ul-fNMam;;=)L8{lq`RR`EQCZT`^6DkQAa`P;LHxv^8%ZX+| zev6aZti6J`>}LHJC(^@iyb;B*9Yl~IXx~g|+l*|(FNV5^p&wJkYZ7S(?(e9FWt(X$ z9C?;uVnlg1>gl6eG)fNC9lVJZV@J8UI$_vFxCqoTot5zN4r4qKv_a>#5q-7^ePPug z>enl05iT4$lZ{vkrxIf^dNdu1nnR*VMW*?MOp}XTMM$a!LaOF3Rt+4Z5m}z$m*Zqg z1pPOksP4`IiKzu5sRSO{s1A>EM&hIN{_1@rf@{@AuJ)3 z%z6%&ASzec;c%Qp)P4wmHae8_hnE+2slvnjz^pB+j40-T9L_Hxx~OA|pku5()6A%2 zjUaQb(yXJa*&6#TxudR35jBDGYJoDlu#Tp zGe$BqW->D-GBXx30#gZ|Kk-yX5+AVamL|vbqZs=X08Z3yq8qpWOLh;#u}Yt;to!%R zANY;JgPxgW9qSDY3v`?gu1tND7BDGD^-?VcDNOY~CI`BWmTURyeu>IX`fmUtEuFa3^$r;DqYC z2&`%_+r*9@>>@mc9c@klq1=%J^o8{R-(E^7?zCZ#*9OB}QV;r+oDweCNq5u`gui-V z26wEs6Y{-~QeFc*4s2UItwIv_c}u7L@-KiAt>ObH5o%Cb9HK);t4km3Ka|M39*&`) zlaM``+=~>7U&M(o5sIE+Bf9L+YJqZ&s^3u)p3|1f^s&f5b5k7Zp&?Zuj}hduS6TBC z5wZN#O(DxJFVW>aM&x`JfBX#SrM>bkRPB*bkyl8i*CgW&o%l*|-ZuvMnS=BS8s1T! zdCzYu;vJs!>WA3ILQI=Emg5ZbUas*F?@Yuy9Zl7Cf~#(uwkCY6j|5=HWmK_>6ijH4 zv}PzBGbo*Q{Dms0kW*E#2QTzMEx`EO)=qaVn&`YFEsWo- zg>zPDMnK`c#g}=GzcimO38RE_cIsFfe%_Gx_$UK-4Ls^uu_A;(3uhJLq=iX&Y2afU zY=a&|Z7oL!Q;10Am4WUg8jllH%rvc?F`+IRfNg@zs=tncc{wB0(BL%7qgEA9`|ars zlz9y`S$NGfox8(K=^m&jNNyrZemQB=_NRJKC9^v+)u-@{W0OMXv=frSRF~36NayEB8qCcBVFgc=Pc2?(c_I+CglKlr0 zRWW(vUDQr*k-E4Qae2$TK0%IMbdO$iKaSU+`tb{)sMinjoqdciq?xp?WBa^-6g9v! z;2^$Nsu@3I(ngcB?M1|TbZ44=K5$H+NVxk0Zh}5orIMcsH`(OtO*IO7{hWX}U!uh= zRBy5=2OJv{<&~un(W_e zO=w%mA{EB%h?N@v!mV4g$e(d&~rqy)VZ=L%ww)wpf?R>cJ zGTNxzS4D&E)g!BGLg;_p(^1et@qDM&YcC_^6g*q9%jx}TS}nrgUr%_L7hN z7Q04}k{*F7`0!`wC_VUFwoqFL@v08G@raA>P2bIyx8BplY{jNO?RxF& zYO7M4M>CUqzgFmas8h`8?6|QC^!$*%Vi$9zIjvg5{zA=NJ?GD2R`_f*EiOHF&0lJZ!B%NvY z3(jF|WDCKC`)prIsR;5X9@0)n9*#-#!;fPwgefhrDr?S_u%?gY=u&=dc-lR9<3Gy! ziFn>7{=HxS{eUXB=SNyNGWNdX?^FKmGQB+XHQupJ*M<1>zSJCdxdiK;{R{0X_%kH) zsp`n!y=hXHeG~Hi^LmFr^4p$a!_JZL0Fx1&drYC)V-)3ETeuyrx5&(+qqsVp)=x(2KHXR@SL4TcAokZPy9IsWcjY4L(|%0* z6vqzJsyLez-;L1EhPiH%&yD4I@=z8Qkd4Kymx0zPp)%=BOn`X zEU6Ql@-JzBs}wiyo->%O2mK8Z}kbTvBNSD!J*gY>?0UmeGbdHDHp-es~^ zWeYf9ZHvjk+Z5hqA%Fi?)6ZKI-?ZfxfR`H^0A{MXnf;(qwDcv{if}?aW-88$)dCT{*L- z-?8?#S;Qde&9`m%Azb^S!2X^1gZyX|(b&t!hU(x5O<9A>ZJWn3w&BaK zY_8{yN%TdJZ^5da$+y(1^iL(YDJjG1GBdvV&kjS`pl2RQPts=U-L2}~zrHE_&G#PT zWUMjVI5(z!)*co+p2M!UT_zVRT({M$)u*%ml)%YZlx>@L)VobLF?~0QU(a3$&W?G( zgnu=6H$8KGLBj!MMZ0o+sa%IyoR?WGI+S~G}hUD zQTavghkhp4H&T?!BVN?B$$9$4cH8vQ$JqaQc!GUOnm7WZ^?86B>^=E##<6L~{oWW+ zP$%`RXfQw=*kZV!Ib^sJevvjGQ{hBBT6y}ULFYJE+FF~@%Dmys+Ryo1C+AhyGUpAb z^6t3Scn*)L+VM)Te>*o0d1r8gqev!~H|QJ+I6#O&qICaLh1&YeE$`_u-u<5FyYM7? zYF^6wcSf=CETN0yJ}~8|b~9EW$e2od2gy~O(-o)S6)5vOy z?(HtjovhiR-n^ZAHKnqf@K+4qQ+jPZ_O3q~uis|+%e24yb4jls0*$8Pp0fT<^ybxg z*faRcC`_k)lQ81bA#$qw4SyMrhPFep>8|Vd(D_CVY-_6 zhis3A%3t%(O~U^D4Wi49tbMBPBMrn|~TlP?>pn+ORvR}r0p$DMlBS#9}~1D{e3j)_|d zW7-AS^t00taQ;F}b?-K;rS)g%WIgt6G04%4%S?_{gD@CJ%*Z%8H&XfzL8D{gXr^On zX{6)g;ACjwWZ|acYNumoYNlskXJ~3=YGh(#YiD9%X=MiZv`_cBQdq2NgI-9_UXTpr zjT$dJVeyqY5Mp+ijtWm9P+@3}CfQ>9+~97q9qbBgqpjB_*yEZo;%-tOBrvE{!6Z{P zjUxg6=%?pKt3p$uYK9rq9tG7Zcbm#zq=)k-Ml>pTl}-`rjG4U@BB&R-$1HKfE?N)e z;Rv)!3&9C>%gA33)k(;IjaKTEnFCC55xP&~0vs*?%;^g`VvxFIrX=oRrD~oC3VA79 zq?e9q#I$q(Yl%plEuN&0xd`PU2s*0)*Fw_@RTLqP^UW7cV+(aNL{l8y$FrCdtjDN2vcatq%q>mwiX^#uFN((iinEt=pSp0Y1>p%I4IiT$I&-nl0Co<~q^0jnfs}&_rbjcB9WXn3lun7^7rV8B@ zEbQ$`GGrZ_O_o_rdCCOfq{muyg|iWWTP4+L5~U&uIIFgn&lO8;IM79 zV5o=P43$aysjU)H4kd^oT7@J!5`sKS?$(rv?(E@L%gfYbI=kxsoiQG&iALEhu^S}a zux1(zG%S-gWrvP7N6cNIfMf|IXlPh!Wg*F&X*rD{$cWAa=hVS`X#{ck6KEPOhC(zz zs#L1H`JhwaFxdrM1v?id86|Z<7E)EYEqUiOKXb&$@$Y4!XagsHqajvvltME?qb3?C z)Ts&B61o9n5Dp464Y;(5wY!3HWj0D_fI?vn*rF6PJ#Y&z3PyQRK!=!uJS7tJnQ|aR zrGPN)Yy=EXK>$F!8A?1Y_MUOp679sx&53i%gBR5l1K^WYyfrt+5Y1^>DfWQUoLtDM zsQx6w(oBiY9fw0`xwOPLZ?_rG}MbS9bT(PX2EocdsND)OeQxxxrmYZYM4 z%t6bgTpqvIQfji+#jI=xcog!ncP~dFYi~l964QmME#GQa6NH znqgiQ(8$VI)FA+ySU4!#ZgSwCi~S*yi5kwTrwg`Vt_eRK0~EQQgq#y-H6|k@)Mk@pVhR{vC>wc)5_k1VF%a>VGcIIH zpO}`0l}(4MBZAPGlvNkgVxCxj(z1`sJW{gkolIZIH9ht(cre=vRI;nU^M`{r&6;AG zwy;7K6>#$FH&c)TgGPvd;A+4@gk~u8K#|nS&h7$wUYd&ez+_TWCt{%0`>#QC>$@Vb zL^er+^Dgl=V8PJC5uoLoj6?h5$v@JlX}5p8<0l9$^CzPy>T*bs=U5t2fS$FiJBfC} zPyG%uOuhIZ>5yHH9+ z=+exB3V&L;UvBPMsUJNZgx%RO-i3J_vWKf*Zo!+UnMOd-eZdrTKkbplJaKpu1bzx~ z)7{BWy(yGVw$~m3cobI7#8?7qGwJJHC zQm$9)mCou_)4D}m&-hF3*OfiqaRi2e<}>dod|Asl{ajoW7(0W(ziKFr39{zz26ZzpfC`lci*vDF2E!dygqmD|}%qe&}^|s$B@>9{4J^T*Z5?8pG!i zQ7i1!dfSyA_$h1^;pJ0`NINISlPP4tA=bi;z%$7?no>QRHjhP7tyVS#S%WRdVVoi2T8d|6v1`Ce?46F%EG9 zu0uTTpTH)qy{O0AG|?F5F!BUQIc{1Xo2X6Ov;|uf#SRCevPNYCsF&Din;xPU z66qa5lNSNEm#B^Al*NO=Ax`K{9Ei-HdWV7ZCClvUy<~@w6iz5<=>R@hF-HNzsay?& zrl40$1N#}}YJj>;>IAlGuQIQ{9>)$DWe`b+oF{C_F+~9z$04O2d$~_l$O2Hfgf26r zszuQn)GEhGdNqME$zZ5CV4>0mbqLT8G>xAQo^n5Vn5R0hm=Z^&d=( zFTg+0*an4bkZG(E_d)SLsp)o-EJEnG)k6?I>WPc0s;FjDi9y+wEb!UH@8^DN$J@4& ztv@M){T7mSLLZ@3BowHpwTYc#sdaeC_yO{}zdN0wp01KmL) zq(uo!%!?bGv7F_16uXVsj1Bk;TRP=nnSyzyOyp81QWXs{ocYk%p9u5vEXauhh&e%Nrm$Xp*{ z%pYP*A7U&YGH|_2aJ>z1y^e6bWlVleRDS!B>$!6`xcPgO@g=fUaezh$0KN>Ka`iPl zd<0_f!0W!{r<7wpmYeJ-WHl7Yrg}3ph(N6!O?5@R(4f4z^7iKsk zD|{6}%kOU@s&+L(;ueOHWw23yw8W^ zZItSqJ8eg~p-2))O6Z@DvzSsHAwhLj4!Ag32>c{|s|1YQg0H7%;23@~U~Wi(Ib<~N z5`3jAQM#+NzY?=i=B4ehs1CG}CcykOL$}!zm7h_&Fn@I!qTido{IH^U)NehWPLx$Q zPbeC;8hj>>KI!%K30vr@;MBw7Ed(PboIy5uc-Rk#k|6}!wPT%;1?k@n@~+qFVfICh z1;Hy@3Rn-zRC|6~I6Dpz&_98GASazwN0h4K-Q}a(csd8jHYj$G5v>RSIp$BFaOR0& z_X#q|8$9x7s_2=b(1vmG`#=0dY$J(l&u=8cI|}*P`#<=}S6{?CDd`ob*v36=jULf? z$4h-P&dF;)^BXEfOeT##1(eP_sY3>_!=Ye1Gsah8`)%b`>`DY~U6Sjjhd}KGxV=?j z>qB8L%G`FuiEUWlHCP~b@jJcn4uS9vB0h*eGuW3A)W;N3+YHi6I5Ex(H3x1ZBpWzH-KzpgcyEg?tzj^~3})2eKw$HbbwT z_h84pi-lD-#`F?3KX*UU{+8mIDY5KjP|oQWYN}O(Gu!A1I=in5Eo?9=IMc{FTgy7* z7PiyPZ6H|J%IV`0Ed{|Dyu2@9a8v8Rv!EniU^NQ(v^V! zT5u$Daq0$EP&dCglgsRX%xDg>S}a7ahbNihMAos~7+F`;2_)dVK#yT06dd_lKX23$*lhKILAD`KPh=3<>LDCoxiO}IDfUNGmoZhML) z8f`AJSB&zH`-?r{^b%pGtl|ZUo~~rCvuq{F`^)og)j6ihyJF9od$dGM9da9MvD3gJ zf+P>Md&m=hz__dUJKFONIQXXShlm*p_3Jxy%3Be$??b)1(cH@-!#4LUP)bkZE%cPa z_o87M_e0-h4pSt)f+m~wwFhmh$>XQBqQ=*GOwe07E%)DVyx+>^*TA!c>rD3B!$n^o z{gyc%i+YVdOSrl>TyQl)+WCIl&Xmq)Z(B9rLMaW$^?n~L>AkLGS_J&u)5rEzCChei zRy6cn{7Tlrg4ZD-{@c>l^3)s7Rl1K`d|S)cakV}uc^EAhr;>ucAP*_NEX|~Pt#wD@ zGpxJYTHGIQ4u*~PIp*Jg6PRLWUp9ZwCxfvPs2Pt}hul5+YA@4TGyN>_40U~-_({yP zr{9F~diTlSYl*XZ)lHAMxpIxY|DA1dewh?Sq0P6px=JaV^&vBTmHRQ#bNXC6O*qub zgWUT1m{#a}wXMgk-%PpO>S?JJ(a7~TO||*F)AIV^elv%)qBL=9pw({YI*fhU{Xw;T z+kEgL)%WKoUw!DcW^c#uT>U5thUmJ4>Xz>IvN!y{Q}1%M{-0Ju@jX4-&&9i0XDE6P zR43c0`)z(g^nX5E&@qh@6<^&3>c=E|(ijj~=q=}S{i~e!wscFF%6pdxeSaYg`1zbp zvPbewei;7j(c(q_*R?rf_aQrSmyfB_W}=#bkKsVN%L>oyb+eWzQeoKkR;5wC_F%~S zxP85+%zL;y-t*UXd@oG9j(2|^ZB*3z8m-Ae#XRtQu~+Z$rZf&o)Au+EE`(M!yD5-f zsG*tC+w$Z#-0sxo>1q*O*B6s_ym!6A@2M{`*d+)_{*>v5-@nQdP6X2Z$k>_T!p5LZ~xV?Arr5VPkIHqa0%&I(*qqG;x5)o3jwATj`#7O z>5zjVKEdeP%c1j#ZLVsDFT@MVv}!nT$hh4rVD;*hi?^gVx7 zg?zDtPq;0djZaLgai9KwdRK~ew;8A&5-y7B>!#;-@?-D`ZMt`M>kxSr-&^|idd9@7 z_*Ec0UK{6ivGe#257nolx3?Y>)7%-j=+Ryj}&Jlv}FUXD1MUJ8sQuwXiP1{%A3l)O?;K_*#zp z!`!w#CExCISH@ZA@zMx;I?>DI`4XlahNpQ`meuQYVlrJbZMS8r|Dd?$(>z^D7=5eX z_L>p=PRLB=@j4e-6*Eygy`!C%`~DCxM$j{Hpj*VeoqxPiO$>|sbv;uz3Yn|F&GyFl zcp`?_4yntDi~QbuY+ro!;pC&+aXOz4JcQWZB39=O_e2VM$-ujGP<9r6gz}CpO$$#L z?7R7j9s|$Ltqks8)AxOUQx|s5pK0jgutYBpRQ>WCyt1d@=;qu))ArFLzG0|CaXhIP zG|@T#!JxqT=(4iFpKzON?bl~IkT=mYcv!KSr#p1m&|rG(;Ay{ID6 z7X2pc$um2*ife_WzPbAx8smiPtPNS`!~0KtnN~RX*3)y&_a^@K4_n;Tq|U3uwti>@ zV{3gQmuhr728;64o~t&c(#0k z1qT6!ytD3iGE+$cuRXaekPA1mnSx)h@~w(4rL{HJXC#|J#iV-8_AD%pNfhp_w|KyA zCpO=s+&&TAV3K|D2^jshh#y01N-(qSFwfE|beJz<_?N{7+|%#O4gUo_aGl03GY@Df zMnq;``rE5B&j#`$cKQ9{; zBfic}!L%bt-HdkvTE_Ufl693CbOoJMy&Zf`Fr_LKY3zRHn-6NKFa(+>m*UBk- zl8Rpr)Kyf3Q1v(5f^kC0zDw-j*Ocl#%YN}P+NGXVN~(p%!Yd-`{ziC!$SI)?nJegt zvUDIDYT(pe7ncP8$fnCx?rWy6v7{G)wc-anw!bk+{dYfT@S2ge(h;O?CPmO$Zto zHjI85MtydFUeHZorWM~$n4@!(UJ{X27NpO;2@dmKA?{`cuOKHyn~CZNv~tee4PlDk zCz7F(n|lXa&H(dG>b8rlz`Kgq;OzrL#*Vd?7efGf9Bp*x!~9->`l}tVMq+<6>c-{M zw#gB6wPB)JT(!XPE>`*% zV|w{%S+Qyc_CrPta-Xtm*8*zeqt&|=^hdl8G?=Y*ryqwLpSp7MiXLA=nX?DKXs&0a zK_C7e_Qr#H@Kup_GTv>+c{A`)Gkt;Gv&WO(Hu!Dr@c-SA|3nt5;3iIyf)b*-BOA&=JPg@XQ(fTV<6(|B&!6r{)IV4yNz)gKE;5ym1rlC8H*u zZawsf8Twvh2o)^b29oYmIxUK6{}D`9O8A76xaN&O(8=GhF_Z$@RaRk_-NqrntWhLg zgml_5mJJ4-)CcUV6*rkw$nl^l9Jh$^K@&$khSfO32^hB_5l11ki|%?&DIE+-k$yhd zEZ0%!q~TNHRV_il9dQuDOdGFNvu87DW+Qx?$-hK=ooP;t~5@!uT@@nDp}?xMkX zb}}kv6NEI8vyCSX2|IEj$;BY4rc=p9gbNcFj^iT^N8UJ$2bT|sID+tGuhlCuL;)&9 zF9>DKqiro-i%NSTjXNPE0mFaE^nO~us{HUE%)4%0Y-=fKA~12_c5)AYf2PP|~YH7y}fHvH?g?(1sw))t4rvNx6&} z8I@Pi(FoKi%t--6(X`aud?}RC7eUN0)cG$8ND)-ht1(bPd?F^^s!P1O3(zmqubgrG z$7H&?s;zcQ&%V!UuI`QVRiS4tySp9K{1Cb^^y?%tN1Tx6&Z}hf3CWCdMtI)_p&D@Ft2Ixie0pqm!}e@AU~<%S>BdYr7c3nM>Y>dIJ*=^b=jISvVo<_Q&j**{>p@KM5K%9y zz{XBePrvp|6Wr1!wbDE8jI20PV-R6e|srAqB+P6jeJjE|aE|MW_Fbv0W7#BW8pNhmX$<$LxHtv!Ph~JH|;28o-IFW^Io}4=azwgqtRY zwHM+835tn6;co&08~!WCCJ2bJu`x=cEC#C-WOU6EUAOU@PdIbTfkpfkVf4U|U%l_2(I}+8ma+o>7jz`u?!^!+o&cR zR6W5YBveX7V_QumZJA9(W7tdkmIAw%i z6f7ir3Cbgoh8p|ibF2-K2Hr4k3ARz6^A!kcA0XvzKVmNWdExvpv?v|T}Q<+qvAjh2(zU) zs{LtS1?^&3)jCYuw?dz^@N4p@@9kbu>WfTmUY0^0}yST*3IUh0+DSJSUd~X?3(tn1?~>j z2qWDP1P_rEt>}Q(nKwC0BMb@(B^)A5w3GVW#!+g}gD2klLy_+n2 z3YO+c?l_aa2<@H=AahuNg##LFDlczQcdi<{=hmTh{y%*16<^-O{tdDf!RU_w&MUug z(c35kf^0>MP$#HpQi(u&ixi4+-ci#!W2WxWUJlF#c1bp7+-9>=!OWihjBwWlSD?7BOZodGc%?{xGaMi^q zo*pf%%_%(gPmryR5mtvaNV>QI!L~WyaWft=%{(wKCchBtgLeG4pcg>2+3AH^56zLH zu>y!TdJJu6DgSSfEfbRx!?ULs<_@n8_RfJlq)Kl$Gp)Lzk0;c5t>FNlXMFa=;z`>J z3WYOO0XUA0(e(Ty2PLQqT{TOQY*+ge=#UHvC9yO8g)zvA|=Y;=fet=up`|Zy$=j#q>rJi z4jdI$*e*AT5f&hkHc}5kkMcv9mmcNkU}hWOL)&_nWRA5uHd+A8l7OmJSt zo}sl)^dpF|LZlSLZ|HtR99!s}%US(G{8fUKC_~zcOQhobU#m2F_1L^k=z9wWfl%B9 z<1&gEYUU)>^h1olV5(@))w;wqpj9*?0o#M9Xl$0(Csx#VC>uWJ51d6tVW*kYJdor+ zBAieY21k@U!ewaX0sUKKE7^D+1Qgkf0^H<0~KoB_FXxAF(4Jy2-Cu z0tQ*-cPsk4aiq%Lwaorzr%dF5C<)sKU6-ntG5kCa{5C2WFC8$UzL$jL}Dhv*pj1QrW z52=g~v5pVjCr4sZp%RP_x#%P(_$H9NqtGX!lK5myT0_*X(%K;Zegkv*Ypta^FUZa6 zBZ?{#$Ms~&DT+Hy@5P~vP(UKIOiQOZoJ`ALxmG0Fh5Jwuk!FF+KprteA)Ow~?OXX% zHmt6))VZbfKH}7R$c2yG8kpFcxI#~4%qu7H|M2zBL6(GFw`X_R)n(hZt4?*Uz3C@@Z1bSrf6+PuY5!4a-LPgT+$yNR(WUM> zXJ848IMfmU)ke;6rrPkOBC=g#C&)k^W4zs>yT0QR_(8Iv{Rhc*u>T(<+iPGPY3|NG z<;S1KNxybTICsA`5yh+jAlXC)b`11uk`0Y`M~Clf_mD17MZ~-iN`IPdknJmfnr*r( z`mTSPZQd!gp#`>T?r0IN1e*Qdt=>MC#;jD1V=8}=qGJMRJa*5EVY96TY#GU|Joc%; zcUwZYH#>f@grB6yq$E2T#IGNgceuYU_b%NOd566cER!{%;Y|p?5*Htcvjk;^fHH^f zho9EoU%g<*m7KIOE=Tt7CZ6tcsXZIf5&I`$cP#dN99cj zqiBlplFDd%sMlIY_8}B_TZTGh0WzdB+EK+ztn%c?rZ~UdP}Gey?~OE{p5V(10o*M5 zn4RFep5WWf46~67vr!1Mkq@&`48xrRbAq1e`$yQwM^6!~jDZq3x21)~8#c6ctaO(` zSPFJgjsY*6=i3MSoMvGm2NEg{vkx8nbQeb31UASyx@3bV2J>Mchm@gYaA!d_J-0L$ z?nx@;ODrAp&W?0sVwu>-1M%XOIKgZ|Jp+{6)9TlG9s-s3YmN=v9x|{e>lmSOg4<0p zv}!Z~rF&X0W%;zJ;*yV?5~{C;qzpy$AfaVQx}Q@#Iy@G`?@eZ7pkQ^!gh)e2@lr@6 z_61g>nz%Fs!@LTWznAr85Sc{Qr*qNCO z2;Ftce6l|0vy)4Ik!U56>zt^q*odvrh^bU%fG<*5dlA< zC&mw)ZI+yAhTOm`;jvgQ$uAiRx4?_tr&j|};3q2b1^G*Y07r-;Y$a-OqrSFPB}1g^TAiCP~Sa4d+$qlO(;# zV9_jO4etT?{g=l!6knO&0VL!#LqHrY5+$r`mjEL&8kI-#V%Z~yfGzcl+>dl|6n)Q- z+Kv$pK3Ye&6+fD{F9$f|O&K_f?C(!hl(!Fy_wo|L)dxweN){IrvCvf-2JJNr!m(g< z0{kG*RS(+T7E=C^w9C`uwkNdirvFP;C?Zq~oN_i{&!NSQ>xas}=UmjbW;qmQ7 z*jtv3JkAe~Ejmj}M{9HVwQQROz*1;@voI5r{O}(CaiaeGcDr>lMjD|VJ8|YXOJIeC zuDW{u$9pHusWZ*wsgeAt!z{JAm#*{kn%eyCD()Zfc~`ntOViR{-uEd@Ymc|VPBMe` zuN7*lrE0E*dMA;i=uRf^#f%h}*68m*t)Yqy*U^g(tm&zi+LqL94x^`BX9vVj?<@Kg znLsw<)i)!&D7Ju?=_=k9=7+yhOOGCW@!Q{+7u${s8Fn&s>)#mNVYW zN^W>w<*k?(D!P3G_^(f{LlzI}Lz03bZY>sa=jc#X*2{$GNU8KWQ&UBX_HWU~{nSU3 zdb4#vbEM9-5t}7>+xT&E9jNQ@xs>*$k-G_I@>fSifR(0pWnOY|z$eQ^Z^~D@=S88r zdj`j|=S5bavQ~X!{py>-GH!L}_BgGy#A?sueQmgL;iS!5QnVHh>&r`lM&U(aL+J5v z^G$947iO6TLq)=8tEHP^u_|7Bm#(MIxxfC?%4J_vGvKDyhKz&v)i^JihT=-yB)>j@ zCd>PF9UKABB=Pj>AE|Rwi}uv=bo+VAPMqyPa82e9e6KXFaAh{W07?oJn}98L#E0M#j&W_^ zeBGq@z@vTq8t3N6v%w85rSProgM?=zT+4O?I+uac?&dZozU{7i8(hxv`LNHLGv|S`k&gaPKJnR=yYyBz((L3=n`lW$L z_50CBBNK^bLZi<9<)g&PX}Xtn(=!U5Muy9m@JS`&Q|jfoF|oVn1<-Yyli8*C%D6-E z!ev8e!sVQ|!rXhK#7e3ZYeqMuhvn$kTNTIl`wAyhN@FfXq2;D^XGT%Ar{(m^h)aB+ z{1PheveD10mBxG9irr-P;KhVy(NvTiVCiu~XR>&5?F1v}*pklLxwh99 z{8XFWrHYH8+=si$cyv@%MI!hN>b}hUwtdYT3cu;iNGiD1UcW3dML>Www)S!Tm|U{z zy|ySG$-80ut2b9-_HtjQ$6LSVdw1SASZm{%^VWNcsp%5wrh3(zzh>XqBul;c=DRrx zi#yAp;dY^t`WN%n*sDPl<_rs`*`}U{73aqoi3jMpwIVvD%wBWAvBtz0CEJ8F&!we; z{mKqUOPSIq7YdleLTK20kya26j!4jUmiJIkRd)f_+W)~~20bML(4q#ynMZ7> zcl6ZUmG~P3)WTIW1V}yg7jlR^YJpZ|s=k8YB+a$h)T4KrH2n})_hC*hgH$%n=V93T ziyzb))5J4d_w^QLw2PjVa^Kq{!?cF4!Lx5;IBv7uFp(48lW9Fx%hyzitBSSFc~6y& zop-r_uK~+4?|X#`%S)r*c{12l8*=*UuPyBwC&#XjK(4LTr3|(dCO7T;3zdNFFUG2F z*J=3oha}9Z?!)>qhT1M{k5^*HTQO@suV8{}Vx9(C-0m;@CA?SFw|K_N;_FN)fI=;? zVNYLk@GLSL7i>EWQ`$1e6{>*MUr*Y_dfRmw;jaSG0(sj&o3Ujhg@ubf9F+&j57s0pYH=JqbVaDlW=WH;ZRL2( z?teXV$G0|cv^EXX9oXSzxtL4(i z&j=-LkpTwwZv}GnB2+lqDISY8XhBQ>5P{ZlpGL@QXRm^Qidoy{2{^J&sdDWl|2&UG&(JFkZ1kYRq4lz6yh z6M=sM13|ImJ(iw81E{HKR|59x%|+GZ6F{+~MK!AesG)vR4xnr$v9Y*ZYW7%syH+_&e|qV0jHokYQ8i^j?o`taN$ZDb6aALeG{PgF`O^VI34K?9gZk^&73cq<1Ll9k8~T2e zBdllXU_@){sH6z_3)njr)c8N^e~C8^)Ls8W5R(Bif|M99n*lPSgDAv$kIVfFolsXm z$>&QTRg_tsLl7Yr)5YTY8wtornopc8`L@FC^+ z(~%Zq;O9h^#!EISLO&O2i-zNsfx)gz0`;ol z6HBV#h?!VPQ(3r#!4;GH2t$f%S#6lr<+`(~(P92m-g$(BV@~2uG{g+XUMP@ag;?|} zYQyK&xR!iOUyraU>*xNHi@R$j0XnJGX8tcv0RB-~`XJ|*ddy`r@%(7cF)+w|4$hnvUx4jFpc$fbG7UGSovtSVG zAb)sK9+v%n2j~y{R-{2cu_uL2kT`6zLF0}R#ec^W3?;Y1CXSox)#GG2YfPDA4~Lv& z^&<#U6d`3%xDwhea>)~&Usr8az%jub#{l2gM5T*_iv#bKJqXZK`-?Ti=5ou78uw59 zo9&ezxAwKt@}>zD=9t35iQDJ|0$@)s!|@C!Blbuu!@(3+e1JZva3FGzY>-qe8nsKI z_%_i0n;VRP0~)>W#mfVmH$nu!Z2o%~Df@PTjDp)qfby1FY8=eH4HxL^)jCJP6e{n@ zU6Ra878q&HdQ{bD=hB;emSPKorDUze~D}Lkz`~aO#Pq!|= zgC+GrDi?CmCkiO;RhEB}6jH@l=>vHRfnTaRJ+ub@GkbdRds_dz)x@ELy(=-!y3t~^5e_OgJgf8{yQnO( zLoZ(eFmyGYmT#t&R7rN-!#~4?CvUPxzphaGLO0zKN^dLMq8*b%Y_FiZ7+JnsB}H{2~tdW*fjLViQX@+J_zz`W*5VVwmF5vo>zO4yH1&SBR`LMK5Z zjw{P1RZf6qTl7+f8tI5qYtYnNMh?_9B93<@s5VLKueyaAVG$=;{TxtG-;x)u9#Pi> z&RGJAR+VX9!e*#%(Q&>xp*{SEzPCcD%QQ2{XE|_6{_H`>$)=}`7O6gzQgb{vA(Tpo zc5I6uY|_f<1`GOTB7!=LA@}IMWvy6uQTU+)^Z$9))x@`|LLMgK(8ECUGUdN# zSLe-B);4Dc5aDyxulXCxlL{+s)@ug2dQDozPw<`}aG?gd$`83p2)XKjSjQ_i zk|o0}?b{v$>Q&Tp)!U=J;3whrx?cy(7QUQ|I+a9uw~$+CguMo-DFUYyx&tPSB(7QWB@VrmzR=n%c$1F0QS{S234CSV z%Muj><1plgeN}bLCkeudoV#3$Mj9babJ2c!AoKSh|hRMb0SsD(IWK{ z`G8D3;e;-mVS7AdIZ#sly=kr|^l*8uf$qZ<;;u*1;ZwkfCo|&#LzU04y2vaN9FIkz zSHAzKT<%5QrQA`{-q& zisYgv-2c~+Bpwrt!cI;z99;#=Gad>en7KkhSsYDunW`|WJK;@m%tCPN{A68h#t(MJ zh}BZG&Hq*%2n(h+8H8g6LdUJgKk^o*7oPR!m>)fi4l=BUqKo)%pYcnaE@b+?1Q3~} zFC9f5%`FLNX(Eigg{6U$y0v>eEKEPr3=)W?z|-0iSE$5rARzN0$V8rtv6LrEaO~Q# zb1TUpY?a*fbz<3r#+%_y;s#+j!eA(JSmSTt8I%%*>>H&P+?di z3b{N8xxCL%L?Wa@A_Ax=&u|`pf6KZ}Gp0Uiqu*ChT<*WG_TX(*fNa_H|G8MQqaF#O zG>E9e>*!H_$YT6)F>397oFLN87j{b~|7O$z z8*>zkIb{?rlE{-x?q$>p8gax3qn$+WxRd$s?0N;VRRzJ|L5h-HdZ>`3UvUTUBk7Fz zHoFq~_v-^ky936VmiG{=M=l?hUMfwl1(x^Zt4FhSlNFZt=&Qz^mJW56^?XgMCt|>( zC#B%_Cvb~;O1J$?(YV{L?Sl{swkBF1)C%!{pL>Zd&tmjIfi2W?6qGr)+6`yKvoHcJ zzmywv6e$_F6tiwdgSn4!2ah~^4>JtpRIzaeo}F8fOp%uuTC&NE-%v3>)otY@Q?vDI zE105Kei+!=s3mwR?2HRsz==kV5fpL_KX%p;`J6-;D$^*Ilvr7hOy|oWDBvSEOJ+v0Fj7@1Oorl8kE3N(6~Y!4kA>Qj?{N(f6eAZhXj+vn zSX#2g-uO{~L9J>Lp=V)W95leqqYU%(WON`sNjkwUAbJ1O^)+i^oSIwziHld1TBPr} zvzEJ;H;F7xHA`^g>9?{{@x~cB`)a#e*!P)bog1ZECXtDsCL#*$Tqc=_P!~4J{70UC z8#W5lK}iaQ$RITbrSZ5NgGf|ZtG(hxeEuGyhPLqFIs#)_ECLjq>& zDpjNTLQ@88mCyK^xZWieX76bfA5@iq&y%keMt=y>pk|d$CPhq|K0YMbe_glP9?9ib{I1S|&w}!NviRwwB_5syrhRU0a_UUox1GM1Vw0P-V@3ny#wuA7$ zSt=bjJRFN=4esm9U6PIf(@P*a_eSko_~vp!5r$biDY#}tMC z#Qw`7Z0qI&+xMT{b^Dgh*#65ZZ0qMAK?|;#w}wN>1L)V*Ff|Eko$`whyR39P{OA^* zD_?DzB?$UU3Vo-}Q3MsjeA_7Bn;_iW_nrhMhs-UBr-bb0JzVw|E`AU1cZH+Y?x!Prrn(scow0q;L}_ zQ4m$l%#xYWj8~Z) zExPd*E^flreYh;fV`0-RMO-4vBbT0yADB>cATTy^VMdRjW6Z!d*As=g5pn3XUyznc<4A6a6=@i-_N_I6a=vZ*wOQ_31Dbz_xs z?kbZjU#7G-&?P0$@X~{gzfxa>r%B`$e|5v1%#tj#-!#7m{p(H=G^q2aB+E zeR1G!qAkeSEr?te?nV!wmUPdoaGE-soaUbHCiXOq4&%PlQK=S1JMU zqns%etCtm|n zQ@*OgU2S!R##&?NQ|{;49?$1Fqf1?yi;VxO0`~t^tT4KZ(Z<*BAH}Cd%3MJ@hIOy zgHNxL=K^M{pdPpiTnEo39PyeHAJANggmLz%YG)Wh8G)@6Pq3l+s9tf?8Ev^#Qmfe_ zAH39!eDELE@ZQ=N-`ZhqbjRMvZ+wTpa_X)nd)`MDnpQ$k_NTWm^WVlVfe~`U`gFSe zUUqLyrq4QnKIM!am}Y0)Sya70jkl}4LErMT4Z4BO*|aQ&I)OeJ4j;SaKe*n`c;}qm zZql2zKvlgP>0rTie7~f--q;@7H=oSDw*tH`Nt@JPp&neG7U4A>xePVAf=^t)2e(~8 z-`Zc!RP+aX(SVgjUyp52mR&gpwf6Oa$4T*tUH1Hjf>}zmDqe3@7NoJAOCB)0vi~F~ zl-zFj;Yhs%8tuAYp_nOyRPZ+F?ORP2tgo*hsZcNd8`4>b+D^+TeLGrayfbVyau;SC zW>tphuHZo4AXb9;n4~iKma(A<7pFA`!(n&%r!y}v<#H6i?|i;!|IVBp*2R9X1Ha`Y zfm+Fdv3?tOG}FQR$S`bUPWhPT@pRi|@UNrNVc%{;`P_!!X_y9vooz+eQzTQG(G^cyy~>nn#Im?GEVifc{lOzHfdCZUp6k56ut(r>CkuTg zNb@LsyY|p$t;$AVT2|>1dFNq-OShWFIvie_!^W~s{g%Zof_3Hc%l$vkzDTc?uFxXB zh>8tq9kad%dp3AeO zbL+T{a{F$MhwWU%S{p>jywJnSbg98&YD49Z$>#+&SPht5iK{iF%3L6DX=KZDmwTq`4 zS+rX1J72I}$(LzeZvjsoM{+F=veptln!!bz%>=kG8q;64uTwci?*r;N8dj@GgC!+v zK$p`ccTi;#nsUflv`d=hNzIc48WM^e?2}vEum}5dxxnSOJ;PXs5SlUWlz}lGkF~bX zS@;=Jb2KIFv`JqSykkcF5yN12X5G@13&XKL-DWxiXYy+D)_CW8y=Ku(zR{2;^nEJH zW${8V8?1_y&duiU$p(=XNPLvW0+by0`N8?~?3ZXO+ zks)hWkEdKx(yMYD_&cxVi&VsKhnrrJtJ3j)IK_3Hv!O4%_av6P?9cDIk7zI2@8a#X zwwGOB*v_x+rj_p})GLecu5W{^^KV*ktU>1Wrj#4}c@%|@>0@shp~&PlaLVqov3J$D zP3Om5Gx0d^s}%{j@Ao&Z*Hb!kM@H|4_p0=DZr=O3`t0PXxeD_ z=VH@YDG&CjeM+4`?UORu*IVhC+E3{nDmpI;uQT?ssBJO%uIXt{Vx9@%G)A+VucLvk z0__5*Yv3L9CQNvU1r5rGNX(xhOHXsOv#7~Jv()JdD`L5d-K6>0(!2Vu(XxqcF?0HQ z1_9~ zIE$suq3rZ_wf27uKsW#Kr31Hbkyz1w{Q~Fu^^5-(_Ah%Q2PaEM2Ra11E_UxE0ezAn${$-|asZJ6$zFHww(Jg)}FHAml z!cEy^f~hQzP*F|Us%a`OEK0VPGgBF5(xps(L0B2BN=aiuuQfk{VwA>EK<71e2dk_$ zB{v9CL5;?Zh)RV|D@n;_p^Tan$s(w%gPbEKFo+U(CQg-EKC==S@PX_qQ_nJ9GJc?O z*rRgl4<_`Bw-B$*+`h7hH?hgb99Z6{v{K%a%>S&ks}vB==cAR+fECFZDP+T1d-o+< z?$2o@(>X*4&caFlR|-0Y>MspgFCzekjsDMG}NY9XZaP;SdXL+6=U)m%n@Nm3$2_p z{56V2Uet`T<8XmFi9iA5{x_zLEbB&=7NmlqpFKc}AvOxErUoiYeZUTjvYGkyC-~57 zafjV}Lj-8(9Efa9M3I+?Qlotnq)N2ri}fLY!wq2U5uqU#OB7Qy!nX?KN9Dq?9ns;o zs6lX0a^mZLBa|*hjHw7zQlgzHMyzs&fo6t2&>Lwbdv(!eJP46vAzrF2ik8owaDoXI zQ32asi!vohMi3(9CrWX*DZaWv=l22Csr?ZM8l+5jg;T(~Uo=on6pq9egWenI`IiMY z{8ug*WG=JB@^;P|ZG@PyA-oP3cT9Z&HB&sCTj9?_;zL)V$7H2Sz?rbHY=^OMz54r| z9y7)gkClYaA1_j*C;oD|VJMYWb9%+&qq7Fj84wOp;jiWVUk@-!c^(8rMVE(tWMg3F z;hg?$c{Nkqmw{AVD@2YdB+Lh{rPMBEH zjdvZ=*T;uxCJC=JCRZ-UD{iuoKdU~Y6Q9Wyq@rN?<5V~0i7KQk>gRI{@v0X~4VP9l z&#cQ_fMAS307Ll@CYTwRyr_;C{(Ht731IxeJ)`)!AnVfiyI02T@;E{urB2-d*b;U# z&$NP`Q}tl3vHLNyUZ2sk3{d)+IQ(`?<%<(R=u-kJfN~&>+!!b(eu1sZmjB`mT8Y@T zVvO6JjFF-qH!+Ijkb(VuZ_jii@Pa&Wel451!Uy*Nu_*gXSge5+O8xjoJsrwd?YBK> zrEd-ka-%V5C5_*bGT%8j-?<0h`K;fP0Z8R=k=0S7SNE@o{YF;ZN@(OAV6@6%Xmyz# zV3EqjJgHGrW3`6p9&A)e;!eCcE093nt%$oN;<+b;Y*`jlD*RMJTO(uvf@FX9gI#T~9_&D)xexe& zj^F}NYyPa}3xIFIg;VR+0(qDb!Rw&-l0elYpmbPbTpQ`2`Pm@rw(b~FfzWACxUEw5 zjw?fJKUMA+Z9{4|3E$K;`XyH)by#|uu0?mGTao|TzPDzDZ(kJ#eerEkTT|}xR`|UV z@up)Xh*G9mK|ZVIAcs>x6y;a7YM@9Id1i^S`YaxzSiaMt0do8)XX{((2W^R@soTB- zlp=W=r8O3SAI3lsU&(b6HuY1)?v8vaC)8Ri{lvKOrQg_}T7>5S}?i5N9LsvXXO zN(sY_d!etX76IDsib_RHeO2p|jjT&Iz1Kxpqw^b%U6osOsajY`1$XM0oE*Nep1VF6 zx)%YIos!;NQuoOuc6A@9o(#lT25KS;F@=YQ-d$MtsiNnC&TQRvZwkfdp1YQdz7`F6 zMsA`va*B^T6+@sgcpY2Ktx~1?}v+MpA1(5d-?>5-(nnBuNiw*=Hd55ky z#T$CagB+(*pETyW+;HMVXVPwAZrGE6Ya0g!=%O|R2}7m zKs3}zbXQ?h;G!)s16@TFS&SlSEl75>-mfzxsmbFL`a67LOII5w=_pR%?QuO=X3WZb zQnlcWiheyCRcK(u_MSD68aPOVcrc{`a%4F>*z+9%`SaoazZ(HOm`g$~c?$jbc$&z| zL}QC0+XX;<1h7~w5@J5R(iGLL;%1@+9nuBN{M*WXsFo4d(0WL78_3fn4ZX%MN%Y|9 z`x@Y`HK1OEAH7L0NwN?cyBd9jFulzIqAQr&h_rfG9ug-3R<|_MCs;2@WTly+^6nJ| zR$Uv;q*&--;5lp%E4~$vQpfmOxU>(id^kA%oUweFxA^j2$EA^O8L&z%BY}D)o6=>> zD1-w8d2#IELHWWRMl81xD5F;}M4B6dbygDBhZx4n(MXZl!go=KLf9m0i*ovcBB3Nv zeg@m7Q_FlxNvq~_ee5oNNO~RdJ0L24NRYi^g15A)cGMGLl%~NoRq}dWFNKUiU55!v zlS-IE1+#+cF0)#3q3sX&lRQE$rzC9>Vd3o$sGEh+C$yeIF0LAm$%?aDZ=qLlq-_#y zC$yhJE~9^&!6dVe(?P_Zh<6TBk7AQF^Ai()qBsI5e$gT-0$?G2uw9M3doID_UO|(> zlKVe$$GNne-Bf4Xk_)aaCHL-mQvwiCXpreOR->eGaB#&k?kCtd;q=5=4^~s6*y%qDi5lmsW2xTf*cFbx{veV!Z ziPe}nzv-fp;cStMK#moFr=r<;?rn-_|La_bypi=|>ubatSk}C&!j=`t2?0+UI?`hN zIM)aPC+Uad$RqlHv9p56=fuNMfk&|jh?RDlwLNTo*}P4ApKeJ+SlTpE)n^T^tu0)> zJOAxZni+O4oY_eg&O#c&PV}Eqrtdf2x>t%Ph7XshA{!c;>b*LG-p{gQh0?{NL=EYi zd4GvQlc04xik4yOon!c;(Oi%lgz9u%pC#`Lc2oE8fF*zW57-lb^k9tdYZR7R3J=0CP#TBH zF-Sy&MGuy|2u6gh>MMFs3J+peq&sA%NF=esH}6{Y`mg~5kA8qXE7BPW#+~u@GX;F` zWY65SQqkyyG*II=v>aIIub8&Xn5_s^1!+mbypyOMsi2QB@eTKoOsz_2na~l{cazq4 zBbcd4o2v1psf0CDAeyON@{czY(rkw|RQzmE&DBX47itu=Z#t!G;BTQW_=;=ql0oivZ$vKd zwxWOR-a{pD@P{};xG;`^Y*e)dtUEDmScmL7E5I~s=(lX5 z_FqP!TYHD?I;+4myZ;EoXA3bPzdU?86HH6Se7zBX!_4k%>Yjq7UcE|iprQ1qi zXa{t`IeU86a`WokSs#Z_YE3T7#oVx$fQ&5>F^`E83!CTCO@cg>i z^HG(QhY6@90f8+qU%-K>j+-0eQ4?P8+f;7ul>kXGh@Q0#b$<XOp)j zo~?Pt{T629h?&z-=K(w-v|Ex9yJ^>4`bKR-c9BoG+v}I=8rFT4&WC9C>vq9ZWT=iO zpQLOC_wj)O)g*R{!5yvl%HPD+9@Qoa>Z*CIA#6q1m^C$6&$IHJl}18XS4q7yEKO1> zw6f2oog2Ja=grNAQ-pu#(VQ1B2{$uwX8J!v=&*m%-7Z^KP} zNn4JB%UBS%S0@8JuJ(dH4#hO1)3Y-!8+nt`E3-G6pEScXTp!LC3FZx20e4(M#1bmj zQ`x6N*+tH~T^mkMyHmMZtyS$!wA*;Aks7QGx|i$YZMK(F>0zws@hw)z>eej)^3Zl7 z-nH|!3EGY9aj0o)YkoU|p*a_(`ZnA7ysnP5Rs~+Md$dcLgSl&GDRgg!H0bK@_QHs>vCeP1@xE!90XxU!TsyLE z>AeG~xZ%t0Kckm!Zvy#WhoKvMFOt~}otrIQ;KE+-PF_IE zodyFrGw}NC-}h4aV?busTQWg0_(J5%&w|^#lTWhPPaT^sSIhmO;NP8N_kNxC9U}<` z_|ZP9^Y*Kcv@N2RPQC(dTcmGNG+ z^GLz(2V_F4rz{4TDtK8Y`Nfv&3l3v~T%M()o<$fnv+KbNn~JRq(%+nt?_$CKxH8R7 zI!P;@k=v+EHv+F^R!*g~(z@>rJQ#3HjjpcKb@QJqWk9?q8)l21I*F|)J-O~6~@+c9=XfCSv%q*yOyqZ z7H{3KJn4?q1o<4ewA#9f9IgT)rRNtX$+h(Isitr(c;2wEtYo&?t?+B+H6$Kg@G7W^ zYCnmBb*@6EpKi8!*R$hTZsxwoAI{s5dd9n$aH|a>@-8`HH1`uRLgE(h>~TgAfDCD0 z81;iiptIRZpgzb|JUBVs$O3P%ngC5jlsEZ2kI8x-pMgtlWj)ciJ=N-81|%=*S(X2W zv#iNnPx|`+ib^}fVmI4s0Lyp}8ljJ9itZ=rA4%fo-aUXc3yZ;jC)@n)lTFDt8~4nNbQFiKs zL+ZNQwQL}Pw`%wMT1$5;dD%ud_c+H|;f3;H6Yu5|RGf08vmnxRQD6 zd9SjH6Xm|58=i%)!!StqtE=LzRHoAd_h`xzFD^Xm@QypCLaR0^F1sx`^Ly;4=1}-( zx$IdqD_Fzfl1&2OEKpv6C{4Pem!ZQLLs^HT*x5|{JNfu6&M>DjPOe{hbXMd2-0!p6 zGFQ>wi0o1XU+-v&TVBDz3-L2{DZ7j}`ySuh)McuYwzF)euGIQqeGO0s9x%Kj=YWx7 zbvm5F13ii81C^^HPSAz+F77Yn$Jrz$I}~6_(XGs5E?X;<1w1*ra$YxMYaJc?LFw%) zm$s{puEB=y)Ec7ju<%ovJBq8Dky6L?a&v)IP^?agq)IW@$`rtJm%baosCZv89oNfc zNSF;Q(5ZO+!ZC1r?cl?Tl`}usldt|me@%u9ikkoZOE%kIh0~g%NQURzT%=fw_3b8z z0A9Q={1l_Siy}+jI6rxta#H+dQ<(R1;qz=}lJ(8)<>yhLleMkCQ%4z2+a`Y+geRn} zc1n1=L*ZVvPY+AW{bDcXG4j%JdY~a=Vjf1&y@=AcU*{Y&EziMLja2Dmyzlw;3 zgW23Zw7|vsCO8c*b8Tu-jI_0alUj$j+mNByerSNzc@_xYK5S0r8jQtywoX~Xo2RYp z?eY59)0KS8OJU`CI_N%q`d&yyS6!uMzwdQwa=$E;3Fj@BR^M zE7x)RP?cAI-QDoHioRCB7~1V$%X0lYiGJ%QsJSn0Zr10QA~sfD=(HyD?CP>bTP8Hp zqOO+eQtBLw(;V@(57s!BVAj^{0Ph6w8f{~+J1#w21lyG6loj)Qa&uTO4N@t&+AlYe z$xu>OZ*6ov5}Vg4T=|N;24KEkKmAOP&(}Hnp0~Ev{CK`<-2Cd+uKk>}lBibn{WQ=v z{?6N!%ch!ALF7fpY`GilzW&}cZ0ywd@$Jpomum|O?5dr*vq@ZAv`h4*QGRYtw~y=4 zNa=6m9H1K6sr91LXFVy=DT6OMBVeE7oFD(Wuphh5Jcj$l#P+>jDaYGS(OcV?$gjKQ zEm2L$C;KE+N9h{R^#zYKT`KO4Nj)kyntB4482MNAU+H9$Z<7Q1JkD}|w?;YMqz~e% zISYlj_Rn{OO)B2l*w|TyTZYiu&{>91hT9VY0!l_wLRL;rP6k#&0!~U+R!#;&LO6?7 zi{%J*k01>3h5_)7ysS9~u{8k)n{PZHuC#}W+IHxD8npBsu zK#>*$F?a>dTH~f0M5;L)iMPHB^;7W1JJfLjhSSp!suk|)UT)@i+`tw;7N}cP>MpQsUMmD zr>4KX@FPlF-&L@oLD+fFd|_0AI@%{`skch?zGLt%#Ki3KJZ!P7K3Nram%7qn^Wo>8 z6QvcphhypJ{agi7-w@_B9;&Si$x_di(vTwQ^1B@GYr9ulo7AL0xq3@1W~;Bd9;vi; z{ZWI%+Y^0CXO{cp+tYA7?#1NGqgCC>OWJ@j3-A4ZIQBe0j(u!pZo2OeCwj_VFEw0M6^57LOcC;(wx4k}RDpq%u0o(F{r;Fo zD#7@IAi_X0vGEC&s)rG`Xp|?ZhihE!buAW(Yn~}fEG`vZ>1&iiT`e`OeHkBZ9c&SA zb{)BXoR8nfxL;ErXF6;-Ol5G*7QCo8k|65ngsy{&wu#C!HXyNx`^%fmq$VJU`^ycR zKe)OXiN-K)R!q&MB;@jmE9c8m3A9KXRftcU11(a-5zhS0OEs@l1XCHeSZ2>s z18h@@tOa0|9nKP}iN|(UW+u#6I-)MF)oRE@8Gx4*4SZ!a?5$E3;$fs$2~_03j0dZ& z!P4?_;e>1dYfNf(!H4yUY<1cf0cJ#Ny;z&_H{KJ*84>AWT9y*BnKYBHG+_^w;vyBw z9i?~6enn(9AxzOoGNpvy+=zCLyis@`1of$K4j36W^R2b(?;2WxDPN;WVybcHmMaX5 zv?4QpXR0u98ah8&5an8n69w6sq&f*g%pgAo4r-CX4i!N4;}M43$=G5r3jIdb(Js=vP?`L`h~YY)Q+w<=oA{F`5J+=$DU;l zV`>cYkomfOSlGoKevvb zMC>95XmCiT26^55VH%HZkeHot(aQa?7;3HY94vv#*w&OxQOt>a6wKlVPVvlVf^lPe zFIx!wWRiWtqO?Bt-c~B*N#TLWG{;}n=AVr#=^`k-%Lld%N%!i3N%u1hYdeB8#}3sN z9L1RBhaScmNe)qpG{YbNXohR$#~D(K4ol=oE}mx*5$B+FgSwqVSe4T&23ZxybA=_W ziViK~N)IPuglNW%4q*(kp!hDc(|xh}3h|}A+s_u@64c+@6?I-ERvSj1}uJT`lHg4F^QHSa z{r{az#R!Wb*=k52+9s(sF$=zRQQXHe-wY9Aiizq_xbW%-7Z1qAzg+aC^;qv;P^QwugE?9*z z3zpC4%6buuhI!=#=te*Th{tWd%xj0Ruz4n;?Bt*gL+S(~cQ=0Nq9y4{#DgxQpgBk1 z_V%j_;Auvt);Tai#q|NK_KiWM7<=EHjVy0NqSGbxVPd`1*5g#i_s!E?np$%*zShE3 zC-ud6FutZf)+<0Uz9v4_yLO~h9d_D4UYg>2GOqS}FPm+QH9t`j?5){Ye_b1FT~^*P zTithnb!`Y1(fKfkcA&iY@Ft~k+&3Bwo5+kfB}R6|@EK391kF{{C%SB|iWQ}M9LNULLaLS-RUVQYXG6m z7qhzUMv{L>=-HKIjLEwmE<3(&mbYQ^3gk=s)BBJ41Ks2qSN8JlXXzTGx0Tj8%;HVG z{ZnFXAHpMq^x?~ohjIcv=TX3-Cjt%FleWRlxflQ90MzS4611KTe>Y+`b;-1X3VkIJ zCuGl!RKAE)FRL|?tHuC>94cTK4m%!5E5q zHu$^L73WszyANHcf`{=t2>-Us0Hpu98pXfm*hro9Fy=pudgieQ32+*d!0Va*!=B3? zM%=_EwtpMfyoa?>Q$-XQRkD4fZ>IMOV^zE*0Wb#?Gsls)_(Yy;@%PT@M5E-Q`us~C zEOT;FmD|6TP zl7uJe?2tP+D@V6p>yOa@EbnKEh zNl&6aFCL?n>I+cYGO4&f7)L_~?J!qxO|rR?{i2R!B}*wbA>(he?R6*X$nc3|eq6r6 zgpzP+K`=K@xU56s&l4)Lhx@@Wyh2%ybYv@x3uyVDeaDB}jha2AU3$QYNh3Kk(_tdaj|8zWxf)o@7p5!l|(-71s0#UKLhj!V%p z=FqFV^SS!bA``g95Ej@T`FS?{rWyLp+9sJreaTl!IVQ7or+YK?@OUoay~J1yb74J9 zI9xHivWXg{ED4n?%)kj_s4RW=8PCexTTY=vRiVS{zzJ!gLslX4q+I8=T<1Tz4J+9& zGuPUvGh388q4VU7lUi>t)ods8R0*u?q?gRdE$+9xaW&>#Tuk(t5Col-iAZLIFtgnG ze=+j7ld?qy0TyZ zSOvM-z*HAHiUmcwxG)eRU%N92KV|HXV)Ua4qGd1tXiUPQUuLy;DJ~2A#CwAf>z_%{ z>tZ7xCCD{FCB@(d=Y-__XO7Fg2g(l?cE%Jk(OAI8c8^|4~JZA!pF zgHAua`o53NW*U2PNA2Uz+1R6%7TYz5DCQYIXiqdm9G#`PxYfnN)3MT2HnxiX4c8P_ z1-XTXbAA3`<{M>y!@LbC)gTrf5dJ~4ft7C*_YLzBPN^>S=wOL5d8&w>+}1>Um?A@n zef({?x=auBmukLS#_a03J#pqHn}vQ1N^7H}k#pbLYDuwi&2R}d6Iojvxjm4fXb3{V)%Tlmuw{8ql8l338@y{_2Xh=} z)_k4zoIS0S1oa<_Fjwe>)SSzuj(j@kGvB^(&eBcyvmYn6%cCP&^t;Yt+I<(5DYEzeDsgC@g& z9{Uz0asyPo!KHboi_=NrS?S47q7yw_D|{3=B%J9+m+9V$^_~6!MoaQdlkVOb_{G1+ zn><)29#ze~cY8R^Bwn!LYZp@;M~%S-q3fQj&Kq}9h-Og@AhQfYVXYu-yvHck~qrtk2_B`zy8=`vj_Ahpaav*R#u#PWQhT(ds!WA0h<GF5P}zrYvtY2R%>Ala;ywQT1_%s@zxa3t0`EpBqT=2P zG4W@g0lq$cfX)e%8SCNEaF{iV^PswsP5l}=t(l$zeQ3xaBjdt+Q^Pn?tzxm&E4J4y zd?rDXVTms$)wtg>Tsynv@~!HQtyVQ0%#Po=c!*FM zk@c&o)Sia|)Dk!v0fz7m%c{E3CCA3S+u_J^F#ISDovn;sO(w(NeqJtY!JeBvG$7}o z+pMr~qdZ;e7!Z}5n^ttY+QV?;A>wtU=;O@zmR608?0}`(VS)zYdB?E0 zsdqId{p)|gyR-K%6Nv$dIKYpR-D##^CRG-B0TM4Po2 zX4Lb$3auI`Q8LdG7bV!666l*}LHg!CKcQ4R-M;B8OL^?>**)0}-51YLY0rRwH-L!< z=r)CBK7BPD(*)kMru^9e&Z}kx*UmA-0-LXCmx~IrQP472wk%KMF|}f13@Q&f=!iED z9WH#^osz58CI1@A0Rw@d4VzC7ebGwIM=^uyZ&Nl@#M$SouHs^ND1tYS?%DLWpoSo0 zWEgk+mdsO_kU1Zw7O2~)|I;s#ms3slF>bB57Xp=EYUQ_%-7TS9a5vy`zs>e?O zSem_8C+>bNGeuB$OV?sUdPV9#8OZBjJP2u*TnZjasDmoErV5kwiIPDum$onfn-PYu zK&x)G3Z-qjsr-(G6E+IkVN~s>D1}8$gWlo97zR*NVCUqY%!K}A{~xxq9Q1K1{^bdp z948oBf+n3w922g;ZZ|S_B^#QvWlD- zUG!C5Rr{;;DgUEz9wriZt$NPwZ}_7J?^8ah$%p_F*M6{fnflKfy#(HQELxBKl8(yC z2lB1Hf}n#MNd_2LU=g1{+X6&mGDdgFc^zN6#;+lprodf=XtvEI9DNzc?J)C!#7QH| zlCeFugf}o-PGn(4sLzh-{B;i8e(i-E>1jIMWpf%`|D!z%6=~Vwc3bVIyv4aKQ>yE$ z`R+Z79RS#gL=_f6Wrpe&OR;C~f_qbR2a`kTyEjD~h;5CnUFp+(7?Ql{EqbZi`VCo; zK5e2TnRddxIeho63cbc72e+(oogdh{pLBTcsJACURYb@+Zx}ATNmo)T1BfFftPXza(-7MZB{pHc5}Pudu{A@X3)X>vO_f3!5Nz*Poqu4}*UR?zG+|>P&9?DbHyV^K;lQ z$h+Ddo?K1wQcJh}9-l8179ZBColCF?FbAAx+e_-`_Sx86-+?b~eNDVZRx&xA+QU74 zY8Hn!pcm6rw6SC>k5u}QUVKiYnRr`)N}?YI_HR;I2_OLJt>N+C%f4=H{zrJWZKtev%_ zTc(k|a%+3Vd%C_qiD?#Cr@qRK(G(-K_v>7&o0PL2F%-l!tTmps^>kz{TU}Q-%*!4x zvSiTdJNEqeWnS^-@~;FlvaxwT8140dnc(L> z(zjKJN8toYa*o)MXP#{CdVUJe`-8hd@use^w+J%y+$62BsWxoRI%CT>(t}O_T)#z% zxu+c5H~vwG*AoO|+~}WB-nON1ls{?<0Z|`x7=5~-Ixw#b@h%v?vYmCbJ@RPxE@UCc zgE>cX`5p68BoLi@ApfJ9S140!N{i(Zk2zYV_5z;}{nX?ue@E*%; zF7xpN>cl+VmVL$g^u*jiw{DP~dain~{?h#v#DZUEU>3dwwBH>wOAt`Q-^Sqm`QErP zcWAAcWfNQm6`7j26@?Y}Q??XQ8hB7i><0|0V^+8I|7hCsGbnc`!KKrb{zcV^6>N!0Tw}DWKL65|dApmXy579&_sY zXXGTeE5)xfdAx+xOQp{dI0hE7xzxu0pz>qD-K<`8##gDi?V{f7yq9^tUCJT#hGH-- zHcfho(!VwKoZk=%C<`hdMa{xVEztd)L*1R&VQ{h`19Eiv@R6DjF;gvB4TrDMH9pzk zP3PTxm;1v;B&Bia9lE?Ucy;2>ggOdN*VlHpA6UNm*KisMJ7i->ssq6&v^11Ux$uW%75U*IF|!c zz&;)-cOUv6>q%FO?jP0zjQ#@4-P<*~%S#D4oBXQs3z)84A1eZj3e)i~*;R-sHC@rJ z>#t{nbDFo+D&4-^c4tA@{s4wS`z%g6)2BJC=Bp^c5aDYfR#m|&?;2b78{)C}M{V>< zb`@kwottJCE90j9f%`S|5#Yx{PQr5dPRjFJ?H2TwMB8q6=_8Q$26Z+HMhQ$rLq1%R zY#v2&IKSZ+kx3QyJURST)m1&ng{G4s=RE_yf{W z3_XVp)kQdc<)OQPOTJ5YyC2qnTHb+v#j{w)AeJ`y$zc@tR1X%lE>O3w9~LDNJ-I5O zM8hsTOd%z+7bA-g+Id#iCvgwSZNs?s+pnLF8;;YJ5hVtBAK4AJL_s4uz5SHJ4C$`- z7<3>^S)aGv{Ins1?P`Oa1|sb7Hcy0w$asIqDs(^_^6I)Q%!d1iZy@@@0oMkWg}W{yqgJuT7IC*hE6lAQ$=ZSu zrv~PgMcGEJ0}U+?u7f8SN1s*Bh*KTxPTm2h2(#;roqxY#LpAJr>8g#gAZ^NEHGIa; z5GWWZ(6f5dp+4Me1sD?&c7FT3T+Q&zotz3j*AMJ-}6IAXn-y=D%}<}_~=ty<+|2C1(?qTiF& zp`kUQps-i!`Be+gDr`(Q)x!Tm*iWowtQD$lzGiQdZl-{;(pU~bTpwKK6IqdxmFl`h z2nfn*&lakLYSb{kXhSO+3FBn-zcn(}$sCep5m$rcT=8bP;-Zkup`%&q<7No(Yk+V% z?ge^tpHR0qK&6>7$70y@3ZafmkF~WnxuGWo}aL73;&A!L=^1)BJ?a zfr9MC@=VJ!^lMa|v2j0zqqN-9{;cffHs`gq1@l-tYiYKtxNwSnZIVHnV*4YM5Oue# z+G(++8L-I1c^7F@hB0emlxyC;m-nEBQE3e~=Vjgou!ZHaj^=K2arK|d z8}g#1bkY#aI(o<{aOsDgnKAeojN-P?l!xE2ssW431`j1TEh5q(Dw+qA3~eKp5M?@V zA{x~uE2w)rb*pt3SPgGl#$h5mV@!J5^B+hD7ve;`HS`$oXVdOWc5Xg4)4UN7$KffZMjSL3Wp@_ z!Y_62@6DhR8lf!8eA!6pKw__^Z1Bq8Kln3*%kk->RPY>~38Lv6gET=W9x7PovVU}v zi6>E1D5l|zg5$U(@kiZVu9fKuui{^0c0@dP#u@kXrnOlAq;#kc$q;cTU&euC_&K6e zGOc(SpFv|Cdj#kn@Xxw8uSj>?`m$2`W zXB1w?W zcq3ZIV%9R8;XXftn5&pycR4E)p-@h=V|i8KT4MnXZkk zB3sZQB7)~X!n^vD28~eYo!&uslD#92{WV4h(XXVZh>w9L<}~+TX(fDgqN{-z0Dr?D zktbD@X?SfX0ZxqY+())XPa#}Kgn%t?ju`<>z89NtKt5G?&^9*uij2|Qdu$&&MPpOLUJVPHC<4@|eX*x5t zHeq~~eW-8xtS5BZbe@^w3n%cWtexEwG%7hYglw5*G+MB@HWWt9ykT=$Af|n`IO+?X z9OFd~U1Ej*7DNlL+}W^?=PQ^Ww*r%#Tt_Efi_SXqM;NY}&LB328EWO+zJ8f~>}NO# zg75zVC$<*k7*^kjgtD4j|81CSpiWsK3nEMO7&i&6s`)UNkRRu=(45|Y?iY?=jNA1) z)q`+5ImY8t?i1tp^Z9&xa*S?T!AOXL|m?s5WieTj*Q0;O%T|0@%0R-FGjN>BEDzVoN2(}C0* zqPRR7IHXhBQO;EbrC@Wr#1i(v3)3lCs5>f@@*Bx(z*-T}Tn5{7B=;9BE-QjADGV~k zw5ufgSH;?mScHX)ct`uNqh_r2xJYl{7jU7NfOq68;w9TbC$DOr*rTcOI}QGAfDvfZ z#F#%Z>bczpB=EV4k9yW%KR_Qz)ev(_YYbaKhmAcXAdvJWuh1h=ADEZ?6;Y9(ufMv3 z_fv2@!ulUX`lt`xk9}jE2oQB~vXcwW`7pe8RxVKMo^(qyDhQ5kS;Dprm2EwAXrl!M zuF$R5wXDaBRn!JXIWAt2Y=EsAmhAIIzNpi_Zyv06g4q})gUFD%RCc0C*0R2q&`>|3 zGE@&{U^(ucEDt7&xb|lPoh+q%voPq_!g&w27hGD9D~oEko3A84L5r1P<56T2%c%5C2)d@SF+ei zqsx&T^w(Aq9RPm*ugd-fWjkO&M}q@0%*IenKYg=+D`nhh{X3_K>%-!_HJnjf{~d<9 zIbE2UZO#E*80rhO%R>hE@OC8iHPgR43?i8BQmSjFsXGi(;q7F!7tA`2Dc|>|k+#%+ zC*+P~!(?&~WK(d{$qPkENc&q*6H~wYM=F2ugeNMSC@7mSE}h^OJFE+z*nM*$syTYA z&%cEdJN$)_r73n87Czx6cBm?59+clOodBaeqK$eW88c_tw7s83S##O3Zq#r2a|vU^ z^hDO$Hs{>}YQwd6}RLVZU!pUig3_4P!9IqC)SppF@^J}ai zKbwee@Eu)e+6%cZ)nsi5=+Kz9scKwgwUN0hKy{`tzt>IrJa_Kz8Rv4bh&VGaKDD!OemBqPw#L^3q%J_rBy*{pS=wFOej|@B8ACbLv27f*&9hUA^r~jsOx*=)z-a7yOyQvnSyQHq|P%!+YbmnT~ zJn9x}W!{k66Y<<|*AJY&sj&$?_S1G${CcT*_pg4Bt$vpb*f&NPqv3uFgBUA8psN5I zXcqL`q(O{nK_|N+(4Asxuv69gRQJ>?1(+s4jF~9}vWrY$f?|C@v8tdX=3#7Um}Q7r zRyqn9O~dxY|4=$+u~n>;K2n(PqyO~}Q3mvpMmm*6|TZX;RaDo5V@)qz-NQ@KG3fvNd*_XEK)|g zMYnj{Ed)hh?caGin%yG!-}xUjJB}oW_M+fbTk^FFs?Q_>JTNEG;Z+kJsGsQ24toOi z4E3e@3u6&py}C~ho}yGj_e`h2RiF{uGS$x~j+5h{`a>PK9Ly@HV-a#mzfcKkr+0se z+R1 zL5O7J2e~4*1k^lIB^eb7!6HIops*+b1{LCHUz3omAd$Lv$XuTAiSmzof4;Q_uAi`v zU4=e(8+ltV0R}SZ5m3rHj@chP{@*y@f<0_;;FOkUJ`QL>j}r(SBxE8vv8bIZkDYw* z6)r^EgL*SlqlNrHD8r-;4F`0Mhre{r+$v&giFg0R$rcc_)X=~$a!zNx?*01jkAtPc z?~5NB$_-)@C?6IBLvBwBeqC`f17zV?C~nD*MxGVvqU=5&UgnJDcy9@Q(Cwi^LkrjI zScY6o9Ap7M&YQi!u(59I)4p7t(yHaK5!!_mvB{pirCLaq5|Oy?>Hy}P*Yk9+oRl`R z`@dXv6JGbH`&hjx#tTA#@JegLqpZKH%rCw!LikfZ$q}2+l7#W7kbEECs}j=iw4d&1 zqD|Rv+r?DSMP%Xnw_@9b#kK=&9wu5^tL*A5z~o-@$^CkVX)k%U=Y7}EY$fH3*DDi? zbwv=ZQ75>^l>9abRowHHqQmM1_A6L=Ccmq0zbj=cY1dK#d(C=nXGlnGaKE|GDFgrH z`0TY?i=R? z{7pA{_{}?mw=zWV*BY%9*r5b=>yHB6o}3!z1E{1AM}7$3yxEeNuRerDX<2y!*FLXb zw%qOZf-~rl0js;+a4pg~Hv!wP zK^Lv5(AGd0HU%e^X+YDW*ic(q@1{(Wfzk>(2Q)QA{1% zd9QE2yExK`g=_cnaEMa}G{ZPLgT>&)BygXh-`JiOQP=T zz^(s5VM?BY;_AZy{ zhNoIvvx71nn_?}!`&zi4#IVs*ZZ!Xa`NcpjaeXOker2)CT62ElR8B0r`tGKLNbW3# zuMeV&uj>O#g99NKZmnrArsGLiTp3?_w(4%bz* zbE9x=3_sv&M@HJ6&O*UoNCt|^$<#}Zke0Mo=Lb5(K{45dFu58%rgrLV>&KNp|Ewu| zKJX@S)JpvRP)ezte~{+JSGKDd>MyUzpBj>0f!lx2gUw$_qOl5ZQfl(uo^7vpDHMc! zMaiFKP4-ZbhwV4Er&dac1#T4Xeg*i^4qhA=6E&_d>tHOFU^egzXsQ@;>~2P>YTzf3OvjZ1iRIGPAFRdt`HyM*&k(r zEWeI?<|q&I2=88RRHm{wnr@jJhX%*#Lmw{Q7?0bg$Bj^gydS1QW~Yd#2%TV`=Bnz> z@$NVVfg44Zz=q3^d!ZCoCjtxRjnvEzwrZWezwGsxcNYpZ5k2k4HpYE^cuvOg; zLBJY%di~q033mG&9<6qL+Y5hS_fI=Ae{USKK!UB+hNmvxJVj(iO9IW*yGhY0O1VTE@~XV@BOjYKdkocnAoa za}V5q>N&Y=mekq}#96tlBU(;Y2l+;Y(pYtecoQl6EKUdrf+@OXkoMh8)uHZDU58p& zrn){n$1_-K6wW+IpFZ>j#NHI{Rdhz1k+m6HQ+fL2Sv5HO#~WX)=COCaD05&W7N2=+ zjQg+(Je=AsuE}nD4=tb7*_o~8ySc-z=*R}?W3kP}`4UKnYfGM#2(dT;)36fSELAI* zL{{2UH@%_sGt-oZU6xW}r|;c<+-@fhzEGkgq&a8FamYw@s$N!4xef$DP+VCoty7L6 zrqxdlsoR`im>nq0BqkP(8%5uFKdL&3exI$Oe_h$lkzB`oc)MO_VmLUvSp~3OsQ14q zb$lomD20GV1&SXCGtSNQ%5bhVeG@ZU-$fry*IyZmUEk(zUc~GUhCA`I>^ip1Dyu2M z{OMa}Mzg-_4?kDN8#8e++1T>_es{WMkc)I?(mdVVtWB8{8?Fc=+K5#<

SBM$HVYnnvQuV1bJq0DNM`JFA(NWEpc-w$noW%X+RMbCy8Wv>b(>Td&gIOqZp8 zfJ<(uc-60i`|+)c1k(-W#@{c5<4s3a5q@R>i_r`uo(hDr zDV!30FY@1)=yJRIdKdRj8Xwqo6#x?ig|wYlDz0E%cEKwg)JLvTsUN^ql> zz|t)l0^9SvK!d`!#g=sL=P}>o;4}&pZ{L;6Z&5pMbYO$-)`CA8kxwDu75C9DdcS6q zkBFviN)&wljc)FB&%vUwm`8W#IiFvx?$#dxzt`z#F>B5iExCNhOHWpP4|Gw@X!X3! zE@L~jNSANc-}^u|HD(-_Kww9m!47TO?CauDN!7{hQ(gDo*kd<(%K9p5cy}Q+;>Gu9 z0c{wy@Ez18kTO{&zqJN#8+%@8mifMm-d^xH za2CE?@Iz?*pcm{Rti))AT#7U`@qYjizkg8W%Zt8dDSv3^J^_r1p_bmEZ4@YHHhg91 z9%jov)M2zzvU5V$d^rvvmVF%cyr?WX(&1~yyO1XKRw`j1yd15R%#7sCFg?UXeayl$ z68MhkiBt_NEG%q{?2Ig}ENzS|jZpS9aBwy7(loVFQ*qOA(r~o!zEHCm$1)!N`g;QmkGaVKgN1?)+mrSt5_1VH-XW!cv(8gHpH=(*o@A}%N zHcYBgs{T8+07N6=QV|P_f2LT;ohz89hO;3|q#@iyD;y`>{_`z__iwP(BC_;3voH{` zwMXbS1ueu47agF_LDe({wa%~?K+Nh#tzwZv-KIMn#BcGQKgYo0(jE)zG4)m?oTqzm zGEUOI!`zGv+n+`7(oSzJbLzTSZ^4Np=!@$p=5z6{yk)^ zSp0tBtL=Xdj0}&Ps8-;)P&Xiul9B@dDpkc#eO_{;PvqK>Sd$e@h`OPP@tzw)xf;St zqGm{3MBL|wPFMaEO{8q3PFn6j(pOV;!#%^j0~)KrbfGS_sX?aC3yl)j$7l~m8fixK zcINEKh?0Y~Y9VM~SR;{Q^+Lc7em8{63wRM+ZNu@hA@&*m$xDkb8bhYRCrbb2fA;ww zKBobLIS6Io{{KP_Oh5l8<-n$IY~r`Q{UOt)I|UPng;y;V2J>q0f+y5|98ki zRYhVBwMAk86T7i`e&V1B6>`kGdg)p}Kx^?Lcz z!UEc2+oe{Ox?@#U?n0-pT34E943KQWw}dOSyDL-VVEiS++wXCs)jpp(EQy8|W4?lx zm%lMTibSmfP-RRP*=tnG^rJU&z{oo%dzW&OL(#fHxQhR8-6|4=`iP)z`nrL%xeYg>b%?BdDH;_7#Xgus8by^DgL{52yZLX5GWuLh zQ!rKu^EUzd`3n4{yK}qnuC}?^2y-cNaL~vt@W{{b?;gUWcEZN^7WoizO_u8NZVha@ z=0-ux4RLpAibM4l#-pl1XWrza?L1>TWm>IIH4GG^&BPK*?A=8mesq@3& zh9N^);lemuvuNOmhiN6|_Q6dt0bG;=CQe{Xq@viZ8)1rBmKBGNsut+5Tz*u*mYoiN z#v7^&88z1bN=`E4x+O^Q!vvF6fF11rU`kW`i4QY8`4Yom*4SHYeg%<62S?dQGB?f_ zYAIX5W~?b*A@nB^r7bQXjsSTdQVcg=(!H^5z`ETuc70@u=)}u|%6fLmMV7l>);BM^ zkVE|Za)DNOApScBW!gx&lppwKI?`!UV+qZrFBW36aJ$sdO;J291l#g*+nujPAljYy z!y+a29kObHM;>u`RKj&we!!qvko312`$2dbp`l$|k>Y`HcEQRhXm}MN0n8%>*+?q? zAm~f(b4xmN8KPBS{eHM4*{fd+i>U7|aTqHFYh({&dc{E4KU)@i|z9SOlB z!Of>4){`k`c$AH2&L#U8s0G;QS@p;uiZD_Wu9cRf#f8T?Fp$*qokzAF~ z5uDF2@E7IBP`s5IQmuw;GFtsa^ivPE{$bds76x&chl=PH>1_5;^C0{a{OXP&dl(al z`$UyGLI~YpW6+G?6G^`P@D!H4x=6qWjB0(cMB&pS95Y>rG_03f0?=_U{4u`c<$q}4 zCKKc)Eqa|b@y4tB+JLgmd8vCVhTk)8^DRz0h=i@0=Jl$HOy?_N><;!p;3Qc;BZ+iC z`n2q`XzddK#bJ9%F_4`3cW_XVB!Hj27U}p?C6Y%Svo#1osC0=PJ)G^K!F&qbuR4>N z7*#(EVxy@|27X2wlB%$)Y#{AT^{>^NbHL$Ttf_w-kRJNy=l-96V!x#^ND3G z@xy#U>yqVstp+v0yTA7;2MBI)VTe#4PWS1={UHe=1f(>0Eg(Zxw|)-o#93~YA`sA; zUvn;Z!|Y8@A5&-}Rud&T@!>`X|Szk}WUd8q;PS-K{?*+~c1r5%r=uT3Hh9Cu-52 zglH#&@sg3Nm@pAQw;dr z{LeW;f5|=6Ig04ws@f3N3Q1B_gQhL8FS9emToar_tk{7kOO#^m9Wd5Fz7H!uFmm}; zcJ);|Ete^SjrvIwzM^2-0>C!RpJ)QufBD14L_CdHDfY!09lOp#NjzNVJNaTQI}ype zFWFt6+)}^Z4zJZ7#rr$G7)G$rd2#wqnFsl|i zBY(&TM=(8OyET}7i9i7DU`zX{E|Yf&ViC@-mfV*Nr*Nz{>@(Y-@|-(iqKM7= zw{Y__xb{^-1ctKpCtMScRPrFPFddzA*my0iRlkI~e@Ts`yg3dX3;IYM{crVR4NXWC zR%x7nxYATii9cKOf+PN*2_Zqhc8zdr!KSfRtjD9Yx1tjulR^g;>_JJO2eza4dK>g! z8@VM4r7$k2w52lomLdIN`e#*lGf!2IC#QZ&o-Si=#PD0vS$|q*N7OkiB~2#Kf&nb* ze9V|W)}G%=7JhJ(ZNVV*xr#Vo7GzDqxU=Zr^X!s}HBcasPqP}YHO)AyL_^twQ~EBf zydR8&29FyF2m@WBr-gn?o6iRU{xYK`jqu7QdbUvA!YLiZ<%1%rZmE>aqNtqHsT$N1 zxjK1~3iE(Or6O#Jq4PbAlU{c()xh)_Ll`@ozni9SpVr@X{WY!p7?sxkbGL)Z)DuKIY--jd!vRgS+G^YlRaar@;%cf*q ztf>L3Q@<;*OlPPH0@RtgGa_M*yz%~{63zTaB|6I1MnGN7eAw$64Z@dJGH+lHje4687F1| z`cp)?y2MnDla*Cyb<9yus)A*7=*%aasu-6EbmJHuIjoOYd zSxoepRPMqx^M8S`I(LkxY5Bi*8bu#rI*Zk$QhE)PeAb8R;Sc*E>YGAZkO7F|J~%j1 zdmL?{Ueu)ViXXVyy+fB`!N~(oTFIX#5qabA4x5q@(3|xlA%suU5Ek9hYoN zLcc1eg&?NomI!M>8r@p?9Tq@}LL&}@yNSXP>Ka(@ryq1wGDO7|GU*Z_8FnY5I3t7b z)8}u_dtJJLyMe^*jEH=X6OAR2f^z(iNkkF@_c?~r@AMJM)_KF+1 zFc%Il3%s?fif_EVH6lyC%~5&C33f8L1M#7DDSBJT+g5FNK4GE*p8R$&L)$8V*}a{% zO*`z!t|-5K9ew@0>&OmO)csY(1sIy&&WE;s&V*Ic*8g=2-KI!Xcn~QE4LaQL26y?@ zIRK-fSNQLzQX~_D&vWW^6?u`L(Dg0r&hLu!kjrmZ;vzX${h5&e(fbRczE>?ivc992 zd|fYdw(c7uyz0}A2yVmauJ%NA4WnL7JfkP@cHpFXM>{*#fs+b)bnZuP`KL;h*IS#; z_!XhC_oRzgk-swFbN*qT@|Q9xL96evD;}OclgY@gPaD`pG5-h=?rsO#$m;%zjU-jS z{g+B~J@Brzi0y5P{yhFG_HN?8R3ds2fKwCWXBWVUC&u8np+)Yi;t0J^>?%H@jekj} z=q|`&19E?Hm)31;fOdaP0bOKJR7!mi06E7o)_R9OE1b<%&d7-tLN|TB1F!s zT~QGr6s2mSG*#myoQpnrbG=G^F6>E&}} zZZ-zwFq{C$=NR=hd7oDEn5yC1`r~{hUT46KEC(+tal#)l*D^piEo|C3_s8FAz!xv+ zQhg|Wq;)Bk>fKE%<;#bt0(_guU-mjP+DDcETjUpN4q_eNGc?qiKU2`H73v!J1^FI`i=@lQ)dNaN=pd z8;LzV@b2Y4UCJeIPVtPPhF$6|_Wi()d@&fRq>%SAy!*3c+jiVoeA!2;7K%M(%w6My z(B(X%W=B?u{M6yEmw-N1j_NWq@w^%Js3KcJiK-8-Ty&sZmteabS<#eVX};*H-VT6R z`3|VNyA+3J)wW;%*$@-7(QM=}mY_i3vYnadN>R*x^yinlf(NiiNW!HpU2ffdN_%Xn zUW6m-uCAd$uBjpGE&zsh=#uCxHzqXFm)kimXya$eZ38zS!V#_`GMG}$?xBf)7BxKJNE%1E+d~fXAD^VzHFXjt<95+Dv;yU zVRbi)%?2Fox`0(r1ibasn-VzL{^g&P0#aI?v0r?`l{xR`F;f>+uipmSG+Z7``pE%Zy_NY{~`d&uvxUm-iF( zzgu;2npMKRNy2ye&2%sfp%s6BOF6xeP=oSKgxZ9KRmDyKR8VthsEefpMbAtxE7cQ7eM%gGr( z)m+3Xl2+=^1Ij!i`!vory=8^D=hCtq40`IKi~Fo^RzxRgFPOh_*58aZzO%hxC27Q} z2xql7glkUgBzBulM*o1!ImbS^Z3c5y*IVW&<|wyT=gAHFBA6hn9$+Ubdo!j{WXf}k zJP&9s%7dE{`@t>peAcRh?-(y_Z)KeHt53(1C|Z7O#-=6kvz8yZQrDOM5N($o@Py*I z9cIpsJ@=@TSX1U_t2JfOJjedypm=^B#3Nd!UtO#G6H+-b&s}E=WR&YSrQ1s93D7Q9 zYY~{9DOqxt~NLI+w;{~l2f2)P!+9HWpM{gc+`(R*cb6Z#qQcO73VAhSIU8wAKCK0{f3@D4 z?CUrGjaI+wBNN8no+sqr>Z_*|ex2mEYSttA)(G$Oc5f^ZHyY9PBrfV~E<+xd9=mR{`_Udi$_nn3T6N$`Q}S zo@WHM>z1(Zm#Xum`-A+XO_eA{cO}Xny0#G&Z+E-ir(1QOCIa7@w_CruIJXD%sggE%H++iamqw)k$ zmhcGylqK+}G5YAw1jyl7+#Rmxof`9y{Leyi_Hk8}(Tb2+08F+PD@qLSJEvlq0>1m0ce< zae^+%SRbuB9NIZNX%v@jd=mh;bon#A23FJ8ztvaBCJ2~%LZj{nV3xiPNYhsIw*ZtS zHy+#gt}7b#Lax+D-w_$cn!bF%tlffRt^ZXgScEG?LihVvfr?~qiaxF_sgFz?jA~0k zi#qO{WV+G>7}wgE6g@&p^6`{oWlV>A*wNnH{2z_zCE`&jYJ`yF<37jAplw5s*9PX7 zB%`MDbCTo`E~y7?wl)BK>DKgYZGTv;7QkMbrk*l9f^VLMq2@IMyd!DC7-Q`_!4J;m zlZB(C;Zk|l+me>@IUzoa3l$q>+r0;*TE55Yj~7Mv<=9f&#~Yn!0RwM|4B`Ay9W#V3`D=H>P#mx?Ob{rPs3 zKDd-{$)fP}b`_hY_bo58_%vdj?i!DCJ-zIZ$w{5*o9qX!lT9WzPU@kOXzAk3NKK@4aO+r8tMr7|t2a9G2YEcK+LkuixN8s??-xSB{2qUDTD zy%9NtH)i#P*o_w66@T2Rx@L3jcMBc`)Lf#$7A4Niz`F;Oj47^^I?epsDYhwe8~AxL zG;J6$8Z8^Pjg60Lgrd+aS-u`E8~(hyDExgl#aH{~hZC^mapqq38=qf|tf&i<8Gjy{ zUq4(8$lQEk4=QRhw+^`TkeL99d5?@4m6Uk^IUVrVI8`7PxKX(z!+onk2CK>+Zemhk z-As^XB0p$s;c-|x(Dh$94x4`JpT=`WoW^Mo8^I(XaZaN~Vzhnzj0ME%KV^;o-1|quZmy;=cjsHnpqVl!=FI`eIo1%8#krx zB2>0x4A^I3N>xth!xc?1@KH1ah2Aq`Hl%CBXsonPVzLuZk&RYmLX?ROBtG=>dEoDn z2N=*94cEVAz4M)O88h(=wp@Q zocI7>mSg~!B?{v&A$KtpG8KTi2Y zjnp#S3IVny3V#I=>%txD*zBF@C~@-9Lj5|kE|~vy%v3MJo)ThBkB*BW1jj7VVkI}d zY<`o#EN8~by816^=vX3iIMAxRQWh$68??q|NX;1Z#%z3Z9)(#>wOLQab7tL(pF{t# z&Seet3vRH~{lX_@feQ=o-BM<(Mk-8c>WJsxsZ#-Gg>K+$qVgE0GURqg3C(HVf+_`7 ze~oz)geyYvULgH>}p^&{<;uXq*kAHCo0OF zRv$N0YRxa0TGCG}^z&C2n)$m4cQv++a-sP;-L>*C>1d#lK!u5PhiG59(XUE0id=(2 zXF6O<2{PW;DiK~o&{W)?Wpr_@Jey&vdMvcC2uuFnOwKZzZbLJU86S(2{3U6O> zK`om25E3QXdHJ6;C+p-5cJFp`r_+Wo>_g4&YX1${P3~%evG+H)JF~JE{~EcX_7z*M z5mZ@m&zw<^Plhw`FqK33>j+kgn94&z?A%(h+>iv^Z~|&N12d_MnA(-f z89{da!EGN#=E_IfED1!*breGd45lUo_8(@+-_p&=l!!NP&UQxm-k9wa0rE-{w=@pp zQ7l9NPSdDZV+8xLJwm{!56)QLj;VCWq8|pmlI<#Cat9hePMo~^z)%_TF0Ayx!5PU738lr$ zpOjUF6?}6vix4GxQ7X4gKz32Pf&Fg8Z-|b;{|h@7k)d2cBwX+}>PjsTE@6}JAJ{>e z)dB+{XEII}>%*Xk{bAv@lv%d2zxLt-q}7W121+4`rqhb)$P_f8iTIB;1lE1Yr_H~b z38$*=v<0!P1MQ%kZcV(c$Pk)`HxONIWsx}(`ca_w#TC%gK4 z=RVT4zlB>OOC(;mj}!d%sF`T3SDyP*9-VB205^pIs<|7s^!0UTQ z8Hj5p1diTD58JdMo&-oDBB?3E8VuYPLt83U0hFb+EQ8jB4Dn{0vo7z{u%!cGqLAO6 z1;R*TVUDhe=5q>$p#{6EY^kK?1PQkF7%cS5NxPsJ0Ls#K+AgS|Jwm>_TqQf2slb1f zCAu;3!-)@B!`sl!nU0Mp+AstbxZ-u?|?3OofxW|Dj26RxoQSZ5Re%mgWG0g#7;m z2_tkfoPa2T36q%~gpGCRhSC4CN0BOeky&oc=~EI|b3 z$-7tmwH(NF?Dvfk<}d^FPee&=Dqc?zaYg#_F;;-o~O!C6>y3X}&)x zBWK8eSp@6<#gL#8SD1uCqd{!5g0(zG;F@2pplOhGs-&Zt!joK%Ql%3Zp9klN~fXtu9B)|p1tj2~BwA9>>i z!bvdy4gCH0bYzQ33_})~rvczBMI8R4M!GY!uaH^Ol&GfZYg;}lRGDlt=m+({l4Qk7 z%)6DK*qq_gp>SvinU-~vcWxJI4e5MIc;RW)E;Ah&ccp z2_vCWIAp_b@IJ_6l^RUhK_<5GMte{phmK{3&>@Ej@M9&RhYP~@nAB~&evSUZOw@nP zwhBl3BtssZ6wd&#B|7#(J>vNuvUxfWs3Zj57!rrdrC!Pq?>%nLF=NKnnX5e$?x5V^ z0V?>#is?O78G2uQjFmzA3WB|7B8zU?;0-v2P0OtQt6~h>mO%h)DR@K9u=R^Ncms!F z^BSN?kb`acFl^=NJ%5z|{`DOIP$Y~R4}=W}T!xqaIKQGcC`+5&y$vODoLebv@S{}BW>*>+bNzV!6i zSbKUNzW44N>&RkbZK?T>vg9BSl~^Xi@6x67mvF#<#}0UL8}_RcEEiZEZ@?XuzYSzC z=PxK?EVw#1ui1pZruTKkxY!6LH0)yUk%#0psY~A(gbJ zDHwJEL10b+0>^qvcJ{xvOR-y$ky-hbym+Mm`V=;_RZuyv;9?ExGt*TRo*+1>vXhqX zE5{uz^Nj(&f$zr_HqGU?9Dv(5+iE%emQHg_;_Bymgzb^xbjmakt)>#cuH8iEU)0aH zw@FxiaP}wVDi1fum|i6SOQi^mcCDMXL{nwZks4b>wUeUUp@D741OCyw*iR6K%{!KhaZNh8 z!}CTcuAci5TkY$Q7P3mKxL&x{T&)LZr%a>mwoVhcRthNXlTJC&EG@K&tFmOC9cGH) z2t7X!s0P;H3T2l`YaWn2*!SFx!+*lp_)NA3sVn(ym<1(C*_5)!s&=&AZ*SqO+6D^t zQx00pFAM#gR>R9N2cmk}+8m|iiPLL*f^co2H|Q$PSqv+Yf;WrNp#;v2tHDF&~bo zW-G3=?(-qot@(iUTs-B`{VW=k{G_Ve3e23LK9I5IqwBz|7W_e;^2w}gn=2%B$h16Z z-{!Ivbt?9^Ngj`q*j$oDg<>EhYQ0F=XzVVH+4J?b{QR6x&)!^l06AXiK^oF|qUZ!+ zo!<-{N7YKtzY~)_s?cJI+#_A7_H;G!WoMf_ReKz&waxqqn=hzCKyG_uvzCBB^0FWQ z$v)1ef#18nq&l;+NgpI}?R=D7omZc}-`FBM?_khg8U1I;!R!a0RoKE@N=sA72mQx_<;%LL zMCR>WSy>Ca%g9S)HquCy^X&@cEh-spo`#qA`(cPhwY}onym#fw=J6;g>v<`=Wq$4r zLJ^*urtsy`w&bmOT~h9b=_bmtDGGl3=V>?(1o}))%ZB}T%bX+~c?b;blg%TS#P;vnvtVe{O>a$}8>yhTv~qlrp$Hvsu=xSrtD$Lgsc zSeH(KW9?i6k#Q|D-7vS4Y6Y%nw`SfG+Y}PuW8LW1`;E-xx^bF5X;F}3(=s2zI%S3U zn31HbR&O;BB+BaP;h=qG9uYO{-PEhEn-IK~y z6X4)F&QH)i9?{T?6z7lAx-<5e5>qyWNYAfNb&BurQLvO_w{Ws=^yR*b0RZfSY>3ad z@~-Lg6%(}Qr=Sc@_m^)c$_@-jOZEMb-NEZiSP!V}KBm2(qoU9X4lqpi{5w_mFb?q_Lw% zQ@6=D=6Q;CtFy!bH0Aa1_*}P5w0u|l^HS5nv^>d9Q0CXUBqqyAiCacwDfeEg+vVn0 z=aENY<3y(}tN;w0xGy$&^k; zPGK^hEz0xi4^jEeb~cVlFpRSPgK*JG5On!l_2pE?N21bA6n1vgw<}8%LAArrKr-Wb z6-COm+DmnwZIDmvf%quFc}235{@@`lDrSz4bFU{JHnE~?l$-gZgQ?mx>|H)Sy8kdRL-}%6-QCS8E$cKXE3jDS;C6lh6b=g`zA(zU7{MJ)#^yJM*C$P+@d8F?h z4@LNH{Ca%DKE8*e+uz0p4~2qCeb;>HN*gC75uoKNJcG|;_|l)O#3Mvl-^YR#oAm?iiNNNx!&-!E2`@IvsP5Gj*-oy@J|MW z6@u2;3oB^Kl%-)o&QWAccJz79=iKj~wVasqlng01zu-eW*eP?DA2RHz$5uZhp8hl6 z>$i<{yh`T}mAj}L(DlKr7BlZ-o5?AS#%r_V~ydK)~>yF=MVwWQpOQd zzbrq6hdRU3g~eh&%yZsSWtlT%r>gMyk(B{T+bO}al*v3b>Z_}b-F~_om8)4^uFU(a zu0YX5qi51y+@-Ynb#|Ego5hWy-RETKdV z%`D1O`^905HTo`6M{3q;!1*Mzq>jT?7^UGHr1`iSj&+ud|K7-m8wa zT2JM6sbJCms(bv#_tSsf#bny~s5g8+8$ruf2h}HKe0syWHBCW?<{Q80sgp&GqAj>O zq05OXEQJ-b%i7QGM>wrU>{-ktGR4|>?h2;@4`+S(?!C|Y%?#JBl$3^6* zqzB8X0ONocI_mZN<|0H`10=>?;Z0Rvo_tyh|j- zkA_#giFf(N9Yj$~SCRqiu4mTfk?rDk^4nqXyOWwP{^gq0=cvD@#;-HsyK@phpr`%1 zDd>mSw;yk}C-)`g?sZ2bBXa(oMKU^WLd~Sxe@3e72Z=*v`6Y)i7 zhiHw2Z2tERYTyGIAt50pCoLxdDFG!X0Vh1qpjhyT$cX5mh}iJ3u*lHx@bJ)J2wCjQ z0@_B*KGULnJ_)Zr+EZe7graS_*Xs|Wc3+`bRwMH4;qnj0q{HobI4MuJq_d#a_At>> zdq{_%gq&_>UUSnEEM-5c%ej*RIepK;bkB2#LM&>*1pP9+V+nTX_Vd6A|f8W%NXjgbf7}7RjZ`DatFd0FVUo@&{LxdVB zT~m};;e%Lzhc|z_Rv3cj)se~z0f{1)QV_H#h6;;a0rwY{r!hc7k`*KcYNin!cxyM`)+U8G%L_=72>_@F!q6VPNUldxnFih&gznU@}j^GOP?GxstV`-&2RHAQ_MC zqX{sfmT7+ogUUawI?nck=1;lU+eMJ+t+W)O*bH;Z=Yri`O+r+8AE&n#Yyi`UF zHj*yb3B40QMdHO!ZN!he9uFHE(w6LsHo0Q{E(LwZk0Zw)ri4t@8oEXSB`RkKSgp(T z;W*U?#+iB=jYYFE2m@OOWkUfQJEBY!AY=5*Y-_I0YIY&DxjP;iJ2x@t9NC+F0n(|T zYO=p@M~7vU#t#lV7EMb3htxp`<^I6X=ga#T)B8IR%xR8Fnb%^z+D z5-q$MH%syZGiQU8kLq(^EYM1NDWfngw-8mq9B1c4l7N&V z7stuwAV-r*PeY^_kmhfZkM#dRMJAT2P9ZHFk(Ws|3nx7n3KubJ=1XD{DM&;nk?+V! zBx(rce?}#c3nW*%S&3u3cQWcn#es{T&C^2>m@xq<5Q`Tc#%!TrG!a{)is(2cW&Po9{qIT9?Bhg#^nh9)1)Z}^3@7&OE^S$=@toO!7jk;J z5JM#Qu>4m>K?c_3{FiWGT^9cpac4rR-;*nA#DQRorUtQ77trd)z;^EBtq~J_NXY8t z0UyQC*NouTT9mDJ6}#O90Ug?4Zb8u39h9v%WxL%qhAoOiwy0bE0WaAI#GPzato|&G zGStHv5H?y;>&t1qp6O!|H1Hj8C!q0kY_H;tFp!ibk&0HoS5vRXn8d(gP(=ciNH@}f zSs9YmtNv)Sn)3gKLkN*VEBWmmTDq|C+}Ae)Fp8-ZlWGYaffs9R6 zO)C!W8e}xY0AelM)2uzdinI!|65e+gP?&Y0ZYZn=sMWAG)vm3#F_Z`r0c z-An^INX_}PmJZ>BUjSik0 zB4<7{sLppkiSLN7zvK|0%?iQHN9;Nrz>2(zu6!yQbPmMFAoPi<^U-&P1Y>&tQHvE1 zgtL!31!5f(A%wyRiV#Mv{Y|TApno5zNNydOksn9dKl<$7?5l!S3uS4rK#P)x%x67% zdP)iXIGq|{$7q4cD)Kc~pAl!mMm7O24%*D$a!4MnlF$2BUkf8@i>b=~HH`wdd9X>H zt`?P&VXSwV;8mai4ycpnU8)geBX3?BL&(x75M>e9YY3(}*R0vl8;d&L075y9M#;d1 zxc^)j_8_&ElgVJ{;@?sb6qP>|RU8ym7!*|+R0S2XDjR7fit^<9U&|scpeiwbH82ce z>Z)vG%P!!}iV>*P@z$Ft8)em_UkvnD-J7U;Ut^f~bP=0is|OTbKBL0#=W7?}li5Pf z$5corH9tP97r+qm&+K5uO3#c!vqsF5{A`C3Sn$Fm<0tt2C;Ii{(or)6|7NHaGjIk^ z~p8NMAc{RUhmn&05+!XVgn;?i=zI$!~{3bZz%cs{?TfL z0$^Y-g0xH!xAYn|nSUbbWlDDEgvGXo(6f4;<$q)82@))W81@XLQ7%%+4Wy;@r4t!R zNf}AAO{Jv`r4t!Qv-?ECFO7pQQM8H4gkP!CR+P2tR5j^rZY2&IS4!&(16|G8WzdBY zIG>MNjk%_2McK20Q^hVa=?H zogRzn5EszH&a_3BxdoS{3g1mcm#HEv)m)6bhDXz9Mm2j!HAhE1v*MaUkOcN)oI>+Sq<1( zp>ZeCx}|L1MOY0`YXGY;fYF^m>x`yx=h3P(1z^W~hf^C$o6Udvu~Ff9KU85GOYEV^X-zkrwudn(HeAU;BN zYnXFf4?DM~Uod>6;2U9m@bO7s#CQuvL$a4>ViE!zWh7*3t)C+w@{(Z2N@lOBq3V?dS&a#(d+{-rX z|4N2>VMe~jB;9O~Zaqo0?=kBC%7%L3ma6z6eR$s!uwcP>%WZroLW@kNqC*7tooBjb zGTPn$ax{VNEbMdFKsZ1JKAKL#}KdbRv*a%uuq`3k5Jw*h5eky zcoiDy&588y!2t1N0>7pQ2mIo}W)Aa1mN4`>V1cNxNBNhxw$|xmhP8Pq39-mZ!Nuem z0-<^J`C?s6H7@4DK*wX1JF(9Tadb{&{kZ_#f){>EdZfVgLHx*pGJ;H_vOKhBnbhuF zIXEzOWMlMGBz_>MXJDjpYRjr>!niKhzfE(YFTQX3cJTYkAV##~gsh34w<$2{Aw-lB zyS3xzj+{wL2h2$#`)E!qPj%|SPYz18wn+PX2ip8>aIL+0XgDU=j^IrwLtyYtQS6n4 z!=P!Tr*xPas@6k{)|wWFw6$rhjt{$WTINJzRzxktL@k6wEyF}BxixUJvjMRB8IlEL zz0K>VR{>%%bD}XzqIPnkH~gYEqh&F&uOE<5+H>Gs76%n}dhhMp(Y+Js7$GvDE5fT9 z4_Gs2`IRttas1xIj+%)i4(#Lc5-w`85%sp2XeJce_K7YhjpP@777EJ=3;C7kB_bM> zfMJJbVnIFbj%MPcp7=C00R^YXcR8|06GoH+ukQz(n+^#P8&+Z!iL#dU4>h&VAPs>$ z>r3bJfk!SCnshjJ^S>Nx~iyj6=*{gR<$>iDdE^ct%5r z#nK7#a$uBM2J}LkdrkA4O8yW6NAO+x~FFZS%)k{0;Wx*E{a@tcNiOlDAjN`p)H+i=h zGC6-$XY`Z=4W7ov@vAASbWGS+wiExIDG#BKp9{B#PDK&ty_&f*pN;Ppgp>1_fogL2 z-!s+RPamZtv3{Q)yK6;QmVA?HU(P`@e#JN^5 zZ}X+DrP+Q*j|{jw2r``8;5s8s)~}XG<#rC$zOs0mzK{1OaC0tW-&HmT3LinDIng+O ztG6AV*q+3ml#)|oY@=_6LQPwJC9E4(VU=(AVwo9$bPlwlBL4jSn4-F061oaLyMh^* zV>x;0bFlCgO6V^dlfb*C*V<=|WPZB4fx=5z zFo>i0&hr9y{nJt%((^Mo)8N~YdU(^*&r$rdbLSgapuai~mj(QA=tc6w*{e_!*fU3Q zw|<0n_Ib7m%>H2LsWB)W;Rc2hTi+z%&pn9+rsdh#R-K| z7=uc~!1zx^I0`2i7{&J&+0lw0`H`E%4YmRY`9>#G@pm!{udm6~5cUU6<*prVD2Drr zYF~*=bnQ(2`)fqXyid?GwC@t#MojhV+KAZJrDfV(V9ZQamt5*jTY#Rs!}Bu%4%O@a z_5)`l_W3~{;{+2|&)psNg^MTEr7_q~lA=1xXKqT4;?v>#=N<32@e^|PJ~v*DUb?Hn z02*BnS=CqoQ%mP(Sz8)W#I)LNPLyrKe*a(SZ_$O41fplJHxV`_U)Z;$5jXqIF^szD zi3ue$zYd#}6oj3}Gq#YNhkL3KUoN-34&Cq1ULOm>cG|bQ&5MR>&o>x=OuPKPJjHIO zNTUCe8PCuAqw1zcQujOPx@x5>?f6K~Vdq@AAt}aA=}CWaGE0}&Mr^#0)o7-86;4I+OIr-y^JOrH77E=S9qZ-`u&-5^ zAKs*%ozUUHU5KJumLzC%Ms};cstib4$l2iTOP$f@huj0CSE~m;L6GIcsw$BU0=Kh~ z*x_@eUTb@c+pdTA`a8+rAEA&EIp#|Rp()3Lf*1H_Ea^)xOL`i;c-aci|EKyzgRk%Bh3$IL3-r5V>mWx? zdNV8di*NgWVkxHAZ~TYpXA3X-M_K4q=uON(FR(~pX8d-kIg7XZX+LIKMoYO*POi-Z z)4FQ6X4J`2JL387f^7v0t)V#}X2WfOnY%JTuV>Ovlym>!q4?U$wnh0i*)7J|+ejGf zNBY>TLx2*86d2Q^3u;!F3~ z#^q5AZuYw)MSC*t_V-5WlWupEpL)V7Lf7ZnZh@(GOTQhWAtFMg*C7 z-iJfE+q)9Eb-qw%kGEqyzq=&%)rp?2uiG35_*@^`p_nP!=Eu7|wWH?=+nK|RM=aDQ zDlEIl`NMN3u8&s_KWQ6X#?1*krbyj}qLLWCjgA)*nDrh@)E|fTcK(-|m47Qp-!C6( z{XQ=>L@NWbi=%gMcij23be3;DD|P|4Kk0T==`H$_(0zM{_j5Viz&}uZ-v!=oMN%Eb zNIz}}q%DBEALwU&GOj<{4+Da$N4RHV~V>th&pnILO}@~5So8fs|>_~KXz>R z@4J$IH!%(px{=6ln+pzAWweO#DgwmAdUA2$L@MP{ zddW4=g_aI=Dm(MHPiOtiXy=b3>+}bvn%i*Wx%!EePHo z;h}`VWh_gM7E;j;Zt)_{VxDUYTPRee15w>V!qq{(!hKNtWzn0)IgZ;Y1TI4F$$N?!Fd3pQ8l;guz}GKYI;1Fv zz-%;}l+7Qwqm#OCS5P#6FUn42I;m~~lvamp^XIKo+ggq$>OR5^xRk#Op{M;+0xO5w9@6SJ;YJ$4@lA|C^}GyJrvE3iK4vXnL$gn z{)sDRxy7YUUPp+?SOnGjlP>n{DOjerlc$(EjLmwnQBzQGlY4C6k%0W5GJ;Qg6*B4K5Xx0mgx+n|L@6h6YK*@hWG*9Od5iD@f<(pU)ECj^|J&=Dp>8VM@)-WS&mp6xjvJH^hd7+;z76 z3XR$*V)R6S70%URNdz*@IZt-1fYiO9_2HATl6{&BtodjWeizA&HHq6|F?O+V4aC~z zUUW8vW&K_LiX;=4>~S-?D+@&VMQ%S4n-)>0tSFBXDfgc{Yz_2;EH^_noOsLsUPLxHgw1W zFNYv$nvtUkI7Y?hv}@!c^qCj{Wm~FRzbN@r@4UIOqu2VLcB;fz<6up-?O&3>$WLkWKW^HRgN9Y>vNA3?psm_>$&U&vXAh0SKq>ny^nw zgDV`TFF_=Q<6PX);^HqBw>P>&jXyKr@j8uXcIxfC%<#5*Z{cTpx1T zr7Z}RlMFq9M*Z>?zMOExbr)>Xd>*}pdC#Q~QL5{H>9&l2!vwRqIVBBZLiQOHN;O^> z;86L1kcT8;SPoCAE5cH#Rdq|D#LP$z>R+YNEL$1w9ea&`1720nYCza)051-g^QjrbS|{0X>b@cVVFd&JTk8VG!3|lFhLgdgKhgE zW55~Z6{jton0PTm=Yq0P2O~PUtzun1IA@ch}<4{S%Rrco;M@J?9h8U{2 zE8o-C_Y2oSbC+A6mMRExqf~SkC-CvH&w(a3gszd3E&ibVPV{V#@%#{ zZNN322sI12*w-V}xm=5RhmTJ8&HOU z0EjmP|9_NW0MXU|1myaEZNqc|nlS&3|0%G#(sENr)>uIZ4Xt~r4uzu@x1(hGn@<&h zL9MWY1A-}GD4xm%wOSh(wzX23mH zT{t}|+ao5~s+!G{QZk1cT4>r_6e6j3CSO;orYZZc5j}<7O_>ON-!siE@C_J)Vy5$t zThE_saDbgUH>lPWOE>zn08^3)Pmg|nB_-{~tFtR6NigAlqIkF!aOlXD+*LUaE1dyB zfH9yZqVBQ~ZA`k8xUeyxevpjCf5vmeVB8ST5XS%)$FmR(l&&mbl2*<2j(-5Nz?r+a z*#t{Wzk-sdj+2U3>zAA3R}KpeNWqzwTXx2ON=vg))8v$8AI8lr)>_W~OD8Q(4k){# zC-_%3AXiEyT^d5VctXZZ5BMxd7eqP})p#W<$Xu-TPFk1xkmhQj(Mo3*vlySBeN(y` z&nR;&uEgMqxF9ze*QXNMmA!FO}9?6u0S@ zF<)Vr=b{0>YPW3`cBjal5(lTp)C9DDm;tA*ed5|QQKkskd)2(#k8hBPSibRv1T?ZJX>>?UC zPto+iV}pp?49aV(MG@mm>C7ldv15z|?&_`eWf$~GylO1zMU2X`af{NRxENy#`q+Rb zOi)Kb(iETx1H=f9aFt&f6kJc56jr=A%LFE%<6y^T!c(IAn-VPs-+k&)H0eDiB+gx3 z`#u#*@D&qw6lTgZ9ktrg5>ej7Y~ftjJ|HtJP(5@D2i`M4(lZ=v3XCLKUJphrpBf*$4e+$2CZe2^z+K#lGcf2mSS_~D2d~q&|N-* zvZSb3d3v}7w=0eHmappK3%A!hdIvTB+uZm$n5FvQ7Jw#!}BA^K)9o$nKLaMMJ2VzsIO5X^+Aw95R z{4bbIh_snHCy6PNgRo5xlRAnG{IYBmFN$1nezzDdJ^KX zQ#N7QN_rAa^!tDs*G&>{a$(vL(Ze>1rnVICHkWw|PW{yan)as8J3fd8?gnWl+!4~k zXPW9Sq3C092-%%Ri>S|^+KyZ@pn^0he$eWJHK0ljYYx!(RHfe-oK&E9iUV|EPApzs zzN7}|jJi$JML>#(y6Tk+O09XqAwZ_m<)d}+4}Ft^p-U+!wqTSd&sHJo%});mooSGK zez7$8tJ|SYE}kl6<8zY~KpZ=nzYcj!B0=5h=@K@1Z9CGLVD6z{S-nUP$T9?O_!~Psq|*j-dsgAfOW@K-;p}F; zWWl5KuZ03V(^DbHW!!TEo(S}6dH16}RtLEi=zH#meK?~$iuJQMB0n;QZ70#+CZ)h0 z0HqhT00zB?VQ(Ug2fhG?9q3_iGK_~V{YiuX2EO}YP{!8rLvCxiU7(wXzhs}sRi5aY zsLFq1pm)J~ikiVt6+l?&ZbgypDHGHKW=3+6E|F!XPEPqh@rkWHrt@&m&O05DPdjh4Nr71-cO~0cYWn zSI~x`Qp5H=fQAfWEl^$UMA~Q#0 z3ePOm!g|l|^!V=$gl3ke81Xeq3E;D);3{&P_c z=R`nr!QIb&kXJa76Uhn*3;f!#oNbO$>`~^ z+`HlyrBDM-ByXhiNUN0P$2Jr9>RB9!e9%uZyFD>Jr@w4VAH@?6P#&wCDKMv5tk!Jcvyp178;0vL;_l7 zxKq717QcG!lIfIwMf_55KNqy8qSbU}w`&|M1MYgt4}@ua13|8jA>%F2YtYaSj^QzC z!SQQyd^yl??@_i=kVz7NF@YNuIH%0WYr6BI5iu!XK!FLIgp{VvsphBsW7cMV*>j;Q5WsYA^SMNy;$J1%4bgv5qS5kp1q4Pf$Tsn z*Ke8;nS0(k2@j-n*-*kcJw4I@b9-rFNLssFTDyOI1}MTH78n)_DNiC57&c>|It`Yl zQi%?6Gf;k$hz@x(P@;t@)BzeZBI0ZxymuVB>xPTL&0sj|u+Hoc7nw?3iN7Vv^#V*G z-#%cZCltjryHPAOf-M|IS5|BD^xL*-ss6xqAYr?r>K>~>jxnQ8dwtodXE93)ctH z3_#`r-$TSV{enq>%@=|q5;=(_b7m(ywI7V=%QBeECuw^|9B+~(t1_&HLm?eq(63h* zfDMwB>@(Oti`FmUIzM$D5ia41_Pq7t^F;vNX=9gXXE$)mP3Q#ao8K<@&)`?hjD-RlHPT{LC6jPK#9cUlP5+cshcyq_+Y%@^nK? z`Ex=ZB<*IY9>S#W0-bl2j>nwZM=SMr7lgcffs;D+XbYBBgA}?T}L~Gi%Pw zkMGA`RrOZwwf3)Cdq3BE-`5a^3*C@5{l=JG%cp<(b?C0ME^IR-{O*$+Y_ljJ@`BcX z8H!=k9k%y2cW)gu>@QoPoK;3poc#P9_C<|S-HuWD-n47JjO|u)nW(ODK7qXggZ+u_| z*#>e#XKpU*3wL({H&m|M_DoU+7h!+`wQs)%i7Y;tZ!1d%ARNz$BZatQ1jdsECc{$_ zp~HRUvwRT1S9%s|8fg6+G>B^b-U$i^#>^$(vGgD$ zzdCb!Kq&`^!dUd;ck%+%zyG2zU>ujCIZa+A-S={%-97|pWF(A~%J7|X^^UUDXFVM7p?&Dgirt zL)@*18yr#CsI7;9@SER8T#KPJp`<`dE;*!5I-2t?OMUd6?q%4X=a2M$M&o+T2s-h(AL8*M-C|k%p%ns&Fr>VvwmL{&{37`vMr*j^T-qsdRYT?;SpHQ+1TXGwVa28CY^bl$2;ToT@G!Q2pfZP=ReEI zy+wy!CvfP?{>1XRCaCS-s4BY?fwpfS-rwlb*+!n;c5GtFats~$JR-Y4J&+VC{kG|^ z0=(Wtx4WC0;1Rz4k|jk_K}B9Y4NZlV%_cqRgOB%uKwUpfDxxeBa&8e|I$I>!cG0De zRj4ztN=$JOlCp~42*dlx!f0%~t>wEdhF6}eoc z^!#KgllaGT(q!wYNAOGM_*WRHEedk3pzOjqiboV%+rCO+hwk)GL+&5P71UJ3TaWJ+ zmm6rup|tCsBdg2}DK|1_sSMTY&<2Z>KP>ao@Q8I(wOh)K^CbuT(A#^~(T_u2_Px27 z_1?`5bhatq!uSUVR^TaVwtQYd>CYdf+-4f$!O#yfIiB30bGhql3wCC}3EI|IjW$UK zg$ijEiybZG1%CN3S**_Un}(9tacdL4X%or3RTM0VWRtQmbG08qd4ne*CUH^_6k9+P(yh1wof zEZ$Cci<TS^3r)M*!2EHS4^zIz&4Xhw zgA~M&_44Fnd|LRLWr6C2t~DiPaqkl5JRed>uYUbID&EcBi+WOWuV{ndsN->7cT`Op z_PoAKZ*B`I?=!uRLez$rxDE0<>2v0tWVZU|6YX`~L+G%BGyo>TSQy6u-8oVo1zF4B z-J9g_HgD~D9`eBa3UL*Wb$jebyP(o_vgf^2WA~ zj%~DITM!oBX`Ck3v8}s##YtEQ(30iS%V*l`>J3JA^%H@GE`OKn_spB#X@zoaK35({WB5&}N^V?NVu$?ntFPLBx!IoU{ecDI-DYU|3bcKhz8hjE)wot?QJ&Az!WTqfJD zl#@Zd15v(jb9B0e@hbY^?mfQVG-xra1gl*#a6-R=>?XU_L(-q3g`SRidDv`u%Ga+f z*iZ0?7qgRBf9_X5ba7Vyy2;nR9Y6mhh5YQd%I8=34jx1NGkP}xeA2OWPaCUfbo-6L z{~0TR&ERc6T;ce^x*C2A_h6jf;qaf`EvLh=GfY zf{2WRjf@Dye3geQmxQDLaabb{)J9(Rw3X^KueJRfp$}8qeF^K>m#J!$SF}F+gQH&@ zVa5Pw=Zw`!wsnesN^PzmPqe~%a&zE^^H5w@4Klo1zt`sAA9*Qvj(w=5n!l!Fix>NPg zc$3Oa@Nh?n_^fC)^CK3u1jdvH>t)0W6>OK@yv*K$Y73dwvdnZn8hsM4O1m)?;46bx zBT93)%aRmZNY-900SygSPJ_ye6OtrJAxTRk(Td>Aje$SVX685-e;Ze{sghljmbzp- z!P`O)k*4N2mc?mZYPIsz+nU*K7gwdJlJ$~QYGusF;HYvqFl8Uw!ASMZWY zOU1XgaKKL|DIMzwng6ueB;7AB^M9aD34U*jH#Nb0Ph{JwJ{%o0_3gjbeuMunvM>u! zeehaCuHsIZKR1__cm6 z>>xEq0fUG`iC;a^K&bVej&T5_254Cv|B9N7h?GHh7LMbdDCnd+A&YvrbkYdYU zEQS3c9Tg0nNLO0)AQcn~9Zv@lM34!s&zTC=I+SygO2ax9@Ti^^=xr@*`2ED#&rCO2 z5@0^2H)Nz_4r>5Gp&(5m&0(ly2FEdS5E$l1MkjaVmt@38c$oTQr%wiP_*|+KOZdk+ z1w<7CI+Ga4hLO!lS`n+Cd+4o*oR4cBS*>B-`LCU2Mq)d$d zM+!52CIVzP2BZxKBn^GY9jnmS82pyxH{z0uwQZ*7bEsH?c15`6+0dvEpc*M`!iAFy zWFjMQ>Y%})ezr4GJIRnApvI9gjv|(0CXOWeTSiR2ETJ@YXf5)E!qZY^8!1IbuoA60 zz-xb!B;=*}AuP}n5Y{C5A=MeVG;GEiFxTZ*`GY@upbr1qT9cd@B{2_xKK>z_R>44> z0pf^1biv<`fiW`L8FCRf^qq*Nk}5WSC{Z@%1H-Z${!L2n-@_E#yMd*w5sa9RMh}$+ z)OHkLqcEd=BrG&GUq>>k&ygSUL!@-x7?TfyfLm>8p(y+ET>OoQ!8WhcZ882GZ7Xo#vEwZNG)(;+df5$Q9R31Z|-0uc8;G26Ju~r zjx%N%Kh1G?WbMnm8P157p`p2g9oK?xEb<2ljvEPS3BswD?9y0Oa!434A(;weq)gV= zalaWuh3%X&E6qoP#zhcGWM7Z=;Dj03@;T}$=qwohNyrB+4Z;BAm|WLS#QCS@U1Vd> z)>){*-~6Y;6cl^_--mLMN(dTX$0QKM1Tbi!-cNga10XasSeA4JxC&Z!DqYhOW+4Lg z@1IMEtw8}qb1A>bkn>d{DI{~{b7xQW%ruI4l9l6ag?C9`giae<32a#16)aVp;bltsg* z$DBI=?w6Z8$65jtM*#38A>BHVN!%8~D2sm|U!kCzC7OBqB*_9(18U4f2$BWQX-dCy zj>?6HD-4(e%nH*WJIU$JMr#hAO83%Mk5xTtpq~I>{yuCCet)Y{2S=8jj4Aa8x}C+&Og&k2EeJwJ?I^0;ag_&z8MP{^_e7G%K6zYRc}ZLke$ zhK6u=caV(`&OE`3fAAc4STPKI)n{F&2G>Pmm28`O zx*i4fkpJGsj^$4bhbHET$6e(A@n1b#W(WUzwz>!uq%-cX_A{O>r@*^*we)z8LAL+U z_>Q7ntr(BN|22?;((gzzgz4^}0$+^jg2k++zRlE@-@IV-gRe`+g;RhZL8PN%6 zKcax;!u_SPR)+x`RYT=Xs&`w0_$%IjpDLX6$Uk1;#)~?^%wZzsS)V7 zl`sz*A-E~K$+Z1M)^TcQ54}gM;Slp><*#~Gc9UxR2_4zCQ6v{agbkxmZr{*O zV(@0`yJ3W1;E8@2iExcaATGZqQzKsj*EBpxVv98bRPY-Gf1@Tdf#9)Yqks{J+`56#d@nN?D3|+yTyC+HTHQ_G$H|EN>>7K zS$0F7{fO5L)+3my6k2w}uDys?NYjbs4Ek-mAUg|AtwvU?3oB@cv-w{nxam=s3*DY<1)x zzIP=WL}bHpgH$7lV)jNl88^WvkHJJ z3<;YynnzXTcgz z5y>BD23JBf70fM9xK-zgV<=q1DHs}=4%KA6+v%DzmD3k00xPH{3QZh^WO7cn3#i=s zDcW0GJu9uiN1|*xSJW@&;cuvykho)}>2COSoWGNJDo^`wh`0lyLI4qy6D{lel+C8{+1QA;u+{zoQ-YXp=bBB^ke?o#^%| zZlZl$ZVOPfrO8Y(eJkxx2qKMu@*lV;l6a7^ z6<_*VWl*L4e??m+`Vw|L%KIOG=NaEkEaDyQ-5{7teBdWYniiL$tj&2@;Qpg%i-SQ! zRs{5c8(rYRT5lZ1tjCv)Yccm<(UwIVE64vz>;b||AR5%b<(HdE$NLvZ zoo-RNopfT+&KOooz+z%!uchI$M)2%DeFQLgDm)gPRhxhOObZB-t_E^O#NgcH3L$lZ z*3A-OiA75#DLQT1x_r^k?hK3b#MIuPrn#EXtJSyh$*FrJK*D?-oF5Dh>=lYUP{zPLseQ(w!}<)L`-b=%wIER z>Qb4$(PhWcvXUxU8m#usSEuF`0CySz#m1zv<36q$T|4HhgY$Li#fRf-V3kAc%Q-`% zqLJ}oYcv5DyNn|`|9ZB5q@%*uU=EM~dbVD}4g$S|cB?Iin;P&m)A_sb^Pl5qx8q(x5u;~+(Ti*qy9e^>1y7B<_oUQ)guc97qUAihroNnyh0JHxg!E#C_ zxErtF>g&`NFmMXL%CY&6GyG5QhYLJB3UIUr&I!gjUrB>YDrofngXopaPR#E;8KmU5 zER186H)4mMv3U%H15&e2e0b1?N+f zr?Lj9&+?ZSrYn2>ucEE5P=w}o&z4UsWSj1rUFT?o=6BB)?w$TiRh|vpyWJ;jJe%kj zJCBKw^=ma8S$?1%(iO?jU?~<)3D}Y1QB$MK>B<75@`gZ+7k^jx?~S0uxYy2?-(I7T zr<9)KB2#!TN!-L{N*=+}U$}a{)i^7Wezt0y!`Qo;QUA0bD6Mga5*iT|JcSt2bKwZ|PQ6<59gKvT=GC)I@#Kq8nt8=L`k}a4gQ6;t?IBIv zAFLO&J|5?kXLr>;9+H#H6pk;Xm4V(kU%P;&t=W8R zCgLtXv`wk}%LB}vl>?~yZMdRgv{y2_s%SVh{C<8jST$AEzfQ+vcy6@U9YC zXea9M-7bm=r4b)cq^o7f+X<-Zs;gU^Fm@#+lLyf%JTMGnA7tHr@y>>Hb#+=%v*Fp8 zKb>8vQhRApcV3%ZSUr*S8EnKB;?-5da2q@Z78QCKQtbzDaNnDwN*q&TR#zPdFTIaF zh)$tzIihRl&%Le_P5|Ed7sOc({h;)swPQ&B6>g<&x@ZmI{d{|xqj8Yj zt`}-Y&A3)TlH#@U+zY2R^iU5|{a_oatH6omt{?3SwOz{x>htkAPTieyGZ7Qu9p#0L z*gOWHR8{F#lOj+^nJ3%}-e{SWb)OHBZ-KT$mh`62jyHkbp4Ny|o^|CfSLJR2jXHx3+03^z^W{6k{OLhLc2UTzZig!0D2{rBrl06@ zc+pp0rCSpMZ3hy~Ds9%_j)SLV{Iu{WbJD)@33@t9|O!bM=aMsAg zL$4`Ac8U}uH?I5{ z=s{#B5%zRe61SJuWpM8Xplxu__sz2C?W5akY#*47KJlHDKyT>_B(ylq4#x@hAKL37 z)*raKULju!0o8i@wl1?yL!a7Sx5^C;PviG5?(eSW`7dvJl1`|~?QG4Tt=JMtO7bDk z2_!r`j=>6UnvOL1%nS6<*F`+pZb=L(IObhsSHR3yv#QOsI^3eDyvEMNU`h&@!r_v4 znkpKHExLc(F=Dj&an6h_oWJuaEKESV+lx~j76S3>v$k5~1(o|xlZL%bOLce*X@p;t z6$FbBG{`yJ!WfoybsmC5i5Kx1sp_Yb>XowL=1n5B?c(1aHsSlsO-nwvJK5J5yW$j@ z;eU9w9C%nvH?@~vKUL(a*y=EvmPcZ z$>DjzQ4}?o7bI;b6g<7}5;^s(pF4}V5-%2oXbQ|MAuqIq3?A#?y!lbgg)0=Za7jjT z=`ucGn-edd1PySW9hi?@=T=Q0wpd6olDI8JwCpdiiQivJJxiRKjp#+YgUTof24`WS zbTq}a;VfOfj6EKg`s#`kA^Mbau$mzU;kY2NSmd*2q9P$UD5KC&kg z(N1eO7f9kP@aBFOeYIwmsnWJ9@lo0uCpf0_MBAWpG2#QE%-!P2BBaR~7Y+Nz-y((| z&2F9_GCf=9h;3`O72Y2;gFifah+{c*rFNY;oD*`m;&zsX+6&(#o4AkY*&WiA*{0Y^ zhViJToQk)fZ!^KM&U1H&biMzHOpV$+(Zy}*k&1q$n_IbJW&GSZ{S)h+mumBIu`0{p zsGZ6Xj(HV)gMhQ@Qlf~TdDsPsB1Jdfg)C#7Wv(Xn{XyVq@PlqXQPZnRTIfbmeK zl3^ZH?ok%W^(qGnyvVyt~#u^tRN*J<+Fc@9_`g=I^e% zhOQAQ^w8n!3uoX(-N-0wHo7ILyOnYb#bb^VAAxV$GrZ0oFY7UG7lEttp{t~Tca-%t7H_Fn+|ag;xnmRc-YxRvsjc!{9&={; zoy76=y4h{@r~|XH(#h)OZOp}QW%a$O4t~g@PyWd5L=6Q;)_c_&uDwnIpIu+1sNSE`E9IG8ux;Muj zLJ9h&gZ%Zw=y9dxwF_Bu_tuJOtPBN7Qk(IO1ytkCbxX|;*Ee*F+s5(E18Ext z8{JH|`MG`7G7-hwsx4rn3^yHG|=&WtAayHS) zDYxSNsWV8TtvZ?n+Wi3~DbafiMr>89TT*j*Ur?@qGiNSQQgXVBIU7 z?&3H*!)$xYi|?`klgBDYWzH8Ivf%-vC8Oxxy~c94ungMc`|?PykW&Ci*#Xuf%AG6- zhO)arw;CyHO-xKo%}vQo%HNclo0J=}ho3lDNQj6yxM(z?ae=`?!hs@zA)>;1)aCcr z&O@dxenOI-J(K6;T*-wvOfFB~{44p;(;Dw*WLEX0t*)t(0u1`*F*R$Ja5BIhW~5wE3}I9bnAA$RQ|PW& z%$H`>YihpPO@(Cwg4P3|WtA?N&JU>60=ZLeUIt_si+c$!djn00oztA0(Wu67rCfOo zic{zHW}o!>nzc1+jbEc1D1+9EUohrl?uqof!6Py#UIMv6oEJJHr z`zueqrFrFcaUO7z9-2z6v-ub_Rh3B-Br;n3D2pC7(KqhJ^!R~(=+)IG`>FM&yIQR0 zMb-Mi#baw==Rb!BXDG{)N%rdf<=fT8FFb}UocHbj#THrsY@xw_n~nV+TL36L{-3FZ z|0>e5a<uRad;(hBs1RhJ? zviDpc_lIsH6C3}c3&$@1q6?|!U5!LYK0cwtAxRC1uyW@jmY9V3tY_3xOomLS>Xo@Y zJwn0^t18k_OMN4Rm@4Iqbn;rH@nsA(YgJf@!l?RFj0Ggkv9KP8rg2PC*7W?1H2$?3 z4-hIteAZ3Mwlj2Sm9fnHMZYy5i1hWxILySUuu8{fLGWzCs0NKDSIS1u;&E;Ec-6%h5pDv8XqXS|4|9^sz}vG-pk#_A1pt3aLf!#Ox52KR49V|?Q` zqdCfmQ1FUNqH~z}%-Qtn)F$OY;Fa^93DW;5@UTi0{wMGls{prlm4us#7KU)=?!X8n z<11kRidIo~&>Cu545FY%pppyvu$}J1F%~1zee$@y^tIOe5_l9JEmj`Tm+jxO6 z_f%XBqna=>n)oO1fYkV9_*0zfx6~xvN;6t#tiDl+FhrlIZ`5E;Jcy$T1X3)8)0P?P z0-Z^Tu_K8hE==3cQ^YY5noliV)j9N1E0G4%RC$$vUMJYUV{$A5o%vF7O!R3|36z@d zj`aDDXgC4j2Q|NgVZ)UnJm3{EMaqAui&P2-&H1u1&}BIU80O>%f}F}IHMyBL!x*tL zJWv}OawRmDP-%z+q>RFx$+&xvrMEr)kP|lo8#FheD`ld^1Nb|*=V`@WZy2%YVJac0 z$90V-ewHw#H>P!&`NI+semzovkn2k%&=L(abZUQu*7=PY_%XzkSq_p@2mrW(SJZ4E z8sEy0KaV1xF1TyDTm*)10QjfXl2y`v+%Q1j(f`xj==0i6=l~slPTWu=MIl%N?v1OG zKU&0+&&MgMHAYdBDm6EjynsSGcF(P&3$DdgHq^aLz*1WH|)53$J7bl$#} zj&FF@Cx=EVWQD5)16IWspChVK0%v$8x2*&gXSh|LBTDJ|jBN^b+5P|{cb|(_VfZy= z);`NqcDPj{(OajiUuJ{`}=;S?M=#`{*0O|lDS~!~{J*sD*-Voj6i6%wT zc^z*J7UZ`Tc((*ua4ATWTQ;l0$ABkMGFV{vW7$uG{`m$Cswm0MJd6|o6;8Dj@+vNC zSZi9X!2igNS%c5NRO)eT{V=i9?3IE^DFmvoCE36_K523CD%ek=S7mJ!3Mnw`yi+#B zsQ(XnhlX8L*6^{nN;t|4oq@~rbd3oz{DCFn@au`jZ#y8?dPE^x)UkyZ`K53liD3ji ze1?xoI=QS{I0*V=>&GR67nv(KmKe@Zr>Yp&(S$ z_z2@g{)azm{14Te;tW^I3~quzDvs2jh^OnALB_lG)SHq_S2?nbb?>P(r|Ydir|TlQ z##w?2bq~y%TA{3V&7SCsR#m`Xf@i2ck(R%$5m$oWbghLTlxb8FPAW{4AkCrWXPAp) zFpcF7>5R75WePOh?!H1uui>*mINg4D4lC-R%K~3W-9~9w!3fg~!QE z`~di02L%;-Wq;VM><(s+zA2ZSQS|&n3bC+B_ZPDUa=AdmuG6^3+?wRBPv~3qq^{Fh z2i)(J*Q)@dkH8|U!&v~+jRSm0EVscl0J>y?0m-S zNcPN0jVXuQAB+bifp#shs=u)Ai{^3xODTr4_7I&%CNpeyIG9Qnz~2CS2PDAW(c=F| zV!EM;W~T{!WjEBxiMpZa`wqYVRpo$n&rk1K0u=3P$a56=DnxgrxRv5_Gvqmhea~(? zf!;y?eGvJIZYo8_UoO=xbph=)#o$JaiVFZsfNw^^Im|L%ddbT zmbk?e_B@BDcR?#E>NkRCU(%!e;c+>`Z71v-z$1?1G|FLQgahxamWC8gW&l6`G3H_hAqXSCtmfD`k zD%>`dR-e;TaswJb8m97bZm5q7Siwj;L!Bz76W6DVK>Kfi9w31GM>RM(AkAUya;M?W z4lou{VbA;_WAEDejA!<(Ms^6$MRJ_6OXn~SeiDXD?0K%BGVf8B7n)Z5bvDH~6+oVC zqV7sIPE+HSVPe1-L?ll8DSRc7fZ<108%W0X1XnbxF=CWnAp#fS;9^G*;ljbzyVx5# z(7X9}&sKl1mX5M42dA$sJV^Xj(wOLCHzDbOB9dUvjG(fZFWE>SS*L(qI7iafkEWEy z@sx0;u$nKq8NBTQ`6P~H<`Bn^<`hJm0j%u-{Uk2b#-a9}Ynw={jue;_TMx^a9=oCf z*2ErO{$~iRozns1 zuTLaRGN>elL}<`4xg-T&Xiy5D!US5LUh{7lyMU4>k+?Z!!ZsnY=;5I5?S?Z#Q7z%s)fXwt*uWocw6T5T{7H1Pwh`ZtNFhW@DQ zvnKOv8H56>is&kjfV$FEZG0)z2 zxKKVfzvZa7c8F&9qX1ChITgfb;52#zrw^Zh;8TdqDFx%$^g)`a9!(k;CfZdjDnK+d zj&dL_nw=QWMWTu*&IdnER&Ug)H6M-$;1cIL#plNTI!o}Dp!#GjexN9Rm=}7(B>1dA z(0$#xS3?@sfa5w&n9dd@O_zcgZso2RQiQSq;kp*;+`+OzhaMvKw^xU1rX<>^iaW9t zXwgRRyX>afbrytbP8Vp|MC!Y2`f1}5XxB~|xXz8gaRsyc)c(^3p@!ot56BmR{vT7| z@f3n3D;OO%x|Ge38C}K>HQZ6`d)NwMq!p66CILIe6NusIZ`L~C8gAYo=l2@M{7;p* zt1Bzw*XU|$Q%|?+^`5R+j!+n@@r5>CX@9!{dD1Z@@wKyp)zzAUD1YB>4icGYxbH`S z4c}~RO;iFJySjEd!B9jU82y>LTtjQm$aYoj#+##pHY4o{9&qdxNF3g#@=Epk^Wo zwRG0@@Nr#&rEK{EH3Ep{<@@YpUGq1&2CVL z7ySN56zET=(f+wRT80PFy#En}&#^cnxK033ur#u$lWL$Mx(llJ+)o8EZ>a4aKt&;Z69_&v3fR_8#``_kR9(Wu$wn zY$dQn)(V>a<*rpp3diWNmVT}x)jCHXR27lc={f7do%xx{l4x!BgPNTkUv4CmVEz|=)l2=}F7sU^as6s`M9c*}J(ci{znc(fU*Pny(m^tijuDY`W+G(y_< zA;B8%UBqbi{HOS9wNgKQ_Al5*$uyxz?A3h#KP&w@o7R(Q9!PIG;bl0cNso1SFO4r< z*BeJqoaGeWHs~wkTlYta6+=F9GVg^aUndMo*;duv?52yOlH8Yy-X7)F#f!7f6CDvd zsMPj*y*||jIx#L2;S$pDH&A~gVn^rVa#`MbHaL|x=Za@7%c8Muv(rS-gK+P#aSZak zcsQR*l*bh~6c}}G^Qt3xFjX{XtCYz<-R&y!f1#c2aB>5o^YvheKOKj_`hxP z(ZQ6Z)OG=n6vrh`OU7%z zK9kl@+N{J5ExQxuuKW8|4#)zdFR~rO^>0^kzurCG73275FQWyzW7|R5R$WNV!3VnM3eoWiP z+v2zfxjmIBaU&gxFj)ot0fyLx0Ab@)^(4Xb-GS5+n%omwItsUm^fRvgAU}8bwhN6w z*9}8g^Qqe&T(@`3W4Uy9KU$|!voYciRo?edAG!$Qs5?eH}Zr$l9)72Mk9Cz$d1 zMyJzqY;nm1j*XhX?+$*{4sJB2%6GhvYUd zddgj>#|pdqH}LZ)Iqti>B%25}k;bR|C6=l-Vd}0Y?abwbZFYN7lkEagC7ePPu1HtT z4R8N5FSg@X>vwtz!?lZkI1ZiMu*rIsDeQ*WHG4BlA*o^Hg&o1{N`vmooZt?g7JY38 z;zjS(#F&!>iL>**q3cCV=diTL{rJ-`+Gs^MuXR?}KNnny4E866`+t8>G%k5;gB4^I zRLLq&rL%vtMf)VDEj)MYH!ol2BFSV^oV#6;I63UxrNkP~XpQZC-m`c`)-j zUKW8aWHr^x4oPu~b1Rq(erb8IF5;i8*w)FjmU_IWrExI5`cb@Uc_5mJ=%CBl{DLIm6zm{P7z^U{Uf{Cxa8P&+G!x@?iY#MU?*kc zR2Z3+*14Okm#y%2I#bcgXp+luK=po}TUFQ~zuM(9%uW128wq$6-nPnL%=UaxdAU$n zbD4VJUEN}FU-XR=|KyO$KY!3faX*5?&P-I&J4dw1zm|^RZuRtwl-ayHGCZZj{*+(s zv0tyZc9f9SeJw7RJjpte*Zs9+oUfYgU41pQht!sH^9~$4g!EQrZ5JI=ZS`>2og$mF4H*<_1wFfsoKT=7;U^>@g7?EP^`^5LGO|-@{NjKQFWpzF>Su-y>d#=@-}@h zm!#{1rTyGXy8bnGa2DyMRcuiaeDlH^0dnw_}mhq z4Jn1yWLmjwIBno16R~&n|yS*}PP2zf8x%H|5YZ_{^=NuSs87vBA) zCF1C3Td}uqcj9-O_q%asz3-Id)$dQ+a;fS`PTi>GS^2HI1>Wm#@wm6GO3*~zuPG<6 zj+=00*>)jU*H58kY-WJ{XJ{K!e%7}7QvEezb++_RdBlQWqX}-!o)13u4Z?ZJ;U&MBbYsP5%gL_A%@p zjIZi%Q?a;j=09NanTrCQV>x(|-bkhoX$uDhK0J9Iholq~61d2C@fhi3(c%%Iq6pBl zvGTFEp?4)MCnGPety)<>@v?skzo1sOL{8K7|4n-FRh?1vq!ITpySn@awG&0oW`1(y z$J##I&S#h9+1wp~loF@0oAmEgtgEtBAODa z%czB#j&nqvB-(_uykHaN^nf%$STN{RuQ_&oK%68h6mDsxT5*)wIdBJMW{-0b_E-zJ z$Xlu^FYQ`^Hh`HMacYlaS(?_imMc%aqq*gkvQLsKS`SUN)`oo+hN{ct$u6ZNWx@{F za_(_EptD12O6vapd_vN8e|<1?q3^OyW=K2R@vzDa_v@P{E^)KP^vbif36HfH9CJGI z6YqZw?Q;P``+)6%v{OtVATxj(_~zsVE(Xy9kqfj~eU4ac zo*r-^v=}=yh1zYjf{+jx`5fZU0x!iU=PRU}Z*IKl{^VnEXXhiY;t%ic=giejqe9hk zTok2p6}d#bp9?Hx>r|1*;O%;inyC0JA~l>9b!OO>$a@BXzmkbnl>%Qd_|b&Jb69@# z$4XvU(32OLnRZcAw>r&?_zC+7I%h^(N-QfD zrKrKZti)K7MF2F^a3R^7mGp=}tg!Xq!?Wna=a%&w4&;(MxN7-L!q&914+FL!?$rEc zVdq#5Rm~0;QXfK|H`TmsWwCD&pTdhN457{|>PZr&qncAgf2LW{2KVnTWYZ3V)hvX> z#*UXL$JvHqCmE!It~2@*R?`@!)a1kkaS~c$urdVH9|;%QIGTv9x32Po6$yuIB=EKA zxX&5a5Xazz3188#>>b9A||uy`h*fSmlM8l{o294UCMIWg@*_*ffNtkYB6` z|L+Y7mG<8!_K~3+A{9oaR>QCu35@F_z2FOqu1u495j)TGAAS^ zDLf`BN;oDa2}LIwdL|2{rxc6mo<$-YNw6D zh+WxpAkU%#{Erz*LrFaAaxvGR?ddb zkTHzjpA2i~ALbAnb}tllPlWnkc8uaOHd#gctg1b&iao73`|Xkv&3hB7w4nUP$iMcaHCqK+v%O z(@9VldwMMcUw{pRM!8pes8_qnzg+>q9yP~%;^|? z@EZ)OsK0g*K#jrrC&{()LU##)kKhUyE?LT%`vXj|u@uVYDc>SoVzF2Xlx3I=5txmm z7*^29Ioanri~w2`{kaST;Be5q3^1)#xxSe@j5c&P=ojkSDqi{Js;A(G^eKH7Dal-* zO8TFT0jCvPz8!-490Zb?AMxOlhx2qMNX(-M`WJUQMeez1-2LMobZT~TN*uR9%8bT} z_zgpqf#=Fd6xV?{58)Kkxkm~WF74`f$iz)-e`_0UhLa@Lx_tyhAr?eYgxDtiE__6k zk@Box&2Wf6tZ@5(G=_FMvRp;!sy|7{`srJl6V5)Q)K)2PGEkh(VIEPTAql#UPK7o$O}#v;~M!qTl~r=cR#47NslXvhIDKW<<@hmI%RlG z%D)jJl_EB4Wv>#Di{tY zk3LP`GzKMn)3xbQ*swH!&A{Cr1H!Rrik$tVJX}rJt^It_e&z49;Tx&%>bANoIt8^X zfj}?=EJdGP#qlt+JiM8tS%{Vlx5jQP@WOAzPXr3q8T?bS!H;WKjw&?2LDm!YR9J)z zj%~-dvF7`K5rlb@%}nw}y{Cc!52Oy^hrOqx0T^h6I~H@0z73X;RMUj?d5Y6=AnZ;$M$)mwj5a5{Bm|=<$Qgl1PJ5GbTSebNj=h66x)kIW?97a{XzZ(?RZ{9`EaWLWi01C#{L3|cFij~C3#1PpmzFl zKtO3QS_$$Gy=4lPm@r;!J!36ope!JOH%od_BzzOvK0*{CwTyRq0jQZVkFnczuHb`g zC|I}vYur1Kf{okV&oS8_mL4KF+3!5rFPRt7pHpqf(<#$wJ9Wr&HK>~QJ;^@e~xx@;eU<$&h zKxb9L-2?O+9D5k%keNw=CE@$pLoS+W2R4TcAQLs;*g-M;+WaG%i(&aB$kKRdUho_+ZbzkLj>_DBY2UhQkh zSoslo<;ddXX^~i5zZ1J!Vj|f@JT|yje}%xL24mG-+i}D)kXu7Db5MCQ6U9?&FadSuwie@%tEa zYOcNAL|D>DX#0j3E?Z(w8yN<4T(5zO_M^q1TZqo5c?_RgwL9;BS{qc#_jwF3J&K|< zNhfgCV?t`^B#_hlMWS!!qQIz_+>nK{^tss)z}w(FfUOV%6&)g}2y%q&Q zywXR^lVn*@u+F@OODdBMd9)8dCz$b75=~p4-OunNFx_?m-FC4fhZDY*TBz3Y7A+Y2 zkFZv)g32Whuq7%muenp)bf2_zA1_es<^i1M37qCWoaQl{<{=#DSyOoMGZsjDt_UBq z?{S*nOk|A{(K1?+6ZoxTu-g8L3g)$D4|`ZtGlPbPP5mioPdaOUGR$Ru*6d|YB1T+|l`V4F1W#I0J9?Mf+30kw+EoC^Rfx7vnUCyix9=-A=2`yl%~HHWeCCy@3Bp9m!ve{%}Vc1 z4sA28WWu6qC+vY{B`uRq#g;iF5@zBtFnwO8Q?+_HGl+%BRdq&y5WURyIvh{x#cgq#V@*1thyPmx?yF!9t(FyO?P9^ zefz;)4;PZPeY$rY?!cM;fXJUwt!8o{D}r7)Z{}{DqSYQ^S)yi1oSftrj^%kB7+B!&woGEIeSMDQbPD| zoewd_eYkbD9*ZxiOnCSz;nXZpWEPl7vJr=+B25|_#rXWAsAEB3N}kY^IQHg7ecS8H(Pd!lZ=(NEQt`KuDn*4w>`kmd~{ zD@)K$_}_0Hlp%{+Sb2DQo>*yZ8l139du_+%_t9!_OSEZqwGJ}KFB7iYPIsO_*{`Fk zL(YKEW5TSwxmfP^r2mYJiBd|onDNTyshdOAo9F$fo;o+`;!h!ug{z-&iRDjeKTqoJ zGQ=1dnJIjOA<5?kkyt2t9Ui zRa;mR8L0aMz1dPRofQGE*RsFXvzbOa7YEJ~ciT8`jQ15gf;Bf%^k+bpW`DD20>txW zhjjyIhWgmT+7ePV)g61=V1Qo*Yvh&5bUno~hTLLX-z;>6hu}nt_!(})-0Pd2qx_wJ zXU*m`OqN%SOeQ34<6cZtI?86pb(8i{75vkt7*>Fu^cq(9tY7c&)uzVBk(`@M(a~%3Zj{CF?QEyrxTeaZ!2Mj5 ztA%S*)oc?b6Ln(|Rtwc?sf$m;&Cpeh6~GjvxA0Qw^~ZX%%bsI82W5 zR6_6W28?HWP7;osjX$i4BGc5_+6VVf^ONH2K*IrH!^}PJ*e5kug`)@CeVRKG2ZJSk zJgxJO9qW;d{>S|avKJ3VFAu*|9#St32iD?0+wI~slYAcMM%qkKXqV4n20FKI)_28s z(RbZ~us0KV`X>F%Tl5@ld(KfO@_MBiYrsQUdnQlP7o?29FGIiw!9Rel>4?XdQ`@+o z*RqD0EPx@%k>{QAI_#-0>R~1Pr1+4)4lwWxqc}*h7cgH7u%zJ&y+JDr_cBGr>6)JY z+!Ouf*;IN~^t`2E-!zhk%}-_Hj!d8p+-ovj^DzHDa^oxOe~x;?HQ zRvK>a$+J`6^436y9g!b_Z+? zp2p?eKl5c9_F~)Th28a&?{!|wbL=#$i6DE4Ev@SE8#=arYw~GYlwfmxS4QMRNj{K% zoszHa2~h9xP1ZfJs;8e#JEurdI$vRJTc1fCJ8y$<46etVW-%AzZczeVwx4fXIVT83 z6OILPcuET<-!J8Gf+P0Nx5cJBYmXO9eEvHNy~8~0Ew3+FaIH9z+Y!Y}5VDlm4> zVV|2p#je;NVnaoKh>rtFBOOUv-Af}!i2DR2PxzC6RwXMpoYwOhD z*&&6NhVF6Xd*kGwMG$jG2h3pRzHLH85a>ui=J22+vo1;i0f8o9krC!>gUOi1Xp~f) zv4q;fK2bez(0@n}t8<$xVb6VG+PYssfA@#9NiNw`Do%SGjVH_SCm>KPGjS>G~QpVpL?u7SK{25tUut`kY@cwF(i<6r5WoP&}a>4 zWVbu&uTYqKR5v`4TCF0@3u#n}^0Sck>6{n8bM*lb)Cd zo`7|l?V4zvH(G?BoAw9EvX5S9Vf6UrGR_*f9a1bM3GoolZ>{-vt2`DmSURGsr0$pZ z9lN0<-dLqj;>PE?_!HO8E8t_r1(5>BD}`#osj&*g!Z|_R{OOdaeqSJ`wxoy0nu1sM zwX4~aWXfySc%MJ2yS`sOs!}ci;nO`N?PqPrN6~NkMcGlL4kT>WMME{w3`co2ng;Gc zR=w4%7lq#02IkN=0vWfZyYg3wv79@fz8ODmr{2sZ#4veZ1clvzJK1TiG$j}JWGrlE7@VU9hjQ1T^s)p+;g^2N1xY$v^)zO1`5*-cw){Tcax59*i zLF%Htyjqo^^q$gspb$Pv;pgwgPW}&)Qb(X*vX%ioq*=vp<}$czJG z3mS4=zm|-D;3XM1KXLJTe>Q}+Po*K)a9Rh70>^NcHM5*7*Kc7}+Ny%CYU@A|xkSmJ zo;Ql9?Fsqsq+@wbHiM^)Lt2t(d%&y=w>zlgMtK>X1wtgiImaoVOpzRJx^=C}>Sym73 z;_U1XKE%@`laBJ4yGi``bFT>); z8uesO0c+(bPY^dA95nT?#!P2WKDT{>^2fvY{GqNzKtZT>t zXp0IF+9>$HfFxmBWEf%C8Ub1q*O6mN5B`;!piRf^w7h&Lr>wz{ON8ft#1tQqh($SS zT?AqI&gRc7Mo%C^IiO`oa7$v|bruxcvy2xx$<;kIz(aPb{Q4;`iMya zM{KZ56Le#4#C30zu$lFecgY6NJ>-(Hj%GyjaUC0HqkB23aHywrR!05e2S${NG5rk7 zO7a<{8wCTf*l5$R2Tw#4EP9CVNCRwZG1OLTbyNEP+j+)#4ubN1>h-{@%=ROAM>ugG-C)y-n6>HblLziH`Vh{EEoX7r%L!r}+SpG;=@ zi~=NKRMRb@5;>Q7M7n=cm)c;fS%TN;3^&Y`CmDU|5L;i@EtGxH~zPX2}VUh@huByzz}@EW6P!wK^43wf`&R9dMBGL*Ad^S za@|MQXtKpgKq8h&$7q^ujz!|5-otjHoKI4&Zs4`Hetv$vc79!r)^6mA*6A{ZMrG^n z>E`8*$7b=#W6H&=>xuVhQ_@kyFOW+!j=YtNKhL4Vfm5kQcsrwg!!+qdc=M!I(l>9p zXXx4mpx#M%XFJnQ3eCos8hIn1EsCvIH=Ic6ltaQ=5Jdk(;5mvAqqG81p@)7IbAdQA zEcETQJ1sk_+DOn3V-5ZEp=X+1$EFh(WTtxoot8)0<4B^IBHEwUB4JE^Fp*<1(XsND zC|d*jY4j%+IJ^(9DY6AGL}n7NJ3OuIJIGhIa9{(iv~u}RV$dyH%G=j6eS;bo823p& ztpF*4WY9q4czk&pgn5M~Q z@j~nGr2nE^-40tt(fU;4xZ2UIn>yvPWf{j*5Ho~eHvMR&${elPio%=k@Patj1?8RC zac&%lt4hHodSuZD3{U%BJ0ALJ@!2u+%vky)5(6=_!Cm7Ho`~*{p$^A;GCahM`R$Br z8I$6i^0eCgf5d~?Axjz{n;y7?S6DdRwxc{?1HV*t1R$_YK>fxmLL|Z(s{27L+i_vr zkN;sv=rGOz46Sl-iu8qsRq9*tS33HqZaDB(ISI+f*72eyZF>lNREV*SFBOb~8EVFe zqcHX*btqFXtuN)AQU_%9s1lsZAW%jiQHT3=(F41M8-s+I(hV+^uBr$3ZI~SVTgWo0 zM=({lErU}+b7Ktb<6AE1^2X;)m`GAC$9HO%PqPMNO!b+jsB6#(fhk<40SZ}aSq_{Z7-Y=GAZ#@3~+t|OP5ouScbVWr`GN==<9#+m)@q?!VIqaHgvA+W46+nhqn?4Tn4HCpc~i-qAE23n?+3@<%lC_+*gBg*BpZP~ zg~pc|#oYdD#f~GBD?A8|mtYnijZYyi=%dh*L z$50p=_j{i+4F;O{CSHv9muP<46vb!$$nq(5qX6MzmkU<_IY7U)Y?urKmMx5QtwPng zANqXQ42VMszuM5Zj!qSnh?;r2Bwg{3zeHY(1EEaO3zA} zca0R6)Yq0$c;}KVxauqq^6jPP7PXtZ-eE$~www)!9^yljS)$)DkoXD6b)liOE6rBm zQN)r_2|h!9?r;9Ja}*>Ky3hCjI4dZ<1mXDdy7eNkf_#gLv62WG9Fg8?{QPr%SgfO(g+y)+wWF;BM3Qci#K-Cf7RIO@#j zcrqMlsiK_pP_(@?553Y-c7KB8?e(3?Y#`=K`wv{_B1hlr^NX}3)W?<5L7Uhgzzo_5 zv;!SUvVIk-zZ+JRhACJiuI3zzGqyteqks-09fD`h|k`U?S8j5nQFEnTD< z0{GyF<4gfv>V#4JDaE;MlK>q(W#^L4R;{>q4}J}Bczpv(Z;|_{p*?#CyUKVX1Y0cy zBZ6@kA5Rj_AQ@JVf%C~-zcz!q>w#ubJ6|YWzlQHTwD>N0p|^L+T(9PBKNR>bDZ^^H zVYEL|IeS!WKSVUEatdEe?h=Y#8CQM->=O8~;m(EosU&UqfDvd!@cxV1Z-5=z72GD; zN~I+yKi7bvOu5B61P!%^=+wUtD9u6aclBqxm|{w3N-S(5kUkqRi8P0Xgb5)s#&l>T zLKcS4#`iu09V;YiXIkk}hQ-;o7IaJb8=$`i!?BoztPrb$g54gHuvrD4RPd=oO+eU% zdk&%Gr|2Z>tO(B~a@X4V@V(P=ZIDG|X3EC%!D{EInSRhhKl#ZJqOdWw>r&<1Bl)Oe zkTTYWpL{C~VrXK|`bH)cB^F7gK$;Eqhm*_8(0kh|U;z_dyxA15te(jh>^(5pqXqsQ zAbie0d}_N44&H+yo7NN8orNA-28`B->v2G91~NZsD{dFUoK72%xd`!=qKyiIl`3{a z9#_G9FHiGtV+2=*@z5pFKw!jX8>AD|TmG)2bmcY36aqyw$SeXUY!Z-v2JTYtu)y~z z3YstDDUvXI5xW2y1tOTRBiqCpyTWUEEyOVwVZ*l5vs@x4Eu{_{bL_r02Y6gffbKiI zR_Qs=o5kY{2S*&VFVCnJ{+3{;ye#87-0MJMgvKGoUZ`adWf(FYQ4O+k?+lv+R4_k+ zctKrbo_Vwy;&?oQ<6~tLkhW};#&basuA9C<|H?|xzv_c{>%-X&%NP@)YnLL88yObd z0w~L1tRsqeb;2$`dTOgF*ruBGx~KlkFoHhmb#`kN4^o1;o!$6}5bR!e9y9D9o(GvJ zGREFNBI!;4u)wfh_RU-gy|Jto^97SNP81Gd4MsL+LlM%;`5Izgu~L=TNz?GBw3AQcvb|mTDcO<2EEUdB~ex;0kpe& z-?fIO_b86}hZeah#PlAetNyT{T>Wtbg2)sR$DLtJE_)^5UT@nuG!qzDg5Cko>lz+t zS`5t0*X1ge<)J|4p@`KqDt4A8cK&W-S5C-t1$^+;d9Q`>j2jU~>thJoVgK#7r4YEq z61dd?&(GXv&DF<{w8QSV!;ZG+l)1|u)_lQ-(e(?~lSg&+T>Ykw@%t!51@u(>b1tJS&`b5flaPH*5I$DkC+a6$5FQ-M08xL#}PL7>0w{Z%yHO zDPfRTNox#bR8rDeI%ZNH_z*n=R5JLG7Wfdk!2S>T5GZWaJ8V?L?+x1oS4X}(3(m?| zTw?d&@-yA_8sIu<=Ly<~WtF_91XRYfjSqRSmD9zTqqt+bX81MAPeBauatS&WMR-~U zoR!jtXPIMp-peJ|_EcwgqLp4^9@91@764yqC|@ClM|v^aXL%2^vy3YeVSXrRY11;DYC*&e5oSYJDpR(1uC;4ii$3#}z@-|5Dr4NuZnpLq zOJ}sT>okkLG+XI`znU(-7CzQH>)#(ABel>F;`W>tHgJ~~eszPMH{Q;Sa9JPs@m}gx znYUNl>A`5fL8MV7PIDOISzsWvm3P`q%27Ylo^{Qq@7bE9mFcUmd5N$2P?kk%|Iv*M z-K*Vzu!ZI2XZzmA;zGiR5W9n62AWByBMIWy#)|h? z6~|S=_w2;3DMZ)Yz-^>&S(xaWLTI}b(#IU$=M3Hl7yg@J2W7_r$IRb1GMbg^*G*(eu;G-Xt9%AXjxIK2p|pXZQaJ z>^?@qg!A6=LdiY_frra%z}I>V{YQWh*0#?aS= z9Yi)Izm-=IAv5I8OrSN)xN#WcxX`BTfnFg-;*A)R)#msUzpB$pTimJ-0i}nTrH9bD zEm$+_xaL+8+PH)Z?ch3DT&w!J+KO&nIu|jekDsNFAi{3~xgOC#7yI5VQL$}UHV&1A zs&)@?+ZH^ZBnC#oJAl%KL=Cv zs*lR94nxqk2%_3VLZWet9taNGs zsSHB0Kmv)}`K19yq`zNqQcupe1}48NiqFFbrP9wTkz8BSq=UTbtlu_xRN1e;o;P@^ z6fiZO_3h0hy1d?PD&INIYtNCdPpL&R&X=KAhPs-xH^elb@!b~)P%~0dOI3d^a$buV z&{4M|0mKjNx#;UxCKHRpJR#s6pd$M1pMlGx-#qZ5} zQanMUs~8<>BH1@9W8_n~J`#7M4X^0)SAhHdCn4Fl3o4W_&ho6UMdlx83rT?B@&+X` zB(MAZVYRD%bGq`mA=@szlQugoM-9uq;b#a*`Pk-OArj0gRz0_8W6&hC7mv-`Q_=>H zfgR7gn8#ll_d!SNVUdmq_e*T=`mGLH3pVsjPGKFT@@=b6X#<7jp%Ntxldt8@t~Zp5 zp1|QqBFV@61;>m9wglVvPmGrfl{n zXF*x#k~+8QPl==Sxi$M%{lbO5q1{i$yGK&mCwsY(9~zfx9av{!e94-4eRRwYYAnuz zTiFhar;UHY2RR0}wx~2UwSA4dA8MF~bTkU_J%8?DblP<5lpkTc6Y?NDOd(2P2YqO{ zofpAnRA+|i8#fGIz$uc%%C5B)_W9c@JSM+O`*F-~?X~qt4%sEIgwT0~5BWrSeMPRo zIQ5<<&&Mx+u^#(+PV%eZ+*HgBS&EOk?rgIVG#UInx>{VEim>KxygjWoyL6A|F`T>O zyS(9-k_;g8q-Q>TxCo+KSf7W%lE=Mzvc;cmts2g_+@l>jGLWeISntDlf#I9qJmO_! zjd=Q()QAXYsd;wvXuYnjhTR&?y>V{yzEbFUdGLTG$*|VGo{-PfS?I>PbQ~|~boWGk z*}z^-)|PqqF9WHlLgTG6NqseYIALXFYM`>p@`+ zL2GLC$Jct{R!&>j7steB$s}U`XQ@b0X7k%)(4s!Un)!OaCyU^BwRcwIWy;s6ZFbtR zbGi1n*!rvg%d7Ia>|vvc|6J)!Lt{PCz@kgDf78Kf%X9aO`BU5}_)ARlj=LtRcN04v zQwTF~(%HH(>dei;@(Ih%eSKs~+ngKa;DxOQ3MxQR(N1-%pTET;B54eu3})Z1_8TumNGWu*wlqvgQj5S%230-b^D`xWYU@i0I9NT z?jp;+&y-&!t!y;E%ct=Y`s|G!0*zM5=6q~$v82VlBtE9i@UeiIQcQTwRUn;tJc7@ z|8;mI7j@Q^={H$LqAZ!|MLIV%!N33EA}Y*^MXYQD){!;F`Mz0P;Nh+@(V9x8&8sj` z;Am}2e5J^sytmnW_UZPHhozT-w=_1DG0T3u$T2xZ#<7?T;5gnpbmDQ*$oex?wa&md z(RFsDeE#L=soAmVa$jZa(cr9k>-VO#?(jKiHdU3T+V{xAnnmKt`E(Nj{66#LgP*t1 zmvX{>6u}?YnTIJ(Tb+;Zv-*7*QGwf47|&jS&qbRwZl-X=%4;tF?$Ezyh@^SB?)#^3 z#kBlQUDq5|Pn^tNNc3n;P0hh?P?dbQU{-5-u9n1iSPs-f2X9BCPCNhPho2~HIQ0%L z=bGKwcZQIv*MEO;(JF6t79P@Get9H1@zbxhW4jWzkn(!GzD9411eG;uF0ylFt-m69 z*)ODajC@^eto!Ab?s9YjfuT7$&jPD>K%iEg(-wRb36)CCEj1nXd`^r>nh0D%^-PMT zOm@2M4>X`l`79-iFt#s>zA~ymcAkJRXsF`~#z^8W3S+z3l;-**?-KDN`~`m(r_)tO z*4EL_rMSBUXL&S=1#IeyItw;i|0;a#O@E#Nrw1l;XF zb6dAMwMnjoNc76@eb8SLE4H<#fhnN(Lx)c0>>NRgfh8p>YQpj<6Lu10f)&Zan&E=Q zddQMWak+ij_D4QurP)7vruRNiB(E+%>NfHINdCec3wsc z&AoNlJyfm+$iWP(HcLzX`iZAA8=7=+KC zcEhlDhiPi~qhwonCBw6x$#Qv`H73^?&&bjOR}*Gf{krkBsuH0WLkjC|$F)Cf%#$Bg zvVXc3IU6v6Z?Z1G!rRPV!*S;b4BtAeOs)(kyFJSi;XR?N&;VV{7i{&tt5*o)cB*qI zEX+KA}VF78fEcI@1L`R~CBsyyzS!n$;G*jn*1R zJ1+)s!4jUXb5LYlt@QZ}T%E4*k&&%2oB{<-6lYAA`XOC|vKB7=uR)?49PgfYF!r`(RwAF9DS5?| z=;^NPXJ-|MzK%^QakKfqxD>Tdcm%UFm1_H=Vdmzq&*zh`^>lQ0zJ7-)TLmXutQ9f;ZbtLzh-BLfLh zTf=jAyQpK_*+>b)aO&gz_Q;YH=dXUM#uq{0+~fi9Ig8NuMER_C@f{-TD|qFOU=L=Y zrNs~4%S%LM)Ax=nYB!0|eTJ>d2jWPQ^F6CUjv`QdZLh#1wf>%gLO!Qw){ zjoI!eJv%=i)S8dKW%}!y{*!QbPDzO7M^bX@m&7x~g5acf?6xuI+f&UUL+(KIx?J>+ zB*^X6gRW?d!DAJXN8NRPk9p*6LU^Dt?dra!zFS4XDN(ssvj1h3A7!7J< zIXNF;dNgrEQ0*EOax55+l=g#{5ytqBjTEikg@vxS5VJd<^vpPqp};zsf(%~UXnu35 zvGR{SMuqo#qO9)PCYNxPsw??qN`8 z)ySF&#v_a7NZ=eUh%m)Ni{|7)<0c3NqwZ0%eCcbo#N?22z$UaZRzb_k;RTecB`aSV zP_FO{h9Z+?4XIcX3%%@8Dp=_r72q+UA2kX>pf}2=@s5b4xBHusStvjeg~lbxsQO2^ zE`-4kzz|aV>u90ma=5_INvsr$Su>$iFyuU-;*udp=lnP6v%JLhSImF6ABoPfx-B%k zABI3}v0A+Bw=k*n{WK`#T}XJp_^HWjpF1jA3+N<(Mf|D4|LUiba)~QF7SWwCauy)!ouK-N%jSeyuR%lbiD(dX@~k z*@l%^qQ+!Z|HAJxeq_IR`+o&KE#H7S<^A@JI_CfXR$%}8qN4vJU>^C6m;X2Zm&j*d z)kOhq=?SS>S3yDJtP@TT#YRXyLl(ILdSKd2;}kpAM;u;!GYzJg4zof4w8Ek6tjSEV zesOiRby3Z)twHVgyx9iUx=mTGiR^pllMz##_r+6s*3t76!x1U-Rma<<$CD@DQzv`e zb27D}A~iq1VgXWxS&_uFohhxpZa8LMw?8vxS&Z>Si4i$Zym&ostg6omVDy^ST*o!o z?@#^Yc-;m(7Z6J?O(zntEW!G!&5zURg+8A;30c#e$3N({QacoF7=` zL(M3o4n`Lu8ry1s`5Y`iFRSF1n<9nmw$QD7=I1X6#IVG$UsyZOHiM`0HtW+@_+_c* zUMKK?y@j1cE7Mo9q&{J!#DHCasr5n6QXS1IjPv08^BKZaKnkRoH5)T%{2yO>F)RH1 zVd-E!iU58t9oqBIvw?4`G~(Z43h&pS$lI($cr3^iEb-(ly=Xtf&{CnZOCZ~G{?>n*fE51pSF zv9|zkpN<7YUV!d=u_%Hs-w#bDU^S%u0vjQ`{C11Qoa>8en@L&xAi zjgmPUuv^MiJ#h-e!;h;l05F>ZFlQ;d?qu586>UE+0eq;FQ?hrV$f< z%RJb;(2`GKUsPUZb%(&Duo+E|3^&y1f+WkA6Tx3!$XL>yMpm7O437kQ#xeQimdr+uinRutqu~Re)8HHW|qz zWvm-7?NDz^Y#!WKU{TG&^#=$1h7!7O&WAkO{A#~nLu_04wQC6CU+~10u`kL`##l7f zvIF73$lcyuju^W<&KL!Kju>Wr&KNub8{*qN4ix=Bv>3dp5SbIbqE|a?|9<;yq!=Z|*)=~?=?hYRK++Y=7;jfL#IF4c;6jjy&KNx^k z8d;URa1~ujcPR+lh&iDP0!Ed~h&k|fmEI+T+9iYBg;)00HFp1De;Iz}3~8=MGT~pFI#&^u+{i+rA*N`=s!C>3QSxP79^Ai;Ytckd_*2|1HPrA>L4ZV z#o0!&KD4$!9`r7E=#UM#`3&svs#ss6e!VJdB0Fz!_D>xB5h;EWD98i+{=W<{OW_G zx5hQQCa7}0|Dm1h{0yxF6DlebjU7|~s!|A|%_*ku!lse~LgNx*=t4ZEWazu_^SYtG zmKAlZQ&y!>NpGbIb!<_b_Dx!E<>`A%lJ*U#w-V)3lNHRe7LCXj%A)RV+9(djhL$=Y zjWyJi?^j{+zU4QZ9T$d=!PSP4;in>qZZDPDPnzN|@(qf3602RHx-^OJf2ihmJBEOE ziJ>nO$^P&=Y0+a z$4OU*!KC|l)vK+z@AkiJbi+-2dYgiO9P-GS)4P$zw5Ba!t{AE`5>QJ#pst#T#VHM> zaOv37xEP?TaO=V#4;bBa8szTRJ`E~7)ti`pZlXV&Shq8=*WXsQ84#Se5ifLB8F{d>T2XV1Jb;T?Cdwf3@-%qI@C@iw7M|iBNjLR#^@~ ztjJlh_+k$EnavRHVlSk)hK{9PHKI657$8GTmO8hOwcPJFPu>>p>mL2Mi3-*RRes=u z`*lt;pDq25k61KhY18HG+(hVQnZ?;*bxHl)0+^Xu^p8~-wQB_XG?=uQ%R^^+AB4v4 zCUDMW)UDPYzY3eMZ7B6UbxYp&oNIW{5Sgr_n$LLnQ?7d4X%l>0<%3S zI17G3Bf~{S_ES~kp7yIN)s437)^PX9Fr+$LtYto(I@4J92Z$i_3FrEw<=Q`yOCSYM z^1{g4T&7i+(i)&vIEH~dmO|SQEUQCu^x|&wfp2&A-!xV4sifd%WN(aaJ}MlB5l{>1 z@~f(QSzDsdh@e@5p|`@5+J}&C$iiwuQrU+*=8owE&T>E{{8EAq0han?Mu7YxTy>8dbY?s?RhX@K#lX059S#yt6^k-3XiLUCJ>C^VxA=DhKtba zT^^7JW7#=~@dHcuChB>Z_dg?{pW2NpQJxtXVm1-kQ)LY3U))W!RFPGRlAb*l2CEQW z70CyAod`t!xZV8)al1??{a2pjge(XXtpwLnyrlbY$MXQh=6jC!-kzk^LNyy;Ro2et z>)}lz$+GnhXN3Vl#JzB~Zng%pP|vA>?q;?ByYH8UO5l!ctZGho*a2)I5hv6LLm0kv zzu8IYE)AW+wTN=}tDegQo8_AS%eJfNj@BYeMhp}<&imHKQqZ|w#DNTAlx9CWy1{n?C!)WMKe0g$?!N8G( z(zrscUScdpT|#+sp25KP?oOFnJ;0;XCdFY~0bRF;vpVC>M7J3DJVQTo z{%c2YckME6;umGLySUCx=@M!6#ttfHwPXiaTR8Y5P3`!>vcduA23TdZan*fhhNw{$ zah)`F?Xz){MPF#`C2Vbm z*q30wQ#9=EZ871?G9l1Iwg6VI7lLGRUyV9=Wy-BTXcDI|YQN~~!p&E5@T_M(quza@(Ys`n!SdL9iB4Rc>6MrdFEG~x?o z`$FQ>wG&TxiijeCPu}x_|A9Kut|NA^6C;u8){|Fz2>N$o$Y3I%_Ha-54kq`7bWuaz zy}?I;naA5pVRjQYGqxe?asIQY{3E>lgZBH^yb1pmNrwCNn@a3ZIfz#DgXC2nlv;(+ zlbf3}%3mTSl5Mh;Q^PC!HbDywGPclo`!gTk-qaOu@0OjvCPcO&QO)fMfcZh7n}~{(g8rNgE<+Vp@ZI#wKt?Vc6BOQE zBAL%*Hhe56>rpZG-kMB&?7AYEbLzU{IWfEO4Zri*>jzSnvS1SuE!N&2xa$Ca`p+Qn zQumv}@R^4jhrF}1um{B(vMI+-j`aoi4JgzZL@rRWx?vfIE!e;bw`N;+7vf?8AJCA)xjq=bc{fr06`b{a5U%TwOj0*8 zDZ2xUF;EHQ`>x*A8>BZy)&QS_UwdBGU<&wOQ(qlq(x@slpOQ4LI%{7yN4T%a5xzP` zH=|5%nl5=d5?+6At+1yd0w_Lg_D zRq`?S#4_~_BZa_vuj_M&HZ1sIEae+SI?aQ}`>;Qz&+(oA)_6+i`Z`LHTPhw-P-N!$ zlcfp2W-OuO51W1^wyo$N-Cc-#(8x}5$5UI z#krD9=CUtXV`i6|t$C3aMUq)=LCQ@RL{(>Nrl)_Ol&)x->MBH;BJoccn!xzny8#v4 z{+;b~FEgL7GxjOh&o|Bw>My0tDI~2|{71LkX&q;vp)^LDg}QzowmUsrUiw;n%KBO= z_uZ}usJj+X(27s>c&*JGx*Zyw%wq3&2wa%0Wp$J z&A;p(avr2z-6xJHvJAOSo?sVYc=hq@OK8ju(AwfQUo1@T*KStqA6)N(GDjSy$qK|r z`Up71-%A#;hW4D?p5LZ!iWCsEetl9toGF0|%zJ$4w?~SnBJ%6>J-=C>6+eXj*N7|U zwzi7c*10vmz2~f&;L-?$M;Ok*`Yp(CJ zJnQXYjc+O?>Vkj=z4h2(`RYK|%T{Y4#{o$g_47r6)9d7W?!xpdBCu-<9TWL&bu&Y~ z9q|nX@aR#!bBI-XApUTqjR4i2yTdmF_CbSASne!wM+~bum=(eiP$hJ@e0$BR(;v8| z;E?6!U{|@BvpApWwtL;D8Q-rw>Sp>>Vibi~;%WKb#-p`tRPS|^ANTKVDDdEHY*3>h zuq1Q=0y8lmPPwv z4Qrmt#eG*|Y2z2Ol{}r~_=y`khOC#;*1A2wt3nPhkRH#5T+7MV_lkSWY*x8MRlD3B zVhXhF?+`xMgf#cT=86C}k(UNNq9 zBlp@Xy+h^b$y(k+P)TFSudpYJW&z7$*@(4(-O!Vz1%^|HbpBp#$Il-KdZTITcE_D= zOsNt4F+x|R)`e2y(yzyXvQeI=i9$t57Zz%<+u3ucTkeN-LqI1kuWGwI6??1Y#j=Zl z9_dw>Z6L$0lf0--KXul3()ar7Mjbi|X;q`8P3BaN7CW&@2gCZGP; zO@mn!y&sMNjOHQEpDJqBUr<#$?pInK=zJGc?dJGc$A2Ff%pG+%PjI4Ks7H?(dwddv@|9l+7k|nJ(#xcI4sO6KK-#)YstM5A|_POxH z*B=r(-}&l@2q8~bR6V%ZP~GMoj+2?=SUh*s$WU?rLg~5IZFdX_5bHWpmD{LrKOgNo zNTrrFJZ-T3a1WNot9Lmr4SKCM>tCDj`JD4sc`Yr7ML54y5@D-Y{JG}xlbf^cx?xwK zx@FQ;D>Y*^ucz@MbDnyGL`LVK&t>T*LVtq8i6=-J zWWx}$|F11go~k4-BRu#tC9vv#2~^B*BMf+D(M3bdc=UccPD2cHkc%HQGANL9$jE9$ z{bZ(0u*inl1T=IphP9VOrY2agpKP~xN`^Ecy`tdMr2VY$f!6?~MTuZ?CU`S+No?$5 zW2|w?xOyKNx^P1~cACB$lQe%ryAyI3+U6YS#8G%T7E!}cP#T#NwP|HiLvYbFmDy>I zf)oKs6Df2AzWU!c5aD!Xxp_z!ngAM^a`Pi^F*Vn)-wC7GFFJ*hqY35Mo7&U&u&A0! zUqvt@JxKgj@5ZqpCrrfs8pSL_lbc5x<|0t&vO6n?RY=@tZq`j11xO!ec_%%#hV9u+ zYSx||5{(G=_FvatKS2Jw$G{G}tPS(q|5F{SwjCmJ%;}=J^SC|Fa9?xD=i*i z_ILX~u4)fx!8oA}F3NF(_6dUh?jk>f|02wu5=JJg0TvX*WQZ+jLBW!B7c}y%ye)JA zI)Io3CYK+8sU{=@0GOa-LauO#si7x{DG+251yBXlq-0kgL)>NM+&($V%`T`uE>?MV zUv8FP$f+JfFL!Vax4Zoh`lE`*#_ z4+p!qhd}h(f>m8eCLz9%TpprVWpH|Jf6_i|<<}Ajry~m*4dj=_Nr-Zq9s{riG7~k} zl%q=f+%~j~O>xj2lP04t@dQ8U08K^e61+cr#=FmI_W`bjCQbDc0>xq?0@!nfQ! zwqY@EM!OZ!P`pI-u#Ty~e7b^Q?E3Rb-cZf~l31BBW zZE7c4W{V60YacMz)vP1E-*r@KQgP%mdC(*alBR2v1t4OLw2%|ua$8#|Qkq(Bo$Bce z56s5F0a{-+CwZr5t*S!+aP-qkL^LExLbUa7i=v2%L>!o|O~hF?6VW4mI&yhm?pIAA za(7dO$Q1o%XrQsQ2QSbdz)!yVxfBS!8cp{?aCUp%O;YC3>U3z zXq6KC8fhG0nn0&PA128@9SrSK#PkG6N80#pm%w%7-_M;Wk1}&{prxUrMi)V~{Doy9 zJe#C|Z`^q-npt^7lqA^Wz)sT|31cr@V=4-%fBw#q`zzcAqM}+2q#@@oG>nKQAIV-2 zCFD;JbpK6pmU2uXvR~d#Zuyo5if9%f-bF+6Ga#oLdbNjKx)z{8Hq4TlFfZwXzQSKo zKrAufui6| z9yFCs0#aGhMT#hINkL~#UXyy$ts!_!l$+X=(r?2(nrRq1X6Aiyev4>A_! zc4NN|x-23+szn_X0Ev1-tCtC+q?(EX1d}GB7nW*340%Ds?^93q3TF z3!&XCe%{*u?4(dAk917OIn*5T6T}p5hYMJtn=TZIA?9#W5g(_asx{eZ0v#7h(W3%m zgdBJvUzqd71~zaWfnA1DA7Qx7f}XK9DvAJg3$(+0swOLm0uFi5nv5PRGM@MpXuYLl zCrDd`tY=VeIJDYn2#nV6c9@uf#hsi zT_upNML6xMmS>wK+8W!NxBfwo#S0X*Au-{5*9StwiwSi|vIh87fA;7R+Yy!^lz?8A zR1+MPiX3+GB3Lc)T$XV)s-#L1j9j|473}hF1*1*}gS9wFF;+f263w5GwJ<`BtRv9M0)HVDWD5W6elz8!p zGm~N5o>!YvV0E{z13NSifTOq~#f~xvRAa5djo2X8#iVFkf)M(C4i&dKq+UpkC4tV&V(iLL9?9a~mlmlf5ZrBwJP7Ns%pmky={$UvY}_aBs*E zUh^;p8w7eCL9Ur~`yGjTSpx6OAh)@o8*jm{$4H(KC;})#^x6Au!w|dk;JXdsyLbNS zGC=g(lJ(o7^=I4vmJwU}8Q{wXf$UE?)i)Sw2XQ zez)i)EHyrCiiBZ&NI2(Im8YL8;{?uQ0W=SPK=)c z#qtkhtPV!27Jn zbaNezG3tk6fI^rl1UBD$OD@SAlu!%mh`S^S=Y}0-b_!CFZN6f_UOkT8s>0aG4`0m< z5$Ip)^10qog0F-sYVAGXT5b6AC2N>u8(m~G+;@KUtLQ#OWdw^A1jOYM;U`~H%7$U= z<>M}Duf2Kc5%PXP7RgoJ8p z;G~2KYQB9zidOq#0X^OK&^kuYIz9?jXOJ}(h8^Wddu%8}IU{Y=0=a4wy|@`Cj2;V_ zqW9|g9+AX$nM7B($6ftjp0bFqLJ|Gj;r#`$!5*=|9Ow z!|X`Q>!|v&3L;DY8>Fe4$&=T*U}BTq&ZU6elgH-a!Q=ta5)MGT_C0K-9GVP9Av#v{&?a2pB@8tvAQ2SGGd_+ zL1_;>y4G7OikZX5VWkkxFHN$uz!7Fyvc(m*I%J@eWdemSd?RXNAqf!4+EK79jRp&S zMnLc1Ap;qluEfR`ii#o=7vDL)hN~2&+Kpv8`pabb$tMaX2$L1;MIU`Wg#mUf5-N=) zrP-&u%OYUBh9LlC%-f82B3%uDuC_4h&lJN-3QIahoU3^}4c<;B@tA%^$@@p#nd!E4u*(!_{R@VKV7vn_lqm1tN z6%~FeiwSQgVglXF5q0rjV()Jqm$BDRJ_@@_RWr`kk&rbM`5$KHr)Kv9dlEGSmVVVT z57ysNBZnB-R|VRT+B}99Ho*^Ke@J_^p%8xKtCJ+I}ND2@&13luN1qa$>u(6wSxx?x^xKCwn4h<>2e% z()F32Y%@01PpIKL2!7zabkRN{-%$o1bKUsKR%LHz9blU>+w|JU$o*1D;dxsJNr8Ez zCe(@aQ1q1Yvporauf3~rY*gTKRqJ7=wH`THtw!!9E4q5gVrcTu&Bt|gAq!>Oy7b-_ zm!n-Th_S%O^SM-38%4==-H4}L^I2N)i0&J&`}>C3#xs5qOuGGTV&jLd@sG(D6V93m z?FQqy+ARIW1GrPJ9Narkdi}l2BFut)%Es%Hp}tXx#a~Nr47`}?4SU-(t6p=$muF)z z?d^>tvE~P&WTLOdfM*lsk*unZ>mooEzg5nxOlHme(`sNp?&HKyU%QFj93`fP)U1S(?SbKL-{G^#*nzW_t}^>i(^$`NQYk&3&A#P}lmzAvwV68HE;WKK&7luO`}l_EeM%fkU7GtFDNkB4o#X9& z%pZ>%*f*)60xmYWV+)N@Iju5W{0^m|pG6{bo}~F5Z|`f(n1zeiH=c}Bob817w&gd5 ziCO)fUOV~kk5LGnUI&cdcW&U3d@XiwFZmsdgFX+;X8q~i34F{Ir_={nk#aKK-}{TW zOucGmP5XV=J)Y!azi^4VzEW*^ zXP1Pf-roz!TIIoA8S!~-a$i73`t+7kiym3%eETVcS|LVwb%iw*O}QA`TVs5E*?;{F z(emEHEnjKjj1Y9==Uy9+i6?n|b(FgUgC6w9d%Lr$?=NTK{NV4W+`mY^D|b%&+UnLe zNv#*+3)l{xKHaPin_v4L&A){@2l20R@4gnl9Kw=*Y?aPE{4u@~AfEQ+HU~HImD-16 zd0p@a)y}`^GL?%m`(Aqcz$GJoNYvH(YbB5-apYZ?jtMwwpXh`>D3D zYtvT>*n~d1Kf2H4U_Nhx(6;i{HQ&rdNQ=9)cK2m|e9ZW$)TPFreCmlF77+O8*JycW z?k8Vc>0><~v>Fn8xJ@3ckv6$MHB(F-o|wBo`^RThEQr4@H4_4Ck#zs;H|t@yd#hS8 z>RY*3&!Crt>en%U{^^h-@cBHzD2bJQyvo$yYB%YTL_(5I&GGU4fh}OabY;pR&HJ{@ zUy^36gU8tYCv!9>z(;4M7{IdE%xjIy!l``)p?v}vEV4!anfvtl~e)6J~@VR@dd-t>A{j{jzVd9j+)7wo~ zrhUHVq5IIb45U3?eG#!HciPDTgP{(N{;i?M>XBSN7<0gRlgcql;WKJ z_nAYpxR1Kw7<%&C>L~sUhuxl;=5?#zeAUL(KIV+#-g=*7fYj0Z2K)D^`y`=|w#V1? zmZY1Fk?9xZ3+B$aK-YrR(6|56v!2Z0qGE;Bbg(E^u6vD5^Sm#M{I>E_WWm}*N(bcv zgd@H(8;b@9Vx$L|2FL*{4U7;(r&EgMATcAP^sceAm{2vo#u=;wGX8x1^9$*~;i6Rz z!rbDs)$@^`|CP`D@-qAK@$B4SF%qXa+H7e9^6lx*CA}>id^E6GwNDruZV@UnrEIy8 z%--VGFIM`^3aa_r%KNge0Lv?%{XAY4vNM8*7~iBV?-D{qQEFTEL>pXJV%HvtB1;-# z*OSb7x>iigWLV7N6&4&kl8Y^Y`vMq24&|v@=D1y#Vd*zhR+d*bLP=LSy30^S$3FvY zAp&Vrp_$3Ali|Kbi-fCIN2O0!7?R@fZ61o0PJK|OomLrX=b(x2P`~8gCyy8`EM}L| zWXB)6>i4dm3^1XiLv0_Tn?*9(Tl1s0CaitUriN253)PrT>+hmZises;7>Dgj)3-oE zPluYMHos^7NR6{L^avZ->5dqCVtK7$%1$^tw86)eGPmieHo-fWLsy^KuMsmzA0Br) z7JH|5Wkh`sHkUBHh#@>`<2mBj z`$ecFe76v}OL^=cRiV3t6eH-pbI|=%AFRqFkTmVo3DHMMOKe$Jv4zY? z+M(xNCMko|6FlU&iazU^t2L|UQF6mDFz`J8f{xg$;kz1s6$G>D=win^h*~uPU;>hv(5|CoZ;4Ojt zd3ASrKKBU8t=^3Jc2?fr40!~|DNn}VZdH-CQPBNEDCV|MNM5re_)e^sS%3jr4h*0M;6$V-^X+|tmhy_be&L4-?Vm2y8v|3!=ju-z0sk2?RFX%h^}7B&W&{K;n8MRK$q*e|I;3E_mLV$G z!x4cvf)ozn&oZ4np)iI7CQe_Srn!`+!_Q#IA88Ew!YN?pc{{L~$r7VVyL5Nr0mnC_ zCS`UQI2nd*lA4ylRVHZ+zQQSH81G`E`DQcvM#5@LlfwPDF5#o3CiV=Btg(h;vyfl% zImuxs^p-16fw)w-up_XR_hdoxVS-T#ZoBahC(G_j1tkj zu88-XfDpcN&|gZ8#()^vQWq_E0JXslWGu^T)2b`aj~>i$gYap{Eb05 zwBk>&SZ-YQQC+c+-FesfQG>^-)`H?F)Jb6kF|tTfQini@c$#x z`F9uL|30()zlsX~IsK#s*irbq{m(?l4OMkXu3rh-bPKK?EhwePDU>9xPEb=?MoP2q zAdX@V7+H^WvU~@v!f1?35_=XD6cPnMK!C2GlLuG__<_|^L;t3z@FJlJ_|&CvWTAh0 zpPIV8eso`Lb-d(yTzA}j{OM_4oc=wT=G1AS{Fvqq%h|?BQ>{+&-!>J*zig`M-r=FE z6$0&QqTB1QK%Ab>qPwhRz946mZm*dHR^z`euEg>jmpIH z3=x+RPHqJ7+{Xz%Ehe5JGLt|?_I(6h5L)%?oxOWKzA(l)8%QS?1k9kH^nvt|rL(8t z{x5#!Qf4yQRE+rI9Qg9$<2hl9K!u#Yfeu84wJR5Szkvm6uED8s^C+YO{U}8F7%%iM+NqgRtyQ$z=u);gF)o+!;2KQf@yRE65Y6s&5(x%Ss!VnuxP z7wyciNG29SZaj`cxH+wm7*5)`MS^hvD*q;vXL(d$Md2sj`bvyb69)$=oFkVz$4R3M zv>+D|u%ZwsNjqh1ZQ7oAh)_BVl0OuHSd?1;x5|KQd8Pn@%yahff-VbV#pK}V4E9fNX;8{s{vO9&X} zkSv9wfh!j-4^hs-|3;xc4vmN>*}bY&Vp@j~5Brx)Rfv^ItW+G0LY*ugnM7?YDji-& zGWg0BPcjyX)@@G$_?>W3LA^U|jtOp6xUBvSJv5BUfgSaWC<=AFIEh5zmDs;!s#fZsU=iO8ItmBe!J=;ml&(5pI&ueEHNtNr=_VPe z?HLqzd8NW{$E1JiEAGCPqZ}%cPbSWFvQ8>T_@@qWAq}sXgPCS<%B$}3(lb*{|FCcs zV#)$!I7!a@35k&o(*yugzOA$SVgH`B8r;6mU7l3oeOE7RrP%D$k)tionGSfQVv<1} zgkZ`PzeYeCETzKf<0=t!TA)yEDD4UUjuvN@@7UG68xoU!;yPMwbQijDy?`fB| z+fojA<%hd4B5#pN0!~5+c=bnFH~MK=vlCRxjXXJ<~* zs6kt2zY7G*AV5T?mjho8%3algZ-NlI4+mQ)NdiYyj|X|SP96RZuFWPS50*2N>q~%k zXTyAz*@G#%5O`uG=-e*eNV_bg+6}L?K^>?|QLEi+B~QI<1*T8!cCLK2I(7A;)qCm} zup|Vi5rNg)qH^=D*mlS_)i+esT@`7fhuCgsF})c>JgAH5{&qmqBnLiyx<11EcMrMp&1zIA913P}j+l{ABGD zYr#VHD99lp1xkbZ%-l-ZjKr{3_@@_Zroq@HNUc|%!JKwO5n%9j0oj|_^;fbuHvJ`W zybkRE`*w2szc3Z-MnVCnS@;b|fU}{Mb+)}&Y)-n70mQ|im%oidq#|S!LN5}Ie4<~o zh%bz*f#-Y%7^#%4%9lWHgNkKCI1e-0fs$S@7#GnjY!q@eO5w^$(XScE6>y|#Q6U<2 zQ5yD98rM-8FhGwgN<%72Ll?WQEzj%)el*zx<<|wM-u@1ektLr<$|9*>r^8P_KJ16i z+L(ZHG!RG}!U$c^&77a0SFRS=GAmccQ3pUof^xj`6a1_ zG-8bO5PahOK1U(EQvDyLV%Kg-;}TMM6bS4c47;;A&Q@O7`!pV_=tq%hVP^f zUg4Ja+G6$Fa`xLo^iPdn;f`G40uN4H;Rar3%3b5i^gaBH*lPGkrfRu)(!}yvfe_HC z4g`~VB(xYbI+}*EG4T+Jltv^d@*g25ZTKZPAfzZu+Rh$*Be{G#vWus2BwJ<4OQ)ck zrKO6g=!~lB_c5ys2!enKcN&9-K@!PV2oyyYG2#_50uwPJ5!rPQ8Gr+FRUreBEmEYf zD3R7MKN3($eu^&MGhb-XR--PxiaM;q)kq%a;^TDXOXW-8zcTA4;HzVTdnFeuP=i|4 zV^mWAOQwo;1G_6jW}FlEq6vRu-R*Ot`=wiv0c~DdUh`d5pR$}K{G=O8qXAl@LFy3~ zzLE>RvK6QH;A`!si9zRJPsUV_R!@&drhj9c|2^X0Nas}dKf@e!PRzbXi`*?rf4xeC zI~0tgB4U5=0jPl;(}A7+@g4N>9V9ly8WY4CBShZ;JiQ4#y?#8sF}#m7u8tV4j%=9RO9MXCiE3RI}>ra0ScO8x3s`b+M;vY5X*RZu;Fs#~nbFXMwyLb`Q!}`w+ zr?!k|=Jdf8XroQF*TMfcOXW(!#V7?)0(H?h=a77yX_2(uQVE&Go>bq)qoDTq1?+qQ z<%m{1*BgnpLdvc?kD1nk%d!Mb!f^B1YX!r?ozDJVjiEB{Hu;in? z_&RD-3nfsc8Vq~oBfUTZMmpH`Q%t)bzr#FJkRS0#FWkk~b2KzN1s9#K6;0VVZ-h+m zQ7EF6XcWw?|)no5y&)hX=yg$*F1zTMy|0&8Tr51nzF0!REkVkN69SEzT+UKXFd} z5_MJaOHUrVPHg&olxQ*6i;P){iFO`DPC1h!ops*iI+R1p;LOSX1aU0;{dVJy7 z3Y?XV5%kF^cxBIk$NAx*w?U%3L87d|m8ij$tig4bHdgNG6H>G2=b+3r>-8q;FFe@B zvS%tZ7e#8!H06P3J2g_UHB;>A#1Fa~^Ky-O3~W z73jR*r5$)vj&owNRPo5ne$YulZBtC7`?pBt%=Ux*3#KYo^jHnllK|UD+IxZLcu5ZA(Md=O;_Sz zOGQPIh>Pz5106aJv?~cLCqLya%bu@fafM%`j@(Eg`1y^5I8(?@9m&WuEw)s|pt_9% zcu}yQI9`aiRs!^OMN@spnB>r-fq~B5Jf7~q0v+QtgwfH$YQ51xyr3$&Ya z5#JO?Rm9oemPAc;N%g~?*TN-AVuli_+joo6gnspQyR?JiD9LWQ$JaIDROOk*1vsf; z&%F=GVKerg$HUcmrhKcBt@mOq#m~Ik>Y?!UaIG!?oagZC@d@M;W^lC(0SCwC=YHMQ z#>bO}?{%NCI;5@KH4mPzsB78$h=9}J{2xMbmup6X1OeNT1htQQo3*B8^y{eZem`#W z8uo7w99S=HRd*JZ-6pSRj5w~}Zl-FSK9lpfq&}>MUBno&<>f03$WF>rvjvpnmadD{##Cdc5Slt3KY}F;?*NyH4fFt?zoG`Vcx$ z3<_|by?VlVl09cqh|YiKVdvVWO!Xvxo-V3`IeE{e1!V1YTh?sW;4ShwWwp0|{@yG< zj9_@&ztCBqb6PZ|=suWR1DBU8%in~mjvvaAxm=Xq8GUqZc_=9IR3em%I`L)dL#;ls zzxC9S@g&ws#uX@B8Ii&@Ubqgnf|Qu@TzQtRQ?b{ti zYsE`D%C*tL9;f6-s26Wxv~qc%amrzh{>d6-tD$KW^+(Oe>XZ;x8JeyWO-V!uFASEUxK8!2?Fqfx@cu;6 z?Kw@Dk4y5L?mxvebxNeVx0Y{^zqYrJwS=5kI)-lDJLH(S@NOA)q~bO??yfLSKjls_ zjvpI7I5d^>$apkf+k49su78XXFuHnMOZ@gK@6t~&#Xus>mQ1Bu z{(?)6)LmC)xW>YMxMF2W6k5K=$4Kn8S+DPbGHtcm8|)#GU{@gglMs$@-WTkl zJ*8o4aQKqR=rx)IvBn2@;9X!Z=gsZuael#z?v?7iqQM(>Ufy$hTy@C#v!2XreTJDK zHHW8qg@(|zJyYzloHbdSz4AS-Nqe+KV5`NeXkm_@fx6M@c-e;k;#s}-5E;{JJ>|?p zU{hyrzf0?S4MFN~??Ttx&S6f9G1ZN_Y&JUBbp0p(lsRF??miy&o=sq9*WVwnBITgq@{B=DKyv8|z+P!=hfUG-| zb>f1j|2pNs*t_J>zogs0l$MXMwP$s^-}$(Gnid%CBfga2xMkgLq4V@c-Llu;Kh#4K z%f{zHRex1$Z!BGM#c@gamEbrj?nBYL+kUkOyrErHz%0r3QEuJgvDAKH_;l(~*M0U$ zPH%3p>eb68O4j+Z-T5QeelLVL&-oDV>&L-X`{iPXqN0FOo%z7Ht&C5;W_0_hG)`@lGy|kQ|$yMIl?a%S9s;kRT-*=8PxV5T^?koNJ zZqro<^S!9%2ir~lkuC<#HJ>_9vdG`Tjt{{rv?^Podi96qn(OAX4kOvmBTu_=&{v7}YZ;>s|A8K%EWH z$kyd+ERgE%SvVEHW@o?Uk~50eDq-i7dNnkyPFvIS%-gioy-?j|ne%e9RK+9s-k$EB z&y%X}H#XIILG9(y&j$bc1CAlbW8oO0MK5aQ)f02)BN9Il#&iUAnc*u@E4VSvo$f`& zZ0ft(2VFax%iBiI#B$k;ZI_v^*J{+sC6WeWsduV9hwi)0=kwswZsVaqvwrFa#i3YF z@Vho$m$;dB$7c7(!GXnLgv3LmBZVx#`)lj_UlF z%~_o<$F@zb4+sZ5DD$1!YmV8Ryuf>s3cOErmRyzT{pIn9wr-o< z(RjoyyM|<%>rMXb{`(xtm?3Tg^ROr|q)Eup*3J6C)Q)1wjIZ`FQaO?t)seZPXAEnX zMS1<_nG~VDFd#%6gCYb2;~*qNyf|g(0PRVD&l@H~SM&+h-FlUv{}AnBPq-Q0y>tb1kOa=Xo<0!Cr(i9F*UKW(XpQa0 zCJ^4NKs%WCCfHKdAH`G+J0G~SY#M5m8~_`vX6OdzE(o_!jh(a_yuq2_Ovxe@ zxlQ*sxSGXB{yc-RQ+pf~rNViFLcufxDsYDuh1FNlGL;&*Bv8)cL#bq%f#vU-wCOjo zGTj5RfmFy~7tOdY`bn#%;Xg2MwxKPN$@8Srb+K09+<74uwBdeH{emqB6DD{SjAXEZ zJ@2E+j~nKi&W}`TXX^XnZUH`_&yBVktSCzSsH}LNAz7x2C5Gle2)Doh?)|UTud1a| zAO4`GE`|`q4w$eD<PFdRXKp~P6`swBW}^foIg$zMMBU@C3=ev{H#e= zzjX;%^|6)B(8WPx*BgWyPyT)dNteHMvFD@E4TYOjr(;lop9>#WAFZ7s-WKzrTFH+C zHxqH*j>}B9Ma;qC4w3-6vmZ?u{NDfJ`m2fHp)|3`K&AB;J_m#QFOC0^!^8t8grsrm zw1i;-!6?4}#~AwmoDlLqnl=A5B1GKK+R>E3&Pi1T1_ay)I3MKi_J0I19hxveFlq_O zzCY|$s|xqWb^u*|?m|>)3sG1vxsfyc$ZrUdghw+lBQXnfDZwgLt17L=Ww=dX}hX#6JU8plATbx`YtT=}Jdpa6h4s>mgWk(`Yi z2PNbdn_D`Ntha;tr4BytK}d!MPjf_@yJ+F+7x6VvOyRpl6iu;Blm~YyUO~)tI%0WC zlz8S~AGTP-1dTvVMaia2&B9ekl!DG@{C0yyj+TiI#{Tq5jw6D(G@l8?IdO zD0#%vDaw}}V6GNWxX zPlV5pHwSDQ915~fE5RH{Q3+0fI|{<#Ta?uV7a_!$FlzdC6%pJD20;cjvtj>oIIb`h zY6rzp;))Lhvye^M3i(Y88$}q)vz9RF@O4VliMN0IGfr(~Rl|@s1 zCFH$@;5ODYKa|t7f6PmpXi+aG@dk)&CB6flZG-F=LCSnSN&PvipE1r9#=S3)1D^i) zm$e!lrh<|`yC4bhZ378VMMeggy!RYLtlKpIXjT$wg6M{6w0un57(vRk5sMf^LyD52 z9!W#AD`Kq&L?9uXa4Kz`*@kw0a{<2hU1MD-5sqjK8?l*m#3ryfjb!xZoXp340}159Gz5tF_{(pETG=1-t6_Ol<7y{`cB%DCnSA>`4?ztv2)P3P9vCFyhbHHt}2do z!i==-kpMDL%)P?|J&a^R{vl3_e8_YjgMXPQoZ4kt0^J@;Dgu5IjeuQbEXloE6^JpX zZDq<<>feAd=WP}s{&bjRO6$-gjjusocLo=3T7jNY6|eD7G*Dkdmf}rcXI(W=?+nzV z;x(H1iJF3Dtwku7CBHX;%vmQQT2rt_(iiP%Gxs)IyyXP`&QFX4N9PX3&kE!eMw_QF zuic{$F-bY|)DBk*72UJ}LQyh*c_@PtQGZ<&08?5vj|=e@icl0Naaj9T2Bj1Z|3Edb z`Y+S`gLQXuiGH?5M{LW{9jl2j4;}_@_$yXlhh|~G#{w_ifpvmjB(yCsRY^c|S0 z3%8*J9#=t`_{OpH?Fb3Mno-b3yG4`u63|XHid@}ZXEcRI=pWn6JAp`*9LN6m7zh_O z$m=uy?W`@24!z#^f03xm4!m&zhaM4pH!{F&9oo1+vH^d5z_SYMfxZ$`4iJiB0Ne(n zji<7vDFmxwd!`SdzT}>MD;4i%El*WcF$6eO1ftK>m|Gi?uM73T(eA+%Cb^3SEE;P7 zqN($W*|@{jB>~!VTX7kdVZ%!}N0Egy+~o<*7QeaIuR|7Ug-|e$B;eI3sg2n3QK_ZH zoLWK_>cY(L+Iw+Y;8<`LMb%-;4Cx8IsH$auRs{Ntz(AL#OUf_k z<0-)aMtesFjM6%qkviYziX3>KIhYd9Spgy6kFh~WNCVJ22vG15t+g>>9UT564;s2C)}m#l$I~6}A zH9szTj_d_K6Xpn{PSaIwKhPvxVSr zauAiJtgC*kc$k_-ACX1Nu}+H@o{Qq-Zs|bJ)Ck5>EA&9xdlUyH=s8$O5jz)nX zVRpClWD20!d(Hn*MKH(A1>bnMY`h2^9%J9gmU@s$#Lg*;`sq~Z|9x54Te*Y1ZK=Kz zf2@c^am)L9gG9N~=TDqGM(>ky*N(hV^4_Po=$kgBdx+nO{rew7_ z38okZ%KLCmm@PWl@9B?cQzK{SCJ)kp6jg{6M#)D@KnfcyoxVr!O)}Wn5-gn+y2c`P ztye)$Jy44Z1RE0vq{zb3Wo`ht0ZTf>0L&aT9TlZfl{k87z%#~QBuccRk3dVP!y!~9 zuBwp2niSF2b!?HA_OzOaC+tp+q%%?_PB<1;DF^{&9y6tah`PmKpvY|@98C#T=`Rvx z^Q*`Kh(vKzH=6!~L`i1kN&%6mpns64M}^sQ^1|22Os6f{!dKAPxI>>CJm|+{QU%U8 zMT%+pB*7z#OBVc#;r&=0{2pqdd3>@YZqWcoEFcmUU?GjV9q*9Q-UmdYjI%R*Q`j)a zGGb9Dy1z_N)*h7KEkG(wz=CAcm2~3rl3NL!m%RgpsLINB_563d8J|rF?&s~tIE(DMxt!%7i2b%)r75+P;gU$8kF$tIn8lbap9Wt|03-kgDYzrZc%q^ z+ntU(wr#s(+qP}n?ASIswr$(?Uj4iu-u>-;PMxarVVA>N6VBRNA&sQJcZ#gM~tjJxtcKB3( zQjpu836M(^_N1{2&I+6h%6}Pnq0R5F$VByNiC^%<%5fe;CXN#dCM_7j27&keG}kYw zg!Ed7&%;$k%eWcw!(I_%*6)wOER9~+d$M#M-g)XeekSi0^kbf9OD;r*I<_5X{krP$ zuPMSlvY=+392MEL>GJ2=QtMq6Gn|&ZC#-(t-pU3|T8YucQ)9}lC>hf5&t;IMs-ek# z#Fchnix*Nw*Z>ql=B!@7TP^-_DHSuB-H0vJk zN@-=M|(12d3}n`Qg5ZaTM6xKUaWX<8AFq%q2_KIsdkz% zS-N_6;;Cxq`qB?*o)GSZ3+G^w?qqV9Ex$;%IzlVk<2lBkfR(uP;k_}_nX>B!Zl`_h z{(J)A(EnoCoro*R+PI0Z>XbjTe1FHYG;xloyb3}az;CeOW+81L-|?=jOkforKHFGs z6%6nd)iUa2raV-kf{z_ayT8eOJmnQUc!R%_TusrwlD|KQC{4!7c`}H z-qK}YM;SOZ&bo+${yK7|#$vy|v0Dolp(G9c(>WbSp`m4aq}L`Sh|!xwdef(-R&d1? zt@CKjWMKco22IbHta?Mu2v>sd{e#Td<;7+kJ}esg^C^(>u2kc}G{d1;YkcawF-`}P z=Fsvi^59PNh@QtOyK>Pfr&kK^-Hq=J-c71gf^+B%e#9UhFc8pv74AuDC;Pi08A3~H zo~#T6hJ`)ts!geijg=>+Ri5zTl)OE`>{o}O#4rm+K^Nvmkz2WEQPrir;8vjL;dM!t z8uwh*_yo10h*Wv%5bovUio3B9e*M=*>Pg&^eVWUP(3Z|Gj^UV`1Yya16!jxlrY-&tU_)>~_E)Kqvv@&C`ObxpL(v zQFe(E^A{^$X{Cpns}@$<;?{Q1_7yJxweiu!_@R!Ll;VXJHkLp_d)fv6mbdwsR#Vfd zZrSbUbF;dn$6L|ySE=&s&GEo{Y)`EJa=$@%s?m8_YMYSFa*X46+S`RW(+T;g!hhtf zLKZY3z9)P~30X@2;5c`=tEu+btQD2~5bgJ#Kw*{84*7sYc;i{NO@K+dUiavDk8c-t{7XhYf+}CK5y8qYzxu2(kvtM47b(Xn(c(zjO1ayIq-o8J*n0ER22IQ zC-i-MaHVC#iSi9%-MU1{9jci-8NVfX%}T5Ka`5i*uyWw84u?kF5#)&%dygw@&7JaH z#tetxq|s{5ms_0zp<$C|Yc9IwI@)}^F=zkArVEebtMd9|wZfP_?g+)&f-**)d*MZE zL3@0?lc{bN%gq~OEy8XRLDu4~q8Qfz8Gh`p`n)VrLH1d>d`;K>~g)@G$i$hV$E{mO@{-iW42Wj2Q&!BVbGvBh-9kats5|XKw*LA#$ImS?E$(T@p%wj?uG?HDFQx8))g!R8 zU{A-xrbo?_2O{Pa`~`G^Hjh`zua8v`GH_~5M};tr1lEu!Y@e_E$04sRrIs7xQp=~9 zc{d^i&T0$UElUpFr!L9WD+p`zuBPom7P5Bl>x0Gak-PdSJRA+ohw`SE>*JrT!b6P- zd|60~yOgU4{_2e#y42JdL;Wdc-m*mSv29(3iwT_o+6ncwJ2U4e;Dk-5LuQ1T$IaOx zC9XO#z}f(b0z(r#)nC8ogUft z^s=H$%l5{_3G8ip^L>?Bcd12KwZ8{Q$r8MJzeda)wO3xRZTqrSZc{VH68s?$#!Z^G zN>LiPdq>taQ^-L z)_?`Jrmj+AM5+Ecq#q#BVpA2zpLnjT=tWq0IJtLpw@j!&{S$ z)*E&$R-;OKJ-$M+#9fs01wgQ(Ov#`vayjnzC&dt$7MqA1f7+;XbDlS^jN7ZIytH(i zm>)zN-0@twcy2v=lx~oD7`!nrTv;GoeAPcYTwm4|znOs2eI9AO&UyR1wH9|vZmm6? zH@7qI^E zETO-5`CJq;e_g3?t#%XM``(#^c|1yTQ{lIFyIJ~t_3pP?u&%FFH{B?g`}Xp@VenE( zs2p`{AcS|nW{?>`4dvhkKpWjaSQ=9xj>mM`G}AnLTLx^9OXI?Y^KJy_T{wpFqUi71 zeC<+ecotL#%qdUzugF3+=bU4@k~?73yo>_5s0`jns%uPkK`b$j`fXj#^?EZJzLn#3 zZ7MFkcIC8R-*akY`E1*=Vc1imGzh5s`f{q0J3!Q6!7xUEXwvwg%6Cgcd}(}tBhW7b zUH$~KlqH+(^_G-+J6Z6d1fiCini4ec#&Pho>|&$kMrG2T2wXDe1t+VsS_JXvWNRd6 zq$jI~?jZgF_&c2#iQY#?_({ja#Kc0+TFcbP#8S&d4`ELg2^-1Zv%bDOKL2NVetx`v zb{Lk6=Xw_v!4y^lJwW0x@=HGK|0%UEnmISMG;4tfbX<&*5h-N*tjWVpVP- z>VL}sZUJ44?sr6o-+&C@lI#Dai}CLn!2etc|IaGM{}ur}19}*LU;jr00O5)cHj4+ubYkz|4OPv+5$?D?tDthclStPIxP+-yh&WssCgQqKC zB?T;D7LL2W_DcmL^Wkt_1%(OpjoC)L{EQL77OkAxf0Ju3}=RW zn^0s5zIUvHAB#}bv9JhuQ|86{36r0hNH>aw46|sV@WOj0_jS&+206&i8?|xCzzf8 zJI-8TYNZ+arBOgEy=^Kum`i4O`hI>9;VZVG)1vv5p_McJ7!7oZT7FI!zGHgjezr7o zs7N-Ns~~zv`dGFEz3dNo^BH=A#`(NouN>d4YKmWmn+`Qj{2+d?Aa+!ml|(7(;DOe| zF2|09;iAiFw0$@$MceAWyB=H0a&jJLOh>I$SfZaW!+`S5Pz$R5$VVg^18&M+8Lh*f zE#Su+%9nX5sfTx(ftV$M8;rySVX0M1HE_$I7Ua`9In8@r<=LB6W#vFGvf-=5SiW(N z7*GKZB0IC_J$A);OvmIW6FCi_gso*ajx*8oV*(PEuQm0W3kO61(R%qk*l>Yjr1M$O zkFmP8Fh5!D3;R0nC%zn)MU8BJu8Qhl2!YLfx337JQ`9b@#k(l3*0ENSM-wDz06cBT z=)FJEhebQfX;2^ntOh|-HE6K$TQi@FSq_ixN(Bsq!iAH(M~n%B2;39vl$2QPd0i8q z^n4Fj4M2V-jnC6w<2xZd!UrRD$er$xNIH$wK#} z6gI}-lwtbhlvtmr6gp!!s1dsk&?G{$pkN3@1&28Lz?dVH=N+z_Jn;~=?AOQ?PloK2 z^;i*Z%HMz#V9gil>&QA*s1TKS<%%Wil|Nuy$ikgw^tx{Khej5Kn$?6T3*KmarAXa^ zYOQ)~CAv}hMcu+m-2!{v0*YZd+2}7>33VDZZgz}L8nrC=Uq`3Ys08I(v6YhvfT0hD zIK>ft6j1x-vx8W^a6i+EQ#FCq*tGk6Xl}z$#c_Ui03^y=(MoEclL0z0n7Ud zN+>eWpZH3dgb3=aT-fa2vqS%o&vQgY9TDw?WrH@B5bdQx>Z}y3??pC?ad0)$xw@WZ zIlKjf&#r)XHo_*YQ9}=fpd<^8EjSu!)!7aOB3;C}bV;}o8yl%d`-%i}Ze;2lgv7aBL-9wu%(;Eypv4+(7gaOfVv^jMGFi2Wl%Nve;xdf!#HuxE>#cyN84-+9y@?sG z3^9zcj8d%;*IU+)*HIISmlLO3O$g_RF=>2bS}PLFQAj80q>Zwf66PF!-r|fSNs7oK z=I}tL2xOB$oh_0!9MXU_qvC#Yo>jhJaB~^i|Bx!`u}%Z_FoZr&qWGrkp0hmQNeN8< zO#sQo;9Q|8rB{zq2_2ZdnC(~FTw#xNzGKuQQu*Ui{!}O3r+;>%Z>^@DFBeD8TRvu# z1TiXtd!DP9kk09}9@IcJ^fVG4$EnB-=0>-llGaM>l(-zeEIMEsV!uEEkAs59Lqg=y z0&K;`(6Box?n6eTNlN6=18ZfDq0Yq6aEi1t&i9QFBP+po1ED8-DG!F{Wlt2#KZ39> z1+2M!`gi!{II*jK`C5+n{p7{;n9y31rsKj`-Vzr)AO~QIOTm3rWDju`T?!Li_|XPr z_6GraF+-u(UD6(q18mIX<&Ye0QuB2mPjfdr_#ALgzEcx?R0;YFPj=99-udm0htx3= zj7)k`ao&ZUj~CZ5<^bN%u%na@(3>svJ&TQwW{zDl9qLf<+*)Z5SwHU7J1r^r@3u5V&{77pJU-UXF+9hin9 zu!TfZ0o9JOrI2rT(N?^+-)cMlPuSX@-+`u_Es1>Q)xPsUmKB6aYuSbYf_%PQ{M*rv z)mdBunk=ZV@p2~3au`47(E?3hvaD)S332M=_IKrGuL_m1CBM}|7ZOkQZ-|lMs`a87 z44gR*>hUn$a5QyRGxu5gVj z26?!CyF7uhrG>V&F?S`P!$q%8fL`rX)wvp&2Y?5`Z*IRLZnUH&RSb^JKhWCx725h| zE`gx6cG231pQnv-1yc9{yAhO*ZsK{8`2s0~&|2}RZN!w0SY^{jn5|L}*FSm0rwwI6 zWD@joXpPm*{lO7ZzRk$hYXji?**PQJgQNH#%xz&-gaEZr^dAf=L3FW!n-9Z zG&vcWCp!^v3#K;ogFrn)OuS(eDvh=A^iUG3-pZ z!_$o3&BFR*qk6tzG_lx~8gj7{=p#gSh$z}}JXv2OET5Akba5Kndsk)V+ZjeA7U~@G z1aexkx7%B%pp;g+_)7=xX;t_kMZlhDunpDGjB2*T1`C`L*soMPzpeXm?=cr=o-FRH zNg6o@zW|Y-1ZRw&^74g31F$(Isii^#R3tw)f92~pA)`4Ymbi+Bg||v5jVtEwYLWc> z1Z#4e41003fXPLO4sv9Joi#g@rOi3*M7oi9fYA%i?8cB+-MkC>8v!6gN62;+MMw2Z zaz|?&o-H5D*HRv{sQ8zKLvcV?-;R54xjdS4c)(?K{_-=z3pxN zA}gE=Rq#{i@H@(oH;e#Pupsxx)5GgJ`iuU?qKaUqw5Rn~Qi4qk=OC6%)blU2^r`LgbcKjLL zxPh7){k=((T7SFbcwlopy7gytp-=_K(CioAb31hTQ;$*hLidyQM}x@&WA?(- z?1E2jdCpw|C4nn}!_N(x8b9Ip_#at+ffQ8C|R8MwW7D_o@`%*p;5U=N;k01X`5%VG--8f)rJ%@mn&)brNw0WDewwW_msEAAFT+DA@j+(ph z|2QEek;4{6RhCatL`4cNFDuP|`~fdF7Jh5TEVq{u!;DX|P#TY|BrdIrzgcgj2fz6i ze>-yr54NNbKx;;WEN1sN1o+0UsR4JRE(#~VGW;~5Qu4$}shJYmai8gVT<&$%OqO>r zNu)VNgsq@Ge@4x3+De9w3ptk^Z|3Y#78)?DbzIY51~e7onQRw<>v4_unDW(0U-tNQ z=3`TeB0LHHWlGZD^eOAp6&Eu=6^;(?oV<0Vu=i7l)$_~Uz zRhrAGF)KJ*xg@+@sS-l+^G71zYI3y;ZA8Fl?$se^@5cbkLSx~C2Tu*3*Mceuq8rrf` zU;IX=R&Q7slnvRSo_TbVABYYY|h2O?e&FpSkY!g_5MN&BN@szJx7z zlc{XerPhe0=qeA?F(wbAv5MJ;53W6+)reIs)>GGWmnYT?uF)BTc9Sou&8Ym9rw|ba zxV9oxJZ;Wli*7f;D{bHotsRqruEI__&V9V=GyjPjvR&Oz$*pk<7ywuA)QxZUg0zsV z;34~9#PO%adu#O9zV{yp# zv2TB#u2S(nVp8$45ph0sKs8&Z=tNa#GMWU6W=qM+?6xo%xbIuvRx6wtZZ4h<7SmMw z9e)kow;9+ny#MxpIgOAxT&O6~3J76Ka}hkZ0uJ;1NFC|i%|@p$SbKl1cmp9zsC7tu zId>qNtL|O-NNPu8=zG)Z*6{4_jkI&7<1sxm%*N&Uyxe_&C#|2^zCdW^NJXobjwWaw z>y^@jVZ|Gz)(?JSXB)b$@J+ic!(a7tVi>|nN;c}=(G?L|*jO=*3kZ|mOt{?Y9SKd@ zeD8Wtf?(vb?;niz${MF6c?0IGc3~(@sG7(M!i{XyeNb3$3UwRIe=17FjV!oQX;P2K z8b8wT2p&*zrW1|<2`?i-qsPkzm3_N5AXD@*zf5P}fJw^@;0#|CLSwK1mX&R*tB6&r z;$@}G*CCv1U382i+w(P9z6~9?PKDa)9%K|*c+A-mS5O}Nf-EUKyTqxf4r^=5&?CD3 zX!lxpHhKFY1S9uysv_UbrTI#A4R#zuUYrva`JOqb7O+^%()H~l&PTr+`aWZ-`B2;U zvn2dghx~J9^HsLEq}^%d)Y0#=gtJxyaOZOK)nnf(M z5^Nl|tb4?-GfKzzGn?nNboVZ4$LiZV?`?B9$XD8~HdJln7#Xput3(22VYYk}=UhGP zD!)VhFF_5P{zZ-H-~&re7^^viU3h-ZL)QSD!ijcObx*X_X#N22VDFF3UA*b+6^v`@ zdySa$7SN~90M3@&iPq^4;)WNCU0S|&rkjMb)%SY5c1z8hu0^Z;xr+qv4fmekl}m4? zrulab&g*#;%4CI`^V(la%T3~fl7_AA_%GXU@+sz6}lO%U8G4~<7%KI=dl z1usVp)CtMduvOLfv=&`m6LGnW*>56;4iDB#yrt69kLxM`?96$Da&7dN68kxpC{{?w zvgp_GN^$$v*GS+FbrHeMa1^iCtM0~hx%$mq?@>7X=XvKAXJ-J#rUxfeE4&rob_d9G zm#5DmP56`G-P5#;xjTXFlw?dh|AkhFwYR7n!{NU0aSWqY0hep6{42^mfxFWby(uF$ zdUnX;M(8JosW$KXT5j9d*DJ-YPg#zf?ruG4q4(*Bu=aI}4Rjil^Jfbw0ehMUH(XhB z;E$rIQ>!IpHZsL^i}BhhmrN!l#6mCN{e;h1pDrOhg3_HF^v z!@bYFDqedAvZm-)FS61cM)bF-)FESZ;(+hCk{ees*eZ>w;J5VBPf!K%jhG>})wV zJ21e2z(_=+#dDZReWluEWscX6DQ;CYERLLet9k_SmZluaycuqkR0)s!PyM3V7Uf8d zFmbm0G>S^7J!0zQ5EndcR@lg=4hTE>6rcldMlY4QDrv{MvshdNhs>kNnc8tni~ zj%DU@`6;+n!mI|4L@+N6Px6eL`uSRMgTq~gm&pVb1&bwaUDuc&gH_bw`lnllHwC

^FP=u~6rKzFeVtL10VM_B}`SE`z`nGg38+~#nE%hMM+OoeCtQDf(ef{%elYsjPi zo3-qV{a)t>ezw~U1?T3<9j%(2(ZfMyXCI}>!>d~NQ-t|3P1e=R*k*m#D}USqng>ev z+bK|G&biNq(c<&_X~0JZ!S6%tCxT_0>GH?ZiBH4shq+Ol^RcVttrPk-Ph!tJukeMa zceR|nX;W)0BS1~6VF3NSE6Q5$-cSmpz1n1p*Ew}nlk#BGOH&6Bh z$?kt{OvZ90GBn*|14@0Ez-^qo)@0XO~f`_FK|<4Ys=`QO5UO>ar6$GOt4U*PH~ z**PKW-mHhfE1q`R?&PK&=}^@ZU9gk;s}&HB?sn$##`-eGXs*ISp2p!B3EU^t_)5B_ zrlywq*7|1VW|sP9`tbXzm{{r9>8Tl5m>HPq*cs~S8S2>S*nXaBcSn0B)0j=*%PF}) z-hsSvL%%QRJ!N)y5FN*2L(=i&sLJ9<*BG9ASOIxKpHCBcQ`m^yKC>5yV|lD-kzD2L zchNNE;NxF}2rSEas!n`<0_AiaL0mO~<|=_df}Hi9VcZa*7OHRnZGb>)412$TLs}B( z3kFi2E^9gS6sW&N1Aah@plF&#@o&iB^-qhy_}G~c;X3z4GDx6(oc>$@dyfAOkVm?*?ocb3Lu|_ITC&>eR%a|{ituqnk3$W0Ye})H#FEo|PQST}0p@}evSj}cU zh4Yj#(=b~Z_#-Gm0NsM&zTpYw`AFsf&+^=;zm*7_s4jA6D6DuZjK@RUfA0vyo8F_+ zKz{Tg{_O}fInm!Q^F7Dvx&C4a#TTz}4^{uSe82(F5#S04Ai(pqI@B1)Y?6Mz%e+S(dYD|6i~s_W#D3Sa^crZ~O8@kB#exlTBT^h^R>f zSWk0!2VTj`&;5VVr#}rlhZt_>#qQN|T6R#ekznk@?7{M3pcz$#B@+{hOICdoC<)i| z9ZT3ltTU?yI-VdDBL`PvR1xg{)eI463>(Y;poi5K>5TQNAV(yagQNg${#x-XrH@e6 zsC|qAE0{0i8`#>nc4Zdd8W~bV8jt{}dE_`XdZEJlLH|{KQxQ(AdBXZ6-!?2llH6?B ze&`3I=^jvQbJShFze74D|={p)R}AwM=l1)5gUXf#g~Tky08@~ZB~DR zz{Zbd6Anz7ajWAsVXhFW%m+1spvNVk?7gd8g45SMyYhPiK zl^-x2aP*34B?ws3EUc|2iD{>j#F6uiA!GXO-I${NuSVG__7toC;WfQ5UlZjHT%cQm z#Q6gie*b)+1qR6 zPfOq@3`ANXBjxDd`3;Y*?HVF898CQQC)q;lI_*Sjbc9i+hy;_YV8`D!bZ5YVac6zu zz7!IcS14xSh7pPPeFNl3cF!ls*O5?>h!iRlKMoGW=vgESrEo|Tenz2FC}yKkkPL>R zUL6UAQ_(8C4~CW!XFBHNKv-{!1lhU8Cs9#<_ z4eaOYb5sgR%@yod1)P#dyG#{t*=)Rn1X4p(?k4=5ci19?9Tzaav1j&oeuI3&{O|lm z5&0kpV19#;!FDe{^pvp2=R0+HJggzgwd;uRjuhP{D5;o4CfO{T>`$Ns>1`c}%!oub zSv|Y#vVY_zCX&f1O27g~zgsZ!5i-f-?=%y$OcQh<+9(J7==v$pQ6@VYm0d2%pa1fj zu7P2gPGa*%!0?g~>!G2k1nt(oX$vJAWB8@`>vM|UI9eyR!vDM1B>UHE$`QFSLLHFF zGQHQ1>n$g(@iioyMeeb{&Jw!jSa2f}Gs%P9Wb$j~VrsET@f9YmQH8oh21re6wqKQ? zPk1CPS3i5Tia=djk=M~AY*l{tddY@u{3LO{nxm$2y?Wv6_n0$BQ575t6zFi9u5C0t z7erANrF6?1&}-gF=Q}-cCm3shWsZbC| z8X+VCy%@++NY3gfFagAWflW)UK)38PF>~N77XF)fmU;YNROH7RM%%1@R{sq)K|E)c zT|z2+2!L}+>!$cGT_V>Efp#ffua<8=p!Heg_%C^bRf9llqd{xyP`ctNUB8O-p2hkv z>48<-4Ez_^1o>}ZlSSsavnw&kGfl2_$^|-v$TX2}!Mxr_RX9fO2blmrQ&>=I# zE(GX5P*|Bvegs_X_-&%D!+Z{qq0nflRu_LtM1YsG8@=SqGl;^Z9 z`@cnTj2MYPM$uhYB4A*&u55m2?V-iVxF~n*qtSYV#egM4b*%YOT%pPZiHO#3F$y4# z&<8<-HuzC!91*}Gu29oH|k|V+G63)L0o}qgXs@g-;j$@C#k`HweZhtNwEbCPD=~usDiM9 zzy>qlpb^slwV4bsqcZ|Wr+qU5Cu-2Gkp=ozTg2Jm&Hy7B%oP#o@@W`iSs@S8!blY{ z?P87H!DZZ*eCUga<=g2wZgHb5KkA}N&v`>qMOmpsX*-6ZC{cTHJ`QE}huEoFpAjv6 zrGe)>Ow)UdS^^BQJz@WXO^koRris5`Q&W$$1T3t#98?hXjhse*_^RO>pGLO;%rbOT zjnNUzbeIhbv7;lJtGm(gAO=P1>~-??~fX|Heyk`apVJV(6c{H%UpZm z_*;wvJNI9MI>%hh9CAcvQj^U`Z&815iq|& zybR`KES{(;i>&WN5}4sJ%|1nr~eV`Te2_hWDBlDZ*$diR>$2qH8rY%tjAo zBQG;ETq=^shw|W2%LIYxEK-gN0V^w0EI<8GW}Oc`t>a$i8~dFw&N_{7M^6mZJQj26 ze_P;q#t_U8C8P)Y*ZcobObY%*>2Eo3QbYl1ETALEd zy1(-qjHb{@h&2+8x)X-FZX?6SR{bcsvQe&SEV~E9yY-I*{8`DGj$<*&%{ zS%OMO3^j*HHHQh8DOjr2bT!%wzKy1f)z{E24FZD>r(6MQV&kR zDHHsR9hJT`hF$SIEe5W)*EoDATrP2g>Mn9;VJ4j}8myaO!^AX{Yt=X}KS$Mgbl>`V+a znWICu!bI!;5u2m|V$&CWRalGM{?3oQMEqAiaYeanJ2Y?qL5aU&QwBh6+M=w&)!jkQ zzs8bZmiP~`X*qaemuiHPuya4qPoavMU1~c-`Pq$V#i`jq=YAimd^>sj=$N7NDm{x8 z-Kymt_}|1Pc+JC!n7yDG>hPlt)!>xrjTo3wtPdo+c*Cn1{Nd`numyryK{cr=@{;uUWOAy!9wqA2IyUMf4~uRLWHdO(!t z9%p#a5LhQFeB%^ZFNkazV(RCf{6}np68XEs!ArNBt>6C<_s!W zl237^YI@Kr6j1Bf%M4gQ4Ol-3xW9`V){LN^#*piJkn2W}#{i{?&>an^Q(HJUU0e7& zDl6+gH3_?mjaf;RiliXi{CJkB=WhS8zX9xF`RyzZ>&ok{Hyc-o5<1vY zN!YUFew!7erYpOq!VkZ`B2T-oPQ*RQDC#D(was3Gs+ZK2JeAdszNVC?Cu8W|Pmr2p zlx6NL@t1?ww%nQkKx~Q^Dnk2Dv5BCX0U$P2PU0eQIp8=QHl2=N43A%ojN@I8@7&h{ zrKhKSi>zSqz&$US{1m!Amp?tvnqT2eSZR-4$(g5YmU;g8M{H^@yQ7r-m)NxPpJG$d z|6OeQ3pBYOFHoJ8B23o(<(oS1&66J%0b-LVKy3O;ICW8tMN*BiB^!OHCjZuT`QyKg z@OE~STu4?8zx@rF?>Aymd{A*|DC%++C|YXO5R+`y^X^HiTBm9}yrK9js`YFpR$#_G zO{epA5VjXPS#RXeY0{ovreP4n)Kb0(9hE;+UfVntq0kp{$d7u4X{5+!mL1D&qeTBq zaM*(xn!2J1hFzPPmt?`_Kw2k*mhc5RZu^92q${w+EqWO}?G-cY=dw!FMYD z?*vEQUKFD13t)l+GrnRfF43~cO92hQC(u_>wZ`Bh3&lV9P#>8BHqVb8)Ri@3!<7vP31~&^VB6m zT(T6cgNe%|rMuw$c!={r2s`AxTFXKdp{m+F4_npiA15Svg zwLWeZX6jIPnZ3-(hDyOlyULp&{6*Bg&sN(?+_eS0_msX?R9Ke#n@LsB+7jy1y^7LV zbBn#G%~$CSZIJhkH{7M}sDI^~$=11pA#pUOU51BPDdr>p5o&|*|kVn> zKW0-1z-&_gm)XP<^@s9@tmds9#7jNVFEO4=*N`wt9W1TEAf}Ga*?9|emyaVq+N&JJ zLGH(cb)u^dl|ki88gn)FjiCXWv#-;7&aqb6YU)x{n6Z`*0v4TFwymMxRGy!^pD@}q z%|jVJ=!Z!TM}1>4F|Fr&*mw@rp_}Ii={?kv1>s)nt?fHL&qQNvFC*{FZ=^3Rb}E%! zY{e-NyFB8{d)M~D8AB6VbhAST7}2K9)~PXb2bZoBUj0L8kKCE}E0KWe-M!e@ik7xa zsQN_%%Z*)UO7%Opg(Ma$<<)Dp+abfHlebsJ0SK*&oAOA`q&H8MOn{7JTi#0ob^|u% zOmU>gO%G4kU5s|sVO%B@gt|v74l{Z)Y|c4}EBm0&4YfBzbT`6ac{R=b&b7dAp<>Sa z_%qMV7fBJzr=FTeuUbMtp{2c%U=n~frAj1iDBE*YeC!1wzIEPJf&OxJrPScQrK zpiRneDnu2JJ+1Dj4fC*ABVdP1dal zT)0`yM)Nd`*6(R)jn>b1_AEWB52e^tC&kZ}GS%3BM{=TT&{aQuCpa>rJ6-Ql)V7xW zYN`r7Gg{vb6?r48ez3!6)7%8-y|;f&kCM9LIoqSjN#||Ru4JgPb<5zLEqiG}d#|X1 zDRymb;gp@)ioZDMYI9R)doGI6u<$k-PZ}C)In?=5pSPn}67_oeu6XSXzu1xKk3Jl5yu2-0 zcdz3ozPvsM%pM7*{@`S(RBVTC;eC?80e;wf{ucz+{g>YYrZr zTedzaBxIA@78;gsPC}nS=mUczvvs4Hf93{5r{_Wzmw2r-RqCFEBC<`QNGwz)x!ukT z_DHW1=jUs2lJ4Hn40_Vg(G;(wd`>!UE^P#@r#c-L**-D=b`c#O%j4F{hpy`4RM~rS zQayxnSL>Q9ee1BN*E7Ge&(tYCftBq0VrrZ9GI8sm{9f+V+S3-U+w$_~2SG-j?ctzZWiSFae;5D@^uWAkM8QRYf!3U$$ri-ulzf@ zneS%aMR}uo*)u18C;tSz_zDl}BTZy&_gw?mNXa9JwU_TeQGS(KPm#)a92PHW3U?8X<7um~JtaGRYtM7bt9&%rJ*I)bxY@(nsAnn{CobGsi;m}dRlA-h z<-Sigr`nz}6)UjqA!;$b!dZ2O*gEUIi=b&qvdZP8`p-4mRr6*V6Tg(rRZ0O_dT#&g z$vwuR$T{2Inh$}`pIE|r{ai;(DipJ>#R&8xZ>X3dw2PZ&zUFAN4bR~}A2cl0P|?{Q z-Yet^@m^e3XUHoZ5SSC=UHUm&I4i5sh5cD0m2`~{{j;fzUgwqT+kJ;(ORF>ccdZ)4 z0q655ZH=`%edo#dCS>sI~q&Gd%j)oCeZuAntx|xVGt9F>|M?>O~9nHyZ%jW zb$+*+AhF{i-~wz+CabOQ1#8Ga5`7E7D^#cVCAJ5XKri-P`Pr+y;EA5hZE){R*Q@@6dx4GLE9|JKd$*^_MJK4-<1c^y< zir_p73H-8O(+ujpC58>@3NsA~6Xc1|jZ}$wXc8VtCn{X3- z$vR?ShNeqigz@_;ROt?3yY1kj5c*tKqk=4=>2Qjt_akJDsz?jSGX?spqPWbBz7dj; z!@xB>FN!Nj%3)yZ>p5|YnvU4Pz*W?=iBW@D=k0)Q-D*ghQrN+K5cZz_XcPe4%c^N3 z4orq_p7w<*kC@Ltvg-ndqBh1qikMf}L&uK3)=1PrOe1-A&vS$9u3(@bBr%mYee}z! z(iM;d1TdK(oLYs++nD(?;J;KL^8h!GGe?{63zKt&^BB>$;yd|Fq0~`1RBm{0xfvKF zZ6Rln!es{(4HLH^^XHIr+Qh}6;JM4(mkn=}~Y7qWHBP2P#J{}HG{{6iG6b?Vj z@>z)!aOvupyzfhYfD%J$0$!#i2p*N5>umK;Wk`&KdQ`i|@Oi{rdzFL0=klY5&Cp|r z+|c49*PEATerNrX1RhdtM*AN7=lW-~*B8)#FQUH!IvZH(SFLe?Or}Ec+c&=dFN^5^ zPo0f_k7OVcz>NRC{?AC}?;v_MtOh19C?q844-53z3gSR&M_{$@#DP$!zI=>nX0r)W z#zkcw$o?XHaB1Rvpuz)Sal&8+K)|EM(dc>SH236g}Ni6JMuClMcQ{u!A96sAfq=rPl zUP$$@;R;ORSv3^C&}Al81@Oud%Y|HNYdL1qGqhTP64+HNevc@DAIE%Xe^7&>Rr64k zTO`4=qH-gC4~su(A2vWcQ#36HIqSfJi3`9mqORf&IP3YV{hXko^R1$H;B!SgMz@0I zufKzL!MbuBqiLq{yKLnzdTg0HbQ<2&QTz>W2h$s zaV5~n0tH0=W`rndh-kIr7FlWOaNPm)fXO$GI$e!@&+6|Xg*TjNENK>mrS67->N;~m zmDKZ%!K0$I(l|4EAg63BvfY za#VhPLR2gB7smAUoqsebZqONj#G@4=_VS~GcYj|K{E3Bc8E)#RYG4nEglBFZ0tjTN zU^>tMfs8387M}I#vTkCU1Q9aP@#MKoeWM+Hbm;FRYF6@{BU@ecwhRBlMDR~2B-rVJUgo*aM?uo@j6(z?~<^^IB+%52W}d*ePQT)%cOqit>1?r zMNq<#Amr+GBbT&@hR3(IrcD~0r9*SnbmMmFQqMfr4TY>FS4OB_A zNiRnHnK&1TGj_hNn2W8vk22EA{buFmiAE<9(b@SU%2yyR-rFCO`Um`$2HYj1|C{ir za)Ou}yS&se2E`m{SfT_%Xd*>|utXTej{%P|vA6@_@GYh|V8cm|mGAmX7|KAB4XG5y zVSB>+?Bz(1KW-AmxaqYDGiXUa3rCiBsaOTSvvj%r6hMg~98x<>lmIJNi?>Sl1Po}f z7&aC{CxV}^TAPQFh8#ktU+}L`D>SFm!S>{dhl}@w#p}nyYO+z}dqQ3lh$Z9`gRDTV z;1EGn~2`ap6ihpp`V{@5;B2ai&DZIba-Q7_QOx2_DPRYN&hwdJUqVe(;J-4dg z+^u&Q%}+5a_XvJy`hT1=`>RLgoT2hgMZnuOz^rB>j+;c2;wey>P0K^kmsgBy`ksMb zv(+`^LUSQ)uLX_DUGfAYvPMoMd)^q6T5=TK5TSAVEi_E@gc#SR?_8{TsB*=m~eQ)b;>Wta5 z23ba}tZVU2X$`fcNfCZI3k`TbBGWnqt)sxs9ojX8!%Nh6&SkX61hhT04Cg?l`_uz_ zfnS$Z*^8IYDF*ftySE#y<54*PA#z&~tU3m@gU8s_D}TFD=bsUcVrcCgiobyjN8$EE ztN#)Pw06kA#3-S;Lm~v*E9C#;>m7q*jo$Um9ox2@?AY3|ZQHgxww)c@-mz`lwrxy) zb56~fIaBrj@K*P`R#$)MuCD4@_j6yD6Pd)Xy8kk0IJI5Wx08XCK3A+8XRO8`Im7Hw zZq_;qjS7zHs@6E_AyX6v7%f2QfpP3wVR;*YZns5lJVdc*Rh%8dth}K0swiaiLIiG8k@|xEag+h~#qQN(anh>+Vvmv!4N5+M3zW~2M?X`j z!e$fjjjpBoj}Lv)K6wU%ZCK^UhYqtrOd8VLBIypRwMU)D#Ii$ez-%2{5j4X9ss>eJ zV5Lpg5Mm?G%4G*OdS6RtsoX2SAyWT|mUL;rU0?g84tD&}k*wSV5)W5U?|W^C&)Y!Q zbz_S$B@b$+jIMS!e|co+4aL?I^L4t(GNDhgAwUeb_4DoswanGXl0Cm$B-Mf1_o)A4 z&>#%%v*%ersUr@;B{(@zGY4wVLu$HE1me2s4+*NS2A0zb5!@Ila#_I}EwB-bv)Ptu zx-5ab@J~OWj?u{H@zd8?LCG-Fp4~Sqc3**cHtg!zDjlK(c?aEvi0&HLjX$B^g^vPI=mWH`uZIA|4?+!K~O5|%s= z#u|r2^B0LG7|9BeM3uVys8g_&DUHJ>EJuxOB|J@2Xml>*wN9-X#h#;|5AK|@R1%=S z6DC?m(|?@UkD&WARy(^->3M2Qgr)f)3Z8F2>TzWe;E-;Qa>4sFm5+F@N8t=k!lKbzJ@Q|My?wwpMBKz11@rsx=< zL7&n2G*FePGgrif3%@9aikw8w(qB9%A6t~o1~VD4IW=K$wQdHv$`YAYNd-?+g+jpv z=|2Jut^Y@$!7h}q9ViSYWC$*lpFqTzNOVgmv_}*oj3%^apk0)mHORw_qc#eMG(~jo z3F>Bz$R3f=LEL)j()o7|h=Rpi8AZ6c?Fo&83}@;$_zNano+a>-Hk~>vEG+_-huHb8 z|4fBvhwAw)*WRs%G0c74jF;x3@k>j2+nBzjL5R^GB18rN+Mfq3CaHd&go#`LGSM>v(p8HZV)m z81th&|CEgT2EL_Uy|FUxuUHLPu7&HaUcJGw?zdVE`5ydXcH6k~uHS$+?Xs_iu-C)c z8RBe3IZ*@7sF97Cy;umQL+iWJ(Oj@VTFryK3MSja#2$9kwW;Gv>o9@EQ?CU zAwgLd+9f|nit4Qsygsv{-IDdPoAv9qHe_!M482PV)fF)~iH5=V^~mXdbk?wO_dQPJW}9zo z*kWq|k3I@tf#pVf{>z~u^OMfVIJh{C zLL)oDDG4!m{><4GSVEC|jq+iiBUxI`fPzWD(LHd?1wOjx&^=1?ROYqtvtot`(&QuN z50~XNW*3F9t*{BaHL$TJ?MTPzgqiK*u6GM}fEqeP zFiE76@^ObAgF#tNeMvS}V@bEhKm($4te%0#(6X2ZU8W}yICOsW30pWDvjApmB2&N#_TL%@&3ZwCo8hl^fV;E+5tfvH_2cS`hyVEM403F>4Q;7W)q zmBphP;sM?8;zTyF{GRh~sB8uuu{oAvoCR4AV{foYLD!#{`vh5$mQV=AmMIDLx+sL$ z#64$Qp0y)~BNsbenFJ*~USePb#NG1)cpMDu0|{JbXQ3I}p2>k!LKwMi3-T*ARvl56 zsFy1_$k^;MqkN^uO*&4D*vK}Q;4X{qW>LhtzZrI3dG;hEA4qPp4vxTXQvH&?a!Kgk zgpzGXNTTws4HMnq_FR19@^?ZWSfe3~qZa70x6=Kx6%rTT7hTJt+dWd-*Z1)Ckw|j= z&HzEdKc@Vz+y2%&qvp|iot}ycd_cXFlQF^fmZwXiIBuZeITV0l7q*(}%9T-DQtiP|Mqi!28(Z~pb}BG^K^LI(ze z|3#@O_R6;5dgG=%B~OUSJCDq!eX9j=_x;s5c7$juFVo#}hMa?B6T&Mbw(>m`KaS$yE<>Ufv88XH(6vWr05N? ztZU70xo)pQAgsO8bt7877Q+(sb`>3_wtk+_ZPVJ}U3wiX^{#u389({?2AEeuF;c== zJ1$=^5Ik&11j}|O#qee+hTTW({+PMv zRR8_^KpAq#POh&8m-;-`M*28T`vm>IS6JDRV9*&e^<^9A@P>3PThmV7Z>%O6_9dG-kV$N~Z5+NEp9|=Y(u_*T$R};} zpXSnJzkjUncg!kPbN@w9Wz@ho>j^L*?&kp4Wyw(n{-#MYC{E0 zQ)BpXxde{S?#Xd)G~MihqGJQ3+J9X%_R%r2V6&5(@b!whKDB6gdOrn~VEgU{jehxd zpkjGys^SnllZn-!2u%=1Pr6+cq|h*&xTGg+Ll@ z`X`sNHyZ}HIlQ;y+bXy@Y0eoVJa61DH#Go;`>Qk>9*43cSvQh!QZaHY4-erf9wT0T zqC=%(=_EHa_jhIaJ{O$8`a89cOH-NKE}k84V$tl>;42PQ&3xjPoHg-MpB5_Vf5uK* z$2qN1Qe!Lj*KVwwRDJgj71zml)^Qu(E3>yB$582)`IX#mI-?u4ZH-3`V?hy?ySFXO z{^2xkOt236G~?gc57k(q{2U>3MwK{U4yb={p?_~-Pw%#MH3w5js_=ee&1}*g|9&x2 zg{ZGq2<=e16}-9LuBv#nse(bOBGXzv_?`Fk75mxZRCd{0Qx#m|w&#*rKRDh0orm&< zaBl9ZbuKOTz7o>N3RS&X@ahIU6O5h zypKk&baTM0*RVXT+8*RL1OXWF>!_#eNqzI&U) zzZrBg*6U*Xwh-gc1wuE@cTYaB@4hao=etS|BVNslgKHigy9?7@VkKNQW>hkKCy+Zf zrg{^fYq+lu&%Cx+orm1%pXHC%9%gfv9fy5&SoBBzJT~BbUaHxvy;|o(F+Lm{i&O3= zIe93D_YV0sIc~?6S~RC><(NWn@9Z0gU6AZ*?(NYsvdvQ)xBGSU@Y%iIKoQujRWF(# z*xXw_0;^(IuI}wOb9`)8+Ai^{A4STxBPv+6yS!{$aI*Bonul#RYS;Q@a;P`xXdmpk zwky-pzjSTYiamyl5IQ%8lL@+giJDT@b98f@R4ez-=qvdXyf?-Va{xyR0}?KoHOqbH z+qe&oI=-2&XKjow8)JEQ5Ro&lF1E)+D60Qae;DqTl8>YLUg?x}6z%U74v=sL4(>%X!e zb5CDhY?~hM;n?rTu0Kues%c`w?)z8&{i_b2%U84@xKQ79Sl_Ci@B5qPkbc{72Cf!C zJ7Uo6m))D|+50%#wYmjQ?IWI3&EWB;XPXbK)LXQS?mm;|MUv-T$2)&n{yfL0@M+gC zzwxx%OqiDK2BTwiZTxjKZ_HLM5)Zt}?Wf7xsi7 z=ULdR%f4=L%8%6*cRf6-zSf%V=-d2HjqbAPN-7}oQ-_fCwsA0E`^v8}^&Fy;Z;8+w zL|iIPR_udk@U8Q~`blCi2;aWm2snRxP7^RaeF0zTh~Lm)HSKocWsnb%bVa3Ril*dJ z&gIEr6tuSWAUR0+@^AF)t4LsC@d zl3VQptM)&2|FN-QpVIq3lUX&n`_;Ckr}@|8dLlN`>Rn%Jfn3Tc8W0;>S18#VZaKJ8u3ygO!Z5a2uLU?1nxjcVDj8?fpYW=9 zI__=eJ%5VNm-z-K>n(nhI)C{;=@j0djwh5yHcM~)y{XxF+6NCtNc7d31({$*Z41BkP$Ai>b(ai`05je`w? zaM;T7jVD>m)@w$@Gxk;p>4!*fKL{^N(}9m}G!D$?@$I<({r9f?`*vu$8rbOb%*c1( zY_zuDq9pz3=uf51jhs!4ld+(P9#K3e)KfeAu&xG6%|HAj>%Tf*Ux)px{wQ)v=?9;o z_O1<%$sasqywy&(sn%-VpCqQ$zS0o1G{3X+UM)NzBvmu%J#f1djv_xguPQlm^lw8& z!zNj}55cz`245A?bMCvVeNCjhCKWILkyHD|dv0`iVm*1{Hd&m0(>lt$G_Cvat#OGi zf4XiwiEE%( zpiZhP8w!ImK*57dQ`CP5kvNd7AFqyu_nSTLJ!!G9Xb)VZmsN-Xq%pe|{qe<;oMmKc z%Ib9Gcf|{O42&Vn(q9`AgQjA@LPkVp&*xE-hHz6O^Rq+VGyC;B`~O5i?mtmbUP=(g z`wza$mizyJ@A_{<*MC&%tp7U-V%D>@vv4xe`~Tp(P=3%||GEBG7IdlQp^l=q99mk6 z^b9u$=FlIF2a0AYJQy5+slCS)Vocf_XMasf*wkPPnRk`n*rch|_+#tXKIj>+VA;1z(IkydrQYAM8zxQnOF<^mkr>q9|*b0a>ya899 zW{MYLq3QHChzng_4eX3l8WQ|57z%Cx3hp*Yv@d7~eWwUmreV}n;F;xL46EZ8)v5S9 z>WCw*W*XC|z9eIPAnV3USGeR%L%;D(7F_yk8E?Gvkg<%>gsN2AVxRw!!_}E*%dEcO zv~>MY{vvaxZsJq`dVWy?EK0n;rt1h0AN7o+4n*vdx)Sct+nH8b9f5i-_^pj3P?gOY zw!3~CxaFKW3=HQ?2yHhVR;c%X8agX6EKO=&iNSwmq`53v?Zf^^y<#4aq3@KKU`|vG z>KkH`St z&Y1uol<2PmhCK!$$t4_jCGHZ*f8pwPAsXVUwmc}r89CI)#C7mDKNr7{U2=1#Of4XS zoP<(`fecyluYl#_vyi`(C6_LmV@_kknK!`0E)*AE38I>fX%k zzU6{;?l_S=1Smx>!0<6k(sK-(clLZ>cJ?q9V5H$zV3f`cOg}CdAt(IvUy|DvBd8!K z6k}T``&B`~HV?ywRe~`QCrC?EXi~)~1mgb{J4pzOq!^d$+j=$tFRA~hv}{$sL)xmK z)FnN)kqRPBg-~Y!J4{nZ!b)~wjo8u#t#JymaSF9Do6s_g%#xQ85R?1ZzPiIJaIReG z(gfXco%;xpzmyo%GkfA}uEvzk34ck#5Dk(jagACZoy)+Grm&SRxmFAXr4rc$obW0y zI7x&z9}K?`;qxD<2fLiOQod0F{A||!-Ga*4C7d<9mH^}kMpFR7V3j`j7PKj}Rxd3~ zS{1NUEqNdI@ma9TtEW&zB=#&n%H5FSa1mp}mP z-1)-)xik}JEl`PCshJ}vH7XJ!sTvXfV_iXyD4$JX-NQ#jL-|lOou4F6u$NT z>&hezI6OV7r7 zUU9(=Z$V}CpM>E#%79CRP30mdvg6ZuX_($SM=m(ySMQ1cVF;VjM@*zdOnf1B%M)HH zvbE46wiBRuiGWpe9mF!l2Ur2}lUp5~{Lj%Q|AxE-6+3zjC6+h_eud508V6=D1#Z>j zER3Wp(t7shHfOO~BBlVRUCnoqFjGx}mgL2b@Z?_(f(3A#Dw zz@eRJ)t3NhJ232*GGT^KLme z#}fXc3ssRfYmT=#%9Uh{clZ>-%0O5L^FZpyDiM~}P)aP47Zzcqj)D-c)lE|woKY** zzjh+)XsJGohcW09?)W&vUgl>n-v2hGNURUio)i+Jh>b)Q9ga%>U_)lzOMb!z!A3T@ z<^DSlj;+5rh;s#ZJ0%f9Ylotbz&hLxDtHEB8yVez##<^ZsOu%g9!hIpOOV1k&<^Zp zP)y6e&=pzYvMwfDu+>J6Z3Y41j~!wyh=!HeJ0Tkg##>*m>|r2RqJYi~p(4P{JPJG$ z4omw$(09R>LF{|>nAxl+Tofu0R1Atd z26^vbK2Cy7ONI;cO8Up{VM)Q!7r!GeXwRPS?pDcxZYqN2+**t8 zXaN18hy_`2F_({5q#%qfQ}~#)K^D>?ML)Np<(SHs&_2M`BBeK~^CodMr{|ao^s^vw zbr*B+`!=DSRQ!rb-Z53!IU{1U;)PrwfftIyB*7z-Y}5QO$v*B+SOj9if%u4icpTA7 z$_Y=>2~X5XWr{QY?^B*Wf#djusRGh0F)6OtcxO}6tYJwmE7Gh)vaH60skS?oxTq~p z!pLlSJXO4Ip7YuB_ATp$UWb-CAV6vvSx1K1h&S(GZ@9$nf!+0Ck~@PLd)gMhNDCyZ zc@#%_B`pgikP%urcv?iN7&HV2zEZ%w1@%-c2QQ3WF}wsus@Y7~TS-5Qp_bC?3iy;! zV`+jY8XQaF?5Q!!pY2w}d^=f6CBjf32lry|L{-Ix!i0|5h(hR(rAUO|H%AC`D}2z7 z%KgPoGZFajqIMh))~ieJ%%gxZ=ACrO@dy*RiJr6ajQvI^FS`E1eMZ>OfC*T-%(!ZO za_m&Gy>a7Ulv?$+di%oQ1eh&PW0mNZN8??QNbWJ=z0_;2w?nT4J=6Vc<lE`< z;@NtYd=o8?0NFv#RH?yH3N(sjoiRdgdv>VEa6#1-dSk@U6(THSiczR-%7&@!6z$ zhluk@k^07xsbR#h#hHhI+-QTH>g;;7KfpMiVqDHiUC#Lu&e0>Crfv?7LF5c4aG1K> zMCf$6w_`YzEey6A$}@u)Tcj*!jm ziY=RoeV1LRwg`pxUX;P>BtPpZeV28pwsM8`?_d>5GU&f<)GmsIg~;Lf$iS244w&no zj$IY!V95nxKcd||b1F5Kfz6x~10I4=3+ZpG5IJjZs_Z3qGyoy=ANW1J%V;anL3W_r z&rLnsB0u_S(is_lc9V(f_WVqpVBqnJLc(iTXzMHLh`Rvc9sx3W=tw^vRM|hh{yKq`zdpK(hY0v8Agvq1~V(PO7q=ZGSweH32J`Gx!&QCCt(F&s);499)1 zdq50qz+#rUpmCFRV<_~#?wD!RG2pOnxZmOmd|pq(yXOI*=c}P_oCwKT$S>TTbvc6JR~?Y?j8T8=5ftowQmhDP4sSHtWN zjM%j3B#~+5vI7&iYw4{y~g|0pp&YN2Q#N?beZ+xCF z7`7%aU+0EKeP)vZ^CgNbZRGGcEeybqa?~1b9YAe&PNL7ZM?&rMe9^1+o~ylIRnxe4 zN9VMglFe%x;d)ICFSe&W)E^E#zx<@UXaOa9Kt+lF?RZgy`Z6>D~vs*C0!^`fv%%zstZ6Z^}7IWZ)Hl~^qK`9fCVbSLZ=bJ7)e0Q0t3KzDV?%Q-=s!J z=dS%O^?1o6eod8hK_tzM6zxLfk?n+wjH<5lOU(~Qun_Q}Qn^N=Dvt`Ug z$uqo{3sIdM-=*3wF5#3_^CL-thoj-w2kOaFu~v|~Q#N`C(+1_$6Cv(Cgdod1ixdF; z&Vf2kXQ3#8scL5QSC_p)n*5QFbnHYG3XJ zQTnz0VK^M~f_9%TPgnk`XkCs25tkY^wzmEPh?-4=wUZj2t8{bo0y8-1zlxa#1xEQ9 zF8pk(qT8~^)faS)vui<-(H$iBr}Uw0CIqIdCqRM!ppWQkXQz*;OKM^pX8n^3hH4jW z9^Qnz@dv%`P8trSm|d9WN+ z90sHP%K5w7&3{a@R#k81ou{n$`+ODxlU-`jlmxu|DHV%$ZE~Jf`+#!sB1SR0mhsC@ zYJ0&K=A~Em#aVm=2zsCWj<@rRZYT7^|H~G0+kPFc( zfGU=&V!9jb!=CeN)d>hD(J>(GdveF)Qmf~hdUzY{;c89=2)Fm!T2r;X*^hu|*loCH zORapT+2P}TKo5A?4UU6}t+?qP-XDyg_tsA^wPL+|Yj9c0V1df}GL_2;tBY@$o>FvK zBDGz+OXRJ8Ice7_Csi*=vF3_)yZguIC+XDK-uPUpXgPaMAiKn>TMW*pv6qeE_V;Xb zqqAyNPqa)j0Kx1V5Y+Zs?zSRqg|%q9e6(s5!6v`$l3kvDrXwnomeXuF25Rp}PpW(zpv-s^F&96k@Y)Ot%*XtdhEI>_Uch5IhQ4x3mv zSunfjlQTTd$w#HTn^wVhjn-td3(GrADo`eyztH3b&=)A)ZJlD@B1X*ov6=PpPnM09eE4}h6yc~s*8N5(z6kiT=~}Tr?#-AQt=s$ zb+uBJl$LG7Z{2SOo-d?(vOat^CCPtIoSzeX9&k09OMQD)G~pNX{AREIqI{-ief^lp ztwBLhg1$Z~`gg2dKyJTH+ox?a>k zC!&1Zk%Jt9&?dh5a58Au>Jay4aa_A66mLdVgW*PQ?2e|;Ha+b&|CU`er7cCdTZ5*Q zC76h^T*E8kQCX3NsG%$kSI)`*ykSOB+CX;gR-x7y^d`P}toYE6f_h)5GaT9(S*E5- zuw2n~NBVfbY1u9|NuLLd+`LV#?wPh$xi5x zyMA|6DbqN6x^3k}hizJrv01Qug=<|v!nct(Wn%3tPq@Ddx=29Zm2-0uJ|uchUX@vA z6CQn)+3M)Lp@?&t%F51pBZaf6KQ$h*cxUs_$*$GZ`44uH^>0BsJ;F#y8N%?l<0T9Z zuOjQw{ZNMWL+Za8Rn23gg8NiUUfHFlS*MD_hLLZg*x&FtuXi_JJa19QH`m9h2eWlB z;LTbpNn#~Qw$!ri*&RN655r72I%6aJ>xK#<Dy~x7@J`NGLMFY}XkJeGc8-mWWPe3sY*9XnTi`eRw5R8%SjURJgii&m{VO)ZqtB}GY`itZ&u_Keh|8R%sfNAnW?#y> z?%j)0Sr=YgG{X%wnJeKBpHIX?)p;^)>-|vf9kKbV z>1@kw)a()Ly4Hulw!)aqXMxy|7rm3E*9?~y)9>nmuYgKR((BKST%XhK8sDJ6uK;QK zfbNO?p$%UG{yT)5?YVa;@GrMn)5Hz{oPyy&qLfm|&b{KG9`SJd<6Ftdm#|ArFnIJV zL;MHvG^TSM}3Q#aX_FTai0)ye_&fkkg#hsWK0+& zT=;-ydeV~03+!U7c{oBE8Smk7zB1UP&WA7Ll>S|T>>AJs@3k+iDZjgmXd;202xl&< z?b0UahW; z%V>0(Jgdys$z&eoFl=m1Y<de>5~4w)B&EwO4I8UaRz5&e06mYSh!?sQ zw7uCu6#gv$k96y27c(i_1h#$s>W<&VCzr7L_jTI$==uHgo4I}cBU3aQF672uD3E_^ zW?o)Du{bdMaD<#68fOJ^L|Hpa>D&(D~n#`BJVzc|5a_u;htFSm_B-o|=&& zh@vu4BpX}ACF*~yqW%5_<;mXW#up*IHF|X9I>>ebr$>!59;7+8c;s5Zr=qYSu-va( z+m)=Zc!|5K4! zOFgqjZxARBwqXEn;|w02OUD|f2)AYs__UB42+Wl(K%o_%QQbLVgOTN^T?bLsg&9#F zMQ%!K-x85!n5Xi4a*91*sxCI*1p4Qc-ojrLLwk#<_fmyNR|H(gsgKsizNkOAI+(b^ zFcDOv-cxu+Mx?xl5d~<0w+Z@zArMoF^vdQD{rv4I$&gkZ0=j{?OJ^`Qb$bri?Lj@S zQCeA;GH}cI%`E{S4y07FH`Xq0LLkNRH*KBFUA{_46qLG`Pn-mrVHFg}se(g0Kwp?u z6!D$#L<{WguYj+V7E|G(D%T|RWdKA(z=3P(*p4leXJ%1jbygD`q0Mc|lksCc!{WK~ z*}Gp5j}*-Y*CLz{EK0aRLXlG-xk0*6JSfkfI@kn>W>l%vkj=^|izTdj{eQ$ns{+)g z9C34q7b8n?qUrN=o3<;JPMl%Rc-E~TZpmmB#L%vV(k{6~g^&1B?VeQ=i}G_Mfz=bI zO>1@=(G;5@hv=We+&EU8}n6-XVmRA|xYq@1h#?XQCUZ?TQB68fN^ zD7NC4Y2v~-{VXJ9TE{BgGK$S9 z3{gErQa?46=mf(wV4nY~p7>Qhb@Ue5A9lE zM*=TmbcZTp4>`!tnpWi60*sb5&`jK!mwJ@Mx;tyF>#FBz0<(q7_!@_&vHtzF=?!aW7e- zjSSE=LyCG8t|ptQ5E2{$3|YlE1tyZYrL4E(kB0 zx46JJCFnymG-O)u@W4Xkoz;wU;*>MtinL5~P@1hu4~6MX+1PRu_03-_@9Mk9;IZY@ zsG1t(uBHlapQ^h?p+l>ZvE|>%HJj9WUdmlh<=&TxXA3e5yVM9kp8SCcq<$P#5^Q_f z^6j+;bI?|iR{vRb^gFP4dyJH%gg z9P0olKy}uWjntdyY}Yqzou92{oGxe$gc)TY`x{+0k+h3uvhcAiM|vBmsA@PI3VU(Hh36)~gr|{s#%xpWT9f z4)!ESMvk?@4ZCf=A@Qpl#s}rHhp&`{AYI(XIxOJLu*SVt)p=SqMlR5V=^~W+-k7@b zOIY0vrJsYm5=dJ8mdk*Q!GMg(fQ-?AjM<=w!JvrA0M58iQfP3aOqu2sv_U{XCs`k{ zt^qu;3=Yb%2A^VwH|jlaMbtzE9vOt$c0>njD@R-4&T&*FjyJoWJxZEXX?p8M{wXv6 z=l>X`7m00;E-

ON|k$m%$hQ1dJUj!qo6g*!d90-qQXpID;Gk-2vak@r!;S_dNE zaK57irSEG{w?kpU-&?hw7n+4z_Uv0Xg8gg2P)_hIyZD|EOY2Qb z8%=W$W4vWB$`4??h5W5(Y{qCz;}D;TxLaVX`_ExeYtHl3rdwFnD+@=G%Q!T|$yvbp zpOIddmg~-iVUUvrqzY2~(dlUV^vGR&3c^Deg-~~hdn5iWcEAg8eNb@AJb9-Dw07ZN zzG8Nt%(UO+#c~Yw*`&t2BIT(+PfU&J7ULgbNzOGymz<(1O`xM6f8Z-X9{EfIlGo*JnNci5lh%O@9DHnA&K7T0Gcy2Hgv5)6QZW zSoaiqM+n^uT+d(wESoa`$M+}`iuhz}Fb*~FY$zFRku|i#+o4qY8$@6FgsECFm z_vHt;`NSx2S%QiFPQCv3W|y%mZMN0!P>C@!vtgBZM6Skf}OrO{x{S=|3&R#_0aG5-{IUM3arVnGf0<_zua`_U<4gQp_G;Pjhxm;=gm( zCwEipZS+Qp4aOl`K~w_AS_QzCu2>KA^UWU_2(vj~13A6I;`fmbIa;3u>O?u`CbNET98f2bhwm zS65K*uSnIT4tf1Bcr7b--B$*}(*dC@D+78it)W;MM0cxSJg&kj9<#7tf)t7Rvzi8(YGRVMBTf*qQ`b^WK&4&DlZzhAo_>0whK!B5*2Zq(djT4m(c|9k3jl$I?fy1kc z(@BrjNr2VKU$Vuu`%FS=FT&lSg`?;5mT>CI9f+W(kIP1+o4E69(u$`fvL1AgB>qKw zuYpqP$Udnk?o3%Wvfe)v-NZuMJgLY@GYRfz3unSYfh}l0{{sJKGmmzRNj>S7b}Xch za(rMM3PFJ%cWRd|oa6@w1__*-4k?xpZe|gc#Hk&GmNsedH#W2G#QY;bfL8q4M1Tt# z!(t+8NW?RBi}*z_x+^o$Ko-O@aTgQXu?yHq#q#!CGHn>_`rnY{!V${KaJ)r2)DoC` zebs_k?g#=`5NhOzf&qval|7G(vEq)8fSAe+0Qe7DgZU4Ym;xm{5#r|N1tWb~SjH~wjv=lMJ+1okd#RK*8d;c$OKzWr-Km?H^eFc0y?MVkp!hdq7aOXC06YAs@Qxw2dtT6) zZkD?}cD{PgEdLo}bOld!^8DxUj%4-4b2*@kMBdA0E@BJh=DMw$`90Tj`lo$jntwi0 zX|PCjvV3E8pTvv>E=0Jj!GPL$&+G*M{HOa^4)g7{)Mu=fnNg^=`_kg>7vKF|K?(kL z4)O`+Kl?lzyI8*@cSo@xU-zCHqu%A&k`ORT>H=VYQLUT_ySe1=kE^Idq+lq`&ps?G zv!R0`(Clr+2hvg8P)#U0%h++PXQk$%#WX#fA7vw4=A&^F$Hn>hthD15u zd3U~?i6b{Ehl7{>C4c`2HVV~=7hcXAqo4B5cpC-8@NU*|efHW-6ct0>^c%sN2|0vi zj;vnucR|#j4vQ(~P%jbiLYnwtKKFc(?$I6oUrR+0nIwky(eH{NHKN)+3mKdowU(oe z9oG~h_l-K)TuSI++V6+ zPg#lWYWakNIlhX3g(@n3A{yp;0eL`9rm1+k>SPXQ;*7~%7VAfP4TjFf!rZhM8^Krp zo;e(WtE1x5PXY03VxmgL=O>y&C#kG^mwkOXjdqM_maN{Tuk?1^KAQw|rGDrhM92cZ z#!h2Pp1u~RV^`H5C)Jg=-Ier3YwobSE}M(VPKU6wwbaiwH~NN;)hHS)xW=Y^8x61M zKWX{8tVC|-NH_ssw;L^+@jjm)dlg=4VhEK-Uf@&Jvh{B-W3Kp8B~pN{!($z-;_B)n zvF*Cm`@qdN*dTMc!0)*2Q9h<74inqBJ~g(vT>$N10`oxkVD zhjdyv+*5C_$wHquCo24Q2wG_X>i+4}&xW0uynX=wJ^R7A$Ye!zv9?X>cIO_!hYfBhk4I2xcXeWyvbR!{{;Q6px3tY;{HHbct1`|U`u?l z*>-x)Xzur2`)2+=Z65fR7mC!nkDU{3Nn!i5o@q6EWeJ!`8JkjiQoSGSo1>bw`uG@R zsW7P4?neL=T?wazJ^g)y_#1E{Z?)#Yr@o^{b%pS(5C(@$K*tM?!4(0h;~M}Kcbwl-EYy}cl`$6z}%%uRP6 z@!-WjR>!)fv<=HpKiraJ)j&XR-`;XB$+di3`B{Dm-$P&JS@8iL(rWk`v!3paq~5M8 zbhIajuXlgPc2=Z!>vA%30bh<^oqG9xo<+p!xtfUOJUNWWO;pNqv%S3B@mpHw#E8A$ z<3HQ!@q{1QDQ0(K?jF-fX?d@lr^VV0)g;2eE`B7S#XkJj)9P>Lp?~%{eSY=cc8Pq- z6a>imypCT%jr(*MEyWC*V!XeaIH^vYUOe1ejnR9nvjt!I&WqBfJK;Qa58zz-YDC7$ zbv2C1O({s^sPEYY0#JmWyDO&Q=xyRBQE<^SWukw+F%+SXnvK?~+MBxJ@NdqHj8D01 zQ+(7xq(K9Dj4;J;NMdj_vR#IN-(K*y?z!5Mz~Vd%z|gap^6>tDlBk_1MNy*nf+=&8 z@^8f@lEWg%`wb-vwA&;1sM`mD19B-#<%uDoIwK3shUU!GUd|UErXJL4n%`Z%w=za? zA^Ey|v50G3SP@jXXSh*nKw}9(pKZ0T>&btexo5ff)MPWMYl@V`J+5M91YU%Jm1M*p zQe-KRa38Hy{$G5Z1x#a)!e<8^bZ~dq!QI{6T?Th|8{FO9ox!ER;0}YkyZhk2<-PxY z-`o8*oAf64mWG6;329Hy`E|v?)3y9wwCbuBW!R-{sv!_{N(pBdIq@ z4DPEWF7ksm5|XQ9-18nFQqmWZ)@PAh16#DepS&a#48DtC$E+>RxOa4Qtg-9XbBI7^ zLu18*NbXM^;VBtnc!4poS~ZjoQ4#{|0ZoC$xM$K_7@Kai7hct9ZBhkQZPiIaSDU|U zl-N*(M`r=H$Gi3KoBFr>RaD%P*T*hb_B!gSz=c&(^+?wvc zdynuS_S8btIWCoYeOrE1))?VDzchF?QqDRDz_^xo#jmrLf61I{j71350bzf*?+=lH zzjgOP_bG0M{M%tXJ5x2by}cV$AiE8NxWBnn-`9p}YZvwQ1nc{qvhTj%YNleVBPad5 zS044M$nj+Qah$#TLsOatO%*1RDe6Yund&Eup5tkq353RwHGnLv?d!#Ix&Mldqj_T{ zNA%$;RF5XFAT&;c=6kcy;6R~bKKyu=YqX_{N9Ks|RFIU-`n=oos?m; zYEDU5hNdovYPCu}=GlgZT0h4P_f5Y)_SDMuuESsD4OSXi{MFT{kaV-TKFdqiP2fy! zfH`XDT5#iE*9Ws~ND;W!B3ob9b>*u+=IP+Fog_+qnmm^F^R+j$*W=bjpMKo;f11p@ za&H}v$7AntyzsqjTw|jI@U$5TxsdJMs-$wtSBHEr9%-^lusqtk%D?BfUfpVu`8u?o zl+ekxLd|v}1gpzmJRz(?2>hJm*SK3udFkOkgw`WYC2-4L{7}l{D_%mXjuZa_R+-r( z6V9rZyjOIKEa$@$7yyr-jN4m@hBT_{agR5b`f9^OqgdBF5ucNHn#oZX-sd4f^qT%D zaLqBn<3zO7m@xXUzN;8j>3YZg3fY|M6hV4wHbC7MHcEIwzGK!$wp5+Rsh*`~vn%Yb z<;vvQYqiW}dNq>T<)-Wjaq`EP34Y?&*V)zWp4(e-F5f?{T~Q!gk*%*rT%A*^$NWd? zKRSa378j&87Ozd_Cki2d(wkhvLq5JrZ@&uqtPHseTC!8CBY41jabUGS9NCR(fSK~9 zz$}k&t#iTUW~9A*^8FKi%jzv0paBlH7|CQ~VeC>q67B#f}vmZUvEcDu6yI(Gn~%7p0SCCr6GlTd*CK`(Gk5A3Xl@BOlv+dgjc91tdWvA^ zWd@#+-k|a@Lt-|#O+}3y&bZ$GEX07~NsSz&K2mGU92z5!0|}VK&K^H8xWbJs{!9rN zCzHObIe2ERatXsMlaNv~jcH3{sz{VyS2}PsTAcB{9MdrJzZnd@!VR+w`xC#@PG(6O zW|;>|Ym?I^V-AL^0lpc@BJxp1q5QJ18_0SB0TseDLv=75&@w5aogmyHjsVsOkn1^& z!eGf$^owZax`+CR$LNuWB2g|K82kT5CJjJ80FDgn55l1}v&>`1a2NQ*x=&D4tud?F zUNLJ0MppaustZBAqocB(Jqg&CT60-qOvYsHQW8#VE}f2Oq`^oK-kSLQ$C+pNndRKN z-Z%XIW7(z+xNOt(L20Fk`Q^)*;Qyzh#`JGJ=96JD{@>~h<3H-me+0k*GsedyvWb7rO7?3(r7V;LL=~bjP@~=QQT?cWcE<*!tu47w?g_hF3f8soz}>8JxR<(B_rl2yJEc zS3T=Q;;D>m_)PR`Y;Ji7X69K48)MAF=(keiz5KX+7moAav;b1}y&IG$0SpwQ@_-W3 z{G0`AkfZYOy4cmseN-lkUr3m=o^w?ADzz#C>X}2Z)Fn<#Wh^YZsrbpZX&}_4I5T(5 z0%%?12d-QYnP>Lxq`L;@70#{5G#Es&<5SIxS{ZPQbnJa{pAI|H<;;|TWoNYR6Jr7Wh7`xA# zHxm|2nCg#8|EAuojA@$eI2(c~7`SASXGmm@YE&L0cBs3-vvHoxtEUMXJ$skJ1(?|u zdhkwBZ4UoUs>KUxUO?Mb=X%fs!uK^0@AAiFCe`V0v`p^D>A0Ax@(>THCKT9 zMwn$3p(~uZF8E5CptdwP){N!Y_gW$DOMg)Mw)IwIb8)fQh9IjI3vI6%bh~nKHTvt_ z4!P21gd>JwC6tyJw?4!^@E>XB778Wd^!CHVv0sN%1dxwYtSwZUGv_r8vMI0GXn^mr zok{Vy6|n>Gma(}K59m_J_t~s;T*-9XH7$3m6ReXVHe!;>;pVa%?`!A2&yVWdk@Cto zo5;1y>-nO=eJ_rNhRlx0HJK}A4U7mM7$<+P) zr!S+Ll8;mr0e>tc{CoHPH_7V07z&|fqQDB=)Fj+9N;uM&GHr(L%khM=lu?<@?-2eH z6gLDC!DTYI#&M&9jOZj4E?kZfrC@i>fj!OElOk<0YQTuviDBMMn42)tT*uHI0JVuaxq7|x<5-yxVXABZWQaDsfRME)gN@9^J z$VMWg4;t}IW06T+4#Z@`84e`@VfmBP;LVtIHEGn}(2yKmnGr<-k|g3W>1PJaP#1I~3!is^?mEVtIj&g=7Ep?|r@g5I!D zlKVfQ4nRN+ENMh+&@gG3L)HKbYHFYwMRk&pHHw*w!q(_xTky;pJH=ac-$BRzY{^2b zwV>LkGTWlXiP*8*{i2EEXNSaX2^oT8m=sMW3twA4;@9O5Be%HoV0pxB`N z{e)L+iOp`~2C(CMPyZWUT|P-R-T>iMkdhVt64!91NjMxDbp&{-NHZ&zwGnNd7FDCQ zOdu#W{x_6gbR_Nsig$uUOZY`tEIpt6kkE!B@Bp93mmBF z6(i$a!7Db)dLiRE_`TAs4^c_(@Rl<3qs+FI9}O~aC9CGznxVoH$D( zb1@MwZduKoGjTFf_k696YqRTL-oeFP8O4f#q?c3MTE*JpW(G)MTh)u_0FSIr-5?ltgd$RwLf79`OhI0ey z*xqe`z-Q+>fu>Q83+Sc8kCArpr2OgWvr=bXWX=?YoS$qACa16INCA~-s@jUA+uBBU z`*dKNKlS5kFVO<@g@b%@|KNwpP9eUMTQa+2TyYog#Ue_*C-vx?Ua^m_Aw=}>o0bV@ z1M_KjWiq6&@MS9CGT0E0|6a;t<16cvZPW9M8nEL#)VL6%r{j zRm(+(EZc_-kT8a~D)=EY$eD0x$k9d>)MSq~a{r*v9NIL}e`0`=b;CR{+lf?UM|%Ep ziFk9A4{;p);AtO1>|JPeFXH=qHX|CLM@)mA<9BKZR#hmCL{Sfgef$x~0uYcvKa$cR zt9C;IB!c}%5G3**K-xb0X)(Zl+GSMwBYAd;q0t<{ z_jYJaSLOP%JP`xbHZpOCD&&0-@YP<^CEnQx5Pl%NI{2hl%>qDrh52g;0sA}jmVADf z2zMc(MXm}^xq*tD6iHn~L0$Qu^eT1a5vOGP z(qeFvIjU+bk`1b*N)}j5_I1V&flhLRG&VWKS#z!$Kt&zs*AQ47*HeX zo_YJaV$be;6zIzIJI};~$F{iJI5EXTix@etEF@kSM~oBvUWbX3;-XQ)Dg)|9MLBih zA9~ews{%q~C`3HZWYjyBHoVB7*qxRJybY(N^{1u%NpnwOR2)uo|As+TuZ&r*{4aXN z!c=qUFktEs`Iw^*+_Ql+A;YT|?!#aaysb6bL~PmXULJrdokgT3HXff!Q2p~sud=oJ z6Z7;%e~Uyu$^<;p)(0&EX7PVnFnKGoW*4&hgr`lAmC!TS9{_<@;t51ll{%&-EUR(H z=&)A*uxE7q9T$leBgrYe*aEWHf|A$*lh{Hg0MK+M4Uy0~G8@In>`oVCr|cng!?ee)0m9Az);ILKJBjucdGpq|b znu+qqavJHlU-mc?(VqX*(^_V~Z4M>Jb69zk<)BZ(^?LEI@AFSpw!gY&p*R!__ecs! z^4SK2DnVJ~-|ULnF|C`v^6Evb z{`8{9iZPD8DnLBIYuZL@QZ{!(zxi%x+1RR%`^saO99*8@#Xh5jWe}xM8+Fplo5A}eH za^XR~1}g)=NVlhF-1Z$0@=Aw(fcZDus&9P8OJAH1{fAvWXaMVJul@}7vSZyYgWb`8 z9Zk&Km4mOvzelw|MYW9lzDP=>Y42{YIF{F%%?&@M@4v-2+EVZB_ECDnG1_t)>?Xj5 zd^d)B9l(6dWW4&xc!gjjV5WdIg`)xq2bt^-13V%C09G^3@Yb2~`~*Q+_+&-KuaUfF z{Ly#h|J2h|;W%dc;M}uS;j6rik1u-W3`im8!vtNxPeCzveM zkql+jk^SFOq(+VEz)yEd6xK=m`#eMPY;zdE5t@!DwKwyWrR1>#LRTOmtBOLv)5VVH zu{0CTpOCF}O!vOkhI2X2PQ@3_#b=h2uv3t<5R!=R##hcPLYkZFf!3O&Por!ppB()X z5|33Bk5v`7lNGZQ7PGTbz|X#VKtcut;591`sc&=^0oEtB#xO8LWy2Olmh|qh?WROD zzwVIuKT5EaDW({AE2hX)aAVI=o*F9@Ry7c@jJ9C{FD!u>1huAaMz!D$1vN)lX3Yi4QL+jZF|4D4Fm>oqZCx%Q>2qah# zDPLh8Nq_{a+>Uk2{F7iIb8SHN!~zKxu5f@0E%eCq3cIR9{$(0rY#ZF;*SEM!>--wJ z9jPu8%u?5As&&@Aa?a}8-Ct;#Z_(ogEc!r##h27yUK44Ee)0YVNU$1=&4t|@gl8&) z2C!Ym7Fec_uP)<4ec9QMS6&IaGRF1a{BK_nIE+9-%HV{&;({p)Z1T_Or|SU8ARzC;z~GSHLH`yO&V#T2!qY;LG74 zT4NV-VOhE17Vd&gvU!PyiJ8XIFrb0*Rcp*CF_g6Oy!cSFB1fCdx7|}FxZ2-A(N6u+ zjf0ipbaJn;3e5)K2|sZV)QU3qzI(4*=>@~4b}63i-BNh2qtp%1%bE7{u(K6-oPaf) zbfaPReH{;!*H&NHms&lXt1JvUtBUV$aK?W#FC$ zJ&WFzF`?gJ09O56#GTJe?KaCW4xIYObumlJn_7IaW1ojMm%F~yJS+u_Y*ILCc+h;q z!0R8ve|@nVt$d&yJ&nsyD$UB%sLylif3@?${q4mT$K^nN*g5n2ptnkMO>%L3ch>1` z?cuOjw+|=T{>pnXeY;c&KrW6y7p<{#NL{_57&rSn$m{+!@tlJ8)?p)l{niTM1t8Y3 zsp)526T2GMaat;jM7Fjjma5)tt=J6`~J2D`*8oO<}D$&a>IV_0Kd?CZLci(>%``j!`I)12e>mYfYoik*E_?E zTYcKXKlw& ze)7FABX>2yvuHKj%J)L>g9gR!j*{rnZk>S#t;-Jh5+3Rbt-c`Hjuq;1)dXD--|TQd zky2%fJ^kedHyCq$*)?7`NHPuvoll)9J{N3eCmP$;@zOu49PCzacRqLnU<*aL@&n90 z$9kxE7I}~sl^nSMLjZ=xZ*C;o*BPt6tDA?& zd;6&+UxXj({a_r+qe}{fS&urO+;5imakk0vy#ur~(4oP}ofY<+gc*T*r!gzn=;2@5 zy`t54iher?WI8QUimTX#Z-&YnfHez7vbFde?r(azm%Hc_v7UZ+iUjQ2-WryD2;Mqf z!lBruqN(;n%~61s4&HY3j(5fL1Q~({apUXq%qCMuvkYIo27~5U9Dtk0A)3wGTI!4b zWus9{ZhHb()yAutch=J~*Wtq3&aUNJycYe(uQVKo%Z8oftX!Int6+*7GXiyIi>I>& zUQ{3fs~`etUP=T$q0K{)GtIrgiQ znx3!9t%me%A5rD?WFK{x{LuTC{EE`64>fIT1l>1bx!%_oMvM9Zt{=VQ#4` zfzE}`sbvE*dF_4j`d3G~v>#oqIjt-{(dFlBxwCi#xy-EN+&Lb8FN`Rd3bh_FjSG#$ z6-O;5(-gAw{KYR-lBKI9l@wpYl$(94AKQ&8Q0j)?{=_Sv_=NtNJ*B8#G2(KYynBhL zSd`_R$z4(KxP@-&pw-F0bCZ#Wv&pZ#+E7&98NqmzY*0%N$v$bWYq-&#Z9B}U;mdgp zUYX?Vu*n5`n=F4Ar@vcwv7d)lR?pmRuWDtNjZUbNC&lHlrsjQbPc1=*+i+-=@wsXP z9A97fTe8c--*+zk1zd|ab>3ybE_Zmn8ANpGufKF{jPsI45XS75loO>Jiz!*=r>N(| zwy(FZR$5C?lM$3Z7R*pa)sl{@8;ahS2q}#TQ6J|A3!+0SQd?UHW@wP7Sa`T*WI1L` zvW%R#%3HdMA=?4opB;wId=A*1ZnB?z+!o2IQ8}vhQ@4*bVVTZiRWJ3{%dAp8YPhxf znh46dt3Zvse)E-p-+|JcZ{HWGp07P*1SZ=7G_K+29IizyO?ff2$BHIfF^~9i2}*;u zmgseOIGvDE?G6OgtSP!Kv#plweNUbPRs4(-PbQDd&)aJcEkYN1W&L#@y= z`0p6$otJb}z>Y9oIfl1Ua0onp_^srCER2>Ej7~p;Qhun|Gn14i&!cKjNH<@Afh0>- zqszAm)U=YAsIS9gMjySF0Lea%?M}O_L^%vgGFRQ5xS&FGDXq;-KC8V}c;eRfMa2xFi zIiOVx%y(F=YcajE{-wz_n_GG?xr$^WxvhN4Z}=uItM}0q)_jS{cz5{m6-Sh;Tx|2h zV*9=3rRVhN>5Yel6jUmTcAp%LbCqCCHend9qy&?DSCRe;U41CDoVs1c@CF9S@t2(_{jj2*-!Hc@f0R7eSBs|rzJ5=?b!YKC-EmJRKW*T{PI%c}g+s&3$2))_Bl5(IZsZ*;Dv zHRWlnTU4)slWo(N`w4@z4Hz-WT6i+jU=EacQc7sH3~%rvb~!e6ru<*#oBig&F>__BMPY2cZ8Zq1;FT+t4mzf z+OR@|#UC`Hi1b07@L>fhRESj+Aze#iVZ$*PHo_pu;3`5A!!~d)_+b^K0tVdx8wlfy zwGoA-FTG-*31~zxvt$5s)B>RaV@jFSt!Jp^YMv2=G`b!##QLvZDEYP5fsuH|s`2r) z+ks=)L{wTrBE4b=bOv!*!xCv_eSbsuNXdu|GrnPvOUN0M%FJy46_$W04U>m zrd@A__EC+)&g$vvJLG@+IFvviCqu0)EFXw6tcCtRDC0jWVE+SU{DV3E4;#lT9>VnV z_#Y?(Rs(f@HS`XWW(yvT75*ABV+~2gT3lrgkv?+4@}%)B=1*^Z`{AFeun6<;+B_2r zEj^`%NRyP3w3Ph{lZ^KcQJ4X?~t_x+&(`Fj!Lfrp}8vY6W@t0W}!zMm^ig@s^?Xqd=Ef1>WI$L{kDn#gB z(CeHCFH~3z8>`^wBn+qg7k?E|{-xuICRol!bp+=U0pKJIb{ckG<-k59d;{cI-gH{k z&#E%ob)G2>=~Ofh1eR+JTDf-J+F`a*;hER~H4vDE9=6D;uY!zXh_qlGNGxdanv4oX z4dg!&>kQSB;Dko3O$cdeYT;OJ!Zh+*;j}OXrGb+$=Avl5Xu%5_`S7adG=Weg=4K-< zN}I6RYCUuV2d!}r9tv@!jSN09__PZ|@V1$egXcwOUic4>SxGIu5eg0Z=nJNI7! zjM#E2bm#Q^R)shSO8;u(r}Z%0o$e$mKm+&jnc;~5`hoKm-JDe-;?H_{$T|_oY4n_- zrBSx8lc$QY`28}Vyki3NB<{m=5i%CdsHo}r$!>!F7}*n`(6{6bLm3$sgU1@USdr{a zL>tnts4eT&$so`qvWD)?5h%}8*KY%a6R*jwS zrXr*?N5bIBRB(KP2g2A`7gi@Km$ie&{KPApbD)t@^_uAJG(Sq16uGeQ3@ml6lF~iX zBrwa&TuYa=x-iB2Ax$rBtBP{_{56l7VG|tZZ(;=-#>g~6i6D4~1zvGfKq1956*`~@ z^YIFLP?EtKXoY!)QMnbw->Qr}Hc}4l3m_1iB@K>v82&(ophU!fQE1pgise=>(5Ywu zZoW%J?O3s9U5#+bMq&j|T1B8>9>`jAFO9U5R9TJ~4U`XWOn=pDZm{dw7vWQg4_!v~mnzwOHkmMa&i%^+Jg#R?a{?GC9rNc{=QUvpk!z{1IbF>Q$ebE(ue zACUj`o2;S?sGJHan8{YJDAK&}&kq4DR5Gk#v8`%CcI7d_cqEy`Bo2&O4taVo<|u;_ z12*cwH+Su@S8-btX!oX9`$gibmT*iWj*Y}zsL%LyIHZjXN&<-vRRc$ z9Fwv4u>abGp*8UT+=OY(FGT*KW|;56Vtd*%J3PZoW{|IMovX^@L$`t&;YMHD7jQzs zvdarYJ0rq~MO&iijV}t^gb}j~hfBfkfhFaRPx+!ZcU1J#M8&K%cLD~hiX|X*jRjCS z+HFaqQ3(EELI$wXwAWV*c&R{MTTI&OtOj?>MqXnFo|S{Ubs)F5BrVzGX{UW`+%$(h zl(+|?2HG-*bp0CV)vM8jg)4bv(Z#T7wOqv$5|gLmf<2({|e6YEaF~7y^vQ) zg8mk6ZN;=Ss_>&D!B3kX6~^3?Lyd*|c8eE$z8Sf89iid@t&A@Zx0X8+eE5)S{Tt0v zKC~yn6%qWixUb+Q6<7_@8F2DjMc=2QowcIe073O_N&XH63%H{r58Q-F_J0$*N)K2B zfv5?DtNEaG$5XoEs@if9={YS6SY(6%q7A)V8ux64t7oAqH8#aeqYC}JF5`Ss2tqZy zgj)?IN4a=i*OsEM`cHE<_2bq!l;$pxZdHMc{IF!N!@2#&h4}|~$Z^+VS$B=@N$VfB}ZiFo6 zfRBV81~zlSo3{Kfz8PPA*#h)OdpyEVp06QZrO3MoZP8k&3eBM|)tB_`>jLm?lE$Op zD7CkWVRV3-Fn%b<*j6H3>n7Jfg5Apl9yQE!2HVfl{(b{dHFsudPe2R%+=QXbgO^$6 zs$mkaZ|SvU9wqe#=I+)^c5$w_iSL_`uR;mxK1eHvEOwQ_7e#F#E@9sb;?}ML_w2CN zSOT;g9XIK|xgX|-JjETd9 z+$P^2|cIbRVC|hK?K7}>X#t_3Ytn7}^Igcf{^`!0tK%~lxRoO}( zy0PK+I+*IGWdh}8H4(O~(RZgXkK3t$$KG~bfDqE}Y1W#w+#l?R+rqW@&G)jGB^HpY zTi+5aYQNhe^L0b5ktY2-w@<7EZ61=9f`jYShYrTOQZVa}K&2b<+gTTFn*2M%=?14dIpA1wZUdn$Xa9`e=2*nql#*siz$QQMX?i87}TIwuy9zCndY9FeC%@=}7I zM?BxPli#-x+Se#7FMd0c?wi3;DUh$v;~xj`L2X(wWW_2w$bdxTWy^Hp7tRWTGHj_7$+1tj9)BwGH&*)x-tSjJKQkdQHw zKn0#NlbSM+nlh0}VInu!CyF&EiMq1cCZG^!2`}7{UM!K?qPMtFD0t)5+#DfiCcT*Vu=vf?=u`#e*}3A{xx)Fg&7R(O z%KEKq0C%Gs*Ef^%^_!DBiIY2u)1&UemFdA1`hh)b^(WI%j~lnAsgCyFLA-6OiX|QU3@!*A(asj5LHvf7WP#&KliJ(iiKPi~=snF?+-iw5zK35@)2k9p|Nh z#7+Yu75pJ@H6Oul?d9;~BocOb0XPYB1Du4(=HnYQ*_cD3kFx#0CShK|fnu15Jz!gp z=lfzDkZC~2j8$w}2vb$&K98R83FW;3Ct(Jmobx*CSH9w_%%1bFICG24oHK+k#Cwif zQu9<);LldzQ&i-l%%jjMcMU1ohMwWKk{0Hx1}C{;g&@o&_2QE> zNohhi%l~8=rsjhy`g}{7ff}NMxq%g7Xvz4m`=D%#NGVNIhAB;R78(murS-J9v1!;; zu>;7~(V?#%iI@nhN8Z0JDsS+BN)DS4MqcB}v3KaoJ=dS2Z=_5Jz<*<=lFf3-218uT z6y0(se-NH0amy#xG_yG){;UQ6Swow|mC?t^EBAozwskR;4_cpJ_IjMe|22VMII)rV zXCv~@dz_qybML0O#14$wl6JPf$4kPt^NJ6>z5%Toky@fbmL;oR=}0~J50dyLiJ5ZE zl&U_>6rr}4qd8jXdEjNq&18Ki7wg#b@f_`GjdBC6ZmH?=m+TA^ga%sXTH0NW6nJCJ z6#sSTzefDHMSa={G2UE110}yC3vmR|@+gZIO&t)Hb?0-*+z<`Oe<1RA5lGJ%(n^Nz zl8>j0+b8lBhd%O^O~*PR|EU18VZs#*fW@T`EVxS6;vK8^y{lnlt_8fS4ef{?XK;NMP zzuBK?!*j%gC!i8T%SjBuvx4CSlOkBImrZBJN%kkSjasLRvLm*^u4ZolMLH-RcS(GQiiJIhCeFT z;lgw{kLwKy42Fvo9!IBLU-vS3c=&8pJ#o$>ls28sW-&(OZi6Oz`PR`{{I1i93Af=U z`h5fK6b>bOKM+uzDI80Xo*px(ZU)fnzCJimaf{sgb6iu*>XSJh`3~NdH4U!2oB2m8 zcjA1bvw5pxackXiW0vUc7v|;hzK7BfRp0BvvF`Ko4xfAHLz_-NhsPmXl`rD@WX^7S z$*(Rb^(tMxoOLXdTHr#>G6+Uve;d@V1biEEHA@-*{`k7^9M9Et^1enK4!f*$yrx} z$BN^-JZl^9EnKd2zxI@1O|v6bFSffQ5U*p0scl#ND}JuuR03wYCn?6G|JrL>O@!du zstlYznY`W~#sI%lt=)3%?OPG>!u(glA(9N=R?AsoFb)Y#=YBlusFHn*Q@(RfJ$GyS zFYL^wbcbG#?&BO2W4#?22Ah>d+^s3u;<@wPfqxE+?f!l6#lkx8+Sz#S`m>D=#G)GZ zsRQ5s_S*F9-MJI*T5x^un_C18Aoh@S@;jf($S){5gmDa>H7bwgjdVk_wUEN-cKGLk zKA-H2+7kC4WMs#y!b>$+&}7nzCnzuco*k z8+bJ!Aki1l^eR%IVB|$M?o9UfQeW!4>ROxl*@FB&Mr@CGmqkUOO+}uz`t)@MC@fvs zI;L69A_rK5YA$ek^j-;=#y?E(9uEA7uX88RL|(gaH+JXV9{449`Lme=mh{&j*BTx+ zeI0r4>pI83dez0w=v}tRw{F7t47H8*x#-^Yq?b;;Gqaz^MsB@HT_oJf zT>0(;V(l% z)f^CXwGAUUReF5%J@q#ZFr9Jvk3KJb0e%BSK#Y z8UL;sH-6wQ^SrOz1EelA7Kb(+t*iDfZut_iL+^8ak=$Zjj-vM#qH=LRG$7bLR5aZ$ za1q=*6crmFpYW>#}lbrJvKV*YB1FX-WHRYYV{LgE?WE#_1K+7PA7OCPmS1*NEzdp|h7K z+)jEMy?3-uHr$2>o{P<+>^qW9etpj~%uYG(4#QYOZM}ly;(Z)=6W|KbIVU%qIQ_?l zjn4_Ru)WswU)V}H>=HY^TZxuveQZl^=H9oC&Yp#a5J(z?6jRY`r9mq=-4$G{&X0R4 z;g`PDNw4a6<0bC`^XL5S_POf?A5(8$w`0b>S=q_th*bof#{lL<+3iyJVzU>o<|PD7 zTT=WcS6S|Y(Zx>&6+M>Wx6Q z+0n&lBi|@ZYv_!|+dM<7l`lgpGq52`fGOMX958q9_gngRGYWz2^o6I3d>=yKXXIlu z^dzg=WgizH5*yBS6Vfjm%3dh^Vt6h5C7ZMNNBu5dL|E@D2d$U!^Of!oI^fgFGBp7^ z;oFs~tJ;KZcRhXWgl+d>SJH^*ro`{4)srWOnFy8>jd^##LZ$F%yrtNThVKsAR&@S<7ylgqn@Rd36@34PA zR#>g4zjvIDjQYXAmX=N2K3~YTW`geu=Vk%- zcuRfc?+~)7^@z3FVaVbIYEY`n3T*=H`5x3>`>P)!dQk9z# zVAtvP;(yFVYzXD(v~L+L9;FZ=yf5czxJFx#5eiQqu@NC?=dY;biNwo>d|s$57c3R! z38l+o<_O_O3N=|m2CEr9!gxGFkE>TCX_^GtqWzUX$*7@h9K~Fx-V=mSsU)4FY#0Rx zUi>8(S4Y({i2y!C0SOo(7ObXOpdHMU<*!92 zX|809Ce|#3-z{)A2l{=~x|cFFZisrSKq`}|l3|o=&k(w?tYC^d>Wc_|n7~c)Hxo6( zB4p{7xR+~duZCS91syB`}lOX`1RXfmf z79RN9p?LSc!DZw|QPx8jDu~m)PrZr&|5g(&$06`>`9aj>AjKjQH+}`1w$G=4jLXpt zj8#}PEzSz1(J9at%`vy<>p+FHv376=JIdDhU-DXFeL|4aMMXR||fJ=_0ydmk<&3`#hiATd9kXqG%06(V)Zo)eWw z(!Le1{)pBdCLCHQWif|@TF5bgFtR{bqA0qT@Ca>n`Bwz%M&hB2w2d|9^CbUnt48Kd z2pg5&74^4LYt+27PthZQxXqXy6(%978xhB}_LMk>-@lQ`vh0T=x65uMRe8W?rp)`FOd0%aA1$i`FjICBO#5G%G6_-YSw?J-IgVevLk*Qmdx5@ISwBlW|B{fZR8VRm0piiERh+K+A&YTxoNAZ%UtU^15$ zi)~R^Y?^I$E&AT0lv<^SUJn#KIuRt!?8-y58`c^68NxBiwF^dBph@r|NVh;7ji+*) zrR!KISI19qE%3--|1EkD;&CheTl9!Om`D*ruvut56Kdl#D)(<6jZj5`;8rT(Qc#~+ z8CNP*p>dkezGawB=3G~M>1)3((vFSt*`K>|YORIokW*Bv5i?|H^yDe%pl4zTP$}xl z3C>fmg4l-z^KWj6kxzoJiU5r!l#M8Xg};t@fsMgU#gYA4CJX6P^0U+QiZ&h-1*~Jq5KQm=7WiFF-)C`E< z-=2V`M{>p!8PN1VMtdWqQWL`V&4>DJ4jD)WG(ALII7Ot+=oO3MlgpCy63UeHQp!j} zB$F<;|J^Ag`RtVKhB_jL@Kqu%{V3j0%DDX<6xWdHPl;4S8X?jwf{`nHRn)q4Kvye7 z$m@OG2rJ7o*m>dIl0^Xw3-8q44Sg5e63}yk71SH9$WDQU=OynK*6FFHx4nYDq zOEO{s6qVqVe}1J9O+o;92eVL0Ld#3@Ulg~6DG7)-y^5&X;fdJcX@iPl3{+Rmf>q5L zV#*pS=ZIBz$jRH`Q5^6{h`2FEJ0wOsq{1RYJuDCI7I&B!;CLC&(+Is`K|4IIBNFh+ z>x1usgJda>+Z}ZwW^qUqhpN@XEb*z;(?Mu zJoJYbLuvsBLOv#j__UWW$j$hcEOPd+6u8~7wNf{zWdlS1fepm#7#z9>6(BOQ8<|-H z`1UFW=7I_G!Vzr^jT&%S0Te!Zbc+LCa$qiAr~#F#XA5OZKL1udgrP5G3DSGCR>;w} z#siywk=PJq_E4}SVrXQ4A1S+KoLIC8=i5FJ)A3DZ-8Siz@W{v2T0eT+{fL^O8pvfN{ecZAoSih5fk zB(PcV^Cyzc`f|k3 zadPyQ#MD|oN0r>4JausX8Hh@XB$lKzv~$V_6p(xr4k-AAFQNyepqHS+7a#5C7{W^%t7Q@e&S<+bczM$#R19l35;7`!q=R;9ovoGzCvv*m; zzq#qQ?Icz|4`bi_s)73NPMK{{=x3*FKk{FlvdsVLlx<>rRk&e2Laqt*cIra;A|T!4 zF?r##2@2b*&zN0JY~lY~^-$^dVSbD>z33_p_-bk{kUhT#-=~l+f$j)YJ^H5gnQ23< z!d(qLbV&aOquDc-8E7DFA|drb>5y`TB>z{ZOl=U@Dbs-X?3Af~cFN`z7}<>YM{a7l zfT~A4Aj0@%m2$NY+xQ1-9rVN#)>rdQts*;{m4{p+^eJK3naHiXEjZCfvBfM!q2}3) zz)#hK;@_%AMT~`Zm@4YqL;sIYY|aU@&DF>wN@hk?7RK_%{`xz=*43r^Noz$EB z43*J{14CsU_`Q0>XR@E6GO=Z81mW1}T7)(t`I*|!P+2aS_dQ9a%;KYW%vHLe zZcXTDu0RP}0gIp7pg#IGLg^`@!SOqo4FHGhR*)M@Z>}LB8dxghEHa772_vPnFx;C8 zRiyLhjg^@Q=k6$GmkoK!D3`W7AuqxI0W+olf0W8lkJ2!KfwEvm#t25n3`WKf#%pS$ zf;dJ-bfD|8i&?+>FV_Q{sU{roBl|Y|wp^dMw4WQmWNpu4 zFb#_bqt}gcY7A;>>`tJ@K%ff;okyUG6jo0Z{S{yP>czUJ(gjwp5B(KH`zqABhoBCk z#spSx2>q2&`-;N4C$}D=W{4Aw-^C<60?oF&l`)Ekf(9AYWaPZDFB(wKHh~>lOfnPu zcD*6*_B7essOyU|5ze8ww?mp=g8wWenW}K*faV*sQqoNi^(|AlZINzWm$$o@KDSOpuzYp8S8(` z9+2_`s}4ZfqaP@HBoVsX69|d{WskC9pzQIEb9WOjVDGX0jfYl0=D%eR*~(4u*%LY9 zJi9y4l>zX^7ek9Cuva@Q(~)VsVF?zW5~mkL&^Q87k8w zg?lY9+d?+lS`6JEmAk5k+(qM!QiqP}iucg{FM<=&Es$b`LP9X;v zDuYn@Gw@?_UDBu{{-02pC@@q89=fO*ZnSQddVwzkFZM$r9aCl7-tvF(b&gSv{C~oo zX}hQG?rGb$ZQHhO&a`dYwr$(CZ9Vzz?z8(pyLZ1#DP-{|pP4mmoZWAj)r_BAkD1LrS`a1u@D2j0GW%oQc)Qq4TWZtH_;Lq2WQerK zjL|gTL1NcNCP~B-_cz)IU?6+Kj;cde*z}*zs~{{ERwt!l8sh+%%A9u_$UH*;i0$~sK%tI#@tYixz!R1_YQ@EO7NZzZjuIKWx;FEe#%J)b&d|qGl|^c(DXrF^y{ZS z<1*hn^*zu~iM`kT_C^Jm${zZK-x73)U-%<^0H!hz42$*tW3rJBAVwvN+hd89Y-k=+ z*qIU>F+JZbsd=fIse$;)qAnm|9{>{(6^a5&v`+xi&51HVeleJlN5Gc)K^A~IIr?kQ zh|K;D5f`_+=Z+7-H;5mc?XnWgEA@vsVr)bdtHI4a;T6Q6SUnsWfx4_OX=SeyP7<;l znoryuyt_>5hectp(`FdLhN{}a_7E=S;bfnVMuYn98uf^)aXGJ%KXGoC&h!&~o8Kav zom*6(Yctv(Jr7|B<%+aCon^7Te!k#5ckS%9W}m*FHd)LP09Bc#lZM6k_tUP{r#%Pq zAo<=ZiZiETH_%-);J=r4n~<~Ud*yCz0S$}8nbKVRX{=^a@R`Q9MnUbFL|N<(wqRCt zgFcM~hTDbndjFlrdL-#+Esd3!=riW})hMt{;E%n{)ok2oh=Hoh)zg_Vm7m*M!+k$h zg5`QYI}OrWwpHt22{oZ0P;t>STjHf5!rdf&@j_iTtbo>@zY!6l?cvvgbRQ#7S3 z6_WDa!!D=NP{%hs-CFYS=nyACKkfgS@Fwv%w9IULGDP7;=5UgRobdM4>D}ny@hY`% zq-3RjE*v^mcwGuqd8;OQqL}w_UFK|MZh7;1xfwkl(gA4A8E%LCsh<@ao)9usJAcu4 zg&euQZM(uFyNDmqE_qq6&M|Mgm!8ggk~S|M3~#=*P5zwee0UqXsfoAjI3Jq9)bNaZ zTq@}s_kPK+ZMz@-y1+)SwKLp@N__JQlVkeKLi~Tdib}& z4%xBtd$lo1DalHC7P2>Z*+VbEkB=$#Zk)?F`bgXQj?pW1?c)2+;Z#&euLB9-o`^!H_$#`B3he@^y)re6%QgjR5VA; zSDV=`r7P34(GoZrDeRHYN@CGmP~IO|%JD2#o~zUFRtmR^#=bI#&~rfHTbjrF-OWsD z1m0YU+dci;!*@Pi<8jQUB+a+?yNZZ;oHTd0#FS6fZ}wL7+qd_fhbqxe_tfoOUcU`8 zv%kqc;}3_x2R~Rj7614yZ4KL&H|9OIq7HsEmWbb0%Anm_^P)e`2b$r1JMM=(Ygj+r zmv>*!b-jV%`EJKG^LXN=f%kO}MW$=szPo(9n|-unS@7b1kv$NO|FUxwKbk{%yhg<7 z@Ijpz1`KxaPUes80|qjo2L(4+G@M;l!k8_Q2 zZ*j%VDFhk{|H7?WwsC%dtl(vL`gEG`82FBZI@a+8`dUwZjQ&)Pu* z_2)C1fmQbz5N7Zr=GCyMIs5py*@o96&n`Oq`kduUy}V~SdMfHRs!DMp*^A-esmNu} zOX)@1Zks)F+B%lb=IWe>hiiR}F7E#Rc&la6(7Y(x^=spTF8JI~47&Hs$I ze*}g0_53b7C-|HVhlb~M^Ys4pBTMuEuh`)G`Au@}k~bW&y-S1O@i&_k#Qo<7$Omcg z8}GwXbvgF3Dbn}rI5;cd^>BWdMR&pWuKR#W{PuOyI==-NGmAaUf9BXHPvF*V2UXFJ zQaA#>>ohyDU6FW7*{s_ZT^l`n+Yskj>g$Ca3ZXUL zJ)tF{NJ;RPugX6;j1DB@r&7yQ(7)Gq<8-C+(<;7XuaB_igwTiW8x`iEpzCYY3QX3H z)-Ibi)mIfUC`8Q5C+>n_E7gR)@~H^xSRV;Mi5aEH3C)NRg=cu{=INXtz;UESAuV#J zm@EtH=1ML}o;L2)mT;FkS1wmJ?^mzgpS*mhuRYrnf<9>EwlhgCZ|A;`JU`Y{LWZ-a z#m>`SI$nZBZJhf=cdoE%t`#OF?fU6_k2|Mr`C8tDWQNVKHe}`l`QGina1%vIv!5$I zkDU~k@dn=VGo*Z@FpEUrwdws*#@&@wy(>4~bBC2%H*p(_dES|X{T=dcvd4 zpb|3p^o{CBqCCun)Vc?h%VHN-R=r}W2^BfuVwEqxjnM*gzB?9Gyx3# zCmJ>|z)k-3%KB2vrcCuZ&jz{}ugrGx689%$XsvRhqtnLcNz3j`Q zi`j5eT2YiaLbQx|BhAaHTAj#cwST6 zCKX4y^x^Nw-^%H@^*=ufrQg;ggwEGM-eVN@KItU*EuC>q>XYP? zt&Ae(AqI}fA%_J_mNfz?Inv=nK|`|?1XUdT5ovl4(*&n%!~9}9pbC8{zGqQW%p zJqn01CNL(KL(LG2X#w0XkpP-5BBoV9g#vgK0a4Xq5Y-Xh5Wooa)$bOJP)8t-YUKh! zJSH^KSA=$93ehj2#s9aD2KLc@Lqi*r#0 zizzuUAQ#=(jRpz{|Lp_8t+EO#xn#nCns;qU;0Nhrh)ncMH03s52YBphis+g;7);9Hxnwsp`vcITtGj-#rLbyHVoce_PuR#uXz#s=C z?cMwFKV8B)a0u2O7yrfa~UurX+kd zK34TNhv4sR*pdsZxL5$bJ_%NZN|~}e+PQZ^6Rw#ipz7YZ=Fx!IsG-Rvr+JHd=DnPc z`?!;ts$G{}Fnqn-f%oNc{~EKI>2A_>&$WH*ezY2DQm|A;9luyf>G(QR$G0li=+p6U zNq2u^0*v@oy;;or2q`tPc=N@R1}Y?kF|`H_SfpN8hEa-q;dRA)17-Zy{fN=fOQZbn zTgE{~vB|Vt4ZJ=z^}tyI(;Sw03pxvQ70bF5{CG4P@VcDeg{&HJpyFjt8auu4v81V$ zsUnwhUCMA=iMV29$O{Tf0;4W-+Pp+q4Qg;Vh_88|Yr; z0>w=i;1tz|3XMki&v%~ve0j`}3atE0Or@5vhpdbpD=R=7q#xzpc0>XpxfxR_R4PrW zJd~?Y8;}4}0XhVX5^uYoCpkZUj*+No7%#GqfSmy+tiY?42zaGC>hXirEi#OVtNki4L7$+uq@$0(ZXDsi zN#AJ%nvwq5MwuQ_Fphz^D>mcyZa~sKf;6u)LAtgiSh-e1B#=un<-~bzU2}D#R5eKAHzIO%>TJc_ zMuvQ3rBJRWRJpwNM@_dUNt4B{a)2E8>eIFCGw zTrE{Xpiw7pbqNU0I3rl*Xap)>$0U$~iQg9#onH$&{SSC(Foz{6=xa%uaaPST*jR}r z-oLIw`n$g)nTo*45C18;7c*nVG=RaVoI8--ScCzJ?n;27I|ULU%UC?DttIm*TVf$Z zXW)QLjDG|j5gu8<0}eYk9<<0-dIed}8iiTUGxBdWPTbdy`Hvln zow>)}CJ|Y#QLK+#_qM0*Q5f9Q2Ws51i4@j}TG8So6X0jRy?MRseE#CD%H{vi;I2*0 zY8Fq*VhxN^OPU_DaLj@N`qWMLN845WRqfjdD7rfUitekgdKp5N`8m#T)s3Y;Qc!-Y z&R{}l2+CWA=P*;6S)(*eAvNIAnte9qJc96_hnf#|EL|T2igzrns-1g*x-Ijcj|MC$ z0;(J4OlFDgqh*vpzzwFslYeI$iFji2g?VU% zmKA2f?dpkx0O|@r(|zn6O4c0EboYbNO@QY~`1OrI52X}oS_U2v{cQr=Y>VYkGtrA1 ztH)mV2OL`Ew#_50s#DpivYZKk$z{-wv6n)n9q95D8SS8;RuD@CE ztVBP*t%SgoX;czUDq2h6<`6~sRX0IM#!KetjkeeMO&X!5+7<1#v;&~MNLzZI1E4QZ zwSL5tZ`Mja{QPSm1)<-OW|-d6VXtAn5b9dK*Cn=>tCe8#XncgEu(}#c0^7Pou}ri zHwN8F0m4R0;l`(X4~o>oj34k++Gx`bwhUuEKd0_=0dbYETnTn=rbI*a;6dG} z(H$Z6rpO8#@*qXL7VAwM2hi(640(~G-zVx#P;8~hg{Y)HVbqd7@IAlIAbH=bbOuyF zS>}p@Rz!9cHU&G|0n^hy^Z|=e#H$5N56@sdh87(;IzhPqLTus^PK?hV>v?uiLle(; zULZV=iFa07$66Zp8=$HA`O`{FV4y$*g&ay)Nvl6ZO~~RCeFcJ5KRP6GHuGemCKQ|| zEFJOC>pzl=i&Rr*k^f^DGnEaxtwhY{EGu)HKw0X~4c-MCOi~Xuv*1@2$|Sj+#AJDvI*VVtgk}<^)o=Qb^y{(<&miY5s^6 zQ)*@O2~FsL_FLQ$(X94c=vCas4h?x;`vcmhvEK;|u&}E*%BFG535|JPdqMaWgPKz^ zj8l5#sH1oM@6jxf46hRQ@JVJ3Y6rF;QXzf_j5Dzjy4aZAf1>UICtPtS70AxG#AjS< z{Ku?9nZtiMy2;Nzl1dl}&$v_;)QQhB$j>TJotKn~{iPn3fE!OB7xf=+#_MrAyUr&? zU?=(_yI!=55Wa1)NN=4p1CiqU$OpD8V-5AgH}yO`k#`9eav1i^>MU~o;n68|nR~1{ zxD?oW>{AdkWb5sH22N9b8J)%2TDSs57I=Y=eyyjK8GC0MFaCSf9qCky0CwhC3Szc7 zm{u_d6nO7u0* zX^;*xebH30k2@UE?hK+gW1g!~mla+ysz1H1m$h80^hw1rYCh2^))XNYM+_X;GWF3} zS8Q#55n22q)5wgyREQ|p<}}Fp22JQF*PKFw*z^=fVe<5{5n-sn{4RX;@!Usy! zs6+-4=_!&7HZR@>z>Um>TVQRWSKSy#NK$#%*%u`-Vhi5jZz1q~eTB?kS7MWl38=>* zso`GJ&{=@0YOq`i^Yz0+^;KhytA^yNAUP0y4Ddcn_fMurPMD1zG-8vSgOaAj?>%CZ zXoWPooN|l<#l}Tqld6R@SV9}DP|a5f=I$EB##LgItO=;Ml}JylG90N_*tivc#@QFbag?_^r@m zYQAD6!A6H53AxwtWsTx9Vx$RlU~C%MMJY!cJ?*NTIm}TUt^d;T@gNgp!H&tj6d+$6 zX4>G{x8Y%D+H?cL?so9**QD3}H=<~O${hG5+yl}zpz8jd^3!+dGg`8wsPPnsgQ@u5&6oX40xD4@u^0i}w-IQ>rgMi%Rx-KNMF`&QPBO z=b3HPu1lz%774oQfL*&p-zBCw;8Of`So#ecj-YjFz)kqBizrNUyFtq)QvanZ{l-=J z?h_wO^B;p2?!L2yM35gJz@Fr@F{A#`p+5(iUV|z=OEVL&14DlSXCug7v+7^j=3Wnn z^S>3N7fjz$E9*Bif$QG7$QbA+_RxFmVbyBjzz-&W9wxnU~-oi;#>jL$=oaUpwnBIfX+E&7xoWA!Y3g$JBs4!g!BK-Ex$QexZU^L7=WY)ug*;3^(+Ohz zl}`kto(hXe@nzl-9%{qE*uy1cAO$kE)btp+E{HKr8cphE(71z*3OtrCY1_Bh`5``j7>HgmaQHpuu00l4&&fxlmA?x-^9!%b8)$! zi$K4XH(a>4oa`(xmx_>HGMO&K$xcx0(uL(vlf|)JcN$nT)08lL z+ex{)AE(^2%4|y3NkDV1t1aJ_%!-RRC{F;#qPUjFS+|+DY5iqA4iVmdz*&JZ>BD$B zgXS1${XmKXG!=Ozblh|^wbt{tax%Eg^~CBe*4dI3vs5pd6Nwu;dvcot_Aufh8%9$V z-jsN$r1Ds5A!W;RrpR+^7IAr56WqUYJb4aDQR)AX#=k@vvEy~D?V&s)#^!czaMrs(^#Y$HRg$Hm|iz6$Lyxd z*tWFb>FttAV4aR@(*3e)F3I6tIf1(JV|P%g6Q*lVaJtKzONfPVNM+-Sx{|&N0jJ4e zVh!KEwy9ZnX)EDjl*d6{3su+PIJsbMkl;lN6bDVX++#HAk)#P?^s zsYu&#s{Uwb?l&%-Om-+|UsO_Ua6{GkUEq8~u+vdK;2RShdCoHoxQvD7@MPHc0t%b4 zJ!XkW=LT=BB<73JaCyi)6}xk{oqe@m*5RQGy!zAL6HXLODl^I3luQ&4=ShCH@lU;( za{ustuS%3mN?RtU`oQcT=x;Bddkj(1Ax+fE>HU+bMv3br+?|tbr}Ca~n&5}QOa3et zBHfEp#sZszlf^*SwJvYwa34aB+PlMj^@pBy_wcvRiCHDP??RNH!4H|H;A5P*?%m$5 z>$$S$4iBl1H%T2hqS?B&A1)VCQ93T(4%OTk*qy|dt0(fi*qfykmX$J-vz(&1+F$oT zQClzl2d`hzY;To6og2>693D!7xm7042WC^M569ArZ&NY8-aWg>)OA^31xkb7zDXud z`>PM1N8L7&kTY5KgO@&sVZ=ty+FNZ7%(fW7y)bIm>rtxH7D0Sk2zhW2YNPvJeEdnf z${;@=R6mUYad&z-O{AAtniuAA-vp3uO?B2lTYAwuE#fUEsrDYZ0&CHkV z$|-0!a2=pG&$pkZQC76%D6 zx4mIwS8}J)m!WsmuB_<1sC~=ti`K!HH>K_aQm0IZ+~Hu$0$vYq;;tvpt~aVin~64C z*@||FN1ik8VOtOO#;V>5orynarCsaKu50n?{E=N8yZ)CV#E-KPn}5<}xn!i*Jl*}j zhZB{A3`9NFr?i#A6^m8iw4SgUpZCTywX-sEe9~UIu3gNC8^_O6yw?4Y77}g1ok=?= zORvTP3fS)q7wk8~N(Nj|77}o%y^jZQa`*Z*Hf4$_OI=YTSFq&mgD@SOT*rVv%`KGG zIzg$<0n@nczT`6kHYKcEoYMqCcL`F(NG&C@GL*c_(RY zX^P-zV5K5=HOFDv=?TC3dm1Y#X8$qm=xUSNjPR{i=8i^MNC@K33?RuY9xCrtEO8tr zfBOjSV&!QOBEGh;)M03jV8Ffb9aw0J!_-0kIDKh9X+8cCY^_SnOjY!8*NZw_BCYbB z9+IB8u!o;mE%B5fzwNnPj%3#rkai<^+TOmLAdgl3wBF@uPt3fgbz`=2xxbLd-Dwk9 zURJ>MR-}~HmYKr=pSFCpx*%auudBLQ`EIN0NIs70t$PtbKhkwwNCjN-$jHB* z3dgM3hY@|gR!w^_;1+$xQSxA~^UJr@!g9BKGK=!5J$mGr6m@X=5CyIh{rwuz^v4t} z*ZCk5d+VzrHGkTY?cbgyNfjKxqiIRxN)}eZVJMk%tE*0Wbcet^yX3alsY<) zS?)XN+jfC>9J5mDLHEASNNofA$>%E^@fytsUX#X6f#K?@liU>7rZ7Q~mj|ynaW%!$ zGyP)Ky*Vh;OJtb`eQ^KAIUbHwi>&niJ}p?&8?(Nj@C&N(JB;~bwwzQ6xr_u^ z6F#AX9{+QbG?P_+9L7~z`ra!m5k zm#Wp}t@Ab8ik$ql*7u+#=VMq&VcYJy?p3xVLXDSJo8NsDulF#!`^HmFpb_1P+G57IIQzgN#{|T-1&FMu8(wjd4lu3O4aRL`@-lF;W>4eJb&h- znxpSiD`MGxPfI%OkTxZ|YobpT=Vy$?;-u|p{K-4fR?`+{`seir@rXtHFdjvqQ60N$ z6ACYT4f*bO)d$KtZ*lnPJZ6nAQIqdJwP-PxXEs%D%ls6s_w)97G>AIzvz`2_>N0MV zdj4=KtEo1T4_b%xlO*hpsN^kaw}tEUwG*SJ&#|{O3-&Xy^U1@}w)?Ix{>Fjs(r(eG zVa?~Otacph^}7qVCh*&^#N+X?mH16~XBAb+&kr`$k2H_((f)6`b*s3AZUl!2FTiOp z8&CD)hvkd?JpOF%<|mD2#l)fH)+IIoboj!Vzl_9zj3D@S7*J11o&$REHq0Cm(q z+ka;cn(k^ys>|RTbTlpDoHS}RVHc7pCL4DAzYHs5*P{GENYLXpI3fR1=cxqk%q=CB zDSI_ORZ`kiE>i5TwJF zZB9(=A2xqn^&kllZ!}|QFd?Bz+5Lw& zP(0AzT5uO%mdd9u@)y~^y>#JlDSUk}j#B=fv{Ot0c;v1 z68krWdd#-hX`$1-gc$b#g7HBh4; zi3^c~*i9O^5vc=_tJt4osevh9r&^lvJOWIH9>$_UuKcuGl9AHSH3xBl88{em!4QG! zd=yGSkk7lxM1YjhjIRL2F9b3Rv}bs14>EEhrxuaPKIIow8Wd7mC{o%!By|uZ^<-G9PNPOGDw>Rj2XuGGM3_6nBg*MjTxPW1&B=$qZ~MY&>mSxjxx;MSqsUK z{uLQ3QIscOMkT^7Sd5%{Wo3AHrqWyvNy{YxI4At^XVU!`_=4j7p{jygW@W58x_p27 zTos%@5KQYsziH_M{g^^}H`1KJLgaH$K_R@rohW})Q}VkiD-tMVve1aaGDkpKU@Vzo z5BuW^dBltj=jxuEi@yaZQ~!O!j<=aT#~JpEAyO9HNVf^93(Yk$NGStAhxmNPdKlE0 zUVl3VDuKl%=M;_X{HenfCT0gB*uO-;`5>59z{EIt;;s>8pVuy&d|mt@PYTRKg6#Ec z6~;^>jKch#;#8u#!0W(*awdrce$FBmW@(9|B(q&g@QWF;)A z4Qc#l0YDGJU(2-c5AbriUc3BKoIU=s32+N0j7olPO_4bzFgVesP8eW>8b5AX;9xSq zz{q>`_6!Do08ybEG!{4n(6&--kF3JP@-6Wy*$LV2{*GlL0WCo;QLe@+UOb;MgGEnD z(jNo-lGBiHDWxWxTL2TWP#QQNvSb!#ONK@E{3DV18v{e%4Ob4VCbP2Q7c`+-@8`A= zu-d^SA#{(X1YH>j^2@ECMFlR;2M|(vNeLs{`lrd=8 zLa6StK0Rcq`)_JLoSvcjesv?kNJKMr14fk^aSqB9{6IAov2pVuD`t?3>vVo}f`Z@t zzUctw0F@(HlIpIPA!XTF;+&MznEL}gv4_Y4R;V(!fSJ_78nv7Uh|@POh7-9A}ax4p~SN4zo;J3vO`;>_(A57vj#?{TUvhdN#V$2O0c-kV7WI zVI{oSx)OeFQEsK55l?~~wAe6HE^wp4cE9FtGAhKmgcAUAa5~N-P^M1r^k3Acga97w zQ=!w|fHK*s)GMk%nP3QnPuwA_2(1~4R@c|ASJ%gIT$M_Q z>;BbglDMl|%uB9B=(MUlF+LZf)ui=MPdzcl5~5v4k7?1i ztCx1ytKTspUYTqn>rEb*MC?X*>mCEvl>g4giserX2PNhRheQ$3*dsehiybdGU&Me} zeeNPrpw7FuLCSP(KLOhQOH(80B7E_ARhsVAF=OV-Lu!sfZEL%2rOYIr*|ud z&sG5Vh>b548<3Wh>e!YjnCkzVDOYMa$z33%1BUeDr_U98uzm(5}O#;KqM=Lq#NMj1ozGP)xKw&H`zw z#>g!H0l(BrnEzP?l`}(^DxWPAW@SE&`k#P`eiK_Ycwof#o;Q%1pO6TGm}O$}WO;j- zEIon;OVI(a_DD}=CxKT)#UYVg_VODkWXmAl@lX(dn5@pIh%s5}CC#tM3PEG%;>2!+ zw^9F-H*A1S6F2lWy(H0rukUJNxz}+2!yDlK;SHZR)cOc60|4Hz0=bPyqi^aZc@m_3 zQ`u9(+l3-_MiKjfxm4RFXR{s;3)2HUg9QRZWO-?VS1>sg+(mcdyq(@?#)sa)XJo%nq zz48K~`9bU)5-kU02m|tV7w3`e8Y2am)L^g?P9$t#BCC*5E<|i0zdpd^JJ3|Hq3d&? z)<`OvwNlW0N?}53UCiyH=Ew*lN#@?fuI7?kBzfmHbRCj^#{|v=60-;kLp}%tP?KfX+@HgOrn!aywlRk z93O?pxxdCk&{<8}%VDZ;^>u}Owf|{LErw6BIax=#>9`kne{ZPxF0#?}A+j5tDJ!~V zG=Wyo*@6eQG%EE1>oKjj8JM3CPk$V5g= zl@2_TlHk_3rU#(2)K+Ah6Bq@x<*q9e+3;|<%^Soq2D%-8#{RtX9;LmvlP&81;x}Li zk!cuq=rW{|C8*^}O|gawu{dMkME23k=3caS`I*WcIYE=az>g3LMQKixlxNU}jKYYW zXHbNUQeRN=A{Q1G4Y*MY3tLrCnuyIas6s}$FDOZgcCNohf*zTZu*2FyWwPV9u8ZS#CLy;;f%LZ$G z!o7kz&`VI{`ggu-9E<|Z_-`&Sq6%84(ch_T`b5SY2cc4NNDSfwlD(NG6LVt@moyoc znKBxMT3AFfauIx^B7qFZJmkUcpvCKg88+4V^Ox}%HoRB(*PjR6zkJU3_z-S5+ktU@ zVM8Q7WzFyKXMBxp;$tHUZRqbQi2k+j65SSJFkLcoyeSDk5ObTLX%(P(ZPC7iY(Iz> zx+5oeWlGXgWn1IR!+`5yq?ya#QEz{6bT~b}oTFVS)FqmuML1379vlPD9*$#Mve-ds z_qye3tDY@s&Rp*=cZ1@O3|YP?Nd&NlOuY|2IvcTFmv{hPnDf$vYW@c74C8P3B=l8E zKsC23v~0%rU6PV-TuA~z161=Lg_cjM;B~@^sAj4kJBAm9LV_gFJosP8L8sf=rFst!IRdI5pHZ3hxbqJb!^L^h@)uAz^7zlth7@Hx;S<{l{%;>2NhyQS9 z5VWO5DEYB8NS6gFrw?8#%6Eno1E$8sW6H=+&GRgLnEwd_%JR_HMAZ6Ma4>P{$rBtG z!n?z+gXOMI{`B1K4uy!fJ|J>727mD%)^J!%$`4=-Rt9*OJ6uXG62A@}hJe!d%)4{V zQ6VkTL4L?_s>Y^gDHyp08;c7EJOI=8Mx+Qdiu;-qf z4J$|bz&6g`(`M)0-q+mJf1PkN#a{>5EnK-)x;x-~xwCIxwG)57DyE+kGe4d62g3Qx zGjZp;={E+xmh;fV+hU)LrmN6e(c4>7`7#ur-HmqWd=yn#;%2@u#MyE>>O2PKsdZT_ zc8CrWaNhe%hipxB^4)jCZJjArhVCe8ybEo$V-0SvJQ_+Ynnws+@2bm`z(GjhLg?%zlsieI*L?7 z?f`W8YG1p-a$Ebt{e|UMtjFU;QBvxd+ZnXtR$Vn?=>4ub+f`N`B4oUT-M`&OU-)=> zu5Z^Mx${p_E4#?NR%+p|ziUL}IbpYUvhSR6r+qlXWcRauKWdbzd`tgK&q@o&ezeTJ zQdd~N-) z3BTPhcfbm=TEQHuR!Eh7Z~3crJ=|44SK0dLbRG9#J?n0T?!<3*O~b9vghb&Yy;|Ct zVe^VqW+AoYBYA&3MnaD#$aiEvsXV0&Zk~#Msj|Ymms5Vri&@Ai%ZYk?apni2R$lbK zH>s6#cTlI;aE30zYi}jX;-;?okDE!o!=`P{H>+}MICFnI)Y|~R_zC4qb>ofsvYgcm z?#|M4JxSefF}M1V`Q2=1yPE`QW?>+1vmW?DpchAfkI_$d)6~yddL`MMHkYLM)YaVP zSV+@LAF?h@O4ZLL4?lVqKPgb+EGKp!Lx-KN+@UO(?y6`sUTDrKNy238t$65~7X5Y-Gj(VvLHYoeyjv+eLbIF5u= z@z|`~@^)y_JWWznOg+?5c^lmn&D5O$m}4lzjyz===L)|>uQ!DDU6dos?z4#~y`ASJ zH|s}LIC4v`E!3J*XsQ#__OMi>rZr7DZt3z;p4!^H1o;{hn}!xA@Vx5(4taH=x4$v* zF!uZUPN-&xNcMBIIo$LPeS-C#B_P=#jqSy!^t;PF|wxb@NV(AY*Y3`C+&M0?69a>>^9qf zybojpu{y)Ek#to@-^2MI$Y`v5{RplqTsXy>b)LJqw%P8PevO2h-m{$CeZSs$V({5> z{_rCGezEt$@wLCirMQvm<{5v~RRCtnx%L{@p?K=t{1gFKk!uo0oA`9QNJJ_wQS&}E zMPP<&vZj9m=(PWx9^*3atDS1C}qRB)j5;7Yimxa-maM}CK zpZqXM8U>uGa}{4h4hdpja~>1>2k|q7_l@_{<0(1!T5BrueH-~sk1v&*ihG?i_w=Ow z;);)~GHZot+qs;8HYQ7DJHxNv&YcvWVwtTd3sJY9w4%*ne7F=~F zIGqir;1@Kv&ci;YvkRT9>uIyestdx7E6?t)YBv}BSZOjcQyc;%@LJC0uJp1fR2#Le z7PRtFojVTQn+BOJ2H|H_r>RzjgzKGwv$VMRopG7$mHI11#dW4SwmVOW z=k+>Gk`u4$oZcJZd8mtuMR;4Gn^5MTTj0-mPRt5bMHcV39p@#Izn_GwOiID-~(SbW%A#QWd{@F>0ebmt~;Ax(@fCj?Uwt8 zh)aCrMf$bpc5GDEQYthpwT%irb=fzTBF<70 zKii~D8dcr`3=E^ClxTy$T5K+FtxI^IXqqOT$&2DEcwW&A!cS3X!Kl~mW+krE+s?-^ z48u?5MaqVomxJDnH^I&eeY<=hi66&Rn)bg!M5B^yaevkM-XGJB33kG1vwiP~yE|oNSy7&;FifmXDLo(o@Kb}X6C&`XDywu zKl^#uRT6MlEeldyz6O{UaW{GToK5Vn(HaBC||0CwEYoGf%ouPmMl4? zm|p&e+NP9O{p&U1EV`+xI&Y4)nZ_fQyV=Xe^?N&R`!mDi8%a+xgYi5LqvftJ_Kpz822@UCv*085&>d3+jV5GRXs?`LQ- z=|4iswXa&-#*I2&3PUb#1(U5!g*PW(^^I@cV9F+671jPQ->t9RfiM2tUgO})s$|0r zdP)V`Z|Bcn=d(T3T1s7uiFXCWrz+i$(C&M#y0`pW-&{Tgz+YV3;(<2l7PsO3uD;ji zrP603Eq}eWZ@Oo!AJES2roZF|z5=Lhq1i1_6%AX&2F8sPi z3EU?oA|e7{1uhZ-5-KhdE+}rHKVW}`K>p;W@0{$6jHJjdprQsea*cw02TaQW|4n-F zQ=e1xp^<1cyE^(7v;&LBbDo3ayKa76*Lw<7HD!9eRjvTm2ye0}U1D6pknp+NgHT@D z;x74RO7=SLk^#J+#><+kwGf9_!Df)@j5aPz`-&e%m{gXZt_ENecR~9F$?ab6B$-1wu z_*+5hItW~XDD08Oh@%dLs|gcq;h%G~`qXreIqVNf5+sqwB{fJZ12zI3APrtk_68Eu z(fneb_TH{fEllDV@1)mCRwK<5`NMBq)AngbKFGM{)bRjbm#>@wz z_|(#~ETNzpz-87!!l1~)paaByJVK}lXnB95mHA?XUDSm@)RB8(b*Z_v=OIrxd5>?8 zGYZPeuPQa2yRWy(&ZhoGSm5*X=jQS&`zQpgfk)Pf3FqznvVuz|7=VT=mPWk{cEKmn z7;({b6c+v)j%p;|07c)Rhz`4H0AH?7FWP!{Xx|ezl-s7%4+euWGXsfqf~w7-SB6kTNz)51 zQp!yQO2g_UOtK=?_Vk~pSUW7yLeuc3K{m#ao}e2$8fgsApLF=zK>+)O%Z}7Q1{N)n za+Wg-`lp@Gt_>wwGnRimLQj!!!^|OD&T5g_Q+A z$&nKT&4NWdf?t>}Sgg=b1XTW>6$+L=ztQEwe+tR`(3ap+B|I^)kP2baybGV0p=ik= z6O%FRf@zDN&6j#~{%oiQKMiMLCN577#s;)1l7G^x7kRX>4nqPX8-^NWkatKV%NZ#W z{e+L949pU$NIsn{`=?dmUPq-i@mEY`CLQ7_m+l306?0GAHK25nyWN=tDb!3?QILv) z(nJ`!;&191pLhZ5>Oz46E#qReSiwxk0P5vkA*A0{@x43j zzc(R5euW``%hl_ZEN4G&bko`Zf+X~RZGswunD0_nA3C8{arr(&z$DrgQ zdM@$dg9~#;AhHsad1Z@ye9Azi=mSd2vcmzphCW$UZ|)&y$Eju6rlmeU8jtr@i_^qq z*~dm#IX*PhV7O<;ToV^GtOPx?G-i$hbkXc-Oe69K{!e3n)j>gz5L#@0 z+{YGz4(_D>*ybs(dT51E)b8+P$3act&BeHu*UOy>=k6sq8yp-7kBwdMoTl6XB~%m0 zLoyHD5zdsMy+K@5$IQ8}1Ja=nP~BW;9hc~FAXLlc_x{7$3bzClK7FsJ*kE#`Yo|;bE-HtIZTAtpe`@H6~NZnH&!-CxJa(&Juw!Fk^ zvrTV>>o-gXx&8LX5cE(kKpm4r#LF7LS&uuKW%PlDg_HCWC)mh>WMD~Es(!D;g%m+X zK>R6f8UoT0le?+|&kz0EG6HrXBoWNjEDk!zx@64&g774B6W3cN>r?XgTr*8>A2dIC zxox8UM|sYP%G+-nm4~};3m34@ebAp(yNw0l=WPDEGMkLB(@WxI{?gI3+) zOCabqH7Xt7%YlBy&8ybnS#5*jhNw++-?yKH2Gu};Tk0$xK|{&)kcy#Qo8xx@z$L?w zb&c8I78$PqPEDT5f@+0!Rq+ycH_+WalV?ZtH>w=Xb|P}7t6BRAfkl(n%*)(mqKUJy zXmKw#iEDBu*1|T}R(zAwEUyK~cbf-zu>ed;T6@6vmS5nQl4g917D9V%dC*-MU<}y3 zPet2xGA>}xpa7x({WuQEj0L^-j@ah`el>z{~nbk1E%WIV%XDEODb z&I_a;$HZ22Oajy`|EvmRlYdr48gk@6tAZ#WL1u%;*YNkYY0`ozn&k5glc3M&0OGcp z=(+abcFH%yEufKOu%{00M&<)4|E*g|0s!}W-wop30Ni(W+@~4s-6T**LPK_w32Ony zggC^KycXxxDv-D($sSW}SXEeG15Et`KA8Omm6#cRP_@rU15O<#DGx*)Cd0r+hddBi zB$8y0vpA?KY^8yyO3LDvz>9cR5MsFejWf>L7tX|MIaa0;S*@2_zzH$QM#i|m^n9yb zLofwn0JHa^4?BE2YIOataP&rI1Y$4b@_;}`MliN@I4-c2$+Si4`CG;wO&+*6xH=5& zFRp4O!(Z26N>tW&USq?XolJf8Yb8?vDbWw>Ib1|Qa+e3yZ6 zgMo09fpDXN)4!n$5y%ysq-umf#6o4_J>HSrkBfe0s)F}iRHmxS*isU3zOV-_W4ip* z&qGsQM~Ic`D1Q!n{keY~kRqA=etd)0`Dsj2=6MeZo^OVU-x@ADgX@lXZ@5hqPEgXr z7Op4G)F$5Fd+fJfXuwmh$Qy#h8xpT-YVQ7>)ZH|GvGN;lNP(vw5r7mLKpFn+HRNBd3qFeq|aZwVk3&s5i>Pd?SyRR^UBNG+l0#lT#axSlQi~CH zI0#>Pgss7N+@sak;~D4ofC6Sk4R&S%xkw9NsgA8NhNUrvtwC$=VziI~OXifY7HN?9 zr30;B5UnSP_tCaFW5@Kf8~a>9utN+(M-HPSoZg*X`xt0+3xxwaYYenXMSuRe<1g|<0A;siOS6XjE&fuL&2T>jx{IUNC+?-HW z?e9=rl)IvFAqj!$p2u+Hy28D~sx1%?6Mlp!0^Vpu1ZtW(z*44|us%vr1KPi%=wyUI zt)gU^E;_ygWmZZKi)w#0z8@T*8MMV2ulvN>ff3zdfUe839!voBHpZ!!^|J}1 z0wE`aF8}NPhFk8gIN9CMIRXV7Ga1>~`OQICtQb?M1^=&y1QFNzH5@M;q41Vy!M9-x z_U(qe;sxKgxzq2$xmli|7lz=gJ~a)ixR*11HZ}a~U1U2=G457he6yB>?t;o6QZ=5j zr3579`t=fgGNE39Y=kBQd_$pcQPC~K*beecyV=GAd_ce=9r+2K^iorN-45Gf3dmTP zSv-KIEe*aEEeK9Q6)s#c8O-Z=gb`hL68Me5`w?Cy(eCY_PeiFd*uKZL+~A}wJ$UVp zg1d_-cb2Jtgq42EsMs-s1Bjv5DJI;oK)YGM0dvIHKSZ_>gL`t|{o4QIR|sVW3o?}u z16!Fu7q}4okaRXuo8t#`4(1iIfUQgjDii(2Q)@y-ok{<1E0Zj+m5B=2$`oO=YmhYf zQPwMnW{DaEjK3hba3|xO8mxhzNWad6$Nicj%^S~O+@(WWu>#W|jM4zs zS9m!g713ac=g}F8l|lKo{zl;Hs|&_6)g+Z0t{ci6s$*I=DzDayFj)q7gSe&Oz~MM} z@30tp`R8x1Wx^?0#n^v}*l>lU&HK9_D*mUHX;B!vsglJJ-nuAioyUHkV|B!)F>Wm` zZY?41$9A06%s+oy2(s2FX~xKa(&m2Bui&S7!B5KqH{t>}@&dQL`3lreeqm7chQ7CxcgGji42E_4zjZ5<1e`+$$u4e!Xm=J-dyqH)f zFqs%8VZi0XO+OmX1zfChP|*?xc6e+y|Q`zKX^g@G-Ia)rw9 z%8C^B{F5qr@rqw^|K4>+WoL}|vt>b?An6n_v!bb=(04|E1>k&eQT9jDOg{$pmnMWz zGbsoC)3-G#I>Sb-3<8J*_1FlLUe>;n$|G>tCSk=Ym zetZmednG0z+B`*z(H@$L_z|xwJ4{kF`_#bjuhul$wxV3eA~?DQV4I{=W5q)ViL6S9EaC2>LKqWQ5n6LuMK4e z^K`8?*i*=^os&eKSD~V9&Y5_d`o7jXR0CR05c(TG8?|j?HtOvZ2ED=#mye<>I%*mw z>+(}jXjX$#q3FmT!!nC=^v%vce>BF^=;_``Kfmw?yqxX4_&Tr6HX4p2FP%abysDYs zp12@dBfKyS)^Hk$T+hb@=d89FAGBu5^HAQbz~TJtSBEs;dwqE9a4dX?zD2VE$X?lU zE!)Xj4xSH)oBhCF8ajGJXw3hi@%T70%fP$Ue6rx*c z>h!^=p0n9WQu=W?QBm$z(*dZ;Tm$rGFL9PwOr@^$Q%v%Off0Uv;@5leA=MkrDj9u_ z<-$GxO-~*581i_*hpWMtAE(>f@>plNHB683eY>++I~;$y-D9MRp2&H7E)!#=7-E08 zbqmLh5#T6|ykZ-ivC^+g*-ET`*hSBYnW}Pi{k4dOXs6NopisFn_VvCNc-u(H_pWJH zubOjtdriza2VdWPcf;9-dDzji+5g4P_-AuhnLw{=ou190Ek*#-ri{NuTUsrdx z`t7;3ld_OO^7fV-*J%#^s7D2q#sW!T1hu%X4UfPjeo~#aO=I^TR{i_hvBFh zo6>i;goo}pK2Ed4&6?OQ)_2*Qr;G=)lws z@NLf(AWr~*AK`lyW7eJf9Wj_vO4a5QdAbqI*S-9pC`akKBmKSY95fsH^P}Bk!K2oL zI6uhp&QbPt|F#mC`zOp;D}SvU<80pbP)U4<==;i>+a^3jzWsFQCkIdE$?v?Nwq|g4H_cjobC;Ar zk0+}>|Ez(fOLBg^VX|i!5m@r!*yq96{V4O?tUUASINiBZc_Yn8*rn#~(St$gOY<@h zg`0hQOBsLmnH{h%Aorzp+}+d9sL#JIx@&_tmBr6#`bx(ru-M?Xwth$OX&YdhkYJT~ zlNHnUsK?~-EVe-Moo{JvY)uM-ADLxpxcQUqvnb)A(|dWR6kT8U=H{rT;mo6?I;A`F zX7U{sbM#sI#H#}T$DYtp41a_r(FdogZ#}@4l<;d{e!uLIi?N&2jH=r$e%F8K`NG-U?9D)MQJ;5lMbYBe;XQn|83xrq`m7pJ zlN9sPJFNfCtWJ02{eG=YSOUMcw{xbK(q7r2JnB~2G^jx~_i-lEEdclR^rn99+vxky zenO~zaibv^_GC=t{IBUsLmF6!q0v& z9e+++@0d|+bJmJ66S0nhz)ToR%k z=`Xv(HzDDiGa{;AQ>=O$klUc?uIBGNf^A0_&1}P_htEk{fn5*k^Z3CDv5an0RTm&* z941HSb-3RmROK9ZdI?jvzW5kTEPXbhK0Qr+KSS3beE*7FlT;zxkF`yn1OH1h4!^#H zD$#FW&JWF=N%(Bx>FS%Fm`_rDOId#_M|Z|i5iyeG`98JNnml~q%fa$6pU$(z=ww!X z*|d~NK0A#z+#ikN3Ihow}Yp-EFayxv~;*_)$T^g zQ&7?k`xg!Ep#VLiC~co6&pWPXPc~3fmQfZ zUX+UkHy6Q;I)3VS9Pol$Lof9QpVvf|1HmHThm35S@N2f0OIIlsYwN5J_aH?jmqqje zJ;P}^+6~^3$!+LWd@8dr`BOA*$AIiYR-aJ20{%;IJ@wHc>J@0{aodYo#lZYDCfVADK#ENIfaZRIxZ8(tbPTr zEn!>uwjie|r+Em#{-z-zG)8&+;)v)o7ldZUnFK8(FOO}aX*622&6wifJ;gjzaCb#uXA`5A7)pRtWZzf<)9X4KFHY_-k5+-Qqu|FaZFw%O$)1(K6#~X z4fn2nn0HjQE_0DogPj+$(eAmQ)wQ>b4}o6vy!zp{9$#1QDTX%}#ZEaTtP2Ur2Ztr5 z2JHraCX~O5&fRw1U25Nsui33OyPZkgXZ$Di!|AsU&@g)LFlP+mS{rGW>ezm1w!b6Z zif^{P#FJLux^?A^tfl8PkG*ons^99#hk`k|tuNye7Y=}nNx}$I#f24umW7f)p!ms1 zDFn(0#uvsSjYu#XD6ZJ+C0J>4K-ZNwR8CGF2X^}OC`P~FL?P{8_iGM*CTQ$mZ&fUDn?IB0EcZPo zLqxiWFN9H@F%=w#-7;}GXu_aJ=P!X^dRh)N)Ik`<+NVC#jXo0xVYKu5T1`!ZR604_ zzD3x4xD?<;`|L5(q9CI>E@Ifp-eo$!Zwdc4^#%d^9>TmBB``Z^upaG9ikTRJ_2e9* z+JPavY($w@#O1&(B*YM5(IhOy!LV!l(SXuEQ3p`o%#~V20u){}%C7!0coy3ULiQ}K zJUXttQ|p8W=B`y1-eZ15SJ@MgA{1`Tsoe`TxgD{=3pj#nI*eOP9C<+pPYr z|HHs1m>wRauZz?mEG!kN52L9Nh8RRdI5ig@n&Th9(PvDdx*;Npu#J+6jj^J-j)aLJ z7ALEiOhlD0FC+vaB#5kH97Y1$a($}y;4DiiwDrQBDR4yAxS`#A3iyw}v0kaw*xyG& z(pUab80dm92F$s|A7fb7EYALNs@0niaEGmDOD6$fIL!!A##sUFV;f}nI+{Mr>Wp8;S6 zhD7(rK>JUjl!lP*$!)tLcb}g>=y7CTRCjd)4J3T4V_tq5IjJe3!30y>(!nS3n=M`ZmU0%y9J)%Z(2 zWVjB76h+8~@jo2?GoXmy6=m|nJ1cD}5W&09 zU5p?6;kLv;EnSX14~o-zZr$h+u8~%1n5ByzY>G$u%bZD-s))0*HO-PpU`75y64~aO z3L+o>-MTz{f=a}8m;=WUe_zZjZ%qV4aHTkRJk7iXA}SZH;QKGr8C`%~S6_>Y%?knf zaVTBeXIy0HYM&y<#dug_x@655x27X^SOZv89cHqO$=ZG=*}AnJiUmjW$d8FMtu2Yo zAjiLpKU0XME7yS(74%2~_Q~+bAC{f#je}zXI2>BmYAutYF$c#fyfkK;j`33Gn(QwzG)h?GrWawL z3LJTnP0bOxpUycYsv#McpE0f{`}$*kkE4 zWSO=+mgznR*`VHtNW5W7xD*j}ym#m}qN${!Vw|Or9-)m)T96d|F+l9@PUY|jYIG(5 z<39HAh-G4Gt|h0$3Xq-m@?tPC6=MznsNCHdrRR*X3rvk?PrdV2z`vyCMD$Dn;9#-d zdA0BEGMABlru-^_DrjAwX%LajMc^n$GDXK+bIh|9zY8rI9-Dqg+G3b?ZDU$oMUrET zS(Ce^Z0f}h!ocoW#-%sa(-H2#!W`~sRuPlem`-5h2}=&lW;wnW8cUs=Bj%YZJ=TQt zoE*!1^XAsF6=F1>)Xn3561!YaVLR%oz#yfv;^RgsVZ3W9Y<&aX3qLE?1?7N#fc($H;Ys!058`6x`n4AC zqA@pyt%HB@`W12b3>Be9p1tGI!u?|D`c z;cj&y43Q9_(l}9>JM274iaK*azmQAGX+!Hbp_;(xS_CW3MELk~)eo8{bQfV3p3zKu zx&avdisKfDtm{Ls6>JM9t`Ug&14T4P6SCqNNt#E5PG-yAkgg)1lKs}(J*3rD{BQ_H zGTQqM8Ru31$?0Bk?`gTi2SXSi0o0FQLIRV*X6VJZeVF&CS?`%0ngDHFauIvIUcih8 zVf(6i2sZw7bPpWA1f6v!rb*L~RGRElz4s=i{`CwW#@Z#Ow2q)hvnUH&Yx+k#x}3y! zG@9fQkYfC-6fUCasz0mZBrxfRd0Mo%ILGS3=VKp45x!oFvpI#Vkw!H?T8q%9wne)^pf5PTR9q4bD{H;V74;ZS!RHgKFFw2D? z_dKWb$%c>>igpvR9wC@;*5UZ<88q+%wu+?ecyAj9G*%hpPR|0n>JiW%cO>u(8sB;ltj56ve`MQ6%k0 z*&HOehbp)nxFtfNG%nhJUc20OFWwv4^o)Y4f6w+j&L7&~yTAD0j2TV-$1w1n#LmPP z;`hP9`A6~VLFMDq(lsER+3r{(Q-!tpB!;CR|oI}62Ht@Q=xBy1n0JZ2XuIMfI=&eq?ZkXR& zC_tS2wCTHlIZI&qyW#$92)?uD-%u%#NS0x=**GzA zqWId%khnQ4ak2S}gp*wzqWeU}Mofl9)eUh~L{<8{^3ZpaX4pt-6xa(jZWO)f0W>g_ zBD}CSZWLv16hwP6f_(|zfjFu?37G(zyL0Eu6>NnfPSJNJ-6hiLo8k_k&Eu34n;IJz zsgTU+XGIaDjpNmnGsVnU-SKB;4B-Q`$~5s>Q-@k_XXA>Qz0N=SPwqX(H!JgdPRAIB z{?lIK3;gcDF?)-m3%{zapem+gErwz(#@r=2US$zpB_;eXzQ4Vm;Q@`20S|0YXV~8t z>+vd&xHP7mYLlI6^?sigpE9DG9~R5A9(HVZBGvqg2E_5ak{1-{A4UVndENaqBsn|6 zZQa?ou5r3{)Ddt2QFiY1Ti0ja{dU&~HG8;#1Uq-Wt!v1xoha}A45E@gW%aJY)oas{ zsGWOm&7?ntBLpd6-4Iz5#8!Cmh+{t;x6jQ=-q&_sskU<^f5Dbj&p zD-z2K5LD8f3n6vO?|ZW$t?Rvm$k5K4+T~0B7STdzwK2DFk!Pv`NWe5KN#w?G`SDj% zS3M4X&GcWO8nyUH_1h3WEPmq5S_-_URll|>K73Q# z+$A^wj(o!2*eNd^1@%#k1L46&-KM$dGj-Uh6oWc8Kc|a)=q6O)J%cj(p#xrh6XJKAp zxUeL+txyZxnDI4s(DNrBJC?tHaj+*v=sh&*d3{${1ZNEm(BCjEpZ-lFnoh*d#&$D5 z4Q;Jl=*v_bOfmw_Efx%}AR5pa8q5--)n1~pTZ3S4#A39-NccV>v-15xbE?|uEF8!c_T=g zmI~6Pn$05_UcnT=>-hZ7>$$`0x!d$g``pSEn?;P=wND^q^?uLI>-*|j3R}!Dy7!Ae zs$h`TMUdCA8meqC)tRIXnIdkdjA!Blhfv}}-iD7X@R0RHG1(e6s_v75Zxk~1o(k7| zw!k`8GN!HYj+OqnHf276xq~Kw#-l`d3r{f7|&~kRJJa0UWTzp4zpI znuco*oQ;sfWwSZV3G$i#zoYu$O z^G5p02=(fGp=nA`6Sh_B3=OSR{8Yv7@7v~l3f`Jf?r;10hmy;yk|TOYPD-?Y=nG_8 z2{4qDw~=Y+zHBSooT9v4d+jYCz(%x%)x7edu+EYoNRukdr6VU$$2xlT3yDayUXJ;h z_|WwARr4;<2qYx-ZS`9g#j?1amlpjs_8$6a^~6qTGWZ_WzZ&<6T`)Ox@AnuDz-BJY z6mXjoo!0Jpy@Kl${F>{ao{-@F8cS6EMQJ(3p9tz|t?dSj{x{(4Lhs8&-tWRT5J})- zba#&Mdo^@ZqmtbdzvI)+9+cPdMNKt40aGoBrrnctHtSXN0~ifI*J7vFo=}=_l1Cux zCHB$TPcxOX8q1?vyUnY+x%ShG#i>+MSb@<>8g=4jhNyRvf`Co^l^x5q;&v}}ysi$1 z*TNH+Ho%koF3V9ALGIZ_H-V3>ss&s==8ePw*hyhSGRKb$xgkp3v=?hoi<-8tkIsI{ zcI5!&t>J{nzns<8r^2g?jTEt^_Fw&#pRn70$x+dlaEC46@i=NmsH zUiRz4P=mqvqta!p;BIXqnjeE)cW2C(ry^Tsp{(MRT0SMEc$a-|!p3T^u+!oY*+E~1 z+Hr-j*{5&4Xex~^%Plr?znUN*n15RZ9Km{mO%97649Bz3Je=-`rZzvdHfN}Rs~clpE8kdlM)em6UKboH&@Imf=4I;O*4l_@~M}n+8H^z^}Knk{dyKJvn zg$h)*=uv}<@O3G_4Nqqq^E(1hn^Eor z<@(3HUkhLZv=3q~56)}z0S0&Oi&xjmUtYqdxwb-Q2Q}K70hrJ}rP~QAY3p3I_LG+Y z>^2UewnGI55w;GsSiS`fF1g-&Yh1%TU9Hs8zISo=B}=0Yvqfgm4}@M!@~#at>PXH1q zUV`qrms9Yy@sLnr0BTFl8TwdRUE)4I_JXC)Et1YJA%HvUR zbCb~_+Tekaf8}jgwYM>~bjMa6E{lJEbLUYF!Pgt^yNbHO!&|&4z9uW9dEElpX#Hgx zaLhV!-1gmsp9u~FwEL@}I+(MOLAc@7TX$vcQJ~KKp7Y15TeS=qvWqEHW&iC76YhJ6 z+zDK#lQuQhK5t^eiM(P1-T=fm5T$+AF3J$qw!$K?q`^CsX5=Ny=(A4AP5 z(gt$o2#>TS0Uu?_)pnbnZ$4J50uNscN1wK?XBSTumiosFR_Zj%b}KJExWH%L0(J8b zJCrXWgDBe}ei$z&2`!nawJyMzMYp}fe%z3JG&Ho$rtU{F4;j&TP9v)wCbzm8gVY+% zmyzH#pOZ!=4+D|4{$ieE=^7(jR|DOs)ao1V808-3YfuP@X|pBOOW+6YknZLzDN*8w z+KTUF#) z&YE%;O!x}MpsECLiK#|7xx8nIgk-26&Xd$YL8Q~wP)$S~o#|#*rHL&6__ox7Q88s> zbhOk1X1CaOaHKx_-K59>Hk?p-Kd|GzD=Hy5h0W9up&K7_# zd3-NGD{CCW4$mWV^~+nm;H-JG>*gI6s{cZ6-meDzS^)9OyyZm}n*vH*g+ zFFubGydIinAd<=mc{}YF13&bS_imi<9F6ZXcs0p?7h^fpFKqn1!O9J zi#CfvRJZ+o{M;0?eeYO34?SSVRgerCJ;hc3;MVitxWiY%*7w1v4XGR+&snQ$Ea3@t zK3q?DqNm?<#rT={a`XEt>!Tcl4=|)`ce#`$Q14ap67*4liynl~?7N)PGJRt-TkQ3A zG$~=Y+HumcW>6o8>R#f&a%;aWlSQ`P9==TQ=sy z*8O@meXgOR+fLL+ro~T)A8NKuOFSn9*4FmU-HA@uDzt|wZ*~khf6m6vSl^E9-N(I? z080Q}nzKm5qowW7phV?clD>x&ga91!8yGr@q|ngGL@u`roFx={o*&~)eIbVzgsAh6 zh*Y#wnJ>uJzMtUT+&~5$E#rf>_Yt@KtfQdy!xZaLz}=VvqUmefN7o^J%`VzN08DqS z8>94*Rp^cX+yM2mC5Go^fi{zofE*60WL?rVmJtR1HrbJW$5IX(h5Qi; z5$B9ps8wr4YfFW^aSC{O;+Ud5E%H36G*zrFVFG@UMoW+&HS9d?AYP^5Lt6IHN|qrB zbcq0xQniWPX&O}Lir6ta(Siuxs{2K{k zVD3w}@PGJpIqGs%zqUlpAVmh^rxb-M7N*>Bnn*{OR=B|QMQk904omtT`{RdsYdFh) zJLw65%wik{fdr0FkQ4HWKoPx^6w}unz>+sY5y^tzYUF=N`HyLzjcX??~`9YKCo{+72*Gip6eKGq{=ncd!3*A~=z3vrf59}4r($cFiT<#-2qt7>SG)G&*{wS)^aR?R9HAo(r-V=zS2o05Yg^9DZv4ISsD5Q#uEbm!&o;#xC*qt0<$@HCV-Q@VV7#^qRMvD^ zu+O##DJ*IgiX+joM4YA;8NUiGxF|TWZ-_q(E0DG}44Y#Cd!WyBoJg46Tn?V#7bGH0 zZ4h=-gGu|!D%`s34E4_=jNOGJ62;`30hzS#>OhjLZuEs3n_`*9lEYdTbWKzXXLkl7 zr9{x)D^WOxq`#?*IJvlRcBk)2xJmVoq(7Y|oY=A)P)onzf;`SERa)MJ|Dn+*X;(q1 zzSLYW-z)5#OGJCPmIU|Xg$_YtSeUjBBQ!kJcsWh38}1?mJvKpvrhRtQHhIz6&^ARO z)C!-4LILK!7f10|p)3+~b9g^FG=+|lzoN3ilhSfOyq8w`3~^P{I|*$aT#y7BS@RG5 z;LhXYndG51*TuyQ;7uk3x&NX$F>_^CU~h%meG*pDxDePwO8s2Y6*#iOaUyZxEP~bW zZ?m>H$sFwMnpt9thMKjSe`3#T2``p&W{{3YmSP1npfpxD{%9UK5?%0&%mz9qp;wT= zx>m}#*u6dC(s~14x@w%t0u{~D zKcixZ_U#ZO`o^Qqq__xY&TR~YNa^IA&{SB3^81{*iD%(ppjB41_smI8=Y@SGeRFLp zeZtbNeZ8lBp{6cq4O>yE3*sG3s$U9!HF!E{ksN=Rm|*dMXb!oUlt%xa^bR;{CKYp) z7FqF_q*9J^7`08^K;eDpKq8BnVSFkLa~EL}R=q!v{gp8U*PO%PNQAy$A)m&ej!KL# zazGKEv<=P|jf=-Z;YQK+W*AH;GR*KwM;AJoj4;y$4r`(uFKX`-|9k{v<)` zPp=I0n~Wdo3vrC0xAej5-!s|k(*)5@*eAhWjsZ<{?v|R;?Jl64o(_9?M$=-YV!s4r z)0KODLo^>6R(LhmC{~+R;G1h@_$N-CmnG$LUv6y( zqg+8m4&ogw>3^%9WLry%)+#svvGleo>TCQ-l$xY~v0^l`UEs$z#x7~w?WyHYQrn9} z<(oh({kKKW2?I)8?8RftxqZg>dw~59FrS}D$3M`IIs0|z=L7DndhSKCEPS7lSvT;F zU;ls#F!%G(=;tK&`DW~x0S_v?eR^MBl@88mlWIC?^!0(~1-Fgz2UgvqOCU)#0W|s{ zg)}axE-l3nAU>%glBj|Wi)0xR@+(;dXgqv%Fa$8EKhQ!JB(d&8uc@?aJ%uHQ{_4#{ zI}TGp!(4NL?Fa}Fc~|sYjb8^+uJu1kRBS}YXNDygD>qWfK`Tp)3qvbQR|Hck^cP2vjI+dHClRBuLJN+F zvxu1f>O zXneHPzB(C@ZyRcn&y9YAUG4c@eG45bsugfN4++(x9)oHQeZ(KPTv|B_VmLkh!%3LD`_ON-Pzuj>H z8%~gP!6tipjVDr$kpnklYB&7o7T4;`_{-S-yUEEj?$k?0&r8P0 zOTGOkg~KO>(Pua(aL>q1=q zd*UDX|Fm=wEo`RN29rP8p&DfWrKJ-D@#V-(O7e8(az=?j{6|YKE)JSU+wDGA@k25Z zE?$5&>+4TTEjG#zWo6D{RUA)q&t$y;H#3AZ%MWC|A){t7HghyKizDG9&R=oXlaC9R zoy9n@$q|+JsgF$cZ{Jo7)c9qxi}J||ik49qqlR+0LfUO-t9nQ%}A!mcYJi|J5@pDK><(Zh=qI6~@@Um*5hU#4r=*0TH#ZV$P7iNA6_H+gHbj`6JAPMY4 zOJe)m4Ug_MPTO{)ckkJ=|JF3UJD>h_hIg;s4TRk`+~x>oM;N0!tNt}c*LL%Ze}#k` zxxDU5zM^hckP69eiSlzzs>P&kfi@$MH|19!}InT=53d5IPXw#S-Id>dcIBz`J z{CvM2)d>nLcXj@g!y4W_$e@9?!c*w!#sV1KsvLr=LO>Q9?HjOBWEgl?KYdtyhmw}G zJQmkJ&vZYyho;yj=dA9NNF4^LPd^RD=rO5xMtsXn!j_tvZuZY+jP`)c5NpHj`#%|l z9@5$@%uQl2!o(+}BhM$pQD|v=(D^YA0*naYDNsJb!4Qor8RmG~WL1z^9P$+WN?IJ*q~OY$R%XQVmvc=59@no&00$Ou|EoOTyFXrv3ZZf z-YF+*XAcP|f`1pUuyce29KpZm8Ewl?_RvrE=x6$^W%_kEAePZ1MBcBO}TpSU0k1{5A3j9%QJ6M<*zP1PG73IHSBkFC8?ln?o?_CUl|O`fip+N6H z*&MRwHEj16|NJnR2b>U3D?OvgHmWlbp8+L82XSEN21hb+aPAcF?+~|{=O(8sP#M}7hJ%QN?>Xrz9Fu<9Ox-J!4B>bGZ(_lLQ=Z%Z9U>;}wQ4Ena zmK80rSV@{+J>S`Ao7<{V+y*kp2QRBQ%`i)n0vAZ84+5@<$nyp#f9|CfNI=L! zbJ2*QQhzHN$>&ZJ&5|jWBOvZiz|;tmAR{!m{uj>iD^?(~Ac?=Z5r_BoCc~dm!KsXy zjkV4LhI8=WqhVlL#!|a){tay2Pf6NdwS$Kns^b>gA^F&Us{3{{qSSYXYI<#?c)2au;m{|dhq?U&DO09wY}x$ z;Vv8VeX+0p!CIyPXqD}j^YDT;)0*LF6 z(S@3tW647U%dJxU>L&pJT}{~6X!yyA?~^$qWIG%krzPUU&!(_WVpBIz_^6prh?-`Z z5#}Z+MgW>X-pjW8R}iH(eBC_3rTSWLWz&_{qYn#;_u^d0%$WeQY ztusYe*4Ny*a?&uR^IbFFCOOU38u4c(Ip3CsIwc5Qjjxm2BT#AKl9zP|9Pr86?gs4K zH=E^1fKQxu+$`QW1iW|8ds>Ol2cr2_6|a7bo>6NK6X4`MtR=4VUnm50rGG6AxahN`Gl{l;bzL0BrG3*$8FLxayRKI3>g>!^xp}l&s-`4dR zUn|=hCHMWFk>f}Pi^C_k+fwhSRrAR3BTXLR9p?b?y2jGC*rJ~S?0s_J?-`2t!vpF; z2p?gm5yp3=+c){qm=?~1CrLTj8stP=0?&upRxR}HcBC`wr}~3mJ~e0CGUD9NCTHg^ zW5V9QMnfkt1-8__Gh|K)Uo|j7t*~}+DJ`9XZLT%1E_UvKK?NhYc6qhqdB5C;CfH~t z2W)s$q_b=8K?IK$6^$XP{vMs;^qCt~_+Cub<3m2=$aMU60yPUkm})?;cX>x!@4Kxk6$SDe;oPUFM)4G!XXFpf*(+;^VRx^2s9lx)axI3O?ZWrUEYhzC#4tLgF>I-|HBYsTOGvIYM zJUnF?6L@hFBe3(8_671Z;G3Vf)A=%NL8>B+3yjHIZ+Z?v0RYeDdqo9fLSu-FT@~=O zP;jHJCV=?@ypAp|3+)=XgP!*HPzByHymO7k$5HzfM7FLbx^@4ndb|0nFF+P-?TW>+ z8d-7xjAqpr)03Wy^hsp=+MMqx6jgc|2M~6F-K<`WoAMd*S=g`Eirh^40$f_vM_?u?zAa4- zWr*{7&i@Zz?;K-E`zYK_+qP}nw(V)#wrx&N+wN)Gnzn7*w(ox5-?`sCIr;8McI~8) zN@f4CtMWXx)-sWf&OzHb%e(Z|c&_>3`TpL7&$tOAZ(5}*Fn0zP`ZwuQLf|=Q;LDQU zeA8*>Bx=L=agtf_c;L^YQvn8UJQIBOT^a%=0zao7*9FK|T))ml#|2n)bl6+ZR60z% z_EYwD?L3T;IUSGEbqYon?G(bNZ7@dB(7SAt{NCs1&&9V2{f5u$-}I%&Tj8G!Px9~j zFJ)*Q5*lwOTvmf`?k(F8pF`l+{&Nv{Jh`=Sg`6L1XqzW~FY$19{&1>OuSXNaX9Cmd zt!IF?`c{u{#QqY+Swaoofj{}ztmC}5OAJSKtrajK$hX3^8UDfm+OT7Sj5cqH8jO$- zUQNU-?!k}W1O;M9u}jPmF%o>yRf}<)bygDENKtFVd3&K~@qq;_RS;G zM2HYY6Lic`MT>3S`a@dm@$h_Ao^rQ4H+`=gdY4fu*HS_#c=Imz`K(IxP6P$`Y-t`B z4;$&`K%3?+TRBY@=MN_Y_2_LWR4488rq4aRtxxWNIFRPA3ViFKSM(ngX8s&ljied6 z^HIHkljdd`dz=nAq>;FvVVu{yX^3<%ML$ytLujNB7)__ zvK(U(8m~1YJ+-5s;Cwth~B@(tY@HGOUpz+LpwFM{wfQ8j7RRzZb{Wz z*J|?^F1i8IRve1u1IqA%c_<+aL)(mlMucFqm?J$w8XqtFN?w~207KyyEcdRLMTKW6V!R@C3PYS=L zck&erFT5`u{{;LJe8`7j{v zKs2U*vurG>97K*$M}%TQjF zrPh(c_3Z(&*4Xgq3I;CyqLF7C@kWI<^KI5gsX>C4Q9ke2{RipowVS((dGtuTPF}n! zS6>?L*`K_&oT|8*)ys+l>^S($$T4&`41EKdJ8O;s%kbbnx%@Q_oO;b)&buefYNQJz zJ*%DtLYM;lZ8E-99Pb>~m4~V#kO+nkWS~4&HMUDP@W0l?e**Af`RLZLf7RQ z(4IszIk!O2V5qgbhMZM^;XDLI^J*^Wm-zMzjVm6ZRF7-Ef+Jsyy~4|UuYGH)Ox;RgG_tL>|Z-dYM*lPa?xDtHcmyq2W z8O4%^ANpg5=8Qh04~}$vy{U#Df2^?Lcde?O^C+y5LbMVJkNx{ppCgnF^i9Qe2+H*wtK%YZfXORVSI#h3G1sYIkAiblDdWrfHb zhSb!RLW37+fFpxpS9toWT|QQF4#vY9c5S*cq^M2eW%OPjG(vNVW?L@osc&@5GNaM~ zwSkb3!9zIm8}It>shSOcmw|1AGwPO4K2N)u)I3BeI)pZ=%CmCU(wc|>uwZ_%1|(^9EsOZ$%cxrkU*Ytv$Phb%Kxqwn_j<9GVC zjM-J!agOu!>*rLPJGlsQg{oRPFtF-x{>neGR%t?M1J3GGHf%CKQO%pu#kP_xciY39 z%iRhiQ(H$I=k9kw6GkZs}VXEkjS3zzy&=sqWQl`wJ40s?4m@hbj_E;jeSxyx20yQZ_<5Ccz zE+1`GOQw&>I;0Ty1PIg_(v~`k#reYGKoFK!nZhv{epDWwmY+9qI3=SY@Rd)ni`z^S zAxwi-8|;ELI#NKA4nP(M+y@@lzani$Gi<3C4;~T&g6AwqK?k`AhWy*Be?ZF1;((cO z8j5{g6v>5rt>4Afcj#0TgBiWrTDB9T4azNgPy^gWZg#P+5XEN3+6Xiqv)o$GG9uF? z1r(l!F_JJU!gan&T}#$Jy(pL137n5O3CV>bXciT`C2vmz7V9eC`Zc_tpWGXHRa_0R5B z&|3`b7AKd7jF1uCM57-j}!TRXEvHS-gbT=5M7 zBM5BZ2#L#}2)&a`ap??nY(`0V88y(!u2E2mO*}6Pi$>8Y28Hs@9tPD|K2I_ZgHrYF zAR7o6oQMquGbj{DxRX&;kcrg_h#?^jij^D%6dN2RlD29LT&DydcsIUZP|O0V;HyKI z!g1yYOMONP7`d0HZDN;vsh*B=fE*07NloAdn`$SzX${$`b;pHk$;Bh->iKB>vWiJf zDfzKVO{r<1d)cz($~i5a$MW#`Y5g*b#okG*cq5gTO+myHQW||wx`_+=s<87vD<8EWLG?4OJVwTM`Fh1gz z&9L*8iZJ{XtTJCQao1D8-ZL-y5Q!ob7(CQ2Pp~fVdr(R#rU2D6!*_1BkV+Lz3p z&!Y#M3@SSTvVjaH&>t!#Kfvd`hv|`n5CryPEEK#U;3e%=H2zBYMe+ zUCAF@U{_A)t-^tP5rK5GK^6Ru`x&r(j|N<01w21@#19=xNO^>z|C# zTMyA@a(RK8Zq$E~&ID{#BhFmyY*Xf0K_%}o?}j5(x}O9-(!*WxK_y%E`C@NCwC&`h ze_X&{k@TTV+~MA6!sLwf`A|dLrz1}sJ2Cnv0wn#o6Wa#{-3h_3%J4_mtZ9m&>ghiv z_n5b&X<{YU&OoG-o2|*t1Ix@` z$6d*CY3X%_vbkaNkx2-y+1KIls3o&Q`Y@pJks%DJx}_6B0^ijECbbE+hf1lX>n3yf z>-LgDyoSH6oUo-RcoyJWDHgw;V+0ah_rohFfXJi@k5AZ#oc}~6U*0ZXk$3?MCUM`r zWXou1F`+;FCuZ1N_CfLxkzge;H*#q|eHn_q)+Ehw)bbTg@yUFAf%KApp^XCY`VCTZkOW9P} zt%jkQ!kv)D%CpXh7qLXJ3ih-i7U(D9s{qFF_8w%T3A+wvlElx%5vkB#$}xIcdou-+ z9efrJ)ZlW7gn0nLyGel$7u(&g=TSdP)_s=WfvNFqj3^xy-gq^!S53I7fu@{DWa)6y z#B>@C$Vtu#AN6ayCY32w()yP9dqFMuH7c>4kFtHyDelSTA*u5|p09C#$7y+!jvD+E zeqKRuKOWqqS9j0AB_T&R;N|1-vc3|L8sEH(;ST=lUDbM@V&o4kr|y!+2!w~E83kT zj8*+=j4Iy3L8?*i!JSSxe})Dgm*}poR5j2NGq^2ZCaq0$XridK_5tZg0d>REI`Xpl z;}jSdgu~mw@iw(3YUVF%=3mfTpWb_iFxMp(Xj%b0&Q74&OjGOgr?*}tkW4?&v=mq- z5O|y(EM*#16BrbCK2PLTrMIkpDG!q1Ta!aP)>Y>4nK`#gJ<%Hf+ zaw`(JTh}SJno3K$Lhy*E3=t0kQeaefe+cPq67sqWl)!AQs^OXZ6aGZMs^^`z{A z>9U{yH0keY>+H%^C|ma*KxG(~tzasJ{S3bRC8&?k4Hu3iOLKQ(`uYy3zm5>K;zPm!aTk zP5)%Ue>wZXH|rB-!4G)e&#Pfiu#yM8`AG)gL^~zk!R#*uJR0>MEP6y6^~u+2UPdVh zKKN4(nCwHy@IUX~PyE<32S2y)^#)ZU7BM|0jUU_X+cyc>?T~QJsvi5Vqu=Rf7IZ*iaj#g{>&i(e5$tJ#K z|43E$>B;-PJH~m_oJca+y&ZFU+pti_TOHaK(AUye%kFusF-X06@x!h$CHgHTH}ve9 zZ@rf9{$59|_9eqe1+1$R?JnUe_<0eoF;&~j$9d-C?Si*maym!3u>Gc8NK{w-4k8`I zAQ0=`HDB#wc0Qh8ecL7S_h~}jVRo^5bO?_-wZ%(R-+leD(nYIp{)BQlaa(xl`i(P? zj$p#S@#NPN{_Ee%_mpt{^DE2epWO#XtuKL<9e8@}-hz?AkBytlZcWeAG?JJ4b=!y1 z{BG520ebkpf4oZYml=-GC$ZN&`tSooQ6Ob`eNE|czh?9(qXrmH)7 z_Lh#gW~&o}>HSzfbWQOb-^mF!_~-*|+bz4;-Ym_Ro7o;yHwZANzkJRVHTP#gn_Vnx zXp{<(HHjKuQe{AJJRaWdZQPIMep_gSy4LH*%B?kC%QCQQH?(%H26-$`&R$<4;P=)0 zE@NZ6+pOhgw>aJ4W%D;DZNFaTg6h+q9h@b4aHoDouJHJ)wEzBRD zbvoV5B5XUb{aiV15jo~=mmZh+NxzMAXp{?q%d5LG*|hMWb}|Y6iTeHQ!kggFykzQ` z@O5b-2OOQwrTc54o=uwYbE&2UdA@gh-q3a}1G8Wv6 zq~n@tRw+*#qZ9S-wVA#pUza1JPbT?hdwIwHT1*I>ZE9k>T||g=%952EZC{G_I7?^6 zm0|cNy}K9?XOt|P%@wk`9#BGJ4wy2Q&Y97;HhwoDrwiO>sft9tMlR2bS6#8xW%_B; zo;1BB%7d)n>@=4SPHCrlTNDP_8-YWUSTYv1Np$X2lzs>?^{O)ijv>Ad9>NK!8>>WT zGhjdSupa+7jKZ^Tj$7g4kXHc^^<9zWI|FXLXH?scY>)hxca8JvSB~Y_h@+ajx*B3f zK+xTEr=pf%*E%;ghIhc=J$5~7>z4XLLj6fU^fFUs?WX3hpDNjV4f?m8-qSVf8d5FY z*6H7;duuWg8I z)n^j0iXv>kjNxzlkK$c$|9OY_7e7~ORN5l2#b59}@D}g!~E_`)~s@6QymQ)&ej(lOvsG zagFO9s|*j6`)+iBjembhz;26Ajp=`w~A4MuTPp2(8`X^_d z*OmgO1kSBPNy-ZAzHq+fIP==AI{Es=XZL-VF+y@Z_Yc$W)Yq5f3^HkWRnXO2ojPN( z*=xiF&X#rNK8tmCN?#c&vL}t?I0l8xN9Xa%-lV^?O^WCoT`O!fmY6tz=dSb9Rcm!r%eX&&dF2tH&4Y7U==yGXr+|--v@l_C2FKzw$z0_0F zIo7S$V$T96Q;ol+(w+YL_7IAWFsK~14655WuwlwxnW^nBRl)Q72v2KlihJb__m@kX zSuBomRC*uA@$*LKBZS#k$!u=V=ehetlzwhw_T!_cxb)klRKP1PiuK7H=Z7E@ORe)< zJ;r2oI>MFP2>jM}v4p!h9{CB;HU)~1#_&VfMHMfi-qhB+J5yrF13wF=mvUhA0>-(xT1-IvSD@xld zByoPb2H_a4794-KNBK`tTU*aKA=cklQWgg3@GknYt=&v&9O{=r;8J1Mc~*aQi1Tf& zlf}n!dzcu{LPBJwI(>$h;M}iF48ufWM8iKRL2{~mhX!|HF1;Z1!)24n)I=V$ z@@sg>kI-%7hBkFEwYajKb7oXN$9Lml3YG$=B6iIGJLT784tm{PKaIs_h4UM(K+Kd9 zfzEb}t-`cGd~@>01N4*t(l^0zZe9q=Y7@#7#w46ohu`Bpg;;F+io1YZt4i`3sa=id zcxXYfC&}s=h;%e@M-6^k)6Gw%rpaJ0=i46CNB+4|c>QM}7Jpf{iK`Nh^aZnwo4M<; z^uR8&r#SCRqF+}C3J70FwI3tAc~2)>r=?FZ z>n)gX%?FnZ8TP0xj&mCC>EARxXt$f|x#j158`cvZJ4(?>8OwV$@6)(#Nf_l1%YO4i zHQ)MPTMhnvi>As~IVkT83KQBF&S$e@^yHDYGAPA;Uz?uHIH#XiL!&wUBdK*Bc9Z+j6?!0N3Aj8J(vy6DBc^i^GG4tkZASE{1nRntYA{OCP05c;)B{sN2h2-a zxmJ}l1F^8Nu`|;$H8Zra0bgk#TZVdQ2<;NWJ2`sx(e%Yj1< zY`xu}a1JCNk-YTh0lUu9&q8_YVZ2Lc5k@Mptgp>|5`ME)BKU$0B6aezVa(H~IC%iz z)24TPj;&h+Bqkgo!_< z1+j;w7@AlfxUu}vg>j*5gdve2X-QPNOqwa4p^t%$-GCQwvIYlS?t%G1GlRCzsgebT zxSP1$alvb}atEUmi_>Nhy8avLT4-W=qE4N~AS#%f(E0@6DmlnM6=_kJ3AdRqCJ%nt zv$NxsojOglJ7kj1o;3YLddpv}M1L zwQdOW#}6Cf|3%^T-v{FV=Q#ZTt=X~x=(YT_{EuPyf1530Ejg_3KZpcZFhqW!5Qf-< z)P`6JO9B%S!9t28!~Wy&N*a~@(@qhAtq#Hh1Up7-M=Nj@5kyN&977m{6I6B)R3AZz zC~9(xq4!U-h4h3czTcCJ;`Ho~_xV!Sw!1xm454U{*Uhdr6w!^%?Q6GdXHs{pH*m|i zl<%;SXXMd!lX6Jrny5SSX{l07n7m~&lR~s93yw)27H*jtHcOiLj8 zCa$W7(H^vD&}rr&?ZSe7wd?3NO93lA2s*0g@SZ#6G)*~*m*H%P6yBP+=d zwGm3Ows@_o9-sBX5H!{#k0WS^n3)DV(q&0=Mzk^$i$1u-q!}793riI!!&9shkvRBB zd`7qs5kgxzR8cG+P2EBcZEYm43aPQ-sH5zfj6QQGp#VlE@i|m6fwYnF6SJdXhA5^< za`2SPYo>b`b|xR!C5Dwvs39}^3b|pNF16hh%4H0exv4kXjiR07SjVKoB1KfD{0BEp zBDS$VFxW{xJ{sW!WP6%$ZR4PBSt1-pM;ar7iG`2{4~&pTO*s=fZIhr(Bq59<@aD)} z2MX5!K;pIW%6J;X=t>CV%r#kDwoFg-!?Xw1C=!;q%8X@5y_sjyjPGnV7#~Gw8hU0N zvOtz@G#TCc)&!<8T_)cEUANi@>VdpLA~VAhFb+?^jBact*O@v2W*06(17g%5*XPmL z3kHI^A7Rv4?i)*VZ_+`VaSIxXEJDVmq#r`5Bhr#M zqdYx!F+y+03o82gcyv3XtWCYboDhUxMEC_q2T`6#uFBv-+St$PQQ%~dXj%qgX#{a* z5#UN|-;BYnYXKBui!7N7#~fq8nU4wegAGR9WAhdHP&xLngVdjpdGt*}nV1>|LaT^{ zXRb)13*NhKrFNs)-Kr+y)@nRZ2ot5VkOK0;j%<3h;h-%BF!8u zCW-P~JQ|7Ez^(*!FDD5mgK~9-=-4=vkxjE?dJ2s)H6OKd2`{;_v@9ysmM5{;60PzF zc_b>K%+fA5F}$xISFLzd{{X9FV+64|3$PGrBC;hZwCI@+Q6vT3kh(@X7;v52aXBO% z?;ET}qzpB>4Z2jbPQz^k{z~Fvfs-QcPE-E^q#QimFUuwfvS-RotG*eK(h|xc5De-l zA!f=!F{DM2fNw!6_pyv9t8r-}BCeaGDda}8vJG~mj$4X7qG;dXgP~g$r2Rmj zrKA^Ex|j_Xw~vI);Rq@wb-)c)I&0g0jABm>Sa0qj! z=S-ce$%QY8rUH#;`k-OUZie95AvnXR8w>Pr72)D0P@4(2<4j?Y$SCGMszh5k@PDc; z8qhZ9^Z5ZK;l^UoDhT|7injC+1mJ>AVu5EQV?g27^*<(G>09Q(o2`O!Ao)v#w$TwE zE}3tz`Y1#fW6vt|UBktj2v(%j1mF}m$%3b23|fMgbD(e9kbLrY9*cvPHDGIM&^n)} zJ$wsyniTr1dxDl*p!Fb7JG)hG-t_v;Ash?qBiFJ`fdctQgr%Sb8@WkO>xvGj6J+r0 zq!8d=7cc8K_j)1Q!5q@KE3*{m6zftK3pbFELr5Kx!FM$U5#z>pBuF@Sr`+fH=8PKh zLCX5cq)tX6WP^W+UQjq0sU{(O$iB%(-Vah|t&L$tku%V%?>yKz3_(sM^%<~Rh~2m6 z|L{wPo5Jd=aqjhuJOjT0`$_ogo7@hWgQm~|@>*j>-(eO|Z5cBY2T!KENrJ#YZ{Hdx z)-`UDEbSoHdx%Ko9T-L)-#=1@DCtxYM8n3L^qYniCLKnlMw$UHvB}oJB5c($YyvYy z?p{PtyQ;r|djUau*XDX*30PSkJ#$=pLF8-&O}Yp0ZRBF3;xFL+irB)&H=f? z%!h}f8N>n4XV0fSP^!gA8h7dDcbD!K_ zh!u1WC^7?{4bsYhOUGZxAi)HR!iEsM3t)8xP^@!Ag?;bSln_$80|9|+0zBwFQBFj4k$Jjg^|%kbykf*&rXI z@y~6osG6&lnyzS5avAVLY7R9asib6)j+taD7P2WeDLD%%zPY5Vp(Jl#f=3e3EviUC z77-H~hownr-H80KoRe?}%SgM~UYzTmmt(L7X=&FJ8x>xyNWWGFY5Q^=!!X&Zqw8#c zsU@@;Vy&5VUq*lLT|#2jS_Fk)4fd_H?Z9zT#54*0#IP_=;~(E$mOoa%)m?#suv0P$*kmW&5kugf%D>k*qtzWC?|&0HDuv5I-3!(q z`m6;yx{dY+Urns*R&PB=s9zKaa`%|+&q6T7TPPdaP{-5A9v=3C>O~1acj-xwi6VDg z`loLieRSW*vB@~;D}YMjiYu?4QWyC`1aqyS9>7RuQk#A+ioX2Lu+z7mw`GuOv6pOo zT2xkl@B937)}p-#BKpWv@Wwa3Kmh58kaQ&y(;AND7;h2bos9I1Npj^VzCrN+=371^ zk)F-PH&kL;OSQE4M3!8i^i0_p?|@7nF=R1|)pJN7KmLgA@QUmR+cq&neU*gW7O=;p zN@6nna#dSarg`tD`Ez!yy;vdoh*01aOLRjaw8MSc-G|sZir88#w8IeA>yHKO&jM=4 z0(!*=>XH>Dh*(P8>qrgk%83@(-r81Yfi~LKAtS;kPY3s?)ByRb%K~BYamvbah7^nn z4vsVH6prh{oFpzXe2eai0nV#nCXJGH*TK?atZoi5#ulY$+EPXYsTy=uECX0kG^s11 zhOc??>J??}dT`S4f6!}8c z_(*d_EBiISH@$x$y`zjJ{8xHMQhLX3I?g}Q7OMtOgJu3UUqe$UY?t@_AWS9%eY$<>pD6U9I{XEBaqDU-KBs`Y0Q_zTdxS1@x?O}+ z=;M=`Sf+x@r)oFF7Jf#nMI9_p}SM)nA&F=)%A1xof*3k8SXg>UJa`t<*%zJn85>FT5v%_~oO($8+EyyZA%D zAx_{B zvRpg+=WT-6Qx1uhytT)}pVKcc!rPpB93GqD5=rmnPp<7TFkJ3)v!6ltg~Um|dke`o z4PAx6N4cs^LdYi$;Upx<#fu8SyiGg=RGaGH`jc;~KV80YPd8@XQ z#_({BxR<$-iNhM&Cc5^MvV-aDwp!0;C5G>#E#7Gi6q?ry4ZY8T*;?uDt=Kq2?|{^A z%FoomLpYk(IFxaMtIqy${VIE^ET4_>@yqK2ZgBNGy!z>%Id|3J*A4B5#i7=`J|lal zI_w4K1oITSX^!Ll2opd3=Dnx-IW+z1QC2qk#riG#t{0JPzQNxj6>~ay_5=))mSI`J0#X@;^meZ+0%9s|NiZS#A8I>ekyK>;>xB zzokCx={q*>`qr=ATYUNX{Yrt>?eY9}x&V7mzp=i*o+G~rb#FdbBrIzXR!ZfMQqk%7 zPj&}Uz-YYXvd3GZauU&j03j7I6>nMI%R^hCtu2ALii3(vo?)4Nn%A4OzHa*}y>3Pi zylK@~?Qi7u#o! zw{#A6`J!MB8O)+hGiP+>LEVj_53HEz~^>(9Er4Z?}ssI^?3>TK_xk20)X^)1U(OlPsv+AFp9t3HE> zbnP-U`W+0R_gC^OoH756!x}RaGfh5i#w7N^XwH-egH)1i4F1~#}{@_L+44h^mZ?0)Yp-v%SjZSxcMPcTzTdN))bcVEsB2-m2; zy_&XLgsigPr_JBpv9wM*a@MQ~{B~p4)5)HnQr)ZkKdskCKmC^3$L{dDI&+&0jDueD zOWzUJB93#V{EZ2?-#0co=n0mR%=;KM!!U_^~aCpUv2ys_EqkI-u0ip&71zXPx%^rFZ-M0OH4z& zrwxJ2sD(O#)O2ir6(FF@2cpQx>TXms5sWD6trQ+V_vsaTS1&JhJ3pbF z3)pz#Iaz(THIW&ncG<1IXI_r_8FZQUT(h^(O`mOD38>)A>D{`pwSM}Uc}uhIzV(L% z#wNW0qZ`Zio_Lk1<*i~EK*&?zR(^w7Z22(?*$VuC`^o5hBvJeMit=$Zv*D*=^Hc-6 zZhujIA9wVWX}I#elbDXW@)i8-I%sLv!xi=k>tZXQowqg(ddX88F82YGL2pWA(1J+j z29i1DVzU!xCl+~KJXo7HF|kQ~_j``|dgkszi`kX=736&`yHe6nk|ZWSOR zI#{iQc=B_!QZh4=H$(G46Y(*N%0#hnj0=s-4-IY0&rd8bu4@Zys|!ufFAt1tD^ITr z%`Z+2tSe5;53R#m`91w~9voKnuU!lNAG=nw&A)an!+-2r$}wxb|7F)=`^T>3|9|XS ztpC`x)XUh>D3wfp5dl^})S4;Tsmc`1P{K+Rh6@y{GKBK~m23G4-~XmI{f}I0F64*? zx{6u61=>>)XptJ~AJNu7>ZuQjdo;jXi3I@vmMY*7SHNZwz%isj2}T1+2BNsT6tfPb8}LziO>;fLd#&^#4<95nw8spozZ_xw|RaMlluv z)LA ziy{la|4pv70st9|RZW_V(SQ87;{IQv_y5Hz*tpr~GyTu`lmD7q(HA$gasEFIlo5be z>!0O+OrRWSd3(Svzi(H{80jJx+s#H<7qSvV?QxS(TS(3o(`p+x@Qm8nxH(TvkOv{I z{d7agP?iRC>{Z56m05L^>F9zUq7fI!u534CWO|O7I~p=A-gyNSKJV9$SF^c}JCA** z-~ITny{9IIh8N1pm0w@W|M+66=`1<~Crmk`Pnj@D{lqk{%MjhlGCOK_f?_vDW^mli z&lNNoN#c^lmZ`!LetdVh^Dh_MqfWLQ0S=O28)J zpw!R2qZUUp2NQ}^4}6gi#E|G@SvE|hnD0p*_Ckbiv1bbQT@cftw878Mh%U1RQ~qk% zD*ur~?vr8SZ^v*b9SvIh8KlPynMHY`;(1e#TfSQ&MJZUtZ5B*OXHf;yJLjd7lxl;M z-voaz7~~bwLI%~?bbe;kr^>fbdV`E0HWNqN!G7C_{>9v%?uvWm4~u1^#FuIPBhAv6}Y_-DQc5nEEjc>J$JnC=*RXsh@))b&5BJ zpL}%v2W0qTEerq|L^D?}=s?_U7c0dJVy(h$9G%HeKi{ zqE9DJ-(VA2PLL8~@_3LggbW>^sQE>cc5~WObGa`}>oJfntE?l$#H1K*vyhln)EB~I zo))__O;woy*?YzXmIgI@pLR@{fj0yl9SM1XXt0JEv!j1N25})@D{6b(7;+0i)V=Kh z*&DC_0vQAuc$K*R1sN=e61dL{@y4A?NTbpI1sQ^!dJX6>g(<~>1t)_zE}B6BAj8^S zRRIkmlgXzD71Cj7uwfAkyJo3Sm)Sh%mW}Y7IHI|v^kkkLHDCtifj!yM3#Qb?sz46K z)0no}H27dPe~d6fLdb&!#UePg*)8zsuS4LRe+VKiA$!knIB}~!%D{Sc3dOKE^!6d> zWM^Yg=|6SAhZF0-WWzkbVt!eIOh=$m2OfdK#RSbIaREY-BcNdhhIxo|om6?4*i8W# zk`tiVzeEGWh$2N(QcQ!{6fK&S$jh&$<2eH@bxlHYgEdXQ)~3T|wx0PI~iY-cXQM z9atj2bEV=Txyc`GY_J@?5|lUhEPcQTI|AfFfp0ETY)C$L7&xuo>oWlgU3*AC=mj1x zk~=J@@o{*Txad*_b?CICB(cS92I=Uu7^W#zd9*s|aO#$QR_UhJCMi{Fn$7ZNt6u4* zI3_8+^bIzJ0N8Wu1Y);52&g@&y?%6@f=VAm7$0R|5W1B3#OKR}DjL|;$COkE!%?WKNj46mFmYoe z_`eeA)Dk|W#GiVY>xWHE6h=mJk`w8ECGwGxczdlJqqg43I)njtK!A9w!%TTD#^;(s zPC<+-q3iAfWBdtc0o#S5s|Iw*=`;ixh^HVgLFcF>1E0^v&Qq3ZLY zgt$)xU<`)_Uqlf1b+{80PK@70eIeA1V}>}Ay!i<2(;-$5GKF4HVrWZ(a&(oEetgC- z*1Pb?dYU*2gvApi$c^|S`m7}Yf946|Tmnq9Spq+q7(x6s+YEwKq3Z(C`AmXq>mJ;> zQ7|ceEk2WGZc)TwfP4`p+Uzd&(lBw(>@^V14*i71N&FS6a{mtkA4)sPEN`ICBost> z%ayV}DUvHIRfl5sbaD|XC89Dc57s0WeMcwkIL(BcfgbdMaLofPpj-##;$`D{h8ny9{Cywj7HU^C7 zf(nSWBwFVcc7QZJ!9%$sMXI7X@dGwG@$fZk*J?-wQ8+>yBk1G$C{?UTfTpMmseGWW zBE?p{w(2xm{3ceI*X)4t`cvoffQ}f-f1wO9h-4d?lt}-DGH~e3dybH4+M<(O=%MZ8 z)Q02iNZJE9h5&D*VQ*sq%76nv8Im7wRsTU5@@s2!bOh}GgED-gwTmLP!xC=R(70fg z&5*4{(+B?tS`f`2$AQ*00<5T=Fw5r4R0^kZhSJ}juww;ld`bnOdM7z#>@JB?E!v>4 z-N->lr2Jssz)3)YCbZwx*}IqAyQkc1kiYe8+`GqQ$Q`EU@2PtEh}*m895e@P?c8h7 zyX6wPUCn#3arqqx`mg}vxgdO8%l)#eB^ZAlv}h9&+1kM5mtrZHJ2}tWnsXp#v#mU}2 z7Hi=xZkn)>wc{vzHMO-Mg{4bDpgM83bP>?oWGLci)I%Dum6)Sn{G$W}!)v<7l!HPM)MawCtQn6{gm68ro&8J5|H`swDP&SRS)?amEf}F&x1r%CEmFQoIyId8I#rc}1$X2~afoZ^I5%z*0g%(QCZqfN|gjoFUv2-3>5) z_0kiK|BRATW0*m5NcVnnx?ZIjx%h4JL=Bd*2%5rHz%?QkKT68j%~YR+qRPqI<{>) z9Xshb>Daby+jhscD#=v;zi-dpGkazqtm~@!tyL#gb+n%Mz8?h^2AVNdn$c7lh_%=- zX)U>YRYRnz?|N;bG^2>hXx$`;*Dn8lrStOd9pMjlBp>W$-^B?r1|twq7X_=;gcBV5 z!sVaJ(Z&iX$P%N~6E+Ow_xPb?Z1! zyH%xHkm7zH``zI~_f$ya+TD0_20Z-)%`50qK;-*1LDcpRQUb2@Oq#bSjG8HWl4^lT zZu|!tNqu3HAf=U^=yuU(vFbHV+nIU}fHnuvuWUo~Z3<9vCiyi|VR;Rr(At#qu&8&m z>^ENZ!8WJ&qcP_;UP0i+mu}2>8+xYwapJn5pi-^jyo;YNtOU6t{>Hpz>3;jvEnu}( z%0@?dzACVA!*t$PT)L$~rd{`5rsZ6weM@ovvN*x6hwAjHBEc>h_&|C75(qoEC%JS) zZQb5Wee*#256aN{{vVVU86!mW@Vy^k?S>O;<%mn z{DXpb0$7{e;X1M=DX}ox?MHto%-~O}%o-4@%;c%g9iI@7?uI~w_)$Fnd?e4N>(?i; z!f`)veTOJWb&N+3S!CtI2nde;1;PSL4(&j2yaF{TpXi#M*s(u#oU_egJ-7a^J9rgm zH~t^j^u(h3RWC$UC#4);4?vP3O+LZo=ytsRUC%-f4@fff=xORFe>DJTSIOCU%Bzp- ze~(uhuXNO=HLR~1l*l}8h~&`Do#3(+y+?ptghSl>S+u);-^i1QN%lT|cAr;;H$1H9 z(7j*xJ1((*bZJqKvvg$f@%47^xN4-BIwHJOa~0V5$U=a+1Ji7_>Z$fEnK&9#QoZDg za(-s>@VB;Z$tH4F$-MDgo#wvnooyU)CDB+aU87BX@&Bpfnq7Vx-;Ly^PwP~Cn0@VL z@O$G2pxBvs7%JY*w@4j~RHXa-C>SATcsdJ;q^CV%qk|IpUU*Kzj%4;WLQ0|JmxoDILCR z-Ij4@?CmS(@}uy&zNig?v~Bpr{N=j*iReOBfgYZsM)GZ>T{tUq7XOMS~+ zPhnO7X{Zb~?*|gD>FH+OdN2uh=UU%(H$om#fjIgK{NtQ=s<4q7p;AF-tN7kIu53TCN(#=r=()MnQQ0T3Oq5S=%^`k z-y;NofSsZMuV=B<^y0Rs&0eS*u!m&AxQ8b0t(|LpI{Qb@$3T!2)cf{rGgg%Ly5_{; z;X)R-+f&}HpO8&)R+dG%`^UTily_nN4u@@tPqT}ZMbNjtzEFr$^O!%T<}-is-&juS zg0l48{VwMUE-%KELU0V>E^L0-ch78wx^cWzePWTw-ml8bQwnnXuL$q=G)~Ba^lS|5 z^u+oLsvej}3Ceu@vihdDmAr7n!_s)!-5YxQe7&r6y|x?>zFTtF45Sb|Ue*#NX8Qaz zFy3uEUi2V0?B|bGhmQ>e0o0!rwZ02$902E9BBiZQ=b@gAk`KMMShbPvCf>S_W^&qQ zKB)IC{>xO}C(7uk6NQ%%*q|eRsCRz8%V)Tczk6Ah4G|w56|@kdXp4|`iCPhM3On3K;4ToC5889}g8?iO}*oxNO>9={S)vGgR=*|T2RW4l*r zeJL#@>(a~CsMi%JXVIp)@|;z%v~1R4K-uZxIVGGb&JVlSmjzOHXBnS*T-r2w(&xKX zT4nmBtZnpoChGG0yUy4*`O>8+RfuUmi!>c}ekoBL8@oPNKyW$=>NLNix0`*SaK7<5 zTWlQNayQAQ>u&6<>TlO4wSBqne(I9Sq;2rOH%7?PjoewwYy0=zQLg|Pcb}@YMY~5X zkA&A!9Xn?IU#&I;RM-LsyDZnclzm*~6v}&7I$KA0Z(X&O#8t0;n@HcfEcEPPAn14M1(0gCOqt)KnBD&wr@QSnH1l?;D3yKe z8TfF1a_x>hWWnon4_e%+v^~81`nZ=|{W(LFt2CJ?{IP(XYtfH3O$E5@r` zZ~D+hpo@PoJ8v2{9(r7-3l$GRrZA46~M+^Qh(>CrdEaWoyZ7To0S6FYsR z+Rx!_=>f&lX6skKjNpUY3&!@fP@Edi_dCs? zr2+h4QXc>hIy@W8UskssO0w-9d2zua=$2()CV@w8tYWSxvhsi}+tG*^}~**BIQmBUT$2FYf9k|MIm zy03c=XE7K8B&xQ#84l#)zYNbW%67Ks+gVz~6PHV7u;?e{by&|wKIEz-&ZrqVJ9OC| zE|LqF?#S*v7LusC8q%b5UZ?(6xHGUsIdNj~d)-2^UaFWpEQYj1Isi7ryIGHN4!$Y@ zNd7cm=G&X?FMZweAD=yXj)1DmNQRI1+c*y^z@>AH{MmP`OYlQu)k0>WNl-2tM90&@p((#>a@tr6CCJ&vux!&&Z1BzvLwWKU((KzEX?fTzkaS0xG^-# z=9y7&1k{Wij2(upbcp1-SNS|l`-S1z?vMD1F29fP=@fdZ*l47xJofO*eU&gi%zJ)N zlPrj&MwO|5>lI_`dzZTvOdi@wZ-J#wTmW&f~7+cgUAah~3Y3Z9~W0_CiwY}S7 ze6D_fkalNp=x9l`AkVRHp^GcAi#E8(F40YuUZhEj&&e&Yp-RX!C6pJPmm99qEUY|J zx`pDH(Omv&sYW|W2=pxUgZgF2WR-a=6q46$*tCnbV9?1nF$WnYJ--hV9ViVOM+0d= z1pyR^d&Y`-28ktohl3>Ls}M7VX*YoGZ}^KsLK^{W2mh|ClCu8xksujChtOc~JHSOr zlgF%Y7W5RCv|c$40t~h=YMg}$&J0y*g)83F2vg_RT_r2cL_b~Y>b`aorrUYdJMS=OXFDSn8Jdh2(hT-WWir(1oA~@wl2PMF)7lYzy|6LpaLwhg!&d&$uo1Z8@BvjkNE z0E^BWt^@Z?9NOcxoJXk=*6D6E(vHn(Za=q9=L^RFS@4YxyxlJ1l*M@gYI-h&{||J) z|LJ!7e^`3{*Me_#prGg9`hQ(+1L*(_71ej5;bg}F-2U52X?vO8P>l*nm`Qy_lN=4vjpFaX!CoD-l|{N0^i&-dIIMA&mC5dg%{Fre1OAzv zk>i>BUjB*q?B~qqXYU>7>8?Vu<+6$ecAL_QTUM17)l@T#dx-6^jV!nHxZNy<4l=Gg zd>Dbn^?^`eauhffS8m3r{k%pcBzyL6$ z5crlY!voVk>bCDWScc!)e3D=KDdG=9DTtWPsp$RH~&85NnurOi<%Uh-Q3)(5~E~!4n!#M7v21 z=mW=%7_B^^oL74PHEgss_#KChxBw%dIKodnH(hcN7KFvZ4wRk^7A2Ql)uM>1aTwON zR74x6ku~6H86Hd5J9dJCtFX3Gr}2%BN(%K)8u8TCq!aWhrlqn^B!~;NFNh1>w=9v# zn7NbAg{*;&O{Z$@lwRWqzaz}nxhpYOfdLY2aXnP}Hc)64zGO3hLhh5>SrTLu(7vN@ zbheJ>mL|MWeP)wjI9kZt({!^##&*FF&h)bhq@$ooLh39Zc4CN*-&xT(-6&)DCke&| zb$$q7HLa6_=UyZW)5QyiCm%C6*Kdv8LoOP9%R>{IA-M^Ld0=TF6V<9ryrZ8TJ{AeU z(yf+8^+<>&G_xx{E%EJ=)^BY?(CivCZzn*kfgT4#p{OvVy8n#@pwyKlaBwL0&zug@x$uHZd*Z&-%_d~fu1b7| z01Foe;HiBx5}l;dKMA)Ev%V2K?IoF*{kA_C>yjt1T*qfWuxWuKBrRQH_ZeNHsrcw+T_O{!bx zoOo`URGphVNy}MCvz~Wgb3C<_3N;gTZP}zYHg8aie5an65Z1yYjX^6DI1(RdA^!43l(R8Ef5R1t5A3CkZ2NI6;^eB zE4@nSvqO84>9bZqS+xdz2TuO0-K@{tyh-$(YeUq40bu})PDE-C0<{~S{9RX-zH?rP z8e^mMe^h{{K&-&Djx?%J+B!K-ZQ&}qJ}A^MqI%F1*^e|R?%NRMJWGDD zu<`u)$UX^^ilt5>%|(z~DmFl|~ed0hMDwqE!kS0q=lhkIg19DtNh;nkJOY zmc)~gLo^u_`&Wikb5B4$zs(PdWzdS<%$&yb@n*96s?rO=jx@6~Nz6 ze3OnSizxt(XJCDOV~1;2i(vOX9&%%!WDm?HEGihamY)GttrxLXDk7loSvs$l^a@s@ z(n78osqvAv@mH22QpOV*2+yslsEDL`Knc4jZZ+9Fix2!*fhkmiso;;4%OYra5mhJj z5jrJ??;2Q;#`n>*UOwiqVOfr}M{pN`GSMux6-3<@E~JRpv7e|{y@aJ~XraHC6^S4p z2i0x^=})Fp!W4qIQGGz!DP)s0x}fW_$s!G-HM(%>`q>B12EsA=!ZF6emr_w^BT*`d zNY(r$DnUxq9>@=6*c{h|7;Eb5(3qPbf0hPCP@`rZ>4 z;Pg<-Jm(ImycFUi1}*0Bj-s+f9n8i^#2K40AKrTR(D}RIfx8iYC%6a)N+Fwtd(bNR z9fO=O?TmU;6ss*^4H&9}c)+X0@$(YkkQF3l&y(} zaFh(iRZ;913IBbQkeHAX0%-?FbbZ7IW&9X-?JQ`O71^YcYL=ENmeLe?`L~av5?E*$ zR8-3p98|kdv24f)pU^>*&_VRj!F%|KFD9~L7BX0D)CGrlFE^fh8)Ak8G!mbH$#e0| zS}h&Y>c{ZEjVpN+MZRtpF9{Ukgf17dj?!+Ky6`uqxFX`+T$KA_@iB?P30@~~ zF!OK7l~!fyow&zF9yIC@GKQ2Abj7sBV8B9j zn7HM3Q&nGlN!MIk4w_EJ@YIOa19=tM{Z7iaT4W~5kA7DTnT;G1SM2ipUJP1p1F*BP zxAHv%uqJL$+do=!>E@!oZ-)Ghl>W}O<-kdpQX#t|R`6}aa_P7+uW+fR)6x}*cvg=- z;E5U3S65r>66(2P?70lj6DAYWlQ;c_`4ak*r~nLC|w9Lhc+pS8B+4@z>D6;|t2wsg49kAD@gV<9_&XTs6$FqI(A4UZI(l3PnlG<8_MJbQ zNn?_!t|WujnI~~bCw3@m*5Ur9r@Ez4Ls}S1DAKI%AAMg$z(1QcY(-rwEM^9X+MjfTJ0Jx%}OxI+k z{uM?%K3G|m#zn5BU95RqR6>=P38SHtv9rw{NZFT;oIk9v-l{DPb(QrAq2r>240~nb z{yEXKvWNsy0klnJ#CYa`e{JMJM=AQUaRcYnykn40#X5IswqwV{6}8nO&Wn!j;W0HB>jeXU8$V5kK3a3Jmbv^ z=Sj-mUC)Xe!g1+~@gBu!Z?Y08CfeEHL>0_VMA)UVfY$3#;j_`cv(c=vQQgr|-P_U6 zhb9o*KdIj?)QlgXS9J-x!mdw<&rb5rPWZ=86n@WnpC(tJeF}(xs@L{WF5M=#Ffucw z4!qw%gc@QPv7-%fb=y8seRj;MmV=fHBy zg%OY6k(dj5@R)WqP%`;J?DHgqSxBn!J9bbUmldW}%KkK_>%YJNisfI;jQHYNlVeQM z0hs>P!DC+6{ML=Iwy=)gH0i|=FFXhe&1HA6=xf4ZzkyBlGBt)&umbW35MvI}D~c%i zviOSNQ|0lFgQFfse{uNu`~s5UAOI+k1iOnZxHp>nQa~Z8OD9Sv5mDXuOrgL>Zq$U! zM*H%9pB-jl!U!@zjNhm`$BsId;nEI^oQk+;dDPqHpMi3HdwPva#!Ae>R~Gc6`#dr^ zUtM_C+81B4D>w7e*+?rx>Iei}%;&a@RyM_wfgWlkfSuhU|Ls36fcd|;03EqF{||?1 z0M|wR1@g^BWT=jXKGE7p4^sA)fo`v;&(bvkA=ZKhD?8iY(q^=5VF*q3V{ows>fE{mN%>MWXe zD)g(Wn{!UtMzepfmq_$vK3?509zdzAFf!`i>)zx(^xn<$Ho(H6#+3V5K51uEe<4@K zF0{A~C73a_cfK!u|*qooVEaO+= z=R17JH$2g7_tpve&NqGQne!w*v-!2jg%@_&(JlJ{HnV`nD;0LW5f=A~v7|m#(ZB&a zMqkC-zTogsiSPfpf^yvN# z5ZPwg0$U{VI*xU9qSnv8nbvGGgg2xE^#v1_dVTM}Pw*TF`Qy)2)8^;Wj$w;z z>sw9nPOSgt0755nt+~Ajv-^R>Qn`dQA;aI0l%Q#yoH2Q7zy*-L!g4+~AM#lc^uttd zIq7=(X~dtIx0ON-qk!d1S##m-wmAhrm`5@lZ z9V*G;vAM>!@w7P1d)m#)yC#J2O0FloBa9rW#qnZ4;80$swJ8S6gnjLNPm3$H``|>s z-!b&eeYRrn_)9Q-aj zqr{WVv()oD$xDN0qu1w!siD!*@-A%!CLhkzxf)-bJVM8hp|3sE8^-(6XT|&Q@Tl8` zm=a@lvh%Mh)wrK=ejo9F9v7H5P~M9k8K=D;p5B*@h&HTeXZ@NRZcZ*GmyEvFU4T>l zW#?y-_C5Nx=aip44JUpRz&HM`?>Z)b5lNeOitpo-&c<{_w19n<KPRJ|>G%g=_lY4Hv3YrNA>^;XuqxsABl)fp@-*R1wzg35khDQPAn=i?MD#CH5bq`Q0PTo3$x!fVfBLFD#k@e|LQDEB$qMib>6$x=Cw zRj_Myod%Pb(bw++c(u?N*DPb%Nb^3g)E@X|&-9$Hm|idr(+?P{#ie=wXxb3*bJd1$ zU!-!)(P!D+xQ>1vf7+3Gxy4eopGezXgrkhZLDI?SX572bJUB(cVFENUM)!e)I39|| zl{yJ>ysh7!wrvfsj7a#`e`JXcBayc2>Ux<_ULje%KXCvi*Ro?iYP_YN-y8w(uLUM( zW$IbP=dd?BzSx42L^^?&SXv+AEHgX#0>d-??Q_(H-TB^>B%JT-MeKuqBSWVz)N;I` z*OVbd`sr>UD+Co+b|mlHn<4yYpC6yMwXc-r%K+yO|E>*NDKta;Zw}PD#1tq2xvl+^ zgDq|y&K=U{t2FdUP2+bE^OLDs!Nn00&;jU0ezWK>%=E#dl}LzQ@l-?&bl|KpwR=KY zW;x^YR+xEB9SGifWR6Jp3(Tz@J0Ce4A34WgJJlRUpMFI+7YvI@Nr&>ZqsjMaM8InRxEyON>9hV#G=UMaT4rpk8%wEm_tYVMIskq49Znt%rA6+&}`t)>smz}VA+ zsyMUYM*E?VfG7Q2J}%N+I%f^0^mf1kj~_H=J)Hgt?z_0rWOj_D;_7XX%^UE&vKNKm`UrCQd9UdRYT(_&OG>UJ8e0VX4H!pyhy!)Pm zU`{;TV)CBcVTY{)zYC*Jf-!Tn{9?YV3TS`R5q&u>_(IxKNW0`C6 z3k0@i*=I`GX#`S!(r7eA zs@CqSjtAM!3^hBpx+L${IDb{6U0q(0RcBuDQ*8$FHl2V5?EI~6n&lm4Zsl$P>kHi+ z&a5)f3EWUDHG$7dz}5-Glh+c%T@|535$@|EOOin-DhMy#eL|6mQBs!44CEEpP|-f6 z!YtV>{wAYIMU7ap3y2fPkdPeON8dc_%g--V;ev2?tu1YyQ#v!e)#sglgC!xou?&hNu zA&Si@0XNS{ZQ3J&AMFHZ?5pF}5Ee%{fWqQk{V7g~G60pzUYaznC_RDA50@oI=F0V9 z$V^>EMp7~m0)vT`C06Lt3J@wXNs%F8;nA@(u~Y@d`Jah{8!&OexqpyJ z0{V!?fj%PP|G%>8|J6tIzZC=ge|1$O183>~t^Yl8@IW7G&SsPCM;5{aStI*}j4Yzo z{B40wlL^kSpT&kFF`a^c%)|`nBLZQg!%CbC7Drc76h?LwMrI1EhXz@Uq6!Rz&i|DQ zT2GrXerI)h`sYXQd-sUD=WBOiMa6X4&tt$Bx9V0_Wg{*-LTD|MXcJLlUKjBO1x7@m ztV1h)Br;>`CZeVbCQnG31@VaW36|;cM2q*MW&-1 z|6=EOjv)kF<)mFz{2yynLd+xQ4(kx&E@O67=)<@H#4Piuktt-P(!i=}kZ8{r3 z_r?6L7r>F^>OzW#S5qq?q)j&v>XF9;JQwx1Fqx7Nu&J7<`Cn7D#G)~H)<_To`K}}D ziIt^XapU@3ROKv#M=q6=oqIQYd;D4f9fHe&OC>K(R5Kg-_C&u}%|cl!2y=SMn}JXP~l$GdznyUGPtKs(}Gy;oMU70HyMtL+bZPdibcu$1Ey_K;+11H1uOj=ut zrgXzxDc+Ii#AHS3XlT{M(J#W&&%}JkECeS8R3*~OKE?XQ_?_FUo;sp##cYIz$9b&Y z+H}%F^h&5I)C-tkwfG6=vLn%>4ZiR1z2H_HB{QqyKs;o;tOheuo|kG+tsk=SM^-1HQ@c=}D<5 zlt&(d*ZhTm!b^iv`W4pn_AxJ|cFiuTg-V71SKObA-7d=z^}o*tA_KUUt*%ooAWdYG z9tMh}U>rTfV6ZJA3&_S*qr^P9wIauos1}B$l4Oj@$CDgUiv~R+_CE^6;xWh~cfcb+ zO{P3bVuM9q;AtWmPy)gSOL#B)xGbk3plOww?ZCy8|YY`&U-f^!Ij7ex* z-qA~c9OYI~$z_x7F$j%6YNf|KigdSbSr$YQwoIaRA>79=)1t-{-CIN(>DU_+X%r61 z*GiS|1${##6_sE4Z3wP-?3U)6A%M$Ekm=CP$!W=Vx( zcHZ!-zE!4qxuc&%jB^C;@qBcPW#&X=K-@4l(#V=U8eh=*=`($U!G^KL$)V$ zu_3_m-hrpFAZKjkA#l?{lj;KWD%9%=3}LW`pLBhl{85Be5eU&O41e@M%)4| zUSHm?c2P%-ENtN#XvCjkOK37IG*6LkN$=U;1Zf6{!?c!84e5v*>1Q7$BOBWV-`>-r( z)>XaBHl^+j3K3joB6u9y&u#L2*qB(eo|2I73C6G(;TB*&Tc!oEfi@!3CJdmBh#gV+ zn&k$eH89+_30nt_HSB9BGl-kvX>253z?(bTy zPD5xbv~dx{CK&^ShZT71eLZ080LKZ89c(w@nXp6mc}0w{Lo+|cFm`Bf zm4-$DN+cX`mj~oUtTl+(Nm;kaT?sjaIEQYCH^s|8R8=kwMeA!NG$2+0(W)xV%<;A& zreEBHpk5ZjJ}8}pI(g9BCx3V~(@q+X_C;$^1n|?gWf$N%h6u@*w0(w zHA~!$UUCdxN=9seMD{kHq~H%R%PvA8SHh9jdJbbC3bKk1xV2*4Lo`d$OW>>$g9}>l zNq}yne2{MjWGPm_OC)Sh3&WoURvCp=oupSG=Rm@lyl|aOCQ;Y5qZ+xAMk+;XY~j@9 zyADPg4n~>|Mj8)Bnh(kt44(CcFD0T>U|E@UO18~sfLAH}QupKvPt%GpvGfI7tr&q> zjWgUKIxj692QkP!tzO00{TRde#u&5-F?&Gm1vV-Ic{YcK9{pcc)#COyy0cLM(m)$g zz!-X0BQnrN^mF!~ji~IOjY#CiC(t9kcrX3O&E)s4bYTxf@ykvFKs~hIKzMHYUMetg zs2;EfAFwAIuty)5>A%7I9~lH**sZqwEuKREQ(@@NHw?i5I$IG{KqY(+ejpLUif};D zJ5r+|v&%`48%tNIq$vquQNS2E>HVY7pop|AX_!FFoz&|6*nXylMZ6w6BBn(-Lsxl5 zsaH6SkpF2Js7PS2TsuZ+a4NA?elb2>3|$UhU&zGbKwv#1e`-Tdee(u$>+LaeKqaY0xF_V1Vn$92LDnmhpLDU zI3y+2en5!}^P}HCd<&l;LCKNV0mcq)C#q<>I`WU2U1xmxZ}>{S+5d?hKu`60d%fZV z9up8NL*OgL|A&T1&(wh4%z)m|pli5SYrI!0G~>LYN9B8R2HzBR1K6<$@LKc13H5~Y zAwaJW^uz35RW+vLi|zgm@8n+cW~G#35_!u4qTpbbyu;GV4<0gt=|OM7twTZBIk(rCR*HR* zcwo$?^)GeM75&&&{OM{Nbnh;lGHyuE;@ zddgaNGPR4t1VT+fvv|EZii#H_1>XE5J0^|Tz3czyB9i}a7m;uYLQ~}@ucDee3aD>2 z<*c1VF5{J&ns+Z_lgmbdLQ9QQ`z5fd*FCj<9nG(*%&$OwOF;z*dC6Y0n2>DnBi4n; zXpnz0{Jkr-RTA6L-(c_~9_5*e^om(x^B}raka<7bV32<-{GFAyq6hdl@T#fD#edDM z_ku-}s7fw@0X2Vs1`H!}dADe-!DTP>d*~4j!;wW}jkPmu6{$Uirm`>XJeK<-FVTuU z(unTj{#mNDanGJF0t{6337inP`QK~Rj{^$X}<-IhI#Pa=sc>^+7*9Z}=Frd6B zHaYZdl|%Tgv77Dhp3Lv`654N`WNY_QG!2zaZE#{w>)$wpSk*r6`nK`K%3z0WRKzbz zA9fkBZir)RD~ZE2m%|t8Dw#{_Dq#rg7L|#jOkbD?VUOf(*lfljJQkwi@U7;Qnz_vt zwc{KDL~Y>^SH7F2Zm zWJJP{H+5DGBmJ@K7g>Skaf@cxl~daxlb>AaU9=?vJbTxqWRBvz7sXRG-M%;{ZoFWO z`UdD~#A*iy?o(E5B@^}7Mx-gL;lMG`sAGo|^50j4nJK3H-1HM(j(wWT84LB5NF}D4 zB*3NrW1`*oONSS}1p{X!h(EYTA3>UfxNCqa)PzhCA3|vzj?Se6 zj*-@H_=i$H+ndD8jVPtCj7~aimtrcN(jifxC=5!VbS}=BMXmzUmQSukn6R=J9zzSz zhHB;%a9o*(+o(x)F)+$9MYg==QTMo{o;!-j6Z|)FboRz76m=Kcsx%8!5)wYSSBdN! z`D{K8j(l`9vBdB{84(>4`mH3k%ddrPt6oR4--XCyCmy7b0s=^X#1|Ptnw`bNBnqvB;BQ{ena&H-G}s^b%2t zs8S}0p00FHV%(y{e`G`}GqbyX-DmD`KL|BAJ%GY1m<8VAo0Ik?y&aH~_9p`)N0FjF z2V?1mOmvq+d&;GgxN6OU(C$^Z7=@iKFMBkXs!UFSGE>tj+m(`6%%{KRIp;Tv0AHo# zcL_$Kb%13x3oQ=|;P0-R5BAPmt)u<YU;|UtQyGi!1U%l~EV`1#m zpN3cXdY=B)zhIJb`Te}b)3@vVQ#oc^bJOx=sQxvsP#Hw9GbOw9c1^~C?LE0xa98j! zSuH3osTxz@HZ*zBeDyH#-@>8bcOx^oyWX@f(|Pa@xi#zt_Z;QvV>hi%>*9vYnYIMa zhV$P}e(&?)yw_bP$BeE50^ciIGj|-#rf$`$CSI?mj0T^uJ^+_JcA6Bvrw8it6Y#V{9VMZ5pgrW|HMku(H-}V^QU?>-rVy>an!7$t54T- zWpx9(i2b$)_<#DDnR5HLtHvNOX3`z#YyRf<^&HUzoJU}IH+uj8#TY0CVt?Z7TMib( zg;#uhroe#4KW^_xeFPWMzxTer8Do03%XEV;*f^96zj_usvkFa`QzzxMr;nU{TK@L%!_nLi+uaC2O$-;uy%szdHGl%-%%n`F5 zsk?4lz)tkRj`zXRj^h32jYtu=EuZM~g@AFVNxvJXP1X}4N3j3bnBB|Anz$IuLU@8; zwUS3rwtCAD8*7GwYkN0Bjq!5p-p>BlZ_g9K$^(IqxS`L5946G~33(m@&u^b25bp}d zHa{*Ht3D>2k$U}nT;EsqEYc1%cyB>g6-w>C=7G+k`nG{~D1)96T>$0nm-E==F!10E z&C_dZYZlqppXQC8BaqeW$FFGr>-T}4oV;z{HAzeTu*;u84K3Y0w^)My%pVRxAr?EH zCilEVG+^Veo~9cO?gDz#jM#2<6Vb1SfP3D)(z33J*IRqS?#7Su93ltytxotQd(Ves zZ`gznz=<{#+_c;Cy>8->|NCDM!5;6cH4IkC*B4)mf*QLEy=g%LLw=APL^>Z1ISu2tAwPNIZP^KZ;MxC;hT z9ZuItty3TedXg-(nvU0RmpNywKNfmQOEaAgew95Q8p<}$0JIip*XL1Rr@2bt={r&wR~=02Edc&V>g#$Q1fGUGe0anAb~E|%T>E=*KL0Mx=Zc+~ zBv(>WGZEL~ptW5mbl>QT&z2fzx&$#~oF}P*pPPk-m7ga>e_)DYnJCt*%MBB@RMD$X zRYGCSFsrc?w?3uA6W3b3%9^|nA0$}*IIefR`nd3(ZtL;x-syNZnMkfy+jrlbz=?OU z5~u%d_Iid_Xq)SlpfKpMwFAE;2c#v2VpX;KoC$})N&WG5ehX@Wwj@3b89VMBXEbu;67@O zM6*}GshUi}>~Z==I(m+dFKxlYobF9V5-WWa)zy^NrFcsj zF12P0>jOvVot^73*@I$~4zZ5J-S51L>FRU(Br6X9a5cl**F-3&MCG?mNz9C6P6zLa z(vjpC`)|}_F1t)KHAL7ZAG=(6-L*FrqY8ptU%|((e|l~ZNc0KlTRgLChF*`%F0teN z-)&c^X6{|N^zZyv;^_LXS1t)42amrn2~(f0L-?a3FEx0OBxF_IDb~uwiz((f&Hsk_ z-<`Y}Hr(h*d*OEaDI%ed4)42)G}7~Vze92VKs{vO)=A1q&y26r0q+GPO$P@Bo$|{-WN5M~)N{Ojc6|GQ1u2>9}SRcMQh48MDaGH>k zD1M$jn?IfDO5GzN1G}Z36re_6TcK_J_L-0)VJOg3e+8y+%nqoyUPYAff=(1=@FBu< z zAC3xPPPFPEUu1$p4m-_VyO(c@;tMT_m&U+N9z~^8t01oV+DMl#)<+0uN?oi}4?O=) z<1(Is;X;=WX9yRPpF(r0D2CK;2P4j|m>-T99D`CUBOXQ~!>0qwteQlX-#?H7^|Pp0 z6a|9Njf1K@7FB%FeRVt+Ygu{NR;9(UU$ZtZTvVg!s^QZv8FM;O=5UJ==?2j`0JQew zPQCpR#j=Qgi2{auEmRT`Fq?Jp3}t_UnJq3z-4y8Hjn@d$NFfAt>JeLr?Pm z3Sj(iaR`&XoxP>A>HpI&gaj;S__zLFdB~x*rw5wqiUM2`svA`koslJ6A!yNi{wxk` zqmYJ7Ir0Qcq4av8eBw5*w5%s}?kWd0h7vL|I?51)xGBo-?)mV@aH=SwcvUKG6sihz zh>xo-*0D*l(re@Fk}n_MY!kC9C%@;+d*0*hwg5TPs1z1WgWSpUxRj8w`o>UHeq*y} zvx4LZ(@9Ly3QmlSv@{Efk>ZI3?etcQ(4})%IkuItLTkB7zuF(hOJW!pY4ZzS6ZKzD z_Srom)=ffH#F2HHF%)XkLcXz67W2$%UFl6i#awELCa@vx>{_T%5>|H)y>S^NM22;T zX%Ct@75Fa|AG5g@vm#rdx=rF7v&z!i!W9uR=`1Rz`XHc5pE5nLv+H?FT5=R!%T|qP zny=uc3!+_i!AjQ2LA_EK7RC@x7#7w-wUB~flmiRL&{ zo)jNHaht!B{zUW=6M*wd3DARezpbn}tT`$aC!P|dh;dSI6)b6O!x|?umPK@oQ`dr74{j;Z2RuDU393aZ-O+GUNtw-vgn(hI z5U1aECu677pd$x3#@TCuH!y1&zm-5F%6cvOneqjp;D)(uxalOxYnHS%QVEhwUX8g4 z@8F;gqz*hnv|_d7he5l>!X?go!EN=Fs7s1xBlG2rMFWbNSw#x?wzz)Q;Ki|OLhkUY zoq(s0LZeOmk}-sK$8J>|WpTLhAJ1j_&LXIbNKw{n0w$@|6hL~y8k%+s8SAtL8T<1m zJici$E|;Agt3zy#-LB3gHkafylSB4Al|6PXlGFGvHA8GmDNZ@I?`UT#LiXP&SRE+F z*d5eHNkb2v>X(%(1A1>9XdOgb!3WE(=QGoU5u>5md*IQ-lQ?c=u$*KvCIZAEB0Y*g zYS_?|%sh*#I_Ej`PL1?VQT0yKJLii!=Npn%BIduS!5X-W?(61FX_zGQA9VCiJ#`i> zTzDEu)9L7~WiqLu}L0}rnx3YJe>@Gjn z2WSr)i56}BDm3nbWoJz%L5qcK$ZpyP?+R7`R1#9>8V_#!nRe2LDdsB1VNZ`ND+hqK zrpJ){UzD9wkR?%+uDiNy+qP}nt}fd)PE~bTUAAr8wr$(CZ_S;DiI|zV5%(eEWbVE5 zDdU`zvDW_n&(lgFZ7BL~_hX*p<5v=4KRrEgb@)e@KL)6!6vnbh%^kg~-E$Tpc(vpO zBD}8n(@nj<>IhzJYzFRDjsGImO;ILKMJb3~Y0o;1d|e~X=oTgTby<%W&*;{%WRo1h zDmc$5-+^RP7Wu09&)JdW6Qq0{EYBz#RYEhwI9sCMB+ht}QIdDrZ!L)Ngl5U-C8io! zm+wwUOqGTeg}8duTqFs3bN?!jLYXSb_~97ZYi6Q_A!Rpi`Aw{>U*Ct2H=Oc+ae07h zs0Y(sDs$l-8C=0{o5%a*3_L(R&4)xmBbq39`*}y}Zvx`l!Sx6#U6cIiMe)AEt?O6% z0SEq#0-T z6+Jk($_~NxGQLMQ9@2+r%JVtmR<%oqUi6*MXV~*O<5u;sM|YOOm(+)A9ZbgFMjLYb zV*}I&jU=9#JA8)fjhLy<((lKuB^r^dK+|N-;aKuy-f6;ydy^giku3DhCzjJm4>e%3 zX=%JC84%c!tS>ct%wq_C?Xs`F#4>e`b=^jPE2^21)hAEVmfYX$I!&D>_Um`9oGtmk zyYHGh#@Fj4YYut0lDco3J~EpRy|E! zvs`O2E+eB{+DE}Qj0B9(*5bd`<9{()DN=_2MpRv_;l(3j^Fg}!o6eAJAnr`AfaQAE zzi4lkr;Yv~Yl)9DQ&V1?V2p8)G$%_%nj+y5C;=(B&9+Cgg1ed2k5|x2o+aG@=Jk`H z!|_7V2(SyoH^As3tqR ze>1{ecW`OqUK9Yu`>TtqPx9w{|4Q)9P`M7!o}K zsUHGpNY|)RUWiQ8py^Vth&*_44@q-Cd?B11A)Jpt#TSVh4s?C)+A67as74CEPx`-L z;mtXv!Z`|}NV2(LNM}i6XH2;?aR_Hj${A7}p=208x_pqfe`qI6VmT2?IpZ3Bbo)Qo z=qF5qInrcplfzT@J|P69Ss)oa<8fBBOsg8486%t`qBwD+E+fkR!dXMjBFB;<$D<-< zjXC$s{!^<+nZ*!X@~F;PWG6g2GpUHqS%fDQh|W`p&Pz~S=h_wo;GNe3yi*ZAXFoX% ztADrFVbS8@e*WDEKG5d9C+o043$_!TfP2p;!U){n%ava(g{RjAxDv=<6*V_ zt#x1po){h{HUXyTq3eBR!9Nzy$ZAzrlAIeX#AM0+g3^$%wsMVxy~8ONRb?qN%L)2N zN0blrRuzcgU9TPc`wvWLKQn()LtGqb_CX{ciR#X>4#3c_*~fhF>6YA#<&6k!ZN^CK zjJ*A->g(YQ+PHlI(?PUw@m>^`X6UR8f6!{bNi&$T&jaub z_%u>IZ%A&N=VVmS{bqf3@)R9;{ZHDD6e048qzZ?GQz8N?bYMwIlE@&mkO~b--kvBS z6&j@CZ5#qBJ+xx2Mrb&xkV*%U$e>b1x>vS_SW**C^R5jcz8%;r$^Efck!P*ANa>d< zPr&pxb*Pq_)aG5UzZM_b#$DJ>60E-#8Qf~kXbi?2x;GzrSo28$hl8zsn&&8W$w(B8+wKHywR z6#SAF^OY03CcrFEOAyS!h;jE0QU@CyhBN|nk;os!XT-=8=|b367Anw>HR|nXpF0dt z9jsCK`13%MV&je}yo91$9Y{Qr5^kz1&3V(t*rHz%UVj|WBKYi!=76-U%jVc|{>~lS zs{Vb|dm6pr9U|->x-L!e$jdkdf08a?TG?iZc6A?@0rA{UijN@8carpxuXRUA^vVzm z0Fk!s$F(Aug@M-3OnxA7U!eUYuYEAGJssYhlSeou4|`Dd0UQRv8%pAwyWBKr@%bWT zn%1UhgFoGv@dV|N^)uTp)X&+MlVco140#Z4(8budOQg|5?k*7ouA4}_!5HvbdxB;}yS786?K+ z0Mj9!!dK_KyLxg)9!T&h3o*BSON-9=d`s&lHZ$eoKK+T%(+94*CF|#F?^;xOBfr)D zQjAD(Kb+3=RF&O5-v0Hap$qMMXL}qyxjDA28e`8<0x~vFob$^UK4-;zWi9(xkfuQn zKfDUux;{p?BZXT)11Z5JgoGCjUUn_2ATQso=wbdl2&n7La1%*usOVtg)}t^u9t8K> zzWe1tbUu@a=NT#~U$uYcYz(7s9=~G|Js)@2-H$yP{aGRCSYeh>Fku)Q`XaoTS76?{ z35Oa+NM12@Y{?H-`UpH*d>!x0`0Fs*w%-`|`!U*?e0+pjhzXo~j(uK)_tdDfCRzS^<7XLjo0uJ> zyqv2Tek^$<220@4j5xPAEsTyb7x5e>*U@+bsW%r#`RBfRR|DnRpJ%aMYV~&*Cpt_f z@*QpZG~GE%hqJ|e;Ed^N+~$^H=32SqioJ}99Fk(`sZOXt_~*4kj%@wrb>FS5qqyrX zSIO5K5@m*rrlg9*VFuw7T&=F^pmK5dNT<*k_wwbhl@?)LAmnG590ZpjCn>n!o? zbC0i~%ZSExyg%E|y0h`l&dD`!O~SmhFGYc2|F9zZiXwj`|bd+8{7w(qzjuQW&)7!u7(cbPG0?-`$x8tQ?#+EA=h!Hr>*Sq!XQCojR?KZO52-@nxw`R(?Okd>_A5Og$wIZRZ zPbKlYI^HD`Gry)!FTnIG^5nA`4x9fq&ErOIjL-_T_^<+{qbj;`d02N|9m#TUwr&qm zMqkv;E7xuFrQHPwOnEtNhR;HQZ~6vWUoK#P^XmLKsbk1-S(>`{wvt?40^xnX*1i%d z%A=pebGK*bfif=6oo>0LejFV9om09vZ}vNH(wDy>i9YD&m5z+9?PGWz?k+04C;hT1`m!H% zb)veQId=74%(RkNzW6d`U(E3Uo$vBmS#@d|L(cMtR@Ht6)=X4&rT)FgmRw8Uq?HC_TF1_S8 z1>eGGUEi#puY&UK+#z_>a0Qs3$406ss}<;-C0eOOq)n+7gRv^PrRi?8A>rioO_G+X zhp;QHi+!YammM*fXzpnLsXfDb9Fc3SdhXy|=uv+wq2lkB(!qFEd+=h1QwW>x9V1>aK5RoSVahLh!d~7N*nx|4t5(mzTZMTP zzvw@UR$II*0`T<h zsJxZ}J{@3}*LHNcau(<+vpUqaxK)DhzH)Lm{!c$&sp-LnTw<%Tlc(j?;cd&Zf@eF= zqbak1@)NB2_Zg-h!E8(h z_1U_HB%Nv=%i?!invPewrpzV~yVn}exXi0NQHiybP^!06L~2_r=!*xE$6ows`iA*k zcuP;-<{>%xXL?!eP4i;mD2gdpF6(0U?zwJ9P)+r%4sL^NyM}pgIvCYW(P>IiWx3_Y zy)*cItyIKG1>XU_iZ6{-N|Tv}x6Q`H+iI1eRtR&5L*70dhWlVf0!X3@ayPBqQ zblvwh*ZaKCRPtin>dui4gd)$zs5!FesE^L?*R>6s_jxz|l6<=CwZoNk*TAyNItm8U zt;Dr-QYUPD?`N(?La!k^@8G3nQ&}0_C-GHzmGZYmAT6)4jAXOVET6`S?bY+m$B2dK zMTKqf>dm^!$;34@+=Ms1tE_6=wOohcy#unc_wd+Qvw$n*iB;&rfDqmMw57@H^%eUj zwi=zyw$^q#MWZN9HrGtYz%m{k9H+-)t%U;*(#OoQb4q*HC8VNn(z6}6=j15e=Bznd zTxL4%50FGu4t;?38za?6q@=^yLw?{lJNi?`{q)mw>bC>@{x`HbzmB)tGI{B@UiHdX zQs~A1{yFz$TlF&xm~{zSkDeQsBh^v8=OH@zqdWTXg+;!9KXHYw(T4 z5mhn!G2$>{n#Jl3Vdm5E(mwj~m3qDC$;ilP1C#(#LQ>iQDFBkEL{wx{q_l*jq(peM z+)w^xsJuj&yv%^+RcoeJbTkvHh%{h|`h=1%tDuwR`SBabJuhq;D?~EJn)jPG?fusR zvK*i%fyxj1>M-3gAfLB(AcRt>LbTI+ApCX!shdHX`du!!%g}?8j1M=~pY|DZLYnzj zG4w|bA6>0qo29?kv7ehdt?%&NJ}uM-0F8JCLC!XJH$P(5@F!1sdFd0bx7X`M;_V$U zL&Zybg-JSqB;(7znW5=ZuQ8TqxLq$nxggE%Rq80A_k4^qEtmWB2Dyh~zO@QgsTGCN ztL>;uY$|f7ZVLBX^(ae-y_5*Wmr*A4uRoj{jB|ztr;3Kwe&k6GYh$ z4u1y=hoZY62xTJvO$TAAkiKLBLl~zok&+4`B07sEVNdT;*}kWgtO;7)l;|+1<1kp+ zVz4>0)>hr^bq$j$Ik)69d4IEy$5Sh+`EhGBrh%4o_!EAFf_9 zml&9&V72H;6gYFX(4Z7wMEKzy1|n=2rz8*Zg4CdtDgjcLDbk=VOzn+y=-KLa<}T|j zqBV>D{W6N=MfJcXm3v19WbFrIVanAJLx29&l+2wmS@YlIEGFbE^YN2~#?YevQH#8y$t4RjTau*$ z?<7)^^ZEvH?u~4c`8Z_7!_U}6gj$}m#9AhCmAob{e$GNx5`@lR-erRF@ivAy#*zB@ zn$THcAg#K&MCOS+s*+8IG^o{C5HJf<)alOgaaT;}9GbQgbr|cqhjalxImHKm?QD=% zdr8WH!HdQyJ;If$3(V($X}gKBz_s-^I9665SzVJFg`npx!c=C;ih%sy${2ej9}0^7 z2?&}!8D)=sNi}OK{KW7Ji{gFOV+x z_5hQmIOvH*8r5g10hbnD6wnPpSiq&SdQaX&i{YJ}VfZ7=k+3xWC`E+mPDWmWes)P~ zWwPp#1q6*Z^-HjqoQ92i<)ODz7=apX(M@s}f`@h`L_ z9CId;nF{(<8a;7!Od_yKUxf3}jlNNh1&m|L_`hnAo*hd0VEM?Sz2OB5Flw@0sDbGl zzldb3NMFPx8$A7)7~zWQe6+6#y5ezu|6^c@Pw*_`N>Q<<87S9y#xJ^40kXr(l8uUq z%P!TZPAt`c|I|K2FE;ePf<`@8Ay%!J{-;zs#i%*R#4k4JC{v~Jc~8R& z5Vdp3)B|I+bK?@1qJ#{?+(#9;a~d}LgRn4t<+&4ctP5flC|Nn-&iPrk-+cm=z#iw) z3s0!&3aI+j0;%vM3)Ty<{jPu2PQ2`)Nt`0B)An$XUF@%{S;jKTE@aban*4t;MQ=ix z+H3X7w4hEHHv6x7Iz}aAK(iVwbI1=?om-){{{yI2DS{RVcPbqqhpNE-1Fwu*>c{zn zO@6l^RUn8(nB(Q2iuns55QPL}L{x!x!1tK0VV}~1r(*F*y*bL)-uP)lz;si@Yts<; zKr%s*%9EFo#ch90g4u4dksK&Ndx6A_|K-JbIB?X5r>9?nqkcG$qHG-t#Zx z9xI7fXWInP_9r%BBW#1lm1+Ikh^!U*;kzCYQlS+?Adk7gpGzVP2UI(rDbxYW1Ih2H zb5o+s&uNP{grA~=v>sGP&}M%s`O#jdz*0_tv<0ZxoYoJ9R z$B`Etj8dV==RS@LE+&HAb3%;GJFtvjq*~XhNM#+S@D4M2Wtlzw9=;HbT9wABH^He- z<}|IgpU^!_=|tlWBE9k?d!?4X@R6`?P3SO)<+pW2*|B{lMuCLKUWs%Jk07{$CtwVPPZVBG$>7{|QFHJfSRP{x*lojR9}f{c{a$rjg8akK+=?J+C`#;#^h8vj62Ckkc`v*`5MUIj^5P z4R@x=T7r$WfD0#d=$59PAG{j#SCsA$c1^ZKiuf0yCRV}#Wdgi;pq85Sx(4FKR6=!7 zIz*GrZl`f*lv=+CaqfqP_^o&plKv@$Uq?O&p}1dd^=u(EC6S=n44G2z5=e*=?*vGQ z1`NJ=Fu3Qb#};v3&kGEJ(vB5 zQY>KF+~B%E2}Hu%q(GQ`NK81p*eT+escgbbla8tEdoEvt+iG8!qII zDI<@}!;UG)jwvYJ+H76ct36O$nKm= zqRIV(BDZs*r}Yc4sP9!dqVN|j(X8^DHC9lvWYdSSUKAkhKg{j(!-A*udu72q5>Lrm zWmk?UfF5U-0)BHUK{3{{S=3NJuZ2>O3Tc--mA|M2t)$v=3 zib^w$VP8?G_B89d+XJ!tO2JhwIke{YSV^ z(8a`NXKkf3i9l%=!yinrDF1;ay{g2SVJdV_+A4=kpw=DsNdlS#z2y<249oU>^l!wG zYeLuvJ^7!vL*p%aW&`=U!7o3<)#xjA8LX7(OafO_4#GpO1S*#*panR)EUHe*T^nAJyG!$xCo__$xq# z=5Jf-b6AkMMT9=~23p*%Y0N(+-#V8vbS5H+$B11RI$wIh27s01xKdV! zfR-KGHK@=)ov6P`Xj~gM*BaTGpmB)DtMc&TH;Wf;T4z?V32>eXvE^4(u?e#LxGA(| z(@lf*p~dUZ1h)J*JP+-pw9vG2;E9# zpCM5?>Qg=D0FkQ#*lMI>A6~(YysD8-<*=GbO!n-F-polSMF0vk69h02;<>anlLV6t zhQlbD2NC9BpfSoVz@FNfQcAFD6WpnCQIa~_s0wnPWX2ix1aVI3DXqiO(e+d)KqEyu+p3SdW5Lu;U>w@Q;v57f)=t4b4 z>2Av;A6UumsDkga9#y^@9<;$)rdTTW@#Cp{VrMSd#a3PyU^&4O!Ub98Gum3 zZ@?J`^zXl(aGfX?5(NtMF+i^@uSk`jiKz)FjcAC%onl#diHz?_*ArQQn6BM`2igu3 z;$z$n6M4upwanf6Zt$4}FKk!gQuvBY@YB+0oc0@ZBq@^MbSC3vLG8gR4VXjD@Y(iE zjI?!0ZPl0>jvNpvrQbPehSUVUdSyu%KrVC2Q-*L9>1D76ICYd^m<4dZOCto%1aEIL zXcLwCob(j<(xw$k-VDv96C%tNv?`V0cpJ2z7kX6PysCw8VYHZfUnEEP zJux@CO#1gU(pU{f`rN!;Owh&V_?q-s!sEMde(~ioo6Bdm_-KK+yM|rGqk~lCnYsAm z84@8PmxsX*`}(Lj+k6G7xuUylO{ zukMA8mLT~TT~xv49UzcfWXeW#wjyp{J~H*x+6g``58;5}@lU5S)?#LsZ{qoxpL@oD z%GX7iIFmyW$J1XJnUa;C;Rl}eHK^fw(oyW(GTyt$mEFl5m1d3LkLHpRQu&SpFb=aK z`fSX4{P$XZ;_w*yGqgO`Jbm_KTCc zxg16p#%Y4pXM-@j9(=jMb{z3+2O9xiwHWnRuPIy8)#42Ay4zNQc9)|R5k-}wiVvn; zRG)ddttl_?Mo}B(G4Wy4&q&2MYCSvN|cucf*24>}-P}ja(%jF9XS^hbqP|CK2>}xleGn z5pSI-B{HW8;g`Ia&9n~FhCc2Ql}`V3JdFe}n3E_o?ss4A_wdylJbclk^@WJ4Q4i?t z&3M`?vhEx5uyq^p-s*E(Unk!gySb8QyZ}V0qm3tY+gtahW9h?JI*kMwvoN~PmiV|s zEuc|ZoxQp2^G4_QQ|p!?j5GE9={Pz(Xdnb1@8r{P4b%0XdBgyITzj=_D?h@#zYjbg zyTaW|MqE=p%^6vr!(X0cJdXmew-Z{-_90&RdHi;EefV-b37fB_h}GDX&u#A8q4M_s zN=xuLfWKQG7jx_8$pG#K>G^gCWIShUCpwq)_x8_}X@TVFk?vO4b+Y(vQv>WMMYI`z zEc$qZTTwT#%?GM00odz~Nr>HbZvXaUj`V%3W!<6!$Tr;GFJw*TWnW_Q;!x!}WjJ(d zig>Pw>cU**e+V3hO`>+R4QE3_uct4QpM6*xz z^Q!!*s!qqatsKsVvGt^e|A|V`=`GA9+nDCPu=*q*=5rhyp&j&+X>p%h|G>4AeaTr< zXQL^z>4UDD=HG%|ns?IN_e6j2XgThCLv2q#-u+x+gVaWVUIKjq>?<{3cbWEecON4o zyVS(X$CRykhby3+KbP9;U@Ar9?+9glGX@yuak*AOb7<1Wf_6cy9zNc|N0tL`ZzJKu zLvZ{R{3yDd_SVCF0cv`SAn0oA=&a+q0k*j*PyQwV^6BhsPvxzbrI=QA_HFLux(NV% zQ31$r(&KU5b(>Dn8N2K}BkZ`9)N)=t{B4EMLt9*K<+>3Dd4WS{QEf%$f&JSZ{XuL` z%WsV3pU>}#!RZ;@&k6S*)FS>&H}8v4xX|* ze!G*U&wQe?bZsv>H2NNMl|i!4_@9p5^~;s0^YW3rKH!zfKuI{Sv#Y_Qu{!qQgUunb z?5*a}nm3rDKi^ZOQz`oxXhkyE&EIrnF3@L-_RL|dHVz&0ygyQlQJ!SRa=oj*D4%_* z_CrUY`;_{3mwvHA40Jn<`N1Ga6K<#|apSe93$QqGj%evszIz2qpYfrb9ZgBUSXvcNTpA1cpDy=+E z5&J7?`tcS0CHX1)d#J3R_{sXxa*Zi<*4m6u6V2)>ljG#Ir6BvxT0SuEq-Cpe%cA5p zaP5F&S}G5J3p7&txJMesYI=@uv6a=SLJhlQxa)KNiUB!}!libfbC#t1ijDksdk|>t zlT0+`M-;XfRH&PlVsy454W_HG-MSrQ*37n*z8*beyP_71EMaRRP)-H(U9r_t=6&Da_bH*zpkjrxVakyd=^&c2$ zzMS=L*8}ks)plNW>_u2-f3`?oo+uutm;0;4J32}!#}@w&b@ zjU9@gJluOne<)I4roBi0^*=99&6qEjNLB;)1;B+mvk}C+z@H ziCwqhe=P-u3p4sT@CQ!0ZfJ_IkMZB#rP)F{)Kt`RJXBT7`Mtr6UDXkT?Ss$VK59{g zw5%qVMT0V4*Ua_M)!MyRrj?sX+1}XWX3h@Vg&VzXa@<_@8WHS+c#~~ee<=BsJ1hq# z%VFE_hccwVI5#Fev^y+ZSB0~e;cKHDFg2#KuwHh|wvyPa?!l3)FM+s# z8xUmt$#JZYRTmmxwcc~D)8X*;=~UG@_b1N@LJ|*$lZ5*ehevjPpZOb1G_BY5iS%b% zBVTPYrtITn{dC()z~jLTHI1=@uHb$~-QIe4@FOIg*FRYGgsb~(Kz_?{B=v9?&Mgu( z1-`q#tEVmv{aOn4>Lm)=Ap*d4D1yJ1>^%t-(F_i?dkbK#oO5DXE9c;mVmJm%<90gV zdE8fJWP?`WZEw4lZ%BT-bXlIhO@mjn{VYcu%}jx(wMX0xNb9zb4e|c)V;aAVzvU>e zGd~>fU+??=e!H%U>ArdwJ5U4m_4^L)W=w=_|ESu~hFL~sU32*=J}5g%I9pJKD zX@*l6QX6#?^LQ<_Ixb(#@eNh*RWo%8?5;B%?6B;CZyc^@Ea){ zj}Pv@x4h*i8V)1+VyFCyAqApYv&8*QxS*^RAlaKlJ4 z9;+)OKbF=-UvcJafL1>w{I7o6owTMVc}{#iE1KQi1k$Bzo6CW_`XMAd4t+F@f9ahX zY~hgd$uzyjS5N+{=43olbs*IB*c01WGAV8qELL{(8a>^|6%)2K0#*#WVKIrOxEG+% zbzsPN-x?OC>>4$!DK{0vQq1sPy`CBYkkdc+)ML{OV9C5R*+8;2ll zysA?9OaxAKbYyLj#uxlPrqZ8E|G-l6AoXR$Dx(q^6*8^o&Q$&>R+_=aj6x*=g=J^w zv8^FRP$XJd?)1{e!m^~qxtx*Ms(=+h!Hb|kSW;N#u#wJ#SPsE4`)h{P#;r`%&hhME zQb#_H05{T7Qi5g5Wg0GEJyck8sFdbvvMQt-Cirpb9OhIZjvVCt#5pgzn{!kN=5!UA zTALA@3$z^muGWawo|(z(g~r0{)pDF!F%V=h@1wmrLT4F-PIG|XV)H+8oFO7z?K#ad zP$+e=7SJS{RIpflxh8Ep98Q518xlbvTiBk(wRfbH387shm3EC6k3Mf1n z?tVjCo(a9gT&%$?RV7~TSOVC84^`KlZSj{VbiimIf~a&dC7G7h0z--*7PJuLpOm~r z>QtrV%8W54N~r@0s+kCcU?+wZ2vA(25$A%uNj*94&(PE-_@3>Ro_sMX*zu1J;z;kQK*qO3rRfcF8V-DGN?qJPVG>Sj%S?7L1T1 zoW$)s%-n!E66;7X6{mt#=p;*qtP*Pm=3J;rYek0Ph>}pws}-|7i8j>ULvyLz1p%-o z2`ewS44Osde&f_m!_x8Z0372xO<-Ls3iHW3O>B=!*^Z?@9ZRbnOUmw*#dn(W5%rFm z^3y=f9F_C6OPr1j^MdC;^YyJgie)M%3UinmXqm(e0iaGJ7ut^hau{Z?6t+B*|8)+N zE}%H0#7u-BS<Ka zzI5n6eW1^KZG!Di|E_&gNAFI*KH{}Xwl&UnuiF&#TvyqVY_s2aA>G{qoRMxb4aCjC zPCa1Tqzu63Y?7nSl(&k_s+i;pm?iHc&qsC#y?VM-M!e)Sn;L| z@u%XJejI!LKswLJE zaqLx%XRKTU&KBNS@`IUAT*w2SezV627K+&;7em5p*L)43Ry1?$|NJI$JK85RU;P*a z_2BSU{=F+0u^(Nx?!@AJ^kUyL+D(xAGkzaLz9z6AF>j?zdl+@?M83wcA5l-GDEPZ# zx`nPFd*&SDP>614s8$n{_hFS#hp^5zuQb)5;Sn4KWw`@Mnh;6bzhW$jIr*2i;@}E! z0s4|))l7{kkZ9?9latx=9K|pJMPVvqQ#RNcN1z!ITckWQHa7iohXo0+Ea& z#vuMCAigD@^+1{RV43|xc)}w#<53YXwf__F2hAmq;G9KqGVwzbAUUf+agjrF{g4Ia z))oYqosR;nXSrMzbUq$SIm>R5g2n!&?laikH1KnO=g; zujE6Z=V}(hiN{OAHfr~zw3GoA?O(?B0m%);>;TfD?uq55H_}Um=LQck7jj>DQUtBb zn`b?_=ZkWruvA%Q1WK48Sp~LV5%TPksFevKK=*EB;K}j}%q7V^@^K|mUnIxf8t5K|;nG$w5VkwG3p zsv~ZZ!QVgV0F>e_|9=t$T?nWVK_xE&|Cb=3LCrJ7T$=Qe14uNfg%01;#zesfILE!t zXDWAMAS#x9LsakFKRf;&AC9U)fvl+@sNp!U`cb5`%-OEU3-z(FMu?mLt zEj}$;;iy-WmM_OHX8$ZEudycR&xWo65cbr~0wu}*rb!3YXo9G*M%7;>>$xlZHSP+O zg!`L@8&spKj`YO&2On_G0L(ckNxF>@=s{x|qi2yBFh-6;6>5VygyO~ty2fYRz$&^& zM=q_WE+q**A)VPlKJvv0N{L_&go7e2B)1#KIA<{I&EwvQaE(HqlI#WT$T1q1h_<@& zPc_Q1R|-|Z?x2svDby+E!UWk#8BiWOgNM%JIy<)s`_JPJF&}hhcYya@YvWMB;MH=^ zC{Fn)SX{?~NAEs}XGQJD5M0P1By&mrbAs|lo~lqx9PFE?%kb)DcAYXC%$Q!~%x-r2 z%Cheg;7B%@a%nAx9Y`|(`sfYv;^;6x_K}?U6z6_+JZIdKjY|qmwpJg@Im(PxaG>?Kl}jVlTsdGJkU1*7|Ps%QQePe zPl2hy^Eb>#y!%^DDRlYo<7YJghf;)s=^J8n&golXH)(}!*O2K?yq-REx)ZU!rQ)ek>grz%8Vdn>-0cMJRJDp^a%MQEsKm)dl@ z=?kqWgzq=TqKKO~4X0QCy3IrdK*x66LwS!ZcD+22^qlheM_v~AOcms5`Tr_rM_U1* z_wq|hl^GQ~iSz`5tZh8UVSGrjhsO1Kq2IOh^*X0N{i)-7NoQHAVBg4VGR|J=-tS|- zoivE$%SE~+tv9y?k;g@TfBBewlIYrgQmrsEkZF>wp&5Fpx-txeuLVDkdFCgAQYw7k7Wy8UVQgP1JAsqlCPV>33*%fQ?7I6x^~t@ zFB7oXZrWGpkXoKT8l11^Hobt?lY-4B`mPtlT1HPir*fz8xo(>CzEO;DmG1vl7RD2w z4ccrs`&V))ZzYsXdy29z?W74`NL_iTcOETy`6=U_@f>iNdGV>kN)~ms-VVO$aIs|| zJS47Vvk$P61^rULIvAW0oox-v{ufmf~qsO8UyI@KT4LT%NtGOsu?)s#Zr6Uj8U4O(tzZ#x?Vt zYt{;|=l+h2^Bx-rzk2$*|3}YOZO8JrXt+|+lk);k6($?ro4cx5wboF2UNPVBL=hF< z=bn3M`|Q1JY>k*Bcx#PTRmaRJ!R0n1V^u|{Wl@=LKbEztdQqCT^9XZk*z-&dV^K&> z3%uZi%&UQMLv#InEMi^NyYeS7b3Rj5aH*u`h}DhsshKYv`qM&_$Yyd|k#7J4-y{;W z{us6!!KU)~QF$oXO2`y@3Q)$3_ySn!xE_t${$ z_rYrJm4~Z>jPuSp@YE?XKcl84o>6Wzz`nV|ZG*>iekEa`oDuY4C;%MpfVN>~zY62A z9S&`Vxz0=;#=b9?cUjY<+KY;GqDX2>1GS0^W;3(mnjT&;soE0!uSWx z^J=9;r(1@7-lMxagBaI5S5|a0iIIDvLH?4vw-I|XUl^|J5-+O>nk{7KVXv1qWT6&b^x9c3Br> z8m04QbYKY`dAsnmF)e7n89|Fg-lC5&83)kY7Tg8Y1orlY#_E^7To>}H$)vOFvO zT_{RTYmY!r=;H2pELnCd@Iznx)mdT$2x{DJ>FXnjosfS()fY0Uy3)Mb2l-A|=b-z~ zN59Bj`L-21>!vxOhpnk$-wHcnce2FSwP5>tHdVylN43{8u$_=ki^SIiGdg@z6oZq; zt|nkNJqDjJ?%EIZU1g!K8R+FX))!@CMYcDV@y9L?33*Ga%X7pNq`4i_Nba!i_+qE- z0KK%;?YFkt?DjBRX}sOU<6)n97PH6P-1|?j^riNi zQ93!^g@do8uT`f@zFBSUy)syyg`Y)j{k`0mg8kj*EfLG{dLEmxy5h+p{dTNfERsgL zTNqWl z*vU_u2ik6PDq1#~MI4}(E#{2ZX!POx5vw^{Gxb_>N?sCPuc&mm(4yC_Z5t*gk+_pBn*_tsMKGX z^$YUM)Zb38_4&`(ckZ&8vKdbP%F3&%Y&A|XQOc$8b~uNOY-1MR!^a*b)#*btvivo- zRArljTE2Vl^Fr2zx(mAj#8+qyFIv2VsJzefnDj(I!s~KgY zX7Sb_`FxYoL(}+C`qI9N+`5<_xYM|#uH7R*XBN98+y7hoQzbPcwd(<#xw2G3XVLqJ zr_G`#BZV&KFJ6nW=`v)u<_T>gZMo?LRnBy}t9DX$-3)CrgD7wpWsdLZ`}h^^JdLy9 z_IW#+99_=m=^*w}G<^>1b91Gf@Z)fv&$}}FyXMI;|0M+%XRi?U6HCS7UPr1NkNZb{ zK_=(};zJz{&zOv_Tl89H#91oa>eidtHrmX%hW&{TTv<8*?us+~$c>uJEz7oP23Ncx zrp5zJbC%g<_bv*p&C%;M?c(q6x38_*eSG?+8QlQ7=WNz3ne27!?MAWGRJx6C+1}k; zQ0M&e=6Rt9YxS*`f9I}+|b(!G|o9vnV1)1ZE3Mc(bR_06+T1|H`%iM7_ zOfi_3|uV+FG<| zTJ!V-KpIuKyVu5&3A{9%E5rj;s&}#R4hbkWeX7=6+iq^fGQNT+rjAl_m8XJZr^DQi^q*(S!;5Nl zbk}Zq9hO7yM14+q+{iDLPDeG-(VBK@W;ma&aLrmXfI$4$v(%F@tsQdgy^~~!Nz(3C z9r?oQ$F-Q-Q@0$inVK!WQ)8P*vxD0DhUjt0Jn)kswK+e`)e3EB&T;v8re#<*< z8Q_DvD8$B2@ySPi)(^t%+^@vge*(om=PLI(4U}Yo_0s?mwnxrn>uipYP{G@!9rU z*R*fImMM#m&*^4|?AyuvVhlo6RnCjlNm~<>a_3^rC7tsf08F3@Kz<~f18;qY7rP}x z-}Mpp6sAeWS0Q!P8}}i`E=5qW+GWA}fi)YYQx}tdaSoPL!=d9WsndD6QTT=cBqCYN z*;~mRW_oD9`M`@MI;Q=U>*zAx@lCavjyQQl#!2>aFtz)~^cJzgk`tJ47#`ZeVRL6A< zp(g-kKcUf5tTiQQs=hl$BccAd+Dt*o+_eDAE99AU7#Mj@de?s5U^I3w$o zv`>AOzt-!Y6%=+GonUglfq{8*$5Y8F%O_9kcEnRFh@cBBn5@+CeuG4^YQz{Lou58Er4}D{Ue@2e7=GHw!3j^zd=ULt ztpfI|rDH#hBjDi+o0k5c=!gF?>*zmU)cxPJX^}5!`rq>3N)`PYP)_K-*W{DG>6_<0 zM#ujNWGf3K0Hr|&)km`zvR^)vXU(v3al9O-1c6^5Z$rLPQV@zG4TYs8D6RcNs`d?} zlmop`k~kEce%p2IY(0w{$SrWRH^n`&-ty>e>z;k&eVLW^mvRXf78)86vE;!J8i*Nd zf&vG=*|UbvkruN{AfG|Le0%O?1fH`~YXZYcM9?GSuXoRWb39FPSCek+=%y3RPg1GW z9q9X!mIv1`EN_O==bmC!C*wf~i^G_)F*@ci;voD(KpF{DSVYcwCXw98d=gcV*m(xY zwUzZu7`I{pEQPAkp|FW#L+L;NFK6bh!Tq2Remw)56?*JemAPZl3@+kxl0e} zS|*~D0Hg*4wOYs;eJvq)2#ydx4lpXWGc*_jOl;MX+>99VD}6vnWLA^KED>J~9@kU! z44E{5QhZu@4;dm|G2}2@1nz7)o~J%u$db&=aKvu5;fSZgUmJF6RHS^$-=91$?0C*9 zn!YX8CfNa1=|0zbS$4V-x*mz0U9jU8QI+x1X~dmT9h4v*Ois?eaKi868NSX6#a)W+ z2qw3{Upd_x7PLf3jRj~0t}RQlym~e|yX=C|mRgNyWG3##TPw&JQ~HBr{JV%_B{7@= z=*Gw$5s8Z=Xj)qHjpDDBzWHd`@#F1Vg$g-g@X;T5Ubv>(B>`ElstMRf-st1qJ z<}Qj0Vor+T-HJ_1iXfl}KZw(lK)O&S^|uwDNjw^}be1wyS^8&~GR@BjWmGh=(Z}zk z#>rSTz6qkzvD^n^#3&*;is1Bl2JNZD(HJNWDrCr_{)v)t!~e8t=xjDD&CF*L=>Bx9 zpK@lqGgRD;S-+z7*kx)bSaF+xeh5+N+TadvD%P9}B3UAiAaW1Hk3UK^<*+@rr^yG* zwne2_6d>KXX!5=Ou@4FPyqiPx`O^wX;|vatPNK2Tp1VAAw()@_rDw=!V_G z;kz=>1J%son`5kQ1JIapKv5k~HNf>ToKE47s^@3=xSmt->UpsqZ58-rydGGHbxw3U zRaQGyn(fZgKW!Qyj|>p&O&E4tirv};f7(=fF0>)q9)XyLU61WdyB+3PD1c&X;UAlP zW^0hMjWf_3GqTyXjcWids#vd<6_NZb2?cKQFK422&qlsfl9ztzkI*aH<>W-X@IM)aE(z0Bno!g3Y_=>GRi#t@9N**}{|XGOWbK&Tf& z9JeSvCVS_+w-d*WV*7KX2c@N$H!XqW9uTr8(VbI07Ot=VJ6bRAqN{uEy;HcLsy+SLxiQi3BoDlrbGFwkJ#+_1e%KEz&R0k%=wysE(w7jY)#7+C`6 z@>0SQ;%sbB*{NAnRN@p*-AI2gB8!%9e;Esk5mULb~y?$*mvGxtFRK=j%UPI#w7!4A#@!7In$one;onH z=B}Lm4oz+oa*L^!jQh7|YBz`=#%4}zuk75&VfZrCs)Bf_wa%MHf!h{h3nu7NPbvbk zEiyfv+nnTQnqs-LKbMw`eGQpA>J3;XhdY+t?hp=pcOY^uMvH z#B;)n8k4G*h!h&A!M(Hh5^KrS!&&Z;#xVf`B7Q*hwLV3MVngnc&aePNB1A_OVj@H* zs(&z|3iUxYOR^#0?UKf^TLp&a(F{ptheXZDp}_b2CSThUULoWlL9-0kwwwIPEo7a6 zvUWKCkVA4fBLX)IcRAXJ6Mhv{sh;pG+r|w?FoHC8g9!Y&R7(l{ta z3bPD}6-KM4sr9$Fd;hgiVHuIrS*XlR@IRdC+CS%ta2YPq-w2-c|CclE`oiXwAUjqK zRx2@fA(^}pM!!advW&A4U%;gjcJs~OTOW&$Z!Y{F_|RS1jCl&-B0@olv&@hD1wIIW z^F@EBSoPyqC`AvBUu716xbH96g~*usuKD8!!l86ERvA8nqCUTP;6esh@OS-t{oe3t zXu5b9x^Nh}bQro|m~tjG6#+`XhpZZy0?PsU!l)qVDsfI_Ela4HMohvNG;RL{P3zI` z`9MD?swH{#3yMUoAMC!5?2s`9tv@gCQG1Gvi0;;(A>5iyF>=)!#np1QLq3q|^BMo7 z>H{CuaUk}H3=RVZ^GWrQE9ZMf6MIFIm&>X=&Kj81ezeGfoT>N>YtA*&S8yhjJE7JynnWAR

C zdstS9SW%wPe2Aj@=VMk0A}|mpOnEF42Fs9Cw@(zrFh7=sC5?q8h=nDI<(l3wKa_<9 z0gI+~4yX3JU0f#ejx1}Hdb2@5v()+-%7}TDwX@Xg`LtalW$1LA@}Np3{7#P>c`KL%ILR3?)YfnTbp$H6-f6T>G{6x$N{-W9bul2`lt8A`$=* z2nhQ7LLt65770*gGh#3>VlXl47#;SAifagryXPUZq#?7!A+zKrvxH!@DgF56XJm83 zR`ByS3vLGw9hd|T{H>9@Wh7XIct=3INebtaLH|V1xXu2BNwdTGq%b`3H*PWYf*@|?z5W$}-hw%NTRv0oOc ztxehg2R=lE{9~qNE&hX<#&SsQW-7mW6|X(L=(C)O;{*t2gFhCm^^=?;t?P6adJ(Q@ z064q|YoR;mh9tNqGI+MbW*EYcZmSWP@`{P55-DDYp#{WUY*3*QvH1S99ryz?%+R;F zDqgZU66;^Wm?{X#L3#dSOc%=v<}4(ty!K2j*u4tl*@Q?n6Ofn@RFG6Wgy@!44%Jr< zg`L9ezFV^cA28WWY9z1a)YYBU7PGj7wM;KF`5Ln{TpM@fgy4UI&x{Goz`MF z32X;p;a;`kRBrbL&%R>-yM!EmopQky8?5U)W~iXFXfQUJNL2+)+}MM31W!IP82fTFd-CTFbJz5Jstuhdk$)kKP3hy}kyZ?wf! z)|=-A;9*_tyoK(#1@E|p_o9W++xk!IA;mYM(nU{nFXi)&5Z}u@i%WfrOMa=#4#~@y z<3tInH}633O1+?tg^w?2+FrhR_#?6}(RVT+G3y(ypP%5Ao4=DgJ zm0lDp6#%;AqiR8TKB@6alnSh3zgC8vEfNtHOBFH8zXqTqSivG#qs3MfFpcfqxO;p~ zLi(KCWM<=jLfLVFC`bif8|}i-lC^L^=|fPSRFCgbO?{H&TOq^El7Cs#c5#E_l3IU% z9wT`DCp4{EzyTSZPyEF{D3%Q5(CwIIU2{t5Lxd0h2Q-b4 zsAHYmc?H9xV2Trci=YgjL;ek2B!Oqxjdd8Dby%s{^9_^^3HFH$o~OIimVJ8;{6D2> zuP zt^ORv5QeFor&six6>%J~w?`YCxih_RD>8LIay$K9oeX zqb=CYL$S2$9)Cvcfr2h>fk7w&SJhj2Izv*Td3xIztnVA!pD4BTXG@NvTM>sJ2wOba0*wclS&=tRveh8y`7TTC_Ed{;tsduzX1$ntV9Kb>i~4nM$b1 zB=Oc$)I=^{b=L8qy}oLfUpT4}6|m@!LfkB3;_P^ny6v~Pb?2FDYn%ubWo&;ps3}K2 z*jN~CqqKK#c0Fp@5DNI4x3E~3$E;_SqX^a!V#}fCGR~E0$?r5AxjipbrtOYoI4-9Y z`B;^}U-yb7?w-=c{{4Z^YV5bwu>86Jm(;mwn`Kvv(FC*Yn)%)yo;N|>3nyzkQH$P- z-u;i`qX=s}vM#ijnrwbs3I{6MWFMC%*0_k{yRoxu*MfGER~}JWbN2Pu^W=FPh+@>{ zTA+X{Q<&^NY@KcnQwZCAbk26`VdtnF?A!gLPJr%rx0;*D0~_i7A;P!UpQD!7f};_# zp_ae+spaW4c^h^dkg)g-lZzL3xz`)Aywsim+*+T3hFERwr#sA%5kh?)x>+%{V{w0|FNb#+t1~TrYU;=kY7mvl7l#v{T2_ z2Z_4i!8sp+Q7jCv-rqwq-Y25tPxlnM=*C<#kM|A_ZGKL@O#=^Ig4(+(Ud548?j8Ym zx_%jLZ*F%3#>X}nZg2THc#DsAhDE+tGR?q42gb|^YSG&)#j9%ojc&373PHfu#a(d7 z>0YO%diwsM&YD;L2Gl{}dT0Jl4wKcvVdd{4UtPZWzTe3oiI{au6IpEpH7z?@NRN_M z`YD+@got~-zzXYvt5=sdPv5r?a5i7&H$`BGGTC-a%xGqGpe7_=%v5?a*>(5RW6t5s zcF&<-ATnSwU~LsB9TA%0IhZ))Cam*ejg&nUdgRdgkCnb*x`p^R54UG0&+Oc^W%?yXzIf7ASgoa%*qxX}oNOMbdxkTo|fKjULLeI_umaiQK9R zzDIlW)%B=a5y9^AO}cIY0^i9$G+g*QFGwNFMvhYO^uIfee8f(kIHtX zQSX(Vw6{SSJ}tMP$G3cULK`8!9#6J9*#^2W>=qyF6VsE@m{+G!J4c7MCm`qR#DRsT z3In(Q%|{|HUi#F*#`>aq04dt^k2*g4{=ti5!i!Y^AunS4(cxJ%90}Iky4JyYgfWdF z|N1PG_*Xo8Tg%n)Ys`k=>rjH_qK!`ILrT0CkWK#UefeVH=jCHg3GQQ# zkLJD7WYk@(qvY8HhU3TMOT_G%!0qWIHLQ;T^+t&6CEp$LSJo|&ujO{qmKKx_`5qC! z`+i&qbEnBCOg~uDYvn@*?B^$!T^$r2wl!Z=IqbK4-o1tB5O`qgy4?+yQx?m^c45xd z`(5np9OTwE21&!Jm);Ls=uBVtnN>~Gq!(BbHPX~~gExR;f=?bEn^x1b)9i>mZTJztu4o@wrH0~y!8(l*Q6+0BpHMP3)q1B;5Qa9p(1xQdKl zZQ(Bwa#VhJ?vZeOUem3@r@q3hJR*K@zif92`109(9BV3lu=rkYZJsm!EoF`bd$RKR zQ%X!4JnvtxRn&M}b3SfdrD0*_Q!CBPB2}Xp%MnVQ-_AT7F-$F7ZART|GaGAD%R@;i z8oD4#Q5NQx&TXAegz;-_rTVdt^|7{{8+y&>$K?*+rTfS2@*Nd}O3mhs=0+?#mB}Xn ze@xdd6z7#8X{Jc%`q$>u(bX~tvO7z-wKX9|-CRbbaU|Hj)B0uTmFKtk^Flc>=kx8a z85AS!kCz_n8^x;*?&4aAGnzkcZWvcWxoscT$xA9;YmTx^6wfjrkv#3Ie*@wsaLM3x z1$VM6#_Vs=n>Sn-*o^HY*_sI2W>N28mV-|q0fRKv$KrPGe@l_p`LH+8)Czg3hv^g(stkUz%Y z(DQRB)YfZh+Fqbl?BVg`K=Q>ccFotgd!2v$BS%oQfJ~lKny>>nNg6`U0 zt-nGM*r>U9Yae$76UZC$+Iox3SQ0Z@?`Cuu$fkRKA2U7lYW>V{=3m;uf6RN&fpZzD zx>O;!VcYH+ZmIR&9F_X@Tr~Q9@J4CycD4&85r>X-u18?-ukV|_nx84S&=@m7W61os zhWvC1Hha0DGGEmWc$WB>1vbNFnZsV#Fth3zAhFEJyX~<}o(qrC7L+=tpqGW~L{5xY zzkF#~`YF=Vu06%yEMU{}f*GfHn;fnX_6vuGm+#SE&_aA*yW$ z9i%~4j~St9w;j09kTtt&2ZxBIV41qO`!$nXvbxnpLBnF*UxW<6s38-e`85S}0Yk#v ze96Akj6t8^NJh&1_-g~+X-sxmbCeXUOw(LrjDDqGMn^%laA!_48ln;Yj<9D==lS)a z?12R^Uwer#Q@p|t*9d}1#GJ|BPJ>(PjHhPE7wd7vXrwC#(i_0+puo1ZQqvef?3lw8 zhU4&rs|mU>_m<`V4aG!i5(dyFN0ajK@!jbC{$i{HLa6W(S?!$AXSQ|}AuX{tm9l$a znzsE&v1-EIswx&F3jH&?yTPYI==Q_tVnKNuLz-j!SG}D&r|$8ytVk!`+jw^opq;uT zNIEv(S9SlZ;NbDq9HIYej12m(IfCzh$dLNqtm*$VL+byED{y@^M*Lg;TgBl&GNe?O ze&&-xDyY|o(rttK4|kRri!0BQmIzsf-xGj^kt3rXQi5=zYpVOJJz7}*wy@N!aJJUA zV7ISmQ{&XovJ=um9$>sU_q@y^`7YJ!rF+Z&c7NbLJihLE@xI~cbCl_MC_Y5qU`B(? z#S3@#<8SxR*z~+{RRvt>LdD}!N$G;$$LJ?~>==a7g?RgEu>2S((#Pnq+!&)yzrt7> zK4LaHo(n&Uun=GjDQWqA~?c0ceD-ivLIruLWRjn9zo*85Xw5ushHs4 zlA3)K$GLy$m4x8ZNOtoyL|QfCM(e}PXqWDrPO4bI82Pp7A(YqbqW`N{RMtnAm}%FW$+!TE{KWK)_3)4!fn(ffL|p>CI|)E zNel^ZclCJL6`?j6>0J5?HKmg|J=rJbp+X<5Ili3EoKYGm$sZf z!oA~ZwTxFd@x%R%`y^X9c84P^U7UD83$y_g{`x(a=hq5_Im1t0UscZ^pNzv=Vf3EZ z`D-7_gE+v;lGWU|U>*YIaP$woM4sw5f(oxnXhtVM zNqS6*a|0$yrK7Q;;}UorCvt9rC$mKNA& zXf8onD#1^@UxG*ovAtgtF^dRH2B^j%Svv?rk4245#T5y8;)|8ja2d3|J#Y~4x2F=4 zXr9zmbBvbBp(8RvPu+bW6Gm(}+73!La#7Ma!YMk#z24k%1+#(rNVD&ds6WyWA zG@&zHQqERTEEWDG>JUyGKP}nIoe>Kfib5Bqfm$k-=?l1a8mDR%_kcx6l~X>IQ!ka% zQo!PzT17F{mVSM4#agVP=U`YYn5 zxE^*%J+o~`#M&V%3_F+&1lMg(phh5XcpBnzxEGSOpOX|#LXp}~KLLO@C0SaNeI_H+zGOgA7fCT1|BE7%;5dmaL;9dh zkLO&!;JOHv9CLaxLblBsI?8L1f{a`{&2--V^esCCZRhkl+4spR^@r0gwznV(V6o zudFym3b+R!K_v}LKz&h0izvQ+F|Ixt6?w*oJV-+)=(GC5h*X3>yIDP>jUV`i&LEOk zBIxT<)J)3NOni;f-ouO=(~Tb-9Kb}@lR#f+Mb1D(&)8wK4U+^a>(JMs^(p|CtXST2 zo0;6PKXc7%O<8>`Sb+?oDw={@hdA|<1_}+r38kv6kR67G(`GM>Y$cLEw?xc^Ltz(R zWqUNHupj-hZDOa9aQ1%C=HXG??NRQ10P3-CQ%GM_Vczj9$Eg4;ISzwfgSfZcmg86e z7TwxGuL0a!*Tqy{=0R}K=Fxqqa3jAKr*R+bi%cAxpndev8r_=dsU$ccHzNGLCkCII1uT&ao|@Hi+6nzxX6(iQ zYu!PfG3pLS4<@Cr^ut5l(G-BJ7n2(<5aM+k;$}Bv5*4fARhb0y*@J}+V!Nu(qQG=h zH#yehmyzHbvvk9>eX{Ph@{_-6gnrsSoHU~^LU0mlO68u$*x-nRYR#h>Cjv&>umt5o zwGPmO>N-V)f%^b49bw*P7>dyDM4R9XI;I&!e%VI<{UIr#TD0Csj)(jQd>aoOy5I8L z66M!^o^TEdtOY9UpzwPvGGo`Rkp;$RFgmS+5VRHNE^U;5pqW!O`6S4@KG|xm*O_5Z z6K{Y59p{eq+MDM!c7=QpY7}-LOEhXU0Y&esA}O~-SZ}nyfo{cw>Ni?#%jsdEdWIf* z;z40M0DF&p>NbelwV$LtTN;SUv|Iwh?oUc#rlh=W=P|46#X1x>4%8`*RL2zXGe!}+ zTZKvOVEc=BvjfIHZdaV1mNCgm?al3ViZI6%#mqKx$BX#hmNB!*udAtR^rG4C8Q}5{ zT-*Jnt28fK4MJ3qhB(6HsjTVfH)d2FD9wan+Ls%!L%;3MggqrHX7g=z;^_k$$DPf|weFjQD~a`QQh0 z^oc}@9wwWz74eL;Q-5$DumbR{H02TiDHT?bfWP`;VD)M|STb{0ZQ$RYDhx-_b4C78 z^VRj*(=lq)+HGD9l!A8L=Lf*mKZEht#y}G-P&+txefI;kVeLksx`gXS8lOf zVT!B4)>a@p^rZks*F z7Z?{{FzDLXTkV>yjL*~t6&TY=jB!LKpW`#=I@Wvj&D0jq2$|+(mX8UKP{C0o(PzNd zCX5lcDR3f29LuV3bsHgexzF0@d}n_b7PPkp(8^SAYq{GsuNZe;zcj%_Z&obO8Pw%Xf(Y>9KLs=kK3-Y0sI)hqrEraBhbYW``W(Gmx3-voqWA8)q!J zqpYtnZ!5mF<2u%9@2uJHu-Wfru@zp^?)1@yLHJsA<5SrZRAvn)MUI{d0l|9nYIYm` z9(_FPHU^QJ*agXOO>121Cw8|4jO!N;J1$eS>lcnYDpNsgV2tay&32x{L2G1R&syv} z$Ai|Uw3^@j=j{(6zCB^M(=5i0f^_MFIhfzT=)5Yy(QwluP(t$cW&f~R_2d*DgR&EP z$%LE50w#xLrT~+S0ru|f1&IbLqaCngx0tD1tW|+tYiG?#*4OFQu3{=U5 zZyS(zTzOGgUFj5+gnGBTQK^+c{4zy31b6as8=%3SkNxUGA!tFZkt3_2Lm7w}vHLLs zn)8llxHTCSavgQmerQZ82x_9#QcQ~RsAa$GO;N zjr1P%Jhrt73Fq;Cy0tl5e2k%Sm!M+ce<|?x1Sw#Vq*JAmn}pHfQPd@pT^34bN0qS4kcZ3UYknFifB$;`%f+*!$4?#$MRBb6aq!dQ!0M zY@rcD(x>McB(oYqHoiY$DbG+m7=NmDo9x}dG$`w`%mz}d!$kb_edT=;LV|M~i3a7!dK&nV_Np%)7sPO6Ho zDY^0{oDJ&DiO->YW~iOra|yf2ALP&;p^j<&J9z57uIu>d6Wg>FHYyQ&%`OHDP#irh(@5+vU)NBj zHN*8x3mAuwZ^3xxI3aQy0mq$@I)9SKH@W_i=MwyB!oqzZdhJn_6xecO+IK2=A$q9e zd-XE@hV7WQEpZFtOz!ap)2EI`_xWx+-3e2 z_N8_KU1!ZwcndhrXeL5PEdJ6U1FvS zkB2Qp@Nj(KT259lc-0(P3)~(C3B`!P8IHd~GIpl&kx$a+j#nT?+7es531qmQ6a2jS z*y%iXv5ovL17*#Saf)k@ji0zO;{Awb&~5vQ9;9tB0VmNgk+HicOgbeif0{lG28sOg z$~zV5H2%F06;1e)ThHo8=YR}hN2ZQArq-=?vwc_`A14{h+2IRoZC;J$b~x0*{xgQ> z*h0c#oDFD=*#Zk$+5!6M)^DzuH^DWU(>EHjm5o!wV(W~^zfCEOkL5S`8V&ZWV|O?jINcYcs3)9cV=GT z5`SliX1gJI7aFB4?k^kiEnN+BH}-K!@!#5s6PH@y&$yi1eHKn8!5<&#Ov23lLY;cF zBp*wGD_*G$eCbh({BpC7bQ&eMTPA|S+q>JXc?AuaYCCY%8`%{f8+F`QeHGUup`iib zy_Y~&8%M`&iRDu!!KX4ypYX%(nEF0josR;yrzTNe&HV+u3Fyz6_IK|LFoE=tF5V}0 zzsCc&Ghb)A9*GG*qP{-eCWeovSExfnuk+R(WykHIo2ww`=Gz7ppf%xp=XFyzAtLAy z1!aAMl$zNtuRkrQ4lGoK^`E8Q7nzl%8ON%&I=UJ52f?M7xG+O6P?##Y_pe$~lT#5*M(o;56JZCG2OB%c+CW*>6(OWdS;uGx>ay z2Chl#J!jGh0#T|Dr!X${v&T)WrAp+z5P*%Nuva$oLgVvauO6iS{+z8 zRg|rjX=)-KIh1eS9OuVGT4H=oYw7u1_F-no`%Bh3IwEIL9FyTBKT#~HI&^qNyaPg& zFPu7d9plw#UdgL%-q=asHsd!wI%va4<-ENi+#}35pDW^eKU6~zWSBfeySjwuVa*_4Ot&t|uoj_iR67Y**UZ-)JlMAxp`yjoN` zsE;ApTW-O?73Yer*8|S5I3gQDd!{!DgPybDp8=s$HVPj*( zssv{rXgj+H9Kem&B+0g0R;a8xJtAl26^wx=PVKpfah%u z-#e+jkGE+~yE%LRzWevTMD%6khaD|_bP831QsStfwDVDQ4Zsud>FH+*Q4yssXTw9W;61xp^M~QJwOUP{!Htg})-iOZauI+KM z+P?d;P3qzf$wRlvy2DXMIZX22oo<M{z zQNPo~Z|wWKf6(#Zj41U*hsMK3?!)zzUDzuiw!3s3`OS#msFk{IaU4bM8#~?|AO9ko zG>q7^P+R=>*?fw<-!^{LwIaP0G&)T_2<9YucqdJkcr&(uNsXIs zk6|=hGW;HZSCHMY039-Lgs7s2rm`VPq;%*{kkgk2OGPPi{Bbwtr?sr2kB^>r7js;> zqDm#3Gk7azf?;ef-`)0!qV46;?;AN;WTaSbmA=W2gZ29BB406XaQz44#~c2(s%f-2 zMFZ1i8KMVi5=>P&otC3I6lwd(J}F#^Wa&QjA)%42F4Va-F5E1;Ps7U(5=ZInm`pjF zZGbrJ^{tBg459m?AF3hzcUo_Oz;hMXn%b?OqPiO<=ig#941{lvvYC9AZKcakc-U&Z zqok^j<&Q%#7Qk1YdypR`e)l)^K|(Gtlu}IQNJhlHenjt`%byP&e2(k3=N1~i^{`43 zr+#i{gi}d0_J4jRwUE9Z-7R{p!au}_K2!m@H1{u@R|D!PKg7n5X9AhuRKh1b~>4!$+vNt8U8`W2tKk^dT5#Y|TcoDsP_0)dq6Ckb% zK(6FGbM3XS02hi|aqeOcPN&9?qps;PEwH`_i%kA6!s2s3&o{MV;p$`ynnn0W8;>I& zPZh>HgF`s+jxJe<-hwl1ZhYzQxoZKKT;-eoKsPAP>aSbqaO!Y#Npt9M^Dy=kG9Do& zAqFNU9w80^8YUhtCLSgsCV^i);P_s~)hQV7PlG7bdS>>Uqx!78qr@jsAOr3vz_}V@ zc6{=aaKhon5^hL9Frvx-ba{Z~;3Z_$;2OynP}At-h)g$*8S&XGucj||u|u7lW$-qK zW65l*E75btwnsFe_BBBen|>WLGRM`5?bZ>Ue%E($1vaFI)Vk-1ik2h&nHLt*7;aGD zN6pN*ezln<^M`s^lnFlpL_`h5UQW!i*&(wUfC2l{dI@*DBc(vkzEST@l|931VMvI% zjd`z2W!b1)P@?9n7NaY;Q3W6o8=&VxGEtRLQA6ub&|e#2<`ZQVb+9odMBn!3aH(fe zjShdHHp0~B+_FzKG(gQ|XS~YM`pyA;j4}P$GXg>P{c9sQ6CVuAgd+TI14|Q4W*?R` z1BgSEm&=}yc3HxN{eATD$;}Ee0k~gO8K z+?COm9+7$=3U-5KY8Xp?S7V|pYo(!JG=$1b<653$$NRwql3z}Mt7>&(!Za^MS!kCv z{I=A#(6$U}wcT25RTa}*w=Z1{uV;MkG-gdeUdz6{#s7G=muJ0g^xW#mnd-Ql3>3Ql zz+g-N^DSzMsNB3riVdc~eR5FU3NtKX-^wLoSo1qT%Se-P#9V%dg0YN&>ZW~#e|P|= z;>%>_kRG&?NM~nktU289eaiqKuuHj^q4shY8`XsKAl^vHM=Xp>wae9uFp~X(V#~`5 z9yOC8O*fa_@gvgCKQ(rxRDTv-EeF#GAWEOde8PL1S=hJphrI@X(;`MM^>aoo$%7^B zHeCD62_E1ljMy~XP>vWhg|+%kkBe-sj~T=}5fjTIl}QQ~L;Z&Mt8#@_GVhX`u0YaR z3SNe0u1?In$pkk)jB@3lj#^R()zE<{>d)gO6x*K5sG|_WSi2Z}#Ga_&Ovnj*9 z96NhgaIsiW2YqJHDGXCHJc-BveeMN*)^EF-CRd-*jelMNLF&kA(rNM{5no;kJZkqH zzQMkDAb3?PUXc|hlNy_V&y3!ac7_#^yF^M2)2s}NBskrzapo)4ttk!Jpqno?7iGs< z24un!{`a+L2nz0;dsqmmnNZ^=WLgMyY-S{{X8fc|aIle~3*M1n2PO&#b;}WPyd?2_ zzdg3*h`}Bg!O4tYbzH79rkfJdD)tF-oVe1e85x@+;;^ItMmD8?Q|O(Y2tt)T52n!I zD((r&sTkvmd4tCk$T=i7=(Slbfd_pgIa~pJ&kvDxvj}oKHNkM`)dX___^Ma=?-~+# zL*bcU$Aw0a1S9=sumPU=79v!v>nqbwg?&1-ZLy`=2n|=timcLnrsCs?5YA);8wk#0 zk!58D!|=fxCSR?`OgV$`=KQEGzmxl>O#0}ZG~5+CPnz|ofp%Aioo0TB{iGlFjbXV{Y}2YpFDT(CPznv(=lG`a zk}dmn-fch^QN9&lUujYIJz_s6L}+%PLe)b2peBP^BMv-UA=luq?+ZHUHsVy_Yz0wD zyw3K?EbZ>!&)ixzKg`1_#!t{gb0Y1kkXUpI=-1b}}a%;hG)%?VXBjmzgs z7aHmHmcT-+*kdb#%NFvj8pt)z6DuZTD+n1h`NbPO09N*J*m74Llf{S*1{D z9e`|dP5v~<=ou^VkoX10a3H?lF=?u>_@_uT3OXbv@2IAd)YjK8Eqz*1kd;WfMBPXa zmq4&1)$<7&e1$Q+$jBfbByEy0B`<$%i+JdiIux|!jyjc*2;%#dB69*EL>)P^Ceopv z43O{$)=8HL)(P)6&?@E2a&DWAGUN)u7z&8OqR1>b8f($rj{`B8M7w|h%ajTPJLcPM z#@B}Xjks;YlDTXxnug2i%zvN)YV${I_}V>fn+$0i3$0ji&up>-nf^HgOpBYtY1@#= zw;oC+PT-Ba8vdD#KJrDs1zZK#L&Z3&mWH5m+*6!CUb~QJoMY@ab*~pSUfaMrwpdF4 zDr~aik!GwrO20;2XH_s>E5|y}*pVr=HbmK#&q}L?tXKM_57Dw}^ z=Z>%DHV7G<1u~HMVz60oLkAbhy15p4hI_!VzEGw=%FP3*Q$*!_0{oSadGqI_Mr4ha z{`T|spY13ED7)Bvw_&h>K2)xJC<&VIq1qmM{--Lufy?q__610*of@Ut-b zs{+Ez#5jHmGEWV$hsek?b(|GP*4iUuBj5PA4q`G-Me&DUy@aq@`jpMGeqWd2JMy6f zJP%@YjiAGYjhP~BT>Z*BFJvX$gfrH1VE!Js0NMYZdr&s->`Z5bi&V5Th@qB<*e2PV zNqjYyE7SQIWKzD_^>~C}0$fWSFQv9S3E!l%UWxJe<^v>bjy@=o4`t5;2*iq2KHPyxkI^?~D{4(Y1lrBUqBT~=u<&niL2sSveM^sePz*W~ zk$Bj7-~@zYpmudzi0V6|l5(JxNh(t3@8YTqDe-j>A%NLqnXm!;E|h=urTa8fSBgcJ zM|Da;1d8A;($E#6!T{iSY@VF^zSV{v@aplMqp-s)f@;Yq{&I76FC)E%SDd`L;Vf6 zEQlsV7)LJZ)AWaGzqtm_z;DylMl*@zJ;2xMZ$qZ#(}4t5mL_4 zb!vPIy>R&WH5;sqqpNClyp$v6mo-Nj2URIYhH}Ql1zT)jwg+*HMhRlMKT$Ts-4o+- zg-L28Z@^WZEU`Do)YY~}I98a{4tix|I1it?GCn1>Vqk$cP9W0yb3zt{&|s zAq~DBZZP=KoN^|Sy+_f{A{{BL6w~SO!QvjLUCj(n8-vhpU1rdMv8u}z8XjvWRXR;x9FdjGHZ zCB8D;XCO`lCqoYP9yHUC=dFYP=$I^D{E4$qteOoJ6>SaQ2yZe~f?Brl`U4T5EPCoh zlIyqnyWispeXR$sdRB!AC{|cF{%lsc_<$2Tt%;l1fb7>psUnMJR5-VV^7!w9T@iNL z!><9|sBj1iW%~X`nxlTw+6KP$keDE4FYLeZ<2f1}3!bFAaVG-3bo*}|sok~xc*!xp z)sj)w+HctHtD+7{Kp_aXUUnCE98otiLZ>wB?%7s7Fhtg zUJ&^Gzz-a945~P@HwIu-rz62nOC0r zUUxV1a)vXGUiuIogO>Q`Y{&9aZ2c&FzI`3j@I+`V-0z<`}g+z;gl_jq1AJhM^DeE?>fTuF8*EA z8WHV%=d?5SQNZTP;57tP8&0W52KIHE@*R~k3>(*ZySDP+HGcN>i}Ia^a$Fmvc{|UN z;59P#bswd^b5|?*KN8=%hf)jsg&7Hwk)g`IY6&ZzrJ&NS1LA-De36SD33GpU@;6@6_8@Sd`iYTb{d(5b^n{rjD z#!SC?&;`b{uReNtqUs#-`8On$<{1}zv{OJc-S&f?ZW~X{uoi!p@)s(>_(9$2Ab1@- z(RP_{h~x|Ter{{a&u07fFMkP_?%L%oK}E}Ixa;jH_yd3`ReR%Rs=iUxsRDC6nA5eT z9!YtX3m>(KoBXZ!1`O6$AtLEGAd?fxb78}2#h zqw?Zqf+B4Q`14! z%lp2V9+>AA0xlM`$l(hS^Q(F@TKd#ugTbp>D#NV|V1V8H3u^vo` z1o#ks9!3X1A$__@L-k^`aEAXY45Vls0=^@G^!c$+EL;-cFepIus4EG4@Cnd&wYQJz zvW;5wEofp_e+Sr^5a}aS-@K6?S#th*l^AI?P;xr%SnX$qfu6|i?zlyP)YD+JxGp&V zHxTZiH+Ct-);!Cyt>f9BvbC6iWy0Jp&^zE6|E@2B`Qg7dKJ2T^%XQ7Ka_29aPV?3^?ft2{m6=SD za;xwzx})D7or91ta_aDpn*OAX7pW&^L*naN6dKokp?t8JxME>#@ z)1l%$Kilj2=}9j=^=XpNFK71wrmUo8q;fPZTh?SKF14%gJ=06yC$86|_?-W%Gs)FQ zy!=nqcCf`0p(lg>Y_`DVHtJ@X#Ytk`O(3VgD9@lzPfSVq$A2;FRWuEhmIXBw=U zd}8;;tESbtbAI*-P{$@{J@Simk!CcOAZS+F)G4;mL zv2<*Nm=LIe1B#Gx89;3<`Z8wX;wu7NMx%AzGw7_px81mEE@}Lvjn47h**8yZ-PT6b zX=4af2l7PjW-|7`veHF9QC^$gitV=^`~2LWY|Nt)b5be?*l_WqcW~c;_Uc= z+T}p1mg^cG6$fS!OgzK%ko|F8 zqp%wLHABnd{EW)*=|`8L*~9zzIP~pK$MChEcpr0SA}A`IqF9-?`ygnVR(A1baDQ0)2>`G* z9aW~kS7@I4dc?jSoo@o((_i}*@Y~KcEM}1jGTJbP0tCKISeNwe2rE8d6`zc(zkgXc zp6DfoBN?21i*TJ>pH%i4iYq^C;gG!^1m8=~mcJx(JFYboihd9_v3;8nV*KY(c$X>I zUtTvxiq+AFh5-}`@0HV#qTf-{E5~WUUo*XLQ|GTFVBaQ9TSrO(;-?Kqgz2Rp^{($8 zRzD;5LG)2rI5D84l%zj@VA|m_q@R-^it%(nkBn%6`)`Cc{0x zZ%d_i(-S2;45i=HUF%P%ErHM`Qp7shc`1qqbo-1uT-}%7G2EViJo)mz`G~eZ6sOEb z$}uOOlFD3skjADiiI*;0Vw|(GEo-yTD);vI)}pga+h;cYy!rY!{O4a~y6zhR$cp2# zr+CS6?c2KX-2@jNV=Q$0A38WJd>6QIs>Q6f(6oI_`wXk_%?n97hM)2hl{E>EjcE^l+V`ATFYzK9>u2t<2SaANm!`FA8!7w^!iQz5FKKizm-$Kr+)?BB zm|BPO6&_-+J`F5pNzv8kH9nWBmov01&@z#}Wcm_xALx8%WZ*e`tD)&5q+AKVw|Kvu-PF{>f&jhq%g9_v-0- z!u{V_vn?=dE?%JJRlo!Rx#9)^5dy&jaW->tvvqZ0P%*LeFf*_>u(G#uHTXZPbqtK% zOfAe@4OkcrT+GZ&4Osqf7Jv_6%>19@e@4w(9%6Dq-VPWBZbf3A_qv9*uxKEwh*H<+zQEgoz&3HG&xjdOwYo3T;R?FVp zuQg9+Yr{TgBz+{xP8)*3IWrtK$HcyFh|-}>9&8ESR<2zHbQ42<-dogg11Q%E5|PaD zi`IxOHOI-gr!moJoYe;)*B%rR6d@qbWD3b(B=Og)z5j3VjFsDtU@n`JE+n zAXp2Jj9kGG-O{Wsfn(zm;T}%;d!5PuZL$NnGG)g;_Y22MeHXzUaWRoN)sP?(RF#7g zZE+#+?JY`keOK}+(XM}G#e^UN@cX8XJUEQr51ZDKLr?^V!dVd{cKi+(jcYitgmiN) z#Fc$%g&3|`ON-}>4GvmxjwD!wno$@Es155c;!Hsu;?=mpT@+PcONB6!2e)m`3zH@= zl*@|x5HX0kU(Pi!Oe)HYkumPe8Z@j`8E3+zEsHGCe+|8xe~Juuj@mPjmF6!+X`M7U z2DvUZfE5hbAItL1fg?nSew?TMC<1+~(65lR?0VmgoD38~FN74mZ>=cSUvA{Jk#AcE zfI@G8157|qD!~YmqY^N+ZorAdYWg_Hidr0zWw;MOA8{6Kn97|o zS(4ZT`A1FPq0h?Zqv!wb3TB!&G)b3}uk+@xjO=MIi{M<=!UuVxhxB3#VvTdS-m+q$ zdP;vIy$TXE{xKjwsRsk|oYdx1z~)YHz#mhYLD;9NguG8-5%H9ci}#HB7~>tuIHXDv z@q$eya@V0E2u{fO5fkX^!Jlj%qR*N<^8)!vp|vuG>ETGISVMZ#E=Wo8USEYP3k>xKx5l-DjJ1!l~VGxFuVAr#yFhs_uv zfwY|%rZPGMq}>>@WjX`sD6&g|R;y^8qo=%jE62QgqwxMk$r9_023BEvHCsXV1Cubs zb#pAG>kN+LKhVw*o@+QIi1!3>!a3>9R%}DTI&`4P{c#mbN9E)=DlwvQcj3@qxUhF+ z7$(VmzKgbk&gNb2aipb&9#`*V{z{%_)qUvj+D*P&)ra^5X7*e*Bg<-3m8z>>d zu$4m+(as_w*s&=PND@D%lv^$&I==x1Wdo2>x`tZD4R}&0)hM}=O4LO|Y_#EG-NieI zX=8dBa}A)KDv}5^Z+jw6Wc#{i4>IzCoTWyogWBsuRZVHuz1mf1bk`_!Y=HXU0*D=n z_RI$I)4I@O)Kx=}Ub69l^_OKZf|_6*Z~;xM9aLx|K3^HdkjGoPAE@m8?cg%2$UCT< zMkKy6D0VRpRFZP&30cO3Q}YLHy0(VDFa;Hm86#?1GiirQ4}$qItgle4g+jEAiJU*= zIW(s`aGG8c;DVLw4yR&RgK4i>WhBpICQiFA(@rj2yY-U%9KH665wu6=^1jw8T(e{!4Kt|iL|%Y1m+?A3|2lI$lbizq*ES5Yu97=*{DKSS&b z&4x?MnT?0Xx)9<7Pyv%2m?D9K%7`Kwcw~iGkG^yxi$_eq}cw_-kENli?SV`Iecs;-Y+5r5WBYc;`E7nCv&E)tx= zzzh4AL)uq^$ca~N7PX+EUD2^yJ9e&0V#h~vQ&fs?M!I)MX`t_4bi)i5U-EK6NSdxQ zH0MkofBW~aku-fQvB57&PK}D02R$y`BH)OJCwV$g&|E>4IxyKZG}_gIN-MF}bc8g& zCZoo51f7hgj9QCHZX)VbTHUwKbOfG^#vFJ-W+G~BmBTClC5YAz@oH#87<(3+lsA4OB=E)dl9>Y!fLZsD0)AB!NfwRNYCxCSUI2xiQ4g1KabYtW&=m2)R zs9sMTz0K)KYg6MFhp@CX(6l>ASE;bH)##e)@BMeVpi?*yX|1rd=jfWtG!3?jhHDl5 zO;Uk}J`ibt^FVWhZ0Zt%q7`82si{J#$1zKTY)6Jj>BQ%&8nV@q^EZhwOmQn=HX?@v zJWZ=H(;ih5@I}2T61T;uBQ~mo{uu^?KmHM;mV~AM=|sivMjW~rf}V6M?Tl+@m7`Jn ziL$aT?4X^hjY}&BAT2O95ytpW$lL`CnUUu||H*Xrz@9$sq1U3h920ZxQ97if$PRSCWu&O3X3L(MFWH{veURKYZV66^vBa3o*xvQo1&gF1?>|2Q)fZ3ZHJ!O@ zrq%br&pmJ6?76)$#LFA1X@^ulu3rCEw!vn6z}@I1BGsNvx@DhuwISWPr8G~sL$L*$ z;@VYRvcX5TbwzXfBq-G$F5SteJb&4kWZwt;OyFGy*Qp>kROVE-339=+k|3$fuQ@fJ zq43gKYtp|18DJK=;xnAtOqHR?qyjXO9x!@KGw-4P6EaVFZ|^O9gMq(vLU*FvDBxI# zhXhES><35|NXK%R#nk1m2iIxGK4PdOTkxT!>6iZF{9&v%2Y-c zdJ{ir$b>N)S=o*5cF9ziy-zri;YRV;=|I`k2aFFN(zS4%-jb+ zVTNEVavIQ7IjJw-_}yb%e9pkC3lsNz2@~E~@4R11ITn6GtEY*9!9{!bkU`Tw41-qh zTGh|7hLb3Ts43BK@|UFqd}TfITou_N1sr@o^>K(V{ri5~(bj*1 zpyU-R_h{be8^`D5EdWQU{;^tAJR)USwX1&+0Q-&O%K46(32z@@8 z#crx6aLPe7>F|T5zAE=iA2$rUX`S8DQ>QF}K>PyDtM{Vz%6Ws@-ZycHiPXYng9XP% z7FX+f8m`v7_N4I%AU!_`c|vs9gO=mm)iRFyd~Y+$hr_yjyUInj`10iN zxFxpp0Dd{ZONcx8?XfmQV+-JB)5Xoxb>wS|v@+{3M`mHwSd!0!*ngX~P`N)PNlN(b zcbAA*ak+`U+Dg%hsJElGfTuTxc-g~-ckaSY-?1xx`RYaDg!eK~%i{CT@M!6@&lQuN z_vKw-y820d+6EtAtI|?f_Z-uC`a3c@v(-I2;Bl39bk6v?1&14p#a*&K;(bI<_7+7W z?=s$%sT6XRp3Wv~1bw>YFEfW56WqJ3Ga=%nF@eFx_t2C=j_iTTn|Cu5XQuzXJb0^3 z&PYu4=Rqf1aL#nIA4XWc!GUqy63t0pVO;bYFW;lIs5Lvu@TDiy5NG5>odd%T4P!P} zXWLGdEm8KK%r+H=_@Qao&RVZD%h<#X9L|I8R{sdR>boA|2VFMIX>^sbCuTDo!KRLX zWppD3uB(M=O|n;CYV}48x0bAKEiM%Vhwk*iferV^UH=3)xSuCY%Jkb+LaxPkMZg*Gh& zxT8A(+7fH+I#9$xeUmG_QNw$OK?2p?VDE*Y7c53bJ}s;MkB}70y|&RFDj!2Vm(Th8*W0c$VrPKhP>9-{z|Xerr>+hL_ty#~(!G3^4$&{3hMk!W z{^zPpz{!2)@K;hg!cOS-WzXfi(NHUWx+e8fegG@5YXx3HR6?Cn_jkj-#h>R}|J?RJ zsy}-5VOPF_>iJa4Xy=!^`u+_&zq?rp1XR>|9Xn+*(acU*I8-_Xwkr65n4S7tG~tM)BZFUe=Ud4^6-24C*Z~WMcCHC_IX;|%Tk9CzcPq18Ah>>K_`@B|;DFq|C#Jp&$DYJV^O@NFM`c%|PVf5TPQHNfj^|f| z^lGaS?s`4p1HXpR43yS%xa_8go&naqOL7^FyoS&USnXtWXV=gB5dNbWbFJx9c?oF^ z7+0p_qgdW-+?)|xA1w<};jm<`j`0A`J41@(E~niEyV|CVs8ssytn6J=_V{eR+w4Pn z^Qp5FNHXd_86Uxr_mL^K=A(>u^7iwjWk%`i3Tdt5=H568xwC}He^Y!YS#|D=iCGS& zPB4x!xiT-!;@#}l2S+wfdktJ3q}ZE=@N$D@_(bX--uNsdqp?Z-TOHj5TxT*c1d1ep zg=lE$dHMOr#`7NAUfTDG$$v0$nVtJ-u@3A#-StL%w5I1sY$USiEtncdHXzlZ3y$MY zXXz@xKVL~6VvplT`}CE?+IIH&|5!dLSqdBzth|;Y``jlj>pB%X>UEd)e7#9;Cy;q* zKkg=}P_%Jh5$M)S9TKJ+ED&^kT^{DE<9Rs5y}94FKJ{5;1|Jm`d-E^Foz?58*lN|i zYX+Kp75*+?hy0W8k@M)=?mmR1B-0+?rsvb3m#h|kCgAd{y?rUz!{g6ohloLl2SdU{k-e>(-A=F2+Y-LMCbjyvaR z9n)6n*@Ata`+N1OcIhndQg5E$d)?zf@l`I8gxlkk{@M0!_oeQ~J^p1nO%am5+|-ge zg9)k*d(SnZf!FrwPmkG8m}iy!E3)`4&aZZN0*068mp;1hq3!YSiRWH}2j87e`fr1J z&aXD{E8K6tkBx4oufg5}DD^l2DNp3Dy_?kU-fs`}RG(_Mf&&dJGyEOZxax1}2GU9} zo$r;dKV7WwVn*G>2=kQ@(oxE1VA?J5y!Kh5H$o6tR2`h}f{8_xl$;UCCr5xF5egLD z4>(=D4=CMpa_)#|`=DqqrCGlA#UznSXI#mo&NQvclSYkmJ_&4#A|HO1pcrTp_jlMOuEl|7^)4Pe6v4X^&#%nUel%h>xMkn z`dL|3#ge=hR?Cu|ITKV$m32SHjxI-~9Ya-S&%B`KwNb&o3D0s+=?b=Okjt2kE=&B# z0|FncbifP6jg5$41DT%zc~FWS$IdF8q^`&aOHRdGl~q!x8WZ&Qjy621Jc3dP6JFd? zJ$Dk-bpScUVxm|yElkUCP^hzlw&(twEFMJ*zS%G#&{atb(tH2_dWu8Q6qNx123rz6 z$;NJl_7_=c5FLV#HHl5i1SS>U zjydaJxg}*`LLteKl5<)Z>e`DpE;V1mmg+V<-QA>8Ylg=Y+q!5ef(K^Pgnh7IC$`7_%V>Wx zp3*;ma#6o-kajftrY zGM21xl9^d0CoEa5$#%+PG)}SQ3|c2klFZCb4h}jcM|*E5cK8^VOhYQQGv{sRPq8ae zt(B*_d$g_QfBqy0bN)%2uokWm^`B^xURh7E|N?c>1?*EPH| z-F%_8@|BxQq@A`@G%r-g4~Ax4iWibM1`Vu~COcX#VWCV_UL=O#BmbQTQIL-Jp#FlK zShW~~hoMT-Tp(V_hQsm`5H6WcrM>>>;^!x2kWhp$m!~Ape*XAmBIuY@1aXyX8&VWW zWNKVd6s(fYEMYNE-T0doG9=MxFG_x9Nc9a*jlNh`jYvW{7@Cs z%f21|-?^!!Kqv>nWQHnvta2=GGS*`L!ziUT&jYL?*f@05f~U;45Y&?!Iv+hYl3FXLR1ke?*Gs+ z3J-3j^|A&cFa^6~e~26far`r+bYe0FLBxb_&Qkd~lb)02V0wm-D7Y+;<09(6_bZW= z9D=%}LalxZaN=g!f)gVtH#K>FQ8`UHuL@ZTpp0FSo2y86q{bb5M#SP4VrCv3 z=lzAW2&vhsOB*xqd?lcbTOqdnVgAEY2621Y8K|{&25N0_b4m_@TH7?B)>ivJT3arl z*7h(Xzv7Tn-I|M>!iuX(HBw`QJzljD5kGJ+tO;IzgOv z%Ir|pqzxAI^8Rl>!k#j6c|gzEFrspSNX4nwFOibgycO=`g>^-c80Ul$8wAhV!UDD- z8@v5h_IquMd+p;JZH2wonZwq+EYHjZZ*78%8LQm5)|upBYPWm}M<;;O4k|c|X%DX_W%e@$lBz&G>eOFgB1hQwmrAg2PJA5_IAQX{bAe$d@0!5%7JqNWP@RUna)r6_ZNK3gA=UMqurBxXL$?xqO5D<>LhQIOxFGKuzCG1;qyHplXiRo&u5;hdKJz!th+~5uY6q1=rhD@7dK_%0@Cyr9DUuqC zMcVarnk(E* z=b>is{blh9vv{JNz2M4QGw?K@y`R?K&Fsn096|A*Li(6Q=^{jX&&Jbjn>ytc%*q`ttbrvhkRIMba8G$`eh9k_C1I@N5 z_CT|3>@9fGq-fgi9g}U)8@P%Km$o>;O9&l)(d4u$*?|w$gJAI-cUF>{N9fT1)6OoS3x<9;=}0Xa(5R2QwUfi+oFHePm)yyIZF!T zi&H31Y?9~K^`&u%OX(8l5&g1YbQ<_N>3-u&x=8S*10Z?!bO!M*VjRvVg7t3D_K?w$ou7-CDmOFG!D2 zKY?j2pItEPS_j}5^0A6=F&q9Y7z(Ji)hS)sv#l5wqfaSxr>UGzi9iMmmfn|L#bN`7 z-oY+45$+a0O^?Zo>~wq?F6CDzdJd?DYubm;zBc(AoLh&`ZP0m{43#unAokrE=rX|apX!yV6RXm zHqCxn5s-FF84l>}U;fu?>i~Vltm>F<6x2luJkxbd7ah<`0{6fs>X2dXlpFiU)hCr; zssJj_J;}M2Qp&-i%$Y8taC@2>8U@N=-3|Hq$#}`l31ai|GPU|j#EZp8n?t!o{l5ysj z9ehMAusoYvr@K0NK|WRl&`*=Lu`R|wy5VIWS6Erf9@P~vhMfYZzq{{lG zmI8|*Ye%UJwJ*o~I(-46Z+eLD)cqYQ^3afT2y=?%(u6C2_>O1TwwW;{e&GU{bA#O{ z?(+ha*@r$;kJ1zrE)tf;iXOGdI1d@){<6q85E%n5w8B#(B1%kHl@_zeI1(A-8b+y6 zH6rR6=(zzhNF=B;doA*%LOcPXo9kD?Fr`|7aDQ;touE zbZ@amwp^nGG)WbhH49E*2BtZHYq!C+*rHjk(F5-6{DS1ixYDJ@CMfVJQeDI0n?%Nc z?d;j3VygV9X~>`Y<=!a5JT9Mv=8h8TvGSjZm!kMNVa`Vik_yEP850b3S-^er z*D0qFY3ascq-zq|ywotjj^M4@RhaJ)J{W$-g+0soi$ zb8ycBK6=g-@)gnb=RZE=uakd*dp8Y#1O4~$pi`ghUG`9NfYG-S9;)aj)4uXpq?5mR zz9_SavIR@SqLec@ZL<_TmJGj9vhO_kD|(`DmLz{L8OLE9dx9ky*sd1EY2r9O5B!)l2E={y6-`C5v64Z6JrctK6*wELeq*8fYPb&B zK!-GA9n7A(*x)6%RUPKwWf-zO$K~K<8nQhbP-qLh;PHPNZa;}aHvEV-2@KtMMSp-0 z8Z(gLP_r1(F%=|`f0Nn-n39mGSo|999~)kPI;O9I&id{|E(w4UdIR5aPQ2&nJ(P#Z zTFT#Z*@0qgn2Fr7d`W3`X@cQDSR2_%5RBy6PGRb@zk0mr>@x8}*qrFf%!2&E=I#Lk3lk)_38Pp5m3bLn%+327?D)>g9z(ujxJu%}Hz+yib___+0PHgXmEM z;edKuVPKqySUEUzI0xoQaQRZ9mAOk4%v%wKma{Ng{Gi8MYqih@!I#|E*!8wC+1B^H zhXd6g@>rCBQVhc5X9k1bLc*RI(|MblNmyg*4G!^DV4buEyWsp_sRP72*N z!TDa$?2whKnFhG$d-0dll(_tLcwJN~?$*AdkNP#4#t`7%qxf^DxY75}%SC~9h3*+| zRFBQ(SpnrlIzY#r;^(8q$g~#^xE|8VvOCT&xldjtzxuD@c)jQQ6aFR+8f?E!ht*jP z8THgJfd3jf_QnV}4`Sn!iJxKoC6)gitwm|!g-X_KUJC96CciQFqYqt3ZEuw>Zy#q? z&syJF!|J3ZJLB8efP^e3?$hg_+;eHL=7dr{d%hoIhPnqI9^3tt<E z@}v0G3yeWT2ko2}jo_c|OZ$r`#n)4-auNc*7pE-0*@kty=SwvN+JxMSi$>b1-0d4| zbp`dIH>gjS=v7pOE@GjiV1#-OR!gDMw@>JbwSvdC8Jx4nnxu3-<7zumhTZM_NE_|E z2;Kb(ytP{b;BlQKNwQV>6Jld>TOWkgDBoWmF01~Cn5>;`j0G{{ww2jrEUlbBA)5CX zLv69@T8k*GyS-XjrMF$wHE8WPWCs45e>+br6soD0)#vipL&t5k#e7`9TG7h)9GYp9 zD_*M`z(>f{5NjX|)r!JW6>U^w;<($I4qi>v;CY(s5^G)q+gZ9=-_9S&3yA*<^-3vt zdmYWYv%)ru6c>N*3_Jch1n;`9P4oFYS=_Z!rfp?*m2|6RB-Uba`&TEMezw>xnuWsD zplu#O+~?AptSh1}hqc+*@QZid^=!YRm!igX@L7SpHNdiTLk*Me?L-&lQib_k>zE_z zgb1xGpsU&r<t&!{jP@)Y;+u3^3>IPb0X zZBY37kFT9-%8YdU>bqgFLTl)D;@lYLRYGW<-_`ZO4r}YYSJd^!)yKB9Lxh(Bm!4he zN}mR&r(0ZV9%scvIul&&Lx*zToC>a%RpxWdpjuDV$(YF2_Qp~8!^v**OEx^s)@fFH znM!D=f31W`yL~&Y>vXT&@d}paX|IkeT+2Bf>k)&~amwCH0r>Y9OF+u-nAn%%nf0mU zFN*go+3q-9uXehhs|TUjDM_B49fsXKX!72=;Xjale7RX4KtgXfUh+2| zm~TXKAJYwew;aB*(gO*PLcX0}k+Aj{UKe&8O99e%tAKe1NZ?b@DE{xSnV-C~qG;bf z&Bk)~z?-QC75rBH4Hu$apucj~zSJ1N1f)Z*!vcA}mZ9?@cdsS%p*JVRc28b9FcD#( z;p)q`AY*obS7RP@b7Nj9OS-; ze7Dw5tRZ@zbi7({3_QeicfyaisMbS{58L&6KgjLta}Bwo>w9goP&PgFyt#U3lrR5; zja_O!(DX`w5o}vhq;fpm_TNNRFG8Ku`;6`IiNprDl#@Sy?o>=v{nOiPVo}}Rs_&~Z zidVW1mY0C%){|bstv(-8LCWK!FIHBMS@TtNi%Rlu{vOTkEmhrmh1|SNrfBsjIc%>CL^|F~fl5;r@bjx~J5t78`dmBpLfWP+cSa<**zD5b zQoB$BtG%D9*v3EK6qn_dja}24qQ6uWmovnNHOno^8eJe1 zvC}gISd-ono#t*)QK--v6YG6UaW>cRRBQO)RQ1oO^x3M9urdzUdKvH3D+=~>ZAFg# zn3c`_hN7BHPc6j7IA)~IJV}g^=9iZ)rhS0%o17GrUgv|Bvv-HQQT_9JR4IP_ zJ9?RET58bKeVfWwT;qCp>sA}iCh;gc&d`&MHB+2C6T-0e-J9BLkh9Kvw?OfYs8Hq{JC*b;QbZ&?FR_9}@7l64WC)8Jw zT8il{crBi;VA}?;a;*=M-_bfz(Tj%+@cb)mqta7%yFz-D; zr*Xh^w<5Dur&ko-hf+}_U%eR|PJ_vIvQ2hJK3!(xjlQba<6#n@qq=$4`%}%i+U31& z0Lni7V>3YhHDo}Ii~!#saY3P`utH`dOI1yPQc4>8P>X3F=h1ZQ)9QWSx}w(KCrF-A zjqI~$T_ctKkHqWZDR~KRRbd>h98aptJALcJW(flpy{9PO+T&guhMskWflai{6r#Ga zo_!WOEp4wvuELw?dT8IXN>kbZn#Xlz*~5ynJmkuDSsp#E6FWd#O77uIu`9q=YvNd8 ztEaUcQMq&rQmgm8Bes*RUF`XWyjt3S+R_L3w~hL+&s`|b^ddH?xaoxODP51j%k?T< zt+&i?QBvluT*zRjpln80kOHx)%vWR8q2n%-2_EE_Hi5LuIiaRJ_7S0MX?m z!@1?jI=3RW4#QONzdfJcdOc5=H;Kr{W79U0muegXoe$KT;@n$<<#!$SuOZiBA=SQc zX(s2_s1}Y}uhw_C#AH&ZZ)`1bRSQXEr%-``fq_D!M5DRA zxk95kqr?*;YEn*0Zc=9Sc5sQJ@`T-r2*sMim9zM`U6u>MA~OD6tOxXbnFaZ*uD5_+ zahChdN18k(r}*DIS(pDdP~?64IrVh}9TM(B1}unT4w@ zFR9T|j0~5{JQ>0YXbj}Aq7jm<1?Re8(+;D^=6K+--g{usK77xtaitFo;tdQ_2qR`e zbiiU>L6h@;IRU248lWW@Z?)os&9`(RqFuq!@~=59kC-)6NHSjR)(LD)Jv0W2@CSzL z{h!vTxd38}<&0>3WvettOABhW<+fyXm6sSa=ZC}@Dw5IX<~}uIe!9cXQI<}zw((2y zN5p9=g3;%OJ~d`_##|#nYj5k=+4_nYZMh@n${$4UKT(J1fA@Pv0_%TaHRc3q>5i;J ztukXZykxDR$m+_HuLZWkeq9^RG9)LX;c#62mARfTh)1aG8FC9w>Eeeys3VtdUtC>g=!-R$SQ2aC=n)gHNA=v?$`pQMc9`9*!DeI5pPwOo7lyv-wGPVV~7rto>5eoVyTl77GKvh6a% z=Th9LbT~O>no2V@=}V~vr( z1+zjPK@}u3KE4W78#P$m8uhV1IJ@Y?zL6~B!AZ9obX<+VGC_c{oI1R29UYgF5W@}v z<4t0sNLyl}JSa-(jxxToAW5XUHAB|BcFB^7CCXp1JltE)qjIzPh3cjI|Uwc+mQ02=_-wr>8KVy|5pR zWdS%sD-5RmvuU)vgYgHdO2Sj5;w+=3Q50t}*IY^b3S+VN2z5VHZ+@`&X+dLr6?IY_ z`$p0dW;WZ1xmka>5=}5hnvEh0q+ApM>mD731zt4#;vxr_CyfwGDv-o9 zl1Y!UbFiuy2{Z^J>~G{iYjdiq-Snd)ERvqrdXN83GkvWc=llmoBC$WZT+$d@g`$-) zQX?-^D-MZbNrxZYJq+vHV$F86Y|UD|1iM%S(mT{Ifn89G+M=>VPRzEhWQ9|qQZCrD zZrB)OEa*eLrJyDj}nL|a>%4ep(>F|3xv@CPiyO|3xu9 zWkK80H!%MT#XLBzq5@IOB<;>%3J!5pjX>6aQA`-?)1wlVV)X>4v;%X|95+BAJA*k) zMa5EVudK09av@_$xVWg?sYQPU>$pkZ*P#UfW1NWcRt{DgUl=;nKO&qH=<@9u9RI`X z{x>|MA`B!pXT2IWClbCb^oh-`uYE+IOO*mb&MZ8e;|$YuKa;>_zf%pnqih_bqpFp$ zx;G9Z49uzExygyqiPedNAp0U!;I^Qx`K~SVe19fUqGp=Rl(tM2BEE+b*Q8Cc1&oZ^ zirZPT>t&8GYSC?=Nw12+e}Cbt{6Sod@TmcePMuAFyy!8fehW zO?AfH9a!_-+Fdw%nwNQ4bLs&NO*H#h1KbX`zs!zPFQbfq?FE4>W+H3u3>_^FsyVN5 zACE5^$YKbn3RI#`zuM~Q0$&812wph}^<4hTVhHDSf`Q}Fs;&q5#y4OY(eMHc_`Vdx z{`^d^izSsZ6hROHO@Tb^2oRc-jS`G61ZzV>u$V+XFK^E7nzj&H(hbA%1ZnbMQz?dA zLq2P;GgDvbKl~Mg2)!6|CVbYY2d32T_1nUpoANywbf$dPxVNX4ffoXwn_~Hys<}8e zQV`i)?AejJ)-cIdr8jBK5mx%>@NFeN1MEcEs&s8B=hl=JiApHqg6bko7D?o?MA_D! z-H|KVfgI*;rYqVbFy9Mn^>0@{!8QbA<~}~p1^`|u28Jjm-FX^z3b!=eTvvf5iUPfPTqRuWJIyzNY|i`(VsBG;k~!43a9E=vTVQZ9&9{h`OUDOq?SvK`vw* z)xfPX=Kc5^Tg9352(aPC%-Sn?R4H=Cf^i`Pn^XQ z@9YIc-dZ%14c9(>Y48+Y<2z%cM#?Uh)K|x(%1t$rgIU?4?yk0$3pojY;h)7krhk=- z1uH1)v969PrT`uJ;H-xu(ds}J%f@ISo}i8K0%w|z;DrzxfH#ngoS7saf{^WMPd%*iC@ z)I^Rv!)jv3il5(*o+~D}rL5zs5BwgcZ=n8BBQ88#BZff*E2gSDWbGNT`c~M$Z9hnW zU{k;|P+6D`MIU8@dT3qfp!{PmA*@xeG-QBVp31FM`3!~XF){`6WJ7#42w~0+^5Zuv z$Dj+0T?11u+4KO?tsRwD1B^fM$3WRjnj&=1Eu9ex`%rs`Ntt<#@r9Co)x^3L81e zOqjLqKVgBzt6yqaI4H*Eic&f7JO$$2W`2t7>Y`mN59u9|mK&HmG{$r>C=@sl@Vj_q z37paii%LNd5->z@z>*tNsRa?Ypb_)sfw(&G@F7Kt0VCz0A((;Oi2dBgea#F6H9+ZL zXGG}4txT!F#aRj_QK~qK6>H*C<1T&TuwG~4(N$2_1M+5R`LLch%+txA^xbmvBl*0w)#GEhrHX zOwD=yn)5=Q(;_?J)dYA&h#o6Iqc^2Tt~iZPfa$`QqS zF~gB{?Ex?j5ffXDV~DB`?77*cl!ciom*N!If0*vclun|jpDloSR0y3IZabQ9bkiNP zRw9d~#<3(SRvEYa-CHgc(TiOt8wd|MyqAeCsVy~^Bzh;#l0wQS4p~<5S&K^e6 zomql7oqvpVF%k=@`0e@zmJcvP(L@{fuR$U2&`T(i^hb#S?-!LDq))+Si^Ti`rFo*)0ReYL7r}xL6+l_XvD0!4y^oCGlv18D0~ zLKVj0S~tgXM^+Pb`%-LfUYPKhSSS-JA64)>N9hEST&w<>3j}EC#4ztccWxKNK%glu zB`CfV*R0{Rzk=m8BN~4HY3v5xFp=O89tpi`7A6IP56W1SYU+632U7y~0C4F6iqTlG z+!#f6f;`e#Y4`y#?P?JDA-+ z;>dPx!491iLE7PncF(~MUW7sG!awQ@)b5i8WGjVG8}IO$3Od*??~v{!i!!;8UL+7` z%R5fZcNhw)F)?N$@E+2fE#t}5-nW?(QqWTxA_@I{ZF(o)k=6|=!Nq^f-dE4Rz-QeXs) z&*@Ll4Ph10$n6=~h~lb%=iMO%Ut<94TpZu^Y$)z!q`drx&J&5o1n0ttYuQOs-^^h& z*wWwmt-PPmhXAxCL3!VL#boMrD?Cq61Y55ch9`M|SD|V3_fOgllyQ|O%$I1TxygO+ z`z@T0j9BGj(Fo1NiD$K;Xi)U`m$#{|DeK0oYF!{;W|Vr*lqi1gQCy_%BqNELFCk@Y zsTa)B|Kx0H17`!gdES{mx8&Bjcc-y>)>Y&l+&NIO^LeqOo?!n=nG6hWmi2sgdg-gI z_a(4!&@->nM`W{lQ}KO>fQipGBKMIsbLWY6o4lu6f!mA2-Gspocd}M1Gka+ggM)9Y&ViA-@jq%opxS-bH)v~gQS zi`C%pMxo{T!Fqx~AIiMco3~WU<(cqlx37L;@2tg~lLnyVSv<3?apP?7fvHV#B3y+?|di^?o>~gPu^`=Pq^iVrqn&+;6+#_FAfuU@f>e%cNQ^xc<`B&E0 zXja62KCiM_i1$uB@iG?9=WBCsir_XWe>%O?xp}5MGg)(iwO+}=jz$I@r)GKoCua#dhBF-zRMg%DX1mWrnrzz^bHcYH#@qwO=v5OZMB~k;9WrxJSRD2WV#!G;NLp zUy>|0^H-IV*zfm==+?^fCn)1?yO~}OJ9|*Cy_bc+saiKfv~(PZoYQwkFYYHRFVRj1 zbg!pkDcPfF0idw&ukl9Vjk^oKoOqyb!W|tx@y_kr>iQAET$-(hf%A76&;Gy5*CADt zMUONLPxog#d8O}vVW;#nB%jB7jWg7V5|=iB+mDDLe0D_MbvP&!`5HbfH{~kv9>N~R z?0eHs^7<3W;bVCqc%@klzm~-Rba-!N7CNq9x@4k9bT+fESc+e)MHARI%)esv==Jz? zXFr|i(550d%s#_gg?fgX|9h%q?YvuBIqmLLnYf>Gk9EaRQtrlkNFYM6bK5W~rQq6w zsqAT(pJ6xYuKF&_&O!DKlH#{4oq;>8=VSn?rfIe9e(UA7cw4XcF}tdp&riizo=b6? zeNKk3v4`h6Nh_#r?p|I!1=$a{KYwk2a7y#);ZdAnQj zEv?Y6o5#WbkzQV^vVCu=^kuKv|6t#1GJ3)X>oebO=iXxxS)bzPQmT4<)8*qX4MMr= z8Yj`Q?*yxSO~7IkRi15i#1*QhNZy(b(KBFlSAXK)fauNw!UGRD|2!ldT?6$NV)8T9e!pEO284h6=;8i8 z<=qL5xShfv_Km7$OW7e<<1hL884P9L%V0-LoqA{f*T?&)KJfA%1dc9q6S(D|paZ$A z?StRJHJU1Ox-Qu(-wL;IA3XimYntA4+x*Y)cKS0hy&<{RGL*DK%X1ij@&0A0Cn=2E z=Cx4U+ph3f^=hjAnP>=}^?+J_P}w)M*4B4dCQAaxw+AI+H`h+(g<0ZlX1Wag!W6eE zc7DfYGw7A`(l9<;MK#I&uWpVIKL?#F7apB1}>OU+)V z&e_<6V=D z9)uUR3a4N;)#RkA?o=Ul?|tBHrMyz`Em`S^J!L@=IY8nw?S|NX=pS%}gK#r5ocf_M z1b1&|yfICl3?GB%rgoydZnJG{W@mMn=cdE+!CZ*9)!0&{_{a!B;a)Nw1N=*cpH`L? z=f}`FNAmRxub%4->fw9*ma+Mgqm`M`_Cj2Q$3m61<+8OrzeM_9kEB*OhP)Va@Mui0 z?_aF9+_oWmmPn6#scM0?K9Q6PUw3@9{p)2f2&dA{pRo!H8|O#ubg_!&Hru+B%tuOj zirb&PQli(bdi+Pr>MrvzqtiG(;-b$mT~fzse{94N4O=vl34RM zlQqCrZ{*vn9nQfEe`;$F#+HD`pJSk6^qj)h&~q^!+CCJmp8D^fD{V&e-17Zqk{0tjN19vAl*APV!L5@ocOXO#XSr&~nuICI<4M;&p#)ut4~HRsS{LGK9d( zU>vxBb%*_$cTUoc-r5@k*U7SKqpn{n_I~ma8I^T;=roI`XVEKsGS< z@+3}DT))07)~0D5rb701HTtYnbnz|gttvXOXT{4qx2gKiTOObBO6T#Pj8}&_ZLEr3 zbi0au6cv+Fb=Y;ol``kq_7PNkApCR}y1|_&Y|cNPI{R6=Ue+8ZN2@I;`4+m8o%Up! zaU1r}W%@2~&a-HtFR@vXXXSpwAcbgUzEYm9gf^lz*TqLhdI^zRRIZCi;? z%Aq~6v6iaD#;$NyN;v5wtLyp;b(^{#?49n@VQ*`5wGD-j5n99k%MInXbIR&9+}WEy zs2s&uC%1-B8vJJ6r88>PYX!L%vP<5xfp$5!e|OSlNmZ8XY*DoNUF=U?TrXBos$MR) zTN>woFZSX!m-S@bb9^kHJUoXZ-WN`dwJB=#RfR;;mAn9tqy&eV+aWz3Zn2(C_r;28 zr_hgKBfWJJ{1Qq0=cZrHmllC6J}99PjPn_3dbjDx9a7qw(u;LJC2#2 zua5Jh@7r5>(@R!czRlON6_-6zqRKD~@$OysY;vE^AFj#GPLcd7W~}e;KX`T%>81OP zN2KrXx0#x>-X|{zx%cZ=m@n~>EWr#>PSuPwm)j|NW1BC6lEDE= zBr?R#z16Ee@yKY$r?v@y0aszDSo9c2%10z*v*I#y!W*F0y(rhu&o6IhcIPJUCU56v zN8tex9TO257Zn?Qi;?3uEhQ-r=@9>K!e7>_HuM>J-ba64OeLkGEWK#7-Vv%wo-l0MfNEvr^_({j+R#RvBE)**j8Gwi8yTrb zwZOxkKOR)yHk~Zw;pXsDMU9l!!l>Q|qDC3on;HtH0sQnDs%z6p24Cs|APi%S{Jg$^ zQ7k}}W=G%n(~%PwN44IFThBew=`vdYPPe8JZR1h&mz(JQ4d6Us>STb|dE^F-+Q}I^ z;dW!?8|44kSE7H^5(b!S=#l>*hyN%0%KuF*`G1H0|9>ViaVCJ7{ipr^$R)^Vj8{UN zqJq|fG~B@SlVK5*0hl^U)zLt}D6mk@6hKIp&1j^Gv`cE`i@pj!@OjsA*K+9s4O{C< z+f4;TAdHQCdNjJl>u$G@Nm5H|z|{TAGQ*7U)em-f#%so7hVywc`tJyfH9c~BM+7ck z(WrD%oQP>Ts#dU8A1S#Y$a=y3Xn=Tcr&?XzPcqAo`MOulx2KtYZsXg zGKe^}vT9RWHt~-~gw#p}>Oz@{E6)M?)%iAhqUNbZU8t>$n1ZZaweYJ0Zjq;? zH^(lr_;BvhXvo9GizJxbWp*CV)P*7w%D)MRyFWYg20CLlJ$qJE-Yt1gu$^ zBst_(cFJ$96c}x=FIW;~VNE64Dj^}Am0VBC?Kxfkd9>Keb7EcK6uO#7H=p^08P+r8 z3XLSiO$cF8%e_`Ypq=AJ6?!XmtaC6rww)djNA2%QrKG{t~7^_>UR*6)2 z<^U9KuzT>ZcIEL-0m&V-->PJeD)HMBGdwgpOm{T{hQAu!BVVx4s76^Zwfqw=--T~DIUr`(Wp2uP^jl$iIn!x zp7#elo)J+wQuTT?r5Xr6fzV-v8o8Nb^>VD)Mm|Tf5w0;j>gO1ucOOb0$l<&IOxf@6IalL<`j9f@&g4oD!2Lhl|SYCqXp2$`tOmHi_ zN$u-hbC{K&4A55KtXBh}sX@F~8wl_J0uMh70JF(D#)NZoxY1L_wf>-8ZJSo111wNF z(P;Nt0){PUC{0=e$UNVCuyli1442!IjFumPhUty5n(O=m{>AH^j;}_*!bLz-N6Z!C zeiEg=W1*=KY7!N_7jg&_Kl!_$Jvh>Lq}=oQ;WPJW*pp2zoTzanbViD@KR{c&?CHE2 z1KrZpEBY}>&#}aR9oVxuoVO4atFNw*r2Qa=iI(Pbg}h!~>i)ds=FrE_Swr7Utpz`D zVSQo^di)n__+Spl^x-%w)poDThWXl5n}cC}++!6ZE7fPrW0h@sqQzOSCGfr>d#gUT z&hAN-zB|&j(dfWTf8u0wWy(ms{?p6pw7c#*H@eI(ITDDROBlQ=P-R8YATy&{VvbT7 z&3$`4v>2P?_%poW@mcSmQ3@EACh14F&#nvo03m{n$;MsrTH8OO<-<21Vc zwC+AeuXy8@EaMj9C*_nj>2?<~=?f&03sWpz(?tFXPH3wxP8w#}qk>wF3}(DLs3zH` zs9`r*@NVe2n!|s~VBpow*eZqpO3tX3^4Kq6xzc)|;Hc;3TNN@83j-e6wClDEqrKGS zw-e`Fs~pcU{`HQXyvzRe5zvf&{T=H|S_n9fdQD?C1F@f^$oda{_#=?njKqABpVOFc zKk7A$l^n5O^>5AJCr~5zY#7p;%;qyz3)NZJt#JY87rG`o3x9lrdteL}pi(A(vK4Pw ztpUER1)G?sVRoH?lo-X5Qkwm_X4JNUIS0Jz8?GXEJlQAkY9zuI7>hul^*zKn^8Wt@ z4-0Cuh**3o%);{u;lr&B(rlFrLNHQCa2p+R_1o(J<;Q{B{V){#I#tJMT9mkqsdVd- zYHrs3R)IHnOz}@*2%ZI;8Ex|@I)*0w^-dbXd3OA-egpE@JBCmL_)d@;A$jJ=$dTRi zr6AotH;3I3xRInfM0f+;p^a)Obdi^@mT6C zXV2Yd30WXhx6`Gfu}y`+sGa;7yzPcmGk}o`ObsNn;6HYRn@nBZqR{%UU_Wd(MCxo| zYKw2V#G;X-Q9=ESCy3qKG-bJ_S7f_l z3UN!d*wX!bcf}OrmWugvc7KID(J764O^@R479N^v8OAfh4wZG`*e8kNLiGk4?H?Zy zPD{ijBN0GHl;|!*UMO94k6LxFQq`EX{0gUcY{|wYf9&d;xaoRZ~c&m(L6m$cFwtr=Sc%mA7=5oCz_16rr_D`geL}rLDGUL1z%a<4#kslR@s#^fBezR zdH$cTgm~*}>nux~zBb7JMILr>cLB$j9lQ?y)n^|A>^>XP6q{*$%l?Ca7JNa~L0jHci@A*$CWv>_m}>p$ z!%rrR$D-8>0*(}rzV#&y-x0Qy<6VuM*r_M_04L`(JjkuaX*~mOZiJcXkw%QW^t@l< z!ASgUw~F;)u@S>;ii)G+!^nKpTgQb3KZRm%74JUrVJ1FmzJtO&5Zkp?zd42W#_5#lRNh~L2hLJAwW`Bx@PTO7UCpqyBOnxGjhi?7n z;=bu($8vRS{tiukf<7mu))HtFr9XqBosoTw$D;T+6M;%`NfE&LwaLDT zlRhd{?<^}1m^JP+XHZv+2k_X)9lpBnPl4df4aSRo$X zcLe-k{=m5F37m%N)Qav0Jlvnd$K@p%{k=!z9mjVV_<-OtCMHMHVImK;#*)3;*bDJ{ zM;_wOU_bNH^#0=x=Lr2+AgHLR`g$UR{%$(j31n!L7yJ)+Sl``q&$^YsUv4RX>q!`_ z6tw4$J#8PH>^q7aGv?2JD!i@Nzavd9pO!OuW^Owzp7lOCTVado^4 ze^ww@?D*MP*oTObS-scD9ARz7%nLI```CQXf({Mq09y$9Sx%wV#TI{0@NZtbpjzKT z^SqobQ~)dJX!yDQN69vxe;6RA_|xM~!Pg6%k<=08LibBgAcG2d9MYP?{?thk9p69B9d;WbW_9W%>Uq6S(k#x7i=DeMmp!FA9jWY zePJyvD$P31{N>jv7<S4MH|$y#i;o`Sqc0;X!zs^Z z(^{qK31CFfiGFU%*@s^mQF$4=ir1c4H0%aS197Tvj`Af=9E+DcHYM4t^E{W-L=Hc@ zOr63A7mRfeeny!8xs;7S@8+zSA=sqrL~h&_=Dhpsk8Pu&f2rk2z(0EQB{`_q~oSH)!o7pfcvSqi}aJzdWN-AuZ z)yO$1^0k8%zxGuUk9+$X<7f+DOfc3ZUm*9w8GBjgm)Bp|5~=Clmdb*UGI?d1_=|K! z?xeleVJR<;HO7E3)h=Exb1u->u1k2-*Olzd&U!H7dxV#imaWsFDY z*tYuoge38}J9Xpke!>|WK(X(5Q$de=CBRUEgOM>YSw3s*uSAdUHGQO%Hh>;g8k^g! z{ZtZ=i}bPm_xuh$>SkJ*Q}Ur8F#W)O$ywj&m-W8u*F3ew(C3YhOQsp1$2_;aeJ>gI z#x+JEquQ-6LK&(04X>r%Qnd}=gt!ct%nK@OPCO5YSyD0yWElAKGZE(2AA^ZSq&zrS zGBRXubQ@(4dPDA&*T5$#3hodTqLcfWAZE zP}mA^{EhpbyM7K*{xcAD-}Bnelj>;=7-sMBK`mPk?8k4Q9!#L;whiEH@VssDFQm4_ z#J*j{_{$4qWaN+cnPCkp5jA-&zN_r+TS@A~a)nQKry0--Urz2ecnR|*!}*yfzUnH^_TgDX z(GVEdHNuqh84`fG|goBk|N63zi&EMj&Bg=@d#l}%ltFP-&!Zpun()yyvX)@mNMg&c`?kO*i`l~*LlsuX(|DUde zl`I0@XYXt10%CK8cUI=}u>5*A*!^O(Aqw6Yf2Le6p+))_VmTF*bN=_6H&|uPdRxOL zwZ+a5_m>=Z8NQ_D^4Ao6``8*nu{Tq3(zLTMaU=knw5Fo$%#|EL61jgu=&vvVna0@4|NYMg;`6+f_-1py$B$c$SdkEmr)Udhx28|{m^;C z<0=jfb@(m2+~1iq6+sznrOhLELo!8IFWtY^q4bEKOPkUkwu*T59t~?Ne_p|ifGNLU zKK^m-b&m$N;`?~`=XG|m>1Zm5S9LhGmN|W!?#0O4>Wqt7)FQ1d;^pX*kDf0ZRg^9u zwQAX8?RwNl+^i?nu^(4+Wi8u3+s1YO*imkJ7vu|(;V(DbrX-f4duMyxtHLi@0Q2~t zkSLl>Hx5Fl0x0c{ng{-Z?6M~P6-=T}IOKP;<9d%I&0-CofysCWH(y8G2Txoay+!v7}wqZY!iFKqvDUhJpPS{ zcr;KZ+l*xF?Y)z)IGTI=K`IV@OI)~Fgyjg${DoH6zj_z?Bn^ws8&W56}#xk zNT$Sl|HZ9D2s*!CX^mleW{x+v=h;%HW>3&omtEua@C)~L?hmNeoR2O&4}|n+i+h7Z=8G2l-|eRW(6S6mb)T5 zE^eGJtz{q6FCobj3$N+t0=7RcaqAJ%-TEhOdU+nIq*)!WPP^r#tz$A{r)RfPqPI=y zb2Ekcs2G@^drY^yJ>1~xn`4P9E+Zh08Yv<~bM&_vw<+-ePj1SE?ec8tk9Km&?Z*Xa zC>VajJi8+n&&MkMoD^@1>+ka0q$Rj&*OE8f#$Po)ow?gO`ho|D#u&ji9M& zx7dn3TgqJ8>ejx#4^~GPWtB(u=UaH36O~afcQ2!8$C=v{PTO9TS2y#z_Z8LRy_)(y z(GvOC#82QCWO9ok=kq;X@{_TX?{8yC+(#tQ^VeVA@;0j+bhPTXBgzFQC;f`LZajoT zh10sdjNeGNQlHn_3;77)f7doN7W>7Ao_5jr?K$WD_8m(@o6d$mM8n=VM+j&e-@1O| z!(997-o30mg?(>-b)w(0pWGw`Gx+!J00y`JJfCZl)h1=mb!23c^!PY_q7qr{Z*&;E zo>}RtORAlwJWBzFekD7kf2?iR7{4xorjIzp&%h4q2b}+5ugyD z;Su12*$w@He(2Uj`ThO=yM}_hME+kuUxtD4Dbh6vtA5jU-SsIae!0ua?)0);7FQSF zAob!XIjpgDf*I@ky@i_cJ(s&d5PnE(4%uE@rCGVXyeT&hGRXO5dx*hc0EAWdr`B_!Rz;KWu9%z z-4CyFl4vrhmaZit9GoNaFs?d?wWZo9#a}LfGpMP~u4OobeN>V*Gz@)dv_(VLCws^V z(!vnWCQ|i+Hbs~R##&#yiqBWhh%>0MZn}AXLX$Ss0{x2akM}Eg_#xut0QC}EUqp7_ zo|T!`Z$m~bbC4yfo!-Dt*K}Nh{k=Obip|1RLK-FZ&LMm>DW{z(9X7Wp3xC;wvt7~}tjS*SR<{$JQ+oFB8v zf7<^;K^fA5a+gtm&(*1qrh_C4YbLcIMIJ(KuOC7tB2Sr?n_BtYe@XF~uzMr`#HTT-S&$<8d zz4n^!X1AnL4S);5W`#}mMTWD5J5{qRpyUASoUegooC}yYO%)aI5k4U|@vvDfARZEf zX`oyS!sH^43c#L*l3SA@TG&3|GuArr5~th%TtX&=84!O!U3Z+^SFS|%nOrS z1q=SD5iif6oe*C{rVT2nGR)C;IOY^~+O!PG;f#nJ0j@f!s$l8rCEy^H0bCIy z!&oFzt0bh32sHF=ZYN2{TUuuI3WJ`={3v!`03~42jAs`EeDVvuPG$|#B zoXrZpkP@IwjH(bisRnvh0*dDE$p=LfM48tE#WG9f0tQzuR8}zyNyAbCEl-&j4#m=J z)_$^eufsM+ByQR{ch#6rIbmV=*QJwrohYVZv_0@&6d|n>xrG{JhIpEF;VM`ug}j3Y zYO8p>U}O@ZGsuReTR}^lsbW8Dx1%=qB;tN@(HHB->M zy;MO`MS&ny#NRCun^cfu3&x=FSb4@sCnmtzQ*dKR=+;*vFilW)l+Xzi$uxnbqZf)t zo*j-{OTkg1nK+4?69b^JgZZgIjDu6IwOh*ts!b}&r5!YBrLr*;u=m(njpMZ7Z4jTJTPDGLmQCsioU zgvC%Mqp72qlzqZQh&R&*P3w6zq9}|c%k?2DcaL3+&^>Vrs#3%ZX`9+M3v7QY{SNRA z1AM~t`#~~~V}S3i`E&6ovei*SvjmuwV{KaaxzaMMG-lH`{Tg7yg3SN{&+J1KV%+;> zg%11Z(O@!OEH&Xa<||$l_lQs^qJjahBzb%T*@*PWbL6x1flCOjIIlurRU@69tn-u_l9Te8N#_ z(jlDb(lT7>6f2y`v`d64o=CAPH<_{_K;(8JiBceCp*Dc*;;l-^AUY0QYV4l`l*hCg zaDfO9nWDT*+{sa6IKL%*OUE4@q6ESg4_bcs2**nDae}jGrl{J*(ysduq)hz4L(lpA+R5&(Ug z1#qIt;tbfrOk!joDoR?#Tf4&JJE1b7gvfoqAHk20I?JK8! z#9gnn7TwNOw!5Wc>-rkayMty*o@ER`@KEk=cLhh-66&924N#^IUeQB0L!5!fQZs!> z*T4>_OQV#m@mH8O5>4UZNU0-0OJ!nM!MytvjJll+en`9#7M>hZ4II%s!k~*wLUnY% zDq>a2T9n~AbWlVoP1BXOxjy%Qm0l|eq(gZNsqj+vzN!99#>ztTOq(j8Cr_DyW~ zJgrHTSLAZ~{VL|35pHs zO_dw1Lr}LnsICKUKyojwz&-5#^EtE#yd-pNI2`~korb8Is8JRU;i2gSoI-5x9o$oH z23`ibzWnN~e2bes6EONoAPm_ZWUQ(Q@M;S^1k`>6=e}6-n|YldTCFR{3$QOyqJ9cr zTuViv1!S(ed+$y}s-Eq_U=(bb_>L-r9IZ=tuarylGAY-l>uH2~#~GQ|WTOo72E2?% zX&4hrN5*Fga*#v}RT4!3hzu{6{ciz>)^RJC2+|FfCw1-eHXvW|p&m`hFG#{Cx&}{N zHeXQ(?SJMsIXm#d2X?QFw0tU8a(m}(<*LPi=O>QJ$UvN~icA!0gYHX+Rp$%xoXe^m!8H6YZXstt8j zq9y1kJn)xZv^X6P4N8RhQ|4Z*UB;+81tr*KHV0b=V{6@pqpRfnQ6z^qX+GPnUzqO=JK z)gn<_ARmF?dQ;WvJF*C(<5mxn`=}!-tgNJ*O<}}iSF*rs5xBEh-!)mM8*BMQ^{-Y) z)EQ=!UVuQXg3&g!_OGjH+s#+ghsq*&n8DH%GG3426Uj4~QJ8wf+ICQ*YWQBxPI(h#fS5Ua9_ z(%8hR_xx=%TxfO1IG&s{WZC`EHQp~bv~VVyvNRIg?V0Td1^eEkAug)=1OkFW5T=XI zmz(8#qa-;Ii!VPXO$>8dDqag9^n{;Fua-dV20{>E0EaI+afFsBIVlFsAF<$oWF1Xp z!Hu#)ogo69$u~)*!p<3l%qcH&bN`h?qQFam*vk^IEkSHy^is_HA$EF$Gj)SAc!M)> z!_55gm+>RV?1lH|GWPa8{mlu(aC|-94#~WOC z;qq_sUaW&&fi6)t9VxaCw)Gosn?Y+=9K8{?5Bl{R&z9Zil@RtiIC~Qu-&|XFl=U04 zmR&lI(!J6eTE+T41P41<;+e4sP*F`MPMaIy4XlX9zmBItE>0YL@26C~tPP3dXR;xE1fc%fMel6FJMh9gHSqrYSik^A&@VH{4P(d+b4W}MIDbMpVZVJA z&eAv6tFny1v7CFX=tl=S){}sMqL4G(m z=TdMI#WGP;D`yVYgP3hX! z%&kPstz^utgUriCwQ#cw0kAuX$!6Ek>(-5KkXgJ!nY>Dw-6ZMVDCynIrF>kw*CeD4 zqB!1CnfgBO|7H%IUi5qVD6533{tnP*ELzB6m@*EDsT+}`%ootc3@R=~ySN=UQJ(i( zDx%MTiD}BqMK7kR6x^x=|F`#e;rpLVK-S0U6PD3$qp@_y4Slds^^WucLbS4j?R`z zg>>}KEhq#5kG#O4_16ypiOyDGVTnia$VTx5mX0vgZ72*87|w#pc~t_9pok}kkQSat zpM1ZBff<5BEtyM7u+8x!s)PsEjni>aW(xm(3lytrHU zTLFxl)jAl26@dt(3YwOOf5Sv>3-95}rvO)%p(Up%b&E|9C}|~y;K07HU*>O5c?geu zpS#^x0gJlq*3ON6u6?&69G}Gw)=(6I2Y^h#hs|LLiD@3+cb@urk^q0_KqEd$m>_Sz~1ZgTFF z_0tC7g1y8R6E9@5_7q1w1^x_yzVjnRIQU?QHGszPi-J=S%B-=oW*IXSSwuNsg!UcejK7OvBjuVDB2~zH9tf1G7CU ztzJhbLhs&Sj;S~U%OSIHLehR^4Taq}TsId{je5Gr1&Vh2LBFrZm&6FZKHVrBmGtxm z_fzqW|JUmT+r_qL<5scz)K&yc_{7@GnGV~_>@AOcZH!iLulLh<(gOS^#0}c}JFZ2~ z`*oE62iN*&+x(#q_37*7>pBtll1C|=?CjcNMNLL7c1}_AvC8KA=eu$`0XfyX8vZKI z1ozc7-P-N0z}I0G!ISK?6lG+tH^YUej?3zmF#EW=+G(tJeZNO7Wrm%%@0wW;j~pJdy@&4D)=96d&t&H7Fd&RW}*T2YX95`wzr#J%uc-tYP&uw zF*|D0?6DM^UWF=+dYnh9O6?&$UhxE{><+hX?`pDwMbH2J?2K=*oS-LteG$x7!(@(4 zzG)0-VXwr|ncS9p=PfdcN}b7u_Q9&>)ARlu3Glj3x*43@&(T2MJ!ELrZUa2Y^L^Fq zsj#s=-(}CFn9J1e?*j;u>iS)#C+zjNcnhWZ8NS;ezXAsF;y>|Nhs(RWKN?mEqB+}! zCTew2@j6!a4`;xuH&#sYW985Bxf$73MgVWeB_Ora_qRXm!0s`8g6+TVZGCeS6El(= z@$lG5H~LXug+92)`xxZ9SDCBbK@4$R(eR-X`PIh5$;?DFgSA&{%`A7( zY|6gt!OKLo>DUq9!W0L+RU|KSOLMI4xqupMlka6=?krE(_gJ_M{_IAm`rd-I;o#VQ zUmer;6}?C7r+M3<5rbZB)A!{P<9D!uOx?_ za9XJV*NiK%-+;fp$A!DO24(jWPl_wcCYCocDcr}!`+9m@yoRQwmSo;tZffDxpPu#uj@RsmkBEY;8iX;C_{~5J7nFpj$>hDxwq+Hym5o) zYwM;mck4I$y>wsM|wh$W}}+t ztMWNFsY%|`OAbWHrRMQI&ej^r_OEJVr&Tkzd8LJ|=f=vp2|i!JeIV9Eaj#d&OmWqA z|LNM~V(|B2N&>*b*73>bG}+q6`MTMZZtO>(qjoy{#rOMt_%V` zA4b9V^T%Zccdrsg6K4AM@9>x1iR!{$FW9F}@U?Aw?pcwm_OKO#G|fMUj4R98`hqJC zjq|Fv%g`VE2;UZMyM~_xWa`eu+sZr|%f3}8RDk2RU1(H=&*}<>WkIME$ zwz20R!wz*M;2ZEj^G^%BfOf6o46ocArSGlx_tMwwcQZ zE7Kfu*U(xD_d;@$8e*BZ?zPdcQv%Zre=p^k$CYQYJrxibwl=G!x2l2CsRoVgr!dRT zD{zy#*TDC(ND8SVJpF2Z3u&sQ>GrPN(%Nv;m!S+Mv%Ll>2Hv^3+ zFO6byiTtcaOAf?iravU5-EP2N+t=N8kY%Te`{<f! z9Dd2kV)|kL_7u14EqQC7r)-U|etpc&V~iXddYPcpzt!sTH_ZXkh3y*BOL?J;2Cnj!N{HceD&Kv$n9uQhGRp0P@9b|WOYeEG%SN-EZy zICO3P$JT;apw`odjQw+!1&&GHxy+V=`_$I4Yp1=#n-5V7!32hE-)J3d!e#pJKwOIk z+jbLrG+!kdBO4bT8|X*&xUv-=?vFmsbTMM4Z?)e z=ldY0@;C-02~c7tsT?l56dg8)(gIR-bAll#uXjHr&LcMmO4yb&A4|g15Yxe^NC--} z7I7a7mc={Ekl|mbVB8)qDxu8y5wQgZjdn0_#SZ-E(TsW%lYJ@#c764Y z_xIIXnBjgUJjFM_{cP8e%b7i%eAn?|;J~+k=vi%z=fV%uukUYKt)FM7Rzv1b@GPE| z)G*AqT)Q4CeFOa3P6`PvU*G$U0|(~1eC6(y7IJSxO8lxncHR4MM#0OiN@effGV1jB z&l`WVVTEhL#;CNz5pM@B81EETC3pYPWk>2Xo8#5KQ-g>~ebOekA6w?HG6^ky4mz-P z-^!pqrPdExnNfRbK3o?nRM^kS-DZ5bb{`e?7$?O|szC?VknPV;l!2^@lQH)D%8#?b(0p-mFJd^wW!9fN_F8 zI)pLa1dLk^uct=BFZ_w95%`V+7(@;Asc%?~FJv{Y8)}S*YdYrn#XoGyD=jn_=xHjr zDrom!dnE_P$d=XU1{hTjpMIQI(q{VulGV_Zjbc4D7|;H|#w&@)N8oYMxGIY=4z7g$ z6BcuIBWMr>o|q{{kJL~|(NHf&!dkpX--(2i(Hpc_>OOUFI#7heBW+DgS|rWaP~fYx z)DsmAQ$%mea`*$tC;K;c2!KhsQ_p@`hkcz zqOqwO3=~mQKbeBN46}+HHfJ5>M?$I zK#Zr)J7O`Cb2SvEaM;hQx^!<+)$VJv-O2r7aE&J<&#Pp3akcw7?g7*O?G7pxbnlPlMCCjEn2pTh66>EPAu0PZDmuw z068|)a;p9Js=<)@i=7lt*_BnGMta@EBE`t<;ZPcCK11hg!^+@~10T7}Gx5mCA@4tSBbbNpA>gZxH zvb0BU?$cnPkZLJRYFv@iu@5kkW@dI8&8{k+U(jGktBMlCgt0`%SPB@Gr!2a#hc%_| zeGP^fQZ+2N!8*$r#y0TGvRj?g*^!MeR`~z#QPp22HHOLsmJb+TZ;Lz>2ic&rl3iYd zfz_Z5#@ga8hx8E)?VN1e&5Wk_y98)K`!X|+l(=H#lvJf!n&UpAl4Td8t3#JH= zhh-3m5xOX&Obwu*M+sAxX(*5=vxnRt<$|R_wQ63d0nQ zsWQhA;Al|a<6>QCFnR%C)Ygzs*kz@BX$=NagU+k! z4UADs#z+K=A>DkdbOJSKpXb-mV3;yW?yd3 zW$wAgCm1FH#Z=Ge(Y4r#OL(2ciUyH`GbKS*<8@Hu@JQEr`+S z7SQ5ST;+408|PZcDW9YQ4TU3Gm|7!I7Nam6F?nRQywZ=IlX>1n_%cUxLd4*v)l1%s z=7AV7c?rd9gC(>?hXraVOaUb&s-j_%qk`;80c4}urRr_UfE*O$t>zAZ&=OUGm_Ulj zicADWYGtpSGK=*kEnEX4`J$QwhcrY^M+C@GL zfidpMt{7YMg!*G$n!s64n`cqJNwF>Pz1;;K zE*@p%VVMQ4EZx@b-2Waj!!vuivW5dShdv$Hm0;PGK7i5S$3Az)vorgssRly~hYBgi z$dT1(2N;W7f+qQ~!}7431_Qf7<#+~Zz6yCr2Jrxp?q@%AO=qig_Am!PNKCnOw3IPU zLP}5nJ#E#$>{+mru^J3%SQLe6ST4%LQr=LbUUv7sY?Ze9)*3_a3QosBSqVKnwTiYIG_vh)qXfSX{Nj4OQ$rX$H2Btwc0w1&= zGlR`&i9a8jG)q!Ga#k( zoZyC!H591P2?b|Lglx)gpg0thb@muLD|yix3UOA{x?=KVfxNz50#Bk_eCmG!IGB@h zGc+7%R)`wP6B9;`Y)T|xj6d8owl{n7e0G8cLo$U;sE>?cT;nO};lDQ+5=u|E4E$b$ zA*rEE!3hnMJ&6a3A#XaDoXr+Tg)e+;hKrY}~*=mzAn_H5^C{ zy1J=wU^((+Q!0ZTsk3sAwJ}Bo-fwBXX7A?a!uKeu4T_;A)|An4^2oZt@-}kcAF*ky z94GxX941@rH^ypt!*ck^htwlLj%#-phKyqO9IpgxFhn`HaQMpM_!h$PbKjb-OWERZ z3)NttaFDg0o!D7&IE>p{HlKGbwH(x-x3~C5S!2*}sEnGzVUxpQEcWNx)$}S4;h-G# zCTTcuI7pGQA>Ai)7}xCY%vl)xF004+Tn!y1FeL)n67wz2HW0Z?O{PvWroY<0B%U4wzs zLF7==f-!c>YJ3V9z3#U^KLbp0fje2vP1>W7E8nEZ6sm^8FonZK<~Rwi)Ia~%@D<<+ z-3UhdYB*4Zky%k0#cJ%58!Q^sh+6I!{i~q{Syv`hv!(!l>`rvBYE+ih2m&?wZR>p9 z6^`EMrsGnGh69Z(QG<)%Bu`i|{ADqW*E19?F*JAw zyDvUCLW6s(q8DUSMt~_k z(V;b$vV-#DS`CFGreKOXvL{A~hBUZcqyt+Uzocp?kQUSvl~9u>jbt@)K#d6#j*Sa~ z7Nvu-_M(OZWrKL4))cIUuS`+Y*amJ|_FER4(2xJrP~a{n7^=NYYLu4uft$dUj+MHP z^<)#e)5FcO%(WO;jq!4c1_MRwmG@i!$*R$`qJ{!_;$Sx7Wmk;zwC5jB8Z^M*AWPep zcQqVhM%BQYT)8iE7iba}ZZpIZnuO7^fGql9t?T1Q_&CCGUg= zLo6_rQIjnerx)r0N3-Zcy(8I8@s^t!4pbe~7PYM4l&+8`_5@(uXzpCj4KV2C&?QCO z`5r|nr5Gw57-OeAvU4CD-XYtow_#l==!wrgXpnJ_H$gy6Lx zYYuXx!iLmGc4Y}*G|$Ps5Xe5Lai^^Y1BaBBhtdPvGFX;lC!{0k?|++*VfQ|ZBQ+eT zNhyX(hsl;Xay)i|9{Fv;Z;ocyx4Gl2IcQ2*UpB~k7>5b-#~%JN3iP1&`%GVK%|YU! zTrtUEahPD71alfyt#AQb>4U%1V2DG@c34T-78`5^oxfe(U@epfy;^?rpoT+^smceA zsi(Xd916C$h5Znc2JJyhti)9fhp3101!Dxt=~xZ6yc*kQZxUPS?eaAkCOPa6sg*4l zqmS&0F)X{|B7f@zYS6PjLwt(ym5zp1KP(vIJ6R3m#C7FjISFOhS?LgHjX`Ebt#77T z87o(5KfsuD`My^WTOGHWT4PW(lqn{TJei{baKt9Ze;vcFaLc-CIK&l>)nm2HkqjKy z2FAG>hYECsiyLaqL2O~=7%GP}7ee|^?J})gSvkhf)L`IQacnzSR+b|Jbr1|yV5W3zmgRUKFfs>u_o&6*!_ae&21ArXr324wimZllYBJ#OUH^IV ztOhw&@cLPUfz$vZyOV;f92O@hPe3}}8{k|glU?8j+_&bSL(8uB7vv7x4jh}uU#?S^ zEssNPFIiS8)q~|&Cv(ID$ArtNaW~k7c1$@92aX37gj?Z8%N&WoG1enDZy0dUb5L2q z8V*!qq&zr^Niv0T%VMR6nfE_tccrI0YAD17j!o$-d1#GO*KX-Ck2|yL{n{uE2Udej z3pcbD=dO*;rhs>-MR}tUP(OFjV{V+l-5@ z&|rvasBoC7^iw$<2EdU?gEwuS>07^zXU zs$dF>gV{=;MqH<)sqsKTPu6C9prJsf5DKonM#@``9iT<(9@hcJL#p&>p_iUw@Y8F!nRlcBMxBLx0f;n z56W=alLVkRRlD|HU$!<%T+>jTec|Ojk4je_=F+j z_VcV&H8=jq7Du431043QaM}1p&c=CAqi1NZEj`)u3!hcfP~hUQr%+XHnv`_e6mP&t ztlg><|5046LtX$wd4eO_S)Nzp!ds~a4_nR$OXwbOPq>D{q58rY79V930m&#=Z`j^t z?9Tk?ml_Q038@XW=`op7T+YT#kmLAUZ7Qt=Ip|*W+8hlBsxNxD#D?Q{IUGifK5P>) z{T-IWV~sTjome$0COs^A)E)H5UJ^7R7n*}EvOD%`I7DB#ls1u5Is)YAk>A_1uptLo zWGi3PV4##zTiAG5+|pslu_N+G#d|)$K`xgkJZSS&t~-SaqsO{SVj* z^L8)J7dqk~J217Zm@o=tj5x5RdGz2%KCsHtY5CP(gMrsKvgu%LiI%6f2FP)xec36S zARhF{ZD2hO2bx+U2kXjQSq|eStg6NCx*ueRc3B5&44Mws7K@v(=!@n6==lM4W|W3Q ev_+K%?sWF!4~jY)zbPL6IvZ-U Date: Mon, 1 Apr 2024 21:28:22 -0700 Subject: [PATCH 41/43] run --- run_experiment_baybe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/run_experiment_baybe.py b/run_experiment_baybe.py index c3dab96..5866017 100644 --- a/run_experiment_baybe.py +++ b/run_experiment_baybe.py @@ -97,7 +97,7 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool): reccs = optimization_campaign.recommend(ITERATION_BATCH_SIZE) y_vals = problem.y(reccs.to_numpy()) - y_real = problem.f(reccs.to_numpy()) + y_real.append(problem.f(reccs.to_numpy())) reccs.insert(N_DIMS_SCHWEF, 'schwefel', y_vals) From 0c8c41b98563000709505cd48e5eadd2b5dc989f Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Tue, 2 Apr 2024 18:12:32 -0700 Subject: [PATCH 42/43] work on 2 fronts: 1. Clean up BayBE workflow into a single standalone notebook 2. Correct range error/add options to set optimization parameter space bounds --- noisy_optimization_BayBE.ipynb | 1249 ++++++++++++++++++++++++++ run_experiment_baybe.py | 31 +- run_experiment_random_baybe.py | 160 ++++ run_grid_experiments_baybe.py | 60 ++ run_grid_experiments_baybe_random.py | 60 ++ src/schwefel.py | 2 +- 6 files changed, 1556 insertions(+), 6 deletions(-) create mode 100644 noisy_optimization_BayBE.ipynb create mode 100644 run_experiment_random_baybe.py create mode 100644 run_grid_experiments_baybe.py create mode 100644 run_grid_experiments_baybe_random.py diff --git a/noisy_optimization_BayBE.ipynb b/noisy_optimization_BayBE.ipynb new file mode 100644 index 0000000..bc8736c --- /dev/null +++ b/noisy_optimization_BayBE.ipynb @@ -0,0 +1,1249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ddfe121c-9cad-4d3f-b518-e42c72a1b70b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "97c15027-9275-43e5-9613-ecbfe31d4914", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST None\n", + "SMOKE_TEST None\n" + ] + } + ], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from run_grid_experiments_baybe import run_grid_experiments\n", + "from run_grid_experiments_baybe_random import run_grid_experiments_random\n", + "from src import visualization\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from src import schwefel" + ] + }, + { + "cell_type": "markdown", + "id": "5bcc2d3b-c62b-422c-bdee-15eee6f1452c", + "metadata": {}, + "source": [ + "## Intro\n", + "\n", + "This work lightly explores the impact of a noisy oracle on Bayesian optimization performance. Here, the 2-dimensional Schwefel function with Gaussian noise is used as an optimization objective. However, this work is motivated by the need to deal with noisy data when applying BO to experimental optimization. \n", + "\n", + "## Implementation Notes\n", + "\n", + "- This work was done as an entry in the 2024 Bayesian optimization for materials hackathon\n", + "- Our team pursued multiple implementations in parallel\n", + "- This notebook serves as an entry point to the BayBE implementation of the project\n", + "- Individual optimization campaigns are run from the run_experiments() function in run_experiment_babye.py\n", + "- Grid screening of parameters builds on this with funcitonality in the run_grid_experiments_babye.py\n", + "- As this was a hackathon project, there are some hacks. Watch out for hard-coded gotchas throughout. Would not recommend directly re-using code. " + ] + }, + { + "cell_type": "markdown", + "id": "02f05875-7a5c-4f50-8b55-fc0e30340897", + "metadata": {}, + "source": [ + "## 1. Define grid search parameters \n", + "\n", + "This is to run a grid search over experiment parameters like number of BO trials to run, noise level, etc. These values were chosen by the team as 'reasonable' sounding values." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5319bdb1-b6a0-4dfa-b05c-bd854456278d", + "metadata": {}, + "outputs": [], + "source": [ + "seeds = list(range(5)) # run 5 replicates of each parameter set\n", + "n_inits = [2, 4, 8, 10] # Number of initial randomly collected data points\n", + "noise_levels = [1, 5, 10, 20] # variance (?) of gaussian noise. Bigger number -> more noise\n", + "noise_bools = [True] # carryover from BoTorch side of project, ignore\n", + "budget = 30 # Run 30 iterations of BO\n", + "bounds = (420.9687 - 50, 420.9687 + 50)" + ] + }, + { + "cell_type": "markdown", + "id": "abdf100a-84fe-466f-9b60-67d698e7578e", + "metadata": {}, + "source": [ + "## 2. Run grid search\n", + "\n", + "Run the grid search over parameters. This will take a minute or 60. Results are written to disk so if you are just following along, skip this step and load below " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8ffedfe-8639-479f-9cee-63f4a0f32e54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(370.9687, 470.9687)\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 8.28s\n", + "(370.9687, 470.9687)\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 16.15s\n", + "(370.9687, 470.9687)\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 23.46s\n", + "(370.9687, 470.9687)\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Unexpected exception formatting exception. Falling back to standard exception\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/tmp/ipykernel_6877/1632482094.py\", line 1, in \n", + " run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds)\n", + " File \"/home/brendenpelkie/Code/project-project-noisy-nerds/run_grid_experiments_baybe.py\", line 40, in run_grid_experiments\n", + " task = worker(n_init, noise_level, budget, seed, noise_bool, bounds)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/Code/project-project-noisy-nerds/run_grid_experiments_baybe.py\", line 15, in worker\n", + " run_experiment(n_init, noise_level, budget, seed, noise_bool,bounds)\n", + " File \"/home/brendenpelkie/Code/project-project-noisy-nerds/run_experiment_baybe.py\", line 119, in run_experiment\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/campaign.py\", line 298, in recommend\n", + " rec = self.recommender.recommend(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/recommenders/pure/bayesian/base.py\", line 141, in recommend\n", + " return super().recommend(searchspace, batch_size, train_x, train_y)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/recommenders/pure/base.py\", line 43, in recommend\n", + " return self._recommend_continuous(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/recommenders/pure/bayesian/sequential_greedy.py\", line 108, in _recommend_continuous\n", + " points, _ = optimize_acqf(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 563, in optimize_acqf\n", + " return _optimize_acqf(opt_acqf_inputs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 584, in _optimize_acqf\n", + " return _optimize_acqf_batch(opt_inputs=opt_inputs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 349, in _optimize_acqf_batch\n", + " batch_candidates, batch_acq_values, ws = _optimize_batch_candidates()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 333, in _optimize_batch_candidates\n", + " ) = opt_inputs.gen_candidates(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/generation/gen.py\", line 252, in gen_candidates_scipy\n", + " res = minimize_with_timeout(\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/utils/timeout.py\", line 80, in minimize_with_timeout\n", + " return optimize.minimize(\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_minimize.py\", line 713, in minimize\n", + " res = _minimize_lbfgsb(fun, x0, args, jac, bounds,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py\", line 369, in _minimize_lbfgsb\n", + " f, g = func_and_grad(x)\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 297, in fun_and_grad\n", + " self._update_grad()\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 267, in _update_grad\n", + " self._update_grad_impl()\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 175, in update_grad\n", + " self.g = grad_wrapped(self.x)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 172, in grad_wrapped\n", + " return np.atleast_1d(grad(np.copy(x), *args))\n", + " ^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/numpy/lib/function_base.py\", line 873, in copy\n", + " @array_function_dispatch(_copy_dispatcher)\n", + " \n", + "KeyboardInterrupt\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 2144, in showtraceback\n", + " stb = self.InteractiveTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1435, in structured_traceback\n", + " return FormattedTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1326, in structured_traceback\n", + " return VerboseTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1173, in structured_traceback\n", + " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1088, in format_exception_as_a_whole\n", + " frames.append(self.format_record(record))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 970, in format_record\n", + " frame_info.lines, Colors, self.has_colors, lvals\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 792, in lines\n", + " return self._sd.lines\n", + " ^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/core.py\", line 698, in lines\n", + " pieces = self.included_pieces\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/core.py\", line 649, in included_pieces\n", + " pos = scope_pieces.index(self.executing_piece)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/core.py\", line 628, in executing_piece\n", + " return only(\n", + " ^^^^^\n", + " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/executing/executing.py\", line 164, in only\n", + " raise NotOneValueFound('Expected one value, found 0')\n", + "executing.executing.NotOneValueFound: Expected one value, found 0\n" + ] + } + ], + "source": [ + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds)" + ] + }, + { + "cell_type": "markdown", + "id": "16aeaf0f-14b6-4827-b94a-cb87cd2a4d7c", + "metadata": {}, + "source": [ + "### Run random search as well\n", + "\n", + "Generate some random baseline data to compare against\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5aac4ea7-cb9f-48a4-a874-e4396d8b9e22", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 1 budget 30 seed 0 failed\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 5 budget 30 seed 0 failed\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 10 budget 30 seed 0 failed\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 20 budget 30 seed 0 failed\n", + "Started problem 2 noise 20 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 1 budget 30 seed 0 failed\n", + "Started problem 4 noise 1 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 5 budget 30 seed 0 failed\n", + "Started problem 4 noise 5 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 10 budget 30 seed 0 failed\n", + "Started problem 4 noise 10 budget 30 seed 0, time: 0.00s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 20 budget 30 seed 0 failed\n", + "Started problem 4 noise 20 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 1 budget 30 seed 0 failed\n", + "Started problem 8 noise 1 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 5 budget 30 seed 0 failed\n", + "Started problem 8 noise 5 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 10 budget 30 seed 0 failed\n", + "Started problem 8 noise 10 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 20 budget 30 seed 0 failed\n", + "Started problem 8 noise 20 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 1 budget 30 seed 0 failed\n", + "Started problem 10 noise 1 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 5 budget 30 seed 0 failed\n", + "Started problem 10 noise 5 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 10 budget 30 seed 0 failed\n", + "Started problem 10 noise 10 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 20 budget 30 seed 0 failed\n", + "Started problem 10 noise 20 budget 30 seed 0, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 1 budget 30 seed 1 failed\n", + "Started problem 2 noise 1 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 5 budget 30 seed 1 failed\n", + "Started problem 2 noise 5 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 10 budget 30 seed 1 failed\n", + "Started problem 2 noise 10 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 20 budget 30 seed 1 failed\n", + "Started problem 2 noise 20 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 1 budget 30 seed 1 failed\n", + "Started problem 4 noise 1 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 5 budget 30 seed 1 failed\n", + "Started problem 4 noise 5 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 10 budget 30 seed 1 failed\n", + "Started problem 4 noise 10 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 20 budget 30 seed 1 failed\n", + "Started problem 4 noise 20 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 1 budget 30 seed 1 failed\n", + "Started problem 8 noise 1 budget 30 seed 1, time: 0.01s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 5 budget 30 seed 1 failed\n", + "Started problem 8 noise 5 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 10 budget 30 seed 1 failed\n", + "Started problem 8 noise 10 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 20 budget 30 seed 1 failed\n", + "Started problem 8 noise 20 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 1 budget 30 seed 1 failed\n", + "Started problem 10 noise 1 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 5 budget 30 seed 1 failed\n", + "Started problem 10 noise 5 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 10 budget 30 seed 1 failed\n", + "Started problem 10 noise 10 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 20 budget 30 seed 1 failed\n", + "Started problem 10 noise 20 budget 30 seed 1, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 1 budget 30 seed 2 failed\n", + "Started problem 2 noise 1 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 5 budget 30 seed 2 failed\n", + "Started problem 2 noise 5 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 10 budget 30 seed 2 failed\n", + "Started problem 2 noise 10 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 20 budget 30 seed 2 failed\n", + "Started problem 2 noise 20 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 1 budget 30 seed 2 failed\n", + "Started problem 4 noise 1 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 5 budget 30 seed 2 failed\n", + "Started problem 4 noise 5 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 10 budget 30 seed 2 failed\n", + "Started problem 4 noise 10 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 20 budget 30 seed 2 failed\n", + "Started problem 4 noise 20 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 1 budget 30 seed 2 failed\n", + "Started problem 8 noise 1 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 5 budget 30 seed 2 failed\n", + "Started problem 8 noise 5 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 10 budget 30 seed 2 failed\n", + "Started problem 8 noise 10 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 20 budget 30 seed 2 failed\n", + "Started problem 8 noise 20 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 1 budget 30 seed 2 failed\n", + "Started problem 10 noise 1 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 5 budget 30 seed 2 failed\n", + "Started problem 10 noise 5 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 10 budget 30 seed 2 failed\n", + "Started problem 10 noise 10 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 20 budget 30 seed 2 failed\n", + "Started problem 10 noise 20 budget 30 seed 2, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 1 budget 30 seed 3 failed\n", + "Started problem 2 noise 1 budget 30 seed 3, time: 0.02s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 5 budget 30 seed 3 failed\n", + "Started problem 2 noise 5 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 10 budget 30 seed 3 failed\n", + "Started problem 2 noise 10 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 20 budget 30 seed 3 failed\n", + "Started problem 2 noise 20 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 1 budget 30 seed 3 failed\n", + "Started problem 4 noise 1 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 5 budget 30 seed 3 failed\n", + "Started problem 4 noise 5 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 10 budget 30 seed 3 failed\n", + "Started problem 4 noise 10 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 20 budget 30 seed 3 failed\n", + "Started problem 4 noise 20 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 1 budget 30 seed 3 failed\n", + "Started problem 8 noise 1 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 5 budget 30 seed 3 failed\n", + "Started problem 8 noise 5 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 10 budget 30 seed 3 failed\n", + "Started problem 8 noise 10 budget 30 seed 3, time: 0.03s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 20 budget 30 seed 3 failed\n", + "Started problem 8 noise 20 budget 30 seed 3, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 1 budget 30 seed 3 failed\n", + "Started problem 10 noise 1 budget 30 seed 3, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 5 budget 30 seed 3 failed\n", + "Started problem 10 noise 5 budget 30 seed 3, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 10 budget 30 seed 3 failed\n", + "Started problem 10 noise 10 budget 30 seed 3, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 20 budget 30 seed 3 failed\n", + "Started problem 10 noise 20 budget 30 seed 3, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 1 budget 30 seed 4 failed\n", + "Started problem 2 noise 1 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 5 budget 30 seed 4 failed\n", + "Started problem 2 noise 5 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 10 budget 30 seed 4 failed\n", + "Started problem 2 noise 10 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 2 noise 20 budget 30 seed 4 failed\n", + "Started problem 2 noise 20 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 1 budget 30 seed 4 failed\n", + "Started problem 4 noise 1 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 5 budget 30 seed 4 failed\n", + "Started problem 4 noise 5 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 10 budget 30 seed 4 failed\n", + "Started problem 4 noise 10 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 4 noise 20 budget 30 seed 4 failed\n", + "Started problem 4 noise 20 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 1 budget 30 seed 4 failed\n", + "Started problem 8 noise 1 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 5 budget 30 seed 4 failed\n", + "Started problem 8 noise 5 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 10 budget 30 seed 4 failed\n", + "Started problem 8 noise 10 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 8 noise 20 budget 30 seed 4 failed\n", + "Started problem 8 noise 20 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 1 budget 30 seed 4 failed\n", + "Started problem 10 noise 1 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 5 budget 30 seed 4 failed\n", + "Started problem 10 noise 5 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 10 budget 30 seed 4 failed\n", + "Started problem 10 noise 10 budget 30 seed 4, time: 0.04s\n", + "only one element tensors can be converted to Python scalars\n", + "problem 10 noise 20 budget 30 seed 4 failed\n", + "Started problem 10 noise 20 budget 30 seed 4, time: 0.04s\n", + "all experiments done, time: 0.04s\n" + ] + } + ], + "source": [ + "run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds)" + ] + }, + { + "cell_type": "markdown", + "id": "34ac9617-f371-40b1-b1a1-5203d84b1a65", + "metadata": {}, + "source": [ + "## 3. Process Results" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ce441669-a569-468f-a482-fec91efef95f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_6054/3776676619.py:17: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
0210True767.079651
0211True767.079651
0212True767.079651
0213True767.079651
0214True767.079651
..................
010200True767.079651
010201True767.079651
010202True767.079651
010203True779.453430
010204True767.079651
\n", + "

80 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 1 0 True 767.079651\n", + "0 2 1 1 True 767.079651\n", + "0 2 1 2 True 767.079651\n", + "0 2 1 3 True 767.079651\n", + "0 2 1 4 True 767.079651\n", + ".. ... ... ... ... ...\n", + "0 10 20 0 True 767.079651\n", + "0 10 20 1 True 767.079651\n", + "0 10 20 2 True 767.079651\n", + "0 10 20 3 True 779.453430\n", + "0 10 20 4 True 767.079651\n", + "\n", + "[80 rows x 5 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load BO results \n", + "sm_list_bo = {}\n", + "bo_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list_bo[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + "bo_results " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "f08502a6-d831-4e88-b4e8-a89ac5f649c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-45.6567, 16.9314],\n", + " [-11.6536, -26.0542],\n", + " [ 35.2966, -27.4438],\n", + " [-42.6366, 22.2730],\n", + " [ 35.2949, 14.1249],\n", + " [ 49.8565, 22.0936],\n", + " [ 4.0415, -8.2770],\n", + " [ -2.4373, -7.2442],\n", + " [ 9.6980, -28.1025],\n", + " [-18.8534, 22.7238],\n", + " [ 36.3961, 41.5238],\n", + " [-24.1650, -10.7899],\n", + " [-45.0575, -28.1582],\n", + " [ 48.0141, -18.1148],\n", + " [ -9.1047, 24.4760],\n", + " [ -2.4491, -24.9724],\n", + " [-40.0584, -35.1531],\n", + " [ 14.2042, -41.7810],\n", + " [ 9.0255, 18.5154],\n", + " [ 28.2799, 4.4241],\n", + " [ 0.3153, 11.6851],\n", + " [-32.4421, 12.8029],\n", + " [ 21.8362, -19.1830],\n", + " [ 38.4819, 26.7066],\n", + " [ 38.3217, 6.8988],\n", + " [-34.9491, 18.5383],\n", + " [-26.3170, 36.3540],\n", + " [ 21.7468, -17.6640],\n", + " [ 14.6553, -16.4636],\n", + " [ 42.6094, 26.6559],\n", + " [ -1.5765, -45.2681],\n", + " [ -7.0089, -1.4098],\n", + " [ 1.6903, -36.2116],\n", + " [ 32.1310, -9.0131],\n", + " [ -9.9266, -25.9650],\n", + " [-19.0032, -35.3150],\n", + " [-46.7624, 15.2622],\n", + " [ 25.8250, -41.4523],\n", + " [ 12.9008, -32.2894],\n", + " [-18.2179, 1.7376]], dtype=torch.float64)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "af01900d-a2bf-4c49-90a0-b3614a59c2fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_6054/1297806956.py:17: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
0210True789.469910
0211True796.131165
0212True802.132568
0213True794.562561
0214True792.798035
..................
010200True789.469910
010201True790.011475
010202True796.409363
010203True794.562561
010204True792.798035
\n", + "

80 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 1 0 True 789.469910\n", + "0 2 1 1 True 796.131165\n", + "0 2 1 2 True 802.132568\n", + "0 2 1 3 True 794.562561\n", + "0 2 1 4 True 792.798035\n", + ".. ... ... ... ... ...\n", + "0 10 20 0 True 789.469910\n", + "0 10 20 1 True 790.011475\n", + "0 10 20 2 True 796.409363\n", + "0 10 20 3 True 794.562561\n", + "0 10 20 4 True 792.798035\n", + "\n", + "[80 rows x 5 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load random results \n", + "sm_list_random = {}\n", + "random_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_random_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list_random[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + "random_results " + ] + }, + { + "cell_type": "markdown", + "id": "a62c64e2-b44c-4cb2-8e3c-4955abb334de", + "metadata": {}, + "source": [ + "Calculate 'performance matrix' to generate iterations vs noise heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f9d3ca6c-22f6-459e-9107-cfa6cc663673", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix_bo = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix_bo[i,j] = best_y\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cd2d4601-b8ee-4d96-8c54-9dad0285151c", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix_random = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results_random_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix_random[i,j] = best_y" + ] + }, + { + "cell_type": "markdown", + "id": "9ba43cdf-7017-4853-a200-945af8e20e61", + "metadata": {}, + "source": [ + "## 4. Plot\n", + "\n", + "### backtesting plots\n", + "\n", + "1. Fix n_init, compare noise level" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "d436acd6-c72c-40cf-b11f-5ff586a63abd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.5455675e-05])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get true minimum\n", + "\n", + "problem = schwefel.SchwefelProblem(n_var=2, noise_level=0)\n", + "\n", + "problem.y(np.array([[420.9687,420.9687]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "8f2b0de9-e2b8-40a6-a910-a4945f06aaba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_init_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_list[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline, noise_level={row['noise_level']}\", linestyle=\"--\")\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(\"BayBE Optimization, 10 initial observations\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('Schwefel function value')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8989570b-6b02-4e6a-99c7-f7ccafb0bb7f", + "metadata": {}, + "source": [ + "2. Fix noise value, compare initial data number" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "409f03ea-c50e-461d-850d-f7723b031f98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "noise_level_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['noise_level'] == noise_level_val:\n", + " mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, n_init={row['n_init']}\")\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['noise_level'] == noise_level_val:\n", + " mean = sm_list[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline, n_init={row['n_init']}\", linestyle=\"--\")\n", + " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(\"BayBE Optimization, 10 initial observations\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('Schwefel function value')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ae3f1ea5-856d-47a3-9010-7a4d4ceb8b1f", + "metadata": {}, + "source": [ + "### Heat map plot" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "e90c4e71-e671-415d-950b-377a463730b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Bayesian Optimization')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_bo)\n", + "\n", + "ax.set_title('Bayesian Optimization')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "5d63c7cd-755c-4fd3-8506-a317bf5bdd43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Random')" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_random)\n", + "ax.set_title('Random')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "caba841d-9015-4097-a8f6-f046370d33d4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_experiment_baybe.py b/run_experiment_baybe.py index 9394d74..98576d7 100644 --- a/run_experiment_baybe.py +++ b/run_experiment_baybe.py @@ -53,22 +53,43 @@ from src.schwefel import SchwefelProblem from time import time -def run_experiment(n_init, noise_level, budget, seed, noise_bool): +def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds): + """ + Run a bayesian optimization campaign on the 2-dimensional + schwefel function using the specified parameters. Uses BayBE with the + SequentialGreedyRecommender with Expected improvement acquisition function. + + :param n_init: Number of randomly selected initial trials to run + :type n_init: int + :param noise_level: Variance of Gaussian noise to add to scwhefel function values + :type noise_level: float + :param budget: Number of optimization trials to run (in addition to n_init) + :type budget: int + :param seed: Random seed + :type seed: int + :param noise_bool: Artifact from Botorch implementation, does nothing + :type noise_bool: bool + :return train_X: Scwhefel X values evaluated + :type train_X: Tensor + :return train_Y: The Schwefel function values associated with train_X points, including noise + :type train_Y: Tensor + :return train_Y_real: The 'true' noise-free Y values + :type train_Y_real: Tensor + """ N_DIMS_SCHWEF = 2 ITERATION_BATCH_SIZE = 1 - torch.manual_seed(seed) np.random.seed(seed) - problem = SchwefelProblem(n_var=N_DIMS_SCHWEF, noise_level=noise_level) + problem = SchwefelProblem(n_var=N_DIMS_SCHWEF, noise_level=noise_level, range = bounds) - bounds = torch.tensor(problem.bounds, **tkwargs) + #bounds = torch.tensor(problem.bounds, **tkwargs) target = NumericalTarget(name = 'schwefel', mode = "MIN") parameters = [ - NumericalContinuousParameter(f'schwefel{i+1}', bounds = (-50,50)) for i in range(N_DIMS_SCHWEF) + NumericalContinuousParameter(f'schwefel{i+1}', bounds = bounds) for i in range(N_DIMS_SCHWEF) ] objective = Objective(mode = "SINGLE", targets = [target]) diff --git a/run_experiment_random_baybe.py b/run_experiment_random_baybe.py new file mode 100644 index 0000000..e0a12a6 --- /dev/null +++ b/run_experiment_random_baybe.py @@ -0,0 +1,160 @@ +# %% +import matplotlib.pyplot as plt +import numpy as np +import torch + +from botorch.models.gp_regression import ( + SingleTaskGP, +) +from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood +from botorch.fit import fit_gpytorch_model +from botorch.models.transforms.outcome import Standardize + +from botorch.optim.optimize import optimize_acqf +from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement +from botorch.sampling.normal import SobolQMCNormalSampler +from botorch.utils.transforms import normalize, unnormalize +import os +import gc + + + +from baybe.targets import NumericalTarget +from baybe.objective import Objective +from baybe.parameters import ( + NumericalContinuousParameter +) + +from baybe.recommenders import ( + SequentialGreedyRecommender, + RandomRecommender +) + +from baybe.searchspace import SearchSpace +from baybe import Campaign +from baybe import simulation + +tkwargs = { + "dtype": torch.double, + "device": torch.device("cuda" if torch.cuda.is_available() else "cpu"), +} +SMOKE_TEST = os.environ.get("SMOKE_TEST") +# SMOKE_TEST = True +print("SMOKE_TEST", SMOKE_TEST) +NUM_RESTARTS = 10 if not SMOKE_TEST else 2 +RAW_SAMPLES = 512 if not SMOKE_TEST else 4 +MC_SAMPLES = 128 if not SMOKE_TEST else 16 +batch_size = 1 + + + +# %% +from botorch.utils.sampling import draw_sobol_samples +from src.schwefel import SchwefelProblem +from time import time + +def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds): + """ + Run a bayesian optimization campaign on the 2-dimensional + schwefel function using the specified parameters. Uses BayBE with the + SequentialGreedyRecommender with Expected improvement acquisition function. + + :param n_init: Number of randomly selected initial trials to run + :type n_init: int + :param noise_level: Variance of Gaussian noise to add to scwhefel function values + :type noise_level: float + :param budget: Number of optimization trials to run (in addition to n_init) + :type budget: int + :param seed: Random seed + :type seed: int + :param noise_bool: Artifact from Botorch implementation, does nothing + :type noise_bool: bool + :return train_X: Scwhefel X values evaluated + :type train_X: Tensor + :return train_Y: The Schwefel function values associated with train_X points, including noise + :type train_Y: Tensor + :return train_Y_real: The 'true' noise-free Y values + :type train_Y_real: Tensor + """ + + N_DIMS_SCHWEF = 2 + ITERATION_BATCH_SIZE = 1 + + + torch.manual_seed(seed) + np.random.seed(seed) + + problem = SchwefelProblem(n_var=N_DIMS_SCHWEF, noise_level=noise_level, range = bounds) + + #bounds = torch.tensor(problem.bounds, **tkwargs) + + target = NumericalTarget(name = 'schwefel', mode = "MIN") + parameters = [ + NumericalContinuousParameter(f'schwefel{i+1}', bounds = bounds) for i in range(N_DIMS_SCHWEF) + ] + + objective = Objective(mode = "SINGLE", targets = [target]) + searchspace = SearchSpace.from_product(parameters) + + + recommender_init = RandomRecommender() + #recommender_main = SequentialGreedyRecommender(acquisition_function_cls='EI') + + + print("Collecting random observations observations") + campaign_init = Campaign(searchspace, objective, recommender_init) + random_params = campaign_init.recommend(n_init + budget) + + y_init = problem.y(random_params.to_numpy()) + y_init_real = problem.f(random_params.to_numpy()) + + random_params.insert(N_DIMS_SCHWEF, 'schwefel', y_init) + + #optimization_campaign = Campaign(searchspace, objective, recommender_main) + campaign_init.add_measurements(random_params) + + #y_real = [] + #print('Beginning optimization campaign') + #for i in range(budget): + # reccs = optimization_campaign.recommend(ITERATION_BATCH_SIZE) + # + # y_vals = problem.y(reccs.to_numpy()) + # y_real.append(problem.f(reccs.to_numpy())) + # + # reccs.insert(N_DIMS_SCHWEF, 'schwefel', y_vals) + # + # optimization_campaign.add_measurements(reccs) + + measurements = campaign_init.measurements + + # get X and noisy y values + x_names = [f'schwefel{i+1}' for i in range(N_DIMS_SCHWEF)] + x_train = measurements[x_names].to_numpy() + y_train = measurements['schwefel'].to_numpy() + + # compile noise-free ground truth vals + y_real_complete = y_init_real + + #for i, val in enumerate(y_init_real): + # y_real_complete[i] = val# + + #for i, val in enumerate(y_real): + # y_real_complete[i+len(y_init_real)] = val + + + + train_X = torch.from_numpy(x_train) + train_Y = torch.from_numpy(y_train) + train_Y_real = torch.from_numpy(y_real_complete) + + os.makedirs('results_random_baybe', exist_ok=True) + fname = f"results_random_baybe/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" + torch.save((train_X, train_Y, train_Y_real, None), fname) + + return train_X, train_Y, train_Y_real, None + + + +if __name__ == "__main__": + run_experiment(5, 0.1, 5, 0, True) + run_experiment(5, 0.1, 5, 0, False) \ No newline at end of file diff --git a/run_grid_experiments_baybe.py b/run_grid_experiments_baybe.py new file mode 100644 index 0000000..0154262 --- /dev/null +++ b/run_grid_experiments_baybe.py @@ -0,0 +1,60 @@ +#import ray +import argparse +from time import time, sleep +from run_experiment_baybe import run_experiment +from datetime import datetime +import gc + +MAX_NUM_PENDING_TASKS = 12 + + +#@ray.remote +def worker(n_init, noise_level, budget, seed, noise_bool, bounds): + + try: + run_experiment(n_init, noise_level, budget, seed, noise_bool,bounds) + # saved file looks like this: results\Schwe_n_init_6_noiselvl_0_budget_0_seed_2_noise_False.pt + except Exception as e: + print(e) + print(f'problem {n_init} noise {noise_level} budget {budget} seed {seed} failed') + return 1 + + return 0 + +def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds): + + # ray.init(local_mode=True) + #ray.init(ignore_reinit_error=True) + start_time = time() + tasks = [] + + for seed in seeds: + for n_init in n_inits: + for noise_level in noise_levels: + for noise_bool in noise_bools: + #if len(tasks) > MAX_NUM_PENDING_TASKS: + # completed_tasks, tasks = ray.wait(tasks, num_returns=1) + # ray.get(completed_tasks[0]) + + #sleep(1) + task = worker(n_init, noise_level, budget, seed, noise_bool, bounds) + tasks.append(task) + print(f'Started problem {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') + #gc.collect() + + # while len(tasks) > 0: + # completed_tasks, tasks = ray.wait(tasks, num_returns=1) + # print(ray.get(completed_tasks[0])) + + + print('all experiments done, time: %.2fs' % (time() - start_time)) + +if __name__ == "__main__": + + seeds = [0] + n_inits = [2, 4, 6 ,8, 10] + noise_levels = [0, 0.01, 0.1, 0.5] + # budgets = [10, 20, 50] + noise_bools = [True, False] + budget = 10 + run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget) diff --git a/run_grid_experiments_baybe_random.py b/run_grid_experiments_baybe_random.py new file mode 100644 index 0000000..aef3e5e --- /dev/null +++ b/run_grid_experiments_baybe_random.py @@ -0,0 +1,60 @@ +#import ray +import argparse +from time import time, sleep +from run_experiment_random_baybe import run_experiment +from datetime import datetime +import gc + +MAX_NUM_PENDING_TASKS = 12 + + +#@ray.remote +def worker(n_init, noise_level, budget, seed, noise_bool, bounds): + + try: + run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds) + # saved file looks like this: results\Schwe_n_init_6_noiselvl_0_budget_0_seed_2_noise_False.pt + except Exception as e: + print(e) + print(f'problem {n_init} noise {noise_level} budget {budget} seed {seed} failed') + return 1 + + return 0 + +def run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds): + + # ray.init(local_mode=True) + #ray.init(ignore_reinit_error=True) + start_time = time() + tasks = [] + + for seed in seeds: + for n_init in n_inits: + for noise_level in noise_levels: + for noise_bool in noise_bools: + #if len(tasks) > MAX_NUM_PENDING_TASKS: + # completed_tasks, tasks = ray.wait(tasks, num_returns=1) + # ray.get(completed_tasks[0]) + + #sleep(1) + task = worker(n_init, noise_level, budget, seed, noise_bool, bounds) + tasks.append(task) + print(f'Started problem {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') + #gc.collect() + + # while len(tasks) > 0: + # completed_tasks, tasks = ray.wait(tasks, num_returns=1) + # print(ray.get(completed_tasks[0])) + + + print('all experiments done, time: %.2fs' % (time() - start_time)) + +if __name__ == "__main__": + + seeds = [0] + n_inits = [2, 4, 6 ,8, 10] + noise_levels = [0, 0.01, 0.1, 0.5] + # budgets = [10, 20, 50] + noise_bools = [True, False] + budget = 10 + run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget) diff --git a/src/schwefel.py b/src/schwefel.py index 2abeacb..5f7a90c 100644 --- a/src/schwefel.py +++ b/src/schwefel.py @@ -7,7 +7,7 @@ def __init__(self, n_var=1, noise_level=0.01, range = (-50, 50)): """ self.noise_level = noise_level self.n_var = n_var # Number of variables/dimensions - self.bounds = np.array([[-50] * self.n_var, [50] * self.n_var]) + self.bounds = np.array([[range[0]] * self.n_var, [range[1]] * self.n_var]) def _schwefel_individual(self, x): return x * np.sin(np.sqrt(np.abs(x))) From eb306fecff8607826b8b971b0778cf0b3eee4be7 Mon Sep 17 00:00:00 2001 From: Brenden Pelkie Date: Fri, 12 Apr 2024 13:57:31 -0700 Subject: [PATCH 43/43] clean up baybe work into some notebooks and repo reorganization --- BayBE_grid_search.ipynb | 259 ---- README.md | 17 +- analyse_grid_experiment_BAYBE.ipynb | 605 -------- baybe_result_plots.ipynb | 144 -- botorch_results_plots.ipynb | 2 +- combined_botorch_baybe_final.ipynb | 6 +- comparison.ipynb | 342 ++++- hello.py | 2 - hello_test.py | 5 - line_plot.ipynb | 521 +------ noisy_optimization_BayBE.ipynb | 1249 ---------------- noisy_optimization_BayBE_extendedBounds.ipynb | 1300 +++++++++++++++++ ...y_optimization_BayBE_original_bounds.ipynb | 1190 +++++++++++++++ requirements.txt | 17 +- .../BayBE_heatmap-checkpoint.png | Bin 0 -> 94163 bytes .../BAYBE_heatmap-checkpoint.png | Bin 0 -> 14190 bytes .../BayBE_line_plot-checkpoint.png | Bin 0 -> 158982 bytes .../BoTorch_heatmap_delta-checkpoint.png | Bin .../BoTorch_heatmapFalse.png | Bin .../BoTorch_heatmapTrue.png | Bin results_plots/BoTorch_heatmap_delta.png | Bin 0 -> 109999 bytes .../baybe_original_range/BAYBE_heatmap.png | Bin 0 -> 14190 bytes .../baybe_original_range/BayBE_heatmap.png | Bin 0 -> 94163 bytes .../baybe_original_range/BayBE_line_plot.png | Bin 0 -> 163925 bytes .../BayBE_grid_search-checkpoint.ipynb | 414 ++++++ .../baybe_no_noise-checkpoint.ipynb | 367 +++++ .../baybe_utils-checkpoint.py | 151 ++ src/.ipynb_checkpoints/schwefel-checkpoint.py | 34 + .../schwefel_functions-checkpoint.py} | 0 .../seed_data-checkpoint.csv | 4 + .../visualization-checkpoint.py | 25 + src/BayBE_grid_search.ipynb | 414 ++++++ .../baybe_utils/run_experiment_baybe.py | 6 +- .../run_experiment_random_baybe.py | 6 +- .../baybe_utils/run_grid_experiments_baybe.py | 8 +- .../run_grid_experiments_baybe_random.py | 8 +- 36 files changed, 4338 insertions(+), 2758 deletions(-) delete mode 100755 BayBE_grid_search.ipynb delete mode 100644 analyse_grid_experiment_BAYBE.ipynb delete mode 100644 baybe_result_plots.ipynb delete mode 100644 hello.py delete mode 100644 hello_test.py delete mode 100644 noisy_optimization_BayBE.ipynb create mode 100644 noisy_optimization_BayBE_extendedBounds.ipynb create mode 100644 noisy_optimization_BayBE_original_bounds.ipynb create mode 100644 results_baybe_extendedBounds/results_random_baybe/.ipynb_checkpoints/BayBE_heatmap-checkpoint.png create mode 100644 results_plots/.ipynb_checkpoints/BAYBE_heatmap-checkpoint.png create mode 100644 results_plots/.ipynb_checkpoints/BayBE_line_plot-checkpoint.png rename BoTorch_heatmap_delta.png => results_plots/.ipynb_checkpoints/BoTorch_heatmap_delta-checkpoint.png (100%) rename BoTorch_heatmapFalse.png => results_plots/BoTorch_heatmapFalse.png (100%) rename BoTorch_heatmapTrue.png => results_plots/BoTorch_heatmapTrue.png (100%) create mode 100644 results_plots/BoTorch_heatmap_delta.png create mode 100644 results_plots/baybe_original_range/BAYBE_heatmap.png create mode 100644 results_plots/baybe_original_range/BayBE_heatmap.png create mode 100644 results_plots/baybe_original_range/BayBE_line_plot.png create mode 100644 src/.ipynb_checkpoints/BayBE_grid_search-checkpoint.ipynb create mode 100644 src/.ipynb_checkpoints/baybe_no_noise-checkpoint.ipynb create mode 100644 src/.ipynb_checkpoints/baybe_utils-checkpoint.py create mode 100644 src/.ipynb_checkpoints/schwefel-checkpoint.py rename src/{schwefel_functions.py => .ipynb_checkpoints/schwefel_functions-checkpoint.py} (100%) create mode 100644 src/.ipynb_checkpoints/seed_data-checkpoint.csv create mode 100644 src/.ipynb_checkpoints/visualization-checkpoint.py create mode 100644 src/BayBE_grid_search.ipynb rename run_experiment_baybe.py => src/baybe_utils/run_experiment_baybe.py (95%) rename run_experiment_random_baybe.py => src/baybe_utils/run_experiment_random_baybe.py (95%) rename run_grid_experiments_baybe.py => src/baybe_utils/run_grid_experiments_baybe.py (96%) rename run_grid_experiments_baybe_random.py => src/baybe_utils/run_grid_experiments_baybe_random.py (95%) diff --git a/BayBE_grid_search.ipynb b/BayBE_grid_search.ipynb deleted file mode 100755 index b95d12a..0000000 --- a/BayBE_grid_search.ipynb +++ /dev/null @@ -1,259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "id": "fc7d83ca-d889-49f9-a332-785d3267512d", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from src import schwefel\n", - "from src import baybe_utils\n", - "from src import visualization" - ] - }, - { - "cell_type": "markdown", - "id": "3e9d8019-5827-4325-b0e4-cc1f80745270", - "metadata": {}, - "source": [ - "## BayBE Schwefel function optimization examples\n", - "\n", - "### Brenden Pelkie\n", - "\n", - "This notebook walks through a quick grid search to explore the impact of measurement noise on optimization performance of a vanilla BO implementation in BayBE." - ] - }, - { - "cell_type": "markdown", - "id": "44701cdf-29e6-47f7-84f5-abfd5112a2e9", - "metadata": {}, - "source": [ - "### 1. Pick parameters\n", - "\n", - "First define parameters for optimization. Here we set the number of BO iterations/cycles to run, the number of random initial observations to include, the dimensionality of the schwefel function to optimize, the noise level of the schwefel observations, and the number of obserations to make per iteration/BO batch cycle" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b158f689-b0e5-478e-b7a6-2cb586acf4c5", - "metadata": {}, - "outputs": [], - "source": [ - "NUM_ITERATIONS = 15\n", - "NUM_INIT_OBS = 5\n", - "N_DIMS_SCHWEF = 1\n", - "NOISE_LEVEL_SCHWEF = 0\n", - "ITERATION_BATCH_SIZE = 1" - ] - }, - { - "cell_type": "markdown", - "id": "3b3e07e3-3daa-4196-b17f-8982e0598db9", - "metadata": {}, - "source": [ - "For the grid search over number of BO iterations and noise, select the desired values here" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0670300a-6f94-4b37-9854-d4b0b5251b40", - "metadata": {}, - "outputs": [], - "source": [ - "num_iterations = [5,10,15]#[5,10,20,40,60,80]\n", - "noise = [0, 0.1]# [0, 0.1, 0.2, 0.5]\n" - ] - }, - { - "cell_type": "markdown", - "id": "4705ebb3-86ad-41f5-8c23-0f883b79a871", - "metadata": {}, - "source": [ - "### 2. Run grid search" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6a78944a-2833-407a-9bbc-86206155b2d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00, 4.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 6.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 7.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 5.12it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:02<00:00, 5.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:02<00:00, 5.63it/s]\n" - ] - } - ], - "source": [ - "grid_results = baybe_utils.iteration_noise_grid_search(num_iterations, noise, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE)" - ] - }, - { - "cell_type": "markdown", - "id": "4f403581-1dd2-4a10-b98e-fcd147ec0bba", - "metadata": {}, - "source": [ - "### 3. Process and visualize results" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a10e5944-1038-4bec-bb54-66a60f22ccec", - "metadata": {}, - "outputs": [], - "source": [ - "n_its, noise, performance_matrix = baybe_utils.process_grid_searh_results(grid_results)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "524e2642-4a6d-458e-bade-05106c17e4ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhsAAAGwCAYAAAAAFKcNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABAqElEQVR4nO3dd3RU5drG4XsSIIRUAqTQS+gdRJogAhKKFEEFjVIlHqV3sYBgCaKAB0VRjwIqCHooCiqIVMFQDCBVaugklBRIIIEk8/3BxxzHBJyB2QyT+V1r7bUye+955xlWQu4877v3mMxms1kAAAAG8XB2AQAAIG8jbAAAAEMRNgAAgKEIGwAAwFCEDQAAYCjCBgAAMBRhAwAAGIqwAQAADJXP2QUYYd62fzu7BACAi3iq3hDDX6NmvREOGWfXtikOGeduo7MBAAAMRdgAAACGypPTKAAA3FNMzi7AuQgbAAAYzeTeaYNpFAAAYCg6GwAAGM29GxuEDQAADOfmYYNpFAAAYCg6GwAAGM69WxuEDQAADGZ276zBNAoAADAWnQ0AAIzm5p0NwgYAAEbjpl4AAADGIWwAAABDMY0CAIDR3HsWhbABAIDhWLMBAABgHDobAAAYzb0bG4QNAACMZnZ2AU7GNAoAADAUnQ0AAIzm5gtECRsAABjNvbMG0ygAAMBYdDYAADCce7c2CBsAABjNvbMG0ygAAMBYdDYAADCam3c2CBsAABjM7OaXvjKNAgAADEXYAAAAhmIaBQAAo7n5NAphAwAAo7l31mAaBQAAGIvOBgAABnP3j5gnbAAAYDQ3X7PBNAoAADAUnQ0AAIzm3o0NwgYAAIZjGgUAAMA4dDYAADAYV6MAAABjufcsCmEDAADDsWYDAADAOIQNAABgKKZRAAAwmJlpFAAAAOPQ2QAAwGju3digswEAAIxF2AAAAIZiGgUAAIO5+wJRwgYAAEZz76zBNAoAADAWnQ0AAIzm5p0NwgYAAIZz77TBNAoAAAYzmxyz2SM6OloNGjSQn5+fgoOD1aVLF+3fv9/qnPT0dA0YMEBFihSRr6+vunXrpoSEBKtzjh8/rg4dOqhQoUIKDg7WqFGjlJmZaVct91Rnw2w2a+3atTp06JDCwsIUERGh/PnzO7sst7Hl5136bekOpaZcVmjpImrXu5lKhIfc9Pw9mw5pzbdblHzukoqEBqj1k41VsW4Zy/F9Ww7r91/26EzcOV1JzdBz0U8otGxRqzFiV+3Rro0HdeboOV29ck1j/tNPBX28cn29zGtZ+s+r/1XCsQu5jgXcjvcGfamU85dy7L/v4Rrq0Ld5jv1nTyRq7X+36PSRc0o5f0kRzzRVo/a172hMe/zTz92Sj1bpj/XWv1Aq1Cqlp8d2vKPXhetZt26dBgwYoAYNGigzM1MvvfSS2rRpo71798rHx0eSNGzYMP3www/69ttvFRAQoIEDB6pr167auHGjJCkrK0sdOnRQaGiofvvtN505c0Y9e/ZU/vz59dZbb9lci1PDRvv27fX1118rICBAiYmJat++vbZs2aKiRYvqwoULqlSpktavX69ixYo5s0y3sDvmoH7+cqM69HtQJcNDtOmnnfpq0jINnPKkfAIK5Tj/xIEzWvj+SrXq0UiV6pXRro0HNX/KT3ou+nEFlyoiSbqakanSlcNUvVG4ln66NtfXvZaRqfDapRVeu7RWzd90yxpXzvtNfoV9lHDswh2/X+CG/m8+JnO22fL47IkL+vKtpareqEKu51+7ek2Bwf6q1rCCVny50SFj2sqWnztJCq9dWp3/1dLy2DMfTWync8IsyvLly60ez549W8HBwYqNjVXz5s2VkpKizz77TPPmzVPLlte/X2bNmqWqVatq06ZNatSokX7++Wft3btXv/zyi0JCQlSnTh29/vrrGjNmjF577TUVKFDAplqc+h24fPlyZWRkSJJeeeUVXbp0SYcPH9bZs2d17Ngx+fj4aNy4cc4s0W1s+uEP1WtZTXVbVFWxkkF6pN+Dyl8gn7av/TPX8zf/tFPhtUurace6KlYiSC2faKiwcsW0ZcUuyzm1m1XWg90aqHzNkjd93Ubta+uBzvVUsuLNOyiSdHDHMR3ZeUJtIpvc3hsEbsLH31u+gYUs24Ftx1Q4xF9lqhbP9fwSFULUJrKJajSpKM98nrc9Znpahr7/ZI3eifpc0X0/1ZzXv1P8sfO3rNWWnztJ8szvafX63r4F7fxXgeOZHLJlZGTo4sWLVtuN36P/JCUlRZIUFBQkSYqNjdW1a9fUunVryzlVqlRR6dKlFRMTI0mKiYlRzZo1FRLyv/+jIyIidPHiRe3Zs8fmd3/PxN3Vq1crOjpa5cqVkySVLFlSb7/9tlasWOHkyvK+rMwsnY47p/I1/hcKTB4mla9RUicPxuf6nBMHE6zOl663ak8eTMj1/DuRmnxZSz9dq0dfaK38XvfUzB/ymKzMLO3ccEB1W1SVyUE3YbrZmN++t0JpKVcUOeYRRb35uMLKFdUXb3yvK6npNx3L1p+7o3tP6Z3nZumD4fO07LN1unzp5mPCtURHRysgIMBqi46O/sfnZWdna+jQoWratKlq1KghSYqPj1eBAgUUGBhodW5ISIji4+Mt5/w1aNw4fuOYrZz+P/eNH76kpCRVqGDdYgwPD9fp06dv+fyMjIwcqe7a1UzlL+D0t+YyLl9MlznbnGO6xCfAW+dPJ+X6nNTkyznO9w0opNTkyw6tzWw267uZq3Vfq+oqXiFYyecuOnR84K/+3Bqn9MsZqtO8iqFjHv/zjE4dPquRH/dRvvzXuyNtnm6qP3+P097Nh1W/VfVcx7Ll5y68dmlVbVBegcH+SkpI0aoFmzX37WXqN7GrPDzumb8v3Y69iztvZuzYsRo+fLjVPi+v3Ne5/dWAAQO0e/dubdiwwTGF2Mnpv5F79+4tLy8vXbt2TXFxcape/X8/ZPHx8TkS199FR0drwoQJVvu6RkWo23PtjCgXd9mWFbuUkX5VD3Sp5+xS4Aa2r92ninVKyy/Ix9Ax44+f19X0a5rc/zOrczOvZikx4aJSzl/SjJFfW/Y361JfzbrUt+n1ajSpaPk6pHQRhZQuoulD5+ro3tM5uiK4ixwUNry8vGwKF381cOBALVu2TOvXr1fJkv/7HggNDdXVq1eVnJxs9bs2ISFBoaGhlnO2bNliNd6Nq1VunGMLp4aNXr16Wb7u3LmzLl+2/qt44cKFqlOnzi3HyC3lLd77qcNqdAeF/AvK5GFSWor1v39ayhX5BuZcHCpJvoGFcpyfmnL5puffrrg9p3TyQILeeOZjq/2fvPytajWtpC4vtHLo68F9JZ+7pCO7TuqJ4W0NH/Nq+jX5Fi6k3q92yfGcgoUKqKCPl/41qbtln7fv9V8ut/NzVzgkQIX8CioxPoWw4WbMZrMGDRqkxYsXa+3atZZlCjfUr19f+fPn16pVq9StWzdJ0v79+3X8+HE1btxYktS4cWO9+eabOnv2rIKDgyVJK1eulL+/v6pVq2ZzLU4NG7Nmzbrl8fHjx8vTM/cFWDfklvKYQrGPZz5PFS9XTEd2n1KVBuUlSeZss47sOan729TM9TmlKoYobs9Jq0v+juw68Y8LPe3VrtcDavnE/ZbHl5LS9FX0Mj02uI1K3uKyXMBeO9btk0+Atyr95TJSo8YMK1tMqcmX5eFpUmAx/1yfGxQakGPf7fzcXbyQqsup6fJz8B8CsNfdvxxlwIABmjdvnr777jv5+flZ1lgEBATI29tbAQEB6tevn4YPH66goCD5+/tr0KBBaty4sRo1aiRJatOmjapVq6ZnnnlGkydPVnx8vF555RUNGDDArg7LPT2Bl5iYqBdeeMHZZbiFRh1qa9uavdqx7k+dO5WoZZ+v07WMTNV58Po88+IPf9EvX8dYzm/YrpYO/XFCvy3bofOnkiz3Hbg/4n/h5EpquuKPnte5k9fXfZw/k6T4o+et5pdTky8r/uh5JcZfXyWdcOKC4o+etyySCyjqp+BSRSxbkbBASVJQSID8i/ga+m8C92HONmvHuj9Vu3lleXha/7f49+/9rMwsxR89r/ij55WVmaWLSWlW38O2jFm+ZkmVqhiq+VN+0uGdx5V87qJOHDijVQs26fThszet859+7q6mX9PPc3/TyYPxSj53UUd2n9T8KT8pKCRAFWqXvtN/JtwBZ9zU66OPPlJKSopatGihsLAwy7ZgwQLLOdOmTdMjjzyibt26qXnz5goNDdWiRYssxz09PbVs2TJ5enqqcePGevrpp9WzZ09NnDjRrlru6RZAYmKi5syZo88//9zZpeR5NRpX1OWL6Vr73y1KTb6s0DJFFfniI5b2bMr5VKuV9KUqhanrwNZa880WrV6wSUGhgeoxop3Vtf77Y4/qu5mrLY8XTl8pSXqw231q8dj1bsXvv+zWuoW/W86ZPWGJJKnzv1pagg5gtCO7TyjlfKrqtqia49jfv/cvJaXp47HfWB7HLNuhmGU7VKZqcfUe18WmMU0mk54a00GrF2zWdzPXKO3i9SnLMlXC5BPofdM6/+nnzuRh0tnjF/TH+v1KT8uQX2EfVahVSg89fr9lISrch9ls/sdzChYsqBkzZmjGjBk3PadMmTL68ccf76gWk9mWagzy/fff3/L4kSNHNGLECGVlZdk17rxt/76TsgAAbuSpekMMf40K3V53yDiHF77qkHHuNqd2Nrp06SKTyXTL9OWoa90BAHAe9/5d5tQ1G2FhYVq0aJGys7Nz3bZt2+bM8gAAcAzH3EDUZTk1bNSvX1+xsbE3Pf5PXQ8AAHDvc+o0yqhRo5SWlnbT4+Hh4VqzZs1drAgAAMdz1B1EXZVTw0azZs1uedzHx0cPPvjgXaoGAACDuHnYuKfvswEAAFzfPX2fDQAA8gb3bm0QNgAAMJi7r9lgGgUAABiKzgYAAEajswEAAGAcwgYAADAU0ygAABjNzT/ni7ABAIDBuBoFAADAQIQNAABgKKZRAAAwmptPoxA2AAAwmpsvEGUaBQAAGIrOBgAABjM7uwAnI2wAAGA0955FYRoFAAAYi84GAABGo7MBAABgHMIGAAAwFNMoAAAYzc3vs0HYAADAYHwQGwAAgIEIGwAAwFBMowAAYDQ3n0YhbAAAYDQ3DxtMowAAAEPR2QAAwHDu3dogbAAAYDT3zhpMowAAAGPZHTbmzJmjH374wfJ49OjRCgwMVJMmTXTs2DGHFgcAQJ5gctDmouwOG2+99Za8vb0lSTExMZoxY4YmT56sokWLatiwYQ4vEAAAV2d20Oaq7F6zceLECYWHh0uSlixZom7duikqKkpNmzZVixYtHF0fAABwcXZ3Nnx9fXXhwgVJ0s8//6yHH35YklSwYEFduXLFsdUBAJAXuPk0it2djYcffljPPvus6tatqwMHDqh9+/aSpD179qhs2bKOrg8AALg4uzsbM2bMUOPGjXXu3DktXLhQRYoUkSTFxsbqySefdHiBAAC4PJPJMZuLsruzERgYqA8++CDH/gkTJjikIAAAkLfc1k29kpOTtWXLFp09e1bZ2dmW/SaTSc8884zDigMAIE9w3aaEQ9gdNpYuXarIyEilpqbK399fpr+0dQgbAADg7+xeszFixAj17dtXqampSk5OVlJSkmVLTEw0okYAAODC7O5snDp1SoMHD1ahQoWMqAcAgLzHzadR7O5sRERE6PfffzeiFgAA8ibus2GfDh06aNSoUdq7d69q1qyp/PnzWx3v1KmTw4oDAACuz+6w0b9/f0nSxIkTcxwzmUzKysq686oAAECeYXfY+OulrgAAwAYuPAXiCHav2QAAALDHbYWNdevWqWPHjgoPD1d4eLg6deqkX3/91dG1AQCQJ5hMJodsrsrusPHVV1+pdevWKlSokAYPHqzBgwfL29tbrVq10rx584yoEQAAuDC712y8+eabmjx5soYNG2bZN3jwYE2dOlWvv/66nnrqKYcWCACAy3PdpoRD2N3ZOHLkiDp27Jhjf6dOnRQXF+eQogAAQN5hd2ejVKlSWrVqlcLDw632//LLLypVqpTDCrsT0c8ed3YJwD1p+MdlnF0C4J7cvLNhd9gYMWKEBg8erB07dqhJkyaSpI0bN2r27Nn697//7fACAQCAa7M7bDz//PMKDQ3VlClT9M0330iSqlatqgULFqhz584OLxAAALg2u8OGJD366KN69NFHHV0LAAB5kgtfteoQ3NQLAAAYyqbORlBQkA4cOKCiRYuqcOHCt7yxSGJiosOKAwAArs+msDFt2jT5+flZvnblu5gBAHDXufmvTZvCRq9evSxf9+7d26haAADIm9w8bNi9ZsPT01Nnz57Nsf/ChQvy9PR0SFEAACDvsPtqFLPZnOv+jIwMFShQ4I4LAgAgr3HzxobtYWP69OmSrn9y3X/+8x/5+vpajmVlZWn9+vWqUqWK4ysEAMDVuflaR5vDxrRp0yRd72zMnDnTasqkQIECKlu2rGbOnOn4CgEAcHHOyhrr16/XO++8o9jYWJ05c0aLFy9Wly5dLMd79+6tOXPmWD0nIiJCy5cvtzxOTEzUoEGDtHTpUnl4eKhbt27697//bdV0+Cc2h40bH7L20EMPadGiRSpcuLDNLwIAAO6+tLQ01a5dW3379lXXrl1zPadt27aaNWuW5bGXl5fV8cjISJ05c0YrV67UtWvX1KdPH0VFRWnevHk212H3mo01a9bY+xQAAOAAGRkZysjIsNrn5eWVIyDc0K5dO7Vr1+6WY3p5eSk0NDTXY/v27dPy5cu1detW3XfffZKk999/X+3bt9e7776r4sWL21T3bd2u/OTJk/r+++91/PhxXb161erY1KlTb2dIAADyLgdNo0RHR2vChAlW+8aPH6/XXnvttsdcu3atgoODVbhwYbVs2VJvvPGGihQpIkmKiYlRYGCgJWhIUuvWreXh4aHNmzfb/NEldoeNVatWqVOnTipfvrz+/PNP1ahRQ0ePHpXZbFa9evXsHQ4AANho7NixGj58uNW+m3U1bNG2bVt17dpV5cqV0+HDh/XSSy+pXbt2iomJkaenp+Lj4xUcHGz1nHz58ikoKEjx8fE2v47dYWPs2LEaOXKkJkyYID8/Py1cuFDBwcGKjIxU27Zt7R0OAIA8z1HrQ281ZXI7evToYfm6Zs2aqlWrlipUqKC1a9eqVatWDnsdu2/qtW/fPvXs2VPS9XRz5coV+fr6auLEiXr77bcdVhgAAHmGyUGbwcqXL6+iRYvq0KFDkqTQ0NAcN/LMzMxUYmLiTdd55MbusOHj42NZpxEWFqbDhw9bjp0/f97e4QAAwD3i5MmTunDhgsLCwiRJjRs3VnJysmJjYy3nrF69WtnZ2WrYsKHN49o9jdKoUSNt2LBBVatWVfv27TVixAjt2rVLixYtUqNGjewdDgCAPM9Z99lITU21dCmk67ex2LFjh4KCghQUFKQJEyaoW7duCg0N1eHDhzV69GiFh4crIiJCklS1alW1bdtW/fv318yZM3Xt2jUNHDhQPXr0sPlKFOk2wsbUqVOVmpoqSZowYYJSU1O1YMECVaxYkStRAAC4h/z+++966KGHLI9vLC7t1auXPvroI+3cuVNz5sxRcnKyihcvrjZt2uj111+3Whcyd+5cDRw4UK1atbLc1OvGXcVtZVfYyMrK0smTJ1WrVi1J16dUuGsoAAC35qzORosWLW76mWaStGLFin8cIygoyK4beOXGrjUbnp6eatOmjZKSku7oRQEAgPuwe4FojRo1dOTIESNqAQAAeZDdYeONN97QyJEjtWzZMp05c0YXL1602gAAgDWTyTGbq7J7gWj79u0lSZ06dZLpL+/cbDbLZDIpKyvLcdUBAACXxwexAQBgNBfuSjiC3WHjwQcfNKIOAADyLJObpw2712xI0q+//qqnn35aTZo00alTpyRJX375pTZs2ODQ4gAAgOuzO2wsXLhQERER8vb21rZt25SRkSFJSklJ0VtvveXwAgEAcHku8tkoRrmtq1FmzpypTz/9VPnz57fsb9q0qbZt2+bQ4gAAyAvcPGvYHzb279+v5s2b59gfEBCg5ORkR9QEAADyELvDRmhoqNWHutywYcMGlS9f3iFFAQCQl7j7fTbsDhv9+/fXkCFDtHnzZplMJp0+fVpz587VyJEj9fzzzxtRIwAArs3N51HsvvT1xRdfVHZ2tlq1aqXLly+refPm8vLy0siRIzVo0CAjagQAwKW5cE5wCLvDhslk0ssvv6xRo0bp0KFDSk1NVbVq1eTr62tEfQAAwMXZPY3St29fXbp0SQUKFFC1atV0//33y9fXV2lpaerbt68RNQIA4NrcfBrF7rAxZ84cXblyJcf+K1eu6IsvvnBIUQAA5CVunjVsn0a5ePGizGazzGazLl26pIIFC1qOZWVl6ccff1RwcLAhRQIAANdlc9gIDAyUyWSSyWRSpUqVchw3mUyaMGGCQ4sDACAvcOXLVh3B5rCxZs0amc1mtWzZUgsXLlRQUJDlWIECBVSmTBkVL17ckCIBAHBphA3b3Pi017i4OJUuXVomd49pAADAJjaFjZ07d6pGjRry8PBQSkqKdu3addNza9Wq5bDiAADIC9z9z3ObwkadOnUUHx+v4OBg1alTRyaTSWazOcd5JpNJWVlZDi8SAABX5u6TATaFjbi4OBUrVszyNQAAgK1sChtlypTJ9WsAAIB/YvftygEAgH2YRgEAAMZy87Bh9+3KAQAA7EFnAwAAg5ncvLVhd9i4cuWKVq5cqQMHDkiSKlWqpIcfflje3t4OLw4AgLyANRt2+P777/Xss8/q/PnzVvuLFi2qzz77TB07dnRocQAAwPXZvGbjt99+02OPPabmzZtr48aNSkxMVGJiojZs2KBmzZrpscce06ZNm4ysFQAAuCCbOxtvvPGG+vTpo48//thqf5MmTdSkSRM999xzmjhxon788UeHFwkAgCtz92kUmzsbmzZt0sCBA296fMCAAYqJiXFIUQAAIO+wubNx5coV+fv73/R4QECA0tPTHVIUAAB5iZs3NmzvbFSsWFGrV6++6fFVq1apYsWKDikKAIA8xeSgzUXZ3Nno06ePRo4cqZCQELVv397q2A8//KDRo0frpZdecniBuLf069NSrVvWVLmywUrPuKY//jimadOX6eixc5Zzxr38mBrdX1HFigXo8pUM/fHHUU2b/oPijp61nFO9WikNHdxB1aqWlMxm7dpzQlPfW6oDB884420Bt+3XhVu0cfFWq31BYYGKeifyps9JT8vQ+m83af/WI0pPS5d/UT+1fvoBVahT9rbHBO5lNoeNIUOG6LffftMjjzyiypUrq2rVqjKbzdq3b58OHjyoLl26aOjQoXYXcPXqVS1ZskQxMTGKj4+XJIWGhqpJkybq3LmzChQoYPeYMM599Sto/je/afee4/L09NCQge318YdR6tLtHV1JvypJ2rvvpH74aZvOnElSQEAhPf9chD6eEaW2Hd9UdrZZ3t4FNPOD/lq7fo/ejF4oT08PvfCv6+c83P51ZWZmO/ldAvYpWjJIPV7sZHns4XnzpnFWZpbmT/pePv7eenRIW/kW9tHF85fkVcjrtsfEvY8Forae6OGhb7/9Vl9//bUqV66sP//8U/v371eVKlU0d+5cLVy4UB4e9v0wHDp0SFWrVlWvXr20fft2ZWdnKzs7W9u3b1fPnj1VvXp1HTp0yO43BeM8P/BTfbd0qw4fSdCBg2f0yvj5Kh4WpGrVSlrO+e+iTYrddkSnzyRp35+n9MGHPyksrLCKFw+SJJUrG6zAQB998NEKHT12ToePJGjmJz+raFF/hYUVdtZbA26bh4dJvoE+lq2Q381vcrhz3T6lp6Wr67B2KlkpTIHF/FW6agmFlCl622Pi3ufmsyj230G0e/fu6t69u0Ne/Pnnn1fNmjW1ffv2HItPL168qJ49e2rAgAFasWKFQ14PjufrV1CSlJJyOdfj3gULqEunBjp58oLi45MlSUePnVNSUpq6drlfn362Sp6eJj3apaEOH4nX6dNJd6t0wGGSElL0wcBZ8syfTyUqhujBJxoroKhfruce3BanEuGh+nnOeh2MjVMhf29Va1xRjTrWs/qDzZ4x4QJcOSk4gN1h48KFCypSpIgk6cSJE/r000915coVdezYUc2bN7drrI0bN2rLli25XuXi7++v119/XQ0bNrzlGBkZGcrIyLDal52dKQ8PPvbFaCaTSWNGdtG27XE6dDje6lj3x5to+JBHVKiQl+Lizqr/Cx8rMzNLknT5cob6Rn2of0/to+eefViSdPz4eT038BNlZTGFAtdSPDxEHaJaKSgsUKnJl7Vx8VbNfX2R+k16Ul7eOaeBk89e1LHzp1S9SSU9MeoRJSWkaMXsdcrOytYDXe+/rTGBe53N8x67du1S2bJlFRwcrCpVqmjHjh1q0KCBpk2bpk8++UQtW7bUkiVL7HrxwMBAHT169KbHjx49qsDAwFuOER0drYCAAKvtXMIWu+rA7Xn5xa4KrxCq0WO/zHHsh5+26fEnp6r3szN09Pg5TXn7GRUocD0Aennl04RxT2j7jjhF9pqunn3f18HDZzTj3/3k5UVIhGupULuMqjQMV3Dpoipfq7QeH/mIMi5f1Z+bc58CNpvN8vH3Vtt+LRRaLlhVG1VUk073afuqPbc9Ju597j6NYnPYGD16tGrWrKn169erRYsWeuSRR9ShQwelpKQoKSlJzz33nCZNmmTXiz/77LPq2bOnpk2bpp07dyohIUEJCQnauXOnpk2bpt69eysqKuqWY4wdO1YpKSlWW7GQ++2qA/Z7acyjerBZNfWL+kgJZ1NyHE9NTdfxE+cVu+2Iho+ao7Jlg9XqoZqSpPZt66lE8SC9+toC7dl7Qjt3HdeYl+aqRIkgPfRgjbv9VgCHKujjpcKhgUpKyPlzIUm+gT4qHBpoNWVSpERhpaVcVtb/d//sHRP3PpPJMZursvnPyK1bt2r16tWqVauWateurU8++UQvvPCC5Qdm0KBBatSokV0vPnHiRPn4+Oidd97RiBEjZPr/f0mz2azQ0FCNGTNGo0ePvuUYXl5e8vKyXsXNFIqxXhrzqFo+VFN9+3+oU6cT//F8k+n6xyvf6Gx4Fyyg7GyzzGaz5Ryz2SyZry+KA1zZ1fSrSj6bIt/ASrkeL1kxVHtiDsqcbZbp/7/fE88kyzewkDzzed7WmMC9zubfyomJiQoNDZUk+fr6ysfHR4UL/+/KgcKFC+vSpUt2FzBmzBiNGTNGcXFxVpe+litXzu6xYLyXX+yq9u3qaciwz5V2OUNFilxfsJaaekUZGZkqWSJIEW3qKGbTASUmpSokOFD9+rRURsY1/bphnyQpZvMBDR/6iF5+savmLdggD5NJ/fq0VGZWtrb8TpsYrmX1vI0Kr1tW/kX9lJqUpg2LtsjkYVK1xteDwdKZv8ivsI9adG8sSarbuoZiV+7Syi9/1X1taikxPlkx38fqvohaNo8JF+Tmf0fZ1QIw/a2H8/fHd6JcuXI5AsaJEyc0fvx4ff755w57HdyZHk80lSTN+s8Aq/2vjJ+v75ZuVUZGpurXLa9nnmouf39vXbiQqthtR/RMn/eVmJQqSYo7elaDhn6uf0W10VezB8ucbda+/af0/MBPdP68/YEVcKZLian6fsbPupKarkJ+3ipZOUw9X3tMhfyvX6p68fwlq/8r/Yv4qfuYTlr11QZ99tJ8+RX20X0RtdSoYz2bx4TrcfOsIZP5r73sW/Dw8FC7du0sUxZLly5Vy5Yt5ePjI+n6VSHLly9XVlbuc463448//lC9evXsHrNmvREOqwHIS4Z/XMbZJQD3nD4NBhv+Gg0nTXXIOJtfHO6Qce42mzsbvXr1snr89NNP5zinZ8+edr34999/f8vjR44csWs8AADuRa68uNMRbA4bs2bNcviLd+nSRSaTSbdqrjhyqgYAAOdw799lTr3ZflhYmBYtWmS5Tfnft23btjmzPAAA4ABODRv169dXbGzsTY//U9cDAABXwH02nGjUqFFKS0u76fHw8HCtWbPmLlYEAIABXDgoOIJTw0azZs1uedzHx0cPPvjgXaoGAABjuHnWsG0apV69ekpKuv5pnBMnTtTly7l/wicAAMDf2RQ29u3bZ5numDBhglJTUw0tCgCAvIQ1GzaoU6eO+vTpowceeEBms1nvvvuufH19cz133LhxDi0QAAC4NpvCxuzZszV+/HgtW7ZMJpNJP/30k/Lly/lUk8lE2AAAAFZsChuVK1fW/PnzJV2/bfmqVasUHBxsaGEAAOQVrjwF4gh2X42SnZ1tRB0AAORZbp41bu/S18OHD+u9997Tvn3XPzK8WrVqGjJkiCpUqODQ4gAAgOuz+w6iK1asULVq1bRlyxbVqlVLtWrV0ubNm1W9enWtXLnSiBoBAHBtJgdtLsruzsaLL76oYcOGadKkSTn2jxkzRg8//LDDigMAIC9w9zUbdnc29u3bp379+uXY37dvX+3du9chRQEAgLzD7rBRrFgx7dixI8f+HTt2cIUKAAC5cPNZFPunUfr376+oqCgdOXJETZo0kSRt3LhRb7/9toYPH+7wAgEAcHmunBQcwO6w8eqrr8rPz09TpkzR2LFjJUnFixfXa6+9psGDBzu8QAAAXJ2bZw37w4bJZNKwYcM0bNgwXbp0SZLk5+fn8MIAAEDeYPeajb/y8/MjaAAA8A+c9UFs69evV8eOHVW8eHGZTCYtWbLE6rjZbNa4ceMUFhYmb29vtW7dWgcPHrQ6JzExUZGRkfL391dgYKD69etn9wey3lHYAAAANnBS2khLS1Pt2rU1Y8aMXI9PnjxZ06dP18yZM7V582b5+PgoIiJC6enplnMiIyO1Z88erVy5UsuWLdP69esVFRVlVx23dQdRAABw72vXrp3atWuX6zGz2az33ntPr7zyijp37ixJ+uKLLxQSEqIlS5aoR48e2rdvn5YvX66tW7fqvvvukyS9//77at++vd59910VL17cpjrobAAAYDBHXfqakZGhixcvWm0ZGRm3VVNcXJzi4+PVunVry76AgAA1bNhQMTExkqSYmBgFBgZagoYktW7dWh4eHtq8ebPNr2VX2Lh27ZpatWqVYz4HAADcgoPSRnR0tAICAqy26Ojo2yopPj5ekhQSEmK1PyQkxHIsPj4+xz208uXLp6CgIMs5trBrGiV//vzauXOnPU8BAAAOMnbs2Bz3tPLy8nJSNbazexrl6aef1meffWZELQAA5EmOmkbx8vKSv7+/1Xa7YSM0NFSSlJCQYLU/ISHBciw0NFRnz561Op6ZmanExETLObawe4FoZmamPv/8c/3yyy+qX7++fHx8rI5PnTrV3iEBAMjT7sUPYitXrpxCQ0O1atUq1alTR5J08eJFbd68Wc8//7wkqXHjxkpOTlZsbKzq168vSVq9erWys7PVsGFDm1/L7rCxe/du1atXT5J04MABq2Ome/FfEwAAN5WamqpDhw5ZHsfFxWnHjh0KCgpS6dKlNXToUL3xxhuqWLGiypUrp1dffVXFixdXly5dJElVq1ZV27Zt1b9/f82cOVPXrl3TwIED1aNHD5uvRJFuI2ysWbPG3qcAAODenPS3+O+//66HHnrI8vjGeo9evXpp9uzZGj16tNLS0hQVFaXk5GQ98MADWr58uQoWLGh5zty5czVw4EC1atVKHh4e6tatm6ZPn25XHbd9n41Dhw7p8OHDat68uby9vWU2m+lsAACQC2f9dmzRooXMZvNNj5tMJk2cOFETJ0686TlBQUGaN2/eHdVh9wLRCxcuqFWrVqpUqZLat2+vM2fOSJL69eunESNG3FExAADkRc66Xfm9wu6wMWzYMOXPn1/Hjx9XoUKFLPu7d++u5cuXO7Q4AADg+uyeRvn555+1YsUKlSxZ0mp/xYoVdezYMYcVBgAA8ga7w0ZaWppVR+OGxMREl7ixCAAAd5srT4E4gt3TKM2aNdMXX3xheWwymZSdna3JkydbrXgFAACQbqOzMXnyZLVq1Uq///67rl69qtGjR2vPnj1KTEzUxo0bjagRAACXRmfDTjVq1NCBAwf0wAMPqHPnzkpLS1PXrl21fft2VahQwYgaAQCAC7ut+2wEBATo5ZdfdnQtAAAgD7qtsJGUlKTPPvtM+/btkyRVq1ZNffr0UVBQkEOLAwAgL2AaxU7r169X2bJlNX36dCUlJSkpKUnTp09XuXLltH79eiNqBADApTnqU19dld2djQEDBqh79+766KOP5OnpKUnKysrSCy+8oAEDBmjXrl0OLxIAALguuzsbhw4d0ogRIyxBQ5I8PT01fPhwq0+WAwAA/8/NWxt2h4169epZ1mr81b59+1S7dm2HFAUAQF7i7p+NYtM0ys6dOy1fDx48WEOGDNGhQ4fUqFEjSdKmTZs0Y8YMTZo0yZgqAQBwYS6cExzCprBRp04dmUwmq4+pHT16dI7znnrqKXXv3t1x1QEAAJdnU9iIi4szug4AAPIuV54DcQCbwkaZMmWMrgMAgDzLvaPGbd7U6/Tp09qwYYPOnj2r7Oxsq2ODBw92SGEAACBvsDtszJ49W88995wKFCigIkWKyPSX1pDJZCJsAADwN24+i2J/2Hj11Vc1btw4jR07Vh4edl85CwCA23H3sGF3Wrh8+bJ69OhB0AAAADaxOzH069dP3377rRG1AACAPMjuaZTo6Gg98sgjWr58uWrWrKn8+fNbHZ86darDigMAIC9w92mU2wobK1asUOXKlSUpxwJRAACAv7I7bEyZMkWff/65evfubUA5AADkPe7+p7jdYcPLy0tNmzY1ohaHSb4vxNklAPekqS8cd3YJwD2nz9a78CJunjbsXiA6ZMgQvf/++0bUAgBAnuTmnzBvf2djy5YtWr16tZYtW6bq1avnWCC6aNEihxUHAABcn91hIzAwUF27djWiFgAA8iR3v37C7rAxa9YsI+oAACDPcvewwW1AAQCAoezubJQrV+6W99M4cuTIHRUEAADyFrvDxtChQ60eX7t2Tdu3b9fy5cs1atQoR9UFAECe4e7TKHaHjSFDhuS6f8aMGfr999/vuCAAAJC3OGzNRrt27bRw4UJHDQcAQJ7BfTYc5L///a+CgoIcNRwAAHkG0yh2qlu3rtUCUbPZrPj4eJ07d04ffvihQ4sDAACuz+6w0aVLF6vHHh4eKlasmFq0aKEqVao4qi4AAPIMOht2Gj9+vBF1AACAPMphazYAAEDu6GzYyMPD45Y385Ikk8mkzMzMOy4KAADkHTaHjcWLF9/0WExMjKZPn67s7GyHFAUAQF7i5o0N28NG586dc+zbv3+/XnzxRS1dulSRkZGaOHGiQ4sDACAvcPdplNu6qdfp06fVv39/1axZU5mZmdqxY4fmzJmjMmXKOLo+AADg4uwKGykpKRozZozCw8O1Z88erVq1SkuXLlWNGjWMqg8AAJfHHURtNHnyZL399tsKDQ3V119/neu0CgAAyIUrJwUHsDlsvPjii/L29lZ4eLjmzJmjOXPm5HreokWLHFYcAABwfTaHjZ49e/7jpa8AACAnd//1aXPYmD17toFlAACQd7l51nDcR8wDAADkhtuVAwBgMKZRAACAodw8axA2AAAwmrt3NlizAQAADEVnAwAAg7l5Y4OwAQCA0ZhGAQAAMBCdDQAAjObmnQ3CBgAABnPzrME0CgAAMBadDQAADObuC0QJGwAAGMzNswbTKAAAwFh0NgAAMBjTKAAAwFBunjUIGwAAGM3dOxus2QAAAIYibAAAYDCTyTGbPV577TWZTCarrUqVKpbj6enpGjBggIoUKSJfX19169ZNCQkJDn7n1xE2AAAwmMlBm72qV6+uM2fOWLYNGzZYjg0bNkxLly7Vt99+q3Xr1un06dPq2rXrbb/HW2HNBgAALiIjI0MZGRlW+7y8vOTl5ZXr+fny5VNoaGiO/SkpKfrss880b948tWzZUpI0a9YsVa1aVZs2bVKjRo0cWjedDQAADOaoaZTo6GgFBARYbdHR0Td93YMHD6p48eIqX768IiMjdfz4cUlSbGysrl27ptatW1vOrVKlikqXLq2YmBiHv386GwAAGMxRF6OMHTtWw4cPt9p3s65Gw4YNNXv2bFWuXFlnzpzRhAkT1KxZM+3evVvx8fEqUKCAAgMDrZ4TEhKi+Ph4B1X7P4QNAABcxK2mTP6uXbt2lq9r1aqlhg0bqkyZMvrmm2/k7e1tVIm5YhoFAACDOeNqlL8LDAxUpUqVdOjQIYWGhurq1atKTk62OichISHXNR53irABAIDBnHU1yl+lpqbq8OHDCgsLU/369ZU/f36tWrXKcnz//v06fvy4GjdufIevlBPTKAAA5EEjR45Ux44dVaZMGZ0+fVrjx4+Xp6ennnzySQUEBKhfv34aPny4goKC5O/vr0GDBqlx48YOvxJFImwAAGA4Z9yu/OTJk3ryySd14cIFFStWTA888IA2bdqkYsWKSZKmTZsmDw8PdevWTRkZGYqIiNCHH35oSC2EDQAADOaMj0aZP3/+LY8XLFhQM2bM0IwZMwyvhbABAIDB+CA2AAAAA9HZAADAYG7e2CBswH4NK5bUc23uV60yoQoJ9NWzHy7Sih2HrM4JDw3SS91aqGGlUsrnYdLBMxcUNXOJTidekiQV8/fRy4+1ULOqZeRbsIAOJyTp/R9j9NO2A854S8Ad69e7pVo/VFPlyhRTekam/th5VNM++EFHj52znDNubDc1ur+iihUN0OUrGdfPef8Hxf3lnBsCAgpp4dzhCgkJVJOHXtGl1PS7+XbgYO4+jULYgN28vfJr38mz+mbjLn36wqM5jpcpFqhFoyM1f+NOTfl+g1LTr6pS8aLKuJZlOee9vu3l711Q/WYsUmLqFXW5v5o+iuqkDm9+oT0nzt7NtwM4xH31ymv+txu1e+8JeXp6aMgL7fXx+1Hq8sQ7upJ+VZK098+T+mH5dp2JT1KAfyE9H9VGH38Qpbad31J2ttlqvImvPKEDh84oJCTQCe8GcCzCBuy2dnec1u6Ou+nx0V2aafXuI3pr4TrLvmPnkq3OqV++hF6a97N2HL1+D/7pP8bo2db3qWaZUMIGXNLzg/9j9fiVCfO1fuUEVataUrHbj0iS/rt4s+X46TNJ+uCj5Vr49QgVDwvSyVMXLMee6NZYfn7emvmflWrWtOrdeQMwlLt3NlggCocymaSWNSsoLiFRXw15XNvfHaDvxz6tiDrhVufFHjmljvdVVWChgjKZpE4Nqsgrv6c27T/upMoBx/L1LShJSrl4Odfj3gULqEvHBjp56oLiE5It+8uXC9G/nn1YL43/Oke3A67rXriDqDPdU52NtLQ0ffPNNzp06JDCwsL05JNPqkiRIrd8TkZGhjIyMqz2mbMyZfK8p96a2yjq5yPfggX0QtuGeue7DXpr4Tq1qFFOn/zrUXWfOl+bDpyQJD3/8ff6MKqTdr03WNeysnTlaqb6f7RER//WAQFckclk0pjhnbVtR5wOHbb+BM3ujzXR8EEdVKiQl+KOnlX/AZ8oM/P6FGP+/J6a/Eakpk5fpviEZJUscev//wBX4dTORrVq1ZSYmChJOnHihGrUqKFhw4Zp5cqVGj9+vKpVq6a4uJu36yUpOjpaAQEBVtvFHWvuRvnIhcf/9wp/3nFI//nld+09eVYfLt+sVbsO6+nmdSznjez8gPwLeanH1Pnq8OYX+nTlVn0Y1UlVShR1UuWA47w8+lGFVwjV6Je/ynHsh5+26fGnp6l31Ic6evycpkQ/owIFrv9xNHRAex05mqBlP2272yXDYCaTySGbq3Jq2Pjzzz+VmZkpSRo7dqyKFy+uY8eOacuWLTp27Jhq1aqll19++ZZjjB07VikpKVabf52H7kb5yEVi6mVdy8rSwTMXrPYfPHNBxYP8JF1fQNqnZX2NnP2TNv55XPtOntN7y37TzmPx6tminjPKBhzmpVGP6sFm1dTv+ZlKOJuS43hqWrqOnziv2O1HNHzMFypbNlitWtSQJN3fIFxtWtXW9pi3tT3mbf3nw+ckSetXTtALUW3u6vuAYzGNco+IiYnRzJkzFRAQIEny9fXVhAkT1KNHj1s+z8vLS15eXlb7mEJxnmtZ2frjaLzKhwZZ7S8fUlinLlyUJHn//19x2Wbr+ejsbLOlMwK4opdGPaqWLWqo778+0qnTif94/o2PDb/R2Rg2+gsVLJjfcrxGtVJ6fVx39Y76UCdOXrjZMMA9z+m/lW+0hdLT0xUWFmZ1rESJEjp3Luf153CuQl75VbZYYcvjUkUDVa1ksJIvX9HpxEv6eMUWzYjqpM0HTihm/3E9WKOcWtcK1xNTvpYkHYpPVFxCkiY9HaE3/rtGSWnpiqhTUc2qllXvDxY6620Bd+TlMV3VPqKuhoycpbTLGSpS5HonLzX1ijIyMlWyRJAiHq6jmE37lZiUppCQAPXr1VIZ6df068Y/JcnqihRJCgzwkSQdiUvgPhsuzt3/jnJ62GjVqpXy5cunixcvav/+/apRo4bl2LFjx/5xgSjuvlplQvXtyCctj8c/0VKS9O1vuzR89k9avuOgXpr7swa0baSJPVrpcEKinpu5RFsPnZIkZWZlq+f7/9XYrs31+cBu8vHKr6NnkzVs9g9as/uIU94TcKd6PNZEkjTr4xes9r8yYb6+W/a7MjIyVb9OOT3To5n8/b11ITFVsduP6JlnP1BiUqozSsZd5OZZQyaz2ey0a6smTJhg9bhRo0aKiIiwPB41apROnjypr7/+2q5xS0VNdkh9QF4TuJ17mAB/t2vru4a/xis/T3fIOG+0GeyQce42p3Y2xo8ff8vj77zzzl2qBAAAGMXp0ygAAOR17j6NQtgAAMBg7r5AlNuVAwAAQ9HZAADAYG7e2CBsAABgNKZRAAAADERnAwAAg7l5Y4OwAQCA0ZhGAQAAMBCdDQAADObmjQ3CBgAARvNw87RB2AAAwGBunjVYswEAAIxFZwMAAIO5+9UohA0AAAzm5lmDaRQAAGAsOhsAABiMaRQAAGAoN88aTKMAAABj0dkAAMBgTKMAAABDuXvYYBoFAAAYis4GAAAGc/e/7AkbAAAYzN2nUQgbAAAYzM2zhtt3dgAAgMHobAAAYDCmUQAAgKHcPGswjQIAAIxFZwMAAIMxjQIAAAzl5lmDaRQAAGAsOhsAABiMaRQAAGAoN88aTKMAAABj0dkAAMBgTKMAAABDufs0AmEDAACDuXtnw93DFgAAMBidDQAADObmjQ3CBgAARmMaBQAAwEB0NgAAMJibNzYIGwAAGI1pFAAAAAPR2QAAwGDu3tkgbAAAYDA3zxpMowAAAGPR2QAAwGBMowAAAEO5+zQCYQMAAIO5e2fD3cMWAAAwGJ0NAAAMZpLZ2SU4FWEDAACDMY0CAABgIJPZbHbv3g4Mk5GRoejoaI0dO1ZeXl7OLge4Z/CzAXdD2IBhLl68qICAAKWkpMjf39/Z5QD3DH424G6YRgEAAIYibAAAAEMRNgAAgKEIGzCMl5eXxo8fzwI44G/42YC7YYEoAAAwFJ0NAABgKMIGAAAwFGEDAAAYirABAAAMRdiAYWbMmKGyZcuqYMGCatiwobZs2eLskoC7xp7v/z179qhbt24qW7asTCaT3nvvvbtXKHAXEDZgiAULFmj48OEaP368tm3bptq1aysiIkJnz551dmmA4ez9/r98+bLKly+vSZMmKTQ09C5XCxiPS19hiIYNG6pBgwb64IMPJEnZ2dkqVaqUBg0apBdffNHJ1QHGupPv/7Jly2ro0KEaOnToXagUuDvobMDhrl69qtjYWLVu3dqyz8PDQ61bt1ZMTIwTKwOMx/c/kBNhAw53/vx5ZWVlKSQkxGp/SEiI4uPjnVQVcHfw/Q/kRNgAAACGImzA4YoWLSpPT08lJCRY7U9ISGDxG/I8vv+BnAgbcLgCBQqofv36WrVqlWVfdna2Vq1apcaNGzuxMsB4fP8DOeVzdgHIm4YPH65evXrpvvvu0/3336/33ntPaWlp6tOnj7NLAwz3T9//PXv2VIkSJRQdHS3p+qLSvXv3Wr4+deqUduzYIV9fX4WHhzvtfQCOQtiAIbp3765z585p3Lhxio+PV506dbR8+fIci+aAvOifvv+PHz8uD4//NZZPnz6tunXrWh6/++67evfdd/Xggw9q7dq1d7t8wOG4zwYAADAUazYAAIChCBsAAMBQhA0AAGAowgYAADAUYQMAABiKsAEAAAxF2AAAAIYibAAAAEMRNgAXULZsWb333nuGjW8ymbRkyRLDxs+N0e8JwL2DsAEYpHfv3jKZTJo0aZLV/iVLlshkMtk11tatWxUVFeXI8gDgriFsAAYqWLCg3n77bSUlJd3ROMWKFVOhQoUcVBUA3F2EDcBArVu3VmhoqOXTPW9m4cKFql69ury8vFS2bFlNmTLF6vhfpxzMZrNee+01lS5dWl5eXipevLgGDx5sOTcjI0MjR45UiRIl5OPjo4YNG9r9YV4nTpzQE088ocDAQAUFBalz5846evSoJOnnn39WwYIFlZycbPWcIUOGqGXLlpbHGzZsULNmzeTt7a1SpUpp8ODBSktLs6sOAHkDYQMwkKenp9566y29//77OnnyZK7nxMbG6oknnlCPHj20a9cuvfbaa3r11Vc1e/bsXM9fuHChpk2bpo8//lgHDx7UkiVLVLNmTcvxgQMHKiYmRvPnz9fOnTv1+OOPq23btjp48KBNNV+7dk0RERHy8/PTr7/+qo0bN8rX11dt27bV1atX1apVKwUGBmrhwoWW52RlZWnBggWKjIyUJB0+fFht27ZVt27dtHPnTi1YsEAbNmzQwIEDbfyXA5CnmAEYolevXubOnTubzWazuVGjRua+ffuazWazefHixea//ug99dRT5ocfftjquaNGjTJXq1bN8rhMmTLmadOmmc1ms3nKlCnmSpUqma9evZrjNY8dO2b29PQ0nzp1ymp/q1atzGPHjr1prZLMixcvNpvNZvOXX35prly5sjk7O9tyPCMjw+zt7W1esWKF2Ww2m4cMGWJu2bKl5fiKFSvMXl5e5qSkJLPZbDb369fPHBUVZfUav/76q9nDw8N85cqVHO8JQN5GZwO4C95++23NmTNH+/bty3Fs3759atq0qdW+pk2b6uDBg8rKyspx/uOPP64rV66ofPny6t+/vxYvXqzMzExJ0q5du5SVlaVKlSrJ19fXsq1bt06HDx+2qdY//vhDhw4dkp+fn+X5QUFBSk9Pt4wRGRmptWvX6vTp05KkuXPnqkOHDgoMDLSMMXv2bKsaIiIilJ2drbi4OJv/3QDkDfmcXQDgDpo3b66IiAiNHTtWvXv3vqOxSpUqpf379+uXX37RypUr9cILL+idd97RunXrlJqaKk9PT8XGxsrT09Pqeb6+vjaNn5qaqvr162vu3Lk5jhUrVkyS1KBBA1WoUEHz58/X888/r8WLF1tN+6Smpuq5556zWktyQ+nSpe14twDyAsIGcJdMmjRJderUUeXKla32V61aVRs3brTat3HjRlWqVClHYLjB29tbHTt2VMeOHTVgwABVqVJFu3btUt26dZWVlaWzZ8+qWbNmt1VnvXr1tGDBAgUHB8vf3/+m50VGRmru3LkqWbKkPDw81KFDB6sx9u7dq/Dw8NuqAUDewjQKcJfUrFlTkZGRmj59utX+ESNGaNWqVXr99dd14MABzZkzRx988IFGjhyZ6zizZ8/WZ599pt27d+vIkSP66quv5O3trTJlyqhSpUqKjIxUz549tWjRIsXFxWnLli2Kjo7WDz/8YFOdkZGRKlq0qDp37qxff/1VcXFxWrt2rQYPHmy1yDUyMlLbtm3Tm2++qccee0xeXl6WY2PGjNFvv/2mgQMHaseOHTp48KC+++47FogCboqwAdxFEydOVHZ2ttW+evXq6ZtvvtH8+fNVo0YNjRs3ThMnTrzpdEtgYKA+/fRTNW3aVLVq1dIvv/yipUuXqkiRIpKkWbNmqWfPnhoxYoQqV66sLl26aOvWrTZPXxQqVEjr169X6dKl1bVrV1WtWlX9+vVTenq6VacjPDxc999/v3bu3Gm5CuWGWrVqad26dTpw4ICaNWumunXraty4cSpevLgd/1oA8gqT2Ww2O7sIAACQd9HZAAAAhiJsAAAAQxE2AACAoQgbAADAUIQNAABgKMIGAAAwFGEDAAAYirABAAAMRdgAAACGImwAAABDETYAAICh/g9eq120OziMBQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualization.grid_search_heatmap(n_its, noise, performance_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/README.md b/README.md index 8ca827a..088a122 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,18 @@ # Project 23: Reliable Surrogate Models of Noisy Data -TODO: Figure out a real readme + +This project was part of the 2024 Bayesian Optimization Hackathon for Chemistry and Materials (https://ac-bo-hackathon.github.io/). The goal of this project was to explore the impact of noisy measurements on the performance of various Bayesian optimizers. We explored this problem using both the BayBE package as well as BoTorch. The BoTorch version evaluated the impact of incorporating noise assumptions in the surrogate model, while the BayBE approach uses the default BayBE model which does incorporate some noise assumptions (?). We used the 2-dimensional Schwefel function as a minimization target. This function has a global minimum at ~[420, 420]. We mainly evaluated the optmimization methods in the range of [-50,50]. Over this range the Schwefel function has high frequency optimizations leading to many local minima, making this a challenging optimization problem. + +## Guide to Project: + +1. BoTorch arm: The BoTorch half of the project can be viewed in the `analyse_grid_experiment.ipynb`, `botorch_results_plots.ipnb`', and `line_plot.ipynb` notebooks. Source code is in `run_experiment.py`. `run_grid_botorch.py`, and `run_grid_experiments.py`. + +2. The BayBE half of the project can be viewed in the `noisy_optimization_BayBE_original_bounds.ipynb` (for [-50,50] bounds) and `noisy_optimization_BayBE_extendedBounds.ipynb` (for [0,500] bounds). Source code is in src/baybe_utils. + +## Team + +- Darby Brown +- Karim Ben Hicham +- Joe Manning +- Brenden Pelkie +- Utkarsh Pratiush diff --git a/analyse_grid_experiment_BAYBE.ipynb b/analyse_grid_experiment_BAYBE.ipynb deleted file mode 100644 index a537a8b..0000000 --- a/analyse_grid_experiment_BAYBE.ipynb +++ /dev/null @@ -1,605 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/brendenpelkie/miniconda3/envs/noisybo_ray/lib/python3.10/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SMOKE_TEST None\n" - ] - } - ], - "source": [ - "import torch\n", - "import pandas as pd\n", - "from run_grid_experiments_baybe import run_grid_experiments\n", - "import run_experiment_baybe\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 1 budget 30 seed 0, time: 22.37s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 5 budget 30 seed 0, time: 40.27s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 10 budget 30 seed 0, time: 57.96s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 20 budget 30 seed 0, time: 75.61s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 1 budget 30 seed 0, time: 88.79s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 5 budget 30 seed 0, time: 101.12s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 10 budget 30 seed 0, time: 115.53s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 20 budget 30 seed 0, time: 129.22s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 1 budget 30 seed 0, time: 141.80s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 5 budget 30 seed 0, time: 156.04s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 10 budget 30 seed 0, time: 166.04s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 20 budget 30 seed 0, time: 178.00s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 1 budget 30 seed 0, time: 191.24s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 5 budget 30 seed 0, time: 202.44s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 10 budget 30 seed 0, time: 214.49s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 20 budget 30 seed 0, time: 226.22s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 1 budget 30 seed 1, time: 240.88s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 5 budget 30 seed 1, time: 252.93s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 10 budget 30 seed 1, time: 268.69s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 20 budget 30 seed 1, time: 281.57s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 1 budget 30 seed 1, time: 294.19s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 5 budget 30 seed 1, time: 307.24s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 10 budget 30 seed 1, time: 324.61s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 20 budget 30 seed 1, time: 337.33s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 1 budget 30 seed 1, time: 360.94s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 5 budget 30 seed 1, time: 379.74s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 10 budget 30 seed 1, time: 401.20s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 20 budget 30 seed 1, time: 421.99s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 1 budget 30 seed 1, time: 444.00s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 5 budget 30 seed 1, time: 469.75s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 10 budget 30 seed 1, time: 485.24s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 20 budget 30 seed 1, time: 508.65s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 1 budget 30 seed 2, time: 534.37s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 5 budget 30 seed 2, time: 555.21s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 10 budget 30 seed 2, time: 570.75s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 20 budget 30 seed 2, time: 582.51s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 1 budget 30 seed 2, time: 596.20s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 5 budget 30 seed 2, time: 612.98s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 10 budget 30 seed 2, time: 628.34s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 4 noise 20 budget 30 seed 2, time: 643.01s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 1 budget 30 seed 2, time: 657.63s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 5 budget 30 seed 2, time: 669.18s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 10 budget 30 seed 2, time: 683.32s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 8 noise 20 budget 30 seed 2, time: 703.73s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 1 budget 30 seed 2, time: 719.24s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 5 budget 30 seed 2, time: 737.70s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 10 budget 30 seed 2, time: 763.14s\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 10 noise 20 budget 30 seed 2, time: 786.99s\n", - "all experiments done, time: 786.99s\n" - ] - } - ], - "source": [ - "seeds = list(range(3))\n", - "n_inits = [2, 4, 8, 10]\n", - "noise_levels = [1, 5, 10, 20]\n", - "# budgets = [10, 20, 50]\n", - "noise_bools = [True]\n", - "budget = 30\n", - "\n", - "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_16892\\636701015.py:10: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
n_initnoise_levelseednoise_boolbest
04100True207.341003
04101True42.158676
041000True120.417244
041001True42.158676
010100True1.374434
010101True42.158676
0101000True102.431664
0101001True42.158676
04100False118.466827
04101False0.138172
041000False118.751045
041001False13.000295
010100False0.007694
010101False0.265530
0101000False25.227522
0101001False0.034306
\n", - "
" - ], - "text/plain": [ - " n_init noise_level seed noise_bool best\n", - "0 4 10 0 True 207.341003\n", - "0 4 10 1 True 42.158676\n", - "0 4 100 0 True 120.417244\n", - "0 4 100 1 True 42.158676\n", - "0 10 10 0 True 1.374434\n", - "0 10 10 1 True 42.158676\n", - "0 10 100 0 True 102.431664\n", - "0 10 100 1 True 42.158676\n", - "0 4 10 0 False 118.466827\n", - "0 4 10 1 False 0.138172\n", - "0 4 100 0 False 118.751045\n", - "0 4 100 1 False 13.000295\n", - "0 10 10 0 False 0.007694\n", - "0 10 10 1 False 0.265530\n", - "0 10 100 0 False 25.227522\n", - "0 10 100 1 False 0.034306" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", - "for noise_bool in noise_bools:\n", - " for n_init in n_inits:\n", - " for noise_level in noise_levels:\n", - " for seed in seeds:\n", - " X, Y, Y_real, model = torch.load(f\"results/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", - " sliding_min = torch.zeros(Y.shape[0])\n", - " for i in range(Y_real.shape[0]):\n", - " sliding_min[i] = Y_real[:i+1].min().item()\n", - " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool], \"best\": [sliding_min[-1].item()]})])\n", - " \n", - "df " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GP with noise\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 best
 meanstd
noise_level1010010100
n_init    
4124.7581.29116.8055.34
1021.7772.3028.8442.62
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GP without noise\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 best
 meanstd
noise_level1010010100
n_init    
459.3065.8883.6774.78
100.1412.630.1817.81
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_no_noise = df[df[\"noise_bool\"] == False]\n", - "df_noise = df[df[\"noise_bool\"] == True]\n", - "# df = df.groupby([\"n_init\", \"noise_level\", \"noise_bool\"]).agg({\"min\": [\"mean\", \"std\"]})\n", - "df_no_noise = df_no_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "df_noise = df_noise.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "print(\"GP with noise\")\n", - "display(df_noise.unstack().style.format(\"{:.2f}\").background_gradient(cmap='viridis'))\n", - "print(\"GP without noise\")\n", - "display(df_no_noise.unstack().style.format(\"{:.2f}\").background_gradient(cmap='viridis'))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/baybe_result_plots.ipynb b/baybe_result_plots.ipynb deleted file mode 100644 index 21a3a1e..0000000 --- a/baybe_result_plots.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 21, - "id": "d6c817ba-cab1-419e-97b0-d5d5f7fd4f01", - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sn\n", - "import numpy as np\n", - "import torch\n", - "import pandas as pd\n", - "\n", - "from src import visualization\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "783c52b7-cf3b-44b7-b14f-445227c15754", - "metadata": {}, - "outputs": [], - "source": [ - "n_inits = [2, 4, 8, 10]\n", - "noise_levels = [1, 5, 10, 20]\n", - "\n", - "n_inits = n_inits[::-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9a3411fe-7c63-47eb-b080-800019cd1458", - "metadata": {}, - "outputs": [], - "source": [ - "performance_matrix = np.zeros((len(n_inits), len(noise_levels)))\n", - "\n", - "for i, init in enumerate(n_inits):\n", - " for j, noise in enumerate(noise_levels):\n", - " y_vals = torch.load(f'results/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", - " best_y = torch.min(y_vals)\n", - " performance_matrix[i,j] = best_y\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f0925240-1130-40f6-9835-51e6a4fcf89f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, ax = plt.subplots()\n", - "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "62d7a546-5ca9-4836-b075-dfa5792c7907", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.savefig('BAYBE_heatmap.png', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "1e8781e6-ed60-4959-91fb-8ea7ef3ec956", - "metadata": {}, - "outputs": [], - "source": [ - "fig.savefig('BayBE_heatmap.png', dpi = 300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce3eb3a5-5150-4b5b-bd41-636edd081feb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/botorch_results_plots.ipynb b/botorch_results_plots.ipynb index 2fd5bed..64bf7b6 100644 --- a/botorch_results_plots.ipynb +++ b/botorch_results_plots.ipynb @@ -169,7 +169,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/combined_botorch_baybe_final.ipynb b/combined_botorch_baybe_final.ipynb index f6b613a..2e606d6 100644 --- a/combined_botorch_baybe_final.ipynb +++ b/combined_botorch_baybe_final.ipynb @@ -347,7 +347,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -361,9 +361,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.10.14" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/comparison.ipynb b/comparison.ipynb index 61d7847..6ec2e85 100644 --- a/comparison.ipynb +++ b/comparison.ipynb @@ -1 +1,341 @@ -{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["SMOKE_TEST None\n","SMOKE_TEST None\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","\n","from botorch.models.gp_regression import (\n"," SingleTaskGP,\n",")\n","from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n","from botorch.fit import fit_gpytorch_model\n","from botorch.models.transforms.outcome import Standardize\n","\n","from botorch.optim.optimize import optimize_acqf\n","from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n","from botorch.sampling.normal import SobolQMCNormalSampler\n","from botorch.utils.transforms import normalize, unnormalize\n","import os\n","import gc\n","from botorch.utils.sampling import draw_sobol_samples\n","\n","tkwargs = {\n"," \"dtype\": torch.double,\n"," \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n","}\n","SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n","# SMOKE_TEST = True\n","print(\"SMOKE_TEST\", SMOKE_TEST)\n","NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n","RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n","MC_SAMPLES = 128 if not SMOKE_TEST else 16\n","batch_size = 1\n","\n","\n","from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"ename":"NameError","evalue":"name 'n_init' is not defined","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[1;32mIn[2], line 22\u001b[0m\n\u001b[0;32m 18\u001b[0m problem \u001b[38;5;241m=\u001b[39m SchwefelProblem(n_var\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, noise_level\u001b[38;5;241m=\u001b[39mnoise_level)\n\u001b[0;32m 20\u001b[0m bounds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(problem\u001b[38;5;241m.\u001b[39mbounds, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtkwargs)\n\u001b[1;32m---> 22\u001b[0m train_X, train_Y, train_Y_real\u001b[38;5;241m=\u001b[39m generate_initial_data(problem, \u001b[43mn_init\u001b[49m, bounds)\n\u001b[0;32m 24\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time()\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(budget):\n","\u001b[1;31mNameError\u001b[0m: name 'n_init' is not defined"]}],"source":["from src.schwefel import SchwefelProblem\n","from time import time\n","\n","torch.manual_seed(0)\n","np.random.seed(0)\n","\n","\n","seed = 0 \n","n_inits = 60\n","noise_level = 10\n","noise_bool = True\n","budget = 1\n","\n","\n","torch.manual_seed(seed)\n","np.random.seed(seed)\n","\n","problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n","\n","bounds = torch.tensor(problem.bounds, **tkwargs)\n","\n","train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n","\n","start_time = time()\n","\n","for i in range(budget):\n"," print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n"," \n"," train_x = normalize(train_X, bounds)\n"," mll, model = initialize_model(train_x, train_Y, noise_bool)\n"," fit_gpytorch_model(mll)\n"," \n"," # optimize the acquisition function and get the observations\n"," X_baseline = train_x\n"," sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n","\n"," acq_func = qNoisyExpectedImprovement(\n"," model=model,\n"," X_baseline=X_baseline,\n"," prune_baseline=True,\n"," sampler=sampler,\n"," )\n","\n"," x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n"," X_cand = unnormalize(x_cand, bounds)\n"," Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n"," Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n"," print(f\"New candidate: {X_cand}, {Y_cand}\")\n","\n"," # update the model with new observations\n"," train_X = torch.cat([train_X, X_cand], dim=0)\n"," train_Y = torch.cat([train_Y, Y_cand], dim=0)\n"," train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n"," \n","train_x = normalize(train_X, bounds)\n","mll, model = initialize_model(train_x, train_Y, noise_bool)\n","fit_gpytorch_model(mll)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best value found: 764.5328592764758\n","Best solution found: [-25.51163547 38.93445618]\n","Best real value found: 801.2781865671863\n","Best real solution found: [ 43.27939814 -25.89256121]\n","Total number of evaluations: 51\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# print trial results\n","print(f\"Best value found: {train_Y.min().item()}\")\n","print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n","print(f\"Best real value found: {train_Y_real.min().item()}\")\n","print(f\"Best real solution found: {train_X[train_Y_real.argmin()].numpy()}\")\n","print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n","\n","sliding_min = torch.zeros(train_Y.shape[0])\n","for i in range(train_Y.shape[0]):\n"," sliding_min[i] = train_Y[:i+1].min().item()\n"," \n","plt.plot(sliding_min, label='Best found value')\n","\n","#plot all evaluations\n","plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n","#vline\n","plt.axvline(x=n_init, color='r', linestyle='--')\n","#\n","plt.xlabel('Iteration')\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#plot model\n","X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach()\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower\n"," upper = -upper\n","\n","plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n","plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n","\n","#plot true function\n","Y = torch.tensor(problem.y(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n","F = torch.tensor(problem.f(X.cpu().numpy()))\n","plt.plot(X.cpu().numpy(), F.cpu().numpy(), label='True function without noise', linestyle='--')\n","\n","\n","# Convert your data to numpy arrays for easier manipulation\n","train_X_np = train_X.cpu().numpy()\n","train_Y_np = train_Y.cpu().numpy()\n","\n","# Generate a list of indices for the optimization samples\n","c_unnormed = list(range(len(train_X_np[n_init:])))\n","\n","# Normalize the colors to be between 0 and 1\n","\n","# Plot initial samples\n","# plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n","\n","# Plot optimization samples with colors\n","# plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', cmap='viridis', alpha=0.5, marker='x')\n","\n","plt.xlabel('X')\n","plt.xlim(bounds[0, 0], bounds[1, 0])\n","plt.ylabel('Objective')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1gAAAIeCAYAAACm6a5tAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZwlWVnnj79PRNx7897cs/a9upau6ironV6qaRpkB2dUcJpNBRQHBgG/6k9mYFDpAUQcQBwUFEVZBJlxGjdAFgVkRHa6sqq69qqsfcvKPe8aEef8/ogbkXGX3G9m3qx63q9XLXkzlnO3OPE5z/N8HmWMMQiCIAiCIAiCIAjzxlrqAQiCIAiCIAiCINwoiMASBEEQBEEQBEFoECKwBEEQBEEQBEEQGoQILEEQBEEQBEEQhAYhAksQBEEQBEEQBKFBiMASBEEQBEEQBEFoECKwBEEQBEEQBEEQGoQILEEQBEEQBEEQhAYhAksQBEEQBEEQBKFBiMASBEEQBEGo4pnPfCbPfOYzl3oYgiAsQ0RgCYIgCIIgNBnf//73eeMb38g999xDIpFAKTXl9h//+Me57bbbaGlpYefOnXz4wx9epJEKglCNCCxBEARBEIQqvvrVr/LVr351yc7/pS99iT//8z9HKcW2bdum3PZP//RPed3rXsfevXv58Ic/zIMPPshb3vIW3ve+9y3SaAVBiKOMMWapByEIgiAIgiBMcPXqVTo6Okin07zpTW/ij//4j6l3y5bP59m0aRMPPPAAX/jCF6LHf+7nfo6/+7u/4/z583R3dy/m0AXhpkciWIIgCIIgLHve+c53opTi5MmTvOY1r6Grq4vOzk5e+9rXksvlou08z+Nd73oX27dvJ5VKsXXrVt7+9rdTLBYrjlevBuvDH/4we/fuJZPJ0N3dzb333stnP/vZim0uXrzIL/7iL7JmzRpSqRR79+7lL/7iL2b9fNasWUM6nZ52u2984xsMDAzwxje+seLxX/mVXyGbzfLFL35x1ucWBGF+iMASBEEQBOGG4dFHH2VsbIz3vve9PProo3ziE5/gsccei37/ute9jt/+7d/m7rvv5g/+4A945JFHeO9738vLX/7yKY/7Z3/2Z7zlLW9hz549fOhDH+Kxxx7jzjvv5Hvf+160zdWrV3nggQf453/+Z970pjfxh3/4h+zYsYNf+qVf4kMf+tCCPN8nnngCgHvvvbfi8XvuuQfLsqLfC4KweDhLPQBBEARBEIRGcdddd/Hxj388+nlgYICPf/zjvO9976O3t5dPfvKTvO51r+PP/uzPAHjjG9/I6tWref/73883vvENnvWsZ9U97he/+EX27t3L3/zN30x67v/+3/87vu9z8OBBVqxYAcAb3vAGXvGKV/DOd76T17/+9TOKSs2Gy5cvY9s2q1evrng8mUyyYsUKLl261NDzCYIwPRLBEgRBEAThhuENb3hDxc8PP/wwAwMDjI6O8qUvfQmAX//1X6/Y5jd+4zcApkyn6+rq4sKFC/zgBz+o+3tjDI8//jj/4T/8B4wxXL9+Pfrz/Oc/n5GREX784x/P56nVJZ/Pk0wm6/6upaWFfD7f8HMKgjA1IrAEQRAEQbhh2Lx5c8XPocHD0NAQZ8+exbIsduzYUbHN2rVr6erq4uzZs5Me97/+1/9KW1sb9913Hzt37uRXfuVX+Pa3vx39vr+/n+HhYT72sY+xatWqij+vfe1rAbh27VqjnmZEOp2mVCrV/V2hUGh4xEwQhOmRFEFBEARBEG4YbNuu+3jcgW+6nlL1uO222zh27Bhf+MIX+PKXv8zjjz/ORz7yEX77t3+bxx57DK01ELj3vfrVr657jNtvv33W552OdevW4fs+165dq0gTLJVKDAwMsH79+oafUxCEqRGBJQiCIAjCTcGWLVvQWnPixAluu+226PGrV68yPDzMli1bpty/tbWVl73sZbzsZS+jVCrxkpe8hPe85z287W1vY9WqVbS3t+P7Ps95znMW+qlE3HnnnQD88Ic/5EUvelH0+A9/+EO01tHvBUFYPCRFUBAEQRCEm4JQgFQ7+n3wgx8E4MUvfvGk+w4MDFT8nEwm2bNnD8YYXNfFtm1e+tKX8vjjj3Po0KGa/fv7++c5+vr8xE/8BD09PXz0ox+tePyjH/0omUxmyuckCMLCIBEsQRAEQRBuCu644w5e/epX87GPfYzh4WEeeeQRvv/97/PJT36Sn/7pn57UQRDgec97HmvXruWhhx5izZo1HDlyhD/6oz/ixS9+Me3t7QD83u/9Ht/4xje4//77+eVf/mX27NnD4OAgP/7xj/nnf/5nBgcHZzzWs2fP8ulPfxoIolEA7373u4EgEvfzP//zQFCD9a53vYtf+ZVf4T/9p//E85//fP7f//t//NVf/RXvec976OnpmdNrJQjC3BGBJQiCIAjCTcOf//mfs23bNj7xiU/wt3/7t6xdu5a3ve1t/M7v/M6U+73+9a/nM5/5DB/84AcZHx9n48aNvOUtb+Ed73hHtM2aNWv4/ve/z//4H/+Dz3/+83zkIx9hxYoV7N27l/e9732zGmdfXx+/9Vu/VfFY+PMjjzwSCSwIrOYTiQQf+MAH+Id/+Ac2bdrEH/zBH/Crv/qrszqnIAiNQZl41acgCIIgCIIgCIIwZ6QGSxAEQRAEQRAEoUFIiqAgCIIgCMIi0d/fj+/7k/4+mUxK3ZQgLHMkRVAQBEEQBGGR2Lp165QNjR955BG++c1vLt6ABEFoOBLBEgRBEARBWCQ+85nPkM/nJ/19d3f3Io5GEISFQCJYgiAIgiAIgiAIDUJMLgRBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEoQ50tfXx5ve9CZuvfVWMpkMmUyGPXv28Cu/8iscOHCgYtt3vvOdKKWiP+G273jHOxgdHZ3yPGfOnIn2e/e73113m1e96lUopWhra2vY8xMEQRAEQRBmj7PUAxCE5cgXvvAFXvayl+E4Dq961au44447sCyLo0eP8vnPf56PfvSj9PX1sWXLlor9PvrRj9LW1sb4+Dhf/epXec973sPXv/51vv3tb6OUmvKcLS0t/PVf/zXveMc7Kh7PZrP8/d//PS0tLQ1/noIgCIIgCMLsEIElCLPk1KlTvPzlL2fLli38y7/8C+vWrav4/fve9z4+8pGPYFm1AeKf/dmfZeXKlQC84Q1v4KUvfSmf//zn+e53v8uDDz445Xlf9KIX8fnPf57e3l7uuOOO6PG///u/p1Qq8YIXvICvf/3rDXiGgiAIgiAIwlyRFEFBmCW///u/Tzab5S//8i9rxBWA4zi85S1vYdOmTdMe6yd+4ieAIN1wOh588EFuueUWPvvZz1Y8/pnPfIYXvOAF9PT01N3vn/7pn3j44YdpbW2lvb2dF7/4xTz55JMV2xw4cIDXvOY1bNu2jZaWFtauXcsv/uIvMjAwULFdmOp48uRJXvOa19DV1UVnZyevfe1ryeVy0z4HQRAEQRCEGx0RWIIwS77whS+wY8cO7r///nkf69SpUwCsWLFiRtu/4hWv4HOf+xzGGACuX7/OV7/6VV75ylfW3f7Tn/40L37xi2lra+N973sfv/Vbv8Xhw4d5+tOfzpkzZ6Ltvva1r3H69Gle+9rX8uEPf5iXv/zlfO5zn+NFL3pRdK44jz76KGNjY7z3ve/l0Ucf5ROf+ASPPfbYLJ+9IAiCIAjCjYekCArCLBgdHeXSpUv89E//dM3vhoeH8Twv+rm1tZV0Ol2xzeDgIEBUg/WRj3yENWvW8PDDD8/o/K985Sv53d/9Xb797W/z9Kc/nf/zf/4PLS0t/Mf/+B/58pe/XLHt+Pg4b3nLW3jd617Hxz72sejxV7/61ezatYvf/d3fjR5/4xvfyG/8xm9U7P/AAw/wile8gn/7t3+rGd9dd93Fxz/+8ejngYEBPv7xj/O+971vRs9DEARBEAThRkUiWIIwC0LHv3pufc985jNZtWpV9OeP//iPa7bZtWsXq1at4pZbbuH1r389O3bs4Itf/CKZTGZG59+7dy+33347f/3Xfw3AZz/7WX7qp36q7v5f+9rXGB4e5hWveAXXr1+P/ti2zf333883vvGNaNu4ECwUCly/fp0HHngAgB//+Mc1x37DG95Q8fPDDz/MwMDAtI6IgiAIgiAINzoSwRKEWdDe3g4E0aFq/vRP/5SxsTGuXr3Kz/3cz9Xd//HHH6ejo4NEIsHGjRvZvn37rMfwyle+kg984AP82q/9Gv/+7//O29/+9rrbnThxApio86qmo6Mj+v/g4CCPPfYYn/vc57h27VrFdiMjIzX7bt68ueLn7u5uAIaGhiqOKwiCIAiCcLMhAksQZkFnZyfr1q3j0KFDNb8La7LitU3VPOMZz4hcBOfKK17xCt72trfxy7/8y6xYsYLnPe95dbfTWgNBHdbatWtrfu84E1//Rx99lH//93/nN3/zN7nzzjtpa2tDa80LXvCC6DhxbNuue8569VqCIAiCIAg3EyKwBGGWvPjFL+bP//zP+f73v89999236OffvHkzDz30EN/85jf5L//lv1QIpThhdGz16tU85znPmfR4Q0ND/Mu//AuPPfYYv/3bvx09HkbABEEQBEEQhJkjNViCMEve+ta3kslk+MVf/EWuXr1a8/vFiOK8+93v5nd+53d485vfPOk2z3/+8+no6OB3f/d3cV235vf9/f3ARDSqetwf+tCHGjdgQRAEQRCEmwSJYAnCLNm5cyef/exnecUrXsGuXbt41atexR133IExhr6+Pj772c9iWRYbN25csDE88sgjPPLII1Nu09HRwUc/+lF+/ud/nrvvvpuXv/zlrFq1inPnzvHFL36Rhx56iD/6oz+io6ODZzzjGfz+7/8+ruuyYcMGvvrVr86oN5cgCIIgCIJQiQgsQZgDP/VTP8XBgwf5wAc+wFe/+lX+4i/+AqUUW7Zs4cUvfjFveMMbuOOOO5Z6mLzyla9k/fr1/N7v/R7/83/+T4rFIhs2bODhhx/mta99bbTdZz/7Wd785jfzx3/8xxhjeN7znsc//dM/sX79+iUcvSAIgiAIwvJDGalKFwRBEARBEARBaAhSgyUIgiAIgiAIgtAgRGAJgiAIgiAIgiA0CBFYgiAIgiAIgiAIDUIEliAIgiAIgiAIQoMQgSUIgiAIgiAIgtAgRGAJgiAIgiAIgiA0CBFYgiAIgiAIgiAIDUIEliAIgiAIgiAIQoMQgSUIgiAIgiAIgtAgRGAJgiAIgiAIgiA0CBFYgiAIgiAIgiAIDUIEliAIgiAIgjAt73znO1FKLfUwBKHpEYElCIIgCMINg1JqRn+++c1vLuk4n/nMZ046tqNHjy7ZuHK5HO985zuX/PURhOWMs9QDEARBEARBaBSf/vSnK37+1Kc+xde+9rWax2+77bbFHFZdNm7cyHvf+96ax9evX78EownI5XI89thjQCAC47zjHe/gv/23/7YEoxKE5YUILEEQBEEQbhh+7ud+ruLn7373u3zta1+rebyaXC5HJpNZyKHV0NnZOe24mgnHcXAcuXUUhOmQFEFBEARBEG4qnvnMZ/KUpzyFH/3oRzzjGc8gk8nw9re/HQhSDN/5znfW7LN161Ze85rXVDw2PDzM//f//X9s2rSJVCrFjh07eN/73ofWet5j/MQnPoFSijNnzlQ8/s1vfrMmxTF8PocPH+ZZz3oWmUyGDRs28Pu///s1xy0UCrzzne/k1ltvpaWlhXXr1vGSl7yEU6dOcebMGVatWgXAY489FqUshq9HvRosz/N417vexfbt20mlUmzdupW3v/3tFIvFiu22bt3KT/7kT/Jv//Zv3HfffbS0tLBt2zY+9alPzfu1EoRmQwSWIAiCIAg3HQMDA7zwhS/kzjvv5EMf+hDPetazZrV/LpfjkUce4a/+6q/4hV/4Bf7X//pfPPTQQ7ztbW/j13/912d0DN/3uX79esWf8fHxuTwdhoaGeMELXsAdd9zBBz7wAXbv3s1//a//lX/6p3+qON9P/uRP8thjj3HPPffwgQ98gF/91V9lZGSEQ4cOsWrVKj760Y8C8DM/8zN8+tOf5tOf/jQveclLJj3v6173On77t3+bu+++mz/4gz/gkUce4b3vfS8vf/nLa7Y9efIkP/uzP8tzn/tcPvCBD9Dd3c1rXvMannzyyTk9Z0FoViTOKwiCIAjCTceVK1f4kz/5E17/+tfPaf8PfvCDnDp1iieeeIKdO3cC8PrXv57169fzP//n/+Q3fuM32LRp05THOHr0aBQxCnn1q1/NJz7xiVmP59KlS3zqU5/i53/+5wH4pV/6JbZs2cLHP/5xXvjCFwJBPdq//Mu/8MEPfpBf+7Vfi/b9b//tv2GMQSnFz/7sz/Jf/st/4fbbb582fbG3t5dPfvKTvO51r+PP/uzPAHjjG9/I6tWref/73883vvGNCuF67NgxvvWtb/Hwww8D8Oijj7Jp0yb+8i//kve///2zfs6C0KxIBEsQBEEQhJuOVCrFa1/72jnv/zd/8zc8/PDDdHd3V0SgnvOc5+D7Pt/61remPcbWrVv52te+VvHnrW9965zG09bWViGIkskk9913H6dPn44ee/zxx1m5ciVvfvOba/afi/36l770JYCaiN1v/MZvAPDFL36x4vE9e/ZE4gpg1apV7Nq1q2KMgnAjIBEsQRAEQRBuOjZs2EAymZzz/idOnODAgQM1EaiQa9euTXuM1tZWnvOc58x5DHE2btxYI5K6u7s5cOBA9POpU6fYtWtXw4wqzp49i2VZ7Nixo+LxtWvX0tXVxdmzZyse37x5c80xuru7GRoaash4BKFZEIElCIIgCMJNRzqdntX2vu9X/Ky15rnPfe6kEadbb711zmODySNK1eMIsW277uPGmHmNYybMNPq1lGMUhMVEBJYgCIIgCEKZ7u5uhoeHKx4rlUpcvny54rHt27czPj7esAhUvXEANWOpjgrNhu3bt/O9730P13VJJBJ1t5lNquCWLVvQWnPixImKvmJXr15leHiYLVu2zHmsgrCckRosQRAEQRCEMtu3b6+pn/rYxz5WEzl69NFH+c53vsNXvvKVmmMMDw/jed68xwFUjMX3fT72sY/N+ZgvfelLuX79On/0R39U87swihT2AqsWdvV40YteBMCHPvShisc/+MEPAvDiF794zmMVhOWMRLAEQRAEQRDKvO51r+MNb3gDL33pS3nuc59Lb28vX/nKV1i5cmXFdr/5m7/JP/zDP/CTP/mTvOY1r+Gee+4hm81y8OBB/u///b+cOXOmZp/ZsHfvXh544AHe9ra3MTg4SE9PD5/73OfmJdx+4Rd+gU996lP8+q//Ot///vd5+OGHyWaz/PM//zNvfOMb+amf+inS6TR79uzhf//v/82tt95KT08PT3nKU3jKU55Sc7w77riDV7/61XzsYx9jeHiYRx55hO9///t88pOf5Kd/+qdnbX0vCDcKIrAEQRAEQRDK/PIv/zJ9fX18/OMf58tf/jIPP/wwX/va13j2s59dsV0mk+Ff//Vf+d3f/V3+5m/+hk996lN0dHRw66238thjj9HZ2TnvsXzmM5/h9a9/Pb/3e79HV1cXv/RLv8SznvUsnvvc587peLZt86UvfYn3vOc9fPazn+Xxxx9nxYoVPP3pT+epT31qtN2f//mf8+Y3v5lf+7Vfo1Qq8Tu/8zt1BVa47bZt2/jEJz7B3/7t37J27Vre9ra38Tu/8ztzGqMg3AgoI5WFgiAIgiAIgiAIDUFqsARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBAisARBEARBEARBEBqECCxBEARBEARBEIQGIQJLEARBEARBEAShQYjAEgRBEARBEARBaBDOUg9AEJYaYwy+71MoFLAsC8dxcBwHpRRKqaUeniAIgiAIgrCMEIEl3NQYY3BdF8/zKJVKAJGwsm07Elu2bYvgEgRBEARBEKZFGWPMUg9CEJYCrTWlUgmtNUopSqVSJKCMMWitMcZUCK5EIoFt29i2jWVJhq0gCIIgCIJQiQgs4aYjTAl0XRdjDJZlYYyJBFZ1lMoYU1dwxaNbIrgEQRAEQRAEEIEl3GRorfE8D9/3gYl0QGMMxWJxRmmA1YILqKjdEsElCIIgCIJw8yICS7gpCAVRGLWqFlJhBAuYdZ2VRLgEQRAEQRCEEBFYwg2PMQbP8/A8D2DSNMC5Cqx654tHt8JjxgVX6FIoCIIgCIIg3FiIwBJuaMKole/7KKUmjSI1UmDVO3YouMIIl2VZdV0KBUEQBEEQhOWNCCzhhiQ0svA8D601lmVNKWAWUmDVO9dkgivuUiiCSxAEQRAEYfkhAku44Qh7W1UbWUy3z2IJrOrzAnUFV3UNlwguQRAEQRCE5kcaDQs3FPHeVtNFrZqBcHy2bQOVgqu/v58zZ85wxx13iOASBEEQBEFYJojAEm4I6vW2Wo4CJC64wubHtm2jtaZYLEZRNhFcgiAIgiAIzYkILGHZU50SuFzF1WQopSIRFaYRhn274oIrrN9yHOeGew0EQRAEQRCWCyKwhGVNGLVaLimB8yVeT1YtuAqFQrRNKLjCCNfN8NoIgiAIgiA0AyKwhGVJdW+rG1FAKKWYzoNGBJcgCIIgCEJzIQJLWHaEva201gCT9ra6GZmp4KruwSWCSxAEQRAEoTGIwBKWDWH/qIVKCZxJxGi5MZng0lpHgsuyrBrTDBFcgiAIgiAIc0MElrAsCI0sjh49imVZ7NixQwTAHJhMcPm+j+/7FAoFEVyCIAiCIAjzQASW0PSEUatQBITNeIX5EwquMM2yWnAVi8WKGq4wtXAmzZsFQRAEQRBuRkRgCU1LeKPveV6UEmhZ1g2XxjcZS5GyOJXg8jwv+n11DZcILkEQBEEQhAARWEJTMllvK6VUZG4hLDyTCS7P83Bdt0JwhRGuMKVQEARBEAThZkQEltB0TNXbyrIsXNddwtHd3EwluJ544gk2bdpEd3e3CC5BEJYlkoIuCEIjEIElNA0z6W11Izr9TcZymOTjgqtUKkWPhREuoMYwQwSXIAjNhtaaUqlEsViUnoGCIMwbEVhCU6C1xvO8KCVwspqem0lgLTfCld+wVi58LEz3LJVK0fsqgksQhGYgXmMazkFhGwtxVBUEYa6IwBKWlHhvq/AGfarJ62YTWMvtudaLOFanFIbvdxjhqhZcoUuhIAjCQlJd6xteq+q1sIg7qorgEgRhOkRgCUtGvcltuolqIQWW67rR5CrMnpm8L6EhRnyfUHDVi3DFXQoFQRAaRb1a37iB0kxbWIijqiAI9RCBJSwJ8d5Ws1kBXAiBZYyhr6+PEydOYNs2XV1d9PT00N3dTSaTkclyAZmJ4ApFb9w0Q94TQRDmQrzW1xgz4/lnNo6qIrgEQRCBJSwq9XpbzWbysSyroTbtpVKJgwcPMjY2xl133QXAyMgI169f5+TJkziOQ3d3d/QnnU437NzTsdwm5Ua4b81UcFXXcC2310oQhMWnutZ3Pul90sJCEISpEIElLBqT9baaDY2MYA0NDdHb20tHRwf79u0Dggm4q6uLLVu2oLVmdHSUwcFBLl++zLFjx0ilUhWCK5VKNWQsQn3igit83+NuXyK4BEGYjtnW+s6FmQouMfgRhJsDEVjCohDeFM8lahWnEQLLGMPp06c5ffo0t956K5s3bwao6a9lWRZdXV10dXUBQc7+8PAwQ0NDnD9/nsOHD5PJZCoEVyKRmNfY6o11ObGQwiY8tgguQRBmSnX7j8VK2ZtMcIXReJAWFoJwIyMCS1hQwpTAcOVwvo5L8xVYxWKRAwcOkMvluO++++js7IzGOR22bbNixQpWrFgBBIIsFFx9fX0cOnSI9vb2SGx1dnbiODfPV2yxxWA9wRX+KRaLU97EiOAShBufeK1vXOwsBdMJrrjwSyaTUVqhCC5BWJ7cPHd/wqLTyHz3kGqnp9kwMDDAgQMH6O7uZt++ffOONiUSCVatWsWqVauAoJ5raGiIoaEhjh8/TqFQoKOjIxJcHR0dN7xD4VIKl/gNStxmORRc8QhXePPiOI7YLAvCDcZca30X8zowWQuLH/zgB2zbto2enh5pYSEIyxgRWELDWch8d8uyZh0pMcZw6tQp+vr62LVrF5s2bVqQSSqZTLJmzRrWrFkDQD6fjwTXpUuX8DyPzs7OSHC1t7dPuTq53CbSZktnnEpwFQoFLl++TDKZZPXq1SQSCelrIwg3AI2o9V0K4vWmYaRdHFUFYfkiAktoKAud7z7bFMFCocCBAwcoFArcf//9dHR0THrcRk9U6XSadDrN+vXrMcaQy+UiwXXu3DmMMXR1dUWCq62trWYMzSZaljPVgmt8fJx0Oh0JrnCbapvl5XKDJgg3O/Ot9S0UCoyPj9PZ2blkqXnhmKWFhSAsb0RgCQ1jMfLdZyOwrl+/zoEDB1ixYgV33333ktZDKaVobW2ltbWVjRs3YoxhfHw8Elx9fX1YllVhmLEcxdVymtzDmsDwxiSMbmmtI8FlWVZNDZcILkFoLhpR63vlyhUOHToUXXenW/xaSOpd+ydzVJUWFoLQnIjAEubNfHtbzYaZCCytNSdPnuTs2bPcdtttbNiwoekmGaUU7e3ttLe3s3nzZrTWjI2NMTQ0xNWrV6Omx77vc/nyZbq7u2lpaVnqYU/JchOE1X27Jksp1FpTLBYpFAoiuAShyZhvSqDv+xw9epTLly+zd+9eOjs7K9K7+/r6UErR3d0dia7W1tYlrzcFcVQVhGZGBJYwL8JIzLlz59i2bduC32xOZ3JRKBTo7e2lVCrxwAMP0N7evmBjaSSWZdHZ2UlnZydbt27F932uXr3KsWPHuHjxIkePHqWlpaUiwpVMJpd62DUsp8l7usbI1WmjoeDyfR/f9yc1zVgsG2hBuNkJo1ZzXdgbHx9n//792LbNvn37aGlpoVQq1Sx+hdkGAwMDnDp1Ctu2axrQN6vgEkdVQVgaRGAJcyZcLcvn8/T19bFjx44FP+dUJhf9/f0cOHCA1atXc8899yxri3TbtiPXwXvvvRfP8yJL+LNnz/Lkk0/S2tpKd3c3PT09dHV1LfnzXe4RrOmYzGY5jN6Gv6+u4RLBJQiNJaz1PXnyJMVikd27d8/qO2aM4eLFixw5coTNmzezc+dOLMuqu3hnWRYdHR10dHRUNKAPsw2OHz9OMpmMxFZXVxfpdHrOz60R14q44KrnqBoXXOKoKggLw/K9AxWWjOp8d8dx5mydPlvqpQhqrTlx4gTnzp1jz549bNiwYc7HbjbC5+o4DitXrmTlypVAYAkfCq6TJ0+Sz+drenAthSV8M76GkzFbgVXNZILL8zxc151UcElfG0GYO2Hdkda6YnFjpniex5NPPsnAwAB33XVXdE2dKfEG9Lfccgu+7zMyMhK5xR49epRUKlUR4UqlUrN9mg1lOkfVcJtQcEn6syDMHxFYwqyol+8eRpXme8M6E6oFVj6fp7e3F8/zePDBB2lra5vzsZdTBCa0F1+9ejUQNFAeHBxkaGiII0eOUCqVKizhOzo65Ma+Cq11wx0uZyq44q5f8r4IwvTEXfTClMDZtu0YGRmht7eXdDrNQw891BDhY9s2PT099PT0AIGACwXX+fPnOXz4MJlMpiLCNV1690LPRSK4BGHhEYElzJjJ8t3jN5SLKbCuXbvGwYMHWbNmDbfddtsN38R3KlKpFOvWrWPdunUYYyqKtC9cuIDWuqYHV6Pfq+UkUGHhP6/TCS6oXxMhgksQKpnMyGKmjeeNMZw9e5YTJ06wbds2tm3btmDffcdxWLFiBStWrADAdd0o26Cvr49sNktbW1sktrq6uubd9H6+zERweZ5HPp9n1apVIrgEYQaIwBKmpbq3VfVFNfx/KLwWknBCPXLkCBcuXGDv3r2sX79+Qc+5VMx14lJKkclkyGQybNiwAWMM2Ww2ElxnzpyJXLHCP5lMpqG5/8uBxVgQiDOZ4IrbLCulRHAJQox4+496c890CzulUolDhw4xOjrKvffeS3d390IPuYJEIsGqVatYtWpVNJ5QcJ06dYpcLleR3t0MC1X1BNfo6CjHjx+PjKPEUVUQpkYEljAl8ZQMoO7NXvyGcaEplUp4nsfg4CD79u2jtbV1wc+53FFK0dbWRltbG5s2bapwxerv7+fkyZM4jlPjijVbmuHGYDaE/XKWinqCK/y+hRGuasEVuhQKwo3OTNp/TCewhoaG6O3tpaOjg3379jWF82q99O5w8evYsWMUCgVOnTrF2NhYlN691NkZ4bVKKUUikZjSUVUElyAEiMAS6lIv332yC2V4g7jQRhdXrlzh4MGDADzwwANLPuksBgshWuq5YoU1A5cvX+bYsWNzLtJeTpPpYkewpiPeSBQqv4NhhCufz5NMJmlra6twKRSEG4mZ9raarAbLGMPp06c5ffo0O3fuZMuWLU37PUmlUqxdu5a1a9cC8L3vfY+uri7y+TyXLl3C8zw6OjqWvJ42/jpP5agaF1zhopC0sBBuRkRgCTXMtnFj+LuFimD4vs+xY8e4dOkSt956K0ePHr0pxNViYVlWNHlD8HqHKSz1irS7u7vr1gwsxwhWM0/29QTX2bNnyWQybNq0KbrBqTbNaObnJAjTMZveVvUiWIVCgYMHD5LP57nvvvvo7Oxc6CE3lPB6vHr16iWrp50tc3VUFcEl3MiIwBIqmCrffTJmU2w8W7LZLL29vQDs27cPpRRHjhxZkJtjudAH2LY9ZZH2oUOHaizhl7oH11xodoFVTXgz6ThOlKZTHeGSRqLCcmW6Wt96VAus/v5+Dh48yIoVK7jrrruW5XUpznT1tGfPngWgq6sruh63trYu2Hd+pscVR1VBEIEllJlJvvtUTNakcT5cvnyZQ4cOsXHjRnbt2oVlWZGj0c1As9wYVxdpV9cMFItFOjo6okLoVCq1LCKMy01gQWXdWDzCFd5khs2/J6uJEMElNCPVtb4zjWyEC3vxXoi33XYbGzZsuCE/59X1tMYYxsbGGB4eZnBwkNOnT0d9uhptYDSfDIWZCi4x+BFuJERgCbNOCazHTNycZorv+xw5coSrV69y++23s2bNmuh3i2kJ3ww0Y9pddc1AmMIyMjLCiRMnOHr0aE0KSzNOlMvxMzRZ76644xeI4BKWB/EobPh9nM3nUSmF53l873vfw/f9efdCbAZm+/zDetrNmzejtWZsbGxKA6OWlpZ5OdQ2gukcVUFaWAjLHxFYNzmzyXefikZFsMbHx9m/fz+2bbNv374aN7uFtISXG825kU6nSafTHD16lHvvvRetdRThOnfuHMaYaEW1p6dnQVNYZsNyFVgz+dxPJbiKxeKUNzHL7TURlifVC3tzqccZHR1lbGyMTZs2sXv37mUROZ8Jc11YsyyLzs5OOjs72bp1K77vMzo6WmNgFI9wtbS0LOiYZoK0sBBuRERg3aTMJd99KhpRg3Xx4kUOHz7M5s2b2blzZ92L50IbaghzI3w/lFK0trbS2trKxo0bMcZElvBhDVfcVCO0hF+Km/obWWBVExdc1Y1EqwVXWBPhOI7YLAsLwlxqfeOExkcXL14knU6zd+/eBRrp8sa27RoDo9Ax9uLFixw9epSWlpaK6/FUVvaLdS2QFhbCjYAIrJsQrTWe581r5bCayexyZ4LneRw5coRr165x5513RrU+9biZBNZynCzq9alpb2+nvb29IoVlcHCQq1evcuLECRKJRE0Ky2KwHAVWo8Yc/85XC66wzjG8wUkkEtLXRmgI8631hSDLobe3F8uy2L17N+fOnVug0d542LZNT08PPT09QDD3hgZGZ8+e5cknn6S1tTW6Fnd1dUWOsUs5586khcX169dZvXo1qVRKWlgITYEIrJuI+ea7T8VcUwTHxsbo7e0lkUjw0EMPTXtzfTMJrOXETN+PeArLLbfcMu8V1fmOeblNwAuRGgszF1zVNssiuISZ0oha3+osh4GBgRtuLljM75PjOKxcuZKVK1cCgWNsmG1w+vRpstksbW1tdHd3z2sRtdFUCy6tNYcPH6a9vR3f96WFhdAUiMC6SWhEvvtUzNbkwhjDxYsXOXLkCFu2bGHHjh2zqi1ZiAt9eEPZTDTbeBrNTFdUe3p6ohXVRlkvi8CanMkEl9ZaBJcwa+Zb6+t5HocPH6a/v78iy2Gh2oMsNUt13U8kEqxevZrVq1cDgWNseD2+evUqpVKJH/7whxUtOpqp7i2RSEgLC6FpEIF1EzDffPeZMJsIlud5PPnkkwwMDHDXXXdFq2czYSF7bgnzZ76freoV1VKpFE3wJ0+eJJ/P1/TgmusELwJr5kwluIrFIoVCAcuyam5iRHDd3MRrfcMWA3Mxsti/fz8tLS01WQ7NFFW5EUmlUqxZs4Y1a9Zw9epVzp49y4YNGxgaGuLIkSOUSqXIMbarq4vOzs4luT7Fa4DDf8VRVVhqRGDdwDQi332mzFT0xCfLffv2zaneppGW8EJjWKj3I5lMVqyoFgqFKIWleoLv7u6mo6NjxhP8ZJbnzUyziMLqCHgouHzfx/f9SW9iRHDdPGityWazHD58mNtvv33W770xhnPnznH8+HFuueUWtm/fXrfGU+aCxcO2bdatW8e6deswxkQtOoaHh7l48SKe5y1Ji45qgRVnuhYW4qgqLBQisG5QGpHvPhumW0k0xnD+/HmOHTs26WQ5U26WSXU5XtwXeswtLS11J/ihoSEuXLiA1rpmgp9sTM0iVmbDUkWwpmMym+VwgefMmTOsXr2azs7OGsEl3FjE07M8z+PKlSvccccds/quua7LoUOHGB4e5p577olSiKu5WeaCZqD6dVZKkclkyGQybNiwAWMMuVxu0hYd3d3dtLW1Lcg1dyqBVU09wTWZo6oILmE+iMC6AdFac/HiRQBWrVq1KBeFqVIEPc/j0KFDDA0Ncffdd7NixYp5nUsm1eZjKd6PehN8NpuNJvgzZ86glKowzMhkMhWfn+V2g9+sAquaasHV399PT08Pnufhum70+66urqUdqNBQ6rX/CB+f6Tw0NDREb28v7e3tPPTQQ9Paht9o6eLLdX6bSYuO8Hociq5G9UScjcCqN+7JDH6KxWJFNF4cVYXZIALrBiJcMXZdl/7+fmzbjlKrFprJJrqRkRF6e3tJp9Ps27ePVCq1YOe6UVmOkZalQClFW1sbbW1tbNq0Ca014+PjDA4O0t/fz8mTJ3EcJ5rgw32WE2Edy3JDax2tBkNzGsoI8yNe6xt3cQt/N93n1hhDX18fp06dYufOnWzZsmXa7+dC1GAtt2vCYjHbeahei47wejwwMMCpU6cq+nTNpyfifARWvXFLCwuhEYjAukGo7m0VXhgWi+qJLp4/v23bNrZt29ZQS/jq56aNh6Xk47zUNNMEY1kWHR0ddHR0sHXrVrTWkSX85cuXAfjBD34QORR2d3c3ZAFgoQgn+WZ6jWeK1rrCjKTRLqbC0jFVrW/473QLYsVikQMHDpDP57nvvvvo7Oyc0bkXItrTyJt1YYL49RiCz8To6GjkUHj8+HGSyWRFD650Oj2jY4efr4V4z6SFhTBX5I50mTNZbyvLsqKO54tBPEVwpvnzc6XepHol/03WZ54T/Xwt/x1WtTxQYeuuTQnbmvwGutkuhs02nqlYDtEIy7KiyXvz5s1861vfYufOnYyOjnL+/HkOHz5MJpOpWFENm2w2A+H3azlGsEIHU+HGYrpa33iK4GRcv36dAwcO0NPTw1133TWrNgzLNZ1uudLIOcmyLLq6uujq6pq0J2Iqlaq4Hk+2ALaYC08zaWEhjqoCiMBa1lTnu8e/+HNt/DtXwolueHiY3t5e2traps2fn++5Qor+IJfzX2dN+mFsFVyAz2c/D8Dq9IMAjLt9XC9+h1vaXxXtl/OukHHWNnx8jWY5RS2WyzjDz8+KFSuiNFrXdSNL+L6+Pg4dOlRjCd+oHlxzYbkKrPAGpJn65QjzJ3Rhm8qhdqoIltaakydPcvbsWXbv3s3GjRtnff0QgbV4LPTrXK8nYii46i2AdXV1RfcXSzlHzqSFhVKKbDYbjVkE182BCKxlSr189ziWZUWriouBUoqBgQFOnjzJzp072bp164Jawscv9qPuCbQpMlx6khWpuwEY985wIft3kcAaKP2Qi7kvsbXtldG4nhx6jLtWvB/HagVgrHQcR6/FtlsXZNw3MsvtJqdeGlAikWDVqlVRE9NisRgVaB87doxisUhHR0eFJfxiioblaswRj24Iy594re90va3Cm8/q60M+n6e3txfP83jggQdob2+f01jCYy+nRajpENEY4DgOK1asiEyxqhfAstksbW1tdHd3z6ndy0JRr4WF67r86Ec/Yt++fZHJT1jDFaYWStr0jYcIrGXGTHtbLWYDxlKpxODgIJ7n8bSnPY3u7u4FPV+1ycVo6RgAl3L/ysX8GRQKz3gMu6c5MvpFbJVg3D1FSY9xfOxzdCfvwlEtZL1znBj9GLd1/RoAZ4b/gVzxGrs7/nvkbjRQ/B4rUvcv6PO5kVguE8RM6ixSqRRr165l7dogyhm3hL906dKi93xZyDqDhWS5Rt6EWubS/qM6m+Lq1ascOnSINWvWcNttt81rkSKeAr7cvhfLjaV+jasXwOJN6M+fP4/Wmh/84AcVEa5miJpXL+KF92bhfVworKpruERwLX9EYC0jZjO5LVaKYGipq5Ri/fr1Cy6uoHaFb8grYllryXpXuFT4f2hacBSAR27sL9HGQ5PCwuLU2N/imf+NJomNQy77/zid/Raea1EsJsgkR/nmlV8Hk6LF6SaROUxP4vl0pneStlZi45Ox19HiLLw7o1xcF465FLKn02nS6TTr16+fsudLaJrRKAvikLAx8nL7XIjAujEIo1azbVofXq+11hw9epRLly6xd+9e1q1bN+8xzaTGS7gxiTehHx0dZf/+/WzatKlpMg7ixLMPqltYhBHY6hYWtm1HES7pGbg8EYG1TJhJvnuchU4RrLbUzefzC3auauLROW18+goDwZh0F9fcVbQoSNtJWlSehNIYFIpSOPLyv8GF1jc+vlfCGEMqnQHLJpMcwdUliv4VSl6JXOmLXMj7KMvCkCZp+9hWBlt1kFBdpOw1ZKwVZJx1tDubaLc3TmmmcSOy3G5w5rsaO1XPl8HBQU6fPl1hqjEfC+KQ5dIDq5rQ4GK5CUMhoF5vq9m8l5Zlkc1mOXjwIAD79u0jk8k0ZGzxCFYjjyfUp1lfnzBVdbqMg2rBtVjX06kWx0Rw3biIwGpyZpPvHmchI1ilUokDBw6QzWYjS91jx44tWs1XPIJV0NmJxy2FxmFM2wz7CmihzU6QokjGzpNSWYwKJ2KDMYG1va0snIQNeOXfWVjKwnaS2Ghs5eD5RbQx+H6Joi5hqSLKGgauYNtPMqIS+IVQxDmkrBVYKkXKWkXaXkebvYl2exudiVuwrNmtoi0n8dKsE3A1jU53qdfzZWxsjMHBwboWxHOpG1jOPbCaIVVHmD1hre98opDGGA4cOMDmzZu59dZbG/oZbrTAih9zqVjq809GM89D9a7n1RkHccF14cIFtNY1Kd4L9drPZnFsOsEF1DgUiuBqTkRgNTFzyXcPsW17QQTW4OAgvb29dHV1sW/fvsjGejFt4eMCK++PV/zOUQk840U/FzUUSDLoKyBDuwNtVpGEP45lXBzHIWkn8VAoUzl+RdkBC42yLGxCJ7QkSit84+J6PtovoRVgaSxLgbJx9XUMPgX/HCMuaNLYuBgMjtVFylpJm30LGWc9XYlb6bS3YVnNYwk+W5p58q3HQtcTWJZFZ2cnnZ2dk1oQt7S0VAiu6Rw3w1XQ5cZyjbzdzMTbf8w2JTDE8zyOHDmC67rs3LmT7du3N3ycCyGwmoFmfT7Nev2Z7nqulCKTyZDJZNiwYQPGGLLZbCS4zp49C0BXV1d0PW5kivd8rt2TCS7XdSmVStHvRXA1HyKwmpS55ruHNDqCZYzh1KlT9PX1sWvXLjZt2lTT72SxbOEni2BBILAME0LJYICJcY56MOw5+LqDjmQXXcalmxIGjSrvZ6I0wuD5GBNcHLWxUQRi17ETKAyWkwZjyoJL47oGY/LYtsYO3zeVQFFAqQTauLh6EFePkPfPoIvF8rlsktY6UnYXbfZmOp1baVO3Nv7FW2CadQKuZrHFSj0L4rBA++zZszz55JO0trZG9VtdXV01lvDLVaj4vi8RrGXEfBb2QsbGxti/fz/JZJJ0Oj3jxsGzZSob+NlijOHMmTOcPn2atra26Lu4kJGN5USzij6YfXRfKUVbWxttbW1s2rQJYwxjY2MMDw9XpHjHBVcmk5nz56CR2Qf1BFe4GBIuclcLrtClUFhcRGA1GfPNdw9ppOApFoscOHCAfD7P/fffH3Vij7OY1rLx51aoimDZysaYieetTZDiB+UbB8/DxsJJJMijyLsO170OMpZLh50krUaxysJKMREJs4JkQaA6DdJgKYWyFQoHMGgNxii0D9q4aGNhOx5K2WCFgi+BqYiY+RT0ACV9mVH3EJf4EtoYvG1pfjDyRdqcLXQ6t9Lj3EFrcv7F4Y2mmSffeiy1I5bjOKxcuZKVK1cClY5YJ0+eJJ/P1/TgWq4Ca7mO+2Yk3v5jLnOPMYbz589z7Ngxtm7dyo4dO/j2t7+9YItvjYpgua7LwYMHGR0dZffu3RQKhYrIRvg97OnpmXctpdB4GlFT29HRQUdHR0WK99DQEP39/Zw8eRLHcWpqamfKQl4Dw/qskLjgqhfhirsUCguLCKwmohH57iGNElgDAwP09vayYsUK7rrrrkkbrS5VBKs6RTBppYHh6GeNxlbg+xrf87Btm5ZEKyVdjLYxQFYnGfZBmQw9jkuXkyWpsoTRL4UFWLF9KqNcuhzlMjhYlgfYOE4S3xTxdAJjfFzPQxMWrWosC2zLCo5mFIYiSjmYsohTJFB2jqx3mqx/mqvFb2CpNkCTttfRbm+lK7GH7sSdZJyehr2+c2U5XbCXWmBVE3fEAqIbvKGhIY4cOUKpVCKTyUSRr8Us0J4v4c260LzMtP3HVLiuy6FDhxgeHubuu++O+hctZMuQyfpszYaRkRH2799PW1sbDz74YPT4pk2bKm60r127xokTJ6JayjDClUo11tComa5L1TTr2Bp9PY+neG/duhXf9xkdHWVoaIjLly9z7NgxUqlUlG0wXU3tYi4yzURwWZZVY5rRrO/tckYEVhPQiHz3auYreIwxnDx5kjNnzrB79242btw4bY5zM6QIpq1adyqv6OEbD8dJYFkKW1WmKwXpgcHFT6O47iUZ9VtRlFidyNNmj5a3U1GyoYmiXGUxpIIol4ddEfmC4GKmsFG2hTGBGNNal0WfhbIM0IKyvOBA0Z42Rmniws43Lgqfce80495pLhW+jq1asFSKVnsjHYkddCduZ2XiKYta0yURrMbS0tLCunXrWLduXVSgfe7cOa5du8bBgwcXtUB7vojJRXPTiJTA4eFhent7aW1t5aGHHqqoJ1zoxbe5Cqx4tG3btm1s27YNCKLJIfVutEdGRhgcHOT8+fMcPnyY1tbWSHDVS+29UWjma/xCX89t246utRAsGg0PDzM8PDyjmtqlrJ+dqeCqruFq1vlkOXFjXgmWEY2Y3Ooxn0mtUCjQ29tLqVTigQceoL29fUbnW6wLcIXAqopgpezW6P9humXCJEkkExPyyFS+vtporKrX3FIJ8trjXKkViwxrkxat1hApNbGPUqospiaJcpkJx8Lgb41SFpZysCwfMFgk8HQRzzMY7eIbjWUH2ynLBxWIP4MOiluVh6UcjAmFXQKDwdWjDOvDDLuHuai+gU+BtL2advsWepJPZVXiHlqche9RtlxodoEVJyzQ7urqIpvNcvfdd1cUaJ85cwalVMXkPp96gUYjEazmZb61vmHd0smTJ9mxYwdbt26tOcZCL77NRWB5nseTTz7J4OAg99xzT1QbOd1xqmspXdeNvocnTpygUCjQ3t4eRbc6Ozvn7LzYjDTLNaWaxb6e27bNihUroijtZDW14fW4ma6BccEVtbvRmgsXLnD9+nX27NkjgqtBiMBaQuab7z4VcxVY/f39HDhwgFWrVnHPPffMeDVu6Uwual0EbWXj+i6e52HZFqlECyWdi7apnrqCdL/Ki5+lJn7WKK66NsasoNVqZ1VinIw1hkULhlj/L6VQsYNrwtdjom7LUja+scqPlftbWBYqmQhqx4wJolxaY/zg2L5nwNYolaS6BkxhoY1X8ZhnSigFWe8SWe8SVwr/jlJJElaGdmcLPYk9rEo8jfbkprqv71xZThfg5Wh5Hr8Jjhdoa60ZHx9ncHCwIfUCCzFuiWA1F+Hi0+nTp8nlctx2222z/v4Wi0UOHjxINpvlaU97Gl1dXXW3W+jFt9kKrNCAI5VKsW/fvnml+CUSiZrU3sHBwYreS2EKWU9PD21tbcvqOhmnWUUfLL3DanVNbVx4nz59mmw2i2VZnDhxYlITo6UgfM1C12ljTPT/UqlEsViUCNc8WPp3+CakEfnu0xEKnpmu7GitOXHiBOfOnWPPnj1s2LBhVudbTJOLqWqwQKE9g+d7OI5TntwrhV8Y/ZnmLBU/aaMBxYjvMOJ3kbba2Zg0ZKwCSsUjVROvgcFHYaI0wgCr4thRLVf42imFYyUxlsHg47olUArf9/F9D2X7+MpgWUHfr+AYGrvsUBie18Ih7OsV1HUZinqIYmmI66X9nFb/AFi0O5voSexlTepe2p1b5uWStJxYThGskMlEoWVZUYH21q1b0VpHlvDV9QLhn0bXjUxFM63eCpW1vuGN1Gy/CwMDAxw4cIDu7u6Kdh31WIwUwZke/+LFixw+fDgy4Gj0NaClpYX169dHvZeqI82Nbj6+2DTrWJvtel4tvM+fP8/Fixfxfb8i0hk3MVrqRaj4vWi9CFexWIzSZ0VwzQwRWIvMQqUEVhO38Jzu+Pl8nt7eXjzP48EHH6StrW1O51usCNaEeNQUY5Ep39dcu34NY5sgJVApMKBV5bh8qsZpiDUgjh6q+lljYqmFeW1zsdRKUTtsSGXpsoexMVB9bJKAV/VY7P2IhImO/VZhCKNc4Dg2FgkcJ4VvChit8T0zUQdm6aDeSgHldEIdE3WKoGeXpRxMeSy+KQEWA+4RBtwjnMw+TsrupNVey4rkXtakHqDd2cJsWE4X2GabkGfCTFdp4zdxEKSvhIIrrBvJZDIVlvBT3SA3YtxLffMgVNZehJ//2fZL1Fpz6tSpGdfmwsKnCM4kQub7PkeOHOHq1avceeedrFq1asHGE1Iv0lzdfDy+8NHT00MymWza61IzL6I1+/XcsixaWlrYvXs3UN/EKKyp7erqmnNq6Xyo104jHuGybTvqwWWMoVgsVkS4QsOMcGG7md+PxUIE1iIy33z32RB+OadzrwmL5tesWcNtt9025xuhpUgRLOp8JDJKJZfR0VHanZW0pdsZ90fL29rUyCUTihhT3saK2VeUN6meTAwECmbicYXCNYozhTZs1cqmpEeH048TO5QiEYmaekS1VRVRrtAZq+LkoMqpi1ZgqOFrXTbMMJRKJYzlokiCbUCZyC/DqLBGzI7GErxusT5myqKkxyjqYQbdI5zI/l/S1hoy9mpWJp/K2tQ+Ms6ayZ9HE0++9Wj2Cbkec3Wichynol7Add2oXiBMX6leTW1k+orYtC891Qt78V46M71uz6U2F5Y+RTCbzbJ//35s22bfvn3TpssuVDZGvebj4ffw3LlzkWGG7/skk0k8z2uKNLLlQLNfz6uzD+qZGIWC6+LFi3ieV2NitNDX0Jlcp8PrBlAjuAqFQrRNKLjCCNfNKrjk27sIxHtbhV+0hf6wxQVWPbTWHD9+nPPnz7N3717Wr18/r/MtRYpgQWcxBnK5HLlcjra2Nrpau8gXh6NSpcA6vTo6ZSoFVlXaHsTrp8LjBAYWlemFE/v4RnG2mMIU17M+lWOVM4StXJRyasNhFWmEhuBrWBnB0vE0QmPKp4qJO2VhWUGEzrIs7EQST4PRNp4u4WsfpUBZFpbyQJmJ4RqFxqfyI2ihcbFJoAnSAAp6iIIepb/0JEfGP0fa6qYrsYtVqaeyNvUACWv2kc5modkn5Ho0SqgkEglWrVoVreIXi8Vocj927BjFYpGOjo4KwTWf8/q+v6ARMmFqpqr1nanAChfiVq9ePavaXFhak4vLly9z6NAhNm3axK233tpUQr/aKCHshXfy5En6+/u5fPly9D3s6elZ8tYMzXzNbOaxwdTX7tDEKJPJsGHDBowx5HK56Jp87tw5jDEVTY8XopZvLg3hZyq4wsjWzSa4RGAtMFprPM/ju9/9LrfeeitdXV2L8sEKv8zhimWcXC5Hb28vWmv27dtHa2trzTZzOd9iR7DGiyOMjIzg+z7dXV04CQdtfCziFwmFrqq5MphAMJVrs+q9H9pUCyxV6z5YpZwU4KE4X2zlQrGVDckc65K1jYmr0wgDETZhDWwUFWYZoRA0NemHE2mEQOCE6DjltEMTCCnt4XpuuS5LYdk+lgrSCU1FNK72f9WRtaIe4VLx+1wu/pAD6uO02WtY4TyV1al7aNHbltUFs9kn5Hos1JhTqRRr165l7dq1ABWrqWGh/nxWUyVFcGmYSa2vZVl154gQrTXHjh3jwoULc16IW4oIltaao0ePcunSJW6//XbWrJk8+t4shL3wrly5Qnd3NytXrmRoaIjBwUEuXryI1nrBb7KXK81+PZ+NCYdSitbWVlpbW9m4cWNwrzM+Hl2T+/r6ItfY8PPQ2to67+evtZ73QthkgktrHQkuy7JqarhuVMElAmuBqM53LxaL+L6/aB+i8INeLXquXr3KwYMHWbduHbt3727Yjc9CCKzJLppKKQqFAkcOHER1Q3d3dxDNATzcij5XQTTIVMSnDLpGhFVHufwqUaaMojrKVT2pK2VQJjiWAS6UMgz7rXTZNquTQ9i45X2qnQAdYEJgYagaj5nYT03sVc8sY2JMCtuysRQEhogWGBtfe3iej698LKVQlo2lFKbqfjkQV7oidTIQpR5KOWjjMeZdJucNcKbwrcD9cF0Hx8f6Wd/ydNoSzX1D0+wTcj0WK9UunU6TTqejQv16q6nxQv3pJncxuVh8ZlrrO5X4yWaz9Pb2AsxrIW6xTS7CBURjDHfdv5eO1o4FO/dCEL5P1d/DbDYbORT29fVFtZZhLeViOIU26zWz2a/n87l2K6Vob2+nvb2dzZs3V7jGDgwMcOrUqYo+XXM1T1mI+WUqwVUsFikUCje04BKBtQDUy3efbTFxI4hPbPEVvb1797Ju3bqGnmshUgQvFU+yIrGBFnuieXDBz0WrOT07Oii2dE7UGhmDZ0pYqlY0KhW4CQZj1FBnmzhBiqBFFG1S5b/q2rCHm1jl1MMJAWWM4nyxjYulDBtTOdYkxlC4VMSMZpBGqEhgVHV6YvwCFI7TxHYtpz6qoFeXraxAUDkpPFMKjEI0eMbFM37wkih7osarSuZNiK1KYWfw0cbFbbnMsezjHM79Le32KnoSO1ifuo9VyXua7ga72SfkeixFLdNUq6mDg4OcPn16Wmc0qcFaXGZT6zuZ+Ll06RKHDx9mw4YN7Nq1a17v32KaXISpjOvWrWPXrl1cKh7GuC4rU5sX7PyLQdwwI7zJHh0drXEKDcVWdaPbRtDMdbbNfj1vZFuQuGssUPFZCM1Tkslk9Dno6uqakfieS4rgbIkLLiASXIFLsl9hmvGDH/yAtrY27r///gUd00IiAqvBTJbvPl0qxkIQirpcLsf+/fuBYCUyk8lMveMcWIhVymPj32N75k62ZJ4CwOVcH9888zgrR+5g1apV2CuHuTBWwlHBRGKrJJ7xsVXtx9rCwkejlF2eKKqiUTGxFDT0NdhY0aOBcKqkJsqlwkhX/LgB2licK7RxqdTGxuQYKxMDsaNbVfvUSyNMUB35qtjHhBGseqmN8eNODNaybSzbwTdB+qM2Pr720b4LxgY7ML+YamLQUXQtPGyQepn1hxjzv83ZwrdpsTJ02JtZl7qTdS3PoMXumvR4i8VS902ZC2GPkqWk3mrqVJN72GRzqcd9MzCXWt/qeSnutnf77bdHNtPzYTFSBMNUxnPnzlWkMo64l/HN8hNY071elmXR1dVFV1cXt9xyS91Gt21tbVGEq6ur64b+Dja7wFrIRabqz4Lv+5Fr7MWLFzl69OiM2nQs1QJeKKigUnD9xV/8BbfccosILGH6fPelimD19/dz5syZhqxETkWjI1jXi5cYLF2hzT7DlsxTGB8f54un/oJiYpQ7NrwoWEn3s1wtnmVDeicACZWk4I9BVUJgeYTlv8N0QFOxRXzstrLwCMVCTNRUPT3fVL+ftWmEfnWdllGcyndyrtDGtvQo3c5gnWdfR2BRPTlWG3foctnVhDugIu5EOLGPqTLUsLAwlsHGxrYT+BiUdihpt/yZ1iilMQq0pQk/QmEaYeU4a6Ncni5xXR8n7++nL/cpkmodGWcbW9MvoCd1W53nv/A0+4Rcj2YUhTOZ3OOT6EKsrAsTtb6zbf8RXxgbGxujt7eXRCIxI7e9maLKffwWCmMMJ06cQClV02ZkzLtS4xBbb/9m+17NlupGt6VSKYoyh8Y1YR1lT0/PnFzpmvl1auaxweLWodq2TU9PDz09PcDkbTriEa5kMtkUC2HxuSKbzc6pZVAzIQKrAcwk39227UWNYIVir6+vb1GKfBsdwTqXv865XBvXiwN8t/8deKMKq30AJ6HwPQ9VsjhXOExR5ynpAkmrBUvZjHiXSVsdWFjosg2FNhM3pnFzizi67CwYeLhbZaOIeCpcdV0U+PgVU7cylREjqDXLCCWNawzHcl20qA52ZUq0WCOEO2pjUDURLLtSU5norwhLJSr6X1UTiqGaxD8Vt28P6tUsy8ZRGmwbhY2vS2hdXkRw8yjbx1IOqPC1tcKB1nPFR5sJcVfQl7haGOKa+yPSVoou56msTD6NpFKsatk36fgbSbNPyPVYDql29Sb373//+9i2XbOyHhpmCHOnXm+r2Xyuw3np/PnzHD16lK1bt7J9+/aGfs4WMoJ1/fp1xsfH6erq4t577y0/HxfbDor1s941HDV1U+2rxUusbdmwIONbKpLJJGvWrInm/Xw+H9VvXbhwITLMCFMKG2GSsJQ0MgVvIWiEgcRcmapNR19fXyRkSqUS2Wy2adoDiMAS0FpTKpWmzXdfzBTB8fHxyCVwz549i+Kg1Og8+6yfQynoH+1HWwO0dABKUfQN/+h/DMdyUNksYDPijrMikQQFWX8YUFjKRhuNrz0MBVCpskoK3p+4yDBl170wuhUJrSkIUg4D575QRNXbw6tJI1QV6YYFY3Gi0IMhwY6WQTLWaDCCqiHUph5Wyz0CwRM7X02z5PIN2ITte/nYdSN+8TFbWJYNlsIigSKBp4ugDW64qFCOsBldu782VaMNUziNwtNDXC99i8vFH1EySdYXvsHalqezLrkP20osmKgQgbU4OI6DUoqNGzfS09MTWVEPDQ1x4sQJbrnllqUe4rIlnhIItfUNM0Frje/7nDx5krvvvju6CWskC2WAdPLkSc6cOUM6nWbjxo3R6vuIe4YeO8hqyPuDJK32mn3jr9PZ3KmmElgLcV1Kp9Ns2LAhsgEP6yhDkwTHcSoaHre0tCza2BpBs1/Pm0kAVrfpCK/JR48e5erVq5w/f75iEWyp0kvD1jvLGRFYcyRMCQxXDqdLyVisFMFLly7x5JNPsmnTJoBFWzWJ59A24kI3nA9uwgBU6wZcUwSTx7GKGAOuymKMizaKXHGQ66XrpKwWDFDUBSwTTBCaLBo3Oq5S4GsXy0xcMMLITig06qWUmCqREESqTLmH1iTvq6k1wogGEdMbnjbkdYL942voSXSxNTVExvaoVFjVkSEDNavCtWmEFVsowFhVTYYrq8smoly6YkdFpS29bVnYVhJl3PIqugXap1gq4iuDrRTKClwzw0bHBhuFjylHu+K9vjQK1xQZcX/EiPsjjqs/AdPD+pZ97Gj9WRxr6lXo2dLsE3I9mmmSng3x9JjQiroRtT03M/Fa33gNw2wYGRlh//79GGPYt29f3bqMRtDoxbdisciBAwfI5/M88MADHD16tCJCNuqdpodAYBX1MAW/M/qdMYacn6PVmXBEvFw8AzyjYeNrdurVUYYpZKFhRktLS4VhRiKREJOLedDMi2PhNfnUqVPs2rWL1tbWKfsidnR0LIrgymazDWkhtJSIwJoDc8l3X+gIVrw4+Y477mD16tV897vfXbS6r3hj4/l++a5du8axcydIZBK0tbUx7mcZdEt4JgkkcbRPQiVIMIaj3HI6ncY3JVxjUfBLGAoYY5GyXCxLT7gp+hqPHJZ2UGVrd02QZuPYYQSoTl+suo56tWmE8Y0spfCMqTlatYDzY2Jm0E2R89eyI52iwx4htG+vjldpUz2iyeq0qvreKKdCcNbUcoVRrrrHDj+/VeJLBZbwvoZkKoWrXdDgaw3aYFRg+KIB2zaYsid8/JUJA18GC4VG6xJD/iiF3Fe5VPhHWp1bWZW8l5J/nN0d/z/mS7NPyPVoxhqsmSA27Y1jJr2tZnKMM2fOcPLkSbZs2cLp06cXtC6ukSmCg4OD9Pb20t3dzV133RVFSOPHH3cvAuUInx6lZMai37nGJa/ztDJx4zZQulRznqW+PiymmIm7gAIVhhl9fX0cOnQoSuVNpVJNUatTzVK/X9OxHK7d4b3bTPoixhtgz6WebzrCtgQSwbqJmE+++0JGsMbHx9m/fz+O41QUJy9m8994BGuuhMXKZ8+epXVTK3YimPSTVrJCJhQN5LSFMWmghZSlSVserQ6Aj1IGbUApj6KxUb5FzuRwlCJplQCDTwmHIG1Qm2JZUEy+gmswFQGjePxnYptKuWLVTTms7aXl1dRpWQx7CbJ+KylP0ZPwqI2S1UtjrJdGWC2UHKgQWFX7qcBu3q/p8RV3I4xZ15uJHVX5d5ayUI6NRQLLWLg6cDVzPR/fdykZF1/Z+EZhnOAwsXbJgMaoMMoFmhJj3iGG3D7GtWLEfyfrWh5mQ+phLJWg6F+jxZldGmyzT8j1aOZV0KlYruNuNmba22oqSqUSBw8eZGxsjKc97WlkMhlOnz69oEX4jZiHjDH09fVx6tQpbr31VjZv3hyrq50QWK7OkfcHAMh6QxhcSno8Ok5Jlyj4+eiYSimy/iBFv0DKrp8Wd7NRbZhRLBYjd8KhoSG+9a1v0dnZGUW4FuIGe7Y0e3S/2ccHky+E1euLGArwsJ6vuhF9I+bW8fHxZV+jKwJrhsw3392yrGjfRnLhwgWOHDnCli1b2LFjR8UXZDEFVvhazPV8xWKR3t5eisUiDz74ICf7L0SaImklyql4E1gmiFsZFEVt4xqHMc9GkyJjeyRVkZRdKqe/Bb2pNJqSNoCFC6QBx7LwTAGFE8kMU34exg6tzyfS46xoMq8f5YqLniiNMHYDEPw9sa+lgqQ5qsSbxgYUYyZBsZSmw26l1c6CCtMZwyhQ7PWuTiM09eq0rIptarcAjMJUJPDVbAA1DoXBo34UsQt+aSsLv/yZTNgOljF4fgLj+xTdEgldQlkWrvFAqXIECyincFZEuVD4Bka9w4yOH+TE+MexVA8rEzt4Ssevzur7KAJrcQgXpZptxXu5MdNa36kIoz9dXV089NBDJBKJaE5ayPdovimCcVF433330dnZWfH7+PW16PdT1DlcnWWwdA4Av1pg6QIAVwuDrE2vwNVF+otX2JjZOucx3siEEY2xsTG01mzatClyKKxuPN7T00Mmk1n0a2uzX8+Xw7V7JmOM90UM6/my2WwU4Tp79iwAXV1dM25EPxm5XE5SBG8GGpHvbts2pVKpYWPyPI/Dhw9z/fp17rzzzqhgMc5iGmvEUwRnSzzt4+677wYLCjr+WinSdoqSV4o9YuEZK6j1KYsBbQw+ijEvMGIwJZ+0XSJtlwJHPBXcsBsUnvbxTQ58i4RlsNVEaqDWGk0OY4L6NRW5CgbnNVQ3/A3QpkpglWM6wT7l2qaqbSyl8GpeM4VvrOjQBsWJXDsbWnJ0JVwSKhcIEUVl36sqwaONHwiUimtb9We3nqmFgzKVn9XqtEZVdmmsfiwSqUbXaFBTTidUlo2jFLZSOKqE9k3Qf0sbCr5P0vbwSZZr2CbwTfB6BiLMR1Nk0B1nxOvlaum19CTvZlPyJxjyvsXW1p+vKW6vGMsyWFGsZjmOObweLLdxNwuzrfWd7BinTp2ir6+PXbt2sWnTpor+jDD3hbGZMFWKoDZBxHsyRkZGeOKJJ2hvb2ffvn1RKmPJHyRpB06VSik8HUSlCn4/JZ1lqHSUEe8aAL7JRzfgQQQrEFgns32sTnXjmxLX3WtsZGujnvK8aHTLk0ZiWRaZTIZMJlNhmDE4OMj169crDDPCCNdkhhmNpNlT8Jp9fHNdCIs3wN60aRPGGMbGxmoa0ccF10wEuKQI3gQ0It89pJFiZ2xsjP3795NKpdi3b9+kF7CFbvAYJ3xdZnO+eC1APO1jxB2t2TZlp8Abi+0b/OuXo0kOVjmCM3GjoFGM+ynG/RQWPhnbo80p4VgeltJQrvcpaadsjpEjqRSWymLwyvbugQgK3fIcO/jKVHbRCgakMShV70YlHoWpTSOE8tCjFLygMXH8uRYNeEZxvZSkxUrQYfs4qlj5etZYu4dRrsnNMoJ9rKr97Kpt6ryndW4CKp0OTY0oMyYQe2FvMG3AshQ4CRwViFkHjcLD8wyuX8J4Fi1JH6Us/Io6reCF8o3BUQrfjNNf/BZXit+lqBO4dLMt83zS5ZswrV0sKxEbS3OveNaj2SfpeoQ37hLBmj2NSAksFAocOHCAYrHI/fffT0dHR8Xv55t5MBOmimCNekN0JWqdC40xnDt3juPHj7Njxw62bt1a8dzz3llsK4OtWgKBZc4Duyn4/eT8UYZKRxn1glRog0dBj5O22ymZIoWyGDufv0LWH8OgGSpda/wTv8God82MG2Zs2bIF3/cZHR1lcHAw6oOXTqcrGh4vhPFWs1/Pmz2C1aiFMKUUHR0ddHR0sGXLFrTWkeDq7+/n5MmTFY6V3d3ddfvtFQoFfN+XFMEblUZMbnEaUYNljOHChQtRv5IdO3ZMOabFTBGc7flc1+XgwYOMjo7ytKc9ja6uruh3WT9Xs31CVV6U/Sox4RuDbwIzBlsFW8RLhDQ2477NuJ/CRtPmFEnbXhDZolwHpEsUlMZSYCuFbzwcZaEM+BSwSRKKpbgYgiD1zqfyQh9FdIxF6PCnjROYPJhEsIVyyqJwYrRaJ8kSRObypSIp0hT9FGNecHM0DoxaSdKWz4rEAJYqAgbPKIKvdJhYZ5WPGUtNrbF7r00FtKqjXMZEToDx41REzzA1ESvK0cLYFsGiRawnVvCfiZtvZdnYyiZhpUj4BmWC9Elf++TdItpSuMaQtDWoBKjKKJcxDjmtOJv/Ahfyf0urs5NOexO+ucydXY/FtmvuCbkezT5J1yN+/RRmTtibSinFmjVr5vRZ7e/v58CBA6xatYq77767bm+bMCNjqSJYw+71GoHleR6HDh1iaGiIe++9NzJfACjpHEkrQ0n34+n12HYLlmXhqsCoougPkfNHGC6dIOtP3LiNudcCgaVd8uUI1rViP2P+KMbAiBc0fHd149P4bySm+xzatl1hmBHvuXTq1ClyuRzt7e1RdKuzs7Mhiy/Nfj1v9uyDhVoIsyyLzs5OOjs72bp1ayTA446VqVQqsoMPI57ZbBZgzhGssbExfuu3fou//du/5dq1a9x111384R/+IU972tMAeM1rXsMnP/nJin2e//zn8+Uvfzn6eXBwkDe/+c384z/+I5Zl8dKXvpQ//MM/nNWYRGDVoRH57tXMN4IVTjqDg4Mz7leymCmC4flmMlGPjo7yxBNP0NraWpH2EZL16gmsyo+qqWqoG69x8gyAhdYWSgWmF+HvADxshr0MI54hpQwZp0iLVcI3duCVZ8DDYlz7KGPj+Bns1AhF36HoZihpje+3YFQOx0pS0i1o38FX5ZtJpYJ0RT8DdoGU7VD0g4nb13ZQ96WDbbVuwVil8mOh02GKpNNCUbdTKpVwnBaMgou52EqPbsGxfYp6NRsSebZmrpC0HXRMHBW9DOlEEV/72IqycOzEssL/Gyw0tqWw8FFKo9AYAvEXl6fVUS4LG594zy1TI97iqZETr79VkURpsCPb9mCb4BhhOiBKYTvB+2KrJGiNb4JaSE8rPDxQFiYRTLA6qk0Lzj3uHeNK8Qq2yuCPfIQdmf9IZ2Ijrhlp6gmvHstRYDXyGnozEK/1HRwcxLbtyM1rpmitOX78OOfPn2fPnj1s2DB1j6fFEFiTHX/YHaj4eXR0lP3795NOp+taxw8UT7AufQdF/xquHiJlB+LT4yoXsico+IMUdQHPFMh7l6P9xr0BVrMdV5eiCNaQO8JwaRCD4VI+cB4cLI7R4yzvtKSFYi7ZMNU9l4rFYtTw+MiRI7iuGxkkhI50c7lWNLvAavZr92IthFULcN/3GR4eZnh4mIsXL7J//37e+ta3snv3biAQSnMRWa973es4dOgQn/70p1m/fj1/9Vd/xXOe8xwOHz4cXQ9f8IIX8Jd/+ZfRPtXXmle96lVcvnyZr33ta7iuy2tf+1r+83/+z3z2s5+d8ThEYMVoRL77ZMwnghWfdB566KEZ9ytZ7AjWdLnjxhguXrzIkSNH2LZtG9u2bav7+tZEsMoXT6t8+x+kmVVGXgLziUp7cV8rlKVQRmEpUzaPqBxvzrPJ+jY2aTJOiTa7iG2FkRWNbeXQVg60hTYuxihcDZoCSWcM31uBNqCNDWWBNVmjYhPVEFUavk/8Xd6uel9TrvtS8W0m9r/oZrg8soUd6SJrWi6V0x+DFMlwJF5U+mVh/ImLqMbBUaZsqlF+LenGM+BYkLAgoQyOlcJWOWylsZRHcOmI12nVe98nmjAHzx+wqoWyVRZFJhpz8G/8qBblQBaWZeHYKRLKBZ1CuRa+DqLNCigaF61stFbY5afpA77JcbX4Ta4W/5UWayvFzGXWeT8F3F1n3M1Js6+C1qPZbyyaibDWN76aPNvrdy6Xi5rM79u3b0ZF4gs9T0yVIjjujeDqEo5KRHPDLbfcwvbt21HlLAILO5onRr3LdPu3UPL7cfVIdPwSWX488nXWJwfwTLCYZfzx2HmuA1DUxchFMOtnuV4axBi4XAgiYNfdcRytaHNq05YWi2YWCvMllUqxbt061q1bFznShQYJ584FpiTx9LGZGmYsB4HV7ONbioUw27ZZsWJFFDDIZrO84x3v4O/+7u+wLIuNGzeyZ88envWsZ/HCF76QF77whdMeM5/P8/jjj/P3f//3POMZQX+7d77znfzjP/4jH/3oR3n3u98NTJi31OPIkSN8+ctf5gc/+AH33nsvAB/+8Id50YtexPvf/37Wr18/o+cnAqtMo1MCq7Fte9bRpHge+lSCpJHnnA9TTdS+73P48GH6+/u56667IgvYelQLrKSVpGRc0nY6+J2BCfOIKEmP6pt8E/tXG4WNDcqr3YAgoXDca2HUS5G2PNqdAnZYT2UUWoGLj1FDaNKkkuMoNRHDmZBvgflEYLRXK5Sg0u49HENFvZLSFal1gbIKxdLEs4pvobE5nm/ndH47OzNDrEpdB2NR/WlRUQVUeT9toxyfikGVI1GeDv7kUXhGYUdpmikc1Y4mScqGhKWxjYfxDKmkjyJsklwpeIP6s+qbbav8TCqjjH58OFgVwbGwlkvhYNsKbEjaSYzWaC/4DOYLHiRcLCuon5u4yTfk/Atc1zYF5yv4wwPsbvtpMvYqrhW/w5qWfTQrzT5J10N6YE1PvP1H/EbHtu1ZOc9evnyZJ598kvXr17Nr164Zp/ssdKbDVCmCBs1A4RrXTgYmCdVzw4g7SIvdSsYOhKI2HoOlPkq6H9ePCSyT5UzuJD12Cc9oSl4Oi0t4ehWOpcj5QwAUvDx5XWDMHcfVLgPFQcCQ1wXO5/oZLI5Rsorsadu0YK/HTGhWk4tGXn/ijnQbN26MDBIGBwejep1EIlHhUDjZ4vJyEFjNfB1slut0a2srL3vZy9i6dSs/+tGPOHjwIN/61rf4+te/zhe/+MUZCaywR221N0E6nebf/u3fop+/+c1vsnr1arq7u/mJn/gJ3v3ud0dC7zvf+Q5dXV2RuAJ4znOeg2VZfO973+NnfuZnZvR8RGBBFLVaSBU/21VC13U5dOgQw8PD3HPPPfT09MzpnK5b3fNo4ZgsgpXNZnniiSeiPl3TuQoV/UrzhoTlUPJdMmWBpTHlG+7KSJGquqFXVQJHGxVZL1hh2mD1W20UOT9Bzk/gKJ/ORIkWyw2S2ZTGUMRKFGPyYUKEJROjlNzOmFiqEnzGAuVHlu+ET6GGamFWFhRVr2/FViZIvStgczC7ks58F1vSI7STrzyW8it21Nioqnq2elO7paq3sfAMBPd/FkYnGCsmaFMapQwpC9I22Hg4lo+tXAw6ajI8cX6r4uy6bC3vV9R32RX6T8f2nRBmFsoC5SRwtCZpJXAo4WtwfR/jerjGLTc8TgbnoMDl4r9wpfgNWu112OSbXmA1wyQ4G8SifWqmWtibqfCJN5l/6lOfypo1s+sJt5QpgsVSie+e/DfWcUvduWHUG8LXXiSwfOMyUDwGpoXzxQuszgTXRZc8477HsFfAYMj7R0D5FLXBsRQFfxhjDJdLFyn4BS4VrqKNZswbwQCusfi7S//O+mQPBScNLK3AakYWWvTFDRLCep2RkZGo39KRI0fIZDJR/VZ3d3dUV9jsAqvZsw+abW4JLdpXrFjBS17yEl7ykpfMeN/29nYefPBB3vWud3HbbbexZs0a/vqv/5rvfOc77NixAwjSA1/ykpdwyy23cOrUKd7+9rfzwhe+kO985zvYts2VK1dYvXp1xXEdx6Gnp4crV67MeCw3tcCq7m21kCHS2USTRkZG2L9/P62trTz00EM1NUozpRlMLq5cucKhQ4fYuHEjt95664y+xAVdKbBsFdygpe0gbcNUJZAR/VQptpSqTrYLZVEYIVFQET0xxBWPZ2wGSxkwivZkjja7iC4nKvrlnkyeNYzx06BsHDuP67aioq+Vnjht8AoBVdGi8i/Dj10wLFNpDhGZZtR/rsHvrArjjxHtcGhsHUNeNxtazpO28+Xtqj6DdaJc9SSWrTS6KqpUuYeZqHUzioIP2qQo6iKQAFpIKGix0iTsLAnLw1IuBq8iWlX2MqLyFbIqXo/wWVY+ExvQZav8IL6nLAvbSpAgUZ7gHIzxKXk+Wgdpk9ryUZahoPtxjear/W9hc/o57My8gKIeIq+vsSL51JrXYylo9km6Hs2yMtqMxNt/1Jt7ZpIiOFmT+dmwGCmC9W7OL126xNlzZ1nTtZan3fq0imb14Wsx4g1ibMMqgpQcH5frpWPlFhQnom1dU8BSikF3PEgzNkW0CZKYW4GCHmHUG2GoNICH4VrxOr72yPmjGAy+VjwxfJSW7tsp+Yu3KLncWEwRY9s2PT099PT0sH379sgwY3BwkFOnTpHP52lvb6e7u7uhLXAWgmYTMNX4vt9UC2Hj4+Pz6qf26U9/ml/8xV9kw4YN2LbN3XffzSte8Qp+9KMfAfDyl7882vapT30qt99+O9u3b+eb3/wmz372sxvyHOAmFljV+e4L/eGfySRmjOHs2bOcOHGC7du3c8stt8zrgraUAktrzbFjx7h48SJPecpTZlWoXawSWGGfFFvZ2NiUwhz72G14ZYJZ8H9VJUMmtgmElQ9orbCUVRYHpiYKBsGN/LCbYcRN02aXaHWK5e0VSvk4yVGMHsegsO0iQQtjQHlYqoiJvmYT72V401Fz2xFGeKoHrqp7UVXvWW78W1GnpbhSSnGusJlNLXm2pi/T4nhVEaxYSmPs0Yrnb2wS1RmYNaG/Oh22TOUF2zOKAdci4dsEgihFxrZwlCJpF0koFwsPU21oYqoElgnHFRfHQQNlz4S1XJVjUEph2U7gzWilsUpeOXXUYFwvSJy0DAV1mRP+Z+jL/R0pZbOh5eGmEFjGmGUpsCSCVctM239MFcGK17PWazI/GxrhcDsV1fOQ7/scPXqUK1eusH7HOpyMXTH2rD9GmxM4po66g8SbzGvj4pev/77xKPgDWJaFp0pYxibrB6Y3JW3jK78cCbco6TGuFa+S93NYVpJBdwSDT97PBgutxqJg8ox7Lq4SJ8F6LHXaYrVhRqFQiOq3xsfHOXHiBP39/VGEa66GGQtBs6d3N5sAnG8PrO3bt/Ov//qvZLNZRkdHWbduHS972cvYtm1b3e23bdvGypUrOXnyJM9+9rNZu3Yt165Vtm4IjYdmcy970wmsyfLdF5rpIlilUolDhw4xOjpaY007V5bK5KJQKLB//3583+fBBx+cdTfuQlWKYNw2fHNmA/35AQaLQ3h4kegIxUookBRWOR1u4iZcVUWGwIAqx8NMYKJRbVBRuXXQV2vUS9Fql2hLFKItlTJ4WlEymgQlDDbJxHBwKJMpH0BFB5xY1a16f4wFSlemEYZ1WrGh1fSvMhNRrmrZqbE4W2jlYn47u1qzrG65FKVITkTx4qercmgsR+0qH6slSCOMH6vyxrr61YeyW6OnsTwHcFAK0molKbtA0goEqo4sTSrFU6VJvIWKnc9E28QdCm1UzB5FKWvCutpY+NrD0wZ8F48RipZi1P0m/aXrPLXjZXQmNnCt+ASrU3fVefYLS/g9buZJuh4SwapkNrW+k80Znufx5JNPMjAwMG0960xYTJOLXC7H/v37UUrx4IMP8qP8NxhwK29kRr2hSGCNeEMk1ETdjTYT4sciwah7GqW68ClhqSTaGLSx0bYTpCSXrwSuHqO/eJW8ztNqJRl1x/GMT9HPEZgAWYBm3MtTrK6dXWSa+TveTGNraWmJDDPy+TwrV67EsiyGhoY4c+YMSqmKhsfpdHpJxr8cFseabSFsfHx81veN9Qhr/IaGhvjKV77C7//+79fd7sKFCwwMDLBu3ToAHnzwQYaHh/nRj37EPffcA8DXv/51tNbcf//9Mz7/TSWwjDHk83mOHz/Ozp07sW170b5wU61GDg0N0dvbW9OtvhHnXOwI1sjICAcPHmT16tXs2bNnTl/aoq4M91ebf3fbnTgaWjvauF4aYsQbjbLuJiJQ1W1uY5l5auJYgeiZuHHXvoVlETUMrhOowZiwgXGSjF2iPVHCLp/TslyMGsSQxLYKGOMQ1qlXGGFEQ6iezC2qRVc4hsrKrvqGHvEIXHV8TOPwZLaDk/lWbm0dDIwwqH59a9MItbHLiZX1zjiBbVWG0Grl1EQqZPSzoaIKyxgY0TaOtoAkkKTdViTtBJbKYqtilCLqx1ZUAwfJic9aGOWqbNhslftmlUV5bDBKBTe0lu0ALmiFb3xKXoHL/re5mv8uabMFEhd57opPkEg05js6UxYr0t5omm3iXkpmW+tbL7I0MjJCb2/vrB1lp2Kx+mBdvXqVgwcPsn79enbv3h18lvOQ97MV2496w6xnC8YYRr0hMtbEjZZvJtL3lHIY8y6gVDc+bjk125C0V+BTxBgfhcHTBm1GOZM/S1HnaaWTMS+HRlPQeZQV9hA0ZL0Sllr6dLOljhbVoxnHFKelpYU1a9awadOmiga3V69e5fjx4ySTyYr6rUZ8d2ZC+Lo187W72RbCwhqsufKVr3wFYwy7du3i5MmT/OZv/ia7d+/mta99LePj4zz22GO89KUvZe3atZw6dYq3vvWt7Nixg+c///kA3HbbbbzgBS/gl3/5l/mTP/kTXNflTW96Ey9/+ctn7CAIN5HACqNWnudx9uzZaZv0NhrbtqOVjPC8xhj6+vo4efIkt956K1u2bGnomBZTYBljKBaLnD59mr1797Jx48Y5H6u6Bqv6rtwpW3232Ck2ptey3qxm3CtwsXAl1iiyzmRQJWZMtXoywQ2+NoCxImv3OgcizNvL+SlyftD0tz2RLxthKGzbxTUKjCmvugbiKpkcolRaEZN/1e9P2aa8ntVguI+xKh6N/64yylWVM0g5wuhbHBhdSafTxS2ZYTqTUxthBGl2lWkzdZIbyxGseFSt9oJdI3qpPbYqC6GQkrEZLChsKxBcLZbBd1rQivLNkA7qtFRMYMWidxPnCv4fNTquEeXhTwo7+owlg6i30ZT8i7huicdPv5X1pWeypeMuuru7sdN5WhOzMxaYLcthkq5Hs6WeLAVzrfWNL8rF08fn4ig73XkWep7QWnPgwAGe8pSnRKvEISVdoKSLJK3ghnfUHQIgr8fxtEtOTwiwuMACRd4fJq1U0JNPBZetrsQtWGackh8sIBW0IWXlOZ87g28C8TTu59FKk9ceacvgm6CGM+8VMaqA67qkUqlJzROa3VThZqP6/ajX4DZseHz+/HkOHz5Ma2trFOHq6uqq24i7ESyHxbFmu07PN0VwZGSEt73tbVy4cIGenh5e+tKX8p73vIdEIoHneRw4cIBPfvKTDA8Ps379ep73vOfxrne9q0J0f+Yzn+FNb3oTz372s6NGw//rf/2vWY3jhhdY1fnuiURgNe37fvT/xSD88Pq+j+M4lEolDhw4QDab5b777qOrq2tBzrkYAit8LqVSie3bt89LXBljamqwqm/mHWVXrKZZymJjei1tToYRd5zrpSHyfgGYMApXYY1S8GD5XKoyLKTAsidiNRqF9hVYYX3WxN+VKMa8JGNegvZEiYxdKqfgBdExYw+idQtOooBtBYJAKbtsaFGVVlcekzEmJixNfNjlmgRdd0WxMsWxjsCKxZVGPIfe0XUMuD1sSl8g42QxRqFqahBqo4HVwlBruybyVT+NsFrk1kY36t23xB0hC1pxqdgGeCjVQsY24LhkbA0UK0bnx1MLy69taKoRblPZEFlV/WuCz4WycOxEsFP7EFfM/+F66V+xLiZxLI8tpddEq6OZTKbOM58fkiK4PKmu9VVKzfg9DCNYpVKJgwcPMjY21rD08TgLOU/k83l6e3sBeOCBB2hvb6/ZRmEx6g6yMhUIr1EvEFiF8jU8H+tnpU2lAUXeH6HVstDlJhCgSFkrQbdiGEShcDEk8BlxB8jYDmOlEmOeH6RrGp+0CepDFYbhwji+P8a3v/1tOjo6SLS1s2XtGjo6Oio+x8evDbBrzfxSM5cbzSwqpxtbdb8l13Wj+q0TJ05QKBTo6OiIoludnZ0Nu24th2t3M5pczEdgPfroozz66KN1f5dOp/nKV74y7TF6enpm1VS4Hje0wJos392yrFn1F2kE4YdXa83g4CC9vb10dXWxb9++BRN6iyGwhoeH2b9/P52dnXR1dc077F4yblX0pjY1wVG1H1vPBEKlM9FOZ6KdhHI4NHqSXLynloIa0TERxpg4V4XeCYSZUqqcJmfKwswwUf81cdwxN0XWDWq0MokCVtnJ0Cm7+GmjsFQRRQKMDcotH7Pq4qbioqZKqpg6aYTxp0hYk6aJu/1F0kHF0iVRXC6mOJPfzOaWPFvS12hNVAvc+N4hVQILG2cGAktVVpdRa41RX85V14BRjnwZA1lP4ZkMZ/NJWuw8bY5P0nIx5KqcD4Nm1D4miFaqsDbPgsgW3gpPWH4CVY2SAcuyAR+T7sdvsXDYSLF4hatXXY4fP04qlaK7p5ue7kBwNSLlNyySbuZJuh43a4pgvNY3vPmb7XsXttn493//dzo6OhqaPl59noWYJ/r7+zlw4AArV65kdHR0yoWHXExEhQKrpEOBNXEN96ui3UWdD2q8jF/+TitQSRwrExn4eNqglUZTZNjN0e9naLHbMGhcbcpmOQqjfUaLWdozDg/eEdRgHLxwgf5rl0gYi66ursjV7gfnLt10AquZma34SyQSrF69OrLizufzkeC6dOkSnudF73d3dzdtbW1zvvYuh+yDZotg5XK5BQk6LDY3rMCaKt99sRvwwsSX6/Tp05w/f55bb72VzZs3L+gN00IKrHgT5B07drB161Z+/OMfzztPu7oHFtRGsGxl1TymTeX72ZFoY3VyDefHhtBWHm1V59WbIJpTHZBS9VP2jDH4KKyKG6WwaXC1SQSMuC2Megk6EkWSlh9FtHxj4VpFIAmmhWRyBF+nwLRE+0ZnDlNJw6qheO0Y1EiVaB8TiJTadz40wqhXp6U4V8hwsXAL29LjbGy9hBNFsupJnqo6Laya16GexLKUX9NEuO5zqDiKqXlfVLnqrfpUroEhN3ApHCytI23ZpOwitiqU3/GYI1kosKrqxuK2H3FBOPGaqYq3Im+ucjr1aTo2beMpu36W02P/SCn7AGfPjvHkk0/S1tYWTdRdXV1zEhzNNgHOlJsxglW9sDcXcWWM4dKlS7iuy44dOxZ0rmj0PGGM4eTJk5w5c4Y9e/awZs0aLl++PKXYzulAYHnGY9wbwxhDyQQCq6Bz0Q10PILlaY1vWRS4jEGjy+0cFA6OSqPDFhQKLha68YyLQVE0oyR0BgOUjMIzVvk6riEJvuWTSqVYt24dlwys7EiRcg3F0XGuX7/OqVOn+FbfNe5pS0Tf60YK32ZeRGnWsc3XpS+dTpNOp1m/fj3GGLLZLENDQwwODtLX14dlWRUNj2fTDmE5LI4120JYLpdjw4YNSz2MeXPDCax4vnvo3DKX/iKNJuzTcOXKFe677z46OzsX/JwLJbA8z+PQoUMMDQ1VpKw04nw19VeAXyWeLMuquXf3TG1am6MsFBa2bkXpNMYqoK1ixb7VaX8KoghVPbQJzBIs5QMKpUAZFaWihcGs8GZ+2E3j4NORLJGyg5sDCx9jDaGdJEq5WMqqEqYTRhiBuCjf0seiTgD1FEYsCz3Ye5o0wuo6LYPNsWwHfflWdrYNs77lKmGaXCVVNVnxdMvoWPUEoKmq76qddMKEn/gjtXKxVtRWfyg0MOJp8BIoEnQ6ho5EBiiUx6+odjqsTMoMjxI+Vk71iMkvE6Umwah3mu+Ovh9HKW5Z/zw2bb+HUqkUTdRHjx6lVCrR2dkZrYTP1Eq42V2oJkNrvWC1Dc3IdL2tZkKhUODAgQPkcjmUUmzZsmUBRjpBI+eJYrFIb28vxWIxSgmM15FNRs4LBFZJF9FoXFOiVJ4LtNGM+yO0O10VNVgjHjhWnnHrn9Ha4JsEQQ89G3AwJAgzC07lO+lOjgdmNpTwtCYwwLAolacXZVt4JT9qcq61Jue6XM2X8DyLfVu2sGXLFnzf50/7Po9RFmfPnuXJJ5+kvb29YhFlvt/VZjSUaMYxhTQyfVEpRVtbG21tbRWGGYODg5FhRiqVqnAonEpgL4fFsWZbCGuUi+BSc0PNfFprPM+b1gLXtu1FTREcGBjgwIEDKKW4/fbbF0VcwcIIrLGxMfbv309LSwv79u2rSAlsxPmq66+gjsCqbnJrDG6NwAI77hJnHCydATeFp/JYCa9WM9SzDKyDIejBpGCi1io4SxD9UIFwUCZIKfSxGSimsVULXck8Tjka49gunlEYo1HR6+Zj2+P4fkdZVE1+0YtHsKrFjEU5Fa5i3NMbYSgTiIaStnhytIfT2U62Z0ZoT5RixyFwWawWSjUvX1UaoXFqLjgzmbJ1HZMNVdWMOHisOsrlEEbaDJDTCQZyaWzl4PgJ2lOB4YVTER0LDTDidWyBWK2VfVZQXB97XCmbknH59+E/Y2XuX3lax8+xevVqSh1n2L17H/l8nsHBQYaGhjh37hxAzcpovWtWs/dRmQzf9xckra3ZmGlvq+no7+/n4MGDrFy5kttuu41vf/vbC177MpXD7WwIU997enq4++67I2Ed3rhNNTeEToJu+fpf8HORwLKUxVCpnza7syKCNewaViQhp85hDBhSGCy0tihhsFQLGLcc0yq7wgKO5ZPzgrRDDxUYXJTTx4PG58F3vy87SMHzGMiNM5D12LduExDcgI+UXFZt2Mie3bsolUoMDg4yODjI4cOHK9LLenp6aG1tXZbf3Xo06/NYyO9I3DDjlltuqTDMCAV2W1tbdB2vNsxYDtfuZotgzdfkolm4IQTWbPPdFytFMJ4qsXv3bk6cOLGoqwSNmjhDLl26xJNPPsnWrVvrujBO9HaaO9U9sAC8KoFVfR5b2VGD2QhjsOOvtVF4vgfGkHBaUT54eBg7T038I16DVSNSJjYKhFYo+Kpv7if+CiZ/RUlbXCu00mJ7dCSKOGVbc6N8fHsQrTPYTg7bLgQCKxRqk7xW8cbCtdEiVfP4hH3HRNpjTT+tSIQFr3Hetzk8tprLhRVsab3CitQA2th15U11tqWpek2mei5xLKUra6fquREqVaPOVHV6p5kQWOGIwueX1xalks24XkHacuhyRkjb40zEM+PHsoNUQROLchKajfjlPyr6XXAmQ3+pl3+6fohOZyuGYTal7yeTyZDJZNi4cSPGmGhltL+/nxMnTlRYCff09ETiZDmsgtZjuY57Nsymt9VkaK05ceIE586dY8+ePWzYsCHKeljom5/5zhPGGE6fPs3p06fZtWsXmzZtqnj+4f+nFFhlp8BQVBV0PqrBsrAZcgfYmN5W8ZUfi7RWKx6KomklY0bQKIpaY1ktoINIddIup2uisJWm4OdJ2EFqc8nY5S+0KffcM+S9EsdG+ykW4Ymx86S8jui8J64P4WnNaLHEqvZWkskka9euZe3atTXpZadPn8ZxnEhszcQevFlvxm+WCNZ0VBtmhFkKQ0NDHD9+nGKxSEdHR/R+Q3PXX0HzLYTN16a9WVj2Amsu+e6LIbDCNI94qkRfX9+i1n41KoLl+z5Hjx7lypUr3HnnnVEn9YU4X3UPLGMMnq6KXlS9vQnl4FW5SxkMTmSHD6WSh2UbWpLJaIUy4beTsNLkzDiaEhN37FNNJDVFW3g6qAkLHfJq9i5nHKqy4CroBMWCQ4vl0Z4sYFsG3xhse5yJxsgeilT558DAI+zzVHn2Snk4IWGCm//4nFhthKFUnbqp8gEr6rQMjHoOPxxaS1eihx2ZQXrSQxW71X3FjF9xeE1gDhGnbh1ZTTpgPSOMauFbbwTVjY6Ja6zo2EOuxbDXiiJDj6PpdLK02LmKbYJd/PiRJl4rZcoisHYMBp9LxWv4JPj+yP/lnvafjizgjTF0dHTQ0dFRYyV87tw5Dh8+HK2MOo7TtDdeU9FsK6ONZra9reqRy+Xo7e1Fa82DDz4YrdzGIz8LLbBc151+wzqEDofj4+NTpr6HvbAmI4xglco26nk/F2UzWMpi1BuosmiH8XIxp29acHUC32g0DtpAQfu0qDQwDigS5XrSIB6tg8WY8qW1pMPrS3gl1QyWcvRlB8kXi5zMXWeL1RKd99DFKwBki7X9srQ2NellIyMjDA4ORvbg4Xc6tAe/kb8fi8VSOhwmk0nWrFnDmjVBq47QMGNwcJALFy5E93znzp1r2ohmsy2ESQSrCZhrvvtCC6zr169H7knVqRKLWfvViFqzXC7H/v37UUqxb9++KYs7FyJF0FZW3ZtwmLioWpYNunLy1Whsy0Jrg+e5WKqFREJV9pZRwf62n8GmBZcSxi5QGxqJ1Q3VrTXSUdqgVU41UcpUCaGJG/AgjdAi66fIFxK0JYqkLI+gzMrgaXBNHsc4YCwSiVG0n2Hi6zpxZAuFhy5HXGo//xURpVgEJvhX1WwT1w4TD02okmE3yY9HNtKV6+HWjmt0JEZrzjnxpKvTNsOIz2QjDH5WVX24dJ3nVYupjWBVC7MacTVhF1I+AqN+kutuAlt1sDJRojORxVYVthaxMccfq3xucRnuoygZl+PZL3Em/21ub/8ZSv41klaG3W0vivaZamX0ypUruK7Lj3/84+jmrL29vakmxXo0W25/o5hJre9MuHLlCocOHWLdunXs3r274mY7/P9CtxSZawQrdJANHQ6nGuNkc0P4HSnoYEEjShHUuej/gcAaqbFoz3pB5oqHTVGnysdLoo2ipH3aE2nC72hSBf6DxhgcTPk6HeDFBZYyWCj6i6McG7mK6xcYdLOssCfE1LevnQdgvI7AGikU6GnN4HoeCcepMEfYvn07rutG6YRHjx7Fdd2KmszwprJZo0XNJgxCmslCvtow49KlS5w+fZqhoaEKw4wwwjUbw4yFopkWwsIocL2WDsuNZSmw5pvvvlACS2vNyZMnOXv2LLfddhsbNmxYUvfCcNVwrhefa9euceDAAdavX8/u3bunvVFqSIpglcBKWA5FXR2dqpyobVV7YfC1JpfL4Xsetu3gqASGYtX9dfw4FgndiVItFFUWgxvl5QfETCyAeJuquDFGkNqmahLm6r36wQKqYtRtwVGGNqdYNsJQKKtESQ1CMolllTDawZg6F2IFSk88/4kYXG2KYK0wCFMb65lRTDxsqo5iAf2lFAMDG1mdKrKz/SoW9Woaq1MEa7cwVfJJU/te1qvvqjXLqBVv1QmJQQ+w2siXUn7sZzDY+BiuuimuuinabUOPo0jZ/eXnWW8RoRwxrBMF9Q2xNNERfjD8CUqmldvbn17nOBPEV0Z7enro6+tjzZo1UbNMY0zFRJ3JZJrmJiOkmSbuRjHTWt+pCLMCLl++zFOe8hTWrl1bs02YjbHQi3KzXRiLNz0OHWSne/7TPY9QYJViNVhFE/YytMj6I1GTYGOgpA2egZyGJJqcF9QuapJ4QMn4OFYr5eUsWuwS4zqBUhaeLtfOGoVBYVkeeDaUW3HYSnF87Bonxq+Tclzy2iUby6w4VrgGWORKtVG/4VyRntYM5wdHWN/VSUuy8hYrkUhE32ljTFSTOTg4yJkzZ7AsC8dxSKfTFAoFWlpaas6xVDSr6AOa1gRIKUUqlSKZTHLHHXegtWZ0dJTBwUEuX77MsWPHaGlpqYhoLkWqXrMthEkEa4loRL77QgidQqFAb28vrutO2lBxsSNYc00xidcD7N27l/Xr18/4fPN9XUfyCTKsxlMjlEyxLJ6qo1OV2NWpYEbTP9hPwSuQTqXwjMb4YY1M7LOiKkMa4RaO3xrURVl5gvhDLD5UJwpSPe1oo8CUHQZVpUCbbD/fWAyV0jgqSWeyiGUFaXDJZBHfKDQuXqmIVgqMh+UUMKalbGZRSygkKuVVOYIVCcOJFMqJbSbiW8HvDNpU1WlFaYQWV4stXC1uZrWdY1fbNdJOofwa2BXCJX7MyhehMo2wXuPh+ul/lY/putGxetcFU/XrSgONoC7NqtizaBxO5VMotZoex6PTKZCyx6sU48TrFT+PMcTqA8u9u5TNqKfYP/ZdrrtXebjr52lzuhksnaMnubnOmCfc+DZs2MCGDRui+q2hoSH6+/s5efIkiUSiQnDNtyddI2i2iXs+hLW+58+fx/M8Nm7cOCdBOz4+Tm9vL5ZlsW/fvin7Qy3Gotxs5iXXdTl06BAjIyOzano8XYpg0c8HTZXLAiuvc1ENlkKR97NRD6yCNmS9QGBlPVDGJ++3QUIBSbQxeEbRYnVgDPjaL6cIJrGAnO8ECdAquFb7BFbtQfxNY6sEP+w/R9bLo22NZww5f+IaMWiytNJKto7AGskFY84VPfr6h7htQ/10eghuvuM1meHN94kTJxgfH+c73/kO6XQ6im5VmycsBc22gBPSTBGsauLiz7KCfmphjyfP86K08L6+vkhYzLetx2xpxhRBqcFaZMKu9vPJdwdwHKehk1bYUHH16tXs2bNn0i/EUkSwYHYCq1gssn//fkqlUkU9wExoxGrr5XGP/dfB0MHaTIJMmyLtjJDXY0wkdE3Y/lbX3Pm+x8jICCkrSXd3NwOjQ3he/TEZTIVNeFSjAyhj4/htKHy0nceUJ/dqS4j6Nw3lcZogkmVNYvlePRqMRck49Bds0pZHW7JUroRSaGUwyQLGTYEaR1kFSkUbZdkY49Rmw5nYDf+kRhix5131UxTjUrV2FqHbooo6Fiuullq5dn0z69pLbG+7RsryUMqvkJWmruCpbphcaxhSL/mxuk5LR02bY9tUGWGYiv1MeW271qEQY1WdsPwsjGLQTTDsJXFUK6uSkLEHsZQbbTPhNBj+G//eWeUzl1O/jOFK8SCPX/vvbEndz+XS93jZmj+qO9FVT4BKqah+K7SOHhkZiaJbhw8fprW1tWKiXoqbs2abuOdKPCVwfHycQqHApk2bZn2cixcvcvjwYTZv3szOnTunfW0WY1Fupqnko6Oj7N+/n0wmM+umx9PNDQYY94fxjUtSJch7Ey6CEDQU1mWn2KwXCCxtFHnfkLGD1gvBglELnvbRxsL3ghRxANeEn31FSTtYlsY3ZaMibZWvl8G1VgGnc/34VuAoqBVk/QJv/uaXOTk0gGf5eEmfbNmExPf9aH4dKZQFVsllMFuYUmBVE958d3Z2YlkWW7ZsqTFPmEuLh0bR7BGsZhVYU10DHcdh5cqVrFwZNK0OHSmHhoY4duxY9J4vdFp4/DO81IQpghLBWiTClMDQJXA+4goa564320hPo139pmMm9rhx4ja799xzz6xvyKZbpZwJWS+YtBSKqzkPX7fSX2ilM9XOhnZIJXL4JlexTxhvKBYLjI2N0dLSQltbB3Gji/BGP/zUBKPUVDa6Veiq8StsdCmDTwHLccuirHx7bkzMDCIehpk4ozGhpbuq2sTUqAYTG2VeJygUEmSsEplkCdvSaFNCpdygpa/S2Fawfcn18C2NpRS2Iagtq6rT0qY60S8cY6Xwqt1CVW0x8WJWVCWVjTEu5TNcym9hfarILe1XaU/kJhV2wfm8quPU6Xllal+r6rhdfZONekYY8WMrao0w6p2r8gwGQ0lbnCs4KLWyHNUqkbLcWPpqWNcVT98s9zYrR+m0CYW4y8n8jyjqBKdyT7Cz7Z46T2XqFBjbtqMbr7DWIyy0PnHiBIVCIXK2Wsz6rWaauOdKvNZXKTWnBTrP8zh8+DDXr1+f0iiommaIYBljuHDhAkePHmXbtm1s27Zt1vPvdHODpSyG3H7yegRDYPxTcgvR70u6GJlcZF1DzqwkaZ1l3E+wyl4JZpjgCtSCBkqu4drYAHZ7Ak2Rok6UF5sMrrZJ2Rq/LLr8+IKKUWgDQ14erPJyiVFkTY6D132yXgFaDW7KI182Bjk0dI07Vq4DYDRfNulwPS4OTVGfOgMSiQSrV69m9erVwTFj6YRhi4e442gz1PIsFc0usGY6tmpHytAwY2hoiAsXLqC1jmr6uru7G2aY0UwLYblc0FhcarAWgUbku1dj2/acXZNC8vk8+/fvr3F+mu68i5kiOBN7XAguTn19fZw6daquze5MacRqa86rfF9sFXzpR4qakSJAC13JVrzRHC0rOykxhjGmvKqcp6O9g5ZUS2SMEVq111TGmPDx2holS4VCS1H0XHxt0DgYHXRTsVKFaM/64kFVGmNQrsMhMMFQdfYKNUTlaBQjbgtZXU4bVBqlyumKBrTStDhpjHLwrSE8N4XWfrCC6xuUY1DKwrbtmnqkynObmv/Hm+kGQrEqFFSxTfXroLhWauVC/2Y2Z4psbbtKxsnWCKfAeVFXPPF6US5dVZkViuOKbbSithSvWmDpWMpmSFVPNUzdNNAKgRU9fz+Kao3rDLbpYEVynIw9xmQCK9i/9rPnG4uChm8O/yVP5r7BI92/QKvdxtn8j9nZ+vRZ91Kpd3MWd7aKT9Q9PT0LVr/VTBP3bJms1ne2oieM/IS9A2dTV7MYi3JTXbfjwvDuu++ODFjmcw5f+9hW5ZfVwmbEHZxwE9T5ighWSZfIei5ZvYEBb4Ssn6WobVy/H9iIMuVrnGphaHAI1zasXLWGgtuPp/MUdBKDi6MsfCyUmugS6BsLpTQlLxBa2hg8PMIAuDGKgirikMQncIv1Uh7jhRJjxQL7B65EAmusUK4hcz2ujIzN6bWC+tGidDodpQjHm99euXKF48eP09LSUmEHvxAR62YUMdV15sHPOjC/agLmeg2Mp5CGaeHj4+MMDg4yMDDAqVOncByn4jo+15q9ZloIy+WCBXSJYC0gs+1tNRvmmyJ49epVDh48WNf5aSoWO4IVOOxNLXpc1+XgwYOMjo5OabM70/PNN4KV8yqdmWrecmMYLnr0jycZvdhCeyrDUGkQK2nR3dONbTsVxhihQKsZq7EwStcdb2jw4PkGV+soQmYM+G4SGwucIsr2gmiWKcexwrS8ujIhuHHXJhBw8XhVcFJQplJihcfwtMVgIU3S9mhPlLAsHextZykaF2PasOwCjkmQUC0Y7aEtG88ECxPG9zHGwljB56Fa3c2kTmuy7YPXJYiOVcf+KEe0zuc2s66lyObWYVoTEwJaGwdb+bEoIOVGx9VUm2VYNSmRukLKxIYQ14X1rNRNtcCifulWTcTMrhqXIes7ZAsZbNKsSpZod8bLtWHlMRqFrYKqvuDnuMAKI4UW/aUTfP7aY2SsFXQ6HZHAmo9QqXa2Gh8fZ2hoqGKijq+GN6p+a7maXExV6ztTgWWM4dy5cxw/fnzOkZ/FWJSbbI4YHx/niSeeIJlMzloYTnWOS4V+NmUqTT0sZTHmD5Hzx4HAtt3TXuz1Moz5o4x544x5RXwDBZ2gxR6hZBQYC20MYyMlUton096Dk0iR8Tsp+EMUtI1j+djljIJ44rM2QRp3TjsogtRdH0OLrdFlh0Gt/KgeVQE66XNybJD916/xjyePs69nMztXrWCs7CxYcD36xyszLRpJdfPbsJbnWn8/p06dIp/PNzxi3cwpgjAh/kq6AEqRqmOYtBQ0yoBDKUV7ezvt7e1s2bIlagEwNDTEpUuXOHbsGOl0uiLCNVP30WZaCBsfH8e27aaoIZ4vTSmw4vnuQEPFFcw97UJrzbFjx7h48SJ79+5l3bp1sz7vYkawYOrVyZGREfbv309bW9usc+pne66Zkq0WWFW/T9oOJT8wRnA9j3PDY6xNt1FS3awYddjYAUk7T5FhYCKCFdqmxxPnQFelBJYjWCh8DLmSrs4gw1KBSDKlJCgH5biosGFweJTomLGExLJrFQROg8bYWFbliKajpB36Cw6tCY9Wp1jWMCU8axhLWyjlllPcFMqysFHYdgJHWbgeuNrDcz0Kno+nAvdFY0XhmPJIJ0sjrCWq08LCr3Hwi/+ruFxo4WphAxvSLutbL9ORHA0Ej/Kj1xvAqFpJp5QmKveinGJXM6DaRsdUNajWxsKOrVxPRpX3SbDAUyMfbSrcLGPKzEdxpZTiWilFZ6IFS40BpUh2h2IqPpKoMqxsjqKNx6VilhHP5kT2CRzd1bAJMD5Rb968uaJXz8WLFzly5Aitra0VzlZzWQ0PI0DNMnHPlOlqfWcyf5RKJQ4dOsTo6Cj33HMPPT09cxrLUkWwwqbyM60Vm4744tvZ/OUagaWwyHpj5P0g6nM1P0p7KkPRzwdjVDZZL/hdUWsUSXyjcI1Dv3sGZWyKxSJdyTSr2lYz7o0z6kKLk8J3LYyx8Y1F0bfL37Hyl9wEC0EGg68VtlJ4ulyLpSbSwZXllx1iy3kLtubY+ADnx0a5msvyjn/5Bn/1n34m6o1VcF2GcwUWi7CWp5B22NN6G4VCIUonjEesQ8GVTqfndE/VrBEsqBRYCat5muYulHiJtwDYtm1bJLIHBwfp6+vj0KFDtLe3R+97Z2fnpItdzbQQFhpcLLd5ox5NJ7Cq890X6oM520kr7AcF8OCDD87J4WSxI1jhOasnz0bk1NdjvhEsYwx5vyp101SOK2lZgcAiSL1pbW2lu2cF1wpZBguawQJ0pzpJO22s7iiRUMEkFzXNpVL86IrMt4nojac1GCr85CwV9JyKVj+NhVfMoBwXO+FSneAWtgfG1DwNDApfU+k0WGXKEBtmbEeL8VKCnGvTlvCwbRcfm2zJxlGGtO0CNoYSlp1D++1YChw7FFM2SWWhfRft+bh+kApjqeBG0lBp8BBG8yrrtKrERiTO6plRxNMI4XIhxdncRnpSRW5pG2Fly/WqSFO9pEtdEeVCBdHHaiq2AVC66vW0y4/FzdRrP/f10wirv7dV7pYq+it2HMVACZRK0mIlcZRLwg7SRYNzh/sEjmgT5yJyH8z6Y/zz4J/Rxib2qmezf/TLbGjZzark1ppxz5X4RA1BVDucqE+ePBmthocTdUdHx4yuy+G1oFkm7umYaa3vdAJraGiI3t5e2tvb571wtdgRLN/3OXLkCFevXuXOO++kY0VnQ+bg+Dku5q/WrZkpmSLFsnPg6fEsT0t1UKQssLDIlcVXSWvscipfwU+itEH7kEqnaM+0YamgKfuI69GTsnG1hUFhjCJbSqCNKkfdgyOHBI9YQcpg+TFNOX3bClowmOgSbRgky4nrgxR8l8u5UT72nR9HAqvoeYzkC3OqDZrPXHylMMbG1i5aWlpYv359FLEO0wmvXbvGiRMnSKVSFemEM4l0NGsEK/xcha9bQRdQdVq3LBWzTe+eK9WGGcViMarfOnLkCK7rRlHN7u7uKKrZbAth4+PjN0R6IDSZwAont/m6BE7HbFMEw2aQM+0HNRm2bUdRucWiWmA1Kqd+JueaLTnfremV5JvK4znKYmxsFAy0t7fRkk7j1Wxjc2m8wKVxxXBxFTnfoSNdIJksxY5fKZx0OUoR3vR6OnCZwhhsZU00pozVVpmycjK+jefbKMfFcrzo0LH4VTmV0FSJkaCgWhlVN5o11XSmjcVYKUnecnDKUSgPg69G0SaJbXvlUEw4konnq5QV1EAkEyhTrjczBu1rXO0GQlITOBeGY6ktwYqOOVV9V+WYJyxFBospxkprSdvd7OkepTN5DTVJamXgRjiR3mcm7nIqqQlh1dq2R5vEBF3NYVS1yLNrjmVhVSYuVh40OltYd1bQMOimGSrZpG0bpXLRDR3Gxy+/wLr8TFE2BiJzkmHVx7dTnyI54vATVndDBVY1iUSCVatWRUYM4Wr40NAQBw8eRGtNV1dXJLgmK7SOp9c1O7Np/zGZwDLGcPr0aU6fPs3OnTvZsmXLvOewxYxgZbNZ9u/fH9nHp9NpRt0xUg2IBsRdBEfcMQbdUVYkOwGDrRL4xsWPRZyv5z2KsadtKYu8Duqz3HKqngayXgLbd/7/7P1p0C1JWt8J/tw94mzvfvd7c18qq7KSKqooassCunuoNiRa05K6ZgCJ+cJI0CMzPvBFMpOZkMkAmfXQMpMGpkeo21rTCAQtMRKoZUIgFgmqKmvPyn27eff9vtvZTyzu/swHj4gTZ7lb5r2ZF8Rjlnnfc04sHh4R7v5/nv/zf9AqIorKaLvCS8TAOg41TRC4IIyZQ9tAmXJsD5ExpTxS0ASVKJxXgbI9cwFBvbb+hucivL6zjVNCHgmndvdpFOVAktyRWsfV3pDjm+9dsv61dFFYo644+uijj+KcW4h01B0opYrhnxSbj2Cdm1zgyZUn3rPz3ypC9X7R75rN5oJgRjmOlyIpdcn4++Wej8fjm5au+JNk9x3Auhsqgbey26UIOud48803uXz58g2LQd6Jvd8RrOFwyAsvvEAcx++aU3+rc70TG+XZwnd1Cp93jr3eDtY5lFZEcXh87dw59cyzY9gZR+yN14nNCsfWLM1WH10Lt1RRmiIqogiFiilUpkrhg1JzUJU5VEpjbfCBKhRiY5yLEOMCwKkBgiphumjVrHw6IZo1d+0Lb0BFQ5PqY+4NqVUYLRXQUipjkDVYiS2Iq3nzLOGVr0d5VFAfVAoxGuV8WLyIYJ3FOY83YYGjdVH4tBbpQ6YAa2kEq+zbIhGZWnRIoejlMd/cOUKsN/jAeo8DzeHMIkYwC/S8pUlSarZgcbif84B1sQD0MtNVIYByvyUTzxIJ+OXH9TPbpGIYZRCpFdYij8zRGMuiymVGmUeq/vXAbh7xXO+PWYmOcqL1aLHPvVXQmveGj0ajaqI+ffp0lWhdekbLcaUcC+6XiftGdqeOvWXzR5qmvPTSS0wmk3edyzp/rvcigmWt5Stf+QoPPPAAH/zgB6t7NvEJ67x7gFAvej9wY86OLhcAC2LdwbkerpBhR6CbCd0sZbVp8OIwKmJguwDk3pNLjnWOiWtwYuUwvX4PXbynwa2hmeQZCk1eFGIXNBMb0dG2eM8UMQ0yUryAUYISsFKLhQvT3Nooh1TKyhSQK676ERKBaE8vSVkp5ozMWrwIp6/vvyOA9U6jRbvprfO+jDEcPHiwcq72xgPGvSCe8Oqrr+KcY3Nzs4pwlQI496tS3zzAOjM+z2OdR9+z8+9lIw61bnyP74ciyPM11+p1FHd2dgD4xje+cU/ycO/USorg/fis3andVwDrbsh8345VE6S/CpKCWgF9aGabeW/e3UDU71cOlnOOK1eu8Morr9w1Tv0ye7d1sOYVBGEawcqzjF6/z/HOOptrq+zu7lbr2dzPRRdqL6YpQk8intQqzncbCAc42Ib1zpi4MaqofyWlzJdgYIYrR4EgfMXFV5TUOl/Q6Ip8pDRCxRqifCYaUo9ehX9nn/Ug262LullSyXjXbR4elOt8KwrnYoyChrFVhKtpJhhfxIaiIdi1avFeP3913IIu2NAR3iu0ich8hvcO5yTkd6lAeRA9O7HVbT5SqJReiE7WI30TG/HS3kHa5iBPrY852L5CrDO8BMUvVbtwWdovMgOe/NIE57lYmwJh8XlVc9TCZQBrWaRtKb+zXmxZBCSIY1iB/VwzdltsRqCYILgpZVVM7ail6mD4t2u3+dfX/yFPdr6bz278eZ7r/iv+/OH/fsn13n1TSrG6usrq6upM/tb+/j6XLl3ijTfeqBKtS5rH+724uJHN5/rermNvHmDt7Ozw0ksvcfDgQT7+8Y/fVfW2e+2U895z9uxZnHN87GMfW3AiJu728oi8yJxja2oDO6nmhlwsubf08mH1u5KwkLOFMNFqtEU/S0gdHDFrDG2Xhm4xcWMaukVWKDuiFRO/AmJQokFNFWOdN4ycpWMOkLnz5WhNYiM6sS8ogxqjGyhShACwtNa4bGaEpXKfmMDpLuuui1f03ASJwWthmKRFPi5k1uO9cH63x+du0Gd3G7B479nPJjf8fSkt0+d8ZfA2f+74xzl+/PiMA2VnZ4dTp04RxzEHDhwgy7L3nIFzOzYPsC6nV5EF9sK9s+1seFOAdT8JSJRWj2oeO3aML3/5yzz99NN0u90qD7fT6czk4d6uYMa7tT+jCN4je68QazVB6mPgzmHGfw/UJqIfAelxpft5Xnp1m4ceeoinnnrqrr0c70cESynF+fPn6Xa7fPSjH+Xo0aP37FzvFiCP3WIEK/ee8XjMuCg8d3DrAFcnwxmgkc0BrPo61yhdgR+gAk6jNGJ3vEoj6vDgukc3+igdJvjc+fnDBHBQUfxCJEQEvAuyvUbrQpygiNL4CJ/pgjI4G8Wo2Ia1c3ivqsV+oKuwCEjmdyrbVQNuY9tgkjdQOuSKCUKu+ngfYfCgcoRGsf1sNEnNFV1WgNYKo0yVR+NdAAfeOVLnsUoKAYr5aNHs57J88OzSZfF9z7zm1e46br/NI6sjTnR6rDQGs9suZQjO185aphg4+8c0LlTbRPQUCc8ca95mtylh3ewWs0qDYZv57xTX0giUsBZBbLLiSPVzFkpmRb0ej0KL5+To65wcvULnfcxxWpZoXfL+z507B8A3v/nNyjN6v9CPylzfdxJlK+cP5xynTp3i3LlzPP300zzwwAN3fQ67l065stRIOSctY2gkNan0m9nIpqzFyxkR58bXqrnB+rBAd7Xi3k4MWmnyos5VP3ekTpE6jy4cJbGKEYTRfkbuHMoYQOGV4aXhLg94RZPpAtB5w9g5tG6T+pjyfc19xInmIwzkRbxoxE9LWGglNHVM7m3x/rkCiBXvrnZAFOYTFwYhW7irnBZGWV5F6K0NdMIr3eVS7WluyZ1jtbUYJXinucwDly6IRJXmveft/jZPbc7O//vZiG/33+YHjnysEhMrHSgHThyhreJKACdNU06ePMmVK1eq6NbNhBPeK5sHWHv5Hs6/uzI8d2I76fCmv99PAhLLrNQ7qEc1yzzc/f19Tp06xXg8rmik5Th+r65pPB6/I42D+9HuK4D1XtmMB1IXBR+lC3af4WCIH57kez/+CO31BiIN8EdBv/sb/l4UjazbZDJhPB6T5/ldi8LdzN49RXB2UBQRtvf2mGQJG4UHpVrAFP9oyijXDfIlCgpcnWqolarAS2Y1F3sxzm+x0XKsr4xRelScYrqPIHivSycpWilcXSxhKnGH0SFvS0SBi7G5wbQSlK4Dt8XFv1LT6klOSmrhzSfaZZEUJxrvDEYJRnliA7nAOG3Q0oJWLoBDLCiLolkBx8WjzYIwY6JwrcbQ1AaxGeJDSQUFiM1RSyIBuoBYswsHqY5bWknV8SjODFe5NFpnqzXhQxt9mtFOrY2z/aKUZ1ZQQy/Q71DFJDwTnZqvzbVISVy21lFzdc7Cl3NKisqATJ/pgNGXZpkB0Lcwck02o02aBkoRDSGoJpYkyDqoTrxi31r+5dVf4gcP/VVWo3Xg/fOaRlFU5W8dO3aMF198kQceeIC9vT0uX76MtbaiH5WJ1u+l1ct/vNNc33Jh8fWvfx3n3G3XQXwndq+cctvb27z00kscO3aMRx99lC9+8YtLn5nJbUaw+nZyQ4B1YbzNURVVESwIkZPSPBCpNpkfsmJWeKM/QEQztpaJH5P5HCOafr9Hnh5DGxPKXSDkImQ+55xXfEiVhXYVuVckzpL4hFxiRBS5C6DsYHyYfhpyHDMfIvZBzRTaJkawlGm91Tsr4IvxG6+CUAaESJaEUSR1NlDLKZx0Ilze7y3vr0mK97IUYL1T2xkNSOdFogq7kvR5YffqAsDazYecHV9huz/iyMbsM7yXjXm4c6ACU71ej6NHjxJFEXt7e7z22msz7/PN8jHvtmUux4mlHbUXANbYj2/7ub0btpffnJbpvX/Poj/vxJa99/N5uGmaVrTw8r5vbGzMjON3674Ph8M/A1h/kq0EOiFk3gE6WNun3++jlOLosRPE+jJkvwEiiH4Q3/ivIfoYqHfeZe8lRbCcQKMo4vHHH39PkgbfrYpgvQZWmYirdcTWgQO1AaAi3IEIsTGkcwuQOpgKAhWzAGt+IFCEaMPeRLE7XkPTotUc0GpmGIKSoA9JPSyuqmfPK14Xm03pGBqDzxso7VBRXXyCcEwl00yb2uLZCTUBjGqHG55/3pwoxjYuqGWhTlfqHUQDtHRAJyG65ptUWd3zuO8GwBRCP2pjiKMImxcgSwWQnboMUWC04FUAZPOtn39URARRMqPcqJTi2rjFfrJKK9ri8fUuG43xPAQqxCRmi/rO00SWVdhaEMJYco+X9/gcvXPJFmqBWri8Qlr9jALs5BGSNVmJcmKdEOTmpQJY9XPZAuddTN/k/3v57/PpjT9PU2smbshnNn9wacvfKyuLVx4/fnyGflQWPD5z5gwnTpx4z9pzJ0IWN7Pd3V0AVlZWeOaZZ+6pd9oYQ5Ytj0q8E/Pe8/bbb3Pu3DmeeeYZTpw4UR1/fqElIrcdwRrkE2hvLf2tn4/o0GTdr5EXEazET69JRKF1A1AY3WQ3GeBFMXE5qXcM0wQ/Smm3OozjVfww5CY2tKccOVMfBCuCKXKvycSSuBHOhzFwlBsEjVENhIhyRDDKoIpxYCWKgaSiZ0vBm1ZQ0f/wgC8dNSU1WCEa0tyRWot1IVP14mB5dGMwWbynJYXvnc6h567vkcvy9cW58S5v93cXvt/Phkx8yte3T/MXNj4681svHwOz5QXiOObo0aOVcMJgMKgiXGU+5nuRx7Od7eHIeTh6cCbHaWjHgGUn3+fRe3LmRdvPbg6w7occrJvZ7UTYms3mzDg+Ho+rcbxkKtQLHr/TMgAQ0nP+jCJ4D+y9pAjC9MEajFsko3O02+2AnHWtzo5SKNlBp/8UydYQ8ykwHwDzTNjuDuy9oAiKCG+//TZnz57lwx/+MFevXr2n56vb3VARhOAtGQwGrLQ7tDqdGcpWOfeUgYiGvgXA0qWwwdQWKG1KzeT4jHNFL10lQljrpBxYT1AmwxOqH4VzzLbdKI0XVwmKU0z83gteQlxESYTkBjEZ6FtPomWqtZdigl8WNWEJFW5mya6Y2BjxQmQ84EFZnBpivaZhauIQUi7j9cyCf7bvatzF6kdV/WaMwWAQXUQWJSy009xjlQpUSr28rl2QiV+ITVV/9bOYF3YOsxLBY2tDDrav09BZkW81B12WguFFeDMPwkSVZMb6Nov3Si2lJM4/+3NFjG/A+axH0aRaw3kGVoAmKhYaTIKUe0VDpZJyDzsackn40v5v4unwzMqHl5zrvbX5ibtOP3rooYfe03zUevmPdwqs6qJHAE8++eQ9p/7cTadckiS8+OKL5Hk+E3UrF3/LznO7OVgDe+Ptrk76dFjlhAi2iCqP7TRXyIkQF+IQ/bzLXhpU/SYuYzRydIcTHthcZ7W9ycVuXkTfPUZL5Y+aOD3j0Ei9IiujSb6JKGGcx2jCvY91GysZCEQqIkSLhRjwHrwr87nUFGQVjjERFSiCAHo6OkRthU2F3dGEzAWWwF46Xpr7NEjSBQ/ThX6Phzc26Y1T1lo3j3gsO+al/S5ZY/n64tKky5Vxn/1kyFZrunjdL3Lhvtl/i7/APMCazeeaH7+UUmyrlCceeqh6n0uwdfHixaqeXhnd2tzcvGvvy5X0Olp5Hm4/ONMX19M9NJ7dfP+unOd2rGdvnPcG92cOVt3uVKJdKcXKygorKys8+OCDeO8ZDoNIyvb2Nm+//TZxHM8IH90J0P4zgPUn3MqXPMsyTp48yYpyPHpinUbxEAjziZwpEKNkgLJ/gOS/j8TfB/GzYB65o/Pey0VFlmW8+OKLTCYTPvOZz7C2tsb169ffs4XMuxW5GOUpo9GI8WTM+to6WyurjOaSauuENRAitTgw1GXbFQH85LWFtJ6LZShmyXElQPOi2B+36E9atBsZq+2ETieZEVOY1rAqvioiUkClthfaLVXelrLNsE2UMyUFLrMpkBEF3oVoVi2Gt3TPxShROHvuQjQvdh6jPWkekTmhpRMi1QYMokfgV6qjLAhhLKO4yfw24Xxaa+JCLCPC4K3Fe0FccU+1wStVqf0F2iULC495S73mte46fn+FB1YmPLDSZ7XRhQX4NGuqHh4ERMxi74m6JY1w2XeyJE9rHthL7f/TbWA2J0uKPp8+rwPXYJxusBobWtpOSxfUFB2ncT/FwAovD0/RNr/Df7H1A9V7+V5P8rc653vRnrLGi7X2XZX/mBc9+vKXv7y0vuDddhLeLafc7u4uL774IocOHeITn/jEjBDHjQCWcAcRrJssMq9M+hzEzFAEB25U/e5E8DJtTy8TPJpBMmYfT7yyjmnEaCIyKaTSi1pWIfCuSKwpch7DG5Y5ReaLqKVv4HVG5gxGGbwXVtQmiWxzqLnCuOpfxZv9jNxrKGthCcTKBEeMngKs0oFTn35MQ+GV5upgSGZD3ayJc7xxeYenHzg80yfDJOXs5X2+89HjxTGFN3Z3eXhjk+uD5JYA6/ndS3zi0IMz313rDcgPLhehuJr06duU53bf5r954GPTvrZjNIrz+aIjdhlwmH++3x7u8MRauLZ6PuYTTzxBnufs7++zu7vLm2++GQpB1+iEq6ur7/h92c73sC7nuzZm37udvItSQjdfTs28FzYPROftvaqD9U7t3eaIaa0XygCUQPvChQu89tprd1S4fjQa3dXyQe+n3b+w+h5aOaF84xvfYDKZ8Mhjn6jAVbAl4XumFDulFLi3Yfw/wPgXEXvyts97ryJY+/v7fPnLXyaKIp599tkqt+HdRpXuxN6NyEWWZbxx5jRJmlQej2jJAqzkuJeTn1myzbxsezy3Tag/tTxOUl/fT6M4imHa4Pr+BhevHWQ87lBVDi4FJuZ2ml+kl+cN1xBELVTWBB+HRf3CVSw2LhQnVlPZYMC5stFlO5bvW27gRTFKm4zSJmXxTacyxtInlR6ickLqNkVuwbzXcvr30jutQCm9gHVUEb2KIkPcaBDFcQAI3pOmaYguWIf3fuacy/ol9HUAcRdHHb69c4JvXHuCveQw3utqx4WnXs2C2eVKg8vOt+T9UfOL0VtPoLIkPle2ZObQYma2C6DVMHSKbt7ESpuQWl+fpMpnPHyXS87X+/+B/+XSz3MxOce/uP6LZP69y0uAO/eM3m0rKYG3Khx8K7t8+TLPPfccBw8e5NOf/jSdTmdpPq1w9/v33TrlRIRTp07x/PPP84EPfICPfOQjC4ubsl+WnSd1twZYIsIwv/G176Rjun4cAFaRe9XLh4yLqJcn5IiWNrGCcz7kR3U26duYzGcoDLmzWB8xSDuUo4PRhtwrpJBjR8AWta+cKLw0SKzGFc4TJ8J6vEWkDEdba4ytIrOaURazlzic0/iiCHykDGtRyO2qGASeavwXXeRwARIJsdJc6vWxrojeK/jqqQsLfXJ+v8vLF65Nrzm3XOiHGlY7w1s/R1/dObfw3e5oRHYDiuBuNmTiMl7vXyKx4Z5OXMbAjtFKMWAxf+tmUcnSzo33bvhbHMccOXKEp59+ms9+9rN88pOf5PDhw/R6PZ5//nm+9KUv8corr3D58mWSZHqu21lD7GV7XEi2q+0rgYush0JmAPwyG99ADORGNroJTXdQe/aXvUP3O0Xwbo/TxhgOHDjAk08+ySc/+Um+93u/l8cee6xiV33xi1/km9/8JqdPn2Z/f3+hz95tBGswGPBTP/VTPPLII7TbbZ599lm+8Y1vVL+LCH/37/5djh8/Trvd5vOf/zwnT86u4ff29vjRH/1R1tfX2dzc5K/9tb/GcHhzMZNl9p9dBEtEuHjxIgAHDx7k6aefxjgJQarCVFHQcGbNpJpza6PioXCvwfhVxDwNrb+MMg/f8Nz3IoIlIpw7d4633nqLp556aqG45XsNsN7Jufr9Pt/+9reZiGNra6uasKIl1dir6FSBX8wtIlgA8TyVU81Fl2r3dR541XYBILOGa901on6HtU5Cp53MHENKOt/MzrOEO/EaZYrFvjWgAhVQR27umSt2LQMvAoUoImVhTKkXxV0SXQqHmZZQLo+buQhBiIzgvGCMIxELNqKhLEhYOHgRPA5NFCh8vn6OxSiXDgirKpy72IPFJ6XQRhNpTVNHJC5D+TDYO2uDopUOHuF583ORKAX08wbf3ungWOWRtRFHWkPixvwCoSjjW4Jg0Yiad3gs9uE8yAzgZi46JQtf3cDmjiXM5uQBSkUsE8dQBGpqP4exXedAHKMYIbUeKaXcy+e4a6/xa5d/Ca2FnewaJ1q3H3F/t/Z+qmd578my7F1Frcqi7Nvb23zsYx+rEr5hedF4kQFUQgt3x96NUy7LMl566SXG4zGf/vSnWV9fv+l5llIEbyOCZcUvVYAF8OLp5Qk9My6iSaHPFIrz4ythfx9qSzVMoDHm1iNEiImJtOHSwPHASg7KYMUyykJmLIBREW3VwUvKMPO0YkApctE4D4mNMEqR2ShQvAWsONbMJi0dxH20rOBkTO41kbIkElRWjRhEoGMi+lZVr6m42pikwvgXa0OmHG2Bq/0RmXXkLsejeP7cFX5srl/O7/c5ub9TfR7mGVdHYd2xM7x1n58d7bGTDDlUo/v10gm5X/6cd7MxuVi20wFX0n0ei45xenSVoZ2E0iJOMbQZ3XTM0c5GyK+aA80LSrEiXJ7MFjYu8y7nTSlFT3kefPDBilbW7/cr8Zs333yTdrsd8rY2Ojxw8NhNoxw9O2A3G1XtKN/vfdtH4xnZmwOs3XRMS0e3DSzO9ns8c+jw0t+GNSfEnj3NocaTM7/f7xTBe92+EmgfOXIECO94mb9VFz66fPkyW1tb71rk4q//9b/OK6+8wq/8yq9w4sQJfvVXf5XPf/7zvPbaazzwwAP8/M//PL/wC7/AL//yL/PYY4/x0z/90/zAD/wAr732WlXD8Ud/9Ee5cuUKv/d7v0ee5/zYj/0YP/ETP8Gv/dqv3VFb7iuAda/DqNZaXn31VXZ3d4miiAcffLCIuszWwApqYG2gHvqd76rp5KqUAv8Gbvw/oqKPo5p/CaUPMG93O4JlreXll1+m2+3yyU9+kq2txSTj9xJgvZME3UuXLvHaa6/x+OOPszJw7I2mof1l9L9pzav6cnLWsrk+XoiEyXydqxrY8iVFSwq8svyanNfsDtrsDtpsdFI2VxNUNC/JXbY0nGsxb6sAIqIRGyGSB9pgLc2pnk41BVIUuThFNKsG4KTe3jJNALUAEkqzTuF8DBLgh/UKpzxeD4np4NUE8CCrN2zPwrFl2Z/1iEyZWybVIbXSxLFBfFDf8hLqyKRpTo6gtOCURysdyJa1oaIs/AxgveFUb52Lgw02GikPre2z0eyh8IRE9loSv1oie6GW52nN5nfNRpjKq5vP+rpR7lb9W60MyLwYx+JTLVLm9pXH1lzPDEKLtUjwkgVp/yIfrQ5wvVL0csVvbv//+AuH/jseaT9WHPPeFg69HyJY7xRcDQYDXnjhBZrNJp/73OcWirIvi2B5P8ToI++qzfP2Tp1y+/v7vPDCC2xubvLZz372lipmy+aJ26UIWnEkNwBYE5eRWEvPT8hsTlSAF4Ar6Q5GhzzCxDnSyYA0SVF6DUGTOUAidiaEyJcYcvEkzrAeB5GKhmqyYlYRMgaZ41DBbvYipHnE/sRglML6QFUGRe49VhQrRWRqRW2xQxEN8RpXOL00EdbBdj7GeTARwcEkQJFHKuVYrWDic1zqON/tkuQ53gSf19n97sIi9mp/yE4tv22U5exMgljC3ji95Ty6n014s789A7CGWYqScJ/n3+2eDQCrl4+5nnV5bCUArJFLcD7MA2Ob8XLvEj/Y2WAnGS2NYHVtQjtPWItbDG3K/hwIe+XaNt95YlHuH+DyeMADKwHka63Z3Nxkc3OTxx9/vJIF39vb4/lTr3L6tZOVSt2BAwcWVOqGbkDfjheutWeHKOUZ+5vT9vazCYebq7RuMT69MvxPfMfqf8n5mwCsev3O7eyNpQDrfqYI3ggU3ytrtVoLghl7e3v88i//Mr/2a79GlmVcvXqVtbU1vv/7v58nnnjitvtvMpnwr/7Vv+Lf/Jt/w/d93/cB8Pf+3t/j3/7bf8s//sf/mJ/92Z/lH/2jf8Tf+Tt/h7/4F/8iAP/sn/0zjh49ym/91m/xIz/yI7z++uv8zu/8Dt/4xjf47u/+bgB+8Rd/kR/8wR/kH/yDf3BHwkz3L6y+y9bv93nuuedI05TPfe5zxHE8nSDV4osjt/JEypLwuWoh9mv40d/Fpf8O72cnnXKyvBvFlAeDAc899xzWWj73uc8tBVdw/0awvPe89tprvPHGG3zsYx/jiSeeoJ/PTubLCleWNa8CbWoRlGsWI1jzAMuo2QhE/W6Uy98yn2V+0TzdaSrI0B+3uHB9g+3dDfI8WjgmKgDBWw0RSjTkTcSForzzkbDy80x7RRX5PxWPEIo8HhEpgNMtnjeBSd4gyWNEdDimykjoYSXHq2mfI4vAYUYvhOUCFjPXWex0wxhXjU7YbDaJoigoQXqPLShf1toAxm54SYrtSYPnrx/hy5ef4OLgGJmbLpKDVscS8LzQLmFeaXA5HXDZd0vEMuZl95fkbul56uKS8UIVx/MIPQvdvEXuV3BSJudLFdH0xXe9fI9fv/qP+T+u/wYXJmf4rev/bEmb7569nxEspdQ7Alciwvnz5/nqV7/K8ePH+eQnP7kArmA58BG5cwrJrexOnXIiwpkzZ/jGN77BY489xsc+9rHbkoi+YQTL3TyKAcHpNbmBPPggH5N5h0f4nye/j62dYjsL9LIst1za2cbmlq2tLYQwBmU+KKkO8vIN1CQuOJE6phUYDNJiRa8H6nOR2ykorPekLmI31ayYJokNAEuUkHlHYj0rJtD+DzcO4UVhvWGcN3AujPsGjRaDUqFERxmvr/pMqYpJATDylm6U88a1HYZ5Fhw4Ioyt5ZtnLs/0y+V+n9GarfpzmGfsTibkztGfZORuuk7oTxbXGkObcHqwS+qmjt5ELJkNPbWTTiM4uXeMXUoujpFL2cl6dLMJbwyuMHYpkywArP10wpnxDkObcGHcYzQHmkWE69mQ1/uB2nh10mdYzNkiwpnBDt++vFxUS0S4Ml5eEwymsuAf/OAHefDpx/nMZz7D0aNHK0fHF7/4RV5++WUuXbrEZDJh5MYkBeW5rONkfY71X0LhbynO0k0n+KXar1N7dfAlXhn+RwAuD5e3PXE5aRGVzf2Enl2kg/7nHsG6mZWCGQ899BD/8B/+Qy5cuMAHPvABHnvsMX7913+dD3/4wzz22GO8/fbbt3U8ay3OuYUxu91u86UvfYkzZ85w9epVPv/5z1e/bWxs8OlPf5qvfOUrAHzlK19hc3OzAlcAn//859Fa87Wvfe2Oru++imDdCxMRLly4wJtvvsljjz1WoeHZWlgbQIPZ3KvZSUnNSz7LuIif1AQVVAuRPpBD9n/g3RsQfx4dfyewqF74Tq2M+jz66KM8+eSTN11EvJfFjcscrFt5xpMkqQpcfvazn6XT6RSUhFmAtQw8hQiWuiFaibXBz0ewFo5z47ZNFxCqaoNIJaJebsUM+CoW68M0ZpI0aAxbbK6nNJoJCgnc/VpOVkX3qkd1VC0G4iK8U6iGRdWUspay0FSQX5elcY8Cank1lzK2uEjyhOP4Qggj0h6thEkeo5VHdEaswqLGqzFKClEMKZMSak1aRrObCempQEtZokpVPwoFgNBKEUURsYNIQVosPpxziLNoZ3Coojho6KUgtBGgUOY0p/qbvNVd59E1Yat1nY7exRTP0QxoVX72ThfgpC6eeKOHb56KuQAhxSy5f3rJ7bg1KJiH/k4019KYpm7SNClCilIGwQbhEELUy+N5ZfQ1Xh2+zFZ8b2uNvJ8RrHfiMc7znFdeeYVut8snPvEJDhxYZCGUtpwiePcB1p1EsPI85+WXX6bf7/OpT32Kzc3N2z7P8ghWoAjWx/ORTViNZ52PTvyM7HrddrNBcHgpSHzO/3L23/HYyiZWLHtZnzUvnL9+nWa7yeEDx0j9BCmi/bkLheYz7/j2dsx/zC9UY+Zq1CEVhZEGK2Yd8YpJVnsfcOTOkOURxrRomxFD78CE9qYeDpgWBkOkIpw3WGdwTiNWgxFEYpRXxT4a5fx0sJDw6pafbZGvqltwdtjjqG/jdWAoOPH84Wun+dQTD3J1f8CxrTX2xxOyLeH3z5/mv37kCYZpyv5kTDeZMEhzRllOZh2Zd1zoDXimPV0weu8Z+5zTwx2+tXeWZw+HiEmGwxSL/XPjLoeaoSbVfjYKbiLxTHzOfj7ghb0rXE16eJ0xzB3Wa3bHI/bzMWdHe1wa9+kukR/ftRP6gzGfOvgIbw22q8LGbw2u86VrZ9gbL19rDPJ0YX6/ke3nA9pbbR544AEeeOABRKSiE169epXX33qD/sEeqXi2t7fp+x5KKS4mL7NiLmPUwVtGXrv5ZCFXex5o7OWXqjH86ng55bCXT/CFQ3fie4zcVAq/Lrt/vwOs+6UQcqPRwBjDj/7oj/KX//JfZjQa8aUvfYmHH75x6k3d1tbW+OxnP8vP/uzP8vTTT3P06FF+/dd/na985Ss8+eSTlar20aOz9eCOHj1a/Xb16tWKzlhaWX7gTlW576u7frfDqHme8+KLL3Lq1Cm+67u+awaMzFM8RM+rlsy3ZX7xL0v49nMPqd8nn/y/ySf/GPHdm8rh3o4553jllVeqqM8HPvCBW/bZe00RhOVeztL29vb4yle+QqfT4TOf+UxVn2tos4X8p/nDtEzM9L4UC/C5jeIlA8X84Kb1bJ95kak8tgcEoiDou/QaphCsjHPN/jbJI67urnD1+hbDUXtpxKOMwE2/kIr2plBB6MDGkBfRtrkoEcX+M0V6CSIY862aXfQHsDNLZZu9Bi+KcdYkyRvFZ02uUoYyIJFhIYQR1Lz0QpyNZfhtpg2BEnjTzafkTZndSCmN1hpjDHEjJo5jjDaIhJybPAvRrdy6mVhk2c7L4wbfun6U5648zpXhJrlrMn8HZ/vGFEep9+ey52LRqbCoRmqWbLPs2Zjfpvh/bd8Sfs63OvGOXq5JXQdX0IVs+WzXjjl2cH4y5jeu/gvSmZpE7z66Xtr9NHHfyrrdbqUM+LnPfe6m4ApuQBGUG3vo36ndbnH6Xq/Hc889h4jw7LPP3hG4ghvMExLG17yWD7ib95m33DsmdnkE61rSxxcOqgYRCkVTNwHhcu8qZy6eZ2JarK+tEalWKN5ePIKZ16Q2QoDtRJF6T1YUNF+LVohUE+shool4RVJrvxVHyyteHA7pZZ71uFUBIwhRYq00Td1ElMJ7g3hTOEkCO8E7jSkUBJVTIXeWurNqirashJpcvgG5FobeVvmxE+945VKgIH795KUQ1XKhHuIvv/YSAGd2uvSTnO3hiNQ6zo+HTHLLpd6AXjJLd7sy6ePEcyXt8bX909P7oFwocAxcHPXYy8J+vTwhjBaeXHK6+YhXulfZy4dkPmdkwXrFbjKmnyecG+9xdTLkejrrMBAR9rIxp4cBRJwd7ZD4EIX72t5ZTvZ22E/COmk3mQVnu8mE5DYdvaV0fGlKKTY2Nnjsscf4xCc+wRPf/RQmihDleOvUSZ4/+TxpmnL+6im8eCKV43x+03VPL0sXmC6TbHa8Tvyoel62x2O8dwvj4342rcmYuD6J369+G7vQ//d7BOv9pnLXrayVWIq0rays8AM/8AM0Go1b7Dm1X/mVX0FEeOCBB2g2m/zCL/wCf+Wv/JX35RrvuwjWuy1WW1qv1+OFF15gZWWFZ599dkGHf2HiUoeAK7Ut5rzrfrREQ6AN1D0b87k+RQHH/Hmy/E1080eA8EDfaWXv8XjMCy+8gFKKZ599lnb79pKp32uKIITr+/b+Zf7o6imGNmMtavLQyiadYYa9tM3Hnn6Ghx56aGax2c8WQ/rzA2DTmIrvXNLV3LygxZK8rUWq4TwJbLqoLml14ouit1oVkY5SeXAqD3wj5l0ZycitYa+3gh502FrLaLcnldTvbEREilaouaMALkgG+6g++MvMYqFuXnThXZ2NxM1cr1CTOC63WqynldmQz6O1EHmPGEfiBSWGWOVAK6iPiceTowohjCVdPPNRUV+c3MCKDprKkJdf10FGUSg0MkRK0dSGxOU457He4W2IfimtiCqMGvZPXcT54Soneysc7eQcX+lVuVozeVpFfav52Npi0/1CTy+qD2oW62ctAWsL9c5KauEsrXWmhpdQLAxDO1Iv7KUdGioGHeqfeQGjwjlzHyKPrw2/zbnJ2/yFw19gJ7/CarTGx9a+m7th7/fC4nbmkpJSd+rUKZ588kkeffTR23L0LVURvEcUwZuN33WGxhNPPMFjjz32jhyVNzqPiJC4lIYOC5z9rMcjnVnvby7uhiIXZwfdEB1HiAnKoVoi+v0+fTvmwYNHGV0L+yoVY71HRBeOIMXOpHRzFc+RhNxcow2H46PsjkY4Ae+n9RC/eu06V4YpsTeIV+znE7biBt6PCycdxEYTq5hINcIc4ls4Wzh+ijwr64qIdEEH996EKhweUKr4d66/Ig9KMxGLFGOwR7jSG+Cc5/T2Htd7I3LvEAUne7tcHPY5t9fDOsuZXo/UOt4aDfgvrOXKYEhuZ5+zb2xfQgR20xFv1RQHrfJIQaG/POlzdTzgYLND3yaIhNphThwDO+Ji35OpnFiCpL3xil6SMLQTLk26XE8y+kuUIfdswqW0B8ClZEBiLYM85eX+Za6NJqyl4T79izMvc7DV5ocfC7W19tPJDJ3xRpZ5y9DdOH8qsznbrlvlwD3x8SdIr4wxFw3DfB+JBIPFesfL59/gA0ceXVr0tp+nC87ZcZKz0pou5Cd+SDkQ76UTduw2q2aNjplG/ru1CFbqe6Ru6mTZzwesRJ37Pgfr/R6n5208Hr8rFcEnnniCP/qjP2I0GtHv9zl+/Dg//MM/zOOPP86xYyE/8Nq1axw/frza59q1a3zsYx8D4NixY1y/fn3mmNZa9vb2qv1v1+47gPVurVTVO3ny5E0nnMUI1mFmBMVkziOnHEILVZfiVdFsZKGs0VFZ3Yszwqe/wvGjLZz7KHD7iXLXrl3j5Zdf5oEHHuCDH/zgHb0M70cE6z9dPcVvXnoNCADord42PrekYuk0mjTfusj6mRbH2+scaa1yrL2GJiTZNpWpvN7Wz1P95tWb6qIXxTZLij/P3301t6iV2nq2XJBVW0iIcGkJRYtFUdRqqu0/+3GBTua8YrfXxvRbrKxMaDbz2VbJbKHj8pi6UBdUKHBRSLCObbXh8siPFGCwVpz4ZpTI8l8vSzcTwHnFJI9oig8TtSicEsSMaKoWnhTRFuUWh5P5fr2ZzeZyhZ39Auxc/Lve10pp4qiUshcQwXkfolpOUNrjZ/KgFNuTFtcmDSJ1gIfWxjyyNkFUrwBwpez79KaKWtb384jSLC6+RC18tyw/Ts1Hcost68BPEVGnNAuCxszkFShg6EKu6WoUI1IucHS1hcIwcgP+xdVfxkuT79n6JKwtNOkdmXPupkpg77elacrLL7/MaDTiU5/6FBsbG7e97/II1vCuC4fcLIJVijbt7e3dktJ4K7uRyEXmHYlPWWcN5x3dPCwg69dpvSPzoT7bvKrryf5+oPNKcC9oMVy9dg1pCI1OgzGefhpo5V6iEMEqzy+a7YkPdGKvKQVAYx2eqRPNB7g6eJ3MCt6Zai7o5hkT54l9TO48AzfmgD5ENmjSWg2O1lgZjIqACC/CitlgLH2MLqLKPoCq0smnRENROD2grXoUvuZUMiA6OIZ8OaYryKzjxdOX2e6N+ObbFykV5Z0I/+nS2QDARDi72yW1jgvpkCS3bI9GRHOO2y9eOYeIBPW6PGM/G7PV6OC0B/EkNudq0ud6MuQZjrKbDnFeEOUR5biSDDg/Tmi1PR0BKwrvhV42YehSrqV9elmgdJY2zgJVtO9StpPgSDjd3WaYpFxNB5wb79FPHHnBerieDPni9TNTgJUlVa7Szayfj7E32e7CZJdr6R5ewsi5k+2RmYxWo8PK4QZqqIi0YBSc2T/P3plrNJvNSixja2uLOI4Z2JCTVrdxmgFT8JT6kKclIvTSlG62SxZlMwCrl4+rMTz1AywJmR/T0B328h4Pto/edwBm3u43psFoNHpXKoKllcWQ9/f3+d3f/V1+/ud/nscee4xjx47xB3/wBxWg6vf7fO1rX+Nv/I2/AcBnP/tZut0u3/rWt/jEJz4BwB/+4R/ivefTn/70HbXh/p393oHVOejf/d3ffUPhB1gycelZJUHFIv8Y1WFW3GI+GX2ORkhOkHcvv89ot0fk6c9hG3+dKP6um16P956TJ09y/vx5vuM7vmMGcd+uvZscrEGe8O+vvsjJwVWup0OaOuZIY40HOwc41t7gsZVDPNg+UNWi0lozFMsXL71euwjhuI85Iylx3MCKYLxnLx1zZdwHBS0d0c+TSsAi1kH5qW0aRBhaUUxLx6xHjWpiV4QFbzp3beY2FjfzS9q6J6uKlMyo84X1tS9AiBIwJpQr9hU6Ww4gprhJ8KLoDdvoYZv11YxOZwLGhSiJqsfRyijP9KhaFKAxtoHVOaJ9yOVStbW/qp8xUF2ovq9fv6qnhIXvZDaYEpYX9aiWIs0jRMCYsMLwKmMseQEGp8C0iJeg0ZSKjbPFiJfX/Zq9fhWELW4jmF0eu+orVeZ3BbEMpRStyCDWYkSTuxxEmKQJTiliHTzTDsOZ/hqXBlt0Gps8uDpkLU5YmBuXgkXHbBbcYkRwuS2JGsxFsHzxfNX7TM/lbkl1zsWoGXiGFiZEbMarNI2b+90BhqETvtZ7k6ONR/no+jO30fab2/02cddtd3eXl156ia2tLZ599tk7ZhQsBT5iESaoWs3Ed2s3cpDVVQ6XMTTuxnkEmLggGODFc3ZykV5B3/rj7dd4qH2Ex9cOVwvV1OV0omk7xjbn3LAbfhPBOE/WH3No9RBmtcl+PmDiHRMr5M4HBVM/HQW8aAZWQqF4q4gbYYxs6lJIKLAK+mleAKyi3QINIrSKsG6CxnHqqsU7Q6uIxGk0WkV4r3HiebR1lGEyYeJcEE0VyDJHLp6GxMy+VyFS7JvhbwqnVllkXnQIgpU/STF3PPf6BfrjlK+9eREXBceZMnB5MmBnOMZ74UKvhxPhaj4htZbt8YS1eKoMCHCqv4uPBMSRe8+Xdk7yfz7xnTjt0SLspxOuJ8MwvwL/8xvfZOBzGsrgEN7cHzDK2hxqOkRMRRkfZBkJlu1kwCjRIcpWzLff2D+Pz3IGJmVoc85297jQ7yNG8Ur3MoM8Yeg83fgaznt20zFJNu2zXpaQ2tsAWHZcPU/LgMnlyT47WZ+yFuJevs/Ej+moVfr5PijBKIfWigMPH+a7nnmGdDBib2+PM2fO8Morr7C2tsal9Cp7+12ONlerc4zTWad65hJycgZZGpQw3T655DzYmuYDDVyKKyaptKAId/OLHGk+RddOnRH3M8C6nxxh3vt3XQfrd3/3dxERPvjBD/L222/zN//m3+RDH/oQP/ZjP4ZSip/6qZ/i537u5yoxjZ/+6Z/mxIkT/KW/9JcAePrpp/lzf+7P8eM//uP80i/9Enme85M/+ZP8yI/8yG0rCJbvzf3RqzV7pxTBbrfLCy+8wNraGs8+++wtOZvzScqyoCSYEPBnfVCYP+Y8cFkMqSvaSC1/S2EQGZGM/yeixvfTbH0BpRYnxyRJePHFF8nznM9+9rPv+IF7pxGs1Fn+6dk/4mJRSDBSipODK7whl7HiiLXBSVj0HWissBG3WY9anOQyLbvBRryCsYqd3X3WV1fYcpA4i1GKSAVPe6wNVjyJs7RNXHm4Mm9RClJvg3coD9GcU+NrweeuFUqEOI/YHo5oqpimiemY5mL+iiwWy13M9ZJK8amyJaxCIVDOgsCCB1EFBS3sUEIkPXOIMMuK1FJoBAbDJv1Bg3Yno9NOMI2aOuKS+yEEsQwBlI/w4gqP6fwqu0BU5T9Sa0lVyGXJ9c2ZL/qp3uZSNsI6hVIGrQRjPGkeoZSnQY6XGMTgmAChIK5WqhJaWNa35ZJq/rVfBhcWSZSzmUgQPMvLXAqKIG5j8FgRoijGO4d3HiuOSIW6W8Yo9pMG3fQAbaM40B7z0MoYY3oFhp2PWVLwhmYBlixRH5zv9mURLJH51i9OzmrOuSNFDGvRpt95YDc3NPIODZMhpNU+Za7ZwE745cu/wXcMXuT/euQv8u92fosfPv6jS457a3u/uf3L5hLvPadOneLs2bN86EMf4sEHH3xHEadlIhcA4odg7h7AMiYIydQXm3cicnS7dqN5Ii0iWIlLOTe+WEljX0y2+feXX+Z/+NiPVEyDscvoRM2qracGu1VUaZQkdMSzsbFBa2WFXPrEKiZ1jknuSHIh9UXEqPD05C7oaWo0aR7TaOYg0CxofrYoqTEoAJYrgJEXaOkYn2tET7C5otv3YBSxKmpcaY1Gk0koXn+kscaHVx/mm93TBSIKzAMQIucRR0X5A6DhyWNBcqbsg/LnKICs8jtR4d374oXzrNCg25/gi1r1Tjzb4zH9NMUpYWcU+rfrMgZpyv5kUrLKGfYmRA3Dfj6hYQTxYf9v75/jLxz/aIicibCTjxnYjPPjIA/fzxMcglEKEZhIEM+wIsFpWNg4T/Gx52oyQLsVLI5MHE0V8c39C2wMU/pxiJr/T9/4aoiYofnm/gWseKx4ci386wsvs5+PSa1jYi3tKKKXJ6T+1o7egZ2QS8jpu9Df56kDs2ID21mfrh3gilljz3ZJfYJXjpEL1EWjQiJ16i3X0hFPHDzIwYMhxz5NU/b29khPfYM3Tr7FzskzbG5ucuDAAfa6GfLQ4ep9GrghnoRempJ5x8D2cGYWhPXTEY2oj3OO1AXnQ98GgNUranH9WQTr9m1U1IIrc7DeifV6Pf723/7bXLx4kQMHDvCFL3yBv//3/37lRPtbf+tvMRqN+Imf+Am63S7f8z3fw+/8zu/MKA/+83/+z/nJn/xJvv/7vx+tNV/4whf4hV/4hTtuy30HsO7URISzZ8/y9ttv3zGHvj6hTDjCtn2SE2aHpuqGY9NBUU/qnc+xmn3ZFCnzoCwoC9aOoKeTfp79AdZfo9X6vxCZh6ptdnd3efHFFzl06BCf+MQn3pV34Z3WUfn3V15mkOUcbmzi8YxdwjPrJ/h2L8iQ5t7RUAalYCfts5P2aWjDdjTApCN86lEC0apBqX3iqEHuHQ5N7iKUGHLvUUWysZMi4gGAoFHYueW1VgorjpDHK6RYxllWgI/Qr6+NLqBFEWlDQ0U0dYx3kJNhCumK+UWtVgqtwDqppL+9AzFSRXWmftU6BCrk0AthDK2XZjwtgCap/TsaN5iMY9otR2slodHIq99mWikhElO2w0io16KUQ/SNJ64S2IVAW5G1VCURzYSsFq1exLje8OK4aR5DBsqEa3TaMXADNAZRlvAexEvzu5bFsOYjeGhNcbOXNaGyecC8CMKWXBqAUkTGEGmD8o5ICal15LnFEqR/I4m40G9xbbRGw2zxwOqQzeaIuNbnAZz4GZpnmSh/K5sHU1KGSGXuu+VXUDvO4nO2cK7i31xgkisauk1DO4xKq1yz0h3x8uAtTo/+EVqljO2ITnTnlI37aeKGUCPlpZdeIs9zPvOZz7yrSdwYQ5rOMhZCaYSbFzi9U6sLI4kIr7/+epUvUC98fDfOs4zpkLoAsMY+4XJyhaEN4+zlyQ5vDs/z4t55jAnP4qTIw3p+7wLffegRzg33sUUUJLGO9UZMs9HE+9BXkYqZ2AmpFSZWmDiH9VOHSe41kQ60Q+s0kTI4sbRNAEnWB0AxTB3ORThCGQ9BaOkGTitGHrJBIyip6iAetBl1Cpq4IXUS6nAlOfnumK2oQ19cwQ4wgOM7Dh/ny5evIUUKgAoBfJQQ0gpMne5NAFcqICuJQRXLgYvZiAfFMxhm+KLms5cgCDHKcqQpdMfBSZt5z9lBj+4koVVE7Pr9CdoonDhS5zEm1At8e3SFySQnbwpGYDcZMrE52+mIb+1cxooliCGqIpqm8Di89zivitkWBlmCj4S+ndB2MWKEzFuaOuLN/nUOJIprSXi+z4/3oekRrzk92i1yygTE8HZvn16e4p3ij6+e5gcefIp+ljK22U0ptEmWM3RjnFj6WcLre9d56sARrM/ZTbY52jnBTjagb0dVrnQv74OkOO1I/SCQupVHISQu4+pkwBNrUwGzZrPJ8ePHMVcSnnn8GR6M1tjb2wsR7dcvwuAqW1tbXOrs0vX7RErYn0zwOPbzPezcmu9M9hqHVq/yx3tfpRMHUYy+Dbn8g3wKsO7nHKz32xFWt/E4OBjeDUXwh37oh/ihH/qhG/6ulOJnfuZn+Jmf+ZkbbnPgwIE7Lio8fw74Ew6wsizj5ZdfZjAY8MlPfvKOlJPmKR6xXufLwxzY4OHGYZ5uZRwwCUamAGtxybQkGVN1QOqgbHaBodWULqUA3NsMhn+fTvv/RiP+HKdPn+b06dPvyrs6c753EMHaTgZ8afvtGQGJrUaHsaQ83D7CfjoC5fHeYWuJa4M8Q0mRByISQgmFl84yxke+AA5h8jElpU001im0eAQT+PYuRmlNmYMkBKpImTDtEaIaWBKRKmcpFUfqLBOVIUXoxDYcpTxGgkZi0BK8mEZMkATOXUhepqjlJIJ3RSazqCUli8oYilS4yxftMEqF9hZiEotR2dmQziSJGSUR7YajvTpBm0VgMZunFdqpvQGvsdrWKI1T8FTSCMuvA61GofV8e5Y0r4bBBAKQnAORQdodwEAEWjtSJ4hEiHHE2heg1uHJiWiWLVs45TwdcPmTXwNhSiHlgqz2nuga1Cj7rOz+4mkpcj7qxwr5ey5SxMqgvMX7kL+VW4/knkTDIF0lNusca3sOrHRZawyYxtqWhD3nPi8qBM6LXiyi9FAgdf7wcxspFnILlx1fMwWkmRcGeYNIKTqRBtIqYRuC9PTEaf73q/+WHzn239KJ7iwycz9N3NevX+fll1/m6NGjPP300+8a+C3PjRL8XRa6KNs5GAx47bXXMMYsLXz8bm35PCGk3pO4lIlL6dshExeznw8Z2DFaKf63s3/EDz/0GSDUBAJ4fv88T28e5+L+LoPxEJoK02hU45NRESIxsdLsuz65i0idx7oYX5tlhUAZjEzQQ4+JOdrZYCUK1576wAwYZzY4kLwu2hAFgGXA5TUeQVHOYjRQROsBRI1yy2A8Yf96lwMHNnnAtDi3dxUEnAOM0IkatLVhVKv1pUSHXMlCxn22MyU451Y8pKZK3crx7OQpNsurfUSEvXRC5gJ4GxUiThbPpWGfQZqy0QgMl729EacG+zh8ePYKGfiJT3jx2kVcI0SkLvUHJM7SyyZ86fo5nHgUPqBBCeqISMj/ss6DGEQLvTwJxd2tI/YeMdDLJqxFLa4kfbBtullCqxGz44dBsMkL28mAXHxwRopwadRj4jKUa/Jbl17m44dOMMgzLiVddicTDnWWjyPX9ocMVYIVT3cy4fQgqBVeSs/y6rU3+MHHTtDNhwz8JCj/AgM7oCEZTjmcTIrnaxrB2s6XOzwcY/bcDh/aPM7q6ioPP/wwV7I2Tz95mG9fe4V/tf97PLxuyUX44zPfxkcpvSylXr5HROj6MwCcSy/wZDTGo7mYnOfj68LAjbgw3LvvKYL3U4RtNBoRx/G7pjzfL/YnFmDt7+/z4osvsr6+XhUOvhOb90BGOqahm2Q+5XyWcT6DQ40HeDxe59H4Gg36MCe7rGTCjWthlV/MnVdPRQoAkATBMBj9r+xc/0OuXX32jhOub2bvBGD9zpVXFtT5tFJk3tHUhn6ekTmLUYZYa5qmQ6QUg7SHKEEJVZHPMNT56hjlYXVR3yl4/CxKCc1YQGwlzOC8LhTvFOI0HoOzwfMoRU6SMjIFAVLw2gtA5kWItMbORAkCPEN7imkHi4QJrwWRg4AKFcYDovFWQW7CYl5UochkwnqhFpxRMo0weS8oCYtuZaZKhlImS82DiQIIpJlhsrdKHDlWVjLiRkE7XaY2V1xNCbSschXkmx53Ma4hovBeFaUGlh427DG/hlfT4srV2dUULllrsNZgdNgqEUGiEQ3VxJIWXt0gi14HnFL0SwVUWczTqmiEc40KoLrs27KhN7ieuf2q66pdX/1ajVE0tMFrT4QidTnOOZy1XMgM5wcbNMwGD65lHOz0acQ1JbXiOblZK8IVL9IB53e7Vb7adLvF7+a3C+/G7Ls9coqRNbSiNkplldOgFBh5fXianzv9i/x3R3+AR9sP8kL/RT5/6PuXtGrW3u+JW6lQnPrNN9/k0qVLfPjDH75tDv2tbDnA8iGCJSksoX2/EyufyW9+85s89NBDPPXUU/ekT+fnCe+D6yrzIQdr4hLGbszEtTk3DgpbkdJsp11eH1wEphGs7WTAH77+LV49d4qoEZFrTV57GTSGcRbRbmjGmcWKwdkGJ7dbPHPkBCJhYa0oHFbFG9DUTY62Nqvj5C6420aZLRxAhsRZhIiOaRbqgCVPOoy5RmnGaUZ/L2Oj0+TK3h7DsePA2jHidptknIR3xCusC/NCO455qLXJG8NQYJcigoXowEWO6/O5QmUKfygP9y6bit14JYyMJarNWV6gmyQ4HCAkztJAcAjXkxG9LOVAQUW9ttPjq9cv4dc9Ih7jCwejUvzBtbcQEyiWV0d9JjbjpfQKg9Qh+FDLsD0ktwZbzKseSGqP8NClYUQSIS0ELn774uv835/8DAOb0XGGxDuaEjHReRD7UZA4h1cS1B8FriaDAOQ87GUTfvXsN+llCbvpkCvj4VKAda034Gp3yGg9wXrHhXGXy0UO2eX0NBfGpwDoZiMSlVWO1aEbE9sAsLykKBVGUKUg8RmTdDnA8iRcnpwGPlJ9l+aOAwcOsJMktHwWRExEeFO9hcRwrXuVXTzn0nOsba4jranK5PVsm0faYxKfIzJknI9JXc6Xd09zhMVSMfeTOefuG6bBcDhkZWXlvo743a6JyP0HsG7VsSJSRXmeeuopHn744XfMoZ+fINtmlaxWoC7ziq+Ocr7GAb6jfZwPNoa066wqBUIHZryWsw+qmqMARSZDZHYba2N6ves0230+8vENVlc/fsfXcyO7U4A1thkv9S7OfLfZ6LCXDQGFVpoTnXUujwbkYkl9uGfb2YQ0F1CGWEd4LzSNwnuHNjpIngsBBMyoKYTwgqKIB6liES2C1q7MHoLIgkCzKFvkfZgEveiQqOwM3ka4qKhlVRwxUkGZarraFaaS10VuFILSxfJUS0WjCypRrpjgcxrFwC22iGjZMtCj8ZMGyofog0QW8gAEgQIkCgZVROVmaW1qBnCFdrk8ots1RKpBZy0jbtpQ/LJ2X0rAU/ad8QaFweMKfnwBOm8EogqlvQqCTbuFWlDuhlZex+zxFbkN99Foj8eS+Am5M8RGioVEGHYcObr4W6sFAfMbtLneb0HZ0S463mfaE76SmZ+0Wtxm2e7lRK61IUJQplA2FLDOkeSec/sRp/YPcrCpOLI2ZKs9CJHqhd6bjXIpzJJtFkmN07iXqn13awte3sUi0HWlQSmjeThGFrSKWCmiWfUyCWOX8KuX/w0bUZtDjdXbBljv58Q9Go14/vnngaAMdTeUqUqLomgppc7LCPwOmAfe9Tm897zxxhsAPPXUUzzyyCPv+pg3snkq+Zv9Kzy+dggvnn4+YiNOceIY24wrScjLNVqTuoxv7J+io9skLtSgO7dzhe38CvnmCjoLtCnrhem0p7g8zHliq8HEh3c6txFGC7EcqwAWlIyAMAI3mHWidvuOJFVMbHhLvWuSuqRQBWyE2lQyHcUUumBBwMXuiHXdxVvh4cPHyUYxY5uROVdQ0afs5KYxrEZNtDeEMvdl8fnSYVGMoQqwCuVK9ATFDmFIVWCNBB9T1ROQWx+qcahAcYxMACq7yYR+kpF0AsC6sj/k7f4+blVClEzKGo7Cm6PrIJDj2J6MyVzIM70y6eO14KyiXcrNu1J2Hmq1mUl8TkOCSm4vSWg24bfPv8l/8+CHsWLpRoGOP8ktubYFC1IKJ6pUzrduPqnmxpHN+OPrZ1jxbVKxXOz3+MihI2z3hhzeCHnl3nteunCdZJwz6iRYcZzs77KdhXXVbnaBoQ/gtm9TXKSqGmMDOyTLJ3xIO4TgHNJFjmzqg2DHMlpiszHkrfTLXEg+yO/t/CYr5gADeQT4MLvpgNiUpQME37mKaR0m7iiSDM7sX+DahesYo7HFvejZHrlkQU1TjfmP3f+N3Hd4dXiVLRoVwBraCRExrXeQ9nG3FUpLe78dYXW7WwqC77fdtyIXN7M0TXnppZcYj8fvOsqzLDepbdbo5bVK3LVF2cuTlFcmTZ5sPs1HW7t0VKmT3wLqAGvuBZjj7BrtEGlUx02SCdt7GRubLTrtDuJfZm/0/2K9/VdpRh96x9dX2p0CrJe6FxcqnLd0RK92XetxC+nA+dE+AMM0IXM2nKsggylgZMPEoyUCESKjcD4jKhXwkKKOkkLp2jmL8EmN2VZtW+YTGS2ggycvjDnFVgLOaQSNcxonIQerpJ+J1xhTFLMsI08YqFE3BSlUBMtP5fmLpW4kIfYVUfI8oR3us/EKpQWfGZTXiNUQOZQWXG81yKEDUSNDbFyByRLmlJN3KfNnnabfbxNpz8qaJWolVY2reSXAUhrcSKj/4oqI1kIUo7ouhZJCqU8x8+gGaqGw8DzXj1Pr8+mtm+7jvGacxEQ6TMLeG3KVh5w3FDkJjUIIQynNzIHmX6Pi30XRi0UYqJZEp3wdYMuNHDmLsGWetqiLRHGtFVFBSW1pzdha+qliZ9IB1eZ4J+fo2pjV1qQSF5EauCyORlAfnN4jQS+AojrEqr5ZEMIAlJ+5BF0pBE7NqBiYzx2NKHvWCyROIdKkZfzMtiJCN89I/Ijn9p/n2a2bq6C+3xTBCxcusLW1dcelLW7HlucseUSGIIMw7qs7Y1XUbTwe8+KLLwYvaBTdceHgO7X5eeJb3VM8vnYoLPSzAcfaGbGOSJ3l0jjMkZHSTCRnJx2waizd8YCvvv5VenaCX11hJx3giucqiFcUgirFM7Y3saQ2OJwGqaPVhv2kWQniCBTiDKHgr/KzIlO5jbh0bYVY2SBwkbdI/RiFoqEjWpEq5PxKUKQwyjDJEq4Nhjx8oMXhgwdYUW32iyK9VR6w19VIZvEkuYWCFhhSJBVaCoZG3TEFqLyg9fqiVlZxHajQBbY5vUBRkFkbmABacE7ICtr9xVGXxApJEcHa7Y/ZtWlwVqqyBqSQe2HkhpWYRi8JQhFIEKtqGCG3pZhN8aZLeNenAEuR6RztCup0MWZdywdcSwdY70miADpS5wpKcrju0slWMgkmPgMXExOo+mPv2XVj8Io3+tv8eT7Aqav7FcC63O1zvTckHVns0QDkL0567GYhH2cvu4JEXc7t7zOwGU0zjdZ38yENkYK9kRc5x8FJmvqMfbfNvt3jQDzNw9pNrxObnLH3/Nvrv0TmG+zl17AreyTuv+Rqtk1cJM4p4GDrOhutJk4lZDE0j62yluc08wi35zACg7TPXr+Pi4TUD7iafYux+y52shbXzLQ+5JVkl95I891H7zySvjMecXjlnavr3cjeb0dY3UqA9Sc9glUCrPsDtt6G7e7u8txzzxHHMc8+++y7ptAtU4Fq61nk7BdUwDxvp/Avu+t8PfkwiRwANYtR1UJx0Vm5d6VAfAsvwqDfZzQac/jwUTqdFVBhUWsQ9of/iFH6n97VNcKdA6xv7Z9b+K6bz15DxzRYbzQ50lpBO8/YW7SKKAJQKEKdkRCpCoVoRQm593gfM8kjkiwizSNyq3A+eNOmFK95rzshCV9NvxChoLmFr6QWFYtjRxzntFsJrfaQ1ZUhG6tj1joTVloJUZRPgW/RYF8EucQrnNX4gkbnvUacwluNeF2LnIX/wlBe0tQkCD4oQTcdup1hVhOiTo5pWRpHejQODoi3BjQ2x6hmio5ytIZ4dUx7c1g1CSBup1QLX6/p95rsXV8hGbQRr6tJsLry2sJEExK6jY9CMvbCeKWqbcO1F3wPqf98q2iyqia66vRSRkXKL1SgpdhQy8Z56GUZPTvBeYUtXJ3lQOSZTmyzJyvPOYt49MwmsnQbYEbF8EawsaIa1q6pBGbl9vNlABShkLHRhkajQdyIiaKY/azNS9c3+eqFI5zbXWc0CcqPs2BXL1zr9K4sXlfd/BwIq3vqq++WFN2eH/KDJ17PbROe+0GuyH2zaqdRBi/CxCb82uXf5hfO/jK9fMDzvRc5N7mw2Mb3eeL+0Ic+xNNPP31PQN6NcrBERigsSPcdH/v69et85StfYWNjg09/+tNEUXTPaxmWdMrS3h5eJnFBxfVaMiLzWXhOlOHSJDgXy5pXY5+xk/R5+c3XOHz4ML5puJz2mLgUKeoVuZICDKA0mYNrk5TcGgQYZoXUuxWsL96LwvljlKGhDTYzM++2szBJIrp9Sz4KY0tLbdAwQeF2LWoVlHRVHS8ZT9gb9nAoJjQwOgKnQzF5oFEISiAlzVuR2Iw3B7s01TTarkItijB9FI9BmQKrbDiXeIVyxQheew1chxlAltqCYqcgc46k6LPdLMV6W8mb740SUlXm2krl/Mm8o+9LBWOhaydYX5y3mCtdMRdXlPvi9LmfOpG9cmRSjI/FxVhlOdXfQYCsEcJxvsiE9oUsfeW8lDCeZOLIvC8YLg5XKAVbcZwZhujnhe1e1R/nd/v0Jgm7gzFjl5J5y7XJkJFP8d4zzHdQyvF7V79G4oIwR/mkWgnCPE7lKJWzm69XACvzllRd5Wpyhbr9xrVfDsBZQDBFBA7yxi4vDV9naMcYnVHSOJQSHtvcwZEwdkMuJ1e4ml0nb1ooKOoSCaoZ8vqc82Q2Z2+0x95kh10zrADDtazLuX6PnfEidXGwpLBz3c71ezf9/Z3a++0Iq9uflghW2Z/3XQRrHrmKCKdOneLMmTN88IMf5KGHHror6HbZBNkxs6pSThar0zd0A+tyXptkvJ4c4BOdVT4QD4gpXpi5fYKy4NRrrJQid4Zu9zpKaba2tlBRB+v3axdtESz9ya+Su6tstH8oePjfgd0JwBrlGd0043Bznf1siBXPweYKe9nsYLAWteglI6JxxkONVT6wskLuhfOjPfbTAMYy71BKVwt7rRS5D976Mr9GRJEVdAqxglGCNh6FRuspbCnTiGr4qpgEao1SRUxmppvKaEP4TWtPHAVVK9oKJMV5g/ehHc5G+CABxcxiVYWJdiZS4zRKC/VAgkiE1j4AZaiiU7qMsImgjEeZcGXxWhomspoHtLE5CGDSCfFKhqJJNumAUsTtCY12xmh3g9EwZmXFEa+MCvngAJCkShNXmNCTQdZd+VBosrie2Zyu4h4UgGjapjkoMh9VkmVfll+FCFmVc4YKIFpNQbH1ipBSl9HRMZYMS05rybAkhLHgdiq6LcKREkjU8aOq/T27ZyWIIeWSsBYRU/UtF09YCqRoDZHWIDGXJw2ujNdo6pQH2ymdzoA4Ko48L0y6vPEzEaxA/JvLB60yVer73QbAmru+6YYaxJE5wYmmoVvE2pOHjEU0ijdH5/m5t/8/GO34c4e/j0faD5H7nFiHyM37PXHfSy/ocoqgBIpgBbDuTOWvXvfwmWeeqfLF3k0tw9s1Ywx5Po1WXs96XE+7iMC1ZEhSLOBFNL18zFrcCc83kNoMK46Dx4/wwQ9+kPGVf8/QJcTSLBagEhRhZQqcFJrdJAtsAy+M8sKxohTtKGKc5zhrEOOJ0DR1RGYFZ4UoDm9DktUHX3BeiNlCfBerPCbSKImQIp9IROgNRzRXOqjUcnJ7yImNdbLco4raDq7wxETGhFIYKHaTcO2xMkyURaNwhHfAo0IYKApjqhKFFo0XC05XLIN6VkD1dz1S1wgiEaLBmtDW1Du8CgAMYJCk2M3AtFDFnOYFJjbH0qjyg8/YHqJ8bRz2ITLudW08lgAGau++0y4EwQVQoai8KPgPF94KVMBIwjEUYe7zumBTTEFaec7ce7TOEXEY0XgveCWcHQXRh8t7g+q8l/cHXN0f0fEG5zNSb9lNxmRYrmeXyH2gJr41fhVREVagURurnU757fTbfMxqdvMVNhpDIuXIxKLNPtv5dXKbE0cxI9vjwuQKqOB0s6IrwQwxCd/sfpuJT1kvULMt1iebrQkT2yb3nuvpDiMZshK1KUnkXjxWZYhSmKgRSr4bR66HWGnx5S9/mY2NDU43L5ElR/jGznn+/MNPz7yD2+mQtXi5eI33nivD5XTHebtTKuH77Qir23A4fFc1sO43u+8AVt3SNOXFF18kSRI+/elPs76+fteOfaMcrLpZvwiwTC1iJQJvpg2+PXqI71n1PBSdRi2rhaU6iATvg/eewXDMxmYzhEJhCSVoqk44Tn8fKyMOdP4qWrXv8CrvDGCdGu5yaRjAlFGGQ+01OjpiqINXKZiwP+jR7XdZWVmh026jUKzEhpX4KHt7u+h2i4l4dtIhQ5sWA5AQqwinfIgSiS/G4oJroAJZyrmguqRVEMIw2gfQAoFyUdAjQk0qqLyTEKiGteLAZeqVUmUUrORuazRBoCEyDqIyTzlMxM6FaEtmNdYavNdEs3Vdi9ytWatHkspojtKqlhMmSK4ril/4NgAjVdSzUpFHF2pPTjTRWka0nqMcVXQsbk9wuSazQhxbskkz5CloAeVQsUPyRhWEUtpivAngUaRIQLpRHKfoJ5lboMoNdlkInCxGUrxMPaKl+IPzOtT10jASS6IsSnmM8XjlgahYGoR7b9ALeVoBzMDMBD93dkWg8/m6mxVm6ZVFk6fgJvxhdFkLZ/Z44TC152yuS0SkipgpFfIWGzoi8xEXknXS0ToHmwmbjRErrTwsVLWaCpLMHXC6WCrbsEwIo3QM1PZj0RYB1uJNnb/FIjC2npQmDWNAZWhCaYVcPKPc80LvNB2zSuYTvmcrqMrdT9z+u23LCw2DMASxKOkhd7BmuVndw3daauNOrD5POO8Y2BHnx7sIwl6SMrFhTrIeBlnKWtxBA3meBxqgUuzHQYY7dXkQFhJLpKIiglLPSdR0ohYXxzs0lQblSIpESqUCBX2SWSQ1+FZOpAwtE6N9VMy/nlhFTOws1dVbYZK2ifSweJ8DvTDzBYtCYHV1nb00A4FRajm10+NE3KBtwryeFe3omIiR9SCKfppSFj5HIEIHymMtglUfOjygvEIVQRCgZG/PDI9hTgI0+LjIA1YghWhGnoexMMlzRIRhlmJNWWC+zC8raxYW+4rglave6qC0WrgqvZoZ83z5fdl0JdjahXgBhXBqtAumcAb52nX46pIpH0+FwroAXrxxgcIvqgBzwsVxn7/4L/4lT6Ub1Xkv7ne54LZ5wh/FuozM53SzBK89b/ROY70P1GW9RyNq4mS1AoplfcjY5OynLRLfwIom0pbEJURmwl6+w3Pdr/J9B7+Hs8lJsmJfJ4rU+9pcLpwanwXRwbmrIBMDOsxZvbwBGvZtn5QhQ7dRRQi9CLlPKJUqvYoR7TCxJclzPvKRj3B2+yI73X0uXxry6vWMJxLDgQMH2NzcJIoittMRj68eWvp+bk/GIbfOO5rm5sv2QZKy3r59ldH32xFWt/F4TOcGKpN/Eu2+BVg7Ozu89NJLHDx4kO/6ru+665WmlwOs2QiWlUB987UwhVFzBT7Fk4nnDwdwKPoA37c6YFVdZkZxUDVBAjrP85x2e4XVlelx1Jw3Gj8FWEqBdSe5MvgfObr6k0T6wB1d5x0BrME0/8wJXBuPmcRNBrniUGudphGu7l9nJx2xubFBHAdO/Gajw37Bl1ZK0dYRH9w4zLWkj/WOXp6wm46ZZI6hT4t4xhQYVV70ApUE71wYAJ1VIAatg2iC0cHT53zIw1JaFiaumQX2wmqxTIKd7uALQFfuFxmP0Y5mXExgEhbaNldYZwpwYBaghJ6rG+W9KhT1qtYwjUXMef1qbZeS+lgqGgJIkLFXoojWUqJigsusAePIsqBn2Vp1oDxoBz7UZ4qaOT4XIhuRicZ4IW5lJFmj6ntdZUtMyyR7z4zS4AKQmLZuCayaverpPlLtGOgUxSKgCFVar8FYGkSAkJIQEyNEs0BpqZVezdltQi8sv4a6lXTA6TJQLUTMStAp9Ts5T1usPUvzUFZphSZi366SyDr5JOdEO6HT2CfSWahLE4XUEV0KrxQ5jbUzLLkQNSfjv/xaRc1+KzML3+X7VsqYCEPrMSpmJQq9U4qFnJlcQfbHPN55OGxb1G16Pz2j9zKCtWz+EDwiY0IE6/brYZVz3eHDh/nwhz+80GfvRQSrPk8M7AQnjpd6ZxGgl+dMigiWQ9HPU3JrmYzG4S0oaju+NbjMXjau6hc6cnQRtQqS5+FcIppINKkLYysoXHFuheJAs8P18QiXghRlExpxTDLxaB8jpDRURGqTmfwn62Gcax5sHWY8CY6dkI9agA2tiHXMIB8gCNbDxcGQg2tbdKIYoxR5oWyx1WgzthkGzTh3KK2ItA6VR4rzKV8wNEShCsl4HEgDSDUqLyRmRU3VZlVtrKw7rRSICd5AMVM5HDHCYDzmueeeozeaIGWkbObuFZQ/mTrHyuP7YuwQgjx7WeculGuoASxReC1QKt9qKZoujFwGhuocSs9rJ9dKSZTju0CgD4b/gqCjgHYM84zzox7OeX7//Gn+Y3ISMZZH8kOMbULmHanPEQ1nhpdCvS7lMdGIVVKujZpERR6WFDWvGtqya1eLyFQoDDL0uwiObr7Hm/0LjPJVosYZXHFLfEFBdeJpKEXuYSwTImOAoIroy1FeIPGalg55X8qMGdphkY8XcrJDnDaocOVi8CpDqYiRtmxubrIajTncPkLim5xMzyAinDx5kiRJWF9f541oyIeiTdbX1xfGrrPdLnuTBOs9zVsMqfuT5I4A1v3kCPuzCNY9tvKhO3v2LE8//TQPPPDAPZkol1MEF29sQzVIpAZ45n53NRGLHev4190OH1v5KB9uvEFU5F85p+h19wFoNlvYOa94mJRrpiyKFiJB0VD8DqKOcGXw/+TIyk/SjB7idk3rQAu4nbDxqcHOzOeG1vTzBFBcGw3p9XtsqJiHDz/CSiuiZ0dVIcJa4wHBFlXbI2042FzhQKND08Tsp2O20xFXx0NSl1ceoGlfzNZtohAV8ALOGXAB2BjlAB9yjYBprlKIYpURihm1OCUB9FSLzNmlfxnlCgV2p/IJWgnaCJEGVXgHnQsiGnmusN4EWt/cGFUX3yjPMW1PsTj3Cm3mF72zkbjp9+XRimgYasr7b4SpNMkNqnACKGVRKkysWnuceGKJiVsTjPHkymElTCadVoZ1miRvUC6vm3FOmjfCYqLwFEo1Pdf7bdYzOl1C1Fy1082rawkHDjfKCQgarYSJ96R2TGwA7dHKAo3qur14tNLMH7x8jhYw2AzyuDHEWhDQWCpxX0LS6Xnn9zNKV4vMajG25N0TwIrh8mQdO+6w0bAcaI6J9QhHhrWgMLDwaBmWybvPt2S+AHN10vo23ABg1ZxKFb2yOJ4TIXUaoUFbR8AYRaBQ7efdcNxiwXy/TNx328qo0uy4WtbBclQ5WGUIfYmJCG+//XY11z344IM3Pde9tDrAGtoEJ57Tw2uICKPc08+DZPaqbjGyGXt7ezRbDXyikWIOvJx0uTbpUY5zzhcCOroo4l45tBSjLKep4kAN9JUWBSjYbLTCOyTgkkBzXovbkGZkVhE3CkdnObxUUYTw92a8yniSMBoO8XmgqmutC8l3XVHuQPAZdNOEg50O61ELrYYoFMc7a1ye9FG5ZqIdGqFhDFrpIl+V8F6X//oi6l+oBuIV2pYRlrCgn53XmA6RmgJ8hoi+VKU/FN4IcbPJE089Re7fCCBsZt6qmVeIU4EyXsyDHo8uOtfZAN7Kc3qvZpjiFViFIhoWolbOuxAfL3N+yzGNUpAJbK6hoH/7AtBKSTMUQbwCI5goPGP7PmVvNOF/fe1bXG/v0XJNBmnG0I7IvSUv5oLr2TZee5xAHKcYsVwWmX2tVFgP9PIW7UYoLuEkIpcuCFxLdxj6EW+PL7Ell7Feo3UZ+RNy72jomKSo4alVGclt4HGYYu4zhSN86CZ0tGXoxmFOLOYyTxH1UwonGudylHJMCmbMfj7AesfQ5vT8hGOPPsq3dcIruz0+pDKuT/p8+4Vvo4vUkQMHDnDgwAHa7Tbne3262YTUWVbiWbGXuomEwsiPsHnDbWYemfvAEVa30Wj0pwJglaD1vgNY165d4+rVq3zmM59hbW3t1ju8Q7sdiiBApBszEaV5syzSCN9KGrw2fpzvXx+zbt/g2vZV1tZiVldXGY9Hi9RDWTy+Vm2cpLXPManb4+rwH3B45f9BJ356YZ9lVi5wbsWzzbzj/HB/5ruNRpvtZESWZfT7PZrNFicOH+fqZMheFpKeD3fWQQxGBcpQOfjOC2OsxS0GeUonavBI1OCB9gbn9gZcy3qhWHEZ/amtyyvvX83K+IGTIJygKACQ1jQiV3nxwjUrjHEVRXA+zBIWjfMwK/xvVsabmYWUoohyGUezoUDlOKdwTpPlCucNoBdU+MSrxZybuSub/as6+QytEARxavpdRfSnigyVxxYUvpQNjgQvGdZqjAvtU+KIVCjOaCLPJI1AB+rkaivkSFgf4b2i2cjRSkhsOcCXYbb552p63Qtr/GLxgar3uVTfBZpncUdEEBXhtaC1ZSWKsWLJSOmwUjtX7UgyR7hVs9lJN4JZ9f3KRYNeArCW2TyQUTNYJxxjaY2qGfyp6KYR/WyDWG+y1U7YbI7QpHifI6KweVYAyyiA8lv4neaFMJZ/p5hXHwxAftm+s+Nl5oW9VGiaFq2i7t3IjUl9iioWdffLxH23rbwu51yNXRFELsChpIsAib1IK150iJWKuJPJ5JZz3XJBjbtrMxGsfIIXz14+wAGJE7p5D4UiG4xxziGrHYbSI8vblOJ+I5tyariNIBgtZKlGF9Q454JohCA4D+PMsRWvYfMhY5+F6Aahdq1SipaOAtnea4ajjPXNNpHSdMc5hzuhlpaT+fG1+EOFwszZSOjELdJ8XD3fznkyVyh3KtA2OA+f2DpAJ4rpRA1aUUSsDVopWhLTTVIaJuQVmgLNhVpZtdfGhfemEsP1xWtVgqUC2PhinpP6EFhehq/NM+Wxi/nCdDqM1kIB4wqnSW2MLec9mTrfwm9F9MiHouyip3OY86FGY2VKTZ1TArmFyIDT02b6+txYokt0cVpVAxxlFC7EdkQCsVmbIEoxUpa94ZgL4z3UAc+4k3J6sMeKm2CdQlQDQdjNd9loenwg9dM02axLqDiZRpj4mNiFSGtLJ0z0AOcdfbdL6j17+S5O7yBFXE8kqDvm3pNKTkqg0CtlQ41MiXHiMaXD0VicZGTiaeEZ2FHRbaUObMiAVgK51xhlQXnSIp+rZ4dYsfTynIycV3au8atnnicTy+qBNisHN2gdeIAPx4fZ29vj2rVrvPXWW7RaLV7p77MrGcMk5UBrlkKXWkuzGIMGSUrmbt8Zc785wsbj8Z94kYv6WH3fAaxjx46xtbV1zyfmZR7Ill5BK42vKQGaucXj/CIj9zmhnk1tH2Xoe8//fslxIjnGf3UC1jrFwKYUmpR61ys8SrWRmUjZrJeiitNIyvb4n7PZ+m/ZaH7qltd5uwDr/HAfO7dQjJRmPB4xHo1ZXVul1WqT+JpnG2Evybg2dsQ64nC7jWNCx7RI5461EjUZ5PXCzpoPHzrM8FrCKM+KXij0iXR5jjK8VXAokGLxp6r8qzDoB/peZg1GCZHxRCZQIcrih1P0MfV0ll75+thSVjUpF8RTR6Ouviu9Z1W0TcCYkD8URwqUxXuFdZrcRnhfSuTOqu6Vx5+1xUijF11oHZVblMCQKSIsuSfVFmXXhQlWKSogpnS5/g9iDN4Lo7QJSmjEJSAVnFe0GjlJHqqBtRs5WgtJHoEKAG1rJWF/2KauAxiIKCXNohaRXGYiM/gwrBQKYGiD29crIdc5E2dROlAgS6AgMp0kVEHhmQd1M6lkS/BSAOnzUbiqQbPNnTluUF4ra+OUVgd083/V6YR+7gSmyDHzorg2anJt1GQ1EjabI9ajIVEh0JJbhxTRX611cd1LAByzwh7ADN25vKD5aKlWhmXy7lbmqMwS6mdNnKBllU7nGKnaZj/vseHXqj7602g3BlhjxGco3wMgdaeJzUGMni6K9vb2ePHFF9na2uLjH//4Lenv76RY/J3aTATLpaAg8QmxDuNlkjuS0QDxW8Qmpu88uUoJdSBV4SATvtU9XRX/9hKiGcaEKHrF9RDNgUaHb+1fpKMjlC/oaUyf1bh8Bj1c3U04vrGO80KeeJoqQlOIExQL/nIs996zfW2bPM956PAJruxegXT6DI4zN5tfmiv6LtRPitB0opimC/e2oQ1N02BSLJiclGNOIb5U4TtVhuBROUhMcBiVw7LU3sFimKywSfmDqv2uZBqG14J1nl6SsHcsD7UQ6w7I2s5CAJlipn0pFNEjCe33tW29J4h7FOaqebYgJVsF0RSsTU8plcCTeFXQH2tzTrGdSHHtpVqGCnnVOTmZEa7tD6EzCc+O8pxr9nncpXgfV1Tm1A+rbvIoIuVC1xTPXDlXeSWkMn2P2lECOC6n6zQZk0uLvt0mU0O0cpVyoJMAsECRVaJAEgASTURypJhAI20RP8TTwouvmE0KQReIOMy5isSDKiJheSH53rNjEpeTeovVlv9w8S2yYkx1qbCXTvhW7xyfefwJ1tfXefTRR7HW0u12+fWvfpndyYDnvv5Vrh84XEW31tfX6aUJR6IQHNgbJ8vkjm5o5Tt/vzjCRqMRhw/fmTjQ/WT1aKD3/v6Taddavyc3uz5BlhY8Z7PoeV69z80vMgjKgnXzXuj2eqRpyu7GYf7Yf4CuPFoeEaUSZEEaeVbAQs/Lv9cW2N7vsDf5bfaSP7jxBZbH0SUH/uYe0HNFTavSRDxXtq8zmUzY3Nqk1WpjlKKXzYp4bMYtFKGQ5JXRmO1UkKzB4XiT1WjKA3ZLVrZRBN959CgfP3acIysdWjomVoYoj4lshPFB+aiEPdPwQ8ljoFyPV3DIiSa1EaM0Ji2k4EuhgmIcxXuFcwrrFM7rIg8gWEk3me2MWSAkTKkfZTNKifMSuBnjaTUsa52E1c6EdjOlEVnCwnU6C81GpoqchMUQ1synKhpX30KktsguJko/HWpFAn3E16XYq2tW1UHDwkhhRTNKm2Q2wmhPK85xPuSirTQzmlFGbCxGe9baU+DciCwbKwnTO0JFuViw2dtYuxZmcoOCZL4msZBmhjyPmTiL8wGQJyTk5MGXegu1zamUe/27MlpVb9pyYDhbIBoiXWpi1vad+Thd0MxbXQgDphLw9W1ziTg/6PDqzmGuDI+Qyzpx3CRuxBgTfKvOWtIkIcsynHNFpACYZhBUFzofwVI1ku20/Yt9uJh7KkWR6NKD3ODCCK4NNtlOxpVD5/0EWPfy3LqI2M2Oq+WTPEYVFEHnByQ2SNiLCKdPn+Zb3/oWjz/+ON/5nd95W7nF9zqC5bybBViFXLQTV+T0CNf294njmCMHj2JURD/P0cT4QlWyWMpzeni1eJcUIroae0VCJEwkRKmdhJpTh+J1psI3vorwx+X85xSTxJJkFusF72HVrBQS7mFAb+oouL887O3vY0zE1tYWGE2so5nne5intKOoisL4XBAvQelOwqw87Oc461iJYgyKjokBqeo9zeRelu+wBeVCRAxAF8uEMrpUNaEYa0u/WBVPKiJA1TaFDDwCkzSjm6bYxnQiqjM1AlAA78rSIVDf0DtdlBZh6uyTxXdjptSDKEpXUbWlApiCTKZbV+NpSa2vjzJSfB8uWLBxjij47YsniZrBUeQFpHCcWbHVOK1MAPsl4PJVrGh6ZIDEGwSNYwqSyi4NgEphVTc4g5Sv+jr3wc01cdOoWNnexLUrQKoJUam2mRBcYx4rZQ1DVXMvhn3T0qmJkBXOqjcHl9hNRziV4/F8e/9y1UfDLGcvHzC207kUglrpwYMHcc0GptPi6Y9+lBMnTjAej3n55Zf54he/yNdefpmLFy8yHo/ZHc+lm9zCynHlfnGEDYfDe8pcu9emlOLHf/zHeeutt+5PiuB7ZeXENuuBDDTBsZtKiCKzCxLrZ5WLACLVIC3UA/M85+reFXRLsbG1hVKasXf81v4Gn1j9KI/zPJChVQeRaYFipRozK6tFh/TsebXKuT7+TawfcKTzl254nfUI1s3sfA1gOWfpdXvEUcSBrQOo4hjrcYv9dJbOGOt5oAip91wYjBERDrTW6MSK0VyNh7ZpMLKBKtmOIp7cOsChoyu8vn+dK6MhwyzD+Aida4h9RSP0qoxwUAsvzYpYT71wGu8gdyZ4mZQjjoRIu2oV7EtqWjmgz7gba0es3RBVRGYWaYT1/LFp4rHWYVCP43C9tsjbyjIz40Es95u/+bqSfZ9OK/MR/TJKJ5RxveJYczEMVU7MUlyTmo+YhYWKqkTpwgSS+xDFElsoOSI0jMVaTaQ9m50xzodoYWwcjSgjKwopH1gdMZw0p9TCaqI2LHR28Xt5G2QOCXkVKI/OCZmdYAygPKKFJk0KtWVyyYlVPLu4Kf+UcPZ6F2qlljoB5q2+haKYmKrFxszSZubv+QUJItMCz7U2TFdk5f5SLMQUe0nMXhKzEsF6K2G9OSGKUhCDNwaNkDsbvK4AyoQ6P6GhLFI5oSyyPYtwFydbvcwXV0i5A0SqSaQN3SxlL8t4RN0/ylT3yhaBTwnexyC9cI/9gNxdIsse4+WXX2Y4HPKpT33qjuo43usI1qVxl0xsdY5yXFZAlnu8KExrhdXVFZJME2nDXmJZbcR4yaeRHIHraa96l3SR8wohgpX64OXPrKOfZjyysknkIjRBEj2zrnpPTPm8uTCmjAuAZVyoUxipUp11GjEWL3TabQ4fPMSF/gjvhZgib0ppvDi8hxPtDc7lXZSFhsQ45Xhzb5cPHzrMZtziQwcP0dEx63ELZ4V2I+byqB/Gh2rsVAUoCNcuuUJFQhGsQLlQAws9HYG9ofJ9qeKdljmHTWXl71aRKkc/SYu8qGkJkAokCUgpWekVNqca3ESBt4BRYews74eEOWT+Ta+mVCn6fsEbV/yvcASqIoKl9WwZk+m7UB4x9JtWYCMHEvHN8WnMlsOJDtfTzop51lfzaMOEeV8VHRecodN3oaQkeqWnaokEp9zANQCFLnLBLGOsVTRr9b9zV0Roa0thAazXWGcKp5cqQJnHKE+kLF4IeeZKQr6blgqYeYHMl3nUglOeXjLm6/tnWFVtYBMrjn07pB3FKBR9m9DPBxyXEOmuC0/0k5RRnjF2FmUMJ44d48SJE4gIw+GQP3r7JNvb25w8eZLXhwnHtrZ4qBkUCuP45sXO7wdHWN3+NFAEu91u1Z//2QIspdQSD2QQutitfZ73+FpJmSd6RVqDg/F4wmg04sjGiVCErnjZMx+8Et8aKt7Mn+Ez5k1WVXsWYC0oJMxGykRmAYr3uyia7CW/j5cRRzt/9YYvye0kSZ8fdYGQGzAY9Dm4so5qN6gPwS0TU5eQD/0xOwALMLAZjbiFUor9NMX5JoMcjq1sgrLs5UNW4yYTN5uLFumIwysrHF5ZIXOOS4M+/bFl6FKMBE68LRZ0XrmpZ6vk/IWOKwCYmqq5CYhS5C7CelASYQoaYZCAn16DL3js3kkRfSloKDUhCqUI1Ihaod/5pXltzVHV0CpbqE3gczcbFvFBmTDNFctfx2mkrPpmSS7X1Bc6bUnAalL9PJW2L34XhTiNNlMZ8HLyrvkcZ86higk2JPRGWDvtA61Cn1qvWW3lQE5mNUYLq+2UbGjwYtB4Dm0MudZfI4iYKELlztpFzYGP8ruK9qZCgWrrFErFoW6aydiMm1ixpIRnJlznYk/N36/57lRL9oNQa6e+1TJq3jJbEMLQegHQVRHRma/V1JlQWOY1V4Ytrg7brDZyDnYSGtGEyET4QolfvCIXh3cOK76gJpc5F7XOVYu01WW2TN5dyfSdi2gQlYVnXXZfKVPdK7sxwCqELhjiZcxgdIYXXtGsra3x7LPP3nLRc+vz3F07O97BZBYpzjFyaYgoOYfLTQB4hZCRRmPQJM6hXARmRJ1Eltichm4iQGQUaRHRcdYwsY79NOOwUiTW8tD6Bld7Q2JlSMWS+Ge8mQABAABJREFU1eYoU5T+LZ9O5ULUK888+5Ocg63mlMpfVolH0Wy1KJ9t5z2RMkQqSG3bIg/soZV1LvS6IIqmikiV52KvxzOHDrNKk612m/1xwma7jVWOqKW5PO5V774QQF0VwVKgvIbco4qkK+WLfKxiWFPCVLa/PgDV/635w1QpI+ghM56dfsj3qVPDZw5RDBIiwQmmm7VzuRi0K8bZcpApxq4ZlYuq68IcUzouqymkdMYtjhezo0jtQmauT6G14LUFmgybAzpaClKHogxaipKClgcNU0RIjUdcqFulyQvGhkc85AWB3jMFkABRcWyND/nGWDIf0aw1LZNQ58/V2h8ciobUeTrRdE5VBYV7JUpQChK7Ujj4Sj5SCQJnC9t7PP/u3BuBTu2z0H4lOISJs6ypFhMsAzeuGBJfvXiBZx9+BICd0YSxzUl8oMlXvawUa2trtLa2+Pijj+Gc48LXnifScPbsWV599VXW1tYqOuHGxsbCmHw/SbTDn45Cw//kn/wTNjc3gf/MAdbtSLX7uQiWIES6SV4TqhCv6PX6WJuzubnJSrtNNx/OHMOoGCeOHQe/OXyYL2ytc1hvT9sz30CZi5TJiAUtMdXByoBu+hWsRJxY+cICtRBu7QFNXM71yZDRcMgkmbC2tsbxzYNcnQxntptP5hcRenORqVXTIPVuJoOsYxr0spTLwxC+Xm2s0KRJUyektYhg/e+GMTy6sUm8FXNtNOTKcEA/TTE+QhkJ8u0E1SUnAjWQsHQFTS2aoEIEKaj/CZH2xJEnMg4vqogOhWHVSaATSjVIs0DrCwdd9pVMqWdTZ2O5ZAgTqRZibYkjhWDJrQm1tyqhDo0xtxLSZuEBEkLkbPpFwcWfb7sUXsYyF2oZRpf5KFfYp95PAuTeBIpG4bU0Oigy+SKyd3BtjBdFmkdoLaw0E0ZJEyWGtU6K84GWOHM9tXtZQj4pr6dsP4EeM3LCJE8x2qFjyFVOu3gSrbc4hGZRBHfe2boQSUTNeG/LpVWZuF2aXvKwzdytSiVxViheq9lJmNpx61/Xo2Ll9/X3cJBFDLNVUCs8vKqI4x5Gp2gdEQEmMmjv8N7jHWR5HsRodKjPhZaKmsiS4890Un0bpLzNGKKg1Fa0f+TS+6J45b32yt4IYJWiReK69HpX6HZ3eeSR/xOPPvroO2rTvZZpvzjZoZPFHCjmiKvDffI80LiUaaCckLipjHqoRZUzLkBAnW7mZepMMNpXC94sVwydsD0wbK0Kk6KwcOocnSgiSVKst9VTFmszXcwL5NZjRRilGTuDMWumUUSUgsCBVgHwOF9xs8hdiHw0iihuSgBdwzSnLTETKaJiSlfCOuX71p2knFhdZd00SY0lViY4KoTpmEMxSpTOq9oUq1Wo+1TNOdUYFkwJoUGlD7MMZ6vSuTONEDnjuLQ3qN43KcCUL/O8pq688ujVGBycYUVdRVfbhAC2KnBWYKLqLS8cjQsjgSw5FzJlfgg1B1ER1apdvFKCigK4MA1fOagEiLQvjqOq/KVYT5/7EB3SRDpER3UhA1+fFwKFMLStpTO0tEL9Rwi5x3M0bI8il9k1kxOFiCHxjnZtTC5f3fUoRSkhEsfAtUE0Rk1d8eGSi1zuQsnx69vnkdiTS14BLAj5dUppRqRMXFoVS/kPZ89UAGt7MCRxjlQc+ZJxYJAGB74xBmciHn7gGB978DhpmrK3t8f+/j6vvvoq1toZdcJOp3PfOcJGo9GfaIogwIED01JK9x3Aei9DlbdVbFhmObEAsWqQF+qB1lqu7V9FYmFr6wBaK6xfnqc1cROUUuQifG10hIcb38V3NF9Bk8F8LawFZUGPVm38jLLglFY4zt/g9OCf8vjaj6HVrIf0VgDrdHeb/W4X710hMDLLWy+tpI6UthI3GM8Ve2zpiHQOHM4P0sMsZwfDIFMcXdlEGct+Niok4ae2FrcY2oyDnTYHO21y5+mPM84M9nBlgUAPJjfohsEheO3D4CWCzMwB9Umh9DSHWcV6g80MEGEQGg1HZPx032Lbarp001VEJQkvy2h7U8pgOS0FD2Rt0C6jW8V2jdjSiC3eBZGMNItq7a7b7MK+muBq7Z3PBao+Lls/1yfDEjHVQeECgFMz0+zU/VdfbGms1+R+GuGKjaUZW5zXrDQtnYYjdzqIkzQzRmlEoLIp1ttj+kmL+ftW74EyoFQE1nAiOBukr3IF3qS0I0iK97VJqHfji4T4G00uy3LxjNYzEawQBZ8HYbP7qeKLMreu/KkOzNTiEabfzONhmQq0l9pVRiush8ujiNxv0Ik8BzsZzXhc5NcptDEY3UDhyLxFfJC7z9MUo8FrCSBJqxoYrJ13HkRK4WUGjAogtnQmjG1233hGlZqlYd1Nm58/pvl9I8QLb7/1DQbqOkeONHjkyGPv6jxZtqhWe7dsYnNiifHec+nSJd6+dBbTCYW9x7bw9LupFz/SEVopEh/EicQTxBfwiNe4onZe6YjyXuM86LzBul4ndZas6LfUuhBZdYrcT/NT47LUBGGoTTKHN4L1nhZNXju/R5KE+aIZN5ikKSIKWwhXAWTWYp1nzTQZi2NkM2zuubzTp+EMutje6BoFu3j0U2uZZJajGytcy4e0ophhAQorSFAsoivfTFFw2BAEolLjlo/cTiFG0Haq0Bc8YrWN6p6uSNgeljWXwiDihZKdW80ry8d1QIeaTjLfGiXgb/CO1sBVqYZbydOzOC4pHeZiKQfi2u8z46GS4OSLPNpMARaiiExebK8RpWiavHJzlcDJiSqAWDiF9YZY51jfCLm61Z3UBagUuqozHafnGAceVWSqThkbQsi3cjXeQTkHShH5y32ozblCyvV0ja3YV/0bsLIU+eNBOfJVe4pOw+FQYe1YpiUoBw6G8RBBcOI53+9yrt+rzn16r4dHyMVX703d+tl0TdhP02pCaTabHD9+nOPHjyMijEYj9vb22N3d5dSpU0RRxMrKCiJClmU0GjeWf3+v7E9DBKtu9x3Agns7KdbtdgBWPi+pTqCy4SBJEobDAQc7x2l3mtN9loCyqIgsBU9ZmICeHymuZt/Jf7X+NrHMnicoCtZLpLIEYE09xF62yaznVP+f8Pj6X8eoqcDEzQBWr9fj9771NbSa5owBZHMgMVaaQZ7ODNGrcXMBYGml5tPWSNziNr0sRYAroxDVOtrepBkLXTuszr0SBYBVtcFonjl8hINrLa52u1wZDkm8h8jgioTTho+wRQM8NRrhjHiEWgAR5e9WNC4rIlvGExuH0XMXVOznJAziIScLlPKzgGo+n6qcoG7mQ5DpAaJIiOMsiE44jbUApgZ2islkCbhblsu11AowWiIU8QW1smqrBEWsJcGI+SDT4mVJAULCJ4fG2RgKz55BiIwj0g6twzkPr4/JXchPW2uneFEMk1YxSXsi7bE+Ko7O1ONbfVH8IQVJyzqGVjDKYCJPSs4KERnCmIyWb9LQMzsX16TmVP4CDWR+eluadzB3rDodsKqjdRNHUn1vV62cdHUsXx0j/DQvJz+2Gj9aIfUtjncUzUYfo5PagkmjTJC6jkyMxpO4PFDCrJATin0rrcK2MLPYgFIBsQRYs1HBkiL4fkew7rUtzB/FuJNlAwb9PZAuR46tY9i5wRFuz+51BMsjOK/Jsow33niD5tYqxvZw4kkLh8IwDD4o0TTUtCZdSPIJtRarfJhiPCk/uzyIsfRTTbMQZhkXi8Lc2eB8ETOzeIz1tM4VEuSnW52IXDyDwYTtbp+8kAVXBcjRArZ4XWJtyFWgsK/ETVAWkjFZEs6xomM0GueDKqEQ6IPlYx5pTXeS0NjaAmA1bjCY5LWBRoX7LarqCyUaJUU+kiq8SvPveYkYAVMUUnYiFZUw0A71dGAREK24OOxVA17o46LfKSTYpVZ8XJVtLD6YgMhEDPNEvsUZovSqFf/65VtVVvR3ea9VGCxK/Akl7FCeUohJG0+0FnL36o6sOCoAOeFetqM8tLGYawVwXhMpCyr0gSPM/dYH5oSIrhxvExfTz1usGheo/gSpeKRq1VJTQGJ15SCoq/+64m5PfBg3c2fIfXDMSgFKHQXzhSlwjxujMHc5ISMH8YhYNrdG2CvruNVJmF/E8/XLl+kn03XexX4PQbDiajXcpjZMp2ukQRrK5yxck1Ksrq6yurrKww8/jHOOXq/HpUuXsNbypS99idXV1Rk64Xs9fpcg8E96BMt7zyuvvEKSJPcnwHqvbGmxYT0LsARPpBrYWlRGSaizkaYp6+sbrLVX6dmpx8GJRat4Vu69BENq1vt7Off86/0n+MGNXZqqOz2HAk2nKFxZHqMxE+eaf5EausXAnuLt/i/xxNp/T6SDMuGNANbFixd5/fXXSVorPBh36LQMXTtmYnN62SxIXG+02E1mFWrMErWxTGZzmhRq4VgbcYv9bDZCp9Cc7w3QSnN8dbOIEC4O7ImzJEmCznMebrfZ2Nzk+ijhymjAJM+LqJZBG0E5VVA1QpHdGRoh5cAJU7Q12yLrDNYFIYbYeOLIBRqh14F+VznfVKGWFUCNJtDzFn0EsjjhMltfq35+ikFbAbHxGA3iBet1sZYz1XWUy/rFaaMWKZmjB4qEhXRdlXHaE1OvpfiCsqGmJ1gOpuq/qNnUuMK8hJyswIgPwFF8yHWLTABQzchidJg0280MpYU0j4iUZ72TcLW7ToGcq3bWu7jC0jIFO1YUNjPkShgR6nnphpCqrKARBsl1iw+UImbzrYTF2mgLd7LYfJ76t5wOuGh+5vegkjY/nRpVj9qFo9S0Ihfasz0xZKN12maVA+2MTiOdOZMQasgZTFAllEDr8uJxtqYwVSL48v6LLsayQBGsn3/s7p8I1r20xflDMZlMGA3Oc2S9xVPHjnB5/DrKd9/1ee6lyEWa5Vy6cIbPyBrf+73fy2+/+Rtgw3PlffDMj+00bhqrmFIWSBHyOCtThGiVAq0DpS7LA1hKHbR1iCAnLiy4c++D80MpsmwqNhRX1YfCMfeTlGPtiMxaur0hptUgH+ZgwjMYKIKhzpUQJNZzHJm1bDZXiHSIiudZ+L2jI0yssOLpFJFppVSlVhdrwyBJK3GX9UaTy9RzpqmiPEapqo6X0hApE4r9WlfhqVmCQRCvUFahUoE1amNwcRxKRkFwdJ2jh48DThQDYgPAQmpUQa/qw2+9obNoqhjkxc+OQhVAKn5TihAlK199VUaz5ie2+ucaYmLq51OmdLYF5dyokxPEK8rBTGG0q+jkKE87yikVVAKdP8x1WoV4lhcd/vMaV0SvXE0EpJe3mLgGHUmqSJmZczrWMWn9F1EqSK/XrkuK46KExMU0VU7qTBXVKmn9ufd4MdX6Qimh08iqedVrW+QM58SRwx26TlYUBHIivLJ7nVE+XW9eG40YyxiHJ6+V2RjnGZ24MbPtMMsX5qllZkwQwcjznCzL+MhHPsL+/j57e3u8/vrr5HnOxsYGBw8eZGtri9XV1feEXfanReTiC1/4Amtra38GsG6VgwUQ6wa2iMI457i2fR0XWQ78/9n781jrsvOuH/w8a+3hnHOH9973rcmucjwncWwnJM5gV6C7RWhwgiAi4o+oAx3SEUMQQkxRIhQkpCiKkFAUFNRISD8hWp0goaZB+v3gBw1B4WfiIY7jqtiOx0pVuare+b3jGfbea3j6j7X2Pvucc6tcdhynHFjSW3Xvvnuvvfa01vN9nu/zfa5fT97FbXofiRLYhDXlzQxTa5oMw+hDWUXl35xe5/uuTXnU/s76GJlsACzZCiMI25GVBFoW/vkEsg7/KqWZ7QCsGCOf/vSnuX37Nt/+7d/Of/vC05yuFpw2aaJ53cExagIP3HzwvJdm15vRxc17p8Ay+g1gca3aVR6s7RWvXT4oKryUFQgf359wozzkxF2S6nkoN0/vsWxW7M1mdF3HtXqGMQWPHeyzcB03Ly6ZrzyrHBHsKXmlr3DeJbqEVTBjH9Yo2qO7ywdI8lSFpLhWiqeqItasj1kvXrmWUehVrpJnt8+Hko1lliuofGs6/hBpy92LpAW8sgFU8D550F7JGze6wWwFOrK3b7ShdxduRW6GYeiw3u2Ci62aKgzXNi4KOfxnq6Winy7me0x6t6UIWKPMKsescviQTrxXtyy7Co2Wo/0Vi1WFi8UOwOx/H97HDE6UbEC0BUYiwS+pC0sk0qmnwCDWoigxJgCCXl14eMfUUN0AU2mBXR93VTZd37Yl4IfaXKNDrsr5GpzOrzCuVTDcWUzR+YRrE8/BpMFIQyIcrr9HQ5HzslIUN5KKWocQEoVLkhS+NTJQAi3lcO3w2opgfa0ogiEEbt69wOiCJ45fx96kwMcHaFxScDmyXr/89vsZwXrw4AFffOFFnJ1CgOl0Oij0iemdUEKTz68KpZnQF88gJoCVAHcuGO8NQSLWprk3uKTyEEjUP9UUEe0pfIW1FGLounWSUCl2+FaE5Fh6SA13NVJMp2gIyXgXIcacRziiCCbBlcDSOYwYDqoyOTpcWge9iUzrgqV6TLqMJFyRn1FpDMvGDY9sVpQ0zqMOailA1w40kbXnaVLYFEUzSeQiGtZAZdTEgUQQn5Q4BwZb39XWHLIw3RpERYjepgCagg8GeyaEGQPTYHjVlFRcODLkyg1paqPzRAXtHYd9i2yxF9bv74j0sNOudBf290HTz2Y6SgjLk1xhI6pJKdAWUBdZKExTRCg57YQiM0VCFHw0RLueFVPZFfBROHdTulgkR18eszV9Lc0rBp336/OwzWivLQhJp5ZSPKgQgkmCG6TvJWgkqB3YG8lJmnKtRJTS+qSha9M6ZCct2lVAQdTI585OaEaRqpPVim62RP2mzfXS4pK3H93gvF07sBddd7X37mVa7wirqopHH32URx99FFVluVxycnLCyckJzz77LNbajfytuq6/dOdfQVssFuzv73/pHV/Dra5r/u7f/bu8+OKLr02A9QdJEZzZ3YdrM70vKexdcjx5lMl+ojoBON2lEZZS0tDsbO+vLWyLWAD/x8VDvGv2HXxz/VT6INnMpdpOOVW2c53WQGYZvsjnLv/ffOPB/20DYDVNw8c//nFUlSeffJJgDfdWi1EfKRn2zuWKwhQ8sjclGL/zPFR1o3AwpMjUgiXj6WhiXt0rNlbHgWRU3FosiMDE1tyYlNw7u0MTW46Pj/He07YdU1NxmfNr9sqKt9+4wWOTA377wW3uzOdcdl1eOEn5ThFM7GmaghYxe+gytXHLQu+BzXqb0GmJa0AkUhWRqvBXCF+kxOtelTBky95YNpTnoqaoxPq+9hPyVm/b9pko1oItAiEoztnUl025Tmk8a8jRg50v1daOz9Ey2d+7DJY0Shpzv3LJ6IjeA/oK/evo/+mX3XunGJpgwKf3vjBhWID3px370w7nTSrubAP3LtaOkdJ4XLwKxGeH8MiTG9TQqND4gJGIsTCPDhFDjEqDw0ZDkazFnatR3YwGWbNNLdwFff2+220zypXusdetPWX84xay0vF7tfu99jPdSVNw0uwzLaYcTQJ17ehzQNf+39RtUh4sMQXoKHfLewXtiAKtOkzp8OVa5vu1ljz9+9H69WM+n/PUU0+x93DD4w8fU2Wqkw8voswRdXTxhMre+IrP89WOYKkqzz77LM888wzXjq9zESz3zuaopnd45ZVJsSZHudg7wCTpCErvKkr19dIkmqnZIeX8FQJlGXFNcuSowlnTEPJENu86fEwOLyuCdzqwPnrR1kcn+zxYLelioBaDMYZF5/j248d4YTlHBHxIeVSguC4QB4CVCiQbSYrBBYYuBIxV2i4wpWSBS5LqVlEfBpGo0lpaNwY+Qi+rLgJGDV5j/kbStytG2CsqzukSsIrQR3RGGhCgShFMAlghA62Y/9YnF7E5R7qMCqQniIQMlhBYGqxLqqrY8YG5WU0AKxqIAQ0kNdERZV3DGpGNRSrGPtw+taEHDb2jrU9vHlPXN9VsswNTU3kPEUVGqryo5jk+DkDKWqU0bnjngtoEpoAiHxs01b2MMZ0talL/m4caq5Glr/D5uNpGokKZmRgaR+tVfjJDDa9oKCWiazdCfg/WqrGqhpAdlCEYCkmR3sT2TNG1fl1P/WaRGIHCRrpgMUUcrq93knqN3FwtaUb20EXXoQcBW0Q+fXYLeCchRm5mgHXSre23pXPsWg8v365yhIkIe3t77O3t8YY3vIEYI+fn55ycnPDSSy/x6U9/mr29vQFsHR0dfVWcaSEEVqvV1z3A2tvb46/9tb8GvEZzsL5W7SqAVZiKytSDtDokadr5fE7TNBwcHHBtdrhBCfSxQ7DoaDbaKcw5fGDJuO+uAmWm4DcuC079e3jv3ieuCMluC2FsAjiNK4R6OFeM53zy7J+D/XZijJycnPDUU0/x8MMP8y3f8i1Ya/nEgzs74+jNIh+VW5eJFvjwZMbr6muchxVLn0LTq638q1lRIrKmMKXr3jUkl1tiGSkna/NaDsuas0wtnLcdL929x8P1jLc+8hjeOu77M3oQs928Rh7Z2+ORvT1cCNy6vOT+omHe9dUf08JhjEG7tMjEIlNeii3X4Rgs9CAnLyhRDa0ztM5iMtgqi5DvwXadrEQjVCCQ5NLXNbe2DPKhsMzmUNLuOpwfSQtZkSXnVaFpC5auwBrJMvS6XhBfJvKxuW3LMO8l4XVt0KsyUGnoQem2i2+EvcZ+wP6d7hflHQBLBoNm3Zeq0IYSF9NCbE2kNCEvdqkGyfWDOU1X0rQFDx/NufXgkE0f5DCEtVGxhVyCWmIENXAaWwRFjBAkUGtgZmo6HG10SeWMXfl1s/X+p553b/SO/0g362KJkMQrhmrEr9BXvspxl33kud/byk68mxBLbs0tJ8sps6plv25zUegk6z4O/Q3RBCOZTlgDgTY4aA2XzSX+YklXClVZclHMv6aCRX8QzVrL2dkZzzzzDN/wDd/A9NFpppStgJIYbiOZgdD5F75igPXVjmB57/nEJz7B+fk53/3d382nbv4md08W3JQLfEyiE51PlKeDYp9L3w7CTev53AJ9vaxUg8hoAXhar9TWsD8rWNg8EeQP44XFBV4FonDZdlkFDhBD6xIFKsbIvXlHbYpcODvlOh3sHcHlBavWsVy6BKIkKQdWRZowFiuXnVZ9LnEYVDKtX7+PzgWOmaY8w+x1OTtruHGYjLvKpLnDhdGaPnKezMoSFwMHk5r5MkUNrBFmdcl5aNP8OArSyMh5J0BNpiVCqiXV52H59Zypo8lRY56a836aC2wFL8SzEi1jAknler4Y6hn2VMB8vER7hVMrzfUog6NRAbygVV6TomDz+qgxgzTpAcRoYjWaaoCN5iol0dH7CFU/M/f0utKGJOGen8WkcNRVFk+XBEB6ANpHg3pgEvuf89J44WfY4HAxqfH6aAE/rCtCDxDX3I84cmq6IEyqgM8MlDiiANp081FSvraQNTskshbCUiJC0QO3KMxKzzLaDCSTSEaZlRJdWNcQO28bXAiDQwPgwjVozgP/ncVtAO5dLrm1THPL2ch2WnpPcK9+rng1VG5jDMfHx6l4N6nWa08n/OxnP0vbtly7dm0AXAcHB1/R3L9YJKD49Z6D1bezs7P/CbCuWrimdm8AWDEG7t25RyddVtizBHajT5WpaOOYErj50g75WHn2DNFjqBhnWfTFFT+/Ei79t/F/ubaVHL0FyqIu2XJpUzDBaQJFIT6gUcv54f/OrXsVJ3eXfNM3fRNveMMbhg/guYvTnWvZjiZVxnJvtYRV8nw+tLfPtbLkhXC+JWPfT+T6sn0ZES6uiHxt0wintuSMlrZpuLy8ZDqb8eiNh7m5SF7WA3OAUb9DUwS2hDEsb7t+gzdc83z2i/c4Dy0dcaBL9MMWLxhn0DIiFrRQej5c740biShttEQLKFg5WHVKWYQEAMoNXdxRXCDlJ6V6W8lMF3Rda2tDkGMXbPUv0XZEUwTq2lPXgveGti3xzmIKpSBgKpCRYMfIeTeca0PgAtZUENb7bdZHSauN6ghZZPCY6LAyWsQYGe55Mdd1Jlx/EkWuCmoNC62PmZMfkzeztJ7CRvanLdPaEVU43FtxsZyCN5SVx0ikdVklKSeUD133K7Mkb6sGkGCHHDVjImdmxZlrk+xX6ahcjTF1kgA2gETEGCa2oMVlgKhIFEortMR1MWs2wY6yWxdrEBF4hdaD/UFAY7R/0N54yMalSVSqfsv4rVLgtCk4bQoOCmV/0lFUjk339e5YjAilqTk8OASgUuFstaTrOn735gtcsxM+85nPcP36dY6Pj7/s+k+v5RZj5OzsjOVyyR/5I3+ERx55hOfOFiBHqL4EgOoDbGYxdOEm8Ee+onN9NSNYl5eXfPzjH2c2m/Hkk0/iBZZdx8oHnmXB3C2pbIEPQhOE47Kki4k7HjTkklNKIUX6TnuJ82ATqBCfWAJYjsopd8wCSzHUS7zbrNI0EWHhfFbzTH9zLnLaLVm0gZNVS43gXIcRg9FIl2lTLkRCiKn+pFGCKoUkoHQ571JOFoILAR/j4ISTPkqjEHxk0pc0CYoGuLzouHEtbSqzR37p3PChlNbg8zdRGENZGurCcpkjVMak74wgRCJFzFOiyWvIIGQhWJ/nQwXrUkDKtJlFodvzff5eQ/4MA+QwGsu2RktFZyAL0CnseAaHubwX5Ej9BycpMp3XIo2Z1j7KzZJOYKoJUDnB1KnshsZ078niJpDAnMYUzbPbmhuwFsLoqad57SmMUhhPn1eFwOsOLtHey5YvJUSzIVaR8q0Ul3MA+xkvqOHC7aWIVzS03kC9Xn9VoYt2kIA3AktXMClSzpzP/Y51F9ManwU08j0MarHisSilSaL8fR7WhkNTIwdVx3xZImKxJiK+TxvQTFtPC+151xBiMUy3rfesTJu9q8Iy21N3L+bcbRIgmY+E2Jqsfvlq21fCNCjLkkceeYRHHnkEVWW1Wg10wueff34AZD3gmkwmX7pT1gDr6z2ClaLcwsc//vHXJsD6Wnk+Xx5gHXDuTnBdx/nFBdfKhzg+PB7G5eMuwCqkYAwbto3fXiRjLaugVKakGQEEMxKNuO2U/3rxDXzv/pJaktdCd6TbA0ZmxBHwsrIGWJGWWl5Hx+e4af4/vPc7/w4PH79ho4dnL842fr8ymlTV3F+lPkWEB8uGWkpoCx7dn9LiOO2WtNFnCmTui92+jqoJJ68iJ0sh1eVarTg4PKSua9q4Tro/aTsul4HDleXR6TUeuEu8RmpT0IQkH9y3/WJCFxbMihK67DXyMC883RY5XlSSF9GnBUxF0VLWHr/kUBsM1CEReVi3UkHjzhUYF6lsoK58yp+6wmCWITlWCD6BppRMPOyRFrctcDd4J0cbhzGRQF5RBGIU2qag60qcL1FRCiK29BR2lJdlIIaknLQRQetVpPI+w4kGj2z6u1GylZD2jTnyZfJ9Sc7TLMvSK2kyupHrO3IlptydEiSrSwneJcO9MIrJidJ1FXi4mtN2BarCpHLcPS3X/W+jrMEo0fWlKdnoMERJXkyVgFWhLTocHgkG6wpi1TFtZ2g0dCO66KQ0OCd0UiCAtYKxieNf5nttolAYWKnfeMZr4LmGTlfVqIoDwErNitmRWrcIbgTwlN0C1pDy4G7NayozZb/qmNVN8jZv7dtHye247p61TKdTptMp183DXIsl1lqeffZZPvnJT3J4eDgsuoeHh18TCuHvx1qyXC556qmncM7x6KOPDoZG1IZgHkJjR2QCeonQEYjEcOsrPt9XK4J169YtPvnJT/KmN72Jt73tbYgIn7j7EvdWK0QMN7Xj0q2ojSVEWHk4KrI8N4aldxjVFLkpJyz0Mo2PlIfiPNhpnhOj4fpkRhcuCSY5mkRhEdzwbXU+Aaw+9KBRWXSOy2XH2XLJ9aLCVSW0LRKFyy7VD/Ihga3SJiPfqcdKmluay5ik3KXIimoMk8dBUbFUR5pTI3Xm02lUglcWbuSYM8mYLq0d/Ay1LVhpMl5LaxAvFDn/J+GnFE2LMaIGjArGg5kJodkFPP1xphNiAaYzhL48SO/46udnn48QSUlUWZwoBJvWAQOUeefR2kEP7Mb0934p6ywUqQCxesmgZw0SJNPpw7C+reeesSuo37JByKjCsH0saNv6AlP0QCuJOFkipkgLax/d8lEyDTVHqdRwZ7HP6/YvU/JErq8I0IUeLOXjJTJ308RIQAa6eD91umg4byfsFV1SCjZJySPNaUrM0Kpf3qIKVhSflQz7mdVFA1isUQqJQ35vvhvD3Tmo27R25HpcJpd2MSZirBK8Ge7jwnWEYHKxZeVi1dIVPtENFbpse969XHC/SbnqqzHACp7Vatc+fbn2e82VFRFmsxmz2YwnnniCGCOXl5ecnJxw69YtPvvZz6b1YEQnLIqrYcdisaCu65f9+9dL6wHWiy++eIWe4/9A7WUBltlnuVxwfnHO/v4ex9ePNxZqp+22vUGxVXtqmxrnc9RrTJEqZTvHarPNA/zbB29irm/Kx6SFcOMaZLrxuxkJYQQfuHf7LqowOfDc5D/Qhovh71GVZy5ONo6/VtU7zurqCoGLNgRciNw8X/DgvONA95hombnvqYPDctdzUV+Rk7Wd3xU1cvPuHdo25VvVdQ2qG5EvAWqxnLQNXzxb4pqSR8ojjsop23eyH78dcfYOtORRM+UhXzOLBSZcYcRHMMEgjUFakwDEZiCSDdiko3+AqqF1JRfLCYumIsRtRKRbuVtCiJYQLd6bNNFmz6IOCE7yGpoMlo3Yz3jh7nsUZTL1HF5bMZm2oIqPlratWazSv7Yt8yQ/urt9X1fYp9u5XCkgNfZXpn99ja9BWHL0TwYv7e4JrvLf7o4jDne+TzL3Ueh8wbIraV1Kpq8rR1Ul4LI/a7DWgwjHB6stlairTIb0birpW3GRZEg6m6g5qgTracsGT2ReLDgvFrTicNIS8AMnv+/PB8V7ZdEEVktluYCuMTTnBW5RoqsK01WYUFDHCSUlRsc6gbsAKwzvW/rBbtyr3Qd5hZDlzo+qwoOm4IXzfU6WM2KcMgakqsmPa0d5om7kLGrVs7e3x9vf/na+53u+h+/93u/l8ccfZ7Va8YlPfIIPfOAD/PZv//bOtbzW2507d/jgBz/I0dHRBhMg5cPGLM/scByg2hE0kJQZ771yx7mdd/OdaJW1ltXvoQ5WL2r0qU99im/7tm/j7W9/+zDuT5/e5aRJDql5UC67FaVJtfdUhdqUOS/UsPQpZ6oNnuvVHv0wjQjR5QLj2kdhDBKFialzRItkQAfNEXChy4p/MYupoHCyWHB6OsdiuHFwbWPdPct1r2JUnE+Rqf0iRaWtmOQ46JTm0iMKF02zcfxDkxkmB6RiBO8TyIoKvou0K78BfmzO3erbpCiG73BWlBgRDsqKntoQJEWOeywjkoiUdZ8uEPq5RQcKtCjYzmCXkqiCGw8u/1PS30IyxIepti8b1h82iUiOpo2bDk6yvGO66UmMpBWiA10lyrNRM0SwbGNo9kbrm1uDho0o13DTNFGss/Mr5XXluS8IoTOEkbT8QJOPKQIkrPORWp/uvYtJTfKyrZi7SfpdBxcRIHg1OaqUooIhGgIlPqZ32Gu6/3OXntX9do8uWjpvWLpiuIc9q8X0xY7zvRryo7KQko7WrohgJT1Ypz0gXEu2C3BUrlL0LlP6bVZPNJk+G3Q9rzbBE2MCoCvnOFk2BBuIeX3uMsC/fXbJabvism3x2teU87gYaJavfq74aqu9GmO4du0ab37zm3nPe97DH/tjf4y3vvWtqCqf//zn+cAHPsDHPvYxnn32Wc7Pzzfmuvl8zt7e3lfkFAsh8A/+wT/gzW9+M9PplLe+9a387M/+7IZt+Zf+0l/KwiPrf+9///s3+jk5OeFHfuRHODw85OjoiB//8R9nPp9vn+5VNefcazOC9bVq1lqc20T7Xddx54v3WUmT0XZ5pUpgaSq6kedgG/jErciIiw7BrnNXVCl28rR065gOp/DvHjzGn75ec2w+i2FGGMnFGtksDtdHwbq24+Ligun0DUQtQWDhv8jHz/5ffPvR/53aHvLC/JzGb17b5IpokruCnrIdmUKFl84XhJXlyEy4Vk6pr/CMXGUgjhM6ffBcnJ1jbcHx8TGSP/6DasLlWO5dhMnI4OtC5IXzBa+b7fOQPaKVhkufF+R8TmvX+4dMYbMI+7GAuQcjNJUS+kBHT4MgL3CdoA50oilx2OoQul+Pa7hQxht9sDhvkU6pikBdelTNhuAFMBKz6GWHhRCgkFRDBBl3v1GyeGOxHdMREzVDqGpHVTva1tI1dZa5Fbpo6KJFNOU32SLJpZs+H229TiZQ2FsPg4d1yOgeXYgkGa3RPZGeemjW/Q6O0QHTbcu9j+/J+n7GKFcWdu63RQwrl+rzlBYKE5jUnmntcb6lKj2H+yvO5nuj+zaKao29x2PPpDAYnGkRTXluxqR3wRddAmWaFKQ69VixFJpEMgyGglQUez3w/CQ1FcYMAYiGCx/pBIK3WGsoS4tYQ2lSQoaoUIgM9Kvh7nyJBWpUVnW41L4F3X3mC2dYOotywPE0MCkaoiSvrx05iZRe2EVZ+W5j4d4uejmfzzk52XTuvJZbjJHPfe5zvPjii7zrXe/iscce47nnnhscdJpZBE7nBHU0KgieQExvVHx1tbBuNSec2RVv3Ht42Gat5YXwlS3yTdPw9NNP473nySefZDabbfz9mYsTzpuW6WxKiIabyzNKk5TYJFpq26tzGubOUYrSBc9xuUchBZEUafCtobDg21TAF4RVEzm0U5amxUsqlDqOpLiQEEISBQAQ7pzOKak5qmfMqoqoOYE/wrxr6YvRNZ1HLbz9+AZ3z5aYPngT4eKk4YWzOXdPl/Svp5CYGHYF1AoRmtYxlSIJt7hI69zG+lQYs5FHOy2LYa7fLysMQpnFKoiglhTBotdeTBF8CZlFnOeRPi0q9vONT+uLTvrocv78+nCJIUuyK5R9RDFPRGNp9p6mXSTgEqMhhrwG5CsbMy40CqEpkKhYb9BJLrmriglmDTKGnKw1cTk6gd70GK9Jw1qYL7anzIUUbSvqmByGOa8pKSwajPGQgUkk5SiJpDyq0noeLGe03tI5k6CVJDphAmWjdV0tbShQtcN2H/saWsK95R6dWkI0rHxFVYeUG61QGYdXQyGapO/zOpXqXYZciqVXNEz3I2AoJAHGVahBsvBGBn9WIqVRPOlZRV0XVzYm0gdw+3XGa0yUWRHO2pYP3X0WX3nEG2wVBlvsqVt3ODtuuN+sCCYpZ54uk72zbL+8CNbvZ4Hhoih4+OGHefjhNJ/1dMLT01NeeOEFAGazGf/tv/03HnvssZ356dW2f/SP/hH/7J/9M/7lv/yXvPOd7+Q3f/M3+bEf+zGuXbvG3/ybf3PY7/3vfz//4l/8i+H3bTXEH/mRH+HWrVv85//8n3HO8WM/9mP8lb/yV/iVX/mVVz2Wfv399m//9v8JsPpq8JCS0p566imq4xnXr6+L7oarig1LRccIYG39/eWk29vYDkqC2wINu6CsAxIf9397cMz/9fhdPFzdI4wW2+1cL4gsFgtWyxUHBwfszfZZnp5lFkYAPB87/Re85/j/wefOHuyMMWwZa6q6U8fqqJ5w1m4CrFlRctE2RODByhEuOt5weMhj9SH3u/lQ/He7MLEdURLbruXy4pKH9w+Idb1hVc9sySWb47hK4CIq3LpcoKo8tn+IFIE2n9OMVktnAxU9dUCxzqAVTFYCKwglBDF0081nYuhzdHLNlCKbqiMsmW2Ftd3eT6KSFpYkjFFgJVBXgdKuw2drYDRuKWcr5tokImveew+02AYhGyff7K0olaJsEj+9KQg+19OSlNvknE2A0uTaXyZibfbNqbkygrX7KMau1XxlY9CSVsY8xOwZlbQIbtfxuErK/ku3bHioEBS8TwZKaSNiIlENRRk52FuxWpVEDMcHSx6c7w/XdEUobePqYkxeVNWk4JXqnyWTsU8G9+JwxiVgimCiJUoqnNwn4q/1q7bPMXrGCs5HnF9bqJPCEoOBIlLYrIRGzEn92yUU1s+sP9N27paqDrPWusZWb1+lUT5YFRTsMSmUR2aCMZtR+NJY2uBpon9Z6omIcHBw8DVJZv5qUARXqxVPP/00IQTe9773DXVarLX47KDqC8CHuEBlj1WcM1GPVyhRjJ6+qnPd784JOt8AWMYY7oRteviXbqenpzz11FPcuHGDd77znVc+jy/OL5h3HrMvVFg+8uA5rh3kaFS0TGyZ31Vh6aGWji4EKms5sDMuQ5fmjU6QUtFYZsE4YdlGDssZXW056VKUd3CykESUhBxNigFVpXOGwhhiiOyVZYoG5Dls6TymSsbwyicJ9cem++m7y5EdicLzt845KTvEZXo3CRAtpMV06/d+3nYcTEvOY0v0inNxQ6CmNJvKoUf1BNELkKRYa0SwYqg6oYtpnEEjKgyURWPMELGT0BOjJdUS7kUWAkimnw0oLMr6535+H6byPpIkKXytmRkRkxNruITxHDbMuQwAKzEyzCC2VERJ4FgV2xoGNduFReow+NAEwJn04EbzuY5y3GKv2JMVGiUmp1SMaans62wlAJfp15rWPx8NSIqodWqZ0HHpJkSScp81fogQaUxRpD6CFdSwDAUxGvosZdVkb7VasGgrDiZdWodDQREjIRjEKhObmA6XYZILGqcL7se1uUanFjHURlnECo/FEkdFj6EyHp9/7jFxfw+NWUfshOTIVsywz9lqxf/y0ocoyqy8pOA0fSfPnJ0S95WT1YqIcrpccbpMc0Sz+oOLYH2pNp1Oefzxx3n88cdRVS4vL/nEJz7Bv/t3/45PfOITWGv5iZ/4Cf7kn/yT/PE//sc5Ojp6Vf1+8IMf5Ad/8Af503/6TwPwpje9iX/1r/4Vv/Ebv7GxX13XPPbYY1f28elPf5r/+B//Ix/96Ef5zu/8TgB+6Zd+iR/4gR/gH//jf8zrX//6VzWWMcB6TVIEv9Y5WKrK888/z0c/+lHe+MY38u63f9tGRKrTdufYQrax6abReaV0e2+MyNUy9DugTIRqZMD859N9vti86WWvR2Pk/r3btE3L8fER9aRGY0+TSOezBJbhPr919i/4/NlLWx0op1vA6Vo9odsSqpgWu8nqfaJymkTSz2erllvnC2Jreaw85FpZ7whcHJbJg7BYLri4uGD/YJ9Hj2/sWOxXFXkdF4LtWw/gRIQ7ixW3zx2+LXmoOsTmiaQIdsNDrwrGy8YjLL2wfwp7ZzBtJFEI+xP3h6qAE0xjoacQ5j8Km2vboJDR/0PwsWDR1FzMJzRdAtJX52mtf06GfaKqhZDpOPQTtzA+c+qv93YyTPp9f8ZGpnsds4MGsbteL1XB+YJVV3G5rFiuqkSPC7v1mV52zL3bVNlQBgTQaIbIDb23lmRI9P9GnWwNbme0u4MYbeqjbi5aWlfSuIKgQl15jq6tOJg1FDZSl+PvtufovHLrLzFoqr+yphGatQcXk3LGbGBhFiyqBRd2ztwsWWqL2ykpzFC8tG8Fa65+f2ZRwXll1UaWS6VtDM1FgXYVNlRItJRYNqKs2Uu9/f3YK+ZdOxgVo23GMneGly5LNG7SgPuofBv9HwqZ9nv37vHBD36Q/f193vve924UwRyLT2hfkkOgKN7MIpwQ1CeDm4jRsyv7b8Pmd3fWLXhxuen4stZyV7sr14yrmqry3HPP8Zu/+Zu89a1v5d3vfvfLPos7i0sWLiny1Vh+5/LOegaJSbSlp+CqznhxfoaLnsoID5WHiJrsdEoUMO0KBKE0BRerjmvFjIOy5rHJftovrt+l3nvfOU/0irGCd3DZJHXBxvm1RaqJiZCigSl/y4ihxFKqTXNFljxfzB3kOb3IJk4lhvm8xcT1vBKDcr2cJsM2Ks6FjRILpd28Z9fqGtEU2bJGsCKUxmAaUK+oS0WWVVJOqhVhVhQD2JL8iavkyFaxDpzvOFiELMner6u6/obHubAN6zlmrBbIaJ84OrZfh6TfX5D8CpbREF0CelWzFlpgYbErkwQvhvCjIXYpgiQ5ohajrNchbyjn6/w1jdmZlM/f50qRc41EGEBSKmuSgF8XLEag9YlW0vgCY5TOS1rP8voRetl6NQRNEax++EMBYF+kSGBuIasMxrjOjfLBsAgVrTM5p6p/95NjUzOYWy/lgjWeVawIavAxgcCYc9mq7MyLkqiMyFrZVvqxaT/27E7IQPqp+zdptMvqjgmcRpTLpuXENbTB82C1IkrkP37s85yscgRr+eVFsP6g5mkR4fDwkO/93u/lAx/4AP/0n/5T3vjGNzKZTPiZn/kZHnroIf7hP/yHr6qvJ598kl/91V/lc5/7HABPP/00//2//3e+//u/f2O/X/u1X+ORRx7hm77pm/iJn/gJHjxYz7Uf+tCHODo6GsAVwJ/4E38CYwwf+chHvqJr/B8+guW95+mnn+b09JT3vOc9XL9+nZPu9s6+O9Lt25TALQPJhQ6RYmNR7KXb14IFW6DsCvGMypRDYiPAp5Y3WNTfwdsmH8/Hp/N67zk/P8fINY6PjoeirzELXvTjcPEBViYs3D2W/G8Y+S6iJpBzVE83JD8B9oqKi3YTFF210M9dL9kredyWi67NUYnIrcsFx9WUY1tSVcKdNiVIV1JwfnGOd57jo+Oc4Lhr6I3lcvOJWGnYqBRmjXDpNoHtYVlz0rTQQOVn1G1S5PE2jhZ63bGjVXtHn1A0UHWGaKATcKKoTbdfUTCsKYR9DKrayq/aviRlEKqIGJrO0HQFhQnUVZJd73cc8HHuI+aCkKo5ATmmvmyxlavUeyphEK7QaNbBnTw8I8p0lnJFXGtxXUre7Wt/9KGnEIUYLK0rEFFKiRRVyPS4zYvbdpL0lJCNosgjsDl8C7mY8CaNULZohJv3sb+e3ddmdxyQpNcjKV9LFaxJlMgQDfuzjtJFlk3JbNKhapgvr1ZBGoa+cZ35y44QJS3eKoqViEnrKz1hR1GCCawIrEwDOcJlVRCtR/eq93xnftQrtP5O+qD4AJUVuiAYBGsNRSFYA7VhEHjpI1iFMXTb+T8iqQYP63GPjcFtGnBff2gV3Nc1wFJVvvCFL/Dcc8/xLd/yLTz++OM7+4xzeOPICWfN2wj6QVYaUpqMgtEFGgOI2Ugsv92cbUSrztycu81mtMoYw7l2hBC+ZAK4955PfvKTnJ6e8p3f+Z2DtPJl13JQpXnehVwryhjO2oY2X0OJ5bRt0azTLdFiMBlkK95X3G8vqKTCitAuCjSaTRDuSqQITKzl1mLO9YlyYzLjuJjxxeUZGjQVJR5/yQomCKY0WSEvElXZK0vqooCuTfOlKupTGKALASsFEhN4Ml7I9jquS9QrAhTZeK2l4PKyHeh8ABqVQ1MSfMxGeqTtXP6GhXorgmXFUIgZcnqTiqbNkeiAceCMhyLlnxbGcFxPOIsdjTikj9hL6isaodC1umdq6xNKBC3WYx0UWFUGkCVdllHvH4AK48VMM4VwqLOV51ztc+aELE4ERbS0XUAqqJylMSHdR5dU+FKJmb5jQZsC6lwAOcgwL/QORbsSfCnEQhPjwygEhshTTxHsj4pqiFl4N2qKYDm1BDWJOYGyciUHdcMyFxDu85NDD+CQnL/c97qej7tgx28dMUfLpEjHqYL6CYu2QkLBtF7kNZ5MO8w2VTRrtUCgqht8tBQmsPIJaAVShCypCxo6n/LPDOkRxjy2PhI33LeRU+3/d+uz9NLvqom2KFa5e7FghadSw2nToKL8+uef5/BGysdvmq+dyMVXs6kqjz/+OL/4i78IwIsvvkjb7gY3rmo//dM/zcXFBd/8zd88zMs/93M/x4/8yI8M+7z//e/nh37oh3jzm9/MM888w9//+3+f7//+7+dDH/oQ1lpu377NI488stFvURRcv36d27d3McGruZ7/oQFWr+d/dHTEk08+OfAxp1cUG06UwLHIwqZlFXQzyiMClVS044U3z4qSY8U7oCw61hUiUiu38rQE+OhFQRe/g2+ZfRzoaJqGeZYy39+bshivXRIwVIQcUVMilZlx4h5Qyjlvvf45Thd/hPtNYFqUOwDLb6mRXUUZrG3BwnWDca8o1+rJRgFjSEnCtxaXsIRpWbNfF9y9fZtI5Pj68QBat6XdUd0ZV20KTrck2g+LKafdcmPbrCi5yONtVLkIwp6z1MGgtqcAKLuxXCVszztdZFIY6pUhiOIKJYhJOVu5CSmqJckiQMtUVHEs4jTsuxmMSBGWUOJXCfTUpacoQgJqX8J5HdRktQPNhnzcjVqwgbnWp+6pIKJUE09ZCW4VcE2B2ORulexBXS8GQhcK2jbV9rAm1TIpirilgphbZE2jHIFFentA1r/uHLeFU1UyvXbTRmPIXhg8tKmw8+a1bp5EJC2yUZJxV5hIXXkmlcNHgzWB+bKg90UZYp9Vc3Ub95+97miim4asBlkaIapHtmmlRILJRF5xqEhKOjdQoEwGS+rlm8hWoeNsVUUgBnBBqawlBksUoSwM1ipG4pY4xm7bhnfJuNw8qP+9fQWK4NeyfSVsiLZtefrpp2nblve+970vS2W8iiIIEMxD7Nf/J+aL38m1zSyKp423mYeKuet4034CbC8s720ArHO/4F63mW9ljOESN0TLOu+ormARLBYLPv7xj1NV1cZ65mPkbrMYANbcdfwfN5/jG48fwkfFhYiiVFguQlI97A1OSGDAa6T1lkYXGCwf+MxtFk3goYfS+2kyCJNgkGh4dL/m/HzBg+WKR/b3uWhakpDuiO7bh9Ml064rwSp0K1jimBZV/pxGDhhNqZ2OSKVKcJGpFHRNSNGpmFQBJUf3bY7qFAjNohsiOGKSGuH9VUMZDDFHDi4uW5Z0VNZQXfH+ltYm0Jfvy+npCm+SGW9X0JV5/otJifCxg33axQWX2UmUy0klFUKTolo9bhocX7D2a203Yf0RRjCtYaipngHmkNMDEBPYG/rqhUham6NPDHXAbBZUIgpWs6AGgnYGaQSdjooQR0P063CkBJsAWG8uBIGSRKHPFG8TDASD5rpmIkm7T0wfJUoXHePaBnLRZspdmr9aKYhAF4pUWJl0PWE0J7edpXWCFmstv7mr1hGyfC4l5beaSZ4fESr28d7SBUVzDbuIsHApZ1kVVrHsX0VQoY0VRiKFCTShyIW005h66fZlVw7RryFqZgPb6e3aP0ARnm3vE/N7HLNzVG3kd27dwYsSVTlrGiKRF0/POV81oOB9nxd6lYNxs32tKYKv1BaLxUYO1hNPPPGqj/3X//pf88u//Mv8yq/8Cu985zt56qmn+Ft/62/x+te/nh/90R8F4Id/+IeH/d/97nfzrd/6rbz1rW/l137t1/i+7/u+r96F5CY5cv2aa18LiuDNmzf5whe+QFEUfNd3fddGstvE7O1EqOwWJXBcVBiulm7fVgnsV5Y+B2s75wrRDUpgOu/mOPrzPj23PLX4ds7O7jOfzzm8dsje3h6Kw2zh5kJqxtaolYJ51xJV8eE+ZflBZlGYUHC9nq73Vd3JtTqsJjsFho/qybBoSjZ8iys+Wj+aTc4XK37nuRfxseabHvoGrtd7w/EXW2DqoKp3gN5BuZmcCFAXuwviGAj3FMEgwkWEbllQrWrEC9Fuym+oKrHcXOGMk+xFjRRRqDvD4YVlNoeyW2s6rDsSxBuktchK1hTCcdsyyBONL03Ay67iYlGzasqBxpB2e6XvI1E1gk9Kdzq4Lcfj2h3CRg9GKWeB6bWOovREp2hnUM+aw791zhANjSuZr2oWi4q2KfB+zSdfp17ocAxjQyBfUy/rPh5cogWt94+jnweP7JD4PT50QPzpDNLvt30D8rGSFKdWrmTlyqG742st02mLmMDx0QLTP+gvAXrH19DToWIE5xUfLN5bgrdras1obELK8QvGE0pPV7Y8KM5YmCWNtIQsk6FbVljCq3F86sGxM95HSBSY1kWWrbJqhG5RYlxJqUm50Gy9Z/231MvCT68wQPt5s4lfnxGsk5MTPvjBD1LXNe973/teMU9sHMHSEcAS8wglM27519NntQUNdP4Fnl99gVvNWvDid+cvbPR54Zc8aC43trUaWMY4nOu3HtzcGcudO3f40Ic+xMMPP8x3fud3bqxnTfAbokRt9PzH57/AZ06TsqECc9dSSUETIkEDFksvsFuIBYXWFSgRHwOLNtXESozvlIuk+Ts0oWBWKSZa7izStSydT1LjEQ5skShyvUuk9x/FmHKKfYom1fn+VimxEVVNKrSa6OFu7nEucM3WxKjYEf2vdIl+LLm4sIlC1wREFW3CULPp1mJO2c+tCotly53zc07un9AtV9y/f4+i8dyI6X6WxjDNQlCiyhdPLnLEPdW26icF0cTiOJxMmJVl+uZizuE1yusfOsRWhrKwm4BIR86pHM3paySljjMo09RfPab/RkFCJoWvVdJ7r9Swz0YLrFVenWC8wTSGMgtESAZceDOaa/N9zn2JQhXLpLo46tdPUwkKMhAqxBD9ZnWpjI8AsCaBKx9kmKfbWNCFVAA+hiQ6UhhwsSDlK0nO4csRJQUXS4Kmd7bPw2pdhcmATgF8chwm4JJewKhCzQFtKBmogfmmO1/g8jna0NtXad72alj4JBQRcsStb33dxpWv0tysZgBayQEnQ7StX7GScm1khRsAb/9tATx9+15aG3Sdg9Vp5Isn5wkAZgf12XJLiOyK9lqKYC0Wi6+4BtZP/uRP8tM//dP88A//MO9+97v5i3/xL/K3//bf5ud//udf9pi3vOUtPPTQQ3zhC18A4LHHHuPu3bsb+3jvOTk5edm8rataz2L5L//lv7w2AdbvZ4sx8qlPfYpPf/rTvOUtb6Esyx1AJyJMzN7Gtm0jRbcFKbRl271st9znOhQbTob6VZTAcgtgbRs5vSRnjJEPvnTJf7/3Ro6OHqaq1gtqsSXdbinXnieSMMY8y/7WxYKyWHB47SPcPD/l7LxlL9S8fnLA62YHWelp3favUJzZHqOq7qgTqsJZ26DAcrni/Pychw6vIbMJd+ZLTi86jmSPJyaHO/3P7O45K7sZ7k8n2dltQzraZhe9zwayV+VSlWZp0ZVBRjK01hm2U15M6BeQvGiE5PU1KlStsLcw7C0NdZMXuN4dqSCa+peVAZc8truhpM37hQLR0HUll4uaxbLG+1c5GUqarkM0+GCSEX/FKWG9oO/AJquUM8fsuKWYOjQoMRg08+81SLpn40VbQcXQ+oJlU3E5n7BaVXhnBrrKcD83PinNxsUG6roaEGWANc7T0pGkcd/dOKG+Bzgbcvdc+cqkfABNFMLGlUQVpnXH4UEquLo/bbdu2BpwvdL9HV1perQx520FQ/A2GRZDHkO/cz/OdLHRBJxpWdolJ5xzbua0kqIOkL7FOFJ0VNUrhHC2xjnC311QVq3iOsE1BulqKq2oKIZ5su/9Kg9/Pxd0+trxjL6apqo888wzfOxjH+Otb30r3/qt3/ol6XibFMFmtP1RvBxzO35TKvgsEDXiwk1uNi9wtzsbFuGb3Z2NPi/dkhO32KBhX4YWR6T1jtZ7fudsbQTEGPnMZz7DJz7xCd71rnfxTd/0TRv3/dmzU5rghig+QOsDnzq5y++c3B2iYkvnqCR57rvoKKVKkteaCvpeq6Z57dFcuyp9c5rnzELW9aBqP8XTYimYdyny1vkAPhnENijvvv4ok3HJDkkKmokBl76Bxjm6znN9ktczhUf395Gg+BjwnWIQDsuaEHWD/mczU1y69OFrFwhtQFSwDYhJhvXCd2gu4GsUzh+knLT9ySEPXbtGPZlC47n71F0u758gMVJLovWpS1+yKokmruRtaaylNXSdY6+qUOkjaWnfNkYeOpxyWFZ4XVMHTY7eJL9YLxWuo7VEMDFFpGzIVPU8j0gEk5dd062dS6qK9MtxXM+Joim32IQEFtuVJ7SR4sJS5DnduAzY3Kazijw+MtA6KmaUxjIQBTWDvUgS0tBMi8zn769RRDE2l7PIIKKnxQH4aJl3k8GxZozSuuQES8NZC1ms32+Ly6IhPURqMq3d9HUac65Z8IJTS5nRrMQZbbD04kV9Dz4alq7OQGxt8YQoCIYm1EPO8xhg9WNsfJnfFUHVZlXI1Fd0W/PkJD3MmNd/HQGsGOFT5/dSPm+M3F1doihRlBcfXKAoq2x73bvcZPNc1V5LEaz5fP4VA6zlcrlzHV+qQPuLL77IgwcPeN3rXgfA+973Ps7OzvjYxz427PNf/+t/JcbI93zP97zqsfTn/IVf+IX/sQDWcrnkwx/+MOfn57zvfe/jxo0bL1vAcZsmuG04bVMCIakEjtvLRZ96dpOLLdvRgG3p9m2egFePc46T01OsMZztP8anmu9mrZn6ctLtY6M1shjJ08dYsT+94Pj4N4HAynluny0wzrCvFa+fHnJUp0Vuu9Cpqm7Ip4sIFnYiX0f1hC4ELi8uWS6XHB0d8ejR8cZ9PV01uM5Q+5rX14eJA8/VRqu/QuBitZUwrqobhoW1aaL3I3UgAOMNbWFpWou5KDDODIm/43NfRdkInU8yt5LyU4xC3QmzubA3N5QdSNB1ThGkhWdloTFIGIOA0dl2Llrw3rJY1ixXJZ0rNiJZxuw+l7Vhn2gJ3qXaWkMhSdhcMGXr57zqiyjV1DE97iinHSpZVy6m+jfRZ8DlEujaiNRlYYnGVVwuE0hMyoWydXIGELgxiJ17PrpffQ9ZPUtUti5hvZL32zdv08sgy63mo6Hx5VCbrCgCB/srytJTiOPoYJ0zcxVg224bn3wGfkkl0uCCwXuLc4YwyvHf6SB7tr14OtOytHMuzSWXLOhwQxRLIctmb172NsU5ZTiM5oi848oHVm1k1UBsLcYVmFBQYplcAbD6d66J7jXhGX01bIiu6/it3/otXnzxRb77u7+bb/iGb3hVx21EsEaKslYeo9UDnF7D03vNIz7e5l57iwfdBfOwIGrktDvd6HPhG5roeWm53n7ZNSDCzcU5513DC4vzYdwf+9jHeP72Ld773vfueFlvzS/5Xz71W6y835iP2+jxMfLrt14Y3q+FcylQEZVlaKky+2LRel43ucZxtce5X5K++/Vc1dfPM5JrRqlQU1GIpdSSGOCkbVh27VC36drePm87vsHr9w6yx3495pg/WI3Kp+/ew7nIw3uJNjQtSiaFRXJXvTjETMpEB0QYCt23+Q2OkqjvZ02WvwaTfaGJRaIsvMNE8C4QFpFgDaFVJrZkf3+fx44e5trBdR7XhxKQ7BwnJw9olyMbIAMd7fLUqTCtSx6cL5kWBT0Q63PC2uhSKpWRRKXLAGkQ+sn5uTm9a52Hm0GWBMG6PNX233cA8enLLvrH3Yso9DUce3szCwD2QJQIoYmoT/WvbF8UN0cAbbQDmFoDPYMsS3RlubE3ozJ25NTKfx8cjEkURL1kpcZ1NuekXhet0Cxw0f/uguVBszfQ6lQFH4QurkU4+rIYmiNlLgidy3N/djQuu3INsFhHT8nvcm08IQovtSHVsot2XWlRU2Rq6SraWOboUw8ADRISldUFi47ys1wwdMFgxdD5It/mxDCRmEBcWpc3HQ1SR4pDn6J2WZhqbO58fnVKLw7yoEu0QGcCd+eLFMHSHmBtpmhc1f4gRS6223K5/Ipl2v/Mn/kz/NzP/Rz//t//e5577jn+7b/9t/zCL/wCf+7P/Tkggbef/Mmf5MMf/jDPPfccv/qrv8oP/uAP8ra3vY0/9af+FADveMc7eP/7389f/st/md/4jd/g13/91/kbf+Nv8MM//MOvWkFw3J577rnXJsD6/aAI3r17d1AJee9738tsNnvZQsMAM7tJDRnTbgD8VSqBsh1p2byOHpQNyaqyG7GyWwBr+7zn83POzs6YzWYcHB4iIrzUTvjo5bfSgyyzRWc0ZlO18Lzd9JAWBKwY6vqEh298nJ7k7WJk2Xlun825uGi5Rk2pZuDyQ6LvLfzoXgjsm4JNwl1KRD49O8UHz/H1Y8qyuFK1zMfIynluni3wK+Gx6nAHhKoqF77PKevvm9nJDZsVFd0oglVYQxUt/XOJOelYcqVWEaEtDI0r0KXFNOvzqup2SlPqoxe7cBETE8UtZoluq1B3hv0zy96lULfJYwij6EsnSGNSIvE2hVDZfoXSOYNltaq4vJzQtGXyom0fqrufdk9NGEe1+sVgfJ4+erbZZfIelhPP7KilnDqQOEpSzp7HYAguq1E5k7zDw3Wlc7euZLGquVxMaJoS77ZV7nh54MdI7n1rlz5KNRTnvOoOCGwEyWJWJhudbw1O1z0ISYyk9cXgDT3Ya5jMPNYqRTFG5JuJYzsY6eVQ2MhJ7JWh4LTLCdK9YTLeb91nehAOT2saFiYBrgUrWrqdeSRuDSKpeI1vQvpPGB8nQvDCqg00DYTzGfurYx6SYw7MNF/C11cE6+zsjA9+8IOICE8++STXrl171cdaa3PyedzIwTLmkFb3iTLFU7HSiqiRGO5x5u9z2l2yCg3LsGQxKvwO8KBbEGLgmcW6MPGlS5H/F5cXXLgVd5vLYdzWWvbf9sYrPb//+fln+J2Te6y848KNAJZfFyYNPj3zpXMoCaQsOs++SYbOynmOqilTW9GEkOucMbx8safgkaOX+T2qTYnFohHuLi44ny/JxZ8wYqhNQRdjFk5I73QfCDckw+/m/JLglcKkfKHjekJtC/Cbc5TEbCiqDKU71Cka0/x0VNTcOZmnKJUkYCJj4RZN0QAjJhetVbrW8eD+IjnNXJpfi7bg2mzGIzdupBqNg+hB+nJ6JUNIgKKuLWeLZkSfB83FlRvvaDQX180AqwdBfT5Wj43Ek0oNjgCOCWCdJqJwBlCRxLyQmHLOgGFNGaJJeY4UVYogFJ1BTWJUaMz7dEr/JZs2Z8BFSfTKLZZA+WCCvVkxqQomtkgsj/x3K+RjUhHlBH4t6i2SK8SpKmURMp1csnIfw3rgouH+fD/lfMUU9Y/Zce1Coqyq9BGv/DwwBNW10IVCF8sECnNNyN733av9RU15VnebJj0TlRSp0swK8AWtKxPbYLRWdb4A3QOEJlTIqP7WaTNl4WokGlxcO4vTipN+Dz5RHzdaGbEzN4BG1SRuYUx6d5zxQ/Dw3K9QoK1jynNUpck27YPFly7tEEJ4TTjC4PdGEfylX/ol/vyf//P89b/+13nHO97B3/t7f4+/+lf/Kj/7sz8LpLn6t3/7t/mzf/bP8o3f+I38+I//OO95z3v4wAc+sEGn/uVf/mW++Zu/me/7vu/jB37gB/ijf/SP8s//+T//ssbS45e3v/3tf/hFLmKMfOELX+D555/nne985wYSHScpb7fZVgRr20BxscNIQRzlBm1Lt8tW3lAv3d4X84OkEujCGqBsmyR9nlZfM6BzjutH17Hl+qMojOWZpRD1W/mew0/s9CHEDS/hebvJ8S/tipATNyeT+xxc+wzLy2/hwWrzAy3EcvMsgbOj6ZTZpMQYWIyU+1LAfLN1zvHSyS0KW3BwcDAsOAu3HXGC05GCVojKvYsVIUYe2t8HG7nfLdgvd5UND6ua03ZzvPtlPYTLIb34tbW0I3qnSPbs9apM+T9eDJ0XpudgSk+cZTrGeLwooc/TEtCo2DlozUAJQRXrhVgKdQdllHRMK7T7DLlfEkCcQQuFYvdc6XyboEGj0LYlTWspi0hVhUF9UCNsB0OTmEXqRUkKhMELtkiT9zjitY0CepYKAEYpp46i9nQLS3TF+gaiQyLVkFcUSDVfJB0rGYSoJBpep0CuC1YVkaKMw3O4qm1IwEv6YRsQ9fnzvRGmw/VvXaGuwelwxUEShWg4WTIscu4yiuCCxeWcgBCFvZmja5WmK5jWjhASkHw5kPyqmub3SPuC0+k6jSRRkXWAXIcXd7N8sOCkw2UJ/pRXZTGhYltAVhT8Ru6Wpjyc0TYhUaX7udBE4c55B+cAhll1iDmacsMKD+LiNbNwX9X60hyf//znedvb3sab3vSmL9ux119fCGFQaxUqRAy1TUaXY8ZFLLhuOmK8zzIYOr+kiy1RA05XtMEn4ABc+g4PPD+Sap+HFgRuLS+4vn/IzYtTPvrRj/K2t72NzxH4/P3bfO8Tb9kZ30duv8id5Zz7yyXn4whWztFQJatdpjwtrQpmpuCh6gYvxGWi6eX5c2bLVBC31OxZT+9Zz8AxYkF99vZrchRGRQOcu4Zje4DgByGGmIGp3YpaJ+dChMJgY6JJOR8wRpgVJQdlTRgp61lJuT2oYONa/0FyhEYQdKWcni4Hx4lEwOcvJShqNEWmRHAx4LvIZdfRLSKr1lF0aW20Kjw8nQ3PXlWwxjCdlpipwxAz3TDl6d5nTrN0vC5eGwBhdAoTaDSiGgkUw6fY07UMDGmhoEm4oxc66gGc1xR92u8nLwELtkvRrcIml1AOcSEepCMpDmbnXt2lublTRY0hGLAuRTG9T3J/xlmsMfigOQKWPVR5Xgsaqbxh2XmmtkS8o0dYYsFcWDjySEziFL20uh1SupLabmkiq5Ck2BPxIIEtFyyNr/K7mkCPCxYRaEOJMSHRDlmvC2ntNQSNa5Xh/LciyylGSWOJZGn1IJx3Ezo1SIxEI7mwcDowNBOUpAwYJQF5JdG7NR6CPafVgiKUqO0yGDR0weKN5NyrNHeTVREBuq4cwlPKGlz7YBMwzVGu8dpsUFRyQffQgVG8UZadx5SGJts3D14FRfC1FMH6vQCsg4MDfvEXf3FQINxu0+mU//Sf/tOX7Of69etfVlHhq1p/P3/qp37qDzfAGqtBve9979t5eGMP5PZLNt2KYAXdypeSFLFqR9x72aIEXgXKwAwiF3AFJXCr+egJIXB+cZG8eMfH1HZGE9YAo5dGfnYlwLt579Emr18kVUnv27zb9JoWNrAIxfARV9UFew99jtt3v5GxdTgpSiAVSr5oOi6ajoenM47NjEldcNatcM6zjJ7+Tq9WDYv5nP29fQ6mk6G3iS0575oN2/N4MuWs2aQWXp9MubdacD97Yw7qGdfLCYucQ9Yr5UxsCWwCrPKKiaMsLIwepTFCGN2bmMMgvZJwa4UYLfWpouXm87RO1vSOfIeNB1/k1bTo6zxtApYiCLaRIQfJ1QzqcSkMIclLKzE5uvIrsmP+DUDD4HwqEGxtpK5CUhG8ql0hI6ia6moJZGn5MflwN9LRLwIA5V5EY4dvLaG1V/afFuMMuPoaWsEghWbAlTqMwbKKBXSKRSmriDUhhQL7C95AetlDmek0G0ONMvAB+8AP0bDB88yPZdjSX9eIQjkcq2YdJiN5qFVTwdDWJ+BaVZ66Tp7HqvS07uqSA7vw9WW2KVuBPck2kxCiZlpWAsbWvnyPGSISxRM1cmFynSAtKNRSUFCZekOoR0m5mmFrDhurN5poMUYGY3vZBRYLuLXwNKfla2bh3m7OOT75yU9yfn6+IWX+5bYxwPLxDAAjm0q0TvfwRK4DbbiF6uu47Fa0saOhBTrudxc8Pr3O0jdEjQiWW6vT4Txz16Ki3F7NiV/4LGfNgm958lt5/JFH+cf/5X8Fu/utqyqfPbuPonzk9ossRhTGNnoKk+tX5amq9UlSfiKGWg+IPklUr7os0hHT8444alNymT+WngAyKQrmXZfeM1UqLWjbVaa6Cb7/boKAGhrvk+hAl9TeAutctTAF2yZwpqJczBsMgguR/aIkFiCiBB+HaHYphhBTeCVRtQ2iKUPn9u2LoTaQkVRM13sIBCh7Cn1yvnTO40Kg04DrlJsnl7xFj/A+UojhxmQ6sCdCjIhJSpxzkvhCzBG6AmGJY2kcd+49oLEOkCFK5zVLoGdmhLeBeKBIq2vnnPReovy/sK4HqVEwrfZT60AjLB34Lkvek9T60KSqKC4r5woQhEknBJ8Ap+b85Mz+TvUkFaqyhhjSvfUm/5wzrTLmMlFYdp6qskk8wqa/OQtqU/kQ0zKSo1cK0zv6DMZ4RJPDb51Tld45H00SeYgJFYVo6GJiEKiCj7kUTh+51H5NiZlelwFmXgsKG1Mes+Q5PEAXLY0vaVyJYDBoioINIh6GprVQwcKXpNTM1G/MBYAhRb1C2CeYsw0nXGNTvS3Ja5di6QsHe1fkiNa69Y8/diY5SUdTu0btNT6JMbJSn7qyiiNQqdDG9M3ePL+kcZ5J+fJm/h8WkYvXShsrNz755JN/eCmCvRpUVVVXgitYL5BXJcJt52C52O7sU2ypBG6P2m/laSmZEigMnuYvRQmcN3NOT08pi4KjoyOsMVRmiwI4+vnZlfDR8yeAkcqetEMEK2ikDcsNCllpDBqL0e+OorrNwbVPM/66261on5AiTmerlttnC1aLwGPlPhO1VMZyeTlnvpjz5kceZTICV5CVB7fu18TuTgTbaoSXbUfbKpWvOdZyqEkSrniGjb+C/llunlVECKb3xqbIiRVDrPooTFpFXBSaWMBZiV1kfrobeV93wiAkz5SPrEmJI9hiE5ATFWpnqJewNxdmC6HMTkAJBnEGVpIWLh31L2y+cHmlDaFguapZLSu61rIRRL3isxp/akrKg/LeDHz3/hjZPh8D1kgL+zRQX+uwVS81OL4P2weSsVKiFEZv0c4QnU0e18zDX3UFi1XNfF7TNAXBJ1rj2Oe9fV+HrcNavnWBPSijH2aigchon22owhVnHPdFjgg6X9C2xZBDsDdpmQYovGFv2oLGrQLKW0Pb2n6VvscYXCYjRPDB0DmLd5JkfXXz4CGqpWuAmbCuozUrFmbBqV7S0AyAKoljvMw4SdF6I2bHidE7KNrXyMK9vZZcXFzwoQ99iBACTz755FcMriB5KkWEEAIhnubzpZppPQPCc8AyswMeuEtUHcvY0oSWuV8g4niQGQX3uvPUB8KDbs0ymIdUePeZO7c4WS6YTKbcMR7nPU8/uM1zl2eDQmufD/ubd24ORdc/9eDuEMFSVTofciQyC1bERM0WhTIWdM5yuUrHdhlBuRCzM1A4rmZDdLyfdqdlSYgx1V6PgcX9S1BlZmu+ff8NdLmOYZ8n6WKksgXSZdCe/VChrzEYkzEJMG8ToGk6R+hyKFmTKEa/PhSsmRMiKdepF3a4c/siyaLnd8GqJPELJAtRKRIjRpTWBYIqjQaij5xcLmkXjlXjsCJJtQ/wnU/CGqQ1KhARci0ugQKL2AK3D9FYugNFJwzFw7sY8MSh7lyYgk6To208P6iJaJaBV9ZCHiYwUNvTjgn0WLWYxiCFrJnKKom61+dheZCoVK1QxLGEe3r7MEm5UhScj0x6sZcMrLSVlDuWNlFKkeru+eyY66mTChRKUaQoXE8tTbWudh1dMfYUwR4sJYCiUcivXp7zEmU0apJrT4CsPybtFPq1MjvHTL6HoqmeVDT9WAxOLfOuom1LOre+BpdrRpamQGWKkGsnZtZLyt1yEHP+sApKMQh1QFI7bJ1Nka58uaWUUHQ0weZCw6PHOJ63fSq2vDYRZV0nUhLDp4th+HaUZAddZjvt7uWCX3/mi7xce7ngwh9UWywWG4Xcvx7b9nrz2rizX8Wmqjz77LODGtS3fdu3vawaVG8AXEUT3M7BCuoTDWLUCrPd76aFFK6Ubq8GKXPYfSBr6XZluVxwenHC4f5hptalfbejXtv5Treako9fvBPJOVlKM1jDKfIjRF2P3WRu9DDGYkGIhsnsNvuHqTJ2aS0nW5TB69Pp4Jnsr8ViOFt57r50wqyFdzz2Bvanu4mLQXcBUXdFPtxiq3BwEq5IVcxP20hYwWPV4YZaYNqPDWliSDV6/Ja3t8DgTUzgSgRjDNanhUREMNakbdlwbQSWwRJOLDRwlTm+MQgycPMxeVT7ibDYfPY2pMm8CMKkNUwXcO2+UDVpm3hBWgMrMySL77Z1fzFamqZmPp/SNmVapHYM9q1iyHnIyZOcaGmxVyDcNv43T5fOLlDMAtWBw1TrqKlsI4qXAWuaF7zoDOpNquqcufCdL1isKhaLMuWdeXs1iBpGdxWaHEY+GoKuF8S8IG+LG14B38Z3YevMiVrS+uRIqY5XTPYa6tIzqQNFVzDtasqmSLlpUTAuK4NFWSejj40eXRtBV4G/BLaSepWLhs4ZXEjRhXgVwB53kkO1ThzOdCzNnLnMmeuK5orcrT7yXmfyw7YDJGhKvva6XcjiD671jIEXXniBj3zkIzz++OO85z3vobpCEfXLbUVR5AhWAli9wNDE7oGA5xqVKGex4pnOUpsLgnruNqcswgIhcKs5AeB+e57fXOFkROO+e3aCaqQxyiNveD0iwufO73HZtQRV5r7h6bupEOYXzlJf//2ltWH1/MU5l64jxMj91ZLby3mK1sSwzlkEDu2EIw6RKCyawEE5weUcLecDRZa+3jeTRHUiqdfFqOyX1ZAf1DnHQX1AVVdMpOCwmhA0U5AjOOfxISTxFQ/jL8rZlJfU5wuJCJ3P4HHecjHKaULXZQGsZnCT+zIqVFGoxLJqPQd1TWFNdpjFVJjYZFAWU+SossVA12oJaIyslh2u9TStwyCU2Vm0vHCITzkxxiTHgmb6mfgEhLqouKmy8AFMiubEHNT2RLx6Fm0qohyr9CnG2RhgyZrBIKSJKf8tXe+mcT7Qr4MgNtEmVWUAZORnbTxITAJMVnLelSRnnyJgBWcS3dGpUhbpHqukguXG22wzJG+UMYIPkWWTopJ9tRQrYA1Ym+T3hwiWmvW8lMGVxiRi0XZFEhIymbCsBiJYSWtVovT1YCoBkiibhYZTrnEGagImFll9sMCrJYS+PIAQvcVFw7KraWNSSjUZsfhc7LiSgjaWCKlOpWQKh2Y1weUi4H2a9y+bkqjr6FcXLBfdJBdLTv0aKUACIYtivJwJ0Y9/YHAowzkBNKwd8mrSP+vSuwtJpv3Xf/eF3Y5z6wMLrwVHGCSA9UplMb4e2oc//OEBT3z2s5997QKsrySK5Zzj4x//OM8//zzf9V3f9SXVoMYeyO12VbHhaludj23v7WY/XWx3jLNCkpTpWmRi8+vy6lCNnJ9f0Kwajo6OuLa3+dKZrWvSLeu3U8fvLoWPX7wLoUz0qexFXTmfRC9GEasQNw1SI4YuK9tM915itv8FjiYTti30q2SaV12Hkj5au3/I/XnL5dzxSLXPo5N9rCRVnZNmE6zVtrhy2/lWrtVBNcme2TQWH5V54zk573ikPOSoTMn2R3lRH7dr1QRrt8LxbUiFKXsVLJIc+9DyrTEjwCUC3gquKwgPLOY8PYNEw9o8FNKtTxETRUJMdVe2bp1xsnGMDYINQtXCdAGTBdRLmCwE06bCj3SyEyzabqpC25VcXNa0jU1GfT9fb0eW2PSgpd+TUljYBlpjhDU+REic+qmnOugwpb8iZHNFGGfEd+8Bg2awFbsMuGKilHSDBHzNalWmZGYVNtQvrqzSuTECQHdBp7JmdPbDVB2BxNH/R5c/CPgLw29RU2QpoLhokupg4YkasAJ15ZAoTLqayhVYn2riiDfsNROss0j24BJlXVMm/9NhfGxOI9mwSiIZFucN3q2T8ftr330O2fOcAddclixYbUS1fF68J9l5sx3BCrqWymhfQSL3a9m893ziE5/g85//PN/xHd/BW9/61q+akFKfx7umCKYIlhVLbaZ4jqhN4Ituxr1QUJmG2pxzszlh7heAcqtJlO4TdwGaKN8n7eWQP/zcrRcxxuAKyzxHpZ6dn9L4gDVCEwK/dfsWAL97noBeMfooT7sVPgQuXcdF1/Lxu7cREWJ+VpqByYQCQVh2Aecj33ztYWwfsQkpQhMVaqk28juDSwXd1a894kcHNxDS/LxXViPjGpqVpwuB6OLwGYkIk6VZR3J9T7cTNJvLbum5dTFSRhNocx5vgUmFcvPbZ2IyRglgjVCXlkKEECIlNolEiDApkjCDNpk+2JfysAnoiINZWdE0SVmjyGvD6qKFDmKIGJvHLVnSvEvrhY+BMFGW0aWcTjv4M1IUxJohpYkqfyuTEcDKucFaZHxlcrHeDDyD3Z5483YxFDYBEyGtLbNphZhUBFqiQJeoiVagcAy0RCWdMxZk8Lv2X6mVLLqRwYxNR1hrcCGwWHkqY1PKgmTwa1O+aNGLo5Cut6cap6l2XRw9ZiAyK/1ovZHkCBQl+ORISoyKPqN4LeueNvRoNLVEpSbVk1TBh6QQKDEJMoVg8WozoDKZ/p2EoAAKmWXhCsF5m59zilYZYzncf4g2FszbCU0gMUAGAZSkCqsIXUhx1toksBb8unZc3wb6/ZBXpvm56vqdkPTNqmeog6ghOdpNJ3iUxnkWrePz9054udbbvf8zgvXVac45fvRHf5SmafDe8xf+wl947QKsL7ddXFzwwQ9+kBgjTz75JEdHR6/quJfTyt+OYAEUWzLs20v0bp6WXindPignAWyBgGW34vT0FFCOj48pipJyK1K2c142I3AuegzCM0vhty/fjVAgGVBdNC3qlYmpqG2BNQYXw6CIlK7TYEaW+2TvRZj8LtttuSVS4dqW53Ohtl7h8Fo9YeEc9+Yr7l6soBOemFzjxmS2Ucz5uN6s3QVwPJnsGMAHV3idD6oKkRQSP71ouWH2OSqnO46h2hYUo2LE3ntc4wagPbQxYJUdXJmjWwYKIVaWVkr0rMCcJ7lUzV58NNE4elXBvmuzDEiI6ePrjfTxa3BFtKIMQumEaU8lXBlsJxRLk1QIh6jW5tjXvxucK1ksK5pVlQo+jgv2bp2bjU39YiED0NpRPhodO8AkoxQzj913SDFyPsTxXnnfV8RDufiyN8SmSBGunMvhgqVpKy4XNc0qKRKOKPn56LyYm7RY9WtYiurpCCvJhhDGcFlqGMsTk6Vzh9ub/20UBB1dmJJz5LylOGgpry+g7pjNOgqfTAEryUAUhCIajAq2N4YUCmeZNTVlVw5jKLzFOJOAV/ZUD+/OUFNL0Ggy2CrxzuBDcqr0svjDK6C7r4IirGiJKKXYYd6aZmeT3QZYIQ45EauXERD6WrYYIx/+8IdZrVY8+eST3Lhx46vavzEGHxYoyREkI2r21OwR5ZhSPM/7GT3wfmTyEvfaB1y4CwTlXpeMoJP2kkCkNJZlcPzqb36Qmzdvcv2xRxAxzH3H3Kc8pxcX5zQxJK+6ek4zu+CLl+cAHBTrcURVTpYrzruG87bl86cPEq3X5OqOKtnRCCjcmS+xWPbKitqUBI24GJEkkYJVu/5gRfGdsri4TKIT1qIi+KwuOORF6doJsexy7TCnyVGAUkahGNVuGpcNqGzBw7M9NMLts02BpkWb3rHCmkENVCSBVO0ivvUp58o5yMqWRgx7tuI73/I4RkwKEETSgXkIwaRZr4yGg6rk4rKhsBbJda4u7zWplIdT+nKXOcBH0SURhBAVFaWJAUzO6+2plUSiaK9BkfyfJo2hr5epfS5wse6/lDzWUnKub+/rSoAp5cgmSXR6MNbB4bW0vtqejmkSKLRiqHwOlPTzvAEtcoRMGFgqMQOsJEUeiSbleFmboowr59Ei5zSb1LcpFGOTiEjvCNbsDFMYIqGKDGIVAJPSJ+W9vM4Yk/4FPy4zsrtQ6vinPJeLURpXEGNSY3WZrWMQ1Fm8s4QoBHIRZBVMLAY1wMIcpKhWEFqXXBc2VvhYAAWlqWlcSRMLZpMpris2QFPIddZiNBixTEyFYPAugbYdxoKsQdd6Sc+/j0RGQtCh5mH0aRdpFa+RZ+6eEFW56HbVru/eS99Qb/e+FgCWqrJcLr+uI1giws///M+zt7eHqvKDP/iDX/8AS1V58cUX+chHPsITTzzxZVM/Xk5J0EpK/N7YthV22BGxYPdlLrZk2CV7d/rJJrA2PJum5cHpfab1lGvXriEDv/xLCWFsReAEapvO+7mF8MnLd6PR4mMY8qha5+lcoNQkmTsr1qHnBCBGBXpNxBcvUs5eHLZV1nLeC1KoMp/P0eWCva0PZK/avP6o0LnIvbMVtjM8Vh/w8GQPf0UUsb0ih6qnEa4N6JRTMFy6CA+WK5arwDUmPDpZj8fHiC1SVRrnHKpKUW+OD9ICs93CFhs0Egnl+pxaWjwVcVlhLwxkfjMtO6BlqH2VI1pW1sbHLkDabD2TswjCbAHTORxeGvYuDGaZolsbeHtER+079c6yXFY0q0y161GH7gx16GJjDJq8b0O+D1ccO9puCiimgWKvywn5uyDm6oSjzXuwfuR97pYhdpKkoqPBhYJVWzFf1KyaEufWOWhXFiwedT6YKleJgGyD397ruHWzNOyeY11cOVk/Phi6IJiJw0eh3GtRidkYC5Q+FzXPh9mYIpkGIUgWI46CDalWTREKbAaV1lsmrqJsSyQmipD0Esm5YGUMlujXYCv4VLyyrwnUF11NwS0zFFgPhA1qch/B2i610MXUgQis/C5F+mvdjDG8613v4ru+67uYTCZf9f6LosCFtZe4F7mAxIKIHFFIoLeSBWVqVzj+A19c/hsicJZzr26uHpDASKTpPLfinPe9732E/IkuvU+qYQp3Vpd0PhdolUib14Cb82Q8FWKHOoyKcrpqOO8aLtqGWVnyxP41HprsrenqqhiT5sbzVUNUpTaW43rKonX4kGhvB+WUgnIQ6FGgaXxisomlyNEIl4ZJiMmbDgwOpab1o/OSKGCjnKkhTzF/X23wPDrdSxTvLWfoqo9gGYNEHaK6ad8khLFsGzR4DmczJmVBjJpytiQpspkuO2D6/K98ajMx1FrwuWfusVx2LJqOLte+cm3IZTaSRazKoINjfD6/pghdU4WUc9tT/ST7azLISCcbXVT+uRecGG9PGg6CihCmeUef5gQAJwHjoWlkcGTZbkTJ64UkbP6bEar8rEwfmTFKLJJ4BaR8uaiKtyAqlH3RcZvESawxiSarqUh1kIgRqIxFi4gxEWvMyG+pSaxDZYjMxGjQsKbLVdbho8FEmyNeMQubrJ1ggxaIsDkXSwZsPSiRVGh4UCHM11WS+naNodOC4PI9UANe6HKqQMEeNke2QrQs2wKjZapZmGmF5PqSqtB1Bdl9mrFRX4BZkABh2WKjIQxymLmm5OB4VNSPHnyQQR5/nBcdswhMXxA5CjR1JAp87m4SuGmcG74/SA6wf/nLH+b2nfNBtfL3oyzSV9IWi8VXXAfrtdCKouCHfuiHEBHKsuRnfuZnXrsA69U89BACn/zkJ/nc5z73FVM/vpxaWDsqgVuUQB8d2yLl29LtEDdysHx0qMJ8vuDy8pLDw0NuHB4znlm3CxbHrahXe0VNrrEQxudWBZ+9eD3zbgRERuGGzgc61yFRqE3y0BRm/VGKCM4r9cGzlNNERTmqaxAhxsjZ+Tld2/L6x15HncFtDyCbbfCqOlABfVTuXCw5n3cslp7H6gNu1DMUmBa7lMGJLXiwWkuPiiSv28mWPLtBOGlWXDQdd84WTEPNY/UBc9chGVyJQFEWNLJ5LxXFjSJOaFroBoCV/2Qd2+KAWJ88f50tYV5TXhY5dyvVZIkxe/fjZv+xC+DjWiGK3mM4PmXy6MYNPChDIcgiGGYLYXop3LhdMDkzSMfLUAiTURCDpVlVrBZVqkXVA59XjCYNp06LWDBrr+JVx42AjRgoZ55i1oFsf3PbKJRdoJMVojZaTBGavtgxTlBv8TmyNV9OWK4qnDNrkNU7Pvvfh/NcfQ26PZDtYQ+G4hWg64pjh3wpX0ChyI1LZG9FddBQ77dEjbisMmZIAEtEhmsvghmiW2pSuU4b87acBN3ndJWhoHQFhSvo6xAVoUC8QCgIvsB3Ja4pEl0mX4NGCD7ifQJ/bfS0LultFmIpM9gyW3OT62sCvUYiWJCUV3+/vLTW2iH/ChhELgCmdg+RgkKmSHaS9QZlCnArXg2LkGhvX1zeQTXNT4hFHjnAWMvSp3mri4HztkFVOXFLGp9UISOKz3NLD6K6EJhm554qPFzusXCO867lib1DJtby8HQ2AGgN2aOvOji2SmN5eLLHwnl8iBhX8MbZjSR2kMFBUnezHB4dU4llIgVRlSZHekJULrMXXTKFzodICIFh+XQpajcUCs/0V4Y/RxbLbmt7+q56gFWb5GQwJgkwJO++4tpUsPWh68fMJhWTMqnOhZzXFQPELueHmaQK2rcwgcIpdZmK2p62Dc/dPCPGmI73ySGRZLF1cMwZl4FKBhGxShGdKDr0308XAzt/HMIfolw6ogMk51qvPIhGdEbOqRJ60VhfKYWHi0UAl3OmogxOWJPRndrkJBQEjAzTbWFlUKWLeaDJ+UNSGgxC2YfsBFQi1hqssagkufAoCUBNbYkWijFJer+nv1kbh/dHJEmSRxU6l/pNfwcVoZQCfKIIGqMYG3Oe5/a6zdDfOL4lMMitS0h5UiEkiqFkgY8YTaIH5hBgKjUjtN0UEaH1NZUxqUhyW3Pn9DpGhNYXuMwMihm8nTVKiMW6viTCI/U1DssaqyUH1R7XZ9fYlzrljfWOyczQ0B5xh/ScNaso9pG8JDkp658NaV+jhDrZKQv1PH/vfPiOb46ivienC1ZNx6/864+yWDSviehV3/4w5GBts+FeO3f3y2yLxYIPfehDLJfL3xP145UA1rZU+3am/1XFhq+iBI5b1GQQ9QCk9Q1nZ2d0XZsk2OsqqcyM2jZmHEe9Up9hR/iiHJ1XgJeaPX7z7psGg89kAzdkY99Iiuh4H2g7T0GgNkkNsDQGm92L9cEXKCZ3Um0M7zk9PUVIRozbGmxlLadbsuvH0+mOmMX1yZTGB+5cLji5bJjFktfVB9yopxu3/Hgy3TG6jyeTnRy0G9PZhqrgyjnaJrI4aykvGkosRVFSB7vjES1a2Qmm2JE3aVB/9Js7KZuYIVqhNZawKKguSwxrT1HQDLbimkoYraIuLdo2e7HCGOgJFH4XddhR1EQ0LaZGDWUn7J8ZJueG6ZlQLK8AD9lNHKPQNiXLRbWmLQwRqF44d+vQ8TAkG+RB1rz5rdONhS6kALsfsFNHbxns6lQoO7xB3br+bMCMAZKqoH3elktRG+8NXVexWFSsVhW+y+pNO9c1KiatW+fZGt+Vfpzh2HXX60tIG0d/AiAgSSnLBKKNUAZEwppbn73g1ve0REnUG8mRqdxPkUVSNBsTRoUimAy+7AC6ilBgo6F0JUXoI1+GyldIV6JdiWsLQlsQXIlzEDrBuchps2TedqwWkVtnl5wtG5puM0oVBvVBGVTs/jC3bYA1jmD1SoJWrmUgM45Taw5iC01Y4rzjpcV9oiplWWKt5bOXd3l2cZ8m+gGcPOgWRJJS3wuLc2JyuySDMARcCLxweb4BsN48Oebbrr2Os2bJRdtyOCqsOcjuK1w2qY5VGzxCWkMemuzRuYBEwbfKm/cfypRyUiFfkyz/qMqUgppUZN77Nc3t7nzBjb0pbzw8GCJWD5YNmlXwtIMKO8opXIu6SExX+GC+JIRseOZP3siapj4pC4ImamBhDK5N22NMz6iuCqZVyV6RwJILSWhDNRJyrpI1Zi0GqzCfBXQRB6NpftHQLR3NpaMX5SHAsrcD+ghVgFldpifsGSjiG6lBkqMPQo5tjFq/T7neFwE1ijE5qmItdpqFPXTt7BOTHDJRQaIZaH/0cwfrgHqby04xkgIvjFnnY2WTwseQKMUoBLsGhALhuvL8jXMCETXJnhBRSmM5riZYm8QpVvkeVXVDUfthPRCUoELjS4JP38h+3Q1FfmspCIsSYxSxkaJSLFDZkbM4ykgCfrjcDSqKAPgEggx1yhOLSZQrBEMkrZu9EqQCnS+xYjjvhMoUuJjqCGq0WEkRrMfqR9MJsmDFvIt4v1YRFIV3HT7Bm2Y32LclB+WUup5wY3Kc1qD8MKIXYpeejiqJDdHP5xHUyzra2V9XzA8/SNYCicNzvHU2HwDWi2cXw324d39OjJHb9y+YX6xeMwIX3nvatv26l2nvAauq8qu/+qtfn3Wwbt++zSc/+UmeeOIJvvEbv/H3hMJfGWBtPuxtXSynV0m3V7SMamNtWWZB3ZCD5Zzj/OKc0s6YHewN0qfbeQ275901XGpTbtDsNoBdTmj++JnwRPEW3nX9GazxiSufF4/COAKWwlo674FA2wImMCkL9uuOVajo8EwOP8fJieX0VJnNpuzN9qiKgot2JOqhyrW65t5ys9jdVVLscQsgNT5wtmo5bVr2iopr05omeObd9v1OtVG2244ICNAulzw4O2Nvbx9z4plEgxFlvuUJS/ksunGsCbIZOZG14dx7ydLmtMptnN0aWiNwKdSiuH2PVLLOB9Kcp5DFN0QhuoD1EOt1jgyQlATLLZw/JHZvbEz7S5Z8R5hcCn4Ky1lES020o95S6Y+KgmsrfKcUpaesw5DPNJwzg6nRadKPKgNVYwAvsr4r4xMN98wqxZ5HnRBWkpDXaCeRLWinG91c2cYe7mQACRpy4nihBFUCBpxiJRU1tkWKHvYe0M2L0yFPawBauq51sTXgnW2a6/EMA++NyC2AGjVFAgWluNYSvUNXJfVhQ3M6yUnPySzvr9GGlJQ/9rrL+EH1/+33zztGJSuGKWWw2bCAIhYEAt5Gri8Osc7QWMd8uiKKUlDgxRFa5bRtOF00XM5bFtExLZMBOy0LZtamCJZ7bUSwfj8pMCmH1w8k7k2K4F7e5xEm5g5dEHp9s3UOboHTlv/vb/wnvPqUF2WSEsLziwf87vwuTUiiPgpcuDbTOSPPzB8kIZ/8Xp63DS4GXlrMcSEwsyVVKHisPiCqcn+54rxtubOYD2MsZC3r/2DVUk7WlGsjie43MxOWwdF1AaMp3xlVxBiMGmIIBHSQRldh49mvnOPRvX3I/arCWbvCu3yeDibTgo4k+rKx3LWgZeRi1SIx+WPGdO1F6zguJkkMI0YKW6A+pHUhT0M9+X1WlYRJxQOSjPeq9XStH/Yr7JpKJ0A0ysVeQG+lCMDysqPtHHJm1vtEWERHX//LA34/4MUPTje1UESLH60hShbYWS8e678ZEs27L6M3+qSzfA6FtcSJhUWOQuW51sSYKNMKOMGWabGamZJKDZIphKGCtowpCl4wALDSWtS4VICaMVsmg8FoM/NQhuXDG2WFy04hARspRXl875Dfnaf5M1gHseD4oSULX+ww1yd1R9tZWiz7VUdP8ZvZGtdF9oqAhAJTKJj+9BHFEkNMtL48rJGeSn7fEnAzXaYXSoE1DhMNhbH46EnVqFIf1iRqeYiWUgounGFaW3CGa3s1D9olJkx5YnLItXI/gbtg0ABtiCDrPLEqJDXZg3LCrHyEukjfxVSnaFyXCIlRwFtMmW04L1AwfJtp/o8pbzjm+ppVsjViSGuv8ev18sFiiQuBAsOt83UE6/6DObcvllRRhvzzcXOdp6y+9rBgPk9z0tc7wOrrYH3hC1/gp37qp167EayrFsUYI5/+9Kf55Cc/ybvf/W6++Zu/+fcc4nxliuDmww5bda2SGtimB2D79+1QtleXPD8hcHZ2xmw64+HjhwdwBVepE24BrHCF/LvZPO8YZIgIrQa6EPjddp/PnL2ZwnQbCmDGRFTteJ5Hs5R7iDEVYGwsNoKNkVB+nqOHlL29fRDhWl2nj1tkWBMK2X0223kZhQj3V5sgbK8sOc30wJX33L5YJE9gJ7x+csBxlaJWU7MbIbNiNmiEqsr5+Tl3z885OjpmNp2CCCsiwQvFXFLB4H7/rSHvxDh0FzT3f4jjYq/5/vc0v2iElRj8ZYlZmVwvJeUBFNEkOSdS0daomlSefEzUwZg4/VfWDtbRv5e5BiSNQxBmc8PeqeXgxFCvZB11GwskqODakuW8xrWjhN0eI2xLy/aLwMbNkpTfE/ocn83xbOxZKnY/YupRDa2rcqauAFivznTeUiV0hhgMPhQ0bcViUbNclbjOsP3EB6XF/qXur1U2d9VRPsErbRuAWBaqkHzf1peW8rSigOy1qEC537HloE2nlxQBjTH9GzzYIphhiLp1k/Ii3F9WBsaDDadC5QrKWGAl5XjVbcXeaspkNUVdQdFWwyBcSN7teeu4d7nk9vmcz9055eZ8xf/+qc/za595jmfunuzU0PvD0qy14B8dft8WuQAo7euYGGVce7D/HqJafFxxp5ozq2Ypj0kKIsr9dsGLqwd9xwDMXUeMKVL4wvw00aUkUQQv2hankduLyyGCdb2YDo6KL56dowZuns9xPkWhemeeRjhtOmKMQ80qg8HHyNRUhKC4LvLSi7fRqNRFvY7GxZzfGgx9GnKKLOU5MChH9YRJYYf8qnnn6HxIhrKDaaa0F51QICmapKCtwkqHb0/CyEEhwuW8SYVUFVQkRbxcWM9VsrYlCpvEY/ogwHy+omvC4OiKui540v//5MBxsUprjA8B7yOL82aYCqIqTgLExOIKE2iuRc59m/bJeTnWCWJYA2thnYM1PN90nZoV8zamQM12NmuQGoWhVtjwPk7LddQqgnaayoO4kDKJRAbA6fvyVmbtNCuNRVWxTVIhTHhXB9piCCnvKtKHzjKoQUdCWQo2pii7zddYRKpJSz1x6/swWm+iCgfTdJ/3ajd8H0ngK8OtTK4wogQ1qVixApKuqL+GEHrqXc5tymt2ESBEkx1MltIUydEZTAYq6V2pjE3fWBQOi32W3nC4P6GLQmEs1lj2wsMclQfDs4vREnyRS1/2a6YwW00J+WImRcUk58ZXcZLn9HTPNCRFQzTbGNkBrDBQu4cXs/8Winz9vez8aIq9bLvhXbs7X9tDd+9dcvtygSGlLWxHsG6/dMofRFssEk366x1g9Vji6aef5u7du18/EaymaXjqqacIIfC+973vqybn2NcxuaptR7CuogSWpiaE9Qu8nfS9LYTRhZZm1RBD5OjoiKqqKE3JKq7ziLYN+O1cr0ikkGqjkPEr1cYS4DJ4yAIbn1sdUsgTvOXoAYzohjEWxDGIzHKoQ5RIha5zWIGqDsjkeY7aYyRe27xuSRTIbYpQaQxnzWYU6ng63Yly9cqD4zYtS87m84ESsl9OKCiYlDVz9cMi+shsj1s50TvEyPn5OQe2wFzbH4wJWySFqcZEGlFihGphCEUkVJvuL1XFl1uGs+ggcNHfX0XXntX+VsS0iI2fjAlCU1r00jAx4GYO64VQJeO4XyRszOpNAoRIiBHrDTKqgwF5Md5CHnE7T0khFLoBuosuRVaqRfqbmyrBpiTvMU3OuwLfQVEGijogJieSb4O4K8BP7+LsjYy0RTdoMv2xIkCh2CIQnaIr4StjMCuYrYFof87sMlbQXppYQExasIO3tK1ibaQ0EVuv8wbGl6RRRpGi/hRXAKyt2mPpUZor711vX40NwxgNsQNjFXNtgbYleMHudehympPq10hPUTxxx+gS2+fXZG+p6hWOhLVBV/Q1xvKGqavTOyTKvp8M81MqUL7Zz5BnE5XfvX/K/3r2WR4+2OP+fMmjh3u84fo1vuH6NZ64fsDrrx3y9d6stUT/EAUFit8RuVCgtm+gko8hYgHfQ1w0Ks4rk1K53A8cLmdZWbAkasvct7ywPOGh8oAv8ABUWQWPVUMgcKedp/dXlIuu5axZ4WLk/nLBrfkc7wLXy3XS+OmiYTotWXQdB3XNjdnewHSIuVTFeesTBTCjji6m+ldN59lfGepixrWjijvzFb3VFzVRFqXJNLI60crqbLxNTJnHKYNwwqrrqELOuXFwUJTcAerOEMskviBGUB+SiEMuPtxPHwqgytnFkrossmMqBX2KZf/d6frTJxk/bS5arBZO500CIXlaCDEy9qdAis5cPgazu+mdvggt4UxSaQ9Aq7XtGw1QpWObPkoZJefJCFrqerkdMFCqEbURwbIMVMiBQpAuB2ciJhjansXSCzrmXbzVXFQ5YjyYMl2cCRFCwPlAf0dSCqglKFnFNt1306wpmpEs/JHNgugkGf4KiGBbCEWgMHaYx1QgmCTZPlABrXJw2Az5V5IHrf2cjDCpHAfaUNiIz+jPGIHK5Tkz9WXy3GhtTKVKCkElPf+k4DtymCqZ/icUavD5nVO1TMoSEUcc0fEshsoWqKbiFI9Vj/EpES67jhA0FVcvCtpRmRsB1Btchq6qFiWpNMamwPUUU+eY1JLfxU0qftelWpg6rAV9Dh/DXKwBxnVgpMhsmSDDOPouF90apN46v+TkbMn1oxlPfeYluhixUXHO7wQoXvriCW9488N8rdtisWA6nb5mKItfaetr7h4cHPC2t73t6wNg3b9/n6effppHH32Ud7zjHV/Vh2CyAs5VbWo2c7BcvIoSuC2hvg2O1kAhxsD5+TmEAmPNoHa4naf1qiiBtthQP9ymxW1EvUS4VA/Sk67hd5ZHlFLw6P66EJ1ogYsjEKnJo9WFJD3etg2mhslkQusDXWc4KZ+CB+/gRvkIj1V7RAsncp/KGC62algdT6bcXSw2tm0XKoXd4sKgO4IXjfecOs9s2XK0t8dhXbEKjrO8n/Oe8/NzqrLk9Y8+tgHiek/m0nhU06TpCsU6gw0Go4F2miYv60k1QUa3swpCtxGtSvuNlcuFFLIPWyKFJpDqYhmhNUKcl5RdRI50A0+IJI8nI69qiJHok9FvypyptVPrWlN9lNHwrJekYjXaZqIOa731QrkwmIXSHEFXx5Q3oIAa1GgCWq7AlgFThB0sNazxY9teZe2qzy3GRIMbuBHDwTJQEU0VoQTtIuqEAaLuALiR8TGcgE1EC2lR2t42ikJpFDSkCKGWkaBCoAAXsTbTCPsC1VvYdfcGfPlNo0kKYWSncHJ9D5cWYyLDmGmbktqrSGh8jjiPFtxoBtneoXkhmJjoO5pAfKk2JUWzpueaKIReRMMXBFXKUWjLWpNAvpj1MT2vcuue9Mt9LhXKskv5nbfP59w+n/P0C7fpQqAwhv/nj/3QV3bTXiPNWksMSmUfpw3PI1dQBKfFG6kkIlKCdvQ3LaqClBRmxf32nINiRuSMiakwGDqNfOr8Rd48ewM1BSsiLgamkiLd99p5YkgIPFgteeH8khAjn7hzl6dv3uHQ1nzLo48M4wlOWYWOZecJPmKirB1jAarKcNa2eJOUa2NU2uBZLlZo43jL9BipDpBVt8FO0EiS8HdxqD3oY6TOxoYZ3ggZmNbq1/nI4hKFTRCkJRVWlWQUExPFd/Aj5Mi/zy9d8IqGwMXlJVGUqZb4eYfUCYYp6zXZhUibCydHk5R0jU/CEEl4ZpPe3U9d7SGYleJDxAt0fq02mBxyOa/arPOtXJYwT7lqWZ2z2p0krmKvqihG18BPRvNkLFLErwsRmyNLPS2zd4JMbYFoTIp1NtUB25/usxSlE6XJ36U3SlM5mqD09ZWsmOF+pShVip6Y/rpikqVX0RyITwWWLSWeSGEFBwQJ66gTCWBN99r1tUivzrC+D5PSU5Z+vWSo4HDYaRzummSHQoiG0npMgGCTGqRiMtAeORvVEFQo1GJU6HK0SqJNeXumS33H5Ny0YqmLgqgtiDDvpgjCvWWTomBiUeDawR5nYW2TeF8Map9gULVYCrpOkrIqsHCOh7L66t1miWAoMHQo6swab0VSNDBuvSAZYPXutCGC1VNCR271RdMlZoPC794+4exyxbWDCZ956UFW2Ux2rynW37HrPPfvpHytnur2tWp9DazXiqLhV9Lu3r3LZz/7Wa5fv05VVTz++OOvbYClqjzzzDM8++yzvOMd7+CJJ574qp/jlSiCE3OIwa7D8iilVBuAZ4cSuA2OYtrXuY6LiwuqquLa/kPcPr897LNDCdzpo2Pbm19uAbttmBJGUa+oSqsBw1qAozCG3z6/zru043UHqdBlXRS4kUEsZB5+jHjvKe0EU5braES0aOGQG5/l5L5Bl8lbalbwaDFF65KTbjWIWhRXfDzblMHSmp3iwg9NZ9xfbQKsh2cz7t1PPy86x6JzPLK3RxeUawq3z0+YTCa8/vg6d5fLjWWtKiyNuG3bHxOEUCkahXIhGQjsSrQXwdDZzXfGOCHUOtw3ABsYSbln8BBH0f4MLEK0hKUyBdw0EItNtSmRrNZW5flWI9GF5LETYaheiSQwVY0OVE1eY7t5sdoXYBk1QSg7KNo02Xe1EsoUrUvGvxC6gtBYTBUoypgmeUC2rezhPmyYLMN1D0nAOUP7qnlVKsWUoC4ku9SYzf62ZYx3zpe36PaVXrVNE6Uk149K66TJqlMpC7ywEVuGkVLV1mn7W/oya8RVf0pYSjbvX/amru2qRL/pqZaus8ikI7YlxhdZKESzN3fUjXBVHWFiSAA3ZsNQJDmKNOeNlKEgxLhBIbYmASzDZorMdv5kGP3uNVJaw6LddJgc1BUPlqsdgZnfr/b7nYPVti118Wba8PxQaBhSBEuAWflWKgkYaryun6sYQbEURjm0BzQh1Ru7Vk5x8QwFztwiKUpGIcZAJ5HKRqLCuWsGk2oRWu7MLzEiPOiWKQ+qXTvgVJXW+QSafGDRJvnmXhwgaloTmiaiZQJY3kfun53iW8f/+fVv4fSljpcu5xzX00zdSvO0ChwVNTfVUBihkEwtzADrRi+93L+POgJYkqInEwoMmur55LkhSw4kGmTIURZNHUmOThHh/u3bVFVFpMGdOmIMVGKG0nC987ENgS4zINTkqF0H1OkbWzQdbJdjzOdwh9CdhSTkgNKL7GqZ7m1aLIehE4jpO8sKfaogQfKY0v6qSQV0+MpUhnpZGjXTxtdGN6F3qKUbGVQzg4EUYcplo8rKIrjhXtu87hgxHFUT7rRNuplWmNuIzPPUFSPq/XrOMECbnif1+rvW4b+5llWhuDbTSjM3WYFOwzABmTJQVT5TNtPMkyhwfV4UiPHMCgi9b48EzOw0DHOnkiiCLgoVKW8qycKnv6qaDdZBz56o1NB1BSEmBk5Uiy0Nweb5V/M3IIa6KInp0XFvkey787ZDxDCxFb6dY62BAPdWSx6fHSKhwA/XK6CWghLXKS4DrLn3QIVBOO1WGIQ9m3IPiTapNCIDMAfZoP0xqouYnBD5Rm1FRUFZtQ41gvOeeyvPsun44osnLL3P9eKU4ANFvbZfH9y9pG3S9/HF5+/zxjd97SJZ8/n861qiHeA//If/wN/5O3+Hb/iGb2B/f5/nn3/+tQuwnHP81m/9Fsvlku/5nu/h8PD3h07yygDrGh994W18+2MrbPkSSqA09UYO1DY4Clt0vi62rJYti+WC/f19JpMpZSw3lO+2jdMQN0FHRClNgYsvTwncNhfdqI+T5QorwtSUrNShZLUghKfmNyhM5OG9e6Rped1EIp1zeO8pigKTPYf9hGFNCqMXRcTd+DR6751InKAI3isnizko3NifMqntoFjYt9LuUgaPJhPuLZZb++1GLHvjbGzfdcFz7+yM5XKZ5O7397heTSnE8qBZDp65J2b7/G5zSmzjoKKlUfFV9nZKjlohlI1QLGA10WEC2zbXkgdsZ4hbjyQvABvBpbxUCSA5orWCKihajyImOTQUymQsaM7dsp0SSkF9ksbGgPFmKEDZj/Sq3K3daNPmhQlC1UC5KGjLgJtkqqRJY42+wLlURNLWfkc+Pa0Puo4UDR2Pl8q8AGbK3eAUH1bX9ItUIAVJzj6MIlrbfW9f0ys1WY+hv+K18EBv1EiOLiUlLJ8LG4soRREobMT0wPUqgHiVXX8VCLsqT2t0bYNHOdMLY0JlSOmIdZciA0bRtq+11OPrMeobet4aalKR9CGkKFk01BRgUqmEEFJ+T3+MGfUX40isIffrQ6QkGc4e5WBSc7LYdI6Uxdc3DWTc+vWjtm8ENkUurFgqM2FiH0qARBUfe65bdj7nXIuJqWhCErA4LCdUxtK4QMATNCW3+wBBItGmukRBIv1H0wTPg/mSpCmYnokbleUoTFIGKIIhushZ09C5voBwjlKI4INmNTzl1q07xFp53SOPMDFTrHHEGLkxm/LCcv0MBWEmJVYSVas2SZ31WjFhv64GoNVHsGKmnCX2QMoDLXIBYAnJA6UopospV5WRZz//bDQXtAX2Z4c0mtgI7dIhBfgyG/q6Zgw75+l6dUPJa4iwztPStUDC8IB0/Xm2nYeZDIuOKinv1khiJTQk7IMkSfY4QgoATtbCFf29G3xjuv44RdE6n2DwsqQ5MhZJBl17B0l//ACKkqx8ynVLB5elpbKWh+0e33j9Ov/m8jP0s0oboDCCCyTRkgx4+/UEhUlhWUpSmFSTKQKZ1agi2EJJAR+hNIY2X0qjfhj+dNYN86GYNC/ZnmqY9ypscrjFaId13aCJ0dAXGaO/x+kcZQlhcNCl/kJI/8QIq85SlsI+U+5ol6mFuci6SdEjURmAmIhkimD6Hu4sAy9erFg5jxHLzNS4GGmzLfbFy0sem+yj3hLUJ/oiIBRUpmQ+UhtehVwUWyzL0GXKrAVyzZf+Gca1CiFBBu+5ejMsn1Ik4SYZKQuv6f3rtcP7QOOUpz/xKeYL6T16aIx4F6hGufv3717Qtolt8PlP3/maAqzFItnHX88RrD/2x/4Y/+Sf/BOcc4Pk/GsWYN28eRNrLe973/soy91isF+tVhQFbbtL/QPYKytcKPjA81OOJm/j2x5bYOxia69tD+6m1/Di8hLfCkdHRxRFug6bE0nXPWzlacUrKIGm2gBY25TAbWDXxpRkHKNyf74kRqVzEVNkpaDsPVSEj50/wvcYz2Ff0r3v068IGijLMi9AIavupPHaosHFlDiLcYSj57Bnb855W3k8AmeLFXZh8DFwNK2ZzSpaDRTWcGebMniFMMZOLpc1PFitNgzYo7rm+Tt38d5zfHxMURSUxvL82TkgzIqSo+kEHyMPwpLuiy2Pv+E6zz53DyNC6QxdvfksjVfcNBkFthUKD90s51pt2OZCLDafoSpXFiyOI0lc8o8bNEIRWisYbyhPI37q0TrlKcSNr1URD1LJ2hZXkDbR63rXsOr2G8pgVFw5tvUuRFJelolCvRQmJOqLm+YxS1IwCssSMUpRB4yJo9ocW9faUx5Gi+qaCtfnn+nGc+0jfwhIlfbXLuSbexWq2d10FSLevf64i4hGBk5PI4SIFoKLyRlhjFIUHlvo5nlGOHIwEPv++ugdyZDsr/+VL2L7zyl/IYZM5ZBALFpwRc5JiKB25zo1WadrWyXTzWI2DK1PEKp1HvGKJ2QAl2jJM2uJwSW6VIwbiK2wBhfiYBw5jVeCqe2o19dzGwBW8SYAxiIXkIsNx4hvSrz3RKmJWQlCVYlZksCFLgEpEmX5sJwy7xxGkqhRaWwSjbE5OjLY4+mHJjruLRe0mc6tQAxxWGdMfhGWK0/8/7P3p7HWZdddN/obc65mN6d/+upcje3Yjo2TmDSuG+XyiggSciFEXHQjIngVIYJAUQRCgaBL8iEkQqAIWUECwZcrAwkSX4BIQC7cwPu+EDuOHTepuLfLLlfz1NOfZjermXOO+2HO1ex9TtlVjsvYkClVPefss/Zq5xpz/Mf4j/+olWXWsGpavA9YI6iPwaZMYruFVh12XrC/X2AZ2opkGBZ+iyIImCZJXotSSlwjZnnOk/sHGCLFtfGuDxZ0QcUyy1AfwVFPi0r7zFqhbTySDe9LZ08KhHUC/nWb+jqF5CxnynIe+jomkxRamxB6pd1Y4wjjWJSwLSyhdFoOIKydj4go+foC2NbgxUeFUpPay6qk0qtEO9SY9YrNvyOlbhSxiEqDo9qa9DImMLM1khx3VBPVBFbo1QqDgWJiMU564RubWYwR9rOSRNDu7WqhllratGthUhbIKlWQWY0CJAJnHfXYEmXrO1/dxCBbiMoVMSsVBLLAwtX9Q5tNEzW2D/rEbTtjrAo22aWgybYR1zBjtZ/rXZPh+HgMpo9XpKBP+q4iEIS6teQ5zJkg4noRj9ZHirRTEImpx3hmknqlCaKBj95dUgfFqVJKDJq03rNyDUGVxjmePT7B98qGSfADy1QKjpMAkTGxjYIPgTzLWGvbCxwBiJpYk7koCC4RTjT27urrZX08P0UhjxRfmmHeqOEctU+JWdLZ7gHPfOaLdD3g7qwWnJ6eMj086Le9d+cM1wRuvviAk+NtP/f1HR1F8Jt5PPXUUzz11FP97//xP/7Hb1wVwTe84Q1827d92+sKruDL12ABHJWRM3Bcwf/5xTmfv/0GbLje/31b4a8TwvDe8+DBAzQErl+50YMrSHQvpY/+bve1arU5B6DOi1hsjlYd95YrvnD/AXcWS2ofX+CXzxa9lG9Mg0cueesCsywnF0sQ+NDxQzyo8n7fTdtSWE+eZf0LK7bZqBczJiBhQBtiGpqjz6LGcbZFC9qbFIgIZ1XDrfsLjh+syRvhejHn+nTOJGWpVlviFoU1nGwpBR6UHQ1Hkoqa5+TePUIIPbgCmGRDuLBynpfPluTGUmQZR5Q8urfPW950PUqW5pEuNb6xNnVyj4sdtAXYFWSVbPS8MgHc9jRNGbH+WaUFbUw3VE01WVs+aNYCIjS5xdUZ2QOLXY84g+mfc716hSQyICm6pRgfCCE6XSE5cBAjr+PsgxJiNHYgyWO89P2YumOWlWG6tMyP47+2EUyIqnztKqddFoTWQIgKeaaLACsbxcf9PjvKQx+F7ZQHY2ZLR2Ckv8YCZBLAhnPvwXlsouc+i5SeLZQbhqhf/2/YLD4XSdt5QV1qPOkNTV2wXudU6xzX2F4Va7z7eNxzHW/i9Z+L2ul5IYyLLi19qpKadU4V9htUPHbe4EMbefh0oCc1vR5lG7tEnibnxvphMnZOtaK0PtA0jnrt8G0gx5CLocwsE2tTBkI2vuf0vOAGxJqsr+d4vSmC3ntycx0jk40MFkAWCj74wQ/imwlFnoOUrFykwsRgcqwbqXTBKgyUvy4OEYJj0a4oraF1saYi4GMGy8daSFWh8i23FktO6wqnkUunIVKwvVc+f/d+FOxpQ1x/fKzNCd3rEZKiKUJwUV2snM1AldVyoBIaFU6bml3b8ZAjWFkumqheSXTafVCuzuY8trfP0STa66rxA01cIlbJrcUGSayIJByRskwz8uhU9h51fG3VBXIj5EmW+uxsUOxjpdSXFddNY1F8Yly0LuC8JmomfeJIktkTMSkQkg7XHS99FjIIkhoKByAJJUrKUmjPDtX+/5HWuDn/xtNRO/EGP/7wvD0AIj1M2KwHMOk8E6VYRbGlxbfDeze1Wb8GAL0wjYridSzzHn2T/ugWLMLezs7QgtCAMx3EA81iY2DFxTlrkt1GeW51jKDszmqKzA8gtrs3fQMvg+vVRdIbkLJKx/6sXztFOjGWuGnQoW602yaMgFJ/LxWsyTAq7GQ5SpRTb3wSbKILnsUTy20UTfEKS+cTpT02TybZ8NxYPnnvLkHhpfWCxo6DB0JBicUkVcHhWSxaT+20b7wsJHGQpG7paoP4WFHGagB+G/NiNAekGa1b6W/S0gcqfOKV3144llUUjhFraEQ4PT3j7t27/NZv/Raf+cxnePFLt6nrls9+6lakt34dx/8MAKvDEb/+67/Od3zHd/AjP/Ij37gAyxjzui6M3fhyFEGAS5NNUvZZk/Ffn93juTvfQsYRAbfxd6ctbe24/+ABRVGyv39AYTebD8uWI+JDs/V3KMymx34us7MVBX7h5Jibp2cs64ZbZws+c+ceX7x7wmlVbaThIUaaFWXVNDgf08R5lvPRk8ssqhlt02CMMJ/bjdUgOu5bSU81Q7Ni26C2YvrYi1RbgiBdwfN4tD5w52TJnftLmjPHZZkwN3m/IAMcTKZsWpBBFEQEvHfcv/8Abw0HBwe9Kk5hzTlBjVmecWsZo7zZlSkLdezPZrzlxmXKtaZwVrxS0yptcd7IWDVUuSJeKBeCbaPoxUbgE8iazdMWOuC0CY7tBb6m8UOEVERoMoPWNgKtalgItu+LkKJY3e8pWihGEK9YFyV7Q/B40/FA0rNz5xssmwsaG0sYAE/WCtOl4eBuFu+Fi8CoXee064zQDuRXIQop9Nc1Punx0A50JdpGiA7Bpp8ePR+ZKlK6iHCjh7UJnGBQCdv4+nm7Ihd+tvU11a3vppqt2qCtJbSWpslZL1OzXr/9zm7ej3glCeGMP9OtSdIde3xCPRgcLboSgV2YeDyCmYXUi1JhLbFkpj1nOnplN4hNhztnutvMiIwad8dtjRFaF6gbj2+i+IoEKMTEBuXWYq2w2hKsya1hUZ9XY/1mHd36EalFjyGjGqwHDx7w/OdeZDqd8tDlN6b3MefExTUlPoco7dxqxal7EKdxGDLPuW14qb5JnT3AKQQfMxhdDVIQjZF657h5tqDV6PyKxIapy6alDZ7jqqIUi3r47oceYj8r0aBR5EJBvNDUNZbYcNWKwfmAelgta7STblchF8NONumzJwZD2/heICFrJTZTV8WKoUzBwXXjyEU2bNskt0zEsnYuxUIGBbtSDeI7qerR9FeYTyexr5PCYpVk0w24THH7OvQpFLhXR4pq7VxynFOGL2X/xMdyJ5Pu2Xbriu7t8HkEVyoROHVJnZQAiiIWOqztGyzkjXduE2QyDZvZ/sGAbxrK7vNOqyr9Z5IQRedUm0JiTVZCMhOTUbeBB/UqPlONwbUwCaxnDYUbBVVQMjGxDjydU2ntcH6SMlv9tQWMUUIIfQC3O/BZW6f64S5j192btB6IkvRNadM5jLB0DEiaKDfe12xtCVi4LpUbP+jrmrV/DkIhOTtZSW6Fq5MZIlAtlXUbZXhi0+G4znTzYqx8C10GzfSA9P94/kXurRxH2TzWxWYjwY7kJ0md6gDTuyPAonG8vKz6uWXTQzRdFs0PrTsMktbb4dl35r6/Dc5sxhA1+g/dCxACmAY+9aU73D2NpRcuA4ylnO3x8MMP89RTT6GqPPfFF3n++Rf4wG/9HvfvP2CxWGwwrV7P8c0OsFQVay2f/OQn+dt/+2+zt7fHv/gX/+IblyL49RpfTqYd4GiyWXjXTbjnTpTnTi7zjivCpb2btMRGacvlkvXCsbe/S1nGaKbZBkdbXWqbcN7hKKSgYvj8HCVwlPXyQbm5PGHsrXY0nDaEtLDHzFVR2qEnTTqP1nmw0Hr48K1rfOd1R2FjSl9DhpgBRLZ+4zBYsbh0/mIc4kps2aBXPo+evLUHZNuvqapyNqq/Eon9OW7dj/exNIbDnQkTydgtCs6auj9wpzIYgrJcrri0t4cUxQYgP7hAsXC/nLBKTTYPLkUJ/iuzOc/tN7TBMbUT2oWjtJYVMnCf08kbB22p2LSONiWggqmjZKwfATLxAr28ezLPbosOOPKZN57utheefmmtQVqwK8GWISZYtnCBZudXdZ+NAISHIhhcAiVxKdRYH1COv9ud2+D86/YppWEQskYonSGguEJxVnBqwUQVPlOkLGra5UBrlPHBti5muAkhSTVHzDja1ghSKBo8VNFjkZH0ciwc39rvWPSju7BXu47I9q+DnG4XBRWxsV+MNyBKbqP8/Pb9G93pC65789ckR7H5eeBc7Vt3zSHExdUXHrFEOo03MTvQxBoOCgj50L9GiFLBnfpflx/MjOmFarbBICQqFDpkxxtPsGCDcHZSc3k+oygsamJj5Ftny15t7pt9jAN0pX0cI0V0Vp57js9+9rM89sbHecfj7+ALpx8BPoFQUCdAqyjWWDyGRtcE1kCO673jyBKo2oz5dInqwXBgIco2EylZbfBUbdtThAprqEJg1TRMbIYgfPb2PZwPXClm3JjvsnYu1nQqSBPtWp6UUbqeQEaFRV0PzmaI86EU29c2da9X56ZnrbBOCNGKsEyF83XjKMSwJPSZ9llRsNI1y7btp71VE8UpQgQ8HUW4e39EocyzHhCt1y1TVcgcvjRJ5GdYY9clLOqG+/eW5CqbiQBNQKmjISaA1cURdWQbQp7sj0kmKGish20Fr4BNdDClp0n3GbBk71RTDifZDL/n0SLA2m6ii40hw05UI8Dqox/xHsc6oggEWgJGU1JMYZ5nNI3neTnlqtsZLl6gyT1BbA+YxBsmeUbVtL3yaGZGNrRz8FUxKmkNUjQL4JSm8UgWEFVqbSiIQCqopP5V0ZZFKmz8HRkAFsR7HNJzyiTE2r708EUNqkLrc7wqZbLjikS6oIuE1O6EVcEGy2ySY3PDPJvEWvQqcGdRY0gspliIh0Go6jba83Q/u2s2EjNXFkPtAzObU2pBCOsRgIyLehuUUMXn1gWgBWHVeha+6oNoVvpitngQLwyFVjrKbMZnLypgwoCvncQWBv3SrX0QtBumgRfvnVKl3ljeRoGLZdWSZRlXrlzhypUr7Mw/x6R0nCxq8mzNhz70IbIs4+joiEuXLnF0dPS6Mcq6Gqxv1tHZ3X/8j/8xjzzyCP/sn/0zbty48Y2bwfp6ja+Uweoogt3oJDe78Xt34Dc/f4P18jFOjxfUdcX1Kzd6cAXnneChWWCKMhCwW1j3K4lYjGu97icJctHhO2WWUbWdnLbGqI4EVm1L7VwyHtHJyaxhVdc0rqWRkt+9/ziWeYxkj/hr1sRC6/Eot2osTAeo8jXN4WfpxMDXW9S/eVFQb933YiRm4ULgzumK5bJhddKw43NulDs8NNvBhcByucB7H6PDl47OZTu3gYBq7BXT/90IkyzjpcUZWW4pyozpJOPRN17BGY1KWo1QptoraYd9hi68rFGevZooXqLUeVZJqr/S0bG7mzM6v+0THAUoL3ore5upgrOGxmewjMccH8hv9OyKGbbt5IztMlMhRrtMl90KCiHSulTDSLVr84aem8/jv6mQN4b5qWV+YiiWlrDMaBc5wY+jgiNwqfF7/X/nLz/dIOlroXpqYfdXAzJTKEOqXQj9M7oYQJ1DShce8it+dgHq7JoaaxszW21jqaqCprb41mzWW4z382pHj59l67M0L42m9TjWqbkgkEWIZowBMQQyQpPhFpZQAZVgaknx5ATMOzA16iumW/9CikGr4n1nz0bALHhOzyru3lty784CqRRZBfal4Nrk6xOx/HpQBAHK7HGCt3zsYx/jC1/4At/5nd/JG248iYgwzx9GUJxaVMvkZMapE9TQak2UtAvUoe0DH0aUqrWIrTCpoa3T0PejU9X4PhBwqmRJhqTIIvVoWTW0PiACZ1XDLM/xPnD/eMVeURK8ok6RAA8dHfVNars7ZoKwbJoolQ7glUKi5HXPJOiymin7JLXiQ0jXJ5xVNfMsp3aeTGLGoCsvmhcFRoSqcj11LcNEEaYAEqS3t/1T9HF964KOZ1XNqmnIJo6QywjVDDbmEw/u0oriphLltLushBCV2kw6zigztT3ZNUviFSZuE1LgSpohgCVZeiZFvIl9Y+Q0tgNpplNCHce2ZPAN+jGyxZoNr37003XDxlXBbWw/sQW181TB87vtbYIMbRsAlru+P3QInkmWMlbpnEJv40ZBqc6pT7ZGrERqncClwxX7e1Wywz7Ve0WE6fzmDPPJTPtg48/pOhTQYBATIsBL12uhr8PyXvpAso4QuCTAEn0cif0ybY4UMM0K5lmJIFRtiDR2JAkHxf3cOl2PInTDuRpiNq9Tji6MpSQ2Je79rLS980pbxSBYxwgQgZXzHDdRQl1SmwShy6bG2msiWxa7FVQbl+71U8jHrNewjfT3L22BbeBktaZKfd5UoFVlsa43+mAtziLwWy4cV69e5fu+7/t429veRlEUPPfcc/y3//bf+NCHPsSzzz7LyclJrMX7Go3VavVNDbC68b73vY8f+qEf4saNG4QQvnEzWF8vNZGvTBHczGBVbpMSqESu+39+ZsH+9HH+72/LmOQNtVtvbDUeTltERqplRErg+suIWOhWxLceZb0erCrUK1Ob443QeB9f4K1r2Slzlqkp79o5gndYm6PegSp5USDOcubho3cf5W0Hn2dqS4J1fd+a7USD3TrIuLuF5kvWe19ifvY4Z9Vmlm5eFufqrbZ9TFXlNGW51o1j3Sy4NJ1y++ZdcoHr5ZSd+S524C8M39sSLtkrS8626Er7Zck6RX1nuxP2Due85Q1XMZnw7Odv4zMIRsjrqERlnWBUY71VCib2EVZijRYI5ZKeo96tm9p9L43u+W8IV3SU0a22T8o489WFU6EtLT4I9jjy4MMEfCEjRBeFMNhkqF4g3S29YTZ+tLgnx2acPdkQ7uhOZbvBrgaCNRFs1fH+YaDNLKEMhInDlNEDkH5d7EOjvaLX+DDn4HPK0JzLahmFkoj6miGSOT5fSMXwkvrUsFmX1IOVcybo/Gd60XaqGw9PvYBT1NiUAY6F4cZ6zHYcJd3tc07XK8BOtv4cmxsnp8On1VQUjEbqSJ+tijU3SqR4BafktWXWQNU6xAjGRJrfTPJIZU27mqYobmw5oFhioKVNnkYQxSAYI+ek2HtZ8VXDcvXNTxUcrx++vspvf+yjFMWEp59+mrIs0VRvNs+fpEjy0qUMQbt1WLGjFkeDSGxP2oSG65M97ldrFEk1HMJ0XrFsy37OBZLEd4p6Bw0Y7Zy/OO9XTRQksUQa4SzPoVHunCx585UdnHPxWQWYZnm0pE57YQn1SuVa6qScq16j6IWP9D9P6N+wiO0FrUMUdxj8YvbzCa3zTImBtKZRNBP2JgUPjFC3rrc7ohpFPVqPoH2T7G6IwqTIsAn4twonyxWmCNEGj1pSiFfIhCUtOgejyliU1ohgEjXc+6Te9wp+o9qttzDrzscgLqA2WjE3p6cNju1YUPp70pkY0xVRqURaXw79i9YfeKBNRmOoQ788ScEwHf7c1fekPVFaQ+MczgbuynJkw+OZuDyKX9FA4wKTPB+kFxWO3Qqd+GhKW4moIdWRd+QcYwI27oIyd4hRducVk0SdC4ly51LANuKZoadTUGh9BuJibVWIWSVRJZNB3dgaQ5+/Cd1alYriulpxGRlPBSOWaZmRZZapLZiZAis1rQ9kXd+vZBetCCfLNn5bN2NYIkJpM0pjWItnZnMmWqQ72dnSNCdrqOsYSh8/i8p5Vi4Fu4gZLPUdsIxCMa3vVFuln4uiYBuJQk/dBJL4PAT6jKsZAWvQCPBdSoSNQLMGaFq/0VN2sagjrXtZE7xijOHo6IijoyMA6rrm/v373L9/n9/93d9FVTk8POwzXJNRacdrHYvF4puaItiNpmk2FM+/YQHW12u81gzW2rcbfk9VVehizWQ6IZvN+M0vwtsuT7h8oDQcA+eFMNrQEHnCgxHNTc46DKBsG1+6LSEMpw4jGeu2pUlppaYJOOsosixFMbOkSBiN1Nq1YGPUZJbneBtYVC1LVXJrMark6dW+31p+9+5jvHn3PnbiKayJRcyyqXkYdBNwirT4MCjZqK1wR89j7j6STEocFzUY3gavO0W50XTYe8+9O3cQYLa7x8npKfWDNVYLZiFjbzbBZFGx6vZ6ubWvYhNgqeL8UPh7dHWX+c6EB1XF3v4U23pCbqBV/CTWpYQ8Xpdxil3HxaUt6ZeJ0V3A5ZCvo8GrZ0reCk22uZ1xURxjw5f2kdI39rttC5q266aMbcGlJKnPBI+lPFWyEuqZ75WHTAD3FXjUKeA3+n2UyXBpGUwLbuy7NVA2VFOSczQpTEjKR6MXJfMGQsC4DL+w+FzRSQs7YeDzd/9uqQ/2GGbkRKDjSOXoQrqNjcJEYoi0hU7mV0fXQreApiiypCirolEq90Jq4da9C7IJzmAANdufKYnWJQQP3kSukc0UawIm0/6+Dhea5pcw+sP4ehM41e68R5u5SKHq7kuoR3+U9I760Dtc0zoja3PyRggaCCGgBE6PWwKxTiDPojJhg+vnbWZNktuOJ2y9IcsM83mGk7AB8Ov2lW3tN+Po1o9bt27xzDOf4tFHH+VNb3pTHxme2Z3076MURqgDzLMdnNoEHiyieaJ8K7n1LN2Kb5k/zGlTccKdFMSzzOY1y/uTGO4Qjcp0SK/aFlLAxyBkiVlQtx4XAlYlNf+11LWjbT1fuHmfrMxo2za9vzI4+mmoh8Z5qg5Eulij0zhHJoYaH1XYANd6los6NnAdrXnXd3ewEmv28iJjaixBA2WZY5M4SlW3qfFw7PFojKGuakRj/8Qg0mdGJcC0yHvqvUepmgq75+JrnrJ7KKmeSlMriPgKD0ENjcIWoviUPgkZfQZLSYCoszsmYYvu/RoDNSe93QDtsxCd5PnI5x38496upR2OexMK9CHYsVEIRFEN1Z5umOUZjWt7G1wHH4+TDmzF0LpIYTdEoNjzmon2w6fG7iEo8zyL2ScTTeWprVCzEe2JojhqhnthAOP7QHBQ2N+pNsC38wbfGZsEEDUZc1WhaS3GBLwaggpNMMxFsUZou5sooa+vCh3VNoGTIS6l/b0CoSQjzzLK3DI1OTcme3zRnOGS/c+NZd1lfrywagMy3P04d9MlOh9omjhPd7OSvN2KhqYfmlqZ+ygfn3cTRYSzxmPSwmIwsaYR03t31gheo9iKNTKIy7SpJ1Ye92m6+9GJwJSjw4/We0nC0EF1MyuJ4lzYyGCtljWTSex/d1HtVVmW3Lhxgxs3bsQSj7Mz7t27x8svv8xnPvMZptNpTyU8ODjYAG9faSyXyx7IfTOPd7zjHfyH//Af+HN/7s9hjPkDgPVaM1iqMdK3bBsWiwV1XfPU5WucjKiDx+ucT965xDtvHDHfeXGDzgeREpgl9Zj+PLbqtLYnuLuoTsvk3K4W/e+Dik0s6DWuxdioitN4H/umpJfdh4AEzywrwAqVcyzqBm0C02k8t3uS8+zJZd5YxuJY55UMRaylDbHRbbt1XkWurOrB3ogoLj/F7z3H9PTx/hyd3wSdgvTZqm7MiqwHWG3bcnpywo39PfbnO4MxFThL3OI7JxFUXdvdwayEg1nJpMzQFFHvpYg09l+5v14nv1XYPZhzNJ1yf70GESazgqZqeeqxq3zq5Tsx46TxTI1X2lGwJauVrE61TgHaWXR0fRTBIqsh80lRtbe1UUgjbKkKZiPg1A3rI7gbIxYTzqseihpaATmDHCXsjHOkXyYZIlHmmK1tNaMHyiICLvZgkbTaqQiZFyg3s6XGG7zVzUUq0Ct7iULWGrJFSVgomnnaPYcWGqOT27Z96/duDekyXZ2b0DkdjGvTbHSMCAlohRHQ6s66q9Ma7asDJsroOkZOySud20UjfmvLQVGgMbFxqVe8WMSGSInJQ8pCJQciqYdtBLU7+bcOWKWT6QMbPq2mXTbLRhpY38A6vQrjfkKZM3RNU40YjIEiS1Re7wkaqGqHzWIU2RhBTHQGxhl3DWCCoak8be57gKXA2frilhiv53g92RDGGJxzPPPMM7z97W/n+vXrG38v7TRtl5OxT1BhJ5tRa0YmbcweqiVIQ8xoKGf+lFoXKI4QBDEx8zWfNtzRMMxciY5sNz8CSgiRdtdR+oKC8z7221HYzXNOV6cosJYsSmP7WHOybmOTWQ3gk5JfSHL8tWsoyGNkG8E1np28ZLFsEsWv4eU7ZwDkwfS0PlVoW89yueaonLJjPBADf4ezdG9EqJu2p0NIAJzvxW7Ew6TMWKU8hmikQNoRfUJNIMt9nO5dpikBnO4dka7438TJ3+nZeRPfGy1i0Kt7RyREMZeNjLQMeEjz4fhmJYSRHZTEUpaUZZEkRLFN5xu/18bD6LUmFSONDhj/M5UQuj5ZJgZ9u/siwFld91Eog8EaQxtCjL3ocOh0+D7AFqzgXWCWZ31GZJ7nLBmv8cO9MNAHl8SAz1qk1QiiRBEbyDKfAFFcP30CZargQ6LTKxFgeUumHq9JrCfEeisJHZBTJBXYhgAaDOOVp3snOrpmB8QKyfEEpkWGEcONfB8jL/U32iBpXVDaNuBcQEZzqzQZVQokLKqaReWwkwiwpDb9sbv7alUiXTokGnAYtgmqfdmsKKhXZibnzKcaRNFE+VNya3DqsSZek/Ux8Bn1CmXogdV1ZpZRTWESuzLp0XVitZKOG+1C2ABB66qlriI474DdKw0RYW9vj729PZ544gnatuXBgwfcv3+fT33qU7Rty8HBQZ/dms1mX9YOf7PXYHVA9W/8jb/BH/tjf4yf+qmf4id+4ie+cWuwvlEogvtFid3KtuQIx8fHtG3L4eEh860O1F1R48duCh977lHa+uCcT5uRbYAos7VF2MpYXSSEUZo8GtOt0dNyJBaet97TesWYQGkzCIHgHGIzchtTI5M8Y29Ssj8pWbQtx3XFomm4WeU8e+/h2HwPRXE0rUdDbERaZpvnaW00LJ21McahQFuest77Uu8MLpttGffyXH+cLIUaq3XFyfEJs/mcyc7uGL3FaNuWl2vT4nmyqrj1YMGtewse3F+R14ZLMuF6Mefh6Q77RbmRSStHP+/sT5nMC6684ZA3vvFajM6rYoLSnpNkF+p5BEa+gLJJtLjuMQShnsQFtFgOUaVzk4Iherr52dC7pB8X2L9eYcgIrTHoymIqwWzRVcb1YWkpwm9l18RpbFw42rdNhe8isZZHIGb4Gh8pcM6DD1iXVM5MQHMlFIGmdLipp5k62rmjnTU0Ry3NnqOdBmgN2dLEejdRsFGtCusj8Mgi+Ig3L6SFNmxeh+8k6pP6oE+bhHQBuaTGN6NeIcq5ZNPmTd5U/pOOxD52krafw6sCXWNAIlH6vbGEKqdZ5bRVFuu1dHAUBhgVvyPb+woyzJFOOrp71ub8dNumYmatbEgKw1AXaayNveWKgmkZEZNznrZpqJuG1kUp7xhNTo2GQ6AZBVJ2JsX/VD2wmqbhmWeeAeC7v/u7z4Gr7WHNZQD28zles+gcitAEQRNwUmIfxDN/NzUZjk+tcgFjApOJi+9HN8m80nnOsRWDclBOUiP56Nx7jT2oAJbHx0yKPIKa1jHLMrLYXInWByapZ1TbeB6a7uBcAFGOmzXOOVwbIo2pDRxNpqQOIKyWbZ81attBjt0oPFitaZxnFiyzPKMIBovw8O5uui8yiDYpSN2yM410IwPgGOTdoe/pNZ7PgiefhAhqbIyOiI7taQrGeKIwjgAZiIVQxiy8t9GR3a7BGtPvOkd2O9RiG4PpJAahr4vpgmBj2z1+m+OjS8qKSdtkw36EATB0VDbZctsGex7tX5tIdD4M+3I+SdOO15cO6yUKooj0/cu6hsgixCz01h0XYg1ShxZEFCkaxKTG5c7S1UX1QiESQVV/eI11VD3F3kflVR9Sm4vO7rV5H3CK0u5m1MS5q0VkxIToVADj/VBVau8oiwIrwtna9f6WkmjOSX63adOakbKGwsgGArX32FS3dZBNRllH6a+JEOXZ+5h7+nfbx4vZ51FwSsd0+1QXFuJ2BukVPPsTT/RA4we7L50Ai1Nwg0rxBhtDo11wzvP8Fx/Ee986msbTNnEShhDbcrzakec5V69e5S1veQtPP/003/md38mlS5e4f/8+H/zgB3nf+97HJz/5SW7fvh0z5ltjtVqxm+zBaxnee372Z3+WJ554gul0ylNPPcXf/bt/d8O3VlV+7ud+jhs3bjCdTvn+7/9+PvvZz27s5/79+/zYj/0Ye3t7HBwc8Bf/4l9ksVhsH+4rjne96138wi/8Ar/yK7/Cn/pTf+oPMlhfCWCJCEfllDuJcta0DfcfnGCtZXd3N9XSbI6xEMayUf77swVPHL6RG5dv0RCjfFbsBjDYdn7OZ72U3GSbzYYRlqOeMoKSWds3U+ysaW4tDS1elWpVoaLsTKcggrQZbTpWmVuCt+xnJY331K3juF5z4oTWHfLmq7cxpoWUfdMQe4wUMkWMDqIVwUZvHMA2aIiLZVsew45hf/2Gc/VX0zzjZLPdFV4Di8WCqqrY39+nLItz/bUKa9g2A+3W85zmeaQfKjxYrEGVG3u7sX+KKruTglmZkwXDjekOdXD4K/ussghgd3YmlCi1VzJv0YlsRHi6ZrEdXa7OYrRUBfImRUWB5U6ikylMVrC6AGCFC95IHWUz+s/MeJlO3zWwsfSL4K1F10oeAmGitKXi803H2jhhW1HcOAip1U3X+0UypSk1OiVd1/i5Eib0NcEqsA4av5uGaOwh0hv5GMqLWbP+siIPvxNVEolw3thNJKOAMZvPV0Pavw7S7l4BZwgd+bwL1VoFK73s9LkLT0fRvoCku/fC5lOQ8eb0E2DrT92fu8+GbJj20dfxYSJmjH3FQlc7ZQI2D7HeYWsqdLTGuIvRs+/l9CWaAQsh001FklFkPksNhrcLl609f3/KPKf2caexQWx04KLdSY41sf/L2BZOy/x/mES7bFGyf7/j+PiYj370o+zv7wO8yvqDa8AxB/kOL1SdTJ2gapLoQIro41n4U7xmhFT447yBHKaTmpUWyaEKqItFGd20CKo8PN3l82dDVsNpYL1q8KvA51zFu554mE8/OKVuPW+Y7nOqa7zGZz23EZjNsLz84gnBRqrYWd0wqwuW6wqj0NSey0czuixRXQ9WWL32Ik35KGh1clZxMFNEcyY2j4yBao0guEyxyabOp1MO5zPOwrJ3QqVzoFMDbRiCsIZYgypFG7NAqaebKLEGhfFU92iXrjcgNmauNoJJGunMkmo8O/pZn+ju3pnC0IwNaSdC0J1vB446kY7QMXgH+zvQ5BLAItXDdnVk4ymbTGEPJHoznwJLPq5FaqJtCbkSEt2rDfFGhA0OMqOMWhTB8iEwzYu+P5crAhvx5S4yKLGG+Y6JfpERRTKPkdgHsG1t3xesdanfmnTy6drPWa8Gk25A6w1eDS5lrpTYfPisrskmw8ommNgnMV27jp9v9+DTyRoRGhc4bWMg2nhD7aoIdpK99UExGtUHG+f7THC3n0IGYY7aOUwi0O/aCSsTYiapxz0aAVWgFwRzLtrKSO2lV5s1COo21zYdPdeQbGun9GlMpDP2ozX9vKI7XaFXwgwZdMVr2r8EcT6qRHDygfc9x3e9+208uLNAUZra9Xbos5+8ybe+89F4Da0jy18dXBAR5vM58/mcRx99FO89Jycn3Lt3jy984Qt8/OMfZ3d3l0uXLgHw6KOPslwumW0lKl7N+Pt//+/zT/7JP+G9730v3/qt38qHPvQhfvzHf5z9/X1+6qd+CoB/8A/+Ab/8y7/Me9/7Xp544gl+9md/lj/+x/84n/jEJ3q7/WM/9mPcvHmT//yf/zNt2/LjP/7j/MRP/AS/+qu/+prP6a/8lb/CE088wa/92q99YwOsr/WieNGwNjoJIWzyUcfjaDLl9nrBer1muVzy5NEVbG/xGfWIiWPtNsGDCNxZWJ4/vsy3PXKImbyATc5JN7azMK2/mBI4Bljrxm0YYJHY16TbIshmuMr7gNEMU0QKR+s81lnyzGBtRw9KRsXGXjbZxHJ3XfGpRUHld3jy0n08Myb5IN3sWkGt6xtNWgydeqAguNb0gKstj6nzCfLg8kYU/7wEtfLCy7domobDw8MIaMtyo+lwF9nZHsutRqbzIqcaFVEjg/qQiLCuW+rGxcLs9J2SnIcfvkYtSplbHuRzXv7SXd74rsf59HO3MYXgRLEuiluMy4aKxtDkIV0vSAlloglKEJopsICzq8LurQgO/EyizG0Z6XTdvlQ74Qo2HGufdY7DsBC7DDYmREhGVgVnLdoq5UrRKdRTTZ4JSAaNIUYtzVBGoFZjfVWHADQ5HP3JpMV5g+2qF7WZ2hwJSG1c0/A6pe/HJsUmjMAbQ5PJ8c6s6Rrzan/P8jEo1cgQJNFOgjd4SYqEIcQFUUzK9qR7ncBq8nvp6DibwEqTI9I5TB3Iil/qL60L8yu9epdu7SrdOTYuMDVa9i6KUGCULAuYLJz77rC7eC6ig3x8t2B2NMFu27GvlbtI9zMhbGaZLjC/4+bBvZNrLLmxKTLq0OBpvcc7xypfkxf5Bc/um2+oKs8//zyf/vSnedOb3sRjjz3Gf/pP/yk2G/4KEsbCVeDTZCYDSoRlrF0j7x+GIhgMdWgR26IuIzOhb8Q6tuj9O5SeMRqdxYKMslvaFaqmZn3WYh0Us4xJnqNeqZuWy5MpX8CgGnt5lWKwGtXSGuPwHtQoK99QVy1nJ1XMYDWeqSkofQxeNCnybRI3KUseZ5GiKqpKtW5jfemho2x2KJOz6JwnlAG7VjKbYdSyUxSDUFNHhwayucOktURGihGhDNh8sFGBwdGM9z4OU3rcyoAVQgbFJNYbiSSWmNOY4UrUQukCJ4Mpic5pAUwNjIJ5vSrqdoC+c3zHa3X3tkpHxzKYlJWxmnZrJWXlBxDWFYV19VWj3WPalLkXcEVAM0UWQu0HlcCNGs1uFz3YizstU22oCLHlRpp1OjI7QgzcdoI6ImBsVLcMGjNYXbP1JmTUTc5s0vRy7Qqpm9sQoPY+w6mJ1MB0DB8EyQKdcmCsR9SePqjEbFVPZWeYKyjkEmmErUbw9OKDBUvnBip1Cv4Zif3fqjYKhI0zPoWN75JfKdWOj+JNEtcGK1GYwqebo/19MrhE0/VdV5zutPrFPYrF9M2St1Vh038WwYVIQyvF9mBaztLCOxK2GKBlDJJ2YLzrj9k/P4XFynF8fALA3dun8Vk1sdosBOUzn3qZt7z9Yaw1rM4q9o6+OgqftXZDLKOqql4s42/9rb/FRz/6UUSED33oQ/zgD/4gDz300Kve9/ve9z5++Id/mB/6oR8C4PHHH+df/at/xW//9m/H+6HKe97zHv7O3/k7/PAP/zAA//yf/3OuXbvGv/23/5Yf/dEf5ZOf/CS//uu/zgc/+EH+8B/+wwD8o3/0j/gTf+JP8Eu/9Euv6Xy68QM/8AP8wA/8wDcuRfDrNToO6pfLYh0WE05Pz1itVuzvHzDf2dlwhLal2yvvztVUTWyGV/id5y3PvvgEvp1s+sJbGatG23PgIZdNPLxds6R9GXD63UQ+bd22sT+HCGUeZXHbEXXHhVjc2jhPbgyTLCMzhtxaPIHdsmRvUvJic4nnHxxwVnlO6prTuo7ZorT4WzFUjQMXDVtuLLk1m9dhAuv8mMXs5sa5t6N76L3n5PgBa+c5PDjsn9HkgmbF2yplmTHnsmOZMSNwFY1MvaU3v1MW53xWYy2nq5o7Jyvs/hQtC8r9GW964w1sJdgFlCuhWArlUpgsoVyAaZViLRRryGso1hHoZKlSNmtBrVCuoJ0L1UzIK2V5AOtBgAZnlWo/NbcEkAjAAlEcw1lordLmisuVaqasZ8p6R1ntK6s9ZXGonB0FTq8ETq8p9x+Be5ehmhvWO4b1zLCaCe0UXBH3G7L0X1cUm8Z4UR8+3PwsMTg2N5Gtr42A/PBZAh/p526bPqKXqH4dx3woEOec4679iQ3naKyS5YFy4pjOG3b2aua7FfPDivJyQzZtoldTayqW2xoq5w/UNYG84DPpL0ojdY8huBoj67KhstZ/d/vQybnrpN9dnVGvMlxl8W5cQs7wgDqAPkx5QGMGq7slaeXt6EWZi2IDdusat7PB8bOtdy6zscmspJoVYkF0nheIMVSu4fT0lBdfusnZ2YKqqqOAxjcZXdB7zzPPPMPnPvc53vWud/H4449jjOnrsL7S2M0PAFD1FLKDEaEwOcrQZFXVYMVSB0eeVakWLvTiAF1kH2JATbfeIxdCBBVdvVGIQCq0MMNwtZyxWNd4VYJXDsuSq+UcIdKEnMY4SwiwFkdw0XGstOW5e6doG4USSonGoXQWPDSN73GIAWyK0ruFY5rn+EQfPXNr7N6KSZ4hPoLxdd0QMkXymHEI3uNqP8Q5gpLZqPZmJ57JYXUuICe5YvMoCd6LTyTHc8w2MGXo66LUKkWZR+dTSAJG8TudvLp1w3tvOoAhoLlsqP4CSQBoyCj0jX+TGeiT8aPXPDfp3VGYFC6ht0jrlj66k76QhDMu784GB7ybOMTfs44WF08I1Zhd7N7zsAWw4pxKhwhpXbJZEkQQmszHGs3OoGyYVd0IiolJcxLwOjT79UFY1iX1CHSBEILts1pCzNKqSgRZOmS4Qhj1wRIAkyiE0dKqRFgVp4rppc8htmKp03Nq2sDv3b7DcVMNQSSVqEwYonhL60J/37oxSf5Huwi0IZAHQykZLmWfrJqhjQGgHqYmoxP8aNsh2CwwBHeJlEzvB53e8etcWNtnYUlgrjRZzIrezZEmZS6Rvl+WosP62CGqtCb3jy7Nx1u3BiGw+3ciHa5pQi8t/+Lz91ktYkB7fTZWxf79jclkwkMPPcTb3/52/t2/+3f8y3/5L7HW8l//63/lscce4w/9oT/ET//0T7Nef+VjPv300/zGb/wGn/nMZwD42Mc+xn//7/+dH/zBHwTgC1/4Ai+//DLf//3f339nf3+f7/7u7+b9738/AO9///s5ODjowRXA93//92OM4QMf+MDv61q/oTNYX48xBlgXRSBXqxX3nn+BEDyHh0dYY841ydzOWEGsT1qMVOvyEdXm3ipwvLjCI3hm108JtLR6PuuVS74hx55tgbazepNTpxLw454IxLqfKrSxIF2SdLY1PcDoI3vJUtbB9XUT0zzDZhbTCo2JIOTEPELR3iLPH9Cq0oaGxXLNdBbYK4djtz5g8UyLnHluOdEhcmM1Yzm5jyDsrKIiTUcdapuWk9MTruztosWm2oMxWyuDpMLsckib75QFx+vhvqhqX7gvo8+2qYaTLOOUTcA6diT3Ducc7EwQYDYr2SkspwsPGDRLrIC0oPsk1xsSKMid0BqQlCETFC9QnsXtXQHLqTA9UVwBqzmxabECwbC8FCl5krIwzYxBpW/0rPtaaBnJ+sooMCbDMwiGrYjq5lAlRlDPFXgPWT5Nn10AR4bMz9aifH6kkzADLOk/3lITlLEEeVo4THcS/XkqwzQZopTWbu5LVZOamGKzABNgXwmtQRqLqy2uyuLing3n09E1N8DmGM2cS99JDwRjhLOLysN2eMvoIArSRxv1ghvsTWwk7MBLwOQg1mMSgIp9kbZGADXav+fRNgghUZGy1hD7Lm0erG7dBobeCFZ05528zsHHUFya69YYyumMWZ6TGWiXK6qqYrFYMJu8Pk0rLxq/XzbEarXiIx/5CNZann766Q1K4FeimXfjqDgEiEGr7DKNvsAbdx7ms2fP95jABcNuPqENZ+TWR3E2UdpO3lrYeLdtFhUpY5+hEOvdnbJMjpF4wZ/FJ2gQrk3mnKxqgsbnNnEWqWNGpK5aHIm6FJRWPOoEjLLyLdNgsA5Eo6O3qmumtWFpfO9EGqKdsi4W0fsGDmclJ0kh14mDWU2J5X3v+zyTGyb1WRK8DYiP1+MqF6nFRGc3KlVCNnGUl9bsFBlRqMIDNqpw2hCz0DZJ2CRAZCJPEoCs9HGeqsZ+UiFAF+kPimmJcuwpE5RVDBIPY5s6au4qDCKMIn6DMtaPtFHXFDbH0hL1uWsvEGCWN5z5HG07cQ2S6ihRLS7tpsk9svFWDj/OJOdU6/i+py9UrRvaOnWS4NBPJO2ypwreKk3d9mWmbsyC6QJAGv2FTjhh2Icixkd1vyRUEYFUXKwW62m0teP9xW/FS0xZKefjs46PREA7MYt4cGUAZdKBv265DIIxXRZL2MsnVC4qP59UFcvaUU5bcrGkaUdItYu5WMDTsTpJf9/NC0SFdh1og6cIlonJaNKct8neSjJnQZMCska1r7YJaV/pekc1fs6lwFdajzsfQoACQ6Xgm/jdzBpyTJzXo9o8iCCsDxR093Rr7d+ejg/OWi5P4h/u34sAy7WezFg0KLdvnbJe1uzuz1idbtVvfI1GlmX8kT/yR5jNZvzTf/pPecc73sFv/MZv8Ju/+Zuvinb9Mz/zM5yenvKWt7ylt8O/+Iu/yI/92I8B8PLLLwNw7dq1je9du3at/9vLL7/M1atXz53X0dFRv81XO76hM1hfD6GLrmD/ogXyzp07vP/97+fh/SQ7mSiE1da2dfC9IEM3SruJXbcjbgHlk7csn/3SI5Th8jmABZESOB7bGa3TanPS2zxsSMK3rUOCRuDYN4Uc/h9HtCReO8d1+H7tfDTOXsklZrZQ4W71EDlXKDPbm7zaO+6u15zUNXUY6HjOB9ZtjVFhYi1WpK/zWE3usZi93NdIrddrTk5O2JnvcC3xc8djW3nQirDaihyXoyyXJoK7C2HjimdlcU46/xx4AxYjIQ6bGd74lof733cO5mjV8MhD+9EJIs4le6Gftblv8ZKyQwJGyJxwdGrIWmGyFKangrSxNsoE2eQFnd9dv+ilq77oBDZAUrdUbQCE7r+OQnbRq3cRCLroXEb7lu3P+3PYsvwyLJTboz9N6bDMmMKTonXdfQpy7l27COGpbl2HRmCSZQE7aykPK3auL9m9vmBnp2KncEwayBqTBE/6HZ0/4S5EuHH2oBs/X/AtHe6BjP7tn9fW9cRnaPCNwa1y2mVOaCzanDfrAtHxDOk/wISYMTAilN704jzdMCJ41Q0hGGPkHCUahnveXbbzoQdrlXfk1uIUZrMZBwf7HB0dcflg/4K78I03unXg6OiI7/qu7zq38FtrX1XTzUtFpMioBq5NHsKI8PSlt+DRJKCnBLXsZzu0GrMxIpYQcsoyKuPEBGkXAVekowV7iVRdhRdu3uMsKapKiIArhQUAaF3oX7cQonNpRFiu2gRIopHwWZRy7qnEAtoGTCrMd62SNVEgwyXVwW4mGA8np2tUYxYrBKWwlulcoVzz4ufvsTiuKPIJiSFHyBVjIu27qWJvxi6IkpmY7bATTzZvCRqbMsteZGnY9KJMbOrXJMT31EJeuj6rZEs/RPctLDvkQmws3/d1aohZpPXwTsR7Fjc2I4XVdOGAkBc+ZQ8HIDxsFeujLMKezWO2OCQqr4dJ0WCquLVlFEHrEFw61eO27uu9pKupTLb4sJz059o56pVzg3pouxl8Gw8lAqyP373brym96EK6f5qeVR/s7VkH6b50DbB7YKV91qqXaO8Oppu22msUuFCkr1XzfrPeWQxJ/GHUKoThuWjKSMWsoWIah/rAns05W8cGv7V3XJ/udk+EkIQyfJfeGz03QShtjmnj+tKoZ6IZU8lYpbrDrDE9bbpbVw/ySa8WWLeepvWD/6bSPzcffN+oeKxdgzC02PBgVGPzcOLcEI3vWNew22zREPtl+AL3AYj18yki9sGPPMfJg2gvnIvtOYLC/ftLlmdf+wzWRWO5XDKfzzk6OuLP/tk/y3ve855X5f//63/9r/mVX/kVfvVXf5UPf/jDvPe97+WXfumXeO973/u6nu+rHd/QAOvrNbYjkKrK5z73OT760Y/y1re+lW9787dsGIKLMlazbBMMZVvF4ds1VrGVhXJaBX7rc3OWJ4+Qs7lwd4XC/RilHFof0C2lwdgIOCqPtU0LAvPJLBXBjo49cgZCimp3whgqsQdEYe25VDYaHafCZDx7dpWpucTV3R12yzwVYkZjtQ4tZ86xaJuoFiiR11y3cTHMJFCmzOFqcpdqdpfF2YLlcsn+/j6T6eRcJB1gvUX9m11QdNm16+jAFSIsmu3vnY+cb4O33Nq+xqwbR5cHlZuDS5GPPDmY8La3P0KZWzQEspGB7sZ2nyQzjiKmEUbXax1Mj2F6quw+gMlSYt8tL31flXE09aKxgZ22QM82thkJNg0g6yKgI5s/yPbnX+5ELvp5Q55rC0KMwNZ5R2V0D0b77FS2IjCRPoJ98emdv8BzVMMk7mGnLdnBmsn1M3auLNgpK6ZtS7FUjBs3R5CeWgOyESne2PUrntQFYcbRdfZO0wUlWFFeW/C1hdMcWdteSXC8v94OqeJjp05sLYiLi3VpTKSmIL0NG9MGo9LYZqYrqKJby7gLAzBbO8fubLPbtTHC7s43dmNJVeWzn/1svw689a1vvbBO11r7qiiCV4oYNAooT87fhMHyv115O9fKIwxJ9S9kXM4PSL1vebi8zCQzLFdFOiczRMD72ETqhWXis3juwQPKyTSisSAbEXOIzU67p3ezXRBCrN1dVU2iicZr9NbHnjiGWL9nBPWwXkd76lygdY6iEUKyn0Vmo46MM5yta3wIHN9fcTib8dDBLuQOY1tcXhP7Z5kITq0SUn9ba6CtPbhoiEyAzMRAQDaJGdUzuUcoHTrvPMwIBLOyBaO9A6oC+aQd5NJtCv/7eE2NJvlyH7BVnN+SqMjGg7aD09wvv4lKtzESBSsvHKbzmDfsnfZ2aSo512c7EYzWyXoozMo2qQ/GnnOiG5blnD1UYhZ6bA2mJu+zX8k3j8INCWGKG7sRqcY0/e4l+gO3VqtoK3RsL6JtlTbOKSMRLvQZrGR7jQmEjvKXMlJhBKx0LLITUpPfUXDG9dcT66q8txvKg3G7jn4o5wQ/MhPZQjtFSSaG3ckEq/Dg7l1q5/EaWPs2Bi4lPg+XAIXvlAlHS7ghzjvrbU+9y8Uyk6IPIpuVib3jtNvecFAM/VN9UO4eV4m6GP0k8cJuWRC8jnzDIcsu0PdM7OoayyzDImQas1j5iMbSA+0+UJwuoqut7m9eOidNdXAh8O/+f89wcryO/p0LiQwSWK1b1usYZF4vXp8MVjeWy+VXpSL40z/90/zMz/wMP/qjP8o73vEO/vyf//P89b/+1/l7f+/vAfTKrrdu3dr43q1bt/q/Xb9+ndu3b2/83TnH/fv3v6Iy7FcafwCw2ARYbdvy4Q9/mBdffJHv+Z7v4aGHHuJoqxeWC2FTzQV6wNCN7WyT28qY1Fs1CJ96Wbn50qNMwpXhvLYogeOsS9W0tK4hFxMzSZIiag7atsFYQ55lGwZNUYzZFuXwG0pPEPs4jB2oQdI0nm/rPavG85GXD3HNhGu7Ey7P58zyWMieG4+itD6wbFuOmwXLpsaFeKwqrGjaELNi1nLP3eR0epfDw0PyIu+PsXFOIhsZJUj3fMtPbrsmeQpI5JM3W87P9rODSKMYj52yOLeNHz2vvMyYTgvynZIsMxwezDC163n6w8IT5X/Hw4wXrm7fCYRFkBqYVanHFLGGa7aUATR0jranpwtcwEy76MeNMQYpW/BmiJoyXMiXw1GSdiYj+/6K2/Z/k43FcfsUxvvY8FV6D0EHRyac33DsGJkRQHmlE9zGofF6omhHv5bZgNn1lNdrZk8s2Tlcs0vL7BTypcT+ZN29SCBrKDBOEvLKuYbFqgpbQFxf6aaPuZ39JgnoBUHUEFqDrjL0LCPUEmXgowfQO08hZSTz1pBlFucDi1VDaALiFatCmcRuxkXhMESwlWhPxvLuYxoiwDplsLbHdsbs9RyvlQ3RNA2/8zu/w82bN/t14JXGq6UI7uRzjGQEDbxj521YsZQ25x+8438no5PitBxODpDkqP3Iw9/FpWKOEgNngcFJFRmi3BpATaxbcqVlrRFEdSIPdkSRdN71NKib9Rlt4qHVlceKSYBaqGhpnQcT65UeOtolU7j5YiyIb1tPbTzF2gz0b5HoCDpYVA3eKetFw9V8ytW9GZrH85o8WsfAQ6B/R4L1XPvOzzN9+pPU+/djqiLE9acXpZ3EH5bZXbRoMNNETSzii1DshGS+Ui2KgWLqhroUG7CF7+uNmhRkyBbxlRQzvEdGpZfa7vtaJburdnCEgZ6aZW3AjoSHgA1ZeQF2bcFOMcEYQWswC6CGPHdk6VlYpG/4LMRss6SHbrp/GxOb1uqw5hhMHxDp9HcaH3pVwh6QbRu7BK6CjRmedLBhAxXsqdlcLwI9ZbtX9bOKpoIf72yqn5KNY2mXmXF2y8cRfLB9Rif2iDO03na4h27l1P484l86jkp8gzJ284J5lrE/nVMWJZePrvXvj/Oee4sT0Fi13gWce1GVHmENflDpYx2AaHwvJ34I0po2Pas0V+e2wKv29z+ocnzWUpqIdFQBB9d3d+P80miHjfZWHIg03UgRBpNqy6wY8tQipcgiIwghKVCObG93r7do9gkKp2COcqeqWSxqzk7XGEnLqGqs2wtKtUoA6+z1A1hN09C27VcFsFar1bmg15hR8MQTT3D9+nV+4zd+o//76ekpH/jAB3j3u98NwLvf/W6Oj4/5nd/5nX6b//Jf/gshBL77u7/7q7mkfnxDA6yvdy+ss7OzvvDt6aef7h/4UTk95+jMtzNWWw9523Wo/aYD79ns7QHgvOVDn5vSnr4By3nRBa/DIl61DozinNI0kZbgmxZtHVmWYVMUugkjKw3k2RZoM+HcfTZs0oW6vw6gJxlVsXzk5SMWVZRonuU5N3Z32JvFPifjHVStcto03FmtWLkGbYXWeRarChVPfrVFDoaiy20lwJ1yq/suF8wP1SHLldaQWXE+y7UNbuB8lusiELYtjPHEk9d6sLB/eRdtHTu5sDPPUR8i3SWcN3JdhHF02jijfb2YkSgMMr4628L+Cewcw/wkyr9LWgT6rI0nOtgJdKHDAji61P6BCgzyx9v3aLxdEGTLSF80tvz+4SAXbvxqP4snEh2ILQCi3ZLLwJsfYZKR77B5jgE2W1SmrS/Y/wZYllSG0IlFiGImDnOlIntiyez6knnu2DsWdu9nFJWQ1yZFS3U4wc5BHoPBi2qtLvqsm0/d9UkEPX3Dz1bAx6i/0QjocDaCrUXsvZeJwQY6pM+8tezYnIkaJmLjv2qZqMU0il95SifsmoKpydjJc3KJM7SryQqq/TtvpDuveJ619+fqVgHWzXkmwDfCOD095f3vfz/GGN797nd/xYX/1QIsgNxMCRrYK+dM7R4A06xgZmMNgNEJ+9kc0Yy5LfgTD72DN82uE53POGs1DDV26pJynAKzOPnvtzVVV1if7IA10kdhKjf0RDppa2qiQIX3HmuiE5dZ4c5ySVVFZ1ssfNvjN/jWR6/h10k0wHla6/HtIK4kKpFZ0GgU0/Ce5XHFyZ0FDx4cY3If7duep7y8xrUt7LSxvjFTioMlJneU73iO8s2LGIQXgSrO1SwBrKZYoJMaKSMdQHKPSMBOY4qmo1uFTMinSeEw2TqT+74m0REoVgPFqszs4LB67e/TIL0eV49WdDOImuZ8ZgNZhwa7GlxN1jwd/1I+xRrDhAz10W5nlSG3jiyLANTqkHnssmrj2hsjkK9iL6p+HSCec6fg2D0U5z0m9z1pINozPReMUoFWAnWHcEZ/7PsJJvdcEHRL4TAEMGYAUM4JXk0fQ+sO51yGiGK0iEEniVmcmMEyPZXQYBJtMJU3dOZQY5AhqgcOZpX0rbmZUIjl8mSGEYPTwMdv3x8Mpxjy2aS/Fhc8QTydy9PX+RKD3BmGeSjje0QUAprbIQCrSVRGfVypDosJzoWkppyYRR58EqXo3tl11fT9vLq1uL/lAuo1CYckxcKUxZ3aPAmWZWS2CwjQRfGGB5p+6RKI/SMdBYrvuJbSGJaLOpY8p8zWyfEaBFbLWJe+Wrx+FMHlMvp98/lrZzT8yT/5J/nFX/xF/v2///d88Ytf5N/8m3/DP/yH/5Af+ZEfAeK9+2t/7a/xC7/wC/zar/0azzzzDH/hL/wFHnroIf70n/7TALz1rW/lB37gB/hLf+kv8du//dv85m/+Jj/5kz/Jj/7oj35VCoLj8b+8yAXEBfLu3bs8//zzPP7447zxjW/ccN4zYzgoJhyPRCWKCyKy47GdsdpWthOBYitD1YG0T7+s7D64wTseC8DxsE+GfXQZFwlCMAHnHK0qudkFE7A2crvbdssZNucdne30h0GothSScmt60YeQCPyZMVTO8On7Bzx56T5YR9AIHHdsgS2ERd3igke9wViPD7GFcrWqMKWnMJYsV3wTuCkvcX0SeNQ+wt1q84UuL1AQjHY1GcNECVw0zQZguOg5bWfH5kV+rvHxdkmWqnK2tc2jb7jCrWQcijIjKzLmRzMefvISt154wM2bx+TBduyBYV+b+Arjkpqbah+NkS5j0hl9T/+2Gg/TBVAKy1mSRRfpHe7+5qCIk1SQ3d2n6OwPxpZzhvk8AB3hg40Pz222OS4Ebx2QGTsnw4W+UlHuBnJKG8griBb0Bcp6bmqnb2q/UHcZgIAg5zJIvR7XxrchAprutGNhdYBCsJMKLlWY1pKdZmR3S7JFTpN5mmmI/cOC4PKtu6ybjwHY7IdzwW2Qje3Sz04GcKNJnaqrDxAh80mC2XX3SsnPYiNznwIIWWZpnEfXoc9259bgtOlpw7k1TEXIMkNIc08FMkJUQVMLplPTUo5XmwIyVgzL6hsPYL3wwgt88pOf5Mknn+TJJ598VUG+1wKwJmZGR6fcyw/7z29MrvH8+iZCxlGxw8ROMOKx1vLUzg0yniP41P21c7xNep811r5IDijUeCbeb84lpLd7rRsobGvvCFlAifVWvZLkwtPsuEgRMorNBaeenbLgubMTdvamNK3H5YpfDGuKBeZlybppqF2sPTk7WXHvfmA6m9JkntDmuMwyu7Gi2btDaZeo3YU80tM1xB3ll2rCZwVjIDQeyV3/jgYN2EkT66wAk8d/s7lHjKWrDhaBYuZSjWZ0WG0ZkAX9PTCJNicK+5OS5dphW0FsaiM0pkz3RnZLszc5/8YGrE0UxQSqcgwVrneEZ1nBflYyVUvtA5nG4rks9+TiITWJ7oRw4veivROBkFTnprXBeEudAq9KzMKY0CkJKkE01v8UiVvugRx6laB0bV1zXW+ATPv6pn6ELsylsaeeBlrnEaO9sJYGg2SuV1NUNbSpz2AIw1rgnSErPEflDsdyDETA6BVcSABXJAKfpCJIEIztqPQpWLCxjqVzUGGmc07Vc1TOegXbO80SQuxvqA5cOcxZkwmOgLhUn9rR9DTaVlsZdrKM0xCPk4lhv5jwINXAq4v10vG5wOVyTtOGmBEVQYyQS8Z65TEFmA5o1fF48UkNzwzi3Ak+UQ4hidLELNZukbMQYVrm+Eapne8z1SYJU2VEcRZJ9300beN2QQlOcSiFGBarhoGTAGcn0f+qq+jzVGeb9vtrObqGvl8NwPpH/+gf8bM/+7P81b/6V7l9+zYPPfQQf/kv/2V+7ud+rt/mb/7Nv8lyueQnfuInOD4+5nu/93v59V//9Y1a2l/5lV/hJ3/yJ/mjf/SPYozhz/yZP8Mv//Iv/76v7X95gBVCoK5rnn/+ed75zneeUxPpxrXZzgbAyrY88G2ntNqipXmNtUuDpHvspTBelse0vLNa+eyLcy7tPYLZvYlXT+MbOqs3Blitb2NhZ5YhjaHxLc5DmWfMMouawNpHSxo47wj4jvCfRpllVH5wfvooYvd7AljdNa9CxnN3r/L45VtRdUjom+ntlQVtCGiT08aqD2CQKm1CoK4rfA1ZqbyUvUSuGXa1y/X9Oa0E7q3X5+SjIan86QCu5mXBaisqfnEt1+azmWTZOYDltpz3WZGf+9628zWZF5SXIp308rU9bn7+FlYtLi+wRujKvMIY8ymIC2guyCgLGrZyy9bDNqNqXgkkhaK2hLYQUt/R7gSHeanSOxGayWbIbCtzcxEw2fxdejQwstnd5fSbdIX0248gQZR4/8LF++lPLyEh7cFa2nLkAHTR1/6X8blov4vewekkhbvrEthQKOyvX7dB3cjPku4k6SlOGxk8Gygu1XCpxrUWznLKBxOkFowR6tJHSk4en3VHP40R3fTOWemPYUzsXYfG96+L2qpooh3GczVeNp9VAt/dti1K0QqmisqBRpSptxt1ma0Lab6OHGcTHYAmgTDnAyqCNdIryE3zjEbBuRjJ9l6jsqkq909W7M2HLPTOtOBk9fry+sfjKwGlEAKf/OQnefnll/n2b/92Ll++/Kr3/VoA1tTs9FTvidnrP3/TzkO8797v4YJyfbLLI+U1xN4D4I8//Fbe+/kP4/2IYhR/ig6axrnRKaoFiTTjPtaiEAi9AWm9j8H8EAVISGqawStWDKU1LFYtQTRui1LYjBWxf9C6inawdh5fBJwLad7GObJb5rywWFAeFJwcn1Itaw4PHuLYOEzucdUMlwuT60vMpRWVy2ENkof+2jQodj7IWzfZClOO7K+CyWuMjfREM4ngy848iKXARCArkE0HFocYjX3kRrbYELO9AdifldyslhS54JpI0QpjCnbKJgUZgjMDGBGsjb3qJCnLWS9RsS51mzcaFYaXx0tyr4O6pQh55slNINecy7MZn1ndBxnonaogDZBs/H4x4awZejqhkTZmqhSkS1PSa8AUHiqireiomUkUpX81slSTnQu0I+Oa9i1pDdE0qboa5a7GKngDWXxemmxo61JQQFOmSiN1EFoO8xkLPYmm3ESwF7yJz4cYiPIpcqMk0KFxEdMUYFOG43VzxWhG7Rv2i0n0PVRpeyWJCFA7WqwKOPF9di43JgLnLrO0VG6e3I11YAHyYJhmed/DLV63xvsVlKkpKGzGuqpSa+FoO3fLCS+sjsnLjLoFkwvrlevPOckZDsuNRL9GEKwakBDl/BF285LbxrAzyXF4Tqsuw5kCbRIVo7tm2dsMGnHgSoNptWc0LJYVVtNappr62tHLz6+Xr5+t7gQuXqkP7Zcbu7u7vOc97+E973nPK24jIvz8z/88P//zP/+K2xwdHX1VTYW/0vhfmiJY1zUf+tCHcM7xxBNPvCK4Arg63UTX2wSjdktFauXPR2fHfZyk41KPRthy6mvn+NRN5aUXHmLGAYFALhk+9awKIVBXLcYYpmXZx0DGY902uMaTacyYeQ09JxxS1mzLm94Gj9vw0aeC2dYN17xyGTePr/UgoivQza3FGmF/UrJbllEEIiiZdMY6mkgfApWP/bU+5Z/lWf8SL9w54d7dJXvkTMRuiFOoKivn8MHHNLvIheIV2/dURM6JZWwLknT3fjym2QXCGFtZymtXDpjuxeJWm1uKIiNrPW968/URNTM2uIwXESOBkbYh47UsNRIenoPRIbLWH3+U3chrOLwH5VrJmu3ZyfkMiTIoMbkYIRx19D0/toGGah8E3axt+jJf61HUttKfbi4AsIFWBonec6fxiqM/px6J8Mp1WtsfdtTAjho5uj45/8X+542eRCOkaXJPdlRhnjhBnjgjXKmZmYLHykPesX+Nd165xvc+/ij/t0cf5VuvXOHhvV32y5IitwlcpWsXwVpDZi3WGvLc9FlvERkogTLcvmHOxH/VRGdSM8EbyOroBozfk4AyKzfnewi6YY8VyI1hPCWdD7StxzvFVR5tQ6QcmoxCDZfnMybpHS3zL88A+HqO9XrNBz7wAU5OTnj66adfE7iC1waw5tlOBDvAPNvvP79SHlCaCZkY3rL7EN935e3s2BiseXz3iHlW4ELKy+imrYCxrZAodjFy1lSVkGsECqp9kE8CtOppTQLNcQOmNqNNQTfN6ePrLgXnmtoRQqBpHb7UjdpgUWV/PkEDWOdpmgbB4B0s2hqbeXwzwYshv7omLxqKvahyJ1n0BrvztrN0ngrh0pr86qp3kt3aYYsWKeJ1Fgc11gakaADFEIMEkgeyLnvT3Z70nT6yb9MxBCQzWGPYm08QazDRt91o0D28WyP8Id1cCNhsUCo0IfZRkhQtss6wWi549vYxVspelAkhZrCsJ7eWPIloWAx705E9G9EhrkyntD6MrGO0FaHtgjXQ9bC2uR9stRmuxySJeBLgCkqsL9uOmo1toIAXpXWB9arAuS5LJYnO1/kLQt0W/Q2K2UlJMu5weTaPAg6ANRFct+ukItjTERNYC7YHGJ3oQ/d3Ge0fDHWjND70NL6ApoBQugBPZNX0WTnZMuzp0+OMfF0idsLU2CjVfrImrCoWZwuci4ycoIo08ZqOsrj+Z8aQJ3uZGctROaNplFxjWtQgNGs3KguQUZYw/RsSLZAktJHOcZbl5MawN5uQ51lvlyWJupg0D0Qi22W7SsAk0NQxG0yAs0VD8El9S8ElhcSOJVItXr8M1nK5ZDabfd1Kgr6e43/ZDNbx8TEf+chHODw85NKlS2QXUNDG49pss4v1tsx3tQWofAhMbLZRexWl2+NEvWgyua2GxR2t8HgV+MhnZ7z90TkyPeW4WuG9xztPkc/RzA/7G71NIWhSD5K0uAZUPVOZ4rJA4z22oyOMxnY2Lsgg6dmNYktlzyCcuBy9d4XrBzc3FrS4U8V7zyQzlPmEkwBruoilYkS7Poo03rMo7nH7bIk5mfHoao+mcrigXN2fIbmwdA1N0+Crinv371HkOTsC3gfsCDBtN0WdF3nfc6sb5+Ck6jlK50VF+tv7fvTJqz1tAGC6U1LmBbOdkre8/RGe+a3PoRh0mg3gSgTJumcXw6HWx8VuAwjIVhhKiXSO0ZQRoiBGt0EzATcBl2gm4zqlHuxIigSOF9BETYFRpmZrSKLxnPss4bSR33B+vOIfur+PPJc+A/dltu+OnzbcAHwKG0zcBCxTRfEr7ke3fu/7Vm0DrdEv/b1SIrWvAzoiqR2EkJeC362Ry4arch0jwmFRgDFMcstB3nDaVszznJVz3L5/j9ZalsGzblrWOEgLbkj3yRYxAlqoZbJTsKpaQhvS89aU5UrnaEBDIHQNgeuY8bIiGyIu220nGh8otwIR2w3LWx9in8BRrWIMfsDdewv2NNrYeZ4x3THc+CooIV/rce/ePT760Y9y7do13vrWt/Z9EV/LeC0Aay/b515zF4Cd7Kj//Kg84Eqxz+Plo1ya7PCDN76DZ6tPD9/LJ9ysklHoBQ8YPPu8C3gESFmZINCKj3TxLG5bNU1cZzqxE1G8VXIsGqBeebIwqAKSDw6gE9+3DTg5rWitJ5RpjnVhkBDBs2tbjkzB4eEhN8OC5VnNfTnhQAK+KjEmYIqA0UCx06D3PJJpUoiLTp6deOxug1QlagP59XVyPBX1ipkGjFOcBIqpIzQGO4u3KDMxE1LsCmIDYkKksJmAJLABoJmiJtbWOI1ZWIDD3RknZzUeTbYuvdjKQPH2QC69vRJJFMEsxP5dKOJhYgtUIpXcNkrlWvZ3dvn8vftkYsmCR23KYGUeIWZJhEil3ZstOQ2zwb75CCZ28oIQPELAmKj6e222w6dvxcynqBBSbW/MCkXD200TFGyQqKWhpDpOTQiK6B2qnKdOpEtvXfe8IkVag+BSz6vOlHifY7I6LTWSslfx7wflDFlFoxmICpW0lk6yQlV6wZUQLJkYGnxUV07PoQda3oLGoLX3keHTt15Rkv8yWO1laC9cU7QvxIKOrlqrcnk250tmwcHkgKmx1HXN8XpJ4+Jc7ALKV/J5lNw3liVR9CiXVDOHxdSg3pNbg28j8I80wATqjODS+yYamQtW4x3p2A07WU5XkBY0yre7JpCdCbmPQMyIUBaWvA0stxReM5HY280r4pWbX7qP97DWmBHPsth42SZE1zaOZr3pM30tR5fB+p9xfENnsF6Poao8//zzfPCDH+SJJ57gne98J1mWfcUF8voWwNqmAMZ+TJse23bWY5wZEtlW80t0jdFwqqMaIuH3nofV3auc3KtTY+SMzHa0kRShHvWxcj7E8ETyeI3EF9Q1gaZ2iI8Ly2QLXG7XKInVc5mgQi7u83Wnznj5/pXeePkQ0KDUbRUBQF4QgN1pzt60pMyyFJn3g9obaWIerXD7C754dsL/9eLzfOLuHf7L55/luZv3mfmMN+wd8tRDD3F4cEieF6xXa+4/uM/9Bw9YLJc0bXuB8uB5IO23ri3Psg3KFJyvyYLzc2C73mu+P2N+eWf0d4N5sGR/r0DVk1nTc7TjPUz38oIyuW0wEKXet1aIURYMItian8H0FGwnijEK1l2EL7ozSaxLuv4rtGnRHRV8n/tiF/js1ukLKHbj5oiveInbAOzLACEZHY/kzOv4jxcXYQ3X1oOx8XYpwjf6aEMyOUWCJdUtnKvTkljrgRGMNdgUCTc2OrSEDEJGm2TCJlnBg6ri5tmSs1XLDhOuZDtcKqbkIlyZzXjz4SX+6Bue5LtvPMzbLl3i4Z0dDiclM5tH8CYwIf48TW0TJmUW3y9k0MuQGLXuLq2s4xKwncXdtgEXPQYjbPSoQc5npjpmTuV8P1/b1rFaNNx7eXHuGF+voao8++yzfPjDH+ZbvuVbePvb3/5VgSt4bQBrP9/rM1i7I4B1tTjk0dkV/tzj3wlEO3WpOOj/fmW6Q5syBRvKsCnQFylWEkEKQpanKP8k0uA0XVonfgEgGgho/Ht6jC8/v+TOrVX8OwKZ9IGTVj2r4JjkGQ+Ol7hUAxNSkiJm4ZVbt26RW8ujh5dBYt/D9bKmoiZ4IbQl1qRMhgmYXMmnDhGNGZR0ebZ0ZDcWMa6RBfL9OtKmfFzLTOEiqJm72L/OerLSIQi7OwW5wHw3BjZM3qVsQIoRfdLEezMtcqyCa1OdMYoJStcAdmMISNe/KoledDu0NpDZQQBDBCb5SOJbhelRyeFkSnDR2Q0e5oUlyx258eTGUtist4OzaR0FRkUiCArx3Zsc3ORPf+9/Y3+y6tdfi/DwdGd4fglIS8f/6s8/nW/KJo2jYtKjb+h6C3bWYWhgq/gQgUXXDDpoBEVj5UCLHearErXgNc6VaH7jOXkn4Eys3QyCSsxVZRKz5BIyZrYAFaxGVUEzrAAp0ChYsdxZraha1wvrBPUbQePAUAKwsWKmxauPVRPrqtato7A5RiNLYDqZcPnSZS4dXUKyPPZp81HRrzlesV6vsBozRRNvmSSfY2IyHpseAEJmbS+O0QEnGfVwLMVgVCgSQyFLATBVmEqGor3/cTCN1Ot8JeDApuDYwaTk0aP9TX9C6bOG4hXbKqcn63h/bJzPWWYoy3g9RpX12RrX+tdNqn2xWDCfz/8gg/X1Hl/rG+695xOf+AR37tzhXe96F0dHcYF7NX1Mrk03Adai3XTcFWWWF6xGn395IQyhDW4jCbD27lz0fJZlA9fZe77w3F0+d7LL0aUM2T/rrX8PCJKIRWZt39vKqMEzFD13L7Gq4n1AK0eWG7IsKu40W7VGxhDTz6PP7BY2l9E53KoLvJtzY3dF0zqcd2SZJeT50F/KOEQN0yLjIJtwuq44XQsqMatls9ibIjuqUQR3r+S4qQkov3v3Di+vluxmBU6VK7sznrp8BYzgylOaNma3VmdnnJ6cUhRF/99FdMDtTNQszzmpNx2mbcwjIudohNvNivcu7WxoFEx3J7S3TpntWq5cv8Fzn75Fg1yYCYo+zfBl3TptE84HFnUIa/fbAORt/E8FpIjCGN522GILBGm31nbx0hT1FO0L6oEoniFxMVLpsjWysZD1WZ/utNj6Wen3sXUH2Jxt5537ruD7HJXl1QwZgacxGEwOzYW7SUhuvPj21AybFmONhe9dQbdqVCnrHDRjorKUSd5uoy0FGSfNJv1i2bZMbca9qsY0lqt2TllGOpGIsFdO2EsNRW9Md3hhecpJXfPtOw/x/MkZXzo7wUqyAYVShMBq3RBcIIjiTJRgz1vDTsjAbD03YFU1Q30Ikaqi2xNu5N90IxPZ+CxSYGONUOMDZWZRVRar1y8ietEYryVt2/LMM89wdnbGd33Xd7G/v//72re1lrZ9dYIdB/khncjFfjY0Uz/K93j7/mO88/CR/rNrZVyfQgjM1lFAKM6rMfJPP3fvmu0ASAJjmeJ3FesFsCzbdqiDJcTMgVECcW6uK8/ly5Oo4irEfk8K6pQm89TqmU1yHqyqWKuiKQvUACiubdm9dsQOZ9wJ99lvd0FhvWqp9x31IifQzScDNiLwctrinB3AiibxmB2HIoTSk088WhlMm1TmsigzYQ/qqEpnBLFKZpKYRb6i8hVTBVsEfB2BiWYRpHQiEBjh8v6M6vSM+6erGIR0AWdj4sqloFI31CbVQQFTRzp3N6LIxZDpg/hO9CqF5BQTw0PlLnv7C6rTOPcOZxkCZNZj1dLHKRQm+QrhCEFjJtI7Dndq3vzO36IJnit7p6yW+wiwrlquFjs8y3Fvd1oUYxUTUVq8B6mvZQ+uhn/SzSetSzEaZTq3vMdKnUCDwQiRPqoGwaearGgfrVia1KdEAE0LmVHpgZcGExe4VOcUWoMtDYIlS3OhlJwDs8s9WSVp95ACsSYFegx4i2+EM78iBOVT9+5wY3ePxkFCqPHMVfp3ULpgW7oeo9HZ6eq6fIhAxhCziePAa/cuCtLT/B0Z2jr8qqJtPKUVrIs0whuzHR6fH/AxuUthbKypT8B7YjMqbfrM1dTmLFzF3qxkGRxZJz1uDaVEqnBXh3/9YIebdxfx/fdKbsB5OJpP+JZLl3nu5JSVdvWMkKVbYVuFOuD6ZJ7E11GE9mQBOzNQZXm8om1aHtw6Zrrz++sLddFYrVbs7Ox85Q2/Ccc3NMD6Wo71es1HPvIRRISnn356Q0Hk1UQgL09mm0p6quzkxQbQ2o50bQszjFWHRIQ6BMYdtiKtMN/IZHUgrW1bTk5OeGR3D7Kcl+4KB6uSg6NFLP5Mzo8aDyGqzXR7MUnOdHiNhuF8wKBR3a8JkTtsBSEKcoSUrjYqfe1VHJsXa2SzgfG99YTdBxl2epssyzBWqBk5tkZ7Jz2oUuYZR0xYh4qqdTjrwOWRZnFYkWlA78ywCEGUO6sVL+sSI8ILi1Puni54aHef/bJglRsu7+5ijHDvbBnB1nrF2eKMrF6zUqEoCmwWDdVqK8tVZLZjcm48m/GY5TnLLZC97ZlPRkX9imJLIc8tR5eOsNYyyS3NvQXza/v4uu33IT3/YUAQ2wArqvqdz5xsPBPdBIaiMFsL6qJaVDsFlw/RbRjTLtJ32F582fhdu1+UGI3rny/94jF23rseUdue+YYiuW5+JtDTFjcA4baseogO1gbO1NE+z+M2+ixXqt4eBC7YkIWP12WwdgBWubHJHiRAK1FhKgZ9Q1+YrmlRF011ew5yUVocB2XJydb8y43hfh25nl6Vu1WFbR27RcGRnVFkhoVvOHM1rUZa3rccXuKnv/3pfh/31ys+dPNlPnbnFp99cI9n7x/jQ2wdsM4cZgHzY4FWadeRTlMYwWSGcppFsRgjfXDHXhCYSAnDjVu6nel2PvS1A7X3lJllPo00xv8R4+zsjI985CPMZjPe/e53UxTn+9291vFqGBDduFJe6t/bw+Ja/7kxhifmD29se2Nyhbqu+ehHP8qVkLGTFSyoeucPIQKUNjmnSnzpPTHToUSH3EBIU6zuJNoh1ueQqIU5aBOdzXmZJ+c1qvMp4JzH5YE6eIo8CpsEiStayIFUk1lMC/JywsOF8Mzus3zL3ViTUq0atPD4ZlSLGkz8HsJ00rJcGvrkXAAfLDKJ5KhqxzMxCoVHVwZMBA0A+dU2Nrc1KXtnA2KUbK9Bj9aRRlVCfUbMmBUy+NtWUSO01ZoCqFyLQVjVLd4oZYh1y701TnEYEyJFO3hiDRfRRpgsYPNEK0w3ul2ve2U/qwaPw+C4ce0+XzzdA4WDeapNzAJWLWb3JXg5Pp9C6ijZoSnbtrtmd1bRgYWj/TNeelFoRDherjmyUzrLLUqS+g6YTsTHCMWkxZPHrEnHSBiJdsT6NInSfiZmsCJFL/SCOiEBz06cQJPN9EmUQom2bNFGWmBIYC4KK1hcanAtIY/7SrWFoTFYyVAxlAlMFSZjEuaphYkFXAr4JSEJsfgqZ10nJUpRbi6W3Njdo3UhsVTSU1TQNs753n6lgrwcQ931kEv2rXEuNeMe2td0Vq51oV/njDfUBg535+zvFBy/9CJzLG615n67woiheTBhYiyltVRBUQ96GmIftzDUxM+ynAUVhVgW6pLoUAwIBBeFKbr+ntMiRwJYK0yyHHFRRfDawS5PPnTEzqdzVnWqwQOyVqPn70DWDi+RSROCIqq4dYNxqWDDB6plRfCBB7dOeOiprz3AWiwWzGazr7zhN+H4XwJg3b17l4997GNcv36dt771rRc2JvtKEUgR4cpkzkvLs/6zaZb1ACuo8vFbt7m2v8M0Ue4q5zZcyXGmRESovWN7Wk3zbANgWWNYr9csFwvmOzvsHRxQ3z4G4HhlWa8PeOSRNU0Wz0sAo3aT3pQMS9AuWTI4hEEVGUm3W2NYtw3BZ4neEP81mJEGILEB5WhkmWGkIo8n8IXjjMe4THFwiqpHvNmgFBm1qPE9HckgFFnGvCxYNy1nC0MrsRDUHFWR33130lO0YoBN8aI8tzjji2dn7GY5i9Dyxr1D3v7wNeb7OXfWK5zOIoA1hgcnpyxXS4wxzMqSOs8p8rx3nO0FfMBmy4EqM8tya8psUw2nWcbaOYIqpycnTPdK8msHPRVpvj/j5PYpk1nG9Scv86XP3mLtOv75pqjAtqpg8p82RpBxRI7E3d7cqnOAbQC7hNwLq7nSFpG51gOp0cRVLsgybf/eAZPuD4laol571CUofRjeMCIoDwdLcO48LYdNZ15FL6RtboM02fjW9k66L6TrkyFnKIluRQ/y0vmNjhnB3ub+jcR7blKazJj4jsVXP/aiWTcejKdqHnBWeW5MdinMYIoPJ1Nur5bDMRAOyknMdI3s1H4xJfeWo2LKm/c2hRmOpjP+2JNP8seefLL/7Esnx3zw5Zt8/O5dvvXSZf7zr/wehzdmPPfSA7wP5MbQVg4bQBtPWaRGoFl8L7f7VqkqmTEbAj+t8/29ggjMumfQOA8lTCf/YwDWSy+9xMc//vELW3H8foYx5tUDrOKor9/ds4cbf3tq/sjG73vNhPd9+H0cHR3xvY+9k89+YsXvVi9BAljScaohUq9y7esENdl2taABXKFkeNauSbMwve/pHdAcSE78xJpIqyMCbq/gXaDF4wjkmY0F/v1+FELA5BnGZjjnObhRsppULEwMFDSVw2cB144EOoJF8XhvKIuW1brAM8RNQhCYROl4P/UxSFF03Lb47hkJZHuB1lkKE7fJbAADdq9B9qOAhslSBF/o6YJRTTQWqO7M5qzdgmPX4o3jZBnvRa8m54lRxC74MvLXuxgNRBVBm3ukHT4/2t8ju5uKY72y9DVr+zyzSUMXb5pNEljMPLla8qMvAo9QmICRlmnpOFtnqIWdwxVT2t42HE7XGBeV6Vof8Cm0GvtnCcGmDJbEbxiB2U7NalkkmiODXeyVPxgK+RIVdLcoWYvbCLaJl9R0WfqIWEi/ihpKk0XwC3RNeoMGJibHhSQ3LxNOXDNEtlTINcNhUnPehpkpaerYo810WTABdbHblFdoVzmJ1QkMPopzQ43gxnnngzi6QKJeGmyQ1DYl2rg6KK1zWEmKgdAvsk3r+/tnWsPCNeyYnGJvhvPK5XLCQ0dHnGQNt148oS5r1Ad2ned+JxLWQHsWgWFhLOJbdrKMWxrFObLU/DqE2M7FNZ7CGFZNS558TRuiD/b/+sPfyr95/ycQIzx0sMsbH77E0c6U20kBWxSKVpEyCmdJEAIhrqVdEfzI/6zXLatEDTy+dcrrMZbL5R9ksL4Zh6ryhS98gc9//vO89a1v5ZFHHrlwu1fLob8229kAWONmtGd1Tes9X3pwwm5RcGVnzu/duo0n8NjBPhCFMHzQqG4k8b2c5vmGqt0GrVCVB/fvs1wu2T/YJ88LTqt6IyPQBOWFl3bZnVumRyfRGIZNVbBOWcwH7bP+EOsuGueTEMYwRASbMlZN67DeUHiLKW3kAaO9fGf/nYGkjfceFWVSljy/yHkiEyY7J4jaDSEP0aiK1kk/D1SqyFE+mk5YSUPVtLQhIHstahW5PcWHGLnvAJtVwQfPIjSowGce3OfWesWOybjdrnl4d5dvf+QGXgJL4jl678mD58FiQQihpxH62XTj2lT1nPz7xaIXm3Nokmcs6pqTkxOyPGf/YJ/p7gGdDsXu0Q7GKXtHu0wmBft7U1bP3qKwJZoV8dpSKDBkMNZVucg3DNtyrBeAFL+d5QpC0Sh5Wt/aKbgs1gzHiz+PpWT077C3bmUbtt7uU6WMVPk6/rnGrNOG6MRQj83WDvqDqk/0tSCp+7zEZtshgqPuMBKIKh99s994Y6RT0zMR9Nk8zrvcSoyOG0HF93WLoctG9cVbScZ8I/U2jv52v2u/DzEmFm7nUyqtaZ3npfWC26sFlyc7PDTdpTCbkuldAf00P08lnNiMm6kP29vf8MoKqN14bP+Ax/YP+DPfEn//wf/3U8wmBWfLiv/vf/s0H/q9F/jCC/d6QZug4BoPNWgVCN4zLSxkBkykTo1jVUbkXODFWhMzdwxBii6Acbi3+Z69nuP4+JhPfOITX7YVx1c7XpuK4JwsgentQF85al76wgsv8NlPfoo3vfFNPP7443zq5A5//OgJfvell+J8CzFj0/WFEqJIgDGpHCVlVWKwXwiTQHMDmpMW7Q6TDW9wyGL9jhKzClGxDbJMaAH1SoOjbiPwjq9eiuZnsa+a5LFhetW03MsfAMrKxjnb1i2aBbwz5EFSVjdDQ4vzhsmkjTRkRmZABYr4JsvU44Mhz3zMOqVm39Z6smmgdpaydHiE3ARsJphpi91pCVjERso5hhhQ7DGBYgrB7nl23Yxju6C8UdG8EIvu2zY6vt4ETKr6MV0wKWisnxV6R93YQJaHPkspwGRSYomZMHxkTDT7L1Hatm/KO8k7ar8nNw22POVwp4LaYownLytYRSd0tlszqTsnWNiZrmARMIcxyLecfRGhIDNRuCNk8aQlGXMRKOYOWQVULSYkEZTuhEdtH8SBWsEaw9Fkxm1dpYeTtu+mfa/iR39vLYZZlqVaXImAy2YsQ8PUTGhCFOe6ku9z0yVhDonUQQkZs2yI+M1NCaTG5uluZ2LRYDFJ+Cd4g5ihFMKHSPWMzJuRKpOCcRIFTEaLmPTxskFFNmgEb7WL59oFRzr73vjYP0UFpDI0NlDjyPYNrQux+XDab7XwHL3xEvbBMY8cXeGLn/9S3JOCr+O87oDbPEuNjY1wWE44rZqYYRODRTC10FhPnsVr2lfLU1cv8f3f+iT/5gOfoDQZj18/5E2PXubhK/t8+u6D3k6I00hZjc21UOIaFVJwd0xPqeqWalkjCKvT16fZ8B8ArP9B4/cTYXTO8cwzz3BycvIVefavBWCNx5iidVJVKLHO4Esnpzx3fMLUWJoQ2C1LDqcTzpqGT96+y7deu0qMQioTm20ALCud5Gng9OSEgyzj6PAQkxz6+1u9Y1Tiwnj3uGC2usTRtdOo1jPmChMVYZxLC6J0/OOULzAe8VnqrxO3iXVbaXEJAROgaeI9KvOMSZbThKERqdPUb6J1SbhhyKR84XjKUyYwKWE9kr3r1HP680wLdnfqIiF2TU+S0SsvrA8igDIvDxRPiA5wJraPphugqhoWWhNE+eKDY5brhivTGd4oN6sF3/uGRzGZQSYr2qbBOUdVVbx48yZLpQdcs0l5XvjjgrlZbTmXvm05Pj5mMp0yTynw6cGMdWqiPN0pmeSW6V68lt2jOS99XiktPPWHHuXmc3e5/2BJcMO9HD3UdB70CoC+c6r6NMz57I3fphqOf1aYVELWKKsptJMItvpjjffWgYgR6DmfUhLOV/dsjY5GOPpuaCWqLUlUcVLopfiDmgHUbJw8oJHuMz5PQ6Rh9A4VcZ5FYYi4n9wYmtRPrEmR/9wYWjwGYj2EBIoMMAGVgBVFNYwywNLXLI2vV7v/91k8yzTLmFmL98qV6SVO6pplW/PhBy9xuZzxucU9npgdxkLodGnbTa5hmIOzPOetR1e+3F2+cMwm0dPenU/4f/7AO/mWJ6/y0U+8QFD4nY+/wO17ixhsMYLzScdKhXbl4sLuPDa3TK1AJuRlxvGi3Xgu4ylY+6GHFsCNK3uv+Zy/2nF4eMj3fd/3fU0ogdvjtVAERYSZfWVnIoTApz71KW7evMl3fPt39JLxVyZzvuPgIeQlQDT2GczYrN90gimjY+kJkfrboQGF9kqgWoQ+G66j1T/k0YioibY0TwI8WaK2alAcnmXVUBQZhmjzJWW/TKsRDBm421Ys6wadKWuTIucBJAu03lAqOAmEYKPggMs0rEgAAQAASURBVDdMbYtXc+7dMYWnmcRzbr0hyz06T3QnUTLrMROlanL2J1WfwZpOM9qdFjvxVI2FzEezIRppj4nSiIFsx3HvxkvsHD8MVxbYqesVOrvkTpiCWY+MSH+SCTwI2AR2bRYlwK10QFKxNjFAgiJZjZkfk7WKtS2+tZS54gSyLJDPbyMCV/ZXVCf7iAnkWfQRstyR5YEpdQz4CMwnNX7isJLhCUwvP4f50pMYNeSi1CbawZgvBJC4H5vqpZJf3/dMguEd7iJVCJfnU1jG+9LrZSSwjKa67BFCNlhmktPBz6nNMGqxxjCVksYpuWTM7XQ4sMZ3JHjD0XzKWV0jwI6d0raKtRlVEoYorcV5E8FB0KH9jMTgWWiVz5zexkw8fjksfJHOF3uQisT64i7wp2gEjTbOe59q9U7qOgpuhOGxQ8rYd/NhpbRTT9O2UTAlxNKLkJQu18uWOvWXMiZl9o0MLQACNMsGCnDrdYqtCddmO5xU98gQ3MrjsugXtekkvA/sU/BH3/g4Nw732MkLpnnO2x65wrTMeeKhI/jkF3vWj4ZIYZXRdZjuc6EH1wB15agXNcWsoJh+7W0nxBqs/1lVBL+hAdZXOxaLBR/5yEeYTCY8/fTTX3FRfdUAa0voouuDpBrpL2Ik8WiF1itL32JU+PTte7EXVFmyrFtePD3l2myGqvKp23fYn02ZZB15LxYKn5yckBcFh5cu86AeIteLqo5c4HE6I70kq8ZQvXDAjUstfvdk+LModhQtVfFsLhRdjZX20fMO+FhrcN6PfSZQZVXVsS9XZjDWULmGtm2x1mLE4L0bqdcIz96f89YrHrItgHiB++1CVP0JJi6kuY21LnuTgtwY6szhsob2haGrblwwZbTfmLHrHF4BTquaO8slmbE4E/jA51/kOx9+iMtmwm8+uMuTlw6ZzmbsljnhdEFVVazXa9zS4qztAZfIxbBh7QagXFU1vl4zn8836v22qYYPP361j/xNdycYY5hf2sEY4cqNA24/e4usVXbffI3Ts6p3TocGKunXQKQ1bAC/8+e4TTWkM7Tjj2xsbGxjcgRBWO9HYYxh2nQrbPet84Bze7/xRDUpEwo4QR2oBy8mFj0nh66FvjYsru+RxqGmw1bJgUxO0nhsHLMHoimLJKSo80jq/JW+qzE71noIavFecBr6jJQqGOMRq1gC1sb+O5iASali6Vax3mExySGJDus8y9nNY63ePMu5Uy15YXHK757eZm5ypsHxtvkut6vN6KEV4c46RpO//fL1vh/W72e84803eMebbwDwv//Id/LCy8f8w//P/0nTeu4+WA79WhgEXVQVV8U5aRvI1rFRaT4xYGPAqEm2qvFRSXCZBC6uX979fZ/zaxmvB7iC15bBAtjJLgZYXb2Vc453v/vdG3UJR8WUKi+iEyuAN6gPKSilyeAZZBr7HNax0C+9LFHVTU1gccUhJ1lqOD7M9q75uUqUZTZp7obEONAQ+2c1bcvC1b38fhTOSdmvABjhODQ0daS/1aYhIycXg8mg9cLyrMHtBXxr8BOhVcMUZTZttvCLIIVycuQpBZy3SN6iMw9VFFKwBqzxrJtJyjRDaVvy0hBmDlt4cIrkIdqeHYfxI+U/o2Slpy1rltfuYS+vCLVFU0YuoisIpcI6vWMjm6ky2CSbGuSaLNLxjEYz13ofgzgKvlV2Zw8IErMFRd7QrKeURcABWeYwO3dBld35krDcwWaBMm8RYD6t8QjTaazBEoU8a8muLFF28KqU0yVXLp3y4NYhV66d8MLdfQxK1+hXVDFFILOBJkBmA96C0yEq2tm3Lulv0rVaI7RD2emQydIuqCrDMzSQhRyDMJE4d7vMaCkTah/YtbOYJR8F6iIAyjgoppxVNSDkkrH0NTYf2sPMbcGJrxAjBBWMGqQ7oZSJWvgmNUjeWp98itApGGeiGqdCYTMq8bjQ1ZjGe2wQdsqCtlcmTH3gVMlT7UWOxblA23gIggtKcDFg4V1AfWBxWqEa1SqtEVrplrCY4dNGKSYGMTFYt16tkUmk5Do84pR107IzKbhfRb/QuUAplktHO+S5pbCWvdmEx64eAHB1fyceQyS2gAmKdb3n1D/fkLYhCUhZI7jWU61qiklBOXt97OdisfiDDNY3y7h16xbPPPMMjz32GG9605teVRbs1S6Q21Lty5R5qp2nahyaXlCTCd7XiRIVCy5bH7i/XkMQXjg+5d5iRRECbe25vVrxjuuRtnLn+JjP3b7DY4cHzKYzqq3eWKerCuMhyzO8hlj4OKLrBYXVyS5hZZheOY3ZqZRU722iDYi3G728jJpecAIGf7BzqDYbvw70EOcDvmnx3rOTTTC5jep6spmdUoTbx3vs7NSYyTodQ/ui0fhBNDobiog+1gPgIUgEepM8o7hucROheiFnXbsoB9/1z4Be+GNDITapBLkY4uXYVfzG579IYYS1eD5e3eF7rt5gb1JybBueaxa8+coR+3nGSyennC0WqCpFnjPRWDjb1VNN85xVkuNZrVas12ueunGN45Eio6pugDCAN7ztYe52/GiEclqwczXOs2KSY3OLXTc89mTMUHz8A59H86HGrssRjRthDvdzEzDYMKL+9c/lPMAIo21EUm+tpDroCmK9Vhes1OF720MVpJXID/OCeiEkGd7+AlSjcm+PD9L1AF3jLoEo55UerkL/BRWSuEa6EisRjBmJdRjWbAGwYc731955EQwRve170v0b8VIXyVaQmIlyamLTToWAT5kfT2FihstmIdJoNape1em9dniKdIK7RcnStbz54DKXJzNurRf83ssv8oE7L+JRHp3uUybO/dXpnJurKHP+h68+dMHd//2PR64f8At//QeZTQpevnPGf/nAZ/m9z9zk2S/d76m5zoU+c9eBf6uCW0W5bC8eY2FqBMlhVuasUxT30WsHr8t5XzReTwng11KDBbCbnQeWJycnfOQjH+Hg4IB3vetd5/oyGmOw1lJgo/Jma2M/JzOasD5RYzXQGiiMHeZu+sHteqigqGI9z7AmxHdADVTr2MIDAU09+qQRqrMWp8o6tMyzDMTHOt1phkpAXCzCr4KnSqoarXEUUkRVQxtwjcFXjiBK2wg5ybEH5tMq2unGYLr6R6us9x0ToGktTMDmAe87oB8wJuDWsdZHFSZZSxCPnXlsolGawiNGsXsOOUtBji6DNY9iTqurx9i5I7iuF5f09s1PtDcAg+0FHOiUZKdS7ZNRsiwQ2niDWxf6tdB5z3Q2qP/mZQsyJbfxfckzjymPoxz3tEYnBmMDZRlrG2ezmqDCpGzGySKuHJ7wYiip73vKsuby5WPu3ZxzeHDKi7f3MZJUuE2kEdokaw8x42ZyxbkCGXWbEejNZSYGp57MCu0oXtTVTWkjMAEN0b55BRMM6oSdrKSrL+rttkbgWZoZD9quj2cnBqSUUrCfTfiSSqLmKbX3NI1Sp9ql3XzCWXOW1oo+mdife6TNJuA/ptcHkETdK162tLlCoWgQvuehh3n5ZMkXFg+4p+uBVW6Eg2nJrToGtUJQ1h1NM82RDEMblLbyrKsmZhhDwPnALMtBBd/EEpG2iaC7E9SA6LO0a8/ksEiy5Q+YTifk5QQ9OWO5XFMEw0IM08Lg19pfzzTLuHwY/YbMWCZF1lOQr+3PEWJJiG0j06g0doPKHoL2lNfpNKepm6T0GahWDcZG3+T1GMvl8mtO2/5GGd/QAOu1LIqqymc+8xmef/553v72t3P9+qtXO/lqKYJr15IZ08tlioD61NleY5o609S/gBTx1sSTblvOQiDXSH/49O17zK3g6oZj53mqLEFg3ba8eHLGw/t7tM7TOk+BjVLqCmWRMZGMqhkceTFwurSs6wOOri7RPErdjpMOJhjcCDQZBDFmlGEZIlnxt8FC+RBitiQIro2t5fMsR4KhaVyUOyWqUbVt6EGbC8qX7s957JJiygpjlHpEZVQTaQ79lUhAGLyAYB0SotPgfICZsvNkxmPHl7hzuuLeYr2R4u6iNt3xuwh897tXhRBY+3h1LY7ffukm2UvQWKVWz7JoePLhAyaXSv6PLzzHQ3tzCiOcrdccr9dA7Isx3duN8tNnC9q25eDggKIsYQSwyjzrHetu5Hm2oVg4258yuTTvDd9kWlAEMwhwoFTHSy5fP6C0wmIZKRQZpi8670bYen2Mwobsip4HFGZME6F7BxOlFMhqJasjjqkmEXAFiSBKGhAv8cBtF+FNDlqHfLsGyqNz2w4uCqO/d6HUfu7GTOXWLojZItPT/iAuPBFnxULePpqfrqufF6Nr1XOfvQLo6hyvBDRMf52xf0v8W0YVosPgax9710gsZFc0NZX0FJKTGcPdatXv/261wgB/aLLHtWs3uLlexDpEYGItL68juPr2K9d54/7h+RP8Go2ORnj9yi5/7v/xHdx7sOT/+tCz5LnhI594ic9/6W6sNdDY8mE8sszQOo936T42bpjH1vDYjYPX7by/nuO1UAQB9rP9jd9ffPFFPvGJT/DGN76Rxx9//BXXPWMMM8kQ8aiLAgZiRgX6qUeQtwHtMtqd3e+4boDuKVQMzcwT0OhEW9a1Q31sMWBtXMtkJSxCRZAMiozZJAMTDdesLHBljanjnK59yyp3cX0oogKbiiJWadSQdcX6tSWo4FLUJ889QYU7zx5Q7rRMrtcxFrMbm/jVbdbTyE0Rz8uaWKAffJQDV5TMBGpdMymjYqLNAjIJFE+s4/2y2hskFbBTHyW5CUgzeOhmdHs0175mpQ/GpPveZ3s6sSgVRBzi4nVVTZtaLMD+1QcY43rRx7KImSmxLQSlnNe0JkN9IC9r9qcGY5WidOTGMJ+0BIQy7wJ10TYeHZzxpfUhC7+mzFuODhYcXDklT89BjIJEURpsHTN3NmaOMhsgD1RNuqixwevBg8URMDrMqc6kC6RrjaAy9mSLDXJr5zmaTDmpaqZZ0YMjrxG0qMto2jrZ6ATyQ8xOTaWMPhOxvEEQThZCk2qFSmuZiGXZBA5Ky2I1vDdCXKPUpTMcmyaNv+fHJgKtLAYQVGGWZ6xdy1E+5S7r/vqNCnuTkrttDA4HVdaNSzg52nhjhIkxuMbz4KyKFEMf38/lvbhOV2sX1aibQJFbaFo6EbIis7TqmGD7YJWIUJQll3Z2WJ01lERfwPqGVj1VXVORMQ3KpcNZshPCvBzA0LWDnSj9XuaYNr6P1w6n3L/fRGAlUbzDAjt7E+YJYGUiuMZRLWvyIiPLXx+48Ac1WP8Dx9jpeaXRNA0f+9jHqKqK7/me73nND+vVAqxZlrNXlJyOis1384IX3MmwLxEcXdQlEBKHNzOGgGJJxZV0tSIxbX9vteSOalLNET539z5PHB7w/2fvT2NtubL8PvC39t4RcYY73zfxkXyckjmPlVWZyZSqpHJJJVWVBLihVqNhwS5Dblso+5MFowU0ZMBwtQfpk4WGDX0RCm1IRsPtNjy1Gy1lWyjLrqxSlZJMFpkTk0Mmycc33vkMEbGH/rB2xIlz30vOzGTKuQHy3XNOzMNe67/Wf/3XqCx441RrISa5ODrESEPUJnQJmlprIozRbFrn9LVeuHl9yv6OR3bUeesmxNI6FumcxPzQsOc/Oyc0DsBYCCp04WtVaNIapagRfJ0rSAHquSdJoswZlzaqAX317ibXLiRcCYOSLBUVoBjuHpt76HQH5aIjuKATg0ksTM3dnQO+dOkJbhyccf3olIPZgvY8rbE7984qdPtIA5ZDd21zVlCAF27d5fbpjDYGZqHh1Tbwy488SjVyfO/wgBfvHvJYFTk5OuKwafAxsb05RYxZ0fnyqJyjbs711zr3bF999NIa1XG0OWJjUG42mlYsZzX7j+xRbo55+blXOTk4w9VCu2HBWhUREFnLOgK5keH6tUj3LMM9kvCdzbV1wjYJFywSEqM2EgIkIwQHiy2DLzN9jk42ed0Q3/Mm90ip+zWd+/H84rJSkbSGaAaPrYjSc7vmq2QltZgVyQBJBmyXeYwZiKe85WG2KgcY+u/Squ4gz0nrYhb6d2n0BdBXMDuDxva6HcYaQgp9j7pTzrBOuLS9cw8NMMbEIkZuLuZcHE35v/zcn2TsCkKM/N6N13hjdspvPPr2svTv19jfnfK/+7OfAeAv/OlPkVLid/5f/5SjkwWv3Tzm1TcO+/dp2BeuqwGdzRvECg9e2qL8gAz2jxpvx5a8m/FOKYI7hdaexRj57ne/y/Xr1/nCF77Q11u92X6mFBgiKcudG2XK0dVZbZiKQ7ckNBGZRqW6oSqw2nAVUpnwua6JqEBLgFiC8bBYen1OSdQmZ8cXBu80Q1Unz7gc9y9H6wOmSpgcH6iLhCfhQqbWAXXlEZvwySqdWSJNo9SuNpn+HQ7REEQ4vTtBtiPjaY2Uem3rWjl7mqlISguTTH0LQt06XJ7PWjtnlLdpbMQWkfLygkVTsOXaledjwIxaYt2J2AjxzHY/rTQcqoQJWTAig4xOxrsLENkO7AqUBURn8XXg6OQEn7SQ9vJjd0loEDYlqCoFSoVVMDga17T54FzR0tqlgsRSBXdGZcMMS5Eba3W8lK3JHFmC7B8iJrG1MWPnwkwl65Nm1UR03jPjtj9eRLIwh4It0zVQzje3Mw+awYoq0JJymmsQfTIZ74XlCLEBcZ7UGA7dgo/u7vHH9W3GtqQNdaaeJowToKCNGhjVZrq6yYkt8G2iwIHUNCESY2SW2RRGNCN2xU14qT3Wa2McbfKQa2x3ijHHfrGi0eaz6jJY5tSQXDY/uXQ0Isx9y6JTOe3vsf5xaaogJiQVcwGlqWpQWZigLS6Ozxbaz7LP9Ks/smxaiomhaT0jV0Cj91dSygIeiTIp1bCbPWsfubK5wQtHd7BlwXQ65fLFDb7x7e9grUVioK6XfPv5p7lw4QIxBEaDhu/OadnGxqQkNgkvwrUHNjh8+VjffyC1AWsMj37kIse3VS3QSiKGQD2vcYW7p8/n+zV+1gfrQzw6asX29jZPPfXUPdSKtzPeSQTy4+4ir3LMG+2pZpCsYzHIUnQurBEZYgc17kFpZdLVaaUcTMn9aUSENgVigMP5gsP5gso5UkxcPznlUqkv9yIFZrSUVH2N0WlbMxanQE46SqAagsOjktHcIttaQIlAKZbF0Nc49+50QhidiEUw2kjQWUPbelrvcRRQGE1Fh7i2idgVEQNtG9Tp94nSWcQIrx/s8MTlGbBKswsdj3pwWEloB1mfPtOWvyuSZWEb/lh+wKVij0cv7PDI/g43T864dTpj1rSrJEhKa1Lqmm1gzY+P3YyaRxuV2tld52Xr+ccvvEJpDDPxtCniF8LECs6OeHp+Aixo5wua2SleDPOYuLS1ed/G0+el3be3JswGNMKN7Qm7btVPa7I15uTWCcWmoq7J5oijW8fs7G7y2Oce5O4bR9x89S4pidbpedMr2BWt4C0ZfJF7imhjXJuvQ7XUZrmmTUibHbO5V35hUrCQUiCVQsjRSCPC+DhiGi0kD1NHPRLCqMtg5Qe9i34OcXwGKZKflxwQzIt00WS9J8bISrUr0TfG7qKfkYSIWQPQg8D98KlSam0XiW6FZKwWHJNIRjMAkoMFdAHvzumgA6hdiGQFxOIAsGuz10GoF22DgBhG1uFjIIbEYrHg9fmCuRGKsqAsSpxzXBhNeCEdsQF86dKDjF3Rb+MXr16756x+EkNE+Kv/+y/3n//onz3DKzeXfO33X2U8Krhx5yzPb+rNH50s2N2d8NiDez+5g36fRwewzrdW+FFjt9ylaRqeeeYZmqa5p97qRw1jDFsUOBpCDhgY09UD6ufHRnvMjm6y2Ky1zOTI9bagtwkCYSdA0hpIyE6W0+e88ZHd8YgEtJIoo4A3pNaTCmglUhRdFjnRxkg1tcidSJJEPY6aPfcKTADOriSsUSptbvlD22WwBgIHPhk6UvvytGK8qc3lQ1KBm9hlYwAGACsmYdkWbIjaBTee91OOkYQpItUksmwLdqqaZPX4bRFyICitlp8E7HYDZ1X/7g+BFUaVeknqrJO0p2PVgVELroyEuQUC4+mUxdEpu/vHjKY1HiGlSIqGsWt1ajCtZtNcyL8pAPLlmWbqCqXBV2XDaRhz1o7YMI32tkq6ngDTPQ32lkXL3v4ptDlgaRLOaNbIZVBnrYJKa6NmuroHoZu2BgCqwKhvkundPTU8aTAVnyXYs10JCKmBI7Pg4ngDw21K47Ci+44RNlzFmW/wSZ/NwhgCAUlCZRxH8zr3x9KWN6dtS4xqo0trKK2jtNpGphRHMlZFN3J/tBAiToS6ixBCH9hWCqz03ylzQ9+Qufd95olWM1w+r39pc8ppVDXoLpN2ZWuD64szNqoSG4QYEmfzWoO3YXUsgrBcthRTQ9MERqWDRa53T0JpLWKFS6Op1r3nOmFS4pHtLb7PnX4uePTSHvZ7gnOOva1dtkLiU5/6FHfv3qVpag5u3+Ab3/gG+/v7RDuiEKGqCmQUmVvDxz92gWd//zqdoUs+YsaWX/gTT/A//XfP6DMlENpA3bRMt6eY+/RCfD/GbDb7mcjFh3G89tprfPvb3+aJJ57gsccee9eR3HfCoX9ke4enn32D/c0JsqXOZe39YAmNRFhjaEPoQcYqc5BfuKSTW4wBIwYnlphUZCJFldNOQGo8Nqvk/LA5xiC4ZAiSqKPysgsM86gUApOEs0WtxZeF1clDhLYW5q9tcuWhRJouV7VVg+MeZlOiaG1F37dKcnQrRtrWY52llIKGsIrsD7YWU1IJ7DysMQSJhIHS3vVbm1zcExq7UKAhEP26A15ZTZt3Y1VJpqN75Wep5uXRDR5ZXqKi5NreDpe3NzhZ1tw4PuN4sSTF1Bdyxlzbo7RBVs5250yfS6R0vT0SKm5St0EdeuB7Bwf8wCnnOaTErdjw2HiLEsvrixPutjX7TaBpPNujkldOz9gcVbjC9uCxk50vrV0DWJt7G+xuTDn0mm3c2JkwHZU9kJ/uqGO2c3kHVzp2Lm7xxvdvINbwwMevcHo45+xoTmw86SziltrskiZqn6wYKZzDx0QyBllG0tTi+2xNBB+QLMKCgAmJYX4wQVboUmNQLhNunhAHTZFoJ4ZQrOq/ur6+CFk9cAB6WYlRdEEHkvbzsSI9DVIMhKxc2WWETQbMPd3kPqMDc8Nsbcr7i0nfAQmS1RYlt1RQ6eoocW29taxX74wMxWf0eSvQyKiIZo7bGHBoIbNzBbuTHUiJUNe0bcPxfKE0kNG0vya/+MCHA1C91SgLw1c++xB/6de+jIgwWzR841uv8dz33uCb336jz+o+9uD+T/hI37/R1WHGGPu/32y4pfB7z/we29vb/NzP/dzbDgpaa9mXiiLVtEGfeWuFvY0p109VkeaRrR1+ePuYJTU0ZpCCYS1bnLKSHn5VyxjKbn7TRqYJlaimVXBjghCd0v1sYft52sdEWSQk6/a3pQpKpCAwjkgILPYjGyYRTG7EnRJnBexE6RvTauDDEI1ANPjWUjeOiPRqd623lEVfoU8SofWWGIVlXbA5bkkxEfZPoUz4YPEYbBEYjSMnswKxSWvOAFeqYqBJZNEcBWxur8EfKU0/SlQKpdP0SnQQfVwBjQSjGdhLnRqd4IrYz5FN8EgDDz6qDnIXAzIRRq7BBGj8GRQJsRG8SsoXJmJGpyDgCq0TK13ggpvlbSugT6I0v9J6NsdL3X6Ena05p8ebkOceYxOjqiBULUjCOAUI1kUKp8CmU1cU1E6m/PBYDE2M2eYpCbqDJ6kRCFCIxRiDR0VSmpgIRJxYnc8yhTsl9X8q41ga3yv0apNd9WmMMTRRGwgDHM+XHDfLzBTQpr9WhJQsmMTIFAqQsl4GSenbBY7WdhzlbB/SWvhOvwtCzOdbR0/I1ELxijQ6QTMnFmgJMbJslSI4KQrMAvYmY06PFhAT80wfTNmxCLnlSt0ESlvQtJFpVeCyarQA25MRdxZzHt3aohgVfPu1OxgxnMxqHt7bxg3mlo9e3uvnmsIYdncq9vb22NvbY2f7u3z08StcunSJg4MDXnn9BZzAsq7Zrxx2s+Lxj+7x53794/x//vtv0wRV9Nja3+CRJy8jWYnXAqEJJO/Z2ld79UGMn1EEP2Qjxsi3v/1tbty48baoFW813kkE8iMXNfJ6clrDKVzYHSMDUBBQLq7N2+koWD3VzkQkarO9hII7E41GOEQ01RxFlYlE1WqiJo8QJMvbJjAwx7P0gUrFqJmllqktaJIqPs0az7aUlJWjKBwHNdy5MWJ3p4A9BmgCkIgfimWYiJNiTZ48tZFgvKaLxWByFDX2uur6jzMmZ+fiKpsgECSqUk7e3sInrr8+Ye9CSzEOSvkKhuVqUxTWwBC/Cuuuc/ZrfYgkk3itvMsDzR671Zh64dkaV2yNK1ofOVnU/ODu0VqfoUinmLhCWEOKnkiXjbzXYe8keEVUMSgFrXBqQ+D7/rivBTNiePnMs9OccbZY8kZq2LQFn5jsELcMDuG5wzv8Cw9fYyNVTJxj1jb4FNna3KCwhrEtiSkil6bYrRkPjjbUiFyY8trdF3j4ly5hXUHahB8eJDY2Sz558SrtbuSPvvY8TZO49vAlDtqGetlSpwYvoj1scnZBAIkrKhwZcBinWcqYn1FZoz5KLxe8RhkJiVQIbhkpvQJa4yPzfUtbSi6e12hwSoLNvZVs7lBvrOkfAmNNrqGSVW+tDIhW0Xn97EMkoXQNVc4M6w9Mfn5WtVaryOZKb2X1PGidXv5oM1010y+1D2dSEHqudms4fA5eFGIoMCp2EfXYfArsjsYcLBeMRiNGo5E6tq3ntFZQPT2Z8dLz32J/f5/9/f1cAP3BGLv3OkIIKkGcj286LvnFLz7Olz97jbJwLJYNf/y9Gzx8ZfvHfmwfJEUQ9NzfCmB1zY7fTVDQGMOTssGztmWe60OMEfanY26czEgJntjd44WNu9w6O80pVTTjmx/RTuyoDwpA/9CGUp3KAMzruQKOCGmZKXxB39cI+ALogoApEYvc2Fy0vqoTGBAD0UXqrcQkZRn4vMtYGQVHKascpoSPhmCNanckmC1LklEpdxL4NgMsVofethafDE1wtMEwSgmxHmzS3xDMyGNtwpmEcYmURXOKyvfvbEcDQ8DtqKAHSamTALEAaSJpv4EblSrTmYRpYeQtY/LMkaAoA1JYToHj+YKLV+8wmizzdKT2WxJUY4+bQQhLpSGaCKKlBdYGTKWgwrqAl0ThPJfLsywqY6lcIALOBcZmyUZZ99GrqvA04waRiJhIZS07G2PajRnLKFirvoRzAVdqLbXWPZv8jKQelBuEw2apTYuRQaN7zVSZJIxMAaXluPVa+xqybciX1WWAJai9dmIoxPS1pS6XPDgZ1BxnKdnrd2ZQoR5r0jYaKWWFP5PYKkpVI/SrrJQkYVQ4khGaRjNKYkT9q3z0BrQRcur6KYKPsQ8sS+7X2I26aVWhM6lytEswrcpMQ08avEzagHgkQsy2srNLTevZlBF12zCdTnDG4NF7MykcpdHmyt2sYI1w6+iMj1+9wMcu7/Pa3WOMGJ68vApQFdZwYWOVAbLGsLc55aGHHuKhhx7i6rUz/stn7pDaSGEDxZYyvz72mV3+yT90HJw1SEpce/IiW3vTfo4wJJpFQyFqgz8IgJVS+hlF8Cc5zhvF5XLJ008/TUqJr371q4zH771ZZRdBfDsRyId3Vcmry1q9fusM7sB0WrIce4JECjq+qhBNxHSNfzMfKnVFv3qGK3eus3ydUQQ6JaOuwS+JngIY0ei9z6IansRBu8RGjbi0RIIkYky0tacR7Yd1fFIQa0OzM1e5VjFYp9mpHnOJRqTanKHy3mO8odgoeyDWvW4dYOknraxeEwbqbOQ1bDL4DLx8iJhkuXl3k8sXTpFRwC8DrjI4p6BR4toGSBIZfqVAUPq6s2AiL49usePW+2QVzvDRK/vsb4y5O5tz62TGybxWiuaa07UCCXB/p0ybzubu8Fl2lsGpdgalq78DOApLTnO02CXDwgeeOz3Anh5Qo6pfz756ky9efYCbJ2c8fXKL0jg+s3WBUVkwbxqeO73LJzb2uPbYQ9x+Y6ac9OAZ7+5AtNy5rVLadjpmc29LgwBANSlpG88Tn3+E7dmc6y/e5ObRjAuXtxld2GA5bzi4cYSI9jJq8yXQHk9GQVgPxKV3IoC+wXQXEOge4UKEVGjT3NG4ZLxZcWk64Qe3D3HB8vBHrnB7NuP2/EyBhKGXP7cu15fkTKP+u54xU4yzkkvvnM1u/wntQULOgtl8bCGpg9DdK5NVPo2smkh2oG1NIGVgW2J2vNqoBjti+iJ2MWhNyMD57PeFPqdtjIzFYazDiGZph0MQLm9ucVPOWNY1v/aZL3DZjbh79y4vv/wyRVH0YGt3d/dd0aI/qLGiA66Prt5qPCr50md/OrJxb3f0892bsCBijHzve9/jtdde4/Of/zwXL77zvmXWWh6WMTvFktdlgUSTMxDaAjcAn7p4kW/fuMOzd26RKo/pcvzDQlMYUip6JzpZCE4DhDeu5Dk9CcFb4lgoWwhON9XkHHFOiNFWEVMIjQTNXLVGazkF2o2WWFkVp5GcyU76d+0dCRW6SAl8NLknuEDU30ejFu8tKQlNa5kM3sUkULeqiNjWjjAySFLKoI+GpbckI5RFUkEME7Pgg55fOVZlh64nlgLPhLgE0xZOy17OPpYg1xrMtofDUb4+sG8rxCRGNlv0BMaFTAeGxs65+Oit/tIvQ8HENUQMRRlwElmEpm8c3SmiFs6zdJ4UBVNEQlmzWSyY2IZ5KGm9Y+RUydFjqZxnWqjUvZiIExhvLHBOaZBVaRgZy3gyZ7mc4opW65lcoCxUVk8sPdBYOSXQpMAieowTNoqSJrT4zg/J6qkjUzCyJcd+hsNywWxynbuEqPS4LoOFQJvnYWcsPtvuwmjfDTuQfW19zO07Mt0uT3Wltfi2VZEISUyl5IQ6UzbpGQ3OWq6UI44XtTbDLnOwgTxfZ/9L4squ+xjXfALx9HXJh8slzxze5smdHSBhxVIVlhQTlXNEq9k7HyMjYwltfna6DFYbuGAtTQ4AllbFQ8ZlwVZVMWlNPu+AM4bpqOTwYM7xbMmvfOJx/u//y9PsTEdUhVN2j9d1L+ytAIoVYXO8Kitw1rI1GbOY1Xzk2lUuPOEoCgWm1npMZhU98vF9Jluj3GIEbEo0dYurnLZu+IAyWD+Taf+QjIODA5555hkuXrzIJz/5ybdFx3g7451EII0Ij+/v8u2bt/WY5nNtSjqL2JlQbDpSptNbUfqDpBxl7xzGJBhrBw6djlWXcDVdq0xCdgRJfTG9OnH0UUjFMiqRm5JmwRA4Cw0hRZX3BU6ahh1XYYLj8PqY0d6S7e2kNTYRZrFlw+ZaDwwhJoL3iMCorHoBgW44Y3qA1QlhdO9hkIDk/XYRoa7CquvzIEAbhVt3NnnkgQVeEjHGvvFrQaQyFkyOLBEZ9vMNpsVREXKaK4hSKZ7ndS7YbXbCKrJzVqvx2d+YsL8xobKW7968y8HZAp/plqABq64vhKCc7+4zKWcVh+BqsHw/0jAPkgFLTGtgLRihSZEkggnww7NjXvv+Sc72CbUEXjk9YlwUHIeGw2bB9eUZD+1uwUnDK4sTDtolT167QJ0vyovzI2S75OLV3f54wk7BqK162tx4U+srrl67gNkZkWLi7qt3GG9P+BN//lO8fPM2Lz/7Kt5HnvjUQ3gi7dLzxsu32NqdcPXxLY6S5+YPblNUBZev7TMeldy8ecjxnTM++aXHuLS5wY3TU57/gxd55GNXqEYFFzcmvHFyypVr+zhjuLa9zaWNCQlYBM/BYsG8bTlqlj2I0WuXjVTPo1+BoN5AZueNjvq5WhAyjVCXX9EJ1+rxuNd4nO+T1WUr09p6mZKYjXOMqmbYPQDqe+cCc6FvhB1yBDSmFtMarlQb3G7mfX87lTTWaMcXrjzIdjXiwQcfJMbI0dERd+/e5cUXX2SxWLCzs9MDrslk8hPNbr1dmtw/T0NE3lToohNhquuap5566l3XGxijmZ390QSRu1qvanS+3CwrvIEn9/f55KWL/L+/+z1iPTDvAyAFQCu99ZdE388pjHQRX+TlIoSRYFpItShvSGAR/SoBlhK+gJPPtIxiVIVDb0ilIRFoLyh4i0oHUGGaHMhb1lr45YPBmKgUQVGqe0pCsoKxEHJm4O7BBgdnY5546E62g0LbaMS/DRbjAk4CIQOsO3c2mOw2SAgQE8Yo+EqlvsPV2BMbQ1vpMbW1w4rO/G6npTkpcxNmiNsBd7VRR90aJGgGZZIs89Tiyqy1mxK2DH3i44FHbmNs/izQRIuxMdPVwU1qkKZXITSiNEQrEbGqrGFsYHKpZtMt1acQCLlrfALqYKlszTg3JEbURzAkXBnAJiaVJYYTJk5t5fZGFpeoPM4FjAvapjA3SzQI0SYubh3zxSee5tbpFs+88jiWEVYUGJDo67EmtmJnNOIHc6FIlraNbDJitmwwoj2wOvDU+oBJQiFGW6ykxI6tuJ0WOLOaP2aL3E9zRRLQe5OE09NTrl3YQ04OmLiKwmS15PxMWxGcFR4ebfAd7mI9UPbYGtBWMkYkZytjzqamfhuCCnh0tuIk9546XWh/rtJYDaLFRGUtJ23MQXQYW8Np7hHY+T8xJibWschBmZF1zGnZqEoulmM2bEkKCbHCRlHQZHXoj1+5yC9/4lH+qz94nof2VSRnbB2z0yW70xGXBz0FjRU2B5LqxgiTquDkZMnWtOLLP/cEN++8zIMPPshHn3yV37/1IlgDZs4f/rM/YDaf4YMnek80Duss1aT8WQ3Wuxg/FQArpcQPfvADXnjhBT72sY/x8MMPv69OxNuJQA7HRy7u9QDrrG6wxuCjNg5pTwKTuoQR1MYTQpYZJZGyc2WN6dpHQO/2reqAOiPYLZKyNZKhGxi7yLjOAtJ1O+xX11+9wElsMEEn5YZImyKni4YmRerbJRcZY/bmED118kySU+eyjcx9rX0VXIH1hmUMBCIW7ZtljNFUDRlgDZM9SbQnzqCBca9KZM2a0l8ThVt3N9kcn609lW1qabqCIBJl4XDi1OkPCooqcZq2R/nyJmnm5LXyLr4NXPBbVM5StyvuPsDOZMyjF3Z4dH+Hw/mCG8czZnWjTnpX+5NUDMHmQi0FharG1Dv8XdHQwPE+rw7Y3ZJzubJzGT6jzk7QCGsErvszkugk6TDcmJ3xj37wMiklZrElkmg2LAeLOXXwvFHPGO85LlzZ5dbZjMNmwY2NwLVmRJNB2GwCi6sVl67scme5QIzQ7Bdc2hxz5+CA5XLJZGPCrGh44Ooup03D6ZFmzB547ALXLu1RnJ1x+9W77F7aYvvCBsYZyqOCzd0JxqoYiissOxc2qEbqrbUxcvXRCxSVfh45R5PdkA1bslGWPLCxwetnpxwuFhwsF8xbzzys1C672sF7aqxkmC1ah0v9a5WzVUk30NNWgsRzoCn1Ec7zW+mUxTqxgDTYl+TihS4zHWPSmgCja7p6gljRhqRGNxYlcv1UJdedMVyaTKnFc9poH5VLrmK7WmVjjTE91/7JJ59ksVhw9+5d7t69y0svvURZlmvZrR832Okogh/G8UECzx8FsE5OTnj66afZ2triC1/4wnvKNnb38vGtXf5n+SGldaheX+KJ3T1enB0xco5PX7oIaD1KNwRZCQAkkEzfWmUq0Oe50LIsDQaJZpoyMItOwCalp7e+t2EhJowFvxE5nczhpoFatD+UQHNRlR9izlpFLZtRoNaWEKFdWqppJERRCfOseJNM1wBX9zWbjTGx7VVBQeuyugzccl7gphF/Zkm24PRkA7t5hoQWCbrT4A1MIq5U9TzfrCxr0zjGTucbt9fQ/nBK6oDogzWQsFUiuaCNnQE/9wSTwPg+6WNcIIZIkkg5adbm+TpaxOSbUySKq0vE+l68Q4h9tkAhasLYxHRnrhL0wZEk0yaB1jsihrHzVM6vgntJQZYrtBk6piXKDazRSqrpqMaWDZt7C1JjGU8CTWtWbUMCJBE2qwWlbXlo9w7b4xlPv/LzJLHUnb3PpzIyjtJaRs4yDgUxJj4xusLRQiW/O7p9SkIdlC1jc2sY7yF4rXctZMVgCDHp9jteab6+0nq2di4wnW7AsVCagsq6vrZXkuBMFnhKBomC9V3EbfW8GxEK4wiZSh5iyA2GByOt7Pms9QhwWqu8fuXcWquO5VIFMCTB1FlOOnZPB7CSKvHafI5dX8NpVSKzwKYp8CFSiOXCZMLpWc3F7Sn/xy9/CmMMv/jRa/z6Fz8KwEZRsLQNu9Mxly+uAJY1hu3pitlljGFaFrTe87EnL/PQI3u8cetFrLV84vMP8wf/8wvYzRF/8he/xO7eiN/9f7zEwZ05p4eH1CKktMG+316VgryPI6XEbDZjc/PH23T+xzU+9AArhMA3v/lNDg8P+YVf+AV2dnbe9328VQTy/OjqsNoQqX2gHCB7a7SeKhxFxjiVqTUpg5EBHXAwUnYO+zoOWX/BkyHXnshAoEGyg5drZnwC2zkRufh22EA1I5uUEoe+1kxa3strtxuuzDeQ3TMwGp0cYZjPF4RKndIxqtpGgLPYsm0qgkSM2F7JLUmAaNcofAaDNbEv8hToJzbOXYuFh/pwwvZ+IBXaeLGlZfWY6nn5ZcSboBEqp3Utw2yDS6oymEi8URziY+QT5ZU+y3PPENidjtmdjimt5eU7h9w9W1B7v+oTkRIpaWRTjGXYoFeEPvPQ37MepA2vxbr4QpfVHEqCixiSiXQ8aI1Uah0eQCtC9GogO2GJF0+OSKQMwlQp8Os3XiPGxLHXHiNne5Z50xBj5DU/o90tssCEZgZnFwsObGSvrtnZ3eHspOXo4srQTzbGpN2KG3HOw2kXUOoh+5XSPFJkNK3Y3Jsqpz4/wA8+sWog2ITQgyu4T30dZNUvYX8yYX8yUYAridvzOUfLJfNcm9bXNA6yWt2V7Z6F7r0yPXexA0UrPBxyTx7yfleoKg3x8n16dUkfLUjD7+55u/U7dXANizYBhlK0+H4ycYQiYkUzwTfOZkycRvUvuzGXBtfrfmM8Hvdc+xBCn9164YUXWC6X7OzscOHCBfb29n4s2a3/LWaw4P4A64033uC5557j8ccf5/HHH3/P1767rn/pyU/wnz//LGVhaZI69Q9v7vDS/JiRdTy4tbWWoZXEykHtnO8sVd0BAl0wZ5pcFg9KYMIqXBEKSDarqRL6DHHXa8h6YVkFRBypyW3fBcK2vl8p0/6Skb6lVIwWji1t4agmLT4aEtKDMYAkHdExfxGFEAzGdDZtZTCXi5K2ccxf2sI+eUpMQuMthYn4LDrlgyGOI1sXZuh8Tg8Sau+YVDUpGWQcYNySOh50bkYrLpGmEZYGG/V4UgFeWlKRMF4wNhAjjLcWFOOgSnv5Uh/XI2ISzXKNhMneQmu5TBfESYQuXpp7UqQgjCY1hQRmqSAitNGRSNTeEZJQSEvRAbcMUgVwhc80wpa9/bkqL6I9xB558ja2CPjGUk09/mzVl7GzOSUh36/E1njBRjXnrNkkpbancnYCEgBbVYmdFSxSZKcYM0t1n5WyXQ1WDISgTXejVwAycQXW0We5tlxJJJGCPqukro2icGFzm7Ise5EIk2z+u8tgCYW1GNEMkwSwoTMBucosKdDZKAvm+aTPZlrzZrKh6M7Jx8iibWnqFpxo31GBaeHwWSHQGlUQjFHteikr8TTvV/Xo9WmLq9SXs9k2jQpHM/dMTUHjAzZZPrq/x7M3bvGv/dLP9UGrv/InP9uLXWyNKg7Ngv3NCXs7KxVSa4SdyYoiaARGzlK3gUceVr2CLhD2+S8/zu7u77OYVOxf2KCqCnb3tnjj9RP2tytC22KN4fjsiKefeYZTjt7XWuC6rgkh/Iwi+JMa3/rWt6jrmq9+9atUVfXWK7zL8U4A1iN7O/z5TzzJf/XM88Cq2ShoFLpz+kwU5AiVSN0yJJuBlOSsVcqOnqyof5AB1aCpoWRgMXTyRP20DGzoJa472l3PRR5KtufoDqwmEIAFnpdPz6iOhbRf0my1jEm4siJKq/SqhMqVhlzbRdR6k6TdyA3C2Dgsgg+RxiRKsuKrMfjQ9e1YH8OsV4gRT+Lw7ga7+zPsSI2iTZpl6DbQqQDFlGjagIuBFJN2KneGsSk5CKueQsd2wfflNrtsYAZHcL5PVRdNe3B3iwd3t1g0LcfzmtePTmi8NkntJjq9d1l1KWdOhiCvp4l19+s+81BK916PlOLQ3wE0EhcGKykVLfU1DmKMfo4r2HfnLPcCE53ITyXwv15/HWfV4a+S5Y+uX8cgzGODJOFIWuqypPWew2kkGeG7JwdcKadYZ2gvl7y+POMTjUZ4Lz+yz7fqIxbScm26w/b+lJAS3zi6wf5U31VXOJbec7M+5TG7kuZeen/vyUOv1tSNylmaFLiyMeVKLuTdqUZ85/A2R3XNrGk0O3ouZdXVUHW1ZN0LZvP7OVy8A76qohV72ogC3vO1WfnepNX71m1s+I7eD2YNYWBEawkXy0RrYl93BrBVVdyYzThqGn5+c+fei/QjhrW2N36g/UW67NaLL774Y8lufZgzWB/kGNqPYdP7z33uc1y6dOkt1n57o3NmrkymTIuCLq6XUuDCdMJmUSIiXN6YrvVw6inqXQYrojLj0mU68tMqWXvFdR9EG4fnn6MxKn2XYB5XtUtJDw7x0kfqUxRVhEVgDNKIAq6kAk6kVSDR3LX4TQdJgVNCCFbWG57DKuCRlFJYmo5Snw8iai3S/GSMP3Oc3t5Act3WdNzgY5fxsWw+MqOOQ9l3vbbL5LJdgxTAXGq0ozpoXVauqZFpgDvqzHqjAkupiCQHvozYpLLqG/sLrI0sT0vKsiEJzHxBExzTaUMrBaOtJY0vKGWeg2aJ7hUKwWCc9mWsRi3WRGK0zGOBWgWhCZrBKuzKf+nCOpCwhUckUY0No+mcBjBZ1GgyWvbAzo08slypQxL0WSn6sKBe58LVmLSpoLGjQKMBNIDdakR9apilJW0btUltBkJWTA5Iqv2V/EgJQmUt09L1k2hlnM7dESQH4ixK8t4eaYYmxqjiKymLY+RDt6J204hQOs1s2WhIUStwTQ/yhVFWOSTB4azWpsFies6/oOJl3zi4xeQE2NPWMc5aJs7RztQ3cMaQvPpKTgwprJ5Z33Y1jYl65rHjgsvVhDtmBilRWEMz97RNoG4841hQesOvffYj/OLHHunv61BJcGc04jUrXNpaD5xZY9ierFgPo7LQflnAVrbLXa3s5Ye2+Oqf+ij/y3PXqXIwr8r0QokBYyxb2zt86rOfYPfqNtNLFQcHB30t8N7eHvv7++zt7b2r7PxspuqnPwNYP6Hx8Y9/fM2x/aDGOwFY1hh+49Mf5ecefoD/7J/8IV9/+YfaQDi/TR31LWQhDJsc5iiRSqVRxJUET5+J6gsv8z4kybqXln9cqZ8NHHTJ0cDsv6VEnnpZC1HKUDyjcyjViySRaEnEgwo/N6SLDSYs6Xpi1CkQJNLkUNwiBTbEQFg1dh3jqKyjCQ21iUxTJ+maaPKE3jmgMWlPqphPXGQFVEMUDu9OeeBKjWehKou2m6BWAKsbPmg0NYRICJGRKZCANjg2QoHldjzl0C141O/jtOsTZ02ztp3NquRosew/j8uCK9MRzi+JbsppEzicL/veYF0WRAvMz7nUKa018VWVunUgkNbueLfcPdVcCrD7B0bfhZRpbSmpU5vonqVVJNHIShmpy5MFT3+sd+YL7SeWNINCIzx/566qFYrgxPD64QmvpCOQRDBq6P/nGz/M0UTBp8CN+YzTWnn2RrSh9O/fvM7YuF6B8WZ9hmCYuAIjwq16xg8Whzwy3aYwKu/7wsldpXgOipzr4O9RLxoXjisbm1zZUFpBGwML73ljdsasaVgGjxWTM46roMOa7Pvq4vbGtYM/KQclfFTHwiYD56cfyS7MIAPZf8e9FFGLXQP0SpnVOaOJgdFgKu7WHFnLlWpdrOWdjMlkwmQy4eGHHyaEwOHhIXfv3uV73/seTdPcU7v1fowfJXLxYRg/Dorge216/2ajY1rEGHlka4eb8TCLrCQuTafsjPRZMcaokuvgeeszDYC0QhdvA1bTTc5g9VcpgxYZ/N5FFuqU+jknmkzZ9V2tSc7YNpZUCzKK2v4hKsAKhTINyfOne0NorzhVYkuSs/OrfkbdoXTHmaLgvaV0Op8qaUuP2nvL0Z0NYoqEqPW/IVitV0qAg3rpEBvx3hGC4HJWLrQKO3qBGkmkSchNiOOq/xZAFbGXIu6Wxe0cszjZAqdBoySQJp6YIts7i2ybTC/IEZNhGR3boyU0jtGkYVk7XFrNQyKq67psHZNJS5sMW9VSsz9JaJIjmkCIhjZaghicXfdfuqO1pdpHUy4wNvskJjc2dp7YgZ+yQdwqY64y/kJhu2rkDMRczWwxbJ1i8jnq7w9NtrllG+7EOcvWM3WOIsutd0JGMSRaHwkpIMlgJVJYy9RVbBh9jm0yvYR+l4WdlAVnwbNdVpy2LYdnSyz6rMeejqfMmco6giSqSmv0TAbNXTA0pIikpNTCPDdo/yohet9L8Cfggc0NXj471AzVgHVRiOV4qbVsNgc69fpKL24WQ8L70CWQWcwbNk1Je6umFBWoKa0lhpam9vig9nhxtuA3/+QX+FHj0sYEa4QL2+v1S9NRQVWu7IkxwuW9DUaFo3DregPWWT73lcf5zo2TfvnJVPu/4SMxO4CjSclkMuahh67y0EMPrdUCv/TSSzz//PNsb2+/4+zW2dkZIvK+2Z8P2/jQA6yqqvDev/WC73G8E4DVjQe2N/ntv/Av8NrhMX/3f/kjfu/lH+Jj7iflNZLrcgTdiECTKA4NqYJ2HBnYhXV0lT+fj4J3i60N6bIpA7Am9JGfPuLezY8yWFE1ypUqkiDFpOITi4L2dYdsL7MlhHlsWS5PNAIk0KaAmEKl21k5sqW12v8LrfcaZ/ezA2ZdLCzEyFlqcFIBgrOG2nsCiZKCNiYO7k4Zb/o13zbG9SvgjLmnHieYACHRtnmfAXCCt57rxSFPpsvgOde/bMWH7sZisaA5qdnc2qQqKy7ly3YwW3D7dM7pstY6mrz7dbXBdYoZkJswrrJcBulBsd6jgbM+GDJYBugl/rtGyV1D25C0zi8J9I/y4H6LrB9jSJGYYjZ60j+OqlCp+2x91/tEt2HQBsMxA3Ijglg4Cy0hQ7dk4KiuOWBBJ5mOwGvzE6XudeuJcHe5yJx3BSsHy0WvNuVEqAlMrHL7nVhKY3htdszEFYycAnonwvaoYjNnuGNSysbLp4cczefUKSBisLJSvIw9XXAAgqVrOLkKYuh91Xejo7UalNqhJFa9Lqk3n+fuVwbZIynWYbMIpdHeMs2gkXZKieNcSP3YxtZaxPK9DGstFy5c4MKFC7007t27d7lz5w7f//73GY1GvXHc2dl519mt/y1TBGezGd/97nfZ3Nx8103v32oYY4gx8rmLV/n/3jlmx02IDVzZ2ODCwEmZFAUL71U+O1l8CHgvUGWAFbQX1vpQ+fKuf0+KgkmDVFNnM8jMgzxhxK4Ra3YoOzMTG0MIBmcjoxRyBovM5Mi7XBhsY2lnToUscigpGrQf1urQ+uhcikJoLYxadeyN9IpEbVNw586Eslj2tjEFoQmWAs1Q14sCWyRCNIRoKAqlWfo29zPDEEwiVbCUBGWLDTKYS8HbQLkT4cgxffCYo/mYuFWvzLlJIJ7p9lLPp9As16IpSAYa72iT4bgZszFaYsUquEP65sGn9YhlXTIee5IVdoq8rbxcwNB4Rx2V1ui90Bl1kdTXmZpJVCaLPaUJHozg8o10LtB1Z3Eu12oNrrlkufih/SlszdKHVfZeEsKKubNbTFiUKgyxbD2TyuGKTBE0uSlK/m220CCnFYMTw6YdsVXqPN52vSajzrmSEmNXsIyRkS04bVuuH51hsCy9J0QFPJ4Wi7IQmhg00NqJjfUMSp27o+i7IqK/LxpPCpEtO+JuXHYLs1lW2LxOCgnr1XZEn5i1rYqFGEPMDYpLYzNbRmjboHVZ2S4sG8/HRlOWhw1uQwHWyDoibba5gSoaxltvDjo+8cBFfu/7r3Jhc325rcm9QbmLO1P2B0BsGAj75M8/yke+9Ub/22hSaomw6DwQfKSajNYCnferBT44OODu3bu88sor2gD5bWS3Oon2D2vbkfc6PvQA68c13g3A6sZDu9v8X//ir/DCrbv83772T3j69ZuMijJn2P1a7Y9BSDWUS4MfReJ0IIs6eMbO+dT3+W61QjdRdL8akb63U1eInJmJPRgbjpWTKXR+YkqGeHuEXXpkt80RtaT9uqz+fZCWFK2DQldTR1b/BVjGlrG3JGNo6YQwYj/xtUQ8EYdmOhZZ620TTVE3AW7ecTy6LVpfhmarhnfJZIrG8GK10uXvMpgznhgMKcExC55JP+Tz8jCXigm2MCyj52hZr13/s7Mz6qbmyv5+xwoBYHNUktCaLYDD2YL5suXG2ZlSHjLQ7YDMmtQ3947zinTn77PWIN1zw9bBuKiBijH3UiMD2ZS3L6uGkR1U0shuwBrp+36JyH2PcVhbJHQtA/LyubaObNs7SqIx+ZhQqXRk5ZRZTA/aspmjazsQU9J/Y+qjnSeh7kF0BybpqXsa/QspKggzhsJY9qqKuFxysSrZ29wmpcg8eA6WC2ZtSxMDNgnJpD7z3KsEkgakmAxM8zXt7m1KKTtRkhterjKW67Vb+qFKjsW5qzt2BU1TrwGsnWrEcaMA64nNTUy43x15b0NEmE6nTKdTrl27hve+z2595zvfoW1bdnd3e8D1dlthxKwG9mHNYH2Qw3vPiy++yOOPP84TTzzxgTkMnZ3a3iiwB4ZP71zh27fu8ODmJg9MV9myzbLk7kKzJ9PoaAKcdZn3JgOSnKyQDonoh9XzG3U5k+1G3102v++SAE9WCNSVh89+jIbkde4uykDd2hXA6jZ1qpml0JaEOKBfmwGuozNL+VPU3lf9fgZcwrZ1SF1gR3WfiU5BqIND8LgyMq8LNstaa7GCwVS5t1feZkiGWKWeqldu18yOMm2qi0K5hIwCqQpEm5h+7hZSZDvkDSkJfvNM1QIFzDhofXNbEEo9ntuLDY7aCWPbUJpEzD0iNdgEt2cbNG3JZqvtRJxoACsk7RN10ozYsYv8OTItG6xEPEqz7K5XWTQgiWLk+/nI2pVioeRUjXMB3HC+UaEUZ9cZFaVrdCZLeRlJmYGSA62ePD8nFrXHj3MNdkmfoUtoDfud2Twfh1LsdssJh/WSWEUOG6HNmSTJ9sWJghGbz+1gtqTaUHDXxkBhLIuokvCdrRIrSKYhrhlzlEEwti6LYQgL7ymxbLiCowywrIHS5OBbUlBlk1Alw6JpteFwDrp1fa8Kq8IaAMtlqz2yjNqWpgm0By0OcFHPvTKGBUptbNrA7Ljm2oVt3mx8/uErVIW7Z77dnd4LsC7vbvDA/kpIYhgIK8clX/jiI/1v1aTqfUQB2sZTjYs3lWkfj8c8+OCD9yjdvlV26+zs7F3XBj/66KP84Ac/uOf7f/Pf/Df5T//T/5Q//af/NL/7u7+79ttf+2t/jb/7d/9u//mHP/whv/Vbv8U//sf/mI2NDX7zN3+T/+g/+o/et+DYzwBWHu8FYIE6q8ub1/kXr2zxr33lC/xXz73A//riq5TW0vioE9vQMIlgZoJbgp9Am7vFd+N8UX0/y+T5Ia5EA/PvGm3qAVXOHIgMMlp59DUoeXu9v74WMEwkEfxZgakN9kJDclElprqp26gynAR1kxfieb0+JRh1VINorcmiaQlWnehlahnbimV2KmsCIwpAekW5XtEpBhYpcXg0Yms3IePAmW9oJDLJ3oGIcCxzRqnq14tRVQtdnhDEGmwjtKLH4CXybXOTC2dT9r1Gf0ojVGPhynjKjYPbBO/Z391bn49Rp/iEFbVwdzrm41cucPNsxumy4eBszuFscY+gRmeXh4BqlfNYv+5DKfC+fq7bjkhvgNcc+XP7SqANelO3lwQx9T3UNIuUqUTDuqdzhWFpsL1u4yl1R7p24BgxRNEMVciGQzArsJRWFBExKiermF53qJTG9fooyMBmcE3Wl0nEFPEpkmJiHvSBPljOVF4/en5YL7JalMVZw6hwbJmKiXWctDXLtsGjz7JjHRQL+k51x5by9exEO3up9wDSi8zkf+MKYKUwrIohn6/+2w4A1qQoeoD12MYmMSsMfpDDOcfFixe5ePFir+p0cHDArVu3eOGFFxiPx2vZrR8FoDqKzoc1g/VBgJ6UEi+88AKnp6dcvXqVj3zkI+/7Poajy2BtVCVWDL9y9Umev3WbB7e2eGBj5UBtVyOEY4qoUtIjI8y8SnjbVrMBIb/YAqQItjV04nwJ9JmW7Jymrj5LkFqfa1kK0aXeLIX1slPtLRQMXdog3HYwAgwqTAHQWHXOl4aQa7YS0su6r56ktKIyppzBypsIKfXL5ZiLCvjkg05BiKLqgRaofcGURjNYuXkygPf6UscgELUnFAKjScPipACU4RGDaFuTMuK3I80osbs9Y9U6JYGB6tJsdd+qRBMsC+9ITlj4gutn22xPa27XG1wbnWQSPbluGt6YbbMZPWfzCjcNOBPyZdBg4d16zK4sIPffOmxGbI2W2jh3cCuqQtcV8f2s7TKoWvqCadmCJJwNKgvf3b/8bDgbVoqTogALVjato6WHnL1ZLDJzJCUWdUPbVNw9WbI7HXPreI6CMv3vuM7KjNkWVVbpfHdPF7SzBUnAiqWjphoM06LM9d5w1rRcNOrGhhCpcu2Udbr9ECI+hSz7f481I6bEKLelIUEdAtOipGkiZW4CbMVQOZfrEYUCKMRQJsPZsqGJUSmBA4BV5h5oiZSzdKvgJiFxdrJkp6qwXt0+R6ecaGiawGLesLf55hmsK1sbbI/v1SXY3bg3IHb14jaPXN7Rc75PIOwzn3u4/3s8LVeuaEo084bRxui+deT3G28nu7W1tcUzzzzD9vb2u5Zo/8M//MM1n/25557jz/7ZP8tf/st/uf/uX//X/3X+/X//3+8/D6mIIQR+4zd+gytXrvB7v/d7vPHGG/wr/8q/QlEU/If/4X/4ro7p/PjQA6wfV+rwvQCsrvkxwFe/+lVGoxGffvRh/vH3Xua/e/o7/P5Lr2oT1IFTPIwSmtOEA8JECKMukriepogrO6A/Z8N33skdYLA+g9FNZkPVs8Tqt257Wui/ynBJXji2lnRjhN1pkCKtbZ/YhTd1pUVos3qRbuhYWlVMipodmSdPs1QRDFAaYYiJEL06q7ICGae5kHqeAuFwxAMucEvmWf1OJ8UmBmoTcCFR5AM/jjVNgu2cCTMi3Ek1Y7EU2RTHFHmuuslHZI9LzQYhRL59cIezw1OSgWpjTCuB1iRaicxDw8J7bixOOQqqwtixqr9zqlmwWCTSbqLYt0hKKj8bsiBISiuPurv2Qz80qUOCzY1ws0iJ1nHrTZUgOaqcsNESPYg3SDRryo1dFm21N9HoU+roham/BrEv0spGknvLjTSL1m1JwZkzZk2QogNBbzU685bCirMPkExXpJwwVohoxLcLTq/q3LosUbe/1NeUdU5ASrFXWOzU1CRpPVcd6cGplZVSoyBUzuWm1QGP0iejLri6mgIOSyCs1ApTl/1LPegySKaAKvUknBNUgUSTv2sG8rd1FoN5YGODnaLk8MdMnRARNjY22NjYWMtu3blzh29/+9t479eyW6PRKlrazZ//W8lgtW3LN7/5TebzORcvXnxfmt6/1ejs1FZRYUX4hUvXeGD8x2xWFY9s7fTL7U/0WDacw0XBJdiVgtsEXDQDDnHebiOMgmFhdeJJiNZMkQN3Hp1yE9gZ+DFIK9hGSBuR6KAu+qSGjoQ2HQYwYA8NzeUETnpVwxRVbIBWaLq+XQlVKzz3GPVJtgR+aQd2qEN20qXSSBiQmBV2c41QVoMKjcVHIWQ1QgHEJpV7d7qcVWV5UkpYF7FWa8jEJGKwWo9loN0M+EozQd2pdzS7yc68RyHJJdo2AyyE10+2ScaxLUt8shwvx+yU2XYmWPiCg9Mp9RlY17I7nVNIQBUWwUfLqR/RRoe4SBstx+0EkUNCEKxVFUdJ2rC4cq2C6qRN163Ra+OjUfBlE4UNTMc1M9b9ii7b1XHcSrvea8uJpbSOsXU4Yzg9abITDykkZnXL6VnD0nuO5ksYax31om6YNw0pgxPXMV1S4sbshL1iVyXWuyBwFDZcSTCwqAOxVrBQGbXzbYhaX91RCskgr/HYTuXwXETTJwVlXZ0uCSosbeuZuIITam2r42PeLpRYCitU3jCPLaQsIOOjNkOm278GEOdNu/L7gMmk7Pshis+U83yJrRGMs/g2srv15vOJiPDgztY93+/dB2Bd2pnyxEMqfnS/QFhRrP4eTar+FXaFoalbRpOStn53pTr3y25985vf5G/9rb/Fa6+9xmg04m//7b/Nr/3ar/GZz3zmbfv855u1/8f/8X/ME088wZ/6U3+q/24ymXDlypX7rv8P/+E/5Fvf+hZf+9rXuHz5Mp///Of57d/+bf7G3/gb/Hv/3r9HWZb3Xe+djA89wPpxjXcLsA4PD3nmmWfY39/nU5/6VP/QWmP4Mx9/gl964lH+nf/sv+eF5RGHvu4n3GFTYZ08DO4M7BzCFEK5HsnvIuLDL9djMd1PQ5rX4K8+azWwf4NtdXLkcu+q/cLhsEIqD3vtvV442jdFMm2qU3aKWWK225enqxXSfXkiN+McaemjqTfSTIucM3Cbx5YZnqObUG1Z7NhzFOt8XmpwF8FzZjsHN5KS4yDpMhK0ENpr1yhdr1UKyR8XtxBzB7t0Khu71alXzShq0czI8Jovu/obNSAmoxLN+OkyLoOQYJPSYzBIzvb1ao7d8sMLnqkWfYuXqL5D6sF2gnyNB21gCAG97lFUUSkI0hhMNMTGII1SYbrnR7KS0xrYT5GQQUIvS5yfGyPnnzRW0ufnvuvk/zXbQw/qB0/JmgIj5NYGmXaYkoqdWGP6gmTBaBYSsE6jiklWdWEdPOnUAm1Xlydd5lbyPjIAyGCtUwjsaLN18H0LGCOiDU87UJzyvYn5jbWr90yzxYZ07oIENKtW4fA+Mi4KEgmft9XVg3XqW5WzHC6VlvLZi5c+FIIR98tu3blzh5s3b/K9732PyWTSg62yVBW7n/Qx/zjG6ekpTz/9NNPplKeeeooXXnjhPTEg3u4wRqWfd6sxTgxTV/FLDz4OwGPbO/1yD2xpNmuvGDM7mXNxMuXq/j6HzavYKBl06AsmrTA+gXLP4UNLY3OgratXiQnxGQilhG2h3UlIa7BLwbsEW4mmOjclxBXASt4wPipYXOzqefLb2wpYwXhoTioYk6OJ95qgwQtHmwGWJKUI2s54QFY2lC4upc0gITcfFoIY6kaFDVqfHUubaLLIRAwGw0p8CaCotFapL0nrpuMq0Mb1512bP6sMO0lFm5xVatk8BwYjWdlOIs5FzoJhJ9tuIXG3nhJbQ+2E48Mpu/tziqw2ElEVxXlytNFQSuS4GTFruhpU7TUVksGh4LA0gRAMTRTGBqzJ9hoFh2Xl8SKMyrafmyWDPWu199aN+SZL77gyyYIIBrbciI/v7zOKBQ/sbXJWNxylumuNSZuFoWJMzBvPovEUlc75d2bzng3RNSH23tPO53jQzMbpMcR8nAEenGwiznJaN9yazZV6l/sKxJS0Tipoz0hBg2uzuqHoQfjgPgnECHdO54Q2IA6lm/tI6yNlaTL7J3JwsIAQ83uRKMuCYpk4TaFT0me5aLVWl86/02e0HgATZw1iVsJgtHrMXSGcNcL+zgb1UcPeW9RgAXzk0t49313YujcjZK3h448oIHmrQNi1Jy6yWeiDP92esjieUU2qXgnxvYwuu/XLv/zLPPfcc/ydv/N3+Ht/7+/xe7/3e/z2b/82Ozs7/K2/9bf4K3/lr7yj7TZNw9//+3+fv/7X//oaQPsH/+Af8Pf//t/nypUr/MW/+Bf5d//df7fPYn3961/nM5/5DJcvX+6X/3N/7s/xW7/1Wzz//PN84QtfeM/n+zOAlce7AVivvfYa3/72t3nyySd55JFH7ou8y8Ly+MU9JncLXqmPudvWnMZGVc5M6pvSJbqiQsGcgnNCO46kcpCHSOugqAva3TvS4P95XWRFUyQbCLOeBdNifuA8/TBvywBp4eCGQfZbUrWiNfYQwORtDPZLjgb2I3SfVxPnIAnW9zDqfHOfi8diEtqjEdNYI5UWDa96HSYiq/5RcZDt6PFKf/30t/68bcRXLdJaZOAod1voGtR29TchruptbKbFOTH4LOAQe9jcXWs9mdKYXGXWOfdpBTg6cDo4cREYKhGSQMw5lB0TUuSLYJNGmaMgUz8AIRCCZOl+Q2oNNJa4FFJtkajSuOo0pJyV6eqR1JMQwxp7cI1yeh/DRaIX0FhfPm+P1XPXKfsNt9ODrqTXPYaEs5bYKg+2y6QVzuBF70dMAbHCyDnavG+lnggTV2jdlRGMGKwIW2XJ3HuMdLVdKjHcUfSWwVMaw3Y14qhW2f9bizlTV0AGkgfNgtAVk+caCr0msXfwJGdvY4hap4geo8sGrk2RNgYul1NueqUVffbiZeLdgw8VWBlmtx599FHatu1rt55//nm896SUuH79Ovv7+x9oW42f5Lhx4wZ//Md/zKOPPspHPvIR3mkfxfcyOhXB7Wrc06p+47GPA/DI1na/3IPbCrDGvqaxBU88eIWHdjf5xkuv44Jgk+WUgHjBLoTSC1Xp8LNIXeRanS6DFehlsiVCqBLJgWSmtJ0b4lYgFLKm8KrKqfrspzPL1BbQeiil69at4MeAaRL+VkF8OKzsSF/wpFsbRpXC0vTgJ3UZjhw06hxn/VF6kBfF4L0Fq815i9LrZ1BbPDJIq5mtIk/+KSvtlhPPrLWkYJGOUZZ7Yvl4H0psXs97wwvHFzC1Z8ssCcb286NOtxruWYSyt1YJ4cZsCx+EEpgvSqUYp66di9BGrUI6qUdcmJxyUo90GxlcRYTTdsR2sWAyblaXMN8fl1UE9VoZrA00wYHpamPpJ2hrAtfPtjhY7DIpF5Su7QUuksCV6SZHp6rmN3aOI2p8VMLjvG3xaMZrVtf6LNTCqLKq2NvvQwhty9HREbuTDd5YehYxUIrNYETtl8Oy4SreODslBLW0TQ2MO5GJqHTX7MTEmDht2rydezMwicTBbEFsEuIMhRWaRVDGQTTZfgnNwuu9SQnxUFkLrSfapGVrMXHr9llmC0HbeoypSKKq0t0wGfR1GSyHMLKW2Hb+hHDt4jYvHN3m0t5bU+c+ffXeFhAXNu+f+drNlMOuVvtH2ZYrD+2xUQqucOxe3mJ5MqealCxOl/dd/r2MjY0NnnjiCf7b//a/pa5r/sk/+Sc/MuP0ZuO/+W/+G46OjvhX/9V/tf/uX/qX/iUeeeQRrl69yrPPPsvf+Bt/g+9+97v81//1fw3oPD4EV0D/+caNG+/+pAbjQw+wPowUwRgj3/nOd3jjjTf4uZ/7ub7vzI8aD13e5sbdUx6ttnm0gslGwT+9dYM7zVyj/X2RvS4vIohPlKeGaBNhCrFY/d6NThigz0ixymp12arhdoegZeXoSp5oc61PXDdY66VfnedsSHdKzEYgbrTdVvpj6jIv/VFFWaPDyX3OQ4FMhiWB3FiZ/vgGcIXZScVo0uI22zVwuD7O72SF4AQVN+hVqUB7jZhIbDtDnobqxf31GMZwElrT1Ckb9op5Jq0ft3S1O7o/izoVStXPgg+imaMUc4POlFac0P7GyT1pSzl381MaUDq6n6JgXC7QMx6p1HFwNubeT5BaS6oNUhukKQhzmx2kQEgrilzn44SOekonqDEUi9BlhuqEw5qm8+90jLkvyYDn2KsusSqAHjkHolTCwhpKY9koS5q2pV0s2N7YZGM0YmtSMSocxgptCpz5Gp/Seo+tlBgVRU/J68aGKxkPpIoB9kdjBVXAg1OlZDww2eSN+SnHTU1hDCGqKtZZ23BreYaJjpaIFcH4LAOcXyRB+4AlFJinlCDA2DgujMZEEtc2t3j5zt0f2/z3bkZRFFy6dIlLly6RUuLmzZv9vPjd736X6XTaq0htb2//xMHie72WKSW+//3v88orr/DZz352zThba6nr+k3Wfn9GZ6f2Nqd9b6GdSh2nVY++RDGfIQn+zOc+wf/03Gs8fmmPT125yMYLJT4ENlPBQd1gjlUoyLXCvhuzpNUpxoHJfrkJKnYgMbMBNvK7nF8n4wVphOQSyQ+oyUmV7kDgyFI4i6sN7ZR+7pVG/3RzSMuCdHVAOz43ha/meMFnPqJ+1UX11oNR3XyY2i7Io5mfaBNSJsYTz/FxzvpYDXClpOqJmjWRvh7Kupg7LecjEFSJ0Qj1wsGq/K3fdzDCy0d7tIVFonAnFMRMAe8WIwOmiHDWlmyXC46bET4ZEAttpA2OdmFZlgWlVTVGH7XR8qkfsW9POIsl87ZcgcIEd5YTttwSZz0+t+jt7JE1CZ+EgDBryt6uWKPBKsMqQ28lUccCaww+WgobSASiCMu25juvvcHeRKXxl62CKZ+Vbuva04reomXjNeg1N4zbkoN4gvOiYD3BwcFdptMp4/GYizTMYosVS5tUtMomoRDDjeMZoU2UxpJItF6PdKuqOKuBoAAoxYQPgXnbUmHw+d1YPUVai3hWNzmAIJQYlr4hxpXQEiTaWntKSkoknyjEYEwmIqYEPnHnaL56ABKMJmPgUAU+ErkvoxqAXthIYNMWhFo/j4qCh/a3uH3jlK37iFWcH5+8evGe7zbuU5c1HG/Vq7CalCDCeGvE5WsXuPvqXcSYe9qlvB9jNpv1rSyqquLP/Jk/86628/f+3t/j137t17h69Wr/3b/xb/wb/d+f+cxneOCBB/iVX/kVXnzxRZ544on3duBvc3zoAdaPa7xdgNU0DU8//TTee5566qm3pd//0KUd/uhbr/WfN4uKT4/3ea0VXg4z2jVAwmoSR42XOYZYJMJIZXT7IWuL3wNEVhmeHJVSb3j9RRlkjuiWyZNtD7QGy6yUnYR46pClQXZaKAZZl6GdG6K+wTGv4YJzp5QsmCjrCogeBS75RJqzSkHCVjPY0mBfw1NMum5HNRDQXmReVphF9Dq1pce2FhfsWhZKN60b12sgvfz4eqowRxlTytHcrDaH9AIPVozSx/KBJmL/fTQJK8pRj0aLUYlrwdvBOaZVbxbpdz84cVn/YnCsMkDgYoDKY6ru+6Wa49ZQkAinJXFWkpYqQ2uqQGwL7Z3VbVpM346gV+DrqCbnbnAHR63R7NK4KLLyoCoCVtaxM6pIwKjrUZIS1pqVqARK5zg+OyPUDbvbO5RVxWZZcrqsOV2uhEh2qoo6BfarEuskq1wmDppVM2qADVdw2jZrz2MhlrvLOefHQc5obZcrY/bkzh43Fmc8xg5/7RO/wLxu+PadO/zg+ISXj4+4OT+jSUJpHbXvlKcMPnkOmwWvHB4hIvz5xx/XDOeHgCL4doeIMBqNKMuSL37xi7Rt2xc2P/fcc6SU+sLnn8bsVtu2PPvss8xmM5566ql7+lt1maUPenQiF/ujcZ8BHQ7vPd/85jcxsxkbZcnHHnqQZ1854Ne/8DEWi5b9UHE3LCiNYXwdwtTgGnCt8NGNXW4ezZDY5rlXXwQTFFhp3ZQCKVibYjFzIWwnaPIxiWKeLgNk7hgYKZBq91jVZgUBI5TzRDBC47W3YnVL4CGBYRA/rf5NwfRiDkDPkkhdJr5X65CeUeFrBzkbT86sMXfEuKIKKiWs4ydmZb9E3tfqjCVT3xFYNupG3T7boEmG0FrqRpvkOiM5UJYjlUJvFLvt6p4MR8spV8YnLENB7TWYlLzgkyM2hmUqGIUWRGijwRg4CRVBhGUsaYIjxI7+LRwsJlybHq7Vh3Wn4EzEB0tKwmk9oioajEmYlDAuId727VeMidrMOKE1UZIoSk/jLXUS3jhd4Hzk1WbG9xc1D+9tq6JuyjXEmYWwrANTWxC9Z2IdySdcMDQpEtqW/SuXmOf7dnW6yVGz1GBkvu8uoUwPEULbiVgpOwFUIMiHADVU4qgbj28Ty6ZlIgWztN73Uu+jsGzDKkM7aKwNsDEqmS1qlrMGY/O5tBrki1afE0kQfORsUROjnmtRlGxMRyRyMHKwz3HpaLLMvaTEji2o5wpMJ6OC3emYh7MgxVuN+wWN3iqQ9FZ2RUSoxiWTzTFXP3qFl55+BREVzXq/xxBgvdvxgx/8gK997Wt9ZupHjS9/+csAfP/73+eJJ57gypUr/NN/+k/Xlrl58ybAu8qi3W/8DGDlYa2lae59AYfj5OSEb3zjG+zs7PDFL37xbUs5Pnhpe+1z1wByz40olomTuedO0TDrKHdd5I1VxN+2IK2qNoWpRhjjoChLUJAmuZaq+9Jk53yIb7Lbf/+DlRXIElSGu4u0xZToGg93q6fWILdK2PakDY00DZiIedybTUqD4z6PU6RbRVa/rVTc9DcJifasIHmh3Kkz73mgnjSsW2IA/AYATzu5r+83AbGIdKJvnVFNHVBaxzP3nGHifJRMd566DE8CJCBGxQ+sCEYsKYeDO8EF8jknUJlZcr+qzDXvetV0YeSeSrhWL5XP3QwRpx6LsefuSZRVD5RucRsRG3F7S9hfQBL80lGOPIvbY8JpqTSKUQvJYHxB5SwxgTPCtCxVPt1ZCmMpreXidEITAqW1PQCrCremuuiM5GzT6ipvVhWn7eD9TAmWNfPZjO3tbYpCjezYOU6b9UzCqCg4nNXM21W26oHpBpsxMqkKxMAiesbOcZaV1rqxNxpxc7Gu5LdTjTiq1+kSpbXcyMv9yoOP87l9naCfunptcMiJl4+P+Gc33uDbd2/z0vEhd+ZzmiZwFloupsTYOf7kgw/SNA0hhA91Buv8GEZGi6Lg8uXLXL58mZQSp6en3L17l+vXr69ltz7/+c//ZA/6bYyzszO+8Y1vMJlMeOqpp/pnbTh+nBTBEAIbRUll1u3PbDbjG9/4BuPxmF//xT/J//POf8/2qOLqzib7mxPmRcMFP+Es1JoV9rbXtbRi+NTuRb7+2uv99voaLJ8gCbaGWLI+keYpw8yFsAk0gwk2g7Q4t9jWkIqIWWgWQLz+ZnLEzhqtpUxLoTxQ8YwwN9CRQzQqpn+mvN2Q+z0BnTZqd1ydWIRmjA3tWUE4KIkXUgZ/ompzdwzxI5JBkoDpxDD0YwzS0+7648gHkVoLNpKS4Y07WxyGMa6IBG/wrQodOesHNVupDzD1AcYOYCW4M59ytlkSMPig9zYFocZgUyJgOWpHCILHYCQxjyUn7ai/BsvgNBOX/561ldIBz40OdEURjhZTpknVBkUS2IAJBi8ecFgTqaNDcgYpJmEyrmkWk9xP0LCzs0M0ibOjOYvFgpPFktgERqagiS1YofWB/cmEwzrTrD1Ik0iTxLiqmE6mzM80mPXQZItbzUyDmBkgu2RXFNCgKrgi0vfNAjQjmrQG67htiEHFhHZsxe3B89rfypT7YYbsZ4TVMpe3NrAYXlw0xADipPd9Qqt0b1IOJIQEEhSEJGVl7G5P1t6R7t/dyZibJ2cgMLFWpdkXWvs2GRVsVAUPnfMZ38/xdnoVjqYVk60xFx7YpZpqbe0HlcF6tyqC3fid3/kdLl26xG/8xm+86XLPPPMMAA888AAATz31FP/Bf/AfcOvWLS5dUqrlP/pH/4itrS0++clPvqdj6sbPAFYeb2Ugr1+/zvPPP8/jjz/O4znC/HbHAxe2lGOcOQ9niyy+IELbqjrR7qJgexG5ueVJhl6GvXPYu8nAtII9UkPnJyk3As5gJScs7len1bvdAx96+O8qrwAdjUkkg7bBpJQSOQu2+k6SkI4LZGmRXX8PYLnnUg0+J9BMTS9T1wGvc/LkPSBbBwbt0hHvQrHR9vK/Csi0ZstI14g4X4i0qp8aQpHzIzjNKhlvsvFe37MkjUz29Mu8j/Py+n3Wa/BdVweWSL3IwhCVak1QJzgh2S7rze3iqEY08tv1B0sMdEdy9qN/RjNlsZPnT2jflCEY7bjj3XXRnagR0wdA/y0nKkm7caWGB5ZIsNhocDYwubHH1I4py5Ll9IxHqkucLlegJqVEkYFVNyZFwfxcw+fNqurFHroxLtwKYKXE2WzGJAS2d3bWAh32PkbgfsGEmBKndctpvQJUo0nBBTOlcIaWwHG7vPfZRaX6zwOsnXLEraXWT31+/4F7V0Lf98d3dnl8Z3d1bCnx6skJf3TjOhK1YezmeMxyueTg4IDLly/TNE3Pmf8wi0j8KMMtImxtbbG1tcVjjz1G0zR9duvHOd4NWL116xbPPvss165d48knn/yR27DW4v27U9l6J6PLYIkI2+WKQnT37l2eeeYZrl69ysc+9jGMMVzb3mZnMuba/g4Ak1HJtChxUbMyPRlCtBbk4Qs72uQ7aVBOcmRO8itil5A2ZM2WSNAolUmCmRtkyT1eRTgqKJKwXLSaBYhor6ic3UgiGGdIPsCpobgtxKmQ+3z09bspH2v3OgcvuCINflz9qfak68sltLcqpWO3ykdLQZexyRKDYb7I9Dph1VdLVPAiRlHqeL/9LE1fCzICgnBwZxPZXvcfEqwzU/KcOjSAMa6CfRHDa6c7FFZFMhCVvg/RsKxLNplx2o5IVgUsxCRwcGOxlc81C1uIZt7a4DhajtmfzPr9d3s2JFIyWVvBcFJPuFCcqt00kWpU46PeT2Miy1RQpYQRaKNlXLUc1dIHXl8Kd9lup/iY2NrephWHnBwysZbj0JJsJLSJnVRwJDnrFIBWn+lRWa7PtSJsFiUz2r72t0ADdRtFyYmfgxEswmYxwmffqiCrDoZE3XqISucbZfn380NAs21R+3iFkPqg8gO7G6QWXjGHxFYDnKENiLG0i5ZlZkI4hCDCpHAsfVz5SyLcb6e7W2NunswwYtgpCxZNw9yoLZiOSzbHBYXboG3bD2TOfyuKICjAGm+OcaWhGmc15g8IYG1vv3swGWPkd37nd/jN3/zNNT/gxRdf5L/4L/4Lfv3Xf539/X2effZZ/u1/+9/ml37pl/jsZz8LwK/+6q/yyU9+kn/5X/6X+dt/+29z48YN/ubf/Jv8W//Wv/W+MSw+9ADrJ12DlVLie9/7Hq+++iqf+9zneqT7ToazhgcubPHarWMAmjYwGRUsFwsaHxQIGIFouHzisFaY2cjJKBDsymnvkxOivUzMkdFsy0QzWqQuIpYBxTmjA+vgq3ekV5tVwzoEDWkQbZPsxHfZoO6YulEbuFXCRrvWsDCdNyo2ZUW7FWzqDyBHRwfJokFGaj381Al3BO9IJ4ZiuyENszCd5ngHqEIi2VXFWC9Nv77l1XFLIhSR5MGl89Du3myVTSaLLayOYSg0octIpl7ot1lTBAaAMqakDZnTqj5Pm+LqNg1KN0RSp3bcHTBJggLgZDNCFiRozwtVONL9J+8g1z1Jh969bksbAQcF0bFTBjSkFLTXU8z0lrz3aAPRCM0DBxzFI8q6xJdnHDZ3uXz2MGVZ4grHvDpio11XPZpkoYnhqOy901JHR0wpcXZ2Stt6rl25wsG5rHN7H5rW8j6O7/xcpgrgrG2YtcPvE0GEK+UmSRInvmYRWhb3Wbebp6au5LHN3Xt+/1FDRLi2vc21gZFZLpc8++yzbG1t8cgjjwBqSIbzkzGm/+/DMt6O4QYoy5IrV668bzSMdzKG6pVvNob1Vp/5zGfe8lh/3BksgJ1qTEqJH/7wh3zve9/jE5/4BA899FC/7BevPsDeeMzjF1fP46QscwAmURiLpND7/Y9e3qUwllEyKkaK0qFM0ICfaUByD63OxBifiJU++/ZUs0YxJ4O6qxxP9H2OPiJOsMuo9Loova0RZ8AHqldNnpeBYFfUPxjYCP03tAbjsi0Z2rps2LRuTDKlOz+XC4FJgsb02aN64Qg+zzkZ03X1ZTEaUhTS0moPL+l3QGgMjpBpfoMmywNjmrqL29HFo1ljCoTOIGf7cmuxjTWRNqhgTnSQWuFsVjG7s88sOa7unCIIxiTEJWax6ncbk8EQqKMhRsPBfMreeDZMmfXXyccsQpIiYiI+WaxJxCJRukDTOkTUVnkEm1IW5jKMqgZMIAZHQ8t1Myct9BxOlzVtq/ZrazrlzqylQYFHnNeEtmV5eIwErUEWYm7RsbKUi6YlnKjYRPd9mcWJRtZSisFZRyGGsS1ySxddhgR1E9W3ikq3r6zt78FwdIFkcuB4ETwpaR+rjaJCCmF3MmKxWCJiNNNlIMwDtVNLHrymvx7Y3+IHN4+IJG3Pks7bIn1vLm9P+c5rd1bHGwwLq0HO6bji4t5WTzf+IOb8t5vBmm6NSbFrPPzBZLDm8zkPPvjgu17/a1/7Gj/84Q/5q3/1r659X5YlX/va1/hP/pP/hNlsxsMPP8xf+kt/ib/5N/9mv4y1lv/hf/gf+K3f+i2eeuopptMpv/mbv7nWN+u9jg89wIK3bxTfy7ifgez6nCwWC77yla+8J67oYw/u9QALNJp4mDNYiMUZQxMDBn2JdymZHgWORp6zcc5f5/86hoYImNZgjiEVECZZlY4hADo3qXTOdP4QjXYlXwu6JFaiF9CLVgxzS0Ngtga2IshJgWkiccur+pxNGo3MhyLZ8e/BXrpP5o01WzDYATnquR4citHgDyvsTqPt0bvjltUk1+Oe3vIruBg2uO048ZINLQKhiJjQqQtKPuS0ntpDgdLwCeojnYPvLFlJsBOEYCXm0C1nkyGcb0qbMm0u01Vy3S9GHDEbd4lCI5IbDOt6TiBI1Kh09lIUq6SsIJlw0RKzqlRKKp1v0dowbbuVrY/odx7666UgN9MtMQRJ+KpGUklrIz/ceB3XOK3rcHN2miUX0hWNxBM5M8d0Pc2GV+78aKNy1k9PTggxsrO9zXmYk1Li9D5CAyfnQJgV4ezcd6Wx58AVlNZxe7aqv0opsT0aM4qOK6OCY7/swdZhoxmtj+9e7JUT381YLBb8s3/2z9jd3eWTn/xkD9y65pAh6HVIKfUZkw9LduunqWbszYb3nmeffZbT01O+8pWvsLm5+Zbr/LgAVifTDrBbjvjWt77FzZs3+fmf/3l2d9eB/RcfvMq0LHjygQv9d5uTkotbE+6eLNRhipDQDNSV3U2mZcGJN7RGCHiKOZgWYpUTMGcJutY7CcpjWOaYo4kCTYSqI+vlSTSIUtJyXMmdqjnoBJXKRSJOABFMtKTs+3WNXddUmlhNu6G1uFHQthRdT71OFr7733kF26WBUa4Szcue3akUPOU1Y1zZw+iF6A0pDnXjs63I9WYpSj8bAgzphF1PyNSlx4JAsQrqdSqLHdBKCEd3xwTnMKKADCMcLcfcfOMil/YPqFuXszqpF0LqrnhIBiOeJjhab7ldT3hsb2AsB3NTCIY2WMqxNhrW7Jd2vi0nLaa2WBdyPZgGM6MkfDKMypZiFPAnBU3Z4oncKs8oKHkh3cIaw8gUhBipjAVnMBZ2d3f5we05VRJMAO8jKSRC29LUTV+z+/LrB9w+OcNvrY7XRFFAZsBi2XIVJXatFthFnQdDjCrUkRI2kmXbV9i3H12dYQI81FbnWSuGRdOyN5nw0O4W3729xJAggHFCvfSkDRSc5tTg2DoKZ6lbT9t4VeMdqMN3FRaTamXvQhtVoj0/o+NRQVWt+i91gbVu/n8/5vy3l8EaMd4ck2JiNFEA/0HM7e+VIvirv/qr98UGDz/8ML/7u7/7lus/8sgj/I//4//4rvf/VuOn3xq+T+O8gTw7O+PrX/86IvKewRXAxx9dz3xZq05x67vamxwFtBq56ubBjdpwdV7x8GnFZGnIfnDOVukyghrB8thgT7P6U57vz0toJ7Puvgoo3W1tofzPfTHtaqdCVoqzq+WzYA5mabC3CxXBGIYzz22m39Zb4OfUnXBHwjb3yW5GS3tYQWNXeHSwjJ7rYPlB5+aUIEq3D/rCaIm6TjCxP4ZOOOR8+l/OfRGlM6zrp72uMTK4id13URsXSrSIt6TG4luLXzqapaWtDa03tMHQRqENqqTkffZgcg2BQK7x0sI+EVH5WtYBdDcJrBdxr5+Lnm+u90qpX34oI98bL5NVEU0iVIHFxoLluKENBbeK23zXPc/35Fu8VD7Pt9N3WcisP5ZEYhHvrYU8qxuOj4+JScGVu0+t1bQsqM85udtVtSYp3313/nHbrO5tKrh5rtGgiFAYy43TOTeO5sxPA9Mw4uFqm71izNgWfHr3nWe4uzGbzfjDP/xDLly4sAauQI2btZayLKmqSrOCzvWRyBAC3nuapsF7/2MRXDg/3k5k9MM+unk/hMBTTz31tsAV/PgzWE3TML91h6OjI5566ql7wBXAkxf3ERH2N1dCTFvTEX/9//BLICpIkxMUlGKpqoK9yZiJUZVOgPIUbIMGoxJUx50zr/PidN5FooAExdn6cychZ981egUI1c2AaQwpCKaF0Z2I8+cdX5TGN4xjdZmzzEwIrWahxHcqtGhtl6C9FxP9XKgHo0Ege+oU2GRaenM4XgGyvGD3W0qidVgZEMIq4xFbp8fpTd8LsTv2bjIc2nI9p8FZRjQ1mFbbjt7Qzgc1Ra2e2/HBBHMgNCcVZ3XJ3eMpN463GI6UlBYvkhRgLR0thcqvDy9sHj4aWm8JSVguVezDkDA24MYN4qICrK4eLLfD8NEwKlqqykOEdqTbbAqPN56Fq5mVNaWxhEzRuzCdsFuMuHnnAAEeufqgqhEbsFEIy5bX37jJwcEBJycnvHH3hLO61QwPajNjjFoC3rE6ImxLiRva+JDBUet5otzqHZLlosUG4Ny8KDkSKRGmwRKiAiZrhFndEGLkod0t9XUyZTUlVRWMXpftVCG7liAkaJuoAQw7eP7yv/VyFchLIRCa1bwxqtaDjcYYiqLo5/yiKN7znP92AmGjaclke0yKkWraUQQ/fADrwz5+KjJYP44xNJA3b97k2Wef5ZFHHnlT3v07GY88sEdVqrINKLBq2jDIZOTjMELo+kIBLhmq5EgpcuGsIBB4Y0czQ53gTefYJpKqDp5AKhJhLIQi3SMkdz5bNJj/39a4J9Mua4ExPaZMATSHDiaRNA49Hz2hTQzTcIVz4gyR9QNcO7a+0jXlfQ8imyK0xyVuqsqGa/U3md84BDVpcCGE/HkVgBzgSckADKyHIuq02jW0PY9RQfsiGVnv76QbXZ2r5F4bKRqSTz1AjaHvQKVZJulyhxkUyaDRbeaMizvXwCyp3HkcNDtLrOTTyds4n3FZA4/9TqQHpN1XJhmiWdVy6X019NXx/XKiFlKEZAzBgI9BjbmPvFK8gDHgUomYyCiO2TNXqOJEjR2JN+7ewRjD1tY2kvn5x+cA1sQVzM9loUbOwbnlyvuAgNLc57v7LTf4TkSYtS1bvuLGbE5KiSc337xlw48ap6enfOMb3+Dq1at9b6U3G0OqyDC7FWPs/+uWk3y/P+js0tulCH5YR1dv9fDDD/PRj370Hc37P84M1nK55Otf/zoXRhO+/OUv/0ixpeI+z+/OxpjPPPEAf/Ern+C//KfP9VpAo7zshY0Jd+uS2mTqYFChpK5EtDpInOYJwDT63pgmEEeaRbJeX/+UxZrEdygjH4DAxivQbguhFYqziKTE9inUojYL1OFNwwxWnmQkx3kSQmhUBU8aINMUpc37TqIUxO5YhiNJBkUa/JPFudCYaFYq5jouMoALS4udhF5YI3qjNEBPrlfrslCQGouUQRsOd/uMAm0H3FB12GD6wGS3zRSkNxOd1k9sHQVCfVxxtlVxtqgwVWcNVtmvNvfkqoMD0W0vY8EGTc9A6f7fBoP3TgOkQu47BhsbS4yNGBcoisgyKJDs3HefDFXRUhaeWQTcyiY0ew2CsiNKYwgxUhrL5c0ph3cPOZx7JjsjREwGS+A8+CTcCImHrm5y9+CMuvaafFzmOidjNdsVoSXPcT5xScbYfNyFMVwtxjxebHGWPFVy7KYK8drwmCZpD7McN3OSn62o9vFqNeUVVHzCGcusbpnVDTtFRfSRxe0GU0AoE8wTIkavWXbWHEJqIik3Gcawyp7mC2RFmLqif96auSe0vl9wPLpXQKcb95vzu7n+nWS33h5FcMRkc0yMiarLYFl503XezXg/VAQ/zOOnAmD9uCiCbdvy/e9/n5dffvlt8e7fyXDW8OTDF3juRW1gdnS2UICVfw9xPZM15O/qi5SjNlh2Z7ARLQsCJ9NI7WJfOyXZJogX7KlokWyVlOLRA5V1hHTPlZVBlqLbHgMgN8g4rbIZq2119UGJPPfMDW4p+K1ALNNqG4Ntci4TkgYNdYVsJM8FaFaBzc7xX1lxPyuRwmO22j6bp2p6pl+Kbn4dAIrO4VgHf4MTlKSgtVXwu9ZaiaQUApSvbJNk+fou+6X1TDZakjfKDzdCCySTC5uBlHuldMBs2JPk3G3qnwuRVTbp/D0ZArNhNDXmXiW+81zQSgWDyQXw69TR/l4MD+Dc9jPraHBN4j1CEaKhZGXM5C3GCCm2iERaaTgrTzVjFEokRkaTLS6UV/r3Y+Qsx+cSXe4+MrL3d5Lv/e58phe475xzPhum56hjs6zYHb1175Lz4/j4mKeffppr167x+OOPv+P1OyPaGc3ztJI4mFs+SCrhT0MG6362JKXESy+9xEsvvcSnP/3pXmXqnYwfF8Caz+ccHBzwxBNPsL01edtKtt3Y2dAmpP+nv/hl/n9//CLHxw2pSL3U9ZWtTW6fldz1i77sdRhQsx0dz0HZ6HxRHUUWly3S6Jtllwk/yXN3J+/ezU85QGWaRGgNxVwn4VRHKg/4RL2jwKifTLK/GpNg6pVcepupcqYWqPL85lEHuhPoSKuA4tDuEAwmqxK65AhxIMZjMuW5UyLMoCccldjpHNOJG2XFOfEGWpP3qftNpxbZy7VmA8MZa4slO8JRSI1msFJWtQqN7ft2JSB5gUrPwbRCOHQc705zhi2r8Q6CtB3AWrZ6P2MU6ujYYNXOpAdDwRGDxZhIGPQB2RzXJAziEs4llr5YC0a2yVDYoL3BTFfXnLdeZJZDrn2KKbFZlCzOTjlbBGqf2HOOECImKDuuaAwz3+ARRgcFp3OvjeZjpIyOlBpS62la4fDwkOXI0IZI8JEdqXqLrv0LhU9M9qCAO8sFJZadqsK3LdKuUGJMkd3RhLO61gb1qLLsy8dniFFb0rSBedOy4ypAVOzFQWoTDoiDGvLOUqYI1mgPLn1hRE8yJAqnbTl2RmVfzzQ/q3EDczSq3t77/KPm/G6+f7ParbcvcjGCxIoi6N7/uf2fd4D10xtu/ABG0zS8/vrrfOUrX/lACrA/NqAJxpjWnDUf4prx18952ZTWPm+0luQT02C5dOgYnarR6nVyhkmbCMVCKI/BLjMl5JwzeY/DIetLnK93ghUQWS1EXxvWLyOr/4iCPXa4Ey1cXoMMQwCTzWc892RK/0t/0NwzBsXXJEi1g+MqUyC1L9h9e1ut7SvXR3Xb6q/B+vH6ItF2tEEyCM7KHCkBLaQlFIsCOXakI0c6tsS5haVFvMEmg+2pKP3e6RsWr12RtHZtI2kNXFmEOACt3TVLrMCRqhKmNUqoiR040n9jPv4YsrphzI02RRWajEjffDH1Os79pQM5h4IjPXheUWjM+nKiTlO0QsQSoyUGIcRALQuaouZk+y4vTZ7nldG3eKN4metyPVeDDe7RfZ4Jfx/aRH0f0YtFe+9352uyABb3WbcT0bi2tfWOs92Hh4d84xvf4LHHHntX4Op+4zytpCzLN6WVvF90wp/GDJb3nmeeeYbXXnuNL3/5y+8KXMGqD9YHFQhMKfHiiy9y/fp1ptMpH/nIR7i6sfXWK54b+5sKsESEv/InPodrYaNxlKU6dg/ubPLAdKxAaNjnLk8MItLT1CcLIEF1mgNOXqnq1a3VOyIeTNSa2WgkbwNcnVRBLtdONaid6RXFc41pT6lLOmXYecKd6W+hVoqgqVfBNWnpJPl6FUIJa1OnzotBkM7IJOj7ZnW7SzmT5AWCIXlDqi2xccRoqM9KBKMAaO6ws5zNQoGICYbUCF0/WVLe50JWNioIHKsiSMzn6VuDBKvAi3xR8vUwXrALy6KuVqq7QxuVoAmqCFI3BV3Xx66nlkhX76vH0HiH95b5ouyvX0pQFKoELDbiipY6dTVf+VCSwVmvcvAu3ufigiShDh7vA6dnZxSF4ywLT2yVlar1des00Lb66frBCafLBkGFrvbGUyTBpByr+EmI3Dk85PbxEYd3j6mXDVkKkZFYUkpUzlEvA0YMToS9coL3gdSu2DwparYWr9k2K0JhnQpviLZP6QN2SZlFpiOCJMFEQeJKMdAYQ9sGQhvZHFUIgi2sipdlkO+MUQXequqzbvVMa3e7fZ2nCL7d0c353Xzfzfmd6mg357dti/f+rQHWxojp1oQY4gemIphSYj6f/3MNsH4qMlgf9JjNZn0zzKeeeoqyvLce4/0YH3/kIiYrtyUSi6WnzynlF9r7FdHbGUPrIyHE1ecQsdbgvfZgaNuGTREuSMXIC8c0nG0mFlYBWS+IkQS7ADOHUGnRcqe4dw+9bS1Nkb8apq0YZJ6Gy3S2Iw2U87oJPU/hsjTaK2Ua8YOASDQpC2EoSOnENgYtSe4rhLHa90o0o19elKcvd0vMTotxOQpnBlmeLOAxPHk9j1VVkKSVQtQQbEZJBJdwraFoBeOVc57iyoZ6SZnJmPuDRVb9vND7I07zVaaLiEnEWO2TlQbgpANGKYNAi2Rax6po2ojps599g+n8q8lS66QV2Ml6I7pEf3tXch7d/YxdKDtfO0kKWDMTiEy2UfpMWhfY1+Tj6pw13ier5xMwyRKNXymIAcFbrE1ZeTEgCRpZ4F3NDziCscGlglHYYBK22Qn3vrfn1QJTSuv9tFCDenKORujuI4RhDPfUfRmEkyyscW1r+579v9m4e/cu3/zmN/noRz+6pv72fo63ym69n0IZPw0ZrOGYzWY8/fTTVFX1nuf9IYB9p1mltxohBJ577jkODw954oknuHNHFcgujt957cKFndU6n/vog2x5x7S1jDb1mB/Z3+Hu4Qbp4C7WCDZTlSWt4mCiWjtsLbT1iGnA1Mogtk1ifDsyv6b7EE/fxBWjtaWIKgma+SqM5FNmYWR/3cQ83wxqjolg54KbQ5hkgBVFpeHzkDYhVT7SINg5fd/I3vkXQTxUUtC9zX2XjLxMQimCJgOs2BrcTAgLQ9uWUGfRBK8zn1kkpSNGlAaYQOaWNMkIqwNYXvTfrIgUW81gRa8Zo7jU5yjNDWxGun5eJhrEa72SpBWgHFLcSULTZbDqqrej83rltAuoKIeFJhia1pIc2tAXneOGck7FKLAcKCxqEM5QFgFr4io4eU/AVWhCIDQtk6rk0s4uz/3gEEkwFqsB47yeWSYaG7XHVifdn4OiI2shQImhQdja3mEZS87ODpBgWCwXXH99znEpbIwjZllQjEtSSmyNK8q5Y9MWvNrGrCqph2qDsFmWxJC0+bHRh3SEIbSokEZHx8t9SyXkwGcObLpk+hCfM4JvAzFECtG6rGSSZqpSBnHGMLKWqrA9Lb+eNUw3Rur7hfiuAdZwvFl2K4TA6ekpo9GItm37+f78nN9lsM6O56sarA+g0fDZ2dk/1wDrpyvc+AGMO3fu8Pu///t9kfD9mki+X2NzOuJXv/gkuxtjTk+X9wCUqrBrWS0jgnNm5YQa6f8NIdD6BuccYwpGuSZo3Fr2D3QynnpLlXnYQ+fW1kJ5IhRn4NpVbVE3OjBwv9FnRwbbvN8CabgwaZUVy2BPZhZ3arTTe96n6Y2FrNa/D9jrMh7ns1ED8aa1Y03JEA5L0nLVe6UbSmccbru7ULkvVG9AFNSkrIYlCVW5CoIn0SL43Ful36+sX6M+Azf4rut3RkJpBR6iR410I5jaYBqNiNpocuSMgSO8fg1WL7QeqyoYp7XrAvfeu2HG7PzNl5WE5D3novUDmk2LMZFC6mk5BsFlad1hnVfKEGpdVKPbrm40pKiyuCIEhJgsKTkIhRqKGAlJM1zHxW1uVi/xtPkjrlffZyHa98WYe7NQ06K4J6t1XsxCv7tXCGOnHN3z3WZZ9vTChzffPsC6ffs23/zmN++R1v6gxwdRNN2Nn4YMVpdhvH37ttYxXbjAF7/4xfccVOtA1ftNE1wul/zBH/wBi8WilxLu9vFuaoP3t1cA6+EHdtgtK5y3vWP3+IVdHpxMaH3QKH6XQcpS6CKCBM1iTRrHCItp87wTYRSEwhrsLCnYacG0ef430gM1VyfMXFbnEfQHExOS1V3FA361f0lCeZwwCdws0daOlAR7Npj7fedEq+CDrSH7zvQ9BI1BkuHJzYt007/kjFAfPItCjAbXaq3W9nVLeQaydHT1XQCpzc7sUpQqmITU1XQthVQP7lEU3U/IRs0LMdeoxRxt9IsMsJY5I5ajlh04kIaVvG0XKBvY3DpYTY6dKlXZiLBsCtaVDfOfwRCXVm3WIOBnM71fErjKs0zFmhn20VDagJEOYKUeAHdgOraJ2jcYY7HOMZtrP6skEBfQ+jAIpKU1XnkH6E2CdhkojcGiAei69tk/EcqiYndnhwcuP0BVVPhly63bd7h18yZnp2fgW5wY5stW5d4zXTWlhInStwP52MX9/vx3pIK5KhB2Qi8nJ2cQw4r5kY+tA7sAo7Lo/162AWO0TMDkyKWkRGksW+Oc3RKhdBbf6rvcZbDerAbr3Y5uzi+KgpdeeonFYsHDDz+s92mQ3fLe98G38caI8eaIFGNPEfwgWib9jCL4IRgfxI1NKfHyyy/z9NNP8/GPf5yPfexjwPtvIM+PX/yFJ/jIxV1++ec+gg8Rh75oOoueW1ju0zg1JdpG1W2qsuodmhRTP0cZDJdOHJeOCx646bh2VDJKRg1Plx0S5XQXM6E6AVOvT3Lnxz3NWmX9z3uECIW1NTqsMmQtSy2YQ9Mb2tWyK0DQg6Vu1h3uWgaf0jkxie6XtPoUTku0o9+5Yx1EZzsPQAbbUCVB5eybIFr0nAb1OEnwRaSp/KqJcdIM1/CYZHhCecdJ0tq1Pf+oJxKBRPQQmkRoEra2Ks/vDdZbbDB9vUB3fVdgdZX5knNbHn5pBtexU4uS/rd71lpdn0F2r3sOOqpriokYgwp29KBLz9HJ+lY7Of2YlDprBLpkSA9YUb8mJEuKjq6QK3mIRHxq8dFzp7zOiT1gs7y3WeDkPgGU0X0yDtV9+ObVfZYbD7Z3bevtUbY6EZ1PfepT75qS9n4MYwzOuTVaiXNOe6bl7FZHK+kM75uNnwaZ9o5q98wzz/DJT36Sj3/84+/LMfe1s++j/Tg6OuLrX/86m5ubfOlLX6Kqqp6K+G7H5mSgUCfClYtbkNRB1DYEFT//0Y/pSxojNqyc7c5EuU7MQgxTW2CRHgAUreCAye2EbQQbDXapRicNCjTHHszpakbqSlaICZeJHSaw6nvh1UbZXJ9k6kRqDcErM0PXBbdMmhlyCmCsh40j1uZ8k1T05RevPorLs5tSE/MEHTUzFKNQNjrPjk6zA13rXNsF2VKjx1PMwLVWAcpZN98aZD4IeeVr2K2D18xYSpIbL4t2FgYkWdLMKrMjaoYwZqEghrTDftpW+NgEBVPF7V0kaVuQTilQF5S+3qoJDjkptH8lhtQxC0x3KwRXBpYxz3HZoEYMIkkBVoeahja1m6itQYwhxMQPbh31RuN4vuzrz23UrN/QzeltGELbqEiGTUJVOl56/UCPLSbaDE5mJw3OFezv7HHxwiX29/YQEU6OjlnOZhwcHhF8IMWEzdR/A9nvAnzqH5C9ssJFoXIOZw2LxYKjw2Oqolxd84wrJWhPzZRgc1yRQsrq0AknXaZR74EkGKGKnmL03KwV2ix61mXL3m4N1jsdKSW+9a1vcXh4yM///M+ztbXFaDSiqqq1Ob8Lsk13J/rZx17k4oM4pn/eKYIfbmv4AY0QAs8++yyvvPIKX/rSl3jwwQfX0qkf5LDW8Jf+whf4y3/+8/yd//O/yMXNgqbxlM7S+thzc2ElfNENHyOt1/4KZVGseeOaQVgtP0muN/a2gdEMpo3h0ils4vSlz8tKFFwtFCdqKIw/h0HuA5buUWaScxkJ7sWLKUfc+qRRUmNnFgZ3ZPJEJzl7tE4hy4vn7aT+swx+k2Gi5T6YXIBYOzgueiOVzmVrgBVvP2jE0cRVFpGc/j9fD5bQHiHNyONtjjAjfZ0UqM1Zl40fHqSsHU8H3syg+DilhBHTbyMmVRsMPhE80BhoBesNzlvNeA0AULe7VU3WADjLyqh1v51HzYn1a6+rDS1j6mmJ/Tpm/fqmCDEp6OqiniYaBfw5i6fGxq7ubb/PBKLU14j2jknJghRYHBItvkk0TcNhvE2zXNzzPhf3UQu8X9+qt9/LSo9tWhTsjcZvufT169d5/vnn+exnP8vly5ff5j4++NHJwL+X7NZPA0Xwu9/9Lq+++ipf+tKXuHr16vu2XRF5X4Uurl+/zh/+4R/y2GOP8elPf7oHgcM+WO/2OIfjsQf3SKhj570npcSFnR02qxGjqmTSBRX6DEfCBM0giEBZ6P0Wr5mnqtXfbKNCFgC2zjONFYgaPPmFT13Dna0iM0o7VtBS1CpwJD5pjVMC8UJ5rIqDIHmbQts4bFS5d+n2FSE5ZRPgYTOWVBnUdIqakuDqZJcNcRgRKuN6myVkde8eYFmKuVGl0WUGQPm4QmORBCVCkWvC3IntL5l412ePurqpWOdQoxdCBlbRG2Krcn4SBGkT6UybxUvMGTaBZPO+UVDV14512RNfqBjI8YRq4SjFUjclPq7ey4TJDZQtYZFFNTpbmzqKYNLMi034TFXvjt8nOFhMWIQCcavwVx/g64CkdOyGyIZx/fU/XdS0nW/i9V7ENPC9OjAvCkDGRsHtyDlu3D4hpqh16Y0uf3Jnzmxe4xutJx1XI6YbUza39tjd2qIsS3wbSEkZOwQN/hViMG3CL0Nv23Y3J5TOMjGOdlkzn8/Z2tymcHaVxSVn90KXwUrsbIxpa481FroAYl7eiNrVIgpX9jYwRpUlC2sJPhJj6v2+94MieH504Oro6Iif//mfZzQQYvpR9bo7l7aUJdW2mEI+kNYfi4Xa558BrH+OxmKx6CkXX/3qV9ne3gZWvFV/nyL2D2pcvrjFX/jqZS6UFZujirYJbE1HvWPZDpzzlBKLZa3pZasvoe8qaEUIIeLDIOtgNA/UZcC254bLM8f4VNi7IVw+K5CofOjKWGyu/zGtcqLNElxNz4Ef+Pm6S340jXC1zI8YiUGdVv5/EMyZwc6kV+ZLZpVt6z6v4RpZAYT77evew8uGyRvSUYnUqs9nIpnulzM/YWVs8sy7XoNwHsUNjisBwSXaMpCIa+AgyTr4WAOH3TKdFHrne+RrRUr6jHaz+2BNE4dRUlXkix6CRyksrfQZLxM6cJgpmf0B3XsdewB+bplhk2UzQGvd+a1k83t5jtXI3HoZnGSK6sAr113Rkwlau2XFYETFNSSZ+4BbQyQRktA2Y0Q2qWQDLBw2BxwcHHB4eMh8NsO37b2ZWFTK/vxowr3GpL2PY9st99DGWwtcvPrqq3znO9/h85//PBcvXnzTZX/S437Zra5o+kdlt34aKIKPPPLI2rz/fg7n3HsGWCklvvvd7/Ktb32Lz3/+8zz66KNrz9V7zWCdH594XEH+qHJ0TV5FhK1yhLWG0mS70L3yIpTRIDFRNw0xq++ZLM/ugmT5a5jc1b5Ypc/AxqiohRHhK599HLcY0Kmh66LBaK7X0DRAdsCJsPk9j3idiN1M3+S2tpgmYZZqN+wiYaPgjoEGbEqMrGXvVOeOsXPZpgilczwx3ebTG3tc294Z3AQN3sQgbDQW1xQ47zTjkWvKpOkyWLavrxmfWVIAc9JFsjIw6k6y66c1yyCtNcRcr0VjiHXugdno/NcHxLJUPhnAyIC2mfL2OxC38I4QLCkZtm+NmdiCFC3L2nWHpOcXtRYs1hZ8ZsHk1imSFawShjYb4C4bo8Etwwt3LtEGh7HnG5ihoDiKMi86Ma+kdHEBZnVL472W9OZspTaqz3bDR1KuGWrbwFZR4n1iVFhCSNy5NUMiWocO1LOWxXFDU7daw57tSl17ticjxuNx93RRJKUbhhC5ef0NyrPEcq6VeDFpD62NqiTMlyyXC7a3t0nJUIismUpBGKPg2hrDpHIsli3W6PvjRIMJKZ/7ZlViPTx0aYec28SKELzavS6D935nsIbg6otf/OIauDo/hr0Wx5OxZsyNZTwdvStGw1uN2Uyp/D8DWD/h8X5RBA8ODvj617/O1tZWT7kY7uPHJbU73Od45PjNv/wL/Du/+cv82p/4OG3tNaJW2H4yjDHStg3GWiaTqn+wU365nTVd4geXHRzp67Vy5BOThSp0PixOI9OZ4YFjxwN1xX6qNBKYhCKpKp1poDjTrJbNFJD7jZWRTPf/4UcMlf9dKfsIKsNbHAtmqRQ7WOEJFb4YZFigr0daQzjnr3P3fUyqgNUVGx8VpLOh1Lqs45fhcUJfyzYEdSrsMNiXiMr8mkRbRoLVWiFyXUCH2da2n78wSTLFbrWPlLNjxnad6LNaIStgNnyJu8xXT1UUPZ8YVfo/etSoNwZTW6UZBtODoXOJubytfIHyjs4LngwvUsrXL3Uf6M67v2P9cv1zk8/RmkHWKkc/lWoYtT4tCv9/9v482I4rv+8EP2fJ5e5vx74RBMGtuAFkkVVlWWVZllsqWV22Wg61PO7WWF6mbU1M2GO3JqJtTfSEHTPjcNutaE+M7Z6w21G2ZavVVmsky/K0SlKVXJKqigBIkARBEABBEtt7D3jr3TLznDN/nMy8ed97ALESQKl+EVXEe+/em3nz5j2/8/39vr/vVzp/HUZzXaMTdjagbxzDLKQ/rGOaIdPTU9TrNYw1rKyu8OHHF1lbW2M4HOLyD36wlargLYhjOOdKIYwdzZsb0n7wwQe8//77vPTSS0xNTd30sQ9b3KrJcVGcehAmx7ca9Xr9vokY3W3+yLKMY8eOcfXqVV577bUtQfi9zFHOOZ47vAPrLPUoKEVOAKZqfvBeoUofJ/Dr20Qco4UijkLiyN8DMnO+MCUcSuDnqBy0PzB8ZnbWdw3w67WUgsf2TqMHGwo6uRGxTnw3TPVB5JRAYaG+YJGpX1zCHiAgGWpE4lB9v6kPuvncl8GLaFiIpWbSxby4fRu1IPBiP853ylu1mN2tNk3t7wm//ntwQSYIReCVYBE04tDnwFQi+74QRCYIU4FUgvqixA0kwaCyIlfntWwuBJRKf60S37lyVngz+aHfsOskL/Llwk3CUoolgPNzTs4Xpkav7f88yELSHDAFXcWL9Z2QwfLSyGzaOTBWYhOFcBJnZAnSXJ4HlLTeVDj3VhT5McB3yeb7LQwCXRv/rpciH4zWcGMd3STJZ5oEiTUMkqycs8L54mm1qFgkh2ToO0+ZM7SikEBKlhZ7Xv02f0I2MCSrCcN+hinUb/N7dabm33eagzFSQSeKfe5wNbSB9Z7vpCwtL9Pr9YiFI1kd0pmYoBZF/vVyVUu/3/AzU9r6HFQLNTJ1GGPLWSqFIB14KpAEdrYaMDQ8/di2suhdCJ4VBspwbztYzjnefvvtLTtXnxTFjLdSkvZU+57P64IXuFBK3dZ5PWrxB0JF0DnHRx99xOnTpzl8+DB79+7d8nEPAmAppXjx2V00m02eemI7f/wPPcnf/Pu/ykp/SBQFOOM3Llr7BBiHmiQZbQiVGt9kCilGykswtvl3xa43X4Bm+tp/+7spdQGDSYkIJaFVBIFiyQwZ2AxtJSLxlSgnwGnhKRgb3s+mn+UIBIDPBYUsQ3l+MPKSLNodTqC6fkA5iyyusuaMlAR9t0QKgXFurKshEBSOvYXRpH+K95YS+dCVQOCG2lNZWgZX0B0+ARjaMdqbKEFM8X4KNUAnwAYgFbihl96yplipq11Bl/dlfNVPILB598sJgcxJ8Q5Pp0NXqBTVMylysKDshBWgSdpKLynvxnmjyzy7JbIU6JBK+MXfOawwfs7c+WTim6aunNPWQpI6T20t5s20kGTOoORIzkIKXxMVgFAQCIHxJVgEBuUCpFIYkSH80JsHlqWmla+supxDXwJJ43IDREFEBAoCpxi6jIWkzz7RJopioij219la0v6AXq/H2poh0JpBFBFUpMy1EJvEMWpaM9iwNtSDoJRt33mDKlzhsfTRRx9x5MgR2rc4p/Uwx1aGl5cuXWJ9fZ16vV4CrU/T5PhhiLvJH91ul2PHjlGr1XjttdduKLZUyC7fbRTzFlOdOq1aRKMejhUyZ2p1LqpVHAkaQVKspzimmnWuZim1OGLXzknev7iKShxOexq7n8fy31vh4JkntvPNN6/jtF8ntZZ0OnW08OtdEbUooJ9kBIMMmSuC2n7eKV+QCGM8wLKO2lAihCTtB0SBYtjzpr6671BpvtHv+u5OLQoQzvHf/fE/xs/9/rf5tfffRwuJloJWLaIlA5YDXygRCPJ6GC5VSCcJUy+pNN2s8dHqOm4oUZlfAx2SyPi10SQOuZYzA/J1qlCOFRIvcgG+K5d6MQzn8tmrvsAKv/6EfRjWyj09GIEMPFUQ57yyITlgK5JI/t+B0aRpUC71/+n2p/nNi+dYXGizf9c1/zwEmVHYYeBzRi7UYXOQJYUlVAYnHVlRQCy6ZcKRGY2zXsZeRwb6OfOAUc4FX5gr1uuByfIc4Nfu1WHiiR/5NRLCq/UJ7Yt9zjqcolTmG2LIhpZQK/pDk0uy++Nkg8wXT6W3/zA5q0BJQZR3fK31nVOTWQ/eESih0FIhVICUGUEY0B/0CZwhcwKZpn5u0AlcapEy77QJSRwF/j530IhD1pf7IDxlNvO7DN599wrCefpfPQgZJH2e3D/Hifmep8Mbn89sPssVBAp1j5T6CnC1urrK0aNHx5oJtxNCSHSgxtbwuzE5rkav16Ner39H54bveIBlreWdd95hfn6eI0eO3LR6/GkDLNjMqd+zc4o/+6WX+ee/+Puk1rDaS2jWYrK81S423osbunslmNlEe3Lee8JBkqZIKQmVHs15OWgvQ01LrISs4agpyRDYHtfpJRmpNawZb9intCATYBTYXAK3MDuunpuwrjypQuRivNNV2fhX/iUEYEB3JQSQ1SxOjUSL8rpmnszy9wy+uqcqQKMAUvmrbwRDDrxa04pG1A2uPr55KbpGoyvrShBbZdgJV6gxjkuUg8cvNvQnowqMYx1WVaTXK69VfH5CgNsw0pLPPEN+HasqiHke9MmwPJCnlLgMRCZzGoivvibWYXPOeEnxK2axcpBorULo/LppgXUj8FNc8/EzL4C8P04xk+WcwOSv64zDCklmcwglFSSQCEXq/MZMCYeS+ARJihQWJfONSqVnKqTfxDmjSRNTXlulBBkZc7UaUknWTUpqDUMMjUaDRqOBNQZtLUu9HuvdLlJKojBkptkiG/vMoRGGDAb96tukocMSYG3VwXLOcebMGS5fvszRo0e/I6kQUkouX77MmTNneOGFF5icnHwgJse3GvdDMKmIO52PunbtGidOnGDnzp0cPnz4pten6rd1p++lujmSUrJjtk2rNr4B29ZsEGiJUoLQSQaYnMEg2Dvb4f0ra0ShYnra39MycVgBOgh58bnt/MbZj8jwa3O/v4S0DoNApRCEmjDWRIGil/vPCWBioo7oJzRXhyjjMNoX2pyD2rclAkOQwcBCQ/lFXlhNFGh015AaCFIvwOGM8Ebu1lGrB8jE0a7V+D9/4TV+/+OPmc/NbDv1iLrQTIbeVFVI0IkiyyTCZEjnDZWVgh1TLd68toBNJCK1EAh03nGSQpA6g76mCZUEl98HFjACocnVA4EUL7Zk8Ikup+pZvKpuYLynUpG/VJ5ryNfVUvY+p3z7zzTPg0LRG4wUUOdaTWQiGWY1hPPfP4cjMxLTz82Iy9eQJQ1QCkccZrk4Rr4W5gAxsx5pWARKOGRgcan2hctSJdG/9bwp6Y2B87XACMvKcIB2EjW2nuffTzuy9MiMw6SWoTEMeglaSWySlmqA4KmAWIuW0osdZaMcvro+ZCKMPTCVHmClmYEUpho1LgmRS8YLhknqfbOmphimGXUh6a2ts34tYdgblhXMMFBoK8mGGYEVHJydZOXqOipUBGFALxkiJKyuDBA4arWAMFTUaiFaK68uaMHmXTVrfAfrXnWvrLW8/fbbrK2tceTIkTsGVwBhLdi0ztyNyXE1vtMl2uERAVh3mkiGwyHHjx/HWstrr72Wc3FvHA8CYG3FqX/tlX2srlxkZrrO2xcyfuv3zyKcJQoUg+H4+W0EUjl5sKzilGIJUvoWdhCCMThjydKUzDhPKxQS7aT3Okgssp/SFqAmAsI+pEoQRSHdXkYgFA18C2tlkJA5SxxKjBAkwmBU/pk5f0K+g5S39CtzSL6Lkm8UnAcbIu+SeXU677PEEPRQQOjVoXyhz1fWbAFociBFRegib4ZR8taqfxhr7eWCGj3lqYPRuGask6MOmSj5EuNosnqHjoHMsmnkHe9xAu0kGkkmbVnRc9arBTrrSjEMjcSIER2UymmX3ariOqXeuNgZ77lhhPIVSSnzSqqrdN58R2nUCczBmht5iZWXiTxJOnApXrLdgghy42vpodS4R5ob+295L1SgtcnnB313bvwelnh54szhZfCd9hU/4SGbFAIlLVI4tPLgz6QjemEmre+iZZaPri/Rsv57P9mogQYdSjLhWDcJ9UBjgyDn+yckSUJ3ZYWlNCGozB8FW1QWq7/b0RhPFMUszcLCAkePHqXRuH3PokchLl26xLvvvsvzzz/P9PQ0wKZKZ3VGq4gbJd1HOW53Bss5x4cffsh77713y3L91Wt7J4Ii1c+jALwHdk1T36AUtrPdItQahCByMBGHLNohIHh873a+Pn+ZMNBsm+sAECSQKF8A3LNvFn32IlYKJI5du7YTnrhEsJIRdx2qCb3eGs16RG+lAFiC6ekW65ev8+f/jz/A/+WXfsuzAfoCd1H5mSoHgfObUy0VymR+bcoc8UAzzLxXXiQEGYIs8d2KuBai8swYa81f/dxn+W9+9atoKZmo14iQTEUjYaIIjXHGU6mtQw8FWgoO75rl18+c86JQXYmZ8HlqV7vFyqDP0IJeloShRhjjBSkKgQrwXSsBOhPIZY0tqmqZwg6lL1wKgXISkZly/fWiFj43Cos3UwaEkTRVjZTEGzLnIGttveZV8xxMt+sEQ4VKJLFssm77nnJnFcqEWGEgk7nQhKDqq1WvpfSHCmMlyjlsIXJhpU/qRe5RzntHkeekArAJSG1G7JQX6DLW51OFF/QC9s9Ncury4qhImO8ZipQinGNhrYesS0xmvfx5XiD04hCQJl40SStBAh6c5l+NfpKyvj4klopt2zos9Lqs5vTEiThCKYlxDucsJjPMzEyXlOjZRpOrSRc9kTF/6QpCOj+njcM5Q5oaaolmMo7IGikracr+HRPY66v0e4n3xKorWnHkRWTqOQ01v1A2F+ko6J/3Yv7qXoIrgPAWQN9WjIZijaka2W9kNHS73e/YvFjEd1aGq8TKygrf+MY3qNVqfPazn/1EcAUPRwerkOZ9/LFtfO61z/IX//M/xP/+y68Q5ptnkxkalWpjZkctccgTr5QlsMqMX61kPqzsnEMrhQ40cRznEp0Ck2UkacIgn00pVJ2ay6DnM+pXLdGyo4lmOo7LVnYs/UzMTNCgbQNaqUb1IBziqROWspRV0CXI/1eY/lEoyhVgyfouhKeCkS+2XtZddEEP8r6SA7NBOMIWXO58Pqds8Izt4fMEUPyyUL8TApdK3LpEVvxLPgngu8rLF2BvVHHMcWYhfYvzIBSHdRYpJEpKlJYIJcrXcp4oX9JsRAYyEX52qqdRvQDWNaxLTF+RDSRp4mWLhVMeNlWAzcYzLj29iktFRaq9uEQVTFp5dyC8kIbJIEvx1yxVCONnNoo5tbGjOjFSKMwpmlKIsWOO4N8Whyx/FBiXm2RmivVEsz4MMen4XE1N+sTQF6O5qUAqllb6LCx0WZrvkV7LqA8020Wd3XGL3a1JptsT7Nq5k87EBFprBoMB169fZ/7qvBfKqMxrFRXUyTgek38vBosXFxf/QICrF154oQRX1ajObm0lCfxpCgp9GnE7+aNgVpw9e5ajR4/eshdadQbidqNqMl2dt3rq4DbajfGN2J5OhyDw870SyY//kedQmUNJweHdc9RCTS3W7N41AXhRhiBxKCWpxQEKr5grhWTf3h1MrUviVUsYacJIcerdt1EiK7/XUgkmW34W47N/+Em08J11ueaI39B+pguHdqByEKAzvCBOJmiZ0M/WArHMKU3O55haPaAWjjavX3zsMRoqRCvJZLNGux4zW/cFEiFgQuczIanyBauhQyvJM7vmvJy79ep3Kl8f/8s/esR3T5wjMoo40rm1ByhHPsUmIPP+YcIJxNoIHMtMI5xEDSW669ePUikQP0+mlRf9kRnemwuQTrInngJcOYMlgJXVFpnJSNOEM++cJO4qdg3r7K3vRAiL1hZjpKfHCXD519AYUenbC7S0ID3oGvYDD+IEZMaLQ3lVQYGszAQ7K0fcdwGDtiUzlsRlXoF+4DDSG0vXg5Dd022f3/L86JwbA1jg6YJa+jxJmm8aMr9HSboZWipsasoiaJJm5doMkGSG2CnmJpo+xw/8HklLhZaSYT5bW2/6v6ep99qSid8ga61xBqLQr/G1KEAKPzOpjWV9dYVIC4JQ8bkXH+Nn/9z306pHfN/nDlGLA7ZNNYnDgGYz9yaTXiQmy7u3f/onP8+OnZPU7rKDZa3lrbfeYn19/a5ogdUIa7c3r/pJa34xu5WmKWtra9RqtfvKKnjQ8Uh0sG43Ll68yDvvvMPjjz++SYXpZvGgO1jFeR86dIh9+/aV5/393/s0x49/xPd935P8j//m91hZ6REqRa7cirAjhTNjHXEYYIap7zrgciW20TVQUpIZXyGTQhDpAETmQQ0Ok2WkmUEISRBojDW41MHQMAXUp2FdOrKmIlAS6yCUkj4QSU3fGupWE1tNZgwrNsFIz73Pcm+oQswByLl9/p+CCt2OESWw+LsSEpEKRGaxYU69s74qJsrnjwQyyteszAgVXZuNkwwjMoSEnh+2Nk1T8vJGs1zl/1UIjhtAjKTkR4j8gcKO6JGp8sO40vmEITIPeEIUSnhevnAOM7SMxM89FcUVhlLOi5eUXa6NECXv+pQdvPw6+M6gxJaGKv5dSCSm1K8vXq8CgFz1/eZdNIoul6/uYcE6vykT2oH0M1K26K0WHVUhN10yW17TKrVwHOUVoHF0bjlY2wAFdV7C7MmklORXW6wD1sD1tRH1zzmHnhLsjScQTUHfpVwf9IgQLK+v0V9e9gpkYchKfh7V7tXGJPedOsB78eJFTp8+zQsvvHDLoh0bK50PQgzjfibzW80fSZJw/Phxsiy7JWZFNarX71bD5WIxxblVwRXAc4d3snB9bew5+yY6COG9h4zOOPTENsJfk0gpmGnUmO40iJY1M1PNfH31whOiCVEceBEaKWnWI5ptX5DTCKabNRrNmO/7vu/jf/u9f8uVlSuevYAjS7o58E5Rxq9xtWUvwV6ozYUIQu2vgc4Eab4Rb4qATlRD0KOlNb0sASFQFpr1CJGNfy61IEDL3JfIOJrNqFxHtjeaXOp3UU76IpcBHSna9RhpPGVRpbmyIpI/8uxB/sOu9/jmux+hrTfEVflaX0ORGekV8xJB2Bd+vieQ2NSv+TLVIEANhVdHbLiRt5jyaooqUPhZVd/REnnR8aWp3ZxbPu9NjvMPYmW1xUQQUgsFu3fvpt5/h9mGwF6ziMBX/0ymfCFWybzwmQt7OL/6liVI59f9JAkIZIoA0iSXoS+KX7nEpCjWZSspMWXNMRx4U2ErfKcT5XPpk9tnGGbGg1YhRq9p/D4kZ9wjhCDIxbxM34EGm1psAMvLfeqhpjscEuQjDdZ5CfTUGIQQDAcpkRO++CwEZA4dSFxqsSbzeVhIev2Ujy8uEQWax/fPIRxM1WssrPfIjKUZxqz3U+IwoK01Wd8yN9UGJ7FZnyzt0wpWEGnEX/8zn+fQ/h38x7/zEY/tnMKsZkQFdBWAAZMYwkjzzIt7udYbsp7eecGpyDvdbveeGKcXEd6l8fHNulu/8Au/wIcffngvTvOhjUcCYN1qUrTWcvr0aS5dusSLL77IzMzMbR3nQXWwsiwr/VludN5//f/0xxBC8NJn9vJvfvkYv/jrJzDOEceBN6yrnHcUKgaDlGIH64UwqsfEmzHm9MKCFaCU9P4QQYATWf6FsAwGGb5S5SuS2WpGYCxqMUW0Amqt2HtKCC+MUJealg4YOl8pbRHStRnbVZ1+kpG4jFUyGkozVI7MmZKr7cALNFQ23xbvJVEs6DYfsCq8Vpz2whNOFLW3YpPuwxUDWQWIE4XU9zg0qsIT4fDDyCsK6jafaXIgxAbYkZ+3qPy27JqJsQf6IxaGwCCMIBNeFUoPQSqNE4I072OFSJQagSLrrAenDrAbbZVh4y8KAOQqragSRLrNT9z4LXP5da8+z2LHrqNzDukkpphszukPOG8w6Tn54JwEa5GhgFL8REAF5tpCpmrDMTdc6q3e6qZzL2JQ6WBlW2xKN0qvCyFY7yesD0fPi4Sm06oxNd0ik45rvXWWeutcXV4iMYZEKD788EOmpqY4c+YMw+GQo0eP3je1ugcddwKuNsZ3IkXwVvLH2toax44do91uc+TIEfQW5tU3i4Jec6t5qtjQlIWNLa55oxZS2zE59rvZRp1YaepRSNyRTE826YQhmTXM1BvMdBrUa5pOu5avp9DMB0ZrtYBQSVIpOPT4DJMdP8jeqIfMTDRI8vmxndsmeeP9q1jhiGohO7Z1OHnuOl/72tdQ1iClIhpa1Lohk34xi4SiGYZAik59sVA6PyP5/GMzvC/P0IxD5rueRycdTHfq9DbMT9aCACUkM+066dDQaPtqfyQ0O5stji1e9TR143DGEWhFqxZ6OxPrctl6gXb+8/gzf+xFvnn6YwKpcUBDBqy4hFBr0szP+GiriLuOXs3nAYnDWCD1XlzSORpGM7AWpBcmcjikFQRakSAJJR6duATlBJ/ZvoP/ednnEqQHiCbx76UWBuzevZtOvcGXPv8czF3gvaVzniKYKawxFKxxawTWyA1Ej1z0KFOQSlyocE6QDjx6sjklUVZEn5wszsWLcTgcSWQwCf45ueiUUpL90xN8cPF6LlzlUAWR0+KpeAEUYiGhUminMJlBaDBDh9OOlbU+s1HM4lqv7GwCREpzbalLp1NjuDJkIgrIBpn3p0ohaEiuXp33gEtInLNcXFzBWUeaAwGMIxSC4TDFWUejFrC4CoGUNGsxq7JLLQqp1xvMzmwnXlph+7ZtLCwssLS0xMKl89SVYM9snSuDLlHs84HM/eCyYcqufZ6SGMWa2h2CGWstJ0+epNfr3VNwBdxTk+Gql9/f//t/n69//et85StfuWev/zDGIwGwbiWSJOHEiRMkScJrr71GvV7/5CdtiAcFsM6fP48xhldfffWGQ3+lt5AQ/OkfOcLH56/x3kcLrAwTjHNEWpOagvOag4HyuRsTsocgBbWwSL5KSTJrvCqfEHmrV0Eua+u9tjKc83431gnkaopYyyDI6NQlYiJgTRkvRZoPm2opaaHRUgEZodC0nKBtNU5JEmMYZBlZ3pmxElLnvMqcpBSmcNXTdyNYIDMIjBfbsKEHWiMp9bwyVp0REpv/Vlbfxq+6v1I9hVMWV3O4MD9wQWnIH1XI6QKlcIUwws9vuRzEmlHXzmOvvEKoIK0LpDEEVpVv1OaqeQWQUVbiVAGI/XkY56uDoqLeMXqveWeqlFCvXoPRu9wE1Cr3hNsAdqQTmA3mwTle3/S7As54006LdBJb0EPx10IJBcriZP5ex9tV46i3PJFx6CXx82fVrmWWHz0VBoNFIRlsUSHsJukn/k5JybXV3ui0kOyrzRB1NAbDwckO8/PznD59Giklu3btotvtltSI76T4+OOPee+993jxxReZnJz85Cf8AYpPyh9Xr17lzTff5MCBAxw8ePCOu2m3qiS4UcziZsfbeJ8KIZhp1JE1Q71ZY2Kyzh/9/BN89etnaAQh0606nY6i1YxyGrYgDCUZglotJNSaoRI88fh2Oh3fodu5c4LJiTqLqx7s7N7e8fYTxlMLn3v6MX7r2x+hlGKqVSPDEPYSXArkSoB1KRFBgCH14hIOHt87i80sj01OcW2yzmS9juh1EfhGyo7pFvPr49erEQYESjI9EdPtp8xONBB44Zr9UxOI8+TzXJogcARa0o4jP/9pHIFUDLClHfrhfXNe/ED5hLVvssPJlQVCZ+lbjcCglaazYhnEFpHPJWNADAP/ukAUagY28UW6zFMvpRMoKQikIBYS6RTCCBSSfa0p/zqZgHxfHTh/f9Ry2nItDPji8we57lr86vK/9wVBo9FSEjvoCpOLglSKhy73ynKSYKhRqSRTggsfToOQXtG1mC3LK4bWAdrnJWE99VA4yEJXGjN7r0cPUoyx9JO0zO1aKhJr/ONykFUQGGKlvbBEatE1iRtm2HpArzvkwJynGfrOV34NlGB1ZUCnUyPpZrQnGyS9FOm8pYC0xptQN+osra95oI73Fk2tpdtLcPU6/ZUBST733mnVEFdXCAPNVLvGJQT1MGCYGOIo4LG9c+zdu5e9e/eSZRnXr19n37vX6F6/ytUrq8xMtLh06ZI39Da+sPzsM54a7C0Pbn87fj/BFUBwH3y5/of/4X/g7/29v8ev//qv88orr9zT13/Y4jsCYFWrgi+99NJtVwWLuBdGkbcTvV6P1dVV4ji+qTTvVvFX/g9f5O13LrF9R4d//C/+I2+fuYLDy4SOZNxHG+vqnJafw5KlUV/RySoSsDF2tM31TRu0Uvnfc98THGmakeGrewaHWQKxnFBXgsZcjTgOWbYpFi+hWm2kRUKBlOWxY+E/M+lypZ80RUpNPQgYYEmcJRV+Z25ELgiRpwOHX4hlCioVmMBBQN4Rc6D869r88RZXEf8mfz8CKyrAolBszB8kE4lLHbImyKLMU9gKIFVc7UyUZsjVLlHRyBrrcpFT1R0l6rIaEmtQma/KljLurvh8/NGs8xVRkddBZUGns/49GZHTP6UoExSiLAaWnysb/j2ayRp5jIyZkI29wA2ieHj5c65+mQ+3ltS//LGp8fQUJWWZqIW0ZRew6NNtCQLzg0gnUE5hxQhAJbbyb5ER24BuMu5jFUjFcMMcUDMOWRuOP64RBiz3x9eFQCoWlz3oeuYz+1i6cJ6JiQl27drF9evXeeONN3DOMTMzU/7vdr7fD2N8F1zdPJRSpOlmwF5I9Z87d47PfOYzbN++/a6P80l5aisxi9uNbY0GvXrCruk2jUbEqy8+xvLKACEEs3GD3TviUSfSjtaGWi2kFmp6yrFrR4cw1CglefrJHZA6rq8PANgx10ErgQt8R6S/toBzlpdffpnT6zH//ltnaGSCofBdBXCYJCG0hnVrCRJfzPqzf+ZVfukXjnGoM8v52TbbJlocv+aFExSC6VYDNTO+gjSiECUlE40a9TAg1gE4QUfHPDXnGSTSeYpkSysCrahFgd+IZxYtBcoJwnwmLgo17UZMkhdxdrViTq9IJhpN+sJgSTnUnmSNFUJpvACIhNl6BENfkBR49dM40Ng0Y5jPKUvhbTO2dVpk2ZAM0JkkCjVTcdNbZWQSF/g8FuQ5up7P9Dy3fzuNOKTuduGcRGCRNqQVx+wJ6yxcv4wz+fxxvjA78B0tB6SCYCgwcVFVFHk+qVDeCg59PlrmUoEN/edjBJ6JkfliItIDoJW1Ab3cK8oJ0FIwzOexhXN+ftufDrHWBM4fP8gExjhqQcCq6VKLNAIYVgTATGbLvYUdZNTCgPVuD5TPR60goF5rstZfxzlHIwoxqSVL/WssLXexEy2G/dTT+IHpiToICJVkaqLuNUUyyyCzxHHA7t2jNVFrzdzcHEefPcyrn9nHL/+749QD6+2C3r5Mf73LmlM88fw2nHPUouC2VQQLcNXv9+8LuIJbZ4/dSjjn+Mf/+B/zd/7O3+HXfu3XvuPBFTwiAOtmH/KVK1c4efLkXVcF4dPtYF2/fp3jx48ThiG7d+++7c1XFAW89OI+AP6vf+2H+B//p9/hq7/7Hs5BmlrqUUg/SUu58mpY68b8FjJr856WD4H3kDDWmwSC36xjKGmAQRBgyTsr0g+GZtaLNkgjGS4MccbREo7aTMygJrDZhm2y20zbCqRgMEhRSqGUQiKJtESmflC1mNcKA0WSL4axVAycQShHklkCq8kywzWZ4AKwhZpT/iYFfti46meVq4/nF8BXE/OcXpwqOO9dIvoKUbM47Uqvr61IaqP5p+JnnzigUFQkN7WshIA0tCjj5we2pMmVEoL+NQupdj/Thd/sCJdLqucdto2XPhcHcTmodLiRsTMFQCx7fGOf2VbnVPlz+T5wow5cVbWxOH1boS46/PX2VcRcaUgVnUg76soVyKwCkIUTm9R6BjYtu2FDkTEdNeht6Ew1os3AKdbBJoAVbVGwKTZWzjounnmPVi3m+eefRynFzp07cc6xsrLC4uIiFy5c4O2336bT6TAzM8Ps7CyNRuORGu796KOPOHPmDC+99BITExMP+nTuOO73DNZgMBj7nTGGt956i6WlJT772c/eEx+0T6IIVtW77hRcAexut7lU63LgwAxCCPbumuTVI/sBmG00OND2G8pAeXlspRRGQL0e0ohC1nTK7t2eQhqEiqee3MnClRXOXVkCYG62jVbKU6ZchhIZ7VaDVqvFtsk2E40YhUALQeC8eujEVJugUWPFdqFncUYSyIQ/9eUXmJzq8LVYs2uyWS43oVbMtOo0NvhdFKa1UkriKKduOcFUVOOZnXP+Z6AWaqbjGsPEz/OE2s/2aK1QzhGr0doQBYo0M6RZSjAcsGOyRcvWWBI9UqH4iZee4//9ra/TCAMGyhKGks89u583z50hkA6Tb+SbcUSaGEQmkPlaKITge5/dz7e+fYHFXp8pUUNKRUOFBEoxtBI5EBAJpuIaXRIaOSXtP3npEODvfZvV0KKHthEy0NRdSM0oskzAQPk1P1/gMytxSMwAalnAQIyMcz0robKmF0wN6XDSIoYjiofDYaXPqeQMjIHMmF9apztIysKnlJVJWgfO5IVOoBZotFU4IOlm7JlqMxiYfEZLIhD5WISPpeX+qMub+pm2pJ8wVAnSCaabbdLEoKWfxW7WI9bXR9/dld6AbOgLFFFubr9rquO7aaGm1YyJAk2vm1BrRtSigMMH5jZ9h/bvmKJVj5mabPHEgW0cfGyWODjHb//+byKcY2ntI77+9SukJiKxEcaYW1IHtdby5ptvMhgMOHLkyENfvHPO8c/+2T/jZ3/2Z/mVX/kVPve5zz3oU/pU4pEAWFtF4TFz4cIFnnvuObZt23bXr1nMQ93v+Pjjjzl16hSHDx9meXn5roe9rbX82R9/hQvn5vmeLz7JL/zqCdbXB0hXDMeC1qrsWBlrCSuqSh5QjZQHIb8WJiu9K4rNqlQSm5nysUIIgkCXm/sisQ8GA98VQtC93ENKQbMZEseaQegYaucHbit7XmstxmYorb1iEPn81RhtbLNYQfFTKDUOg8ss2ki2yYjQaZZtQi8wDLXxMvBQzmtViIKUo73O5eBxxFIr/g05ja2vkAJs3XgfsPwxpXiHK0BMUeXLf1kxCxOVVlf5yFz+3UqHUQXQKvpVxfELiOGfK3N4LPJ/25x+IoQH07boXuY5rxT8EMUkWi7RvuG+2pLcJipOZlXMQ3EN8qphbpQspSzP2214waqYSXlMJ710sIPMFKDRUzRlThe1ophgGJ1AlVqopSSzFu0UmTAkIqUWBJsAVhRoGB/N2DSvCFtBZx/WOobra3QaO3nuuefGaFZCCCYmJpiYmODxxx9nMBiwuLjIwsIC586dIwxDZmdnmZmZYXJy8o4ktz+t+Oijj3j//fcfeXBVxFiH9h7GxgLdYDDg2LFjSCl57bXX7omqV3GcrXLGJ4lZ3G7smejQbS3zxKEdALQ7NV78zB4AdrSaHJj1ypFBoDBS0IwjrqcJ9WZEpxFzzVr27PIgrNWIeOKJbawt9Zib9Z5x27e10VKQJCn1OOBzr73CL//HBQB2TreY7TRZCzVumOECRZZktNt19u7fzUenV7DGsCIcSwvzrK6usrzcwpiETujpb4kQdBoxc+0GrjU+MtCJo020SIVke73JVK2W09IFjShkbqrF1WteBCQONSJx1KOALpZ6pfiiVL5/UPD5zzzNyocfoFagQUhPar743EH+efx7RM2Qq2ZAiGDfZJtLYZsFtV6KDoVaEWee+i6tz7lCwMtP7OHcmQW6WcbjU5O8nywjpSQQCoyk1lXYjmV7u8XZy9do5QDr0K7Z8hxt1oCgR+TqyDAkthqVQZZKxFCDFYTC0zCMUWUHSw/EiM2RFwhdJWcU/oRCCFyQs0oGeZ1NUs4d23yZW3cZWWJIhllpQE3BCim+m8bT/YUTTDXqmCwt6nZc6/WJhSz3AkLAYJASR14xcv7qWtnBkhaSYcr6ahc9GSExxEqhnBdtAZhoxfT7o8LaIMtYvLbG+asrPL5/BoRg+5y/b+MgoFHze5lhklEjIo41jS0U9w7s9Pd/oxaxY0fHPz+KUULx1DM7+eIXv8jS0hKnT1/g4wuX+a3fusbU1FTJethKAOdRBFdf+cpX+Jmf+Rl++Zd/me/5nu950Kf0qcUjA7CqSTFNU95880263S6vvfbaPTMr01ozHA7vyWttFVURjpdeeonp6WlWV1fvGGBVE6oQgr/13/wwUkq+9/OH+Ff/y7f51d94m2SYEQaKZhyw3DWjRXJD4pVSjNEIhfAJozi3YjNbLEjVcy7WQ0/z8s8TwncVhklGZjKkFHTXHGbNP64VaURoCbXA1ARdMozNqDUbZBWajcNt6r5IRNlZg5z7DeXvBAIlhB8Bc9DINDt1g8VejzWdMQgsNq+4+WOMR9HZwY3emyx+X+3fGBBdhVIOW7MlNbA8d0k5l1Ucp5jHKjc+RRer7OTkLbr8Z6vASIfOZ5cKMFUko4J2eKPtYtkJEuCcp00434pEI/05ikJuYlObq3J1qv/1SbDoKlkxMpMtxDg81c/7bRk36oaN9cgsJaVk9PKVrmJxzzlJ5iyjvav3EnOBn2UY0T99SCkq18p3sPQWPla3uvXcQk6EYZqysrzM3onWJnC1VcRxzO7du9m9ezfGGD8EvbDAqVOnSJKE6enpMqk+TMqDH374IWfPnuXFF1/8jgBX9zOqAGt5eZnjx48zMzPDM888c09n8bbqYG0Usyi6HncT+yc7DPZMMpdvLIUQNBv+3tzb6hAEfvsQhZo0M0xO1Fm6mlJvhEy0Y4K1IbV807lv3zStTg2pBHtyKlWWDXAmRQjJRKeJ1prJtt9Q7p6dYOdMi48aoaeoRwGZdbTasT8HIQhT0ELzyiuvkCQJCwsLCPk+Fz78gJoWuJpmIgxpxMGmAkYnjtFy/PpoJHtanTx3eQZBvRayY67NUj6DWQ8DAiFpqoBr2ZBm3v3Ksox0OMQ6RxxFPLt7F7+zcIUn2pNcv9zDyD5CCPbtnKLbhoXlIUIKHp+d5uPOMmdX+6SVXNMmYjnpY+woV081amybbLFzrkNtKuLqdV8d0lKBFdSSkDiQ7KtPcJZrtLbY8MusgwyvEasGkQiJjUYICYlAphprpZ+fdh50OQTWaKIwRtsBmbDlzNTI3NgLXZgRZxuUwAWeseFyqr0DyNV+MyAUiiw1HnQ5z2AQjNgkssy4gqlGzPU1k8/7QT/JGBpHrFV5bw4GKVGoqWtNrLUvLlrvhba2tgpGEAgNLmW6XWf54hqXz10jbEt2TLa4ujhS0nTOce7qEt1B5mevgKf2bQPh7/d6PaQWBwz6fq9yI4n1Ylxl144J6rWRD5YzlkNP7kBKyfT0NM8+W6M9vZ39u9osLi5y9epVTp8+TaPRKFkPnU4H5xxvvPEGSZI8MuDqF37hF/hrf+2v8Yu/+It88YtffNCn9KnGIwOwilhfX+f48ePUarXbnlv6pLifFME0TXnjjTfo9/tjIhx3eszqADOMJ1QpJT/xo69w8fw1rBac+mCeXs+3xnVOY8g2yNaGgaKXVn7nxgFWAWKKlGSMK2dqbIVGaE3eIXGOQGu0djj88G+aZhjjRTTS1FebQqVIVhJqmaFVC6kriUGRKEcmLMZuclRCS0nfVPyI8D5fVQd3mXd00tRinaM/SNFItsk6NrGs2IShsgy08W9K+oFpQ06DzAFMtVtUCmGICggDrza45mXJXc2NOkWIEQhyZc+JcSXC0b8QXuBDjNplJRbJpMNIL3RRbBXGhD82EBWLSh9QyvnLvEsprcIKl6tE+scpKyAsuoMeGCFH1754rWKerKpQWLE98Z1Ni5chrnxmwo3pOpbXQYhc6KM8/8qmZ8M9Vy2yOCcwxittiZUYQkkYSDKdjbqGBcDK5YU3ht2I3NmsKghsEsewxvLx1avoQPP0gf23vXlWSpVgyjlHt9tlYWGBy5cv8+6779JsNsuk2m63HxiV8Lvg6vaiWMsvXbrE22+/vclu414eZ7zAdffzVlvFTL3OSy8d2PJvcxVvt1otQCWS2bk25+eXaTQiZqdbqN5ow/rUUzsIQ029HjG3Z5L5+XneeustWo0aa/2s3KBOT/rX3Tnd4olds1xrxVjrCFsxS0lGZ7JBqxHlIEhSyxfbMAzZtWsXu3Zt46Uj+3n72FucvnKdQKV8/etfL79P09PTHsjV401Fl0BKHut48KeERCJoxgG7d05y5sKiP45WtKKQCRPw0dI6nShiOBxy/PhxL0QRaAKt2NVu0QhCvvTCE7y+eJlMdwF49vAO3s9WqC+v0AgDntw2w4fblvnaRx8SagUJ1IOAibk611aGrFpbgoqZZp3d2yb4wmf2c3z+Mp1enJ+3RlhBXQS8MruTZxtzfJWztOubCzUN5sg4T122qAchcaq9oFWqEalXmBI5GrJpgFMGlXq2hs4UJsipoDYvfOZFOCHdSDRX+F+7yOerQsIdQBRsD+EwmffwdAElTaRgdYiCni98vh+NAosy8RXqikUMBxnb9zVZvt6jHUd8ZFfprwzAWeI4QoqkzOmh8nYy/UFGSwXY0yvUooCl4sWsl1EXQtBpxQjhZwaVkNRCTaMW0axFrK16WmH8CQqA+/eO1FalFNjU8mw+4uGfHxJHIc1mk2azyf79+0nTlGvXrrG4uMiJEyfy53pa60svvfTQgyuAX/qlX+Iv/+W/zL/+1/+aH/iBH3jQp/OpxyMFsObn53nzzTfZs2cPTzzxxH1JXPcDYHW7XY4dO0a9XufVV18d+2LcjuRuEdWEWsj2bhU//Ve+j14vIY4D/tX/8i1+83ff9x2t0AOeRIrc+4qxGRkYSaMXYUqgNdr1KiXIjCv/VlTalJK4zFQ2zf53xkiC3HDOWUuaZbgkwRiHUorACPS6Ra0n1AARSFRLkWpItCOVvpcQaj0GsEwu2pFUrqMkl7bNQaPJqYFpZnDWMRFGhFay2h3S1YZhYDHS8/xL2kO+wBdgylX8uqpYobwPM4FccxCCie0YAvKjV4XQQ+VlZMkYzK+VKI8L44dzAjJlcdZ3bbQYlQbL08mfoIQgy0GdE8Urjz5qIUAKhRN+6BcHzjqvIJnPlSknRgbVOWIraI8jiXaf8AQeVHvfNVW5QOPvY9O1K97bhj9VG4Vb/ap4Pa9MKBgMHHYgAE2tEeFIEMqB9iIX1Y5nEcNs8/eul4yDKefcmKqgMYbVlRWCKKTVbLKtfXcmwkKIMqkeOHCAJEnKpFrQy6anp8c2h59GFODqpZdeotPpfCrH/LTiflEEpZR0u13eeecdXnjhBWZnZz/5SXd4nCJn3C9wBf46NW4g01zNOc1GTE8mbN85gXr7Y8JQs3vPJOHl+fIxR/LNZHuiRhAMeOut93j22WeZ+J1l1vrLpUT1tplW+fp/6Ln9vNU5QZpkTM00WV5cZ3KqTjvf8AI0xPgms96IiOOQvdumOLu4whdee54XXtjDwsICZ8+e5eTJk0xNTeH6BpOmUOkWh0LxZP6ZRcrPWLUaMfv3TBG97jfygVbsnGixp97m7WuLtMOAb33rW0xMTNDptFibXyHM1UN3NptMNxtMx3Vc6NeJ73n1IItvvItZMdSbMc044sk9s4hvCi9KkRhm63X2dNqcP7FML0sQQvDU9llatYgvfe5JGrWINZcwsXgJgFhqMI46Ea9u38sXZvbz/5K/T6exGWDtiHbwsZM0dYtWFFMXGolAZgqVKmzm/RelEMhhiIkThJGIQBD1NMOJ1D9eCpwUuWiGGfWaSlUl/DyWrbBBgDI1COgnCTZzuNj/TjtJgs/PIoVaXdPLMrQUmHS0xyhWbWkcOhTl79PUcG2xSyMOCYxXKbx+ZYkg0MRRDdya74Yi6ScpK4MhUklage8starzdAOHUAqBZbLjrQji2IPnKNQ06iHNZkzy8XWcc0SfIFBR7aAKKWg3Y++9Vtx7oabTHP+uBUHA9u3b2b59O1mWcezYMT96ISXf+MY3mJiYKAt1D+NM76/8yq/wF/7CX+ArX/kKX/rSlx706TyQeCQAlnOOs2fPcvbsWZ599ll27NhxX45zPwDWtWvXOHHiBLt27eLw4cObvgRKKZIN6mY3i9tJqLVaWFI0fup/9wWmmzG//OtvgZR0ewlaCmSgyYzNjYVHG4/MGM/trhxXCoGpCFVI4dXfymqqGP0exjsEIxqhwFqwUmFM5itQwlMOB/0ByWAIUhJGgQcSaxbtLCKzRBJETVObFKSZJFWOVPjhYLVhYqjoshRR0LwCLRkmhiQxKOH51JELcCkkWUo3MAyUJdObwc/Y5zCGfEagUyJwKahUQgA2yEd4y+peFV75J5Uy7tXXr6ILN+oYge9IWeWwziA8G4Pir1UlQFyVRjg6riipfyMA7BNmruKINz82zkLmE0L5XpVX5jL4+0FYT2p0OS1Q5gPHZeer8oEUvylm3YpypxCuArBGnSxXecamKO61nNTvCwIC66A/NCQGIjROCmQAa7Vx6q9zjvUN3zslBf0NKnD1MKCfd7CyLGNlZYXpVhPyeZptrXtDTy4iDEN27NjBjh07sNaWQhnF5nBycnIsqd6PuHDhAufOnfuOBFf3K7Is4/z586Rpyuc///l7RlvfKooO1r0Ss7jb6LRjELBr7xRBTg9/8qldRMffLx8zO9vCWsv1pSs44SWlO50O3/PZx/kX/+u3y/mVHTMjEZBOs0Z7skG/O6SVUwdrtYhWKy4X97baALDqIVEcsGfG37cH98+Us5CHDh0qu8XmvXP8/u/+LpOdDrOzs8zOztLUITubHuA1ggDlUtqNiJmpJhMtf/wo0Oybm+RPvHiY/+Xbb5GsLLHt+f08/vjj1L52AfBdLoC9+XenE0fU6hMAbJttUwu96l0t9Od+cMcUONg+1eapx6f4T7/4DOc/vM7Xj59nUoYMcXz/0wf9edX8urNzosVE5AFUPQhRNqUVReyud2hEIdPNOhONzfM7u5ttLq02mYjqNOOIyAlPs84UDAQ2lYSB72qJfg0RWJTwn2mcBKybIUJLtFAYLMIWrBkHJs8rOZ9eOJH7R4pyvXZqNPfbt5mnrBcUwYEhsN7dJRSSVw/t5rdPXyBUuuLjNrrHRebQOdAXwq/fznlJfTsckmUZQgY06rpk7AjhC5Pr/QRjLK1QM5nPqsUV0Y6gZ5EdAQampxr5+IOgFgcIvHdcqxnhnGMwSG/Lw0oIwff/wLPU6uMUzsLOYGMYY3jzzTcBStZWv99ncXGxzA1RFJVd2snJyQduD/Lv//2/5yd/8if5p//0n/LlL3/5gZ7Lg4xHAmCBT2D3SoXpRnGvAdaHH37I6dOneeqpp9i9e/eWj7kT08g7Tahf/vJRJiebvPDiXv7l//wtXn/zI9YHiV8TtaRWC+j1kvxYm5pahIFmUKnme68sMQJYFaABYCu+FAWNUOXiGVmWIaUkDEKSNENJ6emLSeb9MboDcN6AMqoF9I3zMuTdDCEyVDdDAbEEESuCpmCAxHsi+vOpUgaN890+u6GTkaSGKPDDvJHSqEzSysAOLUPtsIGgK1LfAauCqs1MtrGQCEjBpRIZggusVxyUxXUavVgJOLZ4oVI4o/q7nCVhi8lhLLjcZ0rmHabyPDzt0RXntAGubOx8eXpk0QajlHk31nnahvAeU0L57qcwgHAoIXL/MX/SxTvc2CioEgUZuwrFefirUe1WbRTC8E/ME24lKWoECa4E9hkWY6E/gCXRpyZHSTDSeqzjCd6sdGXDDGYtB1gFuKrVasxNTTHf9ZSfueb9ATng14bJyUkmJyc5dOgQ/X6fhYUFFhcXOXPmDLVarUyqExMT9ySpfhdc3X4UDAWtNWEY3ldwBSMxpnslZnG3MTXZIDOW/QdnS9rd9u0dGhUhpSzLePPNN8myPq99/pVyeP+Hv/9Z/t1vvkM975Tt3DZ+z01MN1hd6lFvRCAgrgVMtmt5bhJMBeOb0kYzIooDHtvuKVnN2ngXp9Fo0Gg00BOTzNZirl27xvz8POfPn6cxtLz33nt+k1qv0cPQafruxcH9eWcrUHQaMS7rI9KMw3t3ceiQV+kraI5x/r4fm5zw76FWY2pitE5E+d8LGfUo0Cgh+MJz+/mJ73ke8OAS4ND0DGevLzFdH3+frTBiJr+GsQ5pKc1kI+ZAw1Mcd021mWpt7mDtanaIrk3TjCOmajVvNiwEsisRViBc4JWArUIKhbIBKqf8CwQ600gNQuW+g7mMrpDO22wApaJF/me/nvsKW6ncK6GXJT5XFAALh+4LUkBryfzyOvtmJxhkhv6qX5eFGGUHkUJUzGDhi73JMCMNh1y+OE+gJGbgaNZjspy5IoWgnneTrHXs7DRoFyqSfV9kxjpE3yA6/kgTnXopftSsRwgE9VpIu13DOlhbH96Wh9XUdIMvft/Tm76zYbD5NYwxvPHGG2RZNmZBVKvV2LNnD3v27MEYw/Xr11lYWODtt98myzKmpqZKEaV7Ja5zq/HVr36VP/tn/yz/6B/9I/6z/+w/+1SP/bDFIwGwhBA8+eSTd62290lxrwCWtZZ3332XK1eucPTo0Zt6xtxIEaoahZiFtSNltjtNqN/7vU8C8F/9uT/M6feu8Pd+7v+HDSVr60Mi7amDUgqyiq9JEWGoxgBW0ZkpoqBhFbRDh0PkHa5CQMNYS5amRHHo6QNjyoWiVCW0+Xt21tFb7ZMaSxBppFIMqzQuC65vsIlDZJYA0DhqTcVQAoHEasgUCC3oV2hhxZEzM/LMiAONEQ5rJdo4QiNpIDEKBsowcBlGei8uYFP3qcBOJdYUApGCSHOT4GhcxKI6czRGj6v+w6OpcdpgBaMV5+Jwm5X/xuiHxYWudJdyWl+hqijyxFgAobIyiC27W9ZZcqG/coarbFE5h3Sex+/pHG6MwlgFTgWYKnTbi/k2xq6JG5PTL65x+bts9I4LUYuCspo5m3vISPo2GwNY9TAg2SDRHgUKNmjcBFKSpimrq6vU63VqtVppcaCEZGqLKvH9ilqttsnIcnFxkZMnT2KMKamEMzMzd+SJ8sEHH3D+/HmOHDlyXwtZ30lRMBR27tzJzp07+fa3v31fj1es/9euXaPVajE5OfnAqUGtZsz15R5z29qVQX7BRNvPGQ8Gg9KS5Avf8+rYvSml5Ms/8ByX51cB2DE3ft9Nz7W5dnWVRtuDhTgOfKU/pzlvj8YLHM1mTBRqDk57hcPmFkIPALPNBpHW5edmjOHbv/nbGGM4efIkatjHZRmYAVmW8eTjXqU4DjRJb52TJ6/Sjmu88PiB8jVHgCnvYLU9WNzWavDU9MTocfmIQL3S9dCI8vngTW3bjZiXH9vF4zumeeGxnWPnL4Rg/4TfV0RCM10L2DnRphH663Rg2yRTrc3FnwOdCSbMLjpRxHS9xtqgTwzYVKEDTWBDJAJN4Gn21psrFxLy0nquiBSSVBg0mtQlHlzpfE02lftRjOd4KoIXa2mGtFAo6EsExqRlCnh3/hpBJti1a5LL564zvW+yLB46/D6jUV4zz8i48MECbntElgaoxJAOMtozMb31AdZ6kBjkcvCdOKIjKyydrj+2zHweFUIgpGCiVUPnoEwr7w3WqIVMtms451hbH9wWwJqYurUCzI3A1cZQSpVdWOcc6+vrLCwscPHiRU6dOkWz2Szzwv2e6f3a177Gj//4j/NzP/dz/MRP/MQDX5sedDwSAAv4VD6oewGwkiThxIkTpGnKa6+9tqXMZjU+qYN1MzGLu43DT2znP/9TL7Nv/zS//Xvv841vnmO163eYUaiJQkWW+Q01bG6wuLFOjN/YOucBSxFKCqRQZMZgjEEaiw4CAq1JkmxLGqGUEpcrIwoh/QBpkmIzi0lTkv4QHWqEUhjnKRnVayLw0uaum9MC8L7DzWaEHqSIUEHg+eD1RkQvTTESMhw2taRYgkAhtcCmftOvDDSMoqMDmjLk6qBHogyJ8LNbRU5x+f8VSWLE3hOIDIRROOkHe1357Ru/jiXNr3qxN4KuLdpdVpQahMgRwisfWwpEbHiecAInqxCIMQENUdYg83tP5BLw5eecg//80Q7rPa1KoOkQ+SwXQiCdn/Ua0QbzY1oByo3RIUUuxVgFZrKKaMeUNLx6pKkoqSkkQgoSt0HUZQtZdCU2d4CGw4SVlVWazUap7lfcs9ONWklR+bSjMLKcm5vzSX5tjYWFBT766KMxz62ZmRlardYnrhkffPABH3zwwR8IcHUv1k/nHB9++CHvvfdeyVDodrv31UexYDDs2bOHDz/8kJMnTwIwOzvL3NwcU1NTD0Tuv9mI8rlOwWyuOAgw22mwtrZWqik++eSTW3ZZf+B7n+LdM1cBxkQLAKa3tWl8UKPZihFAVAtp1CNPaRaCXY3W2OObrZi4FjDRqhMH+oYAa6O3nVKKLx56gqf37cY5x7/r/QfOnrvC+so1fuu3foupqSn63VmSXo+VxYQ//kc/z8Try+ydHnXcCppjAZSK9/rsjjl2Nkab6jinCDZrlRmcIKARjZ/rrrk2L+zfwdP7tragOTTlu3SxDtjXmWTn1OhaPLl7lpl2fdNzdjdbzKpdTMQxbaV576MLzNQirjnNMMtoyAaIJSKRAy0ZYKUg3wIQK+1FlnLWghaKzElfWAssbi0YAax8HS8EKxyU3SqE/7dw+X+NH0uIhSRxlp62RGiSzBfJlq/36Gxv4boZhamlAOr5NXOJxZqMbi+hP2wyGA5xQwPGEQaKtdx4WIp8LCDQTDdrcH1EE48Cjeo6ZOLZLmGgSVJHuxkT6pyKmFuF1GsBk5N1rHX8ke85XKpr3qswxnDixAmstTcFVxtDCEGr1aLVavHYY4+RJElJJfzwww+RUpash6mpqXs60/uNb3yDH/uxH+Pv/t2/y0/+5E/+gQdX8AgBrE8j7hZgra+vc+zYMZrN5i1/KW7WwdoovXs/eLXf+0Xf0TpwYJb//E8d5b/9O7/Ch1dXyDJDMvTdqjDMDf42qKmlmSn9qooIQ0VaUSOUws8Amdy0L65FYwCsKg1fXAcvGQ9CSpy1OCdyOqIiCDwwwzrS1C+2xil0qLHOGx1vfN2xczG+40UflLSIdUeQZgT4TlQYS9q1gGHX4JRAR4pGGNDNUmQoGWaGgUmpK01TBiSJl441WIyGrkkZBrkflXDjSufFeViJG4JIfXLJAlM0cUYdHVsFIFvAqbG1yx9HFkkMx9CZ0WtR3VRW/KiEf9PjxMEtwN0Wx7TGJyGhBFK4/JQdFG4uDqiIXTgszgq09Bx75TyYkrmflYDKbFjlPFyRj8UIjDsP4gQgTPX+c6gNBQuVD78N7fj3usrlL9/Thp+TJGFhZZlWqzlGs0jzLujsfaQH3k4IIWi327TbbQ4ePMhwOCyT6gcffIDWugRb09PTmzbh58+f58KFCxw5coRWq3WDo3w3irDWcurUKa5evTrGUCjW8tJc+x5GlR7earV49tlnsdayvLzMwsIC7777LmmaMj09zdzcHDMzM5+aylizGXmpb2D37hFbox7Ct771LQ4cOMD+/ftveE2EEDz1xPYt/za3a5Jm5zKNdg0HNBrhiOUwNOzZUAxotWLCHODMtBre6+4W44nc10sIwVSzyUK9xh/+/GdpNzXz8/OcO3eOZNCjvX2WpaUl6oEqqXww6pbVNgCl3RPjXYN6PnvVjMPK7/RYBwvgsb3TTDZvXKDdlX9XWzpmdmaKyUo3/cWDO7eUDpdS0olqRMDls2eYbLd4OmzxzTMfM8wydkTTWD6mLmKEE0RSYVSuIuzgB3Y8xgt7dvKPz/4+iUtxxqK0wKQgehqXbCFwVOQxkRMxcnuOVDh0QSPMZ30DCQjvsWWNQUjhZ35Ty8pCF7eWIaXDNv0aVo8Chv0UM8hIhkNwgv7qkMEwxQ4NOud0ZMaUrJhaGBBoyWSzxmoFYAVaoRKHGPjcO9Guka5a6vWQOL+WAkGrHqG1Yna6wQ//sWf58T/18g0/ozuJKrh68cUX7woEhWFYdmmL9aKgmPf7fSYnJ8vuVqFsfSfxzW9+kx/90R/lb//tv81f/It/8bvgKo/vAqxK3A3AWlhY4I033mDv3r0cOnTolm+wG3Ww7qc61I0iDAP+67/2n/Br/+5Npre1+bXfeJurC6skiedpW+kXl94gxViLkjKf2RlFFAZjAMvhSJLEy7YHAcUufaOpsXVethVG+3ilvJdVVQGuUMDTWiOkF2NQQpANMnAWJyVOCIZJLiPvRmdSPS8oQFil8+XyStp6hs0fmwJIQSNQgEVlhiACLUFoaEaBlyQXluWVvueEW0UoNQMMQgu6NmVNZ0ROevqhdBiHrwpaL4ghtMBom0ujV6mDbsPc0lZku6JTJqiqcjh8Vw4HOm+pCTvqVhXQSrgtRCkqMaJ4eD6gdaOZq+LgQniZe+fIaSBFV8v5YwoQuUEw+e/8dbBlk005gcMn1GzD2RSiHcV7tS6nlFYAlnXjlMviPTpguKGDtZVwRlYpdAyHQ9bW1tg5M02y4TV7uRDG9KdID7ydiKIol6zehbWWpaUlFhcXee+99xgOh2NJ9cqVK98FV7cRSZJw/PhxsizbxFAogKsx5p5Whm+UC6SUTE1NMTU1xRNPPMH6+jrz8/N88MEHvP3220xOTjI3N8fs7Ox99VbrVFT9Hjs4B3hz6vXlq/yhP/Iy27dvDZ5uJWZ3dmhP1GlN5PNGOYgJA0WCINogLtCeqBHH/trvnencVt6cqMw5taOIRhQwNdlAK8Hy8jJhGLJv1y6e2L+d1dVV0t4a3/jGN0p6VgGQ6uH4Z7/xHBpRCALajVHRph6GY4AL4JmDO5ho3vhzK153MmhxaPvMWKH2Rr5MANpZLp07y0v792GbU0RXFjlx/gow5KmJXbzDm7RUHYEglpo0gGGWIYRgZ6vF3nabv/3ZP8pPf/NXSFPKPUCUhfQ3lqksQLH+k7M6RE5L9w9xshBjEiQUIkp+9bfCsrK8QjrMWF5cww2ML8nV/ZMDKfnoyjLOOepxnSW7RNpPGaaew65Dnx+KeXAJ1AJNqBQyGy8YKyWR/Qz6FiEkc7Mt1oYJtTgcu54TueDKwf1zHNw/d8PrfCdhjOH48eM45+4aXG2MjetFr9djcXGRhYUF3nvvPer1elmIu52Z3mPHjvHlL3+Zv/k3/yZ/5a/8le+Cq0p8F2BV4k4qkM45Lly4wJkzZ3jmmWfYuXPnJz9pi2NufM1PG1wV0WrF/NiffgWA7/veJ/nfvvoO/+LffBMRSHqDlHSYYTNLGCik9tSAqtp1taPlnN+oCiEItPadntxNsCpA4WmEgizdusuwlalx1SjZAVoHOGNzHyaLsRlae4Am8BXPbENXS2u1SYRBSclwOK4uJ3N1ojQ1KCWp64CVnjeVVANHYvzxalLi8qneQFlC5RNPLDVxXTKZBd7LKlCcj7s44ZhKAlLlGFpHN81yZUcYCotVubM9jAldeABWUACLX4iSWljMgFUhWGadV/7LX6CCjcbgw2ZvKF9NhJyCmM+AeHFeW3bDRvQ9mc9SFQqTxgtt5NVDU1APKzS/kXGly3nyIwBUgq5cfMPlAhoIUE6OASqLRYvNFCnnHEM3/tkn2eYO5yAHToPBkPX1dVqtFlYq74BZvD8hGOSJeaZ55xW/TysKmffp6ekyqS4sLHD16lXeffddAHbu3EmWZeV6850ed7qerq2tcezYMdrtNkeOHNm0+SkAVpZl92RjVDWSh5uLWVSpQQcPHiw/5ytXrnD69GlarVZJKb3X6pOtZlye1+GntnP69GkuX77MF79w9K7AFUCtHrH74FzpM1RreBASRZoubAJYk1PN8h5+fMcUdxrtKGKiWUNg+fa3j6OU4ujRo7z99Tc4dGAfnXrMs2f7HD78OAsLC5w8eZJLH10nzTIw6U3vgVoUIBBjMurNaAuAdWhbaeZ8s5gM6jy1bYbLS2uf+NilpSUuf3SB73v+KZ44eJCPrq3w1K7ZUiziC3sO8u41yXa9DcE16ipkqAyJyKhpT2NsRSF7JzvUbIDLoG9cTsv3RchqeOBUocpX6oM2X2qsKmpyjmFqCB0MyIt2ShKEMc526fdTX+yzQN8XR7trXVZX+37f4TzzIxtknuYfKqL8fikZLcYRhwGBlJhktN8Y9FMuf3QdsTRAxgGEsGvnJJcWV1FK0i4k1AVMde4Pc6EAVwAvvfTSfaf71uv1sZnewh7k5MmTWGuZnp4uAdeNZnpPnjzJj/zIj/A3/sbf4K/+1b/6XXC1IR4ZgPVpzWDBrVcgrbW88847LCws8PLLL9+RGedGT5PqzNWDVocC+KN/5GlcannxyF5+6Vfe4NibHzFM+6SpIXB+YQx1TiHMTEmfstb5JKMUokoVc44g1KQVoYqCv1/EiApG+V+pxsUyikdLlSsD5ou1FIIgCkkTgxaSzKQI5cjSBDK/SRfCz+Wo3JOrim8LtaBqxKGm2/egyxhLr5ciBMRRQK+f5KbJERZPMxDCP05Iic08Va85FChdeHElNNqCWl9RMx6Q9ZShuw0mVzTtgcJFgguTA9/VUnn3x4qRqa8g1ybPf3Y5jSOfjyqQYz5TXF7fwvNK4c2ElfNyu6pASsIPXHvBDE/N9LjNYvGGzjLnfFQpfTkRsaxO+o8t78UJ4WV8cyUqsFhnPJgSHkBVUV6VKViALuVPHoGXpbcIQhegpSw7T5mzaMaTksV/pzJnvZx/TmXqb6C7AvTSjEF/wHq3S6fTplmrMdxQ5WxGIau50uDDQhG81RBClCpqxhjW1tY4cOAA6+vrvPHGGwBjSfVRMLL8tOLq1au8+eabHDhwgIMHD265Lhfr9b0QY7rb2dt6vc6+ffvYt2+fp7ouLJQ0tziOS7B1L4beJ9p1pPB588KHZ1lfX+eVV165K8pRNQ4+vYPu6sDLZOfS1nFOs6vVxu/RIBh9/5/df+fgrhNHzHZqfPOb36TdbvPss88ipaQeBbRyILR9pjUmLpDWTvEbp36X/vpqObdV/L3aQazlHa5OxQi4WQs3zYvdCrgCmI3atGsxjU8QtimA4OED+zn8+OMAzLTqRIEmDjSR1jwxN8fe3l4OyEP8e67T1CEiyFhlSDMOaYQh7TjyIDYTmFWBqPlyn3abizPCt6FGRetq/U6M/ivy4lnd+HxUlhGlp2RLBMaCCwVi4FBdg1OKleU10szP+GZJBsaRZClEyoNy4amHhfCRFhJnHbafISoqx8kgYWWpR7lTEbB7R5tTZz2wKkBVoBRz0/d+3c+yjOPHjyOE4MUXX/zUZym11mzbto1t27bhnGN1dbWc23rnnXdot9vl3Far1UJKyTvvvMOXvvQlfvqnf5qf+ZmfeeB71YcxHhmA9WnE7QCsgipijOG11167YwpGtWtWTagPA7gq4vt/4FkAfuq//EOkacb/4//+ayz1BlxeWKWQzfNdJEmWGpQQDNMErTVBGPgFrULzizYArEo/BqAU1ajOUUkhcBXPrbLDoXyXpAA01e6jzOe2wCfeJElREozNyDIQ2FIdqIh0C/PZQI0nDmOtp/51h1iTUa9FBErTzzIy47tcWsvNoiD4bl0GTK76+0tpP8dUSx31gWSyq0itI0ASdQXDUHB4tcFCLWW5ljAIrU9GwsumF4BGuAoYyumAY4rweZdIOj8bluEwgLIGWQFXJbARIyRnrEVYhdCiBMP+2BaXdxmF9CaTzlVmBRVIHDiBzBwIryyohO9ySZtTIp3NRRLLdlz5LyeqYK24P7y6pM4EbmDRUqC0B82RUKTClp1S62xZUx06Q11IlJBlt6qIRhSysLJCr9ej0+kQBJp6GGwCWJHWUACsxsPfwdoqzp49y0cffcTLL79cSoo750rPrYJi1sk9gh5WI8tPI5xznDt3jnPnzvGZz3zmEzsy90Io6V7P3oZhWFJGi0r1/Pw8x44dKxXI5ubm7tg/p9OuYZ3j29/+NlJKXnnllXsKzmv1yBfqpPBy7fjOlWBzB6saT+6+c6NnbQxm/Tqzs0/zxBNPlPf+XHvUIds1O1E+XgjB7u2zBIHmuacP89ozezd1EEu/rSgE4ZiozFe1opB27c7ktOciP4em1I0/u8uXL/POO+/w7LPPMtQVamI+L1YLA6YbXpL+R7f/MOfnhwgEE2ENwgECaMeR72DF+WcgNOvOeCEhIbzBfJl/8rDCV/kKvxDIuX8+x2RyNBOcWEvDVvOmX/WHQ/9EkxlcTSEGxntkapieneDK4oAss6yvdRHGkaUWF0nazZgkM0xONzh7xqIQ0M8wqWHlWo9WhUURh34fILXE4L9z7UZMO6dozkz6tT7Uirmpe0ulLsCVlJIXXnjhgQjVVEMIQafTodPpcPDgQQaDQTnT+/M///P8o3/0j3jppZf4vd/7Pf78n//z/OzP/uwfyNxwK/FdgFWJAtR8UoIsqCKdTofPfOYzd/WFKBLy/RazuFcRBJr/+md+kI8uXCNuhPzKv3uT109cYLWXoJSg3xsirCEKA8IwQCiBzdxYVVdu+jI6TKWaZIynoWWmdGTy8zYI0gJgFd7GFREHJQVZ7pe1MYx1vnMlBCBR0iGsw5GRpF4cQynJYJB5j6cKVS7ZQF20zqGcw5gMrQOMcQTK+1gIYJhkHrwoSZB397LMIOXmjb0siOnA3DXlc1AOYiZWFesNsAk0+pJWXOP8VI9WEjCIDIPQbhiacmBELnubA99cLKOUQnfgmzj+mEY4jLAoJwhyTxPl8sQnfNfJ5eIRrjLfhfB0QJOfgHWUppMFmPYYxydMPwIl89pkLoqB8qpMFhAOQ+5DInPIrbxHiqh0FUdS7iCNT4bOOrKc7hFIf3OUoEv4jliGZWgNdRlQDwPWNvhdDfu9ElwVxZVQbv5eqwJQIsaGyh+FKMDCxx9/zNGjR8f8moQQpSHr448/XibVhYUFzp49SxiGJdianJx84JuAu4lb3QwYY3jrrbdYWlq6ZQ9GpRRZtrk7eqtxv+nh1Up1MZ83Pz/P22+/jTGGmZkZ5ubmmJ6evmWaozVDlpeuUa/v5Jlnnrkv+SuINFEUlK9dr/lzi28CsG4GOG4Wi4uLXDp/jpeeeYzDhw+P/W3H5GhzvWfHxNjfJpsxUgjfTco7xfv37y+V3BYWFjh//jwiCDBZhjDD8nOebNZK4+Hbje3RxE3//uGHH/L+++/zwgsvMD09zUp/sOkxcaDZMeHf247aHKv1RQQwHdRIAwMIJuoxs806Qf7dD4VGihQpFBNRjRUhqjW6MZzlpJ9vLkI4gcWRxkV+qqi7F7NZ+HU2MZkvIwqw2hcOhfW5c3ZuktqFZXq9BCkDpEuwfnAba4ZopRhmKbVQM600Fy8s0ZyoE8ZBOfMNI1VJHSqMhXY7pt2uMZmb/07mPmZhoGg1752v1MMGrraKOI7ZvXs3u3fv5tChQ3Q6Hf7W3/pbBEHAP/yH/5B3332XL33pS3z5y19mbu7ezqQ96vHIAKxPCyF/UgVyfn6eN95446ZUkduJAtAtLS3dM7PQ+x1BoHjscf9F+gt/7nu4cmmFv//f/Tqibjn/4YA4jkiMIxmm6EB534kcaKSZ9SqAlTAup9TlPwsgjjT9/mgWSpT/lz+n8NqqimXkn0Ux31XgLKVk2Q1TUmBNTlNMDUooQmkQ0nc70sygUVhEboSsNs1uWWcwQ0sQBOXnnxmL0JIkNYTFa0vBMPPDtmGokHI0Q1aeM5SGt4WinhSe9hhlkmAFUHm3LpO01zWNnmSXjEmU5WpzyHqcedCkGIlc+MaRT2x+wMlfPilyhQj/IC9wAVkuNy8MxJnEFcNSAjSSUAjSnIJYzKRVmIhVNudYjCyHC4znNlDxi2zqQCivHmX9h2fT0SB0cUwpPU1RSDDZ+HdFiHyYWY5Al1ISmfnzkJljZ7tOpANiqVhNE4ZZRrfbJXaGiYmJsQS3ldJgEVP1ByfRfifhnOPs2bNcvHiRI0eOfKIZbjWpFkaWi4uLnDp1iiRJxqiE91NA4UHFYDDg2LFjSCl57bXXbtms8246WJ/27G11Pu/JJ59kdXWV+fl5zp49y1tvvcXU1FQpknGjGYxr167x5ptvMj3d4dlnn72v51yvbGzjOPQAYPbedhMuXbrEqVOnePHwkzyzf9+mv++qAKzZiXGq2FS77lX6GuP3SlXJrfgu8dvn+Oj8GS59cMZ3iIW9Y3GUZrj196/4zn/88cccOXKkNA5vx5vv5XoYMNMaFYxm63UcvrNfPH66UWdHa7RuhCIA+sQqZCpusH/7NF+9cL7MV1WktEkgNnMgBTamZFcAJMoicqRVPtxSjgFU4YeSEh0qX0gjL6LmTwqtoNWsMxwmvH/+Y2YnNcsX+2RJxtVzizz2wp6xme/Aq0ARRJpkkDE722KiXWN20r/fqQnfwYpCfc/2aFmWlV3khxVcbYyFhQV+7ud+jj/35/4c/+Af/ANOnz7Nr/7qr/Iv/+W/5LHHHuP7v//7H/QpPlTxyACsTytulCCdc5w/f56zZ8/eElXkVsI5RxiG7NixgzfeeOOe0DUeREzP1vnRP33AGzI++YP8zjfO8//9d2+SOstgmOGkKwGR1hKbWU8TNBZrvM+FroAg8CCu3x8dwzk3Nj5r8k1IWqlCCSHQWpHlNL+Cd10FWAUIK/7rnBdkcA6iMESQEYeKXpKSJgnCqVxAwnfQ/FyZIIiispuWH500zXAOkiRDCKjFAVnf0xDTzJAkGVEUkJnRLFkcBQx64xVFLSVJNgJdxbXLjGFiVSMQWO1VF6cHGj0BM4OIrs5YjVO6NS8+UQwX+0FjNzZwPA6EKvQ76RgGFisc0uRdrbxR5nKtXmuFNxtGloCu7MMJkZv7ell1gcAqn2GF8MbDyonSHNkf3ZsT2/K1hBfJwCsuloDMgcsEqfSftc3KETR/rYpB5+rvhPC4U8BqOuT6Qo9tk03WlnvgLCIb0BCG3fu2MxCwliWliIXZIIwBkOaf26MgcFFEFVwdPXr0tkUOtjKyXFxc5NKlS7z77rs0m83SW+V+G1l+GrG8vFx6N91uR+ZWjOM3xsMwe1ulBR06dIhut8v8/HxpVtrpdEqwVcxWXbx4kXfffZennnqKK0sX7/s5N9sjINGoh9TjYGzm6m7COVd6wb344ovUWq2yU1ON6tzUxvcbaIXWkombdLaL71Kn0eT7vvi9rKyssLCwQHbxw5vObd3J+3n33XdZWFjYslu9MRpRwMHt0+XPk/UaAm+K3K57n7PZZp3JitJinAsKtcOIugr56Vdf4WvnP8CqostfySyVWeFSi0mCiQRV5dthBFF/JHqEhX6WEQlfYOs4wXr+wkIIMmsRSuYiWbYsDMaZo9ms02zW6V7v0mnFrF4e+PVrYZ3Fy4tM6IAoipBCEuSXJG5E9AYZ+/ZO02nX2Dbru9YzU0UH695smQtwpbXm+eeffyTA1ccff8wP/dAP8YM/+IP89//9f4+Ukqeffpqnn36av/7X//qDPr2HMr4LsDbEVgDLGMPbb7/N9evXeeWVV8pK0N1Edd7q6aef5qmnnrondI1PO/r9PidOnCCKolJW9Id/6Hmef3Y3y8td3v/gGt8+/gEfXVwmsxYpBUliCK3EGovSEqUEcRiy1huWLZGNPlbWujEZbSE8PaQ/qCj+OYdUglzjOxdRGD/f4ueCxuZBWLXzJXBWIFFEge86WWu8OlTejBEyzP22qufjPN+8FPOAwSAtfbnyR2Ey/7lHoSbNDMNkM50oDjVJf3QPGueNj9PMlh0f4byrfJZZJpYDMiwtpWmZkKRrWBcpNhR044w0cKRqdC38vFMx28RI3b38u6cSGu2wwnpqh8lnvfJhZGkLKiK5HL7/nZH+uQUg006U11pIgbQQOAGBP6DKh5c1gtT5jlVxnFJGPr8OUkqczc/dOdJh4lUDle82Sikq5sejz7u4Zv2CoyL8O19f75KmKROdDqzD+moPnKNTC6g3QyKj2FZrsJYlpTT7IPP/nX1EAJZzjvfff59Lly7dEbjaGFW1ugMHDpAkCdeuXWNhYaHs+FQ9tx7GdetmQODSpUu8/fbbHDp0iH379t02aLhdiuDDOnvbaDQ4cOAABw4cYDAYsLCwwMLCAmfOnKHRaKC1Zm1traSdtVvX7/s5tSZG37l6PaRxEwnz24kCjMzPz3P06NGb2hV80mcThfqm3lVF1GvBGC13z/4DmGR447mtZvOW7wtrLW+99RZra2u8/PLLY1YCNzyfKODp3SN6lxCCSGtqOkDkl3l7e/wcAqfQUjIRRDR0xMHpSR5nglNuyRf0qBTGJJQlUgfC5ZTznBVZ9KwyLQhxyBHmIsWinCUQgk69zvrqqi9OK2/vUkxlm8yC9fmnJgXtRsRqN2Gw3CecbNFqNRBy3XeKB4ahtnTXu36NykWY6p2YpWs9Dh6co14L2bnN7/VmpjxAjcO7X8/SNOX48eOPFLi6fPkyP/iDP8gXv/hF/uE//IePTPH/QcfDl/1uEA+KIjgcDjl27BgAr7766l3TYW6UUIUQd03X+LRjdXWV48ePMzc3x+HDh8e+dHv3TbN33zTPPb+XP/kjL3Lq7Uv8q5//PXQ95NyFxZGYgnUM+ylWGYR1aO2HmU1my7kh8NdJMepKAYSBpEond26cMuifJ8bAmnO+q1GqHTr/GCnFiAqYv4TWvvOEk2g8BVBJQZKk2CzD5J0apTz4UUqSbRDzWOn5WR8hcnoB3m/JWodSXpihAChFbPRycvjqaLVb5+XSxx8nwFfwEkdLBWgjmRiGSClYz4Z065a05hhokyc8X04UjAQqCjpHKZ2Op2YkeSdJW5DGz18hKlTB8roVP4nRi20Igchn6HLZduf8z2pEBRUUZsQSISuUQgFOOLRQhGGAyhxDazCZAWdwVkAOtoqjFQIZQ2FwOIyxrK2ukWUZE50OUinS/DsvhCAZZCSDjCjQDHO1wXYgabRiJJJmHDJ3j6Wu70cU4Ory5cv3BFxtFUUHfseOHWNGlmfPnuXkyZNMTk6W3a17pSp3P8I5x3vvvcdHH33ECy+8wOzsnQkk3A5F8NMwkr8XEccxe/bsYc+ePQwGA958803W1tZwzvHOO+8wNzeHFPdf5n9icnT/tBox7fbdAyxjDCdPnqTX6/HKK6/cEhi5WdQj3/H5pNj4mDjQEOgbzm2FYVjm/5uNEhhjeOONN0iShJdffvmW9wp7pjtsnxinDde0phkGNIRHQTva439/Yft2Frt9moGiHfj3Uw8CJm3IdTHMKe+2TAul8m1RqCw8GTfwyj3rYpRFLA4bCWpO5wVLn3Oa+cybUgKbWVxmyv1CvR5SF5JVYLg2wGSmYssiCFXAxEQTay1ZlmKWl7wXpxsiFTiX4Jxj984JoEIRvMuO6aMIrq5evcoP/dAP8eqrr/JP/sk/eSTO+WGJRwZgfVpRrUCurq5y7NgxpqameOaZZ+76xrrVhHondI1PO+bn53nrrbc4ePAge/fu/UQA/NQzO/lv/29/EvDdqd//1jl+4X9+HRFKrlxdJTO29JqKIsWgn6GVROgcgCqBFHIMYG3scmUm32BXIo6CUmId/GegA0lS8cBQ0vttFACrWO+l8CIRWZZ6KopQBIHGuYxapOlnKWnqDY6NBSk0znmlPaUk62PH9eIX7XqEFV6t0Fr/fqXyPl1JmqGkZDAcF8IA3+GphnHOqyJVYozmbhyp8XNgMlY0VUjUs8i+8JLlgWM9zBhElix0pLmgRfEauZ1XmZCs8Pz2TDlQoDKLLB4tZPncCibOr6Xb9G8nNv/O3z+jgS6HwzrfaZROlObGGN+lrKNp9gOUhFiBURahYX2YQJqSiRyUS0pvMoej7zKuzC9gjJ+5Evl3sD8c7zpIIRgkaXlfp6kl6ab0rucmw08/3HNHzjnOnDnDlStXOHLkyH0BVxvjRkaWi4uLnDlzhlqtVoKth2neNMsy3njjDbrdLq+++uonzqfdLG4VYD1Ir8M7jSRJOHnyJABf+MIXUEpx/fp1392av8jXvrZcdlymp6fv+UZscmbUWWq1IiYn7y73JUnCiRMnEELw8ssv3xPlw4lm7ZY+y4n6zdePrea2Cpl1a+2W7JZi8y6l5OjRo7fVPX758d2bzrumvSBQPfbdtt0T4yIvP3XkRayDi/Iq7cAD01ApdtoGSzZBqhxPbRjJAvwar5wHWm7EjoCcRkhBE/QAzdQkqu8LZ0WuiGPN9StrkFlsP0Pm6rIO7802XOpBIEm6CTa1aO3XGyfApiNvuYmJFpGWqAsDdu6aYfnygEuXP+a3f3uF6enpUtxHSkl8EwPnT4o0TTl27BhhGPL8888/NOvfzWJxcZEf/uEf5rnnnuOf/bN/9l1wdZvxXYC1IQoO/ZUrVzh58iQHDx7kwIEDd50A74Zj/0l0jcLT5HZoBHcThSrRM888w7Zt2277+UpJPvfq4xx5cR9JYhgMU37v98/xjd87y6X5FdI0n50SMEyMp3lJQSAlWkqk8lWsJDGjMhegA1+hqkqtB3p8QciMJVDjt70UYhy4Wb+oD5OULEuRShEGIYkxpPnCnKQG5wRaB2glvbS8sySJyb2i/KyV1LJMMEoKsswwTDMvMRxqssx4paQkK+Xds2wjzU1sko83ziE3qCVaNxL/KK+18AbOhUqjCgVSK1Jj6XQDOl0vQ7+S9UliR1oXJIHD6EKewo2rB+YdIaN8bdJ7O1qkdVgx8sEqgJMVboS68pepzl+VYEqOgJbLlQsLCfgi21pnQfp5OZkJ0qFBSU2ac0KjRFNPAwItMVgsFlLD0CbYQIISLHaXmXIhnU6nBFdKCf+ZVL47tSigOxg3nI5CRS8Hv1Oth7sb82mDq61io5Hlxk3i9PQ0r7766qd+XtXPudvtcuzYMeI45rXXXrvrTfatAKxHEVx1u12OHz9Oq9Xi2WefLTdaJYWts51WXbCwsMB7773HcDgswfTs7Ow9AS/T20Yb/FYzZnrqzoFwv9/n+PHjNBqNsfdztzF9iya0U61b75RtnIEs5raKLvHU1BQTExNcvnyZRqNxR8rG01usZ40goBGEzMY1AiXH5q/Ag5O/+PJL/D/f+CqdXGgj1IrIaUQG01GdfnfIivC+kVaBtKLM2U7Bpjvf5YBM5iyNPHlmocD0/Ly2FwIWhFpx/eoaqx+t4FLjBZxycBbHmuH6gHC2SdpPSAZDP3ftOeOYisBFGHh5eSUFO3dPc+n9ZY4ceZHd+9ssLCxw4cKF0rZi0Fun2+3e9pr6KIKr69ev8yf+xJ/g0KFDfOUrX3ko6d4PezwyV+zTSkJSSq5cucLS0hLPP//8PZGdvJcJtUrXSNO0pBF861vfIgiCW6IR3Gk45zh9+jRXr14dUyW604iigCgKaLVifviHnudLP/gcb5/8mMnpJq8fv8DpM1d469RlZCAZDDMyZ8lwYPwM0zDJCAPtKW05XU9uACODDTNOnso2DkK0EqXXBniQEgaw3k/RSiGV7xUFgSoBlrEOJQWq0g0LdUgttkjhSDNDlhmUEzjhK29SBaVIggdwHmhNtGJ6SUqSGobDjEArTwnMjxUEKpc5H4UQbPqdxWGyDRRJMU6RTBNLGEikdehQkVpLfzCkFgbUUgkr5O9F0gstazIhCS2pdlhVqDR50YyCFmiExWjIMCgrES735XJeNEMi8kqmB1lG+lm3AneNz3Plv3NeKbBImJBXOvPfaSPL91y+/1yhUFrJ0HkXZmUVgVWIxJGYjKHLSFH0+33CMEQHmnoYsrYRTAWK7gY146pn2uRtbJA+zSioblevXuXo0aMPDS1Pa10WgqpGlg8yrl27xokTJ9i5c+cmivOdxicBrKLI9iiBq6WlJd544w127tzJoUOHtjznqU6dZjNmcnKSQ4cOsb6+zsLCQmlUOjk5WQo43SnNfltFFr3drjF7hwqChc3Ktm3bOHz48D39DLZN3Brom23dWdGjOrd16NAher0eFy9e5Ny5czjnUEpx4cKF257b2ioaYUAzDJnrtGhG4ZavJYSgHcRMhn6dCbSiLgJEBtsbDYZXNF21jNFskmp3hVFjpcOlyFnlNkdhGd5ORQmGyo06WMqLQV2/uoYdZggp/VOsL+hZ65iZ7dCtKWxqGK6nhHHgwZlznlKeh1aCJPOKs7NzbTqtmDDSJYuosK1YWFjAvr3A7/7u795WNz5NU15//XXiOOa55557JMDV8vIyP/IjP8KuXbv4+Z//+YdmLOVRi0cGYH0aYYxhbc3PZrz66qs3HXa91bifCTUIgnL+YSONwDl3T+kaWZZx8uRJ+v3+PeGqbxVCCJ59bg8Au3ZNAnD2/XmazYjzFxY59d4VTr5zkWvLvVIpUDjnfazyOR2dgyydG88WghKFmEQQqE1zQW5DHc0Yg5KOQOuyy1EYGVfDd65GP2fWYnHoQHkRjlBRjzXrwwRrDb1u5sUYlEQIT1sLQ02vnzA0hjBUGOOl4pWV+YbezwzJDeccaIW0Xu22fB9iM0Uv0Ip+Mr7hq+mA4WDAsJeSZilhqImjiEGaYfMnCyeoZxqVAl3/PKUk/TijKzOSIAddYiTEbgU4aXEKpLMEtph/GvENlfNGy4KKOIYbB0r++GwKYX0n0+BQxre1UmsQ0h+nENMoKLgC3+kDgbD+8wvqDeomJEkSVlZWvOrhZJNh5mk5xfdzKw+d4m9xpKndBVXkfkUBroph/YcFXG2MKgX6QYRzjg8//JDTp0/z1FNPsXv37nv22jdToX0YxSw+KS5fvsypU6d44oknbnqdGhVp8qoQymOPPUa/32dhYYH5+Xnee+89ms1mCbZvx8B6bvdE+e92u8b2nbd//xSy8vv372f//v33/DPYNXNre4Ztk3fefauGMYZLly6xd+9e9u3bVwrOfPDBBwRBUILaOym4NsOIZhgQa31TSmM7jJmK8xklrQhDiegLOlHMapAhE4HRnuotqmPOhU5UpYgm8RR2GNEEVQImdgxCkXewfHGuVYv4cK0/xmLxr+VzplKCmBFdPiTwQkv9DJMU5sWeWmhSiwoUL3xmL8d+833CDWIWRWH70MF1vve1Q1tSNou9VrVb+yiCq9XVVf7kn/yTTE9P84u/+Iu3bFHx3dgcjxTA2igGcC+j8D2x1rJr1667BleF9G6RbO93Qt2KRlAktLulawwGA06cOEEQBPeMq36rcTD329q2vcOrnz2ItZbT715hz94p3n3vCmfev8rv/O5Z2pN1riyuMegnuEKpMFIkiSHSvgOllEQJQRAqnPAb8sxYkjRDS0FmHSbLcM6iZExWUQl00g/SViMMNGv9kWGttZYoCkphBOsgSSxCSLRWaO1y6VnDIMmIQk2/PyRUGgSjTlioiLWml3ooIoVXEqzqFm4l6KGURDlHUpEXT50dT0DkIMxa0ixDKw1IIisZJJYo0jjp5cj1BlAeCIlMAsKK/5RUgrUgZRhaUm3JlFd1ssIxDP35aWtz2y2HLeR6xfgJ2Q0wV+RzV1AtcIryrUgzmpfWSDJMSfPMnM/YKjerTrOUWCi0VgwwhFFEFMfgHGmaopWgu7LG2uoaQRgQhiF2iwH64no/jN2roru8sLDAkSNHHlpw9TCEMaYU/picnLynr62UIknGu6GPiphFNQpbkgsXLvDcc88xMzNz08ffLLfVarWSKlqIN8zPz3P+/HniOC5BQKfTuenrtDuje3qiUyO6Tcnsy5cv88477/DUU0+xc+fO23rurcb+bRO39Lgd9wBgLS0tceLECfbv38+BAwcAbntu62bRDkNauQfW1E2k5yfCmJloZMTbCAOkEEzVYhI9QHUhjQHrO1i5PpLPwRpkRpnYpB3NAVvnGRPC+P8lOhd2Ep6KGChJOshK2p/zekyInCqfpcZboFiLSTJoeEErO0wxiRdGSrtD3FSTLPP+ldtzGmp4gwJaLQo2deNXVlZYXFzk/PnzvPXWW0xMTJSdrVOnTlGr1fjMZz7zSHzv19fX+dEf/VHq9Tr/9t/+2+9Ij8NPMx4pgHW/oup7MjExcdevtzGhFiqBn1ZspBEUIhl3QtdYW1vj+PHjTE9P89RTTz3wRUJKyVNP++R49KX9HH1pPz/ypRdRShBFAWvrA37rt0/zrdfPs3PvNAvX1lld7XPp6ooXkBhkDAdZ2cQKQ4W1jkYcsNobICXEcc13Q6rzXUrirCtNgWFc0bCIOFAlwAJIrSWIFMOyiySIdYQKHdYYrHP0k6GfKxOCMNQY4xiYDKG8yEOhHthuxKwN/eYtzUyuuDc6SZ9gxu8z6xyhkCQVsNgbJh5c6ZFpYtExSnOxByEFtUhhtSspl6mxG+axIBIaM3Q0hpXfRZprrk8SOQ+6pCPVzn8PcAjnkIVlpHOYvMFVepPhgVj5zgqglb9XbaQ3FM5DC4GtCKAYLBqJEL6CKKVAao3BYpxj4Aw1oUEIgjBkcrLJ0GmMMSRJwmAw4OKly/RT39UKwxCtdXkdbkWG+dOMKrg6evTofekufyeF1prPfvaz9+W1N3awHrS/1Z2EtZZTp05x7dq1T5Qtv93YKN5w7do15ufnS3GGIi9NTU3dNNdorWi1b42V4ZzjwoULnDt3rpSVv1+xe2bilh63a+ruurcFcLpRZ3FjwbWqSlzMbX2S39ZUPaYZeGrYttaNAeFEVGem5gFWpDWz7QbysmBbo8la2CWwkgG+IqaMIJOjwpnT5DRAv74HUmP8IAA4D7gkYA0g8hzhHM04QBiLzVzuPiygUljMUosxDjvMwHnTeYAgsxggSzLSJMNZh01SsiSj0QjL72gYbn1v1WvjwKu613r88cc3dWuVUkxNTbGysuJFlR7i73+v1+PHfuxJ4tTuAACbZ0lEQVTHkFLyy7/8yw9sdvc7Kf7AA6yNvidnzpzZVIG8nXjYEqoQgmazSbPZ3JKuUXhtbEXXWFxc5OTJk+zbt++eCH3cr6jXR/zgVtPPc/3wDz0/9pjf+fp7zG1r05moc+nKCq8fv8C3j13g+Rf3cH2py+Wri7jU0mzWsECapQRa+g6JljjnqMcBbphX1gT0B6k3Mc439kGgNikAaq18EqiGxC/+ViCEIggktUgzSFOGw8RLuMtCQVESBh4EJoMUnCOKPOXROgiEInU2BxMGveEjstahhRgJTBhDPzPEOsBWZN6roBDAWYfpZxiTESiJDCUmn62q0vkKv5Nq1KSmMQhoVC5FvRZwzfQZhpZEW6y0WAXaCmxlFXIOlJXenBgqvltgpK9oNrINy5ZzKOX9tIpQwDBJvJKg9teoqH52bUpNjl7DWIs3qdZoranX67RqIQtLqyTDhJX+ihcaGdZwUjFxj/x37kUUHj6Li4vfBVe3EVLK2zYEvpWoAqxHUcwiTVPefPNN0jTllVdeua8VbKVU2QkoJP7n5+c5deoUaZqWHZeZmZk7HrAvaLNXrlzh6NGjtNvtT37SXcRGatmN4m7U6C5dusSpU6d49tlnb0lkaqMqca/XG/PbKiibG+e2tjebJcjd0bkxyJ6Lm0xFI4rgjk7T+2PVYupxQIhEYJAIlAETkPP/RO4/Ra4m6GXul1WGUaAyINeukKbC7JeCqXadYS/Npd83+F0KWOp6U+EgDpHGkqWGJMl8JwuwmSEdpChnManFuVGHVAhBGG39OX4SNbxWq7F9+3YuXrzIzMwM27dv59q1a7zxxhsAY1TCh0k0YjAY8OM//uMMh0N+/dd//a6UVL8bo3h4PuFbiHtJESyUtj788MMx35Pb8THZ6jUf9oR6q3SNtbU13nvvPZ5++ml27NjxoE/7ruMLf+iJ8t/b5tq8+NwefuLHXkEIy7Fjx6jVttMfNNFa88rLB3DO8c1vneOf/6vf5y/9+T/MenfIanfAr/76W/T7CZ///OMMhhnzi2t8dHGJMNTMbWszGKZcXVwjzXy3ROJV7xDSS9Hm/AfpRrQaB0RhwCA1uZSs850eLKk1JKnBOkEYhwQoBgMvHx4FCmEhdX7WKkkNmfPKgSanyWXGUxQBjMkwxhJoTag0AzfyfhpkmX8eBY1J+M6Z8PNnpm/RSlIPAvrKkBrrTR6dQwmJqVYPt/iORkJTSzW1HHRZLLbhqZxOQSoNWeCle0Vl3kwKP2emnCgpgRMuYsAIEA6tJRSjiqNzjiRJUJGibEEKT3HEwbpNmdEjIJJmmzfaw8wQxzFxHOOcl+ofDIckSZ+LH5zlRCMt5XsfFEe9AFdFp+G74OrBh9a6zAEPU6HtVqJQ1qvVarct8323UZX4P3z4MGtra8zPz3Pu3LlNPpC3+n0zxvD222+XhrvfCbTZQsH3bjpx9Xqdffv2sW/fvjG/rY1zW7sqnct9UzcGpo+1p8v7u1OP2TnRIlba+2jFIc/smON31i8iBOhMkAChlBhnKPywyNkXsQ5woo/VfvZKGgi1JjMZwnpxCikFnWZMb6Xv6YHGjuUfgWClm5AmhrAV4owjSzPmF1ZLhoczjmyQIbXA5gX1ySl/f0h1Y4DV/ASPsyRJeP3110t1SiklO3bswDm3pUdg0UV8kGv3cDjkz/yZP8Py8jL/4T/8h/tehPiDFI8UwLpXkWUZb775Juvr65t8T+4UYD2KCfVGdI3XX38da205r3W/DSQfVBiTcOzYMWZmZnjyySfH3qMQgs++cpCXjx4Y+/3njzzGRxeXeerwdsBvcv8//9N/5Aufe5wnn/C/+4/fPMu//IVv8ZM/8Spzs22uLq7xC//rMVbXBjz5+DZW1gYMhikfXl7CWkcQKLpXe8RalV5cWiuCIGStP0AKh3OGwVqKUopISe/Xtp6iA0lHK0wyUrwItCC1jijUJEmGE4bQGFJjaNdjTOqQmaWWO/oWJsZainJ+KQo91TEUqpyPilTg1ZgEBAiiWoBSmp5NMciyr6WdIBa67HR5c1+DykGmpzJKWmnAepbisZJmjYwwEQwxZcVSaIHEYYRXHtRWEKPHAJbDESvNIM1yLzXfdQuCAFPpzAnnRS9WTeIlgPPvaH9D11EK4RUe878LIeg0GwRBiMPx8otP0Y5d6UvXbrdLsPVpWSU45zh16hTXr1/nyJEj3wVXD0lIKTHGfGqzt/cqVlZWOHHixH1R1rvdEELQbrdpt9s8/vjjdLtdFhYWuHTpEu+++y7tdrsEWzeiMaVpyokTJ3DO3Zbh7sMazjnOnj3Lxx9/fE8UfIu4md9WP0kwC9uZnZ1l38SNj6cq+XHHRIv9s5M0Qu+hVYtDXtu2i1Onl1gfJNSFIBGGSCiGzpC5XPwiV52thYFf9w1esEIImo2I3mqGcJ6l0arHCCHorSdeVdBJMHkaybthxliurXTZNVUD6zCZZWU4JIo1w0GCzQzZMCVUQSnZPrvdv0eZU/W3ikbtxh2srcBVEUIIJicnS5XNootYMIkajUaZQz5pFvFeRpIk/Bf/xX/BpUuX+I3f+I17PpP6Bz3+wAGsfr/PsWPHCIKAV199ddPCe7sA69MWs7hfoZRienqay5cvE4YhBw8eZHV19Z7SNR6muH79Om+88QZ79+7lscceu+FnthFYNptxCa7AL5w/9V9+Yewxn3/lIJ97efSae3dP8eTj25BK0qiN7rev/d77HDowy/RUg8XrXeYX1vjaN99nZqpJtztkZX1Af5CysLSOs47+MKXbHWCtZZh6RcIsc0RakRqHlpI0MyTDDB14RUKTC3dIHEoH4AQm56FHWjHIDFJ4FSUL3n8En2SyxBEqR2oLVafMi4YoRWIz0mFGGGpsalGxwgpIjGWYePCSVb5Hw0FGKEQpU4/w816h8LNSQghaLsQ5i8n9tfqBodvI0EaQKIs0gmaiQXmTZd/RyjtukIMrD0KFUoRCUlVaT3IqoMGyZhM6KvJeaxs8sOpxMGYSDd6zrDdMEQh2bZtmx3Sbxx57jOFwWFaAz507RxiGZVVycnLyvhQmquDq6NGj3x1EvoO4H2u0c44gCFhdXeWdd94pBQUednPOwjT+8ccfZ+/evQ/6dDZFo9Gg0Wiwf/9+hsNhuTF9//33qdfrJdhqt9u+G58LVtVqNZ577rmH/vp/UhSd6mLG8n7Rt240t3Xu3Dl6vd4tzW3tnmrTadSYqtWoBx5k7Z6e4Cs/8WX+q3/5q3Ssou9WCRBI6/OMIGd0OK+8CyOAhYBmI2J+tevZB90hszP+/ffW/eyyFI4sl2ev8tWTQYJUnvWUDDOGQCNfj511ZElG0IrQuVfmjl0TAESxvuH60Ii3BupJkvDtb3+bZrO5CVxtFdUuYpqmpfpjMYtYUAmnpqbu234ryzJ+6qd+inPnzvGbv/mb93U28Q9qPFI75btNiktLSxw/fpzt27dv6lgUcTsA60GLWdzLqLraf/azny0rW1W6RqGScyd0jYcprl69yltvvcWTTz7Jrl277ssxNt4HrS3mdr7n1cfLf+/c1mHntg4vPHtzyWjnHN3ekItXrvPBhSt8fHmB+fkV5q8Pubo4pBlHIIWnBjpP/XPOIXRApBVVsnpiDIEUVYVbQiEZWlN5jCVUksTaUv69SsdwzmGcRSeSxBqUECjl57SUzOfTnJ9zkpWNjhQSa7wn1zD/vgnw8vvSKwjqIcwNY5y1rOkUB8SJJA0MkfUgz4MzSWIS5DCjGQQoEfiB6kxQcypXNQQnLAqBAZayIR0VUYtC1ofjYCrUmzdkVUXFqslwFEXs2rWLXbt2lRXgxcVF3n77bbIsY3p6uqxM3osqunOOd955h6WlpUceXH344Yc8+eSTD/o07kkUs7edToeXXnqpNNxNkuShLU5VxR+effbZe+L5eL8jiiJ2797N7t27ybKspLi//vrraK2ZnJzk2rVrzM7OPhSiTHcb1lreeuutkub4aXWq73Rua++07wLtbDU9wIpDOo2Y7a0WP/7E0ygp+Cevv06SpsSBoi+t71g5T0ufqtf9aFZlJDjMVYAFsDoY8tzsLnorAwb9BK0FCkU/9cWvIpxzJIMMh6eFZ/hjOGP946wjG6YoObLk2HPAj4lE8Y27VPXa5jV8OBzy+uuv02q1eOaZZ277nguCgO3bt7N9+/ZyFnFhYYEzZ84wGAzGqIT3ar03xvCX/tJf4p133uGrX/1qOSLz3bi38fCs9vc5Pv74Y06dOsXhw4dvWqUrOPSfFNV5K29y++gu5Ovr65w4cYJOp8PTTz89VvHbSNfo9XrMz89z+fLlMbrG3NzcI8FxLzjszz333CO5qHjKRMzhgzs5fNCrKWZZxrVr13jr7Q/4pV85xfy1Ps1WTK+XIi004sjPcRmD1lDLqXreHwoipRHS/8Y5R01pXMUI2FjrlfpyhJVZR6QUQ2swuZpfYjK0lGTOkfQ95S4OFCZQOCkYZhmpMTkd0aCkwNrcXwzvYyWFl2GPpGJgRhk2ySwNArK8k+acK/3H/Pn5AeYoCMgsXjkKSAcZUo3OW0uJFBBYQU8mZIH1VJANAEtvAbBErp5YjwOiG9BHqhXgJ598krW1tTGz1U6nU1Ymb8f/p4gCXC0vLz/y4OrSpUv84A/+IOfOnXvQp3LXsXH2tkoFWl9f5+rVq+Us0fT0dLkxfZC0NWstp0+fZn5+/p5Szj7N0FqPbUwvXLjA2bNnkVIyPz+Pc+6R6SJuFcYY3njjDZIkeeA0x1uZ2yqkyQH2dyZoRAGNOGAit7XYM9dhriFofdOxFkQoBUMxADxNUClBLdSejm4EabFuK+lzg3WkmaMeh/RW+mSJZ1BEgWLFGURFTMo5SBOv0ouAOA7o9VOcdd430Rls6ue6pJJIKdhzwHdw4psArI37vAJctdttnnnmmbsusG+cRSzosVVgW+SQomN7u2GM4ad/+qf51re+xW/91m+xffv2T37Sd+OO4jseYBUSxhcvXuSll176xDZowaH/pNd82MUsbjUKqtyePXs4ePDgJ76Xer1emjQWdI2FhYUxusbc3BytVuuhui6FqMmlS5ce2Q3FjUJrzbZt29j2/2/vvMOjKtP3/5maXkilQ+g9ja4iTTokiFgW177usoh1f4K667rVuguLFVdX3a+LKEkABUFaABVUSCON3gmppCdTz/n9MTnHTOiQZGbC+7kuLmUyIU8ymXPe+32f574jIxl761Cy9h0hec0eTpyyINklbLIFvV6Ht1FPncVhdOFl1GOxNQgRsw2DXoe5wYbcjh1fLyNWy8+GF1qNjEavQY9jNxE7+On02CUZGUeumEbWoNNrsdgcVhkmqx2jpEPWyejtoDfqHNlUGkn93bBLMkaDHrOToLI5Tr/khikuDeg0WjWXTJJlbJKEUafDZLNitdkwGPTIGodBCMiqS5xBo8MsO/5trUaD1W5Hp9Vhs9spM9XTUXu+O9aFfmsVw7ngK7Rob7wx0bNnT0wmk1MroZeXl9Oi5HIbNI3FVXx8vEeLq8LCQqZNm8aYMWNcXcp1c6nZ28aBu8osUXFxsbrZ165dO1Vstebrqcwgm0ymFguNb22U95WScVVZWameAmRnZ6vCtrlOklsaq9Wqtou1tuHI5bjY3FZOTo6atxWp1+Gn1+PrZSSk4ZrZKVDLoUOHCAsMptZci7/RQLnGjCRLGDRaJK0Gk9lKey8fvGQNBaY6NA0zwga9DqtVQpYlqmvq0TU44+r0OvyDfCiyWMDayNFWkrFYbICMXqfFz9dIeb3F8T5tcHR3ZFI6HHi9/Yz4NhhYXOoEqzHNLa4uROP2WIvForYSpqWlOW3ohYSEXNEmgiRJPPXUU+zcuZPU1NQW6+AROHCfd+0VcLW/wDabjaysLOrq6hg5cuQV+fpfrkXQE80sLoZi93qtrXIXatdQ3vx6vf66UuSbE0mSyM3NpbKykmHDhrXpfAez2UxdbTH3zI2ma9cerEz6iR3fHUKy2DDbrdjMMjq9DhkJb4MOrV5HvdmCxWrHoNditTvET53ZgpdBj9lmd2SC6TTo7fLPM1Q4QiXtdrt6mgQQ5OtFpckGGseJlEZjw9/PC5NVRmOTQCMRqNNg9DJg0ekwWe1Idhm95uffD5kGlym7pLbfmq0OEWiVGuoB7DYbNpsNo9Hh+2uXJLz0esySzREoKTnaILVKkLFWA3aHy6INKKuvV0+3GiNJ5z9ma2ibvNaQYW9vb/W9ohjKKDEIkiQ5tRI2DfKWZVn9/fV0cVVcXMyMGTMYOnQoH3zwgcvquN7r9rXM3vr5+REVFUVUVJQal1FUVMSBAwdarRPAZDKRkZGB0Whs9dD4luJCXQmNs4mUU4BTp06Rl5dHcHCwKmzdUVwqM2S+vr4MHjzYrU/fLjS3VVJSgraklPw9eyi1GCgP96LIYuHUqVPExcWx7uQu5Poawn19OWmtQoeWQKORao0Nbz8jEX5+yEaZs+Z6ZGQMeh0+XgasJkc+VlFRNf4Gx2adwaAlslsIR0qqHAVpGrrgNWC1O+4V/kG++Ad4Q2kNGlCjVbRaDbLNjkarxbuRO6D3JYwsFEwmE2lpaQQFBbWYuGqK0WikQ4cOdOjQAUmSKC8vp6SkhAMHDmA2m51m5C40uiFJEs8++yybNm1i+/btdOvWrcVrbsorr7zCc889xxNPPMHSpUsBx8/ymWeeYeXKlZjNZiZPnsw777xzRREE7o5HCayroa6ujvT0dLy9vRk5cuQV30gu1iLYVswswPG9HD16VLWob47hxqbtGufOnaO4uFhdQCpiq7XbNRSRbbVaGTZsmEfOjF0p1dXVpKen0759e/r06YNGo+HRh8bSs1sEp86Uk7HvFLV1JiqrTdisNrRa8PIyorFKGLwM6CTQ6nToDFrqTFZHmHFDNIJOq3USYeAQQgaNFnOjmSyzImhkVOGllzWO2aoGbIAsOXYrHWG/Onx9DdTW2TDYHSHHWO34GvRoJdCjA63DZcqg02OTZGySDbPNho+XEU1D2yHQ0EaocQotNuocJ2TKu9VsU9oZJQpqajDiLP4bi0gFU0N+SnNkYDXO/2m8KDl+/Di5ubkEBwerbSC+vr7k5uZSVVXF0KFDPfr3t6ysjFmzZjFgwAA++eQTt9qVvxqaY/a2aVxGcXGxatzg5+dHREQEkZGR19RKejGU0PgLuaZ6IlfSldA4BzIqKgqTyeTk3ubv76/em1rLAfRS1NbWkp6eTkhIiMfNkDWe2+rRowcmk4nsg8c4ceIEJpMJX19fysrKkBveO10CAzhUVY5NY8Xf6IUkaTB46egRGUp5TT2a0nOgcbQIBvgaqamsR6OB0rIadP4+6HQa/Py88PL3QuldQAYdMpJdVjMU23dph3+gt/raanVa7FbHpqHN4rgveDXK0vS6TNaVIq6Cg4MZMGCAS35ntFotoaGhhIaGOuaymzhtBgQEqJt1ShD173//e9auXcv27dvp0aNHq9e8Z88eli9fzpAhQ5wef+qpp1i/fj2rVq0iKCiIxx57jNtvv53vv/++1WtsbjzzDncZysrKyMzMVE0aruYidaETrMbhweDZZhaSJKlD8sOGDWsRRyLFBScsLAxZll3WrqHs1np5ebldm0VzU1FRQUZGxgVDoSdMGADA/bLMDz8eJXltOiVlNZjMVrSA1WbDanO4EnoZDWCVMdhkdAYtGp0Wu9bRZmG12ml8XKXROGar9Dqtmn0lyTIGnQZzo354k8WKFk0j23bHCZUJQJaxmW3UmG1o9VqMWg0arSPUWGsHs9mK0rQnyzLe3gas9RbsdjtGvR5Dg3W71q5Bo9WgkTRo9Rq8tQZqG9wIZbuMHq0qujQah7WwzS5xqqKKqIAg9WMAlqahy7LcECCtueIWwSul8aKkV69e1NfXqyfBhw8fVjdyBgwY4NGnDeXl5SQkJBAVFcWKFSs89ntpidlbo9Gonm5arVbVuOH48eN4e3urYvxaZy4A1Xo7KiqK7t27e+z9S+FauxK8vb3p0qULXbp0wWq1qi3ux48fV9t2la6L1v4ZVVVVkZGRQYcOHejdu7dHv0ZarRYfHx+CffQUVkrEx8er4raqvAKzzYLeYuYPQ4bz14Ld+BoNGNHh62MkTG/E6K1De0TjCBfWgL+PFwa9FhmJ2noL1ZLjJCoo0AeNzjFbawe0soxGltFpHTEiSDT8V3LMVtXY0et1WDWgkTXYGyI6rvQEyx3EVVOabiI0npF78sknOXr0KBERERQUFLB9+3Z69+7d6jXW1NQwb948/v3vf/PXv/5VfbyyspIPP/yQFStWMH78eAA++ugj+vfvzw8//MDIkSNbvdbmxKNWnFfyy3zq1Cn2799P//79VeV+NWi1WlVQKf/fVswslGwQSZIYPnx4q+yGazQal7RrePJO4NWiLJ569+5Nly5dLvo8jUbDqJE96dUzgpf+uIYe3cI4cbocXx8tFqsNSZIwmczodBrskgZdQ/uETqttEF0SOoOjLcMO6LWO0Ei9VvuzJbvsMKVQDDIAbHYZg9bZnbDe4pw9JWk0GGXH3BYNuSQ6gw4fox47ilW8Bo0kYbfb0ev1aDVabHYZLy8NktkxewWgt2sx2SzYrD9/PaNBj2yV0NkaBpW1Ej46HVqrTFW9mWBfx8mULMvUNcnF8vEyNDrBatmWIh8fH7p06UKnTp3Izs6msrKS4OBg8vPzycvLU0+2QkNDPUakVFZWMnv2bCIjI/niiy/cYgbmWhZGrTF7azAY1DagxtmE6enpTiefV9N2ferUKQ4dOsSAAQPaxEC70pVgs9muqyvBYDCclwNZUlJCVlYWgCq2rnS+5XooLy8nMzNTFcCejjL7XlxczNChQ1UB3LFjR6Lyyyg4fYpgbyPlZ08h2+zYNRb0Bj3n6uqI6xqBr5/DPl1pzfb39cLbaKBeY8fb20BttRmDFgwGHRabHb1Wix07GlluEFwafP280DTchKwmK4HBPtTWmNAbtAQG+RLaPhDL2XKHsVOjE6yLmVyYTCb27t2rrincQVxdiMYzcuvXr+fXv/4169evJyQkhJtuuonbbruNWbNm8Ytf/KLVOiIWLFjA9OnTmThxopPASktLw2q1MnHiRPWxfv360bVrV3bv3i0ElrsgSRL79+/n7NmzxMfHExISck3/jnLKYbc72qPaiplFXV0dGRkZak6DK/q6W6tdo6KigszMTDp37nxFxh2ezNmzZ8nLy2PgwIFXvHgKDw/g1dfm4u/vTdm5WtZv2MeO7w5Rb7KiMTiEk8nsMMOQbXY0Bh1mu2MrUZbsGCSwW+1o9BIGmww2Oz4GDbJWgxYNVhwnVOaGVjupQRw1xmb72f4dQKfVYD9/9AmNWcJmtztserUyFpsVXy8v1VEQwKDTAT+LIptNwsdgwLGniVqD3Oj/kXDY+1qsaO0aVWD5GA2YmpxgeRv1qsC61hmsq0GxZq6trVWz+hqfBCuOdIp9b1hYmNs6eFZXVzNnzhwCAwNJSUnx2BZHV8zeNhZUTduuZVl2aru+kNiSZZmDBw9y9uxZ4uLiVIc3T6ZxV0J8fHyzdSU0/VkrVtn79+/HarU6dV0098aGskHWp0+fa9oUdjcaR0lcyFo+JMAXL4OB6H69iO/Rkf/L/9wRHm8xc6awECIDkOwaR4hww9tMr9MS4GPEpDHRs0c4Z/YVIDfM6JotNowGHRasqp27Tq/FL8D7Z3cioH2HYI6cLken1fLwk7dxPPcM3x13uE16+/18XTJeQGB5irhqjCzLvPvuu2zfvp0ffviB6OhosrOz+eqrr1i5ciX33Xdfq9SxcuVK0tPT2bNnz3kfKywsxGg0nndtioyMpLCwsFXqa0nahMBSTmbMZjOjRo26rsWGIjxsNkf4aFsQV4rg6Nixo1u1HjRt12jcGuPl5aWebF1Nu4YSmnm505y2gLIzHR0dTVhY2FV9rn/DLFFoiB/3zRtFXZWJzH2n0Br1VFbXYzDokSQZGRmNRsZm/fkU12azYdDrsdskZGTsVjsG9FitVjQaDTpZRmsAH70Grd7R7mdDVueedDqHgUXj11Sr02K1/OweCA5BLjeoLrvdjmSV8PX1QtPgbqgzakGraWjpa0ilbECnaSro7OgNzpsKSltgrdnqsJDX6fD20p8nsPS6nxevzTGDdSkai6uhQ4eqpz2NT4J79+6tmiQoeUu+vr7qgHNQUJBbvMdra2uZO3cuRqORtWvXuqWhwOVwl9nbpm3XFRUVFBcXs3//fmw2m1PWltLmnpOTQ01NDcOHD3dbAX411NTUkJGR0eJdCY2tsvv06UNNTY16X8rNzb2iwN0rRTGaGjRoUJsY6ldaN6urqy8aJRES4INRp6N9kD86nY7I4ACCMIIGfIONdO3SniN7DmKzWZFlqKutxdfXBx+jgcAAbzqE+FMf5ENlRR1GbwP1koy3t55aGtZvGkdLe2CgL3LDJl94x2C6RXflRNYphyV770hKz5xDliRkSXaawWp6glVfX09aWprHias333yTJUuW8M033xATEwPAkCFDGDJkCC+88EKr1HHq1CmeeOIJNm/e7NHmTNeKxwusmpoa0tPT8fPzY+TIkc2yo6XVarFYLOh0Oo8XV4WFheTl5bm94GjaGqPs1mZlZaHRaK7IjlQRHJ4SmnmtNDYpaa6d6YcevoW1azOYNm0IqTsOsGlrLmXldSBrMBj0GPR6R5aW5Gif1WDHZHFsPmi1WpBkVTgBWK12tHYtslXGbnOcQBkNOnTI+PjpMDXY5Oq1Gmzyz/a6eo3zSZbVbscu2RzBw3o9Go3WkZolSdhMDUJIr8NHq0PS4QhZliTMVuc5Sq1W6zRnBQ6LeCUMuarOTGiAb8NpGOd9LoBer8XPu+Xa2xqLq/j4+Eu20jU2SVBy0EpKSsjMzARwaiV0xexhfX09d999N5IksX79erdz7rySa7q7zt5qNBo1a6tPnz5UVVWpBhnKyWZtbS1Go5Hhw4d7TCvppVBmTK80TqS5aGy137Nnz/MCdwMDA9WTxKv9HVdyu2JjY6+548adkCSJffv2UV9f77Q51JSwQD+89Toigh3z313aBWGusWI06AgM8KFbt270qwLvvWewmKzYbHZqqiox15lBI1N6shSLyYaXUe8QWEgEeBsok2U6dA6hpLAcg15DUDsf6qvqAYi5qTcabyNDx/YlZ88xIruE4B/kiyzJSDbJeQarkcBSxFVoaCj9+vVzi/f/5ZBlmffff59XXnmFDRs2MGzYMJfVkpaWRnFxMXFxcepjdrudnTt38tZbb/HNN99gsVioqKhwWscUFRW1iXZmjxJYTX+5S0tLyczMpEuXLqpr2vWg3FADAgLYs2ePI88hMtIjgwplWeb48eMcO3aMwYMHe1SobmPr1wu1ayiLR6VdQ5Zljhw5wunTp9tMK8zFaNzb3pwmJUajnrlzHRfiGdOGMHZMH55/Nol6ux30GsxmZQdfg1arw9vHiE0yI0sSdpuNGqsVb28DkiT/LEh0Ds9cRepIEkh2CWudDXtDy52XXofGbsfXX4/epnH00Gu1SBoc7YZ2G1pAq9c7TpRl+efhrgbskoRO1v48c6XRICHh66PHrgEJJUTZ+XvWaDQOQSdJ1NRbCA3w5YKb4g2f2M7fp8VusJIkkZ2dTV1d3WXFVVMa56BJkqS2Eh45coTs7GxCQkLU90xrnCKZzWbmzZtHbW0t33zzDQEB52eNuTueMnvb1CSlpKSE3NxcwPE6ZGdnq50Antqe6U5dCU0Dd5UW96NHj+Lj46OKrUsZkjS+X7WVTEYlFNlqtTJ06NBLivqIIH/a+XrjbXQ8JyoimOPmcny8DQQ0bGDpdFo0aPDxMhIYFEhokC8nbcWYLNWUl1VQUVGLTqvDKlkJ8PdBHx7EiWPnGBTXhR0bKzEaDQSF+FNbVgvAgKFRnDpWyp2PjuH00RK0Wi3+wX4OgSVJeDd2EWwwuaivr2fv3r2Eh4fTt29fjxFXH3/8MX/84x9Zt24do0aNcmk9EyZMIDs72+mxBx98kH79+rFo0SK6dOmCwWBg69atzJkzB4ADBw5w8uRJl9feHHiUwFKQZZmTJ09y8OBBBgwY0CxhaY2td+Pj49W2gIMHD2I2m51aMNx9R1CZRystLWXYsGEeucBRuFy7Rrt27bDZbJjN5jafcdXUOaslF8v+/t5MvG0Aubln6NItlO93H8FkBUtD37vNJmE06LHZ7ejQIcvg7aWjstrc0F6rRauR0TU6PbHa7Gh1zgtVjVaDxg5Wsx2b1YYsO0QckoxNNmOQJHz9vJDtWmSdwyXQIsvoNY6TLzQaZElG1v6snnRKWLhZUoekDUYdIONj0CMhO07NGhlx1FusDe1g5/8s7A3OiS1lcKGIq/r6+qsWV03RarVOpxu1tbVq6+3Bgwfx8/NTNy+ux5XuYlgsFu677z5KSkrYsmWLRy4ePTVI/ty5c+Tm5tK1a1fVJru4uJizZ8+yf/9+goKC1DkjT2nXdOeuBKPRSKdOnejUqZN6itzYkEQRW+3atVMFuizL5Ofnq/fmtnC/stlsZGRkAFzRXFxksD+RgT+vSfp2CKO4uAZ/HyOBDW1khob7hG+DZbqPt5EAX2+qay2E+gRRXmhCskvU1Ncg11fSvUcI+gwdvv5GdDoNXl56AgK9kGw2dDotvv5eRPWJxMvbwB2P3AxAcKhDYNltTQSWl95jxdWnn37K4sWL+fLLL90iyD0gIIBBgwY5Pebn50doaKj6+MMPP8zTTz9NSEgIgYGBLFy4kFGjRnm8wQV4oMBSbMaVHfzmOK1oekPVarVOu4K1tbUUFRU59WArNyp3cMRqjNVqZd++fVgsFoYPH96m+l6btmtUV1ezb98+TCaTKj6utV3D3VF2CC0WS6vlec2aHces2Y6j/Tl3DCUlOY0t3+SAUYcsyw4h9POhEVqtHoPB8YAkSVisdnR2Oza73NCip8Wg0ziJGKmhH/DnTCGHQKq3mNFoQK/TI9lkx4mXBpBBi8M9yibJGL112GSHHbu3XotEQ86J3Q6Nvo4GDRaLDYMNbA0zXlovjXoiJ2kargMXCBk2N8xkBbeAwYXSVmMymYiLi2v264mfnx9+fn5069ZNnXMsLS0lPT1dnetRWgmv95TearXy4IMPcvLkSbZu3Uq7du2a6btofi51uuCJ4urMmTOqe27Hjh0BRxupctpiNpvVrK1Dhw7h7+9PZGSk214rPa0roekpcnl5OcXFxeTm5mK329X5uaKiImpra1t8g6y1sFqtpKenYzAYiI6OvqJrSHiQL11Cft546dcpgr37z+Dv40Wgt+O+ptU63nf+Po6/e3vpMTaMbOgNjjWa3QYdOnXERyvTuWsgev0xCgtPY7fZkGUb3n4GNDL4BTg6D7waWv+GDO8JQJe+7R0zwja7k8mFJNvYu3cfERERzdIZ1RrIsswXX3zBM888Q3JyMuPGjXN1SVfMkiVL0Gq1zJkzxylouC3gUQLLZrOxZ88e7HY7o0aNapYLlGM33rGAutANtbHzndKDXVxcrAa6KTbjERERLhcz9fX1ZGZm4uXlxbBhw9p07pPZbCY3Nxdvb29GjBiBJEnX1K7hCSgmLuDYIXTFCaqPj5F7fjGCH787jJ+/N6Wl1VgkRx+8xe5w6Kurs6jBxMpGhY+Xgbp6M5IkY7PbMMl20Dh2JzVareNUS6tRZ7dkGWxWCwA6nR6NxuEKqNVpnZ6nASRJxm6V1NZAg16HZLOj9wKjRUJnAF+dBrQafLz01MoyGhnsaLDjaDU01TtCh+2yRM3pGjpYjUQajOh89Eh6MNnt1DdcH9o1s8FFY3HVGq9r4znHxq23yil941bCq72W2Ww2Hn30UQ4ePEhqaupVm664GsXMorWdAq8XRYicOnXqkrM8Xl5e5+U/Nb5WKvewgIAAl3/fTbMa3VEAXorGIbD9+vWjqqqKwsJC8vPzsdvthISEcO7cOcLDw91ug/ZqsFgspKWl4ePjw5AhQ664jVar1dI9PFj9u7dRj6+XgWA/HwIbZqEMeodQaxfgAxJ4Gw14WUAng3+gL6GhfhQUVDgysry86Na9CwbjXoZEDyT3xxIMBigsPk5JUSFB7QIpKytzOkkEMBgNRHYNw2a1qzNYdXV15Ofn0LFTe48RVwCrV6/mscce44svvmDy5MmuLueSbN++3env3t7evP3227z99tuuKagF8agVuE6nIzQ0lO7du1+3eGh8Q1UWhFfyZvL19aV79+507979PJvxgIAA9UbV2jcFJaQwIiLiqsOVPY3a2loyMjIICgpi4MCB6vd6te0anoDZbCY9PR1vb2+GDBni0llArVbLX16dQ1CQL3V1FlYnpbHru4PYayz4BnlTb5NAp1GDenU6baNTYQAdGhytgpIsQUMroY+3AWvDSZbNZkOv1eDtbfxZTGkanPw0oMRt2RsZD6j1NX7/ymC3SGi0jrkts01Gstgx6HXo7RIGDRi9tRgkDVqdjF2jxSzbqKu2/JzphcOYQ2Oz0c7Pi3Dv5ttxliSJrKwszGazS0Rz09bbpsP7SmRCeHj4ZRfddrud3/72t2RmZrJ9+3a3a+W6GMpmQFMzC08RV3a7ndzcXKqqqq5qHrNx/pNyrSwqKmLv3r0YDAanrK3W/jnYbDb27duntny7etPyetFoNPj4+FBZWal2xJw7d44zZ86Qn5+vtm2Gh4d7lNOjyWQiPT1djX252ntqr/bOGwG+3gaC/LwIbDix0usd/154sC9l5+rw8dJjNOig3IQUaseo04IsY0BDcIgfRoNjPRjczg9vLyPtO4QzbcYECrO/QqN3mAdJknRejmDHXpEUnTqHt5+Ruro69u7dS4eOkR4lrtatW8evf/1rPv30U6ZPn+7qcgSN8CiBpdVq6d27t9pOdK001w21sc1444HXI0eO4OvrS0REBJGRkdeV6XQlKEPAPXv2pGvXrh5zYbgWKisrycjIuKTl/JW0ayj5Me58yldXV0d6erqaGu8OwjAoyLEI8PU1Mu++UfTuHcH7b6cim+1I9Rb0Xnq8DTpsyI5WPZvk9Pk6g66hhU/6eYMDOxaLrSHzRIOs0Tk5EjoeBRq91sqpVmPU60Kj64NOp0Wy2dU4lMYnZRaTY+bLy6BzzPHpJGyBzu6D3j5GLDV2THWWZmsRVMSVxWJx2YlkYzQajdpK2L17dywWi9pKeOLECfR6vbowaeriKUkSjz/+OD/88AOpqal06NDBhd/J1dN49hZwi/fYlWCxWMjKykKWZYYPH37NJyGNr5UXc2+NjIxslY0ps9lMRkYGBoOhzXRgKELEz89PzZ8MCgpyyoEsKSnh0KFD+Pn5qeK2pdcM14PirNeuXTsGDBhwTXW2D3aeC/f1MhLs602Qj0NQK9EYHcKCKDtXh5dRj5e3gbpzdRgNOmx2CV8fI7Y6C8Gh/ugaBFlAoDc6vZaAQD/8/X0IDgkkONSfMWNuoqqqipKSEo4dO6a6bYZ28efUoULsso29ezNp3769W0XZXI6NGzfy4IMP8tFHHzF79mxXlyNogudfwa6SlrqhNh14VQbL9+zZg9FoVC+czZ1Rc/LkSQ4fPszAgQPbRI7GpVACGXv16kXXrl2v6HMu1K6hiOCcnBx1ns7d2jWqq6tJT0+nfXv3blUYNrIHANFxXdmwfh9bvsmlurIeZBnfIB9MaKDBCh0aTpkaWv00Gg06nQ5vLyP1plrQOGalbDYrdpuMzabMbmka3Al//hloNBp0Wq2TmFLChxtlS6JtMIBXNlMk2WHNLjf8P/xsYiFrwGKzY9T/LCAMjbKzgoOvf4fZbrerM5JxcXEuF1cXwmg0qiccygaF4uJpsVjIy8vDZrORmJjIsmXL2L59O6mpqS53eLtaPHXeSjnBVwbIm+tU+0LurY03phoHGzf3SfrFuhI8mdraWtLT0y+a23WhHMiSkhL27NmjniQqOZDu8vNQvqfrNX9o+nn+3kaCA3zUEyxdw/fbpUMQOQfP4u1lwNtbj4+vF8VnqxgY25U93x+iorCSdmH+jtMtwD/QB29vI36Bjn/Hy9uAb4D3eW6byol9fS8z9d/UkpGZRnC7YI+yBt+2bRv33Xcf77//PnPnznV1OYILcEMJrNbqsdfr9bRv35727ds77QpmZGSg1WpVsXU9u4KKXXdRUVGbsXq9FKdPn+bAgQPXFcjY+CLbu3dvamtrKS4uPq9dw9UuW0rmi9KK6s4LP41Gw/BRjqHhxNvjmZUYy5v/3ExWxknMtRbsVjtGox5JCxh1aDQa7I1EkSxDdU0dWq0GvU4PDc5+3l4GzBaTOh9ps9vw0hid4oS1GpwysyRJRttgu67++ziEmdTIvEKn1SBpQLIpIcaSw6RDo8FktTkJLK3O8dX0Oh3+/tdnLKIYldhsNrcVV01pvEHRt29famtr2b9/PytWrOAPf/gDer2eBQsWUFFRgSzLbv272pgjR47w1ltvMWvWLIYOHeoxdZeXl5OVldXiofGNW0j79u1LVVUVRUVF57nqhoeHX/dJk9KV0KlTJ3r16uUxr8WlqKqqIj09/Yq/pwvlQJaUlLBv3z4AVfi6MjKmpqaGtLQ0Onbs2OyvU5CfF6EBPur3prQItg8JJNDP22Fy4aWnc+d22Ew2eg/pjLefF0dyzhAc6o+mYfMtMNgHf38vvH0cm6Xevkb8LjA7q9jth4WFkdHvAGHhYWi1WrVNVvl5u+tIwc6dO7nnnnt48803+cUvftEm3jNtEY8TWErf/NXiqt3KxruC/fv3V9vVcnJykGVZ3RW8VIBuU2w2m2rrPHz48DbhRnQxGofqNncgo5+fH1FRUW7VrqGc0vXp04fOnTu3ytdsTrRaLTMTY4mO7YK3j5H1azM5c7ocu8kOJhtarQaNUYeXQYdFsmOxWjEYdPgY9VgbzVyh0aLX60DWITVsjNhtNqxWOxrlVEunVU+hFAx6rVPIsGSX0eqcBZYj1+jnz5EBfcM1wWS1E3iBt1NQ0PVlYDUVV57Y/qQY/syfP5/CwkJKS0t58skn2bNnDzfffDMhISHcc889vPrqq64u9bLY7XaKi4uZPXs2gYGBzJo1i4SEBEaOHOm2mYdnz54lLy+Pvn37tuq1oenGVNOojNDQ0GvuAriWrgR359y5c2RlZREVFUX37t2v+vObrhmamtEo7brh4eGttkmjCMauXbsSFRXV7PfD9iEBTu87fcP/Bwf4EN7OD28vAz7eRgYO6UzmT8fwC/Bm7iO3UFZURVCIH3V1ZsARLxIQ5KMKLOUE60LU1tayd+9e4scOJCYmxrH5Z7c7rdEuNLflanbt2sWdd97JG2+8wQMPPCDElRvjeXf5a0A5tXJ1K0jTdrXKykqKi4s5cOAAFovFKWvrYgswk8lEZmam2qfuDm/4lqJpnldzhepeCHdo11AWUAMHDvSoVoWm9OgVQY9eDqODkaN7cfhgEcve+IaqqnqHLbvJhs5qx15vwcfHgF5n+PlYqgGTyer4n4aTJa1Wh9FoQMaqvpfrTSbkhhBYrdYRTGkw6JwEll2S8DLqsTWaBZNkGQ3Or6FWo0EGTA0GHernN3xeu+toD2wL4kpBlmX+/ve/8+mnn5KamsqAAQMAx3Vp+/btnDp1ysUVXhl9+vThf//7H/X19WzevJmUlBTuuusuvLy8mDlzJrNnz+amm25yi9dKlmWOHTvGiRMniI6OdqlDY9OojNraWkpKStQugKtx1W2OrgR3Q1mY9+3bt1nyOTUajZprp4jbkpISTp48SV5eHu3atVM3aVvKEETpqOjRowfdunVrka8RGuhsCqbXO0yN2gX4EB7ij49Rj7+fF7Hx3choEFgAoZGBAOj0OgwGR4dEULCvmmvl5WtUn9sY5TSuU6dO9OjRQ10T6nQ61VJfluULzm0p4tYVG9s//fQTd9xxB3//+9959NFHhbhyc1x/92hBFIcoe4MrmDv12Ws0GoKDgwkODlYvnEVFRRw9elTN2oqMjCQsLEzdFayuriYjI4PQ0NAL9nS3JZRZFZPJ1Op5Xq5o11Bm6WJiYggNDW2Wf9Nd6NUnktl3DsXLy8D+vAJ2f3+Yuvp6tFotBo0OW7UZvUGHFzIaLz1ao446i81xStVwQqXT6aChBU2n0zX80WK12bDZJex2K6DBanN8TmNHQaNBj8n8s3CS7BI6XdM4BofwMjcRWGZLQwbWNQosu91OZmYmkiS1CXH1+uuv8/7777Nt2zZVXIFjg2LKlCkurO7a8PHxYdasWcyaNQuLxUJqaipJSUncf//9AEyfPp3Zs2czZswYl8xoSpJEfn4+ZWVlDB061O1C4xuboyjBxoqrbmBgoLox1dhVtyW7ElyJEt3SUqHIjcVtjx49qK+vP8/FuHEOZHOsdcrKysjKymr1jgqjVotBp0Wn0xLWzg8vo56Y+G6Etw9Cb9CdJ5oMBp3qJBgU4oevX0OLoI8Rnyat3Y3FVc+ePS/6c7rY3Jby874ap9XmID09ndmzZ/Piiy+yYMECt1nLCi6Ox93tr/SXypPcoRpfOJVg4+LiYk6dOqXuUvn6+nL27Fm6d+/eIkf07oTFYiEzMxOtVsvQoUNdekrX0u0aymLj1KlTHhGoea2Mm+hYjA8cEkHXnjaKCrRs23hY7Z23Wu3oALvFgtGow2C1o/czIOkcQcK6Jm1+4AgU9tIY0GgdocKSLCHZJWw2x+mXEm6sbfJWscsyhibvH1lyhBXb7BJ2SXIYYcgy9fWOf+taBFZjcRUbG+vx4mrZsmUsW7aMzZs3M2TIEFeX1OwYjUYmT57M5MmTeffdd9m5cyerVq3iN7/5DSaTienTp5OYmMi4ceNaZcNHCY23Wq0eERrv7e1N165d6dq1q5Or7uHDh9WW67CwME6fPk1ZWVmLdyW0JidOnODo0aPExMS0mmD08fFx+nkrxlrHjh3D29tbFVvXaqyltG/269dPDa9uLfR6HcaG62VEiD8+3gYiwhynVV5GPf5NBJZer8Xo5Xi+r5/XzydY3gb8Grm/KuKqc+fO9OzZ86pqUua2Goe2FxcXc+LEiRaf29q3bx8JCQksWrSIp556qk2v/9oSnnvHvwSeGBjZmMazQfX19Rw6dIjTp08DUFpaqhpleFJuxpVSV1enOmQNHDjQreYhmrtdQzEqKS4uZujQoW1msXExysvLyczMpHfvXkyc2I2I8DDahfqxM/UAufvOoEHGYrGjwWF0obHK2GvMaAAvfyNWWUZr0GKT5YY2P35uLdSAVqMFnQ4vowa7JCNLEja7nerq2gZHQkerITTJzAJskowjLhnMVju+Xlq8vY2YrdcmsOx2OxkZGciy3CbE1bvvvstrr73GN998Q3x8vKtLanH0ej3jx49n/PjxvPXWW3z//fckJSXx1FNPUVlZydSpU0lMTGTixIktch2ur68nIyMDHx8fhg4d6nG/P41ddZXFqNKhodFoVLddTzJGuRBK0PPp06eJi4tzmdlUY+dPu92u5kAqxlqNZ72vZPFfVFRETk6Oy9o39Tod3kbH73xYO3/VBRbAy0ePj5/zqZQjb6xhg1MDfgGOj3v7GvFt+P+amhr27t1Lly5drlpcNaVpaLvS5aI4bipzic0xt5WXl8fMmTN5/PHHWbRokUe/X240POuqfQV4qvXuhZBlmVOnTnHu3DmGDh2Kn5+f2oLReFcwMjKy2VoCXIkSluzu1uRwZe0ajdtjmn4vkiQ5hYS2ZaMSuHCryW1TBwMwdHgP6urMfP1lFhu+zFRPquy2n2epbCYbNpuE3qBDa7VjMGrxMuixSKBr5Ewo22k4fZKgoZVQb9BhsVuw2yVsNjsajQZLg8O7+rpoUIWd2WbHt8EW+FoEliKugDYhrj788EP+8pe/8PXXXzN8+HBXl9Tq6HQ6xowZw5gxY1i6dCk//vgjSUlJvPDCCzzyyCNMnjyZhIQEpkyZ0iybJJWVlWRmZhIZGXldVtjugsFgIDQ0lJMnTxIcHEznzp0pKysjIyMDnU7nFGzszp0mTZFlWW3fHDZsmFMbpCtp/DNtbLefn5+P1Wq97Ky30uo4ZMgQwsPDXfAdgE6nUQVWoJ8XtkaZiMHt/JwiOxR8G0SXBg2+fo4NTv8gX4xeBqqrq0lLS6Nr16706NGjWWvVarXq3JYSBdNcc1sHDhxgxowZPProo7z44osefy240dDI15va28rY7XbVurkp7mJm0RzY7XZycnKorq4mNjb2vIt34yPq0tJSvL291YtqYGCgx33vygJcGaT1tPob07hdo6ysTH1twsPDCQoKcgqajY2Nxcvr+uy/3R2l1aR///6XDKKtqzVTU2OiqrKeTRty2JdxErPJht1ub3APBaO3HovJBjh2vjUNFuwanRattx70jpwts+QIMtZoHb7vznHHMhpZwmxrsGfXajEY9Gi1Wqx2iRA/b9oH+xMW5k/puRoA/t/TU/D1vfwMjs1mIyMjA41GQ2xsrFudwF4tsizz3//+l0WLFvHVV19x6623urokt0KSJNLT00lOTiYlJYVTp04xceJEEhISmDZt2jVdhxWThLbkqnexroTGIfDFxcXX7KrrCiRJIicnh5qaGuLi4ty+fRMc7+fq6mr1511XV+eUA+nl5cXp06c5ePAg0dHRLp8Ffvat9bz22PTzHl+z8kcS7x5x3uP//Nt6nn5hOt+kpDN8TB/ahflTW1WPpLG1mLi6HMrcVklJCRUVFVc1t3X48GGmTp3KPffcw2uvveZRmw8CB21CYDU1s9BoNB6/QM/MzESj0RAdHX3Z4erGLQElJSVOO1jt2rVz+59FQUEB+fn5DBgw4JILcE+k6WujXCQNBgPx8fFtXlwVFhaSm5t7Ta0mkiTx0fKdHD1cTFFhJRaLHYNBi9UqoddpsDWEYOn0Wux2Cb1Oh81ux2DUY7FY0Rn0eAcYMdslJC3YZBm7LGM06pAlGau9cSsxDUJMg5+3kR4RIUREBlBcWo2XUc+i/zf1su8jRVxptVpiYmLceoF4OWRZ5rPPPuOpp55izZo1TJgwwdUluTWyLJOTk8OqVatISUnh0KFDjB8/noSEBGbMmHHZ67Asy+ocT0uZJLiCK+1KkGWZyspKioqKKC4uVk9aIiMjCQ0NdatTYJvNprqCxsbGulVA/dWgOEAWFxdTVVWFl5cXFovFbVxs//yfzbz40G3nPb5zSy5jJg487/H3/7WVR5+YwDcp6YybNgSjt161l+/WrRtRUVGtUfZFaexOXFpaesm5rePHjzNlyhQSExNZunRpq4mrl19+mZSUFPbv34+Pjw+jR4/m1VdfpW/fvupzTCYTzzzzDCtXrsRsNjN58mTeeeedNuME2px4vMBqambh6eKqcaL9gAEDrnqRpvQDKwt6WZZVsXWl/dethSzLHD9+nOPHjxMdHd1m3KQuRn19PXv37kWj0ThynRr1al/Kmt9Tadxqcr3W0mazle+2H+SHXYc5fKAQnU6LxeLYUDF46bFabBiNeiwWG1qdFqmhpUR5zOClx2q2odFr8Q30wmS1I+u1SMjYkdEZ9Eh2OyarDY0s095PT2iYP2YzREVF8OgjYy9ZX1sSVwBJSUn89re/JSkpySPdAV2JLMvs37+fpKQkVq9eTU5ODmPGjCExMZGZM2cSFhbmdI+SJEmdxYyJiWkzofGlpaXs27fvqrsSmp601NfXO2VtudL0yGKxkJGRgV6vJzo6uk1cs2VZVue8/f39qaqqwtfXV103tIZD3oX418pveeLuW857PDfzJANjzj/d/eyj77nnwZvYtm4f42cMUdsC3UFcNaXx3FZJSQl2u50ffviB0NBQhg8fzrx581Th0pprtilTpnD33XczbNgwbDYbzz//PDk5OeTl5aldVPPnz2f9+vV8/PHHBAUF8dhjj6HVavn+++9brU5PweMEliRJWBvmItrSvBX8HFCoDGFe7/cjy7Laf11cXIzNZnPqv3blIlBZhBQXFxMXF+d29sPNTV1dHenp6QQHBzNgwAA0Go26iCgpKaG2tva8dg1P5tSpUxw6dKhFXLW+Tc2nptZCduYpDh8sQpIkLBa7KqbA0fYnSZL6mNFLj6XBqt1gdAgynUGn5lwZvPTIOpC1GmSdhk7hAeiNGqqqaunSxYebR3dTdxubztS1NXG1du1aHnnkET777DNmzZrl6nI8GsUEQRFb6enpjB49msTERGbNmoWXlxe//OUvufvuu7njjjvazCxmc3YlKGZCRUVF1NTU0K5dO3Xx35rXSZPJRHp6On5+fgwePNitNiuvFVmWOXz4MAUFBcTHx+Pv74/NZnMaP1BOWlp7Tu7jdXt5YMbQ8x4vPFNO+07tznt8XUo6M26P44fU/QyI70h6ejrdu3e/prDn1kTJ21q6dCkrV67k5MmTdOzYkUWLFpGYmOjSVuGSkhIiIiLYsWMHY8aMobKykvDwcFasWMEdd9wBwP79++nfvz+7d+9m5MiRLqvVHfFYgdXWxJVyQ+rXr1+zBBQ2RXkTK2LLZDI5ia3W3BVU5stqa2uJjY1tM4uKi1FdXU16evol22SatmsoGTKe6BZ5/Phxjh07RmxsbIvbzkuSRPa+06xZlUZNjZnis5XIyD+LKL0Wu01Cp9epphnKCZfRS6+egqHRQIOLoCxD157hGH30oIGpkwfSsYMXJSUlnDt3TrVADg8Px9/f32lH29PF1fr163nggQf473//y5w5c1xdTptCaQNMSUkhJSWF3bt34+XlRVhYGF988QWDBw/2+PtYS3cl1NfXq/ewyspK9ToZGRnZoveR2tpa0tPT1QxKT3+dwNnFNj4+/oImHU07YiRJUsVWc+ZAXoi123NIGDvovMctDd0KTfl2235uGd+P3dtzMcnFREVFub24akxRURFTp06lb9++jB07lq+++opvv/2WQYMGkZqa6pIIl8OHD9O7d2+ys7MZNGgQ27ZtY8KECZSXlzvV061bN5588kmeeuqpVq/RnfFIgWU2m9uMmUXj0MUhQ4a0ymCpLMtq1lZxcTE1NTWtdnpitVrJzMxU7atd2e7RGijW5MpO2pX8rprNZlVsnTt3Dj8/P/Wm5qp2jSuhaaZXYGBgq9dQXl7Lj7uOsD+vgPycAupNFpAdokmr06AB1aXQ6G3AYnaIMFvDSZbeoMVmlejSPRSvAMdsxa9/dSvtIx1tW8pMndLaYbPZMBqN9OnTh/DwcI8WWJs3b2bevHl88MEH3H333a4up02TkZHB1KlT6d69O0ajkd27dxMbG0tCQgIJCQkemXXY2l0JTa+T/v7+6qZUc7rqKnM8nTp1olevXh73ulwIWZbJy8ujvLyc+Pj4KxKnypyc8jM3mUxOLe7NPYu2M/0oY+Ku3JQiO+MUXXsEsmHtTkbdOoRu3bo1az0tSWlpKdOmTWPgwIH873//U1tPz507R2pqqks2uyRJYtasWVRUVPDdd98BsGLFCh588EHMZrPTc4cPH864ceN49dVXW71Od8bjBFZSUhKSJDF+/Hh8fHw8+mInSZJ6kYuNjXVZDlLTXcGgoCD1RtWcu4JKtouvry+DBw/26MXolaC45zW2Jr9a3KVd43Ioffxnz55VW03cgZKSajLTTpCXW8CZU+coK63B2tBGqNfrsNmlBlMMx2NKK2FYZADBEf7odToWPzsVnc7552y1WklPTwcgODiY0tJSTCYTISEh6umWJ7V5bt++nTvvvJN33nmHX/7ylx59XXV3duzYwcyZM3n++edZtGgR4Ni9XrNmDcnJyezYsYOBAweqYsvdIyvAsfGQnZ1NXV2dS7oSrFaruvBv7Nx6va66Stu+p52GXIrmcEBUNmmVn3l1dTXBwcHqJm1zvP45RwoZ1PPKzTb2557gbPFRAnzDGDpi8HV//dbi3LlzTJ8+nR49evDFF1+4zabz/Pnz2bBhA9999526fhEC6+rwOIH1xhtv8NZbb3Hu3DnVZeW2225zmwyKK0U5yZEkiZiYGLdZjJnNZlVslZeX4+/vT2RkpLoreK0obXIRERH069fP7RcM18vZs2fJy8tr1qDGCxmYKG2eLd2ucSmUnevS0lLi4+PduqXRZLJw5HAJ+/MKOH6shJMnzmEx26ipcdwwFDMMX18jHXqE0KljO3718Binf0MRV0ajkSFDhqDT6ZwWHCUlJWqb58XmttyJb7/9ljvuuIOlS5fy0EMPuW2dbYWzZ8/yww8/MHv27PM+Jssy586dU8XW1q1b6d27NwkJCcyePdst29OsVqsaTRATE+PyBaLdbnfalNLr9U5ZW1f681Ms8/v27dsibfuuQJIk9u3bR319PfHx8c126mQymVSxpawblI1Af3//a/qdLSqrJjL0yk5BKysr+WH3T/Qf0Jd2wWEEBLrvPagxFRUVzJw5kw4dOpCSkuI2jpSPPfYYa9euZefOnU4GIaJF8OrwOIEFjovEnj17SE5OZvXq1RQUFDBp0iQSEhKYOnWq2xsmKLkg/v7+DBo0yG1PcpruCvr4+FyTs5CyC3g1bXKezMmTJzl8+HCLZom0drvGxVBOYSsqKq641cTdkGWZ4qIqDh0q4szpco4fK6WmxkR4xyB6945k6uSfd0Mbi6vo6OiLniAq7UvK3JaXl5e6u3s1i7yWRlnov/zyy8yfP99t6hL8/B7/8ssvSUlJ4ZtvvqFr166q2HIHowWlK8HPz88t72WNN6WKi4sBrshVV5mJHjx4cJuxzLfb7U728i0lhJV1g2JHfq3XPqvNjkF/+d+niooKMjIy6NmzJ127dlVHR9ydqqoqEhMTCQoKYu3atW6RpSbLMgsXLmT16tVs376d3r17O31cMbn47LPP1LbFAwcO0K9fP2FycQE8UmA1RgltTUpKIiUlhePHjzNx4kRmzZrF9OnTCQoKcqtFQ0VFBZmZmXTs2JHevXu7VW2XwmazUVZWRlFRkdqqpgwXX+pnrJzk9O/fn44dO7Zy1a1L4xmk2NjYVrNbbo12jQvRuNXkRsj0slqtpKWl4eXldUlx1ZSmc1uAW5w87t27l1mzZvGnP/2Jxx9/3GOuRTcqVVVVrF+/npSUFDZs2EBERAQJCQkkJiYSHx/f6otKT+tKkCTJyVXXbrdf0LBBySNrS9EhitMpQGxsbKvZyze99mk0GvVUvznCpBVx1atXL7p06dJMVbc8NTU13H777RiNRtavX+82G5O//e1vWbFiBWvXrnXKvgoKClJrnD9/Pl9//TUff/wxgYGBLFy4EIBdu3a5pGZ3xuMFVmNkWSY3N1e1xN2/fz9jx44lMTGRGTNmEBIS4tKbQGFhIXl5efTu3dujLgZNsdvtTq1qGo3GKdhYudEfP36co0ePNksOkrujtMmVlJQQFxfn0hmkC7VrKGLrWts1mmK329m3bx9ms5m4uDi3aW1oKRRx5e3tzZAhQ655MatEJygLDlfNbWVmZjJ9+nSef/55fve737n94ljgTG1tLRs3biQ5OZn169cTHBzMrFmzSEhIYMSIES0u2svKyti3b5/HdiU0dtUtKirCbDYTGhqqvj/j4+NdYtLTEiin7gaDwaVOp4rAVe5NSph0eHj4NTkZe6q4qqur44477kCWZdavX+8288rARd/HH330EQ888ADwc9DwZ5995hQ07A7h1O5GmxJYjZFlmYMHD5KcnExKSgpZWVnccsstathjREREq90UFOvaY8eOMXjwYMLDw1vl67YGF9sVtFqtVFZWusxNrjVRTnKqq6uJi4tzm90oaL52jcbY7XYyMzOx2+03hBOkxWIhPT39usXVhVDcPFtzbisnJ4dp06bx1FNP8fzzz3vc4ljgTH19PZs3byY5OZmvvvoKb29vZs6cyezZsxk9enSzn1a0ta4EWZapqakhNzeXmpoaANVVNyIiwqM3jywWC2lpafj4+DT7tet6UH7myrpByYFUrn2Xa5dTxFXv3r2v2UDKFZhMJu666y51g6Str41udNqswGqMLMscO3ZMFVt79+5l1KhRJCQkMGvWLDp27NhiiwxJklQDgNjYWLefD7seZFmmvLyc/Px86uvr0Wg0hIWFERkZSVhYWJtIvW+K0tdusVjc/iSnOdo1FHMWZaC9Lb6mjVEWKIrzZUsuUMxmszqc33RuKygoqFm+dn5+PlOnTmX+/Pm89NJLrSKuXn75ZVJSUti/fz8+Pj6MHj2aV1991akFRdA8WCwWtm7dSnJyMmvXrkWj0TBjxgxmz57NLbfccl3XJyXHq611JUiSRHZ2NrW1tcTFxSFJkrrwr6qqIigoSDV6coc5mStFCUYOCAhg4MCBbiOuLkRdXZ16sqXkmyntm03NtcrLy8nIyLgud15XYDabmTdvHqWlpWzatMkluVaC1uWGEFiNkWWZU6dOqWGPu3btYtiwYaolbteuXZtt0WG1Wtm3bx8Wi4XY2FiPujhfC1arlaysLOx2OzExMVgsFqcdKsWEITw83K2FyJWiuGdptVqPExvX0q5hsVjIyMhweatJa9Ga4qopLTG3dejQIaZMmcL999/Pyy+/3GonV1OmTOHuu+9m2LBh2Gw2nn/+eXJycsjLy/M491dPwmazsWPHDlatWsXatWsxm83MmDGDxMRExo0bd1XtqEpHSGFhIbGxsW1m591mszkZPzS9LzVttw4ICHDK2nJX6uvrSUtLo127dgwYMMCjTqktFotTvpmPj48qtpTuCU8TVxaLhfvuu49Tp06xdevWNjPbJ7g0N5zAaowsy5w9e5bVq1eTnJzMt99+S3R0tCq2evbsec0Xpvr6ejIzM/H29mbw4MEetfi+FkwmExkZGWobVdMFYGMThqqqKtWEwdN2BRWU79fHx8fjM72upF3DbDaTnp7uErHhChRxpbijufL7bY65raNHjzJ16lTuuOMO/vGPf7j0+ykpKSEiIoIdO3YwZsyYy3+C4Lqx2+189913qvNudXU1U6dOJTExkYkTJ16yrdlut5Obm0t1dTWxsbFuHcNwNSgbRnq9nujo6Mveoxsv/MvKyvD19VWNnpprtrU5qK2tJS0tjYiICPr27es2dV0LirmW0kZtt9tp164dUVFRTvPe7ozNZuOhhx7iwIEDbNu2rU2NiAguzQ0tsBojyzKlpaWq2EpNTaV///6qS9PVXKiqqqrIyMhQL3CecBG4HmpqakhPTycsLIx+/fpd9vs1mUzqYr6iooLAwEBVbHnCzbuuro709HTatWtH//7929zr27Rdw9/fH5PJRFBQ0FW553kq7iSuLsTVzm2dOHGCKVOmMGPGDN58802Xfz+HDx+md+/eZGdnM2jQIJfWciMiSRI//PCDKrZKSkqYPHkyCQkJTJ482WnovrKykoMHDyJJ0gVPeDwVpX3Oz8/vmjaMmgbAG41G9R7mSufimpoa0tLS6NixI7169fJocdWYc+fOkZGRQefOnZEkSRVbjU/13XET22az8etf/5qsrCy2bdsmjCBuMITAugDKLNHatWtJSUlh8+bN9OjRQ80fGTBgwEUvyEo4oZLJ0FYucBejvLyczMxMunbtSo8ePa76+226K+jn56feqNxpV1BBsSbu0KGDR9nsXyvKMLFer8disTi1awQGBra5799isbB37141o87VYuRyXGhuy8fHh6KiIiZOnKgunidOnMh7773n8u9HkiRmzZpFRUUF3333nUtrEThej/T0dDXm5PTp09x2220kJCTQv39/fvGLX/DQQw/x5JNPevQpfWNqa2tJT08nNDS0WYKbG7vqFhcXo9VqL+iq29JUVVWRnp6u3ovbCufOnSMzM5N+/fqppiqNXSBLSkqor69XjUncZQTBbrfz2GOPsXv3brZv394mDGEEV4cQWFdAZWUlX331lRr22LFjR/VkKyYmBq1WiyzLLF++nKioKGJiYoiMjHR12S1OUVERubm5zdYPbbVanXYFvby81OFid1jMK2LSU62Jrxal1SQyMpI+ffqoc0HK66PT6VSx5SntGpfCbDaTlpbmEUPhF0J5fVJTU3nqqaew2+0ADB06lDVr1rRaLtulmD9/Phs2bOC7777zqBmKGwHF7CEpKYkVK1Zw9OhR2rdvz+9//3sSEhJo166dx1/zFBHSqVOnFjnhkSSJ8vJyVWzJsqxeI5sj9+liKBthPXr0oFu3bi3yNVxBWVkZWVlZTuLqQjQ91Q8KClLFliu6YiRJ4sknnyQ1NZXU1FS6du3a6jUIXI8QWFdJTU0NX3/9NcnJyXz99deEhYUxY8YM8vPzSUtLY+3atQwdOtTVZbY4J0+e5PDhwy1mO994MV9SUoJOp1N3BYODg1t98VtSUkJ2drbHDddeK9XV1aSlpdG5c+cLziI2Xkh4SrvGpfB0cdWUgoICxo8fj6+vr+r+NmHCBBISEpg7dy7t2rVr9Zoee+wx1q5dy86dO4mKimr1ry+4Mnbu3ElCQgLz5s0jPDycNWvWkJuby6233qpmSoaFhXmc2Dp37hxZWVlERUXRvXv3Fv96sixTWVmpii2LxaJeI5vTVVcRIW3t3qR8X/3796dDhw5X/HmKMUlJSQnnzp1r9a4YSZJ49tlnWb9+Pdu3bxfXuhsYIbCug7q6OtasWcOzzz7L2bNnCQ8PZ86cOSQkJDBq1Kg201LRGFmWOXz4MGfOnCE2NrZVdsWVxXxRURElJSVOu4KhoaEtvhguKCggPz+fQYMG3RAnk5WVlaSnp9O9e/crujl4QrvGpVDEVWBgIAMHDvS4hWNTysrKmD59On379mXFihUYDAYOHDjA2rVrWbt2Le+//z4DBw5stXpkWWbhwoWsXr2a7du307t371b72oKrIysri5tuuomlS5fyyCOPAD9f85OSkli9ejUZGRncdNNNJCYmMmvWLCIjI93+PaO07vft25dOnTq1+tdXjISKioooLi5utmuksvF3tSLE3blWcdUUpStGyYE0GAxOOZDNvXaQJInf//73JCUlkZqaKq51NzhCYF0HBQUFzJgxg3bt2vG///2PvXv3kpyczJdffonRaFTzR2666aY2EcYqSRK5ublUVlYSGxvrEptaxVFN2RVU7MUjIyNb5OREOamLjo4mNDS0Wf9td0Rpg7yeVpOLtWtERES4VQgzOHY709LSCAoKahPiqry8nJkzZ9KlSxdWrVrlFuL2t7/9LStWrGDt2rVO2VdBQUFu9/twoyNJEhkZGcTHx1/w48ppqJIp+eOPPzJy5EjVebdTp05u9x46c+YM+/fvZ/DgwURERLi6HODna2RxcTHV1dW0a9dOXfhfqatuUVEROTk5bW7jr7S0lH379jFgwIBmNYVQZuWUmW9ANQe61uiLxsiyzJ///Gf++9//kpqaSr9+/ZqjbIEHIwTWNSJJEtHR0QwdOpTly5c7LWSsViupqakkJSWxdu1aJEli+vTpzJ49m1tvvdUtFj1Xi5IXYrVaiY2NvaoMlZZClmWqq6vVG1V9fb1T1tb1iFpZljly5AinT59utZM6V9MSrSaubte4XG1paWkEBwd7XFbMhaisrGTWrFmEh4ezevVqt3iPAhf9uX700Uc88MADrVuMoNmQZZkzZ86QkpJCcnIyu3btIi4uThVb7jCnevz4cY4dO0Z0dLTbZg/V19eri/4rddUtKChQRWNbsv1uKXHVlMbRF8XFxZjNZjUH8lrWDrIs88orr7B8+XK2bdsm3FEFgBBY18XRo0eJioq65E3EZrPx7bffkpSUxJo1a6irq2P69OkkJCQwYcIEj8iAMpvNZGRkYDQaGTJkiNvO19TU1FBSUkJRURE1NTXqrmBERMRVh2ru37+fkpIS4uLinGyL2yrFxcVkZ2czYMCAFms1uVi7hjJX15qLMZPJxN69ez0yiPNCVFdXM3v2bPz8/Pjqq6884roiaDvIskxRUZEac7Jz504GDRqkiq3Wdlxt3MoeFxfnMcHIFotF3TBsvCEVGRmpRjCcOnWKQ4cOERMT47ai8VpQ2h0HDhzYqidySvumIraUtYMyhnC5a6ksy/zzn/9k6dKlbNu2jejo6Faq/MK8/fbbvP766xQWFhIdHc2bb77J8OHDXVrTjYoQWK2I3W5n165dav5IRUUFU6ZMISEhgUmTJrllBpRiaassRD1l+L++vl69UVVWVl5xm5okSeTk5FBdXU1cXNwN0cJUWFhIbm5uq7aaXKxdo6XdtuBncRUSEtIsNs2upra2ljlz5qDValm/fr1LWncFAgVZlikrK2Pt2rUkJyezdetW+vTpozrvtvR7TpZl8vPzKSsrIy4uzmPfD01ddb29vfHy8qKqqorY2FiXGNW0FK4SVxei6YliQECAem9qmjMoyzJvvvkmr732Gps2bXK5wdnnn3/Offfdx3vvvceIESNYunQpq1at4sCBA27THnsjIQSWi5AkiT179qiDw4WFhdx2220kJiYyZcoUAgICXF0iFRUVZGZmXtRJzlMwm83qBfPcuXP4+/s7takp2O12srKysFgsxMXFeWQr59Vy5swZDhw4wJAhQwgLC3NJDRdr11DctppzfrGtiav6+nrmzp2LxWJhw4YNbnHdEAgUlPf2l19+SUpKCps2baJbt26q2LqWkN9LoVjN19bWEhcX12ZOcm02G3l5eWrOll6vd6mrbnNSUlLCvn373HKWzGKxqCK3rKwMb29vbDYbVquVsWPH8sEHH/DnP/+ZjRs3MnLkSFeXy4gRIxg2bBhvvfUW4Hg/dOnShYULF7J48WIXV3fjIQSWGyBJEpmZmarYOn78OBMnTiQhIYFp06a5JBlecV3q3bs3Xbp0adWv3ZJYrVanYGMfHx/11OTQoUPodDpiYmLctg2yOXHHVpOLtWtc7QD4haivryctLa3NiCuTycQ999xDZWUl33zzzQ0xJyjwbKqqqli3bh0pKSls3LiRyMhIVWzFxcVdl1BQ5oRtNhuxsbFtZoNMaXcsKCggPj4eX19fNdi4satuZGQkISEhHiW2lNZ0dzIguRhKdMxnn33G3//+dyRJwmKx8PLLL7Nw4UKXz7xaLBZ8fX1JSkoiMTFRffz++++noqKCtWvXuq64GxQhsNwMWZbJzc0lKSmJlJQUDhw4wLhx40hMTGT69OmEhIS0+MLw9OnTHDx4kEGDBrn9Re96sNvtlJaWcvbsWUpKStBqtXTq1InIyMhWnwlqbZTh79jYWIKDg11dzkW5ULuGIraatmtc7t9JS0sjNDSUfv36efxra7FYuPfeezl79ixbtmxpU+1CghuD2tpaNmzYQHJyMuvXr6ddu3bMmjWLxMREhg8fflVtwhaLhYyMDPR6PdHR0W1mg0yWZQ4cOKDOAzdtd2zqqmuz2ZxO/905KsaTxFVjZFnm448/5ne/+x1Tp05l7969VFZWMm3aNJ588klGjBjhkroKCgro1KkTu3btYtSoUerjzz77LDt27ODHH390SV03MkJguTGyLHPw4EGSk5NJTk4mOzubW265hcTERGbOnEl4eHizLhQbO+fFxMS49cK7uWg8YxYREaG2A2g0GqeZIE/aFbwUsixz9OhRTp065VHD33Dhdg1FbF3qlLe+vp69e/cSFhbWJsSV1WrlgQce4OjRo2zbtu2GiA8QtG3q6+vZtGkTycnJrFu3Dh8fH2bOnEliYiKjR4++pGAymUykp6fj5+fX7C2HrkSWZfLy8igvLyc+Pv6y88CN8wiLi4sxmUzN5qrb3CgW854orr744gsWLlxISkoKkyZNQpZl0tPTWbNmDRMmTGDs2LEuqU0ILPdDCCwPQVkYK/kjaWlpjBo1Sg177NChw3UtHCVJIj8/n3PnzhEbG3tDOOdVVVWRkZFBhw4dnFyuJEly2hW02+1OwcbuvCt4KWRZ5tChQ5w9e5b4+HiPfo2Vdg2lTUar1V5QECviKjw8nL59+3q8uLLZbDzyyCPk5uaSmprqUYuTGwmz2cyIESPIysoiIyODmJgY9WP79u1jwYIF7Nmzh/DwcBYuXMizzz7rumLdDIvFwpYtW9RMSa1Wq2ZK3nLLLU5Cobi4mAMHDhAaGtom2n4VFLOlmpqaa5olk2XZKWurpqbGKdjYle1sRUVF5ObmeqTFfEpKCr/5zW/4/PPPmT59uqvLcUK0CLofQmB5ILIsc/LkSVJSUkhJSeGHH35g2LBhantFly5drupGY7PZ2LdvH2azmdjY2DYzGHwplEDd7t27ExUVddHnNd4VLCoqalEDhpZEsZ4vLS1V+/jbCo0FcUlJCTabjdDQUIKDgzl+/DgRERFtQlzZ7Xbmz5/Pnj172LFjR4vmxAiujyeeeIJDhw6xYcMGJ4FVVVVFnz59mDhxIs899xzZ2dk89NBDLF26lEcffdS1RbshVquVHTt2qDEnVquVGTNmkJCQgMFg4P777+fDDz9k0qRJHv/+VpAkiX379mEymZrNbOlaXXWbG08WV+vWrePBBx/kf//7n5OAcSdGjBjB8OHDefPNNwHH71LXrl157LHHhMmFCxACy8ORZZmCggJWr15NSkoK3377LdHR0SQmJpKQkECPHj0ueeNp3Ls+ZMgQjxEM14NiCXu1gbrKrmBRURHFxcXU1taqu4IRERFuO1QtSRJ5eXlUVFRcUauJJ6OET585c4YzZ84gy7JTm4yrB5GvFUmSWLhwId999x2pqanNFgQtaH42bNjA008/TXJyMgMHDnQSWO+++y4vvPAChYWF6vVi8eLFrFmzhv3797uwavfHbrfz3XffkZSUxMqVKyktLWXgwIH84Q9/YOLEiW3iuqY42SpGHS1xPzabzarYKi8vx9/fn8jISNWGvKUoLCwkLy/PpY6118rGjRu57777+Oijj5g7d66ry7kon3/+Offffz/Lly9n+PDhLF26lC+++IL9+/e7nUPjjYAQWG0IWZYpLi5mzZo1pKSkkJqaSv/+/VWx1XQXv7CwkMOHDxMUFMTAgQPbTO/6pSgoKCA/P79ZLGHr6urUG1VVVRXBwcGq2HKXU8DGrSbx8fEeKzCuhrq6Ovbu3UtkZCSdO3dWTTKqqqoICgpSWwk95RRPkiSeeeYZNm3axPbt2+nWrZurSxJchKKiIuLj41mzZg1hYWFERUU5Caz77ruPqqoq1qxZo35Oamoq48eP59y5c8Ks5ApYvXo19957L08++ST19fWsXr2a0tJSNVNy8uTJHpl9ZbPZyMjIQKPRtJqT7cVcdSMiIggICGi2U0FPFldbt27lnnvu4f333+eee+5x+5PSt956Sw0ajomJYdmyZS4z3rjREQKrjSLLMuXl5WrY45YtW+jZsycJCQnMnj2boqIi7r33Xt5++20SExPd/qLRHJw4cYIjR44QHR3d7MYAJpNJvVGVl5erbnctvSt4Kex2u9r6eaPketXW1pKWlkb79u2d5urg/Dw0Pz8/VWw152KiOZEkieeee461a9eSmppKz549XV2S4CLIssy0adO46aab+P3vf8/x48fPE1iTJk0iKiqK5cuXq5+Xl5fHwIEDycvLo3///i6q3jPYuHEjd9xxB//73/9ISEgAHO+RtLQ0NebkzJkz3HbbbSQkJDB16lSPMPKxWq2kp6djMBiIjo52yZyvzWajrKyMoqIiSktLMRgMTllb13p9PHv2LPn5+R4prnbu3MncuXN58803uf/++93yHiFwX4TAukGorKzkq6++Ijk5ma+//hqr1crIkSN5/fXXiY6ObtOnV43dEWNjY1s8L8hisTjtCvr6+hIREUFkZCT+/v6tcpG22+1kZmZit9tbrNXE3biUuGqKzWZTHQmVxYQittwluFOSJP74xz+ycuVKUlNT6dOnj6tLuiFZvHgxr7766iWfk5+fz6ZNm/jiiy/YsWMHOp1OCKwWoLa2ltzcXIYPH37BjyvzS4rYOnLkCBMmTCAhIYHp06e7ZfyGxWIhLS0NHx8fhgwZ4hbXHrvd7pS1pdFoVLHVrl27K65REVctsanZ0nz//ffMmTOHN954g1/96ldu93sjcH+EwLrB+PDDD3n88cd55JFHOHv2LBs2bCAsLIxZs2Yxe/Zshg4d6hYX+OZCMXdQckRa2zmv6ULeaDSqN6qWCpC2Wq1kZma2aquJq6mtrWXv3r107NiRXr16XdXPVZKk84I7FSMTV7lGyrLM3/72N/7zn/+wbds2BgwY0Oo1CByUlJRQVlZ2yef06NGDO++8k6+++srpd89ut6PT6Zg3bx6ffPKJaBFsRWRZJj8/X82UzMvLY+zYsSQmJjJjxgxCQ0NdvmhWLOYDAgLctk3/Wl11CwoK2L9/v0eKq59++omEhAT+9re/sWDBApf/ngg8EyGwbhBkWeYvf/kLS5YsYc2aNdx6662AY15l48aNathjYGCgmj8ycuRIj7Ukh5/nj6qrq4mLi3P5EHTjXcHi4mK0Wu017QpeCsW0xJWtJq3N9YirpsiyTGVlpfoamc1m1SQjLCysVdosZVnm9ddf5+2332bbtm0MHjy4xb+m4Po5efIkVVVV6t8LCgqYPHkySUlJjBgxgs6dO6smF0VFReqp8vPPP09KSoowuWhBZFnm8OHDqtjKzMzk5ptvJiEhgVmzZhEZGdnqi2gl/Lxdu3YMGDDAIxbxF7o+KptR4eHh6maeJ4ur9PR0Zs6cyYsvvsiTTz7pEa+LwD0RAusG4vXXX2fq1KkMGjTogh83mUxs3ryZlJQU1q5di5eXFzNnzmT27NncdNNNHnUSoljPWywWt5w/kiSJ8vJy9UYly7JTjtO1CCOz2Ux6ejq+vr5tKnDzUtTU1JCWlkanTp3o2bNnswdvK1kyJSUlVFdXq0Ym4eHhLSLYZVnmX//6F2+88QZbtmwhLi6u2b+GoHW4UItgZWUlffv2ZdKkSSxatIicnBweeughlixZImzaWwlZljl+/LiaKfnTTz8xatQoZs2aRUJCAp06dWrxRbXSzuzJERKyLFNTU6Pew2prawkNDcVoNFJYWEhsbCwhISGuLvOq2LdvH9OmTWPRokU8++yzHvm6CNwHIbAEF8RisZCamkpycjJr1qxBlmVmzJhBYmIit956q9sJlsZYrVYyMjLQarUe0SLXdFfQYrE4ZW1dSf0mk4m0tDQCAwPdttWkuWlJcXUhTCaTKrYUe+PGRibX+/VlWeadd97h73//O998881F50xak7ffflt1pIqOjubNN990i7o8gQsJLHAOGg4LC2PhwoUsWrTIdYXewMiyzOnTp9VMyV27dhEfH09CQgIJCQl069at2a8r1dXVpKenN8uJuztRW1vL4cOHKS4uBqBdu3Zu56p7KfLy8pg6dSoLFy7kD3/4Q5t5Xa4Eu93OLbfcQvv27UlJSVEfr6ysZNCgQdx3333ceeedvPLKK3z33XeUlpbSvXt3fvOb3/DEE0+4sHL3RggswWWx2Wx8++23rFq1ijVr1lBfX6+GPY4fP96tLp5KT7tyiuNpLXLKrqCStVVfX09ISAiRkZEXbVGrq6sjLS2N0NBQ+vfvf0PcGBRx1blz58tmvbUEir1xSUkJpaWleHl5XddsnSzLfPDBB7z44ots2LCB0aNHt1DlV87nn3/Offfdx3vvvceIESNYunQpq1at4sCBA0RERLi6PIGgWZFlmcLCQlavXk1ycjI7d+5kyJAhqthqDjFUWVlJRkYGXbt2pUePHs1UuXtw5swZDhw4QGxsLD4+PuqGYUVFBYGBger10R3jMQ4cOMDUqVN55JFH+Mtf/nJD3EObcvDgQWJiYvj3v//NvHnzAEesRFZWFnv27OHTTz8lKyuL22+/nS5durBr1y4effRRXnvtNR577DEXV++eCIEluCrsdju7du0iKSmJNWvWUFFRwdSpU0lISOC2225z6cWztraW9PR0QkJC6N+/f5s4xVFa1IqLi6murlZ3BcPDw/H29lZbTSIjI+nTp88NcWOoqalh7969dOnSxS1sy+12O2VlZarg0mg0hIeHEx4efkXtnrIs89///pdFixbx1VdfqfORrmbEiBEMGzaMt956C3C0tXbp0oWFCxeyePFiF1cnELQcsixTWlqqxpxs27aNvn37qmLrWjayKioqyMjIoEePHm0uy+706dMcPHiQ2NjY84xamrrq+vn5qWKrtVx1L8Xhw4eZOnUqv/jFL3j11VfbxLrhWlm2bBkvvfQSubm5/PTTT8ydO5c9e/YQHR19wecvWLCA/Px8tm3b1sqVegZCYAmuGUmS+Omnn1RL3KKiIiZNmkRiYiJTpkxpVce+qqoqMjIy6NChw2Utuj2V+vp6SkpKKCoqorKyEj8/P+rq6ujYsSP9+vVrk99zU6qrq0lLS3MbcdUUxXFLWVBYrVbCwsIIDw8nLCzsPLt8WZZZsWIFTz/9NGvXrmX8+PEuqtwZi8WCr68vSUlJJCYmqo/ff//9VFRUsHbtWtcVJxC0IrIsU1FRwZdffklycjKbN2+me/fuJCQkkJiYyKBBgy67KC8rKyMrK4s+ffrQuXPnVqq8dTh9+jSHDh0iJibmsi6YVqvVyVX3ek/+r5fjx48zZcoUEhMTWbp06Q0trsDxuz5+/Hh0Oh3Z2dksXLiQ3//+9xd9/r333ovJZCIpKakVq/QchMASNAuSJJGRkaG6NJ06dYoJEyaQmJjItGnTCAwMbLGLZ3l5OZmZmURFRdG9e/cW+RruRmlpKVlZWXh7e1NfX6/uCkZGRjbLPJA7oogrT2mvudAQeEhICGVlZfTq1YuoqChWrVrFggULSEpKYsqUKa4uWaWgoIBOnTqxa9cuRo0apT7+7LPPsmPHDn788UcXVie4Wo4fP85f/vIXtm3bRmFhIR07duTee+/lhRdecGo7bjwfFh4ezsKFC3n22WddWLn7UVVVxbp160hOTmbjxo106NBBjTmJjY09b5FeUlJCdnY2/fv3p0OHDi6qumU4deoUhw8fJjY2luDg4Kv6XOXkX5lr1el0TsHGLS12Tp06xeTJk5kyZQrvvPPODS+uFPbv30///v0ZPHgw6enpF50B37VrF7feeivr169n0qRJrVylZ+De0/9ugNlsZsSIEWRlZV1yWPlGvxlptVri4+OJj4/n73//Ozk5OSQlJbF06VJ++9vfMn78eBISEpgxYwbt2rVrNgFQXFxMTk4Offv2pVOnTs3yb7o75eXlZGdn07t3b7p27eq0K3j8+HG8vb3VG1VLCtvWRBFX3bp1IyoqytXlXBEajYaAgAACAgLo2bMndXV1lJSU8M9//pO1a9fSsWNHioqK+Ne//uVW4krQ9ti/fz+SJLF8+XJ69epFTk4Ov/rVr6itreWNN94AHMJh0qRJTJw4kffee4/s7GweeughgoODhcNhIwIDA/nFL37BL37xC2pqatiwYQPJyclMnz6dkJAQ1Xl32LBh/Pe//2X9+vW8/fbbREZGurr0ZuV6xBXgJKgau+pmZ2c7ueqGhoY2u/g5e/Ys06dPZ8KECbz99ttCXDXiP//5D76+vhw7dozTp09fcNM6JyeHhIQE/vjHPwpxdQnECdZleOKJJzh06BAbNmxwElhVVVX06dOHiRMn8txzz6k3o6VLl4qbUSNkWebAgQMkJyeTnJxMTk4OY8aMISEhgZkzZxIeHn7NAqCgoID8/HwGDRrU5m5eF+NyrSaX2hVsTmHbmlRVVZGenu5R4upyfPLJJzzxxBMMGTKEnJwcevToQWJiIomJiQwbNszlr5NoEWz7vP7667z77rscPXoUQM3oKiwsVE+1Fi9ezJo1a0RG1xVQV1fHpk2bSE5OZt26deoJ9v/7f/+P5557zu3dbK+GkydPcuTIkWsWV5dCaclUTv6VNuvIyEhCQ0Ov++dYVFTE1KlTGT58OB999JHHGWG1JMqp1KZNm/jrX/8KwJYtW5zuR3l5eYwbN45HHnmEv/3tb64q1SMQAusSbNiwgaeffprk5GQGDhzoJLDEzejqkWWZI0eOqPkj6enpjB49Wg177NChwxUvLE+cOMGRI0eIiYnxuKyNa0XZ3RswYMAVtZpIkuQUbAyoYiskJMQjdu2qqqpIS0uje/fubUZcbdq0iXvvvZcPP/yQu+66i5qaGjZu3MiaNWs4evQou3btcnWJgMPkYvjw4bz55puA4/epa9euPPbYY8Lkog3w+9//no0bN7J3717A4RhWVVXFmjVr1OekpqYyfvx4zp07d9n5GsHPLFu2jMWLF3PLLbeQlpaGTqdTT7Zuvvnm82YxPQlFXMXFxREUFNSiX0uWZaqrq9V7WH19vRr8Hh4eftU/x5KSEqZPn86gQYP49NNP25TovV7q6uqIiYlhypQpLFu2jOPHjzN48GBee+015s+fD0Bubi7jx4/n/vvv57XXXnNxxe6PEFgXoaioiPj4eNasWUNYWNh5eSbiZnR9yLLMyZMnSU5OZvXq1fzwww8MGzZMdWnq0qXLBcWWItJOnz5NbGxsi1/g3YXCwkJyc3MZPHjwNVlkN90VtNlsTllb7riLp4irtjRbl5qayl133cW7777Lvffee97vuCzLLj+9Uvj888+5//77Wb58OcOHD2fp0qV88cUX7N+//4Y5MW6rHD58mPj4eN544w1+9atfATBp0iSioqJYvny5+ry8vDwGDhxIXl4e/fv3d1W5HsU//vEP/vKXv/D1118zevRorFYr27dvVzMlbTabGnMyduxYvLy8XF3yFXPixAmOHj3aKuLqQtTU1KhGTzU1NU5ZW5f7OZ47d45p06bRq1cvPv/8c5eKXHeciXziiSf4+uuvycrKUt2gly9fzu9+9zuys7Opqalh/PjxTJ48mddff139PJ1OR3h4eIvU5Om4/xa2C5BlmQceeIDf/OY3DB069ILPKSwsPG+Rofy9sLCwxWv0dDQaDd26dePpp59m586dHD9+nHvuuYeNGzcyePBgxo4dy5IlSzh69CjKHoDNZuMf//gHp0+fZtiwYTeMuDpz5gx5eXlER0dfc/6QRqOhXbt29O3bl5tvvpm4uDi8vb05fPgw27dvJysri7Nnz2K1Wpu5+mujsrKyzYmrb7/9lrvvvptly5ZdUFwBbiOuAO666y7eeOMNXnzxRWJiYsjMzGTjxo1CXLkRixcvRqPRXPJP046KM2fOMGXKFObOnauKK0HzYTQa2bp1q5plZzAYuO2223jvvfc4ffo0SUlJ+Pv7s3DhQqKiovjVr37FunXrqK+vd3Hll8bV4grA39+fqKgoRo4cyU033URYWBiFhYV8++237NmzhxMnTlzw51hRUaEGR69cudLlJ4iNZyJzc3NZsmQJ7733Hs8//7z6HGUmslu3bqSlpfH666/z0ksv8f777zd7PTt27ODtt9/mo48+cora+fWvf83o0aN5+OGHWbVqFSUlJXz66ad06NBB/TNs2LBmr6etcEOdYC1evJhXX331ks/Jz89n06ZNfPHFF+zYsQOdTsfx48fPO8ESu30tgyzLFBcXs2bNGpKTk9m+fTsDBgxgxowZbN26ldOnT7Nt27YbxtDi1KlTqgVuS7RCyrLslLVVU1NDSEiI2oLhit3VyspK0tPT21RezO7du7n99tt55ZVX+M1vfuNWQkrguZSUlFBWVnbJ5/To0UPdFS8oKGDs2LGMHDmSjz/+2KlNWHRltC52u50ffvhBzZQsKytj8uTJJCYmMmnSJPz8/Fxdosrx48c5duwY8fHxBAYGurqc8zCbzWo0xrlz5/D392f//v0MHjyYXr16MXv2bIKDg1mzZg3e3t6uLveCiJnItscNJbCu9GZ055138tVXXzktgux2Ozqdjnnz5vHJJ5+Im1ErIMsy586d4/PPP+ePf/wjZWVl9O7dmzvuuIPExMQ2EyZ8MZSbWksMEl+Muro6VWxVVVURFBSktmD4+Pi0+NdXxFXPnj3p2rVri3+91mDv3r3MmjWLP//5zyxcuFCIK4FLOHPmDOPGjSM+Pp5PP/30vLZgZUFXVFSk7vA///zzpKSkiAVdCyNJEnv37lUzJQsKCrjtttvUTElXiprjx49z/Phx4uLi3FJcNcVqtVJSUsLTTz/Nxo0b0Wq1tG/fnv/973+MHj3aba+/Yiay7XFDCawr5eTJk1RVVal/LygoYPLkySQlJTFixAg6d+4sbkatxLlz55g+fTo+Pj588sknai/7pk2b6NKli5o/MmTIkDYjtmRZ5ujRo5w6dcqlNzWz2ayKrfLycvz9/YmMjCQiIqJFdlcrKirIyMhoU+IqMzOT6dOn88ILL/DMM8+47c1d0LY5c+YMY8eOpVu3bnzyySdO4qp9+/aAY3Ojb9++TJo0iUWLFpGTk8NDDz3EkiVLhDNuKyJJEllZWaoZ1NGjR5k4cSIJCQlMnz69VQN5PU1cNaauro7Zs2dTUlJCdHQ0GzZsoF27dtx+++384Q9/cCtzLDET2TYRAusKuFCLoLgZtTw2m41hw4bRvXt3PvvsM6ej/erqatavX6+GPYaHh6tiKz4+3mPFlizLHDp0iLNnzxIfH4+/v7+rSwJ+3hUsLi6mrKwMHx8f9WQrICDgum/4irjq1asXXbp0aaaqXUtOTg5Tp07lmWee4bnnnhPiSuAyPv74Yx588MELfqzxEqDxUH1YWBgLFy5k0aJFrVWmoAmyLJOXl0dSUhIpKSnk5+czbtw4EhMTmT59OqGhoS12XTl27BgnTpwgPj6egICAFvkaLYXJZOKuu+6itraWjRs3EhgYiNlsZuvWrXz55ZcsXbq0RVoFr3QMpV+/furfz5w5w6233srYsWP54IMP1MeFwPJ8hMC6Ai4ksEDcjFqD3bt3M2zYsEvaqSoX0ZSUFNavX09gYCCzZs0iMTGRESNGuKVD3oWQZZn9+/dTWlpKfHy807CpO2Gz2SgrK6OoqIjS0lIMBgMRERFERkZe0+5qWxRX+fn5TJ06lfnz5/PSSy/dUOLKbrdzyy230L59e1JSUtTHKysrGTRoEPfddx933nknr7zyCp999pkLKxUIPAdl800RW1lZWdxyyy1qzElERESzXWc8WVyZzWbmzZtHaWkpmzZtarX2ehAzkQJnhMAStCnq6+vZvHkzKSkpfPnll3h7e6v5I6NHj3bb3AtJksjLy6OiooL4+PhWmXdqDux2u5q1VVJSgkajcQo2vtxJYnl5ORkZGfTu3bvNiKuDBw8ydepUHnjgAf7+97/fUOJK4eDBg8TExPDvf/+befPmAY4FQ1ZWFnv27OHTTz8lKyuLf/3rXy6uVCDwPGRZ5tixY2ob4d69exk1ahSzZs0iISGBjh07XvN15+jRo5w8edIjxZXFYuG+++7j1KlTbN261a3aAJsiZiLbPkJgCdosFouF1NRUkpKSWLt2LQAzZswgMTGRMWPGOOVNuBJJksjJyaGmpob4+HiPykVpjCRJTllbdrud8PBwIiIiCA0NPe8GooirPn360LlzZxdV3bwcPXqUKVOmcOedd/LGG294bKtqc7Bs2TJeeuklcnNz+emnn5g7dy579uwhOjra1aUJBG0GWZY5deoUKSkprF69ml27dhEfH09CQgKJiYl07dr1isXWkSNHOHXqFEOHDnWb9vQrxWq18vDDD3PgwAFSU1MJCwtzdUkXRcxE3hgIgSW4IbDZbOzcuZNVq1axdu1aTCaTGvY4fvx4l4kau93Ovn37MJvNxMXFuY3ou15kWaayslIVWxaLhdDQUNX+vaqqiszMzDYlrk6cOMGUKVOYOXMmy5Ytu6HFFTh+B8aPH49OpyM7O5uFCxfy+9//3tVlCdyYt99+m9dff53CwkKio6N58803GT58uKvL8hhkWebs2bOsXr2alJQUdu7cyZAhQ0hMTCQhIYGePXteVGwdOXKE06dPu9Xs75Vis9n49a9/TVZWFqmpqW6f1SdmIm8MhMAS3HDY7Xa+//57NX+kqqqKKVOmkJiYyMSJE1tt9slut5OZmYndbic2Ntbl4YcthSzL1NTUqGKrtrYWWZbp0KEDffr0aROi8syZM0yePJnbbruNd99994YXVwr79++nf//+DB48mPT0dLdt0RW4ns8//5z77ruP9957jxEjRrB06VJWrVrFgQMHrjlg/UZGlmVKS0vVTMlt27bRr18/VWz169cPjUaDJEmsWrWK9u3be6S4stvtPPbYY+zevZvt27fTsWNHV5ckEABCYAlucCRJ4scff1TFVnFxMZMmTSIxMZHJkye32M3GarWSmZmJRqMhJibmhll4njt3joyMDMLCwjCbzVRVVREcHKzObblrCOSlKCwsZMqUKdx000188MEHHmOq0ho8++yzvP3222i1WrKzs+nevburSxK4KSNGjGDYsGG89dZbgOPa3KVLFxYuXMjixYtdXJ1nI8sy5eXlfPnllyQnJ7N582Z69OjBrFmzOHz4MFu3bmX37t0eF48hSRJPPvkkqamppKamelz9graNEFhtgOPHj/OXv/yFbdu2UVhYSMeOHbn33nt54YUXnE4HGh83h4eHs3DhQp599lkXVu5eSJJEenq6Ojh86tQpNX9k2rRpBAYGNothgcViIT09HaPRSHR09A2zID937hyZmZn069dP3WU0mUzqyVZFRQWBgYGq2HJXF8XGFBcXM3XqVOLi4vjvf/97w7yWV8KuXbu49dZb2bRpE3/9618B2LJlyw1p+iG4NBaLBV9fX5KSkkhMTFQfv//++6moqFBnaAXNQ2VlJV999RV/+ctfOHjwIF26dGHu3LnMnj2bmJgYjziBlySJZ599lq+//prU1FSioqJcXZJA4IT7v4sEl2X//v1IksTy5cvJzc1lyZIlvPfeezz//PPqc6qqqpg0aRLdunUjLS2N119/nZdeeon333/fhZW7F1qtlqFDh/Lyyy+zf/9+fvzxR2JiYliyZAlRUVHMnTuX//73v5w7d45r3Zcwm82kpaXh4+NDTEzMDbMgLysrO09cAXh7e9O1a1eGDh3KmDFj6NSpE+Xl5ezatYvdu3dz5MgRqqurr/nn3ZKUlpYyc+ZMBg8efN6g8o1OXV0dDzzwAPPnz2fcuHF8+OGH/PTTT7z33nvqc3Jzc11YocCdKC0txW63nzc7ExkZSWFhoYuqarsEBgaSm5tLdXU1aWlpvPHGG5w+fZpp06YxePBgFi9ezI8//ogkSa4u9YJIksQLL7zAl19+yZYtW4S4Ergl4gSrjfL666/z7rvvcvToUeBny8/CwkL1VGvx4sWsWbNGWH5eBiWfKikpidWrV5OTk8Ott95KQkICM2fOJCws7Ip25U0mE2lpaQQFBTFgwACP2CVsDsrKysjKyqJ///506NDhij7HarVSWlpKcXExpaWleHl5ERkZSURERLOdJF4P5eXlzJgxg27duvHFF1+0iTmy5uSJJ57g66+/JisrSz2JXL58Ob/73e/Izs6mpqaG8ePHU1xc7OJKBe5AQUEBnTp1YteuXYwaNUp9/Nlnn2XHjh38+OOPLqyu7ZGcnMzjjz9Oamoqffr0UR+vq6vjm2++ISUlhXXr1uHn56dmSo4aNcotNpFkWeZPf/oT//d//8f27dvp27evq0sSCC6IEFhtlN///vds3LiRvXv3AiK0rrmQZZkjR46oYis9PZ2bbrpJDXts3779BRf/dXV1pKWlERoaSv/+/V0uEFqL0tJS9u3bd1Xiqil2u52ysjI1a0un06lthMHBwa0uVCsrK9Vgz5SUFI+11W8pduzYwYQJE9i+fTs333yz08cmT56MzWbj5ptv5s9//rNbnkwKWh/RIti6SJKkjhNcDJPJxJYtW0hJSWHt2rUYDAY1U/Kmm25yiSmTLMu8/PLLvP/++6SmpjJw4MBWr0EguFKEwGqDHD58mPj4eN544w1+9atfATBp0iSioqJYvny5+ry8vDwGDhxIXl4e/fv3d1W5Hossy5w4cYLk5GRWr17Njz/+yPDhw0lISCAhIYHOnTuj0WjIzMxkzZo13HPPPfTp00eIq+tAkiTKy8spKiqipKQEWZadsrZaWmxVV1eTmJhIQECAGmQtEAiunxEjRjB8+HDefPNNwPFe79q1K4899pgwuXAxVquV7du3q2ZQdrtdzZQcO3Zsq5zgy7LMP//5T/71r3+xdetWkacncHtujB4lD2Xx4sVoNJpL/mna3nfmzBmmTJnC3LlzVXElaBk0Gg3du3fnmWee4dtvv+XYsWPcddddfP311wwaNIhx48bx9NNPM2XKFCorK28ocVVSUkJWVhYDBgxoNnEFjjm50NBQBgwYwJgxY4iOjkav17N//362b9/Ovn37KCoqwmazNdvXVKitrWXu3Ll4e3uzevVql4qr48eP8/DDDxMVFYWPjw89e/bkj3/8IxaLxel5+/bt45ZbbsHb25suXbrw2muvuahigeDSPP300/z73//mk08+IT8/n/nz51NbW3vRvCBB62EwGLjttttYvnw5Z86cISkpCT8/PxYsWEBUVBSPPvoo69atw2QytcjXl2WZN998kyVLlrBx40YhrgQegTjBcmNKSkooKyu75HN69Oih7h4VFBQwduxYRo4cyccff+y0my9aBFsPWZYpKipi6dKl/OMf/0CSJIYMGUJCQgKJiYn07t27TQutkpIS9u3bx8CBA9VU+pZGlmWqq6tVR8L6+nqnYOPrbWepr69n7ty5WK1Wvv76awICApqp8mtj48aNfP7559xzzz306tWLnJwcfvWrX/HLX/6SN954A3AY2/Tp04eJEyfy3HPPkZ2dzUMPPcTSpUt59NFHXVq/QHAh3nrrLTVoOCYmhmXLljFixAhXlyW4CHa7nd27d6snW+Xl5UyePJnExEQmTZrULE6wsiyzfPly/vznP7Nx40ZGjhzZDJULBC2PEFhthDNnzjBu3Dji4+P59NNPzxtGVUwuioqK1MXm888/T0pKijC5aAF27tzJzJkz+fOf/8y9996rhj1u3bqVPn36MGvWLGbPnt3m5rEUcTVo0KDzHMFak5qaGkpKSigqKqKmpoZ27dqpc1tXOzNlMpm45557qKys5JtvviEoKKiFqr4+hLGNQCBwFZIksWfPHnU+ubCwkNtuu43ExESmTJlyTZtSsizz0Ucf8cILL7B+/frzZjoFAndGCKw2wJkzZxg7dizdunU7zy5aOUGorKykb9++TJo0iUWLFpGTk8NDDz3EkiVLxG52M3Pw4EHi4+NZunQpDz/8sPq4LMtUVlby5ZdfkpKSwjfffEO3bt1UsTV48GCPdhYsLi4mOzvb5eKqKfX19erJVmVlJUFBQarY8vHxueTnWiwW7r33XgoLC9m8ebNbn/QKYxuBQOAOSJJEZmammil5/Phxp0zJoKCgy24syrLMp59+yu9+9zu++uorxo4d2zrFCwTNhBBYbYCPP/74on3qjV/exkHDYWFhLFy4kEWLFrVWmTcMsiyTlpbG0KFDL/m8qqoq1q9fT0pKChs2bCAyMlIVW3FxcR4lttxVXDXFbDZTUlJCcXEx586dw9/fXxVb/v7+Ts+1Wq3cf//9HD9+nK1btxIaGuqiqi+PMLYRCATuiCzL5ObmkpSUREpKCgcOHGDcuHEkJiYyffp0QkJCzhNbsizzxRdfsHDhQlJSUpg0aZKLqhcIrh0hsAQCN6C2tpaNGzeSnJzM+vXrCQ4OZtasWSQkJDBixAi3yB+5GIq4Gjx4MBEREa4u54qxWq2q2CorK8PHx4fdu3czfPhwRowYwaOPPkpeXh7btm1rte9r8eLFvPrqq5d8Tn5+Pv369VP/fubMGW699VbGjh3LBx98oD4uBJZAIHAnZFnm4MGD6slWVlYWY8aMUTMlIyIi0Gg0pKSk8Jvf/IYvvviCadOmubpsgeCaEAJLIHAz6uvr2bx5M8nJyXz11Vd4e3urYY+jR49Gr9e7ukSVoqIicnJyPE5cNcVut1NSUsLjjz/Otm3bkGUZHx8f/vOf/zBz5sxWE7jC2EYgcD0vv/yyOp/s4+PD6NGjefXVV51CbU0mE8888wwrV67EbDYzefJk3nnnHbc+wXcnZFnm6NGjaszJ3r17GTVqFL169WLlypV89tlnJCQkuLpMFbPZzIgRI8jKyiIjI4OYmBj1Y427g8LDw1m4cCHPPvus64oVuAVCYAkEbozFYmHbtm0kJSWxdu1aNBoNM2bMYPbs2YwZM8YlYY8KirgaMmQI4eHhLqujObHb7fz2t79ly5YtjB07ls2bN6PT6UhMTOSuu+5i/Pjxri5RRRjbCAQtw5QpU7j77rsZNmwYNpuN559/npycHPLy8vDz8wNg/vz5rF+/no8//pigoCAee+wxtFot33//vYur9zxkWebUqVMkJSXxt7/9jYULF/LSSy+5uiwnnnjiCQ4dOsSGDRucBJZwaxVcDCGwBAIPwWazsWPHDlatWsXatWuxWCxMnz6dxMRExo0bd9XueNdDWxRXkiTx9NNPs2XLFlJTU+nWrRs2m43vvvuOlJQUNYvFHRDGNgJB61FSUkJERAQ7duxgzJgxVFZWEh4ezooVK7jjjjsA2L9/P/3792f37t3CSvw6kCTJ7eaPN2zYwNNPP01ycjIDBw50EljCrVVwMYTAEgg8ELvdznfffae2V1RXVzN16lQSExOZOHHiZd3xrofCwkJyc3PbnLhavHgxX375Jdu3b6dHjx6uLumSCGMbgaD1OHz4ML1791aNfLZt28aECRMoLy8nODhYfV63bt148skneeqpp1xXrKBZKSoqIj4+njVr1hAWFkZUVJSTwBKt2IKL4V7bBIIbirfffpvu3bvj7e3NiBEj+Omnn1xdkseg0+m49dZbWbZsGSdOnODrr7+mY8eOPPfcc3Tv3p377ruPlJQUampqmvXrnj17ltzcXKKjo9uUuHrxxRdZvXo1W7ZscXtxBfDAAw8gy/IF/zRmyJAhfPvtt5hMJk6fPi3ElUBwlUiSxJNPPslNN93EoEGDANTTisbiCiAyMpLCwkIXVCloCWRZ5oEHHuA3v/nNRV2BCwsLz5u7U/4ufhdubITAEriEzz//nKeffpo//vGPpKenEx0dzeTJkykuLnZ1aR6HVqtl9OjR/OMf/+Dw4cOkpqbSq1cv/vznP9O9e3fuueceVq5cSVVV1XV9nbNnz5Kfn090dDRhYWHNVL1rkWWZv/3tb6xYsYItW7bQp08fV5ckEAjciAULFpCTk8PKlStdXYqgmVi8eDEajeaSf/bv38+bb75JdXU1zz33nKtLFnggQmAJXMI///lPfvWrX/Hggw8yYMAA3nvvPXx9ffnPf/7j6tI8Gq1Wy9ChQ3nllVfYv38/u3fvZsiQIfzjH/+ge/fuzJ07l//7v/+jvLz8vNOOS9FWxdVrr73GBx98wObNm4VtuUBwldjtdkaPHs3tt9/u9HhlZSVdunThhRdeAODxxx8nPj4eLy8vJ/c1d+exxx5j3bp1pKam0rlzZ/Xx9u3bY7FYqKiocHp+UVGROgMpcF+eeeYZ8vPzL/mnR48ebNu2jd27d+Pl5YVer6dXr14ADB06lPvvvx9w/C4UFRU5/fvK38Xvwo2NmMEStDoWiwVfX1+SkpJITExUH7///vupqKhg7dq1riuujSLLMvn5+SQlJbF69Wpyc3O59dZbSUxMZMaMGYSFhZ0X9qhQUFDA/v37iY6Oduuw3atBlmX+9a9/8cYbb7Blyxbi4uJcXZJA4JEcPHiQmJgY/v3vfzNv3jzAMZeSlZXFnj17MBqNPP744/Tt25cff/yRffv2kZmZ6dqiL4MsyyxcuJDVq1ezfft2evfu7fRxxeTis88+Y86cOQAcOHCAfv36CZOLNsTJkyedOj8KCgqYPHkySUlJjBgxgs6dOwu3VsFFEQJL0OoUFBTQqVMndu3axahRo9THn332WXbs2MGPP/7owuraPrIsc/jwYVVsZWRkcNNNN5GYmMisWbOIjIxUxdaqVasIDAxk6NChbUpcvfPOO7z88sts3LiR4cOHu7okgcCjWbZsGS+99BK5ubn89NNPzJ07lz179hAdHe30vJdeeok1a9a4vcD67W9/y4oVK1i7dq1T9lVQUJBqIDR//ny+/vprPv74YwIDA1m4cCEAu3btcknNgpbn+PHj55lcCLdWwcVwn8RSgUDQKmg0Gnr37s1zzz3H4sWLOXHiBMnJyaxatYrf/e53jBw5koSEBMrLy1m6dCnJycltSlx98MEH/PWvf2XDhg1CXAkEzYBy2vPLX/6S7OxsXnzxxfPElSfx7rvvAjB27Finxz/66CMeeOABAJYsWYJWq2XOnDlOQcOCG4ugoCA2bdrEggULiI+PJywsjBdffFGIK4E4wRK0PqJF0D2RZZkzZ86QkpLC22+/zcGDB+nduzcPPPAACQkJdO/e/aJthJ6ALMt88sknLF68mHXr1jFmzBhXlyQQtBmUHKjBgweTnp6OXn/+/q2nnGAJBALB9SJMLgStjtFoJD4+nq1bt6qPSZLE1q1bnVoGBa2LRqOhc+fO+Pr6UlBQQHJyMk899RTbtm0jNjaWW265hddff51Dhw5dlUGGOyDLMitWrGDRokWsXbtWiCuBoJn5z3/+g6+vL8eOHeP06dOuLkcgEAhcihBYApfw9NNP8+9//5tPPvmE/Px85s+fT21t7UXDUwWtw4cffshTTz3F+vXruf3225k/fz6bN2+moKCABQsW8OOPPzJ8+HBGjRrF3//+d/Ly8jxCbCUlJfHUU0+RlJTEuHHjXF2OQNCm2LVrF0uWLGHdunUMHz6chx9+2COuCwKBQNBSCIElcAl33XUXb7zxBi+++CIxMTFkZmaycePG8wL7BK1Lz549+frrr51OeDQaDWFhYTz88MOsX7+ewsJCnnnmGbKzs7nllluIj4/nT3/6E/v27UOSJBdWf2HWrFnDggULWLlyJZMnT3Z1OQJBm6Kuro4HHniA+fPnM27cOD788EN++ukn3nvvPVeXdkPxyiuvoNFoePLJJ9XHTCYTCxYsIDQ0FH9/f+bMmXOepbhAIGgZxAyWQCC4Zqqqqli3bh0pKSmqQE5ISCAxMZG4uDi0Wtfu4axbt46HHnqI//u//2P27NkurUUgaIs88cQTfP3112RlZeHr6wvA8uXL+d3vfkd2djbdu3fn8OHD1NTU8N5775Gamsrnn38OwIABAzAaja4sv02wZ88e7rzzTgIDAxk3bhxLly4FHE6H69ev5+OPPyYoKIjHHnsMrVbL999/79qCBYIbACGwBAJBs1BbW8uGDRtITk5m/fr1tGvXjlmzZpGYmMjw4cPR6XStWs8333zDL3/5S/7zn/9w5513turXFghuBHbs2MGECRPYvn07N998s9PHJk+ejM1mY8uWLYwbN44dO3ac9/nHjh2je/furVRt26Smpoa4uDjeeecd/vrXvxITE8PSpUvVrK4VK1Zwxx13AD8bkYisLoGg5RECSyAQNDv19fVs2rSJ5ORk1q1bh4+PDzNnziQxMZHRo0df0GGsOUlNTeWuu+7ivffeY968eR7tfigQCAQX4/777yckJIQlS5YwduxYVWBt27aNCRMmUF5eTnBwsPr8bt268eSTT/LUU0+5rmiB4AZA5GAJBIJmx8fHh4SEBBISErBYLGzZsoXk5GR++ctfotVqmTFjBrNnz+aWW27BYDA069f+9ttvufvuu1m2bJkQVwKBoM2ycuVK0tPT2bNnz3kfKywsxGg0OokrgMjISAoLC1upQoHgxkUILIFA0KIYjUamTZvGtGnTsFqt7Nixg6SkJB555BGsViszZswgMTGRsWPH4uXldV1fa/fu3cydO5fXX3+dBx98UIgrgUDQJjl16hRPPPEEmzdvxtvb29XlCASCJggXQYHgIrz88ssMGzaMgIAAIiIiSExM5MCBA07PES5NV4fBYGDixIm89957aqhxYGAgCxcuJCoqikceeYR169ZRX19/1f/23r17mTNnDn/729/49a9/LcSVQCBos6SlpVFcXExcXBx6vR69Xs+OHTtYtmwZer2eyMhILBYLFRUVTp9XVFRE+/btXVO0QHADIQSWQHARduzYwYIFC/jhhx/YvHkzVquVSZMmUVtbqz7nqaee4quvvmLVqlXs2LGDgoICbr/9dhdW7TnodDpuvfVW3nzzTU6cOMH69etp3749ixYtonv37tx///2kpKQ4/bwvRmZmJgkJCfzhD3/gsccecxtxZTabiYmJQaPRkJmZ6fSxffv2ccstt+Dt7U2XLl147bXXXFOkQCDwOCZMmEB2djaZmZnqn6FDhzJv3jz1/w0GA1u3blU/58CBA5w8eZJRo0a5sHKB4MZAmFwIBFdISUkJERER7NixgzFjxgiXphZCkiTS0tJISkpi9erVnDlzhttuu42EhASmTp1KYGCg0/NzcnKYOnUqv/vd71i8eLHbiCtwWFgfOnSIDRs2kJGRQUxMDOCwt+/Tpw8TJ07kueeeIzs7m4ceeoilS5fy6KOPurZogUDgkTQ2uQCHTfvXX3/Nxx9/rHYKgCMYWiAQtCziBEsguEIqKysBCAkJARwtGlarlYkTJ6rP6devH127dmX37t0uqbEtoNVqGTZsGK+++ir79+/n+++/Z9CgQbzxxht0796duXPn8umnn1JeXk5+fj4zZsxg4cKFbieuNmzYwKZNm3jjjTfO+9j//vc/LBYL//nPfxg4cCB33303jz/+OP/85z9dUKlAIGiLLFmyhBkzZjBnzhzGjBlD+/btSUlJcXVZAsENgTjBElwVdrudW2655bwLdWVlJYMGDeK+++7jb3/7G48//jjff/89OTk59O/f/7z2KE9DkiRmzZpFRUUF3333HQArVqzgwQcfxGw2Oz13+PDhjBs3jldffdUVpbZZZFkmPz+fpKQkUlJSyM3NRafT8cQTT/DKK6+4lbgqKioiPj6eNWvWEBYWRlRUlNMJ1n333UdVVRVr1qxRPyc1NZXx48dz7tw52rVr55rCBQKBQCAQXDfiBEtwVeh0Oj7++GM2btzI//73P/XxhQsXEhISwh//+Ef1sYceeoi77rrLFWU2OwsWLCAnJ4eVK1e6upQbFo1Gw4ABA3jxxRfJyMggIyODefPm8fLLL7uVuJJlmQceeIDf/OY3DB069ILPKSwsJDIy0ukx5e/CQlkgEAgEAs9G2LQLrpo+ffrwyiuvsHDhQsaPH89PP/3EypUr2bNnD0ajEYBly5YBjrmlffv2ubLc6+axxx5j3bp17Ny5k86dO6uPt2/fXnVpapw1IlyaWh6NRsOgQYP48MMPW+1rLl68+LKnkvn5+WzatInq6mqee+65VqpMIBAIBAKBOyEEluCaWLhwIatXr+aXv/wl2dnZvPjii0RHR7u6rGZFlmX1+9y+fTtRUVFOH4+Pj1ddmubMmQMIl6a2zDPPPMMDDzxwyef06NGDbdu2sXv37vMyvRSHr08++YT27dufZ+ev/F2Ic4FAIBAIPBshsATXhEaj4d1336V///4MHjyYxYsXu7qkZmfBggWsWLGCtWvXEhAQoLZuBQUF4ePjQ1BQEA8//DBPP/00ISEhqkvTqFGjhINgGyQ8PJzw8PDLPm/ZsmX89a9/Vf9eUFDA5MmT+fzzzxkxYgQAo0aN4oUXXsBqtWIwGADYvHkzffv2FfNXAoFAIBB4OEJgCa6Z//znP/j6+nLs2DFOnz5N9+7dXV1Ss/Luu+8CDuvbxnz00UfqScaSJUvQarXMmTMHs9nM5MmTeeedd1q5UoE70bVrV6e/+/v7A9CzZ0+1xfQXv/gFf/rTn3j44YdZtGgROTk5/Otf/2LJkiWtXq9AIBAIBILmRQgswTWxa9culixZwqZNm/jrX//Kww8/zJYtW9zKbOB6uRKDTW9vb95++23efvvtVqhI0FYICgpi06ZNLFiwgPj4eMLCwnjxxRdFBpZAIBAIBG0AIbAEV01dXR0PPPAA8+fPZ9y4cURFRTF48GDee+895s+f7+ryBAK3onv37hcU60OGDOHbb791QUUCgUAgEAhaEmHTLrhqnnvuOWRZ5pVXXgEcC8g33niDZ599luPHjwNw+PBhMjMzKSwspL6+nszMTDIzM7FYLC6sXCAQCAQCgUAgaFlE0LDgqtixYwcTJkxg+/bt3HzzzU4fmzx5MjabjS1btjBu3Dh27Nhx3ucfO3aszc1qCQQCgUAgEAgECkJgCQQCgUAgEAgEAkEzIVoEBYI2ziuvvIJGo+HJJ59UHzOZTCxYsIDQ0FD8/f2ZM2fOeblMAoFAIBAIBIKrRwgsgaANs2fPHpYvX86QIUOcHn/qqaf46quvWLVqFTt27KCgoIDbb7/dRVUKBAKBQCAQtB2EwBII2ig1NTXMmzePf//7307htZWVlXz44Yf885//ZPz48cTHx/PRRx+xa9cufvjhBxdWLBAIBAKBQOD5CIElELRRFixYwPTp05k4caLT42lpaVitVqfH+/XrR9euXdm9e3drlykQCAQCgUDQphA5WAJBG2TlypWkp6ezZ8+e8z5WWFiI0WgkODjY6fHIyEgKCwtbqUKBQCAQCASCtokQWAJBG+PUqVM88cQTbN68GW9vb1eXIxAIBAKBQHBDIVoEBYI2RlpaGsXFxcTFxaHX69Hr9ezYsYNly5ah1+uJjIzEYrFQUVHh9HlFRUW0b9/eNUULBAKBQCAQtBHECZZA0MaYMGEC2dnZTo89+OCD9OvXj0WLFtGlSxcMBgNbt25lzpw5ABw4cICTJ08yatQoV5QsEAgEAoFA0GYQAksgaGMEBAQwaNAgp8f8/PwIDQ1VH3/44Yd5+umnCQkJITAwkIULFzJq1ChGjhzpipIFAoFAIBAI2gxCYAkENyBLlixBq9UyZ84czGYzkydP5p133nF1WQKBQCAQCAQej0aWZdnVRQgEAoFAIBAIBAJBW0CYXAgEAoFAIBAIBAJBMyEElkAgEAgEAoFAIBA0E0JgCQQCgUAgEAgEAkEzIQSWQCAQCAQCgUAgEDQTQmAJBAKBQCAQCAQCQTMhBJZAIBAIBAKBQCAQNBNCYAkEAoFAIBAIBAJBMyEElkAgEAgEAoFAIBA0E0JgCQQCgUAgEAgEAkEzIQSWQCAQCAQCgUAgEDQTQmAJBAKBQCAQCAQCQTMhBJZAIBAIBAKBQCAQNBNCYAkEAoFAIBAIBAJBMyEElkAgEAgEAoFAIBA0E0JgCQQCgUAgEAgEAkEzIQSWQCAQCAQCgUAgEDQTQmAJBAKBQCAQCAQCQTMhBJZAIBAIBAKBQCAQNBP/H2zAD64VMpYzAAAAAElFTkSuQmCC","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import torch\n","\n","x1 = torch.linspace(bounds[0, 0], bounds[1, 0], 100) # 100 points along x1\n","x2 = torch.linspace(bounds[0, 1], bounds[1, 1], 100) # 100 points along x2\n","X1, X2 = torch.meshgrid(x1, x2) # Create a meshgrid\n","X = torch.stack([X1.flatten(), X2.flatten()], -1) # Stack and flatten to create [N, 2] input tensor\n","\n","\n","x = normalize(X, bounds)\n","with torch.no_grad():\n"," posterior = model.posterior(x)\n"," mean = -posterior.mean.detach().view(X1.shape) # Reshape mean to match the grid\n"," lower, upper = posterior.mvn.confidence_region()\n"," lower = -lower.view(X1.shape) # Reshape to match the grid\n"," upper = -upper.view(X1.shape)\n","\n","# True function\n","Y = torch.tensor(problem.y(X.cpu().numpy())).view(X1.shape)\n","\n","# Plotting\n","fig = plt.figure(figsize=(18, 6))\n","plt.suptitle(\"noise_10\")\n","ax = fig.add_subplot(1, 2, 1, projection='3d')\n","ax.plot_surface(X1.numpy(), X2.numpy(), mean.cpu().numpy(), cmap='viridis', alpha=0.7, label='GP Mean')\n","ax.set_title('GP Mean')\n","\n","ax2 = fig.add_subplot(1, 3, 2, projection='3d')\n","ax2.plot_surface(X1.numpy(), X2.numpy(), Y.cpu().numpy(), cmap='viridis', alpha=0.7, label='True Function')\n","ax2.set_title('True Function')\n","\n","\n","for ax in [ax, ax2]:\n"," ax.set_xlabel('X1')\n"," ax.set_ylabel('X2')\n"," ax.set_zlabel('Objective')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["prob = problem\n","n_obj = 1\n","bounds = prob.bounds\n","n = 30\n","\n","x1 = np.linspace(bounds[0, 0], bounds[1, 0], n)\n","x2 = np.linspace(bounds[0, 1], bounds[1, 1], n)\n","x1, x2 = np.meshgrid(x1, x2)\n","X = np.stack([x1.flatten(), x2.flatten()]).T\n","f = prob.f(X).reshape(n, n, n_obj)\n","eps = prob.eps(X).reshape(n, n, n_obj)\n","y = prob.y(X).reshape(n, n, n_obj)\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.18"}},"nbformat":4,"nbformat_minor":2} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST None\n", + "SMOKE_TEST None\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "\n", + "from botorch.models.gp_regression import (\n", + " SingleTaskGP,\n", + ")\n", + "from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n", + "from botorch.fit import fit_gpytorch_model\n", + "from botorch.models.transforms.outcome import Standardize\n", + "\n", + "from botorch.optim.optimize import optimize_acqf\n", + "from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n", + "from botorch.sampling.normal import SobolQMCNormalSampler\n", + "from botorch.utils.transforms import normalize, unnormalize\n", + "import os\n", + "import gc\n", + "from botorch.utils.sampling import draw_sobol_samples\n", + "\n", + "tkwargs = {\n", + " \"dtype\": torch.double,\n", + " \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n", + "}\n", + "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n", + "# SMOKE_TEST = True\n", + "print(\"SMOKE_TEST\", SMOKE_TEST)\n", + "NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n", + "RAW_SAMPLES = 512 if not SMOKE_TEST else 4\n", + "MC_SAMPLES = 128 if not SMOKE_TEST else 16\n", + "batch_size = 1\n", + "\n", + "\n", + "from run_experiment import initialize_model, generate_initial_data, optimize_acqf_loop" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'n_init' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 22\u001b[0m\n\u001b[0;32m 18\u001b[0m problem \u001b[38;5;241m=\u001b[39m SchwefelProblem(n_var\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, noise_level\u001b[38;5;241m=\u001b[39mnoise_level)\n\u001b[0;32m 20\u001b[0m bounds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(problem\u001b[38;5;241m.\u001b[39mbounds, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtkwargs)\n\u001b[1;32m---> 22\u001b[0m train_X, train_Y, train_Y_real\u001b[38;5;241m=\u001b[39m generate_initial_data(problem, \u001b[43mn_init\u001b[49m, bounds)\n\u001b[0;32m 24\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time()\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(budget):\n", + "\u001b[1;31mNameError\u001b[0m: name 'n_init' is not defined" + ] + } + ], + "source": [ + "from src.schwefel import SchwefelProblem\n", + "from time import time\n", + "\n", + "torch.manual_seed(0)\n", + "np.random.seed(0)\n", + "\n", + "\n", + "seed = 0 \n", + "n_inits = 60\n", + "noise_level = 10\n", + "noise_bool = True\n", + "budget = 1\n", + "\n", + "\n", + "torch.manual_seed(seed)\n", + "np.random.seed(seed)\n", + "\n", + "problem = SchwefelProblem(n_var=2, noise_level=noise_level)\n", + "\n", + "bounds = torch.tensor(problem.bounds, **tkwargs)\n", + "\n", + "train_X, train_Y, train_Y_real= generate_initial_data(problem, n_init, bounds)\n", + "\n", + "start_time = time()\n", + "\n", + "for i in range(budget):\n", + " print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n", + " \n", + " train_x = normalize(train_X, bounds)\n", + " mll, model = initialize_model(train_x, train_Y, noise_bool)\n", + " fit_gpytorch_model(mll)\n", + " \n", + " # optimize the acquisition function and get the observations\n", + " X_baseline = train_x\n", + " sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n", + "\n", + " acq_func = qNoisyExpectedImprovement(\n", + " model=model,\n", + " X_baseline=X_baseline,\n", + " prune_baseline=True,\n", + " sampler=sampler,\n", + " )\n", + "\n", + " x_cand, acq_func_val = optimize_acqf_loop(problem, acq_func)\n", + " X_cand = unnormalize(x_cand, bounds)\n", + " Y_cand = torch.tensor(problem.y(X_cand.numpy()))\n", + " Y_cand_real = torch.tensor(problem.f(X_cand.numpy()))\n", + " print(f\"New candidate: {X_cand}, {Y_cand}\")\n", + "\n", + " # update the model with new observations\n", + " train_X = torch.cat([train_X, X_cand], dim=0)\n", + " train_Y = torch.cat([train_Y, Y_cand], dim=0)\n", + " train_Y_real = torch.cat([train_Y_real, Y_cand_real], dim=0) \n", + " \n", + "train_x = normalize(train_X, bounds)\n", + "mll, model = initialize_model(train_x, train_Y, noise_bool)\n", + "fit_gpytorch_model(mll)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best value found: 764.5328592764758\n", + "Best solution found: [-25.51163547 38.93445618]\n", + "Best real value found: 801.2781865671863\n", + "Best real solution found: [ 43.27939814 -25.89256121]\n", + "Total number of evaluations: 51\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print trial results\n", + "print(f\"Best value found: {train_Y.min().item()}\")\n", + "print(f\"Best solution found: {train_X[train_Y.argmin()].numpy()}\")\n", + "print(f\"Best real value found: {train_Y_real.min().item()}\")\n", + "print(f\"Best real solution found: {train_X[train_Y_real.argmin()].numpy()}\")\n", + "print(f\"Total number of evaluations: {train_Y.shape[0]}\")\n", + "\n", + "sliding_min = torch.zeros(train_Y.shape[0])\n", + "for i in range(train_Y.shape[0]):\n", + " sliding_min[i] = train_Y[:i+1].min().item()\n", + " \n", + "plt.plot(sliding_min, label='Best found value')\n", + "\n", + "#plot all evaluations\n", + "plt.plot(train_Y.cpu().numpy(), label='All evaluations')\n", + "#vline\n", + "plt.axvline(x=n_init, color='r', linestyle='--')\n", + "#\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Objective')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot model\n", + "X = torch.linspace(bounds[0, 0], bounds[1, 0], 1000, **tkwargs).view(-1, 1)\n", + "x = normalize(X, bounds)\n", + "with torch.no_grad():\n", + " posterior = model.posterior(x)\n", + " mean = -posterior.mean.detach()\n", + " lower, upper = posterior.mvn.confidence_region()\n", + " lower = -lower\n", + " upper = -upper\n", + "\n", + "plt.plot(X.cpu().numpy(), mean.cpu().numpy(), label='Mean')\n", + "plt.fill_between(X.cpu().numpy().flatten(), lower.cpu().numpy().flatten(), upper.cpu().numpy().flatten(), alpha=0.5, label='Confidence')\n", + "\n", + "#plot true function\n", + "Y = torch.tensor(problem.y(X.cpu().numpy()))\n", + "plt.plot(X.cpu().numpy(), Y.cpu().numpy(), label='True function', linestyle='--')\n", + "F = torch.tensor(problem.f(X.cpu().numpy()))\n", + "plt.plot(X.cpu().numpy(), F.cpu().numpy(), label='True function without noise', linestyle='--')\n", + "\n", + "\n", + "# Convert your data to numpy arrays for easier manipulation\n", + "train_X_np = train_X.cpu().numpy()\n", + "train_Y_np = train_Y.cpu().numpy()\n", + "\n", + "# Generate a list of indices for the optimization samples\n", + "c_unnormed = list(range(len(train_X_np[n_init:])))\n", + "\n", + "# Normalize the colors to be between 0 and 1\n", + "\n", + "# Plot initial samples\n", + "# plt.scatter(train_X_np[:n_init], train_Y_np[:n_init], label='Initial samples', linestyle='None', color='blue', alpha=0.5)\n", + "\n", + "# Plot optimization samples with colors\n", + "# plt.scatter(train_X_np[n_init:], train_Y_np[n_init:], label='Optimization samples', linestyle='None', cmap='viridis', alpha=0.5, marker='x')\n", + "\n", + "plt.xlabel('X')\n", + "plt.xlim(bounds[0, 0], bounds[1, 0])\n", + "plt.ylabel('Objective')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "\n", + "x1 = torch.linspace(bounds[0, 0], bounds[1, 0], 100) # 100 points along x1\n", + "x2 = torch.linspace(bounds[0, 1], bounds[1, 1], 100) # 100 points along x2\n", + "X1, X2 = torch.meshgrid(x1, x2) # Create a meshgrid\n", + "X = torch.stack([X1.flatten(), X2.flatten()], -1) # Stack and flatten to create [N, 2] input tensor\n", + "\n", + "\n", + "x = normalize(X, bounds)\n", + "with torch.no_grad():\n", + " posterior = model.posterior(x)\n", + " mean = -posterior.mean.detach().view(X1.shape) # Reshape mean to match the grid\n", + " lower, upper = posterior.mvn.confidence_region()\n", + " lower = -lower.view(X1.shape) # Reshape to match the grid\n", + " upper = -upper.view(X1.shape)\n", + "\n", + "# True function\n", + "Y = torch.tensor(problem.y(X.cpu().numpy())).view(X1.shape)\n", + "\n", + "# Plotting\n", + "fig = plt.figure(figsize=(18, 6))\n", + "plt.suptitle(\"noise_10\")\n", + "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", + "ax.plot_surface(X1.numpy(), X2.numpy(), mean.cpu().numpy(), cmap='viridis', alpha=0.7, label='GP Mean')\n", + "ax.set_title('GP Mean')\n", + "\n", + "ax2 = fig.add_subplot(1, 3, 2, projection='3d')\n", + "ax2.plot_surface(X1.numpy(), X2.numpy(), Y.cpu().numpy(), cmap='viridis', alpha=0.7, label='True Function')\n", + "ax2.set_title('True Function')\n", + "\n", + "\n", + "for ax in [ax, ax2]:\n", + " ax.set_xlabel('X1')\n", + " ax.set_ylabel('X2')\n", + " ax.set_zlabel('Objective')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prob = problem\n", + "n_obj = 1\n", + "bounds = prob.bounds\n", + "n = 30\n", + "\n", + "x1 = np.linspace(bounds[0, 0], bounds[1, 0], n)\n", + "x2 = np.linspace(bounds[0, 1], bounds[1, 1], n)\n", + "x1, x2 = np.meshgrid(x1, x2)\n", + "X = np.stack([x1.flatten(), x2.flatten()]).T\n", + "f = prob.f(X).reshape(n, n, n_obj)\n", + "eps = prob.eps(X).reshape(n, n, n_obj)\n", + "y = prob.y(X).reshape(n, n, n_obj)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/hello.py b/hello.py deleted file mode 100644 index a025bbf..0000000 --- a/hello.py +++ /dev/null @@ -1,2 +0,0 @@ -def hello_world(): - return "Hello!" diff --git a/hello_test.py b/hello_test.py deleted file mode 100644 index 708a061..0000000 --- a/hello_test.py +++ /dev/null @@ -1,5 +0,0 @@ -import hello - - -def test_hello(): - assert hello.hello_world() == "Hello World!" diff --git a/line_plot.ipynb b/line_plot.ipynb index a3bcecf..955ccca 100644 --- a/line_plot.ipynb +++ b/line_plot.ipynb @@ -2,9 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 89, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST None\n" + ] + } + ], "source": [ "import torch\n", "import pandas as pd\n", @@ -13,15 +21,17 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\queim\\AppData\\Local\\Temp\\ipykernel_12420\\2067356850.py:23: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + "/tmp/ipykernel_26460/2834717507.py:23: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + "/tmp/ipykernel_26460/2834717507.py:27: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n", + " sm_std = sm_agg.std(0)\n" ] }, { @@ -56,384 +66,35 @@ " \n", " 0\n", " 10\n", - " 1\n", - " 0\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 1\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 2\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 3\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 4\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 0\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 1\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 2\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 3\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", " 5\n", - " 4\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 0\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 1\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 2\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 3\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 4\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", " 0\n", " True\n", " 767.079651\n", " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 1\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 2\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 3\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 4\n", - " True\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 0\n", - " False\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 1\n", - " False\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 2\n", - " False\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 3\n", - " False\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 1\n", - " 4\n", - " False\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 0\n", - " False\n", - " 790.102966\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 1\n", - " False\n", - " 778.442139\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 2\n", - " False\n", - " 789.921326\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 3\n", - " False\n", - " 778.506653\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 5\n", - " 4\n", - " False\n", - " 767.079651\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 0\n", - " False\n", - " 778.442871\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 1\n", - " False\n", - " 778.503662\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 2\n", - " False\n", - " 778.479553\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 3\n", - " False\n", - " 798.732605\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 10\n", - " 4\n", - " False\n", - " 778.500122\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 0\n", - " False\n", - " 801.972839\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 1\n", - " False\n", - " 794.947937\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 2\n", - " False\n", - " 804.242676\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 3\n", - " False\n", - " 809.956909\n", - " \n", - " \n", - " 0\n", - " 10\n", - " 20\n", - " 4\n", - " False\n", - " 791.936157\n", - " \n", " \n", "\n", "" ], "text/plain": [ " n_init noise_level seed noise_bool best\n", - "0 10 1 0 True 767.079651\n", - "0 10 1 1 True 767.079651\n", - "0 10 1 2 True 767.079651\n", - "0 10 1 3 True 767.079651\n", - "0 10 1 4 True 767.079651\n", - "0 10 5 0 True 767.079651\n", - "0 10 5 1 True 767.079651\n", - "0 10 5 2 True 767.079651\n", - "0 10 5 3 True 767.079651\n", - "0 10 5 4 True 767.079651\n", - "0 10 10 0 True 767.079651\n", - "0 10 10 1 True 767.079651\n", - "0 10 10 2 True 767.079651\n", - "0 10 10 3 True 767.079651\n", - "0 10 10 4 True 767.079651\n", - "0 10 20 0 True 767.079651\n", - "0 10 20 1 True 767.079651\n", - "0 10 20 2 True 767.079651\n", - "0 10 20 3 True 767.079651\n", - "0 10 20 4 True 767.079651\n", - "0 10 1 0 False 767.079651\n", - "0 10 1 1 False 767.079651\n", - "0 10 1 2 False 767.079651\n", - "0 10 1 3 False 767.079651\n", - "0 10 1 4 False 767.079651\n", - "0 10 5 0 False 790.102966\n", - "0 10 5 1 False 778.442139\n", - "0 10 5 2 False 789.921326\n", - "0 10 5 3 False 778.506653\n", - "0 10 5 4 False 767.079651\n", - "0 10 10 0 False 778.442871\n", - "0 10 10 1 False 778.503662\n", - "0 10 10 2 False 778.479553\n", - "0 10 10 3 False 798.732605\n", - "0 10 10 4 False 778.500122\n", - "0 10 20 0 False 801.972839\n", - "0 10 20 1 False 794.947937\n", - "0 10 20 2 False 804.242676\n", - "0 10 20 3 False 809.956909\n", - "0 10 20 4 False 791.936157" + "0 10 5 0 True 767.079651" ] }, - "execution_count": 90, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "seeds = list(range(5))\n", + "seeds = list(range(1))\n", "# n_inits = [2, 4, 8, 10]\n", "n_inits = [10]\n", - "noise_levels = [1, 5, 10, 20]\n", - "noise_bools = [True, False]\n", + "noise_levels = [5]#[1, 5, 10, 20]\n", + "noise_bools = [True]\n", "budget = 30\n", "\n", + "\n", + "\n", "sm_list = {}\n", "df = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", "for noise_bool in noise_bools:\n", @@ -460,125 +121,53 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{(10,\n", - " 1,\n", - " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 800.8542, 791.7416, 791.2345, 791.2345,\n", - " 790.3158, 788.2984, 786.3238, 780.9172, 780.9172, 778.6176, 778.6176,\n", - " 773.8997, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 13.1364, 9.5984, 10.3388, 10.3388, 11.3995, 8.9474,\n", - " 7.4343, 4.9843, 4.9843, 8.0457, 8.0457, 10.1687, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", - " (10,\n", - " 5,\n", - " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 799.7986, 790.9266, 785.5935, 781.9990,\n", - " 781.5400, 781.5178, 781.3954, 781.3954, 781.3954, 781.3680, 779.0939,\n", - " 778.7161, 776.4197, 774.0198, 774.0198, 774.0068, 774.0068, 774.0068,\n", - " 774.0068, 772.3127, 771.3956, 769.1237, 769.1237, 767.0797, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 13.1392, 15.0903, 7.7353, 6.2436, 6.4401, 6.4505,\n", - " 6.5145, 6.5145, 6.5145, 6.4820, 9.1904, 8.4899, 9.9619, 10.4045,\n", - " 10.4045, 10.3789, 10.3789, 10.3789, 10.3789, 7.2685, 5.9237, 4.5706,\n", - " 4.5706, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", - " (10,\n", - " 10,\n", - " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 799.6551, 797.7939, 797.7939, 787.2091,\n", - " 784.4460, 783.4135, 783.4135, 783.4135, 779.7010, 776.2844, 773.9120,\n", - " 773.9120, 771.6389, 771.6389, 771.0857, 771.0857, 771.0857, 771.0857,\n", - " 771.0857, 771.0857, 769.3610, 769.3610, 769.3610, 769.3610, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 13.4219, 14.8111, 14.8111, 7.8954, 6.9691, 7.4013,\n", - " 7.4013, 7.4013, 10.1493, 9.5644, 10.1911, 10.1911, 10.1947, 10.1947,\n", - " 8.9578, 8.9578, 8.9578, 8.9578, 8.9578, 8.9578, 5.1012, 5.1012,\n", - " 5.1012, 5.1012, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", - " (10,\n", - " 20,\n", - " True): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 799.2902, 793.1466, 793.1466, 786.7264,\n", - " 783.2312, 783.2312, 780.6354, 778.1125, 774.2051, 774.2051, 774.2051,\n", - " 774.2051, 774.2051, 774.2051, 774.2051, 774.2051, 771.6734, 771.6734,\n", - " 771.6734, 769.3727, 769.3727, 769.3727, 769.3727, 769.3727, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 19.4279, 19.8661, 19.8661, 17.1929, 12.3169, 12.3169,\n", - " 14.0070, 12.7139, 6.5223, 6.5223, 6.5223, 6.5223, 6.5223, 6.5223,\n", - " 6.5223, 6.5223, 6.2902, 6.2902, 6.2902, 5.1275, 5.1275, 5.1275,\n", - " 5.1275, 5.1275, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", - " (10,\n", - " 1,\n", - " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 800.6844, 789.4686, 787.9406, 785.3496,\n", - " 784.1904, 781.8794, 781.0068, 778.8218, 776.4470, 776.3942, 776.3308,\n", - " 774.0087, 774.0087, 771.8953, 771.6793, 771.6649, 769.3792, 769.3792,\n", - " 769.3792, 769.3792, 767.5079, 767.5079, 767.5079, 767.0797, 767.0797,\n", - " 767.0797, 767.0797, 767.0797, 767.0797, 767.0797]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 13.4569, 10.1973, 11.1412, 8.8125, 7.6181, 10.8091,\n", - " 9.6961, 8.2987, 9.8125, 9.7956, 9.6816, 10.3434, 10.3434, 6.6052,\n", - " 6.2983, 6.2786, 5.1419, 5.1419, 5.1419, 5.1419, 0.9575, 0.9575,\n", - " 0.9575, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])),\n", - " (10,\n", - " 5,\n", - " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 804.5387, 797.1251, 792.2640, 792.2640,\n", - " 792.2640, 787.7631, 785.6548, 784.4960, 782.9075, 782.7043, 780.9127,\n", - " 780.9127, 780.9054, 780.9054, 780.8105, 780.8105, 780.8105, 780.8105,\n", - " 780.8105, 780.8105, 780.8105, 780.8105, 780.8105, 780.8105, 780.8105,\n", - " 780.8105, 780.8105, 780.8105, 780.8105, 780.8105]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 6.3592, 12.9019, 9.8306, 9.8306, 9.8306, 15.1025,\n", - " 12.7848, 11.5005, 10.1976, 9.9938, 9.7140, 9.7140, 9.7163, 9.7163,\n", - " 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022,\n", - " 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022, 9.6022])),\n", - " (10,\n", - " 10,\n", - " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 804.2871, 803.7126, 797.7743, 797.7743,\n", - " 795.0626, 794.2285, 791.1630, 787.4348, 787.4348, 786.3362, 786.1907,\n", - " 786.1907, 783.3408, 782.7313, 782.6476, 782.6476, 782.6476, 782.6476,\n", - " 782.6476, 782.6476, 782.6473, 782.6473, 782.6473, 782.6473, 782.6473,\n", - " 782.6473, 782.6473, 782.6473, 782.5318, 782.5318]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 6.4661, 6.8784, 10.5844, 10.5844, 13.6058, 14.6343,\n", - " 11.2435, 9.4880, 9.4880, 10.2008, 10.3328, 10.3328, 8.7235, 8.9490,\n", - " 8.9956, 8.9956, 8.9956, 8.9956, 8.9956, 8.9956, 8.9958, 8.9958,\n", - " 8.9958, 8.9958, 8.9958, 8.9958, 8.9958, 8.9958, 9.0566, 9.0566])),\n", - " (10,\n", - " 20,\n", - " False): (tensor([821.1638, 820.8365, 815.9865, 812.0001, 810.2044, 810.2044, 806.7228,\n", - " 806.7228, 806.7228, 806.7228, 806.1791, 804.1471, 800.8330, 800.8330,\n", - " 800.7966, 800.7966, 800.7966, 800.7365, 800.7365, 800.7365, 800.7365,\n", - " 800.7365, 800.7365, 800.7365, 800.7365, 800.7278, 800.7278, 800.7278,\n", - " 800.6722, 800.6722, 800.6722, 800.6722, 800.6722, 800.6113, 800.6113,\n", - " 800.6113, 800.6113, 800.6113, 800.6113, 800.6113]), tensor([19.0196, 19.2276, 13.5482, 13.4689, 12.4204, 12.4204, 7.4257, 7.4257,\n", - " 7.4257, 7.4257, 6.8573, 6.8794, 7.6061, 7.6061, 7.5450, 7.5450,\n", - " 7.5450, 7.4451, 7.4451, 7.4451, 7.4451, 7.4451, 7.4451, 7.4451,\n", - " 7.4451, 7.4309, 7.4309, 7.4309, 7.3395, 7.3395, 7.3395, 7.3395,\n", - " 7.3395, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407, 7.2407]))}" + "tensor([835.0243, 826.3714, 885.8375, 861.5043, 852.6030, 872.2106, 859.8699,\n", + " 827.3935, 841.4297, 838.4979, 830.3094, 835.5397, 817.9036, 876.8677,\n", + " 800.5196, 819.3487, 767.0797, 830.2089, 810.5448, 822.9490, 819.5395,\n", + " 817.6187, 804.5982, 837.9658, 829.5634, 821.7871, 856.8130, 821.8134,\n", + " 889.2534, 851.8686, 787.5237, 799.3164, 778.4625, 794.3905, 790.8689,\n", + " 779.4604, 779.5393, 767.0797, 779.5594, 851.0072], dtype=torch.float64)" ] }, - "execution_count": 91, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sm_list" + "Y_real" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([843.8446, 828.3721, 890.7312, 872.7088, 861.9408, 867.3242, 864.6204,\n", + " 826.6367, 840.9136, 840.5509, 831.0296, 842.8110, 821.7088, 877.4761,\n", + " 802.7390, 821.0171, 774.5501, 829.1831, 812.1102, 818.6785, 806.7745,\n", + " 820.8868, 808.9204, 834.2550, 840.9122, 814.5153, 857.0418, 820.8775,\n", + " 896.9173, 859.2154, 788.2984, 801.2072, 774.0236, 784.4865, 789.1293,\n", + " 780.2421, 785.6907, 773.0916, 777.6227, 849.4957], dtype=torch.float64)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y" + ] }, { "cell_type": "code", @@ -726,7 +315,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -740,9 +329,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/noisy_optimization_BayBE.ipynb b/noisy_optimization_BayBE.ipynb deleted file mode 100644 index bc8736c..0000000 --- a/noisy_optimization_BayBE.ipynb +++ /dev/null @@ -1,1249 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "ddfe121c-9cad-4d3f-b518-e42c72a1b70b", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "97c15027-9275-43e5-9613-ecbfe31d4914", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SMOKE_TEST None\n", - "SMOKE_TEST None\n" - ] - } - ], - "source": [ - "import torch\n", - "import pandas as pd\n", - "from run_grid_experiments_baybe import run_grid_experiments\n", - "from run_grid_experiments_baybe_random import run_grid_experiments_random\n", - "from src import visualization\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from src import schwefel" - ] - }, - { - "cell_type": "markdown", - "id": "5bcc2d3b-c62b-422c-bdee-15eee6f1452c", - "metadata": {}, - "source": [ - "## Intro\n", - "\n", - "This work lightly explores the impact of a noisy oracle on Bayesian optimization performance. Here, the 2-dimensional Schwefel function with Gaussian noise is used as an optimization objective. However, this work is motivated by the need to deal with noisy data when applying BO to experimental optimization. \n", - "\n", - "## Implementation Notes\n", - "\n", - "- This work was done as an entry in the 2024 Bayesian optimization for materials hackathon\n", - "- Our team pursued multiple implementations in parallel\n", - "- This notebook serves as an entry point to the BayBE implementation of the project\n", - "- Individual optimization campaigns are run from the run_experiments() function in run_experiment_babye.py\n", - "- Grid screening of parameters builds on this with funcitonality in the run_grid_experiments_babye.py\n", - "- As this was a hackathon project, there are some hacks. Watch out for hard-coded gotchas throughout. Would not recommend directly re-using code. " - ] - }, - { - "cell_type": "markdown", - "id": "02f05875-7a5c-4f50-8b55-fc0e30340897", - "metadata": {}, - "source": [ - "## 1. Define grid search parameters \n", - "\n", - "This is to run a grid search over experiment parameters like number of BO trials to run, noise level, etc. These values were chosen by the team as 'reasonable' sounding values." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5319bdb1-b6a0-4dfa-b05c-bd854456278d", - "metadata": {}, - "outputs": [], - "source": [ - "seeds = list(range(5)) # run 5 replicates of each parameter set\n", - "n_inits = [2, 4, 8, 10] # Number of initial randomly collected data points\n", - "noise_levels = [1, 5, 10, 20] # variance (?) of gaussian noise. Bigger number -> more noise\n", - "noise_bools = [True] # carryover from BoTorch side of project, ignore\n", - "budget = 30 # Run 30 iterations of BO\n", - "bounds = (420.9687 - 50, 420.9687 + 50)" - ] - }, - { - "cell_type": "markdown", - "id": "abdf100a-84fe-466f-9b60-67d698e7578e", - "metadata": {}, - "source": [ - "## 2. Run grid search\n", - "\n", - "Run the grid search over parameters. This will take a minute or 60. Results are written to disk so if you are just following along, skip this step and load below " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d8ffedfe-8639-479f-9cee-63f4a0f32e54", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(370.9687, 470.9687)\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 1 budget 30 seed 0, time: 8.28s\n", - "(370.9687, 470.9687)\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 5 budget 30 seed 0, time: 16.15s\n", - "(370.9687, 470.9687)\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Started problem 2 noise 10 budget 30 seed 0, time: 23.46s\n", - "(370.9687, 470.9687)\n", - "Collecting initial observations\n", - "Beginning optimization campaign\n", - "Unexpected exception formatting exception. Falling back to standard exception\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/tmp/ipykernel_6877/1632482094.py\", line 1, in \n", - " run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds)\n", - " File \"/home/brendenpelkie/Code/project-project-noisy-nerds/run_grid_experiments_baybe.py\", line 40, in run_grid_experiments\n", - " task = worker(n_init, noise_level, budget, seed, noise_bool, bounds)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/Code/project-project-noisy-nerds/run_grid_experiments_baybe.py\", line 15, in worker\n", - " run_experiment(n_init, noise_level, budget, seed, noise_bool,bounds)\n", - " File \"/home/brendenpelkie/Code/project-project-noisy-nerds/run_experiment_baybe.py\", line 119, in run_experiment\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/campaign.py\", line 298, in recommend\n", - " rec = self.recommender.recommend(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/recommenders/pure/bayesian/base.py\", line 141, in recommend\n", - " return super().recommend(searchspace, batch_size, train_x, train_y)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/recommenders/pure/base.py\", line 43, in recommend\n", - " return self._recommend_continuous(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/recommenders/pure/bayesian/sequential_greedy.py\", line 108, in _recommend_continuous\n", - " points, _ = optimize_acqf(\n", - " ^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 563, in optimize_acqf\n", - " return _optimize_acqf(opt_acqf_inputs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 584, in _optimize_acqf\n", - " return _optimize_acqf_batch(opt_inputs=opt_inputs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 349, in _optimize_acqf_batch\n", - " batch_candidates, batch_acq_values, ws = _optimize_batch_candidates()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/optimize.py\", line 333, in _optimize_batch_candidates\n", - " ) = opt_inputs.gen_candidates(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/generation/gen.py\", line 252, in gen_candidates_scipy\n", - " res = minimize_with_timeout(\n", - " ^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/botorch/optim/utils/timeout.py\", line 80, in minimize_with_timeout\n", - " return optimize.minimize(\n", - " ^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_minimize.py\", line 713, in minimize\n", - " res = _minimize_lbfgsb(fun, x0, args, jac, bounds,\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py\", line 369, in _minimize_lbfgsb\n", - " f, g = func_and_grad(x)\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 297, in fun_and_grad\n", - " self._update_grad()\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 267, in _update_grad\n", - " self._update_grad_impl()\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 175, in update_grad\n", - " self.g = grad_wrapped(self.x)\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py\", line 172, in grad_wrapped\n", - " return np.atleast_1d(grad(np.copy(x), *args))\n", - " ^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/numpy/lib/function_base.py\", line 873, in copy\n", - " @array_function_dispatch(_copy_dispatcher)\n", - " \n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 2144, in showtraceback\n", - " stb = self.InteractiveTB.structured_traceback(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1435, in structured_traceback\n", - " return FormattedTB.structured_traceback(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1326, in structured_traceback\n", - " return VerboseTB.structured_traceback(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1173, in structured_traceback\n", - " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 1088, in format_exception_as_a_whole\n", - " frames.append(self.format_record(record))\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 970, in format_record\n", - " frame_info.lines, Colors, self.has_colors, lvals\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/IPython/core/ultratb.py\", line 792, in lines\n", - " return self._sd.lines\n", - " ^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n", - " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", - " ^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/core.py\", line 698, in lines\n", - " pieces = self.included_pieces\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n", - " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", - " ^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/core.py\", line 649, in included_pieces\n", - " pos = scope_pieces.index(self.executing_piece)\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n", - " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", - " ^^^^^^^^^^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/stack_data/core.py\", line 628, in executing_piece\n", - " return only(\n", - " ^^^^^\n", - " File \"/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/executing/executing.py\", line 164, in only\n", - " raise NotOneValueFound('Expected one value, found 0')\n", - "executing.executing.NotOneValueFound: Expected one value, found 0\n" - ] - } - ], - "source": [ - "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds)" - ] - }, - { - "cell_type": "markdown", - "id": "16aeaf0f-14b6-4827-b94a-cb87cd2a4d7c", - "metadata": {}, - "source": [ - "### Run random search as well\n", - "\n", - "Generate some random baseline data to compare against\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5aac4ea7-cb9f-48a4-a874-e4396d8b9e22", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 1 budget 30 seed 0 failed\n", - "Started problem 2 noise 1 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 5 budget 30 seed 0 failed\n", - "Started problem 2 noise 5 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 10 budget 30 seed 0 failed\n", - "Started problem 2 noise 10 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 20 budget 30 seed 0 failed\n", - "Started problem 2 noise 20 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 1 budget 30 seed 0 failed\n", - "Started problem 4 noise 1 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 5 budget 30 seed 0 failed\n", - "Started problem 4 noise 5 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 10 budget 30 seed 0 failed\n", - "Started problem 4 noise 10 budget 30 seed 0, time: 0.00s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 20 budget 30 seed 0 failed\n", - "Started problem 4 noise 20 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 1 budget 30 seed 0 failed\n", - "Started problem 8 noise 1 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 5 budget 30 seed 0 failed\n", - "Started problem 8 noise 5 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 10 budget 30 seed 0 failed\n", - "Started problem 8 noise 10 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 20 budget 30 seed 0 failed\n", - "Started problem 8 noise 20 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 1 budget 30 seed 0 failed\n", - "Started problem 10 noise 1 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 5 budget 30 seed 0 failed\n", - "Started problem 10 noise 5 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 10 budget 30 seed 0 failed\n", - "Started problem 10 noise 10 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 20 budget 30 seed 0 failed\n", - "Started problem 10 noise 20 budget 30 seed 0, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 1 budget 30 seed 1 failed\n", - "Started problem 2 noise 1 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 5 budget 30 seed 1 failed\n", - "Started problem 2 noise 5 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 10 budget 30 seed 1 failed\n", - "Started problem 2 noise 10 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 20 budget 30 seed 1 failed\n", - "Started problem 2 noise 20 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 1 budget 30 seed 1 failed\n", - "Started problem 4 noise 1 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 5 budget 30 seed 1 failed\n", - "Started problem 4 noise 5 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 10 budget 30 seed 1 failed\n", - "Started problem 4 noise 10 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 20 budget 30 seed 1 failed\n", - "Started problem 4 noise 20 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 1 budget 30 seed 1 failed\n", - "Started problem 8 noise 1 budget 30 seed 1, time: 0.01s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 5 budget 30 seed 1 failed\n", - "Started problem 8 noise 5 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 10 budget 30 seed 1 failed\n", - "Started problem 8 noise 10 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 20 budget 30 seed 1 failed\n", - "Started problem 8 noise 20 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 1 budget 30 seed 1 failed\n", - "Started problem 10 noise 1 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 5 budget 30 seed 1 failed\n", - "Started problem 10 noise 5 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 10 budget 30 seed 1 failed\n", - "Started problem 10 noise 10 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 20 budget 30 seed 1 failed\n", - "Started problem 10 noise 20 budget 30 seed 1, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 1 budget 30 seed 2 failed\n", - "Started problem 2 noise 1 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 5 budget 30 seed 2 failed\n", - "Started problem 2 noise 5 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 10 budget 30 seed 2 failed\n", - "Started problem 2 noise 10 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 20 budget 30 seed 2 failed\n", - "Started problem 2 noise 20 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 1 budget 30 seed 2 failed\n", - "Started problem 4 noise 1 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 5 budget 30 seed 2 failed\n", - "Started problem 4 noise 5 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 10 budget 30 seed 2 failed\n", - "Started problem 4 noise 10 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 20 budget 30 seed 2 failed\n", - "Started problem 4 noise 20 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 1 budget 30 seed 2 failed\n", - "Started problem 8 noise 1 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 5 budget 30 seed 2 failed\n", - "Started problem 8 noise 5 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 10 budget 30 seed 2 failed\n", - "Started problem 8 noise 10 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 20 budget 30 seed 2 failed\n", - "Started problem 8 noise 20 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 1 budget 30 seed 2 failed\n", - "Started problem 10 noise 1 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 5 budget 30 seed 2 failed\n", - "Started problem 10 noise 5 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 10 budget 30 seed 2 failed\n", - "Started problem 10 noise 10 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 20 budget 30 seed 2 failed\n", - "Started problem 10 noise 20 budget 30 seed 2, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 1 budget 30 seed 3 failed\n", - "Started problem 2 noise 1 budget 30 seed 3, time: 0.02s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 5 budget 30 seed 3 failed\n", - "Started problem 2 noise 5 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 10 budget 30 seed 3 failed\n", - "Started problem 2 noise 10 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 20 budget 30 seed 3 failed\n", - "Started problem 2 noise 20 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 1 budget 30 seed 3 failed\n", - "Started problem 4 noise 1 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 5 budget 30 seed 3 failed\n", - "Started problem 4 noise 5 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 10 budget 30 seed 3 failed\n", - "Started problem 4 noise 10 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 20 budget 30 seed 3 failed\n", - "Started problem 4 noise 20 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 1 budget 30 seed 3 failed\n", - "Started problem 8 noise 1 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 5 budget 30 seed 3 failed\n", - "Started problem 8 noise 5 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 10 budget 30 seed 3 failed\n", - "Started problem 8 noise 10 budget 30 seed 3, time: 0.03s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 20 budget 30 seed 3 failed\n", - "Started problem 8 noise 20 budget 30 seed 3, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 1 budget 30 seed 3 failed\n", - "Started problem 10 noise 1 budget 30 seed 3, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 5 budget 30 seed 3 failed\n", - "Started problem 10 noise 5 budget 30 seed 3, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 10 budget 30 seed 3 failed\n", - "Started problem 10 noise 10 budget 30 seed 3, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 20 budget 30 seed 3 failed\n", - "Started problem 10 noise 20 budget 30 seed 3, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 1 budget 30 seed 4 failed\n", - "Started problem 2 noise 1 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 5 budget 30 seed 4 failed\n", - "Started problem 2 noise 5 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 10 budget 30 seed 4 failed\n", - "Started problem 2 noise 10 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 2 noise 20 budget 30 seed 4 failed\n", - "Started problem 2 noise 20 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 1 budget 30 seed 4 failed\n", - "Started problem 4 noise 1 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 5 budget 30 seed 4 failed\n", - "Started problem 4 noise 5 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 10 budget 30 seed 4 failed\n", - "Started problem 4 noise 10 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 4 noise 20 budget 30 seed 4 failed\n", - "Started problem 4 noise 20 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 1 budget 30 seed 4 failed\n", - "Started problem 8 noise 1 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 5 budget 30 seed 4 failed\n", - "Started problem 8 noise 5 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 10 budget 30 seed 4 failed\n", - "Started problem 8 noise 10 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 8 noise 20 budget 30 seed 4 failed\n", - "Started problem 8 noise 20 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 1 budget 30 seed 4 failed\n", - "Started problem 10 noise 1 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 5 budget 30 seed 4 failed\n", - "Started problem 10 noise 5 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 10 budget 30 seed 4 failed\n", - "Started problem 10 noise 10 budget 30 seed 4, time: 0.04s\n", - "only one element tensors can be converted to Python scalars\n", - "problem 10 noise 20 budget 30 seed 4 failed\n", - "Started problem 10 noise 20 budget 30 seed 4, time: 0.04s\n", - "all experiments done, time: 0.04s\n" - ] - } - ], - "source": [ - "run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds)" - ] - }, - { - "cell_type": "markdown", - "id": "34ac9617-f371-40b1-b1a1-5203d84b1a65", - "metadata": {}, - "source": [ - "## 3. Process Results" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ce441669-a569-468f-a482-fec91efef95f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_6054/3776676619.py:17: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
n_initnoise_levelseednoise_boolbest
0210True767.079651
0211True767.079651
0212True767.079651
0213True767.079651
0214True767.079651
..................
010200True767.079651
010201True767.079651
010202True767.079651
010203True779.453430
010204True767.079651
\n", - "

80 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " n_init noise_level seed noise_bool best\n", - "0 2 1 0 True 767.079651\n", - "0 2 1 1 True 767.079651\n", - "0 2 1 2 True 767.079651\n", - "0 2 1 3 True 767.079651\n", - "0 2 1 4 True 767.079651\n", - ".. ... ... ... ... ...\n", - "0 10 20 0 True 767.079651\n", - "0 10 20 1 True 767.079651\n", - "0 10 20 2 True 767.079651\n", - "0 10 20 3 True 779.453430\n", - "0 10 20 4 True 767.079651\n", - "\n", - "[80 rows x 5 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load BO results \n", - "sm_list_bo = {}\n", - "bo_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", - "for noise_bool in noise_bools:\n", - " for n_init in n_inits:\n", - " for noise_level in noise_levels:\n", - " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", - " for idx, seed in enumerate(seeds):\n", - " X, Y, Y_real, model = torch.load(f\"results_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", - " sliding_min = torch.zeros(Y.shape[0])\n", - " for i in range(Y_real.shape[0]):\n", - " sliding_min[i] = Y_real[:i+1].min().item()\n", - " \n", - " sm_agg[idx] = sliding_min\n", - " sm = pd.Series(sliding_min.numpy())\n", - " \n", - " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", - " \"best\": [sliding_min[-1].item()]})])\n", - " \n", - " sm_mean = sm_agg.mean(0)\n", - " sm_std = sm_agg.std(0)\n", - " sm_list_bo[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", - "bo_results " - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "f08502a6-d831-4e88-b4e8-a89ac5f649c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[-45.6567, 16.9314],\n", - " [-11.6536, -26.0542],\n", - " [ 35.2966, -27.4438],\n", - " [-42.6366, 22.2730],\n", - " [ 35.2949, 14.1249],\n", - " [ 49.8565, 22.0936],\n", - " [ 4.0415, -8.2770],\n", - " [ -2.4373, -7.2442],\n", - " [ 9.6980, -28.1025],\n", - " [-18.8534, 22.7238],\n", - " [ 36.3961, 41.5238],\n", - " [-24.1650, -10.7899],\n", - " [-45.0575, -28.1582],\n", - " [ 48.0141, -18.1148],\n", - " [ -9.1047, 24.4760],\n", - " [ -2.4491, -24.9724],\n", - " [-40.0584, -35.1531],\n", - " [ 14.2042, -41.7810],\n", - " [ 9.0255, 18.5154],\n", - " [ 28.2799, 4.4241],\n", - " [ 0.3153, 11.6851],\n", - " [-32.4421, 12.8029],\n", - " [ 21.8362, -19.1830],\n", - " [ 38.4819, 26.7066],\n", - " [ 38.3217, 6.8988],\n", - " [-34.9491, 18.5383],\n", - " [-26.3170, 36.3540],\n", - " [ 21.7468, -17.6640],\n", - " [ 14.6553, -16.4636],\n", - " [ 42.6094, 26.6559],\n", - " [ -1.5765, -45.2681],\n", - " [ -7.0089, -1.4098],\n", - " [ 1.6903, -36.2116],\n", - " [ 32.1310, -9.0131],\n", - " [ -9.9266, -25.9650],\n", - " [-19.0032, -35.3150],\n", - " [-46.7624, 15.2622],\n", - " [ 25.8250, -41.4523],\n", - " [ 12.9008, -32.2894],\n", - " [-18.2179, 1.7376]], dtype=torch.float64)" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "af01900d-a2bf-4c49-90a0-b3614a59c2fc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_6054/1297806956.py:17: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
n_initnoise_levelseednoise_boolbest
0210True789.469910
0211True796.131165
0212True802.132568
0213True794.562561
0214True792.798035
..................
010200True789.469910
010201True790.011475
010202True796.409363
010203True794.562561
010204True792.798035
\n", - "

80 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " n_init noise_level seed noise_bool best\n", - "0 2 1 0 True 789.469910\n", - "0 2 1 1 True 796.131165\n", - "0 2 1 2 True 802.132568\n", - "0 2 1 3 True 794.562561\n", - "0 2 1 4 True 792.798035\n", - ".. ... ... ... ... ...\n", - "0 10 20 0 True 789.469910\n", - "0 10 20 1 True 790.011475\n", - "0 10 20 2 True 796.409363\n", - "0 10 20 3 True 794.562561\n", - "0 10 20 4 True 792.798035\n", - "\n", - "[80 rows x 5 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load random results \n", - "sm_list_random = {}\n", - "random_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", - "for noise_bool in noise_bools:\n", - " for n_init in n_inits:\n", - " for noise_level in noise_levels:\n", - " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", - " for idx, seed in enumerate(seeds):\n", - " X, Y, Y_real, model = torch.load(f\"results_random_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", - " sliding_min = torch.zeros(Y.shape[0])\n", - " for i in range(Y_real.shape[0]):\n", - " sliding_min[i] = Y_real[:i+1].min().item()\n", - " \n", - " sm_agg[idx] = sliding_min\n", - " sm = pd.Series(sliding_min.numpy())\n", - " \n", - " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", - " \"best\": [sliding_min[-1].item()]})])\n", - " \n", - " sm_mean = sm_agg.mean(0)\n", - " sm_std = sm_agg.std(0)\n", - " sm_list_random[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", - "random_results " - ] - }, - { - "cell_type": "markdown", - "id": "a62c64e2-b44c-4cb2-8e3c-4955abb334de", - "metadata": {}, - "source": [ - "Calculate 'performance matrix' to generate iterations vs noise heat map" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f9d3ca6c-22f6-459e-9107-cfa6cc663673", - "metadata": {}, - "outputs": [], - "source": [ - "performance_matrix_bo = np.zeros((len(n_inits), len(noise_levels)))\n", - "\n", - "for i, init in enumerate(n_inits):\n", - " for j, noise in enumerate(noise_levels):\n", - " y_vals = torch.load(f'results_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", - " best_y = torch.min(y_vals)\n", - " performance_matrix_bo[i,j] = best_y\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cd2d4601-b8ee-4d96-8c54-9dad0285151c", - "metadata": {}, - "outputs": [], - "source": [ - "performance_matrix_random = np.zeros((len(n_inits), len(noise_levels)))\n", - "\n", - "for i, init in enumerate(n_inits):\n", - " for j, noise in enumerate(noise_levels):\n", - " y_vals = torch.load(f'results_random_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[1]\n", - " best_y = torch.min(y_vals)\n", - " performance_matrix_random[i,j] = best_y" - ] - }, - { - "cell_type": "markdown", - "id": "9ba43cdf-7017-4853-a200-945af8e20e61", - "metadata": {}, - "source": [ - "## 4. Plot\n", - "\n", - "### backtesting plots\n", - "\n", - "1. Fix n_init, compare noise level" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "d436acd6-c72c-40cf-b11f-5ff586a63abd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.5455675e-05])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get true minimum\n", - "\n", - "problem = schwefel.SchwefelProblem(n_var=2, noise_level=0)\n", - "\n", - "problem.y(np.array([[420.9687,420.9687]]))" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "8f2b0de9-e2b8-40a6-a910-a4945f06aaba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_init_val = 10\n", - "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "\n", - "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", - "plot_bo = bo_results[bo_results['seed'] == 0]\n", - "plot_rand = random_results[random_results['seed'] == 0]\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for idx, row in plot_bo.iterrows():\n", - " if row['n_init'] == n_init_val:\n", - " mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", - " std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", - " plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n", - " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", - " \n", - "for idx, row in plot_rand.iterrows():\n", - " if row['n_init'] == n_init_val:\n", - " mean = sm_list[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", - " std = sm_list[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", - " plt.plot(mean, label=f\"Random Baseline, noise_level={row['noise_level']}\", linestyle=\"--\")\n", - " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", - "\n", - "# aaawaaay\n", - "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", - "plt.title(\"BayBE Optimization, 10 initial observations\")\n", - "\n", - "ax.set_xlabel('Campaign iteration')\n", - "ax.set_ylabel('Schwefel function value')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "8989570b-6b02-4e6a-99c7-f7ccafb0bb7f", - "metadata": {}, - "source": [ - "2. Fix noise value, compare initial data number" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "409f03ea-c50e-461d-850d-f7723b031f98", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "noise_level_val = 10\n", - "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", - "\n", - "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", - "plot_bo = bo_results[bo_results['seed'] == 0]\n", - "plot_rand = random_results[random_results['seed'] == 0]\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for idx, row in plot_bo.iterrows():\n", - " if row['noise_level'] == noise_level_val:\n", - " mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", - " std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", - " plt.plot(mean, label=f\"BO, n_init={row['n_init']}\")\n", - " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", - " \n", - "for idx, row in plot_rand.iterrows():\n", - " if row['noise_level'] == noise_level_val:\n", - " mean = sm_list[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", - " std = sm_list[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", - " plt.plot(mean, label=f\"Random Baseline, n_init={row['n_init']}\", linestyle=\"--\")\n", - " plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", - "\n", - "# aaawaaay\n", - "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", - "plt.title(\"BayBE Optimization, 10 initial observations\")\n", - "\n", - "ax.set_xlabel('Campaign iteration')\n", - "ax.set_ylabel('Schwefel function value')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ae3f1ea5-856d-47a3-9010-7a4d4ceb8b1f", - "metadata": {}, - "source": [ - "### Heat map plot" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "e90c4e71-e671-415d-950b-377a463730b4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Bayesian Optimization')" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, ax = plt.subplots()\n", - "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_bo)\n", - "\n", - "ax.set_title('Bayesian Optimization')" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "5d63c7cd-755c-4fd3-8506-a317bf5bdd43", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Random')" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_random)\n", - "ax.set_title('Random')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "caba841d-9015-4097-a8f6-f046370d33d4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noisy_optimization_BayBE_extendedBounds.ipynb b/noisy_optimization_BayBE_extendedBounds.ipynb new file mode 100644 index 0000000..bca8c73 --- /dev/null +++ b/noisy_optimization_BayBE_extendedBounds.ipynb @@ -0,0 +1,1300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "ddfe121c-9cad-4d3f-b518-e42c72a1b70b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "97c15027-9275-43e5-9613-ecbfe31d4914", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST None\n", + "SMOKE_TEST None\n" + ] + } + ], + "source": [ + "import torch\n", + "import pandas as pd\n", + "import sys\n", + "sys.path.append('./src/baybe_utils')\n", + "from run_grid_experiments_baybe import run_grid_experiments\n", + "from run_grid_experiments_baybe_random import run_grid_experiments_random\n", + "from src import visualization\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from src import schwefel" + ] + }, + { + "cell_type": "markdown", + "id": "5bcc2d3b-c62b-422c-bdee-15eee6f1452c", + "metadata": {}, + "source": [ + "## Intro\n", + "\n", + "This work lightly explores the impact of a noisy oracle on Bayesian optimization performance. Here, the 2-dimensional Schwefel function with Gaussian noise is used as an optimization objective. However, this work is motivated by the need to deal with noisy data when applying BO to experimental optimization. \n", + "\n", + "## Implementation Notes\n", + "\n", + "- This work was done as an entry in the 2024 Bayesian optimization for materials hackathon\n", + "- Our team pursued multiple implementations in parallel\n", + "- This notebook serves as an entry point to the BayBE implementation of the project\n", + "- Individual optimization campaigns are run from the run_experiments() function in run_experiment_babye.py\n", + "- Grid screening of parameters builds on this with funcitonality in the run_grid_experiments_babye.py\n", + "- As this was a hackathon project, there are some hacks. Watch out for hard-coded gotchas throughout. Would not recommend directly re-using code. " + ] + }, + { + "cell_type": "markdown", + "id": "02f05875-7a5c-4f50-8b55-fc0e30340897", + "metadata": {}, + "source": [ + "## 1. Define grid search parameters \n", + "\n", + "This is to run a grid search over experiment parameters like number of BO trials to run, noise level, etc. These values were chosen by the team as 'reasonable' sounding values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5319bdb1-b6a0-4dfa-b05c-bd854456278d", + "metadata": {}, + "outputs": [], + "source": [ + "seeds = list(range(5)) # run 5 replicates of each parameter set\n", + "n_inits = [2, 4, 8, 10] # Number of initial randomly collected data points\n", + "noise_levels = [1, 5, 10, 20] # variance (?) of gaussian noise. Bigger number -> more noise\n", + "noise_bools = [True] # carryover from BoTorch side of project, ignore\n", + "budget = 30 # Run 30 iterations of BO\n", + "bounds = (0, 500)" + ] + }, + { + "cell_type": "markdown", + "id": "abdf100a-84fe-466f-9b60-67d698e7578e", + "metadata": {}, + "source": [ + "## 2. Run grid search\n", + "\n", + "Run the grid search over parameters. This will take a minute or 60. Results are written to disk so if you are just following along, skip this step and load below " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d8ffedfe-8639-479f-9cee-63f4a0f32e54", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 9.77s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 20.17s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 29.14s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 0, time: 37.97s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 0, time: 46.36s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 0, time: 53.80s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 0, time: 63.23s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 0, time: 73.00s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 0, time: 82.27s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 0, time: 92.22s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 0, time: 101.83s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 0, time: 111.08s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 0, time: 122.14s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 0, time: 133.72s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 0, time: 143.48s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 0, time: 154.43s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 1, time: 164.97s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 1, time: 173.89s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 1, time: 182.85s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 1, time: 192.51s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 1, time: 201.54s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 1, time: 212.40s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 1, time: 221.70s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 1, time: 230.91s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 1, time: 243.10s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 1, time: 255.56s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 1, time: 270.03s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 1, time: 280.28s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 1, time: 289.97s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 1, time: 299.25s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 1, time: 308.92s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 1, time: 319.49s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 2, time: 329.27s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 2, time: 338.76s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 2, time: 347.93s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 2, time: 357.74s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 2, time: 367.02s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 2, time: 376.51s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 2, time: 385.96s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 2, time: 397.90s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 2, time: 408.44s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 2, time: 419.22s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 2, time: 428.92s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 2, time: 439.04s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 2, time: 449.04s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 2, time: 460.35s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 2, time: 471.40s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 2, time: 482.81s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 3, time: 491.03s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 3, time: 504.92s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 3, time: 519.03s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 3, time: 533.70s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 3, time: 551.65s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 3, time: 566.76s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 3, time: 581.07s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 3, time: 595.80s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 3, time: 609.09s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 3, time: 624.71s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 3, time: 638.26s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 3, time: 656.84s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 3, time: 674.62s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 3, time: 691.10s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 3, time: 705.66s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 3, time: 717.10s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 4, time: 728.05s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 4, time: 739.18s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 4, time: 752.93s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 4, time: 763.88s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 4, time: 777.25s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 4, time: 791.57s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 4, time: 804.90s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 4, time: 816.67s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 4, time: 828.14s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 4, time: 841.69s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 4, time: 858.32s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 4, time: 872.05s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 4, time: 887.12s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 4, time: 898.55s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 4, time: 908.63s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 4, time: 918.71s\n", + "all experiments done, time: 918.71s\n" + ] + } + ], + "source": [ + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds)" + ] + }, + { + "cell_type": "markdown", + "id": "16aeaf0f-14b6-4827-b94a-cb87cd2a4d7c", + "metadata": {}, + "source": [ + "### Run random search as well\n", + "\n", + "Generate some random baseline data to compare against\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5aac4ea7-cb9f-48a4-a874-e4396d8b9e22", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 0.97s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 1.90s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 2.83s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 0, time: 3.75s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 0, time: 4.67s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 0, time: 5.60s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 0, time: 6.52s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 0, time: 7.44s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 0, time: 8.38s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 0, time: 9.32s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 0, time: 10.27s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 0, time: 11.24s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 0, time: 12.23s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 0, time: 13.18s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 0, time: 14.14s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 0, time: 15.09s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 1, time: 16.02s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 1, time: 16.94s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 1, time: 17.85s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 1, time: 18.77s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 1, time: 19.70s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 1, time: 20.62s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 1, time: 21.55s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 1, time: 22.48s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 1, time: 23.41s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 1, time: 24.34s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 1, time: 25.28s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 1, time: 26.21s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 1, time: 27.15s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 1, time: 28.10s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 1, time: 29.05s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 1, time: 29.99s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 2, time: 30.90s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 2, time: 31.82s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 2, time: 32.85s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 2, time: 33.78s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 2, time: 34.70s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 2, time: 35.63s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 2, time: 36.56s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 2, time: 37.48s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 2, time: 38.43s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 2, time: 39.37s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 2, time: 40.31s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 2, time: 41.25s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 2, time: 42.20s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 2, time: 43.15s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 2, time: 44.10s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 2, time: 45.05s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 3, time: 45.96s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 3, time: 46.89s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 3, time: 47.80s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 3, time: 48.73s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 3, time: 49.66s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 3, time: 50.58s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 3, time: 51.53s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 3, time: 52.47s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 3, time: 53.41s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 3, time: 54.36s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 3, time: 55.30s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 3, time: 56.25s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 3, time: 57.21s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 3, time: 58.15s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 3, time: 59.11s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 3, time: 60.05s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 4, time: 61.04s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 4, time: 61.97s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 4, time: 62.90s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 4, time: 63.83s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 4, time: 64.75s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 4, time: 65.68s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 4, time: 66.60s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 4, time: 67.53s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 4, time: 68.47s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 4, time: 69.41s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 4, time: 70.35s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 4, time: 71.29s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 4, time: 72.23s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 4, time: 73.17s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 4, time: 74.25s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 4, time: 75.20s\n", + "all experiments done, time: 75.20s\n" + ] + } + ], + "source": [ + "run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds)" + ] + }, + { + "cell_type": "markdown", + "id": "34ac9617-f371-40b1-b1a1-5203d84b1a65", + "metadata": {}, + "source": [ + "## 3. Process Results" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "7509bc0b-63de-4329-a75f-a8acdf522165", + "metadata": {}, + "outputs": [], + "source": [ + "global_min = torch.tensor([420,420])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "ce441669-a569-468f-a482-fec91efef95f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_22503/1164103272.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
0210True219.849106
0211True23.180445
0212True0.325259
0213True234.490707
0214True0.764173
..................
010200True0.684985
010201True1.410233
010202True0.926888
010203True2.214593
010204True16.486259
\n", + "

80 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 1 0 True 219.849106\n", + "0 2 1 1 True 23.180445\n", + "0 2 1 2 True 0.325259\n", + "0 2 1 3 True 234.490707\n", + "0 2 1 4 True 0.764173\n", + ".. ... ... ... ... ...\n", + "0 10 20 0 True 0.684985\n", + "0 10 20 1 True 1.410233\n", + "0 10 20 2 True 0.926888\n", + "0 10 20 3 True 2.214593\n", + "0 10 20 4 True 16.486259\n", + "\n", + "[80 rows x 5 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load BO results \n", + "sm_list_bo = {}\n", + "sm_x_list_bo = {}\n", + "bo_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_baybe_extendedBounds/results_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " sliding_x_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " min_x = X[Y_real[:i+1].argmin().item()]\n", + " x_rmsd = np.sqrt((min_x[0].item() - global_min[0].item())** 2 + (min_x[1].item() -global_min[1].item())**2)\n", + " sliding_x_min[i] = x_rmsd\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list_bo[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + " sm_x_list_bo[(n_init, noise_level, noise_bool)] = sliding_x_min\n", + "bo_results " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "af01900d-a2bf-4c49-90a0-b3614a59c2fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_22503/129970788.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
0210True382.194916
0211True12.435197
0212True295.999420
0213True2.214593
0214True176.200272
..................
010200True382.194916
010201True12.435197
010202True295.999420
010203True2.214593
010204True176.200272
\n", + "

80 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 1 0 True 382.194916\n", + "0 2 1 1 True 12.435197\n", + "0 2 1 2 True 295.999420\n", + "0 2 1 3 True 2.214593\n", + "0 2 1 4 True 176.200272\n", + ".. ... ... ... ... ...\n", + "0 10 20 0 True 382.194916\n", + "0 10 20 1 True 12.435197\n", + "0 10 20 2 True 295.999420\n", + "0 10 20 3 True 2.214593\n", + "0 10 20 4 True 176.200272\n", + "\n", + "[80 rows x 5 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load random results \n", + "sm_list_random = {}\n", + "sm_x_list_random = {}\n", + "random_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_baybe_extendedBounds/results_random_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " #print(torch.min(Y_real))\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " min_x = X[Y_real[:i+1].argmin().item()]\n", + " x_rmsd = np.sqrt((min_x[0].item() - global_min[0].item())** 2 + (min_x[1].item() -global_min[1].item())**2)\n", + " sliding_x_min[i] = x_rmsd\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + "\n", + " #print(sliding_min)\n", + "\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list_random[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + " sm_x_list_random[(n_init, noise_level, noise_bool)] = sliding_x_min\n", + "random_results " + ] + }, + { + "cell_type": "markdown", + "id": "a62c64e2-b44c-4cb2-8e3c-4955abb334de", + "metadata": {}, + "source": [ + "Calculate 'performance matrix' to generate iterations vs noise heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "f9d3ca6c-22f6-459e-9107-cfa6cc663673", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix_bo = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results_baybe_extendedBounds/results_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[2]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix_bo[i,j] = best_y\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "cd2d4601-b8ee-4d96-8c54-9dad0285151c", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix_random = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results_baybe_extendedBounds/results_random_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[2]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix_random[i,j] = best_y" + ] + }, + { + "cell_type": "markdown", + "id": "9ba43cdf-7017-4853-a200-945af8e20e61", + "metadata": {}, + "source": [ + "## 4. Plot\n", + "\n", + "### backtesting plots\n", + "\n", + "1. Fix n_init, compare noise level" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "8f2b0de9-e2b8-40a6-a910-a4945f06aaba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAHHCAYAAABN18UAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAADI40lEQVR4nOzdeXhMZ/vA8e9M9n0jkkhEiDUi9qVK7ElqX6vyK1pKWxq6od6+aq3WUt10430tLyrU1qoiVOz7kiCxRwQhBInsyeT8/hiZGknIkEW4P9c1l8yZ55znPpOZuOeZ+zyPSlEUBSGEEEIIIYSOuqwDEEIIIYQQ4lkjSbIQQgghhBAPkSRZCCGEEEKIh0iSLIQQQgghxEMkSRZCCCGEEOIhkiQLIYQQQgjxEEmShRBCCCGEeIgkyUIIIYQQQjxEkmQhhBBCCCEeIkmyEGVIpVIxadKkYjvepUuXUKlULFq0qNiO+Sz3+yyoWrUqQ4YMeaJ927ZtS9u2bYvUdsiQIVStWvWJ+nmaGAuiUqkYNWpUsR2vPCnu96wQ4tklSbIo1KJFi1CpVHo3Z2dn2rVrx19//VXi/T/ct5WVFXXr1mXatGmkpaXptR0yZEi+9nk3c3PzIvWXmprK1KlTqV+/PpaWltjZ2dG6dWuWLFnC06zevnHjxufmP9Xly5fz9ddfl3UYRTJ9+nS6d+9OpUqVHpvYXL16lf79+2Nvb4+trS09evTg4sWLpRfsfdeuXWPSpEkcP3681PsW/3ie3rNCiCdnXNYBiGfflClT8PLyQlEUbty4waJFi3jllVf4448/6Nq1a4n23alTJwYNGgRASkoKu3bt4t///jcRERGsWrVKr62ZmRkLFizIdwwjI6PH9nPjxg06dOhAdHQ0AwYMYNSoUWRkZLB69WoGDx7Mxo0bWbZsWZGO9bCNGzcyb968Av/TTU9Px9i4+N6Gnp6epKenY2JiUmzHfNDy5cs5efIkY8aMKdV+n8Snn36Ki4sLDRs2ZPPmzYW2S0lJoV27diQlJTFhwgRMTEyYO3cu/v7+HD9+HCcnp0f2c+bMGdTqJxtv2LJli979a9euMXnyZKpWrUqDBg30Hps/fz65ublP1I8wTGm+Z4UQzy55p4vHCgoKokmTJrr7Q4cOpVKlSvz6668lniTXrFmT//u//9Pdf/vtt8nKymLNmjVkZGTojRIbGxvrtTXE4MGDiY6OZu3atXTv3l23PSQkhI8//pjZs2fTsGFDxo0b9+QnU4CijnIXlSEj589Dv48SExND1apVuXXrFhUrViy03Q8//MC5c+c4ePAgTZs2BbSv+Xr16jFnzhw+//zzR/ZjZmb2xDGampoWue2z9AHkWZGamoqVlVWp9vmsvc6FECVHyi2Ewezt7bGwsMg3mjJ79mxeeuklnJycsLCwoHHjxvz22296bfz9/fHz8yvwuLVq1SIgIOCx/bu4uKBSqYptNGf//v1s3ryZIUOG6CXIeWbMmEGNGjX48ssvSU9PB/6pwZ09ezZz587F09MTCwsL/P39OXnypG7fIUOGMG/ePEC/fCTPw2UAkyZNQqVScfbsWf7v//4POzs7KlasyL///W8URSEuLo4ePXpga2uLi4sLc+bM0Yv14drg8PDwQstQHqxvXb9+PV26dMHNzQ0zMzOqV6/O1KlT0Wg0ujZt27blzz//JDY2Nt8xCqtJ/vvvv2ndujVWVlbY29vTo0cPoqOj9drknfP58+cZMmQI9vb22NnZ8cYbb+QrqzFEUet3f/vtN5o2bapLkAFq165Nhw4dWLlyZZH6ebDeN69Mac+ePXzwwQdUrFgRKysrevXqxc2bN/X2fbAmOTw8XBfDG2+8oXuO857TgmqSi/KeM0RqaioffvghHh4emJmZUatWLWbPnl1oudGyZcuoVasW5ubmNG7cmJ07d+o9fu/ePcaMGUPVqlUxMzPD2dmZTp06cfToUb12Bw4cIDAwEDs7OywtLfH392fPnj16bfJeJ1FRUQwcOBAHBwdefvllZs+ejUqlIjY2Nl98n3zyCaampty5cweAXbt20a9fP6pUqYKZmRkeHh68//77uvc1GP6eBTh27BhBQUHY2tpibW1Nhw4d2L9/v14bQ14Xhw8fJiAggAoVKmBhYYGXlxdvvvlmgb8DIUTJkZFk8VhJSUncunULRVFISEjgu+++IyUlJd+o7TfffEP37t0JDg4mKyuLFStW0K9fPzZs2ECXLl0AeP3113nrrbc4efIk9erV0+176NAhzp49y6effqp3zIyMDG7dugVo/wPfs2cPixcvZuDAgQUmyXltH2RqaoqtrW2h5/fHH38A6Mo6HmZsbMzAgQOZPHkye/bsoWPHjrrHlixZwr179xg5ciQZGRl88803tG/fnhMnTlCpUiVGjBjBtWvXCAsL43//+1+hMTzs1VdfpU6dOnzxxRf8+eefTJs2DUdHR37++Wfat2/Pl19+ybJly/joo49o2rQpbdq0KfA4derUydfv3bt3+eCDD3B2dtZtW7RoEdbW1nzwwQdYW1vz999/M3HiRJKTk5k1axYA//rXv0hKSuLKlSvMnTsXAGtr60LPYevWrQQFBVGtWjUmTZpEeno63333Ha1ateLo0aP5Er7+/fvj5eXFjBkzOHr0KAsWLMDZ2Zkvv/yyyM+boXJzc4mMjCwwAWnWrBlbtmzh3r172NjYGHzs9957DwcHBz777DMuXbrE119/zahRowgNDS2wfZ06dZgyZQoTJ05k+PDhtG7dGoCXXnqp0D6K8p4rKkVR6N69O9u3b2fo0KE0aNCAzZs38/HHH3P16lXd7zzPjh07CA0NJSQkBDMzM3744QcCAwM5ePCg7r399ttv89tvvzFq1Cjq1q1LYmIiu3fvJjo6mkaNGgHaD1JBQUE0btyYzz77DLVazcKFC2nfvj27du2iWbNmev3269ePGjVq8Pnnn6MoCl27dmXs2LGsXLmSjz/+WK/typUr6dy5Mw4ODgCsWrWKtLQ03nnnHZycnDh48CDfffcdV65c0ZVvGfqePXXqFK1bt8bW1paxY8diYmLCzz//TNu2bdmxYwfNmzfXa/+410VCQgKdO3emYsWKjB8/Hnt7ey5dusSaNWuK+qsUQhQXRYhCLFy4UAHy3czMzJRFixbla5+WlqZ3PysrS6lXr57Svn173ba7d+8q5ubmyrhx4/TahoSEKFZWVkpKSopuW0F9A0rPnj2VjIwMvf0HDx5caPuAgIBHnmfPnj0VQLlz506hbdasWaMAyrfffqsoiqLExMQogGJhYaFcuXJF1+7AgQMKoLz//vu6bSNHjlQKe6sBymeffaa7/9lnnymAMnz4cN22nJwcxd3dXVGpVMoXX3yh237nzh3FwsJCGTx4sG5bXlwLFy4ssL/c3Fyla9euirW1tXLq1Cnd9od/d4qiKCNGjFAsLS31nusuXboonp6e+doW1G+DBg0UZ2dnJTExUbctIiJCUavVyqBBg/Kd85tvvql3zF69eilOTk4Fnochbt68me95fvixKVOm5Hts3rx5CqCcPn36kcf39PTU+x3kvW86duyo5Obm6ra///77ipGRkXL37l3dNn9/f8Xf3193/9ChQ4X+/gYPHpzvuS/Ke66gGAuybt06BVCmTZumt71v376KSqVSzp8/r9uW9946fPiwbltsbKxibm6u9OrVS7fNzs5OGTlyZKF95ubmKjVq1FACAgL0nqu0tDTFy8tL6dSpk25b3uvktddey3ecli1bKo0bN9bbdvDgQQVQlixZonfch82YMUNRqVRKbGysbpsh79mePXsqpqamyoULF3Tbrl27ptjY2Cht2rTRbSvq62Lt2rUKoBw6dKjA/oUQpUfKLcRjzZs3j7CwMMLCwli6dCnt2rVj2LBh+UY2LCwsdD/fuXOHpKQkWrdurffVqp2dHT169ODXX3/VfYWr0WgIDQ2lZ8+e+eoLe/Tooet7/fr1fPLJJ2zatImBAwfm+wrY3Nxc1/bB2xdffPHI87t37x7AI0cL8x5LTk7W296zZ08qV66su9+sWTOaN2/Oxo0bH9nn4wwbNkz3s5GREU2aNEFRFIYOHarbbm9vT61atQyahWHq1Kls2LCBRYsWUbduXd32B3939+7d49atW7Ru3Zq0tDROnz5tcPzx8fEcP36cIUOG4OjoqNtev359OnXqVODz8/bbb+vdb926NYmJifme8+KU9zV7QXXFebWnD34Vb4jhw4frfU3funVrNBpNgWUBT6oo77mi2rhxI0ZGRoSEhOht//DDD1EUJd+MNi1btqRx48a6+1WqVKFHjx5s3rxZV6Zjb2/PgQMHuHbtWoF9Hj9+nHPnzjFw4EASExO5desWt27dIjU1lQ4dOrBz5858Fys+/DoB7TcvR44c4cKFC7ptoaGhmJmZ0aNHD922B5+v1NRUbt26xUsvvYSiKBw7duxxT1E+Go2GLVu20LNnT6pVq6bb7urqysCBA9m9e3e+1+/jXhf29vYAbNiwgezsbINjEkIUHym3EI/VrFkzvQv3XnvtNRo2bMioUaPo2rWr7uKjDRs2MG3aNI4fP05mZqau/YP/IYC2rCE0NJRdu3bRpk0btm7dyo0bN3j99dfz9e3u7q5X3tC9e3ecnJz46KOP2LBhA926ddM9ZmRkpNe2qPIS4Hv37un+g3pYYYl0jRo18rWtWbNmkWpZH6VKlSp69+3s7DA3N6dChQr5ticmJhbpmJs2bWLy5Ml88skn9OnTR++xU6dO8emnn/L333/n+089KSnJ4Pjz/sOvVatWvsfq1KnD5s2b81109fA5531FfufOnUeWyzyNvKTpwddrnoyMDL02hnrU+RSXor7niiI2NhY3N7d8r/E6deroHn9QYa/9tLQ0bt68iYuLCzNnzmTw4MF4eHjQuHFjXnnlFQYNGqRLKM+dOwdoL5wtTFJSku65A/Dy8srXpl+/fnzwwQeEhoYyYcIEFEVh1apVujrhPJcvX2bixIn8/vvv+X4PT/I6v3nzJmlpaYW+znNzc4mLi8PHx0e3/XGvC39/f/r06cPkyZOZO3cubdu2pWfPngwcOPCpLhIVQhhORpKFwdRqNe3atSM+Pl73n9yuXbvo3r075ubm/PDDD2zcuJGwsLACR3wDAgKoVKkSS5cuBWDp0qW4uLgUOcHt0KEDQL6LhJ5UXhIQGRlZaJu8xx4cfS1JBU01V9j0cw8/vwWJiYkhODiYTp06MW3aNL3H7t69i7+/PxEREUyZMoU//viDsLAwXS1waU079jTn96QcHR0xMzMjPj4+32N529zc3J7o2CV9Poa858pK//79uXjxIt999x1ubm7MmjULHx8f3ah03mtr1qxZBX4LFBYWlq/uvaAPLW5ubrRu3Vr34XT//v1cvnyZV199VddGo9HQqVMn/vzzT8aNG8e6desICwvTXRj5rLzOVSoVv/32G/v27WPUqFFcvXqVN998k8aNG5OSklIqMQohtGQkWTyRnJwcAN0f7dWrV2Nubs7mzZv1RjsWLlyYb18jIyMGDhzIokWL+PLLL1m3bh1vvfVWkecgfrjvp9W1a1dmzJjBkiVLCrwATqPRsHz5chwcHGjVqpXeY3kfEh509uxZvYvSnmRUrzilp6fTu3dv7O3t+fXXX/PN6RseHk5iYiJr1qzRO/+YmJh8xyrquXh6egLaOYQfdvr0aSpUqFDqU3cVRK1W4+vry+HDh/M9duDAAapVq/ZEF+09KUNeK4a854rC09OTrVu35rtQMa/cJu93mqew176lpaXelHuurq68++67vPvuuyQkJNCoUSOmT59OUFAQ1atXB8DW1vaJvgV60Kuvvsq7777LmTNnCA0NxdLSUu+bphMnTnD27FkWL16sd5FuWFhYvmMV9fdQsWJFLC0tC32dq9VqPDw8nuBsoEWLFrRo0YLp06ezfPlygoODWbFihV4plhCiZMlIsjBYdnY2W7ZswdTUVDcKa2RkhEql0psy7NKlS6xbt67AY7z++uvcuXOHESNGFDhTxqPkzUZR2FRyhnrppZfo2LEjCxcuZMOGDfke/9e//sXZs2cZO3ZsvlGsdevWcfXqVd39gwcPcuDAAYKCgnTb8pLBu3fvFku8hnr77bc5e/Ysa9eu1fvaOk/eh5MHRx+zsrL44Ycf8rW1srIq0tfSrq6uNGjQgMWLF+ud98mTJ9myZQuvvPLKE5xJyejbty+HDh3SS5TPnDnD33//Tb9+/Uo1FkNeK4a+5x7nlVdeQaPR8P333+ttnzt3LiqVSu81DbBv3z692ue4uDjWr19P586dMTIyQqPR5HutODs74+bmpisNady4MdWrV2f27NkFfuh9eGq0R+nTpw9GRkb8+uuvrFq1iq5du+p9ECvoda4oCt98802+YxX192BkZETnzp1Zv349ly5d0m2/ceMGy5cv5+WXXza4VOjOnTv5vgnIW1imoLIgIUTJkZFk8Vh//fWXbjQpISGB5cuXc+7cOcaPH6/7D6BLly589dVXBAYGMnDgQBISEpg3bx7e3t4FljE0bNiQevXqsWrVKurUqaObDuphZ8+e1ZVlpKWlsX//fhYvXoy3t3e+GuacnBxd24f16tXrkSOXS5YsoUOHDvTo0YOBAwfSunVrMjMzWbNmDeHh4bz66qv5ppcC8Pb25uWXX+add94hMzOTr7/+GicnJ8aOHatrk3dxU0hICAEBARgZGTFgwIBCYylOf/75J0uWLKFPnz5ERkbq/S6sra3p2bMnL730Eg4ODgwePJiQkBBUKhX/+9//CvzKvnHjxoSGhvLBBx/QtGlTrK2t9UbrHjRr1iyCgoJo2bIlQ4cO1U0BZ2dn98RL/k6aNInJkyezfft23fzChfnf//5HbGysbq7lnTt36kpNXn/9dd3I6Lvvvsv8+fPp0qULH330ESYmJnz11VdUqlSJDz/88InifFLVq1fH3t6en376CRsbG6ysrGjevHmBdbiGvucep1u3brRr145//etfXLp0CT8/P7Zs2cL69esZM2aMbtQ3T7169QgICNCbAg5g8uTJgLaO393dnb59++Ln54e1tTVbt27l0KFDuvm91Wo1CxYsICgoCB8fH9544w0qV67M1atX2b59O7a2troPxY/j7OxMu3bt+Oqrr7h3755eqQVo576uXr06H330EVevXsXW1pbVq1cXWCNuyHt22rRphIWF8fLLL/Puu+9ibGzMzz//TGZmJjNnzixS7A9avHgxP/zwA7169aJ69ercu3eP+fPnY2tr+0x9uBTihVDq82mIcqOgKeDMzc2VBg0aKD/++KPeNEaKoij/+c9/lBo1aihmZmZK7dq1lYULF+qmbSrIzJkzFUD5/PPPC3z84b6NjIwUd3d3Zfjw4cqNGzf02j5qCjhAiYmJeez53rt3T5k0aZLi4+OjWFhYKDY2NkqrVq2URYsW5TvXvCnPZs2apcyZM0fx8PBQzMzMlNatWysRERF6bXNycpT33ntPqVixoqJSqfSeDwqZAu7mzZv5zs/KyipfzP7+/oqPj0++uPKmECtsGj9AbzqxPXv2KC1atFAsLCwUNzc3ZezYscrmzZsVQNm+fbuuXUpKijJw4EDF3t5e7xiFTT23detWpVWrVoqFhYVia2urdOvWTYmKitJrU9g558X+4O/uww8/VFQqlRIdHZ3vuSjouSns3B88J0VRlLi4OKVv376Kra2tYm1trXTt2lU5d+7cY/tQlMKngHt4Cq/t27fn6/vhKeAURVHWr1+v1K1bVzE2NtZ7TguaAq6o77miTAGnKNr3wPvvv6+4ubkpJiYmSo0aNZRZs2ble/0DysiRI5WlS5fq+m/YsKHeuWVmZioff/yx4ufnp9jY2ChWVlaKn5+f8sMPP+Tr99ixY0rv3r0VJycnxczMTPH09FT69++vbNu2TdemsNfJg+bPn68Aio2NjZKenp7v8aioKKVjx46KtbW1UqFCBeWtt95SIiIi8r12DXnPKoqiHD16VAkICFCsra0VS0tLpV27dsrevXv12hT1dXH06FHltddeU6pUqaKYmZkpzs7OSteuXfWm2xNClA6VojwjV3iIF84333zD+++/z6VLl/Jd8f2su3TpEl5eXsyaNYuPPvqorMN5YTRr1gxPT0/dwg9CCCFESZFyC1EmFEXhP//5D/7+/uUuQRZlIzk5mYiICBYvXlzWoQghhHgBSJIsSlVqaiq///4727dv58SJE6xfv76sQxLlhK2trVy4JIQQotRIkixK1c2bNxk4cCD29vZMmDCB7t27l3VIQgghhBD5SE2yEEIIIYQQD5F5koUQQgghhHiIJMlCCCGEEEI8RGqSgdzcXK5du4aNjU2ZLyEshBBCiKJRFIV79+7h5uaGWi3jfqJ4SZIMXLt2DQ8Pj7IOQwghhBBPIC4uDnd397IOQzxnJEkGbGxsAO2bLG+ZZSGEEEI825KTk/Hw8ND9Py5EcZIkGXQlFra2tpIkCyGEEOWMlEqKkiAFPEIIIYQQQjykTJPkGTNm0LRpU2xsbHB2dqZnz56cOXNGr03btm1RqVR6t7fffluvzeXLl+nSpQuWlpY4Ozvz8ccfk5OTU5qnIoQQQgghniNlWm6xY8cORo4cSdOmTcnJyWHChAl07tyZqKgorKysdO3eeustpkyZortvaWmp+1mj0dClSxdcXFzYu3cv8fHxDBo0CBMTEz7//PNSPR8hhBBCCPF8KNMkedOmTXr3Fy1ahLOzM0eOHKFNmza67ZaWlri4uBR4jC1bthAVFcXWrVupVKkSDRo0YOrUqYwbN45JkyZhampaoucghBBCPI9yc3PJysoq6zAeKSsrC09PT7KyssjIyCjrcEQ5YGJigpGRUZHaPlMX7iUlJQHg6Oiot33ZsmUsXboUFxcXunXrxr///W/daPK+ffvw9fWlUqVKuvYBAQG88847nDp1ioYNG5beCQghhBDPgaysLGJiYsjNzS3rUB4pNzeXn376iRs3bnDz5s2yDkeUE/b29ri4uDz2gs9nJknOzc1lzJgxtGrVinr16um2Dxw4EE9PT9zc3IiMjGTcuHGcOXOGNWvWAHD9+nW9BBnQ3b9+/XqBfWVmZpKZmam7n5ycXNynI4QQQpRLiqIQHx+PkZERHh4ez/QiHRqNhvT0dKpWrVrk0UHx4lIUhbS0NBISEgBwdXV9ZPtnJkkeOXIkJ0+eZPfu3Xrbhw8frvvZ19cXV1dXOnTowIULF6hevfoT9TVjxgwmT578VPEKIYQQz6OcnBzS0tJwc3PTuwboWaTRaAAwNzeXJFkUiYWFBQAJCQk4Ozs/8nXzTHw8HDVqFBs2bGD79u2PXTGnefPmAJw/fx4AFxcXbty4odcm735hdcyffPIJSUlJultcXNzTnoIQQgjxXMhLPOWaHvG8yvvwl52d/ch2ZZokK4rCqFGjWLt2LX///TdeXl6P3ef48ePAP0PkLVu25MSJE7qhc4CwsDBsbW2pW7dugccwMzPTLRwiC4gIIYQQ+ckCHeJ5VdTXdpmWW4wcOZLly5ezfv16bGxsdDXEdnZ2WFhYcOHCBZYvX84rr7yCk5MTkZGRvP/++7Rp04b69esD0LlzZ+rWrcvrr7/OzJkzuX79Op9++ikjR47EzMysLE9PCCGEEEKUU2U6kvzjjz+SlJRE27ZtcXV11d1CQ0MB7Vc9W7dupXPnztSuXZsPP/yQPn368Mcff+iOYWRkxIYNGzAyMqJly5b83//9H4MGDdKbV1kIIYQQ4lk0adIkGjRoUCp9qVQq1q1bVyp95alatSpff/11qfZZXMq83KKg25AhQwDw8PBgx44dJCYmkpGRwblz55g5c2a+8ghPT082btxIWloaN2/eZPbs2RgbPzPXJAohhBCihA0ZMkRvdV4nJycCAwOJjIzUa6fRaJg7dy6+vr6Ym5vj4OBAUFAQe/bsKZO4P/roI7Zt21YmfT/rMjIyGDJkCL6+vhgbG9OzZ89S7f+ZuHDveXUvJYnlm2eXdRhCCCHECyEwMJD4+Hji4+PZtm0bxsbGdO3aVfe4oigMGDCAKVOmMHr0aKKjowkPD8fDw4O2bduW+igrgLW1NU5OTqXeb3mg0WiwsLAgJCSEjh07lnr/kiSXkGs3Yum9ohVfxi9i+6E1ZR2OEEII8dwzMzPDxcUFFxcXGjRowPjx44mLi9MtNLJy5Up+++03lixZwrBhw/Dy8sLPz49ffvmF7t27M2zYMFJTU4vcX9u2bQkJCWHs2LE4Ojri4uLCpEmT9NpcvnyZHj16YG1tja2tLf3799eblevhcovw8HCaNWuGlZUV9vb2tGrVitjYWN3j69evp1GjRpibm1OtWjUmT55MTk7OEz1fcXFx9O/fH3t7exwdHenRoweXLl0CtCsam5ubc/fuXb19Ro8eTfv27XX3d+/eTevWrbGwsMDDw4OQkBCDnsNHsbKy4scff+Stt94qdMaykiRJcglxq+SJu8aKXJWKRUemotE826sWCSGEEAVRFIW0rJwyuSmK8sRxp6SksHTpUry9vXUjtcuXL6dmzZp069YtX/sPP/yQxMREwsLCDOpn8eLFWFlZceDAAWbOnMmUKVN0x8jNzaVHjx7cvn2bHTt2EBYWxsWLF3n11VcLPFZOTg49e/bE39+fyMhI9u3bx/Dhw3WzMezatYtBgwYxevRooqKi+Pnnn1m0aBHTp083KGbQTn8WEBCAjY0Nu3btYs+ePVhbWxMYGEhWVhYdOnTA3t6e1atX6/bRaDSEhoYSHBwMwIULFwgMDKRPnz5ERkYSGhrK7t27GTVqVKH9BgUFYW1tXejNx8fH4HMpKVK4W4Le85/BG/tDOGqRw39XT+Gt/pPKOiQhhBDCIOnZGupO3FwmfUdNCcDStOipyoYNG7C2tgYgNTUVV1dXNmzYoFs18OzZs9SpU6fAffO2nz171qAY69evz2effQZAjRo1+P7779m2bRudOnVi27ZtnDhxgpiYGDw8PABYsmQJPj4+HDp0iKZNm+odKzk5maSkJLp27apbMO3BeCdPnsz48eMZPHgwANWqVWPq1KmMHTtWF0NRhYaGkpuby4IFC3RJ+MKFC7G3tyc8PJzOnTszYMAAli9fztChQwHYtm0bd+/epU+fPoB2cbbg4GDGjBmjO/9vv/0Wf39/fvzxR8zNzfP1u2DBAtLT0wuNy8TExKDzKEmSJJegqofj+XC3DZdIIic7lAsXLbC1sir6AdQqbDp2wrxWzZILUgghhHhOtGvXjh9//BGAO3fu8MMPPxAUFMTBgwfx9PQEeKrR6YLkTUmbx9XVVbd2Q3R0NB4eHroEGaBu3brY29sTHR2dL0l2dHRkyJAhBAQE0KlTJzp27Ej//v11a0NERESwZ88evZFjjUZDRkYGaWlpBq2QGBERwfnz57GxsdHbnpGRwYULFwAIDg6mRYsWXLt2DTc3N5YtW0aXLl2wt7fXHSMyMpJly5bp9lcUhdzcXGJiYgr8QFK5cuUix1jWJEkuQXeWLaNpzF3y3gJZBxZxy8Bj3A1dSbWNGzGyNiC5FkIIIYqJhYkRUVMCyqxvQ1hZWeHt7a27v2DBAuzs7Jg/fz7Tpk2jZs2aREdHF7hv3vaaNQ0bmHp45FOlUpGb++QllgsXLiQkJIRNmzYRGhrKp59+SlhYGC1atCAlJYXJkyfTu3fvfPsVNGr7KCkpKTRu3Fgvwc1TsWJFAJo2bUr16tVZsWIF77zzDmvXrmXRokV6xxgxYgQhISH5jlGlSpUC+w0KCmLXrl2FxuXp6cmpU6cMOpeSIklyCbLt2gVNYiKHYsK5kHkdW00u9Rw7UMW1UpH2v7c9nJz4eG79+AOVPv64hKMVQggh8lOpVAaVPDxLVCoVarVa9/X+gAEDGDhwIH/88Ue+uuQ5c+bg5OREp06diq3/OnXqEBcXR1xcnG40OSoqirt37xa6KjBAw4YNadiwIZ988gktW7Zk+fLltGjRgkaNGnHmzBm9DwJPqlGjRoSGhuLs7PzIlYeDg4NZtmwZ7u7uqNVqunTponeMqKgog+KRcgsBQMWRIwFokzGaz39tTYpaYeDNSN5+ZxsOVqaP3d+qTRuuvP0Otxcvwb5XL8yK4U0hhBBCPK8yMzN1q/feuXOH77//npSUFF1CPGDAAFatWsXgwYOZNWsWHTp0IDk5mXnz5vH777+zatUqrAwpi3yMjh074uvrS3BwMF9//TU5OTm8++67+Pv706RJk3ztY2JidDNtuLm5cebMGc6dO8egQYMAmDhxIl27dqVKlSr07dsXtVpNREQEJ0+eZNq0aQbFFhwczKxZs+jRowdTpkzB3d2d2NhY1qxZw9ixY3F3d9e1mzRpEtOnT6dv3756qxmPGzeOFi1aMGrUKIYNG4aVlRVRUVGEhYXx/fffF9ivoeUWUVFRZGVlcfv2be7du8fx48cBSmUBFpndohTYmdvxZk3tlazh9rdYvnJRkfazadsW6/btISeH61OnFXsdlRBCCPE82bRpk2713ubNm3Po0CFWrVpF27ZtAe3I8sqVK5kwYQJz586lVq1atG7dmtjYWMLDw/UWq7h06RIqlYrw8PAnjkelUrF+/XocHBxo06YNHTt2pFq1arqVhR9maWnJ6dOn6dOnDzVr1mT48OGMHDmSESNGABAQEMCGDRvYsmULTZs2pUWLFsydO1dXb20IS0tLdu7cSZUqVejduzd16tRh6NChZGRk6I0se3t706xZMyIjI3WzWuSpX78+O3bs4OzZs7Ru3ZqGDRsyceJE3NzcDI6nMK+88goNGzbkjz/+IDw8XDfKXhpUimReJCcnY2dnR1JS0iO/cnga6TnpBC17mUSyGHIrl5bdtvJSTdfH7pd15QoXu3RFyczEbc5s7B74mkMIIYQobhkZGcTExODl5WVwnWtp02g0HDt2jIYNG2JkZFj98uNs376d3r17c/HiRRwcHIr12KJsFfU1LiPJpcTC2IJRjUcDsN5BYe/qL8jI1jx2P1N3d5xGDAcg4Ysv0aQUzwTdQgghhCjcxo0bmTBhgiTILzBJkktRz7oD8TR14I6RESqLDfz818Ei7ec0dCgmVaqQc/Mmt+bNK+EohRBCCDFr1iw+LkcXzS9btqxcLNBRnkiSXIqM1caMbvEvAELtzLE6+jlR15Ifu5/azAyXT7X73V6yhAwDJzoXQgghxPOte/fuHD9+vMDbxo0byzq8ckmS5FLWsWpnfG28SFerue54ggUrV6PJfXxZuHWbNlh37AAaDTfkIj4hhBBCPMDGxgZvb+8Cb09yYZ+QJLnUqVQqxrT8FIDVttZ0Sv6eRXtiirSvyyefoDI3J+3QIZI3/FmSYQohhBBCvNAkSS4DzVyb0cq5MTkqFdud7nA27D9cuZP22P1MKlemwttvA3Bj5pdo7t0r6VCFEEIIIV5IkiSXkTHNxwPwl7UV3cyWM23NwSKVUDi++Qamnp5obt7iViETdQshhBBCiKcjSXIZqe1Ym1eqBgLwP0cj/GIW8HvEtcfupzY1pdKn2nKN20uXkXHmTInGKYQQQgjxIpIkuQyNahSCscqIvZYW+FptZeHv27iTmvXY/axbv4xN586g0XB9ylS5iE8IIYQQophJklyGPGw86FezPwDznGwZmf0fpm+MLtK+lcaPQ2VhQfqRIyT//ntJhimEEEKIEjJp0iQaNGhQKn2pVCrWrVtXKn3lqVq1Kl9//XWp9llcJEkuY8P9hmNhZM5JMzMUmzPcOvYHe87feux+Jm5uVHjnHQBuzJyFJvnx8y0LIYQQz6shQ4agUql0NycnJwIDA4mMjNRrp9FomDt3Lr6+vpibm+Pg4EBQUBB79uwpk7g/+ugjtm3bViZ9P+suXbqk9zvNu+3fv79U+pckuYxVsKjA4HpDAPjOwZ5/Gf+PiWuOFmnJaqchgzH18kKTmMjN7+QiPiGEEC+2wMBA4uPjiY+PZ9u2bRgbG9O1a1fd44qiMGDAAKZMmcLo0aOJjo4mPDwcDw8P2rZtW+qjrADW1tY4OTmVer/lydatW3W/1/j4eBo3blwq/UqS/AwYXHcwDmb2XDI14bjdPTokreHrreceu5/K1JRK91fiu7NsGRmnT5d0qEIIIcQzy8zMDBcXF1xcXGjQoAHjx48nLi6OmzdvArBy5Up+++03lixZwrBhw/Dy8sLPz49ffvmF7t27M2zYMFJTU4vcX9u2bQkJCWHs2LE4Ojri4uLCpEmT9NpcvnyZHj16YG1tja2tLf379+fGjRu6xx8utwgPD6dZs2ZYWVlhb29Pq1atiI2N1T2+fv16GjVqhLm5OdWqVWPy5Mnk5OQ80fMVFxdH//79sbe3x9HRkR49enDp0iUAtmzZgrm5OXfv3tXbZ/To0bRv3153f/fu3bRu3RoLCws8PDwICQkx6DksCicnJ93v1cXFBRMTk2I9fmEkSX4GWJtaM7z+CAB+tLdjuMla1u06UqQlq61btcImMBByc7UX8eXmlnS4QgghXiSKAlmpZXN7igvTU1JSWLp0Kd7e3rqR2uXLl1OzZk26deuWr/2HH35IYmIiYWFhBvWzePFirKysOHDgADNnzmTKlCm6Y+Tm5tKjRw9u377Njh07CAsL4+LFi7z66qsFHisnJ4eePXvi7+9PZGQk+/btY/jw4ahUKgB27drFoEGDGD16NFFRUfz8888sWrSI6dOnGxQzQHZ2NgEBAdjY2LBr1y727NmDtbU1gYGBZGVl0aFDB+zt7Vm9erVuH41GQ2hoKMHBwQBcuHCBwMBA+vTpQ2RkJKGhoezevZtRo0YV2m9QUBDW1taF3nx8fPLt0717d5ydnXn55Zf5vRSvwzIutZ7EI/Wv1Z+lUf/jauo11tmZ8vHtX/lkjSdr3m2FkVr1yH0rjR9Hys6dpB89StL637Hv1bN0ghZCCPH8y06Dz93Kpu8J18DUqsjNN2zYgLW1NQCpqam4urqyYcMG1GrtmODZs2epU6dOgfvmbT979qxBIdavX5/PPvsMgBo1avD999+zbds2OnXqxLZt2zhx4gQxMTF4eHgAsGTJEnx8fDh06BBNmzbVO1ZycjJJSUl07dqV6tWr68UFMHnyZMaPH8/gwYMBqFatGlOnTmXs2LG6GIoqNDSU3NxcFixYoEvCFy5ciL29PeHh4XTu3JkBAwawfPlyhg4dCsC2bdu4e/cuffr0AWDGjBkEBwczZswY3fl/++23+Pv78+OPP2Jubp6v3wULFpCenl5oXA+OEltbWzNnzhxatWqFWq1m9erV9OzZk3Xr1tG9e3eDzvdJSJL8jDA1MmVkw1FM2D2B/9rZ8te9PSy7eohFeysz9GWvR+5r4uJCxZHvkjBrNgmzZmHToT1GtralFLkQQgjxbGjXrh0//vgjAHfu3OGHH34gKCiIgwcP4unpCVDs06bWr19f776rqysJCQkAREdH4+HhoUuQAerWrYu9vT3R0dH5kmRHR0eGDBlCQEAAnTp1omPHjvTv3x9XV1cAIiIi2LNnj97IsUajISMjg7S0NCwtLYscd0REBOfPn8fGxkZve0ZGBhcuXAAgODiYFi1acO3aNdzc3Fi2bBldunTB3t5ed4zIyEiWLVum219RFHJzc4mJiSnwA0nlypWLHGOFChX44IMPdPebNm3KtWvXmDVrliTJL5pXvF5h4amFnLtzjv/Y2TIpZzFDt/swqKUnJkaProxxfP117q5ZS9aFC9z85ltc/v1pKUUthBDiuWZiqR3RLau+DWBlZYW3t7fu/oIFC7Czs2P+/PlMmzaNmjVrEh1d8FSredtr1qxpWIgP1ceqVCpyn6L0ceHChYSEhLBp0yZCQ0P59NNPCQsLo0WLFqSkpDB58mR69+6db7+CRm0fJSUlhcaNG+sluHkqVqwIaJPS6tWrs2LFCt555x3Wrl3LokWL9I4xYsQIQkJC8h2jSpUqBfYbFBTErl27Co3L09OTU6dOFfp48+bNDS6JeVKSJD9DjNRGjGk0hpHbRrLczoaByZeZnDWL+OXrqOL46D8UKsClUwUuX7jAneXLsHeOxdzHB5oNB1PD/sgIIYQQOiqVQSUPzxKVSoVardZ9vT9gwAAGDhzIH3/8ka8uec6cOTg5OdGpU6di679OnTrExcURFxenG02Oiori7t271K1bt9D9GjZsSMOGDfnkk09o2bIly5cvp0WLFjRq1IgzZ87ofRB4Uo0aNSI0NBRnZ2dsH/Htc3BwMMuWLcPd3R21Wk2XLl30jhEVFWVQPIaUWxTk+PHjupH1kiZJ8jOmdeXWNHJuxNGEo/zoYMfkWwfhwkG48Ph9rQDbKvYkX7bk+pK/8ey4ElVCFPT6WftHTgghhHiOZWZmcv36dUBbbvH999+TkpKiS4gHDBjAqlWrGDx4MLNmzaJDhw4kJyczb948fv/9d1atWoWVVfF9IOjYsSO+vr4EBwfz9ddfk5OTw7vvvou/vz9NmjTJ1z4mJkY304abmxtnzpzh3LlzDBo0CICJEyfStWtXqlSpQt++fVGr1URERHDy5EmmTZtmUGzBwcHMmjWLHj16MGXKFNzd3YmNjWXNmjWMHTsWd3d3XbtJkyYxffp0+vbti5mZme4Y48aNo0WLFowaNYphw4ZhZWVFVFQUYWFhfP99wVPTGlJusXjxYkxNTWnYsCEAa9as4b///S8LFiww6FyflCTJzxiVSsX7jd/n9b9eZ52NDarE5jhmW/BmKy+szR//63L2SyNl0nrSEyHpkhX2qlCo0gKavFkK0QshhBBlZ9OmTbpRRhsbG2rXrs2qVato27YtoP0/duXKlXz99dfMnTuXd999F3Nzc1q2bEl4eDitWrXSHevSpUt4eXmxfft23f6GUqlUrF+/nvfee482bdqgVqsJDAzku+++K7C9paUlp0+fZvHixSQmJuLq6srIkSMZMUI7A1ZAQAAbNmxgypQpfPnll5iYmFC7dm2GDRtmcGyWlpbs3LmTcePG0bt3b+7du0flypXp0KGD3siyt7c3zZo14+DBg/lWzqtfvz47duzgX//6F61bt0ZRFKpXr17o7B1PYurUqcTGxmJsbEzt2rUJDQ2lb9++xXb8R1EpxV3BXg4lJydjZ2dHUlLSI79yKE0hf4ewPW47JrkVuXe9DSOb9WNMh/zTohQk8b8LSZg5E7WlGVZOd0GlhqqtwcKh6AGoVNgGBWEb0PnJTkAIIUS5lJGRQUxMDF5eXgbXuZY2jUbDsWPHaNiwIUZGRsV67O3bt9O7d28uXryIg4MB/3+KZ15RX+MykvyM+qDxBxxPOM6dzJuYu63mv7FbMD8+mAG1X8Xe3P6R+zq+/n8krV1D5rnz3Euz0G68fNDgGFL37sWmYwdUxfyHRwghhHjWbdy4kQkTJkiC/AKTkWSezZFkgJSsFFacXsU3h/4LxncBMDcyp6d3TwbVHYSHrUeh+2Zfv05KeDhKZirsngtpieBcV1t2UYT65JtzviI3NRWvtWswL2ROSSGEEM8fGUkun5YtW6Yry3jY42aMeNHISPJzwNrUmmH13+DC+QasOr2Riu77SNHEsuLMCkLPhNLRsyODfQbjV9Ev374mLi44DBigvdO5CSzoBJrDUOUVaP3hY/tOCQ8ndecu0g4dkiRZCCGEeMZ1796d5s2bF/hYaS3j/LyRZanLgdeaeZGT3IDEs+/ytf+PvFz5ZRQUwmLD+L+N/8egvwax7fI2NLmagg/g6gddZmt//nsaxOx8bJ+WTbQTnKcdOlxcpyGEEEKIEmJjY4O3t3eBt7yFVIRhJEkuB3wr21HH1ZasHIW4q5X5seOPrOm+hp7ePTFWG3Ms4Rhjto+hx/oerDyzkvScAuYfbPg6NAgGJRd+exOS4x/Zp2VT7dQ0aYcPF/vqREIIIYQQzzpJkssBlUrFgKba+uMVh+JQFIUaDjWY2moqW/psYZjvMGxMbYhNjmXq/qkE/BbAvOPzSExPfPAg8MpsqFQPUm/Cb2+AJrvQPi18fFCZm6O5c4esC0WYpFkIIYQQ4jkiSXI50bNBZUyN1Zy+fo/IK0m67RUtKzK60Wi29t3K+GbjqWxdmTuZd/gp4ie6ru3KxbsX/zmIqSX0XwJmtnB5H2ybXGh/KlNTLBo0ALSjyUIIIYQQLxJJkssJO0sTguq5ABB6OC7f45YmlgTXCWZDrw3M9p+Nl50XKdkprDizQr+hU3XoMU/7897vIPqPQvvUlVxIXbIQQgghXjCSJJcjr94vufj9+DXSsnIKbGOsNiagagAfN/kYgM2XNpOd+1BZRd3u0HKU9ud170JiweUU/1y8d0jqkoUQQgjxQpEkuRxp4eWEp5MlKZk5/Bn56AvvWrq1xNHckdsZtzkQfyB/g46TwKMFZCbDysGQnf9iPwu/+qhMTMhJSCA7Lv/otRBCCCGezqRJk2hwv7yxpKlUKtatW1cqfeWpWrVqvuWsywtJkssRtVpF/yba0eSVBZRcPChvRBngz4t/5m9gZAL9FoJVRbhxAjZ+lL8/c3PM69cHtKPJQgghxLNqyJAhqFQq3c3JyYnAwEAiIyP12mk0GubOnYuvry/m5uY4ODgQFBTEnj17yiTujz76iG3btpVJ38+6jIwMhgwZgq+vL8bGxvTs2bPAduHh4TRq1AgzMzO8vb1ZtGhRsfQvSXI507exO2oVHLp0h/MJKY9s+4rXKwBsu7yNtOy0/A1s3aDPAlCp4dhSOPq/fE0sm0hdshBCiPIhMDCQ+Ph44uPj2bZtG8bGxnTt2lX3uKIoDBgwgClTpjB69Giio6MJDw/Hw8ODtm3blvooK4C1tTVOTk6l3m95oNFosLCwICQkhI4dOxbYJiYmhi5dutCuXTuOHz/OmDFjGDZsGJs3b37q/iVJLmcq2ZrTvrYz8PjRZL+KflS2rkx6Tjo7ruwouFG1ttBugvbnjR9BvP4nbsum9+uSZYYLIYQQzzgzMzNcXFxwcXGhQYMGjB8/nri4OG7evAnAypUr+e2331iyZAnDhg3Dy8sLPz8/fvnlF7p3786wYcNITU0tcn9t27YlJCSEsWPH4ujoiIuLC5MmTdJrc/nyZXr06IG1tTW2trb079+fGzdu6B5/uNwiPDycZs2aYWVlhb29Pa1atSI2Nlb3+Pr162nUqBHm5uZUq1aNyZMnk5NT8HVKjxMXF0f//v2xt7fH0dGRHj16cOnSJQC2bNmCubk5d+/e1dtn9OjRtG/fXnd/9+7dtG7dGgsLCzw8PAgJCTHoOXwUKysrfvzxR9566y1cXFwKbPPTTz/h5eXFnDlzqFOnDqNGjaJv377MnTv3qfuXJLkcyiu5WHP0Clk5uYW2U6lUutHkAksu8rz8IdToDDkZsHIQZPwzxZxFgwZgZET2lStkxz+6DloIIcTzR1EU0rLTyuT2NBeNp6SksHTpUry9vXUjtcuXL6dmzZp069YtX/sPP/yQxMREwsLCDOpn8eLFWFlZceDAAWbOnMmUKVN0x8jNzaVHjx7cvn2bHTt2EBYWxsWLF3n11VcLPFZOTg49e/bE39+fyMhI9u3bx/Dhw1GpVADs2rWLQYMGMXr0aKKiovj5559ZtGgR06dPNyhmgOzsbAICArCxsWHXrl3s2bMHa2trAgMDycrKokOHDtjb27N69WrdPhqNhtDQUIKDgwG4cOECgYGB9OnTh8jISEJDQ9m9ezejRo0qtN+goCCsra0Lvfn4+Bh0Hvv27cs3yhwQEMC+ffsMOk5BjJ/6CKLUtavtTEUbM27ey+Tv0zcIrOdaaNuu1boy/8R89lzdw92Mu9ib2+dvpFZDr5/hZ3+4E6Od8eLVpaBSYWRthXndumScOEHa4cPYFfCHRQghxPMrPSed5subl0nfBwYewNLEssjtN2zYgLW1NQCpqam4urqyYcMG1GrtmODZs2epU6dOgfvmbT979qxBMdavX5/PPvsMgBo1avD999+zbds2OnXqxLZt2zhx4gQxMTF4eGgHuJYsWYKPjw+HDh2i6f1va/MkJyeTlJRE165dqV69ul5cAJMnT2b8+PEMHjwYgGrVqjF16lTGjh2ri6GoQkNDyc3NZcGCBbokfOHChdjb2xMeHk7nzp0ZMGAAy5cvZ+jQoQBs27aNu3fv0qdPHwBmzJhBcHAwY8aM0Z3/t99+i7+/Pz/++CPm5ub5+l2wYAHp6QWsDHyfiYmJQedx/fp1KlWqpLetUqVKJCcnk56ejoWFhUHHe5CMJJdDJkZq+jZ2B7Qr8D1KNftq1HGsQ46Sw5bYLYU3tHSE/ovAyBROb4B93//zUF7JhdQlCyGEeIbl1aUeP36cgwcPEhAQQFBQkF65QnFPaVr//gXueVxdXUlISAAgOjoaDw8PXYIMULduXezt7YmOjs53LEdHR4YMGUJAQADdunXjm2++If6Bb3EjIiKYMmWK3sjrW2+9RXx8PGlpBVx79AgRERGcP38eGxsb3bEcHR3JyMjgwv2VdoODgwkPD+fatWsALFu2jC5dumBvb687xqJFi/TiCQgIIDc3l5iYmAL7rVy5Mt7e3oXePD09DTqPkiQjyeVU/yYe/Bh+gZ1nb3Ltbjpu9oV/UnrF6xWib0fz58U/6V+rf+EHrdwYAmfAnx9C2GdgagVWFbF0yeU2kLZnO0Q3Kf6TKUjF2lChRun0JYQQolAWxhYcGFjAVKKl1LchrKys8Pb21t1fsGABdnZ2zJ8/n2nTplGzZs0Ck1NAt71mzZoG9fnwyKdKpSI3t/BSyMdZuHAhISEhbNq0idDQUD799FPCwsJo0aIFKSkpTJ48md69e+fbr6BR20dJSUmhcePGLFu2LN9jFStWBKBp06ZUr16dFStW8M4777B27Vq9mSNSUlIYMWIEISEh+Y5RpUqVAvsNCgpi165dhcbl6enJqVOninweLi4uejXeADdu3MDW1vapRpFBkuRyy6uCFc29HDkQc5vfjlwhpEPhCWWgVyBfHfmKowlHuZZyDTdrt8IP3GQoXN4PJ1bBhvcBsMxSAS5kXb1JzuJBGJs/+ZvfILW7QusPoXKj0ulPCCFEPiqVyqCSh2eJSqVCrVbrvt4fMGAAAwcO5I8//shXlzxnzhycnJzo1KlTsfVfp04d4uLiiIuL040mR0VFcffuXerWrVvofg0bNqRhw4Z88skntGzZkuXLl9OiRQsaNWrEmTNn9D4IPKlGjRoRGhqKs7Mztra2hbYLDg5m2bJluLu7o1ar6dKli94xoqKiDIqnuMstWrZsycaNG/W2hYWF0bJlS4OOUxBJksuxAc08OBBzm5WH4xjVzhu1WlVgOxcrF5q4NOHQ9UNsjNnIMN9hhR9UpYJu34CpNSRoP1UbAWYV48m8mU1abj1sPUr4j6UmC64d05Z9nN4A1TtAm4/A86WS7VcIIUS5lpmZyfXr1wG4c+cO33//PSkpKbqEeMCAAaxatYrBgwcza9YsOnToQHJyMvPmzeP3339n1apVWFlZFVs8HTt2xNfXl+DgYL7++mtycnJ499138ff3p0mT/N/MxsTE6GbacHNz48yZM5w7d45BgwYBMHHiRLp27UqVKlXo27cvarWaiIgITp48ybRp0wyKLTg4mFmzZtGjRw+mTJmCu7s7sbGxrFmzhrFjx+Lu7q5rN2nSJKZPn07fvn0xMzPTHWPcuHG0aNGCUaNGMWzYMKysrIiKiiIsLIzvv/++wH4rV65sUJxRUVFkZWVx+/Zt7t27x/HjxwF0M4K8/fbbfP/994wdO5Y333yTv//+m5UrV/Lnn4+YsKCoFKEkJSUpgJKUlFTWoRgkPStHqffZJsVz3AZl19mbj2z725nflHqL6im91vd6or7ip05TomrVVuKnTH2i/Q2WcFpRVg9XlEkOivKZrfb23yBFObdVUXJzSycGIYR4AaWnpytRUVFKenp6WYfyWDk5OcqhQ4eUnJwcZfDgwQqgu9nY2ChNmzZVfvvtN719srOzlVmzZik+Pj6KqampYmtrqwQEBCi7d+/WaxcTE6MAyvbt2wvt39/fXxk9erTeth49eiiDBw/W3Y+NjVW6d++uWFlZKTY2Nkq/fv2U69ev6x7/7LPPFD8/P0VRFOX69etKz549FVdXV8XU1FTx9PRUJk6cqGg0Gl37TZs2KS+99JJiYWGh2NraKs2aNVN++eWXIj1fgLJ27Vrd/fj4eGXQoEFKhQoVFDMzM6VatWrKW2+9lS8fatasmQIof//9d75jHjx4UOnUqZNibW2tWFlZKfXr11emT5+ue9zT01OZO3dukeIriKenp97vNe/2oO3btysNGjRQTE1NlWrVqikLFy585DGL+hpXKUoxV7CXQ8nJydjZ2ZGUlPTIrxyeRf9ed5L/7Y+la31Xvh9YeFlCUmYS7Va2Izs3m9XdV1PTwbCaq+RNm7k6ZgxmtWpRbf26p4zaALdjYM/XcHy5doQZwK0RtPkYagZqZ+YQQghRbDIyMoiJicHLy8vgOtfSptFoOHbsGA0bNsTIyKhYj719+3Z69+7NxYsXcXBwKNZji7JV1Ne4ZBjl3KtNtTVOW07d4E5qVqHt7MzsaF25NfCYOZMLYdlU+7VQ5tmzaB6aWLxEOXppyz9GR0CLd8HYAq4dhRWvwU8vw4nfIFdTevEIIYR4IWzcuJEJEyZIgvwCkyS5nKtX2Q4fN1uyNLmsPXb1kW27VNMW2/8V8xe5imEX3xk7OWFarRooCmlHjz5xvE/M1k0788aYE/DyB2BqAwmnYPVQmNdMu6y2Jrv04xJCCPFcmjVrFh9//HFZh1Fky5YtK7YFOoSWJMnPgQH3R5NDD8U9cv7HNu5tsDKxIj41nmMJxwzux/L+RQZlOl+ydUXo+Bm8fwLa/QssHCDxPKwfCd82gkMLIDuj7OITQgghykD37t11c0Q/fHt49gdRNJIkPwe6N6iMmbGaMzfucTzubqHtzI3N6VhFu3TjxouGv2H+WVTk0BPFWawsHMB/LIw5CZ2mgpUzJF3WzvH8jZ92GjshhBDiBWFjY1MuFugoTyRJfg7YWZjQxVe7NPXKw49ege+Vaq8AsDl2M9kGlifk1SVnREWhSUl9gkhLgJk1tAqBMZHwymyw84CU67DqDUi7XdbRCSGEEKKckiT5OdH/fsnF78evkZqZU2i75i7NqWBRgaTMJPZc22NQHyYuLph4eEBuLunHyqAu+VFMLKDZWzDyADjVgHvX4I/RIJO3CCGEEOIJSJL8nGju5UhVJ0tSszT8GRlfaDsjtRGBVQOBJyy5eBbqkh/F1Ar6LAC1CUT/rr2gTwghhBDCQGWaJM+YMYOmTZtiY2ODs7MzPXv25MyZM3ptMjIyGDlyJE5OTlhbW9OnT598a3RfvnyZLl26YGlpibOzMx9//DE5OYWPpj6PVCoVrzbVrpMe+piSi7xZLrbHbSc127CyCV2SfPgZTZIB3BpA+0+1P/81DhIvlGk4QgghhCh/yjRJ3rFjByNHjmT//v2EhYWRnZ1N586dSU39J3F7//33+eOPP1i1ahU7duzg2rVr9O7dW/e4RqOhS5cuZGVlsXfvXhYvXsyiRYuYOHFiWZxSmerTuDJGahVHYu9w7sa9Qtv5OPngaetJhiaDvy//bVAfls20F++lnzhB7iPWXi9zL4VA1daQnQpr3pLp4YQQQghhkDJNkjdt2sSQIUPw8fHBz8+PRYsWcfnyZY4cOQJAUlIS//nPf/jqq69o3749jRs3ZuHChezdu5f9+7WzF2zZsoWoqCiWLl1KgwYNCAoKYurUqcybN4+srMIX13geOduY0762M6CdDq4wKpWKV7y0F/D9GWPYwiIm7u4YV6oE2dmkR0Q+ebAlTa2GXj+BuR1cPQI7vizriIQQQoh8Jk2aRIMGDUqlL5VKxbp160qlrzxVq1bl66+/LtU+i8szVZOclJQEgKOjIwBHjhwhOzubjh076trUrl2bKlWqsG/fPgD27duHr68vlSpV0rUJCAggOTmZU6dOlWL0z4a8OZPXHLtKVk7hC4bklVzsv7afxPTEIh9fpVKVj5ILADt37Wp9ALvmQOzeso1HCCFEiRkyZAgqlUp3c3JyIjAwkMhI/QEdjUbD3Llz8fX1xdzcHAcHB4KCgtizx7CL2YvLRx99xLZt28qk72ddeHg4PXr0wNXVFSsrKxo0aMCyZcvytVu1ahW1a9fG3NwcX1/fYpsX+plJknNzcxkzZgytWrWiXr16AFy/fh1TU1Ps7e312laqVInr16/r2jyYIOc9nvdYQTIzM0lOTta7PS/8a1bE2caM26lZbI2+UWg7T1tP6jnVQ6No2Hxps0F9PFPzJT+OTy9oEAxKLqwZDul3yzoiIYQQJSQwMJD4+Hji4+PZtm0bxsbGdO3aVfe4oigMGDCAKVOmMHr0aKKjowkPD8fDw4O2bduW+igrgLW1NU5OTqXeb3mwd+9e6tevz+rVq4mMjOSNN95g0KBBbNiwQa/Na6+9xtChQzl27Bg9e/akZ8+enDx58qn7f2aS5JEjR3Ly5ElWrFhR4n3NmDEDOzs73c3Dw6PE+ywtxkZq+jVxB2DFI0ou4J85kw0tucibLzn9+HGU8lDSEvQlOFSFpDjY+FFZRyOEEKKEmJmZ4eLigouLCw0aNGD8+PHExcVx8+ZNAFauXMlvv/3GkiVLGDZsGF5eXvj5+fHLL7/QvXt3hg0bpndd1OO0bduWkJAQxo4di6OjIy4uLkyaNEmvzeXLl+nRowfW1tbY2trSv39/vQkIHi63CA8Pp1mzZlhZWWFvb0+rVq2IjY3VPb5+/XoaNWqEubk51apVY/LkyU88WUFcXBz9+/fH3t4eR0dHevTowaVLlwBtOau5uTl3797V22f06NG0b99ed3/37t20bt0aCwsLPDw8CAkJMeg5fJQJEyYwdepUXnrpJapXr87o0aMJDAxkzZo1ujbffPMNgYGBfPzxx9SpU4epU6fSqFEjvv/++6fu/5lIkkeNGsWGDRvYvn077u7uuu0uLi5kZWXl+wXduHEDFxcXXZuHZ7vIu5/X5mGffPIJSUlJultc3KOTyfKmfxNt0r/r3E3ibqcV2i6waiBqlZrIm5HE3Sv6c2BarRpGjo4omZmkF8MntRJnZgO954PKCE6sgsiVZR2REEKUG4qikJuWViY35Snmuk9JSWHp0qV4e3vrRmqXL19OzZo16datW772H374IYmJiYSFhRnUz+LFi7GysuLAgQPMnDmTKVOm6I6Rm5tLjx49uH37Njt27CAsLIyLFy/y6quvFnisnJwcevbsib+/P5GRkezbt4/hw4ejUqkA2LVrF4MGDWL06NFERUXx888/s2jRIqZPn25QzADZ2dkEBARgY2PDrl272LNnD9bW1gQGBpKVlUWHDh2wt7dn9erVun00Gg2hoaEEBwcDcOHCBQIDA+nTpw+RkZGEhoaye/duRo0aVWi/QUFBWFtbF3rz8fF5ZNxJSUm6slzQlt0+WJYL2rLbvLLcp2H81Ed4Coqi8N5777F27VrCw8Px8vLSe7xx48aYmJiwbds2+vTpA8CZM2e4fPkyLVu2BKBly5ZMnz6dhIQEnJ21F62FhYVha2tL3bp1C+zXzMwMMzOzEjyzsuXpZMXL3hXYff4WE9ef5L9DmureYA+qaFmRZi7N2B+/n40XNzLCb0SRjp9Xl3xvyxbSDh3GslGj4j6F4ufRDPzHQfjn2qWrPZqDgyzTKYQQj6Okp3OmUeMy6bvW0SOoLC2L3H7Dhg1YW1sDkJqaiqurKxs2bECt1o4Jnj17ljp16hS4b972s2fPGhRj/fr1+eyzzwCoUaMG33//Pdu2baNTp05s27aNEydOEBMTo/vWesmSJfj4+HDo0CGa3i9fzJOcnExSUhJdu3alevXqenEBTJ48mfHjxzN48GAAqlWrxtSpUxk7dqwuhqIKDQ0lNzeXBQsW6HKEhQsXYm9vT3h4OJ07d2bAgAEsX76coUOHArBt2zbu3r2ry8lmzJhBcHAwY8aM0Z3/t99+i7+/Pz/++CPm5ub5+l2wYAHpj5gdy8TEpNDHVq5cyaFDh/j555912woruy2s5NYQZTqSPHLkSJYuXcry5cuxsbHh+vXrXL9+Xffk2dnZMXToUD744AO2b9/OkSNHeOONN2jZsiUtWrQAoHPnztStW5fXX3+diIgINm/ezKeffsrIkSOf60T4cf7dtS6mRmq2n7n5yKWq8y7g+zPmT4M+sZebi/ce1Pp+cpyZrK1P1rxYc2kLIcTzrl27dhw/fpzjx49z8OBBAgICCAoK0itXeJrR6YLUr19f776rqysJCQkAREdH4+HhoVfWWbduXezt7YmOjs53LEdHR4YMGUJAQADdunXjm2++IT7+nwXCIiIimDJlit7I61tvvUV8fDxpaYV/c1yQiIgIzp8/j42Nje5Yjo6OZGRkcOGCdn2B4OBgwsPDuXbtGgDLli2jS5cuumvFIiIiWLRokV48AQEB5ObmEhMTU2C/lStXxtvbu9Cbp2fBA1jbt2/njTfeYP78+Y8dbS4uZTqS/OOPPwLamp4HLVy4kCFDhgAwd+5c1Go1ffr0ITMzk4CAAH744QddWyMjIzZs2MA777xDy5YtsbKyYvDgwUyZMqW0TuOZVMvFhg8712TGX6eZ8kcUL1WvgIdj/k/jHat0ZOq+qcQkxXD69mnqOBX8CfthurrkI0dQcnJQGZfpS6lojIyh18/wU2uI2w+754L/x2UdlRBCPNNUFhbUOnqkzPo2hJWVFd7e3rr7CxYswM7Ojvnz5zNt2jRq1qxZYHIK6LbXrFnToD4fHvlUqVTk5hY+u9TjLFy4kJCQEDZt2kRoaCiffvopYWFhtGjRgpSUFCZPnqy3XkSegkZtHyUlJYXGjRsXOFtExYoVAWjatCnVq1dnxYoVvPPOO6xdu5ZFixbpHWPEiBGEhITkO0aVKlUK7DcoKIhdu3YVGpenp2e+2cl27NhBt27dmDt3LoMGDdJ7rLCy28JKbg1R5uUWj2Nubs68efOYN29eoW08PT2LbbqP58mw1tXYGn2DQ5fu8NGqCH59qwVqtX7ZhbWpNf4e/oTFhvHnxT+LnCSb1ayJ2taW3ORkMqJPY+FbryROofg5ekGX2bB2BITPgOrtwb1svkYUQojyQKVSGVTy8CxRqVSo1WrdN9QDBgxg4MCB/PHHH/nqkufMmYOTkxOdOnUqtv7r1KlDXFwccXFxutHkqKgo7t69W2hJKEDDhg1p2LAhn3zyCS1btmT58uW0aNGCRo0acebMGb0PAk+qUaNGhIaG4uzsjK2tbaHtgoODWbZsGe7u7qjVarp06aJ3jKioKIPiMbTcIjw8nK5du/Lll18yfPjwfO1btmzJtm3bdCUfoC27zSvLfRrPxIV7omQYqVXM7ueHpakRB2Jus3DvpQLb5ZVc/BXzF5pcTZGOrTIy0tUil6uSC4D6r0K9PqBoYPVQyEwp64iEEEIUg8zMTF3pZnR0NO+99x4pKSm6hHjAgAH06tWLwYMH85///IdLly4RGRnJiBEj+P3331mwYAFWVlbFFk/Hjh3x9fUlODiYo0ePcvDgQQYNGoS/vz9N7pctPigmJoZPPvmEffv2ERsby5YtWzh37pyuLnnixIksWbKEyZMnc+rUKaKjo1mxYgWffvqpwbEFBwdToUIFevTowa5du4iJiSE8PJyQkBCuXLmi1+7o0aNMnz6dvn376pWyjhs3jr179zJq1CiOHz/OuXPnWL9+/SMv3DOk3GL79u106dKFkJAQ+vTpo/vd3r59W9dm9OjRbNq0iTlz5nD69GkmTZrE4cOHHxlDUUmS/JzzdLJiwivaN9fMTac5n5A/IWxduTU2pjYkpCdw5EbRv1LLK7koF/MlP0ilgi5fga073ImBTePKOiIhhBDFYNOmTbi6uuLq6krz5s05dOgQq1at0pV1qlQqVq5cyYQJE5g7dy61atWidevWxMbGEh4eTs+ePXXHunTpEiqVivDw8CeOR6VSsX79ehwcHGjTpg0dO3akWrVqhIaGFtje0tKS06dP06dPH2rWrMnw4cMZOXIkI0ZoL6wPCAhgw4YNbNmyhaZNm9KiRQvmzp1baB3vo1haWrJz506qVKlC7969qVOnDkOHDiUjI0NvZNnb25tmzZoRGRmpm9UiT/369dmxYwdnz56ldevWNGzYkIkTJ+Lm5mZwPAVZvHgxaWlpzJgxQ/d7dXV11Ss3eemll1i+fDm//PILfn5+/Pbbb6xbt0635sbTUCnFXcFeDiUnJ2NnZ0dSUtIjv3IorxRFYdB/D7Lr3C383O1Y/c5LGBvpfz6atHcSq8+tpneN3kx+aXKRjpseGcml/q+itrOj5r69qNTl7DPXpd2wqCugQL/F4NOzrCMSQogyl5GRQUxMDF5eXgbXuZY2jUbDsWPHaNiwIUZGRsV67O3bt9O7d28uXryIg4NDsR5blK2ivsbLWVYjnoRKpWJm3/rYmBsTcSWJH8Iv5GuTV3IRdimMLE3RFggxr1MHlaUluUlJZJ47V6wxl4qqL8PL72t//mM0JF0t23iEEEI8MzZu3MiECRMkQX6BSZL8gnC1s2BKD+2UKd9uO8fJq0l6jzeu1BhnS2fuZd9j15XCrzp9kMrEBMuGDQFIO1TO6pLztP0EXBtAxl1Y9zY8xRXJQgghnh+zZs3i44/LzwxIy5Yte+IFOkTBJEl+gfRsUJlAHxdychU+WHmczJx/LtJTq9S84mX4MtW6uuTydvFeHmNT6LMATCwhZifse/plLIUQQojS1r17d90c0Q/fZAawJyNJ8gtEpVIxvVc9KlibcvZGCl+F6a8qlJck74jbwb2se0U6pm5RkUOHin2C9lJToQYEztD+vG0KxEeUbTxCCCGEgWxsbAxeoEM8WjlYAUIUJydrMz7v5cvw/x3hl50X6VSnEk2qatdAr+1Ym2p21biYdJGtsVvpVaPXY49nXr8+KlNTNImJZMVcwqya12P3eSY1Ggxnt8CZP+E/ncH0KaYAMrHU3kyt/rnp7luD6f2fTfIet7y/3QqMzLSzb7xoLB3BsVpZRyGEEELoSJL8Aurs40KfRu6sPnqFD1dFsDGkNVZmxqhUKrpU68J3x75jY8zGIiXJalNTLPz8SDt0iLTDh8pvkqxSQffv4OcISL4CORlPcbDEYgvrhTJ4A3i1LusohBBCCECS5BfWZ93rsu/CLWIT05jxVzTTevoCEOQVxHfHvuPg9YPcTLtJRcuKjz2WZdMm2iT50GEc+vcv6dBLjpUTjDwASVce37YwSi7kpENWGmSlQnaq9t+sNMhKub8t7+e0h+6nQk5m8Z1PeZF+FzKT4GK4JMlCCCGeGZIkv6BszU2Y1c+P4AUHWLr/Mp3rutCmZkU8bDzwq+hHxM0I1p5fSy/vx48mZ/lq17lPObifhNQEVMVQLuBo7oiRunjnvCwSM2twrl36/b7IDs6HjR/B9ciyjkQIIYTQkST5BdbKuwKDW3qyeF8sY3+LZPOYNthZmvCK1ytE3Izgu2Pf8d2x7x57HLMshYVqML5xkwEL2nPT/umT5LpOdVnRZUWxJNziGefqp/03XpJkIYQQzw6Z3eIFNz6oDl4VrLienMHkP04B2oVFqtpWxUhlVKRbjpkxF121yWy9OFWR9yvsBhCVGEXU7agye15EKXKuC6gg5TqkJJR1NEII8URUKhXr1q0r6zDKTNWqVfn6669195+H50NGkl9wFqZGzO7nR7+f9rLm2FU6+7gQWM+FP3r9YdBxEm7OIXH+AsabdefbQdOfKqYPwj8gLDaM7Ze34+MkE6A/98yswckbEs9pSy68O5Z1REKIcmjIkCEsXrwYAGNjY9zd3enXrx9Tpkx55pfXfhoPnjeAo6MjTZs2ZebMmdSvX7/M4oqPjy/3qxXKSLKgsacDI/yrA/CvtSe4lWL4xWP/zJf89IuKtPVoC0B4XPhTH0uUE673/5BLyYUQ4ikEBgYSHx/PxYsXmTt3Lj///DOfffZZWYdV4vLOOz4+nm3btmFsbEzXrl3LNCYXFxfMzMzKNIanJUmyAGBMxxrUdrEhMTWLCWtOGLwwiEWjRqBWk335Mtk3bjxVLG0qt0GtUnPmzhmuplx9qmOJcsJFO7sK10+UbRxCiHLNzMwMFxcXPDw86NmzJx07diQsLEz3eGJiIq+99hqVK1fG0tISX19ffv31V71jtG3blpCQEMaOHYujoyMuLi5MmjRJr825c+do06YN5ubm1K1bV6+PPCdOnKB9+/ZYWFjg5OTE8OHDSUlJ0T0+ZMgQevbsyeeff06lSpWwt7dnypQp5OTk8PHHH+Po6Ii7uzsLFy4s8nm7uLjQoEEDxo8fT1xcHDdv3tS1GTduHDVr1sTS0pJq1arx73//m+zsbN3jERERtGvXDhsbG2xtbWncuDGHH1hNd/fu3bRu3RoLCws8PDwICQkhNTW10JgeLLe4dOkSKpWKNWvW0K5dOywtLfHz82Pfvn16+xjaR0mTJFkAYGZsxFf9G2BipGJL1A3WHDUsOTWyscG8tnZWiKddotre3J6Gzg0BGU1+YbjcH0mWGS6EeGZlZ2oKveVka4reNqtobZ/WyZMn2bt3L6amprptGRkZNG7cmD///JOTJ08yfPhwXn/9dQ4ePKi37+LFi7GysuLAgQPMnDmTKVOm6BLh3NxcevfujampKQcOHOCnn35i3LhxevunpqYSEBCAg4MDhw4dYtWqVWzdupVRo0bptfv777+5du0aO3fu5KuvvuKzzz6ja9euODg4cODAAd5++21GjBjBlStFn5o0JSWFpUuX4u3tjZOTk267jY0NixYtIioqim+++Yb58+czd+5c3ePBwcG4u7tz6NAhjhw5wvjx4zExMQHgwoULBAYG0qdPHyIjIwkNDWX37t35zudx/vWvf/HRRx9x/PhxatasyWuvvUZOTk6x9lGcVEq5XUu4+CQnJ2NnZ0dSUhK2trZlHU6Zmrf9PLM2n8HGzJjN77fBzd6iyPvemDGD24uXYD/gVVwf+tRtqMWnFjP78GyauzZnQecFT3UsUQ6k3ITZ3oAKPrmirVMWQpSJjIwMYmJi8PLy0qvlnff234Xu41nPia6j/HT3fw4JJycrt8C2bjXs6fVhI939/3y0i4yU7HztRv7U/rGxajQajh07RsOGDRk6dChLly7F3NycnJwcMjMzUavVrFy5kj59+hR6jK5du1K7dm1mz54NaEeSNRoNu3bt0rVp1qwZ7du354svvmDLli106dKF2NhY3NzcANi0aRNBQUGsXbuWnj17Mn/+fMaNG0dcXBxWVtoVXDdu3Ei3bt24du0alSpVYsiQIYSHh3Px4kXUau2YZe3atXF2dmbnzp2687Ozs2PBggUMGDCgwPiHDBmiO2/QJuiurq5s2LCBRo0aFbgPwOzZs1mxYoVutNjW1pbvvvuOwYMH52s7bNgwjIyM+Pnnn3Xbdu/ejb+/P6mpqZibm1O1alXGjBnDmDFjAO1Ict7zcenSJby8vFiwYAFDhw4FICoqCh8fH6Kjo6ldu3aR+iguhb3GH/bEF+5lZWURExND9erVMTaW6/+eFyPaVGNr9A2OXb7LS18U/gexIC9dU/FvIOLPcFpm/PlUcVR3tQJ7OHL9CMlZydiavtgfXp571hXBxhXuxcONU1CleVlHJIQoh9q1a8ePP/5Iamoqc+fOxdjYWC9B1mg0fP7556xcuZKrV6+SlZVFZmYmlpaWesd5+II3V1dXEhK0s+9ER0fj4eGhS5ABWrZsqdc+OjoaPz8/XYIM0KpVK3Jzczlz5gyVKlUCwMfHR5cgA1SqVIl69erp7hsZGeHk5KTr+3HnDXDnzh1++OEHgoKCOHjwIJ6engCEhoby7bffcuHCBVJSUsjJydEbGPzggw8YNmwY//vf/+jYsSP9+vWjenXt9UoRERFERkaybNkyXXtFUcjNzSUmJoY6deo8Mr48Dz6vrq6uACQkJFC7du1i66M4GZzdpqWl8d577+mupDx79izVqlXjvffeo3LlyowfP77YgxSlx9hIzZx+fvT6YS9J6fk/2T/KSadqAHjeu4FdZgpJTzEaeCHeAksLZzBL4Msd65jYLhgz4zJYXESUHpf62iT5eqQkyUI8g4Z/41/oY6qHijffnFX46pkPT38/aPpLTxOWHisrK7y9vQH473//i5+fH//5z390o5ezZs3im2++4euvv8bX1xcrKyvGjBlDVlaW3nHyygz+iVlFbm7BI+NPo6B+nqTvB88bYMGCBdjZ2TF//nymTZvGvn37CA4OZvLkyQQEBGBnZ8eKFSuYM2eObp9JkyYxcOBA/vzzT/766y8+++wzVqxYQa9evUhJSWHEiBGEhITk67tKlSpPdL556yDknVtx9VGcDE6SP/nkEyIiIggPDycwMFC3vWPHjkyaNEmS5OdAtYrWHJjQgXsZOQbve+fMEjQXL/BnWxvM2nd4ov6zNbmsPXaVX07UI9fsb1ZHb2HrITfeal2Ngc2rYGkq31w8l1zrw7nNEB9R1pEIIQpgYlb0gYqSamsItVrNhAkT+OCDDxg4cCAWFhbs2bOHHj168H//93+ANkE7e/YsdevWLfJx69SpQ1xcHPHx8brR0P379+drs2jRIlJTU3WjyXv27EGtVlOrVq1iOsPCqVQq1Go16enpAOzduxdPT0/+9a9/6drExsbm269mzZrUrFmT999/n9dee42FCxfSq1cvGjVqRFRUlF4iXtxKow9DGXzh3rp16/j+++95+eWX9VZD8/Hx4cKFC8UanCg75iZGVLQxM/hm26IZAManjj/R/hVtzHCzt2BkO29+6autizKxOcON5FSm/RlNqy/+5rtt5wwe5RblgMxwIYQoZv369cPIyIh58+YBUKNGDcLCwti7dy/R0dGMGDGCGwbOyNSxY0dq1qzJ4MGDiYiIYNeuXXrJJ2gvgjM3N2fw4MGcPHmS7du389577/H666/rSi2KU2ZmJtevX+f69etER0fz3nvvkZKSQrdu3QDteV++fJkVK1Zw4cIFvv32W9auXavbPz09nVGjRhEeHk5sbCx79uzh0KFDuhKHcePGsXfvXkaNGsXx48c5d+4c69evL9aL6kqjD0MZnCTfvHkTZ2fnfNtTU1NlCWFRrPMlN3VtgKO5I6gzGBEAnk6W3EnLZk7YWVp98Tdfbjr9RHM6i2dU3gwXCVGgkQ9BQoinZ2xszKhRo5g5cyapqal8+umnNGrUiICAANq2bYuLiws9e/Y06JhqtZq1a9eSnp5Os2bNGDZsGNOn6y+iZWlpyebNm7l9+zZNmzalb9++dOjQge+//74Yz+4fmzZtwtXVFVdXV5o3b66bUaNt27YAdO/enffff59Ro0bRoEED9u7dy7///W/d/kZGRiQmJjJo0CBq1qxJ//79CQoKYvLkyYC2lnjHjh2cPXuW1q1b07BhQyZOnKhXl/20SqMPQxk8u0WbNm3o168f7733HjY2NkRGRuLl5cV7773HuXPn2LRpU0nFWmJkdovik52QwPk2/qBSUfPAfoye8vn8bO9nrDm3hoG1B/Jxk3H8eSKeedvPc/aGdq5JcxM1A5pWYXibagbNxCGeQYoCX1SBzGR4ew+41Hv8PkKIYlfUK/+fBQ/ObmFkJNetiKIpsdktPv/8c4KCgoiKiiInJ4dvvvmGqKgo9u7dy44dO54qaFH+mTg7Y+rpSVZsLBd79kRt+nSr7fTNSaNNWg5G6uXEWu+kHvADkJal4W5aFpk5ufAXnAIumxtjb2GCiVH5nf7bpIoHLhMnYuruXtahlD6VSltyEbtHe/GeJMlCCCHKkMFJ8ssvv8zx48f54osv8PX1ZcuWLTRq1Ih9+/bh6+tbEjGKcsa6fXtuL1xIzrX4pz6WMaD9oiWHrFuXdNtNgIoPN04BBch6eHs5knXpEtHde3Ho9Q+4WatBqfZdydacIS9VRa0uw7Ipl/r3k2SpSxZCCFG2nmiagOrVqzN//vzijkU8J5w//ADbwACU7OKpK/3m6DccTThGL++e9PTuWWCb09eTWXv0Kkcv3y2WPsuCiaLhjVN/UvPuFVr8PJWFPq+w2rtt/vmSSpCTtSk9GlQutf7ycb1flxwvK+8JIYQoWwYnyZcvX37k42U1l514dqiMjbHw83t8wyKqbdeb5XuPs976HAPvXxj4sEZAo65w8moSG0/Ek60p/vksS0NUt7aYrf4Fz0PbGXbqTwKN73Dk1ZFozEq2LvDMjRR2nr3Jsv2XyzZJfnCGC0Up1Q8IQgghxIMMTpKrVq36yFksNJqnX2/9efKo9edVajA2MSpaWxUYmz5h2yyNtg6hwMZg8oRtc7I0POqyzwfnvjSobbYG5YEct2XFVphozDibcJ4rt6/h7uhWaNtaFayp1a6G7r6xqVr3etVk55KbW3gQBrU1UaO6X5agycklV1M8bY1M1Kh6zOPOr78SP2MmricO0ztjOpXnzMHUwyNf27zSCI0ml9ycRxzXWIX6fq12QW1vJGfQ4asdHIu5zelrydR2015wmavJRfOI46qNVRjdP65BbXMVNNkFfJCxrQEqG9QZqRjdjQWHqoW3zTuukQojY+1xlVyFnOJqq1ZhZHK/raIUusSuoW0Net/L34iC2z70vn+atuXxb4T6Sdoa+DdCu9KZUuA5qlT/LAShKMojf28PthWivDE4ST527Jje/ezsbI4dO8ZXX32VbwoUAb+MLvxixofXuv/vx7uKvNb9kn/tLXCtewBnTxv6fdJUd//XSQe4dzujwLYOrlYM/Oyf1c1WzTjMnfjUAtvaOJoz6PN/VkZaO+coCbH3Cmxrbm3C0Nn/rLj0x3cRXDt3t8C2xqZqRnzbVnd/088niT2ZqNdmKDMBWH/wNCN/+idJ3rowigtHbxZ4XNCuEJX3H2b4stOc3n+90LZvznoZCxtTAHb/do6TO64W2vb1aS2xraCdTWP/+oscDyv8G5YBE5vh5KZdffDIX5c49OelQtv2Hd+ESlVtcRw4kHMZnhw+eH+Ku9kXgYt6bXu+35DKtRwAiNp1jZ0rzhZ63C4j61PVtwIAZw/c4O8l0fnahKAdrV7zxzkmjGgMwMXjt9g8/2Shx20/qA51XtJOpn856jZ/ziu8TKLNgJr4ttVekBh/7i7r5h4rpOUSWtosplF8JDhU5eble/z2ReFTCjbtUpVm3bSrPd6+nsqKKQcLbdugUxVa9dFOVH/vdgb/+3RfoW3r+VfG/zXtpP8ZKdn89+Pdhbat3cKFDkO0ixHkZOU+8n1fvVFFAof/c/2G/I3Qetq/EQ8a+VN73c/P698IgIi/49i3pvD1CZ70b8SlyERSNJncMUvBxDj/a8i2ggXmVtqV0zLTcki+lV7ocW2czLGwNi30cSHKQlEndjM4SfYr4Gv0Jk2a4ObmxqxZs+jdu7ehhxRCFMDE3R0Olv4CPQdiEknP0mBhWsbTKV2PhLrdyzYGIV5AKkWl/aYlNwcTnm6GIiGeRWlpaUD+ZcEfZvA8yYU5f/48fn5+pKYWPMLwLCvJeZLlq9QnaFvA16MxyTG8+serGKuN2T5wG9am1oW2fdDz8FVqblYWCV9+QdKadQBYd+yI66TPMLG3KbZyC9CWQAR8s5PYO+nM7Feffk08Sr/cAuDQf1GHjceoVkcYGCrlFvI3In9bKbcwvK0BfyNycjTEXY4jR5ODq6srarX+tJrPUrmFRqMhOjqaOnXqyDzJ4rEURSEtLY2EhATs7e11y4oXxuAkOTk5OV+H8fHxTJo0idOnT3P8+HGDgy5rspjIs09RFLqt60Zsciyz/WcTUDWgrEMqdXdCV3J92jTIzsashjfu332HadWqxdrHD+HnmbnpDA2r2LP23VbFeuwiu7wf/hsANm7wYf6yECFEycvKyiImJobc3Gf7Iujc3Fzi4uLw8PDIl8wLURh7e3tcXFwe+wHO4HILe3v7fAdVFAUPDw9WrFhh6OGEKBKVSkU7j3YsOrWI8LjwFzJJdni1P2Y1anBldAiZ584T068/lWfPwtrfv9j66NfYg6+2nOXY5btEXUumrlsZfGis5AOo4N41SL0FVhVKPwYhXnCmpqbUqFGDrKxne+b5lJQUunTpwuHDh7G2ti7rcEQ5YGJiUuRvHQxOkrdv3653X61WU7FiRby9vTE2fqJpl4UokrYebVl0ahE7r+wkOzcbE/Wja4meR5aNGuK1ejVXR48h/dgx4t5+h4qjQ3AaPhxVMYyiVLQxI8DHhT9PxLP8YCzTepbBAkFmNuBYDW5f0NYlV2//+H2EEMVOrVY/88tSZ2VlERsbi6mp6TMfqyh/DP5f1d/fX+/WunVrateuLQmyKHENKjbA3sye5KxkjiccL+twyoyJszOeixdh/9oAUBRufv0NV0ePRpNSPNcDBDfXznW+7tg1UjNziuWYBpNFRYQQQpSxImW2v//+e5EP2L27XI0uSoaR2og27m34/cLv/H35b5q6NH38Ts8plakprp99hoWPD9cnT+Fe2FYyL76Kfe/eT70AR00Uhl27yO3UTPZ9cYqGHg7FFLUBYhQ4ZwV3N8FZu8c2t3qpJea1a5dCYEIIIV4URbpwr6jF8CqVqlwuJiIX7pUfW2O38n74+1S2rsxfvf+SSeqB9IgIroSMJufGjbIOpcwYOzvjvSNcXg9CvGDk/29Rkoo0kvysX90qXhwvub2EqdqUqylXuXD3At4O3mUdUpmz8PPDa/VvJM5fQM6d28VyzKycXP46eZ3cXIV2tZ2xtyzl+u/sDIhar/3Ztw+oC/9TlfzXJnISEsi+ehVTd/dSClAIIcTzTgqJRbliaWJJc9fm7Lq6i+1x2yVJvs+4QgUqfTK+WI95dsUx1h2/xpWmHnzRp36xHrtIZm+GlOswdAB4NCu0WdbFGDJOniTjxAlJkoUQQhSbJ0qSU1NT2bFjB5cvX843PUxISEixBCZEYdpVaceuq7sIjwvnrfpvlXU4z62BzT1Zd/wa649fY0KXOtial/JososvnL+uneHiEUmyuW89Mk6eJP3ESWyDgkoxQCGEEM8zg5PkY8eO8corr5CWlkZqaiqOjo7cunULS0tLnJ2dJUkWJc7fXTsvcOStSG6m3aSiZcUyjuj51LSqA97O1pxPSGH9sau83rJq6QbgWh/Ohz12hguLevW4C2ScPFkqYQkhhHgxGDwF3Pvvv0+3bt24c+cOFhYW7N+/n9jYWBo3bszs2bNLIkYh9DhbOuNbQTt/744rO8o4mueXSqXSTQe37MBlimkF+6JzuV/icf3RSbJ5Pe1rIePUKRS5fkIIIUQxMThJPn78OB9++CFqtRojIyMyMzPx8PBg5syZTJgwoSRiFCKfth5tAdget/3RDcVT6d3QHTNjNaev3+Po5bul27nL/YVMbkSBJrvQZmbVq6GysCA3NZWsmJhSCk4IIcTzzuAk2cTERDclnLOzM5cvXwbAzs6OuLi44o1OiEK082gHwP5r+0nLTivjaJ5fdpYmdPNzA2DZgdjS7dzBC0xtQJMJt84V2kxlbIx53boApJ84UVrRCSGEeM4ZnCQ3bNiQQ4cOAdrV9yZOnMiyZcsYM2YM9erVK/YAhSiIt703la0rk5Wbxb5r+8o6nOfawPslF39GxpOUVviIbrFTq/8ZTX5MyYXF/b89GSekLlkIIUTxMDhJ/vzzz3F1dQVg+vTpODg48M4773Dz5k1++eWXYg9QiIKoVCrdaLKUXJSshh721HG1JTMnl9VHr5Ru53lJ8mMu3jO/nySnn5SRZCGEEMXD4CS5SZMmtGunTU6cnZ3ZtGkTycnJHDlyBD8/v2IPUIjC5CXJO6/sRJNb/lZ6LC9UKpVuNHnZgdjSvYDPtWgX71n4apPkzOjTKA9NSymEEEI8CYOT5GnTphEjF8eIZ0DDSg2xMbXhTuYdIm5GlHU4z7WeDdywNDXiws1UDsYUz6p+RfLgDBePSM5NPD1R29qiZGWRef58KQUnhBDieWZwkrxq1Sq8vb156aWX+OGHH7h161ZJxCXEY5moTWjj3gaQkouSZmNuQo8GeRfwXS69jivWBrUJZCTB3cL7ValUWNTzASBd6pKFEEIUA4OT5IiICCIjI2nbti2zZ8/Gzc2NLl26sHz5ctLSZJYBUbrypoILjwsvyzBeCAObeQKw6eR1ElMyS6dTY1Nwrq39+fqj64118yVLXbIQQohiYHCSDODj48Pnn3/OxYsX2b59O1WrVmXMmDG4uLgUd3xCPNLLbi9jrDbmUvIlLiZdLOtwnmu+7nbUd7cjS5PLb0dK8QI+l/vXOjxuUZH7dckykiyEEKI4PFGS/CArKyssLCwwNTUlO7sUp4cSArA2taaZSzNARpNLw8Bm2gv4fj14mdzcUrqAr4gzXORNA5d57hy56eklHZUQQojn3BMlyTExMUyfPh0fHx+aNGnCsWPHmDx5MtevXy/u+IR4LN1UcJelLrmkdfNzw8bMmEuJaey7mFg6nepmuHh0GYWxiwtGFSqARkNG9OlSCEwIIcTzzOAkuUWLFnh7e/Pbb7/xxhtvEBsby7Zt2xg6dCh2dnYlEaMQj5RXlxxxM4LE9FJK3F5QVmbG9GxYGSjFFfgq3V+kKPkKpBU+s4b24r37i4qclJILIYQQT8fgJLlDhw6cOHGCY8eO8dFHH1G5cuWSiEuIInOxcqGOYx0UFHZe2VnW4Tz38uZM3nLqBgn3Mkq+Q3Nb7RLVAPGPnupPV5csF+8JIYR4SgYnydOnT6du3bolEYsQT0xW3ys9dVxtaVTFnpxchVWHS+kCviKWXFj43p/hQi7eE0II8ZSe+sI9IZ4FeSUX+67tIyOnFEY3X3ADm2ung/v14GU0pXEBn0vRVt7LW546KyYGzb17JR2VEEKI55gkyeK5UNuxNi5WLmRoMtgfv7+sw3nuda3viq25MVfupLPz3M2S7zAvSX7MDBfGjo6YuGkXPck4daqkoxJCCPEckyRZPBdUKhVt3dsCMhVcaTA3MaJPY3cAlpfGCnx55RaJ5yDr0YsWmd8vuUg/IXXJQgghnlyZJsk7d+6kW7duuLm5oVKpWLdund7jQ4YMQaVS6d0CAwP12ty+fZvg4GBsbW2xt7dn6NChpKSklOJZiGdFuyrauuTwuHByldyyDeYFEHz/Ar6/TycQn1TC8xLbuICVMyi5kBD1yKYWvnkzXMhIshBCiCdn/CQ73b17l4MHD5KQkEBurn4yMmjQoCIfJzU1FT8/P95880169+5dYJvAwEAWLlyou29mZqb3eHBwMPHx8YSFhZGdnc0bb7zB8OHDWb58uQFnJJ4HTSs1xdrEmsSMRP578r84WzqXegxGKiOauTSjomXFUu+7tHk729DMy5GDMbcJPRTHmI41S7ZDF1+4sE07w4V7k0Kb6ZanlpFkIYQQT8HgJPmPP/4gODiYlJQUbG1tUalUusdUKpVBSXJQUBBBQUGPbGNmZlboctfR0dFs2rSJQ4cO0aSJ9j/N7777jldeeYXZs2fjdr82UbwYTIxMeLnyy2y6tIlvjn5TZnEYq43p5NmJgbUH4lfRT+898rwJbl5FlySPaueNsVEJfjnlWl+bJD/24j0fUKnIvnaNnNu3MXZ0LLmYhBBCPLcMTpI//PBD3nzzTT7//HMsLS1LIiY94eHhODs74+DgQPv27Zk2bRpOTk4A7Nu3D3t7e12CDNCxY0fUajUHDhygV69eBR4zMzOTzMxM3f3k5OSSPQlRat72e5uc3BzSNWWzLPHt9NtE347mr5i/+CvmL+o61WVg7YEEegViZmT2+AOUM4H1XHC0MiU+KYPNp27QpmaFJzqOiZEacxOjRzdyKdo0cEbW1ph6eZF18SIZJ05g7e//RDEJIYR4sRmcJF+9epWQkJBSSZADAwPp3bs3Xl5eXLhwgQkTJhAUFMS+ffswMjLi+vXrODvrf6VubGyMo6PjI5fInjFjBpMnTy7p8EUZqG5fnbnt5pZpDFGJUSyPXs5fMX8RlRjFp3s+Zc7hOfSt2Zf+tfrjYlXwNyPlkZmxEX0bu/PLzouMXH70qY5lY2aMs60ZlWzNqWRrrv3Zxvz+fTPczGvgBnDjFGhywKjwP1/m9XzIuniR9BMnJUkWQgjxRAxOkgMCAjh8+DDVqlUriXj0DBgwQPezr68v9evXp3r16oSHh9OhQ4cnPu4nn3zCBx98oLufnJyMh4fHU8UqRJ66TnWZ9vI0PmjyAWvOrSH0TCjXU68z/8R8/nvyv7Sv0p6BtQfSuFLj56IUY1BLT9YcvcKtlKynOs69zBzu3czhws3UAh9XkcsJM3OsczJ4a+6vZDjUxNlGm0AH+Ljg52Gva2tRz5fk3/+QumQhhBBPzOAkuUuXLnz88cdERUXh6+uLiYmJ3uPdu3cvtuAeVq1aNSpUqMD58+fp0KEDLi4uJCQk6LXJycnh9u3bhdYxg7bO+eELAIUobo7mjgzzHcYQnyFsj9vO8ujlHL5xmLDYMMJiw6jlUIvXar/GK9VewcLYoqzDfWLuDpYcnNCR7Nwnn1EkKyeXm/cyuZGcScK9DG4kZ3AjOZMbyRkkJGdy414G15MyiFaq0FR1FsvbUYTd+qfWeNWRKxyc0EH3oeOf5alPoijKc/FhRAghROkyOEl+6623AJgyZUq+x1QqFRqN5umjKsSVK1dITEzE1dUVgJYtW3L37l2OHDlC48aNAfj777/Jzc2lefPmJRaHEIbIu5Cvk2cnztw+w6+nf+XPi39y5s4ZJu2bxFdHvqJPjT68WvtVKltXLutwn4harcJM/Zia4kcwMzbCxtyEahWtC22jKApZf4TB0bN87JdJG28/ridnMDfsLDfvZRKflIGbvfbDhnmdOmBsjCYxkZzr1zG5/zdDCCGEKCqDk+SHp3x7GikpKZw/f153PyYmhuPHj+Po6IijoyOTJ0+mT58+uLi4cOHCBcaOHYu3tzcBAQEA1KlTh8DAQN566y1++uknsrOzGTVqFAMGDJCZLcQzqZZjLSa9NIn3G7/P2nNrWXFmBVdTrrLw1EIWRy2mXoV6mKpNyyQ2MyMz3G3c8bT1pIpNFTxsPfCw9sDEyOTxO5cClUqFmXsDOAruGedwv7+YyR8R1zh9/R5R15J1SbLa3ByzGjXIjI4m/cQJSZKFEEIY7InmSS4uhw8fpl27drr7eXXCgwcP5scffyQyMpLFixdz9+5d3Nzc6Ny5M1OnTtUrlVi2bBmjRo2iQ4cOqNVq+vTpw7ffflvq5yKEIezM7BhSbwiv132dnVd2svz0cvbH7yfy5qOnNyttapUaVytXqthUoYptFTxsPHRJtLuNO6ZGpZzQPzjDhaKASkVdV1ttkhyfTMe6lXRNLerVIzM6mowTJ7Ht3Ll04xRCCFHuPVGSvGPHDmbPnk10dDQAdevW5eOPP6Z169YGHadt27YoilLo45s3b37sMRwdHWXhEFFuGamNaFelHe2qtCMmKYazd86WWSxp2WnE3YsjNjlW929aThpXU65yNeUq++L36bVXocLVyhUPWw88bTyp4VADHycfajrWLLnp7pzrgNoY0u9A0hWw96Cumy1rjl0l6pr+VI7m9erBqlWkn5SL94QQQhjO4CR56dKlvPHGG/Tu3ZuQkBAA9uzZQ4cOHVi0aBEDBw4s9iCFeBF42XnhZedV1mHoKIpCYkaiLmG+nHyZy/cu6/5NzU7lWuo1rqVe40D8Ad1+xipjajjUoK5TXXwq+ODj5EMN+xrFU7ZhbAYVa8ONk9pFRew9qOtqC0BUvH6S/ODy1EpuLip1CS50IoQQ4rmjUh41lFuAOnXqMHz4cN5//3297V999RXz58/XjS6XJ8nJydjZ2ZGUlIStrW1ZhyPEM09RFG5n3CbuXhyX713mUtIlTt8+zanEU9zOuJ2vvYnahJoONfFx8tElztXsq2GifoLEee07ELEc2n4CbcdzJzWLhlPDAIic1Blbc+0xlexszjRpipKZSbW/NmLm9ex8ABFCFA/5/1uUJINHki9evEi3bt3ybe/evTsTJkwolqCEEM82lUqFk4UTThZONHBuoNuuKAo30m5w6tYpTiX+c0vKTNL9zP2KEjMjM2o51sLHyQdve++iJ8wWxmBtBVfC4VwtAFwcM7l+24bT8fdo5qWdGk5lYoJ57dqkR0SQcfKUJMlCCCEMYnCS7OHhwbZt2/D29tbbvnXrVlmQQ4gXnEqlwsXKBRcrFzp4ahf8URSFqylXdUly1K0oTiWeIiU7hcibkU92sWJFJ9Bchb0TATCt5AS3PyLqWpIuSQYw9/W9nySfwK5b12I5RyGEEC8Gg5PkDz/8kJCQEI4fP85LL70EaGuSFy1axDfffFPsAQohyjeVSoW7jTvuNu4EVNVO35ir5BJ3L0434hybHItCESu/NDlwYZv25+rt2XvjEFm5iahMbhdYl3wHSD9xshjPSAghxIvA4CT5nXfewcXFhTlz5rBy5UpAW6ccGhpKjx49ij1AIcTzR61S42nriaetJ69Ue8XwA3xdH+7GQkAw/5eTRsTNCIwsLhMVr19SYV7v/sV7UVEoOTmojMt01kshhBDlyBP9j9GrVy969epV3LEIIUTRuN5Pkq9H0qBiA22SbBnL2espZGtyMTHSzmRh6uWF2sqK3NRUMi9cwLxWrTIOXAghRHkhcyIJIcofFz/tv9dP4Oes/dnU8jJZmlwu3EzRNVOp1Zj7+ACQcULmSxZCCFF0RUqSHR0duXXrFgAODg66ZaMLugkhRIlz8dX+Gx+JX8X7CbNpPKgz8i8qcn++ZKlLFkIIYYgilVvMnTsXGxsb3c8qlapEgxJCiEdyvb889a2zOJvYUNm6MldTrmJkHkfUtTr0bvRPUwtfbUKdcVKSZCGEEEVXpCR58ODBup+HDBlSUrEIIUTR2LiCZQVIuwU3ovCr6KdNki1j881wYV7vfpJ89iy5WVmoTU3LImIhhBDljME1yUZGRiQkJOTbnpiYiJGRUbEEJYQQj6RS/TOafD1St6CJdoaLZB5cSNSkshtG9vaQnU3m6dOlH6sQQohyyeAkubBVrDMzMzGVERohRGnJq0u+P8MFgJFFLHfTMolPytA1U6lUmN8vuUiXi/eEEEIUUZGngPv2228B7X84CxYswNraWveYRqNh586d1K5du/gjFEKIgrjcH0mOj6SGQw0sjC1IJx21WQJR15Jxs7fQNbXwrUfqrl1kyMV7QgghiqjISfLcuXMB7UjyTz/9pFdaYWpqStWqVfnpp5+KP0IhhCiI6/1ZLW6cwhgV9SvU58D1AxhZaOuSO9atpGuaV5ecflJGkoUQQhRNkZPkmJgYANq1a8eaNWtwcHAosaCEEOKxHKuBiSVkp0Hiefyc/f5Jkq/lX54aIOvCRXJTU1FbWZVFxEIIIcoRg2uSt2/fLgmyEKLsqY2gkjb5Jf7BuuTL+Wa4MK5YEWMXF1AUMqKiSjlQIYQQ5ZHBSXKfPn348ssv822fOXMm/fr1K5aghBCiSB6Y4aJ+Re3ParNbxCUlkJyRrdfUvJ525T1ZVEQIIURRFLncIs/OnTuZNGlSvu1BQUHMmTOnOGISQoiiyZvh4lwYdjYuVDd14ELWHQKt1nJn62VsnSx1TS3sUkkBMsLXQp204ovBuhK4NwX7Ktqp6YQQQjwXDE6SU1JSCpzqzcTEhOTk5AL2EEKIEuLaQPvvzWjYPIEGFRy5YGNNLau9eB7+S6+peaIZ4ET6qWjYvKP4Y7F2AY9m4NFce3OtD8Zmxd+PEEKIUmFwkuzr60toaCgTJ07U275ixQrq1q1bbIEJIcRjufpBh4mQoF0kxC87gdWZF9lq5kwbu2Y0qvLP9RMW3jkQvp/sVGNyqvfC2NKkGAJQ4PZFiI+AlOsQ/bv2BmBkBm4NH0icm4G1czH0KYQQojQYnCT/+9//pnfv3ly4cIH27dsDsG3bNn799VdWrVpV7AEKIUShVCpo/aHurl/SRVjXg0tmGj7NGsXGPm11jxkBJv8LIDv2Mhleb2H9cqviiyM7Ha4dg7gDEHdQ+29aIsTt197yOHj9kzB7NAfnOtoLEIUQQjxzDE6Su3Xrxrp16/j888/57bffsLCwoH79+mzduhV/f/+SiFEIIYqkqm1VbExsuZedzIW7Z8nKaYOp8T/XJ1v41NMmySdPFG+SbGIBni9pbwDK/RHmuAP/JM4J0XAnRnuLXKFtZ2yh3fdpqI1ApX7gZqT98JB3P9/jD96eooZabQLmdtqbhf39n+31f37wMVMbUBt8rbgQQpQZg5NkgC5dutClS5fijkUIIZ6KWqWmoXMDdl7dSa5ZDBduplDH1Vb3uLmvL8kbN5b8DBcqFThV194aDNRuS78LVw//M9J85TBkpUBOesnG8qxQqcHMVps0WzhAjQBoORLMbR+7qxBClIUnSpIBsrKySEhIIDc3V297lSpVnjooIYR4Ug3uJ8lGlpeJupaslyTnLSqScaIMVt6zsAfvjtobQK4G7lyC3JwnP6aigJL7wE1z/9/723M1hT+eq3m689FkQkYyZNyFjCTthwC9n5P+uZ+Toe03436bO5e05SmH5kPrj6DpULnIUQjxzDE4ST537hxvvvkme/fu1duuKAoqlQqN5in/8AohxFNo4NwAQLc8dZ8HHjOvWxfUanISEsi+kYBJpTK8kE5tpB1pfhFkZ+gnzYkXYNdsSDwPmz+B/T9CuwlQv7/UaAshnhkGJ8lDhgzB2NiYDRs24OrqikrmBRVCPEN8nHxQoUZtksTxa5eAf2bdUVtaYla9OpnnzpFx8gQmlTqUWZwvFBNz7c2mkva+RzPw7QfHl0L4F5B0Gda9DXu/1c5WUjNQ5pwWQpQ5g5Pk48ePc+TIEWrXrl0S8QghxFOxNLGkqk0NYu6d4VzSCRQlSO/DvLmvL5nnzpF+4gQ2HSRJLjNGxtB4CPj2h4M/w+65kBAFvw4AjxbQaTJUaVHWUQohXmAGX2pct25dbt26VRKxCCFEsWjm1giATOOLXEvK0Hssb3nqjJOnSj0uUQBTS3j5fRgdAa3GgLG5dtq8/wbA8gFwI6qsIxRCvKAMTpK//PJLxo4dS3h4OImJiSQnJ+vdhBCirDWu1BAAIwvtxXsPsvDVLmWdceIEiqKUemyiEBYO2tHjkGPQaLB2Kruzf8GPL8Hat+Hu5bKOUAjxglEpBv4vob4/z+XDtcjl+cK95ORk7OzsSEpKwtZWpiMSoryLT4mn8+rOKIqaIW5L+aizr+6x3KwszjRuAtnZVA/bgqmHRxlGKgp16xz8PRWi1mvvG5lC02HaxWOsKpRtbOKZIf9/i5JkcE3y9u3bSyIOIYQoNi5WLlgbOZGiSeTw9QjgnyRZbWqKea1aZJw8ScaJE5IkP6sq1ID+S+DqEdg6CWJ2wv4f4Oj/oPX78PIHcnGfEKJEGZwky6p6QohnnUqlopZDPY7c2sGF5Py1x+a+9cg4eZL0EyexfeWVMohQFFnlxjDod7jwtzZZvh4J26aAY3Xw6VnW0QkhnmMGJ8k7d+585ONt2rR54mCEEKK4vFS5CUdu7SCFCySlZ2NnYaJ7zKKeL3dZUTaLigjDqVTg3QGqtYONH8Hh/8DJ1ZIkCyFKlMFJctu2bfNte7A+uTzWJAshnj8tKzfmuwhQW8QSfS2JFtX/qWM1r3d/5b2oKBSNBpWRLGBRLqjV0HiwNkk+twUyU8DMuqyjEkI8pwye3eLOnTt6t4SEBDZt2kTTpk3ZsmVLScQohBAGq+1Y+//bu+/4KOr8j+Ov2WTTQyqEBAi9SxMkdFDQgKeCIKKiAiqnFJUTTuHnKXigKJZTPBTFAiLqiQgqIqj0DoKAAgKBIDWUhHRSd35/RKKhaMpuJuX9fDz2YXZmdua9w4zzyeQ73y8Gdmzu6Ww48kuBeZ7162F4e+NITycrNtaihFIs1VtCUN28oa4PLLM6jYhUYEUukgMCAgq8QkNDuf766/O7hhMRKQvsbnaqeTQAYGvcjwXmGe7ueUNUA+d/+rnUs0kJGMbvzSx2L7IyiYhUcEUukq8kLCyMffv2OWt1IiIl1jQor1eL2NRLH97zvtDkQu2Sy5/mt+b998B3kJVmbRYRqbCK3CZ5165dBd6bpsnJkyd5/vnnad26tbNyiYiUWNfIdqw69SkpZgxZOQ483H+/L+D126Ai53/WneRy50KTi3OxsH8ZXNXf6kQiUgEVuUhu3bo1hmFcMlJVhw4deO+995wWTESkpK6rcw2Tt4LN8xS7TpykXWSN/HneLfLuJGfu3YuZlYXh4WFVTCmqC00u1v0Hdi9UkSwiLlHkIjn2oodcbDYbVatWxcvLy2mhREScIdQnFLujGtm203x/aGuBItkeGYmtShUcycmkbd6MZ4MGxdqGe0iICmwrNOuXVyRfaHLh4Wt1IhGpYApVJAcHB7N//35CQ0N55plneO211/D393d1NhGREgv3asKRrNP8eHoH0C9/umEYeF/VnLQNGzk6/O/FXr+9diR1F3yOm5+KtFIV3gqC6sC5w2pyISIuUagH97KyskhOTgZgzpw5ZGRkuDSUiIizXBXSEoBf0/ZcMi9gwABsfn4YdnuxXhgG2b8e4cz010r7a4lh/P4A355FlkYRkYqpUHeSO3bsSL9+/Wjbti2mafLII4/g7e192WXVLllEypLuta9hyUlI4xDZudnY3X4feS/gb38j4G9/K/a6U9ev5+j9D3Duw3kE3Hwz3r89DCil5EKTi/3fqsmFiDhdoe4kf/jhh9x4442kpqZiGAZJSUmXDCpy4SUiUpb0qHsVZq4n2DLZeOzSu8kl4de5M1VuvhkcDk4+PREzJ8ep65e/cKHJRc75vCYXIiJOVKg7yWFhYTz//PMA1K1bl7lz5xISEuLSYCIizuDj4YGXox6ZbntZEbuFbrVbOXX9YeOfIG3NGjL37iVhzgeE3H+fU9cvf8Iw8u4mr381r8mF2iWLiBMVeTCR2NhYFcgiUq7U8G4CwM7TO52+bveQEKr9Ntromf/+l6xjx52+DfkTF0bfu9DkQkTESZw24p6ISFnVMrQ1AEfPO7e5xQUB/W/Fp317zPPnifv3M5f0Iy8uFN769yYXB761Oo2IVCAqkkWkwutRpy2maZDJGc6eP+v09RuGQfVJkzDsdtLWrCXlm2+cvg25ggtNLiBvYBERESdRkSwiFV7bWuE4MsMA2HBsm0u24VmvLiEPPQhA3LPPkZuU5JLtyGWoyYWIuICKZBGp8AJ9PPDKrQfA6l+3umw7IcOH41GvHrnx8Zx+6WWXbUcuEt4aAmuryYWIOFWhiuTk5ORCv0REyqJI32YA/BTv/If3LrB5eBD+72cASJw/n/QffnDZtuQP/jiwyO5FlkYRkYqjUEVyYGAgQUFBf/q6sIyISFnUplprAOIyYsjKzXLZdnzatSNw4EAATk6chCPLdduSP7jQ5OKAmlyIiHMUqp/klStXujqHiIhLRdVqxKcnfLG5p7Enfg+tfyuaXaHauLGkrFxJ1sGDxL/zDlVHjnTZtuQ3F5pcJP6aVyhfuLMsIlJMhSqSu3fv7uocIiIu1TwigNzztbH572HbqR9dWiS7BQQQNmE8J8aOI/7NmVTp3QfPenVdtj3htyYX/WD9a3lNLlQki0gJFevBvbVr13L33XfTqVMnjh/P6zh/7ty5rFu3zqnhREScpWaQN/bsvELVVT1c/FGVG2/Et2tXzOxs4iZNUt/JpeFCV3AHvoWsdEujiEj5V+QiecGCBURHR+Pt7c327dvJzMwEICkpieeee87pAUVEnMEwDOr89vDenoRdLi9aDcOg+sSnMby9Sd+yhaTP1Yevy0W0yWtykZ2uXi5EpMSKXCRPmTKFmTNnMmvWLOx2e/70zp07s337dqeGExFxpjbVW2CabqTmnON4quuHj/aoWZOqo0cDcHraNHISEly+zUrtQpMLgD2LrEwiIhVAkYvkffv20a1bt0umBwQEkJiY6IxMIiIu0SIiFEdGBAA7z7iuK7g/Ch5yL55Nm5KblMSp558vlW1WaheaXOxfpiYXIlIiRS6Sq1evTkxMzCXT161bR7169Yq0rjVr1nDzzTcTERGBYRgsWrSowHzTNHn66acJDw/H29ubXr16ceDAgQLLJCQkMHjwYKpUqUJgYCD3338/qampRf1aIlIJNIuoQm56bQB2nN5RKts03N3z+k622Uj+8itS168vle1WWhFtIDBSTS5EpMSKXCQPHz6cRx99lM2bN2MYBidOnGDevHmMGzeOESNGFGldaWlptGrVihkzZlx2/rRp05g+fTozZ85k8+bN+Pr6Eh0dTUZGRv4ygwcPZvfu3Xz33XcsXryYNWvW8Pe//72oX0tEKoGG1fwhM69I3hr3Y6lt17tFC4IGDwYgbtIzOM6fL7VtVzqG8fvdZDW5EJESMMwiPr1imibPPfccU6dOJT09709Znp6ejBs3jsmTJxc/iGGwcOFC+vXrl7+diIgIxo4dy7hx44C8hwPDwsKYPXs2d9xxB3v37qVZs2Zs3bqVdu3aAbB06VJuvPFGjh07RkRERKG2nZycTEBAAElJSVSpUqXY30FEyr4bpn/FyYD/w8DGprs24mP3KZXt5qamceimm8iJiyNk+HCqjX2sVLZbKR3fDrOuBbsP/PMgeJTOv7GUPl2/xZUKdSd5165dOBwOIK+YffLJJ0lISODnn39m06ZNnDlzpkQF8uXExsYSFxdHr1698qcFBAQQFRXFxo0bAdi4cSOBgYH5BTJAr169sNlsbN68+YrrzszM1HDaIpVUi+qROLICMXHw09mfSm27bn6+VH/6KQDi33+fjH37S23blY6aXIiIExSqSG7Tpg1nz54FoF69esTHx+Ph4UGzZs1o3749fn5+Tg8WFxcHQFhYWIHpYWFh+fPi4uKoVq1agfnu7u4EBwfnL3M5U6dOJSAgIP9Vq1YtJ6cXkbKqWUQVcs+XbrvkC/yvuw7/66+HnBzinn4a87ebD+JkanIhIk5QqCI5MDCQ2NhYAA4fPpx/V7m8mjBhAklJSfmvo0ePWh1JREpJs/Aq5J6PBGDHmR2lvv2wfz2JzdeX8zt3cu6TT0p9+5XGha7g1MuFiBRToYalHjBgAN27dyc8PBzDMGjXrh1ubm6XXfbQoUNOCVa9enUATp06RXh4eP70U6dO0bp16/xlTp8+XeBzOTk5JCQk5H/+cjw9PfH09HRKThEpX/KK5At3knfiMB3YjGINPlos9rAwqj72D05NnsKZl1+BXAfYjGKty7N+fXw7dHBywgoi4uq8JheJRyDmO2jW1+pEIlLOFKpIfvvtt+nfvz8xMTE88sgjDB8+HH9/f5cGq1u3LtWrV2f58uX5RXFycjKbN2/O70WjY8eOJCYmsm3bNtq2bQvAihUrcDgcREVFuTSfiJRPAT52qnvVI9lhJzU7hdikWOoH1i/VDEF33EHyl19xfudOTj37bInWVWvWLPy6dnFSsgrkQpOLDdNh9yIVySJSZIUqkgF69+4NwLZt23j00UedUiSnpqYW6HM5NjaWHTt2EBwcTGRkJGPGjGHKlCk0bNiQunXr8tRTTxEREZHfA0bTpk3p3bs3w4cPZ+bMmWRnZzN69GjuuOOOQvdsISKVT/OIINal1sLd9xA7Tu8o9SLZcHMj4sVpnJ3xBo7MzGKtI/vkCTJ27uLEhAnU+2IR7iEhTk5ZATTvl1ck71+a1+RCvVyISBEUuQu4C2JiYjh48CDdunXD29sb0zQxjKL9yXDVqlVce+21l0wfMmQIs2fPxjRNJk6cyNtvv01iYiJdunThjTfeoFGjRvnLJiQkMHr0aL766itsNhsDBgxg+vTpRXqYUF3IiFQu//luP2/u+i+eoSvp16Afkzs7t3ee0uDIyODwwNvJPHAA365dqfXWTAxb6TUbKRdME15rmdfk4vYPdDe5AtL1W1ypyEVyQkICAwcOZOXKlRiGwYEDB6hXrx733XcfQUFBvPzyy67K6jI6yUQql2W74xi5cB4+tWZTp0odvrr1K6sjFUvG/v0cHng7ZmYm1cY/QcjQoVZHKnu+/RdseB2a94eB71udRpxM129xpSLfdhgzZgx2u50jR47g4/P7n64GDRrE0qVLnRpORMQV8h7ey+v68XDyYRIzEq0NVExejRpR7YnHATj98itk7NljcaIyqPmtef/dvwyyNdKhiBReodskX/Dtt9+ybNkyatasWWB6w4YN+fXXX50WTETEVWoGeeNvDyA3sypunmd4bPVjBHoGFmtdzUKaMaT5EOw2u3NDFlLQnXeStn4DqcuXc3zsOOou+Aybj9re5vtjLxcHvoNmt1idSETKiSIXyWlpaQXuIF+QkJCgbtVEpFwwDINm4VXYkVYfN88zbI3bWux1fffrd6w6uooXu71IuF/4Xy7vbIZhED5lMrE//URWbCxxzz1HxJQppZ6jzDKMvLbIG16H3QtVJItIoRW5SO7atSsffPBB/jDUhmHgcDiYNm3aZR/CExEpi5pFVGHzxmg61WpN76tCi7WO1OxU3v3pXXae2cnAxQN5tvOzdK/V3clJ/5p7UBAR06ZxZNgwkj5bgF+XLlT5rUciAZrdmlckX2hyYfe2OpGIlANFLpKnTZtGz549+eGHH8jKyuLxxx9n9+7dJCQksH79eldkFBFxumbhVcDhzfmEqxnUpGOx1xNdJ5pxq8exO343o1eMZljzYTx89cOl3vzCt0MUIcOHE//225x86mm8W7TAXqNGqWYos2pcDQGRkKQmFyJSeEV+cO+qq65i//79dOnShb59+5KWlkb//v358ccfqV+/dPsaFREprmYReU/C7zmRTDF7wgSgpn9NPujzAXc3vRuA93e/z7ClwziZetIpOYui6sOj8WrVEkdKCsf/+ThmTk6pZyiTDAOa/9b9255FlkYRkfKj2P0kVyTqQkak8snKcdB84lKyc03WPXEtNYNK/rDb979+z9PrnyYlO4UAzwBLml9kHT1KbL9bcaSlETpqFFUfHl2q2y+zjm2Dd64Duy88flBNLioIXb/FlYpcJHfr1o0ePXrQo0cPOnXqhJeXl6uylRqdZCKVU5/X1rL3ZDLXNalGWJXiPXhsd7Ph6+mO32+vLOMMnx97nmPp+wHoV3cwf28xigBvL3w93HGzFW3QpeJI+uorTvzzcbDZqP3BHHzatXP5Nss804RXW+Y1ubh9rppcVBC6fosrFblInjJlCmvWrGHDhg3k5OTQrl07evToQffu3encufNle74o63SSiVROEz7/iY+3HHH+io0cPKstwSN4AwC56ZGcP34XZk4gPh5u+Hq64+/pTqifJ+OiG9O+brDTI5x44gmSvvgS9/Bw6i1aiFtAgNO3Ue4sexI2/heq1IDA2tZkaNATuo2zZtsVkK7f4krFbm6Rk5PD1q1bWb16NatWrWLFihXYbDYyMjKcndHldJKJVE5nUzP5fPsxMrMdxV5HVq6DlIwc0jJzSMvK+f3nzFzOGds4H/AR2DIwc3w4f3IgualNC3ze39Odz0d2omGYf0m/TgG5qWnEDuhP9q9H8I+Opsar/8EwXH8Xu0w7uRPe6g5Y3Mpw2DdQu5O1GSoIXb/FlYpdJO/fv59Vq1axcuVKVq9eTWZmJt26dWPhwoXOzuhyOslExFWOphzln6v/ye743QD0rzeYW+sMJzMbXv52P1sOJ1AzyJuFIztT1d+5fc2f/+knDt95F+TkUH3yvwkaONCp6y+Xjm+DpGPWbPvnBbDnCwhvDcNXgq3Iz87LRXT9FlcqcpF81113FSiKu3fvTo8ePWjZsmW5vUuhk0xEXCkrN4tXtr3CvL3zAGhVtRUvdX8JT4K59Y31HI5Pp3WtQD75ewe87G5O3Xb8O+9w+qWXMby9qbvgMzzr1XPq+qUIUk/D9KshKwX6vQmt77I6Ubmn67e4UpGLZJvNRmhoKPfddx/XXXcdXbp0KZftkP9IJ5mIlIbL9X5Rw7MN/d/cQPL5bG68Kpzpd7bBVsiH+wyMv7w5YTocHH3gAdI2bMSzaVPq/O8TbB4ezvg6Uhzr/gPfTwL/cHh4G3j4Wp2oXNP1W1ypyEXyuXPnWLt2LatWrWL16tXs3buX1q1b5/d4ccMNN7gqq8voJBOR0nJx84uSqBtQlzm95xDkFfSny2WfPk1s337knjtH8JB7CZswocTblmLKzoAZ7SHxV+j+BFz7f1YnKtd0/RZXKnE/yTExMUyZMoV58+bhcDjIzc11VrZSo5NMRErTheYXH//yMQ6z+A8NAvSo1YPp107/yzvKKStXcmzESABqvf0Wft26lWi7UgK7F8H8IeDuDQ//AAE1rU5Ubun6La5U5CI5Pj4+v0eLVatWsWfPHgIDA/PbJz/66KOuyuoyOslExArp2elk5Wblv5+xKoZZaw/hbhi8cffVtK8TcsXPHk4+zH3L7iPbkc2/ov7FoCaD/nJ7cVOe5dyHH+IWHEy9LxbhXrWqU76HFJFpwvs3wpEN0OJ2GDDL6kTllq7f4kpFLpLd3NwIDQ2la9eu+Q/ttWjRwlX5SoVOMhEpCxwOk0c++ZHFu04S4G3n85GdqF/V74rLz90zl2lbp+Hp5sknf/uEBkEN/nz9mZkcvn0Qmfv24dupE7XemYWhHhasceJHePtawIQHVkDNtlYnKpd0/RZXKnKRvHv3bpo3b+6qPJbQSSYiZUVGdi53zdrE9iOJ1A7xYeHIzgT7Xv5BO4fpYOTykaw/vp6GQQ35+G8f4+n2593IZcbEEHvbQMyMDHw6dMC/Z0/8unbBXrt2ue2hqNxaOAJ2fgQ128P934L2f5Hp+i2uVOI2yRWBTjIRKUviUzPp98Z6jiacp13tIOYNj8LT/fJdw509f5YBXw4gISOBu5rcxYSov34oL3HBAk7+66m8P/v/xl6rFn5du+DbpSu+Ue2x+arXBZdLPgmvXw3Z6TDgXWhxm9WJyh1dv8WVilwknzp1inHjxrF8+XJOnz7NxR/Xg3siIiUXczqFW9/YQEpGDn1bR/DqoNZXvNO77vg6Rnw/AoAZPWfQreZfP5SXGRND6urVpK5dR/q2bZCd/ftMux2ftm3zi2bPRg11l9lVVr0Aq56DgFoweivYva1OVK7o+i2uVOQiuU+fPhw5coTRo0cTHh5+yf84+/bt69SApUEnmYiURetjzjLkvS3kOEzG9GrImF6NrrjsC1te4MO9HxLkGcSCWxZQ1afwD+U50tJI27yFtHVrSV2zluxjBUekc69WDd+uXfDr2hXfjh1xCwgo9neSi2Slw3/bQfJxuO4p6DbO6kTliq7f4kpFLpL9/f1Zu3YtrVu3dlGk0qeTTETKqk+2HGH85z8B8Oqg1vRrU+Oyy2XlZnHX13ex79w+OoZ3ZOb1M7EZRX8ozzRNsn/9ldS160hdt5b0zVswMzJ+X8Bmw7tVK7xbtoQrNAH5K4bNDf/re+WtQ2DXp/D5cLD7wiPbwb+61YnKDV2/xZWKXCQ3a9aMefPm0aZNG1dlKnU6yUSkLJv6zV7eWn0IDzcbHz4QRfu6wZdd7lDiIQYtHkRGbgbj2o1jSPMhJd62IzOT9B9+IO23ojkr5mCJ1wmAzUboqJGEPvQQhptzh+IudxwOeLcXHN8Gbe6GvjOsTlRu6PotrlTkIvnbb7/l5Zdf5q233qJOnTouilW6dJKJSFnmcJiMnLedpbvjCPKxs3BkZ+qEXv7Buvn75/Pvjf/G3ebOhzd+SPMQ5/ZGlH3iBKnr1pF1+NdiryPryK+kfr8cAJ927Yh4cRr28HBnRSyfjm6Bd68HDHhwNYS3sjpRuaDrt7hSoYrkoKCgAm2P09LSyMnJwcfHB7vdXmDZhIQE56d0MZ1kIlLWnc/K5Y63N7LzWBL1Qn35fGQnAn0u7RrONE0eW/UY3x/5ntpVavPpTZ/iY/exIPGfS/ryS+ImPYMjPR1bQADhk/9NlRtusDqWtT67D35eALW7wNDF6hKuEHT9FlcqVJE8Z86cQq9wyJCS/3mvtOkkE5Hy4HRKBrfO2MDxxPN0qBfMB/dF4eF+abvjpMwk+n/Zn9Ppp+nfsD/PdHrGgrR/LevIEY6PHUfGT3ltrgMHDSJs/BPYvCtpDw+JR+C/10BOBgz6EJrebHWiMk/Xb3El9ZOMTjIRKT9+iUvmtjc3kpqZw21ta/LibS0v2z3b1rit3L/sfkxMXur+EtF1oi1I+9fMrCzOvP468bPeAcCjQX1qvPwKXo2v3JNHhbZ8Mqx9CYLqwqjN4P7ng8NUdrp+iysV+dHnJUuWsGzZskumf/vtt3zzzTdOCSUiIpfXpHoV/ntXG9xsBp9tO8b3e09fdrlrql/DAy0eAOCZDc9wIvVEacYsNMPDg2pjxxL53ru4VQ0lK+YghwcOJGHevEv64a8UuvwD/MLgXCxsedvqNCKVWpGL5PHjx192wBCHw8H48eOdEkpERK6sR+Nq/L1bPQAmL95DRvblB3Ea0XoELUNbkpKdwoS1E8h1lN3Bnnw7daLeF1/g270bZlYWpyZP4dio0eScO2d1tNLl6ZfXXzLA6mmQdtbaPCKVWJGL5AMHDtCsWbNLpjdp0oSYmBinhBIRkT83+toGhFXx5EhCOu+sPXTZZew2O893ex5fuy/bT29n1k+zSjll0bgHB1Nr5kzC/m8Cht1O6ooVxPbtR9qmzVZHK12t74LqLSEzGVY+Z3UakUqryEVyQEAAhw5d+j/kmJgYfH0v3yWRiIg4l6+nO/93Y1MAZqw8yInE85ddrpZ/Lf7V4V8AzNw5kx2nd5RWxGIxDIPge++lzqf/w6NuXXJOn+bIsGGc/s+rmH8cOrsis7lB76l5P297H07tsTaPSCVV5CK5b9++jBkzhoMHf+9QPiYmhrFjx3LLLbc4NZyIiFzZLa0iuKZOEOezc3luyd4rLndTvZu4qd5N5Jq5PLHmCVKyUkoxZfF4NW1K3QWfETjwNjBN4t96i1/vvoesi4bMrrDqdIEmN4HpgG+fhMrYPlvEYkUukqdNm4avry9NmjShbt261K1bl6ZNmxISEsJLL73kiowiInIZhmEw6Zbm2AxYvOskmw7FX3HZJ6OepKZfTU6knWDyxsnl4qE4m48P4ZMnU+PV/2Dz9+f8zp3E9ruVpK+/tjpa6bj+32Czw8EVcOA7q9OIVDrFam6xYcMGvv76a0aOHMnYsWNZvnw5K1asIDAw0AURRUTkSppHBHBn+0gAJn25m5xcx2WX8/Pw44VuL+BmuPHN4W/48uCXpRmzRKr07k29RQvxbtMGR2oqJ8aOI3XdeqtjuV5IfejwUN7P3z4JuZWkuYlIGaF+klE/iyJSvp1Ly6LHS6tIOp/Nv/s2596Oda647Kxds5j+43S83b159OpHcTfci7VNwzDwdPPE080Tu5s9/2cPN4+C/7V5FJhmM4p8byafmZPDySf/RdIXX+DTrh21P5xb7HWVGxlJML0NpMdDn2kQ9aDVicoUXb/FlQpdJG/cuJH4+Hhuuumm/GkffPABEydOJC0tjX79+vH666/j6Vn+Oj7XSSYi5d3cjYd56ovdBHjbWTmuB8G+lw5ZDZDryGX4d8PZGre1lBPmsdvsJSqWA5MdvPBaEu4OeO4+fw7WKlqRb2DQplobRrQeQfOQ5sXKUOq2vgNfjwXvIHh4O/gEW52ozND1W1yp0EVynz596NGjB0888QQAP/30E1dffTVDhw6ladOmvPjiizz44INMmjTJlXldQieZiJR3ObkObnp9Hb/EpTA4KpJnb21xxWXPpJ/hte2vkZ6TXuzt5TpyyXJkkZWbRWZuZv5///jzhf+aOPcPlg99nct1u0y2NjR48Ta3Yq+nV2QvRrYeScOghk5M5wK5OTCzC5zZC+Gt84asDqxldaoyQddvcaVCF8nh4eF89dVXtGvXDoAnn3yS1atXs27dOgDmz5/PxIkT2bOn/HVVo5NMRCqCzYfiGfT2JgwDvhrdhatqBFgdCdM0yTFzLimcHebl204XhuPwUbLueAhME49PZmKrG1noz6Znp/Ph3g/5+tDXmJgYGPSp24eRrUdSu0rtYmdyuePb4MPb4HwC+ITAwNlQt5vVqSyn67e4UqGLZC8vLw4cOECtWnm/vXbp0oU+ffrw5JNPAnD48GFatGhBSkrZ71roYjrJRKSiePjjH/lq5wna1Q5i/kMdMQzD6kgucezhh0n57nsCbr2ViKlFH3DjYOJBZuyYwXe/5vUa4Wa4cUv9W3io1UNE+EU4O65zJB6BTwZD3C4w3PJ6v+g4Cirov3Fh6PotrlToRmFhYWHExsYCkJWVxfbt2+nQoUP+/JSUFOx2u/MTiohIof3fjU3wtrvxw6/n+GLHCavjuEzIAw8AkLR4MdknTxb58/UD6/NKj1f4303/o1vNbuSauSyMWcjfFv6NZzc9y+n0086OXHKBkXD/t9DqTjBz83q8WPAAZBW/2YyIXFmhi+Qbb7yR8ePHs3btWiZMmICPjw9du3bNn79r1y7q16/vkpAiIlI44QHejL6uAQDPLdlLamaOxYlcw7tVK3zat4fsbBJmzyn2epqFNGNGzxnM7TOXqPAochw5fLLvE278/EZe2voSCRkJTkztBHZv6PdmXk8Xhhv8/Bm8ez0kxFqdTKTCKXSRPHnyZNzd3enevTuzZs1i1qxZeHj8/vT0e++9xw033OCSkCIiUnj3d6lLZLAPp1MymbEyxuo4LhMyPO9u8rn588lNTCzRulpXa807N7zDuze8S+uqrcnMzWTOnjn0XtCb6dunk5yV7ITETmIYeV3BDfkSfKvCqZ/h7R4Qs9zqZCIVSpH7SU5KSsLPzw83t4JPFCckJODn51egcC4v1KZJRCqa7/ec4oEPfsDuZvDtP7pTN9TX6khOZ5omsbf2J/OXXwh95GGqjhzptPWuO76O1398nb0JecN9+3v4M7T5UAY3HYyvvQzty6Tj8Ok9eQ/2YUDPp6HLPypNO2Vdv8WVNJgIOslEpOIxTZOh729l9f4zXNekGu8NvcbqSC6RtPhrTowbh1tQEA1WLMfm7e20dZumyYojK/jvjv8Sk5h3R97b3btsFckApgmZKZB9Pu+9uyd4BVxSKLcIbcHkzpMJ8LS+1xNn0fVbXElFMjrJRKRiOngmld6vriE71+S9oe24rkmY1ZGczszJ4WDvPmQfO0bYv/5F8N2Dnb6NXEcuyw4v442db/Br8q9OX39pahrclLeuf4sgryCroziFrt/iSiqS0UkmIhXX1CV7eWvNIeqE+LDsH93wdC/+4BtlVcJHH3Hq35OxR0RQf9lSDBf1tJTjyOFw0mFyzVyXrN8p4n6GZU/C+bNg98trflGnMwkZCYxfO56EjAQaBDZg1g2zCPUOtTptien6La6kIhmdZCJScaVkZHPdy6s5k5LJE72bMKJHxeuFyJGRQcx1PclNSCDixWkE3Hyz1ZGslRIHn94LRzfnve/xf9DtnxxKOcwDyx7gzPkz1KlSh3dueIcw3/L91wVdv8WVCt27hYiIlD/+XnbG924CwOsrDnAqOcPiRM5n8/Ii+N57AIif9Q6V/t6Pf3UYshja3Z/3ftVz8L/B1PMMYXbv2YT7hnM4+TBDlw7lRGrF7UtbpKRUJIuIVHC3tqnB1ZGBpGflMnXJXqvjuETQnXdi8/Ehc/9+0tassTqO9dw94KZX4Jb/gpsH7FsC795ApJsPs3vPpqZfTY6lHmPo0qEcST5idVqRMklFsohIBWezGUy6pTmGAYt2nOCHw2VsgAwncAsIIHDQICDvbrL85up74L6l4B8OZ36BebcRYfdndu/Z1KlSh5NpJxm2dBiHkg5ZnVSkzFGRLCJSCbSsGcigdrUAmPjlbnIdFa9JQvDQIWC3k/7DD6T/+KPVccqOGm3h3i/BOxhO/AifDCbMM5D3e79Pg8AGnD5/mmFLh7H/3H6rk4qUKSqSRUQqiX9GN8bfy53dJ5L5ZGvF+xO7PSyMgFvyHtqLf+ddi9OUMVUbwd2fgd0XYlfD58MJ9Qzivej3aBrclISMBO5bdh974vdYnVSkzFDvFujpWBGpPN5fH8szX+0hyMfOZyM64eNRsbqEyz0cS8odt4Fp4v/xZ7jVq1dgvoFBWBVPjEoyIt0lDq6Ej26H3CxoOwxu+g9JWcmM+H4EP539CX+7PzOvn0nLqi2tTlooun6LK6lIRieZiFQe2bkO/jZ9LftPpVodxWX+tXk2nU/+zLeR7fjP1XdcMj+qbjDvDb0GX093C9KVAbsXwfyhgAndHofrniQ1K5VRy0ex/fR2fNx9eKPXG7QNa2tx0L+m67e4kppbiIhUInY3G1P7tyDY1wMPN1uFfC1q0hOA645uJyIzqcA8w4DNsQmM+mg72bkOi/81LNK8H/zt5byf10yDTTPx8/DjzV5vElU9ivScdEZ8P4JNJzdZGlPEarqTjH4TFRGpaH69dwjpW7YQPGQIYRPG50/ffuQcd83aREa2g/5tavDSwFbYbJW06cXqabDy2byf+78DLQeSkZPBmFVjWH98PR42D1699lW61uxqbc4/oeu3uJLuJIuISIUTMvwBAM7Nn09uYmL+9Ksjg3hj8NW42Qw+//E4Lyz9xaKEZUC3f0L7B/N+XvQQHPgOL3cvpl87nWtrXUuWI4tHVj7C8iPLrc0pYhEVySIiUuH4dumCZ5MmmOnpJHz0UYF51zUJ4/n+LQB4a80h3llbSfsINgzo/Ty0GAiOHPjfPXB0Cx5uHrzc42Wi60ST48hh7KqxLI1danVakVKn5hbozzUiIhVR0uKvOTFuHG5BQTRYsRybt3eB+W+simHa0n0AvHZHa/q2rmFFTOvlZMEnd0LM9+AVmDf4SLWm5DhyeHr903x16Ctsho0afiXbP29f/zY1/Ws6J/NvdP0WVyrTd5InTZqEYRgFXk2aNMmfn5GRwahRowgJCcHPz48BAwZw6tQpCxOLiEhZUaV3NPaaNck9d47EBZ9fMn9E9/oM7VQHgHHzd7L2wJlSTlhGuHvA7R9AzfaQkQhz+0PiEdxt7kzpMoXbGt2Gw3RwNOVoiV45jhyrv6lIkZTpO8mTJk3is88+4/vvv8+f5u7uTmhoKAAjRozg66+/Zvbs2QQEBDB69GhsNhvr168v0nb0m6iISMWU8NFHnPr3ZOwREdRfthTDbi8w3+EweeSTH1m86yS+Hm588veOtKgZYFFai6UnwPt98oavDmkA9y0D37zr7aHEQyRnJZdo9U2Cm+Dl7uWMpPl0/RZXKvNF8qJFi9ixY8cl85KSkqhatSofffQRt912GwC//PILTZs2ZePGjXTo0KHQ29FJJiJSMTkyMoi5rie5CQlEvDiNgJtvvmSZzJxc7pu9lfUx8YT4erBgRCfqhPpakLYMSDoO70VD0lEIbw1DF4Onv9WprkjXb3GlMt3cAuDAgQNERERQr149Bg8ezJEjeUOpbtu2jezsbHr16pW/bJMmTYiMjGTjxo1/us7MzEySk5MLvEREpOKxeXkRfO89AMTPeofL3RfydHdj5t1taR5Rhfi0LO59bwunUzJKO2rZEFAD7lkEPiFwcgd8chfkZFqdSsQSZbpIjoqKYvbs2SxdupQ333yT2NhYunbtSkpKCnFxcXh4eBAYGFjgM2FhYcTFxf3peqdOnUpAQED+q1atWi78FiIiYqWgO+/E5uND5v79pK1Zc9ll/L3svD/sGmoFe3MkIZ1h728lJSO7lJOWEaEN4O4F4OEHsWtgwQPgyLU6lUipK9NFcp8+fRg4cCAtW7YkOjqaJUuWkJiYyKefflqi9U6YMIGkpKT819GjR52UWEREyhq3gAACBw0C8u4mX0k1fy/m3hdFiK8Hu08k89CH28jMqaTFYUQbuOMjcPOAvV/C149BZipkpRX/5aikIxxKuVWuBq4PDAykUaNGxMTEcP3115OVlUViYmKBu8mnTp2ievXqf7oeT09PPD09XZxWRETKiuChQ0j48EPSf/iB9B9/xKdNm8suVyfUl9nD2nPH2xtZHxPP2E93Mv2ONpVzVL563WHAO/DpENg2O+9VEqO35d2lFiknylWRnJqaysGDB7nnnnto27Ytdrud5cuXM2DAAAD27dvHkSNH6Nixo8VJRUSkLLGHhRFwy80kLficow8+hL1mDdyDQ3APCcEtNAT3kFDcQ4JxCwmlYWgIb91Uh/sXHmDxrpNU9ffk6ZuaYRiVsFBu1hdumQ7fjIfsNKvTiJSqMt27xbhx47j55pupXbs2J06cYOLEiezYsYM9e/ZQtWpVRowYwZIlS5g9ezZVqlTh4YcfBmDDhg1F2o6ejhURqfgyY2OJ7T8A8/z5Qi1vGgbJdh/OefoREBFG7fo1cQsMBDc31wYtg2x2d4IGDcAeHl78lbh7gc25rTx1/RZXKtN3ko8dO8add95JfHw8VatWpUuXLmzatImqVasC8J///AebzcaAAQPIzMwkOjqaN954w+LUIiJSFnnWrUvD1avIOnKU3IR4cs7GkxN/ltyz8eQkJJAbf/a3afHknjuH4XAQkJVGQFYa7DtF8r5dVn8FS2WfPUuNadOsjiFSasr0neTSot9ERUTkj8zcXHITE8k5G8+H32xn/dYDBGWlcmeTQGqHeP/1CiqQ3Ph4Eud/hltICA3XrS1TzU50/RZXKtN3kkVERKxguLnhHpLXZvm+hg3ZM38nn/94nK8cBlXPV64Hv909s5nu/iWe8fEMGv8hx4NqAHChVjYMMDAKTuPCPCP//Zz72lMr2KcUk4uUjIpkERGRP2GzGbxwW0uSzmez/JfTnEiqfAON7AquyzWn9xFx8Ge2NAgu1jqyc9UFnJQvKpJFRET+gt3NxjtD2rH/VCpZOZWv2DPCfoW39jHc5yzDRnXmQjtN0zT/8POFpc0C7y9MjgisXM1UpPxTkSwiIlIIhmHQuLq/1TEskdHnOmLfeh23n3fQLMwHw8PD6kgiLlemR9wTERER63k2aoRbcDBmejrnd1XuXj6k8lCRLCIiIn/KsNnw7dABgLQNGy1OI1I6VCSLiIjIX/LtlDeabdpGFclSOahIFhERkb/k2zGvSD6/axe5qakWpxFxPRXJIiIi8pfsNWpgrx0Jubmkb9lidRwRl1ORLCIiIoVy4W6y2iVLZaAiWURERArFt2MnQO2SpXJQkSwiIiKF4tshCgyDrIMHyT51yuo4Ii6lIllEREQKxS0gAK+rrgJ0N1kqPhXJIiIiUmgX2iWnq0iWCk5FsoiIiBRafn/JGzZimqbFaURcR0WyiIiIFJp3mzYYnp7knDlD1sGDVscRcRkVySIiIlJoNk9PfNq2BdQVnFRsKpJFRESkSDREtVQGKpJFRESkSHwuPLy3ZQtmdrbFaURcQ0WyiIiIFIlX06a4BQTgSEvj/E8/WR1HxCVUJIuIiEiRGDZb/t1ktUuWikpFsoiIiBSZ2iVLRaciWURERIrMt1MnAM7v3EluaprFaUScT0WyiIiIFJlHzZrYa9WCnBzSf9hqdRwRp1ORLCIiIsWiIaqlIlORLCIiIsXyxyGqRSoaFckiIiJSLD5RUWAYZB44QM6ZM1bHEXEqFckiIiJSLO5BQXg1bQqolwupeFQki4iISLGpyYVUVCqSRUREpNjyBxXZuBHTNC1OI+I8KpJFRESk2HzatsXw8CDn1CmyYmOtjiPiNCqSRUREpNhsXl54t70aUJMLqVhUJIuIiEiJ+HbMG31PD+9JRaIiWUREREokf1CRzZsxc3IsTiPiHCqSRUREpES8mjXFFhCAIzWVjJ9/tjqOiFOoSBYREZESMdzc8I2KAtTkQioOFckiIiJSYuovWSoaFckiIiJSYvntknfswJGWZnEakZJTkSwiIiIlZo+MxB4RAdnZpG/bZnUckRJTkSwiIiIlZhgGPmpyIRWIimQRERFxCt8/DFEtUt6pSBYRERGnuFAkZ+7bR87ZsxanESkZFckiIiLiFO7BwXg2bQpA2qbNFqcRKRkVySIiIuI0vze52GBxEpGSUZEsIiIiTpNfJG/YiGmaFqcRKT4VySIiIuI0Pm2vxrDbyTl5kuxff7U6jkixqUgWERERp7H5+ODdpg2gXi6kfFORLCIiIk71+xDVapcs5ZeKZBEREXGq/HbJmzZj5uZanEakeFQki4iIiFN5NW+Ozd8fR0oKGbt3Wx1HpFhUJIuIiIhTGe7u+ES1BzREtZRfKpJFRETE6Xw7dQL08J6UXyqSRURExOkutEs+v307jvPnLU4jUnQqkkVERMTpPOrUwT08HDM7m/Rt262OI1JkKpJFRETE6QzD0BDVUq6pSBYRERGX+L1IVrtkKX9UJIuIiIhL+HbsAEDmnr3kJCRYnEakaFQki4iIiEu4h4bi2agR7tWqkX30qNVxRIrE3eoAIiIiUnFFzpmNW2AghmFYHUWkSCrMneQZM2ZQp04dvLy8iIqKYsuWLVZHEhERqfTcg4JUIEu5VCGK5P/973889thjTJw4ke3bt9OqVSuio6M5ffq01dFEREREpByqEEXyK6+8wvDhwxk2bBjNmjVj5syZ+Pj48N5771kdTURERETKoXJfJGdlZbFt2zZ69eqVP81ms9GrVy82XqHLmczMTJKTkwu8REREREQuKPdF8tmzZ8nNzSUsLKzA9LCwMOLi4i77malTpxIQEJD/qlWrVmlEFREREZFyotwXycUxYcIEkpKS8l9H1S2NiIiIiPxBue8CLjQ0FDc3N06dOlVg+qlTp6hevfplP+Pp6Ymnp2dpxBMRERGRcqjc30n28PCgbdu2LF++PH+aw+Fg+fLldPxtOEwRERERkaIo93eSAR577DGGDBlCu3btaN++Pa+++ippaWkMGzbM6mgiIiIiUg5ViCJ50KBBnDlzhqeffpq4uDhat27N0qVLL3mYT0RERESkMAzTNE2rQ1gtOTmZgIAAkpKSqFKlitVxREREpBB0/RZXKvdtkkVEREREnE1FsoiIiIjIRVQki4iIiIhcpEI8uFdSF5pla3hqERGR8uPCdVuPV4krqEgGUlJSADQ8tYiISDmUkpJCQECA1TGkglHvFuQNPnLixAn8/f0xDMNp601OTqZWrVocPXpUT90WgfZb8Wi/FZ32WfFovxWP9lvx/Nl+M02TlJQUIiIisNnUglScS3eSAZvNRs2aNV22/ipVquh/iMWg/VY82m9Fp31WPNpvxaP9VjxX2m+6gyyuol+7REREREQuoiJZREREROQiKpJdyNPTk4kTJ+Lp6Wl1lHJF+614tN+KTvuseLTfikf7rXi038QqenBPREREROQiupMsIiIiInIRFckiIiIiIhdRkSwiIiIichEVySIiIiIiF1GR7EIzZsygTp06eHl5ERUVxZYtW6yOVKZNmjQJwzAKvJo0aWJ1rDJlzZo13HzzzURERGAYBosWLSow3zRNnn76acLDw/H29qZXr14cOHDAmrBlyF/tt6FDh15y7PXu3duasGXE1KlTueaaa/D396datWr069ePffv2FVgmIyODUaNGERISgp+fHwMGDODUqVMWJS4bCrPfevToccnx9tBDD1mUuGx48803admyZf6AIR07duSbb77Jn69jTaygItlF/ve///HYY48xceJEtm/fTqtWrYiOjub06dNWRyvTmjdvzsmTJ/Nf69atszpSmZKWlkarVq2YMWPGZedPmzaN6dOnM3PmTDZv3oyvry/R0dFkZGSUctKy5a/2G0Dv3r0LHHsff/xxKSYse1avXs2oUaPYtGkT3333HdnZ2dxwww2kpaXlL/OPf/yDr776ivnz57N69WpOnDhB//79LUxtvcLsN4Dhw4cXON6mTZtmUeKyoWbNmjz//PNs27aNH374geuuu46+ffuye/duQMeaWMQUl2jfvr05atSo/Pe5ublmRESEOXXqVAtTlW0TJ040W7VqZXWMcgMwFy5cmP/e4XCY1atXN1988cX8aYmJiaanp6f58ccfW5CwbLp4v5mmaQ4ZMsTs27evJXnKi9OnT5uAuXr1atM0844tu91uzp8/P3+ZvXv3moC5ceNGq2KWORfvN9M0ze7du5uPPvqodaHKiaCgIPOdd97RsSaW0Z1kF8jKymLbtm306tUrf5rNZqNXr15s3LjRwmRl34EDB4iIiKBevXoMHjyYI0eOWB2p3IiNjSUuLq7AcRcQEEBUVJSOu0JYtWoV1apVo3HjxowYMYL4+HirI5UpSUlJAAQHBwOwbds2srOzCxxvTZo0ITIyUsfbH1y83y6YN28eoaGhXHXVVUyYMIH09HQr4pVJubm5fPLJJ6SlpdGxY0cda2IZd6sDVERnz54lNzeXsLCwAtPDwsL45ZdfLEpV9kVFRTF79mwaN27MyZMneeaZZ+jatSs///wz/v7+Vscr8+Li4gAue9xdmCeX17t3b/r370/dunU5ePAg//d//0efPn3YuHEjbm5uVseznMPhYMyYMXTu3JmrrroKyDvePDw8CAwMLLCsjrffXW6/Adx1113Url2biIgIdu3axRNPPMG+ffv4/PPPLUxrvZ9++omOHTuSkZGBn58fCxcupFmzZuzYsUPHmlhCRbKUGX369Mn/uWXLlkRFRVG7dm0+/fRT7r//fguTSUV3xx135P/cokULWrZsSf369Vm1ahU9e/a0MFnZMGrUKH7++Wc9I1BEV9pvf//73/N/btGiBeHh4fTs2ZODBw9Sv3790o5ZZjRu3JgdO3aQlJTEZ599xpAhQ1i9erXVsaQSU3MLFwgNDcXNze2SJ29PnTpF9erVLUpV/gQGBtKoUSNiYmKsjlIuXDi2dNyVXL169QgNDdWxB4wePZrFixezcuVKatasmT+9evXqZGVlkZiYWGB5HW95rrTfLicqKgqg0h9vHh4eNGjQgLZt2zJ16lRatWrFa6+9pmNNLKMi2QU8PDxo27Yty5cvz5/mcDhYvnw5HTt2tDBZ+ZKamsrBgwcJDw+3Okq5ULduXapXr17guEtOTmbz5s067oro2LFjxMfHV+pjzzRNRo8ezcKFC1mxYgV169YtML9t27bY7fYCx9u+ffs4cuRIpT7e/mq/Xc6OHTsAKvXxdjkOh4PMzEwda2IZNbdwkccee4whQ4bQrl072rdvz6uvvkpaWhrDhg2zOlqZNW7cOG6++WZq167NiRMnmDhxIm5ubtx5551WRyszUlNTC9xtio2NZceOHQQHBxMZGcmYMWOYMmUKDRs2pG7dujz11FNERETQr18/60KXAX+234KDg3nmmWcYMGAA1atX5+DBgzz++OM0aNCA6OhoC1Nba9SoUXz00Ud88cUX+Pv757f9DAgIwNvbm4CAAO6//34ee+wxgoODqVKlCg8//DAdO3akQ4cOFqe3zl/tt4MHD/LRRx9x4403EhISwq5du/jHP/5Bt27daNmypcXprTNhwgT69OlDZGQkKSkpfPTRR6xatYply5bpWBPrWN29RkX2+uuvm5GRkaaHh4fZvn17c9OmTVZHKtMGDRpkhoeHmx4eHmaNGjXMQYMGmTExMVbHKlNWrlxpApe8hgwZYppmXjdwTz31lBkWFmZ6enqaPXv2NPft22dt6DLgz/Zbenq6ecMNN5hVq1Y17Xa7Wbt2bXP48OFmXFyc1bEtdbn9BZjvv/9+/jLnz583R44caQYFBZk+Pj7mrbfeap48edK60GXAX+23I0eOmN26dTODg4NNT09Ps0GDBuY///lPMykpydrgFrvvvvvM2rVrmx4eHmbVqlXNnj17mt9++23+fB1rYgXDNE2zNItyEREREZGyTm2SRUREREQuoiJZREREROQiKpJFRERERC6iIllERERE5CIqkkVERERELqIiWURERETkIiqSRUREREQuoiJZRMq02bNnExgY6PLtTJo0idatW7t8O0XVo0cPxowZY3UMEZFKR4OJiFRwcXFxPPvss3z99dccP36catWq0bp1a8aMGUPPnj2tjveXzp8/T0pKCtWqVXPpdlJTU8nMzCQkJASAoUOHkpiYyKJFi1y63QtWrVrFtddey7lz5wr8UpCQkIDdbsff379UcoiISB53qwOIiOscPnyYzp07ExgYyIsvvkiLFi3Izs5m2bJljBo1il9++cXqiH/J29sbb29vl2/Hz88PPz8/p683KysLDw+PYn8+ODjYiWlERKSw1NxCpAIbOXIkhmGwZcsWBgwYQKNGjWjevDmPPfYYmzZtyl/ulVdeoUWLFvj6+lKrVi1GjhxJampq/vwLTR4WL15M48aN8fHx4bbbbiM9PZ05c+ZQp04dgoKCeOSRR8jNzc3/XJ06dZg8eTJ33nknvr6+1KhRgxkzZhTIWNht/9GUKVOoVq0a/v7+PPDAA4wfP75AU4mhQ4fSr18/XnrpJcLDwwkJCWHUqFFkZ2dfcV/9sbnFpEmTmDNnDl988QWGYWAYBqtWrQLg6NGj3H777QQGBhIcHEzfvn05fPjwJdt+9tlniYiIoHHjxgDMnTuXdu3a4e/vT/Xq1bnrrrs4ffo0kPfLzLXXXgtAUFAQhmEwdOhQ4NLmFufOnePee+8lKCgIHx8f+vTpw4EDBy7ZX8uWLaNp06b4+fnRu3dvTp48ecXvLiIil1KRLFJBJSQksHTpUkaNGoWvr+8l8/9YeNpsNqZPn87u3buZM2cOK1as4PHHHy+wfHp6OtOnT+eTTz5h6dKlrFq1iltvvZUlS5awZMkS5s6dy1tvvcVnn31W4HMvvvgirVq14scff2T8+PE8+uijfPfdd0Xa9h/NmzePZ599lhdeeIFt27YRGRnJm2++eclyK1eu5ODBg6xcuZI5c+Ywe/ZsZs+eXah9N27cOG6//fb84vLkyZN06tSJ7OxsoqOj8ff3Z+3ataxfvz6/CM3Kysr//PLly9m3bx/fffcdixcvBiA7O5vJkyezc+dOFi1axOHDh/ML4Vq1arFgwQIA9u3bx8mTJ3nttdcum23o0KH88MMPfPnll2zcuBHTNLnxxhsL/AKQnp7OSy+9xNy5c1mzZg1Hjhxh3LhxhfruIiLyG1NEKqTNmzebgPn5558X+bPz5883Q0JC8t+///77JmDGxMTkT3vwwQdNHx8fMyUlJX9adHS0+eCDD+a/r127ttm7d+8C6x40aJDZp0+fIm07ICAg/31UVJQ5atSoAp/p3Lmz2apVq/z3Q4YMMWvXrm3m5OTkTxs4cKA5aNCgK2534sSJl6yjb9++BZaZO3eu2bhxY9PhcORPy8zMNL29vc1ly5blfy4sLMzMzMy84rZM0zS3bt1qAvn7b+XKlSZgnjt3rsBy3bt3Nx999FHTNE1z//79JmCuX78+f/7Zs2dNb29v89NPPzVN8/L/VjNmzDDDwsL+NI+IiBSkO8kiFZRZhGdyv//+e3r27EmNGjXw9/fnnnvuIT4+nvT09PxlfHx8qF+/fv77sLAw6tSpU6Adb1hYWH4Tggs6dux4yfu9e/cWadt/tG/fPtq3b19g2sXvAZo3b46bm1v++/Dw8EuyFdXOnTuJiYnB398/vw1zcHAwGRkZHDx4MH+5Fi1aXNIOedu2bdx8881ERkbi7+9P9+7dAThy5Eiht793717c3d2JiorKnxYSEkLjxo0L7NOL/62c8d1FRCobFckiFVTDhg0xDOMvH847fPgwN910Ey1btmTBggVs27Ytv93wH5sQ2O32Ap8zDOOy0xwOR6EzFnbbxVHSbJeTmppK27Zt2bFjR4HX/v37ueuuu/KXu7h5S1paGtHR0VSpUoV58+axdetWFi5cCJT8e17O5b57UX5pEhERFckiFVZwcDDR0dHMmDGDtLS0S+YnJiYCeXc4HQ4HL7/8Mh06dKBRo0acOHHCaTn++IDghfdNmzYt9rYbN27M1q1bC0y7+L0zeHh4FHgIEeDqq6/mwIEDVKtWjQYNGhR4BQQEXHFdv/zyC/Hx8Tz//PN07dqVJk2aXHJn98Kd54u3+UdNmzYlJyeHzZs350+Lj49n3759NGvWrDhfU0RErkBFskgFNmPGDHJzc2nfvj0LFizgwIED7N27l+nTp+c3g2jQoAHZ2dm8/vrrHDp0iLlz5zJz5kynZVi/fj3Tpk1j//79zJgxg/nz5/Poo48We9sPP/ww7777LnPmzOHAgQNMmTKFXbt2YRiG0zJDXs8cu3btYt++fZw9e5bs7GwGDx5MaGgoffv2Ze3atcTGxrJq1SoeeeQRjh07dsV1RUZG4uHhkf89v/zySyZPnlxgmdq1a2MYBosXL+bMmTMFevi4oGHDhvTt25fhw4ezbt06du7cyd13302NGjXo27evU7+/iEhlpyJZpAKrV68e27dv59prr2Xs2LFcddVVXH/99Sxfvjy/R4hWrVrxyiuv8MILL3DVVVcxb948pk6d6rQMY8eO5YcffqBNmzZMmTKFV155hejo6GJve/DgwUyYMIFx48Zx9dVXExsby9ChQ/Hy8nJaZoDhw4fTuHFj2rVrR9WqVVm/fj0+Pj6sWbOGyMhI+vfvT9OmTbn//vvJyMigSpUqV1xX1apVmT17NvPnz6dZs2Y8//zzvPTSSwWWqVGjBs888wzjx48nLCyM0aNHX3Zd77//Pm3btuWmm26iY8eOmKbJkiVLLmliISIiJaMR90TEZerUqcOYMWNcPqzy9ddfT/Xq1Zk7d65LtyMiIpWHRtwTkXIlPT2dmTNnEh0djZubGx9//DHff/99gb6XRURESkpFsoiUK4ZhsGTJEp599lkyMjJo3LgxCxYsoFevXlZHExGRCkTNLURERERELqIH90RERERELqIiWURERETkIiqSRUREREQuoiJZREREROQiKpJFRERERC6iIllERERE5CIqkkVERERELqIiWURERETkIiqSRUREREQu8v+PutklAM1Z7QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_init_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_list_random[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list_random[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline\", linestyle=\"--\")\n", + " break\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(f\"BayBE Optimization, {n_init_val} initial observations\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('Schwefel function value')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4e856df-7791-447b-8f6e-fc78cf1cd769", + "metadata": {}, + "source": [ + "Note: The random baseline is a flatline because the random search finds the best point at the 10th iteration with our seeds, which is not shown here because it falls within the initial random data collection phase. " + ] + }, + { + "cell_type": "markdown", + "id": "8989570b-6b02-4e6a-99c7-f7ccafb0bb7f", + "metadata": {}, + "source": [ + "2. Fix noise value, compare initial data number" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "409f03ea-c50e-461d-850d-f7723b031f98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAHHCAYAAABN18UAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAADrnklEQVR4nOzdeVhU1f/A8ffMsO9LwoAikIgLLqipmamoGJK7pqJmWG6VS2puueKSplYuuVRaYi659FVLLP2Zu0BIJq4kaogbiCsIss7c3x/EzRFQBoFBPa/nuY/ce88959xh8TNnPvcchSRJEoIgCIIgCIIgyJSG7oAgCIIgCIIgVDQiSBYEQRAEQRCER4ggWRAEQRAEQRAeIYJkQRAEQRAEQXiECJIFQRAEQRAE4REiSBYEQRAEQRCER4ggWRAEQRAEQRAeIYJkQRAEQRAEQXiECJIFQRAEQRAE4REiSBYEAYVCQUhISKnVd+nSJRQKBaGhoaVWZ0VutzwNGDAADw+Pcm3Tz88PPz+/cm2zKBWpL4IgPN9EkCxUeKGhoSgUCp3NycmJ1q1b89tvv5V5+4+2bWlpSe3atZk9ezYPHjzQKTtgwIAC5fM3MzOzYrWXnp7OrFmzqFevHhYWFtja2tKiRQt++OEHnmYV+V9//bVUA2FD2rBhA4sWLTJ0N4QK7P/+7/8YOHAgderUQaVSPfaNhVarZf78+Xh6emJmZka9evX48ccfy6+zgiBUSEaG7oAgFNfMmTPx9PREkiRu3LhBaGgob775Jjt27KBjx45l2na7du145513AEhLS+Pw4cNMnTqVEydOsGXLFp2ypqamrFq1qkAdKpXqie3cuHGDtm3bEhsbS1BQEMOHDyczM5P//e9/BAcH8+uvv7J+/fpi1fWoX3/9lWXLlhUaKGdkZGBkVHp/Dtzd3cnIyMDY2LjU6nzYhg0bOH36NKNGjSrXdiuClStXotVqDd2NCm/Dhg1s2rSJhg0b4urq+tiykydP5rPPPmPw4ME0btyYn3/+mb59+6JQKAgKCiqnHguCUOFIglDBrV69WgKk6OhoneN37tyRjI2Npb59+5Zp+4A0bNiwAsffeustSalUShkZGfKx4OBgydLSssRtBQQESEqlUvr5558LnBs7dqwESJ999lmJ6h42bJj0vPzKd+jQQXJ3dzd0N14YrVq1klq1amXobkiSVPy+XLt2TcrOzpYk6fE/L1evXpWMjY11fse1Wq3UokULqUqVKlJubm5pdFsQhGeQSLcQnll2dnaYm5sXGAH9/PPPee2113B0dMTc3JxGjRrx008/6ZRp1aoV9evXL7TeGjVqEBAQ8MT21Wo1CoWi1EZg//jjD3bv3s2AAQPo3LlzgfNz586levXqzJs3j4yMDOC/HNzPP/+chQsX4u7ujrm5Oa1ateL06dPytQMGDGDZsmWAbvpIvkdzkkNCQlAoFMTFxfH2229ja2tLpUqVmDp1KpIkceXKFbp06YKNjQ1qtZovvvhCp6+P5gYfOHCgyDSUhz8G//nnn+nQoQOurq6YmppSrVo1Zs2ahUajkcv4+fmxc+dOEhISCtRRVE7yvn37aNGiBZaWltjZ2dGlSxdiY2N1yuTf84ULFxgwYAB2dnbY2try7rvvFkirKa78VKHw8HDGjBlDpUqVsLS0pFu3bty8ebNA+eXLl+Pj44OpqSmurq4MGzaMe/fu6ZQpLCd548aNNGrUCGtra2xsbKhbty6LFy/WKXPv3j1GjRqFm5sbpqameHl5MW/evBKPSmdlZTF9+nS8vLwwNTXFzc2N8ePHk5WVJZepU6cOrVu3LnCtVqulcuXKvPXWWzrHFi1ahI+PD2ZmZjg7OzN06FDu3r1bov65uroW6xOFn3/+mZycHD788EP5mEKh4IMPPuDq1atERkaWqH1BEJ59It1CeGakpKRw69YtJEkiOTmZr776irS0NN5++22dcosXL6Zz587069eP7OxsNm7cSM+ePQkLC6NDhw4A9O/fn8GDB3P69Gnq1KkjXxsdHU1cXBxTpkzRqTMzM5Nbt24BeTnD4eHhrFmzhr59+xYaJOeXfZiJiQk2NjZF3t+OHTsA5LSORxkZGdG3b19mzJhBeHg4/v7+8rkffviB+/fvM2zYMDIzM1m8eDFt2rTh1KlTcrBx/fp19uzZw9q1a4vsw6N69+5NrVq1+Oyzz9i5cyezZ8/GwcGBb775hjZt2jBv3jzWr1/P2LFjady4MS1btiy0nlq1ahVo9969e4wZMwYnJyf5WGhoKFZWVowZMwYrKyv27dvHtGnTSE1NZcGCBUDeR+MpKSlcvXqVhQsXAmBlZVXkPfz+++8EBgby8ssvExISQkZGBl999RXNmzfnr7/+KhBw9urVC09PT+bOnctff/3FqlWrcHJyYt68ecV+3R41YsQI7O3tmT59OpcuXWLRokUMHz6cTZs2yWVCQkKYMWMG/v7+fPDBB5w7d44VK1YQHR1NeHh4kQHfnj176NOnD23btpX7GBsbS3h4OB999BEADx48oFWrVly7do2hQ4dStWpVIiIi+OSTT0hMTNQ7v1ur1dK5c2eOHDnCkCFDqFWrFqdOnWLhwoXExcWxfft2IO/nJyQkhKSkJNRqtXz9kSNHuH79uk4qw9ChQwkNDeXdd99l5MiRxMfHs3TpUo4fP/7Y+39ax48fx9LSklq1aukcb9KkiXz+9ddfL5O2BUGo4Aw9lC0IT5KfbvHoZmpqKoWGhhYo/+DBA5397OxsqU6dOlKbNm3kY/fu3ZPMzMykCRMm6JQdOXKkZGlpKaWlpcnHCmsbkLp27SplZmbqXB8cHFxk+YCAgMfeZ9euXSVAunv3bpFltm7dKgHSkiVLJEmSpPj4eAmQzM3NpatXr8rloqKiJEAaPXq0fOxx6RaANH36dHl/+vTpEiANGTJEPpabmytVqVJFUigUOikfd+/elczNzaXg4GD5WH6/Vq9eXWh7Wq1W6tixo2RlZSWdOXNGPv7o906SJGno0KGShYWFzmtd1MfnhbXr6+srOTk5Sbdv35aPnThxQlIqldI777xT4J7fe+89nTq7desmOTo6FnofT5L/s+vv7y9ptVr5+OjRoyWVSiXdu3dPkiRJSk5OlkxMTKQ33nhD0mg0crmlS5dKgPT999/Lx4KDg3Xu/aOPPpJsbGwemxYwa9YsydLSUoqLi9M5PnHiREmlUkmXL19+7H08muKwdu1aSalUSocPH9Yp9/XXX0uAFB4eLkmSJJ07d04CpK+++kqn3IcffihZWVnJ3+/Dhw9LgLR+/Xqdcrt27SpwvCSpH49Lt+jQoYP08ssvFzienp4uAdLEiRP1aksQhOeHSLcQnhnLli1jz5497Nmzh3Xr1tG6dWsGDRrE1q1bdcqZm5vLX9+9e5eUlBRatGjBX3/9JR+3tbWlS5cu/Pjjj/KMERqNhk2bNtG1a1csLS116uzSpYvc9s8//8wnn3zCrl276Nu3b4EZJ8zMzOSyD2+fffbZY+/v/v37AFhbWxdZJv9camqqzvGuXbtSuXJleb9JkyY0bdqUX3/99bFtPsmgQYPkr1UqFa+88gqSJDFw4ED5uJ2dHTVq1OCff/4pdr2zZs0iLCyM0NBQateuLR9/+Ht3//59bt26RYsWLXjw4AF///233v1PTEwkJiaGAQMG4ODgIB+vV68e7dq1K/T1ef/993X2W7Rowe3btwu85voYMmSITnpLixYt0Gg0JCQkAHmj3dnZ2YwaNQql8r8/y4MHD8bGxoadO3cWWbednR3p6ens2bOnyDJbtmyhRYsW2Nvbc+vWLXnz9/dHo9Fw6NAhve5ny5Yt1KpVi5o1a+rU16ZNGwD2798PgLe3N76+vjoj5hqNhp9++olOnTrJ3+8tW7Zga2tLu3btdOpr1KgRVlZWcn1lISMjA1NT0wLH82ejyU9tEgThxSPSLYRnRpMmTXjllVfk/T59+tCgQQOGDx9Ox44dMTExASAsLIzZs2cTExOjkx/5cJACeWkNmzZt4vDhw7Rs2ZLff/+dGzdu0L9//wJtV6lSRSe9oXPnzjg6OjJ27FjCwsLo1KmTfE6lUumULa78APj+/fvY2dkVWqaoQLp69eoFynp7e7N582a9+/GwqlWr6uzb2tpiZmbGSy+9VOD47du3i1Xnrl27mDFjBp988gk9evTQOXfmzBmmTJnCvn37CgSlKSkpevc/PwitUaNGgXO1atVi9+7dpKen67wpevSe7e3tgbw3XI9Ll3mcx9X5uH6amJjw8ssvy+cL8+GHH7J582YCAwOpXLkyb7zxBr169aJ9+/ZymfPnz3Py5EkqVapUaB3Jycl63c/58+eJjY0tVn29e/dm0qRJXLt2jcqVK3PgwAGSk5Pp3bu3Tn0pKSk6qTdP0z99mJub6/ydyJeZmSmfFwThxSSCZOGZpVQqad26NYsXL+b8+fP4+Phw+PBhOnfuTMuWLVm+fDkuLi4YGxuzevVqNmzYoHN9QEAAzs7OrFu3jpYtW7Ju3TrUanWxA9y2bdsCcOjQIZ0guaRq1arF9u3bOXnyZJG5vSdPngTQGX0tS4VNNVfU9HOPjqgXJj4+nn79+tGuXTtmz56tc+7evXu0atUKGxsbZs6cSbVq1TAzM+Ovv/5iwoQJ5Tbt2dPcX3nWmc/JyYmYmBh2797Nb7/9xm+//cbq1at55513WLNmDZCXQ9yuXTvGjx9faB3e3t56tanVaqlbty5ffvlloefd3Nzkr3v37s0nn3zCli1bGDVqFJs3b8bW1lYniNdqtTg5ObF+/fpC6ysqGC8NLi4u7N+/H0mSdN5IJyYmAjxx+jhBEJ5fIkgWnmm5ublA3tzFAP/73/8wMzNj9+7dOh+hrl69usC1KpWKvn37Ehoayrx589i+fTuDBw8u9hzEj7b9tDp27MjcuXP54YcfCg2SNRoNGzZswN7enubNm+ucO3/+fIHycXFxOg+lPTqSXt4yMjLo3r07dnZ2/PjjjzppBZA3A8bt27fZunWrzv3Hx8cXqKu49+Lu7g7AuXPnCpz7+++/eemllwqk1hjCw/18+eWX5ePZ2dnEx8c/8Y2biYkJnTp1olOnTmi1Wj788EO++eYbpk6dipeXF9WqVSMtLa1En3AUplq1apw4cYK2bds+8Xvh6elJkyZN2LRpE8OHD2fr1q107dpV5/ezWrVq/P777zRv3rzcR259fX1ZtWoVsbGxOm8+o6Ki5POCILyYRE6y8MzKycnh//7v/zAxMZGfTFepVCgUCp0pwy5duiQ/bf+o/v37c/fuXYYOHVroTBmPkz8bRVFTyenrtddew9/fn9WrVxMWFlbg/OTJk4mLi2P8+PEFAont27dz7do1ef/o0aNERUURGBgoH8sPBh+dUqy8vP/++8TFxbFt2zY53eBh+W9OHh5dzc7OZvny5QXKWlpaFiv9wsXFBV9fX9asWaNz36dPn+b//u//ePPNN0twJ6XP398fExMTlixZonP/3333HSkpKfKsLIV5NM1FqVRSr149ADmNoFevXkRGRrJ79+4C19+7d09+w1dcvXr14tq1a6xcubLAuYyMDNLT03WO9e7dmz/++IPvv/+eW7du6aRa5Nen0WiYNWtWgfpyc3PL9Ge2S5cuGBsb6/ycSZLE119/TeXKlXnttdfKrG1BECo2MZIsPDN+++03+eGt5ORkNmzYwPnz55k4caKcK9qhQwe+/PJL2rdvT9++fUlOTmbZsmV4eXnJqQoPa9CgAXXq1JEfRGrYsGGhbcfFxbFu3TogbzqtP/74gzVr1uDl5VUghzk3N1cu+6hu3bo9duTyhx9+oG3btnTp0oW+ffvSokULsrKy2Lp1KwcOHKB3796MGzeuwHVeXl68/vrrfPDBB2RlZbFo0SIcHR11Pl5v1KgRACNHjiQgIACVSlVuq4nt3LmTH374gR49enDy5Emd74WVlRVdu3bltddew97enuDgYEaOHIlCoWDt2rWFpiQ0atSITZs2MWbMGBo3boyVlVWRKS8LFiwgMDCQZs2aMXDgQHkKOFtb2xIv050/Xdv+/fvx8/MrUR0Pq1SpEp988gkzZsygffv2dO7cmXPnzrF8+XIaN2782DdvgwYN4s6dO7Rp04YqVaqQkJDAV199ha+vr/zmcdy4cfzyyy907NiRAQMG0KhRI9LT0zl16hQ//fQTly5dKpBn/jj9+/dn8+bNvP/+++zfv5/mzZuj0Wj4+++/2bx5M7t379Z5fqBXr16MHTuWsWPH4uDgUGBEu1WrVgwdOpS5c+cSExPDG2+8gbGxMefPn2fLli0sXrxYZ07l4jh58iS//PILABcuXCAlJUVO8alfv77881KlShVGjRrFggULyMnJoXHjxmzfvp3Dhw+XeHVLQRCeEwaaVUMQiq2wKeDMzMwkX19facWKFTpTa0mSJH333XdS9erVJVNTU6lmzZrS6tWr5em9CjN//nwJkObMmVPo+UfbVqlUUpUqVaQhQ4ZIN27c0Cn7uCngACk+Pv6J93v//n0pJCRE8vHxkczNzSVra2upefPmUmhoaIF7zZ/ybMGCBdIXX3whubm5SaamplKLFi2kEydO6JTNzc2VRowYIVWqVElSKBQ6rwdFTAF38+bNAvdX2IqCrVq1knx8fAr0K38qtqKm8QN0puYKDw+XXn31Vcnc3FxydXWVxo8fL+3evVsCpP3798vl0tLSpL59+0p2dnY6dRQ19dzvv/8uNW/eXDI3N5dsbGykTp06SWfPntUpU9Q95/f94e/dxx9/LCkUCik2NrbAa1HYtY+uFrl///4C9yRJeVO+1axZUzI2NpacnZ2lDz74oMCUgI9OAffTTz9Jb7zxhuTk5CSZmJhIVatWlYYOHSolJibqXHf//n3pk08+kby8vCQTExPppZdekl577TXp888/l1emK0ph065lZ2dL8+bNk3x8fCRTU1PJ3t5eatSokTRjxgwpJSWlQB3NmzeXAGnQoEFFtvPtt99KjRo1kn/u69atK40fP166fv36Y/tSmMf9zD08XaEkSZJGo5HmzJkjubu7SyYmJpKPj4+0bt26J7YhCMLzTSFJpfDkiCA8wxYvXszo0aO5dOlSgVkIKrpLly7h6enJggULGDt2rKG788Jo0qQJ7u7ubNmyxdBdEQRBEMqISLcQXmiSJPHdd9/RqlWrZy5AFgwjNTWVEydOyDNHCIIgCM8nESQLL6T09HR++eUX9u/fz6lTp/j5558N3SXhGWFjY1PovLqCIAjC80UEycIL6ebNm/Tt2xc7OzsmTZpE586dDd0lQRAEQRAqEINOAafRaJg6dSqenp6Ym5tTrVo1Zs2apfM0uyRJTJs2DRcXF8zNzfH39y8wJ+ydO3fo168fNjY22NnZMXDgwFKbu1Z4Pnl4eCBJEnfv3uXTTz81dHdKLP8+RD6yIAiCIJQugwbJ8+bNY8WKFSxdupTY2FjmzZvH/Pnz+eqrr+Qy8+fPZ8mSJXz99ddERUVhaWlJQECAvGQoQL9+/Thz5gx79uwhLCyMQ4cOMWTIEEPckiAIgiAIgvAcMOjsFh07dsTZ2ZnvvvtOPtajRw/Mzc1Zt24dkiTh6urKxx9/LI+UpaSk4OzsTGhoKEFBQfIqSdHR0fK8nLt27eLNN9/k6tWrYklRQRAEQRAEQW8GzUl+7bXX+Pbbb4mLi8Pb25sTJ05w5MgRvvzySyBvOdqkpCSdiedtbW1p2rQpkZGRBAUFERkZiZ2dnc7E9f7+/iiVSqKioujWrVuBdrOysnQevNFqtdy5cwdHR0eDL90rCIIgCELxSJLE/fv3cXV1LbDUvSA8LYMGyRMnTiQ1NZWaNWuiUqnQaDR8+umn9OvXD4CkpCQAnJ2dda5zdnaWzyUlJeHk5KRz3sjICAcHB7nMo+bOncuMGTNK+3YEQRAEQTCAK1euUKVKFUN3Q3jOGDRI3rx5M+vXr2fDhg34+PgQExPDqFGjcHV1JTg4uMza/eSTTxgzZoy8n5KSQtWqVbly5Yq8vLEgCIIgCBVbamoqbm5uWFtbG7orwnPIoEHyuHHjmDhxIkFBQQDUrVuXhIQE5s6dS3BwMGq1GoAbN27g4uIiX3fjxg18fX0BUKvVJCcn69Sbm5vLnTt35OsfZWpqiqmpaYHjNjY2IkgWBEEQhGeMSJUUyoJBE3gePHhQIIdIpVKh1WoB8PT0RK1Ws3fvXvl8amoqUVFRNGvWDIBmzZpx7949jh07JpfZt28fWq2Wpk2blsNdCIIgCIIgCM8bg44kd+rUiU8//ZSqVavi4+PD8ePH+fLLL3nvvfeAvHeGo0aNYvbs2VSvXh1PT0+mTp2Kq6srXbt2BaBWrVq0b9+ewYMH8/XXX5OTk8Pw4cMJCgoSM1sIgiAIgiAIJWLQIPmrr75i6tSpfPjhhyQnJ+Pq6srQoUOZNm2aXGb8+PGkp6czZMgQ7t27x+uvv86uXbswMzOTy6xfv57hw4fTtm1blEolPXr0YMmSJYa4JUEQBEEQBOE5YNB5kiuK1NRUbG1tSUlJETnJgiAIQgEajYacnBxDd0N4RFpaGq+88gp//vknVlZWhu6O8AwwNjZGpVIVq6wIkhFBsiAIglA4SZJISkri3r17hu6KUAitVsuVK1dwc3MT8yQLxWZnZ4darX7iA58GTbcQBEEQhIosP0B2cnLCwsJCzKJQwWg0GjIyMvDw8Cj26KDw4pIkiQcPHsizoj08c1phRJAsCIIgCIXQaDRygOzo6Gjo7giF0Gg0AJiZmYkgWSgWc3NzAJKTk3Fycnrsz434bEIQBEEQCpGfg2xhYWHgngiCUJryf6ef9JyBCJIFQRAE4TFEioUgPF+K+zstgmRBEARBEARBeIQIkgVBEARBMCiFQsH27duLXT40NBQ7O7sy648ggAiSBUEQBOG5MmDAABQKhbw5OjrSvn17Tp48qVNOo9GwcOFC6tati5mZGfb29gQGBhIeHl7ufU5MTCQwMLDY5Xv37k1cXJy8P2PGDHx9fZ+qDwcOHKBLly64uLhgaWmJr68v69evf6o6hWebCJIFQRAE4TnTvn17EhMTSUxMZO/evRgZGdGxY0f5vCRJBAUFMXPmTD766CNiY2M5cOAAbm5u+Pn56TWqWxrUajWmpqbFLm9ubo6Tk1Op9iEiIoJ69erxv//9j5MnT/Luu+/yzjvvEBYWVqrtCM8OESSXIa1WIvVWBtkZuYbuiiAIgvACMTU1Ra1Wo1ar8fX1ZeLEiVy5coWbN28CsHnzZn766Sd++OEHBg0ahKenJ/Xr1+fbb7+lc+fODBo0iPT09GK35+fnx8iRIxk/fjwODg6o1WpCQkKKff3D6RaXLl1CoVCwdetWWrdujYWFBfXr1ycyMlIu/3C6xY4dO5g1axYnTpyQR89DQ0OL3Xa+SZMmMWvWLF577TWqVavGRx99RPv27dm6davedQnPBxEkl6FfFh1n7ZRIEs7cNnRXBEEQhKckSRIPsnMNsj3N4rhpaWmsW7cOLy8veb7nDRs24O3tTadOnQqU//jjj7l9+zZ79uzRq501a9ZgaWlJVFQU8+fPZ+bMmXrX8bDJkyczduxYYmJi8Pb2pk+fPuTmFhx0ateuHaNHj8bHx0cePe/duzcAgYGBWFlZFbn5+Pg8tg8pKSk4ODiU+B6EZ5tYTKQM2bxkzrW4e9xNemDorgiCIAhPKSNHQ+1puw3S9tmZAViYFP+/7LCwMKysrABIT0/HxcWFsLAweenmuLg4atWqVei1+ccfzvktjnr16jF9+nQAqlevztKlS9m7dy/t2rXTq558Y8eOpUOHDkBezrGPjw8XLlygZs2aOuXMzMywsrLCyMgItVqtc27VqlVkZGQU2YaxsXGR5zZv3kx0dDTffPNNifovPPtEkFyG7JzzJqu+d0MEyYIgCEL5ad26NStWrADg7t27LF++nMDAQI4ePYq7uzvAU41OF6ZevXo6+y4uLvLyv09bX/7ywcnJyQWC5MepXLlyidrev38/7777LitXrnziaLPw/BJBchmyV+cFyXeTip/XJQiCIFRM5sYqzs4MMFjb+rC0tMTLy0veX7VqFba2tqxcuZLZs2fj7e1NbGxsodfmH/f29tarzUdHZRUKBVqtVq86iqovf/EHfesLDAzk8OHDRZ53d3fnzJkzOscOHjxIp06dWLhwIe+8845e7QnPFxEkl6GHR5IlrYRCKVZtEgRBeFYpFAq9Uh4qEoVCgVKplFMPgoKC6Nu3Lzt27CiQl/zFF1/g6OhY4jQJQzAxMUGj0RQ4rm+6xYEDB+jYsSPz5s1jyJAhpd5P4dnybP62PyNsKpmjVCrIzdaSdi8LawczQ3dJEARBeAFkZWWRlJQE5KVbLF26lLS0NDkgDgoKYsuWLQQHB7NgwQLatm1Lamoqy5Yt45dffmHLli1YWloa8hb04uHhQXx8PDExMVSpUgVra2tMTU31SrfYv38/HTt25KOPPqJHjx7y62diYiIe3ntBidktypBKpcTWyRyAe+LhPUEQBKGc7Nq1CxcXF1xcXGjatCnR0dFs2bIFPz8/IG9kefPmzUyaNImFCxdSo0YNWrRoQUJCAgcOHKBr165yXflTsh04cMAg91Ic3bt3p3379rRu3ZpKlSrx448/6l3HmjVrePDgAXPnzpVfOxcXF7p3714GPRaeBQqptDP3n0GpqanY2tqSkpKCjY1Nqdb91+4EsjNyqfGqGnv1s/OuXBAE4UWXmZlJfHw8np6emJm9uJ8E7t+/n+7du/PPP/9gb29v6O7o0Gg0HD9+nAYNGqBS6Ze3Lby4ivu7LdItyljDAHdDd0EQBEEQSuzXX39l0qRJFS5AFoSyJoJkQRAEQRCKtGDBgqe6fv369QwdOrTQc4XNLiEIFYUIksuYJEmk38si9VYGrtXFu3BBEAThxdK5c2eaNm1a6LnHLeYhCIYmguQylvUglzWfRAAweFFLTMzESy4IgiC8OKytrbG2tjZ0NwRBb2J2izJmZmmMuXXeO+WU5KLnahQEQRAEQRAqDhEkl4P8RUXu3hAr7wmCIAiCIDwLRJBcDuzzg2QxV7IgCIIgCMIzQQTJ5cDOOW9+5Hs3RJAsCIIgCILwLBBBcjmwV4uRZEEQBEEQhGeJCJLLQX5OcsqNB0jaF36BQ0EQBEHQoVAo2L59e7HLh4aGYmdnV2b9EQQQQXK5sHnJjAbtqtIiyButCJIFQRCEMjRgwAAUCoW8OTo60r59e06ePKlTTqPRsHDhQurWrYuZmRn29vYEBgYSHh5e7n1OTEwkMDCw2OV79+5NXFycvD9jxgx8fX1LrT8XLlzA2tpaBOIvOBEklwOlSslrPbyo3dwVlZF4yQVBEISy1b59exITE0lMTGTv3r0YGRnRsWNH+bwkSQQFBTFz5kw++ugjYmNjOXDgAG5ubvj5+ek1qlsa1Go1pqamxS5vbm6Ok5NTmfQlJyeHPn360KJFizKpX3h2iIhNEARBEJ4zpqamqNVq1Go1vr6+TJw4kStXrnDz5k0ANm/ezE8//cQPP/zAoEGD8PT0pH79+nz77bd07tyZQYMGkZ5e/GlL/fz8GDlyJOPHj8fBwQG1Wk1ISEixr3843eLSpUsoFAq2bt1K69atsbCwoH79+kRGRsrlH0632LFjB7NmzeLEiRPy6HloaGix237UlClTqFmzJr169SpxHcLzQQTJ5STrQQ7X4u5y/fw9Q3dFEARBKAlJgux0w2xSyVP10tLSWLduHV5eXjg6OgKwYcMGvL296dSpU4HyH3/8Mbdv32bPnj16tbNmzRosLS2Jiopi/vz5zJw5U+86HjZ58mTGjh1LTEwM3t7e9OnTh9zc3ALl2rVrx+jRo/Hx8ZFHz3v37g1AYGAgVlZWRW4+Pj46de3bt48tW7awbNmyEvdbeH6INZLLSfzJW+wNjaVyDTu6jm5o6O4IgiAI+sp5AHNcDdP2pOtgYlns4mFhYVhZWQGQnp6Oi4sLYWFhKJV5Y2NxcXHUqlWr0Gvzjz+c81sc9erVY/r06QBUr16dpUuXsnfvXtq1a6dXPfnGjh1Lhw4dgLycYx8fHy5cuEDNmjV1ypmZmWFlZYWRkRFqtVrn3KpVq8jIKHq1W2NjY/nr27dvM2DAANatW4eNjU2J+iw8X0SQXE7yZ7i4J6aBEwRBEMpY69atWbFiBQB3795l+fLlBAYGcvToUdzd3YG8vOTSVK9ePZ19FxcXkpOTS6U+FxcXAJKTkwsEyY9TuXLlYpcdPHgwffv2pWXLlsXvpPBcE0FyOclfdS89JZvsjFxMzMVLLwiC8Ewxtsgb0TVU23qwtLTEy8tL3l+1ahW2trasXLmS2bNn4+3tTWxsbKHX5h/39vbWr4sPjcpCXp6xVqvVq46i6lMoFAB61xcYGMjhw4eLPO/u7s6ZM2eAvFSLX375hc8//xzIexOh1WoxMjLi22+/5b333tP3FoRnnIjUyomphTHmNiZkpGZzL/kBTu7ioxxBEIRnikKhV8pDRaJQKFAqlXLqQVBQEH379mXHjh0F8pK/+OILHB0dS5wmYQgmJiZoNJoCx/VJt4iMjNSp4+eff2bevHlEREToNSItPD8M+uCeh4eHzlyO+duwYcMAyMzMZNiwYTg6OmJlZUWPHj24ceOGTh2XL1+mQ4cOWFhY4OTkxLhx4wpN7K8I8keTxcp7giAIQlnKysoiKSmJpKQkYmNjGTFiBGlpaXJAHBQURLdu3QgODua7777j0qVLnDx5kqFDh/LLL7+watUqLC2fnTcEHh4exMfHExMTw61bt8jKygLy0i28vLyK3PJTTyAvF7tOnTryVrlyZZRKJXXq1MHe3t5QtyYYkEGD5OjoaPlJ1MTERPkp2J49ewIwevRoduzYwZYtWzh48CDXr1+ne/fu8vUajYYOHTqQnZ1NREQEa9asITQ0lGnTphnkfp7E7t/lqe/dEEGyIAiCUHZ27dqFi4sLLi4uNG3alOjoaLZs2YKfnx+QN7K8efNmJk2axMKFC6lRowYtWrQgISGBAwcO0LVrV7mu/CnZDhw4YJB7KY7u3bvTvn17WrduTaVKlfjxxx8N3SXhOaCQSjtz/ymMGjWKsLAwzp8/T2pqKpUqVWLDhg289dZbAPz999/UqlWLyMhIXn31VX777Tc6duzI9evXcXZ2BuDrr79mwoQJ3Lx5ExMTk2K1m5qaiq2tLSkpKWX6RGvM75cJ/+kC1RpWov2QumXWjiAIgvD0MjMziY+Px9PTEzMzM0N3x2D2799P9+7d+eeffyrciKpGo+H48eM0aNAAlUpl6O4Iz4ji/m5XmHmSs7OzWbduHe+99x4KhYJjx46Rk5ODv7+/XKZmzZpUrVpVnlA8MjKSunXrygEyQEBAAKmpqXIifmGysrJITU3V2cpD1dqOtOpbg4YB7k8uLAiCIAgVwK+//sqkSZMqXIAsCGWtwjy4t337du7du8eAAQMASEpKwsTEpMC66c7OziQlJcllHg6Q88/nnyvK3LlzmTFjRul1vpgcXC1xcH12crwEQRAEYcGCBU91/fr16xk6dGih5x6eXUIQKpoKEyR/9913BAYG4upa9hO1f/LJJ4wZM0beT01Nxc3NrczbFQRBEIQXTefOnWnatGmh5x6dNk4QKpIKESQnJCTw+++/s3XrVvmYWq0mOzube/fu6Ywm37hxQ15RR61Wc/ToUZ268me/eHTVnYeZmppiampaindQfDev3Ofm5fu4VLPFXi1GlQVBEITnm7W1NdbW1obuhiDorULkJK9evRonJyd5+UmARo0aYWxszN69e+Vj586d4/LlyzRr1gyAZs2acerUKZ0Vffbs2YONjQ21a9cuvxvQw7FfL7F/7d9cPnPH0F0RBEEQBEEQimDwkWStVsvq1asJDg7GyOi/7tja2jJw4EDGjBmDg4MDNjY2jBgxgmbNmvHqq68C8MYbb1C7dm369+/P/PnzSUpKYsqUKQwbNsxgI8VPkj8N3N2kdAP3RBAEQRAEQSiKwYPk33//ncuXLxe63OPChQtRKpX06NGDrKwsAgICWL58uXxepVIRFhbGBx98QLNmzbC0tCQ4OJiZM2eW5y3oJT/FQiwoIgiCIAiCUHEZPEh+4403KGqqZjMzM5YtW8ayZcuKvN7d3Z1ff/21rLpX6uycxYIigiAIgiAIFV2FyEl+keQvTf0gNZusBzkG7o0gCIIgCIJQGBEklzMTcyMsbfNWArwrRpMFQRAEAYVCwfbt24tdPjQ0tMA6CoJQ2kSQbAD5D++JlAtBEAShtA0YMACFQiFvjo6OtG/fnpMnT+qU02g0LFy4kLp162JmZoa9vT2BgYGEh4eXe58TExMJDAwsdvnevXsTFxcn78+YMQNfX9+n7sfu3bt59dVXsba2plKlSvTo0YNLly49db3Cs0kEyQbQ+E1POn/ki0fdlwzdFUEQBOE51L59exITE0lMTGTv3r0YGRnRsWNH+bwkSQQFBTFz5kw++ugjYmNjOXDgAG5ubvj5+ek1qlsa1Gq1XrNSmZub4+TkVKp9iI+Pp0uXLrRp04aYmBh2797NrVu36N69e6m2Izw7RJBsAJVr2ONWywEzS7HSkCAIglD6TE1NUavVqNVqfH19mThxIleuXOHmzZsAbN68mZ9++okffviBQYMG4enpSf369fn222/p3LkzgwYNIj29+FOV+vn5MXLkSMaPH4+DgwNqtZqQkJBiX/9wusWlS5dQKBRs3bqV1q1bY2FhQf369YmMjJTLP5xusWPHDmbNmsWJEyfk0fPQ0NBit53v2LFjaDQaZs+eTbVq1WjYsCFjx44lJiaGnBzxDNGLSATJgiAIglAMkiTxIOeBQbaiZoEqjrS0NNatW4eXlxeOjo4AbNiwAW9vbzp16lSg/Mcff8zt27fZs2ePXu2sWbMGS0tLoqKimD9/PjNnztS7jodNnjxZDlK9vb3p06cPubm5Bcq1a9eO0aNH4+PjI4+e9+7dG4DAwECsrKyK3Hx8fOR6GjVqhFKpZPXq1Wg0GlJSUli7di3+/v5i+ewXlMGngHsRaTRaLkTf4O6NBzTp6IlSJd6rCIIgVHQZuRk03dDUIG1H9Y3Cwtii2OXDwsKwsrICID09HRcXF8LCwlAq8/6/iYuLo1atWoVem3/84Zzf4qhXrx7Tp08HoHr16ixdupS9e/fSrl07verJN3bsWHkl3hkzZuDj48OFCxeoWbOmTjkzMzOsrKwwMjJCrVbrnFu1ahUZGRlFtvFw8Ovp6cn//d//0atXL4YOHYpGo6FZs2bP1DSzQukSQbIBKBUK9q8/hyZHS81mLtg5Ff8PnyAIgiA8SevWrVmxYgUAd+/eZfny5QQGBnL06FHc3d0Bnmp0ujD16tXT2XdxcSE5OblU6nNxcQEgOTm5QJD8OJUrVy522aSkJAYPHkxwcDB9+vTh/v37TJs2jbfeeos9e/agUCiK33nhuSCCZANQKBXYOVlw+1oa9248EEGyIAjCM8DcyJyovlEGa1sflpaWeHl5yfurVq3C1taWlStXMnv2bLy9vYmNjS302vzj3t7eerX5aEqCQqFAq9XqVUdR9eUHqPrWFxgYyOHDh4s87+7uzpkzZwBYtmwZtra2zJ8/Xz6/bt063NzciIqK4tVXX9WrbeHZJ4JkA7FX5wXJd5Me4FHX0L0RBEEQnkShUOiV8lCRKBQKlEqlnHoQFBRE37592bFjR4G85C+++AJHR8cSp0kYgomJCRqNpsBxfdItHjx4IKej5FOpVID+wbnwfBBBsoGI5akFQRCEspKVlUVSUhKQl26xdOlS0tLS5IA4KCiILVu2EBwczIIFC2jbti2pqaksW7aMX375hS1btmBpaWnIW9CLh4cH8fHxxMTEUKVKFaytrTE1NdUr3aJDhw4sXLiQmTNnyukWkyZNwt3dnQYNGpRh74WKSjwxZiD2/y4ocjep+FPsCIIgCEJx7Nq1CxcXF1xcXGjatCnR0dFs2bIFPz8/IG9kefPmzUyaNImFCxdSo0YNWrRoQUJCAgcOHKBr165yXflTsh04cMAg91Ic3bt3p3379rRu3ZpKlSrx448/6l1HmzZt2LBhA9u3b6dBgwa0b98eU1NTdu3ahbm5fukuwvNBIZV25v4zKDU1FVtbW1JSUrCxsSmXNpMTUtky90/MrY15b0GLcmlTEARBKL7MzEzi4+Px9PTEzMzM0N0xmP3799O9e3f++ecf7O3tDd0dHRqNhuPHj9OgQQM5NUIQnqS4v9tiJNlA8tMtMu7nkJkuJikXBEEQKqZff/2VSZMmVbgAWRDKmshJNhATMyM6f+SLbSVzTM3Ft0EQBEGomBYsWPBU169fv56hQ4cWeu7h2SUEoaIR0ZkBudVyMHQXBEEQBKFMde7cmaZNC1+ERaxkJ1RkIkgWBEEQBKHMWFtbY21tbehuCILeRJBsQPduPCDuaBLGZkY0aFfV0N0RBEEQBEEQ/iUe3DOg+7czid55idjw64buiiAIgiAIgvAQESQbkN2/cyWnJGeg0YjVfARBEARBECoKESQbkJWdKUYmSrRaifu3Mg3dHUEQBEEQBOFfIkg2IIVSIc+XLFbeEwRBEARBqDhEkGxg9vlB8o0HBu6JIAiCIBiGQqFg+/btxS4fGhqKnZ1dmfVHEEAEyQZnp7YE4F6SCJIFQRCEpzdgwAAUCoW8OTo60r59e06ePKlTTqPRsHDhQurWrYuZmRn29vYEBgYSHh5e7n1OTEwkMDCw2OV79+5NXFycvD9jxgx8fX2fqg+ZmZkMGDCAunXrYmRkRNeuXQstd+DAARo2bIipqSleXl6EhoY+VbtCxSWCZAPLH0m+lyyCZEEQBKF0tG/fnsTERBITE9m7dy9GRkZ07NhRPi9JEkFBQcycOZOPPvqI2NhYDhw4gJubG35+fnqN6pYGtVqNqalpscubm5vj5ORUqn3QaDSYm5szcuRI/P39Cy0THx9Phw4daN26NTExMYwaNYpBgwaxe/fuUu2LUEFIgpSSkiIBUkpKSrm3nZmeLd1NSpdyczXl3rYgCIJQtIyMDOns2bNSRkaGobuil+DgYKlLly46xw4fPiwBUnJysiRJkrRx40YJkH755ZcC13fv3l1ydHSU0tLSit1mq1atpBEjRkjjxo2T7O3tJWdnZ2n69OnFvh6Qtm3bJkmSJMXHx0uA9L///U/y8/OTzM3NpXr16kkRERFy+dWrV0u2trZSbm6uNG3aNAnQ2VavXl3stgtT2GsoSZI0fvx4ycfHR+dY7969pYCAgKdqTyhfxf3dFiPJBmZqYYydswUqlfhWCIIgVGSSJKF98MAgmyRJJe53Wloa69atw8vLC0dHRwA2bNiAt7c3nTp1KlD+448/5vbt2+zZs0evdtasWYOlpSVRUVHMnz+fmTNn6l3HwyZPnszYsWOJiYnB29ubPn36kJubW6Bcu3btGD16ND4+PvLoee/evQEIDAzEysqqyM3Hx0evPkVGRhYYZQ4ICCAyMrLE9ylUXGLFPUEQBEEoBikjg3MNGxmk7Rp/HUNhYVHs8mFhYVhZWQGQnp6Oi4sLYWFhKJV5AzJxcXHUqlWr0Gvzjz+c81sc9erVY/r06QBUr16dpUuXsnfvXtq1a6dXPfnGjh1Lhw4dgLycYx8fHy5cuEDNmjV1ypmZmWFlZYWRkRFqtVrn3KpVq8jIyCiyDWNjY736lJSUhLOzs84xZ2dnUlNTycjIwNzcXK/6hIpNBMkVQFx0Egmnb+PVyBnPei8ZujuCIAjCM65169asWLECgLt377J8+XICAwM5evQo7u7uAE81Ol2YevXq6ey7uLiQnJxcKvW5uLgAkJycXCBIfpzKlSuXuH1BEEFyBZD0TypxUTewtDEVQbIgCEIFpTA3p8ZfxwzWtj4sLS3x8vKS91etWoWtrS0rV65k9uzZeHt7ExsbW+i1+ce9vb31avPRUVmFQoFWW/LVZB+uT6FQAOhdX2BgIIcPHy7yvLu7O2fOnCl2fWq1mhs3bugcu3HjBjY2NmIU+TkkguQKQMyVLAiCUPEpFAq9Uh4qEoVCgVKplFMPgoKC6Nu3Lzt27CiQl/zFF1/g6OhY4jQJQzAxMUGj0RQ4XtrpFs2aNePXX3/VObZnzx6aNWumVz3Cs0EEyRWAnfrfaeBEkCwIgiCUgqysLJKSkoC8dIulS5eSlpYmB8RBQUFs2bKF4OBgFixYQNu2bUlNTWXZsmX88ssvbNmyBUtLS0Pegl48PDyIj48nJiaGKlWqYG1tjampqd7pFmfPniU7O5s7d+5w//59YmJiAOQ5mN9//32WLl3K+PHjee+999i3bx+bN29m586dpXxHQkUgguQKwN457w9Rys0MNLlaVEZipgtBEASh5Hbt2iXn8VpbW1OzZk22bNmCn58fkDeyvHnzZhYtWsTChQv58MMPMTMzo1mzZhw4cIDmzZvLdV26dAlPT0/2798vX1/RdO/ene3bt9O6dWvu3bvH6tWrGTBggN71vPnmmyQkJMj7DRo0AP7L3/b09GTnzp2MHj2axYsXU6VKFVatWkVAQECp3IdQsSik0s7cfwalpqZia2tLSkoKNjY25d6+JEmsHHWInCwNfUOaYq9+dt69C4IgPK8yMzOJj4/H09MTMzMzQ3fHYPbv30/37t35559/sLe3N3R3dGg0Go4fP06DBg1QqVSG7o7wjCju77YYsqwAFAoFdvl5yWJ5akEQBKEC+fXXX5k0aVKFC5AFoawZPEi+du0ab7/9No6Ojpibm1O3bl3+/PNP+bwkSUybNg0XFxfMzc3x9/fn/PnzOnXcuXOHfv36YWNjg52dHQMHDiQtLa28b+Wp2KstUCgg/V6WobsiCIIgCLIFCxYwbty4El+/fv36UlvMQxDKk0Fzku/evUvz5s1p3bo1v/32G5UqVeL8+fM671bnz5/PkiVLWLNmDZ6enkydOpWAgADOnj0rD5H369ePxMRE9uzZQ05ODu+++y5Dhgxhw4YNhro1vbXo5U2b/rVQGRv8fYsgCIIglJrOnTvTtGnTQs/pO7uEIJQng+YkT5w4kfDw8CLnMJQkCVdXVz7++GPGjh0LQEpKCs7OzoSGhhIUFERsbCy1a9cmOjqaV155Bch7YOHNN9/k6tWruLq6PrEfhs5JFgRBECoekZNc8YmcZKEknomc5F9++YVXXnmFnj174uTkRIMGDVi5cqV8Pj4+nqSkJJ110m1tbWnatKm8TnpkZCR2dnZygAzg7++PUqkkKiqq/G5GEARBEARBeG4YNEj+559/WLFiBdWrV2f37t188MEHjBw5kjVr1gDIczwWtk56/rmkpCScnJx0zhsZGeHg4CCXeVRWVhapqak6m6FJksS+tbH8b/6fZNzPNnR3BEEQBEEQXmgGzUnWarW88sorzJkzB8ibj/D06dN8/fXXBAcHl1m7c+fOZcaMGWVWf0koFAqu/n2X+7czuZv0AHNrE0N3SRAEQRAE4YVl0JFkFxcXateurXOsVq1aXL58GchbIx0odJ30/HNqtZrk5GSd87m5udy5c0cu86hPPvmElJQUebty5Uqp3M/TslfnTwOXbuCeCIIgCIIgvNgMGiQ3b96cc+fO6RyLi4vD3d0dyFvZRq1Ws3fvXvl8amoqUVFR8jrpzZo14969exw7dkwus2/fPrRabZFP05qammJjY6OzVQT5cyWL5akFQRAEQRAMy6BB8ujRo/njjz+YM2cOFy5cYMOGDXz77bcMGzYMyEtBGDVqFLNnz+aXX37h1KlTvPPOO7i6utK1a1cgb+S5ffv2DB48mKNHjxIeHs7w4cMJCgoq1swWFUn+Snt3RZAsCIIgPKMUCgXbt283dDcMxsPDg0WLFsn7z+LrERoaip2dnV7X+Pn5MWrUqDLpj6EYNEhu3Lgx27Zt48cff6ROnTrMmjWLRYsW0a9fP7nM+PHjGTFiBEOGDKFx48akpaWxa9cunSk71q9fT82aNWnbti1vvvkmr7/+Ot9++60hbumpyCPJYtU9QRAEoYQGDBiAQqFAoVBgbGyMp6cn48ePJzMz09BdK1MP37dCocDR0ZH27dtz8uRJg/YrMTGRwMBAg/ZBX7179yYuLk6va7Zu3cqsWbPk/UffLJTE3Llzady4MdbW1jg5OdG1a9cCGQhlyaAP7gF07NiRjh07FnleoVAwc+ZMZs6cWWQZBweHZ2rhkKLk5ySn3spAk6MVC4sIgiAIJdK+fXtWr15NTk4Ox44dIzg4GIVCwbx58wzdtTKVf9+QN/vVlClT6Nixo/yskyEU9XxURWZubo65uble1zg4OJR6Pw4ePMiwYcNo3Lgxubm5TJo0iTfeeIOzZ89iaWlZ6u09SkRhFYiFjQkm5kZY2ZvxQEwDJwiCIJSQqakparUaNzc3unbtir+/P3v27JHP3759mz59+lC5cmUsLCyoW7cuP/74o04dfn5+jBw5kvHjx+Pg4IBarSYkJESnzPnz52nZsiVmZmbUrl1bp418p06dok2bNpibm+Po6MiQIUNIS0uTzw8YMICuXbsyZ84cnJ2dsbOzY+bMmeTm5jJu3DgcHByoUqWKHPwW577VajW+vr5MnDiRK1eucPPmTbnMhAkT8Pb2xsLCgpdffpmpU6eSk5Mjnz9x4gStW7fG2toaGxsbGjVqxJ9//imfP3LkCC1atMDc3Bw3NzdGjhxJenrRD9w/nG5x6dIlFAoFW7dupXXr1lhYWFC/fn157YeStvGo4rZTlEfTLUJCQvD19WXt2rV4eHhga2tLUFAQ9+/fl8s8nG7h5+dHQkICo0ePlkf2S2LXrl0MGDAAHx8f6tevT2hoKJcvX9Z5Dq0siSC5AlEoFLw3/3XemfMa1g5idSdBEISKKCdLU+SWm6Mpftns4pV9WqdPnyYiIgITk/+mFs3MzKRRo0bs3LmT06dPM2TIEPr378/Ro0d1rl2zZg2WlpZERUUxf/58Zs6cKQfCWq2W7t27Y2JiQlRUFF9//TUTJkzQuT49PZ2AgADs7e2Jjo5my5Yt/P777wwfPlyn3L59+7h+/TqHDh3iyy+/ZPr06XTs2BF7e3uioqJ4//33GTp0KFevXi32faelpbFu3Tq8vLxwdHSUj1tbWxMaGsrZs2dZvHgxK1euZOHChfL5fv36UaVKFaKjozl27BgTJ06Ul8++ePEi7du3p0ePHpw8eZJNmzZx5MiRAvfzJJMnT2bs2LHExMTg7e1Nnz59yM3NLdU2ntSOvi5evMj27dsJCwsjLCyMgwcP8tlnnxVaduvWrVSpUoWZM2eSmJhIYmIiAJcvX8bKyuqxW/60wIVJSUkBymbUujAGT7cQdIkUC0EQhIrt248OFnnOvY4jHYfXl/e/H3eY3GxtoWVdq9vR7eOG8v4PkyPITMspUG7Y12307mNYWBhWVlbk5uaSlZWFUqlk6dKl8vnKlSszduxYeX/EiBHs3r2bzZs306RJE/l4vXr1mD59OgDVq1dn6dKl7N27l3bt2vH777/z999/s3v3bvlB+Tlz5ujk327YsIHMzEx++OEH+ePxpUuX0qlTJ+bNmycvFubg4MCSJUtQKpXUqFGD+fPn8+DBAyZNmgTkTd362WefceTIEYKCgp5435AXoLu4uBAWFoZS+d//rVOmTJG/9vDwYOzYsWzcuJHx48cDeYHcuHHjqFmzpnzf+ebOnUu/fv3kEdPq1auzZMkSWrVqxYoVK4q9fPnYsWPp0KEDADNmzMDHx4cLFy5Qs2bNUmvjSe3oS6vVEhoairW1NQD9+/dn7969fPrppwXKOjg4oFKpsLa21kk3cXV1JSYm5rHtFBUAa7VaRo0aRfPmzalTp47e/S+JEgfJ2dnZxMfHU61aNYyMRKwtCIIgCBVF69atWbFiBenp6SxcuBAjIyN69Oghn9doNMyZM4fNmzdz7do1srOzycrKwsLCQqeeevXq6ey7uLjIaxPExsbi5uamM5NU/vSs+WJjY6lfv75O/mjz5s3RarWcO3dODpJ9fHx0AllnZ2edQEilUuHo6FhgXYSi7hvg7t27LF++nMDAQI4ePSpPL7tp0yaWLFnCxYsXSUtLIzc3V2cq2DFjxjBo0CDWrl2Lv78/PXv2pFq1akBeKsbJkydZv369XF6SJLRaLfHx8dSqVeux/cv38Ovq4uICQHJyMjVr1iy1Np7Ujr48PDzkADm/vid9Px5lZGSEl5eX3m0DDBs2jNOnT3PkyJESXV8Seke3Dx48YMSIEfLS0XFxcbz88suMGDGCypUrM3HixFLv5Ivk5uX7HNlyHmNTlc5ohCAIglAxDFncqshzikc+DHxvQYuiyz6SpvnOp689Tbd0WFpaysHI999/T/369fnuu+8YOHAgAAsWLGDx4sUsWrSIunXrYmlpyahRo8jO1n0eJj/N4L8+K9BqCx8ZfxqFtVOSth++b4BVq1Zha2vLypUrmT17NpGRkfTr148ZM2YQEBCAra0tGzdu5IsvvpCvCQkJoW/fvuzcuZPffvuN6dOns3HjRrp160ZaWhpDhw5l5MiRBdquWrVqie43P183/95Kq40ntaOv0vhZuHz5coFF5B41adIk+ROEfMOHDycsLIxDhw5RpUoVvdp8GnoHyZ988gknTpzgwIEDtG/fXj7u7+9PSEiICJIfsv9cMv87dpXFQQ1QKYuXtK40UnD9/D1MzFRIklTiZHdBEAShbBibqgxeVh9KpZJJkyYxZswY+vbti7m5OeHh4XTp0oW3334byAuc4uLinhjAPKxWrVpcuXKFxMREeZTyjz/+KFAmNDSU9PR0eTQ5PDxcTqsoawqFAqVSSUZGBgARERG4u7szefJkuUxCQkKB67y9vfH29mb06NH06dOH1atX061bNxo2bMjZs2dLPBpaHOXRRnkwMTFBo9HNqdc33UKSJEaMGMG2bds4cOAAnp6eZdHVIumdALt9+3aWLl3K66+/rhPA+fj4cPHixVLt3LMsJSOHkRuOE3YykR+PFn/qGbtKFigUkJ2p4UGqmOFCEARBeHo9e/ZEpVKxbNkyIC/Pdc+ePURERBAbG8vQoUO5ceOGXnX6+/vj7e1NcHAwJ06c4PDhwzrBJ+Q9BGdmZkZwcDCnT59m//79jBgxgv79+8upFqUpKyuLpKQkkpKSiI2NZcSIEaSlpdGpUycg774vX77Mxo0buXjxIkuWLGHbtm3y9RkZGQwfPpwDBw6QkJBAeHg40dHRcorDhAkTiIiIYPjw4cTExHD+/Hl+/vnnEj1UV5TyaKM8eHh4cOjQIa5du8atW7eA/9ItHrc9HCQPGzaMdevWsWHDBqytreXvbf6bnrKmd5B88+ZNnJycChxPT08Xo54PsTU35uM3vAFYsPsct9KyinWdyliJ9Ut5cxOKRUUEQRCE0mBkZMTw4cOZP38+6enpTJkyhYYNGxIQEICfnx9qtVpeyba4lEol27ZtIyMjgyZNmjBo0KACD3FZWFiwe/du7ty5Q+PGjXnrrbdo27atzkOEpWnXrl24uLjg4uJC06ZN5Rk1/Pz8AOjcuTOjR49m+PDh+Pr6EhERwdSpU+XrVSoVt2/f5p133sHb25tevXoRGBjIjBkzgLwc34MHDxIXF0eLFi1o0KAB06ZNK9UVfovTRkhICB4eHqXWZlmYOXMmly5dolq1alSqVKlEdaxYsYKUlBT8/Pzk76uLiwubNm0q5d4WTiFJkqTPBS1btqRnz56MGDECa2trTp48iaenJyNGjOD8+fPs2rWrrPpaZlJTU7G1tSUlJUUnef9p5Wq0dF4aztnEVN5qVIXPexYvxzhs6QkSTt+mVd8a1GlZudT6IwiCIBRfZmYm8fHxeHp66jWjgFB+NBoNx48fp0GDBqhUZZOuUhHlLw4TGhpq6K48k4r7u613TnL+9C5nz54lNzeXxYsXc/bsWSIiIjh4sOhpcV5ERiols7vVofvyCH46dpXejd1o7PHkuf3s1BYknL4tRpIFQRAEQdAhSRIHDhwo11keXlR6p1u8/vrrxMTEkJubS926dfm///s/nJyciIyMpFGjRmXRx2daw6r2BDV2A2DKttPkaJ78JKi9c94UPHdviCBZEARBEIT/KBQKEhIScHNzK3EdgYGBJVrM40VTogmOq1WrxsqVK0u7L8+tCe1rsvtMEudu3GdNxCUGtXj5seXt1RZY2ppgZiXmnxYEQRAEoXStWrWqyIffyms1u2eB3lHY5cuPn6lB3zn8XgT2liZMDKzJhP+dYuGeODrWc0VtW3QOjGt1ewbMe70ceygIgiAIwouicmXxvFNx6B0ke3h4PHYWi0fnxBPy9GzkxqboK/x1+R6zdp5lWd+GT75IEARBEARBMAi9g+Tjx4/r7Ofk5HD8+HG+/PLLQtfvFvIolQpmda1Dp6+OsPNkIkGNb9Ki+pOnRBELigiCIAiCIJQ/vYPk+vULTmP2yiuv4OrqyoIFC+jevXupdOx55ONqyzvNPAiNuMS0n8+wa1QLTI0Kn7Lmr90JnNx/lTotXXnlzfJdYUYQBEEQBOFFV2pPhtWoUYPo6OjSqu75ELkccjPBqTY41QJbN8a84c3OU4nE30rn24P/MKJt9UIvlSSJ9HtZxJ+4hcpIhcpYicpIgZGxkuqNnVGq8iYmuZuUTlZGLiojJUbGSlRGyn/LKjE2U6FS6T2BiSAIgiAIwgtP7yA5NTVVZ1+SJBITEwkJCaF69cIDvhdW9Eoybl5GhYSJSgMmVthUqslPru6EXrDgzwNnuFajN5UrV4VHUiocXa0ASE64T3LCfZ1zXo3/W8ozeuclzkcXvpSoiZmKtya+gr3aspRvTBAEQRAE4fmmd5BsZ2dXIEdWkiTc3NzYuHFjqXXsmSdJhCU34Nz5yrSvkYaP8jRkp8G1P3HnT6Yb/1tu1adg4fjfaLNTLXCqTdWXa9C088vcv52BJlciN0eLJleLNleLUvnf629mZYy1oxmaXC2anLwtN1cLErzkZk3G/WwRJAuCIAjlRqFQsG3bNr2XuX5eeHh4MGrUKEaNGgU8m69HaGgoo0aN4t69e8W+xs/PD19fXxYtWlRm/SpvegfJ+/fv19lXKpVUqlQJLy8vjIzEvL4yhQIr79fg/HYSq/bC5909cPsiJJ+F5FjSrpzk5j8xuHMD5YPbcOlw3vYvJfCKtSuY2erWqwKW/7fbEmj5yJLxkgRalCgUCpQHzCDcHIwtwMQi71/jf/fzvzax/PeYORhb/nfMqVbev4IgCMIzY8CAAaxZswYAIyMjqlSpQs+ePZk5c+Zzvbz2w/cNefP9Nm7cmPnz51OvXj2D9SsxMRF7e3uDtV8SvXv35s0339Trmq1bt2JsbCzvP/pm4Wl99tlnfPLJJ3z00UflFojrHdW2atWqLPrxXHKpXgOApItxoDIGp5p5G2AFLNv1N98fiOU1m1t8HWCO6e1zkBybt6VehfvX8zY9KciLpZ/+BnxhyIECqSCCIAhCxda+fXtWr15NTk4Ox44dIzg4GIVCwbx58wzdtTKVf98ASUlJTJkyhY4dOz5xjYeypFarDdZ2SZmbm2Nubq7XNWW5CEl0dDTffPNNub/ZKdZTXb/88kuxN+E/6mreANxMiCcnO6vA+RFtvHjJzpb9qa4svvkKvDEL3v4JxpyBiZdh0F4I3lHyrf92MrpuJKfzd9B5KQQuAP8QaDUBXhsBrwyE+n2hdleo/gZ4tIDKjaBSLVCZQGIMnN9Trq+ZIAiC8PRMTU1Rq9W4ubnRtWtX/P392bPnv7/nt2/fpk+fPlSuXBkLCwvq1q3Ljz/+qFOHn58fI0eOZPz48Tg4OKBWqwkJCdEpc/78eVq2bImZmRm1a9fWaSPfqVOnaNOmDebm5jg6OjJkyBDS0tLk8wMGDKBr167MmTMHZ2dn7OzsmDlzJrm5uYwbNw4HBweqVKkiB7/FuW+1Wo2vry8TJ07kypUr3Lx5Uy4zYcIEvL29sbCw4OWXX2bq1Knk5OTI50+cOEHr1q2xtrbGxsaGRo0a8eeff8rnjxw5QosWLTA3N8fNzY2RI0eSnp5eZJ8UCgXbt28H4NKlSygUCrZu3Urr1q2xsLCgfv36REZG6lyjbxuPKm47RQkNDcXOzk7eDwkJwdfXl7Vr1+Lh4YGtrS1BQUHcv//fM1N+fn7yqLGfnx8JCQmMHj0ahULxVFPZpqWl0a9fP1auXFnuI/LFGkkubh6NQqEQi4k8xKaSExa2djxIuUdy/D9UrlFL57yFiRHTOtVm6NpjrDz8D90bVsHLKe+BPcxsocorT9X+zuUnuXTyFu2HNqJaAyf9Lv6/KRDxFYQvBu83nqofgiAIz5OczMwizymUSoxMTIpVFqUCYxPTJ5Y1fsoUidOnTxMREYG7u7t8LDMzk0aNGjFhwgRsbGzYuXMn/fv3p1q1ajRp0kQut2bNGsaMGUNUVBSRkZEMGDCA5s2b065dO7RaLd27d8fZ2ZmoqChSUlIKfLSenp5OQEAAzZo1Izo6muTkZAYNGsTw4cMJDQ2Vy+3bt48qVapw6NAhwsPDGThwIBEREbRs2ZKoqCg2bdrE0KFDadeuHVWqVCnWfaelpbFu3Tq8vLxwdHSUj1tbWxMaGoqrqyunTp1i8ODBWFtbM378eAD69etHgwYNWLFiBSqVipiYGDmN4OLFi7Rv357Zs2fz/fffc/PmTYYPH87w4cOLFcTnmzx5Mp9//jnVq1dn8uTJ9OnThwsXLmBkZFRqbTypHX1dvHiR7du3ExYWxt27d+nVqxefffZZoWtkbN26lfr16zNkyBAGDx4sH798+TK1a9d+bDuTJk1i0qRJ8v6wYcPo0KED/v7+zJ49W+9+P41ivUparbas+/FcUigUqL28+efYUZIuxBUIkgHeqO1Mm5pO7Ps7mWk/n2b9oKaltniIhW3eH+ob8an6B8lNP4A/voaEI3D1z6cO2AVBEJ4XS4LfKvKcZ4NX6D4xRN5fPqQfuVkFP0kEqFK7Dr2nfybvrxz+Hhn3UwuU+3hTmN59DAsLw8rKitzcXLKyslAqlSxdulQ+X7lyZcaOHSvvjxgxgt27d7N582adILlevXpMnz4dgOrVq7N06VL27t1Lu3bt+P333/n777/ZvXs3rq55D8fMmTOHwMBA+foNGzaQmZnJDz/8gKVl3jMuS5cupVOnTsybNw9n57zZmhwcHFiyZAlKpZIaNWowf/58Hjx4IAdLn3zyCZ999hlHjhwhKCjoifcNeQG6i4sLYWFhKJX/fXA+ZcoU+WsPDw/Gjh3Lxo0b5SD58uXLjBs3jpo1a8r3nW/u3Ln069dPfjNQvXp1lixZQqtWrVixYkWxc77Hjh1Lhw4dAJgxYwY+Pj5cuHCBmjVrllobT2pHX1qtltDQUKytrQHo378/e/fuLTRIdnBwQKVSYW1trZNu4urqSkxMzGPbeThtY+PGjfz1118Gm2JYPGlXxlyq5QXJiRfOFXpeoVAQ0smH8Au3iLh4mx0nE+lc37XQsvpy9rDh7OHr3Igv+Ef3iWwrQ71eELM+bzS599pS6ZMgCIJQ9lq3bs2KFStIT09n4cKFGBkZ0aNHD/m8RqNhzpw5bN68mWvXrpGdnU1WVhYWFhY69TyaA+ri4kJycjIAsbGxuLm5yQEyQLNmzXTKx8bGUr9+fTlABmjevDlarZZz587JQbKPj49OIOvs7EydOnXkfZVKhaOjo9z2k+4b4O7duyxfvpzAwECOHj0qj6Rv2rSJJUuWcPHiRdLS0sjNzcXGxkauY8yYMQwaNIi1a9fi7+9Pz549qVatGpCXinHy5EnWr18vl5ckCa1WS3x8PLVqFRwMK8zDr6uLiwsAycnJ1KxZs9TaeFI7+vLw8JAD5Pz6nvT9eJSRkRFeXl7FKnvlyhU++ugj9uzZY7AHTksUJKenp3Pw4EEuX75Mdna2zrmRI0eWSseeF1Xr+pJ6KxmP+g2LLuNowbDWXny5J45ZYWfxq1EJGzPjIssXl7NH3i998uX7aLWSztRxxfLaiLwgOXZH3swcjtWeuk+CIAjPupFrfirynEKp+6jPh9+uL6Ik8Mjf5MFLv3+qfj3M0tJSDka+//576tevz3fffcfAgQMBWLBgAYsXL2bRokXUrVsXS0tLRo0aVeD/9IdnK4C8gZ2y+HS5sHZK0vbD9w2watUqbG1tWblyJbNnzyYyMpJ+/foxY8YMAgICsLW1ZePGjXzxxRfyNSEhIfTt25edO3fy22+/MX36dDZu3Ei3bt1IS0tj6NChhcY6VatWLdH95n96nH9vpdXGk9rRV2n8LOiTbnHs2DGSk5Np2PC/+Emj0XDo0CGWLl1KVlYWKlWpTFNQJL2D5OPHj/Pmm2/y4MED0tPTcXBw4NatW1hYWODk5CSC5Ee4etfE1fvJ79iGtHyZbcevEX8rnYV74pjeyeep27Z3scTYVEVOloa7iek4VrbSrwKnWuDdHuJ25eUnd1r01H0SBEF41umTI1xWZfWhVCqZNGkSY8aMoW/fvpibmxMeHk6XLl14++23gbzAKS4u7okBzMNq1arFlStXSExMlEcp//jjjwJlQkNDSU9Pl0eTw8PD5bSKsqZQKFAqlWRkZADIudmTJ0+WyyQkJBS4ztvbG29vb0aPHk2fPn1YvXo13bp1o2HDhpw9e7bYo6ElUR5tlAcTE5MCz6npk27Rtm1bTp06pXPu3XffpWbNmkyYMKHMA2Qo5uwWDxs9ejSdOnXi7t27mJub88cff5CQkECjRo34/PPPy6KPLwQzYxUzOucFxmsiLnHmespT16lUKnDyyPtopEQpFwDNP8r7N2YDpOn3sYogCIJQMfTs2ROVSsWyZcuAvDzXPXv2EBERQWxsLEOHDuXGjcJXby2Kv78/3t7eBAcHc+LECQ4fPqwTfELeQ3BmZmYEBwdz+vRp9u/fz4gRI+jfv7+calGasrKySEpKIikpidjYWEaMGEFaWhqdOnUC8u778uXLbNy4kYsXL7JkyRK2bdsmX5+RkcHw4cM5cOAACQkJhIeHEx0dLac4TJgwgYiICIYPH05MTAznz5/n559/Zvjw4aV2D+XRRnnw8PDg0KFDXLt2jVu3bgH/pVs8bssPkq2tralTp47OZmlpiaOjo04qTlnSO0iOiYnh448/RqlUolKpyMrKws3Njfnz5+s8jSj8R5ObQ9LF80XmJedr6V2JDnVd0EowdftptFrpqdvOT7m4EV/CoLtqM6jSGDRZEPXNU/dHEARBKH9GRkYMHz6c+fPnk56ezpQpU2jYsCEBAQH4+fmhVqv1XhFOqVSybds2MjIyaNKkCYMGDSrwEJeFhQW7d+/mzp07NG7cmLfeeou2bdvqPERYmnbt2oWLiwsuLi40bdqU6OhotmzZgp+fHwCdO3dm9OjRDB8+HF9fXyIiIpg6dap8vUql4vbt27zzzjt4e3vTq1cvAgMDmTFjBpCX43vw4EHi4uJo0aIFDRo0YNq0aTp52U+rOG2EhITg4eFRam2WhZkzZ3Lp0iWqVatGpUqVDN2dElFIkqRXJFapUiUiIiKoXr063t7efPXVVwQEBPD333/TqFEjvebxqyhSU1OxtbUlJSVFJ3m/tMT836/s/W45HvUb0mPSzMeWTUrJpO0XB0jP1jC/Rz16NXZ7qravnbvL+T9v4F7HEc/6JfwhPfsLbO4PZnYw+gyY6pm2IQiC8AzKzMwkPj4eT0/P53qlumeZRqPh+PHjNGjQoFw+fq8o8heHeXgaPaH4ivu7rXdOcoMGDYiOjqZ69eq0atWKadOmcevWLdauXVtuw9/PGhevvEVFki7EIUnSY6d4U9uaMcrfm09/jWXub7G0q+2MvaVJkeWfpHINeyrXeMrJt2t2AIdqcOciHF8Lr37wdPUJgiAIglAikiRx4MABjhw5YuiuPPf0TreYM2eOnKD/6aefYm9vzwcffMDNmzf59ttvS72Dz4OXqnpgZGxCZnoa95KevMz0gOYe1HC25u6DHCZvP0WuxsDzVCtVeTNdAEQuA03O48sLgiAIglAmFAoFCQkJuLmV/JPmwMBArKysCt3mzJlTir19tuk9kvzKK/8tKuHk5MSuXbtKtUPPI5WREU6e1bgeF0vihTjsXSo/tryxSsmn3erQ+9s/+PVUEtm5x/iqT0PMTUr2UZImR8vNq/dRqZRUqmr95AsKU78P7P8UUq7AmW15cygLgiAIgvDMWbVqlTzjx6MeXszjRaf3SPLs2bOJj48vi74819T/plwknn/8w3v5XvFw4Ou3G2FqpOT32GTe+T6KlAclG8E9se8K/5t3jGO/XSrR9QAYm0HT9/O+Dl8M+qWyC4IgCIJQQVSuXPmJs0sIJQiSt2zZgpeXF6+99hrLly+Xp/UQHk/OS74YV+xr2tV2Zu3AplibGRF96S69vonkRmqm3m3LM1xcKuE0cPkaDwRjS7hxGi7ufbq6BEEQBEEQKjC9g+T85RL9/Pz4/PPPcXV1pUOHDmzYsIEHDx6URR+fCy7V8yZNv3npH3Jzij8i3MTTgc1Dm+Fkbcq5G/fpvjyCf26m6dV2JXdrFApIu5tF+r0sva7VYW4PjQbkfR2+uOT1CIIgCIIgVHB6B8mQt8b6nDlz+Oeff9i/fz8eHh6MGjUKtVqtVz0hISEoFAqd7eH1xDMzMxk2bBiOjo5YWVnRo0ePApOdX758mQ4dOsgr/o0bN47c3NyS3FaZsqnkTKv+A+n+yczHzm5RmFouNvzvg9fwcLTg2r0Men4dyamrxZ/32MTMCAfXvJWOnno0+dUPQKGC+ENw7a+nq0sQBEEQBKGCKlGQ/DBLS0vMzc0xMTEhR48R0nw+Pj4kJibK28NTmowePZodO3awZcsWDh48yPXr1+nevbt8XqPR0KFDB7Kzs4mIiGDNmjWEhoYybdq0p72tUqdQKHilYzeq1qmHykjv5yVxc7Dgpw9eo05lG26nZxP0bSThF4qf6vLfoiJPGSTbuUHdt/K+jljydHUJgiAIgiBUUCUKkuPj4/n000/x8fHhlVde4fjx48yYMYOkpCS96zIyMkKtVsvbSy+9BEBKSgrfffcdX375JW3atKFRo0asXr2aiIgIeW34//u//+Ps2bOsW7cOX19fAgMDmTVrFsuWLSM7O7skt1ahvWRlyo+DX+W1ao6kZ2t4d3U0O08mFutaZ09boBRGkgFeG5n379mf4Y54iFMQBEEQhOeP3kHyq6++ipeXFz/99BPvvvsuCQkJ7N27l4EDB2Jra6t3B86fP4+rqysvv/wy/fr14/LlywAcO3aMnJwc/P395bI1a9akatWqREZGAhAZGUndunV11n8PCAggNTWVM2fOFNlmVlYWqampOlt5yMnM5O+IQ/zxv40lrsPazJjV7zbmzbpqsjVahv/4F2v/SHjidU7/jiQnJ6Q+/XLX6jrg5Q+SNm/eZEEQBEH4l0KhYPv27YbuhsF4eHiwaNEief9ZfD1CQ0Oxs7PT6xo/Pz9GjRpVJv0xFL2D5LZt23Lq1CmOHz/O2LFjqVz58XP+Pk7Tpk0JDQ1l165drFixgvj4eFq0aMH9+/dJSkrCxMSkwDfJ2dlZHrFOSkrSCZDzz+efK8rcuXOxtbWVt6eZkFsfmtxcdi6eT/jmdWTcL3lgbmqk4qs+DenXtCqSBFO3n2bR73mr+RXFwdWS17p70eHDeiVuV0fzj/L+Pb4O0sUMJ4IgCBXFgAED5Od8jI2N8fT0ZPz48WRm6j870rPk4ftWKBQ4OjrSvn17Tp48adB+JSYmEhgYaNA+6Kt3797ExRV/Ni6ArVu3MmvWLHn/0TcLJaHRaJg6dSqenp6Ym5tTrVo1Zs2a9dh4pzTpHSR/+umn1K5du1QaDwwMpGfPntSrV4+AgAB+/fVX7t27x+bNm0ul/qJ88sknpKSkyNuVK1fKtL18ZlZW8kIiSRf0++F7lEqpYHbXOnzUtjoAi34/z7Sfz6ApYpRYqVTQ4I2qVPa2R6nU78HBQnm0ANcGkJsBR1c+fX2CIAhCqWnfvj2JiYn8888/LFy4kG+++Ybp06cbultlLv++ExMT2bt3L0ZGRnTs2NGgfVKr1Ziamhq0D/oyNzfHyclJr2scHBywti7hgmVFmDdvHitWrGDp0qXExsYyb9485s+fz1dffVWq7RTlqR/cK012dnZ4e3tz4cIF1Go12dnZ3Lt3T6fMjRs35Fk01Gp1gdku8vcfN9OGqakpNjY2Olt5kRcVecogGfI+whndzpsZnX1QKGDtHwmM3HicrFzNU9ddjMb/G00++i1kp5d9m4IgCEKxmJqaolarcXNzo2vXrvj7+7Nnzx75/O3bt+nTpw+VK1fGwsKCunXr8uOPP+rU4efnx8iRIxk/fjwODg6o1WpCQkJ0ypw/f56WLVtiZmZG7dq1ddrId+rUKdq0aYO5uTmOjo4MGTKEtLT/pjIdMGAAXbt2Zc6cOTg7O2NnZ8fMmTPJzc1l3LhxODg4UKVKFVavXl3s+1ar1fj6+jJx4kSuXLnCzZs35TITJkzA29sbCwsLXn75ZaZOnaoz8cCJEydo3bo11tbW2NjY0KhRI/7880/5/JEjR2jRogXm5ua4ubkxcuRI0tOL/j/w4XSLS5cuoVAo2Lp1K61bt8bCwoL69evLaaQlbeNRxW2nKI+mW4SEhODr68vatWvx8PDA1taWoKAg7t+/L5d5ON3Cz8+PhIQERo8eLY/sl0RERARdunShQ4cOeHh48NZbb/HGG29w9OjREtWnrwoVJKelpXHx4kVcXFxo1KgRxsbG7N3736IV586d4/LlyzRr1gyAZs2acerUKZKTk+Uye/bswcbGptRGu0ubvKjIheKtvFccwa95sCSoAcYqBTtPJjIw9E/SsgpOg5edmUtcdBJ/7X5yDnOx1OoM9h6QcQeOry+dOgVBECo4bbamyE3K0epRVlOssk/r9OnTREREYGJiIh/LzMykUaNG7Ny5k9OnTzNkyBD69+9fIPhYs2YNlpaWREVFMX/+fGbOnCkHwlqtlu7du2NiYkJUVBRff/01EyZM0Lk+PT2dgIAA7O3tiY6OZsuWLfz+++8MHz5cp9y+ffu4fv06hw4d4ssvv2T69Ol07NgRe3t7oqKieP/99xk6dChXr14t9n2npaWxbt06vLy8cHR0lI9bW1sTGhrK2bNnWbx4MStXrmThwoXy+X79+lGlShWio6M5duwYEydOxNjYGICLFy/Svn17evTowcmTJ9m0aRNHjhwpcD9PMnnyZMaOHUtMTAze3t706dNHnr62tNp4Ujv6unjxItu3bycsLIywsDAOHjzIZ599VmjZrVu3UqVKFWbOnCmP7EPetL1WVlaP3ebMmSPX89prr7F371459ePEiRMcOXKk/NJXJAP6+OOPpQMHDkjx8fFSeHi45O/vL7300ktScnKyJEmS9P7770tVq1aV9u3bJ/35559Ss2bNpGbNmsnX5+bmSnXq1JHeeOMNKSYmRtq1a5dUqVIl6ZNPPtGrHykpKRIgpaSklOr9FSbx/Dnp814dpKUD+0harbZU6z4UlyzVmvqb5D4hTOr01WHp1v1MnfP372RIS4fulZZ9sE/KzsotnUajvpWk6TaStLCOJOXmlE6dgiAIFUBGRoZ09uxZKSMjQ+f4lQmHitxufn9Kp+zVKUeKLHvj6xM6Za/NjCi0nL6Cg4MllUolWVpaSqamphIgKZVK6aeffnrsdR06dJA+/vhjeb9Vq1bS66+/rlOmcePG0oQJEyRJkqTdu3dLRkZG0rVr1+Tzv/32mwRI27ZtkyRJkr799lvJ3t5eSktLk8vs3LlTUiqVUlJSktxfd3d3SaPRyGVq1KghtWjRQt7Pzc2VLC0tpR9//FGnP7m5uVJ0dLSUm5urc9+WlpYSILm4uEjHjh177H0vWLBAatSokbxvbW0thYaGFlp24MCB0pAhQ3SOHT58WFIqlfLPibu7u7Rw4UL5/MOvR3x8vARIq1atks+fOXNGAqTY2Nhit/EkxWnncVavXi3Z2trK+9OnT5csLCyk1NRU+di4ceOkpk2byvutWrWSPvroI3n/0ddBkiQpJydHOn/+/GO327dvy+U1Go00YcIESaFQSEZGRpJCoZDmzJlTrNfgcYr63X6U/hP2lqKrV6/Sp08fbt++TaVKlXj99df5448/qFSpEgALFy5EqVTSo0cPsrKyCAgIYPny5fL1KpWKsLAwPvjgA5o1a4alpSXBwcHMnDnTULf0RJU8PFEZGZF5P5WUG0nYqV1Kre4W1Svx4+BXeTc0mpNXU+iw5AgeL1n8V0CSaGIMpjkSw5ZGkmqlemx9r1V7iaGtXsbU6DHlfPvBgblw7zKc3f7fHMqCIAiCwbRu3ZoVK1aQnp7OwoULMTIyokePHvJ5jUbDnDlz2Lx5M9euXSM7O5usrCwsLCx06qlXT/dhbxcXF/nT29jYWNzc3HB1dZXP53/Smy82Npb69etjaWkpH2vevDlarZZz587JD9v7+PigVP734bazszN16tSR91UqFY6OjjqfHD/uvgHu3r3L8uXLCQwM5OjRo7i7uwOwadMmlixZwsWLF0lLSyM3N1cn7XLMmDEMGjSItWvX4u/vT8+ePalWrRrw36rD69f/9+mpJElotVri4+OpVavWY/uX7+HX1cUlLw5ITk6mZs2apdbGk9rRl4eHh07O8cM/C8VlZGSEl5dXsctv3ryZ9evXs2HDBnx8fIiJiWHUqFG4uroSHBysV9slUaIg+d69exw9epTk5GS0Wt2Plt55551i17Nx4+OnQjMzM2PZsmUsW1b0NGPu7u78+uuvxW7T0FRGxjh5VCPxwjluxF8o1SAZoL6bHVveb8Y73x3l2r0MklJ1n2ZWY0J1VNy5nM6fZo//yOWPf+4QdvI6C96qT303u8ILmVhAk6FwYE7eUtV1euTlKwuCIDynXGe+VuS5R3MvXaa++piyuvvqCU2eql8Ps7S0lIOR77//nvr16/Pdd98xcOBAABYsWMDixYtZtGgRdevWxdLSklGjRhVYYyA/zeC/PisK/L9fGgprpyRtP3zfAKtWrcLW1paVK1cye/ZsIiMj6devHzNmzCAgIABbW1s2btzIF198IV8TEhJC37592blzJ7/99hvTp09n48aNdOvWjbS0NIYOHcrIkSMLtF21atUS3W/+z0z+vZVWG09qR1+l8bNw+fLlJ6bDTpo0iUmTJgEwbtw4Jk6cSFBQEAB169YlISGBuXPnVswgeceOHfTr14+0tDRsbGx0/iAoFAq9guQXlf/gYZhZWWHtWKlM6q9WyYpfP2pBxIVb5D4y20XKX7dJibpJh8oOBL9R9PR99x5ks3jveeJupNFteThDWlZjlH91zIwLGVVuMhjCF0HSSYg/CC/7le4NCYIgVCBKk8d/ClceZfWhVCqZNGkSY8aMoW/fvpibmxMeHk6XLl14++23gbzAKS4uTq/neWrVqsWVK1dITEyURynzF/t6uExoaCjp6enyaHJ4eDhKpZIaNWqU0h0WTaFQoFQqycjIAPIeBHN3d2fy5MlymYSEgs/peHt74+3tzejRo+nTpw+rV6+mW7duNGzYkLNnz+o1Gqqv8mijPJiYmKDR6ObUu7q6EhMT89jrHBwc5K8fPHig8wkD5H2qUBZv1Aqjd5D88ccf89577zFnzpwCH8sIxePk8XKZt2Frbkxg3YKj1FdNzfg56iaquzl0qu9ayJX/6VjPlZAdZ/g55jpfH7zInrNJLOhZn4ZV7XULWjhAg/5w9Ju80WQRJAuCIFQoPXv2ZNy4cSxbtoyxY8dSvXp1fvrpJyIiIrC3t+fLL7/kxo0begXJ/v7+eHt7ExwczIIFC0hNTdUJPiHvIbjp06cTHBxMSEgIN2/eZMSIEfTv37/AOgelISsrS14n4e7duyxdupS0tDQ6deoEQPXq1bl8+TIbN26kcePG7Ny5k23btsnXZ2RkMG7cON566y08PT25evUq0dHRcqrKhAkTePXVVxk+fDiDBg3C0tKSs2fPsmfPHpYuXVoq91AebZQHDw8PDh06RFBQEKamprz00kt6p1t06tSJTz/9lKpVq+Lj48Px48f58ssvee+998qw5//Re3aLa9euMXLkSBEgP6Oc3G1AAffvZPIg9fFLd9tbmrA4qAHf9m9EJWtTLt5M560VEXy68yyZjzyVTbNhoFDBxX2QaNiJ2wVBEARdRkZGDB8+nPnz55Oens6UKVNo2LAhAQEB+Pn5oVar6dq1q151KpVKtm3bRkZGBk2aNGHQoEF8+umnOmUsLCzYvXs3d+7coXHjxrz11lu0bdu2zIK9Xbt24eLigouLC02bNpVn1PDz8wOgc+fOjB49muHDh+Pr60tERARTp06Vr1epVNy+fZt33nkHb29vevXqRWBgIDNmzADycnwPHjxIXFwcLVq0oEGDBkybNk0nL/tpFaeNkJAQPDw8Sq3NsjBz5kwuXbpEtWrV5GfN9PXVV1/x1ltv8eGHH1KrVi3Gjh3L0KFDdRYtKUsKSdJv2ZLu3bsTFBREr169yqpP5S41NRVbW1tSUlLKbc7k47t2kHAqhtd69iuXkeWH/TgzijvX0+kwrB4edV8q1jX3HmQzM+wsW/+6BsDLL1ky/616vOLx38ci/DQQTv8EdXtCj1Vl0XVBEIRyk5mZSXx8PJ6enpiZmRm6O0IhNBoNx48fp0GDBqhUZZOuUhEFBwejUCgIDQ01dFeeScX93dY73aJDhw6MGzeOs2fPUrdu3QKJ3J07d9a/ty+g+ON/Eh9zDPd6Dco9SPYfUBtzaxOs7Iu/ApCdhQlf9vKlYz0XPtl6in9updPzm0jefc2TcQE1MDdRQfOReUHy6a3QZirYu5fhXQiCIAjCi0eSJA4cOMCRI0cM3ZXnnt5B8uDBgwEKnWZNoVAUSNIWCqf28iY+5hhJ589BQPkumVmpasmXjWxT05n/G+3A7LCzbDl2le/D49n79w3m96hH05fr5+Uj/3MA/lgOgfNKrc+CIAiCIOTFWoU9bKiPwMBADh8+XOi5h2eXeNHpHSSX1xOFzzsXr7ynektjeeryZmtuzIKe9enw76hywu0H9P72D4KbuTOxyXDM/zkAf/0ALceDpeMT6xMEQRAEofysWrVKnvHjUQ/PLvGiM+hiIi8y9b/LU99NvEZmWhpmVlbl2v7x/7vMtfN3ef2t6tg5l+whTL8aTuwe3ZI5O2PZGH2FNZEJ7PvbjN/sa2N19yysbA2dl4jZLgRBEAShAqlcuegpYIX/lChIPnjwIJ9//jmxsbEA1K5dm3HjxtGiRYtS7dzzzNzaBjtnF+7dSCTpwjk8fBuVa/vxJ2+SeCEFr4ZOJQ6SAWzMjPmsRz061HNh4v9OceVuBkGKvqy1+gr7ewnwQxf+dunGH9VHk2NU/DQPhQKMlAqMVEqMlApUSgXGKuW//ypQKZUYqRR5Zf79WqVUYKzMK2Oo9UxMjJS8/JJlgQUFBEEQBEF4tugdJK9bt453332X7t27y6vBhIeH07ZtW0JDQ+nbt2+pd/J5pfby5t6NRBIvxpV7kOzkYUPihRRuxKdSs9nTr/rXonoldo9uydxfY1kfBa/fn8N4o40EG+2hZuI27K4fYHLOe+zVlu99GkKL6i/xVZ8G2FmYGLorgiAIgiCUkN5B8qeffsr8+fMZPXq0fGzkyJF8+eWXzJo1SwTJenCpXoMLRyPJLiIvqCw5e+RNdXfjUmqp1WllasSn3erSoZ4LPx+/zgnNFL560IneifNRZ1/lO5Mv+MumLducR5JuZPfYurSSRI5WQqORyNVqydVKaLQSORrtv//q7mu0EjlaLRpN3nWGcu9BNofP36LrsnBWBb+Cl1PJH5IUBEEQBMFw9J4n2dTUlDNnzhRYMeXChQvUqVOHzMzMUu1geTDEPMkAOVmZKFVGqIzKPzU89XYGaydHolQqGLyoJUZltBwqADkZcGAuRHwFkhYsHCFwPtTpgcHyIsrI2eupDP7hT67dy8DK1IjFQb60rVX6q0oJglD2xDzJFd+LOk+y8HSK+7ut94p7bm5u7N27t8Dx33//HTc3N32re6EZm5oZJEAGsHYww9zGBK1W4uaVtLJtzNgc2s2EQXvByQce3Ib/DYSNfSH1etm2Xc5qu9rwy/DmNPV0IC0rl0E//Mmy/RfQ872oIAiCIAgGpneQ/PHHHzNy5Eg++OAD1q5dy9q1a3n//fcZNWoUY8eOLYs+vhDKO4hSKBRyykVyKaZcPFblhjDkAPhNAqUxnPsVlr0Kx9bAcxREOlqZsm5QU95+tSqSBAt2n2PkxhgyssUc4oIgCILwrNA7SP7ggw/YuHEjp06dYtSoUYwaNYrTp0+zadMmhg4dWhZ9fK7FHt7PmrHDOLju+3Jv29nDBiMTJVkZueXXqJEJ+E2AoYegciPISoEdI+GHLnD3Uvn1o4wZq5TM7lqX2V3rYKRUsOPEdXp+E8H1e+Wffy4IglCeFAoF27dvN3Q3DMbDw4NFixbJ+8/i6xEaGoqdnZ1e1/j5+TFq1Kgy6Y+h6B0kA3Tr1o0jR45w+/Ztbt++zZEjR+jSpUtp9+2FIEkSt64kkBj3d7m3Xb+tG4MXtqRJR89ybxvn2jBwD7wxG4zMIP4gLG8Gf6wA7fMz4vr2q+6sH9QUB0sTTl9LpfPSI/x56Y6huyUIwnNswIABKBQKFAoFxsbGeHp6Mn78+GfymSF9PHzfCoUCR0dH2rdvz8mTJw3ar8TERAIDAw3aB3317t2buDj9FjvbunUrs2bNkvcffbNQEocOHaJTp064uroW+WZDkiSmTZuGi4sL5ubm+Pv7c/78+adqN59YTMTA1P+uvJccfxFNbm655igbmxr4IQelCl4bATXehF9GQsIR2DURzmyD10aCqoRTqBmZgntzUFWMH++mLzvy87DmDP7hT/5Ouk+flX8wq0sdgppUNXTXBEF4TrVv357Vq1eTk5PDsWPHCA4ORqFQMG/ePEN3rUzl3zdAUlISU6ZMoWPHjly+fNlgfVKr1QZru6TMzc0xNzfX65qyWKkvPT2d+vXr895779G9e/dCy8yfP58lS5awZs0aPD09mTp1KgEBAZw9e/apH7gt1kiyg4MDt27dAsDe3h4HB4ciN0E/9moXTC0tyc3J5tblSwbrh0EfLHOsBsE7oMOXYGINV6JgUz/Y0LNk2w+dYe8Mw91PIdwcLNj64Wu8WVdNjkZi4tZThPxyhhyNWOZdEJ412dnZRW45OTmlXrYkTE1NUavVuLm50bVrV/z9/dmzZ498/vbt2/Tp04fKlStjYWFB3bp1+fHHH3Xq8PPzY+TIkYwfPx4HBwfUajUhISE6Zc6fP0/Lli0xMzOjdu3aOm3kO3XqFG3atMHc3BxHR0eGDBlCWtp/D4wPGDCArl27MmfOHJydnbGzs2PmzJnk5uYybtw4HBwcqFKlihz8Fue+1Wo1vr6+TJw4kStXrnDz5k25zIQJE/D29sbCwoKXX36ZqVOn6nwvTpw4QevWrbG2tsbGxoZGjRrx559/yuePHDlCixYtMDc3x83NjZEjR5Kenl5knx4eAb106RIKhYKtW7fSunVrLCwsqF+/PpGRkTrX6NvGo4rbTlEeTbcICQnB19eXtWvX4uHhga2tLUFBQdy/f18u83C6hZ+fHwkJCYwePVoe2S+JwMBAZs+eTbdu3Qo9L0kSixYtYsqUKXTp0oV69erxww8/cP369VJJcSnWUNvChQuxtraWvxariZUehVKJupo3CSePk3ghDueXvZ58USk6G36dmD2X8WrkRJNOL5dr2zqUSmg8ELwD4PcZcLuEH5VoNZB0Ev5YDg36QyXv0u3nU7AwMWJZ34Z8te8CX+6JIzTiEnE37rOsb0PsLcXCI4LwrJgzZ06R56pXr06/fv3k/QULFhQIhvO5u7vz7rvvyvuLFi3iwYMHBco9Gpjq6/Tp00RERODu7i4fy8zMpFGjRkyYMAEbGxt27txJ//79qVatGk2aNJHLrVmzhjFjxhAVFUVkZCQDBgygefPmtGvXDq1WS/fu3XF2diYqKoqUlJQCOanp6ekEBATQrFkzoqOjSU5OZtCgQQwfPpzQ0FC53L59+6hSpQqHDh0iPDycgQMHEhERQcuWLYmKipKfe2rXrh1VqlQp1n2npaWxbt06vLy8cHR0lI9bW1sTGhqKq6srp06dYvDgwVhbWzN+/HgA+vXrR4MGDVixYgUqlYqYmBiMjY0BuHjxIu3bt2f27Nl8//333Lx5k+HDhzN8+PBiBfH5Jk+ezOeff0716tWZPHkyffr04cKFCxgZGZVaG09qR18XL15k+/bthIWFcffuXXr16sVnn33Gp59+WqDs1q1bqV+/PkOGDGHw4MHy8cuXL1O7du3HtjNp0iQmTZpUrD7Fx8eTlJSEv7+/fMzW1pamTZsSGRlJUFBQMe+ucMV6lYKDg+WvBwwY8FQNCgW5VK9BwsnjJF04B2+8Wa5ta3O13E16QFJ8Oc1w8SS2VaDHyqerY0NviNuVl7rx9v8q1FzMCoWCkW2rU0NtzZhNMURcvE2XZeGsfOcVaqjFwiOCIJSOsLAwrKysyM3NJSsrC6VSydKlS+XzlStX1pmRasSIEezevZvNmzfrBMn16tVj+vTpQN4bgKVLl7J3717atWvH77//zt9//83u3btxdXUF8t5APJx/u2HDBjIzM/nhhx+wtLQEYOnSpXTq1Il58+bh7Jw3j7yDgwNLlixBqVRSo0YN5s+fz4MHD+Rg6ZNPPuGzzz7jyJEjjw188u8b8gJ0FxcXwsLCUCr/++B8ypQp8tceHh6MHTuWjRs3ykHy5cuXGTduHDVr1pTvO9/cuXPp16+f/GagevXqLFmyhFatWrFixYpif7w/duxYOnToAMCMGTPw8fHhwoUL1KxZs9TaeFI7+tJqtYSGhsqDpv3792fv3r2FBskODg6oVCqsra110k1cXV2JiYl5bDv6ZCUkJSUByD9H+ZydneVzT0PvtxIqlYrExEScnJx0jt++fRsnJyc0mufnoavy4vJvXnLi+XPl3rbTQ9PASVoJhbLiBJQlFjAHLu6Di3vzguUaFe+BiQAfNVs/bM6gH6K5fOcB3ZeHs7C3L2/4PHu5a4LwonncKNejn7SOGzeu2GVLc2aA1q1bs2LFCtLT01m4cCFGRkb06NFDPq/RaJgzZw6bN2/m2rVrZGdnk5WVhYWFhU499erV09l3cXEhOTkZgNjYWNzc3OQAGaBZs2Y65WNjY6lfv74cIAM0b94crVbLuXPn5ODGx8dHJ5B1dnamTp068r5KpcLR0VFu+0n3DXD37l2WL19OYGAgR48elUfSN23axJIlS7h48SJpaWnk5ubqLCQ2ZswYBg0axNq1a/H396dnz55Uq1YNyEvFOHnyJOvXr5fLS5KEVqslPj6eWrVqPbZ/+R5+XV1cXABITk6mZs2apdbGk9rRl4eHhxwg59f3pO/Ho4yMjAosRleR6T27RVG5q1lZWZiYiI+MS0Lt5Y29S2VcqtdA0pZvjqpjFStUxkqyHuSScvM5mZ7MsRq8+mHe17s+gdwsw/anCDXU1vwy7HWavexIeraGIWuP8dXe82LhEUGo4ExMTIrc8j+WL82yJWFpaYmXlxf169fn+++/Jyoqiu+++04+v2DBAhYvXsyECRPYv38/MTExBAQEFMiBfrSPCoUCbRn8P1VYOyVpO/++vby8aNy4MatWrSI9PZ2VK/M+oYyMjKRfv368+eabhIWFcfz4cSZPnqxz3yEhIZw5c4YOHTqwb98+ateuzbZt24C8FI6hQ4cSExMjbydOnOD8+fNyIK3v/ea/Wcq/t9Jq40nt6Ks0fhYuX76MlZXVY7fHpTM9Kn+U+saNGzrHb9y4USoPTBZ7JHnJkiVA3ouyatUq+eMMyHtHeujQoRK9MxHAwsaW9xZ9Y5C2VSolldysSfonhRvxKdg5Wzz5omdBy7FwYiPcjYfIZdBijKF7VCh7SxN+GNiE2WFnWROZwBd74sjRaBnzRg1Dd00QhOeEUqlk0qRJjBkzhr59+2Jubk54eDhdunTh7bffBvICp7i4uCfmiz6sVq1aXLlyhcTERHmU8o8//ihQJjQ0lPT0dHk0OTw8XE6rKGsKhQKlUklGRt4gUH5u9uTJk+UyCQkJBa7z9vbG29ub0aNH06dPH1avXk23bt1o2LAhZ8+eLdPR0PJoozyYmJgUyC4o7XQLT09P1Go1e/fuxdfXF4DU1FSioqL44IMP9O1yAcUOkhcuXAjkjSR//fXXOmukm5iY4OHhwddff/3UHRLKn7OHzb9Bcio1XnUxdHdKh6k1tJsB24bCoc+hfhDYuD75OgMwVimZ0aUO7o6WzAw7y5J9F7A2M2ZwSwM+SCkIwnOlZ8+ejBs3jmXLljF27FiqV6/OTz/9REREBPb29nz55ZfcuHFDryDZ398fb29vgoODWbBgAampqTrBJ+Q9BDd9+nSCg4MJCQnh5s2bjBgxgv79+xfIIy0NWVlZci7q3bt3Wbp0KWlpaXTq1AnIy++9fPkyGzdupHHjxuzcuVMeJQbIyMhg3LhxvPXWW3h6enL16lWio6PlVJUJEybw6quvMnz4cAYNGoSlpSVnz55lz549OjnfT6M82igPHh4eHDp0iKCgIExNTXnppZf0TrdIS0vjwoUL8n58fDwxMTE4ODhQtWpVFAoFo0aNYvbs2VSvXl2eAs7V1ZWuXbs+9T0UO90iPj6e+Ph4WrVqxYkTJ+T9+Ph4zp07x+7du2natOlTd+hFptVqSL2lX35PaXD2zMvFulFey1OXl7q9oEoTyEmH30MM3Zsneu91T8YF5I2sfPprLD8eNdy8noIgPF+MjIwYPnw48+fPJz09nSlTptCwYUMCAgLw8/NDrVbrHVQolUq2bdtGRkYGTZo0YdCgQQUe4rKwsGD37t3cuXOHxo0b89Zbb9G2bdsyC/Z27dqFi4sLLi4uNG3alOjoaLZs2YKfnx8AnTt3ZvTo0QwfPhxfX18iIiKYOnWqfL1KpeL27du88847eHt706tXLwIDA5kxI29a0Xr16nHw4EHi4uJo0aIFDRo0YNq0aTp52U+rOG2EhITg4eFRam2WhZkzZ3Lp0iWqVatGpUqVSlTHn3/+SYMGDWjQoAGQly+e/3rkGz9+PCNGjGDIkCE0btyYtLQ0du3a9dRzJAMoJJEASWpqKra2tqSkpOgk75enW5cvsWHKWIzNzHj/m7XlOs1e6q0MwpaewKW6HX59azxfU/xd+wtWtgEkeG83VH3V0D16LEmS+GzX33xz8B8UClgc1IDO9SvmCLggPO8yMzOJj4/H09OzVP7DFUqfRqPh+PHjNGjQQOcT7udd/uIwD0+jJxRfcX+39X5wr0ePHoWu2DN//nx69uypb3XCv2zVLmhyc3iQco/7t24++YJSZPOSOX1DXqV1v5rPV4AMULkhNMjLueO38RV+yWuFQsHE9jXp17QqkgRjNsWw7+8bT75QEARBeCFIksSBAwd0loAWyobeQfKhQ4d4882Cc/kGBgZy6NChUunUi8jYxJSXqnoAkHih/KeCe661nQ6mNpB4Ao6vM3RvnkihUDCrSx26+LqSq5X4YN1fRF68behuCYIgCBWAQqEgISEBNze3EtcRGBhYKrNLPO/0nic5LS2t0ClpjI2NSU19znJay5mLVw2S4y+SeCGOGs1alHv7Wo2W9JRsrB2es48VrSqB30TYPSlvueraXcDcztC9eiylUsHnPeuTnqXh99gbDFoTzfrBr+LrZmforgmCIAjPuFWrVskzfjxKn9klnnd6jyTXrVuXTZs2FTi+ceNGvZ6KFQpyqZ730FaSAUaSb8SnsnLUIbYvPF7ubZeLJkPgpRrw4DYcLJguVBEZq5Qs7duA16rlzaMc/P1RziXdN3S3BEEQhGdc5cqV5fmkH91EkPwfvUeSp06dSvfu3bl48SJt2rQBYO/evfz4449s2bKl1Dv4IlFX8wbgxj8X0eTmoirB2uolZetkTm6OltSbGWSm5WBmZfzki54lKmMI/AzWdoOob6BhMDhV/Hm9zYxVrHznFfqtiiLmyj3e/i6KLUOb4fGS5ZMvFgShVIjn2wXh+VLc32m9R5I7derE9u3buXDhAh9++CEff/wxV69e5ffffy+VOeleZA6ulTG1sCQ3O4vbV8t3+i8zS2N5IZHnbiq4fNXaQI0OIGlg1wR4Rv7jszQ1IvTdxtRUW3Pzfhb9VkWRmPKcrI4oCBVY/gpjDx48MHBPBEEoTfm/04+uIvioEg1VdujQgQ4dOpTkUuExFEolvgEdUapUmD20omF5cfaw4d6NB9yIT8G9jmO5t18uAj6FC7/DPwfg751Qq6Ohe1QsdhZ5K/P1+jqSS7cf8PaqKDYPbYajlamhuyYIzy2VSoWdnR3JyXnz11tYWDx/MwA94/JXdMvMzHyhpoATSkaSJB48eEBycjJ2dnZP/Jkp8TzJ2dnZJCcnF1i3u2rVqiWpzqAqwjzJFcGpA1c5tDGOqj4OdBrha+julJ29M+HwF2DnDsOOgvGz86Di1bsP6PV1JNdTMvFxteHHIa9iY/acpcYIQgUiSRJJSUncu3fP0F0RCqHVarly5Qpubm4olXp/OC68oOzs7FCr1U9806t3kHz+/Hnee+89IiIidI5LkoRCoSiwTvezQATJeW5cSuWnz/7E1NKIgZ+3eH5HTLLSYGljuH8d2kyBluMM3SO9XLyZRq+vI7mdnk1jD3t+eK8p5iZiBEUQypJGoyEnJ8fQ3RAekZaWxiuvvMKff/6JlQE+gRWePcbGxsX+1EHvdIsBAwZgZGREWFgYLi4uz28gZUBpd++QeOEc7nV9MTEzL7d2X6pihcpISVZ6Lik3M7Bzsii3tsuVqRW8MQv+NxAOfwn1+4JtZUP3qtiqVbLih4FNCPr2D6Iv3WXoumOsfKcRpkYiUBaEsqJSqcTH+RVQdnY2CQkJmJiYiFURhVKn92cTMTExfPPNNwQGBuLr60v9+vV1tpL67LPPUCgUjBo1Sj6WmZnJsGHDcHR0xMrKih49enDjhu7qY5cvX6ZDhw5YWFjg5OTEuHHjyM3NLXE/KoIfp47ll88/JelCXLm2qzJSUrd1FZp29sT4eR+ZrNMDqjaDnAewZ9qTy1cwPq62hL7bGHNjFYfibjJqYwy5Gu2TLxQEQRAEoVj0DpJr167NrVu3SrUT0dHRfPPNN9SrV0/n+OjRo9mxYwdbtmzh4MGDXL9+ne7du8vnNRoNHTp0IDs7m4iICNasWUNoaCjTpj17Qc/D1F558yUnni//+ZKb9/DilTc9sbR7zh8IUyggcB6ggNM/QULEEy+paBq5O7DynVcwUSn57XQSE7eeQqt9NmbsEARBEISKTu+c5H379jFlyhTmzJlD3bp1C0yfoW9Ob1paGg0bNmT58uXMnj0bX19fFi1aREpKCpUqVWLDhg289dZbAPz999/UqlWLyMhIXn31VX777Tc6duzI9evXcXZ2BuDrr79mwoQJ3Lx5s9CVAQtT0XKS/9yxlYPrvser8at0GTvF0N15vu34CI6FgrouDDkIymdvBH33mSQ+XP8XGq2EiZESpciA0ptSoUCl/HdTKFD++69KqUCpBCNl3uuqUip0yyoVuNlb8IaPM341nLAyLb+5zQVBqHj/fwvPF73/ovv7+wPQtm1bneMlfXBv2LBhdOjQAX9/f2bPni0fP3bsGDk5OXJ7ADVr1qRq1apykBwZGUndunXlABkgICCADz74gDNnztCgQYNC28zKyiIrK0ver2jLaav/XXkv8UKc/LqWp7S7mdyIT8Wj7kuojJ/zp4XbTIUz2yDpFPy1Bl55z9A90luAj5rPe9Zj4v9OkZUrUi7K2/HL9/jlxHVMjJS87vUS7X3U+Nd2xsGyeG/SBUEQhIpJ7yB5//79pdb4xo0b+euvv4iOji5wLikpCRMTE+zs7HSOOzs7k5SUJJd5OEDOP59/rihz585lxowZT9n7suPsWQ2FUkn63Tvcv30Lm5cqlVvbkiSxaXY0mek5vDXhFZw9n/N35pYvgd+kvMVF9s4Cn25gbm/oXumtW4MqtK3lTGqGePpeX5KUt+VqtWglCY0WNFrp368lch/6WquV0Ej/HtNK5GgkYq7cY/eZJOJvpbPv72T2/Z2Mcis09nDg/9u77/imqv6B458kTbo3ndDFKnuvgjJk40CFR3GCAiqCoogKqD8fQWQ9j+txL0AEByoKiAzZe7TsXaAU6KR7J03u749AtQNooRltv29feZHcnNzzzTU355vTc88Z1CqQAS0Dqe9lvQtwhRBCVI8qJ8m9evWqloovXLjAxIkTWbdundWvSJ06dSqTJk0qeZydnU1ISIhVY7geraMT9ULDSY07S1LsSasmySqVCv9wD+KPppEcl137k2SAzqPNQy5Sj8PGWTBkrq0juikeTlqZM9kGBrUK5NVBkZxOyWX1kSTWHE3iaEI2u8+ls/tcOm+tOEabBp4MbBnIwJYBNPZ3t3XIQgghKqHKSfKWLVuu+3zPnj0rtZ/o6GhSUlLo0KFDyTaj0ciWLVv46KOPWLNmDXq9nszMzFK9ycnJyQQGBgIQGBjInj17Su336uwXV8tUxNHREUdH+74wLahxU1LjzpIYe4qm3W6zat0BEVeT5CyggVXrtgmNFgbPhm+Hwt6voOMoCGhh66hEDaJSqWga4E7TAHee79uEC+n5rDmaxNqjyew9n86hi1kcupjFvDUnaeTneiVhDqRNA0+ZRlMIIexUlS/cq2hFm39+yVd2THJOTg7nz58vte2JJ56gWbNmvPrqq4SEhODn58f333/PsGHDADh58iTNmjUrd+FeYmIi/v7+AHzxxRe8/PLLpKSkVDoRtseB/wmnTpCZnEiDZi3x8PO3at3nj6Sx8qODeNRz4tEZUXWnEf/xUTi+Ahp0hqGfgF9TW0ckaoHUnCL+Op7M6iNJ7DhzGYPx76/cYE8nXh4UyX3t68CPUSEswB7bb1F7VDlJzsrKKvXYYDCwf/9+3njjDWbOnFnugr6q6N27d8nsFgDjxo1j1apVLFiwAA8PD5577jmAktX+jEYj7dq1Izg4mLlz55KUlMRjjz3GmDFjeOeddypdr5xkpekLi5n/yjaK9SaGT+lEQHgdOSYZ5+HjLlBcaH7csDd0HgtNB4FGZi0Qty670MDGEymsOZrEppOp5OvNnQr3ta/P9KEtcZfhMkJUibTfwpKq3PJ7enqW29a/f390Oh2TJk0iOjq6WgIDeO+991Cr1QwbNoyioiIGDhzIJ598UvK8RqNh5cqVjBs3jqioKFxdXRk5ciTTp0+vthjqIp2TAxFt/Ti9N5lTe5LqTpLsHQajVsHW/8KpP+HsJvPNM8Q860WHx80X+glxkzyctAxtV5+h7epTaDDy+eazfLD+FMv2XyImPoMPR7SnbYiXrcMUQgjBTfQkX8uJEyfo1KkTubm51bE7q7LXX6JF+fkc3rCG5LOx3Pn8y1atO+7wZf74+BBuPo48PrN73RlycVVmPOz7BqIXQkG6eZvGEVrdD13GQv2Oto1P1Br74tKZ+MMBLmUW4KBW8fLASMbe3hC1THgtxA3Za/staocqJ8mHDh0q9VhRFBITE5k9ezbFxcVs27atWgO0Bns9yQrzcvnsqUcxFhfz+Nz/4RcWYbW6jUYTJ3cm0bC9H06udfhPwIZCOPor7PkCEvb/vb1+R/NQjJb3gda6s7OI2icr38C0ZYf543AiALc3qcd//9UWfw/5bAlxPfbafova4aYu3FOpVJR9Wbdu3fjmm29o1qxZtQZoDfZ8kv3+n5nE7t1J53uG0fORJ2wdTt12MdqcLB/9FYx68zYXX+gw0jwcw8t+phEUNY+iKPy49wL/XnGUQoMJX1cd/3mgLX0irXvhrhA1iT2336Lmq3KSXHZGCrVajZ+fn9XnOq5O9nySndq9nRXvzsLd14+xH32NqoLZRazBFiv/2a3cVNj/Lez9BrIvmrep1BA5xJws+9xCj79bIOhcqidOUSPFpuQwYcl+TiTlADD6tgheGRSJo0PNWzJdCEuz5/Zb1HyVSpJ9fHw4deoU9erV48knn+SDDz7A3b32TIhvzydZsV7PZ08/RlF+Hg+8OYuQFq2tWv+pPUkc2niRtn1DaNIp4MYvqEuMxXBqtbl3+dzm6tmnszc8+ivU73DjsqLWKjQYmf3nCRbsiAOgZbAHHz7UnkZ+brYNTAg7Y8/tt6j5KtUtqdfryc7OBmDhwoUUFhZaNCjxNwedjiZduwNwfNsmq9efnphH8rlsTu2+9jLfdZbGAZrfBSOXw/g90OUpcPUDnfvN3RycoCADvn8Isi7Z+t0JG3LSavj3PS356vFOeLtoOZqQzV0fbuOnfRfKDXUTQghhGZXqSe7fvz/Jycl07NiRhQsX8uCDD+Ls7Fxh2W+++abag7Q0e/8lGn/kIEtnvIajqyvPfP4dDlrrXUiXnpjH92/tRq1WMWpuD5zddFaru84pzIKvB5qXxw5sA0+uBp2rraMSNpacXciLPx5gx5k0AO5uG8zM+1rJEuRCYP/tt6jZKtWT/N133zFkyBByc3NRqVRkZWWRkZFR4U1UvwYtWuEVEERoq7YU5uZYtW6fIFfqhbhhMimciU6xat11jpMnPPwDuNSDpEPw61NgMtk6KmFjAR5OLBrdlVcGRaJRq1hxMIEhH2wlJl6+b4UQwpKqfOFeREQE+/btw9fX11IxWV1N+CVqMhpRa2xz4c7+dfHs+CWWoEae3P+yzA9scfG7YOHd5hk0erwA/d+ydUTCTsTEZzDxh/1cSC9Ao1bxQKcGt9ajrAK1SoUK879qFahUKlSq6zzG/Fh15XWi5mjdwJPO4T62DqNa1YT2W9Rc1baYSE0mJ9n15WYUsXDadlDgsbej8KhX8VAbUY0O/gjLnjLfH/oJtH/EtvEIu5FdaOD1ZUdYfjDB1qGIGkarUbHp5T7U96o93+HSfgtLqvKy1MK20hMuYSgsIKBhY6vV6ebtSP2m3lw6mcGpPcl0GhJutbrrrLYPQtpp2DIPVkwE73AI72HrqIQd8HDS8sGIdgxpHXTLQy4URcGkgElRUJQyj68+b/r7cdlyouY4eDGT82n5LNwRx7QhzW0djhA1gvQkU3N+iR5av5p1X3xEaKu2/OuNmVat++SuRGJjUmnduz6hLWrPUBu7ZjLBz0/Asd/MU8ONWQ++jWwdlRCiBtpwIpknF+zD3dGBndP64uZYO/rIakr7LWom26xMIW5KWOt2AMQfPURueppV647sFsSdz7aRBNma1Gq491MIbn9largRUJBp66iEEDVQ76b+NPRzJaeomKX7Ltg6HCFqBEmSaxBP/0CCI1uAonBiezUtXiHsm84FHvoBPOrD5VOwdBQYDbaOSghRw6jVKp7sYV4NdP72OIwyXkaIG6pUkpydnV3pm7Cs5rf1BuD4NtskyVmpBexfG49JvmCtxz3QnChrXeHsRvjzFZBRUkKIKhrWoQFeLlri0/NZdyzZ1uEIYfcqlSR7eXnh7e193dvVMsKymnbrgVqjISXuDGkXrfsnM6PRxNJZe9nxaywJp2SOVqsKagPDvgRUsO8b2P25rSMSQtQwzjoND3cJBeCbbedsHI0Q9q9SI/c3btxo6ThEJbl4eBLetgNnY/ZyfNsmbhvxmNXq1mjUNOrgz7FtCZzak0yDZrVrvk271+xO85zJ6/4P1kw1X8TXpL+toxJC1CAju4fz5daz7IlL59DFTNo08LJ1SELYrUolyb169bJ0HKIKmt/Wm7Mxezm7f69Vk2SApl0COLYtgTMxKfR8qCkOWtsscFJndX/ePDZ5/3ew9AkYvRYCWtg6KiFEDRHg4cRdbYJZtv8SX287xwcj2ts6JCHs1k1duLd161YeffRRunfvzqVLlwBYtGgR27Ztq9bgRMUaderK3ZOm8vCM/1i97uDGXrh5O6IvNHL+sHVn2BCASgV3vgdht4E+B5Y8CLmpto5KCFGDjL7NfAHfH4cSScwqsHE0QtivKifJv/zyCwMHDsTZ2ZmYmBiKiooAyMrK4p133qn2AEV5WkcnmnbtgYNOZ/W6VWoVTToHAHBqj1z4YRMOOnhwEfg0hKx4+OFhMBTaOiohRA3Rqr4nXSN8KDYpLNxx3tbhCGG3qpwkv/3223z22Wd8+eWXaLXaku09evQgJiamWoMTN6YoCorJZNU6m3YJBCDuyGUK82Q6Mptw8YGHfwInT7i4B5Y/JzNeCCEq7Wpv8pLd58krKrZxNELYpyonySdPnqRnz57ltnt6epKZmVkdMYlKivlzBfNffJpzB6KtWm+9Bm74BLui0ahJu5hr1brFP9RrAg98CyoNHP4Jtlh/+I0Qombq2zyAcF8XsguL+SXmoq3DEcIuVTlJDgwMJDY2ttz2bdu20bBhw2oJSlROVnIiGYkJHN+2yep1D3qqFU/Mu436kTLtn0017A13/td8f+PbsOtTMEqvkBDi+jRqFU/8Y3ERmfteiPKqnCSPHTuWiRMnsnv3blQqFQkJCSxevJjJkyczbtw4S8QoruHqwiKx+3ahL7TuxRfega5odTKzhV3o9AR0e9Z8f/UU+KQbHFsuwy+EENc1vGMDPJwcOHc5jw0nUmwdjhB2p8pJ8pQpU3j44Yfp27cvubm59OzZkzFjxvD000/z3HPPWSJGcQ0BjZrgHRRMcVERsXt32SQGRVFkXLI9GPA2DJoNzj6Qdhp+egy+6gdxMuOMEKJiro4OPNTVvLjIV9vO2jgaIexPpZLkQ4cOYbpycZhKpeK1114jPT2dI0eOsGvXLlJTU5kxY4ZFAxXlqVQqmvUwz2FtiyEXqRdy+H76Hn57b7/V6xZlqDXQbRxMPAA9XwatC1zaBwvuhMX/gqQjto5QCGGHRkaFo1Gr2HU2nSOXsmwdjhB2pVJJcvv27bl8+TIADRs2JC0tDZ1OR4sWLejSpQtubm4WDVJc29UhF+cP7icv07pLRbv7OJGVkk/axVzSLskFfHbByRPueB2ePwCdRoPaAU6vhc9ug2XPQGa8rSMUQtiRYC9n7mwdBMhS1UKUVakk2cvLi3PnzCdPXFxcSa+ysD3voPoENmqCopg4uXOrVet2ctUS1soXkDmT7Y57ANz1LozfAy3vAxQ4+D38ryOsngZ5shCMEMLs6nRwKw4lkJwtc64LcVWlkuRhw4bRq1cvIiIiUKlUdOrUiYYNG1Z4E9bXuu8gWvbqR1DjSKvXfXXO5FN7k1Dk6mj749sI/rUAxm6EiJ5g1MOuj+HDdrBlHujzbB2hEMLG2oZ40TncG4NR4dudcbYORwi7oVKUyl0Cv3r1amJjY3n++eeZPn067u7uFZabOHFitQZoDdnZ2Xh6epKVlYWHh4etw6lRivVGvnllG4ZCI/e91IHgJl62Dklci6LAmQ3w15uQdNi8zS0Aer0KHR4Hjfb6rxdC1FqrjyTyzHcxeLlo2TmlL841ZPYiab+FJTlUtuCgQYMAiI6OZuLEiddMkkXd4qDT0Ki9Hyd2JnFqT5IkyfZMpYLGfaFhHzj6K6yfDpnn4Y9JsPNj6PcmNL/HXE4IUaf0bxFIiI8zF9IL+HX/RR7pGmbrkISwuSpPATd//nzc3d2JjY1lzZo1FBSY5+etZIe0sBBFUUg+G8vOX763+v+Lq0MuYqNTMBbLeHW7p1ZD6+EwYR8Mngsu9SD9DPz0OCx7GorkIkwh6hqNWsUT3c1jk7/edk4WFxGCm0iS09PT6du3L02bNmXIkCEkJiYCMHr0aF566aVqD1BUjqGokB/efJUdPy0m+cxpq9ZdP9Kblj3r0//JlqjU0gtZYzjooOvTf08bp9LAoR/hyz6QctzW0QkhrOyBziG4OzpwNjWPzadSbR2OEDZX5ST5hRdeQKvVEh8fj4uLS8n2Bx98kNWrV1drcKLydE7ONOrUFbD+nMlqtYreD0cS1soXtSTJNY+ju3nauFErwT0ILp+CL/rAgSW2jkwIYUVujg6M6BICyOIiQsBNJMlr165lzpw5NGjQoNT2Jk2acP78+WoLTFTd1TmTT+zYgslotG0wouYJ6w5Pb4VGd0BxAfw2Dn4fD/p8W0cmhLCSkd3DUatge2waxxOzbR2OEDZV5SQ5Ly+vVA/yVenp6Tg6OlZLUOLmhLftgJO7B/lZmcQfOWj1+tMT89jxayxn98uf6WosNz945Bfo8zqo1LD/O/iqL1y27hAeIYRtNPB2YXArWVxECLiJJPn222/n22+/LXmsUqkwmUzMnTuXPn36VGlfn376KW3atMHDwwMPDw+ioqL4888/S54vLCxk/Pjx+Pr64ubmxrBhw0hOLr1oRXx8PHfeeScuLi74+/vz8ssvU1xcXNW3VStoHByI7HYbYJtlqs/EpLB/bTyHN1+0et2iGqnV0OtleOw3cPWHlGPweS84tNTWkQkhrGD07eYL+H4/kEBKjiwuIuquKifJc+fO5YsvvmDw4MHo9XpeeeUVWrVqxZYtW5gzZ06V9tWgQQNmz55NdHQ0+/bt44477mDo0KEcPXoUgBdffJEVK1awdOlSNm/eTEJCAvfff3/J641GI3feeSd6vZ4dO3awcOFCFixYwP/93/9V9W3VGs1vN/9QOb1nJ4Yi6365Ne0SAMClkxnkZRVZtW5hAQ17wTPbIPx2MOTBr2Ng5YtgkEZTiNqsQ6g37UO90BtNfLdLlrIXdVelFxP5p6ysLD766CMOHjxIbm4uHTp0YPz48QQFBd1yQD4+PsybN4/hw4fj5+fHkiVLGD58OAAnTpygefPm7Ny5k27duvHnn39y1113kZCQQECAOUH77LPPePXVV0lNTUWn01Wqzto0GbmiKHz9/BgMRUXcP/UtAiIaWbX+n+fsI/lcNj2GN6Zdv1Cr1i0sxGSETbPNK/ShQGAb8yp+vtb9bAkhrGfloQQmLNmPj6uOHVPuwElrn4uL1Kb2W9ifSi8m8k+enp689tpr1RqI0Whk6dKl5OXlERUVRXR0NAaDgX79+pWUadasGaGhoSVJ8s6dO2ndunVJggwwcOBAxo0bx9GjR2nfvn21xlgTqFQqhr8+E496fqg11v9Si+waSPK5bE7tSZYkubZQa+CO1yC0K/z6FCQdgi96w9CPoMVQW0cnhLCAQS0Dqe/lzKXMAn7bf4kRXeT7XNQ9VU6Se/bsSe/evenduzfdu3fHycnplgI4fPgwUVFRFBYW4ubmxrJly2jRogUHDhxAp9Ph5eVVqnxAQABJSUkAJCUllUqQrz5/9blrKSoqoqjo7+EA2dm16wperwDz4h7FBgNbvvvmmuX8wxvSqk9/wNwDvXHBF6We12i16Jyc0To54R1Un0Ydu5Q8l3r+HBqtDp2zMzonJ7SOTqjUahp39GfrT6dJjc9h36pzdBoSUfKavX+coyDXUGEszm5aOt/5d9lDGy9SmGdA56RB66hB66RB5+iA1lGDzsUBv5C/V3xUFAWVrBJneY37mWe/+GU0xO80Lz7S9RnoP8M857IQotZw0KgZ1T2cmauO8/W2czzYOUS+Z0WdU+UkecCAAWzZsoV3332X4uJiOnXqRO/evenVqxc9evSocOaL64mMjOTAgQNkZWXx888/M3LkSDZv3lzVsKpk1qxZvPXWWxat4ypTQTGoQO10U532t0QxGtm/esU1n2/a7baSJBm4btmIdh1LJclL3phMcVHpccdaRyd0zs44aEMwmPpxZn9qqST55K4kslILKty/p79zqST52LZLpF3Kq7Csi6eOJ+bcVvJ42X9jSL2Qa06gHTU4uWm57V9NCGzoec33I26SZ30YuQI2zIDtH8Duz+DCHvPwC29ZxlaI2uTBLiG8/9cpTqfksuX0ZXo19bN1SEJYVZUzt9dffx2A4uJi9u7dy+bNm9m0aRNz585FrVZTWFi1i3p0Oh2NGzcGoGPHjuzdu5cPPviABx98EL1eT2ZmZqne5OTkZAIDzT2lgYGB7Nmzp9T+rs5+cbVMRaZOncqkSZNKHmdnZxMSElKluCuj8FQG6UtP4dzcB+/7m1T7/m9E7aCh630PXvP5eiGl/3xWuqxCscGAoaAAfWEB/uEN/37GZMLJzR29WoOhsBBFMS9FbSgqxFBUSEiLMCI6ReDkqi21/1a96lOYV3FPctmyTbsEkp1WiKGwGEOREX2hEUOREUNhMc7upXst9QVGiovMtwIgK7WAv+YfY8QbXXDQ2ec4uhpNo4X+0yG0u3kZ64QY+Px2aP8YOHmZFydx8gBHjzL3Pcz3HWSqSCFqAg8nLQ90DmH+9ji+3nZOkmRR59x09+bZs2c5fPgwBw8e5NChQ7i7u9OzZ89bDshkMlFUVETHjh3RarWsX7+eYcOGAXDy5Eni4+OJiooCICoqipkzZ5KSkoK/vz8A69atw8PDgxYtWlyzDkdHR6vM6axyUGHK0ZO3JwmX9v44Rli3Z1PjoOW2EY9VqqxKpap8WbWapz9ZAJiHOhTri9AXFGAoLERfWIDGQYtvA/OPDsVkQqU2T6JSlTHKHQZWvlfy3hfbU1RwJZkuKGbt10fJSi1g36o4ut0rF5dZTOQgeGYrLH0CLu2DnR9V7nUaXfkEWudqnpf5ZnnUh9BuENIFPENA/iwsRLV4onsEC3bEseVUKkt2x+PprL3xi66hV6Qfbo7W/6uqEDeryrNbPPzww2zevJmioiJ69uxJr1696N27N23atKnyeKWpU6cyePBgQkNDycnJYcmSJcyZM4c1a9bQv39/xo0bx6pVq1iwYAEeHh4899xzAOzYsQMwX+zXrl07goODmTt3LklJSTz22GOMGTOGd955p9JxWPLq2IxfTpO3NwkHP2cCJnZA5XALiUANE7t3F1u/X8iwadPxqGe9HoizB1L587PDOLtreXRGFDobDHWpU4r1sH8RpJ+FomwoyoHC7PL39bnWicc92HyRYUg3878BrUEjnwEhbtYzi6JZffTa1/lU1oaXetHQz60aIvqbzG4hLKnKLccPP/xAvXr1GDNmDHfccQe33XZblcchX5WSksLjjz9OYmIinp6etGnTpiRBBnjvvfdQq9UMGzaMoqIiBg4cyCeffFLyeo1Gw8qVKxk3bhxRUVG4uroycuRIpk+fflPxWILn4HAKjqdRnFpAzqYLePSrG+M2FUVh7/JfSL90gQ3zP2Po5NetdtFHw3Z+9BjemKZdAiVBtgYHHXQefeNyJqM5aS6VPOfcegKtmCD1FFzYBYmHICcBji4z3wC0LlC/45We5m4Q0hmcZLy6EJX1yqBI9EYTeUW3tlCXvU4jJ8S1VLknOSMjg61bt7Jp0yY2b97M8ePHadeuXcmMFwMGDLBUrBZj6V+i+QdTSf/+BGhUBEzsgNb/5n5U1DSXL5xn0asTMRmLuWfSNJp07W7rkERtp8+DS9FwYTfE7zZfVFiUVaaQCvybQ0hXc+LcoDN4hZrHWgshahTpSRaWdFOLifxTbGwsb7/9NosXL8ZkMmE0GqsrNqux9EmmKAppC45SeDIDXbgHfk+1QaWuG2Mmt//0Hbt++QFXbx9G/fcTnFyr909tlXHuYCr1Qtxx97m16QpFDWQyQeoJcy/zhT0QvwsyzpUvp1KDexB4NvjHLaT0v06eMtZZCDsjSbKwpCr/LTotLa1kRotNmzZx7NgxvLy8uPvuu+nVq5clYqzxVCoVXvc2Jvm9aBy8nVCKTajqyKwLXe99gJM7tpKReIlt3y+k35jxVq1/z4qz7P0jjvA29RgyrrXM81nXqNUQ0MJ86/SkeVtOsrmn+eot8SAY9ZB9yXy7sLvifencyyTRVxLoeo0hoJXM2iGEELVMlXuSNRoN9erV4/bbby+5aK9169aWis8qrPVLtDirCAfPuteQXjh2mJ/emgrAg2/NoUGzllarOy0hl59m7sVkVBj0dCsatfe3Wt2ihjCZIC8Vsi5C1oUr/5a5n3/5+vvQ6MzLddfvCA06mf/1aSg9z0JYmPQkC0uqck/yoUOHaNnSeklObVIXE2SAkBatadVnAEc2ruXc/n1WTZJ9g93oMDCMfavi2PLDKRo088HRWS7mE/+gVoN7gPnWoGPFZfT5kJ1QJom+CFnxkHQECtLN0+Bd2gd7Pje/xtnbnCzX7/R34uziY733JYQQ4pbc8pjk2sDav0SLs4rI/P0Mrp0DcG7ua/H67EFBbg5Jp08S0b6T1esuNhj5YcYeslIKaNWzPr0ejrR6DKIWUxTzOOdLMXDxSqKceAiMReXLekdcSZivJM2BrUErY+WFuFnSkywsqcpJcnJyMpMnT2b9+vWkpKRQ9uVy4d6NZa2OI2fTBTSejgRM6oBaJle3uEsnM/jtvf0A3D+5A0GNvWwbkKjdivWQfMQ808bFfeZ/006XL6dSg/oWzn+1A+jcwNHN/O8/71e4zd28cIvOzdyrHdBKhoSIGk2SZGFJVU6SBw8eTHx8PBMmTCAoKKjchVBDhw6t1gCtwdonmUlvJPn9GIzphbj1CMbr7rq1KlxuehoH/1pN9+EPlazGZw0bvj3O8R2JeAe5MuL1zqg1dWdhF2EHCjLMvc2XYsy9zRf33Xiss6U1GQAPficXHYoaS5JkYUlV7sLYtm0bW7dupV27dhYIp25Q6zR439uYy98cIXdHAi7t/NGFuNs6LKsoNhhYPO1FcjPScfetR5u+A61Wd/dhjUm7lEvnuyIkQRbW5+wNjfuab2AeppGXCkbDze/TqDcvxKLPg6Jc0Odc+Tf373//eb8o5+/y6Wfh9Fr46XF4YJF5URghhBAlqpwkh4SElBtiIarOqak3Lu39yd+fQsavp/Gf0A5VHUjcHLRaOt51H5sXfc2Wxd/QqGMXXL28rVK3k6uW4VM6yTRwwj6oVOBmw9lWzm6CJQ/CqdXw8xPwrwWyoIoQQvxDlbOy999/nylTphAXF2eBcOoWzzsjULs4YEjMI3fbJVuHYzUdBt9DQMPGFOXlsXHBF1at+58Jcn62HsUkP/hEHdWwN4xYbJ6+7sRK+HUsGG9t2WEhhKhNKpUke3t74+Pjg4+PDyNGjGDTpk00atQId3f3ku1Xb6LyNG46PO9sCEBeTAqKsW4kbGqNhv5PPYdKrebkzq2cjdlr9RiO70hg8Zu7OL4z0ep1C2E3GvczD7VQa+HoMvhtHJhq3sXXQghhCZUabvH+++9bOIy6y6WDP4rBhEsHf1SaujMMICCiER3vvJd9K37lr68/YVSLT9A5OVut/qL8YvQFxez4JZbw1vVw8ZDxmKKOihxkHmqxdCQc/sk85OKej8zzRwshRB0m8yQjV8faiqGwkAWTx5Odmky3YQ/R44FHrFa3yWji5znRpMbn0LiTPwPHtLJa3ULYpaO/wc9PgmKEDo/DXR9IoizsnrTfwpKq/A24atUq1qxZU2772rVr+fPPP6slqLpKMSnk7krEmHcLV7vXIFonJ/qPeZaOdw6l8z33W7VutUZNn0eboVJB7L4U4g7beCouIWyt5b1w3+fmuZtjvoU/XzbPwCGEEHVUlZPkKVOmVLhgiMlkYsqUKdUSVG1xKuMUH8Z8WOnZQDJ+PU3mb7Fk/XHWwpHZj/B2Hen9+FirDrW4yi/UnbZ9QwDY/P1J9IVy0ZKo49r8C4Z+DKhg71eweqokykKIOqvKSfLp06dp0aJFue3NmjUjNja2WoKqDbKKsnhs1WN8efhLlsUuq9RrXLsEggryY1IoPJ1h4Qjtj2IykRJn3R8IXe5uiLuPE7npRexZcc6qdQthl9o9DPd8aL6/+1NY93+SKAsh6qQqJ8menp6cPVs+kYmNjcXV1bVagqoNPB09GdtmLACzds/iTOaZG77GMdQD125BAGQsi8WkrztXmRfm5vLjW1P4/o2XyUxOslq9WkcNvR6ORKUyT1srQ/SFwDwm+c53zfd3fAgbZkiiLISoc6qcJA8dOpQXXniBM2f+TvpiY2N56aWXuOeee6o1uJruyVZPEhUURaGxkMmbJ1NYXHjD13gOCkfjqcOYXkjOhngrRGkfHF1dUWscKNYX8ddXH1s1WQ1r5csj07vRY3gTWWhEiKs6j4ZBc8z3t/4XNs+1bTxCCGFlVU6S586di6urK82aNSMiIoKIiAiaN2+Or68v//nPfywRY41VdOgw/3Ychq+TL7GZsczde+NGRu3ogNfQxgDkbLmIPjHP0mHaBZVKRf+x49FotZw/tJ8T2zZZtX5PPxer1idEjdDtGRjwtvn+pnfMybIQQtQRNzXcYseOHfzxxx88++yzvPTSS6xfv54NGzbg5eVlgRBrJkWvJ2Haa2Q9/QLvb2uIRz4sPbWUNXHlZwYpy7mFL86tfMEEmb/XnXHe3kH1iRr2EAAbv/0KQ+GNe96rW3piHss/PEBeVpHV6xbCLnV/Dvr+n/n++umw43+2jUcIIaykUouJlKVSqRgwYAADBgyo7nhqDaW4GNeuXdCfPYvjup18usORr3samO7wb1r6tqSBe4Prvt7z7kYoxQruPa9frrbpdPd9HN64lqzkJA5vWEOHIUOtWv+mxSdIjM3i0IYLRN3X2Kp1C2G3bn/JvGT1pndg7evmFfq6PWPrqIQQwqIq3ZO8c+dOVq5cWWrbt99+S0REBP7+/jz11FMUFUnv21VqFxcC/+//CP/hexybNUObV8Qzf5p4aX4m8356HoPp+nMhO3g6Um9USxwbelopYvugcdDS+e5hAOxduQxjsXXnjG7fPxSAw5svUZRfN+arFqJSer0Ct08231/9Kuz50rbxCCGEhVU6SZ4+fTpHjx4teXz48GFGjx5Nv379mDJlCitWrGDWrFkWCbImc27bloifl+L/6qvg7ETzizDmv8dZM+VxTAUFtg7PLrXs1RdXL2+c3dzJTU+zat3hrevhE+yKodDI4c2XrFq3EHZNpYI7Xofuz5sfr5oMi+6HxIO2jUsIISyk0stSBwUFsWLFCjp16gTAa6+9xubNm9m2bRsAS5cu5c033+TYsWOWi9ZCrLWspSEhgcPTXsB512EAjEH1CJ/+Dm63337N1xhz9ORsuYipoBif4U0tFpu9yU5Nwb2en01mmzi5O4m/5h/D2V3LYzO7o9VprB6DEHZLUWDTLPNFfKYrC/C0Gm5OoH0ibBubqHNkWWphSZXuSc7IyCAgIKDk8ebNmxk8eHDJ486dO3PhwoXqja6W0QYH02HBT+x8rieX3UGTeJkLY5/i0qRJGFJSKnyNMddA7tZL5EcnY7hcd3qePfz8bTYdW5NO/rj7OlGQY+D49gSbxCCE3VKpoM80mLDXnBwDHPkZPuoMq16G3Iq/y4QQoqapdJIcEBDAuXPmFcn0ej0xMTF069at5PmcnBy0Wm31R1gLPfLMh3zyciQrO6swqSB71Z+cHXIn6UuWoJRZ8lsX5IpTMx9QIHfzRRtFbDv6wgKObFxn1XmT1Ro1HQaYxybvXxeP0WiyWt1C1Bg+DWH41/D0FmjUF0wG2PMFfNAONr4DRTm2jlAIIW5JpZPkIUOGMGXKFLZu3crUqVNxcXHh9n8MEzh06BCNGjWySJC1jaPGkZkD32XpIDemjNKQ0ygAU24uydNnEPfQwxQeP16qvHufEADyYpIprkNTkxmLi1kw6VnWfPYB5w/tt2rdzboH4R/mTru+oSA5shDXFtQWHvsVHl8OwR3AkAeb55iT5V2fQXHd+c4SQtQulU6SZ8yYgYODA7169eLLL7/kyy+/RKfTlTz/zTffyJRwVdDQsyHTuk4jLlDF0//KwDBxJGpXVwoPHeLc8H+RPGcupjzzQiKOYR7oIjzAqJC7te5cTKZxcKBJ1+4A7P7tJ6vW7aDVMHxKJ9r2DUGjrfJ04kLUPQ17wdgN8K+F4NsY8i+bZ8H4qBMc/BFM8mtTCFGzVPrCvauysrJwc3NDoyl9MVN6ejpubm6lEueawlYD/xVFYdq2aaw8u5JA10B+7Pop+f/9mJzVqwFwCArC61/DcYyIAG0w2X9lo9KqCZzSBY1r3RjakpN2ma+eG4PJWMyI6fOoH9nc1iEJIW7EaID938Gm2ZCbZN4W0Br6vQmN+5nHNQtRDeTCPWFJVU6SayNbnmR5hjweWPEA8Tnx3BFyB+/3eZ+8LVtImj4Dw6XSvcYuvV9H4xWKKXc/Ov9MdOHh6CIi0EVEoA0KQqWpnbMwrPnsQ45sXEvDDp2579U3rVq3yWgiNjqF80fT6Deqhc0uJhSiRtLnw+5PYdsHUJRl3hZ2G/R/Cxp0sm1solaQJFlYkiTJ2P4kO5Z2jEdXPYrBZGBa12k81OwhTAUFZC79mcLjx9GfO4c+Lg6VUxgav0j0setQCjJK7UOl06ELCzUnzuHmxNmpZUucImv+tHHpCZeYP+kZUBQen/s//MKsN81UQY6eb6ftoNhg4p7n2xHSwsdqdQtRa+Snw7Z3YfcXYLwyRlnratuYbCWgJXSfAM3uAnXt7NiwJlu336J2kyQZ+zjJFh9fzOw9s9GqtSy5cwnNfJqVK1OckYE+Lg79ubiSxFkfdw593HkUQ8Wrw9UbP556E8bX+B7QFe/P4dTOrTTr0Ys7n3/ZqnVv/fEUhzZepH6kF/e+2MGqdQtRq2RdNM+xfGAJKHV8jLJvY+j+HLR9CBwcbR1NjWUP7beovSRJxj5OMkVReH7D82y6uIlwj3B+vOtHXLQuN3yNSqVCMRoxJCaaE+dz5sS5KPYM+Xv2AOAz8nH8p0yp0Yly8rkzLJ76IpHdb2fIhJdQqa13MV1OeiHfvb4Tk0lh2CsdCaxjS4ULUe3y06Eo29ZRWJ/RAId+Mk+VV5hp3uYWAN3GQacnwUm+W6rKHtpvUXtJkoz9nGSZhZkMXzGc5Pxk7ml0DzNvm1lhOf2FHLLXx+PUwge3LkHX3F/6ou9Inmneh+ew+wmaPr1Gj1vOSb+Mu089m9S9/tvjnNiRSHibetz5bBubxCCEqCWKciFmIez8GLKvXHvi6GFOlLuNA/dA28ZXg9hL+y1qJ5nbyo54OXkxp+cc1Co1y88sZ8WZFRWWKzqfTeGJdHI2XUQxXvs3js9jjxI0axao1WT98iuXXpqMotdbKnyLs1WCDJgXF1FB3KHLpF3KtVkcQohawNENosbD8wfg3k/Br5m5Z337+/B+a1j+PKSdsXWUQtR5kiTbmY4BHRnXdhwAM3bNIC4rrlwZ1y6BqF0cMKYXUnA49br787rvXuq//x5oteSsXs2FCRMwFdTs5a0zkhI4tXu7Vev0DnSlUXs/AGLWnrdq3UKIWspBB+0ehnE7YcT3ENIVjHpzL/P/OsKPj8GlaFtHKUSdZdMkedasWXTu3Bl3d3f8/f259957OXnyZKkyhYWFjB8/Hl9fX9zc3Bg2bBjJycmlysTHx3PnnXfi4uKCv78/L7/8MsXFxdZ8K9VqbOuxdAnsQkFxAa9seQWDsfRFeWqdBrce9QHI2XThhks2ewwYQMinn6JyciJvy1bix47FmFsze0OTz8Yy/4VnWP3J+xRa+T10HBROUGNPmnaRP4UKIaqRWg3NhsDotfDEamg6CFDg+HL48g5YcBfE/gUyOlIIq7LpmORBgwYxYsQIOnfuTHFxMdOmTePIkSMcO3YMV1fz9EDjxo3jjz/+YMGCBXh6ejJhwgTUajXbt5t7Eo1GI+3atSMwMJB58+aRmJjI448/ztixY3nnnXcqFYc9jmlKyU9h2PJhZBZl8kSrJ5jUcVKp5035BhJn70XRG/Ed2QLn5r433Gd+TAwXnnoaU24uTi1bEvLVlzh4e1vqLViEoigseuU5UuPj6P7AI0QNe8jWIQkhRPVLPgY7PoTDS8F0pdPHrzl4BNsmHrUDRPSE1v8C9wDbxFABe2y/Re1hVxfupaam4u/vz+bNm+nZsydZWVn4+fmxZMkShg8fDsCJEydo3rw5O3fupFu3bvz555/cddddJCQkEBBgPnE/++wzXn31VVJTUyu1AqC9nmTr49fzwsYXUKHiywFf0jWoa6nnM1edI3fLRXRhHvg906ZSs1cUHjtG/OgxGDMy0DVqROg3X6MNsJ8vvMo4sX0zf3w4Dyd3D5766Bu0Tk62DkkIISwj84L5Ar+YhWDIt3U0oFJDozvMU9dFDgHd9WdhsjR7bb9F7WBXSXJsbCxNmjTh8OHDtGrVig0bNtC3b18yMjLw8vIqKRcWFsYLL7zAiy++yP/93/+xfPlyDhw4UPL8uXPnaNiwITExMbRv375cPUVFRRQVFZU8zs7OJiQkxC5Psrd2vsXPp37G38WfX+/5FU/Hv6cIMmbrSZy7B4oV/J5qg2MlpyYrOnuW+CeepDg5GW2DBoTO/wZdSIil3kK1MxmNzH/xGTKTE+n9+Fg63jnUqvUX5ho4tPECagc1nQaHW7VuIUQdlZ8OZzaYp5GzhYJ0OLoMLu79e5vOHVoMhbYPmldStOLUnFdJkiwsycHWAVxlMpl44YUX6NGjB61atQIgKSkJnU5XKkEGCAgIICkpqaRMQJme0KuPr5Ypa9asWbz11lvV/A4s4+VOL7MvaR9x2XG8tfMt/tvrvyU9xhoPHR69Q1C7adGFuFV6n44NGxK2eDHxTz6JIT6e8w8/Qug3X+PYpIml3ka1Ums0dB46jHVffMS+lb/SdsAQHLRaq9WfdDaLvX/EoXXU0KpnfZxcrVe3EKKOcvGB1sNtG0PUeLgcC4d+hEM/QGY8HPjOfPMMgTYPQJsR4FfzV3oVAuxodovx48dz5MgRfvjhB4vXNXXqVLKyskpuFy5csHidN8tF68LsnrNxUDmw7vw6fov9rdTzHv3CcOsWjEpbtfmPdQ3qE/bdIhybNKE4NZXzjz5GweHD1Ri5ZbXo2Rc3bx9y09M4vnWjVesOa+2Lb31XDEVGjmy+aNW6hRDCpuo1hjteg+cPwhN/QofHwdETsi7A1v/Cx53hiz6w+3PIu2zraIW4JXaRJE+YMIGVK1eyceNGGjRoULI9MDAQvV5PZmZmqfLJyckEBgaWlCk728XVx1fLlOXo6IiHh0epmz1r6duSCe0nADBrzyzis+MrLFfVkTNaf3/CFn2LU5s2GLOyiB/1BHlXVumzdw5aLR3vug9HV1eM11iS21JUKhUdBoYBcHDDRQx6o1XrF0IIm1OrIaw73PM/mHwShs83z8qh0kBCDPz5Cvw3EpaMgKO/gaHQ1hELUWU2TZIVRWHChAksW7aMDRs2EBERUer5jh07otVqWb9+fcm2kydPEh8fT1RUFABRUVEcPnyYlJSUkjLr1q3Dw8ODFi1aWOeNWMGolqPoHNiZguICpmydgsFUOjHMi04m+b0YDKlVu7BD4+VF6Dff4NKlC6a8PC6MfYrczZurM3SLadt/MGM/mk+7gXdave7GHf3xqOdEYa6BY9sSrF6/EELYDa0ztLofHv4RXjoJg+ZAUDvzrByn/oSlI+G/TSHOuvPbC3GrbJokjx8/nu+++44lS5bg7u5OUlISSUlJFFxZ7MLT05PRo0czadIkNm7cSHR0NE888QRRUVF069YNgAEDBtCiRQsee+wxDh48yJo1a3j99dcZP348jo6Otnx71Uqj1vDObe/grnPn8OXDfHbws1LPFxy5THFKPjmbqv7nf42bKyFffI5b794oRUVcGD+B7FWrqit0i9E6OuHoYpsrq9UaNe0HmHuTD6yLx1hsskkcQghhV9z8oNsz8PRmeHY33PYieNQ39yQH1J6OK1E32HR2i2tNWTZ//nxGjRoFmBcTeemll/j+++8pKipi4MCBfPLJJ6WGUpw/f55x48axadMmXF1dGTlyJLNnz8bBoXLXJdakq2PXxK1h8ubJqFVqvhn4DR0DOgJQFJ9N6icHQa0i8JXOOHhV/QeCYjCQ8OoUc4KsUuFx552oHG88hZ4luHbvjuedleshVhSF8wdj0Lm4Ety0mYUj+1uxwcii13aSn63njseb0by7jeYvFUIIe2YyQeoJiyTJNan9FjWPXU0BZys17SR7fdvr/H7md4Jcg/j5np/x0JljTv3iEEVns3DrEYzX3Y1uat+K0UjSW9PJ/Omn6gz5pgS89ho+jz16w3J7V/zKlu++oUHzVjz479lWiOxvB9dfIC0hlw4DwvAKsO18oUIIUdfUtPZb1CySJFPzTrI8Qx7Dlw/nYu5FhkQMYU7POQAUnsrg8jdHUGnVBL7aGY3bzfUCK4pCztp16M+fr86wK01/Po6sX34FIHjePDzvvuu65XPSL/PVhDGYjMWMeGsu9ZvJn/SEEKIuqGntt6hZ7GaeZFF5rlpXZveczcg/R7Lq3Cpub3A7dzW8C8cmXmjru2G4lEvujgQ8B4Tf1P5VKhUeAwdUb9BVoCgKamcXMr77joSpU9F4euDWs+c1y7v71KNlrzs4vGEte35fyn3N3rRitEIIIYSojexiCjhRdW392vJM22cAmLlrJhdzLqJSqXDvbV45L3dHIqaimjk1mUqlImDaVDzuuguKi7n4/ETyY/Zf9zWd7xmGSqXmbMxeUuLOWinSv12+mMvar44QfyzN6nULIYQQovpJklyDjWk9hvb+7ck15DJt2zSKTcU4t/TFpWMAvo82Q6Wruf97VWo1wbPewbXn7SiFhVx45hkKT566ZnnvoPo07dYDgD2//2ytMEuc2JHI6X0pRP9pmyEqQgghhKheMiYZy45p0uv113xOpVKh/cdyyjdTNiE3gUdXPUquIZen2jzFmNZjSpXN25dMUXYBUMH/Zq0at65B6HTmscv5+1MozMivuKyDCrduwX+XPZRK4eW8isuqwK1H/ZKyBUcuU5CSW3FZwLV7MI5O5tk4Co6nUZiYg3KlrGIwkLFsGcUJCahdXQmaPhr3JuaLEgtPZVBwMaukbG5GOvvXrsRBo6X9oLvx6dMIRzdnYlYtxyFdhTpHU1L2Kp2zC/5hEbh08MfR04UDa/9AlWpCk12+rNbRiYCIRji388PRy4UjG9ZiSi5Ck+WAwVBMwqksUBQ8/J3RaHV41gujuIEbbQY34tjm9RQn5lMUZ0RvKN3Dr9Y44OkXjiHQhbZ3N+HE9s0UXcxGH2dEX2ahEpVajZd/Q/T1nGl3X1NO7dpGQXw6hjgoKiqzqIpKhXdAI4p8nWk7tDHnYvaQE5dCcRwUFZZfgMUrsBEGH2da3d2YC0djyDqTgPGcisIKynoGNKTYx5nmQxqRFHuE9NPxKHFqCvLLf4Y9/cMp9nElclBDLp8/weVTZyFOQ35e+bIefqGYvN1oNDCCrKQzJJ84jfqshrwKyrrXC0HxdqfhgHBy0+JJPHYc9VkH8nKLypf1rQ/eHoT1C6coJ4ELRw7jcFZLbk75sm4+Qai8vWhwRygm/WXOH9yP9qyWnOzyZV19AlF7eRHcOwQ1WZzbvw/tWR05WeUXTnD1DkDj6YV/zxAcHfM5s28XuvM6stPLl3Xx8sfB05t6tzXAzUPPqV3b0cXryE6roKyHH1ovb7y7N8CrnsKJ7ZvRXdKRnVK+rJO7L46evnhGBVMv2IFjWzagS9SRk1RY7ux0cvPGybMebl2DCAx35sjGdeiSdOQkli/r6OqFs6cfLl0CadDEg0N//Yljqo7sixWUdfHE2dMPx06BRLTy4cDqlThe1pF9oXxZnbM7rp4BOLTzo2mnIKL/+A3HdB058UWYyjRdWidX3LyC0LTypXmPUPau+BVdupbc+CJMprLnsgtu3sGomvnQomdoyXdE3nk9RmPpsg46J9x9GqA09qTVHREl3xH55w3lpn500Dri7huCKdyDVv0jSr4jCs8bMZQ97x20deo7on5kc3TO1X9xs4xJFpYkY5It7J133rnmc02aNOGRRx4peTxv3jwM11g9LiwsjCeeeKLk8fvvv09+vnnhkH70AyDlfArvrHiH4OBgnnrqKQByt19iUfpaclUVr3bkd8iP8ePHm8vuSmRJ4loy1XkVlvWM9uTFF18EIG9vEj+eX8tldU6FZV32uvDKK6+Yy8ak8HPsWpLUmRWW1e7U8tprrwGQfzCV346u44LmH8MWAoFAb/P9xYt4fcIEHOrVo+DIZZbvX8c5zd8LyRAEoGdvzC8QA9OmTWP7T9/RyqU7ifVcOK1JLB/A+S2wBV5++WV2/fIDjdStyajnw3GHS+XLXtgGW2DixInsWf4zwYXhFPgHc9ghHvz+Uc4AJB6ARAju/CzRf/yGT5YfpoAI9jucK73PYiDxICSCf6exHFi9EudkJxwDmrNHG1u6rBFIPAyJ4N1hJIfXr4ZzBjyD2rNDW6anXQESj0AiuLQYwYlN6yg6kYl/cDe2aI+Xf2+JRyER1A3v5+LWTWTuv0Bo/d5s0B0tXzbpKCRBUfAQso/sJHHHMZo2GMT6isomH4Vk6O3bF2P8UeI27qF1yL0Vl005CikQ5X47ztkXOP7HerqEjmC9YwXDbVKPQip0cOyKH5nE/PI7PcNGst4xpnzZy0fhMrRStSfc3cjOH5fQP/xpfq+obNpRSIOmxa1oWd+FrUsWcnfERH5zunbZiIKmdG4WyJbF8xka/iK/OUeXL5t+FNKhfm4EvTo1Zsvi+dwXOpFlrhWUzTgKGRCQ1YBBvdqzZfF87g55lhWuxylWlZmTO8t880kP4P4ht7Nl8XyGNBjLdrdTFKrKfJ9km28el3145F9D2LJ4PgOCR7HL40z574gc8801xZMnRw5ny+L59A16hH0eF8t/R+Sab44rXBj/zBNsWTyfXoEPcMAztfx3RJ755rBSx0tNJrBl8Xx6+N/HEe+c8t8R+eabOlHDtPavsmXxfLr53cUJb0Pp7wiAgiu3RPh3j3+zdfECOvr0J85XU/o7AqDQXI5EmBZ1g++IIiAxGhKhYbcbfEfogcQYSISQLhV8R/xTHfuOqBcaZpEkWQhLkiS5FjIpfzeiTs19UO3VmL+Qb8CpqTfqTI25UbhR2cbeqFO15sbmBhwbeqJJ0pkbvBuVDfdEc9HR3OheQ/yYsYQt+hZdqDuac47mxvw6mvfohXOmG/r8rBvWHxl1O46XdSTlXSeAK5p06Y4mSSEjt+CGZRt16opyUU985g2CBSLad8JwPpfEjBuXDWvTgQLXNFLTsq9bTqWC0FZtydYlkZV2/eOg0kCD5q1wUTzIunz9shoHNcFNm+NQ6EDW5czrl9Vq8G/cFCWnmMzL6dct66DTEBDRiKKu2aSnXH+ct1anwS8wgsZRUTcu66jBt0F9mna//cZlnTR4B9Wn+e29SU++fMOynv6BtOh5BxnJ19+vzkmDh68fLXreQWZK5g3jdfP2oUXPO8i5nItiuvYf/rQ6DS6eXrToeQf5aYWYjNde4MZBp8HJzY0WPe+gKL0Yo8EIFU9bj0anxtHZhRY978CQoVCsv/aXicZBjdbRkRY970DJ0mAouHZZtUaNRqulRc87UGc7oc+/9nFTqVWo1Cpa9LwDh1xXCnOTrln2qhY9++CY40lBTuoNy179jijIzrhh2avfEZeyrn/Owd/fEZczK+54+Kfa/h2h1TndMFYh7I0Mt6BmD7e4KkefwyOrHiEpL4m7Gt7F273ertR+gZJhEVUtazAYuN7Hp7rL6uPjiX9yNKqUFFw6dSLkqy8xajTX3a9Wqy1ZtKa4uBiT6dqJQ1XKOjg4oFar7aas0WjEaLz2hZoajQaNRmM3ZU0mE8XFxdcsq1arSxYDsoeyiqJc8688VS1bnee9NcpCzfmOuJmy8h1hZo3z3hJkuIWwJEmSqT0nWXRyNE+ueRKTYmJer3kMCh9k65CqXeHx45x/7HFMubm49elDg/99iKqSKysKIYSoXWpL+y3sU82d/kCU0zGgI6NbjQZg+s7pJOXd+E+SNY1T8+aEfPoJKkdHcjduJPH1N1Cu05MihBBCCHEzJEmuZca1G0cr31bk6HOYunUqxaZr/zm5pnLp3Jn6770HGg1Zv/1Gytx51/1zqhBCCCFEVUmSXMto1Vpm95yNs4Mz+5L3MWr1KOKz42/8whrG/Y4+BM00j7tOX7CAtC+/snFEQgghhKhNJEmuhcI8wphz+xzctG4cTD3I8BXD+enkT7Wut9Xr3nvxn/IqAKnvvkvGTz/ZOCIhhBBC1BaSJNdSfUL78Ms9v9AlsAsFxQXM2DWDZ9c/S2r+jadDqkl8R43C9+mnAUj691tkr1lr44iEEEIIURtIklyLBbsF8+WAL3ml8yvo1Dq2XdrGfcvvY3XcaluHVq38XpiI1wMPgMlEwuTJ5O3caeuQhBBCCFHDyRRw1I0pZM5knmHq1qkcTzevojQkYgjTuk7D09HTxpFVD8Vo5NKLk8hZuxaViwsu7drZJhC1GofAAHShYejCwtCFhaILCUHt6mqbeIQQoharC+23sB1Jkqk7J5nBZODzg5/z1eGvMCpG/F38mdFjBt2Du9s6tGph0uu58PTT5O/cZetQytH41TMnzaFh6EJD0YWb/9WGhqJxc7N1eEIIUSPVlfZb2IYkydS9k+xQ6iFe2/YacdlxAIyIHMGkTpNwdnC2bWDVQNHryd22DVPejZeBtUj9hmIMCQno489jOB+P/vx5jJmZ132Nxtf3SgIdioOfn3l9WFvQqFG7uFy5uf5939XlH/fN21VOTiUrjwkhhK3UtfZbWJckydTNk6yguID3ot/j+xPfAxDuEc7M22bSxq+NjSOrfYxZWejjL6CPP4/+/JXkOf5KAp2ebuvwbo5K9Y+E2gWVqwsadw80Hh6oPT3QeHqi8fBEc+W+2sPD/NjL01zG3R2VWi6JEELcmrrYfgvrkSSZun2S7bi0gzd2vEFKfgoalYYxrcfwdNun0aq1tg6tTjDm5KCPj8dw/nylep0tSSk2YioowJSfjyk/D1Ne/t/38/NRrjyuFirVlcT5SmLt5gbqm++Zdu/dG5+RI6snNiFEjVGX229heZIkIydZVlEW7+x+h1XnVgHQ3Kc5s26fRSOvRjaOTNgbxWRCKSw0J895eVeSaPN9Y04OxqwsTNnZGLOyMWZlYczOwlRyPxtjdjZKdSXaZYR89RVut/WwyL6FEPaprrffwrIkSUZOsqtWx61mxs4ZZOuz0al19GzQE7Wq7v1J3E3nRphHGGHuYYR5hBHiEYKjxtHWYdUail5fkjAbM68k0rk3P4Y8d9MmsleuxCEggIYrlqOpw+ewEHWNtN/CkiRJRk6yf0rJT+HNHW+y7dI2W4diN1SoCHINIswjjFCPUMI9ws1JtEcYwW7BOKgdbB1inWYqKODcvfehP38ez6FDCZ4z29YhCSGsRNpvYUmSJCMnWVmKorDl4hYS8hJsHYrVKYpCVlEWcdlxxGfHE5cdR64h95rlHVQONHBvUJI0h3mEEekTSaR3JE4OTlaMvG7Lj9nP+UcfBZOJBh/9D/d+/WwdkhDCCqT9FpYkSTJykolrUxSF9MJ04nPiicuK43z2efP9K0l0kbGowtdpVBoaejWkhU8LWtZrSQvfFpI4W1jKf/9L2pdfofH1peGK5Tj4+Ng6JCGEhUn7LSxJkmTkJBM3x6SYSMlPIS47jvNZ5zmfc56zWWc5nnac9MLyU7v9M3Fu4WtOniVxrj4mvZ64YcMoOh2L+4AB1P/gfZnLWYhaTtpvYUmSJCMnmaheiqKQnJ/MsbRjJbejaUcrlTi3rteaZr7NZAq+m1Rw9ChxD46A4mKC//MfPO+609YhCSEsSNpvYUmSJCMnmbC8ihLnY2nHSCtMK1fW2cGZtn5t6RDQgU4BnWhdr7X0NldB6scfc/l/H6H29KTh8uVoA/xtHZIQwkKk/RaWJEkycpIJ21AUhZT8FI6mHS1Jmg9dPkRWUVapcg5qB1r5tqJjQEc6BnSknX873HXuNora/ikGA3EjHqLw6FFce/Uk5LPPZNiFELWUtN/CkiRJRk4yYT9MiokzmWeISY4hOjma6ORoUgpSSpVRq9REekfSMaAjHQI60MG/A77OvjaK2D4VnT7NuWHDUfR6gt6egdfw4bYOSQhhAdJ+C0uSJBk5yYT9UhSFizkXiU6JLkmaL+RcKFcuwjOCDv4d8Hbyvum6PHQe9A/rTwP3BrcSst1I+/obUubNQ+3iQsTy5ega1Ld1SEKIaibtt7AkSZKRk0zULCn5KcQkx7AveR8xKTGczjhdrfvvEtiFoY2H0i+0Hy5al2rdtzUpRiPnH3ucgpgYXLp2JXT+N6jUdW8FSSFqM2m/hSVJkoycZKJmyyrKIiY5hkOXD1FYXHjT+zmdcZo9SXtQMH8luDi4MDB8IEMbD6WDf4caOa5Xf/48Z++9D6WggIDXXsPnsUdtHZIQohpJ+y0sSZJk5CQT4qqE3ASWn1nO77G/czH3Ysn2EPcQhjYayj2N7iHILciGEVZd+pIlJE+fgcrJiYhlv+IYEWHrkIQQ1UTab2FJkiQjJ5kQZSmKQnRyNL+f+Z01cWsoKC4AQIWKrkFdGdp4KH1D++Ls4GzjSG9MMZm4MGYMeTt24ty2LWFLFqPSaGwdlhCiGkj7LSxJkmTkJBPievIN+aw7v47fz/zO3qS9JdvdtG4MDB/IvY3vpa1fW7sejmFISODsPUMx5ebi99Ik6o0da+uQhBDVQNpvYUk2TZK3bNnCvHnziI6OJjExkWXLlnHvvfeWPK8oCm+++SZffvklmZmZ9OjRg08//ZQmTZqUlElPT+e5555jxYoVqNVqhg0bxgcffICbm1ul45CTTIjKuZBzgRVnVvB77O8k5CWUbA/3CKeJd5PrvPL6vBy9GNt6rEWHcmT+uozEadNQabWE//wzTpFNLVaXEMI6pP0WlmTTJPnPP/9k+/btdOzYkfvvv79ckjxnzhxmzZrFwoULiYiI4I033uDw4cMcO3YMJyfzCmSDBw8mMTGRzz//HIPBwBNPPEHnzp1ZsmRJpeOQk0yIqjEpJvYl7eP3M7+z7vy6kuEYt8LL0YvZt8+mR/0e1RBheYqicPHZ8eRu3Ihji+ZE/PADKp3OInUJIaxD2m9hSXYz3EKlUpVKkhVFITg4mJdeeonJkycDkJWVRUBAAAsWLGDEiBEcP36cFi1asHfvXjp16gTA6tWrGTJkCBcvXiQ4OLhSdctJJsTNyzPksfnCZrL0WTcuXAFFUfj9zO8cSzuGChXj2o7j6bZPo1ZV/3RtxampnL3rboxZWdR7dhx+zz9f7XUIIaxH2m9hSQ62DuBazp07R1JSEv369SvZ5unpSdeuXdm5cycjRoxg586deHl5lSTIAP369UOtVrN7927uu+++CvddVFREUVFRyePs7GzLvREhajlXrStDGg65pX0MazqMOXvmsPTUUj45+AkHLx9k9m2z8XLyqp4gr3Dw8yPw329y6cVJXP78C9z69MG5detqrUMIIUTtYLcz6yclJQEQEBBQantAQEDJc0lJSfj7+5d63sHBAR8fn5IyFZk1axaenp4lt5CQkGqOXghRFY4aR/4v6v+YedtMnDRObL+0nQdWPsCRy0eqvS6PwYPxGDIYjEYSpkzFVHjzc0sLIYSovew2SbakqVOnkpWVVXK7cKH8Mr9CCOu7p9E9fDfkO0LdQ0nMS+TxPx/nxxM/Ut2jwgLeeANNvXroz5wh9YMPq3XfQgghage7HW4RGBgIQHJyMkFBf1/xnpycTLt27UrKpKSklHpdcXEx6enpJa+viKOjI46OjtUftBDilkX6RPLDXT/wxvY3WB+/nrd3v82B1AO80e2Nalsm28Hbm6Dp07n47LOkL1hAzl9/3fy+/P1x6dwJ1y5dcG7XDrVLzV3KWwghxN/sNkmOiIggMDCQ9evXlyTF2dnZ7N69m3HjxgEQFRVFZmYm0dHRdOzYEYANGzZgMpno2rWrrUIXQtwid5077/V+j2+Pfct70e+x8uxKTqSf4N3e7xLhWT0r5rnf0Qevfw0nc+nPGG7hr0mGCxcoiI4m7bPPQavFuXVrXLp0lqRZCCFqOJvObpGbm0tsbCwA7du3591336VPnz74+PgQGhrKnDlzmD17dqkp4A4dOlRuCrjk5GQ+++yzkingOnXqJFPACVFL7Evax8tbXuZywWVcta5M7z6dAeEDqmXfitFI4fETUGy4udcrCvqz58jfs4e8PXsoTkwsXcDB4UrS3AWXLp1xad9ekmYhqpG038KSbJokb9q0iT59+pTbPnLkSBYsWFCymMgXX3xBZmYmt912G5988glNm/69CEB6ejoTJkwotZjIhx9+KIuJCFGLXC64zOTNk4lOjgbgsRaP8WLHF9GqtTaO7G+KomC4dIn83XskaRbCSqT9FpZkN/Mk25KcZELYv2JTMR/GfMj8o/MBaO/fnnk95xHgGnCDV9pGpZJmAIebH/WmDQ7G59FH8Ro+TJJtUSdJ+y0sSZJk5CQToiZZf349r29/nVxDLj5OPszrOY8uQV1sHdYNVTppvgkaT0+8H3kE70cfwcHHp1r2KURNIO23sCRJkpGTTIia5nz2eSZtmsSpjFOoVWp61u+JVnNzQy9UqHDVuuKmc8Nd646bzg03rRvuOvcKt+k01bOUtaIoGNPTUYqLb24HJhO5mzeT9s18DPHx5vfi5ITX/ffj8+QT6Bo0qJY4hbBn0n4LS5IkGTnJhKiJCooLeHvX2yw/s9yq9erUOnPyrHPHTeuGo+bmp5N0dnDm8RaP071+95veh2I0krNuHWlffkXh0aPmjWo1HoMG4TtmNE4tWtz0voWwd9J+C0uSJBk5yYSoqRRFYWfiTi5k3/wUbkbFSJ4hjxxDDrn6XHL1uX/fN+SSo88hR59DfnF+NUb+NxUqxrcbz9g2Y1Grbn59J0VRyN+9m7SvviZv27aS7a7du+M7ZjQuUVGoVKrqCFkIuyHtt7AkSZKRk0wIcWNGk5FcgzlxztWbk+dcQy56o/6m97kjYQe/nP4FgN4hvXnntndw17nfcqyFJ06Q9tXXZP/5JxiNADi1aIHvmNG4DxiA6hYuFhTCnkj7LSxJkmTkJBNC2M6y08t4e9fb6E16wjzCeL/3+zT2blwt+9ZfvET6woVk/vwzSkEBANqQEHyeGIXXffehdnaulnqEsBVpv4UlSZKMnGRCCNs6evkoL256kcS8RJwdnJneYzqDwgdV2/6LMzLIWLKEjO8WY8zIAEDj5YU2JOSW9qtSq0GtBo0alVoDavW1t2k0qNQqUGtQadSA7YZ+OPj749i0CY5NmqBr2BC1rnouxhTWJ+23sCRJkpGTTAhhexmFGby85WV2J+4GYFTLUUzsMBEHdfUNjTAVFJD566+kz1+A4eLFattvjabRoAsLw7FJk1I3XWiIDEupAaT9FpYkSTJykgkh7EOxqZj/7f8f3xz5BoAugV2Y23Muvs6+1VqPUlxMfkwMpvxbuBhRUUBRUIxGMJpAMaEYTWAyophM19ymmK6UtxXFhP7SJYpOn6bodCymrKwKi6l0OnSNGuHYpHFJ4uzUpAkOwcFyAaQdkfZbWJIkychJJoSwL+vOr+P1ba+TX5xPgEsA7/V+j9Z+rW0dVq2jKArFKalXEuZ/3GJjS8Zwl6XSam9plURb0gYG4t6vL+79++PUunWtSPal/RaWJEkycpIJIezPmcwzvLDxBeKy49CqtUzrOo3hTYfbOqw6QTGZMFztbT71j+T53DkwGGwdXrVwCArCvV8/PAb0x7lDB1Qaja1DuinSfgtLkiQZOcmEEPYpV5/La9teY8OFDQAMazKMqV2n3tICJuLmKQYDxSkp1MxWU6Hw8GFy1q0jd9PmUkNtNL6+uPc19zC7du2CqgZdyCjtt7AkSZKRk0wIYb9MiolvjnzDhzEfoqDQyrcV7/V5j0DXQFuHJmooU1ERedt3kLNuHTkbNpQal6328MC9T29zwnzbbaidnGwXaCVI+y0sSZJk5CQTQti/HZd28MrWV8gqysLb0Zt5vebRNairrcMSNZxiMJC/dy/Za9eS89d6jJcvlzyncnbGrWdP3Af0x61XLzRubjaMtGLSfgtLkiQZOcmEEDXDpdxLvLjxRY6nH0etUjOq5Sg6BnQkwiOCYLdgNOqaOa5U2AfFaKTgwAFy1q4je91aihMS/35So0HteGvDfCJ+/QVdePitBVmGtN/CkiRJRk4yIUTNUVhcyIxdM1h+Znmp7Tq1jjDPMBp6NqShZ0MiPCNo6NmQMI8wnBzs+0/mwv4oikLh0WPkrF1Lztq16OPibnmfDf9chWNExK0H9w/SfgtLkiQZOcmEEDWLoij8ce4PNl3YxLmsc8RlxaE36Sssq0JFsFtwqcQ5wjOCcM9wnB1styy1ChUqlQo1alQqFSpUqFXqWjEtWW1jniovBUVf8WessrQBAdV+UaC038KSJElGTjIhRM1mNBlJyE3gXPY5zmaeLfn3bNZZsvXZtg6vyv6ZMF+9r1apAczbr/x3s3QaHa5a15Kbm9YNF60Lblq3Utsrujk7OJfEIqrGz8UPrVpbrfuU9ltYkiTJyEkmhKidFEUhvTCds1lnOZd1ruR2NussiXmJN96BENVoxb0rCPcMr9Z9SvstLKlmLhskhBDihlQqFb7Ovvg6+9I5sHOp5/RGPcWmYhtFBgoKJsWEgoKi/H3fpJhQFOWG92+lXr1RT64hlzxD3nVvuYZc8g35pcoWFFe8Ep+4MRlKI2oaSZKFEKIO0ml06DQ1Z9EIIYSwNhlYJYQQQgghRBmSJAshhBBCCFGGJMlCCCGEEEKUIUmyEEIIIYQQZUiSLIQQQgghRBmSJAshhBBCCFGGJMlCCCGEEEKUIUmyEEIIIYQQZUiSLIQQQgghRBmSJAshhBBCCFGGJMlCCCGEEEKUIUmyEEIIIYQQZUiSLIQQQgghRBmSJAshhBBCCFGGg60DsAeKogCQnZ1t40iEEEIIUVlX2+2r7bgQ1UmSZCAnJweAkJAQG0cihBBCiKrKycnB09PT1mGIWkalyM8vTCYTCQkJuLu7o1Kpqm2/2dnZhISEcOHCBTw8PKptv7WdHLebI8et6uSY3Rw5bjdHjtvNud5xUxSFnJwcgoODUatlBKmoXtKTDKjVaho0aGCx/Xt4eMgX4k2Q43Zz5LhVnRyzmyPH7ebIcbs51zpu0oMsLEV+dgkhhBBCCFGGJMlCCCGEEEKUIUmyBTk6OvLmm2/i6Oho61BqFDluN0eOW9XJMbs5ctxujhy3myPHTdiKXLgnhBBCCCFEGdKTLIQQQgghRBmSJAshhBBCCFGGJMlCCCGEEEKUIUmyEEIIIYQQZUiSbEEff/wx4eHhODk50bVrV/bs2WPrkOzav//9b1QqValbs2bNbB2WXdmyZQt33303wcHBqFQqfvvtt1LPK4rC//3f/xEUFISzszP9+vXj9OnTtgnWjtzouI0aNarcZ2/QoEG2CdZOzJo1i86dO+Pu7o6/vz/33nsvJ0+eLFWmsLCQ8ePH4+vri5ubG8OGDSM5OdlGEduHyhy33r17l/u8PfPMMzaK2D58+umntGnTpmTBkKioKP7888+S5+WzJmxBkmQL+fHHH5k0aRJvvvkmMTExtG3bloEDB5KSkmLr0Oxay5YtSUxMLLlt27bN1iHZlby8PNq2bcvHH39c4fNz587lww8/5LPPPmP37t24uroycOBACgsLrRypfbnRcQMYNGhQqc/e999/b8UI7c/mzZsZP348u3btYt26dRgMBgYMGEBeXl5JmRdffJEVK1awdOlSNm/eTEJCAvfff78No7a9yhw3gLFjx5b6vM2dO9dGEduHBg0aMHv2bKKjo9m3bx933HEHQ4cO5ejRo4B81oSNKMIiunTpoowfP77ksdFoVIKDg5VZs2bZMCr79uabbypt27a1dRg1BqAsW7as5LHJZFICAwOVefPmlWzLzMxUHB0dle+//94GEdqnssdNURRl5MiRytChQ20ST02RkpKiAMrmzZsVRTF/trRarbJ06dKSMsePH1cAZefOnbYK0+6UPW6Koii9evVSJk6caLugaghvb2/lq6++ks+asBnpSbYAvV5PdHQ0/fr1K9mmVqvp168fO3futGFk9u/06dMEBwfTsGFDHnnkEeLj420dUo1x7tw5kpKSSn3uPD096dq1q3zuKmHTpk34+/sTGRnJuHHjSEtLs3VIdiUrKwsAHx8fAKKjozEYDKU+b82aNSM0NFQ+b/9Q9rhdtXjxYurVq0erVq2YOnUq+fn5tgjPLhmNRn744Qfy8vKIioqSz5qwGQdbB1AbXb58GaPRSEBAQKntAQEBnDhxwkZR2b+uXbuyYMECIiMjSUxM5K233uL222/nyJEjuLu72zo8u5eUlARQ4efu6nOiYoMGDeL+++8nIiKCM2fOMG3aNAYPHszOnTvRaDS2Ds/mTCYTL7zwAj169KBVq1aA+fOm0+nw8vIqVVY+b3+r6LgBPPzww4SFhREcHMyhQ4d49dVXOXnyJL/++qsNo7W9w4cPExUVRWFhIW5ubixbtowWLVpw4MAB+awJm5AkWdiNwYMHl9xv06YNXbt2JSwsjJ9++onRo0fbMDJR240YMaLkfuvWrWnTpg2NGjVi06ZN9O3b14aR2Yfx48dz5MgRuUagiq513J566qmS+61btyYoKIi+ffty5swZGjVqZO0w7UZkZCQHDhwgKyuLn3/+mZEjR7J582ZbhyXqMBluYQH16tVDo9GUu/I2OTmZwMBAG0VV83h5edG0aVNiY2NtHUqNcPWzJZ+7W9ewYUPq1asnnz1gwoQJrFy5ko0bN9KgQYOS7YGBgej1ejIzM0uVl8+b2bWOW0W6du0KUOc/bzqdjsaNG9OxY0dmzZpF27Zt+eCDD+SzJmxGkmQL0Ol0dOzYkfXr15dsM5lMrF+/nqioKBtGVrPk5uZy5swZgoKCbB1KjRAREUFgYGCpz112dja7d++Wz10VXbx4kbS0tDr92VMUhQkTJrBs2TI2bNhAREREqec7duyIVqst9Xk7efIk8fHxdfrzdqPjVpEDBw4A1OnPW0VMJhNFRUXyWRM2I8MtLGTSpEmMHDmSTp060aVLF95//33y8vJ44oknbB2a3Zo8eTJ33303YWFhJCQk8Oabb6LRaHjooYdsHZrdyM3NLdXbdO7cOQ4cOICPjw+hoaG88MILvP322zRp0oSIiAjeeOMNgoODuffee20XtB243nHz8fHhrbfeYtiwYQQGBnLmzBleeeUVGjduzMCBA20YtW2NHz+eJUuW8Pvvv+Pu7l4y9tPT0xNnZ2c8PT0ZPXo0kyZNwsfHBw8PD5577jmioqLo1q2bjaO3nRsdtzNnzrBkyRKGDBmCr68vhw4d4sUXX6Rnz560adPGxtHbztSpUxk8eDChoaHk5OSwZMkSNm3axJo1a+SzJmzH1tNr1Gb/+9//lNDQUEWn0yldunRRdu3aZeuQ7NqDDz6oBAUFKTqdTqlfv77y4IMPKrGxsbYOy65s3LhRAcrdRo4cqSiKeRq4N954QwkICFAcHR2Vvn37KidPnrRt0HbgesctPz9fGTBggOLn56dotVolLCxMGTt2rJKUlGTrsG2qouMFKPPnzy8pU1BQoDz77LOKt7e34uLiotx3331KYmKi7YK2Azc6bvHx8UrPnj0VHx8fxdHRUWncuLHy8ssvK1lZWbYN3MaefPJJJSwsTNHpdIqfn5/St29fZe3atSXPy2dN2IJKURTFmkm5EEIIIYQQ9k7GJAshhBBCCFGGJMlCCCGEEEKUIUmyEEIIIYQQZUiSLIQQQgghRBmSJAshhBBCCFGGJMlCCCGEEEKUIUmyEEIIIYQQZUiSLISwawsWLMDLy8vi9fz73/+mXbt2Fq+nqnr37s0LL7xg6zCEEKLOkcVEhKjlkpKSmDlzJn/88QeXLl3C39+fdu3a8cILL9C3b19bh3dDBQUF5OTk4O/vb9F6cnNzKSoqwtfXF4BRo0aRmZnJb7/9ZtF6r9q0aRN9+vQhIyOj1I+C9PR0tFot7u7uVolDCCGEmYOtAxBCWE5cXBw9evTAy8uLefPm0bp1awwGA2vWrGH8+PGcOHHC1iHekLOzM87Ozhavx83NDTc3t2rfr16vR6fT3fTrfXx8qjEaIYQQlSXDLYSoxZ599llUKhV79uxh2LBhNG3alJYtWzJp0iR27dpVUu7dd9+ldevWuLq6EhISwrPPPktubm7J81eHPKxcuZLIyEhcXFwYPnw4+fn5LFy4kPDwcLy9vXn++ecxGo0lrwsPD2fGjBk89NBDuLq6Ur9+fT7++ONSMVa27n96++238ff3x93dnTFjxjBlypRSQyVGjRrFvffey3/+8x+CgoLw9fVl/PjxGAyGax6rfw63+Pe//83ChQv5/fffUalUqFQqNm3aBMCFCxd44IEH8PLywsfHh6FDhxIXF1eu7pkzZxIcHExkZCQAixYtolOnTri7uxMYGMjDDz9MSkoKYP4x06dPHwC8vb1RqVSMGjUKKD/cIiMjg8cffxxvb29cXFwYPHgwp0+fLne81qxZQ/PmzXFzc2PQoEEkJiZe870LIYQoT5JkIWqp9PR0Vq9ezfjx43F1dS33/D8TT7VazYcffsjRo0dZuHAhGzZs4JVXXilVPj8/nw8//JAffviB1atXs2nTJu677z5WrVrFqlWrWLRoEZ9//jk///xzqdfNmzePtm3bsn//fqZMmcLEiRNZt25dler+p8WLFzNz5kzmzJlDdHQ0oaGhfPrpp+XKbdy4kTNnzrBx40YWLlzIggULWLBgQaWO3eTJk3nggQdKksvExES6d++OwWBg4MCBuLu7s3XrVrZv316ShOr1+pLXr1+/npMnT7Ju3TpWrlwJgMFgYMaMGRw8eJDffvuNuLi4kkQ4JCSEX375BYCTJ0+SmJjIBx98UGFso0aNYt++fSxfvpydO3eiKApDhgwp9QMgPz+f//znPyxatIgtW7YQHx/P5MmTK/XehRBCXKEIIWql3bt3K4Dy66+/Vvm1S5cuVXx9fUsez58/XwGU2NjYkm1PP/204uLiouTk5JRsGzhwoPL000+XPA4LC1MGDRpUat8PPvigMnjw4CrV7enpWfK4a9euyvjx40u9pkePHkrbtm1LHo8cOVIJCwtTiouLS7b961//Uh588MFr1vvmm2+W28fQoUNLlVm0aJESGRmpmEymkm1FRUWKs7OzsmbNmpLXBQQEKEVFRdesS1EUZe/evQpQcvw2btyoAEpGRkapcr169VImTpyoKIqinDp1SgGU7du3lzx/+fJlxdnZWfnpp58URan4/9XHH3+sBAQEXDceIYQQpUlPshC1lFKFa3L/+usv+vbtS/369XF3d+exxx4jLS2N/Pz8kjIuLi40atSo5HFAQADh4eGlxvEGBASUDCG4Kioqqtzj48ePV6nufzp58iRdunQpta3sY4CWLVui0WhKHgcFBZWLraoOHjxIbGws7u7uJWOYfXx8KCws5MyZMyXlWrduXW4ccnR0NHfffTehoaG4u7vTq1cvAOLj4ytd//Hjx3FwcKBr164l23x9fYmMjCx1TMv+v6qO9y6EEHWNJMlC1FJNmjRBpVLd8OK8uLg47rrrLtq0acMvv/xCdHR0ybjhfw4h0Gq1pV6nUqkq3GYymSodY2Xrvhm3GltFcnNz6dixIwcOHCh1O3XqFA8//HBJubLDW/Ly8hg4cCAeHh4sXryYvXv3smzZMuDW32dFKnrvVfnRJIQQQpJkIWotHx8fBg4cyMcff0xeXl655zMzMwFzD6fJZOK///0v3bp1o2nTpiQkJFRbHP+8QPDq4+bNm9903ZGRkezdu7fUtrKPq4NOpyt1ESJAhw4dOH36NP7+/jRu3LjUzdPT85r7OnHiBGlpacyePZvbb7+dZs2alevZvdrzXLbOf2revDnFxcXs3r27ZFtaWhonT56kRYsWN/M2hRBCXIMkyULUYh9//DFGo5EuXbrwyy+/cPr0aY4fP86HH35YMgyicePGGAwG/ve//3H27FkWLVrEZ599Vm0xbN++nblz53Lq1Ck+/vhjli5dysSJE2+67ueee46vv/6ahQsXcvr0ad5++20OHTqESqWqtpjBPDPHoUOHOHnyJJcvX8ZgMPDII49Qr149hg4dytatWzl37hybNm3i+eef5+LFi9fcV2hoKDqdruR9Ll++nBkzZpQqExYWhkqlYuXKlaSmppaa4eOqJk2aMHToUMaOHcu2bds4ePAgjz76KPXr12fo0KHV+v6FEKKukyRZiFqsYcOGxMTE0KdPH1566SVatWpF//79Wb9+fcmMEG3btuXdd99lzpw5tGrVisWLFzNr1qxqi+Gll15i3759tG/fnrfffpt3332XgQMH3nTdjzzyCFOnTmXy5Ml06NCBc+fOMWrUKJycnKotZoCxY8cSGRlJp06d8PPzY/v27bi4uLBlyxZCQ0O5//77ad68OaNHj6awsBAPD49r7svPz48FCxawdOlSWrRowezZs/nPf/5Tqkz9+vV56623mDJlCgEBAUyYMKHCfc2fP5+OHTty1113ERUVhaIorFq1qtwQCyGEELdGVtwTQlhMeHg4L7zwgsWXVe7fvz+BgYEsWrTIovUIIYSoO2TFPSFEjZKfn89nn33GwIED0Wg0fP/99/z111+l5l4WQgghbpUkyUKIGkWlUrFq1SpmzpxJYWEhkZGR/PLLL/Tr18/WoQkhhKhFZLiFEEIIIYQQZciFe0IIIYQQQpQhSbIQQgghhBBlSJIshBBCCCFEGZIkCyGEEEIIUYYkyUIIIYQQQpQhSbIQQgghhBBlSJIshBBCCCFEGZIkCyGEEEIIUYYkyUIIIYQQQpTx/9jJGhBq561AAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "noise_level_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['noise_level'] == noise_level_val:\n", + " n_init_val = row['n_init']\n", + " mean = sm_list_bo[(row['n_init'], noise_level_val, True)][0][n_init_val:]\n", + " std = sm_list_bo[(row['n_init'], noise_level_val, True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, n_init={row['n_init']}\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['noise_level'] == noise_level_val:\n", + " n_init_val = row['n_init']\n", + " mean = sm_list_random[(row['n_init'], noise_level_val, True)][0][n_init_val:]\n", + " std = sm_list_random[(row['n_init'], noise_level_val, True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline, n_init={row['n_init']}\", linestyle=\"--\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " #break\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(f\"BayBE Optimization, noise level {noise_level_val}\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('Schwefel function value')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ae3f1ea5-856d-47a3-9010-7a4d4ceb8b1f", + "metadata": {}, + "source": [ + "### Heat map plot" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "e90c4e71-e671-415d-950b-377a463730b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Bayesian Optimization')" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_bo)\n", + "\n", + "ax.set_title('Bayesian Optimization')" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "5d63c7cd-755c-4fd3-8506-a317bf5bdd43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Random')" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_random)\n", + "ax.set_title('Random')" + ] + }, + { + "cell_type": "markdown", + "id": "e4ab425a-61a7-464d-812c-502b9ecd39d7", + "metadata": {}, + "source": [ + "## 'X-distance' plot\n", + "This plot looks at the distance from the true global minimum of the best point found so far in the optization. Unlike the 'best point found' backtesting plots above, the values do fluctuate up and down as a new 'best' local minimum that is found could be further from the global minimum. It looks like most BO schemes are settling into local minima that are not close to the global minimum, while high-noise BO and random baseline seem to have gotten lucky and found the global minima early. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "caba841d-9015-4097-a8f6-f046370d33d4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot distance of best point from global min\n", + "\n", + "n_init_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_x_list_bo[(n_init_val, row['noise_level'], True)][n_init_val:]\n", + " #std = sm_x_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_x_list_random[(n_init_val, row['noise_level'], True)][n_init_val:]\n", + " #std = sm_x_list_random[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline\", linestyle=\"--\")\n", + " break\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(f\"BayBE Optimization, {n_init_val} initial observations\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('RMSD of best found point from true min')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "873019f4-a6f1-4674-b2eb-714e6d5f172d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/noisy_optimization_BayBE_original_bounds.ipynb b/noisy_optimization_BayBE_original_bounds.ipynb new file mode 100644 index 0000000..3c86e14 --- /dev/null +++ b/noisy_optimization_BayBE_original_bounds.ipynb @@ -0,0 +1,1190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ddfe121c-9cad-4d3f-b518-e42c72a1b70b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "97c15027-9275-43e5-9613-ecbfe31d4914", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "import sys\n", + "sys.path.append('./src/baybe_utils')\n", + "from run_grid_experiments_baybe import run_grid_experiments\n", + "from run_grid_experiments_baybe_random import run_grid_experiments_random\n", + "from src import visualization\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from src import schwefel" + ] + }, + { + "cell_type": "markdown", + "id": "5bcc2d3b-c62b-422c-bdee-15eee6f1452c", + "metadata": {}, + "source": [ + "## Intro\n", + "\n", + "This work lightly explores the impact of a noisy oracle on Bayesian optimization performance. Here, the 2-dimensional Schwefel function with Gaussian noise is used as an optimization objective. However, this work is motivated by the need to deal with noisy data when applying BO to experimental optimization. \n", + "\n", + "This notebook looks at optimizing the schwefel function over the range [-50, 50]. See the analogous `noisy_optimization_BayBE_extendedBounds` notebook for optimization over the range [0, 500] which includes the global minimum of the objective. \n", + "\n", + "## Implementation Notes\n", + "\n", + "- This work was done as an entry in the 2024 Bayesian optimization for materials hackathon\n", + "- Our team pursued multiple implementations in parallel\n", + "- This notebook serves as an entry point to the BayBE implementation of the project\n", + "- Individual optimization campaigns are run from the run_experiments() function in run_experiment_babye.py\n", + "- Grid screening of parameters builds on this with funcitonality in the run_grid_experiments_babye.py\n", + "- As this was a hackathon project, there are some hacks. Watch out for hard-coded gotchas throughout. Would not recommend directly re-using code. " + ] + }, + { + "cell_type": "markdown", + "id": "02f05875-7a5c-4f50-8b55-fc0e30340897", + "metadata": {}, + "source": [ + "## 1. Define grid search parameters \n", + "\n", + "This is to run a grid search over experiment parameters like number of BO trials to run, noise level, etc. These values were chosen by the team as 'reasonable' sounding values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5319bdb1-b6a0-4dfa-b05c-bd854456278d", + "metadata": {}, + "outputs": [], + "source": [ + "seeds = list(range(5)) # run 5 replicates of each parameter set\n", + "n_inits = [2, 4, 8, 10] # Number of initial randomly collected data points\n", + "noise_levels = [1, 5, 10, 20] # variance (?) of gaussian noise. Bigger number -> more noise\n", + "noise_bools = [True] # carryover from BoTorch side of project, ignore\n", + "budget = 30 # Run 30 iterations of BO\n", + "bounds = (-50,50)" + ] + }, + { + "cell_type": "markdown", + "id": "abdf100a-84fe-466f-9b60-67d698e7578e", + "metadata": {}, + "source": [ + "## 2. Run grid search\n", + "\n", + "Run the grid search over parameters. This will take a minute or 60. Results are written to disk so if you are just following along, skip this step and load below " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8ffedfe-8639-479f-9cee-63f4a0f32e54", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 9.03s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 15.98s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 23.97s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 0, time: 31.18s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 0, time: 37.90s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 0, time: 45.18s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 0, time: 54.21s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 0, time: 64.93s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 0, time: 73.17s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 0, time: 82.01s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 0, time: 88.16s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 0, time: 95.95s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 0, time: 104.06s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 0, time: 110.81s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 0, time: 118.32s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 0, time: 125.44s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 1, time: 134.62s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 1, time: 142.04s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 1, time: 152.19s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 1, time: 160.21s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 1, time: 167.79s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 1, time: 175.58s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 1, time: 185.98s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 1, time: 192.50s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 1, time: 201.80s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 1, time: 209.80s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 1, time: 217.41s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 1, time: 224.21s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 1, time: 232.65s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 1, time: 240.03s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 1, time: 246.48s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 1, time: 255.71s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 2, time: 263.26s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 2, time: 272.02s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 2, time: 279.79s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 2, time: 287.12s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 2, time: 295.66s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 2, time: 305.75s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 2, time: 317.32s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 2, time: 327.66s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 2, time: 336.58s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 2, time: 344.40s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 2, time: 354.19s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 2, time: 363.36s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 2, time: 372.11s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 2, time: 379.97s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 2, time: 388.45s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 2, time: 402.06s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 3, time: 411.24s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 3, time: 419.22s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 3, time: 428.12s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 3, time: 436.11s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 3, time: 444.58s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 3, time: 453.59s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 3, time: 462.30s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 3, time: 471.83s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 3, time: 480.32s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 3, time: 488.51s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 3, time: 496.07s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 3, time: 504.09s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 3, time: 512.36s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 3, time: 521.28s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 3, time: 531.76s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 3, time: 544.60s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 1 budget 30 seed 4, time: 554.02s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 5 budget 30 seed 4, time: 561.61s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 10 budget 30 seed 4, time: 569.97s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 2 noise 20 budget 30 seed 4, time: 579.05s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 1 budget 30 seed 4, time: 586.18s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 5 budget 30 seed 4, time: 593.57s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 10 budget 30 seed 4, time: 600.87s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 4 noise 20 budget 30 seed 4, time: 608.02s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 1 budget 30 seed 4, time: 615.86s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 5 budget 30 seed 4, time: 622.96s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 10 budget 30 seed 4, time: 628.90s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 8 noise 20 budget 30 seed 4, time: 635.66s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 1 budget 30 seed 4, time: 642.71s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 5 budget 30 seed 4, time: 650.22s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 10 budget 30 seed 4, time: 655.89s\n", + "Collecting initial observations\n", + "Beginning optimization campaign\n", + "Started problem 10 noise 20 budget 30 seed 4, time: 663.50s\n", + "all experiments done, time: 663.50s\n" + ] + } + ], + "source": [ + "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds, 'results_baybe_originalBounds/results_baybe')" + ] + }, + { + "cell_type": "markdown", + "id": "16aeaf0f-14b6-4827-b94a-cb87cd2a4d7c", + "metadata": {}, + "source": [ + "### Run random search as well\n", + "\n", + "Generate some random baseline data to compare against\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5aac4ea7-cb9f-48a4-a874-e4396d8b9e22", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 0, time: 1.24s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 0, time: 2.34s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 0, time: 3.42s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 0, time: 4.47s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 0, time: 5.72s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 0, time: 6.72s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 0, time: 7.76s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 0, time: 8.82s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 0, time: 10.01s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 0, time: 11.16s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 0, time: 12.33s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 0, time: 13.38s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 0, time: 14.50s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 0, time: 15.53s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 0, time: 16.56s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 0, time: 17.75s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 1, time: 18.83s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 1, time: 19.85s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 1, time: 21.08s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 1, time: 22.06s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 1, time: 23.14s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 1, time: 24.14s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 1, time: 25.17s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 1, time: 26.16s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 1, time: 27.23s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 1, time: 28.26s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 1, time: 29.34s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 1, time: 30.44s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 1, time: 31.49s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 1, time: 32.57s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 1, time: 33.72s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 1, time: 34.84s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 2, time: 35.90s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 2, time: 37.27s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 2, time: 38.41s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 2, time: 39.53s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 2, time: 40.74s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 2, time: 41.94s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 2, time: 43.17s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 2, time: 44.51s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 2, time: 45.78s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 2, time: 46.87s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 2, time: 47.91s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 2, time: 48.90s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 2, time: 50.06s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 2, time: 51.21s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 2, time: 52.31s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 2, time: 53.49s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 3, time: 54.92s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 3, time: 55.90s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 3, time: 57.02s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 3, time: 58.10s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 3, time: 59.16s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 3, time: 60.19s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 3, time: 61.83s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 3, time: 63.11s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 3, time: 64.16s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 3, time: 65.30s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 3, time: 66.58s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 3, time: 67.84s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 3, time: 69.13s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 3, time: 70.30s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 3, time: 71.39s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 3, time: 72.60s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 1 budget 30 seed 4, time: 73.87s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 5 budget 30 seed 4, time: 74.97s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 10 budget 30 seed 4, time: 76.17s\n", + "Collecting random observations observations\n", + "Started problem 2 noise 20 budget 30 seed 4, time: 77.46s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 1 budget 30 seed 4, time: 78.68s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 5 budget 30 seed 4, time: 79.76s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 10 budget 30 seed 4, time: 81.08s\n", + "Collecting random observations observations\n", + "Started problem 4 noise 20 budget 30 seed 4, time: 82.32s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 1 budget 30 seed 4, time: 83.38s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 5 budget 30 seed 4, time: 84.39s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 10 budget 30 seed 4, time: 85.45s\n", + "Collecting random observations observations\n", + "Started problem 8 noise 20 budget 30 seed 4, time: 86.59s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 1 budget 30 seed 4, time: 87.73s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 5 budget 30 seed 4, time: 88.80s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 10 budget 30 seed 4, time: 90.28s\n", + "Collecting random observations observations\n", + "Started problem 10 noise 20 budget 30 seed 4, time: 91.53s\n", + "all experiments done, time: 91.53s\n" + ] + } + ], + "source": [ + "run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds, 'results_baybe_originalBounds/results_random_baybe')" + ] + }, + { + "cell_type": "markdown", + "id": "34ac9617-f371-40b1-b1a1-5203d84b1a65", + "metadata": {}, + "source": [ + "## 3. Process Results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ce441669-a569-468f-a482-fec91efef95f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_24219/3826372340.py:17: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
0210True767.079651
0211True767.079651
0212True767.079651
0213True767.079651
0214True767.079651
..................
010200True767.079651
010201True767.079651
010202True767.079651
010203True779.453003
010204True767.079651
\n", + "

80 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 1 0 True 767.079651\n", + "0 2 1 1 True 767.079651\n", + "0 2 1 2 True 767.079651\n", + "0 2 1 3 True 767.079651\n", + "0 2 1 4 True 767.079651\n", + ".. ... ... ... ... ...\n", + "0 10 20 0 True 767.079651\n", + "0 10 20 1 True 767.079651\n", + "0 10 20 2 True 767.079651\n", + "0 10 20 3 True 779.453003\n", + "0 10 20 4 True 767.079651\n", + "\n", + "[80 rows x 5 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load BO results \n", + "sm_list_bo = {}\n", + "bo_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_baybe_originalBounds/results_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list_bo[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + "bo_results " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "af01900d-a2bf-4c49-90a0-b3614a59c2fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_24219/2042071605.py:17: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_initnoise_levelseednoise_boolbest
0210True789.469910
0211True796.131165
0212True802.132568
0213True794.562561
0214True792.798035
..................
010200True789.469910
010201True790.011475
010202True796.409363
010203True794.562561
010204True792.798035
\n", + "

80 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " n_init noise_level seed noise_bool best\n", + "0 2 1 0 True 789.469910\n", + "0 2 1 1 True 796.131165\n", + "0 2 1 2 True 802.132568\n", + "0 2 1 3 True 794.562561\n", + "0 2 1 4 True 792.798035\n", + ".. ... ... ... ... ...\n", + "0 10 20 0 True 789.469910\n", + "0 10 20 1 True 790.011475\n", + "0 10 20 2 True 796.409363\n", + "0 10 20 3 True 794.562561\n", + "0 10 20 4 True 792.798035\n", + "\n", + "[80 rows x 5 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load random results \n", + "sm_list_random = {}\n", + "random_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n", + "for noise_bool in noise_bools:\n", + " for n_init in n_inits:\n", + " for noise_level in noise_levels:\n", + " sm_agg = torch.zeros((len(seeds), n_init+budget))\n", + " for idx, seed in enumerate(seeds):\n", + " X, Y, Y_real, model = torch.load(f\"results_baybe_originalBounds/results_random_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n", + " sliding_min = torch.zeros(Y.shape[0])\n", + " for i in range(Y_real.shape[0]):\n", + " sliding_min[i] = Y_real[:i+1].min().item()\n", + " \n", + " sm_agg[idx] = sliding_min\n", + " sm = pd.Series(sliding_min.numpy())\n", + " \n", + " random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n", + " \"best\": [sliding_min[-1].item()]})])\n", + " \n", + " sm_mean = sm_agg.mean(0)\n", + " sm_std = sm_agg.std(0)\n", + " sm_list_random[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n", + "random_results " + ] + }, + { + "cell_type": "markdown", + "id": "a62c64e2-b44c-4cb2-8e3c-4955abb334de", + "metadata": {}, + "source": [ + "Calculate 'performance matrix' to generate iterations vs noise heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f9d3ca6c-22f6-459e-9107-cfa6cc663673", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix_bo = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results_baybe_originalBounds/results_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[2]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix_bo[i,j] = best_y\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cd2d4601-b8ee-4d96-8c54-9dad0285151c", + "metadata": {}, + "outputs": [], + "source": [ + "performance_matrix_random = np.zeros((len(n_inits), len(noise_levels)))\n", + "\n", + "for i, init in enumerate(n_inits):\n", + " for j, noise in enumerate(noise_levels):\n", + " y_vals = torch.load(f'results_baybe_originalBounds/results_random_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[2]\n", + " best_y = torch.min(y_vals)\n", + " performance_matrix_random[i,j] = best_y" + ] + }, + { + "cell_type": "markdown", + "id": "9ba43cdf-7017-4853-a200-945af8e20e61", + "metadata": {}, + "source": [ + "## 4. Plot\n", + "\n", + "### backtesting plots\n", + "\n", + "1. Fix n_init, compare noise level" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8f2b0de9-e2b8-40a6-a910-a4945f06aaba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_init_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['n_init'] == n_init_val:\n", + " mean = sm_list_random[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n", + " std = sm_list_random[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline\", linestyle=\"--\")\n", + " break\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n", + "plt.title(\"BayBE Optimization, 10 initial observations\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('Schwefel function value')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8989570b-6b02-4e6a-99c7-f7ccafb0bb7f", + "metadata": {}, + "source": [ + "2. Fix noise value, compare initial data number" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "409f03ea-c50e-461d-850d-f7723b031f98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "noise_level_val = 10\n", + "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n", + "\n", + "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n", + "plot_bo = bo_results[bo_results['seed'] == 0]\n", + "plot_rand = random_results[random_results['seed'] == 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "\n", + "for idx, row in plot_bo.iterrows():\n", + " if row['noise_level'] == noise_level_val:\n", + " n_init_val = row['n_init']\n", + " mean = sm_list_bo[(row['n_init'], noise_level_val, True)][0][n_init_val:]\n", + " std = sm_list_bo[(row['n_init'], noise_level_val, True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"BO, n_init={row['n_init']}\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " \n", + "for idx, row in plot_rand.iterrows():\n", + " if row['noise_level'] == noise_level_val:\n", + " n_init_val = row['n_init']\n", + " mean = sm_list_random[(row['n_init'], noise_level_val, True)][0][n_init_val:]\n", + " std = sm_list_random[(row['n_init'], noise_level_val, True)][1][n_init_val:]\n", + " plt.plot(mean, label=f\"Random Baseline, n_init={row['n_init']}\", linestyle=\"--\")\n", + " #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n", + " break\n", + "\n", + "# aaawaaay\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.6, 1))\n", + "plt.title(\"BayBE Optimization, Noise level = 10\")\n", + "\n", + "ax.set_xlabel('Campaign iteration')\n", + "ax.set_ylabel('Schwefel function value')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ae3f1ea5-856d-47a3-9010-7a4d4ceb8b1f", + "metadata": {}, + "source": [ + "### Heat map plot" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e90c4e71-e671-415d-950b-377a463730b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Bayesian Optimization')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_bo)\n", + "\n", + "ax.set_title('Bayesian Optimization')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5d63c7cd-755c-4fd3-8506-a317bf5bdd43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Random')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_random)\n", + "ax.set_title('Random')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "caba841d-9015-4097-a8f6-f046370d33d4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt index 097a011..e7022c4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,10 @@ -e . # package from this repo -# numpy -# scipy -# pandas -# matplotlib -# matplotlib-inline -# ipython -# ipykernel -pytest + numpy + scipy +pandas +matplotlib +matplotlib-inline +ipython +ipykernel +baybe + diff --git a/results_baybe_extendedBounds/results_random_baybe/.ipynb_checkpoints/BayBE_heatmap-checkpoint.png b/results_baybe_extendedBounds/results_random_baybe/.ipynb_checkpoints/BayBE_heatmap-checkpoint.png new file mode 100644 index 0000000000000000000000000000000000000000..7d877e120ff811631a1219e04e90e3472b0d3e55 GIT binary patch literal 94163 zcmeFZcT|&U*Df9_V`UH#1OXMXfPjefrlZ0r0wNGVnu36IkrEI>P)DT(M+HQxB1J;) zHJ}1fTBLWP1PCp}5Fn6x?g#Xp_pI}sGxPn{_s8##m$h7zp*+uB_rCVt*R{j08tCrY zF1#HEgYCNX`-N*T7*7Zcwk7qKt>8O#f7%Ry|ET(0H1{#~bo4>oyz2ndzv*+w&C|!t z+3uj9!(DG@PY(rI6}*h|>|Zyd#VA7aDVr6pCG`jYj=dND;S_;rFL%)|v59lQ38(*1Dr zwQnpaW3Le;51iV%v$7DYm!(L-Qo9(k8$w13HvSL!xlj3&jcXqBVBxhTYXCq*r2na} zy%3BPTz9r%F_(J~TVMxn<@~>$_lN!UYkzRn^1z0jXGM$uW)e>8ZvCvg*Va=o-`4Fn z*5CHG+xVyL4`gxnZu(3$47U3;{QnIQzU^Fpbe5#EE^O0P+&`X1;y;8N`WswyxDLBx?a?6(U@tbn4!HfzB>oLZtwI|QeI0>InGytGcb*ph zV;-$`^Bja#k=`qV`DEBoZ4mC{70{ZJ~z~=)R_zo719n4O7!Wxq-tFq zK$~0{zz6R`5RizIRuy;3`+UfQidfc!94-#Kn0~vz;AT|MwgVMz8+NTt@xbEt?c0|z z0h9O?@sCe)R4569I;zIvPZ4%i$c=e|4cQm_7FWBX4w5l!z1Ct@*_XuZ7RYaNz>dE+vj% z>+N^kUVHb~kj2t`q>OC!T#-~Xb1E%*tZFu|Wd@opB9NSV?%~*gQ%bHufET&6&(U2P zIh`J-6U-S+i+Giu8pP&>K8rs>}SwSBSB*t2LgcTJP zh4wF3b}LARG8}8|v?GLIk>F;JIVn z#)i2I2f8v12@_YoHUBo5#3qV!Lo-u?)w(>u8fws573A$2LUU%f@7O_nEaDM*==|db z=gg#i3Cf=6(9mPa{)d~Q#ck6_`!@0Hvpl35JC-bD%VqQxGq6j!o)b35pQ`*Ed@6O=8{ha~ z%Z}6J$Gb$DB;%~ErFoku-wLLWAnX^$Kgavsym>Qd31d4lTak|>yBe9BzjUqx>niU) zSTek0>z<20!iEE9bfU$Qdc;)k-YRh)D$^nPb5=WYiBSr&`VJQ9f z(QN^vM6|jrhP^!GsSAG4y&=d(;i~3O3&!&(T-sZ%cCqslK7XL}ZqKfGQhSofY+pXU zWXFyjX@T^4RlKE@)d6rg+Di2F^z1pTRn=&oXXk$`z>(3&{R(3faklL<)ic=`m!RbE zLo|mt>saGYtZz@m9ZRcyXWNILG)@uZg0P(XRV?~HM#7S z%xzw9&je|cZ7n4`wr?MMrYHNZLv>m4R-RC=lE-jGv*H=&&Rfy+&AWG(g{%jYdveE` zhyRq&)k#ot2cxGFaTMcER&aQ1kL>Z|S$B6u2`e_cc=i{(Zl=R_2mLs82MaG>zC8A) zuGr@pOcXhVQpe@w_?e!c43`d8b{{mE5QbicgNFO#yu(cWMtu7kFA7?lX-P4@bIER0kh4dH)G+1aA>GMUYaHIjv<@643a7QLNZKN#p>@H{yaMdKX=aBx5rdb3qRG7R{Yx}m>698maMES8koXc zgX!t%@`(B2L>xXzUAZ^kHpN+R-#(pkkCALEo;zRpfBN)Gk}Iy-^bb4a8s-Ly703@B zJg69drni2ydPeNjVlFoV>r~9J37CjU#MQADS}e%-H}U95`NCM@Te?+#PDUk7H@e*= zT3Au)dbx*nn)j7Nl|r5KlVGzQOH+``1Bp0zjbC?`QBU3`m4a>S*e2w{2^pEFCt~-G zWAdw{z3Jl|CI#<8as3oDUrBN-6%YFU{`iR#1G5#rv%QTEd9_~lfa^&zt=|2${^-ve zF#&E%`*s6$zd0eNC zcl*B9ORhg9!WLhh?y|kWpTCesZ^{TEhyJX#XYkoCRA%$u0Dqab^>fQkNpY(z&ox zOhX0u5^?AT6{>}7dj2D)^ZOs*8k|`3gMxeaHb72*NCVdFD8^dkkhhnY*Og=q)nFD; zTo;%`>DoN7EM8#so8De}{>HN0L(v8tzjp)t_zi2$c;+(|C!SJzy1Xn1>$3q|F^LF!@e(T-@Iyh?HwZz7Vvhc32GcxX%tHkX)L@wgR z$Z6i0@QyNf+U?G_2M-_a%^mtx8lD&7EPgVwF~Y3JJEs1AYQRmR&~UY$59N#0D?uf6 z?!-i4S;@&s8EXOYR*QYoeg3f_12C8#6=&oaoIPxoYpbQIXg_i;pry-n|+%?E1c_$7`&P{pPa0R>1hNv1V^{ z_%}8*(q=>RSJf*-|8H&ksddpcd-SyPSQb)t1luk=XW=&%OO79-DVC*5P$fkQsTSCR zM|I37dU5jShsb!f(%WLu)`&K(5pI?`2XmdMvujQ;PF(Y3_)y6x8E=+P_?5SA)i07m z8b3Z%<6wJvGc0xc=K3R}8Ttu!6U{M3*RJgWZocs5$H%vaE4=m!2wXHWG6E1x3Bd80 z>MQ+Dz!R(Q{EFM9c_woYC zM}CDX$5P)(jpVz=DN!l58Va(UMK3B_T?|da)mG-HJkTkG?38hYVA`) z$2{&$6}~LBa~RqC3kZ$)Vb4{bAJq>pw8u38NFvacp&wD{P10Ev3pQ$QX^8*<%M^go zkP*{~5*7e4M*hd}*N#_SYG`K8?w0Woq_e+89HzPl1tC zidJ%eqzI8+U^!!rv-Ku&1k-^~bl)q36zuUlb~!f}jw}ymzR7VXo)@tQw8eVjt8Ef`ZeDQseGoKeW&b@N7f}ND!+-^nH=4jyu*g(uLu^9{=9}}=HCT5w)K@>6W3xXLo16>Y$+!@w z5-|1JYd&;?X`aI2vB%ZiebR7J@Z;~S^mf@N>_%}Lq)c+MT5dD3s@!G2tv0PY2U|$F z(iXtN6i+sYAFZ{S2=HE-Fg9r2jw+9h!%=MG51@kGFXu=P3Gwl*1_XHN)M-({&%^iW z3+rCEF#92c`jLhYlcsQuU#JTT!BXMdLR7zwAn7C2S_)DQL`2U8Rx+-=`#t@dX;t4c zRWJ5rnEHH$1i(zmE4}4d0o&DPGZ66hdra0$$a{}2NJXpPyPBV~+`T`-jxm=WC`x~r zZ}n7wqow+7WTh9SEi9tsF-l%?5K+dBBPJW5e<=2Rx_7ITkWi|kl~_&Z^#exDnIc*8bI1qE}RUY-X&#|dh>zww1PbC;+D%kE4J`?)p z&70dG_JXn^gQc#57cXAy0GWoI*~5ODaUd}R&$kh^WzU!qjZ+vVZlg6!?U}RIdg<4^ z=1)8xHVvT?UUEsAd?# z352`;n)RF$0J~=uM}tTtO6ECU9bpa+G(1G}6KPhO*vpxp`#2pj=2RDS8sphOeWH6# z@wWpvxKA%%w7O&G=TH58w|bG4e`mc)a3=3JP5R(s#kcu7({C11qO&b5gAH>QQit2; z3CbAHx$08X3!OYI)Q7@9Q_6|>FvD6VNA??~-7(jEzqYutJcja$7>(WB{d_%tx#5C_UKzS580fmB~<32f?10X&OS9I-dcqXZ5H$Pkn*)PbT zxeb<_fPM@aegIRo^J^EX z0GO#6iX!thx@voTdEgAi)6rdKq{KOGs-(+x^X`+lU2_6SgY&GDrHW!GYON;P`DKcr zEwj(~G}$-Zftt%j-mQaR&O}ynW5?@&;wyo)-dySx#j-#1cC`sBhd<{XD(un=P!pwN z7xA^|z_BE{UB`^5`hFjefy3yTj#{|OA8fePdt2CC+~)cNPsLCys*r-?m0p(-J_(%$ z8eUr*WaJ{DRE^i_L>vx8$ooareENyCYCcoHetf(uqD8JwBbYsFi))QnAOhCk#V%(*_PEzu|Ipn z)HzB0*Rga3jr;7b0EuF=N$mGAW?|KKC$q`1sWcbuTcZ}v(@o~4->mQ;Dd*y@mFj2h zzfhmEymCUkG22&US!J^O=97!naCJ^K>g#~ApDIN#^=P8P!N4Voo`PfJt6dIDCi-Q> z-O~cAyM%8IdbTE|2+YBciWauS;*TwB`OpP7!LCX`%YfywDY^}4g+SEzhvE%?Y~vpr ze{PufClj^sJS9@l#{ZB<9eX+F*Ay$#bJi=&hM{aK?a5n9&YHu*;z4Aa7IhRhs4c|7#F)aEpM!T_qhS#$nNvIW`9 z3Evhl_jY@q80vQZ182ID^t3<^&wlU>;vbSG~f8-&rTtqHaEdf-XUJAE2O+;N^o*}$b*w?to zeyfx^)2`xv=Ztd^QPX9j*_S%jqFC*4)A~`X`mE8g>?>v0XU=i4v5mt4Ev~)>seXw_ zE5hxfE*>=q`D`;@UOw4#5ItW0asy2gNS#c&=TEskd9dhN*tsZ#kl<0=I#j zOOcH6dKGp>a7V5&=Jv>&Q@tRSlh%VGYxVcnm;ejfT+5l<`M;BCEK;MGbtf>)KbeD5w7n~Mdnp5)>o3%lsWR#AN|Sf0Hzh7dtYtP6;c1 ziY|3ks0^)4lFNZ> z!eDdpu$Pn?={o(p2Ws4XXsmwg?UlBrw*JQneNTi->G^y(6U}vJjIGgGuauB z(^5?D{m}p50c&L;u070%bRd4(a+O-Ol47fAEKS=`im9;2s4RS+OFJeeTuvva@QA%) zGn;DCF^3P^byL}6fwn5@AV6$q1XRN>cI25FEmC?;SiTi2o6z?5U^+=!F@vuTg0EY~ig93Hzr-60{{7j=XCrPQ=VXjq1cC_k26$)?GOx%x6RGQ^E~dx4%iIR;#;v~2OF|8P1&}Hl+&tBmWwpFO zQm+O1S!WdW5k4|hBu}mM8Q%a}bL^@A*Sh>DxzXiJ|KhFi0yjkPqvAynRGfmV8}sir zCq=dHb|j=IX=}YIe5*t1xBkTH=g%j1Y4R*m#2)F3I1FxyT1fMqb?l&g&+{5Qu_;YI z!7)BX#ulrT$L#bZ5=|wUXO3Ofmdz<(&}-u(>=`w++Ij+rR!&mdt&dGBW0~lP1X=bi z1Dcp}mYK$48fI({&A_(d{kSMusF)U%CEeE z#|5vtiQ!n9DPtq75SO3TTk9vt^60B{h?p8Kx)>?kuWgf{Acv)oD-`;Wt7=D!eY-L& z$PTmF9F3Bi4=;NkL5bfinTh?68%04 zn5FL&>6h3|>5=bBRzw9vigG<#6U$F3xsM84gS*%jJuC8Y>+&b7uA+dhl^%p#*p57= z=}8-G#$f)<>JLY^cnx|DpuVmQ6?>9t$omWhm+>z-e(s4amLMC5z^=-5kP2i2Ycg`& zyfu~^T&dc1jKG}h)>VeI4NIg4&$r$x6&I_c_tf!KYw3yh7`tz2ArXvl{7k zO%DVLLC`Wa`r0%x*e68@+tn_U`${n?u+GaXvW7ZpUSmhrstIp)tsxH<+ucs_@FL>H zMrO-;>p7%3&On{G=BmdMoC|L(5E~(K^ROqe%ioEC)?q_$Q*Xkn-Ds0i`102uULM`G zL#lEQFrbpK$q*2`rnJ&AX8C9+!u|B8gsVFis_p=wnB*)Tyz;G7Nzi0+>V2)>fNa3r z$}m7V@E2^|mSA;v`;|IW@S$Mp6KZv$1nUXTCaTzQPF%Z+`r-KT{$<09Espv`w1`}& zv?sRio}Aq=C-juKz6fQ-0kw*owHtF4Ut21t?fRdJLkXU~LazM7ynW$4u z<`Rj|$L2fmW@_{A1V7oh|{8hn{)6OqDk4oW9BVKo$mL72>CEUd<3?YAyQ9s&tKWv!eb z=_#B|kTPc$PqhEsJmUGe%D-_C9C3FI+6)gB^TZaNLRfh>HBxImNs3DjxOK(fUMDhe z1zVFw z*7*tXw?p%VVU#8erTz0v^-9U1zyq{}pC~3xR&O*ibP?~-Fn-~kWvd!JQ2E_Y} zzPC3&s!SK0FJ?5sy_h6l2N7cW+v&mz`J_@{McF6le z132&Yql_qdPfoc1!nE?(%Wt*BJaM+`(M8Q11Zm|xT}z~oOG1{O*1k$>S1a?}t28h@ z6=o_){UD-nSF8@})+LKHtXZ*X$6w>sVCo#G?V@!V326ONo(~7P?Q+)4=anwH9DZ?W zy5rDpO2?s$HjC?N-WwDtFSKgYoZqAASuv97RD66f{nQ&r*2I!B@$Kl7rY~b2@z`Rf zxEN7;m`!;#tL#umQf$t@2dhEqvB*kpgG+1ySjekGz}R<}xeu+(+H!?SiQWb;VqzSA z8w}$=*3=NjFLC&d6F#{E;tcfWT0+?aQ20;tA~qRaz4|K@B?GR(aZp6tvSVLEO#p3- zO(#UFdQaY(>{n&EgAA|6%GZY-dDhjJrLwVs^OeW2%n=IQ)h*$?&V`}#hB@ED`w3Qe z2Kg+=*&cB<)!Nsw@SBp&$u-Lc%cYRtjct21UbDsbBFVI$GcaSTQebKo&m8aaZ0OBZ ztWFQK{-ZT!mGpL+O5v^!2_Xqf9jWijc=HqeeGry<%sdFC#S3`Vks$Exp>5;quWgF~l8z0QO zUTymA^D$6f3yYA$s_c`wz865a!)05_`S{lJpWfVVw|kkkcdorNIm#mU7uo}^3Mvdl z$gdr3VdJT;IJkTX7waBZbJuiQ7FjHqH{2FeT|P(jHL41>-#fn-TjF71o(uC|sZZ`G zwZ~iu?8=al_hYG>VtETtphn=kQ}vA#+}7HSYXvY!at*tEScuvEB1f%VMDYr2Cr^ln z==G+8kHsnJVq#{cPSFCcezcRNS=;f~Fqhu|>~?(6SvO8eX1nVd7&_E{B5A;4!ySCP zyUQZyY}+hAUpwb}C86{SrEh?hk53YunD!ZGbL%qqD!S}-58rM-Q1#3@kL{0K?hpad znP~vlPoT-9=GYMn2A7N23iEB;Uw_~WTAQ=tPpPGk0rCeLl>V=TR%_Oe$R-(>F-k|; z?SY9s<34x>%UR0Db-y!yRM(wAbG=bL9#2Dz*wy&3^M-z-T)OmV)^e}nyUrDIySDos zc;JU|UQF#Xp)6FIj#&JX*b=8?mD&+0si>wK*Y3-In$kuij<&h*3Y%Dw6k(=__m?(F z$a&%@TPST-2f9e6R125);noZ(EfI1XI44n!Ev~14D(UbUiSDM4~Y_?55NWbJP566g` zIcor$SG@7%CL6#WB0kY(-3*$LwhiME^FJsSI9K>m#mmlZG;s+2oBEUbT_Q@6P^JZB zPDBK4gS>LxR;@aG`>X{8t$mD2o01W1ic5v?vN9>@S*^o239f%yGySD1qEGwozB4(4 z0s2wfr4oBy5aSW*u7ec^4YaYIaDoK4fUR)gQ@bYVQ_gZ)bYIY^!1=7XrvWoeMe)-l zJI;f-o?!Q(Gx;>(knHNO4fid`Pnv2?Cjwjh+n!8s7lxC~reFET4kol&mN}M+D18s` z1S>Ma{hqVjkI3u|*kvg-m;KE4b0DsFVZprMp8n$PF14<{V)q1pZ_b0nN$395b3V=k zhcTXLP|Z#4{Uzj5B(ObAOMSNDeYPm%i`dvEu+jE|CC*fkp=&B~XnFJY?GZr0c$1TJ zQdrsJ07zUKnV9S)50ytlECt5mG(VfM6!GRwwx@lG9N{TTBP+mtqmIogWCw}g;NBVka{i5LpXgA8$6$$Ma!}Qb__cwA@I&6n9L`9RjKG6^ zH@FHlwq8C2VBOcCs2tX8f*DkDqfr)r&E0a}WX&$0bHNX9_nrxC^mLyK>C7{IlxvEi z)@NS;1pVICQD@|D{ZEFZ&n%2P&BbeOndsHxp7qSn3Bif z9&V5~JTYkTW;`}HW$HkFow9N!6?xAulC@PGzBBGcY-3S$pJ^qUW~g}>qv&hsQ6K|okqKzLfwJ$D#z}hAdWn(~OE>1lyed960h${3kZL527U?9^H9!L!@ zjj`G`$J@UDX{~x^1W3va7G>Azk|Q|QY7XqYa);SobBm(cO43m68GFCP(Q?BK%^F>= zNRE?-c)Q4`0&^u&(5W@st`r6{d8GLowtQY+STRxoO^NHFmpftVtQU&&Yf^ghDgUI(I<>a7Xt#So?#IAAk`r6c6M2g#ay@-!svPHbuQ8GvR}b95ozzWymhtGZ#tiAw#j;dO<<1Hl@^^ z_9eJcKIjHW<+}D&9p_9$zP3Q3@|$|YNJ*zvW_RN08FG=)e%?y*cW$UGz1U!6USVHd zKhe_H)RG@$p61UBjH~g8YOWPH{JZ zt11Iv3H7A!__q<_6sIvzs8AmyPpH$|t5Y>hzda0K0-~Qlxpg4?Oe>)s_Z$tn>2o_} zNGgWBTl1|wixHO+{osM6uzx(Vn!x$OB*Ggx}t^eT=x}RlY)fMAG3H zh<93J^NxrGdsFIWN0?*M7Ig{9nyH?Nqx6Ey`ILbS-=OK|5=D$)`oi1(F_nX`kn4bC z)Q|V^ARI;u_e)^%k8bSZ1_oVgeN`8f8F0NRh07dNp}aT;(vZ3Ze_l?vTS-l(W~SKi z>yk?@SJc#KU}+#qVMV(7>Tl2yvTjS5wq{T?9TpcI4Rw2#_@F$5^KIeN^>n0{9yl_dVh$sb*p zR;l8m)tXznsBNIq5skk_0%-*9%BmodL$TVEqPi3ct@fO3m-9%CbI|hQA!;`Yb*NqR z&)P?`30;B>;#|(XCEMdyF=|Sl`_6|Kxg(c*td-qH4_*D#wvi%ApdU{~URawT!LL(Le=p@2}KV=b_;;+W{U&&N@}aFH8`SLn8V6Ts zdug;Jw%cNbzw_muasv%+$$JYj^|@B0qU4hI<6{Msc1@?-y@B1`bP_Hy9wOKc?u-{v zYK?xbvl#K5!(E+}lyoSxo2W7`_c;Id8sEi>`fv0|@Q#9SdC{Mt%^ISMM+zS8omHkc6dbNWnI1gp@NjE@*N3m?$NqRfG|Ho=>=q7@5~`L&Bn0?!UnxiB zkuB%_<|4O9j}3}z{F+eaHbTbVXCRhCuz!|!9aGPcvDs4$&lx5${cew*)C%nWvNC5h zg#K<(yZ8|?AhmHw7xygHLYI(h-}qn?>)u<&!W zlPhV&g|z6%i>|#MfpTsGc5$olTw8I1|6-U+zTZ~ZLBz%z{Sg-<_BDbaNBkX^y94kX z2K_ki9u^NOR$4E#2DA_t^4VI!)Fix4w5&QDtLSd^Yi*!kKl!CT@PzdF{UW?@s;3Slw>-^Ex>dr%g(*u_s@`!pdhqufHOq|Ak0PnT z*8bO)0k5x+fv5&0>4tQ|e*QYf;KhzK@8#m|n~h|Ix+8H?#%stk8C|DEhnG&Cbi4Me zX2M)B?m?2K#yq)#TyBVC6t(rd;>i*rne=l4K<$+FzG!z_!o??(ZOi^`3%-)nkwR4T zXYXu3#}(j?#`JhK_8_cIoqoJDl{_u?2_G%y?pGJZ2C3|^rYB+%0pj_2omRl%M#F^e zz5u1>L#Mi953#HI7=E9{+pPDIkW|!I^N#2!JzY@&^3WLqs7%f(Hz~;JN7-@+E9NC` z`lXX< zj!-*~01HH2iHJ$5v=r?k$wU{|-SrIe=cUgY^y|##V;yFhUHVEg}%L)Cr2!5J$|X|T-M5#jDqZy(p5(_Nz_ z4tg32OAnD%q*-qgF8>&I56Jahgw7gZ7irqmfs%QKoV8dqcF90vX|yD<3^|$vDl=Z} z6!CCxo?W3RO~$9G(W)Mge9PjS=QS3Z)V;Xc$8e9^v{E<1u9{t&EC9)0dtcS`{PbJVnKjD=}}`QhamZP9II zytl;H)kgImif;V}cTe}oSNXd0LoA|({RBo9%TE9q^8AT#Vj_+edjAR)*DW zVaVR!WF;lgQD>H^l2d^!aktnzG3}1OMvKh}SI(3R1w<>9Ux3l`2P!3&)l;2$7fW0% z_Ua2ONBXGQxjS3roEZ2O;@>F<=Jhk9R8mX#m(6yZEI4LuSsIy_dtK*=W7``GqK?s1 z!og-GcXX+Pi2ZbxFO~B~(x}SWI0_&5dv)0wW6#)J2soQU$MY5-1Uo!xS#qQ-){I;G zlsz==8I+FIQ@L@#Z#j3#`nTNG@9!wRh;6XB$AD|eQwVAR6lBTqQRZzUWZcH9nM$6c z!s0;{r?gh?+yvh^P~SNnig1!egMWF{z&N-5y`K8ELZ13`tu5mo@3VUfZ}iNlr6493 z1mwLddDG82bovQwPDM|U*?ZP$jj-LDe^9uA&a7a|8J@Q`wfA}ktQ;I1e3gFaq~>;P zIK}f8q)9G-T$hiU^)Xt{1h_@e*Hvqf0K4a&Qm%4xL zyCIwIgOc1ekVt@{BPdFC?L7(A=dUNYLaLK4edV4(_Bz2eH3L+U4vGeh|0y5LS_usW zjOH*fq>F%w?c$IpV_DYSjQshV6HtOvR9Dp31kMDM2u$YXwbw2G3wG}Q9TLlgiaOB@ zk~J!|TGszCg#7>v=h;s(Yk@R0z4jz^h=40`81RR2FHRT(r?&PmSQZLQ(HwdX*bF|t z+1}g;Uagf>_#`OEUfL@NK!%r>4`s9#KDqY$&$6++Grqr^s(*hJXq!T2s;EsO_6?zY z@qTGKQ7#V2_R*S3j`(D2Y5F?HKjhUBkkRs;E5tPw;>0Wo4(o5N5B~UreG6pp|0bgP zsZo)+l(|NHRr(u}?Y}f)GVT9__^PnZG*9hS*(?-N<*?$^{=e{dvwhb1yEp9rW>R3Cpe+a)90QV&nV0`` z8d2xLG%WvSH~!klzbMapq)j2pbHP8QJj+~C-y{^G=J-E4jlVh-{H4enUsvS)V|Aon z=Re|B|2$%4-~{~rH2yM9KgsL=4L126=^uq@^S|MH{~S~7ThafXH1e-?`!`|Ye>LWR zSDj`9?W#)uKOIxq+XEYSo*gOr8*SrXH|sC?mdP~W)rUMp|I;(9mkrdIU;l@1$Uk19 ze`|sPvF=kDodG~pwBbf{ujpt7@Gn^=UCB`ZE`(?@7WVG~A7C!OH50zW{`S`W%Xs`V zo$TN2WW+BacK2gJ%iVBs<)_CJ|c|#5A1=gICb_9_0fO3bWbnX1p^Si@} z+ts8!u&a|(o#}e*bi>Rppcvq4L2?(|;&c}x5aco3$0BT5cEdoG3iOZW>5Tiging4I zd8O*@cm;5xG7MA1EX&;9QSPoe#?;=ud-sk9k>6a57+dTzjP}NBDnrOp6Nq7lIHQ~v zTh8fG!2L}mL6c4NYT8eS=MzbhYx2>o6_9fO19PBY!_R0-n0%Wn97E)`@AXr;=4 zRT>O9aTkCh@C|}8>IHdZkkb_d9Klc#&l>s5dU~^|c58#kvXsz8w@z0Okp$2;{X9p`kT3H7`NNVsUC@6Hi0t2XKp{G1)VMW zC4_(eJ&$plOq0GO^eKZD6{-;P>a8nEML(*xCzw)nY$+6e#f=<_#=H@YQl!GS+0W556Y^28HgN_=gPQ3Wk($ezB zhy0p_%h2*iBPNZA% zCkHKj?k{rC%RqKsI(9MNrgm9PR!+_{P9{Yhh!tPS@~WH#XU^^0hev#cWd7f*tNn5p ztH7&>xM)1cgaXyQ(ksCEZ1Ih=4GwVJa;Krpd-K|Ad_7wTF4786cgOa`fs_kWvWGLF zoAkNpH1yV-<>}0nWLkfhfw~N+Hq7-4zuTtr9lR{3q~r_@36#CS=+)9kBTO-Hhq7=j zP@VP*v<6y3NZ%a44DP#27HF0Qrg`h~v6b1g|EI266MaHjIs%ld9R_o+mxiBvxV7gp z(zA964ri`utmnSGKA&ckrW3ym>|YT&c=K*Ca0Z3~5<-)BBlD+X%xpdf5dcpfI?&R! zWk}|7Jf#o3H(FpL^xm^0m0S+9cyL{pOQLwSLQ)_|Pg#Q4jWWx#+ON%B#S~gYs?z#ia#V*1BqLX;i@UfH5_>{{2lnQ~1u=A?z;5XaK>b|H`)~M95}U zO+wWB9*mh&o;s+yOV!#!k}9i;Q(g05f1~==w24oTuPLfeT9)MuI<=O$_Q}qEdOsLz zkZIrgQr-e-W)t9GU|-JNg{+21+_O2OPfO6%dgvm z!`oXyE*o+wOVt!o_ONPAP?qZg>h(}P*~%lCg#w$tDieYfXl$AC;XRP_3ZbgIqRpjg z6Yukgg+jC?CHKL-d_#WykOi{`42|lfOgrf%#Z~THms@lZ|s=*qk8g{D~R27 z!D0hZRwLAxBGJ1&4DxS)5GDlNaXX6zME3&|;!l#I#I?0($l&1MMbLvl)@E(bVRO&J zhqR*)sGbJp+~-~W&A)Y{xO7ysIO{7<^&fRZ) zRN}#CmxcDNwV$Zxq=F=g7)XHRV6Dng=zjL)VSuM_(I^(N7F|16)Z(Xlk2)GiX>(2{ zOFzNb5u2g{YmVKyQ?SM7g8)z%E8sQwfvnpUGNUbGQf@y{;Jf2!_U3>f;GJZVaaMOP zwL2{a1MYn^B;X#DtNH$$0nNmUFEIji89TM#Xa;oNcu89c?`w}&I0lr4l`Z?a1xN#`CGDn-@*8icsB8=2-2fc zE^rPOXoIv#s-V$>5XiYT&+x-OzQ6QC(OxPUb9ufmc>a-~O(;+`It^~wvc>DOWSq&W z7Fg&gs1?kTjCIu+5>-UmBNB*;fW#2+BCFp&{+}Ylm`SV_v%_d(4xkI?3993 zA_Yj6LX7x& zL7fdCu%DH69DvoU6ZuKp4Qrbi!u||FZ6N%O_rsw@iLDCwoMkp=!j>cHy7V2Wgbn~K zL71&*j1a^@w>Sq2o!%H5LY42V3YSGwAOpAoID$hqq*Y)A1wrKXxg;VY0%1|Q6Kc@I z6Q(PUJOh9ZA1MVD3?VG^=jX?8fD3C6K=_{qnV6-u7L)ZhZ}+fD#WA4H3c4(eWIobg zvl~G~5n%tVye`70u6F>?PK59*q@e5S%e5@8SnSlJtMno2m?MA`$VUJTY*3+)TaU2; zzMq2AR~~uMLnnKp)S-?-D!fmvz*VgW-5JDE7~Pq6?;mW5|EdH5UDGAhYmCBrl5(aQ z+yi^y$1RY|A~wwr;=Ua4>%SSl%vz;EJ}?Y)M!5itTt*#0xlo%EoG8Mo=KF^o@f@JG z0F8b;!Qs3UO#|mvIj?f^RSvdAw)!NNmPBa)(Zp#az#wQyp#~AGa-If!t-lYrsAeW~ zhmG5Ah!g%0QTKBTBB>L?LPfukmw&AQc&Y=7WF9|xGN+uecJES;MuCCw;X8AS{DG(gQHdc^RIUzW(edsE% z_gq8`NZC>uv-zO6!qrs>KnM}pK`3L$O7{Y!0v8MVeh#Pd;at1=*$+sd z9E%Qh|KjMZ- zWuU<3Lw23)cLl^IEIgNmm&M$pzlBUUID!q3SQ6O!mPJn>>(gR=|2Tr424X%9fb}}6 z5tKyIhG(Dmec84QqOAttluv)7K;bKd9)rH^u>F;@cDA3YfG#}OS`$?jetKb`qnIMp zp=xOD>QHp7n`w|FRhtJc1r#-cW-9k$J`l*DNr@;FM+^s2YQ3gjKbfaiS$b4TLXo!6 zsT*fwz`JqVziv*@2)KK}ZD>IiNGTz!3pLu%G+XbNT1s3YM&k!Lm1UsM%@OQsn`e_U zP;!9|TgpIoJQ@a=vN|?4HYEV*Kt@bu@}>Qo)YKa(lb?Y+-8H{f2~scm15ghBU^EoE zd%(;9eTPy<9f~WY6|`c57udR67T{r!F0hzn0xaJc$|o^XxNhTrj|Bm>%EBiJ;wOpd zoTW<8K<36Hxu^V=XOH!R2T9lst=54sZpmuB;TG)b5CmjFXwymuoj$T~>!k%)NEN^u zn?{7W<5-VxjY9yGtg!)Lh^E`76D>8VGvNeyNy(pVq1Pb z;Xbftcc3;Qpf}SJpw6$QRnS1!42~+th~TjHv?I3ba!4p=EVFt+3@tx`8UA$cK}o+HKGjm4hpBcCVsq&c@~y<2U459y{4`e9&May191NVNfW*bDu07ygsv#Tl}zN!=P)Ww zJeBVoKYparP(BTmj88*~Pzl*e$deC<~dU!q5RqEI>O&aj*#v-#o=T42!t7Nv)jZm(Ex z(}=8oruqnZZHdkRBzllUxFfPM80=&!g0p!{X%DH5r#2#U<4c0*S~C&m
M}p%i8;12cfhI`vY^vI#@D`Ws3%#By^uS zBu=&+y3P;PwpRd3!aB`p8RhRTZ+TeW2q*{rIY{yZF-8qS;YFkggF{2dZj^ybM`HaN zKxK@81{~(z2Pj~^&hMcW-2*Yoz6viXIElKhqbj}aSI8AD*~Y>9R)e8F1#(rUy-q!r zh#(X)RS1w?621aee;80CKbS`~M|gYxfpT-$e=`c}b$Msj=pnU?b_^_wKoOD)Jur^1 zfyx&sIY5wP)ELP(4>^^{?+)iXcffZb5*5M_xK#^zrou;veyl+Myzyf~&zN^3$k~jA zw&1_2k$Xk}2qZ+h28HAg0?tmqLPFFUL|+gD4c&f@tfl|C4%GA< zVrUV62Fh%R6;GY}jVplzqI;mp^5>}k6;S?9$w_8M$?5;UI;$TW^oHLX0WL=__B_0Zu23CUmNozU!TfW$p5n%P~0*wBUz#xptY>$ zXqd}Nm&x$En9T?IbITCf)c}ykcXf_j$=T!DWV zJz?2QW&VTQg%9~t+)1)z^F8B=o&xV#=wK95-`oCaRNUXF*+4g5JBiRz znq_Oj&Ka1`{P3E0Z4J`J4%$)^{nmRFWx{za=_TpA`LNEW)D1ueU085Pzs&CETCY|` zvG~5`RX*N55Uulu$=AW^1YnRaRx-lH4dS1_4GpE80@6?*=!T($Ox_-<^?RM5fwD31u!6uxiP`-76R9^xQcKcq zjQ)&ws~H(%U_fZ^pktuo*4Hb}W(558p?(Ve*ALk`RNeYMFzKGIq*zH;Iua7L)Hq$V zIl5u~Q%`hx^XK97!}=1G>2KZHHOlw{wikp{>Fn!{`Btl%9QBK*;tbf_9}=T;-K4hW z+K(B{`7{>2mkDXoD=tk6F*h#-Ot!HH+{}|%8t-JK`#H0ACwL@HX6Q#~zyC&ztI&H7 zowW@nMG-HXAE#|UzafF3>$5b%S2K~V87prBJRz6aM04T<$+LX9EfP~=W4e7xtCxAd z^AvqwPhRiTM*Azrg!xYGxti^*MSl&fQ`6j|iqCQB4n|j^%CD<&=rKG@IAb*Re!*0! z^U^CV9)AvBrx(T%iR$V3Y5BMZ6}u32YvhJ(A;dfUF>T z;BYcSM4`p5_>?}MNC1(JjANdpRls@K4s(Mse+dV2F#N_wl^Y6UR zoIbyQscvGOkwLpA1b(j;8c$zCS-lB;B_QH0-{^Sf4#I)j=2`eqG#z}_wkD>giYdlB z7cf-vn$e8q?dg_(fi10XBN0-aXQ_57M!-Nr$D=)VwRS-NML9G=v8z(3;N#2Pg!`jc z4WK|M-eWvJqQ5z1<^7%my3>iY)xOr>Uv|y0zj(MvEPWzX!jWOvIYDmt>!q)FrNj|? z%#YdaRk3u#kN zn2Oh%hrht;JzgI~+#aUM7*>V1UQ$Ske|T?rytSZ8PrZ5sX)n@Pn8#k~%DT32=Ee&i z_Hc$CQf+Fon60( zHF|yPtIP64;qaF!1(+oc$tlVc;cP+XLSKhDt1?twk|OP- zks2K$dW#;Q__osHs~1pHqwX0Z5!h%sR4^rJyu3BjRR|sUMm?i|YWjs{zSQ3ysdAZ( zX)|ZVjz3 z2Kc=fz-9Kiw7i^OU)nd{L>wCxWKwkZA`Y2fTbVIaW$eJor4=prD(9{Jv##0UcvkUO3ZSdC6?<SLdv#)~Nj(0s(01-M$Lb;a)B${we3LMR>zAJs*=WnznEMpyn%B?R z46DCTt1>!UluwI{<9OYrJl<5g_eI4pg@LdYJkMeCa@U*%VlwDg;LR3)|86XQ9Vl^x zxXGBGFUVlhzZnsU)+|fB;kD8J(*@m1G2daWPovwD|9x|MDu(ZJW4Ys!-W*wbSy;VD zKv+Go6hARbIfoZY+ai5TxaMg#JLg>A5%N{Uq%m!w)IAqYIks)+AfKOpNt20KF3241 zYVG#Ac$BLpLDlbjlhw_cr$wtB-l_BJKB;_G?Z>YDTJ2Y5EVQc)cbRlPk-NS>@TSEa zx$-gGy0bs2#^apZ(hPkDf8MwC`Ecqf!`6X;m+CQIEUhCwGzTrZ@|Z~;9m?}_B5M{m z%yDD6k2p(7?VUMhGI%}NJEbCK#^bJap0zCv z+EV>26c1tb6vOJ7-BjLRWh+S^WESYY{G@0C;~QTpSlCB~@axxe<5jp!(pLLH@}Z|>0>Zru)4>z-G24q8!DB^FY6;-QGcR1~ z*F+?tXfkxp6!sDwUKg+BhLwHxb$ThE5i#R5ay4sPCgGgKk=Y1p^Vd(_7-UPII1RAP53SzrcR5a6V%t?t@8opLz0o54NL|7evZ8?Nk@ zmmZ~1f&51@9qp49#lNjsXtjp2fgZP`RHvO}}T2r0R$#{dWJ7p49lkII(eDvco^(WXDw6sY`}v)HMoIJKeS2 zwX62?D4smSuy+C%D!OOy-G6a~uB1VOpa}kVvfaO2%k>uBMkD!VW`*6uzRyJu)V5S+ zdRp86D&*0%)#P<`ch#sjdB>H9Zi{o%Ipz~y^hrBd>nY9luMg^q$#VoXMh#nM#}*L` zlL33u!;x|Q?Ty;eL2FBG+*I;$<%Wr*p2GB1Ne$GIotvPwx~-O?JKV@WKgq{a7ys3 zUYbeH`d7rfFP^vEdvs@m)={i`1p|t<;{Ps47}n3W`s1Q z0w=r5>Nb7kR7=9hOdg(U+}MM^$~1ZMLM1)MF2h%UFmtq^tDqu&m3u{lSN6GEWkUq65pVj(ap<^ zPl$G$7W=X?YYgbM&+tE}{XP;8HXpV)CCRMVIJZ?qSB;?)KbcHav~YziB?}}tZ_o14 zXOh>@Rd=I0=jtBm{+yYxVrNM-Mpvy=hOEpxIO7M$c?fpW5(O(&%UtMU z8}Dzn(RJ6Pg6E%KqN-7)Ijnm=qOP(5uf=hC^T@&?;Zs}--9UNnx$ym$zYa*6h4Jyf zi;O$6F}zeiO5Rn3~1P3bQ#UrX{P8Pt@q&MAkO(Trza z^b%fhuvbPq1vEZ=sNZMb*zb}i zHup^{f|I>#)3dr}pgwMRPQy}uTSpsa5wOW=$Oip%tgJ9fN=m^I=G)5A$BrFS<=r!Q z^;m;gJ8$=9&GuvFp490MoRN(3+Umpusn(E6tXZSyiIamSByzzkU-b>nl3rO2+~&GS zMZj80E{#X{IZpp9rqg@W6&rmIeVXwB``8HW_y)CV(yM*VSH*dbsBOz<$T{kFy&)A# z_Ru8S>3mspZ^u6=nS80usqT}x=KBp6)3beRDcUMreZpQoUOk>FRGzDT0nzfQKd6I8 z3{PRF>K?5Re~lwxr+YnwNL|-`oA$-7h(Fz%V*P2&z2!>=US?Y`&+|-%$1kgA6ssX? z(|7Drc^HH+oV+vb0mDhD++`U$lwyT?tqRUmXC7qKH`aI9i)5tZUCG)5W)k)!_Hj;J zqV4FG>JJTlm#HjyLe4R+;R!vDmz7%diZ!OB?>o$$tLIT3Z@bF9%K_#*4`m>EK;D(r z)Rvb=3KWt?oq zCkO1$KHnk7UmAEbrSsY!+Q^I_j7-j&g-$*?9d8-#e0R<%e0zu?e64e=8{1(kFe9zs z^va(iBuI}aV2rw19V}B4QCAWW-TG+wC3PMLA<--=CGu@lQkXhb%ae{Hg=YOSSaGw@ zks{*(p=E^kPgpGU=JrKt`(D0L(V^w!Y_FPCUWk?8!LO2{+F7rBTi0}R!WfrfMST^b z^W7Oy%hrZ-n8bzXcP0+wvdbi*j@}^gvD)WHXlnZ8T2HLm)?1A&_s^XCA>n!%t`|~_ z+AbbiUd(>qr>jdnKAgbO@P+##_KDVh3N4ZPZ?*WX#qy4~cWOjVlE*qRN6F*o$w$v$ zL@$>13QbY5i4?9nGbmV7<zlxDnk`+FXV=R6re%_?B7)ZxdlMd> z3WX%?0qRwi5YMh-GsZ+vvPjqDH>+dNGs4-pQ_L`YNOu4A$ULVdqk^%HBu=7JA;3f5j+25{G zn*S>N$?Y56uzIfRd$2AuGAv$h)(__umEmhSfc7K6oh8ejV>$wZxx>>%} zXlD-GlxiCYONuvPDCa87O1^uzaF$SdMBo5vkAHBLA%rxeK4wkztlpv-7rnL|qLhyN zah)h~$4=zbZh`%24O6LNxSCXv+3r%wraQ9_9Xtr4+CeMRUGYFH)e?D!G~Sp)y?5FEEHmtuzzN*Epygh}%ByupesXB# z-37GYALs$MA z3$|5b8l)n(!d@NSrOkmvjj8?17HR#uTIM}v;R%nUZ*-SNtZjsp)a{KUyTO>yCc-)3 zq;dH)Pk+2%eYs@AmSe8oDq8i~R+)aeItKGxFO086i}wQ7f8D_}CBrwhupT3b;ot}t zPAyCfoXpVYWoj-LUez>dyUxHP?1R0>zqMFLeoeZeF}v7XLLOu6m7|z;v^u$T%uqSk zJ~%uoUTEHAsim78>-_cKJ}!?pI=YQ>PKTFPT62|a%)7N?@ZapTHDcD z1;ngTkHR}+*VCNaseIOL9h#&Pq8qZ_>-Ooidz#j0g_(DBi*=W_Z^rf~Pf^s0d@X#; zxYARWIt#(2|9+MRGSuWLvimGhh@UfqUL``T@j#g;w{^c@zO}dTvreF#q{j$>_6;4M z-KYFHsG%=apRpd#DC}4A@+w1?22_g1fo+kwad^$Uwzf9>n$dG`Cux*JT32Yc08w=V zu7xAip{9Tk5s{(*v&-Hbjy(6T`Ti?;VOV$YM)EtEKoetkn zi19XIbuTHo+}R=7!X7@kFdKCPYwliL5?!v~KiRFPtNnC}ipk@%)&1CGUH4eG>}!NA ze-6*7-kO>ea9w>(gEJ=u;N#Sv?teX{cZ+HxYM{ZRSw%hKPkbbNbil?Aikzw3Z@NB; z9|oRIJKbi6H1M;Q>ew<(fm`+J#S2((Z-Huk85z&~(#cmiFnav{GB=P3^}z{1&eg^m$CM9iUZD}%n9g0F&TR?~b`b6hPfAJ>UL7O{eG&!Evel0y zs4P#;mdp{LqLP?z-hr-qMUCz+Ep`N2oyQijd^=;qZy#x3B-AM+(|P_hXkp4U*445p z6D#X<>lzyhQ?_YEBATAFy7JgK$}-|*I5?`dH>ZV9W+Xf2FhVLzk*{c&LwQiaBJRGCF zB+M))v%=V#&gs>BWX=O~qPB`E;Re;AW^d~cmw0Jsl|_x-K*;5Jrh)3iQt9{kiJ*7# zN$CDXk<{3)Ep5_=52vQA z2v7vO`{>alRp%=SKs(XGI;5-<=Q7*dRdB0AejpPQ37Y$=htn{<6igoXkC?iedC+|zz5C+AWgZ^2=LWEghZH3BoJBS`S`wF(oE#pCm<$fg#@1zP zi2&)TWvEN{CTeM(pbJ2(6(`nsBB&|xL2!zI(?Vp!)`_6jkgyQ*)WZi;`=3ydyXW0Y zGyY7l)AM@drD5it3V?&mRMx-CbM4(KCJO5&Qv4BGD}sITNz+A=2@fuv@9_vcbmUjI z`4QEZGIv`jL@aOMb=|{;m6-P=3uQbe$MfcdfN|v6fJFK0^A9(vLk_I0ySlP|A87iq zNUtD`@6Feibxa^1K+z>Kq`2;ek}C#SRJw$05&pDlQT2}XgMp$v^GZV zO;IFX^qm^?0ZBcd4D^^NCo!N_yI> z4GoQLNllxvUr$I8_!XF_`j!F@qFX;M47^B<=Rlln$d9j_DACG0sLq!OpFY9z)aC(p zS}%{DUX{ojW8?RIkCc^6O_PB)ojwBX9338Sor?Zx=Aje=Bdls_Y8v7REM-$x;#5b| z=-Tbuw?X4h+Z?sl%h%(4o-E+H*(W~Tg~!Y*Go+ADm9`I`Tw|QYIQjqhVIX|1f{@E6 zvoOEVTqN^-tY3A^4RwRQL#Mkzg@g{-qLN|YzsK{B(VS3SuF6KF;KtlN)t5r=ELh!( zidj3GvIYFfZt6{NIn!G8>5iFSx^#o7?}wg@$qOb0>1$2HqpAPSbsyv*w|UKAe)KML zP&RkqA?3Cgsoqmr;$C`>Mumv+LxCqnM5kvb5+|tiQbzBMY)hypLpUYi_S+6x)SRx6 z|6W;Hx!&K9vALZ}zGqMHn>YVfUQM3}VLL%bmkJC9nVcsT0(DU8cREiAg^oci{f7)9p(BejtGv}0XgF$Bn{ z$Xk__3if3<#(nnUM;p(vy=VCy(fN=$y4h-JpDe zy>sbYsms`1E3Zdo+9uvwcrlhHRu)cuJ;o|x^2JAGulPb#q%sW|tuPD3Yjdk|nE3B| z)_{a_oyuo&f(b93A@(?Zr1bF~_Wt5>`HsO!!%JE1Ladi_XLDjso}^aoJ0Ob7(zw!{ zJsO+Rc(26!Fr-c+^&)Ie-t)7D>dxxA#0nR22lVV4K4}^GyIc({QaImtq|*Qgmi>cr zGGta3PD)IC-*Zt=uos)V0)1;E1(zqjU<^|xgIfYdb%!P#wZ}O6IJmf!$;ilfnly8~ zYx`OQPBJoLe*OA|UUpne@8?Sg+{SCmO!E9Mc$5~KF8CVeA0KN3*8ys$s~A_ysG4>7ve88o5~fpd+eXhvGffI4^zH1 z^=f34cD%5!<_1=Q!d#aKyY|_ZTT+EPzB~}B{W6MqqN|wyozBYiVmP|%LbFOE+og2E z@*cq2nksa&MI=(X95>2)qBFF=;l2`+H^1Bls7P>8$ua-H_3fy4`idH39fvENvNwk0 z#ul*H;^d9yqajOKauC>*xmvzFY8{wMC@#W|cZ>MnD`Ctq7xWT$O7GV<=CyY*uL0-_ zP`|Bjl|x_o9r>B2vo)@~3Jl~dlnZ1OO1DH9I};t*EGw?h)vI=2HOq z&fD{nDi6d4lN}T{fS^O2cO@+=OHneAG5`IO$B!*$54Q&wLcGbO#rp6^>kOXJdIIa2 zxKI?QYcVT8I=&c~q`mSrCHb4ut*M24N-Cx8*|&+jThOuj21#1VVd ze;gj(+7Zd>T{Ev-SIe0k>)1d!*W0Kftl?|IK%S*P;ZV!4k@vPxzkyM(P~(c$(#oNk zo>N8{T$Exb<{0A|w|WSs)2X8w;0}G-lINbd@$Ef@;^5!w69x3dzVy%COG^$>H*G)4 zMgT`ebAXB8;V$fyl44Uj7VRz%Ds}`eTOh2?4N|LPWI~6STyk@ASaqjBNAt>?3sc@l z{b)H0T+4tUcKLj~3&p*jf_!4lam8)`EJvn>iy)2OE>N#!Zsj5R2eel(TBc6Z(o83r z*DPPdZ{<~!m=sw2Coj{64td)-G4ZE}W$#t+sYZKj%P+7&Jjf@O=0u;e;v!<%oB4BT z$Xx{>JMJR$2h2i^hYv5{m(PDUN#N|y%6v?~(+_1;&UMyk7apB4N!2ckiRbZo&dSN6@6P+7 zT!8P~{qa!6L`K6v4e505^xSvU^@|VfJ6?2Z*^OWiqDwSZS^V`Gwjdr z(>l0~5jy2QE-G2I`d_h!He6h>Q&r{qX`CliFbMA8*g~rZXpp=8Q6A>g!rcM|!5|C`9|h+iM~}|AYuH%`=9s zmqY2@#+`cD;E$byyr+QRZC%tI{89KUyO_R3_dT<6Nep8~^2M`G8JU`~<-$eFTKhta zm=ec+PNqS4{~ZwktdN~@{)E9wb`RrCq=gY%dd4m-LXL2{`zW_T&0ZWt}f{=wc4Q~W7H zu+Oil=?0~lO~+l&#cxbElD~5N_;}8bwES5iq(2@**REPF?3^UL-Qgg|)TIsejJm2z zXPRxVQ9Allc8FM=ic4r3$iTdnlZz4A9${rpBs89mMxim{Z_4-QU5l|Vwwaneq@|u= z<`WZLI%>xr=9okh_A1(cC;z?v%f2ewqEpY^^3aJ%i8>gfiB6hm%#e=-z*mghUYa2j zF*VL}pTxb(#J5W1HdUBw=9O!uoK=fl15YUDOC1ogBa4XU?Fl2z(xh!l< z873bioB;}*m-zX$kg39L@MSMpf!c-!IruKEGLK?t9;2S=$Mv}3AfJH#`DHs?(M>rz ze41J-Gg6-jRPcyviY`(8{Ai5H^Z?a-hkb5lOQeW=^L$%vT@$BD{V&m}F=pJtyj@0@ zp*QR2+IY9Hx_VxEMYKd!36boEMTbtSE(f#I=b6`h2~Sv38!`7ndN*(PZPoFvFT_Qp zmsnOl*;;DFsZSwQ4jsc+fa&6Mq+-o@XZLY>`iqYiRvb7Bx6=-LwXw()sBGURYsaR!i$}~VmncLNemu2De0Hv= zci;8AOsmt~>Ol_t+z4z^*H|AKljIvtvF}5xyuPB!gBC6kOnZ)JfoGtdqisuU!7;k_ zl})RP=~55oM3YPGEbx^yYi@4I;)*_{LUrW|xZ)w_hMp{(8|}eZLc)c_sm`)@;A;2E z7G`#5K3pV3OrGQCR=<-p`l`#Z7n8mD#FLs(S9WEuJ1_k`y#gbc9gWG6W5qx%R0aR1 z17!B4Zu88`EG&{i!5=CFz+~41GIap1mHv71FlA*kB#}sK=R}+@V=J`WJv{Xstav;$ zGRLfsiEL{c2LzE>S6;p0v1%h1c0FN*^XRJgJM=Z(g}1{mv1?3B6U*B=nLO-%&LitC zuF1*~i6bku^_mwPOl?AD zj<<0w{iLd2o?>jgL_wGHJ6rI=ngX!@i?G6Q?_O&*j-|f6is5F0LiBq7Y_^*oLF(Ns z!$b9$)`=K%Yy^b7sDN9j)pIt`V5!=b2wmIVIQK$mSY`K|ucAd7G7lYUOk%OOw{L?m zco~$4Z^651y}gsfZA&1kn?(sY%R^JxBksHIw`yavz*FjZZ#f=e64nWFfZ;KnXid<{ zy8nnfSvNY!=#IGf#BiVprtk0`>G2-!kAya6rdM*!cp@%En}w>y|HL|*;EmW`r!dXp zb=Mg^9I5M270V|zb4<1v_FGfZ>U7fYEW%=&!w_{utUMq?_H%3R<{r<&#_UHSPgssS zu8H(_FsM^DGgU9Cwhb;kVLRPbylyt^h0D3`FCQ(JYc>slKIWchw>d1XINq!puEm`RKk5@>r|omlkG+-j7nBG@Wk@7b?nz#)3?C?KHA`8Pt6r zQ#MNl*(~)nR6cDb9bd+eMrq}FT8Eg$$Y5tGGjEhC3miey#*Nd+vGtC)-@0Y+)+t6X z!@6*$g-T%MG8<=k>k4XeAOJt=u4E)18tKNPD@o+H`t3~VYqC^M?yD4UFw90s&^a1c z4jnndoe*z%sf@I4E(E(YbmK~DCQ?@|8Bn_5H_$1r{^03EY;>TPF>6*DhJ%?hv zX82Tid()oVQj3K7_B^(YIKGEKh8s-=>lghzDlu# zM($+Sjk^uE_xv0iIZI0`cDl<|x~Z-uo2$0_jJpJ%YXOB-^Xlpy+rY^i-XYQ)q#UbZKB>_eZJwZ2EUC+ zT8h-Eijrv|wH0+nLz2f;i}r2W;allpZ=Yr zcQVJB;@9N9i2R{6e}dg&^3}fOb`{_Beg2h8!rm!!%s1#}NGrEJIpYZQu3k=_oMb4) zqkAe$mjW1tQpqWff&`irpofU<(Sp_{vlzrQm2Yols-LpN@BQTD3TcVM(912VQ;7^`)hnmPga z=OycuOv1h4bo_jLn#IM%F!CWK?jx58uuA{De4f7s6i6Eu6babwn)M}4d@J<#Opejh z>o}g@JqQ(h%+!UVT5h3yjKepLF(1m)!B=0j7*y6$f-_V3gAXo;gbYqvo0BEM`K%yLS4 zdb-*P<**Saoz`eMT`<4zd9rm>DBAk zDx#u3y?uR_j2%}gLHRD7=RHCb`SXemM+ieO6Y8J>y7tB0uj-r>NYu1pbW3C{Qw_5< zrqMgZeKK|0u-D4Fy^mX`2Bo$CCDxUR~?VQdBTct;j64gDAn*RCf)vHPOzmE_# z`9j8%bLTkZufbEm^ZD=p6;f~Z?B1A9_m|T!zxVg&TOi(EfM@>VcmShfD{mx5~drt-JTR_&K z|4Ig_wO^s}yP&z9dEvA~mG3=pw34vtec6S}P#grFLvUJ}`3%ROjj2&6I=&|$)P{0= zeX^PMB_OsEp6g3UbC!yV%7gXvoQk=9(48FQAbdaa!-r((+m2Hs{CyI!>jzN!d%ZU2 zC3U^Gmf>mV5+Hlvw(M5f+92}I?7`fHx)-;@xT3i$^wH+$sO?@c3Wb-1NyL2d;^gx) zR*Jy9psaiYfCFg9=Nr`=t*WUphuTe$MbAFLhjk~V#-7y3gmPry9d|k_;51oCwn>EU zBQ_zSJ}ob3omzs%tz^hioC3KGzaE&{fOYFJ2VQzZM@v!h&9WyNjrOnkd;NnqkxMZ&{CJ`E zDrG(zUw76BTU@L;09&Hpd?1UQ=+7(QhnnMw8){G-n%@F8obT(f;NSUoe zKfx4sMc91>fc1D;Tv9Rxwd+tgAB-w!6np~R6jk$d=$h1r-CaTrPF<%IgYG>0X~W%{ zIC%Q`A}qcmCy;T4+5?jFKR37}B%}*TMy|kt%|?N_anO8-|2e@4%1G8qEf+r`-H_ueXn0rDz8B-QCC-& z6Me?x5j?6s=Yb3b;k8eCn7;!ciicHp0oFY??A!;QO(BZkF=xLs=U(q4m6sNS8Y#R0 zh{^;pmIf3<8TR|ppQKXw0Jb@p|M?BO6qN3Ajqpfp0qaD9eWfi z)gN{Bf)pSJ3-j|Sa}WQ%kxT}TcIVZ8JfLnLsvWGB{46xR<(7GaAQc{3i_wAk)&s&q zAQ^!8z9nefZg=tA{S@>>`2wD?0rT&{sj--ACD0FyK8Xql4CM8f+|`4FgH1JbevccC zU&5`xoo*`lamBSxGLK_LSw_45RIz5&l?J6)(@91d0TxwjpahF04DYlxBVQWqi@Iv@ z9$9X4V`J)pY=1{Yk#SE&Ru|N^E<`C@hAmy&-1K(3q~T*etsvy6TKD~XJ1Dut!;Bwr zi?#u*b<$N8IqRR7H29Uh-36BB$|Vt2SSfJQJb~uf8nAay0`Q?~fyZN*4K)+;RVZVp z6SQ`|deG3Yod}kRmH{5!AnZAFowAZHmVM^-)}`9wEzoH4Khl-U0=RL~vb%&#!jR zAEeUo^)(dCB4qG#&7G;Sum9W4V&ATV!>DiH0ttw$l&JV%UZ($!_E{H06}?4h z9F&#N%kE0nwb0FXi0tqAVhzs1kX@mX3Av@`S2gsen6fgK3_RgAdHki6&=gv zPM%60%#5%3djo?SdwtFzJ=x81Gyi=d>V_BcQy^wl{hvSGy~$+e{k|x2&|Lk0ha14> z{d-oHTmQ2ZP?3B8=MMg_z~le*!Kd2)6XF1$c^S6{RZ;bvq2ljX4PH0^SGW7}dH3&s z;{iXDf4rk13ja=Kj|9EZWTXFelc@jqg+`Kjysi88P0`H_yScRmfsgvYj#x1YmGr>o zz*SUi05U81`1rK44WAJ(x@ian1K><)d3m*4w;uhrX^)>g!6(Sj9%0~D0Z)Mc^`mlR zW=1!V?XOk1&j~AuRajUL0xc}mV}(HEM01LrMkcHPlxQHLRRk9ednU3=9XfiHpzGY1 zurXVrfW)4;{}YTP3pb*iZX=`+Wc|U!2L?&hyDG!%{a!~ZlYcSScJn~W0(ueY$otT0 z8z;>9_ZocsVdcF2`t=Sdc1R#n%0MJ~n5`j8)_?eOU!7h;&)CSw1oYmQ04X8?JXSlA z`S09H-g`MV>MK+OZ-DV;xM@>C_bud=vas`b^DR&?Pz7!i9&(I*`}fy%bf|}gg-Jtw z9*jjkh;>+Y<%k1QCg?)jDd-k&2En^LyAf&VMo#zQIKDHibYa`uVX(~@zZk0X;Lni} zMB}*|ShZ$EOiWD7EOv^c;lnjC*uE{-wY0Cqc?xu5+CjG@6N)EOE)-OZ?a%<4XWT#s z0YS$Q_3p%mDRERuRNxV`#VdiB#MPP3C_tDI1`a%%&zQl?3($HjhdfU}Ad}27#npN7 zF5pz%K!cLr>#xm0LBqPud1nZXK_tnZpo4|SqhUwiaDqRVU~i(|j0Wxl z7TFq0pr#1J-G8k9@ADrNg@srQ5-z%s+66VnbRimoQy@v^I8G0HTz{A?-}W;1;p631 zhi#Rgi;e9(8u&}m9{J?j;+VGcXo2;B9s~v|8Q&M%l@(Ip;S-8S8zR)%7XKcfEWD?} zU&F#89THjAiOtyoM2BW!X(>IV7@C)tAld4|9=-{z3J9CiLI983eW^fpB4GhP4SFCH z1NWN(yCH+yd2kA>)zt>7-NR6^1~y02sA|zCh(uFKzLeuP>XRppHpnp{92#nf8Td6M zod<8HSr1j76#UxXgFAg;SP3Q0)>2g|A0S|<*NU3(J^!QFu3B2 z6CQNq&}FU(We8x-0;duImCdt?A+AS*Moq@|zoA{M+W{1>9V7@yfNF3`T2{6lvLps! zUx>g!uP_bP;*<_=pToP#vE7FSgUy8uHs~~{1NHz>7rK0XlC96Ko*hVicHdS31;&Cx z+!CmYXgY7NZ){9~mZa^s)xUQI;fu|AS}2e~Lfebw>Hr`Km^Apo&re`A7SQ?QU`-xj z6x2Wvb4Lq^e?a0;^GV~*34Em6iMMbdeZ1rS0d;KyeM7TDdqDn13ZYaZ$&<^xVTAKu zT54)DP_D&-dLZ}x3Y2AksU~>*Aeb)DgqMj-x_no@zZaHTn^8s!?dOzYAlcXkq9;qR zgtebkKr(?qgfv$){&K;27#tr!ML1>pwI$X65Eqg`H*zkOkytRp|MN=~vvSkn0&uRC?dK=X#wYs)uRvW~Oh3e39ElZ3>(-bbtotKSG288E2 zpqR(TFm~HrD)E{XY+(*sdD@V5q$RNJo&ttJ`IC>+IfyXB1)MRXO);5o-u%;cv_I%` z?<>6^*%@{kKd}1Cpg=qEPC@wW>fxPFI$ft61D$-ji(v0f0X_wx&bBkwrEvie&CvbK zxOB5OPK*LaTAxDR05t-|n9#q|yFvoKQ>nkdAGvBr*zXSA6fLw4Ou;-5j+hxJ*M~U= zXob=rZ-vB3a8baik^*J8m(Pzfq=E_sp2+<7CbCWd+8_$4%E$P4N8n+15O?$nkVD$f z{SB2-M|weLDeqxD4a95GFdzUusX~4FhRvX8aq+QPGR4;d|2c#FugP=!ZWbftxY^#~ zqo>)fUsnQ~!A8u5uD zg^uhdMd-TTe^)E&x%lf>@*MMrPoF-Sf^Y_QYo&*ck|f;<5&IH!1t!C4?#?w)V8Hw% zDpqy$z6M0oXh<#iy*7)$l~Dux0-kazsJboVZtR{FjR&G0pekxzU0n#_5471#Q;ENR zDT4m-&xwf?Kr>)^X^Hr~eA$8&9j*HKAvY+)gi^+mK_K=C#JU_nPQumQo$cz?tkwch zX=+7?nBZi;F9lZZ1839$+B%NNRtkh29TY$fdO*WZBVzzV1qmHE&G1R%?;i~8w_%Uu zNrb4|x7a-i`Z8DddF$%vEP>C}M(S16^z>JxKsu!j*wz@>>Q591zd@hCNB~F(zbu0Q zQOU@l60MYgK>LBQ@g!);HG?Ti0jyG^>BIqez#wRa?D4QX(N@zw2CfDVUJ3Y)?T4C} zI*aX-tcpiyJfY=5tH>q}5O&*ykW~l?g~jYZ zsjEE*Xm!uJT4sB1d%-BWCt?*uwp4g~ZJwkwR#u8}8Gly<`4dMFw7bO2EFOf2ba`&h z*I;2~nLqSI(IJt`aZt-bB9>(^IA#z+fk6xUlevQ+9ye6_5YjplAmukbKXS$agk}H+ zK??HE4OCVEW<4tsSptV#>ON-l& zXSA}8Amft^>)YJYA4SK5q%=Qk-yw`^`Dj5+gMZcey{qS7ubxZ@Ns^)b3m?ygPWJQ| z{R5{$y`*yXKLUl;6m@;i{C*7e&hzpK*8no9eB@07_L7nkbI?IuF8XK)S@kXGJvTtU7IZ&aV8k+j znWf!B1)#y=Sd+SrPNbckU2t?X26TPL0eMmZMT?9rus$G+D+Q{P?Zk(>_TzpbER^s% z$e9$O&N^wvZ4BfPo-GncblD)5zs?KL+J{QQh#+8q9;1&0gwAJ*VF9pfWL3~bhJ5O= zDv^WuCJ?3okSX#YMaDrC>IQZQEIJaL0#T%M8zgIJao{TxZG{8{+ac_AoM;t?@o!$8 zAI>*xyD0U0y#l~s2FzJU=eSGiZ_hN@piDbU6-)@QRch?*)3;`qaeGn8?R7>-xS!m) zB)p7!T>a#{&zbkYq6K`iS*7Dms|crFX{QP!@Cq6{YhIr=>So`BuKoV#SCceAJ4>f}pHaTT>$m=5w+wDX`yb!xD+sVBWwy%VD$`h?k_o0R<$a@fbaQ zIs{Yo4Gj(99P78ecWnioAGEM2fuSJSxwsTziiTdf>AdZN&`@sVtl0C;UJ%4CgD5KR z{qXQ`$cGQ($q8hpcMuN`ij8??)*YBA5{X!@-njxxTM-P!1!{`dbH!tf=8A>aA@pbg zOO#&n8Z>Z~DnoV$O|cMZ98+Ec+JA6__VT*~MiDO^SnI8zu962ObGmξIhn0NX0- zf^@j$>>L;^kXQ?wgzHkDSw@+bB3A$_>%9Z%-&KfV6I<_N3{Hg$y5?}dVEgSE8tipJ zVGBZuK!wPg_Zq1G`)(rAECu;Y_@s19gn1f*JV9HP;+;EBL8UDglmcZT;#P)d0qjW! z;EPTI%1~(j9i~j%S~)ytrx>|j;G!z8L21xwnPQM0xfJkG1fr1JQgV(bI1t&9Z{O}l zjG@~S{Sj<3wP9=bX(wAlOvW9uu{046(qQW5>B>p+6A)XBc!9$6HIS5+q`Us$1(J(^ zidaOo?A^OpLqK89jEI#cUKYCO<0rPfNEdtSu7X10?D~;|2UBu9CwM?vmZwQI#}edW z|NEvv!-tgzJ^a;x+?lV;==<#1Wk4G~v!#B3C=5a*i_{2E0nBJB@mRB4p6$~H!Q*!5 zb5b+wL2$FKj*gDmwViK7VoOX`Rx=%H1w2&_uB600e=KJt{r=89RaE@|&8znKKw+(n zGEnV)0ajqRbDyf=+QLX;R1v~(5%$FP4d?+N?1f4z#%8+pvlb^OXGzBxWSA2lP#j19g8V+pY*8dvP>TfT zonPz?gkKPu`ve-EO0ez~Iw--V0}~cvpCrV&fU;8&BM1~zQ&UR?L8a(-5et$ZYO8{} z+S+R&mR(D#NH0Q4WK@(T!d(*C*`WowS$Vee=f!}7b`{?hLE^i4Qv=bUD*imss-vsR zZPG}OEVRUR$X|msC8ZLaH%CWCy7DEY>KYm{9Ar_n6i)wT^orV%s}FGCBhip<;3v<} z(2Ne^Ri=^L%GfwDzZc~0LH4(dFt!C-415G&6D03d30MFx?x+Wd!A&ZoTwr7XkiO(hYW>_D zbjw^jWL6Ays_5q1J>4%=9IST66_BuanDhW;R;!p;uaAYF0_QPNxPegj_#IPw3lg48 zK*b}^y{y%fPRxhVXBaSYDUB;Mzn!Kq27_^0zD6lV<5z{*MRHK>zAyltOA$^0j<o(s9jDu9D%-8e&^Z9 z6if~S4)(LS9s&ZWCW|_-I`07|C%+fM1tn2(+RNZ-EBbXyxpy{>LZO3E9x=w+w*tEX

uPXm`58U#B)H zbay;_g2Td6AX4a7;RPWie*GGxe}k0@k{SrusSC=7Ua`r7SfWm`(zR>X&_FRtScK6) zY)T>)Xd-IXEhsu6$dPp(Jb3UI?DN?_5@B6-PeS5(elLI-2t@6OeU21;A;iCi2VLEi zR^hW}&my=rw~J-A#tleyubZ3>U4f!7(t4Hq{yd=DATW#0IP<%Rx_iComj*ZI($2Es z&aCVh`jxK;1qvy2T@JBVeb1?n0n;r3fj0pQ**ucdrR2O!}jE`U>R6D%@ zXbBf`FG!gMZcC7_uW!TDzCoS;!N-!0ojH>RmmEHc4kE2VoPz}s@b*NxGf1Ho54!Nd z;o(||x=;z@vFMCN+BM+;-m|mIS>~dprNw~)Ye=GZSu7;8SlGuw))C20837V)`v3_! z>}(L|@F&Ep(n9 z+65^v3o6jp0Z?zq`}c`p-Icp15ZVq{s*fPZ+auTkRiAYDAR2POpifYa5(246H|VSZ zTg9xPpiuSoYj9Ym)Vqt&VU?MR>pAUXKX`Qa`%#hAQ0tHz37;lqe23FH!rmIvJvXfT zb&w8V2#}}gdLT?d>Jy3bQK=B=YEQv78xGJ5X}1AjhXjB*NIEkUe%NR6?7sgpiTrAt zA@#~d5ObUWZag6e9zOI_=*3@y#@G}P7FL14EwsGcJ7fl8dW4{agZzabx?1`^4;%*H zlLwO;iKh_oFkuGv8}T*+hdv2SEcv5*26ZT9Y6TCV=t7RFi)anVpnRl@ACBUGhf85m zPmr>(VoJTFD=;`9fSS`=gv9A~{I&6}&?)zW`NmrV+wh@edx({hHeD#WJ8rJpLO;YM z;8vBDl>w-z2D+R`lD=>6-kTn?P#!mfA0Qi$96EfM8faJGhs=;i048G914YLP3wZa5 zc2+>?5MrNJhT{K^vbTVWa{uCf2T`#(s2CuiASfW9gmjolH$x-R-80kxCJF)y0@4`7 z(A}X@3ep_|NHdgl!@D2R^T)CZ^eOO8gWMOf3ns8w3V}QZZ+Ke__CBlk+et!JHkgMcE$`S4grO9}^SPoV(ZKj|+lA zcAJ5_EPQ;O>)TL%yOjSYpb1Agx(|Mp2rhB^3epHbIQ6ggLhROOSkYQx9OZy#FM$|) zz@;`J*jCec`}wJgiHQ}MMmK#l z!F>K-=N{FV{trBj)S?-I6j(nYzQ}m~&!z%Nr<0Ao1jZ1p_kfusg4MSUev{+Rfg8Vv z8jL@fWH~d(X}OGEA`H$3*1wNgUK2K#7qXE8(7s33kO+>TIc{g*F}WFlR~!relSp!W z{&zKjWco!Up*G-l|KIx<7!-q~eu`SgmvscI`#%po2dbn3A=az*cUfoGc&5?0yK2xPvAh)9MF9TbMFV9$WpM(jw7hHxp-HT2&zqbzQNRxU6+ zJgGr+M+;;rslYLO4>X5EJ&;w$>z27g51}H5@}s<8%ga9;Lm&G0o4^$JEPaj?TAP8N zFq1!Y%(6FM0X9-tC@D+9In8n3ashO?CGu-Pqbm0QyV?1A{|Q2_($7I+X%f_t@=#ol z49IU+S#~b1e;I=Z4+co=l{q9e>Xp<8qG#N8tpL>?KHd-{T~6EGa2zOVbra z0&TFTy-rI@n+3AG>vG#ja&SD4+&OaJ2in1k~nWe1~Rd(_ovu0iH~tgZt^ZNBa&dL%$K~+CKwxOpkBw zFsku9qK1McQ3--;j$<>>l;AQJyJF@$GBcA3G+_mJ!U2dM4ptezUBTZwFnbQEOhIsW zmT;iIxa&`={b3INn=&wP-<+R`DenIzakz5f!VM%u3d4?}JV9OdSozhHXV03lU1DPT zUxCAjYX4!eXNZc`vwuO5Nql_Lyl5MF@fg9=25 z5M?p#-~$CV6_42)xGW=@Nf6_t?|=Mf6&>OFrG0q|%R>Qx<8Olq3!K#_d6qqKVB}7| z7N>0jE->q~EBJ-*>cznkY6ls33W=_Vhlel#*>g%Ue{Xcz_Lss7OsNS7RJDM`no@-2 z;^JC@5aKvMUF8i7K7xt%j!7o$>u#=v}@5{Cec+T()eHSN;VdrBC!SG59iz$q;1a~e8|lsY)({^z=K z8TKvS83c7nE2yMWA;E2K({c6Xp@uoOwb0533J_?^MwS5B0vvOVe&3GCB5@-|2U>4> z`W%Bu?mh4ovhBxYw*WRB29C#<@^VPvvABVU1KA1FbqMz5Ky)0u*rL+^rmz{26@Ur? zDOC`ql|a;b5RVE73=Dex`huvaDAbZ7AzWDUmWuxqA6`BWXd8yczl%Q~`Tu3phmbG| z>YDz4{B>{ri#k`lSUD z22%3ti?H0$h`JX?q5l|Sr!O1>fO_jSaQHG{{M->2mkNNlm;;UOTy%cZvf)s>!bt)M zaoSgq)?xo+cp#@8HmZ-ngJ!7)%9|iX0{#SOU;pOd#MmJ4Jq(Wg5^V01(76ZERv{KS z5)guh4#~5ze{7MK-!u2uRcj+j%!{T_5|7mQA&Pq>Ro=Jm04YjVj{9TbCgZcB$AlSWykRwMjW5{C*(2Edt8@yS-3FPreL~J0T2=g8B_n`4) zd*7qqU;lK_W-r`9s_p)tcg{00-9?f}Jgb_VKQ%M8LP1fJ2xeOQJhJzhPr}^({gD?& z)NdeWgZStn#)}u#CPC4)1b!Ie*MO8Fo&Nx$rJUT*bzh5BZU6f)5fP_g0R-2TAYY%o z-wEuMWF)8!JzN68P`F4(M;BgA1q1%$f}qz5SOJK;RulI3ULiRRl06VAsXZ=3)T0pp zMPwE!8Gz0<>}D>;&gyt2|7RNLBc;5E^bCUNh^;gMse(61$gX+;f^A3~eFWM`wmSL# zzO#YvN6MZ5%xs7mgfAhnC1gh*y7u_-|KyNiQ~?SPgIJpFD^XEV$If!`@U*RqoIn5Rg8pB#+vW&tpI5G2 z`M$ml`6M3NG+3UXswFQtoZD1!=#K^yPR{3tNM>^4As!HL5o)Vo*NTmcV}a~c_YjhS zMb1>Bj`H~b@;3f_EAB+F2Uzy5=G zvP)U-v-|v_jgqQt>G6PO$HifFnwK*2dao<>>`82BN!)daO|d!j8c)9VcWms;Ds>6i zvPh6Uke$@BtD8}}K>{)N6yGm5H!05yq(HS?Wwb<7J?@f&z_lDA2`VPo*)=~&{I=4a zr(~?3dZcEo*d-h4m2R3n!&F78y4ktlI4HzOj}K0#T`d!P>aNuz3$?$-f-bZd;bz`H0iR};d%xM3ir^CuQ=NB>yipotCsC!FEDBm>lE+2&l>S+lB zEdwI75z-!L*J9{ORJ|^8`SLv&Lbes04}IFRJVF!9?=IPWRl8LG z(WD|Wt-HfW50)pxjBDSF$Ya6m_GyL*1j0k10nWT$K{U?_xk8yLP1}X+ zr}U4$j0`EPUAqs-w__J+I}+b&cT;)fcbj`3IMgw|T+)_j2w)x6%@g`|m_m2KTy*U* zYb8~8tDG^|sywHW#}66}MED90d+Nas4e><#uef3% z2h-@~`IRMl)DgZ5t}{w{@3vjmKQmKxr#+ULX3%`bVmE(3rQ7CH94Z}XU{Pz;IaKzh z4ndZ#j=qhWa@#lhw+dy*%NA4S4EY13JO`mVu*Vz!9 zB;>Xx%IUJXW*pek{t?q=y^^>}%M;^xNxRG0vrfO@%(KEL;t5htPnIv8>+sTaSFyu9 ztJ^v%5%}KAOsiVN?pmUVHhX7}F52;ILhbv)i4Sa2*<;elb@s5V#kla1c78*&^oTov zME*f2ps=>K9J|(4iYtnf4t+q7fK(z<)zDgHN21)~Ekg$WVX5QWQs=pP&d>c&k|P@} z)e^-JpFJsG>=!Mos;8&m+rBs}ST!*rDtG0?X5IJtmZre5?5$4+k?E{)SJrowq>(RK ztE@i`GtE%G)E3TFyVt|V+uLE(B5aw~yJLp*lB19aUSoyXngfg5oTnMt?;#f@+g`tQ z$ziIZi&J0jT#sg2eZdWO_Gbk9JtpUZr~ROm=3ao7_K_0%j*oVa%kb*_-MGz{xpacD zff9yvPA?9Tw;OweCC;RTw^u5Xw7!ds_wQ~i{rW&bQMJ%7{vr$0#GApEdgiqBsmB(P z<(lTph8-*wMS~VHv4mFFH&s|7nrSs$^Htgjys;zqL^do4$_Cfy=1z={B!xa^O3-Y% zSCqQXX8*Gv#Rv+d0#SIjsh|-{1S&>@AhHOB28faYQT&1vq8y8pixR>kN-U&m1k~#n zBgrrJL$4WPd)+nDc2A!!*Te0^q9DQPnU1(S3A80El1hX}BRxq|9mx`&B5qSmJco~_ zw?|4!xAo}m#4%2;uwLuhtjG)T3U_g^z*B4q5EJ!sr$q_|wyDKUs@fh)JNBpa;yK*A z-m`7^B_*_~m$8+T@?@h0eW{!8xc8~dp4u>PrWkU&nLf3T4D)$Gh&BXV3jS zsD$&m??K?tj5azl%YY79c=%R~j-$k_ma=dYU%+x1KtVW^*=WdP&Fl;Hxn2X3hgJ?Z z`<94@5!)+glCtI8^&T!?qV3pzG9=)@UfS!K=A)N@Gdb)#*@-5E-%+0|cO!K)FUxST zt>aZmO_B3iF66G+43~H7=Es#x@=nV<=h=tb5-R3Nw!~^T;mdgLWEUlc8!gf#d4;%1 z3z)!S1UC38{ZELCm_cyU+gCXHfiai_h%W@@nUQey%9r{+Xxb!gwicWnBJtyL>>g6AuQ`8?rs{ja0hIoeowVOd(QELlraRamDN!UrAbK6gHQg;&ibte#+i> zyM-LnY6Z(~TV;;29rK_SJW0uWhmm{sF{`0@?ybFP9l@sI5n-_%A{ud)u7itJL%iS0 zw>CTTbg}qSjbaJoDL(TvV$=K%>k-EyMU1g!7b5AUN3?B?vqk%sH?(hR#JLL&te%Xs zVrO3;%6}MF7+Z8~(hpn8mK7))Fc{dfG|tPKJ-K?e#7=6C^bt)sZxdySpjp$Y4N^JO zxiMvz0NL0}U4%3xCvzo!cE%f*T3EJwY^O7X*VhJ&MouY5&82*O(DXU$lH<=Q31l&4 z!q8|58LP26np5EkDj$tY6Mfcb9j~mt)KS}VEGY8fn$OH}+PO<3ayo6pOEw_MC+Q;d zr&{rlJB}RxgOdPujS`eYLshF6;TPQB>VYeA5e=N6EM+WA{-4 zQJVEi=H;YEC}{~G)3^+l9&A5}U)%Sz(6Zhb;Y~}`US#BMdQB(ruuTsx8wkfYH?eD?vw8xQ5YchY$Tb)fQ1}L_DUmEg7jda^I)ft|6FB(FL z_P!JnZi8nZZ=?u@!yHqMni#N9)d+&b?5WxQ1ctFe8AqH3CLw0YS;lQoD`)x({7lv~ zYt7N^V2AT_PuvBKN}om5ePA&`w6Efb{|_D2A0shN1Yeh$Iu@PJy}k2lKX8Q74oe1W zYilE_GIK~Knn5GpuFoW`q3?uy5}nmzaw0->6K2uViP`=|;Xd1#S`%}AVJ=W$tlbLb z{_bZ}zUpq)A@E&e+oZ|GQq<->+-yYMfXH;KkkD6+dZ@N0uf-^LR}<<~vS!X1!^9aj zsjuzlUlbV`8c{GZo)?Tx$;QvZC}&|U;>#D(QuVI{G;T&k)b3I7-p)}ADv6X9n|w=pP+>{iYOcM^T5;9lhM_h^2!Om)#R-; zTGW!hJ-ub~8+Wr5qc0i8vR*GBUf~GH=GjiipBtq%P!ffwgGgn4ocv}6po=imi%35>vSa5OK?B6d!O@(#XUWnI-x>?{jIx~z@WSsNok~u>FKWK# zqnjJdKii=Y=R2*Biw@C^!Io~PsKPdFBSY2GqpXv&?Fl3$mT4}#d(gh%ZvCPiM6coB+^%ZwHuIot0h z^nE2^*rKN_jnOc4RzZZe_k#_vDt1+l^{CPVP*o-gJ(XTYIgg%&Jis`l1(1juND9E< z`v5UC@Qu6)rDCK*W7F@hJ+2eo@1}oieGISv{qx&d)ftE3%w!iwGrZsHAMNusbzX16 zZU`6;JQ$oD!Z(GyZAt77*mka2EsdLBk;)UaQxEufiV?#o)%@^n3ua05AN%f;i!}Tn zvx1YmhMvcgB;0a`J1>ssubk+Zq`nIrH_3)?JV7PBmJQbjMy$3MMDP#y=8$yO*Vf%C z0mO3N4lUXl68bh+Tb`h-d3#n9XB|xGTK-Wiy2nHH3gJ3Kh%2<76SJg;xd2tx&2V#* zu4hpeSGNO9oI~#MyIg0ym2{mBhPhV}&|^77G?^KNH8=dbg5THbly8J$%PlWwXde4i zskixaW`21-J-C6%*ktM14lOL4SlgWQ$dkMO+=nfN-KYmTWE%Pb$J<^udJc0Gr9o;(jY zJsPsXm`D*-^MyN4xw(S$m5#HRIQPx9-c~9o@OUnyMMxAPRSS6ksLq}SdRHI`&O|6d zLUAAOcu53al}3ToQ6km8pN6BOUGnqRkb99Sag?CMA#&w8Y8L?r!{2tG2q0Iakom?w1 zMSQWGs6BORe}ve?VuR?Fp42bz-%D;dnD%SYGGbDKj%@GW=VbmRL5IrKG+QI@fWp0) zG)1n=ASZV1aLl>3=Kf`~EMM|)wJW0=u}MC-f*QyN2pV)}9}4pEQD7azlO_l&kvVb& zl@YSjY7kyJ?QmK;O4qrdh=q|=+_)v{%^QU+ZA;AugO2pfDk(SXJ#@_|&c!5cVA{bt zW1T(?v})>Si|N5W-dEN$Nv19K-w(e)F)GbM?Kg{l9g8V7Uv+hvY`#9~IyjpV6zvri z5c#frY_LrPaJTL=sa8~7sUhwn`324HR_v)%ZKWajnLK?1MKJgiWcGU6D)ba&@ljfV z5i&9~q$FV;vwfSJT(vBfB2GZFSrlt+Ej!}ADeZmH^ot>3aaU(?~uZZAl3JQa|kDkUdsb6>~;<7G>HXrtsHSFG4b7Fb#{QT~* z@sDMF+&8V=GBoJi^Ga)+p;-V0+s)QIS8A|mqk;e5n-aJt;yBp4woymP;4=PtBTdya zdhJSK`ad2|pROjEdz~sA>3+A4tJM=;?!O~|8Ptkhc+)pWtvG1mp!sC)Mp#W0?TlI5 z6*b%Iy>ukpM_%jQAoiyQ;b&@>iV}I+4I_I@S^`%RP35~>C3?F~rieOdMW+$)DdI!Z zRC!^8WMF17$zLOH=>uAkE$ZQW8D_+lg%`Y1eI z+n8>e8y4UZpKl8b0`k>>6 zWZk#cS=$jZ{}O_*gJzkn8O1+lDLu9Qhunw_>bY->v=&!QceyH*{jXX&EeTu z-TqoU7l>ORnAT5%k{b^g4dAa)!g|-t$>qVLKVvOUu)Wgl{lJ` z!^OL}XB%}amqzUKCZjHmnNOAuQRW{0xukdxznCZzH50b;J;h9&*jqQhcinkXH_uz^dhIK`ZBk@VCU1WT#Izakj$Yzqe_(cul0``RADSea7{e z_nEJljCzPP$koFx_4H^ho*TEUtWdfvA`$e%Q~2<>6wj z#(8eCKf8cFjV5$kCqK)ZerGu+?jXRRzV>wA{K8K~<$GL5P1?SlH0g6?Qk}Kj#A|dQ zA;}&J*neW<?KW1;?z-M?zlNiV3caik0Q(3UZ=xnTE+4@x1x+Z~H`k7)B^|O$r`5Kz( zJBPeS$zm8w52H~WF`hJhO18R~IL;nff3JN>_!-{t{s}0~Il+#LOE*n@d2-9n#Haln zf$bQg4@$rRse#_q)O39Xu*FcQDBsmJ)^a{LZ!#95ogmc`fIMpH>fm7{D1u0eL3f1* zQedGDCWT?u=b4p_|K%3;Zip}9%DQtMo|2S~ev))38f{;(b}zWQv*iYS_}NkSvKwd} zs+6&rXoI6~_O7{3c5PgmAm3T*jC1}t_@=R1?$Tsx_1MHuTYIW6bgHxM)C=f|*e)gR zY_z8NRtlQDdHyI_wyj*UOvizk?W073DqGlo&01&YeHz8RRb(n|b<9yk!F+O?(+#S` zCI8?ce(}}7ePxs06ojS4#~n50Ll7P%5pjXwU!mjjPEF*cOT4;e*8K1F9(yY_@0eDZ zY~@bK2dR_Ilv%!nEK5!6hLdY>3zgU{?rfzeOIq5XlH_R23!f%Wt+*6#I+4ntfxRkA zHrT*&f#;UG^V-{XDUKzo|blvJGyW{OEt`0I9r9-Ra9hlwc(=pW~s;g;`NZrdISt6Lnl83vqU;wQ!$jNb9OslRp z6g7fp+#f_8JI5+}Vsh}oc%NxVa1X6mxv~Jz-EF|D?>DfE8R~anx~%)*q4f&_7*eIQISbKeHt;J$xA-$xIyCyE5QT?W6%Ty#($yrfj@yD;mbbtLWy!( zoGu54j(MTSzICUGHqF#*XA$)2TOQ|l@0AH-&_TdiCH8Vkr}f&!y1Op2L(nO=W*J2l z++5^=jTDLdF4Qxt&_*$H`lIHCZ(B=9f84_qK^4NgHxNIZ{ASC=ovPn2Stcc+m>}nA zH6^Sl=U=39i=jvr`%UbF(eHjhn`Vv_tI%(O2g$^w_8%T2Xdl=^ws-I4ix+Q$91M!a zl|fkC-0a;_tYhK1-Tw}j;lGR8;fI+%G_#qHz#GTT?4Tg1(k2oU3^gJyFV7Umm!LzP;~wC5gU z=f0V)_j{*kl+3>MitJlLS=l?SM$ z>XtU`63tw$Hn3DQKU|ah7}chISSKg==-GM9io8Qpoll`kBIms?&&VVrUA}JAx_s)5Ht0b=lt~L(^Ue%bbM-g?}YC< zr@KkEo79w_lyrS#BAXXXmr$kad~;hw!fytBnHA4I&kZc=PH#c7-6$|(nmeQ{Hh$O+ zMXAy7+p11$@GwWQ3Iw38HXe9M-7ZtgsCeNLQ>=O2{vM@+Muua-_zk_TV}@g$nCSI+ zFPS)$%?_<4i>2t;$n43k#1}4azM3>dcy;8^=S{Q|wDjfBP2ko|JgT>QmVsFLQn-V_H(>(`gebKV*hK?2B zi(4b4r$y|)zq3~}P*8PG^<#=|ZyIjVuroTwu6zk0XxdIBa&BoV?f4r89I!#`ZMjlbH`Y!H=Q6n#kv$ZmQD zO0tp0?x3PIIvVy21TRoH*4`W|h442+s5gT?K7W3I;I>eQfbWrB9*_)E7nPEeLn_uL ziN|YH)QM)UI=L^^dw^AsRGXNU9J1pw!{HKiJ6<9f`fM}wQ`7L;Kowb(VatsE^_70L zM9ayz&xht$*jrMI#TH(lFb;YXtP)pBA1)cW=KAA^9yD$nwjKTD*eU3BkIuhYL4Avh z6t|qwEwoG;y>RHzW!GpH{(idc^(bWW#?5hM*)64wl{R6eaziB0k+NX5o{T#NXWz!O z=oFO#uHLowdU(w(fxBPZVh78vfkm4lRg|3(OdctayGiUfHs(<#0fWCqVNdNWvqKB`|aj9XOB>+lH=tUF2!5FQi$rHX7~%D)gi_ z-u$7fPcc+K1}NXXGa+!_+bRsh_Oh$x5>l_#$GySb8!*}A58t?S(eN?@E81P8vS{Nb zDXuo+M#P&i{Fg8E1%;j*KBTKTzF5t|5aA;0b4O!TA zZM0^bFt(hD2Jac5dh=|y(T5JsCoLWs>@D6Syt;^9H~2ieUjF6+5~RHO$W6u)LQRW9 z-jVC$NjU=UEE0V>GmsU|bJtN;{x}pLV4-u#bDe^MDuIL%(<0f7hP5;GMGn~zP@FPm zzAu4k;^Y}E8~ZV&XS@9ePG$t{v94XPHbR%9(5)$Tod`cWIc~qL7VcLUo}k+k+a=2{o&-P}nCPzV>x zoOP!37-KQjp3;F_fn43@V@3)0s6HYgv*bFhsQeb9IuLt`NkrE898Xi~PeEj7CUjLkW&F>rs_3dw8S;DPyIL~&CqHH{MyRranci55h z!X4;Ne=0zB8E36v#r_bGlSMK8ao4D*+7J1aOHp>apQsGH0RbI%N2 z(Xoie{-||DC=7IVih* zS0BbiLlfjUx^CsI_;P z0#=m%&VmgzBxyLP)#hySX4_}g6LGTpm=BQC$#L)V6z-jt8dGth)oftiFYm$RH(|-b zvD#HiC<}1Nk08^WDwT7Fy(95CNbNR5PgbJsW}K=MjN;~J9`9B5th!(G3HoEy3iNKd z-f+ifuUe&!c!-&nEr{Qpy-nGQYS@Y}If`c*JJ~?`4plUVI*0U9A z5THR5vaJH)#ZNZ`%0A)|ybfrfkiI=2%U=Smi^-2KPhFsMJz|&%=qkw|l5f!Eq^R~E zBzGI`tc>}5rjboCC7e?9Y$Sw^A>y9t8c|+-A}BDNgQHeuw*DX0ZLj;l=4V zoMYjBFnK@6mP#?khIshT+i>kTTQxWn0Xr79kstiE84wZ^ogZVrxRIueY<}5eX@V-! zV=Y|C4_Wx>U1CgmTI0KZNbeS+?W#sVf;Vq^d%RhiX9>^D5s>sd0k}hn+HU*bNnWHN z2I-h1(G2y~+yMPW>ZOrxJ)n|S~sgL%=A-23OI*~+-i7~a(pF4ocn=|vAMQG^{MXS$+t2HJ*^1K z&ryjLRaF}O*x5e91$|X<03i0^R z9+`dgD82t}eG6#(7qnSS)Z16zjnc{T&iX1TD6Gjj&+^+zUOJ(eJR&A)eRSfiVT;g> z?LMmB@?Oki-_LX?$A+F-?IHF_qr{En(;F?Ky6KC&_J!rwLhhm)Gm~x0zfX1+3g&*< zPhsFHMO@FC%{J5$TE`gt?W8=~RG{sr5t4?FPsne8ROQpmZ(=__7PGpws5v6oS9+Ne6~;f!8Q$(R z#&b)?*r_T~vU!Ff-hy~IX2!RKTceb5M(M>^bB>Yz(oWr$;gXe7_h>1h$3!eGk?#D# z!*d-u=tZu!EoEPawV;Wnf}m}ix_`|18QG+)J#w4h*`sWVSzR38L?b=)UWL62a`kVY zpxW3P_BNP{#%4yTbD z65={1%))a?$+iAm(t@KFWar(4y}3$8P7T9WUP*lb^8UO*;(4=%_EckFg(Yg6_(&q$ z!d9K$9N5o{D=!NmQ#p{ z&dXsJxu)Wicp%v=+oa@ppilH6y3P{cZBCpMr0I^0C-g9d(p`48{w&}HTlfH9VAayK zpfcUu5)ES&omZhjNmx-`!$jBh&*p9KP3510I*G9Kr7WJgv8)0|l=gO4(^%uLND24q zbYn7_9Y0XAu*f!ldCs#&YIf`W$2apWd3#LwQfUm16|?tdr*-$ovrnJH;~y6ST^I2E zVE2v_1TTg8HS+}qyp!v&NXzd3A~X8(0K@wpVnYaeYrwMMtC5QX@v;)3n>0fpGC<)5 zIcmxD-V^?zmS_1}nCN)d8~wr4j;7&Oc05xXzD5Q`QHAdpP)dZFw=^{ziL2NTOSYp^ zifp9eCN60dDpsf?3rAb0w>Ws)A|>S7U)u{UB9R_Vs=Is1bBv(q11ZY}JtdMAhVnXA zu9i|dHqh5Y8%+ZQ^%TGjV_WHJ{=0D?w&z+A5`lU6m9XU&R<&EXAr3+HW&r6Bou7)e zX*}6yc0cbrK*V7s83?STb{xKK%aV3V@8SAa zUE&h5f00FG|9hY__!nvGF>fq%G4d%6oLa#fR6wqp*6WyXdUD0tYkl-f?`tqKBYpgbw5~?u=d>o4$hNT1WR?8|OlLoGoSgg-OOhfm@mWvF5!dh|j!*4uAwsq{wQ`pR?alT(%=AKZRKIc7fha)<1enU7wE@&d&zZYn$; zk5nfB@B&1Btxyas47e;rpnUS=NoBn*0)nE8R#C2Wu>6-&a_Y_rSqY$YaNDiSxM9`X6 zfX=2tXx}z5k8_FAd9ja`2j^DCxdi)`!*xcbZILK%6Iw;4pLUzlnW;M)J8xc9SWz1P z2o6Q}OL;yfxV9YFT5E4=pNITAkK<_gv9m?m25&{2*JMF+6-ER%z+q6id2K_vC5AIv zs=1bKQ^V-At@Ac!Wu(xuI~E#{={%H;kX3at@^ixpQPL9AJdbygv#UYUe_LqGzJA-0 zbGQC8VV_5-Te^|1LN((>lCheb1uaPv;+kNeMs(gTXS^<`GY6( z^x{6i_t&ktgo}b{(By*RfubW5KN~YPBqU7kdaI4%;*MduShKI){EL6kh4<8e_R_#9TuIMz8Ap>%iEEuM6uJ6V1L zN9M(eA6|5rRY!hF*qn0!_=7ZRM9{5BQ8^SFBSpFp>3Y4r(Z6E`3=`fU!0=`{k;n( z!`|63zh=)cbb6)nj4Y_f7*c{^Ym27^#LZMd)(O3nUw7Un%zxT&nQo${ zgqT)NUvwy0IPJQ~0$N%tGinOeA7ekEH$4n3*@S zJDY9Kh_l-%(hK)Jc*s*XKX-C5pLH1sq{}fXn>L=K6$$)^Py62DVJt1*p$c6-9=Vms z2>M7#HPp8;=yJ4lOs;ea(Rmi~@V)oJZ@NzP9nxr*ew7#fNOHQ6tpOq_ict0TCg7uJfa$!OVE92@v`Mf9D{VeXf`nU2gW z+KH>1TU=ZjjPekoTYww+;mB>Jii(eZyuS3(t4uU5yQCn2Ph=%f@PYPrYFQMqsnjt7 zHy*=hF{^XUsUekFlP$zpNx9SL&m=Bv;q-!^!F9{y#^;K4-PXRo%kYq0 zqkFXW-Fu08=pDMxXRf8+Va7TvV-H`+;%p}ayC89@8PgOzxZ0$&a@)OWRJ+u8;brof z-W0j14xUTP#8w7@pe)Y3ORjj-;f%Zsn&^;5@fHRhePYwoc`G?5IELE%DyP8ps>%+##n1RuD|E``+ z-xpr;v3n){#|cyxtDQiePhVuu9e<4Fdl?_2rYj5#^M`#NXuIFIqFHpIPbdA#{2{aD z*|J==O#OSZdE;H%EyhONmdB~DQ&xo)Z#r2w&A%<<%)1*cUVZ@;m#AUL<3E$qBOV`Y z`N2>XW584Q{Cxmh2HwPe*s|}WPkTyot7?M^oQ2CJZ;~ofBiHv-sQT+JZiefWlvPz< z9;p?(t6b46>ozSjl|*Sd@41g6h5>@stl6MCIyw2U?=_U)^VQ)%xk0G(dVzG(K^>7N z+xtoE783-81W2wnzB=g1Du+5Erft99hJgogbL;Hy6o^oODK!6;VGatos}NjHAr&7z zJr`Hj_Mu~6(0wzo3;6@P&XGbzi~?8Zq&SVE@)g}uA;$czD+l_5QlfY7 z!}FO0{6XnLK+wLh)IWq9|TI1Op@>VI3_h|p{r-^_EYv}~c2o*(i>Rz4n`#0b&?(y0DDp`uDcal2gS!lG+h6=)@ zy65b^UZ9}J0EWa{5uA%K`ZwvIm5IbAtOzPUB+*+sww08~8*oWIuzNuXYW=?mobLyUbqv^Gp8td!P4m(IQO%NBde|pW3_y??VW{ zU^&WcyBCtqy7yDj3-D4ZX5Ckc-U?!^E*^gY^B=~MK2$MY!_M@w5LiQ{gQHk~*BWx4 z+cQA;1YjLnNVvEUkBx;q%iE*1d$fC^a9c=~S_S`D#0jYSqo9aQ`VEZmTx3AW+V`)w z=YdO+z`a^mQ{xSw^GYC?Q9)3_8;Yav0`g?CNE$hoXSM;?U1YNI`IbQYv$=Ln>!4?4 zP)0TfeTXaIX{r1j8Zi>M(_+)k&}D$0Ub0~eF=3vT?OxEUDr9Vde;nHncf#)nrj$$a z?+2G)nd>R54^6j##eJKe0*<}3+kxZLOf|(PK&P{Mwx|JvQmU*!-&S8(_4i9@-A7_W zMJ?jyWMF7`hcomgl%m}MQY-FHcac_l`28gGCWvq$;wdN;iJUe!H_3qL4F4DigXVLMy zs_!Ei$xo~EukYRF3{8IBxRT}iYx1YtzgTBSc@q3ke09EOuDy3?`(26pst=B11tr=` z+P@#6yf`vhch7=qJ;J)X4#foXy(34a)$hV*v9N#sfU-UwB>DG;NaZQ=X+3TXt78Xp zY$&tMvFnF-2yKA0QhKR(kKD-vgOL~5bN5<*ihmFIX5ia@V)r)*A|zJ@gQS_H%1&?Y zUXoZjBdYJH1@*v;cf-wo15Wkn-J3!|t>LfH#lp;tpYH%Tzdit0f35rK#*vgVVQ%Zy zA3b)q=RiJi7vOIeU74{_9$dSpiMc{@wZ|0HS&PIr)_S3Z?@kca8H92Szley4w_L`u z=U{J`gBFc#|Gpjmy`QF9Z;;WP1aHp*DWC;6@E<4`b8(yhc4-P{sp$rdt;ok56pu6Dz-n zifiu9v!;Wjkni*7zybN670f~kB^?BfXWlJtzyi^#&$xSbG4(z76EE`e<}GeOXM%Os z-|Jnc>|L0KLmBW0RsoenKet1U9S3~=?}6W#&d+lC*V_%k00`+W%p3#GvlJAt?xFvk z8*s+&vMIV{BBgV_))l%t>mAK)Whojt-VFXinNh62H?&;C!{gk%wu2SgRC&PRS1WaM zE(X-*9Hh9AErOn&K2-WHY!c8CgPW6cbMDXHbo)`rC_n%-3(c@-TcHSP~ltog~G!tnDl2mauO z{`*4%m}SZhU>QtKcOm%aJmcR_u;&D@3ZZbp=Qh26uk3)`fGUxRo|l)`51PK_ z|NL?oduIFY?PQ%2fz-0?^-hnSEqw?o@GQH<%pFI~8o~dcKz8^eGy>cZS)QAI1ZtRw zGotQp#jsu2wyPVr+2{N^i#)Ha$l9=(?+$XIpb$uYtpS~HP6A%iO$fFy<}DDyWx#i; z0fn5WIV}h~F&_YOhLFBfucFJT> z4^9qyaC+^^2JlJgpypA-vr@u3Zc$t75O7bT)=V&+4CyI zi~%j?b68ep<`*7Tff+ffnyPgc*uS@WoyQ_GVE2aygS;wO*J=B=m-83|1&c}nRRJGQ zNoqcu`$x|4rPmBWMQcE_$bcv51EA0J$5LIl?mGt{e!U65aKt7Wgw+9kMJ@e)$jk~9 z(^LRLHnMpIl%m3=i?JNBi-v}Vl`s;AVjds!X7J;q2W}w){UBoe1JbVwaN?MWHL)RM zSQsIwQsF3GlFNmfJJW(XP?zWaIl*A)_O!mhPBZS5g7 zkyKCYH%}|M4lRFZU|pWd5Z@d;U6vs0{OfzrgGsUn@V6m(hduIg`rS7w+46cbwPnD_ zYfB_GDTZZ>32-V_`9GeG1N6NY=Fsbq5Og7t7Zzg-2DACoOBAt{DuLZj19-dC*??aw zMNGYPlo07i@0mG4!&3YX;i{$lBGe2KQ6ISB(8^efu`x_OgE+rzE1XW*_C1a)jdU?C1v=phKCxhJb- z&@--DLSF|ho6q~Mxh_wwmeXzj0|}g@-xq+Pd%?YJ_Od-qmkx|~6UqKms~tD!)KURt z4A>bAf`sRXJKG3z$+(orV=(1Iz_33O4@B3QL;n_!A^D>TvL{5qNEyuI&_Mp^g$6oMCOfIZp+g1hfHf85uKG3UXI9cMnaLxW^ z06ZtE%7_hJ{A1QUAgx{x2?;rk@OmV5Rq{Nx>#?OLfS%y@{5fz69E`uu^JB=Ix$X99;b6|g! z4FaC}vgOWlUYRd2q&rM}DJa0clr#;|rqDWTh~5k?xs>ku$L~;Shj79;XbN#58A`A* z$(cq2LF4sng#sr7n7eaz*6vx^xWN~fs!o@!R)$?kQ|?N~^fVS`)u3ZS=N!X_zB|GIOc1K4hgGxiWth~L} z4pzO4dT23XA{W>hj(BuESIf82lXU&$NuokU4qn`EM=Ibpl?RUlxCsDTe%s6o3#-fj z;qJYoqD-^4-%?v06POSs+L#(jKqO~+L{Lyrau8@lC4)tVA{bgFV*n9>f(FS+awwn- z1SLui1(bqhiV})gggX01duHBq=3Ue0JL~?@ zp(BLf2i~~!xh05v(b=KR`034S{Qe(_nYAUr-$h_;w7Nl{5qv~6nFrfj3UsC90wCZF ztA+WXND8w0Br0IJ$eTIbv?FAPxi&5xamwTduXqjdr1zjEqd!<|neEJNU*T%ajE9G)J& z`_;Z5m-Sc=3^9jM>a9kBlM_@i$aG~F00|@zR^Qa5Va*D8G6Y|xZMajhE?R)7QpVwM z%F36bB?Qsag1eV>`x?alGefm0bFpr{)@Lm%j$ts4Vi%Qv)zM8qa)Gz#I}quP(bEgB zX!_?CApBH?DPz_|D9GU6?KstLaXz9A?uHaFK-9@_gR@IVUdD#5K;33P0PcnDN56ip z%B|%wrf=OGa_>hle*t?_|2<)2owZ0woQHVXHu2)KteHj>T$C`^SKK|;;N={IL)=`A z((JpC{{h~tLXNu+FaMYg^(tiRu0vHobJb$F-CP-EoW1?|4N`)%7)^@I)e_(jUx(N~ z$p?;Zq$WuX`X7U^uo%XLt}TW^))=2%x0U1QpSMv0R>k3uH}VSPA@n{hW_3kgPsbIE zzqa1f&T&}#$nfwg(5LBRpoyZh=i%-n`;n#s(oWLv?|={;2zcVxEtziHY##l%LYs?x zAta9U9S2UnD&TCJpfnd)weZ0>}VH#ct}$jd??#!QOAaqVjXYSfFfH`S)# z`MDYje8TMbp6lEx_y&W9YGBZ*8W}cp5tuUJm1))G3X@U^J_|i10HaKR)SS(9@mbvJ zap>sjKP=~Qn_KU1Vmb>OD-d$Hcl%Vip5(1L`$Q@F`G)kRpun&&$43w{xSVPT2RXsj zaqF~k(P=37@8_0v3x<&^)jvPt+7F;1@maZ}@_*)C93}8x8h_cP6DSz}eQU(D{?7Jh z!Hrr<}m(qz0o^=g-|2^RKE8Qmr<=*-?`dkPKqnWdGC8 zf;xnLKuPc-;Pvjv{B>DL70F8fR&@XQ-zo7=u2R0|%}Qlz$0vxJF>aE7`K#eT%W`ep z`P;^oVeW|i+h2gj?x8=aCqgmX;ty(yR36 zYm8}ChA4XD7h%Q)o}|)tV!HeP@>i?c_gyyr#|H3k^_hRXGRI-iloI!UddvUMe6{@= z@IW2@fA&DrFDAfd`J21NUzV8v(q8b7m*y#&ndsW^&-dAU>zOwW*_Z354(Dk$k9B07 zD{zS51}Q98DCg)QpFBvl&3H1LB|E$G7smTfEHsUC#L5-~-ikVsYwdhB)1cz#5kW&X zULk{h#xMhS;q+0T0_zCw6&lfUsweSWfpv#m5uCn^J<-=&^2BGp^l4t7@11DWwkL~) zncw$P9SzM&w4WTgYM{fV9ywaW95CaG%er8<~{X{D*{% z59E*fYEiqisl8%hTec4?kFua2KV&?b#@e9$W})|1abT!m?|I@>XA7UG?AhpB%1{jd zj%e8tc` zZPtMM+ZRKFlVu0?UVqyvKC{ZnTWi8vak#$AF4f7NySK7LpaMy#4O3xKqxFxXh3i3x~AU7f&AT@fVK68E}ae zXx|xO~C9&xc9Rz23o4qcA_*UknXu<~?$5pp%d^Z{#; zwTK?wbnaA6$SEOpR_>emb-pL1Vl zI^fUK7UUCDqlH@J@(mv>LIP|t{qp?GM8e`=#W1gaTBrqsdw=bM;wv?aqWPMqhBKe= z&)hv>{&U1J%(e?#&Q}`?_}E9XWDUYS4m-v&rk^(H?B(JwyeK7=T(NE*vE{R97&nLF zTJMgQ^z+q@O3KOwADRM>u}<-38Q<7-=jT0aY`VULiN0LdzT3-DxTVpB^!E7*FSlTN z=HB-$!ece6cyPi@@z8_w)e-4Cti01_ zz0#d)J8?r{O~LOKyGUAHqjOV-rR7d#v^p z52jP(90>Qs3X^WsI=4Tb#@D9$Zr5j+Ufv%tM#J}Qh_-4Iz(>Yy5o2jHU5?l4ny2-jm|pKF z8S71(lGELf?fP0hQRsP9>uGRNREK#Jl zpdnNq?{cAZ(Ylsw5yo){UGqT(l`=`)#*Z`6MLP{BZ&q}COA{8?`0QQ_I3oe3p3)WzyQ1hX`f zRc^Jl7jIs1?Gvr^{S<}k3V!t9&6S|&e^^|{YMBFho1*N1!C{iZA>>;2ajCypFr?UmJ#wz(Z)vpVgm z>d3yXpfK+@)0{%_m!y~#^hZCHCl-Xff3y%Z^pIwp*6~iMf2m$EF6`7PlF2ZxVqxsC zzL}Si8pT4B1PN8YOmV-^s!0)_Ld&zMOYeUl z6_`IAzp%C(CtP))xk4LVsBBFKMP59K)}q#=ee*r#CbqNMWKlD0)bu4AwkzC6ex5m+ zc5We5TSeD_z*t@^j!WTLZ-(E8PqBwN-+m1EMWw0^u_-Lvdn04~Tvp8o!`0+>rJ6K1 z<3$IIjOM95vW0f+DXelbNwkm?`2t+p1xZpPnT7|Q^@Ky0OVurwA9h^Td0KW%>6tR0 zQ}4Y0J|C9gO|dKC3+`4e5=}?Eul30fJrU@V8Nbhdz2~8BM5XWhk%`y<7L3v`obD4J zW6oz>Z%U|Ro?8}uG)*~`B=6jm_d?#UTBEjq$uA}OchWP0pRlhJnRLBvB*7_1xceGc zPDSxiQ@rD^t6uNET0Pe-C7o4}e68O8a1@L;BP zvN^1wL6f%DAXJnc>3Xg#H%Xu}MzjGp!oI4QR}>jtfgk-Vh1Z`aDjZOvC_%IJKP%w}zN z0{({FQu`!qwirFTYX`6r*=e&SzTAr!tc49pd_enku z^?KXLs`5hh>pGIl_^J|2SNuKZd24f;@y$IPrtSml-aTR*v zFGU?|m)wfYXDseYd|35`9J9RAmytiTo#cDt(HBQUIG>-8?n_D(M?|%V`L0u+LCUTv zDJdW@u%F%3$MFIDlS#=#It-)uFHiu))s6kZ?-RCWIhrw{Av=BNaAl+6OKnqMMIqvs zyW9^2Zrbzn2dv(Ew=<`7OkFjpAP$eyEB!(%GL4zJ+UG-|CsC(vMd3bfO%%{Q@afEn z{Q*+*-SB}ChHLoVu`Sn^UblNrs7fjrB+}cvD#iGc5ShaZy2Ubq@n+~OMC^5V+t zgnKmt`}S>j;eFh?IP!E6%asquRP002&C5}Aqtgb&(@Bn3y6R$WVU@l=h1q7M{E(H( z*)LlxXwQDgKHyXe$@t5QhTxMk?{jOP7fITk_Rm;fx{(#LXEEu3BKz9H)U{q7<`Ncl znu)J-#eb2pGa!u(B$-$fQ&Xh~eIFMxqmEo2+HR&;Sy1tF&e81U#vyST&B6~&y$&~L zV;40t+PinKRvcW8E_x_&idXb_Ui8gQ1AC=@nW;CvEfw`eIVpidANkpM>z|-I{;hCG zZDZYwld>LorZ?1mm8C_SrsW>s@s)NZa1@d}cXFhybK}nwN3zt()hCPxsW){gDrINu z28z||Sqf*lKDV*XrA)G8_B_KJj^DTZ_oyuOl*Xkqo!JzfFE1=uj_i#k7Yo=k>En{c zvb5sl80XcyNfh=&Y$$Lsz&3B<$ZiXhJ(?s>jp%toR3N<*No1i&%D8qjR7USVyJhD- z;ONSICRP8&Eipm+9D_oIfoPLb>P~duBnM*6dL0O{Y^prv?jfDq+hgSvFee-s_+&$s+rv^@>8&NnTU>IzBHQI8EGPZBaV>kt`XU~FxRCR=;~SI z{ILIG{h`Le7ss|G&&lfr*y}b+;SAJP0xT)nAM^P567XGa{L~(2#kAI?9%ro_MX#kh z+}C!9-4HYu3$kFDtn-GbVX;rA}h= zt+~ql;#%FNA6}k$yEHEYGrGg+&GeVwpWILMbknoOcf2h3SdU=Eay^TP z+V{vIUz}bxzpTq*iA^+#KEzLTxGkwk+m=4KtPh8? zJyG1*K@j3+TW`XIDfK1v0+*0YpLDoPl9naxn!3B;fkt>}$8g1> zBs<=zN$Qpr+^un1&GXn8S*ScYJc%fs*Y4-F%YXXHWO4*o_u(yW^_x$kelbhSAND~8 zk@|a4Jkm8_ytjFMam~YLq_m-iPs5oE65H7Fw`f&Tud;_#NR@+#Me=Q}0n?L}W4i4u)NU$34UEIC%(q&+z+ zQC-);dA){-K9>^KgCT;wSl=$2uouaP8>$3H?R4?Yat@AoW zb;z*{SdhXiB_n-bt1sHTwBq14DhN0f)pPaf>44RQdj@8&MFQ0VZ4-1Z>h8lMf~ zAIDwOesb;WyA@gt9>VH^MlF@3-yFV>py>o9QTnlM)rFK*Z^)FqYP3fevJ#wE^m5He z(<0La=Z1!hM{C60F9j~EcTLS1-rVx}JfsdR_Qq9vnC>>gG9Gc%Y{!lS!?}rppH;Za zHLo#-6NeU{=h)9@H6m7FyVVP?`LVt4w=HUnOdfVzroguFKDNH=50m8i^j*w(L3))=*Qw{eW~e|vDCWRzTo5uq)n;CvXFN45oXuG%hl|7Q0!~co*F4G?sm1Q1goK>eB|{VCor&D7 zVoJxpJ-B*KyO-uuE~aqpHJw`O)VHe6pfE>y2G?d;IEc0F!wQ*D`~nL!1_zyl!)^W~|_B#>6e(lH&?5 zi|K-+`1!FHtpL5`GUIjD>2n$`B|i`tmbNosmM|A7{#zC%b-%dAZLNRgYTL!YW7KQuZm=q zlCG5XzU1QAUpmJy#E#S?il$7r=9d*sl$b1W?rSNy*f!RYZOlbxekz$t&BfU<&+(b> zIgU|^&E5Q>KnvOdVbD$x>ULC7nP+H7zHubqq z$KFUn-I`bKms1PW{P~!a(jfin18tO$F!t==cL{>Imcm+9p{w`ghc(g#9nFc9xHB?E4+R4 zlGk+Xi~QftAB1o~zN|Q`MRl5e`<@=JN>5^u;NXIpw{+7IgZFw*pzE;0Xslrnw0)Mi z5XU>S@+_);#@Ej2-=Y=N@R*RRA3G`htu4V*Pg;M=o(}6o?Jb{G<2*C8hUy-cZ>O3y z$%zm9&CJ|mLplcDjkY&2?C=YS6s-3ZGgnuohB*EDX5{AA6Mi`@Ak^9y-sUD-xGWw@t-AqEb@CE`<2Q*9twAOfpW(%`#5jhJ}WGQ^p!MYx&;depy$d z^Y5n=W^BCIKdcQ#Qe-RU_Iz;5imts(k(U=Lak`#FIIvG7m6<}06lC16zqU$W85(+k z)%H~wUtSz!Wxn7kULR{XtWe-+&FpSt2^lqw<`x|=zLG}Sm+JS3=aZzDi@jN|(CO2m zr?4x(Mx1?H_r|{H8HrA78)O}dBTsubkrZbsQZr+3aYMtF=ONRg>yRY`2iYUzj+m{z zHLLT{csr8du^b|eYyZR8{lrpJiN#RT5#IAN&jXv8;6ZI^DTwLcVI=0L*VLhOZf;zz zBfG%Fz<}#nvW^2m0Dh(YXqsZbtl;_?^Ex+rJB4S>F-FYkcArO2R1#f>2*2tfZ|spF z--02}3ABeLvh>^{aNsYMFL|99Nz0kGBJ9VUv-p(ro~NbKGrH9c_wl)hWzVBRMgvGr zA~&0Ymnf%n{WLC~J+jL=vU)0lZqZUUoa;37@`JDdwDu*O*K?b*Ym?sQGJRnCLbv2i z*V`l(d{yLv;?E~5=WiT;zx);SWkX%=) z;n1CFbPhee=oN1+KwP62=qvcUPGTiBslUXOEavJDy8C&$5w8r3G6@Wa)v4#IS_~HX zc(dJEzXf&7uvWfk<$>*Rsrsg+O4~_U-;1mBBC6FNYgq&rEqfZhpYP`gf@-}c-qr1^ zo*SX}?PLs|7k|QcvFoF3ObX%Ug2T1tNmXt1iH^IuONJ7~*BPJH`KfpD^ZsU_hy{+O z33IchgnL#jMNMTkaEET;2!+q{d+lWUm7T49LR7ZMirtcxhdVOtF#9|NmX zYae5fGT79`6KG904U@J#RLyAQOaxKG?xI7$o@mLM?by*;i~QsoLE%fm%eIlp!MvZ6 zG`V_hZ(nB0eER&)PSvZuug%YKIX8%)HowS+`2ot;i8)aJgl1FJgE0i%`suXRKh%tF zSk{ca*P`t9xZyOcm!Yg|C)DTL!RVb|#?hBP%5tydSkr2-%;?Bg_vsz)>-;pXE@uoe z=RWVs_3)h5uq~f%@=KZH#wn4yEuwX0l-_sn$wCw@eI|*Pn}RLQvQb{9>r9ueE;X7k zd&av@og^K|sT7w?qD?jP7C^%3W9uL%y-W?S)K*~)c|>FA#W+np9&1gz40_~#D!IrX zX_Sf-IU%8nv9?&Hox6f8w0||P>t4N_W*={E116nXo+mPz@WrarQ+1eufs&k1VjjB*tBK@E8~D>g?u$}qdNUfOAYC8r;uiZQ1r9d) z$l!Cq6EnY^Xzq-j8Mya?=%G8;pV-!ENsY{J`*t|huQrS;JdbTQU7U09fj*WL@!NWG zBYFPv%UKOOfz&>^`OmM8;67|JE`FkDb%C}shcqG?rkW!!Q@u`}vU=@KahR!(i_!j4 zlHt2%b&GefBLj%x>xb# zO}jOw+xwA_nJ?+f;*8)M$1Sv5Mhpt3pP~OeaO=jn)q(dz9|nxwaYs66pACx4b<2xa zhd6vT_QY=$mV@|5alDtK#h`Dov(c!^G}fy+lg-)vEC=R}$<3{2LYnB!io6F-X#`g> zDHg}0F4c^}*@gPiQJ*XFC_vk50CYOkLKLE>VZG z9S*WblI#V=e!{!iv&V71C>vY=Z8p1AUT<{7zDv??d;)b)ofMv()z%+%wIe$bCp$CA zhmDI~RX&mNyEq&N(LheaJU6*8wbdhjn%Yv~vzs$UMpO5uafiQuhi!dS%Ap)+)ro-o zt_E%eG0?rX`8{BhVux6%LPjPlkFp)v=7E!EQcO^0Fym|WXqKen{ z^EMirCgB?lOV&jG=E<3%;8)P+Mi8(}xi?$B@^-Lfik(#%Yy{ILy(*8~1doX(TAwy6 zRCu0qEiUixugx?f!*l8EIRiqUHgOQzRP_A_FO3J}bgP*_;!COXl-DJ=TM7s8_J8}l zKnHwnu6G2;RKzX#DbCA9o{ARVQxO~9S6QKr)el50=M%z3{hfPV!@}x+FBJc|_~`t5 zF~R&*JS#B7cHFVnfd-g;=g-GESVvztJ_xf|GtR88U4rgr3+OwYfqv674i)pAaQC7t z2?5X=sQ{g%2T><3b++CW23uG4Sn7Kdx*^L_P{9N{oIrj-x?bC^C~9!ZL!NPg&7I%hf2kyf z^|S8$ypF5cD8!KyFI#?f5vOjEMKfUM9}DP-B(|i{*o7M z-_P>eg7_Rdtjmvql$q#S-_9mrD;e(hyf0Hat}6BZV$X=Q?h3u-=3=C~y`sz=PI@U*OE$LeGZgwdyx z*529jQa`K3|2)=KY3X9sQdD4aE$_sDsRWS+%h2hGFG>#I^IY-}W040P-Z)u~h(cRM z`S${lz-VY_B_+zqGpUoV^R)0Tg3T=c~VM*PpN^Opvo2M$+yV z`(8QWj{d=;kK z$mc`CSiSh^jhFz;GhC~^0uF?&`g(Q^FGOZwl&~^%MWSR6)>f3LWewV|knd+$^@jkZL{oTHmqyox|!ZT`WPlruqq0(cJ$5&j? zU!+Svtnf-RiO>e;CPX^zwq|&D-=xW(CiLi24N?wO@!^%VZsx`v&2o!>pKyDW7}n>w zUEhJWl&Cf7L9f^fA-r2h^=QZ?!{x=Quf>V4Q%h9hHnnGt^hDuS{o6~w=B7}3hgUB9 zrkOWuQw}@pOvugm$%5|j1}gbgpR?Dr83UP*sBxk%#X>;uxHP_U<6L9huQ9Pt*zOle zTT1Z_4v7cZu1aGtb|)~&SJC)(WpA%-o!~*vaLw89N5OU?XzW`?MK2t3l%ku$>uY7K zI?+7yelbK(u*l&+2`mGTFCk&>%f3ucYrV|=Te9uuTkzFC-^bHju7O-PI5niD-y}DG zy&8LQ-S8xaF;aR=KzHc8B^SX-V&L6P+rbLydI9l6sd%^p!bT3FnpxK>em2dl(M!}+ zWSV}>CPNC@C(FW9MrVEOGVw{KNp9Sk--j8cH&30#<}soCh_z%9w$Cw+e<-SM~fOrQwvMnqAU4P}FyK>s zi=y#0nc_w-vnzK>@LpM-@7Jt-**8(Kj)4Xq0aT&G;^7=P_6P!Cy=s^J2n_E zujPK(!?lH2SkwWYytw_Cw$$A`awT&s&_G`Wo`5S;t+L4I*CR$lN0czzZXr=Y!2mLqmm2!X#{td zzJ(!Yt)?Rc$Y?yqqD&}ihf+=C%ZkVj9ppr2+B!3TQGub>U{aD-&|hIoEMc-z{IykC zLoyq(-Xh22IhHZb#es!+DGw`3>-y8DPfHk*VN@3aV=!%dt?1&~QK5?4F1vYNtJba3 zqt>=h?d$Sp&b?S7=bQ)KM{Om}bG^dDs&%qsS^Tcdr$lw4l*a`cY&$le3#%)_Dqfz| zoF8iyTE~&AaYml>_D_{K8m;pAS+nQRh15~3P)YZk2){qnLt-8kTALU+ zODk9+I|>E5k(;!I8-lmToz?vHT&EDxb#DB%s+yT=k)K0!zd%zr?M2CIDh^k7XJ1Rs zdje{WDeWry&=C4_KeX$xDquBw7E`UBc6)h@zFc($^~aV?!Iq{WJrn!B@*q2}x881= z_;FBSA3KFLL%&4e&;1Lbj@*oDsXX_I?M`~5qtkP+dFhzdGzs5*mM_nvAN>FxrKpaHFo)elOI+0ggy)7;8NH7S~JlX0^K|LV;vRm-5jli zj4CB6-Rb8#jAnMXXKSnS?!8=O7_~4kbz){_m93-F(flgTdvOA9G3ZD-L?!!r$(@7t zuc2gYQ)q^eydY}tg0`Uq)h@RrMM|f=UlfVCnq(W)yW zS@kAQ%6;iIOOu>os=im>X0JW{e%`i8a&mmM!U0%)ptM+BgDd z1>Vjigbaca^z#+=i9nYWjv7Y@+zxuST65V*=*3*P3EtF(_u8AOuoliY3sLL1=_4ya zgT|7pqI%@Ky&MH?VVd%stGOw*4%O>1Td_m;X#EFBr0MbxJ&D@Ze$c#>3+lvhC{V;h z<0uQta^*qTfxNu436443jyDaE#y=azjuhUE&V^j1erak6Jf9YhKEoSL?;>z-S=YHp zq+1OTbH?o)Nuwz4V_jPbG4Z-}b0kfXNq+z>HvI5deqx!5DqEvRt5b3!MhiYVu-Uuq zU2>SD-pn8RxEL1}az5H^TsrEqXo5wMn{3526rl(ISUg!<^Ueeh2b=COOmd4fSfG*J z2Ei4O5(MsoS}t2n7^J^{e zZR_0E4-RQ4o^FyIk00yJbuH~09(D{(F(a^%NRD2oO-dai3GcESajWk#QTM_RAW?lCr9hfMSU2r+X3|9* zThh5rw~L+L&Qcc;1GW2bX2+PQ!b=jL#tPIwrVJm|zR<=JQV71jw^nh3@QHjIpV$hY zSZL4Xru46fqbOXnBB(+JUkfG)`=1|*vZ%@N zV-w`Fb~NUQ0-O-CZ5(7{Lm&$UA4V0h(T^QV98qX~?MCgDT3;F(n(s2xG~7`RPG}Ci z%H{5raN9}ZPhdOXyH4`$w}=}sNz^9lU443~=A|pA#g|jILsZs78|R*tqqS6m=4je9(~n@o z_VKBvnWmso*~x+IT&S12OtwoH66#b$x05_=Byo(evUc|!VrK?0qeF$+aZIt-=Z{I?dFfA;J{x8>r()f7w&>*9-z+1KRj}n}B2Bsj>Y}=97PtRQ}adgD$Jk9C+Jsj`7yMd$7v_#t;Dw zo)>(7VMX?26|gc}AO|qOOF4W-z;rbT#?qkbAa;;4%n_+%+wQ}A-#=mvo0^*9@HzCU zsQzZ4p0m)kwd5}H6JYXLLe`m1kWT&o24xAK;Dj|W6 z-b0)SFrb@}4NR`nz99P5f)Cnoy4kqUh7I(Z5CCS%$hc1EWLa(+e|zZD#`V8(5XNF{ zA1R_3ZV2SBAc*g##a2W70cSJ=HQZ{TZ*ilK2dl1bDFH657TFBVmfo?uArb<5{dm{E zJvt12V>uX>tn172*cY0P(VsUCG*CUVTjwDNLzsd_ z_Lp~R6ymE_$D8CA`w&(~n*nxL4j8u{06st!a*@5ZZ;{h*ofNRHhDgjc#|U56qifxp zzmqP|6SVCDpx7Y_ayinp^IZ0Gzw$WA`wk3SUAKV&c+94*{#gJIh*L_!6?%{8rO<_E`xCN9yG5T(^}TkWV+q%1Xk7 zOfDom&WUCN0L@KW{D0?;s)jrq&YYjP>8}-R6&y<`9NemU}V7 zY)#Sw!9Mj**GwLznbfZSxvd{s|7lCy<+|GlDsCrkn?mpZCYMZ5DN?@ zi<;oH2upycdGb>iDSo7Plj>?}gE@X{wrUjdu4`a^ua-T`4HZfxJffz^?!5@e!IrTV z_2X?UB=9~ysWc1e_!sn882d0kuJ{3rX{ZIY^Ces{sO^dI@kf*T!SB2?Cr->-OAQZ?Sk`2X zNG&8I1<$UpBv;Gg?%WTY490C2`f_%_g(>KVR7Yz3)iWEaCm6+T)bQ}8vzaoX>XTCN zh?UoL+;SdP3jn4}P&+{|aH(~@Z$z@W zeW^$E-gXu)gCxu66~301EL+Ae{%KXMfSCtUreGNluLQ?vM_oRmz#yIrSX`3}(2x6{ zW0iM#WPykxps-yUCqp+z##~=s7ofR_YD0JDuE_G*e&1meoKlLl*a5flxK-iXn=KaK zKEEZpT4YLca8ej~u}pFp5LZJ;QG zOv-f9vI+9|wpuao30}S9hzoP**)`wC=KOh%yz%m0U z0NUk{W%#9F>SpcaOBZ`grLkCSP4E%zjOM8y_$O3g)f4z2$k}a^E)+oG)JcF0vzVXgm;pS6qyw;LfQsQbUWnDt z4i@%XuE)Wh*Yjx3!}{*pwJYOV7Yus9mV?Fq_15ofq$@W!T;Gss5ygrC#UOsIF;pIw zKrXC?R@C@C^gT8bv+orHZv5UyJYH5@z^k2EqxmUjmkb6jfeByRt8s(nBjaL)a2LY^ ziO|2nPtC*L!zNAzQu6)_E8vIDpGG_DX7ex0)<%-&Bj6A=3I%ZN0c9ahZN=}~ecmee z%5M*>dJD$gEb1N>LZb*=HP+Sln`P0SvU=jbT*~9;BmXe@yB7Op2O^Ay5uo~dO~G~# z(1oNvTUpnM@cj#i*x0HA*|6zzep?Z8_P6E_fbV-JZ9Ae3`G}ur{zS zfsDd!iU6tGmBd_^E%#me_66xTtqbt_SpZuOl$1Cov;{0!r^MzLaz{{n8B&PwwXA2uwSv zMK1{V#<}+0o?p4rPfN}`zRgW|^KToE;;21B0uk+>W#q5XpPo=31jBnMJo9Si8iiT? z_2V-Xr45@B;n*snRj$C{80wEWj{I2nmAUZ)`X;U&8gX54pO!vq`aW%SuV&M)0#ed` zfW8P)v0ySl;nhP{ULHgvpwe0rRaZZ6S4a4FU|nmMuCqHSTQ`v^N;Qlkdm$gZ!s4RYp2?Ui4H6RALZp0^8tU$vwU^7 zeg*=m=L`L)sR|G*=OH94uqR8vz7UyL;i9Yzu!tcf8bJO@ZKeI~UnmvZe03a*ULs}$ zQ6|F=n+Kpd1F+?5GKTsA%i(K&1^x|f-1*Q`(Vod-o%jHp5p>Y^e1N@cnZ6DEg2@!=N+y-_8K7mLKc%7jr@CKlX1?;sEEP>b|U|$A{Hp_{k)5Qa5 zP8J`Zp15jYa2TQ7fNvnlqPY4EaXxcLH~nLo#y>^j7I1n(_Z?GdA9r3^r17q;EatRX zZVmyj!^YLyT>MT7_~Dty{1@x}pYQd5`OAOJ9sY;?`0%CvSN-@X^_p_~%cl4Lhky4! z`0@43H(lvk?f;Qy|Bw3dpZX&l-y4UTf0~Q`!5!)1FuWQj9fd144rR&j9_ute=0osQzdFG5|Qa7#RKvEZmKhqZhO1f%L|=+_)@E zcg1ml?BrDjZM+fyXlDE=mSFzlqy4x1h5wI#`2*i@ryt>EH+D`0h651VF$e-t5-Roe z@wo&?O69x>&!;zlL3{zts^tK`&GncW{Lt7K0?{0T+gSkfF7Q5!%rANQ`L=UWoDz2a zsQ*C$AYBF|Umrj_$vHVWT}LNsqG}lA?w}5BC`NA_Y+-+y>9x(NhDekHq4Ubu8I!r@ z?;p0tA#}~^GCgB0266J>z~n^az7S5y=lI6g=I=LB%ou>?bolao^2jnFO2GM=y9A_| z0}#muKzv61*4xm~u(Ud=$O~V`8l}%2Ueo2C^uhFu?7;0j9!FscSn-M{RA5j66oDfV ztg#Szg{W$9xi@^+H!1a}XGqFy5}15xn;igpnp5NQn>3#gMfVJ__e$J4RFtm z9G1wy@ficilMA(19FS<&-`?AbP}AYI2+PCk{q0wTb8dmmQB9GK4n1L?arLWczAXjT zc{k)r77%Kp(|E~l&qYvq5yB5q3rm1O<^HAkCro^icp4BmQaR%)TKHGDV!CtFe?KXk zkMv*oWbJo|`sgv0TYDWs4TQO^fk$*5Ah@`hYW9?1AINln!oH41vz|8Kde*J4T3Lh8 zI+=l#s%oMm!vZo~ln^HeBSiCcP=g|T5w+(!0Q~sB&>sJM?m7&b$c2n*gS*+1aDHZe zg;on3@+W73)3s)gvW^Z0_a;m#H;7w%5f2t}I)>1e_;z3r?mzc%ciP-M-bZuAX;MIvTe8j$;(=jH7Je@k+T@C zi`a<2l4t;~%>K3oi)dJKfSQ_Fr8lc8SHX>dc?=E#W`J=N*E;a&n+d@EM7-w4)Z#V` zz^W=)TObc1?XAnqucClSz)$y#lV)x;#T<5ziejkVG+Ji>5&&ZYoX}=T?~RXN(v_scf2AYv{VF(pJE&O z!c%$mS_}k=`p(dL3Qc+lyw(kfY%u-B#&V-*Pn!W5F+o6Q7e>hh4wR{r6N4cd7>vUF z(ok4R0WjF!sfEbGMI^!!NTO;gEFVV&evYg?7Zh(cZ-(I76`1DwQX>e+sYZly4tyFX z1dylgn_q|=yGaD4cp`3t?rL)dQsQxmrZ{OtV-%Noask1-qFWas!bM(HF9DDgK-i>y zWAx-()&dXpTCW4N+wSI7DA#Hhz}v&?2*4@bKa602Q1nJ9X7dTyu8F`$FX)GMQdFEt zss%3N7k(<536!k1rL`T>fTGFC?%W}k9AViUF7Iz|p8%0=d^9QrgW-_a$RgY0Q7oAx z58|Xih%nx#XJu3PJQUns6*xON3CO8;%WcIx9uQ!*D1gWL9JDuu!d_7Wuj(-vWhLV_ zg@g-#!2GwnTVvQ>%H!SC@Q?~+wl3v$2}3TobLUPzfSmRgZWWb<<&Ed^lyxp zgp|4gRybl$04FK_?Q&;vB^yNKAwY=^Uk?rrMz3Xz$6x@GG?gK&olN`t9d2VpNyLZA zFQm3Efpi6?D3yck;QLnwE-7NuqApqs@y+9iPS`u$H-n&vQpsg>ot|YYcmn`r2b*pl zQWN$xfo4>+%kLPXm=6JFG{iQ$^duZ@8wUzJ!S-_xFtX1##20u95jB7kwE{5+P=fyc z1~AI*8kBnZeGqLM-Bq^GjA=RD1qLK@2sjuqy$OC1%1=N20_qX}hA0h>8+&*YM|hrd zYqnkgY0NgaWTgE9WzC@617~dpDfjYh$W2G(VYBZ$3N(@ zacH@6%HtP;VNX*9F(5%Tj0cAZdvWV?DG1_?(j?6zim<1n&0;dm>yFM%#Z;L8XE2Np z?^kox*W0&cd=joOh_X5&$?vnVr{SkdBRI_ZJ=4pnghq$}_aN@u9WP1mrElGkOkrKA z5DM&jc1kn!J&Ww=Bqjod!g<>J3v0ZZQK3yls}SuHJ(i#NG(5% z;|(p$?(j)a!h#VQ9q|+?Xx0TBa+>fg)F>%vQB8`TJ&W=MD<3)GS=pmMEnd){<4VyEsLN29%|b|aEyh)d9OEa z3J>G?X~U*@C%3-1vB-4Lt{Nus!oSJKpG_6G9CSMHpQUXtRaci zSPd-xmwCE$0I?21JQ0$anJGZYNghqxv}K!4DQG=5Vdx&K*urai$vWp+X&ymfQ+K-E z-m|uynF1jQ?1wGqeT`p+q@5jB7K$?>7PxP}*)#)FInG1);XY14D`gJmo?zlz>KYm@ zS+ldTb!eZ!7^AI+pWX(+o&|bsc$h$!Oe%Q9!fm9Tv?-(kO2!r-H0MIV&{~O-P_#i? zk&CE8l;WxKvv6dFXC=AUpl9_O)r71H02ci$yof$}=V3hTjw08q{KG5Z4JgvGfS?Z1 zfRktdg-pxsy;EZW;SLUhj~bB6)K&mCc>sa&3(e>VH3&FZ{oVnX&47aA2o56T`%CZ& z824%bVgZ)1eg+TRndcjKW)h&Xm#icE_jANoqQMOOR78=C;E;p^`~{q+)FZ)k!|+4S{%22yI2jn!7tZCqQbltaEFRTj?7`?+l!-7>qcInUxa5 zqoX-l6<2SoQABFFv^6_0uNd%#zi^TmMGg#zeba8EHfRJ|PI80@sO43FU)lTV8xUQg zvl)#Aur`CuaR{naAyATU?{=z{$}u-dYI{wHgG61CUiXhA7y6ivJbao>T4-4*>VZI{vYp)(*UTUbaa6m&3eqWw-1ghR@U)BU!I z%18H9KpiRt;FYn+*3DRob`}hETi>;K*=-z5-OZ&<5J)Mz+U6 zj1-<-ZVa35HSl8M04pd-T=7u5XwDws5$9E}G4sJU*! zTv^!zbLNxBAbHJxuic^qBY8&wDHIR`_ zK76>amN|%GHH6NXM=k%X3n-O#V zL{uo*xwg*0L{HMJtnE~xmSnv3A- zR*qNb-U3N`5Xh~e;W{~~FpMFU59wB)KHCkHOCts~Vs@fO4RXA(!xA24T`)lL07Sp~@;W=N zNTV7sl(4E1T;B@HFf2K;z-1pqrV==xBwvDP_lRvXk%&^M8Q{fM!*>$sYxRWDwWx4} zz~nv`wqoKpxQzyY*fnQ#Rzcs}UG+ zG^S~0wqCdv#)9)gV-zHwY-o!?G=Ih-&5SaAVjWFv&b8{HFnjiZ!K-bxUFxLXHlobqZ2l)J%ZLB<8Fz+RcriIF6@xT5_ z?*&2TuM2+vv)atR%y;?U`-|8(WZmrG_;1aD*$iFo7x&abA@|uV#d2d_jaDzxROGO}ihCf?H($k=olt1Gj6o2O?yM)nvbDtd8fL{tDY4uJ4iiPhOoq?GL zOMv~=qTGbP#k`J2F`$y*G8zK}@9wu7br5{5xc;*41 zT&cbpe)u;SPzD`7L8#dFp}szYb{2hP_r?g2_=kHSxtLo)nhe^z7+CbdclrZSc45;E zwFuq-<%9wsM3x67X)>1&{j3}N5kz~%yoyG^_Kit3ppovd7hCgBK zX5J!))<_G}a;bHb>qASU3FfuXJ{EEz>yE5d&54s+LoRn%XNhZ8JjEui1!YUA>15H2A6bPQtJIe$u(}w|ZLGr8r*J$%@mx1D>Q? zXWv6q*5u40$&})4@@#E&nd}slmX*zbZ~9uX+xD{hAsd zuLPr$Z3fCqp*7^yojaI9I7&L{?Ck85P%42#?J4oib6ho~rHOzGe>lS68A=Xl#S2_ZQR<4_rt9JZ5cgfN3tbW#i=3>wTl z@9)^%*K7ZP=eK8mVVE&r*Y&wR*Zckcyua5c^+7{2^bRu0*8=R0qqtdl-((<*)d!9u z1R(7G^5ulE5;htX3sdcKv$8nQ5QMQ?P%8up%R=7g;@tC&>TTP$sT&-*h>JVsxBY5z zaz2N%meE!1iqmF_cyU}VZdL7b)4>Jcg^u-;H&s+sMK~q9h0oUU1Mu88d0@RNu~DpA zwG$~Y-@h6mrDI^w#L;uDSncNKmeQJ#n7BqyPw&GU;RYw3Et6TXZ25BT@^M@z4N72S zOpJq_on3Y?C?W@Uy?6tU$tdq|E+HN=%R6!xi*IdVkZgM_YC)yR0c*fxYqr2EWg^Kte!(QK(OITUh` zsH2n%n<&L|LTCEyLQ_*yr;v~kCv+Vb_Jtn6K^jEg2^fk>_(N1l?<6T`IEx&oQ3zY_ zIz7E&esxAhMh+9YgGn(RCvT7^boLTER_VKwWb&o(3m1&N1#1bEAb)ql2(~am$?>0E ztIxgsR4TU>geCtmw;n*|On}j737{O+$;qi|TPBZZPfT28;4N%&R_Q@ZrnbJmEr#H< zzrRVa|A*T7^Ou?sH{o&H#GtcIp=kV|UGVtf!=J#hZDFbsa;bVavikmLng<=kS7-ft zs|5=dsDyW;;YL1Ez35eWab;DNbvkC#d-&rsC)8aBOoR$J&tGTu+_^ehT8{-^dU`m> z_aczS|D>mbzbRICw8SGY)YYB<*V^HNNk{Vd%P!kZtFg$S2s=P+pY0etu$Nq6d1fp(s3j z)^0Mnwa<`fU-mBHGvZo|z9s84HGhI1300?S5Gu3vswZ|Ln)D@aYC_$LD~BT?XGAa$ zTJG3!9vd{>d#T~EXqz;%JT%DGTD`jGX}o4+TwEK5g`JYZ#a2!(Io?<%^6m#vqHO*K zc(KmR%;oas%it&i1mW5DkLJ1F^S4HkqB&t5#cJgUR+P%prOtY+!l*}&c0rN086WK0 z)PY^F054p(X%qF?vu6n5tMCh*og8Bny4-C>hpUjOJDq^U`#zs|RaS_~v|6SV{EcDS zn|kHSF~u{}Q&Xd`BG`9qGBOQ0F2FMXQQoi>gH2B=gphPZ)j+RT&S{S ziPvbAU*8o(aV`dm+csA|ir!?o^Tp^oj%a@h{>XuD%8gpA*IM@)-?cznwPGM(CBT5j zzHhfvCVIboDIz+J>*_X~-8qJy1<5GRR~3AQAI`w_nHjBlHPLq*6#{nJn0RQ&cF&%F zgK6FXSP)^^7M}ise!&?2ryLTAgkj+VQE-~a=S)f@-VlZ*xHVaOZbZ8DCB@!83+Ynm z8Rh4QZ&9s%xky?KOOO0gz|j=fl#koNCT!3FTTBMrEmTSL@EZTR^q#^7 z2$xp5<=m~)TUld1fFWvPDA2hVFaA&kL72$Qc_$&CivjzzO-vji#)UU&tXNTujz|KL z`0uNYgKUs&aP*QLcqk1kPrPv)(u}v_LY7#RjrxHDtMoK@H!o=97$qI$%~s2`u2NGs z=WtI83kxFO@f$TYHDlbH@893C)xX~sV2FM#0E`QQR+-%zgxc@tzjrL=RukigT6Rqg z6EZXtEi>qqO0QnMa>gP4#t(eWWlSB5EU9Jw0AnQqR05N77{l@82Kcw*EZ;ZUS%AJ$(3(L-eBP z1&Q`cE12^0v95a-=<+tB{W5>rI7Qk{OfWBX#;U=x<{wYF9NF)6! zD01COS-yO^>*p<6w874*942%dlbDm}oCl#WS^|wjd|THU4Uc+(eaIx-ajHnwYV&~m zptcc2k=sVUURPsTQPGz^-v9FGRDXAOHoTRsn?nzJj;|zD49@hUV-cE{f`Zu((6#nM z;D}*@Nag6iaKslw;r;vfjSp$Mv_jhPG*w|qMrlZ|txVFdixw`t0ehvI7``l{V9D8F zNGE>D<>xkg)e9TD{rcORCg7Ypn4O*N=R{#Vdi3aOnsZz5o{d!($_sEnLQY=`ulxaj9al0=5X^04-Y_m#UpHBW)vf?LkfnnpO!aJ zSl*d0K1VHbF%W_m@Mx77P6%Yt6#zJ!PIeZEnXB_Km?}1^p(tVvi^UFzZR1esxq+7@ z761cfw9eso?%3-*ZyoTFoJ8@Xb^;Oj=I)u?v89FC!;@(9SyVrXYT89wtSBTAeu<4C zy1KfpSo8-yyO1Q4J~2_<=@yBcXkD;|q=fh5LfR{p(L9VhE}wh_pBYoR*=o302Zdz} z=ge)sZ^M){%noqxLX5L6&CSiNKa;S664KBy0qI=~2d;xxTvSvG`axU=|9jffLO~6z z35hHZkG%X(1&l1!%w%XC2we$YL@6AjqDK>g_b1_&J^2Os6G?3 zyBe46bkqHOUSU~o*x;Lc$MfsbIXp6M3mEJy6gu&-3%3Fx@J@Xo69Bq-8zzp}UcmEm z@tlql4shXq_L)x~KMI0eyV(f|g2`*fLG6=)=;SAka+`X<`ElXGMJragWw;$XmeWhB zBTyLnNU%pb>Sl%_tjgQvjQq@au_rcY304g|Iazxz;F6F)SS|z3KL!K@P*M{UkB>O< ztg?pj0T4L)EG8vHG)S<7GW;h3Re_zt! zQs=xan(^=d(f#K?w}1EDcZ$l&Q~(M9Xfap?9V;yI@?`_bn<^F+25lik0-cMm($dm$ z6gC485pLh{QWM@V^+Y-2l_R2;@#1)_njslNi~a*CuGAY+TaH3izwm`>)Z@`FZ}|yb z5hW#OF8nd!2jX;yz8YZ>J}{RJkBk_KCME)rxgY0jL36rOl;BU#)lzClT17*n2Zt2%A9)lFv7Y`I%m!tep6GE=&SfJ45>^c6l;d(&$DF~zP>l}q@T@Y zFR%A`uZ{CwK>*c?{>xpr&8e;B8`S zT5{v;)~s1WWlBo5v2cx3+pA7@NA|iF8$%O_$%7P|Td3ij4wHsQz7ES`2z-E^kJLg_ zES6EhLAb2(FXZ5F!=3|g?v>w9U%G6WkEucRna?%^p~}kIg$pO&i;9ZEZE(A}pVrG# z(e=49M0Lg)CW-v7K#e&nT=yO^PKW4`a~G9R zAK{t46yw2_N{WgbQ`4+1EnCpINc|jR{$aEm&SBym{Q7zXhtqmhv^{F#tS}3_PNW!9 zWEUXExtY_yEIwq5lvR*C60@mOzsh0)HK-_?R8xo0C^?H(D4oGrW~(scn^ME9t*wn` zpEgc1VI-r=WwZqM$q+hyDRqY%SRaDbSgb~3^3DYZGhBkvv3Nlwu(F+9yu-AmrDZ9y z1I5|a);2ph^Tv&jZ-->Ea|eO;G4G6uF$p+yvf=Sqn`@g7i?2$?Qo5$d;LL_1;N~8P zpls`|Nf?|t;H|yTgl;mQL-eu|gX^G6pD889xXoN~ z<1N5%)xpNBSumI;^{S76ag*TBmY_u;7qHt=5}|<9NU^et?qv2sK>0>PQ)~%pNDQW~ ze5^Q!J|v?-47%K{1+3%|NPeKJrpEEZ@#j}F5Q!`p!W8vVh# z2L@cbE3s2j!6`x9j`0BPX^?17`}*1faKPnlxp%*fO>A>Z3p+jCt(e7C#%jg3`O4_n z^Cg;h8X6iLJv_1yRo%V4psjk)1$)sGjvzT0RMFJy7cXod*Vd~0fi`^|0jslGQ&9Ns z{rek0zTU9zn6kzSR_Adzdr-y@;^MnMeab;sEe>Xb-`Gg?_b)4Qt2xn<$B>Va`_`Eh zwtxi>BYn97_#ZyS2IXv^-8?Ya=tfXC2T$J~fGbQLQ8g}X?cIBMKZSx#&}kr0Sy>qZ?{hwT)3vv6-3gL}IVkqk z^22!=zXWI|9=~-!knz*pl(cUsD-qOAS;LGol(eDEJW*I3%v!$X^e#dT0W_g028;|3 zCtSPc@?plyULr>#+q-9khd@F!&Bcu~)vX_pfKW~?z#C~`ztC>-Z=by9KLBu=L2xbB z#3aO3FJM-6kWVOml%q1_FE1EX@W0>Io|=xsmbuQ@n2a&eH{#KKAu-iR^|otT=C8d}ij+(&?pG2&l`gg%AT$y5HVD!MYJ;xFm=Yt{GLZ zlnjrLm(z-FLnP!KemLjxlP7dUisS5ls2aqs>f=*@nX~VkY1SJSxjy@K4bfq!ojo;m z?(w5XJy_(3iv*xT2u6jND3V~vP)lp~=f?Cl2!E(RaUVR`dH3EuE{KV!DKlc$tXYCP zr{3skRfJ4eAXjBXNB=kQ^)oOd>tv&NXlN)RlO*zNB{m1pujXI=u{a?qDGyCup9+Nz7}DR0i;Ia5geA$M%1Rz? z{i=W7bVfXFzbuoQUzlM-@?RhL^X8bD7yt0L7tfPNs{A+5O@Ab$&p#jTYveoOA5VX` z`Aa#*Pr_0Ob^Nc#)*W($^T%`lKYOjopZEX&2mUoq|2IQ=S@!+bOKG#jWB-!R`rh3( KyNY(YhyO34E1GTq literal 0 HcmV?d00001 diff --git a/results_plots/.ipynb_checkpoints/BAYBE_heatmap-checkpoint.png b/results_plots/.ipynb_checkpoints/BAYBE_heatmap-checkpoint.png new file mode 100644 index 0000000000000000000000000000000000000000..c3d3807f18c8dab1aa9409c22e026034f64351ba GIT binary patch literal 14190 zcmeI2ze@sP7{?#eOeM)^(I7B_rZ@#5vZPc_(kO{+uq8Aw6|56I)S=-Bm&TT8Xb7S; zg#G}psil^NKpfg?3EKKG6utMp+22s`J&=1i&g1UO^Z7o{`|uvDCKsKKQ3nw@6LEQs zXh0+CKebuemG;|M6DKO+|jW-XJNJyzlxkonjBMm*X2%B5A_-nHuK{HKOTmLXNCAPM$kgPTlL$TX&!J`J-@D zezkn$Z*TV4-;5*l(jF5L=^m?JgyE)XP(-dQnMEYKbrH}oZZnjCAUq$CAzTI^!)1Uu z2pM1wFbA*z76^3#b%D?$@gx8XU;!+M8~_Vo0W5$8%#ko@WE5bg%B=un6D|YR3tR^D zNL&V(gOCB{0CNBfU;#5#kpo}>EPw^DK$s(mB*1z>=zw7Y*8%GVE(3-MT!#N)4z2E` z*~8vfi+tU`y)T{FdmAzR?Z{y~&Eh8>z zco;!5BBTZpaDr9Bu#r literal 0 HcmV?d00001 diff --git a/results_plots/.ipynb_checkpoints/BayBE_line_plot-checkpoint.png b/results_plots/.ipynb_checkpoints/BayBE_line_plot-checkpoint.png new file mode 100644 index 0000000000000000000000000000000000000000..0620fd87b12524da798863075544b2af5ed96d84 GIT binary patch literal 158982 zcmeFZXHb>d)-{USYMZdT1tVfYMWP}>B&oC_AUS7I0m(T>Tfr6sN|YdoO56m=8B|0i zBRQjzv*ZllSZMcozxT(z|8CW-I=lLm#eu!|^Q>pCImaAh%ymy%Qe@lay_>11sJ4k- z{!^BUYNHz!)%xeZZovOj@IZ4D9hnPHkn;kv81A6J3{`q#x6q0hH4EJ zmFS=6i%@=^zYG}=G*Dc{6=%}&z`xZuay&1g}iTgji|jAhCdAVEI7A!T7R*m zw$luoSuo}ZF4x5u{_|qu4@jo|=Lh^N&3~C{-M>G(`5auc_1~YUB>(#5(~T}2%V ze_vD-gI`fknVXx-JlxCGH9Q=D>EYfO>IiK>Wtg zxJjYMw)yc=_Oxq7n~xqnO550egX6@>lhV)5TFVBoY1Afa39a9-!T-XmAJ6jBcC~Y; zk5K_WJ}>X@Vjp;aZ-?iD2erLnt_dFxxT7#V4RPvHO$EDJn&+et&uC^T)@;vo16(EYA;-yO-{wyjl zog>Wf?cI{B9G`!_?!Q@ zF=;!aHQT7DnE0inq}rS&%IyV10|R5W>|*Pyjgc!UE>3b-9PcjOE9jJ&oRT7LWT>r2 zyL)%^UM_=1r!ph%!RiQcu`5?V2Cx?_%6|R&b$vRY!(_C}eE;#hmE{FRHIvcSEV;z|`B=fZE}sh@$74O5?vczr4?JkoIrmy>Egq06e4ex<{8CGT5j zr#QzAQKic8guh#KDiC7WGt7f0rQe#hor&_C7u;~hCo?lMnHsVE{C(1TPAaNs&RC_y zgirxT11!Y+RD+g6nt?)LXG~0t!(eF#ishVArmVJ`DAXDE*gu7`t34w8C=xCR3P1Ft#MRcy+uPg6*bFxt)J8wKAS|r# z^3ua^zZP5=L`G0(!+S~(dY2rZ*;D3Sn{@pn$KS7pboBHLThfisA`5kY@H%Tfzdh6W|_%bAh3_(%H~*~J#J&lkxzH{5G7sz)%zuiHr7H`J7J)^;Ss z$;nAR!$gBizxvPpXRRLJ-(t8&OioufGTpQz0sEQx)G3*fk&*F<3Aq&Aa+lBl=>{zvo1l%`+kWj(3u#}V4)zvin_E7{Jr~4M?Cd5QV zpHL_i5|XNE1~KNHA2x5@Dvwq5G_EjAd?I|8x*wM$CMM<`6&1C(xJdKcZ_g#4omF^R zphZP>ZqsR`Ao+vHBd5eUoUBLN^QtnYxg<#>f5ywLd#>@Cm!;& z-{evpVOLU8vcwLayU(6~5+B+kP#MB&8TTQG&(`En=-7&=loU@xyjsIa-5i_YaU&t4 z#*>uY^@&TWq$VDO$?nt)F z+c_zqmFJL_CE`V|yK7efe^_aoZHF;7pKYk+4L!Z!g5`NbO9bJ>VyK;iLr|F0xTxi| zYuA#5?&kVN4MwJREo-#r*qY`nu&E@!@0rmq^=Gy5`kbsIF5L0<_Qu|(`X@6_M%r?A z&Dvf(uVud!FWsKtgL)7s@^G(Cf>wb`eq|J%Ne?zj(8_G5*M)Mqs)_W5SE4>~*HVh= zJL28vm^Q7~xQ{>L5L#KB&^>SwZp2lc?y&EDri(qEqNd zb&6hPI>l7epG_n7&YfR`+iIkQ zG+vz=R`4C=^ZZb6@TVH(uV-}`9WIoetg1JA$^y?iP8)>|SA+>AZWo+AhlE{Q;>Rq0 z>fK)ih1hmDXAlH|>cLuRqnon2ZT8!?h6BYgGc!}9Cy@2E3_9}jG#&bn4!7s=RoXN) zHK`_DbI-OOj6}KD#Ex>mmo7U!j(FHL)Q@9B`QpNAU<9osxZwj2EjwQFQor z?>OpOkCUtG({kO5A|i%EjY$Ueae*$gU+-S19E`^2#vl=AkLE9=I?Y+==;}7tjdm6L z%#XURP(OLbNjDMH~xfQ2Y|nN^*jB)W7v=|-yNVuH!y>zZaDk>D|Kat^McJ( z-)CVYq2wGdWN5&eZDib< z`FX^=B2<8ahsVUmR`KNVeF=Rno(NXkcU-Kj5^Cv2Nfc9=9dFW`6>5Jm)7LtF@7_HE zTm+XU@}0J>aFOa?*5vRM-GuukD$3lynNi&`U2tCcw+P zA1!L}VB@^okNJqA=&_7t9ddVfSE+l(XDjI?w4B*f8OCmFJHzpJY!?Y&g2n0#+wtw# zMbaoTbn6F@)N+|CN^bF8q6_`Pg0|m)zxwED-eUS^SeDA_k~i7S`5v8n$f_M?@Z7&Yo|tLzqmBw5csGFUKOb z0VYWVQQlvbmGus_@7c37KbROXac8BVI^U$^0y0H+e}4=xka!zlQY{jq)53m1r^fM= zGUPGiY^#260*gj>XSr5L1aW?r;Ay*|*EN?j*AvVtnpqPir53+Pu+XjRjgD-#2srd& z_U-E51_vpQqj^fk&8aeN+17r@Bmf6D`>P}TCz=zR@*Fd=ZH8k}!zk*Z=QSh5XbUU# z0bx7})*05tM(i`qO=7u_qF>}mOL`U70%m6?r`jl~19+k_$nR(!WU#XS>{E}7lklgC|=#h<`(u2SM z{*_eh0bRLWyLKV4CA77*4XR(=LyC(@P30v0#y)PNDm0ASEDBt0o2 zBGSuS;YrI}2Be&^S8uRB-ra$k_G|VD@um5x)9+hjPS;zo8&vD0C@+MdeziNrE%0W^QA`9LVsi2{JD| z6js0lbUV5X13ejsN)3;Um^||IOg1zTaitFCHck#2jSMzPK06`Ej!-$xuAW)t)MA_w zsAb>nx3(qhtaV9`N&z^7lLrJGI6KHvq|VLfqJ13pq@|)dvz_qY&o7*~vi8i8BZVQp zzBe8{c);oU{q4q{bmJD)hlh&!fkEzG6P1)?o1A!xid(mcr_A)a14tx(-g&(EICZ&m zfA!H6G(Or7uvusuD?k5YCMo%74*hfwXpLZ3G^`WXy##bm@2QPdV75eNXdCmPV#`MM zuj?`V(u}4#V6NS9l&b)X)B`{pBquBDX}w%6N#(Ws)Vo-%43oC90Cp|5*Q00)%F%(9 zPdF+BZTe!Ug{CHXkjt{Wgf^H*-KaS==1qQw%R;RAzykvkhBU00Sp=F~Mb%CT8f9Uoyk2fHqG1tL5ovhWMMz&c?2`!}xUsp%A@Zv8R z#m&YdN=>r+kF^m>3N(}-?xBD1+qV}4Qz0cWj;B9FqE|$o^PH?hHaJ`){iN0W<9>&) z?z_gF_>UgFD75(fHd>gd7LyzaK|#UIn>SYjX-)#qkdhOz%jox`$BuyxyXcXPgkXrg zj$)&FYiY2K5(Oyuc4U}ET(vxiYienxqh%^+?ECks$^M%70d!jmS39Hx=ibMf;1Lou zs9~NzSuIXL;E;h@P(IokF4OkhH#Vp?NzMM804BV=yyH_-1cw_o_|Jh$cn$oZADy$z z|Fv3HwLwCq#CgO70gfjWpy!y5a3!<9Sm%6bz%jZnXO z>+!c64=C5=eSf!w0IXq8%QtRog1C)?6qX+BIXyJswU{^i?Y_+l3qf~PRno$39^3ZC zrL$HT12-uqvYVQkE+O3@I5fbrph{nv8f@UHMZxb-MaQO*#P{v{*Fp~qJG)Gi{JEYB z4;6r{oDO&)Nz4n3)vKB6_f>`4RBCesPB!UEr$N_i$3Oxlg4A&Gxr9se znb2Dq^K#u?0&I8UwO8?t>b}n}w^AC~LRmhozFc9pi@X0XQx0sYKw((MM)G^~ClN?4`y97M zyr4bTepJv?JaImoJ+(;6gcVW=@ogj8Vf(~Un^G9=_NEG?FcFa z-}hM>Uy|>%(5TAAYf#I0x5w`lP?Zc2j7_GNsQDx>z?aJ=6ZwTZ@u}t6MK*F@u~g(`|sQzqoc(JmyYuBwMfqp`YnNWs=*S8bPv0$ z5>5HK?feI^EAGyBX|7?Rb=u#x%xiCFM>8-}2HyIH^^vcLhWLb}7bVZWKYZ{&UQtmo zsp62RQiA$t^h?{UQIi6|4}^cn>3qZ&_n{-*SS`F0BoOTn*8Z!#gF`Hy_?!#c_hxl{ zhxti;3D89#O%xAgo?!wXj$W${PdgEicmQpfETCjv(shwwHR_gT`nu3xUV~$+Gq?|M z)Sf`A^P|W6_D%NHd&R3)ujY5meED*<4e1w9k7u$Jtv0eoq@eTsO`v~8HQASwcii3e zfj~SxJ*yG-iTg}4--a)PT@B*U@ifU9<_5G3koI-=0f7(pR9M4{UO+xYSK{u+&D7NC zjtiaMf9&4fS2YklxS%om&V9>VdgapB*Tpw1?50M!Y}@Vi!sq?&Ak~(KPp28wd87J< zC*aX5sy*7tfaWz84R%xbDbE+F3BltBZP!kJd3)P_S+o3EW1jzPxxgjOO-1-tN{0q5 z?;3U0hI{dCk`@Q;4cy=Vk-(ZuHbN1|quwgxWyr zdSrwARoOoyE1%J7aX;Lt>(lR~|H?*Fo>Z}8&mR4>3mNzecltySl!|HwD|5YJ?(~4L z@+{q*2brm;+z$V`X;YG_yH9{zl%zfYe-JuL?fW$#RkqQck?wGscqpuAB&sBI)djhZ z6op-L>nt37)>aBG#0f48ofhXkazt3mdCE`IWv-ilAwk$+m%nE`pNVO03Fk3xu4Ve&ATd|*vwu2T)EMnT59*nT@o|n^E!v(k5w(t- z>MN1{_11Fqjbd+>r&~-FnYYx`)NFHIR~x4y2Uy{qaO?$;0xR>cC@6-O39XxMY~Sv+ z+1cMoU*mX|?ICk?9B8SwaM^Z#c-Y=K(@=EVAt6d(#D+k6Yw@f$X6@?;;QlzhvW#9z_J^*HoC4C}w zXvr-P4FUv#9B!k=t1)OmDmk^^{StWPSgFG_HuBD+L9B52>xylX<1f~%+MOyl?_Jh_iO_tTC>GikWQ(;igAv*qEUQwMw)D~=EZ(BIwR*#ntz~qHf2=m8g1e# zn#yC2N4bgp`_!(BBMNVjxzn<>k0uU>q1f0ilm@U50;p7GNL`du9A&@QcIxnq#2(y6 zQ9i4wj_lrs^CO1mn3}Dx4t@v$OGWDdLYUwenP9N`XSlH`tJft(Y@DoB?G4u(dQcADIq9q;LNG z05eDoQ3%FoU6*qqbll($-lj#6nd}g{qr^IU=d8-vJY6I`!c~Qj+&do1IId{zPNeA7 zrhE#bnE9SR*NK23k`)3G7usS{8F)I%YF$a;l-i7DXMev+-!2Uj#i`Wv-2L-n-nx$RatVO=SK%X& z$m_;BKgKurNNz<>q-K0irFkTGLXtvu599IUQCnY}*S-SuNPuE&9iVRsw!#lfgPeS(I$wl|(5XiXK*;)PbznI`bB0aI_xVpOHlgg?DSy;-;o&i~iN>}Q589+~Y?Va2+(eb}7xj^?i-@_O8%#3S-CJQ`bKz*OA(Ux9j}&@JT%D(P06- z7XKHM@s}-3qf~rZPw*Q477-6A#-JhLS@_8OFW-T3xAN@mZeLc(&bC;Z4=$9Im8C(E zw&X9i^Ef#ku&`8AuekK+Q(H%2;Y<07Yi#9~xz8TPvj*R{*xxvy)_6L8|JTmWmr!;F z0L80t=f0rl!2S=Sy;B6|Vm#ec;99U@>+Y**x8HDqwo$Y`n)Hkd`-P79@!Z5JTUuIT z*$Ps!QO$XTn?RzjKtiSqok8D2JyRU7ndguVMq`^bMD$Z}@}vjdrX%Vw-ye0K-&Uq+ zK3{y!byQ~gj)#Y8U0`{S#gkbI^h5=Xk@%|pNw%ZyFYd@`BMI#*0p@q7H$)F`aoa{S z<&oj1D84sDGig$nEH$SZ=Fx+ywlRE|u7nmhaoX35j`v#HaPASC(RO3T7P03V>@#~Z z+$FmF?{=8eDj;!WXP2bCw$NH9`?le{XtV;wANTkiZKWENzgm?3Noe9~ZFGO^+ZWs` zvuA3cE+w|qv8SiSpmIZvqE%f@tCt%7Evjb{`S{0r{fcVoEq$~cC zar5&9#4@9Q*8RhYb-r;gVwEjScR4)_4puuMaxEU=Nt$##&Umf;3cmTYq9d)dC!zP9lOM?a86hT#K49@lt{97Wv}-j4K7YDmJX-3(Ed z-1iOwus}OLS&IhXREJ=esUY(Z_Y^257I`6Mt##fs`}b?~je%#68`zvpEhY?|m(#GG z#Tn)fMK7@8wYp(yCRs(XCdlbU^!xtD11I;LE>e+qA?;*K8EA4<<5G2H!goT=3Lj|> zEJ&~T#Rbiar%wBe5<@N>6;p|AA0fehh)qB%&YfY$Vhmz}cx=Mi4j+CYttRo|!v{ho z6w#>6)n{4iQL;fM^72cfjVEN{w_o9DMcV29`c*qxEk*Ysde}(N`pN^%sruI~qo_m; zTGHdSBd!6@v1w%ANc(FRZVzefanDl@Ek?vDw*Dd?f7p#l<~4Nrw(%o@dX%swf=)=rjk%B#{2T0g&I}XiI5MeE7y` z)hA6c?w@Gh52l4)vEdUQPm={56&20qRnaNtnUHIHGE<)eDtlRq7i8#g^{1@&cd@G8 zuzqAF!w-DqF^*0s&2`14JXD}X8v3p^(n?%o5347(pIRLftj{lI>r+hH&Qh|0V?W*C z@H@|0KR+{S%BYoPadD!z($+47K!nT-Ofy7S@O!g@ewt2jojoLkv*zFaH22uh7y-~h zEJ5KWC}lRLD=Zos`&(1=q{MldnZ?n~spinx`v=+g3;ZuCK2Es2O&k8xOz_Pdy>WtxQ-j%oTV^eX@E>QNMqKGVYj;$lz5 zv%&9Y0NazAALRg;ber%5w({KTvMxvW&CJi=DsB7i{k;oIZ9tso*6v34K(yEzNNRBd zV1GvV7l1$CNVg;ReEaroD0HGM@H7lJA#KCMry$oBirs*PNlDZGA~H5ds$*aH-8jVp zUw3r>s}V-~RPK$%~&ZUar}Y?1c+zkgR`UK4dC@LSc~ zL!h^G`@FLhfCQB`MvI=ZJrDQu)sLJ)1|ODoe4g1YIr8ECd*yge)|-PpXu$WCggH+K z!jfbgWRA0a(jE+0BD>TuoS$NE`TB_u_;q)d|5%E zWP?`Nl4fv5s4iBaCR*m@R%^oTA=hlR?$%!D#dpVox`bsT$zg~tA^{Jpu6F|cEHM7*h$7FLGlyp?&2iC+BG&R7;#JdDVBGmzP>ZM3<~edw<04gcTbssdy^1p12T<`_l-i2F zfxRb4V%fZH8)&yuE12O5uLJu!phOTJ zu5{|gAiwr=&O3MRbmeqjM1FrO*#E9;+Y+?uR8K-GGKWs{V zVq1%jvannsTy;1bNJlTh`RHR(p>Yo$Yzj$5oZ?rM4gjELEaU{6 z6G#A_jz5P0s=|SsN+qb!zpL5gLiU|1+NH&K=+N5)7qlKqC`j=pA;dm1J`911 zpUbGRO4|0zn>Fqp9s_8Ftaa%igTkX9lhhwDa!WIgaGK% z#660paW3KcP;(lk&c5i;QxGvOi(?-z%oMb*EYAp0wzq{eq4Ar93_J+Xpfuc^wrV{C z142w)DA6y`AESJTgKbU4jYL4&?9RG)9kj5q$tI0@7`8YeL2ZNf^7`8ximMV*7ldOe z;3aVw2W3$-SR^``kqz^yw z>w{YK#~**hrPn3EMMU@m>z-pRxi2~4b&N*ACEAFQpvfrHTfobHP+%ALzC1(7*e4?N zr;J3xf`VdyKXQrqW_$_?3eXoCC76_tD&J4$KxmLV41=0ZSaw z($YfR) zL8@swID$E6jn*XRSEk_ff?fF^C%EGa^y%|jNEbJF$7b@?NjQyG!;;2>1Ru* zTrNZZWy0iw z>JH$!^5zF94xJBqh_{4j>v$S?`2Of}i8B(e-~sUG(pRoL5dNrv*zOfxE!%crKAVxI zo_vVbD#*{D{kEkIB)LtRE0}Z|WEdoay#qep-kY!72)c3%*DnsQtot?|DZ=)Z6Earn zo>8eIRFK17q_R6N&-Kn7^WK6A9C)q8ggens#P2P+3m6v)xnrgY$i%K7sm=G?jf*@t zs}4G&mmvMbHqkHO^3iap!dh98k_am)tXlH@f|nta&m+dW2L@sj3znM)#$YN5F5ca5 zFg_EY<+AEym{K+$b)o8tP)gLaKjvm>niSNr+kQ!t9hbgtQrpI$kZhi6Ny$f6zwkC6ZiR+d|O9&yjsSC&l|L6nm6azCbku8^$&=JK)7!T7p$&(^$CbY z>6%{KbbpNqo?=Su0d6DZTWT8q#{d9`GKe%^{o>+%Pv1rN>u{S-qW`4;p%ih1VlTHl z&uCn{bg87WGEw;h%Lzgk;O_gRUp&V(;L(LyHE7uSUA@sAaR2^&Y^^9_HA9`Wnd<-i z@-8u@f{_`3omA0lVRn|r_NkkM{n49-^>O+$VsBBzny=JnW@Y((dZ<1ZgD`~n7lpLr zKahL0sIpRuFgkKl0!MjxKGq+{l5N|6Rvrr4XBbBNzPFOc;(J04YX8tkO}OyJB|sN= zRITjt2LZ^hKyQtxGe7KC2Ze^XXM$Y|cE0cE&?4?^m`0@6K3O{8=(^Hf1RHbXYi(l7 z5zZ+CmnH@a3URD#$s3;Kf}C9qM$MBG{_<$$9}Q$Ux*;f63LrM;r-v!7%M)S5BxW_< zC7QP|lH)FRd3JWT4_3f>$OLs@ADminV@jPLy&4ELjVReQtxto#AO@&QaSARb`FrJo z2YiYd*xA{UXfMNP(ts|U%d+PRp%tL^N<(-nVMr($`moQm_NS412?W0X1P#<$y_wy3 zn>6f9TyVb<&kA}!pPU@WO_wyBJhtw60mWzlGG#fB_Ap2TLVggv5Vl4c&)@x}ggv%? z29c0@qxcV!W1thu0%Uce*g+O%Mhp@PIo+f!7DXfmEwlHHG+7yM;Z1hW8Ik*r0di30 zp+z*Nt(D4l*ztG#9Ya`90vWzT;EjQ%VchuoJbUiwQxkSpbWyQ zNk%m|ExJCf-C+#NVg2Jn!o*UADoeCz_fSW48_oI7Iaugm)3x)EH0#k((C*ozV#Fgw zh8V`i#$XUmpm&~>DgC^~uRm^Xkkl}c4n$gjVLQMnQ3s)purdJZY(`@Oe`o$5UZwyJ zm?QWM9A8U3wzkR9D4mzQ=;Kkyg^63gc7-Q5FONJ0m=*fd8Z_mQr-vO@h$YLUV96$L zs$Q)bE+`V_=&Jbmg%Bf|S9%6#gh1q;3x^}%3bu6)fC?E2iA9Pq05|^E?b`^7eRmZD zY3gyGwa?G51GrKdAd?Z;I)8-iqt$|&gXq5y)tKSSgtxAwq(m0COJ-e=L5WqJr_D+( zM!oVVEBZn5EyQRJEP~mQQsDWwZ{I>Vi8(1cJU#@1=ymhh7trC{86F#`fPHXfeJH-f zq7I4#14T@-kOwJ@>lJ-S+GGwZnl^Em5Pc{kkFk}A)gQfT)Jhu zh1-a)cjE!4*AEV_l@25_6}VZSh#^#Vm?QkP|0HOR>WmNp_EeJZjJPSFw+Y1?k$|N> zYcsT)dCwZ(Z0rvvEGZJSe>U(dQO}tRqsd#T%v?eUi}-~he`VQGcwEUF5h5|Z>s*XD z+K$5QLPnRD#gdz;VFAO7Mo5ti$)33dDHu~OmjJr`vk~abY;2NH-gS2EcH2A*8e0aY zOAILtk60pS2z-CDjxa>w`Pf_J+T=nGVBiK3WdC9FRp@e?*_fEbaEaUuu1EW^>5tEC zd48Gr=D_n@B)I}vkl-;0-pXk?=xV@qyaFj1t&*avSgZwOn=|@023M>cmT-L>nW>0A zCmG5g!#9#C?Cp!@4Y_F$$x3k;boncIGU2%OwfUPWL1Ihstp-fc%;9VK9(A#v192)EOQ zMhY_S;khm3E$2>LCsojGGXqwc7*2dO)YZvG0hEdXuR%V(7RDEj2fw+cz+)@}A2}=z zaS*Ivl2rcab)W$;2Y<~m1Sisw8X5KggK7=OR}30Qj~OQNHB{0L|MwvCRr0O>yv+Vs zjm)1vz7buWeEH{>$IlbU_0KEh7t;0r^ZNhfjd#e<-5YbH}1#x)(vnFoiUy)Bej?h@ThEmIK zI0pX`+u%Xz{O+!Jx( z#Yqy;tb}qH;RIj|EUy3+`gR>Zo>ZHN~L+g z<-;q}(~^k7s#x5FB|KU=O{!XWD>$AU?>I80p)GzBA?A0v!Wb<`bG9`lLOn`yKbbjr zZ7}CRSU4DFb)M=02hM}2+x!6Z*3Z}K(}@C8u&0El)$BIV#0{kmRN6fy#K!6v3VXcj zv*SjD1*pw1)ynC8awF~8)agq8BbXC&TByY?2H#j=3(oxK>pB0yH4pglXe*?rXJ#^T zM#(@IWW(bDbNoH2 z8Zydy@BRI^w1GivL~E|SB^b2E1be`RTD0flyf8Nc6u;(^{P#nib3UfJ6pE~&$2i)Q z^6~Mx@=8SVwr*Jea(ZD9Nj9Vw>75%)EZEu&^&K}w6o)Q8`hPz_y8bfN5h=1FheU&r zzmVcG-?-rN=f4+j-)4gvm*9D#^4_#_AUTd+`rh-e-LkGbeHBz2PNl5~HJo!_LOz1y~m5 zICz+z!FwMrNOUy+tW}^wF6E2*WvW6pOf|@3MD3VlNHiGk~h;{$}`6-3QJ54@G&jMC*k@m8R7k7#||#!KEUgAGQG>H zm9Gx$S5JH+_;+XHGFIEe!lie8%Qxt1j8PF0ytN1Z{Srp~II2te;2g+s98m*hm2zy1 ziG_h^2zA&!NG?p668-%7GwGW^D~be`>tgdtBE+lFl24*N>fw{Z>^Uh(D6=X97cN|w zL^Cx&62NXeK-~4MqolveMAF|114c~K6bi*_K6pk`3kh>BwtKhPN7pI zyn_Ao902eXw35AwtuAm$tqzP&lO6;FCgFL++d$QD>Q^(YI+&mk5-T=B%-;VjhLw^tRkH1Ah5_i=?$fQJ zP2j+G%+E4|7e-qqCLzIU_VEIhpSY@O(6#H=S-asvq#U7NqxW(1)vgnV55If7h&_`4 zw9dAgK8aIrzS4F1rOeAqDgz(94kX^Cclgv3JalK%j;;46pS;{C;Nyw;6??_k zni1H*Fa%kaz-^E5E$q3{SI1|9@NE?I2>upmH{r?Btz!e+BGz|;pb5F$!gUuAsNJq} zn{XS9O+LN#6#e<~}h{rplyba^e~Q0WT{Tg6k{v^dMJh(_ zqpYNagaS}qc{==JJicE0`E+Oa)0?5pzYAafai>&t1iNCeJt8V9fZVD4uQ;$En2h@1 zR(20y*Fx&cYU$NH&n!wTKh<=AnxHoo!P4bVT&~~L0?f8IaW6xv@)9;KRXcONH5a++Lm4vbZ`K~ z>WExy#{B{gwF$i7I;|jjh&RDc4(gWrh?R&HGw-JjWgXbZ|tmM81w*03;d; zI*!@jrTHYO}%w%su+`&DU977!fCqHTcTACm8cro{khS3i*yd~Kn9AS{_tWIJ34>;wz4%^F~ys5Oy=TuJQqR1-y;-c02IWwFw)}&! z&V6l~r-N#kxApKEs^`}TxFHT$I7v>{I*%3YB3{dSct+Fqp20_AkVYEz);g7ZGALX3 zcCg{fv$J)tpIJaUgwG%fL!$#=FcpVwaa0Q0AcM)i&mQym%^(@k{%%hgM+@`00`V0v z;xeEKC!w70ZZ3NNK3XbFkW4{FR6u-!6OrBQpYyyfUgr*uA4phH$L?*^B;an_Vf;Yhu~`O!x=9;Mj0%kq`UC)s!SP0QyNDOS$VeT z_X-2(pRi;^!ABBhox+AoRn3C|V{QRPgr>B2|7qqKnTG;ytwmF4h{`gL$_Zqo`GuPq z#x|mCka-Pa$nr0~k!%=Bm?hE@gRZdxlG)iPh|UN#CyDrJ+bl|1ZOYB_@VuEoh9NnA zn&2fzcc6`vzj9@3F^*OE`SNu{g1bvV5dIJc?h(Q=8bc4WC%pQ7{3fHc^AZ!Ss8d1|iLqo|q4?ol~HKPfd zUFmK=nq0ROLbJHP-C0GYcJAeGDA=_aZNwz>%RhVdkpYR8@xoHQ6Vw}82>%lGe=e8m zoam}d@z2ly4~z$+FzmJ*fmiU~=3jqh95iD|06iZ_7PbB>RRr8e72nV97Id;xxqD;R z>e(PaKeXftCb*58k}twbC5xs3rwEZ#Xn+Hw;kwh=PhY~jN&)`(bGL1np(dw?06eE* zoSR@@q=S;OveeW)nAU=jp~N!r|H1|oaU2i%apupD4}E?aQn*ng6A!FWal%aAAiN-} zYZ58$ufP5x7H)zGF=XS>Wok}v6QMbXOrr}@5phP&0iwVm9mH#?d++@%PcPaxKW~nT z>Wwuyp9Yq(^x7UrqB6nZK$*veiiR3_)_(jIxycw}6@7Z@I=rpmMxV;w-$A?XKTMMw z2Qe%<<-xZRPzOH& zYr5TcSF~YFU4w3{&;NzdRQt#V%o)iRCuSf3R5B|J5s7w??tk~g9>BD4G~7h^k-jE^ z4J`@&hP~cSdMxRG&}5kvI6;YU@7P=cef-RdaaFib)^4Pan;_KD{CJj6|7>(4U|E27 zMbw)c)>=#|N&qOceO8t+fBio{P!h2r#t+E7%1SJfj;OMdy+aUqunti?v45lAK4wBM zufE)BT9U4lQD48uj}H=J5C@IGE4Lb!Rh|l{0z?tPsRAd!U7_%d&&()cVg*SvKAC!Z z0}d-8^R`>9G0~k$8AS#sNl4rt?Rv?lPoFHi&Fn4U?=x=8RzL^f(`4iib`|Yw6haa+ z_O$kk=+OHiyvSp&7lz+%Y%6TsSoCw_V`C>g^sW}b;A4pQl6(pz1G(lH=rJN6f#-pS z;~lK_wI&UudULvQF!8fuhro3%jN3i>p99#K(#ZsL-{(TvP;@tlU=~r;T)QM9u;T)n zh8%ICO&@v{IoNN+kH`n(*^@JIL}h^%|Gzd!p zH@#k6B~mHUU_rZm@BY;`J~Kh;1IQT-nAki%TDo>51}H6Y;qZin-61M)2IFQ~yRidX zmQptT`l~bo${H|6R2Jjg0K(F2>9;Bdv(eF4GbiT0z|Axb^+N2&NQ1%Z@eF|ggP@iS zriYr&TJ=3H=SJg3DlXZaGlK&EP{en)?TdsRj&^q`QPbeYJ6x%p5CdTc4R9RHD1$Lk zp~=jVm;i;#;T-%w$iWZF8~*qoWM`BQ##qQSCM1sX(}*h=B>=r1YHrCmfv}( zbEV}cH_|1&(mHJErKDLFDxgNh#RzrC@#M%iAPUsu2E^31;R1LPFa&Y|6MM3^u4Am6 z0zgCj`{C^{o1jo&BYehR#`io$BZ~|eL1w}l)5|5r#R+%GLP<^pGw=jP5+`S8;vpc; z8axql9)fVE$fZjIq_(2|g_9#Bpd83xO|9XmfGqG7**D~N)2*CaKNsjs2AFI$?*dGa z)qvVez-}T10{NUAZ-#-J=U7(Nd7SDq2&I*k@_36e;U1yL6Gnt|&mf3l?z{u%?j-gD zGqB%i-b`p~TN|r0@#*ul+r9i`nPJ?bhymtcV#*dkmGJmTypN^V)AJ8f{QB`!sHZC1 z1PlB|bh`0*{#~Y!s?x0bWk~~1>@P?kvyLBS>FFFrFSiQtX+HmWuvY zXup^_{#@Lnqma8XQS~HJ@|nR8+MSv%8d*IJ4I$>e{TYlq@-_HuhNNMK5a()taR#6i zEd#6?9E5gL^a9yqfc1`riGbd|>v!=boWVpo*nNK#J|ZO;#QV|Jqc^@LVVf|)$QnL| zsiXwhO0uiQ8VxbW?0!}+Jre^+yH|Np;iY}6gjM%>C#PHnq9dWD4MasZsf)yhN3ZY@ zZV4RQLoHej-)|abP6|uDwvt(tLi;A*Kh*-4Jg9l5b?N9fR%gPS4HNg6xY%KRrrxaE z9*s|NclO-5vNY<-x-yO7YIMPMFD`BgHkLT%gTurKfH@xKBnzP8hsny&>tt;I`-C%6 z!esIButujPz-B*;z#M<#bHvgn{mNYKdAM@%dOm*EWyIp%!?rh3v90P`+RY|v-8pr1 zS_Bi5q8D@dGBn|<>R7RMfUePkG zVG+eJWjguoKT~DMb$DJ|u3x)m1%sgfrN3Vvn%VgV=#j){`)5{n@lvqB(MQGo8?P*D z%0bX2ewTrcf`Zl0gjEM~mgKC%Jp(WdT|YhNmOFf*EnndRObgQr-DDD8@p)u9iv3c=YDT3Xkx zn+8=CCcNrf3R_N??MnUQp7vO0My%!4XKPyoGWw)0((@|4%KFw$Fa2scq8nX6Dy8lA zcEOvMsaDV<9Ho`LcM~V4kimeQW=Ko7{eO9FHI?N}#m1MlBOS%wI@%S}p5yxybM3nQ zl)5@Q<*w$yWegOc>d*RY!jY9tHT6@ng_bvx&I|NPO|+E|FHA+L#EF$SiVV>w?4I!P zpLQJ`y{MuR)`D_$laU-1vEF2l#M<3%H7E6SMd|7PmEYVr>a(p4*RNmSR7bv_X!9_{ zG4!J_9NnXu1fgkRNRk_jR)sX1X{9wrfjo?3w;T}=XnW0S2HPROg|i7A=D%O@dX~bq zy|tT(n%?s1g1ttV8w)!+1bVwKYjT>iv$KiYgAD62te*2GC6(b2>Ya+i4}|RrmH)98 zkqWn&KG+QJS1^Eru9N^`NORn(Mh^4HnkHBbaJC7%rY_sqJofc+ZC4RJ9tej0i2toh@x zI>QqiH*Qpm_hn@V|6-tAguzlUVL%|1v65g+0isrYANi%BG5eBbgD6Mbjeu7mdCt3u6O?-FiXl6 z-!Td`ghi;*E6MbHe9{}VP}b-ypnfnJ)kETb$ehJ@}q!~5zn z9;cFmPH6KY!uyT>Q_C_AJ-qLhevr3xDFV(E%JgGTzS zg;%d$(VU1h#OOmJ$bB+nWq+)z62u(HFLHhYJZnk16(NDW<+~wGyL;kzn>A->Ep;Pr zbe{XE6v&$dADd)MO4>KunDK6SA&QTgIp>C=`n-M9OkiVK_^@B7{>t>rbNbEj;XwFP zd36dWg#wuBogg|VeU{*9YWOQbT6=Ozncsxc06Vzu3BK%Y<|EUrs;P|rnI$9=MkVxuoB3J>;(j7LXF2i{XX4^Svfvj=A1FIOYm? zQ61pVKbxFeb<_^xz_ylirs$&pK2MlAq0JAWZXrkUlGnNZY{4fdL;)de>>dE`!yUD& ze-QdUH05L`qEEz1*f%b#cq4zr0>kVcAhV)5^F1aw2T2IpbVTDd=Fx21Q4_|>V4g}G z{xv;(Ixdt{Yu1cbS@rOya=Z-MTh-*0Vq`A@6s7uN&P+^ZfNV?&H3%`#Q+?`}w@bdA`;;)F?rLgqik# zVNEk@2=cbpSjOs?Y#7TZ>A~WQ3>-qu@J~)o$j6Jh`Bo$P`!@38p`RR_EV=0q)V%^1|2<|9I=#wR`Xg zOF^>+An4+Qf!$YdF&_IKT92uk5KGb(hm(>u52Eg3)G0>EOn)|ar*A9hOhs%QqLrjc zjY&yk1rN7UWP3ZifUQfcVmGK3mfvsm!VPU@G?DJpF$SPQl9VYEYi~(o|xPSK1 z_l^v%1@S{c1URi$yk*8gjdJefcV!$f0(fIo5)*RWku~Z-d4MpCx#LF%wxb*ofc@$) zP9rdho&$hr>Qis`Q98@ry9?MP9Mp=-j3ZIZhEiAyS#v>M(Q|K?#;qvc5K+C$yXM@@ z@fGY;{JEne8fq>*Ak>~|NU1SI3$mlyyP%;AVBt!6@{C}igyXXi%uf;tV)l9vv6!Yg%}nQOef?873$j{DLhj%>Y;JB@ zk%|t#L}IjGma=96a1hP_$|q`|ueCxJf0(cQA!Qz$ecKNX=cpSAssTRy<8})nszpFC zBcn$leKmmM`Mm21MAp*TM2HW^tP3!t*n=b{5DXO)Ky{W}kQ7+0bh!zQ#Szmgb}H@x zvp@zz*noGdxX6Dw^l-~=9Jz!kGezu?#kYXGT{X!D>3INGLr9|1Ap|J&^u}u8v&oME zetbYtZ3P`a^YJsfa7N>2o$qB8zmOt6I%JWWp8i1Z0=ndBCUa_GUmOP0k66FFXX+?w zmO_O}sg9`RwuxH9ZG?2tm+}pBakyVMoLKr@+y5!-h*Jp?QMVMOB~&P$97r?uoabV91V6ElMp_o>kDK6lsDS zz=OQo4>fdVzGZjvzzvK_HE)8>%-`}j{((jPjrh%KiSCCLYw8=O+HC$)iYXlbFYaz_ zA386f^B{U^P{L19I!2QENOSMi`wt(6f|YAU>joOs1MCx;mNFzHLyf-7;1FcNq(pWr zg?!`9+qbH|vaNG_J8C0H7={;vT#|V5sSt-S4r+NY1N&Ku2x8KV0<)%{3NHX6kIB{< zhUyZz#yp*($fPAr4+&l&T*yy)b@m`$%3rwD=?1+5ijPeG)%&CFtn|piVl=eA+W?8+ z$|rwm-uCHuHh5V8!V~v4QdS36AH>4TCB=a7m>y|}Xe1%$*e|(CA}%|BpdA(KE|Sz? zW;)11cl=Go7vsN!!MykdbZdECw={Roa7bhLEc+Kn^zf}TI*uY@J;i3Sh@La8oPK3d z&I;U8%l?A1yL5E7Jt9eDxn{r;EdS4mf3N3VN273>e#|i<;qawPH|*6Icd2+j(-0wj z>VCSNiqxN$j}dUG1q&$dju0GUcq=g;f%D;l!*$H!959A zI`_2yev3)?79NxTK0b!r3MdujxE@yiEovAFlkqnw%x;J@f;T;W-o(3P1nkB0l{+6h z`|oGjNz4E@)B8z(pVE8Rsax<}Eu1Dkms7&JNpk~=|BIGc(~oEQUw`~-`Xs5b|2_`W z)AT#J|MP`>R?x?v{qF)}=#HEsiI@6B7E{`Z1KJPMr$d046VR ztzI1vnH`t13}YDN&z%DwGQ~X&9C&u?DWIS_Y;Ni`dBPC;8aG7pJ_=XhYW@4hW1(7* z{{NL!s<^nA)3?v#t&6+g>P=HK;syIv%pyCb%;_G=}W$15ZdQV$*bv&&8 zN?Xm_(9aYDT9+}-q@o^&XHxiCT!Q5JC3h70{Rdz`qGWyWLRxQcaV70Mx|LpJ^TPxS z?f1F&a<3jn+9G!`34YI%z-g;P&H^Qk`=#gEYi-%43D@x}>c&QL}QuGE2xXmOz z{)G_BknxL%ZW(mCv>ISVK}c^1v){#GTpR?6Qd-DCB;n&40;Ndt!q9HP|9QZ~S2am4k|CZj9k>qfy6d6M~KCr1U8Eds1)G=>8YCt&g)u2uELit8M zb<$pj`=k1#Vw6;>)LE;Wsr%n*1@$pTh&PU8p4=QWU*`qrHZV11AepqhuP;@O}s zmSO<*XbT7ew;*^IK>G!%8BOu6uv!d(Hre%25hYX>;x(Z1E5nV?=EskhfTdIlnJkw2 z8gFCZ+@(mi$5*TDsIUW44CT#fuKBQHk(p+TC(VyIdEwVSzc{sHC>DWFs{oKLhrvrn z=@9TC%6b7BK-H0V49B)r3~oM)4Szei_uSA3-x2M+hCoLzT~MY-YX;q+>c%Jkfbxgih0wT0`tUoV!0 zZZ)SPPD@LR44G(e8@){nz^mf?z`GTsUg#0W-E}*G-Vj@dNAr|adNA-%Vj6M&9y&Si zpQ4kM1KqeCmoW;!#`Ei@(Y8@-c*mQY{RFD{KfmNA)9DFlwOl?L7&U}H^>9iM6OXL8 zPGx)z(ttwa9f7B=v%vTZd>2p>YY?mO6fw5@sg_5%`Xxah;QpwXa3-hbk}+R{(}% zP^LEpvbnsygNZm)0HbV^%)oNR3L&LG4T*uwWD7l`(<)>B6na2DA7C8h%dNwK*o>1~ z3C9u53&Q$9W=so!QaK!}eD=hhgJ1Df{O9Tpa{kw*E1`TIn~wwKD%GGc^bG)YB~ls^ z7Fi6)@DzuLt%$-Dzo%jcM-XXDv%*hJ6C zQSvL^%xuxY-}t#{&;T*z*1`6phXfHao`Zx#not;OeqRYkKa|GYnkk6dxeoCjC*Dcl&nbT@2SO>iXf*9`rHB&UUdRs59MgG=}*Kt^?>{mU2f zH;2Qlx}zHIW;)X`*@@h(=umhtGDS3p9g~Q@G!EiK7?Y&+58&?Mro!!kP!8`Owk7OO z>HR|5M?%$H%TCZut^Ie(@Sf-H$9}H3b5;I{-*=6q&(>VJL6JQByd#c(d)%{`iu>N}fl|&U_ENEO}CD`_?^u;yuUYfq(Au z8|7K<&yhE8on!}YgK5@h^3VkB;a-T-rx@k}Ev0JEJw zk(!#y#nal-(gs5QfwtZooUR=SB9&uG&eZ2(6;$yhhi$qA&LKPb1(DMK^sF$&uX_y} zHz`FkD}4hl_y%XMe?`x%e>z_w0t(A9HX;hAAISMAEW?9tGz)@Ai$*SK2<5i8L#3*3 zbBKCXboi(%xuu~QDZ*|XGi2n%J_U^9X?PZu8EW5$CeMGpU3>cxR07JwtSl_y&`vD> z2puZDr$=jY2sM{yoo@(O!&!#r~o-TTeHr#6W5CqEWb(uEO~Nm8iq#^+1lNBZm8V?;OR86J(d zzwK3UCcDK7EC%YPo!ezQEHdw~@eK41;k60+SWu2W*(@m>gCu}RZlbs_1q4;qY9Sds zN6rtz>O&ijT|7zMnnA$c5FuEDMDs&b-IIX@vx+?*V1VwB@EgFvIg9K%Mf?Df2IhS+KgJ@2!`OyR>{i0ti zE8+XwUK8z;>Gyb^`YamY4c{*|SXDdf;a>l7N5U?Bn)2|!;Z@k#op-=ujQV5uy#}s% zs3Iw6hgV1H*_!vYnK)P9EK)kJRO(TZJaF#(`K4UJtrX2mj_ht&T__F%B2gl|lR%*B zG*;c~Mj6S%f94h>J^^?}&|jAQ{M~cnpiD6w;bZ=AEdUJx#pomctPl>l2s0I_)%_nI zZQLZSVZGtT#cf-VENzbvH4gDY+%QOD__@eGWHoqNv@X`6jvZZ^JTj9%VhRV!X;0&T zISGf&N&KOPR^s&ac3X6ElnZDHwD5r2CXMG~;1)$Aj!lp|vROS%fmEv`)Ytm{DLf7K zQdoPHE?OjDdfUHBGr2reG`btXNR$PRpwZ67E5qm%gn^vpqPbVGE!CLD(zeB&Rd7?k z_Z670t#a6Gnt-(4aa|=5jrbF0_E8FhO1@-*K@@=(P;{g>pcxM+=5PEDn{eaBn>P>5 z^I8ub8}dh@PPR%SxbO;So!eVj;k%}swLA?C4N@d^EXa=n6kH~!0XdN5u*I`NF%9W1 zT;epH5HzU_4M>Yx^c?i8vvB}Sk>QxJ^XaWWP@8LiQILR*(X!eHN$bGIt_2bo^Mdyc z5Q+x@3GYcTEXfAQYjgArg4`k;N*(7k^0P!|rdFe0k%dx^EBRwyI%14j^Ux0@fT*?~ zoEs4$CMs$PjoA_yH#2V~p{(9qbcXY4wP35aEy|(@lKR;n$e}09=LiEH`PXV>W+Q7v(w|YJ& z2Zugn>3Q$I@wbBf#wCU>QQ9*N9WO~mroBgottcEQ`4v6Y^Od%P&@P1pkcrfQrKlUb z+6B=B5+d#ykKhcA3%}m1&B3Q<7lR+l+W!NOG?j?guEn~8f((NjDmT0qv07(-_oD_p zjug%hKx4XARu^<^WW2ckFJS)45G5QAA3h!yAS#sSL#JLEtV~hKoE=!KDp&NMK{ug? zH?U)s2t+i27vK>A#Q641HJmHZaILOOrO*!&=%FWb3xZg3AN_?$)u#XenRJxjNLV{g z{<~5_zoGHA7UR_I0?*$>5_B|v>zh%y&z>`<1nnyJ?tyHBDzFh0=SmtH&0A=zXl&&^ zR}S=$_}b;P4$=3v;A~I=3tx(mD*R}cD*Coa2dIE-+{4$`IBXTNOZ=+OIU+#^=?26; za;Vq7O#`H33uDWeZ3?b?B%2ibVeKAp>u=dnv_bIGvT>;517XF!o#Y*U<6svG(RC4enw#r#5?d=Q6G=^U;+&OBWYKx z!yaNizCdXMHr?3pE6|BFNY5?W@16*ZWW!zlqvUQTXFA$BWGj3McZ9uuJ6t-aZ*w6| zi(q>ki5${kcqwp!H@6ACHB`{=S_aNy3I{rL&&CwM$YoG1g%jlC_5%B^oMw0tHU`lV zjroBxo4PF^h^9<`G186CVF*>E6XMdeIeEhFvvr{ip{YZFY>bqb+9HR*SM+>wICW*D zIHSP|)`e!UxJXu(pHEnN7(f&8;BbBC#BLMe=Rby@RhUo*D4+*Dc0TM5d%wxZ$ncn+ zXu<+-MR$g=ol2SQRODdEkd_VorpK2!m~xt+MDX0E%b4G40Yzyo2+S5Hd?l9SuhM3`*C0& z1x?=Om4o~llEM*x1j`cS(j{j^?x#MFiJNhX<9~51gtpbA4R4#X{N{G&up8+$KA&AV z{~RB>cM<7@!x%=S9mWO~E-ei17z`F7MkEh6DEWp~>{r-{5nse1&08ftHp>zoJv1cH zQrwG)IUD%4yRWYTP4s$cT(nA{Vaoaw-=BhVWdNFS=#vA`6Gc$EEZ#mwGI3*!pZxVT zQ1g@I4kB0zAi+_JS~eHAAA&>f6A%VJ+S1iv5?}0I^#W4NRgkvZez)JD&{a+&vIbPibr)nE<;lP>3w9CW;AZhD_jm zau@4kLc)N04d>UITDeF=+a2RA~aqDv^X`o{Tm#g zMht%5tnpl#T;R19n8OD%%Wmi=&Xcqluoxbz5{j5a=s?qRq~7Sf&*N^Lz^fS>cR*Y1 zHV5!j63nY=$&&(KLuWtDmLdYcldG2Dw4(?@tVA!Mm}EqUS1iQuX_F0q2R>7_0b_%GB;vK?hW|G#)6s@I|cC$p=pc8HM36e*=YI| zYL+PbjktTbzSgU&?-l{fMp*&GZ`wpd4NX5CHVfboJxEqHzyt`=LnOi=A&=d2JX$Rb zC2Hlc51KiVi0?n(Xzm6uPuWJEX;=p>NW3h@DaIiU0w3r6Lu{8q>!Ks?d>(ND_K$2- z9SmPh;PEBlY3#|NEYSQLP`D--0$34v%Jqv>E9(7cW~j^U}@7OLkA1vaxsdhS@AyllQnS z&fhxi=>3zI_})avU40_;@?lejv()W4o<;lgxev}zQnpn|{*fr#KODDhn~0T-?qF;G zxwdbnjvc~gHlN!BDy%SqW*~dj$O41;z+z%DdcBZsako?jr}pCMkEUF7qER_`j4pn| zUzcYUt-B+-NZ_-QhDJ1L>+vW9btU?9Wywyv!74a79*7GYg6fVbnz=s)^M_QE5;kgi)B~@OtIQr3VD; z!gh8j#&4Q+Im@jZSrjhQ99^Ks0w}h*;pAr=CA8M-f1TS?IMQF}NO`r={&S4y>-;4) zcb(YBVBTu{>A6s3Eo48w7dmsL3o@D>-+WbtKJBK+2x^4km34vwQdaTC?=Z1GGO zX+P^2fgtx33zRxmxf~C!R0Zpkhn`D zd9Esl_2s4za;`IIbn{Y!JK&#iqiGb|anQmUfUHKc9R>spao`V2wc__eDOT_9#}qXO z?D3ypuCc}<^(WmOrx&@;WULFu%^#aw(R$d{q&5!7YT!k!H<7_CGMN*w6H{)(|1YvCul&!BO>m5 zL4%VQ+lwk$%QcGiq3<@GgXS|LLmd?w2Ku4d4heQp<9^nY_)9m}{L2|vt)2YhTC1x_$K8_t z_Gh^$d1%?bU3v2O@n9USJ9&2sgI$C6k+0=9Y=fb}!5d0DkjVm7X*bQLL?2xW0RqWS z@SjY#!p39z`e78Slm)qV8b)1F&L2etNN0HBPIgIZwFogw{AE=kIrvKK#l%w#;4^>L zJ*g;-SU225ccZa(E0g)Mw@~&b<@*o3obM2THk}*a5eQ{5!ZAoqMUslZe$+t0(3CuS z*m@SjXd`QpKM6y>0>p{QlP~Ui)kU%oJg7`k?5cyZGs|HYJ-3BcaMqR9RZ%!*G}}>l zlp2axEt~wG&$O0t>q_+V&V!VuhZ#~)sDU?2Q*DM)WU0k_9EO%?qFZ$(233@&o2kP( zvatQ;rnHSqhT7pb*_7V4%@>hM7*kb3Y3n#Qn-QZr*TXTHSEfSE;x;rIRxhV5-&>p0 z+LO1vAXXP==&X}=&wnW@D)xA-$_F&P_EO39QYN2uJ#7JNs*~$Ycd8Ejl?XDV*D-Dd z`M~c+ABkdkX&X4Z97w&0*SlyXK=6wq~Az2^z}_+|3E%yEhBHT2^w-{+xVTH9sMCvKWL>gwNbD;u z8;o;&f*gaQ1UI)u!#E*s3V&+s+9%po${!1#I(vDqZAOpRon)77MSoXG)EdUEluIuI zHf8=kt&RzUMDMI8Q4BF+bXgS_%TJp;3Bocd8tRaVCFpXnfgRaZC`iiCVrWIL;+aFI zlk3csa;vtuoECJJ^I_0^7UZs|tSfajGqDpEK#y#NS#}E;pIQjQ%=O@=3N6 zIgN4vWOBx8lD}aw-Y}Jc5RXE`U{Z7V8=u?x|}0 zM?G6gTSsRfo08zEbil~A?Fp!}Q(6P<c|Y>X!wJNkHHIj{$oPgQ@=Z*C){lNJL~a| zju`k;XDc8e5ZH@Q;1fU6Z+x&c%6B`PJ)I3)PEu=2L{e*bg#5E)&AjAx_b3c%{PSz< zLgc=a^8`HwZW%jH)3vA~O_c3^etvm0#~xFa<{M^#C=U+}Jz#JL-YZEQPl47ZCX@il zW@Ut1S~=NFQPBgbTd6_4=dlvAtDrcRHDLETernLVJ@w0UW3l%1DznNvmcM`H)FVrF zq`+GT?TgW>j(@d8#?$EHf4f5WN)nYRE2KTMhXL+&!(5W1{^J_qzYw4VS|2`q8%l1! zD|X7BPbH8htXb&r^YW^H>doKy@KfE>ixekzGZ#~J0tpfd#aRtRe2)Oe7WvX&G`B{d z+p&M?qobEH1bB`InXLNHX@7e)owuzL@Tw&O z^4|J%J;)m3csz_!=XGr({<{?3qeTWHE9Vh%Yt}f_rYms_@=3{d2wj^{uvooYl(lr} z(t%uAhsV2CFR-<))OipF{cMN09=o4|ef^5<8x2l!PkHg*Yk4*nRWm?f(lNa9qj6DS zfZga^xn}ek8;XWq0-rhb+WsX~#^25~(8wvEd9#x)zYHM3JoSr+ahS`RPSQx|dm)BV z`q)KGE>Kjpnu_ng&e046>?9oCBqtavKOI5oT2|?YP>N8XE_$#uz&YPKE;e{rROuh} zuZYy}jeo(%@t=zDORUyY@yTvz!a@KuwL=Gd1GW*w8HA5#&5Qq83bA_OIj5}M;T)3_4PK*03#3pu;-x;>w5qx8_> z>nWtvqP!$(3)X)67!e*$vqj(fctZ<7$-=Bzi_l{Y#olVUK3z?pk>>=i7%n4&Cdp;EtdSiL7APV?T07s`A_0eeE*! zVlN)g-9COeS?)|WHbnhOeTfx5t06-4I?suk?_3#3X0{NcEHt46tkTqXTJ?294T99V z1<*f`=*Ly4ftg&^q^HU2-8(2beT&;yOJM=%&S+D3czBQ&93}_MY24snh?_(>43B`) zlF+Oyo__tC+0K<|ZSnb**tN|laf>O#3kV463@o$5?5D=R^tE;h z?}ck(kwF$FE_h)8WF~^wst!MN=`dP`bc^Z@&SXr{TfXhBKTA)l zq6$}C@L{gX?0NEkJuJfLrIv-Jq$pE-208FyFzypZycp(CRJeC^q3|@s)g5R}(VFPR z^S}Mt%l6~fIg(b?)j|Nn=Ie5s*Vvh`&*v!RDR*6e%`gpMRcn-`ctUyu|gnz2%Y0 zfhHD>J8bVLc({%0DvO2?xf20UzZC~KvOxiQWk4)>4l1w2?ho?Z%ySL!vB@rvOk0Tl zIFX1!%~q;MsrS<8Q^8M&Yn7XE5FN$``8zh)<$+M z;}eMcDMJ4o1W#Dr7t`&w!&qjmxVTHu|0t-dN1-qzKRaz7Todv?XS+dHw2WF8JXu8@ zou@S>&!B87gXM(QH{>to>8$M0Vu-^L@BhA-;4Zj#V3Q@L4271SM6oP8n*gJ&?zzf- z{ji$bLpisNRpSpIoA)5%XzTbhJrG5f~2O1qf!@{g^JzYD$>46Le zW5kh33|GzSFbg)eTi&?mJvXVdl%MQ#?0fRY*5QhAwX|=b*a--!fG$Y3qYK_JTo;Tx zisHxto^eRy6r-s)mJN?|cjT(Ht}E}us*j-P97@l-DGUVrBw57ZKUe=DHtdi^NGzXP ziX3K;Dwb|Fd3FMH3*pB;-GvZo{g0Q}tArZl1KFZkI_+0~4ILphwdhQo9e&+Bx!!6+ zetb7(%*Pb+v0vb!MUQmRaFPbWwAW2i{G1=QI5Cs8|V#^Jl=ptN07Al?|-BpOiSJKoBY$)_!CHUEZ*%|edy03MW zz86ABFTE^1IjF3r7Kx%<8fC54tIsj(&u3`hf!U>X=JDvPU+DM|+FIIMKvSq5+A48i z_w+4J*4Z7klKS?8+$nZ8drubJSlVLQTY0-~o!)`19V`>O$o3sFY({^K4rj@w!?1(W z2Or52;zbsAykGdc8 zdr zcrjytQ7Q$gDCuULRRIZN(tF`nUPKN%i^)R5g7r&P7=G(DF}iAy3`1@15tT|KlsZ5* zo#W&0mS;XwH~&TVsN%E}``4JumAl-U~@l)DsfUF^@$R zjO%^^e1IGTfhWAu@Gv;cFgi8(scX$CI{pE%f45GzDiEk3fiYdY0B4@kC`W91lKbHc zF7ryjg+1IX>uAvZiPP`g<4+EQ#^kaez%Y`P5ITm~N7?_T(*8FK6%uahlzPgkJz>rcT zA+!@G@ClSdF}(+TAN6xzu4;DGAt(@WDXRrGD-=u47hAGys2zf5(p1uWrlei8<4L4C zhhq%oGbPhtoJ45Cdtsh0wK>?MC1~##EE{4&okDghoB(7nr-W#M#=CJp(=VqT`ZkK%Whn|m8(NuDeXl##X2#h1A4>lvJ^O=Y zVOzAN($_pIm*+0u1&3$lyeSb3Is2qR&;@T(I%#(hWD)|8>*~#$F%?w-n~dUByu4VX z$Lb?|QQvc4uNT;Udb^RETOQ-mNM?*DviY$D0#fke#xj$^61(^jU*kCRZB_Q<>>e^j z5kutG4UERQ=Qg{TD(?U23R~j7u~RBPcda)BaLkmZyQ2(AuM5fnMxM-Q=xb*kNl>=Q zC_mLFxKsp#SB%JWjJL2*i}o*OmqflREyspxvCVU# z;+e8=vPN37P+DwARf~RTc+%|6Qq56&s@YltUD$67ba~&qx0}JdYw#<(+4Gg`ciMJo z2b=WVSbBRNquP_NdVBq~uKjcWyqWSwpXG)6@kw-E#jtpjvo&ZWL)l`X&>{IaM z7jD$;NimJEw@XP3+@l)+Bi;AN8^==ImT zDyiE9?Cwt3=o4+}Y2IhCArc`8EMH0R&Lj7mG5QvWaZAwK?KkI#!lQcAKRe2_0kwu=jHcVmOm3~^;IsWbKIdgi0d)gx+&Az#NJeG~Gd>P>G zm*O*?9q8m~_G%VqDEj=cIM`R8qe{7Tc9DcN!e-p1WKvfReV!pb!~011ebJICjaF!a zR~-*dTQlU~sI2n&(xu5yEBfs_e!kUM`f#nd|K`Z{U*}qI%C`y&Gb&YguADbUb;RMA z<@7PVgr~Yen(}4vr(d_a2jMDS9VrG{GN_Pr^|x0^Xn^;AupTW7QI1rE-q%=%?r7jj zW*_O_P>=QesLMKPsOq`Ya~Q3!G9d5mUqk_qR4vWk;bD=-m8Y5-zoSXS#7DH}x!{_G zBFu-HZO4`>gCS2{ne?5*xI~Z*M-~U^gVGyLS%eoFlEqg!Jt?D{=!YqNapy8L5Rfch z3P|?c-e4m`_@fqjzG1m$o}?-2;P~7AQ%}KalXBm#Y8_c5ceRG)KSahq0M{T{i&2Qj&d791J) zNoqYal~cYi>tt<@>gBgXyazvINf}qq%h&jU$(~@`TYxdzWfIsZAmN3!z@^qgO+g_D z7OQYvqeOlrnSR+rNtW@vJk0`#1z;|Bz_hjRraKooEJTaa!E9YJUC%h*)7a~9+BhQT zw%c0j4;;NCor?3fWmvU%hU-@A~8gW2nXbZy@%ZeTa(N(c;xq1-jk_}&6w%~$gv0Qd= z*AzXQxg~dVDidwGt6m1I+@y5B;}1^YRC@l~W5Gr94Fa^{U8N0b?l+2am6&+FzSQxk zcJX{S%Zi|M+zi)eOx81}TxbxHp&7v9Z42sPZh39Xwi|c9a=J;m>9&^lVUdwWrH@>9 zLe&i#)3(;S+L$aL>)vyhP4|9)y8%+hv9V{=yW@V}KhI^|X(Jrqm*3NgnXaXL_~hft z;PEbQmv=EPBbimZGve)%x)-|5TQY?Y5G@mpD|}M^b8r~Ce7n#L$5Dr;AugPANl}k- z#-vm{YzwBE(jEx3w$EQs!6tzN+}$T4$E0L6c#ZvPIzZE+#rnVBKb%>JL~hVqZ5!%1@1;$u2D^qMt9DRR)U(o6jb^q`U*i*1`AIiBNnHngM+E!Fojng(l!arG_yX!Sqfe+q$Vv0SqfefYL`6 zD;Y%~qAJi(9ZnInLFZ#*q!RBXJoC`>KhjVnZY(?^+#D23qjy^cMxWdfzmcwC2b#&< z-+z^Xl7hk&9eYl#_NpY!c8I8gzUrCL)EZE)ew1zmwCHC6$ zGKkoY*}p#C>VE9VEW<3+<6|XRZI72K8GTSBp%=a4hsJL6+7~#iKE`ePNf82w-yu55<;$SGNOs)-$LB&FLg>J=T#};o5 z7I23u6Dmbtt8x|1LO)b6K@H_>Ht&TR|M~VSsLd#fBC}srLEdr1wgX*Yc6Bz=BelvSaK+>0h0p8-|x_Sqt{rI z8lRQADj*P8!k-VhD++9ga}qVn%Y>z?w~Zm%c*&7~ZQlA?Am5KBeVSG1|8V%7y~^3X z=`+b;M*#M04ogK@HOH{fN9Y)dfo2F?EjR-B6sRscdW{}Jh6KPK@K|iF+Sz`z=W(=u&l0W~M0GhLM?2T81 z3rG_?vy|1jD||jWm`J zY>K&mNPz^%7u+c{-@%C8S#{L!8~{r`vv)y2>}bsF(LsQMuc`>W0aLlystr_g8G6(d zI~qJdXnf?^@Z+dDB7|Eb6G#h<{HvpZX3(L2phz|7cdq!Qs%@fnp2%zt0rZ57b4YWePOxa;W6Fl7kv)c;K-8J`+&e?)Czd}XV_bIv<=_H%)8elF4NJ0WY zh^E;P>x&MIZR@Cy#rqGiGkO*Ct9U-8s3aJ#+=2j6&S}7#aB&~$}7QsyhjE~RE zp1iv;J5|`q;3vEFmIHrJmG#3NHd$2I5()xhu(&}DBQL>gq6*0(ecv$8a_3hbRiw0H1i+Ry?M{ zJC?T_uQ{9=6I@efIliW^W>K`B13ITxy!^SXJexNwVdIAuu;J|G>+-g9O31l=Q~X7| zLV~T;lipaSCr7)+34nkM(^NI;x2er)fpDW7VwE!7+DL|f*8QRC3T$cwcnBl&zut2H z{4YYjN9=#t)s+Bzz7tP@|IL?NHFpgx&Ati);{ci=|n=XS-jgS-Clx>WYUR{@EJ{gFuIV?Z!4`0 z3*%-i(EQ&qJ4(kCgKxO~l-(`#U7_MuG>}A*O&JPrGwm04nHLI4SDhL3sE^PjKWqsV?K2apZYkfKdc zd{HleDkE}V;avVju&?JO+ULxgr)ab-R;w!A|+<1MbHj_j2*P7T*tW?LQxYQ-mdHM&HDx z#!yXoGUWx0;d>|I{6YNZ$>sFhlPXi+NAl{pGB326iwpA4uU&RJ)yaTAxuu1_Tv?0} zCbvAr7I>|s;-%tJ`LmamXtuti?Yp)$lZjRP`0+ds@9F@tjg!X4z~M*eMXj9Y0$`x1 zIA<6CTDt8LbaFj8Tzs&Lk8n5+p7_4H#NDRHp#IRK;#je{k{}^~Id>+G|MjZgsP{PQ z4QU1_+3Zg)XRJplBh1L&=HAf#&oQ~xHidrM9$zR9vwd@8`V1!>nQe2q_@c~aOdOLf zK4?q0PFnmG?jc)5#A%Wx!&&EGIG@Em0TZ>-tpYqs;NYkK*uCNp4uikEh=nMNjurG3 zl_#A_2-WBXRB*85zw88Y(aO&{E<)lbByx_Ti*T3q|U<=)kd zKi~l)QTBG&%9#wli=YVF{Wy*#8;ss-c$X*NFpG09n^MNna_JYo^|r$H+;Up&ZQh!%Kka)8+r@ zbDoa<2Ilf-m8pKaFMn_Ou&yP=Yrp5e>I*$-TsoFBopqc?+E8V(J(+9Sljp&dnO_k+ z(URdEYQl94CEyI1V53U&e-LpgT)#x|d9SK*NOjb^#j*w9-{! z<%;pI!U*_;TB>Qwq5Qkmk2Xl>v52YqZc5oz30(Qzy><2P@(vbkcp32hSI`;0i9g|F zOY0oW#^>j`>aust75ZC~F`SmY!kH>Ow`7Och57%HEbv`NnLjT()@u#J zX#4PZ-SCkZH#MI=?}f0Xl3y4^(yEi%UwECGQ%k< zocllemz#K{0h0QDb5BVgKY#xIkMSZA@h{745oP#O+*4w8LU{Pg zi8G$TV0l$rJcHrXpk3uj&v9XIO|tQ{n>Q6v7=6 zt2}ka$1kQE4ck73Y8f7nylY&2yQ(m`t!j0wj)1{mxuw%ydRC@$dA6|%0!wbv>ieN& zn&Z0vhdyiYyUx!2F@GP?g8B4_Ts6HH7RJB5bW!?`g_$b5rKHqjE#y3L%wg(Q#3D%=IKzE)&Y${S_cHHa$>tbMuX2iCMYn0Uj%t%0%Z^G~({=4w;#FG03^6|I@7k zBDpw6l4ooeE=io5s`lmVMHR5&ObolM-+PW9KAd+c`HYUockBG-UqRYp<7;VGNv!b+ zvTP9}4M9A?c_^%}UV3>-?;Ywuq-K0|Z+nKWm6Ewfp`F!MIleb5zrGj+Rt)J5a5Eps z_0w~tcO|*4)ogz3rQPQx26>YPA9&>MSX(%;XpKLh!dQ8no920eUfr*fs7C?B;hQiu zAU2jf_T>;kKL#XzWt!u$<`~D}2%1xc%m)qwTtJ6PN{Il4k8$2v0O^#n2FhZzOQlzm z2Z_eejwzSMX=rFXFP;%vH74Y&no;fc?mNE$Q}wNL4W_~#0@g;Qzaaei2Z!&f%`70L zJs*TzP#^!6#`$2raQlGB#y^}G*ttgPy>_*=cVW}vrzl3;qn{zKM;*1DW+5WdBk^VR zKqwL$PlgS+`N_7W`3-Gh2x1C?yG4H=htmCH;qLH*_JRV`5YJB#*AX;&WZkI~aNB-x z3GXJ8&o96FtX<%)c=TSU)xy8>b1s3s0mCLm1|g43f3<&{h`JM0S&^c}ntDe)!) z9&t=oQTUjG$h#&~!Sq&F*`8r`2HoXf+!fGn0ZA=mwaz=Ipm#wote$Hg@C+GmF?`3I z+wA_>{YOnmd8imwksieV}f1xGuIAk;hT&Vz#e+OMvRA zgT*$h*qyT<{9AAK@piEn9f?8CtjD`WE~Co5@0jg;t?^Tmq0adX^D`U#b<|d!IiI>x zJ?)=+o;CgB4|e@o02%bUXkIW(`L4iXFV-u^?8Crz)>XG{LoND zZPRYe1F{6?PZ<)-6_u4YK1=`dr3%Cv1ucAlrdUB<{xNj#kY(N{!0GMm0|&P;jHJ#m zWRm(At27cf#;;>`?@K6j7jw9>WQOujr<9S2$t zdFnLNxSTP{L?vNEOW`8u-x4>U+0q=C%=^e%dztA#QJ}e1$4aXs1*m4kRLpg^@ja@q zANn?ais_M5AE>n219fYaci@@ns0Oihgt=6|fXKvSM}+oa$6aF?@>pN8od_2L8W)QH z_3FQJtpUyIe8W32x}6`uR(y@F@Dhbtl|H1_bVm+-^!RCCZ}4=(HmT;R!;#So028Yv z6o_;VQwoc7Al{x*h_AI5C0Cu=(tmDGJn6HGr8(*O9b(4c35FLin=FO&%w1<&R{R85 zoFWOf=?Ou-_AqWL@rj#**Qj`YKh)91=dANj{cJ}b%bmV?!u{HcnZwqvf42ZTs7*d5 z@4R-Ih~7WkoA0c+GF}hd{fM8_Jvvf1IwuJ;93DegmRf59UK3l5w^Y|F*kPc=pQcg> z0~tN>Igv>Tv=W(VK<@_XVgrz|tiG$Ca;o}=N95|Dfv|*hUJrdCP@7{iD_7KgOfl0^ zPLGX^)#z$wp z$b|RuXtzIh!E^|KaU(ET{MPFqwZJzL!>tXW4 zeQ5SGfY&kLsNMO29^=xX1~~!bkTP*9 zh1JZ61BTG5!i_`560=|Sb?X9fKyCme01rrzT-1fo>Q@yR zGf`BLmbORrEUFNaNwQw>(?%dl=wEnt$pD8;6Vt50>YPni&^CK z>zTCt1~8Ff6@p@Do)IOSA*h*UV38z3v-*qtfs9i&5fBI77G)IF;KvC@8HgDj*i375 z(*vG{D&h}5I@b9EF0yS3ba^~71nQRBr+F&5x(rt9Zb#E}VVmOvv6`JyT#t^TbrC*! z!R+!KrB!G4U0r!3WF;5(?mzc?(NX)J!*|2E<~lU`f-x7_Gm;gQJ}O3cV?D zvvS%;3Pj5yRm(y%T&qik^WT*V0EG;FS@(p^t}TxAl4of`MxZV{qL#^?uIrr^0s&AQ{9e{Xg;@O_|b9Mf(mRfGKIO#^%5xJhix@vG0 zYqrx!jQaX|>4MwvH93#eJbX8hhP1tr*j~^?4`Q{q!<1LW$-gnx&F&_@ktDm6w*J*N zvu_G%=i=7qmSv>JzjamYMTfsqOx1Ns=6TN3Cz9XAZ%a+HKKwg)oLk>T;E)0$e@Z`+ zuLh@`^HFbwcsN~xMOE{LKhKajAp8-f7w6Ta6V-+pAXBpQ6a=q8N5wNo4L3ozaQz32 zgZmUJCjX|f>uQo%PvlgUJ}WjhCnG-KN0hW*s*IXt@sXXoQPtjGPf219SrT8Z4i>`} zy`bEWU-w9{E*V~FFe%Vb8Mnz7$K-~-5UWYYECiZszo*U)fB2Z0SsVz4x=;bW{Mq>< zF#AMOFrO(q+SB&?EAF~KGJUunK(nm7?aXH6&{QibaT#{&ou5A6Yw^S+B? zfsEH{k6EGmFV-q4E-pn7GpU8#KMCKI z{T^4fs0lY7$rtAF%KMOIWTS+azrrqXxr^optWJqnWJ>RLzCm91~ClKTpF9nd#7Vemr*IOdO^Z+(W$YFk_5_DX-7g8-)dBLlRnrI`fY zHP!pDZF-QVW`4amtIPNJz4L}ME^XuCIWaKK#O;b$&Ub1_;Rl6Kdq=P z*D$ohsFTIz(cTMv4%Ci1SXCG@v2-PD+&9XeWGupKmjUb_iISl()coj2G7TtkJ(Bhz zeF3^Uaxjnq3QDmuq$c#h6!1@cWKcy-%GROxJv4m^l=2tCP1^5ARc$Mh-g3HTQVolW zfQE{c{o3)yXIIc$?LAe}8$1)2u{9?p3tCM{Mlm`@KN`+u2=3q!s_N4N$gaE~Nqogy zDN9gEk?N|ds-xLuTM?l`SrAl@eC&#k-!?W%7(zlTjG+k!jg$tPEJ;Y$4=vb_2$ak3 z_mI-%!HT;z*MA&})ya)NEcB#iN)5{+4@I|XK3%aNm;b!-rhleqbsf1M^y7+*%t2mdEnHqs|>r120uK1ev4fDo4-7J!< zZx{Hu?%)&@e0Ee<7{K{(H~9$Chzy3aUJsoE5e0NxmCPMoP=w3H3ZC5-w5&xqet917 zh#oPsl-8FX_%h!yRLG)Xz37yCQ}DR;4|sA2EmlewbdbplYcL&H4SfzDT^BLl=jY!qX`btG(d59R?=+)` zYFjtA1U5c&Z79qr$dE%Cb>Wx|0^AvV?S8X(M26wCZ-HCt1Brf;_t(mo5jTouv$*4M zDPz~5=XbFeXT;*Ko|sd;MaT1jh3sD}WH+a;rM$8o^w&LJqnI83Mm;72R30Rm^OJbE zxnIR+-j72TY-}&GcjV0yQ(!j;!o4nwK+#wTkHUDOQ`Sa~!aH*FxBck<#MTS)rw$&v zWW0sJD-DE4&dQJtmB7E%>YuNbhcI%T;u*irHcBxp_ecC)DV(?oQ7A_=F^DNS$XJLUg#Z zIThykdHH@M=1VdACU2D(lnP_EeXD11?>SL2(o41jsH5R0U#f^KJ^SaDy){+kg=bw% ziwBPt6$Rfq^OL?o_;E37Xy41)^kZhl`?p4f>+}!v*-yYJY@~$EuA)@OIle*0k3?)t zlm|h*m1Eg{doKi>ed_O09C8)SspU|<=U`|YL>uyUdIZ^^8 zk*{eCDp3$7S2A5UNz{~+?+5z+S#~=BhLFt3tg#jW^tRZDdyLT>fmyA%EnY~$FbMM4 zU@wKnS2gJ%)#w^bI{2)RVyv4b$(#VVtOV#(eit{v(W! zk9_%LM@Kr(ep>lKiAq4^Vlato-7qFt)i~4 zl_yM4?1-=8-fjZDB+WghDOW}OC=+m6@P}2Dej0+pxEK~8%7&0}!yo_w_$FvvE%fF` zly|g~uMqkC`l!1#4d3}oP%&-Ptpc=!w7o?4F6KN^ii<0f?pf%L@8{RgW`z-Rw-|c; zGyr}&UT2#6(M$gi6u)4uKBYW>KkP~HA3lVV`s)5$%xhe-7qZ9#S|8M%#!>C*YTI>l zgU0mO^*Xm%m&5#kFj^IdET87)+rnqKKL#tZPVJ}>YAcu^F}&o#3Pxx25_G(}yA1-! z3WUn6vU+Y@U0{_sz4F;v|M#L@?}FZDQrZ(5oyTKCg1-1>(^oh-(z%?>kb$}+ox%qr z!w?DDQKC>F*SZGuIlh`JUb9KVM@kvHq`2YccI)acKeh~)#sC1!Rm78#`hC|iaR5FTLL_s$wrlnEIuLZuLrCF z9!->VVPlW&V$ZAMSPMOsyL#@ro?=j9nP6N zjgZ4E-Pcaz(+RK)U*X?iV* zHoNISBQcczPaEh@XrPH@Hj|(pW^|jUFK+zo3XLjubsgNAtC}BbL2l?zr3iYRBGOp{ z@5VJE#nu>m!p>F9X(sC03Lqk@3}Rp*gfRc?%f(60y^8I_KQ10D5j9VJ`tJR#id=JD zo)X^`366CIg8v_DZyi-t8@>%5T9Fh{8Wm9K29Xv7>24&HhJ#2q2T(voP~spUDcur> zE>Y?32K7jD5b2y}gTB9S);DWr&8*q${pVfk+54&cx#PO7JD*LlJqidT2+rkZM~T># zIalp{AdQ-H8k(V38FzvfQHuRBRS1v}rP*-++ehJ8lU}M7{eKc&2|pH{9f1_w|Nq1W zB;-05#=6Eb$PgSpr@PND&t@+`xvxP{QC}@n}ASkve*l% z=@1~+XhXop4ypmuWl7GHfdcQ>qHxUmh{}>oi0qm@96$!P%?W}^gI`KA|4rMcu8G`( z`2w@~weAVk8qTR3__qq60xsVn)ry45=?gQx64CS*h7R?m?4_^%MK<=GjG-V3mtvIa z-FaMqvH568`urADU~4s>tnk3P-Pgz0hZ<$aTr(9|w2`tm+xIQyTz~WTI80E0d<8Hk zZ74W&tpOK{(~ntz*FyKK~$? z^yUHJOvMy6)HJu^7$%^%I~4vRjjsQM@KyT>fa5&?Tpz6lpr90k!0+qVZ=TTX`yJ92 zQ@Hn}^vM(s-B`n;=Ho+JU$NB8@hp~3%+sYOe-{Hr1J!fgo9C4`d$5fZEU^-Gk7|R; z9^)c1K|~~FWHmel9QS{JnOOFs%~0YjW)$l32B|=omurKV5-N=VYX(V*`7Maf+PYi| z1KC+4h_eH*nB?B#5d8oY3vxbu_<)pPhysK|=1L{L?{QC&+CHQulN25g`}lskXMJ7u z@W62OdNh1&Lcz)XKwa(*Cg{@T6daq(RVj6pdE5N2l=Inxxd*)pg7r;oBtBw>gUh!D zlmoW_?WBz{Tg(R7exP?`3}t#DFG~w`3Kd2``Wrx^OBNuAp(AMhgI~B0N{@?Br2^DD zKJEYwokz~?ED*UJq=x%a#ru5e;;Bp7^C?J`Z{dtZq<~JI22Wb$NMXeWOrlgVOq}j8 zYMV8VV!fiNssGF-EH(KK)|=;miy9tDo^A?FSN^aae&4Vc9lmGKlm+k5J(|<|_Pez+K8d~}(g)H1 zYrKNd>fcr>=L9NAqtIJHY44QSM;ELQfKaP|yXpXpyBuOV0hJSA{YC-r`DWz8WUUU+ z*#d$W!#Q2k00;QUrCTt~-8~#(R0<*|5vaY8c`sMZ;!U7MWdNY95EPDtK`aM?YlEux zg*Ut^TF^-Wm0a2iNzK+0qWVwA#E%XpGHaZdip!V#3>7phmWCTi{jUU+Qa-F|h`)Ym zY~G#SWot3V4;RtTpbw1H`PZj*L#i{5)o5hN4usVMm39DD^m49kDSo>SSX+?t4lv5% z1sLGMf|8SYaPP*7>8T+YOdK<0z9Hf-ZJjQlK0*-4oz-iNdoLiLhnSj=d37HG%R$gZ zQ13~z1~jgkrL07nI!R4-Gkn*{)=FEM_K>g!mc!X-^5QO+%eydw=sP$`lpl%84Gh`J z^4R8mQw7V2wRErl4rTc_+l2%`N<$lf*FZ57#6W;?aM@}OG`C(#0D;F?8N?2jw)O=3 z6ViH8j56kmgSsaG&JunhivpCF)W^p_Y8SJ9yANWDC2oRB2owzK>;3^?V>OVw&26@p zTDHivTpBQslUoy|;ut*4%zvVWg13h=&IztDuVXQYqIO70LI*Bg8s2`zKJ;|{K4slj zl(C4&#RZ(gq(@qV|D$3vgaf`7XyE@UaR*_(D@bAA7TisL%|&Pr)SL=u4uS0c3h1Ed zmE#BE3Omqn?Ax#^XW|=Y8*C1(|i0IOEH9iCK_Xo!~Ov!z4zM!!$8fwV{TE z-Fl|U2$#rWYRR(xhc|J?rpvOuPxpIifYMjT>E!H?+m3@JoyP|{)ftn13H=j<1HY=! zKY&H5&W-zl?}5@J-AE4*x%74fWT=)2P*>QVIfY*TAK%xpb`9lS{Hz?*T9!a6m$<^k z*;xQYMgTqDZ3D_iakOq%GgNoG))P*WmNX6EO+mb=Y zwz|6KKM<%_wdZ?}^M>KDL}#ht95W1dc7n7_k`TyB1}sF)6M#fEDXs^irbWPGyE~+s zwdHyfDAgJ2`vAF8g<=u&&$!@2Ura!e0f^CQ-54@@3g*jxs&Dbl^sf2b-jQY3HzK@u zOEec4ejjU}`rl- z*eJ`+oqfae0->IUBBtuXZp!O2O3@$T*}2CmX_=3>wV=v1ve$)znHpMugd?@ZX zK9zG5*YGRzw)f};0w}^t5+iB{AByYBKeWA+Q{R*&g(;zkMDJ8GaaM%lOpjT*RMeGE@c%q!aJVWhe zA>A?1IHMuVY`Gt5-U~p`s!)x8;3<%8bsUCJQ+B5iJ{&9)t|k*t4?i%9^bnoIqO6hO!v;M0e!Q5}X4`ZJ_Q7pmuEoAOV|)AOS@OgBglTY$7r^0NEev zAq2!c=z%00#0IHZ&dUdgw^WF}IS&ZN&{58-kncZJT?1?f{~U)uAf*Mlg54>S zlIsi*7aoL;%nBr-GCrarjrrEKR8F>X38_K%#yI9Wgbuzs zKiX8V2!$(ItzIvFX93&W_z`J;3Y@qt##8m~O*G%mS~TsL`=;_*2Dx( z44INFfV+4AEU3brl^w8~bOE5MsOtNxux$^}*9*!Y3_)_7MF60kapd;%r|it(@}K=> z$}fdL91PwaQUw+gLP~pggA%v^yY0UrQtsK*-vG9l6 zO3L{`fRe!Ko&iSQ6Q4M0#s{dn%&inhgMm>ZN7-*P2A$mm>=h8G z7v6FJ%oZpRWLob({>ts21~5HB0p(WE$>j1DtC$KFR`SO4}#XZWFsfT#s0B8dp=*F-d%uf z8ZrTh{)VL%3S;k~!2-dXOOpV*bue9+tG>GmZP3x9=~|D_ii;B{$AxWiB`*1`fIZ1d zOj4WLb3-Ul<7L*upP*oZalFF|-)a5M;0k>Pcx+<946;q4woQ^WQ2_R$El=6f=|iRVO|yvt8cSHjUez#~0SI^A3LRPS@#}pvzB|=W$;4R_15uRA4o2G4@?C^~_U~d)&uikF0eer)yU3gh z03$4?w)+l<+IqmcGu=!U{i&WX$k~$jN1S?r!K_7f{ZLq37xtdWtX$@QScM~@M#bbh zKt|_gGT<9$n;0~?@%yjLG>ZZssQ%bJhNBA?h$L}Ay7Xx~P@e4g=WWT06`+a2$DaKE z1oVIgmiuRMNx(-L9vXT+0fJbEG5S`U`i9Y6IzR8dOA49^{3_PlA}aiZJ$jOQdgH@_ z^TC{DJDt>P>t=uV9Ey5{!7M=I9k`}j=55+KVTLRKWV(OsoCRn#8s`fQgUte^xg0Tu zNblYCLXg`GNrogxAlH1rz77zC%-29wAhg0~YcUPrJVp*3pp4?@3DB8c(Jl$#ji8Vs z#cwB`83=DE0vx~x$W}ae0rB_mk{ysfTzU4mJY(Lgbt$8$HNWJK$JQ~Hb-ul~wL-k}0-#aK4o1qn3RO*Z~nH{!9{qlX7> z|J@XZ^omkUS^PbBnF{=Xo=qpu<=J311*n_31c+X%Rw|y?V68&(0D!K({sE*5AZa$@ ziX_!+pgJ>srB#N)=`P*DX^3gINNW!fUO=RnhlQ1_YM2XYi9w}>>y;&5?54|>sr?cl zxw^`H>Il>PT*{0wZ`b3(`*BMDxA5;O3w*OdAWOn90(Iyps##_;KvH_l0H713&-DUQ zPYzQBA$3g3rw0F>vXtC_)04Cvh@AyOvKk>;0YK^_gw|i<`UYHmqqLJt%iP>NI)yU# zi4x&^jfSWE{24VY@js?<99T%4tM7Rr+2xSjSZ3vVQ+VN4PbMpxXPFq=e#d_GoBf+B zx(6xfF8x1C^@Rg`4#mhWh-3`7^tMWCy4wMK7Sz&7eNX_X2KfA1XT+ovz^++>>MbDM zD$@)4Mnfnfp-zK$h7D#GFo2MMNbROLI^hndUc5sj`V1uWy&g}+uALsNWc(* zJGxH%R&!=x15pegl~78*f9B!>hKYs~0nc~HL4>Ljetz0>%Mf`OlpR4Q@Q>h(L(;my zx&eIJD;n5)P~QMt!|%Vu+>N1{)HamA4LF>fz29r%IgeMr_x&{_1oOYB@i`;|dj(ON zdE}f(3lq%^`8bput@jp8puCFT$wn=r5Rp+HCI~Pp4qB@6N2L7F-2;&h7c5{Or%PET zS7Mo7AL{o%ud|GrkMwnnuaKTAaD1n8=9mH3j0EJazC)NOh<6PwVL!;^v>X7y-TyA8 zN0Cx`^8X~2mnS8(+)FDEn`m+yw(8oUy8E!oTN7`1@yDf7s{i&~1rjCO{dGc_m!SZi>jC0yn}W=r4Y6@gXqj z|7qv}S7y77DbfZ|6rW+RK82sU>13h!;ZbKIsf5GR6Cfxzwx05}-s)vF!j zB-H+lq0PnJT;l>(L>R{-c*3D|c%0E6+BNO5*?0WJd>r1K2^v0}T4jTef!(VUH+ z^Qf?Q-q|)(BOTFbINRP)>}P|US>7gv1YB+siO&EBOe+=<<+Z}iW`~41>7+)@aW8)@ zSJByHA7T|(BM~eJ2f05ca+n+&wST+myQ4j+H$PpKl2@}t|JJelI=~;+LjteZ*=rA= zb|N4^2bsKtKyDk>KRGLYqp^-7Wu}dJXo3+N*&ecK{#CsSdR-~a7+6Wt-JE4mW-?5X zAY7lJ@4t43ElO3iFfdy!VswMe3@^czv|KF&o-<&YHnpWnOo(lCg7}cdDj&qRB&8V< z;|72YKBg~zIw{yvtGIBTUjwPJ5CuZ!lY@~7Q;r}@%F1}L>2OGCVOj%s9_SA=>`vUt znnNrmYu*`Undp4)J3*Bg4gp9rcG~j zN4vuB+`f5ztCpZf`MnNiYvJ}-q|;x~=)M^US~u^&c5Eo@=l&F=M|K)N;)zHW5=-c3 z{R}7CqCwqIONVWwO6Q$yw1h33q^F0_1DX{Fm}VWLRT`Tr1f{xVYPz(v891#;Nu2UA zqUC`Z`bce;svYsWGdwBW{)n-KIKEmrKSp_er@PE?N+j83JExn;=cDd(IHT_zSQGb! zakVbJ9(Y6U$B6VVnr^^ZPVCp>!;ZSm|2W({ev`TC*951{`!r{qV5?Lr`Z}bXoVx38aUdQT9 z!3`7pjEELA5$T0mW8ARwy`AtdhzuVPAgb({zj;nh2m%oRZoCv*_h21SQm1N~qA@ui&E6KOl^TH>I zfiowYGKPGuZ?w5*GRQ7M*Wql$DaPQK)f#-KI)IcJ|f#2V2kw;EyQ}5vt zaqTEgcMHmal`P!rWnjxvSh9$3h6?_9IYvXo zy?iL%enD})yu{xK>0sdeg*AQ7sdgAGFB!!ytOtmGF%mX!jX{sk1IwYAL)y6+QvuFm z4W>}K4%geJBjaFG0wHw^ygd8H%Yd+?>xLt-ad}ITB<^RsW7B*e%@~r?AY|2Si&WzU z_7~OcpY8#)*WK^2ShKTw|8yEkJ_o;~zJ#cS=1BuwP{YLPEc2L2 zibH-N#w(DmeJN_H-H-Gm{32c$OZ)7-Vp%7vjLz7&(Sg?UT}uyRlqTpi7eNnzsfn1- zv(;Ydj*4XA_s)7}KK${>hGHHJxpLVo_Q>k{<@zXQqtZHshzG5fxKRxsM^PLGf^X_f zCrruz_^^vV(c)-D#l!t z4=eJ_DuNXJH=1@v+EA=&5md@Dn@B|k|44m7lW}(@92*qpeg{h}TJlRUN!YH;*(diY zL+7k-fR6f}>{GM0BiyQ3uNr93F5H#SoDJQr#$ zZMb-H>snuhiWZXl0W})M;?)^7L;H{#VE>_?mEykvF(pNdG1}Gi{TNn1$63+R?lu^b zGym)QOyQQEGltvnec`6m3*ecMjcp2*T5wM=P8JcLjQj`|(0wCZF&>S)vEQjx$?z5z zAO@k|MEn98-%_D*Kb8yl50t|uDl4+|Q{w!nD|&4LIn0XZb$DK;()MjR01IR}@jHM7 zl!n~jVhrfM8W|S*c*2=pj5o^jV^^S41ApDeJI{ig4}QbJC?Pq(r0Zm1w}HNbsN7r- zLmve;#mF~grgOlH0^Hr)D7E{@q5H-S$hF8C2{uU8!NI(rhYJhjC zUa+WDLE}(2_NltY1C1|#EY_t|DA$i4*jsjL#y5&Nf}!qT@CHXrAuz#PeShWj#?`VKf2<$)fAog(IRyL6{?;g@_?;lC zS84@Sl(?+SAAEb4eLZqNOkBYLWrNIee82cs7m~#YN%u@b=?QHJ2~dqCyh6#ku{~;@ zsVqLbA5q#Gq2gIY^DzXxb=iPVXPsNm#M@frXV~hlH@d6GF)%vqtKde_qfSPfhQp8W z+MRHNM=YWFUOTd(fFKv+@$hVXh#|nEh0XGC81Y|f$etWI5lrtbSK%Dr=RSViwtCNn zC{sk7cht3NI?trR_*a??xnE#<`g5$gRLZxbHfH!AY=^9-vD%$7m25vX#zmV?WU(Eg zmt&Hzk>jFNl#sLB*52M9ccf^a&_q?13pE+4sd(=`6@a#AMi*C9@J$h$n-=GUbA%OO zl)@U!m1w5nF$Wg(AH@X^nGQ|?ALEe;kBo@ztQ9W}GPfM}SgyVJsrmlP%X$l3Z~Pl%;74Ju7B;Dz&`=mXq9kb=W*qJL?rIMY%Q zIDDYGy>F5D2@~tc2kq_5@Se+F2{wfPEzR|MELg;2s)4U~3lG-*BuE_F_fF3LDExv} z5C`xlzvMeGV``hZ`bMOIPYO%3=}?@d0#>oM7MoY6z0$6bR3Awm8=2Q^KK+Fbp=A#b zp@}=-k~^wcX8ckCY^lriG3^`jx4x8|m68*JiH(VW&nZL&mj}K@w2z9miJ{}r$)l5v zE;FW=b0OAqO*y?dqV8Y1obq|?EXIg0#2Dzp=Yois1d!AP$0m8KvDkv!C&`5RSUPx2 z!A;1x(x3D}mE}7=Iam(GYVJynq0|z&X_Qn5Ps0PBvm^@4^#T}eDi^!THIlpRL$0F6 z_i8cB;u7||Wa>`4>tPGT4ZEYIcKiXfZ=Vr-v52q0GK1ng zR_bWnC38o%u zZg{V8Hs0H9p*CT{I^CaV&Um|R(QtC9#KK2Rb8d2=D$BBMP5E_ovUL-zeJT7vX;w<( zo!IeV=mhi~D3$=rwdqDFtr8vDA>aXba#r(Yl)JW*pC)UkEJe4>Ya>P3%OlpdJKn_( zZ&}Oz`J<=U975m(D88%sQ`A!yN0d=5tkq(iewWKBe8B;S9svSiF|QLwGAZ>cd#j%c zZpqIAqEj2V()|V@Tq3)HJk$))fPSHRLkftXd~^M)@O3%u(g# z&tk>BW*l&l7XOB;zvb&&(@xFWIZO+57>q&NTT)j-W`oX#uLS$MjL8WMrtwYXK|%5;ekDl zmBtX&aN6|}`1{g`j&ylZvpsv9%r|CU^}LM%hB5Hr2GmGQd^jp!&_D8g`rSjJx0x$# z((y+sAchZkW49F28WIHfoJVxy$56Bi=D?&7)MJwL(GcSrJ? zpj^d~6+9&S(ICR7sQ+HPbi2_+4h!fjfreWGi(VG~6nhF6(|1=0kOiRU%y{+{W$3Ky z{yO2-Dn-tQ{Z=yMzaH93(SeP!=ujoQvhn2tjKBkfoX^+LNv**mD9G!s!7-!&$Az$n z_4pS1-6I@Aon|&Xjz*3EDOCJk3xy4uqfBUVwyi)epVRkx6Oq8ZPU^Sa;(y<`1bG}1 zW5^l@<8O{0%-SnUe6cTpVHV6 z{mqj}0eeK0y2ie*DAIePUau?8oIS6VP$}D5;LXgUbQrARuVAml7wl}2p;R>C6BK+A zyUEt?pzU2ULk0JSo5s)rFJ4`Rsg#kqLB(v@OiNVpeZC`b(X*-RDBLnOvYf22OrfaT zWoap}lfrwBypn?qsw4PF{_jZLf}}hqcYZSs%zo>v_i-Eh(PLVlwmQvDTkqRfZXtX4 zpwJ+(5Y@`27Sy%RQJ?Km5M6o}n0F^AK4bv&=WY(g)Q(paNHEs#xd z8KuxrI$?L=jRhWdX>jwmltL5tmNLv#q^S+O5ZZIdrdS-^x@lqk2dlgTBt@%w2yE{j)b-m z7Y#hP_@*VxV&Sro6Ldd3I&7378v2%} zO`dpiC;Ce%$KrM#m`KdX=ru7ecFz1}qrD(x)|Q0MY1w{Cp3u#*S>)Bve z5V^VgvE>Y?$(h?bnXOg|3dCeF+++( z*6|Pj@wsi4PUPfLKi=tI1p|6$zx%An8;j2l8eKK9ONGUrL=C@$uOj>yj zmf2uVH%HkDcj4~uGnl+Q-Ij3?J znWzC_LvEj z!|UREraH}bI{5jI8(Ib8CZhJ<_yl~it1E3sIMAR}$f}5KgvT*w^O8CYDV9v)~@uh(@hmqkn5-RdZMu~{UN)O#G45{`hqCiL}$va$O%T!3WB3u zPXj}AqtQ0+^R*cB&DWOY(kM>63&vl62p_XuLUxvST@{yCNu58OM6z@$?L3YC&Hz7q zD}p2v0*|4a7Ed*KYORMP)P_yAk5+%n6=E*YBxpGKcb$9{O$OycVZY5pe}z{Ruo;8I zachH$_{QxE8)Kz9lswu$glS#%c>3)2jyU~-bbHH{WAQp=Fsz&|_(p;F{(WEaa&6TO ze1>G~t0SpC{dZzP8VX3l#v(K8v%hiSOz0I5kz37KX&qy?TKUQ5pL zePa|npHDwC?<&W|HG8S6CGdpO3!B2?uT#==!JyBEZ}YgHyA&58R8Z4P73;qs<~F{A z36ltQ*f#NxYe?S7aqy60@?cp8&-lMhb)U{02l+Q-jH1Mle zM^16-`J0M}C2zd&qugejULtlL<(^1;)q#|dX8-xNlrJ-Ut0Y@ukQ?Z(uJpX?T(TRX zz*APrf_I{wn8jQ3ZXH${=x13NMFsbIp>$DSd(RUZHik!57o=)k&APa+bS;xtaMwG> zdHXixG}|8rWkq1bv|G`{FDFiat?!n%)=Ii!F#U8(OU|SWR18P4K+;PPZWQYee1dF; zhMSEJxiwik`6MlTh8fJ|g%i7?~kX2#InO> z5|`_XINPn?-^=f{U0z+f;?{M#<}L0{c1t~4dt;PVKzoVb!r&;Ks!78^L1$R=jc|Hr zDW?uDyAE8yZ*I8U0q=WEMy0R+xJGGg{}+cuj=7NtzZU`_we0Nc)$l{7`>YV)Xg3`p2>{C@be zFy$M492HPj3bC|xh>YHvDB8VwpoE<{VN$P!!|$yS_^L!glq{y&C#8$NmU8NoQZs!m zON!~iaWRb_7mWTbT!~UgQ#UInp1l9j6Hm%=B6JqIZRz2fHLPA*%YY${&UuL8>q@lB z%SohVd=y-fL{BLNw}0cW;8Ow_l)vjpekt7Ho~1w{-!Zam#VhIMwv^>&C5Bxb_GD>^aW1_%vl67MZW-td-hHP#9$Cr> zH`=5{@=bA#BOmNMwKvCS5yqMu2_yX*^Jf8!Fc6|z+sqIEs1oTGO{Az+s|N#3MSx1X zH91RCQmboZ(s7ynQ^j6_R2)Lwj8`2K1AT7#s7#P0s*|$|E*A_0&|}>Qh%ZOUt}Rat2(#)48D3?)?yJ9m}!)UzDU027bNof@8K(^*UX>HuEy#l%jW`jP;{|GGtC#4+<7>L^hq07uEP zw0BxJJ6Ht-Bg~;RjHQL#R&io(RNI1Wr~>n+*@o0!E94|dilx@T zpFKO}vFHx%EYY(0$@f`Df+m($neFl4H4kmBPZn|Fti95BhRq3z?vu#ilyi7+A1)Ag zf@xS_Owt;Zu02Fj_b<5K9cJa!6R{mPZ>%Dwit3z?>NL8=DpuBNO_jGr4I@;UP}-!n5JItm)9(H5uPA%M1rVb-UCD0LY%TDYO=|Sk ziuyUdQAffUV4EY^9iR1~+@YGTD6Cp~Fv{G(Sv1MH>O(1ufjhi?A(a{(S9k0!FDYvo zt8K!HMrVCz!D^}``nYqtX#iopz32N*4E;4KgTo_hQ!FVRKOk}g z-=04tiK`MDnM;^LE)MqX@nb+J0PWXOmp~~)gLNOLzZR~9ZYK0nF&$8v5EPs}8AoDU zU{x|8`v-1I5uv*M#?SYs?O96ikx*Ug(QZJoz*B`WmuyPy6to9hZ&D$Lhwf*$qY#!( z6q9H zSk-Wea(hHw53CUOZzEBF{3|SqjlfsW%FunNp(~hn$hO5rq43G%z%C0*Uggu@(F;GM=LJ%bWstC+*S$dY`f;I=Hw)*^qzuU7G+p8~RHf0P^YVr&DdK0E!XTXBgwRAcl4{&HGSqfAfGY z%e0ztDx=g^?P0{1l%!|iCH(P3b5vM!(?0efZZ^Py(R7w)Y!b5GHrn*OSp%Qc)hRLb z=f6HGj0pq+)0Al5GOGFzi+h7V6@RtxJiJ)hQb^?CZ6W`G$g!OHpWgtj4uXk}Bv$n( zsq_TX>Wt5%l#sP{s2snN@@{vJk;S-U$w{GSB31r{MkaTDr44-BYQGTSKmBy15(h*x zI|LcQECamaG+UZLSI|Krc32b1VreByf4Mw}kdk8OAj8Z;*NLhztxMpfdfb>JyO8L? zB^ZIuA>(^9-+YMPeLlH+Z|Gn-LQjNS0gX{kSSRwZ!ddc2Yv|cHJLB8NQx8kGj>uY* zm&LN&>s~tcN~5`Ll}bCp1`=E%I!_K_&@WoISey0h1@XhK{k~DSS94SebYE-ed2Mzv}Vf=^hK=76W}nueoPz z?dnJsi@LnKDDU#0WiV&s6ZsCy!C)Zy_+{@!Sat4eMmOF84)QIFmZ%%lz9}Sm3iK>WTD1HV5vtqr=H9RGNH_ED0H0 zG>0UtOYT{*+GMxnl2^ArNUAKd$`5DCygSX6+soT8JX@W8LmnTY+iI;}N=qRvry1^P znaufdpCst#e`yiuqk-U|Q=XP#k@G-lH2XfC%Z)g;+tW8EV;L$Y%j~NMD(mRnPANnw z<&u7s9ZhJKeQw%hf`J{VAS0iO zm6kp!&M;XjPex6WqXVqZ4l3IAMCQi5fA;F)TPzUPZ&0KBcMWq*_W4r9)Usm$Qfec4 z;|ZP**u%ZlIodq|j+YJ}6oFfN65apXKqkFs|NEj3qBnv0cz$$%GVY;PBRlaSNwXc2DB@R+Af?4k40@)l#uQF}ZRpP~`jEo}``6OBX6xm9# z(NAMR!hB*aZ;C0jQEWB7Woh80h|I?b?#(sL7aFmfPq3xzf016^u(Pw8@Z!B@tcc~7{2!Ab+?)D;kOY%5LT%P=ia;D^7 zKq>#d(Cz9Ej!EzNIhH{&(odtFC367>F~VhI$j9n4neiPTp#trVFW)Wg&r>i0kh2Rn zY$|abmXsWpe_%=O7g=;S=S50}#D!JyXUL%GODqaQbPVF^dw+|HCp>7A|tw@wy+h=u1dC->6Ur$qt?g5u=~&J zr@%BYX)IBceVoOq`;_(2pIuHwwKHmmvEUdRNExbKb&BTY1f!&I$lQ8Gcz#Y5pf2&S z>#2M+h?Qukm9mfgnWP8+)8-q_Hs3^*b z;RUP%3L-5TYoeJdlNm`q8IOE2r+rugq!RAx9uzYQFBxE^R^zFh5+ z0_?jH1);MWru-yf*Y491Z%`e^G5@XY+E7aGTDE}R4+)AN8?mfU248oKZ3Q^%T?tLL zrkg3)+AK!*(&j`vhuFpiq^NdLXHk@e+Ubv6_xfNv%M~?y``~-C8PMpgkF*`4#!4Oj zl!>rqO}bpgdM2+L)k6LQBQzPsA>+JxN>YCJulc+F6+~!#MQ>J8J|1#Yr4o6pD$iv; zh^;xPNFt35{8CWuhAy4vXU{^?p@hDwq@U8dv?ZTsZ$1eai_VJ5&_jvk6 zwTs%v9s*clEce3~!C7yshy)Cp3b83A&K6MW6_ia{f79MOf-^{Phn;iBXeM+r-*wtv z6m2g{Hr7R(Duv>o8?}r+lcK2|d?`f%RGBQC&^SJWZ)Tj3~}8Iwd$c@a=gtZmY|m z=o)ubkyTwcjZ+3`N&0}ioGplu4cnCNLe7OCgfIDCF2bKu9T`L<-GsF5yrZ8QlLb!@ zdSts(^W(#x{o`kY<-)sDQ{sQTB^Y#iPNu>Wf7bOJ9`AR@zq#Z3+<(QMv5)tP{Dt{= zUomC*cW9J2JhdgS_>)OvEEW$rnk&Wpvl$su9sl!@7{4!Uk5^4p7e+yT_Gkq0!fvHM zvs87HSQpQ~_*IqD#^?tw!MaEpImIUYlWEhhAnOTMWFq0cktT@}rXvxW``R$f6tU-c zQFNGdoF1vxCq<2mq69omLVfWF@V4`Zg$l>)CXI)c4tjp3EJ6MI1TD63%v~o;yh_fU zl2*SHb$BXPs_P2-yc$^^Ub&`}5iS-VxdzgC<<=E;O^R<9MzLwAimA78S$dHyI0 z;5W$%aCPUn&Nq}Lsw1=0P-783J$Cmk&5!1}VT+W9&3Y1@&PMIX0356Lw3`&{F!``g zP7G!@J1hw|77Id@sVLjUnurjC5tL1`=TD3o5#puIRD$mMXFqGTZ+_N-X*G9v=P?ZH z2G#~el|J`0#u~g=gEMiL^9n2B(+mr3VPSl6dYSN8{&Vk2ygLw|FQ3w_L8B3tTJbi|D(5k^rr+Lh?#_{lp5PA+>!*0Kvm;*K zsf^DGiVW2Myg1K>h`sopC~#0A-T_PH?>D}*RN94wg*m@-zS_dS?^%_QLOh{cwL)*R z&Q&1jvvGfPW$)=}_9E|r9164PHkTg4bfZAE>uR5@0EwsXtyNWt?DrQGACP6yN=R+? z2DxfxUA(a`sP^y2n`?<=WyvSR=UR48`d{swwEa?Y?Y%eXT(5phaY8qBdi3R}_YCoc z0AN##DIN{ujoe;a$2-7Jrx7OCK>en^Mf5eVO_qmD0k*1gB4y)H;C=giwbF4P!<1#O z1V%{H$4QI_jS6kg)2gA&tWEdG#`Ih+Y<*WiPAXQ`J+I#9zh1E7`@8wMMCUea3_l_4 z8(uL(mK*N_W`|2QurLcoMJ}-Jf0s)Nn(fbvG$00x=CoM?;J6BRpJ(k9i6!;I%3ov1 zoO;DtoTFXLMDx6bpS;R%c0Obq$G-B*C?yVT%r`iW)6cQ<5kD;DCa@D2SIWQ3r<^Zc z*yWet&-7xGZ$Vv-~T6t=r^(xsI{}a}jK90&*M=v~rt3BmYJNv${&TXjO8q4 z!Ps!}<(9(|YK8=6B~4iu9f6wCtplle7ePZumqZ3v6(U~JjjP~@_U|>p?(*I&#?C4? z2h(}}T>JuiA7(=|D_TP{$ZmY260NrDO8!1yb%rShp6<20ib zrxkapPX^57Xo*4X4PL6(MzVikiQpV4#2;0w{{7LHEoVOpj(pW_fm~M}5j}P#>Ehlu z3NRb3M;pw*zK}*QV8Bn2Vn0rgRxJcszWD6|bN_77?mQdcNaRoC@p)|0=64U@&G$$9RRIj@LSQCwGPM$CWar8_LdN0+ z8l}ThD?@6>J}Cu8KgX|3MLn_Iq)sR!K@a|u7<_&|D}q1BNh2Q7PIecrzx40>FIs?8 za_7)fjG@4!o(9ORKG4&<77`MoZfqQ$m&XQFdl&&bCz$N!uRpcZjs=fuuL9x)@5JOJ zFAom_^tz9Ui-Vf?yccn?!Zv*~3mUGL*bTEeOxIAF6kh{PYny<2j}xH%eMS>L+E@4? z|4FIkX^+hpv>NRI~RB*dB`&MCu(b01&`SFiEY|sRIn2ZJ&SM~~zy)<976X)y4bS_%9)@~) zp@5w}58CQV0aB;z0>A~6{sy%1@0oEXpFuN5#y#J(g*4F7{xcxkZ4^HByHrt8q0t!O zcMP;aTh#&%wCVt;9XScw)II|c_#d|=_b;-s6@UibM|WK9_GbBMeoto`ee#}6MN5b~ z;>3wpQ+uS0haKnbgfz)1Fo{u+2DvOOzjITxSxof2@G9@x=%2!Oox5JDzK@9pQL~Tq_A(cb8U(Nz+;XyNhFzo@>@kN8}deKMpE z$^$GdSr`J2p3#9$HyWnw)KJ%()yC5krcOrxy&FK5Ir>6-ncT9UOVr(HN+h>&w&R!@<06H`Dx`% z>L2A2${!6&qpdnRYO*nxHtA@LS$!|ancv+Srt~$xKWlUGQ?}w5BjIGGvUk;^4U#NtUq~MW$%RXeU;pPI~5(4V*Vq&wohbx>)pmR*e}YSQZco-nb*3#JI~)Fh21go z+WJ}9rlY<=g?}J^uZF;nYK!V3X#kcKYe3HGPCS(p?IOcgTI-rjqh6@dXxeRaiTSW! zSVC^6#!WHK|I395?Bh46HT#d<##6th8qfmb2jDb4P*Wp?`fEJ~JX9M;$8VrH*)t$z z)HLrDzPhsQ{R}h{P0P<`PhVLBE4&9fU%dqS+t=#r>-8J`#dV8}Uji)!+@z$WD!}#9 zFk#S-cLv%ZKY%pg%RNCz-pUj7Wx^iu`$0P^WpB?e}eKvmlqinO%`b*Y}xi)DP&A2{2T*d(<}YFbHnqzM2pWF z+?fWv5Z!%)PU@FgPlK`k9uL`)Ffa|qOW2(-JugLVZi zK(w5(q_Do89thgbz8@6oPzarH(~Ts^CqXEbqL_W3Vj?6&nr2x zp_j+}3whMqMZelm-$jp=4J)%n(OYpOa3luHj}}JU_K5h8 zB#VVhk4qO#N6cvd^Onhs_8!^P8CG(YtU37kiIV{7m9Pj%+w?c2=T2+j=;(-=dI}8V z`@7E`4?O{T6AYlIBr`!%E zsCvL)etv$QTmACSmNMhy#2q_0JCaG!cY+>WnDYEa=X+Ag-ajIKrF^y)9_*P}eXUi6 zJ1{2UYy1%j$A_1V<&xLRXR=)j=%)}~FAiB;kANH1mQUMOCV#?S;CPU4Qyw z|2d^EK7#j^#|xet%HmrjOriU-7FjmZ7&718+-m4}?JHoW6HkvF?*KA#->qXKW;#D-Nm z-haV5IjlkfToiFf#)GMQVm0y6^G^k0_?YZ@3zVC}OCGr*skW?ISEwZ&x@W-Go5%_q%33DWs(Jqge28LNiw8Um-R)$I_?wI=5wI ze?(x5c{^ohq-jenuI$9~sNmZjzVc<5n@Vt(5?1zn!*L&1WVc`QUt--d^(Uq^D zbu!)W-!aEAzn;gNVxcdH6QIbjXT|;TGrqduv2YQ`;-Pri5m;sog&od*i5~w4SziGa z)%L!9W`^#TMnVJy1PMt&LIi^l1f&L$?nZJ@8Ud9QkdhEkN~B|G5G6#q8>GA8zsIZh z{?_-Mr7m>2X7)Mf-S2+hdY;R!$CXeS?qE5c_9o3nca+RX#0LXhay7m|N+?me{?qc} z?&2cTmcR~iqbiudCjS!Jd0lQu75m%&ym@f!-PzsU6$L_@n}9G14oNbTVg`uyfK;V1 z^TMXPJy1Z!`N%LPD@MNet-Y!haQC&0`hr0>SO<{8iTH(4E5CjB?p^dD+dIm)#qqEB z-!-GYQ}?Vr%p-8_4c@0KM2Rdr&sOLoja~_t)Ehz zOXTpGr`So^T=ZSI<`?0Irb%`YmeF^yE(j0iy8AXuwm>n+h@U?!&<{^e)#+us|L5o~ z{wcwGgI~URpT+Q|{*5NpXrbPCz+Obd^SA_W-d>>ra6JVL4?ores@Ji{Zfy^5oHkGv;(+6`O#^w=&7g@(Iro5ouY6Bq& zM)p>X;mgQJ1q_E($Iq{oy6E1++|P)>Nq91y5jOw5#`8k8XzBCoW2st-O3Cae5lapK zML=XYXrZhC!VA-YC86a;-Qj(%%+zIZj3(yE{EfPLpcxqe#FqhI5h_h_sBL(wjFm*6LhSF&gg0y2tBU4!SeE^=oFeykcp$^vo8-61C@wwCRiN#Nrs~92(2~aSZ1OqBNPo}Z(%ST#22eb_{ zK)|_#P!6RKv1McXdSCf10QzL)CqAa`Fi)Q?+?Xc@WB#@Yg_jQ8e)=P^|4lUA!5KeH zy@w&aG6Edl<$yo5g~%6{`sE8XV057aU;}8bkaAcP0qCY(>CEln>8aS?H!v^*n4oM1 zjmK==(8Z19-g7~K5?Y&?o<8)5G1|}He~HKM>(#sF2ZYe8{;t70HtQ}&R~H%=l8_aK zOR;hLd_RLeKschLU=<4Af=h@?(t;)~-zD}C-1!31X@657AZ9n-I`k3+LLm0wgnV-B z>YFzND8}^w%O(bURaRC`?s^p2jEG1A)?(4^Z-R=Cue!OqVqgdcYfUq^c6XCKkXRM^ zXjtd-RR7j3ShCla5{OlUq)gF`Z^-Alfq*B}l9*^A{PV7h(&AF9DL$ zW}tB)Pz>BXToT|v4421XK~@$HpoMJ))7zUny(%*&+g&Ux4c+r#=#7y5KMnFi2h*i|WQhegvyuRqv@(axHH&!epGxQ03 z|NdPRaE($po^IYz`aZEH*6svdNvpTavZzquUlFFNWQb5`4!q3UrB*p-vchwi>zh`K zb5MTGJ)8FQSI>#NGiIh};1|zN(In*t`P}*`Wteb^-~WJNq(*oxp3&DTOdW@djfi6(3-$1C+Uf z?8`#;8|6r){JXs71<6y)oV6uHMQL}5Oa1)(1iI)zD1AjkQ}fcS9#sU&kxF)G+fQRu zuWzM4FOyiA`@}OW1h;?jV4Rgw?V$)R_B$5rRox$ZS6o-!Sfl4Lv2dg`@lDp>Zs!%9 z12+G~drTFNqki=e&nD8B+)E@J6oT>AKU63C2g3H0M;Wii)_&UOy@u9C$E$0Fby$y*6RR@&P;m(@9R9A-^QSEI7=lplhKK$X2I| zD`L*`>vAJ2=|Z^QP8_)uZ&@}NLS+3n?4_4}I?Oq;*m~mh*4+%vt!$bH+ev>t49E)Ws4GtaJ$roZGUufq z!I5$5(?1`o5Nq!P4cptofq=M{9)oSrmB0rv86JMsN*5=EX6NRNfHWIy`LV9N)WeH7`9lRRQW9}kIs*R@YMma7lloy}oHy0vo_ zAk8>?9tpB_%kI_awN8KHM37C@nstH@{KOZ77BY7iab6M;>WN`C^Y7~t9DywU+1D44 zoScku0eVjnzZl=|v*_QFh)p@$$u0+kB#3?tPx3*hTP;NjAh@I3}mIia=>K)38SU61!x%`GhGbV^57!g?`xYSc7&T6pr&NuQ{ZLIHbOZdWwLGjxTx(C@&SIHG5p>rtQTedGnV+TK*`!q zTbqNlrHPE-?(&9=g=-&J-O!F!9~QVuumAN?kjVARjQ45mI5lC4l%0QGE_ncm$z{bs z_6{Dq4#d|MJ)YixX?@i817h4(-?mqmL586|u6E~A{BcQ*1aOlZLDDBMbR!_ZTf5*z>&(gGy1MT+$_H2i^lz%^FmbgUmMw~VZA7t;1#n2&p3i9T9BN={}^ z3#(b}CEajWE$`Y_u=+qS$y5ayu4!42ldGM)G@pk8zaEll!$7>!X7%*H`U`}EruqR$ zFq=_xK$w6G!zh4h_tAKbMyP5LTuH3 zI&|J(#NOcw5$@%{NrvFV{C=xd)69J2Dk$rC-ng5ETzzW!pauF z%oyDJK!VtIe;k!pSxG+uQ0nGyPc>d$Ey?KeQ_Xnop_-Z>%gH1z$TIry7q0=82nCS! z@!5ILl2%$u10fSQ0dc2=o4GO5D)u3SS~WrO!4msUr+7zDN-p?J65x)L&0jn)I!b*Nm(lYy zP)ZXW@W6A>J4)i!P7v@Pzpv<1Ow@oO?ZO!FAfWtes|2^q1fnrXZ~zND+j;`28^_LXqS-Ac1^QTU(2Ak#JoJwul&OX}MieQsO_ho?w+<4+Qu^fHs%u z(=ot#6ow&rH@CKyS5m?ku^IMXTU$fT0QR&EKn|V`*x`N^^@v`!L4SY-O+U}PdFHBj z_U48;XG@GF&Euc)LLu)U6nkLc?NYh~bU#6Ad+uP|HgF|s9MR8ni0+V|*e72SlpAKh z{I=CB6>ao_jJ47x0}iTW{~RV#)uZ^&9Of!FkqrOi;at$0RV(h*XZ6; z1)cBrc`jweSc+N{_l*>y4R2rfV6Zx`uoOu8d;`I zdgz(SnZNOJdn***U}vH>M*xVJ zu>fVOD3Be!;8^$kIb6?m$m9SVBD`|;>A~7qXxTgFveOM>B2?PWiC&|k^p(TMHLkNa z!GRk{fGR75g8~2>r6xp#^ETkfUnE zykbD`lwMWEU|Yrjs04vlUMtXz5CFVyg{+&*7Yw}wFyukABtArdJ6fdk=CQTVjT^?8 zgsGL)i-BbY3kz-#;Yee)08pa0U|^ZU%OgMn;k!6-?ApSVz=^W~yur~3{DZP*WDmN> zu_sAU=jwV`K;E0k;avnD(jKp^YxNm@NHCXA*7b(<5>lgIs`i`~LT`;#sO@jjFTfvV z;ge6T|M+9h@{eethd%-JQ{mHg3Lc$ej+0KEC&MKc@gg>lD}!!TDBn4+^UnL^k0Luu zth$?R>FyI1!t3p)DeWsjGb4J)TuQ|hT)7mM>LQJen7@NAknLkogB} z#)#+|9uR66+>VCv-@Ar-p7HWhZ@D+lRBa% z1bWv80$FON2t`dR#7GIH>H>8&CigW-zU`b{U?H`YQ54wNwX<5}gDzr`aM@3{h9{+f zkc;htH{l`|VumLRA|eh#TNvFwtYH09D5MXFjHM*ce9qXXtgd{gXrK3B(lP|Q&(E$M zN61upY%8y`=@q#{O>yKKT7GZef+Zj(#sYb;K6Zn{9FH7>4S?GSI$3 zu8^>M62?O0MR86W5^Sy);>wH^h=3!Jx+wviw{g_ojqqPfN@*E1 zqJ;Lr1e2;Oh!M5ZsdanHb8$({huB;J_^0R$*8rtebcVk7#@-oYey7jmjXH=UWs{XH zy@>8BiZlHn3e67%q*|;xjinBtI`VI2Ob2|%_@Up|G^Q%erO9&@#a{Q?S~T?aLM0%lWG4+%bWTseh=@3hyrJi^$1-t)#q3sDkiBPxRLBsH6`Go8$d!uEHC zed_I%qY9g5QDRh#`o@8}#=DLye+&7c2oRp7YZRL!c+)??nRyb4?R<9Iia&ES-9Pv= zVob*b>`U-A5sd%kCLMTLC@q!m0sj>p7=OP?bybKbRY8E1-(O}wV@8qY-6d9A`QHzb z4cYX=Qv2xxwnPVE{4w6V2fKUN*fxjECbX6>qBVz9CawwLx%P_zUP| z5BxDVa->sR5tzr!9Uhz1fVb(7+UVMZs3kq#y!YQu9sIphFR`X(&=h`T-4a8^J?K@{ z;wl_X3VN4IB@2&G)dZb6;9P*?Jo#WMOmt4EG^6n&4`po8@wX2~=q2dUP|FSR&PACx z^QNw2JR`CB=8bqPa~GeL<06|Kf*3MP$uqi|yO|}x(DhS{cfV+#2s%Zm zy#wan!ouF~+Bj1Ur@do9U5Igh|6C3juS|t|pMa{u`U979I!6~t6{pcaL-iNs<2GLc zZdQNP%j=LWSFET_ZeSgKSdr~C?ZAvA=qRCzP**MTN8=zSGc^CdyNm{Fyx-{Wg?okI zU7OqB#kp~4fLsJB#pWY~(KU{2Vjo+$_m)C}0UJ2XszZPIyT$iY%9@a${RNz6>>BMR_lr_HmSOVIH?^5PD&L_PysM zRPyNYdT(zv=dW$dSwiW=VBVNG==YR$jDJd)f&qRtiZ}HypmW8%_Ljoz;WD9#h)~(f z1WA51A}5C!J& zM`sA&!KmcYqK62a{^ zTVz2vq9q%~_R9nwZ(5K8kQtWo20eiimk1Wwp(oZ{ay$ar2vI>kL&!EO^y>>iFBA5k z<9P{w>?yw-Ad@U7m#qJS<`y1xk60#(6Z@sA(}B`IMrLdct7$z8dIvi>rRlKQW4h|F zCj9^gF=}4keBZbld%U_VJ#uff@{YP+RfLoOn4Mf?19bt%WpALc;j90PsLC+q z!ojFud*Z}adoNR4*yQESfUaj#57Z~!);C>+V*Jl5>l`ac6MF| z)w3(eaKjrn8~s78lym@_Lspr4{@0%kMMViLSIAaOC`3~61Y=3U_>Dy5KVDkGcxP7H z40R17*Vz1i)J`%SNF3|Q7;FeANIZgn6>m!zwV8fO=ZW5_d<&MD@8Yb^YU0K<_+A7y;;A8}!nWgi)aN(dK)UASuH`N=YVhW&U`FHW` zn8oMg!Dfd}0dotM=A!>}H^*o#87%A7neSJu3@m_kDXz$0WWaa3kKaAOR8pH(MHs(` ztuaZwG`#OiY@yvnsz*BMFS=%!GI@CENaFx7^)MVrKM>S2)yQ5&`|e%17@$V z9mQqsdu?{lbB4At()+@U%IyQF+_|HJYviQs)d+|gRhG}QQ$h_F4e_c{c8HPlb$o$q zJOSB-u;lb9-+-t|Zc7MlAH+34_w^+=6gY*wLZd<-E{Ff=f6$Ns#?j3@*Nv#Rw zk!U9K?Sob`Jf=SNbhhULJ9k1NxLUe{V0XTI5J83Ct%5qp>>CjUS!u&~hMSTXv0R7Q zNX;w#8Po%r8~IX*Ng@N4PFEo)F`Gd|5UJ9)A^n+oSyKvUD%71P zS{KLb3~*I!4`qmG(SCioszxfPL|JNw>|KEx`(LuUUpH<&&+QXbF~ywU~AJkE`Q-|JZJS& zpf)K7x)OtewX2R(BYiPyFO8%K>^<8)m94@H2@7u_9W=4|R+bvQRP0UMsm0YYU7nDi zF%4dm^Wx?^a?4NnqhceY9B&GNXkGcLJMQduw6ya+pVvo#hii|+Ixaae0zLb6u zy;SfhCg)ryF{HW@e@w_IY=7pzGX4_`m~@N)cJtZFe(0grDnqDTl>Ku5jh`{5FKGD^ zDAZ*_4{WJA4xKhznhxD-7RJgu_huTgEyob=M@A;GQ()mgvwBv{>x#&vx=Yr&$N)}~ns9N!AGJR@%q@wJOX z{OP^>Zkv4$J$R9w}91L*891*|(wQ_gAReRz;DZ?+9b7`SEuExHL?|0DZjjX>1 za%FLZ>>pZ46e$TFHj&(`CrJu+E6m%G`IzA3-gcTt$mXd!!+<-yktx=m6MVHR%$jJS zvt+8Ny`4@jM6g(ZQCg|-mS+32l&gihk!fEBny3p7CQIFu=dPvDmhTmQdp40dNLTa- zLXaurE^F>9%l|K^_`U7K>P-eFixqEPv&?--ce}%T{CR+X;6YvtDNhnU3`RX=gFH|DJ63(7#SjP68HblziU7eyOk}`9YG^XX6U`c+{ zZnWPqQG=5|9xKqwf2l=)DCSS0phaYbb!h!b3&K7&*7xxF1+>)%Kdqs!~td2Lf* zBfJ@Kdhgy9Q@o_rZ|-nK_RV#sA=@^q)HdqYeeC~2B+T(fe(AM-6d7BSlwJ;u45CVH zFV~ep$mD#YCA>R7^F>V)ZBj#UzO8Lv&?Qj^enc3@bcTV1%g@+odcH3vfQIv zG8?pOkU2=zFO}wl2O>&$&a&f zx8^c%pDI0tIh-f7eBLeS*>`@%ZE=U%b_JxK);UWp*Sz1cmYwg?hPY)1oj*PrB|vN^ z?nfATzLi;;ZY(r*6-)BgJvl!rlj+6fjntB9isW$*rDq#1NImt|@IJcZZx$X=mdcs_ zk6Rv&NxE{x;su+LJevbXIiU` z2a%~lXO*33&7kX0Cw7*F%vW0G57)mhj?=clB5f~D{7h|rjQ#E3GJPmHCQ#sTGN)i3 zJL1fLL+_u1U^^2-?H^d~ijk4I=j|g7qDdj7Ad0giy`}rsbZu_SAkFtxi)DcwqH_ja zx|aOfpKL37k0;Vpdn7zo(HnI|)THNx&2zxGDXU_dTN{Xxx`W2sFro0G8Z^@o?N?8P zG|T-es-Kl^J-dB`DC3*UhB;K$YY|g2<k;LJZ`akdn#e>)K)@=`!z4_FJc4+i=@S(9 zAQ?5$|FsAf{mwo--4lQ#?=T>Lk*U8Rqe$xrV2{hL*!Wa=ZIa1E?-^ zxjGGxbz_7MD3SLF!A?ZEjUiNGwijt`wa|)BbEzTQ8?+eBgx$gQmvmECB?-5Ea8oaF z8y`6C>KshJe9^ELXLv%3+6VRTOw@!F7A;QUESC)4;xp>*?JNAMU-pysOQc@jm$IA5 z2CnyWIzjd__JNn-wO^iPw-V~P%d`2tOavj(k_(XQdE?33}dyOOX~bp%`e#6$xO1x17#~`~%fT z>}zsCSBM}1a^0on&8%`3#V4}hr;PZyC8g>a)bgT%v){G!Q!CgQ8);d;BYZc5(5)<# zB{_=gjmtIb;XM1dT2pMK+@C&gnhMXJd|53vII+x-LlavICSO6ubEaNeH^cd}@HtLQ z1fpa*(3Jev=LJ{JVfJ0d{I%Dw_}^RR-izk&^&;vfp3Jxe%QIZ@KvXKoJViG#)Z}g6 zUbp=A&8mT$@`6!)$>&rBT2~32GPwi~*}w-mPL+E&YXSh8#}l|azl-Qt_zV|Hq}#+o zpMXrE7BxSn`uh6{h5g~IrYEMG! zF9g}mF!j0l>)BBE}UyYqDiH?|1tofu#L6=WvW!Pp;&A>Z@ctl_RB+OR^}3LU;TeaYp8 z(82UjyO>*s|D82j0yb+2kNI}gNK}%5hNgV!1b!GwZsC#{Q4|CF$ao!{n_XboRSo7rDf43CRLy4$z{tv7oOqQ?%#%L}>}0r0zqM%M=rP>=DLkw463@ z+iUIVkeHcHWe_UD!Ys{rSjU1%PUZ9o=yqn-;Tt&+;>Hpal4b=K&FyH;sfJkA94Iw9Jf0fGrmT}5*Wx*CW#4L|=U4l> z+s_=H`^EQpp}CrG>>mADo(hxBnORuKwi$Qy!sd43r+X%{EuvZ#svZ=U2cJ{m-3xp5 z`3n0^21L(T6{gRhIt_7Ht3?F-$&(f1Vr+}b-u(KkB>I&rS_oogjnahO6N-(B#I~m2 z@Qn-_U$E;!1p0k-x*2v&GbO_5s!G#VvZj0fjPT0NYQ*Ao0)q8DUw^k_Y1#LmX|iLl zHN0-dt!LHYO;~asz#`IM4sO`%yEPJPX*XqdbRmuO{PB{YU#qmReY@X@u%Ir+HNFJT zdAx~)Gw6Vb@@AgxMPo8s7XR3Tc2+EFOyvCga-==~b(u{W#wx$a@agy);^U^x+aGiO z3h1~l7SEJy!<{n!M+DCj2rtJmy)aa%qS3m+)0lK(p{8&9*^FUrXb-Qp;_S9$<#Nhm z|M4`fWTvdj7+c5gTsmpkEG}$0dBi-mXWhOOCQPFV{9*{Q5OVjWW#A?W$^Gb-Hl^^JnbPzWx?uB zoff}-z7UJ3gn2D7o)4@Ra%wU0fNiJTZ8t?m1J*s(S@} z^P=K40>yr*&-G|AL7`|-u?zO40@FmOfE+ms>(yj3B?3$0hqt(!D+d3*JgVPT=HPDu zOtld;E9{@ySwGVvrLmEiZd7x88Dw-D^adbL^6zZTs2; zTA{j`l>4cPy~Tljc@$@(Y!`=bJf+Uol!1Lb_hBR9;5LO|{3c%rssAAnc2GDtm407p zeA7%~U_ZQ8mx+Q0iv4P-BvVyq!0*X;#sBe2K#2NMupE>F+dqTx%VLprOzZ@q4QnoP zVc|eQCB{liM%sGC5DT%$%uocid%?N!2Q^B65pgR4313SJ1Nk+NqUHP5-Mivj8HBjh zYeCowxJZL3pElB-bTKuYjs^5WE^4es!hGcI@G=Z^$4S=IqlW~AsKjWmn^0!n^TkDJ z=gHldDnihA-9GPpB~xk9P*3OgXCFIK{C>iYgB*d?G*kJ7;iKkp)pU2?P8Ys7i9x$b zFOqtEsxJLuc*(0hfVGj06>INWe8eeXmT^@!6A(`g^>r(FuJ{agkmoxTDCC%RebaS< z9zwrbYDomAnCZIZpno8Oz;v=Z9btc`=ZxzM^`*0E+4Sr+a}DbQ-R>-GM<)8F2i9to ziv7e(=!f5NYDE+498DSI=(?YL;?v?p9-z495h_>0a5ai=Q!M=Ii=yTGTM6>mv}Gh$ zBLFFqG|<3fb;dBK{&X|k@~c!3Bc9%-=4AdcOiEtU(Ac{v=DBdEvRCH3b&{bAfx+4l z=~?gb&4??>DQMV2ZTQ-!#h;$f`f1Q2N9;>6N8#$vjofV$$POs2bt(FF-1Adhc zX4?beiOy)dNxqqnC>Km9IPXl1?HYJ8)TGKe9by+yC7~u4lK8a%t9ZBLS_p5dOXrPS z|D0*jpX0BSk!QY=YLY}NTYiMHzPWUtq220m26>YiB<#5Dw~EwKie)+)EvWC>_eQTb z@EuA%7TVJ|k5IT}G^R>;R=sSYH||B5KWa~SR=g}`%{UCFUO34gPsbsO(JouL`OuS? zwr{$siM7xeb?aeidrl6XhHD*7OvV+Yl!mA8A3*Tcqk2YJK3X<`3bc?iGNJD<_(zj9 z)V<@H`Sy<3Yn@^;VghcE;yX5!4ZkZLRm}QR;_`IW?uAQpqYsQC&Fh}%-ulr(mZMoS zg_!Iqb$3OEnc*gno?eR{QFbujglVffL#A^oe!PJqHBv6E7Yoefx{o}H7#=o@SgX32 zb5R_O@(_ppx+K)T|HayvvuI0b?%tSm_OsIK-!n>z9&H4nOUX#H|KKLIz29f#6Ou&# zksG=6Kt@eP7R0V5`KM;Ei8`u-K5w+rU6kMh<)yj1)(WEO?gq*QVhMM}d&O}ImKtZP zJ)D=MXobcE-eGpQG&mx`Z#lnjt@R3@i&7UMMr~2T2TQ?=j5S{rji-mDWr>3P!Cz&YYR?Bi8OK z!?D}<4(1CJ^J5o4l1%p{yKPu@N!a5_JmA*#C*(|~-xF=rdA=4nVlKI}_YVKBH_FEpfuf(deix@z`yujUT1`gA z#I1&6t~fW;ITAVg8J}UErn70Z;q8I)uFl}%_;bw~s|~PoOP;wkJ3lDcN|SL?s6LNC z@ZmpRz}q~#J*3D@sr;6#qB};`xWc=Lt3X8xl3mVI3L`dk197G zOekvi?HhUtbrTuCL>G4okbmjSEnM(5Ul4T9a(V_=tR4uVqGg#X3&D_F+q3F)q^?HILbQo!BeZ z;~2FVa|3nmNp#@MZoh4Z@~d}_G#};u95*d!qBx}(9yh%&=o{FvTpQdNM&kca^!c8? zDr4hSpDq2)Z(F$=yyodw}W*g5vug0763(XUnocE%qOFT&<0C)36FV?tDd9`iSA zrmPvM!9THBh8!~nex2@irh*A732J4_yt(T9Z*lL>4NVt*fIkwEfMjMy1Wm3MTyUQr zQd7CH^8O2P`4V4h_O&lF>8~f| z(UePE(tWb+cs)4n&?fmvXB}SN_3+bgFN9Iq=ST zNVJ_d6~LGP5A&eEV3`7w=*d+2@C+3YtqLaBIW+h*4SNpMG+`$j1in=Z`c1;>6BIowL zC)=3Y_caOA$i00f8@9|y$C{X@_Yi1gb*b79%Ks?%Yn3^eR3_K`l>`(7pXbo{fpZ5I zkD`Jn#YPrCQlQ&t>^NoJh;Uu@b}d0j*Io5$<~NDHqDYs^=$CTM=zxEBBCcU=iD2AN zH&Stokawahw&23S$x{g-a{Ub<&^my>_vS-{Qi<#*NO1ddwhPgNB{bMA0_{34z$9hr zD({*I5098=zE!-~_Qf|Kh`UZGVa??mkRQBt%t4O>f?43ZKuOx}yssujNuwKH4JQjds0Ao)zi+^G(r@?_B_9(PqTJ1X;27DZR1l zi6-QXhNR0GKQO!}UR`@|o`jZ=(;#^GXfa3kX@x;Au7gf9F6%@OvyK&d_T;^Tg=3#i zzMVr^v$rj~);wT{v-aUt%5Ul)a`gYlTDY?L^)Iq1vsan+boR`r9KQzff}nD!ABz`* zd11af7Ub-Fc-60L{oYtPR~i_d2sFA{IND$0iA|$Y?4x1@I)>l#2Ib!CP3^Hbt?4t~ zd9S-3zC2n%1%>_ortUssA5w~cj;=Y1EJEM{W@*R32~siz#e=)K8ybePx`s=?EVtg< zHQbU*DcEn(+Z z5p(hFT)QC*X?Y2sRNVLicNbQ`e^i@!B;%$a7fP^h#xUUMTeW(7q&|zx6~`vZjA-Ntu`vV}6{Vio3t}o=O%d{$9|Ft^=2_dKveyX7NntC`%5#JY#xRd0s>cxb1OZOt+qe$x5n;KavR16I(d zgU$HDQV~0;!Ki|FT%=p94=#T9D(ZE57wq4fzT*qtF~mhSHeZHC)|H1w!q-XpQ+m@J z&_=j?RkWhJ?#PZ(v~pQ`LtCdn5yipqg*VqF2UlOO#-+p^2yo36OiS56czRsmA?-eP zbpOoP*QY^xeamRuPi_H5sZ1gE1F8{Ld;%(^GJxLil5U)Fi=UHyn|z3yD^AdT78kCM zL!bD|-Y}7+vj#MAXc705+K>$QPuqqqk1D@CD`?hRR8A`vf*z=u&;mi1T_t-Q#eYZ#YOaUZoc4xe76y+O z=rh#uT9iyJ5lw64XHr-!kfd#v(895~I=soUGI*X>j+Cx4frt+BPz#S9YkgKX4+lmA zxjQ9+7&iXvxA1(bp~q7R=?=>{dFy}k0k9}+@XAt^U==DRF@=X$NrY!*zEG2Aw&ahY zTl(AfGzqi^h)yPq(R0#p+e$+BCE?WU8%B-zeK?PMan;JVnO3y;98_a=Jx6g@30T%T zf28t-CnSM>IJ-UT7j$NXQ%P@e-BpC0N{#+)7!L(w6yQ6j2FnNrOU+1-wf8C2{BNEK zmCi)N`}1+n4!AZ}SzvVshLbS^e&e;}fxU*pER|!G0d7nEa{#e-5sbZ@fBi*gIQK?) zf?hq${`Di2#j_~>rykSdWqynVIG>%FkulHh`}wICCjWX)7$(Ud&B`c(Uv^W^=+dQ& z9AFWlqtonuZ+lUid44hLhYJrQV%5(s9?q!vN#Dd-xiL1udGrdwo)Y`PjPLw(|_?t2(19YoV!|{p8L#Ux14E{cl%hj*%Ih6_)nyn||hw{b}u*L7ZB= zz*AnN;CCx{dzBi@4AP@$rbfr#awyb)#6W{VzrSZx;${RrxmsYM)$yoxpODP(MSm;` z`XaO+n^lLBKJlg+WzBouQt5H|gWb5EVTKI3 zUWf|eN7}*@a4vCRLHPKBx_L3dT!KIQAT#jm9o%|s&5pcgOA2|M-xeY5oLnb(tHfW% zZj4RC=O`}q{<9i}sYR=RGINTX(j9>;byEmP;{ueDXwg^{PT|^)i@YA7o>hnKz$Y0U z{RL(pdId#{Ie>m&L%%&Tj>F&ytdI;2nuBHUn|Hs?ypb2ewf%Dd+RerZs+Sm+V{sb1 zC19AJy+(^kQ-qe)_>Bo(o|+7ShcR%FBy$yI+>$lkNPJCXB10**u^?AkD*8Z8xZ3vV zQtMxodV3bl#K@LDYBaF7^v%u*l4+Q?<6tAW=wJ8AN$!(x{$#%hvd?l-dL35fx;|D= zju$V?JKu4zb@@r5we+l8J~kBpBjPM1C^g%b{OBqvJ##2pm~h3RhEe&Z+}q$Z5=jKN zFCNclYmp=>>;Zx^vcW8qq5r-dOy975z3BRVVowK#GDZ0N$Fi4=gmAsUhY9r`pTFDI zL2f@n&RP1JY>W;$;F?Iegaw`Zl6zQ1_*-1NgHGk{BcfemmYt3lzsR)2te_En{dKNl z0|?Yx45j~~aGKv6*?54Xy2ak)&CiRNk((S2tnQza=WgTb=;g5E=rQ8fG|wNK__U0f zQR-qp>9tLa;%%~QZ4ChRn28zuZ%*~o;6PX~7ieudD>G%ibkXM3bGttIXut>CiKU^m zbt^Ur`PA5){&`!=z?+7OFc)?gBf&q(f=kQ~F&=DYP;enRT2=PNtS%^VHQ(V5;4>s0 zUpEY^Tlc~DV64whbL65D+I5b+=)wIF-TzSA=5ME62?9iUFkIY}_Y~j$dxwoMCb*D=%k?|1~pAJAhgsxhy(M$vqQG2n0}+aPcsQV6I*wVwfzP&+NywIbo`^igx)p z#i?|$6KYo;5{uT)Oa%=x_H9Gc<1|jdmygJdII=>II8&jg4{P5}RLjSu;ju^A|w{hQP_ya2(Ako@BrWaq7#g*p$nLEthw{Cr;n|^1hBZW|=wgeSyo{3b48z`53LLAu^ZtcQJe&mJ?cc$J zCGM2@2A16F$W-c6w?oN3Xe2r4%nHVP72 zvcEhRs9eqA3*WkV_IGK~Gaun3+&?AJnbjr8#~93iF8=z} z0H(G>!@|@wH3JT}7mUHg3K{+fjh`+|SGDq+FwgIYz6!=+$-c@zk`jM4O@PGq#k1}`Afx{SV?}@&lY4>* zU58dCx5>>`79ihNX_*10!aHJl8>zMQ{ECZJDf_ML|m=kAy z#XLP`FxesC`_)Gu(YphNk#ik!y0&hjjH2f!+YU282PG$mdw!X$`K!@>nUtpN3$p4% zWqFHvM>z2p^!%s#k$yXPgqY-(lM=Di7eck#rMIc~51CjRM1I1g3cQ|< zV0X#DPO!)r9kd)#`2KndD?fD4Il7UXapS`z>9DB^0|I$*`3mZF;ND1O4)XI_!(Z+= zx=;&vfJp-9*@EvGE+GvM8ccZ$nH~@xT< zJAdE%|bJ@1q1}d1FoDb70|gj#$31> zaKQi#hWCleMwt4nDo?gL9Dr%!e5903O-qAqFLWs?C@7%8=sPUQ_w4XsWa5mb&n0;J zQii`=6zb<+tNE`)yqZn+IEzbI8-j%0Jx6ko#Aice3M9g!N{?y~O#3swq@J|2zp z-LT&!V2QPBFGgNdlEp?Rj@cBYXmH05(If@FnU4MHaJSLjg*^!VrxcU!@Epb;z65Nr z6-;&>qg()IOUvi3n_H9mrsdYMi1S8d%<)P7mXg)zTV> zALwymT1fWfb6p>oD$f7-k;wOChNWj{Cu{=lMU(BBij1nv}1 z`{wK`ISgrG5}AR;-r!rQ2$3A!D30H2CVA6 zVXK25akW&cySk4a1!BgtF$POze}b9x++7|B@|FS&hy!{~_tBMhUNa1sVM>1QqaD;P z@CA$p-n*KNfJuyf`RM3qBP!*DW5p$yqW$K{DMzN%w`5S>P}PJ{Q9rf|OCxr+TkTP0 zwe$!Zd8C~$o&Kn?fM!H3MtZM;<**5T|Ga0azts-aw^))7?QvamJrsX(({2q5-Io6h z74uwqQ;s-Gou3Z=CkJmrjTuI631`>@Bz`y_tH3K&RaNDmcW7nyvG=*y{=Ld>y zr!@xGeD*%4fX=voh$>cIwd&c|V;@Xks1q&zxGbJucXlMuiVb0=yJfJ~cb0lFJ`U?k z!T9@(3@nUsAaWB}JQi62#ocA20)=QU*D7LJs9_#O<~72ZE=0-Z%&xV~PSk}~oBW?yL9d*FS z&aXx{!$+D*e{PLD3X8x3*cYq=LuRzoEypgt2S}huv;kumNyR$hwQAJ=@pYC_RdsEn z-kVNoX({QDP6;WcI|K>o2Bk!5(<)(rbT`sSNjK6U-5U{5x;xI?_&o3XedC;Q)(?I$ z)|z|9J@343PQ&U54j}=7r9PAEms9r7{I8Cnty|{r#X|wHfag8={NOn#NDk>+p*V;~ z(C-kaj;De+$Ow|x)E)t2L`<1IAr>KTM?27oSOuzjUi-Q#DKBX|zY!c3bjHK-Xr|e( z#pDVwp>phg5iNbAXiwC#W*xbB_nhmGY*kjf{k3fma4q_kHaocHX+vR% zGx_VxOz)OSnrJeO#(>D!bGAHAgUH0=0 z$@-*ZMTxRGh;8_u3=A!bjlh8@rB8H?XCVkCF0*fkaxYfDWg!Ar^mqK7$2^q*y44R4 zb!e=<729;rerO8dc~2@0pGy#Y<{6C3<+yXsmuCwqT<)H?yr2il%!?|RpbBlL>j;y| z8^LAraL)hKqTn!L68~uMIzWn@EHMfX2q<@M*)F#m{{mY<#lEY*T?54X=2W@4#q=LH zGr%$>c)ypQ6@{hmcXM?jvn}>|_T%GwID=e7hx)b_b-k&r=fiS2Q%!xA?TM?DsVn!1OE8P_EZ4izDhK{!LLOc zkQ6AuDNFf#H8x&VNc{!GXAl#hgsY=~CTbF`blc23FfK$D!Y@9l!$Yyyi>gamylqmu=`Pi9^=M1b&N@>zk! zI;=nH-HROMzO$0(fhX;A?f;kr#FV;#1#p+^*&A?6dH>4IFWIOCyp3|e^qKQH7(m?F zp#TJ%c6yL@6XEQCF&i)HZrAd#fjQX)=^mg5d{|qove#>Q2@ro{=0(N04v$36lf40( zs@uI7;I@w6W|YXpxU?TQ%FSRr5|Vfn$>DB z`7NlZr=syaB!IvHS*r^{h}n;4)yjnfB16|?)3Lx4@^g`(>+5USN?>4Mez(wh{Ky&# zEF*)COdcLNZ&OOr<8D?Ch`{MCqRFePtHIBbDuVElq_L*xG8DvUWerwu80fLUHN_!; zWRrhT6O^irKJ~LO{g7Cw&1eoq&sk}>`crY=2+tA6jkjqaOODIVh3LX`!S0|cb#Wt> zNn+NcRTlvl`W0<#8@}qWyFchv9u*BDd=0nH)1dzcU+b}Yd|jzRG}96w4oYy-9h`7_nZ$NlqGhu#a9`SZdK8LQ{~wHwsvn0&}J}2NLRR7>6B3ioWfu*BF+HC|(nS*~!nV4nZu%mzZ{|9MBeA`L==6&KKoayYm;UcE!T+bWn8gqPJ9*B0Bmk*G zq!#E}<-Qjl5WqDiFR9-aMSvfSjEsP$Jyq>UiJ)#Id#xrg9H9OWkd}0+zZ*#j0+$tW zi^~fAJGXZ9MCu%lR@*OLb65i`2>^SVU=Y<>z@BtY-Zc znS2&|t@mx7*IS)4cS4PpekoJ_cEI{NDy%B5vh=T)N-oS2h!6x94ru!w-}eJiv3CLK zD5I`U)U+AcdmKh}#0ZEM!0$!^(5ip-`f>HFQ~T!h*F8X-75RM$^l)gO?&rFth>{L! z-$C__IVVqTI;Z=SZ#ymHj6z+9#D%_?CnhDm=pwhi|LZ)4F_axfofteu1#_kyKn56q zdpUB81otUcxIkoyf3CR;V_Lm=7WvuL7rey;=k^zoZ}Bo*Lkg6C^*`<&gK}cut57uG za6~xih7yMUNzBn01Sv#xoF+|FKr6cW{!&MWB-Lw`Tw7N+%4^OxFC`QKMA5HynEqZ@ zSW<_Tb}_dWxH>{jop}dGmc}kpqKhqlpsz0|*A}`}h>nJ)ZeWl&ywQ>Q{ufmeIFIc)3HzVgTAZFR+8LqcYH zxtgb0jnV>%v5mTP`k;vRpCa)JBtt2!CqU2DYuWc~I!Gx4_r(zDUXc5Ml zP4h_W+C;Ho*1ffA$JwrjYxlCc?`>7h%*@c&-O zkoRebAU-tadZmAGpmm5*6=nKb#1-V(%*1z8dl%(&IXLz9!{?Zko$mKJXRW`M9Wyp+2?q zfq{Rxv(IaBQ6G@9^}rg$#Q}qBnqCFOMHO2uNdC=h>N3Z+`F|pw(KN^J76irXkMVT++n7*Fj#}!r|)IV zQ4L6ns$Sk67d3Xkyjqy~QRvdy+<3aTqJ>L*p=X)KPK zs@j^TC3ql%i%sv|6GiiUiYk7;es@Hn`|Z>F3M{*(mzURY-H7W}C|})Ap|Q)>EJL}Q z4U?O9rxaNy)P*WXzc(7rR+Grc$lm-G1>OWSG<5XGvDW?Z1l-*`8WotJfVwVPbTb(} z{tfT4hVj~tP&3?H_p7|>g;Dnf8Ri_^JK%yRRA2*KYG`ye)I`;wH3 z3b5w>7Yb|qWivn?ahjgBqSFMwgIlcw0zQ&~= z<15$Ym7iU7XAcC`f%iVxokG>ZqsIz7-M~QKno&lD2u*7+n69&SugrJnKo*fJCbr5Nom`GO4{rC}xPU8dk zfd~^1mVOFsD0u~ip7?GK!)gvwuqMu-vs?EL;sJ5^Rj9z*(i}pVMBlRC*pv9 zw|{uK+m6uZ9#$0DQaIHvg(6H_lZ%;2ZeWCcTv3y}B3``O_LN!ZSO;)-1Xb)+d8|>s2=IX%1^m4GL+amIX=|dV4Fc^{3n& zhJF785uPvpqID*q^pA=(0`frVQeORht6yQE&{BsOA+QSxjQ!45((T~DS9-aw*W5-I zMn%YPva2FAkK&kiT$KfkU>w)ge)6Z=z~MUbm?rWDz=*MHRjsX={eanii^mnd1`e^e zHmb&9?E%0uDPlVBdomve3`@L?NpxKhckE>sKL4Gy2X<0K5jzJ*2M{V`$D*lx2tXz9?n^!3kY z>nP8vcXxVbFG#j;hFeM(M)sD4d`^%1B%YlnlPcY;c79Xy-aR61thEav6}svU^VYrC z-8G;da^&B&N(y){rtGkxM|Rk$=lj(1L6DMx8pH6qm>pvUT0H#x!753Dt!vl~BlQT2!DH5u zpgDI47z_yTx64lBNmP;d9ZfXdZ;OEbury9KYfLo2)B{_x=cp7M#)b5O|eq!l4 zE{045qYYREshvcNZHT%B~vIj|5F5v1S)#OpczvFfr$l z#z+>K4?U+3NYYy;qxwt`E6U(ao7YDz%D-^}y^;vw^o4MH)UJ-f2${KCS=E0M$HS5A zuu%t-$glM-swL8&jJHI_raP8y+XdCIhjQ!1e;-BDNMJ6{Zzb_KF$zL6o~JOZ|Kqan zh?J+?6Mlta6&pNl>(si*r}B&Lus!9UjTvK%1G7~gA0O8*H$z4Ahj2(!Yx%JMv}BM)qJ`T2|4g??w5%BK3k` zc$T!m@rnr!yZR~Wt2R#`oVEtPxkc8TK$nrD=w^tT8{ux?rd8fBLI6!;~2oU9UcSw3uQz}VQ%$YoDOX4bw z32G9ntoKA#L=^+X+IOBRm#fr)>GT+u~FlT({StRSf@@PtYiwfuS zz`KhXT?b8g#2>+A9`Sv1#S|o!PNeyJA;xNE_KwW5zwCUc2lC#lI27h}*cI5(euPXIj{fqLS^2-ohzZP+4-bbBhhgE17V~ zqIX#`7{Rg^MGw-Qj;IN?jHj9?K#nGlx*&ReURq@9{m(mS1uS+O+@)$oy4gM7G^@!rDeuvB-?n1sf-O+e_vvtvP-t9|8d()^$-MEtqR`w zIFLkq7I$b4S3oZJ1zQexHuAhT((+RJ6wMuWH*Z)E27$xepTAJT9z8EX%^~(HuRy-QU!^iipYDtfUDCyD=9N{+wNH=L|3|v7K={1nQ5QE92wy$@Vcy~tuC=z#I&~$KA z4^H~DXC+(FS?i+`F$o~MTQx7k8jleXAb8x%95uekn8E6Xpl`R z{=FlA_n%!1N-l0!F633?lg>iMgmyP0_AGsiA;>N0#m1|sg*Al^|L*|_q$lUhPlhswT+j0EH70dt1F%vvKMk-2#r3J*J6!FFywBZ-X{^b zZ{AeAt*)ucAtwCbVPkM^m_SGn89!msDDe@xdrw-%WJ_bp?Xa|L<-<*@`k%wHFgImm ztQ-*{$ykojUsy)a#%ZFbI!894y_>z$@5Ovh|MG1KPrZ`r4BJ6s zQxqp=pnPtij5M=;uj5w)U0=-1JB=a|11EcsjqNt5j0a;sxStynA&xw_m=v~~;_sQ^ z`8_&Qqf^zgrQ7hod1nzFW3V?Y;TVoPv(+$^Y+NLO@~m+M3f8C$Fzm~oyS^;tL_uS} zBfCK$lnI&zWi{`o!v<1R^4cm_h)+8gJ9E>97yUklNI@9&d<=q;ylmmmwtRON}3 z&8?*uhHu`FJol;hBLp(Xs5RoTJ8onA5>NT@8`IY@K#;eN&j!B3RWqAmUz8dL^q$r2 zC;LydN?Bzg4dY*l>fZU?nRCDZ0DY_gB|MHc_prDY!7dY-#(T(dk~Nq^elwrzTICp! zzl?!m3QH;ce*tO%2vUY+^a!*JkyjKG0AkJy!${c$X52q^NqeQd4x$;te;j7|jPk{w z@ARoRuHLjt)bc4XC^yRIxnut(G^7*t9nq6WdU_Wnm6s9Elg2QV(E4{+zMIyPHGB2X zR2?ziog+&rY@3!Fp1-@BKC|EKws|4TOX*Rh*qmX=^B`g*O|19^OeNU_Q-e2$JnHVdvPlfY$APByS6$*o=2*eQ~aF1!;t zXuw}*r{TaJu@$V?%8&b`AY@TUroZqd{CX&6xtGLfYL2ol@Jx-S(p>31`Xr~5RI0el zy}I5H$9;7frbDS}B7dFDBeej){D!3ugEc)g$e0y{+b3(D3AF(fNZp2TYzVpKQMKhl zq^vzx(OMKUGSA-fcoOu=+Rna=sVtT<8CaSUKXQ1bD2=i}KFGvB_i&{*ICT_;`F2wg zc_ZB6^vb$-uYIz<+%8P$mtUXZ>+T3XP|xYD*Y0-(kQc^1laf;J8Ts$NRk^ zib!FcmLG+TN(yB$RW?G)nBb9qz4G*#9j>CVTm@LkMPWdm+VK$fGo7V024R_LbWhob zlcg}}z2h47hAU2RUIUgm?z#HUi$KNJIIz8qlj@0xB>iYJc_MPNse_2DGHN{Nl)WD* zuf2)OP_~fbr>LS2>(~lshU@Jd{&XIYp*y*-IKht7Z2uE(N3Y+>uhA0Y=7IoFrFHGglamjbv6G__ zz3zB9D>L$U0UnSxxaEuw+hv0%=X*`ab;9}tNy(KS`}O4`u^2^jR0q-0Mr#aHjDKUE zwiO41R0Jchm`(=2GSkx1BF{(xrtxLWs;u59D>&Oi)zmJm4QJzj?rCZ>RbFEs2W+u6 zmwco<_|dhIFG}ink$|O58tP)<)6irzg}Q9`9~J)f6eiR0Bm8($ibzZ^eh1B!eGr07zqd%Q$PGJmB6_oBBgCUF#qLQ z6fiJKdCWckl+c3)!o7+5JJHQdTjoc@Jj+7)kC%qy%>MEl&n2On!jBs=aGuyKdG@2_ z-iezJp^W8EFYkD}*j$6H36T`9hHCXa`*Y8zs}cr$wVgr#7|qeQ`LcppY_QH$Ecg<9 zl8e!hhme3V37?E3EvR*01XH|tiK8$Es|qC&Lg37FNRW zMY}duZUOD|+kaER8UBuhx6He_+WP4Xv>iPO?KFZvA{^97$=FN@Y=p-j9;?JA6-u1( z3F`Et1|MxcsN>zWJDIiFb7;5nq!g^Lc|Poh4Dp+B6R}!Vi?S2W?aiQ(&b?Q5nqZsr zhx{=M`Pl1e>qM7cs)EaZ6^@sP%Fl`jL;x&*DXHn@5bo8|O^pI33_$2&GS1Oqz;ibl zAah6g#O7s})QS&~CGwBfx?3qfLjex0uCRY!A2UF5+XqXgJ!7ZIV9rqky+xi2wdF-q ztcsD7GDjuDVrsT2zYOq|!w|I)lVF7rkA^%v?Kco>PZV1Y7h@(mc2z79DFX@lS=gs= z29UvODuZ3-8|Uw9B!CQ;B}A&XGJ4bFY%EsM8>bgw#3%87p)LzbAPS3-&K!9fq!ucO z>NvZ(aQ8?Z=d+9?>?el4=b`srv9(af!=X?^HHVH4>BmUug36Y|q$9Xtpyu`EPBb^? zJO`NN>Z*CRGZD9bVZ9wru0lMdQ)gLE@1XYkEbbsP&pyhL*E53uJHo+YSlE@&;`uSdd z?Vu#jpyY&&rrLW}V=b9%{0!}=HNNX34&$-e6Y_4V1`V{9=BHK2&Xs8Xjo!E+8#j%N z$1fnY>-h%B0`)#SSeDdv3pGB-sn5hcj*k1^%AM~0xZ-a7!cxMsr6feMiSWMNK0p^Whb!2po7^)7uN{XWV^8t8-RjhqzOqevjlYE=vOg9 zCChTe2b;%UI6$br-FU4LUVgM+m*QLd^W#Z(5pmSrHR!*m0_XNrjO2r!X`yjpvyNC8 zHWZ6L-?GRAW5I+fJE)8`%tW%0-3QtF9LxKWlNEe&A2bogzwW5NDe6lWWT-j5$-8r?Kbt6&$}z z{KXPcXWoddINq+6nui{QG8d}Vsj)(_Qckq*aJs0)yqs;DQ*OdXVdbM!Qz+=pWJ8N+ zd~^Hs5=Wo|3t|M2d1r|r;f-4L_Q^r--|%++Qjp3oLD?|W6eY&u1bHRX#vs(P@ahZ9 zJm1dUhH#Rzq?<8JR>;Em^J7j@WWepsLKjJP6&}h5CSxgulCJ+bQ7+*>QC>kV+r!sqvy$q|#zN#OWk6 zQ%Rx{h2+NIhvMZFKS<9!!<=ep{J!xle4^bM65MPqtMvv{dLX2y(^9`SuE3JpC`^+n z_zh1y|4}|NTpV=O>>7!bv!NMViu@{FJ!ZM*I!_M)d16|-A??hlMF;ClTn+vtZsB3V z3r>HOpFnURAfYsTOGfS=NrTyD0oGdyQzhuf)d3+E2F4SX=jYDqNiX`{pU4si5+iZd z2hWnqyb)-`Je_<8KXMd<1w2ZpmE7Lb!045IF>9ZHdHBEuXN%&g(eN1o*34qJ)c7L^ zOw*7+v6B9f%=b~AP0v2~2@iAexCY2)TiKf6x)=dpki4I*Vf1989tIYQhLMTr`ginP=qAIEC1$b zbMQI%I1!)!ga4c@Maf(gS=pzG5m+z*(5^!kgc#tZNKpoI6l*34N>KyE3=;np%c>cJI4rK zN}CnYMs-QDW^S;GIFPi)$}#iDO?X%(O`LJ;Cm_e-Vj#(Ke4INNjMJ@+#WV}lx!Wg) z1fdvl2!Jw4P>TOXljT7y8yqyV7jlUD^6q2i8dpl_LLvi4jY#N>#>_bqB%M_jwG5NU zP+aVjV3YP<0WiERkrEPZjSGD~rsyf~3+fw}-;cRD!K$PNC$iU0ASSlOjEk1YJcOtIH z7Iu`sS=uL1RVEzMC!KcWcGS(jH2gPysd!5kpH{@idHr}5a+jWk3c&P3ed^KPu-u7% zh>0;STKA2SKqiyt6w7X8-X(BFFsT%8g)%LWiC_8Ug#Px|>CxE!r5yQZj_G{-kf@i& z%cyjq9WTkQvArI>Ji-I_H%u`QkB?7a$x4T$Me4ZNYtjmU>WUJqD9=W;p<+}m*{?So zlhK74XY$gpwOkFH*k@1irsj-nKnrviE&#nRuL-#OeXhET?HwCj!SXDht>T(O|8u$* zZ2I38b}Wszs&SzPNedDoL(VG)WEEM~pB45Wc;E5DflGrAqKerr0HuzOGjNK|B_-LQ z!4(05){|1ZXe*lU&E#R{1e&N8J1ii7MhctG#pVqgyvC4F3+fQh#@wX)kH@6q@47Q) zw>>X26ZaVYKa(7h`>bk6{mxiEG9QU}Jz7r+? zyv+$C6Rk3P4m>;u*Nq9iMI*F0ECd5)D287Cbu#4*!6Tvn_$zX-iIBh!W6BQZJ=VRN z3PwxgMfluVSH}2X382ouNyp;R2C-3xTSegoGJ{gCI8A+CO3?ay8jKmHn71;55775R z0;T+B{q>2EBkbjMDo}5!b71llzlG0fAMnI zu(fxX=}CuXQ(}2si~j2ezk7SG5^|vzC|sI~EfBE%6~8^Kmo>Q$7{cHP;~XBM0Y_N} zL|Cql*%!?+DK)^{MsK)!hPu#Y<&lZ}S@1U-7u|N&EF8tJu*tEHG+U2)0mX^_UF=!` zp{1~q@$Zf*=ApL!b^87yu0Z8B`~3u_{g%bY|IGW3TczzTk+kWeVUs4#qRrRN!!I^tZ49eB*{PiK+g|-_ zQ!FCD*FJ~>`U(ymw%&7;`wEmNG~Gwty9d`1K}Bn?Rf*@}JG6fVitHcI0rd?_l5N0e z0nJT5yu3=NJdP_BAweh0P)VzOyj1PnT10dJpBi-C{t~Z9-QaNIUdVo=TI+Z{tBoPh zm|1N6@sM@U^PXE2eAI%v#Bio-(PhT&{`2-2;)Z2;*_Z)Y_-yT6e8|@=dV-u-a4{I7 zG#d*}AV;s0UFRl!Ko|i)Uit1@OOdlusoQxC{HFOt$i^q5@;P8x^S|aN7vk3LHIv51 zQA1*bp(3015}KX*2jjfX58#d9ZPTTw+oWg+4YaMevKJ)FPY%0&_l;%lYBAlhq)9iX zvN1IEyt9BtAB)9OTFAFd4s;<~@1azG$a{F9B1GbF>He?9vT}RS_(Vh@3u|2L>@e1G zlu+bnW%d;B`wi+@Zr>CuB1?CoRQgZbp7Y7Z@In`q#CE^8-1!-|SkM6$J;?UwxTP;VPa*3{`uD_j?Y0=Bu*% zLtG=>yUg~LL$#33*&F2N8cPPC8rn_OI5Bz$H`lg}+ueruhh#XhDMiA`2c1yene&K# zD$>7~g+|FO;ixnCt`D*S5k-W!(5|CKWfiCmeQbA)wTHn=ofP9Tb-zJW2JQf7&zGOf z9!hsOIPjia(Mk91S_AMRH&xKr@6~5KuaV2A7V8P55awE>TF>uc?qcWQ%vRJ6nzP;b zz%^I5C-MIIu6_|9|QG~2cW+kk%@Y#LFwv#`ZKU=qo% zjJ06Fn0f!*PdXx2l>JRYnFW?FM4ypfpTmCqBtv_=$DPbL_4>PjOPq3u?E_eW@}RQ3#Fxyp;fFupA%kNsDYQ~PxzNsVTW6H zop?>)qB7sC-~X#6E3#g*s>;S}$?IHvr*axqkR_+>Rg{PuKzqL(L~a+Q30e=2Veva7 zB?2C=j|9BQkYi0G?-xZ;yx9Jt=95>S?-7G)A8=*djqO)k8UEB;=;K1yR_e%Cfpr?A zRX)zBcUM&7lXZ8AgxYYmPLe1QA#ktX%;jf>P z=uN+@iNNME#Sj#ng5pd~-CcS7Vn0M3DOjo{1|R&D#=s|>C8OfTe}{<@QO+X>k9zT0 zM|b{9VTI*P69H)8@LSYJnQ`Ja0kK-KKV9?+m z?dxNnZ37BL;{3$T#rwX8uG77p<<(Accl(R!rN%T(98v`tYgUSP)Epe8_?HweSUWfM zy~Hk544}^}k*3J9J}cF^cQ2ortf8NuDD$)BU|;zlL%;)5*U*JRqUYnWM64jl7&;_h z9~$+=nv=eAOcXL?o2WD$IDc0fG-F7WqUUlI`YTrSF=0u>PS1myD%as`*J9=mHb&pF z>*41r-tv`>i%-VaKHnc28e~R8yc=>P6AOVbiA2Xia*@cS-h6oiQPO;<33trfPz=9I zECClGY#A_p;o#SLx9_2hwJytn<~2y6@8lvG8_2V+fx9sL7syy0>q68%c|%$DLj6 zPsds-J9813aLoHpy=O8-?W_!29r23NUQvQJdAGBmZeoD}G5y5H)a?I7h0+G{{7c2C z)I+ni8Xt+*FP2hjBp%@6>bX;ospgHAaOGvetFJp&MIRHDM5Jk`TagrN5DiwPz>DV1 z9Mfsm7a9BmIg=AW7emBYeLvDcU_B*LsNAk#8Nc)j4#)DG6{m;mK$L#WTz@GEnptP( zMSr!C+PzNsggX1u%}Y%1IqI(L+GgsvY!}Mk0ZWw=n`Ftu-wGhNbDG}t9UszSp_fil zI6!3zd7ju}uiXE_;_2fU=!u6?N(j4h0`eTyT~b@*#^)B%5phMX{7Z`H(L&INBUo^) zyMrIhS3F5RW}{y)1AS>(16Y%YQHp;A^#TfmWDI<(p~V?st>T$ZKPb$J{}kwr|LK?x zUP-LG`R75cUy%7&#dY2hE|Z0=B*SMdh1m$s$UX-XBjxZ7o6gI@z*#0ybYXl2Q^Z># z>piw^V24qZarB(XlWrmDv_iOfz$(W&(m^=?FozH)d)X{?(PatgmL2n@S7wD`iY zIpTi1y}{E!Da)a<;*O$k4?b0w^cn7uXEi2mt5@#i>@zqDG&{+l1Zeshlx)S$@Fbx) zta5@TNG_X2a@N2aKEEPnPAy#PI zuwF}|hU#*Qr?C#p-+AS$%PkG7`=`2inoNvyEC{HhN_`m;94z5`>~2z${foBuGmf`{ zDo8nt&P7KRX4LGt#mk=-hxBhgG#=rUSBSnjGB&q#yPmlj8C)(YK69sJq|RRXqK%}l z2`?z1U>i?oJL_khKVNs=kG|@OD3x@a&KteJ8p7myoQsBOhZrA3J(a1>916*wm|7PG zXOX4j`zLd%Z?T27qnQ&=l0@h3xXaa#5p8Jb6)91Vs8-3%ka+%lH^U>MdtdU5U8NSc z((&m#xr()_IIfSIZEJl+#(WtY%FF~rgI`l(%PYABYv9$^&-CUuZuADfZ>yKgW3WmZ zSyBFGNvR$VAyaB98RiV09KsQkA>I+6j|J-LBAZ=)S5x`-D@TZm(er`$R9 zUWk?4&C}U(s<)E0&KH}=+SacT&Egx|_Q>zF)##66HTzcaMPYR!{mAj5Ssz(XZOLY* zFX~B8k)KoBWP)JCNAr*ah`Nc7M(fd#JVV80jFrSmih9W`{-gBDG_G$+m>KiOvZzEw zNFE%8HlKHpuZWZ&dE!70A_kRDO4tJ7MkR|*+pK!^7Co1iL8+RaDKnm0D}3}zHJGTZ zl=Ue0LgtAq@{tmUK%pOBYKh|P4bT*DU-4$zDw9e+CLB`I_6!p8HhE`o1p zo%P!_gmWY2i-g#w)6TH^hw;&8QK>C*k*-*Ck%|cM#KU4QOSbi_P=(~XlbY-CS>aA6 z6Gb20dwT@x7qsv4s0!LX4N@FtIFiF=hB5wmIyoNL|7NB}GXFHyn*WFIGnLYLj7M;G zY71hXWo_*Xm3TN&wf|GX$Ws?@ zD*?y!KC6gie6~N!9?D-C8c}P+H|nzdQ;+(E>{ET5VqUI7gp3GZ1@ggi9PE$z!nL7l zZg*TlMOtpZJ%);D3drGDOfZF_jC&DO%Pe+}$A4CS8Qs$MEP6a&zWvWoICII&EhX); z1?>7*HBpnFUOgKq;iC=~La%ip+2z|_+Ni>Lm{m=U7xMDzwbJL$5T^=}76HYkG@bj< z2HT-G)mxMW=M8@-?&tr#v25?dpr%;du{q@-F@hv5`Q>|GUeD5RX&?1W>^U8(3oyZLVnl`KOcXr4ep2{YbVF1-YGVefKuhR_scL7H!mvWC7G=c-AU$; zO>d;e*N!fwT8Rtr(GkBdJpm!eS9O4c~Q%y2+R~Au%m{v|4MfC7h$B3 zTnzK4qWjgQA2sS{N6KD7l#z8EzY6P)>9|dsG3u2L%*H;wO3oxT(H`AUv5X?o$nA2B z_f95X_YfT?O2}yX{g5+iVDb;8f1y^5ME(Siqc6i-@%8nagkz5S%v_!^L;TbI%ZN=g zJTZPl`+bTo*FW#RiTXK(4gTKHR5RQnJhNTYduH)hL}3sib{QLofdq2?R|-9#^=#If zza(tQEmRuhdE6tibed>l&xUP&r;JD5MC3e+_i<%hR7aF%-^AnlD|&5%eyd3{^}C(> z^T#iS>~q4#>NfTa!o=99cXTq%6~-4!3uud9m763XnP6q-lKUFBCvI9yz zA)}mM1%^+o*SBwMTd(Q`FVj3|uGU`+k&{mspuYDhvEsS=>9>BSinWha!fF4z_bO+M zGg24iJx-rY3Yl7c<|8I8bVhIYhw>u}Y>4R>aNVYFD-T4saOS0ECpa7Bq>`qvC;f6h zx-$qZ2{U@!Y>*nAMypbJVr#m`=>9e%X6pJV@!NU54)w-G)8*cb?S%?IdTl64%X3Uw z!rPq(D--C+T8F?ezb53Nr$admzw8w~Rj7#2;4rhjuacQ>@Vp<&)!`~u>HUW!vFg0K z!O<^U;Be!8x9d+)?lp(4B>$#RZ`J(shds18wOD#k$}_vfx>;Ivd=#(GGXqS^=r3Q* zA|ZZ)lnp|~Bp%rm@`fsFFg0+f&Q*Q>epZy;dq)`K|8y`^ermGiKu2X6GFb#BW?+NaNkq5+(Tr6;z@duI^ ze;WCo&)8;fSOwN$@)5kFa;u|;)#j7QgCb?=x8#q<@1L0q~m{EwW@)i&MGG_d2VNd z`>#aln2JUEVh0)1dPC9@R#{hl%$!)%`|?x%Bo$l+ZLMewp8T1FI@-Y`d(HT@mN0Z& zeMXJdsvFAzvqAxG<%M5(2lEdtMy1o*2l}WSxp__!njLU;N`9~u@GShR{4lX2ZmG}u zv9dt>6eky(wG!_5(*DMg)y`0H|6I5H&5M})cK6Gc$%!%5BY7(ptMS~emtT8MQv+M# zcu)x&{p2g=&e3(A;13S<9A9CIk4dHjOXoMREGoTOa`aLJO?LdJU+APf*t&QZI`;@M zgAmxCcPIcaLDy9wt0f0(xmPpttWRm+44N5>N_0!Vp4&5W> zY8x4|LSSgIl4Kuc$T^R)I*v1?i^rme*!KEe?XEVOKaEe^ zT^Gp1G5u2I@Oj)F$bi zbR1zb+e9$16`|)dhy%kOQp;xUjr=`--Una@CyWn`U%u9Q z+A(&$F-n?Z0xZdniA0ma+(4=FMtlB?9_M`XzD6DDw0-PKG3PILhpW_zjw|KDb)k}F zO?869sWdDYFFb^Azcfk?&}`hFr+`=428LgUM`lhewrztuevOM5MTHi#eydk2dez!D9uN6H?_5(vx0Msf#jV^? z2+%(GF>8miq&-pcY_3>94DDb-a;*8WAO3gV&sQtR3gnXN9ZsQYx8fC|g^d_U2pLe- zZG!QEn!@Ta$Ada)oeFO8j=b>4F*GG z%3A~E@@1fs1$`uVYM9h8?h^gNE;&1G`IyZPOYcl@mI}VKy&_jLUWx1WwI6!t&(a$$ zQjZ>1Ub~;I*XuJ$Q1Xpfo7@ltBr$+$6?iM)y|QGd{HI8eOM)nCuqO>sp8qjV!;uTo0nwp z7iV`uc>U4$u12p7n)zD2f`|u`1)J`)%V!h{0ty?OOlA?kW@3xzNhWW1P>eLfsG*p3 zN=+Julf1QNFbr-Cc1*Zb4leT+Q>U5KA!lialo*;SXS zIvx;H`c4ah59UUUDV=`6RSylKPD!VI36WCJ-HKNE zL@ZXaO9ek%E#{SJf9bd|7|U)ZXZO0Pv%C!xP9Rx8{b$_(DXcTx-PUHc2eroCp}zDB zdfxtw6v$4U{V=0JBUxiqJkln)fu}o-M$u_`DaF95b|L>`MkIc0yEXgH{U3@}4^ah* zR&NzYW$`&4WEg8#u=*Y{gfT=1`s_ZG!?F;z%;jv|w-P^deNo0Ds3#vnBWee>f@U?Y z8=b0M^Nfdt!QPpsd309*&BW<^ZwNnDTX|8YP7~f?G~|w97ao7|ex;{yaSHmU8NQew zro1xJxA*vmFfl6#_qcvTC9(~q4>JcjfbZ*L54ELWEwBbJIl|fCypcdh_!v(zPE`ZU zR;y%WAJaiaFOMR4^&sbowoeo7?&srfU8s-(k$0N`2-(d_!k0#$DSG4U82tK}( z$Dd43ck8(sg?jcvOy40~Htq2rz)m))MG_`cV5xc$eg6xrPv-y&uD^r@H~Is`ecdk5 zsLP3SlAP{V>4IwC9lQ8<9z}RD=kAlVJi<2)93sm?7)*TVwO*(JG5x2aAB2Ry%hODtwCqM@Q##20_*e@q;Na_K@j1VGL(x#7iH za~V8$LR_tvd_iN6b+na0VUbAz(ud~>5IAy#Eq^bqkLsn7GZ;v$ANSm7KaOl?IQ0GY zZMTige&|K#e}nieq>XgYNHiU7qQs~Wu($NZJT`}$d@qQ@JIBVyb3mz-F{sr2VpguN zQhL;ajAwJOI-r=NxxT*s1CU_yK*f1p?j&@*t>g+v6~YouGPv=6rS2V2bHSUA)8|`?~!YoKB}kz54gB zb>!BR&R9FQUU|@UO!SdpibV09*S&M{F7uZv6&AU-ex%{;htKkHFXsEJPxt%CzC|&1 z^Hy*&m{)96(6F6}-imA+4#E}|zzfl-163#XFH1m4%hAb6+??~yLgRUy{~4+8)rQGr zu1cr#a9v3udufB_j^9bW`@~ag9qLIu|5F*?pG0ZH4DTOmx_BEJCg~O%0R6HZ2U9|m371T|kCLnPrx@;??Mo}Sz+IN5v0 zjhTEwI-S^2nypqUk0%aS+zYx9e|h%ZeUG79ZvKmy@qMxLnBV$EQ`grGjzGkYY+L#p zYkaQUxd8T;L*GYYi|EqG?Jfc{Y#W7j+wk@L1V@9fuH4SEK^rGPDaUa_H44YqQQ z=J}v!Qx+gp814A1i5-0#@>TUf)p_4zq3P^*kaSe+>}x-F1qy(VN^j0fZ~u44cgI^4 zV9@%S(1xFMRg+k}TdYKeDU$U)iDEdrJ}u?!>h%l$;Dgv=$5+4ov7HW%X~?cz3)oD@ z{O-8$P<}G(T3q^e^rH@UJY_E4fv{HnrSQFIE(sIS*9s%;C%5~9o3$3R?}{EO9kYdiFy{7&>>WyJSkcDPrG8 zedMxhSlqBvBdyaBM%bZEj@V67jvpcNM$bH^Etj2c#!LNj;+ij46q%w$HX?ZpsvQzQ zk(2;0Z>E6#gpI|oT~H!vIW$vcAJ=p?;2cX5zH{+<%ig4r=8C1}D9!QzF!j|@ zQFl?-gNmrAJQ500DkUJTGz^M#cMc#S(%ms&AtA`n($X#6Aqq$iT|;*dUBfWno#%Pq zcYUsCsW)SD9#+7uuO%Gx&?eL zdnO0?kZ9z1tT~F82)ZQ58ykZ~|4Q7w-2K^b_3UOUO)LjRUQ6Xm&Vd zq)2{?>L6`_9&X?ynDBvKrtEE>t>KlL>m$=yg@%JgD7wGsaX#*((aGcw!rt0GYdj{- zH?MROU-4RI4vR@-WTrQaSF6Gx)0rjO<3Hef?L`i~M4W{jpyIV)@>-$+J1W(P!=lHi zXLdh_MEC%a57*y!1eOX7Dy_8!up~5sX*MN|{48hGp>2mA@DmU3O@rC|l$75TlnIK` z_)^z`=F83I(T$M;nXy^#%_8rKZli3Q;@WI;U%ZId0iqeN>2eeq;rsev#R4^Q)>J}| zU6VO4Xg+7Ro2pSmJQo!$1o92?K&av)n0EyuqIZmDdaeQ2zjd3}U;SHVPQ!v(mFzS^ zaiUiogzW5;1dAM!6U3a~A~oM)`Ysftoo5Hz85jc+Uo!7jyr_kRno3O)u#nEfMDJ63 zxG;}qrO!|x#Uo#Af~*K%fFxf}^--2vg^}vS6QFTce=hZ6Mh!p4|inz$W|JDd(fbQ)ga)qDD&IA5{jakC7R_~l+ioFW96 zJcHEp;T-2tzw@EgQ6lK+uOzovi}fz{l3^er=QiWD`n8V>hzpcouBTqg0j+_>nlXZh z#qVR+AlctO$jvm|nvvXO&QH4bb6NJggoWk3cFNwG1>WuR4S39yfT;%Ilx$ULB_qF% zU+Gy{hmC+(iH0|6^<}Q&8pPsAj zfG{tqrilH$oQhAkeoO+hp9AG_i;#zcwhl2%MPpmZX@F@2J+$^a0=Ih9G|CIbc%rcCJI7Mjiv_ z1HX)(EFs_PKow;rD32XjmSy|_#Pz(V%u@W0Cz6(-xC1EHraqVFFXHD8Gop4t1I=|B zC@zLD2l&m7!Ld5_BmEuX$EAqivIk(5z2}LIbAecxj(U~Fkw-HI*0@NEe#)+#_Ba>P z&S*|!!2Hmx-S}~pU(I)crH_3zUGk8ilXJ5$wXvAEyv?p|a?eGi5SJ$57L7;Y+bMUK zcY86v4_$+(B@>_$d`*5)$Jxi92HD10S22t?GTDvXJ;NQIJpM{^;qW9u=`Ou~oV*s@ z>q1vT$GJbtQj_G;ZQdHWzd=|{!U?!~lLr*(l6Vs2VZb$R>y4E}7l(I7z*K^(lJ+EC z%;$LN8JA(r&#g@k{feh71PvbOnID)LI1W2md$2pz0whE>T8X)yZ3^S>4H>c)#NluH z%Wv1XzZ)=+y_R4KGndLJTX3QpvvYm^^p~!-``WChR~CSNE~c!DFXKwJTH513Lmv&@ z=8>I18Z$1iZD(z|_u8ah2Jrs67mQi#66*SUT5QV7nu0LRo124Yd9tZKOMu}vjt@p6 z-S4tQ8W?niuSEnA(Caiz){CVc+=qN7(;YL9^#d`^=FthHEHuD9iCSw*VGre2wc15;j zS}m8S6m>@}&*t`o+ST-r6d*oCI2|5^hhY}b5U+_cmyvtPT6=kXI5bzq?#zi*=BDSi zfny->0a@!q>i$7F57Sxc{ImTnX;ABL`mOXjYKUsW>q#A-^*lTyEYa)KLGj{X8tpH@ z@>s-X^cf5kGBZVpEJtfN$I^p>?y_Q!xfTL3P~)A^4eUi2U*FR@9FX3NE%ybL_0ev$ za^D3UQtopZiPj~_XB`6ZpBO(;RS(AD{;)9fwjX8nZ~v~ZM~+O&?|blCgD9yDOfy55<2LA!`7P2oi5&aovxY*k2kojSC{Z`;Nt7H%!Bbz0lS458TT%Qfy7rLa_%A&#@X zNLzyP10s=CG*o<8aesc^M zO8=(Oi7>EPX=^-q?TW*_2)M6nZAOM9UTG*~2MS;X&@)&mEHEE+Ovk1I;}H^0wDGk| z{`q$bHw)xi53@fi_c)oiD<5KC+@+o$5bE+hS6{@|Xl{LU8#;;^^HY{II%AMwi@7_f z?kGr*{Y&m*%z?W{MH%fk{x^#q*M{4Q85nDm2`sn&aGdm z6A6;r1Og&`dg9v^LlvfNA!XriFdgW{Jc--R5pEPF)4I@hXVtPK0ZE28T43LP!rDT{S{X4zI@S25&ZhOjkbl$!U{4q zwYf&MI%E$~qJoHNE|$2gOIR*Ka$zwhXDlyY_IsSh9K2$|?G zpz+&=NzQoY+zxy6UZ8Wh=3ECc=r6s0w;EFs+9=a&zT`0Rtlq(?*X-NyE~tXhlvc5u zF5$cX0NsBTGW7BhYr-By=<_iQ0~oKO9b*8`o0tEr#TwXm>Qw{(A*8R=c7j)I|1X30 zN&RdQ%D!Hy2@QmUVCO(cRP!)CHugRY)cK#U$w2dKT~@<=E`R;{)nLZnI6pQyDZC?8 zP-)h2cYCU~{D&FPA=QYoF|+1t|FYVn#?}7yueDVq#aDh|@{c{b78jTabC8Hpbu0dw zW78KWaw87gViL{^9QnsXE)9>&^0g|<&tvPvi4LO@Z*gmNL3c&d>CsIJC0l+@+aGRT z$!f@a^70WCgUzqj4zsd_Xc_^u9RP zqeT4kGZy@W(~ok$8w=28Qta^|Xd{DPn{((sg-gO`{AWHNAvp8Tj+o@!PQTOfM)#~y z;Jj0>N1bsL(QncBvnw+b+&1x6cRL7hDORRJ*zJ^aPl4C(XwM; zILePB$T#;4mJtl2j+~x&Twkr@Zb02RYn8pF*Oz@p_v+1k$2S^YcXypQx}SG!bJTFC zrom@?%a1cpMlb3BN!J2Ry3H|qebS)0X=RLVc??YuHkz9ul>CYQr>_Y%rLK(t()%p3 z6MYTOQh)qZZxa9Q*RPk@IU?v^Jsm5IrCc45dUXX}>w1dMPGqzl43n?ZC=WgBRH?8- z@n@;C-no5y4Y;!(#P=g3atjI+)T^+E0?lVi$-voPtWu*h*h`epVhiU4VjhejdZJ0GjH^Jt&s1n zB)IK^>qnn@1=z{Rn^+whDkiJ3MzBqE?pEu|v@nm;#*AqlMuI7N(NIZPIeP8AxxW%A0t8S4(DRKIc}Q^XA744vhqR%^g*=>~*YxIFL%XwV^FijI!*gA-fQVWjB+YsN72P~8*D(-5=KZy z3gpIxy3N?%ck@&&cx(w3xB>TL?L~=~l2iwfBa_xKbdpjkj$jSD-=9mcC|%ZRMA{^n zIW=~FQ8xb~u;3>Tv7qsvaks>)Q#XjsT#l-|bk-FN>^W+%6OsPYb=VI57pq-cA&*pj z=k(m{Yh`FrJ8{W$(GInf)gU^Vyl+62!^aTv?sMh>Ma!?g}w%ve3v8xtbW>ES%}#y`?~e)q-Egx zNGlT-Nr)kb3}*IY2bM}0{g&B{I~6X&UjQ^Y4agi}AXWp^o;c+uUhe}b$ztIBusPsL zJ16Jo=R+L6&?c;Ly7pQ3et)4*{lJLfr!SbVBJ)Yo_M6g-{&O ztm9R9i63!S>R95w^sw_T@_3<5>f``3)-CKLHL+R*l^`GszzOYcF_{(+$xYq?>dZ-QF;(!(brpC%7*seXJ&=^C+o{{^5_ShNs(3%I zAj!=bcRohEgF>Maqi!qY3{94r$w-yjPn0oPCS-Darvv!}-+~3pL?)Mel342-lb5(c z*lg}Pk|ATh%6Lk=EzA=oI{mh}(x5KWfeEq4o!HF|cz4GwGZEXP$M+MvIM=-7y{zOA z>&dJ``ls_ROPZU@4zz!S%VBG5HZuv639EU40&JPIdJh4<_kPbUt*$eF0s^a2zwRA0 zD2r@AFVa=hJ8CWQKPpIC#uEq>S{!)Ur*j}|C0geTq$oQNL?_HhjrTkafhOx95G9iy z!+WnlafK734?sXwg~(3LVwd>+ch}7H%JdDWX|FTi?Ed<<=98uQ6)#%@Hm#SaOmM)K zCaY*f(r%sJ2oHW*rqYYTN3DrGn7{36!Cb*iDf(h-Z?ud3cT zk4pMuZ(%BCVZ@_4sqnXwfw%pye%b38GI$rNd(&8F-8jpaFyHAn-j@|Cb8?tDml4>} zVq5G6lG~Imhbu$`B|+d2=Z3B{@&#qzzZPg;yBwS3hF8&M7e}5SZMww9ktalG_l#p@ zq@|y8bEnPB%ry8#fDt8K+tpAJ%i=?dmT%eM!KT;h)$hsE(txbprZSUv4~Q)-qgR^L z7Np}5mbllKnHBIca38>uX`9G$d`K{q#V zZQ7!$l&~@ttD&!-;L+wp$5jY-Ry4}AX}uu8i{1Bcx=_o|uaGLU!^wm=@@aqcUza+I z6~LVfS5IjSR9-yFs7Q@uoCuA(Kh=Ty`SPW1@MKyOMN0?LPRF2P7m58)5qKRUr~YJn z*;ND2O0{x}?d+$m*f*W6BrOBsSVJJ%OOcwB!&3ce2FO3M3;S;w8ks512$9rom32GE z0&StbGWhy=8<&4%x$l6~gS?`mT^a!pm27Y(`=pej3XoOtIyabHc5matp~(eX81lavXn?Si=n_wi>UnM% zL=O6>NZaeDb63gg-8`~HI5_JPXX%xx*U|_$#*v1Gf&>?bRoF== zZ{@1uRf$@J)qe~Az@tI66tA=YkcNf^mcnNlZggB0r{z|Rcff_Zo3YytM?k{1snAR# zFB(sjSWO+!c9s=3ygmX-YPujhTBrFu25+nemVr(_%xy%=qU{Etzck&6*H%iNX5BjF z9((T5+B7=3b-&SLo|=LcaXK?E=#48hvS=?vk9(JpM+-kMTOhq>ksUHORBlB%tps+p^*Vd!()O%)5I^hhq9tLNpt` zz0srhK<;j?+gNH>y~LctOYX_*lWc={U{l<1pp{R+$Nc|?YHmXinG*Qz43VhG9;2mG z90%5Jf09`Tb*YCi92;Q~-wRH1!(y?*z~?yR!tHZCr2Jb%Dw{KOeujh>16D<4_I$M3 zxvKGEnQIJ?J$ zqWv70;8I}Yr?6@gJX0g|&HT$KJ2^Hf#n(3&`nDl$?bxAbZ5YO}m*hrk;IDB>ctxdi z&V+icP2twg0_~oEdG^EpE0dHO6}RF+!kpu{Y+ZvrDcJ22F93J)Q@+pT4^3dXLRyv~LdRtlQy$%w}PE-(Uy@J3cz2z*+kmJXavmciBPX z5{5t3WyL3fB)$@HFn7JaIy~Wpr;UN&e>PCBR06QI4hXQhuthHJ)ihzI(bnx@R3lg^ z@Msy^tsW_k9#ulo&heqbJBN=Qz8|dWlNzYHVuE~pD)zpF3(z8QFPWpBqL%_$W>1#S zj0Ud#Y<3HMZ(jBzzI!n3_($9SMVy3Xu0ncQx6k(9UA+U4^EbyFVhXB6^JLj0bYv5l z{7o}@jII&8)$B_*@+?Lb$QM&bp}&2n0NG1O9GZ(oxE) z6`;Jl!4y;iJ}Ai!jOKh6hc!+H7B>dl20Z=uVLa-!Qsovbq0}JIS-8$mbo$R&$XX+@ zcHW3JYmHx3+(2aY8V6VU@hVPrwDxOiQL7S$bwc45OTaQ_pZ_cLN*0^H*<@(Sz?haR z>H`*B%K?(4ngrmuJYtMm45#_t3P@vvBH)I)PPoBfen%rOgsLs{{`KUbfH?I=p=Pqf z!^53Us_hK8yaz%LKhR%DKyhct7aluPA;k|u+gwq0)$4fw7Jm!;z*JUM;oYNt{A~uF zF8KMw1_Dwr5}Z*IDu|Cyv|99robK*EE}x>qHlK|x6*Zd+W;xhR7dI&~Tl%fW`gDmn zeG}uHXEN_iW~sIRHQjKu$xd8TXd}Q(F!g^KhTp)3;|}2mIt!2+-AgqLLD`OgzFn?> z-~GM6+P9?y>(n({& zYCz~4fcY7s8?J7YKk(-HAFJ)|qx_k;4NSfNV}q*VRF_Z1sN*pG_o>U~XF(y?wtLj!2Htk@VNf zZfl)D=FS2KQ%Y`-q~kGgJ2+pgda+p)?Aq^uR7I>f_pL6UA#Doo`j+W68 zC%(QpXG`Ij7B&{2-}yE&`KqWGo>u&x0MDLYZ>+1?w51&?)5R1CZLA|zAk=3Y!1NwJ z^J}YFfwv4H-Hbm`Nwow+gr+B9m=&GvZfUhJ;>YG+QPhn3j!FTlzEmq+wl zv-o85yYiWSl|!ER;P#7lhR@)WYgp@I`jtcJhjvNNm6REm0AgNg%zZ;uM-5we^AC(b zw786;r~glnjo=X2k-k))A*;FcFwwyPEYwBmo`!5U+ydboWTzYF>jKtpPkExdWD5k@x#s(2Eb~p$dGc3}F|i>J`_)S@QTY-;#8aL`6b5uA-JZt>{~UE9TES*)GKa zU7hw2V3q$yb!FU{kx4R?>Gs~2tY1rqb{&*F7vH%@FV7wj`;A4eX`K~yYXJ|Z-l@%c=XA@|0oCzY1;4={bmrFv?R$`a6C@RUGQ$U#;K z!)DU0)|COS3I9y359P>jLOvgVtK_=SO;W>2&UTEHv0T>^TyR7tsGV)W{zFIjjjqzXTo*Q z=_)79RxsDkW^pNK*5$0_E&AxOW@RIIGoT}uQ+$lJ-=}<@t#7H2D)SgTxR9WljnnAN zy_2SWB{lALG!iMh9=VnJwUhZh$EwMz@giIEe?ZFQfBTujOmBIXfj7$K?$77F1px_H zlPo_ErF$TojRfpZ1HmCpH#V<)A5xZp92W{u&_yiLXT> zEGlx@Qu?HG%hxCYDi-5m`!n5`W4@DD}JmNzE*M`f9-w} zn2^c=16cPp{0fBmFOsH^6ln1N;6c2|wySvI1(&%N&11DkMcqhk(!tu|- z*e6q?8NWbnl9SC7qDx&zj2)mC7g6^&52-rX)aNXjD$Ub*3BwPn(5lapqelkS%XMix z5Tzuz+rm{jScXrb@ODm6@Qtw(xZTu2`*aooq}c3ZAJuFmFBi0bZDF36z@4>%DR*o{qW}Xqz zG|{|y4MfxgkTxd)d=i~jUM^upbn;c2;pT1IBE>N_6O{LNb7 z7SXJZjretmPxury58%DF6hn|C{c!XExR>vwXFMw#wH z;-CHNYpQ;W>+^q#^J8Lcvy|0t7*K<}yCQlETuA}>l=ZENh`0MJq#5BHsP9unh3szc z?(Vu;=t-A;$vTeD2+z%kU;N82%`2_#oLboPg>k*)pkuERCKczwR8$nZ}pJl zX^lfZ{KI?ws%m!UeBD3AHkQXR#Hd@vaP#hO>Irg}t~VgVx|0XXLmPM>UF&<3hcozc zzX7R!Yvg&1$zTd{P&6iHVLz1l*HKiVPwAYN$a6jE*X!RMBJati&Xy{`jOIfoKc)_T zwQ0_N!^KnAx07h>XbgX+n~+@3avUf4DxInYVQx+yFzuQnPJhT1Bou~Kr@@eX)uK7clJh;ZxCkAtfzAVc`JIyo(5-?eeK@Zj%~J4nep$u zykiaX(qHDGP|pMjz|YLczv{|o{w@)AdzQg>zhYy@jp$YLsJTG&ex-QK@Oj{ZU0aE8 zo&nr%eo9!HFl~l6g^TM(SqAVMif2{J zDLV=vv5Y8lD3=`9#Wh@)lwG>9S>4l|J5`K!l(JU6JDHP)E(my)}M{ct~SE{;l$q;&~<`-i42d*0ABEd@bkTx6e-0lM2_j)&*qP!-ge_9!O5 zekoQE(EAI=$18lu3O%e9kn#Q49aG}ClJ~i@lxeK$cVdinnDl*E=5bC_a(Uel$zknk z^}5@^fx#i?SLE22IHmRpzfY@>oN8N*Z_3@Tt+_8s!_Wh!&&{h`_L$~VaOGDdZ`ya1frNE+x7;h2^`Wbq4)7*D z_!K|QIjG!A09tr)QIKNoVOI2!)luI^|7M<~Ox$eKyOd(V(t`hH0=t&inEq!so6qMc zyxcNw3eBRjJ<^QxmwC!#SWyVWo$gC9oz&a(`eS>M{GI&LRWV9*BC(Nv+>y2sXXR!V zmY?YL(d6SDRg4PKq4m>c;v}0Vj7R8Z;?;wGs_LWk2fggIvon!l;(lXQ2#nSynoI8y zDu23pHn<{3Hu_(D(j`#2(}MSGo7n6LVDpzsoEzgI!Wx@s%`gjFy;!E^*CO-le{B*A zG?9DG9?<0KZ6zP~^=LBV4CtnqOmgJKamVP?1Y%J(;iBtLy4$8T0(c`WCl>OeKks?i zWl6W<&im{m)}Th^uSJN4CWSP;q~7W@G^EzT{iRZQA8V~~?XZ>5Zw48Q8Z)eSiRT1| zmB1tuK8*aJ+NP9`xb#|8eS3(0UeH6;GU6NFy)3-Ulu1fuB+#mDUp8=X^(=6SD6nky zdnJZS$Rcxh>Y(&E+}w3+68oZlC$g(3OvU_tzv&H0-o*m3$u)dcD|f=Te`7l%V62_)Vj5TP}|7!NO{gsGEZLI4Uzu6*D;BmO;q)@bWsDbdkl)bClRChrT z+@8>}i?~_q(pAU(ir}0L4;4t;J`|N=_uIu^F+=w~=Dof?qD&Qy|)ihDa|z zGaH^>h<AhRLtxND?bo%RF?8y#n^)yIMN8(K+-Kjv-~1rv6Ej(_lQ2`06kMfJ zGH3amuJlFx?Giig?;p&lz@7R|+u$`zbZIjnfGn;sHQK%sjJf%|yvM5`jut9u$*IVm zaPzMJsl3+&j=Ax}s@Ul&L$yVTOlujrOV_tng2j)&_T>h)TlpB*RO??)X2 zIF+vHzQXAE#Ujk~UWRVwU?0h#7R!mI9}Ie?Z}G%Oa_?B%pQu-`<)%~q zV#?OoEi@o6{hS<3`5T%4AOcp@LF4c&$O3^Oe=TxQl|V(yOKW%Qc$Fo*@vA|!zEh!Z zN7ReJAQrZ~K>=Rt;PHp@YIzau%nQW0fo8*;^mobnj~U2@JMUsWswS z9T9uW8(${wM5I5oLdHF~R`$D#8^8Qr?=Bqh`!aqw4E9DnM5rw4;r}lG5%VtjtgFK> zJIR?Zyu@$i5rukXk*ZTiD}`jRMyNZp6X_IcTWe!I@pJVW%~LLyilgpJEOa5abG!0 z=q7Vhw;FUj&|b2uMpd%(r~MBs7E0f*DxJzzUgM$CSmtYp=~*95cW;iB9`sEnNQ(t6 z8N*ND21|s2O_v)?!v5L~KNiWTj5+LOe}T45y5@GMLF3GSG$%jzk55z$+@?&fFeFoZ zMOG-A3{rG|pkGyr9nE?rPKhDe8cU3u{Pa$(=Gwz+}X8P82LuGa)WsCv1t@6GYD z<~f88^dOsJh68MAu;g|G9BP2J7xKlnzbMOfq<$wi*)VT;?u#oD0v7QW`C52#GcB&c zNd09ciL$0slAWSPj}l=(Cuii|QKYf$Kr?ORMGO$VrfS6HBt~~>9)DAQ|1VekG*AQt z`XKS~)@-Wzc4nd3a|j4s+?cpZ-j3Pwj+eV@vO`C-p<{S>iceGQ4rPB`B!^cA))bX| zUf@R3O>22x46Y-J%Zt+vBi19Bs&_|66H`$myQP-Fqc-dQ9g*54Q>JjZTPJqPty%LT z0j+ZC{VCHj^~e0stdTQx^-N~?{O{PS;9r}%nZFLiHJzb8v)3!9Pu5jUZ#*fQ#37Jz zQ^?HgBTlOrd|||K;~_CZqwdJA1BZ*C+L zF}B=oxfa+fDHYWM_46(X-w56SUT3VEIP?bosw-G;#XFc*frG?g8z?G{+%!>qTbw>= zE6U*az-o0_z-sQ30Na4a{5z0|X*A`RtzXzG_Z{tC@rg|JAI(|uhY}j)K`A;IY^oa4 zF7D~&xPZC(hp%^Uno*h^&Obm7?UjeHtl^fMw(Cl85)-S6*>d=P zwIMFg)lJxnzbe}FXz&SiRLjv|Dtx_7jGfrt=lT7i4O!VvH@2!k*4~E`8C670nL7^6 ziX0?Y1;9Mb#EfZe);ccbV;Y1u) zw9Ny5XZxqin~ff}!^Nq4_bj_2rDccxB`yg zJ<5M@H2-l_B2BK`$x6U$0`BDao-g`z%~I3Vu>2|?D60)_MLOu397~_oz-*HzD7m3N z$v#X^wNr2B!nUdaDJ2Mm{I?kfe?+;>^kN=7&<|hAYP~_^w($K6DO-sa(>_f6;FGth zqg|hHH_3CembK#HJYl<>jN#+QS=A&3ix-5}B6jY5O->^D&gUgW(;aVdn?#G~X^&!Z zE7m%pZ6bM}5hm(E7iA8c=AIskhvLghTCj|z+WK28Qyu$n4?o@iFveS6h`oqS={FU0 zraP*^OxVBUF-IC36&yrTjCGxYv-)&2+pqAClTO#e30nEvMjb!Ojukllf*W=+q~nU_ zVIjrXT16^c_(N#X=}p;-#Sbkc~K0f2c_ELO4v0eL2UicaeT{re|)gj=DKvV2?25@JC#e zEQf=?U0+=dt%d@l(ecsNF~45vJ_7#f+N}!9ryQxqj{~z!?w~a<_6*438}11`vJ)MI zyz(4grJq&&LU|iIf+!!iFP;U?cyvf(tKdJ^#OeL_L>u=%?$K+$3!My+$wfmTG~}s| zwRS~XHQTT*;I#uVl-y6&`UHHE?t|cJ`%|O+8b0dd-d9&7_Nkzz;m(=f{Kr&HDda)y zfU>KowDjJM=2usGTOAVe(jPSF)R$b=epdD=@U3nvTnxJ+H8z`9Oy8iq?SWPLtM-Eu z#e4oIm7^a~{BFSSa()bTI6k^ZUmwiAJ)w`SK7{CCN3nHk!F$pR=9= z^YC{)4Nh-MukFS*m^=J)Ol{&sQHBo^hV- z?UY4uR1>~v9k^e4bk_YKpF#grOs$>d@G{kY@PH_!dDK$Wm@b(LG{=420|Y%G+)kPh z6@y|{hj5l#iPYO8RJ1_`KkehSJey4%Wq-9aH5@Vc6WR^ie0#*r8=^*d`*~K2-OyiF zhryjOT*=*}0|Kuim-gA&V4I~sV`Hqs{M~012up<;il|q>j=TEE zo_B8}81Gb3ZnGlTkNIs`YsBZ7xP-Z`89HLw}7$#aO(?c2P`je>_Qb|d`Qye zN-tK&`V;mw$<@wl^`>v$d-sFfJ{x85HUo<*7!q)@&fBkwfFijI3DyWkJlB19u^Jzz zQ#1s|eI{Jly?a-);(__0p_s4$SoIaSx^Q>Fg!{A-Bzy@-FHhcpJ!xbW;QiA z@5v&5QQdgz`KcFkOUG>JI$n@KWfZezxcha`iJwfmb)Ec zVD96Qsj=ECD21pf)g|0rvAY$b>6vv=aba??QSvC)&iRot1vj@46@$beM;L{r7Jh@@*bO=riD_y6bF@;N*xXb!0n1{E+uf_Kg z@~VV*=jubvB1nS?nEd%HZ}7~e;%*9Q_9mWsU2s=)V$nY4QDe`Kj^nJ^F4N;0p1w+A zv)J=w`H)KGa&)`wL2vi$ec50|b&kk|B1tCJUF-(k=+l-;4xy^7+b%4Dd8{{v_t(aV z?aKx~SbJ?zhv3!?9tvG*$v}P%G}?3ji0A!n&LCT5YB3zOuE5ykDu%m%bPAw+ua=;Jt8g8a33-QNKfM;kHwLVR5S6WV7nvcmCSc`b5q< zG`j<5ZEE?!O*gD3s50h^8{^gRnQ^r2#1=-vHqJ&BBmw$#Z(4rbxx`|}r9YZp-Dr|c zH!he7uW?NeKO5EKeaW2kXy()<&&FvTYwwd>;UOu=)38CUK3ki0QolGo*pB%fJiid3 zzyj0qZuSvp5Oea`E`XI?;XL`BwyNaqQS(q4{c^2KiY6I=UNpcv ziTFvO9?_(D$nN8k-!8{GZ2U)X|9Kl(i*!f?vwuOsj~DGvt!w2yJKv)?6VG1x2|3kQ z9m|Hguh2thXyzW>h~7mk>M)L-T5|kH1H4G$x}7F8?lT4~3028%kKRXguVvrxQO*fl zYtPa8<0#b$6%G6I#>4Ii@>%;Wp5Xb|-<|WXYs4BTx4s+}Lt|!;C#|6`5i{r1d3M_C*d5#hE2Ms2mW=g!jJ{9D zMQwG1HX0J}9WRv#KHS_8)@zZO{g($EDh%>q5 zLPc6Y9}O`E41E0TF?Q@5G#O7)7{_ZN&V8|6kojk3vfb(LNVS5CcJvII1*bB3X)efP zc#It6;UZq0f@IU|RDrXZ#-FbYQRyB1H56$kl_@7oG)}2dq}<@SY)ab!aBJUJ+Z5+HX$p^U1A)S%6 zA20b!V;&;ZKd7U5mT6c1WO{lW*T|jE5Njw3M`G#jD%3(#ApvUttqHYzOt2x~XTv%L z!B{f@X6^~^if3EZ)nT=k0z2d@5UWe!RVnF2t=NsL?@zIEam?k_#8BDp7x4lkIrn21NgOq6D z6}Qb7EN<+iMNR^t#Z8kbB@dUG3w{stvttHiA6zOOOClOv`R^ss3CGKd$15t+mL}1~ zB0iWMQ#D07g&N3}Jz3UyrqdN1SDjk=JM^)-@07ItZEa5;U5bm_K@=YtV-D!N z1eZ!79Afh+^pNaF|1B?b7!N{iAhv3eCZVV`;Yqn!PPyC*qH$R#_Usz60-uGGf5yW9 znuooA;up2uM5qNW9xKKu&CU44xIlWNPsJ8th8^m>07^nWZU?Uh9i^n&4!6i&HrK*& z1x5OU6eFKxdUN3>xloZOZI`$6m;bPK zGUZ*aYQL@4sHWyA3#dsYQ^)Lih0>t2~3{o`(z!=BsYuS$o$h}`vkl``Rm$VJd|Wxw8q|Jp8sHv z%Ju7&0Jhz(n|RDpYM1x@+U_`iWqieVue(2OWQI5X9H5>Es1Ao~CqYJKU(KE`Rv&ed zm1x%UDf_fa$a}K3C6CX$J=TUOAk6ff?G*B?jexUavAG$tF%DIwUXLWH^1(MBbc2_6&)`<~9V99aX7Rr0CYcMqn1(~y(7&JN zro>ptQ&X9TwCy!NVhi5D866sfJII9=8SR|!u2pZNj$BnUYMs<>IuL)q`@ z)d1;ti6d(_GGkMuWgrm5zphGJY<_ay~xi!`VMj|`;R+zuf;_;Ycbvl30+{i%L0T>v|j&>F#&0ESAm&$t$J9$89Av>Nl3}Rx)oSwQUQ#yfNs+?rcKS zg81(!ujyA=m>WpPM%tR0>rLS3MyQ8`LQx$YPi~XH!vt2Opklt+F?REok9TWM@DH0e;87J^kOgA1BB<5@!pDcyRH^< zOjvT`&~INBMagzuXJFxt>gJ9WEK>?lRais+K!Zr=#91QZrU~*S`gkyiJsoRe%UQc( z8ea6KJ4)vrHxnc++puF0+TXSXdd*~f)PB*X8O?Xgk=o+i5mWrX#VUuWcE|_=#{fUzrgk zB_a*m{izHxwk~$;mQWNg!1R%@cC?VeNzSdlrQ%NsMd3e`*O9-&e^sa(3BGjgu&a#T zGilwKSA0jp?AmTy83+CaBAVB2E=*b<+ZAU~?G2X1e2`P|Av-fOvVOcmc~o`3Z@=9W zb7$_+f4DE%+CV~Kg3x>>-9lmZ{;CuJ$)a_eRig! z5*6$HV-d-ypj-T8AbjhULfq14j4j!&+%{7E?crs!QW@Fq>}utM!gF$`e!P3kT(JO_f%LR^({DQ1QyTeVK5A5B|=0u}pn*K5Or_)?9PV`OLYVIR;_kC48NOTJbGN0;%a(=`&qt5KQ$*Y1bLefKck< zJ28};?b+7Jg&WIcp@~Co$bl~|kcMMjKo!IiPOPRl^X^ySJ{wC6YWLhjkqOV>Jk6s| zb{$D!bPrzuv$nv~OTVGH# zbucJ9w`}WK`nBtWoQ2wZa_i>Pw;jTR|5iiACou#6g zM2dQYSwMV+`Q*6%2_eN;R#{H`&;mQ{T}JOX$*+Aiiy>OK;CvIpdkJxzRSjHM_m*o` za(5Nv>_>i#3oYS2L}@p6x6bQ4emc@VV{`mukBX)vXxjY>_b?KL@S{dxN)>6{AhyM`=zjo)>@N89Sy5R6X7$|lEmmyf5id>CE zEmEK6-o}WNYRKIQVrt-LLifiPi_c9&zi+Ois&zCS+BDQDl-SKI_wwfG(`a0nW=qJA z#L{*;eS3eE$$@sUvfXDmC4C~8oQo^(Md;JHc-FO`DEzwa#LV``gIYUIFGV+Bi6*kS z-M>9?BjR$QYL&w`7`Ql&Ke!Xj=@fQAg^y>vEk^28%*Az4!y zxAALZ7*~Ao;y$eO+>G?)=P>Sm1%z1Tb|1m)o2C({+!OeS!L@;_*f(8l1SZxb!5dkbR**4R2>#|2NuN6x^dsEQ ze(9UprV!DVv_F5;_*1RmZr$YGZ^6a$Osklw%Rh#qGIB_P7Bc2G(cwc{Z5@P|wF zxDUy6JmL6=|OC;o&OFxNPoS46s>NvMP)-T+8^~$fZp5k_sel{ZxG|aaKzAQ`97C$1nhIIfs9r$yl3K(pp`b?Or+SZ;y0O=Z}>; zq{UBPm$1 z{3Oy!QI;(};kaO2e^LM0nY%n3D}~b(g8EMFM8B_1xvv$JN!})nB5mz%=B-K9*UzV3 zY&L1}5J;mVFg>kg^AO!zJF3zZJbZ&2^r0WpzIsa|w*5v!WYW_#a;ooRJW}~pD*b{k zTsmr;INVQ(XK`5qW>a;~=6bEl6C)#1{2wIe!*zSJPM)YB5X&H6Gpr1x=V{58=h?VH91FG-7#?t(F5 zNy(C&g=#dKuBR@`o%J!klEeSquh%P>sO2l}S1EiUtb4J%@tmAn zqw>vFqss5pI0Y;v669@WcNQTS=j}UL1`VQmBYQ~s_?@G$8=5X+qtbFZRa$NSI zkq3K2D)f?2w>iT1Up5cHs%5L&(~j&^%W?tZxN z^jgrR?KW1yZJ<+#1Esw3c48`Y6?fM}$%`=}Y9- z*@gHxFl{VTQ)BwAS~DBkRxlRTXOujOm=7Arkm)gxCk!&P%6V7E?w(Rm?M4O1bf>xmpwsWG05wNoQi zEVc`NaL6_$?1vtmdN<(JOH4Jx)>Yk@ZI!yx0zwVQ!|)lZYy<08V#0KJ7%bQpA06=S z&94@jnNG?~G+j*WyqwVVmPD__09%{NT%=}t>jO_GwN$Jh!)N^<4fmPoKc0BmrleHw@@V1lW;f5exC=+ON-pa& zdUvIbI=2Kxjpx55W1^HTeeNdC-3gfVeBif_LGfY?}+8sH0ORNIRw!h^4`5S z*4-?+bHf4#7iISB%qVgIq<3dq3Em)JRE+`0wmIEUL9Xuohm_0u(){jbK?=GXSE|hV z-TWSg5#)0;wdX_9$Ze`=o;imxX6EpuG@*!}LJ=`E(*B<`qaeg~ZCz=S;$LVV6;AFl z06!Gnan0S#R;<)s#dVtzAT|hz^M2+kNHMkLu3Q!k>$TvS)va55vwm$AUog57| zQr~#e>=-|etEat5+ZqMnX7er8(|Q7E&D*2_fvHiw@$VOY3VibJVkT4fbK>E?fUFgt z0FFXJLYw84J=+`yl_yuydI$H@P!{dZX=a9c6Ywq6>sMz>*A{wLH=@Fg0dBtuH~$rB z<=&R|Tcz;j^Q*ZNiXBh?B?PUpM1>QX61e;n`Q~l1QB4Roi>?ms)(3q&M-+&b^|;h) z1(uS=MlHHpK%-Hk^i9PgE>TWCx8_FY&rt&N*p4y8+sq(2Gq}HA?pUM72$0}ZE8QHB z`+ff{2`8uDa9%tOtnY8XLS80RER(7FK>Xn&DLE#)huBCF%Q`Yua+lAh${jZmV^q+W zSB|Susvs!i`9F7-#EkLZV=&8qN<#Ieg_D zCPNssrn&o#;7{Ono_=stbedS^$qEa-h|~`;%E0UAV-izVm4C@DN^k#y+!#2ig(TVCylzMvqf+FmhJ6PSDi<8JSZxI{ z@@QSg`2}Ol@R~5k$sC8?xnhD*o<$(dRt^k8#f+zr57_`n1RL2kv|RqvB&IE@9mL&5 z&8g_ap`2r=EMj!i=?b0a4c{T9Dx=+fs8HgrpQ}cpK`5ay`P%~u#snJ4m1>_$K=RYQ zrO(#XcaAOqw$pOj>#2d(Y-IyCv#p&2dx*wUm^E%MEM>&AaF(#-I)lpnFh>3456nz) z7v{bpp(cIhn9ATv!(k#KqE3pF-Zwi@%6 z>w$O~n?*wYBa`^#rd{FpWvc@MYdru#WtU1#@^s15y^k2CmCmVEF%}r=-}l8}Fmps! z5vuWl36?l8BgpVhJa>?9?;9BScye;$7X&muo+)ge8~)`$fivr-+jWeb{zeXpBZOOb zy-tn_JAii|xG_&;llFg_7C+lrdU?3Ly}dxb?)pyn&$k<&N+L-WAeTOk7QZ%#n(xG$hf_{{3|V~8(Cm&emg<#fi7fDRayyAVFgDc4!q`o!GG1e zo3~b>yfx*3m&U~zI#u(1{Gc~?n5UQ}f{0c7+54dNd(|_ZBASP|LPhiM6%AY`UlJ~1 zI_N3d*SPacq+Ti&x+!y&u0v3hP3NdP>`wr;N;Kmi!8iI2nL9yWvq3ld;d+-4YI|qv4>0TxqI;8ROy>%ZDG+s z9z6CU@WAu*^exT)Qi zN|1fG6eGyT)_hVUQF$<)!~u@ zg#QsCHeeG#*b;RSCJCDS0{1-|kInF;-m;ow>*GI7BK?=?;uZV%F;Lz{wEX8aVtcu? zx{SzpN2(pKa1_pHVW%HW-H?tSanC3pBOVSPjw@U3s(Khv*%%j9S)Fs?_n5%ifao*5 zmg7%$i=%|5xV0@aUoQWeusy6w9N%)J=ZF_R+%Cka4PJdN=VDtf5J5yx%tp_IQTeRH z;<)m3{zLsQ4F%21zuGSGyd$W4U(~@2f3q}FH{)Qo($i2o=SnYxr*#z~eQM_hZya?R zgrxCOZ1GE{>rr`Dt-6p%z1bq!0H!`xrmtIvO| zeV^Q49xV(L$llBC=_nh;mAM);8xc7=Bv^d6n5cb4C@W{CJ+B_}l{5zV^Wp!BCVa(2 zlxk@?7d}(``Ot8CgT(G182L&Gqc_JScDqMgpDtt7;?-Z(?`<%S)=}&Cd?>u1)-Jg9 zv$8&iLjs8Gda^zyenNN~fT1o_byj{hG!*-IDCt;p{4S7Ea5Ryn!ol+y)x$jPG6jTb zwLj+;?QVfII;%Z+k`M z7b)YXCwt4+Y+WB{MRkIyZ>23Qc3-8TNoa4>p?vZ!=^07Nj9na$bvHtxa7GUL5)*41 z=q;pxtLn{MdqP~%>F?j4Z@K=$INts!(pOf!lUq3L-r+u;dg&J5C{9};^YQA$N$&XZ znoBm&euYHl#tkJ?fikw?cHDk#Gg z=0p9>vWr|j5|#~{>e~uyGMb|$ZwHNy2DO|4kn<>7fl!nMUdqfxu0;vC8O3W z^4Jy*LTO+#j6?L>%-xlPD7%t~^HpC$(!>W0vifWcKOn~W8pWokyJ6=t=A_=fiHvx- z0)N(wo$a%pIQy^~gw1g$(Dr%i!payzuID=Wru_GJtR3Kf(()BcnYo+jHb;z*cXw#I zEa#Rj?c{rTwDS)*m00vKCb-SL=LedbVpuiO-KoLCpAh~fgo8*{7d)Va-)x(z6t>19 zFQ-~qxbJ*C+MxL0Fj6$};E_ge8TTNZzde<3Tjpt=C>GD9@HjOi-PJ76P3UpY@T_2p z|N7Y}QbwBE`^6l#b@aLL1gxvcU&&-r0zE`L=_hH8Df+KIqNe@2(S0bt?ecbmuWQpF zv*Ax{3da}Y#+y?EbwLq?ugIqkbEFNM#4I+%(|x(ge&VBDK#7K{*>(qtNY31b$(lI=iHOxb@NqOicFW7yZF@M zoVn?ym(OM5wuX%or&oBqcH`n7rfo2d7-#!g?>C~Xd-aK0$-RwD19}dA{eM@?+&I46 zsSeT(-8Arts(z<(*D#Xs0qu-exb}}s_N#|N&c(SuWq#ZQ$5YmgBj%9;!|7fKXj?2HwAgcPagxzgp*{!|lU=Yh5Re0N4SV@$SJwxGQ|qp6ksz>2 zAsb_AD+DH13@Mo0MoB0BNT8YAfnSDNnv}rN_9Rkv{JNRzEo z(d0{IEmJS?B46b%w=0~)#B`_XLy-<@7WWTdDjLM}@PEuXR@=@~O#9iPnNHxs%xYPa zS{~Fbd!UOWbG6BZLa*NwYnbpgL0woojq&OL?lL3?uW7hw9+rq{{pCV?!+_8qu1XsvEZrC=p%^uwV$;v2^QF|VHGDED$#TNG}lv9yUJvekRv5^d9XpJ z^@rbNW}@Lvn2JA2TVPjJ1pqp2GO~ zz+j+SZG-o*>Mx&`j^bi;3Ka^kbJCf94y-VVI=fS{5c-dzZ7V=|0u;-q;YE4zyB!li zylNjc@%t@rFK3RD$noMshbTQD@Qm_Uk<|@5Ebb$B8C4380Y$cH(bYxuGb%8K$S_Ug zBi`!GmWjrv(JV|~#^yf+{JXt&^Fl4p5JSDkfism<7@1Ob-;;@IYRj<<$E zpwy8+7pUriejQCxCYh?lLi$K&1GrkFJp|!6RAiR)IoG2-Tr(6MV~OKdiTODjz;tS? zD_)AFw0HGltJWd|fWiY<=I;MNZY#LoXVQ9x2^3Gv$ z*s&0no|2BWypzWwv@P(_TB-dW$PX9qqEh!9dj- z6hFJaH6Jawr(qaO><*T5NQZ`+_2z48Z_b8-@{H=9;@O^G zbwg+f;PTE8>)?^oxOt&`HVW9Y5UR5r!@ae!xT5DBLVGhmmk?HVr=31^gxcLtB2^2> z4LyIqqL?Z68ZR;pX0180NA3WYC#7%8SFc~6V=gN#jl!RBZ3+-FEpc8MRN4@nEbw(e zp<>?`Fk>c)ds3+B#fukpPC<6)wQJ15b&jqVVY!xe+~@txI*8ahtjs8Kq6Z zh_I8t*ZPT(f$4ZmEbG^Cdtc!4m)0o!$Ll+`DmO1U1!V-G`Kth0xeq?~oma139elgt z8B&xty4#^VDKomZX5%r(mBav9lHgSkyId|#Ef9ltgIcpdPpgu=7KCvb_1Sdd9PT7>C>KAlU$EyK8cawiG;NP4 zb1)s}u|2*h-p*Hoq0}eMpIf_Uxp!8Hr53N&wk318+-ccyy=o7btkaH*B65jt0cU>@ z!{HwwWSgZia9J4)Et?p;unZ3Fk7|hmL#g3&D8&pd8k4KGIu8u*DObkLtWy){6sG-p zW_$dWU$Pq|{kNp-Q|h&p4;DbW=Kqpvm_5;X*j%f?KwNyD@8r+)R=<;@ql#lt!QqXo zGadJ|%N@-Xj5nK`eVhq0Lx?(HDL6zS6qLX@4W}JHnPakBboiRgA8+m_^o|0qlegDz z%lyTY@p4#;#5?p*iaxr75%38Pc7bI{V>$-*MWbIw(ewSyN~e}9w!Z=r=101_!^$2~ z<8&2r@HQku;fBKeP&wKXo3ZkECq(w?aWDUxGm>-ex3zj3vnQ~RA9sGmdNk*Dc$NdV zlk;bE-ZU%^N_fnuLx+%7jpy+~;c|KT!BRFbX#&X?zW;0~$!N8Q`%n{vJ)nLFo9Wo# ze$Bgh`yUMVTK6Upnnt%jlkXgGdK~84(~%m*vniO4TLo4O<22%%6!CR4Dv%5RKH#WL zdzn;@suP_U4XIzF;ty2+4>n0U;M<+@XKUBo`(1Y<+T)1g^pB)lB5(O9lwg3F!ynx% zY-MfLJeg(l(%f}wTyx)_jgIyJ#H9xa1%~lrlrbKYLYQICQk;bRvPH8_u~gye%aIIV zJh^>_>Z81Y|0~tt!Gi|}<9pT7MK(~fM>T03N>lvQJUQ8`(c3L3oA7jR-*lT#psQ#O z%$H@W;x@hh8yM9S2Kv8--2Mkx4|##)5TS{54gfq~0y3h+@qdebwN}pY@0i9jq6~#v zF(}*P41UQTR95lJ+h%7d9Euxr?I_t`0=%)ie0+T6f~M_cRV&79*$2-NgVN#CyY-57 z;p2mW8dkT_QtOdziqj2>d&}T7eM&iWadBDpW6C4ax&Bg=;&8A@Mt2^m>CiVZAyPg% zFc7U?autP=q2KgYYsfs87gUh;=W2_!wIlUEN(DTf_fH!^g8#v=PDX%N6raxpc1E63 zl3iGvEm=9aDw2}#PUdV+Xidj}D?TaQk=)YNRU_Z)co`wf>vF;(WbFlmjBBmLrgB3< zQ&Q!&2kfuCu;HpXo2&0B+UshY)Gi3Xi3S@yBJFTg@9RRGZ@T{dt)y1IdM`@8s%h@?Nk{ojg^JX4EZ?6 z-67Luph#Du3?uX{C zWNjV1Pxf-w1cha=W4d8hqr;7ap|pwP@y>wy;lKqTv-~8EW#)V3myEbpnrpO?h02S- zj5$_qTHIi!^52)io-CE^{wTb|;IZ(4f^nI@ zuCA{9*GsI;DX6gmP=FFQNgt>$`{Bp3%C@%oYakWm4nH~YPueWj!N}_cc#yCB2%^^u zuW=R3^uxKcc zh0BK?VWRXoIn@HSL?2;1jwKJB8l(35cxRdq0dw(6J~sUmej9jJ5ZQz?>8Vx-_i2gq z>TnlqyomiWuuTF-vQln&9oeBFsnM?v{%ZsM{TZs+YUVpjeQqoXtzBbdxj@n#eK0&w z9TolbR>j0S;q5zMdV3*+myFfdLGGLLlnym5Ns0>+{-KMq-j_=uGt_8|vXp;C{ghMu zY)Wg3C9%x7S)ig)Gga`yMia%r?&^rKW|T>Ld=_F|g6S5u+`o3QKj~zf4x#BEF13ym z?05tP2owZ2#1f#Q7tQqH z!OJUfg892j8jE$`{HCQ#dG~+-<&gVA%_k*U%jBTP0*v9e)D#COk`J)L7;tKl5%o3? zM4PGK)w2mY!;HwJ@3id~Q-u{s?;6OULp3y{GBWioj~IHZQ$6ccooM9CTeNj#Z^E{okSmp?wj{CpPB zL9q*+W%YO{VqEQ&j{rUshTqRI(L}c}HC%tYN?A5BF?#jnzt;S#WM z3;x=;F*au}>51GEi1cEo2PqZ${6G17Er5OmDYTUtXBG;y)Er=%;{;wuL;?n4@;Vv( zTmnLNMc#%v)W?HEd?QZe0p~i@Llk)V(L;e+dU@6iMcv55WZh9pD2~XOz8%E^H*OqJ z|J~bq&b$1AwiJ0A=T_+y*=+Rmx;`DAz3#@#|MMo1V(*JfjeZjNnxB(@mG&O&AWgjt z;9b?yLr+l#-5FVm&DG2L$$2T_5NyhLg87+x-xmo>BG+xl(HBSn^%x1P{?I8e{&g@@ z?&K7dcJIP}t1m0~?`z{g01-bM)owLH>t&YN-o>pO(*D;LVxP6U1PTh}WC%osa;bHw zj6ef89~Tk-)&UCGN-6&phT*euu7|_j0I?V*U|AwvEhNXu*IH5tsT z;&UD01*;P8(1rn&3qrr+4p?rYxc~gGNlgFc5Ok0CLCVy1QQY5dWU|en6qz1lYV;L4YpTdm!t>IdDRMJ(KS2$pVJN zn4X;r>?pEtehu7P3F}tPC6D5Gm-nGeT$a-^wX}f9-aYYXW~J>I!+)#RAlKq;J9?14 z8D3A=BihA_S{p^iy09mBJ@dY^yfuTh@$Qot6zk|{D`<9GoTTOW{}r#vD~h(9L$w3UVAXw|k3 z>-K*t*}{$lIr59jpg@m#Cv^Zv?9Ry^sKk6&OPG5@Cs_|$j+fMDS+($Fu0j>Tmnw`A z4#;fn7&J_C=8eEuabuhfo(M#K$-Y^bcf~*SRF0cCP}_g-z-{E!eZCl z>FIL=H#iet%6%z%3!~-z`MsHnpv^VW6vX2|k-Q+TMKwMpWbRynS}-XikB*OrH{N&9 z|ML<^$dI?JovAw;G;%}?x}U9mU4oJhDI=HGtF_&0wi5Uy(34R+bEffjzz#r(JLYSp zM-{a*mV4}}*L<3kyp?4L<`!IXte{+vPFKg=A|m?U^PPPBnFXZm* z+&O%ekDqOxoLy0#U2(8=uZhdSwra2XVUojFw`F0cbu5%T>*POX`dB#P;$6p^t*Kws z^M~0o^MY>Vmot7MV6z#ng;z^{^Lg_kN>rCREa=5W3-#V=kL{zDhm=ZcYRmI|NzZX8 zbnV1d^0f!*Ltzv}x9DJB74_u1NHXYGsN)i7L#R{S&@oW8rSSZDD~mi4HndoL6`7nxtt zGf~HGv(l)pG(QhQ;%)Nd z6Qswxg+9H&tti=`Q!W-vamttSOG*0A9k%KHekJ4sipx?UjW z#ECO=`O#*kZUxW8B-|ky{`tR#0&hpkG;AKO2j2i_oF`8Md)c%-fJWrhEloM=`8(vP zfq8rrAOfAw?wuoPE?K&3X|S;~(DaGn5$BRRfO-+D1(J?O!vmXm$Vq$dYfLsE1CW0c zVn_j%PvU62dXkPeqm}j>N#)59DdA>8-w_DYpXWp@#P3((lzm(mNIG4|lpkZMetmU$s#{FXE6vZg#;Fy<_2Zw9$3)8n^V%K3%XN zPLlAx5iWXV%OmUByK?iEesY1;sop8RvHMCv0}#Qu8s@m~jnM`m2`D5mD9zZE{lQPut7}vNHr)*amKhadsNJps`Qt9+Ib0>wX%}|)Ps(>&OxNJCdEA4E=$^pL zv-$WzLk_o92YpN=Ssr(zQl;Xlbr1`DoBSxVLO zi6fOZ`^S4t<%>O}G;ddf?$Hk4vTx?eKiqDiGi<|RgRfg(=es>j;dR85uYEk_U$DvM zWgbEF!+Ao8Kf&_=J`^yN$oaB2F5&y(i+0(tnyA zn#kU0OvW^WT_+0#gPgWqLS2oI%W6<_RK;skI<>xYqqrv}?fQUf^`=Zt z97n2>ftkynNvCp~)nfC4gT;@~wL+m?A8WP`k~v9tv%4C_ls+mrmFHPZewXn7@NBF+ zQx=ajn$^0PNXBI-U12~qUw6!mBZlgv0eNWyT!(tvvwn1|IEYoG2IaVDCx7O%y#dQ` z_`CZ(40{$$C2XO*>T(5vCoqS}>1aihk8@eT`$fj1;R+YW)5#lKWhqgpA;R{pPQPJV&>H?9eDGJvt2O-k5y649d$o`=bRp=)wkCJ z3r~(=G^TW`mK=5BzP(x|vn~DMJXWGG5V`)qQdQ1jO110M4i^vRmi)y}%9x7I-eVicYpq$|9e}X&!f#Dd&EKWJ}UbA z+rQ}FWS=o3I^Fy6=WbBM<1{$F|9lihOcl{r$cEL)OihLqq<|zssP6{5=HK@;W6!pN ziN!*{E@-2(U(b%9qdN7~-~fr)Eu35OpaM|IA94yYke2)eV-W*a^ns@FdDxj?e?uDP zj!}h9#J)K@QT^Yqm(Zr`|8v6xIKE=kVZLXhiDHRGI^Fh8rxt0g^LE4jbyVyt^PY?b zs5XuR6`uT{CW}Ngs}3?Te&=rpvitcTb76SXhRaFRH$F!w0Z=HGsGkJd-1algL{L;* z;b5uf@+4M5HxL(r^XZ`{2Jgvs973#E^RhzMNQ4BiCNay2CDf?`p2h}6C`JRyL#ZVD1%l?2yo7BkR!e<+AR?orr9}dj7;{~`5D0fV zJz7^LI=sc`2cE@~qyB zGK@r*akmYq?#cj}3F`SLP=+Jn%XE+c=MvPnPUM5aHJnQ@u!b|up&YKifAMX1J}`uu z71-Dt9bP9~v~n>wQzZgF4IE>5TmPnFLLP2*Q=7Rt3lb9l9ItyNDGh{-)j&itkeJIv zOiT=lI8P&`WFLI@2l?UTu^EkFMM`5InzT%GjKGSXhTz>ObH=AnpDsexz~28|s%Bl( z-Yr?u;j|5qL)1MR78XXtVIWp@@C<6|O+md9sc0k!+Tc85t-d}8fbFdtB1cclE#I1(47zL2rWu($P zT1X9U{QRVj1n&mw^Vip4I|@ogMc;dQ3*}AF;bQTN3#WGGC&bT5f(19FBy>E2y{aMP zI1OR6Kp&s$#qF4KZV57v{tw_56daAz_2N!bwJnM zdK+SR-!>qNDzX<9Kjtpz&fYoLX*;w&|pIHz;oDKrF%Gu)&|O36t<~2x}7H z&K!;losZuTVq?RiXrqtrpRUroi`yL;8F|g`GHUSLI_hF!q>ogoIcW+2v`QFndH$IUD7OmsI_UtpMwG9Z7?1`E*?gIN`}j??nnTk%l+-I)GlU{a+N-#z z=4%zZ-u$v+)J%+&L#4U(kX48}0+XJ*8RV2GTRmSPy254sR^8ic*$#(D{>ui)dRi+I zTyR|KW%$S;Ayy2Nwzsbj0b1M+z;U+CWWD!vVeamSc?MAz1K= zmsmoGMTOOQhC3+3GCbZ+rjX*`GE)K6nq^Sj|C zNe{IKRNHvI%Aj19`&l5S5*1#JO1?SRaLG!(Fo!#Abyz$lXacdXl1nxM-#?2Ja(5Pl zVr9fmLu9AVA<)-wiQ@T9Y;2H%K&0H`*Q;y~+Ab`Ae}h%DmTE#Ot&K7h^fjVX?gNhs zgPY2?JB;39fE9;^X8$^3O~l?EVahDKj7uxVxRd{8=&uYfk9vY$UOe@J$VCB^7L%k5 z9h~!mbkNp?d5C&;{5g6Fj?Rj%$Kh_<;QPm#n#2$j$~vSV#on6l=%C+QpAgC3MTruS zEnYbiK_0o*%i$;B6b3;Sls7+&jxH>8xfu34sW!ctN^X88BQi2FXDb@E^L&Rxr{(?z zP%T_bV1FlL)8Om}p_+@tr0rnweP-Cu){0$!ii`*(wPfffdLAssaG5HUKZf0pN+O`y zP;8`hz5383LJyJ__{%(IBh?K+Di-abfGW5xs6hHBm)MK7e5X?AmpX;Pu?{lQ)U2)p z>%yC$Nx(ZRdSklDG~yxOxAocU_poQVOp!e zYVo7o)wdw!%XYWyvSWuYE}aRw8LNLUq6#Co1;ov)al~6dzHwW4tT)N8 zKo^S5aB7XLVVE~MAKWnR4J5nAx$Nl-l`jXON?4JNx~^`b5>gp{`g7g&5t9noOj-T> z{S85Ev9URvmU^L@yfdZU2?R-6oL}4#=^NWZhfnq6&f(8$k&bUP!+R0x`X(qmZs_*9UH5(U^|Q7(PVI z<)aU`7jY%YomAG&UqGESV0U(!;WUVc(zBQooYH{grR3>Q!77_UR@saXd96|IoXsGl z4Ga1CYd0#A2sm2_9{sLU($OKu#;yUg!*KBP6T4yakA2UMw-m0C5syU>jkJ z9CQU6KpN3vIc)euu&t0m;oAbJ&h2955F|j0> zotVTiC@e$RUtrg^HL9HEqclL$T3f>DnjoG!9`WIvXf~wTP+2Lzj+cSW`TJoBh-mUJ zY1AWANS=E9Gxjc%4l4^wAU2i63`ferG6<_&JRb+Loee@vv+UUzi znd$-x9E7itW<9hMcaWEpuhlH=9s2O@R;=BTLp2!DymZIZ;Vpl5%WBA>hIQF=8}CGb zx}L{f0v0ViQeK+?R-N*CUH4V|OSfG0BO)SZW;{`%QDH4UfqnZy=dS#l@`XMuJ$)Y? zJ2E?mQ+pHp{H5!~bwKa)9=yT*(?w#uw`pU@KGA8xrw;DvTnlz*2rg(RoY~pGU!zp( z1FJ8Q+WQV24nBUU6!j+BFYOe|`GhzoG{_&q@bUN2aT!2X{@(rJTbOZrC_Up37sP)O z#Vz~e-vC3fz|h4OLF(TOVD9N8A&!vGZ5dD}N^3J{$+0VHgb?5nAYI5}acw--0o z^xzY-guhRNzKK<*dPgyY%88{^3ZmUzlp7K$5G|S&s#O&6_4AXWK6;5b4q8-9Nb`E) z;-IXwsGCH5NXcP76Eqg@NMt-jBt?6E z(EMPcM34rwz+!$r`wHxScfrCL;X6U#&rDJ#a}WS8oZ4c8mQ8z+m+%>C=_ezML0i%c zVXnsCVNPB{zr+dv2vXElrxE>hN?JI+VPR)ZdNG=$r1+{cM$eU@V>lv!GHPXnzOcY) zvs^jESl!(GdndbE7jy|52Oh}jYk$_86-{wiOUB5XNyD3+p~XW*e$YjK{Y&h*(uI|y zF_3+OT59lv7h`#4M3gJ_--~(Xq`>HK;^EHNr7N+@|}xSyNy`6Zf<9bMvFZgV3p#3U<2&AGKlU3H@dh4iv;B&k=YduN=on- z;3c?25I}#=LdRN_R3g^@NmimBDQBP?;@w_H zFEe{-WI;#O33xMb`}&-@{UHz3I$>-%$S~wTz$%seeuEUg>rGbJ4Xw9AfaQwZ|L8S5 zB1BxZt_sjFP$Of8NBDQ;)oR>_xe%A8COQ9%D2YblV4tB|$(QlaP-L?j<@*3M zig%xtni9wNG`ee||1{A0aa=X4Qjz8fKA}K0I{pE&3lqyHPA(vk1_02j+#v~0-maWN zB*_K!1`Wp(&?&8OK)eNr2d1xbB0pgw&Yj5*{8tgJI?f7+vTNrR@Ah%ls+=@Kl3*d>}g?ZB4zp2q$tVLwgQE(ZVHa7>Y9Smk1&wo^A^6 zkk<1!*!KPEFyDWm>Ez^u7=R~7^CC?EyiDbmjSpMsoxgb12iz1kV}hDM06)uIugw;^R7fban+cU{ zgs)b%&De_7BNi$Y3ik(#oEIri|EysP?8K%Qmu?Xe>OJ2e2(Q7$#v)+$K@NCa=U+(9 zMbZnAtL?=Fc6oz8zb8xjYXG+^FzZUu5<$$gt3>R#FW$IChV~t--(4ps=@8tKQ!8NB zt>n8da`gKTWKO@AQ>$eYLAq7X`WUJK zVQhqS=kD3f&CM@>@l20EFz-NWu`gS_3GTXulwr=%so*(wL6XW4;4e#I?`#IWGiWh> z5&&9r`{Gri5T^>TYE2{d;DpQ|Adh9P9_Hre@gBQF8vyHxI0H_|qFV+foEzYH77_S1 z{?oxC!cedskb)!#Wj%hjnC|pUZv(0b;{$S|*MlH-t}s`I%^`P)2TH6*cvvfce<$KL zQzQd`OG#A~Fh#GEtt7)A5ex{bYP+R-HddXzpo|n$)zzEUA!r&{B=_YN>9 zoHcG{0f<~LNTtEImzaz8($dla04w?r+#unmKiFOj-|=XNLoW-RA09w)?6L9~Kx5TL zD&aWWMz<_mdMo0=`?FQM#_f@CnaCAGr*u3g-w-16(aw*yEy`Q5(_;UNv#O*a}xJQO+Jre|ffiEz)=t>V`OXg#1D)QN}xKt~)eTYrvb zIBOXU3S_f?T!9bI#l*+lb1)HvtX5(tfRinX50Mhw z1#k}B@hgaa+-mo6CE&+K5iX;4O*!kDMe=RJXEJ+x0^Y?J?fhT5MuG>6i93MTRytHY7c{Z6JYY&* z2bfmrFx^=LGjUYO<{^Msv8(0lCa{946@pyjs2=y{AR~4^IhyLH8vJ{l5a?A9a+AA2 z>GSV&<>^!e*td~?b5e3>vTYMyUWQWufsYjK(PZ;*LB;@rSD=U}0B_!&ZVZGq zfXvt9_=+bbRzrn+b1{Yn?eX^(%V9YQ(<;OhA9Zge_7g`mbR9l%4?869JW6L4))-!KMZA09Q&l8&i{W znEjsJ^YnNR0r27gZ!jH`%qLl79OxIz{O2*cc9fW@8bi@+Zb|HkS-C!{^VbY zu}q~@`|dq}6{eTw9` ziTuXdAaP&3tiNQ8hS8y(#T|YqC!Z|*C`%xlt$~8ClkYi|? zo4f`+1#Pb`-3W@-^Snn%i7dncz5lDd?~ID_Y}dv3CGKd{s0gAUQDPSeAV?8VL9hTy zPz<1u0MZdhKmjT4Bq}JyC<4+fNKsmF=rtM`KswTEoS{e)hu%5YJtq4*=lnT8&syhf z*UHKgb>^M-eV+Td+I^3g2fl+ywZkR{t!XM^)3fMy+?8C~>Fw<=qQpA3BO6Mf5+W3^Z9U+ONUJZo9Ck zvftOoHwq*V;0~Shnjhz)35kWW-Sq34PapQ+1h7qn2t-dIh@-DF8Jv+T36}x1e1&AT z(;?)72gj!K7oLo!x_ZXNc2fw=e$f1nNcUY``{`0BNSEqXYA#Lq6ico0>dsGpQ2kB1 z+^0ACKXVp!M*dPht2ipPNx36$e5LnUviAP(i|yZQulm5ZP_K7)9{Ti7eASmeS*iH# z{~h||&30eLSN%2G{ch9EN8j+*WSZ;1_FG(-=lu1{YJWgt_gl-VcVldhoH2X`7ihPn zK9dEXcWu(2(hD;aLt(=o`7TQU2?6!<^wiH+2o$2*<0J#> zn5AQV5VaL2)HQWoU0t0I8E~FmxJ7~-Fs0kbK4{>l#KtDz(^Bfi@$%~BMtT7TRz3-$ z2RYE{44=W^*oS1;qr&*)e8YiWs*1z}9-K86I-hgJp8cLLG#Dow3w>w&lKE4nxv)F9 z0bX-rxR8enxj<%8sNY|7S|U|IW+7?uD9G6jw)Cc%konp0C~Q8?^Nm>Jj&Z4IE>14a zdr7&DbWKfh0y13e0iJ~slViO4T(A&;d6QEoy0!}gJjbG3{`${lrjX9VJR)(j08R7W zLfZW3FCUKlWc@-)3Gdwm;d>*$?d}IQr>-q8Mz0h9{q5Sfc{dmL4c^0_`pLt1*>HeK z22LbRJk?EKjeWpVMtZa;4vRebjUj7Q9XkHyK>Ryp49NK z!SuOeuQ^Rb0_IMpI&`WGq^~~k@gOA)fUKDnw(ES;)>!ZSbgSo~YeQFnji$Y<+{^yGxtXY?q|u z$>b%c!7vfB5GFfx>fJ%?PG1ovIWqDx@je4}!?@RMZ!!O-y;}&~(&2JiU!R2{5%uE5 z?)hf&h=>P+HY>?Rh}$Gct>oE}LQ#b5yyYwg0F2kOaISf$LXPu7v z@Yra;!nsSYK9|BcEN|~0m=@aM;Dg)-BZuiT5(n)J3k}n3&gjfw@Qf=SL}$s=NJH$& zN115RXQP2m%S*xN_a6^YwuuYEFXz$z=W12q{q0VA?u)aXRmb+G+G$TmeO?kx_`-XQ z7#lk_0Lg(eBAn5xt(gonGN`#x2%<)U<2%h$p%6o4@YZIxIGxE5SzCQx!eu%K25KSM zY@}&qUxXFLXuc3!gtrS%$^kFtl$k7Cb|11eu`PLYY>D-4saaQwv7Ezr1O=anhUb8= z3@9PRcekjqAOEuI_W3K#+Yp~&$(9962H?$h5^*c0h|f5U)*(Mo%BC(Zv4i~6upteS zV%pFw%X`qMIO^(qjnemGxR^;Hnn~|R-d`MET#&?Nt-}-#+oT0^!_1fs%twvb7ZjIx zd7JUbW1_U*%`gMJT|SSQ2MTX=;H9e(O6gk!C9AjYJ)gbr3OILBCzJq>O`hkw z*EbmNpWLz#J~V=x6n98-{g*KN`FYPyq$@#rV;V)4SJqV!)EjhkATF zD4mHstij%Z>y<@&JbpX@ zjJc8QRe&`l;uuJDu|lNGFkwt)0gq+fi6yCgFkO!wQJ*Q;qIUDyi}-l(aX?QYd%DMU z*tKoyYu>h;ckO;4#Bl&pn_lIkPpteof{M`ynPoOx>cwc4ZXho8yir*|(m33vG3mU9 z32s4)O&HALIxI-(mUDRUvJ^HkfqJ4Ip=|c- z^)w8omQ-qrPiUc-*m#korggbQ(<<+cDo-OAMbm+V@DP1cZ@C?7v!~zaT=VLZoY#AXKXhO(u|I6wkSg0U$*{1$v)~0?s6q_C>n3vp@hq{*0 zvi-NCg}@UJb+00sc_DZ4c0(T;g}o^X3UFjfvK^YAi_s=iKta0^k&SjeRAy>xYtci$ ze*J~PAn&HJ4<+_l14%j3{+`+FA%RJTBVSKN7HgsL3#=fcc(1EphEPq)HSKrJoxl(>uFAFfm6i4HlH7O)a z(r}95eA?G=0Ymcyja{|`?KQY3F*($_pN@aUG!ex6G^T4bMJs+X=SwuIn0(n7?wHX< zaT)oIBdBS<6ns$d^9zdI<(bMiJuxl1L&MIkqBK<2Hoc|iq}Pm|MplyJ(Efa`pA}d* zdM3&wi*fd!Dn9zt{TW{at5vwA!MY1`IUcqzrnQt%$rMF8Td~hJfJJ7X!*PZpZ1U2V zU#`>wpMaDF4(f*vFU&}!w!i%mK#80F+3Oa=kQ+*Nih}((dj`M3RY&L&x**>f7=HBSC1FTNY^n=Kzz z?40a-YZ*U$LALRpL$iN#qAS)Vhn5Fr9(yYzq98&MIqco4RdR`Thp=mBf!Ct^U)Pt~ zup!N+>v z%(Tq^^C=%Bg!4k(An6z58++Z7#)Y-gUy3l(T#-Dc*Cl_XLZuZCn9@#nhTVIW7}bZC z)hp>tm4jPzaI9(b;>;t#1#PCEB|I4&Bwz;7vyXGFG1)|vpf50pcv*>cBE2xBF7Zqr zH$3|RH64a}-h;y(<1LmjOVQ)b$9 z0y99|_Q&&5FN~24g+ktLS9y~)M(e3F--FKbFpmc4KD3#A5d-6;veY%7U`sE&Y|g2I zx0O{dw=%-kw(>dBcBLYf;-#J#g=-iHmKOt72aYRSDHEUc z74;szD0ep#$k!v2wV9drJRzZt z?O%eq0!65y0)#Bba4>v8-J}bhU)Zp`1FyIdZc3*Z+k_(}s8o_Dde9U8W<|@Vcxp>( z>CCGrf<{B@LSF}UDChk{Ns9zce6gA(NSq*QF~2+5BDv?Bp<_~e5DL!v_3KkEcfS#z z_|nfFY%B#buA%BI3MvP8Ibe3UK$YZDY6GBrlaJW5Qv}~CA-ecxqrkg{6)VX(B_+Cg z0De=(oeR3f56CSQ&p{m8NAXns{K}wHJ%{@MK`t#qJi}oLXbN= zj8w1B9qm!#yPo)bTD50fp#`iS>il6xU8q8;m}bic{*l(Po+z(M-44R1%0Ud(mjv?8 zC)J

ZE8JcJ)!7*nBZ>yum)*{{1;4$H>IQlX1_tm`#kjO@0tTM}y7*a276ZNipdQ zpb;t<_rp5`U}gnC9I!|=NVhWxcd9+c#q_|gE{7-1irs;F>|3gHKSivyj7% z7JpS9;yVX_$rr6v{lBvq8 z`Rh=n{k^q^pX3j|EW6y0ktHw!Bi*XOW^=HSp<$@AsO(jFkR7?5+vDQ#lu6#8!sLgz zBnw!bWat!3*7yNV(i~ApOzXJDT-NzqFP62xV7j6S{33GeEqfW&xA&ZfT4c%dMK1Rw zPsYU(8DqF77c98VQIW7)k_3uaDbMk(@K?KGp9S>Afo)=|(N2^56!T^io;cduD|u9; z*_}{h*%ii^b1^g{HUuTD4dxk_=6Kbh_>yOb-K~iN$V14)K)6t&d74ORS3yuL=Bcf* z3}CzI^5Wwy)tw`_k&IR2)bkOdo}WrS7?N z!kAkWK=DEY%ZT-0M!d*m4Wjl=tw7bN4E``(e*bM@t6?bQ2bt~Kq5NyY8eS6mbE~*z zWSVqi4zZKav{0WEGG5w?KN&CkZknNQj`SiIC>0Ecp?nmLE8>y4AunCYnasm=blN;?fuUXfq5lUy{Ta3o@keTeTmU!vYTmivu`%Fvk=ZvOmp~WDNp6z^l$d|#_JVf zitpXu>e}x;efl&L7*j5%mwaz>oFayg4KNThpn|-Zff-;fban4pc;$n1~EXNoi*N2{V^Wq`?aLE2eM+DCYN_bKG z0kK(yyj%aZa#hM3EMKMdNWSi$x&GBk2D~bYXd1`bLPiiFX^AxBs0Cyj{DGbKX}DDX z*kfr(b93`53*=Q%K{&WM6-P};uEQ0W$m+ZB-=!WLjQjhUXB4t<*Nl2qHYzqYq|3ZM zR~FP|THeqI9Y|E|)~brQSs3M=?wr3-g?vIJQ zbuAq-;@XK%%+mAo$D$>Rz<|(%17Q&AOJ$#jl`Hmk?wHE}ZX0RI3I+fEtP26tK%_5K zmJc!~&)PjdX|)O`yV>hszr+#^Pcp)hO3s|C@ACA7zf1M$-Q6pM#a}mKI@}Z#u1R0T zV_&9s(^CQMkl|#F+nUT8&Y_3lj`~*JUQhanx%ruyO&7nkc@Qtv`Zdkf1N?-|3EuPM z*az)*6kj!C7y1Zk21(+MQO4ge+2QXBY%bP{e?&zWL?F5hGZf?BUNl?YNV$G$g~>)! zc!s&KVC0^L)!(RMJgr3{EKQ*mt8fgYxNzACGgMkMvT&Fy_^iBa4~*ppnMx{3=d+NW z7d_{)e|+_GLW>uGpwOLc2;tdRhC*@D!x7uofz_`m?{<1HH?sgRPrZKRzVn41irS?O~-Lkw3*ATZATBT#O09EjuSuJo%`z zd!H?xli(;9>MGz8$E(;F6JGqH_;bH*$Ytd_XQee9$4SSGYOBu5f;xL8y2fs$u>5ys zHCuswbB6Q7ioLCpj8W6%F@sdbKTU}u5da0xM{78bf(No19KVyrK9WPO-kva>kTU>? zb?t_o!X}uVAme!o8$z@Y7NT5E5H!Q~;W&?5|WH6cohp8(&(o8k0<#&UqG_rP? zxJj|U2$fnPXHoQ$STxp$3tCo$u1ASV9`&%PWC8+~xg@TxJ98cf{6ovqWmT!47)Zwd z)J$+5lPD=EeO_YME}tb!zv$X2U7}2vM&_C?myF83<)%d6}vNbJL^ z@kcN!b^yj!0A*LAu&>8IWc0L6f#+F{>zsBJH>&*s&_vFz>1)8lia19*g{;$~Ug*yw zD-+@KqrqVCg2}lf$0KI;rY+|lFBlFxYGJ4k!z!@L!?|a<5NyXiTEV#ZA1X1vO zF9`cFx5;)sazhM86CCjJX0fi@l4hy=lnePvNcvj@y$hlT$PvdZx5bo-bm~3Q4h_`3 zim#mNI-pUqZB)ic+My#?zCSL?l*=ya%O;%-|DfqFMlBtVfZl|M;77tzmdyQl7%yuc zW)DO!0+(ZSr)b}UE$0i5 z=h*-quv5@a1)vV7>3m>d(X*g+qICpEvGZ}hPMrKgnzN)f`XX-C{q1(cB&dtyXGU{c z4=oen2dgdB+n4G@K^$mVPOezu0U#O;ZbRViAyNyNeD;CNl=SJSQP_q>IBg;%f&&8! zDT(t3Q6M4axHBBSBGmKRZXERTU3b+kyR9}1g5qS+9@8T=3GT$G)77+RMvyQ7*DW;9 zTv`ZPZF$@#G;OecVc669z?a~tgh@DLv|phvQ8^9oyX(jWS6$!+2svwRb~TTD`k(f* z&|n^N{r@1@0Ps4uUJfgX*l z0e~0Zd7_A0)X^Y2Pb;)KUQP< z>rHT&J4ivU?<33gt4(ryy_UR^b3#v7{$$?pg*yEG&fBE?-LZ$q66ylSxwf0SG(m#e ztfUUB<)P4VM=|LmO6U8Z|JRfWz!go+MBz}r>_&sl_i!|Q30l$lN-buF$tTgWLtYs$ zMR3vp>;y_qDEQq8iiS3deq)|w*MGjA8vkgXd}q)WbgIFXU?8~a3-Pp>xR{j`?h`G+H!bVwH7HVB>7*c zi$HH2k2?i0<6#g{LmnA(#mKb1ZA5$29%v2%(2(rj+_jFTl^{R!KFSQ}FZt~_W|(`^ zkaO&$xvfzP1^Dt0Hbya8`+jY`jk-SLz+n^eC58v5wUUkVsEyeuw-}$&6uNS`i>eI- zMmD?=KT4kD+1ew#$G}9tVxkDX6UbNONnWzkt2nC)!D$D5&%hZ`JOe{57=Oey6^ET% zsBM_qu3TaWQbP_?Jf87D2^~*nh|~!=(>@uPt1PL)q?UO)K#lT9P^Qp{xo~SHxgZq7 zq#oex50s#VVX}|c@HjzKf-FCSVvToJFgJW<0>CD0Z!&_T;PG*? z!8AzGBLYfXb8H|=#St~N2)yO@`fET?MIeB|Trr=@2&H1OZqRUdRMa&a#4EEeCX8*V zI%~knr0;FuoGq9+qqg|TGr_UzN2dmguON7ZsXJ1f0g@hlC(=~HP*7!X?Sk}m9~|)Z z(y2jo7nZmb{j2VbT7DRLUFf1o-B4d&&%jsG6p;d{Yl3x2*$CP};R=q@S59fz8OBA( zGoDU{R-2O|>jI~M=hP!LDob4beP_X<#qcpq2{_4XbS2=+C2`TcfxHTMcaL}h|%y@!IVc785sBx=u zQE~V5Q#CN$r!ehNfN&BKldyG?HRt^GrG z2up)av+R4Qpc?*e9w%UE)2FM9-O&E`T*$0;T96c#Gw7D&HG6Qe2) zWCL4KYM5&S5O-FT7(=tjz4Vt~OL#3vv1Iz|FtYB|QqGiGRrNa>!be^esjRXWg{&Pxv zWIv#}0;|H0o)#QufA6M1tq)kmb~p=DZ0cX2yuA|$sGEgR=zy#03G1A*z-djr{;bv8 zU=h45`n-dJ;YkGxGcn88q+5YO3uy*4#8_5EN+AO!UOD`AO5cV#$OLwy*_h;6ObGR`U0x6 z%~kBzDJJW^g;nl1-a5iaFOMdbVf#RekHUP z<&oxmzd9_W-4EvE4FQA~TPJl+F8Sp4`=rWGRXBLAgZ>>jw++1O<1O;yNlCkn8|W}P z16c_N;Z>O9E2Su(3U%yd-nvd6_06=OyzP$J>XB^U+67kU%(7zFH5#d#48v|E#eOll3C(rkn+eRv5W z%{{Vc2zCMZ=HT7^kjJoOn%UZrVMrK(ut5=4X6Vl2RtK;@xOi@b5H&?mSdNNbyMn9> zFHbo9(TcTkv}pwOgoWB91Ot*{Vb)BJi0xZoaQT4*Cbhyg$aU5%*B$~=@q=0;0ns7v zhpfvN;E9nO%um7&9UarFRulC@@WYfo0mVDXb|WMM{`4L(a6)P1GuQ1p_8AH<7q3)= zj3fC3TxdTUI0h)=J__#m+9?$3Q>i+Flb-% zDhT2WNLXkRp~G?^vwVa6r5_;&bSs^{u79B;*Ohm{01svq$%51hpv!xGT#YaiME-1w z3mbm>@7|ktpT_p!r~hxZ=};>c@7MpIT}uD`@&Ehj;|?q@TwMLpZz;3KP#}|T$gv|j L>Pd%x{P}+XFX#`l literal 0 HcmV?d00001 diff --git a/BoTorch_heatmap_delta.png b/results_plots/.ipynb_checkpoints/BoTorch_heatmap_delta-checkpoint.png similarity index 100% rename from BoTorch_heatmap_delta.png rename to results_plots/.ipynb_checkpoints/BoTorch_heatmap_delta-checkpoint.png diff --git a/BoTorch_heatmapFalse.png b/results_plots/BoTorch_heatmapFalse.png similarity index 100% rename from BoTorch_heatmapFalse.png rename to results_plots/BoTorch_heatmapFalse.png diff --git a/BoTorch_heatmapTrue.png b/results_plots/BoTorch_heatmapTrue.png similarity index 100% rename from BoTorch_heatmapTrue.png rename to results_plots/BoTorch_heatmapTrue.png diff --git a/results_plots/BoTorch_heatmap_delta.png b/results_plots/BoTorch_heatmap_delta.png new file mode 100644 index 0000000000000000000000000000000000000000..e022f972c7cae344c33ed42541cb96787051a1c7 GIT binary patch literal 109999 zcmeFZ2UL{Vwl!L|sZDsgZ4pJF+bkIskZ9W|1|%psi{u;&BpX^$Nv#M-R&qvi4lN3j zB^04ZDgu&~oOyG5PT%vNJKnheIQNAy-gvjh=%5r;-?#VLYt1$1T;D@EnF~9%?%PVC zP1EdMdseHi`VQ`@7qJbDFqwao4$X8`oXE`fK|2wj`DGTf3LC zHJG(_r?$WR`2gd&b!VTxd)y--`%Cm6J@reQd2Ux-c@k80NaFs4VT!6}rchz+r|I4f z=aB8?yc_W4|9HhuiE4EJ^#}a-mhImtC`C6&P^d&POyj+9jC*<8-2Q1T9%4(j<;9kR5v9HVC^sp;n$HNHx&&C5GI zcxsW;U`+V6-iZXamBq`lvS~^kT3TAu^P6$KCX{v8pWlBP5WvpCA?3lqa_zU@?ugI7 zUoRbe>e3EoftL1k-E8+YH)X*eteg)YK3rW@byZC**nYe#;mq2ye1zL#Lio*(TXwLC zYy9xT55qmhg<39O&$EcTwA1QRsI7N~G7ai%sF@TBwf6PHT@CrSn1*9Ne!L+z+pe2x zaliBJ+qdtghnh9Jx09t)LhzWV>gc=}s11s;AEOWX$%oBS1T7<=335qnR z3m#e=No&KNXmoSpms>u(jX^YY4Z zsb$4~S@V4K=xI<;LevG{#H_5Ak88HJw*1(5|EEtsS1?;9SH;q~jvc!gCgPC8(eSme zlFMRwq%AG&h;(qfqEGS2`fL45eGhjXB_B~+du8{YJ<2MQSFb)cXb4Xs7ttIi+vYmg ztt^qU5fFnzZ1U|vwG6$7Ng*E?C#R-nKKaR>ctmd0D&QB&_S5kfd|A~B?2P?K`UeI~ z`m6mHKS%jazuBYxme(s|?c{cexh`AZO*{5Q$4ZB^UN;?Uj=HeZy#3{$siIiZjPLb) znfUH(UKhVRXWX}{s!A=}#9t{zE#|x@=j(WTcsY{rYtnCqR`hduAp2%<6pkd9Ls99dfLQ^nh;rPV>pxSpg|2 z8JX6{2QO%}9H0bhyXE6BN4|vH^8+S}>?C|{8zC`7;@W{x>mSokF)R9(cR#xgrnD47szon>UUnXbp^5sW1Ha0)8 zvZltzAIrAv)#6gmN%ptjf7n)B>PVB}-rs%qof&)7%a?Md zt;vlqBs_4QW2dL>78V!T4;?yJ7b2)!Xg}8S{OtO;+?Dr#-c>7h%ATF*P3!8?xOeZ~ zaeT-g4XfLcQBh%9+M0}e_Qdg&E?>hPreD5%xw>iU@3A|K)0m*N5I5T><~)0hpI_0~ z*m&#q?FxE&(H}p4Y{@i~%?-6|=iNTmTdLieYe`P%Q-A*$+}6GO_Y;wMOnQp)3#cOl z0lh0`fgvGDzyH30Mabq{LPA1yLxVCthAc_8X`4xy-)OpV^AYo|ysMWkUBaTvAqCQ2 zNNk=Nrb(5QlnD4=)zC;L@0=Q_6Sg0HCMqiW6AMcUw`M`Se4LDoWhO^`{^p&mEfwC( z)cG$u`uh3_&!7Lgy1GiP_sSJ7a=nR)9G(eB5)TRrDkmwYwKhgwppLfl=^GeS_x5VP zc=5udBO`{MZE8rJ-f$srm?__|fi<&tro}N|IZaDu$)YuHxLxJiwMflEd*kL<>C4j6 zib_fWSg)V{_P6JM{PDLp6%~o9nFhFL$FM?+tT(Beg<0mE0xTl-S1Ns26%?Y)J9CW5UAZmiWn^UNod~L)ok9LNCN8dJZEc-uVUnF~ z-l-(mb2CbO`Ad057OK+cGiwXJtS(>9?#a1Qjf9%0nDq4N)998K1?073?Ch7XUcEZp zkr_t|{7G{R2dMS@9?j@px1|(Lu3vsR+v_srUE#I2?^VF19FUcj zJ%9c@2@cdOrHdCINOd|?Bl~4xYsR|r<@fI0TU}otkei!}==_2?y&}oHM9~Kd>5X+Jdqj%45pARy0dsSZUJ(5~juY&qhK9*Wr z8+4Luxah-u=KbuZjgjXo9{h07>-qENmrLbcT#EP{C-wS1H%2kgnz2e-JJnTGjEa`V z3nj7T9LeszC9eJf+8P>WE|XP9E8e}3@V<5H*5y*qLs7^A6{V{)JRz%+{@LBU+jYmg z@+BvFiaC;N^iJ#@408zo+AMoU=u1t0K;~TK4m|t8}T68e(Eh*+Rr=L1_@?`T1 zC-%-GQYew&Lt7>61{y_ zvR&)NT;^Z$=~w@BkejliM;Yx#| zr8?!C`eH*Y5~pw44)5Bx&x9=J{A3M}_nkZ6d5k2##j==O9dzh*RZclU*5KVcxy%#J z)1R;Em%Q=Z!%WighlvTx=3e##2VUhZPu6(v6rV48xT-Olifl!H**%Cge0} zif-azkJitaW9hS zN%6(c2cz=x+FukT+HwQ?3gFP_Yux;B@4knJhxC#*poQ0#T}PODUFX6c2L=wxh3w%{ z)AQPUvf|^-tE;-BFv>AvdHcV`=Syp;%QGGA)`z&_oz)fL*ROlnudV11bBG^*LF31jT8xa%TQulriD zYDr=^e|utl{M&hydDHFtPQ_Q7R%3O(?R`^I(`afXeI+Gx{7(uce&l<~dI2l@vIL|R z#{RMH!nV{CsGD9Q<9Sg{ArX(bCxNgn_n&d=`Y@~B=SBGvF6Qjz$y3svI&rY-`|aCL zg*y)Hjf#$bow~?gklvAR8%Js<`$)=Y>__APKD8_(ojC`g8OO2gw*4l~l9KoNT;{C` zhYN80N1ZPAneekvdI}xVXdzRGvF218;C*it5|QaSHZi9x&7B8!^o?$z%put{c~~gA zE=~{0>%4ry-paa9XtC_o4~EW^n}}V>hH$a5)sS=N&M{4tu9bApC}v4pbQjQ!Xo>!H zGr5+%J;Uqs@ROeJw{6SJ5B*T?A+rCS^&6qNvE1I&$@B4v;AJLXM*+xTL2pzcWi^}Y&k$vBy?#O$@=xgKZx z?3oDnRX#vtJqNMy@bC)sQ%XrZEDAqSxVgCaNk0Sn*cGG}$e+qW-A=m&Xu z->GC7JudI9R9td2F-f$3vs<}caZpiNQ~%SaPfYhegi^lXP!GPF`h#Q%tZhyb_jhF! zih>t~_xgInO`$%lVkTdIPQ+T-&s;&l=SNGYn8I9%3MSO=-725ROwr0TkoGTjPh2D? zCY4#iR8XKZ$2=+PsH&piZnRGocXx`bq)hyxxbn9@)jEAyL=5EL)9Hp?dDa4J7O6_j z>#mpcdI6QHweab^{aeV?8qSfVf`USJ&h6F|b!LFLjC>jO+!w(N6y;pAOOkeWS?S1E zA*D;lH{9ZwooYD4eEaro+G1gs^F*02BJ3Y%-w{@2zk}<=^tkj~( z%Y|-&sL zo1}5t7@e_x-kI)f#(C@*@1~us8e>i)Hz+s1etaXK-Jm#aouzE3DCxzdqvQ*0@c7A- zk5-Nn_qX$LG&D4%z1nlghwY5k*n2;Px%Fj$5R9E3rKP2HJgN$U6d-T5&XwO9gV@kI z6d`lk(^NJ6H{O~X?a*y3Ognpblcc?Ujc*Wewb zdxegr)&O6zb?+CZ29=i<(;OyxByDUmG)sM>*tb$-gHKhx$Z2@S7nM8;fEVIN4+ zbux0;_{7J=m1T6i3~Yl-a}$>6!4)_t8ft1LSHgs|>75HHxjiPfJ6-=C6`zkPhr zU<5~8(RFE-hC}-kBjdpQ9-BJ^{Jk%`4Oo>kUmC!rZqgEe)ub`<-fzGCMi3ofqVekT z0^^ALRGlC&a~nv6_i`dOXkt;*V`5_bW2m3soh`q=Lmm)PvDD2qCOX;(iFOekb4mc8 z&Z`ZsIKfJ5X%Zd``%dUR2o|(V1<9~`_wFH_W;)6!L0mH#-98X|Z z6I9kmzP`#vM)BCcR6zH-FcD=yID>^V=nK8~3pMpmQNy?p4#|#F13@t<8u{1m>nP-> z0eUHrD{^xyQ7u4`S)3V(L$)VyZ0n94idZoQ7Gct+NtCXw7It)%i`b98iwV?9#9p4J zG9VrOWv`uO~g5SGU8 zo}UHme2m*e3E)TB1DJf)-(BKbNH`0J9>)t(f!ws{OMVJ!YV4Ot`$u|R99L~b1{$1q zh>TN)_4^MWITGwYMM4B9qlu=hZ7v_8X>o zcwD8?NNb8cZ7@++&&jEP#4d}l-P@0ASl$z;ej*V~&0>LMArkaP@gZf@0eb)+M*pXf^BLC!(pKpW?D+P!n#U3vrU!tneX1Ylc-Utg)oSc^x1Db^K1_q3Qz8Wi(*dGa%c^g zmzSSDe*6O6j!|7OKf<}x4jph@?$8y%ZaY8l1KK$n=$_E=E8u!cyY;`_hNY;@r?(lU zQjUH{Wm~~l=|1j3+ox&Qc;2Kr)*H~ie`F+Sbks~=PmdfEdXZ!1k3as%S-*{{SnM=2 z4YI>@@>7+*v7@77urM`I98s|^#TjHt!BA`nXOr|1zxp$4E}+S-ASfp%Cz}vN*+z{A zxKuM1*9p3EB(On~^AaFX!OCnWV#(4!2DM-)yG^UPB|+XaYuqrxwbg_L zoLm7Y6>8)8J){*P=MTF!1K{tk*&K0AMJ3+4uc8X*abRS<^X{L1GFqPhihhq*>dcJ8 zL}zXqIn5|tgcbmohwhvlXaS3^cr;FQ*NAzX1=Cl*Z_#g)ikqLGM}u>An3fcXFj53K zCOsA(-&Jx3SC^+`GiQ`jH4+d3rd3Z4gD+ zJSeec%NCMl{Qdp)1LjJW#+wfZYDA*{HXUtGHyEUicNdO4k>b7zeEHM<{mD3uTv|mc zQ-ckHOLbT{yBe-sBhZ$UlNn<_Bqq&H$+y|H`b>H6<8Sg;)=I$XU`GQ;XfAXJK!eKm zt6Di`L9935uL~JYe9Pj{^ba~R#ezaXD zXLU21%X2{X;^`(Snh990LD4;%B5n(p?-gT^l9EcPU+VAc(>3eJcr##QQ8*^xc|uf7 z%p1)~@~d$1H*ell)bNy=QA;YZK^-$<>>rJGzF%H;iLxE6XLKxo^~$Rw(@^=@1i?n& zJ5x%y7QrjFw+u{L=7cZII%;TyreFW_&ekcAEb7}sma93^RLmR7Zm&0YGMz_5Sb@N{ zcwJf9*}c9U)pE2K(E5ZMP{7uy*yv~`r0r}Y!w>=UM60liIw#y(wL{GMV9Fd-SDUeS;D1sl2_2L+={y!rEasUdtZQkYVnK ziCWutXrMU4PTCV5y!Z%NCLHt-*9bt5+*jci=;$l3F*=Pt;3Yd++z8faQ}eCc%zCrz zZFP0x2}^*9bc4EmeJ?y%PrQ2d%4~R@OPi*H>WYXjTCVmIS7-kPhcUXo=v3`VARRmZ z%ALxr2QBkHZ1OAu6GVEMsI61cv9ZBB%uUQw71W(6ZL{fo05Lbd|9a~drLWKTOisd4 zcjUXTSl4}RTFxnrrS5C&Z9HpBO1X2%QDV2q=VyCp0!MxAah^~V>OW834b;UPsO6OK z0~p?v3Ax8;tUOu2gb7n^;*$r%o$Egqt|Hcz+Qh8h-+FWG+~dcOE0!0g%o908L^|ik z3n#qDTIG!wjO;hDG*3+4{8A@$SzWzwPkWK0t-rnqef`b0R81w1*$HfU%v6Xa!2aj% z!rb%XAn$c?YXUS)P5&8+0J_AQ_wTP~Qo$9O4|k6v*pS7VhqJf<98#u(SJFyXPY_)9 znCH}~mVrj$QL#O>;bNT!WOFUL&FDSb`#vq$g4~hB*O?EmD{`DNIQa9=Wy#_^JUnF8 z6DKV*+*X{D#Pr!fABE7>SXxv2Wi@3tZrH$tenRVB@o#=lo_KBJ&MP~xM!-+eEZtPU z2AQnFvUEjBhnbKM)TckHJ3lP6Ig|Ez&iU(`Jsiv38JU^h?kgkO`XeD%j(D1?s87Tp zcj|gBk`sc1s^BE822@oWCek)Mv0A?KjDjkU7<9yVrghiLHu-D_-MVd?cUIP^gJ806 zuFSNhY!W)$!JqaKu;2J`Z+Ut7Jh*hT>eB0feplYv*%`Urd0;f@LDahUh}NEA=~u`* z=81FZm8_Xgezrg^>{Cjo>~v7+yvsdyb}FnL#}Ud{q8+fA+XOJPg81#`O zuFRkMX>d@Th1+RivfnCxY|oz1I^x4c8f(K%EFMlAULfp*WKh=pVC1hSu+lIFyp(Po#m{1ny!S zd;%_xuAuFhpP#QN!}AqjLOrViY*AXtVq*79KS&e^3@LUj$}9)hfMz^J&z?P7fgamj zo-|RG_c}9E*1e~T3vjEvEQ?GbI!*kJxPcAHwH-fFas)J-(S#)xrDnt)VPlmI7ww1) z19r+T3LBDR7L!u0&fk+1cT)tmf7={RH$NoGqUsEm9TasD zwwce3<*|Jd0DCk?4bvN`CZ>s7Da`k0UObz?9PcL4apFe$S zPqA3CW{uR*yQLs2l>HY_Gr8_OfR3SGex3Bwu^*BHd9*DWH>t<1x1qU}IL}#lj>wQh zQ8-bOnb1v&97j=WdcT_qI6FojmMjxmb*lLQf`n0?ngwE}dPMb~y`kyZC&O<01TD;_ z-pn6Y)^dzFF9g7mlk|-qi}b?I^roIjw#Gez4?Jbo{_a3Tir5b_`JbLhaaKP&p?}$b zA$g-hQBjc>+T;$!QXM7(vEOA}O)?yQ&(7(bYAw)VssOUlyIQeJR`jZ_xqSfrd#s*L zb|Bq-ZK-!_n>IwAH;X>CoeKB@(9EsL`_-Lx(3gI$<~2D&(O?07ku)<)R&_SpG8twg zu;~_>Qd2u{jC3~B+piNz3iW3AIn0Gpf@}~{Dh~kJ2r0<3pdnN~)b;8>>0w|Q(Xg&x!KveH%{{C#7Ef+9vV>Gi$zhv*KAY*!jf_;~)~Vd0 zrlxCAvJv7+x#!i_m(f1FP5+8~azecquh>02`>6sOZtdaLF#0<8knHh(p=&8QEx)6l zsAtgve(eZK-gt~%B*%ZJa4t%7fp%ZAffOYV%+0V=K}`TxQkL0;@5;!bjz4P6}%H*DIp53Oi+l3V%9)$axXVe$Td2AJWdTR4rTBwvnuSV1Am$P@B;^eAKG zNE@$=_-U?Q^0 z)kC#~ZSP)VJp%)}WThYR<+Rk zWFM;x2>-s7z6{lot~dudo3r}M^mNvU{+@DPPN!W8)KqJlki(ltyBSC5lRwjcz^-P- zC*eZ*84uaf_$JYbGL}DdKn}0&InMeWsz=W?M)adi=LUC!yBI z_}$|h_d9MeAc&@aJDYgdxXq}(6d|?xkTWxldch5FaCLrr;U0N*-Ex`>;$xXw0~nTfSRC z?aMkAm1rEMksUDzMovdZ$6%!L_3L)g{@2H$ZKMm|yQJ7ag(SnN6?=0yA#OBr=}Lc` zM1skw{omrFR)S(eLtRV|nJAJ+IZ8%rXI7tk^Xm+;V><)Jv+Vy?HcJZpHd%&O$%09*t_FswsHpjP^E0PF|^y)7FuIgDi`Y}2o6^chb-UYRc6ro zhQ!QF^lAg~>>fB?Ds4v3x`g5@kkjg_X;XEW$l|JW)YO!G+d<*%@X1P7ZEbCjaghoi zmL!B-X4YY;0F}^8jecAd5r%7vubiq%aOf2Iui` zm#)!T^2PZ08b9Wi;5YYkPh`|^XC8A0cg_$v_<&qys;xcrCiHKHk1Zxv4aW9mC5~KB z9veg(Mx;YkI_{z$w3c!)F>#<(8#7Io5#M>-Jz}(W%mVU1Lm)gW`92ih_s>y5!LZ~v z$)XY9;_f;O8Sqkr=#&>xKfnH(bpWvA@|CP ztDu-dBg`qZFcmzs>&TUtg`vYn5GZhCpX5qTe*O7N7Sg|G)+1K&Lx&E2)H6Hj~#6elRfZ z=;)y5S?gxgKXW3!87n!;d8gkV^gW&81AuV+%$Z(J^G|OdGZdG#yws8K9C-v)xqT)w zhpT$R9a3f-tNXHboVjk?)16*YG?88b)7IJpDLE6D7QtycgirEp-LzqYO3}QA|74rb zwf;6=s4<$ZDe`WGrbK{hI3jbrv>Qy6lH(ZU(q!9Jj@2@uNGyp!>BF1Qxk%?ugo=*N z5`@Y=s0GAeqKO6Z54c()n1sa?b{IEvD2(yj^9j8XsPdGT%fSJDeo=7N1jk@QX&*LI z;UdVom^mi`ezfYv;@awxWoAEdV1x_v*EKFypZWDD#U(g9r1nuyWDa93DJAI4`>a-g zcMWRL@!IBbD$k=+BQ?$F!>fDj?Cdsqp}_Kl$~boH?ZcELF;vaWMM@Qs&h2ONki9*i z57jm$Han+B>L!crd`M_ShvGl~{FDEhjAEs*a+s}{sHh!UjuCIco?`&^Qt~);{2=4K zL0gla5UlQglbYio1{rr+6@oZ~hHK)l{sUUmnz`S>*gl4jf+exJ$D^{fr8PYr#g9nd z9leg=H%uVuq?Rn2XaY-+B{S=FQ3-osQ^dOxj<*fEu9|Fbj}PtN{>6nt_nXaVgYpvEB^U>>b#{6d~Jz zr!iyg=@M+N(~m)CzYghXxHToDTwl1EkSr*PAsb^rHaLLze0QVe z$c2{I6OIF}#V}uib>S}fe88iI)AHl%hsad9dQ6h4bXd1$kdQ~P17ND7U>~Y9Y118Z z5drOH66hRDFF9&mLDhyoV$AJq8J*uq|68!tXpcaT6msbvew!_`FgA{TA%v#H3=n&F zKe0%_f58yA1qtdAd_mi=288-DhD@^9;JzC5t+f<%$N3>VTnlpVWoHBb(#A)go|&Kd zuls_83@T1g?G&^D31%Qz_M|1pT-~F_6r3NNBDGHD^rbM+L;Dj&4j+DF*0EP`V%FwU z-Hl6(I~PJ%7@af&!ow{dD>c6CAxP^YuyTleXWwY}_xy*!s{t!EDQmm-XMqPkIbud> zS4?JEOSJVe2eT_sgqs?&ef(?W!@5O^kg`Ipy9bj^ z-&JdM+4S41uBycWi!*5F=9qO{gYsv;gG1Y`u%@ai;qh4g<3QN-Pq*=HI3-s@3=(3~ z?<5S2aZU?B*|66si!&*9=d#uGZUA+vG98<6sr8O2Z9#^Wm6h>NA!m;)TfO?8T}Ne9 zy1p;?c(xq=AFN>2fNRkJpponN-WG@_KV``%Z;r@LH6v*e6MxQPUl> zRQ4O>Cb`3R;JUfiuj)d+M@n}$?Py8W6p@A? zRqe>oAdp=wEm8v_j_W=z2=Bp9!L6n4?w*dKf@bZ{3Am)| zx6yj12yy+X%8z4t?dj8>hzh@~+B#$l8wQGU(x_x*chdLdypN6JuZ z#j- zb5bR#rlYs2XQJy&QA z_8t3A%i(agJUJ}Iu34JXpqVNvDLiF4WTd6c%FKgQNRldUJKgy67pQhaK>3PD6#U>@ ziM8m3lW9n{c|pOFx%rnbU*7Ey?7AKR5?jDis7sb_y9Ao{wb~|FGxgi7+CX`Rftr2+ z0H0ayu?$LxC|0bl(^vV)u5{UWc391h8^C_ZSow;u3-sc%4K&+H9I#_McTQc9kriKv z24R+@XrZ|RQc7$Fv0_Y1RNN=-a?^h<9N=UTK$hApEqEMYmj@b;|G{TgvLA(0ySPpJ z3bGHFVLT_a8)gb_IVb@HG#zk7`N1x%-HKiA@|n#D=nWSXOR7=RtY4MM{_2`MvoA-$ zaK|vCsqL2gzyBVO7DA~lSHR;V?va!Az4D;EqVLbj#FI&Azs#)X&wqA>-;|E}yNdW6 zNlZ$;2Gau#=;hw;zpI3;`Q;Xdm3LICC`X($>FltopP8Ng=90!*nnLe!!g;nsW2-Er z_6E$^sheM}i@_+iJ5|2(+u;dY$hA8((P3q_ecQHX6eTK9$_gwyPGA1qOzm+TR21Hv zsl&85+i6LQkqW$=CfmT+nlm&!o|~gj}QR$~I+R)g}%k{MOlp2yEj^AQAeDtU( zfN=pFT(o>!LkKHM&*I|aFOp&^48PL{(E+Nag2Gecz01r>PUaMY#e&8@ry+LK0Xl4( zwtKOv8vNlsFa-l5w7KJ+olT(N3c~_LoHzti;P#SE5wr13yRI+EhK$8x4SHiPA#mTc zOpVFQiYeASNgvB6Wb+ZMZccACt?1rbEAFACvDnfIgf#Xfv`rvLC1zz z-6)VFM5;tsp9h++uC8tZyq*DhU<4gp$vFzv%^`>+EpUG&X}U`%j+xVgKKAu-4O|X8 zY3KthGFL0tTIAihxVZDT>R2J(l4fdkDq_tDBnB}r%LOihjBEMx&h&-Pum%5jBIKi23*icN3xbP10P|^J^yd?%o{(`t}kjpGeB+siTS0 z8w6?^&2ZzBr+@tM97=7$s}uQ0tyz>U0_%%f`L>s*vVH-AVQky^?PQ6OlXYY4wz+<} zCrpsRToCJIQI{CnN~t4CnfmJYH`>KQhWFeYffQn!R|EtUPqxa34vlVoWmHg_B`y2LN zoAF5}{~3!AG->W|sh|1sMGo+p3R-y>vWODQ#>7GaY+8Mz`vR-u>tU;5N8sMfFL>OXnfAZuBsV1tT7wfC48etV4 znqCY`Sk8W?klC4)=|Ax%1oAB~Og$%;h=@9IpQ_TKe!v0Smu0_Uobw*Cex2Eo)QX&=ZS zCS8SV*KSNLf&lzj$R_TRSN)-S9p%@tpDEk(9%Sd9;9#~-QO~t7r~y|-f2ng98>|8CLC;-?ZBq4F;Fi zkuNhdH29Acb;61NDjJ<>?U1=h2#e^=cV-=Q-npEY0?PuUZN#bUxV~iRkWhu zH%;Ne#l^*8q8bQ%Qo%rz0D6k5I^JfWN=eJ~f8n7!2?uwt#%V<(#u_!S9#LYj?> zQis}^fKNQw+*CM}Q?r<4YydQ9PFo^l{Yx4BqIYvBj}p*uerGLOmfuJhA73#;fc`F(9jtAY~yeJNiA;K9`^7N zIUx$AY~kD4N!jx-yJuS*GJEBLG3=>z{5X^^dhDdB+JZ1x-5In&rxgM?O8v4Ij4%eE z$w4j8XXH|g{w50hG7HuZt9Qo{n#;=6494rmALPkqYC0yiWL}yY_{K!#=}M?9Vfo z%zyM$v-F69h1#x;zwKEJ90HcYcAw=sY!)1EL+OIE=Yg8BHkt%Z$=@I@`+}D;jaj9` zB&+{}9i~KlMJFFf^q;?QfkRxphtnK#J;M$^HJAlJk%hz{%_P7Q&ybxW(2}ODO$@YB zHFb5=o7^3s$5f{;M+jPmODs7fKmwqM1hm4SpJ{S&jO2kE&z2t5kHNZ?e$n<**T$uM zn==kG*$ka56pA4a#+$Z9$Hl3S`4jKT)~W5RqU|5+mT-lg-Rg09c`5{may20^ZWLBq z-v=yIi!Mf!Zy%Ux*(>f@W(~VzN^!m`oHXj9cAR3UYILg8{X%_PwnhNkILBu@9)gMy%`NCM!!hhR|$tLBy$_DT=+WC7Dh}%YXO8Yc>#0wp%eJwmjF(-Zk0OY%;j z@nIBe;-JqFf-Zd=cKv3X)*{V; za^12Ae#Pu_JtkMJZWHIp@SB_ZmR~_StB58`%gCgj;8pCw!PVhg>)yCW<2s+2o;ee$ zD!Oo^fE?mp7(A8u7Enjmy3eiCJh!T|vyg6{bshp!YT>wWW)=nyx~(f&vag*0MUvM88)Ri!-;fdU^i(>&DrD-auj$^}Oz+PXGymXU#U= zLd5~7RdxCgM_3@ng7;*C;Jq;}eo3jh>yoAzT0Ezg11XV>+nnacQ_%W`#K7wD#)RjF zp%sk}yLq3nO__+6BR6%K?w5DEXo?BYGr2BhF!k`mu`)C?Bx9+ppFC4vgmRwZ7MOW8 zLfn-z_JR=qcDscS&trnwR`ZJ{OQWjvgiQy-sEnpl`_G`-*_Zx$;1Xa?+JK6vl_pVRya| zphZu1Ued4Zt7LQUIzWhP>-eMWqkV}hU2Sd3=tWOSfk}DrW>(W5{o}hAL#)U>iLhrs ziy^oOtc%^}n_Gb0J;0LG^xW**Q4C)K;}CkpYbbQ=bX87P0svG#177!s>z)a z^@jmR3UFt2O~|)Yn0o2cP&T)B+thF|+CBh@U<@aejQL2%+ueg*NPDKl?PynL2G(dmIBr5LE81O;qcCg05rAUq@edY8AycF&6Wf_b2N10T+#1 z$-V)k_q?SGS`O>3rV|e7SuJ{Hx>=TiHW9y#r|8|sIdM!MaU;iNzIbZx|Loc4=zxw? z7dZzA7(nBys`_J?xZap~>f}lJC`s>(gi05*uyAweTCa|`B=`|RuCTKY>aTk-d1t`D z=XZ64p*){94UUKKu>#(Dk1>1$^Y*I)*G1u7_hikY`G;_Rhk=MuIsXK|NNZYuLvAty$ z2tX>rbv8(PMC~McB8;2`$3VB0CCH$>E&JiahlGFOlsteLJbPY>0_I3u?;px+0AFBM z@BZoS54tFelH}x7Ki0eH?98m5W0szf3=e<#fl{;wd_eC}kPJ-1iA@8fCO=eY&Pktr z{M7>!SA&918mc{Yg`Y_wC=KuW3?A`(4`}2*M0+x?Hsa43W?A(WF%a|8A#}K*45;KpdBZ3zvqdU@mO3hDNkN;-JtNJV5aT3%gnT9SDA>th^mA5;`xScAk5 z3JcT!(Cb2KP#oaUHdO3S<#T1_r zmr(^Ipp>+?nu?&9o`55aYZ9q%FJ`BF zFsY-gzk)wipxyNX&$t&see%%WG`Rfz7ZRg{=VA?QruSb9FCKI$w;FS^7@M1#nc>%R z?DyX~VQPz!88WbiEcAySD7tcRu*uuu*&?$+)QE8$sjtMNb+g+x78$V*RqteK-p5B7 zD}{*@mSd_4=3y9tJ>41PyYzjT0k%A0vTh0U!7NB8O&)q(>|!?{@Fq#AGmkeJq4ozVfh9iO9UP! z=6BEtyJ0d$D?q*&VL@geq5SvBHApFxoEiA^=@?J~)LsZni5baINeRKNV|Z2rH0~2YR}2;~X)8e-Y~vG(kD)6M;{Y^c z?z6Uj0gmK?v7FB7*Slnmh%Fz!;{yi|_74tz+jh~La%9`#&QKM|HP9CeHzkn+zRP4* zBw*r~2^fv}W*&&aa!z<8u#B=O{7Mi@$Q(YHBKA|K6q>4Bb#WJjVj<>?tCG0OnKbv6 znhydkhtcFT5&NK%jz}apSSJi-aY+uggP4HxB>}e0UjsF49>U?>kCqnJ#}QPT=8tbY zQ)W%V#9a&I=OXA<9r}T)`**R>;$+#-0N33()EXs>(5CjmD}f_))7kQDiDV+=aOC$0pbg&A(;Y~yB05Xs8Otr%a5hEDL83!A1@ zPSi@shT6uOaOuB83ld944a`MQTcP_Ge)HXz-C9SaC&+XPX1yCFeRyQoO}jMR3MQ9ngl8sM#2NaY+F+`ldwJss6XQcpoBJR zu-x7>{GF*7L)cf0f7~V+vb{STH`)hZO_EYKzC3$jhoSmu`uh2&v?OI_OnT53WuVFA z44Dtehr;2_D^rrNQnr4lf^+Bd%~o$rOx&k2&vZm4)M!7}o^DP?w;I9z#AT-(aVCbp zE1{ zHav1nmG?8t&QMavkHTb(!*#}N(G@nl8*|pNkt_KH#ku61XCHkj#-{E}748JxA#Yqr zM#|;#(5Cg zBLE6nHR*P1M}2v@1hL?jz+MlQP)Ol^mEJYj>TH<@^uD!)o#5y~#SN9DPX=8f!%L(m zgxi9sW+qU$vunoQy|_!}un@0>^iDm%Au0~3VxhD@X@kk(mttk56>=IiML%TZ(Mm>M z==4v6GX$Gjis_s=6PoWMg5c4D1WAb9!<@?u%?HU)ZCVmf6 z$e{VU3JiWlSs-@*wdKJxj~+d;iPwT7_MV4FXI5&G&tqWvHiXv;Vg^qY9xs}bA(9Sb zv9fvpBVApzoTK&fTEO-swZbyh8#{x^o;c#l!auas!UEFWK#o%!S>DUSGWMFW!(#r+ z2mf&?GQcCzFRUBF1in=XQzxL9l?#&#FfDb2NH1YZJ<@{R(Iksv8X`l)5<~;$TSJ7! z)Gi}6*r6X3PALM*H4H^e4hgo~QI;haF+7+x`_!;uA^iYa-C$A248XxXkek{=)8M2c z&yyh!DZ$jZ$B1Ci6{SJd3vXb;yKwHD(btb}NHeRs04_a6+il^^4OZ<71hK1?xa4D& zRz1aLd1Zwk5IA!a|O>t7DNu%d~WMi}J!qZzSkQiAKDM54NZ3@4`&#RdLl?mlVjb32)l|shj?{OO8%FI_7 zT4$z7b*4mIBQF1-la+a&BR$#2>JC*I!6m_mHZf!%A+us{=ckT$>*udzTh~j=$_j!D z-PVVmU4V|pSm3C-Vr2n&0ss+aH4o~j_=Sa;lcCTGz(D1Gz8bUzqyhU1yVyP$HEiNb z=)#iWMC1B4I&Ozz(KI1|yCL>)1IMIHO)?#VmfpGDW{gC|a8R8O$&-mUKiURmYk_1l^U zSx=^y)}S|;!CnZM?;?oY0<$Y2&UYfJ)Euel$Z<>5^Se-}tq7d|wnEkGfRAh# zc~Qc$x!|&lj1MrA9YkK@6+#5cAq``ykcPGmft*UWyx$g!M^jt{-F;kr7HJKzf4|ez zs>TzrA)u>{9X@;>)*#4@*RUdJrHB$u9<_z;pXMa7W_E+=J`un0xTUj@%H#oW$(z6( zP|@an(3-UJL4QL=T;YmSgn$P5o%9lTS`Tq6qIF5N_^6sB&_E_NfcgVx$Gh=NmY?8E zOM({%OH4*z;Rhu^Z{wy-!Z9T$NfUw?!ObZF-D4*1Ck%JO9|>LVGD0~s>F5)#8QLtL z4MbNWJ;5U%35xL8>C-B>YGMFFAq!P%2-2XMiNZKW1}{Map$XFv5)zt(=m)A5Zz0wr zptD$d@w*14xYDtNgJ6Jrj?hbj_{D9mjpGL*C<>PYgPR1|8dx7Ygwjw2hTGGl&?hP!oH+_b9=0{| z%pdripMuFkTZWttVqcPsbfPJDl403tdKV*vz`Le!K?sA{K}StSW%Ke*hv9Vd=Xk4T z+78MfUr8ehU^^uP59H2&{E<9gjF@!xkKi#~Cy2wka0iR90{U*#j!DS)abVlJ?|plo z6nW|sGV&+Cy#fC!GxqA=KzS*JDQ?v-%1aX+;l3FMSZP#R?940JLXk`8nqN${_>Q7*EKmMcQV50O?=){Ct1P&V z!fvOT>z-e*g?eA)OnGP4I6wKz>l^=HSij!`lbHY7qFbFO^3hG)vfc6D{$j0Ei@!dN zVo{@KL*0g%RwgF|)nBh$A&{^C`U8bxapzwjR5(ES>q)rJV|h37Ql1Ai{%cJAS5L#` zB{=%;FaCQ%2)=AD*0R6;hC+FNgoN*3um8=3@Zh-)f4%1P@#Mb${*Bycy0UINC0>8} zKkf}5{9ixiw-&$f_t&q|ItU$1ItJN|lanR(EaREu?# z_xmMwUjOSa{~vpB?ziy&$G5!C!58CFHU8UQ{GTE z{~9g-%+j~^l^-gj+@hcV??%Ia+LV7kef-Of;H3YzZ{)_aXB+VAh5yDn{2STeUl;IS zfAPz=&g-3TqFmd z7ynGFc(Ut%nR>1xSN-EVsgzQ9MJlnqb04`?s9P7N_e)~@E z$y4e9*{Z6&^gQFzjEqT*?sF9Cl|M>-KQf<>zp5v8p0@KzXf?Pi^8xR@`Gv&zXoV`(qgg{%PZ$iXYS&$dtx-QZT(0`|DSeiP zt8NyzMTo)?Pf_z9VmHOwH6c!Z$Fr=w)WTY1_Fl^;6f(?2P;! z8IBl!4mr%szZ6KTuB~@)3R$H3v?U)+%e+`>K{q>W)-f@?NO|VDmEqdbvrWV8dAEgM zpOPzlX0&l3TwI2w>TOGmrR%4s1;d-?M{-sNP0t0|Z~gK$y`Vk-k7b$2*ud4DU-^Hq z_7+f4c5U16fC{$-7NSy$fl8^Qq(O*CcMC|jbfeyq0*V6C2-4k14N8M_=Ma*^fOOA% z=LO#H^So=l?|Qy({r{|mG0a@Cuf5NG)^QxSYzJ=F_ln|lFC{;Y|A>C1)zke`{?~ zIU@OQExgQlPu0Dr{>OUL=xE~3M@6JAQfN?|IWIrv6fp3+&d!y9*YR56GNX$3krU;~ zdI-aA4s2ONn!w@v$3KVS2!I0{T3e-{b?Vr$W3oa*f4kgvcz&oWk!WvF<*@s34Mmmn z+?R%{@>$Vl7N49Q9Fpbg66d;dQZlIqr2+-;VPw>Nid7khpG$1?_}i{{kIW6xi=6!N zSbdn9tzG-OlYYmY(&cIquQ`0qA@;GL1*c`SAh!2!8c7o=kC>up^xnOsVA4uX4IN$f zFZTAfrd`r9S6axGl{@2&bj$gsUcNn|=w{X_koaokg7aIqzG7i1LiUrI1da_^HHYUO z3#G|119j}u>O!CSE*PKTW`tIQdl_1}3fF52afF2H2?G`SF@;(ENiBP-CVkaz%O@@8 z!)kmc?uT#S7Y9mb-%DVV7z~WovLd=tO*$_fMRt+c^;0O%L9r)K+Mtr04_!w{Bm#|` zN=DboRRN&_@CZ31fexs*cE|@o;FP6mdiU~?BU`f#x0Ha}m?Kemg@l5{y|Y8|CT{a6 z1x4`9d-sH7KCJ!3qjkM*g!5x*CCVmgaD$<;RLw^$Jp-6^*z=XBl(?!`!ulPbinS`* zX})qPFF&Us_~i7LDe}8lAH7*!|Ha5LS$<^OZdOn-W#fz0o37-cyC)tro6w!j_->}w zqv{GnxEg~MX}}B)GVwFAYI(8UqZpq~nsNHRy!`v-4tgwVunAKqO76Dz*uQ|0{-$&l zZ&6mxG7-?ct^iGBY!C+^wwx@OFS?=~wtHul>g!qu z@jv8 zkT`3AjhKVfv?7!vR7#k+?m((_0D8$FmzacP4x#J)HQ=(4CivO!q;&FzhK45#skvug z?XW1V638{eBh+F&*gWMk%s!gCV={qIMRLBHi4Fn&M8|( z{gZjw*dzav@v_|f?Cpn61e{pOcw=aAjMfV!SCp|N4DRb)4f`@4y*m84y&Sh9MGM|! z|I)yl%xrZ!QMEE(E?H|E4Bv~h?;om+j;3`2a72OIv^I`ENHK%c#$s4`!fXBgHRv@? zO)eI4t61kxJ~HZZ|H#{8tF|MCg5TXLoVFU9nqQ+u@7-HlW)*MtbPPB~*74P?+(sd= zMbC(`z$|5{ymYoR7#rC`t9F0K9yNLaETrvBr))+Er@}|hlap9?Co~hZu)V^G7W~30 z@MP~9s%($(-wNLtSuEMci<@RLu;Km|{>^obN!nG^PAP)6`JB37o9SNMYkHcIk8x4h z4U^6o-n6BYxX$Ti7UPjyJyUCY#2W6y(2pdobP6RBwE^uEGhqhNGWz<7DwVx2?lrua z&NfI|W&r9V$S<}*dOM9F+TjsUCD<89yn^h{hhZ%aFQnn&IjP3fOv zjs6;0-?JjPm-#jQY>9z1CUxmRw0UZ>$TzgwmgFFk#-NmH;=OV`WxAW-+nqg1j< z&V!=kSA=E%a5f|Mfyb3u9@ zpa2-oZWIJOHb03QJ+-0u;8+3$5{)g}vBZT6-Ou6G(I+lZ-*-|cI8GZ-7A5PaW3pQ_ zM;C9=wW^wOdfD69B&)JpjQ&g{O8)Wcmkmzf#l_u>Cbv!Nyr)kk>C}k#T+f{yQ+}Up zrk;Ppe@3H_4_6fv{839;j11LccavYvIpeT*L7d14Vw_o7nAO zWZ(;0ON>|Uz&wh_8ZCYBrGh4&U61qVU z?AqH}G~0HSXg(27Ub#k6Xl`D}XR&u9VmU*SFZuE8MZKmE-yhC(6@OuiAgMgbVRlxx zFcO@vxYuz{S4_3?r0$0~>NO3M6y<$hqy*P#nuNGA`D3)}x{6rR^gP1USYR_Ns{vi> zlDFIe39=3Q+bWPvgdKSmS1{7 zWp~Pg!ydd#%v=ObMaha`y`g*KF1Cgv*-V5^y1PRBqnW$q<=uM(FDrq`MgASLmYD1f zkN%g0kR?eYO4P25C^WtF~i9FPr?|bf-=>aFyiczFhmkxS~E7o2M-Qbt^ml zV%R$Q^^+(AeurRHj!4+ozC^YGP#8RAjVw)y#)4cNm!+8jY5uE-K6X>#y~^+$ph|w>9A4TK4B^ z#b#Y%_fP&%2+4@i`Dul<+m-#-0rJ<;FoK38GXf?t&aXgb7a+T2`uV;>Roypm+TWF% zr9vhD#;}V_|1#EM4=_1rSxi49#9kJxVNcH*eeOOvb^hj6HYJFIZZl^Li#?#K`TG{n z03XU_v@+*d4+&~;$W~7l*u zC>zO5tMw~O6k!wdao2BVjZ2RLh0b2u{d^W|q7R2um|Ff@GicrFNmQGG8a zsj&=+jeuvX>pm3CZtvJbHC0E)c>P8Kl0t`yGCWx5?)?hcbET-G$sP?aI@mfhLs|*s z{3Uk7w61LS34UsN3u&zkX$noA%d6~k*MQ}1T5XiN1a0I zOBB$GpRW_@w!2AA@rg=+hk*@QUr(R%CH@^lkjW$Sx9ykMqZql9zwKYtzU*kSwn7L= zyVL4ot_ydYd_&uJ@j}`P%R+Kn{iv$RS!vyWLh&o8cRWeizkf;iK^-5Ah)F^Vv=jMl z6N$bnEzIJv_x*ScV8Ynh=?W{eH|d6hi66RGD<251;W|ZJcknT^ z8+xE+CGXhqp~Opj7JKoysmFEp7uym$`075gHHWzFx#qi!s4_=?d@dKY!%5d6Du?dw zBV|?i*mDwRlKm!tK~GhJ>%`-Roz72Xf)Yms3Gtbfh23pODeDKl>ORB#DI9M+YTJ?$ zo27kW04k*!U5@(v1|)BYoY{SkcJkYkF{q{vUvkX~^mf7z8K*r!%5t$b!GDkcr5M8<^TVT=?+hMzW+;&(oew4RBSxoon?OvJ1hmh;V7^VMoGO za*wapFbdADQ)Vfad*{XXni+|u^xzAHN$sh-H*Zx@fCt|dz(7NgFoRkbXn z>;*+&7NAqSl(vs|fR^9>m(W#YlFm|GWF)1Y;6Wh2G(Q}gPH8a77u*YJk%Xw|ZzgH{y=R0**c9R^rLF*O& zH(h8g-n{&2&Ch9NE>5F|lA~Y8>Fg~|cW=LJcPf2@R$9fz9xpBWbS*gKl9NuY)NLmH zO~p}lX#te_Xk~?#w0!zyOy|{epIFbL-pM|;#%xKb)LA!nxEyk^X{fGHm}+iizN04j zl#gZ?$D13$LTw$**C?^l>8xyEhBpbFD&Hu5>MPYk7a1cL>$u3=b5crECMrudg+$_N zSFV~|O`&mYy@>X)zt%q{g?cUL9{=?6jzlTpezL7!QTR!nBqDia1>C3w z!5pVPLS3+xgzj&o+6ncui=JPg$fbnvBG}kWT2%3jLru5@`b72ag}u1o2{5THXjDt; zJ+#3!=euI#wx}5N6|yw^fS9-~ApV*p#--#LmTN&yKT+@U*WzMnFEYB`FI9DG6Rp^; z0&%wTaS4MW z>+Q`|&e*~0RBOBG!i_m&lR>}Vqxh{RYV?}4SVv+N)X2MD$Pq;fyOjO3i1MTUTw1j2 zg09dU3OICU18d-Jv_Z0h^=q=Y80$2quu;qWvq<3H!&X0D!`~d`j6Zw7NQnoBe3~n@ zmqN!z7O!QKE%2rGDYnoYE74Z}+dRrG_!6gI{x;zuM9s%eO1so4ua(9{OtmV<;Y=h0 z>+Ni`QbSzIpFU<#AMLV((Ja0in5sn7!UD9&2uD2(7(~s$NNfuo1%^QSBbi|Uf6n!0 z#en*YmSQh7YG1~%jSP^QLXSc^=^~wGA)meG>z<6PsIexq92g0R+D&>a`jV(DT$SoN zr9c;AZ0w8k!X-s9mO#nrUi%8eG)h9EwZ|{7HCl!m z)m8RmTqkdm&Ff`hu+%rccb8G4&yJ#Zx9c%8aB8s zg(+NDR>Al{&F6%SJ=ZjewSlW8MnZ4DW0rT{?AomDg(l-1m-J^%&YLOrSWQWR^i}e) zEb4ekCxDr(0;=E^Gc&Y#i2-i58`u=Of%ah7Wu>_U;CT=X>DhlnxMzVr9zktGD{4B^ zZ<7n2z*&l5i<8_z@8ldv`|x}!D!!?tIYK09emWAj1dzO6@@ zqIb`vvwua$mu>PF zLIgJTYfyoSS3}j;v5zFin2@T**8BIXZLMT{3Nwe|M?&rFte}*RYB7hnl0jHVXa*s+ z)(h201QbW^WgLig04>-(Fm6TR}O06z2%9C@j zZFZ!s*wB|Qke-}kRgT{w*oOV6k|YxbwC9%<=YozO!c=MXMjG*-?=(AXaK7~<2}!uV zAk~etca1gW+AK#Yh4Gt3He)&IR@6@x{+iDjk8v%Te9Ou)-A~mhLUp05JOOzijQE@r zuSt(=&V`93iQZQ(F)uowZs`_yVN{(%_s4~ZUfl9ev`O~$U`9IB`bMEUzG3;XKq%|k z%6_BuRXS5m`vFS(0S$bMtq&w(bQ-v$de+MvPNHpNVI4P5I;b684O#3~3o9<1!sK#Q-Xo>22NcL2b&1E|}RbF$Cp`?lIJCKX4<0 z*pBF9)&<#+^Sxa;l-KC5K3c_Wn9JG7m*?iki*|C%25bb^wrQe_E4QP(*7la0FY*6g zJ6yeeD)Qmb8@HZ|jT#xaSFnfGwMgGFKE@>l_n*SEBN?bgF>9(a;4se>r z7J-XQw+SKhVc&uuxmC_PRlz@PQY!wr{jG;CrQ z+J5uKPkPMfoGha{$zHOC;7^g-h3aUx_rrRBdy>qd7cq%_ecrkG5H5t<)a`&*mdt$> zCExkuZ=62SsCmXEH4WBlqpR7gukZq@csxH@_r_M|*`FZ>EKl03^UmLwKkqS6B#0~S ze_7?Ue#C>S=lESUf$*?|A7>JaQ7w92b`01iC3o4Si=D&Ou;H5AApe+jsOqOEd#;Q5 z7}cn*(!~nA0Gvs#iltQbN%2;>e#=qKSlAwtkAd6o2+RcG(EJT#Dd_;MmxWNc92ip) zD#wszI8zS;n@7n^4+skF2D{q#Kk zbs9E7{c6#_2ZPZnLZACz>OikdWI6T4o^Q7sb^>SfC@DW5QKPCBo+%M!$#ZNOS*Fi8 z?9qCHWiYNdxzDtJur8;Ndoaa_SMnCKJu9NpCJtF(PUvm2fd+UiJUm6{5Ce*!ILNCT zLI2iMF)G~8W z$x)4L>2Ozwx8+JS>+EbHdGGw*_u+_9vle~n+d24GtU0&cHI2L^!t9YQEhW})lc+E&goV5fJFZg7~bP$BICKf9w)|8kD5O89d1v_6)a zduE$9SEH1ZO^rqHnvHN-Ise)e+CsjFZFzc{a(1MFZLKx&P>U%&w9DM+aFHy_e};V+ zDMgvtJzh|XV>Qct?RD0m}&b@eYJ0sP3ZOn19??_WMI-4g$?_QPbO#uYMWCEj4= zx?(x7^X|n;>Y0?P54Cd?GY2NqZ`>CSG+#1)SzA^f^y&FyDqzG}9T4xu?v7+Mc0i}r zTIE$jXKMF4sWPfk2J(aKY9keb{c}z}qJfM{GgfH?rLfm8AKx?|iTL_A^A4jj)dP*+ zbuHzvpgw5uLQZetB4t|b+T*5>K)q?h&MP8(L@gXpsaS1T!7ySsXG2?FR}tIpO&%JNU_7{0D8`{jn}(0ZB5+ zN2EK5ySEtslnf=Ew7Ii2)9xi2mH>{bkQbQ}?d7{+&zu%!b4o0qd*$XsMN-0X+?rX< zTy60p%F=Vn|A+15+bQzL?rPs=t*{TK6sSE-Xgdzo7nH>2IpK5#GP-|8b-{ zyukUkvYOMc?~JQU>DQm~hbWBk7(fGL7bB~rG*)q{%Da38|Ks+G0Di_$!_8@8+-DD>Fwc&ym&Tzoab;X`WM#Gs`VzlKszxSS;&Q&IdN#Y+->2TM7Vc2v%jF<0iZ z9CrESZy5|@iBY?OduT$l^P>GQx7pW6S2IY%{fV7_~S&aG&?biUx{QMGp*rd6- zU5SDDG(Ll4Ip8}*Cfcc8Uzq(wLE6RwM$o)1$U-qJpGS5zh$fT=1V14CqyU)S z_mJd8WL%J9AgD(Hjc`m_1SyKc6Q+J-Rjn4Fvostl@aj1ler^O&EZ2PJhxpm^Scq_p z``TDa+=fuXA(nV{X< z-Qdg^c6&k^#2lzSqDec~$0u@i$G%J9O-X&-&g#&7LV{;B-tfz@V}8ubM9I7*)wT7# zP0Qsd~w~3Fep5Lfuq! zII}F|E#Kc*!zQFTeGcUOJ+`dS*w|V$%X^<*fIdClOkws6ReWEzwBPFMaUC;mhw29A zLoFIw726*J=Ed%cV_l65CZp>+MGYE8VSl_}W=QCRf7P!^GfgR(hac9>1=7)!rfraYsiP-c7S!MyBTgzj?y&{$se)iX?LWFP1Pwg;wXjWDc3He;| z_cuIGgCryCJ9BgyZ0LEVpgkFMT@-EP`O_SGGY8{EX$kp_JT2IEAQc$D5@ow|YH%u^ zr?Zsp{9O4(^gtB3#{_;a>w_^hh3cf!hqX8C&I8R#+`Y-|8!FpaTP z%8I-NrC+ui?{c=?SGY{uyS9P@je~+?vP3KPWEKNTW!(sZK@Xl8@|WMY`v zEH5(WlS>fnj7@V+5vIhlHgZ2+##fO}U-z(Q9y7C??Yk@u}vf#EP28tn_Z9-)$H z4tu=1O%2#jh;6{h4!TBMY3cm^JN_8idX`NTnC?A`b=ke?;TQ#VQ%7quROT7#(X>$% z=63I({WS@xpPOQgj2Cn+p>F3cRB8{e)Yv=e{hT~@lH0ZU-1uVBDR(UYirYZGMCa5B zF_p)_X{h3zP15{wpMbA1#Sg{3)=8kw7wC8ei5Qmh9CXP^VJl$ufS1 z{uMj)(1@H0nnbB4ncqYZPf(=oC@W8TdaWH&@C97l&C)&DonLn6Der>`?)G}{wY*C zFU+GHDYv*+!{KT6Ufc!#ew-1#z2QIN&SZ?(J-{&aCu`wH5`T;bsTwn*?DKe*Vnxs! zg2N-|B2RmBR(Hm#U*a8w@eSLH%ZHgqDoR7t{u))Mq@c? zg>WEEBWvoCrJmGV5t3eIXQvahm!bYm)8{7NK-!VMzRdqIv?vKFghD1JS+6&dFOcZ@ zN8liBf@wvnp3 z{D}v?z84Bm27fJ_8=bpRd2-XqF~!>SF^At@O4%`4zT!_m1qEkFczv|F!(Y|i{Fr9r z_hd_y7sd3A_^!5g*OaFM?-S>Ls)zACJ9c7SX>aBfyB?M`S~gtS3^g-tm8t!uZtnx7VNSq$l* zhdB<4Og_2!KxX_fP+&W`dySkphOiR%bzUQt;+E@LOCz*Z|aErNKIT25L$U15jJxVz#_rui|HvflEYTZxXJbizfy zmD~!tCdDenVR-2aWFFOr&&Bp!Sk=k0La4%2eiy+4bOL^7Agza*1_B~SB$a{_5ONeG zl?b>_&3NFp9~d2jX7PVs+;LDa0Z2BxxHF`LQmF;pevf{KRGC))G6-!TDWdKtR7A0b zr&0g)9or5q_q~L!uCCn4%}oac4W3wrkUjM;17}ZU-#wHiji-l5)HymjEfZS+1^23I z{ByQJL`hq}3CNXFayJ&ku+c`4m_x+X0m;%iK#IzI0C`ba;0}bq|3p=C@V0Rt%b)Lh zm~oo+Jue)BJZQv9ZFM!GU=J)CL0^GT9H?9rznXNt@0u#m)6~cn&4ohA`tyBj{ec2tLQpgb zkk|nv*)*>@<%7hd9!BpW;dUBI;JyHZ0t}ls6zFn$MzDai@&j^@vYshKzaAC44Mbm` z3Lo4Aup+{O$sq~=>W%>*qEG%XfVwA!diEwCc;{*%`*D?C2mGpbXs0s9^eZR;hfcvg zR^%8D9wWR)AcH$*=MU)1#|2db5`Za_ageB!Q zD^@*z(BNFCc0Pv19>|btpcIYP&;_y90Yv}DZf1|`q{;DP$5Q!4fjGF3hvC8FPakKA zpF7W^3HZ}FW2PgJnbFVP9}d)0tMt}`Lj?6 z7g4Bexo?oqoamC18qUlC=yl)N2I$B-sycU4d{@Ys^~Fd`A2#$pT*dEt{ouKKBApS*MoIs+{D|gIpf)y5y^d?IR&PaGhW$o?JWg;n@v%Mu2ug zM6zZ8fE+yinaw~_mG|Q-&Ib~wQJv2LjKdB9`taF%$oWzA9?&`=1Y$=zfHzJtf#sX7 zh~k4hR=%Ct{Jo>ny)W(VQF(cq?Xrv+1M)$@}|%KAC*IL1-E^uTkT-pJ)GX4zLA4eIwUKpV+Uj1WE zE`5!RjO5;#&+E)YIu{1jk09b;)sWnbHZB55xr!x!i!Ko#l&$0+EC;)?;NGlMXQ4SX zKG+P|bV>xT$G})c0>H_r*X{Qky0016NDS)_SMFblwAvY!-DC!+*3k)qwlbUq7+9=; zQne0di(D^IT#RD~c>5DDyopjV(Iq25s$D#v+ccM|?xD)?&o$=9;8&BZhOFumG)iGQ z#sigKJ1{Er!4yLnOsqP@7-|6SDd!3v#2Iq_Rq1>nnG0YfN#_5rp$4*MAHW`ROA79R z`UkXz0vfdIf}ooVlUgAYt^laJYk*#Xij>Ainnv-M^v<8_fAB_0TKW`#uHE^T>#yd3 zl$XK)h@a{2-vX$K=m3`3R*~+3Mnkg=WV+z>zI?7-`x~#1oaFuMlJ_S+!OUkJheVbS zaH3ZOiEJDIrlDWKx%wd~%{fR2g|VEC?fL6qIeVn%4VstM0AYFvOtiU3W+Z!tymJ3W z{pm5kEdJ-S$eU~jUyBX7b~Y>%@L>KFmHQt@p;R9I1HE~F{m-qyn?Eid{9alulIX+( znmm9L?7#loLH~6o?mvy~#{Q2zh?3P5|I9Abi=#w;&^Hf4PW?}B*8kUFWCZymxPsa%m8dRe z#)EPAU&rZRxAaTDs5F>y8r3%z~v){dthb-U$p)C zA0EiR-O|6F(tmpMTi73GAy<+9!|X9sUxV`h?Ss5F``7>5c>QZu|Jxw`k8l2KWHa_h z*5LnoWJCV`|1j}kEIgtD{=)?L*NQx||M~x)SMa~tihr$v|L~@eIZVGoOs*@he#v2v zj7GBsO}1FPD&EK{5hRIHDYrRVZH}UXd8`+oDYbcAJF{sA5-c;j&6e7ee>}=G#KYtj{V?Cr9HTUWELXJ z^Uj-NUUF9!0A))Hzjsb>wJ2ZfLDXRA!=8!IfAAL4sluuG2+>2p?gGB36=2wVg=Y^A zP-%AxWsm~UYzGj)kkwx3c|8cU$D1{*ZXf1A=nxT;wi+->s?qkMIS)ALlm`yLEntOl z-RSPFJ2(O=^&u)U0l@L6fEr}q+Zl)OBr6}H%)Q0@j3^*&8T!oU_!8lMSOPlb8sGy! z*mY7)ZGzjGH9Wq*59JCcw$9p;+^Lilg`OMLJ5UvE zcE;e(fr{$Wa)B51?~8#5Qf^Qz@FLp{;0;lCt~u~hP=ms77@!|Hdm7uaI1D@&jJVKe zH*`T_CIuJM%02N8m_{1(_bOJR7QaRv=YtljaMnu)H`I59c$7&2h~u0_pp^S`#}&pa z$7Z&pOR*jH*7@^01wd2>^id(kQlMDidHcx0*NlBp+~S}Wn-C3{bEN?b7^jqJxK3Z7 zIW6v}0EXw3X<(tld_Q)u@p^=ZM9dN=(;#7hx2+l_hANxJB@&Q ze<>B|ZG>0yEx02Nhu@>#%0;@3U>;8GWY3hPy!Va_(h+tLLQ;gnvG>7WtZ>2H-ya|cBTT5y8kC=!v} zcY)MW4!36g=V7dQ!Bj_J>d5r?@T3|bB03O>TFvLw)q+y{g*PN8eF46opeYDUqw#=_ z4{ia#p`oEZP$I*QU){eJc2gfFH4V2gzJ9%i5Oz2~ zl&jO9tC!*i{S5D*D({qL%@4j+iX&)vmvv|VaY^b=z@lktZ4HFD{tED{Mybx0YQj2! zm<(X@{vhUh8N`0nXZeto4{{?{IowRtg#X-2*i}@^OTg>l+I<|fq@EOzf)Kr2!v52L!DTQ>EM0fF(o_!0+4nU5SVZX~g&u!m z_kVYJw2#k3^}&z1pJ`*^Q?}g!YMd?2z+sp&S>#1IKYY8DCB=&>o%^=~A2eW7NJJ=x(?HQH2;TpfNz`OA8eK7~AYahbvVUDAAk3(0$7cGf7U{0@^*t5o3vr zT*Q$dY8_x7^37s^Q)yN;4e39ISDzb9{&i6Mu7fz14$R;gAE9&S&Q-?%C(vhpXFJ3$ zArv;kLfwi)F*Y4yr*q7)(4i$N3Rr-Q9x~(@?W$)bWtn26m(g z;QR{Aa$8FqtC!N?iIc~Yjq3US;s0E;o_ zC>cONx>(@#!1ZcPlPuDl&(8|HfFJI2kp&|X=2xx%%rXwdCii4*LpKpZe@PQ<0bF&+ zzaYLneVQXTf&Jh>2PX>TUw^`{m7^nE2He=Z>=+=3=r=d=Gfb3eCA#MtbU`UsPm8r zfE^7{DqMm%$h?;ih~dPZg|pv+cZGzIppir6#{Pw|I{+b@o$;0Kbv_`yDtYF97>P>q z`apPT1sVTqXMB(q0vCE8)Xm3k2ZC^hQ6$X#rCv0iqaeruh!=g3bzPuv5Tm2^Cu2H9 z!Ak@@h9Dj_SPIEyZs5_K0NSkpAUS12aunX)U5z8KgVn*dOa;;m4j$=3NH-p-UjOp0FkkmZatW zw5grhUe8$=2e+L;Ozmk=YMIc<{i(rj8qhdw1Zu?uUohp0-`s&z$~foJa|3#0(T!80 zy@8;l2IS1ozzhuVKAGm$Yk@0UDp>37B}F4P5ZSwteZSjmY<~pX9?;mdJ%mLO_$-uY z{%=#F@{iopk=EnTz8?USS`a}&_V!vVQs+2cKq$$(xX1RhQ1F!96;X>qz}Z$%H4Bxi?`CL+~fy7YUgYa~`HQ zQ05*8Q)v5!)U`7a!#Vrutjd89R^YpIn?5|*1`LlAh>3+{EE^mG z&-&83hO;tYMWO?B{bR$k(EWQrnbLD{ae0!{(biF0KZ6vP0I)t`3}L&14tC@+5x%S? z5M`-(OwRwQux;)YDXlkft<^!TC;I!`E7K2<$m9 zW)S;J*c>BwzW_ocelW2_fb|gBuG^vR$36K;ZX71PKd_)*2CmN-PJ96Gf;TXl-hSMD zsK5HyD2XAkIQ~#ZF4V*GvY&e}qO4t)k`!H{q05U9i`lBf@vWMei@N$;g8O?4yG5z3 zgo39BV1?^~>aG>AeAhx^>O+Ye@T1iN9XrDzl$|%KmVCaJTHXIApQ{aoHVk8MA&;kgz-avv<~ej8%En!No_};c6(geoAvZD9rwo zz%a}X047vJI8p|>6)Hx?dLvb2yi6moYsl}w)XxguAhM(VGlyco^Nf36O|`|FyLJMS zB!3--oK61dduCIjllzzV{t9d=MmYGLe)5^iYRW{<($&A+12K6DsJ{e4*7rrpLEf~_ z$OU!@BODnG&kN*}e|gy718s|UU_k|ey^;d-7yUsALI^yIxiv_$%>e7rJdjse)nDo+H(Woq;4XKgmYhCbbyWZx!0%A&^pK=?%f1B zQw?Cd(x7(v{rzJ4v3e5N3OArWS9iCL`Yfa(5uv&B3&84&tVWx~k^SQ#UKgT_q3Neg zc?N(UpTTdYcAn2!g1td+d18MI9+X=nlLq3e)FF4^q*}sRxbp%{VNLTC0J*bfmp!81 zfs+c#UtCBU4=F%o%p?09XjK#gkr*UWFXn(w^JgAgV;3+9NQzGx=+F_vvH*JD;4KN= zW|zEnMn+clj6tN82nZW39XjV!Z= zPn5=19wAoCf!!W4<;am>rO^Ur(i@uadSnm0-G1%89G7_)2!nMXbQqks?@=+R0btc7 znD|+5Qaz$xF~{a1ii=RQVlQ~R0{n>bxZn2W!*+wr2yt82w@Pq0e1Jl_=f$`EN80ur zX=k=t?>DG}38$FQhU{EcAghJQ&rg7Rm-L%5Sw<0p7YLLRqCuyk8lb!19* z2(Sa}ke*%#&~&`)2d>f!KX^9SB`_KM^gXz?``SPLhG& zd_y7(j(9p5sj)veVZftFPv2w4+5w_Uh6DMtWX-*>ijdvx+2=b~z~eM_+rQ@LZ(vAj ziAbqWky2YeNE9cslP&aUk%c2l@UwzMJosMGNpKq+_Zap+kCL`V#vYhsPoD;t7op7N zxHk=&1G25ab+7#2Jv1`zQ4ge}SV)LTvy2z;sL2gdW9;C6eUEDlLi0Qj}YBoo+ZkxY5VRAa-EJI%qBBwKuSP(fTZKS*te+zWdL zWk*&?p4qQA!DsBD21B%;D~Fj*dHOVx>sGyRkVr*!(ZMPXiHM*A1>R@W;9$X>Ou(b? zfepV_3%?u+3#nhRjs>#i3&j%wNL0rMRfE%~tst&I*d5P87@hiNZr}LiKA`ubLv@Km zKG0*p8c6bmgsS1DE+9@f7{qGG{ZD{97(ll>N_y9@E%w5l=MVBtdRd?w4!+LoDEYYsi`~ne*V!)& z<9CX}Sibc9EL*D!V3)Hu>UD_8EmT_WXZJ$i8xH%0 zAtVbR``rTkw;_;Su>w1zSMn!7i46g_A2BkrYJ3c5e2~Y@x_gkn@n8xC-zS*{WR4{G zkrHAgDA-&~|KolpABTe%EX`pMa|GG4d+MhT&MG}4HcnO<>&n{&fFsJDq%D zk_LE@DZ!QC_*jCV5jTFT)x8#+Nj8oBsOrJG_=}}O9!|HoI*&;2xUJ{K)PR#S9%<-ir^o5J=V$ zvMCUmKql`Dkke=MsKbA3Gyg>Y(r9=NLuaMI;K_S+}UZSce%^H&q9y#mTS@s zRMSQskC($vwO?apecBUJ6Q>}2*KDlgGq-_u`YqO08z0*^@raPS&wB&f6C|1p!(%8- z8k=;NQoHZEBA>F?PS?!i?7U8VQrR!jU)OT6rM)2AHs7>|8|;6&y2d3f7R`%vl3`SS zA(VP0Lb1`tgI^t2O$YC)Qn33aZu!TT#qlX-G5c>ZhSsbm9FP0T#3mS@Z#F>B!)ZZ* z3orE6-#XG>EMpGW;x}GkqJvNU33%MLHpqr`LFVnkL|vfNTQuSfA=O(D>i9U@;eoTk zIua-t=8HrJ3RL_;>e^xm35uAFFHMqxhj#1+7nTG|u>B638B(bniMnZ1OT$$gJ{Biv zF5pzzGxD`$8p0iPhN?HQ;)qQRHMRGDu*P8$Nohb+W*=!+Svk{_X`8V3w5f4CK1{1N zk51Cpy#nv%-@CM}fbr89EKL3IjrQV>*zV@5=`@ZTSu&E5R->cb6O|9!EkenH*BKeJ zyrrg@yg!^xs-@GSKFg{1YCB#x;{08QqL!Rq^C;cl@bf%NBTl%p7Ltvu%ICGpf3;pr zTTI^DuC)&s;3AvB@V|U{TT5`YA)j@kB6K9sOT_KeBt^xG*b3v$uPUp?gR_Ex0quEa z+UyO3zoYW94TaV5KQ(xF1sw1D>o_dFx*NAqIWv@NmavD^i{n`hs>MGFdP_~U4vlRL zhqEvb*1Bof2M!O~2P~K!i&t@*$XL&Mazl6gwaU`mr@)EfeyuKJGZd;{1a?wTaG*N< zfoEd_oQSmFl$WxrNY_S~cAFLJ$Y&|hd@J>a!|Bf>Tq7uc)rD!%P$yAm8|*GLaE-51 z*7iJnTj`B2AFs8brl3*f9Ms3ue*1R!Yj0+IylaH&Q4f=j0pm>hk+dC_r5y{}v-|@^ z{rZ?8k^6Ux)sij@WylTT8+mgQO{bM9POfzexZ3-#1PltU4`hhj7!T5SPMXSKrzFPh zF@gdj^W|#6+vGF-x7=*;cdfRywR{NQCYL_nbvI&G`b%CR#;&xw-E|{i#Ee%sBC*+I zZ?B}loQ{&_cR>aT8Vdk{YRxF;TI5HKO(4*{Z099R$F$%QvY8&PfJL-c@XqwLGdbXq zAwPGu@#&k`K1yA&rBv0Flc>vsi1`IKQ??ZXzEeoLm?VwJPRQ@Z)>4`UZP@I+x#|Ye z)j&*)>%4xNT4N)l&cN%0oZMpeaEf1|tv@(F#0=hCbDAg{+T6NT6|XX}TfEi4HCJNV zmlDZq8dJQyITo?U&#Y3)ImOEAn;={x^FEXchSSfknNjPz7F{`ywsG(dEpM^KF4}_j zaOhd{eAi6ym;$qMGp{?G?hEH%(P`&@)F;4Wt(}#%K1n^-A77<**2%+fa8D zIWb?*q@TsCs(EhZd={9vIJv>|GJQApc=eGFSC~{#V2T%_Kj&FGY6PD!6>cr0(Zy(_ z*|F6H3=}AoZq8mw5?ZN^z(|+EL`=V_Li6puf867|kanAo6vd==i&V_V!)q5c3HG0q zx|(HpDH>idF>6w!Jr&n66INkwXbvQ6Ruy(L-_By%v7f)BM7_PXxt1)d+b|bwD@)at z{I?uZONX7RyAW4W%6HaB2nms@x>3C2jdEoE8UyKSVY2eMH()dk@}t?@2+s?)rNT=M z2cziYtOw68Vc{8^zs<~eI{>r0v*xghlhU=F^~{U8G|lO&yMdz;6m)d3F)bepjSi)@ ze*P}{4s)&R-6T~GVucm|J<{rK2HJ2tvtQLIORc?_z#@j$f%$aIkC0{^!x(d`UmgN? zlhu+`Q)WBd`OhA4>?V9zH+f?pXhAm2*sYnLds9R*T@}TbZV|Y-R}|n=CPMWCvys&5 z&np_>*DDxP(XWEeh&W$ORBG#AwK2q8!D+Z*+R^AAjD zt%R>2-?Hkn&jNxtr$rXrI9iJ5rsVEiWmiE9POUZzZU4+=T4|$Kth{oaQM2?$_^Q;vt zqQki}MT`f!!k2SD-*H!brhbR<26H%7UMTB`X^oX6{pr%8MQSZK$u?xh8G71M7zG~v@4j)H8_s7ZFpCoJLJH17 zx1Z~Ro&U~QJ841zVQ-}Rc2rn@s?o#Ug`ZgoZ_#KM9r<0e)zaVqmBNy1(FDVne1!I2 z#rP#9Q^JY0`o`{rrG|XRrS?ybOJkJQ6Y;mKb$fsGEL9d@T9hSls+|68fqlHV0Ydqv zruxbTbBv6DUd#CAScVkDcjDusNi;zv`0nrJdIp0H+vh4&MUY9nngZ3T?2`cbc=iR0&3j6Zd-FzRxjy#L}Fsx!&C21|6 z&lT_=v-f`(RZ=Ia)zg9&Cqm`Eg@=W-YC+at7XoLb*Xt%E_+Gy@i%B}#MR&yU+tT17 zb(HF9=iao%ItC9))1NTgL%eDy+_^a2w^BE| zpid`>T9{J_4S7mfpZz1l9`OC$+%(oeAvYk#>*B8!y z^M5Ie6K?VwS0ZdIbJh)SZ&EF3k*);_l$!tLPBxenh`KQMihN4a5B9!yj*KDIiA52% zhCR8pBL<5s<($qK=C^1nMk&Q=jm3&HX#Vy2`eqgTH)zL`|mcr7uQw1Ox9w6wZaTz$x&VbD46Hi9sRP6Fu zvH;q*W=JePKDji=pxJ6ET#$5`m8-zlzI?h)iCNicsftwxCo%t~ z!a$N*>fTS%7$wHt3=7GNxdy5`ANyA(@*D?8GTj1OXvVwtNWKnC82t1{2c$;fPQM!9 zu*Ta~y>qZK-L|(5lr%HW=X}1ybW@eqy66H1*=wHnXN={LDfk3mZNH^wKxX6fI)f)YtT?UkAtKr zgs-4JRK2^kQgoL%nU%zT@f?QV6eGWrx}(`LN0E^cVOH_@`_OfTQr@x`5-Ln83Xygh zg@xg7qq=8F$s|oM%RfG!P{^G)W&m^g$8TY)mUwl;W9B* z=LIklpCj@d;4<{&N*{c0`Ksc!joG@MrljaNKQ6J&*_89kSD?%c10jbZjvB!yaaip{#p7}}Keq`c|6Ut4}`N^N93$x<6LL;6)L zL?(@m6FwV^faxx;b^K71;|7XKY|Nf% zWwYZ3MrljsjZDGCXg7}UW^VHJ)!qJ)pmvc+zV6DN)7L2Btscs}d&488DxvJi-dko}86jjG<5)$qooo(=`~5le``!QlegE(ExIM1A$Qj@B{eD01&wIU| z@4c3Tk0&O?geWHm-oK2EVyciII-K$)>qVsRliNS@|F)Phv=P&Ex6Toq@J``f%a^rG z-(T71tYD=A=Iv@226Ks7qnh^lz7(jBL>cH6+*9g@WQsk^^0r;6fRL-PvDkYbt*W=T ziv>OGztZWXse0M^e`v2UWSTeWUTO#d!+$L!IVdsJ$>yAa2J=FAcHlFXo`Xwiu2KPt z!6N5zRp0zh%+=K}^f9lHa%SSXe8!tqtQ!spn5%X#8kH(Vmw%@ZAeA4jOIxS(BY6@S zWAsV_H~MAs+;EextL|G=EUzuPIc%8mgBgPnm^Yi5#_C_}A-*&4bkfJU$o1mmu7)gU zjZDF_Z+m;nj0y52x{qvVS6DiKG0Ybqe0_-{cJtf9>z{Q;q_TdiJh+KhLbrxb%*`bUgT=*V-!JvV2rdQkd}Nn-gl za|VdbTNKU&dYec=m7)~BH*6Opc+!hRGZNt@=l4o%JxV=Sx9HE1z7G@s9hzM7e#q{> z4smG>mg??x1=)B*1x|LK^_axOIc)JoAG7w%Qo_>Vi~6{qvE`GQJto~$ZKqFp)A(zg zv>G1ZyH{U8PVPB z6?@UgtL|-@Rms-)E8(r5Phpo{C>Bn3jc}RkZcF5K{(&WHKHm!>(FipJfsoM4P0yDY>dJ7Hi)n$#HWAbWf#YBsFKXS;3k11SNg1 zZ`}!NvSyoAk~UXvq{(H3?%H0misYtfE6zm&DJ{X%JUl_rRdWv2x=>#2Z+(?OO{AO+Q z*SR=Ei)^SVsK#04X1e+29fhVb&VAKGfz=Wu<>W?+3**5haUpS(QYXubZP}=)y~UjT zhnN>)+{}KydtGR5n0d~CCCGvCTK&(g;j=NG*Q)<9xMiZ))KzCR=>E24cURqe_qOtx zCsEU=DdNY^YwP!TOhTy2>pmEi$<4}+ZB7=o-A^$T*ybZ?J#85KAzJBki8Wz65AR|f9^u`g%*MfXi%K_yh z^k%HAPbsHasHqL`#eoFujITmHc*OqehFj-17pL{|(re!TR1H1i@7%{_H;oUKefQ7M zvuCCiqPGH+mA>_ z(PG_Ji;gWcES8<6OV!fMI^)k|=zd-q^61QXE9oBsjX_-QK_)0;QL*x~yhJ8bZ)trt znr}EyWiWUxBOIrb#S&HWERIUbJ&Ht^smrRO!c3o-(%Z#~iBCuQW5{}<^fT%dLn+bP zfBv3o8Pco1;108d=U~Chd#uPxH?e(MNd{+NLOtVp?Ee7 zEd?HE2r25A!PUy$IpsQ15~2{ZAm~0gT)Na-azS6M?4^L!$ddoXJ3mg{8tKqEc!vlv zd_w7^c}J#e>vT#c1Ve-2$E0OH>OCIUlv|!uYtge78J*+d4(^N1As%?86A)3~i}kov z#qT7o?>+gptxP9WVLhAIfMzAoLu%HO#POZ4^C8{dppH#tIT`Ln{d`MjeYb8Jep_AQ zn^&z~zufzY?R|4iw?h1ARwm-gHj6Ho*Eg&$htv8BGj|@Jep@u66`0 zO?k4W#%&U6SB8^ZPt|V4t`rGd$4TnVh|L!0%$1}J`Ikt>7z)+yTIzz>u+(*994$y; z*P%}diWx3?Gg~d0@^ka_Y+V+sdKwE_yaA_91qZp_Q)+ZN0}6|0nAiKp04M7QmxNzI zvD;>$Y;LV?3d;O$(26($j!iI30Oe(82Q^OiDikuK7z?U~k_CNcVA51DRE%q2*pZ=C z90hDY#)qbcIM}0 zohWzqm%mmhPLbgO)%=O(m zH|Ay6Qq^Jq7WCR!2S&ZF<<( zhCeO0YGb_Gt4JOAkl>8L1269}#jsT5u??*E-}_GRxC@oaj1*b9Ca$27!aah~T0XP6 zl_PeEgF(XrEqnHHcfYeY+!$wiS|bDzWu*E%OJUv#Qd!+|AA^wqk$~a)5-D1?8?ky6 z8oQU&z^aXWseDqaCA#t~ntS??t7?eEa6ITFC>&F2Q9RS6FxhHP%c55pOqg*sjTRjL zR=V}5`_7jy>1-GOSW;NFZU0kR;v^>dvJPqt@?P#$)733&{tITT)j`MFF)Xy-48t_Z z5Y&feVd4VqvBFU8O@yR5>Iwwyjz1_dPeOlFmU?P56u;6lvY*i?3!fk^_7YiEawQJv z2?S(R`Ix7BZ!xje_VC>^%S~vmT>MV2F)KD3ATk#eMF_!eP3yH~U+eJm``jR3gA8W$ z`dEFj$mToo2$fcqLX_5$#VG}hN#d6+ zUu}8cQTMXBKV-fZr%{s?CFpOmk6<|S7h{k6(P9hM6+=>UPnP0(XVDo)x>|$Qpnwv| zH>?ZcnSo%j(F3j$or4O3S&uyOAV9BKxw8e^VS|;J)lC9hK*V6t9V!<=lIS#}l<;_| zd1UqNm6s}Gt${CYNd}(@s16)W%HZHTB_w*uieZnKa|j3VP;jwQ_rQm8E$~?es8i~5 z{> zdgS+FjP*cNcDkhBK@mpJNzwigwCp@xwLU@VMlOkf+7eYvqioC0 zKo!RXx-y}lg&L6N#-z4HP>)5kCe_gWPn4*sV)ds`H3RB(@biN{kveowp`K^;vA75# z;hRL6)_VdIjhVH}41|O1xx-ltmedr+C+ z-0@Jm*keJXW-6u-M8%Vxl($zCXs_9~#IXe&PHtA5DzSE!uej*$g8pnw6&)U)RaEw$ zWx+#bbyvA-kWq}g>Be|mYuB%ohfk*BB?r>`SDG6xcHpmtWvY6An+eIdG?aO=X{1^H zV!I~Lvpl;+5BsY0nvw&hfytG&aHxxLMcU@?COjySN^kGcNU|E9I4l`(@i23ASuvK7 zz1z4)nSyd!ZM4eHN4&CYGL>h|J!m-gZ0q%dST?u2UXUsp6Y4v8uj}Dmeyfl9Zqr2} zBl#Uv{#%l3-(Wd3*&M}geDt^PZ{1|Q_48bI2k}JTBHy&`XcY}z=S-_`)MRIZ^Tw}l zThAU=IG2~ARw$_`Uk<6h1urX4(O1iN9pQ8o|0@WZxXH zQj339HehfD>W^)a?XUBHG@(CkKJDxFlOG-pTY0Z2FNsub`S6?c{^REAg0oZKhv{Je zS*7ac=1O&n=1)QBeFZu~ukhm6s~tpCghN-H+%Scz;Hc#$NNDgF_0%jElH3hL4LoB} zf=cAa^JCE1bco>sr)H)$HPp{$K_?3GO!{-$&qbR`%Nhf;t|f9cTa~h>k8HBVYK~T+ zVzlmhk2kTddwEa-S2g=U>kb%zAY8b7NCioLW8>A;1n;JM)YK5$oT3^`^b{uzhL=oP zwsA`N*w=lx=;ke3Xn+)|lG2)kd^9OVZe%T^HMq`wYBSVj^6M`9*3^}eM-zHRF@K5$ zU=0?7xL0P@5~DisNmfk>u#q@0*=Vno<-fZm8FR6^(Xr~{T^C5{j!T)1b%iCbx!W(s zFB*umvaHJ&jh*2(V4Bf;XXI(7fOEBWGi%H&#JC~%+VQY;Z*?~DUfBAw_)J8Rw){-# zug7oON~~_DlYZ8B43A;G1&ed%)FOu@ghfS5>=;fR)+-tHy6j`9zc%w}q@Rf`v)SRq z6v=6|L1VUj&a&%b~oxOhYk{>Hb}r@E1;j61ErPShW+&@vE87h9T*1FuRt=nvo=4< zfBVWzw2m86*!u3;Q#_St`OqeWG5@RzL#)T?Y&z*yNTgZ1wqoZ-UqYsS(a17-4)-x^abLO>R3t5C7y`m=EHJ%< z9X}H(l;xkFKK=TmcSp+w!_tJ)vfhGO_=R)2Z+z$m^KrF#g{h^DBPqPvkzKY%^;jzD zPtQZI*FcHw)C``$%4{%77>Hs!MmtK&q`9B%LaFO?dv>j+ET%WXDq}gXUN$34D=b`H z`@9DX7;}d z4W*$saB9;iIr@wXJiLwpzm(OWJ)V5+`lO zmf)-oe+jxWL|R|Hi|KsD_meL;I7@I^FfZLysi@8lU#$^Z7q*zml5(i`Viu`pod#;M z#EffI+PR#|6TK&K*_&}!iByMqqK>M!)AYPqnld~#vsv&+L8fMy_8Q7waEHW|xZS-G zop*fSqJBx=70ALv*Ns3d<1I0vg567UTfq}s`yxw?HBr@Ue)aH}QUFN79X1`+HB;!a z5;ZP&XdC8xFmYHJ&EW-5`K-sRd*Eg_v9Rx?WQ>&K*~EAsdF%3nn9kF?ekuQi{>}h? zle!w{cQS!~IMknZnkQ>8yx?C2`dkMPjbE=s{l5vIX6<7xfG(|+y&s?}%r0yw2l|ds z5Blp1cNL%qTDzjBSI646!zQNJ*v6gcuI3)ZJ>-;NwXSI~1GQtVsmgmiy5U{3o%pWt z+`;5NQLS$J1^W8S<#wg=98x7}MRh`h@33?D^euJ=xfy)1#&kFIKw2HPt6y%VrZ)0= zv*{>Zw8cE%j-BS!DIZ-3dthVb2d|<5B?60?-h&w{7u9OY93OO3e4wU|!RziiB*xx> zeh>az2mWAyrn=R}Bow}vB43&hmda1+MsYPyT?5&AUi@j&dalNU0d?T`$|6Am;ID^a zRzn~t(QV5YG?Dm%^MYe5{uwRP=G@INum%axysc+rrwDB%cVMDzR3%mkkui6acE; z&3v%v-OsTv3ffja$`w=v${X?Gv)@bh{^%dkR63ZTrkv22;V(2m^srECaKY^|Em5-L zO`C0%p{BkY)&DpmbmYgW9)=&I@32^P&XSS!M0W2R%1?Z@ZqeTDn_I(RT)3dNr)6l! zO0R$aG8*1X{etxymyqanOU1Xd#fCXnN|Dy32a8HeEvv-`p?a^T1|Z7(mxFq2Myje} zdIHcrD0}-h00C?|Yq;y5Ojq==hV*A;J{x$sSx#4nOR*JRhq)&jbC0m%p0F9z* zf`}j~r!1=c-F(z(LCVLXnvQ!3$!C2^=mhuRE;+bLDZEY6^lCIu(eXEFP zg<=-ZpH(^0`x!$`-6XiikZck)nKL+3*zo$~*~$7ziFlVR-O$Yfohhzn?uqwzQQg#i zWO2wd^IVqHR<>pLC6cSi*~x}RQ;aWB0TVsSTe;W{nwm{e`|JEFw@)p%?yDq4CWeyXYzip~-#G+J>pzkw^U7+5^?o#9}F zhWoi(XO!(;NpAKhdDsbS&5l3t9{%VmIry_jR>WV}@|&$i_uyIramc&RWNWZow!|gt z)=YbTzS}U#zfZ!*{n`aXl9Oe0McbQyjF>u88h8Eb-nXJ;)s2hW^EKDHY}7G=Dl0*+ zzKb7<8{9#5n$OuDdAKT<3p)CcN=5!kH3$&woquoi)Zi?fIFhK7e}9lBbq+Lpu^vQhM{24Zh%cef)^CJz_@{P z&^y_FWdJ%;Mc}(TD2afyeY9Ho)JW+jNm^eCnH#B>gSNm|cKJ!#2E8UB5;t2xKgq%e zmqxH&90DZjyc1u1e!ej)?viMRCV3MeS){pusY(~8bGfH9zc5Vfpi(Rsv|oPs`hA$A z_fQWfv!qL0Ra=q`PIIcnR`;(V=3*_l08_V5Q&IMM+j+n0lHB@w;>D18pV_xqpAYe; zt>54Nd-K_FyW7lyg&u>Nqw=6@YvFQ6oO0HD`e)ukSBXo3 z9|r_8AM-+I%9vzi;ngBcuLZv1B)_Yghu=cw2vzF*DuJ~Gd#PpUe%#9BO95o`88Z&2 zR&#w(seYg7T@apU)GevOinXrx?!brAHwWcE`?9fVcB_7dpEGjQPCG_lw^&fp;QYl$ z(CXERg$2C~ITcX|(O=s@jz)Z`^OikyHP%2)hIMUiEo!M1TngnFQL+NFdSjFibt0n~ z%yKg1dI$8>&w*B`d%~=zN2_W+%p5xW!ImSf8Il<}pB;xjwo{-jrH2a$aB|e1i?pSP z3?evm7JdRd5@9(8ogL7|tha_SKYnjuEwr#6T>k;fttn+DY8E{WZ?T_(%~3T{Ral2b zLS$9e+Lq^hb5^h?D$8j!M5>oVv2N4h`~J3ci;=8gE4(nDZS}Xu<{e&FF$K_J5qqqJ zl(FI4EFt1^$4k)l6IRNTwm8Tr)-x$daiuf-=#SC5?tO$zB z%3|Cz_yRT`){FF7KDQ1%H%OLnyNfs3+8h93?U3C&Lhk*a-#zOaKJBwz@TTf3SJ_yR zr${pJwEXf|`ul^ssu?P&jbE0{fWTv`hSoiD&KsycwWxLbU8wRi6)Dms7z%g2mQ*0o)KYw5 z@SU4$PSBnnzV77cYFM^-9(80hsHmNhQTgLO6LuM1{5U>25XTP?eA|;!0 zHcwI971ujgerm{XtWM<_F3IXv4lP{1+mAfzM^7;`mpfZV2zGntV9gJjr`~Qbt>KG3 z#<1o3G3`ZY!EAMq(U6_AlO;~v*5l{Ws_F8E`VigTwK-@msY{Y$IofZcG^T(H^ zGovL_?*<{0z{+z#uwOdgxHkP5NvAneylQTu8}*w&M8n6(tiRq`*bc{JF5jX5=H}#B8=F*xTBzE@kySotAoe4c$ zEh6`>drf`zzByJTm#IfuPm#Omx`*$1wo17HdrsTWA&~*y6pBJ8}au4X={rCCX)#&*WVe`K9Nk9r#f zTQlzl3}-RKY0QOcD1l2f5@jU9sf=InJtpSq5Eyq+;NCgCzphy+2u<~!t3JNZWs>zN zLGU!OP-Upg=VSWl9F*$?beS;Hg3xoD1`-mnA`M5l#SZdS+%F$1`}Xi4xxNa`TEgMH z28>!p-p>6nu3kXHvS?#mPU;zw%R&PxxL7d7-5N!!E}&T&hY{g0s+}ySf`)$dfFu4$ z%S%>5Avpx<>_rKZJ0K7bB;T(9VYvp=wK+h-^s^;~35LyEZh+dzPepQ#v#4Ddgu@`t ziGI;~aOv`8LtAE2*ucnmPNtqx2_%>4&Kc1{HHV`_Ephy7f`yZ1$DII^p*NS#@~-uD zXn4d)NvvxEL;Otev5M~%U$bZQYS~&O1by^;oS^=DPLnP|w&3B$_~2XTqd#mm<8t$i zz65W1yZvzSQZUx5yAjJ!?Y_LU+H3#mW;d!7pg(k(+O!MdS84~BBEj%=dKf}7ZX_03N z)zt#0Yd=N>pV`{z$#vAZTbKlNX9lrtOijG5_QHiH!{|h|&GpAUMY&6Y>(1r_;+xJm zp^{ZRN1|jP+mK;VoO8P6H8pwt)pEJv*tobi#W4$MpD60lpM<*^cLraEy|^LkWZfYy zT0F2ZmOKCKS?QvRN=@{&sG(8{)4IJB%q%RCHtVmWx|AYoRi4IG)OH)j37*&#%$^^~ zl{6AQ#k#rLENt`j36mAm*w5NCd#D0$?PET6P>UyoHmUYSRL5z&kQGDoywH%n#kGdX z($VIWgnK>?czy8JhCfSs*1qswh9TccwHXQKO7gEV4Vc`69H315(7ey7X{$j)LZfKTWvpysRuKgu?E5ko!xP7iO6L5Dlp$S?IeDDXLjUT}>6hx;tCH%G7}l zIv+QDasA&sQ7JJ?<#}fyd~yf=Pcomb<|zRe+W~f}ejaa6oLrr^ZX#i>bF~$vReT(Y zZ5A2PeS&t3Hc>(Y!>`wxv}*r$C5cJCUb`FAwc9i$kH)MP2$O@WB)Th;%UMx!>s?`N zZF%>5(^ng2RO(X?C#&NrJ{z|BEE{GT-eNSH6*$K@mOK>L_0~OO{A-0ql1U2B8#Yi8 zd7V3#ULGZfvR^il*6TfQOYV8GuK1i6)eP74ISTrDCe*?C^5j(U9}qFNW%Y%(z6OGz zQq~dPGiQ1yy^x3rgn@O?^^yppd~j80IAjA;dlNFoK}ze4bSdPd*cJSeAl2WF4i3Yh zXAJ`N9e6WP@i>O$&B!xM!&U0#&oKk{EfCz76c?$#I!&bBkF5!(8m;uWmhiSE%@h+f z5`EGE&d=%Z1+A2z);$CC7T~)bs$h09Q5DY*fWP>pAslwJ+7x zWQj3D!#CY?Wue!8F=UHz=or6ni?|n|C#$KebuPNdSVw$ylER*#NT|=(*nOnuO;99J z-fLqtP}r;Fqy7C5iG;f!!|hZ9v|Tx73r{YL;LfnsqK4af)0vI_W#Z!gyo5sGea@4t z&|z4CJMK|Qdop8WK;+h)#ZHww#P+>KH|a6b#R{Bf6+fDPMe?at@Zwp)14JqpH*gGu zrhu1Kt_|f3SOujp?g|kHdM2ZR-sg{mJ&?BQhjN8I_#-&YH4Tuhh19@kjlbeR)p0Ke z^}!+bf$(_zqxS5Gu&`Qm@gPwx#m?UT&&X2ORv1clj(|tm^x3^$1Gls>1X>jr_N*vc znK%RdwTR-Z1cLxBs1+E#(&98(n|!IApDrvJBO-N=SJyC+@TTwdDI!7l80vL_s8Xij zVZ~{~TXanU3U?+qPR`AJS<9;C1C2*{t}6x!uD8q0afSA;GI;_{W;}ylv4bYV{h$^N$$C?;WN<%T z(gLcphh_&v4)h6zgex6{olyII*(Ew2zGopRUjp6-*sMecJ&DPm344*t10`4J3m26M zs8dRiHyk;gb20Q#w5TJ1em0CK2@b+&}!SA=@WGi`W@xXaU^0&7cBjNl1w zXl@Pyv7p^tmYw(6O}JnPB51NUPx$}`Ji8-E&gnI!H}s4nL_9xR^1!7(p-OnCpWg9~ z{+G0Zm2(glh1~OEU~A63%iSrLq0n7p6{i%^ea9nNu_3j+=W?)kgP|bb*N9VKNx}XxDO*4Z@u;z=?y#=}^pmdJKGkO4L5(;vR0ND$u zsAw#S+)?xpTdD$eEWy3FArU9$I!mXu3#s8CUCZe+5+npMYa^iyX*tPp=gbRNfT~3D z2tNXLK240M^O4PM70uUgAh1vmVU6z`cmyxSeQ+h z-DGFl+#|M)<1-cgF!*KrC@>dPpld=l@YoBFO-L}Ghl{{WMsrQetkmK z_@u1iAU|^Q)a*M5WLH5aH3A{I(o})uY7i7jngXEwl-6ZMrZ0Tn7qy{4hp~*I1sM_O zQiEXI6hqqFW{@F6O3}J&EW?Oo+3(V412~Mc;}HKt{b5NPD*;sym@kJsz&sd1h?^MO z7VTit>kfQ7*Hvmi!V^m>Pdmt4dK{HOk&18_065n@ZM^^VLIQ5+CtwDb(b(n{nsNYN zotRzcp;r|fUwt2U2z37yq3#OiMkM3|>ySDca-l&8vpCigGg7@dRXq$c^hPI(?N|CB2%B6F zD(r&JT~PuaSBTpL+zi_tP*=`@=&Ba_3Xx2e?81WnbOf3w0`0t~^dRG=CWqTbFn~&G zSrU^PhY(>!V^GDDLn>LQ`=N`U2MJg${0h|RgOTbI92prIPQkStSEv#q5ir7p!ddXl z>e^NiW31WE!Z88TCej0Lk`%ilLWs{|B7#GGgDNy+^=<>xUeX>Zji6hi*ZDdqA}3O6` zB2giu-oA#;Rq^gpNW~Rtifw<`T<>V0N;x<>js#w8@dHjI5A6SeY*LcT07XCqPYxi_ zvI3U_1#AF&3K5-59S4?a(gHvTxUvSSv0J)XJG7ZpNuVnsNX`hNK_EFPIZ-uYCbh1k z8wUp`0zHc$EVXvdmSgk2wB!Xj48<6JvR3$mCTK`8pdr1L3uDTH1^;^)WH&PHIIdR+W&DJ8EgVdY)MF?yoVY>DaxE6z=q!< z226A^(dJC&K-X{N5(x~LLxiD+1vE#3{VR@3&kw^7V8bq)v zq#a&*v3;6-vrsoQYQY5ay<`iv7wvw}m~2Z=pe_4zYwJ)Z7L1$IWak+wvFThg{sY9z ze(*-(?Cf01(PaKhw-L5dS!(-qUkxV{4M9&p#$3UWhnIl40td7nB;tlyBB*d6RK2Ez z{0W3H7;OwA!vY{ov`VwwJ}hH76k0<2C<~aaG@fX%nE%YgPC;1WcS}V=fSvt!_9>|I z&+TV)p4(0l$WFQg!Qu`;67*(_pfL;Aol`O22=+H-5tcd=oEtGi&h4kDCZfF%`fmWI z03A30`_-Ecjf#r$sLGOdg?Yqn;2@|Efdka33Z5x^_5NEbu?_bVvg)!9@J4g<@a!Id z!Rm{4gd0;yVhboGJ39f~1i<<(P=|wpiU(vbY9#@XqbxDx)->J--6!v2`Sgmd%dK{< zvn9~+6tjkwMc^hWel(~KSc#jhL*QU#wV+xba)@fM%S$M+PpR?SX?oRf6g)l!yvf%`oCek>M9DGYWMb7T$)$kQuo*H}Rx-Kj7P_Yi=q6=!G!t7DY$K4h+bTce?*lY+PZC`DVq^Idb%fXu0@Pc z1Ix%{V|7kVUq9M|^!zH7KPVt@pFLrJF#0|4ktCrJS(AgX zztavJ6G2320U|oY483M?1LrWT;M5#q7CtYz_9N8K53x+h!Vqwc_=w9hOo$|czL10h~u=IKhQ^$TuE_x6Nqt@FfCx zvzAfFmKiRGEsCz@0KKQ>VN2Lcum2ZH@1XuCT4HvI)XN@Q0NCxJPlQ1tW=!x5m;>mp#71qoeOq$op-HH&WS zd}y2uaPES-{^(>Y_T^${r;=Fw>b6B%LFCFgXj88T;{#ZC*%aj%iZdb!PGWys1h-;{46o5%4877dv97Z0J7vLh544|KMlMD0Y+}(rb z~WKCvBU7eHZRRHwU2>D!W4JUR7!byBlB3nZJ5DW*tn?2!t=!i=qUq z=pcFmy@=8}#DvJ1v8<3izVOEf^2=f?Ka+d zV`G?rVKD<+iI&wyGD!&iPNSOVu?IrYL>Wv0JS>ced&N_^?ft2GN`fIQbO8iIhJa7U zW>gD>DFtlaoq&eA&MC;8`y8xpW~Ox%$x`7ifqccK%N~DnNV|;sRICWl(2Qdof=mS& zo#k4}6~9T15i)Kt&6iy#uMuet9_?J?@;x=s<}3qI52e;C65zAKx@@~Lbx;cr7P0_n zQ3Npw4SvV_60h#S%GFW#6&-9^B$ptVWMDM{Z*rCiA!$LQLmp=QY9v!fOF5GN21$BI`UX1jvX%1v4w7%etTmOL(Q<(W(;c26E$< zhF}|~L)*_y*Q1oj&L7_a!$^}B0P1BoM}+wR?Rdoq-mF49)Y}EYoSt_a88C>5dz#Q@ zK}`*GzJS}Gp46h4{1^a!)6p$Xb`O!_%4P#KokBR@M--Oca}s0w4U$x9Nj|{3)PeL{ za^nJ647Ags2o&&Yugbz<_f1U<#%Jr2ug38x7W*1%4QTL{W7EK|08}psR)cNE2pSr) z3ZW6JS*-g#*joMkR)GjTf@n2p)uPcBh#%^?Jl#1pW#G_o(jJICnrX`Mw~t_i?t?$7 z&^Lv>HC5dsMI2AGY^|Kb9_^||G)RQx#Kmr7Vp2t*r0oH(zB=n#%>>o&w5fyiynz5e zv$g*Mc@@3X7CO1fTgA0Fi93tJ%~F^w0rA41A7bi&zm*ste;o3-bO@6LWa*@qsofXk zF#R6@z^qQxRaRD>RE`B>lGZr3cYF0yDne>z1T<9*P7Kl##s|mGMfjHok#B5_?-#(! z22jHB7VYloY9{|9!?#im7!d&aUWk0T0YtU^<@}lM>&~}2{~g-?cSu^qiDo6h(@J-F z%oKGCmcvw@T<)ceGkP9U+fO@Q0P)*bD3EdFwb_`!ii5>k+?$iEzP!yh!${xT@5_`6 z3p|_~{M(FSdXDTwa*!VW z?Qsfukr+l0{cdbpyZs~o%XAJ{o!CL;l* z2ms@a6UZY$D@CTpC2Msj?K>$2!AUu&t;o1P?nNLWLd5a-!}eKt{x>qg(Bng9#j>;0 z5@E5K1Kt!ltuXQjrJWeEk8D4fjPG`Vl!;8ulV7d~oQ+~nz`at7h_sk)+ZkVLls^Il zPzXE|B}DxE5P=?X{(yPfaDO{XDi*S}@r({L8F)8dXA;t~0Dg>F@&xkx0qpjof0|@$dGL?sGzeT>n|WP_{B+w{2;L)89FIQ!@~KV zC6l$m^$P*wz8gfRCJ^!2Lq@9(`1(6MEDUBR@n;4~__Q(PcOz9iBrAs#0(hUH#u<=k zpdL3hRaMn2cVMhQta(ro% zhabNQM776%ly3h--j#XZ+)G_lUB&K=D5K>)E_XN{3zuDxox>Sp>d4G zM?c%98NsxoZEGY=;(+bqWy24_tAnuAIS8<7(ii__0si!5N=<)KCP2H{wWz;+sM5_e zkl8`35!osi?-^1Jjuo4OsEtPx_xuW%$={lK;D`mF~;?fBt_M zPJ+0Dldvwjfxrks5k&Wm#sWLX>wN-D8;S?PFtW2FS}ua9)2;8{SwhBR;9AB*+{pb$ z_6?0kcgRn(tQYhNI*1WyVfaAVS^Uq?68xS=Yv&H{q=#(UX=sR{oeqZ*Q<2^8XMv_2 zol)p!m6wPEiut~?zgH;@T)09A&{JZEAUm+*hxlg6Bg`9-jCu2hlDr?{2*WV~BvKd( zNFy+6IRHGIi@L?u?`v)1#-RFwCQs!t!n)Qp`HeypxQgk=7yvbuJn90LqKr%+v&aS6 zVCMI=Ra318HoHoyG)xF_*Ex&bmw&U~R;+goV4SCW_&4W5u=DJ`7US)mR4fI}M@lsT^Yx!Fj3PU8jete1 zy{jS=re^zt%nMDBGJ$;_O&&w!TJW#9%3KMoaGC6&Zev<@WBWMb84;7ey}cbJGln5G zLWXQY^6u;mB&nFt!%=u^4>c?q2u5L=6nfrs7G@Z^WKghoQF$22Mxj<#59uR%bjpC_ z2}PnX$*ybzCW!J}5Y-Sl&?r-QfwTiL^tC}~N1wR>qZ-871q5Ocm@R5*|C%m>nBJBL zSXN{N!Ovqs9jM@?ZXd4kdhc%0CO{(E*u#R5mGMaJ0}m4oL@*5S1MT@I5fU zhM|BH2)`17*$^Fd`VeEZK}Mq#cF2p%?%S{D+X%Z)51gwJz>F#$v=@Vn6C~FRqTvI> zSqo68&xQ$ha0#nQVeX*`KpJ67MRx$Ig$e(BZGsYd&P!-wGkp1S=kIl*D+iV*s@36G zfzay(2F)G}zn#EFke@k%Ji^(WgM>>h+D*YUgP-^+JRG&eYBhzC4V9@sdH;xj+aMjd z;N&D);{aMJXsa@Z5=7BY3phA&fWM={7^EGatwJs&0FpcS)8tujFoR$h+$jS0zP#3k zWRvCSEP}g3MYb;+C5nduvS>hdlhJQsr%C2&+PVELePPQ*X|Sb=r7bf;)kq@myi{&d8s{V_Nlr^bMzgM%DXKq;P11-!aeH2K|xtK zx9t;&{cQ6ba>M*O|2wn`|rPzvhdjWsuKZY@4#Ae%F8CI0eG}Xc{er9JzNPYK-r zCjIx5CymDEKbv2&gnU`!=g+Ba+6)W~kSTh4W9-kTj~|;u6}I@$@2w{K<;QOod>Cnx z%Z7=^7FMr{z;!|LbDk)$jLW{NJw=eQ>|=?_W>Xd>e=FgQ(1QyczTh z-_YZ~r-`8-l^_1GWB<`l!uxk7ufg{p456?DNZx-9A43oL&ksNH0sZUt*Ei1Jqwe5m zvG0ExW26oKH6#o@M0o#yeE9YM#bNzJUW8yk;j-IsdwO~{4GauCpx(K8q(@B73ybc? zNSk~*V0>x`%=H;q%8U5i+m8~j?l=_ihQ?-`zA>FJ4AJxw7A- z;SnENu4BZ`NJ&Y_zx-&d3X_l?CI0*VRHYZ*(<3ingYRrRfsP-2p^u2p$@IH2rwMlX z3yeXc-o(QH_$+wIG$<6Nfr)GOEj?hf5Pn#2I4YE?vvoQG=!1` z)Xwz*B$lp~Q%}D#Mtsb~M4_ojlDFVaNzPEH!Cq z-~NGtEXH+6WtYf(CO@70m*s;$a%rNZuvLeivuJj2hg1`8x(u7~sY>_E7| zh)-b^IJ#dc*hM-HDGezGd|ujBt~1fH8sHb7cIle?PYK1e_yl zh)ja1{(V8DSsd)0{{0xhko~878BM~VTUeM^UH_~qaF|k6CO*zfeBh)!gNVCA%jYgT z`>~_g6Q4e+!3lq5$wZyW+sx5<>9f(`tSwNj+Sk4?*K!$reOu7LeVRMFK)YXGzq450 zPgM2^_m?3pA4As07Cu#7msqM8rLGr`O&Nlm>8LOi=NW?<Hb5h+@PcBlTydi#jo*HJh*b3TSW&BrWd!SEm#mO zJM4(&OT0;=KYGQJsR!`m9qTC(8F9P5cNh!Bw8q^XpY%6GN|ON1H}`C0`(0z$E$xHwpRbq3?`bNTn%)GzF?01YbKGB8 z=Zf?QJmu60dM!bISfIK4_J5PDgH5eIm<6qC$fv!7wAX0~2?NMc6RWq@rIsc->f282 zPW><*JJWa)J9Wi0K5o-{K|6z3c;I0B)o!Ye%nagWTwL~7mGTh$IaW3mSBaIU_JT}@ zeV6_rZFCk{yekt3Q14EDF?pm{rYbfzd4;CE!$iI^Hc9#3IipAF#iZ&wl@xnyCeJYZ~&2xp3Z7TXv+D- zZ1jaAyq~piN|hWHUhSY_S+y@u@0&k9b|rcF96mL)FT{MDh2{s%u#p@!-1nEfIDMJ? zVcIlQ7o?%kl@9cVFM}5ugDxC91lvtkE&=8Vm}Tj|zxfi}*(8{n)gxhuN!qBCSp34X zbavuRcudN>#dCK@9loX=#bC1jl9#!xPW!oIK!f^$&jG%LlU8oNIfE7Jx)K|04&IgS z)k@jxpD8&y*)M(NGTD`@-}FFQocpF}R+j7@*>WXG?MgIUT^>~J=6kB=S@Xz6!7V|aDg#j1Z8iy1@;wtZG+nf##} z>gf_u9ibJYf6&+Q4S(rV*6I`ZOaPu}d*}M36f=t%%w`VxVT>qejwR(u^kryeB)fzV zI@d0F7m$1fSw+$+?ftpA&nW%rURFLaX~-gAHXLT@-uub6glZ~r#QzM@{L`$i(blZm z9$eNn8u^ad@PI{OB5)- zl&nQjbjU>D{5NywFqzZbyqbA!=EYZ9+I;%4s!drv4@Ij}Q;D(|OHS@{*Hx5R?sC%~ zrL}5Fk+N*a{uH(>-}t#}n1!Oqt^V>}H&0D`cCk;8NTL>YFoE)^iYA_`I`?{jD|2W1 z9)6nmxKF`D6= z?2+{hjp|^BrPS4ez>l@ps97SEtKr7Q|(O}F)X zx(kzcMw9Q!zkC@IhkG+nk4sQl7Y%@@G5&43)KL7#pHLTef~J78XU;S%9=!{$7^~a2 zT|qF@3tG%x#l^)UHX#{r;k_o~Pb>@SsSPzJ&dxY+}p1ZGRl@z;k>{FOo_F?2(2ZXz<&7N0#SEx(Pidfoj1@?PI)vIgkXG&GQ zzn*cpPM5YmhH$P6j~%$}QOfnTUXjMbxszUR87CY*X2;0Kw-Qjjxm7`U?SuDx^~ZTt zxiSL@RVC4qR{>FX`*N7TBXK4EnG`FT(|JoinMk8Yr5S~BS#aw3Ty2o^E&RK~LZ(sq zNtU79A?pAu!f`3e(!1*Ke-K8!uI{jQYs)w;#e>LDrapEnslU7FYV(ynPQ&`OWPTQ9yub&}?EBAwtqO&TZVOh@`J?Ks#79Tu{$4Gi`V3OCyWy z;`zSagfAGN;1J3!&|=yf!jxGv0!m#(36e%JB}gTs=Fp2BTYy&~PXLg-wN< zFfX)O^8lN;`I^g9OlEh6^E+F%fgyDt>+cGqKj=lzvrn0h(w#2Nh>L%5i{ZmVV)si8;xQ)>OW&0 zB4VOxOUJC5Xdk(EeToqJB&v~bGxxH;g-+2kO6h?=?aX`x-7mYuK0CnJLU54Q-e)t}T$sDky(tNV=#=s`E*b^_;gZ=_6N*Sp zi4<@g=UTP!oKT1U#vJ(v(ZX%4o%5du7ss5=@z*|}?v?Fb@F7)Ny2#EIT&!y(tkAlY zD@Wt%nOwU?1u@Q_2KqAe2S)Wy3vs@m=)DmB>UAUUZ1#M>pZTR&sfdx&Gh(yUzbyXD;`4ZqtCYQa)9%XQ%bZSg}+Ku=|;RxTp{e9!)g%2OPMa*7PkIq|-4vtW@ zk%emx|3+%S92({Rz>@yS`#4}Pf37`XDzv^9Vlx~O9IPxZgW|!)o2SlI5|1da_laAE zMz#M4idmJb_>fWYaNq`In8vFAH@{-}S7G(F(MOBUTu)Hwnax!N)T%~m*^1I!O@t=a zh*%0Kq#RwB?vwsvm8B7s3*?;cCIRvnQ!KVdgNdFK=b|xZ-z%LVHM8_AGPY7mSKYd` z<-WXjO#N+xZT7aRBt^>r+&c6(ciG39eaxTq*{jd^ceaJdt10~#HHJUdyFZu6!2>kDVw;gQ=}cRLc)vW zhn$uRsBC(tM^ZDsO^v6QDoa>2P~zm!6I^)&uVLE)izsc$mG-y*HYTZcAMuEdFABW= zCs0;tng(%?Jv_c!a9+B=p>SEVui^XY`*HHiFF>RcD;Rqb^2O#@ip*2$%U61;jLqV- zo*l5znCN!>fsc2!ot3ZL{C1I2^_7m@Nl^}RU0ivvCNAPhceFEb!0sMdA-rLXz9WtG z^0HW)IKS7w`FW;qQ-fT&gutbs{-qy0J9_D7ne~}!YwaHtUWt#6)%2u(`tmklANUj| znKzSg6j%p*S@gCL`3Q;KX{d8qgoFl{^VzoJ0ru&h>P#9)gRYxud)eqDuDp0kswdy&XkFYp;+{1(q}oX&sKpgKm#)eu zCz?qPnzQv+sx!B6`I4+qP*Pc`@zN$`?>GgQ6)Vn$Z=q)l2o{uSqSdR>c}quU zDU(&_=-qt!Ofy3=PG|O?x&L4XBKCBINVMFTmuKnCb@5tW|Kr{sOsDzNmg-w0u!Rg= zr9s_S@$qHx3LKdN_Oh)zn|{|8`7vZUOiyksE-iCnB1L)n zLpIqiU$wwKqRAAX!8|RHU4K}D(e}Goc27)Q{J?r7k#u@cYsYyBTebltM(*tw%a)z8 z&N|rPjJd+t;!D{%pWkh7>oK^x_M0iMPg`#kuJ>M!tUFXwbtG@8V95_{f^K##x_f6D zf5s{;TL*_b*}-!2GBbr7I>nhG{}PE6H!a$=C)b`JPWT>AkIH$Lot@q1JX6FCVmYX=PbZlUpEk=ozYRq2QXS!4qT#JGo7i0z`MYc5eXK0YL zd6&|V(qc;!%6R>|_Ue0Kf-e)6Mj7JHEv_zx`1!4yt~0Q4*c|pa$13Suxe@K@@6YGv zH8pp^b#{Wyb)_!1EXzw>dF$yAJ^G`!4RNJs?+SSZMZBkot93VHX=zzFe&rL>WCEd_ zjp1AFboMo-;#O0W>8V;vD+XrN#??18g|pv68{}ApV+U~qw>*-`Fz?hqwpEzY*WWKWYzRa>o%%gIhPk3R>!gzvOWq_k=SaQkq<+af2 z!2$*Cz?iIe2T}YIkQQ0Lc#fUxG(G7iBAzB7(7S$y<0(hp0a1%J;ovb!#RX^8 z39{Jk7+XHIHN9M)>laXj{nHAxfpBWDGVsT;fx9v>sfgmXhZM9Ilx78@IX}?x%)mlFB;j|M@)FRPlva@m7v$IA_SjZ`aVSUcG?8qrQi*<2Iul1eUHy)uE zy*RGYCP`LnrJ;AqZe_7ks}C00Ql!71)wY;kDDyxy8@$q6|9G2VJ20rCuA=h}N(k8p z6P;IRKQ`jzpsZ+7(Q}@BN;S`KHmyj~+n!tKhIVY$%P*5p*hn?5CL??CxoEIb7&~1S z-@jg~rL0TrJGr$vGl3UM4~!sKvBnyRhMo+)Bz#W%!2tR6r#H8*y`hoYI_j__=YbPq z+p#%{!Yct^?KJ%!)E&qRyFd$=c>IC{a-uFELt1%8&kr1pBA6yxSPPP2bq&;7@Uj@Fx)b(74@n;5RX_5BUWIP>3$-JssGMu!L8?kbu!~iiWbNY_ur61@7Hwc zU)`db-Kbi;|4NH|?%juqJ+%eKH%wH>^t5XwRR&&Jg9WIU{#&U>r&0@_SQ914u;yvc0A&6HBo z(n?^7*7@hGz}-)X@# z&7Ca?u*@~-d=b$uJYcC2*4=M6X;iwhJ;dNTKAxMrh#xjA_}+61P02p8eBgFrUdb%f z!Rfa9Fq^i`uDX3$nOe{GMdIt6r*H0|S;Kv(mM<=`1`IhObm^#{ii(b*lZlOqUKLqX z?&f??(YKUliW*^Up4^gbwoQ)?PMp25ZGKrwwRS>w@oaX*jv) zDTxCtx8vHicZd{z1M+UO448V+@}0yYuRQ6p8H_K05a~Njn5e~j%T-(wpZU7+ z=M2-(Nj@!GoHW)J-N~CTc{9g9Q)6aj3DBHJIl(*m*wnrBNZtaNj3d`2$Q8fOn`#+P zP)-6`or>XnBr#e@m!dW3^kZo!_dcV)a&CKSw$og9)YbOnvKyIf`U{8REG}ybSWsvX z#f)_9BQn-C&P0Y-sF4vcYp12%)*2@tm6(azPsxUGPcT6mbeY43~uL*LgXy@)xcozm`s&C{2p*R9kxU5!;)aap2Q z(;3w1D$uq>)3hs((zDwH>MM@lIamFoM$iwM%W>zR6?q8wZ#RHW*9DBbREY2j(63BR zxx?xjv8)u`ZWe;UU~GVzQ3-}Pp)rBehoJEc$w(NRV4a)UoNne#S2E zv0|=CS$;kSlW%(Y`c97P%QyEPjS>Cb7WdK%MXOA&Sxl$sO2Vw!>gUPM(e+lC8vHsv z7N6O=lO)lzK^%Q+GUn{n7tdU!qneP5s(I6pm0@%0^!IQv6tkny)!Hj0QEyFgHY~;i z2Z>$H^Fjz_SsE-Ej;$Kb z;8!dIJs7wFm#$ye2C54d6dfr*nwAz3DRk4ShJz<)fhj^b_NXxg*L^DKYfxuX!*P|V*>dcrK$ETw>IE=rKsdnr&6Kb%Tw#vN!{4#BSnxkrEN+i1at7}?LmKJt8e6WBTO3cm=T$0U z6EnwKt>-a8Il`LF;;G*x|3z`Zcq!jm5j#r~cU3Fb&<9B5$xoj?wVyAv!D+!s+FhVI zNn*{i%UT*EQ3-$^W9K{Zqd-q_a0PlFa9NV!IhUB2#6kDGxUg^+SOeg1kTBlNs+Trl z2Ftn>Bq6c`pD{w3e9xfPG0>K8sxe{&_^n=v`w0jF^3G_=d)Vu))a$-Mx z4Z_or{?-{$^P>ZJPBEv)P2eYemSg4MB+>51#W7x%Q*nrm-K~O+%M7`&hE% ziVk{9Nvj1oeX6rm+<22*cDPb-(vPiLb)W}Z8}~r?dN|fvz$Uhp0>GTM$YOoVB5}fa z!UJOC#xN!|(WyLf<~hc_P|{fI>An!mfMbEDXY#%%aU0j7?#h))`_4RB^w5ne+Jzp= zG5Sv*aiLN|G$|n`o+sNIz6Zg+WESfs;o4eYvTtSWf=Y4BV?<@m0=j*k#Y^wq?@sND3}*Tyf0@55zOT$l@a za-Jlr^Fy;+bAz)ud5CYMW#N_$ZAYUO-)jKQGesRgj4u$(`fktT&`&CarCSMu=&u4qyV@u#wI2#tgJGyZ7r^@Iy>^KEiEpB`AB6V(#r$Jbq@&0n4~~0 zrVIvegjTO@WsvIqH8{97OwYg7BHGl@si&hcm9D9;K)&VBaHz7#9Ydka#a1PeVsei- zS!KTH$gy?Zndn~jW|m(mKaM1hgl@Rdk0@TqjnVdMB3jRrsLX9d`#P?s-oSSV=>&CU zN&OY`_Np2=hmPJDz7p-XF8fvWSm&zABSTF$SC3j}8e;8yKP!4{ zhNUg8vIzGq)x33xAdK@hT)vdCsD+BUbx-e6H^;oH_TRsbvyjK)TLoL>8awKW-&|%Dz^U}4dmZGN8q9m4 zhdO-^TApwoxt2finXm7_l>l)3&_|li&2<=AkG>*7etwPMi*k9XnUEQP4@&mPhye~1 z3duy6Vt80>SP*8;oMb{tp1;uj*~r8M4`z}*P&O6<;D{(;zUQ&k>*(kpooYBK+XDC^ z;MEj)@;_~yF%tBg2HtRNauR5BB#^EVG?<`|l)b(*F%9}|M1P%m?$$GYLD{(XcIc~< z-M|&7yH{B-S96$cz(wPFKs_tFK&FnivpO}w3a6rcz`64!t6M~7+2zYu@c?tPWap%k zB^>ll$6Z}Mf_pGVnY@^#oWirQl+9`_S|-CxNl9%vd$%#T_xjlhCOm4UJWwg5z-~ z*e3~;+Sat{x4pAPqJ`z4i;!G4v$>~oz0rwaX1=wOWYtc}EgLzseK#$oskLj41}C$w z=_2UdTV_4+MBAtFV?CyUS97AXS&dddtE6e(C@m$(=t|Z@I#)MFEsiMZj&5>KHH;@c z_MNk~Pmwi?GshBFQ+`kkf%?<}#UsoWt(#a~(rIHH` z>hL71yZNtg$qqLSx-*#?v0h+S03ze|P>jWKT8&XPKPD!Lt*zkL<;JS;GBP@Ee}dIe z)67hRjNt8X(cIZld@{-1OqSCZ1XyL5mDyF+hM3~3sx0WJv_{>w^~Uud79BpCxvb^Z z`Uw-)>!gh-B0JnpudWy0Nv%|f|8dv)<#X)9_wV-P#U-UBsp@{?5$)B1&zw0$j|yk! z+~LU3FwPH0h4ecvhs5ZY4Oywq=d@(+Q1H7?AR%R{49 zz3-yU#&LR`ZH)N0*$o?qq5AyBO%9J>q00}>sW>hY=1;?T2?Ia?!2Keb;=~C# zDg7t!&)(vR6$17bl#|O~JZ`q|wU!8EO^EjDSnKJ~CXs?e84=>h1zQtl#VuMaIB0 z(hloe2hvIsZ%e(^Y-ngz_q_XhQ%LyassCK!SY14md(_X5$7?E3(2n>0_*IgU$!7f` zR$!3?>N$t@JuwFgi;3x$-rQR0N!<2Iv7qLAGG^d1a!CtNUki1JU%cQOK`_hQ=1Lfw zn$Y!qL}KpY@y?yUelc4SYSXto4jGO@g*$iJ;CRPEv-b0H;hgW!ts1gH z&j<;eNLh;PteINq2eooHZ$3sCuCSD(%fctK*iVshOPnfQk05?9ElZcYer&2sT31^l zJbGID_2YMj*4JIN^sTq$MCXLwJw88>Sp^b_I&7`D++NJEDkJ;YV}_kkNeECX*4n7T zrBNcT-;Q5?Re|Q+LZzTu$*t#%(SotbtKf)l8o8316Ev$pf-0iy5>lDXn#p?lcxM)@Zfw%iON1g&t zG8d?hc$h^hHa1z8-$O-2Nly=Twuw3D5GdtmfRxbzvW#UQc$yRN*yJh@aUucU@yEyw zU?yeA&nNLBNj@}4@E|_yM4Xdg!-jS>kOrD&%$eiP^+KwqHMH*Tzo9+gJdn%P?=JJ0)ed`e|&5cc8K)7FJNTKA&FH+i4b(?gMv9<2=v{<-U(9Z)GW?ydxfP zENxibi?gr3g( z^A3)Oj@5l~)_dT1$SHrOWrLpn=92~RPQeFyZ6>4%LHUxYTV5o8AAApjii?Y1uy1(q zT)ZfO_&{~?qk6#{ELufE;u#f(o|W`A^nEQsY-Z7u6|MU1B^{qU9iL5C+>Ix)tftf) zdKoWYlJ4K9QayIhtl|E<}8bnAvfNrVSlu2siMby z_yNkF!M8QOs<#Y{(Om2S&riVJ$X*`ut}NjfMD^A~`Vkr%YpaDLg8P7W^kyX-t+#^I zpt9Ob)F)4ZBO^}{X>kjP8RVGdqZA<>p5n3U<&^aO&C>SPtpIGqWQ;Ydb7*KN**ds2NJ>x{(dD`(D@@v1<)DJfre5+ z&|-Ik)n(k*uZGIHFpg;O*&DcU8%02w0)_e2tN(Pig5??SsinmSkDolTzIO4~+wk-3 zO+oQptlnId@gaaVjGHwjIav)%w+D(WfW_uBUt3>4t>8rrMm7L*SfCL?E_tA+$JEq_ zsw*0CK$2I1%^`Uea=2w+p5$+FjeJ7r(_ob*69k(K*ufhIU;Xy=tE}ki zWc$a9_kUBRN3K9{xqr7DhF-Pb3G^)yqF$b%Tkxd`!0(z2Ro8pX2yz6SbLY-2Lg;}R z18}O1w@%dGLJ-2p6Y_`#I1%mY z!0<3F0AUearU88#?ChJrZI6KZ{30Nr8!*H8*B2xkhKH;BSJ;7eI#8TFgMlufL9y%W>-*JzZfWTTWGL0R@r_haR>h(; z1W}2stgMwIbZTZU6H5q-I=!o+vOM7y%3GT0Q_l>E6e^X~tE8?Ixb0#|F$`|83AfBR zFIH}(C)~=3JTtcz3IE>(A8lx8=(xG|AX>yj3y>91Xaz%O5vBt+&sgTzX?{f#5|RKi zN-_HTN6nbWp^kz}KyEn^@;f+_;~E;@B?Sgd)!g^%NWpzVwV#iTO-|o^!3QiL&6yY( z?*!Ea&||WzwIm(?uHg9+qxn7zar%d zel6CX`oBLBg|hHvi=#UW?WI$s&uCYxH$`2S!<90gL2RLg%9u*eITZ|tE# z{{H(BwQpArf>R;caaJkag$HGgO*y7oha#V=y3 zLD~~mUS9s>!tY1~G80=XlG#LnD@}-{iHV;c{c;~eiG8a*UIU+82O1Tghd&IYsMYt> z*VevO+xKfaz}4C5LGM!qj^^PuYlEJWt7{qf%uI~@_4Cqy{;QV`zT-+0SL}jN zlpcXqr&Z}n=s=)FP)bSq?oQmAy1~=*s2N-E-Jun6FWCnFTs)XpAtf2Qp58eXcDEzs z`=giD&S& zy}M$%P(36*BRzGVoSYn1D$*VQd1HSof{;r?(g)b;kbRc%G6#pku=|q#KU37Zu6~#B z_bJ8|f7!*&O(uxJ<@<4fSz7)jO=5;=o&kJ7trEL|rw<#SsPL}{;hhQH-_Dgn)44;3A0Ax>r7>s3U+2hB1 z;FLg9BAAw12xWot3fyF^Z|+V8gM+Xt!hxQ>Wd%)SPMU_loo0UymmLGFw(sl;L_Jrm z)0N#3B%FZMuovK5ss7!0=#K=5BBxm`N#EUP#*zJO`ThJsjeKw7aZqpmAzeeE{%h0( zMB|@V*GT`e za7+RgYjgeXy*8j@^uEgk^d;ikPyg(OD8*`cjeQuio#j`hjvh1aZr9&m+ssNseJ|_) zKZBRDl9C~T70PWydF0~6(#3`RX z-QG=cp&z&hzDg$GZ&M%<4rbIXvAY3*A8mL0*JrnJ(vY@n>_K%YFnZs64JF@~+FEhw z2O^CcV0O{d|FQ&ur+Z(!gZg+LJ_?e{Oh!1>2Oa1_6OAV0Uj8(}ZuB1T_W@`f5bT_8=l{ZEkWyu76N+H1`1XXg%oUEKza;I;19lfrk50l^V_4(Bz`)(& z-Bl5H1g0oVC<+TxRSIxhRaAUMO4cj-$$&Ez9xd6ww?3GS7LbSWC$vxgOJjHf`Jr1j zikH^kr047?>93)GgV_*1O@i{LKRj|E1MHKW6i|pz8=8Y;eHMS4g(1)MMnl480E?|) zpcj#(!D-dYV2|B9yZaQBmES(j22HTg1U3BwghH=0#oHNHXe; ze70{Kua1IPS@!ub*y7-aceabcr(AOERE?T^Ic2w1T{v2N;ddp=z6k<7L~mZ7Y*NDsBIK z7|MVxFdl3J>sNKyD+T_1Ng=%Ca8gRjG+-lLVEnCbY-$=Nvq{|8m4DRPB0smBoDDO;&+IwIZ zbOjHiW!ULh1O!TTz9M*n^N>plgWJqiaR~`Qeyv}lU?xNgDJZIK@)gRa287Rq?Tz_q zu&Y&acXu!Sl9mFByG0a=Sf<%dZ)Yjf{H7`k|I{I<=b>|JaqOw>E2wNe=k({t`yIPMa6Zr4*y<8u<8~dbul7!@{D@9Z#UCd z^m+~7P^-|wc=hS-Cg=U^349AD@Eh3Os#EvK0L2~MT-&}odBLFJ$-gD*e-tFB5ypRu z%IzE+{~0HK)+g`VVKivzc1tK&#sAxSc==xe?SCbo|M#^(Qejq8al(NfNOdYxI%>H; ztM60ig%OsW%2el>rJujSPvr&##Et$M$^R1a{$0QQI}>?@FqeKmm`BGafB!eiPU`?t zHbQ>zKf2oAum7K`_eV$#e!qC;(?99%?;jzh=l|-`|6Q{_jZFBxwV`S>PeC1mifg~O z|6e`@{~qXn-}L`?*Rp%K0;>10Boy07dA~dVYNT>@_oSLAs)@*Yrn9}aFmJACDj6@T2Z0kl0Ss4Dy>_-;!G~8BtiBSa zfq09-4_8+sPr3(Bf@p;FZV1yo3Wy7>f7af(dw<*yCqu-Hq00wqLe+3@w6n8197J^z zxO5d2LJ*Qzn3?OBJ*A|cgH03@y|Al_9~E0rWaOB6x5 zI|gCbaDpcAR^fy-%LN`@+o6nVAWfN|5h@Y*U?Q-=Y;W!@#OVYI;dCIoh8IQxMh6?M z-V)tD#rfST0hO8r8QU~`LN{Qu%O8$%ToM%20&LX88j2(y^A1L&F6_~9@b7a&h*@aR z()nc(-~urvCA}&!z_9S#UNi{ftwqOVFKRNuY*1AE)N|5(nc zJ_vgbjQ6n+%h~$t`WhQ$5YPu%q$hmiiKSsK2+%) z`uOn{)X`Ot&&r{vd$N1Lh7nUq<^AmWZrns}+5FKYgwRo1q7i1lKp?O=Q_X|zl#X>Z zII8qyxWj2OmxP2W8blD|67by${f4q*_+t^DHh=>jOva#ARP7TH6|L3@T0Q!d^WG5xg{Lbb#bgFpXXrDFbe zD(vZb_dXuTw+H(GjFlH0k&$l?HBe@mUf+sw{oaw&e*{LwdStr=$BRg;F7R$uR|M?8 z|D{<9K&F~*zSTzFHxfItKfXA=7fCdFdo`5%a5!DWJ1kE@NhwIP6tXjT0T4%20}j>* z{I;os7?`64zYhuyw(Q~)64HV3Q-YBGYKg_ z02{wA0txE_!_Z@;q@preohCgaQX#QL#jc}a6FLuGr_0bl&=7{hJf0ffzb`iiH&_EJ zDk4%f_z)FEzIz3225=&X1XzwgaHOFm!k8dUwa^ft(k9EkeTOXH15XHn2jOf1DKLWJ z2~~hrhU!ku&^oIAFd=utON~6~ zfeDXzLx3GSU>HJ!gYTzLBk`VH7j(b#TK>Y&W>_Z@BO2gz3ssO>iF+u-2&F-x{I+3x zdz)6k-YD}c^cnvMkU5V4TDJh>e&CMrVL=6;i})Yy*|Wz&xC%f*!1yiIR$-8|yNa#K zq0sEH;JJoCzW;3MVr&So1;g161RXgv0$D)ZqNJs@$e$kc+?X{P14u*n%a<>zD?n!N z0Kszl>4V-&e~g9VQ6!YZ(KSe9SJs7d0tzg!ERbXOFKZ#ql9BgM7-+ll^2E_C9WQ6v6k81O#Q43ScCnqJpg~0uK&fJkE1E$TkLJQ)p4a znq8p;%T2Y&1rY;7`x2(WognbUKZr2)5u520aEFWNpB*e!hGtMZw6mKG-9~+=GhQ4P z`v~a7?^${MD$-rlg-Ahmn%_W8T}LMay0JZG6>#mT=;?0NqEaY72vA7i;fN*QG#fD1 z!uMpyA~adpK9TYiGLTC>t6+@BseOaAW_|aDqs$DDu4f|_tBM?52kJSibWzxA`_#I^ zO3KRcNnv^&TdTwRzIXT7?c240LYaAkDOUmj{AS>dZvP;Se+*i2?<})QC14gat{>$F ztm~f?G<e>qawf!5JcS3cA5*6v0ES2TSe(;M>sB@&@`w&^t|mX$-^PbZ>zq)onMd z=A78;i%JN1>f6|NE)$AIkyEx)2ks-_vU(gY@0;(Ehu78i?KhOV#=ByYOEe)(|xC*TXyC9v1yC8?)a( z{O@X||F%B)W7+@TXX@@oZoqtijN|~=-~N$TLp5vwcSs}N*eJ)<$tvq}8wwB?mJ6&3 zxuz|aJ@K6^m#)}5KE!utrIi)>3S5t@1UfbL(4-2v0FXog%qQ; z#V=j^q)#HE2YPuWSEm^Hi-{JtGn$kqsjds&_BCUmikq{JNO#fjcCQdpQPfBbd3ico zRu&sy;?VGg!_o0Cv4k#OAr0LK$xGMT6owe){&wiHioPR{jbAgOJbB%w#r`TySAy{g zv>8RZqSu+y(vnLlaf*k{-wJxpJvIzdGUD!bcYkujg8F!RR`W;7uhVQmx*emWy3EF` z0Aly)AALrYp&`9<&rn@=2L3WYn^VPqKI`RDUcM!wFgAI6(TB*TApVS{t5w}5iybaX z@ut?BfLNI{f)K*w*g)YAsTd=*u8f5MJOu6)`l0W@tSFo$fS zg*1xg7&1QRcpn*HWK5Y(VA3WmkPoP+SgnV-?=3IxPH7!d`_|hMC8NAdpRIvwJvD#W z?DHP?O70>rN&iW#n!2N9W*aJ2dVmTXVVdW@5e?R}`<*6L=p|ehjyA}%eZ-b~a1kte zWGnmmlzpl0*`qJxmd8VFc_*>rbXHDsz+lC3rw7{5VkP0OsPt#0mfSulf6=0Oj%I6h z;F$Z}BfW3IC(Pv7G=}crsv@Q>PI_RBn$mne?Ka!6o64DtVO7(G!b697!+uZMBlaI}0DONX3%m6=?tq-j+pM{GLPduuI z+w0wbIXcBPT~$LdX%_g~1$^vx8&?_9He6U-y)UlL8ukelx@4;^si>NeO(mHo2ePSq zjRo6IHTjT6>Hr{6(+Bwm0Hx)n__rHb*g|zeMqYGmV9GY9`Kv63ZrxRp)ljF?e_?{x z&VSsqTz8)@O}jiF%J(z%=0w%X?^8B? z^%CTK>i%?XQr`|ZcDy5OaPNGiY-nK1v3%(j!>y4(bW$wtmizu~>|T$hZkF?giR*gI z7Yq7RO4izZ>S2CTQ%=pxbwSPkPNCe2)bGlRuuEKZmxXGI`-@_1Y-oFneEOg6?~yXB zHF&j6A*|>{$M@--3s22}@_jc)MbCS=wa-JPJOrnv((b3r_Ue3{o{EnI1~A|?FTUT!RGJF8!Lea8g10;DE~?=N+~ z9X-5^W!|ZhyW8A!g1mIyM=&~^h5E@2c)^#(PTaCyAX)SB0$1@=U3Ink1ZybU=*!i8 z+>S8qFh3frkq<~Ro^p3B0ytjcS$9-Wo(EwI%Sa~GR10V1PAKSOEm+X_r zk~x+K^~Y|386EZZ8ivZfR8Oipo0kXl@WQFV+b$Cic>)gwF|I#!nZ$a@c7Cd6SA>Xx z`|7s6J>5i+ZlViD7buraKTcy~Z zO&fxqN3m;m?d;N#44F0lZ^9%kjKZwyGQ4NH@VI)y^k`>83#)G5v>~UN*u)!GyMDW4 zGaU^o!{r_hmCIpL5nBz#Xa?H{QqPYZ@f*{9!YHl!Y}G0uZ2s+))y5#^@_?B30hwa+ zk5#?)U*^t&tYWTTI$;Cd_Vm$Mwc>sQy3T{>p6aaf2Cxcyui;U0FYKpP7-=(N6*ko- zjZErnwbx)3woX_7mdOiPw}o~~B5Xv0_e!)px1}ZL9qW&`xGNki*jMjAF+%%%jZI?0~j(0z6uug&)50~ zS-hoX&vf0|DHB0#%aCjFG^Jcp38`B#%htQAI{(V(XzMWd(3N$!2QmE^@c~R^il8>9 zjH<2uFgq=-vJ$AET^s%9-b41-rU5&r_mO?;Zqpo{zLD6<<#rC&jYxkJG$7!t#SGn5 z2@6_lA7Z=mq;eAd22Sium9Z`l`&iCW47Z8Qa=-IBs9YMCqu~{?YF?U|;;?j-FuJOZ zK6~-T86g*p&>+V}R*7Ty#2dm|mIJoTnPqcRpH!@e=(MNTSiizD;`i&6GA9myl_Pp~ zl8jMPsrDv+GV3b3mEzc`96OaJ>fh7U?jbubN7JSGq+G&$`o@M$pw~qKxeY^#0uJ6A zZUM2_?Gw)VS0unGgPpk^hPKl!ru zZJ)|{lA3c?*yZCTxb##FayFeTrgC!ie&)*;w1;Pf66c*5@;0CVBcj_wNN}=ap9*Ag zD=X*e<@xpRxh33vAypO!p`B>3#qRgDoYmF4FMYINMn`Uaeu6S)Hd0?DqxL$PMQ1xd zTh)#U4>{Q@#?lfR$Kl@hWrR%e?xFME7Po!&Pkp&;Jdt*$&`2G<)OEw7c*%giNm;kC zRoRxUWAy3wBLh7~G3nA^S}=}Nj5xj1x+pisfQPi!drSvGhPx!42RT(dMUtv(Y=*t^ zbQV|bC)kbfxjXDvG3B%UtS7(ETZgU9RrhUDj}}u~XDkkm>aW&MMT?Iw4138qGG)?# zg#Fg;K(>3nUIK$VXxebv=I|zvC=vX@4~6$n#ZITO z>P4@{hMaH1%?A3m`HT`Ef;6evHct97{6d3bIDA;WSvSuByK*GBI!p%q-W)_TxG;2s zRiW-eR^<+RypN2hDZEj8yUEr#x70;29#7lxxNE*xe7?}%cKcX+GF$d)v~(BvdUjg~ z!SK`tKp7NrkCTRbEHBhDXgOj(t4s0B^?3c&u!~o=+=X~X8*EgUQ{M*MJ&wnw`J_cf zYsaltWo~B_IalKnX9hcr?o?-Gc4di&H-Ez%HmzW;(ah+hPx$ba0z(X;@ps+u%``!O zbSo@0)-Z88w8z+`=; zBt6aR(&~UyMos|}M;m!r=G-IXvPY-pOtfmu{PlNUR1zSDg|JiYEQT4nFYAy+`Q+QO|e>9U6=>lgI5Gbj?+cta21$)CJ; z(FZSD1oI4OFP4$3HJcY?@YZ$evobGQdQ)>)APx!P0lnA2@}m^FA{~w z)zxur)yt1+5?@|n8?5LV?>W4W%r`;ffFElT0QeuzhYA&A8mssUu6@F5z)P z=)Ue&si~E>DJlZ5@{2B~CgOFghyg;t~>SCUQ8IyS@Lu0Ei+klf| zVE5um!7P>Qg@Yl_ZJl4%dNkJY*^5a=ep1m!Qkwf=ucOtoT82|bFP;>_;kaGys4ELF zU67m>5It>Ia`BNT?NC}_n)^tT0KJUt106Brm(zWE@2ip|A8+Ptv=-l0)_FlI7x!|qm}{)gHYYTt8q3Cz zB*IDV=4K-{$(VQW>CNf@wv&_U#Ieb2UjTVkWn|0H_%Is9t zmuBaSPvq+aAvE(SoxxKuckDkLMsX@mSW4}S;GO* zg;G|{7ak+YAB*V()unR+xUbu$!JVPO>ISqQdj@XFecYQ#dn-tb_psWXSJ^tcq|&WT zs`eIYKA5d5$V?KWZ7WUET$iN5Mkz39YiRSMp%vIsNL9M7;W2#)w7m-J?85$zFlU zq*9)}<9PwqY4X^vAe=9nUAVDop)iAA1Twde*9*HLa(5MqKaDs*1m&p4WzAB-|HLMu zedQ;8alV<-Pp{w{ZhO&l^{P}^en(jgYI&TW;7B+S5n4Uu={-A3LdU|Vpj%Y=pm_Xv zgkWm+X4Kt9!akPlp}_#u5NOBiCXWY%Hi4%+$i{hRSXmepn~Xw~wTXFAQkp zR&!X&rO13aYZyyfXnov-H#zA`$Fu~YgRO^D#m-xybk$Iv*>bw-1aYQbAH5keCglJD z3e~avdfe1LtMC4L_Z;DFYti`Ok{`NFGazIA;R#x9 zUOs~BUTTbf(GBqFESPki&%nh`Jt<>7U!xGj{Z58XSim?WhG-+_Lq0-7+A&;5*1mir z$xD7A?Ubu^E!DIwl~__kRvllnGT&{>x{;W%esz8Ywqcdkr7pG;tl=ptf(nGG3;}ih zw282+;)gM1gL}XErdxliwM;RYz<;T=wD1HN3w6mCVv5d1{+jybeR+&fi?cGZ4WwUf zNeXB7zO~oGnj(_X=fK;*P>qXyR?i(SkAG3J{95jA?4UWT^$eriXousz=j}PKnFAAXW>yL4#C4t5+KQ>p3=zq%D+fT;+>90Hl3cx; zy&^}bpQu*J3*E^=rRu1JuMam>F%0-YZk=GF<25FI#wkbXGLuSz|Fy*i;CRQ4H?%5usILws z&aq`ir3~$4&5fsRm}-eA;I7@JzwmK+vx7jTU+e~kUiXc>l-J)fS%wCM-q-yBqnZkw zTK-Vzb^@)0m75#HOocnc*TJb88hAXRzT{{0rmxxjJ!hFE4Q2JY&Xp##^gJetVX^r^ys{ya~w3;)0!eG1c+1Dt+)Q3yo10 z$xybebnM)>HTIE-*PjFGoeVGd(K%03MX{U=9&-1^_7=E6zb7F!rrrQ!+tx3g6JQwO z#ahh58moH*rJZgU1J*1dn>@|R2k&#VD96m#1m@L(l63Pfa?gf$3mO(Szb_pSHC#UQVirM1inY)G{ASs&$n)~3z0iL-ZLVU zP_bu&=|ba0nWdu{fj}a6Hx3@6d@k&uXXz!JKD+VFaYiMGJ<*hvqxuesr=CTpkjgDs z94{BPP99exGKnw!b+(dg-bBuAGMYvG?scf!z;fkmrF*Lp&(UL?SYxA!spsVC{G2QU zKUR5m9xp5XG`S|PsJbm`Eo!*3y*S(Pc%FB9o-d#)x+f&&OJB+@hvq7qRJ{$SgsmqB zqN1w9Qu-Qrr0@$HZ`!Ey?3X9Vt9r6?7fj|kAkJrJ_ND`{(vIFLi@-B4<5TH~&e&p} z-nxQo#wS>BR1F(Qy@!#jGWo!6TCuM9m<+|kZeogeg!F5?d5#O^qB9Lf}R zS7HuQ##GL|Jvv3~ygqEvUo+?GxOk|OhczfzKQ)kVuA5rh>%#ic-W!EWT^qxpu_;bx z+AUd&Cf`ooN9*ofTk87T+>O^Sdxk|n-vzJoZq9S3{KZ6CsP=NpY)@pXaQq* z=mJuI`(s6I8Ir(zo-fa*}Dbq&OUd7eXIPt&gi-#Gu^rPMnjMsRI9B@W^~i{L z?V+_Y9i_1#uC!AT>vcWS4WF-J5%295%oVp(!iyEQLaUZD%U|WbVPv=UUbgG522|d( z+;%BvD0fN}($NJJ3DwwT6QiYea7m>$RucAWeN_X zL40W*t0xYZu|v`H5m%ugr*>RDc?PfHR!-R>tR9vS9R|JRCT&+k8a0t9CHK}5hE%fI3Nfp!5Kjm1SBT` zMGz!Pkeo(GNdhV$q6i9-5hQ1DlptAKyaBDvI#^kfA zo#{`eiysGAT2yA_#|=xR zdm>-1VO`Pku9Ra-sN3XdfaWfv@|gpDx2IM5W0&o!&sOWp3iOkhs$1;R=P<_Dt)J@- zozBTf&m20qX1C8_qp^DlbB!;rRP^U-<)~D<yu*mSJin3-sAJ@!Jg*~naJ!!O%P*-iKXAcYxS51*<$dv$8uEg^l^ z38ixbW9hp*k4aIqPn_OvFepMVxALNN<@mSFf_%s;w86! z#b%Dcs-D!_gEz-QC9Izc8JWM?Ul6V8Aj4{C8`Th*&BDnc6F=i%_qmiUyrO?`u4Y4h z!e2h%gGBSjUphO^DBERg&f8xqgO#`KG97vudGx)i<8i8|UH{^?%Ch4@Q9hwu$7G@P zW_BZ)Lx4{0w`1p$dwVkDc)b#@EOHI+Pj7JxDhoAf57#_o_Xt(BuS3vzx2_85_I>9f z846q$$3xRFuuuBw2_=mEyzl1bhNi=CROTq5X09H|=daB)$7{gd?RNa~Cnl0T58FpI zsa(^0@A3Wc2M=oNKd}ratX`LF8gRq!Jtf6LQaxQ-C(eN6X(ZK^6D~UTrF)G$fHF;W ztfwKP{51`-@=4oU`6Lr*E_uxjLp!I6`ppuZq8>0$Dbij4OYTY_{TnT8DsXucLwZ~V1|4MgdDgxdd2P)ZYq0J$Q+H2{ zBZuhePXV)QKbquJZ)B)eNcSBX%i`mCr%exCdJ=G7D|K{K^hrSI#a#7~M5-DT;gB@m zwdxY@;ey4vxYcWphRC73c>;yg>Z#tA5G%ksM!K=spFLVIpNu6#%^pUr%V6LUYNZPD z-X^~PM=tE?#A~jBROu5|S8B%EMzfM5w2)Yn@>dpJ&61Atk1fX6NK+5c z?H!`kY@byfj8ge99(MKPpI7x&uYy(h`%Qks;Uny!^X8UuwjT)N_Dm$EG`*_6ua*7c z?uw^-3(E5=s4n~DTv=fF%t=N$W)u=&K?XJvy++Y{aH0`Fukhbgf&;u zpred$x%Gipe?{fk(R2Tx4sBFKqq~@V>Rvl zm!tra*NbLujAD-W zZ0JGz%Hqh|%(W7Au!w8JSoHB~^ZCBukgJV{2~ zdCH&f&8^A;-Xm<>?403a0m(oOt|^98Ua`=5%gCGak~Y->);+mbh3U6>US4FN-WwFX zERyl_oLy;s(X-CY^5d>jz8YjfWbdpX;S7%2cKg(JGgZp1bP7 z5Z5)%Pm768o+&$M?D?|Q<9YOX2bDD}$tx}eE!VFQJ*}EkpWD_ObF8IaR1JfFH zHU?(n4r(b2?$TFsUT6osFt#v^^ps*>+E+^m?K71~f|iCKtE@^CzQLvBVte&Dm*Vw1 z!N+DNzpzEdd-9}6|qoIfkv%Ow*dpw zi&MLgM)ayabp2ctRza&gFi@2rUsbkH!l!pCqdcLpFm|!KGwzddRVvS_=LeM&Iyk8! zMa8QQKI7)#m%XhfD4WI`%3l_erJ=zwU1Cu7dN`sn#X2N;PyW(;=DFP_O(r*tLn}i} zl^3apq$;a8(zA~uk@eAuxa*74YY~u{n(olU%WT670qM~r<@ITZ_ zJ;UVpK9tF(|H`ngDdBX~jlFpj6*Oh()4b6VMqYC=yFN6==Ndj4^1B~ben6mP!;oS6 z3JX2QJYimE1OAk3?ZJ(q2c&m>D2dP28}VSLl9;UOpwK&&P>~Y~m48T8x5}I?eP6kx z6>S-#w*GFs>DIvQY4(lgDcP%*pVVk|d9!Ccow|P1ZuzU*5I`Ri*kq>6pD(e}Yc4x` zJ8y!%LrV5b?kE$+CkMcQ$=j`npgy<0#`3)3^6!k3LmPL*uD{=Na;u53mbs%om+qs0 z#Lw?*V+Gu-e{*?1gfTmDbIYSI-X2$!<>-mwu~gR^KP8Qi341v1KfCEHLy?WZUbQDa z#cB)eM!g=kDHvbH^D;*bXYkBlKmW$$kJ;3rXt|<1-T35;GIhi=#Dw>mLtnFun&7N@{-~>Wcur=~XnuHv z_MVwp+kPLm({V#K$aQuPXg*eKbDBxjG0qiEJ0Q){tZ>UAojZ=MH}H|2{w*>2zEO(Y zOm&GnkNT^k^anOnJ0={n7O>38pL0x`Eei+=rytI&n2KlEQ4{ylKF#PV;nECdkN0eR zt;}fJuCJVr+>+46IMKIbk$(#_Y`Ty=#&~Nq3>HzPK2qr6nvJ~fI!Q0a2#rF>!J>da$Fre6r$06> z6&-vEI_S##41q<`Dv83v40UhsJ3~*!pZEtTWOsrnYq(1NBe4EJp|&iIzd3#w#UBBW zNVq&Qn__CpyOHLkwP5V6Hk-hv66=x?EvYWGJX@l#imhj~V63#CJ(1g7)y6w7 zOe!tu(d3SH&87Ut`Rb3C&fiov7E}aXMr_})FOy^MB`nPpqaH@+zm3rE?r+c+Rk`b( z!EMUI7`-KQ>BC^X(2@6%jnUq_B&rPR6Rt#BxEJ_pEexcMT~C}}(;9v7Z~MjSEtUPw zt=4Cw9@Gvp9DM)zNVC&fx2lIClm@PSV%6t&hsVlz^|i6fTORTT5oPs(MNiq%s-!e< zH;=$;-AI4*UeY=OHh`-nAC_nes~ z$v@=^$L0F?y5%rdJbrPBR_ms=+S4;lcU!0>MMFI~?xvk#aZ_w>Omrkeg+fkTXq1*+ z(;#1F-iq-$S){%|fScSi>1Bftuj8zuoVisSF0tW5HPIHqu()F1-P&>7DdyJL3u=Mu{m%_@_AT_zfT^|c4>zcFU)`Or-086VZR|6QqN zS>;h)qu$}3SZ7U*zJ_zRc%2vkf=ho<-LMHx5y3s&sP?22%zbeNEaT^d_PcNoF?Vy(Who_IhmO4 z;@Bsd)vJG3VVY5TqNji8s>Y+^YsMM}Yi{w5uO4T}qKyc>n3RxFw)X~an3vb1A}sLS zYGr`d%YBnwr!)FR%lZ4FoJ#yH)htRrv`L3&r`HFsI$fXOF?;+&s9AHA0-LwK&B|w7 z+r4WQyi^806r4XQD!cG1e_n_$Qy;{jq(hYg^~Tant>CBG9bI4$-L+gc3m-gUc(%WID1EcL`Jw!0rz7uY+w`1G$?imPO{DH&RPWf$ijPfCDvrB9vuM2c;^rG2;h9c; z<(K|9iX99GS1-hhmhYIPdQ>l6OGz?y9Q(jtoo@mo;GNiZMcJl1FOg%>vEIY|cTM#^ zuM)E)2gktkOtnf!X~)zOxO$%SU3^y|=V+1L+IF#?eP48|S`S_#yQ?TMZT5AkddX_) zZ)bQU1pRWtR~n*Trn}#dC)xkrLskq^!B|TFs3)_Qgmbj|a5K^Bz(>clMuFL}(^tE9 zE=@;>_x_TDY9#v90B^R&$&|SVo5N+a&#D+&C){+(V^I1#>q)^=nYz16PS+m3PU~TM zKqh8oj$bRQs0>UDbPN0A)~7zLMY}KBSE@fhY}X&)R$0csII_m8YTfb5y=|4%XOJ4PAM!HnWitxM3*-oO zjB5?*y&j*u7oYh6)n1xmEeDx8T z#fCEoG8ey8Z@6*zcujme^@HZF@O^d9mBg|R^!)wk=Y@|o8#r4YXYWf$;4);{^rFgS zUww|{nlz&wT9?Ckrz-3!ZD%y&wnV_>Q-Z5EJlW9Su2P_&oG`0@Z==iZNdu}P%KQAK z7DJ>e>nfr|2>V*|Bre0KJtI!>!a=(on>wO!#p?I+q+XO~?YZb_YJ20k__=_V!`w`K z^joZRSLx0v_87Vt_U2~Y$=GYR&jkb&?8-Pi>(IwE?U*UH+pJY{SlC8Ih2&sFUOtJr zqN{qll!g+IeqS>esj+RZ!ueH98gs=9{6nLDTBwUtQn=y1HVzBkonkK}yDJ61f{T~- zmhrv8by(;`qO1#q46=(TY2BcjWHjpzV#u*c)2rV}BLGw%r8!({Y>}qw`#X@aFRF4S zpTpes8zzmZZ>*cMR?L}BvgA$8p3pp-*w$RvAn+tOA;z-3Ccg9*pV8PJzE$g@U$tfV zuXUPPPFa44uM#tXYu8+;too*a+7#m>%^ zH0OMv7wN<*{n@FvElxsO({0xy5_Yw`y!dANfn`jPdZuAUyJohu;H)F}>L#khPHUsC z`vPsErtZa)gL^h)cvgQF33w1W-8clbyy@6iK8sFA4|6q~4qbY&+j@6{cFEcX*K|&H zV-?=)Z5x`K1B+|RuHuf*g;SF1ks zbn;`Ykw5*ucB|;)fS8!wdV2GgCY=#y6`URGFE2~sm8Fq!*PPY{p@RAEMAt2JChduKuK^c!`N$bCx6LhF0%P7+>ye@M@`#6JKmFNwPTYJa5#ujoYQ@u~pzU?}0i`1$BO}V0$<5 zG(5fOc!O$^^Of_OU)17TGF?tS*&3h}BcED+ZMy_t!tNV}ioQ%rafJ_uPU>hbK73T5 zBRy|ax-DslLO;1)eCDC_8EUt1nu$Yv>#IK6NKEeXL186Uj)rbC4~7B$qPi93WrM;+ ze^)ZTFVcr575ih#qg%BQ{@M)G>d+ofD zvV0}avfr-19_QTIK{-g_%6BN%HKok^CH!>Bb@_eHXM%?prZNgRryc5Il{lcTrZ7DH zt$V;VS)Q$pq|eCDh2uZJ{`wCT%8oaX!6H8wWH$G4QCv^^{8JRa;}6RBevEs2|MmO- z_J_%}n%_P}H&$$48w2Hf^gsO?drtrQqu-Y2FUt4+m_4U|D72J$pa4JG;Ge2>UA*15 zk2-Y+@A&;wfBjzQeoF8h#+bXVzWSr_;J^IRfBG-o>v*ACzAMZB) zbm`j{xUStx`QDuJZsYfk8CMG(21W`)rHjM2-=X~bwZ?n}V3{i~PY z|JJf)uc36@`=>YLzj?18?Iiwwf1Sp%UHV@yn>h*v7%Er)r`P?DYoLY`@b!mBY2W?f z)f9oce>yDxnl{X z-^Q!@`VyE`)3s7`=6CWRvu0OA-zpsBF*p)iNytq8kG+l^hFecLGJ(hybWPyBo z+ek6=S2e@vEaruqYS7vxU%y_L@n82#_sh}60rZgHg>?FF_S3_f@g~7gmSjO1#E}<5 zW6c>5X|(ML(l1Ln-?8z>wK;rSE6-j$LXy?%qG3b)4q~Si=GyHCUM3$|BOtcwy9KE2 zxO=SX?6sp>xdPGwlpwM1R^{F=P=l4amZ*`zMgCZqwzKBj#iz4=e06b3r#V@VZ3rhM zDrWN@zaKZRwgX8vscXBT)JW%(g=0FR`f3kUZFE4ynWOhz0zKu{YM0kCGySkw4957Y zQFPozPCRv4oMjE^MKaCuUUo zZxQy`Z-`wfwh)q(YMpjyGtH!a;oql&f%l^jb1OvK@$Nu$#q&NO$ln8>SSwI)KF@{AAj4)_2cO#eC7 zf&p$^)63Sc{l4AL3L*7%e>;!vJ7@1*LblgiA)~FQ@&5OHID6QyU68vxI|TtKb|@04 zhuQgv%ze)M@@7pJH0SPK@fALf@7@YgvZSk(j~}61|1OL038cCdU4alQGb$;Z z&p+m6W8ycxc(wXxX4QWdkjQ3YAGSfVKz2$`Pp`g$y9Pi=?3+633N{MS2P zpFOa-dok{a@{y&_5~7~a(Tto}=vOK5#0PD2@Igo72=_c^EVgU(bGUEu>pzz(%%PFK z=+TKAhDjrV3^z7`7JF#Uw=y51hx{08@V9?HplFHfvgKt9?b9?D zm)xlqBW|Ss{btsm&GQ=89mdM}5A&>F4fz_?`A>&L&-KyK zhN*qIcaJ{S0#euuGx+z=K?e{=9G}`x6>a(Mz*fFo!t_HHS zu#6JPBdwOz-cU&p>qcX4ZV1lwU@f`Ys}q+WcKPmCP6;Mu4L`s3=C4&XFac1|HA)`A z4R*%~XNy&S`S{^uk)`=@f9rks-O2TgV0`AN_T3F!ID;-$#%&j`|Ng1B_YfVUBeoMO zq^qwV^)dk+!%?Ydb!JjiYgqNi!Oh>7$^TEh$$=^ifl>UpZL6qLnYz-)Gpi&Zv0!SS(=dJ%opMzBx%qF z3Vx{WI;L+T`t_`Rb0w=nou<^RM%sl%=z}S`+Qlq=CX%#shn{Yw%@p&O#dCGM-uE3iG|3{IG_MluC4Ruq3BtTSNMG_1 zCxAv_wi-umdP`#%PHxVEF*b->_uV~#v3L8slFPZgFpT6k8G(;~MME^B>;}w`7gnew z(Rnh?7K6hSMB-pv=Z9?yIILaQGCTgbj&w)^9z`bfer*P%fwup4>9uoZ#-+b+V#wR1 zR$>spnbv&gwgPu;{J&g`d<3yBbuTselT%Wz;H)KG|M;c}1Tt4wtYXY&wC!JXoQ%_M zOKfqyb?ZA!>(~PUk&PIu;)8mSk;!ZfDmnql&rQ&>ihfq+YPIS6zA^vIl{1*!ly0JN zch|||kTGY08*Lw$ga(57TwhcPM2R=T(fHtuDPD*;b>g3^C7{PQj&VRIaN;>WX`xBn zmptsI(GinKa5cNed#imTqymd^wum2=gCCDpNz@_{2xL*1V8j^YcV6%K0TY>Z*;Nvl z$){j^QdG{?4vND62z1di`RwM8GfVj&q0Nr}7TWx9OaBEW>PF;zJ#UnEiXMQWulGV$ z^4lN$7r^;11orW}w>f-hl^bRGg};B8Gs^!nQ1xH%``@2TESrPUap8XtJ;k<0=KM@q z|F2Hd{}HnvsMTCXar>9>&c9>A|G01ma3a=x(EORw7Z4b@4I@|1CJVzW8`MS1F>(Af zb9Cfz$EjZ770jP=qbQX1oLE2|ggEA{<|V9DB2U~2UoCw_oU+ut>+3JME`K_`mGb&| z(f1Gs(ayg`FF!8S|A&9Lj{_mtJ!j%0;f$hEm#yF0F8BJ+Du>kq@9m(*&#b-~LxXSk zasBlDyws+w;QjWG|4nG{;}%fv_;yO{YK!Lh2@R@dS+bB@O<#W4crd4>C&V3!e|tc!+C3u?DS zsSWq#H-8Z%0wga2;}i~!z8dJD7~mU%+SgEA$G(Ehj(cgw&B>31>?{a2IVc;#jB-G4 zEZ_lAGsp(v>t5AOt0n1(uie3O0BY2IP0%GVP3(h+368}s;=tr-8w6}2)O-*ECEYUv z+Dl9>bSg~;4$vVtT{3{({O=Xp3V|sIR60`cwC#xMMp1fmbtlIDSQ^6s1y*nI5DT{J z2_nY(Gm27ThF4Hd%}Q=%WMquY`IW-1EbbJ(C;n=@40R4~RsZXpwIrwU`@4|TK5;H`Kq~Tyk##_YpZMyZ#I!}k;tV5eU#5_jGlcoH$ zy%0j*Iv3Lh30{AMIYb2V1A=dF)U?qo zYa-K3U;X5ki5Dscy6c>5)RtX*^$yfj7bZ#E#jz@S#C@fcgdjVP*^PbrOe=6n8X4`2 z3N41Rrr+kp5Zg|V(TsuxPJ|JiP!4p5_H7~NE_6Mi&0j5RLh;C9Er3mVVbtDtzlA9e zQ+}zQgmBI(aM?Wwg}xHNPI);wVI|6vnD!{tCup8LVfA?F3;_SOc9J>1UoGn(ycPy_UZ5)`j`y73MOg=!>2=;*n5C10Z$WSz@iuyl01 z$Gc@hovaI@Hg^XH2ivq1vMjm<2I5n6Djso@z{M82-+rOq2#^Wq6K~VJ%ck|d1=lQb zm?JosT-39>Ei1UWEk3==#Hc)lVLnJeeugxOuHG!;e#+}V3zwcQW7a97?|e$pgSN+d z^9D8jh@7>1PHS4#h!cDxhMN#BA5rI>1kL{o2d5F#h|k03%Jg9pwo5_dZq?aRS?A4W zd6UYR;lQYML`nIklj50^aNk?5;Qjl z$46|BU9E5zUFef7wZ@)zAy8}X+VRFiY#%U@HrYlUNLd3YFccC^mTfeQar9I=ilYNM zn&Rf}#g%;+$Y>K5+M7frkaXQf5D4}wB8L~rfO?=)16uw9*GDroGJ{CLS{1-t%4(p& zB=HLF&sr8Cfrt?-nq)}MD&2g*_h(HXM_d%~xEP%4Upw8PVK!vhsvs*IkUTjK0ZkU@ zw@?QjA(kfr&*R!@yodiYbhZffgg^42_II7~#&Wlr)J7_0j%v6V8BvqkhbA4o<-Rz zHAe8WUAQPme@~-OzGG_t=NNug2n^*ngz-%VwJ**kmDmio3PuP+TQZq_-Mb68!_hPr z4C@UO^;bE-%ju?fE*b2mP!yj5)E5IcU%_S*-X|KQ4le|l5GCFpuBA*FLP&~jh>=s> z-@vhEU>m3UR*iIH4=i?I_VGQ(;7t_RhA>OaC9yGZ(joSmQvgN;s;Z%N5-1|DD8&f(`fmyY}Mo|;ygLfezl9jXdjYR)C!iK%@AUl$JvNzb0&?Us{&-BTW6(olxYvOAH~j{&qssTxQ1-riqk56YN1`|1x=oh2H~6akZ^am`2QSHM`Cp zOJTgF5`O2XB%SRP<|1!*>#DX=`ZlHhgg|J zVGr`ERx+!DkO~)oFVaC)qcq=7RzwgD9^Qz3dz>r#gaM12zdaV>(g(Hez!~2j3)EHj zLMozG)qF?0q!QUjzwM21sy%loKz@ztuY>GLV=6WR{_$FQCkTxaib*cX ziKxOOG|IcmO3oCzeIL35}P+y(#2@xcLT#@xJFFOZdrrT2mdDgrrRMWCb7 z02|y(Z%o`eNf=fCQ3B$aAs)wS&(+b<0IkGFJbIFnv<)sZZ&iWYSZLx7wZK>)HQKcj zv*^P)T}NGJrGV!p#0oJgGYp?L`u_BWB9N*9xu%%sH|iHUe}G_6|55TfilBAGZeZsZ ze?*I}o=F*IR%E`1Ogg4#F~4=&KpSGF&z(7R>fF;C4Qiu^E`-T52WncY0qt;N%+pRa zwHVR3Ow+b(Ttog&l(u?dDo2!K1B})q-fQx8iP{A}ajaZ_m2kYc+X03?WZ^%^ism&M zAxPNP8{%M!bz+Suz-Qcl@?y`E&?1jYRXSRiAR>6?ml+)yrg^1 zA(BzJ2s|V=q45_9gmI?M;DQ~|%(e=wUZO40T}C0f8mN|s3$zRQr1g83#zmJD;?1m_ zk>?<{z0FxU7{Se;FO+hN5tQ!V7Y3tLgTzuaoE`yd0DKG>h;|oTsb(=zb|s^jJ|4Y( zsvq|~7XX-W#Kiy-ehcKGNgqPaE=$akE(Ww?onOITNU$BQ%jl4q%gOGQcU0n4H(`Eo z)6gTx_LZ2!b0{f#-~sQEG@w9-aa%SYA@#rF*&rTPOhGL`?;p5zUy@JpInf`@tGMcd zkbbC>H8G3k&|nt>TA#9QL(YYnq>HecTmT))e=_{!mW`oe`^ea>BIFyg8abclSoLiN zL+y9E!noZ-XCg(c4)U2A|46vdE4j4m2+= z&1}UB0TjI}keTH+?<{;IXnFN(aIXB4iR8ip5*=B6gvQ=k1+Lr(tyy0}LqgT`3V@Ov z+?xS1F;+l=kWj4EWT8-s_7EiEN)SV+2%+&B0T&b{{np01>jEJV z{6#~NgN)31ahEW){-RX$DMjUwB%(mak9^v9vQaVyK{Q+3R)JRxZ6hJs{H*3`Wtbc&an!V zGsIjsD{veKT?kj(#<~pu^{bVHv^*?<@jSA|t(n|ydgL>&yAu}b)DD<)g|TT|6L%6b zI~9HXyR(EYKD8qUW{$ipm4t){X5~=?lKGVd*( z(f8=Ry}czfqg_`@AFw9r6nPR7iFg(t;7t;IbG*2-1aNbD9^A-X1hr(kAHmIXxLAZb zPQ?q5;|P@BJ~E4-W(OgW0HnoXc*xylLgESmc@0WSqhULCh)B<>=h~W+&@_Lh(~H0h zyxSqrA)5hAeUK}7k&~;V0e1)Y&yB+$GT%#lr#bGn1Y(~LOyGnP&v4a+F?@Ce%n2VAQ&xcyzk>B40CR|CawBrl$dtZJ-EV#ruC(y=UvnPhU2 zC)QK1243DF3WmixCISm*4!WmArC_d}oPI*FvGpGK>UYCQsDja6gPV_lgA^s%rO()j zPoF-$a3qr*Gr$ zxJrmiZ^48LdoXHIVrj&R&;+(MM85{T^7Vy1nZv@eZW1KC)P8Gp| z$CMMbcKM1f+Lnb@e=7AOB`SZL_L`!~jE8C$K4ZCnTBhCp2W*5ORq}GMh4bbKqGNuHkfy27+`@o+#%1}GL z3m;&R(fdmVToVhbHD*X6HGqAAo+y0MdeD8RY36fYUsXf$&luw;#5%tV^x!S( zOMw9};f+2cj=d*Gaa;@KBz6T1x?nnIS19O2(-AqVBGVd%Jcn^N(fPf4lT+==ON*p* z0Ifv_soONIKxzUu+1>;ca<=#)93Zv!1A6&K;D_p)o=oQrnA%7TPIi2D z;LGHzk`|K*TaD?X_l-lZzgabk*mE$s-ck;Xn%Bp-ZW4G_KPMQ9L_$Ip#s1q7Ng;V^ zGQz@m34Dwvj%uc&B~lv2Ff~E7{NS}UtM0Zv1>P414e7_z82C1KVY2(T6Z_<=M7g}N z(viVRt#~VPijqob7zf1W!Ud1Z?w37Pa(aWX|GX0*ZfA(YFxylUD!xR>!8R_yisY=5 z0q4a+f|2Ar5`Z95ofcezOVL3qur`+Dp^}}T z`#qejG?pKX>ey16S!-s}`HVlg>>p0Fr@J^KmU>WN2KE#s22P>lF3o|V`}a9%Z^1*p z6C)29O(B(L;kbjPQ!ro;HvjUl4sR~s0{Wt@_xKy_1|+}zm6FI@4U+$+Ys!ihB5>Y? z+5(w$v_bz(Zf8ZPXo2E52-JaKW1AH zk3s=9{a<`h^Ft~(gsXC3acseV`ysqGiVCi$>%nlHYc8JiSCBc^{5V^{lg^9>K2`el zTKvRp-E`T!oHOIaM4L6$gf2`&IgfnHq2TL(coeI|DU)dOiLn`Q6ah;M35WLK?-_IA zk1XN>ZZl~~_3+tsvX~SEndOe20pXA>MxJ~pDv~_J!o&)+Y(L)qGy`BoQvTCRzfUXF z^lpc_^$6{{xrXa(&=oV6hbU2zUUVjvTqs}}8f`&I2B%&EP!(`sbQ0AFp8M%$-Gb5L zovhA2q&$xA3(C%~9yP<7T(h6QGHXWuA` z{Su}jWHM4v4Zx;p-aSL$4W{q4n4K1HBBVRGKAzMgfD?lzjzVrL@JhqtFnLrOF28~UUH8~%UHUhaf*JKNLtzx93b`!Md z?oOg<3`fZ?Z^)<1!}thBNl7PJ|J0wAMq+{fBbbOKnq&c<5vE}ZqK_9dinS7^b3Z+h z>crIh^LG}=^CG@E%6e*Ptl>S?cuF@&byV3Sr>`jH>}_2!_(78=)3SM1?`WtdYcr zJON)9PZinSD=>6yPCrr=MIl{r+6#t~4lumLo(ztH`_?K;FCdVEq^O&^%HQnJ58gz$ zC`nqN%8DfkJfK1gNvoHoC5$~t==}`zDM>Hqa4wjqw`t~_BsoqH`8>@bCv8lRs3pJf zS0apno;V={uD!<`Bmo>KzaivChO8$CLYR@PWaF?#%E#ZLVIZHNdA1=0K`BjF%H?4N z6hPJQC%JrkNji!zWmhF`U<8x#trUb%nYpOq?RKVNU}j|eb{CXWU4f~SLtPW7G1)17 zTt;x^>hXeJ&eG0^9{i<=kie-Ja##1PLlD%8+yI*srqi%lzY4;+2Z3rWNrQj57_U@F zYBYh*Mf4KD>8Gnsa~c&1x@7`Qvc>5KBAJc>Mh;nx$}Fb&vpxP(C@mnBSKap2NY)cO zk(`GxdxqrkL6gcj0%DggnxW#16udDQRqdYdSjpD=D=BF4K`;`aS%XJ=?M$i)Pdz3H zd^V0t+qf=1+Wc;)oYYvMAgXi<3_vHcCA0zM_oQ+QV?&}Yet?rTK&-YZj#du_brW9Q z7h@o{4$L71VT&Gr`8lwnKo=tz))1}OSK#851$eB3)4;@@s(JUTp%{q3qDtHG$@WH6A zAp9nwF7N~a!@o;ChixUrtOS!KkHCidcKMv-A|HcvNApl7NztGSF^y!=kViZ;H$Ac& zGiZ)XIr)MMB#0?l7@4r`Ff7SW{rt8d8+NoNMF`iFjr!;)0_toqex%BSB#4`aHvB}` z7Gh@!w@jil02xM-Y4R9egXJ64Y{)-~(5kE)Df1n|_7m>lNki?Y}=fKEC{=90Lt3Db(pnov`)9oa7AUgH(3FDQZ)5kj_x6x*V&5+^A9_glZn zfm%dyjRL8@Kq51K=*nc7JWNV^V(e}cu6b_SrZR<@vk+2Fi?c_k*8(g zg9x>t>i|?+O4Ghkuf)a???pNYNF_fp`dy^6z}+3Eyb!>37oo7~;?<5};?|}c{dFD~ zEJ?VWBO2-PVsYtMFxLm)uGIp-7HxzgaQ|!}tjrej_2lhVuU`Ei6eHYpeE+_TtZpYz zzk8z7kP-a`?E%S0f%L0fF?s^1gyf1u#nY1+6>n)IDjcm-kS!tw#)K~Gog@^`Met1AIN3V4 z3zkR|gKFmW%dMCfY>rT7WPA?5UYxXHkc&_9YcePLUg*%`gwhcEk&-R+2pW~g-x3;+8D)2x%$NBDVQ4tcF0+ie(o)kd|DGhx2{);7a zoVYPDOBKr-kH!OVB8+$_Puz-PQp8WnNaN#c%_LJv)fv(@pSrt?uu|kK6Z0$%>q0mh zgz*ZUL*5_uB3>r~Uxg1?rF7uPNo`d$SbQ=F-bb+K5%JvQ0JiB3D*&vWe{!6Ylm?Rr zL#QHw_$UnVPtCWo6eluuxJ?g^@l^4T;%JJl`|_od4jfhTbC{7Zisa}2NIBsDzHspW e*T3n$w839_-N2C=nM$(M=fq{i68^Y!^Zx=^+uA7r literal 0 HcmV?d00001 diff --git a/results_plots/baybe_original_range/BAYBE_heatmap.png b/results_plots/baybe_original_range/BAYBE_heatmap.png new file mode 100644 index 0000000000000000000000000000000000000000..c3d3807f18c8dab1aa9409c22e026034f64351ba GIT binary patch literal 14190 zcmeI2ze@sP7{?#eOeM)^(I7B_rZ@#5vZPc_(kO{+uq8Aw6|56I)S=-Bm&TT8Xb7S; zg#G}psil^NKpfg?3EKKG6utMp+22s`J&=1i&g1UO^Z7o{`|uvDCKsKKQ3nw@6LEQs zXh0+CKebuemG;|M6DKO+|jW-XJNJyzlxkonjBMm*X2%B5A_-nHuK{HKOTmLXNCAPM$kgPTlL$TX&!J`J-@D zezkn$Z*TV4-;5*l(jF5L=^m?JgyE)XP(-dQnMEYKbrH}oZZnjCAUq$CAzTI^!)1Uu z2pM1wFbA*z76^3#b%D?$@gx8XU;!+M8~_Vo0W5$8%#ko@WE5bg%B=un6D|YR3tR^D zNL&V(gOCB{0CNBfU;#5#kpo}>EPw^DK$s(mB*1z>=zw7Y*8%GVE(3-MT!#N)4z2E` z*~8vfi+tU`y)T{FdmAzR?Z{y~&Eh8>z zco;!5BBTZpaDr9Bu#r literal 0 HcmV?d00001 diff --git a/results_plots/baybe_original_range/BayBE_heatmap.png b/results_plots/baybe_original_range/BayBE_heatmap.png new file mode 100644 index 0000000000000000000000000000000000000000..7d877e120ff811631a1219e04e90e3472b0d3e55 GIT binary patch literal 94163 zcmeFZcT|&U*Df9_V`UH#1OXMXfPjefrlZ0r0wNGVnu36IkrEI>P)DT(M+HQxB1J;) zHJ}1fTBLWP1PCp}5Fn6x?g#Xp_pI}sGxPn{_s8##m$h7zp*+uB_rCVt*R{j08tCrY zF1#HEgYCNX`-N*T7*7Zcwk7qKt>8O#f7%Ry|ET(0H1{#~bo4>oyz2ndzv*+w&C|!t z+3uj9!(DG@PY(rI6}*h|>|Zyd#VA7aDVr6pCG`jYj=dND;S_;rFL%)|v59lQ38(*1Dr zwQnpaW3Le;51iV%v$7DYm!(L-Qo9(k8$w13HvSL!xlj3&jcXqBVBxhTYXCq*r2na} zy%3BPTz9r%F_(J~TVMxn<@~>$_lN!UYkzRn^1z0jXGM$uW)e>8ZvCvg*Va=o-`4Fn z*5CHG+xVyL4`gxnZu(3$47U3;{QnIQzU^Fpbe5#EE^O0P+&`X1;y;8N`WswyxDLBx?a?6(U@tbn4!HfzB>oLZtwI|QeI0>InGytGcb*ph zV;-$`^Bja#k=`qV`DEBoZ4mC{70{ZJ~z~=)R_zo719n4O7!Wxq-tFq zK$~0{zz6R`5RizIRuy;3`+UfQidfc!94-#Kn0~vz;AT|MwgVMz8+NTt@xbEt?c0|z z0h9O?@sCe)R4569I;zIvPZ4%i$c=e|4cQm_7FWBX4w5l!z1Ct@*_XuZ7RYaNz>dE+vj% z>+N^kUVHb~kj2t`q>OC!T#-~Xb1E%*tZFu|Wd@opB9NSV?%~*gQ%bHufET&6&(U2P zIh`J-6U-S+i+Giu8pP&>K8rs>}SwSBSB*t2LgcTJP zh4wF3b}LARG8}8|v?GLIk>F;JIVn z#)i2I2f8v12@_YoHUBo5#3qV!Lo-u?)w(>u8fws573A$2LUU%f@7O_nEaDM*==|db z=gg#i3Cf=6(9mPa{)d~Q#ck6_`!@0Hvpl35JC-bD%VqQxGq6j!o)b35pQ`*Ed@6O=8{ha~ z%Z}6J$Gb$DB;%~ErFoku-wLLWAnX^$Kgavsym>Qd31d4lTak|>yBe9BzjUqx>niU) zSTek0>z<20!iEE9bfU$Qdc;)k-YRh)D$^nPb5=WYiBSr&`VJQ9f z(QN^vM6|jrhP^!GsSAG4y&=d(;i~3O3&!&(T-sZ%cCqslK7XL}ZqKfGQhSofY+pXU zWXFyjX@T^4RlKE@)d6rg+Di2F^z1pTRn=&oXXk$`z>(3&{R(3faklL<)ic=`m!RbE zLo|mt>saGYtZz@m9ZRcyXWNILG)@uZg0P(XRV?~HM#7S z%xzw9&je|cZ7n4`wr?MMrYHNZLv>m4R-RC=lE-jGv*H=&&Rfy+&AWG(g{%jYdveE` zhyRq&)k#ot2cxGFaTMcER&aQ1kL>Z|S$B6u2`e_cc=i{(Zl=R_2mLs82MaG>zC8A) zuGr@pOcXhVQpe@w_?e!c43`d8b{{mE5QbicgNFO#yu(cWMtu7kFA7?lX-P4@bIER0kh4dH)G+1aA>GMUYaHIjv<@643a7QLNZKN#p>@H{yaMdKX=aBx5rdb3qRG7R{Yx}m>698maMES8koXc zgX!t%@`(B2L>xXzUAZ^kHpN+R-#(pkkCALEo;zRpfBN)Gk}Iy-^bb4a8s-Ly703@B zJg69drni2ydPeNjVlFoV>r~9J37CjU#MQADS}e%-H}U95`NCM@Te?+#PDUk7H@e*= zT3Au)dbx*nn)j7Nl|r5KlVGzQOH+``1Bp0zjbC?`QBU3`m4a>S*e2w{2^pEFCt~-G zWAdw{z3Jl|CI#<8as3oDUrBN-6%YFU{`iR#1G5#rv%QTEd9_~lfa^&zt=|2${^-ve zF#&E%`*s6$zd0eNC zcl*B9ORhg9!WLhh?y|kWpTCesZ^{TEhyJX#XYkoCRA%$u0Dqab^>fQkNpY(z&ox zOhX0u5^?AT6{>}7dj2D)^ZOs*8k|`3gMxeaHb72*NCVdFD8^dkkhhnY*Og=q)nFD; zTo;%`>DoN7EM8#so8De}{>HN0L(v8tzjp)t_zi2$c;+(|C!SJzy1Xn1>$3q|F^LF!@e(T-@Iyh?HwZz7Vvhc32GcxX%tHkX)L@wgR z$Z6i0@QyNf+U?G_2M-_a%^mtx8lD&7EPgVwF~Y3JJEs1AYQRmR&~UY$59N#0D?uf6 z?!-i4S;@&s8EXOYR*QYoeg3f_12C8#6=&oaoIPxoYpbQIXg_i;pry-n|+%?E1c_$7`&P{pPa0R>1hNv1V^{ z_%}8*(q=>RSJf*-|8H&ksddpcd-SyPSQb)t1luk=XW=&%OO79-DVC*5P$fkQsTSCR zM|I37dU5jShsb!f(%WLu)`&K(5pI?`2XmdMvujQ;PF(Y3_)y6x8E=+P_?5SA)i07m z8b3Z%<6wJvGc0xc=K3R}8Ttu!6U{M3*RJgWZocs5$H%vaE4=m!2wXHWG6E1x3Bd80 z>MQ+Dz!R(Q{EFM9c_woYC zM}CDX$5P)(jpVz=DN!l58Va(UMK3B_T?|da)mG-HJkTkG?38hYVA`) z$2{&$6}~LBa~RqC3kZ$)Vb4{bAJq>pw8u38NFvacp&wD{P10Ev3pQ$QX^8*<%M^go zkP*{~5*7e4M*hd}*N#_SYG`K8?w0Woq_e+89HzPl1tC zidJ%eqzI8+U^!!rv-Ku&1k-^~bl)q36zuUlb~!f}jw}ymzR7VXo)@tQw8eVjt8Ef`ZeDQseGoKeW&b@N7f}ND!+-^nH=4jyu*g(uLu^9{=9}}=HCT5w)K@>6W3xXLo16>Y$+!@w z5-|1JYd&;?X`aI2vB%ZiebR7J@Z;~S^mf@N>_%}Lq)c+MT5dD3s@!G2tv0PY2U|$F z(iXtN6i+sYAFZ{S2=HE-Fg9r2jw+9h!%=MG51@kGFXu=P3Gwl*1_XHN)M-({&%^iW z3+rCEF#92c`jLhYlcsQuU#JTT!BXMdLR7zwAn7C2S_)DQL`2U8Rx+-=`#t@dX;t4c zRWJ5rnEHH$1i(zmE4}4d0o&DPGZ66hdra0$$a{}2NJXpPyPBV~+`T`-jxm=WC`x~r zZ}n7wqow+7WTh9SEi9tsF-l%?5K+dBBPJW5e<=2Rx_7ITkWi|kl~_&Z^#exDnIc*8bI1qE}RUY-X&#|dh>zww1PbC;+D%kE4J`?)p z&70dG_JXn^gQc#57cXAy0GWoI*~5ODaUd}R&$kh^WzU!qjZ+vVZlg6!?U}RIdg<4^ z=1)8xHVvT?UUEsAd?# z352`;n)RF$0J~=uM}tTtO6ECU9bpa+G(1G}6KPhO*vpxp`#2pj=2RDS8sphOeWH6# z@wWpvxKA%%w7O&G=TH58w|bG4e`mc)a3=3JP5R(s#kcu7({C11qO&b5gAH>QQit2; z3CbAHx$08X3!OYI)Q7@9Q_6|>FvD6VNA??~-7(jEzqYutJcja$7>(WB{d_%tx#5C_UKzS580fmB~<32f?10X&OS9I-dcqXZ5H$Pkn*)PbT zxeb<_fPM@aegIRo^J^EX z0GO#6iX!thx@voTdEgAi)6rdKq{KOGs-(+x^X`+lU2_6SgY&GDrHW!GYON;P`DKcr zEwj(~G}$-Zftt%j-mQaR&O}ynW5?@&;wyo)-dySx#j-#1cC`sBhd<{XD(un=P!pwN z7xA^|z_BE{UB`^5`hFjefy3yTj#{|OA8fePdt2CC+~)cNPsLCys*r-?m0p(-J_(%$ z8eUr*WaJ{DRE^i_L>vx8$ooareENyCYCcoHetf(uqD8JwBbYsFi))QnAOhCk#V%(*_PEzu|Ipn z)HzB0*Rga3jr;7b0EuF=N$mGAW?|KKC$q`1sWcbuTcZ}v(@o~4->mQ;Dd*y@mFj2h zzfhmEymCUkG22&US!J^O=97!naCJ^K>g#~ApDIN#^=P8P!N4Voo`PfJt6dIDCi-Q> z-O~cAyM%8IdbTE|2+YBciWauS;*TwB`OpP7!LCX`%YfywDY^}4g+SEzhvE%?Y~vpr ze{PufClj^sJS9@l#{ZB<9eX+F*Ay$#bJi=&hM{aK?a5n9&YHu*;z4Aa7IhRhs4c|7#F)aEpM!T_qhS#$nNvIW`9 z3Evhl_jY@q80vQZ182ID^t3<^&wlU>;vbSG~f8-&rTtqHaEdf-XUJAE2O+;N^o*}$b*w?to zeyfx^)2`xv=Ztd^QPX9j*_S%jqFC*4)A~`X`mE8g>?>v0XU=i4v5mt4Ev~)>seXw_ zE5hxfE*>=q`D`;@UOw4#5ItW0asy2gNS#c&=TEskd9dhN*tsZ#kl<0=I#j zOOcH6dKGp>a7V5&=Jv>&Q@tRSlh%VGYxVcnm;ejfT+5l<`M;BCEK;MGbtf>)KbeD5w7n~Mdnp5)>o3%lsWR#AN|Sf0Hzh7dtYtP6;c1 ziY|3ks0^)4lFNZ> z!eDdpu$Pn?={o(p2Ws4XXsmwg?UlBrw*JQneNTi->G^y(6U}vJjIGgGuauB z(^5?D{m}p50c&L;u070%bRd4(a+O-Ol47fAEKS=`im9;2s4RS+OFJeeTuvva@QA%) zGn;DCF^3P^byL}6fwn5@AV6$q1XRN>cI25FEmC?;SiTi2o6z?5U^+=!F@vuTg0EY~ig93Hzr-60{{7j=XCrPQ=VXjq1cC_k26$)?GOx%x6RGQ^E~dx4%iIR;#;v~2OF|8P1&}Hl+&tBmWwpFO zQm+O1S!WdW5k4|hBu}mM8Q%a}bL^@A*Sh>DxzXiJ|KhFi0yjkPqvAynRGfmV8}sir zCq=dHb|j=IX=}YIe5*t1xBkTH=g%j1Y4R*m#2)F3I1FxyT1fMqb?l&g&+{5Qu_;YI z!7)BX#ulrT$L#bZ5=|wUXO3Ofmdz<(&}-u(>=`w++Ij+rR!&mdt&dGBW0~lP1X=bi z1Dcp}mYK$48fI({&A_(d{kSMusF)U%CEeE z#|5vtiQ!n9DPtq75SO3TTk9vt^60B{h?p8Kx)>?kuWgf{Acv)oD-`;Wt7=D!eY-L& z$PTmF9F3Bi4=;NkL5bfinTh?68%04 zn5FL&>6h3|>5=bBRzw9vigG<#6U$F3xsM84gS*%jJuC8Y>+&b7uA+dhl^%p#*p57= z=}8-G#$f)<>JLY^cnx|DpuVmQ6?>9t$omWhm+>z-e(s4amLMC5z^=-5kP2i2Ycg`& zyfu~^T&dc1jKG}h)>VeI4NIg4&$r$x6&I_c_tf!KYw3yh7`tz2ArXvl{7k zO%DVLLC`Wa`r0%x*e68@+tn_U`${n?u+GaXvW7ZpUSmhrstIp)tsxH<+ucs_@FL>H zMrO-;>p7%3&On{G=BmdMoC|L(5E~(K^ROqe%ioEC)?q_$Q*Xkn-Ds0i`102uULM`G zL#lEQFrbpK$q*2`rnJ&AX8C9+!u|B8gsVFis_p=wnB*)Tyz;G7Nzi0+>V2)>fNa3r z$}m7V@E2^|mSA;v`;|IW@S$Mp6KZv$1nUXTCaTzQPF%Z+`r-KT{$<09Espv`w1`}& zv?sRio}Aq=C-juKz6fQ-0kw*owHtF4Ut21t?fRdJLkXU~LazM7ynW$4u z<`Rj|$L2fmW@_{A1V7oh|{8hn{)6OqDk4oW9BVKo$mL72>CEUd<3?YAyQ9s&tKWv!eb z=_#B|kTPc$PqhEsJmUGe%D-_C9C3FI+6)gB^TZaNLRfh>HBxImNs3DjxOK(fUMDhe z1zVFw z*7*tXw?p%VVU#8erTz0v^-9U1zyq{}pC~3xR&O*ibP?~-Fn-~kWvd!JQ2E_Y} zzPC3&s!SK0FJ?5sy_h6l2N7cW+v&mz`J_@{McF6le z132&Yql_qdPfoc1!nE?(%Wt*BJaM+`(M8Q11Zm|xT}z~oOG1{O*1k$>S1a?}t28h@ z6=o_){UD-nSF8@})+LKHtXZ*X$6w>sVCo#G?V@!V326ONo(~7P?Q+)4=anwH9DZ?W zy5rDpO2?s$HjC?N-WwDtFSKgYoZqAASuv97RD66f{nQ&r*2I!B@$Kl7rY~b2@z`Rf zxEN7;m`!;#tL#umQf$t@2dhEqvB*kpgG+1ySjekGz}R<}xeu+(+H!?SiQWb;VqzSA z8w}$=*3=NjFLC&d6F#{E;tcfWT0+?aQ20;tA~qRaz4|K@B?GR(aZp6tvSVLEO#p3- zO(#UFdQaY(>{n&EgAA|6%GZY-dDhjJrLwVs^OeW2%n=IQ)h*$?&V`}#hB@ED`w3Qe z2Kg+=*&cB<)!Nsw@SBp&$u-Lc%cYRtjct21UbDsbBFVI$GcaSTQebKo&m8aaZ0OBZ ztWFQK{-ZT!mGpL+O5v^!2_Xqf9jWijc=HqeeGry<%sdFC#S3`Vks$Exp>5;quWgF~l8z0QO zUTymA^D$6f3yYA$s_c`wz865a!)05_`S{lJpWfVVw|kkkcdorNIm#mU7uo}^3Mvdl z$gdr3VdJT;IJkTX7waBZbJuiQ7FjHqH{2FeT|P(jHL41>-#fn-TjF71o(uC|sZZ`G zwZ~iu?8=al_hYG>VtETtphn=kQ}vA#+}7HSYXvY!at*tEScuvEB1f%VMDYr2Cr^ln z==G+8kHsnJVq#{cPSFCcezcRNS=;f~Fqhu|>~?(6SvO8eX1nVd7&_E{B5A;4!ySCP zyUQZyY}+hAUpwb}C86{SrEh?hk53YunD!ZGbL%qqD!S}-58rM-Q1#3@kL{0K?hpad znP~vlPoT-9=GYMn2A7N23iEB;Uw_~WTAQ=tPpPGk0rCeLl>V=TR%_Oe$R-(>F-k|; z?SY9s<34x>%UR0Db-y!yRM(wAbG=bL9#2Dz*wy&3^M-z-T)OmV)^e}nyUrDIySDos zc;JU|UQF#Xp)6FIj#&JX*b=8?mD&+0si>wK*Y3-In$kuij<&h*3Y%Dw6k(=__m?(F z$a&%@TPST-2f9e6R125);noZ(EfI1XI44n!Ev~14D(UbUiSDM4~Y_?55NWbJP566g` zIcor$SG@7%CL6#WB0kY(-3*$LwhiME^FJsSI9K>m#mmlZG;s+2oBEUbT_Q@6P^JZB zPDBK4gS>LxR;@aG`>X{8t$mD2o01W1ic5v?vN9>@S*^o239f%yGySD1qEGwozB4(4 z0s2wfr4oBy5aSW*u7ec^4YaYIaDoK4fUR)gQ@bYVQ_gZ)bYIY^!1=7XrvWoeMe)-l zJI;f-o?!Q(Gx;>(knHNO4fid`Pnv2?Cjwjh+n!8s7lxC~reFET4kol&mN}M+D18s` z1S>Ma{hqVjkI3u|*kvg-m;KE4b0DsFVZprMp8n$PF14<{V)q1pZ_b0nN$395b3V=k zhcTXLP|Z#4{Uzj5B(ObAOMSNDeYPm%i`dvEu+jE|CC*fkp=&B~XnFJY?GZr0c$1TJ zQdrsJ07zUKnV9S)50ytlECt5mG(VfM6!GRwwx@lG9N{TTBP+mtqmIogWCw}g;NBVka{i5LpXgA8$6$$Ma!}Qb__cwA@I&6n9L`9RjKG6^ zH@FHlwq8C2VBOcCs2tX8f*DkDqfr)r&E0a}WX&$0bHNX9_nrxC^mLyK>C7{IlxvEi z)@NS;1pVICQD@|D{ZEFZ&n%2P&BbeOndsHxp7qSn3Bif z9&V5~JTYkTW;`}HW$HkFow9N!6?xAulC@PGzBBGcY-3S$pJ^qUW~g}>qv&hsQ6K|okqKzLfwJ$D#z}hAdWn(~OE>1lyed960h${3kZL527U?9^H9!L!@ zjj`G`$J@UDX{~x^1W3va7G>Azk|Q|QY7XqYa);SobBm(cO43m68GFCP(Q?BK%^F>= zNRE?-c)Q4`0&^u&(5W@st`r6{d8GLowtQY+STRxoO^NHFmpftVtQU&&Yf^ghDgUI(I<>a7Xt#So?#IAAk`r6c6M2g#ay@-!svPHbuQ8GvR}b95ozzWymhtGZ#tiAw#j;dO<<1Hl@^^ z_9eJcKIjHW<+}D&9p_9$zP3Q3@|$|YNJ*zvW_RN08FG=)e%?y*cW$UGz1U!6USVHd zKhe_H)RG@$p61UBjH~g8YOWPH{JZ zt11Iv3H7A!__q<_6sIvzs8AmyPpH$|t5Y>hzda0K0-~Qlxpg4?Oe>)s_Z$tn>2o_} zNGgWBTl1|wixHO+{osM6uzx(Vn!x$OB*Ggx}t^eT=x}RlY)fMAG3H zh<93J^NxrGdsFIWN0?*M7Ig{9nyH?Nqx6Ey`ILbS-=OK|5=D$)`oi1(F_nX`kn4bC z)Q|V^ARI;u_e)^%k8bSZ1_oVgeN`8f8F0NRh07dNp}aT;(vZ3Ze_l?vTS-l(W~SKi z>yk?@SJc#KU}+#qVMV(7>Tl2yvTjS5wq{T?9TpcI4Rw2#_@F$5^KIeN^>n0{9yl_dVh$sb*p zR;l8m)tXznsBNIq5skk_0%-*9%BmodL$TVEqPi3ct@fO3m-9%CbI|hQA!;`Yb*NqR z&)P?`30;B>;#|(XCEMdyF=|Sl`_6|Kxg(c*td-qH4_*D#wvi%ApdU{~URawT!LL(Le=p@2}KV=b_;;+W{U&&N@}aFH8`SLn8V6Ts zdug;Jw%cNbzw_muasv%+$$JYj^|@B0qU4hI<6{Msc1@?-y@B1`bP_Hy9wOKc?u-{v zYK?xbvl#K5!(E+}lyoSxo2W7`_c;Id8sEi>`fv0|@Q#9SdC{Mt%^ISMM+zS8omHkc6dbNWnI1gp@NjE@*N3m?$NqRfG|Ho=>=q7@5~`L&Bn0?!UnxiB zkuB%_<|4O9j}3}z{F+eaHbTbVXCRhCuz!|!9aGPcvDs4$&lx5${cew*)C%nWvNC5h zg#K<(yZ8|?AhmHw7xygHLYI(h-}qn?>)u<&!W zlPhV&g|z6%i>|#MfpTsGc5$olTw8I1|6-U+zTZ~ZLBz%z{Sg-<_BDbaNBkX^y94kX z2K_ki9u^NOR$4E#2DA_t^4VI!)Fix4w5&QDtLSd^Yi*!kKl!CT@PzdF{UW?@s;3Slw>-^Ex>dr%g(*u_s@`!pdhqufHOq|Ak0PnT z*8bO)0k5x+fv5&0>4tQ|e*QYf;KhzK@8#m|n~h|Ix+8H?#%stk8C|DEhnG&Cbi4Me zX2M)B?m?2K#yq)#TyBVC6t(rd;>i*rne=l4K<$+FzG!z_!o??(ZOi^`3%-)nkwR4T zXYXu3#}(j?#`JhK_8_cIoqoJDl{_u?2_G%y?pGJZ2C3|^rYB+%0pj_2omRl%M#F^e zz5u1>L#Mi953#HI7=E9{+pPDIkW|!I^N#2!JzY@&^3WLqs7%f(Hz~;JN7-@+E9NC` z`lXX< zj!-*~01HH2iHJ$5v=r?k$wU{|-SrIe=cUgY^y|##V;yFhUHVEg}%L)Cr2!5J$|X|T-M5#jDqZy(p5(_Nz_ z4tg32OAnD%q*-qgF8>&I56Jahgw7gZ7irqmfs%QKoV8dqcF90vX|yD<3^|$vDl=Z} z6!CCxo?W3RO~$9G(W)Mge9PjS=QS3Z)V;Xc$8e9^v{E<1u9{t&EC9)0dtcS`{PbJVnKjD=}}`QhamZP9II zytl;H)kgImif;V}cTe}oSNXd0LoA|({RBo9%TE9q^8AT#Vj_+edjAR)*DW zVaVR!WF;lgQD>H^l2d^!aktnzG3}1OMvKh}SI(3R1w<>9Ux3l`2P!3&)l;2$7fW0% z_Ua2ONBXGQxjS3roEZ2O;@>F<=Jhk9R8mX#m(6yZEI4LuSsIy_dtK*=W7``GqK?s1 z!og-GcXX+Pi2ZbxFO~B~(x}SWI0_&5dv)0wW6#)J2soQU$MY5-1Uo!xS#qQ-){I;G zlsz==8I+FIQ@L@#Z#j3#`nTNG@9!wRh;6XB$AD|eQwVAR6lBTqQRZzUWZcH9nM$6c z!s0;{r?gh?+yvh^P~SNnig1!egMWF{z&N-5y`K8ELZ13`tu5mo@3VUfZ}iNlr6493 z1mwLddDG82bovQwPDM|U*?ZP$jj-LDe^9uA&a7a|8J@Q`wfA}ktQ;I1e3gFaq~>;P zIK}f8q)9G-T$hiU^)Xt{1h_@e*Hvqf0K4a&Qm%4xL zyCIwIgOc1ekVt@{BPdFC?L7(A=dUNYLaLK4edV4(_Bz2eH3L+U4vGeh|0y5LS_usW zjOH*fq>F%w?c$IpV_DYSjQshV6HtOvR9Dp31kMDM2u$YXwbw2G3wG}Q9TLlgiaOB@ zk~J!|TGszCg#7>v=h;s(Yk@R0z4jz^h=40`81RR2FHRT(r?&PmSQZLQ(HwdX*bF|t z+1}g;Uagf>_#`OEUfL@NK!%r>4`s9#KDqY$&$6++Grqr^s(*hJXq!T2s;EsO_6?zY z@qTGKQ7#V2_R*S3j`(D2Y5F?HKjhUBkkRs;E5tPw;>0Wo4(o5N5B~UreG6pp|0bgP zsZo)+l(|NHRr(u}?Y}f)GVT9__^PnZG*9hS*(?-N<*?$^{=e{dvwhb1yEp9rW>R3Cpe+a)90QV&nV0`` z8d2xLG%WvSH~!klzbMapq)j2pbHP8QJj+~C-y{^G=J-E4jlVh-{H4enUsvS)V|Aon z=Re|B|2$%4-~{~rH2yM9KgsL=4L126=^uq@^S|MH{~S~7ThafXH1e-?`!`|Ye>LWR zSDj`9?W#)uKOIxq+XEYSo*gOr8*SrXH|sC?mdP~W)rUMp|I;(9mkrdIU;l@1$Uk19 ze`|sPvF=kDodG~pwBbf{ujpt7@Gn^=UCB`ZE`(?@7WVG~A7C!OH50zW{`S`W%Xs`V zo$TN2WW+BacK2gJ%iVBs<)_CJ|c|#5A1=gICb_9_0fO3bWbnX1p^Si@} z+ts8!u&a|(o#}e*bi>Rppcvq4L2?(|;&c}x5aco3$0BT5cEdoG3iOZW>5Tiging4I zd8O*@cm;5xG7MA1EX&;9QSPoe#?;=ud-sk9k>6a57+dTzjP}NBDnrOp6Nq7lIHQ~v zTh8fG!2L}mL6c4NYT8eS=MzbhYx2>o6_9fO19PBY!_R0-n0%Wn97E)`@AXr;=4 zRT>O9aTkCh@C|}8>IHdZkkb_d9Klc#&l>s5dU~^|c58#kvXsz8w@z0Okp$2;{X9p`kT3H7`NNVsUC@6Hi0t2XKp{G1)VMW zC4_(eJ&$plOq0GO^eKZD6{-;P>a8nEML(*xCzw)nY$+6e#f=<_#=H@YQl!GS+0W556Y^28HgN_=gPQ3Wk($ezB zhy0p_%h2*iBPNZA% zCkHKj?k{rC%RqKsI(9MNrgm9PR!+_{P9{Yhh!tPS@~WH#XU^^0hev#cWd7f*tNn5p ztH7&>xM)1cgaXyQ(ksCEZ1Ih=4GwVJa;Krpd-K|Ad_7wTF4786cgOa`fs_kWvWGLF zoAkNpH1yV-<>}0nWLkfhfw~N+Hq7-4zuTtr9lR{3q~r_@36#CS=+)9kBTO-Hhq7=j zP@VP*v<6y3NZ%a44DP#27HF0Qrg`h~v6b1g|EI266MaHjIs%ld9R_o+mxiBvxV7gp z(zA964ri`utmnSGKA&ckrW3ym>|YT&c=K*Ca0Z3~5<-)BBlD+X%xpdf5dcpfI?&R! zWk}|7Jf#o3H(FpL^xm^0m0S+9cyL{pOQLwSLQ)_|Pg#Q4jWWx#+ON%B#S~gYs?z#ia#V*1BqLX;i@UfH5_>{{2lnQ~1u=A?z;5XaK>b|H`)~M95}U zO+wWB9*mh&o;s+yOV!#!k}9i;Q(g05f1~==w24oTuPLfeT9)MuI<=O$_Q}qEdOsLz zkZIrgQr-e-W)t9GU|-JNg{+21+_O2OPfO6%dgvm z!`oXyE*o+wOVt!o_ONPAP?qZg>h(}P*~%lCg#w$tDieYfXl$AC;XRP_3ZbgIqRpjg z6Yukgg+jC?CHKL-d_#WykOi{`42|lfOgrf%#Z~THms@lZ|s=*qk8g{D~R27 z!D0hZRwLAxBGJ1&4DxS)5GDlNaXX6zME3&|;!l#I#I?0($l&1MMbLvl)@E(bVRO&J zhqR*)sGbJp+~-~W&A)Y{xO7ysIO{7<^&fRZ) zRN}#CmxcDNwV$Zxq=F=g7)XHRV6Dng=zjL)VSuM_(I^(N7F|16)Z(Xlk2)GiX>(2{ zOFzNb5u2g{YmVKyQ?SM7g8)z%E8sQwfvnpUGNUbGQf@y{;Jf2!_U3>f;GJZVaaMOP zwL2{a1MYn^B;X#DtNH$$0nNmUFEIji89TM#Xa;oNcu89c?`w}&I0lr4l`Z?a1xN#`CGDn-@*8icsB8=2-2fc zE^rPOXoIv#s-V$>5XiYT&+x-OzQ6QC(OxPUb9ufmc>a-~O(;+`It^~wvc>DOWSq&W z7Fg&gs1?kTjCIu+5>-UmBNB*;fW#2+BCFp&{+}Ylm`SV_v%_d(4xkI?3993 zA_Yj6LX7x& zL7fdCu%DH69DvoU6ZuKp4Qrbi!u||FZ6N%O_rsw@iLDCwoMkp=!j>cHy7V2Wgbn~K zL71&*j1a^@w>Sq2o!%H5LY42V3YSGwAOpAoID$hqq*Y)A1wrKXxg;VY0%1|Q6Kc@I z6Q(PUJOh9ZA1MVD3?VG^=jX?8fD3C6K=_{qnV6-u7L)ZhZ}+fD#WA4H3c4(eWIobg zvl~G~5n%tVye`70u6F>?PK59*q@e5S%e5@8SnSlJtMno2m?MA`$VUJTY*3+)TaU2; zzMq2AR~~uMLnnKp)S-?-D!fmvz*VgW-5JDE7~Pq6?;mW5|EdH5UDGAhYmCBrl5(aQ z+yi^y$1RY|A~wwr;=Ua4>%SSl%vz;EJ}?Y)M!5itTt*#0xlo%EoG8Mo=KF^o@f@JG z0F8b;!Qs3UO#|mvIj?f^RSvdAw)!NNmPBa)(Zp#az#wQyp#~AGa-If!t-lYrsAeW~ zhmG5Ah!g%0QTKBTBB>L?LPfukmw&AQc&Y=7WF9|xGN+uecJES;MuCCw;X8AS{DG(gQHdc^RIUzW(edsE% z_gq8`NZC>uv-zO6!qrs>KnM}pK`3L$O7{Y!0v8MVeh#Pd;at1=*$+sd z9E%Qh|KjMZ- zWuU<3Lw23)cLl^IEIgNmm&M$pzlBUUID!q3SQ6O!mPJn>>(gR=|2Tr424X%9fb}}6 z5tKyIhG(Dmec84QqOAttluv)7K;bKd9)rH^u>F;@cDA3YfG#}OS`$?jetKb`qnIMp zp=xOD>QHp7n`w|FRhtJc1r#-cW-9k$J`l*DNr@;FM+^s2YQ3gjKbfaiS$b4TLXo!6 zsT*fwz`JqVziv*@2)KK}ZD>IiNGTz!3pLu%G+XbNT1s3YM&k!Lm1UsM%@OQsn`e_U zP;!9|TgpIoJQ@a=vN|?4HYEV*Kt@bu@}>Qo)YKa(lb?Y+-8H{f2~scm15ghBU^EoE zd%(;9eTPy<9f~WY6|`c57udR67T{r!F0hzn0xaJc$|o^XxNhTrj|Bm>%EBiJ;wOpd zoTW<8K<36Hxu^V=XOH!R2T9lst=54sZpmuB;TG)b5CmjFXwymuoj$T~>!k%)NEN^u zn?{7W<5-VxjY9yGtg!)Lh^E`76D>8VGvNeyNy(pVq1Pb z;Xbftcc3;Qpf}SJpw6$QRnS1!42~+th~TjHv?I3ba!4p=EVFt+3@tx`8UA$cK}o+HKGjm4hpBcCVsq&c@~y<2U459y{4`e9&May191NVNfW*bDu07ygsv#Tl}zN!=P)Ww zJeBVoKYparP(BTmj88*~Pzl*e$deC<~dU!q5RqEI>O&aj*#v-#o=T42!t7Nv)jZm(Ex z(}=8oruqnZZHdkRBzllUxFfPM80=&!g0p!{X%DH5r#2#U<4c0*S~C&m

M}p%i8;12cfhI`vY^vI#@D`Ws3%#By^uS zBu=&+y3P;PwpRd3!aB`p8RhRTZ+TeW2q*{rIY{yZF-8qS;YFkggF{2dZj^ybM`HaN zKxK@81{~(z2Pj~^&hMcW-2*Yoz6viXIElKhqbj}aSI8AD*~Y>9R)e8F1#(rUy-q!r zh#(X)RS1w?621aee;80CKbS`~M|gYxfpT-$e=`c}b$Msj=pnU?b_^_wKoOD)Jur^1 zfyx&sIY5wP)ELP(4>^^{?+)iXcffZb5*5M_xK#^zrou;veyl+Myzyf~&zN^3$k~jA zw&1_2k$Xk}2qZ+h28HAg0?tmqLPFFUL|+gD4c&f@tfl|C4%GA< zVrUV62Fh%R6;GY}jVplzqI;mp^5>}k6;S?9$w_8M$?5;UI;$TW^oHLX0WL=__B_0Zu23CUmNozU!TfW$p5n%P~0*wBUz#xptY>$ zXqd}Nm&x$En9T?IbITCf)c}ykcXf_j$=T!DWV zJz?2QW&VTQg%9~t+)1)z^F8B=o&xV#=wK95-`oCaRNUXF*+4g5JBiRz znq_Oj&Ka1`{P3E0Z4J`J4%$)^{nmRFWx{za=_TpA`LNEW)D1ueU085Pzs&CETCY|` zvG~5`RX*N55Uulu$=AW^1YnRaRx-lH4dS1_4GpE80@6?*=!T($Ox_-<^?RM5fwD31u!6uxiP`-76R9^xQcKcq zjQ)&ws~H(%U_fZ^pktuo*4Hb}W(558p?(Ve*ALk`RNeYMFzKGIq*zH;Iua7L)Hq$V zIl5u~Q%`hx^XK97!}=1G>2KZHHOlw{wikp{>Fn!{`Btl%9QBK*;tbf_9}=T;-K4hW z+K(B{`7{>2mkDXoD=tk6F*h#-Ot!HH+{}|%8t-JK`#H0ACwL@HX6Q#~zyC&ztI&H7 zowW@nMG-HXAE#|UzafF3>$5b%S2K~V87prBJRz6aM04T<$+LX9EfP~=W4e7xtCxAd z^AvqwPhRiTM*Azrg!xYGxti^*MSl&fQ`6j|iqCQB4n|j^%CD<&=rKG@IAb*Re!*0! z^U^CV9)AvBrx(T%iR$V3Y5BMZ6}u32YvhJ(A;dfUF>T z;BYcSM4`p5_>?}MNC1(JjANdpRls@K4s(Mse+dV2F#N_wl^Y6UR zoIbyQscvGOkwLpA1b(j;8c$zCS-lB;B_QH0-{^Sf4#I)j=2`eqG#z}_wkD>giYdlB z7cf-vn$e8q?dg_(fi10XBN0-aXQ_57M!-Nr$D=)VwRS-NML9G=v8z(3;N#2Pg!`jc z4WK|M-eWvJqQ5z1<^7%my3>iY)xOr>Uv|y0zj(MvEPWzX!jWOvIYDmt>!q)FrNj|? z%#YdaRk3u#kN zn2Oh%hrht;JzgI~+#aUM7*>V1UQ$Ske|T?rytSZ8PrZ5sX)n@Pn8#k~%DT32=Ee&i z_Hc$CQf+Fon60( zHF|yPtIP64;qaF!1(+oc$tlVc;cP+XLSKhDt1?twk|OP- zks2K$dW#;Q__osHs~1pHqwX0Z5!h%sR4^rJyu3BjRR|sUMm?i|YWjs{zSQ3ysdAZ( zX)|ZVjz3 z2Kc=fz-9Kiw7i^OU)nd{L>wCxWKwkZA`Y2fTbVIaW$eJor4=prD(9{Jv##0UcvkUO3ZSdC6?<SLdv#)~Nj(0s(01-M$Lb;a)B${we3LMR>zAJs*=WnznEMpyn%B?R z46DCTt1>!UluwI{<9OYrJl<5g_eI4pg@LdYJkMeCa@U*%VlwDg;LR3)|86XQ9Vl^x zxXGBGFUVlhzZnsU)+|fB;kD8J(*@m1G2daWPovwD|9x|MDu(ZJW4Ys!-W*wbSy;VD zKv+Go6hARbIfoZY+ai5TxaMg#JLg>A5%N{Uq%m!w)IAqYIks)+AfKOpNt20KF3241 zYVG#Ac$BLpLDlbjlhw_cr$wtB-l_BJKB;_G?Z>YDTJ2Y5EVQc)cbRlPk-NS>@TSEa zx$-gGy0bs2#^apZ(hPkDf8MwC`Ecqf!`6X;m+CQIEUhCwGzTrZ@|Z~;9m?}_B5M{m z%yDD6k2p(7?VUMhGI%}NJEbCK#^bJap0zCv z+EV>26c1tb6vOJ7-BjLRWh+S^WESYY{G@0C;~QTpSlCB~@axxe<5jp!(pLLH@}Z|>0>Zru)4>z-G24q8!DB^FY6;-QGcR1~ z*F+?tXfkxp6!sDwUKg+BhLwHxb$ThE5i#R5ay4sPCgGgKk=Y1p^Vd(_7-UPII1RAP53SzrcR5a6V%t?t@8opLz0o54NL|7evZ8?Nk@ zmmZ~1f&51@9qp49#lNjsXtjp2fgZP`RHvO}}T2r0R$#{dWJ7p49lkII(eDvco^(WXDw6sY`}v)HMoIJKeS2 zwX62?D4smSuy+C%D!OOy-G6a~uB1VOpa}kVvfaO2%k>uBMkD!VW`*6uzRyJu)V5S+ zdRp86D&*0%)#P<`ch#sjdB>H9Zi{o%Ipz~y^hrBd>nY9luMg^q$#VoXMh#nM#}*L` zlL33u!;x|Q?Ty;eL2FBG+*I;$<%Wr*p2GB1Ne$GIotvPwx~-O?JKV@WKgq{a7ys3 zUYbeH`d7rfFP^vEdvs@m)={i`1p|t<;{Ps47}n3W`s1Q z0w=r5>Nb7kR7=9hOdg(U+}MM^$~1ZMLM1)MF2h%UFmtq^tDqu&m3u{lSN6GEWkUq65pVj(ap<^ zPl$G$7W=X?YYgbM&+tE}{XP;8HXpV)CCRMVIJZ?qSB;?)KbcHav~YziB?}}tZ_o14 zXOh>@Rd=I0=jtBm{+yYxVrNM-Mpvy=hOEpxIO7M$c?fpW5(O(&%UtMU z8}Dzn(RJ6Pg6E%KqN-7)Ijnm=qOP(5uf=hC^T@&?;Zs}--9UNnx$ym$zYa*6h4Jyf zi;O$6F}zeiO5Rn3~1P3bQ#UrX{P8Pt@q&MAkO(Trza z^b%fhuvbPq1vEZ=sNZMb*zb}i zHup^{f|I>#)3dr}pgwMRPQy}uTSpsa5wOW=$Oip%tgJ9fN=m^I=G)5A$BrFS<=r!Q z^;m;gJ8$=9&GuvFp490MoRN(3+Umpusn(E6tXZSyiIamSByzzkU-b>nl3rO2+~&GS zMZj80E{#X{IZpp9rqg@W6&rmIeVXwB``8HW_y)CV(yM*VSH*dbsBOz<$T{kFy&)A# z_Ru8S>3mspZ^u6=nS80usqT}x=KBp6)3beRDcUMreZpQoUOk>FRGzDT0nzfQKd6I8 z3{PRF>K?5Re~lwxr+YnwNL|-`oA$-7h(Fz%V*P2&z2!>=US?Y`&+|-%$1kgA6ssX? z(|7Drc^HH+oV+vb0mDhD++`U$lwyT?tqRUmXC7qKH`aI9i)5tZUCG)5W)k)!_Hj;J zqV4FG>JJTlm#HjyLe4R+;R!vDmz7%diZ!OB?>o$$tLIT3Z@bF9%K_#*4`m>EK;D(r z)Rvb=3KWt?oq zCkO1$KHnk7UmAEbrSsY!+Q^I_j7-j&g-$*?9d8-#e0R<%e0zu?e64e=8{1(kFe9zs z^va(iBuI}aV2rw19V}B4QCAWW-TG+wC3PMLA<--=CGu@lQkXhb%ae{Hg=YOSSaGw@ zks{*(p=E^kPgpGU=JrKt`(D0L(V^w!Y_FPCUWk?8!LO2{+F7rBTi0}R!WfrfMST^b z^W7Oy%hrZ-n8bzXcP0+wvdbi*j@}^gvD)WHXlnZ8T2HLm)?1A&_s^XCA>n!%t`|~_ z+AbbiUd(>qr>jdnKAgbO@P+##_KDVh3N4ZPZ?*WX#qy4~cWOjVlE*qRN6F*o$w$v$ zL@$>13QbY5i4?9nGbmV7<zlxDnk`+FXV=R6re%_?B7)ZxdlMd> z3WX%?0qRwi5YMh-GsZ+vvPjqDH>+dNGs4-pQ_L`YNOu4A$ULVdqk^%HBu=7JA;3f5j+25{G zn*S>N$?Y56uzIfRd$2AuGAv$h)(__umEmhSfc7K6oh8ejV>$wZxx>>%} zXlD-GlxiCYONuvPDCa87O1^uzaF$SdMBo5vkAHBLA%rxeK4wkztlpv-7rnL|qLhyN zah)h~$4=zbZh`%24O6LNxSCXv+3r%wraQ9_9Xtr4+CeMRUGYFH)e?D!G~Sp)y?5FEEHmtuzzN*Epygh}%ByupesXB# z-37GYALs$MA z3$|5b8l)n(!d@NSrOkmvjj8?17HR#uTIM}v;R%nUZ*-SNtZjsp)a{KUyTO>yCc-)3 zq;dH)Pk+2%eYs@AmSe8oDq8i~R+)aeItKGxFO086i}wQ7f8D_}CBrwhupT3b;ot}t zPAyCfoXpVYWoj-LUez>dyUxHP?1R0>zqMFLeoeZeF}v7XLLOu6m7|z;v^u$T%uqSk zJ~%uoUTEHAsim78>-_cKJ}!?pI=YQ>PKTFPT62|a%)7N?@ZapTHDcD z1;ngTkHR}+*VCNaseIOL9h#&Pq8qZ_>-Ooidz#j0g_(DBi*=W_Z^rf~Pf^s0d@X#; zxYARWIt#(2|9+MRGSuWLvimGhh@UfqUL``T@j#g;w{^c@zO}dTvreF#q{j$>_6;4M z-KYFHsG%=apRpd#DC}4A@+w1?22_g1fo+kwad^$Uwzf9>n$dG`Cux*JT32Yc08w=V zu7xAip{9Tk5s{(*v&-Hbjy(6T`Ti?;VOV$YM)EtEKoetkn zi19XIbuTHo+}R=7!X7@kFdKCPYwliL5?!v~KiRFPtNnC}ipk@%)&1CGUH4eG>}!NA ze-6*7-kO>ea9w>(gEJ=u;N#Sv?teX{cZ+HxYM{ZRSw%hKPkbbNbil?Aikzw3Z@NB; z9|oRIJKbi6H1M;Q>ew<(fm`+J#S2((Z-Huk85z&~(#cmiFnav{GB=P3^}z{1&eg^m$CM9iUZD}%n9g0F&TR?~b`b6hPfAJ>UL7O{eG&!Evel0y zs4P#;mdp{LqLP?z-hr-qMUCz+Ep`N2oyQijd^=;qZy#x3B-AM+(|P_hXkp4U*445p z6D#X<>lzyhQ?_YEBATAFy7JgK$}-|*I5?`dH>ZV9W+Xf2FhVLzk*{c&LwQiaBJRGCF zB+M))v%=V#&gs>BWX=O~qPB`E;Re;AW^d~cmw0Jsl|_x-K*;5Jrh)3iQt9{kiJ*7# zN$CDXk<{3)Ep5_=52vQA z2v7vO`{>alRp%=SKs(XGI;5-<=Q7*dRdB0AejpPQ37Y$=htn{<6igoXkC?iedC+|zz5C+AWgZ^2=LWEghZH3BoJBS`S`wF(oE#pCm<$fg#@1zP zi2&)TWvEN{CTeM(pbJ2(6(`nsBB&|xL2!zI(?Vp!)`_6jkgyQ*)WZi;`=3ydyXW0Y zGyY7l)AM@drD5it3V?&mRMx-CbM4(KCJO5&Qv4BGD}sITNz+A=2@fuv@9_vcbmUjI z`4QEZGIv`jL@aOMb=|{;m6-P=3uQbe$MfcdfN|v6fJFK0^A9(vLk_I0ySlP|A87iq zNUtD`@6Feibxa^1K+z>Kq`2;ek}C#SRJw$05&pDlQT2}XgMp$v^GZV zO;IFX^qm^?0ZBcd4D^^NCo!N_yI> z4GoQLNllxvUr$I8_!XF_`j!F@qFX;M47^B<=Rlln$d9j_DACG0sLq!OpFY9z)aC(p zS}%{DUX{ojW8?RIkCc^6O_PB)ojwBX9338Sor?Zx=Aje=Bdls_Y8v7REM-$x;#5b| z=-Tbuw?X4h+Z?sl%h%(4o-E+H*(W~Tg~!Y*Go+ADm9`I`Tw|QYIQjqhVIX|1f{@E6 zvoOEVTqN^-tY3A^4RwRQL#Mkzg@g{-qLN|YzsK{B(VS3SuF6KF;KtlN)t5r=ELh!( zidj3GvIYFfZt6{NIn!G8>5iFSx^#o7?}wg@$qOb0>1$2HqpAPSbsyv*w|UKAe)KML zP&RkqA?3Cgsoqmr;$C`>Mumv+LxCqnM5kvb5+|tiQbzBMY)hypLpUYi_S+6x)SRx6 z|6W;Hx!&K9vALZ}zGqMHn>YVfUQM3}VLL%bmkJC9nVcsT0(DU8cREiAg^oci{f7)9p(BejtGv}0XgF$Bn{ z$Xk__3if3<#(nnUM;p(vy=VCy(fN=$y4h-JpDe zy>sbYsms`1E3Zdo+9uvwcrlhHRu)cuJ;o|x^2JAGulPb#q%sW|tuPD3Yjdk|nE3B| z)_{a_oyuo&f(b93A@(?Zr1bF~_Wt5>`HsO!!%JE1Ladi_XLDjso}^aoJ0Ob7(zw!{ zJsO+Rc(26!Fr-c+^&)Ie-t)7D>dxxA#0nR22lVV4K4}^GyIc({QaImtq|*Qgmi>cr zGGta3PD)IC-*Zt=uos)V0)1;E1(zqjU<^|xgIfYdb%!P#wZ}O6IJmf!$;ilfnly8~ zYx`OQPBJoLe*OA|UUpne@8?Sg+{SCmO!E9Mc$5~KF8CVeA0KN3*8ys$s~A_ysG4>7ve88o5~fpd+eXhvGffI4^zH1 z^=f34cD%5!<_1=Q!d#aKyY|_ZTT+EPzB~}B{W6MqqN|wyozBYiVmP|%LbFOE+og2E z@*cq2nksa&MI=(X95>2)qBFF=;l2`+H^1Bls7P>8$ua-H_3fy4`idH39fvENvNwk0 z#ul*H;^d9yqajOKauC>*xmvzFY8{wMC@#W|cZ>MnD`Ctq7xWT$O7GV<=CyY*uL0-_ zP`|Bjl|x_o9r>B2vo)@~3Jl~dlnZ1OO1DH9I};t*EGw?h)vI=2HOq z&fD{nDi6d4lN}T{fS^O2cO@+=OHneAG5`IO$B!*$54Q&wLcGbO#rp6^>kOXJdIIa2 zxKI?QYcVT8I=&c~q`mSrCHb4ut*M24N-Cx8*|&+jThOuj21#1VVd ze;gj(+7Zd>T{Ev-SIe0k>)1d!*W0Kftl?|IK%S*P;ZV!4k@vPxzkyM(P~(c$(#oNk zo>N8{T$Exb<{0A|w|WSs)2X8w;0}G-lINbd@$Ef@;^5!w69x3dzVy%COG^$>H*G)4 zMgT`ebAXB8;V$fyl44Uj7VRz%Ds}`eTOh2?4N|LPWI~6STyk@ASaqjBNAt>?3sc@l z{b)H0T+4tUcKLj~3&p*jf_!4lam8)`EJvn>iy)2OE>N#!Zsj5R2eel(TBc6Z(o83r z*DPPdZ{<~!m=sw2Coj{64td)-G4ZE}W$#t+sYZKj%P+7&Jjf@O=0u;e;v!<%oB4BT z$Xx{>JMJR$2h2i^hYv5{m(PDUN#N|y%6v?~(+_1;&UMyk7apB4N!2ckiRbZo&dSN6@6P+7 zT!8P~{qa!6L`K6v4e505^xSvU^@|VfJ6?2Z*^OWiqDwSZS^V`Gwjdr z(>l0~5jy2QE-G2I`d_h!He6h>Q&r{qX`CliFbMA8*g~rZXpp=8Q6A>g!rcM|!5|C`9|h+iM~}|AYuH%`=9s zmqY2@#+`cD;E$byyr+QRZC%tI{89KUyO_R3_dT<6Nep8~^2M`G8JU`~<-$eFTKhta zm=ec+PNqS4{~ZwktdN~@{)E9wb`RrCq=gY%dd4m-LXL2{`zW_T&0ZWt}f{=wc4Q~W7H zu+Oil=?0~lO~+l&#cxbElD~5N_;}8bwES5iq(2@**REPF?3^UL-Qgg|)TIsejJm2z zXPRxVQ9Allc8FM=ic4r3$iTdnlZz4A9${rpBs89mMxim{Z_4-QU5l|Vwwaneq@|u= z<`WZLI%>xr=9okh_A1(cC;z?v%f2ewqEpY^^3aJ%i8>gfiB6hm%#e=-z*mghUYa2j zF*VL}pTxb(#J5W1HdUBw=9O!uoK=fl15YUDOC1ogBa4XU?Fl2z(xh!l< z873bioB;}*m-zX$kg39L@MSMpf!c-!IruKEGLK?t9;2S=$Mv}3AfJH#`DHs?(M>rz ze41J-Gg6-jRPcyviY`(8{Ai5H^Z?a-hkb5lOQeW=^L$%vT@$BD{V&m}F=pJtyj@0@ zp*QR2+IY9Hx_VxEMYKd!36boEMTbtSE(f#I=b6`h2~Sv38!`7ndN*(PZPoFvFT_Qp zmsnOl*;;DFsZSwQ4jsc+fa&6Mq+-o@XZLY>`iqYiRvb7Bx6=-LwXw()sBGURYsaR!i$}~VmncLNemu2De0Hv= zci;8AOsmt~>Ol_t+z4z^*H|AKljIvtvF}5xyuPB!gBC6kOnZ)JfoGtdqisuU!7;k_ zl})RP=~55oM3YPGEbx^yYi@4I;)*_{LUrW|xZ)w_hMp{(8|}eZLc)c_sm`)@;A;2E z7G`#5K3pV3OrGQCR=<-p`l`#Z7n8mD#FLs(S9WEuJ1_k`y#gbc9gWG6W5qx%R0aR1 z17!B4Zu88`EG&{i!5=CFz+~41GIap1mHv71FlA*kB#}sK=R}+@V=J`WJv{Xstav;$ zGRLfsiEL{c2LzE>S6;p0v1%h1c0FN*^XRJgJM=Z(g}1{mv1?3B6U*B=nLO-%&LitC zuF1*~i6bku^_mwPOl?AD zj<<0w{iLd2o?>jgL_wGHJ6rI=ngX!@i?G6Q?_O&*j-|f6is5F0LiBq7Y_^*oLF(Ns z!$b9$)`=K%Yy^b7sDN9j)pIt`V5!=b2wmIVIQK$mSY`K|ucAd7G7lYUOk%OOw{L?m zco~$4Z^651y}gsfZA&1kn?(sY%R^JxBksHIw`yavz*FjZZ#f=e64nWFfZ;KnXid<{ zy8nnfSvNY!=#IGf#BiVprtk0`>G2-!kAya6rdM*!cp@%En}w>y|HL|*;EmW`r!dXp zb=Mg^9I5M270V|zb4<1v_FGfZ>U7fYEW%=&!w_{utUMq?_H%3R<{r<&#_UHSPgssS zu8H(_FsM^DGgU9Cwhb;kVLRPbylyt^h0D3`FCQ(JYc>slKIWchw>d1XINq!puEm`RKk5@>r|omlkG+-j7nBG@Wk@7b?nz#)3?C?KHA`8Pt6r zQ#MNl*(~)nR6cDb9bd+eMrq}FT8Eg$$Y5tGGjEhC3miey#*Nd+vGtC)-@0Y+)+t6X z!@6*$g-T%MG8<=k>k4XeAOJt=u4E)18tKNPD@o+H`t3~VYqC^M?yD4UFw90s&^a1c z4jnndoe*z%sf@I4E(E(YbmK~DCQ?@|8Bn_5H_$1r{^03EY;>TPF>6*DhJ%?hv zX82Tid()oVQj3K7_B^(YIKGEKh8s-=>lghzDlu# zM($+Sjk^uE_xv0iIZI0`cDl<|x~Z-uo2$0_jJpJ%YXOB-^Xlpy+rY^i-XYQ)q#UbZKB>_eZJwZ2EUC+ zT8h-Eijrv|wH0+nLz2f;i}r2W;allpZ=Yr zcQVJB;@9N9i2R{6e}dg&^3}fOb`{_Beg2h8!rm!!%s1#}NGrEJIpYZQu3k=_oMb4) zqkAe$mjW1tQpqWff&`irpofU<(Sp_{vlzrQm2Yols-LpN@BQTD3TcVM(912VQ;7^`)hnmPga z=OycuOv1h4bo_jLn#IM%F!CWK?jx58uuA{De4f7s6i6Eu6babwn)M}4d@J<#Opejh z>o}g@JqQ(h%+!UVT5h3yjKepLF(1m)!B=0j7*y6$f-_V3gAXo;gbYqvo0BEM`K%yLS4 zdb-*P<**Saoz`eMT`<4zd9rm>DBAk zDx#u3y?uR_j2%}gLHRD7=RHCb`SXemM+ieO6Y8J>y7tB0uj-r>NYu1pbW3C{Qw_5< zrqMgZeKK|0u-D4Fy^mX`2Bo$CCDxUR~?VQdBTct;j64gDAn*RCf)vHPOzmE_# z`9j8%bLTkZufbEm^ZD=p6;f~Z?B1A9_m|T!zxVg&TOi(EfM@>VcmShfD{mx5~drt-JTR_&K z|4Ig_wO^s}yP&z9dEvA~mG3=pw34vtec6S}P#grFLvUJ}`3%ROjj2&6I=&|$)P{0= zeX^PMB_OsEp6g3UbC!yV%7gXvoQk=9(48FQAbdaa!-r((+m2Hs{CyI!>jzN!d%ZU2 zC3U^Gmf>mV5+Hlvw(M5f+92}I?7`fHx)-;@xT3i$^wH+$sO?@c3Wb-1NyL2d;^gx) zR*Jy9psaiYfCFg9=Nr`=t*WUphuTe$MbAFLhjk~V#-7y3gmPry9d|k_;51oCwn>EU zBQ_zSJ}ob3omzs%tz^hioC3KGzaE&{fOYFJ2VQzZM@v!h&9WyNjrOnkd;NnqkxMZ&{CJ`E zDrG(zUw76BTU@L;09&Hpd?1UQ=+7(QhnnMw8){G-n%@F8obT(f;NSUoe zKfx4sMc91>fc1D;Tv9Rxwd+tgAB-w!6np~R6jk$d=$h1r-CaTrPF<%IgYG>0X~W%{ zIC%Q`A}qcmCy;T4+5?jFKR37}B%}*TMy|kt%|?N_anO8-|2e@4%1G8qEf+r`-H_ueXn0rDz8B-QCC-& z6Me?x5j?6s=Yb3b;k8eCn7;!ciicHp0oFY??A!;QO(BZkF=xLs=U(q4m6sNS8Y#R0 zh{^;pmIf3<8TR|ppQKXw0Jb@p|M?BO6qN3Ajqpfp0qaD9eWfi z)gN{Bf)pSJ3-j|Sa}WQ%kxT}TcIVZ8JfLnLsvWGB{46xR<(7GaAQc{3i_wAk)&s&q zAQ^!8z9nefZg=tA{S@>>`2wD?0rT&{sj--ACD0FyK8Xql4CM8f+|`4FgH1JbevccC zU&5`xoo*`lamBSxGLK_LSw_45RIz5&l?J6)(@91d0TxwjpahF04DYlxBVQWqi@Iv@ z9$9X4V`J)pY=1{Yk#SE&Ru|N^E<`C@hAmy&-1K(3q~T*etsvy6TKD~XJ1Dut!;Bwr zi?#u*b<$N8IqRR7H29Uh-36BB$|Vt2SSfJQJb~uf8nAay0`Q?~fyZN*4K)+;RVZVp z6SQ`|deG3Yod}kRmH{5!AnZAFowAZHmVM^-)}`9wEzoH4Khl-U0=RL~vb%&#!jR zAEeUo^)(dCB4qG#&7G;Sum9W4V&ATV!>DiH0ttw$l&JV%UZ($!_E{H06}?4h z9F&#N%kE0nwb0FXi0tqAVhzs1kX@mX3Av@`S2gsen6fgK3_RgAdHki6&=gv zPM%60%#5%3djo?SdwtFzJ=x81Gyi=d>V_BcQy^wl{hvSGy~$+e{k|x2&|Lk0ha14> z{d-oHTmQ2ZP?3B8=MMg_z~le*!Kd2)6XF1$c^S6{RZ;bvq2ljX4PH0^SGW7}dH3&s z;{iXDf4rk13ja=Kj|9EZWTXFelc@jqg+`Kjysi88P0`H_yScRmfsgvYj#x1YmGr>o zz*SUi05U81`1rK44WAJ(x@ian1K><)d3m*4w;uhrX^)>g!6(Sj9%0~D0Z)Mc^`mlR zW=1!V?XOk1&j~AuRajUL0xc}mV}(HEM01LrMkcHPlxQHLRRk9ednU3=9XfiHpzGY1 zurXVrfW)4;{}YTP3pb*iZX=`+Wc|U!2L?&hyDG!%{a!~ZlYcSScJn~W0(ueY$otT0 z8z;>9_ZocsVdcF2`t=Sdc1R#n%0MJ~n5`j8)_?eOU!7h;&)CSw1oYmQ04X8?JXSlA z`S09H-g`MV>MK+OZ-DV;xM@>C_bud=vas`b^DR&?Pz7!i9&(I*`}fy%bf|}gg-Jtw z9*jjkh;>+Y<%k1QCg?)jDd-k&2En^LyAf&VMo#zQIKDHibYa`uVX(~@zZk0X;Lni} zMB}*|ShZ$EOiWD7EOv^c;lnjC*uE{-wY0Cqc?xu5+CjG@6N)EOE)-OZ?a%<4XWT#s z0YS$Q_3p%mDRERuRNxV`#VdiB#MPP3C_tDI1`a%%&zQl?3($HjhdfU}Ad}27#npN7 zF5pz%K!cLr>#xm0LBqPud1nZXK_tnZpo4|SqhUwiaDqRVU~i(|j0Wxl z7TFq0pr#1J-G8k9@ADrNg@srQ5-z%s+66VnbRimoQy@v^I8G0HTz{A?-}W;1;p631 zhi#Rgi;e9(8u&}m9{J?j;+VGcXo2;B9s~v|8Q&M%l@(Ip;S-8S8zR)%7XKcfEWD?} zU&F#89THjAiOtyoM2BW!X(>IV7@C)tAld4|9=-{z3J9CiLI983eW^fpB4GhP4SFCH z1NWN(yCH+yd2kA>)zt>7-NR6^1~y02sA|zCh(uFKzLeuP>XRppHpnp{92#nf8Td6M zod<8HSr1j76#UxXgFAg;SP3Q0)>2g|A0S|<*NU3(J^!QFu3B2 z6CQNq&}FU(We8x-0;duImCdt?A+AS*Moq@|zoA{M+W{1>9V7@yfNF3`T2{6lvLps! zUx>g!uP_bP;*<_=pToP#vE7FSgUy8uHs~~{1NHz>7rK0XlC96Ko*hVicHdS31;&Cx z+!CmYXgY7NZ){9~mZa^s)xUQI;fu|AS}2e~Lfebw>Hr`Km^Apo&re`A7SQ?QU`-xj z6x2Wvb4Lq^e?a0;^GV~*34Em6iMMbdeZ1rS0d;KyeM7TDdqDn13ZYaZ$&<^xVTAKu zT54)DP_D&-dLZ}x3Y2AksU~>*Aeb)DgqMj-x_no@zZaHTn^8s!?dOzYAlcXkq9;qR zgtebkKr(?qgfv$){&K;27#tr!ML1>pwI$X65Eqg`H*zkOkytRp|MN=~vvSkn0&uRC?dK=X#wYs)uRvW~Oh3e39ElZ3>(-bbtotKSG288E2 zpqR(TFm~HrD)E{XY+(*sdD@V5q$RNJo&ttJ`IC>+IfyXB1)MRXO);5o-u%;cv_I%` z?<>6^*%@{kKd}1Cpg=qEPC@wW>fxPFI$ft61D$-ji(v0f0X_wx&bBkwrEvie&CvbK zxOB5OPK*LaTAxDR05t-|n9#q|yFvoKQ>nkdAGvBr*zXSA6fLw4Ou;-5j+hxJ*M~U= zXob=rZ-vB3a8baik^*J8m(Pzfq=E_sp2+<7CbCWd+8_$4%E$P4N8n+15O?$nkVD$f z{SB2-M|weLDeqxD4a95GFdzUusX~4FhRvX8aq+QPGR4;d|2c#FugP=!ZWbftxY^#~ zqo>)fUsnQ~!A8u5uD zg^uhdMd-TTe^)E&x%lf>@*MMrPoF-Sf^Y_QYo&*ck|f;<5&IH!1t!C4?#?w)V8Hw% zDpqy$z6M0oXh<#iy*7)$l~Dux0-kazsJboVZtR{FjR&G0pekxzU0n#_5471#Q;ENR zDT4m-&xwf?Kr>)^X^Hr~eA$8&9j*HKAvY+)gi^+mK_K=C#JU_nPQumQo$cz?tkwch zX=+7?nBZi;F9lZZ1839$+B%NNRtkh29TY$fdO*WZBVzzV1qmHE&G1R%?;i~8w_%Uu zNrb4|x7a-i`Z8DddF$%vEP>C}M(S16^z>JxKsu!j*wz@>>Q591zd@hCNB~F(zbu0Q zQOU@l60MYgK>LBQ@g!);HG?Ti0jyG^>BIqez#wRa?D4QX(N@zw2CfDVUJ3Y)?T4C} zI*aX-tcpiyJfY=5tH>q}5O&*ykW~l?g~jYZ zsjEE*Xm!uJT4sB1d%-BWCt?*uwp4g~ZJwkwR#u8}8Gly<`4dMFw7bO2EFOf2ba`&h z*I;2~nLqSI(IJt`aZt-bB9>(^IA#z+fk6xUlevQ+9ye6_5YjplAmukbKXS$agk}H+ zK??HE4OCVEW<4tsSptV#>ON-l& zXSA}8Amft^>)YJYA4SK5q%=Qk-yw`^`Dj5+gMZcey{qS7ubxZ@Ns^)b3m?ygPWJQ| z{R5{$y`*yXKLUl;6m@;i{C*7e&hzpK*8no9eB@07_L7nkbI?IuF8XK)S@kXGJvTtU7IZ&aV8k+j znWf!B1)#y=Sd+SrPNbckU2t?X26TPL0eMmZMT?9rus$G+D+Q{P?Zk(>_TzpbER^s% z$e9$O&N^wvZ4BfPo-GncblD)5zs?KL+J{QQh#+8q9;1&0gwAJ*VF9pfWL3~bhJ5O= zDv^WuCJ?3okSX#YMaDrC>IQZQEIJaL0#T%M8zgIJao{TxZG{8{+ac_AoM;t?@o!$8 zAI>*xyD0U0y#l~s2FzJU=eSGiZ_hN@piDbU6-)@QRch?*)3;`qaeGn8?R7>-xS!m) zB)p7!T>a#{&zbkYq6K`iS*7Dms|crFX{QP!@Cq6{YhIr=>So`BuKoV#SCceAJ4>f}pHaTT>$m=5w+wDX`yb!xD+sVBWwy%VD$`h?k_o0R<$a@fbaQ zIs{Yo4Gj(99P78ecWnioAGEM2fuSJSxwsTziiTdf>AdZN&`@sVtl0C;UJ%4CgD5KR z{qXQ`$cGQ($q8hpcMuN`ij8??)*YBA5{X!@-njxxTM-P!1!{`dbH!tf=8A>aA@pbg zOO#&n8Z>Z~DnoV$O|cMZ98+Ec+JA6__VT*~MiDO^SnI8zu962ObGmξIhn0NX0- zf^@j$>>L;^kXQ?wgzHkDSw@+bB3A$_>%9Z%-&KfV6I<_N3{Hg$y5?}dVEgSE8tipJ zVGBZuK!wPg_Zq1G`)(rAECu;Y_@s19gn1f*JV9HP;+;EBL8UDglmcZT;#P)d0qjW! z;EPTI%1~(j9i~j%S~)ytrx>|j;G!z8L21xwnPQM0xfJkG1fr1JQgV(bI1t&9Z{O}l zjG@~S{Sj<3wP9=bX(wAlOvW9uu{046(qQW5>B>p+6A)XBc!9$6HIS5+q`Us$1(J(^ zidaOo?A^OpLqK89jEI#cUKYCO<0rPfNEdtSu7X10?D~;|2UBu9CwM?vmZwQI#}edW z|NEvv!-tgzJ^a;x+?lV;==<#1Wk4G~v!#B3C=5a*i_{2E0nBJB@mRB4p6$~H!Q*!5 zb5b+wL2$FKj*gDmwViK7VoOX`Rx=%H1w2&_uB600e=KJt{r=89RaE@|&8znKKw+(n zGEnV)0ajqRbDyf=+QLX;R1v~(5%$FP4d?+N?1f4z#%8+pvlb^OXGzBxWSA2lP#j19g8V+pY*8dvP>TfT zonPz?gkKPu`ve-EO0ez~Iw--V0}~cvpCrV&fU;8&BM1~zQ&UR?L8a(-5et$ZYO8{} z+S+R&mR(D#NH0Q4WK@(T!d(*C*`WowS$Vee=f!}7b`{?hLE^i4Qv=bUD*imss-vsR zZPG}OEVRUR$X|msC8ZLaH%CWCy7DEY>KYm{9Ar_n6i)wT^orV%s}FGCBhip<;3v<} z(2Ne^Ri=^L%GfwDzZc~0LH4(dFt!C-415G&6D03d30MFx?x+Wd!A&ZoTwr7XkiO(hYW>_D zbjw^jWL6Ays_5q1J>4%=9IST66_BuanDhW;R;!p;uaAYF0_QPNxPegj_#IPw3lg48 zK*b}^y{y%fPRxhVXBaSYDUB;Mzn!Kq27_^0zD6lV<5z{*MRHK>zAyltOA$^0j<o(s9jDu9D%-8e&^Z9 z6if~S4)(LS9s&ZWCW|_-I`07|C%+fM1tn2(+RNZ-EBbXyxpy{>LZO3E9x=w+w*tEX

uPXm`58U#B)H zbay;_g2Td6AX4a7;RPWie*GGxe}k0@k{SrusSC=7Ua`r7SfWm`(zR>X&_FRtScK6) zY)T>)Xd-IXEhsu6$dPp(Jb3UI?DN?_5@B6-PeS5(elLI-2t@6OeU21;A;iCi2VLEi zR^hW}&my=rw~J-A#tleyubZ3>U4f!7(t4Hq{yd=DATW#0IP<%Rx_iComj*ZI($2Es z&aCVh`jxK;1qvy2T@JBVeb1?n0n;r3fj0pQ**ucdrR2O!}jE`U>R6D%@ zXbBf`FG!gMZcC7_uW!TDzCoS;!N-!0ojH>RmmEHc4kE2VoPz}s@b*NxGf1Ho54!Nd z;o(||x=;z@vFMCN+BM+;-m|mIS>~dprNw~)Ye=GZSu7;8SlGuw))C20837V)`v3_! z>}(L|@F&Ep(n9 z+65^v3o6jp0Z?zq`}c`p-Icp15ZVq{s*fPZ+auTkRiAYDAR2POpifYa5(246H|VSZ zTg9xPpiuSoYj9Ym)Vqt&VU?MR>pAUXKX`Qa`%#hAQ0tHz37;lqe23FH!rmIvJvXfT zb&w8V2#}}gdLT?d>Jy3bQK=B=YEQv78xGJ5X}1AjhXjB*NIEkUe%NR6?7sgpiTrAt zA@#~d5ObUWZag6e9zOI_=*3@y#@G}P7FL14EwsGcJ7fl8dW4{agZzabx?1`^4;%*H zlLwO;iKh_oFkuGv8}T*+hdv2SEcv5*26ZT9Y6TCV=t7RFi)anVpnRl@ACBUGhf85m zPmr>(VoJTFD=;`9fSS`=gv9A~{I&6}&?)zW`NmrV+wh@edx({hHeD#WJ8rJpLO;YM z;8vBDl>w-z2D+R`lD=>6-kTn?P#!mfA0Qi$96EfM8faJGhs=;i048G914YLP3wZa5 zc2+>?5MrNJhT{K^vbTVWa{uCf2T`#(s2CuiASfW9gmjolH$x-R-80kxCJF)y0@4`7 z(A}X@3ep_|NHdgl!@D2R^T)CZ^eOO8gWMOf3ns8w3V}QZZ+Ke__CBlk+et!JHkgMcE$`S4grO9}^SPoV(ZKj|+lA zcAJ5_EPQ;O>)TL%yOjSYpb1Agx(|Mp2rhB^3epHbIQ6ggLhROOSkYQx9OZy#FM$|) zz@;`J*jCec`}wJgiHQ}MMmK#l z!F>K-=N{FV{trBj)S?-I6j(nYzQ}m~&!z%Nr<0Ao1jZ1p_kfusg4MSUev{+Rfg8Vv z8jL@fWH~d(X}OGEA`H$3*1wNgUK2K#7qXE8(7s33kO+>TIc{g*F}WFlR~!relSp!W z{&zKjWco!Up*G-l|KIx<7!-q~eu`SgmvscI`#%po2dbn3A=az*cUfoGc&5?0yK2xPvAh)9MF9TbMFV9$WpM(jw7hHxp-HT2&zqbzQNRxU6+ zJgGr+M+;;rslYLO4>X5EJ&;w$>z27g51}H5@}s<8%ga9;Lm&G0o4^$JEPaj?TAP8N zFq1!Y%(6FM0X9-tC@D+9In8n3ashO?CGu-Pqbm0QyV?1A{|Q2_($7I+X%f_t@=#ol z49IU+S#~b1e;I=Z4+co=l{q9e>Xp<8qG#N8tpL>?KHd-{T~6EGa2zOVbra z0&TFTy-rI@n+3AG>vG#ja&SD4+&OaJ2in1k~nWe1~Rd(_ovu0iH~tgZt^ZNBa&dL%$K~+CKwxOpkBw zFsku9qK1McQ3--;j$<>>l;AQJyJF@$GBcA3G+_mJ!U2dM4ptezUBTZwFnbQEOhIsW zmT;iIxa&`={b3INn=&wP-<+R`DenIzakz5f!VM%u3d4?}JV9OdSozhHXV03lU1DPT zUxCAjYX4!eXNZc`vwuO5Nql_Lyl5MF@fg9=25 z5M?p#-~$CV6_42)xGW=@Nf6_t?|=Mf6&>OFrG0q|%R>Qx<8Olq3!K#_d6qqKVB}7| z7N>0jE->q~EBJ-*>cznkY6ls33W=_Vhlel#*>g%Ue{Xcz_Lss7OsNS7RJDM`no@-2 z;^JC@5aKvMUF8i7K7xt%j!7o$>u#=v}@5{Cec+T()eHSN;VdrBC!SG59iz$q;1a~e8|lsY)({^z=K z8TKvS83c7nE2yMWA;E2K({c6Xp@uoOwb0533J_?^MwS5B0vvOVe&3GCB5@-|2U>4> z`W%Bu?mh4ovhBxYw*WRB29C#<@^VPvvABVU1KA1FbqMz5Ky)0u*rL+^rmz{26@Ur? zDOC`ql|a;b5RVE73=Dex`huvaDAbZ7AzWDUmWuxqA6`BWXd8yczl%Q~`Tu3phmbG| z>YDz4{B>{ri#k`lSUD z22%3ti?H0$h`JX?q5l|Sr!O1>fO_jSaQHG{{M->2mkNNlm;;UOTy%cZvf)s>!bt)M zaoSgq)?xo+cp#@8HmZ-ngJ!7)%9|iX0{#SOU;pOd#MmJ4Jq(Wg5^V01(76ZERv{KS z5)guh4#~5ze{7MK-!u2uRcj+j%!{T_5|7mQA&Pq>Ro=Jm04YjVj{9TbCgZcB$AlSWykRwMjW5{C*(2Edt8@yS-3FPreL~J0T2=g8B_n`4) zd*7qqU;lK_W-r`9s_p)tcg{00-9?f}Jgb_VKQ%M8LP1fJ2xeOQJhJzhPr}^({gD?& z)NdeWgZStn#)}u#CPC4)1b!Ie*MO8Fo&Nx$rJUT*bzh5BZU6f)5fP_g0R-2TAYY%o z-wEuMWF)8!JzN68P`F4(M;BgA1q1%$f}qz5SOJK;RulI3ULiRRl06VAsXZ=3)T0pp zMPwE!8Gz0<>}D>;&gyt2|7RNLBc;5E^bCUNh^;gMse(61$gX+;f^A3~eFWM`wmSL# zzO#YvN6MZ5%xs7mgfAhnC1gh*y7u_-|KyNiQ~?SPgIJpFD^XEV$If!`@U*RqoIn5Rg8pB#+vW&tpI5G2 z`M$ml`6M3NG+3UXswFQtoZD1!=#K^yPR{3tNM>^4As!HL5o)Vo*NTmcV}a~c_YjhS zMb1>Bj`H~b@;3f_EAB+F2Uzy5=G zvP)U-v-|v_jgqQt>G6PO$HifFnwK*2dao<>>`82BN!)daO|d!j8c)9VcWms;Ds>6i zvPh6Uke$@BtD8}}K>{)N6yGm5H!05yq(HS?Wwb<7J?@f&z_lDA2`VPo*)=~&{I=4a zr(~?3dZcEo*d-h4m2R3n!&F78y4ktlI4HzOj}K0#T`d!P>aNuz3$?$-f-bZd;bz`H0iR};d%xM3ir^CuQ=NB>yipotCsC!FEDBm>lE+2&l>S+lB zEdwI75z-!L*J9{ORJ|^8`SLv&Lbes04}IFRJVF!9?=IPWRl8LG z(WD|Wt-HfW50)pxjBDSF$Ya6m_GyL*1j0k10nWT$K{U?_xk8yLP1}X+ zr}U4$j0`EPUAqs-w__J+I}+b&cT;)fcbj`3IMgw|T+)_j2w)x6%@g`|m_m2KTy*U* zYb8~8tDG^|sywHW#}66}MED90d+Nas4e><#uef3% z2h-@~`IRMl)DgZ5t}{w{@3vjmKQmKxr#+ULX3%`bVmE(3rQ7CH94Z}XU{Pz;IaKzh z4ndZ#j=qhWa@#lhw+dy*%NA4S4EY13JO`mVu*Vz!9 zB;>Xx%IUJXW*pek{t?q=y^^>}%M;^xNxRG0vrfO@%(KEL;t5htPnIv8>+sTaSFyu9 ztJ^v%5%}KAOsiVN?pmUVHhX7}F52;ILhbv)i4Sa2*<;elb@s5V#kla1c78*&^oTov zME*f2ps=>K9J|(4iYtnf4t+q7fK(z<)zDgHN21)~Ekg$WVX5QWQs=pP&d>c&k|P@} z)e^-JpFJsG>=!Mos;8&m+rBs}ST!*rDtG0?X5IJtmZre5?5$4+k?E{)SJrowq>(RK ztE@i`GtE%G)E3TFyVt|V+uLE(B5aw~yJLp*lB19aUSoyXngfg5oTnMt?;#f@+g`tQ z$ziIZi&J0jT#sg2eZdWO_Gbk9JtpUZr~ROm=3ao7_K_0%j*oVa%kb*_-MGz{xpacD zff9yvPA?9Tw;OweCC;RTw^u5Xw7!ds_wQ~i{rW&bQMJ%7{vr$0#GApEdgiqBsmB(P z<(lTph8-*wMS~VHv4mFFH&s|7nrSs$^Htgjys;zqL^do4$_Cfy=1z={B!xa^O3-Y% zSCqQXX8*Gv#Rv+d0#SIjsh|-{1S&>@AhHOB28faYQT&1vq8y8pixR>kN-U&m1k~#n zBgrrJL$4WPd)+nDc2A!!*Te0^q9DQPnU1(S3A80El1hX}BRxq|9mx`&B5qSmJco~_ zw?|4!xAo}m#4%2;uwLuhtjG)T3U_g^z*B4q5EJ!sr$q_|wyDKUs@fh)JNBpa;yK*A z-m`7^B_*_~m$8+T@?@h0eW{!8xc8~dp4u>PrWkU&nLf3T4D)$Gh&BXV3jS zsD$&m??K?tj5azl%YY79c=%R~j-$k_ma=dYU%+x1KtVW^*=WdP&Fl;Hxn2X3hgJ?Z z`<94@5!)+glCtI8^&T!?qV3pzG9=)@UfS!K=A)N@Gdb)#*@-5E-%+0|cO!K)FUxST zt>aZmO_B3iF66G+43~H7=Es#x@=nV<=h=tb5-R3Nw!~^T;mdgLWEUlc8!gf#d4;%1 z3z)!S1UC38{ZELCm_cyU+gCXHfiai_h%W@@nUQey%9r{+Xxb!gwicWnBJtyL>>g6AuQ`8?rs{ja0hIoeowVOd(QELlraRamDN!UrAbK6gHQg;&ibte#+i> zyM-LnY6Z(~TV;;29rK_SJW0uWhmm{sF{`0@?ybFP9l@sI5n-_%A{ud)u7itJL%iS0 zw>CTTbg}qSjbaJoDL(TvV$=K%>k-EyMU1g!7b5AUN3?B?vqk%sH?(hR#JLL&te%Xs zVrO3;%6}MF7+Z8~(hpn8mK7))Fc{dfG|tPKJ-K?e#7=6C^bt)sZxdySpjp$Y4N^JO zxiMvz0NL0}U4%3xCvzo!cE%f*T3EJwY^O7X*VhJ&MouY5&82*O(DXU$lH<=Q31l&4 z!q8|58LP26np5EkDj$tY6Mfcb9j~mt)KS}VEGY8fn$OH}+PO<3ayo6pOEw_MC+Q;d zr&{rlJB}RxgOdPujS`eYLshF6;TPQB>VYeA5e=N6EM+WA{-4 zQJVEi=H;YEC}{~G)3^+l9&A5}U)%Sz(6Zhb;Y~}`US#BMdQB(ruuTsx8wkfYH?eD?vw8xQ5YchY$Tb)fQ1}L_DUmEg7jda^I)ft|6FB(FL z_P!JnZi8nZZ=?u@!yHqMni#N9)d+&b?5WxQ1ctFe8AqH3CLw0YS;lQoD`)x({7lv~ zYt7N^V2AT_PuvBKN}om5ePA&`w6Efb{|_D2A0shN1Yeh$Iu@PJy}k2lKX8Q74oe1W zYilE_GIK~Knn5GpuFoW`q3?uy5}nmzaw0->6K2uViP`=|;Xd1#S`%}AVJ=W$tlbLb z{_bZ}zUpq)A@E&e+oZ|GQq<->+-yYMfXH;KkkD6+dZ@N0uf-^LR}<<~vS!X1!^9aj zsjuzlUlbV`8c{GZo)?Tx$;QvZC}&|U;>#D(QuVI{G;T&k)b3I7-p)}ADv6X9n|w=pP+>{iYOcM^T5;9lhM_h^2!Om)#R-; zTGW!hJ-ub~8+Wr5qc0i8vR*GBUf~GH=GjiipBtq%P!ffwgGgn4ocv}6po=imi%35>vSa5OK?B6d!O@(#XUWnI-x>?{jIx~z@WSsNok~u>FKWK# zqnjJdKii=Y=R2*Biw@C^!Io~PsKPdFBSY2GqpXv&?Fl3$mT4}#d(gh%ZvCPiM6coB+^%ZwHuIot0h z^nE2^*rKN_jnOc4RzZZe_k#_vDt1+l^{CPVP*o-gJ(XTYIgg%&Jis`l1(1juND9E< z`v5UC@Qu6)rDCK*W7F@hJ+2eo@1}oieGISv{qx&d)ftE3%w!iwGrZsHAMNusbzX16 zZU`6;JQ$oD!Z(GyZAt77*mka2EsdLBk;)UaQxEufiV?#o)%@^n3ua05AN%f;i!}Tn zvx1YmhMvcgB;0a`J1>ssubk+Zq`nIrH_3)?JV7PBmJQbjMy$3MMDP#y=8$yO*Vf%C z0mO3N4lUXl68bh+Tb`h-d3#n9XB|xGTK-Wiy2nHH3gJ3Kh%2<76SJg;xd2tx&2V#* zu4hpeSGNO9oI~#MyIg0ym2{mBhPhV}&|^77G?^KNH8=dbg5THbly8J$%PlWwXde4i zskixaW`21-J-C6%*ktM14lOL4SlgWQ$dkMO+=nfN-KYmTWE%Pb$J<^udJc0Gr9o;(jY zJsPsXm`D*-^MyN4xw(S$m5#HRIQPx9-c~9o@OUnyMMxAPRSS6ksLq}SdRHI`&O|6d zLUAAOcu53al}3ToQ6km8pN6BOUGnqRkb99Sag?CMA#&w8Y8L?r!{2tG2q0Iakom?w1 zMSQWGs6BORe}ve?VuR?Fp42bz-%D;dnD%SYGGbDKj%@GW=VbmRL5IrKG+QI@fWp0) zG)1n=ASZV1aLl>3=Kf`~EMM|)wJW0=u}MC-f*QyN2pV)}9}4pEQD7azlO_l&kvVb& zl@YSjY7kyJ?QmK;O4qrdh=q|=+_)v{%^QU+ZA;AugO2pfDk(SXJ#@_|&c!5cVA{bt zW1T(?v})>Si|N5W-dEN$Nv19K-w(e)F)GbM?Kg{l9g8V7Uv+hvY`#9~IyjpV6zvri z5c#frY_LrPaJTL=sa8~7sUhwn`324HR_v)%ZKWajnLK?1MKJgiWcGU6D)ba&@ljfV z5i&9~q$FV;vwfSJT(vBfB2GZFSrlt+Ej!}ADeZmH^ot>3aaU(?~uZZAl3JQa|kDkUdsb6>~;<7G>HXrtsHSFG4b7Fb#{QT~* z@sDMF+&8V=GBoJi^Ga)+p;-V0+s)QIS8A|mqk;e5n-aJt;yBp4woymP;4=PtBTdya zdhJSK`ad2|pROjEdz~sA>3+A4tJM=;?!O~|8Ptkhc+)pWtvG1mp!sC)Mp#W0?TlI5 z6*b%Iy>ukpM_%jQAoiyQ;b&@>iV}I+4I_I@S^`%RP35~>C3?F~rieOdMW+$)DdI!Z zRC!^8WMF17$zLOH=>uAkE$ZQW8D_+lg%`Y1eI z+n8>e8y4UZpKl8b0`k>>6 zWZk#cS=$jZ{}O_*gJzkn8O1+lDLu9Qhunw_>bY->v=&!QceyH*{jXX&EeTu z-TqoU7l>ORnAT5%k{b^g4dAa)!g|-t$>qVLKVvOUu)Wgl{lJ` z!^OL}XB%}amqzUKCZjHmnNOAuQRW{0xukdxznCZzH50b;J;h9&*jqQhcinkXH_uz^dhIK`ZBk@VCU1WT#Izakj$Yzqe_(cul0``RADSea7{e z_nEJljCzPP$koFx_4H^ho*TEUtWdfvA`$e%Q~2<>6wj z#(8eCKf8cFjV5$kCqK)ZerGu+?jXRRzV>wA{K8K~<$GL5P1?SlH0g6?Qk}Kj#A|dQ zA;}&J*neW<?KW1;?z-M?zlNiV3caik0Q(3UZ=xnTE+4@x1x+Z~H`k7)B^|O$r`5Kz( zJBPeS$zm8w52H~WF`hJhO18R~IL;nff3JN>_!-{t{s}0~Il+#LOE*n@d2-9n#Haln zf$bQg4@$rRse#_q)O39Xu*FcQDBsmJ)^a{LZ!#95ogmc`fIMpH>fm7{D1u0eL3f1* zQedGDCWT?u=b4p_|K%3;Zip}9%DQtMo|2S~ev))38f{;(b}zWQv*iYS_}NkSvKwd} zs+6&rXoI6~_O7{3c5PgmAm3T*jC1}t_@=R1?$Tsx_1MHuTYIW6bgHxM)C=f|*e)gR zY_z8NRtlQDdHyI_wyj*UOvizk?W073DqGlo&01&YeHz8RRb(n|b<9yk!F+O?(+#S` zCI8?ce(}}7ePxs06ojS4#~n50Ll7P%5pjXwU!mjjPEF*cOT4;e*8K1F9(yY_@0eDZ zY~@bK2dR_Ilv%!nEK5!6hLdY>3zgU{?rfzeOIq5XlH_R23!f%Wt+*6#I+4ntfxRkA zHrT*&f#;UG^V-{XDUKzo|blvJGyW{OEt`0I9r9-Ra9hlwc(=pW~s;g;`NZrdISt6Lnl83vqU;wQ!$jNb9OslRp z6g7fp+#f_8JI5+}Vsh}oc%NxVa1X6mxv~Jz-EF|D?>DfE8R~anx~%)*q4f&_7*eIQISbKeHt;J$xA-$xIyCyE5QT?W6%Ty#($yrfj@yD;mbbtLWy!( zoGu54j(MTSzICUGHqF#*XA$)2TOQ|l@0AH-&_TdiCH8Vkr}f&!y1Op2L(nO=W*J2l z++5^=jTDLdF4Qxt&_*$H`lIHCZ(B=9f84_qK^4NgHxNIZ{ASC=ovPn2Stcc+m>}nA zH6^Sl=U=39i=jvr`%UbF(eHjhn`Vv_tI%(O2g$^w_8%T2Xdl=^ws-I4ix+Q$91M!a zl|fkC-0a;_tYhK1-Tw}j;lGR8;fI+%G_#qHz#GTT?4Tg1(k2oU3^gJyFV7Umm!LzP;~wC5gU z=f0V)_j{*kl+3>MitJlLS=l?SM$ z>XtU`63tw$Hn3DQKU|ah7}chISSKg==-GM9io8Qpoll`kBIms?&&VVrUA}JAx_s)5Ht0b=lt~L(^Ue%bbM-g?}YC< zr@KkEo79w_lyrS#BAXXXmr$kad~;hw!fytBnHA4I&kZc=PH#c7-6$|(nmeQ{Hh$O+ zMXAy7+p11$@GwWQ3Iw38HXe9M-7ZtgsCeNLQ>=O2{vM@+Muua-_zk_TV}@g$nCSI+ zFPS)$%?_<4i>2t;$n43k#1}4azM3>dcy;8^=S{Q|wDjfBP2ko|JgT>QmVsFLQn-V_H(>(`gebKV*hK?2B zi(4b4r$y|)zq3~}P*8PG^<#=|ZyIjVuroTwu6zk0XxdIBa&BoV?f4r89I!#`ZMjlbH`Y!H=Q6n#kv$ZmQD zO0tp0?x3PIIvVy21TRoH*4`W|h442+s5gT?K7W3I;I>eQfbWrB9*_)E7nPEeLn_uL ziN|YH)QM)UI=L^^dw^AsRGXNU9J1pw!{HKiJ6<9f`fM}wQ`7L;Kowb(VatsE^_70L zM9ayz&xht$*jrMI#TH(lFb;YXtP)pBA1)cW=KAA^9yD$nwjKTD*eU3BkIuhYL4Avh z6t|qwEwoG;y>RHzW!GpH{(idc^(bWW#?5hM*)64wl{R6eaziB0k+NX5o{T#NXWz!O z=oFO#uHLowdU(w(fxBPZVh78vfkm4lRg|3(OdctayGiUfHs(<#0fWCqVNdNWvqKB`|aj9XOB>+lH=tUF2!5FQi$rHX7~%D)gi_ z-u$7fPcc+K1}NXXGa+!_+bRsh_Oh$x5>l_#$GySb8!*}A58t?S(eN?@E81P8vS{Nb zDXuo+M#P&i{Fg8E1%;j*KBTKTzF5t|5aA;0b4O!TA zZM0^bFt(hD2Jac5dh=|y(T5JsCoLWs>@D6Syt;^9H~2ieUjF6+5~RHO$W6u)LQRW9 z-jVC$NjU=UEE0V>GmsU|bJtN;{x}pLV4-u#bDe^MDuIL%(<0f7hP5;GMGn~zP@FPm zzAu4k;^Y}E8~ZV&XS@9ePG$t{v94XPHbR%9(5)$Tod`cWIc~qL7VcLUo}k+k+a=2{o&-P}nCPzV>x zoOP!37-KQjp3;F_fn43@V@3)0s6HYgv*bFhsQeb9IuLt`NkrE898Xi~PeEj7CUjLkW&F>rs_3dw8S;DPyIL~&CqHH{MyRranci55h z!X4;Ne=0zB8E36v#r_bGlSMK8ao4D*+7J1aOHp>apQsGH0RbI%N2 z(Xoie{-||DC=7IVih* zS0BbiLlfjUx^CsI_;P z0#=m%&VmgzBxyLP)#hySX4_}g6LGTpm=BQC$#L)V6z-jt8dGth)oftiFYm$RH(|-b zvD#HiC<}1Nk08^WDwT7Fy(95CNbNR5PgbJsW}K=MjN;~J9`9B5th!(G3HoEy3iNKd z-f+ifuUe&!c!-&nEr{Qpy-nGQYS@Y}If`c*JJ~?`4plUVI*0U9A z5THR5vaJH)#ZNZ`%0A)|ybfrfkiI=2%U=Smi^-2KPhFsMJz|&%=qkw|l5f!Eq^R~E zBzGI`tc>}5rjboCC7e?9Y$Sw^A>y9t8c|+-A}BDNgQHeuw*DX0ZLj;l=4V zoMYjBFnK@6mP#?khIshT+i>kTTQxWn0Xr79kstiE84wZ^ogZVrxRIueY<}5eX@V-! zV=Y|C4_Wx>U1CgmTI0KZNbeS+?W#sVf;Vq^d%RhiX9>^D5s>sd0k}hn+HU*bNnWHN z2I-h1(G2y~+yMPW>ZOrxJ)n|S~sgL%=A-23OI*~+-i7~a(pF4ocn=|vAMQG^{MXS$+t2HJ*^1K z&ryjLRaF}O*x5e91$|X<03i0^R z9+`dgD82t}eG6#(7qnSS)Z16zjnc{T&iX1TD6Gjj&+^+zUOJ(eJR&A)eRSfiVT;g> z?LMmB@?Oki-_LX?$A+F-?IHF_qr{En(;F?Ky6KC&_J!rwLhhm)Gm~x0zfX1+3g&*< zPhsFHMO@FC%{J5$TE`gt?W8=~RG{sr5t4?FPsne8ROQpmZ(=__7PGpws5v6oS9+Ne6~;f!8Q$(R z#&b)?*r_T~vU!Ff-hy~IX2!RKTceb5M(M>^bB>Yz(oWr$;gXe7_h>1h$3!eGk?#D# z!*d-u=tZu!EoEPawV;Wnf}m}ix_`|18QG+)J#w4h*`sWVSzR38L?b=)UWL62a`kVY zpxW3P_BNP{#%4yTbD z65={1%))a?$+iAm(t@KFWar(4y}3$8P7T9WUP*lb^8UO*;(4=%_EckFg(Yg6_(&q$ z!d9K$9N5o{D=!NmQ#p{ z&dXsJxu)Wicp%v=+oa@ppilH6y3P{cZBCpMr0I^0C-g9d(p`48{w&}HTlfH9VAayK zpfcUu5)ES&omZhjNmx-`!$jBh&*p9KP3510I*G9Kr7WJgv8)0|l=gO4(^%uLND24q zbYn7_9Y0XAu*f!ldCs#&YIf`W$2apWd3#LwQfUm16|?tdr*-$ovrnJH;~y6ST^I2E zVE2v_1TTg8HS+}qyp!v&NXzd3A~X8(0K@wpVnYaeYrwMMtC5QX@v;)3n>0fpGC<)5 zIcmxD-V^?zmS_1}nCN)d8~wr4j;7&Oc05xXzD5Q`QHAdpP)dZFw=^{ziL2NTOSYp^ zifp9eCN60dDpsf?3rAb0w>Ws)A|>S7U)u{UB9R_Vs=Is1bBv(q11ZY}JtdMAhVnXA zu9i|dHqh5Y8%+ZQ^%TGjV_WHJ{=0D?w&z+A5`lU6m9XU&R<&EXAr3+HW&r6Bou7)e zX*}6yc0cbrK*V7s83?STb{xKK%aV3V@8SAa zUE&h5f00FG|9hY__!nvGF>fq%G4d%6oLa#fR6wqp*6WyXdUD0tYkl-f?`tqKBYpgbw5~?u=d>o4$hNT1WR?8|OlLoGoSgg-OOhfm@mWvF5!dh|j!*4uAwsq{wQ`pR?alT(%=AKZRKIc7fha)<1enU7wE@&d&zZYn$; zk5nfB@B&1Btxyas47e;rpnUS=NoBn*0)nE8R#C2Wu>6-&a_Y_rSqY$YaNDiSxM9`X6 zfX=2tXx}z5k8_FAd9ja`2j^DCxdi)`!*xcbZILK%6Iw;4pLUzlnW;M)J8xc9SWz1P z2o6Q}OL;yfxV9YFT5E4=pNITAkK<_gv9m?m25&{2*JMF+6-ER%z+q6id2K_vC5AIv zs=1bKQ^V-At@Ac!Wu(xuI~E#{={%H;kX3at@^ixpQPL9AJdbygv#UYUe_LqGzJA-0 zbGQC8VV_5-Te^|1LN((>lCheb1uaPv;+kNeMs(gTXS^<`GY6( z^x{6i_t&ktgo}b{(By*RfubW5KN~YPBqU7kdaI4%;*MduShKI){EL6kh4<8e_R_#9TuIMz8Ap>%iEEuM6uJ6V1L zN9M(eA6|5rRY!hF*qn0!_=7ZRM9{5BQ8^SFBSpFp>3Y4r(Z6E`3=`fU!0=`{k;n( z!`|63zh=)cbb6)nj4Y_f7*c{^Ym27^#LZMd)(O3nUw7Un%zxT&nQo${ zgqT)NUvwy0IPJQ~0$N%tGinOeA7ekEH$4n3*@S zJDY9Kh_l-%(hK)Jc*s*XKX-C5pLH1sq{}fXn>L=K6$$)^Py62DVJt1*p$c6-9=Vms z2>M7#HPp8;=yJ4lOs;ea(Rmi~@V)oJZ@NzP9nxr*ew7#fNOHQ6tpOq_ict0TCg7uJfa$!OVE92@v`Mf9D{VeXf`nU2gW z+KH>1TU=ZjjPekoTYww+;mB>Jii(eZyuS3(t4uU5yQCn2Ph=%f@PYPrYFQMqsnjt7 zHy*=hF{^XUsUekFlP$zpNx9SL&m=Bv;q-!^!F9{y#^;K4-PXRo%kYq0 zqkFXW-Fu08=pDMxXRf8+Va7TvV-H`+;%p}ayC89@8PgOzxZ0$&a@)OWRJ+u8;brof z-W0j14xUTP#8w7@pe)Y3ORjj-;f%Zsn&^;5@fHRhePYwoc`G?5IELE%DyP8ps>%+##n1RuD|E``+ z-xpr;v3n){#|cyxtDQiePhVuu9e<4Fdl?_2rYj5#^M`#NXuIFIqFHpIPbdA#{2{aD z*|J==O#OSZdE;H%EyhONmdB~DQ&xo)Z#r2w&A%<<%)1*cUVZ@;m#AUL<3E$qBOV`Y z`N2>XW584Q{Cxmh2HwPe*s|}WPkTyot7?M^oQ2CJZ;~ofBiHv-sQT+JZiefWlvPz< z9;p?(t6b46>ozSjl|*Sd@41g6h5>@stl6MCIyw2U?=_U)^VQ)%xk0G(dVzG(K^>7N z+xtoE783-81W2wnzB=g1Du+5Erft99hJgogbL;Hy6o^oODK!6;VGatos}NjHAr&7z zJr`Hj_Mu~6(0wzo3;6@P&XGbzi~?8Zq&SVE@)g}uA;$czD+l_5QlfY7 z!}FO0{6XnLK+wLh)IWq9|TI1Op@>VI3_h|p{r-^_EYv}~c2o*(i>Rz4n`#0b&?(y0DDp`uDcal2gS!lG+h6=)@ zy65b^UZ9}J0EWa{5uA%K`ZwvIm5IbAtOzPUB+*+sww08~8*oWIuzNuXYW=?mobLyUbqv^Gp8td!P4m(IQO%NBde|pW3_y??VW{ zU^&WcyBCtqy7yDj3-D4ZX5Ckc-U?!^E*^gY^B=~MK2$MY!_M@w5LiQ{gQHk~*BWx4 z+cQA;1YjLnNVvEUkBx;q%iE*1d$fC^a9c=~S_S`D#0jYSqo9aQ`VEZmTx3AW+V`)w z=YdO+z`a^mQ{xSw^GYC?Q9)3_8;Yav0`g?CNE$hoXSM;?U1YNI`IbQYv$=Ln>!4?4 zP)0TfeTXaIX{r1j8Zi>M(_+)k&}D$0Ub0~eF=3vT?OxEUDr9Vde;nHncf#)nrj$$a z?+2G)nd>R54^6j##eJKe0*<}3+kxZLOf|(PK&P{Mwx|JvQmU*!-&S8(_4i9@-A7_W zMJ?jyWMF7`hcomgl%m}MQY-FHcac_l`28gGCWvq$;wdN;iJUe!H_3qL4F4DigXVLMy zs_!Ei$xo~EukYRF3{8IBxRT}iYx1YtzgTBSc@q3ke09EOuDy3?`(26pst=B11tr=` z+P@#6yf`vhch7=qJ;J)X4#foXy(34a)$hV*v9N#sfU-UwB>DG;NaZQ=X+3TXt78Xp zY$&tMvFnF-2yKA0QhKR(kKD-vgOL~5bN5<*ihmFIX5ia@V)r)*A|zJ@gQS_H%1&?Y zUXoZjBdYJH1@*v;cf-wo15Wkn-J3!|t>LfH#lp;tpYH%Tzdit0f35rK#*vgVVQ%Zy zA3b)q=RiJi7vOIeU74{_9$dSpiMc{@wZ|0HS&PIr)_S3Z?@kca8H92Szley4w_L`u z=U{J`gBFc#|Gpjmy`QF9Z;;WP1aHp*DWC;6@E<4`b8(yhc4-P{sp$rdt;ok56pu6Dz-n zifiu9v!;Wjkni*7zybN670f~kB^?BfXWlJtzyi^#&$xSbG4(z76EE`e<}GeOXM%Os z-|Jnc>|L0KLmBW0RsoenKet1U9S3~=?}6W#&d+lC*V_%k00`+W%p3#GvlJAt?xFvk z8*s+&vMIV{BBgV_))l%t>mAK)Whojt-VFXinNh62H?&;C!{gk%wu2SgRC&PRS1WaM zE(X-*9Hh9AErOn&K2-WHY!c8CgPW6cbMDXHbo)`rC_n%-3(c@-TcHSP~ltog~G!tnDl2mauO z{`*4%m}SZhU>QtKcOm%aJmcR_u;&D@3ZZbp=Qh26uk3)`fGUxRo|l)`51PK_ z|NL?oduIFY?PQ%2fz-0?^-hnSEqw?o@GQH<%pFI~8o~dcKz8^eGy>cZS)QAI1ZtRw zGotQp#jsu2wyPVr+2{N^i#)Ha$l9=(?+$XIpb$uYtpS~HP6A%iO$fFy<}DDyWx#i; z0fn5WIV}h~F&_YOhLFBfucFJT> z4^9qyaC+^^2JlJgpypA-vr@u3Zc$t75O7bT)=V&+4CyI zi~%j?b68ep<`*7Tff+ffnyPgc*uS@WoyQ_GVE2aygS;wO*J=B=m-83|1&c}nRRJGQ zNoqcu`$x|4rPmBWMQcE_$bcv51EA0J$5LIl?mGt{e!U65aKt7Wgw+9kMJ@e)$jk~9 z(^LRLHnMpIl%m3=i?JNBi-v}Vl`s;AVjds!X7J;q2W}w){UBoe1JbVwaN?MWHL)RM zSQsIwQsF3GlFNmfJJW(XP?zWaIl*A)_O!mhPBZS5g7 zkyKCYH%}|M4lRFZU|pWd5Z@d;U6vs0{OfzrgGsUn@V6m(hduIg`rS7w+46cbwPnD_ zYfB_GDTZZ>32-V_`9GeG1N6NY=Fsbq5Og7t7Zzg-2DACoOBAt{DuLZj19-dC*??aw zMNGYPlo07i@0mG4!&3YX;i{$lBGe2KQ6ISB(8^efu`x_OgE+rzE1XW*_C1a)jdU?C1v=phKCxhJb- z&@--DLSF|ho6q~Mxh_wwmeXzj0|}g@-xq+Pd%?YJ_Od-qmkx|~6UqKms~tD!)KURt z4A>bAf`sRXJKG3z$+(orV=(1Iz_33O4@B3QL;n_!A^D>TvL{5qNEyuI&_Mp^g$6oMCOfIZp+g1hfHf85uKG3UXI9cMnaLxW^ z06ZtE%7_hJ{A1QUAgx{x2?;rk@OmV5Rq{Nx>#?OLfS%y@{5fz69E`uu^JB=Ix$X99;b6|g! z4FaC}vgOWlUYRd2q&rM}DJa0clr#;|rqDWTh~5k?xs>ku$L~;Shj79;XbN#58A`A* z$(cq2LF4sng#sr7n7eaz*6vx^xWN~fs!o@!R)$?kQ|?N~^fVS`)u3ZS=N!X_zB|GIOc1K4hgGxiWth~L} z4pzO4dT23XA{W>hj(BuESIf82lXU&$NuokU4qn`EM=Ibpl?RUlxCsDTe%s6o3#-fj z;qJYoqD-^4-%?v06POSs+L#(jKqO~+L{Lyrau8@lC4)tVA{bgFV*n9>f(FS+awwn- z1SLui1(bqhiV})gggX01duHBq=3Ue0JL~?@ zp(BLf2i~~!xh05v(b=KR`034S{Qe(_nYAUr-$h_;w7Nl{5qv~6nFrfj3UsC90wCZF ztA+WXND8w0Br0IJ$eTIbv?FAPxi&5xamwTduXqjdr1zjEqd!<|neEJNU*T%ajE9G)J& z`_;Z5m-Sc=3^9jM>a9kBlM_@i$aG~F00|@zR^Qa5Va*D8G6Y|xZMajhE?R)7QpVwM z%F36bB?Qsag1eV>`x?alGefm0bFpr{)@Lm%j$ts4Vi%Qv)zM8qa)Gz#I}quP(bEgB zX!_?CApBH?DPz_|D9GU6?KstLaXz9A?uHaFK-9@_gR@IVUdD#5K;33P0PcnDN56ip z%B|%wrf=OGa_>hle*t?_|2<)2owZ0woQHVXHu2)KteHj>T$C`^SKK|;;N={IL)=`A z((JpC{{h~tLXNu+FaMYg^(tiRu0vHobJb$F-CP-EoW1?|4N`)%7)^@I)e_(jUx(N~ z$p?;Zq$WuX`X7U^uo%XLt}TW^))=2%x0U1QpSMv0R>k3uH}VSPA@n{hW_3kgPsbIE zzqa1f&T&}#$nfwg(5LBRpoyZh=i%-n`;n#s(oWLv?|={;2zcVxEtziHY##l%LYs?x zAta9U9S2UnD&TCJpfnd)weZ0>}VH#ct}$jd??#!QOAaqVjXYSfFfH`S)# z`MDYje8TMbp6lEx_y&W9YGBZ*8W}cp5tuUJm1))G3X@U^J_|i10HaKR)SS(9@mbvJ zap>sjKP=~Qn_KU1Vmb>OD-d$Hcl%Vip5(1L`$Q@F`G)kRpun&&$43w{xSVPT2RXsj zaqF~k(P=37@8_0v3x<&^)jvPt+7F;1@maZ}@_*)C93}8x8h_cP6DSz}eQU(D{?7Jh z!Hrr<}m(qz0o^=g-|2^RKE8Qmr<=*-?`dkPKqnWdGC8 zf;xnLKuPc-;Pvjv{B>DL70F8fR&@XQ-zo7=u2R0|%}Qlz$0vxJF>aE7`K#eT%W`ep z`P;^oVeW|i+h2gj?x8=aCqgmX;ty(yR36 zYm8}ChA4XD7h%Q)o}|)tV!HeP@>i?c_gyyr#|H3k^_hRXGRI-iloI!UddvUMe6{@= z@IW2@fA&DrFDAfd`J21NUzV8v(q8b7m*y#&ndsW^&-dAU>zOwW*_Z354(Dk$k9B07 zD{zS51}Q98DCg)QpFBvl&3H1LB|E$G7smTfEHsUC#L5-~-ikVsYwdhB)1cz#5kW&X zULk{h#xMhS;q+0T0_zCw6&lfUsweSWfpv#m5uCn^J<-=&^2BGp^l4t7@11DWwkL~) zncw$P9SzM&w4WTgYM{fV9ywaW95CaG%er8<~{X{D*{% z59E*fYEiqisl8%hTec4?kFua2KV&?b#@e9$W})|1abT!m?|I@>XA7UG?AhpB%1{jd zj%e8tc` zZPtMM+ZRKFlVu0?UVqyvKC{ZnTWi8vak#$AF4f7NySK7LpaMy#4O3xKqxFxXh3i3x~AU7f&AT@fVK68E}ae zXx|xO~C9&xc9Rz23o4qcA_*UknXu<~?$5pp%d^Z{#; zwTK?wbnaA6$SEOpR_>emb-pL1Vl zI^fUK7UUCDqlH@J@(mv>LIP|t{qp?GM8e`=#W1gaTBrqsdw=bM;wv?aqWPMqhBKe= z&)hv>{&U1J%(e?#&Q}`?_}E9XWDUYS4m-v&rk^(H?B(JwyeK7=T(NE*vE{R97&nLF zTJMgQ^z+q@O3KOwADRM>u}<-38Q<7-=jT0aY`VULiN0LdzT3-DxTVpB^!E7*FSlTN z=HB-$!ece6cyPi@@z8_w)e-4Cti01_ zz0#d)J8?r{O~LOKyGUAHqjOV-rR7d#v^p z52jP(90>Qs3X^WsI=4Tb#@D9$Zr5j+Ufv%tM#J}Qh_-4Iz(>Yy5o2jHU5?l4ny2-jm|pKF z8S71(lGELf?fP0hQRsP9>uGRNREK#Jl zpdnNq?{cAZ(Ylsw5yo){UGqT(l`=`)#*Z`6MLP{BZ&q}COA{8?`0QQ_I3oe3p3)WzyQ1hX`f zRc^Jl7jIs1?Gvr^{S<}k3V!t9&6S|&e^^|{YMBFho1*N1!C{iZA>>;2ajCypFr?UmJ#wz(Z)vpVgm z>d3yXpfK+@)0{%_m!y~#^hZCHCl-Xff3y%Z^pIwp*6~iMf2m$EF6`7PlF2ZxVqxsC zzL}Si8pT4B1PN8YOmV-^s!0)_Ld&zMOYeUl z6_`IAzp%C(CtP))xk4LVsBBFKMP59K)}q#=ee*r#CbqNMWKlD0)bu4AwkzC6ex5m+ zc5We5TSeD_z*t@^j!WTLZ-(E8PqBwN-+m1EMWw0^u_-Lvdn04~Tvp8o!`0+>rJ6K1 z<3$IIjOM95vW0f+DXelbNwkm?`2t+p1xZpPnT7|Q^@Ky0OVurwA9h^Td0KW%>6tR0 zQ}4Y0J|C9gO|dKC3+`4e5=}?Eul30fJrU@V8Nbhdz2~8BM5XWhk%`y<7L3v`obD4J zW6oz>Z%U|Ro?8}uG)*~`B=6jm_d?#UTBEjq$uA}OchWP0pRlhJnRLBvB*7_1xceGc zPDSxiQ@rD^t6uNET0Pe-C7o4}e68O8a1@L;BP zvN^1wL6f%DAXJnc>3Xg#H%Xu}MzjGp!oI4QR}>jtfgk-Vh1Z`aDjZOvC_%IJKP%w}zN z0{({FQu`!qwirFTYX`6r*=e&SzTAr!tc49pd_enku z^?KXLs`5hh>pGIl_^J|2SNuKZd24f;@y$IPrtSml-aTR*v zFGU?|m)wfYXDseYd|35`9J9RAmytiTo#cDt(HBQUIG>-8?n_D(M?|%V`L0u+LCUTv zDJdW@u%F%3$MFIDlS#=#It-)uFHiu))s6kZ?-RCWIhrw{Av=BNaAl+6OKnqMMIqvs zyW9^2Zrbzn2dv(Ew=<`7OkFjpAP$eyEB!(%GL4zJ+UG-|CsC(vMd3bfO%%{Q@afEn z{Q*+*-SB}ChHLoVu`Sn^UblNrs7fjrB+}cvD#iGc5ShaZy2Ubq@n+~OMC^5V+t zgnKmt`}S>j;eFh?IP!E6%asquRP002&C5}Aqtgb&(@Bn3y6R$WVU@l=h1q7M{E(H( z*)LlxXwQDgKHyXe$@t5QhTxMk?{jOP7fITk_Rm;fx{(#LXEEu3BKz9H)U{q7<`Ncl znu)J-#eb2pGa!u(B$-$fQ&Xh~eIFMxqmEo2+HR&;Sy1tF&e81U#vyST&B6~&y$&~L zV;40t+PinKRvcW8E_x_&idXb_Ui8gQ1AC=@nW;CvEfw`eIVpidANkpM>z|-I{;hCG zZDZYwld>LorZ?1mm8C_SrsW>s@s)NZa1@d}cXFhybK}nwN3zt()hCPxsW){gDrINu z28z||Sqf*lKDV*XrA)G8_B_KJj^DTZ_oyuOl*Xkqo!JzfFE1=uj_i#k7Yo=k>En{c zvb5sl80XcyNfh=&Y$$Lsz&3B<$ZiXhJ(?s>jp%toR3N<*No1i&%D8qjR7USVyJhD- z;ONSICRP8&Eipm+9D_oIfoPLb>P~duBnM*6dL0O{Y^prv?jfDq+hgSvFee-s_+&$s+rv^@>8&NnTU>IzBHQI8EGPZBaV>kt`XU~FxRCR=;~SI z{ILIG{h`Le7ss|G&&lfr*y}b+;SAJP0xT)nAM^P567XGa{L~(2#kAI?9%ro_MX#kh z+}C!9-4HYu3$kFDtn-GbVX;rA}h= zt+~ql;#%FNA6}k$yEHEYGrGg+&GeVwpWILMbknoOcf2h3SdU=Eay^TP z+V{vIUz}bxzpTq*iA^+#KEzLTxGkwk+m=4KtPh8? zJyG1*K@j3+TW`XIDfK1v0+*0YpLDoPl9naxn!3B;fkt>}$8g1> zBs<=zN$Qpr+^un1&GXn8S*ScYJc%fs*Y4-F%YXXHWO4*o_u(yW^_x$kelbhSAND~8 zk@|a4Jkm8_ytjFMam~YLq_m-iPs5oE65H7Fw`f&Tud;_#NR@+#Me=Q}0n?L}W4i4u)NU$34UEIC%(q&+z+ zQC-);dA){-K9>^KgCT;wSl=$2uouaP8>$3H?R4?Yat@AoW zb;z*{SdhXiB_n-bt1sHTwBq14DhN0f)pPaf>44RQdj@8&MFQ0VZ4-1Z>h8lMf~ zAIDwOesb;WyA@gt9>VH^MlF@3-yFV>py>o9QTnlM)rFK*Z^)FqYP3fevJ#wE^m5He z(<0La=Z1!hM{C60F9j~EcTLS1-rVx}JfsdR_Qq9vnC>>gG9Gc%Y{!lS!?}rppH;Za zHLo#-6NeU{=h)9@H6m7FyVVP?`LVt4w=HUnOdfVzroguFKDNH=50m8i^j*w(L3))=*Qw{eW~e|vDCWRzTo5uq)n;CvXFN45oXuG%hl|7Q0!~co*F4G?sm1Q1goK>eB|{VCor&D7 zVoJxpJ-B*KyO-uuE~aqpHJw`O)VHe6pfE>y2G?d;IEc0F!wQ*D`~nL!1_zyl!)^W~|_B#>6e(lH&?5 zi|K-+`1!FHtpL5`GUIjD>2n$`B|i`tmbNosmM|A7{#zC%b-%dAZLNRgYTL!YW7KQuZm=q zlCG5XzU1QAUpmJy#E#S?il$7r=9d*sl$b1W?rSNy*f!RYZOlbxekz$t&BfU<&+(b> zIgU|^&E5Q>KnvOdVbD$x>ULC7nP+H7zHubqq z$KFUn-I`bKms1PW{P~!a(jfin18tO$F!t==cL{>Imcm+9p{w`ghc(g#9nFc9xHB?E4+R4 zlGk+Xi~QftAB1o~zN|Q`MRl5e`<@=JN>5^u;NXIpw{+7IgZFw*pzE;0Xslrnw0)Mi z5XU>S@+_);#@Ej2-=Y=N@R*RRA3G`htu4V*Pg;M=o(}6o?Jb{G<2*C8hUy-cZ>O3y z$%zm9&CJ|mLplcDjkY&2?C=YS6s-3ZGgnuohB*EDX5{AA6Mi`@Ak^9y-sUD-xGWw@t-AqEb@CE`<2Q*9twAOfpW(%`#5jhJ}WGQ^p!MYx&;depy$d z^Y5n=W^BCIKdcQ#Qe-RU_Iz;5imts(k(U=Lak`#FIIvG7m6<}06lC16zqU$W85(+k z)%H~wUtSz!Wxn7kULR{XtWe-+&FpSt2^lqw<`x|=zLG}Sm+JS3=aZzDi@jN|(CO2m zr?4x(Mx1?H_r|{H8HrA78)O}dBTsubkrZbsQZr+3aYMtF=ONRg>yRY`2iYUzj+m{z zHLLT{csr8du^b|eYyZR8{lrpJiN#RT5#IAN&jXv8;6ZI^DTwLcVI=0L*VLhOZf;zz zBfG%Fz<}#nvW^2m0Dh(YXqsZbtl;_?^Ex+rJB4S>F-FYkcArO2R1#f>2*2tfZ|spF z--02}3ABeLvh>^{aNsYMFL|99Nz0kGBJ9VUv-p(ro~NbKGrH9c_wl)hWzVBRMgvGr zA~&0Ymnf%n{WLC~J+jL=vU)0lZqZUUoa;37@`JDdwDu*O*K?b*Ym?sQGJRnCLbv2i z*V`l(d{yLv;?E~5=WiT;zx);SWkX%=) z;n1CFbPhee=oN1+KwP62=qvcUPGTiBslUXOEavJDy8C&$5w8r3G6@Wa)v4#IS_~HX zc(dJEzXf&7uvWfk<$>*Rsrsg+O4~_U-;1mBBC6FNYgq&rEqfZhpYP`gf@-}c-qr1^ zo*SX}?PLs|7k|QcvFoF3ObX%Ug2T1tNmXt1iH^IuONJ7~*BPJH`KfpD^ZsU_hy{+O z33IchgnL#jMNMTkaEET;2!+q{d+lWUm7T49LR7ZMirtcxhdVOtF#9|NmX zYae5fGT79`6KG904U@J#RLyAQOaxKG?xI7$o@mLM?by*;i~QsoLE%fm%eIlp!MvZ6 zG`V_hZ(nB0eER&)PSvZuug%YKIX8%)HowS+`2ot;i8)aJgl1FJgE0i%`suXRKh%tF zSk{ca*P`t9xZyOcm!Yg|C)DTL!RVb|#?hBP%5tydSkr2-%;?Bg_vsz)>-;pXE@uoe z=RWVs_3)h5uq~f%@=KZH#wn4yEuwX0l-_sn$wCw@eI|*Pn}RLQvQb{9>r9ueE;X7k zd&av@og^K|sT7w?qD?jP7C^%3W9uL%y-W?S)K*~)c|>FA#W+np9&1gz40_~#D!IrX zX_Sf-IU%8nv9?&Hox6f8w0||P>t4N_W*={E116nXo+mPz@WrarQ+1eufs&k1VjjB*tBK@E8~D>g?u$}qdNUfOAYC8r;uiZQ1r9d) z$l!Cq6EnY^Xzq-j8Mya?=%G8;pV-!ENsY{J`*t|huQrS;JdbTQU7U09fj*WL@!NWG zBYFPv%UKOOfz&>^`OmM8;67|JE`FkDb%C}shcqG?rkW!!Q@u`}vU=@KahR!(i_!j4 zlHt2%b&GefBLj%x>xb# zO}jOw+xwA_nJ?+f;*8)M$1Sv5Mhpt3pP~OeaO=jn)q(dz9|nxwaYs66pACx4b<2xa zhd6vT_QY=$mV@|5alDtK#h`Dov(c!^G}fy+lg-)vEC=R}$<3{2LYnB!io6F-X#`g> zDHg}0F4c^}*@gPiQJ*XFC_vk50CYOkLKLE>VZG z9S*WblI#V=e!{!iv&V71C>vY=Z8p1AUT<{7zDv??d;)b)ofMv()z%+%wIe$bCp$CA zhmDI~RX&mNyEq&N(LheaJU6*8wbdhjn%Yv~vzs$UMpO5uafiQuhi!dS%Ap)+)ro-o zt_E%eG0?rX`8{BhVux6%LPjPlkFp)v=7E!EQcO^0Fym|WXqKen{ z^EMirCgB?lOV&jG=E<3%;8)P+Mi8(}xi?$B@^-Lfik(#%Yy{ILy(*8~1doX(TAwy6 zRCu0qEiUixugx?f!*l8EIRiqUHgOQzRP_A_FO3J}bgP*_;!COXl-DJ=TM7s8_J8}l zKnHwnu6G2;RKzX#DbCA9o{ARVQxO~9S6QKr)el50=M%z3{hfPV!@}x+FBJc|_~`t5 zF~R&*JS#B7cHFVnfd-g;=g-GESVvztJ_xf|GtR88U4rgr3+OwYfqv674i)pAaQC7t z2?5X=sQ{g%2T><3b++CW23uG4Sn7Kdx*^L_P{9N{oIrj-x?bC^C~9!ZL!NPg&7I%hf2kyf z^|S8$ypF5cD8!KyFI#?f5vOjEMKfUM9}DP-B(|i{*o7M z-_P>eg7_Rdtjmvql$q#S-_9mrD;e(hyf0Hat}6BZV$X=Q?h3u-=3=C~y`sz=PI@U*OE$LeGZgwdyx z*529jQa`K3|2)=KY3X9sQdD4aE$_sDsRWS+%h2hGFG>#I^IY-}W040P-Z)u~h(cRM z`S${lz-VY_B_+zqGpUoV^R)0Tg3T=c~VM*PpN^Opvo2M$+yV z`(8QWj{d=;kK z$mc`CSiSh^jhFz;GhC~^0uF?&`g(Q^FGOZwl&~^%MWSR6)>f3LWewV|knd+$^@jkZL{oTHmqyox|!ZT`WPlruqq0(cJ$5&j? zU!+Svtnf-RiO>e;CPX^zwq|&D-=xW(CiLi24N?wO@!^%VZsx`v&2o!>pKyDW7}n>w zUEhJWl&Cf7L9f^fA-r2h^=QZ?!{x=Quf>V4Q%h9hHnnGt^hDuS{o6~w=B7}3hgUB9 zrkOWuQw}@pOvugm$%5|j1}gbgpR?Dr83UP*sBxk%#X>;uxHP_U<6L9huQ9Pt*zOle zTT1Z_4v7cZu1aGtb|)~&SJC)(WpA%-o!~*vaLw89N5OU?XzW`?MK2t3l%ku$>uY7K zI?+7yelbK(u*l&+2`mGTFCk&>%f3ucYrV|=Te9uuTkzFC-^bHju7O-PI5niD-y}DG zy&8LQ-S8xaF;aR=KzHc8B^SX-V&L6P+rbLydI9l6sd%^p!bT3FnpxK>em2dl(M!}+ zWSV}>CPNC@C(FW9MrVEOGVw{KNp9Sk--j8cH&30#<}soCh_z%9w$Cw+e<-SM~fOrQwvMnqAU4P}FyK>s zi=y#0nc_w-vnzK>@LpM-@7Jt-**8(Kj)4Xq0aT&G;^7=P_6P!Cy=s^J2n_E zujPK(!?lH2SkwWYytw_Cw$$A`awT&s&_G`Wo`5S;t+L4I*CR$lN0czzZXr=Y!2mLqmm2!X#{td zzJ(!Yt)?Rc$Y?yqqD&}ihf+=C%ZkVj9ppr2+B!3TQGub>U{aD-&|hIoEMc-z{IykC zLoyq(-Xh22IhHZb#es!+DGw`3>-y8DPfHk*VN@3aV=!%dt?1&~QK5?4F1vYNtJba3 zqt>=h?d$Sp&b?S7=bQ)KM{Om}bG^dDs&%qsS^Tcdr$lw4l*a`cY&$le3#%)_Dqfz| zoF8iyTE~&AaYml>_D_{K8m;pAS+nQRh15~3P)YZk2){qnLt-8kTALU+ zODk9+I|>E5k(;!I8-lmToz?vHT&EDxb#DB%s+yT=k)K0!zd%zr?M2CIDh^k7XJ1Rs zdje{WDeWry&=C4_KeX$xDquBw7E`UBc6)h@zFc($^~aV?!Iq{WJrn!B@*q2}x881= z_;FBSA3KFLL%&4e&;1Lbj@*oDsXX_I?M`~5qtkP+dFhzdGzs5*mM_nvAN>FxrKpaHFo)elOI+0ggy)7;8NH7S~JlX0^K|LV;vRm-5jli zj4CB6-Rb8#jAnMXXKSnS?!8=O7_~4kbz){_m93-F(flgTdvOA9G3ZD-L?!!r$(@7t zuc2gYQ)q^eydY}tg0`Uq)h@RrMM|f=UlfVCnq(W)yW zS@kAQ%6;iIOOu>os=im>X0JW{e%`i8a&mmM!U0%)ptM+BgDd z1>Vjigbaca^z#+=i9nYWjv7Y@+zxuST65V*=*3*P3EtF(_u8AOuoliY3sLL1=_4ya zgT|7pqI%@Ky&MH?VVd%stGOw*4%O>1Td_m;X#EFBr0MbxJ&D@Ze$c#>3+lvhC{V;h z<0uQta^*qTfxNu436443jyDaE#y=azjuhUE&V^j1erak6Jf9YhKEoSL?;>z-S=YHp zq+1OTbH?o)Nuwz4V_jPbG4Z-}b0kfXNq+z>HvI5deqx!5DqEvRt5b3!MhiYVu-Uuq zU2>SD-pn8RxEL1}az5H^TsrEqXo5wMn{3526rl(ISUg!<^Ueeh2b=COOmd4fSfG*J z2Ei4O5(MsoS}t2n7^J^{e zZR_0E4-RQ4o^FyIk00yJbuH~09(D{(F(a^%NRD2oO-dai3GcESajWk#QTM_RAW?lCr9hfMSU2r+X3|9* zThh5rw~L+L&Qcc;1GW2bX2+PQ!b=jL#tPIwrVJm|zR<=JQV71jw^nh3@QHjIpV$hY zSZL4Xru46fqbOXnBB(+JUkfG)`=1|*vZ%@N zV-w`Fb~NUQ0-O-CZ5(7{Lm&$UA4V0h(T^QV98qX~?MCgDT3;F(n(s2xG~7`RPG}Ci z%H{5raN9}ZPhdOXyH4`$w}=}sNz^9lU443~=A|pA#g|jILsZs78|R*tqqS6m=4je9(~n@o z_VKBvnWmso*~x+IT&S12OtwoH66#b$x05_=Byo(evUc|!VrK?0qeF$+aZIt-=Z{I?dFfA;J{x8>r()f7w&>*9-z+1KRj}n}B2Bsj>Y}=97PtRQ}adgD$Jk9C+Jsj`7yMd$7v_#t;Dw zo)>(7VMX?26|gc}AO|qOOF4W-z;rbT#?qkbAa;;4%n_+%+wQ}A-#=mvo0^*9@HzCU zsQzZ4p0m)kwd5}H6JYXLLe`m1kWT&o24xAK;Dj|W6 z-b0)SFrb@}4NR`nz99P5f)Cnoy4kqUh7I(Z5CCS%$hc1EWLa(+e|zZD#`V8(5XNF{ zA1R_3ZV2SBAc*g##a2W70cSJ=HQZ{TZ*ilK2dl1bDFH657TFBVmfo?uArb<5{dm{E zJvt12V>uX>tn172*cY0P(VsUCG*CUVTjwDNLzsd_ z_Lp~R6ymE_$D8CA`w&(~n*nxL4j8u{06st!a*@5ZZ;{h*ofNRHhDgjc#|U56qifxp zzmqP|6SVCDpx7Y_ayinp^IZ0Gzw$WA`wk3SUAKV&c+94*{#gJIh*L_!6?%{8rO<_E`xCN9yG5T(^}TkWV+q%1Xk7 zOfDom&WUCN0L@KW{D0?;s)jrq&YYjP>8}-R6&y<`9NemU}V7 zY)#Sw!9Mj**GwLznbfZSxvd{s|7lCy<+|GlDsCrkn?mpZCYMZ5DN?@ zi<;oH2upycdGb>iDSo7Plj>?}gE@X{wrUjdu4`a^ua-T`4HZfxJffz^?!5@e!IrTV z_2X?UB=9~ysWc1e_!sn882d0kuJ{3rX{ZIY^Ces{sO^dI@kf*T!SB2?Cr->-OAQZ?Sk`2X zNG&8I1<$UpBv;Gg?%WTY490C2`f_%_g(>KVR7Yz3)iWEaCm6+T)bQ}8vzaoX>XTCN zh?UoL+;SdP3jn4}P&+{|aH(~@Z$z@W zeW^$E-gXu)gCxu66~301EL+Ae{%KXMfSCtUreGNluLQ?vM_oRmz#yIrSX`3}(2x6{ zW0iM#WPykxps-yUCqp+z##~=s7ofR_YD0JDuE_G*e&1meoKlLl*a5flxK-iXn=KaK zKEEZpT4YLca8ej~u}pFp5LZJ;QG zOv-f9vI+9|wpuao30}S9hzoP**)`wC=KOh%yz%m0U z0NUk{W%#9F>SpcaOBZ`grLkCSP4E%zjOM8y_$O3g)f4z2$k}a^E)+oG)JcF0vzVXgm;pS6qyw;LfQsQbUWnDt z4i@%XuE)Wh*Yjx3!}{*pwJYOV7Yus9mV?Fq_15ofq$@W!T;Gss5ygrC#UOsIF;pIw zKrXC?R@C@C^gT8bv+orHZv5UyJYH5@z^k2EqxmUjmkb6jfeByRt8s(nBjaL)a2LY^ ziO|2nPtC*L!zNAzQu6)_E8vIDpGG_DX7ex0)<%-&Bj6A=3I%ZN0c9ahZN=}~ecmee z%5M*>dJD$gEb1N>LZb*=HP+Sln`P0SvU=jbT*~9;BmXe@yB7Op2O^Ay5uo~dO~G~# z(1oNvTUpnM@cj#i*x0HA*|6zzep?Z8_P6E_fbV-JZ9Ae3`G}ur{zS zfsDd!iU6tGmBd_^E%#me_66xTtqbt_SpZuOl$1Cov;{0!r^MzLaz{{n8B&PwwXA2uwSv zMK1{V#<}+0o?p4rPfN}`zRgW|^KToE;;21B0uk+>W#q5XpPo=31jBnMJo9Si8iiT? z_2V-Xr45@B;n*snRj$C{80wEWj{I2nmAUZ)`X;U&8gX54pO!vq`aW%SuV&M)0#ed` zfW8P)v0ySl;nhP{ULHgvpwe0rRaZZ6S4a4FU|nmMuCqHSTQ`v^N;Qlkdm$gZ!s4RYp2?Ui4H6RALZp0^8tU$vwU^7 zeg*=m=L`L)sR|G*=OH94uqR8vz7UyL;i9Yzu!tcf8bJO@ZKeI~UnmvZe03a*ULs}$ zQ6|F=n+Kpd1F+?5GKTsA%i(K&1^x|f-1*Q`(Vod-o%jHp5p>Y^e1N@cnZ6DEg2@!=N+y-_8K7mLKc%7jr@CKlX1?;sEEP>b|U|$A{Hp_{k)5Qa5 zP8J`Zp15jYa2TQ7fNvnlqPY4EaXxcLH~nLo#y>^j7I1n(_Z?GdA9r3^r17q;EatRX zZVmyj!^YLyT>MT7_~Dty{1@x}pYQd5`OAOJ9sY;?`0%CvSN-@X^_p_~%cl4Lhky4! z`0@43H(lvk?f;Qy|Bw3dpZX&l-y4UTf0~Q`!5!)1FuWQj9fd144rR&j9_ute=0osQzdFG5|Qa7#RKvEZmKhqZhO1f%L|=+_)@E zcg1ml?BrDjZM+fyXlDE=mSFzlqy4x1h5wI#`2*i@ryt>EH+D`0h651VF$e-t5-Roe z@wo&?O69x>&!;zlL3{zts^tK`&GncW{Lt7K0?{0T+gSkfF7Q5!%rANQ`L=UWoDz2a zsQ*C$AYBF|Umrj_$vHVWT}LNsqG}lA?w}5BC`NA_Y+-+y>9x(NhDekHq4Ubu8I!r@ z?;p0tA#}~^GCgB0266J>z~n^az7S5y=lI6g=I=LB%ou>?bolao^2jnFO2GM=y9A_| z0}#muKzv61*4xm~u(Ud=$O~V`8l}%2Ueo2C^uhFu?7;0j9!FscSn-M{RA5j66oDfV ztg#Szg{W$9xi@^+H!1a}XGqFy5}15xn;igpnp5NQn>3#gMfVJ__e$J4RFtm z9G1wy@ficilMA(19FS<&-`?AbP}AYI2+PCk{q0wTb8dmmQB9GK4n1L?arLWczAXjT zc{k)r77%Kp(|E~l&qYvq5yB5q3rm1O<^HAkCro^icp4BmQaR%)TKHGDV!CtFe?KXk zkMv*oWbJo|`sgv0TYDWs4TQO^fk$*5Ah@`hYW9?1AINln!oH41vz|8Kde*J4T3Lh8 zI+=l#s%oMm!vZo~ln^HeBSiCcP=g|T5w+(!0Q~sB&>sJM?m7&b$c2n*gS*+1aDHZe zg;on3@+W73)3s)gvW^Z0_a;m#H;7w%5f2t}I)>1e_;z3r?mzc%ciP-M-bZuAX;MIvTe8j$;(=jH7Je@k+T@C zi`a<2l4t;~%>K3oi)dJKfSQ_Fr8lc8SHX>dc?=E#W`J=N*E;a&n+d@EM7-w4)Z#V` zz^W=)TObc1?XAnqucClSz)$y#lV)x;#T<5ziejkVG+Ji>5&&ZYoX}=T?~RXN(v_scf2AYv{VF(pJE&O z!c%$mS_}k=`p(dL3Qc+lyw(kfY%u-B#&V-*Pn!W5F+o6Q7e>hh4wR{r6N4cd7>vUF z(ok4R0WjF!sfEbGMI^!!NTO;gEFVV&evYg?7Zh(cZ-(I76`1DwQX>e+sYZly4tyFX z1dylgn_q|=yGaD4cp`3t?rL)dQsQxmrZ{OtV-%Noask1-qFWas!bM(HF9DDgK-i>y zWAx-()&dXpTCW4N+wSI7DA#Hhz}v&?2*4@bKa602Q1nJ9X7dTyu8F`$FX)GMQdFEt zss%3N7k(<536!k1rL`T>fTGFC?%W}k9AViUF7Iz|p8%0=d^9QrgW-_a$RgY0Q7oAx z58|Xih%nx#XJu3PJQUns6*xON3CO8;%WcIx9uQ!*D1gWL9JDuu!d_7Wuj(-vWhLV_ zg@g-#!2GwnTVvQ>%H!SC@Q?~+wl3v$2}3TobLUPzfSmRgZWWb<<&Ed^lyxp zgp|4gRybl$04FK_?Q&;vB^yNKAwY=^Uk?rrMz3Xz$6x@GG?gK&olN`t9d2VpNyLZA zFQm3Efpi6?D3yck;QLnwE-7NuqApqs@y+9iPS`u$H-n&vQpsg>ot|YYcmn`r2b*pl zQWN$xfo4>+%kLPXm=6JFG{iQ$^duZ@8wUzJ!S-_xFtX1##20u95jB7kwE{5+P=fyc z1~AI*8kBnZeGqLM-Bq^GjA=RD1qLK@2sjuqy$OC1%1=N20_qX}hA0h>8+&*YM|hrd zYqnkgY0NgaWTgE9WzC@617~dpDfjYh$W2G(VYBZ$3N(@ zacH@6%HtP;VNX*9F(5%Tj0cAZdvWV?DG1_?(j?6zim<1n&0;dm>yFM%#Z;L8XE2Np z?^kox*W0&cd=joOh_X5&$?vnVr{SkdBRI_ZJ=4pnghq$}_aN@u9WP1mrElGkOkrKA z5DM&jc1kn!J&Ww=Bqjod!g<>J3v0ZZQK3yls}SuHJ(i#NG(5% z;|(p$?(j)a!h#VQ9q|+?Xx0TBa+>fg)F>%vQB8`TJ&W=MD<3)GS=pmMEnd){<4VyEsLN29%|b|aEyh)d9OEa z3J>G?X~U*@C%3-1vB-4Lt{Nus!oSJKpG_6G9CSMHpQUXtRaci zSPd-xmwCE$0I?21JQ0$anJGZYNghqxv}K!4DQG=5Vdx&K*urai$vWp+X&ymfQ+K-E z-m|uynF1jQ?1wGqeT`p+q@5jB7K$?>7PxP}*)#)FInG1);XY14D`gJmo?zlz>KYm@ zS+ldTb!eZ!7^AI+pWX(+o&|bsc$h$!Oe%Q9!fm9Tv?-(kO2!r-H0MIV&{~O-P_#i? zk&CE8l;WxKvv6dFXC=AUpl9_O)r71H02ci$yof$}=V3hTjw08q{KG5Z4JgvGfS?Z1 zfRktdg-pxsy;EZW;SLUhj~bB6)K&mCc>sa&3(e>VH3&FZ{oVnX&47aA2o56T`%CZ& z824%bVgZ)1eg+TRndcjKW)h&Xm#icE_jANoqQMOOR78=C;E;p^`~{q+)FZ)k!|+4S{%22yI2jn!7tZCqQbltaEFRTj?7`?+l!-7>qcInUxa5 zqoX-l6<2SoQABFFv^6_0uNd%#zi^TmMGg#zeba8EHfRJ|PI80@sO43FU)lTV8xUQg zvl)#Aur`CuaR{naAyATU?{=z{$}u-dYI{wHgG61CUiXhA7y6ivJbao>T4-4*>VZI{vYp)(*UTUbaa6m&3eqWw-1ghR@U)BU!I z%18H9KpiRt;FYn+*3DRob`}hETi>;K*=-z5-OZ&<5J)Mz+U6 zj1-<-ZVa35HSl8M04pd-T=7u5XwDws5$9E}G4sJU*! zTv^!zbLNxBAbHJxuic^qBY8&wDHIR`_ zK76>amN|%GHH6NXM=k%X3n-O#V zL{uo*xwg*0L{HMJtnE~xmSnv3A- zR*qNb-U3N`5Xh~e;W{~~FpMFU59wB)KHCkHOCts~Vs@fO4RXA(!xA24T`)lL07Sp~@;W=N zNTV7sl(4E1T;B@HFf2K;z-1pqrV==xBwvDP_lRvXk%&^M8Q{fM!*>$sYxRWDwWx4} zz~nv`wqoKpxQzyY*fnQ#Rzcs}UG+ zG^S~0wqCdv#)9)gV-zHwY-o!?G=Ih-&5SaAVjWFv&b8{HFnjiZ!K-bxUFxLXHlobqZ2l)J%ZLB<8Fz+RcriIF6@xT5_ z?*&2TuM2+vv)atR%y;?U`-|8(WZmrG_;1aD*$iFo7x&abA@|uV#d2d_jaDzxROGO}ihCf?H($k=olt1Gj6o2O?yM)nvbDtd8fL{tDY4uJ4iiPhOoq?GL zOMv~=qTGbP#k`J2F`$y*G8zK}@9wu7br5{5xc;*41 zT&cbpe)u;SPzD`7L8#dFp}szYb{2hP_r?g2_=kHSxtLo)nhe^z7+CbdclrZSc45;E zwFuq-<%9wsM3x67X)>1&{j3}N5kz~%yoyG^_Kit3ppovd7hCgBK zX5J!))<_G}a;bHb>qASU3FfuXJ{EEz>yE5d&54s+LoRn%XNhZ8JjEui1!YUA>15H2A6bPQtJIe$u(}w|ZLGr8r*J$%@mx1D>Q? zXWv6q*5u40$&})4@@#E&nd}slmX*zbZ~9uX+xD{hAsd zuLPr$Z3fCqp*7^yojaI9I7&L{?Ck85P%42#?J4oib6ho~rHOzGe>lS68A=Xl#S2_ZQR<4_rt9JZ5cgfN3tbW#i=3>wTl z@9)^%*K7ZP=eK8mVVE&r*Y&wR*Zckcyua5c^+7{2^bRu0*8=R0qqtdl-((<*)d!9u z1R(7G^5ulE5;htX3sdcKv$8nQ5QMQ?P%8up%R=7g;@tC&>TTP$sT&-*h>JVsxBY5z zaz2N%meE!1iqmF_cyU}VZdL7b)4>Jcg^u-;H&s+sMK~q9h0oUU1Mu88d0@RNu~DpA zwG$~Y-@h6mrDI^w#L;uDSncNKmeQJ#n7BqyPw&GU;RYw3Et6TXZ25BT@^M@z4N72S zOpJq_on3Y?C?W@Uy?6tU$tdq|E+HN=%R6!xi*IdVkZgM_YC)yR0c*fxYqr2EWg^Kte!(QK(OITUh` zsH2n%n<&L|LTCEyLQ_*yr;v~kCv+Vb_Jtn6K^jEg2^fk>_(N1l?<6T`IEx&oQ3zY_ zIz7E&esxAhMh+9YgGn(RCvT7^boLTER_VKwWb&o(3m1&N1#1bEAb)ql2(~am$?>0E ztIxgsR4TU>geCtmw;n*|On}j737{O+$;qi|TPBZZPfT28;4N%&R_Q@ZrnbJmEr#H< zzrRVa|A*T7^Ou?sH{o&H#GtcIp=kV|UGVtf!=J#hZDFbsa;bVavikmLng<=kS7-ft zs|5=dsDyW;;YL1Ez35eWab;DNbvkC#d-&rsC)8aBOoR$J&tGTu+_^ehT8{-^dU`m> z_aczS|D>mbzbRICw8SGY)YYB<*V^HNNk{Vd%P!kZtFg$S2s=P+pY0etu$Nq6d1fp(s3j z)^0Mnwa<`fU-mBHGvZo|z9s84HGhI1300?S5Gu3vswZ|Ln)D@aYC_$LD~BT?XGAa$ zTJG3!9vd{>d#T~EXqz;%JT%DGTD`jGX}o4+TwEK5g`JYZ#a2!(Io?<%^6m#vqHO*K zc(KmR%;oas%it&i1mW5DkLJ1F^S4HkqB&t5#cJgUR+P%prOtY+!l*}&c0rN086WK0 z)PY^F054p(X%qF?vu6n5tMCh*og8Bny4-C>hpUjOJDq^U`#zs|RaS_~v|6SV{EcDS zn|kHSF~u{}Q&Xd`BG`9qGBOQ0F2FMXQQoi>gH2B=gphPZ)j+RT&S{S ziPvbAU*8o(aV`dm+csA|ir!?o^Tp^oj%a@h{>XuD%8gpA*IM@)-?cznwPGM(CBT5j zzHhfvCVIboDIz+J>*_X~-8qJy1<5GRR~3AQAI`w_nHjBlHPLq*6#{nJn0RQ&cF&%F zgK6FXSP)^^7M}ise!&?2ryLTAgkj+VQE-~a=S)f@-VlZ*xHVaOZbZ8DCB@!83+Ynm z8Rh4QZ&9s%xky?KOOO0gz|j=fl#koNCT!3FTTBMrEmTSL@EZTR^q#^7 z2$xp5<=m~)TUld1fFWvPDA2hVFaA&kL72$Qc_$&CivjzzO-vji#)UU&tXNTujz|KL z`0uNYgKUs&aP*QLcqk1kPrPv)(u}v_LY7#RjrxHDtMoK@H!o=97$qI$%~s2`u2NGs z=WtI83kxFO@f$TYHDlbH@893C)xX~sV2FM#0E`QQR+-%zgxc@tzjrL=RukigT6Rqg z6EZXtEi>qqO0QnMa>gP4#t(eWWlSB5EU9Jw0AnQqR05N77{l@82Kcw*EZ;ZUS%AJ$(3(L-eBP z1&Q`cE12^0v95a-=<+tB{W5>rI7Qk{OfWBX#;U=x<{wYF9NF)6! zD01COS-yO^>*p<6w874*942%dlbDm}oCl#WS^|wjd|THU4Uc+(eaIx-ajHnwYV&~m zptcc2k=sVUURPsTQPGz^-v9FGRDXAOHoTRsn?nzJj;|zD49@hUV-cE{f`Zu((6#nM z;D}*@Nag6iaKslw;r;vfjSp$Mv_jhPG*w|qMrlZ|txVFdixw`t0ehvI7``l{V9D8F zNGE>D<>xkg)e9TD{rcORCg7Ypn4O*N=R{#Vdi3aOnsZz5o{d!($_sEnLQY=`ulxaj9al0=5X^04-Y_m#UpHBW)vf?LkfnnpO!aJ zSl*d0K1VHbF%W_m@Mx77P6%Yt6#zJ!PIeZEnXB_Km?}1^p(tVvi^UFzZR1esxq+7@ z761cfw9eso?%3-*ZyoTFoJ8@Xb^;Oj=I)u?v89FC!;@(9SyVrXYT89wtSBTAeu<4C zy1KfpSo8-yyO1Q4J~2_<=@yBcXkD;|q=fh5LfR{p(L9VhE}wh_pBYoR*=o302Zdz} z=ge)sZ^M){%noqxLX5L6&CSiNKa;S664KBy0qI=~2d;xxTvSvG`axU=|9jffLO~6z z35hHZkG%X(1&l1!%w%XC2we$YL@6AjqDK>g_b1_&J^2Os6G?3 zyBe46bkqHOUSU~o*x;Lc$MfsbIXp6M3mEJy6gu&-3%3Fx@J@Xo69Bq-8zzp}UcmEm z@tlql4shXq_L)x~KMI0eyV(f|g2`*fLG6=)=;SAka+`X<`ElXGMJragWw;$XmeWhB zBTyLnNU%pb>Sl%_tjgQvjQq@au_rcY304g|Iazxz;F6F)SS|z3KL!K@P*M{UkB>O< ztg?pj0T4L)EG8vHG)S<7GW;h3Re_zt! zQs=xan(^=d(f#K?w}1EDcZ$l&Q~(M9Xfap?9V;yI@?`_bn<^F+25lik0-cMm($dm$ z6gC485pLh{QWM@V^+Y-2l_R2;@#1)_njslNi~a*CuGAY+TaH3izwm`>)Z@`FZ}|yb z5hW#OF8nd!2jX;yz8YZ>J}{RJkBk_KCME)rxgY0jL36rOl;BU#)lzClT17*n2Zt2%A9)lFv7Y`I%m!tep6GE=&SfJ45>^c6l;d(&$DF~zP>l}q@T@Y zFR%A`uZ{CwK>*c?{>xpr&8e;B8`S zT5{v;)~s1WWlBo5v2cx3+pA7@NA|iF8$%O_$%7P|Td3ij4wHsQz7ES`2z-E^kJLg_ zES6EhLAb2(FXZ5F!=3|g?v>w9U%G6WkEucRna?%^p~}kIg$pO&i;9ZEZE(A}pVrG# z(e=49M0Lg)CW-v7K#e&nT=yO^PKW4`a~G9R zAK{t46yw2_N{WgbQ`4+1EnCpINc|jR{$aEm&SBym{Q7zXhtqmhv^{F#tS}3_PNW!9 zWEUXExtY_yEIwq5lvR*C60@mOzsh0)HK-_?R8xo0C^?H(D4oGrW~(scn^ME9t*wn` zpEgc1VI-r=WwZqM$q+hyDRqY%SRaDbSgb~3^3DYZGhBkvv3Nlwu(F+9yu-AmrDZ9y z1I5|a);2ph^Tv&jZ-->Ea|eO;G4G6uF$p+yvf=Sqn`@g7i?2$?Qo5$d;LL_1;N~8P zpls`|Nf?|t;H|yTgl;mQL-eu|gX^G6pD889xXoN~ z<1N5%)xpNBSumI;^{S76ag*TBmY_u;7qHt=5}|<9NU^et?qv2sK>0>PQ)~%pNDQW~ ze5^Q!J|v?-47%K{1+3%|NPeKJrpEEZ@#j}F5Q!`p!W8vVh# z2L@cbE3s2j!6`x9j`0BPX^?17`}*1faKPnlxp%*fO>A>Z3p+jCt(e7C#%jg3`O4_n z^Cg;h8X6iLJv_1yRo%V4psjk)1$)sGjvzT0RMFJy7cXod*Vd~0fi`^|0jslGQ&9Ns z{rek0zTU9zn6kzSR_Adzdr-y@;^MnMeab;sEe>Xb-`Gg?_b)4Qt2xn<$B>Va`_`Eh zwtxi>BYn97_#ZyS2IXv^-8?Ya=tfXC2T$J~fGbQLQ8g}X?cIBMKZSx#&}kr0Sy>qZ?{hwT)3vv6-3gL}IVkqk z^22!=zXWI|9=~-!knz*pl(cUsD-qOAS;LGol(eDEJW*I3%v!$X^e#dT0W_g028;|3 zCtSPc@?plyULr>#+q-9khd@F!&Bcu~)vX_pfKW~?z#C~`ztC>-Z=by9KLBu=L2xbB z#3aO3FJM-6kWVOml%q1_FE1EX@W0>Io|=xsmbuQ@n2a&eH{#KKAu-iR^|otT=C8d}ij+(&?pG2&l`gg%AT$y5HVD!MYJ;xFm=Yt{GLZ zlnjrLm(z-FLnP!KemLjxlP7dUisS5ls2aqs>f=*@nX~VkY1SJSxjy@K4bfq!ojo;m z?(w5XJy_(3iv*xT2u6jND3V~vP)lp~=f?Cl2!E(RaUVR`dH3EuE{KV!DKlc$tXYCP zr{3skRfJ4eAXjBXNB=kQ^)oOd>tv&NXlN)RlO*zNB{m1pujXI=u{a?qDGyCup9+Nz7}DR0i;Ia5geA$M%1Rz? z{i=W7bVfXFzbuoQUzlM-@?RhL^X8bD7yt0L7tfPNs{A+5O@Ab$&p#jTYveoOA5VX` z`Aa#*Pr_0Ob^Nc#)*W($^T%`lKYOjopZEX&2mUoq|2IQ=S@!+bOKG#jWB-!R`rh3( KyNY(YhyO34E1GTq literal 0 HcmV?d00001 diff --git a/results_plots/baybe_original_range/BayBE_line_plot.png b/results_plots/baybe_original_range/BayBE_line_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..3bd67afa190977726e167f6f2911d4d76ce00c77 GIT binary patch literal 163925 zcmeFZXH=Ehwgq_9vMd8wDkv(!fC{3ZC^;%B0+N$R63ID(WV8g!1c(SCStKXPk}Xlm zpajXFk|ZlgPCYkT_r2Hsw?~ie9*=Ra7RWj0`}VijT64`g*S;esBTltx&n60mLM3tW zoC1Zi!IMH+ANj}c_({n_tycWSXLtUxouZ|IouiJ8K1Eu`&dSWv&dm7QJ_mgpTVqR$ zQ|!F#r;hD2va_?Y<>%lq|IaV5TiO_Ml=WX$#G7oix~OJLp|Bq$|6g-GM#O=#hC-1z zcSh-Y=t!s2<;K3{mG8}zyLUbJ{{8IE!@cZz`||qz)zysEERMg-YQ1LP!9TEAnZh5V z7ONj#$<;9OQu%3KZ)%<>gLH?YQpcY(=aoLrEq!k0bEga3O-Dx;?mPS?zi>P+Xol)k z{ABn-v9v*E>oHu&uP@Q`f&P(yzafA7oLNivU*Bu|w(;tJeP^|e*%Md$>+98dKMGl% zUthca_l5tvIR9=23gv%y&i_Wo)&JTK|C>7gKbRgj1PeH(UON2fGaEmD{;PRDNv#2< z(?)(#vT|~AH_GmQ@g1PT6P9}+7|QVTNqhCZxwE6Kz%lL1moIjGm9(8Nt;PB(A3q8T z3R=5%Ee#FL^R{f$s)7w z9m4J8qdV4-w@2{0RrtcdbJaAhE9uwH-?*>YXx>$*rtPtmt&*mdGCMn~(H?T?@cIoK zlJu(rP1EccO1SX*PdfeOHZHNnrT>N#w{T5 zW;@5xqmt%rS@LB*4B1!zSsUVC;;+XYVn5Py^7xgki)qF4iDP*Kj9jK4lRcx?lZ$wD z@*-s&{$^)6bm;uOT^uSGFaF(M7co9!B^l6D=4<-(ebmtVsK3~?i%0OI)J%u1Y>sbSZ0u0wV>W)ru~1G<&Sb_C z_oe*bsrEeo@#Du@fh#S_WMN$ACe(xtaD}67TyNjL9UN*(Qp;)0w@>1C7>Q3&&+Q); zZcS89I=XM)zPcsL%$e)ozl^k7mNb3(=dVmx>Dl@DM5!Qllltd(Od8|&TUuHkI&x%q zqOa=iojdY+de09WJownpPj2=GxkJh)%CF6E?DT1+5N`9z?9Qp-=4{&mnVP3uQulTq z=cE@g;|`f>Gfhjd9jL{}s;s#%e5H7K-jpn0v%?YD@W!noi;=z}%Pm9Yqize+gG?*4 zw}lr*G6l=N*Gq92cf7QA8q={hT$>raF`|WF|V|P=7tjKX$**f~Nay;rB z^Y$#WR?g#U8RraZLs`rH4t3_=c77juX1%a)bWNCmDn8xbfsV!Cde`dG-dkN-$}YK7 z4L0nQu#yaP3|7=KMT={a3 z!)|&+?Z`P5&ZWtSl}5zC7W=88riO4Km(zA%-)lT7@$c>Lk6joq(N>I;QMR+oDqdN3 zvBzVNJi7|MUPb-dApQSrU~0UzaYyoyYVzfWeGgw+-M8thH2(hOtx6e}p`M=6+(e(u z-9$YJ8JUk6`c+%(4?mLFyo+7w!5*%O)3LX2-wt=3kXZUwy4B~8ZREB**NU$qL8~(y z#k0>OA0O3^FR=Q&RzQC9lib|gaJOl-qpYL?#5%7 z8i?>1>d3Rrcb-agb#o(ux-eEC`}nA`&8+KsR&H*U0*BFh#8AMKC-GRy1UyrdhS z66^W+tiRkkaNqz9GQwUSi+HRKwqqc_-Pf!3oucycEp>ZwKj4M54Z(rp>K1hGae5b?OFi4<;F#yn;|*$_-hJ>OyO9fdM9$$$|IC6!;v z;>wY6^_qFDt*xJ)(79Q>Feac`c>QD*H!Yh=W8|5?e$14tXu7+*J5j0mpEi2GuO@`2 zP9YWd@hm#pc61kBd$9%k!W6kQZ~{@wWn9lZ+L3pvY8hMSxOz_GOY5GDLYH<%V`Jl| z!fyE_5*c`{_Em9@jg1X`ObJ<@tMf?2ntSt(opu-)*!1+NZvbHq(^lL~NdKv%nC2tbH`Xixeao@m_l9F)udE=T05s~KJG&|EW zopa}Ig?lVJA*-1Ve@x-7;kx>Yk`!it%CI&o!O|g+UDM?87J6>8%f;`ql--NR%OjE< z4jN>$wJ5vIbUKm(6Du1LZa$&vk6fE9D?E1&SNfi#WKrSoZT(}&qs~7*h?6>H81m*3 zb7pIDXMy8z)NRIkEObC%U|`(b@9}^ch zU|f7Nz~BGbK)8!cXTE*Iy9awSmp(~2JLkJEeiuhoxP&a2;k-D~nmN>%@aT1EX{PkD zq=JHi|MJqfN79+Ues9aQj41v1aF^FoA&J4e{M%pQFVD^V9?OM33<3u6lT%aKPCTDc zDD5ZvYXs|>BN2yKPFXl}*Ue(Yl`2 zgGp~3v1>Q$kNLAF>6&}Y_3b)z_%Q$er?02_g0xLOy}EJN*Ecs$*44F;(W!S^ibh@r zNOi}@RyEvNQ{XtR5+@Uu<+0)(vhbwF0A*XcPH^&mwLxa5?aqL# ztkXi%ExJ#+Oap7&{2x7iJWzgN?vQFvWGOu#Y6c!T8`mXV)u zgx>16)41tRH-%?EtxM`XH{Mm0;=`8hRiUTQ^X@^@OKV*&qxYiN_Y>d0Kg7=4zGH{| z;;c1E7l>sg6O+UkZTvwt3ztdbMHHVZYo8!(cQvvbb;{ayX}d<@xz~?fkAC(n+5ZKu z`cf4AdiotZbRYlV4!$&mCx4@kDgk$-Vjsk=Sx>NnW%1mF7|F*rXO-4l4%Wvw55@!~ zYfX|k!Gp|q7)^ZjN;C!q`Nv?xl{}jmhw-lCgDPLFUU{zBbMo4)Q)hE8N=Yf=PN*tc zkN^@~X2$Z7tdyh9uGh@BlOTx-Ih{Z*u@%dbC6ywVnU6bU1$7!+KYaK=La~Y0zYz&i zX#Q(7d12q^XbO@)3n!<7$kLBT*wV)&u4EZKe9B`P?n^81FYIJqUFej_$IIJNUu@Qz;q+8w(licLBX9a-NKOBhY?Iydk69JpO$;FYJw@qj zrf=>ja&b($KHQvU+0e0j_wIhg;;TdtQBhB))(rjDX{k$>PM0jt5A(U#ZR(kNI_IK= z+E6v#c~m*^Ny*Ccg4qJ2fJ3!LnwC(~%WjVqw@K5qA_G<)6k+;p+a8YcCaI=z2eGNv znuIb4IP@A*z5V<%XCC>PGih`a5)$(52K}+$QW6y7NP61CXY*XSz_=lnm6`cXzjY9Y zb}BND5n?-0A?B{MTcT3JGyJP@Z784j{L;dVDV}uRw8vDFDplX}=gc8KezhHj%Zqc~ z`O~BAN%GMb93DK{i+qTGBYOjoA$m2m%v#nWlYThtcqFo!zzgQ6u*OZ$tQK+UG5aUR;{#P8)5{O_B-Y z_kWJ3L}L`1m*>{&O1cIhQGyg(bEYOI8?YU&PUQ>Ref0yr3mn$KauvBRO{t1$Vzs#~ zyOPToU5>T8_f-WoHzp`@=~vQ7)UJeg2KxISz}0(tQc%dGk@xj-%>Vl9uWaWjlca@= zxJ$v$>O@vF{2SAC#m!qY&I87mJA^U{IqNx{73=R_S)Xzuf(N@=)cWSs` zYGg|*yf)c^;B7=pLSqDqQQB)+7=`C;Fn9ghwP%5N#JFv#{He@D)wJ*{Ic5oXbi>OFfq^P=I&H~ky*6FR(7Pj@wj0fv z^wZN4TZMl3MY$t~njjhYxQQ%`oJz{)v+ZwLv|T|RjK!lf#ZKqW<~WAJpjhNQWn->d z;9yR&$|-~D7mc080{MVf^N2k&e$JJIxbS+H@uIlDHf+5zG*&44@&(PuNdkmfWPah` zcx)9?Vq?V_oz5{bdacwg8%DT&^rLiP)1B&jO2w^v~`}`|~MMb37Fb38pDU*S--)r+OyU(w^Tpv7)xaDqr8e8Nshql&u$x88O zt-)kHJ-x5!d-$(^i`1XFzBE6zTC?!#L`1&Mt~ckVbc_Tu8A-S;o&r8M!rK5o>Ef=1cxnaKM?AEIw-rn^s6 z93fq8YF*lEJdhv_5k1>DVYhj@?XPmly*UUCEc_UZcIg2m6kC+?Qg*Rb$*o^W3NG63D20zMgZ7p)i zbzfO>6zlZcFQ!Bg>b$^NY#!moZ(>D1>ZPvaT4+YqY(B`t!y_HaE02yq8BOl!n8(-m z`$=n~WM%aNc*c;=ASb?}u)V&aL9tGFA-S`s5UAH@6RS2!3I%PbBI*o^uCw1h(=V8{ zy!a>40t@I+!zSU_X&;0-Y2sLN^V;|xf(!JQa3vE$G8Q81#M$outox&3X3o?MZrZ8=DNIgjm!}la=Kq z(!-}}6(yQBr?LtObtbw=O5RoOB+Jnm(>FYv1Q>H=WqGFLxV=>1u@6WUww?0RZ+B_a zRa#hB%t@O4YMWby!vh0j*;G@%B5)+7rL!C3;^IsZPgtlhlHfd67P^5zDs1V?3%JeO zjDSt<{{8p+o9j1c*$pbZeeYE=w~ z{9`i}Ra2~Vh?$AYSO=eZ%ZoF}OQ-3r_w3!9)K%nSisZkC*D6wTl1aef5|it<8%SyX z<2>c&(whqBhmyR1v%-=Z)P<)Ynd`L=ptHDAcs)HT6`MN|!5e<vHC zb$#J3!Q5^|Qi_@iySloZo(dFbW_hKcrYC9@<##S|9`UqYcp>kgtEcj$qVYe=BQCm1 z)jUJII}atqk-9}-?cqli6PE$vy|?Us79Yob5vWt-WqH5GNZ=$hopIo zUqDNCJhL^_JR;9_;L4%z-c)-HJp7#G5N|*tCKJ6CckkX+K*FW+9dFB)0t~f@dh=z= zwr$%?+OiTfwMn{d_(DZZ{gL!{*p)`OLCH&(l)$zK&-L!ew(RcumY>&vYBFi3WOe2a zI>|(U1g-G0B<&J+_tzDASze>cH7EY{~y4_N>mN6-__AcJ10}w2M-j2rey`(d=X6uc}8Lt7?6?zxx#Z zo<~9wH&nHp&ZGUy36KrtjRL~Zb5R-&6Y!2vpRvX6K4p2MUkqh~)Y+yq?QXws%hTss-bj}5xn0gu1Zya_gWNN& z3l}nJ&4}4?fU9!I<%yGOw9rID^R^60=AE}t!S+_ZopW&ri7s~ZnmT3m{0rSFJYGK%$4keGl=AFQQL}$Cj&(_zF!wX)5vw9-l2TN3!R4oqZ8C=Cw=9FC@fvxnReie@3cmyEP@c0|m+a z{rj}Z`^X#a$V+Cvo#9-jDxZH3cp%_AR#a0}#a7qT7iVeh9~x@Lg0_OcPS?rVxu1s~ z*GOUc^QSVmi)1JiZ}p}$wamlq!hm;sgq*XKm((qbVxByqa|7rEFZzPCW7wq$sMkjo zWA7_3bc`w0JV3Lr<@)`+SSQE~J1{6oIl((hN!KzjUqsS|a_9Wi@Z*OMKi@L)+l4Bo zoNLjUJOm)CdgPq$@uULPNB;g3f?DqLJC6iAjv73^nA-%5GP!q^f~5K5S1+2R80Vv` zDN{CO0X7zy=~QFUberjXbIEcHdYCrBag;N&t(sX^dR_NYExCAHp|;zFMo33T zN5$SCGw&kv^5W7`$`B`$mxZs`ZR)-wq1U3(!+F~%DJg}Qp<~x}FC~qS=h~kVft8Nh z=#Z_WV_lA0|G_{c`Di?jHlcC!bq0A{{m3Qk~H1;7<@yWZkyoQkA$D%;%XYY++RqzFVtOw_yV^9OBKhpC8+w;!8|1y z?b3rI(Fr;cz@(R(9vo$7J9u_g5x9NuSI{9WO&6v|Ri+3bl;evwE-EzW;Jsb!8b>94JO zD$yY|@^riE&hToxO+Ai2dvpRVnZe^%D?>?XBgN~SK0G@Qj@nv}@c)!gCw>JNm-Ux( zorO*fiOV0vnQC%mvrU?|^c@uU!TxLO=i|_L8Ts&_cw0x;&UL?UIWcmMJEX>WI3+jB zpk|L)!Sd2V5~7VdOn?JGGkW(agUd}bBdw1PMs98af=osJ;MYq=#d8CXq%tMsGHq8r z>vIAC3{k#k=$$WZHmb3IRRWs>hiaOhY184nvdwL$-rqvqC@Gs zrRjElt52TNOJF5_Kn99OZ6UNtQxBTAO;l9&b}#29KHp|aL=pAQN9`x=CmMpQIZFg# z0=1}(C9$jLnB91%7SNcirugWvbVg#tX=DG_uU`|*L9@iY^V?;x{9S0~K~ue&aIG8? z)*-u)Wz#R2&2!}ZxpVPo!TE9|ATA(V+JR9rYrlT|`d4(k$`>zQ^wCvLzAO*UDHdq= zYJ3FHvP$yhs3Q@s3Ltq^kwu}qL=yc7#JTtLIG}Ffr5}n^jDox~>)TjZSS|uc$OSqM zOV+>8DW&IbHV2jtZDRjNASNb;h(O>IU3~;t+Otg!?X}Z|3~ECYKq~j55p`dlGgMB~ z>fEnOy8#98JrG+Gx)4>;If6-zL*}dK?$&QYfTkjieo`KrpeaUb7qr2n$B#>c2&zYN%rb3ems$XhG(|A% z5pgff43z(>Ez~6GWur-W9xy|i4xNn~*$rFt3vpOMdD@OK zVS1oJkJI?w{oO+?>AG5_H6Ot=J=I^^(%hUY>eIJ0oK`X+^v-k5x*ynm{ZB5|)}WE3 zcjzz!3>JIG>VwwgIqIY-F2Z5P{9|%3;CVDSt6idcbIozGOm5%b?m~HavVUVJS_+~Z zfO2V_p8EFfJxQVYJT0_OE*-{$l?9(j7`&z))P3ZyiVRC6!5<(42GKs}I9Gp&VfF!j*l(0K?=3u_C8bAY9ZdZX-i{rXE>mFhm* zesHPvgm@vu5E|k+$>rC=;A1}EPE!FZ`1Ay^!qVY_Y9J>4(dvr5JH|p(q~fJ%b%;C_ z{lY|o0HlaSlm47?kS)KXqvN#Wn8tC9mvU>?tl^#-L~lb#DTK@*9;}M*!u&ix|0Qzu zFZK!$qmAdaRP1}g;ijdft*WL#f4B(wnXyiMMuOmjL0}=G#DX-hN2-g5AgJH|5t|Re zO5z=5c{2aLM)0M>1UQ1jJlRNU+Int7i|+ww?12R)W@eg;X1bEl9K@q5MthQ@ceL_T%dTZ4tFq~Ch3mka9QjW_FI?APZnt{_AuOhbltDUPzy?zV z{A_wIu_O3W>{{Q?c$5f*rxqPAbrObK(xX9!UjAeoJ$ujBw;AmLp~Md{GqapLDGzxk zklFp0#MX%*_L^{IH^NVe%xw4N%|Z}IVSF!{l~9G+)`LPL`Y;ftT?+0zrnfWo_Zb!Sm%jPKmJLsV-BVaM%p$Z7?bXwljOIzUl5?Z^|4hRtJ2^wn;NF3OxJ|T2%R4(YH*VZWXpZ{&`V0ocFK_P5d#sGF(AvEui*WVSR>KYC zKPdi0ivlas-}0`sR1ECZ{<+POqR?uPJCAa6b2sMzAJ!uiB4{{h&+3uaTov%~Bu`rI z*mV~dQ{;xBw(O)#u{EF8f@XdO7PVj@FE4NK&XMr$x2Jqa|bMFccV}kk{rjoYN=NP0Wa`8U=9R; zDjt+sO4&sFlePHyMxOVsZa6U#h|oaqEVf}QV9 zceHA#7ggoU*b8_h7?K)Yska<j<-Lq& zr;Mm#;NJT0cPj9zJ|H-}^{5ORr)(oFazp>?cmhp=D}9U9oWV=SHgllDGjB z0(QVd-FCuKgHw=)b7kadj|}*uCV}z7XavI1g9l58tQ{R4r$$;8q0}e=XiW(EMnh(A zkkd-P=0ys|k@Nqqi0B6^I7muq?*@0vZjfC2sFk>N{-7~OMwu@YS&piBbi^>2xkd)w z0WMUjsjnYOW2d34?KyGP6CISXsw={~3E;GMeB4sCL{?-;32lMWl`B_L&4mO7e?Z6> z&TiJqH1)Z;ot8ERqK8@~AP-5%VRWl*3$hTkWeWooM@s4Mq*M}KHL2G!&!!JDyRMg) z7Z$E%sAT|t`mf}iQ^)sUh=_or##mNp(aCQ=SbrXDM2?d*(Ji~K z_fX;O(6JB$1*%PrTm--bvWzK+2kttOTYi4gaBqKA{=xr{)DNVpy+dcuo`p_#R5Q=! zJle4akZN{VD$SQxX8<2n0X&bJ18vhVGKQPl5E@hosWQsq+oyj>nkJnNI+?HN7*sVh zLJ4b`ois}bxvw9h&99JpncU_u(jt!s&+jsw3`+&E!;pLlcgTsXz0f{^0?Zo(|Gm1^ z{^maY116C!Icm_55VEJ6{Dz{YNPLRFj|^&qJN4;u8MP z-ABH7NV$+h%_MBb(i|@@1``I%3(zaJ^ns3P6_f>BO&Qx!D+&7yNt_`A5B2Orryv;} z5D_V%UzlB37zPHdwO%ESR^K&}W#5|nX22#Da;a*Wu{Cbvz&2uh`}hAdILUnEh$*hh zJ0G5ameaER4qysZ09wt@toG`wulKt3+ldUYPNpo~FRe@ai-X%$5)|GJPMWo4N#HBc z29k~vz)eX1#!Z_PaQDah&``pN*V@nfpH1iaT68U4E1@u>T)%xfBes_72uND)fKt@h zaNsT>?i!tO!4;SssQSQ_w+Q+&17LBNegDtGPz(>^Hk#t)OZ}Lo;c!A#vKb{M9$oOB zA^2#XnlBW)E$kUv#H}X4HS};;5PjGOLaMNCfC~(&SY^)AKYwkY>htsS(-|J zcj1dbxh6Ipx8=D3=f&@3=(2=}v<|ikzZkTWT_YZ5JGFVo(Kcm3-b(v$l83;N6CcT; z&Nj_{H&LV~fM=#^NSsN%R%#(OM7VWqfilFXOjyMo#~ISADnTSR04lzDuP@{ z(JWAbb0<6&aw(oZayx+rS6*7_1iAtFH)5+2(TIqr_C63^c2{a?Y2{5amx0hv+`CIFw+!ha=6P`bpIHo-zTw z|C*EtFzZ;eC8Ro(Pw}6B{(+0a*XGi)WIcA*53C;>I_6Bnx*R7Tc#zOVU!Li5u}rPd zmLNEuxI1717(|bims0|G7Zuvz_K({4A3sh9SOR^kdi;Asl*q{Rb##1Ci<;0a5*bKJ z6A}cN}kJ9T`27G{T?sy*v zseXBkZ2W^*X0fY1H3=8b!23<*^uzB(DR3B7wj5fp5$+jf>*X3*g)t4U=)4U z+g@IS=)L4bMMV(-a53Rs>t6IYpcD{)Zr~_~R$=^M>8D>8=OzR5@qkTWyl7q4_XG5V z(MBFmH@k6eZq69l70p;(hAe6@?cTkntScz0$QZ^s!{sBWFFf=-n&4)|s+}G;3wQzw zn6r*|muO>;Ry(df#k)+9@ezco@CRITKtzPL&l02C4|x)Yg#C}PXpEO1>Mkj163X{KjZham zU=On>*dz@f+W>aWE5vb(j6;B!RQ}_Oj64>h*kBL!Bhc9H-Tn$$lCSomTK_blR&J7q z1KQF|=_{+;mrSa5Nh&thQBbh0QDmFsNlHk_gHQX?(*uabiX5N`BxUPx*|X|1^;JDm z%)KX|?msl0n8`u2lz?0mXoDA^n>1)*2OJ~r)R+-?N{Kcc%LnOX3NGv2LvR&GjK+1I zI-;g~?V8!sfgFf>y}YZvB~2_0Dj=d2S9=h-W5lS0@4S=tuHpTC<5j&1Gj3Br8Si{_ z^Kbf>0p5pNGvlHx(1)pv!YXj>dge0#lTBN;WI2T+e-k%lpbczmFaT$osUwex&EfsH z>mDgDKw)TaT4gEd`y3{M)WdMoa-qRB2L7)^*L?cJNRQ9~`8Iubqf)^QI6Qcm=hn}V z<&QS>j%$q$XE3QXZ_COCczyN4UI>A#)3jv}y#6bBMV>(S5+>yG@#vg7R*$&H!HuI^ zP`c3(N7|NNuU0b^n7;sXVo~h&aC>(y&zM_TomlZ+Pfsdc0Mr-dfzLYK4KYdy3f?x( z&d#ZmEdsYwCNHkaa(zgyEnIa#rndB}=yp>n9(#(9+cXr`t5^3~5|2}4IU%CZfFBVN z5g~jh_G4g~f}-MM6fC%gc~72wWef=^hfUGL{eKojvg z$%O+Cp|F_^q46~UziP?nb3_99=n>@d7}v>uX*Wu1qEJDmH*d-TE^{79FWX{2UO4IVF-<$d)RlN5`)k8Y z<2P^JT9+;haDt4)CdG%)LaUOUFEX5iqzO7*2{yODRTC7lQQo^%$mkQ;v{J^&6h^+G z*??-RVxQN?L}~HH)*^t7C^>R7O{&@nc&~W?@Dzu27?&`u3}6MloSk1q81A5g0++za zOHWSxV-;$HyCTcJ?cjy4n7vRL1^bmm96*41+>Hd-JmBdnx7U1hL^d#_eIle9sR5AA0NtY{ldDggiWLtdNTFL@e2vauf02%0!TcB6;&C-R9e&hVpt>R3~lZ zUwUS;@n-`!82WyL6f<0*I38l zL9>AOKvz}mAdO5*JS?=kcS}s{`u8&_=NZLCOD&L7h@S*7hH(8Te3fNo;%JRk&2Lf#n=NpI?RU`rUYG`1Thnhh& z4(gCHdEVEn98U004ks0)%8i?)D}U4FSNdF-@y~-*Vh_ms z&lP*#{OOMS^@C3beoB!)zxBMg?-y<3dEgL$77_{mcjnBQTI#Yd4!|7}$hme7yrNO} ze0;bIso|CH9r3&S7`taru?GNzy~9OzukH_&Zz6zjKi@O*BB42vr4eEttgzVtZ*HQd zRso5tj8@F>HUd=c`F|$(uI>PKKtviNy4qlbLgk48o6L%`Tz^bqluvx5*3P`WK{JW@|H<&`9n!5sj3|5U)J!Y#mTyP5ckBdMk{GX z`NRUm<~*9+oaHiOF$x$2&{s>n(R1^^D@oZWF0%OTFZdsu&xVYHr2hE#wu8w-NyU;G1RQ7mAjqq|d!logm2`~NyANmADr25c>`*cx&$E?SWRqK5J zwn3JA-ofHaJ9~iSmQQu;vc`IcmKrmV-J>7bwj8 zpd1tNorCctR4;fa(s>|OvKk>=8RIvoA8ep(`U`p&@!z1^8@|U;EQia137nRW?(4{V z3XRxLh83Ua7fqfhBt}(q!JF;EiAaOW8VLCE7}CcvXx;o()4%$4+U?t)AqPs-18HRu z{sl!$RChf^{M*DtJ!pg`@GjuL_yp*|JTkocbwf5-kjb^OXeF=_%~?ty?oUikR;Bx5 zfB` zk(yD9s3BGWe%t>0nH9P1+1lpV-Zlf`S@IbTwRoMD#~=R&ocn zlcEgyd${6AM57c(k)Glupqwi&uHL{PODfzTX4+E3-H+&;8p*7ytVx+=xIg&S9yeA> zn!uPsc=d(8y;|q+7H%do9R(isV*Zp1*Wtq#pwaP7w->t=pmh);>KC??TF!TGvZ$*g(MaNb&Su3D+*y9jU zG{~$Y41LSUYG-}^RGS<3STSYEHEwv9x{PvEV_o=64;aJ*=p_ezHtA@8-^!T0OY8a> z+$Fa!187!1zdzxzK3aCm^vac|#Oe+EgO-^07I^@CP16@|u~v;)#)?H#AJy~lse^dp zZ7+2GB2^jAH;r~CJSRLNRp_1|CT$k?y+y(xvsFagBaDP8L#@i6!&|m)Q*{$j_J(4cKO<@`Qx6}ntAWO~z z03YwWOUsrh@#wHgS79FUcN&F+ho@pDDD`sjU^r?y_930W2N{%u=@D?KcW}_yh-ufZ zIDjrCh=<2}FFtJj#bo_iFmGrP;Z*qS8u(3Tr2EI=;p~GSQ=3sGiF<(*d*uc21Cr1T zvyDgo{`(KIN@OOYk@zc6!R3vNi~?<|gHMSA-NXY~k7&z5EPNS->i`fJ^LBQ8Qv@ zDA{k)3lqeBsSZZZO$VSRW%@(>Pn@+UfiJ13(T)bzZOWL69d zeE0r+B@n(;OI_{@=BrJt1B74VfPkdjs-Oyo6Yvs+$B5`i{FbW|VJFr4lB<@B`+8`O2884t<8H49Izx;LTrUtsTIJEADtl9w9 z==(GIasU*JD*O)x+7J%{2s49LBH#eIs{(Sy)YjF>qAB?Zlt!$EAhf<>E>aHHNt`mZ zVFK!HS|#q9a8i{|aZxCFyw@A!n>uN*yE=2rIZ-JVK}g}q4fxO(-fPY>;SvG&V7i8p9d}M?rQCXiI7z)1GThwU2z`y@gq@_!nPm>CnG9hN zr=j;e#HL{oT6H}`_hHV%R|07Mujoi}+di{)<{#mMf<_b~B2TQ_Kn*=ts68EhgW2p{ z@weBT0s7@YmZdoC2!iYnlu9td=gZ$CJeJkiRFccfHf`2AlXhy-r?XnX*){`?uhNQ* zm{!PiX7A>9n$R=vc=3(9`+cFjVcGAF6c^kmo7JXQM2zM#SjvlU@j|oy=Sx z@55**;feXC)0+@P$2`X1G(pO;wCyJQH-xtxQ^f>WO3jghYD6Vf!F;9yd_u=H^Oax* z56ov(jBTM;)M2}CpJUP_i2gW@~X5a%oX-4zmqckcvtY8pv4v2?aJJ?~cBO9le7 zvc&rr{6t_k4{=l@6ZW9oGnw;pExS{RdWlw$`d*#Hj%wM?{wmQ0fhTArC1!L)I*!*jzj#5tLr02t?x$kl4VSqUfUHK{1&d(e!q zmxew)?BvWZu-fOb*bqw}U_eM4Hzqkjw@5COBsoAP*Yu0x>p zp*4cKGs~8dXu|+tl%QVupCP(rW*8798W8%TKgwnnPBO3`@9OM7nV|rZqv~NJ5umUi z>`1e3T8)ian%eT>#R=S5UFu03S*G~{8QWYQ`TRKwj??zS-zA(s-PnL51Ptz2B7CW(D7Gv76x9oB z={&1W#Km6B7{5-o8--=#Surtl3&M&j$IG38rsV$|tt^ew<@K+_vkioAX2qv)k%7QB zMQki^jO*+&Mbgt;di3Z~$~!g_f^73_#^QE#1UA{8PKvf2@2FsxAQ~uJDxvxODS^Fy zzgfYHfjL2KiF{f|n0J04z~bRdaO&E&i0ZmWD{i{5%d#r;mM}0@_%+sxUU{{E!ihk( zfeb~289^Oxgh$-0tax(P@KMEcRNKQ<4zM12Cx2TJnlyM^uK5g? z-y0dz-at`(g{DiRY{iX#^{|qw0qvUif;*jpOyoPx2k&tC_;jwex$20>EOss*~n1;by)vy9jq6qoQ!E)_e@gVf7Lww1~Q z2x}c1DYp82Q>P2Ly`_lyQH7qwa(@mr<<$!4h)RDeS2U&jZ<(VNBk7Uj?#54xW0JK)e9mMjczaVg!#l#fyp=0<8@F;tODB%;rS!J% z!2p!lzNRfNbnQ%d8X*x7r^M($Vb1xmWj@Q9)IPdsFO1@bNuiv8UFfOc;62t} ziz5T%YC{E!6(#1J)`-f(-sLHEGIKLI_W=$)*2%FJq{V{y2tM1xW#2WLfQ7g8RTp@h zKg^TGh)D6Z{84NI7~un8F!nOrF+mUpo-pwZ6G%zyz0;+1$mi5|hGnKlMA?sd71iR= z?jGCV11N`7n%o9C!UTrxKpUcZL%B37JI*S?L1DRz^CxgMaj}x&;XmCkEE73CV@g+b zRVTB}Z@2sFq&yDPIw8QmRA|27@z0MNH36ZOAr&l72_yrjy>m4afCp>ZzA@fcUUZ{M#ot|eg!!Iq0wxmMUl|;Vk%FR#r?Y(jC0tCLm3Fu-?aK(v*;NSM* zTpO&24-;>Bp%2oU+x%dh!#Q&@mozuz5!XJ$xa;)Tp5KWjVa-b#Tkf(3@)%AeswHTQ zJ=k=aOS`P-PKfGq*PMV4Ez3nxpZG1Z_Yq|O*Y8wEzd6}F;?r^Y^lA;ZbTLo%b7oHf zl|H17*4@EDx{f_xzHA|_2|sg=0EmnPbQ%C(LCnMtOoqZi;>4W4w{y2o7>1M+h|~*8 zcJI4#S0#HOKn>2k{`>Z@H7{*~4*(_+W@9{Hz;PaW*6=xtPoX{@fyvD}+#&ZG>yn^d zdU0-T-b}Ij9d@!$SAKGf*ifsJ8T=o(n5+N*3KNMhttNoN-Zp6G z>9<|+foJju+#!R=iNqyFdbiS^2-k@_%3qSf;dxJ>0o^1hV`%k`A5&W7OcNrI*Oc}>GmY?DEH#<&jnbd%NwY)#|i6 zG{CblPQPhYkdc(+>`)&*8k$`T&pa-pRskJXE;R!`vxG|7hweOe-XnPP69EX-@H!TT zxD)Or6Q7wVwP#6DI%&IQ)r(MrzdqU z`1HKYhhvsb*zN`DWXk3A*RjP=A5H{-E@IQx))x0RJf%K%fRW22AW9G?kboIV6z#G3 z-7w|UWKl8vv!svOBH-ALGK~r9meXCfbwb1>dAX7dcOtvcZ)c zJpr97j7H*neWsyo>Gx9PNl3v)IWyj|&z~EiD<I!rK5ENsm<(Wf>Yk zu5tmHO!zig1Oq3*2(1Yw_W&Jm^Jj^UkSD8S`;i}eT%xq72wlgEHn&hdiO?NbjnYLx zEn-cANfO3-~pvK9p%=^bu+~;De&Dbb&n@*!e2AsTl%b7?41&ifBwx z=aCNPJP%eSHw#zY2-bPk&=>|r7A(15!&8kKGTx1&LL|T_`#;AR5INC{nEJ_*wMZ9` zu7p8A1wH1e`zK-7B1CL=I+a zU0XLzHxAm74gLu1#6Y`Yx?ymIq|s_de@u+hNejjtIP3+>gcKkNb4?SHNQzG_yGdxe z43Gvnp#&zDDCR*-a1(~G?>p4nc;G{a6kUi!Z8#{)9U_y20@`@znXwea!7%WTGWZHm zw#Hv=fiTI0D+00^8)^zqR?2@Thn87$$J@79)D-uiuE4qF0bD_hzU|s;@+$?uiGGHQ z_0`l9ONMpk6Q5L9om{W@&`7QxEd^szdLvpmd34{^8E*0=M@R)BCR}NApsyr^qrqGY ze1kScwlC4uld(Jf7B2FTvyDESMUhcOy(1<(fVbMj@#hkcwg~(!wH;#4(tyYc(R=NX z7$tJYd>CM%U&bJG5;7?W<`1Nk@2}pSw2a_>52HK%9z_TtkpG{df4%5W=tz*iQk;bH z1^UZyUM7ZVkc!m~8X$DZ5)-DLSv(D<2VzaZCP*L$Gttqp;Nx+z(%5*F6LSiUd_Q}K z-(Kil5}h7uso^&R!PJ)$2n_&_ zElht{*^~H()@E)ZVAES}#>6({5kz4T)Dx$?Wd1h{7Kr;3R!$=Q)l$c_?8Y!93O5&K zmBhAxB{B)LKV=teF*3I0-1C6PEd1bPYJT!q$Z-oGYSJFSB31PwlRI}ykq$;HBjVr$k6pVs*c7lwij!#@E3Lac=O668+>S9rl#KHE%W zSoQAVk@}c>YuByIT#WP-Dii6hz~L;wzYWkNPn)-$E~Wn1+uKVY5s^#ydRbd=E3H=ViX&wzZi175(S2Va$d-Kx!sj7oviGXZ#^mR{xtjYRyvxGds3b!z#x ze+k*l2%SqC*kkyjOd13+=`)W7$--I@WoKeyqB6Y*r}bnS)cp9UZfV?I?DkZ|qgZ;9 zS6wHqp{7O}n&~H-9hkB*9KWn#@454WCuvcNL7@s7tSN>VF#u=rBSiD8gUjvSuD@T zHIcmJ91N_O9hr?dbf^MH>A?&}P7cJw^p871F%s?FMF*b5S z-gDDXcTS-PH8F-CT3(wO$hYR$Bc)1j*=FYl|{zkfgRKkXo64_3zrQMYsEr%L6S+!0K{5jAEx zj$5$tXv7z*e)p_`sfrXCN_jQH6q=I%s#za8g`I^ceoX!D&>6hNFKy8;7+V1j4|vtl zrjk+2m&bn27*NX8OWcb}%aXxi!=_9x%uZ<;LO}DoFTN}-L%IutZKCI!^%_b!xv|tL zSGJ92N0lpEAL5-To>gCw6-&!N-!}saF_2fAkfRGwYD&w5u+nI1$$L`x;H;vsKlp{9 z@KR9;0#&4b4qB4;qDXEhIJN%ehSkQ0e)Y7+-zZk7Ht&pa=-_JiaU0fpN|1q!R8P&u z)feFpxof1UYpeF>{hdretbPUjN$ye5=f-M}M}Bl@J%Mm_)wqmbUvg*3>n8ry4sbXA zHwhRul#zmY-2vL{zhCQp2s39G_(tf}zdwrn4Ki@#xleSf)2-yEa@+BCatH9ozrS;6 z4LDlovSiZkc0onC*1w*gs1;sny>zS?W?yB@ov|ww*Gv|__38e zZYhASW8E+LokG!IAn*VC&v_g2LeC^DXXKj`aWSil_n%+%Tq}l;t$2O%{eRxxi@g1_ z|Ni#+3c`4j#-r9 zzsXTPhPT(zRyOuiPaxjiU{k*^f_rKxoUWHi(D#}XF=MgZ`FM=ED^^R(` zwd(x*sJ%py2L6QKqlBDm2(FnN2?C%*Vn4GKVZfq5F2+eg@zAe2(^6x`Jq#&j)mwn6 zH`F?k^~kXySsS-*b!@492WoqWxQ=1fdHnG09j*VXy@2Hs5EEhQi{G6HsiD66yJr&o za+E_pb3Q8ptA=g4KS7Qg^7_~z=4z=~wE>u-A>xn|RFOzNpfH`*eYXXrXODl0&kx|W zqrgTO1knwI#70ySF!b@jNJh{j*KdbKLe#nxd-9ME-|?E({j0YxmkjxED{(GuHZR7n zUj`h1k(h=-Gh`rQWh5sq2F~MnH%xzNfVqLafFp}I!S5cxVo1{4I(t{|-p!e3{m1Gh z)13chETB&h=E71U?eduwf>JXAzVgmTYh;2TCrViiJ8UsXU5(J%kK5y8<&soVXm;-G ze{ubr2vqSxYR|P$Xn4T7SK?W*^pj`su-e#|)B4v&b=dgFA9BEzv^#gkLc~vmJcdJM zpTdhV{(=wg{TNK#NX1=Yr^(lJJM{TR$!bgBN^DZQJaZVE=XBbNW~T*zS+l%MJXU235ku&M#IF! zAwdqjBMK8lwVg)ESnTt(0}*Rkg6kE+7=Fg++Y9ItHw(_ONf5_r)M6!DS~;pmnJ~y_ z=efQhTY5aQNM@Z4Cp@=Tga-DH{7D|!s|1{|m;~tp6{-@h3rM?U(jTD_%J5W-a4V3~ zaq2}0hL12s9ZeWHbYqE^)1PIQmNMXMzHBn!PL2gK8xjEX2`Bdl1=NphaGn{Rd>R3v z{PvQ>2Lh@SN^&2@(4gLt8^_4d9ODXv`FT(#G&ns5S~+nC5floRx_5YZ*ps$p42K4g zGliiE#lbx8bp1LeEToA13BwLbo1oyta-cmb1SWA3fa%zv(!u%{1P2ANG2t+&4XJPP zH1xxo)3k}@#~vV++$#_e7KVtCST=@<#SNbRB$Huj!uQ)=@V;b0e`|GprvC5n6zm?0 zzn-h%_FD3n9VabLg;(+xQJBPXuKSs(M5f z1~lm>C*TLB%5bSA7XS*J943a1@>qW_njxaFg3ivIn_^^OFu!<$@K0#?^WJgdgi<$< zwyp1M)FI6_<=dN*AqH}Qq1jzl2zux--=qD3q8dDEnt+cW2aXUbC6Bgr<}do9THIHQ zjbjdTigt+>2KzlVPr}WMlsF8(qQAwG)&m>{412iGQVs+HP>`}9MdWO3Yy!>Ugr33) zxi~vmp3shLsKppoWBI2#fqoqoo=s%$MG1)y7;jDiN-+q5;|JL<0mMt*Vf(uoxW9NH zf9HWP#|(J&$vGfst&(91BDNJT`u!Ob-7Cw@au(R_noDTOLVB*BufX6)BVeT&4&EiY zBr)@ogGewPVa6x`33UJd{eX{3J{y~n!-%Pm93Dna_Gw&uJ)#%9Dw$#-S_V0~*AA0& z#2r!UO{MshDlV5eKmH$a6tJZK*Bbo+fCD6F%8GM=<+1a~b|(i%inSx3VVKYM?S%9U z03%6Oev6y8Y*B*m0{6&_BizImppvg18i@?GU;J)0Ifsnp_N`kjO(M1n)ewxxE+S*! zcrS@uhazqqt%-I|2~AH*8#xq^w8hf8;yAGY1Oh>3QN?I{65+5#dz2Zfi#~yzoch?` z|JusU4B#ayGPnlPfU~h6FK_kCT?nxxka5t-V-D@EvF{jgi6Kn{VfviWdUb-dA!i07 z7oZmCUU!A#h!{iCCD!h0W3BCMundfrWNuBPz$l6D^W`fiQwPSFnp3{^aS_Y;yGD*a!h!T&Fe z2A__SLqkAl52qH45ohvP$%;EU1s0WpIMRs62m=)V2Xk)%&1KuJjsFl8QVLOq5+yWg zGDU?H5;Bt^L>V$fWNbndrH~>~9wHes&yt8rD)Shj%tNM3-*KsDzt7(L+yAw{wf_5C zpY^^^@9Hhj&wXFlIUL7v9_RZ8V`_m8Qb45q8C^bR=eoZg?i-27UEq8*y_fg{(ju4- z8hjLf_94H31nLcl3-t!@NN>m0B6IGYRzMSLjXQsjfUXuzz`h`j(O;PDkzO%Q zR2p!oc~n-z(USccUndD~2}u2XddbNg#}oE&_9M1YP+cB@Kh15Z(=$v~a!b|^kGX%n zzLr1Lv`>9l)dp)OkOPXjT#}S9@ zBr=8*qe=A<-meg&_R2I=R#v{44{I{#PKwLIi6YhyB|h6kK&*xnb7GlOpw{0y#bcR6R^XMbl?i1bD1se#yLW}hd5jpN~P<33q9 zJL(@Z3s<9aI6mH|=5z?W41-=`a-!G*Si~yeSu*PzalQ0&F-UB>6+i~n@yL#km=)|6 z&FzdNa2wQSIu+n!K4Ugl*wb@?v+SaS!)H7!qODPE0nh~zxzR~C{$kVJqPdsF4u1^}eD_C2R-_pr{|Zs`1VvE@Y=UP=Yu>`Ft&xj>DAgjqQbWAZTH-4!_jg0t}(Lb0AwgIJnV(r#r&^GP6 z=L0a_K?!G33pXnbO5$s?L*_iV(1E+Op_Yg!nlyl}e}cIS?9J?nb3 zS8ye3*n5Nw59NmApYa%>=@NWp0w%Uqq2&HuN+q2|9Sa9A5WQwQR5 z96bdYzO{FFDV3HE%9ym@s5!77pN+AOncS22VL$ownn7!JUbSTF$%@yKxM~J34>+Y;=Xel(P7u0+n zM+W!)$^fQ7RK?^s_P08hsPRB0Nxo~|7>`$9C+YO{9^ATkmmIr;yF&hd(NCz)2lntF z?|gmKi5dC?P$xidwGinsyRQUlq$<_Fnl0?EC%k}f!@#spXe?k-B5=!gL3-DSu%V&% zZiPb;#FiF9_ck?1xlU$C8;h#!g8kAl-rrvlE~2xj=SF^zSk)DAhWjT2NTz zND8nZDc~Zf&LS%QLO@z#FCjqkVq2tt_4gM0o94$8Hn+&UmI^?>5N-h zu#Rfe^bXc}aTqh$xwyLORS>1+xpU`2tb4H7M!;N|yY1Q2*}QNws8PK~5pjj29zWY>3MtSoVjOiKfY9VHK#0~}> z%;AjX)5KE`JsdG-CG$8Sv&?6VZ{h~{yms8;>a<~P$SVVLI8HE5E)Ja`_G=E1K>~|v zUd3$i;bPok`f45qA3kL}n3qHpV#0__iLBW~t#Z$nfC^RbwshibsI1N}NNRsGu{*YaXG0bItcyvIoeT-NjlNk|WL~FmKN6(#4L3@mfC@_@fGaMl^^M-Q_S9aHOaC5(a54Cm$YJDvtpS8wPAZyV8 z5;(;?+Bb}e19%}ASR-?KIut402@4T6ZLazs>3d%o1BfEpI9uTfNM|tJ^`1Ix<-rkv z!Uu1ZEp`O=jym$P(fGALV31B_l$eD5_EV-%Vli3#EGiFyKKBdp&g5R2o^iSpD z)9~LgPXIo-t^xyIvp(1Ur&Jew%N3Sl?CIsNA|E(?V31Sg3xTaW!gjy=jR#O{1@`an zY_g%vrg^6Uo04kV@O}{dWWwX&uOssEMB*2cnwfd=#oMh~Fpd+FaY1M9G+xy#HRenU zi&aHKkLeg7U&4;iSZ6A@!PK84fVCA49X72O@=azV(mxO{aogDb2nmlu>{EfMjdleQ z~?Q6p!thw8F@tZ>tF*`I6JdfG{%T2zJC~$fZ+cp<1qRy8Uj`~Lke3SW#^Z8jwCwv_fn@fU=LM zatQXK2=jxQMISC%Uy9#W@)Z7X3uO>MFG@MKo+qw1W{DocJ1Ke)c)8}T zw)sVDW`HzJ*0A-?*6)FpMCwUX4A3f011N^h?>ezELdf4=X>yUMGf;!}Va`JxP$;)+ ze+sWBPvY*{9{{Q`PWw&`miTEf1wXg-j~G7Pk_%vIExI<-ty?y4j)(k|w8-hkC3Kkd z(i_i)KL_z0gsj<+%(DOrgAbx>Zud4O(pbX?hqz-BXHAU2#Gzo%`hyIW$1IvkgYgd;U>JRL zRGti16QBE&@YY;3deo8HhYk`x5wApO* zLf)GqtiM7FkKZLPAmmglh#(x8I-DOe$_Nyk7>(!PU4V4JlC8U4OX>7$FsiZ#VL~zW z0Bpf9I1z+V0OHgSB>e|!AI&+);>f!`+DPL?dKIaTB?n+o`Nuax_rA*jbEb0x%!~4vg$S z0U=mk;=QsN!+A%Dqz1861GD1~yGO8f1rGH@{_`EI*`3f(t$+dOEWsfCMeNy$pBOH; z^@FkoRP#jq28~!^FAX&{(TU~0$+?SvFNr1K2>Xa*dSRjG3wNaFpRj?qz<8}+K){Ew zW;qS?L3=%Bofo$Nt~_uw(__LA|6G70V|xnrmauJ#iy|8l_T{HAQA&0G%qF2v5&Cx%<0~$1Ya))`m=wFi z2W;qss;VlA&x@w@a<5>^^RtY{QvjMLlm%Ldn~*cbK`{lvPNvG~qY4UYP;Fqaa|f|h zgBS!_mfOV8v|z>tEuIpzG1HH>Ksy8zj#{$OZS7ifmlx_l@}U?aR=-h+*brSIj;@7B zKQQ%(vJt+WWPTcHmSC2sW7RQlVD6)1frgO$t3aPB;rePq3k40|+35z1$ee@%2r4c+ zr#PmQDhnB1a}KbM98t_+ zZ^7Pl6hJS;$a)|mZJ#`Git1u?lM=2Dr1UOMoWCUa{Kd%)-u zUJd-=eX@fN)ap+pJu+#l3$gY4&L>m&3}O;w^a>4_7hZJN++zeKGX49vs)otvTQ3~? zzK}_C4Puv0bP3RolbJ|}4IO)eKfzvI1z1(u=~8-vK<;}_j5QB{(kI+UjR~=jfb1#{ z%T+K{ASW;1^Wf%(56H{oFxKGcMf@duK8dLfC|}Hwgp>tg1~$o1nrH{BO#%;u#EPj( z{*jp!rK8XS3$r+^vncZrBfEn%M)zYloG3>LJQSK@h!t8(Y#rcIXaHs*8JR0 zgZ0O-5^$&wgUzl14*6t04QFFp7ph4#=Nd#ndpyOUz)h@<&jHXH+P_!`QWRS-%Ka2B zgQj7_xj-TaMF2cqu?4QQWbyy3TI$RyE0W|g;8gC+w?8|aKJE$GcA|YAmUEF61C}*$Me>eix!oemXZNs zOgadC9rMljJ=W#rK;$WK2 zL~0@BAy}UP6Q1#fTn;jQ``#{U>gzw$N&5dEriWei^Pss!ntNs-+^wvts(N$OI&@mJ zuMlp5+1$mbAEHq?>r`Nl@&OiUmYB#?8!I1obRxzvzrS42w>OYU+$epNnGa`LVZP#R zkEoepjoHvxoO#^})Bu-Y5oq?q;eia`l7lTAi@dlAu9fh>_dt7N$%2K#hp z7aDShrsCp%dbUSy4cil>vS}4H)l>FWXFmlq391Qy(Ky??y+gNPS;xGlUsf)?fOcq- z2dgZts$cZcV5SGRy*A`8Sr&Eu+Rta1v-9j6_OT~K?uPM2PWA=Y*$*D*X?EEEmbNFm z%0V+x&6LU29xl{!5@KQveJLM)HC4%LFicWy#?bzsujmS2DkJ+o%nM_i-%Ob)P9e5V z?a-gVc_||u)Up5wE?CfvI5sZe%zx|~&u~J&vWkt(8!4;uy1U zQi4I<6pYWpj+PEjs~EjsUQQ^A(XEXI{b&217tXnP%nZi9R|aB4XgPbG$w~ zZ7-tY1TfWgA7za(?znSzm^ll_>j{{Ea5a)>yKPDj zO~K9e`#YzP)eH#V-heMtv&k0p$;T zCMv&*f4Gtka@3~o%npc}JHyRJpa>y5!#%?MXWyNsx{za(bj9~yU*(p~H23pTVN1N{ zd@Cn(x);{Z(t5pnrz8pa1Zucf7+)OBNJ`j1v{ETBfAMJ_+J^sYCGa3`tbA%pX>Qh^ z1$61y*k-Xuob&TEdp+#Kw9;14T|LqHY}A;;;-9C)3hyXWn2muO_^?u#2rXJBTFRFf zr`Dr=K78(+AQaL@`ySdM1wS_|qF)s379{Q3!rx=ifZIb+KGfBlTUg6l)PHfN&VzJkKDz7g|0#c=5NLe>-d zg-cDr?I=azlmc52EZk@eal|_4!x zw&?1ysFfLD!H6^0lj`p$SJg>%)-~XVy?mX{s&urL z21zNH*2#h?f*sy{QwlUYJ``J1Q@%axZbiE}9y_)i=*W-pbeI*fSbNfXb#`_#^Y9FX zW|tS59=ax6nep7|A1@|arEK-bZ=W!wkz_$O1#n+2%!Caa70!KHx`Ts53bcSe9v7SK zd`eP2b>?AyNJvgXLZa!c^h~5^C5n9@J3}K8@=aSqsQi25c?AAmHUZ3JK+zhNXVHckg!332Hk) z%m4hQ737-=?%1)CmX_Af1W982EYr;V1V_Wk?5(5aX1y8HIqXGfcXy>5zVLOy#; zP&J{snYj=FdMqm^v)Kv)wQNp1*g-n;qsK>aE&XncCL9=^sA-CTm<>$OXLu!=UXhU0@y>m+vm>jpz1uvp*!ib`Qmpc!^7rFSj#(>r{(YeF zs|Fh*<*p)e!$<=@$XHxypM@Io6&z`MoeK2OB70-7t-kS6J~u&$$}EotT_gR0T|ZL9 z^&?O0#Lkdqa1&@Nx{`g8^YO-vP&H;`=qA2FGLd`{DCo71kyiS-&v(B1gcHX`+8qCV z;8(bU(?NXrRnh{v5BLFHWeVcU=DLW%{!na zdzr2w$ZFs|u90c-$fcVDg>HQ-n)UR0k~kgq!-I(mgdpVb6g6A!%u#*^T>PQpo; zU?8FyP~gFdogPt?JPB1WSwDx`cra6xGOn6Xqm4GOWPyi8zj{()B}K8cbmt?{uc4OZ z(MuX+x+Q);>gf||YOB=+S@9qAWmob!AL?1EJyZVe3G@st|Bp|>^gE-zz8`m>3_59qr)i-a(rchs=ZAsh(@xpgNvKGGz-oAfd z3UJ;Q%P66Z-5@T$4!KYsf<06L5k@-#edE+1KW2yDwH>-y-)LX7JdaUdWBx-ZP1pO5 zui&Qn?jU*4nNt`;N`{)5cw5wCX0^%P3scWKZJI^NmQuW_;1hKaE^j77eb4o?SHqbv zgO?_APokzjj=vG`itn)eJ5fh@-jIjc-0k2Q{NVFpg3qEn^W%c}O-`}ZP+yvKpZ8Vk zyWl(6$Xe>OLx56Cv5kn0oT$1k%;rV)r(TTjHr089q;}`Q13&E7LMhk|2~hIAMk!)X z=22-S={B_!)!B96^KXCjsII$32a=)~FW@6)iwgDc`U+_OymSCRv~^V-z5&^kUTu!@ z#N(}Ph#3cf$5N;)21KaH=H&Y#X7oVhZ+!p$-QMu7uOJhSIBUA@Es$rYk)8zv1rO`$ zu7d{lc4XwrC)K_{hEcjM1q3H!Z!hwtH&tt`)-!eX{WhF9#Xh*18~5&I)zQ%bY7uub zmI)W@8v5InKm@u9lJKgc(KnS>RJ?*N>`qwNa(qXmqcn?ZYFwMcR#sNoNivsN-3@od zQW<^z@p+ZKL)3W5!=mP;f65iNUFIJ+-$h8&)lSqcohVKPzvBjaA1VS=HseucAqC4U@OWnkv_=p-@(&xT6pv3H*BIS(g0>XLDNlDsEoh16WE8Bp%3+w zH*=GnIsOj6XqK*;DWd5r@b2%H-go17r4^rpEd*51Y$;wpHG~nbQsBIt0sB#2!F1#` zlAtY)Xp`NcRhVPjaWKRz55KZzDDy^-im)?Iq3RL(c?liK5m=aYWGTnW;6lGDDFK3e zIVfm#Q#M31h@fkMI+P(|>KI${N2dz8KG@8x>idy@Gw<_bYhuYo^2qiUR=C}APio8W z)VPps`<2>M$6L5E3pHJGsELE+@l;`x*t-hqHNl0ISHR=&K!Ksc+4nhiCAmi^J)g$J ze1)(@)V7^MWRBnWW9-o9F&&99K^>DthA{;<%Cm=uZ3~BrXQUU#r2TrHA~!)QObp(? z;`oT#e_ICl@djG|Xuy6M9tht>=&}U)WWsza0&x-ISD(wOXVCR*_i9UplgxuZk1!V{ zsR@cCScGvy2Yd~+HF@10`EQ60oyr@C;%VHn?^tRSZHO3^0*wG8hsbLurBzEvd7RX@ zkvD!BE(~HoDO5lskb$oST11XHI;FN3b|8#>tUBR}Mo-p>^42GO{;|{e)e^7-OHmuA zaY|xee&U_$;Mo3eXGt|$|NYZ8X9qbsNS(hqDc~w$*fW$d` zshaIZ2jd18qN7SmjNcuB4EUk|Xlew(Sqxs8E<(8{tx!3(1l>dcMEbzzsVN|%76U1q z?5R!(G2fAvm6taG-vlM&-8Lb?dIJR7hm7Q8rs_I(8SJrE%h859Zg2TtEU&EW{k+kT?UGX`M830;YHN@ zR!te(Rw?rFN}s0NiKinN!NknfSTg+3Bv~sm=!*E=Euk~(<^}nGCsr1ECLXDq$ib%O z=9?rXIgw*9czNJ9WUT!$)4`>uixDX7etuWQ3ZqT?_OT&Se{adnjBe(qjeKkR3b&B8 z4AbM^YU|SSG$Z(lWKcQ+bzT~$9&cae2?rNW z@e9iu8X8bn9s+?=oop=gv$uu;!OA0xW(eDD)&XmL16*HKU4026Jy1A4KEB7e_eYhL zx7T^Z;_ptO9wa+y@yep1Qt=H8l!qvTR0z_o$80r%XTJ1}*JqyR*=|^Hv%lZUV$ZtQ zWo4Vh#5e%O|E#Q%Zpi=vxhzJitsfT*#b38wI4%p$CTnpAU`y_mQd347LXOFLyrST7 za(tb1vR_Bp7a>l&amUI=`+1*yTxU6E!CQankNs|(Xd8hJRKtBx4;Xtx#!4H^58oiC z0T)lX4EzHtx>#92y@IEoDPySWD>O?KFFeF{^wHaAKA@UH7*9{X*k6AcsjbLV#2B-Y z+Av?hmHn|(`AKy{`?*9^RYf?^M z+>YOUdkqhO0!xNs{0QL_)#uXj{`Z3h?;IbR0l3RT^#(Ak2@ojmx>Sg4-9RL5ud`P~ zqX%EM0Hm>w^PV#gwJd}9mSd40D-qv5jg8I4G7!jeNyQ>L{^g`ff{>xHl4n~FpqZNp z$Mub9XJycFxa-~(uC!>)6Z;S;y|U<+{`-oG!$=7&0Vp4N2qkNbBegS3NqGiuwG&R| zLa^8jKuU|OmHQAp??F=py-yyu1Waw@C{sX4N9$631r7?fwOx>v-Tqzt;sqDw$OpZl zmOB}KUq(S8)Q8YSQUm62-~iNsn}FC>W}>p?9;^U4a7;_EvnHI*lj*?8QTi4Jla{eyoPot1ki$TM(bifk}8z z+zt=F2{l;miY)poUeLhMB31I>Ff8#eUC=W$J98!)!yEVEKw8$mgTl^8hd!0-)QI+X(&ZGfzNGb} zGJ=>9*bNRJ``&XbpjWS>SIA+Ur>+(5!R3lV@rmT(Rb{zJzRTr}Q}37MUt==b0D3FL z%pBm2w?RJ>EhuB7veJ0@a^J(Hys(^Rp3{_c1 zkVdsCRq2I&spysf`+g3um4ImyzzG<4w zjNGA)O>#?Mz{e}Ay2R-HrF@(qTP!z;2GGDX2MqV5ut8pZLiXoRpKPI8-w1BqPEuHy z5q$kYB%=re6Nqiu_gN8}`l0>bt`r-geW0;L$|hZK(^%LfNy*l)$zvycUPZJv&bhhky7r-7l#ZYYmX?f`P1U%X>g8p zajfU&4nTYr3b@|8JH+?-?`M8nPS$at_hZFNfy(LaOAieRZxHRnc9rgb9AlG+e} z-!Pcd4g2@6C7}wl7}iRfnr!5{1%oU(ApGTyD>7#;!ENJ2 z=fF&E<%N|YVD~#fdMz>9R+Ed$zB$ij4Y&(=plBo(gYdb4L5W?17+96Jv3b#y?eFKe z)Bf90$gvv&u(6m#y)37Jb?}V41xCCbpuZ>zZ(>@_UI#pJ61`e;FD|A2h@5_?6s!EL*U}!bI3Z7@@`f4x_-tB!i?fIFlZj zXS!_(ix|J>zhF254!$l{Ot1v6-KLYfNLKxNx$pNz4qD~;6hT(DR~JV4cpKBUw()CI zFVZ z;aZ#MSu<0qFo5WoH}YmZQ_tRYEAUh=9gU5J%>LDabU3p#~GAE-3(g#zx18F?gnib6}ee<8-?{I1f{0 zCJv{|$>1W7+!a`=-_Q`qJbz%XAAby;mz7S}97${N++XzMyQly#|hgXH+PZtY^CR0c8XuteF9 z@oZdd%Y%z)EV-A}H$UWV`lfO4!t9%oEH!)n4VF`G!#!D<^ZDE}$J_RDovr&^;hr!3 zW8H&rrmC<1+(KSner8W=0r;1wXt4I$n9TkRJRWb8AV9;=L^qOeT5U!V zMZ*Vo&g_nQWW(+~Q+kc0<3oRS8i|+kJnR8Vl~>pg@?&5ii%f$yMIzDniA?p~%*D5# zSWk?ZED7hJ4dS}Y$N=O=3W5waIj=_Spm@PPev2th5vyG!GPVq6A*|aHQ1NXL5~@rH z-O)eE=%g{PlT=5H95b9SoI4Cz-V>BWHa&oUNwI~?I*i~>MUjH_73p}5o2W;h=T&K$ zn;ahJP?Xgq*p!AJ=U;NBGZV^HKTe*Ub69bPY7tsf<^Kt8xhcUTM)3-D5gesxQo_BC z9X(ov5-`74uVE-XK4s0du((Zc&$<;v zf1#I~mBo&UlZkV}TZ1R3>D3>Z3p4BNLemn@a%lCcn)C$T`t>2Vz2aV+V(yB~%rf(* zT{fw&5U|)P3aB>&+(VT<5q}<~91@JRD=p+3TW>3l<$^T$(r0wiHFvGA%z%M8Hhu}5 z{Xbc(dH9BXeV?h*c8nT$_g2bej zp*yW=UjFqGEGzFy9YQj-nt7?2PztM^yn>~a?`?(BU(TglyQA8&b9RY+9{+j1ugkrF zI=B<(CKEV5&dmn@xa!39(P}3X54F8G6*Xp8wc&)Re`(U#Vimo*Ibae5PhCTKsW8Va=C!2O^qxh25KR3FR-@_7M=H3TU{b z8HpJV$Bgs6iE7=MKVdh9WvxSPZEWx_38f5!)f|Dyo;vwOE-)c-KOs`{6)|5z)_eK2 zoiy}lJD;-hFl^wWl`NM(^~ks5-*mF`!~blw1A zT0B_1Fb?i>r93zD0VQ!2FDolQFf}XP+||)&LGtG~PE!C*Q_GvxA7#~J%5OhDZsFxU z*7Ky>Byjow`e38wzgf9>({S3G&0F)U43S?(x(o&;cR#n9)UCl-x=eLOmG;={`5AL* zLji%RA>Q^`bM+82xrs|wR>F7yZaD#SQf+8GrEXn3gE#=HKPn^_)@DALXh2 zWx-1f9CPa8-$>n8QB&*q>^w6c#U3tj{!RGH$m%OssO47Ggfe#O_0}dPSoCytO&5qt zpnxqx4Z$TMg~B-nDo{dAk~$23$RA=`MyO$bPOUxyAzyZ#*^VzF3zz*0t8mR&AeqRR z09Isdr%E+M7H*^sTrG>y2p!TgQ)%rL!156R9Ndky##}Ta(9s9!g#md?FD&I2_-$cBKcED$~2lV8=~;mj;&@9U7wDd1G@G*A0R8j?PY? zVaAq(Z`(7Ea*b(BV<|9dzYO=<girK2YckzKRAl~8 z-A03+cLttFs!^F3HL;)XtEwg|a}>t-X|ki$f4U!$llw}VU!c@f+BBI^VHXk0tT`~Y zaG^eH2`ZF^aO9OP36+xxBFUcIjnX^vRZm5x$Og00ppxoZrq-^wr&5?^5&j6)KYZ(& z(GIHdCw)s@mm4hgyK&_!GzeK}bc&Kt8?PrzAKU9jo|V z-@1YV)Tk87;fnMERE{B*ZX1?#N})nN&p@H675O+p)sDX?Sg}hQUUxQzqREAUsL)7x zAoaXa)zs89`%S2mWn1S#hU@v>5BFNqC2BH!t*Y9yf{q3O?lQ_@mjm*S?r&CWXf9R_ zo0bS%eY~eZj)U_LDd!mmYcHAXnu8!PwqlHT)o`ElX&xm}I0c}FpFEzpz4!(sjd3;S z^v;;-R19{1`zFu`4f1GZqXPE*yaB(z3-k|`KpAg@k%>S0b|a`P{ZffBbTQTeUxipo zpgVbo#woDpx0k(`*4sHa2H}-Fpi`9S6bhVgY3v;wSouo_Ch45C<~eNhR_cVv^v>*s z41zveS-@%s(ShKkbRu(Rn2`1Xm2>{O^Y9H~UFX$1c9TE;dDc<@Gh!lqM=vu`@BSN8 zD^F_7JfCENMTBj!Be=H?5{4QvYR|yGT3D`SV{5$P+)CEuMz>(#+Ee&_B5-w5+7gPy z_QxlFCG5ns^T3P`)8&`oA_W`Km93Z(zq*lq@|oH*LcugNo_WbZeB2TvLPLF^xU8C9*=bNol1Yu)+P-nvdyI7Yy#A@i)dKzX(JBgJ;AQ;iTLZ}MMuS`F zLz9>ZKZrzcZ5{QY;AS5hh&*mrK!S1;7o43%p~LS@ig7ufP$RBiSFk4?cso{2TMYne zFD*#dkV_tX383OT2D1{O3nxW9wU><8uTKrH1|_jL#cu*;_9`DVew~n$G?e+#6$xMa zWx4|crw(0&vA;@f1BXrs*Q2eO3fXH^zn>=Ynom`Y&O9b)H4AEl{^>D0yKhJJA4qbH zM!I(1BavhA>ze}b4Aucit(TtV53c<7_AcuHHghZkd~PeTSwdnxjzLP3OFY@8DQF;G z|I&OV8DU6zqx^8+@oH{hEwGTA&6Z4ZdAW9kAm&)NY`zIvg2#RI-bO94a|H#!Y07{H zv4-Ws%kdzpy1WNN$kInp-&zj<2{l0}LPNPYtez4L6aw0BCItAUo06C7qMYETO<)6b!DI z(8!s!H9HBMM+pu)c2-#21D3(J$qp!h=911Y=k|i3?y^tnK|>DC%kPA19p>*S#MU_- z;o=7PG_jw#3-dFqZEYwQi1$~9lE!&(?*xQJJ^2`RFKH)C%Bvw6W7T7Eg9Vc|_Q6)pBZ8D+Cv&vu1i%rk z1bPP9;NV(^wY?wV;&vcaPs{(p0TIAb?OVv3g5CF}+{Gc_R2fXaue6!KWY8QfC&(WDGAEjWQW(h6 z-a7K+yDNNN$n&_r+nnjOT4dWz?DICeeHKjs!Ql`vmK5o}$pH>Vgl9XkGA9i-!p5GB zd3ln+ZOQS|etBjE{@gbeafvU;SSBVWBUrvns}EU?9UCnk3R;w9`2 zOec0z9>>KkgFhHa+QUfkPY_k%k)w5U*ScGTlOmceusrQBfu(pM=T2W3sK6XYiJ91$ znYl=W(2ZGfO5cAB1tAeiLb5r9z-3kT$hl*AxTqa9)Fi0`V#X=j!+?}JJ?DEo!I_@# zYXZO%X+YT!-A+VT@xuP5m$!?ozBABReEZR$tpPZZ#;)n5{#M&)5lW2dSZJk;+t?65Vaec8TLIEtDq^PXio;XcLi$=V~boA zwC>pdT#6H92ftUJ5e%zS2r>Y#h#N< zinib$RXM;yAq8<)nM6$!yigKhwpHqdm;pIt0LRD1^G=~d<1-ajl<#B9nnwDY{%wyp3QpS%ES$Qc@A4A-2z=Hs(z(AFf5Tu_|V|-GWa-N z4MBq#F$Q+$xJ8P#ZR2}QZHyEYfyuj)oSf|SqRMEevoJ#oV0-d&qlUJbX(8?Gi4$c& z7;ix1AoKxd;gFaVbzZ{Ue!olgA-0<{oI8yuc)Z~S0xGrrRVbYZzJt&u2=8@wIl2T4 zUd)WSD8d9c?8R{ffFkvIF1v|f2GjOU0T17wfn8h0kNeOP6LBE|>+?9X)v984E5RJ5 zzLuV(3K(pxaanro%6!%SmI?(YRh#R-dD)}jgdo^?4U^+t7}EmAMvDjQDB!5@0i5It z5Uk`VoUW?)ggDIYF=dRKJi`KV101MO<9z+hK9)4=CN>fr5v0Ngqg?$p6XZa@q+rpJ z@Tr_HNyTl*H^xxvJHuECn=`f~iO{(M7bmYBznj$=Ad#1&3=KgtkXdp@5h~?iHQzKZ z7)+0{p$M8EJ8Y#B#@eTZz9o8cZLwh4)|`<1YKvuOzi06C{&g zz{o5{+6*uYmX_Ej#)90oyuQTDOWP^Tg_s;FzmzUa> zStbQf=Lnu+Pt#~$VN6PzcuuH&bFd{{7t zJ{W{~^7QEv-9$EIHjCQl90=S{EVtK^tLf*&ICQ6v&C zO}b@R-a0)k#*CsTc=ZCc!#80HBPD&)IT_l$sckMxulLnSgx7s}aqH<`%g6R3n^WWk zH@x%DWL+#^XD^dvxgZKb8bXAbr6+nhXjAl8Y01i}&+Virv4}3hW)Ex=7%ybnC|CLK zKmY2R@eoI*X`j@eB1=}?>lI8566d9!U z!t&oV-q95?7bwo)TenZL6D4N+&8Z#h$=`VUAR-~a@33jElkyDy{lBBTx_f!*P4ITq z6f*1uX^==319{b>yKPp>%4$52%i#*Y)!WDl1v=L+_COvVZS^I`m6XC>6a%mL{T1w^ z5Odhc$tgMX5PpMY=VFVsS>_7ka=|#}nsgz1ERHQxj$S6CyYAf20lf^%Wkltn5F^-g z?sutwxDDBER*DRz;LY?;y4>*1!+Q72(2*2YHDSXJzU`E|0Y25m#a7|O?IqWqIez5l zKeV1_W$|0R0zkiJOcWWq4a=Z0m}dK(xYU<3Vj^tF0Rf#GCu3|TPgb@S_SRx#wq(+! zDpEQS;ia%jQC9XF(^4`<_}i2_zZaIMXZz89+XvQ=JUn)IxQ<@L%iO87Mpsb1J7!B zM<2N&u?a<=3ig?}BJ2xM1S)49#eYbLc}EvNu(fXxB~hG_va06Un$tcjWN=q}`#)0Q zu`KAnhDcs~;omfPz_I{R+uHRd3spM6yp- zXW7)=|L~L+e>m%9YP3tdt``|z{l}v&XI=c(tIU>*eDgApVUsPlH%cN${_n4E^yIw#wwOz+@y5g zqph@F{8y>GpU79balJM5#@p>4`TO5Vel?3)F+uI?D;Ol8NFH=>N0xA9vgw(_sON(2 zv9JenUKT!PG0_>QA}<&GLs#tvx8N({N<><-9UJ9ui2q%+A1o8kOckft9h}sit!ouHqvx3Wn*7NZsgkc&T&_{85uQJ<-0}9qhyH|R(FA%qUa`L@kPS676+;5UY~31qMzY>=UIuKw_{6 z1FKg-upN$3n;yR;tG;XTxxM9G{?X!IU=hfP2s*D7@&N}w_06%RvHJT_C9<81_^^XC zDCA3nMJI%a&S3M-3*{tyDM%wC%;uk11tf4Tq`?{1_Mn;>ATLUSXh#rENdYDV??6W?)6LA*%%mon5HLxO;6vZE!+Et;jh_=m?wrQ|2klJUU-1)7`XFmPm)H#?@D?)EOi^&15iC$fb_?tD*=Uf z)$93HUjY@2*WN@E8|Aq$k586DL?)cl9-L_Zj#=7BEDeaSD`&r#IPgJ3-7q_aIL)Tx0UcQD`uxQHM!PLN?hsG!xRWEVTDky4ZkC|{PdyAZ9V*ppF6&vV3j%{ z7#G9;Sk>QE-%LbF!BS7^B2oIyiMK%_N4nh76hL+x4jkY@?{NsCU6>ABL7(^-w3D9) z3NRTcM-A&_GEwVo^;kKp(fiPs0x}wEiw>~PB@h~rO9D`sNO*zyy@taPVc8*BYhu4z z>;?Ft1AU}{p*b$7DD0mSxd5u+4{ZWH1Z04$ExB{|?p5?tTFlF&AZ7qg@GbHoe>8@) zkieO$LxWC=$TrM~e0-H|iC7t8XKbTsoN-W4_$R3o7QZg56neNbVj>SR&JrLJ(iWkD3^x!f z=#Kd}_HW3pPOYB5h3X4LH3*r%<=kf7DPm5s$(?lWz z-6C4KnxsBnUixcr?q~;g=L})joqPAb)!fJHCp5C33C0Hx+u5a*rC^jKC;9+v)S9t$ zYx@ic%k>1o>Zk`ea3ZPpm~3$=$p&0Ow8K&1sRCjlmup{_Zp?GEjGdb+SZVtsoh9_3 zNEr1#>EAI*)rH8s&{a?sejq%<)<$wa(QGvb+z1RTg9mSq6E_MfVxPa~?5Ec~c2I~N zf@eaawMdwx&2b{;5m5LBa^6*A7nv#aQ(GW9WvHv%40bbX2rrf_-}Z|jN#Kk=tLro`)}Kc*kEnfrZUy6g}jp-A2c_!G@W zoME99DA=!#bZTaRVv?~lh!f2h)HT2~@bePQ6p^S12FEv#MT$Gp`!I2#2;T>lqY5lS z;b^uBe@bo1ZNXPaBIJYV=U-xl9^m7-bl-r0BZm)DldJ>u$Rgcrp^1%)pj$FkI97gY+;GTL+PbbC0ANeEZ z?n-y>{#-KQC_OuTHTnP{8DGD4&eMe082$aT`mq^Iu9X3T>uQR=5qEg;-1?i+wZj<0 z=^cd*&%I4Q=gY{oQtFZ@8Q;O`C!7oyWHRV3?@d2^`qfkJ5{m+O?2OK9fei+GuZsAHz>|gNQ@xEq6&y+_{S3?7!t?aR9+k)^KyT&Is zZ{50_5R_<<;fyaQSD$XVmX3*&{dW4VS2b1d$@D000(6TlKeE6e6Uov2oyJ$H>h^>w zArGxghAt}{8;{QNK;a7ngZNS#EEE{Rl!Ms_$taIk;M=)v*dp}?^37iRZJ8v%17~Fy zv*#oJ0WRsAj|B~5x6@a6Hy(u5cx77*mh~_I5-I{gPn+&PPGkz*YmG z=t#noN<0=~My5*Pd9~EY{lytGGg58XlmU)exq$Ud$skwm>#M)^$0ajd_@zY@>jFmW4BT=)5>uH(CF>Q)Wk3nUx8* z4{Ihqex~#J2fO<-k*$|E_^*C_g?irS_Z6RxXs{w^$YrFpZ0Il#qBqISZxOUs9~3IuP0xUR))cr67; z?KNEV4%kFo=R0Olo&_6ef4Y ze8|0fM!0MQ{eTevJ8BB;m*MbV^Z}mQ0a=1nQlfa4+jMI5sY^R9xTXqsV*JL>q(IVS zATkMp@a~>a*^P@Gpsp;{@KHV1%L%kUNSBqjYrlUZvytv1c)6>&q0yU^#yCH%EycKVvJLiSjMYS}ND|&Pp z0K&9M_xc-#wuYfyUu5lbd!z_uxZ-G>kwa8xqw#wmeaQ*Ux5{Fb(S`M+8 z*ns*|2Cl*#&PZ=Sk}l&7O60#mZM6%6S46zF^X0V2@|Znr2jAk?c`kh&88{IHUs;@2he88O1z z@e8W^QR7ls^=eXp$CvpC8>!l#qHE;%TEVKFnp%-2YWUu7lZ2v1Pp!lSLz-3j{xG)6e?o^r2yG z@y6W;cI~Q;9hsPGG0Rz62`X*`uaY`Bq!)bk3Njx9Df1@OV%Us&1pEy2>@V0S!5eMb zzrSvv8KI7ZAfWeFWiSBR={fI4m;r)HBj(uxx+gyh*@kknE$!?Y5L{i6>uHA(g&Gy? zX$~S$4HLE&#|zdk2T?B)=_!OY&^~v7Fd_036vf}{hU)M}j6s#-k)&|pe{+$TKRfG& zRdRHsUjc5250MO62!dI2?Ic<(1lYsn<~!*|r67IWb?K+YqL>SRcl?qy3<0q5J7bY` zgK%1GKWAE%j&V1UEK6Xh~_`^B#V^%foP6p43o_(KkW9R(%$W*Az1WQ`ZhY`kHKGgjuiwc z{8TxcuF{lWe2gTwZ!5c>S3Z&mR@I)9c>L+oe0j0`z&g|7X~nRvm_)6$y|z^grwD8L zR(<~F{!>q`*;g?i{jWu=#UcFn{%=ZHZ$F+rkQTXvrCG2+{I`Nt^N#98MS#DA1eB|d zh0GUJg&704#2BaxnG{1OP%%!J@Bk((`V$MYxNtswJh6q|FO&xk@m`pdjY5fGmM{14 zQ`jRERzy$&S^7X)65uBk=LB+|1v^+u^xM@I`X8`2OSW`!5^=s1mMGtUPVVRBr6x0V zn1CQ1E1ATh5Se2qsYNygN~+cBF=icX>p`DOw9$Gq&gb(6z(U4kOntkx5?T9D3s4*ZWmz^XA+lK;9+3q1|4z4bwiyA3WI0+?umj zNx4Gq$4NDnv5~UMJV`@PKq}8_RDN7wwLd^g`go}ia~x0UuXQ(NxUjCu7aiP@3<3A; zy?f?67-TOFbfmO(ErikK^HgxQIg0F2d>^t8GL_ikwokelRyT+{4Pm0J%aD7tpHUZ4 zhOy~R3|VO+v~ZRd2aS*_2cK&oO=U_Z$KwgzTvHp^44s z7Zjw&>UK76%ndiCEY49-kW4!9ptU$LbX$c{5k*(*7PUhZwQ9~{_S$>W0m2gEm64y% z6$*K~^^5~Jlv8w`5q5QxxKpI>vKoMbVHM_XM%drM6=^fF|5ZfbXb`BBVQ?hZtSDK$ zgD(-;EE-Z{23*r2hBh@bb7AF7YujC~F>Ri25D2cZsd2@+Jw=F?H<1EzBMd?7fu6;< zW(fvp=_pf(th?a5>0`|h|Bx&r97&Z&zcr7!^Bk^|zc2K!oBX^&@TZ85mk)aGO67XU zd&yh5RrMMi79~8M2Q!>`Ot-J)Sj9lU!T<7GzYXO5ME7=Lt}jz-P9by&ZF_Q=QJmuc zvG(VCZ=YwH7xIikwwd?g8yY() zGI^)xO86-3S!k>6KEdi38AFo^OB4`;qoNCcNO`?n47f0vRi4)YFivdPEUUtHmzfMt zvZ1`MGjM%hyM6oiH)wJ5*Xd~_;%E^MAJ#Hl&IQqyrrfkqDZ|SZkRI_05zM-VPnUA* zVxZ_Dw#O&MLRkGUTI^C8LPgo**vA{MxE2S}uUt9e@o1-KmNT!S$m>}*3z(@Tf7)c1ox`XKiaHWBDD5c8Fe1e?xts!_yjt28paSC$$cPJC z*?I*!5RbEXv_#HP=OFqSLwI^29q_)ueDDqY)lf_2V?iuBDR3n@gfs8@1$n1^};v?rj;H^>e%&1`Eb9&B^t7`Ld-bW^>6a` z8omcqvX}blC|CFn_dMl3?3|z(H`eUniz%g??)O*MxdqcQpZ1VSi)o$x4d9Gwg7N)7 z(9$+!&Sk?ZvBWKkv^F5eMsf;YMyi_6{M9OG=Aj53lAA8^Nw=mlqIfK)tmG73g-P(O zl)fA^ivF-yWI)mva5ypymMv=Pz9NWx>pUby2dD801wVI;|CYiMIJ0kWHEIjvu zS$SPob4;alg1TVn3|(dFO}y`hM&Kc`*sWDJNztxkBH#p(8M*S9?b_hgWIWY`dSw>` zA95el5w91c$**^x=3Tz`jr}jy-ZQGIE$bHDo1Bwm0RhQbk_d=oMFc@|kPIRqQ9$wr zNdh7nOk|V{k~0{Pcmz~3f{2J@$sp;Dg`TQ=zt-Ms?Y{QbkE&LuIBTyp*PLUH*+=hv z(Bw0n#8w1MbVv}u2>1=^Q9Mue(7!F7FX1cxX0=I-L)68#68Bq%U+o`caqo>$Tj%qS z`v-b8BxlT51_Lj>?e;3u(*Us&NSIPWIOMx_lWeOJbAi zsoZ2VGyxgg`#WQ+t0rS`JV3OjCI}%}NURZQk5RV*s8&D=dGb|T;v?UNM}-Rjfg!Ak zipt769ng3SkrVG)J2(+RhT5Oxy%vv{sEk&*7i=BJJMd?o|#3x7ANV)6WCea11{fleQWT3`~OVR<<|nI zKw#{Ve+3y-jWv|K0U509^HUtZL>>T~`N16qbzP$qz$BI9-WR<%+@E}ED0);4k_f2m;A6)8`Sfd^50wcn{n(H2Z^_-B2B?qSqe~Cs$Yws^@mXwC)o9ry$!A( zNLLS&@nO+-+lMgT%c-7W4e~bI$sh0p%K#0Z4gwwa8kp!=K~)?9_7NpG$f1;|lZDaH zOAZ@M{jq?C5C#+_g&gfbeVv9}aa0Vm3~eHp2H)^+ApIDYt_4#7BLv_JK@^E$kNh}3 z29|F=Psai5X!QZpZAWqdWh2C#y>f@kz?+_BG#{%$wU8JX8*MX6(v#fVosIzf9XfW@kY8S|+2Q=rN!QdU{BRSP%N zLTQv3Yx%eZooqA8x_bzvENjAn75E&~z;;1~0z@eAgcC6P_h0CzepQB?6oddk%IG}8*aFLg*U0ANF_Kyc zQaHQEA&yx>^gD?9^(+KYIN_jB$V-67epJG@Z9nWr{$moU$q0cSwyi@(DOZay(~jX~ z++I5|aFRX;1d?$t0g$6l1sz}X1z(&O5`b&sz$n#-E&_aBcW;dyXh`1%cL5esGb6zcl zSMzT0Vx0>M3#(yGuLZ_-vAy4EYQ9JRk9^<^AL#O|J0Rz`?bJ5RK=QioI$-$Fgs6K+ z^65Xjpn-E*2CCcv7_-!P!F1>c&&yg5D8ZP=zw(2_bE+S4Vlqz@V%P!sr@ah9c>%>{ z62C>sjRM3!r2U+E`m61}VVGYR1V3piXCeHU_hZd?T9%pE%zw>d8MPA znxdh1soodCAL3E=vn2%ggr6yUZ&v4uhRVsSYHKSiS-h8p34kH-j)81?=XO&-(Ec8l zAROTlk|1a$oDg4`9~8StXY1_ZUjS%ZiZk*%Jf`6?+}Q)JGjE7*o>*Y(-jCC1|I}W( z<+t9&eGw1!_RSk=h9ba6;Sd^V>Nd#UBMJqFP?!!Qu?2$;2yZBh$=gU4JWQju;ds-` z;yC3E<`J`IN;6$Qy{IVeF5|dQd4pklhYiUk*|XbT zP0>}}oxkP+`yok%0R90#b@wG~c~IxUiq4hV2DdvuiJ_J$Xn&0Y8;9EJum(U8LGcXq z2j3;=d`UeT0Ou#DU=_8d{E#ToUv#~>|M4>b{7?<6tX2pAvHc-^n%50|kn!b!I;zGC zB&wR}vV%Y zMwVsuzCZ}25oSi%mauS|4Xj^kJbhvR2&9k@R*y{6hnq&D?;%nc;K@`%cE`}~iK|q; z@+r`K3gGP-C~8FYeu11Y6O4h({@DSoHsY;Z18v@mz{?G9Xw}FuI{=ToSi`3zEtZ;scmsmbjvu?Ew2tlr%V1)rJ5PB zA%jJh^>@3xN;xE|C)79bp|l{nqy6Wr6A_+Jk=0W-IfjuIk35&2K_Lo`x{VX(KRZcU z!x(_HxtbYeIgY{#I>m%LnbcW=7;1w3~eNW#9203F&DcxSaUz?3Z^cn|Zk z>0rHQ)~pXOQ4!m2>WuBui{PUI?UO(~0>o$FS$q6Mx(D#Lv?8u3aCmllTsRrWL5Rd6 z#CWT$L$}J}%N%4hH2d3uK!+n6?bueZ@&+Oo^>8W&A)@Ml{H{428wK)61boSOuS*NZ zElKE2DcJrB@-~^KXVc_}ze)BXoINc8trOZ`9jhjWKdSjQ0z%079Z$;mW5o7}JsWeX znK&xS8LMsKr~OaH^WpQKc_TcKTD$-Ecv0a#7aKs!LxQhvyOB8kK$c!Xwae@ly+oI4=gC4JIIRp2S2lQzWXWuu-U&7=-p#YKU z&$&zv!bv)af;VhF!MvgdF8F%{iUjtS95Xy5&yMjmu2YCD*mb!*96-e00096g)_wy! zt`OB6?Md}r8vs&7PyY+CJp>J}Js4HL zJ*onuK7_7`>{1{LHEp=tm37xEpYVOa`}gm^fhGED$Z1e0z%1W@{l=X+fKHJy%2)x; z83V%GM|`GGLkVyG@7T&(J0N(s06u<{HN1@?n0%PZUA}o6T48kbPT;fLLm@IlIL4z; zK;;7_!+S813%ix>^S);1E-(q@z`~#j`rJj)xw82FWP|h3)Js@Bu76gK4cdmGd>{YS zMQ?^SUT;V$P+3Q$~6wfHBSmEmv3Faeg%pYb5pC&-fh_fgnmG^RjF9!i4 z;?q&f(kTWKKy%&#*514ThhbV1*VmJPL|iPe{J+!}248cTzMnV?jAm%q1I`y>={X4hH}O6o9nw=k2!K<3<-D z!6O?Dp5;`df>i2Gk4Q{Rj57HB2-1T!Dk8~@$T$YRumzBWh2|bYwFN#K0$G{3>Xr@9 z0FU7<9FZb`Pz&xatXy1pKpjRDtDiDbwdjiflVsh0Rf}+XwLgxY2JD<)zZjvJI~h%a zof0et=-$B$1?K_K`+yNCF>v*vCJ8jn8%-2idKdO(ETPh6cF9HkG^A_>`$15*VEWSN zU3*8vLY#38;b~M4n<;^g3A|F_qMq_@Yq(q`ydQl^+w~+n%^-24Eg#x5$*FBri(^xm z&6W-XYFejB%Y@srE}WV8vypt&5vn3xcEq`F6zxN*%>P;w8J*BqVcTAV5*Dr>Z}JDo zqcR8Ve=QW1@rB3(m*6!V9hiQD`Gf$_4}_aH6NZP&$(^OfU1}=kf!={6rwD6Xv{I0k z7|^6;_X)()7QvPU1Oze{#pp>`dK7_#z>E;Mp>5T8Aiyc-VF@F6IAYfnQSN}}xzhncG^n%uxO)wb#Rzct8DQsZaMGE5$C#z8d+}a9dHGi^VkubbnWfrq z=j#)al0*ghwK#GJt=!c9@G5~dS^{baM6ZAV&Fc^Ci&xM*zu31{}B;?9$R>cY+}K z0|*yc@(6DzMaE~~61CoDK;fW+?Ap6iWF5ffeFUL%Pu$=(lr6wek_`AxVTc0^l$u#O zIKcl0L2Mx=IL-c3aDb5O0Rd?`+#a`wsL>OMc#}b=hWLbpHz-+c&Yb@@W^;x&X_&!8 z6L0noHb~~l~{lkIpfI1pEgx$Ekn@;YdkXNz7{7=O+ zq>^mcDR7{mpnBpyX%|5utbU%sy)ggBTH;SjkIZ94G4f`MiZhYrMZ6Gm~D zv$Egn6)RL+pdx!&Q!{q?Rvy$k0pR3>vl9POI(9+f#7lu5q~d|x2OLchTPipwAWUhn z#DKs0g2&x;i9<~h>NSY770|6wL+dk8(Y*}e?`8{ThCKLpt>C3g+@Si(zwyo7B32d; zgJvz1{5pL`K)RajT!Ufi9PxQ)W$^dBaB=wiQx}S%%;1h{sxWp9Q-RNvlz*}^pEf`l z;)9-nI>a_pQz()j;ORi9fDtjh2&dHEVl$TYqU0D@-pd!dX`22i!Vs9J z=2!5P%2OeMg-h2GX%n~)hb>Xqcuu_=MBtb`IwUy`h~#>76Ii6v4I&{mn#`fK_S2iD z`ODbXz7vl+1v{-BB;cH+huV5t(kNNbnqyL=5WuU@{vefc8ZyB zr3lui|HuhP^XUF-bySFLl=2F?WTDzX&4m~a;(4DCI|JV%2+ElXlE{z_XInvR!P6un z(8lIP9%UF-mwl*!+P{gZwtRsu9~g%zrPxAK3LL&x26Ugxmu;7uZi>e5MhubmSk(Bb{)!yk9p;|8{lc(q}@>Mz0IcKD~T_ zrSmey$4B`!7Xyiw*dNlj^Zx}eT3!B>_7=NQabD&Z&tL+Em3`&L5&RMVX)J-^vec9N zMpJPl@}BUPk%xvWTl7Rlh?6|lbLeYxFBwdFpLtUD!;J3!pYC#F_L4v55muKXAv zkNVq*K1K{-5b+Y^D4WOR}*z5{m}A7*8?tCA+H zzzsG&5(`EihpCIDkPmo}s?c8MHAH}rW)gUx*00=+->ab{2(I4*5Vym-G<^DDI{;{w z0dXt=1bex5(H!cX;4ll0>^e-~jqdgM*saEKSO`hhAo0BGYxeS!CDf|#%{)9`XKpRdGCf7`489x$IWFRiqLjeVz6P)i0nbE)z8M>Tm^3F1W+81FsP|4*@L#K`BhY-4p)hd#2$1WID`!Ov!NG$KnnupypogRG00P+~NaT>?LX9;X%F~c}T6X=&miC{tokdxuzKbia&dXcN z6cfol+g4{RqUmfib4-(hX*bqcY3cl!u2Xk|AGyu;LB*eNdlGZQ$l!tV;OGS0HeI(R zIJIo21&>3wl|Q8m00jg-G1fSZhQM0ELLu24&`iK)DCxEk3UN}WW@thvQ&72L>Nc37 zqN9s|KU9n#JV2C-!2Yl`!`{2DYe@aRie+YVf>d43>w8{3i$D+Nr(7FzrS(-zO0D}HqJ@8-aXfT!ih#(=$2)YTF|xOk%`K)TC4Ge?q`KN1&I)(kwVj7ewr7jY!21sUp7`!g>%eBjnYi7l#%%{`Dw% zI6xx0#*8^0LEv@Zle2(i_;Y?Rl*oM~hU88T1iIBPIJ?V;jJ1llK z>y9jZNRTc};KUtMf2rgacXMOer_|G*K-~U#?n)Jzec#TzjEqz^1*clV+j9nk(hoY% zBmUrSUTDGl3zS#3=Eo4iB!Dh9j($46V~Mb`c*dY?wdo71%8#9ouX{+&bAcMD zYdWP{amO((Vk~@8#}ThL_2uo5S zb1IBo!S!HiaJ1F?L8teD27Bs>e*w6!U;*H1_E1AZL$9iFVvKu`80>R_XI8bmoB>~D z2FhgXzD@kC$48CXU*N=D*5OcX9N|}<@hIe_n$xL?;quf^9<6lG-xnlAf57klm4+*D zMIb<(LtWG+=iHwy%utN|uJkM|4;-r$j7gD)(HI0DCixHei zIFduao)-vdZ}quey?Ui!OsPGX1L_l=D)2NVMz{~K^$-C=oB->Df)9=WNZAL#^DuC8 z1G_g?PEK6pJPN|mJ2UuXOp$P+^Ys*j5PJI~$2j<0?yv6fdW<9jpvN-?CAan4{(t+1 z++2}}4T z*G!s&?!QK9>#oc)GBYoKV%mnbg?;*e+KnUv{_}fwOiu%>Bv{!B(L#%5($B`9?enKQ zNa46yN-*IQ(aO)sqDVR^Y=}XRb1amSPnB?uE1#2)FE9 z2oJ8VkgfAx(_P_8O!!yrP^CdEIBKE#IyI>B++ihJ9DBo-&_XPRxJ^8Jhuv)~WKr#z z>*`V?VqprL6Tw&EH86vR{~eQ`pKl=?mVecfh}_2(oaF4m?vz%@^rs|fEx^C%9@H4Y zk2e%Z91}AWz=njaB>c4n3WcyZAteel+7VRE8JJG{&>{L_dzRa|Bvr8|VC|xoI^Epn z1o>#ACH3+Eh&qH5d4aRay^1Cazyy73b*(Y|*;wg4y|g@%Ql+f}D#?fugpdC)m-?fV&4&%FN7w*Kg5?Yb1h+~a+0%*rqfss5i-{<1G@tzx2?B3S z1Q<5ihfP!kNGped&{th*Wgkcw0Yu-%ZXHNJtrP$&#S5S`{rK`6@LJjdnL2PkEVQ?{ z-(u)J&rPq~(Yrc09Q0ag%U2<%f*-^B}NQbf9Cr9B+~OM;z;oF z8Boan0op+#m{zdh<>ANx;!+N^;<1wh92;0CDU|4oeO1?0K!P`b+Yg{$M(x%i!hrq8~>3;%676F|9U}E^+5$iZ$T@fcadsG;lDywA~o3lbpSMM zyvaKb`-|^IGhj`D17wtS?e<z2b&x&wl=vqE8+#H|gARcZ=6EN#tFh<1T5S4E;KowMK30r``l$ z3JT+6R67%>mu&xg?ubV+;{qrsQd3joMZ&o3`5?T_E;*B9T%EF;ti#vtH~)^2G3 z^pToYfIYUkW^gUj#%pxyDXRQxN%)8MRYEaLL3cd*u~FN})c-U8BcKOKHyG$KeR2pG z7nSaHcIkxfI(YX3?2z(bGYdZf7WAM0^5JsUOSQY~(nHR3uBNy&Z$29nBz)=1eHs9K zy~d0wR|B+#9ZHAho#?;1Xt;Y;M{OjF-f1AtxJh||U42FcW?3w7dFLyzz=1L7nGiM) zKpZjRoMEyLFy%X7>DwZmUZj|B{in|iC%z zTLw$ts%U&Kh2-^qCtch%$jl;oe3sZ$74+)YJ!(XLt1NNi`dUH;n$7g8&(N3EGySVqeR+7 z`*kG%t+o+9c4l2CR>LAWkG4izxE*_+5H67YR@)PXu72~~sp~vri2e+ONcd_A6R|ST zQ3` zUmZD*MQ8_5UnKX>g+5u=`+WCcV&VBaCPlx`_Pku^x=CS zqc2gppXL!BC~quuE5$)rvnP%ZwJuY!Nc#+sIn-DK{k{#H(Y4v&2}O;Wun{3|WI6v$ z{vL2^(v14N_0euRJg?opG$%mcpDT^6o;?4~9;s(b z5HRU=yej;g3rXm2+2*%s_CK7QBjqbeC%=y9ZkEK z@!TfiK?1?Oe7jP|q2777QTtHWExtEYRNYm9=ACii+a8XSqQWWMDIZ*ba+`LuCBy0o zA?R`nH-5RX@hyK$Y!R*VYA!H@P=ppm=@xAMpP8*q03yz&Z2h8h;E|1VvjHpvc)TDf z!GqR-z}KKWeL5OxuLA{n@Ldg;#xTSJ1>!9js~w0Mz-mBR5&%Qk5}1oH_Mrpz%Aig| zNNs>7vVmQv@No9mM06SsWxQqg<&3L2uT|LS>~siMVy;%PPz%VXS+lCgncl*m%2vNx zf%oXS&EykNQ9cfosgsb^uT+2bZgwJ#ut?HJ_la!zbNCmxP>_OOhu5I-oti4f|7#}s`p7NR1H@aosGjf_3os35iP0-u$-+&718H!C;DoE~I zDEO|dB0tX5cgb4DJ9n2vlh#~*kx^}^?fxmS8LS|v?he4H4_6gzpnmOb%o?|Hhd2eB z_2X|c>Q)=s5J&x^5;>4u@;exgWPr$I7^n=!a-<0E`bD2Rqcw~cw`6HNIq@Tdp(k-- zciAXU1+05s;tbMNJelT-{#rlydIwr$m zDj*oL8dkQwmD0;2N{77WX_B~{oGkZ>2+4Q@4#NJaD!Kb#JjEEl;72~>Vy&q8G$*Bp zN+#;J&%9Aoa@=zZ>+iSF4mZ2nqrY_Mrc{FWo9T(krYX14w1=UQ_vA&e8?Hpi zAzWL_r;ay2z0iDQQc(e=1T9>PjL%jc=WMP7W~3H!SK6q)$|I0?W984tjX(>Og;zSJ znAsoMj6P{zv~2Z6EF5%~d|IfmDU67DW?F{6U*dac%s??@PtSUC9D?vbL_y9*!k%4t z``)+vnvZ(oUj6v(<*0PG2u|9E;ZSH=;mKw`r{StSGjDE>Z(uKSW2;y}m$wq4xP~<&a_lXo&iv@0y(kYNov&r2J|9Vw`=?kJQFLCy4 z$kN<(`2J*_>BFI`^?B97Gm%%2U1qvTr9%ECnYT>P^9QF6t94WBR)zJbo3*f@a!Q~P zYH-MzNa&zlWj9AzZ+*_VGN_sLbWB_xINkQPV~nxwoA()Bbhp#8;-PfxogZYz5=3wb z3H_(?lI?&g%Y@IA7mjz9k=)C-^a{`G9~0*20iPG~tb1C4;_}F^RZ*#*yOupi)r>+h z#F3b7=Pjs<_%&ZH6HtrWoSiTq)BWilK zj=?07JE;f*^Oj?=r8T`Lk8024RUQtlu(@m;sc)xfPhui*Hi~{7B^;#6(TuJz{eXHR zfc+jzp6p0nR+n_m=gQyP(}W_NzM(g|l=KNBQ}gwXFHD!Z_P^YwE$56xe;waHqQbo& zqv%12)Agm+~JozH`mh1BXKY}L+wxMWl=5n)%4d1txR%#njqTVnHn?41LF0Bs<&AMBi6%xCP zZ==N%CcjQCsi~o^;T}DG)JcpOHT}70!jQ+^cCdHI4tkre+4p?l*0-I5OV83zg~Z`H zQIM41qr7{TNR}b&1$$)0{uDM+o}BIzNVe+SxP8XV=$G}j%k7X+W3ngt+2vT`f8&lG z#fh;@qOHZfZ%RUaLEfH4pG5mZbYJ;hafhWXA<99WM#Sbbay{9o&Bj^)g)`M1qRxr- zUm;X&>z(7%WBQM$jJ68mPejnhJWbkPZRYoKj*Ob=vZ`hHTQ`?&`7OGYef6xX;i3LI z!Q-suHsYc`6)*+#B8vfx>?Q(ny_2_yQyXspL|0R8{@}3NBhPcPLrp-nqn68~v!*^6u zSnqA^_%;g!B(xtR9Ff1HDSvtimx3}ZigrM-pRe?SCe1SssoJw(#>D7BaYlRC>l)%j zsp~wM7mtfRKj}yQBZefcFT)^7bnuH0cXs35wK}2Z%sKdy>l~3hA~dW*OE5p98Erjdr5XDcxB=lEsB#t2x2#F$ur;ZR zjl7j7`$MrUOM_CH*^u2I7j}OS0<4~FoGzOXY1t;z;moV5)F`ENiME9t%*x?ni~u^^ zpS6NE(<+{ch~j^nwa94yye^8-^pwuF{LEaJ-MZp>H0LPuOZnrK&jgLn+j*|Azma`M z_~xm^{Ye$n2(uckwvEuX!!nuj>nH!{hi4M&>4XrL=cDgPWj;iGw(SX_{x<=T#tG(Z zPDg)TP^Ln^)o`>J9L`cbYcsIb@)3QO5L1AP%qJANo``kgTZYsD*b7nDTv*xpR46bV zi7f8EA6OaJJ|&lQA0^DK=OQgMS$3#VLZYc|Q|4+_E4RA73h!M_Kvkmq=g{F;j`NeE z<(;(DPnmc8lK-LY3~}+hrNhbDGt_I|wZmE}&=be!uxfq_N6bQiWOA$EYT}-^*uTpG zg%rfD#@jFiEbJ(n8peFuFygYcEB~tEssx$FrGm z3DeYzgDu#DkZ}6v;zPkqXRkOLNfj>67{=F z6yku@JL})TrGcs*n~!LLWdQWYn(CgW)iMEWz<(!Om4jK|GJZaF4WmkI&yFc~m7fjF z(9N+Ay_%<*B-t(b$Vo&V5b#|Iopv!AbV>!jD`zv>onf1Vj9!N=^9DBK&2j@pJsfk) zAI>Rzc2pZQX=CTUTgX$qbcjA0TM({A6!%ZEqM~#+tFNrwFJ1WM8YD;fsab# zwf&>+ifUaKhMp3ty=&CPvu<1Un1Yd+XnZ^c^82nbJe{KbdPznuhdw-Kji}ielmuq< zQ{WW+b^5~f?-KuG0dx{ZvnrH)luD1|$p6eI@7IV4Vf7(vYM8*6I{S9}R&!pi6YSzq z^apugFwb^yy+&2s_zG6+u zDiWv4CSPS}t_lgIU9j|CGyIsygCUaZM&{uGrM%(~XxripN_mkIDZ#6ysgE4feW>&I zy>;b8#o#wiu6(Q${^C``0b6CWdk8$YEe1xC9D{Vqsjzd`yk%<8)Zp3|06eowJ z{YQeRK>5fM&&$+TA$g=J?%NmRH{1(Re3eHJ&jop;fN!8rh~hI2_ z^%F)Pl;i6E)8`kL1xe>6I;k9<8%En*lDi-J#YO&((7s$!ZDayTVkIis9?yoMu(R_< zz?alWS>a<88ii6*R?v0!(!*k`h&d4J2$a>wqZ!U1GTO$V2SSrYk+vv(`Rti_jug z4P|h5>j-{yC|ce1?YojO1I9j>kDaOCaEGww9sw#s!ihldUfm~;9~%ssj~Z^)uaS$|61ArYqW48g4Z@eXaRpboeRiV|GCg7 zJRaxPK$3%6{HoMX)De#9>t{g1A;=?0Crl33Rz71?+}uPN zLTBV!+ASYVC*)g8L9G7h1`YdzI1Lq^I$tbBwb*J?aXqs4iml0+saN@4Pqu#Otk^on zzhon?Bk>Mds3#YZ$REQ!p~EA>XOjdIcLj2oUbF`9ZXUN+uN>=?s;A)+R%k+h%`5e5 zGA$R6&&Ga#UiJKVt(!JRjVZS7*Ap#;S$7t|KQtdj&xu_WRunN2`w&@3-({s>c4Ygx z)G}1{G}`_wDtU40ERm=hs`rR+?5gT1n=C4PE!(aqgv>^#wbq_rMQZGS$DP^&$=GLK zL}P^q;rPreX}x}xYgHn4DXUz~<>zTHz70+r1`$H`!1{*Mt1SDUr~Tve=uej|x4rve zov9aA|BQP=1!dUs7{8#LU}1!Q4jY%)f-f|2{A?kW@_qDPgW~59A_qGx1L6t+CB6cz z*(FX&zzU4#{F)4;s`2_mJB>RzjlP-x{P$@LPo8Et7r&P|U3oWaR`dG#T~D?T2>tU#RCy6qKZ@0?GqmC6X>RUNQT!lW zwGbnV6iPiTT6BN7f(sSp%jn=Iqe*P)McQW-i+FcrD+UPa3vCpl>{^#1mgpFnDY{Uu zory)AeR6*nMVJ6GIj)LKoM$c+Gs)s_S#Z@faA)2k3yfO4X)dsFkj9=DcP{_(~g+V1E%@hZgUjm(O@$Tn2} zmb$vfK2I9=7|lRm(aY+}sbyO+^*2uP*qjiF!&?IJzgIIYtoz?TB+jfzy13XYa5bO7 z>=ZWDiihu!lO^5_4|3^?23EY`Jkk9e5?;P$l&BPrHp8DD;c;rz+V)lQ6nn2l^%N^e z(irO8J2;-ix$t6m9w^+5m^9s9>`B1sYFnDZe3KjdJ42KQPl9EhjrVVUfq^yXWfWD$ ze$;M zh>M6pRj1s5VqIBHxh1b{`x@Z~Mw_&r-RuXs!>I+u7x!3ox6g77r=kDP2*rEDkh;{% z`zBGtP?u+9)6wO}bLR=E7>Dg6S=OFEOTS(pAFdO2>&_i*S}iBuSJfc9t!7+!|B%AV z+*?^z0&n0`2EH<-2#wLs(N+Sh8jmZv-h6;?W zcIZ3Sps#8N%tSs&{&R9^V=hc}CPd~_@!uX{@bQ)lHNF+(CG|2S25+D4>p}l@R>!mc z>H9-RsX0&NVh%^=HTV4=*E71*JSSujjp|k5d7e^KU&(7FXGWl5g{3R~+}+AL>w2CH zDecOZx}KJ$HVjFKuDmV>1!bv}1GNT)4duBp#{PT>ng;et9)%=ZdtJV=jw_tKM zN2}TA($d6iKr(!1|0v}J5qU(7j}Fk^e)0ZiQqpj4^sK4lUELfKmI>j&$>XRZS*+|| zJPJW|y<3_hdMnz`cHQp3dlv<=cX6sUFxHJ72AQUp1^7#_yD;&8nt=5gXB%@{^7EdL z(Yw?+1D`W-YizrYR4UYrf)w3#$6SxqC ztf39%7gqAWn(!Dzul8bXa#5PSH;K&Wt00h9i2u}_@GzS)EJZP*8#~y&h@*u&VU%R_ z4aO$yn_OEK($e_vcENfQei|qDE*P>eO2JshJ!ohZ=Ks`0s~tkb7vu1|{L_YNSLm5C zC(la{3g`i_F;kD@pk~BPYbL0o(3W6T8@gtZIMZ{MR!u&*p5g})AG-LwADN`O=2Zh7 zj2i8)J6Zku@uR>0F0K>TW3UvaX1VY9#mvj6<-?i_WxU77IUie;s=~X>@SUs|LPNJR zRx2eN$lj!Fnc*U#Ohpx)@Qys{K|VEuyc=QWd8ua!HiTg*>`4T{gAknMW_s!zv&Af4O&9!hh&e$-OTKy z`)*(k>u<+$_o_OgMo86Cv~8#?s(xWxvAe&uz=?_~L0{U}2|?rj2*IR=N;Y(1rEK;d zkut7eGmXK=`ZJQ*s1uANY;i|3j6%C-`kuDpdDE3c-^V|aY#6w--<7{pi0luQS@!t) zW3?Nb_LPNJ>-lnsPZm}3u8De=m2IqRP`F{3pY)N{$)PD&bR^=@RLb0F`z{O2l8uJ0 z0$)CfR5B@WasMt-RwOzOhR{<+INmgG`ZCo1)xGa==faLKf%aCnY}o)io4x_|;pxr0 zzH)~@;>XXd8~0)gI%iEqZIaR;K(W_4bFfcEQfX=77&2iuCkQ|)MG%0ws@Q{yp|#C4 zNU^=5*{!34Yeo38A4#14T)1(PBgCxig*+-6E0--3<+RjjO&5Pth2~4ort45}5+sBQ zZH-wg4S7Xu)c=q*<*5&SLAizY$olrosYbrZpY&UMPq}Gt$m}=UHhZk2y&LtzD$0X# z0@z{gNIYPeb~P5Jw}{7i7dnVBo}KbWkS|aR-lEi&u#v>lYP3H{{@;( z-b>r3K)1hu-8JY?i0mcf<vgO-s*hAd}z-9$$gY!mWkQ=|Se@th~&RVyBJHpLenbjn7A4=;FKIu%wc0ZCy>RjHQET4?vC7JT zPkv{?h&_}ACs+&jb$_%K8q5M6m8QRKu^Uq0wD+ideaY@iqxB?;RZ`^csfHwqkA72T zFP?849-iyss+6e|tCSIw5!?5bWASObFz5GS&$(;qN7LNK-q3Fcj+sx|Ipl+7`m)7) zGqL@_Ta}*q7tL}GNeO2j#z>znWj}XTgPyy@czi$mv?Pt)I;uP4+zk^G|C?VeRy~8y zIxGLvIE4E_Mg3>#4=+6vT}fI};lg{wQ4?M*ZEXWsj40&#ehMvu+SKVMEF3f7?5X(f ztNO|D<>Kh4Gf4&#iVsbu5^vvDqd&7D>#1z&Gg<0t^Kth(`j;=31?qwghv(h)aRmdt zZHN1-4A)BrB=s&N7jEipA4`@Nx!y|UD>|s~m~c3AXmhQJv??)s1_$p^e9XCu-A1gF zUtY)1_M_sIx8AouDgSBQ<>AL%DQMz(WIVWAbc$s{)hfaIf_uXgPEP&pP^)v*VOD-` z%z0T*+)5}C)-#Hh1=SaCFB)0*VoVgD#%eOCWzi=;R0;FMMAPZDo!p-FdOjc`%_Gy2 z4}JE?B#IA;I0_b@;}6yyM>Ia#ztie)Fz7Dua=2c5Sf_X6d*vOYSi!ShVrwaV+UL8J zCLhcsT+c4K>2N!7!O>%l4c*4_%J5(-uf}+N>qx@YTm7eWP>H|Sk5;ZH_PShD@f=~< zrF@4q0%K%!_>WL^_CD#~H;)4Tyr)Gb@*p=IhFUWNSPpf)clp(F%vvu8r+6mh@|ou? zV0)^)o%+&{y%uFTzQ+5l-hs3|A)+-w{G1AI`8(-0)OyE8?vNxelUm@&J%*tOiQfCp>-MjbdbvpWnNcdI-oIp`1`9KPgZRA?6%I2yaAG|oYDRXLj+)Q`I zd^dO@V6bpqFRxis>PMHD>U6&L<&nkxY3G0v-PG*vRLOqSde;?))wpdU=Tl^-b7Jiy z#3t~=+I8TUY1gLN2b2o(?U0N0IspbPe`7q!diHlwB1_T4QS8E` z4{OdM{aFbwRq6+7?xOb|Xbj>#LD9>z-3^-=Y~u~dFF&pCQKjOXu+{G^#@wuAGb~on zcqa3gA%2|{B?=q4Ek>3Ng@;mmwJw=)8SVPLWggc`N$*+H>Cf=wD zo-DtR4N+u#Z+$Ggf@6b%m?l$FdL;9r3j3%Xz%iHG4EEa%a@~ z+w&u*P#CKL*%|S{Qls1_|CDV?+tphR${h+y2p@#mJvq@SMXVFP(ZXTvu; ze{ffcR<+0+By$*~Pc9g?+_ETFWV|T4r(%dt-)8YZ*{g*3*|%yT>ZpjQd3xT21;)+` zeH-6O>J!~^x^}Gla}SJ976Xce0-lqtWXvG%xBPP^u;}|#G3d4ADcbT~Pz(F1x&E)` z`1?&ORZn;sf4E%nU7BL(*9X>M4cMbB&eeNu;D&qsx~ z+t+=U9&US|^1jBDalpP9Sq89Q^*G|eTvY1FhyGCvI)5{(} z$qBi96j3A=odvFZ;sX7wt;?$(ruHjgQ86;&^XK@7^2Tnpe7QyZ`Tk|5w7bVcKQ-?> zaGI*Lz7a+ARdC$bU=rPC@$+ZGm`twWHKJSQYK$+4>MTqX*?(@C@(!LOA)5KmUVx7b z*4qjJ2*G-bW%0H~4A07oXdrgeY`zcT-Ca4$d;OBlNN`0GNU5q;2l0I2CoK9BCGTil zx{l%r{Z;B2ywA4a>u$7K#d@O#lL9N6fT8g3Ah95WMCD&|o%F~TS0}W-;NDi^pxZ7T ztnQ;>a@$&NmqFR$Y=wPY&1=2_Y$z5nWA|sdGgZM5m!djRq7zqQ7CtFn8O1VAFdae* zNyB17E>>~~KuOJ+r*xZJHDjN%+i2uC9!FC%duV;M_NqE-%WpqkAyMYlnmgA#Sa163 zONmrKta#wH^`~W1pEG!|ur0H>Cl_UIR0lgdd~mNo zY30UHyAd|7|3iPBORw>c@C$UZ+u6ArfhEYzQ7F8&lR#wZe(`Ka&G*{F!mZ)X8utx3 zx?0OZX|hH|iD(xM-=-h!Ce+XGNb)0ILCmN$6@|iZ9YZ_Q9{h@pPk;}CV zzGeF)uGmzrhtbF0-=qp&NON!CW2`8md<(A(uCrX2VZ1KK-!(UywkzGHnfWVG(fvPz z2V0Ytq~f_87um_&%TqD=a$2dCzr1TpvE@pC<{u`qRGz(4=(8Q@e_Z%j zt#1$7wxWV8f;Xqkb*sZRWv$!qCTdH3UHfeou1N$9J zVgYkEk!@keDE-h{n2E?#LCduRUibtf$WmCpT_-cM$I zRfp5X6*SE`-(IjVN)hPb&dL8V*!$6?_PBmHE&svo+jD%(x?(2Am1e%)VO4>NRhE@i zZk5!rS1$LZ3hm!Bn~kY~uk zqL&}N5a|2dS^7@GK|p9@P++af@R6YBm1q3=b$YLBwi0p!LQd%|>T)v=f5H3RZjkGD zCO&;HwaVd+>_PH3OA%C3))fH_tp|Z_8c!A$bf0r;-Kz@5JGuP@RTN340CVH@66d3S z4&2{A@egZvjJN)2b<*nn$Bn=#=HRKB{<6+gf8^A&+*iVfS8g;hZ=whHxkyM21DSO% znm~|_T1}G@+LJi%JdHkwsi2PX5iP#_)WfU+HS&c*8eq#d5)rr~gqIzY~wszb0YJF$!T9+`(!}r5zmh*P<9ypk6>)go2f)Lue3$`)w z=h6lE+xsME)Xc6`?9H`ho`q}@xh|;UieM_d-hF30+>PNS`hCLyOW1huMG=d6*%c=X zni{V<6$y1`b%G#oN5kV>j@t@{(+#%2^?vWBiHVol>03eWKGSifVCao$>uvcBY>X96 z)W>PoGaqY%VNd1hpFWw7sPFW!@+!PXavi>S{h-#{(`o&FvG&DHb3gxPMQnoLFTZ(V z=N6cM_4{F8r~BKA8)1!&xL6-Yw7VX?A``)g&ibf=71^_BpW8zQUPkzJJinUBPoCaA z21VZ%=lUxY?mI-Sw?m25T32(^;OO=j4BvoaWuIol)C13aubzZnA<=Uxr|lsH6VmB*2zIaO5QD;WfA&m|m}~=Kleo`QHHC0slV-fzk{&NwWb+fa$eMJ-q6^SsmX19pG!^CX5xK?a?BHp$2{KfNtB)q=Q3j( z)t$^}2w5j@+IbF=W~C2H1IY~=&k733R|tc-2l4ODjp|5Lj9vP=_|gGeP>u9-`qiLd zUBS$U@sf5Ftl07oh}vZt_-=T-*BHh!9@E2(d9D0!+Up1rJUlnxGOp)vy^ciF$AMkx z))lE+g{p%juv&6QIuEIt&&j|i**Z>Y5-0!O5tD?)`{NB9zq%jCzd2XP=JDR90+QZe znK@X0!z=QU*&N?TX0AwkKUtB!Rrr4B2iL(DLGk@(x%-FHs)J5JC98>dOrD5H?!6*? ziZ3OG;&H}ltDk*AO!vjmk}Kr%zj;P1@-;rbAT8^!Ymom?b~=4cf#H8K_0?ffZr$4h z3LKFzKmkEixFItBw#qy*`d?q-n200g9C=#XX@hK`|rd+?n1`{ugN zKj*@k=h=Jh75852zH=X}mcQFmV|MUu8%*qzJvDES#_lJI?sjoC$1#U013_2q$0I^te299uvP{bNK~+uX*7?TpVFzD*)jS2I z##^V=tHP)Cr{dLLVN>ljGM;OH6y!P-J{?*og06(&GK4Ky++4U8o`?m zuT;(?jQ7R%MBWjzf&`MFSKhA`!VPlK=!D`psziT}fz#M6?dC08ScKr(SS<9$cTPXY#m6s3^{NZNiY^3su|5q?Uo*zCTd%BZ^q zE2BmI#rD-|&YA=BO6vleDmvS!8(>%PK}jR4jYs}D&i1N}1oBNtuf$&iWda7eBJBn! z{m*~U6x)%otj~S*Bd7}# z7~Zf&IN3+6Rs1|(i9Oa}=l)-0(Q8!G{ z$6?_e+|F_DcUHTJqm6RFkCMo$%6!a+OHv8{$Xy8}Vy`5grEn5?YHqnLfd6+@LA3_S z(xBg6WwBNpPUeYWYhPavy)c3hI)$fZ6S-hrqf?<{6`H|g4AFNDHJcgsvIP5p0rXu1 zK~-jauSG!CxIexwgGWYOO}15}m*l(5#lcAbGC+6n8w-(6Wh{XMH_3wcfNG2_m`Ap= zv(tn^z#k2!ccjIN|ZytPl515B?07DllFEd>8QGDI@O=&sfzJ9fy#0>5;m*VNk zftZ)dT5*NF;uW%z!oDFd!W2n9N>}!14x^goeWqXMgw=6$Ot^?}Wc8KV5hRKH5v7%* zk(5n*Y*L_FE^!gmf9qs7KMsKHg=+kCr*vULPH|LC=;S+0#QGtatdVg4{n_Rj-ppHl zntpU<^^YZbrNeMw3hpEP^X6U4b-=OIUtBX?03@yD=bYE}R9Tec)0MazwN0@Dar}OV zo1rDR(T`O?a@8++wndKP<Yc;Jt|%rDAoerLZ++ksPLlwGg?Hwf)Lm zo92Gsr)D()A>S>?bX!M|bqCa48WZt9v_*=xUFD4JABev9K&Dxd^le_e zZCUU)sdqk|5g4(=niqBfU530DPA{Aud3QI_MlooDa+t&W)icO74~}DX`>Q{lTxLB#p{Sa|)1$+xs*j-QOLpy+r;-5o+s@ zLl)C{XIb@HguYRsiTy2P`}w~(AvovEGg=EH z{2Iot;f%ud_3|>r9>Q=b&e$Wtv2HcTWX?%PCx-0tETlV4(sVFa4MV98o2t_RG-EkH zD*3}_q@$gkBK4Ur6lxDRXA;5CG+@hPYFF^H85*jt*ov&AztJ`|O>@Q#NMUD*B=Z23El;8E7cm_y_yOmN@c7Slq;9^9cW(%}Z|JM> zh@mZug}3Vss%HfSR3$3`9F+CO?gdMus#cK>>U)x#peKY?XHM_TaDj$8$QX*Ai%%5I zTqCI$CWkY1#Czq@hWM=gQ){;oTEIl1owA|EguJOma2@XOf)o^&6e&^$aknm6IpY|J3bsGDQtKCI&dNHKWN z;&G;Sx)lLe9zCe-^;ZSlT7v~Tg}cBh<(HRh07{DY%xbBV;yIO-TDOgyUl|w}@HXA< zg!Vb1g0Hfm^4J8}+W9?dvG9*I?oNi3!^C0n98CF3DOv*3p!Ql28jmCO`wmh61I|NY zZX_qbl_P!*O~y3$3DNcPg|vye%ZOD6{lR-_|2QXHUfCR};}A$+pR_8{VzOrSI8u)J ztPN)z0E#ub2a!z!Z;gG6{y`?H3>fA6ZF5tY)3{x(tm3R4Jn%43p80gKt93v$4cpL* z(?kJ_qv6zal+y$x$Wf8fWS?Lx0lp){!>fP=y}<-sa-I&czLZA9N!*osXHk2$fb>AYe$Q(y}MZ~xXt3(HlA)jis7<41S)z)i+;yS0!! zh6Q$-ozL?b6L@aMYMV74#IS@Ry)JMA>Jo^0@;sL_c|03yie6uW|$Xj57 zPQU=FG975Hqc4X7OS1FphmE@fZ~fue!L+DwIv`q}3P1A}-ygN`nK_=0>uEQS#6GlQ?6sv$sc0OMt&~2ec!(w36oE-Q53oY7E-@Rh~(^Ci_6`Z%OXE>@EjrN+EGuTLoi81bbvPRQr4Ag!X4l1GT{!Ztz* z#3{70{*j)Mpk_l9y1%p@)lq`-pOvo@2Qj1;<}EXw-VAA_aBtj&Klt>!Ev7Fm>dBpd z0b+)xUFk>KT!yiu9v*-VQ3T8}^NAcxy7dm9AH&Z}eSN_>P-qVW{t+-8cKA3F(Y=gR z72nf71(g3A(+9}rGaVp+Hu)#v4+$CxTqHUOYOG3g&VZgrPN`4NQ=Y3{m*ZoF+}u&q6^W; zzs2_1MX{|&r(J2Px=aqqX1(F2v)X$C9gV+Tni2LjhtqJq4S7>Aq%I1}I$h1F7JFMs zJl!{)C9@#8yOe532TSR!d49r00}2D&=bl_1L{>2@EbPhzU<>IyoNPq)#AGckEg31Z zedB4TjV@~hq;V4M&A_3J0~N7@eTn1sOrTo*Fw=Lh$mZ+^{aIwcH>+CPen%ngeJu@S zW2#e7Z-1O>a>912v>+`k*fGx$$17W-a)4*G_D#~03}NTzg0a?uNIU)Csth> z_9gy1Pbue*83(F)tC8oUUQu!8^(!~}soBCJtn=Uz7l~5fy4~~|o1UpL+=K(WRR?CgpV{6AG>6P zi>$FWf(?$!a(-Mi;aIPNG;&i(TMqhRetOt0sXB_R403(AZW8TT19+QidbgC~9;%F{ z@>#3u9whNuhXaX?Xy-;{=RptFD_n&-_%2rXQPn=DGdW#G+|4udtw4GSb6?5wV&4NHy^h0#1Ji z@|m@l&yKWxq3-gFG3OZbf7bTO+TixS^#cQq%;aR5w7 zFQ5zLzXD27+!7o6ZM7=23keCKfa1B=)6qI0cLdao9_8*B0LKKxFIM0B=cafVbZ!{~ zyBhhY=Y`6=<$N4hCX-Vw-x+&?6zo8xIRU%QbG+i@fp6F!i)>39QW&;9SQ7W>Id7(C6jx4{H~0 zoG!3P_6sj3X99kw{E7-qQIGXLK*s7m<9`4`X!-F8;1kgY+Gg;cW%q6YB%`(hAQsE* zwVMR`kBp%Q$EP&nBHo2l+RV!GMV*rfn4#`+=1Zyx7A*}H;Zt9EBhy7>sib#a+tIjB zDIfZA1(#APPn1<5s6Z|Eyzap@<>F+IY509l@)M!rO7r-cmT0%;skLNCf5U5lK?93hH$er$5&`2DUmFe*~@@2ow?M1N@V}wJ3#7;sl_xF|{Vz^YFx*|1&SP?=1AJvYu1!aX9S>L&w_e-c4u}U>ncn zDEaynBpt)t0b3uFyT5>>?CR}@+V=ML2Tfo`e=S$?)OOrW`iuH^k9(2+i*jI`_PYlZ z3LQa8U9SMge~&ljhZ_x;d01b-l$WQ7AK22nzt5oj5fu)w#f8+c4{Yo>z@89d`MbM{ zwCawMWX>~p*^pg}|ACcHA)yj7nHsd|M;rdHq@-|JFd2}<3@y>n z(uyvds(kSHrZ`7{uSq2k_V`1b0{%`vrSf?zafg6z_Bl!V){vx=hAxe^NCjPOiPj2x+`X0^>c<0$Z&JKQ5RP{b{`@-?3 zKv2&T6C|*8&FdhE?E*`KU<<|4R_?KY)GZ6}!qxlw0@6KP8qaSxlqbin8=ZFY z3>;qDgQilz@`v3FmYlSwn|c|_Q+N31;~vm#aL2gO$^oMDQi;u1ytq8iVlXZCgo0iJ$)Ij&JfAr3$+v z0UZu=I-oIZ-lB$0VCH#MgZOP5c6)pqx#e-5Co;~3pv=;nC&pgI?(tGVr`rO>gh5Lx zi?Fas)L*29L|A73=cU#K#Dt^L=-GB$bF$#c6go=jVLwnAoM>zWte)cA^4rAiO-3%Q zly!imxvmCWVXMKhS~zpmH?{`(#ualXewtxG=vPtK2DbMm;tEXZqYM6af$Xo#1y^qZ z(~mj9+o%uYF|K7TNHPtvwU)=ioqi5I-ujtkk)juDSHt1Bu|L?(RuPZ9)cRK;J^_8hags-f+3iIOfX;WQQ zrcb6OqRJ26wup*k)SY4O>~+Wq+1t*iCKkBEWXWH4$Ds-peG=a7oxeZt{pr$yxfw1m zO5-L7gGe29`jMI`q;kY;)m6>KbJYC8hvH5%?o%rT3C#k9;n2W9RGAZ~eD#epmk(1Z ztNf38Jex8BKfAE`3}EUj<%S=1>35quiC>xktIATi1Ng+pm-p2DLQ6GGhUGibj8R+3 z75i^{k_<9^9BkbN%nusu?T<$-K&#F4QEUdxeoxV#{ysMhV7>{DWK9HhS{xbSZfy86 zPy!>%SAWw1R+Y;w^6a!^2jj~-R#5TP1C|o_T3v#;*)ga-ivZiUp!p2IgvRVm5oDl5 ze~Sjy*8O=mA*bmgK+*Kl>G(9D8<1H@?L0Z7n)n12He%M})_-%tTLT^5YH-mJao6=9 zFh^^wLgxq?_azOqia)l8jj)WIM>vk3*~4t*UD8OC+#cyeTGS)aA}x!Yv~G)(TEkrb z0hD<7W{dz*dv9_q2$RZFCqkPptD|0(Nne?)*b*hFjH1C>Pp z{tW=4hx!_5u|YNvL$G?e0Vr})%KSmtTe4LBb$^1T)b6cvx@?8!+pITVBc)1Y zN@p+rQ5!fdpTDu+*v#>{tnuOSF{>cxLbz~IkWsEwXMWQMt=a_1Dv$qO2}R=LJ<=2b zMA0`9I1Vz7TV3n-X9v|Ji*e9IFA9wYIMg7E#MTvW-|9wDR8i$_m+6@%A|=U5YXafk z!4${1UJrD526gcyf&P}y{+M-r4Tx~=-w19m;GVO6hQoo2SYc_mUs~42!F?Np5;X|d z{5?J>aT@5XC*XV9s25j&@6{#9W^krs0J@~yD)lev<*ZAaG34`UGSV~rcN;-=4Z@He z;lFNiWdexZ#RBgg@-|X10nnGMWPE(6I4T4re(AgyJ}ID&>20`f$TZne+ zXHFlY+=B;e0lD@~10l)N-9dm-C25Awe09l`I9*j^Sw9|#Yx=CNK(gCW;ZrP4yE}$G zxnHdiTvK2GM_!+r2vFELD8N@8!yn(v>YgAJGX|%(>mg($(&iFf_$@igvi||3>Tefa zS#RYgyM}}Vwvg(qNH0jhooLbm5t7$6?O!Lef z${sY?m(wISbCr@s) zs)zzOVL;S6k)4C18UzQx?Z<8_y2w%5pt+plX)=HU1UX#*&rlkz0n&H@#pR+v8lxD< z4x+yTx+4Rick~+Q@|17*gA=$6{5ov0AA_tl%LO*sp6!Tc{50A4eh*T679>=5f#zEbdSTq{v4k|g| z|59D73>#;X<32+f(VuoWY29_`HajXbUrw-I{;ySwtPh{YpGMT92q*PN=Q-)p7+zgd zg8~XVLx5DS%5|~RWUh1ugA5az&I5kBR;sc)6gJ_r`Hg6AjvW5jyctyoXc=PwepPKU zhXE{XyFlwe7#7468LEH~EQi_4z5%R}Oup2J-TKk3DZ}XJCCr%N2jW(DR&aNKTEGj= zC33!&?WMaBlP^<#YxCNn*xx93!T^yq0NBF-s9N%Y)PqzzooBK`J;o^si}f)MI9l`p z8jv`oh}@A|1uG86e9$|X&7#Wns@Ur zP#u76-#?&g{hiuuFNdxs+pNJm{Mi&amJVFf(lS~4RtiaYQc6WCZ2R$t)EF1(Y07PTOK!9L@$u~?e#TjKNJ*;JH zj>i0`T}~|o)&m8^mAJC38i43B53Yj^wgH;yg8<|U0CL4olIT01RS=u8+=U2f>tR;{ z!UM2QLUWrQq=S-;Z`><(LbALdClhrB3IlZ-Y#Wu83Tx|81)aVrvXGV*?=}@l8}8WK z(Hb(TFSNuwpmpw}R`h=8F0}ug=|rebA9?c8?hRuwQ6k>YL8|+vr~IQH1NFI}}o* z+gA9hkXda|f^S(Vx=rbo)HexEh}5cUB}w3-45QAn)x%b13U?iWczZnG)c-mk2|^l6hT}iB^zDRoA({u5ly?n4`T}=cpV_&&k;ft#C2x@ury=&Y4Z{tDVB_}STz^7%t zRZ&&2v60lcS?TuiL`lIdrm>hz6`K6E$lZERxzm^gnBb>kjF@#cQTpTyS9GKinFEzUnMx{0AMhE6HanIHpie6 z{G5OBA1YRG=gayz6$Q!7gT!W?%8l2}#RujL?MVbUw=QHYH;grjP4`51-iJsLD#Hy# ze5DKV^+MnwPy6w8@pjy3FW!tq9TUWi2k|y{U~!?G1uYx-hoXY^>xPnptLU!Xc9%>h z3;rVG#D*!GZ4cXRgrJN;bFP_1{cbcyNf5Jz2iffwD52%}2*2^Q#B#`9|JW5t?LxUL zT0y+^O6%X7?!Vi}WJ}<@L>@IIl~O&3 zh}4){7z;}wU+6+>Rw`;dPS_&!sRlIW>;yjf?tj+xIT%ms%cum?XXU+qd=cti)8Y{q zxq|QV^mdrOV|;Wzl5w)k*369nLUJV)dtK0{e^M>$@=Mvt??nU-pttz%tCQetBeMKp zNMApeDc4%ZnT9J`^)sWty>)a`|3FsAIEDR<5*Dy?S<4CQNJRBJ8;C=w#$$u@HKT&R zm?F^Efn1Wh&t!WEW~D(eDeM>iK*yrKhGR`j?Cnfhtz_jT2nmm)B)(a#G#}WaaQUJ~ zLZDOO;^!|(;Y?wO1AiAI(mjRN}uH6Ip9n^1-J+a3SynfXAsCf zd!MH4Z}gc2J#>Lj*Yxom#wsdp@J=h??JvjN1bha>^)%@(T5Mow3@11R^6+sfLz7nq z_ix|ty_o+kCsCRh{P`ra&E}@GBXnBZpEH)!1IxQX^TxN8g5IS)6duY5K}te5bf0e1epGi`a|}uG8{g`z8ql z>?cW}g<%0HmcJtB>0V605uITGOSe)X8gpg}yo7%2I_ITX1$RVG{Kg7cX9K~Uh-1(} z2DwLPVTrwoUw^s9yYT-x1qg5ozoMAfR^0fD*wa4=P!bh3!c8{2k(^yxzK@RLd)x~vn;rshz6!l#Gk;xMaV-RN#o*RE@DJ!nhfupZC<##3 z7x)*R5FbW0za2*~rA^7nlv*kHmL8)m*Nq7r z&;~?b=~WY6pt#QS?e2a$c~V~EXwh796+&eow#N-YCObAwmQCQzzZktXYx~obiiSI0eDR z>{lh1QVgD4On{s!;eBmcWEGb%f=OtYCIHcV4Ck7No$9^SSC5AO)E}UCRP_1XY*pTK zK4=@2b)Z(Z*L`pl9X>J9Ea+Wl?h-J7OdySQ6y=HZnC!fj$t@m}wp;!1-l?Ox%(x5e z`s$B}MJN9*xgL@{-ZyDK>CfGseGwxF@JwAm4q|pi*H)c`c+pF(gR;8ow z+-|S3R0_cV)J49#8cMyoy!lbc0y|i6@_Q~hAzXrw`&8&>CWrCV>5G7-`@H^PQb$|Z zQ!?_KrT89)O&>1qL*R7}?@{x%MHc=w)&Nl8J+dUAHH>-n3VNLL$J3$KBqTBiT>tw9 z(jB#{CFd;}D3QV%>1Ul_R~2I#d-JC%^mseo6xnvCO4uAon)&pIO2G;gMN52+Q56=( z3K3!U^rN&U`}@Nlq)MVC6)yRHb4yw6$$9-m=rf`@Ogx)`eOnG4w7){}RZlTnMA~y1 z%GKqXUv_+|n8Xh1uxbX_Hh%SK{q?|s7olSForsF_1RHN2YJ4*VO>4#+Z0D|%Yv(`z zCK>5z7cG{tOgh;4;ie{9ot3N`&JIBtZ1~C`d*w;BoQSgOsBlQ2c;&$Sbbc)xv1D@0 zA|Rw-@gPdU@1FSC-uWjqvnjn-MpiV@<_UITlYHpK08xv@bgdjE`i^Fg`59zsv*Q$1~7%nWoefuWa)Dg4(uf=6%-O|J{K6eWNP@ z?YcTG8bQ51Hr=2Iti&7xd}+~VOWR<}UC-juO4*&{y3*b0sJH&t(-x>0;8S;8>vLEw zC&sD5n|F!)!rOde3;1k~Yg=805@dYW<87m0Q7pMU?E`JjV#-x?wneZ)i8sNMq7H_| z`eN!B%|Zt!!vTvnZWu}X^0EO>ST!u{w;351qBuqW_ZJnqij)Pn$SjrX&96(cBfp0{ z4sWYlVVJND_j$duxP)Emy;`> zuwF_@WLFMKNa%}Y!#rrJrOs7Ho2Tz&TQT1yY+hCw@_iAd8^1-&A;+bz@pAluY($Ck zUNB_|8Lbs6Nluf_1pI3jNv{PLm8dnGvzv~ zo)o*cOU8v_0xdWwl+L849!u!ghvaZhM8QbcpOpLm5)+qPx+ad??hGt>^M#E$6Rr*I z;E@e>P@AR^OHkPNP#N$@$pWoswQU0ie9U)=lRo%NC6%x=O7HN%DAesoR}MIg#OuE6 zESDflmW8|xrvBI(h<`H`-sBIA{o(kmV)dUgLvU#8W+5~GNdMbW^!M>(!FICGF1(m( zq}@8^4AOLOtJ?Ruf`IAM95#>_rOH4;@s7L*heuZ*$M#E%+(Kt^iO6u<8~%7@zCkdKqA{TyWa4TvjqX z7?&Et{(0e)v)LaxEFeeGi+YIdMLQ9cF?RiY*0^@JHVC#Ton@ls==;mv&T_ z7c^i^1;gy!a2Rvt-?Ur2Wd8!(Fh$QYQ)LDpFILW51`) zLw}nleWPXH79_F|rtbc>w^%y0(V(tJ^o~KexrEQ5B1b5T{K;!B4xL7{05!|evT)MU zgm`tBxKV#Wxo!KfgMN{e^_oC55pQt#^NOVxGe2R6{=yx8Y`gG-T_H4U~t-M`l*_SXs3=%;!I7et=UYA%y9I=^%~AJlmCA^BIN?wW395i-r0*raVp5VuH)4N#%2+kri7PBKI`4j!@oOix0^=3 zU$RcKN?*E2U9~R8GM=T?IAl*?>TZjrXNl#hTfN=5{OGrkVpobb2O-AgWs%O#}h)ZYtPor+1p&hx%Noit={NC44{8`X6Uf^LEP_R<-yOBQ=^3|iO zncoj>y3HG$GT8Hr6;d*OglKCxhF>`@iOe#_NP0c;C~CS}uN;L-*1L@SPo42+U1z;Z z9O~(v^0ph+O{m@lI{(&*S^l-^^uMzA!glWU;)fSIa~??>S%_9N#9Na3ktVoWSmRU$an7>*S*@`sv=P&7uZCcd&0avme6SAMpPqg&3%K}2etEq><5EUQMkpc?6Bxd!T8F4 zYSj*R2|?F@T`vFb4CxI_Wz)Qi^^&tp*aGg zhfv#oUg01YYsKMS>%7FClHT-aE)zR>NM1M<7M!?`kX4YR?Q_>E=TwrMI>7E;5?335 zFI$mCy_}aYjiK(3^e{@n5Op#-Z$y-;V6!Iu2ccdg+GolGi4DK!qAhy++xNvEgELS^ z1Ski~dU<8BV~tTL#r9^?3eHw`Id)xPO}fkjqn+@u16%hlC2Y^HIqcT2ei1(1;)tqh zyXPq8F%t}hm{D|7g?<$K_=puhS?k<7`GnsLaxo(vLn6b}J@?@#9jNFxlI1=*`JIn5ZPK&R;b`|frjeXw15@<0 zqn2{>R`tr6IbvF9kv~O87Mw64U1wF zuk9I>Ii?OPW-em4O6@31dCKefY}d+@!^#VU;firiej2fbz`jH^l75> zy*Hf>3gxr!8@lh`qK)l;T$3STPIlH)?D{;Vu`LB9Rwz2jM~NZ0cj_OOOWrF;V=P1I zobTEVnSN{8;AJ+JmR|XLuZDP(is4M~Vz+^65h#lA4*mTJT67P7xN?}HKiC&!N2DaB z*;{v3Y>Ni3kxYcAo<3ot=;@`Ejri>_*>N@N2YF{NZH`KZ!=y&A(jc=E80LY)MfSeV zoblYnPjd+YFQL2B(hu!7 zCUP+FE%miW&to}J>twz>H=6{nWCP~A$VPF$;Y^y!U1(#QN{x_xYPI6I;2@I%4}*9W zXATD$7`W1=o^M5FU@&!+APB#yP0^-)|I^63jYV{#*FH5)4QI{}$l?c3N)oqVvI1u9 z9SmcwEyrRVgw*hyb+TV}x*XMdwppf}a>EKji zZ%~$$V9GR|dKc${xcX)tR8g#?j5AKL$y$DyOFPCyFeDQ0| zspai!dpdZiEca=35hK@HuS@E1v&CS2&3Q%JU{Gb4toHUEmrm-I<$+tVYuC?h^&h6# z&@67X2EpN2aThnW_a!TR%W=4({$>2`?b5!C3@Wf^~=eV$g2Qv>R~tl()k_pw(eiTVXN z7b%HMac8JaQKrTfSsit+cE1tJSP9pf(teNn_tqegx2+kAmk#0VQanv}FLe*3Gl}KV zQ#HBgzx}*Ft8)@ngKJXd>#Dla1O!#oh!#@4V^k#??w7F7n$>Sd*YXKylQS@TNjq2sojnq zT$r?ak30<|T?$NL3)W9KjNhVcp)z8jBB zYE)t0zCf$qKxeQKQyx>&GlA6bC$Pthqwx&HD_d-}q&28tewO;$0LjPOR(4DD!pf{d za7TqPGKZkbYJwN=8Qe&r=E)0B9Q@qyzYoQpx~&;ZWPb9`x#|$o8WSk1SC*TP+*@dl~MZ z(z9x&LmZc5vjWk`EmFCauh=7>dbgmvGe=b>Ft}V!JN_3f_BMOf;axGmYl|o+74DT% zmEQaTvbEX7Q~u_C2xv1-E6}X6>51vN?nX%({#mjr6IouFvU~$n>^?8E3*8RfweQ@s zWM^vg+9HFT7L)BguTAMwAKCGIM;qduieL24W!xG|>Eq_n@QDu{+-0KP*2k6}KdJBj zp-1tNfh-(~KNFxPwxY5$*1E+$*Cmn4%UQvx1eR;)Y?!ZFxWQPBUm_DO$gMH@jIp&4 zZY!NA-0S6&k5*C8jJrK>wYYROXau=XNlT~RSV~qZE)_hyQA%hE+i-FmvrBLel`T2( zFO}XwTP0e;hJE;Qd`+Rs*m+_oebSH8U2ZE%pL>mPW-6C2<=zJ$H&0LyK4&Yy<|zOEs_AUh z%Ia@7SzG74YwJ_7YKn!TP(JXf`@+f2M}}amzfDi6^b}kdRqto}6#M4~AdbMax3Ekw zvS;w5?o>eV5xez)Q$pE2TY$w{PxPV$50XKHz24l~dO~=?>D!a+p8~0bH)agJEE%a- zwI~m>=4&^fZja)NwE5ip>oK`B2_dZa;34jTVQ6){&_C{*X9G|PhNI{Pr}fuC14L@S zuu;RR**#R=4UVJB9NQBO^jvr93*pDlEvt&`!vZSN8vZV=q;;-~Dp)i^(q0GYlYm!( zp~+9lD#0#r)%SX&#`shnmXPa>4?OA9w(GQG!`B+8|Jb2L>CCC)3%_^@^=+>|#1H~t zr4C(DNWQ>w{|XR;C?&}#QoP-=N==xjuv%<4!Z4x-FKy?Dhy{Z_P+IK9e7L>5I8GTW znd#!*v<5=GmXZNLV8n~bo%*qxx5O*vF*I}beRJ+aHUVh7MC0lRzf+sR3Enw99YoQ3 zH9VtQdaziLF&Wpp(<*Id8X}bY zHy0R?0Z9zOt<5oF8DDLNvHI^UT^R)YA;`1J>{8>0r<)H!_LNzqn4FiR_fe!#y}6{EZ=|T1U!oBApIyCT@JLVp$@nGQ1h>xUtI(2u zku$0=eJqE5{nPIy?-J}1%A`DN=^z*nJn4YI%B7-l zjM8tu?HFglzi>$u=#dP>zxr4K->fT&)6msnY`Vmw&)aYI>h44UU-q4nPqJZD>j@+Z zcoU`Es)zBQCNp7mGw|;L`Ls8%&()P#gl?gE+ob;3zkEcHURUUP%?LOwwiql~fI6Ui%o``#CE zz&-JA3?sR0C>rIsWz!MGhW26lT-nb++PiIQ(#_djn2@a&CN5X_6jhzIr_wmI zpCc%X*h3{=zo}Dw^S9SY?4^gOYVOECd!*UzRBCNr6`fx=g4nhokJBhy9yZ(mwW@I3 zrDKaWnl6a*&>`f; z7$gl>1J`g5O@;jG?fWK#NlBH^{)TT`bgo~ctO0fLN25@;U%NYTY{;+MG18FacH;5Z zzbqiMTzkEL!Ebq7h#-6tG|x(*dj}+_IO2T zy`w8uYEMb>n4C#WMRFs=qT+;UHTmSug^&1b+Z#ukw1tY#y-k)Lgc{XB=E_sXH>(lKOZEi5!GE`J`hOJYAtX_juyih#riUy~NECJN-80^yANVzw-|jF&&Hw>{6jv=IzKP0^<~;T1 z|BEM=G6z@2E%&xWV>#K(Ri0WLGfqT_bs#v)Iq4Nge$5TQ{Xuo*G?wc3I~F}~;HYHE z6Pm#*^2jLj(g#!St}r{SBzK(T~Gyg+b zryC1btGK=+MqopBQI1HB&6*xT9?65`w$z%k3?04hl}!mt*VukX9^JIXWP1`nWPw1x zhvMfJP{=Ag3OD`ojvCyUjGZ+|JYw@co(erN=9{P?N`@KvqY#gCP3T=z1lg~l*|%B8$hU%IhvSa5MR=SF*(c=-wKqg% zyRKg>dnfTKK7`~!b97cpCU?_517!>Aojvyfp)={k&kc$iQB{>}CRZA~?dO()lI`gI z^NdX02Q0Ka#fs;dS>P&$$QQdOZM+4%beFZ*qt2)7TqcHT?dkfeyVxKI#UqOjw;;#g ziKQnLECvRv~|^lLHOf z>0@^Fy7!U0TefJ$>es8M7)=7PYx(Sf+2H0NZWx*U*-&cv2xa!{pTCD*c#CxZGQ^Cv zl2b`yLea>#7kXczqF7twih71!VYdC5H1?ZXmJTW^!qzsXq*11z@Pm_D3Zwt(;A&~3 zPs+=QsoEse^qO5ZsyQ~mNU8AU6!{Kc@bw?r(&Uj6UQYu`(ipgIh7Xpx!!gw}ye0eg zaE*3lG5uaf{T)yDkNtnTQ^RR76wec_a(kee=1o2Vtp+}HhBwE-#O^-oyfy5Twb7U^ zwI8hEMhT?H?WzsnkSi-CY5IM!GPuqGLgN-|j$30p>f?u(qHOTOcy)}Kj86$xmqfEE z3!@`GJo|&fSJIUBe}N2Z+fR?qE}7Y^ zl5lOwSGWYY>TYcww~bYJ?I<-XlZHdJd@CT2Z=)c-i`OA{xJ}j!LS~+OI|9}SKm^%m zN@!|*@jy~=4G5LPSt=~uvnKFia4;G$^v4Y7K;@4&_(u8IxGsPK`Q8eUBaCJRvm5H# zm|90qOWr>!P3uYuGZ}w+b^I(*^HT%q?s#7gp6LXa)Sm6P&ud`17LPq}ir4PoBAm8d z!*xBpDME_0GzEIZvFK~#Wg6)aW|x}YM+>=?S!>eZ2dt)_^4+=fVafBMEzZQZ^-kaA z?tL1|pP6)>sV`9$E*3VF(t0x=y)i>Emh=8LbzgO(CaFp^TfckY)vbdE27!^(7jHyC z*a7UE7gsX*Vl@n6nS{3je}184@6<1QTDR>sFsc7WJo=C4?BwAx$IY2$mAbRwftzXy zqDkALrHmQF^Fw2iC-w%-tJEr`annK%k4n?KXu?b;WFeFET5mcy0>eT_f@(-s#vQ2v zl$5W_cl%zlfb2GLpWVMaqx87;lvjbJ?Oo-PcXt70gH`E+d;WX8tG}d)e3@Ih2KRd> zJQX=cex6Sr_^x=%*q?IL9~E!@tQkkVa07j#EM)E<@j76(-TuyeYxC_cTxQn~`6?At%Y)2&4Zn|%+bH$z!)0*@ z1s*&CR4(&zOdS~-m)=FO-JFMGGn;fp_Wf&9)DZ0WZzO}6O85Ow8g;fLo4xhMJ$)J~ z4maOqy{k)B{c&}uz<=mV!9?9xWcfJP`9SQcWbw!S9TrkBUO~CppwUsN|7ra5>|qw( z<~SZ9m{Fq!+zgtZ?SkxIEz!Bo_CN3*^Bn8C~iZ%q@ zK!JNgn1HMS;wiS7D4RP#t8PwkmA+Szt5`TmwLq{>wMa|A&~zk#!GTUpHKqZ}?Y^@K5ubqYV^vI{sJa#hvkky} zatPb)<4a9+$woHzI2^mOtU=a$tMWq5TE9R)w16O~G=hY*NJxW}gmia_G$Jr`haeJygaXptIfS$V(%n+h-Cge+@c!R> zXSwdZu65VUnR9l3d;j)2Pt?{I(+Op4ia$t1)NNt0y#f-o^CW9oH&MNUdht9(-eda$ zYg?v+Y+g*B^Nkr?8|4k+T>*MM<=0$0=su*@>*r8ZhQFj%JV)Be#-CM2i))?) zrwWO*M8r8|!0IV}=KaVRrS@)^;Ws6iBoo0!=Yh4bE7+3}cg50n#Ih8t=Epz?g3uQ- z9NMH;66tKl$=>1CpQ>3Lz`A>dFH>C1{H(QNwdtArb56XbHWW=a7*Qri8I<|8D8FqD zc?(m<(1aC&4y3MZ{H$@1<|AZj5-qgq;Kcn*h?3!kuqvXI{1?&Y-u_Iy-Bu*SuCg~y zg$k>h;l_*uK_ie5F#I$4YsfvQSqGLav=Z!p;rSyLy`2sVw2|2k9Za%Ym0^>ctW`_H zz`r(5rD0@#vuTSY8sTcFHy%$%9=4<8eTb-`JwLlmwIS1;bLiNR=m(AE8fIPwB2o4* zuO|?ik^&c<73qZqE0p4?r!1~==VSSUFmm^{krA14v%?GSfjstgvW8^PCMgpoFjaia z9+Yd^f>;^B=Q5_stS2}tobmweY=uPODhG*8l#T*%iWWv9@9Srk?s9L_L1y#d?wq6+ zUh>D?(L;Y53F#6-3@QSCD_K%m^m)dpDU1kyy&`g2XpE{aF zu_Skn7Vw5*(}jsTEMXrL*tdU@b}}q6izGA=AY5lLV(6%`Y zosx`vS4~xxFkpj!kili<1klXm4wy?WDu_E}^M1dKp?UX)0n?V@Ll%Q(=4mN`-PYf) zI@Kk2&Tf$+7ZF16t!bO<1SA3WZw{2PAglByq48-fZuOqI*Ch*-Ay|)39p#7v{q(I% z%s$s6svEXrXxiL{QV{zHK(AALa~Vzu2(~cVu1IFtSkpL-4LOL&jAN~y+ne^;N)Rnu zi6spGg*j95gW^9OTKmaHEhzp$&AU=L0yRPin*uRS1x*%RC1G+$chIOzc-wMFsHxUI zFN_+-!V8in6Ri-XP^6S`N2qix3kuV|SAMY7VcbYke2e6xz9o(E24w3-P^ApjJ;LS% zLF^GfB=)aOisupzgjC^qlnlcx^Db#~T2eMf0(=AlTu&tg1cA9&bS`j#SZhU@cEQLP zI{ZB#Pl-%2Sf(8&3N~ z{Ssb7NV$jN0pr^gv_hC#QnW!ysqnn<&dls`-!W+5V?(g}#pUK0m>(4P!xDREVuxg{ z`Na6VrTIE>wmk7QmOLom(@({Py-{nW?fN90sXlM{`m)Ce1;?T5>E^9tG7P3tg7{Dq z*jZ~$6E+EH)#)M;SueEH%{Cez`LL0j&*ms~ZL6f%=$!~nMN#O)5M6H{k8veAIpWG6 znaYH4_gRUOnxZPUZo~|@9jY^UG3(DSld;Uz-^#9tZRAtn^+4fOh=1l<3#K41&7yoS zg}LWzyvI!&i?W%apO%_8ecKB{Q-dhwQ7`g8x(`r4J;*|(riU1){9jd~o(*~~kx1Ipyl@v0ql)2@*1tD}UZ$T~rU1!)!Qu)Nn^Vf5-h zEbrsWXFkuQc10-xC%xNjRRsBORoH2XAdjjUU&#E!d&uB@1d5UCW8$mzw-NrxMnqr6)0VG8VCD8??1Qa@#evKia`ZODBq^%?tsiUY|9#4&FDmpY6q;=V{)|As14 zIN^3Hvdl&{d|`9uvmEGPEa{ONKy_P+FUVsM^p2Tq>s4?oaN01QC>6BAKqHe~+1j8X zCw0mU%0ZN87~yL|vOr*o(IQ<{)@gE}NQl)9^{n@2=b^iTpopSynvrjIV)hbv9Avm%~_hI2xZHB2^@RFqF1XbCnpDRW6A&|B^g0cD2B}| z4)$Dsj`5di)nv{Bupux*KQq|-oqq{5p@F$+047p(Sn2I)epS{E{(kiL6PxAMWaTtv zADGt7Pmd7N=Hg-)r%H6%tBRk%94w<=65Ch8^SesT1sq?XR_!U zqQ?3Ak?l6L0Jvt$Z!R7uPW@(Uf4+rpdoj6I?s&@T6yzSOu%itEy4g`0B!oNON7&al;yUX8bpAd zT}1NjnGu|jqkKEf2FBOR8x-8#f7sUTNlv@2e7Cf=e$Dq5pfhU0cprQs@%~e50HVl- zPD95m2GS&?BhU~L^jzix&yTAb3v*Snk1vEoyiqRqVA;U1YYBLuJp1iO2!f8mNv!$^ z76TSw)4GdI`7MdPDn+phIUrYoxwtiz>gf4m8ZaGobw1Vz=Y^jI*jkn z@-nC@n6!!0{<=kY?Le7{bvG168^noVq31MV3ZJ7~JTm9iP*cl9!sZbHl$J3t*y^tK z7fek55BL?jU2Z+`{JUKXx^_L^9)i7`T-b6?LN=HQ?%X+{C%XWAxV#5myZ&G#Ow04N z!R6maP%x@SzX>W4@Od_>AhNVbz=9*&SY7Q8e59W}IMZ);BxXmFH_MFh=A7?GdC^xv%tB>YgaV<@~an+ zCWI7Y*=*iO0Apm?Y{7_Pn=4@G|7`ZFj-;>f?fVi#MtF99*e~{A4UFlzwlwfMEU)gh zQ)&49Z{tBPf2GISeg&lhc!nby2H#6|9Fi{*DLi&x_9XIh7~OoNPs#xp zImsY$C(V-vfPnJc(3u4+ug7thKdyx)V8T68pv__6N#QHBhRgiSt#^pqcKM72U{O5xRJ6O7q4KcEuS4@Nrq@T%OwGPbu%EFeI*h)1aKWlaENNAw~j-P2t` zZDqB=+u!CxgTMe}jM>Qj|3h}kjq8^hOLUe3AcaAt*VXy<7O5j(p?wu640Bs z{2vJA#Ky)7^{q|xp2u4iDLo_FJV=DANed?1=8Th`24H_FOr9a|noRBU!$Pem7-k9} z5EL#?%l#{+(L<(0OSu8m$wwASN8w{CNiUeq*Dl&mp??>L8wbys*PfII*48$~&D4Xn}UHF>a=mLJ% zogshf<&uT;FduHZ8j^)c9>HgCmrk)rxmSU-ZOs(r<_@4d(*1la6y>JId6LNSSp)VY z*+v$)b>$!ea047FSd*wXP-;>X9 zxZi8*H88l72*^!IojKGM(O?v0@NtF7zk$7!iu+sHMyo2Go}RbVm$sE-GJg9v`t)wUMMi)HncH8;JiY!m0_QZ7gSk zESr*wuz%&&DpRSWI7-K=*i*~1*~FEO7_oJgcB)A4I{*NVvvQBxW2t1n-lQwDK0C)w zPWq#dV&WiNK^sP#Jjg&SmPmNPFd6!YQq^F#j;FKfjzf;i=V}wCoO=<-0Og?F#~VGrSYV8FbNI-T?+?iZl3o& zr!jvsv`5aCnreaG>S^vQNQhL4XneGQ>ZSrXsAmDsa!Owu9w`lQ&AgZyhzLKia*S~t zC(?=QeR3J2tO3~x<;tJ#*>wK#rtYP=MB<~iFER{W6ajP7;0UJ%`TACv?}JA!WdQ^k zoyvlTY}BS|I`+5@0J(tCY)8LvgDK9~0BS4x6bP9VALIOX2?tJB8ZPO9#-)okmu@rp zD>W0~A4fhO6VrIpE8}H}-8Wufo5yXq*c$JpS7fq?5Tbrx7HFQ>tWC-Zcj3(F4H;#> zH^uyypS5dI8M)1ZMpx!HcI5i*KE0ICkOuBOm3^IS;7t=p5!4wxnf+D6NdM)x5~G@iQf*~kH}^Z+(c%0_As~`O}d=UZ&D~A_Lj?>YaM3pJG0fa z4G-JNUz$xFM|L$DX9c|r-MGZ;9{pyc$oRNn?L(0P_>6b}Rt>;}*WkG}`YPy$$PgGTB?jYz@u*IOWyIX;teu z`B6EA9SA%Q9IoVD1Z1j;L(N;DUyIA}2fm3O^?xeL z07f>&rZt*UaDxUxmyC!G=U0yBq?*BY+ehwrQG(HFH%$<@o(1n@A1gh_`c&a?&bv3( zM8@8>+B;_A=9)jyrDLK!W_kFrCVcaG5o&K{nI*#3(T*IZzwu|kaiV|JjHkq4c>Omz zePRkA3rUaZyBFHlAC2l8tn&YYpLd`8smW*ZapafG?UZ{p2G z+1=fP&{7PjvZs7e;PbarWdcvycj9PeB?SUN*vu?jdzhAJ05fgz3jp3|ASHIMJ1rSM54AskFU*QF$4VY&n(qiLfXke(0jEtaV6X zcJ+$C=Tm|J94=HyUN=BTZc?|uCZ!~^6@~_yGjOTy`Z6;8K)e$ona;OUcK~NoSsg6* zL}kqX4BCK$O9As?cz8HNiY5)vNY{YSj{pIguGhCSF-gD8S%BwMaJ@=WtB#bS+2H-~ zb6%+Uo23Elq;pY7q(M`@t}w5!)O70lWfB%-QGuD~b(g|xEm{sFq8R09?~_VjVkF;|ZbY1(J`qcX3$Gx6Zcf9I ziDSU#y$U1e=QjFN{eGe14%ep`A#|R@TDRM>oi_z|WfJh5gI@ToLw~F|H&=RGUOhKK zzE9>)(%9=tHUbP&!oP3rldl19g$W+Xnn8O*2$>v5wak_lub<-nVDv!>XPH8m8VG+0 zwiqo&yf53i7Zry45_oR%>8q@SvQ#ez&D!;EISn4c3iR?Fp7-=v1r~Cxh4}Sf+sn+! zsr!$+@vpmKoOdlV-S*~)Oi%+){^=phAc%gZ>jeQ)8;(LH;j&asUV)>?&N~#m(g2~X z!hIINLZP03AH7`}Zrg`bnf~NLF@J>@h;L`73~%_C{)XE0bTKR}EZ%wr@6i9@u@oB$ z^bkrw6)mRarv=E|V@=ooczVWbTESysXr|^h*Rpg~9of*Ndb;ef^P3eHtiSeiohlQT zs*EuZ&8WS)?O800j?6obzfY71rg?_rluZ?_HGnZ0$dg`gJAo|J96$uOW@c&}vMB(S zMt{K#HJOy#`nOwB0=LcjLHv)SM0eQ9Wc7O!$rFTqIQ%9s?7QLHKU#trvh%(gDGHxsXins5Fmv(Nw9V8PReo7OY91g1}&~EBf z+Zna3Ou-5P!$;6)mZMK#$m&mlRyiJQtWYPel{phwBif2EY-GeqV8{S%cD3FuJXz4) zNqq$gsnj_}p}+5Z2=bXpWy9v`G{EPWa>5z9)5z^!Yp`o40XGUE-Q< z+9}oFp+!absF0r%>hR8D>KWCF+=%&cPFq95)OJW2ENq0wnUt?yzFcFCJ zkZo78sg=Zs%qCZVn4Ha6|WEGlASSB0g-yt(>yYoww z8d~P7m2#ykPaE{Wz#qt47$fI-08fuU8SUuJ2iW6F`yVo1-d8K<4o*HGg2TGN_0$c7 zIhFv0T7P_`YxUxL5@BQR=!5ME#&fubZ97@zg%xv;2<{w!Ks+Zs{YmV8idvqkZC27>SgSO!7ecPR@$wKXe_YPfo`)-Vf{jJ5vSX?w?1SM8m* z7?)0L24weUY5>UAz4Upt(v2k=ZVoplKXaK z$DGl6XABd*HXmSzeRrC)U}#r}=adHgO1l#$Q2PPNv2VpmB%GzkZ*&0=Zr!T_kK+pJ z^L&^;?^upO4vJHjovfeROP{ox92gi*) zudu>(_djAgHRrvb*rAfL^f(D5iJu*BCcV_bjJ^&xLWztS`P@s}f9wB3NdJFe*62aA zErx>dRfRrC!-a~grWmpl(*WMl>0Pl_x#Vr`bC)W;+=KIM&d<+<`f5NVfe(8J3Rr=n zsX~2PwT`)7CqHJP?||OI;Wt~CK8aIS#&mj-MBk~BE!ejixJ zxpQs+!efxdrUiJy^HqTM|K{i+ArW}J(Rj^dG&|OC%vBGvcZQw!!0xTVIaDXG1s-^r z-~wd2a180dV#k*sRkQ9pf}TrE2GVCZ`IVJwZ~{KTNAcPdJwW*c97p*{cZ8a^w8%}( zE;{Crr>H}8^mWp-&HG$PwT_%TqJ%aZQ506=CFUYy^4Se7gIFL_>>O_JHZ8)`%e$Ia1r|m%aL1 zR_?XnMRGZB|JmNk%AzB8=w?4}yt!x$xZvdGUWfnJZQMS8*~R*UIi1TD$=3%IE!#lp z0Kh_7tLG3|43ab@;C6fMt4a7k7hB_>`r@oCNjQ~mXa?=~r}`GcslARB(AM=7uNlL_ ze&0}uN#nulpshqbpCm=}q2|`|ifPRNf}SvWqs^LZ(COiXt9++HA}M1=3fq;{{<2g0 zk6HVppK&JVJ1gnu5%lTjr^!#WPcMRdy*Uf|u36HGg~$I@!wS-kevUs?|g zE1H>Dvrze=Wael%5|@7U6D;s!ds0F-+z9oVRz{cOP-0|BLF09VBV^!LNCJEz#$PA+ zi-&XT)<@haW`uy`^Q~okQB_e%w;0O0rRxIb835Er34N~H?_`3`xUL+zQZ~*O1Fcqk zMd?{VLb^~$PD)A&-D`cc0f(N!d8z*2u3r$551CupBtu^n;*>Sp& z`1cqVa>XWn)>AoBD#9(gT`HMFxk@pxhp#cR$QufWMmR{%$87n&GbU#a9p`S|pw!`^ zm!s97I1C@C<_^ExzHR)6_@P@cx63y>=}E&@tzR19CmEA%J+= z3XaH9Q4q`o5rB%-Sdm}LF!U_Mp)RY zjt_)9Np(ygFhjWYFIBWR<+xF6P&ocU4hR5J_(`Gk7rHf|w8L=RF<=49JLPd*JLLjS z9GsR6wo0gI75#cs_{w=#EirS-Y-DrmQHz< z^P|Us5x`{OXwW10ViP}1*5BtsBJx|)h?Dk<2WKHS2wXm?CT~NqiQA-@#_q2Uw&f~L zRf7dH`IcS^LyxY4+8U3AF)Cq zyb?mHH85CbWS+JQHHav5Jo@`q0pId@A*9y;qMK|)YAJ0mwh*;lwHaK{B>(Mqj;>rj zrz?90!@f7&w^Qx@iMoO3*;N_>$j7{O$#=n}vFf4%w_+$@2mbIM^4Pz)g5N9@5Q0~Z zgrYd#{}pHSP`U)6QaJu+6WIYgts)$eZ{-DGlVvfT7KL$ZJc2=k!>Lz@YPfoel}_@) zI*eI;I=0o45?bQa&7+|A)9k@1z{y+7n)Mv66@2@aJfvu3Vfj6CxkSl|r>YY6v+SNv z{Vox)2_Q$J4a&E^ZT$ca&O2nwF&rXy>jIXjFj$s}&ooj0MZ$1O7i~+~mP^-)o&pHQ zDU0hqDkI!ak?Ky26@cj1&{3#krHBPH>r9z>tK#eb;7Xt8nlexUZJfjG9z{;w0vg|+ zEVqiSzjPe%64I;4$C>1kw3&BNvs2NuL?`764ApK?RvFLXB@iC1+qijRp}$UAW{$vu z|M?LyL$^^Owp^cC^tnX-m|pd{T`h!DI~okBlCjO!N+&2WEND%Yo;y=>xLf znIalHyU)L^w^gatpV~mVx`)GwSMod$jr=K)WkJJ&QKimIbyG2oa3soi87c`l4SDBo*=gQRr-1Z?$+9&nB6>Ec>j zA|=fGUJw@Kp%)pyhm<6nsu`Qf*r>V`*Jf5dW5@VP&dzZd`IYxLyYz|8g>&%&v@!uY zmg;-`TKOj;7BSX$j+WwCFZ12W*RuysLDyw;G}4okYis4+?}n=)^2XTlonw^fFRoc6 z{RR4R{_7?xB@v+%f{3JQxvLfO0y0pzM_m-s(}#oz7W)B-uYVN6#g-^ zHMGbrOY4MX>oYsujP^vdNHy#8fmG=ea?ic%2l9Yb7HGYdV}FZ2;s9x&A&E_rk3Rx;_PXdNXp`1Lu50-_lFBA``9 zf;^>)EeGEk#WEqJRkp}emk7cx4i)64D9}UUwPT~TPUf02N`7VoRtmWT`(W2H-m2w_AJ)J3<-7>u?P?{S?CIe@T1DV`PZxPB z?%(2wr}8wY5Jed`)r(26xEto-=!>ezEE)EB-SP`%*DKOhg4z##qvvO{!|UnZDWj#Y zejlmjzF$Y<;Gg&e$ngeaInFyV`0_K)tI1xY(;1+-*yJ>_q$JPEh$Z*|DO zAgh9LYfjwPR zhT@f@jldyeAiO(i} zI1?SR$da<*$z>V(*5GV2Mia+nQYfbODqudZSn7%{@2lD2boPtYa9W*^odqU8aPoEO za2HO6d;(D`1oAs^i3fcOwj>p+05k1?vABZ5rsDm#8TbCPZ3`Iu$;`vH*Kv$|ZdD?% z`mY}fFA_*bZ^T#@B1U?N&6t!q-s8Oe%D=!QX_Y)DE>}R5pWBQcugtEm;dffJr`rqxOo*~z1nX)~P=cuD}0BiV448bQRoNs`>s8W$u(6Hd@c z?^lc+1(I+re>Zr;8I%;drLj^nVM*>?0eS@+i`W&5_EeDv z%ttMeU|x=Yo?Y!Osb6_XXmq~c*RjoQ}4Q-=S0knLs0&kg(@Y7fUZ?(qaA%i4_>0~j` zwWSs`W?@;caC2a((@E=8<^1cgM!0Yo+O9C@mwOcW{f$D>3$Vh@Sdi)J_N_LhFcoIo zLwo|z>SHT6ZfromO_bWN7%1e!S3FE_=L3%z%S5kObu#ZDSa-eZz})ZVB;`@~kic-6 zJKEBWEjk*|Fs)eDM76?%2ph#1wxhmb#qQO3a7*uI##v2JOA(!(?bN$tP$S<}wZ@~D zzwkncIyVJ3Hnqy$bjZUSbBTBsRC6rWEwtZXZcREiErqCtk#91XnvT~tcFmU7%$-0# zz0sTWb-M{j@jCa7B+>a&n_3xpco_e%RXGOZk)H-x^3KbvJKCp@{+*mU8UNnUguMZt z)&|JQZ-%Mj*baJ&CBLbPy%aVkReskJvuNC|YLd#$YroksqT;7{$__@7{^>vZz82M_ zeEHsrG92JZQrSiRV<@NrHdn> z?7H!=L*Q1bMH26ZBLqdaOqTQW_Q=(?JX9L&tR{zy z36Ct=rJ^>9)rS^U%HpH&E#N^Tm{IaNd71D1G*B%JIQ`9ecXOenVVppOWQ zH_FlD-9C^Iz=elIlkk_iH)s|s`wg@qbSM3OZq0c{vtdgz3u?*+BJ#cNB#`6vus&dDOT1LjViv3sO%h`lqe=c zBf+-U+Ler`y>pGOn>|AuStBG>S5y?^Ge!WO)H7r$Q7+af>xt^z@hDhN_rZk;tycc` zk>2fI>!Fn%Sxx>UC7r@$j@g|d(%Haf<+nVF4dx^s}Wwb3O!@Z0d~(ed56dFF7|)YUZ3qiCRU z&OVZ(xxq-LYxjH|Ti_EC)2vTwk%pMXjA;AzuJIV88vODTo@jeWCPWG8p!wlxX(+9= z$Knf5qEKz{$K|3A_E*?oIThx!^YN74^bTp zI!BYn2utI?s4ettVz-jvj;=B6Q`+%K51b=9a@`su)ZeVF6K)_4x5rj^F{kuLda+(X z@xBFtGo&4nbEmz8JW%1v3k`Ok!Zz3QvE{s=v&~QXK25)chXfReFFsTpIt^+u`n*b$ z{uMQ0aBuh<3CT|hFa5VAYBat@svJALLHtS4AYw z275QSV1=(*Gh4nM(hB!WWrzF|TgIx+YL?{S;Qi+ZqQ!{?*D<*lZjK$Fip5o_0wC(g z)x}eTI)kZ-PO@T{kLRv`qOLxk350K$3YC!{ZbSj}ZDaj7S#9%%qN;i^TQw%#W?9@?+4z zZX{jlDEYE#B%at6IZ`o_>lG-|`|eA;UYqZ)L5AdwPv~I zot1d|3)anMD|HxwSFZ<8tJ<{fq|oY+2wY!pFI5Jj2ROKD>BI;n+{BPiKUIEMCHhAM zGfMP5f2^b77w0C+N6^VSCD1gY4+zp9jq`jkb1b>IK6+17Bk%ArG}e5KHTEJW07>DL5ZGuNdtekxLxsTPP`v5=_?jxn1F zY- z$7ym$lPv(J`uJ3y`5o3mSHyNpQYp2Lk{P-F`^Jr+wlCvl&II|(k8Y0Cqyn4xvQr=5 z*)K*W)FyFpHK}_D_cDqqj3>#OhFIk>?{Nm4aw>>lzmIN_s%XNoGF3Q+e3cO$m##Rx zK?VF@M8WqskOMPc_pIO&7d)>1TQ;ROVSpWQYwUbV%SPWz0#UW8&i!MMOkI(dnVSlMKd1V$^BO894Vy@k-wyP>teFH|k92Yf5weHJVq!e)G5kqPU1pzz`wN=}&u+re*aj*z@TE zPQ4lhXzczYOr@uI?sA2WCY{$Wi6+Sqz zr!c&GVQKp&G9u!tnZH6Inhq97a^j0Gd{qhUX80z3mg!EQ)=16l zH8n|;-Crieil3yZ)6l8p{EL>(o+}5dlA6D`@8eI4d|cGPx3TV;@GhuC#ev@}A3_nl z^Rk7(?s`QgRdt?Hj8>uW^Q)Ztw?Q*EaV|lm?^d+IC+K<)s5iEpx#mOd)mk|NkH^2~V)T7aW`yjkA{fiMxJZqXgq?upfP(2v0Jj;Sh2x%X@=%S07p9 zS=_kh0nwt3N!T=sAa_6!^8sW`6y=$s3#qwpr;Sy?eh#AksCrPZqIuZ*oG;8ih3mZZMy6zKFR2md3anr73E3HwlYvsRuD-LdX4z2E?H z(u$gbVUw);5>LLYgu6dR8{z)q2&(oy-y^L&AIhDhY{YRVRXLAhoV(CL?at`PNPQb{ zQ+v(&`?>cM+{jsE0Jf6>^)H9c2 z3?gh2S5>Bx6BAV_v3{TyJOTEP7+O)?N#{SmG4cEx)@pGNhS!YLJeeR2Ydl zlP9^OjGw=BSPe8fO&=nsN}7~Nl=c@}9Q+vZ5|2|khjchP_@ z_0O(ev8m%9Mv^CQ3v>?m={#0XtJ3O^h?uchM5LC9qt$%_Oj22Azej(;@Rguo>64}H zv#YJA3Q$OFBzeVZ{worbgbL~OYgD-Zy;HY9+M%eJ0dW-DR;fi$EGu2=xJ(rQrz@6yK%ktx~!qS zA8erQywC5M678SyB_Q&hM7arqHj4VoN4po-Q47;6ZpWWY54i6Aixg{~fw&{@t+tzK z7{So;d^Dfoqi5|@xJtg%A3g+ZL2B%iWqZ+B>mizD&!9gsVp^LeveP`L`f!D+1x@s> z<{WZnPiWfs6qtYhmPyY-oD+SiH-)?D#nfV-bc0w(RwWiG3qTHH*^fCj2y$NggmS$AAl3Qy_Q-q z3_)N)S8=#Y1AO!|NERa%-8KE2HaRR+qv-GDgU_tZrN%MBe;yZ)7+TM~C)(mc#k#nO zFJ<+}q3l6=VDhG;ZSX+>aaVpjAniqU z2?dN5gWMJx%hh+18*E7qG#dU^x-t9X`wbQJo5l{zt)#`HKMgAr{-tVY z@Zaa0JC}md!|x53HrR)cNS^itWHN7tVx_RbFoDNi4a1rv$DH+%NbR+MF!4y5{u|g} zkeb+e5vz_N!w`(VC_JBXlm%;WHppe-P)4d<9KYZu(7?l9}t%ti-;l$l;W9$o?x{ zqltZe{wdl`^;Xwv*p^?|;b{MqF~1cZ1V&C8ZX)J}#h^F&MgzHBx}NU$(2N*+-Taid z_@7Men~rNLYk{)r;&M}_fKv_nf0``4r|_OS&GJS1Fw8Tt5Nf}1Rz?~ca|G|2&D)TNzWWk;4OQc+#<6cgfEijX+!A<7W+FbBMm z`w_EK;ZR9<7lom7{$TDd8BB=|4h&#AbzUA(Clic<1|qwrw;CEi)1 zN9b2P6ZW{gjv4Z%?1ry-x``HJ6owrwh^SIt>lkb;;iuc7OZBCCrbKdfQSyjJESoka z*`iE7nD>1GamM?HKK!_eKZDK;^J{MAe0IDz`7JCIDK9r!ap8Bhz+Njo@&4M>>>lV@ zc=J=I2ysjyR>-)#rI+#Dd!%M0&q4;#to$3~rUDRoWRKE!Rj?PiUq<>0+*bKKR zlNR5MLg$9~aZx0K{g#(UG6QmdY*v-Edj_%8yC(ZzjuAQ)Lr|4LJP?)fWw+ryEozfQ zK*1IkRYBs|T&?ZU@!u&ChyMkegT`?GxfJkwGdZsmixU?mw@DreSFFqW%mZXvZ9nu; z+uAVs9-L)sy%W!$S*$I30W1HNjvl+DsYsD;j>2fCKSjMV*)xiI{&D6dj|9waS>QiQ zo7o!R18%oQ6$1Y{3{_v7luVp&g(>A~06;W4n?*bC-ysQdCjE-7PnRH103QAB`tKtvj7Q*A2$bC5m9Hg5Vx1PIP2@HrJjWp37gtkYbxmefth!1X#HKTpZbz(B9aJ}# zHHp~DFVE$G7U=xhtSuUM^&uoe{O@z4^tTaka3ddNAd|VTr!FfK0uxGx!0Wn`VvE?( zl5EzZ+%k%ShffvHXUsc{@7?pq&Wa5g(!9c{5bd|ZYke?Kv#ZurWJBC#L-nj~!8V|_ z*WZNm)N7_p5d)nbU4_HJ<|~FT)0+DbLqpI0ha_%+N)sNFGnK2KQkvMW>vto@>+RhVRCDXwi?}+w9 ziAjyP`Yk#3yxBKW2@~@_92hnegMNJ+aWvm)B)_!wjI6)aKOhM}MgB{Duu(6e&115m zQ2Op@q#?OVH;NG*zXn9BljAe!!5E zI_(wCU`u~yB=I$(M3wJohWNM?xrWXRlKFOfIrlq;bpeNuRDLy8Di|L#=L3s2^qS3ePua#ms2KFFriXENA2ZrT5sJUSl(tks04)gi1ghGD%zzikrqA;ui zE@0xSKa~l8WO3pSQ0EM7448AL(GL5;?D!{JY|ODMQwFR>zv~B;Jp4c<_gl0@mr{Ec z_z_V^3uh|SxyIJS#w&OApqp4!B(C`nWSCHEx%RU4Gr}6pvz<&zRD1X<1Z>;orM)5KMAbJzjsIoza=h_;O-KQeTJkQRCFoxX91u@Ao*QC0yQ0> zC-rSb;8Z__?JLOpL1^SrPAHur9!D{AykC|&6L&DeCz!(BKRqu|)b%R!mrs$=N7M9!R-Mv= zj0iOQYh_tJb;j4wnr2HG+UsyNY@feL^xTKlXiedWNt=`{ZBO_yg`5W8<6z{-eE%m= z*tek(rml9WrLx!{Yn^h?Z*Rof{uEhqWpkC(;+>dWRT7~OJkTt0;}Kk~u_+yx2!(UO zyOn&?5B(~snGa%8hM<9ekPIO%1DvJ5Lek#%Ed<|h@G+mVH0aaAmf@9R#Qg9zGi@@r z2{ImpF=c84UA4Y%^q*_{1iA=xsD3qy{Bv-K?axW6(?}lcBF=^65oGYb63JRl9ofiQ zPO*7VmCift=a!CuD~PB^a^E}un*!)oGPERV!Q`XTj@KL0Nti&L%UKG`bw;!QZEg^GBd* zTdj%hYfzuYi$g?`Vq z@ws)K3+ z9h2Tt)xzi#qu}f4E_RP;g$q35v~li!p>CCbiMe?BLW~Y|ns%y%kLPJiF;1?f95owf zt{6UE?J)6Xl(UdJ@bWH8As<)Dy4RJg4j!6!#h$x0#B;GfD9OAmZ0S)4)x;?*r1my^ zm53FaYXdS}N0R7=`_TKD53BHl%_BES7cqimd)n=wo(fE`O;QvH*}#O)k)X|Z|MZ82 zSQr6Rw@dxmc*ehM@2O&4i>$cnlYr>2UK1K3!2U2!xdfYIf4;un@%mj|by?%boJ+sm%KvpmE*)%`7B6DaS z7HI06Es^W~s>7r8f>KPcSW;(4uxP64_zD#*+~sFMJ7?NO$zliFIk)7C(kzNio;8ya z>Vy567xRneNL!jd>Qvt9YR#9mv3f>oL;qqEu3!rU@cZFsCbaY(4}3Smg;5V?Tv;X! zpp2X!8Uqu)qE!?8AEv%KtjaIg`q14ijf5iI4FUp!fHcww2uK{d;~=e4DoCevN_Pq< zjUe4f2pm$nzJ2g_@BPj{@Q4pD@7{Z6)~uOX`-J`geah#R%ssoE&gZA+EL3t_%fYj} z(4!danMr3Fk*b<84|W?mGx|rc;Out7|A^%WHe@cvVnjq_s;hoJ^4jQ{D1Pa7OOps%g0{xC~ED&rr-2`6b32foh4=e7L%mY8Q+6hP}cDZOk%@wYl5P)8_^So2x8omM4H6(niAlG83S8W`|siZJ_f% zM#_JJ!@^WVhJeH_u^plBk&)feGiI>`1qqz69!S2QS8T&6lD%n;)y4gq5Z@Zo1~C&C zqKVH2=#WtWs2h>B5zsx`4^Pog?TSQ2V}t+lk}kkTkV?(1{Z z`5$Jn6p{PM9NGLsv4bZY2yM1RSb z0NtYHI7_^%Xy*K9Awf$Sa{z&U(VS5y9-Uj!CHiyU&W7?Uh0y(jop%u}`@~z)hXLQi z!eoYD@Q1*-1oZpb$Y*3R{j4?yDJvEDCXRd1UyDK1YJ!G9c2|^{LV|3A)KWQiz2VSn zM%tv9tv^Nc<1&$a1L;4sgpG^9^#+}~Z{@Y|H8BWJk7Ly&f?7;(@m~%@!)mOdGh&yy zrLn#A+uP@_lepaVquX2`c!pD$+l)&>%B&LlexlE83`2Q8Y3}>Jn*Z2nP*vvJ?1@<} zSqp>Iqq>s)cd!R_ILH7!#VC8>|KoGSca%Ti0^Lpl!t3oSs%377ev0YgwO`Eu;Z2}_ zqwG85&!^oU=K)-%4@39&$p->Y&;fyOG;)L0n>+9Qqy}~(t?vNEv?@ggtxSMut!xJUyqs z{hPm7G=%?&*F@Vv)zWEd?QOyPJL2_Ikn-fs(fzPg|5NOVxc6!)}pK(hl_1apiQ39x_Nq$xH zTe6SQq#%KW{&TyXFMYVMCo-jRyZ%|mdf^-nn~gP`X;sj z{qa~O3GdNAKyuVaPTz>W9%DD0z`wfj|Wkj zU&Y%&Xnf82XH+BQKOt4HA$(|G+akd{VdcHs_)Qq?8f?)mKROMNF}m^VUfM5wE!$4^kk%ZD(gMf$k*(A$Y(}H}#Z<&a z?B(#qDq3w&-VTQ%V{!#AvFgi38yXCSRS{AZCdcXLQo2X~>{kQ6OvVqk>OOM886#8Q z!vgFj4kVt~BIM{qKhf=YuB+?o3XWz%KJAR^l6)8jugQ!C&hI$uJ32Q9H5v&HsC>K<~k8Dk6<2 zB9I9b82Od)pg|brC)k&!j<}H^#juv-M2snQ<1CPTJ^G5QZ_7wyaE3kU4hUS>v*=38L8Yke9#Khkh}B4FB;D`4=#I)gayxR0XnKur|-x z{C*Ra^;@KvzNw_uMcT;H1_h;_N9D+r-xtsg#GVSkZ!8viRR%C&Xza{3I2FiVFAQnL zsO8GGg`7cR!%Kx3)5D1y%~Emt9Jgx~>pdGK=D)2QL~GVmkk_`j^N075lQF!afX{i` z6T7*H1k-6ZIX6L~0c6-h22E#yh^Te1g9vsG*JCD@%k6Gg3?p2vPcQ5>}lS<5aCI9O(n^fE-zr-g@1M#{?GBhp%~B+W1_d8ZzK!6 zIm-Czxo7hJ{d)!){G6H^GP{YA4xlv@<0P`P-5}&J6OtzG$rPuQ34Ti*cYw6tg$;RT;U2w^g>4~3bsHL(kjK^`6Cfdl*4bZWL(2W|gwdo=)R@JuS= zTE*q`IT;s|GCgcFvm=M_@{KvQD4K6sf#>!N77$p%ZHuUNXgMRE{_RPPHkMf&DolMq z|0Hm+)Li}1NJ=sCSwaS+=@hceLx_(Q7a5o@o!-j9=V1Juqki>Cb0N;xJjW1%lUF5n z3+D{@C1*{|MPF!pgW&ojU+`seJ11Q{S1~KV_3PEbAr&qrC?H83)IU8tdE9)We>$uF+Ml55K4ZWF6S}w|*7B`Lt!Po1*RPiH*0mWY4N|^EDjW3#5m{wmouka?Z+cFSxJ3 znB%vYz$=SXFlnDY{8(E}b}X}GhXM{7ju^i{+-RLrr!x#gp0%J6N`>l`cYYrZ{L?rj zKF#>S zDkB>a8K||*Dh(Z@x3=$9=R&NT#vs67FZ}=J(0A+T8ONUnaJT#=E(6M4)mA_-#9;zX zKr0qfT;3|id4(6=b@n;%oTB+CZBO3!av2-9zjD9d^oE%J{JUa>_tARcTHTxrj>%E- zD%++gPYSf`s?d!2<+g%*(l(A z&1*f$8mxHgk%asj^A}4D8zyBo&3PmSmXNaig$!I@!NJ*XQ*H-`Cp@K4&hI@jqF^K^mWoeN#s`x6F4MaXe>e=qNs6qhJghj`mT{{REq2fS~2gc z1!x?KSwrJaFZCWxc-EQwdp`0J+d`3+{?#g5^uEyP}?J$Ju-Zo9HF0&{#->EQ)Chc=OF@`9F3zXuDJ(u{ODxRENx zkw`VBlR^oRIX@ePT;E<_r_SEHR}AZaN^;Mq6K9mn1lOU~W2{>nvO$csHfL#hkOXnW zam2OCqhULyA&eE%>g^pejVh}Zqp2ytYSdl#j)^RI@KLl$`5Jnm$^KkUNzp&z^E@JM z6-b;ZfAU)sQB!!dK3x=uWU0N{x=Op}gcq1^XK()v3{b+WJqA*Qp*=vO?(1uv6Y@Bq zc6b{p7mhzn;?+ZCy<|ausXqay;5K=W_(6y0Mk;^J4lpY0h0BH0JRMdNoqM&;_lt~W z-~R2Jr`p5tbu346;z{NX!29H|x+K~5*yS|+MV%Fy)48AXxndS~qV%PLGScWFq5zeJ zMomauhanv1;z|P*b|77@>3vnW3o^%VAA~@72OoCoR$>;@dEdR3sRq}Laf>bnR-5<`8F05?_NDu$yVkLr`5(vGG+gJk_iTjPKyf^9=>lN}j!i^ek#RBxXxk>O z>YtFgya$)P{VaGH#15bw>Db|HpIL~{37ArwkoEr%O(~$fDbO#+WJ$yT5uvh*CiDxu z-7FmS?uAj%+kY3QI4ZYgdXGM#$jZsFnD9r(z!;sZHvdRK121RuLCxn3jK%jr64?z!QJAv*FlEAIW8*MGY!iG1J_X0} z3I18MZ_tF1wcA=8IOX&Yu$wW;PeWr1LwF&D!x`p@N;5w`q#bQ-NTaK$;_T}APOHd> zuy$fGe1LRf2_A40K7A0fTDS(akw8&>dhHZpWV&Jd?DZeM5RzfK01mtw$=PQs#f{zubrTV$krA84w0Q9L(fPWfG`MAfVRm{t5{IjvVI!cI*3)QawU<6{ZtNT%xa5tD zz<{l<@IKE`W`9W2WqDcFeWIR?szu+^jFKn=`4zda%MZoBAKArT@0KeB*%0dY6S61^IAb4>BUp@Rx(((1{P5_^} zOJ)^wH2$8R=5zL2mzmeWO@Z`B^9dWfpsucF!;e_3(p*upE1`;+x>sIBkg}3KO9HD4;2sNwV%q+;=@YWZr>jSw$ObwbW}Ku3t!%u!xF(y zrM-ThAlM)GHiz^PsPeEsmash%y=#qBE~_jX()fi?U;Rm!0d!UM>m1Ya{Z+@l4F{a? z+t1Y#fq;PunFI2;UO+_`;oZ!@V7Cnvst6#^(Xez3LJQApt1v(BWN&k`eK_#O_3z)m zmXf^y4vpO`@!_JRrJWCw)Oj7ORQLLx?he`oW`7tHfE}4=Mq;VThTM;h;|US+e3L8M ztzxEcp2f6*JXAJ*1o_-w;ZA5e@6Df1y@(AtTQ8g4k7I!b?>AgfKt4pHO@+_;zn(V9 zF(6M3N*ifx^ruIGRq8=v*f=EWwi(p6h8D8IBW$>yLsk-kD>Pn|$J|AmT3$$A_QPB! zpb4AL*#yx2kysDVpxOu0F)JwaXAsA>YY&m=;OooNRiGs6GvN=Uk)4iz>8%2_TvDKy zC$fE@sHn()GI;vN)ZhQQq?coSp~d$rbg{MhWV+Jo9rs4w3~LBvk3V0NC=cm{9tIV5 zyzc{29!ANlo01A_GX@>B0f@*)MmJPFoRr;Fm0JRBnYJWwg;UA43FTr~`A##I+(bvA zl{$?NdYg!ow8^A%?QO6IHkLTP+(Q}6?<^{8j(vTpC_fikCF>#e-+sMHSY0|d4zDQm zB#eR5e*;vCnJBjO<)v0dg{bDwub_oJ`G4IRi;lP6M#0t`0&2kH!48w@8O5DCgTNXN zw7bxteyh~ZKD$ofhG#4*&`gw?4zM469jo64^U=!t{rt>a9@K_0baw z5_B9pKJnK$YOq`2nyMv_x9-Q8sDY|PHYd%e>8UuZ9=K1juxGUgmcX>N2x zBTu9)+a@J#Z9HPdWeo@K()mu?!6>G%*8 z98vJ0yJSF(@GfzEysX2+-nY5=V7K{bsM_khHV_>KG!JRnGW_@unzfL^iJEeto$5Sk z>}h_wJFmn{OnL8Kr$vlpSm4b@pn{#WLOHaTsaTn3W85QqejgQ>qd?03GsK5arL z23fjojI}PRFK^X~=Q=MALaTeZvY)HpE(560E`3npI#|2X?gF94kKx&UvJ<}j+@~fW z?Zjuer7nBZ(*WbAl6^ES(Y{t8&`h{z;3nEC<@9d*f+DA--fEcJ3I0Y!3+I!p<>nIs z0*Q#c`50N1=*~D+g@klG#nXj85!)x`nP@U6*k(0Q`qWVkr3@3jJbI(Y4|OUxc8?`X z>m9TLdk(V>m}_@vyNk}FSg0_FG;)<^@Hu9KhEhiFd=j`|LQ4ao=2P9PXX4HBSHd7@ zrZ&p6d6iZRlm`4RXBUSs1Fa3JZHRVTFCA(8|BgK0M|v=GzSa@B(^wHU3b0zncT+_ zL9BO-`=2z2P>i_p1v4TBS6j}GN9c_vZ(a5oOWwMzKJ#ZxaJax7IFPj6&iyE}+8@~Y zop|r)n04pD7VE-fV^fvm#$U&937@o$)%9*?We>Tj0~Nz86{P;$C%f}0llmCw=s)TBt8?G>Vw?4S~a13v~P&IVuofuM!9@>Kqha#1(XqDko7BLE4^J z(`#`8f{r0cARt&xOZ=$JMJO_n+vPk&~AYA_U_*j*5dvCAz z>ikGDqqt4FC3k-7SFz-tiZ;Hi^7o4i7lPoT5CJ^QG5_di*|^E*zl$S$hkPQ^hPe6QjPF;B9<{$B{8DTezr@mN=*JLbIjiqzdt^{yhWGv zfauoGuTqhH@W9P`C4-4HJ%)0b)MjOGb?VrEDt9oHdrw$tyJ;4T7dZ7C=3j)zRnR1g z?Mbs{HwLWKC?KdE+`$S=V}8>P>GABvfr4s72@Fy71mxP z{$CgR+q)hFFzaTzlX$I}j5g2!akdxEhXSu(TY{q82QwfwUsf5JisX6w|HJY@tB=&! zzXOTsMDm5?yvRpo^-O!K-&X%{o*X7gQ$lb*pL`kHAV~+@Ro@I=ANhEWT}MwrA*azLgQ&?UrKU|n|_h2`;_s3;9dDQzpm?^$JE<RZJY zN$1z2&HOR!W>)u?zlbf^7|*{DcJ&FiMJeqYdE@V2=L@;I>p3fhd*?+)G{0q|x+@B5 zRCsm2J3Ike-Kc<()QX|t(1%SnuM48lZUfbLHQy^hWg-P$jAcl`Qu<`AS-#j;&&N zvM$_41<0*ZFGV8mR5X~~-soDT;|ZJ(f^pm_C) zFT~aE`0x3e8nEyZ5sez%mv_*Hpp+n|t0_AS4WLLAiZ@g4>7QMcYzsf>JS(4AueMU~ z!vsEL9gfvRP1i0xlkc{of2!N3x-wSAh-C zBByfrh~sdJ3(2!x0i?S9Oy$CYNh(M%KsMJJl$G_DJ;;(T()9LXQUqQ-<^--8$t!IF z9u$zp)&oAR(b4#RhaWSZ8J%Z z9WfFU5&cTPR|G)^PBKjcEvsd5=(Q+S`D^bd6UV~U0;@NTN;8$fi!^7*Oh*()f+j-A zvXrSY6X-<|E@x&a;frDzVsv`ceDq75Bk>(33k43#yWxC2Wz2vz!)qlHk>khfAGe-Q~O#$3wT) z7$DnU4@8oGCisJ(-UCSCikIEkh$R8pR4+pIUpJB_Qhp!kgkf@Wa)$b6fYC;D{X)A2USS3LIN=Z%KKXnRVX%|pJgPojssBe7GTtOhUl!d=})oZm57PGYu zhzyWNLL#-ctfhsv?P`tN8T`ZgX%KkMF9t@`r5c+FSA8Rs5@fX`yCaA?Uv3ZqX9M|h z<&gWe-)!e!mA^luvkumJC1?#r3fbqre1uJ`(m}eM&Gr)gu|K^LS+nK#(bTh>aF4kg zg3Nx(tBZuF?`g;st-htv36Z{9CYS=6bK@3c0?r^uJ#(>t$Rc%IsJ_e{c#gAhT;`co zSZF%|7gX&kHaTWTq_bzo<$+P5I27ydtxh8E5nx;T<{-v-&bilWzO1#C4xBk!!0d>E z9GJJ<6-Oq0G|T^*o0&evcVC&bq_doMkPV(1oL zzx2R$S!QY%Fcz+=gAPO6(}#@HA@yK9EB@%7(WK-M!fx^^{@aRNv9j+PPY{;yb4D7& z9Pq+tL!Pa{P;e=Q*U7Z?0gxvq3l0tz@!DtTT;?$_Ft|H9Za<&e*f_Nne2M`-KlTCw z(J=_n3nc!#+S?(-k*J9O_-gQ8s?Sy-W_XvBpCvd^f(ptIIxYjykZTcI{pxzHH`16H(v3ff61Ox`0kWc$=hWcb^?SLRd>*rGcWtpo4 zI;n&~qiwzYLa{T0=zCWK(?}ind65ScnJvQn5OSxH>TPpeu0VK6FG)22aTRfu#fYyD zXbaO=gaFu+WUWX&Oq0)1IyHG(y;7^1p>W<=MU70cYQMG&rFK)g401K0}sy1={$NdCDLAATzp4LGF&Z$oq*t>=M=;SZop8L`!U zMX)ZPW7LVqyhS^9OGIJG;{`|&4KZ85_p zW0BrbU@FV8>&>eZVV0s7(Z&-gs;?ipOq16CxxI|o=g%URt({%5nsJY&cRJd z7=#4&9FHR?iE|DbW}`<5ulUXLg5M2;#sO14T%*O;Oa8v}a$Q$im3tDUpi0V|O3B2I z{NgRnwN)|-C8Tj?d-RuyY3&h-280Hu;v-=+nh41)-_`LGqd%D2xJVlujI3|WuALY- zs>dw~c^N>s&Tc;F!$+*|8aKG*vvJ^0=_ToYFgneWoQ*9}*OzHCy%_ z)jW1zs*?nxpB_sObdq-owbN**VR)9l-eP3f>bk`0I1!=p``lvS=>;DU)k?(MV=*#( zfgKWa>6jycoY@dc>Yvb@vhEG^02t@joA>Ug_}4_mO!HGyl^s^<@r^d(@K>Px%A6`9 zYw)0*2*pXiTUDN4gxrh`zs~v9lp*g*LR5rCT6szVGU+`+_3M7b%N8W;>#!i!m`tH> zdUjlusPLU~dTl9(YXf%T5yZNP-_Ts?{`SQl{D!x&>)j}=Nos@UG~{RTvX%}IrEIrf zRTZe4{?X-n%(qkOHTor3>BN*@zWWCmVU=>C1PScbv*x72KyaW}_KWVel1xF_vh9t4 z>+BXTY{P|&X9H>I3l<)|z6%~A;i9a?w<=LDVs5QoRk-RsB*->N;cYALySk~PrN$D+ zx?Xy)v*1wS>&qy<(Is�P--5HqXd)j<6P;VGmM{-k|hhZEE=d4g3tg@8Z44A4bP7 zWxu>JD^y4Ad=;RD4vdK5KL_+UtfcG>awWS zWW12~7QGlUC^Xi&S+8@mXPryx5zTBSDx+@L?oa__bCfv#WG--=K8T2T`&*wxr;|jO z2^;ATX=trjcvNshPCK;CL%VF z@c!5URBwBuPj_W3;c+*3y19XiVFGx`)hZX)SHWz6b`G5S`1R_5*23x2o8R41OxX{= zei?)$M!e3j$Y0>8Quewq>ip$@pO;8O^xzlsok_4RLhAca(j$0A590|p6rB{>p(>w(_hdojJ%_)_bQ^R5=N`}1ki-R}Jhv9(5+ zNDk94r??#lr`W!>3@>&x`VEJ<M}gh`Thiq4Hy)uw`o*GbD~&Mxjp<8_dEs zlidwpU^7)qa4WyvA|RG)L|4`Ie6;PSI)oyj_w*s55c9n*v1D1X&PIGyOu*`&VSXV) zgZNoGVfKEjtmC`U+~eGQ*P2@UY|mh&(ILBzpl|rZ;{`sMQ1u6oSBn+Z9q#8JQ7n}7 zbpkvwiQ6RGOnk}{~Va=Z<4SHC6+%%sW@I`gD4y!GD_;9t%K%ZZZmvW z^MDR6X1C>!(*F5mSFspwqCxFSTpzIQ$?MpgmtbrB&bZFKr^g&F{(KIDH)@uq9m2ps z$g8YpTx)LIvVB;eyZ>#7_dT1vI@AOu&gSQV-+0cBQ#BdUdTnL~{ld5@=&cYK#c`qAF+%T@9 zd}1Ibv*Qz`swH~r-sO|Fjn%oMTAfXM9v8>cixkt&)~{ykw6__;f0w47xQo_ZUS=Gx zlsDx7)$oN+nCEIl^57v=#Zr@g37Yyj_Pp*`PwSlt(YsapFopf&TJlL_IvOA1 zd_3ClC3%cNhYdtS5`pT-CFrDGK;$YpNM!feKo^i@C7)lO1Y zA@<5pn6{O(b#RdPapCKg^$PS3&^yZ1{Xu9;)ua`H^|P<`$^Q8%wf?Htnp-eq>e7%6 z1G0*T?%#^f?DFl1!xFIlukBM(?8S+7yJR8DC;{byI|GG>QbQs)g6V+(JrIv?{K6{0_#glLVuNqMA$ zDgQYwv}eOn1x)!q@H<$W_ZnLnAaCiv|Bk3e%RF^rO^U$D#Y|Y656k?F=R7BTZ?rKs z?96K)$;s0WlR9C-Vl4V=`>@Wo7C4poJWv2Ssy=eQ8Y14^EW` zd)zml(U9I>k11t)sY-iSSBCg2YFn+@+lR>^UA07ei;??p)3bXwa^35=Se~geX}n=y z3KX^Gy*Zc^KlnVq8kA9-FnFYow8H}7(pEY>xvKUy#SPpdVEudBtH;^NqFA^7ZxMzw zsPQdvp@xe=r_C+_L-1W6N6N+Xu?3kPr-`=;R~w=IE{>kJ3BhOI$~|OAkdvQ<_BN7^ zu)eW$=(%isHb^GbVB|Ti@DUaw-FU|t;!6K?7wox_okUfWJSbr$&L5K*_5a&cVaY_Qz|cwc}V?GK2+E5lbJ+%RK@3PKPzMkR_}Q< zO5AE#R74>o5#G(Vc-v_EtT5T;L7bbRZL}hy8Q9CGhXuxtv zH5Zd_uHVy8xM!UDq7Pi4W`$`STtYqbg0#_!C)p=tOOh$@ccx5^tIt!WM3l`%URx~c6T83KWtejzMehy9fwkyD*mjl- z*M{BGQOvr_p9pGVN6ZSzo-cUat+^|Cc{}2~YAmmNq3s)4D4{r~AXTIQA3XAR+>=rJ z)fD-kOUVyM(L;^pwf0}_Aoj<*GM#Fj<9?V=<53t+hfRU(j&?RA@eoKS8K&*p4jBUJ z;AO21j-G|De|~)Hk&)-ll)sEn_P#zRNp0yp*rc?>HJ znB8zzVf=F_x!i)BSZFbFXp5x!>!G>$^XG)=>4OLmU{i*eNCTfIpxC?S`Zx*6>DP)x zd(}ISw6%MX{@;|cO+t*Ve+4G5tIJr*`hP9)5O;M|h%t@4bI3^V9k%&4!gG(H%z;M- z-5A?Vh!Vt5r%&N<+T7R*(iIPOLN$HtBZ;9ppV4IU64PY)IZYBMKC#EvV!fpET;yqf zxk_40P%ecDirG8P%;E&u*IY{#Ln&Vo{^Z}NFL13iswk-volG4#pMSjvV3ESKo-TS6 zl0-A8Px0sSnga<81@SPhZ%R04(VpX@gl#w6c!t zHXS&ujbQFoMRiK8=-!kE=}x4@vsUSbx#R<{!|Ut!rF$5zJuZX+nf)iI<=&A*aXK@*&mJS2stnj46Uh*$s&9@8&pbKok{!( zfv<|F)^2eS3ms9EJaUMeD-vU`v-;o0HyS;lj-w@LDxIfR2wm=?^!1?Ghu&EK;f0BhnqdV?D^O%dN*j`VsVa!IP+wR z>)z^&k6^|3Ko!im(jTwn0&S$%WJsT4*B7$-h&$P**}H_^6Yfh%fGZWG#cdE4<9}#J z7+10FY@tTu&W_87-5f5Q;oZnPy{pHR{ChGf;=4M5qj*H%`5?qTcfUpWubHYfbNMy! zYGVAE9f)UY3@-f=M!YHe+wXOFStFVbIiejlzxYCfDBqSSmFmbcOwP?vGd~;9k(Q3aJ&7~^ywqN!qbH6eF_LE>F&a5_;ie2Zx(iob2jW+fRX*>f;N!P zp{Sy{I)Y;Cm)ZiCxkFpCKkDnjV7})0QSH4$CgMarz%XgJ%P?0+HG?#5s11uSSy7f= zOh>(owGk&?UwWt2Ps8~hXy!P5>*TNS+as)fg28uk3E%ZSxN!&!6Ybw=(zJ>7zPS=n zG{JUDOY-f?hoaRkWYiWsMlz?L@$dB=dTc*P?c!+dcuC8|ef4GYsNw05H2x}EtN4b; zMZbFX&5u2FKJ+GADh2gR%OQAmoRomXVS`Lh=6fF+iBlEPI+92Yf%(g2nH~t_{)Ru= zh-+P|!^Pkwc6L924A z5(e=*bp^j}JIJ6f+Qaw-s3}bc)P%;___XeG&e`eWUQhnei{A~!%5$1FnCGIhi2W}T zY<9WJKc5hVA}GHp+4|R5jG_ci_+QJa%y3&nzP(u`H1;50d1s0wINEyt5_iSUKZ!Ft zJhU@7C&hVms=nMpLq#oUH5b{-iF)KzX@&`_J*JSgbRs^zc1*#uWA0p$ekk$jANTay zkE_8!QH;~PJGSC+lsNXdY6a7)VY+4(5XiDf`pi4(Cc5Bf7nl6Jg6NO7Y{e~xO*07` zuq<&$a~6Kviu>Q=+UnUVF*}IjlU?rzm&m$V;<$A-Ck4PT$ja zLRco2)irj2=!SF%!T!^EH*D>Stip{j=mHTmjCSq8XC1GrrsbQ4I$PgCzm5C>il8;l z>{n>8s@aJgwZ?D(>DS=0RT*Wn1*zl+_GFus|HY1%3DGsX%#U=7Xn}>N@og=I7#dGLspc7S zTwH40Ep(zxiGp>y>)Mn25}=}$@5(FBf!9uE?nXjozlHTnGAFZ^sQ4XZTWuD2-d_KC zutXo&LedhvAoc~YDt%hCcSv*a$$!4n#=9;E2;&ulM{x~xrvWOO!ida?C!7VSgphS+|0-x!U9%^5zTZZ+@9G^+ zF8)*`Lzo=POKcQgFFT#5NnGikV(C=#$+geKusP+BPuZfut-)^0C+`^>tZ7uOIUaXm z9gWpS*)V$jX)BwFes;ZGFkQDRM#i&MS*9k2{j{l~g0W%AOMUjm(S-R`eI1!wbZW1o zJHecFrC}p7b+EDU@R*01v9Fm$q7?mc$*k=wGYci!MZ@Bw&BkKJR7(bn?-P0cc~e^| zj5!#{RKfGr17jWsYa5-8hN8#%(R+$E+vSWBS9D}*#Bm#YGH0RUM5f+IJu$x2I}>YY zF-dPdJGRK}B?-F*13hkAee8QFTTjA2A7t^95Ba2mo^TCp&EMh_7zp3|ODyXX_vr_q z-YI#xQ}ut{uOeFoIeiG4I(d617-L!9;v7y&jZ`9Wn)Q~rj)YP*I^LI3=g~5=^P1@` zR@cPlc;$AWnR%2-oXO@Jy{uh0^qC>~XlDUGQ~GeeppZ;hOK6}YDS(F`oX*&oHJ-$- zUJ{;f4@O9MVuX+#H)p(&$=@tjLKtb(iycbc!!0Mx~#SJjZls zqbT)k#fmx&WLnbD6iOyN+bRB>nJ@UsJ>~Z7+EJp#?u5GZB{{V3&doo&+P;&edCQ88 z4HLY@kY(xihQ0G#ZXRRE6MVIs@tG4s_Z`zWUNd52RKZxZdzg%hSCBw8KeFJHc0JlE z+qEQm`I(@Ca5I70m%*b}6T|0PJojYpwYnW>3WUy?l7M+r#l7jE&jeP1Wp(x#LgM@g zGa1EgHn{Vp-4F$YuaORVXjzqi~{%YUQC+KdH0N8B6K-V zg+`)R8O+&Q@V-2ha2a2>-!qxKcdhi%K=7(tS1><+FZpah{E}bWGhN6 zF*&Zr_#|vSXT-J^8KSU_ie6>e?Xd{kK1`%N6-aMh$4L)mMSYeQs7$OQF(_kyoJ-lb zV=Z!4bPN0C@ok(vHz|V7wWbazF?`RNmmpiOw3u=Fp;7u`a=tcK~}I1wiziExDAfwSdlnlYAHT*lS%$` zX2MU|u=)>1ZpQM#auo2N3$hj6)0FdDJE!!yj^S}xn2b&J6nLp2wy>=Ss6qXcGT_a-QB+Vfi}F2vIZ%FiciFN3X%o`tds^P z!=Z%4uM&oCvP=j#u)m&S%6RT6@qBDp zC8ESIN%Y~ckShpWX&dpFJug`N>mIRb_B#%_Ai6iezI<}w#lov7a?{Px9(B?jo*&cXknYdx@ z^R3A+%BsTBTeNyUO<~5ao!X2J__Rm-Uf`0#*5|z)DyAxQJ?c>cZ+u8(`r%zaqFD0UmM6Rosdbb@aw~)=X4%P>J2(9ACDn%;qbe*4eAjiyC0*@Ides0B z?J9422!Lp-^~l9L(5YXm=P@sVYvK+af?K*t&p;))V|o7{TW=i})%V4X4jqD&NC`-n z5>nC#NJt6NNJ|MgNOvfybSm8)($Xj?-Q7rcH{5;j`+J|~-uuo!Ff(V)S$plZ*IuKRf<{#WnOcPxAHFy7HqHFEc95U7^3a8)L z9Lz2;&b78y1a>4s+D+Vhj%D4wTwUe5ci6<~c;@LLgjG;Y{QR`6x{7=jq3&u-#oBTd z$)tJVH26X#+B2zG32YHLm3v>vpjC379+oFY^@b?h?S5v?5msumWH4G2Vt^bgwM7`eb{PPNxNCo`wa7rw+Zg`{U*rG_WsQVRsBvjiNju5 zFPrK{s@vI13C0^Imb?xhn4>%-Pj;={M{3$>MCL@LL44wByt9pO)D+VGiR3eG(?OXH ze099(1nn~kQdkXN^U^Q^FqQcJ1$n|nFs+cD9M5Z=EBzrJw8rMPcqz@Pl zT#IpYbz}M@@?&!oXrlf`f1;r~`CcuXE9gfz@Cf0D&7(6NoCh3SL2XERuV~Ur0}JaP z?eBoS94;byR!#YWzP(EbK%1c>h#L{ECgA6tR_Yj9o*Z|gE~34GpKCetS?`$Iz{gI} z=4TaGJ>A%96y#NXS4&U%=2yqS{EhCY*t@Z&yM7}3Yy#W8gqDE7Pap>Zl!&DKw(!$6 zUawz;qB;Vv#}I}cyPX=FweuZ;1f%@Z`3JsXD7q<6endnt%NF(`S-wZzC`ad_6cN^W zuc>V^wu$Gq-*Ia6r=*X1f2)skRNGy^RH#z<{-OgS)zhxl3K+9}vtNsoSd#&rh*FP| zJvp(!ITEqRiB*80s{nQ1G$zS#LY+iZ-tS{-b(oyWeE|w2)TD3j@vn`T(TglZ9$}94 zISG=Gaz3G_X2$%FA}Ok;Dc*t3UI5$It|+I%*~>mRT{lz|$1G_hjEiGOLT8LzbI$kO z>5HG-I;xlc^FhJlqhrQG4&5Q9I9}1WhsT)c5?CNMV&%3S$QX&qYjoA(NK>=(-Y5B% zgBlpLT6Uh*{x4Nr%?f1yN&bAfzG)D<`jlh`=kGBq zEahWrmD)I(Bf<;$OZ@&V>#G>u47z&yDg^dmuoo|d2F zJ^W+IqZQ9L8yx0fee;oi!rM98)HtXcA?4g+t27(uM2#i@N5%L=;Ubt#WB4P z@9m@T#%tJx^c{%;b*N1;fJ*N+oHN(DKRheqQwUv4*v(Zs_gnm~$=fqnc3=++y6R_i zZi&VZE!jaeU4B#E7_zM;bsk^c%s;PBU>fLVT@Qb`c*wT#*TNsKmJm4LhkOwKLZTPx z1q`c{-eEjh`cgt}0;OPOd-5Nfh&AlRQuC|W`Vi&6PFhcJSh0%PkADlY*?vbjY<)(5Tt0|C)gu5WY1ioW_iZmj4aUk(6 z;oHe`zWXgwrk0#WwSS+(mJ3^-!lavBTWuPS0;;xs9!?VnZ4FsvguX!KMg891XZ;#D z)@;S$?MO)vQ;LMfmjLF~V_mDeqA@*mDwub{Q!CR6PF3GGG`8)%#;V*nKVa0)f&wU5 zo%aO(3}d0oiW;xS7uY{#dJZ50a|orbgTBUgVibv~VUVHus);&3A&R!_E zF;v6r2I$3w97qph=+iW{cunHbKoHPNnc38u+;%bN4X1Fg7sGlXCpYA_()AM$hLz7G z4@HLjMgvhO#W{R2_5Ai%uc*@uhaF+r?vHJ&l|?OS36Vp0lhFfA>qIKyh7-V6J`8{8 z5DX$@uw*{SGwl$GLw$e`>$7|#3;HFmBJgk{9lOw$J+&e6Eq^_Wz@El2eGxmgSCh{6 zGF@2KRSczN3z^#GY_AjsQ1L_o9z15> zS`~}<@bS{Gcm?qW9?`WdU>=_kmcjEAnho|<==Qyv;QF-Gd8+@1B>}%zZvT?ca5v{P z6=zam53l2`5k8kHp|2>gDyuNwLD;h<-)osHyvWkf{rEt4^YQ$(Q2v zkco)LDqN;^26quFW$DT&fA{=)6dUkfd08j%>67BqaW!s?RRvizk+%5Za^^jR%Vx z^hzaJrf4e=8wAiLJsPak2E+{HpUE$%u2MhZB*8;d8{g<>2;1X{)a)9(ovi*i`t<@p zeLk1*;j*>y&ORAv1#n6_kv}w00KVW)Udl1Qr)Y*hH>(ZCjva+~gGLTd=64l!UQdGJ z*l2Na(`qT$a7C;uvV;U^O9olI;mMeZZz>S;C3CIKpZYUp|1dF}4*6Z^*04a}8QPAz z(-!Km3k$nBte~kx^W;0i53$hV-rP@j*s_LUW#myK79oEn#`lNrg`$A&9SVON(2)B4 zrbgh4S;sdY^88@P?QRL7FK=eybP*fePk+ zFs>|$iJ*h1oKYz&q)-^k+8D`=F&tkF50jh39C*jx2+xF(idDDk3t$>yc(y-A)|W;Q zCm0eIS=S3&*GvAcm$y!?_-stxlf8@MfS65*E|8cc^q!$IJg(G+GWz!TwcU`-Y;JlRGt_iy1 zA-_9MUW4qSr#Uk3wX^4Aqn~E-_eLF78oqBk#{2J2xH_Ud^HYCf(ZtKT-dR19Uhlux zpm&7!%&$m|K&0U!AYtnGe%r^1r`m`lNHD>xByM!qZ|xV&j@iOEe)Rh94HxzJ2x%#G z9h_$VUbdCnXVDK;4rMg`mPCs4Z?ttFm;2fKXWQTTm|x6gr>}W-fEM3@{j=Fuvz$X~ zqu<^iS$>ca#5I$W3Qd1we^kM~aTtsdNh%i@Bfax?#RLRqJ@aHFUZ|{g4*U#W&{*MO z^FZH3BtPC^f`4Oc3^YUhZ)aCb1y+a(dI4e_OJL6Q@1CY7g*nX*ocMuwWA=&mg-$Oo zF`SN?k~v3(o?WRw>i0V7-?EKp&A^};j&gZDhWYJREXuI2Wst8-Z`qkgNvPAXwb^oa(39zH<@6k3!2Um%h(GOD~Us9zr1FdBBz0ENs_tIsP>T zsmTmcp|aD1>amvA<1f`8ea!B^Nu_Tpq1tk)_7@{u?C3=ocHc2{TT~b55{k;B7jh^! zxFHj?O7nGKjY&T{ehi#Qs`)lO$cyC4exQob?Mje~`u*4|yjBYd_FL;DCYGU+;`7Ue z^PCBD3Oa04CSH+tGyJ`UeI0to^~=4nK_^UKKl0;>Tgbwt*d*0$TLDJg!K(Oi5F*m0!g2Vmi4V&$YzcE zK9bW`QzTy~Wfg%YOVDS04({$zV$s&m=1S6BrsxQSA5f6lcKZ!(SN}RY^@LYXl}8A$ z5oUv(LiX2iolq5>WhEZu9tWyVKw-2FUNLl07~|hh-``~rnV^2Uqtn>TM9#4M{Il5bmsEw~E{nIVxVQnt{A~o9E2j$dsgKao zzQs!gtnh=5s8wQ;XiDR3`gz>S=x!@@t>Zt+DMC+z4O^BHGQP%PbNSdbQfPCteM&WB zKTL3_@)Zm=aCSxCj2$I%qsW9wF^%5`fHv()KUDJa9ABh+hta2c zL`PyqlA~Bm#uvh-)P6*Z4XXKzn=({%mqu^yw!6GZo9!m|X)Mjh6RBQ7PZlYC@P;6% z?MEri)+I5NUUlc>napJucuPGSWn+CpswM2 zg2!Vc(vBr%)l#V6){h&J!)qJ5@Ot`}6ZN-778*Z^H_uOr+X(L8Z~aX2Irm<<CthP$gUTt}&>x6)ReFpw=QW-B;?Eyqo3=w~E6@D?;!pf&+y~iWZ_%gl+Ozr0y zcYX{z8>#n7D+lAGiDOj`5AYXsdp;t2Y zep|v5tCXdO`o3S|#S;RQGZ0e0r?((3-%;K;s{N| zhm;f*CI^q1t{`&C5;(q*KES1zJ!MD`x;;v3pXb$yL^sF(DA0wHD}jjaFDt;9Mw;1; zDmsCyCV1MWqKd4XT5K-;E$)dkFIt)w)#DV5pP6kEs%#lV9-Pd0BM)^A?reNdLV`tr zDx0TfLX+a_#ur5;A3)-nXFk*Qwo0dxw;um*k@wvBGrz|^rWRvFrpqGX$x#!?nS`F= zn9<2F{rC9T2IPgu^rX$GVp{9k=a4a_WDkLYf>#56Yqt=CbJa6dsxk_8>guX4aSg>{ zzY&+3w^iDeY(g-PlliaX!jrq38dqr+_>z^?n+3E@m6;MZc!!&--+LgtA|!&3>jT1QuO0Nb{MO2n*dgh_lDqbL~s9Kf;&KUm!@+ zgX-F}I0jnKy^$de?xgjI~lR^G@YMV{yr>yT-?%Er2T>jWT_KE+PaSmYw z-!UTxK-^ykexk1k?3~kQ8;#d8+j#Vwi;9TNZsvmt@Uk6uFP^IS`sGrIf3~|EW`YGu z zXi!8!7K;NZtWD4Yq#i`nO0A5pop$C|QvB4)3v3ZUdQM3{N(m>^ze^~H?`0$+a2{-1 zL}gnfTJ-<%JM-6#%N@T(m=M}ot@RotGBN3paX%ntgu7}d`&6)lQkMrnG{W5027;_& zwrz`#++Eg49*wxYOIc!38SlrH=l@Qg%rGU&`8b#H_pr*CE z1Iu-PnWsWuRrV#KS0D};pqrXh)L&9uDLqM`N49g|zVkkwEsSvA%}a6=3=`W+6G4u! z(-OJ-_0G0ihAa|3>7#K?7Ozztq=lbiPiS9fzqIbX;jn1dvwjOP-ET~;WE*On%&0;bNc{~T zh8_#rP(tm5z{`yxL*iFiDA?ba!!>b;!%GwR3;a1g1RG;}AN`KNcO6z2Jy3q? zmwz~|>{Wn!21*gqqR^W3D!!6v7_xh;GP`{#E>oZkOyj+mtEhu=Mbv3=|5MiL zM~|AK3F-zO(E!oS>xjn{b3uRB^{7b{yV5%Z*x$%$)pXMdaYT1h9FRxx-;fhQ%dR}N zRC>{SV1q5PGZ2NFecIZx)`6e)QoyDU&o$6l7X47PX&6whmaLbP?lZQ=z=K`$A_Yqwzxk zrs}8b*zPM1xm#;6{)2w`$HN*sO8@Bf{;vS?F*1^V9%@{8o?Sk5c(l|bkH>U>BVMvA zc+&k=*G1KM$lpx9mZ!H^vkTYrz~!j@BBq*|67dHpPqPotXmbBaUmm|D!X2>tul_Gs z)_iE}DDv+Kq8mrJCyaH*Y=%AecBx_=Uuah%Q$Oe&Us-&)x**Hu!C-Wr!|jK5BK&3S ziD3Q%@nhdFtyEa))6I|L;>Ci!Y`oqP&OHA&^ncA@()7`l-huP##ndR$9V*LkLbW*A z8POpWB2f`TUkC_?OhEC)XWiYJqK!#W2K;3Tgn{XY?_b%={*&@NsFf6WeNwW#XO@f9 z>enIh!3XwRUe!JFg|s7%2ZL_~Zf~Ne-)0RFj#VoN)_x*ZV}Imtl@iz~6QELs?67io zpFGlkMl>UFYo!-zUMYWMo@fq6zX{H3mPA%8Gd>StRxP^^qxO#2SEIXBD6d*IQRGzA zl(WS2XTO2Npuh?x%sY*7jN=3l6MN%Ep$6cHH8BN+UzWLbr8}2`@cFwneor|t7EC3(duEUAe@!smuuHrF_6bg` z>w^Y#bAuplBh4|ke7&G0t&rbC&3z^Xm)EmWdqsrm>A$Su1UerVKc=LA?pN`{Niao;Q2^DhDL+A0p? zSPT5wKLi_9(ypYvT&+QE`pDqjCwOUW=OHzjjJyfLEQiOk^N`8J93MQ*q2%$9^UT%C_A9tUI;-RtH}7 zuS6Wfp&fZ}-MDl6V%^RmI7~4-n{5rBtM?8{t*s`DEv$L6>M$i)xxf(biQhr2)m-S* zUgHRUDtZLFnd7_#89FnuW;DeM{od^-?Z4G;t_VF9cR<&Li_cnTZlBq1>YjJlx|35 z?8=QGfzmdpkD@b(%UZQ^+@AEcD6W49kN|cCts3n&)vm@ouZdXe_Qd)S_V)JJSsSko z5>}sE&%o_P^@Q=cI=m*tr2cjwn%L3qw*Bl44OMo{_6yE2Kii2RRDs z(mC3X@7rCBgek(sPrjzvi1SWvqp&l@ z+FPGhv}BxjSPZ=)18-ngM-AEgO(CG)9X~~6phC(Ggubf9VEV*+VrKH-@vvQ8GeAI+LU@dhUm^}z})T)jUWCmL8-St zk1Q1zdXrCteOIL_k)Qby2!!lE*bx*u6QpoD5A_Z*khvBE7p#O^ogFuQ_6~XxTa#^V zx;kFe)b^uci5Pr!Gp?_1q@mT=@#ZI|NiZ4@syJy9qP`TLcQC@dZ40fiRK^}3HQm~3 zfI6#iVVD8vKe70zNd=!s$1Xg3JIVb-vp}y=nC2UynEpW8%jY*|OCJ{l8Pc+|(Mp@X zb`F?5DE2tc=j=6zqgN}`^0nU_HCDO&Fja{S0i;w~VyyT+276B-erX3!e}pY85{HXs zq_P~<_h@4E*=T0SAyGq~L^_7Xry1DY;tmL?)0aUpJ{}15c%ZIhuR42Ke>5ojbEjr= zFh<99yMpg@s>BGbS729gYqG-P{X_(v?A{+m5B%7-HW&ae?px*c+1Yala2w!4qE*eh zjE_H0~Z3wspbpQ&SXnLfQ8O3&l9L%Ic5b_&sJvW$4`ZnG{#WiE|zDsni zDk{29#`~eAEtqV-y3hT7iN=#vK3%5;6t`X1IazIouBTP=J#Y`($!7qVVY_0?@cQBy zf;j+K5m+z?Gy=lnTPlRRTlCsOoAv!$D!sj%XUAEk{@Nbq!e}@q zYq1^RB3oAO)(%)HH*Y8t-#5>a>@+cvZ|?hF|LVS{tsIC9Y@-|-A5sK5p=9iQ`Q6nR00C9r@?=k5~vELR`&L#N{wOQxd$C z)S)Sd7kK#nLde}OZY_aWwS7e#mZhsN?csLcN;>W_*IE>Yv2Mv&P;t8hsf=RmChE48Up~n*WQ)Fn4YIl zQOyQ3G3u@zI8EL8(v0*$L@T+(hF8yMvFZ!y$J5AWWmR%Y!I7%D!znj{+*@JixUljWb8ZJLg+1F5Kw2P0f- zAAcq+>zyM0RPcU?X0|QNXu~Vbj0$eVj^suW>sQ6$W1}a|{4H#@O&{e-pt@Jw*Py?+ zL>!xfg$W7EtA`(Mgo_|7wPpY}LVxBqB2Ll%`US5II0>pm42vw@sXx`-ny$9pC@~=Z zx%h>}WQGC2g#u*W@!_ScEc&Zc&)ce9s~CSjKkL;*M+G6!-DxQK#tz8S#b_0T4)f&V z;sOQ(pfNvBXI(!6XzWXVVaU+^+n#t-_qm$i%!K(0`(TkIb3jWwe7u4D_4mTW?QM5+ zguPGF3=LH`vS%YZmnF7trv*>bvpF-Xek=y{s_S=?p7QJoa+AsXQ;zdQ8NXQj?%DZ4 z;|GE)X)MrOD985%t*VNQ$gx6`%iQdF?Oi877;nBxn6m%y*hpijvTeu#x{${&X@x>@ zs=|K?iA-GkXU~v>y`+x5QNN(@7$y}W84~Qat~Yl ze2beRvW$UFe6fVDXrwXb{3OT=aeQZ#5Q@fKODDvmukocVF@moKr^&Z$ef&Af$NF$i z__9yui*+B%n3UhnH|X&zjGE6E)0Vj(@0L2Fd8-kvE}z{F?TyIPkv@>yo|u^UbNwrbEtcAB z6$h}keBCck&bhgO1WqnX-7`aNik8_B&3MY4O%g3{c5XAad~(u&ADZFw1+;=ptn-Crat8Ccu6ov`dNU`@>Mu9s*k3b@l`VRlhm| z>D(KaPo1JT2=-Q2lPWMyS=iRrEE3_@XeTbZmVY&B;7ZW(K9a4{a^jN|g)^v)m)!Ul>q#qx?T6(W^o)LA8&62T6~PHi83@rq2mV)7bC@s-JE?b z08smLPdx9cU>|_()0fZ350Ib+fJb`=sq&gNcIJ~yM0#!<#Sk{y3atpDKDru$IwqB} z?X{nFQ%qkpl{V=AqJO%>xUK(6y7k|Q8Ne&!X22?_MMI{LFc?0B0h4gqQ_yJzU~wC7 zFB_M>uoN|QbV&DJ@7C@L*BwL`ccUkpPS!fYCzo2k60HiJ$Z?*2ca{Tk(Mg+e#K7}359Sp1K3+Nkot&2_;o485*&uV zaH&c+%6=Bv^&c^iWEuH4$fCPMl=QLYcTzP?rrb%oUxRwC&WDvJI? z_JDZXq(}LfF8GlR##{ps{`T}x z;f)&iCl6&^E#tDX_vMK&eFrI|(g*57fv?X+gdwtmU~+G5kN`iRoc*d0apenPQP=jY z2V>?d!8m+F2omq^rHvb;%TP6g{WHkYR zxO5a(zZX2}l@g>#KK=&W#VB7k1a^>L{Gie$4PZWz1DLCL7eUV(w*=3d^Q2R_Sw{h5H8cTz1D61bi2z4%uW5J* zjnb`u$l2r{-sqUsmM>%qT2C0qE?4vc%q(jFNquJH0ajnrIUejDHZEe$^82<|1`=Mc zX<$k_;bBs#P~RW`m{3_2ZOxULxrh-!@}}Z{0$>jfn|Id9XmW|AxbB(7lnL(#stuqn z)}3#^PXRF7MF2fc@-)XZ$<)Pcd!*YPVDSnF2n;9r{^uKbz>nl!P#9;Z#FJ=Q>lE<#R>FT}Ht6Y{aai`&bS?#(Z-$o>2& zJ)T=>3U4QStF-RP<*OAQ)ZGFc@MoXK>s?PxEO|Ng$%%>g+(Ilb*~{rbAgiNTUve+g zLOJS_u%QO&eENy+A2t)Q^?k5KIhs#f7L3|HmPj6D>hq|9w1|H+2)3QllO#-(2;!g)r#9&_-=3W-@MM2 z{l+SnzJ~BHTyz;+@yAbINIb--ePW=57S$w;$c)(QbwA}XI(p~EkD#P&)^_eJ;cZ^8_G&$> zUvt?GjlEi3t7O5w@#c2BztbNFUha9Fa2|X@6lrH~pDGT6!Mq$^B7S=)lRd=YeDF7@ z0fT`4oltK_I1TSceyY1&A@qGW#aLY6K|j2a*`FQ&aLTp>KHH6{ z<>h6L57R#j+DZ6q-l?dW#E`n})}O|?yW88_yPYlglelBg-CmY}I0Se4_3#@-?n_|+ zw)$4m&}F{K`;{n?s4Wx1%nfMomggcBWp&le$Dftu0Bf(YSE`y%yh440-YJR%gNX{| zo0WHD$V5Jylp?oRIa(iL*!-B6m$x|o8K-v*!n==izXt6NhymVbG2C%?RtOcFba<`W zs6C{|O!51(%!yNTryXERjBHsTq?E}j=T2L)S0Fv-ebvQVx67-lwkF+?dSF!<$jD@V zq+#lWV_hQ&n#I9j!t@AoqrizH1VD^>l5=PKOWK075NLYP$*#v~t%)A6x=&5Y!MAga z=7tNK0W={UPB?pjKwjp}>2G%YWO|aRdJfr#jEe zD$lNQp(_9x-d1it#N#>xmc}B$U$d8-11*;3{+_#E90dYk=E{adh3q8&Ks>SK{-Jie z@|%F;2Y1(wFeY)4Q|fvbEDdD za$w3V((l@@XU0ly5D0`pn5JjjaL=i~=bR?p0k*w-4C%XRUs=B#9305cqQN?%XsdG& z$Q_C*C1ji>VukVO2z^6*c+@y7HPWv)SGs%Pk5M8(BPBa@KuCt6ATmpyZGXUR%US} z9v)I!D&N@k;crLyKEUAI#JAfz02zQkBV7w{@m9i>albCifkCm%1jSZ?;0QdT7-q>+*gEip=;(==^WL!|2 z6_4Sze<5Y|IGv2d%g}(qPBAGlip;UXuhN;M?yDzp_7;Z6HIJ^yEmtFUW@vKDr$Bjs zZ?2ScXwH#P{{^29dDA=d!3-Ia?YCPu*W*C-7+oB#m&`a!86On`@z4c6f)ntncwgPb z0D7NRO%HDUg}3hCXeIN-RR0ga00J#Kjvg87RUFMwb zW7~Yl3M_V~^W|8h;T$E+IW*Xr2pu1@Nc;veKq5>pw-Nti8fZf*O-HAcw1F%$zd?F8 zziUTPURg7F`T1WV)L~uV4>hFd~_V zzZr;jw7CJafzsJ@uBmwT`RY=~+VGdh1)55n7HTSeN#xwND-!DuwOhgjb{OrPoWqM` zrD`g9A`TA3{K4>G&I}+GDe4hbo?>VFChy`(kIC$hu9?~KkYamn?AuZ$&+ShrO6oe~ zxe5m#rjI5o@bH_!uk6Gye**$2tKEo{^0;U{Q$GR)!t0Sz#QJ`vx5wg%Z`A)3-fLXw z2Y+8iMEvk|W1M=QPcKV3)|~`x7z{eGAvJMvV*X%m{zu2(Qe>|FjEoH0PXGH>M;c%p z&LLSpwh&QauoTV+7%J>BTSUu!{f^iJgJ#n0T{!_SntyW(SO=JhBj7~^XQuT6#Srxk ztb_lU^+y_F79|s<5S8A421u!K$%pkpL8&(y_6`VOlkWec;fn;_n>O$@3h6;wE`C2= zqBYSEU|Yaoi9ygG=SW5*Y-c6lL`8uua;;=Tv+UrJya5xm&4`-PKwP|mu6QGCXO@1Q zXoY4iA(8m+_W^{@fnd;w!~E)U%@&245;+hObZaa7sld%eXEnURlRaqx;c+fF);-hJ z;ks3a;VF_1B(yNm@Ujb3AiFX! z@mbnj6%{P<$+eQWyJ@)$E7BoaKi>SuymFF!IAEbFH3F8i>g=9S6VQ9YmKsq<+uGl} zCcS}F``(9Sa8k}Z6$nF0Bs5bSHK()T8w_z&;OAa$cZ+W~LIr=l29zMegsD_DNwlw4Jsq&h$EA8;f*f5OTak(0=ZU#;pPgRYDnOXTYgRgu8vA@4o&#fwE3b zOH8aD#l42}o*eCp_vuUPBl$}+z?l36nn7mN*!?-b9@-q0a?zP;umDELnNFtB1sR;2OIpcRE`q z4d`MYpo<|nkGfAfe1>6fzrRBlE3v)UdX5cNAdDZK1+1(G5)sO%q-JkrLMb@*O1r3Y zbIgvMX8bAJAfd1y8F3DQuALNt_<$)+Vx~{PTsg&hk!-6F*`5zV_`)CpA}9%I7V^)K z#`FyzN5HbwIu*wWPSU%viEI~f+52_2r|8*fBmpk!zk$}lYh0iOMLCZImV$Bs9yNVv zqh~XA$w|f7!QB|j;m{Z!NOnpo=^sW64vrs0$fjk$=h_r=3WFWlY8XdBr?ok72`nYC zbOWf77qozFJt~t`aLe;_%9p@Eb8(xw+-*k_xztQb2V14ri43&9e!oT}?GPJL5NLfk zcjJ)W&J5hci$rIWB%1%vHOiqzp}{-;pXDXgmjPmA-J@hfcE|%xk)I5sY*J+7H1G z6*NC0f$z3psFPY?MwM#Klz_N`f!3A$?=ma1lyVzftrgUAL?0PlXguJj+u%@Ebc_z~5@M zDi`}F4Hp|4d{$bLw_p4zL%xtZ1nBBrD0@=_qP(tt{O{3xC%@P{N|X+&ZpQmO9$M(S zF!kXI=WSp9aJQYg#XFp4WVa5|$rNCgdsfE)Tl)fAx7LHF`nA4ZRyK zy9_g7_yc~-6yoFO2OVo4hn3_hZuh*=7SsJH{z+yS<=tFG0gYs3{Pp&#F9$fikYsLG zC|$3_I|H``%z4rX4_F8J85#X}u$RnCz@`Tc=Aw=^M%MNrRd}(0V{|P=xy%b~fo9@& zcT|bULBK9g9K5+UYNCtfIyZ{6|N5}iHCE>#*yIk$Aa}nU?{0a?Vwj}_+fP_kt+`H` zgMnOkbC%dGwp^h=%gZXKydR9Y|4(L=7wV*L@lt5$#+Y7c^qXuT>_Gh~ak@ItW|B3~ zGk{Rb{O^0b0v~kj?VsHIQM4i5WH=Vtt&_Iju9-NTt=-6zF4mo)=Qz4v;Z3*9FXVBZ zMQglJEpS~;;O(2v}w-CVlzt zyY(72L-JPmpG{jDog^*iNlLs;TTqkmZWl>PUNj`()!$xctc?a;!dexf75nLxG7vp0 zHkk-$WkITYxcmFi8ecx}^YvEMRa?DQu8cdWj-d7uUJHX$_|7;+Pd@G<2WAOV$5$^OprGv)5)t>O*`urdhAwi25 z&T2<1Y8sKDJaBtC7y7}dttE~~F9_H?&q8BkdEqG$5o@4e$O_lM;p5TstG(bGtZHSj zz{+|zc}(O~f`|v`IiRhg;IL+^M5=75N3GRiihHxJlibm=j8Cchu(yZ5*ln$!=WD9r zTE9u6I={YXeCunVb<28L)(6si4EKW0i24Gs1!;Kpg2tOT7xln@9b=qnZXIC{_3k~0 zrA<`Oe*gLY%80sNFlfn|+i(pU+_r#z3K~rTuoU}I_q`VxV`GH;(3Z3I%X@+o&O$`uW1YfK}Q;zb1=ap1*HhEeqrc&fQo-#x=XupLH489XlW-<@3mo3 zXJ&Uq7aB(cqEB96n1OF$X59Ifch9+VN?_%2sz41*qCr~Y|9pu9kphvVHUT~(0`3rP z^n*KA!v49bW+HdK7|ctJ3260xin@!9{C$viYz0v~s5W7s3O)zLY79Z7G-y!SQy%sh zuACd7p|{2e-8iK=Vj@jusXa7QCX#;nT>+K2UkgZlYjd|jkraP&hjGd4p)C2Y*QawL zoBJS~I1M67A4gQxTR~)FVvP?Z7K1@bP^jVddRJBx&gsIpr*E=_zKzf zNg{|uKeBE@;ILikPH2I_xLCu1uL|fJMn*=URwI)9^$S*Brb*5wS0GFm1Ug7pnAJh$6(I2)6VxEE zTgM3nNrM`|xQRtP+_pfIj1th=yA2c&(oio#MT1bxl3wmxf7&GyEPg>s4dP@!bEQVu z8xT(i86wAbM-?WgyP#1EDZFkUoHoaz5Pa@&wM1D(U-uI<{W1dLbG+?&d+IsIcz!7Z z((p!5MvkmiMO%9+?>Wq?jZQ{+4Hvp?ig+Z9KAXb%Z@9(}{TJ@F1qDw)0nJ!i38e=$4H^1VkMeD|8^R2@@SCsgdlJOL)$a4)$vXN%NSm zOzU?14L8Td?I8b7Lv5JseiAsOAW%3`GBP^a*8QE6^FlJ@(Ztp!3p2Cf+1>()dvX1V zR{jzqjFbxaz2O;kPhPp4ei@vtr(t4avjcGxSU-J1Q8)poX>SnJ%`qMR+Xq84^zf_R z6*Z{q+6Op)VAVkR=m=VKd$%&vzFwzWxT>n^=q~*%s3zi^goF)x08b3intU-)F%mMx zNl+XogVqD$lg)dbaDqsB`A##C8{n4=CXF5;hJ!|?puhB7G29c>VRgNa-N|~QTlYM@ zend^%SV*$jf;0pJ_E%a)CY1*EMM62^i%Qjp@VtU2c950}B7K1yyWKJu#wGx%LR@&zKm+LDWumELy*}EdF(fazlnHyxYyRz2?F7d9U%tOWeNlnG-AO1 z+Rz9J5Yj+Y-IRj|WPvL6(L_~MN9e)*<3H3efu81r2|*~Zm%r}or}##wlv3?aWqxg8 zgKi!JZa(_s>Tn#S0YGEco(7No#a3Vxn~qf}@-0V!u)SQ-Zvb`&6qQMNm-gOlu^+}d zx5a2VGd(N?V-~nGEP;Sp(p*;6S(^JmMzR?Y=LvVG%z(a zDi$&B*jroqqTl&r*6nZ*MISX%QCElC$iW`-ZcQS^Jq?srRehOfp&qCQs5=t@tnI8n zoh1Wl)jT*3G5h2DB3FARdbjN&Hyj|BmUMA)Jf=qp60N=FL3nsGHTGR*ywiBokFXuD zcpjBy_C8+Q_e&mO`a zAl06E$;+rlN#$u*NynYnL$~E8oBL*70+x0=C<;s(h?PHI-7KnQodvrPSn^=WT!G@Y z4r3=0QhE&{Q2%&Lj9naFj^b4rnGqp92AUS4_J`1dH8@zPO>1oYFKjf)J;h>H0%T2! z|GLb-n?}m$x|%>^-YoywDH%bAQeNh1Z`9#?R~fpfMy^v9#zl9zNE&G$5r_i#8^*lz9`E}H)N5hcj%&;fKJ4LW(FC`ffQ>f?~meY{le)HZbX& zhFjw$nsb*>o(vSp6*stpYRWd&+AZa|S(A7x()thJ`;QM+;UlUsMiNu=iI>qI}o&4Iwm0t0qXeV?g~mogPQLP6nNorD<%uOL$z6j@a&=W zpMN7{L3$Nz^$%@Vdkf(>B+qE?3!lwTqoKpDzT)@yn?wvoNOyKkwu7-}qH3H|3 z`EGvsfFY}C@ssSNg+MWAa4>`;8qye0ZA?0d>r+!hDN$%+Tk;`LFgv<}GJl}XDRRIO zw6uTe?Cc!hjtNVQ1CO8ww9~m?NCPN15CAMIl~e=ml8J`075mmKWX@N`pv*f|(*@G8 z>lu-Ypr@b#s17P(QBw{@C4RM2r!oT^cE0Bv!kV9;_=dXd9>l_0$N6c1ag& ztps3}A=pevWEMQGFIs_8nKEz?K!WBI%ZeK8Aa}Ng7mtbY|I^-gMpb#P+oC2hZdDQC`p8cfHVtTiGYA2ASk`v z`Fyzd$sOa2amW32#~J6Y9|^AZz3VMcnR7nR7npu1LUL=uo2$+_BGb{YNp$M*xowc$t)@~r2ITO`?` z18$WOPH+wG5iy9c#obdYx6BxB!75jl$)p_LRyYu7Uo4IP5(C&i))gD9476Z;sD})C zHr#OuZp_9DIfd*aL9Q!_$G4fXjp7r$Z(V_JOwG?HateIMx;cxu#m>Civdd#mT(laZ z)5#;%aWc0*O(WOvKD^&(H?oJ#U=lWCjnyc{qE8JLr>;Sc75d%J9ZKMn)k0>ohjmBN zQ<1mq+z3ixc}i;PEVkWoRa=U_WWg_1_i1_v2&XgrY(v@^1g;02x%T%YK6cv5Fz0(> z^t6!)u|v3gbkxe#nec$p;lDpW&__<9JL=oTfQV^zZ;3Ng*HRO{etq`4w{TIy(`o$Pd8AzLcaau(ai3c2?JXKFU*KDvj=>c~gY^cLAH^+B0D>|7XdqDIeQZm(j(3V5aXuv8s;M@nu(2?@g@6 zv(HXRA<=XeQEPnjW1yP$aV67B@_4#t_SZ&!w_0-Bzt5BN0m5XQDJ=m3|w$cY32j-oDiGCuDTID|139*ABln+MwPQcJ20mj&A-h8!78P zvcO92(d~QNLWIF@;evZ+pLZ6$%9SK>P)N(@nipk=5vA&MV4z}()=jZ^3e3>l0n&sFP9KwtMWhT8XLkEIt)pM=w8(kR%LR?h+?}e}hOZ?! zJ^Bzitbd(PIi56t0Zu<&W?neW?B6R;DAA{v{qD3$O)1r%!3VxQ`J*$}%bT~iZLf62 zxZ;%45#QE`9q_5Wd^?pf1YG(Y=0f`^6!y-?)(1(p{o4-)lLhy9h1e7C{JYFfv06;4 zA@{?^xDVfjgdE2=&mNVUf9fe?U+zBu1;X-gZxvzQ8X%mwNK$#nPmj)xFb$<~Ed|KK<-PXa3%phu|DB}rrho4;KZZ}{_lXW!5lXB0;>9;3 zz`LE+VT+nNua(~g<{a-aV8&&piJyA~n@n3%lL7j?g@kEtOJOe#m4>ILZksicSDDG# zul9*6DdC{dGMYcGxIkw7*|$CZBhl<2Vrf$W+S9hhsrVniIyfHwsKIJHv&Vq>v>6bK zB|El?`rppYo%ICDhv*yzDSt4OyNk$Y7B1UzRtF#IKAOMldeFA5YRaj%Zhd=q)kl~r ze(WOtYv*RjwvPQWvLH7_U0Ka+a+!?B`K6bM&|EBHQYdfiIriQKr9)-_M>Gu#_>eFKLDwWc3@ndF=GR{i_lHA$ zN1?-NW0&=-M2**SbIk*|?+<3^QW0e&b?zIx71MU9TI_vsgq7n(srCCh6KiJsWG zz7~~WKuaZ%O0?s)xW@=&830ggb2k0z22~C;xx9n|?lgLwE0YAZ8fl>0#(T1YaYtE} zTNO$i+JR*SfkK%HxOeZ~s#a!E5nlkzWbnP(=}Q7`TYgw7uy#Jb(@`qMh|~bC_xy&~ zhAP~4x8b*XBPwUL)s&}P{u44o2c(Crn-E znr0vZBs_D??mA=-JP1)4w5yWUEa}#JL(HrV*jL52FeK`wbLOx8X$D}7#CRGodSF}u zfH2on$R9*PCnu;HZ-|mEXJG70S81U1TOQzXY^vT95<=j(oYbWG!%fy=RyA=JCG`0lQr0@` zC-?4&XXX!<(|w{fO|;yQ|2bfxOTS-j_>*;gX2y(T^x5*Q1IU-N^@!QW!`ma2D@kpG zYES^y#)AFkRmw4>EWLRKpK#k^jTjRs(GXGVdyIgiL^_dI_9d^;BJv@w-TwLM(%=oj z^5PJU0S1J8-4CL$BEA0YxbgR;_AIX{YX+SYaO;k5z(R;u^&rUwl-YWj^3?U0)-Jn$ z|2{YO0OXHblQbe%%|~)-(NAbL9%D)0Ny=(N0eP;)K~<$S$$18qQg2E0#=&$vEbHjs z%@=PiRV|u{*L5^V}jp#P-)Fr*4aZ!=T$t2NVM2x~u0vCJ6_;P>n0} zSg`!yfgQSc;|h&*bXGxaXFWJcmN`v_MfNoZdvCGTL+Zj) zUNh4U#oVg7jZ;)(S=-nAz&^Aq>E#C!8H$e&0|vBcwFYumW>o}S_Uhr}NH&6fdUPP5 zkd<&~D-IaGyg7y^F)`8hY!+_OO$9cwwGWW=NV`twyM8ep`uKXNH`C||hXrZGr-{YL zJs&%L2yvKs(CWF%-*Vn4I8FBq!R zYt12$jHpcP+I;|lH_zgX~5WC%wBNa^Gd3%>|fp4g({}K9ZjO#u5nGbt2_{JM%nNvFKk4PU}$jso@7jfhNFq zc-bcm9h4WXk+ce}mjW)QqhrDDhZtNVs8Ne(07GalWqD_9op+VS0xSKDlVe@5N-EG- zI(o(epx<Rs3RIC&M@Xqaohsh6_}q8Ws!#cg2-B3P zlGy-;?qPc7O<6~aweFv?WgjVio3i~F;7w?iRVHS^h8B&q#Dn3S_)7w7K&GVhH=Fv4 zn3iALnmtr!AnhLIQ`U53Q$}RkJE=$NTqFG;g-7~hL$hbUth)u~wS)kbV8v+{6_cG6 zidnkZLQ8giy6XZX(KF#T{D~KJVn;p3U&dCQN%vV$_x-|Er}(S?ytq(WcGK*atcc+M zJ-4BwEObNk;ra)vaCQkQf#qclgppw-I62h{2CelNqs!+%=9{4|y}~WuXF)q{>>jZ- z#uBE*zUigUuKV1IBN^VGU`eMn#_N06yuR)8q9;btBiq!Z;{% zSV^YCzf>bc*cpgqKp_Z^)W&*62<3TuNka~V-Qk#I^mrB$HJ@#a7{BWeOcxA*% zf+^pJ8F0$4Z||9y{+g_4d+nFXI*JqMT&T3t{7~VuzEoc+DU%z_!;L;R1K;%rwxNt2 z$8voGeBBhK8QEuEr;Zn%06Pz=;b!PVebceQ_XFTr>K>qkyywoHJ6m++FS4+^0t6RK zg5V1V){^<_oIfmXIL`B2mk)(OMy-s&{!Pn6{P{Z%M09w%B>e2 zwR(#R3;fnrb78LsEoUoaKHYt)FV%^tsYr8hq{%v~Qg2Y&bbn*#?%{?FD7{BtKZ5M2 zj&vYFa0)iS2!HaHwN|tH9KWaV1tO5`L9(4U&U0#F;Ax$++k4Inzm z(`;RSM4p>=X`NZL$6y&>?_s;=IH_hJk^BH4D=8Jj8Zwb19Fn5XV!lntUjEUnX1vpzf&#WrE&#qvI9hW9F?+GDgmPC z#K0z7Sz-S-r<(p>$@RDYq9h`H>e&?+wE1gGP0RG#d*KHu*SB$dFSLD3%L+=4^_0uQ z9+X%4ads4q$L2;@G^0vaqFk}-&*IC=7wYId0;7YL2>CKyQI2hJ+Wu5HIU9mcO^gK7 zU?>Dnxz?&S`z6T8xn6!q)JlQ<*lilTdw2`OL*Yg6udLY><@!J7K1w8hwaO65YY^I5 zINBD#%C*E<2$Vgjdg|?qS{x0fPLaR;vqnJ}b8FPSZgvb@R=YPMYC{ggEuaXs3!iT9=DMn_(y~JQAQZCg_;V(=z zkNBwrpAe{0U>^0>zwMZ>?7|FfAjMfl({9hBcS$4a;@Pf~PA3F5TJ>vhvosl8XvO$% zL2kKu!~o=g{M6T%lra^fCk8$H!;-Z;42=NnUG~xZ5|LKNRdPZ-^J|Dq-ch1 z=-g;NfZ`@;w|snq7%~Fl)e}%p;~KOij;;6b*ON$%BnG}hY9&!EzZ`vZ6ivZO4wYV7 z#JddevF#9>_}+&=v(4IyFOwlvscuAI-A?sRWxout%RzecJ<%}Y|B<+!Ch^;0+Us4R9pzh3>Gc36nVKuz8iTDIc2=spw z^*dPDN!<_3x@+Py{G_4}Klte8SBi_SChH;(?=yhJ5M^~eELYF!nB7D=_~ul}ZO5|K z-g3_bv&iH-C8D0(0OxJLMj_#RIe-dZQBJU+pjt?7x5A2Kk4k9b-mpMF)VIkb<1A0~ z7kT+-B|FNYfVwU7Nnd%l^#`}}L-VZM)tZve#LY{|%nSniBsJYSGvQS!=jAVw=L>68 z-L~5G9LiLSp<`%j>p6n5Dyp>Vn|te}>tNaDb|f;VnWe)Cwi#?O6!u0ANg;j9rq!W} zAFNF#^x-K)G>whlrcP)@H4UmR%rLr5^JIgJD`U-tpnm8+0!(Jc>jO9aBHcwFU~~Xq zyK6V}SodR#-aychZd~A&)3r%$8D$@a4kar?mUsMCrOp+4@WTA1K}e!4*hUC`4M86` z43w<499rZ-T)+*ppv$n6l0B28WFfjs6*HO>bhYhTmhAR9hYF|ASilea*ezi?%tJcI zlWXiGG{+}jA(cN-ICy>Zso?aLS`#g;Wz9A@8fbVRfoO8Mpy%kxsci6vibTe`bX-+6 zg+VSq_l)cNFUaCK^OnkA33uN;P`sSdj^YT2$;RD>K0+kEB#`zJRlQO5qaq6hpyVN2 zU=WurS5k6T)c4-!KK>z@PvbV*PyjUma|oVnNtKfzV=PLZ{?L1c~^y2-$j;M)kEsnaFr-ivH`r#;2 zP1?zb-WA&~%|mADfQ*Pk!$aU;ox@?HJ3$*Vu$C?BYcmWUK<}XobF+!;1l~MUN@*_f zVpRH7p7io|UPR6nQJS47&+!kkycOefI)b6&J!$AzDaE(ZtB6Da%Bziz&&1?Be*Bmw z_a{0#bbTQqoh)PiQb8{dhhA&s%b*-<>Aq zJn9B;qKXRp3B*D2`DGwcl$|2wb;$34Lg%N={FoWampg@><&xnO89M=z)Z`PI2#7g# z*BnUH;_U+Ww*%^vLdxJ)EL^qYPJaC`3dv(&R%Qq9)IANHys~MQPO3?K$&)C`KTAQ#$}$;6a_P zmye=hn1*&WKS9@uQ)Qi%+1T)jP`$4wb%M_l{s_(K0rXPvC2JQLQ}pY*KO4wbt6w3b zW}~fU{7mKPvKqpXbWHA|Zg&Mh6dV7VZsE+L>G4sjdq-LyOhuBz@Xe#2EEW04aX<&N zdBe$?h_jEW9+HmlP;dPHOB_Tunln$Md^`t3V~eRp&iHWYYd*J;zBjifj>#*;M-tTR$# zJQ>=r0eQP}yxjJnXv&uDj#$ALJsWP zet$s*)i#!pw4k8fnlx2Nf2ah|q*eOYzkUa(_J_)XhesacO6|EX52x8<)VrXty}YR? z6e8iG%lL8Gg+}`NYp9C$2>au)O25#y>shiBWy@+Fxg;)?G)EPq1Dd_5Kstluf#Sv} zXj+C!b8dRoEnQdrf{Xum{O<4|-*GRhlyx#Pdb24DA~03)%n#VZ;}@1HJUdqGOgYzCI)|s<*t+ZW>MHWTdFH(-DPDdcl_zLgGaz%=6{UUb!RkC>BZ-^W$SBjQw zjCFLbijw86k3u*h60k1!I11dSXss znR6Auo3BU<&ki>7u*`wIe0cZtQhoJ(c*(Sq*2;9jJ=0QC5<=e2x4?tj6-^BX6f#1% zq9pn5xC?F66@u-KK)?a2&0MtruHH%rIm}R8969;g#Tu59Ab>FGVvOpGG;r>ArWK&qRZ_Casbo38^*wOe(KG)Fn7utu zMkw@yu`+2DfB`32M&;vSJXdK&YR~GEr?{dGo{rp;_UL52hlP8#W$_q{5~mffMsZY1 zLxTlH_QbOXOD}Wt89Uws;G|*vh2%#GZ(1yS$xD)uhdlxGIS|`7`R3I>e-OEuQzNiQ zP(eOns_fhZ>(yt8UZ`dwv(8p=m}LMEY~U_rdYDr=3qNn?_;e*x?xiB=i3%M9+*uC8EBOyuEY>2brwk)gd& z7l%y|E%Wvw-jH&&dunOssQ6OFCcpD92@#R&m2jOpaL9}@k^!a(t1O3!L3WWw^eX(rk=RP=aK;O$Rc zj|z=6HMtX1gYAY3XCRO|2Cl^$y0H@3NGB51~&G zE-8(QeLm`502Np%a>c9uKd*m^pBtt*=n2iqKx4IMUacda!s-Xjc2CkfU7mJ*f+H}) z%IZz&&cAmvCvxzQ`GUpgzhB4z=>ogml&Gru^Iv~dX5PYGLdkAY@5`ayw9Lob(oWf$ zc_dx@^YiKNtm=smAeC}=GbE2WYZBg#&8i;Hhe0+KM0zvIA|R6;AMWL&RX{F|WBE+~ zspVx~zhh_P)-@QIYLRg+-b8iH{cdKGqz~5-}`5K~t z7SWxTFU5Fb9;2Th0#h{uWhOH5^ypVR^e-4iJ5>ShnTX&=4SVgTP^>qX$Eu#9&e}eR zj_L&EA3OIxE)!cX7~wX8-YPw9;7Axy16}x18N8y+UvxOlu&KbO`h`z*Q>9vnFfw9F zGJz@E+9FKLcKYx_C76I6NL<%d?-gf=CbrrI>K&X^N68dre-q^U7)+t!? z#hAd*zz1q-YF>RUZC*nm?S&-Qwd$za!m>h8z2wZS7;iPFIfH*(e^)~<7FZ%mg&bQy&L4d}$y z4kAUwX^Yby(!WFgUxJhUk%6Gmqo~~?%eFTs@10{_wpe_8-SfsHYk)vdp5Q=947EnB z2p0_|5DmMxjhqSWq#PivFo#O41(V8=K!3B>D!F99ggX%~3QBIj+9@H@yO-rS9P0M9 zo15|n>Zu4#|Kx_S9eTvJ4UmqNzMpa$rb9?Qk&1<1qWg=1EnTlroU8m79o%&N``d(A z`Ip8K+&^nlJxUF_{LkSq`M>E3gAHL`z4o znHYl~xau#O=*`hiFI3(iJ?U5la&@%FWG13^1Tx%Iy?zC2%w=|TVa$D-!`7WP#XhG7 z$Wq>byuZcir3TY(OANq#H%uXpII$`16rx2T{Ac(?0!UL{6@?`e&V%b{aniik@sWC- zW+=PWbD{*cTG}!I(baqI{s&XI?fp< zP1Di;u}2K-D(H#aizt$jGzQh}LP!IpT;$aAn$D!%Nlo>`gGbWpXaK-=PuLp{m72gU z9^nj8b2gfW@BIW=)n9G~eMDvy0E~bG$0dMnH>Tjuwjs;lr?ID<$YP3O2!$Z-pT5gt zhvchQuZod3u=IcTPHJ&!4`i|}UN*rG9`CoCoUR+nVArucs#vT|9$MC2HcCqUZwuPd zthfBYM^+*%_0C5fA%T5kR4at(X#M2=!j)Kiu!}u2dIfI3<>UMgARU!u_$_`@VsMCe zr>&b%HsFzzm;qDfN$p?!VSuH!`-q|+@BlgoNwphe&_T4|jWhq;B1bJrK;k7APEl(d zY7X7?Rp8&d_w!SmgR2brriIcU30{epBDRwXc_M%7BhdlE)XUW5Bw>b{tzZj3P~Vtd4zH)KZP@xx;$RpC@0u(0a1b>Ud_S zjWzXgKB3|c2!tGEeoJHd$G($d35 z*oRK&DtiA3xmVMDc+3W9(#6L+t7Btd^DM3wgfU5LKc9EDX77CSTsCh)!8jPQ=vuqp z99PI~oD*y90&#@RY7ym>q83K)cVDY4v|9YYOhkE8FWF|yrR0;ZqnqE4>-9MPycmZm zPX!cr0$PsovZ;d1*euS)11+|nkM8{g(8+Q{g=lwCr(j8O(!fYEXbEyZ=D9VKvd#Lj zEvMJMlWlHf-+MI*GsL47Vlx~&Z;8x0GpZi5b*iG0B8Q-FG1wdHhexHb_tP^`3ti8W z70BIO7*MO}!g|?AWnc-tV~6&O7JSuOi_J(Xj`2e584(0|MM@Z>fn<`Uh;51pb{DD) z%ySV1EnGIA-vmh3EkuPFKFBDQE-8-UZuKNj5q;S58t1h7(~)EW6mk#_8CCk@qqYhjeq}%|X2|aSiy#abVpBsX zYM>ukKyC*rosimdApYOWt32CSZ5bhM|2AI+!G#&>dxqGA#cn;8l^A|jOsp6_L$ zS3iOAmEC+l|FS2ci%RtzdNLl=F~t?s;Vh?5f{H8h;7^aU9P<+4;z+Mm*=?4O|8z>yAkK;FOhRWpt+F&qO(ok0Jg|0?zF;wx~xB^Eom zY&AFC-2jlrmF?76OQktntSJZZwp{zd7GNn&>7u_`KV(r&d+u ziw~RLYA-K^+4gCD1GQ9EHv+eR-il9f2Xw}$3m!C>`jgxaGlb@DykGi(YX$r7d~B7Y zC>IO-LLP=}s22h_D&5oPm5tV>xdQZ>sSB6Meu4roRA$9Sy}b}*jyPV=P;8oq_PA1T7W0<70l0wN6N{$e!}=>?KxDScJA0D zmjsq3?1lo{jXH|*vP%WrZBCw#e`)~LlA*^y2`?K>5p4&*bW$lV5q4~MAaM@Zn)bsA zXc@1@d0ZS`H2MDg^`psV70y+8#aItM#nC^OijOaW>O(Piy**)JW07;6er%n1J0h+w zt%w=sHIL`h2SM;e(3#+1SBVdL^?=}GnJ08 zl`lJ_NZ2C}z+k8snIs1^O|ymAK5N7h)&r>clT0|Gb$;b`onl~FtFj8m&#=jOwGmTJ zioxU&gcZ@^p2fNVMo{K>mb6}hF|p8Kk_-}&dRn1;lzn3cL|W79#&m2_2`^)XG%U{vj9Z@w}0WM-U%J4J;X{yooN1Yhmvw z^c^omFG4hEEZe4IwV1#)H8)3tZuH67p7~IS8qft4Qmr;_>G0?HS!X0cBsvtULjPo4*Np+|_eXPO_t{8uYGy2UhF7}f*Zl_nabPpk z9}pNS>!W?uE_VeMkO$IfhK}eW#ibCDw_lYXG%Q7`BzAh;uWta=ry*3Y1IQS*wT~m2 zYFaJBN_&?kh)Q50KP%5L@hQp7BZ&g+n-Z4W5}V8Tsm>cPPe7sI zRJUrSr)9_HUbr7(hBaa&fTdBYDPGR%LzhH{siFE(V83BVbm%wjFH zS9+x?1JCNCqABf1Q8LBy6oj-vtrCPZ!ajHVS*$6OzS%wgkVFlAQG%5@t&2bgnFo62 zmxx;M^OcyxqXm*q01Y*&IRrw=eExl&87Wd>Jp#S}BV&9P<(3FQbZ5rz1e4Oup;D^e zX3!XEVK&UF`Q!oer^JnwAlUzSFa|i0B*@Xnqqr+Wh?spf0Gv@^FmIa_#rX|E?0n;G z$qP}e(GHyBtVf??to7HN4+L(3MIN9A^;9w$U`FvVXZxnONBUphJcn@#Co+tW zY`(@^TdFwuGX)IAz@hmGbmU|s04T*4SD2!bq4!586uxU0%M~ZZHZq zW(TZ^C<#X-{>@5~A%s9v!g*N9#zk}j1m@kwk+fRZ-1ot&6pZh5Low;`;h>2hr69;r z+-;8Yqc24%x(+WRbxVuKmr6||qiBaoV#5f9-{>XpcVK6%0kIDU+F0)~r+yCY9NVvJ zu94t^^t8J9E)MWAza+HJAbI7cKE_pTiDu>XQByK=T52F%T!~$GnM9SCf)xb!xu_0p zZqFy_wBmHT;@MTA{Pm?}xkt8%iUw!oHEqMeS_#Y@ZOZ*zU?k0e!UP66Aa^XqMAW69 zji=gcCcj0{9Wae(oeDV302UDpcPAsGQf3;UB?}rL0c0wA(+jq;#%|;YZuaK;h#B|_ z^&No>B#a{)0Z6^In}^h^WZ}!ZUkGJPIRJ!v7W<+yc5aOVIvmjb3CvJ)=YZ|{dW6_1 zh;!a_OHp43Z093`;pge57H!Cp{d^ajmyo_265N20kPzK_r_@M1(SD*`uo(f99D|5JASqG_}mX1Nnp#_UsNAsR8q0A?$Wl^r3g9n^nvMF6H%Q1Qzg zxob%0s5>nmjHbya1O^&0_3*0%w30!uY|@-#oALOu9DtgY)rvD7gp`Z94}Ve#EmB?U zpp*}R$Uw>wb}ER;4}1Z#r9i}6BMj9; z5g*}@k`a@=FjeTm6-{?2S+#E8XgCqG)HM}_jV&tgg%wXzREmzbrBd~VX7K^}axhNq zpc4ZD1t>!ET-f*pjV>rZVxS9Hj*VVrpPO5!Sq!j0sowA7fjIhqU40BB7 z*fB#AzXE}~OgDO|Qk_Isfn+Ql9iwYinalu4<+Kv(VoPY`tU;DzmQ)b`W01b!nOw^j*=%- zCa}Ct@)a3ET(uozcm4#D%GGaK6Os~ZW=n%876HIyY%**^{ZX(tGi%<}2HiJr9K;O` ziV=~np>UM&=!C(bK;D00NWd}JIsH}_JtnAI1opBoA_AnJy4*;IvQd~&jAHKP2DbOL z*Q;^#VTBcg(SIuc_+ z5^kaIfD*!8A}L58h44LDYZ4l?J(s9Hig_Fr&Y$<+x@hNrc+JxP|M|1~_: Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from src import schwefel\n", + "from src import baybe_utils\n", + "from src import visualization\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "3e9d8019-5827-4325-b0e4-cc1f80745270", + "metadata": {}, + "source": [ + "## BayBE Schwefel function optimization examples\n", + "\n", + "### Brenden Pelkie\n", + "\n", + "This notebook walks through a quick grid search to explore the impact of measurement noise on optimization performance of a vanilla BO implementation in BayBE." + ] + }, + { + "cell_type": "markdown", + "id": "44701cdf-29e6-47f7-84f5-abfd5112a2e9", + "metadata": {}, + "source": [ + "### 1. Pick parameters\n", + "\n", + "First define parameters for optimization. Here we set the number of BO iterations/cycles to run, the number of random initial observations to include, the dimensionality of the schwefel function to optimize, the noise level of the schwefel observations, and the number of obserations to make per iteration/BO batch cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b158f689-b0e5-478e-b7a6-2cb586acf4c5", + "metadata": {}, + "outputs": [], + "source": [ + "NUM_ITERATIONS = 15\n", + "NUM_INIT_OBS = 5\n", + "N_DIMS_SCHWEF = 2\n", + "NOISE_LEVEL_SCHWEF = 0\n", + "ITERATION_BATCH_SIZE = 1\n", + "SCHWEFEL_RANGE = (-50,50)" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e07e3-3daa-4196-b17f-8982e0598db9", + "metadata": {}, + "source": [ + "For the grid search over number of BO iterations and noise, select the desired values here" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0670300a-6f94-4b37-9854-d4b0b5251b40", + "metadata": {}, + "outputs": [], + "source": [ + "num_iterations = [5]#[5,10,20,40,60,80]\n", + "noise = [0]# [0, 0.1, 0.2, 0.5]\n" + ] + }, + { + "cell_type": "markdown", + "id": "4705ebb3-86ad-41f5-8c23-0f883b79a871", + "metadata": {}, + "source": [ + "### 2. Run grid search" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6a78944a-2833-407a-9bbc-86206155b2d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:04<00:00, 1.17it/s]\n" + ] + } + ], + "source": [ + "grid_results = baybe_utils.iteration_noise_grid_search(num_iterations, noise, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE, SCHWEFEL_RANGE=SCHWEFEL_RANGE)" + ] + }, + { + "cell_type": "markdown", + "id": "4f403581-1dd2-4a10-b98e-fcd147ec0bba", + "metadata": {}, + "source": [ + "### 3. Process and visualize results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a10e5944-1038-4bec-bb54-66a60f22ccec", + "metadata": {}, + "outputs": [], + "source": [ + "n_its, noise, performance_matrix = baybe_utils.process_grid_searh_results(grid_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "524e2642-4a6d-458e-bade-05106c17e4ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualization.grid_search_heatmap(n_its, noise, performance_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", + "metadata": {}, + "outputs": [], + "source": [ + "test_result = grid_results['5']['0'].measurements" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bf8e4a92-88a6-4218-b7a4-dcbd83477b2c", + "metadata": {}, + "outputs": [], + "source": [ + "x_names = [f'schwefel{i+1}' for i in range(N_DIMS_SCHWEF)]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "9d2c5714-e918-4dfc-a379-051c78f34e08", + "metadata": {}, + "outputs": [], + "source": [ + "x_train = test_result[x_names].to_numpy()\n", + "y_train = test_result['schwefel'].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "5da6d938-47b0-450c-a4c4-80590aa1af6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fe513817-58d2-4148-baff-fdc5cb531a1e", + "metadata": {}, + "outputs": [], + "source": [ + "schwef = schwefel.SchwefelProblem(n_var = 2, noise_level = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "3f4c8244-65bf-4e23-a57a-a1c04d49f3ed", + "metadata": {}, + "outputs": [], + "source": [ + "y_test = schwef.y(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5e207b2d-d704-4305-8d3b-e883dd2eee9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-353.1162661 , -115.77679551],\n", + " [-100.48640485, -327.1966037 ],\n", + " [ 139.7688944 , -438.20087406],\n", + " [-486.63820259, 378.28871931],\n", + " [-325.39646572, -219.090224 ],\n", + " [-180.23321345, 500. ],\n", + " [ 500. , 500. ],\n", + " [-500. , -104.2417874 ],\n", + " [-132.38714138, -500. ],\n", + " [-448.33851406, 95.06174804]])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "1ddf0eea-504b-47f5-9100-3c626ffa124f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schwef.f(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "58182f79-46a5-4567-96d3-0c8dda176487", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ]])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schwef.y(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "1c39cc34-8209-426e-b1f4-4761456e7022", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-353.1162661 , -115.77679551],\n", + " [-100.48640485, -327.1966037 ],\n", + " [ 139.7688944 , -438.20087406],\n", + " [-486.63820259, 378.28871931],\n", + " [-325.39646572, -219.090224 ],\n", + " [-180.23321345, 500. ],\n", + " [ 500. , 500. ],\n", + " [-500. , -104.2417874 ],\n", + " [-132.38714138, -500. ],\n", + " [-448.33851406, 95.06174804]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f78a53d0-a21f-4ac4-91bc-eb786e1ad5fe", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/.ipynb_checkpoints/baybe_no_noise-checkpoint.ipynb b/src/.ipynb_checkpoints/baybe_no_noise-checkpoint.ipynb new file mode 100644 index 0000000..8eb5fcb --- /dev/null +++ b/src/.ipynb_checkpoints/baybe_no_noise-checkpoint.ipynb @@ -0,0 +1,367 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1-dimensional continuous data BO test using BayBE" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno 11001] getaddrinfo failed)\"))\n", + " warnings.warn(\n", + "c:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from baybe.targets import NumericalTarget\n", + "from baybe.objective import Objective\n", + "\n", + "target = NumericalTarget(\n", + " name=\"Yield\",\n", + " mode=\"MAX\",\n", + ")\n", + "objective = Objective(mode=\"SINGLE\", targets=[target])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.parameters import NumericalContinuousParameter\n", + "\n", + "parameters = [\n", + " NumericalContinuousParameter('schwefel1', bounds=(-500,500)),\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.recommenders import (\n", + " SequentialGreedyRecommender, \n", + " SequentialMetaRecommender, \n", + " RandomRecommender\n", + ")\n", + "\n", + "recommender = SequentialGreedyRecommender()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.searchspace import SearchSpace\n", + "\n", + "searchspace = SearchSpace.from_product(parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe import Campaign\n", + "\n", + "campaign = Campaign(searchspace, objective, recommender)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv('seed_data.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
schwefel1Yield
0356.991347403.045423
1-309.004730123.758482
2-55.674007470.423643
\n", + "
" + ], + "text/plain": [ + " schwefel1 Yield\n", + "0 356.991347 403.045423\n", + "1 -309.004730 123.758482\n", + "2 -55.674007 470.423643" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from schwefel_functions import schwefel_1d\n", + "\n", + "df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "campaign.add_measurements(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "example_data = pd.DataFrame(columns = ['x', 'y'])\n", + "\n", + "example_data['x'] = np.linspace(-500,500,5000)\n", + "example_data['y'] = example_data['x'].apply(schwefel_1d)\n", + "\n", + "fig, ax = plt.subplots()\n", + "example_data.plot('x', 'y', ax=ax)\n", + "df.plot.scatter('schwefel1', 'Yield', ax=ax, c='red')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEiCAYAAAD9DXUdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABjZUlEQVR4nO2dd3hUVfrHv3dqei+TnhBCTYBQFYTQpEZURFTQFQQVaSKyKIsrLMuCoBSFVVdXEUXKb6WIivTeIaEktBBI73XSM5OZ8/tj5t5kUmeSmbkzyfk8zzyQO2fuPXfK/d63nPdlCCEEFAqFQqFoEfA9AQqFQqFYFlQYKBQKhaIDFQYKhUKh6ECFgUKhUCg6UGGgUCgUig5UGCgUCoWiAxUGCoVCoehAhYFCoVAoOlBhoFAoFIoOVBismB9++AEMw+D69evctkOHDmHlypX8TUqPeQQHB2PGjBlmnQ/LRx99hOjoaPj5+YFhmFbNY9WqVejRowfUajW3rbS0FAsXLoSfnx+kUim6dOmC9evXQ6VSGXH2lsnKlSvBMAzf02iRL7/8Ej/88EOrXvvdd9/Bz88P5eXlxp2UpUIoVsu2bdsIAHLt2jVu27x584glfKzNzSM2NpYkJiaaeUYa7OzsyBNPPEHmzJlDJBIJef311w16fUZGBrG3tyf/+9//uG1KpZIMGjSIuLq6kq1bt5KjR4+SxYsXE4ZhyIIFC4x8BpZHWloauXTpEt/TaJGePXuSqKioVr1WqVSSsLAw8vHHHxt3UhaKiGddolgJFRUVsLOzM8q+IiMjjbKf1lBaWgqBQGMo//TTTwa//vPPP4eLiwsmT57Mbfvll19w5coV7N27l9v+9NNPo6ysDP/+978xb948dO3a1TgnYEGw3wl/f3/4+/vzPR2TIhKJ8Pbbb+Of//wnPvjgA6P9FiwV6kpqR8yYMQP//ve/AQAMw3CP5ORkAAAhBF9++SX69OkDW1tbuLq6YsqUKXj8+LHOfoYPH47w8HCcPXsWgwcPhp2dHd544w0AwJ49ezBmzBj4+PjA1tYW3bt3x4cffqhjYrc0j8ZcSampqXj11Vfh5eUFqVSK7t27Y8OGDTrumuTkZDAMg88++wwbN25ESEgIHBwc8OSTT+Ly5ct6vUesKLQGhUKB7777DtOmTdPZz4ULF8AwDMaPH68zPjo6Gmq1Gvv3729x3yUlJViyZAlCQkIgkUjg5+eHRYsW6byvc+bMgY2NDWJiYrhtarUao0aNgre3N7KysgDUuhiPHTuGmTNnws3NDfb29njmmWcafNYAcPz4cYwaNQpOTk6ws7PDkCFDcOLECZ0xrLsoNjYWU6ZMgaurK0JDQ3Weq0twcDCio6Px+++/IzIykvuu/P7779wcu3fvDnt7ewwcOFDHHcpy/fp1TJo0CW5ubrCxsUFkZCT+7//+T2cMe66nTp3CO++8Aw8PD7i7u2Py5MnIzMzUmc+dO3dw5swZ7vsYHBzMvYerV69G165dYWtrCxcXF/Tq1Quff/65zrGmT5+OkpIS7N69u/EPsT3Bt8lCaT31XUmJiYlkypQpBAC5dOkS96iqqiKEEPLmm28SsVhM3n//fXL48GGyc+dO0q1bN+Lt7U2ys7O5/UZFRRE3NzcSEBBAtmzZQk6dOkXOnDlDCCHkn//8J9m0aRP5448/yOnTp8nXX39NQkJCyIgRI7jXtzSPoKAgHRdObm4u8fPzI56enuTrr78mhw8fJvPnzycAyDvvvMONS0pKIgBIcHAwGTduHDlw4AA5cOAAiYiIIK6urqS4uNig98/e3t4gV9LZs2cJAHLo0CGd7W+99RYRCoVEqVTqbD9y5AgBQF555ZVm91teXk769OlDPDw8yMaNG8nx48fJ559/TpydncnIkSOJWq0mhBBSWVlJ+vTpQzp16kSKiooIIYR8/PHHRCAQkKNHj3L7Y78XAQEB5I033iB//vkn+eabb4iXlxcJCAjgXksIIT/99BNhGIY899xzZN++feS3334j0dHRRCgUkuPHj3PjVqxYQQCQoKAg8sEHH5Bjx46RAwcO6DxXl6CgIOLv70/Cw8PJrl27yKFDh8igQYOIWCwmH3/8MRkyZAjZt28f2b9/P+nSpQvx9vYmFRUV3OtPnjxJJBIJGTp0KNmzZw85fPgwmTFjBgFAtm3b1uBcO3XqRBYsWECOHDlC/vvf/xJXV1ed72RsbCzp1KkTiYyM5L6PsbGxhBBC1q5dS4RCIVmxYgU5ceIEOXz4MNm8eTNZuXJlg8+qe/fuZPLkyc1+nu0BKgxWjCExhkuXLhEAZMOGDTrb09LSiK2tLVm6dCm3LSoqigAgJ06caPb4arWaKJVKcubMGQKA3Lp1q8V5ENJQGD788EMCgFy5ckVn3DvvvEMYhiEPHjwghNQKQ0REBKmpqeHGXb16lQAgu3btana+9TFUGNatW0cA6IgoIYRs3ryZACDnzp3T2f73v/+dACBjxoxpdr9r164lAoFA53MkhJBffvmlgRA9fPiQODk5keeee44cP36cCAQC8tFHH+m8jv1ePP/88zrbL1y4QACQ1atXE0I0guTm5kaeeeYZnXEqlYr07t2bDBw4kNvGXvwb87E3JQy2trYkPT2d23bz5k0CgPj4+JDy8nJu+4EDBwgAcvDgQW5bt27dSGRkZAOxjY6OJj4+PkSlUumc69y5c3XGrV+/ngAgWVlZ3LamYgzR0dGkT58+DbY3xvTp04m3t7deY60Z6krqIPz+++9gGAavvvoqampquIdMJkPv3r1x+vRpnfGurq4YOXJkg/08fvwY06ZNg0wmg1AohFgsRlRUFADg3r17rZrbyZMn0aNHDwwcOFBn+4wZM0AIwcmTJ3W2T5w4EUKhkPu7V69eAICUlJRWHV9fMjMzwTAMPDw8dLZPnz4dbm5ueOutt3DlyhUUFxdj165d+OKLLwC07L76/fffER4ejj59+uh8NmPHjgXDMDqfTefOnfHtt9/iwIEDiI6OxtChQ5vM/po+fbrO34MHD0ZQUBBOnToFALh48SIKCwvx+uuv6xxXrVZj3LhxuHbtWoMsnBdeeEGftwoA0KdPH/j5+XF/d+/eHYDGVVnXR89uZz+/xMRE3L9/n5t/3blNmDABWVlZePDggc6xJk2apPO3Id+JgQMH4tatW5g7dy6OHDmCkpKSJsd6eXkhNzcXNTU1Le7XmqHB5w5CTk4OCCHw9vZu9PlOnTrp/O3j49NgTFlZGYYOHQobGxusXr0aXbp0gZ2dHdLS0jB58mRUVla2am4FBQWcv7cuvr6+3PN1cXd31/lbKpUCQKuPry+VlZUQi8U6ogQAHh4eOHz4MF5//XU88cQT3Bw3btyIWbNm6VwcGyMnJweJiYkQi8WNPp+fn6/z98SJE+Ht7Y2cnBwsXry4wXxYZDJZo9vY9zMnJwcAMGXKlCbnVlhYCHt7e+7vxr4XTeHm5qbzt0QiaXZ7VVWVzryWLFmCJUuWNLrv+u9JW74Ty5Ytg729PXbs2IGvv/4aQqEQw4YNw7p169C/f3+dsTY2NiCEoKqqCg4ODi3u21qhwtBB8PDwAMMwOHfuHPejqUv9bY3lpZ88eRKZmZk4ffo0ZyUAQHFxcZvm5u7uzgVO68IGD+vfofOFh4cHFAoFysvLdS6WADBgwADcvXsXycnJKC8vR1hYGBckHjZsWIv7tbW1xffff9/k83WZM2cOSktL0bNnTyxcuBBDhw6Fq6trg9dlZ2c3uq1z5846+92yZQsnaPWpfyNhjvUK7LyWLVumk/1VF2NmeYlEIixevBiLFy9GcXExjh8/jr/97W8YO3Ys0tLSdKybwsJCSKXSdi0KABWGdkfdOyVbW1tue3R0ND755BNkZGRg6tSprdo3e1GoLyL/+c9/9J5HY4waNQpr165FbGws+vbty23/8ccfwTAMRowY0ar5Gptu3boBAB49esS5KurDWj6EEGzYsAG+vr548cUXm91vdHQ01qxZA3d3d4SEhDQ79r///S927NiB77//HlFRUejbty9mzpyJAwcONBj7888/67h+Ll68iJSUFMyePRsAMGTIELi4uODu3buYP39+s8c1J127dkVYWBhu3bqFNWvWGG2/Uqm0RQvCxcUFU6ZMQUZGBhYtWoTk5GT06NGDe/7x48c6f7dXqDC0MyIiIgAA69atw/jx4yEUCtGrVy8MGTIEb731FmbOnInr169j2LBhsLe3R1ZWFs6fP4+IiAi88847ze578ODBcHV1xZw5c7BixQqIxWL8/PPPuHXrlt7zYN0GdXnvvffw448/YuLEiVi1ahWCgoLwxx9/4Msvv8Q777yDLl26GOGd0XDmzBnk5eUBAFQqFVJSUvDLL78AAKKiouDp6dnka4cPHw4AuHz5cgNhWL58OSIiIuDj44PU1FR8//33uHLlCv74448WhXHRokXYu3cvhg0bhvfeew+9evWCWq1Gamoqjh49ivfffx+DBg1CXFwcFi5ciNdffx0zZ84EoFmRO2XKFGzevBmLFi3S2e/169cxe/ZsvPjii0hLS8Py5cvh5+eHuXPnAgAcHBywZcsWvP766ygsLMSUKVPg5eWFvLw83Lp1C3l5efjqq6/0fm+NyX/+8x+MHz8eY8eOxYwZM+Dn54fCwkLcu3cPsbGx+N///mfwPiMiIrB7927s2bMHnTp1go2NDSIiIvDMM88gPDwc/fv3h6enJ1JSUrB582YEBQUhLCyMe71arcbVq1cxa9YsY56qZcJv7JvSFhrLSqquriazZ88mnp6ehGEYAoAkJSVxz3///fdk0KBBxN7entja2pLQ0FDyl7/8hVy/fp0bExUVRXr27NnoMS9evEiefPJJYmdnRzw9Pcns2bNJbGxsgzTC5uZRPyuJEEJSUlLItGnTiLu7OxGLxaRr167k008/5bJPCKnNSvr0008bzAsAWbFiRYvvGZtx1djj1KlTLb5+6NChZMKECQ22v/POOyQwMJBIJBLi4eFBXnjhBXL79u0W98dSVlZGPvroI9K1a1cikUiIs7MziYiIIO+99x7Jzs4mZWVlpFu3bqRHjx46GT2EaDLAxGIxl9XFfi+OHj1KXnvtNeLi4kJsbW3JhAkTyMOHDxsc+8yZM2TixInEzc2NiMVi4ufnRyZOnKizupvNPMrLy2vw+qaykiZOnNhgLAAyb948nW1Nfa63bt0iU6dOJV5eXkQsFhOZTEZGjhxJvv76a25MY78BQgg5depUg880OTmZjBkzhjg6OnKpt4QQsmHDBjJ48GDi4eFBJBIJCQwMJLNmzSLJyck6+zxx4gQBQGJiYhqcV3uDIYQQsyoRhWLF7N27Fy+99BJSUlJaDCrzxQ8//ICZM2fi2rVrDYKnlNbz2muv4fHjx7hw4QLfUzE5NF2VQjGAyZMnY8CAAVi7di3fU6GYkUePHmHPnj1Yt24d31MxC1QYKBQDYBgG3377LXx9fXXKdVDaN6mpqdi6dSueeuopvqdiFqgriUKhUCg6UIuBQqFQKDpQYaBQKBSKDlQYKBQKhaIDXeAGzcKVzMxMODo6WkWLQgqFQjEUQghKS0vh6+vbYmFHKgzQ1OQJCAjgexoUCoVictLS0lrsuEeFAYCjoyMAzRvm5OTE82woFArF+JSUlCAgIIC73jUHFQbUFodzcnKiwkChUNo1+rjLafCZQqFQKDpQYaBQKBSKDlQYKBQKhaIDjTFYKt99B5w+DYwaBcyYwfdsKJR2g0qlglKp5HsaRqextrOthdZKgiZa7+zsDLlczn/wOSYGePJJoO4XVywGrl4F+vThbVoUirVDCEF2dnabW9FaMi4uLpDJZI0GmA25zlGLwdJ48kmUMSLEhETAu7QA3fJTNCIxcCCgUPA9OwrFamFFwcvLC3Z2du1qMSshBBUVFcjNzQUA+Pj4tGl/VBgsie++w1XvLnjnuWUosHcBADwffxLr//wcYqUS+OEH6laiUFqBSqXiRMHd3Z3v6ZgEtoVsbm4uvLy82uRWosFnCyL13DW8MWUFCuxd4FFWBKFahf3hI7F6pKZ5O06c4HeCFIqVwsYU7OzseJ6JaWHPr60xFCoMFsTHncagTGqHful3cf4/s/Cfff8CAGzv9wxi/LppAtEUCqXVtCf3UWMY6/yoMFgIMSmFOF0hhVilxKeHNsOmRoHRj65i6u2jAICNQ1+jbiQKhWIWqDBYCN+cfQwAmBzmjE5ledz2hRd2QaSqwYWg3ojPkPM1PQqF0oHgVRhqamrw0UcfISQkBLa2tujUqRNWrVql00uXEIKVK1fC19cXtra2GD58OO7cuaOzn+rqaixYsAAeHh6wt7fHpEmTkJ6ebu7TaTV5pdU4djcHADD72f6a7KNt24BXX4X/5+sxNlJT+fX/rqfxOU0KhdJB4FUY1q1bh6+//hpbt27FvXv3sH79enz66afYsmULN2b9+vXYuHEjtm7dimvXrkEmk+Hpp59GaWkpN2bRokXYv38/du/ejfPnz6OsrAzR0dFQqVR8nJbBHIrLgpoAvQNcEOatrXw4Ywbw00/AjBl4eYBGGPbfyECV0jrOiUKhWC+8CsOlS5fw7LPPYuLEiQgODsaUKVMwZswYXL9+HYDGWti8eTOWL1+OyZMnIzw8HNu3b0dFRQV27twJAJDL5fjuu++wYcMGjB49GpGRkdixYwfi4uJw/PhxPk9Pbw7eygQATOrt2+jzQ0I9IHOyQWlVDS49KjDn1CgUCo/8+OOPcHd3R3V1tc72F154AX/5y19MdlxeheGpp57CiRMnkJCQAAC4desWzp8/jwkTJgAAkpKSkJ2djTFjxnCvkUqliIqKwsWLFwEAMTExUCqVOmN8fX0RHh7OjalPdXU1SkpKdB58kVtShZiUIjAMEN2r8UUpAgGDp3t4AwCO3s025/QolHYJIQQVihpeHoYUm3jxxRehUqlw8OBBblt+fj5+//13zJw50xRvDQCeF7h98MEHkMvl6NatG4RCIVQqFf71r3/hlVdeAaBZqQgA3t7eOq/z9vZGSkoKN0YikcDV1bXBGPb19Vm7di3+8Y9/GPt0WsW5h/kAgAg/Z3g72TQ5bkxPb/x0OQXH7uZg9XMEQkH7TrujUExJpVKFHh8f4eXYd1eNhZ1Ev0uvra0tpk2bhm3btuHFF18EAPz888/w9/fH8OHDTTZHXi2GPXv2YMeOHdi5cydiY2Oxfft2fPbZZ9i+fbvOuPq5uYSQFvN1mxuzbNkyyOVy7pGWxl9Q9+xDTQbSsDDPZscNCnGHg1SE/DIF7mXxZ+FQKBTz8uabb+Lo0aPIyMgAAGzbtg0zZsww6ZoMXi2Gv/71r/jwww/x8ssvAwAiIiKQkpKCtWvX4vXXX4dMJgOgsQrq1v7Izc3lrAiZTAaFQoGioiIdqyE3NxeDBw9u9LhSqRRSqdRUp6U3ajXhLIahYR7NjpWIBBgU4oYT93Nx8VE+wv2czTFFCqVdYisW4u6qsbwd2xAiIyPRu3dv/Pjjjxg7dizi4uLw22+/mWh2Gni1GCoqKiAQ6E5BKBRy6aohISGQyWQ4duwY97xCocCZM2e4i36/fv0gFot1xmRlZSE+Pr5JYbAU7maVoLBcAQepCH2DXFscP7izRjwu0gA0hdImGIaBnUTEy6M1d/qzZ8/Gtm3b8P3332P06NEICAgwwbtSC68WwzPPPIN//etfCAwMRM+ePXHjxg1s3LgRb7zxBgDNh7do0SKsWbMGYWFhCAsLw5o1a2BnZ4dp06YBAJydnTFr1iy8//77cHd3h5ubG5YsWYKIiAiMHj2az9NrkevJhQCA/sGuEAtb1ujBoZriX1eTCqGoUUMiousTKZSOwPTp07FkyRJ8++23+PHHH01+PF6FYcuWLfj73/+OuXPnIjc3F76+vnj77bfx8ccfc2OWLl2KyspKzJ07F0VFRRg0aBCOHj0KR0dHbsymTZsgEokwdepUVFZWYtSoUfjhhx+M1rTCVMSkFgMA+uthLQBAV29HuNlLUFiuwO30YvQPdjPh7CgUiqXg5OSEF154AX/88Qeee+45kx+PNuoBf416hnxyEhnFldj55iAMDm0+xsAy56cYHL6Tjb9N6Ia3hoWaeIYUSvugqqoKSUlJCAkJgY1N09l/lszTTz+N7t2744svvmhyTHPnach1jvoieCJLXomM4koIBQx6+7vo/brIQM3YG1prg0KhtG8KCwuxe/dunDx5EvPmzTPLMWmjHp6ISSkCAHT3cYS9VP+PITJQ43aiwkChdAz69u2LoqIirFu3Dl27djXLMakw8ERsSjEAoF+gfvEFlgg/ZwgFDLJLqpAlr4SPs60JZkehUCyF5ORksx+TupJ4gi2h3csANxIA2EqE6CbTBN6p1UChUEwBFQYeUKsJ7mpXL/f0MzzYzcYZYrXuKAqFQjEmVBh4IK2oAmXVNZCIBAj1dDD49ayVcSeTlsagUAyhbq+X9oixzo/GGHiAvaB3kznqtbCtPj18nLT7ketVN4pC6ehIJBIIBAJkZmbC09MTEomkXf1uCCFQKBTIy8uDQCCARCJp0/6oMPDAnUxNfIG9wBtKmLcDRAIGJVU1yCiuhL+rnTGnR6G0OwQCAUJCQpCVlYXMzEy+p2My7OzsEBgY2KDUkKFQYeCBu1qLoadv64RBKhIizNsR97JKcDezhAoDhaIHEokEgYGBqKmpsZrujoYgFAohErWuFlN9qDDwAOtK6uHb+gqpPXycNMKQVYIxPWXGmhqF0q5hGAZisRhisZjvqVg0VBjMTH5ZNXJLq8Ew4NJOW0MPXyfsjaUBaAoFAJCQgLT4RGwqdECKUoRhYZ6YM7wTpCLLrpdmqVBhMDMJ2aUAgCA3O4NWPNeHdUPdpcJA6cgUFgLTpiHpahyef+0zFNtqSr/FpBThekohts0YAFErEjw6OvQdMzMPc8sAAGHerbcWAKC7NnCdUVwJeYWyzfOiUKySadOgOnES8579AMW2TuiZnYgVJ76FnUqBcw/z8dXpR3zP0CqhwmBmHuZqLIbOXoavX6iLs60Yvs42OvukUDoUCQnAkSPY03Mk7nqHwqmqDNt+WYmZ13/F6j+3AAC+PP0IeaXVPE/U+qDCYGYe5mgthjYKA1BrdSRo90mhdCgePYKKEeCrJ14EALx7YSe8yosBAM/fOYXejkClUoX/nn/M4yStEyoMZuZRHisMbXMlafahEZeEHGoxUDogoaE4GdofaS4yOFeWYtrNI9xTDIB5g3wBAP+7no7qmvaXnmpKqDCYkcJyBfLLFACAUC/7Nu+vi9ZiSMylFgOlA9KlC3aPfg0A8PLto7Ct0bqMhEJg7FiMHNEbMicbFJYrcPRODo8TtT6oMJgR9gLu72oLO0nbE8LCvKnFQOm4yCuVOOvaCQDwQvyJ2idGjwZ27YJIKMAL/fwAAIfisviYotVChcGMsEFiY8QXgNoAdm5pNc1MonQ4jt/NgVJN0MXbAV0uHgcOHdIEpA8fBlw1fU7Gh/sAAE4/yEOlgrqT9IUKgxnhAs9tTFVlcbShmUmUjgtrBUyM8AXCwoDx4zX/1qGnrxP8XW1RqVThTEIeH9O0SqgwmBHWldS5FaW2m6IzzUyidECqa1S4+KgAADCmp3eT4xiGwdM9NM+fScg1y9zaA1QYzAgrDKFGciUBQBeamUTpgMSmFKNSqYKno7TF0jJDwzwAABcSC8wxtXYBFQYzUalQIbukCgDQyaPtGUksbGYSdSVROhLnHmrcQk919mixmujAEHeIBAxSCyuQVlhhjulZPVQYzERKYTkAzYplV/u2NdGoC2t9PM4rN9o+KRRL53xiPgCNMLSEg1SEPgEuAIAL2tdRmocKg5lIztdcuIONaC0AQKinZn9Z8ipUKGqMum8KxRIprlAgLkPT7OqpsJaFAQAGawXk0mPqTtIHKgxmIilfY8IGuxu3qY6LnQSudpra8sn51EymtH9upBaDECDEwx7eTjZ6vaZ/kCv3WkrLUGEwEykFWovB3bgWA6D5gQDA43yamURp/8SkFAEA+mkv9vrQJ9AFDAOkFlYgv4wW1WsJKgxmIknrSgoxsitJs09NnCGJxhkoHYDWCIOTjZhbWBqrfT2laagwmIlkrcUQZGRXEgB00sYZWPGhUNorNSo1bqUXAwD6BuovDHXHx1J3UotQYTADFYoa5JRozFdTWAydOFcSFQZK++Z+dikqFCo4SkUGl5aJDHQBAMSmUouhJagwmIGUAk1Q2NlWDBc746WqsoRoLYbHeWUghBh9/xSKpcBe1CODXCEQNL9+oT6sxRCXLodKTX8nzUGFwQyYKlWVhQ1ol1TVoLBcYZJjUCiWAJtV1Fd7928InTwdYCMWoFKpom7XFqDCYAaStRZDiAniCwBgIxbCz8UWgPXGGQgh+O1WJubtjMWq3+4iS17J95QoFki8dv1Cb38Xg18rFDDoJtP0Sr+TKTfmtNodvAtDRkYGXn31Vbi7u8POzg59+vRBTEwM9zwhBCtXroSvry9sbW0xfPhw3LlzR2cf1dXVWLBgATw8PGBvb49JkyYhPT3d3KfSJKzFEGSCVFWWECuPM6w5dA8Ldt3AH7ez8P2FJER/cZ42IKLoUKlQcR0Qe/o6tWof7OvuZpUYbV7tEV6FoaioCEOGDIFYLMaff/6Ju3fvYsOGDXBxceHGrF+/Hhs3bsTWrVtx7do1yGQyPP300ygtra0NtGjRIuzfvx+7d+/G+fPnUVZWhujoaKhUllF/PanAdKmqLNacmXQ4PhvfnksCAMwcEoxuMkcUlCswf2csbclI4biXXQI1ATwdpfDSc2FbfXr6OgMA7mZSYWiOtrcRawPr1q1DQEAAtm3bxm0LDg7m/k8IwebNm7F8+XJMnjwZALB9+3Z4e3tj586dePvttyGXy/Hdd9/hp59+wujRowEAO3bsQEBAAI4fP46xY8ea9ZwaI8WEqaosrOhY21qGKqUK//z9LgBgTlQoPhzfDXml1Ri3+SzuZ5di15VUzBgSwvMsKZbAHa0bqbXWQt3X3sksASGkxQJ8HRVeLYaDBw+if//+ePHFF+Hl5YXIyEh8++233PNJSUnIzs7GmDFjuG1SqRRRUVG4ePEiACAmJgZKpVJnjK+vL8LDw7kxfFJdo+JSVQPdTC8M1rb6+eCtTGQUV0LmZIN3R2marHg6SvHe010AAP8+/QhVSmo1UDQXcwAI1971t4auMkcIBQwKyxXc79KaUKkJ1GbIqOJVGB4/foyvvvoKYWFhOHLkCObMmYOFCxfixx9/BABkZ2cDALy9dRtxeHt7c89lZ2dDIpHA1dW1yTH1qa6uRklJic7DVGTLNaW2bcQCuBmxqmp9OmlXPycXVJjli2MMCCHYdiEZADBjSDBsJULuuan9A+DjbIO80mocudP450jpWMRntt1isBELucKT1hiAvvK4AF3//ide/e8Vkx6HV2FQq9Xo27cv1qxZg8jISLz99tt488038dVXX+mMq2/u6WMCNjdm7dq1cHZ25h4BAQFtO5FmyCjSZNf4utia1Gz1dbGBSMBAUaNGTmmVyY5jTOIy5LiXVQIbsQAvD9D9DCQiAV7Sbtt1NZWP6VEsCEWNGg+yNXHFcL/WWwwA0MNHIyz3s62vh0lGcSWUKgJTe8B4FQYfHx/06NFDZ1v37t2Rmqq5EMhkMgBocOefm5vLWREymQwKhQJFRUVNjqnPsmXLIJfLuUdaWppRzqcxMoo1wsCmk5oKkVAAP1fNMdgFdZbOH7c1PXtHdfNudOHf1P4BEDDA5ceFtMFKB+dhbimUKgInGxH8Xdv2Wwrj2uFanzBkFmtu+nydTXs94VUYhgwZggcPHuhsS0hIQFBQEAAgJCQEMpkMx44d455XKBQ4c+YMBg8eDADo168fxGKxzpisrCzEx8dzY+ojlUrh5OSk8zAV5hIGoDaGwQa7LRlCCH7XCkN0L59Gx/i62GJQiDsAUHdSB+dOhsbd29PXuc2WN9f10Ar7pGcW13ogTAmvwvDee+/h8uXLWLNmDRITE7Fz50588803mDdvHgCNC2nRokVYs2YN9u/fj/j4eMyYMQN2dnaYNm0aAMDZ2RmzZs3C+++/jxMnTuDGjRt49dVXERERwWUp8QnrSjKHMLAroK3BYojPKEFGcSXsJEKM6ObV5Lhx4Rqr8XA8FYaOzAPt3X13n7bfxHXx1sTjHuWVWV1pjEw5KwytS9fVF17TVQcMGID9+/dj2bJlWLVqFUJCQrB582ZMnz6dG7N06VJUVlZi7ty5KCoqwqBBg3D06FE4OtY2AN+0aRNEIhGmTp2KyspKjBo1Cj/88AOEQmFjhzUrtR+k6YWBTYdNsQK3y9k6PXttxE1/TmN6emPFwTuISS1CbmkVvBxN+4OgWCas24e9qLcFf1c7SEUCVNeokVpYYdL1RcYm01yuaZPuXQ+io6MRHR3d5PMMw2DlypVYuXJlk2NsbGywZcsWbNmyxQQzbBucxdBGv6g+sK6kVCuwGM4maIRhaBfPZsf5ONsiws8ZcRlynEvIxwv9/M0xPYqFwa6CDzOCMAgFDDp7OeBOZgkSckqtRhgIIVyMwac9u5LaO2o1QaY2XdUcriS25EZyQblFV1ktq67hqmQO06NnL9vX9zxt5N4hKa1SIkv7O+rs6djCaP2ojTNYTwBaXqlEpXZNj4+zaS1nKgwmJL+8GooaNQQMIDPxBwnUWgylVTUorlCa/Hit5crjAihVBIFudnrVjxrauVYYLFnwKKaBtRa8HKVw1vY3byus5fHQiupxsYksHg6SZt2vxoAKgwlh3UjeTjYQC03/VttKhPB2kgKw7DjDlaRCAMCQzu56je8X7AobsQB5pdVIsMJMEkrbYC/e7F2+MejixaasWs/3iXMjmThVFaDCYFK4nGMzuJFYgtzYzCTLTVlle/b2D3LTa7xUJMRAbdoqdSd1PFiLobOBHduaI8wKM5NqU1VN732gwmBCMoo1d+3miC+wBLpbdgC6ukaFuHRNKQJDmrk/0UkjIteTC00yrw5LQgLw55/Aw4d8z6RJ2IwkYwSeWQJc7WAjFkChzUyyBsyZ4UiFwYSwFoM5MpJYgrXCkGyhwhCfIYdCpYa7vcSgarMDgrXCkFJE4wzGoLAQGDcO6NoVmDABpEsXzd9FltcPmV2IFuZlPFeSQMBw636SrKTwJHc9ocJg3aQXmU/hWQK1X/bUQst0JbFupL5BrgatYI3wc4ZEqIkzWMsdnkUzbRpw/DhSnb0x7aV/IfSvv2Kc3zO4Nnsx3zPToby6hgu6hhnRlQQAoZ6a/T22klL1rCuJxhisHPYL7W/WGANbFsMyL5618QX93UiApipmhL+meNq1ZMu7q7UqEhKAI0eQJ3XElOnrcTG4N9QCIe57BmN6yCTcvhjH9ww52I5tHg4SuBq5OrG1dT2kMYZ2grnqmtSFdc/kllajQlFjtuPqAyEEsWwzdwOFAagVk5gUGmdoE48eAQCWjZuPXEd3dM5PxcHtixD1+DoUIgneO56KGpWa50lqYN1Ixgw8s1hTc6salRo5JdSVZPWUVddAXqlZS2DOGIOLnQTOtppcb0tzueSWViOvtBoCpnXNVvpr4wzUYmgjoaG4HBCO42FPQKhW4ctfP0Gv7ER8cfBTuJcX41EFsOe66SoOG0JCLlsKw3jxBRZraoebU1oNNQHEQgYeDlKTH48Kg4lgrQVnWzEcpOatPMLVTLIwd1K8tjVjZy8HnaY8+sJmMSXmlkFuwQv4LJ4uXbD1mbkAgFduHkaXfE2Ze+eaKswrug0A+PbsY4to+JTIBZ5NZzFkl1ShvNqyrOv6sNcTmbMNBALTtyOlwmAiMngIPLNYas2ktrZmdLOXIMBN837GZVhf9y1L4XFeGc47BoIhBHOu/FL7xOjReGnDX+EoFSG5oMIi1ow85NYwGN9icLGTcF0VLd1q4NzSZgg8A1QYTIY5+zDUp7bKqmV92VmLoWcbOnD19ncBANxKLzbCjDomO69oLISR3b3hf/0CcOiQJiB9+DDsvT24QoV8u5MqFSqkFWluboy5hqEunTysw51kzlRVgAqDyagVBvOXiQ6y0L4MtRZD62vqs8JwmwpDq1CpCX69lQkAmDYoEAgLA8aP1/yr5flIPwDAyXu5qFSoeJknoMlIIgRwtRPD3UT90rnMJAsPQHOpqma6nlBhMBHmLLddH0tMWS0sV3Bi2aMNwtBLm7J6K426klpDTEoR8kqr4WQjwtCwxkue9/J3hr+rLSqVKpx+kGvmGdbCldr2cjRZv/QQT+tY5GbuDEcqDCaitqGG/qt7jQVrMWgah1tG2uGdTM2FPNjdDo42ra+QGe7nDAGjCRjmatP3KPpzKE7TTvXpHjJIRI3//BmGwYQITbvVP3nsnMeWwuhsIjcSAHTy0Ozb4l1JcvPWXaPCYCIyzLgYpT5ejlJIRQKo1IQTKL6JZ3v2tiG+AAD2UhGX034rnVoNhqBWE/wZrxGGCRGyZseO0rZbPZ+Yz1t2EldV1QQZSSxsyurjPMvuYWKuzm0sVBhMgLLuYhQeXEkCAcNlJlmKO4m1GHq2wY3E0ovGGVpFfKYcOSXVcJCKuOZHTdE3yBUOUhEKyxWIz+RHgGu7thk/I4kl0M0ODAOUVtcgv0xhsuO0hfI6a6JM3aCHhQqDCciWV0FNAIlQAA970y9GaQxL6/+cYMRm7r3ZOAO1GAzi3ENN+umToe6QippfRyIWCvBkqKbUOduG1ZxUKVVc6XhTrGFgsRELubtwS3UnsdaCo42oTW5YQ6DCYALqupHMsRilMQK1fRlSLaAvg1Kl5n50xviR17UYzGX+pxZUYOkvtzBj21XsuZZq0W6HpjivFYaherRTBYBh2n7cZx+afz1DUn451ARwshHB09G0N1e1C0L5/600hjnbA7PotSR38uTJeu9w3759rZ5Me4HzB/LgRmKxpNXPKQUVUKoI7CRCoyzQ6ebjCIlQgOIKJVILK/RqD9oWEnPLMPnLCyip0qyOPf0gD3cyS7Dq2XCTHteYVCpUXAHDIZ31E4YhWovhZmoxqpQqk7eTrMvDOm4kU2UksQS52+NCYoHFlZBh4aPmml4Wg7OzM/dwcnLCiRMncP36de75mJgYnDhxAs7ObQssthe4Vc9mWqXYGFzDHgv4sidq69109nIwigUlFQnRzUfjd75tYndSjUqNeT/HoqSqBhF+zpg/ojMEDPDjpRT8ejPDpMc2JleTC6FQqeHrbMMt6mqJEA97eDhIoFCpTf4+1+ch25zHhG4kFktM766LOauqsuhlMWzbto37/wcffICpU6fi66+/hlCouYNQqVSYO3cunJza7j9uD7Cdlni1GNxqhYEQYvK7ruYwRaOVXv7OuJ0uR1yGHM/09jXafuvzf9fT8SCnFK52Ynw/YwA8HaUQChh8fuIh/vXHPTzdwxt2EvPWwmoN5x9q4gRPhXno/V1gGAYDgt3wZ3w2riUXYmCIfq1YjQH3nTFh4JmFs64fZwIPHXQW+1kCGWbsw8BicIzh+++/x5IlSzhRAAChUIjFixfj+++/N+rkrBU+GvTUx9/VDgIGqFCokFdWzds8ACCBcwsY7+6vl58LANNmJqnUBFtPalpeLhwVxvm6544IRYCbLXJLq/HjpRSTHd+YsIHnp5pY1NYUA7iKtuYtdf4w10wWQ2Ehgj5aAgBIyS0BLLCTnblTVYFWCENNTQ3u3bvXYPu9e/egVlvGYiq+yeShQU99JCIBd4fBdzE9U7gF2KY98RklJsuzP3k/F5nyKrjaifHKwEBuu1QkxMKRmrvK7ReTLWYRYVPklVbjfrbmM2DjBvrCWgkxyUVQmWk9g6JGzbWmNVWNJI5p0xB45FcAQLGtE+RSe+D4ceCVV0x7XAPIMvPiNqAVwjBz5ky88cYb+Oyzz3D+/HmcP38en332GWbPno2ZM2eaYo5WBSGkTlYSf8IAWEYAukal5jpkGdOVFOblABuxAGXVNSbrwPXTZY01MLV/QIPA66Q+vvBwkCBLXsXr6mB9uKCtktrDxwnuBtby7+7jBAepCKXVNbifXWKK6TUguaAcKjWBg1QEmZMJ/eraTnb2VeXwKNNYCKkuMkClAo4cAR4+NN2x9UStJsgqZoXBfDEGg4Xhs88+w4cffohNmzZh2LBhGDZsGDZt2oSlS5fi008/NcUcrYrCcgWqlJo7SHMVvGoKS1jLkFZUCUWNGjZiAfyNGHMRCQXoqS3fHZdRbLT9suSXVXN++brWAotUJMT0QUEAgF3aaqWWyjkD01TrIhQwiAx0AQDc0HbfMzV1u7aZNDam7WQHAEHFmhXhKa4+tc8nJpru2HqSX14NhUoNAQN4m1Ik62GwMAgEAixduhQZGRkoLi5GcXExMjIysHTpUp24Q0eFLY+rKUvB7/thCWsZWDeSsTKS6hLhZ7qCekfv5EBNNEHu4CayeKYOCADDAJceFyDNArK/GoMQwlkMLa12borIABcAwK20YiPNqnkSzJWRFBrK/TeoSCsMLnWEoXNn0x5fD9jribeTDcRC8y07a9ORnJycaCZSPTKKNRcIvt1IgGVYDA9zjZ+RxNI7gLUYjC8MbE2h8eE+TY7xc7HFkFDNxfaXmHSjHDchpxQztl3FsPWnsPLgnTaXvX6UV4bskipIRAIukGwovVlhMFMJkkQTJCs0SpcuwNixgFCoazEIhZrtFpCdlMVlJJnX+6BXnl1kZKTeJl1sbGybJmTtZBTzVyOpPpZQL6muxWBsIrSZSXcy5ahRqSEy0h1VUbkCFx8VAADGhzdfbO7F/v44n5iPX2LS8e6osDZZRQk5pZj6n0so1rYt/eFiMh7lleGHmQMhbOV+WTfSwGC3Vi9QY1eaP8wtQ1l1jclb1XIZSWZIVcWuXcArryAoTRMnSnHxAUaP1my3APiKV+r1CT/33HMmnkb7gevDYEEWQ2G5AqVVSrPVWalLrcVgfGHo5GEPB6kIZdU1eJhbZpQ6TABw9mEeVGqCbjLHJt1ILGN7yuBoI0JGcSUuPS7Qe1VxfZQqNRbtvoniCiV6B7jgL08EYfmBOJx7mI8fLyVj5pCQVu2XLYPR2nkBgKejFH4utsgorkRcupyroWQKjF0+pUVcXYHDhxF04TbwWxpSu0cCf//Q9MfVE3N3bmPRSxhWrFhh6nm0G1hXkiUIg6ONpvNVQbkCKQUVCG9jyWtDUakJ5xboYoK7P4GAQbifEy4/LkRcutxowsBeTNlaQc1hIxZiUm9f/HwlFXuupbX6Avz16Ue4m1UCFzsx/vuX/vB0lKJSqcJHB+Kx6VgCJvf1h7OtYcKuqFHj8mON5dOawHNdegc4I6O4ErfSi00qDMYun6IvQX26Ab+lIau8xuzlP5qDj3IYQCtjDMXFxfjvf/+LZcuWobBQs/AlNjYWGRnWUyLAVPCl8E3BZ2mM9KIKVNeoIREJEOBmmoZFXEE9I2Um6QRr9bzIvzxAk7V0OD4bReXNl25OLajArzczkCWv7ZPxMKcUW05qMmBWPtOTW0j3ysBAdPF2QElVDXZdNTzz6UZqEcoVKrjbS9CjjaLJ9do2cQDa2OVT9MXVTgxHrYvMkhIJ2CoKFi8Mt2/fRpcuXbBu3Tp89tlnKC4uBgDs378fy5YtM/b8rA5LWcPAwmcdGDbtMNTTodU+8pZgM5PijFTL53F+OTLlVZAI9Q/Whvs5oYePExQqNQ40Uz/pcHwWRm08jXd338TIz87gl5h0KFVqfLD3NhQqNUZ09cSzfWrLewgFDN4c2gkAsO1CEhQ1hi2kO59Y60Zq60W2t5kykxLqpKqaE4ZhuJsoS6qZxN5omjv4bLAwLF68GDNmzMDDhw9hY1M72fHjx+Ps2bOtnsjatWvBMAwWLVrEbSOEYOXKlfD19YWtrS2GDx+OO3fu6LyuuroaCxYsgIeHB+zt7TFp0iSkpxsnQ8RQKhUqFGrvGC0h+AwAgdrKo6mF5k9ZNWV8gYW9k72XVWrwhbMxWDdS/2BX2Er0cycwDIOXBwYAAHZfTWu0JHdyfjne23MLShWBk40IlUoVlvzvFqLWn0JsajHsJUKsfj6iQZLHpD6+8HSUIqekGifv5xh0LmfbsH6hPuF+zmAYTQno3FLTtVRlU1VN4XpsiWDtbyXZQspvVylVyNeWszG3B8JgYbh27RrefvvtBtv9/PyQnd26FaDXrl3DN998g169eulsX79+PTZu3IitW7fi2rVrkMlkePrpp1FaWsqNWbRoEfbv34/du3fj/PnzKCsrQ3R0NFSqtqX5tQbWWnCQiuBkYxmF1Xi1GHLZH7nphCHAzRbOtmIoVGo8yC5t+QUtUFtTyLCL6bO9/SAVCfAgpxTXknXr7KjVBEt/uY1KpQpPdnJHzN+fxuKnu3AXWolIgC3TIhv98UtFQkzp5w9AU9BPX4orFIjTppcONbA+UmM4SEWcwJti3QhLbcFF81oMgGVVJAY0Db8AwFYshIudeRNHDBYGGxsblJQ0XBr/4MEDeHoa/gUsKyvD9OnT8e2338LV1ZXbTgjB5s2bsXz5ckyePBnh4eHYvn07KioqsHPnTgCAXC7Hd999hw0bNmD06NGIjIzEjh07EBcXh+PHjxs8l7ZSt9gVn9VM68JnWQw28NzZBGsYWBiGQS9t3aS2xhmUqjrB2s6GfZed7cSY3FdzAd+iLbyHhATgzz/x8+/XcTW5EHYSIdZP6QWxUICFo8Lwx4KhWP1cOI4sGoaR3byb3PeLWmE4/SCXaxnbEhcfFUBNNBdYmZHcEGw8J85E6xmUKjUe55suWaElLK38dt1y2+a+nhgsDM8++yxWrVoFpVKTa80wDFJTU/Hhhx/ihRdeMHgC8+bNw8SJEzF69Gid7UlJScjOzsaYMWO4bVKpFFFRUbh48SIATR8IpVKpM8bX1xfh4eHcGHOSYQENeurD3gVlySuN4mrRF7Wa1CmdbNq7PzbOcLuNd7K304tRVl0DFzsxerSiN/Xc4aEQCRice5iPsy++CXTtisyX/oL1p5IAAEuHBeoE4Xv4OuHVJ4IQ0kJKbCdPB/QPcoWaAHtj9bMa2HacrV3t3Bi9TNxSNaWgHEoVgW2ddpvmJIhzu1qGMPAZr2xVraS8vDx4eXmhsrISUVFR6Ny5MxwdHfGvf/3LoH3t3r0bsbGxWLt2bYPnWLeUt7funZS3tzf3XHZ2NiQSiY6lUX9MY1RXV6OkpETnYQy4Bj0810iqi6eDFHYSIdREkyVkLjKKK1GpVEEsZLg7MVNRm5nUtgsW60YaEurRqmB5gJsdXn1CUz/pr15DcbJTf7w5+SOUSu3ROysBr332fqvnNrW/Jobxv+vpLbYVVakJjt/TxCNGdvNq9THrw1kMGXKTtDateyPBR0tc1rpOK6xAjQVUzOWqqvLQ8MtgYXBycsL58+exd+9efPLJJ5g/fz4OHTqEM2fOwN5e/xaLaWlpePfdd7Fjxw6dIHZ96ptQ+jSdaWnM2rVrdbrSBQQE6D3v5qh1JZn2QmgIDMPUroA2450Q60bq5OFgtBXJTcHeySbklKJK2frY0vlWxhfqsjRUiLD8FOQ4uuONF1fijqwzXCvk2PrrOgiPHG51xc6JvXxgLxEiKb8cV5Oa741wM60I+WUKONqIMCjEeGsOuvs4QixkUFiu4HqOGJMEEzR0MgSZkw0kIgFq1IS7KPMJX2sYgDbUSho5ciSWLFmCpUuXNnAD6UNMTAxyc3PRr18/iEQiiEQinDlzBl988QVEIhFnKdS/88/NzeWek8lkUCgUKKrXVKPumMZYtmwZ5HI590hLSzN4/o2RzkMLPn1ghcGcfRlqyxqYPojo42wDDwcJVGqCO5mts/5Kq5S4oU3F1Hf9QmPYpSZh166/YcL983CtkOOp5Bv45eelCJBrM4paWbHTXipCdC9NKuue681/X4/eqbUWJCLjibJUJERXmeaibYr6VAlmSFZoDoGAQYDWDWwJcYYMHq8neqXOfPHFF3jrrbdgY2ODL774otmxCxcu1OvAo0aNQlxcnM62mTNnolu3bvjggw/QqVMnyGQyHDt2DJGRkQAAhUKBM2fOYN26dQCAfv36QSwW49ixY5g6dSoAICsrC/Hx8Vi/fn2Tx5ZKpZBKDatLrw9cgx4LijEA/ASgzXn3pwlAu+Dk/VzEpRejX5Bryy+qx5XHhVCpCYLc7dq2GC80FB4Vcnz56yeNP9+Gip1TBwRgz/U0HIrLwspJPeHUSIkTQggO39HcTD3do+mbo9bSy98F8RkluJVejAkRTRcYbA0PeUxVZQlyt8ejvHKkFJbjKRgvPtMa+OjcxqKXMGzatAmTJ0+Gv78/Nm3a1OQ4hmH0FgZHR0eEh4frbLO3t4e7uzu3fdGiRVizZg3CwsIQFhaGNWvWwM7ODtOmTQMAODs7Y9asWXj//ffh7u4ONzc3LFmyBBEREa2yYtqCSk249DJLciUB/KxleGiuCplaIvyccfJ+bqvjDOcNXO3cJGzFzuPHNQ1fWIRCTXG2NlTs7BvogjAvBzzMLcNvtzK5fhB1iUkpQkpBBewkQozoarz4Aktvf2fsvNL2QH99dGok8WQxAPxY141BCOGlcxuLXsKQlJQEFxcXbNmyBUlJSaaeE8fSpUtRWVmJuXPnoqioCIMGDcLRo0fh6Fh7R7Fp0yaIRCJMnToVlZWVGDVqFH744Qez94bIKalCjZpAJGC4kgaWgrnT8AghSDRXTX0tXMZMK1fmntM25THGYjC2YieOHKndZoSKnQzD4KUBAVj9xz3supqKaQMDG8TS2KylCRE+sDdBFVS2om18hhxqNTFakDg5v5yXGkn1sYSuhwAgr1SiQlty3VjpxoagtwNyzZo1mDdvHl544QUUFBSYZDKnT5/G5s2bub8ZhsHKlSuRlZWFqqoqnDlzpoGVYWNjgy1btqCgoAAVFRX47bffjBZMNgTW7PNxsTFZ+YfWElRn4Y6p+iPXJUtehXKFCiIBw6UAmpo+2pINj/LKudXn+pJZXIlHeeUQMMCToUYQBm3FTiQkAIcOaf49fFizvY08H6lZSBefUYILibq/w+IKBQ7ezAQAvKBdU2Fsung7QCoSoLS6BklGXCGcUGdhGx8ZSSyW0MMEqI0veDhIeCnop7cwzJ07F7du3UJRURF69uyJgwcPmnJeVgcXKOLxbqcpfF1sIRQwqK5RI7e02uTHY91IIR72Rg1+Noe7g5SzTlrK2qkPm43UO8DF4AqmzRIWBowfb9SGL+4OUq7V6OcnEnTSRn+4mIxyhQrdfZzwRKfWNeVpCU1LVc0aj9tGXOhm1h4MzVC366EpUnL1JbOYPzcSYGBWUkhICE6ePImPPvoIL7zwAnr16oW+ffvqPDoqlri4jUUsFHABrBQz1IFhg4jm9hUP0l4MryQZZtGe08YXhrY1vmAm5kSFQiIS4FpyEfbGaor2ZRRX4puzjwEA7wwPNelKWW7diBEXurFrGPjKSGIJcLMFwwDlChUKDLQ8jUkmzzeaBjshU1JSsHfvXri5ueHZZ5+FSGQZNYH4hl3c5m8hVVXrE+Ruh9TCCqQUVGBQJ9PV0wfqNnM3793foBB37LicapDFoFbX7Ync9ppC5kDmbIN3R4Xh0yMP8NGBOJRUKvF/19NQoVBhQLAroo2cLVQftqWqMYXhQY5lWAxSkRA+TjbIlFchpaACHg78xAv5KrfNYtBV/dtvv8X777+P0aNHIz4+vlW1kdorllZuuz61i9zMYDHkmjfwzDIoRGMx3M0qgbxSqZdb6G5WCQrLFbCXCBEZ6GLiGRqPt4d1wvXkQpx6kIdVv98FoPFHb3ixj8l99MZuqVqlVOFxnuZmoq19I4xBoLsdMuVVSC0sb1XqszGodSXxsyZK70903Lhx+OCDD7B161bs27ePikI9Mi3YlQSYL9uCEMLFGMydj+7lZIMQD3sQAlxP1s9qYMtgPBnqDrGJV2gbE5FQgK9f64fFT3dB30AXPB/ph/1zh3C1sUxJJw97OEpFqFKquc+6LTzILoWaAO72EnhZQEZfkDbOkJzPXwCaz1XPgAEWg0qlwu3bt+Hvb5psB2uGEFKnTpJlCgMXVDNxtkVuaTVKq2ogFDAI9jD/eo5BIW5Iyi/HhcQCjOre8gKvU/dzARinNLW5kYqEWDgqDAtHGS+4rQ+alqrOuPS4ALfTi9vcUvVelma1encfJ4uoSmwJ5be5LEceUlUBAyyGY8eOUVFogpLKGpRrc44tpaVnfcxlMbCNVoLc7SAVmT/NLkrbp/nUg9wWxxaUVeN6isayGG2CVcLtGa7UuRHiDHe1wtCairamgG3YY45EjcZQ1KiRrS2v7u/Kz2JZ67GdLZj0Ys3Flq+cY31gYwzySiXkFUqTHYfPRiuApgCeWMggKb+cW0nbFCfu50JNgJ6+ThYr6JaKMTOTai0GfgPPLEE8WwzZ8ioQAkhFAng4SHiZAxUGI8B3zrE+2EtFXIaFKQPQte08+fmRO9qIuV7NJ+83bzUcu6spNmeKmkLtHdZiuJ9dguqa1le0VasJ7mVprMwePs5GmVtbYV1J+WUKlFXXmP34bHl8f1f+Gn5RYTACGdoP0tLvOoO1X/hkE7qT+FrDUBe2B8Hxu033SJZXKrlmNmN6yMwyr/aEv6stXO3EUKoI7me1vqVqelElyqprIBEK0MnTPKvkW8LJRgxXbStNPmomsSXN+XIjAVQYjIKlp6qysD88NjXQ2NTNSOLLYgBqL/SXkwq4wob1ORSXheoaNbp4O1iMC8OaYCvaAm1bAc3GF7rIHCwqK4yPwpMsdS0GvrCcT8KKYV1Jlm4xdPLU3MU/yjPNlz2vtBrySiUEDHi9+wt0t8OAYFcQAhy4mdHomL0xmmJzL/T1t4hMGGvEGAFoVhi6yywj8MzCZ/9n1mLgM/WdCoMRSLfwNQwsoVphMJXFwBZCC3a35z0IzxaR+7/raQ0KB8ZnyHE9pQhCAYPnIv34mF67wBgB6HhtmfSeFpKRxMJnMT3qSmon8NlQwxBqXUnlJqmymmAB8QWWib184GgjwuO8chytF2v4j7amUHQvH3g7WVa3PWuCtRge5paiQmF4kJYQwpVJ7xPIzwrjpuCzL0OGBTT8osLQRqqUKuRpK5ZaujAEutlBJGBQqVRxedLG5GEu/x24WBxtxHj9yWAAwMZjD6Co0TR3j00twm+3NKWp3xrWia/ptQu8nWzg7SSFmqBVLVXTiypRUK6AWMhYXJyHLRdvjhIydVGq1MiSU2GwetguS7ZiIVzsjFiy2QSIhQIuFe+RCdxJXE19CxAGAJj1VAjc7SVIyCnDioN3kJhbikW7bwIApvTzR09fy0iPtGZYd1JrGiTd0gate/g48bIYsjlYV1JmcRWUKrXZjpstr4Jau4bBk6cCfgAVhjZTt0aSNQQxO3mwcQbj3gkRQmpdSTwtbquPq70E/3o+AgwD7LqaitEbzyK1sAKBbnZYPqE739NrF/Tya30AmhWT3tomS5aEl6MUNmIBVOracjfmIK1O6juf1xMqDG3E0msk1SfUyzQpqzkltTWSLCUfHQDGhcvw+cuRXLvVJzq54efZg+Bqz8+K0vYGe1GPTS0y+LW3tH2je2utDkuCYZg6FYnNF2ewhIwkoBX9GCi6ZFhJ4Jkl1MM0Kat810hqjkm9fREd4YOqGhXsJPQrb0z6BblCKGCQXlSJtMIKBLjpl0mjVKkRp81IYvs7WBqBbvZIyClDakE5APMUWcywgIwkgFoMbcYSMggMwVQWAysMXXhc2NYcAgFDRcEE2EtFXHbSFQMaJMVnyFGpVMHFTsy5Ny2NIDNUCqhPbaoqv9cTKgxtpLZuunWkPbI/wkx5VatSDJvCUlozUszPk9qOgJce6d9SlRWRAcFuJm8s1FrMVZG4Lpaw6hmgwtBmal1J/Jp++uJqL+HqwBgzAJ1gIc3cKebnCa0wXH5sgDBox7Jd9ywRbi2DGVNWqcXQDlCrCbJ4bsHXGkK50hjGcScRQpCYw0/XNgr/9AtyhUjAIKNYE2doCdX9B7ieqClg+ISJ+4+3hSD32uZWhBh/QWh9alT892FgocLQBvLLqqFQqSEUMJBZ0QpaThiM0JYR0KzlKK3WZCSFeFhORhLFPNhLRVx2EtsqtVEKC4Fx43BnRDRKVYBjVRm6z3oZKDI8o8kc+LnYQsAAVUo1crWLWE1JlrwKKjWBRMjvGgaACkObYGskyZxs2twQ3ZywJSse5LS+XHJd7mdrVr2GetpDIrKe94FiPEZ01WTtnLzfdKlzTJsGHD+O0536AwCeSI2D8Pgx4JVXzDFFg5GIBFzaqDniDBl11kTxHXehv+I2YG2BZ5Zu2kqWD7KNIwx3teUQerSx9y/FemH7a597mI9KRSONexISgCNHAJUKJ0M1wjDy8XVApdJsf/jQnNPVmyA387X5ZN1wlpD6ToWhDbA5x5bwQRpCV5kmDpBSWGGUzCS2A1dbm8JTrJduMkf4udiiukaNC4mNuJMePQIAFNg64ZZPFwDAiEfXa59PTDTHNA0m0IxtPtljsMfkEyoMbcBaGvTUx9NRCnd7CQipTTNtC5bWzJ1ifhiGwajums55f8ZnNxwQGgoAOB42CIQRoEfOI8jK6mQxde5sjmkajDn7MrDHCNJzkaApocLQBjKtpA9DY7BWQ1vdSeXVNUjWmtnUYujYPNPbFwDwZ3wWyuv3Su7SBRg7Fgd6jgAATLx/XrNdKATGjgXCwsw5Vb0xZ18G9hhB1GKwbtKt1JUE1ArD/TYKw/3sUhCiKTrmwXMmBYVf+ge5IsjdDhUKFQ43YjVk/ucHXA6IAAA8e/e0ZuPo0cCuXWacpWEEamMMqWaIMbDH0LesiCmhwtAGyrX+eWsUhm6sxZBjeB39utxjWzNSa6HDwzAMpmg75/14KblB7v9P94pBGAZP+NjCf/d2TUD68GHA1bKa9NSF9fcXVShRUqU02XFKqpQoqtDsn10/wSdUGNrAuaUjEbdyDNdL2ZroaqTMpLtUGCh1eHlgIGzEAtxKl+NMQh63XV6pxI7LKQCAN0b3AMaPt1j3UV0cpCJ4OGgq8Zqymxu7b3d7CRyk/Nf0osLQRhxtxBBaaK2X5uji7QCGAfLLFMgva/3iHS5VlQaeKdAkNkwfFAQA+Ofvd1Gl1KSufnrkPkqrahDm5YDR2tRWayHQDAFoS8pIAqgwdFjsJCLuC38/q3VWQ41KzS1u62FhrRkp/DF/RGd4OkrxKK8cs7dfx+rf72LH5VQAwD8m9eR98ZahmKPNpyVlJAE8C8PatWsxYMAAODo6wsvLC8899xwePHigM4YQgpUrV8LX1xe2trYYPnw47ty5ozOmuroaCxYsgIeHB+zt7TFp0iSkp6eb81SsknBta0u2Lr6hJOSUoUqphoNUZLGlkynmx9Vegi9ejoSNWIDzifn47/kkAMDCUWEY3NmD59kZDmcx5JvSYijXORbf8CoMZ86cwbx583D58mUcO3YMNTU1GDNmDMrLa5V5/fr12LhxI7Zu3Ypr165BJpPh6aefRmlp7V3uokWLsH//fuzevRvnz59HWVkZoqOjoVI1sgKTwsHW0Y/LKG7V69mevb38na3uLpBiWp4Mdce+d4bg+Ug/jOzmhS9eicR7oy0/ptAYwR6ai3WSCTOTal1J/AeeAZ47uB0+fFjn723btsHLywsxMTEYNmwYCCHYvHkzli9fjsmTJwMAtm/fDm9vb+zcuRNvv/025HI5vvvuO/z0008YPXo0AGDHjh0ICAjA8ePHMXbsWLOfl7UQ4d/6fr2AZffspfBPD18nbHqpD9/TaDOm6pNeF86VRGMMDZHLNRcoNzdNjfakpCRkZ2djzJgx3BipVIqoqChcvHgRABATEwOlUqkzxtfXF+Hh4dwYSuOEaxu5pxdVorBcYfDrb7LCYIE9eykUY8H2MM8vq4a80vgpq4oaNbdYlsYY6kEIweLFi/HUU08hPDwcAJCdrVkk4+2tm8Xg7e3NPZednQ2JRALXernQdcfUp7q6GiUlJTqPjoiTjZj70t/WuoX0pUJRg4fast19qMVAacc42ojh5ahZvGnslriAprSOmgA2YgE8HS1jkajFCMP8+fNx+/Zt7GpkFSTD6PqvCSENttWnuTFr166Fs7Mz9wgICGj9xK2cXlqrIc5Ad9KdzBKo1ATeTlLInK2ruiyFYihsDxNTuJPYyq2BbnYtXtfMhUUIw4IFC3Dw4EGcOnUK/v7+3HaZTAYADe78c3NzOStCJpNBoVCgqF6zj7pj6rNs2TLI5XLukZaWZszTsSoitG6gWwYKQ2yK5v2mbiRKR4C1rI3V9bAuyfeSAQBBUtN3idMXXoWBEIL58+dj3759OHnyJEJCQnSeDwkJgUwmw7Fjx7htCoUCZ86cweDBgwEA/fr1g1gs1hmTlZWF+Ph4bkx9pFIpnJycdB4dlchAFwBATEoh1Gr9v5hsM/eBFtyzl0IxFiaxGLQd7R7/exsAoNP/bQfGjbOIjna8CsO8efOwY8cO7Ny5E46OjsjOzkZ2djYqKzWBGIZhsGjRIqxZswb79+9HfHw8ZsyYATs7O0ybNg0A4OzsjFmzZuH999/HiRMncOPGDbz66quIiIjgspQoTRPh5wxbsRBFFUok6nk3pFITXNMKw6AQy+3ZS6EYC9ZieJxvRItB29HusbsfACC0MAM4ftwiOtrxmq761VdfAQCGDx+us33btm2YMWMGAGDp0qWorKzE3LlzUVRUhEGDBuHo0aNwdKxdabtp0yaIRCJMnToVlZWVGDVqFH744QcIhUJznYrVIhYK0C/IFecT83HlcQG6eLe8gvleVglKq2vgKBXRUhiUDgFrMSTnV0ClJm0vg8N2tAPw2E0jDJ0K03U72vFYS4p3V1JjD1YUAI3VsHLlSmRlZaGqqgpnzpzhspZYbGxssGXLFhQUFKCiogK//fZbhw4oGwrrDmLdQy3Bjusf7GqVdaIoFEPxc7GFVCSAQqVGepERVkBrO9pViqTIdNI0OOpUmFH7PM8d7Swi+EzhF1YYriYVNiiV3BiXHuVrX0fdSJSOgUDAIMTDiAFobUe7JFdNcyOXyhK4VdZJm+e5ox0VBgr6BLhAKhIgt7QaCS20+qxSqnAhUdOSMaqLpzmmR6FYBEYNQGs72j321Hg2OGvBQjraUWGgwEYsxOBQzd3/yfu5zY69/LgAlUoVfJxt0J1WVKV0IEKNnbK6axce9x8GQBtfACymox0VBgoAYKS2Rv7J+znNjjulFY7hXb0sZjEOhWIO2IZcj4yVsurqisfjNTXgOk0aY1Ed7agwUAAAI7tpAmAxKUVN1k1SqwmO3s3RGU+hdBQ6e2mEITG3TK9YnD48zteITKchfXl3H9WFCgMFgCbroqevE9QE+ON2ZqNjriQVIkteBUcbEYaGWV9dfQqlLXT2coCAAQrLFchrQ9dDFkIIF69g3VSWAhUGCsdkbSP3X2Iab3J04IYmQDYxwgc2YrpGhNKxsBELEaztl9DWXukAkFtajbLqGggFjMW09GShwkDheLaPL0QCBrfS5biTqVs7qbBcgV9vaYTh+Ug/PqZHofBOV5km4cIYwsDuI9jdDlKRZd1oUWGgcHg4SDEuXFO48MtTj3Se234xGVVKNcL9nGh9JEqHhRWG+0YUhm4yy6seQIWBosP8kZqFNYfis3BVu8I5rbAC35x9DAB4J6ozzUaidFi6GdNiyNHsQ58yNOaGCgNFh24yJ0zt7w9CgPk7Y7H/Rjpmb7+OSqUKA0PcMCFCxvcUKRTe6Kq9u0/IKYXKgGrEjZGgFYauMoc2z8vYUGGgNODv0T3QxdsBuaXVeG/PLTzIKYWHgxSbXupDrQVKhybQzQ42YgGqa9Rcg53WoFITThioxUCxChxtxNjz1pN49YlAdPF2wDO9ffHr/CHwc7Hle2oUCq8IBQzCvNruTkorrECVUg2JSIAgd8tKVQV4LrtNsVxc7SVY/VwE39OgUCyOrjJHxGXIcT+7FOMjfFq1Dza+EOblYJEViqnFQKFQKAbABqDvZZW0MLJpWGuDzXKyNKgwUCgUigFE+DkDAOIyDOuTXhdWVLpaYHwBoMJAoVAoBtHTzxkMA2TJq5BbWtWqfbCiwoqMpUGFgUKhUAzAQSriejPEt8JqKCpXIL1I09e+JxUGCoVCaR/08tdc0G+nGy4MrLUQ7G4HZ1uxUedlLKgwUCgUioH0YuMMbRCGCH8XY07JqFBhoFAoFANhL+q3M+QG92ZgxSTCz/JqJLFQYaBQKBQD6eHjBKGAQV5pNbJLDAtAsxZDuIXGFwAqDBQKhWIwthIht57hWnKR3q/LKalCRnElGMZyM5IAKgwUCoXSKgaFuAMAriYV6P2aa8maisXdZU5wtLHMwDNAhYFCoVBaBduXhC1Prw/XtGMtvacJFQYKhUJpBQOCXQEACTllKCxX6PUa1u00IJgKA4VCobQ73B2kCPPSLHRjXUTNUVKlxL1sTSkMVlQsFSoMFAqF0kpYl9ClRy3HGS49KgAhQIiHPbycbEw9tTZBhYFCoVBaSVQXTwDAifs5La5nOP0gT+c1lgwVBgqFQmklT4V5QCISIK2wEom5ZU2OI4TgzINcAEBUVyoMFAqF0m6xk4gwOFSTtnrifm6T4xJyypApr4JEJMAT2jRXS4YKA4VCobSBUd28AACH47ObHPP77UwAwNDOHrCVCM0yr7ZAhYFCoVDawLhwHwgFDG6mFSMxt2EfaEIIfr2pEYZJfXzNPb1WQYWBQqFQ2oCnoxQjtHGD/8WkN3j+WnIRUgsrYCsW4uke3uaeXqtoN8Lw5ZdfIiQkBDY2NujXrx/OnTvH95QoFEoH4cX+AQCA3VfTUFql1Hnuu/OPAQDP9vGFnURk9rm1hnYhDHv27MGiRYuwfPly3LhxA0OHDsX48eORmprK99QoFEoHYHR3b4R62kNeqcS2C8nc9vgMOY7ezQEAzB4awtPsDKddCMPGjRsxa9YszJ49G927d8fmzZsREBCAr776iu+pUSiUDoBQwGDhqDAAwNaTiYjPkKNCUYOlv9wGIcCk3r7o7OXI8yz1xzrsmmZQKBSIiYnBhx9+qLN9zJgxuHjxYqOvqa6uRnV1Nfd3SUmJSedIoVDaP5N6++LAjQycepCHqf+5BBdbMTLlVXCzl+BvE7rzPT2DsHqLIT8/HyqVCt7eukEdb29vZGc3nj62du1aODs7c4+AgABzTJVCobRjGIbB5pcjMTDYDRUKFTLlVfBwkOC/r/eHzNmyS2DUx+otBhaGYXT+JoQ02MaybNkyLF68mPu7pKSEigOFQmkzzrZi7H7rCVx+XAB5pRJDwjzgZMF9F5rC6oXBw8MDQqGwgXWQm5vbwIpgkUqlkEql5pgehULpYAgEDAZ39uB7Gm3C6l1JEokE/fr1w7Fjx3S2Hzt2DIMHD+ZpVhQKhWK9WL3FAACLFy/Ga6+9hv79++PJJ5/EN998g9TUVMyZM4fvqVEoFIrV0S6E4aWXXkJBQQFWrVqFrKwshIeH49ChQwgKCuJ7ahQKhWJ1MKSlIuIdgJKSEjg7O0Mul8PJyYnv6VAoFIrRMeQ6Z/UxBgqFQqEYFyoMFAqFQtGhXcQY2grrTaMroCkUSnuFvb7pEz2gwgCgtFRTQ50ucqNQKO2d0tJSODs7NzuGBp8BqNVqZGZmwtHRscnV0pYCu0o7LS2t3QbK6Tm2D+g5WhaEEJSWlsLX1xcCQfNRBGoxABAIBPD39+d7Ggbh5ORk8V/EtkLPsX1Az9FyaMlSYKHBZwqFQqHoQIWBQqFQKDpQYbAypFIpVqxY0a6LANJzbB/Qc7ReaPCZQqFQKDpQi4FCoVAoOlBhoFAoFIoOVBgoFAqFogMVBgunuroaffr0AcMwuHnzps5zqampeOaZZ2Bvbw8PDw8sXLgQCoVCZ0xcXByioqJga2sLPz8/rFq1Sq8l8aYmOTkZs2bNQkhICGxtbREaGooVK1Y0mL81n2NTfPnllwgJCYGNjQ369euHc+fO8T0lvVm7di0GDBgAR0dHeHl54bnnnsODBw90xhBCsHLlSvj6+sLW1hbDhw/HnTt3dMZUV1djwYIF8PDwgL29PSZNmoT09HRznorerF27FgzDYNGiRdy29naODSAUi2bhwoVk/PjxBAC5ceMGt72mpoaEh4eTESNGkNjYWHLs2DHi6+tL5s+fz42Ry+XE29ubvPzyyyQuLo7s3buXODo6ks8++4yHM9Hlzz//JDNmzCBHjhwhjx49Ir/++ivx8vIi77//PjfG2s+xMXbv3k3EYjH59ttvyd27d8m7775L7O3tSUpKCt9T04uxY8eSbdu2kfj4eHLz5k0yceJEEhgYSMrKyrgxn3zyCXF0dCR79+4lcXFx5KWXXiI+Pj6kpKSEGzNnzhzi5+dHjh07RmJjY8mIESNI7969SU1NDR+n1SRXr14lwcHBpFevXuTdd9/ltrenc2wMKgwWzKFDh0i3bt3InTt3GgjDoUOHiEAgIBkZGdy2Xbt2EalUSuRyOSGEkC+//JI4OzuTqqoqbszatWuJr68vUavVZjsPfVm/fj0JCQnh/m6P5zhw4EAyZ84cnW3dunUjH374IU8zahu5ubkEADlz5gwhhBC1Wk1kMhn55JNPuDFVVVXE2dmZfP3114QQQoqLi4lYLCa7d+/mxmRkZBCBQEAOHz5s3hNohtLSUhIWFkaOHTtGoqKiOGFoT+fYFNSVZKHk5OTgzTffxE8//QQ7O7sGz1+6dAnh4eHw9fXlto0dOxbV1dWIiYnhxkRFRenkWI8dOxaZmZlITk42+TkYilwuh5ubG/d3eztHhUKBmJgYjBkzRmf7mDFjcPHiRZ5m1TbkcjkAcJ9bUlISsrOzdc5RKpUiKiqKO8eYmBgolUqdMb6+vggPD7eo92HevHmYOHEiRo8erbO9PZ1jU1BhsEAIIZgxYwbmzJmD/v37NzomOzsb3t7eOttcXV0hkUiQnZ3d5Bj2b3aMpfDo0SNs2bJFp093ezvH/Px8qFSqRudraXPVB0IIFi9ejKeeegrh4eEAat/z5s4xOzsbEokErq6uTY7hm927dyM2NhZr165t8Fx7OcfmoMJgRlauXAmGYZp9XL9+HVu2bEFJSQmWLVvW7P4aqwRLCNHZXn8M0QZlTVVFVt9zrEtmZibGjRuHF198EbNnz9Z5zhLPsa00Nl9LnWtzzJ8/H7dv38auXbsaPNeac7SU9yEtLQ3vvvsuduzYARsbmybHWfM5tgStrmpG5s+fj5dffrnZMcHBwVi9ejUuX77cYJl9//79MX36dGzfvh0ymQxXrlzReb6oqAhKpZK7k5HJZA3uTnJzcwE0vNsxFvqeI0tmZiZGjBiBJ598Et98843OOEs9x9bi4eEBoVDY6Hwtba4tsWDBAhw8eBBnz57VqUwsk8kAaO6YfXx8uO11z1Emk0GhUKCoqEjnjjo3NxeDBw820xk0TUxMDHJzc9GvXz9um0qlwtmzZ7F161YuC8uaz7FFeIptUJohJSWFxMXFcY8jR44QAOSXX34haWlphJDawGxmZib3ut27dzcIzLq4uJDq6mpuzCeffGIxgdn09HQSFhZGXn755UYzNdrDOdZn4MCB5J133tHZ1r17d6sJPqvVajJv3jzi6+tLEhISGn1eJpORdevWcduqq6sbDczu2bOHG5OZmWkxgdmSkhKd319cXBzp378/efXVV0lcXFy7OMeWoMJgBSQlJTWZrjpq1CgSGxtLjh8/Tvz9/XVSOYuLi4m3tzd55ZVXSFxcHNm3bx9xcnKyiFTOjIwM0rlzZzJy5EiSnp5OsrKyuAeLtZ9jY7Dpqt999x25e/cuWbRoEbG3tyfJycl8T00v3nnnHeLs7ExOnz6t85lVVFRwYz755BPi7OxM9u3bR+Li4sgrr7zSaCqnv78/OX78OImNjSUjR4606FTOullJhLTPc6wLFQYroDFhIERjWUycOJHY2toSNzc3Mn/+fJ20TUIIuX37Nhk6dCiRSqVEJpORlStXWsSd9LZt2wiARh91seZzbIp///vfJCgoiEgkEtK3b18u1dMaaOoz27ZtGzdGrVaTFStWEJlMRqRSKRk2bBiJi4vT2U9lZSWZP38+cXNzI7a2tiQ6Opqkpqaa+Wz0p74wtMdzrAutrkqhUCgUHWhWEoVCoVB0oMJAoVAoFB2oMFAoFApFByoMFAqFQtGBCgOFQqFQdKDCQKFQKBQdqDBQKBQKRQcqDBQKhULRgQoDhWIADMPgwIEDZj3mgQMH0LlzZwiFQp32ks0RHByMzZs3m3RelPYLFQYKxcJ5++23MWXKFKSlpeGf//xnq/bxzTffYPjw4XBycgLDMCguLjbuJCntCioMFIoFU1ZWhtzcXIwdOxa+vr5wdHRs1X4qKiowbtw4/O1vfzPyDCntESoMlA7JL7/8goiICNja2sLd3R2jR49GeXk5AOD7779Hz549IZVK4ePjg/nz5+u8Nj8/H88//zzs7OwQFhaGgwcPcs/169cPGzZs4P5+7rnnIBKJUFJSAkBTw59hGK6mv0KhwNKlS+Hn5wd7e3sMGjQIp0+fBgCcPn2aE4KRI0eCYRjuuYsXL2LYsGGwtbVFQEAAFi5cyM2/MRYtWoQPP/wQTzzxRNveOEqHgAoDpcORlZWFV155BW+88Qbu3buH06dPY/LkySCE4KuvvsK8efPw1ltvIS4uDgcPHkTnzp11Xv+Pf/wDU6dOxe3btzFhwgRMnz4dhYWFAIDhw4dzF29CCM6dOwdXV1ecP38eAHDq1CnIZDJ07doVADBz5kxcuHABu3fvxu3bt/Hiiy9i3LhxePjwIQYPHswJyN69e5GVlYXBgwcjLi4OY8eOxeTJk3H79m3s2bMH58+fbyBgFEqr4be4K4VifmJiYgiARnsg+Pr6kuXLlzf5WgDko48+4v4uKysjDMOQP//8kxBCyMGDB4mzszNRqVTk5s2bxNPTk7z33nvkr3/9KyGEkLfeeou89NJLhBBCEhMTCcMwJCMjQ+cYo0aNIsuWLSOEEFJUVEQAkFOnTnHPv/baa+Stt97Sec25c+eIQCAglZWVhBBCgoKCyKZNmxrM/9SpUwQAKSoqavIcKRTa2pPS4ejduzdGjRqFiIgIjB07FmPGjMGUKVOgVCqRmZmJUaNGNfv6Xr16cf+3t7eHo6Mj10502LBhKC0txY0bN3DhwgVERUVhxIgRWL16NQCNe4jNLIqNjQUhBF26dNHZf3V1Ndzd3Zs8fkxMDBITE/Hzzz9z2wghUKvVSEpKQvfu3Q16PyiU+lBhoHQ4hEIhjh07hosXL+Lo0aPYsmULli9fjhMnTuj1erFYrPM3wzBQq9UAAGdnZ/Tp0wenT5/GxYsXMXLkSAwdOhQ3b97Ew4cPkZCQgOHDhwMA1Go1hEIhYmJiIBQKdfbp4ODQ5PHVajXefvttLFy4sMFzgYGBep0DhdIcVBgoHRKGYTBkyBAMGTIEH3/8MYKCgnDs2DEEBwfjxIkTGDFiRKv3PXz4cJw6dQpXrlzBqlWr4OLigh49emD16tXw8vLi7ugjIyOhUqmQm5uLoUOH6r3/vn374s6dOw1iHxSKsaDBZ0qH48qVK1izZg2uX7+O1NRU7Nu3D3l5eejevTtWrlyJDRs24IsvvsDDhw8RGxuLLVu2GLT/4cOH4/Dhw2AYBj169OC2/fzzz4iKiuLGdenSBdOnT8df/vIX7Nu3D0lJSbh27RrWrVuHQ4cONbn/Dz74AJcuXcK8efM4S+TgwYNYsGBBk6/Jzs7GzZs3kZiYCACIi4vDzZs3uaA5hVIXajFQOhxOTk44e/YsNm/ejJKSEgQFBWHDhg0YP348AKCqqgqbNm3CkiVL4OHhgSlTphi0/2HDhgEAoqKiwDAM9//NmzfrCAMAbNu2DatXr8b777+PjIwMuLu748knn8SECROa3H+vXr1w5swZLF++HEOHDgUhBKGhoXjppZeafM3XX3+Nf/zjHw3muG3bNsyYMcOg86O0f2jPZwqFQqHoQF1JFAqFQtGBCgOFQqFQdKDCQKFQKBQdqDBQKBQKRQcqDBQKhULRgQoDhUKhUHSgwkChUCgUHagwUCgUCkUHKgwUCoVC0YEKA4VCoVB0oMJAoVAoFB2oMFAoFApFh/8HgHpaW4mrrHcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from copy import deepcopy\n", + "df_cumulative = deepcopy(df)\n", + "for iteration in range(5):\n", + " df = campaign.recommend(batch_size=3)\n", + " df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n", + " campaign.add_measurements(df)\n", + " df_cumulative = pd.concat([df_cumulative, df])\n", + " fig, ax = plt.subplots(figsize=(4,3))\n", + " ax.set_title(f'Iteration {iteration} ({len(df_cumulative)} experiments)')\n", + " example_data.plot('x', 'y', ax=ax)\n", + " df_cumulative.plot.scatter('schwefel1', 'Yield', ax=ax, c='red')\n", + " plt.tight_layout()\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "BO", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/.ipynb_checkpoints/baybe_utils-checkpoint.py b/src/.ipynb_checkpoints/baybe_utils-checkpoint.py new file mode 100644 index 0000000..b22e1d5 --- /dev/null +++ b/src/.ipynb_checkpoints/baybe_utils-checkpoint.py @@ -0,0 +1,151 @@ +from baybe.targets import NumericalTarget +from baybe.objective import Objective +from baybe.parameters import ( + NumericalContinuousParameter +) + +from baybe.recommenders import ( + SequentialGreedyRecommender, + RandomRecommender +) + +from baybe.searchspace import SearchSpace +from baybe import Campaign + +from src import schwefel + +import numpy as np + +from tqdm import tqdm + + +def run_optimization_campaign( + NUM_ITERATIONS, + NUM_INIT_OBS, + N_DIMS_SCHWEF, + NOISE_LEVEL_SCHWEF, + ITERATION_BATCH_SIZE, + SCHWEFEL_RANGE = (-50,50), + recommender_init = None, + recommender_main = None +): + """ + Utility function for running a bayesian optimization campaign. + + NUM_ITERATIONS: Number of bayesian optimization iterations to run for + NUM_INIT_OBS: number of random initial observations to make before starting BO + N_DIMS_SCWEF: number of x dimensions of schwefel function to optimize + NOISE_LEVEL_SCHWEF: variance of noise added to schwefel function + ITERATION_BATCH_SIZE: number of observations to make per BO batch + reccomender_init: (BayBE Reccomender): recommender to use for initial sampling. Default random + recommender_main: (BayBE reccomender): recommender to use for main BO loop. Default baybe sequential greedy with EI + + """ + # Define Schweffel oracle + schweffer = schwefel.SchwefelProblem(n_var = N_DIMS_SCHWEF, noise_level=NOISE_LEVEL_SCHWEF, range = SCHWEFEL_RANGE) + target = NumericalTarget(name = 'schwefel', mode = "MIN") + parameters = [ + NumericalContinuousParameter(f'schwefel{i+1}', bounds = (-500,500)) for i in range(N_DIMS_SCHWEF) + ] + + objective = Objective(mode = "SINGLE", targets = [target]) + searchspace = SearchSpace.from_product(parameters) + + if recommender_init is None: + recommender_init = RandomRecommender() + if recommender_main is None: + recommender_main = SequentialGreedyRecommender(acquisition_function_cls='EI') + + print("Collecting initial observations") + campaign_init = Campaign(searchspace, objective, recommender_init) + random_params = campaign_init.recommend(NUM_INIT_OBS) + + y_init = schweffer.f(random_params.to_numpy()) + + random_params.insert(N_DIMS_SCHWEF, 'schwefel', y_init) + + optimization_campaign = Campaign(searchspace, objective, recommender_main) + optimization_campaign.add_measurements(random_params) + + print('Beginning optimization campaign') + for i in tqdm(range(NUM_ITERATIONS)): + reccs = optimization_campaign.recommend(ITERATION_BATCH_SIZE) + + y_vals = schweffer.f(reccs.to_numpy()) + + reccs.insert(N_DIMS_SCHWEF, 'schwefel', y_vals) + + optimization_campaign.add_measurements(reccs) + + return optimization_campaign + + + +def iteration_noise_grid_search(iterations_list, noise_list, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE, SCHWEFEL_RANGE = (-50, 50)): + """ + Utility to run a parameter grid experiment varying noise level and number of BO iterations. Runs full grid in serial. + Params: + ------- + iteraitons_list: list of ints - number of BO iterations to run + noise_list - list of floats - noise values to run + NUM_INIT_JOBS - int + N_DIMS_SCWEF - int + ITERATION_BATCH_SIZE - int + + returns: + --------- + iteration_results: an abomination of dictionaries. Outer level: results keyed by number of iterations; next layer: results keyed by noise level. Values are BayBE campaign objects of completed campaign + """ + iteration_results = {} + + for its in iterations_list: + noise_results = {} + for noise_level in noise_list: + opt_campaign = run_optimization_campaign(its, NUM_INIT_OBS, N_DIMS_SCHWEF, noise_level, ITERATION_BATCH_SIZE, SCHWEFEL_RANGE=SCHWEFEL_RANGE) + noise_results[str(noise_level)] = opt_campaign + iteration_results[str(its)] = noise_results + + return iteration_results + +def process_grid_searh_results(grid_search_results): + """ + Process results from iteration_noise_grid_search function to extract a performance matrix of best result for each campaign + + Params: + -------- + grid_search_results: dict of dicts of BayBE campaigns + + Returns: + ------- + n_its - list - iteration numbers used + n_noise - list - noise values used + performance_matrix: matrix of best observed values (min schwefel val) for each campaign from grid search, arranged with iteration varying on axis 0 and noise on axis 1 + """ + + + n_its = len(grid_search_results) + n_noise = len(grid_search_results[list(grid_search_results.keys())[0]]) + + performance_matrix = np.zeros((n_its, n_noise)) + + + + # fill out performance matrix + iteration_vals = [] + noise_vals = [] + + first_pass = True + for i, (its, entry) in enumerate(grid_search_results.items()): + iteration_vals.append(its) + for j, (noise, camp) in enumerate(entry.items()): + if first_pass: + noise_vals.append(noise) + best_result = camp.measurements['schwefel'].min() + # hack to flip matrix BO iterations upside down for plotting + performance_matrix[n_its - 1 - i, j] = best_result + first_pass = False + + iteration_vals = iteration_vals[::-1] + + return iteration_vals, noise_vals, performance_matrix + diff --git a/src/.ipynb_checkpoints/schwefel-checkpoint.py b/src/.ipynb_checkpoints/schwefel-checkpoint.py new file mode 100644 index 0000000..5f7a90c --- /dev/null +++ b/src/.ipynb_checkpoints/schwefel-checkpoint.py @@ -0,0 +1,34 @@ +import numpy as np + +class SchwefelProblem: + def __init__(self, n_var=1, noise_level=0.01, range = (-50, 50)): + """ + y = f(x) + eps + """ + self.noise_level = noise_level + self.n_var = n_var # Number of variables/dimensions + self.bounds = np.array([[range[0]] * self.n_var, [range[1]] * self.n_var]) + + def _schwefel_individual(self, x): + return x * np.sin(np.sqrt(np.abs(x))) + + def f(self, x): + return 418.9829 * self.n_var - np.sum(self._schwefel_individual(x), axis=1) + + def eps(self, x): + # Assuming the noise is independent of x for simplicity + return np.random.normal(0, self.noise_level, x.shape[0]) + + def y(self, x): + f = self.f(x) + eps = self.eps(x) + return f + eps + +# Test code if this file is the main program being run +if __name__ == "__main__": + # Create a SchwefelProblem instance with 3 variables/dimensions + schwefel = SchwefelProblem(n_var=3, noise_level=1.) + x_test = np.array([[420, 420, 420], [420, 420, 420]]) # Example input vector + print("Objective function value (f):", schwefel.f(x_test)) + print("Noisy objective function value (y):", schwefel.y(x_test)) + diff --git a/src/schwefel_functions.py b/src/.ipynb_checkpoints/schwefel_functions-checkpoint.py similarity index 100% rename from src/schwefel_functions.py rename to src/.ipynb_checkpoints/schwefel_functions-checkpoint.py diff --git a/src/.ipynb_checkpoints/seed_data-checkpoint.csv b/src/.ipynb_checkpoints/seed_data-checkpoint.csv new file mode 100644 index 0000000..4ca1035 --- /dev/null +++ b/src/.ipynb_checkpoints/seed_data-checkpoint.csv @@ -0,0 +1,4 @@ +schwefel1,Yield +356.99134680215377,403.0454233719017 +-309.0047300290056,123.75848185395103 +-55.674007109553486,470.42364275539364 diff --git a/src/.ipynb_checkpoints/visualization-checkpoint.py b/src/.ipynb_checkpoints/visualization-checkpoint.py new file mode 100644 index 0000000..2f75dbe --- /dev/null +++ b/src/.ipynb_checkpoints/visualization-checkpoint.py @@ -0,0 +1,25 @@ +import seaborn as sn +import pandas as pd + +def grid_search_heatmap(iterations_list, noise_list, performance_matrix): + """ + plot a heatmap + + Params: + ------- + iterations_list: list of number of iterations used + noise list: list of noise values used + performance matrix: np array of dims len(iterations_list) x len(noise_list) with smallest noise, smallest iterations in lower left corner ('origin') + + returns: + ------ + matplotlib ax object + """ + + df_heatmap = pd.DataFrame(performance_matrix, index = [str(its) for its in iterations_list], columns = [str(noise) for noise in noise_list]) + + ax = sn.heatmap(df_heatmap, annot=True, fmt = '.3g', cmap = 'crest') + ax.set_xlabel('Noise level') + ax.set_ylabel('Number of BO iterations') + + return ax \ No newline at end of file diff --git a/src/BayBE_grid_search.ipynb b/src/BayBE_grid_search.ipynb new file mode 100644 index 0000000..cba3cef --- /dev/null +++ b/src/BayBE_grid_search.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "01039780-75fe-4d45-ad43-a3802d21f6f2", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fc7d83ca-d889-49f9-a332-785d3267512d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\": Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from src import schwefel\n", + "from src import baybe_utils\n", + "from src import visualization\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "3e9d8019-5827-4325-b0e4-cc1f80745270", + "metadata": {}, + "source": [ + "## BayBE Schwefel function optimization examples\n", + "\n", + "### Brenden Pelkie\n", + "\n", + "This notebook walks through a quick grid search to explore the impact of measurement noise on optimization performance of a vanilla BO implementation in BayBE." + ] + }, + { + "cell_type": "markdown", + "id": "44701cdf-29e6-47f7-84f5-abfd5112a2e9", + "metadata": {}, + "source": [ + "### 1. Pick parameters\n", + "\n", + "First define parameters for optimization. Here we set the number of BO iterations/cycles to run, the number of random initial observations to include, the dimensionality of the schwefel function to optimize, the noise level of the schwefel observations, and the number of obserations to make per iteration/BO batch cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b158f689-b0e5-478e-b7a6-2cb586acf4c5", + "metadata": {}, + "outputs": [], + "source": [ + "NUM_ITERATIONS = 15\n", + "NUM_INIT_OBS = 5\n", + "N_DIMS_SCHWEF = 2\n", + "NOISE_LEVEL_SCHWEF = 0\n", + "ITERATION_BATCH_SIZE = 1\n", + "SCHWEFEL_RANGE = (-50,50)" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e07e3-3daa-4196-b17f-8982e0598db9", + "metadata": {}, + "source": [ + "For the grid search over number of BO iterations and noise, select the desired values here" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0670300a-6f94-4b37-9854-d4b0b5251b40", + "metadata": {}, + "outputs": [], + "source": [ + "num_iterations = [5]#[5,10,20,40,60,80]\n", + "noise = [0]# [0, 0.1, 0.2, 0.5]\n" + ] + }, + { + "cell_type": "markdown", + "id": "4705ebb3-86ad-41f5-8c23-0f883b79a871", + "metadata": {}, + "source": [ + "### 2. Run grid search" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6a78944a-2833-407a-9bbc-86206155b2d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting initial observations\n", + "Beginning optimization campaign\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:04<00:00, 1.17it/s]\n" + ] + } + ], + "source": [ + "grid_results = baybe_utils.iteration_noise_grid_search(num_iterations, noise, NUM_INIT_OBS, N_DIMS_SCHWEF, ITERATION_BATCH_SIZE, SCHWEFEL_RANGE=SCHWEFEL_RANGE)" + ] + }, + { + "cell_type": "markdown", + "id": "4f403581-1dd2-4a10-b98e-fcd147ec0bba", + "metadata": {}, + "source": [ + "### 3. Process and visualize results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a10e5944-1038-4bec-bb54-66a60f22ccec", + "metadata": {}, + "outputs": [], + "source": [ + "n_its, noise, performance_matrix = baybe_utils.process_grid_searh_results(grid_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "524e2642-4a6d-458e-bade-05106c17e4ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualization.grid_search_heatmap(n_its, noise, performance_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6f476b1b-3931-435c-a85a-73b76c5ecdd5", + "metadata": {}, + "outputs": [], + "source": [ + "test_result = grid_results['5']['0'].measurements" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bf8e4a92-88a6-4218-b7a4-dcbd83477b2c", + "metadata": {}, + "outputs": [], + "source": [ + "x_names = [f'schwefel{i+1}' for i in range(N_DIMS_SCHWEF)]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "9d2c5714-e918-4dfc-a379-051c78f34e08", + "metadata": {}, + "outputs": [], + "source": [ + "x_train = test_result[x_names].to_numpy()\n", + "y_train = test_result['schwefel'].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "5da6d938-47b0-450c-a4c4-80590aa1af6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fe513817-58d2-4148-baff-fdc5cb531a1e", + "metadata": {}, + "outputs": [], + "source": [ + "schwef = schwefel.SchwefelProblem(n_var = 2, noise_level = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "3f4c8244-65bf-4e23-a57a-a1c04d49f3ed", + "metadata": {}, + "outputs": [], + "source": [ + "y_test = schwef.y(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5e207b2d-d704-4305-8d3b-e883dd2eee9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-353.1162661 , -115.77679551],\n", + " [-100.48640485, -327.1966037 ],\n", + " [ 139.7688944 , -438.20087406],\n", + " [-486.63820259, 378.28871931],\n", + " [-325.39646572, -219.090224 ],\n", + " [-180.23321345, 500. ],\n", + " [ 500. , 500. ],\n", + " [-500. , -104.2417874 ],\n", + " [-132.38714138, -500. ],\n", + " [-448.33851406, 95.06174804]])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "1ddf0eea-504b-47f5-9100-3c626ffa124f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schwef.f(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "58182f79-46a5-4567-96d3-0c8dda176487", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ],\n", + " [ 704.85928745, 555.62241542, 1314.4367682 , 590.92000549,\n", + " 774.57368413, 1154.99341253, 1199.14411706, 583.68789976,\n", + " 541.86150018, 1195.2620041 ]])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schwef.y(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "1c39cc34-8209-426e-b1f4-4761456e7022", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-353.1162661 , -115.77679551],\n", + " [-100.48640485, -327.1966037 ],\n", + " [ 139.7688944 , -438.20087406],\n", + " [-486.63820259, 378.28871931],\n", + " [-325.39646572, -219.090224 ],\n", + " [-180.23321345, 500. ],\n", + " [ 500. , 500. ],\n", + " [-500. , -104.2417874 ],\n", + " [-132.38714138, -500. ],\n", + " [-448.33851406, 95.06174804]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f78a53d0-a21f-4ac4-91bc-eb786e1ad5fe", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_experiment_baybe.py b/src/baybe_utils/run_experiment_baybe.py similarity index 95% rename from run_experiment_baybe.py rename to src/baybe_utils/run_experiment_baybe.py index 98576d7..2355137 100644 --- a/run_experiment_baybe.py +++ b/src/baybe_utils/run_experiment_baybe.py @@ -53,7 +53,7 @@ from src.schwefel import SchwefelProblem from time import time -def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds): +def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds, fp): """ Run a bayesian optimization campaign on the 2-dimensional schwefel function using the specified parameters. Uses BayBE with the @@ -146,8 +146,8 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds): train_Y = torch.from_numpy(y_train) train_Y_real = torch.from_numpy(y_real_complete) - os.makedirs('results_baybe', exist_ok=True) - fname = f"results_baybe/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" + os.makedirs(fp, exist_ok=True) + fname = f"{fp}/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" torch.save((train_X, train_Y, train_Y_real, None), fname) return train_X, train_Y, train_Y_real, None diff --git a/run_experiment_random_baybe.py b/src/baybe_utils/run_experiment_random_baybe.py similarity index 95% rename from run_experiment_random_baybe.py rename to src/baybe_utils/run_experiment_random_baybe.py index e0a12a6..706a842 100644 --- a/run_experiment_random_baybe.py +++ b/src/baybe_utils/run_experiment_random_baybe.py @@ -53,7 +53,7 @@ from src.schwefel import SchwefelProblem from time import time -def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds): +def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds, fp): """ Run a bayesian optimization campaign on the 2-dimensional schwefel function using the specified parameters. Uses BayBE with the @@ -147,8 +147,8 @@ def run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds): train_Y = torch.from_numpy(y_train) train_Y_real = torch.from_numpy(y_real_complete) - os.makedirs('results_random_baybe', exist_ok=True) - fname = f"results_random_baybe/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" + os.makedirs(fp, exist_ok=True) + fname = f"{fp}/{problem.__class__.__name__[:5]}_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt" torch.save((train_X, train_Y, train_Y_real, None), fname) return train_X, train_Y, train_Y_real, None diff --git a/run_grid_experiments_baybe.py b/src/baybe_utils/run_grid_experiments_baybe.py similarity index 96% rename from run_grid_experiments_baybe.py rename to src/baybe_utils/run_grid_experiments_baybe.py index 0154262..60c42fc 100644 --- a/run_grid_experiments_baybe.py +++ b/src/baybe_utils/run_grid_experiments_baybe.py @@ -9,10 +9,10 @@ #@ray.remote -def worker(n_init, noise_level, budget, seed, noise_bool, bounds): +def worker(n_init, noise_level, budget, seed, noise_bool, bounds, fp): try: - run_experiment(n_init, noise_level, budget, seed, noise_bool,bounds) + run_experiment(n_init, noise_level, budget, seed, noise_bool,bounds, fp) # saved file looks like this: results\Schwe_n_init_6_noiselvl_0_budget_0_seed_2_noise_False.pt except Exception as e: print(e) @@ -21,7 +21,7 @@ def worker(n_init, noise_level, budget, seed, noise_bool, bounds): return 0 -def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds): +def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds, fp): # ray.init(local_mode=True) #ray.init(ignore_reinit_error=True) @@ -37,7 +37,7 @@ def run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, boun # ray.get(completed_tasks[0]) #sleep(1) - task = worker(n_init, noise_level, budget, seed, noise_bool, bounds) + task = worker(n_init, noise_level, budget, seed, noise_bool, bounds, fp) tasks.append(task) print(f'Started problem {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') #gc.collect() diff --git a/run_grid_experiments_baybe_random.py b/src/baybe_utils/run_grid_experiments_baybe_random.py similarity index 95% rename from run_grid_experiments_baybe_random.py rename to src/baybe_utils/run_grid_experiments_baybe_random.py index aef3e5e..69fd07c 100644 --- a/run_grid_experiments_baybe_random.py +++ b/src/baybe_utils/run_grid_experiments_baybe_random.py @@ -9,10 +9,10 @@ #@ray.remote -def worker(n_init, noise_level, budget, seed, noise_bool, bounds): +def worker(n_init, noise_level, budget, seed, noise_bool, bounds, fp): try: - run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds) + run_experiment(n_init, noise_level, budget, seed, noise_bool, bounds, fp) # saved file looks like this: results\Schwe_n_init_6_noiselvl_0_budget_0_seed_2_noise_False.pt except Exception as e: print(e) @@ -21,7 +21,7 @@ def worker(n_init, noise_level, budget, seed, noise_bool, bounds): return 0 -def run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds): +def run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds, fp): # ray.init(local_mode=True) #ray.init(ignore_reinit_error=True) @@ -37,7 +37,7 @@ def run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budge # ray.get(completed_tasks[0]) #sleep(1) - task = worker(n_init, noise_level, budget, seed, noise_bool, bounds) + task = worker(n_init, noise_level, budget, seed, noise_bool, bounds, fp) tasks.append(task) print(f'Started problem {n_init} noise {noise_level} budget {budget} seed {seed}, time: {time() - start_time:.2f}s') #gc.collect()

bPM7g=UYL8nQ_cRQ(8T^o{q^gP!dMv}gXeR#i6KX2 zGli0gWa|U;`ot#a*Nb}q-ZH=bM4^oS=UAF^bH#-a#=rcZ88X8^(g|H|jc+wLj(j`Y zxPrdSf_kw$hu~}7q=Sm77-sl7>*$rTs(-1eidt?%2%3Fy3E{uvn=m?M;!xG$db`)> zKf=tP6lM8Br}W-s4)wz8XMYLb0d?&qgpD}O7#y04S!33N|2`t0`=y0v!rk}>fU z&9ToyZbBCi*)C9Rh^sK`y-Jrh3vQy*Zzk#n>{|vQ( zeQN-mFWUh#a32J(3|2Vp=s(2}9FKJ3;Y!%c#z@Kk%^X{{*=57Nmvy5Kpx|pzD->bR znWTPj#ZfRY~Q3EkT6DBShJ znWYri;I{!gVi*jJiIYZ%;JP!Y06HqwOp|WqkVfHTf8I~yM9jmuEDUx!@z4sE_6!jO(bM{e*$nf+k?p1~{I$ z>K6p)l1TFJOZEb~?VeTl(lRS@+=qS+r-2B?hN#H#If3~9wO4u&0aGHEEuzT$_7w1a zAVVbJ6nR1rh%#uWoTGkd2&&eQBwXHn7}~&ho}V$l!6IG`VAOwA+}Tb4%um>N{uYp9 z*L^Y?zcU|~ec+>^mwP#mCgpx=|Zu5t|Pz&h?Rc_qO4BCe&RWR!rIlM zKAk!Y_Ildcq#+0R9g(>u*c4O@A_#>+6%nJMQY5}<u zkGherCtgWKBJ|S$4$Fb5u}b*c4&1e$>^}kirxL_(3SerICj-mFc{WxI0^qB4jc}B< zn;vf??V19rDa!rwE?8!$W>`c)0v&-emW(wZZr5=C?;F@cjCp=R`KcT`b_A8O%LM+^ z@Ab6a%h&yvzI)*Ew_{!(^ij-^<^g$#0Tjuj$_2U|O#~UAZBXe1fgY|XBh-Hjn_J;o zsV^B*0srsgnC)H+KoEuwd)>Y)Y5raX8;8F3}BJfoz*Lu*!NgpQELEDA2T<}c@M$K@ybd;H1ZCp5&o@~RUJ9;1o{>+-nA z=IK-7YP{gjb`io9+K99-qu!~ZIiRf@wFoeGss@-G>+e%6OPX-%-fD*DIN4O=C3iVS@`#kH;vym z9W;r&y__i=&pk0Ve)aM%D{`rDTkx5oNb6$|6Cf8Im5$St73JzM#a`)Kf8dDRZM|O^ zq^vy2d$9jw)y=8&He%q2u}hZ|xzpSKhVH&0%!V*zNc-?g54!eL1A9F(RK{}z25R7a zJ1&QAmGA0w!wgikBicLu4VjJuWH8n$BqzU(>UkZsj|kW#>ERL4R}4nK;M7sA!7huB z+-c#3z@k!9=yPyIu3y#s^i71Yfo9(h3Dvmsx8Vi#5|kVbpzg8=Vv z#IjSZe9%0Sto0bY8RY&tlGQDeA~HMcXM`Waa>%OlU8<7Blqv@fb~~vuj(+p((uSD) zUj-dg{7>ok_7kq){M{;OK6d=LY7GcpT=fp{rkAYU`(Vw5^tU(-ID5XS9a#{{kKGdn zZ3f{Bk?C(sF3vt@hh$)}y`JOjxirMXcc)x{QJ72GV6+`qz1HN4uV|`yDX&&$MK(oP zZ!I;Lb$z!cEFXFF(kt?Cfy@E_g`5I9}4iQhPch`D}mn=*| z`G0DGMQl@R+vdp%qq-79MpTaO(i&P(W5YL+C_%Q8rSdBQ71=jCatKsaG6!whU-q5kQ^1WeUROHg34NHUT3-e=FgW0sDZNY3cZs-n4ubTbA2JqNPi}ln69BG<_Rt96v=2Xw;&%;qPDI>MY z!GI=WJWFa|Vzj2Dq;z*5TFOS%X$e9R$}yD)UOOaSzkZFGK4CcP(ff+QNz>DqqjmfC z?H#@MG4+R(;z*57*~?KZh}i@>!qiO6DN6W-9XUS#grSu&>@4w0I>UYAgg*`_vAxe> zCPPQj1Sc*8@EW~Y2#RS4Un*he4j`RDObLr^B)nS8o7|0ob}js1)S>Ry025%YkfOw> z05#sh@h?@Zo+lZ|jo-CzONn8OH7ZA5EE!~XPpTUm8@0%EwrTkE7!I|U@c3NY)I>^9 zV!#aFczsNTLiFC9;y{KpFl>#+2s^hH6V9JJrC$|LaxsPlxovgXuytz;(eIO^9VV9t zrvMrgfFAy+aJqa6l};G!X30nrFr(Br!Poo+srgwuaMTcHFIurNczkzbv>#TIWL5-0 zfNL|5=#RM6wOiMU@xd4SD6wzDCaeS!O4C6_8W`}Wj z$b%TLX3ZKQ9(xoU<=9J@p;o`Wc>(N0n>MjxanVn#z=b5?EWVQBx@5@`Sqx_&WkV-q zc}lXf51H8iIb3G^F+({fUSRUjv|aqihNXhqeX(BMFP7X{uvj7X9<{u*#n8yX_qiisl#bsKV^Sn{`9tO%DQDZpMTVEZk~siSAlLq#W;3mFhS4-8^>MKE&HOu zzpGT0~Vvf3m=BRfa5^_aT4bzG@d>OG8SOAp=R%6=PreJ!Ms>;@GMn;&*9e{Cd&N3cXidiO18DL8x9ejk~rWk^-}_k>`c&n z4)mb-K!5X+M7tSbr~}dbrY6*6K!MJ4KvuLDW zeWcCXC$FGo+{#4oK!x^bxp@u*285^CtU zPf&I%8~xVXR9!Ux-2F|^55Nutqz0@zx!G_@I1mo zo)>$YY*Uo@?p%xB;J}kZ`kIIg5zO)U2Ajw3?n9_8h_^Rk?Fs)N{*C}0dk5lXj|CWw z`?!THrqE!JO~!$Q!~VWnxeo(g+R1{Pp&nQzjP_`(g8JJQQUW8>q4dZ=p-uR5kqN3HXjy+!EN2IUvmt^N1pp5Ywib0a}yPs z{&vC&Q&PYEMz)|(&i=(-lBz^zILFC9nBkwXo3RuDAcDT z{=nd`S)cs16cqIbc#x2&=puDAR8k;^k@G&7Mlbpl8Pd-d9mW@Hb73w!ZO!#rzpb| zJC;-=({%78g$~Vu^ORnsEZU@M240SfCKF)EQ|!kw`*+mOtXMunp{VXebRQfU0lD7x zCCKHU5WCab{K{@ET}wFTG7NJqVaMfT*Km~i!pr8AJ1 zENGfXjvj64>gtlMg+YHRyVI{4ixRLAp`sg&n1|X5#_zO$h8orm$W>IcZLJ>H?)(js zRb`pq#QwSEx|w{{rDWaT&X8O_yhm^8z1hxyBR$S<5v9rGkKFai$kt^T<`&)&BLLK@ zfT9Wu&!P1c6F33Q{T&L$=eMbo6bi=;GFgU;sdekC-bX!*om)f28V|7T;Vw&iqrSsW zM&91wQHb;6fQ39O48-!1J4+6nI`jJaz+k_UlJ+B4f8iL7PX=r;kyi~J76>FM=D6;= zw`|oDR^I~l@)zZ&dqf}imfKU#O6L98J8Vo-T7CWcGLz;I_j}7)7@62R>J~Cf6v-Nh z-!7v+xc@*lrfS1`r#}iBY0B?gTGC}@L*<#`uX1dc*WqekE7k>K%*3dEOs{Vs<3<(^ zQ7u!Qw)GujVJmw0ZI8Dw7EW;>({c19kdaV(uQpBpw5@1JA~pIiWl^nuYnKVl66WE@ zOH%}b7$mGMrsx9JTtTn9R!D3Y7_J=Bd|S>h6kKVnJQGI>Sc52t1>3fq}8`LyR&crp;CTlrpq={GGelNxH z*v-qU*Ke%D=4?^korPpr4%=lcAKbW1EVY(rm3qmu5aGSQ{C)aAMrBFm_w|#o0#ETA?TH(wp(yUDnE zy+RjX+PJuC@|6@#dp+Oy!A&z_z2WZ}!sTo49M{tF*I~EpXmgk@-eZjY60j2+^1&C~ zve_mb+#Ax(zTQBqM?pm;OmwP4W)sgleut+Nvz&XUMk7Z;^^Z~3lQH-a*u|yHE6+l|dRi);-zb2h`Xqk9sV!8(E~E3dV6?oxqeB+$_M)*azq@a# z&BgW;uf6^CjlLN;=aq>F9gDGT+qts6Jh`Du;MlEBri0R@!$;}u(++l_Lq4|7^!ddC zyt;gKcg6};d5xUTAHA_Tv$81fMA^h}Nx4W?;>A3!tXS(THe*+Z(v7sN2>X2(p1-_d z#Be2ilt0yc7uEF+@MwpOTS62}fJ+&^-2L1z{h?;)|} zx>`Y9yLPp|Zp;iZrn~#_=6^8ytalXAi&wW<(?rEb1|8%4@zzYJzJ&A#XHZfD zy}r+08MW{e*7smVq20)}m79{+BWzRJV)uPLQ|CpcDfY8RYX9hLd)&$<@GRI(pR8w3m2y0P<4t#r)kde_(M{15{{z-WqlR7&m>u<^1g^bopqXx|QFeHK@Mwdk zX7I!fcR61X|G-c`O`%OBqd4W7m0j zc9f5A!8;3_xSiifqfOW37WNDkj<&XY=41v&&QFeB9I4;jKhYMpy5D!WtyVX}#cjN( z{gk(qUVOZVntJ2`5s}U z@gRYBPV9dt8b;q)#!wj{)A)jJKkQk1v?)og%R$t!LV+HFYVH&(EYWpa4 zNE{fGZdu1v)3ts64Zc8{nN;_I;_0B)cP2{(9#NINA2S><$fWpmZ8b{ndduuO(-$Xs z`j%hX@Fw~C(jIzOX11yGhAE3eUimR^caMlh`*>;gQRWo?7X}+jLwkLP9cVHNN7^jh zSsk>rriKf<_?oNV3wUXrHKJDt+t9DdkEy9E1vfgRAD*d?SAMV4;{J*vs#Pyy9bkW@ z{*cYXGQImtu zD79ll&@TJE1q!3D^XsXajUj4+!c&2pPk%KvJf1xLqsD8Zv+M0h#akzB{fkF3?XCNg zHWd$kGm)A$wjG|FsSobT8FeqzV}iAy|D<+U&DeOQjg)b)TUUsw#=$q5B3>Rb)LJ+G zx>a}}H=*nQjmTYyKhXaDW!a@P-Gpa1nw*X_A&DU!4}mM+@33|N)Z9T?yWLMe^p`dl`9rba#xBftc|r*?yk}i$QCxNZ8DP|W3OQ5(c%$^jK0)F7!jWjR!2qibZ_ z$K}oy{m1mz#E3-w>hMK*2eOqjcaHYxa(j6yw?{j0R3xv}J56K6!>rY1*)Db}yZ~uaufjMl@&m20QZ@Js-E;l_qf($7ZMWEL;Hu<|DJj~gqLjlpj1J|3w1l*r! zwhiZ8JLfcia;m%Kv0h<^tABR1xk_swJBqO6Ua8U^x6EKK|5S};r;&$OFYoyArgm3n zhnHxCrMe))fu6x4w`}2Rh3Si58jdq_BFAwI9yAFJBx9xaIS&_0>q{H)DwLTP5zUDKE|^$|Co-pfBVH5S#v_olJH z19#$eif7jw9d`R1KI;GhailaO{zHKaE^z`f)W&g|&{O*J}8`s~NulG$iK8R@PQyb14 zY2NCfWICp%p(!B|7_+cA9!%lQA**;QDpDpa&HSY|Ju7~%866s0z8&-Pj*r=-mBwEY zo^TGz&ks}W7u5T(muk4VpP~O#z>jF=3gH9seu9+5i`z5aMHuT>=!M1KTNZWb>2P7&A3{2()wSfUOlu@syw=k$Q_F@7 zj(n_R4-O3T@$q=!^KGK&VLGd@s9MLk%E*)H7{2<$ob*~ zv+@G{j%dlfG1K+eCU0H2*Ez$%*xF~x7Sh(rm!30Jr!LR`E@!&undzn{=h$Y<%RG;d zE({osV0ju{Z=s-)M(umR^DQ1N{zPlZ=B=wV6Dn+b_%2LTUe+l)l>U5UEYI86UZ7Mu z1f@bDPkPWJgLV#6U0MY+TFUi|PBMYkl{w;#@4I*JtfLn-u}zEEZ>oEpx$tYNe4)c% z7cQ+m()!WyPxd|VmfTfg}@~DYaZ@XC*p^ zhw|vrofRHWJR?G@CLJXkdwaT4Yc9J+>jF=A9ORvj(@A7|_KZpS9YPZNH~9q-<0>;P zdW)FW4_s&dacke@R?g)qej=9y1Zf-b)s^#wR&nzuGdxVu_6wTLIweY*l2)hp*B?si#4OPyT?)zKex^Z}GGP2wt-GlbaX-^{689kp= z*Df(^4q5ee+p45_xr5l>Me^*cwkU~j7m5G{#O%e*En53hBjJH6Pk1Zq3`Yi6&uu^yv4to~Y%wZwuIMQ&W@P z*->!w#FJ#kacE2S5%dr3bh7E_W z8(*a+aRyaSBQ)gNL9ln`y_a2Baj?{s-f2E1wAZFaf!>BBb{GIEPn!ADFfi!;(q-~R z?W=;*r9KrlPN!La%nW+8&0IT)F>~AWb*j2bM)85xU9`XjM_Za(dSo|Kgm#GtsBcfF z@>FEy*g4!B)lpXK-^^S(mbRTYz-7|*^WZVJ~o%qG~av2r$AiskJOca3OYy*Wy&@tW`asZJ5yw&24{)AhRbZTNFi zk8ww9Dw~Sh)p{$h54qU3JC~-M zuIfa6#&Fis3r0d476ef57;O#Ho@$n&7AWxLeCDmUGZ!iE$3c~AMOJX%KE>KKeu8aP{60xxp`pbcWs0?K;wJ># z80O1tM(_B+OcXDXRT2y5udC-#H?K9dw-(@M*VvO*D7-d0`Jt8h_M@$zZ)}lIle|`6 z8(Xd2PVH@YKz>HWn*}<%~kdCkJweO{1m0n#7)}SmG5AaleZpK@V8Kmuu53A ze7L>T?aA}UYRXLRuL{3@4ZafeCD`G!{{_PcuIcRTbeqq+hm9JgMd?i!7?o=O-YQn0 zWS8-MDo>8eyC-nuKDW65r)1B|h{9^A_KGvLqdN|U7tIqEEDycEjKQQlYh~E9y@*Oe z%pFZV(`gG>(*|Q5QD|)<5v(Fqpthl5MZA4wSxS-hT zOCx`7(Ek*DZ~x+4@5kOboXXTMOP{&8qP-%ik+-v<%Xuj53{%yI7*BScnrRie$2T5z zwyjh)T`N{_z}%6aqS^4VBFF31#kTg^n4BQzVCm9C`>d9pmY_SiVs=Jj#R7eM*f`7D z7O(28a8bLO9Wd=&v!cT1ElZkiyzpdHVs&Ew*ivI@p|A&;LgVy_aLd}WUp5rW#BEc& z{snu~dNRGky8EWg7HbEJP}hgBo2OeCvTN!?>Dl~dr?#(2uPUHPZ3uAStgj2T(UUqA zdWLC(EM0HUiK`}CUwmEgIpUe--#{*`gO%Eo+V-^vEPlg~Bs zsK&O>XE5uOwz+J-H1qa|)=+nJfD#if59Z}B$dhWh?ptf{A=hauZ&wAE>cLe#YJu6& zvIjy{L*?W&OG5iKqEjR5>VmhLuPau4aHlVFL|buC!N4j_Y0IH*Gr?+can+%Ywuw*i zZXSMWGt%>07#Sz7_1P0ZY0+CPiDf2b!5{eB?Pi#!b(z+8T%X}636vBqjjy*!+SmUo zTfkPqK`rG>O4dMn_Mvlr<{?A9Vb2yA7Cxe=2AAB-2)|HkW|Ni}sqPQZjzohQzZ~{koC)E zAr$~pl@say2RAEHq7DzkN_VT!ndVE4>D~NPrP?(z-ktUbEuTBRONse^h&vCcsIqNc zmjSU2&;~?_+L!SgM#)G4ilCBnk%LGoqCkaOzik!URy4sxE6c=FssaR%4w5RhNJ}R-Z zwYkd8|A;%@6%LsW^r)+~vOt;&Xb(VTgqTE#4*-awZ=)Rx0Mw<+E;H{IQ4%q+=xEWgm}@BsrC zsY2FcGlSp63fN#%{0Zyab}o9}g8T%R?*GYdWML5tJDJmYp2ZI0_@-=vd!qEmGJg(= z*s@*I7uL-)VhXeVoZR8|Zu`urS^%o7)5K0cpeOV3KQ)(c9L8Gf~V%KN5aN!j8b@foVO?kibK+>#vE5LD6mKB~d- zyV;&%+`OM(AQ2Uh$6=9r=|Dc$>`dxK_U=@3b77aR?CF{O1p^zHQ+Xu3j24Tw3E1#z zwOuRQHAPm^)O%D;9**m9r&3mRD~9e5v_BLhbKW@gj?epPVI30`SC74v7U|BEqim>Y z5#$Yx=}A?MjqM?5bZgT_&P?p5Nql7)o;8E*uTi4xJ#v4|r9eMr@xG2kuC&WiY;D%J z#pQ47#o;%hxi~v1+L^k3c3qq~y)F7cd~IUkYlZOkYr`Suo$<9CcMj4q9F?s2!mglt z-Hnj?;q~C^US5ZrDzK5e#VPpq$BqLm;;jghVf6>>hhd>sA(LcyvoJc6*AIL2oo?ni@W!paDC-)!1DfS4&^I<4LURl{r^x zSkKn2%^r`R>r3%zI~l6owf5LWpijAMheyY#qqtp)wSW3?e--6nGNTTOQl(2biSrD* z<-I+aPUFF-?l>jW;Cu>o$AuU%QwCYV_(;OLE3Spk?oI?w_Y@bA?gGaT=emS%sfN!p zN>$f=5~LnA$UNCPYV`~k$+t_YN=IK=XJ6;xW|fsgP5XGa8LGw2jl`Bz=9q{^>O`d< zU<{3SFUL0=AlCZSu77C$`j%^TrBp6EQC)EH#0h~QTV=||8eChN@KQeqUy{zUt#6Fk z`TC`5nar%I@Oj*LbVAA;|3GQiG96Dj^x#e91Y+yE3b3BDWNk zZhQ6O)qBErO*Y3_wTkbpv8+zKXq)rsTyb)?8Ip;{!+%E$C#U|*gzch;@KU((7(Sn( zy2FSRmrNO2;g#~EME)J4(TNCQ(1V2buyjG*YddI?K;%pKDuYgF^~ut6v!OjJ;^4=P zMD{8;X{wqHsn$mVkTnoCI*t)EYt-(K4P3U;G8MRBk|qPGL!hY)wK12MjoA8u)Z;*v zsspnc6xpw(POaOGRQd47=UpmRTTYvxKd-ggQOaN&Qe@5K9}4+SF6Jy6tjy`L>juusKqV}()ylsRV9{{)CoV zZM~OVMP-Z!fq#v`T!h2+GEgWPmOZ5@_+Czr2Th%0P$&eSgLJvdB^-`6AjE2{SjTE) z>6n=NFdVZhU%GV3Q$(n&?R`q}!|sAY$TvU%7NItiKKw*nt7eT}&fDRVIy!a(t<2R- zYq7xx4Y!4hN5sh9o{b;Pwl$xMd%4xABPi_)TLrO2(StnjU8{nf73<-1y>Zn3#5FcH z7R@c{Z);xMqN{IqD(YPBU<5q06}|UK;}7XN&Efs-hVb0b)v>k|`H!6F3A<76nmg9q zFix9m?zxl8l~+0b7atB87k1TU8pt6nHJKnB#I@~$O^ zL{@;S|1vi>cOrj?3CzCM_z3UO-(ax^jT*$PzX07<_cvas;cvSZ!_7)@-<#|hDp_(M z?dc?YdgYp3NSJ?`U+0YMc&Y!fnfuQb1$-=}wB27Nl-zv0ptT40_UVkjmY@03l}zUZ zevE95tX;)nOkPbj%Ti@!GR~{rZYjkpCfry#6g9r@R@)NZ>c6|Pvf6a^%+|Zksivzsoi$thM}t>dzyCoEJJEcG`@YN!>gYqaHE3e<@f*no%G$?c8QQdTt>&*okW3|ayU2_@ZUb~3m0|O2K6v-POu=%2HIH?5M+2egD49D z1qz=cEH)sXYiWK)kqz&Ihrzf8e=Zik4)v|*j^?9+*a zpr|BHhK-Vc7D|3aVLjWjLyey2Gqh+G?R(medq3ZHssA~tTg_6wZ_Y~6K3}BW{&VV` z;Vuz%MwTSyfrzTSn?r}{d~GgrD2ja*Y^@Y~XJcrC5lS(pR9v*ct?I-<+9p&VP3MI= zwNXp|=|c}jvv`M{qr8Bw?M|$mSO;$z`V;9U>uFr2gG+B~`4{B(9hDZKzaNV`= z4wdl-SaszzIpUc0=wS!^pGqFB7LJSGf~^IbKXOEw7dT|t^UTyAWRxC!r43h4?ii-% z!DHCk0Hr5_$-NYy+zu$<1Ysi3BQFW|FJI7j$AUa|_%W^aO%oH7ZK+udpy`oeGB0_N z9wtPWb^OlPYWC5&2;syJZ+!*Q%;@YMSMB|C?=BUms~V2{JoAP7Ib-sj88CE{0sB^= zO;0I%I=+r@xs?2HIjCaNEO~vU5B@A@Yp*5YEBQ-W`Qyv|4*2u_DvUQwI*)F-SB2kJ z6&cIRAo{BF_NSpl(lMNkg$1KZ%fvKCc^~g#Yu3i^{|)%no%fR~=@V5!1Dv@o8t7WhZ&(-9vDA%UIxgwQzAyUnYNk zQbPafh?Eb1jLV!iFBZsG@m5byt?4+&o!Fi&C>L(28-wXQ5#^a4=Y3(FV-Ak$%oxS6 z_k1SuTw(~CFe<1+4!3(ac`sD`oZ7=HO8 zhP(UuePNn09Uh6h-v*7l`d*ri_MD4}HzTOOf{F~Ep4OITZU30uZFg}zY%VORUF&FT zk6ceU7n}f`U7GmzVi!)4>|%E^mUOScx>ky^R};D_+^dswnl42}A}^X^a*8BH?y^x& zbV_a$$TDvth-hnv@L2^VnmK0AC28bTuJ8|g$(IgvJjrx3rwh|n4-eDPe4Q?Lk_Q(C zb!~f|%dTB1OAIe9kIB&&@oU#I)&QREkh5#Rl?N;1_;il06l=9b8JxN8Zq1&iC%z^2zhh3!;0A z$W6DDTHYwcBx=%dc)^!Q<&h}FoUU#wK7nqo75+mh`3%V(IS37bWsZYwBgQldh_bw@^(ed4G2A!H5 z=b)0)_Nb*T4uhW~kn3&Olx@1=G~3}}-jE%bdP!DRI6bgin;28H+OZJQ?%P?svBlxp zhtatOyVThV_H;wwp^@sNJwUWd1Cb5DK>=WZW>HZQ`a{6D#-l3>7ERN<`a_Ujz7L5g zk(zdKNxQS=O-#&HIz-={){E$JeI8LXC|f-y ze1%agS`WKi(BSi`yFPrjBq3P+qCTXjp+YUDaSjd`(CkDsmdV0DG zO*=Xk77WR?ll7vL!KtC{WF?#Hx}L@#wyuI+Xp8#ymlSa`9?zsIXX`X7mDIcQC?#5$ zWgC_%mD!gU?~Qs+cE6_R_o(Q}#fj{inrZGj;Ewygn)|sn9yicMdUz^p@$k&SI^XQw zd+X~i&Ipl>4&*}UCt=WdqLHAYia(0Ah$uQ?&B<@I}X(ymD17gZ0nQls+yl~ zah6Fp6>_=qPBy-&BT}YeCb=a+c=|I7qRF|yITHJs6iaq%p)b%~LpnPoKkWjLPDzL7dj5N|W$n_-Wpa1XURiqRyye;76ScQjs+M<@i)^mX917`5 zgV7r|H<^~W`VNTu*1P>)gr-kRg}xTs+RV$XD$+e@K)YMIVwS)s(lDWDz(^W>kt<4U zV6Iiv#O59U0tL)7eYtwMboz9~A#1(E3YyrU73b=qZKuL38|1nd3rEcNyJ=K0)YZVv}EcwdFL)_xkAXIz<~=T5B@rBhui z@MVZ|_={YzA=b6yrQB$2Ha+>_m;P~lbV0c>mUu6`vQ|Z0y9heY&8$KBUn zn{D-oOv=bg`E&A+8+#WwK4Wz!yJWj7_q9*H7tjaJ4UBtEN@b09fDg?1jr9V%4h@a} z(_PdGCfFxVNv5ZCR5g>Bg=f;{m=f3D`z>MN>>@uocuO+ZMRrQ*3b#)JL)-n8k(7@cAlgqvLhNFw{WsjqCWD=MT2 z5HCyW$V~^A7O8LFR%q=TXtTcM&tEg$8u|eOmQI*DIW-^Smg@SWBU$VERnMv1n8F+D zU751$-Puw#UEk>3*gJ(j6sD3I^wZW-rEeJeEZO0bFI$c`o&O^X4$NoA_(6A(o4sb? zTWT6v$=OsG7%;5+bg{Ap)?~67bE@|EYvV`7lr=HN-O|!FuiR$F+yW*Fa2=wq1-1N< zv1?NUY=pPBGAr1t1gY*h+OwYJx&lj#ig)WU9PM+Q@|`W|m}HI5%d1}{bMxuRTg2 z&*tg#=UDKCfGpgm?*m0+PHkYCoV0W!z&hi>_=W?5K9fDC7f9E@zZzv$_+1#333b*58?nbe;!$uk~wY&%kLmu8^dVd+WZa(>0*jjrJI_i5$HVcEC~ z?#Y#$tPKH;uor$4Ucpvjr@oHSG`L6fBJJ107in%(xNR$4+oUQg$$^~3Eq*RtP4QyS z3kf6_@$b4)?%nNK8qzuA#x41!tZMlMBa6AS;lGt;>3+DGy*qjJOl>B=y@T_rETPE7 zqk>JfdTW-=LjnBFp(m*IT&T%T#bhhh!7I7mnRP9hin+P7PYfD>%oNMHi8D(uZH^1z z#V5x*Q?n&P4`rNbd!wNtcXG7IQpLz>GGaRQP_L%1Z+NO*&Sb+|Ek|rkg?BHPKAWEC3H^#^ln%+rk*gFze;kmsX(yd*jC9N`so0IAajia*q zvN82>N8U;{W!6=_g2N@D@eXc-#-wi0B@H8pe2nBvF!B%4%_Dmda^!*6?^ScMO6bJi zy?dMAKcsAaB}SQBL~C#l98t3`BUi4%{=`?$K?h`00~P)VKTatzoVOf6=p8wR!E@;) zD60Ck?~phxbX3)}&>4Fx84FDy&^L`@ZR1Q*7PAY}dEc%cXE^Y6GTw!;gH1%>#=iSo zkhm9w0}5}gvgc>jV($*|6XZ&L_6uYy_E3SmwMgS1D|dU;K; zs_IF*DIN=iMZ5x~Z&(YpdO#SSKZ0qidT?(EFNsJXd~KIt6XL$)QY6B5noS9mVsnf# z^=;Pqd-=9E&X6pt34FcE! z`&K==Lls=52#*Jz#rB5{aIHxY(`wuIS!)Q1vVjyPvN9#MRTpEi4gQDY$d+?%0RYmv zBrT5Vyoa~|cK!6!&!E0&hRvQXwJXaXe`Faop7emowgPZ5qjVJ53w3lX^**G&(Q!5R zB%qlZ0UI<6jtI6NiBiA`Q3OY|Q*tS5i>8t<$vx+7WFq z`kO0gpw0WF%e>%n{(I0lsRIG@g2$+BZkKH+*m4rVRI^+Q91vmQGq4Bw@rqId%{a=q znCn?xT?M;gp?)?W#1l|#-`<)lBLX(c%=TY?l3pj!lH-k@>6e~s$~IO3b~0}=0dZZ& z+nbAaPJ@geZ8$hZp-2F`lc9nJkQGpR!wpk=`|puR4EWl+x1A}6H7{6s1uBlx0#Sq_~d&F=1Q zv%|uh?*xO9SVXRZPDMH4fjY8^7TzCzFF2a?V!<~Akrhkdyv=@O6}UI0>*-(Qe(PdqM76$ggY&N+r3v!W53-RW zsqpF8goV2fc53f>5c-yknxa2mZUN8WTU-`OnMp9|z*@U;R-6c@a>%~#=i?Ivk);S= z?hv&3PoSUr2pl;Iii#+lw)LTotE($vWx5i=jNb_f!&TmR<=wV;&4elGf@dEG6VDt+ z1f29hg)Fh~(*(U-eG| z&Yj(c&k*r(bsp+5YOhU`Ntz}DviIDm-ZwUvWaez@7M&Sc*?tDUNh{NCxS0biqLCO{jTyBqcAkRTd$Ex0N%g=YjO#^+` zqd=}kj8V`WLz))&rQkm(F%Tk&E;hkrkr~PU>pDc;1+`cnbjPmSby|ABqdnfovE}T~ zAE+yXY&~}?4?(!71bCo)^>kJ@Z=MBpKtGT@t1BC~xs5ulFZZaxRN!2e4o+!ALvn^F zVhGyvgGmki86#72*RDPG==s~@*`Ber^)>RqBCVRrYWb&6qrjlS0;Wm$=MaK7a`wY6 z-Dk+#e7vo86*9V2e)}6)?iq4w0ve@e%@N3*X+=voYOl?!nRa7`2S9y0fp&ce(XNpJ z6tuWZO#eE%V8&~AblqOsZManF0dJS4Z=nvfLs?q0~n#I*&$dS zR^$k#zm3sT-@ngddIezG=OF<_febE72}FPh=}fzI7}`7LHFdu}Q68WoAA=opc@pM; zbI<9>zy2!aAx9L)#B|+!gPe88ua&Qk3-E0p0AqZ~aC2N>Fq)U)^O(RR55?23pTPHv ztyOZZHD({op2&sw^L6O*-=;UrzkK!nNXh0m=IxX~x7G6(LQQG}qxn3&@z%%y7u0#U-O zVB}=DuwzSO>90$CbN5zVZsc=c8sKz={1I?RArv9C_vWIePWhKAb{Ymnoj@mSo*aO3 zsRaHhL>)l025<$0epPNlCt)%{L40t)3q~s-A*BP?fjr)!0Kzaa2S5&0jJgpC%s3Z)M^5tZz??fElUpI zs|DOh6l5Z>3J4T@S;b^f<+zDQUlSK^ca^(wWA@jhZE;pqw0K9l8f+pFCgT&cYhgDe zy`yIXW?LNKn9(o6%FFlF-2>OO%_l}X1kk~|7yCj)51=;4>A+NoY)RLdyvdA7`4e;4 zGax+Nbe)iw*J{0Ob1p9V0*4GrlcO+0H=x}t;4Kshw+^GWptu)9A}}4H_6^bo3wjd- zC8ksCKE&;C28huif_7b@RCA`l48Q2l~ZJFon_XK-*ZB7zqIB1wFHTS-XkRyt2# zI68&`5hNCp3RtY)uNft;VRbfMw3*Savv0~7YRkqCk9W)9$rM@OwO^XcEl&0I-R~F< zcMp*O(2hE8vZ}JOlH6txIdV#L@lVK&z@@taVV@hWJ%nEtzyFTV*I;1vzJW*iUoPZi zokW;?e9yXnWOkl?fl@Wl9?=(4FB9No#Skd7JX>#LRH$G;R&cs5O2ecgNgJt8YRioO z`};&R5@1ePFBG9|Wd#hXdO)s^7OnxILtb4ygfIpWDrm+i05&DvGS%DL8;u?WPC9V% zMje=}#?uibZ10W|D9(v+$Y`wC1zvCsgvX8(031qngs=txF`6LFd~Ap|lHYnm)PP_K zEgjUDQ6Q!^u|p+>K9siWDt?*N)7#stzAnGH8;OcdhFb(>n7N{sSjPnw9Zz7K%=0b? z+e3Dgw#(vO#V*(^$Rs~7p79V}xr|^2)6n;u&5T@4sTqYQCjp5l;tGZ$9A)7xrssmH z8EAgH9f7(ZYNI1B{{n2yK$kk3s+q3<^&kQYAo&Gs*eX7GtpBVDQ)?0CANXvol~4u2 zX`eP>o`K)$BNkwoq4do}D09$eyfObh%KJ^%Cg2Ewl2Slqn8XP%{AO_0B~^k3p2ub7 z9wK@*lyM{no>6-}={)J^Np}ike5@=p1C7E3?<-O~9&DXuBY<5K+tS z3)mF8m(h00KOmsJlCR6=JqwNp#p9L1dz1EO@k@d z-^Zse0cCj40)+$zO(XK%v{ZL2>?{-~xhFYQ0#TAaVMV5wH z5J0*{1n5r&0xk7Kq6(bNdKF#+As1le!jLd7A zj^-+WU+{OzLM@62|A@!|4@g$}jx_L^dT)qJrb@Q$buU(^&S(J~Pv3V~1)4fey0Je* z+n~+6ri6r(>MuzhqP0K+2COVqZYDrI(Wxeo-NXpMT?6ULD9o$RAc!dn@JovP{QP8X z9ZgLWw&le|exV|O_y$$L1*Xc~7#Ev0F(v@QUebXi6-;92T2u2y7sQi}2Q~Lz9tU|P zl3^ltVnag%R$U#6mopIRs8qbvoPKcMzGEX_=Nb{%jt7{l4!KonMPGBXvYPtN2l)AI ze28rtmvV2zF$&YDAUAi@9p}wSRLC4U2?gUh9U(U6aA{sjDCR<~BnKFrIjfEwQy$=< zd$g+pc(DlN3dyfuUuh^y4M4jjh&;f@jiU510H1g=@GGEs8nuB@sETh4(DrdqdO$Ud zPc>NHK?w@CW|i%H;$)iQ=8o+acaLJs%24Ql2&Vf8kZgb*(y_c*#%&z=KlSt^&z_^p zg4hT}#H*YBZsa7S0Q+v3$ zgi|=qobjvHw?0joCp4UvipETio7n)=loCI|w1y+d0}5b^Aav!?%mu|>R^amNNHpBP zO!;}>Hz=bncj5$Cc2ddh!^4J(tr`F*f@~L$@(^F&+ibb+ZcJ`7`!Kmy?zi%0;~khc z2TVDfeK*n-!cq`-O$Oep5(xFgOjBSw!4^S#kzuK51wc}K{(PqGdg?Mxl9w*Ief|3N z1|EVNtaM{|(kCHs6#PCJ=xvw5-)J&b1o->2^6>co%p0pqYBk|Sjmx-#caudOeYVi+ zGh(xYmfWl5WE~wWHUF}1C4XI1~JurDR(m+C0 z>wr>c3C_h*fqT(#_h-AJmkkEug~z$D=R$&((s9Uq4yy19V?HAZs~!&tr?KUXB;55D z2q7LDfHPB+b&A7^!fAF~X9-LdsFpBH!KB>hh>|Zw33AZAKrIFIe$v+nSu*7^d|lt( z*Kd|-bs4}MDaE3)3bu$N7J~4w#aTf!Tx?!NG>`~eAX9BFKmaQ)0@;Sk^p;j66eA7T z2!P`NY=rSJD<&C|zq-)E1+lJ1Fc{H{w#rNct2i_SjMMVPVSuA7J2?IO>uwpI5OTgb zN#n570+1+Z@>A@3K(RZd2_dy_3_u+uXyZ!0@CedDM>VerL6UD{OC7*u>!1}&-&_kx zoce5l=JurWJm7}^9U}%%eIm45;iT#eqS3&bnni-X&>I9YRZdZT1%M~Vds&-vOOyFX zLo9%3asxZEjpKn|#g~{0;MkA~V4^mGq@50LZe)~*&bf#k-2;V%g}Cj_Xuf-x?52{c z9t-2QwGv=Kz`v1=F5g+=fS@%!PBWfqggJFomWA?}=d5qLy+Ia1xJ2kt8bjg(;xT(j zY}Wjj`p;+=wqhdycUfvMugwKD3GAMONoK{XwopbUbuU*`RLq7?I!Q=)F`RGiu^$R2 zch5sfRh5OG0d>MG^rDMuMnOhw1I1Vf$psGCLzVY$%X|AfM&~}jPouylq82Ug$ra9Q z!MXro$y}gez6Kd9%4B9@;@G?zo`GRuns++B#hI<2k%U3#urv}cg9imNf~u`e2Nr{L zq;ggr`Wi(GUv~rcTo!GeZ=|DnY46!Q)Cyi-?O6{dxj>O;43iujrbEg+87+J$*9D+U zL^~)EP-Hoa`98NY?r}@nEGZx*AypwdP=?j0J=dY=;EF9IAtEurL|#7?bc;~yS%5@vf(uBvNzj?{Pft&e_xO2(Un)_ng=Q|z zTe&WG^Z-Lq5Jh}Kw;`9P0?RK9S8}%?YDjArs=^cJNrK&nG~CJvM8{u+c_Evq^5ft= zyBpDKLA|2`I~`LjY`C7?jW`q#9GvLEL(ozy@>W&aAb+Zc4+;9`i2VF0wH@k29}SO?1xPZeo*vbbYarv(qK!vIl=v)|0OX$15 zdHQr0$SKwv&~6?ke7g7Pa7uul14Jf+>iYYb?&f?AJurqX7VIsM*cc9|H*v_3gha?> zpdho7M;br96#Law>lNQ#lBx=cNP9}pQ53`IX+|hA5LRFJwX(L>YOsS-FX{HpasY#= zvw_b5y`e17K|%AaEOr&#qrkTdoZI~6%DNlzX4TM4`moRw|3sSo{^no7N3n-LF1PsJ zueZHw^V!8*s=S8jd7EE5~->N|PAr`QG{`s>o z89V>oSBP`B`AX*6Bj*kOJoeUqo!a#`T-YC*-Sf@*4GvHK1rYX6co^Wq>YmC>dsl8xw+&^HhW@bh)%!7r#bfd=%{lDl}x=Md>-7@~`*W_2okj1`-D5=4$ ze3$O#n}cAue>+N0?#quC9AKefhX2L^);|xorl5nk`O07`otlLnqYB;s5HW|EyZ;@; z-2Y~<{&VOerrPF9)&JH#hwT_Q_Pzgz&imunE*Dz$|MJz};$3O`cIsl>-u*X*_y6iv z{&O<;=i?7&NSG2D$iLnEXsAQJ{oj1`s}b+^?C}3*THs&r*$sBV@e(lMH~bGD4isdW zNB=*&mFF*Rhlc}nVw;Z(ys3YCy8D0e;n)ZF?SJ}kxW8wGe?5QuzrGIqgXOqEZ~S#X zBA6hX?`i4(Y`WPXbbIt>iT9RMn@F?n|b_j)J=+o zoELC}$Hp#iD_`XMw!vRR0R%@I{YCZPVRo2`qSn!l6(04z7@L|g{JZc%pgA!?P9W$t zU&PvHR_E&tTEzwOT1)tp8FI>VO~%fUgaga6qfIX|**H1UXWDu1yU<-s3lx`OPDq&R zlci_D45^aa*%;fLT3JmWUwu5>;hL8z?p)D#(0O*uLv}>W*KFmB45>AKf^JSYWJ>dznPjC8qD#UYjE?OBLQ+W*}F7v18{t*TlMNBItZ`z3W}WCQMKl zmP&Kx`7x4?is-;y8ugVX4=MhL?%^D%UDDaBm8r-jJI&NjNo z)!B(a!$hA%)pdua@%(G?Cz|rwiT>-0>XC$^_ak?9)N$DyX)POY-u`Guf+?|mD4|?R z^hFv$XB^+~1e0}gxhBHKo;knlu~dbC#v~80ZI-?Vg#t~=T6&j1xYfR;*Z?!G>7KC2 zYk->CJ$t=XER0tl!%_Mkm~8U)Cmsv@gIP_h>pAz$^PXzpTwtByD|fF z!}&XV4^{J#>7y5R&Xk^c*SMcN@+wsz{H0NnIjFRQ3F0`cW-|l*k;AVAcgQvG4iz}0bGF}Cn2?J-K6oZ z*e5-(U*`lBu1E3C*W|VGN_N(!S5?P7I<{aim--LHiP?lZx-o z*5l}zwMMhIyk8>))V1}A)WqKn{h*UL;j?HJ?wH&7G1MbjO+BqL zgzV6yJ9-)`TSs3%9shwH^0i#i)qc(>#!t9Mhx4ro~{6p5T4 zv>(F$ol|EJVVmdO`jb<)|0`|OZ(7yVA5w+is7#KjDUOm2X5P-GGqT(8ic0g%y{pjf zNjEJlxR%rS=k5u$FO*TXBa5_t5t9oH%@^8*B*MRZxv8!_ZjF_<|I;hn&dKZPZ@c&& z7b|pu##&O%h-#iiveD6-*Pnh@2wcB9Clk;Iran(?h$GN@1(9~hnP?Bohjxut$t4kK zxuj`pcNDFjx32h;aosjwekG4UV>xpZ$|Tw3PI7shY|9y7uk%oJwvFGe*k z9|0dYHjPuS==dvV6?;de)!+yXg@pNeKBueYLlQZx;2}#i(|u<~_59E!>08(7TIz)) zOhx-w_;FWH%`hnGeC}j8f4PrDSN|SMyjce#3AaHO%nmo@!H;W=6@;7k!O+0m!2RX3 zHaKRP-+~Fwg`qZ7me=)scF3H|4bPkRD;>ue9+71hN_~*x+ep0vK&Fa@W z%rLJL+{@X|ahD`Ihqbzv4;-5r_`00M=Wkszw1GpI_^dxOvE}>2g40Jo=36%fiy4H` zaZRe_ON^XqI3ek>#UtHtF7qwC8PaRMqP>rP5ejaLR=zum9cM(l|9VmF>nwlyt!DQ1dEBbI-X z`)$0+x@oR4t#z%9d@S=@Q4~@_ft6_6C3Y*_N=yGhWz%hk57I?NhjSknTeHMJ<%SL=xYc3GPF=L~va55UXf5<5@4HjQ2$-(O#Ym8}ry7cftCzeu}g27eWW1hHUA za4u{8%e#8}uDzo3ktN~Vz<6h3C*v_|e7sjoaD_a4XFD^7c(E|CePT;=n5YE*1wE0x zwI~Iucwqf|&VGA$@=NdU>?IB5A46+jciIkS0LM6f4&IFYWZO%&v+3=>#Y$i)@y|?D zY8h$Ewm&wy?vZpUKI^q+hiqmAm7!@;WZ*lNvPEk@!7KWp>=%kkb$p;7#dB`M-WLot zVw8WL1+PTBcaVzJo`~TU|NM!a!JH}zs!0zu4#?nzDc|v0?C5pkA88YC>|8k~5ozf> znpZG_dsG!2$}2&mM>{+^&)ZQc7Dp|w_bEZ&oBC2E&4Gh);2pSu2UGE?HQC30bflc} zB5=u;zJBJ5E12{ul>Od^Pv4H5I?P`A^;uf85|n)G=~?8ZJ49f5u%apLUUx z$apN%=DeZV9f!klB_}Q;l_ie2u^HajErV0~Gg6sB3x}`attJ?hj6VzI7#*8g%56!Enfj_0ddswwdu*w%nu*yh zU_9yKjMEEP$(xYxJQ_e!GpwJshH-kCsVvuB_ar%ezo2^WQicn!rPo|+WYeozMFAbZ zQ*b8n#7&p%H4<}-(c$x_$_|%xn2tt)A{(-hVas zI3`QUjX9`&AEs0RRB-+;Uq*u_ba8=uU33!qhU0CSpR^;+-MW(5u|4DiCI4$iy=@b6oi!`ZFT`6ae|BXF}Ndf>DJ?|GqQExZ{5q>ouwp5nCtggq2y+Wh~SpF`6s`bw>tt5<;R2oeGZQ)NW(%NV(>UJA(K}Q?!Qu0JBm4cGntfe=Yy#G~BGYI-0aGx!_qa z;>k)N$N^c0A)Na7o^?6zl#hgJpwx9(90=rboHHRzeb|jcT}@@0lO5iQ#;Kaq!mkAqU|N8ZqK;lrnWWd!wqtTjofTFm*zp{TW8yv3Ut)%~2 zXbx<4oVMy_NrCDH>=Yqzf~rq#=&3FuspT+^A}JwehzuspVZb!uM^*0+vT>Ykd}Vs- z2L9p6i>}7aaK8TT550in8nH6WE930pBI@L_n7+HigOfVZpKWi+PExxW`-5@tW6f1X zEoPYF3%>|S@SM<-?G8+d*6?btyffrw?QnB%)PPa{c=9pEDveXF=N*`R|iBdjQ(9S)C%6iULg&pK3`edrF`MLNT&{% zHH+pr#|ayqB9XRU+WmECJo!FaIe2MJ0)<|`gyrZOTme2q=u2DzVRb#*DkMua0Z$rS zzk86cD%m=AB9@q$naKl!5;*D+Z8Ue8q383?GdptRNJUkZNdtkOcKYCqG&@P6strz3aptK1qTH-cr&PqdB_3_a|GiiPx^3 zdv9u9DO6mx5dW;Hj!nXwr)3BB@A#NVG$f8$fp+pp%bWNW=asR6u&-Ok<>ahz%r(1$ zoeK-e{cre!KlJRCTqf5{@FZm@g`^3q1SMO?TV)S49qVeHXR98&py2y$d?3|PqN*;P zSeTk=uO@Sk57TLMb2$QM-Npc<@2Jwv1q*AvY@k9>4U=pF^Rmh7W;GC)pd6^>r$DiZ zgToMGh#W)NVV0I}AeJ{9&iNsc{W6@99z2MI3`97@@3@^~>jIIb=veLKsuQ_eT#lOA zEW;pt(P-q+n~mjfqyuL4T$(Tlbx$}mrIHOu7L=cR$?KXDaf5gAs+5X*F-R^GW>VEw+=pnFw*aDIES zZ81UUYx#>^o>ZwrR2b!bjE9q8wUxn#YQ0^xo)Oa*b3civH%wX{uG8nq@%N@OQy#c` z*>XMS`_r8}Z4Ck6#LjgTtHn}n-DFF02j18{dW8wqw>t@_!nI=@jRx!GGKO-Y~Q|sY>vwJg$c_wqQPe9`V5E zk1!Hdv;$XDeKwbFmI}#Z*6|sJkF$Qu3Y?MJh5!1YO}r;k$uIo^|Ik5NCU(hbu07-@ zq^NNNFJ6mN7`Y?$Fb#$O%Ob5s!@bRo9cccQ1I{1I%W>bX7aiEvtG>O&P$|c}C++Q9 zE=k(jIcHL0JPC->e?ECq2bLi+2s%li%mXi`j>}>!I>Sd6p<~A)_@(WBN_HlfvZyzx z7@(_iL~oA$w?{I^9(@lCxVkiC!qtJB?tS(zt}_b%Ae_;0v{*wwTrIF27i~ zm{BRFk7?l%s9_M#(<7(9yfQd^bf#we(C!%qvwqFXX~LL*7zH>fqRCm#uSgmuGls3z;zq)pD*&id~kU7@_k3#upCFygF>I?)wMo>5SZlKFVpD zZGL?tMavVQ3-BcffxT>T;o_m{i|uxxb7L%EawGqaiB{r=JlWNGMD$}8EvlcVbAN@B zBS4@>6r&c_qrMLknHc1Me8yRH6YIBJeACO*Qwbm;rL--#<-VxMGVN9O{U(-JQS&)F z5cXYu=W^m<9>jlkvM_!vvh;~g@m`~=ja`>T&o^=5!Yxsd`{J9_X{}`K7w8@6;6;K_ z)k(v(Eh7#BK%nXbZn~16amNU3xazro%#kt!6+3EHQ)M2fe!$LsojFu@8}B`qfVu}s zg|9U?riA95!CH9$zo~QL?OB8xvndYz$w-u3I-jZyJbflEy(bW@sydntI>!RgP%}>S zgFrNHN+n59FHKHyX@>>K+r^W8{ryjj+zz5=X6}i>j7W$sy@32BRQ&)#X496&ZR87A zHW6G){En@cUXH1muk{zB0%7MI6bNa1g1yIOTizC@`K7*PTOH>=uAN!zn!Sg!==pZ_ zca^zU0%FcSEU4)DVvQxq4?U{7_2yQ=+6#JyqRG#LECNgTX3frgSA)F~A~852SrK+d zH)|~#gSGt@Xr}<^HXO5XbQmqyP~f4^3gDkp<;pvknQI%#zbxNcLKe5-s+FhYLM0wM zZ18Bgx24kPJj@dMOIl=_WGr>A!>8;SZDW=vgsE+AGT?!%oFg7nQPv4uy(5vAq7c@noW~`p8P5UkQB~_f^7i)H`ymq!#taPzg z$V_kc^xXHfAz2_es!0a#FFp@D0M%eey_eQk^mSZC@e{3l+CSh;op}M3;9v*;o zJE7;`;E>r4Sh0WrPMx%z~L>pn=n zGT8KS4`Jq0Emm9IEPb>oGu3SU%Zckvv2i5f zamjR#rlbZ~(4VaK39+D^Ca$L0g9%x>_nq8o}?#v$vGmP4Jps+-W<5b0c_b z)BE3@G_)CVXLdQ#nwG3T$8;;H!(}j`f}Nd|x6SR8l1}hDmgXOuSjlPfbbXZV#Bb=X zf%bs>o{~9;)Cr7Zc67gXOTVd0I)40kt7|#{gdzGDRQI#`+57Dl zFM@06q)bSR2eV6FzSs_ifbih>7%|q3=EK;S4?kz}CG*v}12h9F;W|Sd~0u1!yiK?MNpdeZ@U_sCLZ#<>9}NnM9ErD(mptVxw}2(4aUOr ziQDgZZ+Id*H;?^B;0`k4TY0}3HNP&ulMn&vyQwBFU~H@ zr8yH1m)UwjnF%+iHvTRTn4LWyZx1H zEJKIStsi|%xt(Yj-JqOEELyxm`*a#txT9po-40p+@tKWy-ku*nU6O7hF6xNk_;Yv8 z`}8m%1b-|^d~B950AULaF59~X2B6nL8YsxzgbHe64t?a>l_db-j4uF62j*Ug z98UG!KNQB^2o_({&OCWA%=sUGpmVP(f;^~Xo~F7w>VptfXk;FIw+qxsT-NB*Dt7Pz|7^l|O z))GOgnsvKum+;y`Khm7zJ4|zV0NUH4+p7BYiSkK7K@E_*|K*}aP&aU^T*QZZftps! zaXfnol2gGl3IXllB5Pc-aL=_XLNJ9(1g_ooK09E4aqgw)<2r#yIlo~8pX8uDWt2Jn zmvdOG^6Nt1D<|&WZOwh`PTsy~56Xs{xuQDrcjTyFH7`_?XhBRC$g1Uf`9I3pp@IY1 zRXylJ$MBp*3$KwkSRgx6XO*PlFc@qwHtPNMZq290G7{C z8&1rqF2y0Zww+s-^oB-~9aAhG=rn)`n)UK!v-HtUO>d`O7-Q;|URY@;$wiT9|F z`n#My+#x3FTaiybFXA+0)`}z}y|(r|GIxOw42UZ>m4`7|XSC62{$DR;AU?!rClqnn zZHVA4CzdY47LS|{7g8oU2`O4Zlb)aIis9J(OCOLC1CC-t5ckFj{8&-)ML-@1b|2rw z2{4G5jlx=b<(E(6%t$p~52)lb1K~+Y0s~z9oFya==RT_1I|8xJiDALPBXj5B*y5K8 zz-{2C=Df1@3qAm8wGVPp)ALr19J9W&u(T9X0ZW1z^ES7D`@atSRck;1i$aYZ+iGj& zpvtqxF=s!nicno;mo%u2;PK}a{Xflp30%!-`}c`y%rJvWDs3YYMNOe12~%o9M60w+ zNf|8??b~CSaasqFMhj($Xyuejh0|s!F)E!BCo0uxu~b^$?{!T0zyEpOdEV!J-p~8? z`ONZ}$vOA$cVG8)eXs9zUEfgP1$~DX-Qd&RWGDUC z#oeK<^u$DWm7HC*MsSY+(Or{cB^O7^jf#(GEd%cJ}B%L9INb4vqK>bOyg#$eKJOz>@yct1Fpw_S>{b=s%a zC%dn43{CK|`GbNqd~+j_I;Bm#%*uyB{n1m<1(^^)Xngh#j`k7V!*)P+coh!EZm6(c z4f@m;m3z4Q=(M}nPUE7kShJSAI~cs*_OOc@SP}0w^2I*0K0Js0qqzJBu=TP6NyNr_Ki|KEGG{!3TZ|5-DX z*-ru(nGW{9EnH)#ROFMe^IE3#WG^Pv6= zp}XHIiTyES_kwMM)+gn+hL&@a&z!f6Rf_sO_FQiA0>y)evqEQ`N(l*R%TUTM9v*FL zm+ZgCSbXoBi5ls(`+TiEEBjZrjy9}syi?E`_rtP^?TUF57=7v=y9m!796#3I$$xxN z3uce|Km(?tsP%^@I%|49X$r$G{o_5gs|-Ir(B_X`=r`d%8z4jW9QF9>dq)1rk1cYa zed^caXJ?h+_-nYsoD(8Y_xNK!n89bi^OxUas+jj-VFsz|&nGD{?A-t5m2uHV=(Y4? z-`eM^A2iU7^B#xY`mWJPr`TWI?Tj9&O&C{_m)qizm zBB>dSy2r!$f6lf2^$vda9!)N`@tcI>v+buX~SsC;Xp(@;?99?(DO}@aa{4=DhxLi~i~(jnBaup61dW zqu>EgI#29WE=Ue(jPIY3k^d%P0;4W}`)drkD&>s}Ll{=g9G3z4yRH3q*7M0@|K%f& zTZ?}f#+u789pnJ)ooQbZngXdmn${_N6W`4im?X8S`lUdAEGYkz7=RA|#>m|L{FcS_ zJ&q=+W{W_8%1)B;vD*u7_!36o&-c^NV>1NRB7q?0gDy)=F-~4f;vkr1o&m00>5;zx zW7A50Wa+#%bJ5lrK(yh|-C;w7!{POdir#m%woW?kk1v_tJEf8SN55s(_8-5QwPe1G z?~5ZA**i8Isij*TF@rnZDZzKg#qLR+*HKIsf?Cfu;IpyAfFg?_51Ds=7r}2$-qlB3 z2zEt6E~wC?ANby_Veg|2y_k2>R(ET#XnV7Mo(S|P0_ZCs@3Q$4Ew4}w+86Idt-oO8 z*Q5=uR#@dhichM}4^GF+H067D`#@mqPTF_hJ$@QA#LER@cer)%>s|hxy3?*3e(yqIy5C;{&v|KRf`3A4TArc|^e*Hk)=9s@=;1JmVo^XmIcx{2 zkCkDJNecSxjnA*euAc|F3|UOu?AoptE8H}N5%Ste@cUw$q`5k6UoZ@^e>^s?1ldOZ zx~`>e4Y`_7WpDr@VvhQ~Yu9sgbGvMjDF=m)=@<~K41zjCR0&eNW~jT*Oe?n8fhFtx zQgmF&JQC&h($S%AS(M&4+)LEa!Ag(kwq%ycgjr_qD1iW>5UC~fX(#%%U)03xlEd8- zi*x9YG15Vg3#q+n>3pn zejb>SFGZ4*U9?cm_w@@O2>&>slB9}xC{0W*2|Iv$6bFsBpig_{`xnP@2e@G(&%2RI z%3v~m#|sDnV9LxM^XaP4upFq43W^r@?{D-j!-&$BOHN-fP6uGTWEBn0nWfuUS35Gc zQ{BS@7B4n52xXDu4og>UeGK{&X*6+^8{ zWdh$;yC%$ztJuzk+`e_VjP`OA1w@3#Y*r5kkM#%lLfP`H#k&cNj9rjju~YFVm2hH1 z%+LWJPdhHI?qU%KxvT0+rm3OI-!klCegb-v6 zF(&5s<_+Rfi3DwL5rd*fMp{?uo_7bEq-|SRbqNQaA$2Oo> zuK?TbxK;AEm4a0hY*;Rqmq8%rJK%EgL%R97-4_$I{ntWzB8!J3EYAX7JI(!308#Ap zH>f5p&yRQ$9EWEpnRN;;=!xQj(=b0095RID<9orB56YAB4_M|C47OVW0&znZ1^ zt-(c{W6}0q4^H1yPgfbRS=MsWA<6K#Vo70Q{|rWP*Z8MkgP2~BA6&Pj@Yy@T)`|1m zuH-y5ylQpr=WC7G2X9v7EdG--RKSX$tN8FcR8}rWp(z5^t!(<_42>J2k zJuyc6$f77HFsTTutOhZr`7(M77pZ4Be3I#=Lk!LdOfxz|5v4%48FQ>-DVxt3y3}7? zAScIic_Iw&6$WiOwNxR4C=5?te{j7Sw)v#%>o zby$0*M?=mY2B!Mct%JE3$WMmT@_4jCqXbl$(&;rJu2#@PNU}zyL^z^qyBb1X1h4Yz z=|Y(S?_alZa@>=;C+Q5Vmh!x(%8@p&?>D*GQG`!{jMxv%7Hs-3L65pQVD*$Sz%8s0 zzxSx!7j;)=1Ss(C)!d(8XQxl=7PImBY2miq?>XkUQXm)NE?%0rEAis~?v$h!xGygM zLlz-Yv_W)+?mJe6YLi9o?w??L>d;P-g|`kBvkg!k?BiLrO^S9X4QC>DRTM38XL8bZ?&Sudz&2O8-^~-YtT0pBhJPZ5TFFG0|efwQ$mtCzxZ@2)s;ojotgb3vggrTB%M z-CvZF1so_`WxQMIalED8H)m^cBa8@fh9ZFwONqj9nYk-qXVVrv*ZxkzheHIIH$5j% zL0TeV`&>afH3K?CIYeozSL*;HK-|LBH+s@0IRr1Gt6|F@1?W#sJFBFrkkRkQWhxLV zFnWs#Rt2|w9?QPU7*A%L8c1gBB}=HUPZPm8s!Yaz;U$xcM|l{R4y(2CzcL|p!a&ac z+2ttk87^6=qhrhXW(HbWY(k&j@Ye^#Wp$&dYrNv^gCCAmY!B7mh(!ygr*A$NN@;Jt z5LvPRS@*yWE4cS4-9(0Xo%9m72F+?4Y!tIJ7(HT{J0G2Q$%n~SWAa?>;AK-i0;bxHlNy1tgd^ZZ0=_0oasZ#Ts2VTLOVD&ekL@Js zy_=FofQF7c2i)wyd3AJ@?tBRWx#?hJ3|t;Jz*qZ#M^2`_Pe{Mqv<0e)i#!0UqhD14qp5b&)nGfq5s-Fj@oJserwp~A)p2|7EXV}#aA`WCFCziThCP2= zGTn8dzqSEAJTpf z0dFYR2&ZR_-^JmPSgSa;*hpkKUI0J*h%wu+L`wny_9vk;mi^Y}T3cQuQ$6Qo5&jk|uCDm@FO9D8!Va&jmm&mcg8 z%7B)U=JSJ{`=H`xJlt+x6pBPJmf^in4~sh5<}PW_PhxAQX7q6cp3FDJ!IoM3`VN8bv8%T$4Cf zG}b@Hg}By&Y(D|Ud@ax>oq8lABGxe}9=%YG*vcE_RiSSV7=^^E1`Q90>jVYp>Qb5s zVHz1MH3FRmWhmp=;|7pSAd5B4sC)>g7rYMb5vyCY@6|(j==@2v#(5eov@Kl0DcbY$ zjt>+rLZv*hCN@CDp`v1C59lxH!B21Vdv%U!D{cd$)Fzv z8Fm^wrZ|V(C{L?zpMH8Sj;v_aiZ|;EAaSXRy4f1e>6s?^UOYbDanc#GL=E3j%=GI` z>gYxlgI-@0wA4!RmdKMIId)8>>3yX)7fNGhdNGrx+XlWf4VTnVCZKJLUPHpRv_nXW zBY}rPm8UU6>?y8ha42>{Ol1z>jTowBk%)w4P^w8n8fIJ+G~$e_jHHml8eiY@GE|wb z9Ot#}6H_uylrsL7O#6)X`YTT5Z#azq3$)08;3G4vfhL(qIYyjdr=r#m-CiQ%84bTj z@$`JsI>QJAJo!-kUc%XRHZO#r98f%Yo8izz2Mi}gVH$1HiQT$5=yGO97cp(ieho@I;i5`r zR7OfhB36yd0G#2_^D7jhYpxP@pSakz?T-5?}AVb_vkuh=IF?fKI&p&OWhM!26C(Q zW$&{fTPq1<@U-hB<;_s^b9hmHBoh&lXLIV*Dai}*UR$?$l!%s0{i+p5lJ*!eQyC3r zHZPU}sf1G{Q`M*E8GQAknu7d#)W4dkb#p~e@kH`Va_JS@)hJaG2YCQ;z#;#Q^q%tB zsxVcTA$nVAGKo$(kB`zlHg7|glX*9|j7*MPaZ%t9T$Ev+-xy4N>1hi)}WXi*mBbO^CzyZ4cQY0iq z$q1BT*#DR$BMt*lit!^H-iGM20=lPK-vrMYm7YKLd|L2fg#@1@i#OAxmoJ&RyzR93 z%76j&+f9dq(+>vWmfK{6yIvTISpKNBBPgwE061D#j>PEN;h`ZdFh!csK>{|D$%J2a zwc#Mr=?;P1Qu43{q$;PiK-cN1*X<@)OdE1|;!Xf!^6&R(aGO%&(yzDtuGyJ!n|o~I z$^{;-c+lcVLfNO|zLC=W93!l*(sa0B^3X@9+h|)NEcfXnJNV*e?-CEI9|@7QzVV7Ofz~Dous!n(FRP zrO2rWO#E`jObIlyu6a%~ooW-%TS8BAFJ4Qd z+VB%#x(V06}`-lz5xP0)(X_-~5FpT8KeAf+3FULSMu2NOtU zsdl4GcC_c$HK{A2w7PPAAh|sUv>`6{6kfdJ-eLK5C)k)B5yyYF?E{3VDG?OgHa#(T z)Hn8zER?$2Kxid(U(T)!g;rJ>(o`;f`!Lj#6~J81OhoYdrERv zt+Hj-T))0f@^$mQ0zVm@6lswYzu+!nnr)q2a+qWKEHG zmGc+kMIW`8osV=N7bTawXA{uXF(*^r6X1@EC=xkUdijj&^*KPk9>|PKAz~~#8?w#^hjzUK^&3%6RhyMn3i#a*qA96IYO!0jJm`6*L%rWd3i zU7e5}STxkwZ4ct3@YvM_?AUsN3TRy_{JzQKhX?=UW1gY8f zbs+ebg8SRrnMv-7%=$?m5Cl9G4MA}dxnSYA>8fSbG%=#aCzSG6`pX`LI2;mgvD}Pu;@QtRmp2h5{__( zF;a@g34+%4owI32fq*U7$7!l^h*g~8dc0r`^fCAJbpQp$yi$K4SatO2)4nR=Gx|Dk zRM|f zJrK8N+rQ`e-SpR~^&8jy|U7ef@8c+PSbJlA_&af{8+%Qm=*V3>E?^>U*h$l{Hm@*C2^E^bXB&Kx;)N zOL^hI1M*!~^{s~wvn2|wI>0b+?>&PvK*=Z|^S-Z!TZ1@t7P73db!^KUR#utzdiIhX z6n8V1?(>+@zYNO_IZA9vv2sRt;ZP}b(qr24ahZabUm^4i73#^@n5E`Ox=3W+)x9ob zb|IIGElI~ zs0tV#{2<<>hp2X2ZYve#ggsrltFk}^JV^&nD@E7RvwV5D7%uu`N1SIR6B{_wmxs$y zL{|5aA}qHx1s0W=NQZhpE@c7$mD3q|Qk8_0{iF{jF9VeGrbgK!bDh&=8NBc;6mdz3i{YSr##cPUC;Vs zpXgQCxLTs8zSlcXDx6Z1?=nOY505IE=Z3LTrsoMyPtWGu=15LU!Or(2c)Mz=>62c0 z{L}I}(J^$akZ0)W5{E@t@(`=9@dzM+LOt@x1s;n8%Rmxez)O-pR>4LQLl%0fL6q`Q z&WTsagsqjJZ*Gb;F}31aUM}HM%d*O!5r|?gI%SJM#eQF+MX>qYtfIw}-7XSu6EiSm z^8iKTjK(SumC`_6l`L8Gfv&kZEV6Nh5EU3%jUa${2@2Ld%g}l|t7!CH`npZ~enQHk z^4JFt@~nuCe8v=0cwILrt+yd2v%1kF7|t&UoUvLhI59i^$%!00aVweG+u&W3F>p|w zZd+6i3XBStkZYYu?w4|1|3*q4z+89{33lif4{&VWglBXjh`MXJb|PoLl^dv|B}U6% zo!xVz)zT?m6jw5ts&@t>6xzc|n2f-VD`ytRuIF|>OPUc72@n@ExB1E!K0@6$r zT8SGDGQpMf(xo>KavK^Nbp8D~dfkx0WIz`dm|0c?PJ!tf9JRbX zg@Q_ESaEfT8V5U)(iRyK;ZD>7 zOWUb3&LYEZ^@3FLhIc|KlUFPT_LA0@@!ihShQZ017rEVCJxMy+A`>0?!)(iREwzAw z9^D~?GL^o%EV}YeH$P?ZeyIL~qoazaT*1>VFJ8Q0gC9KVNsCQzo@nM~#KNk+F-!@EQ(fP}leB#|#?2W9QdxQ4D1NJ# z9=S0seql}Y1svsu%OUZodCUFAIii%7D~HO-5!a2NoRmRi^4tW8yD!0C@wqMc7FUH+ zu9h))i+Ui)aiFqw(W15$5gutwY+DDi$0M~yK(bt_(eF0dBj zA(@3h4D*fP4t+o+h>XBCm>90nBnfBD^nFDmePM(Rw5)H}pr`dVy;~|JrGZx)z-ooC z-Aa2KK}Fdk&UnBo%78IqccA|I2Fi|vq_+UFn`GoEPx9C zAnYb_iQc3fg@7)(!^Q(A)_`AefT+JQYHKb{0B0hh2~`Oz@||V%<%0(h!&gMOTNKGoIc7CT%>-H zfKWN*wqIDFVn`kemuvh&&SNi1Hn=VQns6g^an%(nY8B!*%~zr36e79dm#=MUPS0z< zbP~489ezelCP-r?p(P&*dWVh1X@{zTkp>^gK3u*u_6z4h_p8PPE@VpM)>z?TaN zRpY`!ihn53D4}pCLaaC7j@T@3B*JTrj4IVzZ$O=!QZy);r@1ttae_PirYZ{MRz2)M zGR_c~9QYWE0v`|1+pP78R~Aa`eXyEOg}{X#|2EAWO*t^TNT~PfBMYQJVT>~~fU=uC zLKv=pIJ&th2Ny6=O2JPQpQPh5oLlV49bD=e!oN=O!0c=(%tKsAh;TdH$y{m!A|!AX zw+FP>T#9SbU1zZmS>t3C)H#%DTnvT-rKqlMgRAOz4@f><~6mPqywhjVFOX^E`=)Ogi0s|Gx zYr@B%KS>3&Pfla+zV<(uI47dnuV2%}A}4YEMjSBUA=X)&yuCZmWFMJw2kZ+~yBpu2 z)g^+wKDKL0pxIGgVonVP34QS9xv%f4L=g+k-0h8hQ-x(pS5|{(iOPdPCKuPG3618rb>iQ&09{zk5 zRdNEy`mMp~JG87H^^A1W*4XX;9|~4tod5s; literal 0 HcmV?d00001 diff --git a/BoTorch_heatmapTrue.png b/BoTorch_heatmapTrue.png new file mode 100644 index 0000000000000000000000000000000000000000..6fde24cac6bcce08341b4edd64768a6687fd7440 GIT binary patch literal 101793 zcmeFZXH=Ehwk^8MRmN=@$WWFMP*9PajHm=9BUwd2vZO^0CMcCqqJU%-$w?$<6#*qm z&Y%(&8Oa%LpH|gA;l6j?zNfukx1H5mg{838_su!x7=85K$M-tK>z@C#9)D8sSgRTT5wtpY#Y(|U&&uYSg)T+rn$<01Gb>|*>%UTUEi4Vp zOnJBjxOfi#deh44mZcCkx5+<$fXmE6pSz^@iUL05=UW$4Eh!YPz2u)&wh`h~$|?%w z;@MM*w!uT~HdZ^8x{Am2YcKqC@8rh38LfMt<92SBef{F6JHzL;5ABlMxhpzj? zD?GXxU-b7&QiRQH+uv{S-!BYn-~QX%tK0wQ-Jh%RKP&NfRQ%6M{Le~IDE|`$|2IUz z+pdASn7u(2eiJ-;<@9FTex``%^_B-j8U_cz9>rbG%^ z54=j&DH$l-CSLdH$-%s(TN_i2n~(dmB}F;UrVDTU);C(MkmAprH*F`g`s61*z4B)w z;x>oX)8l1LW`?v%r_(J*z3MzZ|gn$7DHAm94{t z{Vh7)qP@d=C|j2nC#o2aUUj_`CT7BsBp>lmIsVEEF3oIPgB#@j_TdXoa`pH3&ri3S z)yJ#yI88U}cd%}yP)^3AnYIf#tt`!Su&%#4KGmpg)c5&?q>K!&Z)1YGN}O_ne3{>o zyc5UC$I#~N94Hjmm)n_{D+ciE%o#J`v#k;U5`lPEkwI z^Awx!+v~J6)p&gKUYd>DwVUtn{X9?`e;_vQ z1frs93qH|SIz-4?I!sD3&SiP-Jc8+Srg>Mc=+oj*CGOT7=H!&h=yc90Z0y8tC51W9UwrrOofbUsBR02Vf z{nd?W^3%bvrlpZe;n6z&PRDRpR|>ZWS@4smn%2-$uKx1NFE1rL84a+Mk>ffgUNLxM znqfrmmTuY}OIB`dY)r^*{1t~{^z)A&FVrJ+5W`QrFMpcXlDD_dZb(p%LSS=pab03( zXWzJKlfsP~uLAka65X~i?Jg78$|@3FuskodVJq|J03QAB!NIsvUrwXh?_W7^j1){> zMMW7Ex@{41oPCWeu^Omj_hna**t2I(tad?uAg@s*hf=JqtN5@?3)voQ^Ul`WM#$bClZrsQ* zek>TjXw;ZkpQxEbzGrT3E=JOq^U{qkWd>c>R{Dd&T#+Uy=eUU zKyyme`@+IU#%<@lS(Wg~H8nLm@yS?MK*8y_U%8CD|P5*Mtf-l$Nv52 zkR0k?o_jRpJcCagi161MzaUOq@qBGFs#R1{!ikkfpg;WmcUc_Sx)*2Mf6H4hwUWQV zxFc6pm^$g z$(T=lyU51DTv7?X>{(BF{^8Ms2gmjWINMJT^-~=}gl*D|%y7S^X=z4{TwO?={IQBL z*Bg6_#IIeuR{Z<+y&f__d|9F+i;m-;4pN=GSVdbB(w$e9Y`S9O5|1N-{YQ0TRgyx- zzTDl$h#YA;rI14%7AXk|4K>YO7|D@9YMmGoKXBkc=QIxwk7t$GLODIMxLmq%vm@K1 zec|R^#l=oD?X(Xa9>ecb8DUK@z8`iI9Tw0jn*Q*L0Ru=bKp5}5k8EMO^r5Ch#wVj^+8I2x0 zqks6y>t8#aQ8lh2DGOG-*A{k2h>>N|b*OL=@PbZ0ts>eRaB1j*wF3eR_UH!vZk9{twU)jnW3+L2d$ z@8=yJSTXy$xlE1^f4m|XPN1;$k@Hd zfBf;Kp)30e4%V@2@7M1A?YG~mmQh7+ihO^&MnXeF<5J!ot;V@em6d$T32M3`^L^nG zii%Ia(r7AQCe*_vwshq$jPRg>%0`z21q2w7I?&gilkv5ytEI6Quts$3c$B%9q!nf-1m&4_NkgHIHJep<>ftMmErT)cR&Yqncen4d&mystm^1QbVe!er!J|h%lN}ywH7m%f zzkE%QF>LS8KmRO&)662~=C5V{X|IlIfpgw5!@5J^7CohNV){L0{``qqy^I;lN51vV zr&{&b1b4Ct+z^g=Hf6tbgbBe;mIg(B!0O#rAlnD!69OA z(Nqg(Uy3NgoJ$ma@zqtKTZ5U~hdMLN!lX=Iy?RA&)9I9`!>ntdc@;>i_x^IkXR`jv7)FP%J*EDsgBd_&l^~-a&mwM?W$^&`J zZhn4#yUe?1vC1eZueYIT$BrGId(OCJ);0E3hkF7n_=gIcas0gMySiDPFRh|-(OJl5 z#CUOOrju*RS6g5|L zEd+e^G)l}WPFN9d)Ufn4M-_@%DCMRs137G3JpMc9s_(+1xlcy@n=MxAViZc6@*FZ+ zt8~l@rhjq;K_VIIlh$y6^86!>Z68L zB+NHAH`{kVK5x}--PpY3LOmxh?^9-t>Rgwg&c8HSdzq_#0skgw(0}1>UVQvvPZnY2 znGS~}B;p>6CGnL-1@g_v8yiLaP=5zP9Y)Oi&AwIech&(A23Q7%h9)A*58!$&vpvhp zr9ByVBrIu_)&{vglY>bGu?{mMYB(!p)|YW-IV0IpGwn8EK+z$wmRPf?fqE(N<=ODE zrL4ixf)!P)4^%!oc4n`H zhwSOocWTL9ow)Jw9~EKZo%Jtze}C|x8rRj97#0u^gZ!(dqV1F>wc>Aa9IlG){TI;{Y8{oJ{82Gc{$ruAW>4(aGj6r&6D zFWUF`s#uk<*z^Gp-#4V?KG<>4aB+4_QcB7)yAt69IL6N+Xz`(EIexYUh})Hxyx}P2 zZFdo!_S0+hk#BG}LU}4mhK8~BGb6)CpKso_t?~7>!n#-`&T|qH94Nfi)z$M~-PtUr zHAKvis|Fepg80lj;(?5^R$N7X6%tZIVnaoYmuL2HFJicx>0r_uluTd&+c2bJJB4=)H-hznEUs-l=H}PQP zmDF~c@-6I4yIFl2#YX}E5UfHP4-q?gQuk3M0EAtd^Z0^&u_L&dKrK4NXua#IHHt}E zqUg8=u+eHc)CAPPgxaXfK?5#8>x@EH(a6g}8S^Ct%M)QMeaH>zoZB{UuG^)e@dEo3 z2V_S86IPJdW<(?B?ICvdi)%J)twYS#0aRubAJea7R`^3b%R+YxtH>2V1qEB%%*tTF z@FVK!eL17~wZJ4Y*4mC^=eDp2Day&oWg}@%BFA(uCIZPZirBu!ay^>}L_6e*Y@@p| zFeN3$8gU$lb~RS^#T^A5t!%3px6KUSj#?SjMtb7^USiu19XfOYfI=+ZNj5eg3w5C#r z)%*)?y)mdgFx6Lm2C$r3u<7TY zh=9)EOj3;T!Rkkqapg!qe*BnV^Cz#3QCXq6$gsAYh#H#stz zjR)GYQgEswalxGX_MOeo&j$#X$N8oXHNC)o-2@n$(^@F=<%~kNLSr^-ep58L01!u) z8DZZ|1TSoYXt>O!`>E(q{ zBix!`3D7yCz^&&(R(;QZ|NWoezB8{8j!n zF43~zyt}A<|MS|K8V{^|YkdO3Y7D(LO?4FKw)ojG9cHea>0%}`0p8v=sRYg359){m zkGTmNO_iUAr`EK&TSd#R=&P;$)PQchdPd^gF=jExCR;E9p7>4~DJ>prvnf@w&B`W()fnuq55!q`e(fM|LUsCHj}h?Z@H61L z)=kggH-vK&kv|vg@>w@`bAU6-#G&~@2?p)7;R)*u8MY!|Q zydX`uMZGRy|8w!V?j7M;dG^H>Z0YKxO(TMik_Kp?N-sswdUzR}({OQ1 zqNZ@vOre*jCvS(#ic>fe?^b`;$|E+~>+-a_qmD|Qq@y`#UF*qMJ84HcD&d0#gH?7T zT&spX2RrN%xmIbmHI|{p3`cFCdYgU!@(wjvcszlLiOI-=S+M3S@5hjcc1_>LUp|9p zh$>9jgI@mzTARqvOU1>-LT!UHglW2EHbFr6qJ}L}%r0zHl?1K4(VVyQpmnH8>p+n1_yz{CK1OsBs6l6D{}V*yt}9hXH+O;R zNo8_D3JGZu=N1-j{ko+Qk=F$XVb|b53$Ym*0EJl|GuEptYYU?1nB(lNoMpqh=&$3U zRc_aI(%&oo@S$lqrwM1ue|oQo?f2($=(H4=Q0ck9PdN@IWVDD@qKeWMR=(weGGf@m z#Ah{Y?-#tu^@)#9@O+SEHximef?AriQ-@Wp%(@0ZxIt-a0_c&%GkMqRYe{8=(V4A% zTeK9aa;0+ZTTbQp*TPZOgAMD%SZ&r^HPe`>wB#iiJ0jePrfuK9-+$hG=ROp~3{%hM zU0z~gM>H}&|Fm^$I%kq{LSU`-PWF%ZN*R-d`FTd<;E38OR9hCU0{};k$1zq==a-RRfrUeDkU6!!9SQ#)k zYB?lm3U3peeJ*8^JNxYs)x>I~tvP~^+58~o(&fwivC0XCDpk!4`)QhJv8g5^V;^>` zYp}F>Qdw11)s$-Rgdp;3ZCDDbi6Bo|k)~HMDSv~LW*C$_md;GTyEtPwU5zurJerSinz!H*#G^{R6xQEb0`cyMWE2@QOY zU4q55TO1l0O`CRI6&IxSxrPVc-ux9;#a+7QlM0qCvO58;!gb61s7}Z%3w8~5=u2d1 ze8x?m`{luUsnfA?){e5+TlmRhoF8X=#Z7zJqXA-E%Vq==g zg_E2Zg4VUy&UvKS2G!Fd>#}}3wbg{G6U1kt_IPp^deIEWah@uXE~-OTVZt$;;%>VU z04d`cflkK>8UKagqu1^f78HH^$u;H8n<2l~%QzI{2~QS8r%-UXYmV)nZgDwgv-+x8 zE^U`z%Bt^xm_w}UKo*{H>0)vJ9OEoxa7 z5$zKxE-TL4yqgXu?NZ}fjzJyY&crmBEN1C)Disyb^nQ}twSrepSFc{pPg==c{RPBG zPJKZ*B@?BJE9vkb3sReWJi1D}Y3e1dNuoHS6jz^;^Aw84jrv$6Mt;*^G?;>*R0TB6 zKKbk~EiFCn+ip>IWaHMY)EKbkF6{_JY7Z%@}V zyDKvhv|q}Z$Ph#_^-tUauFSA4Gg&h$&Se`yIE|CDkGCgg@FWgpJ=iscW*l+r#aVNA zn*06x%^?o+lLr0^*2R~_mu?7gsU!xEfY$o0-{{?)bw31~cJmSz(+vKbpMR|#1414b znE}W;#^MjQHKWbqQJE78YeSDvDCoFwoSUH1hIrK{W+81!5n!P095wuXq8&F9M^aO>pCLg$J|i+4t%!w5&^U=Ql9bO~WMO8vP~w9 zfRf_JpkRT_f=ak zdM>#!ncj+Bdg+%_h2JCD)ikZ=LCMaYJ70lp$ZE?7XA%mR@U+MZc^?hI0F>;Aa^9m| z^;ZdTdjtgs;z>3d|on{<;oqr9BLIenrqyduL)r!@=aV~ zw&X4j#qT%8X-Bm4+pIKls3sYSTWCGbq9dIJonKdKhlY$3vE*r(a8eHk?d>3wV2j@>HjrMnbz*ER z*}-f9#7diKm1b7bjH;?CBRJyx`GL688F}ay0pBVWm7me#$$oTMPYDjz$N9G|3D+E4Htf(%5+e*G|~ z$nAS2x?3ein&X$3mkB9f*5$XnWi82qFYXDa=x*AJJ`Wu$pD%=zhGB+HUV7@;^Uu&P z_b9IrK-ez;{ewUYZ0@a$f;Rp+Nkd8-uE@JQ{z|%W0AV!bSkYC7@&Jb9r!T-X^^r ze5vmIO9K0+(47!%6(USw9Zvaa&#&?p?8vzh2$w!2gjy-?yr&9&fBf;BPTNrq%_XPR zLPsuXOcVXR?hF#b$}D%xt<_|tIiZLJBL?_wu5$UKQ|)scPAZS0280TpTIu9(`RpM2ukyzw8`MUcW;DLK zxMOa1_K>i!a8!AH$xa4_e&tT!iAWu@wmzE^wQUSpW({iA#cFaElR}*3Mp;MX+{IYDR*ZD|hty?%1y4$!&MrNq+`8#*+Oak=wWe+CuXg-2co`CC88O>ivEcH7Q z2W~SCD0QGIIXv5DbYy(u+fDX{m$?$8ll*b6K@r%ddf&UZHm6Z%WbpYu_p*n=aUgL_ z8<#*Fov@$0ISi_Nn2$^OGo219MYzLgo{CxiTx5*n%JNd6h~1k5mqX*B2;%-_ScI%( z{kb&jl6B5Q3@hH-#cAsQ-a$*wVR5Wv>fHvmXL_0W2q{pKjxY4;q&o8c+`p_OZ6A?o z8^=)2)+Ad=@CzW%UZHgh$d~mA2}!69KhFsypU^YDzobgl$|^PIjP=KQT6ud_t(`fI zCo!CRZ}-33D?iuulxNb^30abIca$ zBHFyqbF}GUT;tAP15SOG3t_;~B3983Mq+km}X|H+iSfjI`A={S= z&edNw_0s#aTQ9)KW)PgTA~dX-KF@{4(|0Gd9EN#C!E)Ixiy@PMNo?40S>y$RljFn* zMI6u1P;9OT9&?GwQAGYzLxl>p{r0nhDn1!|pwX9}=hSYgVy?mcXz#C_UPiC>0>CyB z<#E|XS6_dSoG#p1yWxzyW2HzQo2|JcYu3&%KnaS*JmJ`>ATpwH_5w@&xHJtjM!ytU zbpBd7xIQ?Axt_Z+=(E@S_@s`-)Y$A?R{pK?AjUOSvn+ZvOgHRT8EFit&`ViZpx)0| z;d}HXDrwD#hz)xC&){rMn6?Sz=)SfrtE_AQX*4z4f2nm}~~OYxCy%1s73? z^Uw?vehn0&{FjA`YHESIxiq3%O><+`4s=BX zx~3`!>fzOu@0TYB>dO+tzRkL*{(+EdStm`l8RvWo;#)sJE5UVj$UXP(-)9#PxFRMt zy`sTcemG$(ixA4MosdQM@5EVxG$@ZVTai%-g^Abv+gZFxc)R45mvADFMx<Qvbl5~AVr)UA;fD=0#GkT0@;Ou!edQO9O{%vSpnD`gGw~370_`T5w!u%Bs4S0#U>ETA3S(4 z5B`?dZv2KdP|LL|wnI(FwlJPJRMoTBva{R&;Rxe)Mn*&aiP%GacPipW+cvfc^NWwv zErBbMF5xw5sKZ%d&hP9k*-@nmABSnqFrU-%91Vv#3=lICiMSuyScRnsF;Z;dZ#D!ZQm$vb^s0je%zvwzfEX-Ya_rbK2#LY1OQTK6g;qm-#pmVV zRq(IS1JLLULE_7-03I2yG2Uixm^e`wmBF%dJlzu8gK+Y77AQ zSoG9om}!enl=Bd45k%wX2SaU7-@bi2a)R6(;VrK*WhHI%nS|DtBO18~(k_QK38uN| zP3kQyhh{U*a+|3c_-C#VHh4Zj`vjGazzgj^^GoH&kFSkNb`U!UBvNHkAE=`p320^q zKyaoCkK#icxeFG*jV+7DAihum`ykV#a=Jh$YB!ybFBr)x+tFACUb>Y4^ye3f2Tc;x zGb(x3<_{+3oN$`AifUn(sZy2uX15j{kTA(tTLlB#pLUMhAp&_=z!(w>jOmoq_b;ai zZC=WiGju$o#X^NRnb2n{H1}2n`TF|yC_4-`CXsz9se>R;YYzDx*Q$Bgsc?a;Tn&W$ zHdF2yBSB2O>_A0kt4)ft-o_$yL#6j--2FR?kamcUl@%HXFct-VLXA83 z`kYaLjo znwkiy;ryLf>D`c9qln`DmQS+PQd#joh84eHAUjnHwRxx%f=rKcXQ#_bp29F)TAR~> zg9iuE+*`*TF%vs*e&L!kV!o(`Key_MOAr&ksiL&BM@&528Pt@Xg!gC!q4z|-0cbXa z{l+v+MWgXH=rE&?f9#2Tljkra)V7cXphM+(jU3?0kSeZMJiPpGi8<;)|65f0B$ zJMO$mAI;_u4|cTMk4O#Rge7WltEU<2W(2;4UtnD0c*{n$)SE9Nr2Jb%O$(1&Rf*2@ zh1hg_%5aAEGwingQ?)#Zw@`NcL2I_xQtyvrYh4Tr-;V zpvkEYtLY|tT*rILg1nwQc_J#7xcIbqCKrwx(osgf>Fc|hq0PO2Dx1copXARMB%`8f z?He-$a@2mJg8z}RvHu+$5+j_Ltt$mGfKX<6Q_7*%XHK6U%71D;`~6y(3wvR$ll{H z^hnu+Gvo-uev~b+U3zx(ovC*jWTVo~&VtW6RuXVF@m~M%lY%NvCgB3E_p^1_U^kn5 z;h)rUyC%?H48@D6-U_O>ZoM|mn`(H!b<_v6Pe$|bc0uF{|LIQ^<>hs-ZD_Bt+%yVdSaDdg|&_6PE^uGgiV@Ot8Wlc5^`y_H@x2g>c zXH;F3&|AscOp@6(^FOrh1(NX&kQPVFWyv~c9F~V@&_{C(PG4?s7%C;$(FuEaX=(|* z+W>5j$gO=Z+=F9B4bXyaHB;;WDMZ>Y1rrhY7>K7c=J3&@vV^h!NFOrwVaMgBb(KVv zW_A32%KwZ%P?r0Gs+QrOj%2-gRUFt1_nuwgP}0>+so11Toxh9U`r=B&X5h=9QnP|3 zDpYBE!)N}lzJI?(Sg6Q1U?Vlb>rr3a-s^U)&r5jZGk}A14|E%J;Wt93LhX7PGRy{8 zg{&?RTSH7dl82ph|0~=Z^|mU|Tw?CHWKOd~WfS_i`~o8N4Bu%IwCwE@Uzzg;?4en> z&a9aC7_Dqj?!hf8{D9GfzE+p=$B}C=)vibHrU0sy8oT_IU11W=rtCH| zG_S07-P>72Y(Mv8`<#Fz{wIBa7;_wiCA!8c7gCbH${P0gn%Ph|2+j~nE%FT(;)iye z(UU-!hY5Q)oSbsF#h|PVq7R@BX8A2Ud6G@pD!xWrGVp}KOR&!|=<%rq4%oCwcwhzN ziKi;3XXP?%e4L4yrvaEuJ@+3uA_Kcc{xVpqY^8#I92Gy(Ri-VTcNU)A&JNy=IGl6FSC&Ry2z>;GZ%ky6sA4JFly@*#G?OQ)g zG*&`yA@ioqPxk9%w3(cGQ3ehn0$4hrbSs-!JRk~jSXyMV_5QGoTJ_75cHi{Vmo{8Q z9I-JaE+Pde!Z&%sW!W+6bT&wlKIpnNpI@A*e|~CB-bAJFy7gJ4*tD1DlMTBZCN{Tz zJF$#sn6{_FVtP?tR#x_QSI)D7f`WGWR(wx z20(2nL76@QOAfJ9Um6|_Mpp|%u#jW>&Plb>yn;bky?8;MX53}9dD<|O3q;=PRQ*=- zZ{?k=>2QAEEs&;AL8QDzr=J0|^(wBQidMh6_@oXO-cA&j+j9}o032#CR}@wyBZs`! zvg_I9KBZR~Y@h;;WLXZ~HIhzmo%N@qo8i@0+a~he6-PWAjBi|B#cy1Hg<56O7fi+N zuSNkIx>MGMT4*)ieG%T2<5iZ6i;Lvwpv382np!{O&kY;mV@PWd4;*wPfZvNv zJP}-_A!%4Fdp$HApec{HjJxkRApIE!a}wb3St;O5EV`O#)SZFGq%dHmDC8a88#nsE zC_VJ@YQUAGUMpOm!|?o&plRmU^-D0jQXNiL2J%X6+I^VVbL#UPY?NZ;&lwmPJcI!% z*KQ&ep8A~h#igYwd`C2fC5leik3t{z5nEZboEq)yObvDbBm#jOg{o!%+!L!1a_?oMY|sHYIB(ePeJ8mXY&W<~uVxD!}Ci<}6MF20;9m!d9*HGm=^W}$vGt+E;# z!3y3N=WUejL6wk$tT2b7&I_j(=shVwG-Wh;XxAdq>JNY~6!f-65rE^wC{+7!snprC z_aZNcC8COS>^!~#zXI$+zBq2-`A!S5IB2CPtRQC7uD)B1^2tYAge9~8g*&T--J`ov zq4tu**k!POj2OYvP1Ff%{bOjMF8T#ZK~ZrrsdvC>j}4S3O)}aeaXp4GiE&MZ`1(d5 zt>1*t2vj2((Ev_T=QXG~i$ztuzj4=7_-WwKFai4vQ$3R3Fd8>yv?CM4gMR9!C=N(4!!V(cL zbwK_};@iOztCCua_A8!PrjS!f2-MWp66-h!H3Re-Et%%w3Mnz%=SU(1=Bg(och#u3 z*nBkXdQ(k0-T|d?N^y}{mNanlT9La}`8;_-I_CX8m~GYn5>0+AvC+uLC|$q) z5?LtOi#dn^n_0ea%#DC>Fv#{+bRjrBI*Lp@>P zvK^5)!)yjuC=g0z6Tz6FAP+;j!NRoSbF0zy}+bTAm?W zC3`r*YD;wz=A5D5#~|g;Wo^Wqcx!T|i7?Koc_=QY^Sp?c8v=cb?8+z*p$F&^!S*Ov zN1i>S_h(yx6;{Ti4xiq`w>kV)QpLJWyXj$Uzi+>MK>FF$8@jr?np6;Yo0e=iKEamk zmJIncimeNM$nbZwl6p3Ml6fVN7S6NA?bCtQd1*=*P_9rfC95qzEG+DpS%*l5b|WCM z%|h}N%+wd8qeR8S?$DP%|)B!%3U@@nb7}`vLeyDlOaPD#_i{@qG`-m#ON?$Ys zxjie%%*O*HeOA3Pa@y@M6%D!I#=Hp)J5MIJnMD5$+gMLPgy>5yNCp5AY|qGH*6eIg z)30JBuGy_@Wt+Hjzr3zQr|pT5up1K0f%9#gB^U`on+IcxJX zcJASBsgod#?zy`U!f4n4yuxey{i^>U3^h05YX@nY1GiGk^&J4?gf9a|d_jhY+HB+u zI`i{TJ}*yU*s3QROKA^0It7M;`gz!YLu;5wAvCNy3FIY1XV;rd9B)7UWj3d zA-(hsYuE0~JdsdOj9pNz2*M|0KK_F?19ca1yhzc~S{t4-scF~atCoEIKG8HR+F!pu z3Tvu^J)92RT?*gNe@DD!@|=AF;rb>(A2M=Qt+iUZF(JGSx<5X?IlXUkp%}ieDSTPWuxCgZS~Ww+>5Rv3{GL--NB#yjXpUcheH&q) z(M}CQ^QoT`kg{$J^BXWOOIce?%*-_2-6j?d60p9%Co0T-fSsHlF7Zp3IggCm(Q%?$ zgyIjq(^P`dBHInxqb|vSEkAu(bV~7s%7>dRsWEQb*tDYg9ujL_8Z6N6jqTu4<>8LW z)wFxG`$($ul0ABh{Lf(zgy9Xt*6)h-^odO^_%TDl4EFy}5!CseaA+d$8@+X1W&iEL zLBJI8euO>oscy7B^8%hCd(^*F1UAv2*L4KPA!nFo_a6WkAV-`T>R%P*V1W!J!xS)a z$ewZAEO+bHt=lwlIR0Bw^u0sTH2;=2*B=XaveC>)yFDzn^*Do3rZpLu{vcBWC^``i zBUw@O68-t$S6Ys|bAqj4yISHJ|~3=(+H=Cs?^?L86ksN z4Psq&Lbnag&FuDVjTF6#N!*MP`eYK2!6P)(l6utbN#IyRc$cUP4ZRU4g^Wlme%#uL zb`!l>Q-R(d9(BMJiD>Nlp+G1iIzk3!0*w-@@#`iT+E%BKoJd5#=Oa&4lUj#Y$m=p7 zQ)-AtMvyFkCD;8b;)|q?w8g+zN@MyCWA`c4d0CV%0{){~tID9YB#^v_(J2&%s)<}o za&2Kaeiqi5&w!xa7PFj7IU0TV{WN@^Ot65QsQsiYDoseNIz&lP@Tmk35c|IT3Yg_U z0rQu{|7p`J3St18JL1cIT6Ia3I|I#VELaOxqohnI#N)kF$KhB8A6kvkj>&J2 zxMRWfU2>YM2KpxG6?VQfM9^?k@|hoctcw}hWb`?a=<`~aMq;8uEi#$y00;?JIow&G z4OlIY`IC@&4`!&j#Mp@pUW*kcJF|sZ;PYPZ@O>P^f3g(idh~QA&g&>Aw;$Vlh9Evj z0z2vGqR{(eQ0owcLL50Iurb^*u5}E6{RC|Q7rmODwT*c7>JX--V0<*JetDlrufuzU zjzD|_hpB|Nm}q!Fz=uE-zj*P&5OWTgJ5%mcy;0N;_)`Y~;;|(&J$*D`c32Mk@{v(C zl*(OfY)R-H4q;i)$)TMO1R&|Qx~j;CFC=N=p{J)3fCvgEu{HlWFxC+By-pPwwyq=3 zAq}R8{Ra*ZyDQpL*g54uF_Nr;0nDAB(yUO{$%cXPCzDmp#(ciq+FHOb3Rt;1c(ahy z?Bff-!E*`;siEVOyl_EQLc$IDebOI){DTbY$poG-XCFFpK~FD|WKm3x9pd9tcDr^6 zu=KCtv575)KZIE9Rs+xtLAdxPrC7y_P+k>@ex9fy=sgnvY)fDYjt3dl^YQU{8Wv6RVaM3ui|>(3ah#Q}ILFVZsn!C(nL(YXJi4`Tb3F zz09bbOZGTFc>=$o(2&<5)*nA2PlobfpoM}YId3C44wps@K$;dSnWZGJoq0cIDJUmb z0SyH0SeC32ooPEWyXnureO~t~{6Q4IH#a`{1g{W72CLJg9Jd#l=_Rj+bY!~t^-~aS zy^Axb6>$LF|NgHXAd&q2ay@qxgO>k%9lk>xNPoZnuYdQxs&x(JOTG~Q<*Dg^P=3|! z3Ez<`x|Z@$q4=#~2kUA|cbo*Tw%N8-l=0NQP)v8<`z2gA$30i{-ft<^wMOJ8-mcR7 z535NAVgG))p8Nmj-7Fb@{_AzcuL74$dCFH#V&L!BqaP9RFTMLdL2Blouh)lzDq8pA z(~ke^pLTWI)xUrJ;YM}b?L!70GXL|}>+Nqprh)!n-jSi+KVPmd-?P4@xLz;beD$wC z!GB)=gi8L;*MI%)htK1$pB;SjV?bQqjp6M-fBXOAi3>xk$e1yCA!|3Srl-i3Kl|q& z;ZJv;0(tt+*MEBV4wwGdYv-8KZ4-v;kLj%c&rf>z`}6;N z?!&)(_it%g_D0iML}c#29+CesTEEgrLHhG`Rh93DBdX>9AkT~b^FSZsAP41;-hVGY z{C-Vm2Nq%M`oF#i|J#Ex{=Sd@ZMpu7pS%~ALAvrEFIU}W;|?ge&!1lV*RSH=ZH40h zX>#~4wnE8`T-PD%e|=y7L#FwEPS#id?Y3N*rbpW2fL=I2tb=w^Mteb?dI3T)7EQp$ z&6_80YNN-mMfc*1F-P=zv9J;i)I>ZwzpzSnoOmbxTD4yc%AXx0M6YO#GWapT!R6Ie zQqt0OFfk;+Y{|4Zs5?uFh*h?^?f= zIT|yhwf)ZcGD02v`EvcGzxq5|J=A#3#2K`$H`_AJBi}5pZUJ_`2|Ns*O5>6$wgU~M z!4ge;MefR#E0^qmeMv(tI{tAlh7WBn&A~hOg2)W$#w>4lIiPVRdWS+YbzeW^LGmmO(%0s=(x2@AcM&N03baTJ8g8ye^*1*j^k*Urz!xfo z(yB0Nicvnnl2xl#sb$`J0r#1yO(J-l#OwhMGOL9KjdT*0&iuNiPgk$`0t*`H>mxC_ z7fEa^{Jt;=+-!P%jXb0R%)7-jw;0ej(L@HC^}{MM*eDG}zkL2{fhf=GFj(DycGQ%E z^QK*ITZ;K;X4h8tucJkzCjg;QrI_w8V_F-3^}H6W^O%)AK+6QRYCn`*GA>d;+&B>O zWpp9MX9I|j4irE?KA5Gj=)3SnNCg z+u0*(b@Fk$)1WEI@S3S9V7;Nab>wI+HUVoy|Mh%u*;? z>{=Tfj zoLRN-WU0^n`-V&ukv*8Y{ZsLL-V?Sz2JA}YeqRLzg^&@RnXI|{6(F*YvS9cj>1}fX zU6?~Mq$qZD%isYodF^yn`UjxL zO+{Rb>s+Ni!L_2l@y6c0d##a>c3(*X)1#JaYhF19?y4SY&+0+)>=sC)f+gT?3_J6k zLTP3lxy@E>=s{O62qb|qB_ho9XeW1nr$ZOUr)Ax@hR#{=yZYRv(EFvpy@a=sfn>_PJWG;+eesMm$=w@WybX7 zpE`1BzSCW;c;v)c-8G9WsjJ^|Gz@Xib6W=s1-%R@@dlk){MeT_UWut{^G~n;kw55c z5Flt7**^;j#V<_zo!lpk|H!QR4u5kDw4}05@s+A;Bo!jWEWJ>RV=?yBW4By%EqNcW zQE!XkFGv?w6&P2cdMDXQg@xv#npVgtva^?~T#U z;?1WU&6MZedj@%DIax+^uKe}HRC0fagL}aH&hmI;r^zxQr`a;KqJxp$PgN2WwQRKO z&+X+lJIEI1;-8!~H%@KIrA6mgbtO{{X|cIvD^9p{^jYMorM;i3J3AqN&(cLLj5dcO zR$WIkJ$}EyzeDK)IEMT#t|=tPV=7{OU{G1dYt&Hi?YU=TLze(^X@5{g|!xr+(}UWeIH8V74~)BA1J%a09<>>T~x_A13-UT4ACe<4~u!iRm)cYJF=RZgV3l(n%u zCryA;SbaxFK)80paH=r>bT@;j=mfvXBf&`j6`n&;i`8HAPPmw)%2(-TBK?P^arb6U z7cq0se$aUNlJS|B%~`gF5ts4MF={(+$W8|Huf00<_L0RM6Zw2gy5hCa$JEP|jT^JK zIGmU$bf`DKlj95D*jR{@bCG$1usm;gW);}6#cvDR#g1fpd z>RZvuo9(=+kG^`g#5@tQEuAt_3}WnMnpyc!!hYc0 zSf|T)mSv08>>p)Lib?-4+~^XKdqz*+GxM;db>VV*?QyX=55M8&guzw~A09j_x-li?6_;+!+bCz(x7fbXaAQ^XQLJ_Nea^@vnX6g_%1pz@$&la z;(KO&_63C*S85I2Z>F884O z*XZ5E7KV5fn(@>C4MQ^4PKNf#=oC>2`7t(3tFJ!4H&B=SB3AL|3PBi%>#7QteQnlV zv^ES{c~@}rlXBGjyu;ur7H{E%(#LdqNp-OSE)0q7>)zP+_sVUi2BIb3=~{YeuQ>TiedTubNzklNdzgEZq1q$<@R8=>uSQQrn2xvZ z6K%^0TkW`#SCMHk%9`NSEJfL@QJ%J#c-9Wj0ht;~smfNefh2C<8=x;D{M_-d>YER# zxfYP8B|PZAS_orb<{<=~!1bUX=F;bTz1P{|!E=gOMeT3m!3{*SBTpG1jyZyE^1mkSgLy#}<20X-WVF46 zZAlwX1tKn~I<#>NZjWo1;ZTTyv0!)Y-nHXY6C2B;Io(O3LcYNR)neIvNnI8n+md7K z*ozX5B|nt18d;-Pp8VvYAoZF-DkGi@C>Ql`)86&BVlwS-ZKxd^_Z5C>TfN-rb?@F& zJd{zGTH3#C5vHE+D9ft9`fUN1mYS-M-*8LRhm_Ez!}VFA4tr7xl9Jrg^ZvnH5^}L2 zV^E}^W|OOw%F2OWZtwVJw(wV}C%mVt)7|vX^kOtasg`5Kjgg&J*&nh?YLlo0<WklmZ7<@s#j9!VOo}yN7qVpGV*8~GT@1krjXG&gRI24RjU$5BeWtt z?1cZQQ>=1sSSNe@AC&O3_9I!eeVR=vGxzDVK0}tNgAcvBi zjo6n&i&Lu@Kj$aM#6NekYd3FS;Vy9u<=Hb)bUS(9IW{X3m&bi~dz~Z}G#frg%u{l? z1u{M6Crz($1PPm+xjp6cq+;dm5ryKpBXkyF4zaGU8BX@^);Ms|DJWoueI=RR&xOi& zW27674W>-Jid>U%wajvbc)nx6;vg{o`*%oka^Yd<{`O8}Qln z(D&r_IT5{-=yDzmJo_$!S*d{-hDQwL{no$j(8uRqGP|tgnzjQ8OYx76-DDeaE8k^V zku7Cqt~?&Y(>oDVU+FT9!dK<$Y_fBw)z{WfKmV+sI_}-KdT_YK>1>D)U#XyJb*>+E zWrzCJJnT=jR{N&Lhw<^oCY|OVi{#cEl3AZ_{J&^>3#h8sZEbv^+r-!yAiV`K06{=H zRYE|zrBqT@KJMI;mjBo~5oNjD2cLTYWgLy(Ro-SEv9-RGY3ANT*=^WFP>Ym7a{ zIk@6i?|kQc=JPx=Eyjx!7cYMy(X?vLgKjqN+Ic%d^%&M9t-pr*_+>d)9@8YTUDD1Z z6%&=*6)``l%^&u)TVllWqZ_*%)`7w)F1?z0n9G)k1dpgo>BVGZRRyYBuyiutz9rRH zOx7vN25@CUnHqV{00HG~a4^&4V0;KDquoFyQfsdVf-1D_ojn^5WH?!5f5l_5wp!ne z+?YV_5yK=K!q?YZ0QNx%BYj*?qF%dIThH;3ygdP1oekUzmdQbDIdIl|2&s)k&!CEe za6E8dRz7%{v05JZ!@AP; z>aGyhF1JQh_UuJNE3L}X1JxM@yBSHt206**l|58F`9gXD){t1c3MG2et^8`_qbVw8q;N;4tr<_2?*Jcij!KxJSHQ*W|u zl?tG_(z4@K+LocCL`&6qJ=jD4SiLC0hSN~~4hNQzgS9g{`oNKsM|fyE{SK>n;WA|{ zF+ZZixhvZ;EI|St=`tOeBDk7(d}BP85`Uapy}iCwG8vd4E*(X4;qp{&Ciut^E)(VD zo@vn{J~K?bbh-?f{K`A1bkD?U?(l2WZoPSP-mZv~B;~X^$2cDsi?-BhLXu?F(T#;B z>g7)p=G;kYr`!FLK4g>^e4TBmC|n>$V)OV zm)s_wT{l;qd*#wR7iHz$5L_41jxDw)@bAoJ9v67DVEDe~5cJ}V`*El4!H$5O1d34tSV3Rtd_1J6a8FL70a zt*oN7GQM7WGTU)^X^}l5*K+ebQ?$HN80SukMnU$5?tn~Vct>qAc0o0J&SU5_Eq|Sy z6vl zg6sMQOZy+3I=i}s*%gZv=$FE~QNtW>% zyayu3NJk;bTRM#b4nl(iB$*QTZ;rhZTTD3w*3lbRD{Vgy=1fwCH+P}h>DHju-AT&$!ViWXS!0M;RW%@`<|iwFu(8-lFn44oDJz+Iqh+#&e^ zN9UV+f| zM4z{)uPOP<8DWk|mj2bhE(tiYatzvWab_=#L^^CNOshS`V~(~Z>s?jLbpBRvJQ)@S z7e@d`HFpKJXOUVO%=3`r%8fGbgFaYF1w8zV8aFY?k2fC}GfnTJjdO{JTW$*^!CE)BmeQ5#r0jtn}d%Ynrp`;Fo)tNsDgu&nM#~oO6e7zHk>!w$6fB089AaKS72WM zy3$1hgJFjZtLRGU@xzDz0?%C?E7X2SFwj^z8FFFw<%+&2J4+xt0jlL{`%>3sv&798 z{{G0bPi~tB?3jp0F|02kkCz7y^cfl&XtZAh$rG^8rqFhGc=x;Fj78CHI?Ol^In9Ut5w5UUo+*_Z^Zrk zHIw#TK9WJzHvkHV(G%c^-ZAP(#ha24XwsMNG*BpPa%G|v45FsSB8sPLi1Zz#Dptxx zwg=b)kH#$333EAhH+jh1ky|vSr=4zRRjt+Gsn|(~xlna6y4jMRpgi-iM&FS?=-men z_&+7n!l2SRr5L`?QI`jbgIu_<9< zTU6cI!VjZ(X0NQBe|7$rYO->?P*amO5C9MEN_0!Ip{Jz-LtNc#c}j0~XI zeqZ8-_kcb&VW`gQQ1I1}OcsIbJi+`W?3pYX)6I4EGxD`tmA>hBsVxUgzR#r=Pn0A| zJ`~%mK1zB8LGlt$51KzpOb8boRrjCil@GEPs4CCQ8Sk>AE^LERA<3G4F;sPICwE}f z@!$#5)J%~Q1=^n7lE{0Y>>y|BiFKRXR4m&Nj>}gHOp*<}yO^uYs zajQ&%XjqKz`%m6jF$`;>g%^H<+uW3SbDo`$`GGSn%4H=p_55Pdof*A8pq3{7 zt$vpmCMwH=gAJQIdazH`74mT%5Su4mTGc4nIDW?nfA-S?VSo5L{NWM#+P?Sdw96%z z(>$%)k4Eu_?k*p}lpctY=I}#%9;T%qE*~W?cCDFLV`nB2gS-i89DqZ zz{5T3O^th(W~9C1ntKw$S@*N|M~Ds0P7402QOp$Oa+K{KjH7ehnXW*odQuLTd6K?( zKsEa9=f>ZLlzC(H&4aJHeXCcxB!A&yJH4Q;S=_eKGWT*(1Tc45q=C8Jc`MK(Avm>x zYrN?~;=|sRTRr*PugW8q69-(k)$>gCWP{pbJkOSGv|KIrm~r!V7ssTH`I7seac|B) z1>kM&1E)J8+&begUaeacZ&M7OA|#l7Tfs)A>@IfbhVvykHAR1V3^pQaalRcylZWa< z${jhLw+^i97fMD6MZ`KBT5*H3dfISfXQvpifr}i0znFg&jn+?~!U#=1r}tE}UGP>@ zMuwBn`}>@pk3^1uv0oi`hE^St14A*Pdar+J$$onkznf>y&JHj;6_>Fe0{SAek*)*u zD|K4QEw7=EYCc*=s(+LfXtyA#c($+RP(}D*FC_+Vpg1FMP+-r#4G9T3-I4(mTo(1M zk}XAWWBVIa;5KVh1kE0jOZ@(O4E!RPFrw!NL|Gk3RRCJM)}6x?&+*X8*ns;CDv59N zH46Y3qNblis+Bjn$C(~x;z`nv&-%FR#?uBe(k#Y9JZwTP-5M+WVt71iD|vtlo9CLF zrZKdBNj`Dn+v>Ra=8l0QURGeb+lYX19%#QM@GgL2D_dF%x)q(P^I4PKiPp78uuPNd z4@JAY@kLd*+bwIGA}upSdZl>ZT~A8plw13cPjueJZKVbs3z2c+ zghTIZ zKNpho)ZR?;*?`VmPXjOJ$1@NFg$Dqg+c=${N-49pE~d? zS7-KPA{Dt9RB5RqOq$4R76{(Q!%n|Zaj1Vem?TwSOx0ZwCBT~*w{T-@v^6kFD2^X% z(fV98v-QP?Gu&xbJ7TU24O&c5MhCAwx{|6?7SxlMZq97yus%-LAD*n<_%bBlmmg{z zUOqM6Z-&|BYi8baW#e^`+2+q37%&H4#X+iFoaT63r8_Xf*K^&(>E_r|!_BhRRUF@_ zGBw8~`LKOMGNX0Hs`O&>DO9IoL)ud!>c}PCJlDrRKWk8{dv1FBCF!p5mPJ2K!1Ll) zgOU9*`x$Xs5tpfe(fU!Fr@J4Ta94oNiu7-ifpk$S`M^*G1(G}jmJaB<19iSCauBpd z2(Uqf13=77%*-KOgcv1Nz73DRIoQzwvG{>}H}dU3GZ~-|MBLa9*a8KZ3K0!Yv4{$v zZu(trVUXZiiRxjP=t-{00Oz;(At3Iq&>&t0BK3E7M&?!JbCM#vANjF!RV3N>&w-m) z2W;$U9zpm(DI}DZt04>XML%2zs7|NqKJU}!6k_LQU_PT&Tl&D3N z2Coyg8ZFm-X%jGoa?=6}^I2DVD|0q6Z|~gd<>B%O>GAwt=twX6tyv^Rv-%YMaf72%b4L4sm&zi@*n5z z9))ox*aXb>+%hN`qu1f%jCsg)+|>2FpS*~Z{4G(#%@Y_2WQ@+$f4Y1oyB?V{+{|;S zzgjQ>Fh?7D*y~`g*h_gJPH4a*4ck8x8jt}Hhm5pzmU$dFHzJBj)YRVkKvH__&Q@eL z)TavI->(R~6Sem@49iJNP7_9SE9I{jQOiGz60(OaKi_0l|CDGRn(S_Y+1Kr4)))cYlyAuHU#pa`ecR)Gy+jSkwKNJu$XtowBaoomEjJj6?7lCl{Ap9e7HD4kp@t8oI3dKw?k@hDrH& z)Ht}VfmT%ysiqMc5V-)tVQ?fN+>Zh!36%1(z>s%HIf?0zz#OYT3>Mt*9CLeaA!fQM zaMls94604UXaCj52J!1Z^|66Ejzn|7=2im96`-O;4``i$D-K45q$15EgB8QHEWR3v z33;k43!i5ee-#R{I0slK6x>( z^$}BOQpzrKam^A|m+!l9W0Y0<^&7H zjPkb3r!gknX_eMKI#RFL_Tsz8)zTHu!zWl&WF6ghsDcFz^)!Dw&xO%3gP=EmNq zar9@VFVvC`CI|?g2cSw16!szII~(RqOrvG2(+=#^M)=rlmTg=^an+yi`!+d-i+A+P%f_lC>%B1 z9pOwHaYmt5=aoP(ONvo{3V+Qm4gY)pl9Lphd>B6B)cLV)JhZz&WtdVw8`;FI!pjhQ zlg>ndobsWFsGy+si^SR0=HC=Ij#-)yggQ+&c?isY6U)6tOL*2G>p0i`*UOdDR(g$j z5l)S%8a%W$UrO>*_IVArX1?Hmr+fNz99L0@4SSk;dy3lyLlIj6rNE}`Pou8eRJ>~V zmn-xGkA8TSZ<^m~vN3kdueaRi#RYX_2r+f14AGc`WAFykL+jjI;TL4+79XnWC9M(@Zx}z9zA|s4YU<#TF!xk+1rP_NP$2+ zbn1+1wf_FN@OO2-4~*v=gndN}9jhyOm#))dbL{Q10!h3UpX}S;MqE%&$Sx8sD&NYv zK>LFG`DwtOCi*Aw{JMA*)myFe&0Q*Wb;HICiaiI`Hy?_$vUO5}V>e{Py(inSve?*2faQto4xOBW4+3n4}`z`@!Zd1_>r)^)WI$ z^oWOf&-&i;YGw-6i{pHAs@WlK&eGq9kA{(Q#&|FupR@l~{bk4C(sEU-f0f7yOw+WR zi|m0z`fJAEffBT%SyrE(-;u#Zd~L7Q>uT1X9irl}z2)vu-#Etp>buoRvB@^P3*&*% zBenbif^X7-cfWvfSnT026)sM0YrEX!nK`{E0NFB}Oj_rQJ)YZhRxJlpg4SSenL5Ry z!rNT+ri^a~eIKz|#Fxbo$mJFBFF*{ycB6Co8X+wr$2B55Te!qtPRYB$Ayw6OqmN{k zdNSSZS$N$=kA>7Gqr&_{m%WA&C+JQMsb}~1m9$>}h?Pmx46O=wIof1qwVR9SDF8qR<;)|1pU^cm z?+IV%Y3R((ok8}#J2l^|IRU#w_;xxWf>MMhLVzbdUTK@HHMq$55A53F-SnP(u(!Y{ z+Cxf^bRx8!H@K(-o+~53&Z3;K?QP?d-`}<;(IpU}@zQF-+O~z`Wu9#CrnK<%6qy2K zW2E|oVrs~k`6%>ja&216_ib9V76HU7OKtf;?GGZsCoNbrnx7>tRtsRip>`n|GjNa;tByeRBAiFSR&h*D%; z%ibfK3%9p|Jrxg@H^P8L$~`?qXxeFmXw`y=C!}g_l=GuLitYFU@{TVx}o9L>Nqr zuK%lkkgfF+zDV@X-^J#O%zRuRi$$9FKzhJcfnz%^FsaMTcn&NPq};4 zLDSl1iB5Q-pCAws{q4spbi7s@`eF|ozhwNmMEx=69tGV$$AA8sqG}Is%6kEd+4pX2 zj4E~-ZaWF1+!E(F33ulU_HDVhCqKN}N>^DFC19s+x6_reN|Dx_^~PjD=wwBFoMl9D zirV653W1hZuDqd8Il70v*N(~IvJ^?NgRv3TQwfjv_kfrxMuJ*NqmMN_pWa8iyPCye4T2Q1jU*VVye%$8)gs0!ko+Bd+fyqL$JnrZfq`FV(n;yo`;t{i)YD-tCqs-UzS znYp2M^lhMV0!g1iPB&EgM%-qaa%XSWF&FAkMhS){LtnvR;?WcI6t}h6Tc145qyze& z1GwLG7#MEVC0jP8q>xj^Vz8o2@d;OU?)dW1^2I`n<^ZqPsbop+!6yp)Fzj0PgzW*k zn=hKz>R{yF&DY(_i#h+H5Aqeyzi(Rem~WQv_wm{Hn9Gj=niX{mfUMrTSN|L)je+fo z7T$8VrneFvMyqR@z#dd=a4$uUrhmV@O#W5e0X%A%572)(tE~^-2vr>vf4>o-srIrkSNUg`1*3vW`Y5&EaNZ1f|q+2E8zcy7hnO3BUj81q;88D zU^Es{`4340hV2MC!+&PccQFF1Nfin=WN{Q^cb{t=JEV`Nj!tI#Z>7;P3x?DdIl~RyZy8fnY`N-xsX>t90&PIVuyFEOy-;HZ8L>Si3SUE0u;`KIN z8(qeP#@Cz5oP%aen)tsT{#(%^7VwV^;J^3B+RR@8_z#nKH5JKn5hAI6bG9hTqT>;LDOG5PuLV%*X+ z|96l0d#5AGOeA1)RG`W61+Rr_Kv(J3*LPHYZpvO!QD-aoGhoonb`GO-&=;@{qXDhG zFL)2s+&Dyg20ZCBP?w3VY^eqGS}Bix_daKPoB25w({<&DmtB|s&O#_mB=Z4>ZSdFk zggGS-OSTmRXLHw@13k|G8ujvmbc-k?BmRScmSq7*Q4N0M=1HzUuSiVnZx#GAz{$At zyb!EieBhU@bBI8(raCFSq)Sve=rZ@`BQaEhC*QWV_D?|bCXRFmafrm~F1`PFeTnr3AY(EV> zGUuWVk*yzLel@Ll9D6-bZch*HtQ5dyT9f=f{?hZFh6(8e4Vr-F*$*=#ELS!G zkbeL^K9iz+0YDbv+(af`FdZ%2N&)(7vcY>9sn{$FD{J4w=p5(yVLo6rkF=>M_``8n z@NW5c6`D?eCS~{Ae}eCiggaqD>B^4JS%i=S@K#In;_I&S5 zFQBDM(ju@&SQrN4@rUG7}%$^W`bF5WR=<8;)xmF zSec~;U@-lM2bl6TnG)go;CM7t!MO=Th&h47tMttTE$GzX!rr4{yfF6UMjs47q3mQU zo`@}+&F`(7!Gm>V6?&d0K-!`MozTf?v_^mz$bAAP^d9Xz-sX|-)ue>3laD`(o2;zt z2nq!19%$_17a%&em>jYo{KwZKxEq4S;d>^^eSD`rbN%I zq?_CM#fxC!=3Jc>076QCP~*5fCv0>RJjqeGs?MvSb4%N*+Q%;ePY9~bfI{czNi2romb88JP|KhTT7JGLwNfsu?6upBt7V9c1 z{%#U!BdLy4>VD+{?JLSzfN%tWKAu~-xv;P>{8mN&YatH^G-)Z0e!oES=!76Z&=5{< z$17*u`}E`-+;1A}*1QdjcBlb2Jp=HGn_MOEjrf6Z^BYeM^pNjtH~=wv33O~1MSiY^ z{*bzFH~}-Tj-s4BEzfrzG1=Qqgy4&VkA{V^Y6UgV!R;)eMhX#$ITp%YgDU7WfCl(1Yoj(?sFOH4+rLmb|QcTdr4BGYFA~ zUrW(_EmS|H{_8(EI$eBQse^@&*`Gf&PZEok|{sG87r6ICWd@WL$P>OtMy zBHSjZVGMJDiMne$Ncx?e!OAKj<52Yq2Rum^xk z`)t5`@8Av(SVg0Y9&|w-rfX59{4^2BHmMITf>mG)Gz61nO0^?l3Vh$5B4u##HjHWd z0`6eNm75S$PQgQoZ=D68*l=6NAZG4&3~?VI$G-!G+@%zpIP3%1EstMQ=d_!JTNmS_Z`Qy;f-9SD9)Ig;$1BbE`T9&10q9*mP*3)vD zn4N0-pjEdvselc)nT1H#KPNMURykZ63ebkj>X(3)I2M{hqwG|*SGu?FlJY-=xq*fr*$xu#!dZR#H|zg={!SV3;$JhbRbh8b-h@ z>m_3VU#xQ8{vu?Cj9#i;(3>X*4S;U^Tp7&L9EO%eqt%UG4%jT;fwfYxF0hl9xomSN zITMwW*P5KlPhX0%SJts_mY9dh2-XF=kTKA}@Q8;fMk^Ec+rGp&Q}CEHMQUanN(1Xc z2p$Gx4~-&W58Kb&Hrosyg-el-ZX)}rRKht}b{#jzfXrM7!)S+LGYtfL`H8d)W%X>?@%>mL~v9!B+%ygb>-~7zmv$hw3a1hm;VNnZG@;<&{ zAI1kTeqqwwV`J$B5xyVB?){hmzT5ZF(JDBPVPGumr4I9OtWx4%z_@uBJZk-+?Rrff z@|P5MnwS%(F+>I!p89@mV6~hA)UiH3{@O|wY{4>?ivwuAWPqQjj}OY!8el4~%zbn> zymb4(#)?LNy-tJK!5a3#9G%8|G-Ct#pjs6`{udy;(v>E{sC|AB*5pW1TtMAnEc86L z)>?5pXJCW^r3ZxO)h-xUKLV7|02q114BM5yxcC>a2^fZ5DG)rwE&(?`(-(U#rU?#m zA6wD)ptVZ+omVG3cu%^iB)6&G{}I9l(?Zrp*|C%$rg||vJiOM4-#Lr+y1b6P2dAP5 z+%z4VVRP?cGkBh_6XpiW6G=}`$C1|--VI=ors57h-;*n#(;Q*PuFJxevs{c;Wj+gI zt8T%Jau&3iNPVgPW4%R|-5=cTvOjZU90v1Ej@f=jDPtiJMIg(x{{a`Fmv1(=br|%R zgZI~{$(kX=`dQ+f?7M`}CBw`d13w3HFu+x=&I|9_DdIeD2VaZQ+jtN7>1 z2QZg?(h)=o!(RJOTwdJwea>qhpzP|Sf6Otd98U+Ql?$Te$cP9->lHFOR+ zCqzSmIAK_NM+37)eFx!|tEZ61SfQf@ksmxJiykn2Dw=FST^(5(WNN^K>d|?0B``4p zpn#~lbyE1PO?ozqUBNh<2^<`gA-VvPZXgBZ0KZ9~m4o?)GsyF}GM2$0LOBoP5P{*7 zvh-AR2gYAE=NK%HmdwS4t;D?>ThTNLxsel!5&d(Jv(eZ!aKP#3-x`B&l>)anu8e;7 z#m3Qcn0_^m<;;I=1^X_|qY9GXj6+E!ADsD9pf;K8gyyyurQM>UOMUv~y`#Nnk~Lb_ z2lM@Mluf3r6day5m_#|KOK!pvsaH2S7CH*dsF&1Mh37|lI?Gq`P;>~MBAL1fn{ZF= z6t8s98Z6;dp#j(vhM}17hXqCUI{0e*-sSzH7eo$}0O~gGB@QNxhULS)ES&kTnaALi7sY`cQI zavt`}FCeLdhXh4hpRTp%a(a4t?%zoDlWhI>&gzvVS^*cNgRD+ca$eY2CE_5d(5UT$ zmFf(`>4#C6>S|htI}-quJ3)wC6flq>01h6E3H`z6#FASQzOoNOS2Gkn%$@t&&5NL%fp7A|n`~uLMP5`rKTM1wmktsSx?lsl`xi*R zwUceiSIdZ1;M3cJeHR6Ol-;bS_f9u^7~DYvP+8dv5f>JQiQzWCc65Ej?5p)xw@$nO zsrWU|)sn#~l(>SjwZth9-BJ;#rTAcmeD+;Z#nlf%jriD`@>;p3N=q}{TtK=w3%?GV z*Dx?Ad^6ogStdnLm8|yp*M^ojs9kLT(0yER@C9~zS|19a&2h-_s)2DG`|;yPW6@(q z#l$FWvy$JuW{Wquv5goI7P_>$x(^J|C_JstY62jt++^@OK^I=2LIx2${X8Lbf}?#w zd!F3*&PC}{`1MBX;abMPw66<$%V3H}PynM?8A(z~y0%Wh(qT7y2V?J7KASX;XgWA> z=7-f$7v61g@r}DsQjb6Z8c4r2bSQ5L+26v;3q}qT8BmV621(h-6H+!G*cU!zT2roB zd6q#3XL3^>FcqrIU+#oS=d{I$Hz8XhD;V^2+nUnA`+^;3HH?2d16tHTAfVR3GKd9k zS2J8ZV0lYHqQnXn9T_xQQfIUG4jJQRdkRB*0_<(d#7JA2NZYzAdAdh`9?&a^e6Rlm zi~COjy4U)>d;0@MtVyQbkpwei_FIL6B5=*jme=7bLY1zV%Kh_eG1{jwoj0U|+8lqK zc!hi|Wrc$(v)bb3v$krkU&pnOv#;<3m4JQHHFEU17`7j&IvBHe82F>0qlIPYuP>W< zYJui8qt}0ldm@N4W(7=EInh%OOsN-eVLBZ7e|}ty>4=vT2Z)6H02&a5DbzsvoR@5f zs{hPqF>ga5{{-@*?;5H@VT_0dARiYQ z7=kWzr|3gsfT{+B;OuU6;)hZ00mkRNdnpn;^ybADKaV12A9{S8zj;$U$Shm&1p|ny z@Tf_*82`nuj_rw7dhCYVMr|XtI*SGb)RKMZ_NR8K84;2XWP=>UR)%?5zn0!?00g;ay zt+NMEc})hF!3u!H$^dFo4!f}i7_g{?P79?oFl_cNOpIm)yon6hw0@8ZC*B%?)d=DM ze*{0lH);3M|GHO~|HSY;*M-4{=z{&swNPq+!sg3N2s6!KSi(u%EB_$o1VYup2HwTV zrjV3vl!4&78s?CxtI+>C(j>2~9yYHj^j#?Iw|uh(m}~?l1ECUC-82%_>R@Ixy7rl( z{C9+)U64j)?h9ZjibFx1lDvmRdi8yr)D*!aT|qklEG0=$=)01RM_3gnfBr_i2yt7g z&w0du((Z95j9!Kq@zuEK*P}yz*vqaB%!0x_`+?RCWj$BQ^#Lf6(a`HVSZzy~Tc$<@ ztRwWQ6@fh!Y@{W`E&B96K$6r9B1>j}7)~FD7OCsm9|`ln)|WCPL%33HksU=9?qez7rkr$^C#|^Fjm#yNAIDr|Lex<3(}>a@3yJCk|+){ki4q_HuhDK=Vt=3 z@~n>!A5;}wpYQ$p<%c( z0h{#_!s_5#rjp{|ebGJu@3f2kL`wPp__+d5dm)7YvS4~LGF1ECi5B8V2@2ZtVTe6n zIaHg z5MxZ4t?T?eda-WhuL(~BBj1FEQY5!^=vVe^?T!Y!;7gg^#;%Pz4GI>ZAlK`#YQ7Ur zGE?fS9uemir5zotRv|wVFUy`hduqxrV{lszc1KI&epeTWYGRaWn1KVcm)p{1CaAMG z#4@ISwJyY}AX$h!FtGQ_d9`;bQ846t4pNeYeA%CegE4yrf1!2kx}T@JS8t*nZo%wV zuLXn2{=4@6H$$Z{cmatHKt+e@@ifgW1_*bjrGr$RBN1dmmGY~jk#ZA?;>%aAyZ|g( z7*Jq*6$DURFvo$4y~7d=fgRz%-j2X;1x^9;@$4Q~5Z0cqzLCH@&l`9_n%RN==NTw& zBlrsCmng)-q;MQrpViSPEIG?uKi}|i5fc87L=U3dJjfhS?F119f*Vf6V;G(wKX~xP z0X#vB<~-etKR@$|Y2OeHH{|%vU}4ts$r^CTVW{-%BJG;^^K|#X8n{q$@XKa1U0IV? zqg9es(cM11^YbE#1=Od`?n|PB$43rt1ypr+z`2vE%zl2mSNI>8CZ&kXoqmHrVJM+u zLRfxX`;>^QF?OoR$|VyZPxGuj+{Q~+GX)xL_cRO+JPHsP^4ibvU_@pJBn=4~zx}$N zMY0IBT$!QNhj_)n@Bq-4eCm&JM<+Y4upZ@ntqU+MIHvj40(KUj+ zqY7+>Xdz<5Mn;D4uQc6~{QUaAkM0GAuSOU84wJzZ;3IhN7z_YJL!>mz-QhXh+xK5> zD)VpcuaLij_RWoLKuZIlu&Ra>hyuv|0Vq|n0lNSQVZyP15H&;SJL(@Q6F&^YFftQ< z0?Zh%I!G|~q5c!7aAnbM0pSLkAaGnTjQ~wbJxj$R9kf*R`RCbHGy!Nk0#3@!eA8(; z4`6jq{!sV&#hWRThAjz3IOVNDxc=BgR-WV856gx->*cR?+&k{{afO zbEr;@gqYHd$L^Yf!Uo36e9W)^&2gW(db&Tq3i+3y1N8=$N;Uu#Nc>s7493F7&E@9P zrY*5b!PWX@ww4XyeC+C1uljOJR4K9J^>R&nuM5mJvxOH}g~u!WjKnDX!$VIGX{(jM z)+}tV2$^q&t-6Qpcvl0k>D^0rhrd8;rBtqU?Grs-@4ef_a$!b2?8cw(=D$YXmMJK^ zC04)E_#p5`|M06fD=&=G!{54XeI+*fh0?hQ8Y@KO8)**>i!M+wb@~FLs}dTNUZA zJB!KGE)k(Cnk{M=8n32Ddx^oS#eVjZWNg z?g=$`1iU4>T?z%^(+Zwj((%MY+2e^)^k-{~Z2~r?cCCb^X8Vimwm4qewXN$Pj6FW(sLN<3=U#*Xv67|kg~f>9O38xfSCY?r}XLEhkZjw6;lmBj3(i^gAUYxdGx zBN~BKb@38m)*l$c^G#{|BY6E#y5j3=u>Lvttcaj{f6ubb>hkUjmsSd^WxYinxj37} zXIc44d?8KxuQ^r@vCH{#r}~bVhtg8JxQw36d0c$|)Oz{ANOUye>RpBz9C3GW2-Draxlgi?wcwIm0;90fIyI0niWyq(>}2?EW+{Q)xgv@y2ix|QE?R^9qu5mu3&J_qx>u&%?OKGDZ0UwTjT42;{(!QR+@GBHq~Vo&V7BI!Qs)%DSHbMPQ;XFhUmqWubG zXMn(T4BwBnG8n*6s8xAxC3rhdHuHqdZli+axA^_xZEct z}PH^;Q+8!nw@cGn*dxjMYS7+71l@?FY^ zprS&~>PHk+EOMEWk_z+*XcWaIM$?L(mtp2pP+RJ5rC`rc#Kvs&t)kDb`4W_L@8KF}o$|*Zl37l{x%>j%eFgp0BV*Whg`}y-{O+cFO z;6#K$_(2WU8%eX-UaYre-mDG9zo6_~f%^+rb%6BrgB~dyZnVwq(dt@@lhj+clF?FE z7bQ*WO%MHbq9En0$mYsrE@1M~xjFgYlBc3!7uix57q`&VKV`7HZ5-+R>7#A?0acuq zZ0MJ?cv)um!GS1Ff_FseqZ@bhOOqPPQ+;2K^mpUb*C?aH3u+Hz8S`m5LpvvLtpw7g z^LmCEe@h9T|NK^Zezfel>ccxRu8o?d7mQgm)`Yk}n2!l@+w5$;D^8kmF*CBUDTPeJ z8`m6f^wRGh(}Ujgiz}*Q-4#vutAm(045Zt~lcamY;?&Pl)bwhF$1&USSQ|wj$A~T4 z)jkM7cBv?ilaXZstd3^#m=!Vnp`GBd^e|0xVV!vZPMTvVMz6Ol(eReEkkfAJq~rE8 zZ=28S&yAMT`W?(Wt@Ak7|9W#Mbb4TaAoZC8%c8sTEzdLW1T0rg=crRF+9{K9#1ix< zJ0<0k?q1UlL2<8nK(AJZuK#YnZ|gx~gw^h3d{&lmnag2YC1!?zxd?s*wTtiiUyp1o z*PB|i8y@VsaF$HUl3z-V0sd-G@GdT&0!ZsyudxVGQ_xHrvNhco;?~ zl=Px`n5yM)cfF53j_UU8FP7Xi4qI$XI?|0xG-EJ4XiF$Ze@h6nYqM*=CeArJPs2EnK44PASDmoUjfTmr2eUnCA_lmo z?&D$j>!!G_)XHl1_h;ZKNM{d7w)L-Qn(p$1BP~pBeXt%r)1Uf0eOy9hu_7aZQ;C`_A zJ~Q>WC!hI}qzHcIgpP_80`wCFubY#P&tMgcJ;u#5zXY{Q^cL+;s;4?jUj3Jzo1so> zfEo;LLWdkJ>Fv#7AK1X3!a2S$SF5i7H2E12-@ST*@gr<)YLM*oPQie&>0xM#`a@^5 zpk*XMIT;?B$^gNATB`#u!v^c<9^EF^rWR}y7uym_!S z-TL6`lhG{Ks&Lc43O%n|cJmd_wsWlXSg%gxYt-1bhx9xc#C^!Mrxvb<(N00ldRyHK z&;V$h;;#~0rYe@oGzB|mD|TJoGu91}}aG&AaUu5D;Y1d}n&LM@)79$3&%JGlD{mF!kS|GA}OF+?yb=|_n zU0^;3o)r50ky+gM9o<*WR?Qh&;^OXWb>E$bn`&rm?>;B2|}6J<-T={e+?$bcF<6TfSUw zTo@G0rsSm0(ZF{l+I9ML6)t}x)(C|LQ$3FA3`rf>3)tC03TjfdeT5&d3BYZXqiPG} zp6;kw$TNTNtxin~XYjZuZxli_)mYNh;jmUa)$NTe#IVoc@E0)7eGxyzy*R^1E9sn2Q!Qc?lvCIv#f8&mn@rVXV^K0 zHt0t;XAFM4rnf=p<=D{A44p1&6%D7wmTU~p=hX+i%iF8^2<4tM>%GG51`JxR%Zy5d&r z?4dyq7GpMD<Aj7%J*aBuz#SM3?!Y}fwc zwR=r{=L8?x3T=N~)5KR$+kW;JPp}g^Nqska&mp*lw8UPMG1Js^qsD z@%{?wfmaXIM)zTgSmWgHc2LIH0N1Go$|Q0a5%dDIClb(}+5^;J?3^gbuM0Ve)X=BV z0jMSmI6$0(c<2|#0i$O=RezgVo`3Z1)Yf4|D<~Zx_i8BQF*U{6_G|0BE&A*42*bDsGg;T8^uuY@Jp2qtL}t4q;>YcM)?r#%eYw%N^!@kWGYPF1$T}75IY{aL8v6@_o?x@7NqhYwT2eo}A z9wnTayv(eA`(5n=(SM64MJ{`ay58{%yIv$i*k?xI@ZcIxda;;l641kG=$Ybs-j3+l z8y#2vSDc&sd#Tm6u^We~h&6sMve3n)VuXteO_%i9Qz?zm**QHPX(!LBQ)p8)lo0$C zfgLdM>H1zJ1?b8M)OIhS*OWlqM65hyY+k&pZW6LHxD2j5Re-gO7J__uI3dC+MAEtja6SHR8_k9hj z^}wLx)cgDMB&z0dqJaT+83M@-<7e8~eWLdZ)O=qxj?eUW`Qbmo|53KBesJT?LZkmq z=d{t>wLdV=iVehZWt=?(`Gy@W+wP-b&sS#79wI{P!>Pc}V<6>B*8!Rw35*V{iC%|z ztxgD!997NnOVPitmaVIAO?ro>Y`ci9w11&dJAK|x>n`s@8qwY1BR0Dc)>W45;c*y@ z*%Qn=fpI|OUc6Bd;rD&X`|NH6&bm}O=)yd-+jz^D;@PXfhYVa45OBLS#*3F8%t;;$ z;lUEY`8NQNvpvMo5_hO>WczA9c22m^?Ydb{jLxbRi+DdVX~*Wv!$TH}R9Kcm1dg-2`P)SoYo61*nxnP`d2n1 zhNwF%yNn^S8W81mV?A041&{SLvd>E@gcm{(zC?ZAu65)1u;6pLXe&|Soe^j3+)PTy z!I|&R>}OBWhFkI|Sa$tR2*758L$o)Dk6!%csxpaxApcSlI$TyDrBX zK}!HXV#V=*AYwB#C(K}-gC&j18Z6IpZ|qsCp|)0^w8LLPtF-+u2ebm*te$q;({WUEupi)9xf2adB4IAhEQo z4?4_#bJWw0cHy_`S<=knDOqeAV(UHj0bsDauZBSYaVGmR~ zeesKzJncx&2m}by6Wx4La@?rIWkFXRtS?V3juR9P6+r^=au1f7N=`)mGXp^F=fRP- z2E7G!g#h@51?~$w5@N}GP)m zx^;dB0HBVwac>`=K>dhQ(=SC&N>KA!pDSCLyF0g8e^hCczOAJ1+tkpvZO(l|VI$*K ziB4aR0F>}jRGZE&k?pp=_K(RRzu+%M>%#>n0!?;A*cdTZkfIcs{br`{=hhQG!<^Z- zj0}=$3pxX{-L{+Kbx#{M9cH7meAiYq^!$RWsvxbZJ#d`#=wZmJQ6Wko-~|{0Zi8h#|6RuY2|V=d|Pyr2PRA zX9p0K3>F3WO=(+_{D4+^iwUJh+X53yoJ(qg;N0z~l^xPBmcD~DeQpuGS3=`s{9+2+ z1_MUR@tb;K9-&utC{r~u`p3xIypJCp?n_k&sF^#L)0iGrFoSL2fBq%ry<4@x?&i&E z@M%2ab>D>7K2WduW6ZUFz3k%41Mj#QQwg<_wS6CB{y>#+>nyKHlMlz69K0O&$3@DH zM2+jrTkg?!byUy50~y_J(gsdcA^i0rHJ0q9XG8^8CYJK{yV(aO9G|MG%ParBPq5^E zSwVE1Ugk6Yl#4gVvKzGWg{<7>C3?69Agv zy~4`shXoE*Gqh?op}UN_Yyb-)Qb6x+GxB za@yPhpAR0OG&w?%u36U4sF4<(uX5TcdO@*slE1pwH|SZCTQTM z>nvI-6jCNR(6)Be!Fy89q7@&6(ql4CYk~?r_7nxZqp9oJM`fj@U76Yi=Y(yP{o66B zOloIEeo!CZ_nXS2EWCV6Q%1&YsplT0l&= z-q}G|qp06B44%ev`p`UHtlLJ61NMxAzC|)ouI3MgD6_7*d zR1)P8qXkHN8P3g$2VjkEvKx6a7RRPy85w#)$S~Gj?(vrV7Ml3;ak`3{<%@svN{`0J zO7t2x$Ij+!EU~=<1;5iwbN)>N#%$oV$A+Zd)S|x89ldL!#;jlGaG|u}>cWOrs+yv4 zO{6ENs3>Tz=YGzk$uk+`Gt0^G(STAnbZ2G!XyE>~NP~^p;CcM+5nN;7!wQ-BD=(lpIM-Wcg;fp_`XxL)gShF8It*ehqivzfBN{e?1R}U2g*(= zp2nT-Qb?o2ngFVn}8%+?laIRxuz)XY$gB@Hzem~Nb+&~v`u2hP!CuPKKv zMp&+@s3(=gt(xDhY%HfOh7miq{W(T>;F&>Ze#^wy$`kwN!Cnz!E;C~OAlgG)8IeLX zbkwXX4B_$ri?sKEYBK%SN2889>Ih7xRo zKpYT3s`O4Q5Tr{-T9nYM2qZuVx%&+`zd8SP?*E*-u4~OK!PM`2-~I0W?B{uQh}+D& zzC`oKK}`4SI!}d4*+=;w_b(~mh*QFw-|b1WES@G{T`nI=A~bFNbLWnPTXm#;nU>x` z`dPs>Grp!~s9P|zS_+(EHMgCUT?%gYt4p2(6X#D=KCRi4vl=UYdU(9cg7>2q{q7il zoT0;diq^EMjM7xkJg45HiRJ0BOq-2%)%|fwn$@uCFYih93rh`cCvtFP@Wr6a^%3-G z7+W(|!G+g6ouavnxx?PmrQRAC<<-#FxG%W-wXQSO zMr{aOQd%LKo+)1>6w2rwWmkUbKFGCvaJ!ogfHuF_m=UB@^z^=6e<;B7Df_D4Z^OlP z(N*EVXaS`IAZWNjn-O;6NqZaR#eSnPYLu9Pe_UNQL;7^M{wuBUNc&t^)P2QQVNnlB zy}3HfnKh@M-m9T~!}bjn+L_M)CjU96&hjm%OIwuDZ>Jm682KkLlNKv(DgkKl(QGo7 zaZF&4larGKY$!oQV?$sM5Va$GXApnjG@HVqsN)8^g6CvHrr|5Us;jQvmK6_Z_k#d zrKrXbxicI-mbrac_Hd^r@2kjSI>opQ0zb7!Sq825W0a9LRPzwdpzz>^UwOB*A)5(>5-JzJGe;7DB|WL5-K9MLNoN> z8l!iIMA3dEp7~n}i6wx-?Ai;Hu7Mem4(uZYWS|9kQn#Gqh4ROF1ro|{5I+l@@FBj4 z`wYC3W1tOkPRcU5QzHVXH;kD@#6gRiM>4ofi-KjTJDhF8|l zx?qL&@0+cfV-NRQS}35$7HZ)47OoJ9)pJZA@!^XxLLz+UrTeuX9kS7m5ejFxI`rcE z{G>Myms80qTiD6=bqDUn)klfBXllprN-|KDNI1+2^PO0icrTFJ9Ig4TtSfL}jus#w z=qUPaCTw^oU!~R&!rt=k{Dz{{LVeSWD*|c@-H`X&9LaW~bW&whifmH}A6pqC}l##V^x+2^QxqRmiS?XAe2&Mb6kgZ=ly25Fv zsSh7-*%{dRswpu0@!{&NF2f{Sv4Z~WB$L*|EP$Xp;z0AOb2i*_?+1^~4m^3So4P4Q zb?P}ZVO16`Q%4XeN|g2>$m72FdoY;8DeNOo-eHOfO(ccX1&H9mkCQ7e07~$yZ~F#T zcRmGi9n9olyaUj6iG8b8O7hw>wphA>GVp?6gP`!tDmPI;k2DmsfU)XG8d-&CVRz3y zIdskkh+{5;P0I(uz^qRV4Nw3<8NVXUY z^s#p4*Dp*s`2PrTJEg~TYAJ9@lc$Y89x!9h-95fEIVWD&!Q~vdnGjVjT><$HzkB~D zZsjuPn3@E4zO_MQ%!q=GuzK&2UqP7-@M8Cv{}PCUf-%9jQIR=UCuiaPE)T z;y|w8nQKEZ;5>o>Q)R4RuAflALia(oiG1K}rHe|0Riy=4w3iHgY^n>^3+2`9d8Jib zMk|M;IA2>iCQ{LV9Cwi@J)BZy5TBST#d`i}?n)XL$W}jC+c^G{CxxsQS9C}aHkP%se1PTdECOJ(m z5trMuEWp}j7WR_3K#Nn3=h0YTH35y*o*%R-92~Py1S=t4u?MiDTGPDia+aXaG9L)P zN%q3-aInHoW;CJjgN(aas!4Rm?9}s1!TJOb$$lSdB3kP)jO~1T)yb0c zAs;UFNgC!k8cxsBdYx(4D{ksuq~CNSQ5%g!QwTx~wyuLL#OBr2y6t{!FWf@9TZS8dqAUkkxCgV0g%mfeSHdC@k<1z5^kW< zaECu)1ubOxg(5N`iVlk)X)PFc`2>(G*pBW63C?ZO0-_qTt`tf-=C2!d@BM`xqB_(V z6*s}2{-n?Lqeh)7?!orcS*zmJP=s2J9;1`~KC%;$3`i>m!Itgc+yAP)aE9_~XQDOT zra8$yKp>uG6b1M{>Hz?A(W1FGDf>3H<$AW2Zn{yfFJ-ZTSL695@x-f5Zjrn~DJl%b zyCIhvj9&JvXQBo8(;}gxk3Bg(AWcwn;_Z5EdDrQ)aGz4oFS!2_O{Fo)=;V*h7he-;ylbBnGvzn z*%TJ^&ZM-`KTIGIuK=qxEoflr;U3CYTK#1V=A?L}gFVk7*KVYhW(m1qpTg!B0z1T2 zJv?GMfp9b&t~z$PIeY*i(QeiJ(pG27K*9#O!@vVRr=u?P!R`~Fih2s_ZjoZ{7MB z&xke{rL2T@2u-F9yt0AQ8AfMNC41(>gJu9F&p$q-2Tk|7&h&@pc5sq zb-RKd?1Uct3Lc!8Gm_#toDxfGNs0!lpQ}bwopUCF-swZC~Iw%}2+xuK#mn9cIFa>Y~EFU!y zrwzDeyrMs0Fey)gQ%gdT8vg5WJBBeb$aZt|k+*g7;YKka?y@PU%oavFv&@zkKs#m$ zjdP=g&v3O4T3DW4frO#Dx438Z0{vjP@@`j0`z>7D%j7xpa^$ z*c(6DyL6aa8V+LwD}|Vg)gCbBhk|~AevtC`_65xxfNmOS zXHkwh&)NEe8TNq(*nt9d`2d%0_P)@{{=}wN zU%;OcZl_bp*ekm%0rWcixg2K23@|Bf!18?qniN%-PRkFHgHVugR~F$Yu%&@oKtkd~ z4!*+QA&|nMv~1hDuqKcK8AVtZNVM$dUDiGoW(b&kY-xZE#@I5Q#*juGlseTwk>v%K zc~g@2^WVN~Vpl%l1g4*;^Ou%*ZS9Hscl`7g)N#<;9RR^MS_akjS>>Dn}?2aDE%y7l)LefN*Cjl%zn2gt<-a{V3vmNVu@0ig#1 z+zA*G%#Bid3dnxardrls%3lB!5N|-0aq0n^z_ZmD*;_4cbfk$5zt5(46ozQsL-K4` zj@3xH0k_l&tp!|%f$bT{MtAD62+lgRq$|brO>Z05ScAQg&(6Ywmh*@}=txCFgLydk z^ckFc_f~@XYcFWaFOt1$M557V*b7Jt^pp^p`PoVPRN5xiXBqq*)OwgVS5ch+=?_4} zx~0HF2nO!5V=hwM^Fc&J_$Q(%Lgt#W>;r-)Z6e6s!r-!C>1Wnn2cs(tTvL!JTqJ@r zd+8J%;C(RxPEB=mAFQbpur$!{;lB72=D)QEL#RN1a*$0zP;hW-R$<|?J6s`_f+sK# zX;pJ)hQ;oKQdU$fv*9`&~tx>I$(kb6fQy_*RPd|kM(_q;ZqE*h-H9;FD z;v~Ai5KV4=&asBt9@yHMptBBfxp(3Gd8Ba%vw^ff;F@r03b?kNs(udCBhvOSfCQFz z+pUi!7p;bfB8tUg(cVD7GLl9kFcD0t!w|X;!}kX6Z50ee6+rni4UYUUaQ}98DFf8e zIuC4ljWQP2@6Em=(2mtNan(f`ERrGw26AJ4eUQmO<0!;`B;f@@Zh2q!D#)Xg&5+mf zn9Kv#HS7wzhJ0``Nt~Y)(jgelzTL#G4&p~pwwX?Wc;gTU2M*@fRRe*(umOj(QrapD z*U*;`}5jt zUUx+MAMKU`LHn`xA@1n?pjQ9ipL=_KQv0<%nwU*Gt?B=rDdN zAq5g_Z9T{6U7$mT-uv2n`-}hVKk;I^uT)c1*V~K_0O)LBU>ka?uKlylsSURUq7H!$ zZtZ6ba%)-ptE(D_gOFfiw&n<-5^3(C;nob|KcxGf1P738P`@CRqGR;7Hfea7yCA9j zA)b12dP-op&|V&(79`3s;7J3C5868!ncUH^8U&1m%wDf&QAkGu886xsZT~4N~DPaw)q#m8SpPu z21uk3X{Zn=f&?1Cmu_?`Rud3eza!%>|MwOuj!8|*bM+saB+X>!;QPrOTxoy`eTQV( z38X+p5YxfPWuN=A$YezJ))jcH4BBN@Wm_k`)^CN5*5K{-G-q-J(ntZ63EH|4D)P30 zaVE!ek*3HJBr5xSNpN!t6t8-K7Cs69Ex$^j5YXh&sF4gkP2EGq_InJSTJ<3~=YJT?Up7(j9=wTwyF z@KL3b5#Il3IsBh}hcB}BS@dDB1O3U9++{F2*^yxO}IYUA;0G}@u zeKMpbEGjDc%@X1cg1XXP|At=LpC1I8akc-ab%-y-Ap}*8QCWIoI4}z8HrUHBXsk^1IaHR4~!H%*{JAQ); zPC=ZAhzJe=J|^yc`#54YI6+F_YyD@5lGmD)Wc-o1{&2kKaLiu>LSM>D(F1c}5(9P_ zV8o*CuwueQhc1$-!Mcz`ZaV0E2bM<{WHtkE)k9k^DT$-$pyliv(PrSMp@b3@b%B8y zzmpqI_ceUz*K(U*+um*a5TQxm)Dxf;$akU_MjUvg39cOmLtxYb%}cO~I$wxRB6R43 ziBOe+ZQMRI)+L5^F-BTM5G+nuqzCXn2|+N2?&>dUeNp;7n|1JL6|gK8)-nXxg(T3m z@u0?6zI~_7A0=?_j6v9O3JLX)VwV8yQsjU?j9A?Gm+yK*2Nh zAJ2dsz&xFp%CIUz$USn9fgy1YH~5Oge4U9xFALX%<&~4E)1Wq;y-_}ZfyAb8y1LKJ z&7ll380@kfa{b`If)wEZ($s45Nw;w;qT9<9N zihYWZs{B7rBk0o~heQV28c;6*j9K>1;S5;Cn1b3+LkSLOw1(?|O%WKV5z1S|V;jo> z*>WfxOw~x+2E#1_k@tyZ-582gKu+IFruWlQspkHQNLdpU1jXqHLED+)^KLU2cuR?2 zfDgU`s?0D@3zAS0heV)Au!e-wsBCwA!jdhj3E&`LRYChn|Fw5k@$fT*VCNcrazkAP zXg)>CDuGEy5!0cR!g^XJdE^qlblEpg`jb=3cY4xJfzLil|#c5T-+c+J7O zj&|<>dS&_Az#7vdAlom|KkFf15}oq`*8oe9xPf}$IqH<4jgLITmJLDR*o({rd{41_ zgY1AWG}hVhTrM0mh0cY3INYoonTgb_9DKxgXo3U@a)30`L#%hamJ1%QRnRSo#bqcK z_yN7FRHl31&KN$}V#5n$;7}pMjH!Nrw4S3@q)a9Q@}8zHUfwR(u8oSC;!wp4pNi*r zli|D>Mk8v%DX3n_wy6)a_ZpB`S+1@uA)+_8oRje{LaNH8YdP@X(`#%(8-2jNA=y1V zJq5O`P4P2FZ}1381W*4yC%9YupwtA*gj2r_LrLf0ngJgZ2Yi^QiB+_zk@zpU=@(P* z@hO8(h3hg8&`nu4sDO(0rI{Q!y40(JB}FJ?;u=u4Y~H~+dAlAA)v8yP<_-#5fc?{2 zPz5a)1Sw?ElW#&|b1ard*>1?n$|ULFXXs#U_XirtTQJ5u+R1>^F%Zn-u&`hwE2z+` z2Yv<@WLy00(7&IbaE#;o1ns%`iH5?E{wO%2<|oA26In^Be_R8IGm-I2Ez4X%ALdq) zD;Rp6XrcTq1x8Yjn&;4L#Ti8n7!!`LL{YZ-$7|B~425-n(L42`F6$e4OD04+#nckxG4t>xgYQ63X2wkN`w<}p~J{%K>@$1fq_7KkY6vF)S?ibNea@QyQMH_qY=(Z=<}pU6pI>NWCydiL={kHZSNxvMKyr~ zMCM&_wLTF@CCInksbK}072oax&>^* z=q=F|Pkb2wY}h#bnq31Xh|hfE{sn1-feR060u9I!m$Z;1voCoa-WV6>XI^98l|ZR=D>$&4=ls8g;)UEk)=!VGy{y3ETNUh(}|M9^PS+q0Y{x1 zkn^g+cOwyHLXhgf4(!##vw`z6t3m?HyH9W!p|ZlEB>vF3y-)`Wxc72^{>?AcmyPuy zQdr(d*&x}tTZ)j3dZ}uw1)g>eX?Q7lWQiAIkL-Agr^~@sOQst%tJm}3wx3_XxIP96 zYkQU%wjaPImJtF(nBL*yauoTLALc%`6^1MX0 zR?FRR$_SyCS{c>E27yua%d94-*P-QVR)y4?AyTO7L|x}JW0n_4%_m5J5x(OX&00N} z$UAVlTTQwtSbdBbB@_L?ey;Dp9BON!m7@%&6V&pBjmR_*olIwt5Yu&{197K8j^C|E zV}k{b5UX^sS5f)l=5U7uW13{T+q0JoOHL0127yd$_9F5y2|zByW19ueYZT9FJirl1 zd8!WiOO76Rhpao&z9@NlXVLb^JVFwIc+ffp-C|Y>x*7}h`;kNw0T^n&yg&ieI}FAy zi?Tt&??^?Q97MiB1}GlTEc=t_FaCn{J^g!ce>@Oim0q7A0lLyms;$EvSTL1n!6N!R z^4~z!0WuPROc)una3ZuFtr*hy1~=c0H_+_>;L|wyQrtkD+3Fp*fNdL~gh9oHkqNR9 zC|N)g4#tx5jPGn-ODQkp*In*XQBR4nv53j@mjNti@BmS0iedI>=+^0!#~V->LPt?MT!FCMr1uKh;MX&0DVAP%t}ef?2j__XE_(S8Vwb+?1D zuKW)}?f+s&3u56e2-gH(H1=E*hAxV!bU^t{fdaz21nfuj^^;`R-sA-@(i0(X$k$Az z8w23{^7;yfmA}pYC@SB~WpBP4cHxm#vfvNf0YR`G=*hEKLv}GA@f&Z{CLz~XMad?= zQ)@5$M&{0z6GJ&ZOW)@wU_f-Ig$V2St^r!S8&YmX-MJ!x*4?rzG)nB&h6ZmhLr=NiCX&`Z5pjc!Fgb{w3x;b?%rKUU1po4vV>_wb;5q1ml%+;zw4j zpcJmUtSPV$rMZ6={Iza*Vi$!e3KJ6-?;G}J+2NEyaxF^pe7F7)`<|fmyj-;8`}ze< zDNSF~I%s8K3S2>s1kp(R_Rl9UCoh6#8KQ&{^aIxZocy?M9)!XC8?pUiWYT_U2<(no zK!X?LIfW&sAgjK!P#2XGzYbfqNgx;iU~Wyj+^o=~HH8ox-MX&ezdQo)hJRL}s}X7n z4E^l4wkrL_4K^5WMe=WhDb|asEqq)(fXBSS!>&2IzF;v=s{ZdEwCVsLI{^Hxuk?SL zS9fjn;>w3Bi{#|U2j$VB*WVZjFnN%=b`ktjk=;#{gb6R8{@8)n$?-wRAdXYQ`1)8j zV4*;=JrLY}*}%QRaQ2(vi^E#Jj^LAkd84%uy|)v&Dzw|(%*ekauT|eUf95Gk2vR=7txf>sy{BqC_e%aNdB3286Bm zUocQF%WH}o7oA{~z;?(6K0>Fd)Y|n^3q`KST1HrKM;wJHEUv#Eq1t}3g@1q=>XliR zVSXy780#W*{i(2t1*?JU!03zn;5xwb7G}Ez!g105UyKD(;GjZH2_X3(@;GvY`7i(e z2DIi<(0}0K;6Q9%knZLGXSc_ph7Y7mEHoYZCLuytRYY*PUVO*eF`A{*e^YmM=o>pm z=?H`)x*v0fS%uny$UYh_l_Kg;BgzI8O?d?u*1u8Cah3rAssb1#U^IZp#7`AmY!Lqi z2Rj4>%S3deSOx^o*9-`n+_aYF$gCIepMwxnSYZMkb0{g?9vXA#9O>!WHGSK7 z8kW)SFRbGoqvizbqUg{$aJEAK>~9jaAP+z$+n;4_!Xa2ev=2Do{OFK@r=Dn}Xy@Ge2kAb5$xN)}8@CZ#X`o(T`=uHcy&7P6 zVrh)^hxP8qWZp+mSvr+<*i%^KQNBoyB_us&yb^|{{)Oxn>?fS$29ol{z zOBjWJoY}Rmpv&_A*UFw{=`?^&pZN)}$T@?8e)hVh#|>Nf=J!!%3n)1U2cr0KtNM7v zFn+39`-pGsIWV1<&pmp1`itW+XC2llt+vqhE(^C|{{-9*&8ed+eE`ARWv;hUx(pD= zyRx!!_f08aQBzd$^=Dx)Z76W2!0|TL37q!Ks`G1a;UD)9r5fPGw!8pf#?|0R*6-h_ zyyjosaAV{N*ktx^TzF}V4#shUAZQj{~Np|<7@A?i=)6lvz1L@FalhVz)%b5z@YbsQWBZcnu_?KCIsYZdUg9)i)qs0iqiB(%;!Ux#tpFPkU6n~D;{vD zu}*zs+!@%}Wru$llh{37PYW@aJAZ&RedE&GwfBEBf!dP*Zof2R2QmDQz;-f;r)ffUZ*q#@G=sBfIQ&%6olwuwUJ_pP&Fcy0vTdW0#=41NVmkEwf9TG zpq>VFr4V}qwHF7b3dvzWr*)(vXc>HQ~g`*BE*r4q?_sC(+7P8}6Ah1=*Rm_Htg3%=M0x!U>g&oYBO&hi9%ldNS2PG;VDiMujuG0MTCTVDabJ9Q*bqm7i(XI!cDlmCGHs3^ z97f*?l}tCGLoW-(7ocAVAD#uWZZ+x>0n@OiXaw#fvYf9*L^?zIn!PFJZ2=0LaCjrP zvWA~=N`UJDAmtbf9-+;ENjUbGLV<@Q;Yf}V3~q|Zj|ZxJ5DS^}Llg%FQs=YfAO1xO zpEoK7m_Qt`@&g>v%p9S;1m-^FGz#~}hV6a>MB;A6@j!Glct_5yu5Tr>$=&p?) zD^K3vsUXVb|I7v~fl>h|UtR#RVXpM>@W|@$(319g(&u*SN*RAUoa^wKK*DT{H5l6E z0Sk%M?IHbmkRT);P(fzRPat#-+6Q_`v#8=iCvXg9{XkXP3SXrgSPY49{3CWQw9g?s zw*=G8NM3vRKO)>EHE}orWHAXOo%L(5|Hyi4A6$46QM=l$`xY!sXa}Iv7_nCYK)m`l z4q?U!07ePEN(Xq+#k1Z2k%@t$SpZ5q(ECBTF@ zMTHiJ2Sllsd(NN}scWV;82=zvn_3l_zoV26Sz*4^pWy^U)}3;_yAZZ;ccMca zWfK5vKZIIF6P(D9*BMxeGSH_58^#JK77*{52m(c9k!ZMTwYEUi3?Pk}2FbA)ptzHT9^DNh=ryd_ z3Tf-DQ1m5E)^7(|2n0h=7y{h`mUzdVwQe^?O$+7aQ2CsJJ~|}PF!TZ`Fv06%{iCql zyHKx{vHZ+F^YuTO0-|o3qtl!p768hfc~6Q;fKPI_1gmdzBl=P8*L5onaD*&JW@f1L zM{H(rJvKqj6;_p*^y542XJyUCFkl(j0|0YZ4c0tHsQN52y+^}P5ET@iV#q@ja(!g1 zzde+NG-%eWf7pHhAPsFJe7H51g|IO6L~{WfIwGo4@MBfDsiFl4517}scGw=l`V21$ z(6kF4A}zC48&dQ-A2cu|mkKURk&%|X5arSg>Q429eUUKkfXDA%!r9{x*2$u|DDp)Mnn3%3z@afUpzKTe4t%n15~c zT6jMfI>JDLiO6{9k6L~)I1>+m|9JTfsuet?wjf&y*S8T{8s4j>9?l;GE_#fK7O zAWl{RO)8W17H zR0JacgxG?)5#5n~nZaE|cWO>@48GZs;yVa*ggaWedZQd^)u*Qi6 zN)&9vxC;j3wi$IF>*H0F`Xr!$15gG@OyS!Z8uFqBkR^m0SbJauSqa<*e#D2{m-^G4 zNVsPH(tSSfW@jT$i!mDp8}KE!4fwx*z&g7!{8dUxL|-=>y?A(Lx8!ta=iz!$eX=EW zN??Rv;SgI_uF>F7Qo-k$L>@vFR)fgLUiQuR$&h6DUm?M#KXeq0e`@t1R33oWNa5n&8E~jmOQ7olay5 z3e@@dwT};Y3@*_II_Rc>Nn(2X%RmP!eFPAJO&+Vq23H%Pd`RnTPg)^1j}Xu~z0}oZ z?K_S;l`nLNcK$pl0mT%l{+(wNfAvbv2UYQBvLR#>kKcYFdxzf%{Y>-@D4OM~Ch-K!2#^`A ziBc}_T2iC7P5#m0+%>y``|?6?c0wqA_f(H;k2(4M?j2k?~mYSZxNt5k<4 zdS5Oa+Pt(d5RH{Tc8RxuAvqv>>l(IVwMw!Q7eEc4VSK;mF+qtL^4+3So~bxJD(|aC zg!|yU^j&_4N_2F^-;Qp?_3)TApkYE16*KodMp_-2-ZKTQ#NGEx-CoZxV*LijJO&cA zMe$g*Q>eKi=sEaTi2ccIhKufR!>GXn(08O06Lp`%1nO3Q->>!u#v>Y8{jo&B$O<|} zM#k?z1S=`IUXnLyW`i2&sPqdAL`goXjk+f`i#nYi3-h}bvvVT%TkKrL?N54NB_nz? zh;m8YgzVd%m&@c_r`_nXw{*CfMFV-E&3@lbzP&HWS^AQajY>;zZ?>mjHeT(~EZ{G=2q%!!(yYEkqW)=S<{u_nc0GJ- zmve2k_+(hk$zWaz`N;D~6Vq^TPvh&1blDlaz08?FZvgL`Og_(;l!_ zh0mSxr6@1>`Au}S^~a;$=_)kI!9f;s3ko3B;gVp*-mZT_=U7(5Q&aGUD}hm*_0071 zmh-ndRWI=rxw~!OnA&b8=qE1dIYik`?o2#gw^&tNZDs8JEs4E$l3tU(8Al4G>8Dma zukduZd#gvd&-ZR`wNh`wQc#v)h3+DKvgh#tExe3hs413!!-t1uP)d&o?oH^>Wk?5(hj%;0HTHGG#OqG>h@199=5o+>)|Q!I0l^jEg`NRz%ttgMet*&_9chr?umQu@rw zRrYStmGYT~aujHEbMJ^shiO90S9~}%!s6KGYV~9{abe+v%lF?G4ON>xq)1kX>t#sv ze|7OMp+vrJg~RCra1I_9>(Xh0Y9?>~=}qPGMOw+gplv3Dj4Z3b4_IRv=pXjWG&-gS zaaAz>1~V>1g$FaS2{Yzy%;~6=iN3;trE?T9A2Wyx z6nX%AQxUcPkDS8hXC)ry#$?vcl}-(+*q-SnWPQhP#{W5LrL3&TVezlapwJTr zhgUN$Fj#=2M5p|4lL`Oh5sA!i_^k_5QO&D2Nw!j7TmQ7Cnq+%9mVKWM*^W)r4AA5G z>Rbs%V_JrC_IqvFnv|ypW}3?P=!f~ds$ac2EDIRQZr!0up6}WaX zB_q60)urrZo~#B@Rmzlk!>iZ3)w8*9WW-Eysc|e|>IBBAInUhqZuci>FOnI?y|Z8H zcsikwyqt9@ko|DOL%J^`bhPDfYjyR>jsi~~%9BkLc3N29w2S{Zjn+vHNycU_5@{uQ z4jlUYD#DcRJwj28q_P?i0MJ&KbdtvDHBy=y)tm%~S~1#()z58amg_g$Y8zdfB^qtC zYDKdRfSmJBhui1@Ysf;r+%0>y@lw1d5np zu0-Zi>z>&!%veH2f%nL%=5ou4r`d6mow3ZJ`-$fgm&d4lLXBObU{SbIB}S>muE=Su zevh5cchOcoF}*Z1!6Wwg-ZMGN*+);aiw2ned@bU0WbZxL=R#LGqZl*g;0yv*H&acT z)?+O>XEYZ1vZugAL3GZ+zk99y4$W-KxBeS?MokpfU7L{99hn8Ft3xV{tR~VeT;FDb zndJurpdf;v;un>nCszkRlc0$p`}dY8S)GLz7eXcwH4eB&sC@n`!E-d=ntI4+Im{hP z-I8mq;FVRuGc^S|(Pp5YEOtlWTc@xU@~5abTMJq!8NGLa&> zT30#s8q~gYrwr+CN#7G-_t^911O3sVftbR>Fb%q|r|`IK{@cdk7v=8Zecir-Ug<_= zyTgfZ7z!^nlk5HH%1W6&V$1ymd8J`VL&)lVR3+Uey6m2BxD9y2RZXkuYu~Err0H)8 zSru57{*{|HlbwDPwqLY(+rur{r?_a?CB5SDpWFmr2g(p<2H8MGT>bFKD=y1Z4@KG!O+Rzktz{7u;Si+4Yw55bib6MaHLn?0 ze{=5X8TQT{O_&e$w`ZQz(HUM16IBXx%1uj+5(;-l=Ag-4Zq}^B2W=P#ypH1$$t#B~L z_evMq$M|e>s(H5aq@c^}_+Y-H;o_&ATJ2l4g`#d^87=kKOU+&KX?c!uvDm20^re+- ztD$9eOmE3h|42AT04*$R%Fw2JM^xrRF7i|TMBFrfCGsMMp94pd3fSiV_cq@4Ea$t9 zk5|fQT|N0^jS|I$ebSqhhURb?$Oj#eSh%bmZZl#-b5sd>T%{9jDfGXJnkQW01 zG^+_3H53;D-S`zM>#Cv2Rze)wur=a$yxDJ8WdVsrap&+G253CYM!tbo2KEHbrWW=eY**pAM#vR@~dVL)!z+s8QN-@(f}< z9lnbc;vJd%9NDkaYgd}nIp;LNby4%A`P{KYwsUy|Z`Vtq)vvv#M*n6c9sg27lhyQTeL096Pw`Bzs5 zkpof)H5i7zy$2JRlF>kqdV*2D-9DJ>NAkty;vwZ0sPLP{=nxI}Mpw=<*e}hin6md^ zXS6@|mIH;&ZXOpyoiX*0&~ujP$y)X&js4ynaOC}i$cZ|O_YIrhr1>>7}-`8wndpKO0 zK5SN6eB3k^E0<7RH$ z?3oH6_{xa7Pj-#qnX&VF{BkQVF48Gnp=-b0{uJc+Ct{A{pVrmuZK z=JprF-=qLel+juqr_@QVvqSu#Cy`Ue5uX6MMx^>&MZ_ZnH4MpCbuQ*29X#V}mQS^> zt)YVHoGV$Nc`)!CMkDn6oFHF2seekqxhz{IkoUw5HQ~Vrq|r%G;bTv_XiT29l{2of z`RFtKRkJeC6fZnb?g(Q@$ByJ)IrwRDeyT-PW9MYtXsP!Vs@Y-+uQYbC*XSf$jGTi+ zxS6V~LT2gYlZ)gmRpz_JHb+_&p9`%l!Cfsp3^&4sSVljSxI!-V?mGcg~dYlkg$7<5=h|R`kGJ*vi#&TiBs>6_T)H*(|iVF|C$vN zQc`H`zg}?CcfPcmrl^toC}Fg@*fGI*EOo)V*sCcuUaOa=?^Y=L)18Tx12vJw;pL1= z*KC&NOeKCBjuH`RE_Zwmg^Yp2>y*F+qk_ty(C0a;2K#QtmfRiN4J!LvqtevZVlzxI1AxVD6D! zrjsH7cY>^rI-)0`0d2fR!d<0;;L!I)ux4tD(DQhPTz=YvdhJG^iCZnr-iD_z2~bX&hyM(of%P z-$~7&+~dOvQp~*N;yvx1jvMA55RMDW6fmsD-CrgtXzmPol<>ZuLGd)2eK?k=?_A2@|Eaxox!PAZ#Rybu@<~g?tZ{L0;hFTC*d%Zl$>OL$Y;=wdz8 zGZ0#JVPGsKcYxqrF0d~$(p7NN4ZnsMnIniRuWp8SU>f&CctX$V;aeJVK7`uZ`nBR0 zHSn6e+E+@X)jhbYD1bDIKmyh5DR89WI{V8(Q~Izl8ZiXn1{t=D-97x%1JY#ry_y?t z!$Ct*?hWJVVcE$MOS5CHJ*!J|M+lr5Kr#*tJTuWjBA9YMRa2#Z+ID}>l(#)keTO=1 zxVwr2JKUA`UbPhulJxoZ$7tIt>%IFpD-8~^hvsTbGstq14cEB~`9^laoFW=zvH`U5M@j05pLu4H1M z&n?pXqPgZ{Ql2H>xSpk21}ucfn{-=u_lOo$f6(sqWix=R#%HR7PU)UuoP@l_*Lyf< zDiSwC$s!d6nD*MDF^LJD(&^$^U8WPu1x{(ZF>+j((^qlR95qcA;bk$5cH#nu^vpTf z-2+>>%(w|ELoZ{)H|yvs#)W~VPGlKPgG&#Wfor1fp#hZgzDUhw~@>^~tNdJ1vu}K_5CI40KrPfAG!t@_EAURF?)azb_WUNf$0vu5?_J^g_*E8~RhTP{ncSTfALkIz0m!xb7|d3`BV=jyO3P7EDu(wg?bL*(ZdblBO~>pF=Be8g!~B$ zOHa=ydpR}e(1<+SG>sen8c>DXN(=wp#I%HHF@HJRw=Qat5-d5@*|b6HuZ1H!ZJ}E) z;{@A8mEGsV^N4kSo2XR-6S_49&FLz zqYDW+InuDZq4!dWn2H}#)7(L$JT&c3uN5~u9apejT@9kHQ1 z>32HbTAiQ|KpOeCkG);X;HJq-;A{w@#3pVz zylm$!;jDG7Z?!n{fJ?Mn#(-p}-|;Q;lTXIH-D}1dVZLY8;H^2!vF^}pOHIX+Jo|l` z?2`ieaSGB81n2vkc{h2v1$kGvo-w8eZylI@U2x)$R|#k49?8!Y4HA>4vxcZmkTVgK z<$dRqf)igZmWMYhxxJ*!*0fSqkC+7)_M7aUIOP!5GAz;-4+?MsAaV;-HIrv@n5g_(O9Q?@D(`MQll)Q(W;dGtTEX zWvs1JS*bKlEPR8oaQ41C>XjgH;RZVL$8m1BVYTN!DuV_Vbz|)CWF*&ufxMQl@F2<- zDG1Bd62uI$!~HD<4m@S>v3A(QJx7qHAwbaUD5$ChSXL>}Ys{kdqk(Z~!Z>Sm-E%o2 zZ3kKmX!bG8L@(<0cDV(^pko{}a)%hUFqFw5tF!Cyvj=TuMe>*AJ=79rviTv?q?*?h zzMI%B9X{SF+1M+RUP1YME<4+2M$E+j)yzsFdvH{#gH(w{-z$&hg~=Q@-0u|8LPrV}U!2g>*1u|#TuJhR6y z&^_9U#_zUYu2I2UXTYZqf_c@;XmUooOqgJcr0@4g_A{5idM6)s{A|c!Rx~v2v79tm zx1X=B0JJDv)#yJn=F*+s*{N_+#A&jU^ISxQ$$%}nNES|q>{1o%K>6J7?(OMCRH4r2 z(TDts3q9)!%8j?lawY*Oq4sa6L5jOpmsSpd+bhA-{ZeQa9zP4FR!y|ZI3~6ECeu%; zW#7vyvoVju!n68mG{f+nI!4yk)};cWPlF?i5l1+|q%)!z$y1S4W=C~zHX_oR^^|tMR!I<_eu{{h6kseum)j9^;0+!LiDmd&a{TS3V;5~L% z5y_2^r3>$*?PuMBjh{{K50@5RCvZ`0t^J0msgD5JkQxstTHcsS(}kTyA&iS&{HXLq^xjzvFACQkA3|Zu06xOvt-MQ zRs3iO#LW*76Ke&Os6MlTd-uK^pc9yChgU)ai^iyalbox1`4$bN#rX!mFij`oDaAaz zv-=av2h?!TIf22U8|`*-`uXcd(NoVaJlEgmU%1`>?`);1H>VTN)HM2yr1xLrY-)-z zY7ofRH#;yFf84N8!dJ@tiVuCoMH*i%*oN(vb&g-qZN%t;z!9Gf?S+e_AfCkm2kx4S z=2W}>$6s`eHhxj=1X4e%*$PS%7DaW>Fi<3AS7E}VP3DEOGw5yg?iQL+%nF->R;z8< zN304W`mbE(z&@8(eJ-*wRA;7fYGCxV_mSm0ISd!h--ds`CYq^iX_YY3;eBLEW(7~y zo{r(>5^`7H%UQ;SiBleYG1~vP>v5CZY&}=ZLYjLa71zdr--FKwr}8J412o8jO8U3! zO^c*aMZnuv>SbckyFlX_s1qU=Z)&w33F)%LfbHCHSN1`WM9!2yHN0ZXEzpx6(mqjZ zXKddY=D2&RyLX3d$XuEObET`NHBq{bKpQ`3mD$L$)uB9joN&61rniBMb8lseFMi;) zyG@7D&bRmeo%cU%(}ZiwweicQw+90FcO&ynq*k3RB{L*oL~6XjR7tXK)(kDFb?hh@ z#UgzMk~d5(`1C%AxRjiTb_`U`aop(H99h6MzT^M0ts|roBKO!$*G6SIe7W!n6$@Xf%=>hAu?<| zo-Z{CY*Q1CJ~w~im(XV-yDJzheK#RX6%;6E&}Fqr*rX129KDb_&LSZ+8ZpUIXgfn^ z(p0~k4W`8-p64P+jrKC=G&1Gn&aVfBmd+xZ<6sE+5TjUT17LKqvPjHf=rgPD0YI-Z)!%IJ0It=5e3lj2}$iHON(uW5U#?*_mp>wF$#U zRUe&L{9@w?udAM(#w$Hab`1U(zQ%Xne^IB$pZjda@UGc*diU1`K9_M$X{Z+Dfk`h+`g*rpgd%G;`cYFjwE2=2zI)Q}8-E!qZI8 z;q3IYmWsPf-+)bTBq^lO3oFy!z)VC&GAGZw!OX2c+TpdjUtf^Hrh?4>u|vU%C*$qwsUgy%m>w zb9{n!bll%(J*%i5-RRp8z4*-e;WEg#)tlPR##sZVp@A!c9DHQpu02|l2zYNHeJCIjoFo7{? zi+oSEWabP5R&Zs+>q5M*a9#VSr@u@ziq?pC> zOr+IsKAG)g`8_^Uz-VTuRwd1g5hgHpz0kbL_2N@$8CCq)wiqhz1AFSM{E-lO`DWkQ z(Bf>*i2}oG+uM>k%DvAtxlKt=IrbD*Q=Dy?bEfT37#8vl&zV+7iSL=pe{PQz+SJyD z6y^Y>Hs<}vxP@$iPWZ5(ov3ChjCpm=4$vYrXy73b_$q<@_IQbX8BQVrn5pA|Ohdv@ z+}mj^GD(nOzu>FmXFlEAb=@?+UFrdKG$t~_b9UJk*GHa7Tfp10DJwVj4y}BR=Bf`b zHNz{kW-dp`f0BQ4N-S(nW9P;BcEIQHOWz*C!OhqfljgM4n|8Hlv^B}(`1R~e4O$_f zbEoO<+Vc%&mrEUQ+*`idoi5*~>Z_A51Bq~2Yc8jxS@caInLNLhCZ?(hg`W1HC|Ex| zxVC(SZzOVThb2H2qo1hdFJ5ZX*rdE}5=U1C2Q$^OTI?GiPuu?*a}j z-^pjpWsdVoJ7=Uo2o+CX=xo`^x4X%}L@#P_mG1)QOlQ;9K~gaQIeJMA(o_2;PE}RP z+2}(8V69{oU+SQB+#oL2MHO$_;QrfETd}6*+@rXeOHLAG&a4WfITwX{%dXVf`w6>PtH*yT$50L)F4T4c#L~A-2CNJEI1>-ty#k)6sU?g}b zkiFz?{Hf!8M9}AK-qB+TcQaZG#Pj@0g6oTYur+1;kp_jZFRSeP`QE#&TEI%?=inF`8e*+DG~^g&mF_e?&}Q5oS*+dQbHvTcbK)5s#`jiQ zb1k3C55K8+Sm7P-d2oU;~ZM`xXzGpYSyfi<2 zs~>e{)|pFCz|ruewog{doBfyg751-wf2>`3Ci!q-4&j8gO_{QzZ$2fX?~Q^lo+k6K zBFofOqt|0F2hrxd8sZZepoa zrOPoxP|kpr=iTLF&98Oq@MepC-BG8)2|l9(@-Hups59f5&>WRJ4LonR1|sJ-#D)Va zuGL*e1!c70CY8ARItB(jNUw=pQIVV0BEl4q91EFQg%>!c`F$vtLbHHbGwD~pvR5OI zP7e_99290z>mOfIJeCPUU6zCvc)Pd0oHw6=N?727*Wto_K4pSyDb66_!X@tAn>Q=5 zzwxqcY6CTQ}2<%{c2i90KnuCfL*U zn)C4!eesuzEj4{ssR0(VSpTR+;_rt@VezekO``2731OzgLh&NZR)a`)DH3(YC6H8gYjc*i(2sr9xAbF)jOB;6jDm80o+9q;&;cL`HL(>wj=Wv*$ zb-P!Q<4TL8dvjUnMFYdKY~#Nyuxm0hw)Y3lH;Zj#t#l_*!lO#9UXG4lwYVOsT6kgg z&ADsH=H<+Sk-%J!6yk;ug$l4W4S!ZZ#{A~`N{Ae!*#Pi>MNT)=q=MM`0~+7t_qx#e zf`JBUM28WL74aqla&mG&u_;H~rBKFTYusatm9u!{iKE6 z3eRP%&qx|T1x=hlTbFS-T0*SV{kEjq_Uz=C=+^PnP79yy<T5*#77;6wIY_^jKt>&ESRe99|LwqSJHH`BQ?}ad$_DG9FY$ z!_d_hKPEpbL6Fxg;TTJ+huWd`)5a#y=De0BuRizeke8R2{OD-5iL%D)pb$BwOPBI2 zdw5^~=3`y-bc)z4TvuW=l6M0MYQN`vKhm9}q!>>s^k!Fi+xGSPZ~dSmM`R5oK(4Q^ zM+Q1?^B8=kJBWZ$r(7(kZI404+~V;DM}83a)O__e^pT5=xO*WB0|0@cPNEHs(TSaP zt6eI!d(>$UEu!O-_MTiwo$g*8hl7V~Wrk?samcbV9hQjMq-JLpIqAe{2{N1zBTk>( z?Rkf}7q6bxj3yomBy;KBa#BC2p5a?5vFW^VeS!ee4}ShIZDqrS9X;EmGla zB%`8)&p?iuYm0}Q$!%$7Wrh4Qb}#GUfX)jMghl8{AUgC;aJ(jFi~DS}<5rYrU}3tx zi|Z75C!IUaN^!!BJbko`dSO{*@bY{kH3;t^SB<=FalyGVUtj(=0Hi zf#h}|#XT*9_n-3TI@8XgaC4K1fO3)WUp$Jd%x>c*db8z^9;2?U%}M8W`BERcw)CM} zEnEDc=XGjFkM~{p``_4?{Ttke2?S4#0_~N6E zmr}zWX7WzbA($KKhgfGs?>>IolHfEyfzOt zsk85Hu07>^#o+Q~O3z~ANVwlTVdK7Z$aCI6?fIIg-~krT+9d@2ud+@e#pCsrK0qHi z0^4K=zE4JnO*81#k!=E{(!zpr(YlA+#l7vH&2hB}DCYx!GA!8hs1Uayt#6c~9U53Y zWk-g5yIWk3NoLk}UU9=wOPGOG6HPzlL^Gf$j_On$Ic;^icLi zr^K-ImxbniG~i~cGb~=5g(9;*+804N!>b3wQD1t;4U567L%ZulB=B3c0rR2(C^yy@ z%TW6L>a}aUeJp@THm>~P>b?s;X5wX@ItBKd1)wIvzh>*A8jfEDSa7F|4L(_3g66J$iRnT_PXR`w)I(gO-=GN7P`Y{27=R+>eUa!a^DMtMI(H1?58RMI zYViLc`V2@qE|aFX{^zQPb0Cov0r9W;OgR85ef0oe#r@!O)e4d1Nr?c#fHFJ{Wx`vB zJ4kmgU$wnu?+q9HYO{lh-6kNe=#gs>akD|gJp|T0em&N)ykM}t#_fS0!><$ zDFo~{JyxTmb2ABYGEq1xmeS^xQAPTaJ)R~_&UdcC#K}@`>U=GTCe-AEn-MVlZ)fVR ztcB2*{K+!>0l$^@=2~6YGs(p#q=~VD$;`?`4`X2%@j*-wcPRHCsUUM-({ zXB*}QeovKr3&>{ZN=RncSW2kfT|&op_{^O@e7~b%Px{kyhlFSx@d6i6lBF7kbniy? zlQ4G}sp#K+{?G`+d>eIgLY64hln0q1p^V^dE21+sj&6x?(hfWZ)6ER2K`BT>Z6KvL z$`>F9jnQb>uG>4GZR`qrgax?$=H483V;cl2=78E77_2lUxuO)hzDn%;)`?}blYq<4 z3iM^z@1qS=?`PI8J zm|VUP@a(*4-4V#kYeMfdv5jvZ!#)>+vJ(<*4%MLh>}lDt-Y{H@Od^+Z1IJM^vb5*c z*g(MrNJ#Q4q%QVO8L@8;o)Cl{B`;TP0MRu>6|*9STq>H zQ^XBEABZn1-Q6HG%>Mbd@|Z|0%rj=0^NWM~PUBZ<1)!L4D=}{FL z$y^bw4795Pwi?9SLLQ*Pj@plJ{e>Q6If}B=KN#GKwOWWFuJ}TDzR={p4H+JKRU3F30D}mcyV4ZFB!Gn`@ zrO`-&3aJL{dCfzItR^rZAGG1b3c>($aI3RPW16t zrTZ>R<84LC@i^i-A=Gk9{qJ$hC~Dun1_~+;M7c)dHdi>Z2RwSTyyf#NlmT>|MuX4+ z4^qz*kWdFOiQTS!K(etVL+Q zXYvaaX99W|tu~01h1M+T5YoP+auSnp86p{Gaz@)5FiP4|@P5CEPT#zXx;5o~rh` zLg>TuM`9aVW=eDGuPaI-(F6*o8nwTVhEh%Qqpk!&GfF8j`Qc5AQ%?bdQ#L^ESKq(gE zNmtwfBM>)3H`X~?0r;ir9-|n>E^QlmkTweUa1cS4kWK^8Fw;7ucWz9%K&XifvyhLW zLY#cWXT0?A$~ewA{|%_D7?ClpE4FbwAVpFiB9Clr4$ zu&mFF{&pKP8Ae@2`gTqq4t!=YgIfxL5NgO_X14PWw{Z}lAl~As4$S&9(5iM<| zriJxBNj;#DP^glY_8b0>o^pED4!6oN`y;f_y$~H5m{7Pq74QHd$cO~F9rwNZ*9f0L zS9Fv3^;k{?Vf8S4SJg07NQ#3Rxgu<%mm}9V`FA(NJ}<8f2L>XZu+^t4to#q&k-o9r zo+|Pz&}j%)tTtKJLVx4ql%kLE%OCLwqfoJ2PN;#|+E*1s3M2UM9GuwQ0`~hMY38*X z2wfTt^!P?K9D%$NK*xRnj1UF0?fDJjHi+?LsXp*|cOC4l!$@w9e2V}B4;WC}K-dM; zSOIob_PE%p(w#fM&OVx^4G$Evg2{pvoY?OJX&qpmx)pvdbXlIAOSg1mmsm_zlm-5% zbO`AQw!eS?J{^b*C={ti6kIkgF8qa+8CalPH>dOhaqdgGtw76s%1K%PyA@$$&x6o< zfd&?;4+BLvcbs9zKFr7)4stSg zAK{VHHV}6DB9NrU1GSJs`@+s0Ml>g}EZA{~5D-MS0CI#iTnzXQi22IS!U}4z?wEcy zA>M;S49QP9F`l~vFh%>3d==rL=LQJwf;3#ZWm?~~Zru0{38wl1qkxhx)NOQ zzUXWQIxwmfJh~gJ3a<`D=gR4@SIB|7Qm#QUvjq zVb`xx;<>v>B$a=$qW<%n+0n8W^Pq~76s++54o74J3;{f(CW2d_0uo~21u%-%LgEsU zOqFij&Q(8gg~Wj4yMi+KsJ{+7ZW6jDHp1*F+(PFXbRv_=da)LZ!-#lISQuyKTV#sI!k7{Riy-~uRcGi&;bZHV?d1rze{AB^^v zGe8!%1G}C&D6>H1Ujw*@h>6n4l+-218Pv4Jn>9M=nv+!%f7h9UpbC&c1Ay4D_4@YK z<}hkq%!?D_MH@8~qQVH~JvnC)MTp1*L8r3?JRDlB08N91WP6WD$H5VD(im$w1CUm> zeH3&mQ|k~K#~3q%2^u9D#C1_x3vo70#B0HV>maJ^XnArzBS3O%1s%{J z2It)XM}paB)TNmSy9H!u;<3x1H(n!6w&$ml#kNdR z(58YquO#R|VGTyyUz4=pKtZiJz&4+`cnkFoLt$R)GT_!b)`X?De&h;;- zCFt&SF4aH`?2p2S!yN43p0o5qYp%MUTT;WUB>35(U+4wQyb#K@Rh_v{avU0hslsWC zY6b?*xEV+692IHslmCgfHk47m?*Lu!nxUU6v7R`e0qb(4G5Afm6NkzCBS=At&pBJYXMLV&yO2Fa7Mue%Q=$-@V%+7N&`DupdayiKL<^WprL&OY9 zg$fw=IZQkRL@TlaPc_S$HKWk(zds`>AGJ>a($JKxC8XBkp_Z$Wk>rffmw9r2w3L7w zhWqAnAk3zGbAB22@^0|;Bn*HP0%<3#MOa#j_V3UaJvB?Ag~TvO_A}bbfi-1-YMMP4 zlIURBhrlyrqCxhe7BdVNv^ABYubAe(=ak` zY#YxqRl9q$;NE4DmF>}CR%bqGBU6pQU8s-@cQJE&Ewc!Kkhu~xEw(=>f3PWie##Ho z2}eMP9C2F&gaLSDv@D9xS`?24%is~ENN?>^NRG&%mW3ZL8hHl_cv(30{ll||Lwb`_ z-3DkKc;F)~&#YB^d%{)SB#4jfu=3nEL7D^K6(G$>l+RmdtA2<13~jUFES*co69@@> ziREG#lM-{@iq+88Dmb6pbK?g&@Ov|=pn%;Y%pUmbNUoG>Fn#WB^VlRF1~V6W&6!bR_RCosO2;N%Gyj^|t3C45cC9Udk`rTh|`+^4fX4YIS0uTR@dHeY8qfKy{Ls zhCuB`2;;j~o7hhgN2-)=F~rXLkW>h>hZt`NS+gc-iw}h9xv!2Ef!^5wj-O!_1_8n> z2!{>nq*ZrG<~omEw}~%5k@<6a+Y8Dz$<`r)t`K05^^uC80@@>C0nb7QyJK&r5d_9R z8rV1y89I>pXI+~#SRdb{2m@~+9m*mj>}XpT;J}JzOQwck#AGbaBi^`3*DgMD(#O_O z%3-=H|9;K^49B6>5SZnl_HNV|N_j1qrJraiU1Xm<GzH>fFSl2Rju4JYJwLcpF~qyxMcs%|Y%>7APhEB-s zoU8xidmLna04}BB-(1Q++ymJkx&4t;6^O>G$guzBwf}c-11V+p-&0~gcK&~H|NbG; zR=t$+hW-C!s0~r3RP)id2jZ@!E`gu_nejlHEFb%MZ}T8(r2_M796ebg!RFf3ka##E zbekG}&lLUMqKl2neB4Vme1%PbCN!`&F>Xjm&t4gz&Ni%B>(*3bLhWYC?&TWe-^ts~ zv~g?Zn?e~6UuI>>5t_{>kbc4U!o5+YTPn0OB836byGvyH0@8CAlrin{u+FO8l5Pmz`&VeZD(7&53f~ zpEb=+G1CEGPR{W6FGjT!98F58f|gzpF}-d9bS>FNu3JUZO-kFmtAmaX%r@=cn060k zHM4Ml+Hzy42$$|g z7d{Huhy@lhIT!*m7cj}GBw9aW!$N@s%* zXcVTiE3;xRO1Mm)ZOwFBrjty~E7FMUw!FpAx9;V5w|0JRAyPd@m8GRlMc^BQs>%)8 z7|9HK8x`LnL#vzHcdWEyP7g5?#F&Shl%97T@HKJ&{ZlRfy}8es(LTbKoGGn~rXiMI zAxs{;hs1L%{davf*J2-f*capHpc`I_Gdg#6m!!dj_p{n3F(PMuuDUipb5*%@ zOER&Dy8S_WFtM|)JbUV> zEHk^8xqeyhs{P)SeS08B+&%N6YF5|ndmymwF3RT+P&M;5d{WbzEZfI+zJE0)Hts`Q zk#UQ6Yqo4e5g{GUULq}32^Vmi4Eb+dZ5r_n460dy!X_U(bJ+R{&R+HLiJxa0OY#kL zv<;U?eG2LE_xe7e*#dXx53zNBBg5KY`O&%7%xL<(dl#vWk}W3UTHJ-1 zEWYo~vX+jkOnS?v>0m+y5wJi;?{7iWqIwu@evf-%u2YY# zS{FlP?oWM6>pJ8g<=+D3%U!oU_wl(ewTG4zEonWnGwI>vWVF_dv#kH{)uqh-ucxY3 z*n`Vm$rTMxqnQ}B@|yAzIo(<9+rNc`rZ#k9ulXim{ZtR?zb_fhK3#63s%v%W&aX2P zCz|OUwQY@CS=u~)ue_7(+7|9;KS*zva;z>RaWIOG>q5#VL&v*&rA21O)&b5|TsG}P z6sbeUa9!pBYu#DrZQA3)aZ0K6o!Hm1Z3eaVd$~Oh+q7rR?jG9s7~mUF+Ni|3#7xs5 zknU~NsMKQ2;PK@J_rjmjrf&GjdsD+Pl`*d;=-oe6xeXS&FV+rxzzZ;H*6xQ{^D$tO zIlZs&y0)H@E~T$grdL>IbD&^Mltz3n(^k`;@S9&)nQ2(v9^B0%l-M-0_hp?;5L+@7 zTW(=jD&8y?HtS5CHyCo`!X}3>>BMJy_S3mHzp$#~&X0-j%)G^n+agrU;H!oY_YLPW zy4d;6S1vph=@7i%|7UlRvq6IR?S(h|nq*>M4lADhfd4S`fp+ta!EhE8xvW+0azIxS zTc+37R|z$*$#v`Yyq{7}3-mU?VufSGD-{-ZVQ$!<2bBkR@>a!t`E&w(OSVH?x$&mY ztr9DomZf%x4w&0%o?T@A6*0_v~3N(@hw2AgYAHB4TVW9&UA32ePIG>t1^T+$qH9AFeeUB7c3V{j1}VY^==__@MP$Zx8Nrp; zfB&!C52cG!M|IZid&k`lZ<_pmYD0Xen{4i(Vhr8cre)V?N2}bS3Be?z1^Ra0=9LNV z?DDv^nQ!9?S`3!r+ZJ(zH1+^r0~~WjU;c%@*R^ol8OslUrST|UjLDAwUjCco_m7#i zb#41(BLWIEo;>ZWkb7o~k6(h}8>6FLAh6syKS{>9t`}U_^L;tSowx2;LQTO}1o)g39=Y<+$HFGIgb@Z+ojQiOL59WDW7|xU zF0;{)2GT(fR&@EI90jl3rB}g&F9HOk!(PDX{$3#+*u_#oWi!}&yq-ZMPS15B*Jzr5 zBRwTH>vl|+#PW^rX>$+d7V!ovrKw~e*Q@s*^RLS*R9C5OTP|0L5BIEPEkDhetNX_+ z?Sl!c_2_sL)7p^{Uz?66jit`31TL1|k2uC`EqF20ZkO6`HkZ$=={kR_Dc7dJNEJYo z79J`sy6C<1E}eH~M)dq}Pz`TNas1Nc5gzpfhw47|u@2`niqU&>pW;Tim=8_$7k1*v z=$<`{a)^mf7`q*iVqRA!X`|9HaQKiQT$jLj`jhNCP0gxhd!Ov(9x0MeDNnR^xhJ+7 zGR`|=`0lLL`$UE2NsZFJ0=4M^p4Dn!x#`i#w+1+Rnwlnii*Ji#Ho0+eWv5K@$rg=T z7oI2;*efUK+_?4I?tqL7|YJKHjnZO=f*&)FwVnAFR7VZ<&4{(`nMkJ=1DY*w8K2W^J1} z0)2K@``P+}2ZT)&xjq0LWUyoUyp4(rZ2I>z6|XiQdloQ3f1&ranhQ>p3+JD+sKqlw znRvK&rVPKCf4lZmBTX_TWqNPES2VdpdTV);mVmFs?oy-PKCnt@WOkSp?vAwz6JCvy zDTM`JskdZJIHiE5GB-)#6de=|ki+rCSJ(bUePgV)bU zA*!(QEZL%2U$8lM=G8ipi^l-{5fa&ELdS#iu+fbhFuf5&7pSoqIkalo*f$R)$h%K4 zw6Xh-4{knZJJ;`I-w&HIcljZ$aJ9q=_=5T7gXdd0`wH%Hsqgadc_9u4il^V3?0W5s z5e|ruq7x)+k}G^5JncSL89m%9T%K0JIe+PZG7SR`FUF}t+SSxm5`)QC&LdpI7;^;7inFywm#jo}zZgB-1j1BxyBp70#3O59$|OVn2Zp`!ou;o|YNZ|${?fw6mbO(sac^GzY=Plyr^9%Ce-nLS zZ3M2#?<-fcH$QtPc8o$R&Z)t$wZyGq!o@LA$hG}a>+&}y{*()l|kV?&n&ng&s^7o$)p*<{i+0FVERvs3Oi#wzb5dDX_+P0MwH~NAE>v zm%F-{Y33%Y;XD%>m7sUAAQ~bl;t@)c{(j6$**a@7-NbX=`p~XO9exP*?+m;ezcecH zU|&wAvTuRagCOC_Ct{)T;m3lizcKwKJUKHuZ!N&|T+}o;J@5%{@Y%JeF=Yf*OW0Ze z$m_GaPen;-DIv(tw7OZ%hA+D?w+Z7v&TKRImVl*d*~$cctH*;(p=~NwS?ugg3S(lP zsT8cTD%)TO3{qbslq8Hb&*puG0S{%<;2IQ)X`Bnu5fog_dsxAL-D9rW;tL_V(alt8WA^E~{(HZ79lBjx+mA+K2#q{$jTTWR zQuNkmt*o~xy(?G;-Oio2H^VnDN*UPM2CWX)JlPqXgR0w01HiYPga|%C1 zjDwCW$1{Evy?-0*J6zYEmdz(}dBb?U*URXV=X(;%cqGcj&g(ryziUBUy`?Sw& zrx_VFr?shb%B@|l>xjjNi%upG_1Ndn`3D9H2_A3|4zbuOxi{C?-<4W?(Ae+LOx`-f zqV=J%ByhgqyHeirWiV*m3(zd(U+lI9!%L7iDfcX$W)KHF(kF39=H+_`kebtvgz zoZHuC>Wf-%wpGmBVE^>R!?dMcg~AYD|DM6F4vs#Mb)%$|-TP!NS?QR}Gl=1D!I09( zRPWhZZK2$ZlbsslFLAIuIhmBZ5Brr_*f7(`=#a$h^OdSZMfrs$7Z)pz$l~Sn*g$a) z$GCE{!IYKt%vvM0y@bEeToa6m$j1`Q@nd_{j1K_sY(_LskIzyyS2!ghxGdMP^7pZN zpL&_n*sMcj4}wB8&KIiAj$W;k{99Uc|0SyULHW)Zm2JWv2!J$dX>n^A>85FU?*4&# zms9A7v}<0uWA*Ed{ew8dJBCOnCtJcLZ+l2!dwHqIXmf8&PgFJtM-!#{x^5h5=DL;3 z^bap5zs(YQYNyz&q-=dr&Od`kZuAt?kSqh%3Qyw{BA@Ak<;D+1Z~5k?ve#pHSX%|B z--c&8v-f^HU-(I_X~-{-u@vX>EL!N|8pYLU9Z(3A<6dl%FZ;m2dO6`TU#u}VW!cYY zDaLzRAg$+UP#=4|*~j2eZaZDS^(|@cElaM;)h?;wzE@wSPF)S}a`#n>byqbTps~@5 zikcARBkz;Mg+{tkCKQ~s>7cF@J#Wf9sAi!!s2rkN4gFjEF2vq{i8AFisA{OyQp!s3 z@PMNJ91^-$fY$?y#K_JWjs&%|Ta?ftHm$8ZqRZTmW@gFtDebC(vwGGkq|Ip6O9@GnsBtn} zOvT&q)YF_R?dP(cd)998xX310)_mZftC~?%b-_RBiaZjyX=Sb7VL2b%WTJAq<$DhW*{#S?pefZ&T8NwIv7aRs2-m^(PVu+R|ZNY@tXd5DTzQ4WJ)UoHRm$v|*e zz%~aMU?U?&eT>agpz*NXoFBvx7yg<$eK){MXgh0osHj5vh}+@v&Fj}SUPqiDuPbj= z9ZL$axhU5m2eluoFL$st`!`|5erzd8#Wluewr>(UMfD1W7F#Xl7JX%`H7>t6r)Es1 zD^z}b)s>-D%q?KqU($6U>00e0V_R1o1{+M21ZUJft( z4I^zJm|I`pFMYYoA|S;ZFc_*IdrKnmT8q)AN{>Q{*AElQGM*^8d0$4xuy5RBd)Xrp zY1EW%WXHR0cKV|HcgZFz8$$y#YTDh-8#_1I9VgGw z{>BJRvp?U#mKEdvP06a(Q^~sm9WklKE5f17vzD6({Xy>B2XD2}uR>t{IwLG6E;3x> z=o|pSUrv5Q7k13f0WDraQZ`wfgAr_!8*`w)?t~Lf4ASK8~5-PH=Lb z^EvFc)JW^r+~WA9RZzb<;-JBN?MT7$yY%oHN-Wb6Y_ueYh^;w295K$QkGwn8>u{e+ zYB>MS=-Mq2c40Y2=l(A)#U-;qQwk~Il+SDE4DKiiXqtMttm4KplKWKe(XOh8m%q!t zx~Z$n6e#xn55v(}D3sHp$DhycgoB!^6%~$lt&n^+A#Al;~k8(AFRxIS9sg#`g=6Fo=hFaEKW^8_p}^F zo|tY+W7pVvhDAul4J*N#>y#4|$9fRosz2X%o z@b|~X4VCLff^rRMqLky!BSB8VjP4Q977qgP3sk$HmOpF-RuwF=F{En=HfofhmOI+e z7YcZv9=dKPz!l%^HyO5ZJK8t>rQBk(xu=Z^?&Fa3W5p{AdQSMxr!J4XJQ%Sj*hQ@LH zm8XqvQ#3qa60}-lyke;Xz!9weRqkIR$OMD-4bprYO!}i#--|8ZxmsF zd*(ad5NH`AI-c|h-r?d}3sCT|&sU2|o-~5fKxAQ>mVJy9w{XG3LBoId=X(UsYk8te zlPdAY*2pkP5xtBnWE5s#ZKs?YBo74XN9Wg%ABap2P)O5)uO{p_$I13&Vs{0oIWBEt zl33%NDX!)vriao;u$vU<+?HqTKk0CC{!Y|-Z6%|V{n1%#iDiiKA#jnP=@BocGX6zQ zJuH4uGkR0_e8|c9FFI?rK`=lHgK_>=V{CQwKaTFl-lZ?O&(01W1xVo~Y3H+R!Hqf^ z!|Ne9J?gKFqDH|lKMffVK*&Coq_8?>&OOx;MfL!88vT}5@_Tl_o+vd`5e_LNv}{4i zV`e;Q67mDH5Vt_uZtVjdzI0+{BwsaV?`p=0ph}QGD!_*G`uq2&%3IJ4dZ6HbDIN4Q z>j#!;D-V9b!I#F zqtvtg#`>KpPprDy<@Dv8u}}pzzIFDAEO(h4Bwe}5ZB#OTAA=E5%bMD##H)V64_^rf zf7fgXQ_41nre*YW3OwBLcjV4~Z2zik2*8K|`P7?oF%Jg-16Imh!zwPspZ&Yj#QtZO zmAok`W3y8rfmy8l%0^m8eN2tD@1za>%jS_8VJ=*NPkJAqjyMa`Lr@~?J#zUF<+GHuyG_YK$|KI zu32D!qwg8kVjxy)(#hp>=dEGbyLxx+bq~7sgn<+tU)qalicv9w#tZM2dQHX) znhqLq_!hU(5auiVO&f+f_WxBp)Lda^2X|FGp8&k?{W+1eFXtBWQBd2H|?43s6qldQ+#Psg5a{Lmsz{?J!*X0UO34# zkI;Ij64aEj&S*5~mfKsD7ubIN#Kz9vmpL8=_(W~$vFp)Q% z1*QYzQ_~uwh>3~j*@U-njivdMzr`@QS@CfVt{(g(JEdNospopJKvhk}hPCCjA6$KT zTbY zw6O0PUyV1)YrKA(uA0k$P>83tHbW0zPJQ|5y58!NIX9F(CfYJg(_;i}RGVq~;@4F(rRCi^$xXyYaq!+%CM?4r0D zmj<*3rB}1`!4Ev}h@8td7=jUUw?JEg@CUZ6@ECU3Bb=dOEVFmjQ>7Xevkv$kNM#%! zWgYankE^7YR%D=k#@HcQvN-j+h!u{+Lp|&;&f13IztiOn{f;ZGbfZZ zRKe<(VSfpyAo>yX-!^)Hhy1p~*mXQ3_?2TM-PkUBOmquPS6@=;n#oFM9;^s%A)!DN8zC7F+qau0NczY=0>!Ey?dM z4;O(1v2VZU&9<`u8J8keK<1w8cSnDLsJ`lR>M_yzBC+S@^o&5D3+F3q)LLZ6NHL5K zcMRp1KBWgta1sG4oqKc$j#|U5ffTX}H*f+I=|8DB2sk z*Nc7nnpZfU?6BFl>;+XRaA>m^V%dF-rZ=C8qy#=cUMERz*XjKu&V|)-K`n=UJkzVg zR}C9X5pnudT%Nq|1R%g{C) z=)ZNG!n-&d+@aQDqi1Q7^d};2?oFQfRA~x@>HIg>9$s!>c}z?+%YQ;*=E%U-6xGzw zi+6r+#u7zb7*8{+KYk;smj0`4mLxm-e$9pUp|7{#e=JPeQI7qe z(Pw2JIOw@HRw=%7r~Zo};HLoWN0VWYBCANNW2zq3{u?FJ|KC(l|KkJw(LR?h8%&-E zZ&U{0&}{u0@Z3$nwuSn1UIuOPIT!!kr6lE*RDjp0^;Ak~h7TwV4M#PQ&kHU~W!idnML7j}fc>j8$y zcNZ$|VxKSoZ(lSTtThDf8oj|W2aa2i5fh;}+WosoRehbYv^2W!-ojP@j3GM(|NYro__Jz~@ zP^prGWy8K2y4PT3^yDA)^ujGDJu)*1MMXCt`zYi=!@4!ldFVueOn(l9%;K%fV9QD! zd$P+(jwHusxcJXB{1jn$2(&|a?p;k7fMe%MZ&yVDZXI0SCC1PNIzb!^YoX92x9Q-% z?>;}2^tX|Jq_qMh>v2GJ&|n;f5tX7`w}DLAtrgQb4ZtAjTrbOnzYz$LWsM2!>fzJ} zM;J-U$p=y>aVGYyYPWaATK86Q!pV6E&R!bF*mY{~Gr_IEUC-NwoLl>f?up8y;4ddG z56?3}Vu{%giEaloFkrOJ0?;JVVbDo~F@C4aiFz`(0#Y2?;n+VMC@InE4!J302RlD% zdGe=d^w2M=>SMZ4P*GBOJ&^QVhNuDc0Qw9dd-CppZWk2`= z2iwCyUfyy2K3_&~UPL50;s&GFt`nO~$Aely@gG2{-xtQv9kaC4K)pP1lK0{6$3OJd zF8|#JVCr1^4d%g-8E9PF;k*GRZ?*O7pQ}FqEl72haJPd7zS-8J^^hC^Lk?;q70ljS z3Tpv^TP}3)g{J!h1yInu_qA~$+?USb|I6nl<@Z@uid$5yj)@GoAkHe__g)V?NLx6EBQ@WF}2Q_%A9R(2D zIwYusE_RcI%GPIM)bIQQLbEwa)TlVTyc94?abSFIn}sD0kv5?p4*f|)TR1Y4!v4rK zZqf%26}3aZl2pC1o&Q6ViVV8W*|oCp6z4z;?V;NZ`cy4ApqPFklkzS4{OLE4c zUZwx#eu(}saFh}~2F-tq`=^fbK#^Y%VPa;$))Xq~;qFg}fxpe5=GA5alW_r!D!ZTe z*7{6PJwCJ6VuuCaf4%hW+cO~4f7QXgbd;yd&i5;Xu&^x2FUUuW71u6Hf??cy3C=#J zJC1HK@4XxK;}QKc-M^aj80u-;>W)CVj^{_zJIdc zu1m*zX$zVOtyGa8nSm$7DgXj}+&U^yA**^E0f5UXHjGkjEI(Wh^)QPx1raDLSOFa>BAP zMp6>_^ob2K5Mc?-2j02OBJ6F*Pz>ZxQ825<)PU=rcN5kF;x>txyFi$zTZn-r$ZG9t zA{^|c+RpyCwpvtQ0boJzX}JJDAo3VNGUr(%Oj+Np&2=Mal4IeI=_ zstV+nNQHje*P}%E5j5i#n2msR2QDqt?d9}kKd(o-rZzpJeuQ}bw=P|}gc>A+!>*&{ z9Y)?${dZDC_>U_3pKHX94e~z@fd88*{ofcAz&`N5YfwOj0HM!6%#8oa-0-*A70iyH z>Tmov@$BEqe9F`;fK_evk^Bk&pIRrb$Rb+CXUuF4dx$>C^F}x6cXu6 ze!{FDKeVj2)vy9Rlbr z%eY(j8FMr$5wC_$bp9wDWX=Ww)`3EY#I(Sy<7is`$CJ=M9k>7Q{m_T51bw{F4aAB; z&O*?jD5U9>3ZPc)(BK;(hlD0#AJdhH#lE-#tbCw8BX+L=Y?9D!y2Kb5PI;Dii9^C8 z&(R`|I1{M`uv2nJ!-fzlY-5N(ZLq~5_=pt*YO;X!%Mk&;d@=FJ_SrKZ7y~F6En6A# zMLY#Zki8hd;uJ+_&+rU+tShzZOBy8sl=QvoCctn4wJJUp2Fx==pv zJvp-kxKnwn_5dsYZw*Usopy&newN#F zAOt#o49~%_3n4m`r(D1lH;W932EdbBF9U@~4Q#c?BmpIGI_PSLGY90Xa0hOC?v^ok zZ{3f1j?;zWB7%}n-b+IrD3C7}K}q5&bxQ3~N)DhK=IG!CYCB#$y`Y7*S#RFCS?J~c z0o5gOE!eyNa1+;<^4hy}Z%+OwD=AOJ%gCQK4DVLMJ4Fj`p-`TOOU z?m@+bD9@nW0``v}Iy9he$7SE^*`e1>e<*~d8|#(ZE<=M5PwHjuA}@lp6!LM?qq54_ zy%z|YLELW2oq_y)@g$jaqM`!= z7Cn<#cvD?2x%pLbcx#=2&y1nOc=l%j@yEC$MHTK-@|y-sUD0BvmI3U${1p|jHwvfi zt5Zi7ScC;ZKH7=+zyL(gK`wMS-!|3HdwhQtb4Isf3cB&3yl82YM!EB%58~FLzP~E~ z4G;Ue4Mk)sbeKT%hkX$vFJ0-rIi!VOQ$sFJm@>WJOh7$eI%FT_M&==jmCv-$z0~51rNAZ} zj^(GbtO2=F19MV$MjR?}7G-m_GI;cz8;c{PwnVo#=1m#4Dl)UOE~1mL3?4v;a4JS} z7}f%Jq=lh-#_aBxzl;{R3^;nr;4MAY`!rP!Vx)l9wVUC@=&)RZNLj~?B413bVdF&xDhoOL@g)ne{@e9J0~-V|f+HFnkuoK)NmtW)N%d<9!q<9h<6| znQLO6Da0;xrLfp{M4vT-J-p76-y{Dp1~aJym`#8lu&QP1G9tD&aQ=xl7mNR!SER!t*T;7Y8O))#7cd4V%|eH$IRZts#4RL|(pTANK|9r|awu z=g4^)(%i4*w(o2Dy(C=2xtv1t>rLL=2*YN*UC=o3l+E?TyCY7g^SK=R!r237jF(wpMPB>h{?DQZBr9UOuI6kFcGHLU(Yzh z17Oo4My*9((7GhzzEQ-(!*hMu!ouPJ1%)bDhZo=!(tyKd`iboEe028Anb1jCL`C^3 zA+)u&u~D|SFF12fP*D9Cr}hmgDbF_NJ)cFOGh)zq`S6!7U!F9d`|4J|OdwPh`gw6%da<+K4WonVkx69}HhM`q*N@L^oXPxjlQeTN1 zaFzTyad91(ayLMOz5g<%|AraiN=;7*c*LcY;Olo(dU(L&I&L5#ArTH~p%*sSoMUpz zg>{(D=&2TweoRYo7g#CcNO>g$we>j~^GY8S?{XWCnC99a|+EeM$l7 z7DeXhJ^LPf%o=!KH83#ng4G6nE{~HS+cAxx=pnw9S5Foln|ZN7m--x?rKR6xw|tn>U=w@H}23B5QR z?#(KC2s%5H;=bKP%X<}OK$7^0ka=$3Z;5@FEMYhKT6*3*FjGB~NwJfxun0EG6yQcB zq=$d}@FDN+Uq^~dOGV3~a|y~PPo6xq3~%T8iI_L`j8KE>?R7hSef{HhyD-}Z^nXs{ zEAekuVOL|b6~`0?LyVFF+MyEbQ6KJ3KtY1L|0<{yIXc6MLqwZ?=c+XU)Ebdo;k;-< zd>1wGk zSB>agHs-KVo%Eg&*uP$}c0iy6>jJF-oOv}N7W3YgRQ%%)uSbs``-|}NtG2eb+BeC| z%Xb7{(DLim&rX7Dz}ZQ1_`GJ>ow1>&qVhVqVBiQHrv55q8s!&3NHZN?=)UP3C1mv> z$+SJ`4FpK|>n(FM+ofCdC45yKE;;RGk}Vn{}In-x4&JgS788m?aJ8 z#K~ZA3llS350tMeD0D(gba?~d%do{;V5-s-V>wr(4o zc%t>_N}}ik_F`CTpmEMy*eH%0xVyW<^`$?0^eECk8YXCiem3nQ5JTq6`nuFEUw#U( z;k$G$z)_Ftq=L(feC#2+3#LHA0)WZbgoS&OU_I!7dO?oqFaN8(EB~iD?cbWEnwm6b zCR5R>9!nW5Wb0^=%D(Rhl_@!PW$7Sll4mR}Vno@(!Ql`(B^=B&S_mhOh=e5jQg*^~ z-RIQz^?Lq==k+~5%yf>kd_MQ*zTfZby58^4bqnPf0@HDTf*E<6)Gzly?v`;UUKebS z&ci7aA3nSQ^3q}*5mHP*w?scIgBs9b6`ltx7ANSEw5Ges#HE+$cQLPCOre{BMVp&}t6QLB1x0Rh2^#>+6g(7hU=&M{$C#L^~1jt>xjCMQJ$X5q;$NQMDX{I4L%gd`-SSF#EauE^Y z%0>{WH%u5^VozPnZE}Mq-KZV6KTguDf#Ir@J1n;dcR;>edT4 zG5P95dB;fC;=w&3GZ^6gy#*F5Gbc;MMM8yY%VrOBgPI=aDbRRa!2D60l#C5$zY0o0 z8R9sCsT+>e7>R)t`z(rKP2<*tBR6#BAt6x73yk3zM8=iq+TOq#Sh1 z^4PH?y@b7%cNE}zl&XB@{m=C0U=HI?C9XdO}iHdU{T4966#LiS%NEMK?V) zmEk}AvBv^x*0=pK8B3Vg8P)C`4gb})_mcnmm(0$cJKL1qNh(D6pvb@kyY{}u!NM&40JZ}cDNDr_cK_NQE+K`% z)LfSEqH+=ZMgzQRGcY-NF}u&*KfuNW+UnI?rvdY3CgPP}JY1r?kLv52pFMj|SX?RT z-Milql(%i%xPP)Ou#D{FobS3)hzNMty|x!tG)4Z?`7$OMNoX2iqRg{w=~Aiw37qD! zC4!i^1MYkG0vFC#Jbo;rre2 z7k;i4d`w8_*Na~iD)P$z@DJ`!{`=9RlX({5`;Y)nw$`oaf{MR;ZVr?TSwhs>yEh3X2r|6wox256aMXQ)>oX2fY#UNmn>{dsyzj`di1idJ zVAp)7){znS>Uh=04E@_m$>rtc2fGJeKQso!wR-58hBDPM${s1HqgN>tHv#5P`RrdU zCYFWDLl16As)JATDCKcgm9{=*JPMU>eZ|1^~R6@fUOV8m3{iCyQ+pi5#4tm=Q}1P{Vea?WPsg66#IBQo{`^IO*A$Q zyED*U+zEzVu+_1tZJO}+dV!kCS)^c)VW!DUEb=B%#$+w?Q=g8Q&!`&Aqk)1z_~MsW z!^2I$b>%UHUp=zx9hOW+g3i5m?FZnEs-gMZ7{FYaEn5zu+99>$Q}q1&ylY#j4Q%oZ zfZ3ws;;h>9@bO&@Q1#9f=3=bGI&!zbhclf!qPS_(0cc^ycq8!5WB%8t-#&6hJQEwz zfZo^(PYTRCLl5(7`%#e}I-m64Z*kq-7rWb`55Pi?64F33_4O?zIr{Z$YP~~gX(>6? zwR@a1z;17H%A9+S+Aa2zG)u58;cyyKHGiU@HeZCAt4X@vx_ZCJ z!Rt3}q+lw*3Hn$4yHu8yf`Uu43Cd0;;IO>~;7=>o>^cOFAp1rm`eIjA4EC@0R`Gay z$?PdK_syJh&Z0LFSQ zELqRRoYLt8nI!AxReo9=E=RZHD5_go*p@-Xf9kF}haXge?tBh5TQC_>sk~_DaO#xI z=FNX+c2D7gjqdGw#n2VQ&4ZR$Y0Y`|ArsK{{`uz+yz0%FR&1sRl};~$bd?TdTP4l; z?bIn6_7-8}p&n1eMAXvMe1VC{K3OFSO~RUB7A>t%G!Xp4fw!!;OT;=6k>krG-GH!G zY*3@(6ZP0yg6&YdzT%&M;B%%2nXmzcSKUpc5II(@TQ?ZxB>M+C3^IL&-)QOUD?Wy% zlLFo%g-Ekx<$4~#lvjKCBAXIlL%5%%^$t5gnWhi28$a3B{@Ll zF3ZlSA3R8yeHZS7wu*yDe|%Zq?-wu1pLwriQ|Nv`Qt!5bj!yW*P-_-S#4KBm6>I`` zT+!9hF&TctL40!b3FJq}q|=|{#adPG?7b991RehSDxUBDZTu1>u^uL7I`){fb#7&I z3}|D#yw-RbgduY3D`{M6CptB?~kka?$DmW-&kSpbyyulff)f zdwbr%fHRiKlb`2H7v^VYQ1TqZeY0KXj9OY+tjV><)|JW7yz5sd?zF!Qg*bXx**UR^ z9dPHt4z#*N^!5(^oiHDO&JZ%hDd$1W*gnIy926XwMronF7K)0B6m%#duD(Pcx??zo z$QrBUVe8y~&$SF(2|A?za1}6u&L>xWDx1mMq>q+h-HhQr=3k1~ov!Hwixw@iD)G$|Ii+0(jDLGL4}DLU{QdoPy0;H-xQ#^gBe^QTSuRSud$!;v%~=#&&XUsR$gQ0ckY}cS{J-)L9hNGbCw4tU#+Ob zGDyCN?z59T&9)R0p&16%p+N=&@{^2=Ly=RXeJxZv$784WW zxV#^`C6XoUT`jP?)_}}`bJDUz{YT#O-O-eq>T2-7POW?I!APU8T|34Dd^N=`2?Ggk z#jPJfQ?z+kw-7#T0@<leK^m~;Rt6AluGgkx^Oce z@wBU~R`yIqIpq`?0)^lUSDK5wvNBzPA+EP9wada!Fw#%>3^u1_XJ?<7l+Z+*nfdg` zt_V6J0Uc@73vFmWvYf<(n+wJZSit0K?KbT7H89I!AEK-}0=b~KWGhElli@RBn}yS5 z_${H8}8^xMubnPdH$RSF)6ea!w4Ov_C^J;TrlK~1_%wD z>G!t?cLb^G;TGNRI0ixvEGj-Tiq7iQqrgNU8EJ3rPVanWOBCUCrKDl*`Mtx(17l#? zC|_Fh?AZ$(gLQizgw7qTarBBkt*YAAX1@|!1T4$PXm^cxnPgkM7{ZM>*s<1*6<1>@lnHiOm%Y))A3Sss$W4(05gVAto$fCaC{8aLKzk!|8 zGc!zx^Yq!l2W%+Wn;T2qI}ZLan;#t+`F2bp%#)K#wxVRD|LyX?xAf0J(HFrk3j;E3 zP`UA-uHr8x_jq4Aorayw7W*rP(W>s`H6Hcv3 zh$~%w;SIC~^!q4yygfwV5RovUk1wb0R#txaPr$3n>T0iOgr;+s2ah#K?UC}WCAN7 z%m@M9I6f|}&t2~R8DN2Rwi5f0fRU?AK|0xBbS%sqIP@A|R@Ef#OYELPNr;9DE%}Dh zzf#C5ta?v0+c$PpaiE^9adzVOt!1u4^XwQdW|3PmW~R}@1E+U-N&;$n3CGT3-<2Zz zIMZ;YxYAwjq-Kll5p-^kL48o=dfA#XO{8BCEnG-wWFJ32m3zG#8p=UgOCOT?<0b&2 z21JE}>WbF)FIalq?_#g8*Os^h9qjsA1zIYUkSZFx56l-LD(UGBtU0$N{ z@bOX$;1L7#AxxABMYP^e*5$snYh$*hF=|oU8_u8%V2sY&P>~5ai@W;~V)lI7)9AXw zCC@@tB;4tsl(uVPQ3f*mJx`qqLq#GyyOx}CY_^*YA92-^R zIifDp?9$S*bL`Ro%9v@EJfAzKQf5DD3{9mK1`LM-AhO4UVO+BLia#0-g5_6PUa!B8 z1y&_Cj<`D-FvH#mRJenZ=lAiQW__`3*e<^bXX81p`pQ?7##k^M7v`wnkr6ny(wIQ= z+Qz;C1xuGMHNZrRn85|Nf*hEc_y8!AA^A8))|vsuqU-&8+We>K$A#(X=|r_IetB;$ z!5=B4#t{{KJfc<$^4emIG{iGq`{A;M($>Ei6(?SR0J*IdQ=moJ=!?KdTo(mld6vR& zPk$(`>35ze2~m=l=Z%dG0G=ygn2m;6@@QA8G{LMsh%}W(*9)t-v*_!^tG%FW&d&`8 zI$^9R&26fxEjdKXRGXi& zueUgT`gG|Ny_-7@bXCp`C(2>Qn&H~3x7a?ZsBi@Pgs7P3ccp*=v8)J<4-qriAh)kB zEe!kUVEZ>3IvHn19<3Zq*bM+@OhFxja6tJ}wG*`qrm$?Om)n?{o}L9|x;g)3SgX^~ zK)2uL-iFDk$P=AfNa*J)d@V&veS`nVfxK=~9wCb+f}k#1TPf%K!kkaNFO9}Ts7r&M zSwCw97{NqR&u*en`yp0wk#B$g;fJK-k1qEDom@qxz1Cd_CIkH`6HNvL7=(L(RztFt zYh&X`;=6}wNP|TP3v2{v$oE~bH^5quHF}_W^Uz>?Cn;&8ZtO0N%*@QK#oQmI>`F_} z><)2%gS|8G83BC>W2AW1<_E-hy0i@1&Rob(SAX@?Ar-KOe*N>h*_Q6o8k z9kM)nO8A47LgkT z=Qad>$^*||MxBK3w8_DP7tsMh3&+MB>LkoR^z1SLj|Rcgjf}c|I~%71g;{yL`F1ik zl*LwQf6yle9KUIV9)UX$uw&09{07K*E;_g{g|t-y=SzP4=_hUfH+Z|Wib`|F?%lg1 za%U%p-ROTeuZKiVtmccn2blnr>XE29v7iv22GL+z~y+8l~v1LIZ5S{&ej&fnqa}UVr z#zg-4Cj&_r#XK!wlbDc@5T*{Qs2y%7-MMU-*CZSs+XahU$MqU;nhv_{Zk)J9~VS$XRJ> M=pD*Ac5nU=>Fe+Zs>4Z`EcOq^Rq(7pI?3aaZ%f{<(cYb@^Za z&u;7g{^I{G&OhVfeC2KdoouV; zT|KSZ;f!VHBYJgVOZ8P%iYHH=ym$BRgxizqb$etcUK?hdb+fI^HB7Cnk{oyH`)L(JI4;(m9SzX-}dCK?cAAdx!$2^=tDQXpG z-&=0Jvb;pM+52JUo}V8rG&H%XNo}MvPu2U|xUcN|=dCg`ZN@u~UHNVA-n~sl9xg>+ zUvHGWK9`yptlOKkuFxJQ%f6Nle{Lb%g!rpI_F7N_Eisu{-Qy%hN-xnvF3^ zBRmz|)2re?EscME_oVXe+Y1T`KKJh3tK^SLO}*gwrMkY=&^sAVqCDH}VJniwjt{3# zy+6ffKG+aVxAD<^TUFKA`%td-un_*`1pg@thuwvT+%lO~?dj)QtKPkvz8=n_M5|1f+ZpASH*44 z9({QB>{*>6cd`h{Wq#Xv_w4!eN@!=NUYARcC;9JKMt(s-Wg3Ojv|qP&@Ps;_ZuWV( zZ(nZ<*Dmpo3D%|IYj1BkFNz_PgU}RZRyQr=2t_o=?F!sWbW z*>+R54|Eq5e5rmOwQeK(VQJ~kPh*b#wau0(MPF_O;MZPh<6Ke8wrM_#wq|6W#V*)i z^kZh{=898C7|nbmzPr8NkCm*9zF;SxaoCS?^g^jk$&$01hevQUB7X4gqkRGvn{QMJ z6r8tgrK?}P`m(Ht+jHvTNKZ-6y5!YZfeh)DZ<7;KQ^JbDlGKEW$w~dD7x`6Jg$D0z zH%L9>JrL=u_kJhdoYHbMZO?_$Uw-+eDW7U--JV{?x73zqY|-%Y_tL(dK9NenqUNo1 z{c~r}*8jfskQ#CU4ne%z18xgnra6{CpEn;bLb_h|3ucd~Srx!0+tXsRbO-7{lrtgn~sQx zsGFOcPfSm%pE+|^C-Yk5+?=b(wfCs+5Mj(Eo@Fu*64xKhKGP?KRfkscU=Qb{` zk*Nh{dRv>8=kk1iK8~@vo}OM%SYTjm#cprC46{1=rTN&|dKr(I!v_w?$H&KSU|~_i zqFpgIZq6`M$qBJ-JIaE!GwjH*ZN0f>{nO{q<8R$seaFWq!E0&3qW;C5eCp7bfS#gk z1pLKce!2Bvm&A*wPk-*~>pLPObP3POMCj0J&(5@Enm<`yUbN+say~ylKTpz@q|-oz zL6Q5r*4EY-A)LHSn=VP~?n3Q~hr0uPeSKd*}5txws7(Kqtf0{{gp5LT|-G-hKN{k=L=Z zsw0wr-o4wFI<@w6E|VkAtd2XQXL`ylPcy|pdC{gNm)WMJrBV3S!!*DuJl?hxgY^U}jN);xRm_glAbH^*Jvmu20dk*u4O z{o~*A3tqqe#kwO~DOk+51yO?KJM!e6T7HU{HABtb-X4{TN>xoHADKTG6Fh|q5)i{B zZg;}AXgYw~^~0*|+2_xxsI)Sh6RE>($D&`pjLXf{!m47mF4cv}Bp1INu-Iv@GL_ zO}$@&ZAEtqU%PzqOMCd&uXeo*%M=kj&zM~SuaM)H^L~=Hh2sjol{$s4Y4-Ddft;w- z+q`0tcZ?@KzbmhgI?a_3tDRxmmz@(4@yd0!%fa#i=a_A`bQGXMUyF&*^ZS#vQgw6K?pW{7!S zzyAFRU4aSivvP`xtC`u@(8pVJZFj8sf#vA+>(_ab>aGgy2$gaXjXD4P%5Z01W8{Jb zlfy0h@#8JMs3^k7D;hSQVPRqAUq6=m_f-W3_hn6MS^D!n^haVBu_>N6jY9GewN~7- zXHWUM?RuBbhs$<}BNa9aC36K1FY)Q;H(9isJd9PBsI%?Nt$!wNf8|a^aAWxU_ZP@= zmLo)LKKJ)a>6y71xbE7KFW8TnV7QE&CP97v%0#;>?}LK4u_8s$52c1@$(0twD;)b z*jWF}&d$(tZXX*RSPpyg#JF&(K~Z6Lw2PnNG*eMkRW;C4QZf#Vu>-XS??Q7tdGX?- zW0oy^#$P}D>4PMyv}ic_rN)0KrKEdqro+}B5W<&H=sJGIc(^T%y}~d3`)Tmuv^kn> z-SOpujQTMInBPo@^)$1&yf4tu#?{S@*0GEr+6la>d1snWEA`a?szF}I?++h7w4ZL) z{BlTv+8fB!N^uxAe-9^r{N1>tC--ZlV z625+kE%f^~L@%fnxBv88=}1obj#VF!A2>(~)bBa{crypbHR@tsoZI;qbDw#G~Q? z*9bjB2!CXVKhU!;+osMlnNcU3ac!|+tXOw+;gE93Q3d~<>d`AXi7CbT=d9^J^{ut9a>S2-(C=CG6^j7)RmS=*jWb5&y`V&_&(^?$sUp7&*ELH3{NA)6`7 z-zMw)%l$b!mOQd;d%EYAW_w2HDOM8MVH*h;h(dxxw_3ecGtNb2k$JZ#Ie#o8W`Zvws;P)gaTtL1;!WS%>daO^?P?!IHZndUHE#F7nd_<#} zsJ7#!ayYu`j4)C?>SPzI>5^3eMpb#2feC0c#aVsEDiqg0Z>>`=ULFE`dGz$@)z-1z zxQW%TNMn1-I{cWif_XcT2}zJ60;N4YJz7BDT|H&$i}T~X6HYd=?uLtn!^6cOLE6!mfuwB;WKUe!rmn<(>0ymp~e0^tA);<9FKtr#*ORB1h zjRYv8eb(aF$-IEvLeLy4ClXk3x=#6Yt6>UREoKjSnNg`vyu2Sf;TepKA_EN__ERum3KI2CISrMpmK^+ip5A4&Xc?F3z1f^S;n+^68^TMgc3NHAFL| zFKei2jd-|F6E4@oXWg8jj#jq8liHqcl0Wy!j}{nc7BKqpW90JEVxmg8ED%}VFQ-n? zf`duVGKk)?)wX2GRCM+$;F8n31A*7DnaZKJdHDFw;}HU!ENqxKOV5{N4jznYF#e?~ zfZsZysMMX7$|0Uh0;=ERtT$YfLD(mXRbZ2YCdVs;xy1f>y zeHo8HA&{mK;q!REt*tHV4xwL>i!K6re#YaydGjm3R_Y1hNmI8R+aCH6)tG@$m+ty> zlPag7(WweMJ#9>(l+C3GEi&ur}LN`&jSKt{=B_@=zZiVgkO7lw8BGf zYv<%~pdTihDNSeR?EQqHV4^SA@5z!Dp;}!>uQ-jYsr|Cz&6|PO8wJ&{6X?eJ`uk%; zLUy6|vcL&r6uM@GYCT~iWoX4De{mx=&382#qa1HVP1$e*wZJ%mBJkq=PH~<0aHl(h83ht9%>IsB;1`2s983{nFbT z1zIN#{p`Kpf4_*JdWv;G>;l-@E`Y15ZZXWVpdkX1@i3Jg9UYNlhT90tSzhc}2@nuN zyGM?X?hA$JT@AQx~!a0H|$LgR6?$-Df)b)6&w)U&#Aa*66CKT|H(sfo{wyOXl_utm%Wv zACU)lG{&lkefsoicy!fpf4}C!7?=u9bQ`*PZcm;(@&8!rtD8w61uZ*GH12oqS7>mu z7QFKiP8%k|1NO7aJlns+rQ0o5pW3*ltpPPKdDHJ8VpMw(^n7AH$P508f<;YCucO|} z*Mpe?!DGXY?~y5ZLykKWMzZK28XJG?Zm?%Kz(;Inm<`&M@{;9gK|g2EHH?@UP9MwK zycTyWpxh+|Q&ZDuH^p=3?gKLIT$!))zMq}f2LfbR6WygMV#bb_- zSM2U+dLkz%ST~1sdf$?sSI%+t@`>o7z$MQ_hDS?1_ba@AI-^uTQj+01Ry4~KInphC*C&7qV_sI?rglQtgL_nz=Vsd>E?M8H;&+Tm4_ zte~J^uiVPKO7zMhPEo5=hU(1C_1vlP$?8TUI?GejYGn0rjA_W&|TcQ?C9+i^2Pw_U)6i^CZjwp&QY z5X|%hr;ybBd6%S9Tti_WtA1OI5@#$=!c{V)T;bWwS;%z$Q;qJaO+-o7eW;y_U zhgg@>6ZxWTGJN7S-Rm#GQx4-T`uarRglZKy#BC^w;nUMBXKY*V#Y4>-^h08t2{TE z|0yIG$je^fIn)cKX&UXx6V<_~Ed>6{FM3`uYSz6?8e>fY zuBf2wBYHVDpj_X& zQbRfyD@9jo_w+!0X-w}ae5YnNy+mWWg(c5M3a8I>B-A^N#0=lvADOP8)tZJJ4% zA;??U+rlED*<+9H@(OF|^3Z6SaYaz_?#-KxcFB2l#^hHfSgr612xQOM+`M_S{P6)r zEBZb;xt=Q?)8pgz*96=>vec6b|Fl6~Q7Y9=(Mo&$ zf;C%t{pWp&^&t)ZyE<c#S>M^t%g%=jZd_4Sz~8J2X@7tloy?QvRA4K3Q2l+_iM z(}Mp19I)O`h}&a0&B8Jxqv=-)==Q{-rtjsrU>U6ee~Zi2?ilg`;D}B8+8Qk1{}qP` zZ)<8qu3WiN%`jH{V=R-wL9uD2ht}sg_Vairt=q!;FV zG6#s3J-0%b5QqbpqO;X}QBZUsq_&qFxzKM-Bzzz<6q=4%TO;d((+z4363T4DBHi&53i8$k0WWA z&7R5yP|)t&by{A&o%+O*BbGZPV1)ng5-md?tRAer0e0TbslOai_IMn+l@Yb`}U2;^TUU?GZ#8mzw$Af|N1G&l5TJAF>J(7 z(F+Z)B-$Cf8_vYl*;%4-%S&WW{hHeHMMZZ04Lv^voVA}2Aj61zd)ZMIzwWiPP)j{( zt$vADS7(jK5K`TP!Wmrl?z-rZSPvwROR>S~2rnb=t!|9()r2`h;%#<~{DSqa*iI zE?OceL|KOR?NOU?NZPmRvZd+<@rfT9b)GDJ!Pq&2HA2AEYRp@tW( z7T@yu`lsIy@m1y_0(fi8#_}SDvtAL6(?!=>-FfZ{zLA7p3)*9A4Gk)*zjsg;#SY(l zMA8gprh+-o=#E%~OnL31f=Q!fo`qZ;I5|)O)h)J7agSzoF$)DDwOV_uux9SORtSkSVBkvYd;S2S}>1}iD87;zI%LtM?gSS zvEZW*=g;82G?!2(^QT*M`5U$i8dzyc)Lu@m1+mkvuk6RpFAO;%BNIJgGV;%8I-$%9 zCa*vA2!3$l1dJezK&qGafh)c?kfvjd))n7SF-&8AEzG;{E1te0u5%fOv1H(Lr z(7S8uQ)&8c`OYI2kDzuQ6+x4Fu20Q5$+1~N3?lH}k%w~2sfo(Eq;uG7jjA*;G2!4& zQjLH?A?cY+&2u^({7?n;sm?zo zzug=RwW&Gi?m(Q({wDWI?paSyPp=~c5K+ZN^c_UL zjBUPn;lky#60vLVPk=f|FGwQn(kDGNuGun^*%GE?&Y^<`b-uNbrz!2zcOQ&q?;C?O z{c&QKJ=_ZI)Y=DreyV8P_!>sKdXn^SYd9|rfZE)%=Om$K6$?i5hdzT^(<}ll+;n&I zzG2HGWf`_39^u2)BQ_vqHU+Muyvln(IrJ2O6(NTA~< zGNpMn>6vFZrJR#aS?A19x^?E8ldVEgcJl{b`0e7U+R^i_r^gW4E4#qPQfxm6-=eNQ zHDELTrd)et--%HF^*7D1_F6A#vtQAno`kAvYVNr>L(Lrp>i1i}9T3zmS6cI&l2Y@x z`N?VYYD19Sn#;BuCP6%oN0-BFi1sV%2O>IN<^A=;$?aK%HmLrjUDwtDv&p|YFFT$6 z*R)xVjsh23X#5>CBEqqoLxs#qWEoWZ`+yY!avOat!O;fc$Vcbob|2W23=9yN&wFX6 zvu|W18HZbjZ+MYssmU{-{;!*cFsJ@zf!irZ+&m8 zfk$$w;Etx>&{imTd3l+dA2+MbJTgYiAWsgd1Vz(W4+p9{2fwYYZNV?{-TI;b1@yN1 zPdmpRo#sw+b$fG>uzKA^9yJX=uyI>W%ccnx7Z>M)wXSJMPfwo$X48!KuO+y^zEFte z6r0S{AB{|{60afzeRYGvgI$B~H=f7#X6DkdW4`)=79B5!muCTz2cYy&8KTZQ?XPmx zvL;E3$KPPF5O(#|jaAP5M;2dP2EZ@#tD_z97&)wdeQBU_sk6CN(j=#Vs8CZ@pEA=s z^nnF)*pJ7UrR{qu8k;(Ff7$`9Hn2u~|ewasgB zM63wx(gMU|buFy{I|)MnRaRcQaN&=wx|)t)0nMgYJuZUWIKs3Ch};BsMR2rw^z0S4 z3M~)rEm=h7ald{0c4Bl$Z;>$0<2bmpAI=v> z=Ng@d+ae#zE}Q|n^`KSdI9I1ICGv(vQ{k%u>r(SkDAXKn8PWQ+FEH_i#&D-A)ToAFl%xI zlyIWnXgh-L&5l-2w22vu>sDE|Mr_lU%Xg3y^hoXnh5eP9xbJ(|!G4j5mE6=U7`nkZ zkos-T9yFn(-a!hbpJ(-}6RcSONa!Rf*mba|-3=uxOJfbeC7(WBQ3?^i2!fcNYNpZ7 znT%}iXRv1G?!$*-9;z8Jwx7X+)!5uP5D!qiDX=x$TG!3h_1QEdb5;s*BBAEu;E>*) ze%z*er0;ET*<(KL35iAiseC?u{+hkXg+@heT#{EGLGTof&8}#d*_@z=x?xi^t>FoU zLz(yZzI{=(KitqSV~nTpRJ4KI3}(jN%`$4{HRk`@$NLUEQZ0ECA&YIs56wXjuos;n z>>x~9FcHNg|1le5AL0ze6G|M|)j6vlk`Q~*fyBwqzDnb(?T(p6CC%+zM;r_?F@&AnM&Q+;8zZ%IiBA+`}aEyFvdVSxdGO7F%;!5=Vy<_Q#G zQ4`*IdaS1>(%43S3N#|IQ{O_|4(Gwe%s)9Hh64;Gu;TJ-}L0;SONP zto7EWj?WGweUIk#a~F-5bD9qR{`>DiqSis^!DHg$4#Q-pLY^R8){}hB{K5i59RZY_ z;o;jlCG-1n`eF!`Rj1xhm)JnxY))%{jZPy4QlA)}4xpS8lmwsXnjF&CSjJ zr-X-ZrsLJxZe(tq&L8tXoovr*(~Xh@524kiV0`6D65Vhs+b@;D-npXhV3q`p5W4fj zA3R=dtA|27?`JvdWz2F6CH1N)?Hbc_t7TkH|Uq7ZI%Q9*ffDqLah(xB_b1B@VV zP-!ryL?F_XLM5{r+%{PT2X)7Unt?HVjLhia_8=PR9LlXN1;OITnqJivE-|&_f@*BHA@R*vD{0^I)Mqj#RuS8xxiKr{w zb@1mf0cDdN+NsmC*CFPMxJ`U^SP4V=ht5gG)V31?2w(Sp!+fWBQFwW5!qUY& z`($#{Xj$K~yR{^5XXS@1g$^&tKB(rTJ%4WgNI|76-xeyVhR2WPWo6p>Jq*^BylVi2 z8vPOI5NTlWWXT~VwmM=pyMLbryIysaCF5c8Uq|}NtU-|%NA15 zI+~29QA&f38T zS1_`JgR$WnAQOqo1Xx=a>24X6Wu2d}Xsz`1>(}@GV%^sQNFMQ%=u|n*a%u%MdUXTw zZM5385+p$~H1N3k!XG7&h|nf9qLWC1(PJP$*X9^!ZFkrCP|4;`T65rxHS@#<^4yiZ zaKJu)G+KT;2>7pw4v$uuoLBlWE%WKT*Yv z8#Xi?3cvmWp1y&sR)a>Gra?RwmmCDR!_p*I4%xa$`DQ#!qsy8#Wisq)r)OqbMsj-S zSW4pba{uz&%n6UHA*oXamzoG zBjBJ)c`fk__)VFVQkYVn